About Me

I am a Postdoctoral Researcher at the Leuven Economics of Education Research (LEER) center at KU Leuven, and a Visiting Fellow at the London School of Economics and Political Science (LSE) within WISPPRH.

I obtained my PhD in Economics (joint degree) from KU Leuven and Pontificia Universidad Javeriana, defending my dissertation on March 12, 2026. I was advised by Professor Kristof De Witte and Professor Gloria Bernal.

Jaime Polanco-Jimenez

My research lies at the intersection of Development Economics, Human Capital, and Behavioral Economics. I study how policies, technologies, and institutions shape education, gender equity, financial inclusion, and environmental outcomes in developing contexts. My work is grounded in Randomized Controlled Trials (RCTs) and field experiments, combining causal inference with data science to generate actionable, evidence-based policy insights.

Job Market Paper

The Tailoring Premium: How AI Design Unlocks Student Engagement and Learning

with Kristof De Witte. Under Peer-Review at Journal of Economic Behavior and Organization.

As schools increasingly adopt Artificial Intelligence, policymakers face a crucial trade-off between deploying inexpensive, general-purpose models and investing in tools tailored to the curriculum. We provide the first large-scale causal evidence on this choice. In a randomized control trial with 2,440 secondary students, we find that offering a curriculum-tailored chatbot increases immediate learning by 0.126 standard deviations (ITT), while a generic chatbot has no effect. This difference is driven entirely by student engagement: the tailored tool increased module completion by 15.5 percentage points. For students induced to complete the module by the tailored design, the effect is larger and more durable, increasing long-term knowledge retention by 0.23 standard deviations. Our results show that the learning gains from educational AI are unlocked by deep curricular integration, which succeeds by first solving the fundamental problem of student engagement.

View Paper (PDF)

Work in Progress: AI & Research Productivity

My research agenda extends beyond the classroom to academic production itself. I am currently running a Randomized Controlled Trial (RCT) evaluating how AI tools affect the productivity and well-being of researchers.

Artificial Intelligence and Productivity in Cognitively Intensive Work

Polanco-Jimenez, Jaime, Almudena Sevilla, and Ivan Vicente. 2026. AEA RCT Registry (17749-2.0).

We study whether access to expert-style AI feedback raises the productivity of early-career researchers engaged in cognitively intensive work. In a randomized controlled trial with more than 100 early-career economists, we assign treated participants to a structured, multi-agent AI system that delivers diagnostic feedback on the high-context evaluative work that determines publication success. We measure productivity as the quality of research relative to the effort it requires, using within-person flow items collected at three and six months. We test whether the productivity effect operates through a reallocation of effort toward judgment-intensive tasks, and whether gains concentrate among researchers closer to the knowledge frontier or accrue disproportionately to those still acquiring expertise.

Interactive Demo: The Treatment Tool

Below is a demonstration of the Referee AI agent used in the treatment arm of the study. It simulates the rigorous peer review process of a top-tier economics journal.

Access Required: To test the tool or discuss the experimental design, please contact me at jaime.polancojimenez@kuleuven.be.

Research Interests

Substantive Areas

  • Human Capital & Education: How policies and technologies–including AI–improve learning outcomes and expand opportunity in developing contexts.
  • Environment & Development: Evaluating the welfare and behavioral effects of environmental shocks and natural resource policies on households and communities.
  • Gender & Development: Analyzing gender gaps, peer effects, and the impact of targeted interventions in education and labor markets.
  • Behavioral Economics: Applying behavioral insights to understand decision-making by households, students, and firms in low- and middle-income countries.
  • Microfinance & Financial Inclusion: Studying how access to credit, savings, and digital financial services affects poverty and resilience.

Methodological Focus

  • Randomized Controlled Trials (RCTs): Designing and implementing large-scale field experiments in real-world policy settings.
  • Evidence-Based Policymaking: Translating causal estimates into actionable recommendations for governments and international organizations.
  • Causal Inference & Data Science: Combining quasi-experimental methods with machine learning to identify policy-relevant effects at scale.