for providing detailed information on courses, minority student programs, and registration procedures. Thanks also go to Alex Haslam, David Levine, Doug Miller, Uros Petronijevic, and seminar participants at the University of Calgary, University of British Columbia, University of Manitoba, University of Victoria, the Gender and Academia Conference in Sweden, the NBER Education Program fall meeting, the Presidential and Academic Senate Leadership Presentation at De Anza College, Northern California Community Colleges Institutional Researchers workshop, Case Western University, University of Colorado Boulder, the 2013 American Economics Association annual meeting in San Diego, and RAND. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.© 2011 by Robert W. Fairlie, Florian Hoffmann, and Philip Oreopoulos. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. ABSTRACTDetailed administrative data from a large and diverse community college are used to examine if academic performance depends on whether students are the same race or ethnicity as their instructors. To identify racial interactions and address many threats to internal validity we estimate models that include both student and classroom fixed effects. Given the large sample sizes and computational complexity of the 2-way fixed effects model we rely on numerical algorithms that exploit the particular structure of the model's normal equations. Although we find no evidence of endogenous sorting, we further limit potential biases from sorting by focusing on students with restricted course enrollment options due to low registration priorities, students not getting first section choices, and on courses with no within-term or within-year racial variation in instructors. We find that the performance gap in terms of class dropout rates, pass rates, and grade performance between white and underrepresented minority students falls by 20-50 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. Potential mechanisms for these positive interactions are examined.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Keywords: minority, college, community college, affirmative action, diversity Terms of use: Documents in EconStor may 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. 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. * We are extremely grateful to Bob Barr, Andrew LaManque, Howard Irvin and Stephen Fletcher for providing the administrative data for students. Special thanks also go to Lydia Hearn, Kathleen Moberg, Mallory Newell, Jerry Rosenberg, and Rowena Tomaneng for providing detailed information on courses, minority student programs, and registration procedures. We also thank Alex Haslam, David Levine, Doug Miller, Uros Petronijevic, Daniel Shack and seminar participants at the University of Calgary, University of British Columbia, University of Manitoba, University of Victoria, the Gender and Academia Conference in Sweden, the NBER Education Program fall meeting, the Presidential and Academic Senate Leadership Presentation at De Anza College, Northern California Community Colleges Institutional Researchers workshop, Case Western University, University of Colorado Boulder, the 2013 American Economics Association annual meeting in San Diego, and RA...

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.

In this paper we study the relationship between task complexity and the occupational wageand 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.

M5S3G7 CanadaThis paper analyzes the importance of teacher quality at the college level. Instructors are matched to objective and subjective characteristics of teacher quality to estimate the impact of rank, salary, and perceived effectiveness on student performance and subject interest. Student and course fixed effects, time of day and week controls, and students' lack of knowledge about first-year instructors help minimize selection biases. Subjective teacher evaluations perform well in measuring instructor influences on students while objective characteristics such as rank and salary do not. Overall, the importance of college instructor differences is small, but important outliers exist.JEL No. I2, H4

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In this paper we study the relationship between task complexity and the occupational wage-and employment structure. Complex tasks are dened as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate eectively. 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 signicant 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 eects of automatization.