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Professor Florian Hoffmann, Ph.D.

Professor Florian Hoffmann, Ph.D.
Aktuelle Position

seit 2/25

Research Fellow im Zentrum für evidenzbasierte Politikberatung (IWH-CEP)

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 07/19

Associate Professor

Vancouver School of Economics

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Forschungsschwerpunkte
  • Humankapital
  • dynamische allgemeine Gleichgewichtsmodelle

Florian Hoffmann ist seit Dezember 2016 Research Fellow am IWH. Seine Forschungsinteressen umfassen Bestimmungsgrößen für Lebenszyklus-Einkommen und Karrieredynamik, dynamische diskrete Entscheidungsmodelle für Humankapitalbildung, die Einschätzung von Gleichgewichtssuchmodellen und die Bedeutung der Interaktionen zwischen Student und Lehrer für akademische Leistungen in der Hochschulbildung.

Florian Hoffmann ist Associate Professor an der Vancouver School of Economics. Er promovierte an der University of Toronto.

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Professor Florian Hoffmann, Ph.D.
Professor Florian Hoffmann, Ph.D.
- Abteilung Zentrum für evidenzbasierte Politikberatung
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Publikationen

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Growing Income Inequality in the United States and Other Advanced Economies

Florian Hoffmann David S. Lee Thomas Lemieux

in: Journal of Economic Perspectives, Vol. 34 (4), 2020

Abstract

This paper studies the contribution of both labor and non-labor income in the growth in income inequality in the United States and large European economies. The paper first shows that the capital to labor income ratio disproportionately increased among high-earnings individuals, further contributing to the growth in overall income inequality. That said, the magnitude of this effect is modest, and the predominant driver of the growth in income inequality in recent decades is the growth in labor earnings inequality. Far more important than the distinction between total income and labor income, is the way in which educational factors account for the growth in US labor and capital income inequality. Growing income gaps among different education groups as well as composition effects linked to a growing fraction of highly educated workers have been driving these effects, with a noticeable role of occupational and locational factors for women. Findings for large European economies indicate that inequality has been growing fast in Germany, Italy, and the United Kingdom, though not in France. Capital income and education don't play as much as a role in these countries as in the United States.

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HIP, RIP, and the Robustness of Empirical Earnings Processes

Florian Hoffmann

in: Quantitative Economics, Vol. 10 (3), 2019

Abstract

The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.

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Complex-task Biased Technological Change and the Labor Market

Colin Caines Florian Hoffmann Gueorgui Kambourov

in: Review of Economic Dynamics, Vol. 25 (April), 2017

Abstract

In this paper we study the relationship between task complexity and the occupational wage- and employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980–2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.

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Arbeitspapiere

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Who Benefits from Place-based Policies? Evidence from Matched Employer-Employee Data

Philipp Grunau Florian Hoffmann Thomas Lemieux Mirko Titze

in: IWH Discussion Papers, Nr. 11, 2024

Abstract

We study the granular wage and employment effects of a German place-based policy using a research design that leverages conditionally exogenous EU-wide rules governing program parameters at the regional level. The place-based program subsidizes investments to create jobs with a subsidy rate that varies across labor market regions. The analysis uses matched data on the universe of establishments and their employees, establishment-level panel data on program participation, and regional scores that generate spatial discontinuities in program eligibility and generosity. Spatial spillovers of the program linked to changing commuting patterns can be assessed using information on place of work and place of residence, a unique feature of the data. These rich data enable us to study the incidence of the place-based program on different groups of individuals. We find that the program helps establishments create jobs that disproportionately benefit younger and less-educated workers. Funded establishments increase their wages but, unlike employment, wage gains do not persist in the long run. Employment effects estimated at the local area level are slightly larger than establishment-level estimates, suggesting limited economic spillover effects. On the other hand, spatial spillovers are large as over half of the employment increase comes from commuters. Using subsidy rates as an instrumental variable for actual subsidies indicates that it costs approximately EUR 25,000 to create a new job in the economically disadvantaged areas targeted by the program.

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