Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/91694
Authors: 
Bryan, Mark L.
Jenkins, Stephen P.
Year of Publication: 
2013
Series/Report no.: 
ISER Working Paper Series 2013-14
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.
Subjects: 
multilevel modelling
cross-national comparisons
country effects
JEL: 
C52
C81
O57
Document Type: 
Working Paper

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