White Paper

The 2016 election revealed a dramatic gap between two Americas—one based in large, diverse, thriving metropolitan regions; the other found in more homogeneous small towns and rural areas struggling under the weight of economic stagnation and social decline. This gap between two American geographies came as a shock to many observers. While it is true that some members of the media and policy analysts had grown disconnected from a significant portion of the country, something else had happened, too: the nation’s economic trends had changed. For much of the 20th century, reality conformed to economists’ predictions that market forces would gradually diminish job, wage, investment, and business formation disparities between more and less developed regions. As recently as 1980, the wage gap between regions was shrinking while growth in rural areas and small towns led the country from recession to recovery in the 1990s. Recent decades, however, have witnessed a massive shift in the relationship between the nation’s biggest, most prosperous metropolitan and non-metropolitan areas. Globalization has weakened the supply chains that once connected these regions. The rise of the information economy has boosted the returns to urban skills and diminished the importance of the resources and manual labor that non-metropolitan areas provided during the heyday of the manufacturing economy.1 And for that matter, high-tech manufacturers that still depend on supply chains to produce physical goods—and might once have sourced from the American “heartland”—have instead moved production and assembly functions overseas.

First authorized by the Workforce Investment Act (WIA) in 1998 and reauthorized in 2014 by the Workforce Innovation and Opportunity Act (WIOA), the Adult and Dislocated Worker programs are two of the nation’s largest publicly funded employment and training programs. To rigorously assess the impact of these programs on job seekers’ employment and earnings, the Employment and Training Administration of the U.S. Department of Labor contracted with Mathematica Policy Research and our partners at Social Policy Research Associates, MDRC, and the Corporation for a Skilled Workforce to conduct a national evaluation. This report includes interim impact findings from this evaluation of 28 randomly selected local workforce investment areas from across the country.

This report summarizes the two-year findings of a rigorous random assignment evaluation of the WorkAdvance model, a sectoral training and advancement initiative. Launched in 2011, WorkAdvance goes beyond the previous generation of employment programs by introducing demand-driven skills training and a focus on jobs that have career pathways. The model is heavily influenced by the positive findings from the Sectoral Employment Impact Study (SEIS) completed in 2010. A major component of the WorkAdvance model, in common with the programs studied in the SEIS, is formal training offering industry-recognized certifications, reflecting the hypothesis that skills acquisition is necessary for advancement. The model also requires providers to be far more employer facing than traditional training programs, taking into account multiple employers’ changing skill requirements, employee assessment practices, and personnel needs. This report presents the implementation, cost, participation, and two-year economic impacts of WorkAdvance. The economic results are based on unemployment insurance earnings records and a second-year follow-up survey. The WorkAdvance program operations and evaluation are funded through the federal Social Innovation Fund (SIF), a public-private partnership administered by the Corporation for National and Community Service. This SIF project is led by the Mayor’s Fund to Advance New York City and the NYC Center for Economic Opportunity in collaboration with MDRC.

We develop a framework linking organizational and industry architectures to value creation and value capture and apply it to the case of Industry 4.0 (the coordinated use of digitally-enabled technologies like robots, sensors, and artificial intelligence) in the automotive industry. We argue that if factory owners develop automation methods that capitalize on their greater access to the context in which production data is generated, they will be better able to prevent value from migrating to “digital entrants” that offer automation consulting and data analytics. Manufacturers can do this by adopting an organizational architecture that empowers shop-floor workers to combine their local knowledge with digital tools. Conversely, to the extent that digital entrants develop a more abstract version of these tools that they spread across industries, they will capture more value.

A much-publicized Freelancers Union/Upwork (FU/U) study in 2014 claimed that freelancers comprised 53 million workers representing about 34 percent of all workers; a second, recently released study finds 54 million freelancers. The FU/U estimate stands in stark contrast to what the Bureau of Labor Statistics (BLS) tells us: there were just 14.8 million self-employed workers in 2014 representing 10.1 percent of employment. Who is right?

Our paper offers several predictions about how Industry 4.0—the coordinated use of robots, sensors, AI, and other digitally-enabled technologies in manufacturing—will affect which firms and occupations capture value in manufacturing. We develop our insights using in-depth interviews with manufacturers that are part of the automotive value chain, including parts suppliers and automakers, and with integrators who provide robotics and other advanced automation to manufacturers. Among other findings, we highlight that value migration within firms likely affects whether and how value migration occurs across firms.

This paper establishes that the rise in the income share of information and communication technology accounts for half of the decline in labor income share in the United States. This decline can be decomposed into a sharp decline in the income share of “routine” labor—which is relatively more prone to automation—and a milder rise in the non-routine share. Quantitatively, this decomposition suggests large effects of information and communication technology on the income distribution within labor, but only moderate effects on the distribution of income between capital and labor. A production structure calibrated to match these trends suggests modest aggregate welfare gains from automation.

In summary, RPA can be used as a tool to increase engagement and satisfaction and is an enabler of ongoing transformation that touches upon many dimensions in the workplace. It therefore needs to be connected to a broader talent strategy, and companies will need to change their operating models to maximize value. Simply put, the benefit of RPA easily transcends headcount and cost reduction.

Individuals and businesses alike are embracing the digital revolution, utilizing mobile interactive tools to communicate, make decisions and facilitate purchases. Impacting virtually every industry, this global trend is certainly top of mind for automotive companies. As they rethink what customers value most and create operating models that take advantage of new possibilities for competitive differentiation in areas such as connected vehicles and mobility services, the most pressing challenge for auto companies is how fast and far they can go on their path to digital transformation.