Research

Working papers: 

Identification of a Rank-dependent Peer Effect Model (With Myungkou Shin)
Abstract: This paper develops an econometric model to analyse heterogeneity in peer effects in network data with endogenous spillover across units. We introduce a rank-dependent peer effect model that captures how the relative ranking of a peer outcome shapes the influence units have on one another, by modeling the peer effect to be linear in ordered peer outcomes. In contrast to the traditional linear-in-means model, our approach allows for greater flexibility in peer effect by accounting for the distribution of peer outcomes as well as the size of peer groups. Under a minimal condition, the rank-dependent peer effect model admits a unique equilibrium and is therefore tractable. Our simulations show that that estimation performs well in finite samples given sufficient covariate strength. We then apply our model to educational data from Norway, where we see that higher-performing students disproportionately drive GPA spillovers. 

Examining the Fallout: Who is hurt by educational gender biases? (With Ragnar Alne)
Abstract: This paper shows how to credibly identify gender bias for equally skilled students. This estimator is simple to implement, and allows us to identify gender bias for students with different skill levels and parental backgrounds. Using detailed registry data, we show that gender bias depends both on the socioeconomic background of a student, as well as the students own skills. We then show that students exposed to a negative bias are less likely to attend university and more likely to work after high school. Students with negative exposure have higher incomes for the first few years after high-school, but they start to earn less than their non-exposed peers ten years after starting high school. 

Fairness in grading: Randomizing Ethnicity and Gender in Teacher Assessments (With Ragnar Alne and Rune Borgan Reiling)
Abstract: We investigate the impact of ethnicity and gender on teacher assessments through a randomized controlled experiment involving 203 teachers grading a single class's assignments. Within teachers we randomly assigned names, signaling gender and ethnicity, with a control group grading anonymized assignments. Our results reveal significant variation in grades assigned by teachers for identical assignments, with some evidence of both ethnic and gender biases. Additionally, we examine how teachers' implicit biases interact with grading biases. Through counterfactual policy simulations, we demonstrate that our estimates imply an economically relevant trade-off between precision and accuracy for grading policies in schools. 

Published papers: 

"Residential Investment and Recession Predictability"
International Journal of Forecasting (2019) (With André K. Anundsen and Knut Are Aastveit)
Abstract: We assess the importance of residential investment for the prediction of economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1–2014Q4. Our approach is to estimate various probit models with different leading indicators and evaluate their relative prediction accuracies using the area under the receiver operating characteristic curve as our forecasting performance metric. We document that residential investment contains information that is useful for predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer confidence surveys and oil prices. It is shown that residential investment is particularly useful for the prediction of recessions for countries with high home-ownership rates. Finally, in a separate exercise for the US, we show that the predictive ability of residential investment is — in a broad sense — robust to employing real-time data.