STRUCTURAL UNEMPLOYMENT IN THE UNITED STATES: THE EFFECTS OF INTERINDUSTRY AND INTERREGIONAL DISPERSION

JEFFREY PARKER*

The effects of sectoral shifts, measured by dispersion in the growth rates of employ- ment or earning across industries or regions, on unemployment are tested in a spec- ification controlling for the effects of other labor-market variables and shifts in the demographic composition of the labor force. Interindustry and geographical shifts in labor demand have significant unemployment effects, with adult males the group most strongly affected. The estimated equations imply that most of thefluctuation in un- employment over the period 1956-87 was been due to microeconomic causes rather than aggregate demand.

1. INTRODUCTION

The unemployment rate in the United States increased from an average of 4.9 percent for 1956-73 to 7.2 percent for 1974-87. It is of considerable importance for purposes of economic policy to evalu- ate the contributions of various microeco- nomic and macroeconomic factors to this increase in the unemployment rate. If the primary cause is deficient aggregate de- mand, then the appropriate policy re- sponse may include stimulative monetary and fiscal policy. If microeconomic factors are most significant, then the appropriate policies would be microeconomic in na- ture-job training programs, restructuring of minimum-wage laws and unemploy- ment-insurance programs, etc.

The period from 1974 to 1987 differed from the earlier postwar period in several widely recognized ways. Oil prices in- creased dramatically in 1974 and 1979 and

Associate Professor of Economics, Reed College, Portland, OR 97202. I wish to thank Paul Evans, James Hagerman, Carl Stevens, Jeffrey Summers and an anonymous referee for their helpful comments on this paper. Shehadah Hussein stimulated my interest in and increased my knowledge of this subject through his dissertation research at the University of Houston. My-Linh Ha provided valuable research assistance. Remaining errors and shortcomings are, of course, my sole responsibility.

Economic Inquiry Vol. x)(, January 1992,101-116

decreased substantially beginning in 1982. The steady postwar increase in the female labor-force participation rate became more rapid in the 1970s. The large population cohort associated with the postwar baby boom began reaching maturity and enter- ing the labor market in the early 1970s. Inflation in the United States was sus- tained at levels higher than ever before. The dollar began floating against the cur- rencies of other industrial countries fol- lowing the collapse of the Bretton Woods system in 1971 and fluctuated consider- ably in both nominal and real value. First West Germany, then Japan, and most re- cently Taiwan and South Korea developed impressive manufacturing bases in indus- tries in which U.S. exports had formerly faced far less competition. Government expenditures on income-transfer pro- grams accelerated dramatically in the 1970s, while regulatory policies were changed in many industries.

The influence of these disturbances on the natural rate of unemployment has been modeled through three main chan- nels: (1) the effects of labor-market distor- tions such as minimum-wage laws, unem- ployment insurance, and unions, (2) the effects of changes in the demographic composition of the labor force, and (3) the effects of shifts in the sectoral composition

101

@Western Economic Association International

102 ECONOMIC INQUIRY

of production and employment. A grow- ing literature has emerged following David Lilien’s [1982a] finding that sectoral shifts, measured by interindustry disper- sion in rates of employment growth, ex- plain much of the increase in unemploy- ment in the late 1970s. However, the stud- ies relating unemployment to dispersion measures have largely ignored the effects of demographic changes and other tradi- tional labor-market variables. The present paper tests whether dispersion variables help to explain unemployment when the effects of several market-distortion vari- ables and changes in the demographic composition of the labor force are taken into account. I also examine the relative explanatory power of several alternative measures of both interindustry and inter- regional dispersion.

The second section of the paper sum- marizes the literature on the effects of dispersion on unemployment. The results of my empirical analysis are presented in section I11 and applied to current eco- nomic policy issues in section IV. The final section of the paper summarizes my con- clusions.

II. BACKGROUND

Search models of unemployment imply that workers having substantial industry- specific human capital or other kinds of high mobility costs may remain unem- ployed for relatively long periods of time while trying to find jobs similar to those they lost, which would enable them to avoid the cost of changing industries. In his seminal contribution to the empirical literature on the unemployment effects of dispersion, Lilien [1982a] uses as a disper- sion variable the employment-weighted cross-sectoral standard deviation of em- ployment growth rates of eleven sectors of the U.S. economy. For a 1948-80 sample period, this variable and its lag have sig- nificant positive signs when added to an unemployment equation whose other ex- planatory variables are current and lagged

values of Robert Barro’s [1977] measure of unanticipated growth in the M1 money supply, a time trend, and one lagged value of the unemployment rate. Lilien inter- prets the significant, positive coefficients of his dispersion measure as evidence that greater intersectoral inequality in employ- ment growth associated with sectoral shifts in the economy leads to higher ag- gregate unemployment as workers (tem- porarily) resist moving from one industry to another. His estimated unemployment equation suggests that most of the rise in unemployment in the 1970s is explained by an increase in the natural rate due to increased dispersion, not to cyclical unem- ployment associated with aggregate-de- mand shocks.

Because the employment effects of changes in aggregate demand differ across industries, aggregate-demand fluctuations such as those associated with monetary policy may increase the measured degree of employment-growth dispersion in the economy. Using data on job vacancies, Katherine Abraham and Lawrence Katz [1986] rebut Lilien’s conclusion, claiming that the positive coefficient of his disper- sion measure picks up the effects of aggre- gate demand rather than interindustry supply disturbances, and that, therefore, aggregate-demand expansion was the ap- propriate policy for reducing unemploy- ment in the 1970s. Stephen Davis I19871 attributes the contradictory vacancy-rate evidence of Abraham and Katz to their use of vacancy stocks rather than flows and .provides a model in which their result is consistent with the sectoral-shifts hypoth- esis. Further, Lilien [1982b] shows that a dispersion measure that has been purged of intersectoral variation induced by mon- etary shocks affects unemployment with a significant positive sign. Davis also finds that interindustry variations in employ- ment growth that reverse the pattern of the previous year’s variations tend to reduce unemployment, supporting the sectoral- shifts hypothesis.

PARKER DISPERSION AND STRUCTURAL UNEMPLOYMENT 103

Kevin Murphy and Robert Tope1 [1987] provide a highly detailed study of the increase in unemployment during this pe- riod, based on a large cross-sectional sam- ple. They find that most of the increase in unemployment has been in long-term un- employment spells and that intersectoral labor mobility has declined at times when the unemployment rate has risen. Based on these findings, they deny an important role for sectoral shifts in explaining the rise in unemployment.

Prakash Loungani [1986] examines the impact of changes in oil prices on employ- ment growth in different sectors of the economy and concludes that most of the increase in interindustry dispersion in the 1970s can be explained by oil-price move- ments. In additional to oil-price changes, Shehadah Hussein [1988] finds that aggre- gate-demand fluctuations and movements in the U.S. real exchange rate have contrib- uted to interindustry employment-growth dispersion, and that the components of dispersion associated with each of these sources contribute significantly in an equation explaining aggregate unemploy- ment.

Lucie Samson [1985] and Janet Neelin [1987] have shown that dispersion helps explain the Canadian unemployment rate. Unlike Lilien, Samson includes a demo- graphic variable (the percentage of women in the labor force) in her unem- ployment equation. Neelin examines both interindustry and interregional disper- sion, concluding that interregional differ- ences in employment growth have the more significant effect on Canadian unem- ployment.

111. ESTIMATION AND TESTS

Testing Dispersion Efects In additional to updating through 1987

the sample period of previous studies, this paper seeks (1) to establish whether the effects of employment-growth dispersion

on U.S. unemployment are robust to a specification in which several important labor-market variables are included, (2) to test the sensitivity of the evidence for the sectoral-shift effect to demographic factors by determining the degree to which dis- persion measures affect the female, male, and teen unemployment rates, (3) to eval- uate alternative specifications of interin- dustry dispersion: earnings-growth versus employment-growth variation and disper- sion at a less-aggregated versus more-ag- gregated industry level, and (4) to exam- ine the relative importance of geographi- cal versus interindustry dispersion.

The theoretical basis for the effects of sectoral shifts on unemployment was de- veloped by Lilien [1982a], Davis [1987], and others. The theory asserts that dispa- rate growth in employment causes unem- ployment through workers’ reluctance to incur mobility costs in moving from one ”job situation” to another or through un- avoidable time required for this move- ment. The crucial characteristic of ”job situation” is that moving from one situa- tion to another is time-consuming or in- flicts costs on workers. In addition to explicit moving or retraining costs, these costs can include wages, working condi- tions, or living conditions in prospective new job situations that the worker per- ceives as inferior. To the extent that workers’ skills are industry-specific, inter- industry movements are costly and re- quire time for retraining, and industry boundaries can define job situations.’ The relevant boundaries could be quite broad sectors such as manufacturing, retail trade, and services, or much narrower industries, depending on whether work- ers are able to transfer easily between

1. It is quite possible that there are sufficient firm- specific skills to make the individual firm the relevant boundary for the job situation. However, data on em- ployment growth disaggregated to the firm level are not readily available.

104 ECONOMIC INQUIRY

industries within the same broad sector of the economy.

Moving from one state or region of the country to another is also costly and time consuming, so job situations may also have a geographical dimension.2 Thus, although dispersion has usually been measured as the interindustry variance of employment growth, the geographical measures used in this paper are also con- sistent with the hypothesis. Furthermore, in additional to employment reductions (presumably layoffs), reductions in aver- age weekly hours worked or in wage rates could induce a worker to leave an estab- lished job situation and become unem- ployed while searching for a more suitable situation. Thus, interindustry dispersion in the growth of total compensation of employees- the product of employment, average hours, and wages-may incorpo- rate relevant dispersion effects that are not captured in measures based strictly on employment growth.

Although these alternative measures are all consistent with the basic dispersion hypothesis, the theoretical foundations of the hypothesis have specific implications for the coefficients of the equations pre- dicting the unemployment rates of fe- males, males, and teens. Most fundamen- tally, greater dispersion in employment or compensation growth, whether across in- dustries or regions, should increase the unemployment rate for all demographic groups. Furthermore, the greatest effect should be felt by those with the greatest mobility cost-the highest level of indus- try-specific skills or the longest expected retraining period. Because males on aver- age have more work experience than fe- males, and may therefore have acquired more job-specific skills, the male unem-

2. An additional possibility is that job situations could be defined by occupations, in which case dis- persion in growth of employment across occupations would be a relevant measure. This promising possi- bility is not explored in this paper.

ployment rate should be more sensitive to interindustry dispersion than the female rate. Because teenagers have little experi- ence and are less likely to have depen- dents which complicate geographical moves, teenage unemployment should be the least sensitive to both interindustry and interregional dispersion. Women and teenagers are also more likely to respond to layoffs or wage/hours cuts by leaving the labor force (e.g., to pursue parental or home-production activities or to return to school) rather than by searching for a new job. This is an additional reason why fe- male and teen unemployment may be less sensitive to dispersion than male unem- ployment.

Measurement of Dispersion and Other Variables

Annual data on employment and com- pensation of employees (earnings) by in- dustry are published beginning in 1948 by the Bureau of Economic Analysis at a sixty-five industry level of aggregati~n.~ The variable SE65 is defined for each year as the cross-industry standard deviation of employment growth rates (as an annual percentage change relative to the previous year) across these sixty-five industries, with each industry weighted by its current share in aggregate employment. This mea- sure is similar to those used by Lilien and others, but uses a finer level of industry aggregation.

3. The data are taken from Table 6.6B of the National Income and Product Accounts of the United States, 1929- 1982, updated in July issues of the Survey of Current Business. Most other studies have used Bureau of Labor Statistics (BE) data rather than the series from the Bu- reau of Economic Analysis (BEA). The BLS data are available at a slightly finer level of industry detail, but series for many industries begin in the mid-1950s or later, making it difficult to construct consistent disper- sion measures over a long sample period. Measures were constructed based on the ninety-eight industries for which employment data are published back to 1948. The results of the equations using these measures were qualitatively identical to those described in the paper for the BEA measures based on employment-growth dispersion.

PARKER: DISPERSION AND STRUCTURAL UNEMPLOYMENT 105

Because the relevant industry divisions (the boundaries of a ”job situation”) are unknown, two related dispersion mea- sures were examined: SEZ3 measures weighted employment-growth standard deviation across thirteen more aggregated sectors: and SE65X captures variation be- tween industries within the same broad sector by summing across industries the weighted squared deviation of each indi- vidual subaggregate industry’s employ- ment growth rate from the current growth rate for the aggregated sector within which the industry lies.

To incorporate the possibility, men- tioned above, that interindustry fluctua- tions in average hours and wages also lead to dispersion effects, a parallel set of mea- sures (SCE65, SCEZ3, and SCE65X) were computed based on growth in industry employee compensation rather than in- dustry empl~yment .~

Data on employment by state are pub- lished by the Bureau of Labor Statistics. Data begin before 1950 for nearly all states, allowing dispersion measures to be constructed for 1950-87. Two measures of geographical employment-growth disper- sion are used: SEST measures the cross- state standard deviation of employment growth rates, weighted by the states’ shares in total U.S. employment, and SEREG is a similar measure defined over eleven regions corresponding to the Cen- sus Bureau’s standard regional classifica- tion.6 Figure 1 plots the behavior over the

4. The thirteen sectors are (1) agriculture, forestry. and fisheries, (2) mining, (3) construction, (4) durable goods manufacturing, (5) nondurable goods manufac- turing, (6) transportation, (7) communications, (8) util- ities, (9) wholesale trade, (10) retail trade, (11) finance, insurance, and real estate, (12) services, and (13) gov- ernment.

5. Compensation of employees is given in Table 6.48 of the National Income and Product Accounts.

6. The Census Bureau uses a nine-region break- down. However, for this study, Alaska and Hawaii are treated as separate regions rather than included in the Pacific region because their geographic isolation im- plies high labor-mobility costs of moving into or out of these states.

1956-87 sample of two representative dis- persion series: the series based on disper- sion of earnings growth across broad sec- tors (SCEZ3) and the one based on interre- gional employment-growth dispersion (SEREG).

Previous studies, beginning with Lilien, have used Barro’s I19771 original specifi- cation of unanticipated money growth, in current and lagged form, as the sole aggre- gate-demand variable. Most have also fol- lowed Barro’s two-step procedure of esti- mating the money-growth equation prior to the unemployment equation and using the residuals from the former to represent unanticipated changes in aggregate de- mand in the latter. Adrian Pagan [1984] has shown that, because measurement error in the unanticipated money growth variables is neglected, the two-step esti- mator is consistent but inefficient, and that the estimated standard errors printed out by typical software packages are inconsis- tent. To avoid this problem, Leonard0 Leiderman [1980], Frederic Mishkin [1983] and others have estimated the money- growth and unemployment equations jointly, imposing (and testing) the cross- equation restrictions implied by the ratio- nal-expectations hypothesis. The present study follows Mishkin in estimating the model jointly by iterated, non-linear, weighted least square^.^

The specification of the money-growth equation follows Barro [1977], except that a third lag of money growth proved to be significant for the sample period used here and was added to the equation. In addi- tion to three lags of money growth (DM) , a measure of federal expenditures relative to “normal” levels (FEDV) and a transfor- mation of the lagged value of the unem- ployment rate (UALL) appear as explana- tory variables. Because the money-growth equation was reestimated for each alterna-

7. The model was estimated using the SYS(H) com- mand of MicroTSP 6.5.

106 ECONOMIC INQUIRY

FIGURE 1 Dispersion Measures

tive variant of the unemployment equa- tion, different estimates of the coefficients of the money-growth equation were gen- erated for each variant. All of these were qualitatively similar to the estimates of equation (1) below, which was associated with the regression of the log of the aggre- gate unemployment rate on current and three lagged values of unanticipated money growth, the labor-market variables discussed below and two dispersion mea- sures: earnings-growth dispersion across broadly defined sectors and interregional employment-growth dispersion.8 Stan- dard errors are shown in parentheses below each estimated coefficient.

8. This unemployment equation is reported below in Table 11.

(1) DMT = 10.42 + 0.055 DMT-~ (3.43) (0.15)

- 0.092 DMT-2 + 0.547 DMT-3 (0.16) (0.13)

+ 24.802FEDV~ (7.21)

+ 3.944 ln[UALLT-l/(lOO - UALb-I)] (1.21)

+ DMRT

The residuals from this equation (DMR) are interpreted as the unanticipated com- ponent of monetary growth. This is the ag-

PARKER: DISPERSION AND STRUCTURAL UNEMPLOYMENT 107

gregate-demand variable used in the un- employment equations.9

In addition to the aggregate-demand and dispersion variables discussed above, three indicators of government labor-mar- ket intervention are included in the unem- ployment specification. Increases in the ratio of the federal minimum wage to economy-wide average hourly earnings (MWRAT), by raising the cost of unskilled workers relative to their productivity (which is assumed to move over time with the aggregate average hourly wage), are expected to raise the level of unemploy- ment, especially among teens, who partic- ipate most heavily in the unskilled labor market.

The unemployment equation also in- cludes the ratio of unemployment insur- ance recipients' average weekly unem- ployment insurance check to average weekly earnings of employed workers (UIRATE) as a proxy for the replacement rate in state unemployment insurance pro- grams. A higher replacement rate in- creases the subsidy to job-search activities of workers, lowering the private cost of search and raising unemployment. This variable is, at best, an imperfect measure of the effects of unemployment-insurance programs. Unemployment insurance is administered by the states, so replacement rates, limits on dollar benefits, and dura-

9. Changes in the Fed's operating procedure, in its objectives, or in its pemeption of the reaction of the economy to monetary policy should cause changes in the way that agents having rational expectations fore- cast future money growth. However, there are insuf- ficient annual observations to permit different equa- tions to be estimated over several subsample periods. Because the effects of aggregate demand on unemploy- ment are of secondary importance to this study, sub- sample regressions with monthly or quarterly data were not performed. The choice of M1 is also poten- tially problematic. Recent volatility in the velocity of M1 (see Darby et al. [1987], for a discussion of the evidence and issues) casts suspicion on the stability of the linkage between M1 and aggregate demand. This study uses M1 to retain symmetry with previous related work; using M2 or a Divisia index number of monetary assets may be more appropriate for the 1980s.

tion limits vary from state to state. This makes it difficult to construct an aggregate measure of the legal unemployment-in- surance environment. At best, this vari- able may capture changes in the average replacement rate across states. It may also be affected spuriously by changes in the average income of the pool of unemployed recipients. For example, if the proportion of high-income workers among the unem- ployed were to rise, the average check (which usually depends on previous earn- ings of the unemployed) would increase, leading to an increase in the proxy vari- able UIRATE.

An increase in the size of the armed forces, whether through a draft or through enlistment, not only provides a source of employment among young men and women, but may absorb a relatively large proportion of workers who would other- wise be likely candidates for unemploy- ment-those with little experience or edu- cation. Changes in military participation may have significant effects across the entire labor market, but should be felt particularly strongly in the market for young, unskilled or semiskilled workers. A variable (MILRT) measuring the number of active military personnel per 1,000 pop- ulation is included in the unemployment equation. An increase in this variable is expected to lower unemployment.1°

10. The variable MILRATE used here is not the same as the MIL measure used by Barro [1977J Barro's variable specifically attempted to measure the effect of the military draft, and was set to zero after 1969 to reflect the end of the draft. Barro's estimates of the effects of the sudden drop in his M I L variable on un- employment in the early 1970s were shown by Small [1979] to be implausibly large. Because the variable used here is not subject to this discontinuity, its pres- ence in the equation does not imply a large jump in natural unemployment in 1970. Another method for dealing with the effects of the military on the labor market is to use the ratio of real federal expenditures to GNP. When added to the unemployment equation in place of MILRATE, this variable had an insignificant coefficient and resulted in a poorer overall fit for the equation. The principal coefficients of interest (those on the dispersion measures) were not significantly af- fected by this change in specification.

108 ECONOMIC INQUIRY

Estimated Unemployment Equations As in most econometric applications,

little is known from economic theory about the appropriate functional form, lag structure, and stochastic error in the rela- tionship between unemployment and the explanatory variables described above. Because the unemployment rate is neces- sarily positive, I chose a semi-log specifi- cation with an additive error term. The choice of how many lagged values of explanatory variables to include was made on the basis of including one or more lags of a variable only when adding a lag resulted in a significant t-statistic. Only the aggregate-demand variable proved to have significant lagged effects. The time-series specification of the sto- chastic error was chosen in a similar man- ner, allowing for an autoregressive-mov- ing-average structure.

Because monetary policy is only one dimension of aggregate demand, I tested several potential fiscal-policy variables in addition to the unanticipated money- growth variables, such as the ratios of real government spending and real govern- ment deficits to real gross national prod- uct. None of these variables approached statistical significance, so they are omitted from the results presented here. Addition- ally, the actual rate of money growth proved insignificant, confirming Barro's result that (in this specification) only un- anticipated money growth affects unem- ployment."

11. There is a long literature testing the ineffective- ness of anticipated money growth. Among many other studies, Mishkin [1983] finds that in a more general specification of the money-growth equation, antici- pated money growth does affect real variables such as unemployment significantly; this study does not PIP- tend to offer new evidence to this debate. Since the contribution of aggregate demand is not the central issue here, relatively little experimentation was done with alternative specifications of the money-growth equation. Several minor changes to the chosen speci- fication for the money-growth equation failed to alter any of the conclusions.

As discussed above, several alternative specifications of the interindustry and in- terregional dispersion variables were tested. Table I compares the estimated coefficients of aggregate-unemployment equations with several measures. Stan- dard errors are in parentheses below the coefficients; the subscripts on the variable names indicate their time periods relative to the dependent variable, which is the log of the aggregate unemployment rate in period T. All equations include the current and two lagged values of the un- anticipated money variable and were esti- mated jointly with the money-growth equation by iterated, non-linear, weighted least squares for a 1956-87 sample. A first- order au toregressive process proved to be an appropriate stochastic specification for all equations in Table I. The hypothesis of white-noise residuals could not be rejected using the Durbin-Watson statistic or the Box-Pierce Q-statistic at lags up to five years. The column headed "SEE reports the standard error of the estimate for the equation. Adjusted R2 statistics for the regressions ranged from 0.93 to 0.96.

Unanticipated money growth and the labor-market variables enter each equa- tion with the expected sign and, with occasional exceptions, are statistically sig- nificant. Each of the dispersion measures has a significant and positive coefficient when entered singly. This verifies that dispersion measures affect unemployment even when such labor-market factors as minimum wages, unemployment insur- ance, and military participation are taken into account, corroborating the conclu- sions of earlier proponents of the sectoral- shifts hypothesis.l*

12. Moreover, when these labor-market variables are included, a time trend added to the equation is not only insignificant, but its coefficient has a negative point estimate. Thus, the variables included in the equation provide an adequate statistical explanation for the increase in unemployment from the earlier to the later years of the sample.

TABL

E I

Est

imat

es w

ith A

ltern

ativ

e D

ispe

rsio

n M

easu

res

Con

stan

t D

MR

, D

MR

T-1

DM

R,,

MW

RA

TT

UIR

ATE

T M

ILR

TT

Dis

pers

ion

Var

iabl

es

AR

(1)

SEE

SE13

, S

E6

5x

~

-0.0

53

-0.0

090

-0.0

280

-0.0

224

1.29

5.

28

-0.0

96

0.05

1 0.

840

0.06

27

(0.4

7)

(0.0

034j

(0

.004

7)

(0.0

049)

(0

.40)

(1

.00)

(0

.012

) (0

.009

5)

(0.0

73)

-0.0

80

-0.0

090

-0.0

281

-0.0

227

1.30

5.

32

-0.0

96

0.05

1 0.

0034

0.

841

0.06

47

(0.5

1)

(0.0

035)

(0

.004

8)

(0.0

054)

(0

.41)

(1

.04)

(0

.012

) (0.011)

(0.0

25)

(0.0

73)

?? 12 8 SE

65,

-0.4

60

-0.0

100

-0.0

295

-0.0

277

1.31

6.

08

-0.0

95

0.05

1 0.

841

0.06

59

(0.4

7)

(0.0

036)

(0

.005

0)

(0.0

050)

(0

.43)

(0

.99)

(0

.012

) (0

.011

) (0

.078

) b

SCE

13,

SCE

65X

, 3

0.25

1 -0

.010

9 -0

.028

7 -0

.025

8 1.

21

4.58

-0

.094

0.

044

0.82

0 0.

0605

2 r?

SCE

65,

3 1 g

(0.4

8)

(0.0

033)

(0

.004

6)

(0.0

049)

(0

.40)

(1

.04)

(0

.011

) (0

.007

8)

(0.0

86)

0.35

9 -0

.010

1 -0

.029

2 -0

.023

6 1.

36

4.07

-0

.097

0.

043

0.02

4 0.

831

0.06

12

(0.4

7)

(0.0

032)

(0

.004

5)

(0.0

047)

(0

.40)

(1

.08)

(0

.011

) (0

.007

8)

(0.0

17)

(0.0

82)

F

C

0.06

16

5 0.

359

-0.0

099

-0.0

300

-0.0

235

1.37

3.

99

-0.0

98

0.05

7 0.

837

(0.4

9)

(0.0

033)

(0

.004

6)

(0.0

047)

(0

.40)

(1

.12)

(0

.012

) (0

.10)

(0

.082

)

2 SE

REG

, SE

STT

-0

.678

-0

.014

0 -0

.033

7 -0

.030

0 0.

765

6.83

-0

.080

0.

123

0.82

9 0.

0682

(0

.49)

(0

.003

8)

(0.0

053)

(0

.005

5)

(0.4

4)

(0.9

8)

(0.0

14)

(0.0

30)

(0.0

85)

-0.6

89

-0.0

145

-0.0

341

-0.0

336

0.71

2 7.

06

-0.0

88

0.110

0.82

8 0.

0691

(0

.50)

(0

.003

9)

(0.0

053)

(0

.005

5)

(0.4

6)

(0.9

8)

(0.0

14)

(0.0

30)

(0.0

88)

-0.6

77

-0.0

139

-0.0

336

-0.0

296

0.77

8 6.

81

-0.0

79

0.14

6 -0

.024

0.

829

0.07

06

(0.4

9)

(0.0

038)

(0

.005

4)

(0.0

056)

(0

.45)

(0

.99)

(0

.014

) (0

.083

) (0

.085

) (0

.082

)

s

110 ECONOMIC INQUIRY

Although the different measures of dis- persion yield quite similar results,13 com- paring the performance of alternative measures shown in Table I yields two broad conclusions. First, the measures based on the broader industry or regional definitions have somewhat larger t-statis- tics and yield slightly better-fitting equa- tions than those based on narrower defi- nitions. (i.e., SE23, SCEZ3, and S E R E G outperform SE65, SCE65, and S E S T , re- spectively). When measures based on finer aggregations are added to an equation along with those of the broader aggrega- tions, such as in the second equation shown in Table I, the latter dominate with the former having insignificant marginal explanatory power. This suggests that mo- bility is considerably greater among jobs within these broad sectors/regions (e.g., durables manufacturing or the East North Central region) than between them. Sec- ond, the intersectoral measures based on earnings-growth dispersion (SCEZ3 and SCE65) lead to smaller residual standard errors than those based on employment- growth dispersion ( S E 1 3 and SE65). This result is consistent with the hypothesis that some dispersion-related unemploy- ment is caused by quits in response to industry-specific reductions in wages and/or hours. On the basis of the regres- sions reported in Table I, the dispersion measures chosen for subsequent tests are the measure of earnings-growth disper- sion for the broad, thirteen-sector industry classification (SCEZ3) and the measure of interregional employment-growth disper- sion ( S E R E G ) .

13. As one might expect, the dispersion measures are positively correlated with one another. Correlation coefficients between measures based on broadly vs. narrowly defined industries are in the range of 0.89 to 0.93; the state and regional dispersion measures have a correlation of 0.93. The correlations between the employment-growth and earnings-growth mea- sures are in the range of 0.81 to 0.86, while regional dispersion and earnings-growth dispersion have a cor- relation of 0.53.

Table I1 describes the results of regres- sions of the aggregate, female, male, and teen unemployment rates on these mea- sures of interindustry and interregional dispersion, along with the unanticipated money-growth and labor-market vari- ables. The aggregate equation suggests that intersectoral earnings-growth con- tributes more significantly to U.S. unem- ployment than interregional differences in employment growth,'* though the latter contributes a significant positive effect. Note that the greater strength of interin- dustry dispersion variables relative to in- terregional measures found here is oppo- site to the result obtained by Neelin [1987] for Canada.

A comparison of the aggregate equation in Table I1 with those for the various demographic groups yields further sup- port for the importance of sectoral and regional shifts in explaining unemploy- ment.I5 Most crucially, dispersion mea- sures are significant explanatory variables in the subaggregate equations, implying that the significance of these variables in the aggregate equation does not result from a spurious association with omitted demographic effects.

As hypothesized above, intersectoral dispersion affects male unemployment more strongly and significantly than it affects either female or teen unemploy- ment. Male workers are likely to have greater industry-specific human capital because they are, on average, more expe- rienced and better educated than females and teens. Males are also less likely than females or teens to leave the labor force upon leaving or losing a job, implying that

14. All of the results reported in Table I1 were quite insensitive to the choice of intersectoral or geographic dispersion measure.

15. In Table II, the specification of the equation for each demographic group was kept symmetric to that chosen for the aggregate equation. In some cases, this implied retaining an insignificant coefficient, such as the autoregressive error term in the equation for female unemployment.

TABL

E I1

U

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ploy

men

t Equ

atio

ns f

or D

emog

raph

ic G

roup

s I

s’ ~~

Con

stan

t D

MR

, DM

RT-1

D

MR

T-2

MW

RA

TT

UIR

ATE

T M

ILR

TT

SCE1

3,

SER

EGT

AR

(1)

SEE

6 F W Ei

Dep

ende

nt V

aria

ble:

Log

of

Agg

rega

te U

nem

ploy

men

t Rat

e 0.

082

-0.0

122

-0.0

297

-0.0

247

1.05

4.

82

-0.0

87

0.03

42

0.06

21

0.83

8 0.

0583

(0

.45)

(0

.003

1)

(0.0

045)

(0

.004

8)

(0.3

7)

(0.9

6)

(0.0

12)

(0.0

087)

(0

.030

) (0

.078

) i! $

Dep

ende

nt V

aria

ble:

Log

of

Fem

ale

Une

mpl

oym

ent R

ate

1.27

-0

.013

9 -0

.032

0 -0

.034

9 0.

253

2.32

-0

.051

0.

0234

0.

0667

-0

.306

0.

0419

(0

.35)

(0

.002

9)

(0.0

033)

(0

.005

4)

(0.2

1)

(0.7

4)

(0.0

059)

(0

.007

6)

(0.0

28)

(0.1

7)

3 D

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dent

Var

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f M

ale

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mpl

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F.

-0.7

45

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157

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328

-0.0

259

1.36

6.

73

-0.1

10

0.03

68

0.07

91

0.87

6 0.

0714

(0

.54)

(0

.003

7)

(0.0

056)

(0

.005

5)

(0.4

4)

(1.1

6)

(0.0

14)

(0.0

103)

(0

.035

) (0

.049

) 5 6 i

Dep

ende

nt V

aria

ble:

Log

of

Teen

Une

mpl

oym

ent R

ate

1.88

-0

.004

6 -0

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0 -0

.018

5 0.

887

2.74

-0

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0.

0190

0.

0411

0.

873

0.04

93

(0.3

7)

(0.0

027)

(0

.003

6)

(0.0

035)

(0

.315

) (0

.81)

(0

.010

) (0

.007

4)

(0.0

24)

(0.1

00)

11 2 ECONOMIC INQUIRY

job termination is more likely to lead to unemployment for males.

Among the groups shown in Table 11, the effect of interregional dispersion is smallest and least significant (in the sense of the smallest t-statistic) for teenagers. If teenage workers have lower costs of geo- graphical mobility (perhaps because of fewer dependents or lower likelihood of home ownership), this is strongly consis- tent with the dispersion hypothesis.

Thus, the evidence from Tables I and I1 strongly corroborates the conclusion that sectoral and geographic shifts in the dis- tribution of labor demand raise the level of unemployment. Moreover, the pattern of effects among demographic groups in the labor force is consistent with casual evidence about the relative degrees of intersectoral and geographical mobility of members of these groups.

IV. APPLICATIONS

Estimating the Natural Rate of Unemployment

The distinction between microeco- nomic and macroeconomic causes of un- employment lies at the heart of the ”natu- ral-rate hypothesis.” In his seminal state- ment of this hypothesis, Milton Friedman defines the natural rate of unemployment as

... the level that would be ground o u t by the Walrasian system of gen- eral equilibrium equations, provided there is [sic] imbedded in them the actual structural characteristics of the labor and commodity markets, in- c luding marke t imperfections, sto- chastic variability i n demands and supplies, the cost of gathering in- formation about job vacancies and labor availabilities the costs of mo- bility, and so on. 16

16. Friedman [1968, 81. It is worth noting that the natural rate need not correspond to the socially opti- mal rate because government policies (such as unem- ployment compensation) and imperfect capital mar- kets or other market failures may cause divergences of the private costs and benefits of search from the social costs and benefits.

Thus, equations that estimate the micro- economic and macroeconomic causes of unemployment, such as those presented in Tables I and 11, can be solved to yield es- timates of the natural rate of unemploy- ment-the rate attributable solely to mi- croeconomic factors.

Figure 2 shows the values of the actual and estimated natural rates of unemploy- ment for the total labor force over the 1956-87 period. The latter is calculated as the fitted values of the aggregate equation in Table I1 with unanticipated money growth and the residual set equal to zero.I7 Several implications emerge from Figure 2. Contrary to conventional wis- dom, the equation implies that unemploy- ment was considerably above the natural rate from 1956 through 1964. In the late 1960s, the decline in unemployment to below 4 percent was driven not by mone- tary policy but rather by microeconomic factors, mostly the increased size of the military associated with the Vietnam War. As noted by Lilien, the spike in unemploy- ment in 1975 seems to be entirely attribut- able to natural (microeconomic) factors- partially sectoral shifts associated with the sudden rise in oil prices, but also due to the reduction in the armed forces after Vietnam. The monetary accommodation of the oil shock brought unemployment down below the natural rate during the Carter administration, with the natural rate rising in 1979 and 1980 both due to dispersion and labor-market factors. Con- tractionary monetary policy in the first Reagan administration moved unemploy-

17. This implicitly assumes that the residual is part of the deviation of actual from natural unemployment, rather than error in estimating the natural rate. This seems to me to be more natural than the opposite as- sumption. In any case, because the residual accounts for less than 3 percent of unemployment variance (the unadjusted R-squared value is higher than 0.93, the estimates of the natural rate are not very sensitive to this assumption. The next section examines the contri- butions of various factors (including dispersion and the residual) to unemployment for selected sample years.

PARKER DISPERSION AND STRUCTURAL UNEMPLOYMENT 113

FIGURE 2 Actual and Natural Unemployment Rates

7.;- -. :.... .... Natural Rate

ment well above the natural rate, which began to recede substantially in 1982. The rise in the natural rate in 1986 and 1987 was substantially due to increases in dis- persion, probably caused by falling oil prices and changes in the real foreign-ex- change value of the dollar. The quantita- tive contributions of the variables in the equation to unemployment in selected years are examined in the next section.

The natural-rate hypothesis of Fried- man [1968] and Phelps [1970] implies that inflation will accelerate-or, more pre- cisely, rise above the expected rate-when the actual unemployment rate falls below the natural rate. According to this hypoth- esis, the gap between actual and natural unemployment in 1986 and 1987 should have stimulated a rise in inflation during the late 1980s. Although some acceleration

occurred, its magnitude seems smaller than that suggested by the rather large unemployment gap shown in Figure 2.

Using the natural-rate hypothesis, some authors have estimated the natural unem- ployment rate by estimating the rate that would be consistent with an unchanging inflation rate-the nonaccelerating-infla- tion rate of unemployment (NAIRU). For example, David Stockton [1988] estimates equations representing the price-setting behavior of firms that are subject to micro- economic and macroeconomic shocks. He then infers a series for the NAIRU from 1957 to 1980 from these price equations. Stockton's estimates of the NAZRU are much more volatile in the 1970s than the estimates of the natural rate shown in Figure 2 (reaching a high of nearly 10 percent in 1974 and a low of just over 1

114 ECONOMIC INQUIRY

percent in 1977), but are more stable in the 1960s) hovering between 5 and 6.5 percent. Although there are sizable differences in the magnitude of fluctuations, the broad movements in the two series have similar shapes from 1965 through 1980, lending some support to the natural-rate hypoth- esis linking the natural rate in the aggre- gate labor market to the nonaccelerating- inflation rate of the aggregate product market. Unfortunately, Stockton's esti- mates end in 1980, making it impossible to ascertain whether the increase in the natural rate shown in 1986 and 1987 would correspond to an increase in his estimated NAIRU.'8

Decomposition of Changes in Unemployment Another application of the estimated

aggregate unemployment equation is an examination of the contributions of vari- ous factors-labor-market variables, dis- persion variables, aggregate demand, and the unexplained residual-to the devia- tions in various periods of the unemploy- ment rate from its sample mean of 5.9 percent. Figure 3 shows a decomposition of the effects on unemployment attribut- able to these four sources for six selected years.

In 1967, the unemployment rate and the natural rate were very low-two percent- age points below the sample mean. Nearly all of this low unemployment was caused by labor-market variables. Military partic- ipation was high as the Vietnam War ap- proached its peak and the unemployment insurance replacement rate was quite low

18. The unstable demand for M1 in the 198Os, men- tioned earlier, makes the linkage between aggregate demand and my unanticipated money measure some- what suspect over this period. M1 growth increased from 5.7 percent in 1984 to 12.4 percent and 17.0 per- cent in 1985 and 1986, then fell to 3.5 percent in 1987. The estimated equation translates this erratic behavior in M1 into large movements of the actual unemploy- ment rate relative to the natural rate. The 1987 number in particular may well overstate the effect of the rapid expansion in the two previous years on unemploy- ment.

relative to later years. Both of these effects tended to reduce the aggregate unemploy- ment rate. By 1971, the unemployment rate had risen to near its sample mean, again due to "natural" causes-a reversal of those labor-market factors depressing unemployment in 1967 coupled with a contribution from above-normal disper- sion.

The high unemployment of 1975 is caused by a combination of labor-market factors (mainly the end of the war) and extremely high dispersion associated with the oil-price shock. Aggregate demand is also relatively contractionary in this pe- riod and there is a sizable negative resid- ual-the equation overpredicts the rise in unemployment in 1975! Unusually low dispersion in 1976 through 1978 is re- versed with the second oil shock in 1979 (though the dispersion variables surge up only to their sample mean values), but a high real minimum wage and continuing demilitarization push the natural rate up- ward.

In 1982, as in 1975, all three explanatory sources contribute to the upward spike in unemployment, with dispersion playing an important role as the dollar rises in foreign-exchange markets. The con- tractionary monetary policy of the Volcker regime is a major cause, but not the sole cause, of the rise in unemployment. As shown in Figure 2, the greatest contribu- tors to the increase in unemployment are the labor-market variables. By 1987, both the dispersion and the labor-market vari- ables have lessened their positive impact on unemployment, but the driving force in the decline in actual unemployment is the rapid monetary expansion of the two preceding years, pushing the actual rate well below the natural rate.

The results reported here verify a sig- nificant contribution of sectoral and re- gional shifts to unemployment, as shown initially by the natural-rate series con- structed by Lilien [1982a]. However, a large share of the natural-rate fluctuations

PARKER: DISPERSION AND STRUCI'URAL UNEMPLOYMENT 115

FIGURE 3 Contributions to Unemployment Changes

- . 1967' 1971 ' 1975.1979 1982 1987

m rn m m

Demand Shocks

Labor Mkt Variables

Dispersion

Unexplained

in the present model are caused by changes in the labor-market variables rather than by dispersion. In particular, the military-participation and unemploy- ment-insurance variables seem to explain most of the rise in the unemployment rate from the 1950s to the 1980s. Because Lilien did not include these variables in his anal- ysis (relying instead on a time trend to pick up these effects) he may have some- what overestimated the quantitative im- pact of sectoral shifts on unemployment.

V. CONCLUSIONS

This paper attempts to extend testing of the effects of sectoral shifts on unemploy- ment to control for the effects of other labor-market variables and of shifts in the demographic composition of the labor force. It also explores several alternative measures of dispersion, including interin- dustry dispersion in earnings growth rather than employment growth, disper-

sion across finely defined industries ver- sus more aggregated sectors, and interre- gional dispersion in employment growth. Significant unemployment effects result both from interindustry shifts and from geographical shifts in labor demand, and these effects appear to be more significant when sectors and regions are defined broadly rather than finely. Moreover, these results hold for major demographic groups in the labor force and complement the effects on unemployment of such labor-market phenomena as minimum- wage laws, unemployment-insurance pro- grams, and military participation. The es- timated equations imply that most of the fluctuation in unemployment over the 1956-87 period has been due to microeco- nomic causes rather than to aggregate demand, and that the actual unemploy- ment rate dipped considerably below the microeconomic natural rate in 1987.

116 ECONOMIC INQUIRY

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