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Abstract  A comment on the article by W. Erwin Diewert in this volume.

Abstract
Linkage errors in probabilistically matched data sets can cause biases in the estimation of regression coefficients. This article proposes an approach to obtain consistent estimates and valid inference that relies on instrumental variables. The novelty of the method is to show that instrumental variables arise naturally in the course of probabilistic record linkage thereby allowing for off-the-shelf implementation. Relative to existing approaches, the instrumental variable approach does not require integration of the record linkage and regression analysis steps, the estimation of complex models of linkage error, or computationally expensive methods to estimate standard errors. The instrumental variables approach performs well in Monte Carlo simulations of an environment highlighting a many-to-one linkage problem.

Limiting the spread of contagious diseases can involve both government-managed and voluntary efforts. Governments have a number of policy options beyond direct intervention that can shape individuals' responses to a pandemic and its associated costs. During its first wave of COVID-19 cases, Singapore was among a few countries that attempted to adjust behavior through the public provision of detailed case information. Singapore's Ministry of Health maintained and shared precise, daily information detailing local travel behavior and residences of COVID-19 cases. We use this transparency policy along with device-level cellphone data to quantify how local and national COVID-19 case announcements trigger differential behavioral changes. We find evidence that individuals are three times more responsive to outbreaks in granularly defined locales. Conditional on keeping infection rates at a manageable level, the results suggest economic value in this type of transparency by mitigating precautionary activity reductions.