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Household Income, Land Valuation and Rural Land Market Participation in Ecuador

Permanent Link: http://ufdc.ufl.edu/UFE0022369/00001

Material Information

Title: Household Income, Land Valuation and Rural Land Market Participation in Ecuador
Physical Description: 1 online resource (109 p.)
Language: english
Creator: Castillo, Maria
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This research provides an economic analysis of agricultural land access at the household level and its relationship with rural markets and poverty in Ecuador. We find that land inequality and land market imperfections have a direct effect on household income per capita and that there is a synergy between these and imperfections in the labor and credit markets, which magnify the effect of land inequality on rural household income. In addition, the presence of multiple market imperfections intensifies the quasi-fixity of factors other than land, which affects the contribution of land to profits and land values. The labor advantage of small farmers explains the remarkable difference in reservation prices per hectare between small and medium and large farmers. However, this effect is reduced for credit constrained households. Lack of land titles is not found to discourage investments in land or to cause land values to be smaller than for households with tiled land. Consistent with these findings, we also observe that the demand for land by small farmers is significantly larger than the supply of land by large landowners both in the land sales and rental markets. Small farmers are found to be more active than larger farmers on both sides of the land markets and sharecropping arrangements are found to be especially common among the land poor. Land titles have a significant and positive effect on the likelihood to sell and similarly, credit access on the likelihood to purchase and rent in land. We conclude that, given the difficulties that prevent desired land transfers from large landowners to the rural poor, it seems improbable that the market be able to achieve an optimal distribution of landownership without assistance from the government. Also, that for the potential benefits for rural development of increased land access to be realized, such an increase must be accompanied by better access to services so as to improve the competitiveness of the rural poor. Policies regarding the liberalization and stimulation of land rental markets and increase in the supply of credit in the rural sector are recommended.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Maria Castillo.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Deere, Carmen.
Local: Co-adviser: Espinel, Ramon.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022369:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022369/00001

Material Information

Title: Household Income, Land Valuation and Rural Land Market Participation in Ecuador
Physical Description: 1 online resource (109 p.)
Language: english
Creator: Castillo, Maria
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This research provides an economic analysis of agricultural land access at the household level and its relationship with rural markets and poverty in Ecuador. We find that land inequality and land market imperfections have a direct effect on household income per capita and that there is a synergy between these and imperfections in the labor and credit markets, which magnify the effect of land inequality on rural household income. In addition, the presence of multiple market imperfections intensifies the quasi-fixity of factors other than land, which affects the contribution of land to profits and land values. The labor advantage of small farmers explains the remarkable difference in reservation prices per hectare between small and medium and large farmers. However, this effect is reduced for credit constrained households. Lack of land titles is not found to discourage investments in land or to cause land values to be smaller than for households with tiled land. Consistent with these findings, we also observe that the demand for land by small farmers is significantly larger than the supply of land by large landowners both in the land sales and rental markets. Small farmers are found to be more active than larger farmers on both sides of the land markets and sharecropping arrangements are found to be especially common among the land poor. Land titles have a significant and positive effect on the likelihood to sell and similarly, credit access on the likelihood to purchase and rent in land. We conclude that, given the difficulties that prevent desired land transfers from large landowners to the rural poor, it seems improbable that the market be able to achieve an optimal distribution of landownership without assistance from the government. Also, that for the potential benefits for rural development of increased land access to be realized, such an increase must be accompanied by better access to services so as to improve the competitiveness of the rural poor. Policies regarding the liberalization and stimulation of land rental markets and increase in the supply of credit in the rural sector are recommended.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Maria Castillo.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Deere, Carmen.
Local: Co-adviser: Espinel, Ramon.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022369:00001


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HOUSEHOLD INCOME, LAND VALUATION AND RURAL LAND MARKET
PARTICIPATION IN ECUADOR




















By

MARIA JOSE CASTELLO


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008




































O 2008 Maria Jose Castillo




























To my family in Ecuador









ACKNOWLEDGMENTS

I thank my mom and dad for their constant love and support during this j ourney away from

home. I also thank my husband Santiago J. Bucaram for his love, help and wisdom, and for

keeping pushing me forward during the progress of this dissertation.

Special thanks I give to my chair, Dr. Carmen Diana Deere, whose valuable support made

possible the completion of this research work. I thank my cochair, Dr. Ram6n Espinel, as well

for believing in me ever since I met him. I thank my other committee members, Charles Moss,

Pilar Useche, and Grenville Barnes for appreciating the effort I put into this endeavor.

I thank the Instituto Nacional de Estadisticas y Censos (INTEC) for providing me with the

data set that gave place to this work at a time when it was not freely available on-line.

I give special thanks to Ing. Julia Carri6n from the Sistema de Informaci6n Geografica y

Agropecuaria (SIG-AGRO) of the Ecuadorian Ministry of Agriculture for providing me with

'hard to get' geographic information which contributed to my study.

I am also thankful to Dr. Jeff Burkhardt, Graduate Coordinator, for always appreciating my

work and being one of my advocates. Also to Dr. Ronal Ward for his willingness to help me

understand econometric applications every time I looked for him. I also thank Dr. Andrew

Schmitz for offering me the experience of publishing two papers on an interesting U. S. topic.

The Department of Food and Resource Economics deserve my appreciation for the

financial assistance they made available to me, without which my Ph.D. would have not being

possible.

Finally, I thank my friend Lily for her faithfulness and for providing me with a place to

stay during the final stage of my dissertation.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ................ ...............7............ ....


LIST OF FIGURES .............. ...............9.....


AB S TRAC T ............._. .......... ..............._ 10...


CHAPTER


1 INTRODUCTION ................. ...............12.......... ......


Obj ectives ................. ...............13.......... .....
Hypotheses............... ...............1

2 THE IMPACT OF LAND INEQUALITY ON ECUADORIAN HOUSEHOLD
INC OM E ................. ...............16.......... ......


Land Problems and Rural Poverty ................. ....... ...... ........ ...............16.
Multiple Market Imperfections and the Household Income Problem .............. ..................19
Statistical Analysis............... ...............20
Credit Access............... ... ...... ............2
Value Product per Unit of Labor ................. ...............25...............
Labor Allocation................. .. ............2
Value Product per Unit of Land ................. ...............26..............
The Land Rental Market ................. ...............27................
The M odel .................. ... ..... ........ .... ....... .............2
Household Income Per Capita Estimation ................. ...............35........... ...
Conclusions............... ..............4


3 UNDERSTANDING LAND RESERVATION VALUES IN THE PRESENCE OF
MULTIPLE MARKET IMPERFECTIONS: THE ECUADORIAN CASE. ................... ......50


Introducti on ................. ...............50.................
Data and Methodology .............. ...............51....
Restricted Profits .............. ...............52....
Land Reservation Prices ................. ...............56................
Re sults ................ ...............60.................
Conclusions............... ..............6


4 RURAL LAND MARKET PARTICIPATION IN ECUADOR AND ITS
DETERMINANT S .............. ...............69....


Introducti on ............. ..............69._ _........













Data and Methodology .............. ...............72....
Land Supply............... ...............72.
Land Demand .............. ...............78....
Re sults ................ ...............83.................

Supply Side............... ...............83..
Demand Side .............. ...............86....
Conclusions............... ..............8


5 CONCLUSIONS .............. ...............100....


APPENDIX PRIMARY ACTIVITY OF FEMALE HOUSEHOLD HEADS ................... .....104


LIST OF REFERENCES ................. ...............105................


BIOGRAPHICAL SKETCH ................. ...............109......... ......










LIST OF TABLES


Table page

2-1 Number of farms by farm size, Coast and Sierra regions, Ecuador. ........._... ..............44

2-2 Credit access, type of credit and operational farm size .............. ...............44....

2-3 Mean loan terms by credit sector (all farm sizes) ......___ .... ... .__ ......._._.......4

2-4 Agricultural labor productivity and operational farm size............... ...............44..

2-5 Household heads' primary activity and household' s main source of income by
operational area ........._ ...... .___ ...............45....

2-6 Mean land productivity by category of farm size ......__.............. ........_. ........45

2-7 Farm size distribution of land tenants ........._._. ...._. ...............45.

2-8 Variable definition for household income per capital and credit equations .......................46

2-9 Summary of explanatory variables (income and credit regressions) .........._... ..............47

2-10 Credit regressions for the probability of obtaining credit and the amount of credit..........48

2-11 Household income per capital regression .............. ...............49....

3-1 Mean and median quasi-fixed factors ..........._._ ....._._ .. ...............65..

3-2 Classification of owner-tenant households by category of owned farm size.....................65

3-3 Credit constrained households by owned farm size............... ...............65..

3-4 Summary of variables (land reservation value equation) ..........._ ..... .. .............65

3-5 Returns to fixed factors equation (quadratic function) .............. ...............66....

3-6 Log of the land reservation price equation ........... _......__. ...._.._ ..........6

4-1 Farm size and land sales by owned land category .............. ...............92....

4-2 Incidence of land rentals (landlords) by owned farm size category .............. .................92

4-3 Mean and median statistics of variables in Equation 4-1 ................. ............... ...._..93

4-4 Farm size and land purchases by owned land category (prior to purchase) ......................94

4-5 Forms of land acquisition by gender............... ...............94.










4-6 Summary of variables in Equations 4-2 and 4-3 ................. ...............95........... .

4-7a Land rented in by category of land owned............... ...............95.

4-7b Choice of rental agreement by category of land owned ......... ................ ...............96

4-8a Multinomial logit regression results (owners' decisions between farming, selling or
renting out) ................. ...............96.................

4-8b Multinomial logit regression results (owners' decisions between farming, selling,
renting under fixed-rent or under shared-rental contracts) .............. ....................9

4-9 Censored Tobit regressions (amount of land rented-out and sold) ........._..... ...............97

4-10 Logit for probability of purchase and censored Tobit for amount of land bought ............98

4-11 Logit for probability of renting-in and censored Tobit for amount of land rented........... .98

4-12 Multinomial logit for probability of renting-in ................. ...............99........... ..

A-1 Primary activity of female household heads by farm size ................ ............ .........104










LIST OF FIGURES

Figure page

2-1 Mean labor productivity by farm size ........... _......___ ...............49

3-1 Shadow land values............... ...............67.

3-2 Land reservation prices per hectare and non-price rationed households ...........................68









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

HOUSEHOLD INCOME, LAND VALUATION AND RURAL LAND MARKET
PARTICIPATION IN ECUADOR


By

Maria Jose Castillo

August 2008

Chair: Carmen Diana Deere
Cochair: Ramo~n Espinel
Maj or: Food and Resource Economics

This research provides an economic analysis of agricultural land access at the household

level and its relationship with rural markets and poverty in Ecuador. We find that land inequality

and land market imperfections have a direct effect on household income per capital and that there

is a synergy between these and imperfections in the labor and credit markets, which magnify the

effect of land inequality on rural household income. In addition, the presence of multiple market

imperfections intensifies the quasi-fixity of factors other than land, which affects the contribution

of land to profits and land values. The labor advantage of small farmers explains the remarkable

difference in reservation prices per hectare between small and medium and large farmers.

However, this effect is reduced for credit constrained households. Lack of land titles is not found

to discourage investments in land or to cause land values to be smaller than for households with

tiled land.

Consistent with these findings, we also observe that the demand for land by small farmers

is significantly larger than the supply of land by large landowners both in the land sales and

rental markets. Small farmers are found to be more active than larger farmers on both sides of the

land markets and sharecropping arrangements are found to be especially common among the









land poor. Land titles have a significant and positive effect on the likelihood to sell and similarly,

credit access on the likelihood to purchase and rent in land.

We conclude that, given the difficulties that prevent desired land transfers from large

landowners to the rural poor, it seems improbable that the market be able to achieve an optimal

distribution of landownership without assistance from the government. Also, that for the

potential benefits for rural development of increased land access to be realized, such an increase

must be accompanied by better access to services so as to improve the competitiveness of the

rural poor. Policies regarding the liberalization and stimulation of land rental markets and

increase in the supply of credit in the rural sector are recommended.









CHAPTER 1
INTTRODUCTION

Latin American countries, including Ecuador, are known for their severe income and land

inequality. This explains the persistent interest by the region's governments as well as

international development organizations in land redistribution and in enhancing land productivity

in Latin America. In Ecuador, according to the 2000 agricultural census, the Gini coefficient for

land was 0.8, the same as for Latin America as a whole. Land reform in Ecuador, which took

place during the period 1964-79, did little to improve land distribution. More importantly, access

to land via the rental market is very limited as well. According to the agrarian census, only two

percent of the farms are under fixed or share-rent tenancy and 16% are under mixed tenancy

(owner occupied combined with leasing or sharecropping). This is due in part to current

Ecuadorian legislation which impedes the free development of land rental markets.

Though agriculture remains an important contributor to national income and a source of

employment for about 30% of Ecuador' s working population, agricultural policies have been

deficient and unstable. In 2000, about 70% of the country's rural population earned incomes

under the poverty line. It is commonly argued that an important reason for rural poverty is the

limited access to land of the rural poor. On the one hand, large landholdings do not use their land

intensely enough so as to generate sufficient employment opportunities for the resource poor. On

the other hand, poor landowners must exploit their land more intensively than environmentally

desirable, which worsens soil degradation and lowers productivity. Furthermore, not only is land

ownership and access to land unequally distributed, but access to capital, technology and product

markets is as well.

The land market also suffers from segmentation, where the rich trade land among the rich

and the poor among the poor and where ethnicity and kinship play an important role in land









transfers, especially in the Sierra (Lambert and Stanfield, 1990). Consequently, land, credit and

other market imperfections (as in the labor market) affect farm income generation and land

prices, finally determining who can participate in the land market (buying, selling, renting in or

renting out). In turn, low farm income and high land prices for the poor strengthen market

imperfections and inequality.

Since the 1994 Land Law was approved land redistribution efforts have been left to the

market. Given current restrictions on renting land, and the market imperfections just described,

how well can the land market perform this task? This dissertation explores this question through

the study of a national household survey data that covers all coastal and highland provinces of

Ecuador.

Obj ectives

The obj ectives of the dissertation are a) to understand quantitatively the role of land

inequality and rural market imperfections on rural poverty, through an analysis of the effect of

these factors on the level of household income; and b) to identify the key variables that explain

the persistence of land market imperfections and inequality.

Research question 1: What is the effect of land inequality, land rental market restrictions,

and holding untitled land on capital access and rural income generation?

Research question 2: In the survey year, what was the role of the land sale and rental

markets (fixed or share tenancies) in land distribution and access?

a. To what extent do land reservation values reflect land quality and productivity, as
opposed to inefficiencies in land and related markets (credit, labor, technical assistance)
or other non-productive factors (such as holding land for status)?

b. What variables determine participation in the land sales and rental markets and the extent
of participation?










Hypotheses

Hypothesis 1: Market imperfections and land rental market restrictions sustain land

inequality and consequently rural poverty in Ecuador.

a. In Ecuador, farm size affects household income directly and indirectly through its effect
on credit access and labor allocation.

b. Land titles contribute to farm income primarily by facilitating access to credit.

c. Insecurity of property rights and restrictions on renting land out contribute to
segmentation in the land rental market, hence limiting the amount of land the rural poor
can access and consequently contributing to rural poverty.

Hypothesis 2a: Land reservation values are negatively affected by restrictions in the credit

market, hence lowering the competitiveness of the rural poor in the land market.

a. Provided that small farmers are more productive (higher value product) per unit of land,
the contribution of land to restricted profits (shadow land values) is decreasing in
operational area.

b. Given credit market imperfections, land reservation prices per hectare are lower for credit
constrained households.

Hypothesis 2b: Large landowners do not make land available though sales or rentals to the

land poor.

a. Due to land sales after the agrarian reform, and a likely process of reconstitution of
latifundia (Jordan, 2003), small farmers are more active in the land market as sellers and
large landowners as buyers.

b. The land rental market is friendlier than the land sales market for the rural poor in
Ecuador.

This study is divided into three main chapters. In Chapter 2 we test hypothesis 1 through

the use of a theoretical model which shows the effect of one more unit of land on household

income when there are multiple market imperfections. Descriptive and econometric analyses add

to the discussion and corroborate such effects. Hypothesis 2a is tested in Chapter 3 by analyzing

the configuration of land values. First, this chapter presents the estimation of a restricted profit

function and land shadow values. It then categorizes households into credit constrained and










credit unconstrained and uses these pieces of information in the estimation of land reservation

prices per hectare. Graphic analysis helps understand how land values per hectare vary with farm

size. Chapter 4 studies hypothesis 2b using descriptive analysis of household participation in the

land sales and rental markets and econometric estimations which highlight the variables that

influence the likelihood to sell, purchase, rent out or rent in land (as well as the amount of land

involved in each type of transaction). Here we analyze the role of the land market as a channel

for land redistribution. Finally, Chapter 5 offers general conclusions as well as policy

recommendations.










CHAPTER 2
THE IMPACT OF LAND INEQUALITY ON ECUADORIAN HOUSEHOLD INCOME

Land Problems and Rural Poverty

According to the last agricultural census (2000) in Ecuador, the Gini coefficient for land

was 0.8 (with 1.0 being equal to perfect inequality), similar to that of Latin America as a whole,

the region with most unequal land distribution in the world. The agricultural census shows that

64% of the total 843,000 agricultural production units in Ecuador are of less than five hectares in

size and farm only 6.3% of Ecuador' s total cultivable land. On the other hand, 6.4% of all

productive units each hold 50 or more hectares for a total of 61% of the agricultural land.

Land reform in Ecuador, which took place during the 1964-79 period, did little to improve

land distribution (Otafiez et al., 2000; Chiriboga and Rodriguez, 1998).2 More importantly,

access to land via the rental market is very limited as well. According to the agricultural census,

only 2% of the farms are under Eixed or share-rent tenancy and 16% are under mixed tenancy

(owner occupied combined with leasing or sharecropping).3 This is due in part to current

Ecuadorian legislation which prevents the free development of land rental markets.4

Though agriculture remains an important contributor to national income and a source of

employment for about 30% of Ecuador' s working population, agricultural policies have been

defieient and unstable. In 2000, about 70% of the country's rural population earned incomes

SHayami and Otsuka (1993) show that the Gini coefficient for operational farmland distribution in Latin America is
higher than 0.8, much larger than the coefficient for developing countries in Asia. Also, de Ferranti et al. (2003) note
that income as well as asset inequality is higher in Latin America and the Caribbean than in Asia, Eastern Europe,
and the 30 countries of the Organization for Economic Cooperation and Development. Average Gini coefficients
from 1966 to 1990 (obtained by Deininger and Olinto, 2000) are 0.81 for Latin America while those for the Middle
East, North and Sub-Saharan Africa, and East and South Asia are all lower than 0.7.

2 Chiriboga and Rodriguez (1998:16) note that, compared with the agrarian reform experience of other countries in
the region, Ecuador is among those with the least redistributive results.

3 IH COntrast, in Asia some 20 to 30% of the land is rented, in the United States, 40%, and in Belgium, 67% (FAO,
2002).

4 Legal and normative types of limitations in the land rental market are addressed in Section IV below.










under the poverty line. More specifically, the 1998-1999 Living Standard Measurement Survey

shows that 50% of agricultural households in the sample had an annual income equal to or

smaller than $1,300; 75% reported incomes smaller than $2,500 and 99% indicated incomes of

less than $15,000. The average annual income of agricultural households was close to $2,000,

which represents only 46% of the mean annual income of the total sample of households (5,8 16

households).' Since the agricultural portion of the sample (1,898 households) represents mostly

non-urban households (90%), this is an indicator that rural poverty is more severe than urban

poverty,6 a finding that is common in developing countries.

It is commonly argued that an important reason for rural poverty is the limited access to

land of the rural poor. On the one hand, large landholdings do not use their land intensely enough

so as to generate sufficient employment opportunities for the resource poor. On the other hand,

poor landowners must exploit their land more intensively than environmentally desirable, which

worsens soil degradation and lowers productivity. Furthermore, not only is land ownership and

access to land unequally distributed but access to capital, technology and product markets is as

well.

In addition, the land market in Ecuador suffers from segmentation, where the rich trade

land among the rich and the poor among the poor and where ethnicity and kinship play an

important role in land transfers, especially in the Sierra (Lambert and Stanfield, 1990).

According to the literature, land market segmentation is encouraged by land insecurity or little

protection of property rights (Marcours et al. 2005; FAO, 2002) and by high effective land prices

5 The year of our survey data (October 1998- September 1999) represents a time of severe economic crisis in
Ecuador, just before the 'dollarization' of the economy (which officially took place in January, 2000). Yet, that
should not be considered an abnormal year in terms of poverty since poverty had already been on the increase and
continued increasing in the following years.

6 Similarly, a FAO country profile for Ecuador based on year 2004 reports an agricultural per capital GDP
(agricultural GDP/agricultural population) that represents only 40.5% of national per capital GDP.









for the poor, beyond the productive ability of the land, driven by imperfect capital markets and

the existence of high transaction costs in the land market (Carter and Salgado, 2001; Carter and

Zegarra, 2000). In addition, low farm income and high land prices for the poor strengthen market

imperfections and inequality. As argued by de Janvry et al. (2001), under multiple market

imperfections like these, improving land access for the poor can improve both welfare and

efficiency.

The obj ective of this chapter is to understand quantitatively the role of land inequality on

the level of rural household income, at the same time as we explore the hypothesis that market

imperfections and land rental market restrictions sustain land inequality and consequently rural

poverty in Ecuador.

The development literature has already addressed in different ways the effects of farm

size and unrestricted land rental markets on farm income. This has been empirically analyzed in

several countries of the developing world; however, the results tend to vary from country to

country, or even within the same country, depending on the variables included in the analysis.

Moreover, previous studies of these issues in Ecuador have been only partial, limited to certain

sub-regions or variables. This study, which covers a sample of farm households from all

provinces of the coastal and highland regions of Ecuador (an area that represents 77% of

Ecuador' s cultivable land), will provide a comprehensive analysis of these issues.

This chapter is organized as follows: First, we summarize the importance of market

imperfections in household utility maximization. Then, in order to test for the existence of a

relationship between farm size and 1) credit access, 2) value product per unit of labor, 3) labor

allocation and 4) value product per unit of land, we perform pair wise analyzes based on our

household data. Subsequently, we provide a brief description of the land rental market situation









in Ecuador and its possible influence on poverty. Then, we model the effect of additional land on

income for an average agricultural household, followed by an econometric estimation of

household income per capital to test for the effect of farm size and credit access among other

relevant variables. We offer conclusions in our last section.

Our results suggest that land inequality and land market imperfections have a direct effect

on household income per capital but also that there is a synergy between these and imperfections

in other markets such as credit and labor, which are essential for agricultural production and

productivity. These imperfections magnify the effect of land inequality on household income.

Multiple Market Imperfections and the Household Income Problem

The agricultural sector of developing countries suffers from multiple market imperfections,

including the credit, insurance, labor and land markets. Development economists (Singh, et al.,

1986; Bardham and Udry, 1999) have observed that multiple market imperfections invalidate the

classical profit maximization approach used in order to find (or understand) optimum input

allocation of rural households.

More specifically, models of profit maximization assume that production decisions are

independent of consumption decisions such that input choice depends only on input and output

prices and the available technology (Bardham and Udry, 1999). Under this condition, the farm

household's problem is separable and can be solved recursively: production decisions are made

first (profit is maximized) and consumption decisions afterwards (thus utility of consumption can

be maximized subj ect to a budget constraint that includes taking maximized profits as a given).

This implies that production decisions affect consumption decisions but not vice versa. In other

words, preferences (e.g. between consumption and leisure), household endowments (e.g. assets

and labor), non-farm income and prices of consumption commodities do not affect production

decisions (Singh, et.al., 1986, Bardham and Udry, 1999).









This structure, however, applies only when markets are complete or when there is only one

market imperfection, not when there are multiple market imperfections (Bardhan and Udry,

1999). In order to reach equilibrium, households must equate demand and supply for each

commodity. The non-existence or incomplete presence of markets impede this equilibrium to

happen at market prices, instead it happens at what are called virtual or shadow prices which are

different from market prices and are endogenous to the household (Singh et at, 1986).

Virtual prices and consequently the farmer' s maximization problem will be a function of

household endowments, such as land and family labor; market prices; off-farm labor market

characteristics and non-farm income, among other factors. Therefore, the appropriate agricultural

household model under multiple market imperfections is that which jointly considers production

and consumption choices (Bardham and Udry, 1999).

According to this analysis, land inequality or rather an improvement in land distribution

would influence input choice (including allocation of family labor), hence having the potential of

affecting farm productivity and household income as a result. The model below develops this

idea following Finan et al. (2005); first, we perform pair wise descriptive analyzes with our

household data in order to observe if and how significantly farm size affects credit access, labor

allocation and labor and land productivity in the case of Ecuador.

Statistical Analysis

We use data from Ecuador' s Living Standard Measurement Survey (LSMS) 1998-99

provided by the National Institute of Statistics and Census of Ecuador, in order to empirically

observe the effect of land access on credit and farm labor. The sample includes 5,816 urban and

rural households from the Coast and Sierra regions of Ecuador; 1,898 observations of

agricultural households are used in this chapter. Relevant sections of the survey include

questions on economic activities of the household members, credit access, land tenure,










agricultural production, farm labor, variable input expenses, and ownership of machinery and

equipment as well as household demographics. Here, we perform pair wise analyses of credit

access, labor allocation and labor and land productivity with respect to farm size.

The analysis below will classify farms in four categories based on farm size. The first

category consists of operational holdings of less than 1 hectare. These are considered minifitndios

since such small farm sizes hardly allow for the subsistence of a household. These farms are

treated as a separate category here given their predominance in the sample (the national agrarian

census also reports these farm sizes as a separate category). The next category, farms of one to

less than five hectares are still considered small and, as noted by Lopez and Valdez (2000), "If

not irrigated and intensively farmed ...this amount of land cannot support levels of consumption

above the extreme poverty line without other sources of income (Lopez and Valdez, 2000: 203)."

Farms of five to less than 40 hectares are regarded as medium size given their higher

probability of being medium capitalized units, that is, units belonging to farmers who have been

successful in agriculture and have been able to accumulate land (or access more land) and other

assets over the years. Finally, farms larger than 40 hectares are treated as large. Table 2-1

summarizes this information.

Similar to the data gathered by the national agrarian census, Table 2-1 shows that the

largest category is made up by minifitndios and that the maj ority of farm households operate less

than 5 hectares. Moreover, the median operational holding is less than 2 hectares.

Of the total number of farms in our sample, 73.8% are reported as owner-operated; 15.5%

are partially owner-operated, and 10.6% are held by tenants only.










Credit Access

The dependency of credit access on land wealth is a well known constraint in the

developing world where dualistic structures7 characterize the rural sector (Bardhan and Udry,

1999; Carter and Zegarra, 2000; Feder and Feeny, 1993). Besides the fact that collateral is

usually necessary in order to access the formal credit market, and that land is the most desirable

type of collateral given its characteristics, land ownership is a sign of economic -and at times

political- power which facilitates market immersion and participation (de Ferranti et al., 2003).

We explore the existence of a relationship between credit access and farm size in the case of

Ecuador. Table 2-2 shows the proportion of farm households classified by operational area who

obtained credit for a positive interest rate.8 The proportion of loans received from the formal

sector and those from the informal sector are also reported together with the respective credit

amounts and interest rates.

The credit variable is total credit received by the household, which includes credit for

agriculture, for a family business and/or for consumption (purchase of durable goods, house

building/remodeling, sickness, etc.). The reason for including all types of credit received by the

household is that since credit is fungible it can be used on any household need regardless of the

purpose for which the loan was obtained. Besides, ownership of/access to land (which is our

focus here) is a signal to lenders as to how much debt responsibility a household can acquire.

Thus we expect to find a relationship between farm size and credit access even if we include





SA dualistic structure in the rural sector refers to low productive small family farms coexisting with capitalist
farmers who hire labor and where the mobility of farm operators between the two sectors is severely limited (Berry
and Cline, 1979; Bardhan and Udir, 1999).

SHouseholds who received credit for a null interest rate are omitted for purposes of this analysis since such cases
often involve small loans provided by relatives or friends, or credit received in-kind by input suppliers or NGOs.









loans for family business or consumption. The interest rate (r%/) is the average nominal interest

rate faced by the household including all types of credit.

The chi-square statistic for the hypothesis of independence between operational farm size

and credit access (null hypothesis) reveals that there is a statistically significant relationship

between the two variables (the null hypothesis is rej ected at 5% level of significance). The null

hypothesis is also rej ected (at 10% level of significance) when owned farm size is used instead of

operational farm size.

The same test was performed for the case of formal credit, in which the independence

hypothesis between farm size and credit access was rej ected at a 10% level of significance. We

also tested the hypothesis for access to informal credit but this time there was a failure to rej ect.

This result suggests that, as expected, non-institutional lenders pay less attention to farm size

than formal lenders since the former tend to be much more familiar with their borrowers, hence,

facing lower levels of imperfect information.

Also, statistical analysis of the relationship between category of farm size and the type of

credit obtained (formal vs. informal) reveals that such a relationship is significant for

minifitndista~s (5% significance) and small farm sizes (1% significance), with the odds of getting

informal credit being higher than the odds of accessing formal credit.9 For medium and large size

farmers formal and informal sources of credit are more equally accessible (and/or preferred) than

for minifitndista~s or small farmers.

In addition, when analyzing the difference in the amount of credit obtained from formal

and informal sources, using a t-statistic we find that, although the amounts of formal credit are

greater for all operational sizes, the difference is only statistically significant for the small size

9 The data indicate that a minifimdista is 75% more likely to get informal credit than formal credit. The likelihood
for small farmers is 164%.










category. However, taking all sizes together (Table 2-3), the mean dollar amount of formal credit

is significantly larger than the mean dollar amount of informal credit.

Analysis of the interest rates reveals that, except for the large farm size category (Table 2-

2), informal credit interest rates are significantly higher than formal credit interest rates. This is

in line with what was expected given the theory and typical empirical findings (for example see

Andersen and Malchow-Moller, 2006).

A somewhat intriguing finding in Table 2-3 is that only 20% of all formal loans required

real estate as collateral (compare this to 58% in Per-u in 1997 as reported by Guirkinger and

Boucher, 2005). This, however, can be explained by observing the structure of the formal credit

market in our sample. The bulk of formal credit is offered by private banks (33%) and

cooperatives and associations (49%) and the rest by governmental institutions (11%) and NGOs

(7%). The latter institutions typically not require borrowers to put real estate as collateral.

Similar is the case of cooperatives and associations. Finally, although private banks would be

expected to act differently than the other lenders, asking for valuable collateral such as real

estate, the evidence indicates that the loans offered by banks in the rural sector are in general

small compared to those offered in the urban sector (Espinel, 2002); this could explain the little

need for this type of collateral.10 This result together with our findings of a statistically

significant relationship between farm size and credit access suggests that land ownership is not

necessarily functional as collateral for formal credit but it is also a sign of economic power

which facilitates credit access.







'O The average amount of credit received from private banks in the sample is US$2,143.









Value Product per Unit of Labor

Evidence in developing countries has also shown that there is a direct relationship between

farm size and value product per unit of labor (usually referred to as labor productivity). Our data

conform to what is expected (Table 2-4).

While all farm sizes reported the use of non-remunerated labor, medium and large farmers

hired a significantly larger amount of labor than minifundista~s and small farmers (Table 2-4).

Larger amounts of hired labor reflect the capitalist nature of medium and large farmers, which is

manifested in higher labor productivity. Capitalist farmers hire labor up to the point where

marginal labor productivity equals the wage rate, while traditional family farms usually have

larger amounts of labor per unit of land which, given labor and credit market imperfections, they

must allocate less efficiently to the farm (Berry and Cline, 1979).

More specifically, since moral hazard and hence the need for labor supervision is not an

issue when using family labor while it is when hiring labor, family labor tends to be more

productive than hired labor (Binswanger et al., 1993); however, the presence of imperfections in

the labor and credit markets (i.e. unemployment and credit rationing) cause small farmers to

make a less efficient allocation of labor to the farm compared to larger farmers, resulting in

lower value product per unit of labor for small farmers.

The t-tests of mean differences indicate that the most significant [consecutive] difference

in productivity occurs between medium and large size farmers (10% significance). However, the

differences in productivity between a minifundista and a medium size farmer and between a

small and a large farmer are highly significant (1% significance). Differences in mean labor

productivities for the four different categories of farm sizes can be better observed in Figure 2-1,

which shows a clear increase in mean labor productivity as farm size increases.









Labor Allocation

Table 2-5 shows the distribution of the primary activity of household heads and the

composition of household income by agricultural and non-agricultural sectors for each category

of farm size. 1 Table 2-5 illustrates that as farm size increases so does the proportion of

household heads who primarily work on-farm. Similarly, considering total household income, it

is more likely that farming is the main source of income for the household as farm size increases.

Also, both minifimndista~s and small farmers rely more heavily on wage income (either from the

agricultural or non-agricultural sector) than medium and large farmers. Our data thus suggests

that as farm size increases so does the importance of the farm business for the household,

resulting in a higher level of income obtained from agriculture (compared to other sources of

income of the household).

Taking into account the sex of the household heads, 16% of them are women. They are

over-represented among minifimdista~s and under-represented among small and medium farmers

(Table A-1). Of the household heads who are not economically active, these are slightly more

likely to be female rather than male (53 vs. 47%). Also, female heads are more likely to declare

agricultural self-employment as their primary activity than male heads (55 vs. 45%). The female

heads most likely to declare agricultural self-employment as their primary activity are

smallholders and large farmers.

Value Product per Unit of Land

The hypothesis of an inverse relationship between farm size and the value of total product

per hectare (usually referred to as land productivity), often found in the developing world, is also





'' The decomposition of rural household income follows Corral and Reardon (2001).









tested here using pair wise analysis. Table 2-6 presents the mean land productivity for each

group of farm size.

The t-tests of mean differences in land productivity show that the difference in mean value

product per hectare between farm size categories is statistically significant at a 5% level of

significance, indicating that small farmers tend to exploit the land more intensively than large

farmers. This result was expected given that, as noted earlier, small farmers usually have a larger

labor to land endowment ratio than large farmers (Berry and Cline, 1979), hence output per

hectare tends to decrease with farm size.

The Land Rental Market

It has been argued that land ownership should not necessarily be the main obj ective in

order to improve the livelihood of the poor, but that access to land via other forms of tenure,

friendlier to the poor, should be earnestly sought too (de Janvry et al., 2001; Sadoulet et al.,

2001, and Currie 1981). In accordance with this idea, international development organizations

advocate for liberalization of the land rental markets in developing countries. This process could

be considered to be only half-way implemented in Ecuador because of legal as well as normative

types of limitations. 12 Among the legal limitations are the following:

a) Sharecropping, a form of tenure that, although regarded as inefficient by some authors,
has proved effective in overcoming imperfections in the capital and labor markets, was
abolished in 1970 by the "Law of abolition of precarious forms of labor in agriculture",
and it continues to be illegal.

b) Fixed-rent tenancy is allowed by the law but the law also contemplates the possibility of
prescription of the owner' s property rights under certain conditions, namely, 1) if the
landowner does not have a valid land title (properly registered), he/she can easily loose
the land to their tenant; or 2) even in the presence of a valid land title by the owner, if the
rental agreement is not in the form of a written contract properly registered, and if the
tenant has been the land operator for at least 15 years, the landowner' s property rights can
be prescribed.


12 Based on FAO (2002) study.










c) The Constitution still includes the possibility of expropriation based on the concept of the
social function of the land (art. 30).1

This legislation conserves the spirit of the agrarian reform era; it gives more protection to

tenants than to landlords and discourages supplying land to the rental market. In addition,

normative conditions that hinder the rental market are as follows:

d) High transaction costs discourage land title registration by landowners;

e) Proliferation of land conflicts (due to conflicting inheritance rights or lack of titling);

f) Lack of knowledge about the relevant legislation and abundance of corrupt lawyers who
increase the costs of legal processes;

g) Lack of formal enforcement of property rights; and

h) Unequal ethnic and socioeconomic relations

Points (d) and (e) reflect the impediments landowners face in order to obtain land title and

consequently to satisfy requirements of the law in order to engage in formal rental contracts.

Conditions (f) through (h) point to the high risk of losing property rights that landowners would

face if they decide to rent-out their land. This analysis would explain the low incidence of land

rental agreements in Ecuador reported by the agrarian census (see the first section of this

chapter) .

As a consequence, it has been found that land rental markets in Ecuador are segmented

(Lambert and Stanfield, 1990; FAO-COTECA, 1995; FAO, 2002). Landowners prefer tenants

they already know and can tr-ust and vice versa, hence the more economically powerful rent

among themselves and so do the poor, with the ethnic component being of importance too. 14

This alone suggests that the landless poor would at best be able to access land of the poor, which

13 Causes for expropriation are precarious forms of farm labor; technologies of production that endanger natural
resources; abandoning farming for more than two consecutive years; and lands that, while not fulfilling their social
function, face demographic pressure by peasant populations.

14 Lambert and Stanfield (1990) and FAO-COTECA (1995) note that land sale markets are also segmented by class
and ethnicity.










given the potential effect of farm size on poverty, would imply both that the amount of land they

could access is small and that they would continue to be poor. This implication is strengthened

by the previous analysis on credit access and labor productivity, which are influenced by farm

size.

Finally, our data (Table 2-7) indicates that the large maj ority (84%) of tenants (either

tenants only or farmers that combine farming their own and rented land) are either minifimdista~s

or small landholders. Given land concentration in the hands of large landowners, it is the land

poor who usually engage in the rental market. Although we lack data on whom the tenants in the

sample rented from, the findings about land segmentation of the studies already mentioned lead

us to expect that these tenants likely rented from small farmers.

The Model

The model we developed has its roots in the agricultural development literature (Bardhan

and Udry, 1999; Feder and Feeny, 1993; Finan et al., 2005) and has been adapted to fit the

situation faced by a representative farmer in Ecuador as observed in our 'statistical analysis'

section. The purpose of this model is to show the effect that an additional unit of land would

have on total household income.

This model illustrates the optimization process of an average agricultural household when

choosing productive inputs. Three market imperfections are considered here: incomplete land

markets, dependency of formal credit on land wealth, and unemployment. The first market

imperfection reflects the fact that -as argued in the previous section- land markets in Ecuador are

segmented, hence demand for land by the poor or marginalized segments can only be partially

satisfied. Under these conditions, land purchases by the average household and even access to

land via the rental market can be regarded as unimportant for the purpose of this model.










Therefore, following Finan et al. (2005), we ignore land transactions and consider land as

exogenous.

The second market imperfection occurs because costs of information and [consequently] of

repayment enforcement limit the ability of institutional credit to reach small farmers (Feder et al.,

1988). As a result, these farmers are rationed in the formal credit market, while this is generally

not the case for large farmers. Hence, different from perfect capital markets, borrower risk is not

necessarily the cause of borrower rej section (since institutional lenders do not have information

on how risky a specific small farmer is); instead, the borrower' s farm size is. This is supported

by the Eindings in our 'statistical analysis' section which show that there is a statistically

significant relationship between farm size and formal credit access and that the mean dollar

amount of formal credit is significantly higher than the mean dollar amount of informal credit.

Informal credit is thus ignored in this section.

Finally, unemployment is also a crucial problem in Ecuador. In 1998-1999 national

unemployment reached between 11 and 14%15 (Instituto Nacional de Estadisticas y Censos,

INEC), among the highest rates in Latin America and the Caribbean (ECLAC, 2005: Table

1.2.17). This is an important constraint for household income maximization since, together with

incomplete land markets, unemployment produces inefficiencies in the allocation of family

labor, which is reflected in low farm-labor productivity (Berry and Cline, 1979). Moreover,

segmentation of land markets is encouraged by imperfections in the credit markets since limited

access to capital cannot easily make up for poor households' liquidity constraints enough so as to

purchase larger units of land (Carter and Salgado, 2001).




15 Although 14% was the highest unemployment rate between 1990 and 2006, rates were in average 10% annually
between 2000 and 2006 (INEC).










The assumptions in this model are the following: 1) no land transactions or land rental

contracts; 2) hired and family labor are perfect substitutes, and 3) off-farm wage equals the wage

earned by hired farm labor. Two limiting conditions are of importance: a budget constraint and a

labor market constraint. The budget or cash constraint includes the additional limitation that the

amount of credit that can be borrowed depends on the household's total land endowment.

For our model, a household is expected to maximize returns to its fixed assets (land and

family labor) -income that will be used for consumption and savings. Income for the agricultural

household comes mainly from three sources: farm profits, off-farm wage income, and non-labor

income -which would include remittances, governmental transfers and income from investments-

minus the cost of borrowingl6

Max pQ(A, L, X; y) wH qX + wL,, + R iD(A)
L/,Lm,H,X

s.t. L = Lf +H

wH + qX < wL,, + R + D(A) (2-1)

L,, < M L,, +L ,= E

H>0

Where,

* Q= total farm output
* A = land endowment
* E = family labor endowment
* L,, = off-farm labor supply
* Lf = on-farm family labor
* H = hired labor
* M~= maximum amount of labor hours the labor market can accept from the household
* X = variable inputs
* y = household and farm characteristics (human capital, farm location, soil quality, etc.)


16 Total debt value cancels out in the objective function since the household repays the same amount of money it
receives; the only thing left to affect its income is the cost of borrowing.









* R = non-labor income
* D = amount of credit received by the household
* p, q, w, i = output price, input price, wage rate, interest rate


The Lagrangean is represented by the following equation:

r = pQ(A, L,X; y) -wH -qX +w[E Lf]+ R -iD(A) +
u, (w[E- L]+R +D(A)-wMH-qX +p,2[M -E+L,] 2)

Kuhn Tucker conditions:


,L = LL I1W +4 <02I; Lf > 0;' Lf[pQ!L I1W + U] = 0 (2-3)

S= p~x -q4- pq< 0; X>20; X[pL!x-q4- puq] =0 (2-4)
8X

S= pQH 1 uw <0; H > 0; H[pQH 1 uw] = 0 (2-5)
8H
y= w[E-L,]+R(+D(A)-wH -qX >0; pu, >; p(wu[E-L/]]+R+D(A) -wH-qXJ=0 (2-6)


=M~- E +L,. >0; pu, > 0; pu [M~- E +L,] i=0 (2-7)

Since the households being modeled are agricultural producers, Lf and X should be

positive .

Now, ifH > 0 -> pQH 1 w* u

From (2-3), pQLf 1 2~ H Lu 2- 2u H Le ef+~,~~,=p,-p Since

family and hired labor are assumed equally productive (assumption 2), then pu = 0 This implies

that when H > 0 the labor constraint is not binding (Lm, < M~); in other words, the household

would not be suffering from unemployment. In such a case and assuming the financial constraint

is binding," the indirect (or maximized) household income function (F) would be as follows:




"7 This assumption is based on the fact that credit is scarce in rural areas.









F = ~(D(p, q, w, i, A, E, R; y)

= pQ)(A,L',X';y)-H'w-X'q+w[E-L*,]+R-iwH' +qX' -w[E-L']-R) (2-8)

In order to observe the effect of one more unit of land on the optimal choices of inputs and

consequently on total household income, let us obtain the total derivative of household income

with respect to land.

dF dL'7 8H 8 X*
S= PL!A [LL W i [LH (+il +[LX q(1+ i)] (2-9)
dA f dA dA dA

This is equivalent to (from the first order conditions):

dF dL'7 8H 8 X*
S= PL!A WI1 ] +WI1 1 ] + [ i] (2-9)'
dA dA dA dA

Provided that pu i > 0 which is the case for credit constrained households, Is we see that

an increase in land endowments contributes to household income both directly through affecting

total production and indirectly through an effect in the optimal choice of farm labor, hired labor

and variable inputs.

On the other hand, ifH = 0 -> pQH 1 uw From (2-3) we have pu,w = peq w + p2 *

Replacing pu,w in the previous equation and solving for p2 We get u p2 H Le This

suggests p2, > 0, which implies that the labor market constraint would be binding (Lm = M~). In

other words, the household cannot send any more members to off-farm labor, therefore it must

use all its labor on-farm and neither would it need hired labor nor could it afford it. This result is

more appropriate for an average agricultural household in Ecuador, where the labor constraint is





's The Lagrange multiplier for the budget constraint, pu,, represents the shadow value of capital. For households who
are credit constrained, this shadow price is larger than the market interest rate (Cater and Salgado, 2001).









very likely to be binding, hence little or no hiring is done. 19 The maximized household income

would in this case be (again assuming pu, > 0):

F = A~(p, q, w, i, A, E, K, y)

= pQ(A, L'7,X*; y) -q[ + i]X + w[ + iM + [1+ i]R (-0

The effect of an increase in the land endowment would be

dF; dLf ,8X *
= ~rpQA LBa X [1+i]iA (2-11)

dA BA dA



From the family labor first order condition we can see that ifpu, = 0 and p2, > 0 ->

p2, L p > 0 -> w > pQ~ Hence, given the labor market restriction, the productivity of

on-farm labor is smaller than off-farm wage labor (this is what causes no hiring to be chosen).

Because of the labor market imperfection then, even if capital markets were perfect, the shadow

value of land for the household would be greater than simply the direct effect of land on farm

product.

Now, if both pu, > 0 and p2, > 0 u ~ u -> p2 1 > 0 -> w(1 + u, ) > peq That is,

labor productivity is smaller than the wage rate times one plus the shadow value of capital. Thus,

one more unit of land would have an even larger effect on household income than if credit

markets were not constrained.

In Equation 2-7 we see that one of the ways in which one more unit of land will contribute

to household income is by allowing it to relocate labor between on- and off-farm activities

(similar to what was observed in Table 2-5), however this effect will remain smaller than

19 Our data shows that less that 30% of small farmers (less than 5 hectares) did any hiring in their farms; less than
3% used more hired than non-remunerated labor, and less than 1% used only hired labor.










w(1 + u, )-as long as the labor market constraint is binding (pu, > 0 ). In other words,
dA

inefficiency in labor allocation will continue given the labor market imperfection. However,

since L,, = E Lf the more A increases, the smaller the labor market constraint for the

household (the constraint could go from binding to non-binding) and the closer

pL,f to w(1 + p,) Hence, increases in the land endowment should improve labor allocation and,

consequently, productivity (assuming that farm labor skills are standard for all family members).

Our results therefore suggest that imperfections in the credit, land and labor markets affect

farm labor and input allocation, inducing low productivity and hence worsening rural poverty. In

turn, a poor, low productivity farmer has limited access to credit and land markets. Since this

type of producer makes up the great maj ority in rural Ecuador, inequality in the ownership of

land (and lack of access to land in general) is therefore strengthened and so is poverty.

Additional land alone would not necessarily turn smallholders into efficient, modern

entrepreneurs as many land reform experiences across Latin America have shown. In order to

raise agricultural incomes, reforms in complementary markets are essential.

In the next section, we proceed to estimate the effect of farm size, credit and labor on

household income per capital.

Household Income Per Capita Estimation

From the previous sections we see that land is expected to affect household income both

directly and indirectly through its effect on credit access, labor and input allocation. Hence, we

need to deal with an endogeneity problem when attempting to estimate the effect of these

variables together with farm size on household income. In order to perform a consistent

estimation, two-stage least squares (2SLS) estimation is used, where a credit regression is










performed first, followed by a household income regression. In the household income estimation

a prediction of the credit variable from a censored Tobit is included and both the value of inputs

and intermediate assets eliminated. This is because credit would have been likely used in part for

the purchase of non-labor inputs (fertilizers, seeds, etc.), machinery and equipment. In addition,

we run a probit regression for access to credit prior to our Tobit analysis with the purpose of

confirming what variables should be included in the latter.

The system to be estimated is the following (Table 2-8 defines each variable and Table 2-9

summarizes all variables included).

HIPC = P, +P, County +P, Periphery + P,Settlement +P, Disperse +P, Landeual

+P,Male + P, Age + P, Edu + P, AdultsPC +P, SizePC +P, SizePC + P, Tenant (2-12)

+P, CreditHatPC + A TechAsist + A %HiredLabor + A Offlnconze + P,Rnat + 5

Credit = a, + a,Province + a, Nonthban + a, AgCon + a, XCrops + a,Male + a, Age

+a, Edu + a, Size + a, Size + a, NP lots + a, Owner + a, %Title + a, Formal (2-13)

+a, Formal Title + a, Assets + a, Animals + a, Of~f~armnzh + a, NonLaborhz c + u

Given differences between the Coast and Sierra regions in climate, production and

agricultural land distribution (the smallest farm sizes are mainly found in the Sierra), two

systems are estimated, one for each region.

Like in Table 2-5, our income variable includes all sources of annual household income,

namely wage income, net monetary income from self-employed members (either in agricultural

or non-agricultural activities),20 Other sources of income such as rents, interests, pensions, etc.,

and non-labor income coming from remittances, governmental and/or non-governmental

monetary transfers.


"0 Net income from self-employed members has been calculated using individuals' reported estimate of annual
monetary income.









The variable farm size refers to operational land holdings. As noted earlier, because of

restrictions in the labor market, more land in operation implies more employment absorption of

family labor and more income possibilities. This is so regardless if the land is owned or rented-

in. Also, given results in our 'statistical analysis' section, this variable is expected to influence

credit access and the amount of credit obtained. The sign, significance and relative magnitude of

the effect of farm size on Equations 2-12 and 2-13 are the main focus of our analysis since they

will allow us to test for the effect of land inequality on poverty. On Equation 2-13, this variable

will indicate how limited small farmers are with respect to access to services and on 2-12, it will

show the development potential that larger operational holdings would have for individual

households.

The tenure variable is also of special importance in the household income equation. It will

gather whether or not land ownership makes a difference on household income compared to

tenancy. An aspect closely related to the importance of land wealth on access to credit is the role

of a land title. More specifically, if land ownership facilitates participation in the formal credit

market, then those who have title to their land should be preferred by lending institutions (the

interaction term between formal credit and title to land will help capture this effect). However, if

only farm size but not land title had a significant effect on credit access that could suggest a

rather indirect effect of farm size on credit access (Table 2-3).

Farm machinery and equipment (assets) ensure better productivity of land and labor and

therefore could influence credit access. In addition, machinery can be used as collateral for

credit. The value of owned animals is included too because empirical studies have shown that

farmers often use livestock as a form of savings in addition to their sometimes being means of










farm work. Specially for accessing informal credit, ownership of livestock or small animals can

be an implicit type of collateral21

The effect of household location is expected to be captured by several variables in each

region. Location in an urban or non-urban (periphery, settlement or disperse) sector represents

distance from maj or markets. Also, since land distribution and productive conditions differ by

county (Lambert and Stanfield, 1990; FAO-COTECA, 1995; World Bank, 2004), county

dummies are included in the household income equation (Equation 2-12).

For the credit equation (Equation 2-13), we added province dummies. The province with

mean income closest to the mean income of the Sierra is Cafiar and that for the Coast is Los

Rios; thus these provinces are chosen as a base for comparison in each region. We also included

a dummy indicating whether or not the household is located in an area of agricultural

concentration or in an area of concentration of export crop production. If the household is located

in an area of agricultural concentration, it is likely to have better access to productive services

(marketing channels, variety of lenders), hence having better possibilities of accessing credit.

Due to data limitations, agricultural concentration is assumed at the county level, more

specifically, if a household is located in a county in which over 50% of the land is being

exploited22 it is considered to belong to an area of agricultural concentration. Likewise, if a

household is located in an area of maj or production of export crops such as bananas, cocoa,

coffee or flowers, then it is considered as belonging to an area of export crop concentration.

In order to capture the effect of operational holdings distributed in more than one farm, the

variable NPlots was introduced in Equation 2-13. The fact of having two or more plots in

21 The word 'implicit' is chosen because in our sample there are no cases of animals explicitly pledged as collateral
for credit.

22 This includes fallow lands and shrimp pools and excludes the area of moorlands, mountains, forests and
infrastructure.










operation could have two opposite effects for farmers when trying to access credit: 1) the

possibility of benefiting from economies of scale is reduced, hence lenders might be less willing

to offer credit to a small farmer with several plots; and 2) the risk of loosing a harvest is reduced

by not having production concentrated in one location but distributed in two or more, which

might be attractive to lenders. The final effect of this variable on credit will depend on what type

of effect is stronger.

The land quality index is included in Equation 2-12 so as to capture the effect of the

agricultural potential of the land on household income. 23 This index varies by parish (smallest

political division of the territory). In addition, given the presence of plant diseases, pests, and the

predominance of traditional methods of production, those households with access to technical

assistance are expected to do better in household income.

The percentage of farm labor that is hired for a wage is also expected to be positively

related to household income since, as noticed in our 'statistical analysis' section, large portions

of hired labor are usually a sign that the farmer is of the capitalist type, who hire labor up to the

point where marginal productivity equals farm wage.

Also in Equation 2-12, having a source of off-farm income should contribute positively to

household income. For Equation 2-13, the amounts of off-farm and non-labor income (which

includes remittances) are incorporated as they often compensate for the lack of collateral for

credit provided by NGOs, cooperatives and associations. In those cases, households who can

show a steady income flow are likely to obtain more credit. Finally, household head's age,





23 Because the LSMS survey did not include questions on land quality, our index was formed based on information
at the district level provided by the Geographic and Agricultural Information System (SIGAGRO) Office of the
Ministry of Agriculture of Ecuador.









education and sex are usually important variables determining credit market participation and

household income.

Estimation results: The credit analysis for both regions indicates that farm size matters

not only for accessing credit but also for the amount of credit obtained (Table 2-10). In addition,

in the Sierra, other variables have a significant and larger effect than farm size on the probability

of obtaining credit. Location of the household in a non-urban area reduces that probability while

younger and more educated household heads are more likely to get credit.

Also, having more than one parcel increases both the likelihood of accessing credit and the

amount of credit received. This result seems to indicate that the second possible effect (discussed

earlier in this section) on credit access of having more than one farm is stronger than the first.

Hence, operating more than one parcel appears to work as a signal of risk diversification, that is,

it could indicate less risk of loosing all production if nature is unfavorable. In addition, compared

to the province of Cafiar, households in the provinces of Chimborazo, Cotopaxi, Imbabura and

Pichincha are less likely to obtain credit.

In the Coast, the value of farm animals held by the household exerts a slightly negative

effect on the probability of obtaining credit. However, since 95% of those asking for credit

actually obtained it, this result suggests that households holding enough farm animals seem to be

less likely to pursue a loan than households with few or no animals. Thinking of farm animals as

a form of savings, rural households in the Coast would prefer to sell animals in order to meet

their financial needs before asking for credit. Also, households in the province of Esmeraldas

have less probabilities of acquiring credit than those in Los Rios.

The effect of farm size on the amount of credit obtained by the household is such that one

more unit of land would increase credit by $18.30 in the Sierra and $24.60 on the Coast. The









dollar amount of credit obtained both in the Sierra and the Coast is significantly larger if it is

obtained from formal sources and more so if the borrower was a farm owner with a land title.

This suggests that the effect of land on credit is a direct rather than an indirect effect -as was

hypothesized earlier.

In the Coast, the level of valued assets contributes to accessing slightly larger amounts of

credit and being located in an area of concentrated agricultural production considerably increases

that amount. Younger and more educated household heads receive more credit in the Sierra, and

this is true also for households with larger off-farm incomes (although this effect is small). In the

Sierra, households in Cotopaxi, Loja and Tungurahua receive less amounts of credit than Cafiar,

while in the Coast it is again households in Esmeraldas who receive less credit than Los Rios.

Results from the second stage of the estimation procedure (household income per capital ,

reported in Table 2-11, suggest again that farm size is positive and statistically significant. One

more hectare of land per household member would increase household income per capital by $22

on average in the Sierra, which represents close to 5% of the mean household income per capital

in this region. The contribution to household income per capital of one more unit of land per

household member on the Coast is $43, that is, 9% of the Coast' s mean household income per

capital. Mean household size in each region is close to five people. Taking this into consideration,

an additional hectare of land would increase total household income by $110 in average in the

Sierra and $215 in average in the Coast. In addition, as expected, households that received more

credit per capital generate higher per capital income in both regions.

In the Sierra, location of a household in a rural area means it makes on average $406 less

per capital income than if it were located in a city. As expected, the largest negative effect on

income for this category and the most significant is for households located in dispersed rural









areas. Education of the household head is again positive and of importance, this time for both

regions. The effect of the number of adults per capital (interpreted as the inverse of the

dependency ratio) is positive and highly significant for both Coast and Sierra and, as expected,

so is the percentage of hired labor and the fact of having an off-farm source of income.

In addition, technical assistance increases household income per capital in the Sierra but it

is less significant in the Coast, perhaps due to the small number of households who reported

receiving this service. Finally, the regression results did not conform to our expectation that pure

tenant households would make less total household income per capital than landowners. Although

the sign of the effect is as expected, the significance is not.

Conclusions

This chapter has shown that land inequality and related market imperfections have a

statistically significant effect on rural household incomes. Farm size increases the probability of

credit access and the amount of credit to be obtained, which also increases household income.

The total effect of farm size on household income is composed by a direct and an indirect effect,

through its influence on credit and labor allocation. The effect of labor market imperfections is

gathered by a positive and highly significant effect of the percentage of hired labor on household

income per capital. As explained in our 'statistical analysis' and 'the model' sections, land

inequality together with imperfections in the labor and credit markets (i.e., a labor supply that

exceeds the demand, and credit rationing) is what causes traditional family farms, who hire very

little labor, to have lower labor productivity and hence lower household income per capital.

On the effect of imperfections in the land market, our section on the land rental market

showed how these imperfections cause the rental market to be primarily chosen by the land poor.

Also, results from our 'household income per capital estimation' section showed that landowners

with title can obtain more credit than landowners without title and pure tenants. Therefore, if the









land rental market were liberalized and property rights better protected, together with reforms in

the credit market, the poor are among the ones that would benefit the most as they would be able

to experience increased land access.

We see then that land inequality, through its effect on related market imperfections, is an

important contributor to rural poverty. However, since unequal land access and imperfections in

the credit and labor markets form a synergy (because limited access to capital and low labor

productivity also contribute to poverty, hence limiting the probability and size of land

purchases), increased access to land needs to be accompanied by other related market reforms.
























Table 2-2. Credit access, type of credit and operational farm size
Received credit Formal* Informal**
Operational area Yes No Total ptf p $ r% p $ r%
Minifundio 124 666 790 15.7% 5.8% 686 66.31 9.7% 515 102.36
Small 117 579 696 16.8% 4.9% 1187 64.00 11.9% 329 107.20
Medium 76 287 363 20.9% 8.8% 1880 68.70 11.6% 1115 115.15
Large 7 70 77 9.1% 3.9% 8063 60.22 5.2% 6052 56.88
Total operators 324 1602 1926 16.8% 6.0% 10.7%
Jf Proportion of households in each category who received credit. Formal sources of credit: governmental
institutions, private banks, cooperatives, associations and non-governmental organizations (NGOs).
** Informal sources of credit: input suppliers, exporters, packers, individual lenders and relatives or friends.



Table 2-3. Mean loan terms by credit sector (all farm sizes)


Table 2-1. Number of farms by farm size, Coast and Sierra regions, Ecuador
Farm operators (owner-operator


and/or tenant)

40.8
36.4
18.9
3.9


Farm size
Minifundio (less than 1 ha.)
Small (1 to less than 5 ha.)
Medium (5 to less than 40 ha.)
Large (40 ha. and over)
Total


obs.
775
691
358
74


cum.%
40.8
77.2
96.1
100.0


1,898 100.0


%Requiring
real estate as
collateral
20.5%
4.2%


Mean
term
(months)
17
6


Number
of HH Mean $
127 1,256
216 651


Mean
annual r%
66.12
106.21


Mean
monthly r%
4.32
6.22


Source of credit
Formal
Informal


Table 2-4. Agricultural labor productivity and operational farm size
Operational Number Average value Non-remunerated Hired Total Mean labor
farm size obs.* product ($) labor days** labor labor productivity
minifundio 747 417 1,326 10 1,337 1.99
Small 678 1048 1,905 31 1,936 2.55
Medium 348 3417 2,099 145 2,243 3.41
Large 73 4141 2,027 317 2,345 6.80
Total 1,846
*Only farm households who had a positive value product (from crops and animal husbandry) are included.
**Includes both household and non-household members working for no wage. In the case of non-household
members, this is a common practice in rural communities where farmers exchange labor, hence avoiding hiring
wage labor.














Table 2-5. Household heads' primary activity and household's main source of income by
operational area


Minifundio


Small


Medium


Large
Head's HH main
main source of
activity income
% %
73 52
8 11
81 63


Head's
main
activity


HH main
source of
income


Head's
main
activity


HH main
source of
income


Head's
main
activity
%

70
9
79


HH main
source of
income

52
15
67


AGRICULTURE
Ag. self employed
Ag. worker
Subtotal
NON-AG SECTOR
Non-ag. self employed
Non-ag. worker
Subtotal
OTHER
Not economically active
or unemployed
Income from rents and
financial assets
Remittances & transfers
Toted


10
14
53 24


10
8
35 18


10
1
27 12


2
9
100 100


2
7
100 100


1
5
100 100


Table 2-6. Mean land productivity by category of farm size
Operational area Number obs.* Mean land productivity t statistic
Minifundio 747 8,351.57 -
Small 678 506.08 2.45
Medium 348 270.41 2.92
Large 73 64.04 1.96
Total 1,846
*Only farm households with a positive value product are included.


Table 2-7. Farm size distribution of land tenants


Tenants only
Obs. %


Owner-tenants
Obs. %
86 29
151 51
52 18
6 2
295 100


Total


Farm size
Minifundio
Small
Medium
Large
Total
% of total farmers


Obs.
184
236
71
6
497


37
47
14
1
100
26


98 49
85 42
19 9
0 0
:02 100


2











Table 2-8. Variable definition for household income per capital and credit equations
Variable Description
Household income per capital equation
HIPC Total household income per capital in US Dollars
County Dummy variable for each county in each region
Periphery Dummy for location of the household in the periphery of a city (base: urban
area)
Settlement Dummy for location of the household in a rural settlement (base: urban area)
Disperse Dummy for location of the household in a dispersed rural area (base: urban
area)
Landeual Index of agricultural potential of the land (includes slope, soil texture and
depth, ease of mechanization and irrigation)
Male Dummy for sex of the household head (base: female)
Age Age of the household head (ordinal variable)
Edu Years of schooling of the household head (ordinal variable)
AdudtsPC Number of individuals fourteen year old or older in the household
SizePC Farm size per capital (in hectares)
Tenant Dummy variable, 1 if the household is a tenant in all its land holdings (base:
owner)
CreditHatPC Tobit model prediction of credit dollar amount
TechAssist Dummy variable, 1 if the household received technical assistance
%HiredLabor Percentage of hired labor (based on total farm labor)
Offlnconze Dummy variable, 1 if household has any source of off-farm income
Rnat Dummy variable, 1 if the household has received any remittances
Credit equation
Credit Total dollar amount of credit received by the household (includes both
formal and informal credit but only cases with positive interest rates)
Province Dummy variable for each province in each region
Nonh Uban Dummy variable for location of the household in a non-urban area
AgCon Area of agricultural concentration (dummy variable, 1 if the household is
located in a county in which over 50% of the land is in production)
XCrops Area of concentration of export crop production (dummy variable, 1 if the
household is located in area of major production of major export crops:
bananas, cacao, coffee and flowers)
NPlots Number of plots owned or operated by the household
Owner Dummy, 1 if the household is owner of any portion of its land holdings
Formal Dummy variable, 1 if the loan received is from formal sources
% Title Percentage of landholdings with title
Fornzal*Title Interaction term, Formal times %Title
Assets Dollar value of assets (machinery, equipment, small productive instruments)
Animals Dollar value of farm animals
Of~f~armntc Dollar amount of off-farm income made by the household
NonLaborhic Dollar amount of non-labor income received by the household (includes
remittances and monetary transfers)














Obs.
1876
1360
516
179
1697
81
202
1414
702
458
1876
1584
292
100
722
723
331
362
1305
154
55
1876
1876
1674
202
1087
111
199
310
1830
1724
35
632
1421
1090
287


ep. Variable Household income per capital ($)
Sierra
Coast
Urban
Non-urban:
Location Periphery
Rural settlement
Disperse rural area
Agricultural concentration
Export crops
HH Adults (14 and older)
Male
Female
Age: 17 to 25 years old
Age: 26 to 45
Age: 46 to 65
HH head
Age: over 65
Education: zero years
Education: 1 to 6
Education: 7 to 12
Education: 13 and over
Size (hectares)
Number of plots
Farm Owner
Tenant
Land title
Received formal credit
Credit Received informal credit
Total credit amount ($)
Value assets ($)
arm wealth
Livestock ($)
Technical assistance
Hired labor
her variables Off-farm income ($)
Non-labor income ($)
Remittances


9%
91%
6%
10.9%
74.1%
23.5%
12.7%
3.02
81.6%
18.4%
5.3%
36.8%
39.0%
18.9%
20.8%
68.3%
7.6%
3.2%
3.89
2.0
91.0%
9.0%
64.1%
7.3%
10.9%
790.42
227.57
351.42
1.6%
30.1%
1,991.07
257.85
16.0%


11.8%
88.2%
0.0%
10.1%
78.1%
74.1%
55.2%
3.35
92.0%
8.0%
5.1%
42.5%
37.7%
14.7%
14.9%
71.8%
10.1%
3.2%
7.57
1.22
84.7%
15.3%
41.7%
2.3%
9.7%
855.34
486.76
292.04
2.5%
43.2%
1,759.39
210.41
13.3%


F



Ot


Mean or
frequency
486.03
72.5%
27.5%
9.5%
90.5%
4.3%
10.8%
75.4%
37.4%
24.4%
3.11
84.4%
15.6%
5.3%
38.5%
38.5%
17.6%
19.3%
69.6%
8.2%
2.9%
4.90
1.59
89.2%
10.8%
57.9%
5.9%
10.6%
803.61
299.8
335.69
1.9%
33.7%
1,923.57
244.01
15.3%


Sierra Coast
487.55 482.03


Variable


DI


Table 2-9. Summer
y of explanatory variables
(income and credit regressions
)












Table 2-10. Credit regressions for the probability of obtaining credit and the amount of credit
Explanatory Credit Sierra $ Credit Sierra Credit Coast $Credit Coast
variables Probit Censored Tobit Probit Censored Tobit
Azuay 0.082 197.253
Bolivar -0.150 -206.300
Carchi -0.289 -61.254
Chimborazo -0.626 ** -813.950
Cotopaxi -0.900 ** -480.558 ***
Imbabura -0.496 -831.628
Loja -0.248 -142.438 ***
Pichincha -0.489 -410.020
Tungurahua -0.271 -362.834*
El Oro -0.126 89.716
Esmeraldas -0.838 -489.137*
Guayas 0.199 189.723
Mlanabi -0.245 -156.020
Non Urban -0.343 104.663 -0.049 -109.625
Agcon 0.042 270.825 0.278 457.649 **
Xcrops 0.153 -158.539 -0.206 -98.238
Male 0.192 56.079 -0.045 6.025
Age -0.200 *** -165.633 *** 0.056 -21.790
Edu 0.302 *** 238.852 *** 0.077 -83.297
Size 0.026 20.772 0.062 ** 34.104 **
Size -0.0004 -0.322 -0.001 -0.640*
NPlots 0.186 *** 150.108 *** 0.158 -76.575
Owner -0.176 -165.450 -0.089 100.187
%Titled -0.002 105.718 -0.220 -13.122
Formal 1473.730 *** 1092.042 ***
Formal*Title 602.740 *** 813.476 **
Assets -0.00001 0.018 0.00003 0.033 **
Animals 0.00003 -0.060 -0.00038 -0.264
OffFarmlnc ($ 0.00004 0.056 *** 0.00001 0.021
NonLaborlnc($) 0.017 -0.009
Rmt -0.157 0.014
Constant -0.781 ** -1200.427 *** -1.772 ** -786.920
R2 0.34 0.66
*** Significant at 1%; ** significant at 5%; significant at 10%











Table 2-11. Household income per capital regression
Per capital HH Income Sierra Per capital HH Income Coast
Explnatry vnabest OLS (robust standard errors) OLS (robust standard errors)
Coefficient P>|t| Coefficient P>|t|
Periphery -408.379 0.019**-- -
Settlement -332.328 0.072* -95.994 0.602
Disperse -476.745 0.005*** -219.228 0.253
Land~ual -29.729 0.420 89.438 0.263
Male 1.044 0.978 55.588 0.321
Age -19.732 0.343 -38.875 0.119
Edu 159.395 0.019** 74.376 0.050*
SizePC 25.122 0.019** 69.902 0.001***
SizePC2 -0.391 0.092* -1.773 0.007***
AdultsPC 504.534 0.000*** 400.848 0.000***
Tenant -67.702 0.203 -62.124 0.168
%Hired labor 601.851 0.000*** 383.302 0.002***
CreditHatPC 0.531 0.030** 0.829 0.092*
TechAssist 237.380 0.099* 241.940 0.140
Offlncome 231.235 0.000*** 346.803 0.000***
Rmt -23.526 0.583 -43.865 0.304
Constant -565.125 0.002*** -355.581 0.271
Degrees of freedom 1288 467
R2 0.30 0.61
"f Also included in the regressions were 22 counties for the Sierra (16 were significant at 10% significance) and 20
for the Coast (only 3 significant). *** Significant at 1%; ** significant at 5%; significant at 10%





8.00
7.00-
6.00-
S5.00-
4.00-
.m3.00 -
S 2.00-
1.00-
0.00
minifundio small medium large


Figure 2-1. Mean labor productivity by farm size









CHAPTER 3
UNDERSTANDING LAND RESERVATION VALUES IN THE PRESENCE OF MULTIPLE
MARKET IMPERFECTIONS: THE ECUADORIAN CASE

Introduction

The presence of multiple market imperfections causes land reservation prices to differ

from the present value of a stream of residual returns to land. Rural areas in Ecuador are

characterized by land inequality, segmented land markets, incomplete credit markets, high

transaction costs inhibiting land titling, and dualistic structures in the labor market (Chapter 2).

All these market imperfections can have an effect on land prices causing these values to diverge

from the productive ability of the land.

Schultz (1945) explains how agriculture is an activity with a high share of fixed costs

compared to other sectors of the economy. Such additional Eixed costs represent factors of

production whose supply and demand are hard to adjust to macroeconomic conditions (quasi-

Eixed factors). Market imperfections such as those mentioned above further limit the

marketability of factors of production, hence worsening the quasi-fixity of factors. As a result,

the Classical (Ricardian) notion of residual rents -which regards land as the only Eixed factor in

agriculture- would misstate returns to land and therefore would fail to fairly represent land values

(Mishra et al., 2004).

In addition, imperfections in the credit market create capital constrained households to

have larger shadow capital rates (or discount rates) than unconstrained households, hence

reducing the present value of land for the former (Carter and Salgado, 2001).

In spite of its importance for land inequality and rural development, the study of the

formation of land values is not common in Latin America. This is partly due to the unreliability



SReservation prices are defined as the minimum payment a landowner would accept in exchange for his/her land.









of land price information in the local land registries -when this information is even collected.

Thanks to the Living Standard Measurement Surveys, however, information about land

reservation prices in Ecuador is available. This data allows us to empirically analyze the

configuration of individuals/households' land prices by examining the variables that affect an

individual's valuation of land.

The purpose of this chapter is to understand the factors that come into play when

individuals are asked to value their farmland. We would like to find out how closely land values

represent land quality and productivity, as opposed to inefficiencies in land and related markets

(credit, labor) or other non-productive factors (such as holding land for status). Similar to Carter

and Zegarra (1995), we recognize that we are dealing with a hypothetical question and so

answers need to be interpreted carefully. However, these answers offer a "first window in the

economics of land market competitiveness" in Ecuador (Carter and Zegarra, 1995: 15).

Land market prices are the result of buyer and seller interaction, the first having some -at

least implicit- limit price and the second a reservation price (Currie, 1981). In this chapter we

study only the formation of reservation prices; however, this should shed light not only on how

market prices are likely formed, but also on who is more likely to end up selling their land.

Data and Methodology

In the Classical tradition, returns to land are commonly measured as residual returns, that

is, after discounting variable production costs from farm income. However, in order for this

approach to fairly reflect land values, we would need to assume that all other factors of

production have the same value for all farmers, that is, that their values are equal to market

prices (Mishra et al., 2004).

In the context of developing countries, such as Ecuador, this assumption is not plausible

since land is not the only quasi-fixed factor in farm production. The presence of imperfections in









the credit, land and labor markets (Chapter 2) results in family labor being trapped on the family

farm and similarly, for intermediate farm assetS2 and farm animals,3 eSpecially for low-wealth

rural households. As a consequence, market imperfections produce shadow factor values to

differ from market prices and they dictate the efficiency of factor allocation. These prices then

affect agricultural profits and land values. Thus, the Classical measure of residual returns

misstates land prices if labor and other factors are discounted at market prices instead of farmers'

shadow values (Mishra et al., 2004).

An approach that would take all those factors into consideration is one based on a dual

profit function with a flexible functional form. We use data from the Living Standard

Measurement Survey carried out in Ecuador between October 1998 and September 1999, which

gathers household data on farm income, for households who harvested at least one crop and

provided land reservation price responses.

Restricted Profits

In our case, a restricted quadratic profit function fulfills our obj ective as it fits our data

well. The restricted profit function specification (Equation 3-1), which takes into account quasi-

fixed factors, is as follows:


TI = p, + C S X, + C C 7:,X, X,X, + k=11 kZk +-- =l 1 k=6,X,Zk


kC=1 =1qlZZkZ +B,Provinlce+B,Quanlity* A+ONP*A+ B,%Owne~d*A+ (3-1)
6, %Title K +0, Age + e






2 For small family farms especially, the acquired farm equipment is mostly the basic necessary for production, such
as tools and simple fumigation equipment, which have little value outside the family farm.

3 For poor households, farm animals are a common form of savings for times of need.










The symbol T represents returns to fixed (or quasi-fixed) factors Zk, Where k = operational

land holdings, family labor days, intermediate assets, and farm animals (Table 3-1 for average

and median measures). More specifically, TI is total value of crop production minus variable

costs including the cost of hired labor. X is a vector of output (temporary and perennial crops)4

and hired labor prices. It is assumed that the price of inputs such as fertilizer, pesticides and

seeds are the same for all farmers in a province, hence there would be no significant variability in

these prices and their effect can be assumed to be captured by the province dummies (Province).s

Intermediate assets represent household reservation prices of farm equipment which

includes mainly farm tools, fumigation pumps and animal plowing equipment. Also included are

water pumps and trucks (reported by less than 5% of the households in the data set) and animal

sheds, irrigation equipment, electric plants, tractors and sowing machines (reported by less than

1% of the cases). Farm animals include cattle, horses, pigs, poultry, among other farm animals

held by the household at the time of the survey, valued at the average selling price reported in the

data set (by households who sold animals) for each type of animal.

Additional variables in the specification which affect the contribution of land to profit

(land interaction effects) are land quality (Ouality); the number of plots (NP); and the

percentage of land owned of the total land operated (%(Arned = size owned/size operated).

Based on the concept of scale economies, for a household with more than one plot of

land, returns to land should in general be smaller than those for a household whose holdings are


SThe output prices consist of two Fisher's price indices, one representing temporary crop prices and the other one
summarizing perennial crop prices. See Castillo et al. (2007) about the methodology used in the replacement of
missing price observations for the formation of these price indices.

5 It would be superior to include district-level prices in the profit function; however such data is not available in our
data set (it was not collected in the LSMS survey).

b Land quality is measured by an index which takes into account slope, soil texture, depth and ease of mechanization
and irrigation.










contiguous. However, it is important to consider an Andean production strategy still practiced in

the Ecuadorian highlands which date from the time of the Incas. The optimizing strategy consists

of the exploitation of small, dispersed plots of land at different elevations with the purpose of

taking advantage of the different ecological levels offered by the geography of the Andes

(Alvarez, 1995). This strategy also spreads risks due to weather and disease.

Our data show that the mean number of land parcels in the Sierra is 1.9, while only 1.3 on

the Coast (the median is 2 parcels in the Sierra and 1 on the Coast). Also, 77% of the households

with less than 5 hectares (in total operational holdings) are located in the Sierra. Since 72% of

the households in our sample are from this region, the beneficial effect of the Andean strategy

could prevail in our results, hence making the returns to land larger, the greater the number of

land parcels held by the household.

It would generally be expected that the effect of the share of land farmed which is owned

would be positive on the returns to land, based on the hypothesis that owner-operators make

better investment decisions than tenants,' especially since over 70% of the owner-tenants in our

sample had shared-tenancy arrangements.8 Nevertheless, under pure land ownership, production

and price risk are totally internalized by the farmer and, in cases of poverty and restricted access

to credit, the risk bearing capacity of the farmer is usually lower than required in order to reach

efficiency in factor allocation.

In addition, our data show that 99.5% of owner-tenant households fully exploited their

owned holdings. Also, Table 3-2 shows that 49% of owner-tenant households owned less than 1

S20% of the landowner households in the sample were owner-tenants. Among these, there are two types of tenancy
arrangements: fixed-rent (cash payment) and shared-rent or sharecropping (with in-kind payments, a mixture of cash
and in-kind payments or payments with labor).

SClassical and Neo-Classical economists widely considered sharecropping as an inefficient form of tenure because
it would create a disincentive for the tenant: He/she would only use a factor up to the point where only his/her share
of the marginal value product -as opposed to the total marginal value product- equals the factor' s price.









hectare and 88% owned less than 5 hectares. In light of this, households with smaller shares of

owned land relative to their operational holdings appear to be small farm owners who need to

acquire additional land. This effect can thus speak to the competitiveness of this kind of farm

household compared to those who only operate their own property.

A related hypothesis is that households with title to their lands, compared to those who do

not have title, are more likely to make fixed investments in the land, hence generating greater

profits. Moreover, titled land can facilitate access to formal credit, further increasing profits.

Thus, the variable %Title is included interacting with the value of assets (K). In other words, it is

expected that the effect of intermediate assets on the returns to quasi-fixed factors ( T) would

vary depending on whether the household has a land title (or a larger share of titled land).

The evidence about the effect of land titling in the developing world is, however, mixed. In

Thailand Feder et al. (1988) show that land titles improve tenure security, increase investment

and enhance land values. On the other hand, studies in some parts of Sub-Saharan Africa (Migot-

Adholla et al., 1993) and Latin America (see Gould, 2001 on Guatemala and Carter and Salgado

2001 on Paraguay, Honduras and Chile) have found very little and at times ambiguous total

effects of land titling programs on farm productivity, the dynamization of land markets, credit

access and land values. We shall then observe the direction of this effect in the case of Ecuador.

Also included in the restricted profit equation is the age of the household head (Age) so as

to capture the likely negative effect of less efficient farm labor and/or management of older

farmers (Carter and Salgado, 2001).

We estimate the profit function and then test for monotonicity and convexity in prices,

properties of the profit function. If the function is not convex, we perform a bootstrap procedure,

by which a new sample is randomly created each time based on our data set and new estimates










computed. This is done 1,000 times, providing 1,000 coefficient estimates out of which we retain

those that are convex in prices. Our final estimates are the average of the convex coefficients

(Terrell, 1996; Moss et al., 2008).

An appropriate measure of returns to land, which includes farmers' heterogeneity in

endowments, is obtained by taking the derivative of the profit function with respect to land (A),

as follows:


= aA + 3~, ~iX1 +C 1rhZ, +8, NP + 8,uality + 8,%(Aned (3 -2)


Based on the hypothesis that small farmers can be more competitive in the land market

(see Carter and Salgado, 2001 about the 'peasant hyper-competitiveness' case9, we expect land

shadow values to be positive but decreasing with the amount of land in operation. This

hypothesis has its roots in the labor advantage of poor farmers (abundant labor and little or no

need for supervision given their use of family labor) by which they tend to be more productive

per unit of land (Carter and Zegarra, 2000; see also Chapter 2) and therefore should be willing to

pay more for the land. However, we will see below how this competitiveness is expected to be

undermined due to the presence of credit constraints which primarily affect small farmers. This

will be reflected in the level of land reservation prices.

Land Reservation Prices

The reservation value of owned land for the ith agricultural household (y,) can be

expressed as Equation 3-3, where r, is the household's discount rate, Tis the time horizon the

household expects to hold the land, and Pe the expected future land selling price in period T+1.





9 Under the assumption that all farmers face the same cost of capital, Carter and Salgado (2001) show that farmers
with the lowest land to labor endowment ratio have the highest shadow land price









rn 8n,,/ BA, P"
Vl = [ + (3-3)
S(1+re)' 1+

Now, taking into account land insecurity, which arguably (as indicated earlier) can come

from lack of land title, land values would be negatively affected for the case of landholders who

fail to prove ownership through a formal land title. In such case, land value can be expressed as

Equation 3-4, where a represents the probability of the ith household loosing their land and

where the selling price component is omitted.


-/t (1 + rt (1

Another crucial aspect in land valuation is the discount rate particular to the household.

Imperfections in the credit market cause some households to be credit constrained. These are

households who either have being denied credit, do not have sufficient collateral to secure a loan,

or had the opportunity to get credit but preferred not to for fear of loosing their collateral

(Boucher et al., 2005; Guirkinger and Boucher, 2005).

Credit constrained households are also called non-price rationed (Boucher et al., 2005;

Guirkinger and Boucher, 2005) as their ability to obtain credit is limited by reasons other than

the interest rate. On the other hand, price-rationed households are those who either obtained

loans or decided not to ask for credit because the interest rates were too high or due to reasons

different than those of the non-price rationed households.

It follows from this analysis that non-price rationed (or credit constrained) households

have a shadow price for capital larger than those who are simply price rationed, hence they will

have higher discount rates and smaller land reservation prices. In addition, they are pushed to

produce with less profitable capital intensities (reflected in smaller shadow land values), which

further influence their ability to pay for land. As a consequence, these households tend to have










lower risk-bearing capacity and this, inter-temporal considerations aside, 1o guides them to

choose safe but low-yielding activity portfolios in which land is not abundantly present

(Zimmerman and Carter, 2003). In other words, capital constrained households will be more

likely to sell their land than unconstrained households.

Because of the small share of landowner households in our sample who received credit at

a positive interest rate (18%), we are unable to use interest rates on loans in order to approximate

households' discount rates. Instead we concentrate on the reasons why households did not obtain

credit, which leads us to classify them as price or non-price rationed households.l Like Carter

and Salgado (2001), we expect credit constraints to be correlated with the risk-bearing capacity

of the households.

Table 3-3 illustrates the incidence of credit constrained households in Ecuador by farm

size. As the last column shows, the largest portion of rationed households is made up by

minifimdista~s and this portion decreases as farm size increases. Similar to the findings of Carter

and Salgado (2001) for Paraguay, and Boucher et al. (2005) for Honduras and Nicaragua, land

poor households are the ones that are most likely to be constrained in the credit market. Thus,

like Carter and Zegarra (2000) suggest, we expect land prices per hectare to fall very quickly

from the small farmer advantage indicated in our 'restricted profits' sub-section because of credit

constraints.


'0 The possibility of trading off current consumption for assets usually helps resource-poor households accumulate
capital but risk and other dynamic factors undermine the benefits of this strategy (Carter and Salgado, 2001;
Zimmerman and Carter, 2003).

11 Based on the information offered by the LSMS survey we classified households as credit constrained (or non-
price rationed) if they reported to have asked for credit but did not receive it, or if they did not ask for credit due to:
a) did not know any lenders; b) already had debt: c) lenders asked for too many prerequisites: d) did not know how
to ask for credit: e) did not have collateral wealth; f) did not have land ownership title: g) fear of losing the
collateral; or h) did not have any guarantors. Price rationed households are made up by those who either obtained
credit or did not ask for credit because: i) they did not need credit; j) interest rates were too high; or k) their income
was not stable enough.









Given data limitations, we can only analyze one year' s worth of returns to land. However,

making the assumption that our year of analysis represents an average crop year in Ecuador, we

can estimate a log-linear function of the following form:


In ~= 4, +4Co~uu~n~t/ast4Nn:ba+4gn+XiopI lns.10hwAV+

A In A OL''Endow + PNonPRat + P %Titled + u (3-5)


Where,

* V Ao= land reservation value per owned hectare
* Coast = dummy variable for a household located in the coastal region (base: Sierra)
* Non~rban = dummy variable for households located in a non-urban area (base: urban)
* AgCon = dummy variable for households located in areas of agricultural concentration
* XCrops = dummy variable for households in areas of maj or export crop production
* .10thow,~lA V= shadow land value = 80 / dA
* AoLoEndow = land to labor endowment ratio (owned hectares/family labor days)
* NonPRationed = dummy for non-price rationed households (base: price-rationed)
* %Titled = percentage of total owned land that is titled


The means of all variables in Equation 3-5 are shown in Table 3-4. Location effects on

land values are represented by the region (Coa~st), the non-urban effect (Non~rban), the

concentration on agriculture (AgCon)12 and the major export crop production areas (XCrops)13

effects. Land speculation is commonly found in areas of export crop production (Lambert and

Stanfield, 1990) or in areas of agricultural concentration and so location of the farm (as

approximated by location of the household) in such areas is expected to increase individuals'





12 Based on results from the latest agrarian census in Ecuador (2000), counties in which over 50% of the land was in
production are regarded here as areas of agricultural concentration. The census added fallow lands and shrimp pools
as land in production and it excluded moorlands, mountains, forests and areas with infrastructure.

13 These are the most productive counties in the production of major export crops, namely, bananas, coffee, cocoa
and flowers.










land reservation prices. High inflation rates during the year of the survey, 1998-1999, (between

40 and 60%) may make these effects even stronger (Carter and Zegarra, 2000).

The percentage of titled land (%Titled) is included in this equation in order to observe the

probable effect of land insecurity, arguably represented by lack of land title.

The land to labor endowment ratio (AoLoEndow ) will reflect how land values per hectare

vary as farm size increases relative to available labor. Just as shadow land prices are expected to

decrease with the number of hectares in operation, land reservation values are expected to

decrease with the land to labor endowment ratio. However, the advantage that farmers with low

endowment ratios may have with respect to reservation prices is anticipated to be weakened or

overcome by restrictions in the credit market (NonPRat dummy), where better endowed farmers

are expected to surpass those who are poor. Following de Janvry et al. (2001), failures in rural

markets cause landownership to provide side benefits that increase land pricesl4 and many of

these benefits are more likely to be enjoyed by large but not by small farm owners. Credit access

is one of those benefits. On the other hand, the reservation price of better endowed households is

expected to eventually fall again due to disadvantages in the labor market overcoming their

capital advantage (Carter and Salgado, 2001).

Results

The profit function estimates (Equation 3-1) in Table 3-5 reveal the effect of land and other

quasi-fixed factors on restricted farm profits. Conforming to the theory, more land increases

profits but with diminishing returns (see coefficient of A2). Returns to land are also significantly

increasing with the value of assets, and decreasing with the value of farm animals. They are also



14 Those benefits are that land serves as a store of wealth especially in times of high inflation: it provides a source of
self-employment; it serves as collateral for credit: it can have speculative value: it can offer tax breaks and it
provides political and social capital (de Janvry et al., 2001).










larger the better the quality of the land, and smaller the greater the amount of land that is owned.

The last result confirms our expectations that owner-tenants are more competitive than pure

owners as they strive to expand their operational size. Also, the number of plots, although

positive did not significantly affect the returns to land.

In total, for a median farm household, one more unit of land would increase restricted

profits by $5,306.23. Figure 3-1 shows how with a few exceptions, shadow land values are

decreasing in operational farm size, which conforms to what was anticipated in our 'restricted

profits' sub-section.

In addition, the contribution of intermediate assets to restricted profits is also positive and

decreasing. It also decreases with the number of non-remunerated labor days and with the value

of farm animals. The effect of land titles turns out to be positive but not significant, which

implies that titled land does not significantly improve the effect of intermediate assets on profits.

Shadow values of all factors indicate that intermediate assets and land contribute the most

to agricultural restricted profits while the value of animals has a smaller contribution and non-

remunerated labor contributes the least. Taking into account the size of the median household' s

quasi-fixed factors (Table 3-1), the shadow price results seem to reflect fairly the reality of the

median farm household in Ecuador. Also, as expected, households with older heads made fewer

profits than those with younger heads, indicating the lower efficiency of labor and/or

management caused by age.

Results from Equation 3-5 show that shadow land values contribute positively and

significantly to land reservation prices (Table 3-6). As expected, better endowed households

have lower reservation prices per hectare than households with low land to labor endowment

ratios, but this advantage of poor households is undermined by their being constrained in the









credit market. More specifically, while a 1% increase in the land to labor ratio would decrease

land reservation prices by 0.31%, a credit constrained household (which is more likely to be a

land poor household) has a land reservation price per hectare 0.55% lower than an unconstrained

household.

As anticipated, Figure 3-2 shows that land reservation prices per hectare are very high for

households with low endowment ratios (less than 0.02 hectares per family labor day or less than

2 hectares in total) but they decrease rapidly especially for those who are credit constrained. As

shown in Table 3-3, most credit constrained households have less than 5 hectares.

Also, while reservation prices per hectare range from $46 to over $50,000 for households

with less than 5 hectares, for households with 17 hectares or more, reservation prices range from

$3 to less than $2,000. This conforms to Carter and Salgado (2001)'s simulation findings that

reservation prices would be larger for credit unconstrained households but smaller again as farm

size continues to increase due to disadvantages in the labor market.

In areas of agricultural and export crop concentration, land reservation values are

significantly higher than in other areas, which as indicated earlier can be indicative of the land

speculation usually found in these zones. However, land reservation values are smaller as we

move away from the urban centers and this effect is stronger than the effect of agricultural

concentration and export crop areas combined. This suggests that the value of land close to urban

areas is highly influenced by the advantage of being closer to maj or markets. Also probably

included in this effect is the possibility of rural land conversion to semi-urban settlements

(Lambert and Stanfield, 1990). In addition, households in the coastal region have smaller

reservation prices per hectare than those in the highlands (Sierra), presumably because there is

more competition for land in the Sierra given that land is less abundant.









Finally, with respect to the percentage of titled land, this effect is again not significant (see

results from the profit function), which reveals that although almost 30% of the land in the

sample was not titled, this does not seem to be causing serious problems of land insecurity. One

alternative explanation for this result is that in many cases land titles are likely to be only

certificates of possession or similar documents which have not been properly registered (see

Chapter 2 about the high transaction costs discouraging title registration). Hence, the existence of

a title-like document is not capturing information on security of land rights, causing higher

shares of titled land not to make a difference on either investment or land values. Another

possible explanation is that, like the findings of Carter and Salgado (2001), land titles do not

contribute to easing small farmers' credit constraints; therefore, even if providing security of

land rights, in the case of small farmers (which make up the maj ority in our data set) land titles

cannot be used to improve credit access in order to increase investment, then the effect of land

titles on investment and land values turns insignificant.

Conclusions

This chapter has shown that the presence of multiple market imperfections intensifies the

quasi-fixity of factors other than land, which affects the contribution of land to profits (shadow

land values) and consequently, land values. Also, incomplete credit markets leave some

households unattended, affecting their shadow capital values and risk bearing capacity, which is

reflected in higher discount rates and smaller land reservation prices. These households are

therefore more likely to sell their land in moments of financial distress.

We have also found graphically that the difference in reservation prices per hectare

between small and medium and large farmers is remarkable. This can be explained by the labor

advantage of small farmers, which makes them more productive per unit of land than larger

farmers. This effect, however, is reduced by the credit constraints mostly experienced by small









farmers. Yet, for farms of 17 hectares or more, reservation prices turn smaller again as farm size

continues to increase, reflecting the disadvantage of large farmers in the labor market.

In addition, although land values are higher in areas of concentration of agricultural

production and/or export crop production, the relative marginalization of non-urban areas

severely affects land values. This is likely due to the distance of non-urban farm households from

urban markets, a disadvantage that is exacerbated by the need for more and better road access in

rural Ecuador. Another reason is the possibility of rural land conversion in lands close to the

cities, which increases land values for these landowners.

Finally, in this Ecuadorian case, lack of land titles did not effectively discourage

investments in land and did not cause land values to be smaller than for households with titled

land (or having a larger share of titled land). This was likely due to the possibility that land titles

are not registered, hence not providing the full benefits of a title, or due to the pervasiveness of

credit constraints which limit the potential benefits of land titles as a device that would facilitate

access to credit by allowing small landowners to pledge the land as collateral.









Table 3-1. Mean and median quasi-fixed factors
Quasi-fixed factors Mean Median
Operational land holdings (ha.) 7.95 2.00
Non-remunerated labor (days) 1,911.94 1,152.00
Intermediate assets ($) 419.75 42.30
Farm animals ($) 415.44 222.97


Table 3-2. Classification of owner-tenant households by category of owned farm size
Owned farm size Owner-tenants Proportion (%) Cumulative (%)
Minifundio (less than 1 ha.) 79 49.1 49.1
Small (1 to less than 5 ha.) 62 38.5 87.6
Medium (5 to less than 40 ha.) 19 11.8 99.4
Large (greater than 40 ha.) 1 0.6 100.0
Total 161 100.0


Table 3-3. Credit constrained households by owned farm size
(a) Credit (b) Total HH Row % % of Total
Farm size constrained HH by farm size (a/b) (a/c)
Minifundio 44 302 14.57% 5.35%
Small 41 304 13.49% 4.99%
Medium 22 179 12.29% 2.68%
Large 7 37 18.92% 0.85%
Total 114 (c) 822 13.87% 13.87%


Table 3-4. Summary of variables (land reservation value equation)
Variable Obs. Mean or frequency
Total reservation price (V) 822 5,088.90
Size Owned (Ao) 822 7.62
Reservation price per hectare (V Ao) 822 83,601.44
Region: Coast 229 27.86%
Region: Sierra 593 72.14%
Area of agricultural concentration (AgCon) 370 45%
Area of export crop concentration (XCrops) 233 28%
Urban area 78 9%
Non-urban area 744 91%
Land to labor endowment ratio (AoLoEndow) 822 0.019
Non-price rationed households 114 14%
Percentage of land holdings with title (%Titled) 822 0.703










Table 3-5. Returns to fixed factors equation (quadratic function)

.xlntoyP~ Coefficient P>|t| bl

Azuay -0.042 0.036** Pt*W
Bolivar 0.008 0.346 Pp*W
Caila~r -0.022 0.197 W2
Calrchi -0.048 0.051* Pt*Labor
Chimborazo 0.016 0.217 Pp*"Labor
Cotopaxi 0.013 0.423 W*Labor
Imbabura 0.053 0.021 ** Labor2
Loja 0.053 0.007 *** Pt*K
Tungurahua 0.024 0.161 Pp*K
El Oro -0.016 0.304 W*K
Esmeralda;s 0.040 0.030** Labor*"K
Guayas 0.006 0.429 K2
Los Rios 0.013 0.336 Pt*A
Ma'~nabi -0.024 0.121 Pp*A
Pt (temporary crop W*A


Explanatory
Variables
AniW2
Quality* A
NP*"A
%Owned*"A
%Titled*"K
Age
Constant
D. of freedom
R-squared


Coefficient

0.169
0.051
0.229
0.084
0.206
0.307
0.002
-1.604
-6.099
-2.323
-2.823
-11.569
0.783
-0.056
-0.289


P>|t|
0.048**
0.334
0.138
0.195
0.036**
0.025**
0.496
0.116
0.000***
0.113
0.046**
0.001***
0.062*
0.463
0.370


Coefficient


price index)
Pp (perennial crop
price index)
W (hired labor wage


-0.739
0.922
1.027
-2.812
0.923
-0.055
0.106
766
0.38


0.274
0.010**
0.226
0.000***
0.223
0.028**
0.001***


0.259 0.001 ***

-0.145 0.040**


Labor*'A


-0.340 0.308


K*"A


20.092 0.000***


rate) -0.201 0.015**
Labor -0.032 0.369 A2
K (intermediate assets) 3.511 0.007*** Pt*"Ani W
A (land) 3.084 0.000*** Pp*"Ani W
AniW (farm animals) 0.154 0.358 W*Ani W
Pt2 0.389 0.005*** Labor*~AniW
Pt*Pp 0.099 0.120 K*AniW
PP2 0.509 0.003***" A*Ani W
***Significant at 1%; ** significant at 5%; significant at 10%


-5.638
1.076
0.589
-0.490
-0.531
-12.853
-3.361


0.000***
0.015**
0.133
0.284
0.212
0.003***
0.005***










Table 3-6. Log of the land reservation price equation
Explanatory variables Coefficient P>|t|
Coast -1.212 0.000***
AgCon 0.508 0.000***
XCrops 0.664 0.000***
Non Urban -1.681 0.000***
InahmowA V0.825 0.006***
In.-ffo-0.312 0.000***
NonPRationed -0.551 0.001***
%Titled 0.141 0.246
Constant 5.491 0.000***
Degrees of freedom 803
R-squared 0.37
*** Significant at 1%; ** significant at 5%; significant at 10%


Operational size


Figure 3-1. Shadow land values


** *

*H
30l 50 00l l100 00 150O 00 200 010 250O 00 3010 0(P









































*




0 5 10 15 20 25 30 35 40 45 50


1,000,000.00

100,000.00

10,000.00

1,000.00

100.00

10.00

1.00


0.00


0.02 0.04 0.06 0.08 0.10 0.12


A/L


1,000,000.00

100,000.00

10,000.00

1,000.00

100.00


10.00

1.00


Owne d he ctare s


Figure 3-2. Land reservation prices per hectare and non-price rationed households.
A) Relationship with respect to the land to labor endowment ratio. B) Relationship
with respect to the amount of hectares owned. Both figures show only 95% of the
data and are drawn in logarithmic scale for observational purpose.









CHAPTER 4
RURAL LAND MARKET PARTICIPATION INT ECUADOR AND ITS DETERMINANTS

Introduction

Land is the most important asset in agricultural production and this activity still employs

over 50% of the rural economically active population in Ecuador. Land inequality and abundant

availability of labor in a context of generalized unemployment suggest that an important means

of reducing inequality would be by increasing access to agricultural land by the rural poor. There

is, however, a lack of dynamism in the land markets. Large landowners do not make enough land

available through sales or rentals to the land poor so as to satisfy the latter' s demand for land (see

Lambert and Stanfield, 1990 about land market segmentation). This situation seems to be

encouraged by a number of factors that are worthy of study.

Decisions to supply land in the land market depend on a variety of factors that first affect

land reservation prices (or reservation rents). Households that supply land in the sales market

may be those facing financial distress or may be households upgrading to better land or deciding

to migrate to the cities for better economic opportunities. Households offering land in the rental

market may be behaving as risk averse (Currie, 1981) or may be households experiencing a

temporary or permanent change in their supply of family labor; for example, those households

that become female-headed after the male heads migrate out of the country to find a j ob that

would provide more and secure income to send to their family back home.

Changes in the macroeconomic situation affecting the relative profitability of agriculture

often put rural households in the position of choosing between participating in the land market or

not (Currie, 1981), as do severe climatic conditions. Given the presence of market imperfections,


SJordan (2003) notes that for the decades of the 1980s and 90s about 40% of the economically active population in
the rural sector was working in non-agricultural activities, which reveals a loss of importance of land as a focus of
household reproduction in the rural sector compared to earlier decades.










for those households facing harsh economic circumstances, their wealth level as well as their

ability to access credit and affordable technical assistance could make a difference between

selling their land or not.

Demand for land is also affected by households' abilities to face uncertain economic

conditions, imperfections in rural markets and risk aversion. In addition, unequal land

distribution and certain characteristics of the land market, such as market segmentation by class

and kinship, may also influence how active land markets are and what type of individuals

participate.

In spite of the accomplishments of the agrarian reform in Ecuador,2 the available data

shows that agricultural land continues to be highly concentrated in the hands of a few large

landowners (Nieto, 2004). The last agrarian census (2000) reveals that agricultural production

units (UPAs) of less than 2 hectares (ha.) constitute 43.4% of the total and they hold 2% of the

cultivable land. In contrast, units of over 100 ha. represent 2.3% of the UPAs, while they control

43% of the land. Since the 1994 land law was approved land redistribution efforts have been left

to the market (Santos-Ditto, 1999; Jordan, 2003). Given current restrictions on renting land

(Chapter 2), rural market imperfections and land distribution results from the last agrarian

census, the answer to the question of how well the land market can perform the redistribute task

seems to be: not so well.

The literature on land market participation in Latin America is relatively recent (Deere and

LeC~n, 2001) with important empirical studies having been carried out in Nicaragua (Deininger et

2 The main achievements of the agrarian reform (1964-79) were that it eliminated precarious forms of labor, such as
feudal-like or servile relations on haciendas. It also changed the agrarian structure by eradicating the latifundia
(Jordan, 2003; Santos-Ditto, 1999; FAO-COTECA, 1995) and facilitating land access to peasant producers and to
some indigenous communities of the highlands (FAO-COTECA, 1995). Even though the agrarian reform
redistributed some land, most of the land adjudications were of state lands in the Amazon basin for colonization
purposes (new settlements). Chiriboga (1998) notes that by 1994 the State had redistributed only 9.3% of Ecuador's
cultivable land, benefiting 9.5% of the rural households; while 68.9% of the cultivable land was adjudicated to 9.9%
of the households for colonization purposes.










al., 2003; Boucher et al., 2005), Honduras (Boucher et al., 2005; Carter and Salgado, 2001) and

Paraguay (Masterson, 2005; Carter and Salgado, 2001). These studies aim to understand the role

of the land sale and rental markets in asset inequality, and explore the factors that encourage land

market participation.

In Ecuador, comprehensive studies of the rural land markets (FAO-COTECA, 1995;

Lambert and Stanfield, 1990; Jordan, 2003) are few, qualitative rather than quantitative, and are

based on data mainly up to the 1980s. These studies are compilations of more local studies and

they show the effects of the agrarian reform on land distribution and the role assigned to the

market after the reform period, together with an evident decrease in governmental intervention.

They also note two different results with respect to small farmer participation in land markets: 1)

their propensity to sell land contributing to the increased tendency towards the formation of

minifimdios-; and 2) individuals who have succeeded in agriculture have been able to buy land

and advance towards becoming medium capitalized units, especially in the more productive

regions. These studies also emphasize the decline in the importance of agriculture as the main

source of income for rural families and the difficult path out of poverty, as well as the marked

social differentiation among small and large farmers which worsens the segmentation of rural

markets.

Rural household data for the period October 1998 to September 1999 give us a window to

observe the incidence of land market participation in Ecuador. In this chapter we use a

quantitative approach and test the conclusions of the above mentioned Latin American studies in

the case of Ecuador by examining the determinants of households' decisions to purchase, sell,

rent in or rent out their land.











Land Supply

Supply in the land market can be in the form of land sales or leasing. Landowners have the

option of putting all their land into production (or none at all), leasing it all, selling it all, or

doing a combination of the three. The factors that influence landowners' choices among those

alternatives are analyzed here. We use data from the Living Standard Measurement Survey

carried out in Ecuador during October 1998 to September 1999. The data show that out of 1,738

landowners (90% of all farm operators) only 4.8% chose to rent out at least a portion of their

land and 1.4% chose to sell at least a portion.3

In the analysis below we use multinomial logit4 regression analysis by making the

landowning households' alternatives mutually exclusive between farming all land, leasing at

least a portion or selling at least a portion. For households leasing and selling land at the same

time, we categorize the household according to whether the largest portion of land was leased or

sold. Our model is as follows:

OwnerDecision = p, + a, Coast +P, Nonthban +P, AgCon + P,SizePrior +
A %Titled + A ValueAssets + A AniW + AAdults + Pf Credit + (4-1)
fAge + A Edu + AFemale + A Of~flnc + Pf NonLhc + u

Owner-decision refers to the owner' s choice of renting out or selling at least a portion of

the land. The comparison group is made up by households who exploit all or do nothing with

their land. In addition, we break up the 'rent out' category in two subcategories describing the

type of rental arrangements made by the landlord, namely, fixed rental or sharecropping. Thus


3 Some of the households renting out also rented in some land so the percentage of net lessors was 4.6. Similarly,
some of the seller households also purchased land, hence 1.3% of the landowners were net sellers.


SLogit regressions estimate log-odds, that is; log (i(x) j=,sc a x.SeAgrestui (99)


Data and Methodology










we run two multinomial logit regressions where the dependent variable outcomes outside the

comparison group (owner-operator only) are (a) rented out or sold, and (b) rented under fixed-

rental contracts, rented under shared-rental contracts, or sold. Following Deininger et al. (2003)

and Masterson (2005) we also run censored Tobit regressions so as to check the effect of the

same variables included in the multinomial logit regressions on the amount of land sold and

rented out.

The independent variables included in (1) are

* Coast = dummy variable for households in the coastal region (base: Sierra)
* Non Urban = dummy variable for households located in non-urban areas (base: urban)
* Agcon = dummy variable for households in areas of agricultural concentration (counties in
which over 50% of the land is in production)
* SizePrior = amount of land owned prior to selling
* %Titled = percentage of owned land holdings with title
* ValueAssets = value of machinery and equipment (in 100 U. S. Dollars)
* AniW= value of animal stocks (in 100 U.S. Dollars)
* Adults = number of adult members in the household (14 or older)
* Credit = dummy variable indicating households who received any type of credit for a
positive Interest rate
* Age = years of age of the household head
* Edu = years of schooling of the household head
* Female = dummy variable for female household heads (base: male)
* Of~flnc = share of off-farm income relative to total household income
* NonLnc = share of income from remittances and governmental or non-governmental
transfers relative to total household income


The amount of land owned prior to selling (SizePrior) would give us a first sign on the role

of the land market on land inequality. A positive effect of this variable on land sales would

suggest that, for the year of the survey, larger farmers contributed by offering land in the market


SWe could not easily conclude from a positive effect that Ecuador moved towards a more egalitarian land
distribution during the time of the survey because we have no information on who these households sold land to.
However, a positive result would suggest the presence of incentives to sell land by large owners and hence a stimuli
to small farmers (or to the landless) to buy could successfully improve land distribution. The lack of information on
who the households sold to is of importance given the evidence of land market segmentation by class and kinship in
Ecuador (Lambert and Stanfield, 1990), a phenomenon usually observed throughout Latin America, where land
inequality is a prevalent characteristic (Carter and Salgado, 2001; Deininger and Binswanger, 2001).









while a negative sign would reflect a reluctance to do so -likely due to non-productive reasons

(Deininger et al.2003)-, and a more active participation by small farmers. The effect of this

variable on the supply of land has been analyzed for the cases of Nicaragua (Deininger et al.,

2003, for the year 1998) and Paraguay (Masterson, 2005, for the year 2001) with different

results, namely, a negative effect on the amount of land sold in the case of Nicaragua, while

positive results on incidence and amount of land sold in Paraguay.

In the case of Ecuador, it is probable that the tendency to sell by small farmers who

acquired land during the agrarian reform period continued during the year of the survey (1998-

1999). This behavior by small farmers was evident after the fragmentation of production

cooperatives, especially since the new land law, which removed restrictions for land sales, was

issued in 1994 (FAO-COTECA, 1995; Santos-Ditto, 1999; Nieto, 2004). Also, Table 4-1 shows

that 56% of the land sales were performed by households holding less than 5 ha.

On the rental market, the effect of the area owned was positive for the amount of land

supplied in both the Nicaragua and Paraguay cases. Also, Boucher et al. (2005), using pair-wise

analysis, show that the incidence of land supply in the rental market was higher the larger the

amount of land owned both in Honduras in 2000 and Nicaragua in 1999. In Ecuador, Table 4-2

shows that the majority of households supplying land in the rental market were again those

owning less than 5 ha. (71%).

However, farms of 5 to less than 40 hectares represented an important share of the land

suppliers, especially in the sales market and in the rental market under fixed-rental contracts.

Also, in the latter case, those households offered on average significantly more land than smaller

farmers .










In Table 4-2 we can also see that sharecropping was more popular among households

owning less than 5 ha., while those owners of 40 or more ha. that chose this type of rental

agreement dedicated on average less land area to this purpose compared to smaller farms and

especially compared to farms using fixed-rental contracts.

There are no clear prior expectations on the effect of the share of titled land (%Titled) on

landowners' decisions to participate in the land market. Land titles could stimulate more

profitable own production due to credit access and security for investment (Feder et al., 1988) or

could facilitate participation in the rental market.6 Land titles could also facilitate land sales,

especially by poor households who are credit constrained (Chapter 3).7 These may be agrarian

reform beneficiaries selling land after land titling programs were implemented (Deere and Le6n,

2001; Deininger and Binswanger, 2001; Carter and Salgado, 2001).8

The number of adults (Adults) in the household and the value of farm equipment

(ValueAssets), as well as the value of animal stocks (AniW) should increase the likelihood of

farming compared to renting out or selling land since more of these factors increase the

advantage of farm producers. On the contrary, we expect older household heads to have higher

odds of selling or renting out given their decreased labor/management efficiency (Chapter 3)

compared to younger heads. More educated household heads would also be more likely to rent





6 The importance of land titles for the supply side of the land rental market in Ecuador has already been discussed in
our first essay (see also Boucher et al., 2005 on Honduras and Nicaragua).

SIn the case of Paraguay, Carter and Salgado (2001) explain how land titles were unsuccessful in releasing credit
constraints for small farmers, similar to what Boucher et al. (2005) found in Honduras and Nicaragua. Given the role
of land titles in reducing transaction costs for land sales (Boucher et al., 2005), what land titles may lead to is land
sales by small farmers.

SSince the LSMS survey did not provide land title information on land sold, we assumed here that the percentage of
land still held by the household at the time of the survey that was titled is a good proxy of such percentage at the
time the land sales occurred.









out or sell than to produce as the opportunity cost of their labor is higher and therefore they

would look for more profitable opportunities off-farm.

Landowner households with larger shares of off-farm income (Of~flnc) may also prefer to

rent out or sell depending on the household's composition. In other words, off-farm income can

be a good source of financing for agriculture; however, this depends on the total amount of labor

hours available in the household and the amount dedicated to earning off-farm income. In the

case of non-labor income (NonLnc), that is, income from remittances and/or governmental or

non-governmental transfers, those households receiving larger shares of this type of income may

be poor households more likely to farm all land rather than rent out or sell, although for

households whose skilled labor has migrated, renting out (or even selling) may be their choice.

The sex of the household head (Female) is also included here in order to observe the effect

of gender on the decisions to participate in the land market. The participation of women in

Ecuadorian agriculture is such that, according to the latest agrarian census (2000), 25.4% of farm

producers in Ecuador are women, 30.5% in the Sierra and 14.8% in the Coast. Also, our data

indicate that 16% of the landowning households were headed by women (Table 4-3), 19% in the

Sierra and 9% in the Coast. This reflects the importance of women in agricultural production but

also suggests that women are underrepresented as principal agriculturalists in our data set,

especially in the Sierra.

Nonetheless, as noticed by Deere and Le6n (2001), in Latin America rural women face

disadvantages when trying to access services like credit, technical assistance and marketing

compared to men. Therefore, they may be less competitive than male farmers, which may lead

them to be more likely to sell or rent their land than men. This aspect, however, may be less

important than it seems in the decision of women to participate in the land market as sellers









because of the additional benefits that landownership brings them. Namely, ownership of land

increases the bargaining power of women in the household and the community; it provides food

security for their children and it constitutes an asset suitable for renting so as to generate income

for the household (Deere and LeC~n, 2001, pp. 327:329).

Hence, female headed households may or may not be more likely than male headed

households to sell, however they may be more likely than men to rent out rather than to farm the

land themselves. We expect to find the latter result especially given the overrepresentation of

female headed households among households who rented out some land in our data set,

compared to the share of female heads among all landowning households (Table 4-3).

We also need to be aware of the dynamics of migration by gender in Ecuador and the

possible consequences this could have on the effect of the gender variable on the decision to

farm, sell or rent out. In the leading provinces in international migration in Ecuador -which are

in the Sierra-, the migrating population is mainly made up by poor rural men (Camacho, 2005;

FLACSO and UNFPA, 2006). This migration of poor rural men, whose income-generating

activities are mostly related to agriculture (Camacho, 2005), leaves their wives in the position to

choose between farming the land under their own management, leasing it in some form, or

getting rid of the land. The fact that the year 1999 witnessed a large increase in the migrating

population (Camacho, 2005) may have influenced female household heads' decisions to

participate in the land market.

In addition, harsh economic conditions in the agricultural sector due to, among other

things, weather phenomena such as happened with the effect of the El Nino phenomenon on the

Coast during 1997-1998, may have lead to some land sales, especially by female headed

households in 1998-1999, the year of our data set.









Access to credit (Credit) is also included in our model as it could make a difference

between farming, renting out or selling. Also, if renting out is the choice, landowners who

obtained credit could opt for sharecropping where they would provide a share of the inputs and

the tenants, the labor.

Based on Chapter 3 we also hypothesize that capital constrained households (i.e., those

who needed credit but were not able to obtain it) would be more likely to sell the land than

unconstrained households (i.e., those who got credit or did not need it) because of the former' s

lower land reservation price. When testing for this we replace the credit variable with the non-

price rationed dummy variable.

Nevertheless, looking at Table 4-3, it is interesting to notice that over 33% of the

households who sold some land reported to have obtained some type of credit during the year of

the survey (3 8% of these cases obtained credit for agricultural purposes). This share is much

higher than for all landowners or those who farmed only. Nevertheless, the average share of sold

land was smaller for households with credit (25% of total land) than for those without (3 8% of

total land).

Land Demand

Demand for land can be observed in the form of land purchases or as land rented in. With

respect to land purchases, our interest is in landowner households who bought land during the

year prior to the survey. These households correspond to 2% (1.9% net buyers) of the

landowners in the data set. We expect the same variables included in model (1) to affect model

(2) below, except for the percentage of titled land, which is not included here since it should only

affect supply and not the general demand for land (Deininger et al., 2003).










Purchased = P + 9, Coast + 9, Non Urban + 9 A gCon + 9, SizePrior + 9, ValueAssets +
PIAniW + PIAdults + PICredit + PIAge + PIEdu + PIFemale + P,Off~nc + (4-2)
P,NonLnc + P,Tenant + v

Model 4-2 will be estimated as a simple logit regression where Purcha~sed is a binary

variable representing incidence of land purchases during the twelve months prior to the survey.

The effect of the amount of land owned prior to purchase on the likelihood of buying land and

especially on the amount of land purchased will tell us how disadvantaged the rural poor are in

the land market. In the case of Paraguay (for 1991-94), for example, Carter and Salgado (2001)

found a direct relationship between the land to labor endowment ratio and the probability of

purchasing land. In our case, however, although the total incidence of land purchases is again

small as in the case of land sales, the land poor seem to have been more active in the land market

compared to larger farmers.

Table 4-4 shows that an important number of households who purchased land were

landless prior to purchase (41%)9 and that 53% were owners of less than

5 ha., while no large owner (40 hectares or more) performed any purchase. Nonetheless, 72% of

the land purchases by households with less than 5 ha. were only of less than 1 ha. and so were

43% of the purchases by the landless prior to purchase.

Assuming that in female headed households the landowner is actually a woman, 1o these

households may have been less likely to buy land than male headed households. This is because,

as analyzed by Deere and LeC~n (2003), in Latin America the land market is not the most



9 This is, however, a heterogeneous group of people which seems to contain wealthier, urban households entering
into the agricultural sector, as well as poor rural households being able to acquire some land. As such, 21% (or 3
households) of the landless prior to purchase acquired between 24 and 63 ha. during the year prior to the survey.

"' We need to make this assumption because the 1998-1999 LSMS survey did not gather data on landownership by
gender. This is a shortcoming of most LSMS questionnaires designed for Latin America and it has been addressed in
Doss et al. (2007).









important channel of land acquisition for women; instead, inheritance is. Yet, these authors note

that Ecuador is an exception due to the large share of women who obtained land through the

market compared to other countries in the region.

Table 4-5 shows the most important form of land acquisition (50% of the land or larger) by

gender based on our data set. There we can see that, like found by Deere and Le6n (2003),

inheritance and purchases are the most important form of land acquisition for women in Ecuador

while the market is for men. The difference in the share of male headed households acquiring

land through the market and that of female headed households is, however, very small (45.4%

for male heads and 44.5% for female heads).

Credit access is expected to significantly increase the odds of purchasing land and the

amount of land purchased. Table 4-6 shows that 50% of the households in our data set who

purchased land obtained credit. This is a very large share compared to the total share of farmers

(last column) who obtained credit. Looking closely at these land buyers with credit access, 59%

of them reported to have obtained credit for agricultural purposes, 29% for household

consumption and 12% obtained loans for both purposes. Credit was provided by institutional

sources in 3 5% of the cases (private banks and cooperatives) while the rest was provided by

family, friends or other informal lenders.

Given that credit is fungible, we thus expect that having access to loans in general would

increase the likelihood and amount of land purchases. Particularly, 43% of the landless prior to

purchase obtained credit during the same year they purchased land (most of which bought less

than 2 ha.).

An additional explanatory variable in Equation 4-2 is the dummy Tenant, included with the

purpose of testing the hypothesis that access to land through the rental market may help the rural










poor scale up the 'agricultural ladder' toward land ownership (Binswanger et al., 1995; Sadoulet

et al., 2001). 1 The 'agricultural ladder' is a sequence of progress of a farmer that goes from

being an agricultural worker to becoming a sharecropper, then perhaps a fixed rent tenant and

finally a landowner. Even though the appropriate way of testing for this hypothesis is through the

use of panel data (the same farmers being interviewed over time), we attempt to observe any

contribution of tenancy to the probability of purchasing land in the year of our survey.

As such, we expect to find that tenant households had a higher likelihood of buying land

during the year of the survey than households who did not rent in land. We expect this effect to

be significant since 41% of the land buyers in our data set were also tenants (Table 4-6) as were

43% of the landless prior to purchase. It is also worth noticing that 86% of the tenant households

in our data who purchased land during the year prior to the survey were also sharecroppers (as

opposed to fixed-rent tenants). This is relevant given Lehman (1985)'s findings that

sharecropping in the Province of Carchi, Ecuador was more a "capitalist partnership rather

than...a form of tenancy (pp. 3 51)," and one which allowed capital accumulation by peasant

producers .

On the determinants of the odds of renting in land and of the amounts of land rented in, we

estimate Equation 4-3, again using logit and Tobit regressions. In addition, we run a multinomial

logit so as to specifically observe the effect of the same variables on the likelihood to choose

fixed-rental agreements, sharecropping or no rental agreement at all. The total number of farmers

in our sample who were tenants is 497, which represents 25.6% of all the farmers in the sample.

Of this, 76% chose shared-rental agreements.



'' Authors like Sadoulet et al. (2001) and Binswanger et al. (1995) conclude that, given market imperfections, access
to land through the rental market would allow poor farmers to accumulate knowledge and wealth, helping them to
acquire land in the long-run.










Rentln = P0 + P, Coast + P, Non Urban + P, AgCon + P, SizeOwned + P,ValueAssets +
PI AniW + PI Adults + PI Credit + PI Age + PI Edu + PFemale + + Offlnc + (4-3)
P,NonLnc + e

Table 4-7a shows that 41% of the tenant households were landless and 53% owned less

than 5 ha., so we see the land poor being more active than large farmers in the land rental market

too. Similar to what we found on the supply side, households owning less than 5 ha. make up

most of the sharecropping cases. Table 4-7b indicates that these households actually prefer

sharecropping to fixed-rental contracts.

As was the case for those households who purchased land, the share of tenant households

who obtained credit is also larger than this share for all farmers (Table 4-6). Among tenant

households, 37% of the credit cases were for agricultural purposes, 40% for household

consumption, 7% for family business and 16% for both agricultural and non-agricultural

purposes.

Technical assistance as a dummy variable is not explicitly included in our regressions

because of its low frequency in our data set (Tables 4-3 and 4-6) and especially due to its null

incidence among households who rented out and those who purchased some land. However, we

will comment on the effect of this variable on the multinomial logit of the land supply equation

(model 1) and on the decision to rent in land (model 3). It is worth noticing here that 66% of

those households with access to technical assistance chose to put all the land they owned into

production; 20% had all of their own land in production plus rented in some land during the year

prior to the survey, and 11% were tenants only. The positive impact of access to technical

assistance on the demand for land is evident.












Supply Side

Multinomial logit regression results in Tables 4-8a and 4-8b reveal that the effect of the

amount of land owned prior to transactions on the likelihood of selling land was statistically

significant but very small (only a 0.3%12 increase in the likelihood to sell relative to farming all

land per owned hectare increase). This variable also slightly influenced the odds of engaging in

fixed-rental agreements (one more hectare of owned land increased the odds of choosing fixed

rentals over farming oneself by 0.4% and the odds of choosing fixed rentals over sharecropping

by 0.5%).

The share of titled land significantly affected only the odds of selling land. A one unit

increase in this share would increase the likelihood of selling compared to farming all land

oneself by 596%. This is the most important effect on the likelihood to sell land, which reveals

the impact that land titling programs may have if not accompanied with policies aimed at

releasing small farmers' credit constraints (Deininger and Binswanger, 2001), among other

market imperfections.

Households located in non-urban areas were more likely to choose exploiting the land

themselves rather than renting out compared to households in urban areas. This may be

explained by the fact that urban households have more off-farm opportunities and therefore a

higher opportunity cost of labor than rural households, hence they would prefer renting out more

often. In addition, for households who chose to rent out (Table 4-8b), rural households were

153% more likely to choose sharecropping as the leasing arrangement rather than fixed rental

payments .


12 The effect of a variable is obtained as follows: exp( P, )-1 and is read as a percentage.


Results









As expected, older household heads were more likely to rent out than to farm all land and

more educated heads showed higher odds both of renting out and selling relative to farming all

the land. Furthermore, more educated household heads were 4% more likely to sell than to rent

out. More education raises labor opportunity costs, apparently leading to these results. Consistent

with the effect of education, the share of off-farm income strongly increased the odds of

choosing to rent out land over farming it.

Households receiving larger shares of income from remittances and/or governmental or

non-governmental transfers are even more strongly likely to rent out compared to farming the

land themselves. However, they are significantly less likely to sell than to farm all land (95% less

likely) and also less likely to sell than to rent out (98% less likely). Since households with larger

shares of non-labor income tend to be poor households, these results suggest their need for

production support and their lower risk-bearing capacity. Furthermore, these households showed

preference for sharecropping as the rental agreement, which is a sign that they want -and need-

to be somewhat involved in the production process but they cannot do it all by themselves.

Female household heads had a 136% higher likelihood than male household heads of

renting out under fixed-rental arrangements rather than farming all their land. In contrast, higher

values of animal stocks would decrease the odds of choosing fixed-rentals over farming all land.

These results also show that households in the Coast were less likely to sell their land than

farming compared to households in the Sierra. In addition, sharecropping was a much more

predominant type of land rental in the Sierra than in the coastal region, where there were no

cases of sharecropping (Table 4-3). The FAO-COTECA (1995:45) study observed the tendency

of sharecropping to disappear on the Coast, especially in the rice growing areas. This region is

known to have a more capitalist orientation than the Sierra (Jordan, 2003), which would support










these Eindings. Also, households in areas of agricultural concentration prefer farming to

sharecropping. This behavior can also be explained by the more commercial orientation of such

areas.

Also interesting to note from these results is that for households who rented out, one more

year of schooling of the household head increased the odds of choosing sharecropping rather

than fixed rentals by 4%. This result speaks to the benefits of sharecropping agreements. More

educated household heads seem to prefer looking for jobs were their marginal value of labor is

higher while allowing others (usually family or friends) to farm their land but still being partially

involved in the production process so as to benefit from its profits.

On the determinants of the amount of land to be rented out or sold (Table 4-9), non-urban

households rented out significantly less amounts of land than those located in urban areas

(approximately 10 ha. less). Female household heads rented out about 5 more hectares than male

heads, being the second strongest effect in this equation. Also consistent with results in Tables 8a

and 8b, more educated household heads both rented out and sold more land, and older heads

rented out more land.

Titled land facilitated land sales up to the point that a unit increase in the share of titled

land increased the amount of land sold by 19 hectares and again this effect is the largest in the

land sale equation.

Finally, when replacing the credit dummy variable with the non-price rationed dummy so

as to identify the specific effect of being constrained in the credit market, we found that the new

variable did not have a significant impact on the likelihood to sell. We also found that credit

constrained households were 61% less likely (at 10% significance) to rent out their land rather

than farming all compared to households who were credit unconstrained. This result must be a









consequence of credit constrained households being mainly poor households who own small

plots of land and have abundant family labor (Chapter 3); hence they need to farm the land

themselves as they would otherwise face unemployment.

Demand Side

On the demand side of the land market (Table 4-10), we see again that households in the

Coast were not very active in the land sales market compared to those in the Sierra. In fact, the

estimated odds of a household from the coastal region purchasing land were 85% lower than

those for a household from the highlands. Since the mean size owned prior to purchase in the

Coast (13 ha.) is almost twice as high as that in the Sierra (7 ha.) and, as shown in Table 4-4,

transactions were mainly performed by small farmers, this result makes sense.

As expected, the size owned prior to purchase has a negative effect on the likelihood to

purchase land and this effect is much stronger than the positive influence of this variable found

on the supply side of the land market. More specifically, one more unit of land would decrease

the odds of buying land by 16%.

Also as expected, access to credit was highly significant with a strong effect on the

likelihood of buying land and on the amount of land purchased (the strongest effect in both

equations). More specifically, households who had access to any type of credit (for a positive

interest rate) were 253% more likely to purchase land than households who did not obtain credit.

Also, households who got credit were able to buy 9.6 more hectares than those without credit.

This result has important implications for rural development, such that, even if land credit is not

readily available, increasing the general supply of credit for the rural poor would contribute

towards land acquisition.

The dummy Tenant also had a positive and significant effect on the incidence of land

purchases. A tenant household had 148% higher estimated odds of purchasing land than a non-









tenant household. This result provides some evidence that tenant households are in a better

position than non-tenant landless households to become landowners and than non-tenant landed

households to purchase more land. Hence, the importance of the land rental market in providing

[progressive] land access to the rural poor is stressed here.

These results also show that the values of farm equipment and animal stocks had a positive

effect on the likelihood of purchasing land and the effect of the latter was significantly stronger

than that of the former (6% versus 1% increase per $100 increase in animal stocks and farm

assets, respectively). This reveals the importance of farm animals as 1) work animals and 2) a

form of financial security for the rural poor. In addition, older household heads were less likely

to buy land and if they did, it was in smaller amounts than younger household heads.

The main determinants of demand in the land rental market are specified in Tables 4-1 1

and 4-12. Areas of agricultural concentration were more active in the land rental market than

other areas but, consistent with our earlier results on the supply side, households in these areas

prefer Eixed-rental agreements rather than sharecropping. Similarly, households in the Coast

were more active in the fixed-rental market and less in the shared-rental market compared to the

Sierra.

As expected, more hectares of owned land would reduce the likelihood of renting in land

(18% decrease per ha. increase). This effect is again stronger than that of the same variable on

the supply side of the land rental market. The amount of land owned also reduced the odds of

choosing sharecropping agreements (24% per ha. increase). Also, one more hectare of land

owned would reduce the amount of land rented in by 0.21 ha.

While female household heads were not significantly less likely to purchase land, they

were significantly less likely to rent in land than male heads (37% less likely). Moreover, female









heads rented in 1.2 less ha. and were 40% less likely to rent land as sharecroppers compared to

male heads.

Consistent with our results on the supply side, older and more educated household heads

were less likely to rent in land and rented in less land than households with younger, less

educated heads. Also, the share of off-farm income reduced the amount of land rented in.

The value of animal stocks increased the odds of renting in and the amount of land rented

(a $100 increase in animal stocks, would increase the odds of renting in by 3% and the amount of

land rented in by 0.08 ha.). The value of farm equipment also increased the amount of land

rented in but with a smaller effect than the value of farm animals. Interestingly, a $100 increase

in farm assets would increase the odds of choosing sharecropping by 0.8%. This effect, although

small, suggests the relative importance of owning farm assets for sharecropping arrangements.

This conforms to what is noted by Sadoulet et al. (2001): "with increasing capital-intensity in

agriculture, landlords look for tenants who can help share capital costs. (pp.210)," and to what

noted by Lehman (1986), that sharecropping is more a partnership rather than a precarious work

relationship in certain areas of Ecuador.

Once again, the credit dummy was positive and highly significant, being the largest effect

in both logit and Tobit regressions. As such, households who obtained credit had 107% higher

estimated odds of renting in land and rented in 2 more hectares than households without credit.

The direction of this effect was true for both types of rental agreements; however there was

preference for fixed-rentals by households with credit access (19% higher odds of choosing

fixed-rental agreements rather than sharecropping).

Also, households with more adult members had higher odds of renting in land (13% per

adult member increase) especially under fixed-rental agreements (21% increase). One more adult









in the household increased the amount of land rented by half hectare. The effects of this variable

reflect the need for land in a context with land inequality, abundant labor and generalized

unemployment.

Finally, when including technical assistance in our analyses we found that, as expected,

households with access to this service were significantly less likely to rent out rather than to farm

all the land, and were more likely to rent in.

Conclusions

Our results show that the incidence of land sales and purchases in Ecuador was low during

the period from late 1997 (year prior to the first day of the survey) to late 1999. We also found

that the amount of land owned prior to these transactions influenced participation in the land

sales market; however, the effect on the supply side was minimal while that on the demand side

was much stronger. Similar is the case in the land rental markets, where larger landowners were

only slightly more likely to rent out but significantly less likely to rent in. Hence, although there

was some indication by large owners of their willingness to offer land in the land markets, the

demand, which was mainly performed by the land poor, seemed to be largely unsatisfied.

Conforming to the abundant availability of labor and the need for land by the rural poor,

we found that the number of adult members in the household had a significant effect on the odds

of renting land in and on the amounts of land rented. The importance of active land rental

markets was also perceived in that tenancy contributed significantly toward land purchases.

The share of titled land was the most important determinant of participation in the land

market as a seller. This reflects the role of land titles in reducing transaction costs on land sales

and the effect land titling programs may have on poor landowners if not accompanied by policies

aiming to remove relevant market imperfections, especially those found in the credit market.









On the demand side, credit access was the strongest determinant of land market

participation both in the land asset and the rental market. This conclusion on the significance of

credit for land market participation is also noted by Masterson (2005), Boucher et al. (2005) and

Carter and Salgado (2001). A good portion of credit in our Ecuadorian case was obtained for

non-agricultural purposes, hence suggesting that increasing the general supply of credit in the

rural sector could contribute to more active land markets.

In addition, taking into account that households with larger shares of non-labor income

tend to be poor households, this type of income was observed to contribute to land access by the

rural poor in that it decreased the likelihood of selling land and increased the odds of leasing,

which provides land access for landless or other landed households. Hence, this paper showed

some evidence that funds coming from remittances and governmental or non-governmental

transfers help the rural poor hold on to their land. Furthermore, the fact that these households

strongly preferred sharecropping (rather than selling or farming all land) as the type of rental

arrangement may also reveal that they want to be involved in the production process but are not

able to farm all the land by themselves, thus showing the need for production support.

The probable lower competitiveness of female household heads in agricultural production

was perceived by their higher odds of renting land out under fixed-rental contracts, their lower

odds of renting in, and their larger amounts of land rented out and lower amounts of land rented

in. The participation of female household heads in the supply side of the land rental market may

have been a strategy to secure income for their families -at the same time as perhaps they sold

their labor in off-farm activities-, however, it may have also been the result of male migration

from their households or harsh economic conditions after severe weather (El Nino in 1997-98)

-or a combination of both.










Finally, the land sales market was less active on the Coast than it was in the Sierra. Given

that owners of less than 5 ha. and the landless prior to purchase were the ones who performed

most of the land purchases (Table 4-4), this difference between regions is probably due to the

larger degree of land concentration on the Coast; while small farms predominate in the Sierra.

Also, landed households in the Coast and those located in areas of agricultural concentration

were significantly less likely to engage in sharecropping as opposed to farming all their land or

choosing to rent out under fixed-rental contracts, which can be justified by the more commercial

orientation of these areas.

In conclusion, the rural poor seem to be the most active on the demand side of the land

sales and rental markets in Ecuador. However, given difficulties that prevent desired land

transfers from large landowners to the rural poor -such as transaction costs for large owners,

who must subdivide their holdings in order to sell smaller plots (Carter and Zegarra, 2000), and

the unsatisfied need for credit by the rural poor-, it seems improbable that the market will be able

to achieve an optimal land distribution without any assistance from the government.






































6.2 2.4


Owned prior to selling
Farm size category (ha.) N % Mean owned Mean sold


Table 4-1. Farm size and land sales by owned land category


0.36
2.76
11.45
113.67
18.27


0.10
0.76
2.41
76.67
10.24


Less than 1
>=1 to <5
>=5 to <40
>= 40
Total seller households


6 24
8 32
8 32
3 12
25 100


Table 4-2. Incidence of land rentals (landlords) by owned farm size category
Rented out Fixed-rent Shared-rent


Farm size
category
(ha.)
Less than 1
>=1 to <5
>=5 to <40
>= 40
Total renters
(landlords)


Mean
owned
0.5
2.2
11.7
93.0


Mean
rented
0.4
1.6
9.6
50.8


Mean
owned
0.5
2.2
14.1
100.0


Mean
rented
0.4
1.8
10.5
100.0


Mean
owned
0.4
2.2
8.8
86.0


Mean
rented
0.4
1.6
8.6
1.5


83 100 8.2 5.5 34 100


11.2 9.9 49 100










Table 4-3. Mean and median statistics of variables in Equation 4-1


Owner decisions
Total


Coast
Sierra
Agricultural
concentration area
Urban
Non-urban
Size owned prior to
transactions
Percent titled
Value of farm assets
Adults
Credit
SAge of head
SEducation of head
Male
Female
Off-farm income
Non-labor income
Remittances
Technical assistance
% of landowners
who ...
Net sellers or renters
Size produced, sold
or rented-out


Total landowners


Farmed only


Sold


Rented out


Shared-rent


Fixed-rent
34
# or % or
mean median
16 47.1%
18 52.9%


1738
# or
mean
458
1280

627
172
1566

8.61
0.65
338.32
3.13
289
50.37
4.24
1456
282
1516.14
146.44
276
31


1629
# or
mean
439
1190

600
138
1491

8.48
0.64
347.85
3.13
262
50.13
4.13
1374
255
1375.05
144.61
249
30

93.7%


25
# or
mean
3
22

8
8
17

18.27
0.90
678.96
3.36
8
50.32
7.04
21
4
9116.59
58.00
3
1

1.44%
1.27%


83
# or
mean
16
67


49
# or
mean
0
49

4
14
35

6.17
0.80
61.62
3.06
11
55.82
5.24
36
13
1886.20
234.18
15
0

2.82%


% or
median
26.4%
73.6%

36.1%
9.9%
90.1%

1.06
1.00
25.80
3.00
16.7%
49.00
4.00
83.8%
16.2%
817.12
29.69
15.9%
1.8%


% or
median
26.9%
73.1%

36.8%
8.5%
91.5%

1.01

26.85
3
16.1%
49
4
84.3%
15.7%
785.76
30.12
15.3%
1.8%


% or
median
12.0%
88.0%

32.0%
32.0%
68.0%

4.05

47.98
3
33.3%
48
6
84.0%
16.0%
712.91
6.22
12.0%
4%


% or
median
19.3%
80.7%

22.9%
31.3%
68.7%

1.41

4.47
3
23.5%
54
5
72.3%
27.7%
1278.69
41.87
28.9%
0%


% or
median
0.0%
100.0%

8.2%
28.6%
71.4%

1.06

7.03
3
22.9%
55
5
73.5%
26.5%
1005.17
35.14
30.6%
0%


44.1%
35.3%
64.7%

1.8084

0
3
24.2%
50.5
5
70.6%
29.4%
1594.79
43.12
26%
0%


15
12
22

11.19
0.62
39.62
3.35
8
54
5.97
24
10
2176.90
179.49
9
0

1.96%


8.23
0.73
52.61
3.18
19
55.07
5.54
60
23
2006.73
211.51
24
0

4.78%
4.60%


8.48 10.24 5.47


2.41 9.88










Table 4-4. Farm size and land purchases by owned land category (prior to purchase)
Owned prior to purchase
Farm size category (ha.) N % Mean owned Mean purchased


-- 11.66
0.44 0.46
2.19 0.81
14.03 11.56

5.84


Landless
Less than 1
>=1 to <5
>=5 to <40
>= 40
Total buying households


14 41
7 21
11 32
2 6
0 0
34 100


Table 4-5. Forms of land acquisition by gender
Female HH Male HH Total*
Main form of acquisition N % N % N %
Purchased during last year 3 1.1 19 1.3 22 1.3
Purchased earlier 122 43.4 633 44.1 755 43.9
Inherited 129 45.9 541 37.6 670 39.0
Adjudicated 11 3.9 95 6.6 106 6.2
Usufruct 16 5.7 149 10.4 165 9.6
Total landowners 281 100.0 1437 100.0 1718 100.0
* 20 households (1 female headed and 19 male headed) reported a mixture of any two forms of acquisition (50 and
50%) and so were not included in this table.


SThis average goes down to 2.9 ha. when not taking into account the 3 households with the largest land purchases.










Table 4-6. Summary of variables in Equations 4-2 and 4-3


Purchased
34
# or % or
mean median
4 11.8%
30 88.2%


Rented-in


Total farmers
1940
# or % or
mean median
537 27.7%
1403 72.3%


Total incidence



Coast
Sierra
Agricultural concentration
area
Urban
Non-urban
Size prior to purchase
Total owned
Value of farm assets
Adults
Credit
Age of head
Education of head
Male
Female
Off-farm income
Non-labor income
Tenant
Remittances
Technical assistance
Size purchased
Size rented-in
% of current owners
(1,73 8) who bought land
Net buyers
% of farmers (1,940)
who rented-in
Net tenants
% of tenants who owned
land


497
# or
mean
132
365

200
44
453

1.53
256.14
3.18
119
44.95
4.27
444
53
1352.88
113.54

57
11

2.22


% or
median
26.6%
73.4%

40.2%
8.9%
91.1%

0.18
29.52
3
23.9%
44
4
89.3%
10.7%
876.20
110.87

11.5%
2.2%


29.4%
14.7%
85.3%
0.27

48.80
2.50
50.0%
38.5
6
91.2%
8.8%
944.87
16.93
41.2%
17.6%
0%
0.71


1.96%
1.90%


37.1%
10.7%
89.3%

1
23.43
3
16.9%
49
4
84.2%
15.8%
843.29
38.39


10
5
29
1.62

441.34
3.06
17
39.82
6.56
31
3
1937.72
532.59
14
6
0
5.84


720
207
1733

7.57
321.37
3.11
328
49.53
4.25
1633
307
1509.58
143.32


300 15.5%
35 1.8%


0.71


25.6%
25.5%

59.4%


Table 4-7a. Land rented in by category of land owned
Total Fixed-rent
Farm size Mean Mean Mean
category (ha.) N % owned rented N % owned
Landless 202 41 2.6 54 45
<1 153 31 0.3 1.1 18 15 0.4
>=1 to <5 110 22 2.1 1.9 31 26 2.1
>=5 to <40 29 6 11.7 5.4 15 13 11.2
>= 403 1 47.3 11.8 2 2 45.0
Total renters 497 100 1.5 2.2 120 100 2.8


Shared-rent
Mean
% owned


Mean
rented
3.9
1.0
2.0
7.2
15.8
3.6


Mean
rented
2.2
1.2
1.8
3.0
4.0
1.8


0.3
2.1
12.0
52.0
1.1























Rent-out / Farm
Coefficient Robust SE
-0.315 (0.367)
-0.848 ** (0.377)
-0.342 (0.304)
0.003 (0.002)
-0.024 (0.344)
0.091 (0.087)
-0.155 (0.110)
-0.033 (0.028)
0.369 (0.313)
0.035 *** (0.010)
0.093 *** (0.035)
0.486 (0.304)
0.817 ** (0.387)
1.036 (0.608)
-5.135 (0.910)
1710
0.13
-423.94


Table 4-7b. Choice of rental agreement by category of land owned
Farm size category (ha.) N % fixed-rent % shared-rent
Landless 202 27 76
Less than 1 ha. 153 12 86
1 to less than 5 ha. 110 28 70
5 to less than 40 ha. 29 52 45
40 or more ha. 3 67 33
Total renting households 497 24 76


Table 4-8a. Multinomial logit regression results (owners' decisions between farming, selling or
renting out)


Variables Sell/ Farm
Coefficient Robust SE
Coast -1.568 (0.882)
Non Urban -0.571 (0.667)
Agcon 0.164 (0.537)
SizePrior 0.003 (0.002)
%Titled 1.941 *** (0.713)
Adults 0.214 (0. 172)
ValueAssets -0.001 (0.005)
AniW -0.028 (0.024)
Credit 0.742 (0.569)
Age 0.012 (0.021)
Edu 0.136 *** (0.047)
Female 0.640 (0.609)
Offlnc -0.425 (0.789)
NonLnc -3.040 *** (1.141)
Constant -6.878 (1.689)
No. of obs. 1710
Pseudo R2 0.13
Log pseudo-likelihood -423.94
*** Significant at 1%; ** significant at 5%; significant at 10%










Table 4-8b. Multinomial logit regression results (owners' decisions between farming, selling,
renting under fixed-rent or under shared-rental contracts)


Variables


Sell/ Farm


Fixed-rent/ Farm


Shared-rent/ Farm
Coeff. Robust
SE
-37.685 *** (0.272)
-0.313 (0.506)
-1.127 ** (0.545)
-0.001 (0.003)
0.521 (0.431)
0.065 (0.103)
-0.170 (0.181)
-0.015 (0.018)
0.460 (0.405)
0.039 *** (0.012)
0.115 *** (0.044)
0.210 (0.396)
0.699 (0.531)
1.325 (0.697)
-6.385 (1.162)
1710
0.17
-459.62


Coeff


Robust
SE
*(0.875)
(0.669)
(0.539)
*(0.002)
*** (0.713)
(0.173)
(0.005)
(0.025)
(0.570)
(0.021)
*** (0.047)
(0.608)
(0.789)
*** (1.140)
(1.691)


Coeff.

0.468
-1.242
0.249
0.004
-0.461
0.142
-0.132
-0.164
0.201
0.030
0.074
0.857
0.769
0.462
-5.443
1710
0.17
-459.62


Robust
SE
(0.564)
*** 0.464)
(0.427)
*(0.002)
(0.541)
(0.146)
(0.115)
*(0.096)
(0.479)
*(0.016)
(0.050)
** (0.425)
(0.540)
(1.057)
(1.411)


Coast
Non Urban
Agcon
SizePrior
%Titled
Adults
ValueAssets
Ani W
Credit
Age
Edut
Female
Qfflnc
NonLnc
Constant
No. of obs.
Pseudo R2
Log pseudo-
likelihood


-1.555
-0.555
0.153
0.003
1.948
0.214
-0.001
-0.028
0.743
0.012
0.138
0.640
-0.430
-3.035
-6.907
1710
0.17
-459.62


*** Significant at 1%; ** significant at 5%; significant at 10%


Table 4-9. Censored Tobit regressions (amount of land rented-out and sold)


Variables


Amount sold
Coefficient
-13.041
-12.688
-1.574
0.058
19.276 *
2.187
-0.002
-0.248
4.415
-0.006
2.127 **
14.263
-8.184
-53.473
-95.005
1710
25
0.08


Amount rented-out
Coefficient SE
-0.439 (2.437)
-9.609 *** (2.620)
-2.004 (2.224)
0.016 (0.016)
1.453 (2.041)
0.340 (0.623)
-0.793 (0.498)
-0.087 (0.159)
0.722 (2.349)
0.153 ** (0.066)
0.589 ** (0.264)
4.889 ** (2.295)
3.476 (3.026)
5.413 (4.585)
-33.930 (6.936)
1710
80
0.06
-516.17


SE
(10.445)
(9.144)
(7.919)
(0.037)
(10.194)
(2.085)
(0.115)
(0.381)
(7.555)
(0.251)
(0.879)
(8.757)
(9.045)
(33.073)
(26.138)


Coast
Non Urban
Agcon
SizePrior
%Titled
Adults
ValueAssets
Ani W
Credit
Age
Edut
Female
Qfflnc
NonLnc
Constant
No. of observations
Uncensored obs.
Pseudo R2


Log likelihood -201.01
*** Significant at 1%; ** significant at 5%; significant at 10%










Table 4-10. Logit for probability of purchase and censored


Tobit for amount of land bought
Amount purchased
Coefficient SE
-4.287 (5.378)
-1.305 (5.720)
-1.244 (4.141)
-0.659 (0.477)
1.012 (1.275)
0.034 (0.084)
0.398 (0.290)
9.604 ** (3.963)
-0.355 ** (0. 160)
0.874 (0.487)
-2.714 (5.857)
-0.257 (5.680)
10.416 (9.373)
5.688 (3.921)
-38.661 (12.674)
1710
33
0.08
-239.77


Variables

Coast
Nonh Uban
Agcon
SizePrior
Adults
VahueAssets
AniW
Credit
Age
Edu
Female
Offlnc
NonLnc
Tenant
Constant
No. of observations
Uncensored obs.
Pseudo R2


Purchased
Coefficient
-1.876 **
-0.763
0.148
-0.178 *
0.172
0.008 *
0.063 ***
1.261 ***
-0.050 ***
0.056
-0.238
-0.121
0.673
0.909 *
-2.453
1710


Robust SE
(0.727)
(0.731)
(0.473)
(0.103)
(0.157)
(0.005)
(0.020)
(0.422)
(0.017)
(0.058)
(0.626)
(0.701)
(1.129)
(0.487)
(1.414)


0.21


Log pseudo- -108.28
likelihood
*** Significant at 1%; ** significant at 5%; significant at 10%


Table 4-11i. Logit for probability of renting-in and censored Tobit for amount of land rented


Variables


Rented-in
Coefficient


Amount rented-in


Robust ~


SE
67)
24)
45)
54)
47)
03)
14)
67)
05)
22)
12)
18)
79)
54)


Coefficient
0.069
-0.393
0.597
-0.215
0.522
0.021
0.082
2.091
-0.092
-0.163
-1.203
-1.122
-0.221
-0.434
1918
494
0.03
-2124.91


SE
(0.519)
(0.701)
(0.466)
(0.040)
(0.138)
(0.011)
(0.041)
(0.503)
(0.015)
(0.065)
(0.608)
(0.634)
(1.149)
(1.341)


Coast 0.132 (0.1
Non Urban 0. 172 (0.2
Agcon 0.346 ** (0.1
SizeOwned -0.196 *** (0.0
Adults 0. 119 ** (0.0
ValueAssets 0.005 (0.0
Ani W 0.034 ** (0.0
Credit 0.726 *** (0.1
Age -0.034 *** (0.0
Edit -0.063 *** (0.0
Female -0.461 ** (0.2
OfJlnc -0.220 (0.2
NonLnc -0.179 (0.3
Constant 0.585 (0.4
No. of observations 1918
Uncensored obs.
Pseudo R2 0.13
Log pseudo-likelihood -976.74
*** Significant at 1%; ** significant at 5%; significant at 10%










Table 4-12. Multinomial logit for probability of renting-in


Variables

Coast
Non Urban
Agcon
SizeOwned
Adults
ValueAssets
AniW
Credit
Age
Edu
Female
Offlnc
NonLnc
Constant
No. of observations
Pseudo R2
Log pseudo-likelihood


Fixed-rent tenancy/Nothing
Coefficient Robust SE


Shared-rent tenancy/Nothing


Coefficient
-0.500
0.224
0.229
-0.275
0.086
0.008
0.035
0.688
-0.035
-0.087
-0.516
-0.298
-0.235
0.832
1918
0.14
-1226.01


Robust SE
** (0.200)
(0.268)
(0.161)
*** (0.078)
*(0.051)
*(0.004)
*(0.019)
*** (0.176)
*** (0.005)
*** (0.025)
** (0.242)
(0.257)
(0.452)
(0.542)


(0.268)
(0.350)
(0.255)
(0.060)
(0.081)
(0.004)
(0.017)
(0.304)
(0.009)
(0.035)
(0.407)
(0.367)
(0.626)
(0.755)


1.374
0.109
0.590
-0.122
0.188
0.001
0.037
0.859
-0.032
-0.013
-0.475
-0.101
-0.256
-2.126
1918
0.14
-1226.01


*** Significant at 1%; ** significant at 5%; significant at 10%









CHAPTER 5
CONCLUSIONS

The three essays presented here offered an economic analysis of agricultural land access at

the household level and its relationship with rural markets and poverty in Ecuador. The severe

inequality in land ownership and access has consequences for land and labor productivity and for

access to credit, modern technologies and land markets. Land inequality has generated

distortions in the access to agricultural markets over time, which have become institutionalized.

Thus, the effects of land inequality on poverty are augmented by imperfections in those markets.

The main findings of our study are summarized as follows:

1. Given the abundant labor availability of land-poor households, conditions of
unemployment, incomplete credit markets, and segmented land markets cause small
farms (less than 5 ha.) and especially minifimndios (less than 1 ha.) to be more productive
per unit of land but less productive per unit of labor than larger farms. Ecuador conforms
to the traditional findings for developing countries, many times emphasized in the
literature.

2. Land reservation prices per hectare decrease with the land to labor endowment ratio,
which may explain why small farmers tend to be more active on the demand side of land
markets than other farmers. However, similar to the findings of Carter and Salgado
(2001), constraints in the credit market reduce and even overcome the advantage of poor
farmers with respect to land reservation prices. As a consequence, the demand for land by
the rural poor is better satisfied in the rental market. This is suggested by the result that
the mean amount of land rented by households with less than 5 ha. is larger than the mean
purchased, and by the finding that lack of credit limits the incidence and amount of land
purchased more severely than it does the incidence and amount of land rented in. This
supports the argument of Carter and Salgado (2001) that the effect of credit constraints in
the demand for land in the rental markets would be magnified in the case of the land sales
market.

3. For agricultural households, the effect of being able to access one more unit of land is
such that it would improve the probability of credit access and the amount of credit
obtained. More land would also allow for a more efficient labor allocation and increased
labor productivity. Depending on endowments and output prices faced by the household,
among other factors, one more unit of land would likely increase agricultural profits
leading to higher household income. In addition, improved credit access would increase
land reservation prices and the ability to purchase land. More credit would also help
farmers acquire more and better intermediate assets, which would reinforce the process
described above.









4. Contrary to the findings of Deininger et al. (2003), there was some indication that large
landowners were willing to offer land in the land markets, reflected in the positive effect
of farm size on the likelihood to sell and rent out land under fixed-rental contracts.
However, that effect was not nearly as strong as the demand for land by small farmers
(i.e., the negative effect of farm size on the likelihood to purchase and rent in land). In
addition, 56% of the land sales and 71% of the land rentals (lessors) were by small
farmers, which show that in general, these farmers were the most active in both sides of
the land sales and rental markets.

5. Given the difficulties that prevent desired land transfers from large landowners to the
rural poor -such as subdivision costs and a restricted supply of credit- it seems
improbable that the market be able to achieve an optimal distribution of landownership
without any assistance from the government.

6. Because of the severe land inequality in Ecuador and the distortions this has generated
over time, the results regarding the estimated effects of one more unit of land mentioned
in conclusion (3) represent the existing differences among farms of different sizes. To
represent the potential of increased land access for rural development such an increase
must be accompanied by better access to services so as to increase the competitiveness of
the rural poor. In other words, since unequal land access and imperfections in the credit,
labor and land markets form a pervasive synergy, a governmental policy oriented to
increase land access for the rural poor would need to be complemented by other market
reforms and by the provision of technical assistance.

7. Female headed households were more likely to offer land in the rental market (under
fixed rental contracts) and less likely to demand land as tenants compared to households
headed by men. This result reflects the lower competitiveness of women in agricultural
production, which conforms to Deere and Le6n (2001)'s claim of the disadvantages
usually faced by women when trying to access credit, technical assistance or produce
markets. Still, female headed households were not significantly more likely to participate
in the land market as sellers than male headed households, nor were they significantly
less likely to participate as land buyers. This supports Deere and Le6n (2001)'s analysis
since it reflects the importance of landownership for women, which may go beyond just
obtaining agricultural profits: Landownership provides women with increased bargaining
power in the household and the community, food security for their children and
constitutes an asset which they can rent so as to generate income for the household. Also,
the analysis showed that female headed households acquired land through the market in
rates comparable to those of male headed households, a result that, as already noted by
Deere and Le6n (2003), makes Ecuador different from the rest of Latin American
countries.

8. The value of animal stocks and the share of non-labor income are important variables in
the determination of participation in the land markets which have not been considered in
previous studies. The role of animal stocks as both work animals and a form of financial
security for poor rural households is reflected in that they increase the likelihood of
purchasing and renting in land, as well as the amount of land rented in. They also
decrease the likelihood of renting out land under fixed-rental contracts. Similar to the









Endings for female headed households, those households with larger shares of non-labor
income, which tend to be poor rural households, are less likely to sell their land but more
likely to rent out (in this case under shared-rental contracts). This again suggests both the
importance of land as an asset that can be rented and the lower competitiveness of this
type of households. In any case, funds from remittances and governmental or non-
governmental transfers help the rural poor to make up for immediate cash needs without
resorting to selling their land.

9. Land titles increase the amount of institutional credit obtained but only for households
who are able to access formal credit. They also increase the likelihood and amount of
land sales. Unlike the Eindings of Feder and Feeney (1993), lack of land titles did not
effectively discourage investment in land and did not cause land values to be smaller than
for households with titled land. Also, contrary to the Eindings of Deininger et al. (2003)
and Masterson (2005), land titles did not stimulate the supply of land in the land rental
market, which suggests that the low level of development of the rental market may be due
to the titles not being properly registered, or to the lack of knowledge about the relevant
legislation as to the proper ways in which a Eixed-rental contract should be carried out in
order to avoid losing the land to the tenant, and/or to the deficiency of formal
enforcement of property rights in Ecuador.

10. Conforming to Sadoulet et al. (2001), Binswanger et al. (1995) and Deininger
et al. (2003) we found that the importance of the land rental market is that it provides the
rural poor with an alternative to landownership, and the possibility that it will play a
ladder toward landownership. In addition, sharecropping was found to be especially
common among the land poor (less than 5 ha.), not only on the demand but also on the
supply side of the market, with 64% of the supply and 81% of the demand cases being in
the form of sharecropping. This suggests that sharecropping may be more a type of
productive partnership rather than a precarious work relationship, which is emphasized
by the fact that, given land market segmentation, rental relations are mainly among
family, friends or in general, members of the same class. More research is required,
however, as to the specific characteristics of sharecropping arrangements in Ecuador in
order to confirm this proposition. This result also suggests the need for production
support by small owners so that they are able to work the land directly if they so wish.

Policy recommendations: My results suggest that liberalization and stimulation of land

rental markets are among the most urgent and important institutional changes that should take

place in Ecuador so as to benefit the rural poor. This would entail the elimination of restrictions

on sharecropping in the land legislation and the reduction of bureaucratic steps that cause high

transaction costs for individuals wishing to rent land. More importantly, such reform would

require the effective protection of property rights so as to ensure landowners of their rights to









land rented out. This calls for a stronger judicial system and the creation of effective mechanisms

of conflict resolution (Barham et al., 2004).


Also, the general supply of credit (i.e., credit not only for agricultural purposes but also,

non-agricultural), the most important stimulant of production and demand for land, needs to be

increased in the rural sector, especially in communities where credit markets are missing. An

innovative strategy such as the creation of credit bureaus which would turn information on

borrower reputation public (Barham et al., 2004) may help overcome asymmetric information

limitations faced by formal credit institutions. In addition, increased provision of technical

assistance by the government is a crucial complementary policy given the traditional

characteristics of small farmer agriculture and hence their lack of knowledge of modern, more

productive technologies.

Results from this study provide evidence of the conditions sustaining rural poverty in one

more country of the developing world. Our conclusions conform to many of the findings for

other developing countries and hence add to the plea for sound institutional changes in the rural

sector, which governments should promote. The need for improved data collection by

governmental institutions must also be emphasized. For example, including survey questions

aimed to gather data on who the principal agriculturalist in the household is; asset ownership by

gender; the titling status of land that is sold and purchased; who in the household rented or sold

land, and if possible, follow up surveys with the same sample of farmers over time (i.e., panel

data) would be improvements that would facilitate and make more accurate the analysis of

agricultural development.













Table A-1. Primary activity of female household heads by farm size
Total Minifundio Small Medium Large
# % # % # % # % # %
Ag. self employed 163 18.1% 80 35.2% 57 15.7% 18 7.1% 8 14.3%
Ag. worker 34 9.1% 27 14.4% 5 3.4% 1 3.1% 1 16.7%
Non-ag. self
emloyed 53 20.3% 37 25.0% 8 11.9% 7 18.9% 1 11.1%
Non-ag.worker 18 5.5% 16 8.0% 1 1.0% 1 3.4% 0 0.0%
Not economically
active or unempoed 31 53.4% 14 58.3% 8 44.4% 7 58.3% 2 50.0%
Total 299 15.6% 174 22.1% 79 11.4% 34 9.4% 12 15.8%


APPENDIX
PRIMARY ACTIVITY OF FEMALE HOUSEHOLD HEADS










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BIOGRAPHICAL SKETCH

Maria Jose Castillo was borne in Guayaquil, Ecuador. She graduated with her bachelor' s

degree in economics at the Escuela Superior Politecnica del Litoral (ESPOL) in Guayaquil, in

July 2000. She came to UF for her master's degree in food and resource economics in fall 2001

which she completed in fall 2003. Immediately, Maria Jose continued with her Ph.D. in food and

resource economics at UF.





PAGE 1

1 HOUSEHOLD INCOME, LAND VALUATION AND RURAL LAND MARKET PARTICIPATION IN ECUADOR By MARIA JOSE CASTILLO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

PAGE 2

2 2008 Mara Jos Castillo

PAGE 3

3 To my family in Ecuador

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4 ACKNOWLEDGMENTS I thank m y mom and dad for their constant l ove and support during this journey away from home. I also thank my husband Santiago J. Bu caram for his love, help and wisdom, and for keeping pushing me forward during th e progress of this dissertation. Special thanks I give to my chair, Dr. Carmen Diana Deere, whose valuable support made possible the completion of this research work. I thank my cochair, Dr. Ramn Espinel, as well for believing in me ever since I met him. I thank my other committee members, Charles Moss, Pilar Useche, and Grenville Barnes for apprec iating the effort I put into this endeavor. I thank the Instituto Nacional de Estadsticas y Censos (INEC) for providing me with the data set that gave place to this work at a time when it was not freely available on-line. I give special thanks to Ing. Julia Carrin from the Sistema de Informacin Geogrfica y Agropecuaria (SIG-AGRO) of the Ecuadorian Mi nistry of Agriculture for providing me with hard to get geographic informa tion which contributed to my study. I am also thankful to Dr. Jeff Burkhardt, Grad uate Coordinator, for always appreciating my work and being one of my advocates. Also to Dr. Ronal Ward for his willingness to help me understand econometric applications every time I looked for him. I also thank Dr. Andrew Schmitz for offering me the expe rience of publishing two papers on an interesting U.S. topic. The Department of Food and Resource Ec onomics deserve my appreciation for the financial assistance they made available to me without which my Ph.D would have not being possible. Finally, I thank my friend Lily for her faithfu lness and for providing me with a place to stay during the final st age of my dissertation.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................9 ABSTRACT...................................................................................................................................10 CHAP TER 1 INTRODUCTION..................................................................................................................12 Objectives...............................................................................................................................13 Hypotheses..............................................................................................................................14 2 THE IMPACT OF LAND INEQUALITY ON ECUADORIAN HOUSEHOLD INCOME .................................................................................................................................16 Land Problems and Rural Poverty.......................................................................................... 16 Multiple Market Imperfections and th e Household Income Problem.................................... 19 Statistical Analysis........................................................................................................... .......20 Credit Access...................................................................................................................22 Value Product per Unit of Labor.....................................................................................25 Labor Allocation.............................................................................................................. 26 Value Product per Unit of Land......................................................................................26 The Land Rental Market......................................................................................................... 27 The Model...............................................................................................................................29 Household Income Per Capita Estimation.............................................................................. 35 Conclusions.............................................................................................................................42 3 UNDERSTANDING LAND RESERVATION VALUES IN THE PRESENCE OF MULTIPLE MARKET I MPERFECTIONS: THE ECUADORIAN CASE.......................... 50 Introduction................................................................................................................... ..........50 Data and Methodology...........................................................................................................51 Restricted Profits............................................................................................................. 52 Land Reservation Prices..................................................................................................56 Results.....................................................................................................................................60 Conclusions.............................................................................................................................63 4 RURAL LAND MARKET PARTICIPATION IN ECUADOR AND ITS DETERMI NANTS................................................................................................................. 69 Introduction................................................................................................................... ..........69

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6 Data and Methodology...........................................................................................................72 Land Supply.....................................................................................................................72 Land Demand..................................................................................................................78 Results.....................................................................................................................................83 Supply Side......................................................................................................................83 Demand Side...................................................................................................................86 Conclusions.............................................................................................................................89 5 CONCLUSIONS.................................................................................................................. 100 APPENDIX PRIMARY ACTIVITY OF FEM ALE HOUSEHOLD HEADS........................ 104 LIST OF REFERENCES.............................................................................................................105 BIOGRAPHICAL SKETCH.......................................................................................................109

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7 LIST OF TABLES Table page 2-1 Number of farms by farm size, Coast and Sierra regions, Ecuador ................................... 44 2-2 Credit access, type of cred it and op erational farm size..................................................... 44 2-3 Mean loan terms by credit sector (all farm sizes).............................................................. 44 2-4 Agricultural labor productiv ity and operational farm size................................................. 44 2-5 Household heads primary activity an d households m ain source of income by operational area............................................................................................................... ...45 2-6 Mean land productivity by category of far m size..............................................................45 2-7 Farm size distribution of land tenants................................................................................ 45 2-8 Variable definition for household in com e per capita and credit equations....................... 46 2-9 Summary of explanatory variable s (incom e and credit regressions)................................. 47 2-10 Credit regressions for the probability of obtaining cred it and the am ount of credit.......... 48 2-11 Household income per capita regression...........................................................................49 3-1 Mean and median quasi-fixed factors................................................................................ 65 3-2 Classification of owner-tenant house holds by category of owned farm size..................... 65 3-3 Credit constrained households by owned farm size........................................................... 65 3-4 Summary of variables (la nd reservation value equation) .................................................. 65 3-5 Returns to fixed factors equation (quadratic function) ...................................................... 66 3-6 Log of the land reservation price equation........................................................................ 67 4-1 Farm size and land sales by owned land category............................................................. 92 4-2 Incidence of land rentals (landl ords) by owned fa rm size category.................................. 92 4-3 Mean and median statistics of variables in Equation 4-1..................................................93 4-4 Farm size and land purchases by owned land category (prior to purchase)...................... 94 4-5 Forms of land acquisition by gender.................................................................................. 94

PAGE 8

8 4-6 Summary of variables in Equations 4-2 and 4-3 ................................................................ 95 4-7a Land rented in by category of land owned.........................................................................95 4-7b Choice of rental agreemen t by category of land owned ....................................................96 4-8a Multinomial logit regression results (owners decisions between farm ing, selling or renting out).........................................................................................................................96 4-8b Multinomial logit regression results (own ers decisions between farm ing, selling, renting under fixed-rent or unde r shared-rental contracts)................................................ 97 4-9 Censored Tobit regressions (amount of land rented-out and sold).................................... 97 4-10 Logit for probability of purchase an d censored Tobit for amount of land bought ............ 98 4-11 Logit for probability of renting-in and censored Tobit for amount of land rented ............ 98 4-12 Multinomial logit for probability of renting-in.................................................................. 99 A-1 Primary activity of female household heads by farm size............................................... 104

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9 LIST OF FIGURES Figure page 2-1 Mean labor productivity by farm size................................................................................ 49 3-1 Shadow land values............................................................................................................67 3-2 Land reservation prices per hect are and non-price ra tioned households ...........................68

PAGE 10

10 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy HOUSEHOLD INCOME, LAND VALUATION AND RURAL LAND MARKET PARTICIPATION IN ECUADOR By Mara Jos Castillo August 2008 Chair: Carmen Diana Deere Cochair: Ramn Espinel Major: Food and Resource Economics This research provides an economic analysis of agricultural land access at the household level and its relationship with rural markets and poverty in Ecuador. We fi nd that land inequality and land market imperfections have a direct effect on household in come per capita and that there is a synergy between these and imperfections in the labor and credit markets, which magnify the effect of land inequality on ru ral household income. In addition, the presence of multiple market imperfections intensifies the quasi -fixity of factors other than la nd, which affects the contribution of land to profits and land values. The labor adva ntage of small farmers explains the remarkable difference in reservation prices per hectare between small and medium and large farmers. However, this effect is reduced for credit cons trained households. Lack of land titles is not found to discourage investments in land or to cause land values to be smaller than for households with tiled land. Consistent with these findings, we also obser ve that the demand for land by small farmers is significantly larger than the supply of land by large landowners both in the land sales and rental markets. Small farmers are found to be more active than larger farmers on both sides of the land markets and sharecropping arrangements ar e found to be especially common among the

PAGE 11

11 land poor. Land titles have a signific ant and positive effect on the li kelihood to sell and similarly, credit access on the likelihood to purchase and rent in land. We conclude that, given the difficulties that prevent desired land transfers from large landowners to the rural poor, it seems improbable that the market be able to achieve an optimal distribution of landownership w ithout assistance from the gove rnment. Also, that for the potential benefits for rural development of increas ed land access to be realized, such an increase must be accompanied by better access to services so as to improve the competitiveness of the rural poor. Policies regarding the liberalizati on and stimulation of land rental markets and increase in the supply of credit in the rural sector are recommended.

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12 CHAPTER 1 INTRODUCTION Latin Am erican countries, including Ecuador, are known for their severe income and land inequality. This explains the persistent in terest by the regions governments as well as international development organizations in land redistribution and in enhancing land productivity in Latin America. In Ecuador, according to the 2000 agricultural census, the Gini coefficient for land was 0.8, the same as for Latin America as a whole. Land reform in Ecuador, which took place during the period 1964-79, did little to im prove land distribution. More importantly, access to land via the rental market is very limited as well. According to the agrarian census, only two percent of the farms are under fixed or share-rent tenancy and 16% are under mixed tenancy (owner occupied combined with leasing or sh arecropping). This is due in part to current Ecuadorian legislation which impedes the fr ee development of land rental markets. Though agriculture remains an important contributor to national income and a source of employment for about 30% of Ecuadors work ing population, agricultura l policies have been deficient and unstable. In 2000, about 70% of the countrys rural popu lation earned incomes under the poverty line. It is commonly argued that an important reason for rural poverty is the limited access to land of the rural poor. On the one hand, large landholdings do not use their land intensely enough so as to generate sufficient employment opportunities for the resource poor. On the other hand, poor landowners must exploit their land more inte nsively than environmentally desirable, which worsens soil degradation and lo wers productivity. Furthermore, not only is land ownership and access to land unequally distribute d, but access to capital, technology and product markets is as well. The land market also suffers from segmentati on, where the rich trade land among the rich and the poor among the poor and where ethnicity a nd kinship play an important role in land

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13 transfers, especially in the Sierra (Lambert a nd Stanfield, 1990). Conseque ntly, land, credit and other market imperfections (as in the labor market) affect farm income generation and land prices, finally determining who can participate in the land market (buying, selling, renting in or renting out). In turn, low farm income and hi gh land prices for the poor strengthen market imperfections and inequality. Since the 1994 Land Law was approved land redistribution efforts have been left to the market. Given current restrictions on renting land, and the market imperfections just described, how well can the land market perform this task? This dissertation explores this question through the study of a national household survey data that covers all coastal and highland provinces of Ecuador. Objectives The objectives of the disserta tion are a) to understand quantitatively the role of land inequality and rural m arket imperfections on rura l poverty, through an analys is of the effect of these factors on the level of household income; and b) to identify the key variables that explain the persistence of land market imperfections and inequality. Research question 1: What is the effect of land inequal ity, land rental market restrictions, and holding untitled land on capital a ccess and rural income generation? Research question 2: In the survey year, what was the role of the land sale and rental markets (fixed or share tenancies) in land distribu tion and access? a. To what extent do land reservation values reflect land quality and productivity, as opposed to inefficiencies in land and related markets (credit, labor, technical assistance) or other non-productive factors (s uch as holding land for status)? b. What variables determine participation in the land sales and rental markets and the extent of participation?

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14 Hypotheses Hypothesis 1: Market imperfections and land rental m arket restrictions sustain land inequality and consequently rural poverty in Ecuador. a. In Ecuador, farm size affects household income directly and indirect ly through its effect on credit access and labor allocation. b. Land titles contribute to farm income primarily by facilitating access to credit. c. Insecurity of property rights and restri ctions on renting land out contribute to segmentation in the land rental market, hen ce limiting the amount of land the rural poor can access and consequently co ntributing to rural poverty. Hypothesis 2a: Land reservation values are negatively a ffected by restricti ons in the credit market, hence lowering the competitivene ss of the rural poor in the land market. a. Provided that small farmers are more producti ve (higher value product) per unit of land, the contribution of land to restricted prof its (shadow land values) is decreasing in operational area. b. Given credit market imperfectio ns, land reservation prices pe r hectare are lower for credit constrained households. Hypothesis 2b: Large landowners do not make land avai lable though sales or rentals to the land poor. a. Due to land sales after the ag rarian reform, and a likely process of reconstitution of latifundia (Jordan, 2003), small farmers are more active in the land ma rket as sellers and large landowners as buyers. b. The land rental market is friendlier than the land sales market for the rural poor in Ecuador. This study is divided into thr ee main chapters. In Chapter 2 we test hypothesis 1 through the use of a theoretical model which shows the effect of one more unit of land on household income when there are multiple market imperfec tions. Descriptive and econometric analyses add to the discussion and corroborate such effects. Hypot hesis 2a is tested in Chapter 3 by analyzing the configuration of land values. First, this chapter presents the estimation of a restricted profit function and land shadow values. It then cate gorizes households into credit constrained and

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15 credit unconstrained and uses these pieces of in formation in the estimation of land reservation prices per hectare. Graphic analysis helps unders tand how land values per hectare vary with farm size. Chapter 4 studies hypothesis 2b using descriptive analysis of household participation in the land sales and rental markets and econometric estimations which highlight the variables that influence the likelihood to sell, pur chase, rent out or rent in la nd (as well as the amount of land involved in each type of transaction). Here we an alyze the role of the land market as a channel for land redistribution. Finally, Chapter 5 offe rs general conclusions as well as policy recommendations.

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16 CHAPTER 2 THE IMPACT OF LAND INEQUALITY ON ECUADORIAN HOUSEHOLD INCOME Land Problems and Rural Poverty According to the last agricultu ral census (2000) in Ecuador, the Gini coefficient for land was 0.8 (with 1.0 being equal to perfect inequality), sim ilar to that of Latin America as a whole, the region with most unequal land distribution in the world.1 The agricultural census shows that 64% of the total 843,000 agri cultural production units in Ecuador ar e of less than five hectares in size and farm only 6.3% of Ecuadors total cul tivable land. On the other hand, 6.4% of all productive units each hold 50 or more hectares for a total of 61% of the agricultural land. Land reform in Ecuador, which took place during the 1964-79 period, did little to improve land distribution (Otez et al., 2000 ; Chiriboga and Rodriguez, 1998).2 More importantly, access to land via the rental market is very lim ited as well. According to the agricultural census, only 2% of the farms are under fixed or share-rent tenancy and 16% are under mixed tenancy (owner occupied combined with leasing or sharecropping).3 This is due in part to current Ecuadorian legislation which prevents the free development of land rental markets.4 Though agriculture remains an important contributor to national income and a source of employment for about 30% of Ecuadors work ing population, agricultura l policies have been deficient and unstable. In 2000, about 70% of the countrys rural popu lation earned incomes 1 Hayami and Otsuka (1993) show that the Gini coefficient for operational farmland distribution in Latin America is higher than 0.8, much larger than the coefficient for developing countries in As ia. Also, de Ferranti et al. (2003) note that income as well as asset inequality is higher in Latin America and the Caribbean than in Asia, Eastern Europe, and the 30 countries of the Organization for Economic Cooperation and Development. Average Gini coefficients from 1966 to 1990 (obtained by Deininger and Olinto, 2000) are 0.81 for Latin America while those for the Middle East, North and Sub-Saharan Africa, and East and South Asia are all lower than 0.7. 2 Chiriboga and Rodriguez (1998:16) note that, compared with the agrarian reform experience of other countries in the region, Ecuador is among those with the least redistributive results. 3 In contrast, in Asia some 20 to 30% of the land is re nted, in the United States, 40%, and in Belgium, 67% (FAO, 2002). 4 Legal and normative types of limitations in the land rental market are addressed in Section IV below.

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17 under the poverty line. More sp ecifically, the 1998-1999 Living St andard Measurement Survey shows that 50% of agricultural households in the sample had an annual income equal to or smaller than $1,300; 75% reported incomes smalle r than $2,500 and 99% indicated incomes of less than $15,000. The average annual income of agricultural households was close to $2,000, which represents only 46% of the mean annual in come of the total sample of households (5,816 households).5 Since the agricultural portion of the sa mple (1,898 households) represents mostly non-urban households (90%), this is an indicator that rural pove rty is more severe than urban poverty,6 a finding that is common in developing countries. It is commonly argued that an important reas on for rural poverty is the limited access to land of the rural poor. On the one hand, large la ndholdings do not use their land intensely enough so as to generate sufficient employment opportun ities for the resource p oor. On the other hand, poor landowners must exploit their land more inte nsively than environmentally desirable, which worsens soil degradation and lowers productivity Furthermore, not only is land ownership and access to land unequally distributed but access to capital, technology and product markets is as well. In addition, the land market in Ecuador suffe rs from segmentation, where the rich trade land among the rich and the poor among the poor and where ethnicity and kinship play an important role in land transfers, especially in the Sierra (Lambert and Stanfield, 1990). According to the literature, land market segmenta tion is encouraged by land insecurity or little protection of property rights (Marcours et al. 2005; FAO, 2002) and by high effective land prices 5 The year of our survey data (October 1998September 1999) represents a time of severe economic crisis in Ecuador, just before the dollarization of the economy (which of ficially took place in January, 2000). Yet, that should not be considered an abnormal year in terms of poverty since poverty had already been on the increase and continued increasing in the following years. 6 Similarly, a FAO country profile for Ecuador based on year 2004 reports an agricultural per capita GDP (agricultural GDP/agricultural population) that re presents only 40.5% of national per capita GDP.

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18 for the poor, beyond the productive ability of the land, driven by imperfect capital markets and the existence of high transaction costs in the land market (Carter and Sa lgado, 2001; Carter and Zegarra, 2000). In addition, low farm income and hi gh land prices for the poor strengthen market imperfections and inequality. As argued by de Janvry et al. (2001), under multiple market imperfections like these, improving land access for the poor can improve both welfare and efficiency. The objective of this chapter is to understand quantit atively the role of land inequality on the level of rural household income, at the same time as we explore the hypothesis that market imperfections and land rental market restrictions sustain land inequality and consequently rural poverty in Ecuador. The development literature has already addressed in different ways the effects of farm size and unrestricted land rental markets on farm income. This has been empirically analyzed in several countries of the developing world; howev er, the results tend to vary from country to country, or even within the sa me country, depending on the vari ables included in the analysis. Moreover, previous studies of these issues in Ec uador have been only partial, limited to certain sub-regions or variables. This study, which c overs a sample of farm households from all provinces of the coastal and highland regions of Ecuador (an area that represents 77% of Ecuadors cultivable land), will provide a comprehensive analysis of these issues. This chapter is organized as follows: Firs t, we summarize the importance of market imperfections in household utility maximization. Th en, in order to test for the existence of a relationship between farm size a nd 1) credit access, 2) value product per unit of labor, 3) labor allocation and 4) value product pe r unit of land, we perform pair wise analyzes based on our household data. Subsequently, we provide a brief de scription of the land re ntal market situation

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19 in Ecuador and its possible infl uence on poverty. Then, we model th e effect of additional land on income for an average agricu ltural household, followed by an econometric estimation of household income per capita to test for the e ffect of farm size and credit access among other relevant variables. We offer conclusions in our last section. Our results suggest that land inequality and la nd market imperfections have a direct effect on household income per capita but also that ther e is a synergy between these and imperfections in other markets such as credit and labor, wh ich are essential for agricultural production and productivity. These imperfections magnify the e ffect of land inequality on household income. Multiple Market Imperfections an d the Household Income Problem The agricu ltural sector of developing countri es suffers from multiple market imperfections, including the credit, in surance, labor and land markets. De velopment economists (Singh, et al., 1986; Bardham and Udry, 1999) have observed that multiple market imperfections invalidate the classical profit maximization approach used in order to find (or understand) optimum input allocation of rural households. More specifically, models of profit maximi zation assume that production decisions are independent of consumption decisions such that input choice depends only on input and output prices and the available technology (Bardham and Udry, 1999). Under this condition, the farm households problem is separable and can be solv ed recursively: production decisions are made first (profit is maximized) and consumption decisions afterwards (thus utility of consumption can be maximized subject to a budget constraint that includes taking maximized profits as a given). This implies that production decisions affect cons umption decisions but not vice versa. In other words, preferences (e.g. between consumption a nd leisure), household endowments (e.g. assets and labor), non-farm income and prices of consumption commodities do not affect production decisions (Singh, et.al., 1986, Bardham and Udry, 1999).

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20 This structure, however, applie s only when markets are complete or when there is only one market imperfection, not when there are multiple market imperfections (Bardhan and Udry, 1999). In order to reach equilibrium, househol ds must equate demand and supply for each commodity. The non-existence or incomplete pres ence of markets impede this equilibrium to happen at market prices, instead it happens at what are called virtual or sh adow prices which are different from market prices and are endogenous to the hous ehold (Singh et at, 1986). Virtual prices and consequently the farmers maximization problem will be a function of household endowments, such as land and family labor; market prices; off-farm labor market characteristics and non-farm income, among other f actors. Therefore, the appropriate agricultural household model under multiple market imperfections is that which jointly considers production and consumption choices (Bardham and Udry, 1999). According to this analysis, la nd inequality or rather an im provement in land distribution would influence input choice (inclu ding allocation of family labor), hence having the potential of affecting farm productivity and household income as a result. The model below develops this idea following Finan et al. (2005); first, we perf orm pair wise descrip tive analyzes with our household data in order to observe if and how significantly farm si ze affects credit access, labor allocation and labor and land produc tivity in the case of Ecuador. Statistical Analysis We use data from Ecuadors Living Sta ndard Measurement Survey (LSMS) 1998-99 provided by the National Institute of Statistics an d Census of Ecuador, in order to empirically observe the effect of land access on credit and farm labor. The sample includes 5,816 urban and rural households from the Coast and Sierra regions of Ecuador; 1,898 observations of agricultural households are used in this chapter. Relevant se ctions of the survey include questions on economic activities of the house hold members, credit access, land tenure,

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21 agricultural production, farm labor variable input expenses, a nd ownership of machinery and equipment as well as household demographics. He re, we perform pair wise analyses of credit access, labor allocation and labor and land productivity with respect to farm size. The analysis below will classify farms in four categories based on farm size. The first category consists of operationa l holdings of less than 1 h ectare. These are considered minifundios since such small farm sizes hardly allow for the subsistence of a household. These farms are treated as a separate category he re given their predominance in the sample (the national agrarian census also reports these farm sizes as a separa te category). The next category, farms of one to less than five hectares are still considered small and, as noted by Lopez and Valdez (2000), If not irrigated and intensively farmed this amount of land cannot support levels of consumption above the extreme poverty line with out other sources of income (L opez and Valdez, 2000: 203). Farms of five to less than 40 hectares are regarded as medium size given their higher probability of being medium capitalized units, that is, units belonging to farmers who have been successful in agriculture and have been able to accumulate land (or access more land) and other assets over the years. Finally, farms larger than 40 hectares are treated as large. Table 2-1 summarizes this information. Similar to the data gathered by the national agrarian census, Table 2-1 shows that the largest category is made up by minifundios and that the majority of farm households operate less than 5 hectares. Moreover, the median ope rational holding is less than 2 hectares. Of the total number of farms in our sample 73.8% are reported as owner-operated; 15.5% are partially owner-operated, and 10.6% are held by tenants only.

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22 Credit Access The depend ency of credit access on land wealth is a well known constraint in the developing world wher e dualistic structures7 characterize the rural sector (Bardhan and Udry, 1999; Carter and Zegarra, 2000; Feder and Feeny, 1993). Besides the fact that collateral is usually necessary in order to access the formal cr edit market, and that land is the most desirable type of collateral given its characteristics, la nd ownership is a sign of economic -and at times politicalpower which facilitate s market immersion and particip ation (de Ferranti et al., 2003). We explore the existence of a relationship between credit access and farm size in the case of Ecuador. Table 2-2 shows the proportion of farm households classified by operational area who obtained credit for a positive interest rate.8 The proportion of loans received from the formal sector and those from the informal sector are al so reported together with the respective credit amounts and interest rates. The credit variable is total credit receiv ed by the household, which includes credit for agriculture, for a family business and/or for consumption (purchase of durable goods, house building/remodeling, sickness, etc.). The reason fo r including all types of credit received by the household is that since credit is fungible it can be used on any household need regardless of the purpose for which the loan was obtained. Besides, ownership of/access to land (which is our focus here) is a signal to lenders as to how much debt respon sibility a household can acquire. Thus we expect to find a relationship between farm size and credit access even if we include 7 A dualistic structure in the rural sector refers to low productive small family farms coexisting with capitalist farmers who hire labor and where the mobility of farm opera tors between the two sectors is severely limited (Berry and Cline, 1979; Bardhan and Udry, 1999). 8 Households who received credit for a null interest rate are omitted for purposes of this analysis since such cases often involve small loans prov ided by relatives or friends, or credit received in-kind by input suppliers or NGOs.

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23 loans for family business or consumption. The inte rest rate (r%) is the av erage nominal interest rate faced by the household including all types of credit. The chi-square statistic for the hypothesis of independence between operational farm size and credit access (null hypothesis) reveals that th ere is a statistically significant relationship between the two variables (the null hypothesis is re jected at 5% level of significance). The null hypothesis is also rejected (at 10 % level of significance) when owne d farm size is used instead of operational farm size. The same test was performed for the case of formal credit, in which the independence hypothesis between farm size and credit access was re jected at a 10% level of significance. We also tested the hypothesis for access to informal cred it but this time there was a failure to reject. This result suggests that, as e xpected, non-institutional lenders pa y less attention to farm size than formal lenders since the former tend to be much more familiar with their borrowers, hence, facing lower levels of imperfect information. Also, statistical analysis of the relationship between category of farm size and the type of credit obtained (formal vs. informal) reveals that such a relationship is significant for minifundistas (5% significance) and small farm sizes (1 % significance), with the odds of getting informal credit being higher than the odds of accessing formal credit.9 For medium and large size farmers formal and informal sources of credit are more equally accessible (and/or preferred) than for minifundistas or small farmers. In addition, when analyzing the difference in the amount of credit obtained from formal and informal sources, using a t-statistic we find that, although the amounts of formal credit are greater for all operational sizes, the difference is only statistically significant for the small size 9 The data indicate that a minifundista is 75% more likely to get informal credit than formal credit. The likelihood for small farmers is 164%.

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24 category. However, taking all sizes together (Table 2-3), the mean dollar amount of formal credit is significantly larger than the mean dollar amount of informal credit. Analysis of the interest rates reveals that, except for the large farm size category (Table 22), informal credit interest rates are significantly higher than formal credit interest rates. This is in line with what was expected given the theory and typical empirical fi ndings (for example see Andersen and Malchow-Moller, 2006). A somewhat intriguing finding in Table 2-3 is that only 20% of all formal loans required real estate as collatera l (compare this to 58% in Peru in 1997 as reported by Guirkinger and Boucher, 2005). This, however, can be explained by observing the structure of the formal credit market in our sample. The bulk of formal credit is offered by private banks (33%) and cooperatives and associations ( 49%) and the rest by government al institutions (11%) and NGOs (7%). The latter institutions typically not require borrowers to put real estate as collateral. Similar is the case of cooperatives and associat ions. Finally, although pr ivate banks would be expected to act differently than the other lenders asking for valuable collateral such as real estate, the evidence indicates that the loans offered by banks in th e rural sector are in general small compared to those offered in the urban sect or (Espinel, 2002); this c ould explain the little need for this type of collateral.10 This result together with our findings of a statistically significant relationship between fa rm size and credit access suggest s that land ownership is not necessarily functional as collateral for formal credit but it is also a sign of economic power which facilitates credit access. 10 The average amount of credit received from private banks in the sample is US$2,143.

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25 Value Product per Unit of Labor Evidence in developing countries has also shown that there is a direct relationship between far m size and value product per un it of labor (usually referred to as labor productivity). Our data conform to what is expected (Table 2-4). While all farm sizes reported the use of non-remunerated labor, medium and large farmers hired a significantly larger amount of labor than minifundistas and small farmers (Table 2-4). Larger amounts of hired labor refl ect the capitalist na ture of medium and large farmers, which is manifested in higher labor produc tivity. Capitalist farmers hire labor up to the point where marginal labor productivity equals the wage rate while traditional family farms usually have larger amounts of labor per unit of land which, gi ven labor and credit mark et imperfections, they must allocate less efficiently to th e farm (Berry and Cline, 1979). More specifically, since moral hazard and hence the need for labor supervision is not an issue when using family labor while it is when hiring labor, family labor tends to be more productive than hired labor (Binswanger et al., 1993); however, the presence of imperfections in the labor and credit markets (i.e. unemploymen t and credit rationing) cause small farmers to make a less efficient allocation of labor to the farm compared to larger farmers, resulting in lower value product pe r unit of labor for small farmers. The t-tests of mean differences indicate that the most significant [c onsecutive] difference in productivity occurs be tween medium and large size farmers (10% significance). However, the differences in productivity between a minifundista and a medium size farmer and between a small and a large farmer are highly significan t (1% significance). Differe nces in mean labor productivities for the four differe nt categories of farm sizes can be better observed in Figure 2-1, which shows a clear increase in mean labor productivity as farm size increases.

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26 Labor Allocation Table 2-5 shows the distribution of the pr im ary activity of household heads and the composition of household income by agricultura l and non-agricultural sectors for each category of farm size.11 Table 2-5 illustrates that as farm size increases so does the proportion of household heads who primarily work on-farm. Si milarly, considering total household income, it is more likely that farming is the main source of income for the household as farm size increases. Also, both minifundistas and small farmers rely more heavily on wage income (either from the agricultural or non-agricultural se ctor) than medium and large fa rmers. Our data thus suggests that as farm size increases so does the importance of the farm business for the household, resulting in a higher level of in come obtained from agriculture (compared to other sources of income of the household). Taking into account the sex of the household heads, 16% of them are women. They are over-represented among minifundistas and under-represented among small and medium farmers (Table A-1). Of the household heads who are not economically active, these are slightly more likely to be female rather than male (53 vs. 47%). Also, female heads are more likely to declare agricultural self-employment as their primary activ ity than male heads (55 vs. 45%). The female heads most likely to declare agricultural se lf-employment as their primary activity are smallholders and large farmers. Value Product per Unit of Land The hypothesis of an inverse relationship betw een far m size and the value of total product per hectare (usually referred to as land productivity), often found in the developing world, is also 11 The decomposition of rural household income follows Corral and Reardon (2001).

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27 tested here using pair wise analysis. Table 26 presents the mean la nd productivity for each group of farm size. The t-tests of mean differences in land producti vity show that the difference in mean value product per hectare between farm size categories is statistically significan t at a 5% level of significance, indicating that sma ll farmers tend to exploit the land more intensively than large farmers. This result was expected given that, as noted earlier, small farmers usually have a larger labor to land endowment ratio than large farmers (Berry a nd Cline, 1979), hence output per hectare tends to decrease with farm size. The Land Rental Market It has been argued that land ownership shoul d not necessarily be the m ain objective in order to improve the livelihood of the poor, but th at access to land via other forms of tenure, friendlier to the poor, should be earnestly sought too (d e Janvry et al., 200 1; Sadoulet et al., 2001, and Currie 1981). In accordance with this id ea, international development organizations advocate for liberalization of the land rental markets in developing countries. This process could be considered to be only half-way implemented in Ecuador because of legal as well as normative types of limitations.12 Among the legal limitations are the following: a) Sharecropping, a form of tenure that, although re garded as inefficient by some authors, has proved effective in overcoming imperfectio ns in the capital and labor markets, was abolished in 1970 by the Law of abolition of precarious form s of labor in agriculture, and it continues to be illegal. b) Fixed-rent tenancy is allowed by the law but th e law also contemplates the possibility of prescription of the owners property rights under certain conditions, namely, 1) if the landowner does not have a valid land title (properly registered ), he/she can easily loose the land to their tenant; or 2) even in the presence of a valid land title by the owner, if the rental agreement is not in the form of a written contract properly registered, and if the tenant has been the land operato r for at least 15 years, the landowners property rights can be prescribed. 12 Based on FAO (2002) study.

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28 c) The Constitution still includes th e possibility of expropriation based on the concept of the social function of the land (art. 30).13 This legislation conserves the spirit of the ag rarian reform era; it gives more protection to tenants than to landlords and discourages suppl ying land to the rental market. In addition, normative conditions that hinder the re ntal market are as follows: d) High transaction costs discourage land title registration by landowners; e) Proliferation of land conflicts (due to conflic ting inheritance rights or lack of titling); f) Lack of knowledge about the relevant legisl ation and abundance of corrupt lawyers who increase the costs of legal processes; g) Lack of formal enforcement of property rights; and h) Unequal ethnic and socioeconomic relations Points (d) and (e) reflect the impediments landow ners face in order to obtain land title and consequently to satisfy requirements of the law in order to engage in fo rmal rental contracts. Conditions (f) through (h) point to the high risk of losing property rights that landowners would face if they decide to rent-out their land. This analysis would explain the low incidence of land rental agreements in Ecuador reported by the agrarian census (see the first section of this chapter). As a consequence, it has been found that la nd rental markets in Ecuador are segmented (Lambert and Stanfield, 1990; FAO-COTECA, 1995; FAO, 2002) Landowners prefer tenants they already know and can trust and vice versa, hence the more economically powerful rent among themselves and so do the poor, with th e ethnic component being of importance too.14 This alone suggests that the landless poor would at best be able to access land of the poor, which 13 Causes for expropriation are precarious forms of farm labor; technologies of production that endanger natural resources; abandoning farming for more than two consecutiv e years; and lands that, while not fulfilling their social function, face demographic pre ssure by peasant populations. 14 Lambert and Stanfield (1990) and FAO-COTECA (1995) note that land sale markets are also segmented by class and ethnicity.

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29 given the potential effect of farm size on povert y, would imply both that the amount of land they could access is small and that they would continue to be poor. This implication is strengthened by the previous analysis on cred it access and labor productivity, which are influenced by farm size. Finally, our data (Table 2-7) indicates that the large majority (84%) of tenants (either tenants only or farmers that combine farmi ng their own and rented land) are either minifundistas or small landholders. Given land concentration in the hands of large landowners, it is the land poor who usually engage in the rental market. Al though we lack data on whom the tenants in the sample rented from, the findings about land segm entation of the studies already mentioned lead us to expect that these tenants li kely rented from small farmers. The Model The m odel we developed has its roots in th e agricultural development literature (Bardhan and Udry, 1999; Feder and Feeny, 1993; Finan et al., 2005) and has been adapted to fit the situation faced by a representative farmer in Ecua dor as observed in our statistical analysis section. The purpose of this model is to show th e effect that an additional unit of land would have on total household income. This model illustrates the optimization process of an average agricultural household when choosing productive inputs. Three market imperfec tions are considered here: incomplete land markets, dependency of formal credit on land wealth, and unemployment. The first market imperfection reflects the fact that -as argued in the previous sec tionland markets in Ecuador are segmented, hence demand for land by the poor or ma rginalized segments can only be partially satisfied. Under these conditions land purchases by the average household and even access to land via the rental market can be regarded as unimportant for the purpose of this model.

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30 Therefore, following Finan et al. (2005), we igno re land transactions and consider land as exogenous. The second market imperfection occurs because costs of information and [consequently] of repayment enforcement limit the ability of institutional credit to reach small farmers (Feder et al., 1988). As a result, these farmers ar e rationed in the formal credit ma rket, while this is generally not the case for large farmers. Hence, different from perfect capita l markets, borrower risk is not necessarily the cause of borrower rejection (since institutional lenders do not have information on how risky a specific small farmer is); instead, the borrowers fa rm size is. This is supported by the findings in our statistical analysis section which show that there is a statistically significant relationship between farm size and formal credit access and that the mean dollar amount of formal credit is signif icantly higher than the mean do llar amount of informal credit. Informal credit is thus ignored in this section. Finally, unemployment is also a crucial problem in Ecuador. In 1998-1999 national unemployment reached between 11 and 14%15 (Instituto Nacional de Estadsticas y Censos, INEC), among the highest rates in Latin Am erica and the Caribbean (ECLAC, 2005: Table 1.2.17). This is an important constraint for house hold income maximization since, together with incomplete land markets, unemployment produces inefficiencies in the allocation of family labor, which is reflected in low farm-labor pr oductivity (Berry and C line, 1979). Moreover, segmentation of land markets is encouraged by imp erfections in the credit markets since limited access to capital cannot easily make up for poor house holds liquidity constraints enough so as to purchase larger units of land (Carter and Salgado, 2001). 15 Although 14% was the highest unemployment rate between 1990 and 2006, rates were in average 10% annually between 2000 and 2006 ( INEC ).

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31 The assumptions in this model are the followi ng: 1) no land transact ions or land rental contracts; 2) hired and family labor are perfect substitutes, and 3) off-farm wage equals the wage earned by hired farm labor. Two limiting conditions are of importance: a budget constraint and a labor market constraint. The budget or cash constraint includes th e additional limitation that the amount of credit that can be borrowed depends on the households total land endowment. For our model, a household is expected to ma ximize returns to its fixed assets (land and family labor) income that will be used for c onsumption and savings. Inco me for the agricultural household comes mainly from three sources: farm profits, off-farm wage income, and non-labor income -which would include remittances, governme ntal transfers and income from investmentsminus the cost of borrowing16. 0 ; )( .. )( );,,(,,, H ELLML ADRwLqXwH HLLts AiDRwLqXwHyXLApQfm m m f m XHLLMaxmf(2-1) Where, Q= total farm output A land endowment E family labor endowment mLoff-farm labor supply fLon-farm family labor H hired labor M = maximum amount of labor hours the labor market can accept from the household X variable inputs y = household and farm characteristics (human capital, farm location, soil quality, etc.) 16 Total debt value cancels out in the objective function since the household repays the same amount of money it receives; the only thing left to affect its income is the cost of borrowing.

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32 R non-labor income Damount of credit received by the household iwq p ,, ,output price, input price, wage rate, interest rate The Lagrangean is represen ted by the following equation: ] [ )(][ )(][ );,,(2 1 f f fLEMqXwHADRLEw AiDRLEwqXwHyXLApQ (2-2) Kuhn Tucker conditions: 0] [;0;021 21 wwpQLL wwpQ Lf fLf f L f (2-3) 0] [;0;01 1 qqpQXXqqpQ XX X (2-4) 0] [;0;01 1 wwpQHHwwpQ HH H (2-5) 0 )(][;0;0 )(][1 1 1 qXwHADRLEw qXwHADRLEwf f (2-6) 0] [;0;02 2 2 f fLEM LEM (2-7) Since the households being mode led are agricultural producers, fLand X should be positive. Now, if)1( 01 wQpHH. From (2-3), 21)1( wpQfL f fL H L HpQpQ pQpQ 2 2 Since family and hired labor are assumed equally productive (assumption 2), then02 This implies that when 0 Hthe labor constraint is not binding (MLm ); in other words, the household would not be suffering from unemployment. In such a case and assuming the financial constraint is binding,17 the indirect (or maximized) household income function (F) would be as follows: 17 This assumption is based on the fact that credit is scarce in rural areas.

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33 RLEwqXwHiRLEwqXwHyXLApQ y R E Aiwq p Ff f ][ ][ );,,( );,,,,,,(* * ** (2-8) In order to observe the effect of one more unit of land on the optimal choices of inputs and consequently on total household income, let us obtain the total derivative of household income with respect to land. A X iqpQ A H iwpQ A L iwpQpQ dA dFX H f L Af *)]1([)]1([)]1( [ (2-9) This is equivalent to (from the first order conditions): A X iq A H iw A L iwpQ dA dFf A 1 1 1][][][ (2-9) Provided that01i which is the case for cred it constrained households,18 we see that an increase in land endowments contributes to household income both directly through affecting total production and indirectly thr ough an effect in the optimal c hoice of farm labor, hired labor and variable inputs. On the other hand, ifwwQpHH10 From (2-3) we have2 1 wpQwfL. Replacing w1 in the previous equation and solving for2 we getfL HpQpQ2 This suggests02 which implies that the labor mark et constraint w ould be binding (MLm ). In other words, the household cannot send any more me mbers to off-farm labor, therefore it must use all its labor on-farm and neither would it need hired labor nor could it afford it. This result is more appropriate for an average agricultural house hold in Ecuador, where th e labor constraint is 18 The Lagrange multiplier for the budget constraint,1 represents the shadow value of capital. For households who are credit constrained, this shadow pr ice is larger than the market interest rate (Cater and Salgado, 2001).

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34 very likely to be binding, hen ce little or no hiring is done.19 The maximized household income would in this case be (again assuming01 ): RiMiwXiqyXLApQ y K E Aiwq p Ff]1[]1[]1[);,,( ),,,,,,,(* ** (2-10) The effect of an increase in the land endowment would be A X iqpQ A L pQpQ dA dFX f L A *]1[ (2-11) A X iq A L wpQf A 1 21][])1([ From the family labor first order condition we can see that if01 and 02 f fL LpQw pQw 02 Hence, given the labor market restriction, the productivity of on-farm labor is smaller than off-farm wage labor (this is what causes no hiring to be chosen). Because of the labor market imperfection then, ev en if capital markets we re perfect, the shadow value of land for the household would be greater than simply the direct effect of land on farm product. Now, if both01 andf fL LpQ w pQ w )1(0 )1( 01 1 2 2 That is, labor productivity is smaller than the wage rate times one plus the shadow value of capital. Thus, one more unit of land would have an even larg er effect on household in come than if credit markets were not constrained. In Equation 2-7 we see that one of the ways in which one more unit of land will contribute to household income is by allowing it to relocat e labor between onand off-farm activities (similar to what was observed in Table 2-5), however this effect will remain smaller than 19 Our data shows that less that 30% of small farmers (less than 5 hectares) did any hiring in their farms; less than 3% used more hired than non-remunerated labor, and less than 1% used only hired labor.

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35 A L wf 1)1(as long as the labor market constraint is binding (02 ). In other words, inefficiency in labor allocation will continue given the labor market imperfection. However, sincef mLEL the moreA increases, the smaller the labor market constraint for the household (the constraint could go from binding to non-binding) and the closer fLpQto)1(1 w. Hence, increases in th e land endowment should improve labor allocation and, consequently, productivity (assuming that farm labor skills are standard for all family members). Our results therefore suggest that imperfections in the credit, land and labor markets affect farm labor and input allocation, inducing low productiv ity and hence worsening rural poverty. In turn, a poor, low productivity farmer has limited access to credit and land markets. Since this type of producer makes up the great majority in rural Ecuador, inequality in the ownership of land (and lack of access to land in general) is therefore strengthened and so is poverty. Additional land alone would not necessarily tu rn smallholders into efficient, modern entrepreneurs as many land reform experiences across Latin America have shown. In order to raise agricultural incomes, reforms in complementary markets are essential. In the next section, we proceed to estimate the effect of farm size, credit and labor on household income per capita. Household Income Per Capita Estimation From the previous sections we see that land is expected to affect household income both directly and indirectly through its effect on credit access, labor a nd input allocation. Hence, we need to deal with an endogeneity problem wh en attempting to estimate the effect of these variables together with farm size on household income. In order to perform a consistent estimation, two-stage least squares (2SLS) estimation is used, where a credit regression is

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36 performed first, followed by a household income regression. In the household income estimation a prediction of the credit variab le from a censored Tobit is included and both the value of inputs and intermediate assets eliminated. This is becaus e credit would have been likely used in part for the purchase of non-labor inputs (fer tilizers, seeds, etc.), machiner y and equipment. In addition, we run a probit regression for access to credit prio r to our Tobit analysis with the purpose of confirming what variables shoul d be included in the latter. The system to be estimated is the following (Table 2-8 defines each variable and Table 2-9 summarizes all variables included). Rmt OffIncome HiredLabor TechAsist C CreditHatP Tenant SizePC SizePC AdultsPC Edu Age Male LandQual Disperse Settlement Periphery County HIPCi i i i i i i i i i i i i i i i i%2 0 (2-12) uc NonLaborIn OffFarmInc Animals Assets Title Formal Formal Title Owner NPlots Size Size Edu Age Male XCrops AgCon NonUrban Province Crediti i i i i i i i i i i i i i i i i i %2 0 (2-13) Given differences between the Coast and Si erra regions in climate, production and agricultural land distribution (the smallest farm sizes are main ly found in the Sierra), two systems are estimated, one for each region. Like in Table 2-5, our income variable in cludes all sources of annual household income, namely wage income, net monetary income from self-employed members (either in agricultural or non-agricultural activities),20 other sources of income such as rents, interests, pensions, etc., and non-labor income coming from remittances, governmental and/or non-governmental monetary transfers. 20 Net income from self-employed members has been calculated using individuals reported estimate of annual monetary income.

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37 The variable farm size refers to operational land holdings. As noted earlier, because of restrictions in the labor market, more land in operation implie s more employment absorption of family labor and more income possibilities. This is so regardless if the la nd is owned or rentedin. Also, given results in our statistical analysis section, this variable is expected to influence credit access and the amount of credit obtained. The sign, significance and relative magnitude of the effect of farm size on Equations 2-12 and 2-13 are the main focus of our analysis since they will allow us to test for the effect of land in equality on poverty. On Equation 2-13, this variable will indicate how limited small farmers are with re spect to access to serv ices and on 2-12, it will show the development potential that larger operational holdings would have for individual households. The tenure variable is also of special impor tance in the household income equation. It will gather whether or not land ownership makes a difference on household income compared to tenancy. An aspect closely related to the importance of land wealth on access to credit is the role of a land title. More specificall y, if land ownership facilitates participation in the formal credit market, then those who have title to their land should be preferred by le nding institutions (the interaction term between formal cr edit and title to land will help cap ture this effect). However, if only farm size but not land title had a significant effect on cred it access that could suggest a rather indirect effect of farm size on credit access (Table 2-3). Farm machinery and equipment (assets) ensure better producti vity of land and labor and therefore could influenc e credit access. In addi tion, machinery can be used as collateral for credit. The value of owned animals is included too because empirical studies have shown that farmers often use livestock as a form of savings in addition to their sometimes being means of

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38 farm work. Specially for accessing informal credit, ownership of livestock or small animals can be an implicit type of collateral21. The effect of household location is expected to be captured by several variables in each region. Location in an urban or non-urban (peripher y, settlement or disperse) sector represents distance from major markets. Also, since land distribution and producti ve conditions differ by county (Lambert and Stanfield, 1990; F AO-COTECA, 1995; Worl d Bank, 2004), county dummies are included in the household income equation (Equation 2-12). For the credit equation (Equation 2-13), we added province dummies. The province with mean income closest to the mean income of the Sierra is Caar and that for the Coast is Los Rios; thus these provinces are chosen as a base for comparison in each region. We also included a dummy indicating whether or not the househol d is located in an area of agricultural concentration or in an area of concentration of export crop produc tion. If the household is located in an area of agricultura l concentration, it is likel y to have better access to productive services (marketing channels, variety of lenders), hen ce having better possibilities of accessing credit. Due to data limitations, agricu ltural concentration is assume d at the county level, more specifically, if a household is located in a county in which over 50% of the land is being exploited22 it is considered to belong to an area of agricultural c oncentration. Likewise, if a household is located in an area of major producti on of export crops such as bananas, cocoa, coffee or flowers, then it is considered as be longing to an area of export crop concentration. In order to capture the effect of operational holdings distribute d in more than one farm, the variable NPlots was introduced in Equation 2-13. The f act of having two or more plots in 21 The word implicit is chosen because in our sample there are no cases of animals explicitly pledged as collateral for credit. 22 This includes fallow lands and shrimp pools and excludes the area of moorlands, mountains, forests and infrastructure.

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39 operation could have two opposite effects for fa rmers when trying to access credit: 1) the possibility of benefiting from ec onomies of scale is reduced, hence lenders might be less willing to offer credit to a small farmer with several plots; and 2) the risk of loosing a harvest is reduced by not having production concentrated in one location but distributed in two or more, which might be attractive to lenders. The final effect of this variable on credit will depend on what type of effect is stronger. The land quality index is includ ed in Equation 2-12 so as to capture the effect of the agricultural potential of th e land on household income.23 This index varies by parish (smallest political division of the territory). In addition, given the presence of plant diseases, pests, and the predominance of traditional methods of produc tion, those households with access to technical assistance are expected to do better in household income. The percentage of farm labor th at is hired for a wage is al so expected to be positively related to household inco me since, as noticed in our statist ical analysis section, large portions of hired labor are usually a sign that the farmer is of the capitalist type, who hire labor up to the point where marginal productiv ity equals farm wage. Also in Equation 2-12, having a source of off-fa rm income should contribute positively to household income. For Equation 2-13, the amounts of off-farm and non-labor income (which includes remittances) are incorporated as they often compensate for the lack of collateral for credit provided by NGOs, cooperatives and asso ciations. In those cases, households who can show a steady income flow are likely to obtain more credit. Finally, household heads age, 23 Because the LSMS survey did not include questions on land quality, our index was formed based on information at the district level provided by the Geographic and Agricultural Information Syst em (SIGAGRO) Office of the Ministry of Agriculture of Ecuador.

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40 education and sex are usually important variable s determining credit market participation and household income. Estimation results: The credit analysis for both regions indicates that farm size matters not only for accessing credit but also for the amount of credit obtained (Table 2-10). In addition, in the Sierra, other variables have a significant an d larger effect than farm size on the probability of obtaining credit. Location of the household in a non-urban area reduces that probability while younger and more educated household head s are more likely to get credit. Also, having more than one parcel increases both the likelihood of accessing credit and the amount of credit received. This re sult seems to indicate that the second possible effect (discussed earlier in this section) on cred it access of having more than one farm is stronger than the first. Hence, operating more than one parcel appears to wo rk as a signal of risk diversification, that is, it could indicate less risk of loosi ng all production if natu re is unfavorable. In addition, compared to the province of Caar, households in the provinces of Chimborazo, Cotopaxi, Imbabura and Pichincha are less likely to obtain credit. In the Coast, the value of farm animals held by the household exerts a slightly negative effect on the probability of obtaining credit. Ho wever, since 95% of those asking for credit actually obtained it, this result suggests that households holding enough farm animals seem to be less likely to pursue a loan than households with few or no animals. Thinking of farm animals as a form of savings, rural households in the Coast would prefer to sell animals in order to meet their financial needs before asking for credit. Also, households in the province of Esmeraldas have less probabilities of acquiring credit than those in Los Rios. The effect of farm size on the amount of credit obtained by the household is such that one more unit of land would increase credit by $18.3 0 in the Sierra and $24.60 on the Coast. The

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41 dollar amount of credit obtained bo th in the Sierra and the Coast is significantly larger if it is obtained from formal sources and more so if the borrower was a farm owner with a land title. This suggests that the effect of land on credit is a direct rather than an indirect effect -as was hypothesized earlier. In the Coast, the level of valued assets cont ributes to accessing slightly larger amounts of credit and being located in an area of concentr ated agricultural production considerably increases that amount. Younger and more educated household h eads receive more credit in the Sierra, and this is true also for households with larger offfarm incomes (although this effect is small). In the Sierra, households in Cotopaxi, Loja and Tungurahua receive less amounts of credit than Caar, while in the Coast it is again households in Esme raldas who receiv e less credit than Los Rios. Results from the second stage of the estima tion procedure (household income per capita), reported in Table 2-11, suggest agai n that farm size is positive a nd statistically significant. One more hectare of land per household member wo uld increase household income per capita by $22 on average in the Sierra, which represents close to 5% of the mean household income per capita in this region. The contribution to household income per capita of one more unit of land per household member on the Coast is $43, that is, 9% of the Coasts mean household income per capita. Mean household size in each region is close to five people. Taking this into consideration, an additional hectare of land would increase to tal household income by $110 in average in the Sierra and $215 in average in the Coast. In ad dition, as expected, households that received more credit per capita generate higher per capita income in both regions. In the Sierra, location of a household in a rural area means it makes on average $406 less per capita income than if it were located in a c ity. As expected, the largest negative effect on income for this category and the most significan t is for households located in dispersed rural

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42 areas. Education of the household head is again positive and of importance, this time for both regions. The effect of the number of adults per capita (interpreted as the inverse of the dependency ratio) is positive and highly significant for both Coas t and Sierra and, as expected, so is the percentage of hired labor and the fact of having an off-farm source of income. In addition, technical assistan ce increases household income pe r capita in the Sierra but it is less significant in the Coast, perhaps due to the small number of households who reported receiving this service. Finally, the regression results did not conf orm to our expectation that pure tenant households would make less total household income per capita than landowners. Although the sign of the effect is as expected, the significance is not. Conclusions This chapter has shown that land inequality and related market imperfections have a statistically significant effect on rural household incomes. Farm size increases the probability of credit access and the amount of credit to be obtained, which also increases household income. The total effect of farm size on household income is composed by a direct and an indirect effect, through its influence on credit and labor allocation. The effect of labor market imperfections is gathered by a positive and highly si gnificant effect of the percenta ge of hired labor on household income per capita. As explained in our statist ical analysis and the model sections, land inequality together with imperfections in the la bor and credit markets (i.e., a labor supply that exceeds the demand, and credit rationing) is what cau ses traditional family farms, who hire very little labor, to have lower labor productivity and hence lowe r household income per capita. On the effect of imperfections in the land market, our section on the land rental market showed how these imperfections cause the rental market to be primarily chosen by the land poor. Also, results from our household income per cap ita estimation section showed that landowners with title can obtain more credit than landowners without title and pure tenants. Therefore, if the

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43 land rental market were liberali zed and property rights be tter protected, together with reforms in the credit market, the poor are among the ones that would benefit the most as they would be able to experience increased land access. We see then that land inequali ty, through its effect on related market imperfections, is an important contributor to rural poverty. However, since unequal la nd access and imperfections in the credit and labor markets form a synergy (b ecause limited access to capital and low labor productivity also contribute to poverty, hen ce limiting the probability and size of land purchases), increased access to land needs to be accompanied by other related market reforms.

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44 Table 2-1. Number of farms by farm si ze, Coast and Sierra regions, Ecuador Farm operators (owner-operator and/or tenant) Farm size obs. % cum.% Minifundio (less than 1 ha.) 77540.840.8 Small (1 to less than 5 ha.) 69136.477.2 Medium (5 to less than 40 ha.) 35818.996.1 Large (40 ha. and over) 743.9100.0 Total 1,898100.0 Table 2-2. Credit access, type of credit and operational farm size Received credit Formal* Informal** Operational area Yes No Total p p $ r% p $ r% Minifundio 124 666 790 15.7%5.8%68666.319.7% 515 102.36 Small 117 579 696 16.8%4.9%118764.0011.9% 329 107.20 Medium 76 287 363 20.9%8.8%188068.7011.6% 1115 115.15 Large 7 70 77 9.1%3.9%806360.225.2% 6052 56.88 Total operators 324 1602 1926 16.8%6.0% 10.7% Proportion of househ olds in each category who received credit. Formal sources of credit: governmental institutions, private banks, cooperatives, associati ons and non-governmental organizations (NGOs). ** Informal sources of credit: input suppliers, exporters, packers, individual lenders and relatives or friends. Table 2-3. Mean loan terms by cr edit sector (all farm sizes) Source of credit Number of HH Mean $ Mean annual r% Mean monthly r% Mean term (months) %Requiring real estate as collateral Formal 127 1,256 66.124.3217 20.5% Informal 216 651106.216.226 4.2% Table 2-4. Agricultural labor produc tivity and operational farm size Operational farm size Number obs.* Average value product ($) Non-remunerated labor days** Hired labor Total labor Mean labor productivity minifundio 747 417 1,326101,337 1.99 Small 678 1048 1,905311,936 2.55 Medium 348 3417 2,0991452,243 3.41 Large 73 4141 2,0273172,345 6.80 Total 1,846 *Only farm households who had a positive value product (from crops and animal husbandry) are included. **Includes both household and non-household members working for no wage. In the case of non-household members, this is a common practice in rural communities where farmers exchange labor, hence avoiding hiring wage labor.

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45 Table 2-5. Household heads primary activity and households main source of income by operational area Minifundio Small Medium Large Head's main activity HH main source of income Head's main activity HH main source of income Head's main activity HH main source of income Head's main activity HH main source of income AGRICULTURE % % % % % % % % Ag. self employed 29 11 52 30 70 52 73 52 Ag. worker 24 25 21 27 9 15 8 11 Subtotal 53 35 73 57 79 67 81 63 NON-AG SECTOR Non-ag. self employed 19 10 10 10 Non-ag. worker 25 14 8 1 Subtotal 44 53 24 35 18 27 12 32 OTHER Not economically active or unemployed 3 3 3 5 Income from rents and financial assets 2 2 1 0 Remittances & transfers 9 7 5 5 Total 100 100 100 100 100 100 100 100 Table 2-6. Mean land productiv ity by category of farm size Operational area Number obs.* Mean land productivity t statistic Minifundio 747 8,351.57-Small 678 506.082.45 Medium 348 270.412.92 Large 73 64.041.96 Total 1,846 *Only farm households with a positive value product are included. Table 2-7. Farm size distribution of land tenants Tenants only Owner-tenants Total Farm size Obs. % Obs. % Obs. % Minifundio 98 49862918437 Small 85 421515123647 Medium 19 952187114 Large 0 06261 Total 202 100295100497100 % of total farmers 11 16 26

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46 Table 2-8. Variable definition for househol d income per capita and credit equations Variable Description Household income per capita equation HIPC Total household income per capita in US Dollars County Dummy variable for each county in each region Periphery Dummy for location of the household in th e periphery of a city (base: urban area) Settlement Dummy for location of the household in a rural settlement (base: urban area) Disperse Dummy for location of the household in a dispersed rural area (base: urban area) LandQual Index of agricultural potent ial of the land (includes slope, soil texture and depth, ease of mechani zation and irrigation) Male Dummy for sex of the household head (base: female) Age Age of the household head (ordinal variable) Edu Years of schooling of the house hold head (ordinal variable) AdultsPC Number of individuals fourteen y ear old or older in the household SizePC Farm size per capita (in hectares) Tenant Dummy variable, 1 if the household is a tenant in all its land holdings (base: owner) C CreditHatP Tobit model prediction of credit dollar amount TechAssist Dummy variable, 1 if the househol d received technical assistance HiredLabor % Percentage of hired labor (based on total farm labor) OffIncome Dummy variable, 1 if household ha s any source of off-farm income Rmt Dummy variable, 1 if the household has received any remittances Credit equation Credit Total dollar amount of credit received by the household (includes both formal and informal credit but only cases with positive interest rates) Province Dummy variable for each province in each region NonUrban Dummy variable for location of the household in a non-urban area AgCon Area of agricultural concentration ( dummy variable, 1 if the household is located in a county in which over 50 % of the land is in production) XCrops Area of concentration of export crop production (dummy variable, 1 if the household is located in area of majo r production of major export crops: bananas, cacao, coffee and flowers) NPlots Number of plots owned or operated by the household Owner Dummy, 1 if the household is owner of any portion of its land holdings Formal Dummy variable, 1 if the loan re ceived is from formal sources %Title Percentage of landholdings with title Formal*Title Interaction term, Formal times %Title Assets Dollar value of assets (machinery, equipment, small productive instruments) Animals Dollar value of farm animals OffFarmInc Dollar amount of off-farm income made by the household c NonLaborIn Dollar amount of non-labor income received by the household (includes remittances and monetary transfers)

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47 Table 2-9. Summary of explanatory vari ables (income and credit regressions) Variable Obs. Mean or frequency Sierra Coast Dep. Variable Household income per capita ($) 1876 486.03 487.55 482.03 Sierra 136072.5% Coast 51627.5% Urban 1799.5% 9% 11.8% Non-urban: 169790.5% 91% 88.2% Periphery 814.3% 6% 0.0% Rural settlement 20210.8% 10.9% 10.1% Disperse rural area 141475.4% 74.1% 78.1% Agricultural concentration 70237.4% 23.5% 74.1% Location Export crops 45824.4% 12.7% 55.2% HH Adults (14 and older) 1876 3.11 3.02 3.35 Male 158484.4% 81.6% 92.0% Female 29215.6% 18.4% 8.0% Age: 17 to 25 years old 1005.3% 5.3% 5.1% Age: 26 to 45 72238.5% 36.8% 42.5% Age: 46 to 65 72338.5% 39.0% 37.7% Age: over 65 33117.6% 18.9% 14.7% Education: zero years 36219.3% 20.8% 14.9% Education: 1 to 6 130569.6% 68.3% 71.8% Education: 7 to 12 1548.2% 7.6% 10.1% HH head Education: 13 and over 552.9% 3.2% 3.2% Size (hectares) 18764.90 3.89 7.57 Number of plots 18761.59 2.0 1.22 Owner 167489.2% 91.0% 84.7% Tenant 20210.8% 9.0% 15.3% Farm Land title 108757.9% 64.1% 41.7% Received formal credit 1115.9% 7.3% 2.3% Received informal credit 19910.6% 10.9% 9.7% Credit Total credit amount ($) 310803.61 790.42 855.34 Value assets ($) 1830299.8 227.57 486.76 Farm wealth Livestock ($) 1724335.69 351.42 292.04 Technical assistance 351.9% 1.6% 2.5% Hired labor 63233.7% 30.1% 43.2% Off-farm income ($) 14211,923.57 1,991.07 1,759.39 Non-labor income ($) 1090244.01 257.85 210.41 Other variables Remittances 28715.3% 16.0% 13.3%

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48 Table 2-10. Credit regressions for the probability of obt aining credit and the amount of credit Explanatory variables Credit Sierra Probit $ Credit Sierra Censored Tobit Credit Coast Probit $Credit Coast Censored Tobit Azuay 0.082 197.253 Bolivar -0.150 -206.300 Carchi -0.289 -61.254 Chimborazo -0.626 ** -813.950 Cotopaxi -0.900 ** -480.558 *** Imbabura -0.496 -831.628 Loja -0.248 -142.438 *** Pichincha -0.489 -410.020 Tungurahua -0.271 -362.834 El Oro -0.126 89.716 Esmeraldas -0.838 -489.137 Guayas 0.199 189.723 Manab -0.245 -156.020 NonUrban -0.343 104.663 -0.049 -109.625 Agcon 0.042 270.825 0.278 457.649** Xcrops 0.153 -158.539 -0.206 -98.238 Male 0.192 56.079 -0.045 6.025 Age -0.200 *** -165.633 *** 0.056 -21.790 Edu 0.302 *** 238.852 *** 0.077 -83.297 Size 0.026 20.772* 0.062** 34.104** Size2 -0.0004 -0.322 -0.001* -0.640* NPlots 0.186 *** 150.108 *** 0.158 -76.575 Owner -0.176 -165.450 -0.089 100.187 %Titled -0.002 105.718 -0.220 -13.122 Formal 1473.730 *** 1092.042*** Formal*Title 602.740 *** 813.476** Assets -0.00001 0.018 0.00003 0.033** Animals 0.00003 -0.060 -0.00038* -0.264 OffFarmInc ($) 0.00004 0.056 *** 0.00001 0.021 NonLaborInc ($) 0.017 -0.009 Rmt -0.157 0.014 Constant -0.781 ** -1200.427 ***-1.772** -786.920 R2 0.34 0.66 *** Significant at 1%; ** signifi cant at 5%; significant at 10%

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49 Table 2-11. Household income per capita regression Explanatory variables Per capita HH Income Sierra OLS (robust standard errors) Per capita HH Income Coast OLS (robust standard errors) Coefficient P>|t| Coefficient P>|t| Periphery -408.3790.019** ----Settlement -332.3280.072* -95.994 0.602 Disperse -476.7450.005*** -219.228 0.253 LandQual -29.7290.420 89.438 0.263 Male 1.0440.978 55.588 0.321 Age -19.7320.343 -38.875 0.119 Edu 159.3950.019** 74.376 0.050* SizePC 25.1220.019** 69.902 0.001*** SizePC2 -0.3910.092* -1.773 0.007*** AdultsPC 504.5340.000*** 400.848 0.000*** Tenant -67.7020.203 -62.124 0.168 %Hired labor 601.8510.000*** 383.302 0.002*** CreditHatPC 0.5310.030** 0.829 0.092* TechAssist 237.3800.099* 241.940 0.140 OffIncome 231.2350.000*** 346.803 0.000*** Rmt -23.5260.583 -43.865 0.304 Constant -565.1250.002*** -355.581 0.271 Degrees of freedom 1288 467 R2 0.30 0.61 Also included in the regressions were 22 counties for th e Sierra (16 were significant at 10% significance) and 20 for the Coast (only 3 significant). *** Significant at 1%; ** significant at 5%; significant at 10% 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 minifundio small medium largeMean labor productivit y Figure 2-1. Mean labor productivity by farm size

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50 CHAPTER 3 UNDERSTANDING LAND RESERVATION VALUE S IN THE P RESENCE OF MULTIPLE MARKET IMPERFECTIONS: THE ECUADORIAN CASE Introduction The presence of multiple market imperfections causes land reservation prices1 to differ from the present value of a st ream of residual retu rns to land. Rural areas in Ecuador are characterized by land inequality, segmented la nd markets, incomplete credit markets, high transaction costs inhibiting land t itling, and dualistic structures in the labor market (Chapter 2). All these market imperfections can have an eff ect on land prices causing th ese values to diverge from the productive ability of the land. Schultz (1945) explains how ag riculture is an activity with a high share of fixed costs compared to other sectors of the economy. Such additional fixed costs represent factors of production whose supply and demand are hard to adjust to macroeconomic conditions (quasifixed factors). Market imperfections such as those mentioned above further limit the marketability of factors of production, hence worsen ing the quasi-fixity of factors. As a result, the Classical (Ricardian) notion of residual rents -which regards la nd as the only fixed factor in agriculturewould misstate returns to land and therefore would fail to fairly represent land values (Mishra et al., 2004). In addition, imperfections in the credit market create capita l constrained households to have larger shadow capital rates (or discoun t rates) than unconstra ined households, hence reducing the present value of land for the former (Carter and Salgado, 2001). In spite of its importance for land inequali ty and rural development, the study of the formation of land values is not common in Latin Am erica. This is partly due to the unreliability 1 Reservation prices are defined as the minimum payment a landowner would accept in exchange for his/her land.

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51 of land price information in the local land regist ries -when this information is even collected. Thanks to the Living Standard Measurement Surveys, however, information about land reservation prices in Ecuador is available. This data allows us to empirically analyze the configuration of individuals/hous eholds land prices by examining the variables that affect an individuals valuation of land. The purpose of this chapter is to understa nd the factors that come into play when individuals are asked to value their farmland. We would like to find out how closely land values represent land quality and productiv ity, as opposed to inefficiencies in land and related markets (credit, labor) or other non-produc tive factors (such as holding land for status). Similar to Carter and Zegarra (1995), we recognize that we are dealing with a hypothetical question and so answers need to be interpreted carefully. However, these answers offer a first window in the economics of land market competitiveness in Ecuador (Carter and Zegarra, 1995: 15). Land market prices are the result of buyer and seller interaction, the first having some at least implicitlimit price and the second a reserv ation price (Currie, 1981). In this chapter we study only the formation of reservation prices; ho wever, this should shed light not only on how market prices are likely formed, but also on who is more likely to end up selling their land. Data and Methodology In the Classical tradition, returns to land ar e commonly measured as residual returns, that is, after discounting variable pr oduction costs from farm income. However, in order for this approach to fairly reflect land values, we would need to assu me that all other factors of production have the same value for all farmers, th at is, that their values are equal to market prices (Mishra et al., 2004). In the context of developing countries, such as Ecuador, this assumption is not plausible since land is not the only quasi-fixed factor in farm productio n. The presence of imperfections in

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52 the credit, land and labor markets (C hapter 2) results in family labor being trapped on the family farm and similarly, for intermediate farm assets2 and farm animals,3 especially for low-wealth rural households. As a conse quence, market imperfections pr oduce shadow factor values to differ from market prices and they dictate the e fficiency of factor allo cation. These prices then affect agricultural profits and land values. T hus, the Classical measure of residual returns misstates land prices if labor and other factors are discounted at mark et prices instead of farmers shadow values (Mishra et al., 2004). An approach that would take all those factors into consideration is one based on a dual profit function with a flexible functional fo rm. We use data from the Living Standard Measurement Survey carried out in Ecuador between October 1998 and September 1999, which gathers household data on farm income, for hous eholds who harvested at least one crop and provided land reservat ion price responses. Restricted Profits In our case, a restricted quadratic profit func tion fulfills our objective as it fits our data well. The restricted profit func tion specification (Equation 3-1), which takes into account quasifixed factors, is as follows: eAgeKTitle AOwned ANPAQuality Province ZZ ZX Z XX Xi i i i i i m k m j jkkj n i m k kiik n i m k kk n h hiih n i ii *% *%** 2 1 2 1 2 111 11 11 1 1 0 (3-1) 2 For small family farms especially, the acquired farm equipment is mostly the basic necessary for production, such as tools and simple fumigation equipment, which have little value outside the family farm. 3 For poor households, farm animals are a common form of savings for times of need.

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53 The symbol represents returns to fixed (or quasi-fixed) factorskZ where k = operational land holdings, family labor days, intermediate assets, and farm animals (Table 3-1 for average and median measures). More specifically, is total value of crop production minus variable costs including the co st of hired labor. X is a vector of output (tem porary and perennial crops)4 and hired labor prices. It is assumed that the price of inputs su ch as fertilizer, pesticides and seeds are the same for all farmers in a province, hence there would be no significant variability in these prices and their effect can be assume d to be captured by the province dummies ( Province ).5 Intermediate assets represent household rese rvation prices of farm equipment which includes mainly farm tools, fumigation pumps a nd animal plowing equipment. Also included are water pumps and trucks (reported by less than 5% of the households in the data set) and animal sheds, irrigation equipment, elect ric plants, tractors and sowing machines (reported by less than 1% of the cases). Farm animals include cattle, horses, pigs, poultry, among other farm animals held by the household at the time of the survey, valu ed at the average selli ng price reported in the data set (by households who sold animals) for each type of animal. Additional variables in the specification which affect the contribution of land to profit (land interaction effects) are land quality6 ( Quality ); the number of plots ( NP ); and the percentage of land owned of the total land operated (%Owned = size owned/size operated). Based on the concept of scale economies, for a household with more than one plot of land, returns to land should in general be sma ller than those for a household whose holdings are 4 The output prices consist of two Fishers price indices, one representing temporary crop prices and the other one summarizing perennial crop prices. S ee Castillo et al. (2007) about the me thodology used in the replacement of missing price observations for the formation of these price indices. 5 It would be superior to include district-level prices in the profit function; however such data is not available in our data set (it was not collected in the LSMS survey). 6 Land quality is measured by an index which takes into account slope, soil texture, depth and ease of mechanization and irrigation.

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54 contiguous. However, it is important to consider an Andean production strategy still practiced in the Ecuadorian highlands which date from the time of the Incas. The optimizing strategy consists of the exploitation of small, disp ersed plots of land at different elevations with the purpose of taking advantage of the differe nt ecological levels offered by the geography of the Andes (Alvarez, 1995). This strategy also spreads risks due to weather and disease. Our data show that the mean number of land parcels in the Sierra is 1.9, while only 1.3 on the Coast (the median is 2 parcels in the Sierra and 1 on the Coast). Also, 77% of the households with less than 5 hectares (in total operational ho ldings) are located in th e Sierra. Since 72% of the households in our sample are from this regio n, the beneficial effect of the Andean strategy could prevail in our results, he nce making the returns to land larg er, the greater the number of land parcels held by the household. It would generally be expect ed that the effect of the shar e of land farmed which is owned would be positive on the returns to land, based on the hypothesis that owner-operators make better investment decisions than tenants,7 especially since over 70% of the owner-tenants in our sample had shared-tenancy arrangements.8 Nevertheless, under pure land ownership, production and price risk are totally internalized by the farmer and, in cases of poverty and restricted access to credit, the risk bearing capacity of the farmer is usually lower than required in order to reach efficiency in factor allocation. In addition, our data show that 99.5% of ow ner-tenant households fully exploited their owned holdings. Also, Table 3-2 shows that 49% of owner-tenant households owned less than 1 7 20% of the landowner households in the sample were owner-tenants. Among these, there are two types of tenancy arrangements: fixed-rent (cash payment) and shared-rent or sharecropping (with in-kind payments, a mixture of cash and in-kind payments or payments with labor). 8 Classical and Neo-Classical economists widely considered sharecropping as an inefficient form of tenure because it would create a disincentive for the tenant: He/she would only use a factor up to the point where only his/her share of the marginal value product as opposed to the total marginal value productequals the factors price.

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55 hectare and 88% owned less than 5 hectares. In light of this, hous eholds with smaller shares of owned land relative to their operational holdings appear to be small farm owners who need to acquire additional land. This effect can thus spea k to the competitiveness of this kind of farm household compared to those who only operate their own property. A related hypothesis is that households with ti tle to their lands, compared to those who do not have title, are more likely to make fixed investments in the land, hence generating greater profits. Moreover, titled land can facilitate access to formal cr edit, further increasing profits. Thus, the variable %Title is included interacting with the value of assets ( K ). In other words, it is expected that the effect of intermediate a ssets on the returns to quasi-fixed factors ( ) would vary depending on whether the household has a la nd title (or a larger sh are of titled land). The evidence about the effect of land titling in the developing world is, however, mixed. In Thailand Feder et al. (1988) show that land titles improve tenure security, increase investment and enhance land values. On the other hand, studie s in some parts of Sub-Saharan Africa (MigotAdholla et al., 1993) and Latin America (see Go uld, 2001 on Guatemala and Carter and Salgado 2001 on Paraguay, Honduras and Chile) have found very little and at times ambiguous total effects of land titling programs on farm productivit y, the dynamization of land markets, credit access and land values. We shall then observe the di rection of this effect in the case of Ecuador. Also included in the restricted profit e quation is the age of the household head (Age ) so as to capture the likely negative effect of less effi cient farm labor and/or management of older farmers (Carter and Salgado, 2001). We estimate the profit function and then test for monotonicity and convexity in prices, properties of the profit function. If the function is not convex, we perform a bootstrap procedure, by which a new sample is randomly created each time based on our data set and new estimates

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56 computed. This is done 1,000 times, providing 1,000 co efficient estimates out of which we retain those that are convex in prices. Our final estimat es are the average of the convex coefficients (Terrell, 1996; Moss et al., 2008). An appropriate measure of returns to land, which includes farmers heterogeneity in endowments, is obtained by taking the derivative of the profit function with re spect to land (A), as follows: Owned Quality NPZ X Ai i i m j jAj n i iiA A%1 1 (3-2) Based on the hypothesis that small farmers can be more competitive in the land market (see Carter and Salgado, 2001 about th e peasant hyper-competitiveness case9), we expect land shadow values to be positive but decreasing with the amount of land in operation. This hypothesis has its roots in the labor advantage of poor farmers (abundant la bor and little or no need for supervision given their use of family labor) by which they tend to be more productive per unit of land (Carter and Zegarra, 2000; see also Chapter 2) and therefore should be willing to pay more for the land. However, we will see belo w how this competitiveness is expected to be undermined due to the presence of credit constraint s which primarily affect small farmers. This will be reflected in the level of land reservation prices. Land Reservation Prices The reservation value of owned land for the ith agricultural household ( Vi) can be expressed as Equation 3-3, where ri is the households discount rate, T is the time horizon the household expects to hold the land, and Pe the expected future la nd selling price in period T+ 1. 9 Under the assumption that all farmers face the same cost of capital, Carter and Salgado (2001) show that farmers with the lowest land to labor endowment ratio have the highest shadow land price

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57 1 1)1()1( / T i e T t t i iit ir P r A Vi (3-3) Now, taking into account land insecurity, wh ich arguably (as indicat ed earlier) can come from lack of land title, land values would be ne gatively affected for the case of landholders who fail to prove ownership through a formal land title. In such case, land value can be expressed as Equation 3-4, where i represents the probability of the i th household loosing their land and where the selling price component is omitted. iT t t i iit t i ir A V1)1( / )1( (3-4) Another crucial aspect in land valuation is the discount rate particular to the household. Imperfections in the credit market cause some households to be credit constrained. These are households who either have being denied credit, do not have suffici ent collateral to secure a loan, or had the opportunity to get cr edit but preferred not to for fear of loosing their collateral (Boucher et al., 2005; Guirki nger and Boucher, 2005). Credit constrained households are also called non-price ra tioned (Boucher et al., 2005; Guirkinger and Boucher, 2005) as th eir ability to obtain credit is limited by reasons other than the interest rate. On the other hand, price-ra tioned households are thos e who either obtained loans or decided not to ask for credit because th e interest rates were t oo high or due to reasons different than those of the non-price rationed households. It follows from this analysis that non-price rationed (or credit constrained) households have a shadow price for capital larger than those who are simply price rationed, hence they will have higher discount rates and sm aller land reservation prices. In addition, they are pushed to produce with less profitable capital intensities (reflected in smaller shadow land values), which further influence their ability to pay for land. As a consequence, these households tend to have

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58 lower risk-bearing capacity and this, inter-temporal cons iderations aside,10 guides them to choose safe but low-yielding activity portfolios in which land is not abundantly present (Zimmerman and Carter, 2003). In other words, capital constrained house holds will be more likely to sell their land th an unconstrained households. Because of the small share of landowner hous eholds in our sample who received credit at a positive interest rate (18%), we are unable to use interest rates on loans in order to approximate households discount rates. Instead we concentr ate on the reasons why households did not obtain credit, which leads us to classify them as price or non-price rationed households.11 Like Carter and Salgado (2001), we expect cred it constraints to be correlated with the risk-bearing capacity of the households. Table 3-3 illustrates the inci dence of credit constrained households in Ecuador by farm size. As the last column shows, the largest portion of rationed households is made up by minifundistas and this portion decreases as farm size in creases. Similar to the findings of Carter and Salgado (2001) for Paraguay, and Boucher et al. (2005) for Honduras and Nicaragua, land poor households are the ones that ar e most likely to be constraine d in the credit market. Thus, like Carter and Zegarra (2000) s uggest, we expect land prices pe r hectare to fall very quickly from the small farmer advantage indicated in our restricted profits sub-section because of credit constraints. 10 The possibility of trading off current consumption for assets usually helps resource-poor households accumulate capital but risk and other dynamic factors undermine the benefits of this strategy (Carter and Salgado, 2001; Zimmerman and Carter, 2003). 11 Based on the information offered by the LSMS survey we classified households as credit constrained (or nonprice rationed) if they reported to have asked for credit but did not receive it, or if they did not ask for credit due to: a) did not know any lenders; b) already had debt; c) lenders asked for too many prerequisites; d) did not know how to ask for credit; e) did not have collateral wealth; f) did not have land ownership title; g) fear of losing the collateral; or h) did not have any guarantors. Price rationed households are made up by those who either obtained credit or did not ask for credit because: i) they did not need credit; j) interest rates were too high; or k) their income was not stable enough.

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59 Given data limitations, we can only analyze one years worth of returns to land. However, making the assumption that our year of analysis re presents an average crop year in Ecuador, we can estimate a log-linear func tion of the following form: uTitled NonPRat EndowLA ShadowAV XCrops AgCon NonUrban oastC A Vi i i i i i i i 0 % ln ln ln00 0 (3-5) Where, V/A0 = land reservation valu e per owned hectare Coast = dummy variable for a household locate d in the coastal re gion (base: Sierra) NonUrban = dummy variable for households located in a non-urban area (base: urban) AgCon = dummy variable for households located in areas of agricultural concentration XCrops = dummy variable for households in areas of major export crop production ShadowAV = shadow land value =A / A0L0Endow = land to labor endowment ratio (o wned hectares/family labor days) NonPRationed = dummy for non-price rationed hous eholds (base: price-rationed) %Titled = percentage of total owned land that is titled The means of all variables in Equation 35 are shown in Table 3-4. Location effects on land values are repres ented by the region (Coast ), the non-urban effect ( NonUrban), the concentration on agriculture ( AgCon )12 and the major export crop production areas (XCrops )13 effects. Land speculation is commonly found in areas of export crop production (Lambert and Stanfield, 1990) or in areas of agricultural concentration a nd so location of the farm (as approximated by location of the hous ehold) in such areas is exp ected to increase individuals 12 Based on results from the latest agrarian census in Ecuador (2000), counties in which over 50% of the land was in production are regarded here as areas of agricultural concentration. The census added fallow lands and shrimp pools as land in production and it excluded moorlands, mountains, forests and areas with infrastructure. 13 These are the most productive counties in the production of major export crops, namely, bananas, coffee, cocoa and flowers.

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60 land reservation prices. High inflation rates du ring the year of the survey, 1998-1999, (between 40 and 60%) may make these effects even stronger (Carter and Zegarra, 2000). The percentage of titled land ( %Titled ) is included in this equati on in order to observe the probable effect of land insecurity, arguabl y represented by lack of land title. The land to labor endowment ratio ( A0L0Endow ) will reflect how land values per hectare vary as farm size increases relative to available labor. Just as shadow land prices are expected to decrease with the number of hectares in opera tion, land reservation va lues are expected to decrease with the land to labor endowment ratio. However, the advantage that farmers with low endowment ratios may have with respect to reserv ation prices is anticipated to be weakened or overcome by restrictions in the credit market (NonPRat dummy), where better endowed farmers are expected to surpass those who are poor. Following de Janvry et al. (2001), failures in rural markets cause landownership to provide si de benefits that increase land prices14 and many of these benefits are more likely to be enjoyed by large but not by small farm owners. Credit access is one of those benefits. On the other hand, the reservation price of bette r endowed households is expected to eventually fall ag ain due to disadvantages in the labor market overcoming their capital advantage (Carter and Salgado, 2001). Results The profit function estimates (Equation 3-1) in Table 3-5 reveal the effect of land and other quasi-fixed factors on restricted farm profits. Conforming to the theory, more land increases profits but with diminishing returns (see coefficient of A2). Returns to land are also significantly increasing with the value of assets, and decreasing with the value of farm animals. They are also 14 Those benefits are that land serves as a store of wealth es pecially in times of high inflation; it provides a source of self-employment; it serves as collateral for credit; it can have speculative value; it can offer tax breaks and it provides political and social capital (de Janvry et al., 2001).

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61 larger the better the quality of the land, and smaller the greater the amount of land that is owned. The last result confirms our expectations that owner-tenants are more competitive than pure owners as they strive to e xpand their operational size. Also, the number of plots, although positive did not significantly affect the returns to land. In total, for a median farm household, one mo re unit of land would increase restricted profits by $5,306.23. Figure 3-1 shows how with a few exceptions, shadow land values are decreasing in operational farm size, which conforms to what was anticipated in our restricted profits sub-section. In addition, the contribution of in termediate assets to restricted profits is also positive and decreasing. It also decreases with the number of non-remunerated labor days and with the value of farm animals. The effect of land titles tu rns out to be positive bu t not significant, which implies that titled land does not significantly improve the effect of intermediate assets on profits. Shadow values of all factors i ndicate that intermediate assets and land contribute the most to agricultural restricted profits while the va lue of animals has a sma ller contribution and nonremunerated labor contributes the least. Taking into account the size of the median households quasi-fixed factors (Table 3-1), the shadow price re sults seem to reflect fairly the reality of the median farm household in Ecuador. Also, as exp ected, households with older heads made fewer profits than those with younge r heads, indicating the lower efficiency of labor and/or management caused by age. Results from Equation 3-5 show that sha dow land values contri bute positively and significantly to land rese rvation prices (Table 3-6). As e xpected, better endowed households have lower reservation prices per hectare than households with low land to labor endowment ratios, but this advantage of poor households is undermined by their being constrained in the

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62 credit market. More specifically, while a 1% incr ease in the land to labor ratio would decrease land reservation prices by 0.31%, a credit constr ained household (which is more likely to be a land poor household) has a land reservation pri ce per hectare 0.55% lower than an unconstrained household. As anticipated, Figure 3-2 shows that land rese rvation prices per hect are are very high for households with low endowment ratios (less than 0.02 hectares per family labor day or less than 2 hectares in total) but they d ecrease rapidly especially for thos e who are credit constrained. As shown in Table 3-3, most credit constraine d households have less than 5 hectares. Also, while reservation prices per hectar e range from $46 to over $50,000 for households with less than 5 hectares, for households with 17 hectares or more, reservation prices range from $3 to less than $2,000. This conforms to Carter and Salgado (2001)s simulation findings that reservation prices would be larg er for credit unconstrained households but smaller again as farm size continues to increase due to disadvantages in the labor market. In areas of agricultural and export crop c oncentration, land reservation values are significantly higher than in othe r areas, which as indicated earlie r can be indicative of the land speculation usually found in thes e zones. However, land reservation values are smaller as we move away from the urban centers and this effect is stronger than the e ffect of agricultural concentration and export crop areas combined. This suggests that the value of land close to urban areas is highly influenced by the advantage of being closer to major ma rkets. Also probably included in this effect is th e possibility of rural land conversion to semi-urban settlements (Lambert and Stanfield, 1990). In addition, households in the coastal region have smaller reservation prices per hectare th an those in the highlands (Sierra ), presumably because there is more competition for land in the Si erra given that land is less abundant.

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63 Finally, with respect to the per centage of titled land, this eff ect is again not significant (see results from the profit function), which reveal s that although almost 30% of the land in the sample was not titled, this does not seem to be causing serious problems of land insecurity. One alternative explanation for this result is that in many cases land titles are likely to be only certificates of possession or sim ilar documents which have not b een properly registered (see Chapter 2 about the high transacti on costs discouraging title registra tion). Hence, the existence of a title-like document is not capturing informa tion on security of land rights, causing higher shares of titled land not to make a difference on either investment or land values. Another possible explanation is that, li ke the findings of Carter and Salgado (2001), land titles do not contribute to easing small farmers credit constrai nts; therefore, even if providing security of land rights, in the case of small farmers (which ma ke up the majority in our data set) land titles cannot be used to improve credit access in order to increase inve stment, then the effect of land titles on investment and land values turns insignificant. Conclusions This chapter has shown that the presence of multiple market imperfections intensifies the quasi-fixity of factors ot her than land, which affects the cont ribution of land to profits (shadow land values) and consequently, land values. Al so, incomplete credit markets leave some households unattended, affecting th eir shadow capital values and risk bearing capacity, which is reflected in higher discount rates and smaller land reservation prices. These households are therefore more likely to sell their land in moments of financial distress. We have also found graphically that the difference in reservation prices per hectare between small and medium and large farmers is re markable. This can be explained by the labor advantage of small farmers, which makes them more productive per unit of land than larger farmers. This effect, however, is reduced by the credit constraints mostly experienced by small

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64 farmers. Yet, for farms of 17 hectares or more, re servation prices turn smaller again as farm size continues to increase, reflecti ng the disadvantage of large farmers in the labor market. In addition, although land values are higher in areas of concentra tion of agricultural production and/or export crop pr oduction, the relative marginalization of non-urban areas severely affects land values. This is likely due to the distance of non-urban farm households from urban markets, a disadvantage that is exacerbate d by the need for more and better road access in rural Ecuador. Another reason is the possibility of rural land conversion in lands close to the cities, which increases land values for these landowners. Finally, in this Ecuadorian case, lack of land titles did not e ffectively discourage investments in land and did not cau se land values to be smaller than for households with titled land (or having a larger share of titled land). This was likely due to the possibility that land titles are not registered, hence not providing the full benefits of a titl e, or due to the pervasiveness of credit constraints which limit the potential benefits of land titles as a device that would facilitate access to credit by allowing small landowners to pledge the land as collateral.

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65 Table 3-1. Mean and me dian quasi-fixed factors Quasi-fixed factors Mean Median Operational land holdings (ha.) 7.952.00 Non-remunerated labor (days) 1,911.941,152.00 Intermediate assets ($) 419.7542.30 Farm animals ($) 415.44222.97 Table 3-2. Classification of owner-tenant households by category of owned farm size Owned farm size Owner-tenants Proportion (%) Cumulative (%) Minifundio (less than 1 ha.) 79 49.1 49.1 Small (1 to less than 5 ha.) 62 38.5 87.6 Medium (5 to less than 40 ha.) 19 11.8 99.4 Large (greater than 40 ha.) 1 0.6 100.0 Total 161 100.0 Table 3-3. Credit constrained households by owned farm size Farm size (a) Credit constrained HH (b) Total HH by farm size Row % (a/b) % of Total (a/c) Minifundio 44 30214.57%5.35% Small 41 30413.49%4.99% Medium 22 17912.29%2.68% Large 7 3718.92%0.85% Total 114 (c) 822 13.87% 13.87% Table 3-4. Summary of variables (land reservation value equation) Variable Obs. Mean or frequency Total reservation price ( V ) 8225,088.90 Size Owned ( A0) 822 7.62 Reservation price per hectare (V/ A0) 822 83,601.44 Region: Coast 229 27.86% Region: Sierra 593 72.14% Area of agricultural concentration (AgCon) 370 45% Area of export crop concentration (XCrops) 233 28% Urban area 78 9% Non-urban area 744 91% Land to labor endowment ratio (A0L0Endow) 822 0.019 Non-price rationed households 114 14% Percentage of land holdings with title (%Titled) 822 0.703

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66Table 3-5. Returns to fixed factor s equation (quadrat ic function) Explanatory Variables Coefficient P>|t| Explanatory Variables Coefficient P>|t| Explanatory Variables Coefficient P>|t| Azuay -0.042 0.036** Pt*W 0.169 0.048** AniW2 -0.7390.274 Bolivar 0.008 0.346 Pp*W 0.051 0.334 Quality*A 0.9220.010** Caar -0.0220.197 W2 0.229 0.138 NP*A 1.0270.226 Carchi -0.0480.051* Pt*Labor 0.084 0.195 %Owned*A -2.8120.000*** Chimborazo 0.0160.217 Pp*Labor 0.206 0.036** %Titled*K 0.9230.223 Cotopaxi 0.0130.423 W*Labor 0.307 0.025** Age -0.0550.028** Imbabura 0.0530.021 ** Labor2 0.002 0.496 Constant 0.106 0.001*** Loja 0.0530.007 *** Pt*K -1.604 0.116 D. of freedom 766 Tungurahua 0.0240.161 Pp*K -6.099 0.000***R-squared 0.38 El Oro -0.016 0.304 W*K -2.323 0.113 Esmeraldas 0.0400.030** Labor*K -2.823 0.046** Guayas 0.0060.429 K2 -11.569 0.001*** Los Rios 0.0130.336 Pt*A 0.783 0.062* Manab -0.0240.121 Pp*A -0.056 0.463 Pt (temporary crop price index) 0.2590.001 *** W*A -0.289 0.370 Pp (perennial crop price index) -0.1450.040** Labor*A -0.340 0.308 W (hired labor wage rate) -0.201 0.015** K*A 20.092 0.000*** Labor -0.0320.369 A2 -5.638 0.000*** K (intermediate assets) 3.5110.007*** Pt*AniW 1.076 0.015** A (land) 3.0840.000*** Pp*AniW 0.589 0.133 AniW (farm animals) 0.1540.358 W*AniW -0.490 0.284 Pt2 0.3890.005*** Labor*AniW -0.531 0.212 Pt*Pp 0.0990.120 K*AniW -12.853 0.003*** Pp2 0.5090.003*** A*AniW -3.361 0.005*** ***Significant at 1%; ** significant at 5%; significant at 10%

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67 Table 3-6. Log of the land reservation price equation Explanatory variables Coefficient P>|t| Coast -1.212 0.000*** AgCon 0.508 0.000*** XCrops 0.664 0.000*** NonUrban -1.681 0.000*** lnShadowAV 0.825 0.006*** lnA0/L0 -0.312 0.000*** NonPRationed -0.551 0.001*** %Titled 0.141 0.246 Constant 5.491 0.000*** Degrees of freedom 803 R-squared 0.37 *** Significant at 1%; ** signifi cant at 5%; significant at 10% -5 0 5 10 15 20 0.0050.00100.00150.00200.00250.00300.00 Operational size Figure 3-1. Shadow land values

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68 1.00 10.00 100.00 1,000.00 10,000.00 100,000.00 1,000,000.00 0.000.020.040.060.080.100.120.14 A/LU.S. Dollars Reservation price per ha. Non-price rationed A. 1.00 10.00 100.00 1,000.00 10,000.00 100,000.00 1,000,000.00 05101520253035404550 Owned hectaresU.S. Dollars Reservation price per ha. Non-price rationed B. Figure 3-2. Land reservation prices per hectare and non-pr ice rationed households. A) Relationship with respect to the land to labor endowment ra tio. B) Relationship with respect to the amount of hectares owned. Both figures show only 95% of the data and are drawn in logarithmic scale for observational purpose.

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69 CHAPTER 4 RURAL LAND MARKET PARTICIPATION IN ECUADOR AND IT S DETERMINANTS Introduction Land is the most important asset in agricultu ral production and this activity still employs over 50% of the rural economica lly active population in Ecuador.1 Land inequality and abundant availability of labor in a cont ext of generalized unemployment suggest that an important means of reducing inequality would be by increasing ac cess to agricultural land by the rural poor. There is, however, a lack of dynamism in the land ma rkets. Large landowners do not make enough land available through sales or rentals to the land poor so as to satisfy the latters demand for land (see Lambert and Stanfield, 1990 about land market segmentation). This situation seems to be encouraged by a number of factor s that are worthy of study. Decisions to supply land in th e land market depend on a variety of factors that first affect land reservation prices (or reserv ation rents). Households that s upply land in the sales market may be those facing financial distress or may be households upgrading to better land or deciding to migrate to the cities for better economic opportunities. Househol ds offering land in the rental market may be behaving as risk averse (Curri e, 1981) or may be households experiencing a temporary or permanent change in their supply of family labor; for example, those households that become female-headed after the male heads migrate out of the country to find a job that would provide more and secure income to send to their family back home. Changes in the macroeconomic situation affectin g the relative profitabi lity of agriculture often put rural households in the position of choos ing between participating in the land market or not (Currie, 1981), as do severe climatic conditions. Given the pr esence of market imperfections, 1 Jordan (2003) notes that for the decad es of the 1980s and 90s about 40% of the economically active population in the rural sector was working in non-agricultural activities, which reveals a loss of importance of land as a focus of household reproduction in the rural s ector compared to earlier decades.

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70 for those households facing harsh economic circum stances, their wealth level as well as their ability to access credit and a ffordable technical assistance could make a difference between selling their land or not. Demand for land is also affected by house holds abilities to face uncertain economic conditions, imperfections in rural markets and risk aversion. In addition, unequal land distribution and certain characteri stics of the land ma rket, such as market segmentation by class and kinship, may also influen ce how active land markets are a nd what type of individuals participate. In spite of the accomplishments of the agrarian reform in Ecuador,2 the available data shows that agricultural land continues to be high ly concentrated in the hands of a few large landowners (Nieto, 2004). The last agrarian cen sus (2000) reveals that agricultural production units (UPAs) of less than 2 hect ares (ha.) constitute 43.4% of th e total and they hold 2% of the cultivable land. In contrast, units of over 100 ha. represent 2.3% of the UPAs, while they control 43% of the land. Since the 1994 land law was approved land redistribution efforts have been left to the market (Santos-Ditto, 1999; Jordn, 2003) Given current restrictions on renting land (Chapter 2), rural market imperfections and la nd distribution results fr om the last agrarian census, the answer to the question of how well th e land market can perform the redistribute task seems to be: not so well. The literature on land market participation in Latin America is relatively recent (Deere and Len, 2001) with important empirical studies having been carried out in Nicaragua (Deininger et 2 The main achievements of the agrarian reform (1964-79) were that it eliminated precarious forms of labor, such as feudal-like or servile relations on haciendas. It also changed the agrarian structure by eradicating the latifundia (Jordan, 2003; Santos -Ditto, 1999; FAO-COTECA, 1995) and facilitating land access to peasant producers and to some indigenous communities of the highlands (FAO-C OTECA, 1995). Even though the agrarian reform redistributed some land, most of the land adjudications were of state lands in the Amazon basin for colonization purposes (new settlements). Chiriboga (1998) notes that by 1994 the State had redistributed only 9.3% of Ecuadors cultivable land, benefiting 9.5% of the rural households; while 68.9% of the cultivable land was adjudicated to 9.9% of the households for colonization purposes.

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71 al., 2003; Boucher et al., 2005), Honduras (Bouche r et al., 2005; Carter and Salgado, 2001) and Paraguay (Masterson, 2005; Carter and Salgado, 2001) These studies aim to understand the role of the land sale and rental markets in asset ine quality, and explore the fact ors that encourage land market participation. In Ecuador, comprehensive studies of th e rural land markets (FAO-COTECA, 1995; Lambert and Stanfield, 1990; Jordan, 2003) are few, qualitative rather than quantitative, and are based on data mainly up to the 1980s. These studies are compilations of more local studies and they show the effects of the agrarian reform on land distribution and the role assigned to the market after the reform period, together with an evident decrea se in governmental intervention. They also note two different results with respect to small farmer participation in land markets: 1) their propensity to sell land c ontributing to the increased tendency towards the formation of minifundios -; and 2) individuals who ha ve succeeded in agriculture have been able to buy land and advance towards becoming medium capitalized units, especially in the more productive regions. These studies also emphasize the decline in the importance of agriculture as the main source of income for rural families and the diffi cult path out of poverty, as well as the marked social differentiation among small and large farm ers which worsens the segmentation of rural markets. Rural household data for the period October 1998 to September 1999 give us a window to observe the incidence of land market participation in Ecuador. In this chapter we use a quantitative approach and test the conclusions of the above menti oned Latin American studies in the case of Ecuador by examining the determinants of households decisions to purchase, sell, rent in or rent out their land.

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72Data and Methodology Land Supply Supply in the land market can be in the form of land sales or leasing. Landowners have the option of putting all their land into production (or none at all), leas ing it all, selling it all, or doing a combination of the three. The factors that influence landowners choices among those alternatives are analyzed here. We use data from the Living Standard Measurement Survey carried out in Ecuador during October 1998 to September 1999. The data show that out of 1,738 landowners (90% of all farm operators) only 4.8% chose to rent out at least a portion of their land and 1.4% chose to sell at least a portion.3 In the analysis below we use multinomial logit4 regression analysis by making the landowning households alternatives mutually ex clusive between farmi ng all land, leasing at least a portion or selling at leas t a portion. For households leasing and selling land at the same time, we categorize the household according to wh ether the larges t portion of land was leased or sold. Our model is as follows: u NonLInc OffInc Female Edu Age Credit Adults AniWsValueAsset Titled SizePrior AgCon NonUrban Coast ion OwnerDecisi i i i i i i i i i i i i i %0 (4-1) Owner-decision refers to the owne rs choice of renting out or selling at least a portion of the land. The comparison group is made up by households who exploit all or do nothing with their land. In addition, we break up the rent out category in two subcat egories describing the type of rental arrangements made by the landlord namely, fixed rental or sharecropping. Thus 3 Some of the households renting out also rented in some land so the percentage of net lessors was 4.6. Similarly, some of the seller households also purchased land hence 1.3% of the landowners were net sellers. 4 Logit regressions estimate log-odds, that is, x x x )(1 )( log. See Agresti (1996).

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73 we run two multinomial logit regressions where the dependent variable outcomes outside the comparison group (owner-operator only) are (a) rent ed out or sold, and (b ) rented under fixedrental contracts, rented under shared-rental contracts, or sold. Following De ininger et al. (2003) and Masterson (2005) we also run censored Tobit regressions so as to check the effect of the same variables included in the multinomial logit regressions on the amount of land sold and rented out. The independent variab les included in (1) are Coast = dummy variable for households in the coastal region (base: Sierra) NonUrban = dummy variable for households locate d in non-urban areas (base: urban) Agcon = dummy variable for households in areas of agricultural concentration (counties in which over 50% of the land is in production) SizePrior = amount of land owned prior to selling %Titled = percentage of owned land holdings with title ValueAssets = value of machinery and equipment (in 100 U.S. Dollars) AniW = value of animal stocks (in 100 U.S. Dollars) Adults = number of adult members in the household (14 or older) Credit = dummy variable indica ting households who received any type of credit for a positive interest rate Age = years of age of the household head Edu = years of schooling of the household head Female = dummy variable for female household heads (base: male) OffInc = share of off-farm income re lative to total household income NonLInc = share of income from remittances and governmental or non-governmental transfers relative to total household income The amount of land owned prior to selling ( SizePrior) would give us a first sign on the role of the land market on land inequality. A positive e ffect of this variable on land sales would suggest that, for the year of the survey, larger farmers contributed by offering land in the market5 5 We could not easily conclude from a positive effect that Ecuador moved towards a more egalitarian land distribution during the time of the survey because we ha ve no information on who th ese households sold land to. However, a positive result would suggest the presence of incentives to sell land by large owners and hence a stimuli to small farmers (or to the landless) to buy could successfu lly improve land distribution. The lack of information on who the households sold to is of importance given the evidence of land market segmentation by class and kinship in Ecuador (Lambert and Stanfield, 1990), a phenomenon usually observed throughout Latin America, where land inequality is a prevalent characteristic (Carter and Salgado, 2001; Deininger and Binswanger, 2001).

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74 while a negative sign would reflect a reluctance to do so likely due to non-productive reasons (Deininger et al.2003)-, and a mo re active participation by small farmers. The effect of this variable on the supply of land has been analyzed for the cases of Nicaragua (Deininger et al., 2003, for the year 1998) and Paraguay (Masters on, 2005, for the year 2001) with different results, namely, a negative effect on the amount of land sold in the case of Nicaragua, while positive results on incidence and amount of land sold in Paraguay. In the case of Ecuador, it is probable that the tendency to sell by small farmers who acquired land during the agrarian reform period continued during the year of the survey (19981999). This behavior by small farmers was evident after the fragm entation of production cooperatives, especially since th e new land law, which removed restrictions for land sales, was issued in 1994 (FAO-COTECA, 1995; Santos-D itto, 1999; Nieto, 2004). Also, Table 4-1 shows that 56% of the land sales were perfor med by households holding less than 5 ha. On the rental market, the effect of the area owned was positive for the amount of land supplied in both the Nica ragua and Paraguay cases. Also, Boucher et al. (2005), using pair-wise analysis, show that the inciden ce of land supply in the rental ma rket was higher the larger the amount of land owned both in Honduras in 2000 and Nicaragua in 1999. In Ecuador, Table 4-2 shows that the majority of households supplying land in the rental market were again those owning less than 5 ha. (71%). However, farms of 5 to less than 40 hectares represented an important share of the land suppliers, especially in the sales market and in the rental market under fixed-re ntal contracts. Also, in the latter case, those households offere d on average significantly more land than smaller farmers.

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75 In Table 4-2 we can also see that sh arecropping was more popular among households owning less than 5 ha., while thos e owners of 40 or more ha. that chose this type of rental agreement dedicated on average le ss land area to this purpose compared to smaller farms and especially compared to farms using fixed-rental contracts. There are no clear prior expectations on the effect of the share of titled land ( %Titled ) on landowners decisions to participate in the la nd market. Land titles could stimulate more profitable own production due to cr edit access and security for inve stment (Feder et al., 1988) or could facilitate participa tion in the rental market.6 Land titles could also facilitate land sales, especially by poor households who ar e credit constrained (Chapter 3).7 These may be agrarian reform beneficiaries selling land after land titlin g programs were implemented (Deere and Len, 2001; Deininger and Binswanger, 2 001; Carter and Salgado, 2001).8 The number of adults ( Adults ) in the household and the value of farm equipment ( ValueAssets ), as well as the value of animal stocks ( AniW ) should increase the likelihood of farming compared to renting out or selling land since more of these factors increase the advantage of farm producers. On the contrary, we expect older household heads to have higher odds of selling or renting out gi ven their decreased labor/managem ent efficiency (Chapter 3) compared to younger heads. More educated household heads would also be more likely to rent 6 The importance of land titles for the supply side of the land rental market in Ecuador has already been discussed in our first essay (see also Boucher et al., 2005 on Honduras and Nicaragua). 7 In the case of Paraguay, Carter and Salgado (2001) e xplain how land titles were unsuccessful in releasing credit constraints for small farmers, similar to what Boucher et al. (2005) found in Honduras and Nicaragua. Given the role of land titles in reducing transaction costs for land sales (B oucher et al., 2005), what land titles may lead to is land sales by small farmers. 8 Since the LSMS survey did not provide land title information on land sold, we assumed here that the percentage of land still held by the household at the time of the survey that was titled is a good proxy of such percentage at the time the land sales occurred.

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76 out or sell than to produc e as the opportunity cost of their labor is higher and therefore they would look for more profitable opportunities off-farm. Landowner households with larger shares of off-farm income ( OffInc ) may also prefer to rent out or sell depending on th e households composition. In othe r words, off-farm income can be a good source of financing for agriculture; howev er, this depends on the total amount of labor hours available in the household and the amount dedicated to ear ning off-farm income. In the case of non-labor income ( NonLInc ), that is, income from remittances and/or governmental or non-governmental transfers, those h ouseholds receiving larger shares of this type of income may be poor households more likely to farm all la nd rather than rent out or sell, although for households whose skilled labor has mi grated, renting out (or even sel ling) may be their choice. The sex of the household head (Female ) is also included here in order to observe the effect of gender on the decisions to pa rticipate in the land market. The participation of women in Ecuadorian agriculture is such that, according to the latest agrarian census (2000), 25.4% of farm producers in Ecuador are women, 30.5% in the Si erra and 14.8% in the Coast. Also, our data indicate that 16% of the landow ning households were headed by wo men (Table 4-3), 19% in the Sierra and 9% in the Coast. This reflects the importance of women in ag ricultural production but also suggests that women are underrepresented as principal agriculturali sts in our data set, especially in the Sierra. Nonetheless, as noticed by Deere and Le n (2001), in Latin America rural women face disadvantages when trying to access services li ke credit, technical assistance and marketing compared to men. Therefore, they may be less competitive than male farmers, which may lead them to be more likely to sell or rent their la nd than men. This aspect, however, may be less important than it seems in the decision of women to participate in the land market as sellers

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77 because of the additional benefits that landowne rship brings them. Namely, ownership of land increases the bargaining power of women in the household and the community; it provides food security for their children and it constitutes an asse t suitable for renting so as to generate income for the household (Deere and Len, 2001, pp. 327:329). Hence, female headed households may or may not be more likely than male headed households to sell, however they may be more likely than men to rent out ra ther than to farm the land themselves. We expect to find the latter re sult especially given the overrepresentation of female headed households among households wh o rented out some land in our data set, compared to the share of female heads among all landowning households (Table 4-3). We also need to be aware of the dynamics of migration by gender in Ecuador and the possible consequences this could have on the effect of the gend er variable on the decision to farm, sell or rent out. In the l eading provinces in international migration in Ecuador which are in the Sierra-, the migrating population is ma inly made up by poor rural men (Camacho, 2005; FLACSO and UNFPA, 2006). This migration of poor rural me n, whose income-generating activities are mostly related to agriculture (Cam acho, 2005), leaves their wives in the position to choose between farming the land under their ow n management, leasing it in some form, or getting rid of the land. The fact that the year 1999 witnessed a large in crease in the migrating population (Camacho, 2005) may have influenced female household heads decisions to participate in the land market. In addition, harsh economic conditions in th e agricultural sector due to, among other things, weather phenomena such as happened with the effect of the El Nino phenomenon on the Coast during 1997-1998, may have lead to some land sales, especially by female headed households in 1998-1999, the year of our data set.

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78 Access to credit ( Credit ) is also included in our mode l as it could make a difference between farming, renting out or selling. Also if renting out is th e choice, landowners who obtained credit could opt for shar ecropping where they would provi de a share of the inputs and the tenants, the labor. Based on Chapter 3 we also hypothesize that capital constrained households (i.e., those who needed credit but were not able to obtain it) would be more likely to sell the land than unconstrained households (i.e., thos e who got credit or did not need it) because of the formers lower land reservation price. When testing for th is we replace the credit variable with the nonprice rationed dummy variable. Nevertheless, looking at Table 4-3, it is interesting to notice that ove r 33% of the households who sold some land reported to have obt ained some type of cred it during the year of the survey (38% of these cases obtained credit for agricultural purposes). This share is much higher than for all landowners or those who farmed only. Nevertheless, the average share of sold land was smaller for households with credit (25% of total land) th an for those without (38% of total land). Land Demand Demand for land can be observed in the form of land purchases or as la nd rented in. With respect to land purchases, our interest is in landowner hous eholds who bought land during the year prior to the survey. Th ese households correspond to 2% (1.9% net buyers) of the landowners in the data set. We expect the same variables included in model (1) to affect model (2) below, except for the percentage of titled land which is not included here since it should only affect supply and not the general dema nd for land (Deininger et al., 2003).

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79vTenant NonLInc OffInc Female Edu Age Credit Adults AniW sValueAsset SizePrior AgCon NonUrban Coast Purchasedi i i i i i i i i i i i i i 0 (4-2) Model 4-2 will be estimated as a simple logit regression where Purchased is a binary variable representing incidence of land purchases during the twelve months prior to the survey. The effect of the amount of land owned prior to purchase on the likelihood of buying land and especially on the amount of land purchased will tell us how disadvantaged the rural poor are in the land market. In the case of Paraguay (for 1991-94), for example, Carter and Salgado (2001) found a direct relationship between the land to labor endowment ratio and the probability of purchasing land. In our case, how ever, although the total incidence of land purchases is again small as in the case of land sales, the land poor s eem to have been more active in the land market compared to larger farmers. Table 4-4 shows that an im portant number of households who purchased land were landless prior to purchase (41%)9 and that 53% were owners of less than 5 ha., while no large owner (40 hectares or more ) performed any purchase. Nonetheless, 72% of the land purchases by households with less than 5 ha. were only of less than 1 ha. and so were 43% of the purchases by the landless prior to purchase. Assuming that in female headed househol ds the landowner is actually a woman, 10 these households may have been less likely to buy land than male headed households. This is because, as analyzed by Deere and Len (2003), in Lati n America the land market is not the most 9 This is, however, a heterogeneous group of people which seems to contain wealthier, urban households entering into the agricultural sector, as well as poor rural households being able to acquire some land. As such, 21% (or 3 households) of the landless prior to purchase acquired between 24 and 63 ha. during the year prior to the survey. 10 We need to make this assumption because the 1998-1999 LSMS survey did not gather data on landownership by gender. This is a shortcoming of most LSMS questionnaires designed for Latin America and it has been addressed in Doss et al. (2007).

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80 important channel of land acquisition for women; instead, inheritance is. Yet, these authors note that Ecuador is an exception due to the larg e share of women who obt ained land through the market compared to other countries in the region. Table 4-5 shows the most important form of land acquisition (50% of the land or larger) by gender based on our data set. There we can see that, like found by Deere and Len (2003), inheritance and purchases are the most important form of land acquisition for women in Ecuador while the market is for men. The difference in the share of male headed households acquiring land through the market and that of female h eaded households is, however, very small (45.4% for male heads and 44.5% for female heads). Credit access is expected to significantly increase the odds of purchasing land and the amount of land purchased. Table 4-6 shows that 50% of the households in our data set who purchased land obtained credit. This is a very large share compared to the total share of farmers (last column) who obtained credit. Looking closely at these land buye rs with credit access, 59% of them reported to have obtained credit for agricultural purposes, 29% for household consumption and 12% obtained loans for both purposes. Credit was provided by institutional sources in 35% of the cases (private banks a nd cooperatives) while the rest was provided by family, friends or other informal lenders. Given that credit is fungible, we thus expect that having access to loans in general would increase the likelihood and amount of land purchases. Particularly, 43% of the landless prior to purchase obtained credit during the same year they purchased land (most of which bought less than 2 ha.). An additional explanatory variable in Equation 4-2 is the dummy Tenant included with the purpose of testing the hypothesis that access to la nd through the rental market may help the rural

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81 poor scale up the agricultural ladder toward land ownership (Binswa nger et al., 1995; Sadoulet et al., 2001).11 The agricultural ladder is a sequence of progress of a farmer that goes from being an agricultural worker to becoming a shar ecropper, then perhaps a fixed rent tenant and finally a landowner. Even though th e appropriate way of testing fo r this hypothesis is through the use of panel data (the same farmers being in terviewed over time), we attempt to observe any contribution of tenancy to the probability of purchasing land in the year of our survey. As such, we expect to find that tenant households had a higher likelihood of buying land during the year of the survey than households who did not rent in land. We expect this effect to be significant since 41% of the land buyers in our data set were also tenants (Table 4-6) as were 43% of the landless prior to purchas e. It is also worth noticing th at 86% of the tenant households in our data who purchased land dur ing the year prior to the survey were also sharecroppers (as opposed to fixed-rent tenants) This is relevant given Le hman (1985)s findings that sharecropping in the Province of Carchi, Ecuado r was more a capitalis t partnership rather thana form of tenancy (pp. 351), and one which allowed capital accumulation by peasant producers. On the determinants of the odds of renting in land and of the amounts of land rented in, we estimate Equation 4-3, again using logit and Tobit regressions. In addition, we run a multinomial logit so as to specifically obser ve the effect of the same va riables on the likelihood to choose fixed-rental agreements, sharecr opping or no rental agreement at a ll. The total number of farmers in our sample who were tenants is 497, which re presents 25.6% of all the farmers in the sample. Of this, 76% chose shared-rental agreements. 11 Authors like Sadoulet et al. (2001) an d Binswanger et al. (1995) conclude that, given market imperfections, access to land through the rental ma rket would allow poor farmers to accumulate knowledge and wealth, helping them to acquire land in the long-run.

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82eNonLInc OffInc Female Edu Age Credit Adults AniW sValueAsset SizeOwned AgCon NonUrban Coast RentIni i i i i i i i i i i i i 0 (4-3) Table 4-7a shows that 41% of the tenant households were landless and 53% owned less than 5 ha., so we see the land poor being more activ e than large farmers in the land rental market too. Similar to what we found on the supply si de, households owning le ss than 5 ha. make up most of the sharecropping cases. Table 4-7b indi cates that these househol ds actually prefer sharecropping to fixedrental contracts. As was the case for those households who purch ased land, the share of tenant households who obtained credit is also larg er than this share for all farmers (Table 4-6). Among tenant households, 37% of the credit cases were for agricultur al purposes, 40% for household consumption, 7% for family business and 16% for both agricultural and non-agricultural purposes. Technical assistance as a dummy variable is not explicitly included in our regressions because of its low frequency in our data set (Tab les 4-3 and 4-6) and especially due to its null incidence among households who re nted out and those who purchas ed some land. However, we will comment on the effect of this variable on the multinomial logit of the land supply equation (model 1) and on the decision to rent in land (model 3) It is worth noticing here that 66% of those households with access to technical assistance chose to put all the land they owned into production; 20% had all of their ow n land in production plus rented in some land during the year prior to the survey, and 11% were tenants onl y. The positive impact of access to technical assistance on the demand for land is evident.

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83Results Supply Side Multinomial logit regression results in Tables 4-8a and 4-8b reveal that the effect of the amount of land owned prior to transactions on the likelihood of selling land was statistically significant but very small (only a 0.3%12 increase in the likelihood to sell relative to farming all land per owned hectare increase). This variable al so slightly influenced the odds of engaging in fixed-rental agreements (one more hectare of owned land increased the odds of choosing fixed rentals over farming oneself by 0.4% and the odd s of choosing fixed rentals over sharecropping by 0.5%). The share of titled land significantly affect ed only the odds of selling land. A one unit increase in this share would increase the like lihood of selling compared to farming all land oneself by 596%. This is the most important eff ect on the likelihood to sell land, which reveals the impact that land titling programs may have if not accompanied with policies aimed at releasing small farmers credit constraints (Deininger and Binswanger, 2001), among other market imperfections. Households located in non-urban areas were more likely to choose exploiting the land themselves rather than renting out compared to households in urban areas. This may be explained by the fact that urban households have more off-farm opportunities and therefore a higher opportunity cost of labor th an rural households, hence they would prefer re nting out more often. In addition, for househol ds who chose to rent out (Tab le 4-8b), rural households were 153% more likely to choose sharecropping as the leasing arrangement rather than fixed rental payments. 12 The effect of a variable is obtained as follows: exp(i )-1 and is read as a percentage.

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84 As expected, older household heads were more lik ely to rent out than to farm all land and more educated heads showed higher odds both of re nting out and selling re lative to farming all the land. Furthermore, more educated household h eads were 4% more likely to sell than to rent out. More education raises labor op portunity costs, apparently lead ing to these results. Consistent with the effect of education, the share of off-farm income strongly increased the odds of choosing to rent out land over farming it. Households receiving larger shares of income from remittances and/or governmental or non-governmental transfers are even more strongly likely to rent out compared to farming the land themselves. However, they are significantly less likely to sell than to farm all land (95% less likely) and also less likely to sell than to rent out (98% less likely). Sin ce households with larger shares of non-labor income tend to be poor hous eholds, these results s uggest their need for production support and their lower risk-bearing capacity. Furthermore, these households showed preference for sharecropping as the rental agreem ent, which is a sign that they want and needto be somewhat involved in the production pr ocess but they cannot do it all by themselves. Female household heads had a 136% higher likelihood than male household heads of renting out under fixed-rent al arrangements rather than farming all their land. In contrast, higher values of animal stocks would decrease the odds of choosing fixed-rentals over farming all land. These results also show that hous eholds in the Coast were less likely to sell their land than farming compared to households in the Sierra In addition, sharecr opping was a much more predominant type of land rental in the Sierra than in the co astal region, where there were no cases of sharecropping (Table 4-3). The FAOCOTECA (1995:45) study observed the tendency of sharecropping to disappear on th e Coast, especially in the ri ce growing areas. This region is known to have a more capitalist orientation than the Sierra (Jordan, 2003), which would support

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85 these findings. Also, households in areas of agricultural concentrati on prefer farming to sharecropping. This behavior can also be explained by the more co mmercial orientation of such areas. Also interesting to note from these results is that for house holds who rented out, one more year of schooling of the house hold head increased the odds of choosing sharecropping rather than fixed rentals by 4%. This result speaks to the benefits of sharecropping agreements. More educated household heads seem to prefer looking for jobs were th eir marginal value of labor is higher while allowing others (usually family or friends) to farm their land but still being partially involved in the production process so as to benefit from its profits. On the determinants of the amount of land to be rented out or sold (Table 4-9), non-urban households rented out signifi cantly less amounts of land than those located in urban areas (approximately 10 ha. less). Female household heads rented out about 5 more hectares than male heads, being the second strongest eff ect in this equation. Also consis tent with results in Tables 8a and 8b, more educated household heads both rent ed out and sold more land, and older heads rented out more land. Titled land facilitated land sales up to the point that a unit increase in the share of titled land increased the amount of land sold by 19 hectares and again this effect is the largest in the land sale equation. Finally, when replacing the credit dummy vari able with the non-pr ice rationed dummy so as to identify the specific effect of being constrained in the cred it market, we found that the new variable did not have a signifi cant impact on the likelihood to se ll. We also found that credit constrained households were 61% less likely (at 10% significance) to rent out their land rather than farming all compared to households who were credit unconstrained. This result must be a

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86 consequence of credit constrained households being mainly poor hous eholds who own small plots of land and have abundant family labor (C hapter 3); hence they need to farm the land themselves as they would ot herwise face unemployment. Demand Side On the demand side of the land market (Table 4-10), we see again that households in the Coast were not very active in the land sales mark et compared to those in the Sierra. In fact, the estimated odds of a household from the coasta l region purchasing land were 85% lower than those for a household from the highlands. Since the mean size owned prior to purchase in the Coast (13 ha.) is almost twice as high as that in the Sierra (7 ha.) and, as shown in Table 4-4, transactions were mainly performed by small farmers, this result makes sense. As expected, the size owned pr ior to purchase has a negative effect on the likelihood to purchase land and this effect is much stronger th an the positive influence of this variable found on the supply side of the land market. More spec ifically, one more unit of land would decrease the odds of buying land by 16%. Also as expected, access to credit was hi ghly significant with a strong effect on the likelihood of buying land and on the amount of la nd purchased (the strongest effect in both equations). More specifically, households who ha d access to any type of credit (for a positive interest rate) were 253% more lik ely to purchase land than house holds who did not obtain credit. Also, households who got credit were able to buy 9.6 more hectar es than those without credit. This result has important implications for rural development, such that, even if land credit is not readily available, increasing th e general supply of credit fo r the rural poor would contribute towards land acquisition. The dummy Tenant also had a positive a nd significant effect on the incidence of land purchases. A tenant household had 148% higher estimated odds of purchasing land than a non-

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87 tenant household. This result pr ovides some evidence that tena nt households are in a better position than non-tenant landless households to become landowners and than non-tenant landed households to purchase more land. Hence, the im portance of the land rental market in providing [progressive] land access to the rural poor is stressed here. These results also show that the values of farm equipment and animal stocks had a positive effect on the likelihood of purchasing land and the effect of the latter was significantly stronger than that of the former (6% versus 1% incr ease per $100 increase in animal stocks and farm assets, respectively). This reveals the importance of farm animals as 1) work animals and 2) a form of financial security for the rural poor. In addition, older household heads were less likely to buy land and if they did, it was in sm aller amounts than younger household heads. The main determinants of demand in the land re ntal market are specified in Tables 4-11 and 4-12. Areas of agricultural concentration were more active in the land rental market than other areas but, consistent with our earlier results on the supply side, households in these areas prefer fixed-rental agreements rather than sharecropping. Similarly, households in the Coast were more active in the fixed-rental market and less in the shared-rental market compared to the Sierra. As expected, more hectares of owned land would reduce the likelihood of renting in land (18% decrease per ha. increase). This effect is ag ain stronger than that of the same variable on the supply side of the land rental market. The amount of land owned also reduced the odds of choosing sharecropping agreements (24% per ha. increase). Also, one more hectare of land owned would reduce the amount of land rented in by 0.21 ha. While female household heads we re not significantly less li kely to purchase land, they were significantly less likely to rent in land than male heads (37% less likely). Moreover, female

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88 heads rented in 1.2 less ha. and were 40% less likely to rent land as sh arecroppers compared to male heads. Consistent with our results on the supply side, older and mo re educated household heads were less likely to rent in la nd and rented in less land than households with younger, less educated heads. Also, the share of off-farm income reduced the amount of land rented in. The value of animal stocks increased the odds of renting in and the amount of land rented (a $100 increase in animal stocks would increase the odds of ren ting in by 3% and the amount of land rented in by 0.08 ha.). The value of farm equipment also increased the amount of land rented in but with a smaller effect than the valu e of farm animals. Intere stingly, a $100 increase in farm assets would increase the odds of choosing sharecroppi ng by 0.8%. This effect, although small, suggests the relative importance of owni ng farm assets for sharecropping arrangements. This conforms to what is noted by Sadoulet et al. (2001): with increasing capital-intensity in agriculture, landlords look for tenants who can he lp share capital costs. (pp.210), and to what noted by Lehman (1986), that shar ecropping is more a partnership rather than a precarious work relationship in certain areas of Ecuador. Once again, the credit dummy was positive and highly significant, being the largest effect in both logit and Tobit regressions. As such, households who obtained cr edit had 107% higher estimated odds of renting in land and rented in 2 more hectares than hous eholds without credit. The direction of this effect was true for both types of rental agreements; however there was preference for fixed-rentals by households with credit access (19% hi gher odds of choosing fixed-rental agreements ra ther than sharecropping). Also, households with more adult members ha d higher odds of renting in land (13% per adult member increase) especially under fixed-rent al agreements (21% increase). One more adult

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89 in the household increased the amount of land rented by half hectare. The effects of this variable reflect the need for land in a context with land in equality, abundant labor and generalized unemployment. Finally, when including technical assistance in our analyses we found that, as expected, households with access to this service were significantly less likely to rent out rather than to farm all the land, and were more likely to rent in. Conclusions Our results show that the incidence of land sales and purchases in Ecuador was low during the period from late 1997 (year prior to the first day of the survey) to late 1999. We also found that the amount of land owned prior to these tr ansactions influenced participation in the land sales market; however, the effect on the supply si de was minimal while that on the demand side was much stronger. Similar is th e case in the land rental market s, where larger landowners were only slightly more likely to rent out but significantly less likely to rent in. Hence, although there was some indication by large owners of their willingness to offer land in the land markets, the demand, which was mainly performed by the la nd poor, seemed to be largely unsatisfied. Conforming to the abundant av ailability of labor and the ne ed for land by the rural poor, we found that the number of adult members in th e household had a significant effect on the odds of renting land in and on the amounts of land rented. The importance of active land rental markets was also perceived in that tenancy c ontributed significantly toward land purchases. The share of titled land was the most important determinant of participation in the land market as a seller. This reflects the role of la nd titles in reducing transaction costs on land sales and the effect land titling programs may have on poor landowners if not accompanied by policies aiming to remove relevant market imperfections, especially th ose found in the credit market.

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90 On the demand side, credit access was the strongest determinant of land market participation both in the land asset and the rental market. This conclusion on the significance of credit for land market participation is also not ed by Masterson (2005), B oucher et al. (2005) and Carter and Salgado (2001). A good portion of credit in our Ec uadorian case was obtained for non-agricultural purposes, hence suggesting that increasing the general s upply of credit in the rural sector could contribute to more active land markets. In addition, taking into account that households with larger shares of non-labor income tend to be poor households, this type of income was observed to contribu te to land access by the rural poor in that it decreased the likelihood of selling land and increased the odds of leasing, which provides land access for landless or other landed households. Hence, this paper showed some evidence that funds coming from remittances and governmental or non-governmental transfers help the rural poor hold on to their la nd. Furthermore, the fact that these households strongly preferred sharecr opping (rather than selling or farming all land) as the type of rental arrangement may also rev eal that they want to be involved in the production process but are not able to farm all the land by themselves, t hus showing the need for production support. The probable lower competitiveness of female household heads in agricultural production was perceived by their higher odds of renting land out under fixed-rental contracts, their lower odds of renting in, and their larg er amounts of land rented out and lower amounts of land rented in. The participation of female household heads in the supply side of the land rental market may have been a strategy to secure income for their families -at the same time as perhaps they sold their labor in off-farm activities-, however, it ma y have also been the result of male migration from their households or harsh economic conditions after severe weather (El Nino in 1997-98) -or a combination of both.

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91 Finally, the land sales market was less active on the Coast than it was in the Sierra. Given that owners of less than 5 ha. and the landle ss prior to purchase were the ones who performed most of the land purchases (Table 4-4), this di fference between regions is probably due to the larger degree of land concentration on the Coast; while small farms predominate in the Sierra. Also, landed households in the Co ast and those located in areas of agricultural concentration were significantly less likely to engage in sharecropping as opposed to farming all their land or choosing to rent out under fixed-rental contracts, which can be ju stified by the more commercial orientation of these areas. In conclusion, the rural poor seem to be th e most active on the demand side of the land sales and rental markets in Ecuador. However, given difficulties that prevent desired land transfers from large landowners to the rural poor such as tran saction costs for large owners, who must subdivide their holdings in order to sell smaller plots (Carter and Zegarra, 2000), and the unsatisfied need for credit by the rural poor-, it seems improbable that the market will be able to achieve an optimal land distribution wit hout any assistance from the government.

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92 Table 4-1. Farm size and land sales by owned land category Owned prior to selling Farm size category (ha.) N % Mean owned Mean sold Less than 1 6 240.360.10 >=1 to <5 8 322.760.76>=5 to <40 8 3211.452.41>= 40 3 12113.6776.67 Total seller households 25 10018.27 10.24 Table 4-2. Incidence of land rentals (landlords) by owned farm size category Rented out Fixed-rent Shared-rent Farm size category (ha.) N % Mean owned Mean rented N % Mean owned Mean rented N % Mean owned Mean rented Less than 1 30 36 0.5 0.4 12 35 0.5 0.4 18 37 0.4 0.4 >=1 to <5 29 35 2.2 1.6 9 27 2.2 1.8 20 41 2.2 1.6 >=5 to <40 20 24 11.7 9.6 11 32 14.1 10.5 9 18 8.8 8.6 >= 40 4 5 93.0 50.8 2 6 100.0 100.0 2 4 86.0 1.5 Total renters (landlords) 83 100 8.2 5.5 34 100 11.2 9.9 49 100 6.2 2.4

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93Table 4-3. Mean and median statis tics of variables in Equation 4-1 Owner decisions Total landowners Farmed only Sold Rented out Shared-rent Fixed-rent Total 1738 1629 25 83 49 34 # or mean % or median # or mean % or median # or mean % or median # or mean % or median # or mean % or median # or mean % or median Coast 458 26.4%43926.9%312.0%16 19.3%00.0%1647.1% Sierra 1280 73.6%119073.1%2288.0%67 80.7%49100.0%1852.9% Agricultural concentration area 627 36.1%60036.8%832.0%19 22.9%48.2%1544.1% Urban 172 9.9%1388.5%832.0%26 31.3%1428.6%1235.3% Non-urban 1566 90.1%149191.5%1768.0%57 68.7%3571.4%2264.7% Size owned prior to transactions 8.61 1.068.481.0118.274.058.23 1.416.171.0611.191.8084 Percent titled 0.65 1.000.6410.9010.73 10.8010.621 Value of farm assets 338.32 25.80347.8526.85678.9647.9852.61 4.4761.627.0339.620 Adults 3.13 3.003.1333.3633.18 33.0633.353 Credit 289 16.7%26216.1%833.3%19 23.5%1122.9%824.2% Age of head 50.37 49.0050.134950.324855.07 5455.82555450.5 Education of head 4.24 4.004.1347.0465.54 55.2455.975 Male 1456 83.8%137484.3%2184.0%60 72.3%3673.5%2470.6% Female 282 16.2%25515.7%416.0%23 27.7%1326.5%1029.4% Off-farm income 1516.14 817.121375.05785.769116.59 712.912006.73 1278.691886.2 01005.172176.901594.79 Non-labor income 146.44 29.69144.6130.1258.006.22211.51 41.87234.1835.14179.4943.12 Remittances 276 15.9%24915.3%312.0%24 28.9%1530.6%926% Technical assistance 31 1.8%301.8%14%0 0%00%00% % of landowners who 93.7% 1.44% 4.78% 2.82% 1.96% Net sellers or renters 1.27% 4.60% Size produced, sold or rented-out 8.48 10.24 5.47 2.41 9.88

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94 Table 4-4. Farm size and land purchases by owned land category (prior to purchase) Owned prior to purchase Farm size category (ha.) N % Mean owned Mean purchased Landless 1441 -11.661 Less than 1 7210.44 0.46 >=1 to <5 11322.19 0.81 >=5 to <40 2614.03 11.56 >= 40 00--0 Total buying households 34100 5.84 Table 4-5. Forms of land acquisition by gender Female HH Male HH Total* Main form of acquisition N % N % N % Purchased during last year 31.1191.322 1.3 Purchased earlier 12243.463344.1755 43.9 Inherited 12945.954137.6670 39.0 Adjudicated 113.9956.6106 6.2 Usufruct 165.714910.4165 9.6 Total landowners 281100.01437100.01718 100.0 20 households (1 female headed and 19 male headed) reported a mixture of any two forms of acquisition (50 and 50%) and so were not included in this table. 1 This average goes down to 2.9 ha. when not taking into account the 3 households with the largest land purchases.

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95 Table 4-6. Summary of variab les in Equations 4-2 and 4-3 Purchased Rented-in Total farmers Total incidence 34 497 1940 # or mean % or median # or mean % or median # or mean % or median Coast 411.8%13226.6%537 27.7% Sierra 3088.2%36573.4%1403 72.3%Agricultural concentration area 1029.4%20040.2%720 37.1%Urban 514.7%448.9%207 10.7%Non-urban 2985.3%45391.1%1733 89.3% Size prior to purchase 1.620.27 Total owned 1.530.187.57 1 Value of farm assets 441.3448.80256.1429.52321.37 23.43 Adults 3.062.503.1833.11 3 Credit 1750.0%11923.9%328 16.9% Age of head 39.8238.544.954449.53 49 Education of head 6.5664.2744.25 4 Male 3191.2%44489.3%1633 84.2% Female 38.8%5310.7%307 15.8% Off-farm income 1937.72944.871352.88876.201509.58 843.29 Non-labor income 532.5916.93113.54110.87143.32 38.39 Tenant 1441.2% Remittances 617.6%5711.5%300 15.5% Technical assistance 00%112.2%35 1.8% Size purchased 5.840.71 Size rented-in 2.220.71 % of current owners (1,738) who bought land 1.96% Net buyers 1.90% % of farmers (1,940) who rented-in 25.6% Net tenants 25.5% % of tenants who owned land 59.4% Table 4-7a. Land rented in by category of land owned Total Fixed-rent Shared-rent Farm size category (ha.) N % Mean owned Mean rentedN % Mean owned Mean rentedN % Mean owned Mean rented Landless 202 41 2.6 5445 3.9 154 41 2.2 <1 153 31 0.3 1.1 18150.41.0 132 35 0.31.2 >=1 to <5 110 22 2.1 1.9 31262.12.0 77 20 2.11.8 >=5 to <40 29 6 11.7 5.4 151311.27.2 13 3 12.03.0 >= 40 3 1 47.3 11.8 2245.015.8 1 0.3 52.04.0 Total renters 497 100 1.5 2.2 120100 2.83.6 377 100 1.11.8

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96 Table 4-7b. Choice of rental agr eement by category of land owned Farm size category (ha.) N % fixed-rent % shared-rent Landless 2022776 Less than 1 ha. 153 12 86 1 to less than 5 ha. 110 28 70 5 to less than 40 ha. 29 52 45 40 or more ha. 3 67 33 Total renting households 497 24 76 Table 4-8a. Multinomial logit regr ession results (owners decisions between farming, selling or renting out) Variables Sell/ Farm Rent-out / Farm Coefficient Robust SE Coefficient Robust SE Coast -1.568 (0.882) -0.315 (0.367) NonUrban -0.571 (0.667) -0.848** (0.377) Agcon 0.164 (0.537) -0.342 (0.304) SizePrior 0.003 (0.002) 0.003 (0.002) %Titled 1.941 *** (0.713) -0.024 (0.344) Adults 0.214 (0.172) 0.091 (0.087) ValueAssets -0.001 (0.005) -0.155 (0.110) AniW -0.028 (0.024) -0.033 (0.028) Credit 0.742 (0.569) 0.369 (0.313) Age 0.012 (0.021) 0.035 *** (0.010) Edu 0.136 *** (0.047) 0.093 *** (0.035) Female 0.640 (0.609) 0.486 (0.304) OffInc -0.425 (0.789) 0.817 ** (0.387) NonLInc -3.040 *** (1.141) 1.036 (0.608) Constant -6.878 (1.689) -5.135 (0.910) No. of obs. 1710 1710 Pseudo R2 0.13 0.13 Log pseudo-likelihood -423.94 -423.94 *** Significant at 1%; ** signifi cant at 5%; significant at 10%

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97 Table 4-8b. Multinomial logit regr ession results (owners decisions between farming, selling, renting under fixed-rent or unde r shared-rental contracts) Variables Sell/ Farm Fixed-rent/ Farm Shared-rent/ Farm Coeff. Robust SE Coeff. Robust SE Coeff. Robust SE Coast -1.555 (0.875) 0.468 (0.564) -37.685 *** (0.272) NonUrban -0.555 (0.669) -1.242 *** 0.464) -0.313 (0.506) Agcon 0.153 (0.539) 0.249 (0.427) -1.127 ** (0.545) SizePrior 0.003 (0.002) 0.004 (0.002) -0.001 (0.003) %Titled 1.948 *** (0.713) -0.461 (0.541) 0.521 (0.431) Adults 0.214 (0.173) 0.142 (0.146) 0.065 (0.103) ValueAssets -0.001 (0.005) -0.132 (0.115) -0.170 (0.181) AniW -0.028 (0.025) -0.164 (0.096) -0.015 (0.018) Credit 0.743 (0.570) 0.201 (0.479) 0.460 (0.405) Age 0.012 (0.021) 0.030 (0.016) 0.039 *** (0.012) Edu 0.138 *** (0.047) 0.074 (0.050) 0.115 *** (0.044) Female 0.640 (0.608) 0.857 ** (0.425) 0.210 (0.396) OffInc -0.430 (0.789) 0.769 (0.540) 0.699 (0.531) NonLInc -3.035 *** (1.140) 0.462 (1.057) 1.325 (0.697) Constant -6.907 (1.691) -5.443 (1.411) -6.385 (1.162) No. of obs. 1710 1710 1710 Pseudo R2 0.17 0.17 0.17 Log pseudolikelihood -459.62 -459.62 -459.62 *** Significant at 1%; ** signifi cant at 5%; significant at 10% Table 4-9. Censored Tobit regressions (amount of land rented-out and sold) Variables Amount sold Amount rented-out Coefficient SE Coefficient SE Coast -13.041 (10.445) -0.439 (2.437) NonUrban -12.688 (9.144) -9.609 *** (2.620) Agcon -1.574 (7.919) -2.004 (2.224) SizePrior 0.058 (0.037) 0.016 (0.016) %Titled 19.276 (10.194) 1.453 (2.041) Adults 2.187 (2.085) 0.340 (0.623) ValueAssets -0.002 (0.115) -0.793 (0.498) AniW -0.248 (0.381) -0.087 (0.159) Credit 4.415 (7.555) 0.722 (2.349) Age -0.006 (0.251) 0.153 ** (0.066) Edu 2.127 ** (0.879) 0.589 ** (0.264) Female 14.263 (8.757) 4.889 ** (2.295) OffInc -8.184 (9.045) 3.476 (3.026) NonLInc -53.473 (33.073) 5.413 (4.585) Constant -95.005 (26.138) -33.930 (6.936) No. of observations 1710 1710 Uncensored obs. 25 80 Pseudo R2 0.08 0.06 Log likelihood -201.01 -516.17 *** Significant at 1%; ** signifi cant at 5%; significant at 10%

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98 Table 4-10. Logit for probability of purchas e and censored Tobit for amount of land bought Variables Purchased Amount purchased Coefficient Robust SE Coefficient SE Coast -1.876 ** (0.727) -4.287 (5.378) NonUrban -0.763 (0.731) -1.305 (5.720) Agcon 0.148 (0.473) -1.244 (4.141) SizePrior -0.178 (0.103) -0.659 (0.477) Adults 0.172 (0.157) 1.012 (1.275) ValueAssets 0.008 (0.005) 0.034 (0.084) AniW 0.063 *** (0.020) 0.398 (0.290) Credit 1.261 *** (0.422) 9.604 ** (3.963) Age -0.050 *** (0.017) -0.355 ** (0.160) Edu 0.056 (0.058) 0.874 (0.487) Female -0.238 (0.626) -2.714 (5.857) OffInc -0.121 (0.701) -0.257 (5.680) NonLInc 0.673 (1.129) 10.416 (9.373) Tenant 0.909 (0.487) 5.688 (3.921) Constant -2.453 (1.414) -38.661 (12.674) No. of observations 1710 1710 Uncensored obs. 33 Pseudo R2 0.21 0.08 Log pseudolikelihood -108.28 -239.77 *** Significant at 1%; ** signifi cant at 5%; significant at 10% Table 4-11. Logit for probability of renting-in and censored Tobit for amount of land rented Variables Rented-in Amount rented-in Coefficient Robust SE Coefficient SE Coast 0.132 (0.167) 0.069 (0.519) NonUrban 0.172 (0.224) -0.393 (0.701) Agcon 0.346 ** (0.145) 0.597 (0.466) SizeOwned -0.196 *** (0.054) -0.215 *** (0.040) Adults 0.119 ** (0.047) 0.522 *** (0.138) ValueAssets 0.005 (0.003) 0.021 (0.011) AniW 0.034 ** (0.014) 0.082 ** (0.041) Credit 0.726 *** (0.167) 2.091 *** (0.503) Age -0.034 *** (0.005) -0.092 *** (0.015) Edu -0.063 *** (0.022) -0.163 ** (0.065) Female -0.461 ** (0.212) -1.203 ** (0.608) OffInc -0.220 (0.218) -1.122 (0.634) NonLInc -0.179 (0.379) -0.221 (1.149) Constant 0.585 (0.454) -0.434 (1.341) No. of observations 1918 1918 Uncensored obs. 494 Pseudo R2 0.13 0.03 Log pseudo-likelihood -976.74 -2124.91 *** Significant at 1%; ** signifi cant at 5%; significant at 10%

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99 Table 4-12. Multinomial logit for probability of renting-in Variables Fixed-rent tenancy/Noth ing Shared-rent tenancy/Nothing Coefficient Robust SECoefficient Robust SE Coast 1.374 *** (0.268) -0.500 ** (0.200) NonUrban 0.109 (0.350) 0.224 (0.268) Agcon 0.590 ** (0.255) 0.229 (0.161) SizeOwned -0.122 ** (0.060) -0.275 *** (0.078) Adults 0.188 ** (0.081) 0.086 (0.051) ValueAssets 0.001 (0.004) 0.008 (0.004) AniW 0.037 ** (0.017) 0.035 (0.019) Credit 0.859 *** (0.304) 0.688 *** (0.176) Age -0.032 *** (0.009) -0.035 *** (0.005) Edu -0.013 (0.035) -0.087 *** (0.025) Female -0.475 (0.407) -0.516 ** (0.242) OffInc -0.101 (0.367) -0.298 (0.257) NonLInc -0.256 (0.626) -0.235 (0.452) Constant -2.126 (0.755) 0.832 (0.542) No. of observations 1918 1918 Pseudo R2 0.14 0.14 Log pseudo-likelihood -1226.01 -1226.01 *** Significant at 1%; ** signifi cant at 5%; significant at 10%

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100 CHAPTER 5 CONCLUSIONS The three es says presented here offered an ec onomic analysis of agricultural land access at the household level and its relationship with ru ral markets and poverty in Ecuador. The severe inequality in land ownership a nd access has consequences for land and labor productivity and for access to credit, modern technologies and land markets. Land inequality has generated distortions in the access to agri cultural markets over time, whic h have become institutionalized. Thus, the effects of land inequality on poverty ar e augmented by imperfections in those markets. The main findings of our study are summarized as follows: 1. Given the abundant labor availability of land-poor househol ds, conditions of unemployment, incomplete credit markets, and segmented land markets cause small farms (less than 5 ha.) and especially minifundios (less than 1 ha.) to be more productive per unit of land but less productive per unit of labor than larger farms. Ecuador conforms to the traditional findings for developing countries, many times emphasized in the literature. 2. Land reservation prices per he ctare decrease with the land to labor endowment ratio, which may explain why small farmers tend to be more active on the demand side of land markets than other farmers. However, sim ilar to the findings of Carter and Salgado (2001), constraints in the cred it market reduce and even ove rcome the advantage of poor farmers with respect to land reservation pri ces. As a consequence, the demand for land by the rural poor is better satisfied in the rental market. This is suggest ed by the result that the mean amount of land rented by households wi th less than 5 ha. is larger than the mean purchased, and by the finding that lack of cr edit limits the incide nce and amount of land purchased more severely than it does the in cidence and amount of land rented in. This supports the argument of Carter a nd Salgado (2001) that the effect of credit constraints in the demand for land in the rental markets would be magnified in the case of the land sales market. 3. For agricultural households, the effect of be ing able to access one more unit of land is such that it would improve th e probability of credit access and the amount of credit obtained. More land would also allow for a more efficient labor allocation and increased labor productivity. Depending on endowments a nd output prices faced by the household, among other factors, one more unit of land w ould likely increase ag ricultural profits leading to higher household income. In addi tion, improved credit access would increase land reservation prices and the ability to pur chase land. More credit would also help farmers acquire more and better intermediate assets, which would reinforce the process described above.

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101 4. Contrary to the findings of De ininger et al. (2003), there wa s some indication that large landowners were willing to offer land in the land markets, reflected in the positive effect of farm size on the likelihood to sell and re nt out land under fixed -rental contracts. However, that effect was not nearly as strong as the demand for land by small farmers (i.e., the negative effect of farm size on the likelihood to purchase and rent in land). In addition, 56% of the land sales and 71% of the land rentals (lessors) were by small farmers, which show that in general, these farmers were the most active in both sides of the land sales and rental markets. 5. Given the difficulties that prevent desired la nd transfers from large landowners to the rural poor such as subdivision costs and a restricted supply of creditit seems improbable that the market be able to achie ve an optimal distribution of landownership without any assistance from the government. 6. Because of the severe land inequality in Ec uador and the distortions this has generated over time, the results regarding the estimated effects of one more unit of land mentioned in conclusion (3) represent the existing di fferences among farms of different sizes. To represent the potential of increased land acces s for rural development such an increase must be accompanied by better access to servic es so as to increase the competitiveness of the rural poor. In other words, since unequa l land access and imperfections in the credit, labor and land markets form a pervasive s ynergy, a governmental policy oriented to increase land access for the rural poor would need to be complemented by other market reforms and by the provision of technical assistance. 7. Female headed households were more likely to offer land in the rental market (under fixed rental contracts) and less likely to demand land as tenants compared to households headed by men. This result reflects the lower competitiveness of women in agricultural production, which conforms to Deere and Len (2001)s claim of the disadvantages usually faced by women when trying to acce ss credit, technical assistance or produce markets. Still, female headed households were not significantly more likely to participate in the land market as sellers than male headed households, nor were they significantly less likely to participate as land buyers. This supports D eere and Len (2001)s analysis since it reflects the importan ce of landownership for wo men, which may go beyond just obtaining agricultural profits: Landownership provides women with increased bargaining power in the household and the community food security for their children and constitutes an asset which they can rent so as to generate income fo r the household. Also, the analysis showed that female headed households acquired land through the market in rates comparable to those of male headed households, a result that, as already noted by Deere and Len (2003), makes Ecuador differe nt from the rest of Latin American countries. 8. The value of animal stocks and the share of non-labor income are im portant variables in the determination of participation in the land markets which have not been considered in previous studies. The role of animal stocks as both work animals a nd a form of financial security for poor rural househol ds is reflected in that th ey increase the likelihoods of purchasing and renting in land, as well as the amount of land rented in. They also decrease the likelihood of renting out land under fixed-rental contracts. Similar to the

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102 findings for female headed households, those ho useholds with larger shares of non-labor income, which tend to be poor rural households, are less likely to sell their land but more likely to rent out (in this case under shared-r ental contracts). This again suggests both the importance of land as an asset that can be rented and the lower competitiveness of this type of households. In any case, funds from remittances and governmental or nongovernmental transfers help th e rural poor to make up for immediate cash needs without resorting to selling their land. 9. Land titles increase the amount of institutiona l credit obtained but only for households who are able to access formal credit. They also increase the likelihood and amount of land sales. Unlike the findings of Feder and Feeney (1993), lack of land titles did not effectively discourage investment in land and di d not cause land values to be smaller than for households with titled land. Also, contrary to the findings of De ininger et al. (2003) and Masterson (2005), land titles did not stimulate the supply of land in the land rental market, which suggests that the low level of de velopment of the rental market may be due to the titles not being properly registered, or to the lack of knowledge about the relevant legislation as to the proper ways in which a fixed-rental contract s hould be carried out in order to avoid losing the land to the tenant and/or to the deficiency of formal enforcement of property rights in Ecuador. 10. Conforming to Sadoulet et al. (2001), Binswanger et al. (1995) and Deininger et al. (2003) we found that the importance of the land rental market is that it provides the rural poor with an alternative to landownershi p, and the possibility that it will play a ladder toward landownership. In addition, sharecropping was found to be especially common among the land poor (less than 5 ha .), not only on the demand but also on the supply side of the market, with 64% of the supply and 81% of the demand cases being in the form of sharecropping. This suggests that sharecropping may be more a type of productive partnership rather than a precarious work relationship, which is emphasized by the fact that, given land market segmen tation, rental relations are mainly among family, friends or in general, members of the same class. More research is required, however, as to the specific characteristics of sharecropping arrangements in Ecuador in order to confirm this proposition. This resu lt also suggests the need for production support by small owners so that they are able to work the land directly if they so wish. Policy recommendations: My results suggest that libera lization and stimulation of land rental markets are among the most urgent and im portant institutional changes that should take place in Ecuador so as to benefit the rural poor. Th is would entail the elimination of restrictions on sharecropping in the land legisl ation and the reduction of bureau cratic steps that cause high transaction costs for individuals wishing to re nt land. More importantly, such reform would require the effective protection of property rights so as to ensu re landowners of their rights to

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103 land rented out. This calls for a stronger judicial system and the creation of effective mechanisms of conflict resolution (Barham et al., 2004). Also, the general supply of credit (i.e., cred it not only for agricultural purposes but also, non-agricultural), the most important stimulant of production and demand for land, needs to be increased in the rural sector, especially in communities where credit markets are missing. An innovative strategy such as the creation of credit bureaus which would turn information on borrower reputation public (Barha m et al., 2004) may help overcome asymmetric information limitations faced by formal credit institutions. In addition, increased provision of technical assistance by the government is a crucial complementary policy given the traditional characteristics of small farmer agriculture and hence their lack of knowledge of modern, more productive technologies. Results from this study provide evidence of the conditions sustaini ng rural poverty in one more country of the developing world. Our c onclusions conform to many of the findings for other developing countries and hence add to the pl ea for sound institutional changes in the rural sector, which governments should promote. The need for improved data collection by governmental institutions must also be emphasized. For example, including survey questions aimed to gather data on who the principal agricultura list in the household is ; asset ownership by gender; the titling status of land that is sold and purchased; who in the household rented or sold land, and if possible, follow up surveys with the same sample of farmers over time (i.e., panel data) would be improvements that would facilita te and make more accurate the analysis of agricultural development.

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104 APPENDIX PRIMARY ACTIVITY OF FEMALE HOUSEHOLD HEADS Table A-1. Prim ary activity of fe male household heads by farm size Total Minifundio Small Medium Large # % # % # % # % # % Ag. self employed 163 18.1%8035.2%5715.7%187.1% 8 14.3% Ag. worker 34 9.1%2714.4%53.4%13.1% 1 16.7% Non-ag. self employed 53 20.3%3725.0%811.9%718.9% 1 11.1% Non-ag. worker 18 5.5%168.0%11.0%13.4% 0 0.0% Not economically active or unemployed 31 53.4%1458.3%844.4%758.3% 2 50.0% Total 299 15.6%17422.1%7911.4%349.4% 12 15.8%

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105 LIST OF REFERENCES Agresti, A. (1996). An introduction to categorical data analysis N ew York: Wiley. Alvarez, Silvia (1999). De Huancavilcas a Comuneros Guayaquil, Ecuador: ESPOL Press. Andersen, T., & Malchow-Moller, N. (2006). Strategic interaction in undeveloped credit markets. Journal of Development Economics, 80, 275-298. Bardhan, P., & Udry, C. (1999). Development microeconomics New York: Oxford University Press. Berry, R.A., & Cline, W. (1979). Agrarian structure and productiv ity in developing countries Baltimore: The Johns Hopkins University Press. Binswanger, H., Deininger, K., & Feder, G. (199 3). Agricultural land relations in the developing world. American Journal of Ag ricultural Economics 75 1242-1248. Binswanger, H., Deininger, K., & Feder, G. ( 1995). Power, distortions, revolt and reform in agricultural land relations. In J. Behrman, & T.N. Srinivasan (Eds.), Handbook of Development Economics, Volume 3B (pp: 2659-2772). Amsterdam: North Holland. Boucher, S., Barham, B., & Cart er, M. (2005). The impact of m arket-friendly reforms on credit and land markets in Honduras and Nicaragua. World Development, 33 (1), 107. Camacho, G. (2005). Migracin, g nero y empleo en el Ecuador. Informe Organizacin Internacional del Trabajo OIT Septiembre 2005, Quito. Carter, M., & Salgado, R. 2001. Land market liberalization and th e agrarian ques tion in Latin America. In A. de Janvry, G. Gordillo J. Platteau, & E. Sadoulet (Eds.), Access to land, rural poverty and public action (pp. 246-278). New York: Oxford University Press. Carter, M., & Zegarra, E. (1995). Reshaping class competitiveness and the trajectory of agrarian growth with well sequenced policy reform. Working Paper, Department of Agricultural Economics, University of Wisconsin, Madison. Carter, M., & Zegarra, E. 2000. Land markets and the persiste nce of rural poverty: Postliberalization policy options. In R. Lpez, & A. Valds (Eds.), Rural poverty in Latin America (pp. 65-85). New York: St. Martins Press, Llc. Castillo, M.J., Useche, P., & Moss, C.B. (2007). Mi ssing agricultural pri ce data: an application of mixed estimation. Working Paper, Food and Resource Economics Department, University of Florida, Gainesville. Chiriboga, M., & Rodrguez, L. (1998). El sector agropecuario Ecuatoriano: tendencias y desafos. CONAM, Ecuador.

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106 Currie, J.M. (1981). The economic theory of agricultural land tenure New York: Cambridge University Press. De Ferranti, D., Perry, G., Ferreira, F., & Walton, M. (2003). Inequality in Latin America and the Caribbean: breaking with history? The World Bank, Washington D.C. De Janvry, A., Platteau, J., Gordillo, G., & Sadoulet, E. (2001). Access to land and land policy reforms. In A. de Janvry, G. Gordillo, J. Platteau, & E. Sadoulet (Eds.), Access to land, rural poverty and public action (pp. 1-26). New York: Oxford University Press. Deere, C.D., & Len, M. (2001). Empowering women: Land and property rights in Latin America. Pittsburgh, PA: University of Pittsburgh Press. Deere, C.D., & Len, M. (2003). The Gender Asset Gap: Land in Latin America. World Development, 31 (6), 925-947. Deininger, K., & Binswanger, H. (2001). Evoluti on of the World Bank's Land Policy. In A. de Janvry, G. Gordillo, J. Platteau, & E. Sadoulet (Eds.), Access to land, ru ral poverty and public action (pp. 406-440). New York: Oxford University Press. Deininger, K., & Olinto, P. (2000). Asset distribution, inequality, and growth. World Bank Policy Research Paper 2375. The World Bank, Washington, D.C. Deininger, K., Zegarra, E., & Lavandez, I. ( 2003). Determinants and impacts of rural land market activity: Evidence from Nicaragua. World Development, 31 (8), 1385-1403. Economic Commission for Latin America and the Caribbean (ECLAC). (2005). Statistical yearbook for Latin America and the Caribbean United Nations Publication, Santiago, Chile. Espinel, R. (2002). Formacin de si stemas financieros rurales en la crisis bancaria ecuatoriana. Ecuador Debate, 56 20-27. FAO COTECA. (1995). Mercado de Tierras en el Ecuador: Estudio Integrado Regiones Litoral y Sierra. FAO, Roma. FAO. (2002). Arrendamiento de tierras en Amri ca Latina: una alternativa de acceso a la tierra para los pobres rurales? FAO Investment Centre Occasional Paper Series, 13 FAO. (2004). FAO Statistical country profile FAO Statistics Division < http://www.fao.org/es/ess/yearbook/vol_1_2/pdf/Ecuador.pdf >. FLACSO & UNFPA (2006). Ecuador: Las cifras de la migracin internacional Quito, Ecuador. Feder, G., & Feeney, D. (1993). The theory of la nd tenure and property ri ghts. In K. Hoff, A. B raverman, & J. Stiglitz (Eds.), The economics of rural organiza tion: theory, practice, and policy (pp. 240-258)). New York: Oxford University Press.

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107 Feder, G., Oncahn, T., Chalamw ong, Y., & Hongladarom, C. (1988). Land policies and farm productivity in Thailand Baltimore: The Johns Hopkins University Press. Finan, F., E. Sadoulet and A. de Janvry. 2005. Measuring the poverty reduction potential of land in rural Mexico, Journal of Development Economics Vol. 77, pp. 27-51. Girkinger, C., & Boucher, S. (2005). Cred it Constraints and Productivity in Peruvian Agriculture. Working Paper, Department of Agricultural and Resource Economics, University of California, Davis. Gould, K. (2001). Land titling on an agricultural frontier, Pet n, Guatemala. M.S. thesis, University of Florida, Gainesville, Depart ment of Forest Resources and Conservation. Hayami, Y., & Otsuka, K. (1993). The economics of contract c hoice: an agrarian perspective New Cork: Oxford University Press. Instituto Nacional de Estadistic as y Censos (INEC), Ecuador. < http://www.inec.gov.ec>. Jordn, F. (2003). Reform a Agraria en Ecuador. In Paper presented at Seminario Internacional: Resultados y Perspectivas de las Reformas Agrarias y los Movi mientos Indgenas y Campesinos en Amrica Latina, Universidad Mayor de San Andrs, Julio 2003, La Paz. Lambert, V., & Stanfield, D. (1990). Case studi es of rural land markets in Ecuador. Working Paper, The University of Wisconsin, Land Tenure Center, Madison, WI, and the Centro Andino de Accin Popul ar, Quito, Ecuador. Lehman, D. (1986). Sharecropping and the capitalist tran sition in agriculture. Journal of Development Economics, 23 333-354. Lopez, R., & Valdes, A. (2000). Fighting rural p overty in Latin America: New evidence of the effects of education, demographics and access to land. Economic Development and Cultural Change49 (1), 197-211. Marcours, K., de Janvry, A., & Sadoulet, E. (2004). Insecurity of propert y rights and matching in the tenancy market. CUDARE Working Paper 992, Department of Agricultural and Resource Economics, University of California, Berkeley. Masterson, T. (2005). Land markets, female land rights and agricu ltural productivity in Paraguayan agriculture. Ph.D. thesis, Universi ty of Massachusetts, Amherst, Department of Economics. Migot-Adholla, S., Hazell, P., Bl arel, B., & Place, F. (1993). I ndigenous land rights systems in Sub-Saharan Africa: A constraint on productivity? In K. Hoff, A. Braverman, & J. Stiglitz (Eds.), The economics of rural organizati on: Theory, practice and policy (pp. 269-291). New York: Oxford University Press. Mishra, A., Moss, C.B., & Erickson, K. (2004) Valuing farmland with multiple quasi-fixed inputs. Applied Economics, 36 1669-1675.

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108 Moss, C.B., Schmitz, A., & Livanis, G. (2008) The effect of incr eased energy prices on agriculture: a differen tial supply approach. Working Paper, Food and Resource Economics Department, University of Florida, Gainesville. Nieto, C. (2004). El acceso legal a la tierra y el desarrollo de las comunidades indgenas y afroecuatorianas: la experi encia del PRODEPINE en el Ecuador. In P. Groppo (Ed.), Land reform: Land settlement and cooperatives (pp. 96-109). Rome: FAO. Otez, G., Maldonado, J., Rentar a, F., & Camacho, E. (2001). Ecuador: breve anlisis de los resultados de las principales variables del Censo Nacional Agropecuario 2000 Cmara Nacional de Acuacultura y Sistema Estad stico Agropecuario Nacional, Ecuador. Sadoulet, E., Murgai, R., & de Janvr y, A. (2001). Access to land via land rental markets. In A. de Janvry, G. Gordillo, J. Pla tteau, & E. Sadoulet (Eds.), Access to land, ru ral poverty and public action (pp. 196-229). New York: Oxford University Press. Santos-Ditto, J. (1999). Derecho Agroambiental en Amrica Latina Guayaquil, Ecuador: Editorial de la Universidad de Guayaquil. Schultz, T. (1945). Agriculture in an unstable economy New York: McGraw-Hill Book Company, Inc. Singh, I., Squire, L., & Strauss, J. (1986). Method ological issues. In I. Si ngh, L. Squire, & J. Strauss (Eds.), Agricultural Household Models: extensions, applications and policy (pp. 48-91). Baltimore: Jon Hopkins University Press. Terrell, D. (1996). Incorporating monotonicity a nd concavity conditions in flexible functional forms. Journal of Applied Econometrics, 11 (2), 179-194. World Bank. 2004. Ecuador poverty assessment World Bank, Report No. 27061-EC. Zimmerman, F., & Carter, M. (2003). Asset smoothing, consumption smoothing and the reproduction for inequality under risk and subsistence constraints. Journal of Development Economics, 71 (2), 233-260.

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109 BIOGRAPHICAL SKETCH Mara Jos C astillo was borne in Guayaquil, Ecuador. She graduated with her bachelors degree in economics at the Escuela Superior Pol itcnica del Litoral (ESPOL) in Guayaquil, in July 2000. She came to UF for her masters degree in food and resource economics in fall 2001 which she completed in fall 2003. Immediately, Mara Jos conti nued with her Ph.D. in food and resource economics at UF.


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