Research

This paper examines the short- and long-term effects of education expenditure on economic growth using a global panel of 107 countries from 1970 to 2019. Employing a CS-ARDL model with an error correction framework, we estimate dynamic responses to aggregate and disaggregated real education spending across primary, secondary, and tertiary levels. Results show that public investment in education has a statistically significant and positive impact on real GDP per capita, both in the short and long run. The long-run elasticity is highest for aggregate expenditure and comparable across primary and secondary education, though smaller for tertiary education. We further explore heterogeneity in long-run effects and find that higher educational achievement, proxied by PISA scores, amplifies the growth impact of education spending. Simulations confirm that countries with higher PISA scores experience faster and stronger returns to investment. These findings underscore the role of education quality in enhancing the productivity of public education spending and support targeted policy efforts to improve both the quantity and quality of educational investment.

This study analyzes schools’ institutional readiness to adopt generative artificial intelligence (GenAI), using data from ICILS 2023. Given the limited empirical evidence on its adoption in schools, we propose a novel measurement approach using principal component analysis (PCA) that identifies three key dimensions: positive expectations, perceived risks, and institutionalization of GenAI use. We observe consistent socioeconomic gaps across countries: schools serving more vulnerable students are systematically less prepared to adopt GenAI, which could exacerbate educational inequalities if the technology improves productivity. We also find a positive statistical association between institutional readiness and students’ digital competencies. Our main contribution is a replicable metric for comparative research and monitoring, while also underscoring the importance of education policies focused on technological equity.

I propose a school choice policy to recommend guaranteed school alternatives for students left unassigned under stable matching. By leveraging revealed preferences, I design a personalized recommendation mechanism that offers voluntary allocations to unlisted but potentially desirable nearby schools. Using rich administrative data from the Chilean school choice system, I simulate the proposed intervention across the full applicant pool to assess its general equilibrium effects on the resulting allocations. Results indicate that in the main round, the mechanism is able to (i) reduce the proportion of unmatched applicants by up to 50%, (ii) increase expected aggregate utility by 2–6%, and (iii) concentrate utility gains among applicants who apply to oversubscribed programs. To facilitate implementation, I develop a targeted submarket implementation that yields comparable improvements while preserving all original assignments. Overall, the policy offers a cost-effective and scalable solution to improve match outcomes by redistributing excess demand within centralized assignment systems.

This paper studies the effects of a large and gradual increase in the Chilean school day over students’ academic achievement. We exploit a gradual and exogenous variation produced by the reform with an innovative measure of exposure to longer school day treatment. Using longitudinal data at an individual level and a fixed-effects strategy, we find that in the long run, this reform had no relevant effect on students’ standardized test scores nor higher education enrollment. However, this paper found heterogeneous response to additional instructional time by gender, type of school, and socioeconomic status. These results are robust to the inclusion of several covariates and insensible to the cohort selection.

This study reviews evidence on implementing the extended school day (ESD), a policy adopted across Latin America to improve academic results and educational quality, especially in schools serving vulnerable students. Drawing on the experiences of Chile, Colombia, Pernambuco (Brazil), and Portugal, it finds that ESD requires high funding and maintenance costs and detailed planning, and that its effects on standardized academic performance are marginal in most settings, though Pernambuco stands out as a success. Beyond test scores, the region shows ample evidence of socioemotional, family, and community benefits, including lower school dropout and teenage pregnancy and greater female labor-force participation. The note discusses the main implementation challenges (infrastructure, cost, organizational planning, national coordination, pedagogical redesign, and community engagement) and proposes strategies to mitigate them, such as starting with pilot programs and phasing in the reform gradually.

Published by Chile’s Energy Sustainability Agency (AgenciaSE) for the third consecutive year, this report analyzes the costs of charging infrastructure (CI) for electric vehicles in Chile, with particular attention to projects on public land and volume-based discount estimates across categories. Based on primary data collection, it documents a rise in prices over the past year, most pronounced for higher-power projects, linked to global inflation, higher transport costs, and supply-chain constraints. It characterizes the Chilean CI market as incipient but growing, with new infrastructure providers, more trained installers, and emerging cost-optimization strategies, and aims to reduce information asymmetries and foster a more competitive charging market.

I develop a model of supply and demand with imperfect competition to study the primary education market in Chile. I use this framework to analyze how voucher policy affects incentives for schools to supply quality. First, I provide evidence that introducing a voucher targeted at poorer students led schools to improve quality. Next, I estimate demand and use the estimates to quantify the mechanisms that incentivized for-profit schools to improve. My estimates indicate that under a flat voucher system, schools leverage market power to reduce quality below the competitive benchmark, particularly in the poorest neighborhoods where competitive incentives are weakest. My results indicate the introduction of a targeted voucher policy reduced schools’ market power and increased marginal revenue, improving observed school quality. These findings provide evidence that targeted vouchers enhanced academic achievement and equity by increasing resources for schools and intensifying competition where it was previously lacking.

We study how information about school quality affects school choice and what happens when such an intervention is scaled up. In a randomized trial in Chile, families offered a short video and personalized school report card choose schools with higher test scores and value added. Treated children score 0.2 standard deviations higher on standardized tests, with gains persisting sixteen years later. We embed the experimentally identified demand shift in a structural model of school choice and competition to study equilibrium effects at scale. Capacity constraints and centralized assignment attenuate the demand-side gains, but the same shift in demand strengthens schools’ incentives to improve quality, and the supply response more than offsets the congestion effect. Extrapolating directly from the experiment would understate, not overstate, the policy’s equilibrium impact. We use the model to guide the design of a follow-up cluster-randomized experiment, focusing on the tradeoff between statistical power and equilibrium spillovers.

This article reviews the growing literature studying the industrial organization of education markets. It synthesizes theory and evidence to study markets both when prices clear and when centralized mechanisms allocate seats without price adjustment. The review demonstrates how modeling families’ problems—alongside schools’ supply decisions—yields policy-relevant insights. The pragmatic toolkit estimates demand and supply primitives using revealed preferences, detailed microdata, and instruments drawn from policy changes or natural experiments. The empirical industrial organization framework simulates equilibrium under alternative policies to quantify welfare and distributional effects. The framework extends to entry-exit dynamics, allowing policy evaluation to account for supply responses over time. Focusing on K–12 education, it examines vouchers and targeted subsidies, charter entry, funding formulas, information interventions, and assignment mechanism design. The article concludes with a research agenda spanning supply responses under centralized assignment, entry and quality-investment dynamics, teacher and staffing markets, and platform design.

Governments worldwide increasingly deploy algorithmic systems to allocate scarce public goods. The dominant transparency paradigm focuses on expert audit rather than citizen comprehension. We propose a substantive standard, Meaningful Algorithmic Transparency (MAT): information delivered to affected users must be proactive (placed into awareness, not merely published), tailored (referring to the user’s decision context), and actionable (delivered before the decision closes). We develop the argument through Chile’s Sistema de Admisión Escolar (SAE), a national school admissions platform serving roughly 470,000 families per year. Two measurements document the residual comprehension gap. Linking the SAE Satisfaction Survey to administrative truth for four cycles (2020–2023, N≈140,000), we show that families systematically overstate their placement probability: among applicants the platform can pre-identify as high-risk, the median family believes the risk is around 20% when it is in fact around 80%. The bias is uneven: at the same objective risk, lower-SES applicants understate by more, with a conditional gradient that is statistically stable across all four cycles. In the SAE Parent Surveys (2023–25), self-reported familiarity with a key procedural rule nearly doubled across cycles while verifiable comprehension barely moved (2.3% to 4.9%). The comprehension gap is therefore persistent and unevenly distributed, falling more heavily on the families whose outcomes most depend on effective information delivery, even when the underlying mechanism gives those families priority. We propose six operational components and show how Chile’s existing regulatory architecture (access-to-information law, algorithmic-transparency recommendations, data-protection safeguards, and sector-specific regulation) can be articulated to make the standard enforceable.

We study a 2021 reform in Manta, Ecuador, that replaced one centralized assignment rule with another: a rule minimising home-to-school distance gave way to a deferred-acceptance mechanism acting on truthfully elicited, rank-ordered preferences. Because a single, coordinated, largely digital algorithm cleared the market both before and after, the only change was the objective it optimised—from proximity to preferences. This isolates the welfare value of incorporating household preferences while holding coordination fixed, a separation the move out of uncoordinated assignment studied for New York could not provide, and the counterfactual faced by the many systems that assign by proximity or catchment area. A distance rule looks nearly optimal, since most families rank their nearest school first; yet more than a quarter do not, and acting on their preferences raises the share of applicants placed in a listed school from 50% to 78%, and in their first choice from 42% to 70%, at a cost of only 0.32 km of additional travel. Estimating preferences from the submitted rankings, we find that deferred acceptance recovers about 99.6% of the attainable welfare in the entry grade—an essentially unselected cohort entering the system fresh—while further efficiency-enhancing mechanisms add almost nothing; the gains come from eliciting preferences, not from the choice of algorithm. They also appear progressive: lower-socioeconomic-status families, proxied by neighbourhood schooling, gain the most. As coordinated, digital choice systems spread across the developing world, our central qualitative finding—that the algorithm matters far less than whether the system acts on preferences—replicates the New York evidence in a very different setting.

This paper analyzes how monetary policy surprises in Chile affects the real and financial sector separating between a pure monetary policy shock and an information shock. Using inter-day movements of futures of interest rate in the banking system, we identify an information shock when labor data is released and a pure monetary policy shock when the central bank reveals their interest rate decision, and their effects are quantified through an external vector autoregression model. Our results suggest that a pure monetary policy shock produce an appreciation of nominal exchange rate, and contractionary effects on the economy. However, an information shock does not necessarily produce adverse effects. This paper contribute to the literature in two dimensions: studying the effect of the main driver behind the central bank announcements, and their transmission to the banking sector and consequently to the real and monetary sector.