This paper examines the implications of artificial intelligence (AI) for employment, wages, and inequality in Latin America and the Caribbean (LAC). Using individual-level data from the STEP and PIAAC surveys and AI exposure indices from Webb (2020) and Felten et al. (2021), we estimate predicted AI exposure for workers across seven LAC economies (Bolivia, Chile, Colombia, Ecuador, El Salvador, Mexico, and Peru) and a set of OECD comparators. We then link occupation-level exposure to changes in employment and wages obtained from harmonized household surveys for the five LAC countries with comparable data over the period (Bolivia, Chile, Ecuador, Mexico, and Peru). The empirical strategy follows an expectation-maximization procedure that recovers individual-level exposure from occupation-based indices. The paper documents how AI exposure varies by skills, education, gender, and age, compares LAC patterns with those observed in OECD countries, and assesses distributional correlates across wage quintiles.

Employment to output elasticity has risen from 0.65 during the 1960s and 1970s to 1.25 in the last two decades. We study the role of recent technological change in the evolution of this elasticity throughout the business cycle. Using the COVID-19-induced shock and an instrumental variable approach as sources of identification, we find that recent technologies have increased employment to output elasticity. We find that employment in sectors characterized by occupations at a high risk of automation are the most a!ected and that this effect is larger in sectors that have undergone a technology capital deepening process in the last decades.

We develop a growth-theoretic framework to analyze how expropriation risk, exemplified by corruption, affects innovation incentives at the country level. Our model, building on Romer (1990), predicts that higher expropriation risk reduces R&D expenditure, diminishes the share of human capital engaged in R&D, lowers patenting and scientific publication rates, and slows technical progress and economic growth. We test these predictions using a novel dataset spanning nearly two decades and apply a machine learning-based instrumental variable approach – IV-LASSO – to address endogeneity in corruption. Our empirical results provide robust evidence that greater corruption (i.e., higher expropriation risk) significantly hampers innovation inputs (lower R&D spending and research personnel) and innovation outputs (fewer patent applications, scientific publications, and a lower Economic Complexity Index). These findings underscore the detrimental impact of corruption on innovation and highlight the importance of strong institutions and anti-corruption policies to foster innovation-led economic development. JEL Classification: O30, O43

After-school programs (ASP) that keep youth protected while engaging them in socio-emotional learning might address school-based violent behaviors. This paper experimentally studies the socio-emotional-learning component of an ASP targeted to teenagers in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala. Participant schools were randomly assigned to different ASP variations, some of them including psychology-based interventions. Results indicate that including psychology-based activities as part of the ASP increases by 23 percentage points the probability that students are well-behaved at school. The effect is driven by the most at-risk students. Using data gathered from task-based games and AI-powered emotion-detection algorithms, this paper shows that improvement in emotion regulation is likely driving the effect. When comparing a psychology-based curriculum aiming to strengthen participants' character and another based on mindfulness principles, results show that the latter improves violent behaviors while reducing school dropout.

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Recent evidence shows that programs targeting the socio-emotional dimensions of entrepreneurship-e.g., resilience, personal initiative, and empathy-are more highly correlated with success along with key business metrics, such as sales and survival, than programs with a narrow, technical bent-e.g., accounting and finance. We argue that programs designed to foster socio-emotional skills are effective in improving entrepreneurship outcomes because they improve the students' ability to regulate their emotions. They enhance the individuals' disposition to make more measured, rational decisions. We test this hypothesis studying a randomized controlled trial (RCT, RCT ID: AEARCTR-0000916) of an entrepreneurship program in Chile. We combine administrative data, surveys, and neuro-psychological data from lab-in-the-field measurements. A key methodological contribution of this study is the use of the electroencephalogram (EEG) to quantify the impact of emotional responses. We find that the program has a positive and significant impact on educational outcomes and, in line with the findings of other studies in the literature, we find no impact on self-reported measures of socio-emotional skills (e.g., grit and locus of control) and creativity. Our novel insight comes from the finding that the program has a significant impact on neurophysiological markers, decreasing arousal (a proxy of alertness), valence (a proxy for withdrawal from or approachability to an event or stimuli), and neuro-psychological changes to negative stimuli.

This article evaluates the impact of an Art-based program, which consisted in bringing artist to do workshops in public schools, on academic achievements, creativity (i.e., the skill) and the external manifestation of creativity in action (i.e., creative behaviors). The main contribution with respect to previous literature is a quasi-experimental design-propensity score matching-that makes the causal link between these aspects more plausible, and which had a sample of 297 children between 14 and 16 years old. Four main findings are derived from the empirical investigation. First, substantial practice is crucial. Participation in at least two semester-length workshops is a necessary condition to observe significant impacts. Second, participation has a significant impact on academic achievements. Grades increased by 0.61 standard deviations (sd) for language, by 0.36 sd for math, and by 0.33 sd for art. Overall GPA increased by 0.55 sd. The program also increased participant willingness to consider postsecondary education. Third, the impact of the art-based program on various innovative graphical psychometric measures of creativity was positive and significant. Fourth, related to creative behaviors, the program had a positive impact on certain cultural activities, such as time spent watching films at home and creating cultural goods (e.g., handicrafts, poetry, music). In conclusion, our study presents substantial evidence on the effective enhancement of creativity, the fostering of creative activities, and the improvement of academic performance through the deployment of art-based programs.

Violence and delinquency levels in Central America are among the highest in the world and constrain human capital acquisition. We designed and conducted a randomized experiment in El Salvador to measure the impacts of an after-school program aimed at reducing school violence. The program combines a behavioral intervention with extracurricular activities for 10-16 year old students. We find the program reduced the participants' violent behavior both inside and outside of school and indirectly improved their attendance, attitudes toward school and learning, and academic outcomes. Using state-of-the-art technology, we measured participant brain activity and show that the intervention fosters emotion regulation, enabling treated adolescents to remain calmer when faced with external stimuli.

In this paper, we examine the factors influencing labor productivity and sales growth among micro, small, and medium-sized enterprises (MSMEs) within a middle-income economy. Although MSMEs play a pivotal role in employment within middle-income countries, they often display a lower economic value-added, pointing to underlying challenges in labor productivity. Using a longitudinal dataset of firms and employing both quantile regressions and machine learning techniques, we find that SMEs led by older, male, and more seasoned managers tend to exhibit higher productivity. Similarly, companies with a larger proportion of highly educated employees, affiliation to business groups, and engagement in R&D activities demonstrate superior performance. Finally, to improve the performance of MSMEs in developing economies, our results suggest that implementing targeted, well-defined vertical public support programs would be an effective public policy approach.

New technological trends, such as digitization, artificial intelligence and robotics, have the power to drastically increase economic output but may also displace workers. In this paper we assess the risk of automation for female and male workers in four Latin American countries Bolivia, Chile, Colombia and El Salvador. Our study is the first to apply a task-based approach with a gender perspective in this region. Our main findings indicate that men are more likely than women to perform tasks linked to the skills of the future, such as STEM (science, technology, engineering and mathematics), information and communications technology, management and communication, and creative problem-solving tasks. Women thus have a higher average risk of automation, and 21% of women vs. 19% of men are at high risk (probability of automation greater than 70%). The differential impacts of the new technological trends for women and men must be assessed in order to guide the policy-making process to prepare workers for the future. Action should be taken to prevent digital transformation from worsening existing gender inequalities in the labor market.

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.

This paper analyzes the Covid-19 pandemic impact of the global process of automation on employment in a developing economy. This is particularly interesting because developing economies characteristics, such as having larger informal sectors and weaker social safety nets, shapes the impact of automation on labor markets. We show that occupations with a higher risk of automation exhibit the most significant employment contraction. More specifically, we find that one standard deviation higher in sectoral share of employment in occupations at risk of automation (OaRA) implied around 7% less employment on average between the last quarter of 2019 and the first quarter of 2021. The effect on informal employees is three times more in comparison to formal employees, and the estimation for self-employed workers is not statistically significant. We also find that employees in sector with relatively low compared to high wages, both vis-à-vis the US, exhibit a 20% smaller reaction on employment due to the pandemic restrictions. We do not find robust evidence showing that the employment contraction has been larger among female workers or in jobs with higher at-work physical proximity, but we do find a positive relationship related to the capacity of working remotely.

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 labor markets, although all have focused on industrialized nations, which consist of more service oriented and skilled labor force. This is the first paper to study automation risk rates for developing nations. We examine automation’s effect on 10 developing countries throughout Latin America, Africa, Southeast Europe, and Asia. To address the heterogeneity of occupations across countries, we apply a task-based approach and re-calibrate the effect of automation on labor market while analyzing the task structure between and within countries. Modeling off previous studies, We followed an expectation-maximization algorithm to predict the risk of automation at worker's level. Individuals whose risk of automation was 70\% or higher were then considered to be highly automatable. Our results suggest that these developing countries have higher levels of predicted automation risk compared to developed economies. Countries range in their level of highly automatable jobs from the lowest being Yunnan –a Chinese province of 50 million inhabitants-- with 5\% to the highest of Ghana and Sri Lanka with 42\% and 43\%, respectively. We also find that occupations containing relatively more routine tasks are more likely to be automated, while workers with a higher level of education reduce their risks.

Research on the causal relation between extracurricular activities and the development of cognitive and socio-emotional outcomes is scarce at the individual level, despite increasing international evidence that such activities are highly relevant and even, in some cases, more effective than traditional school improvement support programs. Based on an unprecedented intervention in the area of music, this paper evaluates the impact of intensive participation of children and young at-risk students in the creation and development of the first youth orchestra of the municipality of Curanilahue, a small and poor county in the south of Chile, South America. After confronting many potential sources of selection bias in the impact estimation–self-selection, parents selection, orchestra selection, payment capacity–we found positive effects of this experience on participants at both the cognitive and socio-emotional levels. We found that orchestra participation positively affects both language and mathematics in SAT-like tests. The estimated impacts are in the upper bound of the impact evaluation sizes range in the developing world (e.g. Duflo et al., 2013; McEwan, 2012; Ganimian and Murnane, 2016). Furthermore, having analyzed the increase in test scores for those students participating in the orchestra who take the test more than once, we find results that indicate a progressive increase in their scores, both in mathematics and in language, an indicator that can be considered as a proxy of socio-emotional outcomes related to the orientation and persistence to obtain personal goals.