Resilience in technical efficiency and enabling factors: insights from panel farm enterprise surveys in Kazakhstan and Uzbekistan

We assess how public goods like information (through training) and an enabling environment (autonomy in production decision) improve the resilience of technical efficiency against economic shocks. We apply the analyses to unique panel datasets of cotton and wheat farm enterprises in Uzbekistan and southern Kazakhstan, collected in 2019 and 2022, during which these enterprises experienced significant economic shocks in input prices. We employ Paul and Shankar’s (2020) stochastic frontier model, which identifies associations between time-variant technical efficiency and input prices while controlling for time-invariant heterogeneity. We also estimate the extended Karakaplan and Kutlu (2017) model, combined with the Inverse Probability Weighting (IPW) method, to address potential endogeneity in two aspects: (a) the extent of training received and/or the level of autonomy granted and (b) inputs use variables in stochastic frontier estimation. Our results show that receiving more agricultural training and greater autonomy in 2018 enhanced resilience of enterprises’ technical efficiency between 2019 and 2022, despite facing significant increases in chemical fertilizer and oil/diesel prices and the need to reduce the use of these inputs. Our results are robust against potential violation of the conditional independence assumption of IPW, definitions of the extent of training and autonomy received, and sample attrition.