Three essays in Bayesian microeconometrics: embracing causality and heterogeneity

Trinh-Thi-Thuy, Duong (2025) Three essays in Bayesian microeconometrics: embracing causality and heterogeneity. PhD thesis, University of Glasgow.

Abstract

This thesis leverages Bayesian methods to address econometric challenges in microeconomic settings, with a focus on causality and heterogeneity. The contributions are provided in three essays.

The first essay (Chapter 2) proposes a novel approach, Bayesian Analogue of Doubly Robust (BADR) estimation, to estimate unconditional Quantile Treatment Effects (QTEs) in observational studies. This estimand offers valuable insights into treatment effect heterogeneity across different outcome ranks. By incorporating Bayesian machine learning techniques, the framework can effectively handle high-dimensional covariates and nonlinear relationships to achieve better accuracy and appropriate uncertainty quantification. The simulation results show that BADR estimators yield a substantial improvement in bias reduction for QTE estimates compared with popular alternative estimators found in the literature. I revisit the role of microcredit expansion and loan access on Moroccan household outcomes, demonstrating how the new method adds value in characterising heterogeneous distributional impacts on outcomes and detecting changes in overall economic inequality, which is also appealing to other applied contexts.

The second essay (Chapter 3) introduces a new approach that harnesses network or spatial data to identify and estimate direct and indirect causal effects in the presence of selection-on-unobservables and spillovers. The proposed framework nests the Generalised Roy model to explicitly account for endogenous selection into treatment and goes beyond to capture spillovers through exposure mapping to neighbours’ treatment. This allows for heterogeneous effects across individuals and enables exploration of various policy-relevant treatment effects. I develop Bayesian estimators based on data augmentation methods, offering efficient computation and proper uncertainty quantification, which is supported by simulation experiments. I apply the method to evaluate the Opportunity Zones (OZ) program, which aims to stimulate economic growth in distressed U.S. census tracts through tax incentives. The results show both direct and indirect positive impacts on housing unit growth in designated Qualified Opportunity Zones (QOZs), but unselected tracts (non-QOZs) experience no beneficial spillovers, remaining at a disadvantage. Moreover, the model predicts that offering investment tax credits to non-QOZs would lead to negative outcomes, making the program’s expansion to these areas ineffective.

The third essay (Chapter 4) is based on a joint work with Dr Santiago Montoya-Blandón. We develop a new econometric framework for modelling network interactions with heterogeneous effects, while addressing the issue of network endogeneity. The proposed Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) model overcomes the limitations inherent in the standard spatial autoregressive (SAR) specification by achieving these dual objectives. We incorporate a finite mixture structure to capture rich heterogeneity in network interaction effects and explicitly model link formation, with latent variables playing a crucial role. For estimation and inference, our fully Bayesian approach effectively handles the computational challenges arising from the complex likelihood function and latent structure. We present a simulation study that validates the proposed approach. In the empirical application to an innovation network among American firms, we reveal significant positive yet heterogeneous interaction effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight different firm behaviours and reveal notable transmitters and absorbers in response to exogenous R&D policy shocks. This framework enables quantification of firm-level direct and spillover effects, thus providing valuable insights for evidence-based and targeted policy design.

By utilising recent developments in Bayesian econometrics, my research seeks to overcome the limitations of conventional methods, particularly in handling high-dimensional models, endogeneity, heterogeneity, and several forms of spillovers. Ultimately, the proposed methods enable more flexible and robust microdata analysis, contributing to a deeper understanding of individual and group differences in economic behaviour, as well as causal effects. This, in turn, can lead to more informed and effective policy decisions.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from an Adam Smith Business School (ASBS) Scholarship, University of Glasgow.
• Subjects: H Social Sciences > HB Economic Theory
• Colleges/Schools: College of Social Sciences > Adam Smith Business School
• Funder's Name: Adam Smith Business School (ASBS) Scholarship
• Supervisor's Name: Korobilis, Professor Dimitris and Montoya-Blandon, Dr. Santiago
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85674
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 13 Jan 2026 15:54
• Last Modified: 14 Jan 2026 11:41
• Thesis DOI: 10.5525/gla.thesis.85674
• URI: https://theses.gla.ac.uk/id/eprint/85674