Field Experiments on Seasonal Products and Markdown Pricing - Draft coming soon.
Marcelo Gallardo, Carlos Noton and Marcelo Olivares.
Abstract | Coming soon
EPU Index: Leveraging X and DeepSeek - Draft coming soon.
Manuel Loaiza, Marcelo Gallardo, and Gabriel Rodriguez.
Abstract | New version coming soon
This paper develops a new political-economic uncertainty index based on tweets from influential figures in Peruvian politics and economics. We use DeepSeek to process the tweets.
Information and voting: Evidence from Peru’s 2026 presidential election - Draft coming soon.
Marcelo Gallardo, Nicolás Velarde and Cristina Gutarra.
Abstract | New version coming soon
We study how election-night flash electoral estimates shape voting decisions in Peru's highly fragmented 2026 presidential election. We exploit a natural experiment: on 12-April-2026, 187 polling tables in 13 voting centres could not be installed, and the Jurado Nacional de Elecciones (JNE) extended voting for the affected (near 55 000) electors to Monday 13~April. These voters cast their ballots after observing the flash estimates of Ipsos and Datum, while otherwise comparable Sunday voters did not. We develop a Bayesian-persuasion model of voting in a multi-candidate plurality election with two public signals about the identity of the second-place finisher, and derive testable predictions about vote reallocation toward the three candidates the flash estimates rendered viable: López Aliaga, Sánchez, and Nieto. The empirical strategy combines the natural-experiment design with matching on polling-table-level pre-treatment covariates: distributions of voter sex and age at the table, district-level demographics, and a single Right--Left index constructed from 2021 first-round shares. The estimated treatment effects also discipline the strategic-intensity rati b_i/a_i that governs how much voters trade off ideological match for runoff viability. Our results inform the design of electoral information environments in young democracies with high political volatility, a question of first-order importance in Peru, a country that has had nine presidents in ten years and persistent institutional instability since 2016.
Preprints available
Congestion and Penalization in Optimal Transport R&R at Decisions in Economics and Finance (Springer) New version!
Marcelo Gallardo, Manuel Loaiza, and Jorge Chávez.
Abstract | Pre-print
We propose a new model that transforms the classical discrete optimal transport framework by incorporating heterogeneous congestion costs and replacing traditional equality constraints with weighted penalization terms. The resulting formulation is a strictly convex optimization problem that better captures demand–supply imbalances in economic matching contexts and the congestion phenomenon. We first introduce the model and establish existence and uniqueness of the optimal transport plan under general conditions. For interior solutions, we present two analytical methods—based on the Neumann series expansion and the Sherman–Morrison formula—and develop a practical $O((N+L)N^2L^2)$ algorithm for computing the optimum. We then address the case of infinitely many types, corresponding to optimal transport on measure spaces, absolutely continuous with respect to Lebesgue, and prove existence and uniqueness under reasonable assumptions via infinite-dimensional optimization methods. Finally, we illustrate the applicability of our framework with examples from Peru’s health and education sectors, showing how it yields allocation patterns that differ from classical approaches and provide more accurate predictions. Pre-print in arXiv differs from the last version.
Heterogenous Quadratic Regularization in Optimal Transport Marcelo Gallardo, Manuel Loaiza and Jorge Chávez.
Abstract | Pre-print
In this paper, we build upon the optimal transport quadratic regularization model to develop a framework that incorporates congestion costs, particularly in matching within the healthcare and education sectors. Specifically, we introduce a model with heterogeneous quadratic costs. We analyze the model's properties under specific cases, extending the existing literature. Furthermore, we explore key structural characteristics of the model and provide numerical examples illustrating why this formulation more accurately captures real-world phenomena, particularly in the Peruvian context. The main result consists of identifying a specific type of corner solution when matching the same number of clusters, i.e., N=L.
Irregular wave dynamics driven by a random force within the Burgers equation
Marcelo Gallardo and Marcelo Flamarion.
Abstract | Pre-print
In this article, we study the classical Burgers equation as a model for random fields. First, we consider initial data defined as a sum of harmonics with random phases and compute the blow-up time. Several simulations are performed, revealing that, while the critical blow-up time is approximately distributed according to a Gaussian law, the statistical tests reject the normality hypothesis. For the viscous case, we analyze waves driven by a random force. Using the Cole-Hopf transformation, the averaged wave field is computed numerically. Through a change of variables, we demonstrate that randomness primarily affects the phase of the wave field. Assuming the phase follows a uniform distribution, we show that the averaged field spreads and diminishes over time.