1. Guy Blaise NKAMLEU, AEA – November, 2009 THE IMPACT OF FARMERS’ CHARACTERISTICS ON TECHNOLOGY ADOPTION: A Meta Evaluation Guy Blaise NKAMLEU African Development Bank AMERICAN EVALUATION ASSOCIATION Annual Conference 11 – 14 November 2009, Orlando-Florida, USA

2. Guy Blaise NKAMLEU, AEA – November, 2009 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 reduction Any 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 development In 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.

3. Guy Blaise NKAMLEU, AEA – November, 2009 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…).

4. Guy Blaise NKAMLEU, AEA – November, 2009 Route to technological progress Technological Progress 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.

5. Guy Blaise NKAMLEU, AEA – November, 2009 Usual Research Question Why some farmers adopt improved technologies and others do not.

6. Guy Blaise NKAMLEU, AEA – November, 2009 Envision the whole What - Why - Who - How - When - Where - So what Challenge for Evaluator

7. Guy Blaise NKAMLEU, AEA – November, 2009 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

8. Guy Blaise NKAMLEU, AEA – November, 2009 However, many of these studies reached contradictory conclusions and therefore sending inconsistent message to policy makers. Inconclusive conclusions CharacteristicIncluded (%) (n=186) Significant and positive (%) Significant and negative (%) Not Significant (%) Education8444155 Age68142561 Gender2522771 Experience33271558 Household size 41251065 Farm size73421345 Use and significance of farmers’ characteristics in adoption studies.

9. Guy Blaise NKAMLEU, AEA – November, 2009 Objective of the study Determine and explain the differences that induce the divergences among adoption studies. Methodology Meta-analysis and Multi-stage meta-regressions.

10. Guy Blaise NKAMLEU, AEA – November, 2009 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.

11. Guy Blaise NKAMLEU, AEA – November, 2009 Meta-Analysis: Studies characteristics Spatiotemporal context of study design Publication Year; First author based in developed or developing country ; Year the data used in the study was collected. Methodological issues in study design How Adoption was measured ; Sample Size ; Number of Variable Characteristics of technologies investigated Type 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 sample Study conducted in developed or developing country ; Affiliation of the first author (University, Research, development agency)

12. Guy Blaise NKAMLEU, AEA – November, 2009 Meta-Regression: Logit & Multinomial Logit Models First Stage; Logit Model. Second Stage; Multinomial Logit Model.

13. Guy Blaise NKAMLEU, AEA – November, 2009 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

14. Guy Blaise NKAMLEU, AEA – November, 2009 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

15. Guy Blaise NKAMLEU, AEA – November, 2009 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. Some featured results worth mentioning

16. Guy Blaise NKAMLEU, AEA – November, 2009 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. Some featured results worth mentioning

17. Guy Blaise NKAMLEU, AEA – November, 2009 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

18. Guy Blaise NKAMLEU, AEA – November, 2009 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

19. Guy Blaise NKAMLEU, AEA – November, 2009 END b.nkamleu@afdb.org