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Assessing the quality of institutions' rankings obtained through multilevel linear regression models

Working paper n°: 19

Author(s): Bruno Arpino, Roberta Varriale

Year: 2009

The aim of this paper is to assess the quality of the ranking of institutions obtained with multilevel techniques in presence of different model misspecifications and data structures. Through a Monte Carlo simulation study, we find that it is quite hard to obtain a reliable ranking of the whole effectiveness distribution while, under various experimental conditions, it is possible to identify institutions with extreme performances. Ranking quality increases with increasing intra class correlation coefficient and/or overall sample size. Furthermore, multilevel models where the between and within cluster components of first-level covariates are distinguished perform significantly better than both multilevel models where the two effects are set to be equal and the fixed effect models.


Bruno Arpino

Universita Bocconi, Dondena Centre for Research on Social Dynamics

 

Roberta Varriale

Tilburg University, Department of Methodology and Statistics

 

 

Keywords: multilevel models, ranking of institutions, second-level residuals distribution

 

Download: The paper may be downloaded here.

 

A published version of this paper appears in Journal of Applied Economic Sciences:

Arpino B. and Varriale R. (2010) Assessing the quality of institutions' rankings obtained through multilevel linear regression models, Journal of Applied Economic Sciences, 5, 1(11), pp. 7-22.

The paper (pdf, 1.46MB) may be downloaded here.

Last updated 17 July 2015 - 09:21:18