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Posts
Blog Post number 4
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Blog Post number 1
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media
De Grote Boze Markt
Venue: De Standaard, 2020
When bigger isn’t better: UK firms‚ equity price performance during the Covid-19 pandemic
Venue: Bank Underground, 2020
Political polarisation impedes the public policy response to COVID-19
Venue: Vox EU, 2020
Biden’s plea for masks will fail. Blame political polarization.
Venue: Washington Post, 2021
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
Published:
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publications
The COVID-19 Pandemic: Government vs. Community Action Across the United States
(joint with Adam Brzezinski, Valentin Kecht, Guido Deiana)
Venue: 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.
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Science Skepticism Reduces Compliance with COVID-19 Shelter-in-Place Policies
(joint with Adam Brzezinski, Valentin Kecht, Austin Wright)
Venue: 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.
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Unmasking Partisanship: Polarization undermines public response to collective risk
(joint with Maria Milosh, Marcus Painter, Konstantin Sonin, Austin Wright)
Venue: 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.
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Public response to government alerts saves lives during Russian invasion of Ukraine
(joint with Austin Wright, Mark Polyak)
Venue: Proceedings of the National Academy of Sciences , 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 civilian sheltering behavior, using high-frequency geolocation pings tied to 17 million mobile devices, 60% of the connected population in Ukraine. We find that, overall, civilians respond sharply to alerts, quickly seeking shelter. These rapid postalert changes in population movement attenuate over time, however, in a manner that cannot be explained by adaptive sheltering behavior 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 that 35 to 45% of observed civilian casualties were avoided because of public responsiveness to the messaging system. Importantly, an additional 8 to 15% of civilian casualties observed during the later periods of the conflict could have been avoided with sustained public responsiveness to government alerts. We provide evidence that increasing civilians’ risk salience through targeted government messaging can increase responsiveness, suggesting a potential policy lever for sustaining public engagement during prolonged episodes of conflict.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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wps
The Cost of Staying Open: Voluntary Social Distancing and Lockdowns in the US
(joint with Adam Brzezinski, Valentin Kecht)
Venue: 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.
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Using Mobile Device Traces to Improve Near-Real Time Data Collection During the George Floyd Protests
(joint with Austin Wright)
Venue: 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.
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Work Effort and the Cycle: Evidence from Survey Data.
(joint with Vivien Lewis)
Venue: 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.
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Isolation and Insurrection: How Partisanship and Political Geography Fueled January 6, 2021
(joint with Konstantin Sonin, Austin Wright)
Venue: CEPR Discussion Paper 18209, 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.
Media:
The Daily Beast, BFI, UChicago News, Salon
https://bfi.uchicago.edu/wp-content/uploads/2021/02/BFI_WP_2021-13.pdf
On the Non-Identification of Revenue Production Functions
Venue: Bank of England Staff Working Paper No 1015, 2023
Production functions are potentially misspecified when revenue is used as a proxy for output. I formalize and strengthen this common knowledge by showing that neither the production function nor Hicks-neutral productivity can be identified with such a revenue proxy. 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, only those that impose assumptions on the underlying demand system can possibly identify the production function.
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https://www.bankofengland.co.uk/working-paper/2023/on-the-non-identification-of-revenue-production-functions
Free Discontinuity Regression: With an Application to the Economic Effects of Internet Shutdowns
(joint with Florian Gunsilius)
Venue: arXiv, 2023
Discontinuities in regression functions can reveal important insights. In many contexts, like geographic settings, such discontinuities are multivariate and unknown a priori. We propose a non-parametric regression method that estimates the location and size of discontinuities by segmenting the regression surface. This estimator is based on a convex relaxation of the Mumford-Shah functional, for which we establish identification and convergence. We use it to show that an internet shutdown in India resulted in a reduction of economic activity by 25–35%, greatly surpassing previous estimates and shedding new light on the true cost of such shutdowns for digital economies globally.
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disco: Distributional Synthetic Controls
(joint with Florian Gunsilius)
Venue: arXiv, 2025
The method of synthetic controls is widely used for evaluating causal effects of policy changes in settings with observational data. Often, researchers aim to estimate the causal impact of policy interventions on a treated unit at an aggregate level while also possessing data at a finer granularity. In this article, we introduce the new disco command, which implements the Distributional Synthetic Controls method introduced in Gunsilius (2023). This command allows researchers to construct entire synthetic distributions for the treated unit based on an optimally weighted average of the distributions of the control units. Several aggregation schemes are provided to facilitate clear reporting of the distributional effects of the treatment. The package offers both quantile-based and CDF-based approaches, comprehensive inference procedures via bootstrap and permutation methods, and visualization capabilities. We empirically illustrate the use of the package by replicating the results in Van Dijcke et al. (2024).
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Return to Office and the Tenure Distribution
(joint with Florian Gunsilius, Austin Wright)
Venue: Revision requested: The Review of Economics and Statistics, 2025
With the end of the COVID-19 pandemic, debates over return-to-office mandates have intensified, though their economic implications are not fully understood. Using 260 million resumes matched to company data, we analyze the impact of these policies on employee tenure and seniority at three large U.S. tech companies: Microsoft, SpaceX, and Apple. Employing a distributional synthetic controls framework, for which we establish the validity of bootstrapped uniform confidence intervals, we estimate a reduction in tenure and seniority at firms returning to the office. Our findings suggest return-to-office mandates may drive senior employees to competitors, posing a risk to firm productivity and innovation.
Media:
Washington Post, Financial Times, Time, Forbes, Fortune, Inc.com, Cheddar, The Register, Ars Technica, Computerworld, KCBS Radio, MSN, Psychology Today, Stanford Institute for Economic Policy Research (presentation)
Regression Discontinuity Design with Distribution-Valued Outcomes
Venue: [Job Market Paper](https://davidvandijcke.com/files/r3d_latest.pdf), 2025
This article introduces Regression Discontinuity Design (RDD) with Distribution-Valued Outcomes (R3D), extending the standard RDD framework to settings where the outcome is a distribution rather than a scalar. Such settings arise when treatment is assigned at a higher level of aggregation than the outcome-for example, when a subsidy is allocated based on a firm-level revenue cutoff while the outcome of interest is the distribution of employee wages within the firm. Since standard RDD methods cannot accommodate such two-level randomness, I propose a novel approach based on random distributions. The target estimand is a "local average quantile treatment effect", which averages across random quantiles. To estimate this target, I introduce two related approaches: one that extends local polynomial regression to random quantiles and another based on local Fréchet regression, a form of functional regression. For both estimators, I establish asymptotic normality and develop uniform, debiased confidence bands together with a data-driven bandwidth selection procedure. Simulations validate these theoretical properties and show existing methods to be biased and inconsistent in this setting. I then apply the proposed methods to study the effects of gubernatorial party control on within-state income distributions in the US, using a close-election design. The results suggest a classic equality-efficiency tradeoff under Democratic governorship, driven by reductions in income at the top of the distribution.