Juan S. Muñoz-Morales

I am a CNRS researcher at LEM and an associate professor of economics at IÉSEG School of Management. Additionally, I am a research affiliate at IZA@LISER and a J-PAL invited researcher.

My research focuses on development and labor economics, with a particular interest in education and policy evaluation in developing economies.

I am the organizer of the IFLAME Seminar Series. Please contact me in case you want to present.

Contact:

Email: j.munoz[at]ieseg[dot]fr

Address: 1 parvis de la Défense, Paris-La Défense, France.

Link to my CV: Here

Publications and Forthcoming

Signaling and Migrant Labor Market Integration: Experimental evidence from Colombia

Joint with Matías Busso, Carolina Gonzalez-Velosa, Juanita Ruiz, Cynthia Van der Werf, Juan D. Valencia.

Conditionally accepted via Pre-Results Review at the Journal of Development Economics.

AEA Registry | Pre-results | JDE Pre-Results

Labor market signaling plays a crucial role in the job-matching process by reducing information frictions faced by employers. In this paper, we analyze whether the returns to signaling vary based on the accuracy of ex-ante signals. We estimate the heterogeneous returns to signaling by comparing locals and migrants, who differ in the accuracy of their available education signals. Our analysis employs data from a skill certification program in Colombia that provides both locals and migrants with a common credible signal within the labor market. Selection into the program was randomized, with 30 percent of spots allocated to migrants. Our estimation strategy leverages this random assignment to estimate the heterogeneous returns to signaling for both locals and migrants. Our findings offer valuable insights for policy improvements aimed at enhancing the labor market integration of migrants by assessing the heterogeneous returns of labor market signaling.

Signaling Specific Skills and the Labor Market of College Graduates

Joint with Matías Busso and Sebastián Montaño.

Forthcoming at the Review of Economics and Statistics.
DOI | Replication Files | Online Appendix | IZA Working Paper | IDB Working Paper

Awarded the first place in the IDB Knowledge Excellence Prize

Using administrative data and a regression discontinuity design, we study the labor market effects of a government-sponsored award given to top-performing students on Colombia’s national college exit exam. The award signals field-specific skills, leading recipients to earn seven to ten percent more than comparable peers without the signal. The benefits are concentrated among graduates from lower-reputation institutions, who enter the market with weaker signals and gain access to better job matches and higher-paying firms. These returns persist for up to five years, driven by an upward shift in the intercept of the wage-experience profile of those with weaker signals.

Complements or Substitutes? Labor Market Effects of Foreign Inputs in Developing Economies

Joint with Leonardo Bonilla-Mejía and Roman D. Zárate.

Journal of International Economics, May 2026, Volume 161, 104263.

DOI | Replication Files | Online Appendix | IZA Working Paper | Tariff Data (.dta)

Press Coverage: Voces

This paper examines how import liberalization affects labor markets when labor and intermediate inputs can be complements or substitutes. We embed a constant-elasticity-of-substitution production function in a dynamic trade model, showing that labor market responses depend on sector-specific substitution elasticities. Empirically, we exploit tariff reductions in Colombia using a difference-in-differences design that decomposes trade shocks into import-competition and input channels. Import competition reduces the wage bill, while cheaper intermediate inputs increase it; these gains are driven by services, are imprecisely estimated in manufacturing, and reverse in agriculture. Combining the model with reduced-form estimates, we use indirect inference to recover sector-specific elasticities. We find substitution between labor and intermediates in agriculture and manufacturing, but complementarity in services. Allowing for this flexibility relative to a Cobb-Douglas benchmark amplifies worker reallocation toward services and away from agriculture. It also increases welfare in services and reduces it in manufacturing and agriculture.

Flooding the Brains: Natural Disasters, Student Outcomes, and the Urban-Rural Gap in Human Capital.

Economic Development and Cultural Change, October 2025, Volume 74, Number 1, pp. 85-115.

DOI | Replication Files | Online Appendix | IZA Working Paper

Press Coverage: La Silla Vacía.

This study provides evidence that natural disasters negatively affect student outcomes, potentially explaining the lower academic achievement of students in rural areas compared to their urban counterparts in developing countries. Using data from the Colombian school census, I estimate a difference-in-differences strategy that exploits variation from an unusual rainfall shock affecting over two million people in both urban and rural Colombia. The results show that these disruptions increase school dropout rates and reduce learning outcomes for at least a decade. The effects are concentrated in rural schools, while students in urban schools remain unaffected. I explore several mechanisms and rule out the possibility that the effects are driven by selective migration or a loss of educational resources. Instead, I find evidence that the rainfall shock exacerbated poverty, pushing poorer rural children into unemployment and longer work hours.

Unbundling Returns to Postsecondary Degrees and Skills: Evidence from Colombia

Joint with Matías Busso and Sebastián Montaño.

Education Economics, 2025, volume 33, issue 1.

DOI | IDB Working Paper | IZA Working Paper

Using longitudinal data of college graduates in Colombia, we estimate labor market returns to postsecondary degrees and to various skills - including literacy, numeracy, foreign language, and field-specific skills. Graduates of academic programs and schools of higher reputation obtain higher earnings relative to vocational public programs. A one standard deviation increase in each skill predicts average earnings increases of one to three percent. Returns vary along the earnings distribution, with tenure, with the degree of job specialization, and by gender. Our results imply that degrees and skills capture different human capital components that are rewarded differently in the labor market.

The Unintended Consequences of Merit-Based Teacher Selection: Evidence from a Large-Scale Reform in Colombia

Joint with Matías Busso, Sebastián Montaño, and Nolan Pope.

Journal of Public Economics, November 2024, volume 239, 105238.

DOI | NBER Working Paper | IZA Working Paper

Press Coverage: NBER Digest; CATO Research Briefs in Economic Policy ; VoxDev; IZA World of Labor; La Silla Vacía.

Teacher quality is a key factor in improving student academic achievement. As such, educational policymakers strive to design systems to hire the most effective teachers. This paper examines the effects of a national policy reform in Colombia that established a merit-based teacher-hiring system intended to enhance teacher quality and improve student learning. Implemented in 2005 for all public schools, the policy ties teacher-hiring decisions to candidates' performance on an exam evaluating subject-specific knowledge and teaching aptitude. The implementation of the policy led to many experienced contract teachers being replaced by high exam-performing novice teachers. We find that though the policy sharply increased pre-college test scores of teachers, it also decreased the overall stock of teacher experience and led to sharp decreases in students' exam performance and educational attainment. Using a difference-in-differences strategy to compare the outcomes of students from public and private schools over two decades, we show that the hiring reform decreased students' performance on high school exit exams by 8 percent of a standard deviation, and reduced the likelihood that students enroll in and graduate from college by more than 10 percent. The results underscore that relying exclusively on specific ex ante measures of teacher quality to screen candidates, particularly at the expense of teacher experience, may unintentionally reduce students' learning gains.

Some Children Left Behind: Variation in the Effects of an Educational Intervention

Joint with Julie Buhl-Wiggers, Jason Kerwin, Jeff Smith, and Rebecca Thornton.

Journal of Econometrics, July 2024, volume 243, issues 1-2.

DOI | Replication Files | NBER Working Paper | IZA Working Paper

Press Coverage: VoxDev.

We document substantial variation in the effects of a highly-effective literacy program in northern Uganda. The program increases test scores by 1.40 SDs on average, but standard statistical bounds show that the impact standard deviation exceeds 1.0 SD. This implies that the variation in effects across our students is wider than the spread of mean effects across all randomized evaluations of developing country education interventions in the literature. This very effective program does indeed leave some students behind. At the same time, we do not learn much from our analyses that attempt to determine which students benefit more or less from the program. We reject rank preservation, and the weaker assumption of stochastic increasingness leaves wide bounds on quantile-specific average treatment effects. Neither conventional nor machine-learning approaches to estimating systematic heterogeneity capture more than a small fraction of the variation in impacts given our available candidate moderators

Do school shootings erode property values?

Joint with Ruchi Singh.

Regional Science and Urban Economics, 98, January 2023.

DOI | IFLAME Working Paper

Press Coverage: UI News; OZY; Realtor.com.

We examine how residential property values are impacted by school shootings, which are crimes that have a very low probability of being repeated in the same location. We exploit the exogenous timing of eleven mass shootings that took place between 1998 and 2014, and use a difference-in-differences framework to estimate the causal effect of these school shootings on property values in the affected school attendance area. We find that house prices decline by an average of around 2.4 percent in a four-year period after a mass school shooting. We also find that enrollment falls in the affected school district, suggesting that families subsequently avoid schools in areas that have experienced such events.

The Economics Behind the Math Gender Gap: Colombian Evidence on the Role of Sample Selection

Journal of Development Economics, 2018, 135, pp. 368-391.

DOI

Featured in: Marginal Revolution.

The literature that has previously shown that boys outperform girls in math tests has failed to explain the underlying causes of the phenomenon. This math gender gap has been documented to vary across countries, and shown to grow as students advance through school. In this paper I suggest that these patterns may be explained by sample selection caused by gender differences in schooling's opportunity costs, which lead lower-achieving males to drop out. I present and test the implications of a labor supply model that examines the opportunity cost of school attendance and, thereby, the observed math gender gap. Using an exogenous policy change, the launch of a conditional cash transfer program in Colombia, I estimate that sample selection explains between 50 percent and 60 percent of the gap. Estimates of non-parametric bounds show that selection in the lower quantiles of the male distribution explains a significant portion of the gap.

Enrollment, Graduation, and Dropout Rates in Latin America, Is the glass half empty or half full?

Joint with Marina Bassi and Matías Busso.

Economia. Fall 2015. pp. 113-156.

IDB Working Paper

Press Coverage: Huffington Post.

We use 292 household surveys from eighteen Latin American countries to document patterns in secondary school graduation rates over the period 1990–2010. We find that enrollment and graduation rates increased during that period, while dropout rates decreased. We provide two types of explanations for these patterns. Countries implemented changes on the supply side to improve access, by increasing the resources allocated to education and designing policies to help students stay in school. Despite this progress, graduation rates are still generally low, and there are remarkable gaps in educational outcomes in terms of gender, income quintiles, and regions within countries. The quality of education is also generally low.

Working Papers

Life-Cycle Wage Growth and Firm Productivity in Developed and Developing Economies

Joint with Kerstin Holzheu.

Revisions requested at the International Economic Review.

Life-cycle wage growth varies significantly across countries. We examine the role of the local distribution of firm productivity in shaping life-cycle wages by introducing a random search model that disentangles the effects of firm productivity distribution, on-the-job learning, and labor market frictions. Estimates of the model for Brazil, Colombia, and the United States suggest that the shape and scale of the firm-type distribution are key factors in explaining life-cycle wage growth. Counterfactual simulations suggest that equating the firm-type distribution in Brazil and Colombia to that of the United States would substantially reduce the cross-country gap in workers’ life-cycle wage trajectories.

Compensation vs. Reinforcement: Experimental Identification of Parental Aversion to Inequality in Offspring.

Joint with Felipe Barrera-Osorio, Leonardo Bonilla, Matías Busso, Sebastián Galiani, Hyunjae Kang, and Juan Pantano.

AEA Registry | NBER Working Paper

Parents may invest differently across children by compensating the disadvantaged child or by reinforcing the child with higher expected returns. We study this question using a conditional cash transfer experiment that uniquely randomized transfers at the student level, generating exogenous variation in transfer exposure across siblings within the same household. The transfers increased short-run attendance among treated students but generated negative spillovers on untreated siblings: untreated siblings of treated students were 3.7 percentage points less likely to graduate from college, a decline of about 30 percent relative to the control mean. We interpret these effects using a dynamic model of household schooling decisions that identifies parental aversion to inequality in children’s educational outcomes. The estimated model implies limited aversion to inequality in children’s educational outcomes and reproduces held-out treatment effects not used in estimation. A decomposition shows that the negative spillover is primarily driven by substitution in educational investments toward the treated child.

Overlapping Calendars: Colonial Legacies and the Consequences of Misaligned Timing Institutions

Joint with Leonardo Peñaloza-Pacheco and Abu Shonchoy.

This paper studies the economic consequences of conflicts between timing institutions, the rules that govern when schooling, work, and other activities take place, and local economic conditions. We focus on the overlap between school calendars and local agricultural cycles, and document systematic misalignment between the two across countries in southern latitudes. These patterns reflect institutional legacies, as inherited institutions continue to shape the design of school calendars in previously colonized countries. Exploiting this colonial variation as an instrument, our cross-country estimates indicate that greater overlap between school terms and agricultural labor demand increases school dropout rates. To identify the underlying mechanism, we use school-level data to study a calendar reform in Colombia that exogenously increased overlap in two departments. Using a synthetic difference-in-differences approach, we find that calendar misalignment induces a trade-off between schooling and labor, resulting in higher dropout and lower enrollment in rural areas. Our findings highlight the importance of timing institutions as a determinant of human capital accumulation and suggest that aligning social institutions with local economic conditions can generate substantial gains for economic development.

Complementing Teachers: Can Artificial Intelligence Improve Student Learning by Addressing Learning Variability? [Preliminary Results]

Midline collected, endline still in the field

Joint with Felipe Barrera-Osorio.

AEA Registry \ Pre-Analysis Plan

Education systems in developing countries face a persistent learning crisis characterized by low average achievement and substantial disparities in student learning. This paper evaluates a teacher-level, artificial intelligence-enabled intervention designed to support lesson planning and promote within-classroom differentiated instruction. We implement a school-level randomized controlled trial in low-income public secondary schools in Bogotá, Colombia. The intervention provides mathematics teachers with access to an AI-based lesson-planning platform that integrates learner variability and growth-oriented pedagogical frameworks, reducing preparation costs while improving instructional alignment with heterogeneous student needs. Using baseline and midline student assessments complemented with teacher surveys, we report preliminary midline results based on a subsample of schools. The estimates suggest that the intervention improves math achievement by around 0.2 to 0.4 standard deviations. These results are cost-effective relative to prior evidence on educational interventions. They should be interpreted as exploratory pending endline data collection.

Selected Work in Progress

Information Restrictions and Migrant Labor Market Integration

In the field

Joint with Matías Busso, Carolina Gonzalez-Velosa, and Cynthia Van der Werf.

AEA Registry 1 | AEA Registry 2

Teachers or School Leaders? Artificial Intelligence and Teaching Complementarities

In the field

AEA Registry

This evaluation seeks to analyze the efficiency in the delivery of access to specialized LLMs to teachers while varying the target of that access. For this purpose, we randomly provided teachers in the Dominican Republic with access to a specialized teaching LLM. Schools in the intervention were assigned to two different treatments. In one group all teachers received access to the technology. In another all teachers and school leaders received access to it. Baseline measures were collected in September 2025. Endline measures are expected to be collected in June 2026.

How Many Children Left Behind? A Meta-Analysis of Treatment Effect Heterogeneity in Developing-Country Education Programs

Joint with Hanna Han, Jason Kerwin, Jeff Smith, and Rebecca Thornton.

Extensive research on the effects of education programs has left a key question unanswered: how many students actually benefit from these programs? We answer this question by re-analyzing the microdata from the universe of published education RCTs from developing countries. Despite the current enthusiasm for replication, we were able to obtain data for just 42% of the studies in our sampling frame. Our analytic sample includes more than half a million observations covering 119 different interventions run in 24 countries. Using this data, we construct non-parametric lower bounds on the across-student variance of treatment effects for each intervention. Our meta-analytic estimate of the standard deviation of treatment effects is 0.08 SDs of control-group test scores---over 30% larger than the average treatment effect for the studies in our sample. Moreover, the standard deviation of impacts varies widely, from nearly zero for 20% of programs to almost half an SD of test scores for the top handful of interventions. The average intervention leaves at least 1 in 20 students behind, with some programs having lower-bound rates of nearly a third. The variance of treatment effects is strongly correlated with a range of study-level factors. It is systematically larger for programs with larger treatment effects, and for pedagogy-focused interventions and programs targeted at entire schools. It is smaller at the preschool and secondary school levels compared to primary-school programs, and is larger for government-run interventions as compared to those run by NGOs and private companies. However, we can explain almost none of the variance in treatment effects using \textit{individual}-levelcovariates from the studies, even if we use machine learning methods to estimate and partial out CATEs. In other words, some interventions have more variable effects, but we cannot identify who benefits from them and who is harmed. Our results suggest that education interventions in developing countries are leaving a large number of children behind.

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