Understanding Unemployment Across California Counties

MARK L. RODINI (*) Unemployment rates differ widely and persistently across counties. This article examines equilibrium forces related to this geographic disparity with a focus on California. We show that although seasonal variation in employment can account for some of the differences, it cannot explain the total variation. Factors such as educational attainment, age, and gender appear to be strongly related to unemployment differences. Individuals living in higher-unemployment areas also have a lower propensity to migrate. Because migration is usually considered to be the main equilibrating force, this evidence helps further explain why unemployment rates across counties are persistently different. (JEL J6) I. INTRODUCTION California has been one of the fastest-growing states during the postwar era. Though it faced one of its most severe recessions in the early 1990s, the state has rebounded strongly, and projections are for continued growth. Underlying this strong aggregate growth, however, is substantial variation in economic performance at the county level. One measure where this is most evident is in unemployment rates. Figure 1 shows average county-level unemployment rates between 1985 and 1997 using data from the California Employment Development Department. The figure shows the wide distribution that exists, ranging from a low of 3.7% in Mann County to a high of 26.2% in Imperial. (1) This wide distribution is not unique to California, and its existence often puzzles both the local business community and policy makers. The unemployment differences shown in Figure 1 are also highly persistent. The rank correlation between the 1985 and 1997 California county-level unemployment rates is 0.90. This suggests that during this period, for a little over a decade, the rank order of unemployment rates across counties did not change much; counties with relatively high unemployment rates remained high. Data from the U.S. census shows that this pattern of persistence is not a recent phenomenon: the rank correlation for unemployment rates across California counties between 1980 and 1970 is 0.85 and is 0.73 between 1970 and 1960. This high degree of persistence has been somewhat disheartening to those who had hoped that strong growth would trickle down and bring the high unemployment rate counties more in line with others. This article examines potential factors that might help us understand the persistent differences in unemployment rates. There is a large body of literature on the geographic variation in unemployment rates across space and time. Macroeconomic studies have generally focused attention on the impact of shocks on local labor markets and how these shocks affect local unemployment, employment growth, and wages, as in Topel (1986), Blanchard and Katz (1992), and Martin (1997). Micro studies, such as Da Vanzo (1978), Gabriel et al. (1993, 1995), and Berger and Blomquist (1992), have instead investigated how geographic variation in economic and social characteristics are related to labor market differentials, examining especially demographic and industry differences, along with the decision to migrate. Ultimately, all of these studies have at their core the notion from classical economic theory that labor markets--meaning wages and unemployment rates--should equilibrate geographically, arriving at a "natural" rate in the long run. Indeed, the fact that geographic differences in unemployment rates exist and are persistent is considered to be an unexpected anomaly. Researchers, such as Marston (1985) and Partridge and Rickman (1997a, 1997b; hereafter cited as P&R), have pointed to a number of causes that have broadly been characterized as "equilibrium" and "disequilibrium" forces. Disequilibrium forces are those that affect shifts in local labor supply or demand. Equilibrium forces, on the other hand, are regional characteristics such as demographics, industry structure, or psychological attachment that result in each "local labor market" having its own wage and employment growth rate and "natural" rate of unemployment. …