Labor Turnover Bias in Estimating Wages
Cross-sectional earnings function analyses typically do not recognise the potential for misrepresentation arising from a relationship between unobserved ability and the probability of labor turnover. Our point is that if individuals staying with the firm for relatively long periods of time are more,...
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Veröffentlicht in: | The review of economics and statistics 1988-02, Vol.70 (1), p.117-123 |
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description | Cross-sectional earnings function analyses typically do not recognise the potential for misrepresentation arising from a relationship between unobserved ability and the probability of labor turnover. Our point is that if individuals staying with the firm for relatively long periods of time are more, or less, able on average than recent hires, the coefficient estimated on tenure is necessarily biased. This is an important issue for wage determination modelling given the relevance of tenure to wages. In this note we examine the theoretical basis of our claim, and propose and implement a solution to the problem with a novel use of instrumental variables on a large sample of workers employed in the Australian government. |
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(North-Holland)</publisher><subject>Coefficients ; Comparative analysis ; Consistent estimators ; Econometrics ; Economic statistics ; Employee turnover ; Employment ; Estimation bias ; Instrumental variables estimation ; Men ; Statistical analysis ; Studies ; Wage rates ; Wages ; Wages & salaries ; Workforce ; Working women</subject><ispartof>The review of economics and statistics, 1988-02, Vol.70 (1), p.117-123</ispartof><rights>Copyright 1988 The President and Fellows of Harvard College</rights><rights>Copyright MIT Press Journals Feb 1988</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c239t-e13e8df636f07d5f66d321e711913189c09cd91e86c80fea071e7f7e13b65a1e3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/1928157$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/1928157$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,828,27846,27901,27902,57992,57996,58225,58229</link.rule.ids></links><search><creatorcontrib>Beggs, John J.</creatorcontrib><creatorcontrib>Chapman, Bruce J.</creatorcontrib><title>Labor Turnover Bias in Estimating Wages</title><title>The review of economics and statistics</title><description>Cross-sectional earnings function analyses typically do not recognise the potential for misrepresentation arising from a relationship between unobserved ability and the probability of labor turnover. Our point is that if individuals staying with the firm for relatively long periods of time are more, or less, able on average than recent hires, the coefficient estimated on tenure is necessarily biased. This is an important issue for wage determination modelling given the relevance of tenure to wages. 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Our point is that if individuals staying with the firm for relatively long periods of time are more, or less, able on average than recent hires, the coefficient estimated on tenure is necessarily biased. This is an important issue for wage determination modelling given the relevance of tenure to wages. In this note we examine the theoretical basis of our claim, and propose and implement a solution to the problem with a novel use of instrumental variables on a large sample of workers employed in the Australian government.</abstract><cop>Cambridge, Mass</cop><pub>Elsevier Science Publishers B.V. (North-Holland)</pub><doi>10.2307/1928157</doi><tpages>7</tpages></addata></record> |
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subjects | Coefficients Comparative analysis Consistent estimators Econometrics Economic statistics Employee turnover Employment Estimation bias Instrumental variables estimation Men Statistical analysis Studies Wage rates Wages Wages & salaries Workforce Working women |
title | Labor Turnover Bias in Estimating Wages |
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