Regression Analysis of Country Effects Using Multilevel Data: A Cautionary Tale
Mark Bryan () and
Stephen Jenkins ()
No 7583, IZA Discussion Papers from Institute for the Study of Labor (IZA)
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.
Keywords: multilevel modelling; cross-national comparisons; country effects (search for similar items in EconPapers)
JEL-codes: C52 C81 O57 (search for similar items in EconPapers)
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Published in European Sociological Review, 32 (1), February 2016, 3–22, doi: 10.1093/esr/jcv059
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Working Paper: Regression analysis of country effects using multilevel data: a cautionary tale (2013)
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