I am a Lecturer (Assistant Professor) in Economics at the University of Edinburgh. I do research in macroeconomics, with an emphasis on quantitative models and labor supply questions in the short and long run.
I obtained my PhD from the Institute for International Economic Studies (IIES), Stockholm University, in 2019.
email jonna.olsson (a) ed.ac.uk or jonna.s.k.olsson (a) gmail.com |
tel +46(0)70 765 5237 | address University of Edinburgh, School of Economics, 30-31 Buccleuch Place, Edinburgh, EH8 9JT, United Kingdom |
We formulate an economic time use model and add to it an epidemiological SIR block. In the event of an epidemic, households shift their leisure time from activities with a high degree of social interaction to activities with less, and also choose to work more from home. Our model highlights the different actions taken by young individuals, who are less severely affected by the disease, and by old individuals, who are more vulnerable. We calibrate our model to time use data from ATUS, employment data, epidemiological data, and estimates of the value of a statistical life. There are qualitative as well as quantitative differences between the competitive equilibrium and social planner allocation and, moreover, these depend critically on when a cure arrives. Due to the role played by social activities in people's welfare, simple indicators such as deaths and GDP are insufficient for judging outcomes in our economy.
Structural transformation of the labor market and the aggregate economy [Paper]
Women's increased involvement in the economy was the most significant change in labor markets during the past century. In this paper, I show that a macroeconomic model that takes into account gender and household composition in an otherwise parsimonious off-the-shelf setting captures a number of stylized facts regarding historical labor supply patterns for different subgroups of the population: the increase in female labor force participation was mainly driven by married women; single women's labor supply remained fairly constant during the same time period; married men were not crowded out of the labor force by their spouses; labor supply by singles displays a more volatile behavior at business-cycle frequencies; and both short- and long-run patterns are mainly driven by extensive-margin responses. The proposed model that simultaneously addresses long-run trends and short-run fluctuations across subgroups allows me to ask counterfactual questions: I evaluate the economy's response to aggregate shocks at different points during the transition from low to higher female labor force participation. I show that the underlying trend growth in employment, driven by the growth in married women's labor force participation, contributed to the perceived quick employment recoveries after recessions before 1990, and the absence of growth thereafter consequently helps explain the more recent slower employment recoveries.
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.
There is a substantial heterogeneity in life expectancy in the population. However, an individual's consumption-savings decision is not necessarily guided by the objective statistical life expectancy, but rather by the individual's beliefs about survival. In this paper, we document a systematic bias in survival beliefs: individuals with a low survival probability relative to their peers underestimate their life expectancies, while individuals with a high survival probability overestimate theirs. To gauge the effect of heterogeneity in life expectancy (objective and subjective) on savings rates and ultimately wealth inequality, we introduce shocks to survival beliefs into an otherwise standard overlapping-generations model. We show that with a bequest motive calibrated to match asset decumulation in old age, such a model exhibits a counter-factual savings behavior as individuals increase their savings when their life expectancy drops. Nevertheless, the overall wealth inequality in the economy is virtually unaffected by heterogeneity in survival beliefs, contrary to previous literature which finds stronger effects of heterogeneous discount factors.
Papers not intended for publication
Confronting epidemics: the need for epi-econ IAMs (with Timo Boppart, Karl Harmenberg, John Hassler, and Per Krusell) [Link]
We discuss what tools would be useful in confronting epidemics, especially from the perspective of economics. Our main proposal is for policymakers to employ “epi-econ IAMs”: explicit Integrated Assessment Models, where epidemiology is integrated with economics. These models are under rapid development, but arguably not yet quite ready for quantitative use.
Research papers in progress
Labor Supply under Heterogeneous Agents: The Case of Complete Markets (with Timo Boppart and Per Krusell)
Most of applied macro -- e.g., newkeynesian models, but also long-run modelling -- uses a representative agent or abstracts from labor supply, and often both. Sometimes the models study frictional labor markets, but rarely with active labor supply and heterogeneity, and then there is usually no frictionless benchmark to refer to. This is the gap we fill in this paper: we consider what we believe to be highly realistic and a priori relevant heterogeneity and its role for aggregate labor supply and for who will/should work. The findings have bearing both on short-run macroeconomic analysis -- in particular the sensitivity of ``the'' Frisch elasticity to heterogeneity -- and on long-run growth, where the growth rate of the economy can depend nontrivially on working behavior. The class of potential utility functions to consider is large so we restrict attention to those that are consistent with balanced growth (BK/KPR). Moreover, we focus especially on the functions most commonly used in the applied macroeconomic literatures. We conclude that, although a realistic model of the economy would feature incomplete markets and other frictions, the results we derive here will reflect a benchmark first-best allocation also in those economies and will, likely to an important extent, be driving the positive and normative features of those frictional economies as well.
Can Stable Preferences Explain Postwar U.S. Hours worked? (with Timo Boppart and Per Krusell)
Cross-country and time-series evidence suggest that, along a path of approximately constant real output and productivity growth, income effects on labor supply slightly outweigh substitution effect. Yet in the postwar U.S., where these approximate growth features are satisfied, hours have not had a trend, unlike in the average of OECD economies. In this paper we try to account for the U.S. facts with a standard neoclassical -- and, for convenience, frictionless -- model, using preferences that do mean that income effects exceed substitution effects. It turns out, first, that it is possible to account for the data if one defines ``hours worked'' to include hours worked at home. Second, to account for hours at home and in the market separately, one (i) needs to consider women explicitly and (ii) needs trends in drivers of women's labor supply. We briefly discuss possible such drivers, some of which can be quantified and are shown to contribute part way toward the total.