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Regression analysis of country effects using multilevel data: a cautionary tale

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  • Bryan, Mark L.
  • Jenkins, Stephen P.

Abstract

Cross-national differences in outcomes are often analysed using regression analysis of multilevel country datasets, examples of which include the ECHP, ESS, EU-SILC, EVS, ISSP, and SHARE. We review the regression methods applicable to this data structure, pointing out problems with the assessment of country-level factors that appear not to be widely appreciated, and illustrate our arguments using Monte-Carlo simulations and analysis of womens employment probabilities and work hours using EU SILC data. With large sample sizes of individuals within each country but a small number of countries, analysts can reliably estimate individual-level effects within each country but estimates of parameters summarising country effects are likely to be unreliable. Multilevel (hierarchical) modelling methods are commonly used in this context but they are no panacea.

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Paper provided by Institute for Social and Economic Research in its series ISER Working Paper Series with number 2013-14.

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Date of creation: 19 Aug 2013
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Publication status: published
Handle: RePEc:ese:iserwp:2013-14

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  1. Germán Rodríguez & Noreen Goldman, 2001. "Improved estimation procedures for multilevel models with binary response: a case-study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 339-355.
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  1. Regression Analysis of Country Effects Using Multilevel Data: A Cautionary Tale
    by maximorossi in NEP-LTV blog on 2013-09-25 12:20:31
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Cited by:
  1. Olympia Bover & Jose Maria Casado & Sonia Costa & Philip Du Caju & Yvonne McCarthy & Eva Sierminska & Panagiota Tzamourani & Ernesto Villanueva & Tibor Zavadil, 2013. "The distribution of debt across euro area countries: The role of individual characteristics, institutions and credit conditions," Working Paper Research 252, National Bank of Belgium.

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