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.Download Info
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Bibliographic Info
Article provided by International Association of Agricultural Economists in its journal Agricultural Economics.
Volume (Year): 37 (2007)
Issue (Month): 2-3 (09)
Pages: 201-210
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Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Yigezu, Yigezu A. & Ahmed, Mohamed A. & Shideed, Kamil & Aw-Hassan, Aden & El-Shater, Tamer & Al-Atwan, Samman, 2013. "Implications of a shift in irrigation technology on resource use efficiency: A Syrian case," Agricultural Systems, Elsevier, vol. 118(C), pages 14-22.
- Kassie, Menale & Shiferaw, Bekele & Muricho, Geoffrey, 2011. "Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda," World Development, Elsevier, vol. 39(10), pages 1784-1795.
- Gitonga, Zachary M. & De Groote, Hugo & Kassie, Menale & Tefera, Tadele, 2013. "Impact of metal silos on households’ maize storage, storage losses and food security: An application of a propensity score matching," Food Policy, Elsevier, vol. 43(C), pages 44-55.
- Kijima, Yoko & Otsuka, Keijiro & Sserunkuuma, Dick, 2011. "An Inquiry into Constraints on a Green Revolution in Sub-Saharan Africa: The Case of NERICA Rice in Uganda," World Development, Elsevier, vol. 39(1), pages 77-86, January.
- Dalton, Timothy J. & Lilja, Nina K. & Johnson, Nancy & Howeler, Reinhardt, 2011. "Farmer Participatory Research and Soil Conservation in Southeast Asian Cassava Systems," World Development, Elsevier, vol. 39(12), pages 2176-2186.
- Simtowe, Franklin & Muange, Elijah & Munyua, Bernard & Diagne, Aliou, 2012. "Technology Awareness and Adoption: The Case of Improved Pigeonpea Varieties in Kenya," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126760, International Association of Agricultural Economists.
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