A two-stage productivity analysis using bootstrapped Malmquist index and quantile regression

Abstract

This paper examines the effects of farm characteristics and government policies in enhancing productivity growth for a sample of Greek farms, using a two-stage procedure. In the 1st-stage, non-parametric estimates of Malmquist index and its decompositions are computed, while a bootstrapping procedure is applied to provide their statistical precision. In the 2nd-stage, the productivity growth estimates are regressed on various covariates using a bootstrapped quantile regression approach. The effect that the covariates exert on productivity growth of the average producer is analyzed, as well as the marginal effect of a given covariate for individuals at different points in the conditional productivity distribution. The results indicate that there exists large disparity of the covariates effect on productivity growth at different quantiles. Thus, policy suggestions should take into account the productivity distribution involved, as well as the selected policy objectives.Malmquist productivity index, quantile regression, bootstrap, Research Methods/ Statistical Methods, C14, C21, D24,