Brain drain refers to the selective emigration of highly educated people, who often have stronger incentives to migrate and face fewer barriers. At first glance, this seems to be an adverse situation: losing doctors, engineers or teachers could hinder development. However, migration can also be beneficial by spurring investment in skills, fueling remittances, fostering innovation, business links, and transfers of knowledge and norms. The net impact depends on the skills involved and the context, creating an opportunity for policies that transform emigration into a driver of development.

We analyze how local exposure to populism affects population movements across Italian municipalities by citizenship, gender, age, and education. Using two causal designs—an instrumental variables strategy for national elections and a regression discontinuity design for mayoral elections—we find consistent evidence that both populist attitudes and policies reduce local attractiveness and increase out-migration. These effects are concentrated among young, female, and highly educated citizens who relocate within Italy, while the foreign population remains largely unaffected. This selective migration supports a ''foot-voting`` mechanism that reinforces partisan sorting and the persistence of populist strongholds.

We propose new ways to measure populism, using the Manifesto Project Database (1960-2019) as main source of data. We characterize the evolution of populism over 60 years and show empirically that it is significantly impacted by the skill-content of globalization. Specifically, imports of goods which are intensive in low-skill labor generate more right-wing populism, and low-skill immigration shifts the distribution of votes to the right, with more votes for right-wing populist parties and less for left-wing populist parties. In contrast, imports of high-skill labor intensive goods, as well as high-skill immigration flows, tend to reduce the volume of populism.

International migration is a selective process that induces ambiguous effects on human capital and economic development in countries of origin. We establish the theoretical micro-foundations of the relationship between selective emigration and human capital accumulation in a multi-country context. We then embed this migration-education nexus into a development accounting framework to quantify the effects of migration on development and inequality. We find that selective emigration stimulates human capital accumulation and the income of those remaining behind in a majority of countries, in particular in the least developed ones. The magnitude of the effect varies according to the level of development, the dyadic structure of migration costs, and the education policy. Emigration significantly reduces cross-country income inequality and the proportion of the world population living in extreme poverty.

We investigate the bidirectional relationship between immigration and right-wing populism, which we characterize as a self-reinforcing dynamic process where anti-immigrant rhetoric and populist policies lead to a deterioration in the average education and skill level of immigrants. The deterioration in the ratio of high-skill to low-skill immigrants in turn fuels populist support and anti-immigration attitudes, creating what we call "the vicious circle of xenophobia." We review some historical and contemporary studies that are suggestive of such a vicious circle. In particular, recent cross-country evidence shows that low-skill immigration tends to exacerbate populism, whereas high-skill immigration tends to mitigate it. Conversely, populist policies and xenophobic attitudes have a strong repulsive effect on highly skilled immigrants and result in adverse immigrant selection. We use the empirical results from those studies to inform a theoretical model of joint determination of immigrants' skill ratio and right-wing populism levels. The model displays multiple equilibria, with the inferior equilibrium-corresponding to our vicious circle-characterized by high levels of right-wing populism and a high proportion of low-skill workers among immigrants. In this framework, structural trends such as Internet penetration, economic erosion of the middle class, demographic pressure from poor countries as well as adverse cyclical shocks make the good, efficient equilibrium less likely and the inferior equilibrium of explosive populism and adverse immigrants' selection more likely.

Existing empirical literature provides converging evidence that selective emigration enhances human capital accumulation in the world's poorest countries. However, the within-country distribution of such brain gain effects has received limited attention. Focusing on Senegal, we provide evidence that the brain gain mechanism primarily benefits the wealthiest regions that are internationally connected and have better access to education. Conversely, human capital responses are negligible in regions lacking international connectivity, and even negative in better connected regions with inadequate educational opportunities. These results extend to internal migration, implying that highly vulnerable populations are trapped in the least developed areas.

We study the effect of local exposure to populism on net population movements by citizenship status, gender, age and education level in the context of Italian municipalities.We present two research designs to estimate the causal effect of populist attitudes and politics. Initially, we use a combination of collective memory and trigger variables as an instrument for the variation in populist vote shares across national elections. Subsequently, we apply a regression discontinuity design to estimate the effect of electing a populist mayor on population movements. We establish three converging findings. First, the exposure to both populist attitudes and policies, as manifested by the vote share of populist parties in national election or the close election of a new populist mayor, reduces the attractiveness of municipalities, leading to larger population outflows. Second, the effect is particularly pronounced among young, female, and highly educated natives, who tend to relocate across Italian municipalities rather than internationally. Third, we do not find any effect on the foreign population. Our results highlight a foot-voting mechanism that may contribute to a political polarization in Italian municipalities.

Throughout our project on premodern academia, we use a heuristic human capital index to measure each scholar’s quality. This index is built by combining several statistics from individual Wikipedia and Worldcat pages. The question we address here is whether this measure is correlated with the actual wages professors received. This note is a technical appendix to our paper on the academic market (De la Croix et al. 2020) but also has an interest as a stand-alone publication. There is considerable evidence that compensations for academic contractswentwell beyond paid salaries.1 They included payments from students, prebends,2 and many forms of in-kind benefits. Yet, it is interesting to examine the relationship between scholars’ human capital and existing data on monetary remunerations. Such remunerations have been used by Dittmar (2019) to show that professor salaries increased significantly relative to skilled wages after printing spread, with science professors benefiting from the largest salary increases. In the two sections below, we first review the available data on salaries, and argue that such data are imperfect proxies for the overall remuneration for academic services (i.e. a scholar’s market value). Keeping in mind such limitations, we thendocument a positive correlation between monetary income and scholars’ human capital.

We construct a model incorporating labor market frictions to elucidate income disparities among provinces, sectors (formal vs. informal), and skill categories (skilled vs. unskilled) within the Democratic Republic of the Congo. Through quantitative analysis, we demonstrate the significance of technologies, human capital, infrastructure, and labor market frictions in explaining spatial and intra-province inequalities. Although technological disparities emerge as the primary drivers, our findings underscore the presence of strong “O-ring” inequality patterns. This implies that effective development policies necessitate a mix of coordinated policy measures. When considered in isolation, policies focused on enhancing education, infrastructure, and mitigating labor market frictions could potentially escalate poverty along the intensive margin. Additionally, a development policy disregarding the informal sector also yields counterproductive distributional and poverty outcomes.

We argue that market forces shaped the geographic distribution of upper-tail human capital across Europe during the Middle Ages, and contributed to bolstering universities at the dawn of the Humanistic and Scientific Revolutions. We build a unique database of thousands of scholars from university sources covering all of Europe, construct an index of their ability, and map the academic market in the medieval and early modern periods. We show that scholars tended to concentrate in the best universities (agglomeration), that better scholars were more sensitive to the quality of the university (positive sorting) and migrated over greater distances (positive selection). Agglomeration, selection, and sorting patterns testify to an integrated academic market, made possible by the use of a common language (Latin).

Background: Detections of mutations of the SARS-CoV-2 virus gave rise to new packages of interventions. Among them, international travel restrictions have been one of the fastest and most visible responses to limit the spread of the variants. While inducing large economic losses, the epidemiological consequences of such travel restrictions are highly uncertain. They may be poorly effective when the new highly transmissible strain of the virus already circulates in many regions. Assessing the effectiveness of travel bans is difficult given the paucity of data on daily cross-border mobility and on existing variant circulation. The question is topical and timely as the new omicron variant -- classified as a variant of concern by WHO -- has been detected in Southern Africa, and perceived as (potentially) more contagious than previous strains. In this study, we develop a multi-country compartmental model of the SIR type. We use it to simulate the spread of a new variant across European countries, and to assess the effectiveness of unilateral and multilateral travel bans. Results: Multilateral travel bans do not buy much time, by increasing the time until the infection curve peaks by a few weeks at best. This can be achieved with drastic travel bans only, and this fails to stop the propagation when the virus is already circulating in the country, or in regions not included in the travel bans. Conclusion: Given their huge economic and freedom-killing consequences, travel bans have negligible effects on the timing and severity of the infection response. Managing new waves of COVID-19 with local sanitary measures applicable to cross-border movers is the most effective option. It induces better epidemiological outcomes and smaller economic cost for all parties concerned.

We analyze the long-run evolution of populism and explore the role of globalization in shaping such evolution. We use an imbalanced panel of 628 national elections in 55 countries over 60 years. A first novelty is our reliance on both standard (e.g., the ”volume margin”, or vote share of populist parties) and new (e.g., the ”mean margin”, a continuous vote-weighted average of populism scores of all parties) measures of the extent of populism. We show that levels of populism in the world have strongly fluctuated since the 1960s, peaking after each major economic crisis and reaching an all-time high – especially for right-wing populism in Europe – after the great recession of 2007-10. The second novelty is that when we investigate the ”global” determinants of populism, we look at trade and immigration jointly and consider their size as well as their skill-structure. Using OLS, PPML and IV regressions, our results consistently suggest that populism responds to globalization shocks in a way which is closely linked to the skill structure of these shocks. Imports of low-skill labor intensive goods increase both total and right-wing populism at the volume and mean margins, and more so in times of de-industrialization and of internet expansion. Low-skill immigration, on the other hand, tends to induce a transfer of votes from left-wing to right-wing populist parties, apparently without affecting the total. Finally, imports of high-skill labor intensive goods, as well as high-skill immigration, tend to reduce the volume of populism.

General equilibrium models are frequently used to estimate the effect of immigration on welfare and inequality in the host country. Existing studies differ in the way they formalize the labor market implications for natives, which in turn govern the strength of the other transmission mechanisms. To assess the extent to which the choice of the labor market specification influences the findings, we build an encompassing model that distinguishes between broad classes of individuals. We calibrate it for 20 selected OECD member states, and compare several specifications involving different assumptions on labor supply decisions, unemployment rates, and wage formation, as well as different calibration strategies. The size and the sign of the average welfare and distributional effects of immigration are robust to the labor market specification. Endogenizing unemployment and participation rates leads to slightly better welfare and distributional effects in most OECD countries but overall, adding margins of labor market adjustment barely affects the findings of models based on simpler assumptions.

The question of how people revise their decisions about whether to emigrate, and where to, when facing changes in the global environment is of critical importance in migration literature. We propose a cross-nested logit (CNL) approach to generalize the way deviations from the IIA (independence from irrelevant alternatives)) hypothesis can be tested and exploited in migration studies. Compared with the widely used logit model, the structure of a CNL model allows for more sophisticated substitution patterns between destinations. To illustrate the relevance of our approach, we provide a case study using migration aspiration data from India. We demonstrate that the CNL approach outperforms standard competing approaches in terms of quality of fit, has stronger predictive power, implies stronger heterogeneity in responses to shocks, and highlights complex and intuitive substitution patterns between all possible alternatives. In particular, we shed light on the low degree of substitutability between the home and foreign alternatives as well as on the subgroups of countries that are considered by potential Indian movers as highly or poorly substitutable.

We use a multilevel approach to investigate whether a general and robust relationship between weather shocks and (internal and international) migration intentions can be uncovered in Western African countries. We combine individual survey data with measures of localized weather shocks for 13 countries over the 2008-2016 period. A meta-analysis on results from about 51,000 regressions is conducted to identify the specification of weather anomalies that maximizes the goodness of fit of our empirical model. We then use this best specification to document heterogeneous mobility responses to weather shocks. We find that variability in Standardized Precipitation Evapotranspiration Index/rainfall is associated with changing intentions to move locally or internationally in a few countries only. However, the significance, sign and magnitude of the effect are far from being robust and consistent across countries. These differences might be due to imperfections in the data or to differences in long-term climate conditions and adaptation capabilities. They may also suggest that credit constraints are internalized differently in different settings, or that moving internally is not a relevant option as weather conditions are spatially correlated while moving abroad is an option of last resort. Although our multilevel approach allows us to connect migration intentions with the timing and spatial dimension of weather shocks, identifying a common specification that governs weather-driven mobility decisions is a very difficult, if not impossible, task, even for countries belonging to the same region. Our findings also call for extreme caution before generalizing results from specific case studies.

RÉSUMÉ The question of how people revise their decisions about whether to emigrate, and where to, when facing changes in the global environment is of critical importance in migration literature. We propose a cross-nested logit (CNL) approach to generalize the way deviations from the IIA (independence from irrelevant alternatives) hypothesis can be tested and exploited in migration studies. Compared with the widely used logit model, the structure of a CNL model allows for more sophisticated substitution patterns between

Background Assessing the impact of government responses to Covid-19 is crucial to contain the pandemic and improve preparedness for future crises. We investigate here the impact of non-pharmaceutical interventions (NPIs) and infection threats on the daily evolution of cross-border movements of people during the Covid-19 pandemic. We use a unique database on Facebook users’ mobility, and rely on regression and machine learning models to identify the role of infection threats and containment policies. Permutation techniques allow us to compare the impact and predictive power of these two categories of variables. Results In contrast with studies on within-border mobility, our models point to a stronger importance of containment policies in explaining changes in cross-border traffic as compared with international travel bans and fears of being infected. The latter are proxied by the numbers of Covid-19 cases and deaths at destination. Although the ranking among coercive policies varies across modelling techniques, containment measures in the destination country (such as cancelling of events, restrictions on internal movements and public gatherings), and school closures in the origin country (influencing parental leaves) have the strongest impacts on cross-border movements. Conclusion While descriptive in nature, our findings have policy-relevant implications. Cross-border movements of people predominantly consist of labor commuting flows and business travels. These economic and essential flows are marginally influenced by the fear of infection and international travel bans. They are mostly governed by the stringency of internal containment policies and the ability to travel.