arXiv subjects

Augustine Denteh

Publications and source records attributed to Augustine Denteh.

3 recordsLinked to original sources

arXiv2026

Misclassification in Difference-in-differences Models

The difference-in-differences (DID) design is one of the most popular methods used in empirical economics research. However, there is almost no work examining what the DID method identifies in the presence of a misclassified treatment variable. This paper studies the identification of treatment effects in DID designs when the treatment is misclassified. Misclassification arises in various ways, including when the timing of a policy intervention is ambiguous or when researchers need to infer treatment from auxiliary data. We show that the DID estimand is biased and recovers a weighted average of the average treatment effects on the treated (ATT) in two subpopulations -- the correctly classified and misclassified groups. In some cases, the DID estimand may yield the wrong sign and is otherwise attenuated. We provide bounds on the ATT when the researcher has access to information on the extent of misclassification in the data. We demonstrate our theoretical results using simulations and provide two empirical applications to guide researchers in performing sensitivity analysis using our proposed methods.

econ.EM↗

arXiv2025

Estimating Program Participation with Partial Validation

This paper considers the estimation of binary choice models when survey responses are possibly misclassified but one of the response category can be validated. Partial validation may occur when survey questions about participation include follow-up questions on that particular response category. In this case, we show that the initial two-sided misclassification problem can be transformed into a one-sided one, based on the partially validated responses. Using the updated responses naively for estimation does not solve or mitigate the misclassification bias, and we derive the ensuing asymptotic bias under general conditions. We then show how the partially validated responses can be used to construct a model for participation and propose consistent and asymptotically normal estimators that overcome misclassification error. Monte Carlo simulations are provided to demonstrate the finite sample performance of the proposed and selected existing methods. We provide an empirical illustration on the determinants of health insurance coverage in Ghana. We discuss implications for the design of survey questionnaires that allow researchers to overcome misclassification biases without recourse to relatively costly and often imperfect validation data.

econ.EM↗

arXiv2023

Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment

We provide new insights regarding the headline result that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The individualized treatment effect distribution includes a wide range of negative and positive values, suggesting the average effect masks substantial heterogeneity. A small group-about 14% of participants-in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and previous utilization. Intensive margin effects are an important driver of increases in ED utilization.

econ.EM↗