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1.
Lab labor: What can labor economists learn from the lab? by
Material type: Text Text
Publication details: Cambridge NBER 2010
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2.
Bidding for brains: Intellectual property rights and the int by
Material type: Text Text
Publication details: Cambridge NBER 2009
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3.
Employers' preferences for gender, age, height and beauty: by
Material type: Text Text
Publication details: Cambridge NBER 2009
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4.
Competition and the Ratchet Effect by
Material type: Text Text
Publication details: Cambridge NBER 2010
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5.
Gender discrimination in job ads : Theory and evidence / Peter J. Kuhn and Kailing Shen by Series: NBER Working Paper ; 17453
Material type: Text Text
Publication details: Cambridge NBER 2011
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Do employers prefer migrant workers? : Evidence from a Chinese job board / Cavallo Eduardo by Series: NBER Working Paper ; 21675
Material type: Text Text
Publication details: Cambridge NBER 2015
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Pay inequality, pay secrecy, and effort: Theory and evidence by Series: NBER working papers
Material type: Text Text
Publication details: Cambridge, MA NBER 2005
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11.
The expanding workweek?: Understanding trends in long work hours among U.S. men, 1979-2004 by Series: NBER working papers
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Publication details: Cambridge, MA NBER 2005
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The narrowing of the U.S. gender earnings gap, 1959-1999: A cohort-based analysis by Series: NBER working papers
Material type: Text Text
Publication details: Cambridge, MA NBER 2006
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15.
Gender-targeted job ads in the recruitment process : Evidence from china / Peter Kuhn, Kailing Shen and Shuo Zhang by Series: NBER Working Paper ; 25365
Material type: Text Text; Format: print ; Literary form: Not fiction
Publication details: Cambridge NBER 2018
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16.
How costly is turnover? : Evidence from retail / Peter J. Kuhn and Lizi Yu by Series: NBER Working Paper ; 26179
Material type: Text Text; Format: print ; Literary form: Not fiction
Publication details: Cambridge NBER 2019
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When is discrimination unfair? / Peter J. Kuhn and Trevor T. Osaki by Series: NBER Working Papers ; 30236
Material type: Text Text
Publication details: Cambridge NBER 2022
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20.
Measuring bias in job recommender systems : Auditing the algorithms / Shuo Zhang and Peter J. Kuhn by Series: NBER Working Papers ; 32889
Material type: Text Text
Publication details: Cambridge NBER 2024
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