The Distributive Effects of Education: an Unconditional Quantile Regression Approach
DOI:
https://doi.org/10.4067/S0718-88702014000100003Keywords:
Unconditional quantile regression, income inequality, education, ArgentinaAbstract
We use recent unconditional quantile regression methods (UQR) to study the distributive effects of education in Argentina. Standard methods usually focus on mean effects, or explore distributive effects by either making stringent modeling assumptions, and/or through counter- factual decompositions that require several temporal observations. An empirical case shows the flexibility and usefulness of UQR methods. Our application for the case of Argentina shows that education contributed positively to increased inequality in Argentina, mostly due to the effect of strongly heterogeneous effects of education on earnings.Downloads
References
Alejo, J. (2006). Desigualdad salarial en el Gran Buenos Aires: Una aplicación de regresión por cuantiles en microdescomposiciones [Wage inequality in Greater Buenos Aires: An application of quantile regression in microdecompositions] (Documento de Trabajo No. 36). CEDLAS, Universidad Nacional de La Plata.
Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton University Press. https://doi.org/10.2307/j.ctvcm4j72
Behrman, J. R. (2011). How much might human capital policies affect earnings inequalities and poverty? Estudios de Economía, 38(1), 9–41.
Buchinsky, M. (1994). Changes in the U.S. wage structure 1963–1987: Application of quantile regression. Econometrica, 62(2), 405–458.
Bustelo, M. (2004). Caracterización de los cambios en la desigualdad y la pobreza en Argentina haciendo uso de técnicas de descomposiciones microeconométricas [Characterizing changes in inequality and poverty in Argentina using microeconometric decomposition techniques] (Documento de Trabajo No. 13). CEDLAS.
Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69(5), 1127–1160. https://doi.org/10.1111/1468-0262.00237
Firpo, S., Fortin, N., & Lemieux, T. (2007a). Decomposing wage distributions using recentered influence functions regressions [Unpublished manuscript]. University of British Columbia.
Firpo, S., Fortin, N., & Lemieux, T. (2007b). Unconditional quantile regressions (NBER Technical Working Paper No. 339). National Bureau of Economic Research.
Firpo, S., Fortin, N., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica, 77(3), 953–973.
Hungerford, T., & Solon, G. (1987). Sheepskin effects in the returns to education. The Review of Economics and Statistics, 69(1), 175–177. https://doi.org/10.2307/1937919
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643
Martins, P., & Pereira, P. (2004). Does education reduce wage inequality? Quantile regression evidence from 16 countries. Labour Economics, 11(3), 355–371. https://doi.org/10.1016/j.labeco.2003.05.003
Mata, J., & Machado, J. (2005). Counterfactual decomposition of changes in wage distributions using quantile regression. Journal of Applied Econometrics, 20(4), 445–465. https://doi.org/10.1002/jae.788
Monti, A. C. (1991). The study of the Gini concentration ratio by means of the influence function. Statistica, 51, 561–577.
Ruiz-Tagle, J. (2007). Forecasting wage inequality. Estudios de Economía, 34(1), 141–162.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Javier Alejo, Maria Florencia Gabrielli, Walter Sosa-Escudero

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Article usage
- ViewsTimes this article's page was opened since the journal started recording statistics. One person can visit more than once.
- 827
- DownloadsTimes the full text of this article (usually the PDF) was opened or downloaded.
- 1,044
All time, as recorded by the journal.
Among the top 13% by views · last 2 yearsCompared with all the journal's articles by views in the last 2 years, this one is among the top 13%.
Last 2 years: 112 views, 172 downloads.
CitationsTimes other scholarly works cited this article, according to OpenAlex and Crossref. The two indexes cover different sources, so their counts differ. : OpenAlex 24 · Crossref 11
1.26 downloads per view (journal average 1.66).Downloads of the full text for every abstract view of this article, compared with the whole journal.
Explore the journal's metrics
