Peer Bargaining and Productivity in Teams: Gender and the Inequitable Division of Pay

Pierce, Lamar; Wang, Laura W.; Zhang, Dennis

doi:10.2139/ssrn.3123915

Cited by 6 publications

(12 citation statements)

References 76 publications

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“…In addition, mental health concerns might increase because of the cultural change commonly associated with P4P adoption and other incentives (Gneezy, Meier, & Ray-Biel, 2011). P4P systems can motivate competitive behaviors and disincentivize prosociality, particularly in systems that rely on individual performance (Chan, Li, & Pierce, 2014a, 2014b, tournament-based (relative) pay (Garcia, Tor, & Gonzalez, 2006;Garcia & Tor, 2007), or the peer-based division of rewards (Pierce, Wang, & Zhang, 2019). Tournament-based P4P is even thought to generate sabotage behaviors that are toxic for organizational culture (Drago & Garvey, 1998;Lazear, 1999;Charness, Masclet, & Villeval, 2013), even when performance is defined at the team level (Gürtler, 2008).…”

Section: Employee Responses To Pay-for-performancementioning

confidence: 99%

Pay-for-Performance and Employee Mental Health: Large Sample Evidence Using Employee Prescription Drug Usage

Dahl

Pierce

2020

AMD

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This paper provides evidence linking pay-for-performance (P4P) adoption by employers to long-term and serious mental health problems in employees. Matching survey-based data on P4P adoption by 1,309 Danish firms with wage, demographic, and medical prescription data of 318,717 full-time employees, we find a four to six percent increase in the usage of anti-depressant and anti-anxiety medication after firms adopt P4P. This change is strongest in low-performing and older workers. We also find that workers select in and out of P4P firms based on mental health considerations, which implies that mental health effects influence turnover. We similarly show that low performers are more likely to leave following P4P adoption. Finally, we show sizable but imprecise response differences from female and male employees to the mental health threat of performance-based pay. Women with latent or potential mental health concerns appear to leave firms after P4P adoption, while men do not. Although we cannot claim a causal relationship, collectively our results suggest a model where performance-based pay forces many employees to choose between leaving or else depression and anxiety. Our study expands existing work by showing that the mental health costs of performance-based pay can be severe enough to necessitate pharmaceutical treatment.

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Section: Employee Responses To Pay-for-performancementioning

confidence: 99%

Pay-for-Performance and Employee Mental Health: Large Sample Evidence Using Employee Prescription Drug Usage

Dahl

Pierce

2020

AMD

Self Cite

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“…Our paper adds to the long-standing literature on gender equality, an important topic in social science. For example, the literature has shown evidence of fairness in parental leaves (Lundquist et al 2012), inequality in tenure evaluation (Sarsons 2017, Antecol et al 2018, recognition received (Ghiasi et al 2015), compensation (Pierce et al 2020), andjob hiring (Fernandez-Mateo andFernandez 2016). Researchers have therefore investigated business innovations to help empower women (Plambeck and Ramdas 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Our paper is closely related to the stream of literature on productivity, a central topic in operations management. The past studies have examined key determinants for workers' productivity such as peer effects (Huckman et al 2009, Song et al 2018, Tan and Netessine 2019, task variety (Staats and Gino 2012), task sequence (Ibanez et al 2018), incentive schemes (Chen et al 2019), clients' emotions (Altman et al 2019), workers' perceived workload (Tan and Netessine 2014), fatigue , and unfairness in aligning workers' compensations with productivity (Pierce et al 2020). In particular, multitasking has been shown to reduce productivity for workers who perform complex tasks because of their limited cognitive capacities (KC 2014, Bray et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Gender Inequality in Research Productivity During the COVID-19 Pandemic

Cui

Ding

Zhu

2020

Preprint

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We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academics' research productivity in social science. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries in a span of two years. We find that during the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35%, female academics' productivity dropped by 13.9% relative to that of male academics. We also show that several disciplines drive such gender inequality. Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries.

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“…For instance, it has been shown that historical decisions on creditworthiness can be biased against people of certain age, race, and gender, even if the sensitive attributes are unobserved by the model . In part, this is because other attributes may act as proxies for sensitive attributes, such as gender; for instance, salary may be a proxy for gender when men receive a higher salary than women (Pierce et al 2021). From an ethical perspective, such disparities in the access to credit are especially harmful, given that credit (e. g., student loans) is an essential instrument for enabling socioeconomic opportunities and economic mobility.…”

Section: Algorithmic Bias In Business Analyticsmentioning

confidence: 99%

“…In spite of this growing body of work and evidence, algorithmic decision-making often enjoys a veneer of inherent objectivity, deemed an evidence-based alternative to biased and idiosyncratic human decisions (e. g., Cui et al 2020, Pierce et al 2021). 2 The above examples suggest that algorithmic bias is widespread in the deployment of BA and that it can arise across a variety of contexts and for different reasons.…”

Section: Public Sector Operationsmentioning

confidence: 99%

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

De‐Arteaga¹,

Feuerriegel²,

Saar‐Tsechansky³

2022

Preprint

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The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or shortsighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA.

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Peer Bargaining and Productivity in Teams: Gender and the Inequitable Division of Pay

Cited by 6 publications

References 76 publications

Pay-for-Performance and Employee Mental Health: Large Sample Evidence Using Employee Prescription Drug Usage

Pay-for-Performance and Employee Mental Health: Large Sample Evidence Using Employee Prescription Drug Usage

Gender Inequality in Research Productivity During the COVID-19 Pandemic

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

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