Inequalities in Healthcare Use during the COVID-19 Pandemic

Nature Communications (w. M Verhagen and A Tilstra)

The COVID-19 pandemic has led to severe reductions in non-COVID related healthcare use. This study investigates whether the reduction in administered care disproportionately affected marginalised population groups. Using detailed medical claims data from the Dutch universal health care system that covers all residents in The Netherlands, we predict expected healthcare use based on pre-pandemic trends and compare these expectations with observed healthcare use in 2020. Our findings reveal a substantial 10% decline in the number of treated patients in 2020 relative to prior years. Declines more pronounced for individuals below the poverty line, females, the elderly, and foreign-born individuals. These inequalities stem predominantly from declines in middle and low urgency procedures, and indicate 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.

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

AAAI-24: Association for the Advancement of Artificial Intelligence (w. J Baumann, A Smith, and C Vajiac)

We are partnering with Allegheny County Department of Human Services to better allocate rental assistance to high risk individuals in order to keep them from falling into homelessness. Our project consists of two interconnected goals: (1) to more efficiently allocate rental assistance by prioritizing those at highest risk of entering into homelessness, and (2) to do so in a manner that is equitable. Our model is trained on rich historical data and predicts whether an individual who is currently facing an eviction is at risk of falling into homelessness in the near future, to prioritise them for rental assistance. Initial comparisons suggest that this system constitutes a considerable improvement on the county's current decision-making process, ensuring both a more efficient and an equitable resource allocation. The efficacy of this system is currently being tested in a field trial.

Nowcasting Daily Population Displacement in Ukraine through Digital Advertising Data

Population and Development Review (w. D Leasure et al)

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.

Learning Inequality during the Covid-19 Pandemic

PNAS, 2021 (w. P Engzell and M Verhagen).

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.

The impact of mass shootings on attitudes towards gun restrictions in the United States

Socius, 2021 (w. D Kirk).

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.

Faces in the Crowd: Estimating Protest Ideology with Twitter Data

PLOS ONE, 2021 (w. C Barrie).

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.

The Impact of Terrorist Attacks on Native and Immigrant Sentiment

Social Forces, 2021.

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.

The Effect of Threatening Events on the Frequency and Distribution of Conflict

European Sociological Review, 2020.

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.