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on Education |
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Issue of 2026–04–13
twenty-six papers chosen by João Carlos Correia Leitão, Universidade da Beira Interior |
| By: | Cygan-Rehm, Kamila (Dresden University of Technology (TUD)); Westphal, Matthias (FernUni Hagen, RWI) |
| Abstract: | This paper replicates and extends the evidence on the lifetime effects of school starting age on earnings by Fredriksson and Öckert (2014) for Sweden. Using German data for individuals born between 1945 and 1965, we examine a more rigid system of ability tracking in secondary education, a potential driver of long-term effects. We confirm negligible effects of later school entry for men and positive effects for women. These gender differences arise despite similar effects on educational attainment. By unfolding the gender gaps over the lifecycle, assessing fertility decisions, and maternal employment around the first birth, we show that childbirth postponement and increased labor market attachment after the first birth seem to be plausible mechanisms. |
| Keywords: | school starting age, lifetime effects, education, gender gaps |
| JEL: | I21 I24 I26 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18503 |
| By: | Gallegos, Sebastian (Universidad Adolfo Ibañez) |
| Abstract: | This paper estimates the causal effect of a large language model–based study assistant on student behavior and learning outcomes in a natural field setting with real academic stakes. I design and deploy a course-specific AI assistant (GPT-UAI) for undergraduate econometrics and evaluate it through two randomized interventions implemented across seven coordinated course sections at a selective university in Chile. The first intervention targets the extensive margin of use, encouraging GPT-UAI adoption prior to the midterm exam. The encouragement raises the GPT’s awareness and reported usage, but does not change its perceived value and does not improve midterm performance. The second intervention targets use at the intensive margin, providing guidance on learning-oriented usage for the final exam. Guidance shifts interactions with GPT-UAI toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, improves final-exam performance by 0.21 standard deviations, and raises the probability of earning a passing exam grade by 12 percentage points. The findings suggest that learning gains arise less from adoption than from guiding how students use course-specific AI assistants. |
| Keywords: | generative AI, large language models, higher education, field experiments, randomized controlled trials, student learning, human capital, AI-assisted learning, tutoring, technology in education |
| JEL: | I23 C93 O33 D83 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18513 |
| By: | Li Kathrin Kaja Rupieper; Stephan Thomsen |
| Abstract: | Lifelong learning is increasingly recognized as important for individual well-being, but causal evidence on this relationship remains scarce. This paper evaluates the effects of non-formal adult education on life satisfaction by exploiting the substantial expansion of courses at East German Volkshochschulen (VHS) following reunification. Combining individual well-being data from SOEP with administrative VHS data, we use quasi-random variation in individuals’ exposure to courses to identify intention-to-treat effects. Estimation results denote small but significant and robust effects of VHS education on life satisfaction. Calculations of average treatment-on-the-treated effects suggest considerably stronger impacts among actual course participants. We furthermore reveal effect heterogeneity across demographic groups. In contrast to formal education, which is commonly found to raise aspirations, we find no corresponding effect of VHS education. Overall, our findings suggest that non-formal courses and training provide an easily accessible, low-cost means of adaptation in times of transformation. |
| Keywords: | Volkshochschule, adult education, transformation, SOEP, Germany, subjective well-being, natural experiment |
| JEL: | H52 I26 I31 N34 P29 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp1237 |
| By: | Berlinski, Samuel (Inter-American Development Bank); Giannola, Michele (University of Naples Federico II, CSEF and the Institute for Fiscal Studies); Toppeta, Alessandro (SOFI, Stockholm University) |
| Abstract: | We study the relative effectiveness, cost-effectiveness, and interaction of family- and school -based learning interventions using a randomized controlled trial in Colombia that assigns children to a parental engagement program, a teacher professional development program, both, or a control group. Both interventions are grounded in a child-centered learning approach that emphasizes active engagement and the progression from informal to formal mathematical understanding. Each intervention independently generates sizable and statistically similar gains in early numeracy (0.17σ and 0.20σ). Combining them produces no additional learning gains, suggesting that the two interventions act as substitutes over the time horizon and skill domain we study. When benefits accruing to future cohorts are taken into account, the teacher development program becomes at least as cost-effective as, and potentially more cost-effective than, the parental engagement intervention. Our results suggest that, in this setting, strategically concentrating resources on a single binding constraint – either at home or in school – maximizes the short-run learning gains per dollar spent. |
| Keywords: | numeracy, childhood development, teacher development, parental engagement, randomized control trial, Colombia |
| JEL: | I21 I25 O15 J13 C93 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18485 |
| By: | Yang, Xi (University of North Texas); Zou, Jian (Cornell University) |
| Abstract: | This paper studies the impact of US interstate bank branching deregulation on school finance and student achievement, leveraging the deregulation as a state tax revenue shock. Total revenue and expenditure increase following the deregulation. The revenue increase stems mainly from higher state aid, with spending gains concentrated in capital outlays. Deregulation subsequently improves student achievement, with no distributional effects evident across students’ ability, race, or free lunch status. The findings highlight the spillover benefits of a centralized school finance system in channeling positive tax revenue shocks into public education funding and human capital formation. |
| Keywords: | banking deregulation, school finance system, student achievement |
| JEL: | G21 G28 H75 I21 I22 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18514 |
| By: | Badini, Sofia (IIASA); Gehrke, Esther (Wageningen University); Lenel, Friederike (Potsdam Institute for Climate Impact Research); Schupp, Claudia (Technical University Munich) |
| Abstract: | We implement a randomized controlled trial in a low-income context to investigate whether students in lower-secondary school acquire information about potential career paths more effectively if this information is preceded by a task that allows students to explore their own interests and if the career information is ordered by the congruence between the careers and the student’s personality. We find that self-exploration in combination with the personalized display increases student information acquisition. Students also read about more diverse career paths and, low-performing students in particular, shift their focus from occupations that require university education towards those that require a high-school degree and are potentially more achievable. |
| Keywords: | information acquisition, career guidance, education, field experiment |
| JEL: | C93 D83 D91 I21 O15 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18490 |
| By: | Bagues, Manuel (University of Warwick); Makany, Milan (Erasmus University); Vattuone, Giulia (SOFI, Stockholm University); Zinovyeva, Natalia (University of Warwick) |
| Abstract: | We study how faculty promotion decisions shape women's careers and the academic pipeline, using data from 4, 000 Spanish university departments across all disciplines. We identify exogenous variation in promotions using the random assignment of evaluators to promotion committees between 2002 and 2008: applicants whose committees included a co-author or colleague were significantly more likely to qualify for promotion. We document two main findings. First, failing to obtain tenure has asymmetrically lasting consequences for women. Those who narrowly miss tenure are 57 percentage points less likely to be tenured fifteen years later, compared to 29 percentage points for men. Second, when women do obtain tenure, the effects extend well beyond their own careers: promoting a woman to Associate Professor increases female faculty by 1.5 members after 15 years, leads to six additional female PhD graduates over the following decade, and raises the number who subsequently remain in academia and reach tenured positions. |
| Keywords: | academic promotions, women in academia, natural experiment |
| JEL: | I23 J16 J44 M51 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18477 |
| By: | Lutz Hendricks; Tatyana Koreshkova; Oksana Leukhina |
| Abstract: | This paper studies the effects of expanding high-quality public university capacities on student earnings and welfare. Using a quantitative model of college choice, we find that expanding the most selective colleges by 20 percent increases aggregate earnings by 0.8 percent and welfare by 2 percent. The gains arise because a large number of high-ability students are rationed out of selective colleges. When admitted, these students graduate at high rates and enjoy substantial earnings gains. The earnings gains generated by expanding college capacity are eight times larger than the fiscal cost of financing it. These findings remain robust when we account for peer effects in learning and general equilibrium changes in the college wage premium. |
| Keywords: | college quality; human capital; public finance of higher education |
| JEL: | J24 |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:102965 |
| By: | Rivero, Rosario (Universidad Diego Portales); Sánchez, Rafael (CUNEF, Madrid); Valencia, Edgar (Pontificia Universidad Catolica de Chile); Rojas, Maria Eugenia (Pontificia Universidad Catolica de Valparaiso) |
| Abstract: | Research on teacher preparation programs (TPPs) continues to debate whether program quality meaningfully influences teacher effectiveness. Evidence from the United States often reports substantial program-level variation, but the external validity of these findings for other contexts remains uncertain. Using national administrative records and value-added models, this study examines the contribution of TPPs to student achievement in Chile. Results show that TPPs account for only about 5% of the variance in student outcomes. Rather than reflecting uniformly strong preparation, this limited variation reveals a paradox: programs appear remarkably similar, yet convergence reflects alignment around a mid-level standard rather than excellence. Interpreted through the theoretical lenses of teacher learning trajectories, accountability, and equity-oriented preparation, the findings suggest that regulatory reforms may yield uniformity without quality. This study contributes new empirical evidence from Latin America and advances theory by identifying institutional convergence and bounded instructional learning as mechanisms linking accountability reforms to teacher effectiveness. |
| Keywords: | teacher preparation programs, teacher education, teacher learning, institutional isomorphism, Latin America, Chile |
| JEL: | I20 I28 I29 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18505 |
| By: | Congdon Fors, Heather (Department of Economics, School of Business, Economics and Law, Göteborg University); Isaksson, Ann-Sofie (The Institute for Futures Studies); Lindskog, Annika (Department of Economics, School of Business, Economics and Law, Göteborg University); Sepahvand, Mohammad (Lund University) |
| Abstract: | Abstract This paper examines whether and how Islamist violence affects educational content in secondary schools in Burkina Faso. We combine geocoded conflict-event data with nationwide school census data containing detailed information on teachers’ subject-specific teaching hours and mobility across schools. Using a staggered difference-in-differences design, we estimate the dynamic effects of violence on school availability and instructional time. We find no change in total hours of core subjects following local conflict, but evidence of increased Arabic instruction. We also document effects on teacher retention. Following local episodes of Islamist violence, teachers become more likely to move to schools in other municipalities, with the strongest mobility responses observed among those teaching French and mathematics. These findings suggest that violent extremism can reshape educational content even without formal curriculum reform, through changes in school availability, the allocation of instructional time, and the composition of the teaching workforce. |
| Keywords: | Conflict; violence; education; schooling; Sub-Saharan Africa; Sahel; Burkina Faso |
| JEL: | D74 I20 I25 O12 O55 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0862 |
| By: | Daniel Duque (Department of Economics, Faculty of Economics and Administration, Masaryk University, Brno, Czech Republic) |
| Abstract: | Do public investments in compulsory schooling translate into the same long-run economic gains for all? Using a large intergovernmental transfer reform in Norway that mechanically shifted municipal school revenues based on pre-reform student age structure, I estimate the long-run impacts of school funding on human capital, earnings, and family outcomes. First, municipalities primarily allocate additional funds hiring more teachers. Secondly, average earnings effects for students are positive but moderate: nine years of exposure to an additional $100 per pupil per year increases annual earnings by about $200 in the mid-30s, implying an internal rate of return of about 6% and a marginal value of public funds between roughly 1.2 and 2.1. However, these mean effects mask sharp heterogeneity by gender and parental background. The labor-market return is concentrated among men, whose earnings rise by over $350 per year, while women show little response in own earnings. Economic gains for women are instead realized primarily through the marriage market, experiencing significant increases in partner earnings and couple per-capita income, alongside higher partnership formation. A simple model with skill-increasing labor-market discrimination and gendered norms about partner-provided consumption rationalizes why similar human-capital gains can map into gender-divergent earnings channels. Finally, returns are largest for boys from low-educated families, consistent with partial parental crowd-out of public investments among highly educated households. |
| Keywords: | Education; Intergovernmental Transfers; School Funding |
| JEL: | H75 I21 I26 I28 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:mub:wpaper:2026-02 |
| By: | Ejermo, Olof (The Ratio Institute); Holmström, Peter (The Ratio Institute) |
| Abstract: | Using population-wide data on Swedish university researchers and teachers, we identify the effects of parenthood on academic careers. Leveraging staggered event-study models that compare mothers and fathers around first birth, we document widening gender gaps in publication output, wage income, promotion, and PhD completion. These gaps arise across all scientific fields. We further document substantial gender differences prior to first birth and among never-parents, indicating that child-related penalties explain only part of the overall academic gender gap. |
| Keywords: | academic careers; child penalty; parenthood; gender gap; Sweden; staggered event study; research productivity |
| JEL: | I23 J13 J16 J24 |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ratioi:0389 |
| By: | Sylvain K. Assienin (UJloG - Université Jean Lorougnon Guédé); Auguste K. Kouakou (UJloG - Université Jean Lorougnon Guédé); Christian K. Nda (UJloG - Université Jean Lorougnon Guédé); Loukou L. E. Yobouet (Université Alassane Ouattara [Bouaké, Côte d'Ivoire]) |
| Abstract: | The aim of this paper is to analyse the impact of school governance on learner performance in Sub-Saharan Africa, in the face of persistent low performance in the region, revealed by the PASEC 2019 report. The study uses an econometric model followed by machine learning models (Regression Logistic, Random Forest, Extra Tress Classifier, Extreme Gradient Boosting, Artificial Neural Networks) to explore the relationships between school results and governance factors measured by school management, pedagogical practices and relations with stakeholders. The results show that artificial neural network models perform better than conventional approaches in terms of accuracy and explainability. Explainability by Shapley values shows that the quality of administrative and pedagogical management, benevolent school-student relations, and activities to promote the best students significantly improve performance. The study suggests capacity building for managers in order to improve the quality of administrative and pedagogical management. It also highlights the need to promote rigorous administrative governance, based on effective practices and adapted to local realities. In addition, specific strategies should be put in place to reward high-performing students, while encouraging professional collaboration between education stakeholders. Finally, a review of parental involvement practices is recommended in order to avoid inappropriate expectations likely to be detrimental to learners' performance. |
| Keywords: | Shapley values, neural networks, school performance, school governance |
| Date: | 2025–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05547822 |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI Prague); Schroeder, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Orebro University, Ratio Institute, GLO) |
| Abstract: | Using surveys of Danish firms and individuals linked to employer–employee administrative data, we analyze AI adoption across technologies, business functions, and workers. We show that AI adoption is driven primarily by firm capacities rather than performance. Adoption is strongly associated with firm size, digital infrastructure, and workforce composition, particularly education and STEM intensity, while productivity and capital intensity explain little of the variation. Conditional on AI adoption, larger and more digitally mature firms deploy advanced technologies more broadly. Moreover, AI technologies diffuse across multiple business functions while other advanced technologies remain function-specific. Individual-level evidence mirrors these patterns and points towards workforce readiness as a key determinant of AI adoption. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict actual adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence, technology adoption, digitalisation, human capital, AI exposure measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18515 |
| By: | Daniel Goller; Enzo Brox; Stefan C. Wolter |
| Abstract: | Why do young people sort into poorly fitting occupations? This paper shows that imperfect self-knowledge about skills is an important source of skill mismatch at labor market entry. We use unique data from standardized professional aptitude tests linked to administrative records on educational trajectories and early labor market outcomes in Switzerland. The data allow us to observe objective skills and subjective skill beliefs for many productivity-relevant skills in a high-stakes setting. We document large differences among individuals in how well their beliefs align with their skills. Imperfect self-knowledge predicts misaligned occupational aspirations, higher realized skill mismatch, and a higher probability of dropout. Guided by a Roy-style model of occupational choice with imperfect self-knowledge, we interpret these findings as evidence that distorted self-assessments at the school-to-work transition contribute to the misallocation of talent. |
| Keywords: | Information frictions, Occupational choice, Skill mismatch, Self-knowledge |
| JEL: | D83 J24 J41 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0253 |
| By: | Manuel Arellano; Orazio Attanasio; Margherita Borella; Mariacristina De Nardi; Gonzalo Paz-Pardo |
| Abstract: | We develop a new approach to estimating earnings, job, and employment dynamics using subjective expectations data from the NY Fed Survey of Consumer Expectations. These data provide beliefs about future earnings offers and acceptance probabilities, offering direct information on counterfactual outcomes and enabling identification under weaker assumptions. Our framework avoids biases from selection and unobserved heterogeneity that affect models using realized outcomes. First-step fixed-effects regressions identify risk, persistence, and transition effects; second-step GMM recovers the covariance structure of unobserved heterogeneities such as ability, mobility, and match quality. We find lower risk and persistence of the individual productivity component than in prior work, but greater heterogeneity in ability and match quality. Simulations show that reduced-form estimates overstate persistence and volatility on individual-level productivity due to job transitions and sorting. After accounting for heterogeneity, volatility declines and becomes flat across the earnings distribution. These results underscore the value of expectations data. |
| JEL: | C23 C8 D15 J01 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35027 |
| By: | Philipp Dierker (Max Planck Institute for Demographic Research, Rostock, Germany); Ariane Ophir; Nicole Hiekel (Max Planck Institute for Demographic Research, Rostock, Germany) |
| Abstract: | Objective: This study examines how short-term fertility intentions evolve before and after transitions between relationship stages. Background: Prior research has primarily compared fertility intentions across partnership statuses, while giving less attention to within-person dynamics surrounding partnership transitions and the question of whether shifts reflect anticipatory selection or post-transition changes. Method: Using longitudinal data from the German Family Panel (waves 2008–2022), we apply an event-centered fixed effects design to estimate changes in fertility intentions up to three years before and after transitions to dating, cohabitation, and marriage. Stratified analyses assess variation by gender, age, and subsequent relationship stability. Results: Entry into dating from singlehood is followed by a within-person increase in fertility intentions, indicating that dating functions as a turning point activating fertility planning. Entry into cohabitation is associated with rising intentions prior to the transition and sustained increases thereafter, suggesting that cohabitation consolidates fertility plans. Marriage transitions are characterized by anticipatory increases in fertility intentions. Fertility intentions increase after entry into dating only in relationships that persist, underscoring the role of stability in consolidating early adjustments. Men and older individuals enter marriage with high fertility intentions, while women report higher intentions before cohabitation. Conclusions: Different partnership stages act as distinct mechanisms for the evolution of fertility intentions over the life course. Dating is an activation stage where fertility planning emerges, while cohabitation reinforces and consolidates plans and marriage reflects anticipatory selection. |
| JEL: | J1 Z0 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:dem:wpaper:wp-2026-010 |
| By: | Portes, Jonathan (King's College London); Springford, John (Centre for European Reform) |
| Abstract: | This paper estimates the causal impact of Brexit on migrant employment in the United Kingdom using a synthetic difference-in-differences (SDID) framework. We construct a counterfactual trajectory for the UK based on a weighted combination of comparable European economies and compare post-referendum outcomes to this benchmark. Rather than analysing migration flows, which are subject to substantial revision and comparability issues, we focus on employment stocks of foreign-born workers using administrative payroll data. We find that Brexit led to a large compositional shift in migrant labour supply and a modest change in its overall size. Employment of EU-origin workers declined substantially relative to the counterfactual following the 2016 referendum and the subsequent end of free movement. However, this decline was more than offset by a sharp increase in employment among non-EU workers after the introduction of the post-Brexit immigration system in 2021. By 2024, total foreign-born employment is about 0.6% higher than in the absence of Brexit. Brexit did not reduce migrant labour supply as widely expected, but instead reconfigured its composition, and highlight the interaction between migration policy and labour demand. |
| Keywords: | immigration, employment, UK, Brexit, synthetic differences in differences |
| JEL: | J61 J21 F22 J23 C23 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18478 |
| By: | Leland D. Crane; Paul E. Soto |
| Abstract: | We evaluate whether LLMs have had any discernible impact on the aggregate labor market so far. We focus on occupations that are computer programming-intensive, motivated by data showing that coding is one of the most LLM-exposed tasks. Linking O*NET to CPS we find that aggregate employment of coders has decelerated sharply since the introduction of ChatGPT. Using a novel control variable for industry-level shocks we show that the deceleration is not attributable to the exposure of coders to slowing industries, suggesting instead that coders experienced an occupation-specific shock around the introduction of ChatGPT. Coder employment has continued to grow in recent years, though much more slowly than it did pre-2022. We validate the industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks. We also provide statistics on the agreement rates between different measures of AI exposure. |
| Keywords: | Labor demand; Machine learning; Shocks |
| JEL: | J23 J24 O33 |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102997 |
| By: | Ejermo, Olof (Department of Economic History, Lund University); Holmström, Peter (Department of Economic History, Lund University) |
| Abstract: | Using population-wide data on Swedish university researchers and teachers, we identify the effects of parenthood on academic careers. Leveraging staggered event-study models that compare mothers and fathers around first birth, we document widening gender gaps in publication output, wage income, promotion, and PhD completion. These gaps arise across all scientific fields. We further document substantial gender differences prior to first birth and among never-parents, indicating that child-related penalties explain only part of the overall academic gender gap. |
| Keywords: | academic careers; child penalty; parenthood; gender gap; Sweden; staggered; event study; research productivity |
| JEL: | C23 I23 J13 J16 J24 J31 |
| Date: | 2026–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:luekhi:0266 |
| By: | Pulito, Giuseppe; Pytlikova, Mariola; Schroeder, Sarah; Lodefalk, Magnus |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer- employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence, Technology Adoption, Digitalisation, Human capital, AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1732 |
| By: | Dziadula, Eva (University of Notre Dame); Zavodny, Madeline (University of North Florida) |
| Abstract: | The educational distribution of U.S. immigrants shifted significantly to the right in recent decades as the share without a high school diploma fell and the share with a bachelor's degree rose. This improvement coincided with a shift in immigrants' origins toward Asia and rising global education levels. This study examines how much of the change in immigrants' educational distribution over 2000-2019 is due to changes in their distribution across origin countries versus rising attainment among immigrants within origin countries. We demonstrate that within-country changes account for most of the observed increase in the educational distribution. In contrast, changes in where immigrants originated played a minimal role. Finally, we show that economic conditions in origin countries can explain little of this rise, whereas demographic trends and the skill composition of U.S. temporary worker visas are significantly related to changes in immigrants' educational distribution. |
| Keywords: | immigration, education, human capital |
| JEL: | I21 J15 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18480 |
| By: | Ana Petrovic; Maarten van Ham; David Manley; Tiit Tammaru |
| Abstract: | There are relatively few comparative cross-European studies on segregation, and those that do exist often use a single measure of segregation at a single spatial scale. This paper investigates ethnic segregation in seven European capitals (Amsterdam, Berlin, Lisbon, London, Madrid, Paris, and Rome) using the five dimensions of segregation (centralisation, evenness, exposure, clustering, and concentration) at multiple spatial scales. For each dimension, we found very different levels of segregation. Moreover, the impact of scale was different in both between and within cities relative to their cores and hinterlands. Crucially, we found that segregation does not necessarily decrease with spatial scale. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03287 |
| By: | Shaban, Morsi Abdulla |
| Abstract: | Abstract Educational disruption in conflict-affected regions is often quantified through descriptive statistics, yet rarely analysed through causal lenses that account for the sequential nature of household decisions under survival constraints. This study introduces a causal data science framework that combines causal inference with machine learning to estimate the causal effect of resource-based interventions on school attendance in closed-system scarcity environments. Using secondary data from United Nations agencies, the World Bank, and peer-reviewed literature (2023–2026), we construct a synthetic population that replicates the demographic, nutritional, and water-access conditions of the Gaza Strip. The framework estimates heterogeneous treatment effects through a two-stage procedure: first, inverse probability weighting adjusts for observed confounders; second, double machine learning with gradient boosting and causal forests captures non-linear interactions and effect heterogeneity. Policy implications are derived from optimal policy trees that partition households into subgroups with distinct intervention recommendations. Results indicate that decentralised water access increases attendance by an average of 32.1 percentage points, with gains reaching 38–45 percentage points among households initially spending more than five hours on daily survival labour. Nutritional supplementation alone yields a smaller but significant average gain of 11.3 percentage points, primarily through cognitive recovery. Critically, the two interventions are complementary: a formal interaction analysis reveals a synergistic effect of 12.4 percentage points ( p < 0.001), such that combined water–nutrition packages generate substantially larger gains than either intervention alone. Policy trees recommend water interventions for high‑labour households and combined water–nutrition packages for those with elevated physiological penalty scores. All causal estimates pass refutation tests (random common cause, placebo treatment, data subset), confirming robustness. By relying exclusively on secondary data and simulation, the framework operates without requiring primary data collection or direct human subject involvement, thereby avoiding the logistical and institutional review complexities of fieldwork in active conflict zones. The methodology is readily transferable to other humanitarian settings where secondary data are available. |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:osf:edarxi:3vckt_v1 |
| By: | Lodefalk, Magnus (The Ratio Institute); Löthman, Lydia (The Ratio Institute); Koch, Michael (The Ratio Institute); Engberg, Erik (The Ratio Institute) |
| Abstract: | We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden's largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden's seven-month gap between the Riksbank's first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer–employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers. |
| Keywords: | Generative artificial intelligence; Job postings; Labour demand; Employment composition; Monetary policy |
| JEL: | J23 J24 O33 |
| Date: | 2026–03–16 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ratioi:0388 |
| By: | Congdon Fors, Heather (Department of Economics, School of Business, Economics and Law, Göteborg University); Isaksson, Ann-Sofie (The Institute for Futures Studies); Lindskog, Annika (Department of Economics, School of Business, Economics and Law, Göteborg University); Sepahvand, Mohammad (Lund University) |
| Abstract: | This paper examines how violent conflict affects the supply of education in one of the world’s poorest countries, Burkina Faso. We combine unique nationwide census data on all primary schools with geocoded records of Islamist violence to assess how local conflict exposure shapes school availability, staffing, and the mix of education provision. Using a staggered difference-in-differences design and teacher-level panel data, we find that conflict significantly reduces the number of open schools, teachers and pupils. In affected areas, primary schools experience declines in teacher experience and female representation, though effects on qualifications are limited. We also find evidence of sizable geographical spillovers, and that violence increases teachers’ likelihood of moving to other municipalities or leaving the public education system altogether. The results highlight how insecurity undermines education supply through both direct disruption and teacher displacement, with important implications for service delivery in fragile settings. |
| Keywords: | Conflict; violence; education; schooling; Sub-Saharan Africa; Sahel; Burkina Faso |
| JEL: | D74 I20 I25 O12 O55 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0861 |