Martín Navarrete

About

I am a PhD student in Applied Economics at the Wharton School at the University of Pennsylvania. My research is in the fields of environmental economics and development economics.

Prior to graduate school, I received a B.Eng. Sc. in Industrial Engineering and an M.A. in Economics at Universidad de Chile.

Publications

• Conditioning Out the Poor? Consumption Inequality and the Design of Cash Transfer Programs
  (with Santosh Anagol and Thomas Fujiwara)
  October 2025. Forthcoming, American Economic Review: Insights.
  Abstract

  Conditionality can exclude poor households from receiving transfers. Reanalyzing five randomized evaluations of conditional cash transfers (CCTs), we find that 9–37 percent of eligible recipients fail to meet conditions, and they typically have lower baseline consumption. We assess the welfare implications of budget-neutral shifts from CCTs to unconditional cash transfers. Conditionality exacerbating inequality among eligible recipients can be quantitatively important for welfare impacts. We quantify how large conditionality-induced human capital gains must be for the welfare benefits of CCTs to outweigh the inequality costs generated by conditionality.

Working Papers

• Selection versus Exploration: How Price Information Frictions Decouple Clicks from Purchases in Email Marketing
  (with Marcel Goic and Daniel Schwartz)
  May 2026, R&R Management Science
  Abstract

  We study how withholding price information in promotional emails —deliberately obscuring prices as an information-gating technique— reshapes the composition of market responders and the relationship between engagement and revenue outcomes. Using a large-scale field experiment with over 2.4 million emails across 15 campaigns, we show that price withholding substantially increases click-through rates by 22.3% yet generates no increase in conversions—either directly, cross-channel, or over a four-month horizon. This aggregate null masks two opposing forces: price withholding triggers curiosity-driven exploration among light users, expanding purchase conversion, while price transparency enables self-selection and increases conversion among heavy users who are more purchase-ready. We further show that repeated price withholding creates dynamic costs, reducing engagement in subsequent communications. A targeting strategy that directs price disclosure toward purchase-ready customers—identified via spending levels or multidimensional RFM profiling—increases revenues by up to 8.6% relative to a uniform no-price strategy that follows the CTR metric, with minimal losses even under mandatory price transparency regulations. These findings inform ongoing policy debates on price transparency, demonstrating that the revenue implications of disclosure vary systematically across consumer segments.

Work in Progress

• Chasing the Peak: How Flexible Is Data Center Electricity Demand?
  (with Eliana Sena)
  October 2026.
  Abstract

  Data centers are the fastest-growing source of U.S. electricity demand, and power systems are sized for a few peak hours, so data center flexibility in those hours determines how much new capacity their growth requires. However, electricity rates charge data centers far less than the social cost of supplying them at the peak. We study how data centers respond to peak pricing in PJM, where load during five summer peaks, identified only after the fact, sets a year of capacity charges. Using site-level, half hourly consumption data, we exploit daily variation in the ex ante probability that a day becomes a peak in a triple-differences design to estimate how load at the forecasted peak hour responds to a higher expected peak charge. We then develop a framework to evaluate whether these charges close the wedge between social and private marginal costs at the peak, or instead shift production from the grid to on-site generation.

• Selection in Environmental Screening Tests
  (with Peter Lugthart)
  May 2026.
  Abstract

  Measuring pollution is central to enforcing many environmental regulations, but accurate tests are costly. Are inexpensive screening tests an effective replacement? We study this question in the context of vehicle emissions testing, where some regulators measure the emissions of passing cars with roadside remote sensing technology and use these readings to exempt low-emissions vehicles from costly in-person testing. Unlike traditional applications of screening tests in public health, where tested individuals want to learn their true disease status, screening in a regulatory context introduces an adverse selection problem because higher-polluting individuals may use the noisy screening measurements to conceal their true state. We find that remote sensing emissions tests are affected by adverse selection in Virginia: drivers of older, dirtier cars are more likely to seek out and drive by remote sensing devices on days when weather conditions are favorable for testing. In ongoing work, we use an instrumental variables strategy to estimate the effect of a marginally more generous screening threshold on air pollution and use un-selected data from Texas, where remote sensing is only used for program evaluation and not for screening, to explore how the policymaker can improve test performance by accounting for selection.

• Electricity Price Increases and Household Response: A Natural Experiment in Chile
  July 2025.