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on Human Capital and Human Resource Management |
| By: | Caldwell, Sydnee (University of California at Berkeley & National Bureau of Economic Research (NBER)); Haegele, Ingrid (Ludwig-Maximilians Universität München und Institut für Arbeitsmarkt- und Berufsforschung (IAB)); Heining, Jörg (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "Whether and how workers search on the job depends on their beliefs about pay and working conditions in other firms. Yet little is known about workers’ knowledge of outside pay. We use a large-scale survey of full-time German workers, linked to their Social Security records, to elicit pay expectations and preferences over specific outside firms. Workers believe that they face considerable heterogeneity in their outside pay options, and direct their search toward firms they believe would pay them more.Workers’ expected firm-specific pay premia are highly correlated with pay policies observed in administrative records and with workers’ valuations of firm-specific amenities.Most workers are unwilling to search for a new job—or leave their current firm—even for substantial pay increases. Switching costs are equivalent to 7 to 18 percent of a worker’s annual pay. Attachment varies across firms, and cannot be explained by either differences in firm-specific amenities or switching costs." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | Bundesrepublik Deutschland ; IAB-Open-Access-Publikation ; Auswirkungen ; Betriebstreue ; Einkommenseffekte ; Einkommenserwartung ; abhängig Beschäftigte ; Kündigungsabsicht ; Lohnunterschied ; Präferenz ; Arbeitsbedingungen ; Unternehmen ; Arbeitsplatzwechsel ; zwischenbetriebliche Mobilität ; Arbeitsuche ; 2022-2024 |
| JEL: | J00 J30 J30 J31 J32 |
| Date: | 2025–03–12 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202504 |
| By: | Jana Kim Gutt (Paderborn University); Kirsten Thommes (Paderborn University) |
| Abstract: | Employees are frequently evaluated on a numerical scale by their supervisors. These numerical assessments inform far-reaching managerial decisions, such as promotions, training opportunities, and dismissals. Yet, they often lack accuracy, are subject to supervisor bias, and do not provide justification for the ratings. In this paper, we address the limitations of numerical ratings by letting individuals provide spoken assessments of others and use a Random Forest algorithm to convert the spoken assessments into numbers (algorithmic ratings). Through this method, we combine the advantages of qualitative feedback and numerical ratings while potentially mitigating common biases. Our results suggest that the algorithmic ratings more accurately reflect the distribution of competencies (as measured by psychometric tests) than assigned numerical ratings (assigned ratings). The algorithmic ratings are considerably more nuanced and less skewed compared to the assigned ratings. Our findings highlight the potential of combining spoken comments with a machine learning model to enhance the accuracy of employee assessments in organizational settings. |
| Keywords: | performance appraisals, rating prediction, machine learning, spoken comments |
| JEL: | J24 M51 D91 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:130 |