Assessing the Impact of Agricultural Technology Adoption on Farmers' Well‐being Using Propensity‐Score Matching Analysis in Rural China

Abstract

The present paper assesses the impact of improved upland rice technology on farmers' well‐being. The study uses propensity‐score matching to address the problem of ‘self‐selection,’ because technology adoption is not randomly assigned. It applies this procedure to household survey data collected in Yunnan, China in 2000, 2002 and 2004. The findings indicate that improved upland rice technology has a robust and positive effect on farmers' well‐being, as measured by income levels and the incidence of poverty. The effect of technology on well‐being shows a diminishing impact on producers' incomes. This implies that newer innovations are continuously needed to replace older technologies that have reached their saturation points.

The Impact of improved maize varieties on farm productivity and wellbeing: evidence from the East Hararghe Zone of Ethiopia

“…For instance, the large proportion of studies (e.g. Mendola 2007;Becerril and Abdulai 2010;Wu et al 2010;Kassie, Shiferaw, and Muricho 2011;Awotide et al 2013) relied on the propensity score matching (PSM) approach. However, this technique only works if the difference between the two groups can be captured by using only observable variables.…”

Assessing the Effect of a Crop-Tree Intercropping Program on Smallholders’ Incomes in Rural Xinjiang, China

“…In order to reduce the estimation bias caused by the possibly existed endogeneity of land tenure security, only the village level tenure security variable was introduced. Market access is found to be positively correlated with intercropping adoption [84]. Meanwhile it is also relevant to income, since market access is crucial in deriving incomes from fruit and farming.…”

The Impact of Village Poultry Technology Adoption on the Livelihood of Smallholder Farmers in the Central Oromia Region, Ethiopia

“…According to Caliendo & Kopeinig (2005), propensity score is the probability of the participants for observed characteristic X. Propensity score matching method compares average outcomes of the adopters and non-adopters based on estimated propensity score values. If technology was randomly assigned to farmers, the causal effect of technology adoption can be assessed by comparing the difference of variables between treated and untreated, however, the technology is rarely randomly assigned in non-experimental studies which results self-selection bias (Wu et al 2010). When treatments were not randomly assigned, it was difficult to determine casual inferences whether the difference in outcome between the treated and control groups was due to the treatment effect or other characteristics.…”