Impact of agricultural innovation adoption: a meta-analysis Kolawole Ogundari Dept. of Applied Economics & Statistics, University of Delaware USA Olufemi D. Bolarinwa Dept. of Food and Resource Economics, University of Florida, Gainesville FL USA

Outline Background information Research questions Meta-analysis Meta Dataset source & data description Empirical model Results Conclusions

Background Information Agriculture is the largest sector of the economy in many developing countries. The sector is viewed as a principal component of programs that seek to reduce poverty in developing countries (Ogundari, 2014). Many of the citizens of these countries continue to live in poverty and are largely food insecure (FAO, 2015). Promotion of agricultural innovation and technology is part of efforts to reduce poverty and food insecurity in these regions.

Background Information Cont’d Agricultural innovations are often promoted as a “package” of technologies for farmers to adopt. Benefits include improvement of the soil fertility, conservation of soil nutrients, water, and other natural resources. Other benefits include high yield/improved varieties, weed and pest management, etc. The impact of agricultural technology adoption is central to both the academic and policy makers. But the literature reported mixed findings/inconclusive results.

Background Information Cont’d Sources of the mixed findings may be due to Differences in methodologies Some studies used experimental design/non-experimental design Geographical differences in terms of study area Differences in the technologies considered and nature of data Potential impact outcome of adoption varies across studies Some studies focus on direct impact of adoption: production Others focus on indirect impact of adoption: Social & economic These forms of study heterogeneity might explain the mixed results

Research Questions Is the probability of finding a significant positive impact of adopting agricultural innovations and technologies increasing or decreasing over the years in the primary studies? Is the probability of finding a significant positive impact of adopting agricultural innovations and technologies sensitive to publication outlet, nature of data, differences in methodology, and type of agricultural products considered in the primary studies? To what extent do research design (experimental vs. non-experimental) and regional differences influence the probability of finding a significant positive impact of adopting agricultural innovation and technology?

Meta-analysis (MA) The study uses MA to address the above research questions because: MA provides the same methodological rigor to a qualitative literature review. MA uses quantitative technique to survey homogenous literature. Qualitative review can be sensitive to the subjective decision of reviewers. Subjective decision of the reviewer is replaced by statistical test in MA. MA addresses how study attributes are related to reported effect size (e.g., impact of agricultural technology adoption). It’s been widely adopted in medical, pharmaceutical and marketing researches It is now been used in applied economics literature

Meta-analysis (MA) Cont’d Methods of analysis in MA Descriptive analysis Vote counting analysis Regression analysis The regression analysis: Meta-regression analysis (MRA) MRA involves regressing effect size on study attributes to identify sources of heterogeneity in the reported effect size. The extent at which variations in the study attributes affect the effect size (e.g., impact of agricultural technology adoption) is the focus of MRA in the present study

Meta-Dataset source Compiled from different sources: Google Scholar, online database. Including journals articles, working papers & discussion papers etc. Over 1000 studies were retrieved But 138 studies were selected published b/w 1999-2016 (why?); For a study to be included, the study MUST report: standard error or t-value of the estimated impact year of survey of the primary data Some of the studies reported more than one impact (How?) subgroup of population different econometric techniques different data (e.g., panel data, cross-sectional data) Hence, we have in the sample 557 estimates taken as observation

Data description

Data description Cont’d Potential Outcomes of the Impact of Agricultural Innovation Adoption from the selected studies Economic measures: farm income, farm profit, or farm revenue [183] Social Measures: household food security, poverty indices, dietary intake etc. [189] Production measures: yield, production (kg), or technical efficiency score [185]

Frequency of Agricultural Innovation Adopted from the selected studies

Empirical model Ordinal specification where: - Y* represents unobserved latent variable ( potential impacts) - X is a vector representing study attributes which include DATAYEAR, SQR_SIZE, PANEL DATA, EXPERIMENTAL, JOURNAL etc. is the error term. Note: The study uses ordered probit model.

Study attribute effects with production measures as potential outcome outcome

Study attribute effects with social measures as potential outcome

Study attribute effects with economic measures as potential outcome

Conclusions Probability of reporting a significant positive impact increases (decreases) with direct (indirect) outcome over the years. Use of experimental design consistently decrease the probability of reporting significant positive impact; Probability of reporting significant positive impact consistently increase in the Middle Eastern countries relatively SSA countries. The findings provide information on the development of impact of adopting agricultural technology in developing countries over time. The findings identify study attributes essential for modeling impact of adopting agricultural technology for the future research.

Acknowledgement I gratefully acknowledge the Young African Scholar Program (YASP) of the African Development Bank for the financial support towards my participation at this conference

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