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International Benchmarking for Country Economic Diagnostics : A Stochastic Frontier Approach

Author

Listed:
  • Kumbhakar,Subal C.
  • Loayza,Norman V.
  • Norambuena,Vivian
Abstract
This paper discusses and illustrates the analytical foundations of international comparisons (or benchmarking) for assessing a country's potential for improvement along various dimensions of social and economic development. By providing a methodology for international benchmarking, discussing various alternatives and choices, and presenting a cross-country illustration, the paper can help practitioners be less arbitrary and more systematic in their approach to international comparisons, as well as more realistic in their expectations for a country's improvement. The paper presents the stochastic frontier approach and applies it to estimate feasible frontiers or benchmarks for each variable, country, and year. It then interprets a country's (one-sided) departure from the benchmark as inefficiency or potential for improvement. This contrasts with the literature that compares countries by looking at raw variables or indicators, without considering that countries differ in structural endowments that constrain the maximum performance that a country could achieve in a policy-relevant horizon. The Stochastic Frontier approach also improves upon the literature that uses regression residuals to measure performance. Regression residuals are hard to interpret as inefficiency, because they are mixed with noise and take positive and negative values. As an illustration, the paper uses a panel of 142 countries with yearly data for 2005-14 and considers a set of 10 development indicators. It finds that the potential for improvement does not follow a simple relationship with economic development, with some lower-income countries being closer to their own feasible frontier than more advanced countries are.

Suggested Citation

  • Kumbhakar,Subal C. & Loayza,Norman V. & Norambuena,Vivian, 2020. "International Benchmarking for Country Economic Diagnostics : A Stochastic Frontier Approach," Policy Research Working Paper Series 9304, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9304
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    References listed on IDEAS

    as
    1. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    2. Ricardo Hausmann & Bailey Klinger, 2008. "Growth Diagnostics: Perú," Research Department Publications 2005, Inter-American Development Bank, Research Department.
    3. Ricardo Hausmann & Bailey Klinger, 2008. "Growth Diagnostics: Perú," Research Department Publications 2005, Inter-American Development Bank, Research Department.
    4. Kayser, Mark Andreas & Peress, Michael, 2012. "Benchmarking across Borders: Electoral Accountability and the Necessity of Comparison," American Political Science Review, Cambridge University Press, vol. 106(3), pages 661-684, August.
    5. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    6. Norman Loayza & Pablo Fajnzylber & César Calderón, 2005. "Economic Growth in Latin America and the Caribbean : Stylized Facts, Explanations, and Forecasts," World Bank Publications - Books, The World Bank Group, number 7315.
    7. Ricardo Hausmann & Bailey Klinger & Rodrigo Wagner, 2008. "Doing Growth Diagnostics in Practice: A 'Mindbook'," CID Working Papers 177, Center for International Development at Harvard University.
    8. Ricardo Hausmann & Bailey Klinger, 2008. "Growth Diagnostics: Perú," Research Department Publications 2005, Inter-American Development Bank, Research Department.
    9. Subal C. Kumbhakar & Gudbrand Lien, 2017. "Yardstick Regulation of Electricity Distribution Disentangling Short-run and Long-run Inefficiencies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    10. Bogetoft, Peter & Heinesen, Eskil & Tranæs, Torben, 2015. "The efficiency of educational production: A comparison of the Nordic countries with other OECD countries," Economic Modelling, Elsevier, vol. 50(C), pages 310-321.
    11. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107609464.
    12. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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    14. Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Panel data stochastic frontier model with determinants of persistent and transient inefficiency," European Journal of Operational Research, Elsevier, vol. 271(2), pages 746-755.
    15. Badunenko, Oleg & Kumbhakar, Subal C., 2017. "Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter?," European Journal of Operational Research, Elsevier, vol. 260(2), pages 789-803.
    16. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
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    Keywords

    Health Care Services Industry; Financial Sector Policy; Inequality; International Trade and Trade Rules; Health Service Management and Delivery;
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