Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error

Meeusen, Wim; Broeck, Julien van den

doi:10.2307/2525757

Cited by 4,673 publications

(2,513 citation statements)

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“…The stochastic frontier production function model originally proposed by Aigner, Lovell and Schmidt (1977), Battese and Corra (1977) and Meeusen and van den Broeck (1977) to account for random errors and non-negative inefficiency effects can be expressed as follows:…”

Section: Methodsmentioning

confidence: 99%

Technical efficiency in primary health care: does quality matter?

Zamorano

Petraglia

2010

Eur J Health Econ

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The accuracy required in the measurement of output is an issue that has as yet still not been satisfactorily addressed in empirical research on efficiency in primary health care. We exploit information retrieved from a newly constructed database (APEX06) for the Spanish region of Extremadura. The richness of our dataset allows us to consider original synthetic measures of output that take into account both the quantity and the quality of services provided by 85 primary care centres (PCCs) in 2006. We provide evidence that neglecting the issue of properly accounting for the quality of health services can lead to misleading results. Our main finding is that adjusting output for quality influences efficiency analysis in three senses. First, inefficiency now explains relatively more of the deviation from the potential output. Second, the average technical efficiency in the sector is lower, while its dispersion among PCCs is significantly higher. And third, the efficiency ranking of the PCCs is also affected.

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Section: Methodsmentioning

confidence: 99%

Technical efficiency in primary health care: does quality matter?

Zamorano

Petraglia

2010

Eur J Health Econ

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“…Hence, we give more weight to complex treatments; that is, we adjust each acute discharge with an indicator that takes into account the amount of resources allocated to each treatment, using the diagnosis-related group weight, which takes into account the treatment complexity. Concerning methods to estimate SF models, many contributions have been continuously developed over time since the first publications of Aigner, Lovell, and Schmidt (1977), Meeusen and van den Broeck (1977), and Battese and Corra (1977). 9 They cover both cross-sectional and panel data.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of transient and persistent hospital efficiency: The case of Italy

2017

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SummaryIn this paper, we extend the 4-random-component closed skew-normal stochastic frontier model by including exogenous determinants of hospital persistent (long-run) and transient (short-run) inefficiency, separated from unobserved heterogeneity. We apply this new model to a dataset composed by 133 Italian hospitals during the period 2008-2013. We show that average total inefficiency is about 23%, higher than previous estimates; hence, a model where the different types of inefficiency and hospital unobserved characteristics are not confounded allows us to get less biased estimates of hospital inefficiency. Moreover, we find that transient efficiency is more important than persistent efficiency, as it accounts for 60% of the total one. Last, we find that ownership (for-profit hospitals are more transiently inefficient and less persistently inefficient than not-for-profit ones, whereas public hospitals are less transiently inefficient than not-for-profit ones), specialization (specialized hospitals are more transiently inefficient than general ones; i.e., there is evidence of scope economies in short-run efficiency), and size (large-sized hospitals are better than medium and small ones in terms of transient inefficiency) are determinants of both types of inefficiency, although we do not find any statistically significant effect of multihospital systems and teaching hospitals. KEYWORDSclosed skew-normal stochastic frontier, determinants of in efficiency, hospital persistent and transient efficiency

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“…Due to the nature of the fields examined, the use of stochastic functions is much more present in the literature relating to transport and the technique employing this approach is generally called stochastic frontier analysis (SFA). SFA was created simultaneously by Aigner et al [11] and Meeusen and van den Broeck [12], and is applied to measure the deviation of a firm's efficiency as compared to the best achievable target. In order to do this, first it creates the production function leaving room for random shocks and measurement error; and then evaluates the performance of the individual firms as compared to this frontier.…”

Section: Use Of Production Functionsmentioning

confidence: 99%