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Mismeasuring Long Run Growth. The Bias from Spliced National Accounts

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  • Prados de la Escosura, Leandro

Abstract

Comparisons of economic performance over space and time largely depend on how statistical evidence from national accounts and historical estimates are spliced. To allow for changes in relative prices, GDP benchmark years in national accounts are periodically replaced with new and more recent ones. Thus, a homogeneous long-run GDP series requires linking different temporal segments of national accounts. The choice of the splicing procedure may result in substantial differences in GDP levels and growth, particularly as an economy undergoes deep structural transformation. An inadequate splicing may result in a serious bias in the measurement of GDP levels and growth rates. Alternative splicing solutions are discussed in this paper for the particular case of Spain, a fast growing country in the second half of the twentieth century. It is concluded that the usual linking procedure, retropolation, has serious flows as it tends to bias GDP levels upwards and, consequently, to underestimate growth rates, especially for developing countries experiencing structural change. An alternative interpolation procedure is proposed.

Suggested Citation

  • Prados de la Escosura, Leandro, 2014. "Mismeasuring Long Run Growth. The Bias from Spliced National Accounts," CEPR Discussion Papers 10137, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10137
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Where is the growth?
      by tambo0986 in NEP-HIS blog on 2015-11-23 23:21:08

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    Cited by:

    1. Olivier CADOT & Jaime de MELO & Patrick PLANE & Laurent WAGNER & Martha TESFAYE WOLDEMICHAEL, 2017. "L’Afrique subsaharienne peut-elle se développer sans usines ?," Working Paper 084c8bee-b301-4412-8ca4-c, Agence française de développement.
    2. Olivier Cadot & Jaime de Melo & Patrick Plane & Laurent Wagner & Martha Tesfaye Woldemichael, 2016. "Industrialisation et transformation structurelle : l’Afrique subsaharienne peut-elle se développer sans usines ?," Revue d’économie du développement, De Boeck Université, vol. 24(2), pages 19-49.
    3. Abildgren, Kim, 2014. "Far out in the tails – The historical distributions of macro-financial risk factors in Denmark," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2014(1), pages 1-31.
    4. André A. Hofman & Patricio Valderrama, 2021. "Long Run Economic Growth Performance In Latin America – 1820–2016," Journal of Economic Surveys, Wiley Blackwell, vol. 35(3), pages 833-869, July.

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    Keywords

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    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • N13 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - Europe: Pre-1913
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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