Estimates of the level of inequality of opportunity have traditionally been proposed as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence of sampling variance. The magnitude of the latter distortion depends on both the empirical strategy used and the observed sample. We suggest that, although neglected in empirical contributions, the upward bias may be significant and challenge the interpretation of inequality of opportunity estimates as lower bounds. We propose a simple criterion to select the best specification that balances the two sources of bias. Our method is based on cross-validation and can easily be implemented with survey data. To show how this method can improve the reliability of inequality of opportunity measurement, we provide an empirical illustration based on income data from 31 European countries. Our evidence shows that estimates of inequality of opportunity are sensitive to model selection. Alternative specifications lead to significant differences in the absolute level of inequality of opportunity and to the re-ranking of a number of countries, which confirms the need for an objective criterion to select the best econometric model when measuring inequality of opportunity.

Upward and downward bias when measuring inequality of opportunity

Brunori, Paolo
;
Peragine, Vito;Serlenga, Laura
2018

Abstract

Estimates of the level of inequality of opportunity have traditionally been proposed as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence of sampling variance. The magnitude of the latter distortion depends on both the empirical strategy used and the observed sample. We suggest that, although neglected in empirical contributions, the upward bias may be significant and challenge the interpretation of inequality of opportunity estimates as lower bounds. We propose a simple criterion to select the best specification that balances the two sources of bias. Our method is based on cross-validation and can easily be implemented with survey data. To show how this method can improve the reliability of inequality of opportunity measurement, we provide an empirical illustration based on income data from 31 European countries. Our evidence shows that estimates of inequality of opportunity are sensitive to model selection. Alternative specifications lead to significant differences in the absolute level of inequality of opportunity and to the re-ranking of a number of countries, which confirms the need for an objective criterion to select the best econometric model when measuring inequality of opportunity.
File in questo prodotto:
File Dimensione Formato  
SCW_2018.pdf

accesso aperto

Tipologia: Documento in Versione Editoriale
Licenza: Creative commons
Dimensione 625.83 kB
Formato Adobe PDF
625.83 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/227473
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 18
  • ???jsp.display-item.citation.isi??? ND
social impact