Article by Kerstin Hötte, Melline Somers and Angelos Theodorakopoulos:”New technologies may replace human labour, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. At the same time, technology-driven productivity growth may increase disposable income, stimulating a demand-induced employment expansion. Based on a systematic review of the empirical literature on technological change and its impact on employment published in the past four decades, this column suggests that the empirical support for the labour-creating effects of technological change dominates that for labour-replacement…(More)”.
The Rise of the Worker Productivity Score
Jodi Kantor and Arya Sundaram in The New York Times: “Across industries and incomes, more employees are being tracked, recorded and ranked. What is gained, companies say, is efficiency and accountability. What is lost?…
In lower-paying jobs, the monitoring is already ubiquitous: not just at Amazon, where the second-by-second measurements became notorious, but also for Kroger cashiers, UPS drivers and millions of others. Eight of the 10 largest private U.S. employers track the productivity metrics of individual workers, many in real time, according to an examination by The New York Times.
Now digital productivity monitoring is also spreading among white-collar jobs and roles that require graduate degrees. Many employees, whether working remotely or in person, are subject to trackers, scores, “idle” buttons, or just quiet, constantly accumulating records. Pauses can lead to penalties, from lost pay to lost jobs.
Some radiologists see scoreboards showing their “inactivity” time and how their productivity stacks up against their colleagues’. At companies including J.P. Morgan, tracking how employees spend their days, from making phone calls to composing emails, has become routine practice. In Britain, Barclays Bank scrapped prodding messages to workers, like “Not enough time in the Zone yesterday,” after they caused an uproar. At UnitedHealth Group, low keyboard activity can affect compensation and sap bonuses. Public servants are tracked, too: In June, New York’s Metropolitan Transportation Authority told engineers and other employees they could work remotely one day a week if they agreed to full-time productivity monitoring.
Architects, academic administrators, doctors, nursing home workers and lawyers described growing electronic surveillance over every minute of their workday. They echoed complaints that employees in many lower-paid positions have voiced for years: that their jobs are relentless, that they don’t have control — and in some cases, that they don’t even have enough time to use the bathroom. In interviews and in hundreds of written submissions to The Times, white-collar workers described being tracked as “demoralizing,” “humiliating” and “toxic.” Micromanagement is becoming standard, they said.
But the most urgent complaint, spanning industries and incomes, is that the working world’s new clocks are just wrong: inept at capturing offline activity, unreliable at assessing hard-to-quantify tasks and prone to undermining the work itself…(More)”.
Use of Data in Public Sector Human Resources and Workforce Management: Solutions and Challenges
White Paper by Katherine Barrett and Richard Greene: “Across the U.S., a growing number of cities, counties, and states are using data across agencies to improve management and make decisions—and HR and payroll professionals in particular stand to gain much from this data to help drive staffing and other strategic decisions. In this white paper, industry experts Katherine Barrett and Richard Greene take a deep dive into both the benefits and challenges of using data with real-life examples of how data has been instrumental in building a resilient HR apparatus.
Data can be used for positive change that includes shorter new-hire onboarding, fairer overtime distribution, and even improved employee safety. However, obstacles to using data in an optimal way to improve HR management, such as insufficient funding, lack of training, and lack of software access, can keep government organizations from making the most of all it can offer.
Despite barriers, many organizations are moving toward creating a culture that is conducive to the use of the data their computers can create. Examples of how data and data analysis can transform workforce management practices include:
- Studying existing hiring and onboarding data to facilitate more effective and efficient administration
- Tracking turnover data to document employee departures and reveal information about those most at risk of sudden departure
- Reducing overtime by using the data to ensure fairer distribution of overtime
- Uncovering equity issues by assessing and comparing the demographic makeup of a workforce to see how closely it matches their population…(More)”
Your Boss Is an Algorithm: Artificial Intelligence, Platform Work and Labour
Book by Antonio Aloisi and Valerio De Stefano: “What effect do robots, algorithms, and online platforms have on the world of work? Using case studies and examples from across the EU, the UK, and the US, this book provides a compass to navigate this technological transformation as well as the regulatory options available, and proposes a new map for the era of radical digital advancements.
From platform work to the gig-economy and the impact of artificial intelligence, algorithmic management, and digital surveillance on workplaces, technology has overwhelming consequences for everyone’s lives, reshaping the labour market and straining social institutions. Contrary to preliminary analyses forecasting the threat of human work obsolescence, the book demonstrates that digital tools are more likely to replace managerial roles and intensify organisational processes in workplaces, rather than opening the way for mass job displacement.
Can flexibility and protection be reconciled so that legal frameworks uphold innovation? How can we address the pervasive power of AI-enabled monitoring? How likely is it that the gig-economy model will emerge as a new organisational paradigm across sectors? And what can social partners and political players do to adopt effective regulation?
Technology is never neutral. It can and must be governed, to ensure that progress favours the many. Digital transformation can be an essential ally, from the warehouse to the office, but it must be tested in terms of social and political sustainability, not only through the lenses of economic convenience. Your Boss Is an Algorithm offers a guide to explore these new scenarios, their promises, and perils…(More)”
Stories to Work By
Essay by William E. Spriggs: “In Charlie Chaplin’s 1936 film Modern Times, humans in a factory are reduced to adjuncts to a massive series of cogs and belts. Overlords bark commands from afar to a servant class, and Chaplin’s hapless hero is literally consumed by the machine … and then spit out by it. In the film, the bosses have all the power, and machines keep workers in check.
Modern Times’s dystopian narrative remains with us today. In particular, it is still held by many policymakers who assume that increasing technological progress, whether mechanical or informational, inevitably means that ordinary workers will lose. This view perpetuates itself when policies that could give workers more power in times of technological change are overlooked, while those that disempower workers are adopted. If we are to truly consider science policy for the future, we need to understand how this narrative about workers and technology functions, where it is misleading, and how deliberate policies can build a better world for all….
Today’s tales of pending technological dystopia—echoed in economics papers as well as in movies and news reports—blind us to the lessons we could glean from the massive disruptions of earlier periods of even greater change. Today the threat of AI is portrayed as revolutionary, and previous technological change as slow and inconsequential—but this was never the case. These narratives of technological inevitability limit the tools we have at our disposal to promote equality and opportunity.
The challenges we face today are far from insurmountable: technology is not destiny. Workers are not doomed to be Chaplin’s victim of technology with one toe caught in the gears of progress. We have choices, and the central challenge of science and technology policy for the next century will be confronting those choices head on. Policymakers should focus on the fundamental tasks of shaping how technology is deployed and enacting the economic rules we need to ensure that technology works for us all, rather than only the few….(More)”.
Tech Inclusion for Excluded Communities
Essay by Linda Jakob Sadeh & Smadar Nehab: “Companies often offer practical trainings to address the problem of diversity in high tech, acknowledging the disadvantages that members of excluded communities face and trying to level the playing field in terms of expertise and skills. But such trainings often fail in generating mass participation among excluded communities in tech professions. Beyond the professional knowledge and hands-on technical experience that these trainings provide, the fundamental social, ethnic, and economic barriers often remain unaddressed.
Thus, a paradoxical situation arises: On the one hand, certain communities are excluded from high tech and from the social mobility it affords. On the other hand, even when well-meaning companies wish to hire from these communities and implement diversity and inclusion measures that should make doing so possible, the pool of qualified and interested candidates often remains small. Members of the excluded communities remain discouraged from studying or training for these professions and from joining economic growth sectors, particularly high tech.
Tech Inclusion, the model we advance in this article, seeks to untangle this paradox. It takes a sincere look at the social and economic barriers that prevent excluded communities from participating in the tech industry. It suggests that the technology industry can be a driving force for inclusion if we turn the inclusion paradigm on its head, by bringing the industry to the excluded community, instead of trying to bring the excluded community to the industry, while cultivating a supportive environment for both potential candidates and firms…(More)”.
Data for an Inclusive Economic Recovery
Report by the National Skills Coalition: “A truly inclusive economic recovery means that the workers and businesses who were most impacted by this pandemic, as well as workers who have been held back by structural barriers of discrimination or lack of opportunity, are empowered to equitably participate in and benefit from the economy’s expansion and restructuring.
But we need data on how different workers and businesses are faring in the recovery, so
we can hold policymakers accountable to equitable outcomes. Disparities and inequities in skills training programs can only be eliminated if there is high-quality information on program outcomes available to practitioners and policymakers to assess and address equity gaps. Once we have the data – we can use it to drive the change we need!
Data for an Inclusive Economic Recovery provides recommendations on how to measure and report on what really matters to help diminish structural inequities and to shape implementation of federal recovery investments as well as new state and federal workforce investments…
Recommendations Include:
- Requiring that all education and skills training programs include collection of self-reported demographic characteristics of workers and learners so outcomes can be disaggregated by race, ethnicity, gender, English language proficiency, income, and geography ;
- Ensuring participants of skills training programs know what demographic characteristics are being collected about them, who will have access to personally identifiable information, and how their data will be used;
- Establishing common outcomes metrics across federal skills training programs;
- Expanding outcomes to include those that allow policymakers to assess the quality of skills training programs and measure economic mobility along a career pathway;
- Ensuring equitable access to administrative data;
- Mandating public reporting on skills training and workforce investment outcomes; and
Providing sufficient funding for linked education and workforce data systems…(More)”.
The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty
NBER Working Paper by David Autor: “This review considers the evolution of economic thinking on the relationship between digital technology and inequality across four decades, encompassing four related but intellectually distinct paradigms, which I refer to as the education race, the task polarization model, the automation-reinstatement race, and the era of Artificial Intelligence uncertainty. The nuance of economic understanding has improved across these epochs. Yet, traditional economic optimism about the beneficent effects of technology for productivity and welfare has eroded as understanding has advanced. Given this intellectual trajectory, it would be natural to forecast an even darker horizon ahead. I refrain from doing so because forecasting the “consequences” of technological change treats the future as a fate to be divined rather than an expedition to be undertaken. I conclude by discussing opportunities and challenges that we collectively face in shaping this future….(More)”.
Governing AI to Advance Shared Prosperity
Chapter by Ekaterina Klinova: “This chapter describes a governance approach to promoting AI research and development that creates jobs and advances shared prosperity. Concerns over the labor-saving focus of AI advancement are shared by a growing number of economists, technologists, and policymakers around the world. They warn about the risk of AI entrenching poverty and inequality globally. Yet, translating those concerns into proactive governance interventions that would steer AI away from generating excessive levels of automation remains difficult and largely unattempted. Key causes of this difficulty arise from two types of sources: (1) insufficiently deep understanding of the full composition of factors giving AI R&D its present emphasis on labor-saving applications; and (2) lack of tools and processes that would enable AI practitioners and policymakers to anticipate and assess the impact of AI technologies on employment, wages and job quality. This chapter argues that addressing (2) will require creating worker-participatory means of differentiating between genuinely worker-benefiting AI and worker-displacing or worker-exploiting AI. To contribute to tackling (1), this chapter reviews AI practitioners’ motivations and constraints, such as relevant laws, market incentives, as well as less tangible but still highly influential constraining and motivating factors, including explicit and implicit norms in the AI field, visions of future societal order popular among the field’s members and ways that AI practitioners define goals worth pursuing and measure success. I highlight how each of these factors contributes meaningfully to giving AI advancement its excessive labor-saving emphasis and describe opportunities for governance interventions that could correct that over emphasis….(More)”.
The effects of AI on the working lives of women
Report by Clementine Collett, Gina Neff and Livia Gouvea: “Globally, studies show that women in the labor force are paid less, hold fewer senior positions and participate less in science, technology, engineering and mathematics (STEM) fields. A 2019 UNESCO report found that women represent only 29% of science R&D positions globally and are already 25% less likely than men to know how to leverage digital technology for basic uses.
As the use and development of Artificial Intelligence (AI) continues to mature, its time to ask: What will tomorrows labor market look like for women? Are we effectively harnessing the power of AI to narrow gender equality gaps, or are we letting these gaps perpetuate, or even worse, widen?
This collaboration between UNESCO, the Inter-American Development Bank (IDB) and the Organisation for Economic Co-operation and Development (OECD) examines the effects of the use of AI on the working lives of women. By closely following the major stages of the workforce lifecycle from job requirements, to hiring to career progression and upskilling within the workplace – this joint report is a thorough introduction to issues related gender and AI and hopes to foster important conversations about womens equality in the future of work…(More)”