Clocked In: How Surveillance Wage-Setting Can Affect People with Disabilities

Note: This article is the second in a two-part series on the impacts of surveillance price- and wage-setting on people with disabilities. You can find the first part here.

Last week, we chronicled how surveillance pricing — a practice where companies use massive amounts of consumer data to set individualized prices for goods and services — can impact people with disabilities, potentially violating their privacy and increasing how much they pay for necessary products. But price-setting is just one side of the coin.

While companies may be able to use surveillance pricing to charge consumers the highest rates consumers would be willing to pay, they also have the ability to analyze personal data to identify the lowest possible wages that individuals may be willing to accept for their work. Like surveillance pricing, this practice — detailed in groundbreaking research by law professor Veena Dubal — can be particularly harmful for people with disabilities.

In general, surveillance wage-setting works the same way as surveillance pricing: through analysis of personal data about a person’s financial history, personal associations, demographics, and behaviors. While Professor Dubal originally described this practice in the context of the rideshare industry, as the so-called “gig economy” model expands, so too can surveillance wage setting. Workers currently have no legal right to know when companies are using surveillance data to set or change wages, and little to no ability to find out. But, even in the absence of transparency, surveillance wage-setting may be expanding to industries ranging from food delivery to babysitting, handyman services to nursing, and dishwashing to any other service job that can be managed through a gig platform.

While surveillance wage-setting can undermine the rights of all workers, disabled people may feel the negative effects of this practice even more acutely for several reasons. Disabled people may be disproportionately likely to work in “gig economy” jobs, where the practice is common. According to the Center for American Progress, disabled people experience an unemployment rate that is twice as high as that of people without a disability. Those who do find employment may confront discrimination, lack of accessibility, and exclusion at work. Such limitations on participation in “traditional” forms of employment have historically pushed disabled people into self-employment or gig work, the latter of which often touts workplace flexibility as a benefit to attract workers with specific needs, including those with disabilities. But working within the gig economy leaves disabled workers vulnerable to surveillance wage-setting.

Another reason is that many disabled workers are more financially vulnerable to begin with, which can be revealed to companies through the datasets used for surveillance wage-setting. As Dubal’s research noted, when workers are subject to surveillance wages, algorithmic analysis can cause those workers, including people with disabilities, to receive wage offers that do not properly reflect the value of their work, and instead reflect what companies believe to be the lowest amount the worker will accept based on data that company has collected about them. That personal data may include, for example, financial data regarding income, loans, or debts, as well as employment history, or lack thereof. Because people with disabilities are more likely to be low-income than people without disabilities — the poverty rate for adults with disabilities is more than twice that of adults without disabilities, 27 to 12% — this personal information may be more likely to reveal financial vulnerability when gathered from workers with disabilities. Companies using surveillance wage-setting could exploit disabled workers by offering them lower wages, knowing that their financial situation may leave them little choice but to accept. For example, if data suggests that a worker recently took out a payday loan, or that the worker was recently unemployed for several months, the algorithm may suggest that a company offer the worker less than it would offer other workers. Such wage-setting perpetuates low wages for people with disabilities and limits economic mobility.

Third, surveillance wage-setting may not only lower wages for workers with disabilities, but also create income volatility that is uniquely difficult for workers with disabilities to navigate. The higher cost of living for individuals with disabilities (due to increased medical costs, high prices of accessible transportation, or other recurring expenses) requires thoughtful budgeting. If a person with a disability does not know how much they will earn in a particular month, budgeting becomes impossible, and necessities can be out of reach. The opacity associated with surveillance wage-setting leaves workers unable to estimate earnings, compromising their ability to plan for their futures.

Finally, surveillance wage-setting can exacerbate other types of systemic discrimination, which means that multiply-marginalized disabled people (who are members of other marginalized groups, such as disabled people of color or women with disabilities) will feel the negative effects of this practice even more. There is already evidence of significant wage gaps between races and genders in both disabled and non-disabled populations. These wage gaps show up in the gig economy, too. For example, a recent report published by a group of advocates and civil society leaders explained that algorithmic systems often cause immigrant workers, as well as those from particular racial groups, to be subjected to lower pay rates. Another study identified a roughly 7% gender earnings gap among Uber drivers, with women making significantly less than men. This means that disabled people who are also members of marginalized race, gender, or ethnic groups may face particularly low wages within the gig economy — regardless of the value of their work.

When people with disabilities are forced to accept lower wages as a result of surveillance wage-setting, this can create pressure for other workers to accept similarly low wages, thus lowering wages for all. This should not be the reality for any worker. Just as we have seen with state-level efforts to regulate surveillance pricing, disability-inclusive regulation can mitigate the harms of this practice for all workers, both with and without disabilities.