×

Robust efficiency analysis of public hospitals in Queensland, Australia. (English) Zbl 07645403

Daouia, Abdelaati (ed.) et al., Advances in contemporary statistics and econometrics. Festschrift in honor of Christine Thomas-Agnan. Cham: Springer. 221-242 (2021).
Summary: In this study, we utilize various approaches for efficiency analysis to explore the state of efficiency of public hospitals in Queensland, Australia, in the year 2016/17. Besides the traditional nonparametric approaches like DEA and FDH, we also use a more recent and very promising robust approach-order-\( \alpha\) quantile frontier estimators (Aragon et al. 2005). Upon obtaining the individual estimates from various approaches, we also analyze performance on a more aggregate level – the level of Local Hospital Networks by using an aggregate efficiency measure constructed from the estimated individual efficiency scores. Our analysis suggests that the relatively low efficiency of some Local Hospital Networks in Queensland can be partially explained by the fact that the majority of their hospitals are small and located in remote areas.
For the entire collection see [Zbl 1498.62004].

MSC:

62P20 Applications of statistics to economics
Full Text: DOI

References:

[1] Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 64(6), 1263-1297. https://doi. org/10.1016/0304-4076(77)90052-5. · Zbl 0366.90026 · doi:10.1016/0304-4076(77)90052-5
[2] Aragon, Y., Daouia, A., & Thomas-Agnan, C. (2005). Nonparametric frontier estimation: A con-ditional quantile-based approach. Econometric Theory, 21(2), 358-389. https://doi.org/10.1017/ S0266466605050206. · Zbl 1062.62252 · doi:10.1017/S0266466605050206
[3] Australian Institute of Health and Welfare. (2015). Australian hospital peer groups (tech. rep. No. 66). Australian Institute of Health and Welfare. Canberra, ACT. https://www.aihw.gov.au/ getmedia/79e7d756-7cfe-49bf-b8c0-0bbb0daa2430/14825.pdf.aspx?inline=true.
[4] Australian Institute of Health and Welfare. (2018). Health expenditure Australia 2016-17 (tech. rep.No. 64). Australian Institute of Health and Welfare. Canberra, ACT. https://www.aihw.gov. au/getmedia/e8d37b7d-2b52-4662-a85f-01eb176f6844/aihw-hwe-74.pdf.aspx?inline=true.
[5] Australian Institute of Health and Welfare. (2019). Hospital resources 2017-18: Australian hospital statistics (tech. rep. No. 233). Australian Institute of Health and Welfare. Canberra, ACT. https:// www.aihw.gov.au/reports/hospitals/hospital-resources-2017-18-ahs/contents/summary.
[6] Badin, L., Daraio, C., & Simar, L. (2012). How to measure the impact of environmental factors in a nonparametric production model. European Journal of Operational Research, 223(3), 818-833. https://doi.org/10.1016/j.ejor.2012.06.028. · Zbl 1292.62149 · doi:10.1016/j.ejor.2012.06.028
[7] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi. org/10.1287/mnsc.30.9.1078. · Zbl 0552.90055 · doi:10.1287/mnsc.30.9.1078
[8] Besstremyannaya, G. (2013). The impact of Japanese hospital financing reform on hospital effi-ciency: A difference-in-difference approach. The Japanese Economic Review, 64(3), 337-362. https://doi.org/10.1111/j.1468-5876.2012.00585.x. · doi:10.1111/j.1468-5876.2012.00585.x
[9] Bogetoft, P., & Otto, L. (2019). Benchmarking: Benchmark and frontier analysis using DEA and SFA. R package version, 28. https://cran.r-project.org/web/packages/Benchmarking.
[10] Cazals, C., Florens, J.-P., & Simar, L. (2002). Nonparametric frontier estimation: Arobust approach. Journal of Econometrics, 106(1), 1-25. https://doi.org/10.1016/S0304-4076(01)00080-X. · Zbl 1051.62116 · doi:10.1016/S0304-4076(01)00080-X
[11] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8. · Zbl 0416.90080 · doi:10.1016/0377-2217(78)90138-8
[12] Chowdhury, H., & Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega, 63, 111-122. https://doi.org/10.1016/j.omega.2015.10. 007. · doi:10.1016/j.omega.2015.10.007
[13] Chowdhury, H., Zelenyuk, V., Laporte, A., & Wodchis, W. P. (2014). Analysis of productivity, efficiency and technological changes in hospital services in Ontario: How does case-mix matter? International Journal of Production Economics, 150, 74-82. https://doi.org/10.1016/j.pe.2013. 12.003. · doi:10.1016/j.pe.2013.12.003
[14] Clement, J. P., Valdmanis, V. G., Bazzoli, G. J., Zhao, M., & Chukmaitov, A. (2008). Is more better? an analysis of hospital outcomes and efficiency with a DEA model of output congestion. Health Care Management Science, 11(1), 67-77. https://doi.org/10.1007/s10729-007-9025-8. Council of Australian Governments. (2011). National health reform agreement. http://www. federalfinancialrelations.gov.au/content/npa/health/_archive/national-agreement.pdf. · doi:10.1007/s10729-007-9025-8
[15] Daouia, A., & Laurent, T. (2013). Frontiles: Partial frontier efficiency analysis. R package version, 1, 2. https://cran.r-project.org/web/packages/frontiles.
[16] Daouia, A., & Simar, L. (2007). Nonparametric efficiency analysis: A multivariate conditional quantile approach. Journal of Econometrics, 140(2), 375-400. https://doi.org/10.1016/j.jeconom. 2006.07.002. · Zbl 1247.91133 · doi:10.1016/j.jeconom.2006.07.002
[17] Daraio, C., & Simar, L. (2007). Economies of scale, scope and experience in the italian motorvehicle sector. In Daraio, C., & Simar, L. (eds.), Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications (pp. 135-165). Springer Science & Business Media. https://doi.org/10.1007/978-0-387-35231-2_6. · Zbl 1149.91003 · doi:10.1007/978-0-387-35231-2_6
[18] Daraio, C., Simar, L., & Wilson, P. W. (2018). Central limit theorems for conditional efficiency mea-sures and tests of the “separability” condition in non-parametric, two-stage models of production. The Econometrics Journal, 21(2), 170-191. https://doi.org/10.1111/ectj.12103. · Zbl 07547390 · doi:10.1111/ectj.12103
[19] Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor efficiency in post offices. In M. G. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurements (pp. 243-267). North-Holland: Amsterdam.
[20] Färe, R., Grosskopf, S., & Logan, J. (1983). The relative efficiency of Illinois electric utilities. Resources and Energy, 5(4), 349-367. https://doi.org/10.1016/0165-0572(83)90033-6. · doi:10.1016/0165-0572(83)90033-6
[21] Färe, R.,& Zelenyuk, V., (2003). On aggregate Farrell efficiencies. European Journal of Operational Research, 146(3), 615-620. https://doi.org/10.1016/S0377-2217(02)00259-X. · Zbl 1037.90516 · doi:10.1016/S0377-2217(02)00259-X
[22] Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290. https://doi.org/10.2307/2343100. · doi:10.2307/2343100
[23] Grosskopf, S., Nguyen, B. H., Yong, J., & Zelenyuk, V. (2020). Healthcare structural reform and the performance of public hospitals: The case of Queensland, Australia [In Progress].
[24] Hollingsworth, B. (2008). The measurement of efficiency and productivity of health care delivery. Health Economics, 17(10), 1107-1128. https://doi.org/10.1002/hec.1391. · doi:10.1002/hec.1391
[25] Hu, H. H., Qi, Q., & Yang, C. H. (2012). Evaluation of China’s regional hospital efficiency: DEA approach with undesirable output. Journal of the Operational Research Society, 63(6), 715-725. https://doi.org/10.1057/jors.2011.77. · doi:10.1057/jors.2011.77
[26] Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14, 783-793. https://doi.org/ 10.1017/S0266466698146042. · doi:10.1017/S0266466698146042
[27] Kneip, A., Simar, L., & Wilson, P. W. (2008). Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory, 24(6), 1663-1697. https:// doi.org/10.1017/S0266466608080651. · Zbl 1231.62077 · doi:10.1017/S0266466608080651
[28] Kohl, S., Schoenfelder, J., & Fugener, A.,& Brunner, J. O., (2019). The use of data envelopment analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 22(2), 245-286. https://doi.org/10.1007/s10729-018-9436-8. · doi:10.1007/s10729-018-9436-8
[29] Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435-444. https://doi.org/ 10.2307/2525757. · Zbl 0366.90025 · doi:10.2307/2525757
[30] Munson, F. C., & Zuckerman, H. S. (1983). The managerial role. In Shortell, S. M., & Kaluzny, A. D. (eds.), Health care management: A text in organization theory and behavior (pp. 48-58). Wiley.
[31] Nguyen, B. H., & Zelenyuk, V. (2021). Aggregate efficiency of industry and its groups: The case of Queensland public hospitals. Empirical Economics. forthcoming. https://doi.org/10.1007/ s00181-020-01994-1. · doi:10.1007/s00181-020-01994-1
[32] O’Neill, L., Rauner, M., Heidenberger, K., & Kraus, M. (2008). A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Socio-Economic Planning Sciences, 42(3), 158-189. https://doi.org/10.1016/j.seps.2007.03.001. · doi:10.1016/j.seps.2007.03.001
[33] Park, B. U., Jeong, S.-O., & Simar, L. (2010). Asymptotic distribution of conical-hull estimators of directional edges. The Annals of Statistics, 38(3), 1320-1340. https://doi.org/10.1214/09-AOS746. · Zbl 1189.62055 · doi:10.1214/09-AOS746
[34] Park, B. U., Simar, L., & Weiner, C. (2000). FDH efficiency scores from a stochastic point of view. Econometric Theory, 16, 855-877. https://doi.org/10.1017/S0266466600166034. · Zbl 0967.62102 · doi:10.1017/S0266466600166034
[35] Parmeter, C. F., & Zelenyuk, V. (2019). Combining the virtues of stochastic frontier and data envelopment analysis. Operations Research, 67(6), 1628-1658. https://doi.org/10.1287/opre. 2018.1831. · Zbl 1456.62054 · doi:10.1287/opre.2018.1831
[36] Paul, C. J. M. (2002). Productive structure and efficiency of public hospitals. In Fox, K. J. (ed.), Efficiency in the public sector (pp. 219-248). Boston, MA: Springer. https://doi.org/10.1007/ 978-1-4757-3592-5_9. · doi:10.1007/978-1-4757-3592-5_9
[37] Productivity Commission. (2010). Public and private hospital: Multivariate analysis (tech. rep. Supplement to Research Report). Productivity Commission. Canberra, ACT66. https://www.pc. gov.au/inquiries/completed/hospitals/supplement/supplement.pdf.
[38] Queensland Health. (2016). Health funding principles and guidelines 2016-17 financial year.
[39] R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
[40] Shephard, R. W. (1953). Cost and production functions. Princeton University Press. · Zbl 0052.15901
[41] Shephard, R. W. (1970). Theory of cost and production functions. Princeton University Press. Sickles, R., & Zelenyuk, V. (2019). Measurement of productivity and efficiency. Cambridge: Cam-bridge University Press. https://doi.org/10.1017/9781139565981. · doi:10.1017/9781139565981
[42] Simar, L. (2007). How to improve the performances of DEA/FDH estimators in the presence of noise? Journal of Productivity Analysis, 28(3), 183-201. https://doi.org/10.1007/s11123-007-0057-3. · doi:10.1007/s11123-007-0057-3
[43] Simar, L., Van Keilegom, I., & Zelenyuk, V. (2017). Nonparametric least squares methods for stochastic frontier models. Journal of Productivity Analysis, 47(3), 189-204. https://doi.org/10. 1007/s11123-016-0474-2. · doi:10.1007/s11123-016-0474-2
[44] Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31-64. https://doi.org/10.016/j.jeconom. 2005.07.009. · Zbl 1418.62535
[45] Simar, L., & Wilson, P.W., (2011). Two-stage DEA: Caveat emptor. Journal of Productivity Analysis, 36(2), 205-218. https://doi.org/10.1007/s11123-011-0230-6. · doi:10.1007/s11123-011-0230-6
[46] Simar, L., & Wilson, P. W. (2013). Estimation and inference in nonparametric frontier models: Recent developments and perspectives. Foundations and Trends® in Econometrics, 5(3-4), 183-337. https://doi.org/10.1561/0800000020. · Zbl 1281.62243 · doi:10.1561/0800000020
[47] Simar, L., & Wilson, P. W. (2015). Statistical approaches for non-parametric frontier models: A guided tour. International Statistical Review, 83(1), 77-110. https://doi.org/10.1111/insr.12056. · Zbl 07762797 · doi:10.1111/insr.12056
[48] Simar, L., & Wilson, P. W. (2020). Hypothesis testing in nonparametric models of production using multiple sample splits. Journal of Productivity Analysis, 53(3), 287-303. https://doi.org/10.1007/ s11123-020-00574-w. · doi:10.1007/s11123-020-00574-w
[49] Simar, L., & Zelenyuk, V. (2007). Statistical inference for aggregates of Farrell-type efficiencies. Journal of Applied Econometrics, 22(7), 1367-1394. https://doi.org/10.1002/jae.991. · doi:10.1002/jae.991
[50] Simar, L., & Zelenyuk, V. (2011). Stochastic FDH/DEA estimators for frontier analysis. Journal of Productivity Analysis, 36(1), 1-20. https://doi.org/10.1007/s11123-010-0170-6. · doi:10.1007/s11123-010-0170-6
[51] Simar, L., & Zelenyuk, V. (2018). Central limit theorems for aggregate efficiency. Operations Research, 66(1), 137-149. https://doi.org/10.1287/opre.2017.1655. · Zbl 1457.91211 · doi:10.1287/opre.2017.1655
[52] Weisgrau, S. (1995). Issues in rural health: Access, hospitals, and reform. Health care Financing Review, 17(1), 1-14.
[53] Zelenyuk, V. (2020). Aggregation of inputs and outputs prior to data envelopment analysis under big data. European Journal of Operational Research, 282(1), 172-187. https://doi.org/10.1016/ j.ejor.2019.08.007. · Zbl 1430.90418 · doi:10.1016/j.ejor.2019.08.007
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.