REENGINEERING WORKPLACE BARGAINING: HOW BIG DATA DRIVES LOWER WAGES AND HOW REFRAMING LABOR LAW CAN RESTORE INFORMATION EQUALITY IN THE WORKPLACE

Oct 13, 2026

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Employer deployment of big-data analytics is increasingly linked to wage suppression and weakened bargaining power, extending from pre-hire personality testing that screens out employees likely to demand higher wages or support union activity. After hiring, datadriven surveillance and algorithmic management assess turnover risk and shape pay increases over time, while fragmented scheduling and monitoring of subcontracted or contractor work shift downtime costs to employees. The analysis argues for reinterpreting labor law to restore information equality.

LanguageEnglish

CategoryGeneral

FormatPDF

Pages68

Reading Time24 min

AccessFree to download

I. Introduction

A. Collective Action Largely Ignored as Solution to Privacy Harms

B. The Battle for Control of the “Social Graph” in the Workplace

C. Communication and “Voice” in the Workplace versus “Marginalized” Workers

II. Minority Report in the Workplace: Big Data in the Hiring Process

A. The Explosion of Pre-Hire Screening

REENGINEERING WORKPLACE BARGAINING: HOW BIG DATA DRIVES LOWER WAGES AND HOW REFRAMING LABOR LAW CAN RESTORE INFORMATION EQUALITY IN THE WORKPLACE Nathan Newman * Abstract While there has been a flurry of new scholarship on how employer use of data analysis may lead to subtle but potentially devastating individual discrimination in employment systems, there has been far less attention to the ways the deployment of big data may be driving down wages for most workers, including those who manage to be hired. This article details the ways big data can, and in many cases is, actively being deployed to lower wages through hiring practices, in the ways raises are now being offered, and in the ways workplaces are organized (and disorganized) to lower employee bargaining power—and how new interpretations of labor law are beginning to and can in the future reshape the workplace to address these economic harms. Data analysis is increasingly helping to lower wages in companies beginning in the hiring process where pre-hire personality testing helps employers screen out employees who will agitate for higher wages and organize or support unionization drives in their companies. For employees who are hired, companies have massively expanded datadriven workplace surveillance that allows employers to assess which employees are most likely to leave and thereby limit pay increases largely to them, lowering wages over time for workers either less able to find new employment because of their age or less inclined in general to risk doing so. Data analysis and so-called “algorithmic management”has also allowed the centralized monitoring of far-flung workers organized nominally in subcontractors or as individual contractors, while traditional firms such as in retail implement data-driven scheduling that resembles the “on-demand” employment of independent contractors. All of this shifts risk and “downtime” costs to employees and lowers their take-home pay, even as the fragmenting of the workplace makes it harder for workers to collectively organize for higher wages. This article addresses how we should rethink and interpret existing labor law in each of these aspects of the employment process. The * J.D., Ph.D., Adjunct Associate Professor, John Jay College Criminal Justice program, and Lehman College Sociology. I am indebted to support from members of the Information Law Institute at NYU, particularly Katherine Strandburg, as well as a wide range of feedback I received from the Privacy Law Scholars Conference where this paper was presented. 694 UNIVERSITY OF CINCINNATI LAW REVIEW [VOL. 85 NLRB can reasonably construe many pre-hire employment tests as violating federal labor law’s prohibition of screening out union sympathizers, much as the EEOC has found many personality tests violate the Americans with Disabilities Act by allowing indirect identification of people with mental illness. Similarly, since big-data analysis can reveal pro-union sympathies of current employees, under existing prohibitions of “polling” employees for their views, a reasonable extension of the law would be to prohibit sharing any personal data collected by management that might reveal protected conduct or union sympathies with line managers or outside management consultants involved in advising in labor campaigns. The Board can also level the informational playing field by making both hiring algorithms and those determining pay increases more available during collective bargaining. The Board under the Obama Administration began moving to expand its “joint-employer” doctrine to allow workers to challenge the fragmented workplace increasingly driven by algorithmic management and a clear recognition that algorithms establish exactly the control of nominally independent contractors or subcontractor’s workers that entitle them to collective bargaining rights with a central employer, strengthening worker bargaining power. Such a “collective-action” approach to the

FAQs

How does big data affect wages during the hiring process?

Pre-hire personality testing can screen out employees who may agitate for higher wages or support unionization, helping lower wages before hiring even occurs.

What workplace practices does the article link to reduced employee bargaining power?

It connects datadriven surveillance, algorithmic management, and data-driven scheduling to assess turnover risk, limit pay increases, and fragment the workplace so collective organization becomes harder.

What labor-law approach does the article argue could restore information equality?

It suggests reinterpreting existing labor law through collective-action measures, such as limiting sharing of personal data that reveals protected conduct or union sympathies and making hiring algorithms and pay-increase determinations more accessible during collective bargaining.

Oct 13, 2026

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