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Arizona’s subpar performance on measures of productivity and prosperity can largely be traced to three factors:
Traded clusters accounted for only 40.6 percent of total aggregate earnings in Arizona in 2021, compared to 45.2 percent nationally. Aggregate earnings are calculated as employment times average earnings per worker. Per capita aggregate earnings adjusted for the cost of living was 21 percent less than the U.S. average in Arizona in the traded clusters.
Arizona compares particularly poorly in some of the nation’s largest and highest-paying traded clusters. Of particular interest is that adjusted average earnings per worker is far below the national average in particular high-paying industries in these key traded clusters. The large differentials suggest that the nature of the work performed in Arizona in these industries is fundamentally different from the national norm, requiring fewer skills and therefore lower paying. For example, a manufacturing facility in Arizona may primarily employ lower-paid production workers, while the typical facility nationally may be better balanced, with more higher-paid workers in business, finance, and technical professional occupations.
Very low adjusted average earnings per worker in Arizona relative to the national average is particularly prevalent in the financial services cluster. In five of the six largest industries in that cluster, Arizona’s adjusted average earnings in 2021 was at least 38 percent below average. Similarly large differentials existed in the Internet publishing and Web search industry and the research and development in biotechnology industry. Differentials of 20-to-25 percent were present in such high-tech industries as custom computer programming, software publishing, semiconductor manufacturing, and data processing and hosting. Other high-paying industries in which adjusted average earnings in Arizona was at least 20 percent below average include property and casualty insurance carriers, management consulting, and marketing consulting.
Among metropolitan areas, a strong relationship exists between metro area size, as measured by employment, and many economic measures. When Arizona’s metro areas are compared to similarly sized metro areas, each of Arizona’s two populous metro areas - Phoenix and Tucson - and most of the state’s five smaller metro areas compare poorly on traded cluster share of total employment and total aggregate earnings, per capita employment, average earnings per worker adjusted for the cost of living, and adjusted per capita aggregate earnings. Not only does Metro Phoenix compare poorly to its peers, on each measure it is below the average of metro areas approximately the size of Metro Tucson.
Dennis received a B.A. in economics and mathematics from Grand Valley State University, a M.S. in economics from Michigan State University, and a Ph.D. in economics from Michigan State University in 1978. He has served on the faculty of the Department of Economics at ASU since 1979, as director of ASU’s L. William Seidman Research Institute since 2004, and as the director of the Office of the University Economist since 2005.
After receiving his Bachelor of Business Administration from the University of Toledo, Tom earned his Master of Business Administration from Arizona State University in 1976. After working in the private sector, he joined ASU in 1980, working for the predecessor of the L. William Seidman Research Institute. Since 2005, he has served as manager of research initiatives in the Office of the University Economist.
Summarizes migration data by state from three sources: the Internal Revenue Service, the American Community Survey, and the University of Wisconsin.
EXECUTIVE SUMMARY
Examines the determinants of the locations of high-tech clusters in the United States. Summarizes the development of high-tech activities in Arizona, Metro Phoenix, and Metro Tucson relative to selected states and metropolitan areas.
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Examines the extent of healthcare worker shortages in Arizona, calculates the economic impact of eliminating worker shortages, and estimates the direct medical costs and productivity losses of ill health.