25 Years IWH

Professor Florian Hoffmann, PhD

Current Position

since 12/16

Research Fellow at the Department of Structural Change and Productivity

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 07/10

Assistant Professor

Vancouver School of Economics

Research Interests

  • human capital
  • dynamic general equilibrium models

Florian Hoffmann is a Research Fellow at the IWH since December 2016. His research interests lie in the determinants of life-cycle earnings and career dynamics, dynamic discrete choice models of human capital formation, estimation of equilibrium search models, and the importance of student-instructor interactions for academic achievement on the post-secondary education level.

Since July 2010 Florian Hoffmann has been an Assistant Professor at Vancouver School of Economics. He received his PhD from the University of Toronto.

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Professor Florian Hoffmann, PhD
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Publications

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Complex-task Biased Technological Change and the Labor Market

Colin Caines Florian Hoffmann Gueorgui Kambourov

in: Review of Economic Dynamics , 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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A Community College Instructor Like Me: Race and Ethnicity Interactions in the Classroom

Robert W. Fairlie Florian Hoffmann Philip Oreopoulos

in: American Economic Review , No. 8, 2014

Abstract

Administrative data from a large and diverse community college are used to examine if underrepresented minority students benefit from taking courses with underrepresented minority instructors. To identify racial interactions we estimate models that include both student and classroom fixed effects and focus on students with limited choice in courses. We find that the performance gap in terms of class dropout rates and grade performance between white and underrepresented minority students falls by 20 to 50 percent when taught by an underrepresented minority instructor. We also find these interactions affect longer term outcomes such as subsequent course selection, retention, and degree completion.

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A Professor Like Me: The Influence of Instructor Gender on College Achievement

Florian Hoffmann Philip Oreopoulos

in: The Journal of Human Resources , No. 2, 2009

Abstract

Many wonder whether teacher gender plays an important role in higher education by influencing student achievement and subject interest. The data used in this paper help identify average effects from male and female college students assigned to male or female teachers. We find instructor gender plays only a minor role in determining college student achievement. Nevertheless, the small effects provide evidence that gender role models matter to some college students. A same-sex instructor increases average grade performance by at most 5 percent of its standard deviation and decreases the likelihood of dropping a class by 1.2 percentage points.

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