Martina Viarengo

We re-examine the effects of negative weather anomalies during the growing season on the decision to migrate in rural households in five sub-Saharan African countries. To this end we combine a multi-country household panel dataset with high-resolution gridded precipitation data. We find that while the effect of recent adverse weather shocks is on average modest, the cumulative effect of a persistent exposure to droughts over several years leads to a significant increase in the probability to migrate. The results show that more frequent adverse shocks can have more significant and long-lasting consequences in challenging economic environments.

The paper examines the patterns of economic integration of refugees in Switzerland, a country with a long tradition of hosting refugees, a top-receiving host in Europe, and a prominent example of a multicultural society. It relies on a unique longitudinal dataset consisting of administrative records and social security data for the universe of refugees in Switzerland over 1998–2018. This data is used to reconstruct the individual-level trajectories of refugees and to follow them since arrival over the life-cycle. The study documents the patterns of labor-market integration, and highlights the heterogeneity by gender and age at arrival. Refugees’ labor-market performance is compared to natives’ and other groups of migrants’ labor-market performance. The empirical analysis exploits the government dispersal policy in place since 1998, which consists of the random allocation of refugees across cantons, to identify the causal effects of the local initial conditions. The study finds that higher unemployment rates at arrival slow down the integration process, whereas the existence of a co-ethnic network does not consistently lead to a faster integration. It is shown that in locations where refugees face relatively more hostile attitudes by natives upon arrival, they integrate at a faster pace, probably due to a greater effort undertaken in environments that are more hostile. Together these results, highlight the importance of an early entry in the labor market of the host country, and the need to take a longer run perspective when examining the effectiveness of policies, as the effects may vary over time and different complementary interventions may be needed in the short vs. long-run.

The idea that children have a “right to education” has been widely accepted since the Universal Declaration of Human Rights in 1948 (United Nations, 1948) and periodically reinforced since. The “right to education” has always, explicitly or implicitly, encompassed a “right to learn.” Measures of schooling alone, such as enrollment or grade attainment, without reference to skills, capabilities, and competencies acquired, are inadequate for defining education or education poverty. Because of education’s cumulative and dynamic nature, education poverty needs an “early” standard (e.g., Grade 3 or 4 or age 8 or 10) and a “late” standard (e.g., Grade 10 or 12 or ages 15 and older). Further, as with all international poverty definitions, there needs to be a low, extreme standard, which is found almost exclusively in low- and middle-income countries and can inform prioritization and
action, and a higher “global” standard, against which even some children in high income countries would be considered education poor but which is considered a reasonable aspiration for all children. As assessed against any proposed standard, we show there is a massive learning crisis: students spend many years in school and yet do not reach an early standard of mastery of foundational skills nor do they reach any reasonable global
minimum standard by the time they emerge from school. The overwhelming obstacle to addressing education poverty today is not enrollment/grade attainment nor inequality in learning achievement, but the fact that the typical learning profile is just too shallow for children to reach minimum standards.

We examine the role of financial aid in shaping the formation of human capital in economics. Specifically, we study the impact of a large merit-based scholarship for graduate studies in affecting individuals' occupational choices, career trajectories, and labor market outcomes of a generation of Italian economists with special focus on gender gaps and the role of social mobility. We construct a unique dataset that combines archival sources and includes microdata for the universe of applicants to the scholarship program and follow these individuals over their professional life. Our unique sample that focuses on the high end of the talent and ability distribution also allows us to analyze the characteristics of top graduates, a group which tends to be under-sampled in most surveys. We discuss five main results. First, women are less likely to be shortlisted for a scholarship as they tend to receive lower scores in the most subjective criteria used in the initial screening of candidates. Second, scholarship winners are much more likely to choose a research career and this effect is larger for women. Third, women who work in Italian universities tend to have less citations than men who work in Italy. However, the citation gender gap is smaller for candidates who received a scholarship. Fourth, women take longer to be promoted to the rank of full professor, even after controlling for academic productivity. Fifth, it is easier to become a high achiever for individuals from households with a lower socio-economic status if they reside in high social mobility provinces. However, high-achievers from lower socio-economic status households face an up-hill battle even in high social mobility provinces.

The learning crisis in developing countries is increasingly acknowledged (World Bank, 2018). The UN’s Sustainable Development Goals (SDG) include goals and targets for universal learning and the World Bank has adopted a goal of eliminating learning poverty. We use student level PISA-D results for seven countries (Cambodia, Ecuador, Guatemala, Honduras, Paraguay, Senegal, and Zambia) to examine inequality in learning outcomes at the global, country, and student level for public school students. We examine learning inequality using five dimensions of potential social disadvantage measured in PISA: sex, rurality, home language, immigrant status, and socio-economic status (SES)—using the PISA measure of ESCS (Economic, Social,and Cultural Status) to measure SES. We document four important facts. First, with the exception of Ecuador, less than a third of the advantaged (male, urban, native, home speakers of the language of instruction) and ESCS elite (plus 2 standard deviations above the mean) children enrolled in public schools in PISA-D countries reach the SDG minimaltarget of PISA level 2 or higher in mathematics (with similarly low levels for reading and science). Even if learning differentials of enrolled students along all five dimensions of disadvantage were eliminated, the vast majority of children in these countries would not reach the SDG minimum targets. Second, the inequality in learning outcomes of the in-school children who were assessed by the PISA by household ESCS is mostly smaller in these less developed countries than in OECD or high-performing non-OECD countries. If the PISA-D countries had the same relationship of learning to ESCS as Denmark (as an example of a typical OECD country) or Vietnam (a high-performing developing country) their enrolled ESCS disadvantaged children would do worse, not better, than they actually do. Third, the disadvantages in learning outcomes along four characteristics: sex, rurality, home language, and being an immigrant country are absolutely large, but still small compared tothe enormous gap between the advantaged, ESCS average students,and the SDG minimums. Given the massive global inequalities, remediating within-country inequalities in learning, while undoubtedly important for equity and justice, leads to only modest gains towards the SDG targets. Fourth, even including both public and private school students,there are strikingly few children in PISA-D countries at high levels of performance. The absolute number of children at PISA level 4 or above (reached by roughly 30 percent of OECD children) in the low performing PISA-D countries is less than a few thousand individuals, sometimes only a few hundred—in some subjects and countries just double or single digits. These four hard lessons from PISA-D reinforce the need to address global equity by “raising the floor” and targeting low learning levels (Crouch and Rolleston, 2017; Crouch, Rolleston, and Gustafsson, 2020). As Vietnam and other recent successes show, this can be done in developing country settings if education systems align around learning to improve the effectiveness of the teaching and learning processes to improve early learning of foundational skills.

We study the relationship between housing inequality and crime in South Africa. We create a novel panel dataset combining information on crimes at the police station level with census data. We find that housing inequality explains a significant share of the variation in both property and violent crimes, net of spillover effects, time and district fixed effects. An increase of one standard deviation in housing inequality explains between 9 and 13 percent of crime increases. Additionally, we show that a prominent post-apartheid housing program for low-income South Africans led to a reduction in inequality and a decline in violent crimes. Together, these findings suggest the important role that equality in housing conditions can play in the reduction of crime in an emerging economy context.