This study endorses the use of data envelopment analysis, which uses benefit-of-the-doubt weighting to evaluate the social, economic and overall performance of social enterprises. This methodology is especially useful for creating composite indicators based on multiple outputs expressed in different measurement units, and allows for enterprise-specific weighting of the different objectives. Applying this methodology on a unique longitudinal dataset of Flemish sheltered workshops suggest that social enterprises may face different types of mission drift. Further, our resultsshow that top-performing social enterprises are more economically and socially efficient than low performers. These top performers also have a stronger economic orientation, which sheds new light on the balance between social and economic orientations in social enterprises.
This paper introduces a decomposition of the additively complete Luenberger-Hicks-Moorsteen Total Factor Productivity indicator into the usual components: technical change, technical inefficiency change and scale inefficiency change. Our approach is general in that it does not require differentiability or convexity of the production technology. Using a nonparametric framework, the empirical application focuses on the agricultural sector at the state-level in the U.S. over the period 1960 − 2004. The results show that Luenberger-Hicks-Moorsteen productivity increased substantially in the considered period. This productivity growth is due to output growth rather than input decline, although the extent depends on the convexity assumption of the technology. Technical change is the main driver, while the role of technical inefficiency change and scale inefficiency change also depends on the convexity assumption of the technology.
This paper introduces a nonparametric measure of coordination productivity growth where the subprocesses are explicitly modelled in the production technology. The coordination productivity indicator is decomposed into a coordination technical inefficiency change component and a coordination technical change component. This decomposition allows assessment of reallocation impacts on the different sources of productivity growth. The empirical application focuses on a large panel of English and Welsh farms over the period 2007–2013. The results show that coordination inefficiency significantly increases with the proportion of resources allocated to livestock production in economic and statistical terms. Coordination inefficient farms should generally allocate more land to crop production. Depending on the region, the average coordination productivity growth ranges from −9.7% to 15.9% per year. It is driven by coordination technical change rather than coordination inefficiency change.
Consisting of the difference between an output indicator and an input indicator, the Luenberger-Hicks-Moorsteen (LHM) productivity indicator allows straightforward interpretation. However, its computation requires estimating distance functions that are inherently unknown. This paper shows that a computationally simple Bennet indicator is a superlative indicator for the LHM indicator when one can assume profit-maximizing behavior and the input and output directional distance functions can be represented up to the second order by a quadratic functional form. We also show that the Luenberger-and LHM-approximating Bennet indicators coincide for an appropriate choice of directional vectors. Focusing on a large sample of Italian food and beverages companies for the years 1995 − 2007, we empirically investigate the extent to which this theoretical equivalence translates into similar estimates. We find that the Bennet indicator is a close empirical alternative to the LHM indicator for the sample.
We propose novel tools for the analysis of individual welfare on the basis of aggregate household demand behavior. The method assumes a collective model of household consumption with the public and private nature of goods specified by the empirical analyst. A main distinguishing feature of our approach is that it builds on a revealed preference characterization of the collective model that is intrinsically nonparametric. We show how to identify individual money metric welfare indices from observed household demand, along with the intrahousehold sharing rule and the individuals' willingness-to-pay for public consumption (i.e. Lindahl prices). The method is easy to use in practice and yields informative empirical results, which we demonstrate through both a simulation exercise and an empirical application to labor supply data drawn from the Panel Study of Income Dynamics.
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