I am a Post-doc at the Department of Finance, Stockholm School of Economics and at the Institute for Social Research (SOFI), Stockholm University. I received my Ph.D. from Stockholm School of Economics in 2018.
I do research in empirical and experimental microeconomics, using econometric techniques to study how real-life decisions are impacted by psychological biases, non-standard preferences, and the lack of information. My current work involves topics such as college choice, social preferences, and household finance.
Work in Progress
Intergenerational transmission of university program and institution choice
Early draft available upon request.
The study of intergenerational mobility has long been a core concept of social science. Even in relatively mobile countries like Sweden, an individual's social origin often determines their socio-cultural status. That the transmission of educational attainment carries both a strong genetic and environmental component has been observed for decades. While the revolution in behavioral genetics has been able to pin down the specific genes that drive heritability, data limitations has so far obstructed social scientists from estimating the causal pathways that define the environmental effect. In this paper I build on recent findings in sociology, that have identified strong associations between fields of study choices of parents and their children, and estimate the environmental transmission of education across generations that is directly caused by the choices of parents. In a quasi-experimental regression discontinuity design, I study individuals who apply to Swedish universities between 1977 and 1999 and evaluate if their enrollment in specific programs and institutions increase the probability that their children study the same alternative. I find strong causal influence on both the choice of institutions and programs, with children being 50-100% more likely to enroll in a topic studied by their parents. While some topics are more often followed than others, children are almost never discouraged to study the field their parents chose.
Business Education and Wealth
Relative Returns to Swedish College Fields
Being reworked with new, long-run, earnings data.
Nowcasting Covid-19 statistics reported with delay: a case-study of Sweden
Under review at Scientific Reports
The new corona virus disease - COVID-2019 - is rapidly spreading through the world. The availability of unbiased timely statistics of trends in disease events are a key to effective responses. But due to reporting delays, the most recently reported numbers are frequently underestimating of the total number of infections, hospitalizations and deaths creating an illusion of a downward trend. Here we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the removal method, a well-established estimation framework in the field of ecology.
O Brother, Where Start Thou? Sibling Spillovers on College and Major Choice in Four Countries
Quarterly Journal of Economics, 2021
Family and social networks are widely believed to influence important life decisions, but causal identification of those effects is notoriously challenging. Using data from Chile, Croatia, Sweden, and the United States, we study within-family spillovers in college and major choice across a variety of national contexts. Exploiting college-specific admissions thresholds that directly affect older but not younger siblings’ college options, we show that in all four countries a meaningful portion of younger siblings follow their older sibling to the same college or college-major combination. Older siblings are followed regardless of whether their target and counterfactual options have large, small, or even negative differences in quality. Spillover effects disappear, however, if the older sibling drops out of college, suggesting that older siblings’ college experiences matter. That siblings influence important human capital investment decisions across such varied contexts suggests that our findings are not an artifact of particular institutional detail but a more generalizable description of human behavior. Causal links between the postsecondary paths of close peers may partly explain persistent college enrollment inequalities between social groups, and this suggests that interventions to improve college access may have multiplier effects.
Predicting the replicability of social science lab experiments
PLOS One, 2019
We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman ρ of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists (Camerer et al., 2016; Dreber et al., 2015). The predictive power is validated in a pre-registered out of sample test of the outcome of Camerer et al. (2018), where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to ρ = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative.
Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015
Nature Human Behavior, 2018
with Colin F. Camerer, Anna Dreber, Felix Holzmeister, Teck Ho, Juergen Huber, Magnus Johannesson, Michael Kirchler, Gideon Nave, Brian A. Nosek, Thomas Pfeiffer, Nick Buttrick, Taizan Chan, Yiling Chen, Eskil Forsell, Anup Gampa, Emma Heikensten, Lily Hummer, Taisuke Imai, Siri Isaksson, Dylan Manfredi, Julia Rose, Eric-Jan Wagenmakers, and Hang Wu
Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
Evaluating Replicability of Laboratory Experiments in Economics
The replicability of some scientific findings has recently been called into question. To contribute data about replicability in economics, we replicated 18 studies published in the American Economic Review and the Quarterly Journal of Economics between 2011 and 2014. All of these replications followed predefined analysis plans that were made publicly available beforehand, and they all have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We found a significant effect in the same direction as in the original study for 11 replications (61%); on average, the replicated effect size is 66% of the original. The replicability rate varies between 67% and 78% for four additional replicability indicators, including a prediction market measure of peer beliefs.
Using Prediction Markets to Forecast Research Evaluations
Royal Society Open Science, 2015
with Marcus Munafo, Thomas Pfeiffer, Adam Altmejd, Emma Heikensten, Johan Almenberg, Alexander Bird, Yiling Chen, Brad Wilson, Magnus Johannesson, and Anna Dreber
The 2014 Research Excellence Framework (REF2014) was conducted to assess the quality of research carried out at higher education institutions in the UK over a 6 year period. However, the process was criticized for being expensive and bureaucratic, and it was argued that similar information could be obtained more simply from various existing metrics. We were interested in whether a prediction market on the outcome of REF2014 for 33 chemistry departments in the UK would provide information similar to that obtained during the REF2014 process. Prediction markets have become increasingly popular as a means of capturing what is colloquially known as the ‘wisdom of crowds’, and enable individuals to trade ‘bets’ on whether a specific outcome will occur or not. These have been shown to be successful at predicting various outcomes in a number of domains (e.g. sport, entertainment and politics), but have rarely been tested against outcomes based on expert judgements such as those that formed the basis of REF2014.