Regression Analysis of Country Effects Using Multilevel Data: A Cautionary Tale

Mark Bryan (m.l.bryan@sheffield.ac.uk) and Stephen Jenkins

No 7583, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: cross-national comparisons; multilevel modelling; country effects (search for similar items in EconPapers)
JEL-codes: C52 C81 O57 (search for similar items in EconPapers)
Pages: 81 pages
Date: 2013-08
New Economics Papers: this item is included in nep-ltv
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (60)

Published - published in: European Sociological Review, 2016, 32 (1), 3–22

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Working Paper: Regression analysis of country effects using multilevel data: a cautionary tale (2013)
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