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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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