Public response to government alerts saves lives during Russian invasion of Ukraine

(joint with Austin Wright, Mark Polyak)

Published in Proceedings of the National Academy of Sciences (Forthcoming), 2023

War is the cause of tremendous human suffering. To reduce such harm, governments have developed tools to alert civilians of imminent threats. Whether these systems are effective remains largely unknown. We study the introduction of an innovative smartphone application that notifies civilians of impending military operations developed in coordination with the Ukrainian government after the Russian invasion. We leverage quasi-experimental variation in the timing of more than 3,000 alerts to study the sheltering behavior of persons using high frequency geolocation pings tied to 17 million mobile devices, 60% of the connected population in Ukraine. We find that civilians respond sharply to alerts overall, quickly seeking shelter. These rapid post-alert changes in population movement attenuate over time, in a manner that cannot be explained by sheltering underground or calibration to the signal quality of alerts. Responsiveness is weakest when civilians have been living under an extended state of emergency, consistent with the presence of an alert fatigue effect. Our results suggest 8-15% of civilian casualties observed during the later periods of the conflict could have been avoided with sustained public responsiveness to government alerts.


Fast Company

WP version

Unmasking Partisanship: Polarization undermines public response to collective risk

(joint with Maria Milosh, Marcus Painter, Konstantin Sonin, Austin Wright)

Published in Journal of Public Economics, 2021

Political polarization and competing narratives can undermine public policy implementation. Partisanship may play a particularly important role in shaping heterogeneous responses to collective risk during periods of crisis when political agents manipulate signals received by the public (i.e., alternative facts). We study these dynamics in the United States, focusing on how partisanship has influenced the use of face masks to stem the spread of COVID-19. Using a wealth of micro-level data, machine learning approaches, and a novel quasi-experimental design, we document four facts: (1) mask use is robustly correlated with partisanship; (2) the impact of partisanship on mask use is not offset by local policy interventions; (3) partisanship is the single most important predictor of local mask use, not COVID severity or local policies; (4) Trump's unexpected mask use at Walter Reed on July 11, 2020 significantly increased social media engagement with and positive sentiment towards mask-related topics. These results unmask how partisanship undermines effective public responses to collective risk and how messaging by political agents can increase public engagement with mask use.


Vox EU, Time magazine, UChicago News, Washington Post

WP version

Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies

(joint with Adam Brzezinski, Valentin Kecht, Austin Wright)

Published in Nature Human Behaviour, 2021

Physical distancing reduces transmission risks and slows the spread of COVID-19. Yet compliance with shelter-in-place policies issued by local and regional governments in the United States is uneven and may be influenced by science skepticism and attitudes towards topics of scientific consensus. Using county-day measures of physical distancing derived from cellphone location data, we demonstrate that the proportion of people who stay at home after shelter-in-place policies go into effect is significantly lower in counties with a high concentration of science skeptics. These results are robust to controlling for other potential drivers of differential physical distancing, such as political partisanship, income, education and COVID severity. Our findings suggest public health interventions that take local attitudes toward science into account in their messaging may be more effective.



WP version

Replication materials

The COVID-19 Pandemic: Government vs. Community Action Across the United States

(joint with Adam Brzezinski, Valentin Kecht, Guido Deiana)

Published in Covid Economics: Vetted and Real-Time Papers 7 (CEPR), 2020

Are lockdown policies effective at inducing physical distancing to counter the spread of COVID-19? Can less restrictive measures that rely on voluntary community action achieve a similar effect? Using data from 40 million mobile devices, we find that a lockdown increases the percentage of people who stay at home by 8% across US counties. Grouping states with similar outbreak trajectories together and using an instrumental variables approach, we show that time spent at home can increase by as much as 39%. Moreover, we show that individuals engage in limited physical distancing even in the absence of such policies, once the virus takes hold in their area. Our analysis suggests that non-causal estimates of lockdown policies’ effects can yield biased results. We show that counties where people have less distrust in science, are more highly educated, or have higher incomes see a substantially higher uptake of voluntary physical distancing. This suggests that the targeted promotion of distancing among less responsive groups may be as effective as across-the-board lockdowns, while also being less damaging to the economy.


Bocconi Knowledge

WP version

The Impact of ECB Quantitative Easing on Income Inequality in the Netherlands: a First Assessment

Published in Bank- en Financiewezen / Revue Bancaire et Financiere, 2018

This article looks at the impact of the 2015 European Central Bank unconventional monetary policy (UMP) on income inequality in the Netherlands. To that end, it uses a panel survey from the Dutch central bank to decompose the contributions of selected UMP channels to the change in household income between two periods (11-13 / 14-16). It finds that UMP's effect through these channels was strongly equalizing. The only two other papers on the topic find similar results for other euro area countries.

Working Papers

Free Discontinuity Design

(joint with Florian Gunsilius)

Published in Draft coming soon, 2023

Regression discontinuity design (RDD) is a quasi-experimental impact evaluation method ubiquitous in the social- and applied health sciences. It aims to estimate average treatment effects of policy interventions by exploiting jumps in outcomes induced by cut-off assignment rules. Here, we establish a correspondence between the RDD setting and free discontinuity problems, in particular the celebrated Mumford-Shah model in image segmentation. The Mumford-Shah model is non-convex and hence admits local solutions in general. We circumvent this issue by relying on well-known convex relaxations based on the calibration method to generate global solutions. We derive deterministic and statistical convergence properties of this convex relaxation and demonstrate the utility of the resulting free discontinuity design (FDD) estimator in two empirical applications. Unlike canonical RDD estimators, our FDD estimator (i) extends to settings with running variables of any dimension, most notably the spatial setting; (ii) does not require the location of the boundary (the cut-off) to be known precisely, but rather estimates it jointly with the response function; (iii) does not depend on bandwidth selection rules.

On the Non-Identification of Revenue Production Functions

Published in Bank of England Staff Working Paper No 1015, 2023

It is well-known that production functions are potentially misspecified when revenue is used as a proxy for output. In this paper, I formalize and strengthen this common knowledge by showing that neither the production function nor Hicks-neutral productivity can be identified when revenue is used as a proxy for physical output. This result holds under the standard assumptions used in the literature for a large class of production functions, including all commonly used parametric forms. Among the prevalent approaches to address this issue, I show that only those which impose assumptions on the underlying demand system can possibly identify the production function.



Profiling Insurrection: Characterizing Collective Action Using Mobile Device Data

(joint with Austin Wright)

Published in University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2021-13, 2021

We develop a novel approach for estimating spatially dispersed community-level participation in mass protest. This methodology is used to investigate factors associated with participation in the March to Save America event in Washington, D.C. on January 6, 2021. This study combines granular location data from more than 40 million mobile devices with novel measures of community-level voting patterns, the location of organized hate groups, and the entire georeferenced digital archive of the social media platform Parler. We find evidence that partisanship, socio-political isolation, proximity to chapters of the Proud Boys organization, and the local activity on Parler are robustly associated with protest participation. Our research fills a prominent gap in the study of collective action: identifying and studying communities involved in mass-scale events that escalate into violent insurrection.


The Daily Beast, BFI, UChicago News, Salon

Work Effort and the Cycle: Evidence from Survey Data.

(joint with Vivien Lewis)

Published in Deutsche Bundesbank Discussion Paper (Forthcoming), 2020

We use data from the World Values Survey and the Work Orientations Survey to analyse the cyclical nature of work effort and attitudes to work effort. Our aim is to test two competing theories of labor effort, the labor hoarding view and the Shapiro and Stiglitz (1984) shirking model. Self-reported work effort is found to be strongly procyclical, while attitudes to effort move slightly countercyclically. We provide evidence for the presence of labor hoarding by showing how the cyclicality of effort changes with the strictness of employment protection legislation. Finally, we document heterogeneity in effort cyclicality across occupations and individuals.



Using Mobile Device Traces to Improve Near-Real Time Data Collection During the George Floyd Protests

(joint with Austin Wright)

Published in SSRN, 2020

This research note presents a method for using mobile device trace data to improve collection of data on spontaneously erupting protest activity and related events. Based on this method, it presents a highly granular dataset of such activity for the George Floyd Protests in the United States. We use anonymous aggregated mobile device trace data to identify device surges: anomalous changes in the number of devices in a small geographic area, consistent with the assembly of a large number of individuals. Preliminary estimates from ongoing data collection of protest sites and scale are presented. Establishing better measures of where and when protests occur across and within cities improves researchers' understanding of the downstream political and social consequences of mobilization.


USA Today Ipsos

The Cost of Staying Open: Voluntary Social Distancing and Lockdowns in the US

(joint with Adam Brzezinski, Valentin Kecht)

Published in Economics Series Working Papers 910, University of Oxford, Department of Economics, 2020

In combating the spread of COVID-19, some governments have been reluctant to adopt lockdown policies due to their perceived economic costs. Such costs can, however, arise even in the absence of restrictive policies, if individuals' independent reaction to the virus slows down the economy. This paper finds that imposing lockdowns leads to lower overall costs to the economy than staying open. We combine detailed location trace data from 40 million mobile devices with difference-in-differences estimations and a modification of the epidemiological SIR model that allows for societal and political response to the virus. In that way, we show that voluntary reaction incurs substantial economic costs, while the additional economic costs arising from lockdown policies are small compared to their large benefits in terms of reduced medical costs. Our results hold for practically all realistic estimates of lockdown efficiency and voluntary response strength. We quantify the counterfactual costs of voluntary social distancing for various US states that implemented lockdowns. For the US as a whole, we estimate that lockdowns reduce the costs of the pandemic by 1.7% of annual GDP per capita, compared to purely voluntary responses.


Vox EU, video