Events

Statistical analysis of historical data provides a natural complement to traditional historical analysis based on qualitative research and on the descriptive use of (typically univariate) quantitative information. At their best, econometric methods applied to individual-level historical data can identify causal relationships. I will present three empirical studies based on data from 20th-century wars that are meant to contribute to economics, demography, and political science. First, based on data on prisoners of the Auschwitz-Birkenau concentration camp, we ask whether humans cooperate and whether preexisting social ties increase one’s chances of survival when facing extremely low survival chances. To identify causal effects, we rely on quasi-random composition of transports into the camp. Second, we ask about the formation of political identity of newly re-settled areas in Sudetenland after WWII. We exploit quasi-random differences in the presence of individuals with strong political convictions, to explore the theory of the small seed of political identity. Third, we measure and study the sources of the willingness to die in battle to defend national independence, based on data on the Croatia-Serbia war of 1991-1995.

The large influx of refugees over recent decades challenges many Western countries to devise effective policies for long-term integration. A key policy question is whether refugee children should be directly integrated into native schools or initially separated into migrant schools. In the Netherlands, refugee children arriving at primary-school age are taught either in integrated or separated schools depending on their randomly assigned municipality, whereas all secondary-school-aged arrivals are initially separated. We exploit this setting in a novel difference-in-differences framework, leveraging rich administrative data. Results indicate that on average, integrated primary schooling increases cultural assimilation as measured by hormonal-contraceptive usage but decreases educational outcomes at age 18. The negative educational effects are restricted to children from disadvantaged backgrounds, whereas the positive assimilation effects are strongest for children from advantaged backgrounds.

This presentation explores the use of microsimulation modeling to inform health policy, drawing on the work of Bryan Tysinger and the Schaeffer Center team. It will introduce key models—including the Future Elderly Model (FEM) and Future Adult Model (FAM)—used to simulate individual trajectories in health, healthcare utilization, and economic outcomes. Applications discussed will range from evaluating obesity treatments and dementia risk reduction to analyzing rural health disparities and pandemic mortality. Attendees will also get a preview of new research directions and learn how collaborative modeling can support evidence-based policymaking and effective communication with stakeholders.