Author
The demand for labor in an industry or occupation can experience both temporary and permanent shifts. The decline of jobs in the U.S. manufacturing sector from the 1980s to the 2010s is a classic example, a shift that hit Midwestern states particularly hard. These days, there is debate about how AI might take jobs in web development, legal services, sales—or possibly all jobs. Of course, new technology also creates jobs; few people worked as software developers in 1980.
Economists have long been interested in how workers with different characteristics or in different situations respond to rises and falls in demand for their labor. One way local economies vary is in how concentrated their economic activity is. Some places are dominated by a handful of industries—their economic activity is concentrated. Other places have a greater diversity of jobs because employment is less dominated by a few industries.
In a new paper, Institute Senior Economist Illenin Kondo and co-authors François de Soyres, Simon Fuchs, and Helene Maghin study how the degree of economic diversity in a location affects workers following a labor demand shock (Institute Working Paper 106: “Economic Diversity and the Resilience of Cities”). In the past, economists have often been limited to studying how overall employment responds to a shock. But aggregate changes might hide a significant amount of churn that takes place.
With their detailed data, Kondo and his co-authors can look at three choices that workers can make when the demand for labor changes: They can change occupation, they can change industry, or they can change location. Of course, these choices are not mutually exclusive—workers can make no change, or change one, two, or all three at a time. The economists want to understand how many workers make a change along each dimension and how workers’ welfare responds to these changes.
Changing occupation, industry, or location when labor demand changes
To analyze how workers adjust to labor demand shocks, Kondo and his co-authors analyze administrative data for a random sample of private-sector workers in France between 2005 and 2019. The data includes 30 occupations, 90 industries, and 300 geographic locations, which the authors call “cities” for simplicity. The economists collect the occupation, industry, and location for each worker in each quarter, which allows them to calculate how many workers make a change along each dimension.
Their analysis of this data shows that in the median French city, 4.5 percent of workers change their occupation, sector, or location each quarter. Among “switchers” who make at least one change, 61 percent change sectors, 65 percent change occupations, and 49 percent change locations.
The economists then use a Bartik instrument to simulate local labor demand shocks. This instrument uses employment growth rates for occupation-industry combinations at the national level and weights them according to the occupation-industry’s employment share at the local level. This method means that shocks are experienced differently in different locations depending on the relative importance of the occupation-industry to the local economy.
The Bartik instrument also means that shocks may be correlated across a worker’s options. For instance, when demand for steel workers in France fell, many towns in northern France felt the effects. “We wanted to know not just how exposed I might be to a shock, but how exposed my options are to the shock, too,” Kondo said.
To capture the level of economic diversity, the economists use the Herfindahl-Hirschman Index (HHI) to measure the market share of different occupations and industries in the local economy. This reflects how concentrated jobs are in specific sectors and occupations. Lower HHI values indicate greater economic diversity, while higher HHI values indicate greater economic concentration.
Kondo and his co-authors then estimate how workers respond to both positive and negative labor demand shocks. They evaluate the response separately for an economically diverse city (evaluated at the 10th percentile of the distribution of HHI values) and an economically concentrated city (evaluated at the 90th percentile of the distribution of HHI values). The results are shown in the figure.
Source: de Soyres, Fuchs, Kondo, and Maghin, “Economic Diversity and the Resilience of Cities,” 2024.
Following a positive labor demand shock, more workers switch their occupation, industry, and city. This is true in both economically diverse and economically concentrated cities.
When hit by a negative labor demand shock, internal reallocation (that is, workers changing occupation or industry within a city) falls—and that is true in both economically concentrated and diverse cities. But there is a large difference in the magnitude: The decrease in internal reallocation in economically diverse cities is about half as large as in concentrated cities. In this sense, diverse cities are more resilient.
The final exercise is to use a model to assess what these worker flows say about workers’ welfare and the insurance value of living in a more economically diverse location. When people start making different choices, what does that say about the value of those choices? What the economists find is that the insurance value is what protects more diversified cities from larger welfare losses in the face of a negative shock. The economists conclude, “A more diversified economy may facilitate labor reallocation, reduce costly spatial mobility, and allow for more stable employment outcomes in the face of sector- or occupation-specific downturns.”
Read the Institute working paper: Economic Diversity and the Resilience of Cities
Lisa Camner McKay is a senior writer with the Opportunity & Inclusive Growth Institute at the Minneapolis Fed. In this role, she creates content for diverse audiences in support of the Institute’s policy and research work.
