By Suzanne Hultin
America at Work: A National Mosaic and Roadmap for Tomorrow, a new report from Walmart in partnership with McKinsey & Company, examines the economies and workforces at the county level, and policy interventions that can help communities prepare for the future of work.
The report was released earlier this month in conjunction with the second annual Bentonville (Ark.) Conference on American Life. NCSL was one of a handful of stakeholders engaged with the report, providing insights from the state policymakers perspective.
The rise of automation presents many opportunities but also many challenges and disparities, especially when it comes to how workers will be affected. And it will affect every worker differently. According to the McKinsey Global Institute, roughly 5 percent of occupations today could be fully automated with existing technology but “60 percent of occupations could see at least 30 percent of their activities automated.” This means automation is not necessarily taking jobs away but can drastically change the way jobs are done.
Policy approaches to address the changing work landscape have traditionally been tailored for two community types: rural or urban. However, this strategy fails to address the many counties and regions that do not fit into the rural or urban mold. According to this research, almost 190 million people, roughly 60 percent of the U.S. population, live outside urban areas, which have the greatest capacity to respond to automation. The communities outside these areas must develop community-specific responses to jobs and work in the time of automation.
Using multiple data measures, including workforce participation, gross domestic product by industry, educational attainment, poverty status, access to education and real estate data, among others, the report identified eight distinct archetypes of counties: urban centers and core suburbs, urban periphery, smaller independent economies, Americana, distressed Americana, rural service hubs, great escapes and resource-rich regions.
What results is not a map of 50 independent states, but rather a national mosaic of diverse communities scattered across the country. According to the report, on average, every state includes five of the eight archetypes.
“This is a very informative report that policymakers should use,” said Senator Barrow Peacock (R-L.a.), who attended the Bentonville Conference. “There are always times that we need to take a step back, pause and look at the makeup of our country. We can see how there are areas that are similar in nature yet geographically separated across our country. We can use that knowledge to apply best practices for duplications in other areas of America.”
The report goes on to identify six different policy responses (below) to growing automation in the workforce, noting that every type of community can benefit from all the policies, but communities need to determine what strategies are the most important to them, taking into consideration their strengths and challenges.
- Fostering economic development and creating new jobs
- Retraining and upskilling
- Boosting mobility within the labor market
- Building and maintaining infrastructure
- Modernizing the social safety net
- Strengthening education
The communities better prepared for automation and a change in the nature of work, such as urban centers, core suburbs and resource-rich regions, will need to focus on policies that keep their communities trained for the changing work landscape.
However, other communities, such as urban periphery, Americana and distressed Americana, will continue to have challenges when it comes to attracting new industries and creating jobs, thus needing to focus on economic development and strengthening the social safety nets.
Regardless of how communities and states choose to respond to the future of work, the report cannot stress enough that it will require collaboration by multiple stakeholder groups at the state and local levels.
Suzanne Hultin is a program director in NCSL's Employment, Labor and Retirement Program.