Publications

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 – Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

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• Open access pdf file

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typically, funding is distributed by a reactive allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous County, this study can inform the development of evidence-based decision support tools in similar contexts, including lessons about data needs, model design, evaluation, and field validation.

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In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022, forcibly displaced millions of people from their homes including nearly 6 million refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged social media data from Facebook's advertising platform in combination with pre-conflict population data to build a real-time monitoring system to estimate subnational population sizes every day disaggregated by age and sex. Using this approach, we estimated that 5.3 million people had been internally displaced away from their baseline administrative region in the first three weeks after the start of the conflict. Results revealed four distinct displacement patterns: large-scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics.

• Supplementary material
• Open access pdf file

School closures have been a common tool in the battle against COVID-19. Yet, their costs and benefits remain insufficiently known. We use a natural experiment that occurred as national examinations in The Netherlands took place before and after lockdown to evaluate the impact of school closures on students’ learning. The Netherlands is interesting as a “best-case” scenario, with a short lockdown, equitable school funding, and world-leading rates of broadband access. Despite favorable conditions, we find that students made little or no progress while learning from home. Learning loss was most pronounced among students from disadvantaged homes.

• Code repository
• Pre-registration
• Open access pdf file

Is the American public more likely to favor stricter gun legislation in the aftermath of deadly mass shootings? The authors leverage the occurrence of several mass shootings during multiple survey waves of the General Social Survey between 1987 and 2018 to examine whether exposure to a mass shooting sways public opinion on gun legislation. The results reveal that mass shootings increase support for stricter gun permits among Democrats but not for individuals of other political orientations. An exception to this finding occurs with school shootings, which mobilize broad support for firearm legislation among both Democrats and Republicans.

• Code repository
• Supplementary material
• Open access pdf file

Who goes to protests? To answer this question, existing research has relied either on retrospective surveys of populations or in-protest surveys of participants. Both techniques are prohibitively costly and face logistical and methodological constraints. In this article, we investigate the possibility of surveying protests using Twitter. We propose two techniques for sampling protestors on the ground from digital traces and estimate the demographic and ideological composition of ten protestor crowds using multidimensional scaling and machine-learning techniques. We test the accuracy of our estimates by comparing to two in-protest surveys from the 2017 Women’s March in Washington, D.C. Results show that our Twitter sampling techniques are superior to hashtag sampling alone. They also approximate the ideology and gender distributions derived from on-the-ground surveys, albeit with some bias, but fail to retrieve accurate age group estimates. We conclude that online samples are yet unable to provide reliable representative samples of offline protest.

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There is growing academic interest in examining how terrorist attacks shape the majority’s attitudes towards minority groups. Yet, little is known of how these minority groups react to the backlash such events provoke. This paper leverages the exogenous occurrence of a series of terrorist attacks during the fieldwork period of two surveys to estimate how such events affect the sentiment of both citizens and asylum seekers in Germany. Results of the natural experiment reveal that the 2016 terror attacks in Nice, Würzburg, and Ansbach substantially increased anti-refugee sentiment among German respondents. In line with this increase in hostility, refugees experienced more discrimination, felt less welcome in Germany, and suffered clinically relevant declines in mental health in the aftermath of the attacks. These results provide a more holistic understanding of how terrorism corrodes intergroup relations and how it affects those that are blamed for the events and thus suffer the brunt of the backlash following their occurrence.

• Code repository
• Open access pdf file

In this article, I study the role that threatening events play in shaping both the occurrence and the distribution of intergroup conflict. Using the case of anti-refugee attacks in Germany, the study finds that the 2015 New Year’s Eve (NYE) sexual assaults led to a dramatic surge in the daily rate of violence, far surpassing the more short-lived effect of domestic and European terrorist attacks. Importantly, this effect was more pronounced among districts with low prior levels of anti-refugee hostility and far-right support. The NYE event both increased the frequency and changed the distribution of subsequent attacks—mobilizing new, previously peaceful communities to behave aggressively towards local refugee populations. Together, these findings reveal that threatening events not only affect the amount of intergroup conflict, but may also alter the structural conditions under which such conflict emerges in the first place.