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

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

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 women’s 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.

Suggested Citation

  • L. Bryan, Mark & P. Jenkins, Stephen, 2013. "Regression analysis of country effects using multilevel data: a cautionary tale," ISER Working Paper Series 2013-14, Institute for Social and Economic Research.
    • Handle: RePEc:ese:iserwp:2013-14
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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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