I am a lecturer (assistant professor) at the Adam Smith Business School at the University of Glasgow. My research is mainly focused on heterogeneous-agent macroeconomics, household finance and wealth inequality.
I received my PhD from the Institute for International Economic Studies (IIES) at Stockholm University.
Recent evidence suggests that lifetime experiences play an important role in determining households' investment choices. I incorporate these findings and the fact that household portfolios are underdiversified into an otherwise standard life-cycle model and examine to what extent they can help resolve long-standing puzzles in the literature regarding stock market participation and the fraction of financial wealth invested in risky assets. I show that experience-based learning about returns creates a positive correlation between a household's position in the wealth distribution and its optimism about future returns. The wealthy consequently increase their investment in risky assets, while participation is limited among poor households. I find that in a reasonably calibrated quantitative model, this mechanism is able to close approximately half of the gap between participation rates observed in the data and the predictions from standard models. On the other hand, the average conditional risky share remains mostly unaffected.
This paper explores how heterogeneity in life expectancy, objective (statistical) as well as subjective, affects savings behavior between healthy and unhealthy people. Using data from the Health and Retirement Study, we show that people in poor health not only have shorter actual lifespan, but are also more pessimistic about their remaining time of life. Using a standard overlapping-generations model, we show that differences in life expectancy can explain one third of the differences in accumulated wealth with an important part driven by pessimism among unhealthy people.
In this paper, we provide improved estimates for age-dependent health transitions and survival probabilities for different subsamples of the US population. The estimated yearly transition matrices can be used in any life-cycle model where health and survival dynamics is of interest. The results show substantial heterogeneity in life expectancy in the population. For a 70-year-old man in excellent health, the probability of reaching his 80th birthday is around 75%, while the corresponding probability for a man in poor health is just below 40%. There is also substantial inequality in life expectancy between different educational groups. In the group with less than a high school degree, the life expectancy at the age of 50 is 75 years, while the average for those with some college education or more is 80 years. This difference is due to two factors. First, at the age of 50, overall health is worse in the group with lower education. Second, even conditional on health status, the health dynamics and survival probabilities for this group are worse also from the age of 50 and onwards. We estimate that the difference in life expectancy across education groups mainly stems from the worse health and survival dynamics after the age of 50.
Work in progress
"On the Redistributive Effects of Government Bailouts in the Mortgage Market" (with Dirk Krueger and Kurt Mitman)
In this paper we investigate positive and normative implications of bailout guarantees for mortgage lenders. The implicit guarantee for debt issued by Fannie Mae and Freddie Mac in the US prior to 2008 led to lower borrowing costs for financial intermediaries in the mortgage market, which were passed on to households taking out mortgages. This in turn might have affected the distribution of real estate ownership and leverage among US households. On the other hand, the bailouts in 2008 required injections of $187bn of taxpayer money into these companies. The paper attempts to quantify this trade-off of lower borrowing rates and higher taxes to fund bailouts in a model featuring heterogeneous households and aggregate uncertainty in the form of (severe) recessions, identifying beneficiaries and losers of such a policy as well as overall welfare effects.