Stockholm university

Markus JänttiProfessor

About me

My research centers on income and wealth inequality and poverty and socio-economic mobility, especially in a cross-national perspective. I have recently spent much time trying to understand and quantify the importance of family background in the distribution of economic resources.
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Research projects


A selection from Stockholm University publication database

  • Habit Formation and the Misallocation of Labor: Evidence from Forced Migrations

    2022. Matti Sarvimäki, Roope Uusitalo, Markus Jäntti. Journal of the European Economic Association


    We use a research design created by forced migrations to examine the costs and benefits of leaving agriculture in mid-20th century Finland. After World War II, 11% of the Finnish population was resettled from areas ceded to the Soviet Union. Entire rural communities were moved to locations that resembled the origin areas, and displaced farmers were given land and assistance to establish new farms. Despite this policy of reconstructing the pre-war situation, forced migration increased the likelihood of switching to non-agricultural jobs and moving to urban areas. Consequently, forced migration also increased the long-term income of the displaced rural population. By contrast, forced migration decreased the income of the resettled urban population. We examine the extent to which these effects can be explained by the quality of the new farms, human capital investments, networks, and discrimination, but do not find evidence supporting these mechanisms. Instead, we argue that habit formation toward residential locations provides the most compelling rationalization for our results.

    Read more about Habit Formation and the Misallocation of Labor
  • Inequality measurement with grouped data: Parametric and non-parametric methods

    2021. Vanesa Jorda, José María Sarabia, Markus Jäntti. Journal of the Royal Statistical Society 184 (3), 964-984


    Grouped data in the form of income shares have conventionally been used to estimate income inequality due to the lack of individual records. We present a systematic evaluation of the performance of parametric distributions and non-parametric techniques to estimate economic inequality using more than 3300 data sets. We also provide guidance on the choice between these two approaches and their estimation, for which we develop the GB2group R package. Our results indicate that even the simplest parametric models provide reliable estimates of inequality measures. The non-parametric approach, however, fails to represent income distributions accurately.

    Read more about Inequality measurement with grouped data
  • Intergenerational mobility, intergenerational effects, sibling correlations, and equality of opportunity: A comparison of four approaches

    2020. Anders Björklund, Markus Jäntti. Research in Social Stratification and Mobility 70


    This paper presents and discusses four different approaches to the study of how individuals’ income and education during adulthood are related to their family background. The most well-known approach, intergenerational mobility, describes how parents’ and offspring’s income or education are related to each other. The intergenerational-effect literature addresses the question how an intervention that changes parental income or education causally affects their children’s outcome. The sibling-correlation approach estimates the share of total inequality that is attributed to factors shared by siblings. This share is generally substantially higher than what is revealed by intergenerational mobility estimates. Finally, the equality-of-opportunity approach is looking for a set of factors, in the family background and otherwise, that are important for children’s outcomes and that children cannot be held accountable for.

    We argue that all four approaches are most informative and that recent research has provided insightful results. However, by comparing results from the different approaches, it is possible to paint a more nuanced picture of the role of family background. Thus, we recommend that scholars working in the four subfields pay more attention to each other’s research.

    Read more about Intergenerational mobility, intergenerational effects, sibling correlations, and equality of opportunity
  • Money and Happiness: Income, Wealth and Subjective Well-Being

    2020. Conchita D'Ambrosio, Markus Jäntti, Anthony Lepinteur. Social Indicators Research 148 (1), 47-66


    We examine the complex relationship between money and happiness. We find that both permanent income and wealth are better predictors of life satisfaction than current income and wealth. They matter not only in absolute terms but also in comparative terms. However, their relative impacts differ. The first exerts a comparison effect-the higher the permanent income of the reference group, the lower life satisfaction-the second exerts an information effect-the higher the permanent wealth of the reference group, the higher life satisfaction. We also show that negative transitory shocks to income reduce life satisfaction while transitory shocks to wealth have no effect. Lastly, we analyse the effects of their components and find that not all of them predict life satisfaction: permanent taxes do not matter, while only the value of permanent real estate, financial and business assets do. Finally, we use quantile regression and analyse to what extent our results vary along the well-being distribution, finding the impacts to be larger at lower levels of life satisfaction.

    Read more about Money and Happiness
  • The Determinants of Redistribution Around The World

    2020. Markus Jäntti, Jukka Pirttilä, Risto Rönkkö. The Review of Income and Wealth 66 (1), 59-73


    This paper reexamines the determinants of redistribution in light of improved data and methods relative to earlier literature. In particular, we use the latest version of the UNU-WIDER's Income Inequality Database to have the best available estimates of both pre- and post-redistribution inequality for the largest set of countries and periods. We tackle head-on problems related to model specification that risk generating large biases in estimates because of mechanical associations between the dependent and explanatory variables. The results suggest that the bias in the earlier work can be substantial. The descriptive analysis highlights, in addition, how scarce the data are when it comes to information about the extent of redistribution in developing countries.

    Read more about The Determinants of Redistribution Around The World

Show all publications by Markus Jäntti at Stockholm University