High-Skilled Migration to the United States and Its Economic Consequences

Gordon H. Hanson; William R. Kerr; Sarah  Turner

Book | 2018

High-Skilled Migration to the United States and Its Economic Consequences

by Gordon H. Hanson, William R. Kerr and Sarah Turner

Abstract

Immigration policy is one of the most contentious public policy issues in the United States today. High-skilled immigrants represent an increasing share of the U.S. workforce, particularly in science and engineering fields. These immigrants affect economic growth, patterns of trade, education choices, and the earnings of workers with different types of skills. The chapters in this volume go beyond the traditional question of how the inflow of foreign workers affects native employment and earnings to explore effects on innovation and productivity, wage inequality across skill groups, the behavior of multinational firms, firm-level dynamics of entry and exit, and the nature of comparative advantage across countries.

Keywords: Immigration; Policy; Economics; Outcome or Result; United States;

Format: Print

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Hanson, Gordon H., William R. Kerr and Sarah Turner, eds. High-Skilled Migration to the United States and Its Economic Consequences. Chicago, IL: University of Chicago Press, 2018.

Case | HBS Case Collection | September 2019 (Revised December 2019)

Google: To TVC or Not to TVC?

William R. Kerr and Carl Kreitzberg

In late 2018, evidence emerged that many of Google’s temporary help agency workers, vendors, and independent contractors (“TVCs”) were unhappy with the company. TVCs, who reportedly made up 49.95% of Google’s 170,000 person global workforce, had raised concerns of mistreatment, citing instances of pay inequity, social exclusion, and physical endangerment. “Flexible” workers, such as TVCs, were often seen as a key cog for Silicon Valley’s IT companies: they made workforces scalable, they helped firms get access to specialized knowledge for temporary projects, and they boosted innovation by creating “knowledge spillovers” between firms. But, at the same time, many onlookers worried that flexible work arrangements were aggravating social inequality and making more jobs precarious. Google employees, major media outlets, and politicians demanded that the company change its policies on TVCs. One suggestion was that Google convert all of its TVCs to full-time status by early 2020. As tensions reportedly escalated between Google’s workforce and its management team, some began to wonder if Google was still an employer of choice.

Keywords: workforce; independent contractors; talent management; Silicon Valley; google; employee attitude; Employee compensation; employee engagement; future of work; innovation; innovation and strategy; inequality; talent acquisition; Labor; Talent and Talent Management; Strategy; Technological Innovation; Employees; Attitudes; Innovation and Management; Human Resources; Equality and Inequality; Information Technology Industry; United States; San Francisco;

Citation:

Kerr, William R., and Carl Kreitzberg. "Google: To TVC or Not to TVC?" Harvard Business School Case 820-048, September 2019. (Revised December 2019.) View Details

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This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends - digitization, connectivity, the continuation of Moore's law, and advancements in machine learning algorithms - which led to the inflection point in adoption of AI. Then, the primer describes the range of use cases of state-of-the-art enterprise AI organized in a taxonomy designed to build generalized understanding of the types of work to which AI can be applied. This knowledge is useful not only to managers looking to best leverage the technology but to those working to manage resulting organizational change and prepare the workforce for transitions into complementary tasks. Finally, key issues and implications of AI adoption are also discussed.

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms to “pick and place” individual items into boxes that would be shipped to consumers. Despite receiving significant attention in the robotics space, the problem of universal “picking” had not been solved. Osaro believed deep reinforcement learning was the key to allowing robots to handle the complexity involved in perceiving and grasping a wide range of objects. The case discussion will focus on the decision to choose the warehouse automation market, provide an overview of key machine learning concepts, and introduce students to some of the contours of competing in the machine learning space during the late 2010s. The case can also introduce key concepts for thinking about how the combination of artificial intelligence and robotics will change work in the future.

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High-Skilled Migration to the United States and Its Economic Consequences

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