Rasmus Duret - Research

Working papers

  1. Cradles and Cash: Lessons from Estonia’s Law of Large Families
    with David Seim (Very early-stage, currently getting data access)
We use administrative data from Estonia to study the effects of the 2017 Law of Large Families, which quadrupled marginal child benefits for large families. Exploiting variation across birth orders and marital status, we examine the LLF's effects on fertility patterns, and estimate marginal propensity to consume out of increased child benefits.
MSc Dissertation Bunch

Older papers

  1. For whom the constraint binds? Behavioural Responses to the Removal of Income Limits for Study Aid in Sweden under COVID-19
    MSc Dissertation, May 2023
Sweden provides generous financial support for students in higher education, and disintentivises excessive part-time work by reducing support received if income exceeds a certain threshold. This threshold was temporarily removed during the COVID-19 pandemic, with the intent to support students’ finances through the crisis. I provide the first examination of the effects of this reform using individual-level administrative data on the entire Swedish student population, and combine three research designs: bunching, simulated instrumental variables, and differences-in-differences. Consistent with theoretical predictions, I observe significant behavioural responses. Students increase their incomes by 10-12% on average, driven especially by high-earners and partially by individuals switching between student and non-student status during the pandemic. These results help inform the design of study aid systems balancing academic achievement with part-time work, as well as policies aiming to support students through economic shocks.

I use administrative data from Sweden to investigate how a COVID-related change to students' stipend conditions affected their choice to work part-time. Using bunching, instrumental variables, and diff-in-diff methods, I estimated large behavioural responses: a 10-12% increase in average income implying an ETI of ~0.1, and significant flows into education.
MSc Dissertation Bunch

  1. Unravelling Network Capital: Exposure and Friending Effects in a network-based model of Moving to Opportunity
    BSc Dissertation, May 2022
Social capital, from its conceptual roots based in sociology, has since the early 2000s attracted substantial interest in applied economic research. Despite extensive work documenting its significance in determining economic outcomes, the concept remains largely detached from mainstream economic academia and almost entirely absent from economic theory. In this dissertation, I aim firstly to provide an overview of modern empirical literature on social capital and its more analytically tractable component, network capital. To substantiate its importance quantitatively, I develop a stylised network-based model of social capital which extends the Jackson and Rogers (2007) model to incorporate the latest findings of Chetty, Jackson, et al. (2022b). Calibrating my model on data from the latter, I simulate the Moving to Opportunity experiment –a policy aimed at increasing cross-class interaction and decreasing cross-generational inequality– in the presence of biased interaction mechanisms. My model successfully replicates the empirical findings of Chetty et al. and captures a rich variety of endogenous behaviour, most strikingly that of emergent segregation by socio-economic status. My findings indicate that network-based models constitute a promising avenue for future research in both the specific area of social capital and for the field of economics at large, notably with potential incorporations of recent advances in complexity theory and the growing field of complexity economics.

I developed a simple network-based model of social capital to provide a theoretical background to the empirical findings of Chetty et al. (2022), and replicated their key findings of emergent segregation using county-level US data. I apply this to examine how social networks can moderate the effects of Moving to Opportunity-type interventions.
MSc Dissertation Bunch