MULTILEVEL MODELING FOR ECONOMISTS: WHY, WHEN AND HOW

Aleksey Oshchepkov and Anna Shirokanova ()

HSE Working papers from National Research University Higher School of Economics

Abstract: Multilevel modeling (MLM, also known as hierarchical linear modeling, HLM) is a methodological framework widely used in the social sciences to analyze data with a hierarchical structure, where lower units of aggregation are ‘nested’ in higher units, including longitudinal data. In economics, however, MLM is used very rarely. Instead, economists use separate econometric techniques including cluster-robust standard errors and fixed effects models. In this paper, we review the methodological literature and contrast the econometric techniques typically used in economics with the analysis of hierarchical data using MLM. Our review suggests that economic techniques are generally less convenient, flexible, and efficient compared to MLM. The important limitation of MLM, however, is its inability to deal with the omitted variable problem at the lowest level of data, while standard economic techniques may be complemented by quasi-experimental methods mitigating this problem. It is unlikely, though, that this limitation can explain and justify the rare use of MLM in economics. Overall, we conclude that MLM has been unreasonably ignored in economics, and we encourage economists to apply this framework by providing ‘when and how’ guidelines

Keywords: multilevel modeling; hierarchical linear modeling; mixed effects; random effects; fixed effects; random coefficients; clusterization of errors. (search for similar items in EconPapers)
JEL-codes: A12 C18 C33 C50 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2020
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in WP BRP Series: Economics / EC, June 2020, pages 1-46

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