Martín Brun

We explore whether complexity shape which fairness views people follow. We report findings from a lab-in-the-field experiment conducted with school students aged 10 to 15, a sample with significant cognitive variation and a uniform background. In the experiment, participants decide how to distribute earnings between workers who completed tasks under unequal opportunities. First, we examine whether differences in cognitive ability predict which fairness rules people follow. We find that more able participants are more likely to follow meritocratic fairness views and prefer to compensate workers based on effort. The difference is driven by those who account for the unequal opportunities workers experience (a more complex decision rule), coinciding with a decline in egalitarian responses (a simpler decision rule). Second, we analyze the procedural aspects of the decisions. We show that more able individuals are better equipped to handle the counterfactual inferences needed to account for unequal opportunities. A between-subject manipulation shows that providing this step closes the decision gap within meritocrats, whose decision rules differ only in the use of that step, with the treatment effect concentrated in low able individuals. These findings highlight the role of procedural choice in distribution decision-making and underscore cognition as an additional determinant of fairness pluralism.

We run an in-curricula field experiment in 53 schools in Uruguay. We investigate how the exposure to different role model's characteristics impact early interest in STEM for 5th and 6th grade students. We manipulate the features of an 8-week long project on a scientist biography. Students work (i) either on a male or female biography, which (ii) includes or excludes growth mindset throughout the story. The intervention increases early interest in STEM, particularly in Engineering. The effectiveness varies depending on the gender match and the inclusion of a growth mindset component. We shed light on the underlying mechanisms by analyzing how gender stereotypes and norms, and growth mindset change when exposed to different role models.

We survey the literature on attitudes towards inequality and redistributive preferences. We focus on the effects of misperceptions, social preferences, and social identity. Recent contributions addressing these relationships have produced novel findings that are hardly covered in previous surveys. Before reviewing the empirical literature, we discuss how the theoretical literature has embedded in canonical models of social preferences the three issues we focus on in this review.

Individuals with higher cognitive ability have been found to be more politically influential. But it is not clear how their political preferences regarding redistribution play out, as they tend to be richer and more pro-social. We asses empirically this question by exploiting two cohort studies from the United Kingdom that measure cognitive ability during childhood and preferences during adulthood. We find that the top 10% most able individuals are more supportive for redistribution, even without controlling for their higher income. By controlling for a rich set of variables, we unveil a partial positive effect of 10.7 p.p. that prevails over negative ones. This effect appears to be focused on individuals that have volunteered in organizations, suggesting that social motives may be a consequential factor for this pivotal group of individuals.

We analyse individuals’ preferences vaccine-distribution schemes in the World, the EU, and their country of residence that emphasise circumstances rather than outcomes or effort. We link preferences to previously-measured cognition, and find that high-cognition individuals are 35% more likely to always support such schemes. These preferences are not driven by scheme convenience nor vaccine hesitancy, but appear to be caused by prosociality. We argue that this latter is linked to the perception of less equality of opportunity in society: despite having similar ideals about the role that effort and luck should play in life, high-cognition individuals perceive outcomes to be more determined by luck.

We use a rigorous three-stage many-analysts design to assess how different researcher decisions—specifically data cleaning, research design, and the interpretation of a policy question—affect the variation in estimated treatment effects. A total of 146 research teams each completed the same causal inference task three times each: first with few constraints, then using a shared research design, and finally with pre-cleaned data in addition to a specified design. We find that even when analyzing the same data, teams reach different conclusions. In the first stage, the interquartile range (IQR) of the reported policy effect was 3.1 percentage points, with substantial outliers. Surprisingly, the second stage, which restricted research design choices, exhibited slightly higher IQR (4.0 percentage points), largely attributable to imperfect adherence to the prescribed protocol. By contrast, the final stage, featuring standardized data cleaning, narrowed variation in estimated effects, achieving an IQR of 2.4 percentage points. Reported sample sizes also displayed significant convergence under more restrictive conditions, with the IQR dropping from 295,187 in the first stage to 29,144 in the second, and effectively zero by the third. Our findings underscore the critical importance of data cleaning in shaping applied microeconomic results and highlight avenues for future replication efforts.

This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators’ experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.

We examine if and why export responses to real exchange depreciations are lower than those to appreciations. We document this asymmetric response using macro-level data for Pakistan and show that export adjustments after depreciations are less than one-third as fast as those to appreciations. We use product-destination level data to examine three complementary drivers of this asymmetry: (i) information frictions that increase the search costs of finding new clients; (ii) supply constraints related limited access to credit that reduce exporters’ capacity to scale up after relative prices become more favorable; and (iii) reduced prices in US dollars offered by international buyers after local currency depreciations, akin to a pricing-to-market mechanism. We find evidence of the three drivers explaining the dampened export response to depreciations. Policymakers in developing countries should consider addressing these issues to maximize export responses to real depreciations.

How is research on social issues shaped in Latin America? How much attention do researchers give to poverty and inequality? What is the focus of research on these issues? The paper aims to analyse the main patterns of academic publications on poverty and inequality in Latin America. A bibliometric analysis based on different sources is used to review the main trends of publications on poverty and inequality in the region between 1990 and 2014. We find that although Latin America is widely recognized as one of the most unequal regions worldwide, poverty—not inequality—has been at the center of the region’s research agendas for many years. We detect a gradual shift in research from poverty to inequality, both in the academic literature and in the publications of international organizations. These findings provide new elements to better understand how and why researchers choose certain topics over others. This understanding is important both to gain knowledge on what researchers are prioritizing, and to shed light on the relationship between those priorities and public policies to combat poverty and inequality.

We present comparative evidence for eight Latin American countries regarding the design and effects of cash transfers (CTs). On the basis of household survey data, we analyze their coverage, importance in household income, and effects on poverty reduction and income redistribution. We present a static microsimulation to analyze the potential impacts of alternative program designs, including perfect targeting and higher budgets. Our results illustrate wide variation in terms of design, coverage, and importance in household income. CTs account for a significant portion of household income in lower deciles. Nonetheless, their effects in terms of reducing the incidence, intensity, and severity of poverty are moderate at best, and although their progressivity is high, their redistributive impact is limited. These results are mainly explained by the meager resources involved. Even under perfect targeting, the budgets allocated would be insufficient to achieve full coverage among households in the lowest part of the income distribution.

The lack of agreement between empirically-based works and theorization could be a consequence of the application of assumptions and methodologies which are not entirely suitable to understand some conditions about the studied phenomenon. In this study we face the challenge from a different perspective, using the unusual but highly suitable tools from qualitative comparison analysis developed by Ragin (2008). We center on the study of the presence or absence of sets of conditions that prompted investment booms between 1970 and 2012 in 11 Latin American countries. The technique allows us to confirm that the presence or absence of certain conditions is important when studying the relevance of the remaining ones.