arXiv subjects

Santiago Montoya-Blandón

Publications and source records attributed to Santiago Montoya-Blandón.

4 recordsLinked to original sources

arXiv2026

Heterogeneous Peer Effects with Endogenous Network Formation

This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome equations, addressing network endogeneity for credible estimation of heterogeneous spillover effects. We propose a fully Bayesian data augmentation approach for estimation and inference, overcoming challenges posed to standard likelihood-based methods. A simulation study validates our approach. Our empirical application to an innovation network among U.S. firms reveals significant positive, yet heterogeneous, peer effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight varying firm behaviors in response to exogenous R&D policy shocks and and quantify firm-level direct and spillover effects, offering valuable insights for evidence-based and targeted policy design.

econ.EM↗

arXiv2026

The R&D Productivity Puzzle: Innovation Networks with Heterogeneous Firms

We introduce heterogeneous R&D productivities into an endogenous R&D network formation model, generalizing the framework of Goyal and Moraga-González (2001). Heterogeneous productivities endogenously create asymmetric gains from collaboration: less productive firms benefit disproportionately from links, while more productive firms exert greater R&D effort and incur higher costs. When productivity gaps are sufficiently large, more productive firms experience lower profits from collaborating with less productive partners. As a result, the complete network -- stable under homogeneity -- becomes unstable, and the positive assortative (PA) network, in which firms cluster by R&D productivity, emerges as pairwise stable. Using simulations, we show that the clustered structure delivers higher welfare than the complete network; nevertheless, welfare under this formation follows an inverted U-shape as the fraction of high-productivity firms increases, reflecting crowding-out effects at high fractions. Altogether, we uncover an R&D productivity puzzle: economies with higher average R&D productivity may exhibit lower welfare through (i) the formation of alternative stable networks, or (ii) a crowding-out effect of high-productivity firms. Our findings show that productivity gaps shape the organization of innovation by altering equilibrium R&D alliances and effort. Productivity-enhancing policies must therefore account for these endogenous responses, as they may reverse intended welfare gains.

econ.GN↗

arXiv2026

Probabilistic Targeted Factor Analysis

We develop Probabilistic Targeted Factor Analysis (PTFA), a likelihood-based framework for constructing latent factors that are explicitly targeted to variables of economic interest. PTFA provides a probabilistic foundation for Partial Least Squares, allowing supervised factor extraction under uncertainty. The model is estimated via a fast expectation maximization algorithm and naturally accommodates missing data, mixed-frequency observations, stochastic volatility, and factor dynamics. Simulation evidence shows that PTFA improves recovery of economically relevant latent factors relative to standard PLS, particularly in noisy environments. Applications to financial conditions indices, macroeconomic forecasting, and equity premium prediction illustrate the measurement and forecasting gains delivered by targeted probabilistic factor extraction.

econ.EM↗

arXiv2025

EASI Drugs in the Streets of Colombia: Modeling Heterogeneous and Endogenous Drug Preferences

The response of illicit drug consumers to policy changes like legalization is mediated by demand behavior. Since individual drug use is driven by many unobservable factors, accounting for unobserved heterogeneity becomes crucial for designing targeted policies. This paper introduces a finite Gaussian mixture of EASI demand systems to estimate joint demand for marijuana, cocaine, and basuco (a low-purity cocaine paste) in Colombia, accounting for corner solutions and endogenous prices. Our method classifies users into two groups with distinct preferences over consumption: "soft" and "hard" users. Nationally representative survey estimates find drugs are unit-elastic, with marijuana and cocaine complementary. International marijuana legalization episodes along with Colombia's low marijuana production cost suggest legalization is likely to drive prices down significantly. Legalization counterfactuals under the most likely scenario of a 50\% marijuana price decrease reveal \$363/year welfare gains for consumers, \$120M in governement revenue, and \$127M dealer losses.

econ.EM↗