Economic efficiency in farm households: trends, explanatory factors, and estimation methods

Abstract

Factors explaining differences in economic efficiency between farms are of major interest to owners, managers, and other stakeholders as they strive to improve earnings and improve the chances of firm survival. This study is undertaken to improve our understanding of interfarm differences in, and opportunities to improve, farm household efficiency in utilizing their land, labor, and capital resources to achieve household objectives. The technical, allocative, and scale efficiencies of farm households are estimated using a nonparametric, output-based data envelopment analysis (DEA) of a panel data set from 1993 to 2006. Single and double bootstrapping procedures are used to estimate technical efficiency. Initial technical efficiency assuming variable returns to scale (TEV) is estimated to be 0.83. Using single bootstrapping, the average bias-corrected TEV estimate is 0.70; using double bootstrapping, the TEV estimate is 0.72. Allocative efficiency is estimated to be 0.81. Scale efficiency is estimated to be 0.93. The only factor that is consistently associated with higher technical efficiency across analysis methods and years is larger farm size (as measured by the log of farm income). The significance of other factors changes with analysis methods. Copyright (c) 2009 International Association of Agricultural Economists.

Joint measurement of farm size and farm performance: a confirmatory factor analysis

“…The agricultural economics literature has recognized the limitations with physical farm size measures and has used financial indicators, such as gross sales (Olson and Vu, 2009;Sumner and Wolf, 2002;Barry et al, 2001;Purdy et al, 1997). Financial measures can provide an indication of the physical size of the farm as a function of the amount of product sold while jointly representing the management capabilities of the principal operators as determined by the prices received for their products.…”

Does Farm Size and Specialization Matter for Productive Efficiency? Results from Kansas

“…There is an emerging consensus that technical efficiency and overall performance of farms are influenced by farm size so that larger and more diversified farms are more productive or efficient than small farms 1 (Byrnes et al, 1987;Chavas and Aliber, 1993;Featherstone, Langemeier, and Ismet, 1997;Kalaitzandonakes, Wu, and Ma, 1992;Key, McBride, and Mosheim, 2008;Olson and Vu, 2009;Serra, Zilberman, and Gil, 2008;Weersink, Turvey, and Godah, 1990;Wu, Devadoss, and Lu, 2003). Byrnes et al (1987) investigated the relative technical performance of Illinois grain farms and observed that the major source of inefficiency was scale inefficiency, particularly for the large farms in the sample.…”

“…These advances have not been included in many recent studies that have examined farm level technical efficiency of U.S. agriculture. An exception is the Olson and Vu (2009) study which estimated technical, allocative, and scale efficiencies of farms in southern Minnesota using the bootstrap output-based DEA approach. This paper used the Wilson (1998, 2000) smoothed bootstrap procedure to investigate the bias, variance, and confidence intervals for technical efficiency scores for the Kansas farm sector.…”

“…Results indicated that an increase in decoupling will likely decrease technical efficiencies. Olson and Vu (2009) estimated the technical, allocative, and scale efficiencies of farms in southern Minnesota using bootstrap nonparametric outputbased data envelopment analysis. The authors found that large farm sizes are consistently associated with higher technical efficiency.…”

On the combined estimation of technical efficiency and its application to agriculture

“…Th e results of the DEA procedures are often employed as the dependent variable in the further analysis searching socioeconomics sources of diff erences between the units' effi ciency (Assaf and Matawie 2009;Hu et al 2009;Olson and Vu 2009;Assaf and Agbola 2011). However when comparing the non-parametric approaches with those based on the regression analysis (RA), which are presented in Section 2, the diff erences between them are easily visible.…”