About Me

I am a PhD in Economics from a joint program between KU Leuven (at the Leuven Economics of Education Research center) and Pontificia Universidad Javeriana.

Status: I defended my dissertation on March 12, 2026 and am currently available for academic and research positions.

I am fortunate to be advised by Professor Gloria Bernal and Professor Kristof De Witte.

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

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 new Randomized Controlled Trial (RCT) evaluating how AI tools affect the productivity and well-being of researchers.

Evaluating an AI-Powered Research Development Tool

Polanco-Jimenez, Jaime and Almudena Sevilla. 2026. AEA RCT Registry (17749-1.0).

This RCT evaluates the causal impact of an AI-powered Research Development Tool on the academic productivity and well-being of researchers. Participants, primarily PhD students and junior female economists, will be randomly assigned to one of two groups: a control group receiving feedback from a general-purpose AI, or a treatment group gaining access to a comprehensive AI-driven "Research Development Suite." This suite offers detailed, structured feedback on research papers and integrated workflow features. Over a 24-month intervention period, we will measure changes in objective productivity metrics (e.g., papers submitted/published, co-author networks) and subjective well-being (e.g., job satisfaction, work-life balance).

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.