Which Connections Really Help You Find a Job?


Article by Iavor Bojinov, Karthik Rajkumar, Guillaume Saint-Jacques, Erik Brynjolfsson, and Sinan Aral: “Whom should you connect with the next time you’re looking for a job? To answer this question, we analyzed data from multiple large-scale randomized experiments involving 20 million people to measure how different types of connections impact job mobility. Our results, published recently in Science Magazine, show that your strongest ties — namely your connections to immediate coworkers, close friends, and family — were actually the least helpful for finding new opportunities and securing a job. You’ll have better luck with your weak ties: the more infrequent, arm’s-length relationships with acquaintances.

To be more specific, the ties that are most helpful for finding new jobs tend to be moderately weak: They strike a balance between exposing you to new social circles and information and having enough familiarity and overlapping interests so that the information is useful. Our findings uncovered the relationship between the strength of the connection (as measured by the number of mutual connections prior to connecting) and the likelihood that a job seeker transitions to a new role within the organization of a connection.The observation that weak ties are more beneficial for finding a job is not new. Sociologist Mark Granovetter first laid out this idea in a seminal 1973 paper that described how a person’s network affects their job prospects. Since then, the theory, known as the “strength of weak ties,” has become one of the most influential in the social sciences — underpinning network theories of information diffusion, industry structure, and human cooperation….(More)”.

The Labor Market Consequences of Appropriate Technology


Paper by Gustavo de Souza: “Developing countries rely on technology created by developed countries. This paper demonstrates that such reliance increases wage inequality but leads to greater production in developing countries. I study a Brazilian innovation program that taxed the leasing of international technology to subsidize national innovation. I show that the program led firms to replace technology licensed from developed countries with in-house innovations, which led to a decline in both employment and the share of high-skilled workers. Using a model of directed technological change and technology transfer, I find that increasing the share of firms that patent in Brazil by 1 p.p. decreases the skilled wage premium by 0.02% and production by 0.2%…(More)”.

Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”


Paper by Eleanor Drage & Kerry Mackereth: “In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward…(More)”.

Working with AI: Real Stories of Human-Machine Collaboration


Book by Thomas H. Davenport and Steven M. Miller: “This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems…(More)”.

A Massive LinkedIn Study Reveals Who Actually Helps You Get That Job


Article by Viviane Callier : “If you want a new job, don’t just rely on friends or family. According to one of the most influential theories in social science, you’re more likely to nab a new position through your “weak ties,” loose acquaintances with whom you have few mutual connections. Sociologist Mark Granovetter first laid out this idea in a 1973 paper that has garnered more than 65,000 citations. But the theory, dubbed “the strength of weak ties,” after the title of Granovetter’s study, lacked causal evidence for decades. Now a sweeping study that looked at more than 20 million people on the professional social networking site LinkedIn over a five-year period finally shows that forging weak ties does indeed help people get new jobs. And it reveals which types of connections are most important for job hunters…Along with job seekers, policy makers could also learn from the new paper. “One thing the study highlights is the degree to which algorithms are guiding fundamental, baseline, important outcomes, like employment and unemployment,” Aral says. The role that LinkedIn’s People You May Know function plays in gaining a new job demonstrates “the tremendous leverage that algorithms have on employment and probably other factors of the economy as well.” It also suggests that such algorithms could create bellwethers for economic changes: in the same way that the Federal Reserve looks at the Consumer Price Index to decide whether to hike interest rates, Aral suggests, networks such as LinkedIn might provide new data sources to help policy makers parse what is happening in the economy. “I think these digital platforms are going to be an important source of that,” he says…(More)”

The New ADP National Employment Report


Press Release: “The new ADP National Employment Report (NER) launched today in collaboration with the Stanford Digital Economy Lab. Earlier this spring, the ADP Research Institute paused the NER in order to refine the methodology and design of the report. Part of that evolution was teaming up data scientists at the Stanford Digital Economy Lab to add a new perspective and rigor to the report. The new report uses fine-grained, high-frequency data on jobs and wages to deliver a richer and more useful analysis of the labor market.

Let’s take a look at some of the key changes with the new NER, along with the new ADP® Pay Insights Report.

It’s independent. The key change is that the new ADP NER is an independent measure of the US labor market, rather than a forecast of the BLS monthly jobs number. Jobs report and pay insights are based on anonymized and aggregated payroll data from more than 25 million US employees across 500,000 companies. The new report focuses solely on ADP’s clients and private-sector change…(More)”.

Reorganise: 15 stories of workers fighting back in a digital age 


Book edited by Hannah O’Rourke & Edward Saperia: “In only a decade, the labour market has changed beyond all recognition – from zero-hour contracts to platform monopolies. As capitalism has re-created itself for the digital age, so too must the workers whose labour underpins it.

From a union for instagram influencers to roadworkers organising through a Facebook Group, former WSJ journalist Lucy Harley-McKeown takes us on a journey to discover how workers are fighting back in the 21st century…(More)”.

The fear of technology-driven unemployment and its empirical base


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)”