A rapid expansion in demand for post-secondary education
triggered an unprecedented boom in higher education programs in Colombia, raising concerns about
their relevance and quality. This paper shows that the penalty on student learning and labor market
outcomes of attending a recently created program is large but, to a large extent, it is driven by
student and program selection. Using rich administrative data-sets that match higher education school
admission information, socioeconomic characteristics of the young graduates, standardized test scores
pre- and post-higher education, and formal labor market outcomes, we characterize this selection process
by disentangling the relative roles of demand and supply forces. The main factor behind the learning
penalty is student selection in baseline ability. In the case of labor market outcomes, the penalty is
due to a combination of student and program characteristics.
Minimum Wages and the Uncovered Sector in Low and Middle Income
Countries. Coming Soon!!
Giulia Lotti, Julián Messina and Luca Nunziata.
We present new empirical evidence on the implications of minimum wages on
employment in the uncovered sector in developing countries, analyzing a unique dataset assembled from a set
of micro surveys collected in 49 low- and middle-income countries. Our identification strategy exploits relative
bindingness in minimum wages across labor market groups within countries and across years.
We find that a higher minimum wage is associated with a larger self-employment share. The estimated impact of
the minimum wage on the uncovered sector is economically significant: a 1 percentage point increase in the minimum
wage ratio is associated with 0.1 percentage points increase in the self-employment rate, 0.182 when the
non-compliance rate with the minimum wage is taken into account.
Partial Identification of Treatment Effects in Observational Data
under Sample Selection. Coming Soon!!
Dimitris Christelis and Julián Messina
This paper partially identifies population treatment effects in observational data
under both non-random treatment assignment and sample selection. Bounds are provided for both average and quantile
population treatment effects, combining assumptions for the selected and the non-selected subsamples.
We show how different assumptions help narrow identification regions, and we illustrate our methods by partially
identifying the effect of maternal education on the 2015 PISA math test scores in Brazil. We find that while
sample selection increases considerably the uncertainty around the effect of maternal education, it is still
possible to calculate informative identification regions.
Gender Identity and Peer Effects at the Workplace. Coming Soon!!
Julián Messina, Anna Sanz-de-Galdeano, and Anastasia Terskaya
We use rich Brazilian administrative data to examine peer effects on wages for
an entire local labor market, distinguishing between same-sex and opposite-sex peer effects.
Both individuals’ and peers’ average ability are unobserved, so we estimate them by taking advantage
of the panel structure of the data. We also control for workers’ sorting into peer groups and firms.
We find that same-sex peer effects are remarkably larger (about double) than opposite-sex peer effects
in the workplace for both men and women. In addition, differences between same-sex and opposite-sex peer
effects are attenuated in contexts with more egalitarian gender identity norms. We argue that these patterns
are consistent and best explained by economic models of behaviour and interactions that incorporate the psychology and sociology of identity.
Universidad de Alicante
Fac. de Cc. Economicas y Empresariales.
Carr. de San Vicente del Raspeig, s/n
03690 Sant Vicent del Raspeig, Alicante
+34 965 90 36 70