Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects

Almanidis, Pavlos

doi:10.1007/s11123-012-0306-y

Cited by 16 publications

(8 citation statements)

References 42 publications

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“…For example, in a differentiated products setting, the distance may be an index representing the quality differences of a product relative to a benchmark. Alternatively, firm size can be included in the heterogeneity term (e.g., Almanidis, 2013). This would be in line with many productivity studies that model heterogeneity as a function of firm size.…”

Section: Time-varying Heterogeneitymentioning

confidence: 90%

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A time-varying true individual effects model with endogenous regressors

Kutlu

Tran

Tsionas

2019

Journal of Econometrics

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We propose a fairly general individual effects stochastic frontier model, which allows both heterogeneity and inefficiency to change over time. Moreover, our model handles the endogeneity problems if either at least one of the regressors or one-sided error term is correlated with the two-sided error term. Our Monte Carlo experiments show that our estimator performs well. We employed our methodology to the US banking data and found a negative relationship between return on revenue and cost efficiency. Estimators ignoring time-varying heterogeneity or endogeneity did not perform well and gave very different estimates compared to our estimator.

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Section: Time-varying Heterogeneitymentioning

confidence: 90%

“…The data is publicly avaiable in archieves of the Review of Economics and Statistics website. 30 See alsoTsionas (2002) andAlmanidis (2013) among others.…”

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confidence: 99%

A time-varying true individual effects model with endogenous regressors

Kutlu

Tran

Tsionas

2019

Journal of Econometrics

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“…Inspired by Hicks' (1935) quiet life hypothesis, we examined the effect of multiple airports in a metropolitan area. 2 For the sample of airports, we find that the median cost efficiency for single-airport and multi-airport cities is 88.2% and 86.9%, respectively, a negligible efficiency difference. 3 In general, when we control for airport specific heterogeneity, the median cost efficiency for the whole sample is 87.6%, which is not very sensitive to ownership type.…”

Section: Introductionmentioning

confidence: 76%

“…A potential reason for such deviation is the principle agent problem that the objectives of administrators may not fully align with cost minimization. Another reason may be optimization mistakes that are done by the decision makers 2. Quiet life hypothesis claims that, holding other factors constant, higher competition increases cost efficiency 3.…”

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confidence: 99%

US airport ownership, efficiency, and heterogeneity

Kutlu

McCarthy

2016

Transportation Research Part E: Logistics and Transportation Re

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All US commercial airports are in the public sector yet not all have the same ownership type. For medium and large hub US airports we use stochastic frontier analysis to analyze the efficiency differences for alternative airport ownership types. We find that while form of ownership may matter for cost efficiency, in general its effect is relatively small. Yet type of public sector ownership does have cost efficiency implications in certain environments. Further, when heterogeneity is not controlled, the results change substantially so that type of ownership matters much more which demonstrates the importance of controlling for cross section heterogeneity.

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“…Our paper is also related to the literature that focuses on bank competition during the crisis (Montes 2014;Fernández et al 2013;Weill 2013), but competition is determined by the density of bank branches rather than by the concentration of banks. Finally, the research is related to papers that explain differences in banks' profits as a function of differences in productivity (Caiazza et al 2016;Almanidis 2013;Fiordelisi and Molyneux 2010;Berger and Mester 2003;Grifell-Tatje and Lovell 1999). These papers tend to use productivity measures estimated from stochastic frontier models (SFA) or with the application of data envelopment analysis (DEA), while here, productivity is a parameter of the production function of banks, estimated with the methodology proposed by Levinsohn and Petrin (2003).…”

Section: Introductionmentioning

confidence: 99%

Productivity, competition and bank restructuring process

Llorens

Martín‐Oliver

Salas‐Fumás

2020

SERIEs

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This paper analyzes how differences in productivity across banks and the evolution of industry productivity over time might determine the intermediation costs and the restructuring process of the banking industry in the Great Recession. With data of Spanish banks, we find that less productive banks are more likely to exit than more productive banks, and that surviving banks acquire target banks in order to expand their branch network in local markets where they are underrepresented. Competition among banks contributes to the translation of industry productivity growth into lower interest rates of loans. Nonetheless, we find that the industry profit margin in loans increases during the period because of the modest industry productivity growth and the lower intensity of competition from branch closing.

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Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects

Cited by 16 publications

References 42 publications

A time-varying true individual effects model with endogenous regressors

A time-varying true individual effects model with endogenous regressors

US airport ownership, efficiency, and heterogeneity

Productivity, competition and bank restructuring process

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