Technical efficiency and technological gaps correcting for selectivity bias: Insights from a value chain project in Nepal

Florian Neubauer, Tisorn Songsermsawas, Joanna Kámiche-Zegarra, Boris E. Bravo-Ureta

Research output: Contribution to journalArticle in a journalpeer-review

3 Scopus citations


Strengthening linkages with markets along the value chain is a promising pathway to increase agricultural productivity, welfare, and food security of smallholder producers in developing countries. Our study investigates the impact of an integrated value chain project, which linked producer organizations to markets and provided various types of training to their members. Using primary survey data from Nepal, we apply propensity score matching to ensure common support and statistical balance between project (treatment) and non-project (control) households. We implement the selectivity-corrected stochastic production frontier methodology to control for unobservable characteristics and estimate a simulation-based stochastic metafrontier to account for different technological levels between the two groups of households. Comparing results between the translog and Cobb-Douglas production frontiers, we find that the more flexible translog specification shows no signs of selectivity bias while this bias is present with the Cobb-Douglas, motivating the need to correct for selectivity. Our main results from the metafrontier analysis indicate that project impacts are reflected in substantial technological gaps and significant meta-technical efficiency differences in favor of treatment households. Thus, this study shows that strengthening linkages between producers and markets can have a large positive effect on productivity. Heterogeneity analysis suggests that more vulnerable producers, including those with fewer years of education and smaller farms, receive relatively higher returns from the project compared to their counterparts.
Original languageEnglish
Article number102364
Number of pages15
JournalFood Policy
StatePublished - Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd.


  • Value chain
  • Technical efficiency
  • Stochastic production frontiers
  • Metafrontier
  • Selectivity bias
  • Impact evaluation


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