Differences in state unemployment rates: the role of labor and product market structural shifts.

• Author: Partridge, Mark D.

1. Introduction

   State unemployment rates diverged dramatically in the 1980s. Differing unemployment rates across states are an important public policy concern because of equity concerns and the pure human consequences of higher unemployment. Moreover, at the national level, a greater dispersion in state and regional unemployment rates can increase the natural rate of unemployment and shift the Phillips curve out because of an inefficient allocation of the labor force [3; 38].

   One possible cause for the divergence in unemployment rates is national industrial restructuring. U.S. industrial restructuring was suggested by Lilien [26] as a cause for the rise in the national unemployment rate. Moreover, national restructuring can have differential impacts across states because states can have dramatically different mixes of industries from each other. One of the difficulties of national restructuring from a regional perspective is that it is difficult to avoid. On the other hand, if regional differences in unemployment are due to state idiosyncratic causes, state and local public policy can potentially play a more active role. For example, the economic problems that plagued the Northeast and California during the early 1990s have been typically attributed to national downsizing in the defense industries. Conversely, economic problems in these states may have been driven by factors that are located within the state, such as the collapse of their real estate markets.

   Even beyond the issue of national restructuring, the 1980s represented a structural break in relative regional economic performances. Though Neumann and Topel [35] show that state unemployment rates can be very persistent, even over the course of decades, state unemployment rates for the mid-1980s are not strongly correlated with their respective unemployment rates from the 1970s or the 1990s. For example, based on data introduced later in the paper, the correlation of the 1977 and 1987 state unemployment rates is -0.055, while the correlation between 1977 and 1992 state unemployment rates is 0.74.(1) Thus, the high correlation of state unemployment rates between the 1970s and 1990s seems to represent a return to historical patterns observed by Neumann and Topel. Moreover, annual measures of the coefficient of variation of the 48 contiguous state unemployment rates rise sharply beginning in the early 1980s, peak in the late 1980s, and then fall back to the levels experienced during the 1970s. This also indicates that there was a structural break in the 1980s and that long-run patterns reemerged in the early 1990s.

   This study advances previous state unemployment research by unifying several possible causes of unemployment into one empirical framework. First, by using shift-share analysis, we separate a state's employment growth rate into the portion due to national factors, such as industrial restructuring, and a separate portion due to state-specific idiosyncratic factors. This allows us to assess the relative roles of national restructuring and state-specific factors as causes for unemployment rates to vary cross-sectionally across states within a given time period and over time within a given state. Another closely associated issue examined is whether workers who lose their jobs to national restructuring have less options for finding work elsewhere than if they lose their jobs to state-specific causes. With the exceptions of Gallaway [12] and Treyz et al. [40], there has been little examination of both interindustry labor mobility and interregional mobility together, and none related to unemployment.

   Second, we also separate earnings into a wage-mix component that arises from the industrial composition of the state and a wage-competitiveness component. This allows us to better separate wait unemployment effects from cost competitiveness effects. Third, while carefully controlling for employment growth and wage structure, we also utilize the within state variance of sectoral employment growth rates to account for matching difficulties for workers and firms that can occur when a large number of workers are forced to change sectors. This is closely associated with the analysis of Neumann and Topel [35] at the state level and Lilien [26] at the national level. Fourth, we consider data from 1972 to the early 1990s to fully evaluate the industrial restructuring of the 1980s.

   Finally, to avoid spurious relationships between the above variables and state unemployment rates, and to identify other state-specific factors behind unemployment, we appraise a multitude of other possible causes for unemployment identified in previous studies including unionization, unemployment insurance, and demographic differences across states that can influence a given state's natural rate of unemployment [6; 9; 17; 21; 27; 28; 30; 32; 35; 37]. Thus, by simultaneously considering more factors than has typically been the case, the results are less subject to an omitted variable bias.(2)

   In what follows, section II presents the underlying theoretic model and empirical methodology. Section III contains the empirical results, while specification issues are addressed in section IV. The final section presents some concluding thoughts.

2. Theoretical Considerations

   The unemployment rate is a reduced form function of the factors that affect labor demand and labor supply. These factors can broadly be categorized as industry or product market variables (IND), non-demographic labor market variables (LABOR), demographic variables (DEMOG), and regional characteristics (REGION). Thus, unemployment for state i in time period t may be written as

   [U.sub.it] = [Alpha] + [Beta]IND + [Gamma]LABOR + [Omega]DEMOG + [Psi]REGION + [[Sigma].sub.i] + [[Sigma].sub.t] + [e.sub.it], (1)

   where [Alpha] is the constant term, [[Sigma].sub.i] and [[Sigma].sub.t] are state and time fixed effects and [e.sub.it] is an error term. [Beta], [Gamma], [Omega], and [Psi] represent coefficient vectors. However, the error term [e.sub.it] could exhibit first-order autocorrelation. This can be represented as

   [e.sub.it] = [Rho][e.sub.it-1] + [v.sub.it], (2)

   where [v.sub.it] is i.i.d. and [Rho] is the degree of first-order autocorrelation that is assumed constant across all states.

   Variable definitions and sources are shown in Table I. Unemployment rates and the independent variables are measured for the 48 contiguous states from 1972-91. The independent variables and their hypothesized influence on unemployment are described below.

   A primary factor in determining unemployment differences is employment growth. Indeed, Abraham [1] and Medoff [31] argue that increased regional dispersion in employment demand is one reason that the national natural rate of unemployment drifted higher in the 1970s. For instance, if a state has a mix of industries that are faring relatively better at the national level, then state employment should increase relatively. However, employment growth at the regional level may not reduce the unemployment rate. This can occur because in-migrants may absorb all the new jobs [7], leaving unemployment unaffected.(3)

   Moreover, different sources of employment growth or decline may have different effects on unemployment. To investigate this, employment growth is broken into two parts using the shift-share method. Stevens and Moore [36] provide a thorough description of the shift-share method. First, state employment growth that would occur if its industries grew at their respective national rates is calculated (INDMIX).(4) Second, the remaining employment growth, or state idiosyncratic change (COMPETE) is calculated.(5) This component may reflect employment growth due to state competitiveness or state-specific restructuring. To allow for more complex dynamics, the lags of INDMIX and COMPETE are included (INDMIX_LAG, COMPETE_LAG).

   INDMIX and COMPETE may have differing impacts on unemployment to the extent that worker mobility across industries differs from their mobility across regions. If workers have limited mobility across industries [26], the impact on unemployment will be greater if the loss of employment occurs because of national restructuring (i.e., the magnitude of the INDMIX coefficient will be larger). This follows because there is less incentive for workers to migrate out of the region if their industry is in national decline and this causes unemployment to rise. On the other hand, workers displaced by state-specific employment changes have a better chance of obtaining employment in their industry outside the region. Thus, there is greater incentive to migrate, leaving unemployment relatively unchanged.

   Table I. Variable Definitions and Sources

   VARIABLE DEFINITION AND SOURCE

   UNEMP. RATE The civilian unemployment rate for each state. Source: [46].

   INDMIX The state's employment growth rate if employment in the state's two-digit industries were growing at their respective average national industry employment growth rates for the non-farm private sector. Source: Bureau of Economic Analysis (BEA).

   COMPETE The difference between the state's actual non-farm private sector employment growth rate and the industrial mix employment growth rate (INDMIX). (COMPETE = ACTUAL STATE GROWTH RATE - INDMIX) Source: BEA.

   INDVAR The employment weighted variance of two-digit non-farm private sector employment growth rates. To avoid possible wild fluctuations due to very small industries in small states, industries with employment less than 500 were excluded from the calculation. Source: BEA.

   WAGEMIX The real average annual earnings per worker (1000s) in the state if each of the non-farm private sector industries in the state paid their respective national average industry earnings per worker. The earnings were deflated by the U.S. Consumer Price Index (1982-84 = 100). Source: BEA and [45].

   WAGE_COMP The difference between real actual non-farm private sector average annual earnings per worker (1000s) in the...