We focus on determining the impacts of government programs on farms’ technical inefficiency levels. We use Kumbhakar’s (2002) stochastic frontier model that accounts for both production risks and risk preferences. Our theoretical framework shows that decoupled government transfers are likely to increase (decrease) DARA (IARA) farmers’ production inefficiencies if variable inputs are risk decrea...

The cost of milk production by farm size was decomposed into frontier and efficiency components with a stochastic cost curve using data on 755 USA dairy farms from the year 2000. The estimated frontier function is much flatter than the composite cost curve, and although the frontier cost of production decreases with farm size, that cost reduction is not as pronounced as a cost curve that includ...

This study investigates onion producers’ technical efficiency level in Kebbi State. Purposive and simple random sampling procedures were adopted selecting 210 producers using structured questionnaire from seven local government areas the state. Descriptive statistics, gross margin stochastic frontier production models employed describing analyzing data. The results showed that generated an aver...

introduction: in this study, total factor productivity growth components in rough rice production are assisting by econometric approach and stochastic frontier function during 2005-2013 for several provinces (mazandaran province, guilan province, golestan province, fars province, khoozestan province). according to food and agriculture organization statics, iran is the 3rd importer and 20th expo...

“Within” estimation of the fixed-effect stochastic frontier model does not identify parameters on time-invariant explanatory variables. If time-invariant variables are important production inputs, then standard efficiency estimates are biased. This note details bias correction, when time-invariant inputs are dummy variables. © 2006 Elsevier B.V. All rights reserved.

The stochastic frontier model with heterogeneous technical efficiency explained by exogenous variables is augmented with a sparse spatial autoregressive component for a crosssection data, and a spatial-temporal component for a panel data. An estimation procedure that takes advantage of the additivity of the model is proposed, computational advantages over simultaneous maximum likelihood estimat...