University graduates are two to five times as likely to hold a degree in the field that their parents graduated from. To estimate how much of this association is caused by the education choices of parents, I exploit admission thresholds to university programs in a regression discontinuity design. I study individuals who applied to Swedish universities between 1977 and 1992 and evaluate how their enrollment in different fields of study increases the probability that their children later study the same topic. I find strong causal influence. At the aggregate level, children become 73% more likely to graduate from a field that their parent has quasi-randomly enrolled in. The effect is always positive, but varies in size across fields. Engineering, medicine, social science, and business exhibit the largest effects. For these fields, parental enrollment increases child graduation rates with between 6.0 and 9.5 percentage points. I find little evidence for comparative advantage being the key driver of field inheritance. Parental field enrollment does not increase subject-specific skills in primary school, nor do labor market returns differ by access to parents with the same degree. Rather, parents seem to function as role models, making their own field choice salient. This is indicated by the fact that children are more likely to follow parents with the same gender, and that parental enrollment in gender incongruent fields is more impactful.

Using university admission cutoffs that generate exogenous variation in college-major choices, we provide causal evidence that enrollment in a business or economics program leads individuals to invest significantly more in the stock market, earn higher portfolio returns, and ultimately accumulate higher levels of wealth later in life. Underlying these effects, beyond differences in risk-taking, innate ability, labor market outcomes, or scale effects, is the enhanced ability of business educated individuals to acquire and process economic information and make informed investment decisions. Early investments in financial literacy thus play an important role in generating higher returns that significantly alter individuals' life-cycle wealth profiles.

The COVID-19 pandemic struck societies directly and indirectly, not just challenging population health but disrupting many aspects of life. Different effects of the spreading virus—and the measures to fight it—are reported and discussed in different scientific fora, with hard-to-compare methods and metrics from different traditions. While the pandemic struck some groups more than others, it is difficult to assess the comprehensive impact on social inequalities. This paper gauges social inequalities using individual-level administrative data for Sweden’s entire population. We describe and analyze the relative risks for different social groups in four dimensions—gender, education, income, and world region of birth—to experience three types of COVID-19 incidence, as well as six additional negative life outcomes that reflect general health, access to medical care, and economic strain. During the pandemic, the overall population faced severe morbidity and mortality from COVID-19 and saw higher all-cause mortality, income losses and unemployment risks, as well as reduced access to medical care. These burdens fell more heavily on individuals with low income or education and on immigrants. Although these vulnerable groups experienced larger absolute risks of suffering the direct and indirect consequences of the pandemic, the relative risks in pandemic years (2020 and 2021) were conspicuously similar to those in prepandemic years (2016 to 2019).

The COVID-19 pandemic has demonstrated the importance of unbiased, real-time statistics of trends in disease events in order to achieve an effective response. Because of reporting delays, real-time statistics frequently underestimate the total number of infections, hospitalizations and deaths. When studied by event date, such delays also risk 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.

with Andres Barrios-Fernandez, Marin Drlje, Joshua Goodman, Michael Hurwitz, Dejan Kovac, Christine Mulhern, Christopher Neilson, and Jonathan Smith

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.

Universitetsläraren, Aftonbladet, Animated explainer - Econimate

with Anna Dreber, Eskil Forsell, Gideon Nave, Juergen Huber, Magnus Johannesson, Michael Kirchler, Taisuke Imai, Teck Ho, and Colin Camerer

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.

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.

Science News, Vox, The Atlantic, NPR, Wired, The Washington Post, Nature, Science Mag, The Guardian, Buzzfeed News, Marginal Revolution

with Colin F. Camerer, Anna Dreber, Eskil Forsell, Teck Ho, Juergen Huber, Magnus Johannesson, Michael Kirchler, Johan Almenberg, Adam Altmejd, Taizan Chan, Emma Heikensten, Felix Holzmeister, Taisuke Imai, Siri Isaksson, Gideon Nave, Thomas Pfeiffer, Michael Razen, and Hang Wu

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.

Last Week Tonight with John Oliver, The Economist, Science Mag, The Chronicle of Higher Education

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.