• TABLE OF CONTENTS
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 Front Cover
 Introduction
 Human capital and technology diffusion...
 Empirical model and data
 Empirical results
 Summary and conclusions
 Table I. Variable distribution
 Table II. Summary statistics
 Table III. Maximum likelihood estimates...














Title: Evaluation of a multimedia extension program in Honduras
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Title: Evaluation of a multimedia extension program in Honduras
Physical Description: Book
Language: English
Creator: Martin, Michael J.
Taylor, Thimothy G.
Publisher: Dept. of Food and Resource Economics, University of Florida,
Publication Date: 1993
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Spatial Coverage: North America -- Honduras
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Table of Contents
    Front Cover
        Front Cover
    Introduction
        Page 1
        Page 2
        Page 3
    Human capital and technology diffusion in Honduras
        Page 4
        Page 5
    Empirical model and data
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
    Empirical results
        Page 12
        Page 13
        Page 14
        Page 15
    Summary and conclusions
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
    Table I. Variable distribution
        Page 24
    Table II. Summary statistics
        Page 25
    Table III. Maximum likelihood estimates of technology adoption
        Page 26
Full Text



0/. 727




Evaluation of a Multimedia Extension Program in Honduras








By Michael J. Martin and Timothy G. Taylor*


















*The authors are Graduate Research Assistant and Professor, respectively at
the University of Florida, McCarty Hall, Department of Food and Resource
Economics, Gainesville, FL 32602.
lq13








INTRODUCTION

Technology adoption is a critically important issue in developing countries

where rising populations depend directly on dwindling resource bases. New

technologies have rapidly become available to small-scale agricultural producers

in Central America whose production systems, rooted in ancient Mayan traditions,

provide daily sustenance. Slash and burn methods of field preparation developed

reflexively over generations, as farmers sought to maximize efficiency within the

physical and institutional parameters imposed on them. Abundant land resources

of Mayan civilizations permitted farmers to abandon fields long enough to become

naturally rejuvenated. However, unlike their ancestors, moder-day Central

American farmers are not afforded this luxury. Colonial institutions and

population growth have tightened land constraints and forced farmers to cultivate

the same land year after year.

Reduction in soil fertility has provided impetus for farmers in Central

America and other developing areas to seek alternative production methods.

Expanded markets and complementary technologies have provided further

impetus. The capacity of farmers to respond to such pressures often corresponds

to their ability to purchase complementary human capital, either in themselves or

through hired labor. Small farmers, however, usually lack the funds to purchase

necessary assistance for technology adoption.

Education and training have long been considered indispensable to

technology diffusion and the correction of attendant economic disequilibria.1








Lack of skill and knowledge lie at the root of production inefficiencies which

have restricted production systems in developing countries from reaching their

technical frontiers.2

Local research stations often demonstrate that new and feasible

technologies enhance production efficiency, but they are often not adopted by

agricultural producers. Early studies showed that locally developed technologies

were economically important and that human capital enhanced farmer efficiency.3

Human capital developed by formal schooling and extension training has also

been shown to have a positive influence on both technology adoption and

production efficiency.4 Nutrition and religion are other forms of human capital

that are considered to have an economic impact.5

Several studies have examined the influence of education and training on

productivity. Jamison and Lau (1982) showed a strong link between education

and productivity, but a weaker link between extension services (as measured by

site visits) and productivity.6 However, Shapiro and Miller (1977) found that

a farmer's exposure to extension and other non-formal education have a positive

effect on output.7 Kalirajan and Shand (1985) compared the impact of formal

versus non-formal education on Indian rice farmers. Their results indicate that

farmers' non-formal education, as measured by "understanding", had a significant

impact on yield, but the effect of education was insignificant. "Understanding"

was determined by testing how well farmers could identify the "best practice" as

defined by local agricultural experts. Interviews indicated that understanding was








achieved from mass media, neighbors, landlords and only slightly from extension

agents. Most farmers, especially those with low educational levels, relied on

mass media as their primary source of technological information."

The purpose of this article is to examine the impact of a multimedia

extension program which promoted a variety of technologies that varied across

crops and regions. Several previous studies have examined how extension

influences the adoption of specific technologies9 but none to date have analyzed

the effectiveness of different types of communication methods used by extension

services to diffuse a wide assortment of technologies. In addition, rather than

measure extension impact by the usual procedure of enumerating site visits, this

study allows the farmer to identify the source of learning instruction for the

technologies upon which he relies. Farmers were scored as either adopters or

non-adopters of technology through a survey instrument that asked series of open-

ended questions regarding their agricultural practices. No producer was asked

directly whether he adopted a recommended technology, but all were asked

where, or from whom, they learned the technologies they employed.

As well as investigating the relative effectiveness of different extension

methods, this study examines demographic characteristics related to human

capital, land quality and institutional structure. Demographic information is

included to enhance the explanatory power of the model and to provide insights

to policy makers regarding which groups may be effectively targeted.

Thus, two types of exogenous forces that contribute to farmers' decisions








to adopt new technologies are deciphered. The first are demographic

characteristics that affect producer amenity to new methods, the second is the

direct training obtained from both formal and casual sources. The following

section describes extension challenges as they pertain to Honduras, the country

in which the study was conducted. The methodology used to analyze the

influences of dichotomous choices is discussed in the third section. Empirical

results are then presented, followed by conclusions and implications.



HUMAN CAPITAL AND TECHNOLOGY DIFFUSION IN HONDURAS

Technology diffusion is hampered by low levels of human capital in

Central America in general, and Honduras in particular. Functional literacy

levels in rural Honduras stand at about fifty percent.10 Institutional factors also

influence the accessibility and appropriateness of technology. Land tenure

arrangements of small farms in Central America are tenuousu, increasing the

risk associated with new technologies. Credit is available on a sporadic basis,

reducing producers' capacity to sustain newly adopted technologies. Farm and

household size vary in the region as well, playing important roles in the selection

of technologies.

Technology transfer in rural Honduras is confronted with some formidable

obstacles. Assistance programs to introduce new technologies are often criticized

for not taking into account a common farmer's opportunity set, production

constraints and, perhaps most important of all, mental preparedness to respond








to assistance. If outside technology is introduced to farmers that lack the skills

to interpret and screen relevant information, then the "spill in" of the technology

will not occur. Compounding the human capital and institutional problems is the

geographical dispersion of Honduran farmers. Many live far from main and

sometimes even remote transportation arteries. Providing training and visit

extension services (T&V) is usually prohibitively expensive. One logical solution

has been mass media. Radio and printed materials offer ways to reach widely

dispersed farmers at a much lower cost. However, most farmers in the area are

illiterate. And though many farmers own a radio, most have found generic

agricultural radio programs unresponsive to their specific needs.

Problems such as these form the basis of a multi-media approach to

transfer technology that attempted to respond to particular production needs in the

face of funding constraints on extension. The program, located in the Comayagua

region of Honduras, ostensibly tailored assistance programs to fit the

geographical, cultural and capital-base conditions of small farmers.12 Vehicles

of instruction included T&V, elementary pamphlets with self-explanatory

illustrations, and a regularly broadcast radio program that covered common

agricultural problems. The objective of the program was to design and diffuse

key technologies amenable to farmers who operate with diverse production sets.

All recommended technologies were designed to respond to the farmers'

individual needs.







EMPIRICAL MODEL AND DATA

Ultimately, the effectiveness of an extension program can be gauged by

the increased efficiency or profit of farmers operating in the target area. A

necessary intermediate step in that improvement is the actual adoption of the

technology, the simple yes or no decision of the farmer. Evaluating adoption

rates requires far less data than evaluating economic responses and may be

conducted at early program stages to monitor the relative effectiveness of

promotion techniques.

A common procedure in technology adoption studies is to examine

dichotomous adoption decisions as a function of factors related to the farm

production system or the farmer's background. Most studies enumerate site visits

to measure the impact of extension services.13 A few have attempted to directly

identify the source of the farmer's knowledge regarding a technology.14 This

study analyzes adoption rates as a function of primary learning sources as

identified by the farmer. It also incorporates firm, human capital and policy

factors in evaluating farmers' propensity to adopt a range of technologies that

differ across crops and regional boundaries.

Farmers are divided into two groups, commercial, who produce tomatoes

and rice, and subsistence who produce maize and beans. Regressions were run

separately for commercial and subsistence farmers because they constitute two

distinct target groups for extension services and consequently merit separate

examination of the factors that influence adoption.

Analyzing the decision to adopt or not adopt a given technology in a







regression equation requires the specification of a binary dependent variable.

Probit or logit models"1 have been used extensively to investigate factors that

influence technology adoption. Assuming the basic linear form of these models

is given by:

Ri = 3'xi + E

where = 1 if adopt

{
= 0 otherwise

It follows that

Prob(9i = 1) = Prob (ei > -P'xi)

= 1 F(-3'xx)

where F is the cumulative distribution function for e.

The program was designed to assist farmers who produce standard crops

in diverse regions that require different technologies. Farmers operating in high

altitude zones on poor soil received different recommendations regarding seed

spacing, fertilization practices and weeding techniques, for example, than their

counterparts producing the same crops in other areas. Evaluating each technology

individually was not statistically feasible because there were not enough degrees

of freedom for any one technology. Thus all technologies are examined in each

regression as functions of common variables which influence adoption. Careful

selection of critical technologies allows for the assumption to be made that all

technologies are equally weighted in importance and accessibility.16 All

technologies considered in this paper were determined through fieldwork to be


7







key technologies by extension agents, researchers and farmers.


Model Specification

Farm size, human capital, labor availability and land tenure are considered

important characteristic variables that explain adoption rates.17 The

communication program in the Comayagua region was designed to promote

technologies that complement local farmers' cultural and technological

environments. The communication program thus attempted to tap into farmers'

propensity to adopt improved technologies.

The following logit model, which applies to both commercial and

subsistence farmers, includes two sets of variables which influence farmers'

decisions to adopt new technologies. The first set (variables associated with

parameters [,3 to 39) relates to the propensity to adopt technologies, and the

second set (variables on parameters f3o1 to P36) relates to technology promotion:

ADOPT = 3o + PRESIDE + 32AGE + 33MEMBERS +

SCHOOLING + 3sLITERACY +f6SOLD + 37REFORM

+ 38HILL + PPLAIN + 01oRADIO + 131EXT/GOV +

312FAMILY + 131PHAMPLET + 314SALESMAN +

15FRIEND + P16EXT/PVO.

Where:


ADOPT = 1 if farmer adopted the technology, 0 otherwise.

RESIDE = Number of years producer has lived in area.








MEMBERS

SCHOOLING

LITERACY

ILLITERACY18

SOLD

REFORM


PRIVATE18


HILL

PLAIN

RADIO


EXT/GOV


FAMILY


PAMPHLET


SALES


FRIEND


EXT/PVO


CUSTOM"


= Number of household members.

= Number of years of schooling.

= 1 if producer is literate, 0 otherwise.

= 1 if producer is illiterate, 0 otherwise.

= Proportion of output sold.

= Manzanas19 of agrarian reform lands used in
production.

= Manzanas of private, non-agrarian reform lands used
in production.

= Amount of substandard hill lands used in production.

= Amount of preferred level lands used in production.

= 1 if producer learned the technology from radio,
0 otherwise.

= 1 if producer learned the technology from government
extension agents, 0 otherwise.

= 1 if producer learned the technology from a family
member, 0 otherwise.

= 1 if producer learned the technology from a pamphlet,
O otherwise.

= 1 if producer learned the technology from a
salesperson, 0 otherwise.

= 1 if producer learned the technology from a friend,
0 otherwise.

= 1 if producer learned the technology from a PVO
extension agent, 0 otherwise.

= 1 if producer uses technology as matter of custom,
O otherwise.








Birkhaeuser, et al.20 (1991) offer a virtual litany of common

characteristics that may bias results of extension impact studies. Endogeneity

may introduce bias because successful farmers are frequently sought out by

extension agents. Similarly, extension efforts directed at productive or fertile

areas may show unjustifiably high adoption and productivity rates unless the type

of land is accounted for in the model. Indirect or secondary information flows

present another bias in that many farmers may learn from friends or sales people.

Generally, the presence of secondary information flows causes extension impacts

to be understated. Farmers who have had no direct contact with extension agents

often learn from farmers who have. Institutional surroundings also influence

adoption rates because farmers in isolated areas, out of communication with other

farmers depend directly on site visits, whereas farmers who inhabit more densely

populated areas or belong to cooperatives could learn from their neighbors or

colleagues. Site visits would thus have a multiplier effect.

The model and the survey technique attempted to account for such

estimation biases. In order to avoid spurious correlations between T&V and

adoption, farmers were asked to directly identify their source of instruction.

Thus, farmers who received a relatively high number of site visits but learned

new technology from a friend or a salesman could be so accounted. Human

capital factors regarding schooling, literacy and age also provide some indication

as to whether or not "more productive" or "well-known" farmers adopt

technologies and thus may be sought out by extension agents.21








Inferior (HILL) land may inhibit farmers from adopting new technologies

(although the extension system was ostensibly designed to assist such marginalized

producers). Similarly, farmers who operate on agrarian reform lands (REFORM)

may be more amenable to new technologies because they are operating in a new

environment and because they are more involved in cooperative associations

where new technologies are taught and demonstrated. One shortcoming of this

approach is that partial influences of learning sources are disregarded since

primary learning sources were scored as the sole source of instruction. Radio and

pamphlets, for example, may supplement the identified source of instruction, but

remain unrecognized by the model. On the other hand, this method allows

adopters of new technologies to identify the most effective instruction method.22



Data

The data used for this analysis were gathered in the Comayagua region of

Honduras in early 1990. Variable distributions and frequencies (for dichotomous

variables) are displayed in Table I. All of the surveys followed the same format

for inquiring into the adoption of technology. Farmers were asked a series of

open ended questions about the technological methods they employed, and were

then asked to identify the source from which the technology was learned. The

intent of the survey structure was to provide a consistent manner of evaluating

technology adoption for different crops and geographical zones covered by the

communications program.








The samples of farmers were randomly drawn from a larger randomly

drawn sample used in ongoing research to update and adjust extension

programs.23 Four crops, corn, beans, rice and tomatoes were investigated in the

Comayagua region. The former two are traditional subsistence crops and the

latter are grown primarily for commercial markets. Each interviewee was queried

about three technologies for the preceding two production seasons of one crop.

Technologies were selected based on their availability and importance to

improving yield. Fertilization practices were examined regarding all four crops;

planting practices were examined for corn, rice and beans; weed control was

examined for corn and rice; insect control was examined for tomatoes and beans;

and soil preparation was examined for tomatoes. The number of farmers

surveyed for each crop varied proportionately with the total number of farmers

in the area who produce that crop. Data were gathered in January and February

of 1990. Only full adoption of a technology was considered a successful

adoption.2



EMPIRICAL RESULTS

The model was estimated under the assumption that the cumulative

distribution function was logistic.2 Thus, a logit model was used in the

analysis. Estimation was accomplished using maximum likelihood in SPSS

(Statistical Package for the Social Sciences).

Summary statistics (TABLE II) indicate that the model provides an








acceptable "fit" to the data. Correctly classified estimates (based on a 50-50

classification scheme) amounted to 90.7 percent for subsistence crops and 70

percent for commercial crops. The model Chi-Square, which tests the overall

significance of the model, was significant at the one percent level for both

regressions.

Coefficient estimates are shown in Table III. Seven of the subsistence

crop coefficients and five of the commercial crop coefficients are significant at

the ten percent level. The coefficients indicate that the propensity to adopt new

technologies is different between commercial and substance farmers, but that a

common pattern exists for learning source influences.



Learning Source

All of the statistically significant learning sources coefficients are positive.

This indicates that the survey instrument succeeded in distinguishing between

producers who employed traditional methods and those who adopted advanced

methods without asking the question directly. Survey questions were open-ended;

farmers were asked to describe planting techniques, fertilization practices, etc.

Respondents who improperly adopted a technology were scored as non-adopters

by survey takers prior to identifying the learning source. Farmers who thought

they were adopting a new technology promoted by the extension program could

not be scored as an adopter unless the method they used satisfied all the

recommendations of the technology in question. Correct practices varied by both








crop and by region.

The most salient result of the estimates on learning source is that personal

contact is important in promoting new technologies. The coefficients on

government extension agents (EXT/GOV) are positive for both subsistence and

commercial producers and statistically significant. Salespeople also positively

influenced adoption for both commercial and subsistence producers; the

coefficients on SALES are significant at the ten percent level. FRIEND showed

a positive and significant influence on adoption rates for commercial producers

only.

It would be interesting to decipher the impact of FAMILY between parents

and siblings (in the context of Central American agriculture, fathers and

brothers). Technologies passed from father to son are almost by definition

traditional technologies, but brothers may pass new information on to one another

- particularly older to younger brothers. This could account for the insignificance

of the estimate on FAMILY. It might also serve to explain the different

outcomes of the constant terms, which represent illiterate farmers who operate on

private lands and use traditional technology.26 Tomato and rice technologies

were developed relatively more recently then maize and bean technologies and are

less likely to be considered customary. Customary practices for the traditional

subsistence crops of maize and beans may actually represent recommended

technologies in some zones, especially for young producers.

PAMPHLET and RADIO were cited as primary learning sources by few








producers and do not bear strong results in terms of adoption. However, their

marginal returns may be much higher for extension services in light of the large

discrepancy between costs of mass media programs and personal site visits.



Propensity for Adoption

Coefficients which measure the propensity for adoption show no

immediate similarity between commercial and subsistence crops. However,

results do conform to the Boserup hypothesis that population pressures induce

technological innovation.' The only such factor which showed a significant

influence on commercial farmers is the proportion of output sold. The most

curious results for subsistence farmers are the negative and statistically significant

estimates of both types of land use, HILL and PLAIN. Subsistence farmers with

relatively extensive land holdings are less inclined to incorporate technologies that

are designed to improve land productivity.

Subsistence farmers who operate on agrarian reform lands also

demonstrated a positive inclination to utilize new technologies. The coefficient

on REFORM was positive and significant at the five percent level. Farmers who

operate on agrarian reform lands are obliged to belong to cooperatives which

provide credit, inputs and technical assistance. Moreover, farmers in

cooperatives are more likely to see technologies tested on collective production

plots. This has important implications for cooperatives which, in the agrarian

reform sector, are often considered to be net social losers due to administrative








corruption and financial mismanagement.

Oddly, a subsistence farmer's capacity to read and write bears a negative

influence on adoption. The same estimate for commercial farmers is positive, but

less significant. The opposite signs could be attributed to the fact that maize and

bean production is an aside for some farmers in the subsistence crops sample.

With their main source of income in other endeavors, they have not considered

the marginal product obtained from increasing basic grain production adequate to

cover the cost of learning or implementing the necessary technologies.



SUMMARY AND CONCLUSIONS

Technology diffusion presents major challenges to international

development. Efficiency enhancing techniques will have a greater impact the

more they are targeted to receptive groups via effective communication methods.

This paper analyzed the effectiveness of a multi-media extension program targeted

at Honduran farmers who have low human capital investments and whose

traditional farming methods are being threatened by radical changes in their

resource bases.

Dichotomous, yes/no decisions were examined in a binary logistic

regression. The survey through which the data were gathered asked farmers to

identify their source of learning to account for bias problems which enumerate

site visits. Institutional and human capital variables were included to diminish

other forms of bias and to identify which groups of farmers are amenable to








adopting new technologies.

An entire extension program for a region in Honduras was analyzed to

determine what types of farmers are responsive to new technologies and what

types of training promote the adoption of those technologies. Producers of

commercial crops demonstrated an increasing inclination to adopt new

technologies as the they increased the market proportion of their output.

Subsistence farmers, on the other hand, demonstrated a negative correlation

between land extensiveness and technology adoption. Both results conform to

Boserup's. Cooperative association, and perhaps more importantly the

communication and credit amenities that accompany it, also had a positive

influence on adoption.

The results indicated that T&V extension methods have a multiplier effect

through personal contact of experts and friends, and are very effective in

motivating technology adoption in contrast to impersonal multi media techniques.

Nonetheless, mass communication media cannot be discounted and in fact, given

their low cost of making contact per farmer, present a viable alternative. The

relative pay-off between personal and mass communication offers potential for

further research.








1. Schultz, T.W., "The Value of Ability to Deal with Disequilibria," Journal of

Economic Literature, 13 (1975): 827-46. Nelson, R.R and E.S. Phelps,

"Investment in Humans, Technological Diffusion and Economic Growth,"

American Economic Review, 56 (1966): 69-75.



2. Birkhaeuser, Dean Robert E. Evenson and Gershon Feder, "The Economic

Impact of Agricultural Extension: A Review," Economic Development and

Cultural Change, 39 (1991):607-50.



3.Grilliches, Zvi, "The Sources of Measured Productivity Growth: United States

Agriculture, 1940-60." Journal of Political Economy, 71 (1963): 331-46.

Schultz, T.W., 1954, The Economic Organization ofAgriculture, McGraw Hill.



4. For review, see Jamison, Dean T. and L.J.Lau, Farmer Education and Farmer

Efficiency, (Baltimore: The Johns Hopkins University Press; 1982), Birkhaeuser,

Evenson and Feder, 1991 Op Cit.; and Feder, Gershon, Richard E. Just and

David Zilberman, "Adoption of Agricultural Innovation in Developing Countries:

A Survey", Economic Development and Cultural Change, 33 (1985):255-98.



5. Bliss, Christopher and Nicholas Stem, "Productivity, Wages and Nutrition,"

Journal of Development Economics, 5 (1978): 331-97, examine Leibenstein's full

wage hypothesis; The link between childhood malnutrition and schooling is








examined by Moock, Peter R. and Joanne Leslie, "Childhood Malnutrition and

Schooling in the Terai Region of Nepal," Journal of Development Economics, 20

(1986): 33-52; Selowsky, Marcelo and Lance Taylor, "The Economics of

Malnourished Children: An Example of Disinvestment in Human Capital,"

Economic Development and Cultural Change, 22 (1973): 17-30; and Jamison,

Dean T. and M.E. Lockheed, "Participation in Schooling: Determinants and

learning Outcomes in Nepal," Economic Development and Cultural Change, 35

(1987): 279-306.



6.Jamison and Lau (1982), Op. Cit. conducted their own study on data sets from

Thailand, Korea and Malaysia. Their findings also lend support to Schultz's

hypothesis that education is important in a changing environment.



7.Shapiro, K.H. and J. Miiller, "Sources of Technical Efficiency: The Roles of

Modernization and Innovation," Economic Development and Cultural Change,

25(1977): 293-310.



8.Kalirajan, K.P. and R.T. Shand, "Types of Education and Agricultural

Productivity: A Quantitative Analysis of Tamil Nadu Rice Farming", The Journal

of Development Studies (1985) 232-43.



9. See Birkhaeuser, Evenson and Feder Op. Cit for review, also Jamison and








Lau, Op. Cit.; Feder, Gershon and R. Slade, "The Acquisition of Information

and the Adoption of Technology," American Journal of Agricultural Economics,

66(1984): 312-20.



10.World Fact Book 1988, CIA.



11.Stringer, Randy, 1989 "Honduras: Toward Conflict and Agrarian Reform" in

Searching for Agrarian Reform in Latin America, Ed. William C. Thiesenhusen,

Boston: Unwin Hyman Inc.



12.Ministry of Natural Resources, Government of Honduras, Tegucigalpa.



13.See Birkhaeuser, Evenson and Feder, 1991 Op. Cit.



14.Kalirajan and Shand, Op. Cit.; J.K.Harper, et al., "Factors influencing the

Adoption of Insect Management Technology", American Journal ofAgricultural

Economics, 72 (1990): 997-1005.



15.A detailed description is provided by Maddala, G.S., Limited Dependent and

Qualitative Variables in Econometrics, Englewood, N.J. Cambridge University

Press 1983. See also Amemiya, Takeshi, 1981, "Qualitative Response Models:

A Survey", Journal of Economic Literature, 19: 1483 1536.










16.Accessibility was never a problem because it was a criterion in the

development of recommended technologies. Some potentially useful technologies

were discarded because they could not be reliably imported.



17.Feder et al. 1985, Op. Cit.



18.Deleted from regression to avoid a singular matrix.



19.A manzana equals roughly .706 hectares.


20.1991, Op Cit.



21.This does not wholly account for the endogeneity problem that results from

extension agents seeking out farmers who are likely to adopt new technologies.

However, endogeneity is more vexatious in studies that consider productivity or

technology adoption as a function of site visits than in the present study that

examines adoption rates as a function of learning sources because identifying

learning sources puts the efficacy of extension under greater scrutiny. More

productive farmers may be more exposed to T&V, but ineffectual site visits are

discounted.



22.Price instability, the subsidizing of certain technologies and inappropriate








technologies also typically pose problems in dichotomous adoption studies. None

characterize the conditions of the region from which the data in this study were

drawn.



23.The larger sample was drawn from a census of all farmers living in the area.

The survey used for this study was administered on a smaller subsample of

farmers and focused more specifically on technology adoption than surveys used

for ongoing research.



24.Dichotomous choice models have been criticized for their inability to account

for partial adoption (Feder et. al., 1985, Op. Cit.), but partial adoption holds no

assurance of positively influencing yield. Increasing the number of seeds planted

in each hole, for example, may actually be detrimental if seed and row spacings

are not expanded. Partial adoption of complementary components may weaken

a given technology.



25.The logistic function is f(91) = e"/(1 + en), where f(%9) varies from 0 to 1

as 9t varies from -oo to + oo. A common alternative distribution for dichotomous

choice models is the cumulative normal distribution used in probit models. Logit

and probit models render similar results in small samples.

26.Those characteristics were registered in the variables deleted from the model

and thus are embedded in the constant term. See Johnston, J. Ecnometric








Methods, McGraw Hill, New York, New York, 1884.

27.Boserup's hypothesis holds that intensification occurs as population pressure

forces farmers to extract higher yields from their fixed land supply. See Boserup,

Ester, 1965, The Conditions of Agricultural Growth: The Economics ofAgrarian

Change Under Population Pressures, New York: Aldine Publishing Company.








TABLE I VARIABLE DISTRIBUTION


VARIABLE
RESIDE

AGE

MEMBERS

SCHOOLING


LITERACY
SOLD

REFORM

PRIVATE


SUBSISTENCE CROPS:
CORN & BEANS
Percent* Average"
31.2
(18.2)
44.7
(13.6)
6.4
(3.4)
2.8
(3.3)
66.0


COMMERCIAL CROPS:
TOMATOES & RICE
Percent Average
33.4
(22.5)
50.2
(12.8)
6.3
(2.9)
2.1
(2.2)
59.3


0.3
(0.6)
0.24
(0.75)
1.1
(1.0)
0.42
(0.72)
0.9
(1.1)


HILL

PLAIN


0.84
(0.27)
0.26
(0.51)
0.80
(0.91)
0.0

1.10
(0.82)


RADIO 1.1 0.4
EXT/GOV 12.3 18.2
FAMILY 10.3 2.8
PAMPHLET 0.0 1.6
SALES 1.1 2.77
FRIEND 13.0 26.5
EXT/PVO 3.8 0.8
CUSTOM 60.4 43.5
OBSERVATIONS 172 253
Percentage figures represent the proportion of occurrences in the sample for binary variables.
"Standard deviations are in parentheses.









TABLE II SUMMARY STATISTICS


SUBSISTENCE CROPS:
MAIZE & BEANS


-2 Log Likelihood
Model Chi-Square
Improvement
Goodness of Fit
Correctly Classified
Senstivity
Specificity


Chi-Square
88.053
47.276
47.276
143.549


Significance
1.000
0.000
0.000
0.7538


Chi-Squ;
272.
33.
33.
234.


90.7%
52.17%
96.64%


COMMERCIAL CROPS:
TOMATOES & RICE
are Significance
229 0.0577
581 0.0039
581 0.0039
554 0.5327


69.96%
14.86%
92.74%









TABLE III: MAXIMUM LIKELIHOOD ESTIMATES OF TECHNOLOGY ADOPTION

VARIABLE SUBSISTENCE CROPS: MAIZE & BEANS COMMERCIAL CROPS: TOMATOES & RICE

BETA S.E. WALD SIG BETA S.E. WALD SIG
RESIDE 0.0338 0.0218 2.3933 0.1219 0.0004 0.0091 0.0021 0.9638
AGE -0.0960" 0.041 5.4905 0.0191 -0.0016 0.0171 0.0087 0.9255
MEMBERS 0.0790 0.0926 0.7275 0.3937 0.0900 0.0616 2.1367 0.1438
SCHOOLING -0.0568 0.1557 0.1329 0.7154 -0.1206 0.1112 1.1766 0.278
LITERACY -1.4729* 0.8912 2.7315 0.0984 0.7726 0.5313 2.1149 0.1459
SOLD 0.0581 0.6376 0.0083 0.9273 1.7087* 0.9378 3.32 0.0684
REFORM 1.279" 0.6273 4.1574 0.0415 0.2955 0.2903 1.0365 0.3086
HILL -1.8457* 0.9711 3.6125 0.0573
PLAIN -1.4139" 0.7008 4.0711 0.0436 0.1363 0.2232 0.3728 0.5415
RADIO -6.3014 62.6672 0.0101 0.9199 7.3872 22.2419 0.1103 0.7398
EXT/GOV 2.1516** 0.7691 7.8271 0.0051 1.7383"* 0.4888 12.6496 0.0004
PAMPHLET 1.2918 1.1767 1.2053 0.2723
SALES 3.2543* 1.7094 3.6246 0.0569 1.4723* 0.8449 3.0367 0.0814
FREIND -0.4132 1.141 0.1312 0.7172 0.8900* 0.3804 5.4741 0.0193
EXT/PVO 1.4977 1.4501 1.0668 0.3017 -4.3742 15.7348 0.0773 0.781
FAMILY -6.8030 26.5146 0.0658 0.7975 0.5311 0.8974 0.3502 0.554
CONSTANT 2.4446 1.9328 1.5997 0.206 -4.0472" 1.1901 11.565 0.0007

*One, two, and three asterisks indicate coefficients that are statistically different from zero at the 0.1, 0.05 and 0.01 levels of probability, respectively.




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