|
on Human Capital and Human Resource Management |
| By: | Mitchell Hoffman; Christopher T. Stanton |
| Abstract: | This chapter surveys recent advances in personnel economics. We begin by presenting evidence showing substantial and persistent productivity variation among workers in the same roles. We discuss new research on incentives and compensation; hiring practices; the influence of managers and peers; and time use, technology, and training. We emphasize two main themes. First, we seek to illustrate the interplay between these topics and productivity differences between people and work units. Second, we argue that personnel economics has benefited from exploration, which we think of as the willingness to use new data and methods to shed light on existing questions and to raise new ones. As many personnel studies use data from individual firms, we discuss external validity and provide concrete guidance on how to improve discussions of the generalizability of findings from specific contexts. |
| JEL: | J01 M5 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32849 |
| By: | Jirjahn, Uwe (University of Trier); Rienzo, Cinzia (University of Brighton) |
| Abstract: | Working from home reduces real-time visibility of employees within the physical space of the workplace. This makes it difficult to monitor employees' work behavior. Employers may instead monitor employees' outputs and provide incentives through performance pay. The crucial question is what type of performance pay employers provide to incentivize employees who work from home. Using British panel data, we find that working from home decreases the likelihood of solely receiving individual performance pay. It increases the likelihood of receiving collective performance pay – with or without individual performance pay. This pattern also holds in instrumental variable estimations accounting for endogeneity. Our findings fit theoretical considerations. Working from home means that employees have less opportunities to socialize at work entailing the tendency that they focus on personal achievement and neglect collaboration. Solely rewarding individual performance may reinforce this tendency. By contrast, employers reward collective performance as it counteracts the adverse effects of working from home by providing incentives for collaboration, helping on the job and information sharing. |
| Keywords: | remote work, face-to-face interaction, helping on the job, information sharing, individual performance pay, profit sharing |
| JEL: | J22 J33 M50 M52 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17234 |
| By: | Adrianto, Adrianto (University of Minnesota); Ben-Ner, Avner (University of Minnesota); Sockin, Jason (IZA); Urtasun, Ainhoa (Universidad Pública de Navarra) |
| Abstract: | Do employees fare better in firms they partly own? Examining workers' reviews of their employers on Glassdoor, we compare employee satisfaction between firms in which workers own company shares through an employee stock ownership plan (ESOP) and conventional firms in which they do not. Focusing on workers in U.S. manufacturing, we find employees report greater satisfaction in employee-owned firms overall and with specific aspects of jobs such as firm culture. This satisfaction premium is greater when the ESOP is the product of collective bargaining or employees own a larger stake of firm equity. Employee well-being can thus differ by ownership arrangement. |
| Keywords: | ESOP, job satisfaction, collective bargaining, culture |
| JEL: | J52 J28 M14 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17233 |
| By: | Daron Acemoglu; Alexander Wolitzky |
| Abstract: | We propose a model of the interplay of employment relationships and community-based interactions among workers and managers. Employment relations can be either tough (where workers are monitored intensively and obtain few rents, and managers do not provide informal favors for their workers) or soft (where there is less monitoring, more worker rents, and more workplace favor exchange). Both workers and managers also exert effort in providing community benefits. The threat of losing access to community benefits can motivate managers to keep employment soft; conversely, the threat of losing future employment or future workers' trust can motivate workers and managers to exert effort in the community. Improvements in monitoring technologies; automation, outsourcing, and offshoring; declines in the minimum wage; and opportunities for residential segregation or for privatizing community-provided services can make both workers and managers worse-off by undermining soft employment relations and community cooperation. |
| JEL: | C73 D23 J00 P00 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32773 |
| By: | Grant Miller; Debashish Biswas; Aprajit Mahajan; Kimberly Singer Babiarz; Nina R. Brooks; Jessie Brunner; Sania Ashraf; Jack Shane; Sameer Maithel; Shoeb Ahmed; Moogdho Mahzab; Mohammad Rofi Uddin; Mahbubur Rahman; Stephen P. Luby |
| Abstract: | Globally, coercive labor (i.e., forced, bonded, and/or trafficked labor) and child labor are disproportionately prevalent in environments with weak regulatory enforcement and state capacity. Effective strategies for addressing them may therefore need to align with the private incentives of business owners, not relying on government action alone. Recognizing this, we test a ‘business case’ for improving work conditions and promoting human rights using a randomized controlled trial across nearly 300 brick kilns in Bangladesh. Among study kilns, rates of coercive and child labor are high: about 50% of sampled workers are trafficked, and about 70% of kilns use child labor. Our experiment introduced a production method that increased kiln productivity and revenue, and we test if these productivity gains in turn increase worker “compensation” (including better work conditions). Because adoption of the method requires important changes in worker routines, we also test if providing information to kiln owners about positively incentivizing workers to enhance adoption (and hence business revenue) can lead to better work conditions. We find no evidence that productivity gains alone reduced labor trafficking or child labor, but adding the information intervention reduced child labor by 25-30% without reducing revenue or increasing costs. |
| JEL: | J28 J39 J46 J49 J59 J81 J83 O17 O53 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32829 |
| By: | Ning Li; Huaikang Zhou; Mingze Xu |
| Abstract: | This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Through comparative analyses across two studies, including various task performance outputs, we demonstrate that LLMs can serve as a reliable and even superior alternative to human raters in evaluating knowledge-based performance outputs, which are a key contribution of knowledge workers. Our results suggest that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability. Additionally, combined multiple GPT ratings on the same performance output show strong correlations with aggregated human performance ratings, akin to the consensus principle observed in performance evaluation literature. However, we also find that LLMs are prone to contextual biases, such as the halo effect, mirroring human evaluative biases. Our research suggests that while LLMs are capable of extracting meaningful constructs from text-based data, their scope is currently limited to specific forms of performance evaluation. By highlighting both the potential and limitations of LLMs, our study contributes to the discourse on AI role in management studies and sets a foundation for future research to refine AI theoretical and practical applications in management. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05328 |
| By: | Shuo Zhang; Peter J. Kuhn |
| Abstract: | We audit the job recommender algorithms used by four Chinese job boards by creating fictitious applicant profiles that differ only in their gender. Jobs recommended uniquely to the male and female profiles in a pair differ modestly in their observed characteristics, with female jobs advertising lower wages, requesting less experience, and coming from smaller firms. Much larger differences are observed in these ads’ language, however, with women’s jobs containing 0.58 standard deviations more stereotypically female content than men’s. Using our experimental design, we can conclude that these gender gaps are generated primarily by content-based matching algorithms that use the worker’s declared gender as a direct input. Action-based processes like item-based collaborative filtering and recruiters’ reactions to workers’ resumes contribute little to these gaps. |
| JEL: | C99 J16 J71 M50 O33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32889 |