Group Title: Working paper - International Agricultural Trade and Policy Center. University of Florida ; WPTC 05-02
Title: Farm mechanization and the farm labor market : a socioeconomic model of induced innovation
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Title: Farm mechanization and the farm labor market : a socioeconomic model of induced innovation
Series Title: Working paper - International Agricultural Trade and Policy Center. University of Florida ; WPTC 05-02
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WPTC 05-02


I '-ional Agricultural Trade and Policy Center



FARM MECHANIZATION AND THE FARM LABOR MARKET:
A SOCIOECONOMIC MODEL OF INDUCED INNOVATION
By
Orachos Napasintuwong & Robert D. Emerson

WPTC 05-02 March 2005


WORKING PAPER SERIES


'V


i~fr


UNIVERSITY OF
FLORIDA


Institute of Food and Agricultural Sciences









INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER


THE INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER
(IATPC)

The International Agricultural Trade and Policy Center (IATPC) was established in 1990
in the Institute of Food and Agriculture Sciences (IFAS) at the University of Florida
(UF). The mission of the Center is to conduct a multi-disciplinary research, education and
outreach program with a major focus on issues that influence competitiveness of specialty
crop agriculture in support of consumers, industry, resource owners and policy makers.
The Center facilitates collaborative research, education and outreach programs across
colleges of the university, with other universities and with state, national and
international organizations. The Center's objectives are to:

* Serve as the University-wide focal point for research on international trade,
domestic and foreign legal and policy issues influencing specialty crop agriculture.
* Support initiatives that enable a better understanding of state, U.S. and international
policy issues impacting the competitiveness of specialty crops locally, nationally,
and internationally.
* Serve as a nation-wide resource for research on public policy issues concerning
specialty crops.
* Disseminate research results to, and interact with, policymakers; research, business,
industry, and resource groups; and state, federal, and international agencies to
facilitate the policy debate on specialty crop issues.









FARM MECHANIZATION AND THE FARM LABOR MARKET:

A SOCIOECONOMIC MODEL OF INDUCED INNOVATION






Orachos Napasintuwong
International Agricultural Trade and Policy Center
Food and Resource Economics Department
PO Box 110240
University of Florida
Gainesville, FL 32611
onapasi@ufl.edu

Robert D. Emerson
International Agricultural Trade and Policy Center
Food and Resource Economics Department
PO Box 110240
University of Florida
Gainesville, FL 32611
remerson@ufl.edu








Selected Paper prepared for presentation at the Southern Agricultural Economics
Association Annual Meeting, Mobile, Alabama, February 1-5, 2003










Copyright 2002 by Orachos Napasintuwong and Robert D. Emerson. All rights reserved.
Readers may make verbatim copies of this document for non-commercial purposes by any
means, provided that this copyright notice appears on all such copies.









FARM MECHANIZATION AND THE FARM LABOR MARKET:

A SOCIOECONOMIC MODEL OF INDUCED INNOVATION


Introduction

Hayami and Ruttan's (1970, 1985) theory of induced innovation has been widely

applied in the study of technological change. The basic concept of this theory is that the

development and adoption of technology to save an input is induced by its relatively

higher price. The empirical studies of technological change in U.S. agriculture by

Binswanger (1974a, 1974b), Antle, Shumway and Alexander are also based on this

theory. These early works of induced innovation theory assume market perfection; in

other words, the demand for technological change is the same for all producers since they

face the same prices. Moreover, the previous studies ignore the importance of the

socioeconomic environment (e.g. political and cultural), resulting from institutional

changes and its impact on technological change.

The new direction of induced innovation theory emphasizes the role of

institutions and their relationship with technological change as suggested by Binswanger

(1978), De Janvry (1973, 1978a, 1978b), Ruttan, and several other economists. In this

study of the change of U.S. agricultural technology, we take into account structural

changes in the agricultural industry as well as the political influences. Some examples of

political factors that influence the farm commodity program are found in Gardner and

those that influence technological change are found in De Janvry (1978a, 1978b). The

paper emphasizes the demand for farm mechanization (capital using, labor saving


The authors are grateful to V. Eldon Ball (Economic Research Service, USDA) for access to unpublished
quality adjusted data on inputs and output. Neither he nor USDA are responsible for any interpretations
resulting from the use of the data.









technology) through the change in farm worker demographic characteristics, more

specifically the legal status, which may result from changes in immigration policy and

political interests in the labor market. The other socioeconomic influences from

institutional change include farm policy through government payments, and the demand

for new technology by large producers as expressed through the experiment station

system and the political process.

A vast majority of the agricultural work force is unauthorized foreign workers

(U.S. Department of Labor); changes in the defacto legal structure of the labor market

imply changes in immigration and labor policy and enforcement. The passage of the

Immigration Reform and Control Act of 1986 (IRCA) was intended to reduce the extent

of illegal employment. Even though it was designed to decrease the flow of illegal

immigrants, all indications suggest that the number of illegal immigrants has increased,

not decreased. Changes in technology, particularly mechanization, may be influenced by

political interests in relation to the labor market. Different transaction costs associated

with different levels of legal status also imply differing demands for technology by

producers.

Government policy has a major impact on the direction of technological change.

The supply of new technology is a result of the combination of government and private

funding for research on new technology. Government can also indirectly change the

direction of the demand for technology by farm subsidies. A government payment

associated with the demand and supply of inputs can influence the type of new

technology. For example, a conservation program is paid by the government to conserve

land from agricultural production. This payment directly affects the use of agricultural









land, and may change the demand for land. There are other factors that may influence

the direction of technological change. As Binswanger (1978), De Janvry (1973, 1978a,

1978b), and Ruttan suggest, changes in the structure of institutions will also induce a

change in technology. The institutional change of different parties in the market such as

producer and consumer organizations, public research institutes, and governmental agents

could change the demand and supply of technology. The demand for technology by

different types of producers could also be different (DeJanvry, 1978b). In this study, we

hypothesize that large and small producers have different demands for technology. The

percentage of large producers will capture the effectiveness and incentives to lobby for

the change of technology.


Methodology

A cost function model of biased technological change is specified using a time

variable to estimate the bias in technological change. Socioeconomic variables are

included to capture their effects on the rate of biased technological change. The

parameter estimates of the translog cost function provide estimates for elasticities of

factor demand and elasticities of factor substitution.

Model

The model assumes a single aggregate agricultural output, constant returns to

scale, and that the level of output does not affect the relative use of inputs. The

production of the aggregate agricultural product (Y) requires n variable inputs X = (Xi,

X2,..., Xn) with a vector of input prices W = (W1, W2,..., Wn). Using time as

representative for technological knowledge, production cost is therefore a function of









input prices and the technology variable. The translog cost function C = f(W1,..., Wn, t)

can be written as


InC =vo + IlnW,+-1ZZy, InWl nWj +t Int + t(Int)2 + co1lWnWInt (1)
1 2 1

Since a cost function is assumed to be homogeneous of degree one in prices,the translog

cost function parameters must satisfy the following restrictions:

IV, = ; Zy7, = 0; Z7, = 0.


In addition, a symmetry restriction is also assumed to hold.

7ij= Yji for all i,j; i #j

Utilizing Shepard's Lemma, aC/awi = Xi, a first derivative of a translog cost function

generates a share equation.

nC X =S i=l,...,n (2)
1lnW, C

S, = v, + In Wj + (o,lnt i = ,..., n (3)


AS, = 7 Aln Wj +o,Alnt i = ,..., n (4)


For a discrete time period, a change in the factor share is a result of changes in factor

prices and a change in technology. The direction of bias in technical change is measured

by the change in the factor share, holding relative factor prices constant. In a many-

factor case, technical change is biased toward factor i-saving, neutral, or i-using if the

share of factor i in total costs decreases, stays constant, or increases.

i saving
relative factor price 0 neutral (5)
at S, >









Thus, changes in factor shares as a result of changes only in technology, ASi* can be

estimated from

AS* = coAlnt i = ,...,n (6)

The sign of co determines the bias of technical change, and 0c, can be interpreted as a

constant rate of bias of factor i during the study period. If the rate of biased technical

change is influenced by socioeconomic factors such as the political and social

environment, coi can be written as a function of those factors. The vector of political and

social factors is M = (Mi, M2,..., Mm). The share equation can then be written as

S, = v, + 7, In Wj + (a, + Z PMm) Int i =,...,n (7)
J m

The Allen partial elasticities of factor substitution (oT) and price elasticities of

factor demand (rlij) may be calculated from the parameter estimates of share equations as

follows.

1
( = +1 for alli, j;i zj (8)
S Sj



S-) for alli (9)


lJ = +J SJ for all i, j;i j (10)
S1


1 =- + S -1 for alli (11)
S 1

Estimation

The estimates of biased technical change for each factor are obtained from the

share equation estimates. We assume that there are seven variable inputs: hired labor,









self-employed labor, contract labor, chemicals, materials, land, and capital. Although

there may be several socioeconomic factors that affect the rate of biased technical

change, we include three major factors of interest: the number of illegal workers, farm

policies, and the market share of large producers. The time variable is separated into two

periods to capture potentially different structures of technical change before and after the

Immigration Reform and Control Act of 1986. The first period is from 1969 to 1986, and

the second period is from 1987 to 1999. In order to solve the singularity of the

covariance matrix, a system of six share equations is estimated using seemingly unrelated

regression (SUR). In this estimation, the share equation of capital is dropped, and the

independent variables include the prices of factor inputs relative to the price of capital,

socioeconomic variables, and a time variable. Each share equation can be written as

6 W 3 3
S, = v, + l In +(11 +tPlmMm)lnt+((12 + Pm2Mm)T2lnt j =1,...,6 (12)
j=l WK m=l m=l

where j includes all other variable inputs except capital, K represents capital, and Mm are

the three socioeconomic variables. T2 is a dummy variable, equal to one for years 1987

to 1999, and zero otherwise. A system of share equations requires that the summation of

seven factor shares equals one. As a result, in addition to the homogeneity and symmetry

constraints, the following additional parameter constraints must be met:

7 7 7 7
Z il =Z 2 = im im2 =0 (13)
1=1 1=1 1=1 1=1

for i = all variable inputs, and m = all socioeconomic variables.

Data

It is important for the study of biased technical change to use constant-quality

prices since unadjusted-quality data will result in a biased estimation of parameters in the









induced innovation model. In this paper, input prices are obtained from the unpublished

U.S. data prepared by Eldon Ball, Economic Research Service, USDA. The difference

between this data set and the published production account is that these data have more

detailed categories of inputs, particularly contract labor which is included in the material

inputs category in the published series. The input data include price and implicit quantity

indices of aggregate inputs, providing total variable cost and input shares. Due to the

limited availability of other socioeconomic variables, we use the study period from 1969

to 1999. We select seven variable input categories for this study: hired labor, self-

employed labor, contract labor, chemicals, materials, land, and capital.

A detailed discussion of input data construction can be found in Ball, et al. (1999,

1997); the following is a brief summary. Prices of all types of labor take into account the

change in demographics of farm labor force such as sex, age, and education, but not the

legal status. The price of contract labor is assumed to be the same as the price of hired

labor starting from 1996 due to the unavailability of the contract labor wage data.

Agricultural chemicals include both fertilizers and pesticides. Agricultural chemicals are

adjusted for variations in fertilizer nutrients and physical characteristics of pesticides

such as toxicity and leaching potential using a hedonic regression approach. Materials

include petroleum fuels, natural gas, electricity, and open-market purchases of feed, seed,

and livestock inputs. Land price indices are adjusted for changes in the land stock quality

using hedonic regression. Capital includes autos, trucks, tractors, other capital,

inventories, and buildings.

Figure 1 illustrates the real price indices of inputs during 1969 to 1999. Prices of

hired labor and self-employed labor are very close to each other, except for the last ten









years. They are gradually increasing over time, particularly after IRCA in 1986. The

price of contract labor increased more than hired and self-employed labor, except for the

last four years. Price of contract labor was decreasing between 1979 and the passage of

IRCA, but was increasing thereafter. The prices of self-employed and hired labor are

very close throughout the whole period. The price of capital fluctuates considerably, but

has a downward trend after IRCA. In the early 1970s, the price of chemicals

dramatically increased until 1975 when it reached its highest level, and has had a

downward trend thereafter. The price of materials was also increasing in the early 1970s,

and then gradually declined. The land price increased dramatically during the mid 1970s

to mid 1980s with high volatility, and decreased thereafter.

Figure 2 shows the expenditure share of each input during the same period. The

share of hired labor is slightly increasing after IRCA, while the contract labor share is

relatively stable over time. The share of hired labor, however, was gradually decreasing

before IRCA, but slightly increasing afterward. The share of capital increased during the

early 1970s to mid 1980s, except in 1982 and 1983,and gradually declined after IRCA.

Shares of chemicals and materials are relatively stable over time. The share of land has a

pattern similar to the share of capital.

The socioeconomic variables that influence the rate of technical bias are specified

to reflect the number of illegal workers, farm policies, and the market share of large

producers. These variables are argued to represent the demand for and supply of

technological innovation and adoption in a political market. In the U.S. context, a lax

immigration policy suggests to employers that relatively abundant supplies of unskilled

labor will continue to be available. Correspondingly, the political market response is









expected to place less emphasis on funding labor saving technological innovations; there

would be less demand for such innovations than under the opposite scenario of highly

restricted border crossings and stringent internal enforcement that all workers must be

authorized for employment in the U.S. Data on the number of illegal farm workers are

unavailable for the time period under analysis. We use instead, the number of deportable

aliens located available from the INS statistical yearbook to represent the number of

illegal workers. The number of all illegal workers is expected have a pattern similar to the

number of illegal farm workers since most illegal workers are employed in low-skilled

jobs such as in the hotel, restaurant, garment, and agricultural industries. Moreover, the

level of apprehensions is a strong indicator of the political market. In the presence of

large flows of illegal workers across the border as the U.S. has experienced during the

1980s and 1990s, high levels of apprehensions reflect a lax policy. By contrast, with a

very stringent policy, there would be few apprehensions since there would be few

attempts to cross the border for work. Although apprehension data at the farm level

would be closer to the farm labor market, the stringency and frequency of enforcement

are considerably less than at the border. More details on the apprehension data are

available in the Statistical Yearbook of the Immigration and Naturalization Service.

A second socioeconomic variable is farm policies. There are several farm

policies that could influence the direction of technological change, but many are not

easily quantified for an empirical study. Farm policy is a direct impact of the political

market on a change in technology. One of the most relevant and appropriate farm

policies is government subsidies or payments. Since land is a major factor of agricultural

production, a government payment that influences the use of land should also influence









the bias in technological change. In this study, conservation program payments are used

to represent farm policy. The higher the payment, the less incentive for farmers to use

land for agricultural purposes. It includes payments under the conservation reserve,

agricultural conservation, emergency conservation, and Great Plains programs. The

conservation payment is obtained from farm income data from ERS, USDA.

The last socioeconomic variable is the market share of large producers. A higher

market share of large producers is hypothesized to increase the effectiveness to influence

the political market. Large producers have greater power and higher expected return to

invest in time and efforts to change policies that may benefit them, but not small farmers.

Their influences in the political market could influence the direction of technological

change. For instance, large farmers could put more pressure on government to increase

investment in research on farm mechanization that may not benefit small farmers who

cannot afford the expensive machinery. We use the percentage of total market value of

agricultural products sold and direct sales of $100,000 and over from Census of

Agriculture. Since the census data are only available in 1969, 1974, 1978, 1982, 1987,

1992, and 1997, we interpolate the data for the remaining years using an estimated power

function1.

Figure 3 illustrates the socioeconomic variables. The number of deportable aliens

increased over time with the peak in 1986. Conservation payments were relatively

constant until 1986, increased dramatically in 1987, and gradually declined after 1994.

The percentage of total market value of agricultural products sold and direct sales for

$100,000 and over gradually increased over time.


1%market value = 33.224 L" "









Results

Parameter estimates of the share equations are summarized in table 1. The

estimates of coefficients in the capital share equation and coefficients of capital price in

each equation are derived from the other estimates based on homogeneity, symmetry, and

adding-up restrictions in equation 13. The parameter signs of the socioeconomic variable

times the variable in each share equation (Pim in equation 12) show the direction of the

impact of the socioeconomic variable on technical change. Before IRCA, the number of

illegal workers significantly induces contract labor and materials saving technology, but

significantly induces capital using technology. Although the signs indicate that it also

induces hired labor and self-employed workers saving technology, the estimates are not

statistically significant. Conservation payments significantly induce capital saving

technology, but also induce hired labor and contract labor using technology. The

proportion of the market share of large producers significantly induces contract labor and

materials using technology.

After IRCA, the number of illegal workers significantly induces contract labor

using technology, but does not significantly induce other inputs. This result is consistent

with Huffman who shows that there is an increasing amount of contract labor after IRCA,

particularly in California and Florida. Even though IRCA was designed to reduce the

number of illegal workers, all indicators suggest an increasing flow of illegal immigrants.

In contrast to the pre-IRCA period, conservation payments significantly induce hired

labor and contract labor saving technology, but induce capital using technology. It is

somewhat unexpected that conservation payments do not have an impact on land-saving

technology. The conservation payment may counteract the effect of the incentive not to









use land for agricultural production and the incentive not to save land because of the

government subsidy. The market share of large producers does not have a significant

effect on biased technological change for any input during the post-IRCA period.

The rate of biased technological change is a linear function of socioeconomic

variables. Table 2 summarizes the estimates of biased technological change calculated at

the means of the variables. The signs of the rate of biased technological change in Table

2 indicate the combined effects of socioeconomic variables on the direction of

technological change. Before IRCA, the technology was biased toward self-employed

labor saving, but neutral (or insignificant) for hired labor and contract labor. The result is

the similar to Binswanger's (1974a, 1974b) and Antle's results that the technology was

labor saving in the early and mid 1900s. The technology was also biased toward

chemical and material using during the pre-IRCA period. This result also coincides with

Binswanger's (1974a, 1974b) and Antle's findings. The technology was neutral for land

and capital, but most other studies have found that the technology was capital using

during this period. After IRCA, the technology was biased toward self-employed labor

saving and contract labor using, but neutral for hired labor. Similar to the pre-IRCA

period, the technology was biased toward chemical and material using. Lastly, the

technology was capital saving and land neutral during post-IRCA period.

Allen elasticities of substitution and own price demand elasticities are calculated

from the parameter estimates in Table 1. Except for hired labor and self-employed labor,

elasticities of demand have the correct sign. Only the elasticities of demand for

chemicals and capital, however, are statistically significant. The elasticities of demand for

machinery, fertilizer and land are close to those found by Binswanger (1974a), but the









elasticity of labor found by Binswanger (1974a) is close to unity, thus much more elastic

than our results.

Although the elasticities of substitution between self-employed and hired labor,

and between self-employed and contract labor are elastic and negative, they are not

statistically significant. We also cannot say that self-employed workers are compliments

for hired and contract labor. Elasticities of substitution between capital and each type of

labor are positive and significant as expected, indicating that capital is a substitute for

labor. Elasticity of substitution between capital and contract labor is very elastic,

suggesting that the adoption of mechanization is very sensitive to changes in the price of

contract labor. Elasticities of substitution between capital and chemicals and between

capital and materials are also positive and significant. This implies that capital is a

substitute for chemicals and materials as well. This is in contrast to Binswanger's results

(1974a). He found that machinery and fertilizer were compliments, although the estimate

was not statistically significant. However, he found that the complementarity between

fertilizer and labor was significant. Our study also shows that the elasticity of

substitution between contract labor and chemicals is significantly negative and very

elastic. The elasticity of substitution between contract labor and land is negative and

very elastic, implying that contract labor and land are compliments.



Conclusions

This paper introduces an alternative set of socioeconomic variables that capture

the political and social influences on U.S. agricultural technology over the 1969 to 1999

period. The socioeconomic factors are found to be important in determining the direction









and the rate of technological change. Incorporating these factors in the empirical study of

biased technological change broadens our understanding of the mechanism of demand for

and supply of technological change.

Our results show that an increasingly illegal workforce significantly induces

contract labor using technology, and significantly induces capital saving technology in

the post-IRCA period while it induces contract labor saving technology in the pre-IRCA

period. Conservation payments significantly induce capital saving technology, and hired

labor and contract labor using technology in the pre-IRCA period, but they significantly

induce hired labor saving, contract labor saving, and capital using technology in the post-

IRCA period. The market share of large producers significantly induces contract labor

and materials using technology before IRCA, but does not significantly influence any

biased technology after IRCA.

The combined effects of the socioeconomic variables on the direction of

technological change show that the technology was biased toward self-employed labor

saving, neutral for hired labor and contract labor, and chemical and material using during

the pre-IRCA period. After IRCA, the technology remains biased toward self-employed

labor saving and neutral for hired labor, but becomes contract labor using. The

technology remains biased toward chemical and material using, land neutral, and

becomes capital saving in the post-IRCA period.


























& %


SMaterials




Land


0.6


0.5


. 0.4
C,
0



C
S0.3
I

. 0.2
x
LU
0.1


0


-- *


" Chemicals

Contract labor

c0 t- CO LfO I- c0 CO LfO I- 0 CO LO [I- c)
(D [ I- N- N- NI- N- 0I 0I 0, 0, a, C) II) 0-) 0) 0I)
0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0)

Year


Figurel. Expenditure Share of Inputs


I ID I Ir^ II I'r o0 o0 o0 o0 o O OI Oi Oi i O
(0CO CO CO CO CO 0) 0)Year
0 0 0 0 0 0 0 0 0 0) 0) 0) 0) 0 0 0

Year


Figure 2. Input Price Indices


Self-employed


3.5

3

2.5

2
- 2

w
.O 1.5

1

0.5

0


Land
,


-II

*\1


Hired Self-
labor employed


Chemicals Materials














" 10
-

. 8
E
I 6

S4
o
o 2

0


Figure 3. Socioeconomic Variables


S LO I- Mil C CO IO- C LO )


Year


Conservation Payment


Deportable aliens










Tablel. Restricted Estimates of the Coefficients of the Translog Cost Function and Standard Errors
Factor Shae Hired Labor Self- Contract Chemicals Materials Land Capital
Factor Share
Fnptt. Pe employed Labor
Input PriceLabor
Labor


Hired Labor

Self-employed Labor

Contract Labor

Chemicals

Materials

Land

Capital

Constant*ln(t)

Aliens*ln(t)

Conservation Payment*ln(t)

Market Share*ln(t)

Constant*T2ln(t)

Aliens*T21n(t)

Conservation Payment*T21n(t)

Market Share*T21n(t)

Intercept


* Significant at 95% confidence level.


0.1006
(0.0571)
-0.0880 0.2075*
(0.0567) (0.0577)
0.0119 -0.0175 0.0024
(0.0077) (0.0090) (0.0056)
0.0016 -0.0042 -0.0062 0.0206*
(0.0049) (0.0078) (0.0038) (0.0105)
-0.0230* -0.0844* 0.0028 -0.0249 0.2223*
(0.0078) (0.0143) (0.0062) (0.0135) (0.0278)
-0.0130* -0.0395* -0.0230* -0.0106 -0.0537* 0.1428*
(0.0046) (0.0087) (0.0047) (0.0082) (0.0141) (0.0158)
0.0099 0.0260 0.0295* 0.0237 -0.0392 -0.0030 -0.0469
(0.0086) (0.0157) (0.0101) (0.0151) (0.0256) (0.0244) (0.0515)
-0.0011 0.0060 -0.0108 0.0249 -0.0380 -0.0217 0.0408
(0.0075) (0.0126) (0.0087) (0.0135) (0.0265) (0.0213) (0.0403)
-0.0030 -0.0060 -0.0073* -0.0054 -0.0160* -0.0009 0.0385*
(0.0024) (0.0041) (0.0027) (0.0044) (0.0078) (0.0074) (0.0130)
0.00003* 0.00001 0.00003* 0.00003 0.00003 -0.00002 -0.0001*
(0.00001) (0.00002) (0.00001) (0.00002) (0.00003) (0.00003) (0.00005)
-0.00003 -0.0004 0.00026* -0.00017 0.0011* 0.0004 -0.0012
(0.0001) (0.0002) (0.00013) (0.00021) (0.0004) (0.0003) (0.00062)
-.022263 -0.0420 0.0010 -0.0407 -0.0119 0.0546 0.0613
.014516 (0.0227) (0.0157) (0.0235) (0.0424) (0.0364) (0.0708)
0.0041 0.0080 0.0082* 0.0109 0.0076 0.0023 -0.0412
(0.0034) (0.0058) (0.0040) (0.0062) (0.0112) (0.0103) (0.0191)
-0.00003* -0.000008 -0.00003* -0.00003 -0.00004 0.00002 0.0001*
(0.00001) (0.00002) (0.00001) (0.00002) (0.00003) (0.00003) (0.00005)
0.0003 0.0004 -0.0001 0.0003 0.0002 -0.0008 -0.0003
(0.0002) (0.0003) (0.0002) (0.0003) (0.0006) (0.0006) (0.0010)
0.0399* 0.1652* -0.0684* 0.0218 0.1960* 0.5956* 0.0499
(0.0164) (0.0306) (0.0160) 0.0279 (0.0488) (0.0532) (0.0823)


Table2. Estimates of Rates of Biased Technical Change and Standard Errors

Hired Labor Self-employed Contract Labor Chemicals Materials Land Capital
-0.00022 -0.0186* 0.0039 0.0156* 0.0228* -0.0044 -0.0191
Before IRCA (0.0021) (0.0033) (0.0022) (0.0033) (0.0059) (0.0054) (0.0103)
-0.0012 -0.0255* 0.0028* 0.0109* 0.0328* -0.0015 -0.0183*
After IRCA (0.0016) (0.0025) (0.0014) (0.0021) (0.0040) (0.0035) (0.0060)
* Significant at 95% confidence level.












Table 3. Estimates of Allen elasticities of substitution, own price elasticities of factor demand,


Hired Labor Self-employed
Labor


Contract
Labor


Chemicals Materials


and standard errors
Land Capital


Elasticities of Substitution
Hired Labor


7.0269
(11.4457)


Self-employed Labor


-6.1971
(4.6431)
2.1498
(1.9244)


Contract Labor


21.6624
(13.4198)
-11.3688
(6.3737)
-84.7177
(83.1261)


Chemicals

Materials

Land

Capital


Elasticities of Demand


0.4961
(0.8081)


0.3721
(0.3331)


1.2907
(0.8929)
0.6841
(0.5838)
-8.8893
(6.0493)
-8.50238*
1.76132


-0.6924 -0.6563*
(0.6794) (0.1360)


0.3259 -0.0311 1.7488*
(0.2292) (0.3681) (0.6516)
-0.0070 -0.2725 1.8027*
(0.1714) (0.2802) (0.4863)
1.7190 -14.6849* 20.3049*
(1.5735) (3.2141) (6.6191)
0.3345 0.2364 2.6451*
(0.36091) (0.5957) (1.0433)
-0.1171 0.3805* 0.5672*
(0.1188) (0.1628) (0.2827)
-0.1331 0.9094
(0.4924) (0.7299)
-5.6931*
(1.4759)
-0.0567 -0.0238 -1.0640*
(0.0575) (0.0882) (0.2758)


* Significant at 95% confidence level.









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