Income Risk Asymmetries over Argentina’s Business Cycle
DOI:
https://doi.org/10.4067/S0718-88702024000100003Keywords:
Argentina, asymmetries, business cycles, collective bargaining, formal workers, income risk, unions, quantile regressionAbstract
This paper explores the aggregate income risk of formal workers in Argentina, using a longitudinal database that contains information on approximately half a million formal employees in the private sector for a span of twenty years. We estimate quantile regression models to measure the sensitivity of real wages to the business cycle along the conditional and unconditional labor earnings distribution, thus capturing the asymmetry of aggregate economic impacts on wages. The main result is that income risk decreases along the conditional and unconditional labor earnings distribution, showing that individuals located at the lower part of the distribution are more exposed to the fortunes of the aggregate economy. In addition, low-income individuals suffer a stronger fall in wages when economy declines than the increase that their experiment when business cycle is in its expansion phase, which, in a very volatile economy like Argentina, implies a deterioration over time of their remuneration.
Downloads
References
Abrevaya, J., & Dahl, C. M. (2008). The effects of birth inputs on birthweight: Evidence from quantile estimation on panel data. Journal of Business and Economic Statistics, 26(4), 379–397. https://doi.org/10.1198/073500107000000269
Alejo, J., & Casanova, L. (2016). Negociación colectiva y cambios distributivos en los ingresos laborales en Argentina [Collective bargaining and distributive changes in labor income in Argentina]. Revista de Economía Política de Buenos Aires, 15, 65–97.
Alejo, J., Favata, F., Montes-Rojas, G., & Trombetta, M. (2021). Conditional vs unconditional quantile regression models: A guide to practitioners. Economía, 44(88), 76–93. https://doi.org/10.18800/economia.202102.004
Beccaria, L., Fernandez, A. L., & Trajtemberg, D. (2020). Reducción de la desigualdad de las remuneraciones e instituciones en Argentina (2002-2015) [Reduction of wage inequality and institutions in Argentina (2002-2015)]. Cuadernos de Economía, 39(81), 731–763. https://doi.org/10.15446/cuad.econ.v39n81.76672
Beccaria, L., Maurizio, R., & Vázquez, G. (2015). Recent decline in wage inequality and formalization of the labour market in Argentina. International Review of Applied Economics, 29(5), 677–700. https://doi.org/10.1080/02692171.2015.1054369
Bell, B., Bloom, N., Blundell, J., & Pistaferri, L. (2020). Prepare for large wage cuts if you are younger and work in a small firm. https://voxeu.org/article/prepare-large-wage-cuts-if-you-are-younger-and-work-small-firm
Blanco, A., Diaz de Astarloa, B., Drenik, A., Moser, C., & Trupkin, D. (2021). The evolution of the earnings distribution in a volatile economy: Evidence from Argentina (Documento de Trabajo N.º 280). CEDLAS. http://sedici.unlp.edu.ar/handle/10915/119596
Broer, T., Kramer, J., & Mitman, K. (2020). The curious incidence of shocks along the income distribution (Working Paper). http://perseus.iies.su.se/~tbroe/Incidence_and_MP-1.pdf
Canay, I. A. (2011). A simple approach to quantile regression for panel data. Econometrics Journal, 14(3), 368–386.
Card, D. (1996). The effect of unions on the structure of wages: A longitudinal analysis. Econometrica, 64(4), 957–979. https://doi.org/10.2307/2171852
Card, D., Lemieux, T., & Riddell, W. C. (2004). Unions and wage inequality. Journal of Labor Research, 25(4), 519–559.
DiNardo, J., Fortin, N. M., & Lemieux, T. (1996). Labor market institutions and the wage distribution, 1973-1992: A semiparametric approach. Econometrica, 64(5), 1001–1044. https://doi.org/10.2307/2171954
Etchemendy, S., & Berins Collier, R. (2008). Golpeados pero de pie: Resurgimiento sindical y neocorporativismo segmentado en Argentina (2003-2007) [Down but not out: Union resurgence and segmented neocorporatism in Argentina (2003-2007)]. POSTData, (13), 145–192.
Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica, 77(3), 953–973.
Freeman, R. B. (1980). Unionism and the dispersion of wages. ILR Review, 34(1), 3–23. https://doi.org/10.1177/001979398003400101
Galvao, A. F. (2011). Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics, 164(1), 142–157. https://doi.org/10.1016/j.jeconom.2011.02.016
Galvao, A. F., & Kato, K. (2016). Smoothed quantile regression for panel data. Journal of Econometrics, 193(1), 92–112. https://doi.org/10.1016/j.jeconom.2016.01.008
Galvao, A. F., & Montes-Rojas, G. V. (2017). Multi-dimensional panels in quantile regression models. In L. Matyas (Ed.), The econometrics of multi-dimensional panels: Theory and applications (pp. 239–261). Springer Cham. https://doi.org/10.1007/978-3-319-60783-2_8
Galvao, A. F., & Wang, L. (2015). Efficient minimum distance estimator for quantile regression fixed effects panel data. Journal of Multivariate Analysis, 133, 1–26. https://doi.org/10.1016/j.jmva.2014.08.007
Günther, I., & Launov, A. (2012). Informal employment in developing countries: Opportunity or last resort? Journal of Development Economics, 97(1), 88–98.
Guvenen, F., Ozkan, S., & Song, J. (2014). The nature of countercyclical income risk. Journal of Political Economy, 122(3), 621–660.
Guvenen, F., Schulhofer-Wohl, S., Song, J., & Yogo, M. (2017). Worker betas: Five facts about systematic earnings risk. American Economic Review: Papers and Proceedings, 107(5), 398–403. https://doi.org/10.1257/aer.p20171094
Hampel, F. R. (1968). Contributions to the theory of robust estimation [Tesis doctoral]. University of California, Berkeley.
Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346), 383–393. https://doi.org/10.1080/01621459.1974.10482962
Harris, J. R., & Todaro, M. P. (1970). Migration, unemployment and development: A two-sector analysis. The American Economic Review, 60(1), 126–142.
Huber, P. J., & Ronchetti, E. M. (2009). Robust statistics. Wiley.
Kato, K., Galvao, A. F., & Montes-Rojas, G. V. (2012). Asymptotics for panel quantile regression models with individual effects. Journal of Econometrics, 170(1), 76–91. https://doi.org/10.1016/j.jeconom.2012.02.007
Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89. https://doi.org/10.1016/j.jmva.2004.05.006
Koenker, R. (2005). Quantile regression. Cambridge University Press.
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
Lamarche, C. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157(2), 396–408. https://doi.org/10.1016/j.jeconom.2010.03.042
Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413. https://doi.org/10.1016/s0304-4076(99)00044-5
Lombardo, C., & Martinez Correa, J. (2019). Convenio colectivo, sindicatos y dispersión salarial: Evidencia de Argentina [Collective agreements, unions and wage dispersion: Evidence from Argentina] [Ponencia]. LIV Reunión Anual de la Asociación Argentina de Economía Política. https://aaep.org.ar/anales/works/works2019/lombardo.pdf
Machado, J. F., & Santos Silva, J. C. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145–173.
Maloney, W. F. (1999). Does informality imply segmentation in urban labor markets? Evidence from sectoral transitions in Mexico. The World Bank Economic Review, 13(2), 275–302.
Maloney, W. F. (2004). Informality revisited. World Development, 32(7), 1159–1178.
Neyman, J., & Scott, E. (1948). Consistent estimates based on partially consistent observations. Econometrica, 16(1), 1–32. https://doi.org/10.2307/1914288
Palomino, H., & Trajtemberg, D. (2006). Una nueva dinámica de las relaciones laborales y la negociación colectiva en la Argentina [A new dynamic of labor relations and collective bargaining in Argentina]. Revista de Trabajo, 2(3), 47–68.
Parente, P. M., & Santos Silva, J. M. (2015). Quantile regression with clustered data. Journal of Econometric Methods, 5(1), 1–15. https://doi.org/10.1515/jem-2014-0011
Parker, J. A., & Vissing-Jørgensen, A. (2009). Who bears aggregate fluctuations and how? American Economic Review: Papers and Proceedings, 99(2), 399–405.
Parker, J. A., & Vissing-Jørgensen, A. (2010). The increase in income cyclicality of high-income households and its relation to the rise in top income shares (NBER Working Paper N.º 16577). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w16577/w16577.pdf
Paz, J. A. (2007). Retornos laborales a la educación en la Argentina: Evolución y estructura actual [Labor returns to education in Argentina: Evolution and current structure] (Serie Documentos de Trabajo). Universidad del CEMA. https://ucema.edu.ar/publicaciones/download/documentos/355.pdf
Rios-Avila, F. (2020). Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition. The Stata Journal, 20(1), 51–94. https://doi.org/10.1177/1536867x20909690
Salvia, A. (2002). La estructura social del trabajo en Argentina: Desempleo, subempleo y precariedad laboral [The social structure of work in Argentina: Unemployment, underemployment and labor precariousness] (Documento de Investigación AE/Notas/SL01). Universidad Católica Argentina.
Scanlon, P. (2020). Aggregate risk and wage dispersion. Economics Letters, 194, 1–4. https://doi.org/10.1016/j.econlet.2020.109366
Stiglitz, J. E. (1976). The efficiency wage hypothesis, surplus labour, and the distribution of income in LDCs. Oxford Economic Papers, 28(2), 185–207. https://doi.org/10.1093/oxfordjournals.oep.a041340
Trajtemberg, D. (2009). El impacto de la determinación colectiva de salarios sobre la dispersión salarial [The impact of collective wage setting on wage dispersion]. Trabajo, Ocupación y Empleo. Serie Estudios Laborales, 8, 123–148.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jorge Eduardo Camusso, Ana Inés Navarro

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.
- 150
- DownloadsTimes the full text of this article (usually the PDF) was opened or downloaded.
- 153
All time, as recorded by the journal.
Among the top 12% by views · last 2 yearsCompared with all the journal's articles by views in the last 2 years, this one is among the top 12%.
Last 2 years: 117 views, 108 downloads.
1.02 downloads per view (journal average 1.65).Downloads of the full text for every abstract view of this article, compared with the whole journal.
Explore the journal's metrics
