Adoption: a new Stata routine for estimating consistently population technological adoption parameters

Aliou Diagne ()
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Aliou Diagne: Africa Rice Center

SAN12 Stata Conference from Stata Users Group

Abstract: In Agricultural Economics 37 (2007) 201–210, using a counterfactual outcomes framework, Diagne and Demont showed that observed sample technological adoption rate does not consistently estimate the population adoption rate even if the sample is random. 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. In this talk the author present a new user-written Stata command called adoption implemented by using Stata estimation commands internally to carry out the various estimations and by computing the correct standard errors for the Average Treatment Effect (ATE) parameter estimates; population mean potential adoption in the exposed subpopulation (ATE1), population mean potential adoption in the non-exposed subpopulation (ATE0, population mean joint exposure and adoption (JEA), population adoption gap (GAP) and population selection bias (PSB). The ATE adoption parameters are estimated using the semiparametric (i.e. inverse probability weighting) method or a parametric method that fits adoption outcome on independent variables using one of Stata parametric models such as Probit, Logit, glm, Ols, Poisson, Tobit, etc.

Date: 2012-08-01
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