Taking a new look at empirical models of adoption: average treatment effect estimation of adoption rates and their determinants

Abstract

This article shows that the "observed" sample adoption rate does not consistently estimate the population adoption rate even if the sample is random. It is proved that instead the sample adoption rate is a consistent estimate of the population joint exposure "and" adoption rate, which does not inform about adoption per se. Likewise, it is shown that a model of adoption with observed adoption outcome as a dependent variable and where exposure to the technology is not observed and controlled for cannot yield consistent estimates of the determinants of adoption. The article uses the counterfactual outcomes framework to show that the true population adoption rate corresponds to what is defined in the modern policy evaluation literature as the "average treatment effect" (ATE), which measures the effect or impact of a "treatment" on a person randomly selected in the population. In the adoption context, a "treatment" corresponds to exposure to the technology. The article uses the ATE estimation framework to derive consistent nonparametric and parametric estimators of population adoption rates and their determinants and applies the results to consistently estimate the population adoption rates and determinants of the NERICA (New Rice for Africa) rice varieties in C�te d'Ivoire. The ATE methodological approach developed in the article has significant policy implications with respect to judging the intrinsic merit of a new technology in terms of its potential demand by the target population independently of issues related to its accessibility and in terms of the decision to invest or not in its wide-scale dissemination. Copyright 2007 International Association of Agricultural Economists.

Determinants of agricultural technology adoption under partial population awareness: the case of pigeonpea in Malawi

“…The analysis in this paper is guided by a theoretical framework of technology adoption under partial population exposure proposed by Diagne and Demont (2007). The framework is relevant in this analysis because although a number of pigeonpea varieties have been released and disseminated in Malawi, a very small fraction of the farming population has been exposed to the technologies.…”

“…It suffers from what is known as "nonexposure" 1 bias and it yields inconsistent and biased estimates of population adoption rates even when based on a randomly selected sample. Diagne and Demont (2007) show why taking the adoption rates within the subsample of farmers exposed to the technology does not address the selection bias problem and why such population estimates are not consistent estimates of the true population adoption rate even if the sample is random. Such estimates may underestimate or overestimate the true population adoption rate.…”

“…This fact also suggests that the relationship between awareness and adoption cannot be linearly specified. Indeed Diagne and Demont (2007) empirically show that when a technology is new and the target population is not universally exposed to it, the observed sample adoption rate is not a consistent estimator of the true population adoption rate. It suffers from what is known as "nonexposure" 1 bias and it yields inconsistent and biased estimates of population adoption rates even when based on a randomly selected sample.…”

“…We follow Diagne and Demont (2007) to address this problem by employing a programme evaluation methodology based on counterfactual outcomes to provide unbiased estimators of the rate of adoption and the factors affecting adoption. The rest of the paper is organized as follows: Pigeonpea production and utilization in Malawi discusses pigeonpea production and significance while the data and analytical methodology are described in Methods.…”

Why NERICA is a Successful Innovation for African Farmers

“…The mean impact for women farmers was significant in all four countries (including Côte d'Ivoire) and significantly higher than for men. Papers containing these adoption and impact estimates have been presented at international conferences and some have been published in refereed journals (Diagne, 2006a and b;Diagne and Demont, 2007;Rodenburg et al, 2006), while others have been submitted for publication in refereed journals (Diagne, 2007b;Diagne et al, 2007a and b;Adégbola et al, 2006;Adekambi et al, 2007a and b;Agboh-Noameshie et al, 2007).…”