Estimation of growth convergence using a stochastic production frontier approach

Kumbhakar, Subal C.; Wang, Hung‐Jen

doi:10.1016/j.econlet.2005.01.023

Cited by 155 publications

(125 citation statements)

References 3 publications

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“…The latter have initially focused on either persistent inefficiency (Battese & Coelli, 1988;Kumbhakar, 1987;Pitt & Lee, 1981;Schmidt & Sickles, 1984) or transient inefficiency (e.g., Battese & Coelli 1992;Cornwell et al, 1990;Kumbhakar, 1990;Lee & Schmidt, 1993). Later models have added other components to the error term: a firm effect treated as persistent inefficiency, a transient inefficiency component (Kumbhakar & Heshmati, 1995;Kumbhakar & Hjalmarsson, 1993, and an unobserved heterogeneity component together with a transient inefficiency term (Greene, 2005a(Greene, , 2005bKumbhakar & Wang, 2005;Wang & Ho 2010).…”

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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Section: Literature Reviewmentioning

confidence: 99%

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

2017

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“…However, there are considerable variations in bank size. Moreover, the use of output growth to assert the increase of productivity faced the potential criticism that the increase of some DMU outputs were mainly from the accumulation and increase of factors and less from the technology advancement and efficiency improvement [8]. Therefore, the introduction and decomposition of CM are essential.…”

Section: Resultsmentioning

confidence: 99%

Effect of Fintech on the Productivity in the Taiwan Banking Industry

Yang¹,

Hsueh²,

Huang³

et al. 2017

IJEEEE

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Abstract:The development of financial technology has fostered a wave revolutionary change in financial service industry worldwide. The objective of this study is to examine whether the adoption of Fintech contributes to the productivity growth of the Taiwan banking industry in 2015. To investigate such potential effect, the study use the preferred Cost Malmquist Index to estimate the 25 listed sample banks over the period from 2010 to 2015. The empirical result suggests the observed period 2014-2015, the ΔPTE, ΔAE, ΔPE, and ΔCSE are improving by 0.01%, 0.13%, 0.42%, and 0.10% respectively. Moreover, the degree of ΔT deterioration is much improved relative to the other observe periods without adoption of Fintech. It provides the positive evidence to support an adoption of Fintech contribute a potential growth of competiveness of the Taiwan banking industry.

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“…Laurenceson and Zhao (2008) found that differences in efficiency levels between the two groups were small. The inefficiency of the JECBs stemmed mainly from their failing to realize scale efficiency, while for the SOBs, pure technical inefficiency accounted for a bigger percentage (Kumbhakar and Wang, 2005;Wang and Wei, 2000). Unlike the US banks, which had a flat U-shape average cost curve, the large-and small-sized banks in China appeared to be more efficient than the medium ones.…”

Section: Efficiency Studies Of the Chinese Banking Sectormentioning

confidence: 99%

“…Chen et al (2005b) found that the financial deregulation of the mid-1990s improved cost efficiency levels including both technical and allocative efficiency significantly. Dividing this sample into two sub-periods, the deregulatory period (1993)(1994)(1995)(1996)(1997)(1998), and the post-deregulatory period (1999)(2000)(2001)(2002), Kumbhakar and Wang (2005) failed to find evidence to support the effectiveness of deregulation as a means to improve banks efficiency, a conclusion consistent with Fu and Heffernan (2007). Both the JECBs and SOBs experienced a decline in technical efficiency between the first (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992) and second stages (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002) of bank reform.…”

Section: Efficiency Studies Of the Chinese Banking Sectormentioning

confidence: 99%

The Development of the Chinese Financial System and Reform of Chinese Commercial Banks

Luo¹

2016

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Estimation of growth convergence using a stochastic production frontier approach

Cited by 155 publications

References 3 publications

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

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

Effect of Fintech on the Productivity in the Taiwan Banking Industry

The Development of the Chinese Financial System and Reform of Chinese Commercial Banks

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