DaCHE discussion papers,
University of Southern Denmark, Dache - Danish Centre for Health Economics

Abstract: Much research in health economics revolves around the analysis of hierarchically structured data. For instance, combining characteristics of patients with information pertaining to the general practice (GP) clinic providing treatment is called for in order to investigate important features of the underlying nested structure. In this paper we offer a new treatment of the two-level random-intercept model and state equivalence results for specific estimators, including popular two-step estimators. We show that a certain encompassing regression equation, based on a Mundlak-type specification, provides a surprisingly simple approach to efficient estimation and a straightforward way to assess the assumptions required. As an illustration, we combine unique information on the morbidity of Danish type 2 diabetes patients with information about GP clinics to investigate the association with fee-for-service healthcare expenditure. Our approach allows us to conclude that explanatory power is mainly provided by patient information and patient mix, whereas (possibly unobserved) clinic characteristics seem to play a minor role.

Keywords: Multilevel models; random intercepts; nested models; Mundlak device; correlated random effects; 2-step estimation; estimated dependent variables; fee-for-service expenditures; type 2 diabetes

JEL-codes: C01; C18; C38; H51; I18

30 pages, June 20, 2013

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