Ahn, J. et al., 2019. Work in Progress: Improving Youth Labor Market Outcomes in Emerging Market and Developing Economies. IMF SDN 19/02.
Amarante, V., Arim, R. & Dean, A., 2012. The Effects of being out of the Labor Market on Subsequent Wages: Evidence from Uruguay, Uruguay: Instituto de Economia Serie Documentos de Trabajo DT 10/12.
Amarante, V., Arim, R. & Yapor, M., 2016. Decomposing Inequality Changes in Uruguay: The Role of Formalization in the Labor Market. IZA Journal of Labor and Development, 5(13).
Amarante, V. & Gómez, M., 2016. El proceso de formalización en el mercado laboral uruguayo, Montevideo: Comisión Económica para América Latina y el Caribe (CEPAL). Series de Estudios y Perspectivas N°20.
Antman, F. & McKenzie, D., 2007. Poverty Traps and Nonlinear Income Dynamics with Measurement Error and Individual Heterogeneity. The Journal of Development Studies, Volume 43, pp. 1057–1083.
Baneriji, A., Saksonovs, S., Lin, H. & Blavy, R., 2014. Youth Unemployment in Advanced Economies in Europe: Searching for Solutions. IMF Staff Discussion Note 14/11.
Bassanini, A. & Garnero, A., 2013. Dismissal Protection and Worker FLows in OECD countries: Evidence from cross-country/cross-industry Data. Labor Economics, Volume 21, pp. 25–41.
Bell, D. & Blanchflower, D., 2011. Young people and the Great Recession. Oxford Review of Economic Policy, 27(2), pp. 241–267.
Benigno, P. & Ricci, L., 2011. The Inflation-output trade-off with Downward Wage Rigidities. American Economic Review, 101(4), pp. 1436–66.
Blinder, A., 1973. Wage Discrimination: Reduced Form and Structural Estimates. The Journal of Human Resources, 8(4), pp. 436–455.
Brussevich, M., Dabla-Norris, E. & Khalid, S., 2020. Who will Bear the Brunt of Lockdown Policies? Evidence from Tele-workability Measures Across Countries, Washington D.C.: IMF Working Paper.
Calero, C., Mejalenko, J., Mitnik, O. & Ripani, L., 2018. Labor Market Trajectories in Latin America and the Caribbean: A Synthetic Panel Analysis, s.l.: Inter-American Development Bank.
Caliendo, M. & Schmidl, R., 2016. Youth Unemployment and Active Labor Market Policies in Europe. IZA Journal of Labor Policy, 5(1).
Card, D., Kluve, J. & Weber, A., 2010. Active Labor Market Policy Evaluations: A Meta-analysis. Economic Journal, 120(548), pp. 452–477.
David, A., Lambert, F. & Toscani, F., 2019. More Work to Do? Taking Stock of Latin American Labor Markets. IMF Working Paer 19/55.
Deaton, A., 1985. Panel Data from Time Series of Cross-sections. Journal of econoometrics, Volume 30, pp. 109–126.
Edmiston, K., 2007. The Role of Small and Large Businesses in Economic Development. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=993821
Fortin, N., Lemieux, T. & Firpo, S., 2011. Decomposition Methods in Economics. In: Handbook of Labor Economics. Amsterdam: Elsevier, pp. 1–102.
Gelbach, J., 2016. When do covariates matter? And which ones, and how much?. Journal of Labor Economics, 34(2), pp. 509–543.
Gracia-Rubiales, V., 2004. Unemployment in Spain: An Analysis of Labor Mobility and Young Adult Unemployment, s.l.: Stanford University.
Hanushek, E., Schwerdt, G., Woessmann, L. & Zhang, L., 2017. General education, vocational education, and labor-market outcomes over the lifecycle. Journal of, Volume 1, pp. 48–87.
Hijzen, A., Mondauto, L. & Scarpetta, S., 2017. The Impact of Employment Protection on Temporary Employment: Evidence from a Regression Discontinuity Design. Labor Economics, Volume 46, pp. 64–76.
IMF, 2006. “Uruguay: Staff Report for the 2006 Article IV Consultation, Fourth Review Under the Stand-By Arrangement, and Request for Waiver of Nonobservance and Modification of Performance Criteria, s.l.: IMF Country Report 06/425.
IMF, 2019. World Economic Oulook. Global Manufacturing Downturn, Rising Trade Barriers, Washington, D.C.: International Monetary Fund.
Isengard, B., 2003. Youth unemployment: Individual risk factors and institutional determinants. A case study of Germany and the United Kingdom. Journal of Youth Studies, 6(4), pp. 357–376.
Kahn, L., 2010. The long-term labor market consequences of graduating from college in a bad economy. Labour Economics, 17(2), pp. 303–316.
Kugler, A., 2019. Impacts of Labor Market Institutions and Demographic Factors on Labor Markets in Latin America. IMF Working Paper 19/155.
Levy, S. & Székely, M., 2016. ¿Más Escolaridad, Menos Informalidad? Un Análisis de Cohortes para México y América Latina. El Trimestre Económico, 83(4), pp. 499–548.
Loayza, N., 2020. Costs and Trade-Offs in the Fight against the COVID-19 Pandemic: A Developing Country Perspective, Washington D.C.: World Bank Group Research & Policy Brief No.35.
Oaxaca, R., 1973. Male-Female Wage Differentials in Urban Labor Markets. International Economic Review, 14(3), pp. 693–709.
O’Higgins, N., 2017. Rising to the Youth Employment Challenge: New Evidence on Key Policy Issues, Geneva: International Labor Organization.
Oreopoulos, P., von Wachter, T. & Heisz, A., 2012. The Short- and Long-Term Career Effects of Graduating in a Recession. Applied Economics, 4(1), pp. 1–29.
Pastore, F., 2015. The Youth Experience Gap: Explaining national Differences in the School-to-Work Transition. Heidelberg: Springer International Publishing AG.
Székely, M. & Karver, J., 2015. Out of School and Out of Work Youth in Latin America: A Cohort Approach. Washington D. C., World Bank.
The authors would like to thank S. Pelin Berkmen, Bas Bakker, Natasha Che, Frederik Toscani, Metodij Hadzi-Vaskov, Swarnali Hannan, and other IMF colleagues and participants in the Western Hemisphere seminar for insightful comments and advice. We also thank Luis Herrera Prada for excellent research assistance.
In fact, the unemployment rate has barely increased, also partly due to a fall in the labor participation rate, as discouraged workers stopped looking for jobs due to the pandemic. As the economy begins to reopen, the employment rate has started to gradually improve but remains well below its pre-pandemic level.
Similarly, (Calero, et al., 2018), build a synthetic panel for nine Latin American economies from 1990–2014 to explore labor market trajectories and earnings over the life cycle.
During the 2002 crisis the labor market rapidly deteriorated and unemployment grew to a record of 19.8 percent. At the same time, real wages fell by nearly 11 percent, due to the higher inflation and the government’s attempt to consolidate its fiscal position (Mazzuchi, 2009).
In the early 1960s Uruguay had already established a welfare state as in many OECD economies, including pensions, unemployment insurance and contributory health insurance (OECD, 2014).
Wage councils were implemented for the first time in the rural sector and for domestic workers in 2008.
Since wage councils had been suspended in 1992, firm-level dominated sector-level bargaining between 1992 and 2004. However, firm-level bargaining was not as effective in achieving consensus during collective bargaining, which led to a decline in negotiations.
Since the onset of the pandemic, the unemployment rate in Uruguay has increased to about 11 percent, although the full impact of the crisis is yet to be seen as it is not clear how many of the workers with a suspended contract under the enhanced unemployment insurance will ultimately lose their jobs.
Brazil, Spain and South Africa have higher youth unemployment than Uruguay, but this is partly explained by a higher overall unemployment. Whereas in Italy, Portugal and Argentina, as in Uruguay, youth unemployment is high although the headline number is relatively low.
Skill mismatch could also occur as a result asymmetric information or other matching frictions in the labor market.
Youths also lack the necessary skills and thus are also much less likely to work for the government where employee benefits are usually higher relative to private firms. These further limits their opportunity to receive employment protection, thereby leaving them more exposed to economic risks.
Data on sectoral job losses since the onset of the pandemic is unavailable for Uruguay. However, it is well-known that the retail, tourism and hospitality services remain greatly affected and the timeline for their reopening is still highly uncertain.
While there is evidence that vocational training lowers youth unemployment by around 0.3 percentage points in OECD countries (Baneriji, et al., 2014), more recent studies show that conditional on education level, vocational training facilitates entry to more automatable jobs because the skills associated with this training become obsolete faster than general education skills (Hanushek et al., 2017; ILO, 2020).
In 2006 there was a dramatic change in the survey sampling procedure. Surveys were administered across all 19 departments in Uruguay instead of Montevideo only, which was done prior to 2006.
The methodology for constructing synthetic panels is predicated on strict comparability of the underlying cross-section surveys. That is, the underlying sample across survey years should be identical such that the time-invariant characteristics remain the same over time. We have checked the questionnaires and validated that they are largely comparable across survey years.
In the absence of group-specific fixed effects, variation across cohorts can lead to biased synthetic panel estimates.
This might imply that age is highly correlated with other characteristics which are not controlled in the single-equation estimates of Okun’s Law. This result remains if we replace the GDP growth with the output gap (as in the Okun law estimates).
The explained portion is the unemployment rate that would be expected given Uruguayan endowments per age group if the returns to those characteristics were the same as in the average LA5 country.
In the second to fifth columns, the LA5 average includes Uruguay, which inflates the difference in youth unemployment rates. The decomposition results without Uruguay are qualitatively similar and are available upon request.
These results are not shown but are available upon request.
Except in Chile where marital status and region of residence account for nearly 11 percent of the unemployment gap.