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Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis

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It has often been argued that the results of efficiency analyses in health care are influenced by the modelling choices made by the researchers involved. In this paper we use meta-regression analysis in an attempt to quantify the degree to which modelling factors influence efficiency estimates. The data set is derived from 253 estimated models reported in 95 empirical analyses of hospital efficiency in the 22-year period from 1987 to 2008. A meta-regression model is used to investigate the degree to which differences in mean efficiency estimates can be explained by factors such as: sample size; dimension (number of variables); parametric versus non-parametric method; returns to scale (RTS) assumptions; functional form; error distributional form; input versus output orientation; cost versus technical efficiency measure; and cross-sectional versus panel data. Sample size, dimension and RTS are found to have statistically significant effects at the 1% level. Sample size has a negative (and diminishing) effect on efficiency; dimension has a positive (and diminishing) effect; while the imposition of constant returns to scale has a negative effect. These results can be used in improving the policy relevance of the empirical results produced by hospital efficiency studies.

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  • Kim-Huong Nguyen & Tim Coelli, 2009. "Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis," CEPA Working Papers Series WP072009, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:74
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    3. Dinesh R. Pai & Fatma Pakdil & Nasibeh Azadeh-Fard, 2024. "Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022," Health Care Management Science, Springer, vol. 27(2), pages 284-312, June.
    4. Francesco Aiello & Graziella Bonanno, 2016. "Efficiency in banking: a meta-regression analysis," International Review of Applied Economics, Taylor & Francis Journals, vol. 30(1), pages 112-149, January.
    5. Fall, François & Akim, Al-mouksit & Wassongma, Harouna, 2018. "DEA and SFA research on the efficiency of microfinance institutions: A meta-analysis," World Development, Elsevier, vol. 107(C), pages 176-188.
    6. Francesco Aiello & Graziella Bonanno & Francesco Foglia, 2021. "On The Heterogeneity In The Judicial Efficiency Literature: A Meta-Regression Analysis," Working Papers 202102, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    7. Francesco Aiello & Graziella Bonanno & Luigi Capristo, 2017. "Explaining Differences In Efficiency: The Case Of Local Government Literature," Working Papers 201704, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    8. Phuc Trong Ho & Michael Burton & Chunbo Ma & Atakelty Hailu, 2022. "Quantifying heterogeneity, heteroscedasticity and publication bias effects on technical efficiency estimates of rice farming: A meta‐regression analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(2), pages 580-597, June.
    9. Francesco Aiello & Graziella Bonanno, 2019. "Explaining Differences In Efficiency: A Meta‐Study On Local Government Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 999-1027, July.
    10. Tin H. Ho & Dat T. Nguyen & Thanh Ngo & Tu D. Q. Le, 2021. "Efficiency in Vietnamese Banking: A Meta-Regression Analysis Approach," IJFS, MDPI, vol. 9(3), pages 1-15, August.
    11. Productivity Commission, 2009. "Public and Private Hospitals," Research Reports, Productivity Commission, Government of Australia, number 37.

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