Preventing Violence in the Most Violent Contexts: Behavioral and Neurophysiogical Evidence from El Salvador
R & R The Review of Economics and Statistics
With Lelys Dinarte.
Violence and delinquency levels in Central America are among the highest in the world, and constrain human capital acquisition. We conduct a randomized experiment in El Salvador designed to reduce this problem. The program works with 10-16 year olds and combines a behavioral intervention with extra-curricular activities. We find the program reduced bad behavior and absenteeism and improved student grades. By measuring brain activity, we show a key mechanism operates through emotional self-regulation, whereby treated adolescents become calmer when facing external stimuli. We also find positive spillovers on educational outcomes for other students in classes with treated students.
Feeling, Fast and Slow: Entrepreneurship Education and Emotions
With Paul Sajda (Columbia U)
Recent results from studies concerning impact evaluations of educational or training programs have proved puzzling to many researchers. The most questionable findings have been regarding social programs that have positive impacts on educational or labor market outcomes: they seem not to affect —or even negatively affect— measures of socio-emotional skills, contrary to expectations. To investigate these findings, we undertook our own study with a hypothesis that predicts that social programs designed to impact socio-emotional skills affect subject’s emotional regulation abilities. In order to test our hypothesis, we studied an educational program that focuses on fostering entrepreneurial and socio-emotional skills at vocational schools. Combining a randomized controlled trial at the school level with neurophysiological and survey data from lab-in-the-field experiments, we were able to assess the impact of this program on subjects’ socio-emotional and emotional regulation. Two main findings resulted from these experiments. First, the program had a positive and significant impact on educational outcomes —i.e. dropouts rates— and no impact on the expected mechanisms, which include socio-emotional skills and creativity measures. This is consistent with the findings in labor economics literature. Second, we found that the programs had significant impacts on emotional regulation. In particular, we estimated a decrease in the arousal (a proxy of stress) and valence (a proxy for intrinsic attractiveness or aversiveness of an event) dimensions of a subject’s emotional state from neurophysiological recordings. We finally found that the program reduces subjects’ emotional reactions to negative stimuli: in layman’s terms, it makes individuals more resilient.
Can Art-based Programs Nurture Creativity and Creative Behaviors Among Youth?
How Much Should We Trust Self-reported Measures? An Applied Neuroscience application to Social Program Evaluations
With Paul Sajda and Nicole Moscowitz.
Measures of cognitive skills —i.e. GPA, IQ, etc.— account for a small fraction of the variance in salaries and other economic outcomes (Bowles et al., 2001; Heckman et al., 2006). Therefore, there is an increasing interest in elucidating other factors that might explain that variance; in particular, the role played by non-cognitive skills. In the economics literature, empirical attempts to measure the aforementioned dimensions —cognition, personality traits, and creativity— have been insightful but are noisy due to the low reliability of the proxies used in their measurement (Calero et al., 2014; Cunha and Heckman, 2008; Cunha et al., 2010; Attanasio et al., 2015a; Almlund et al., 2011). This chapter is intrinsically related to the impact evaluation done in the Job Market Paper, and was designed as a deeper proof of concept of the relationship between emotions and self-reporting on measures of non-cognitive skills. The purpose is to show a plausible correlation between self-reported psychometric tests and transient emotional states, with the latter estimated from EEG recordings. To do so, a couple of relationships are established: namely, (i) showing the behavioral correlation between self-reported psychometric tests and transient emotional states, (ii) detecting emotional state and responsiveness from scalp EEG recordings, and (iii) showing the correlation between self-reported psychometric tests and transient emotional states using features from the EEG recordings. I argue that there is a positive correlation between emotional state and test scores that rely on self-ratings. In particular, I claim that self-reported tests used to measure both cognitive and non-cognitive skills are usually biased because of transient emotions that arise during testing.
A Theory of Stochastic Choice and Overconfidence
This paper develops a testable model of stochastic choice and overconfidence with parameters that can be estimated from stochastic choice data and elicited beliefs.
Automation in Latin America: Are Women at Higher Risk of Losing Their Jobs?
With Monserrat Bustelo (BID), Nicolas Soler (BID), and Laura Ripani (BID).
We study the automation risk faced by workers in Latin America using employee-level microdata. We have four main conclusions. First, in the context of LAC, automation risks and the share of worker under high risk (>70%) of automation are higher to those in developed countries. This is further supported by existing literature, given the routine and task-based nature of work individuals do in developing economies. Second, LAC countries automation risk rates follow characteristics similar to previous studies done in the context of developed economies. Third, there are interesting heterogenous effects: Age, experience needed, gender, job type, and education. Fourth, our results concerning gender are particularly interesting. On one side, females would experience on average a 2% higher risk of automation than men, which is consistent with previous results for OECD countries. Second, we observe interesting heterogeneous effects by gender that are consistent with our hypotheses based on the literature.
The Future Of Work In Developing Economies. What Can We Learn From The South?
In recent years, there has been an escalation of concern revolving around the effect that automation will have on the future of work. Numerous studies have begun to investigate automation’s impact on the labor economy, although all have focused on industrialized nations which consist of more service and skilled occupations. Utilizing the World Bank’s STEP Skills Measurement Program Database, we examine automation’s effect on 10 developing countries throughout Latin America, Africa, and Asia. This data has proven to be especially helpful for the discussion as it was collected at the individual level and contains a significant number of relevant factors. Our data set also consist of 28 tasks which represent all four quadrants of the routine vs. non-routine and manual vs. cognitive matrix. To address the heterogeneity of occupations across the country, we apply a task-based approach and re-calibrate the effect of automation on labor market while analyzing the task structure across countries. Modeling off previous studies, we created an expectation-maximization algorithm to predict the percentage of tasks which are likely to be automated. Jobs whose task automation output was 70% or higher were then considered to be highly automatable. Our preliminary results suggest that these developing countries have higher levels of predicted automation risk. Countries range in their level of highly automatable jobs from the lowest being Yunnan –a Chinese province— with 7.7% to the highest of Ghana with 42.4%. Their mean number of tasks likely to be automated per occupation generally corresponds with the number of highly automatable jobs and ranges between 50-66%. These results follow similar patterns as other studies including higher levels of educations correspond with lower levels of automation, more experience equals less automation potential, among others. From our initial analysis, it appears these countries consist of more jobs containing routine tasks which are more likely to be automated. This is the first paper to estimate automation rates for many developing nations. It also begins to address how individual-level tasks and job data can be used to determine heterogeneity between jobs both within and between countries.
Can COVID-19 Accelerate Technological Transformations?
With Alejandro Micco
Available at SSRN: https://ssrn.com/abstract=3688690
In this paper, we present evidence about the short-term impact of COVID-19 on the labor market in the United States. During the second quarter of 2020, the pandemic destroyed 18 million jobs in the US private sector. For economics policies we inquire as to why some sectors, occupations, and demographic groups are more affected than others. We find that factors directly related to the epidemic are essential. Employees in occupations working in proximity to others are more affected, while occupations able to work remotely are less affected. We would expect that employment in these occupations should recover rapidly post-pandemic. But, we also find that sectors with a large fraction of occupations at risk of automation present a significantly higher contraction on employment. The contraction is mainly driven by sectors that underwent a capital deepening process in ICT and Software previous to the pandemic. A sector with one standard deviation higher in the share of employment in occupation at risk of automation cuts around 5 percentage points more employment during the second quarter of 2020. The same sector, but with one standard deviation higher increase in technology capital, cuts employment by around 7 percent. This evidence is in line with a similar cleansing process during the Great Recession. COVID-19 is catalyzing the automation process, and employment losses related to this phenomenon could be permanent.
COVID-19’s Impact on the Labor Market shaped by Automation: Evidence from Chile
Covid-19’s Impact on the Labor Market Shaped by Automation: Evidence from Chile
This paper provides evidence of the impact of Covid-19 on employment in Chile. During the last two quarters, the pandemic destroyed two million jobs, almost one third of the labor force. To formulate economic policies we must understand why some sectors, occupations, and demographic groups are more affected than others. At the same time, Covid-19 is catalyzing the automation process in emerging markets. We find that sectors with a large fraction of occupations at risk of automation present the most significant contraction in employment. Employment in sectors with a large share of occupations at risk of automation fell between 12 and 8 percentage points more relative to other industries during the second quarter of 2020. This impact is larger in female workers. We also find that factors directly related to the epidemic are significant. Employees in occupations working in proximity to others are more affected by the pandemic, while those in occupations able to work remotely are less affected. We should expect that employment in these occupations should recover faster post-pandemic.
Non-Cognitive Skills Development and School-Based Violence Reduction in Central America.” With Lelys Dinarte (World Bank) and Claudia Martinez (PUC and MIT JPAL). AEA RCT Registry 3976.
Economic effects of Entrepreneurship Training: Evidence from a Randomized Controlled Trial”. With Sam Flanders (ASB) and Melati Nungsari (ASB). AEA RCT Registry 4013.
“Innovations on Off-grid Energy Technologies: Evidence from Papua Island.” With Johannes Urpelainen (Johns Hopkins Initiative for Sustainable Energy Policy) and Ryan Kennedy (Johns Hopkins Initiative for Sustainable Energy Policy).
“The role of art-based education programs in fostering employment and career success in emerging economies”. With Mazzelli, A. (ASB) and Nason, R. S.(Concordia U.).
“The Role of Emotions in Entrepreneurship Education: Evidence on Labor Market Outcomes from Administrative Data in Chile”
“Reactiva: Measuring entrepreneurial and emotional regulation skills on real life context using AI-powered computer vision”. Software designed to conduct research on the field.
“Neuroscience and Education: Measuring and Developing Creative, Cognitive and Socio-emotional Skills in Educational Settings”. With Marcela Pena(PUC).
“A direct test of family firms’ attentional structures: Evidence from field experiments and AI powered behavioral and emotional data” (with De Massis, A. (IMD), Mazzelli, A. (ASB), Shardul, P. (ASB), and Vogel, P. (IMD)).
“Labor market in Latin America and the Caribbean: The Missing Reform” (2011) in The Oxford Handbook of Political Economy of Latin America, Oxford University Press, edited by Javier Santiso and Jeff Dayton-Johnson. With Alejandro Micco.
“Financial Sustainability of the Pension Reserve Fund of Chile” (2010). Ministry of Finance of Chile, Budget Office Official Reports. With Nicholas Bärr,, Gilles Binet, Manuel Agosin, David Bravo, and Jaime Ruiz-Tagle [Spanish].
“The Impact of Art-Education on Human Capital: An Empirical Assessment of a Youth Orchestra” (2019). with Dante Contreras and Juan Pablo Valenzuela. International Journal of Educational Development.
“The Future of Work in Developing Economies” (2020) with Connor Joyce. MIT Sloan Management Review.