the impact of farmers’ characteristics on technology adoption: a meta evaluation

Guy Blaise NKAMLEU, AEA – November, 2009

THE IMPACT OF FARMERS’ CHARACTERISTICS ON TECHNOLOGY ADOPTION:

A Meta Evaluation

Guy Blaise NKAMLEUAfrican Development Bank

AMERICAN EVALUATION ASSOCIATIONAnnual Conference

11 – 14 November 2009, Orlando-Florida, USA


Few things we already know

• Poor live in rural areas In most poor countries, especially in sub-Saharan Africa, large majorities of the population

live in rural areas and earn their livelihoods primarily from agriculture.

• Agriculture: Crucial for poverty reductionAny serious discussion of growth and poverty reduction in Africa must begin with a look

at the role played by agricultural development.

• Agriculture: Engine for economic developmentIn most developing countries, because of its importance in overall GDP, export earnings

and employment, as well as its forward and backward linkages to the non-farm sector, growth in the agricultural sector is the cornerstone of the overall economic growth.

Basic Economic teachings: agricultural surplus is a necessary condition for a country to begin the development process.


Few things we already know

• Technological change at the root of agricultural growth During the 1960s, a series of technical breakthroughs created rapid increases in

agricultural production in many less developed countries…. The Green Revolution. Until today significant agricultural growth is possible only through changes in technology

(new husbandry techniques, better seed varieties, more efficient sources of power, and cheaper plant nutrients…).


Route to technological progress

Technological Technological ProgressProgress

Technology Adoption

Technology generation

Adoption of existing improved technologies is still problematic. Many farmers are reluctant.

Since the 1960s, a series of technical breakthroughs have created potentials for rapid increases in agricultural production. An abundant improved technologies exist.


Usual Research Question

Why some farmers adopt improved technologies and others do not.


Envision the whole

What - Why - Who - How - When - Where - So what

Challenge for Evaluator


• What are the main determinants of technology adoption

This question is at the core of agriculturalists’ longstanding concerns over agricultural growth and many studies have been conducted to investigate farmers’ characteristics affecting their adoption decision.

Central concern


However, many of these studies reached contradictory conclusions and therefore sending inconsistent message to policy makers.

Inconclusive conclusions

Characteristic Included (%)(n=186)

Significant and positive (%)

Significant and negative (%)

Not Significant (%)

Education 84 44 1 55

Age 68 14 25 61

Gender 25 22 7 71

Experience 33 27 15 58

Household size

41 25 10 65

Farm size 73 42 13 45

Use and significance of farmers’ characteristics in adoption studies.


Objective of the study

• Determine and explain the differences that induce the divergences among adoption studies.

Methodology

• Meta-analysis and Multi-stage meta-regressions.


Meta-Analysis: Analysis of Analyses

searching through mountains of potentially contradictory research to uncover the nuggets of knowledge that lie buried underneath’’.

Data collected from an extensive search for published articles related to technology adoption in the agricultural sector.

The search was done in well established agricultural economic journals and limited to articles published in or after 1990.

186 analyses of determinants of technology adoption in the agricultural sector have been gathered.


Meta-Analysis: Studies characteristics

Spatiotemporal context of study designPublication Year; First author based in developed or developing country ; Year the

data used in the study was collected.

Methodological issues in study designHow Adoption was measured ; Sample Size ; Number of Variable

Characteristics of technologies investigatedType of Technology (hard vs soft) ; Technology target (production oriented vs post-

harvest orientation) ; Product concerned (food crop cash crop, rearing…).

Geographical and socio-demographic context of the sampleStudy conducted in developed or developing country ; Affiliation of the first author

(University, Research, development agency)


Meta-Regression: Logit & Multinomial Logit Models

First Stage; Logit Model.

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Second Stage; Multinomial Logit Model.

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Systematic differences exist in the literature in terms of the type of farmers’ characteristics included in the adoption analyses.

Expected results of analyses are partly influenced by this large heterogeneity of farmers’ characteristics that authors have included in their analysis

A given variable is more likely to come out as significant determinant of farmers’ adoption decisions under specific study attributes

There is a consistency behind the inconsistency observed in the adoption literature.

What have we learnt so far


Studies undertaken in developed countries have a greater probability to find a negative correlation between the age variable and technology adoption.

that there is a higher probability for the education variable to be positively correlated to the adoption if the technology under investigation is a hard technology, a production-oriented technology, and/or if the sample size is larger.

Some featured results worth mentioning


Studies which measure adoption as a binary response are more likely to find a negative correlation between age and adoption.

studies dealing with hard technologies were less likely to find a positive correlation between gender and adoption.

Studies conducted in developed countries and studies dealing with soft technology (managerial techniques) were more likely to find a positive correlation between the household size and technology adoption.



that the larger the number of variables included in the model, the less likely it is that the household size will be positively correlated with the adoption decision.

authors based in developed countries were most likely to find a significant (positive and negative) relationship between the farm size and the adoption decision.



Conflicting research results with respect to the role and importance of farmers’ characteristics on adoption decisions may, in many cases, be simply the results of differing study-specific design and spatio-temporal contexts rather than empirical facts:

There is a Consistency behind the Inconsistencies

Main Conclusion


Take-home message

Adoption study results should not simply be transferred and interpreted beyond geographical or social clusters, and neither beyond different types of technologies………

Context matters


END

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