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E C O. N O M I C
FEDERAL
RESERVE
CLEVELAND
BANK
OF
The Impact of Regional
Difference in Unionism
on Employment
by Edward Montgomery
Edward M ontgom ery is an assistant
professor of economics at CarnegieMellon University. The author would
like to acknowledge the helpful
comments from Randall Eberts,
Harry Holzer, Kim Kowalewski,
Kathryn Shaw , and Mark Sniderman, and to thank Ralph Day for his
excellent research assistance.
Introduction
Almost 20 percent of the people in the work
force are union members. Just in terms of
numbers, trade unions are an important influence
in the labor market and in the U.S. economy.
Further, unions are widely believed to play a
major role in determining workers’ standard of
living and how work is done and in affecting
firms’ profitability. Freeman and Medoff (1984)
recently presented evidence suggesting that
unions affect labor markets in a variety of ways.
The beneficial effects of unions include protec
tion for older workers, reduced quit rates,
reduced earnings inequality, and increased pro
ductivity. Unions might adversely affect profits
and stock prices and might increase the number
of workers laid off in cyclical downturns, as well.
Although the impact of unions on
these measures of economic performance has
been studied, the majority of research on unions
concerns how they affect compensation. Freeman
and Medoff (1984) show that unions increase
fringe benefits, and there is a large body of
empirical evidence that suggests unions raise the
relative wages of their members.1 In addition,
unions have been found to affect the wages of
nonunion members, although the direction and
magnitude of this effect is ambiguous. Despite
the attention focused on how unions affect
wages, little attention has been paid to how this
change in the relative cost of unionized labor af
1
fects employment—clearly an important part of
assessing the welfare costs and benefits of union
ism.2 (By “welfare costs,” we mean social or
aggregate costs and not simply private costs and
benefits to union members.) If unions succeed in
raising wages only at the cost of massive employ
ment reductions, as some analysts believe is the
case, the welfare implications are radically differ
ent than if wage increases could be achieved with
little or no impact on aggregate employment.
This study7examines whether
changes in unionism affect the aggregate level of
employment in the economy, and in particular,
whether an individual who lives in a standard
metropolitan statistical area (SMSA) where unions
are rare or weak is more likely to be employed
than an individual who lives in an area where
unions are strong.
Whether or not unions have a
harmful effect on employment is also important
to analysts of regional unemployment differences.
Murphy (1985), found that differences in sensitiv
ity to demand conditions in the product market
and in wage differentials are vital in determining
regional differences in unemployment rates. Since
unions have been found to affect both of these
variables, differences in the extent or impact of
unionism could be important in understanding
regional unemployment rate differentials.
2
There have been studies of the relative wage effect of unions
across industries, occupations, and race and gender groups.
In fact, Freeman and Medoff s
study (1984) suggests that unemployment rates
are 1.0 percent higher in areas with a high degree
of unionism relative to low unionism areas.
However, since they also fail to find any correla
tion between the degree of unionism and the
employment rate, a further, more explicit analysis
of this question seems to be necessary to deter
mine what effect, if any, unions have on aggre
gate and regional employment rates.
I. Previous Literature
Most studies of the employment effects of unions
have been on the industry7level.3 Industry7or firm
studies, however, may overestimate the disem
ployment effect of unions, because they ignore
the fact that some or all of the displaced workers
may become re-employed in other industries or
firms. Consequently, these studies cannot provide
estimates of the net or aggregate employment
effect of unions.
Lewis (1963 and 1964) provided
the first analysis of the relative wage and
employment effects of unions on an aggregate
basis. Lewis divides the economy into a union
and a nonunion sector. Industries with a rela
tively high degree of unionism, like manufactur
ing and mining, are part of the unionized sector,
while those with a low degree of unionism are
part of the nonunion sector.4 Using time series
data, Lewis estimates whether changes in relative
employment levels across these two sectors can
be attributed to differences in the average union/
nonunion wage premium and to the average per
cent unionized. His results suggest that unions
have a significant negative effect on relative
employment levels and man-hours worked.
Pencavel and Hartsog (1984)
recently updated and extended this seminal
work. They failed, however, to find any consistent
negative impact of unionism on man-hours. In
fact, they conclude that the hypothesis that union
ism depresses man-hours can be accepted only
for the late 1920s and early 1930s. This basic
result is not sensitive to whether the employment
and wage effects of unions are estimated with
Lewis’ reduced form model or with a structural
model that they developed.5
These results might be ambiguous
because aggregate data are not suited to testing
the employment effects of unionism. Aggregating
industries into two sectors ignores the effects of
unions
these sectors and, thus, may not
yield good estimates of the overall effect of
unions on employment and wages. Further, the
absence of controls for changes in labor quality
across sectors means that these studies might
overestimate the impact of unions on wages and
underestimate the effects on employment. In
other words, if firms respond to the union wage
demands by hiring for higher-quality labor, then
“quality-adjusted” wages will not rise as much as
measured wages.6 Since firms may substitute
skilled for unskilled workers, the effect on total
demand for labor could differ from the effect on
a particular type of labor.7
Kahn (1978), Kahn and Morimune (1979), and Holzer (1982) provide crosssection estimates of the effects of variations in
the extent of union membership across SMSAs
on employment, hours worked, and unem
ployment stability. In these cross-section stu
dies, the fraction of employed workers in an
SMSA who are union members is used as a mea
sure of union strength, because it is believed
that unionism affects all workers in the same
labor market, not just those in the same industry. Workers who may be displaced because of
union wage demands are likely to seek
employment not just in that industry, but
throughout the local labor market. Studies with
detailed cross-section data, either from the Cur
rent Population Survey (CPS) or the Survey of
Economic Opportunity (SEO), offer better con
trol for individual characteristics and for labor
market variables that affect employment. These
cross-section studies avoid some of the aggrega
tion problems that crop up in aggregate time
series studies, and thus, are preferable.
Nevertheless, results of these
cross-section studies are somewhat inconclu
sive. Kahn (1978) finds that annual hours
worked are significantly reduced for nonunion
females, but not for nonunion males; these
effects did not differ by race. Holzer (1982 ),
within
The structural model of the labor market that is used by Pencavel
and Hartsog (1984) w as developed to test for the wage and
employment effects of unions without assuming that employment is uni
laterally set by employers or that the union wage premium is exogenous.
It should also be noted that their model also differs from that estimated
by Lewis (1964) in that they use only the percent organized variable to
capture the effect of unionism and not the estimated union wage
premium.
3
See Lewis (1963) for a review of some of these industry studies.
4
The union sector was made up of mining, construction,
manufacturing, transportation, communication, and public utilities;
the nonunion sector was made up of all others, except military and
government relief.
6
7
The potential importance of these biases can be seen by the fact
that the estimates of the quality-adjusted union relative wage
effect differ substantially from those derived in cross-section studies.
See Pencavel and Hartsog (1984, p. 216) for a further discussion
of these limitations.
3
reduction in supply in the nonunion sector that
results from the drop in wages.
It can be shown that in a twosector model with constant factor intensities, the
changes in nonunion wages will be a function
of the elasticity of labor supply, , the elasticities
of labor demand in the union, 77 u , and
nonunion sectors, 77 , the percent unionized, k,
and the change in union wages,
Thus:
e
n
wn
(4)
—
wu ,u
-h (nu - e) w
(n u -e) [(1 -k) + ew J
+ kt (nu- n j w u
From equation (4) we see that
unless the elasticity of labor supply is zero (e =
0), nonunion wages will not fall enough to pre
vent average wages from rising and total
employment from falling. Falling wages in the
nonunion sector cause workers with high reser
vation wages to withdraw from the labor force,
thus causing total employment to decline.13
Since previous research has found that unions
tend to organize industries where the elasticity
of labor demand is low, it is interesting to note
that the greater the elasticity of labor demand in
the nonunion sector relative to the union sector,
the smaller the drop in nonunion wages, and
the smaller the aggregate employment loss.14
Using equations (1), (2), and (4), we can
express the change in total employment as a
function of the union wage change:
(5)
dET
1
E
A
t
where
A ~ (Vu +
"I
_L
- k) + e w u]
fce (Vu - n n)w u.
e) [(1
See Welch (19 74, p. 304, equation [6]), for derivation of a
Ld
similar result under the assumption that demand elasticities
do not vary across sectors.
"I
O
1
It is possible that the existence of a union wage premium
m ay actually draw more workers into the labor force than exit
because of the depressed nonunion wage rate. This will occur, however,
only if the turnover rate exceeds the elasticity of demand for labor. A s
noted earlier, this condition is unlikely to hold in the union sector.
14
See Freeman and M edoff (1984).
The higher the elasticity of
supply, e, or elasticity of demand in the union
sector, 77 M, or the greater the percent organized,
k, the greater the disemployment effect asso
ciated with an increase in union wages. As the
percent organized rises, more workers are in the
union sector, and hence, are affected by the
increase in union wages. However, if labor
supply is inelastic, total employment will
remain fixed.
In a general equilibrium model
with variable factor intensities, the effect of
unions on wages in the nonunion sector, and
hence total employment, is ambiguous. If the
unionized sector is the intensive sector then, as
shown in Johnson and Mieszkowski (1979),
both the substitution and the scale effect will
result in a reduced capital/labor ratio in the
nonunion sector, and hence, a reduction in the
marginal product of labor and wages.
However, with a capital-intensive
unionized sector, nonunion workers will get
higher wages if the scale effect is greater than
the substitution effect and lower wages if the
converse is true. In either case, increases in
union wages or in the percent of the labor force
that is unionized tends to be associated with an
increase in average wages and a drop in total
employment, as long as labor supply is not
completely inelastic.
The theoretical models discussed
in this section imply that increase in either the
percent unionized or in the union/nonunion
wage differential can lead to a reduction in
aggregate employment. The size of the disem
ployment effect will depend, in part, upon the
elasticity of labor supply, where the more elastic
the supply, the greater the reduction in
employment. As seen in equation (5), the
employment effect of unionism depends upon
the extent of union strength, which is a function
of both the union wage premium and the per
cent of the work force receiving it. Based on this
theory, we would expect an inverse relationship
between union strength and employment. We
would also expect this effect to be small, if the
elasticity of labor supply is near zero.
III. Empirical Results
To test for the employment and unemployment
effects of unions, we used data from the 1983
Current Population Survey (CPS) Earnings File
and Census data on SMSA characteristics. This
data set was chosen, in part, because it contains
detailed personal characteristics for each
respondent, which allow us to control for dif
ferences in worker quality. In addition, it con
tains earnings and union membership data
across individuals in each SMSA. To ensure a
sufficient sample size in each of the 44 SMSAs in
our sample, we combined the survey responses
for each month over the year, yielding a sample
of 104,409 observations.15
To examine the disemployment
effect of unions, we initially looked at the effect
of unionism on the probability of an individual
in the population being employed. Because
displaced workers from the unionized sector
may either become unemployed or withdraw
from the labor force, the employment and
unemployment effects of unionism need not be
the same. Since the distinction between unem
ployed and not-in-the-labor-force may not be
pronounced, and since some of those displaced
by unions may withdraw from the labor force,
the probability of being employed might be a
better measure of the “true” disemployment
effect of unionism than the probability of being
counted as unemployed. An additional benefit
from focusing on employment status is that we
can examine whether unionism has a different
effect on the likelihood of getting part-time
work than on getting full-time work. These
effects may differ substantially if unionism
affects the length of the workweek for those
who remain employed.
As shown in section II, the effect
of unionism on employment is a function of
both the percent organized and the union wage
premium. Consequently, the measure of the
effect of unionism that we used is the product
of the percent of employment in an SMSA that is
unionized and the union/non union wage dif
ferential.16This index is similar to the Kaitz
index, which is widely used to examine poten
tial disemployment effects of a legislated m in
imum wage increase. It appears that unions
impact aggregate employment via their effect on
the average cost of labor. The distortion in labor
costs due to unionism is the change in wages—
that is, the union wage premium times the
number of workers who receive that wage.17
Previous cross-section work by
Holzer (1982), Kahn and Morimune (1979), and
Kahn (1978) has implicitly limited the effect of
unions on employment to differences in the per
cent organized from SMSA to SMSA . This is like
constraining the union relative wage effect to be
-1
^
Beginning in 1981, the C P S reduced the number of surveyed
±
y
individuals and asked detailed employment questions of only
one-quarter of the sample each month. A s a result, there were fewer
than 30 union members in m any of the S M S A s in any given month.
W e restrict our sample to the nonfarm economy when
calculating both the union wage premium and the percent of
the same across SMSAs, which may be inapprop
riate for theoretical and econometric reasons.
Recent theoretical work by Lazear
(1983) suggests that the percent unionized in
an industry or region is not a good measure of
union power. He shows that to the degree the
cost of running a union differs across industries,
different wage/employment packages are nego
tiated by unions facing the same opportunity
locus or having the same strength. That is,
unions in industries where costs are high tend
to prefer higher wage/lower employment share
packages than unions in relatively low-cost
markets. Consequently, the percent of employ
ment that is unionized or the union wage pre
mium varies across industries or regions, even
though union power is the same.
Greater union strength is indi
cated by a better wage/employment share pack
age, not just a higher percent unionized. Con
sequently, it is necessary to control for both the
wage premium and the percent unionized to
get a measure of union strength across markets.
To the degree the union relative wage effect dif
fers across SMSAs, failure to control for differences
in the wage premium will yield inefficient and
potentially biased estimates. Since the union
wage premium may be determined by many of
the same exogenous variables that determine
employment, this term is likely to be correlated
with the independent variables in the model.
The result may indicate that the estimated coef
ficients in previous studies are biased.
To construct our measure of
union strength, it was first necessary to derive
an estimate of the union/nonunion wage differ
ential in each SMSA To do this, we estimated
separate wage equations for union and non
union members in each SMSA:
(6)
In W ik
Wik
i,
-
/3 X ik + e{
where
is average hourly earnings of indi
vidual, in SMSA,
is a vector of individual
characteristics that determine wages, and , is an
error term. In estimating these wage equations,
we included controls for schooling, experience,
n
k, Xik
e
Because the multiplicative form places strong restrictions on
how the percent organized,
k,
and the union wage premium,
z , affect employment, w e also estimated our employment equations
using several other constructions of the union strength variable. In par
ticular, we estimated an equation where these terms were entered
separately and equations with multiplicative indexes that rise more than
proportionately with changes in the percent unionized
{zzk).
(zk
/(1 - /C)) or
employed who are union members. The sample w as restricted to civili
with the union wage premium
Because of their qualitative nature,
ans age 16 to 65, working for wages and salary.
our results were not sensitive to the use of these other indexes.
7
Thus, the fraction of the popula
tion employed in an SMSA is inversely related to
the extent of unionism and to the union wage
premium. The magnitude of this effect can be
captured by calculating the change in the prob
ability of being employed for a base case or
average worker when the value of the union
strength variable changes by one standard devia
tion from its mean value.22 The expected
probability of being employed declines from
0.829 to 0.825 with this increase in union
strength. O n the other hand, the probability of
the average worker in the SMSA where union
strength is highest (San Bernardino, CA) being
employed is only about 2 percent less than it is
if that worker lived in the SMSA where union
strength is the least (Atlanta, GA).23 Thus, it
would appear that changes in the extent of
union strength have only a very limited impact
on aggregate employment.
Given this reduction in the prob
ability of gaining employment due to unionism,
it is of interest to see if unionism also affects the
length of the workweek for those who remain
employed. If unionism has no effect on hours
worked, then the effect on the probability of
working full time should be the same as it is on
the likelihood of working part time. Conversely,
if employers cut their employees’ hours, then
the union variable should be positive in a
regression where the dependent variable is the
probability of working part time regression and
negative in a regression where the dependent
variable is probability of working full time. In
regression (2) the dependent variable equals 1
if an individual is employed full time and zero
otherwise; in regression (3) the dependent var
iable equals 1 if an individual is employed pan
time and zero otherwise.
We found that the union variable
was negative and significant in the full-time
employment equation, while it was positive but
insignificant in the part-time employment equa
tion. In addition, both the point estimate and
the degree of significance of the union strength
variable are higher in the full-time equation
than in the total employment equation. Using
these estimated coefficients, a standard devia
tion increase in union strength leads to a 0.7
percent reduction in the probability of being
employed full time and a 1.5 percent increase in
the probability of being employed part time.24 If
our base-case worker lived in Cleveland, he
would be approximately 2 percent less likely to
be working full time, and 4 percent more likely
to be working part time than if he lived in the
lowest union strength SMSA. Thus, these results
suggest that part of the disemployment effect of
unions comes through reducing the number of
hours worked on that job.
As a further test of this hypothesis,
we re-estimated the employment equation with
the probability of working part time if an indi
vidual was employed as the dependent variable.
Unions may reduce the workweek by increasing
the relative frequency of part-time jobs. As seen
in regression (4), increases in union strength
increase the fraction of employment that is part
time. A standard deviation increase in union
strength increases the likelihood of working
part time for the base-case worker by about 3
percent.25 Given these estimates, the conditional
probability that an average worker has a full
time job (as opposed to a part-time job) is
about 8 percent less in the Cleveland SMSA than
in the lowest union strength SMSA. Thus, these
estimates suggest that increases in union wages
(or the percent organized) might have a bigger
effect on hours worked per week or on the mix
of full-time and part-time jobs than on the level
of total employment. This shift toward more
part-time jobs may occur because unionized
workers are more likely to work full time than
nonunion workers, and because unionized
workers are more likely to accept layoffs than
reduced hours.26 Thus, an increase in the cost of
union labor will primarily cause a reduction in
the number of full-time jobs in the union sector,
because unionized workers tend not to engage
in work-sharing arrangements to reduce hours
worked. Some of the displaced workers, how
ever, will find employment in the nonunion
sector where there are more part-time jobs.
Employment will thus tend to fall by less than
the drop in the number of full-time jobs.
In section II, it was shown that the
disemployment effect of unions was a function
of the elasticity of labor supply. The greater the
elasticity of supply, the greater the disemploy-
The base-case worker is a single white male with 12.6 years
of schooling, 18.5 years of experience who lives in the EastNorth-Central region of the United States in an S M S A with an unem
ployment rate of 9.4 percent in March, a population of 3,479,000 where
5.5 percent of the population receives A F D C , and the union strength var
iable equals 0.031.
The union strength variable equals 0.0367 in Cleveland and 0.0016 in Atlanta. In Cleveland, the probability of being
employed is 0.827, while it is 0.837 in Atlanta.
The probability of being employed full time and part time for
our base-case workers is 0 .70 7 and 0 .104, respectively.
The probability that the job a worker has is a part-time one
for the base-case worker is 0.1429.
See Freeman and M edoff (1984) for a discussion of this
issue.
ment effect. Given this, we might expect that
the disemployment effect would be largest for
groups with a weak labor force attachment or a
high elasticity of labor supply. Teen-agers or
young people may be more adversely affected
than older workers, and females may suffer more
than males. To test for differences in the disem
ployment effect across groups, we estimated
separate employment equations for part-time
and full-time workers by gender and age group.
These results are presented in
The basic predictions of our the
ory seem to hold. Based on the point estimates
from these regressions, we see that the disem
ployment effect of unions is smaller for primeage males than for teen-agers or 20-to 24-yearold males. In fact, prime-age males do not
appear to be adversely affected by changes in
union strength at all. This probably reflects their
strong labor force attachment or the low elastic
ity of labor supply. Interestingly, the evidence
does not support the hypothesis that teen-agers
are more adversely affected than 20-to 24-yearolds. As expected, the disemployment effect of
unionism is greater for prime-age females than
for prime age males.27 In general, increases in
either the union wage premium or the percent
organized affect the workweek, or the likeli
hood of being employed part time, more for
females than for males.
appendix II.
IV. Conclusions and Implications
Results of estimates of the effect of changes in
union strength on the likelihood of being em
ployed are presented here. They suggest that in
areas where the unionized percent of the labor
force is large, or where the union/nonunion
wage premium is large, workers are less likely
to be employed. Besides affecting the number of
workers employed, unions reduce the likelihood
of an individual having a full-time job by altering
the mix of part-time and full-time jobs in the
economy. Thus, unions appear to adversely affect
the average workweek for those who remained
employed. These disemployment effects are felt
mainly by females and young men, with little, if
any, negative impact on prime-age males.
This disemployment effect was
quite small, however. Unionism has a larger
effect on the mix of part-time and full-time
employment (and hence the workweek) than
on the number of jobs. All of these effects are
dwarfed in importance by other factors: the state
of the local labor market and the level of the
individual’s human capital, or skills. Changes in
schooling, experience, and local labor market
conditions have a much greater impact on the
likelihood of being employed than does union
ism. For instance, a standard deviation increase
in the number of years of schooling increases
the likelihood of being employed for the basecase worker about 10.6 percent, while a stan
dard deviation increase in the number of years
of potential labor market experience increases it
by 36.6 percent.28 Thus, a standard deviation
change in these measures of human capital is
approximately 10 to 30 times more important
than a similar change in union strength. This
result implies that differences in union wage
differentials, or the percent organized, are not
the primary cause of regional differences in
employment rates.
Data Appendix
The data for this study come from the Current
Population Survey 1983 and from the Bureau of
Census,
is the product of the percent unionized
and the union wage premium in each SMSA.
Unemployment Rate is the local unemploy
ment rate for all workers in the SMSA
Population is the number of people living in the
SMSA
is the proportion of the population in
the SMSA receiving AFDC payments.
Schooling is the number of years of schooling
completed by the individual.
Experience is calculated as Age -Schooling -6.
Race is a dummy that equals 1 if the individ
ual is white.
Sex is a dummy that equals 1 if the individual
is a male.
In addition to these variables, each regression
contains a dummy term that equals 1 if the indi
vidual is married, nine regional dummies where
the omitted catagory is the East-North-Central
region and 11 monthly dummies to control for
the month the individual was surveyed. The
complete regression results are available from
the author upon request.
UN
County and City Data Book, 1982.
AFDC
The adverse effect of unionism increases with age for
females. Whether this reflects a greater attachment to the
labor force is a question for further research.
The standard deviation is 2.9 years for schooling and 14.4
years for experience.
References
Brown, Charles, Curtis Gilroy, and Andrew
Kohen. “The Effect of the Minimum Wage on
Employment and Unemployment,”
vol. 20, no. 2 (June
1982), pp. 487-528.
Diewert, W. E. “The Effects of Unionization on
Wages and Employment: A General Equilib
rium Analysis,”
; vol. 12, no.
3 (September 1974), pp. 319-39.
Duncan, Gregory M., and Duane Leigh. “The
Endogeneity of Union Status: An Empirical
Test
vol. 3
(July 1985), pp. 385-402.
Freeman, Richard. “Economic Determinants of
Geographic and Individual Variation in the
Labor Market Position of Young Persons,” in
Richard Freeman and David Wise, eds.,
Journal
of Economic Literature,
Economic Inquiry
"Journal of Labor Economics,
The
Youth Labor Market Problem: Its Nature,
Causes, and Consequences. Chicago, IL: Uni
versity of Chicago Press, 1982.
-------- “Longitudinal Analyses of the Effects
of Trade Unions,”
vol. 2 (January 1984), pp. 1-26.
------- , and James Medoff.
Do? New York, NY: Basic Books, 1984.
-------- “The Impact of Collective Bargain
ing: Illusion or Reality,” in Jack Stieber,
Robert McKersie, and D. Quinn Mills, eds.,
Journal of Labor Econom
ics,
What do Unions
U.S. Industrial Relations 1950-1980: A Criti
cal Assessment. Madison, WI: Industrial
Research Association, 1981, pp. 47-97.
Gramlich, Edward. “Impact of Minimum Wages
on Other Wages, Employment, and Family
Incomes,”
2, 1976, pp. 409-51.
Heckman, James. “Dummy Endogenous Varia
bles in a Simultaneous Equation System,”
vol. 46, no. 6 (July 1978), pp.
931-59.
Holzer, Harry J. “Unions and the Labor Market
Status of White and Minority Youth,”
vol. 35, no.
3 (April 1982) pp. 392-405.
Johnson, Harry G., and Peter Mieszkowski. “The
Effects of Unionization on the Distribution of
Income: A General Equilibrium Approach,”
vol. 84, no.
4 (November 1970), pp. 539-61.
Kahn, Lawrence and Kimio Morimune. “Unions
and Employment Stability: A Sequential Logit
Approach,”
vol. 20, no. 1 (February 1979), pp. 217-35.
Kahn, Lawrence. “Unions and the Employment
Status of Nonunion Workers,”
vol. 17, no. 2 (May 1978), pp. 238-44.
Activity,
Brookings Papers on Economic
Econometrica,
trial and Labor Relations Review,
Indus
Quarterly Journal of Economics,
International Economic Review,
tions,
Industrial Rela
Lazear, Edward P. “A Competitive Theory of
Monopoly Unionism,”
vol. 83, no. 4 (September 1983), pp.
631-43.
Lewis, H. Gregg.
Chicago, IL: University of
Chicago Press, 1963-------- “Efficiency in the Labor Markets Rel
ative Employment Effects of Unionism,”
American Economic
Review,
Unionism and Relative Wages
in the United States.
American Economic Review: Papers and Pro
ceedings, vol. 54, no. 2 (March 1964, Part 2),
pp. 123-32.
McDonald, Ian, and Robert M. Solow. “Wage
Bargaining and Employment,”
vol. 71, no.5 (December
1981), pp. 896-908.
Mincer, Jacob. “Unemployment Effects of Min
im um Wages
,
vol. 84, no. 4 (August 1976), pp. 87-104.
Montgomery, Edward. “Employment and
Unemployment Effects of Unions,”
Federal Reserve Bank of Cleve
land, January 1986.
Murphy, Kevin. “Geographic Differences in U.S.
Employment Rates: A Variance Decomposi
tion Approach,”
vol. 23,
no. 1 (January 1985), pp. 135-58.
Parsley, C.J. “Labor Unions Effects on Wage
Gains: A Survey of Recent Literature,”
vol. 17, no. 1 (March
1980), pp. 1-31.
Pencavel, John, and Catherine Hartsog. “A
Reconstruction of the Effects of Unionism on
Relative Wages and Employment in the Unit
ed States 1920-1980,”
vol. 2. (April 1984), pp. 193-232.
Welch, Finis.
Washington, DC: American Enterprise
Institute, 1978.
__________“Minimum Wage Legislation in the
United States,”
vol. 12, no
3 (September 1974), pp. 285-318.
American
Economic Review,
"Journal of Political Economy
Working
Paper 8601,
Economic Inquiry’,
of Economic Literature,
nomics,
dence.
Journal
Journal of Labor Eco
Minimum Wages: Issues and Evi
Economic Inquiry,
11
The Changing Nature of
Regional Wage Differentials
From 1975 to 1983
by Lorie D. Jackson
Lorie Jackson is a public affairs
The author would like to give
advisor in the public information
special thanks to Edward
department at the Federal Reserve
Montgomery for offering valuable
Bank of Cleveland. A n earlier
comments throughout the
version of this paper w as presented
preparation of this article, and would
at a conference on labor costs
also like to thank Ralph D ay for
sponsored by the Federal Reserve
excellent research assistance, and
Bank of Cleveland and by the
Mark Sniderman, Sharon P. Smith,
Regional Economic Issues program.
and Michael S . Fogarty for helpful
comments.
Introduction
Over the past 30 years, a great deal of research
has been done on regional wage differentials.
The subject has received considerable attention
for a variety of reasons, notably because of its
implications for understanding the degree to
which competitive market forces lead to the
equilibration of returns to labor, and also because
of the possible effects of labor cost differentials
on regional economic growth.
For the most part, the work on
regional wage differentials has had three goals:
(1) to estimate the size of regional wage differen
tials at a particular date or over time, (2) to iden
tify their sources, and (3) to provide a theoretical
explanation for their existence.
Estimates of regional wage differ
entials vary considerably as a result of variations
in data sources, in measures of regional wage dif
ferentials, in measures of payments to workers, in
geographic divisions, in time periods considered,
and in methodologies used. Despite these inconsis
tencies across studies, most of the empirical work
done confirms the view that, while some intermit
tent convergence has occurred over time, money
wages in the northern United States have tended
to be significantly greater than those in the South,
at least since the beginning of this century.1
I
A different conclusion is reached in the study of real regional
wage differentials. Recent studies that have adjusted for
regional cost-of-living differences (Sahling and Smith [1983]) have
the real wage differential between the North and the South has not only
been converging over time, but has been reversed in recent years.
Most of the recent work on
regional wage differentials defines the regional
wage differential as the difference in wages that
exists after controlling for differences in worker
characteristics. This is because what is of interest
to most researchers of regional wage differentials
is not why workers with different characteristics
are paid differently, but rather why workers with
similar characteristics are paid differently across
regions. Evidence of regional wage differentials is
consistently found in the literature even after
adjusting for the compositional mix of the work
force. These differences reflect differences in the
way particular worker characteristics are remun
erated across regions due to variations in culture,
tradition, degrees of discrimination, the bargain
ing strength of local unions, amenities, and pub
lic goods, as well as to temporal variations in
supply and demand pressures. The differences in
the way worker characteristics are remunerated
across regions are referred to as differences in
wage structures.
Several studies have separated the
overall regional wage differential into the portion
that can be explained by the compositional mix
of the work force and into the portion that can
not. This separation makes it possible to isolate
the regionally-speciflc source of the wage differ
ential, and to determine which work force charac
teristics account for most of the difference in
wage structures across regions.
Studies by Sahling and Smith
(1983) and by Kiefer and Smith (1977) discuss
found
the importance of differences in race and sex dis
crimination, and the effects of unionization in the
wage structure component of the regional wage
differential. To the author’s knowledge, however,
no study has been done on the changing impor
tance of differences in the compositional mix of
the work force and differences in regional wage
structures on the overall size of regional wage dif
ferentials over time.
The purpose of this article is to
estimate wage differentials between the East
North Central region and two Southern regions in
1975 and 1983, and to discuss the changing
nature of the differential over this period. The
Southern regions considered are the East South
Central and the South Atlantic. They were chosen
to examine the widely held view that wages in
the East North Central region are far out of line
with wages in the Southern regions, and that this
has been a major reason for the relative decline
in manufacturing employment in the East North
Central region over the past 20 years.
The East North Central area
includes Ohio, Indiana, Illinois, Michigan, and
Wisconsin. The South Atlantic region includes
Delaware, Florida, Georgia, Virginia, West Virgi
nia, North Carolina, and South Carolina. The East
South Central area includes Kentucky, Tennessee,
Mississippi, and Alabama.
Weighted Mean of Hourly Wage by Division, 1983 (in dollars)
New England
Mid-Atlantic
East North Central
West North Central
South Atlantic
East South Central
West South Central
Mountain
Pacific
1983
1975
8.92
9.39
9.11
8.56
7.76
7.69
8.64
9.02
9.98
4.80
5.63
5.49
4.87
4.49
4.47
4.85
5.36
5.80
Current Population Sur
SOURCE: Data from 1983 and 1975
Department of Commerce, Bureau of the Census.
veys,
TABLE
1
Two different regions of the South
are considered in order to investigate the differ
ences in the nature of the wage differentials be
tween each of the two Southern regions and the
East North Central region. In order to analyze
their changing size and character over time, the
differentials in two time periods are considered.
The year 1983 was chosen because it was the most
recent year for which the data were available. The
year 1975 was chosen because the national econ
omy was then at a point in the business cycle
fairly similar to where it was in 1983, a fact that
eliminates some of the differences in the magni
tude of the differential over time due to cyclic
variation in the demand for and supply of labor.
I. The Magnitude of Regional
Wage Differentials
In the two periods considered, 1975 and 1983,
the East North Central region had the thirdhighest average wage level of the nine census
regions, while the South Atlantic and East South
Central areas had the two lowest. The average
hourly wage of a nonfarm worker between the
ages of 25 and 64 in 1975 was $5.49 in the East
North Central, compared to $4.47 in the East
South Central, and to $4.49 in the South Atlantic.
In 1983 the average hourly wage had risen to
$9-11 in the East North Central, to $7.69 in the
East South Central, and to $7.76 in the South
Atlantic (see
). While money wages in the
Southern regions were well below those in the
East North Central region in both 1975 and 1983,
the absolute percentage differentials declined by
3 percentage points over this period. The abso
lute wage differential between the East North
Central and the South Atlantic regions went from
about 18 percent in 1975 to 15 percent in 1983,
while the differential between the East North
Central and the East South Central regions went
from 19 percent to 16 percent.
table 1
II. Theoretical Framework
Two basic theories of wage determination are
posited to explain the existence of regional wage
differentials: the neoclassical theory and the insti
tutional theory. (Unless otherwise stated, the
term “wage” will be used throughout this article
to represent total labor compensation—wages
plus supplemental benefits.)
The simple neoclassical model
predicts that wages will be equalized across
regions. This prediction rests on the assumption
that labor and capital will move to where they
can maximize their respective rates of return. Dif
ferences in wage levels across regions are
expected to exist only in the short run when
regional labor markets are out of equilibrium:
both capital and labor take time to adjust to
changing market signals. Since it is the purchas
ing power of the wage that is important to indi
viduals, it is generally understood that it is the
real, rather than the nominal, wage that neoclas
sical theory predicts would be equalized across
regions (Sahling and Smith [1983] )•
Elaborations have been made
upon this simple model to bring into the fold
nonwage factors affecting the location decision of
labor and capital. Workers attempt to maximize
their overall utility rather than simply their real
wage. Similarly, firms attempt to maximize profits
that are affected by more than just labor costs.
Examples of nonwage factors affecting an individ
ual’s location decision are family considerations,
such as employment opportunities for the spouse
1 4
in a two-income household, amenity levels, and
the quality of publicly provided services. Workers
may require higher-than-average wages to locate
in areas generally considered to have negative
characteristics, such as air pollution, high popula
tion density, severe climate, and poor public ser
vices. Individuals may find that they can max
imize their utility in a relatively low-wage region
because of compensating nonwage considera
tions such as mild climate and good schools.
Similarly, firms take many factors
into account when making location decisions.
Among these factors are differences in the quality
of the labor force, access to raw materials and
markets, and proximity to the center of industry
innovation. A firm may find that it can maximize
profits by locating in a high-wage area because of
cost and market advantages.
Since individuals and firms take into
account nonwage factors when making location
decisions, even if wages were driven by competi
tive forces, the movement of labor and capital
would not necessarily equalize wages across
regions. Rather, neoclassical theory7would predict
an equalization of utility and profits, which are
composed of some mixture of wages, cost-ofliving, amenities, etc. across regions. Because of
the importance of nonwage factors, some differ
ence in wages across regions would be expected
to exist even in the long run and even after tak
ing into account differences in worker and indus
try characteristics across regions.2
Many economists and industrial
relations specialists believe that a satisfactory
explanation for large and persistent regional
wage differentials must go beyond the neoclassi
cal model discussed above. Over the past 10
years, there has been a growing body of work on
the importance of institutional forces on the wage
adjustment process. Institutional factors include
unions, racial and sexual discrimination, market
concentration, and other noncompetitive forces
that have a strong bearing on wages.
One common view within this
literature is that wage changes, to a certain extent,
are transmitted across regions as workers, and in
some cases employers, attempt to maintain the
wage standing of one group of workers relative to
another across regions. These forces occur, both
formally through collective bargaining, and
informally through custom and convention.
Some researchers argue that one
outcome of the existence of institutional factors is
that regional wage differentials are decreased
through comparisons and parity-bargaining
between different groups of workers across
regions (Martin [1981]). In some cases, workers
adjust their wage expectations to maintain pay
positions relative to other worker groups. This
process is facilitated by the fact that unions and
other labor groups are often organized on an
industry-wide basis, or are represented in several
industries or firms. While there is currently dis
agreement among labor economists about
whether institutional factors have a long-term or
merely a short-term effect on wages, their impor
tance in the short run is widely recognized.
One often-cited institutional factor
affecting wage differentials is unionization. Union
ization affects an area’s wage level to the extent
that union workers, and perhaps some share of
nonunion workers, can earn a wage that is differ
ent from what it would be without unionization.
The actual effect of unionization on a region’s
wage level is the difference between a region’s
wage level, given the existence of unionization,
and the wage level that would exist if there were
no unionization. Thus a complete measure of the
effect of unionization on regional wage levels
should consider not only the difference between
the wages of unionized and nonunionized
workers, but also the amount of spillover from
union wages on the determination of nonunion
wages.3 Capturing the spillover effect of unioniza
tion on nonunion wages, however, is a difficult
and slippery process that is avoided in most stud
ies of regional wage differentials.4 Instead, many
studies measure the effects of unionization on
regional wage differentials as the proportionate
union/non union wage advantage multiplied by
the proportion of the work force that is unionized
(J o h n s o n [1983]; and Kiefer and Smith [1977]).
3
M ost of the literature emphasizes the positive spillover effects
of unions on nonunion workers when nonunion firms must com
pete with unionized firms or workers. Positive spillovers are assumed to
be most acute for skilled nonunion workers who are costly to locate,
hire, and train. Some researchers have also argued that a high degree of
unionization in an area m ay lower the nonunion wage if workers are willling to accept a lower wage (a reservation wage) in a nonunion job in
anticipation of future union employment and higher lifetime earnings
(Johnson [1983]). Another possibility is that the existence of unions m ay
have little or no effect on the nonunion wage. This m ay be the case if
there is little competition between union and nonunion workers resulting
from a low degree of local unionization, from a slack local labor market,
2
Within a competitive model, in order for industries to be com
petitive over time in regions where workers require wage pre
miums, there must be compensating cost factors associated with locating
in those regions, such as nearness to raw materials, markets, and suppliers.
or from workers waiting in the queue for union employment choosing
unemployment over nonunion employment.
4
For further discussion of measuring the union-nonunion wage
differential, see Moore, N ew m an, and Cunningham (1985).
III. Methods of Approach
As stated eadier, the regional wage differential
can be separated into a portion that can be ex
plained by differences in work force characteristics
across regions, and a portion that cannot be so
explained. The latter portion may reflect more
regionally-specific differences, notably differences
in the remuneration of particular characteristics.
While both portions of the differential are poten
tially interesting subjects for investigation, the lat
ter portion of the differential particularly concerns
those who expect wages for similar workers in
different regions to become equalized over time.
The methodology used in this study permits a
breakdown in the overall differential. It is the
same methodology popularized by Oaxaca’s 1973
study of the male/female pay differential and has
become a standard decompositional approach.
The percentage wage differential
between two regions (call them Region 1 and
Region 2) can be decomposed into its composi
tional and wage structure components.5 In order
to decompose the differential, one must deter
mine each region’s wage structure. This is done
by estimating separate wage equations using mul
tiple regression analysis with the log of the wage
as the dependent variable. Worker characteristics
are included as the independent variables. The
resulting regression coefficients indicate how par
ticular characteristics are rewarded in that region.
In order to determine the portion of the differen
tial due to compositional differences, the average
wage of Region 1 workers can be compared with
5
M any studies of regional wage differentials estimate a national
w age equation that includes regional dumm y variables. The co
the estimated wage of Region 2 workers in the
absence of wage structure differences. To deter
mine what portion of the overall differential can
be explained by differences in the wage structure,
the estimated wage of Region 2 workers, in the
absence of wage structure differences can be
compared with the actual average wage of
workers in Region 2.
Since the actual earnings structure
in the absence of regional differentials is not
known, it is necessary to make some assumptions
about what wage structure would exist if all
regional wage structures were alike. There are
two extreme possibilities: one is that the struc
ture would be that estimated for Region 1, and
the other is that the structure would be that esti
mated for Region 2. The fact that there is more
than one possible estimate of the regional wage
differential results in an index number problem.
To deal with this problem, some researchers,
such as Sahling and Smith (1983), averaged the
estimated differentials resulting from using the
bases of the two regions being compared. The
exact meaning of the average, however, is diffi
cult to interpret. Since the primary concern of this
study is the effect of the East North Central’s wage
structure on regional wage differentials, the
results using the East North Central as the base
region are emphasized. This avoids the difficul
ties of interpreting the averages of the two
extreme results. The results using the Southern
bases will be discussed briefly to provide the
reader with an idea of the range in the measures
of the regional wage differentials.6 The procedure
is illustrated below:
If the East South Central (ESC) had
the same wage structure as the East North Central
(ENC), workers in the East South Central would
receive:
efficients on the locational variables are interpreted as the estimated pro
In
portionate difference between the wage rate in the region and its value
in the nation for comparable workers. One major presumption behind the
/\
use of this approach is that regional wage structures are similar to the
W esc
-
national wage structure, in other words, that the earnings of persons
/\
W
esc
=
—
J
enc
(X
esc
),
the estimated wage for ESC workers
given the ENC wage structure,
with the same attributes do not differ among the regions in any system
atic w ay. This view is based on the premise that the United States is,
Jenc
= the wage structure coefficients esti
mated for the ENC,
X esc
= vector of the mean values of the inde
pendent variables for ESC workers.
geographically speaking, a single economy, operating within a single set
of institutions, consisting of people of different ages, sexes, races, skills,
and attachments to the labor market and engaged in a variety of occu
pations and industries. Regional divisions are presumed to have no sig
nificance in and of themselves, but merely to represent different group
ings of human and material resources (Hanna [19 5 1]). Hence, regional
differences in the composition of these groupings are presumed to be
the primary reason for differences in earnings across regions.
The assumption of similar wage equations across
regions was questioned by Denison as far back as 1951. Hanushek
The portion of the percentage
wage differential attributable to differences in
worker characteristics is measured by:
(1973) performed Chow tests for the equality of coefficients for regions,
and homogeneity within broad regions w as consistently rejected at the
one percent level of significance. In other words, Hanushek found that
worker characteristics were compensated differently across regions. With
a nationally estimated equation, differences in the w ay worker character
istics are remunerated are lost in the intercept term.
For further discussion of the appropriate approach for
measuring regional wage differentials, see Kiefer and Smith (19 77).
6
Decomposition results using the Southern regions wage struc
tures as the base are available on request from the author.
1 5
In
where:
—
W
-
enc
In
/\
W
compensating factors, particularly amenity levels.
Studies have been done that estimate the wage
differential across regions after adjusting for
regional differences in the cost of living. Up until
1981, the Bureau of Labor Statistics published
family budget indexes by three income categories
for about 20 large metropolitan areas in the Unit
ed States. Because no such data have been pub
lished on a census region basis, the data restrict
analysis to a limited group of major SMSAs. Stud
ies that have looked at real regional wage differ
entials have grouped the metropolitan areas for
which data is available into broad regional groups
(Sahling and Smith [1983] )• These studies have
thus considered only the real wage differential
between regional groupings of large metropolitan
areas. Cost-of-living data are not used in this
study because they are not available on the
desired geographical basis.
esc ,
= the average wage of ENC
workers, and
W enc
x\
W esc-
the estimated wage of ESC
workers, given the ENC
wage structure,
while that portion attributable to differences
in the wage structure is measured by:
In
/\
W
/\
where:
W esc-
W esc
16
-
esc
In
—
W
esc ,
The estimated wage for ESC
workers, given the ENC wage
structure, and,
= the average wage of ESC
workers.
IV. Model
In keeping with most studies on wage differen
tials, a standard human capital earnings model
developed by Becker (1975) and Mincer (1970)
is estimated. According to this model, individuals
attempt to maximize their income through
investment in schooling and on-the-job training.
This standard human capital earnings model is
specified as follows:
In
W
=
Bo
+
BiS
+
B2S 2 + B 3E
+
u
where:
W = average hourly wage,
S = years of schooling completed,
E - potential years of work experience, and
u = random error term.
The model is also specified to include a squared
term for years of schooling to take into account
diminishing returns to additional years of
schooling.
Other work force characteristics
associated with different wage levels are also
included in the wage equation. They include a
worker’s sex, race, facility with the English lan
guage, marital status, union status, public or pri
vate employment status, full-time or part-time sta
tus, and occupation and industry affiliation.7
Including these variables in the earnings model
provides some adjustment for productivity and
skill differences, for the existence of discrimina
tion in the labor market, and for the wage effect
of unions.
Some studies have attempted to
adjust for compensating nonwage factors in indi
vidual location decisions, such as cost of living
and amenities. Data limitations, however, make
it difficult to construct measures of many of these
V. Data
The data sources used for this study are the 1975
and 1983
that contain
information on worker characteristics and earn
ings from wages, salaries, commissions, and tips.
Subsamples from each year were created to con
sist only of civilian, non-agricultural, private sec
tor, and government workers between the ages of
25 and 65 years who worked either full time or
part time (10 hours a week or more). The sub
samples are limited to so-called prime age
workers, in order to avoid addressing the unique
characteristics of teen-age and elderly worker
employment. Only workers who were recorded
as working 10 hours or more per week were
included because studies have found a large
Current Population Surveys
7
The dumm y variables are defined as follows:
Sex:
Dum m y variable = 1 if the individual is male, and 0 if
female;
Race:
Dum m y variables for white, black, and other, with
white individuals as the reference group;
Spanish origin: Dum m y variable « 1 if the individual is
of Hispanic origin, and 0 otherwise. Serves as a proxy
for not having English as a first language;
Marital status:
Dum m y variable = 1 if the individual is married with
spouse present, and 0 otherwise;
Full time:
Dum m y variable =1 if the individual is a full-time
employee, and 0 otherwise;
Class of worker:
Dum m y variables for individuals working in the private
sector, the federal government, the state government,
and the local government, with private sector workers
as the reference group;
Union coverage:
Dum m y variable = 1 if the individual is either a union
member or covered under a union contract, and 0
otherwise;
Occupation:
Dum m y variables for U .S . Census one-digit occupa
tions, with operators as the reference group;
Industry:
Dum m y variables for U .S . Census one-digit industries,
with durable manufacturing as the reference group.
chance of response errors for those registering
fewer hours (Sahling and Smith [1983] )• The
hourly wage rate is estimated using information
on usual weekly earnings and usual hours
worked per week. The data series does not
include information on years of work experience,
so the conventional proxy (age, minus years of
schooling, minus six) is used instead. Also,
because data are not available on a worker’s facil
ity with the English language, Hispanic origin is
used as a very rough proxy for English language
difficulties. While the type of information con
tained in the 1975 and 1983 surveys is not identi
cal, some general comparisons of the results for
the two years can be made.
VI. Decomposition of Wage
Differentials for the 1983 Sample
In 1983, the overall logarithmic wage differential
between the East North Central and the South
Atlantic was 20 percent, while that between the
East North Central and East South Central was 18
percent (see
Using the East North Cen
tral as the base wage structure, we find that dif
ferences in compositional mix made up only 30
percent of the wage differential between the East
North Central and the South Atlantic, and only
about 20 percent between the East North Central
and East South Central.
The decomposition indicated that
70 percent of the wage differential between the
table 2).
Decomposition of Regional Wage Differentials
(East North Central base)
1983
Absolute differential
( enc
s)
W
-W
1975
East North Central/
East South Central
(S = ESC )
East North Central/
South Atlantic
(S = SA )
East North Central/
East South Central
(S = ESC )
East North Central/
South Atlantic
(S = SA )
$1.36
$1.50
$0.89
$0.98
0.18
0.20
0.20
0.23
0.04
0.06
0.06
0.09
23%
29%
29%
39%
0.14
0.14
0.14
0.14
77%
71%
71%
61%
Logarithmic differential
(In
W enc
- In
W s)
Portion explained by
different characteristics
(ln~Wmc- In
Ws)
Percent contribution to
total logarithmic differential
Portion explained by
different wage structures
(
In W s -ln W s )
Percent contribution to
total logarithmic differential
where in 1983:
W e n c = $8.27
WESC
$6.91
W sa
= $6.77
-
In
In
In
In
In
TABLE
W sa
= 2.11
= 1.93
= 1.91
W esc
=2 . 07
W enc
W esc
/\
W e sa
where in 1975:
W e n c = $4.91
W e s c = $4.02
W sa
= $3.93
= 2.05
In
In
In
In
In
W enc
W esc
W sa
/\
= 1.60
=1.39
= 1.37
W esc
=1 . 53
/\
=
W esa
1.51
2
An important limitation of the
wage information reported is that it does not
include supplemental benefits. Studies have
found that supplemental benefits tend to be posi
tively correlated with wages, so the estimated
regional differential using wage data alone prob
ably understates the actual differential in total
labor compensation across regions.
East North Central and South Atlantic and close
to 80 percent of the differential between the East
North Central and East South Central are attribu
table to differences in wage structures. A Chow
test verified that the wage structures of the South
ern regions are significantly different from that of
the East North Central region.
After taking into account differences
in work force characteristics, the wage differential
between the East North Central and both the South
ern regions is the same, namely, about 14 percent.
If the Southern regions are used as the base, the re
maining differential between the East North Cen
tral and the two Southern regions after adjusting
for compositional mix both fell slightly from 14
percent to 13 percent. Regardless of the base used,
differences in regional wage structures appear to ac
count for the lion’s share of the wage differential.
While this is an interesting result
in and of itself, it would also be useful to know
the variables responsible for differences in wage
structure. Most of the differences in wage struc
ture, however, appear to be buried in the inter
cept term. This result may be partly explained by
the omission of controls for regional differences
in the cost of living, in amenities, and in supple
mental benefits.
Wage Rate Equations, 1983
(estimated standard errors in parentheses)
Dependent
variable: In W
East
North Central
East
South Central
South
Atlantic
Constant
0.9883
(0.0245)
0.8019
(0.0377)
0.8513
(0.0255)
Education
0.0397
(0.0015)
( 0.0022)
0.0458
0.0413
(0.0015)
0.0149
(0.0017)
(0.0011)
Experience
-|g
Experience squared
0.0153
( 0.0010)
-
0.0002
-
0.0002
0.0128
-
0.0002
( 0.0000)
( 0.0000)
(0.0000)
0.2588
0.2780
(0.0109)
0.2443
(0.0073)
(0.0098)
0.0003
-0.0900
(0.0125)
-0.0997
(0.0083)
-0.0314
(0.0256)
-0.0603
(0.0695)
-0.0391
(0.0346)
Spanish origin
-0.0309
(0.0217)
-0.0467
(0.0802)
-0.0859
(0.0165)
Marital status
0.0413
(0.0065)
0.0552
(0.0109)
0.0494
(0.0070)
Full time
0.1837
(0.0195)
0.1105
(0.0151)
0.1372
(0.0099)
0.0311
(0.0195)
0.1195
(0.0239)
(0.0177)
- 0.0616
( 0.0110)
-0.0707
(0.0174)
-0.0123
(0.0118)
( 0.0068)
0.1487
0.1755
(0.0118)
0.1691
(0.0088)
0.4373
18,880
0.4551
7,009
0.4389
15,702
Sex
(0.0068)
Race:
White
Black
Other
Class of worker:
Private sector
Federal government
State government
Union coverage
R2
N
SOURCE: Data from 1983 and 1975 Current population Surveys, Department o f Com m erce, Bureau o f the Census.
0.0688
Even though the major sources of
the differential appear to be buried in the inter
cept term, differences in returns to a few variables
do stand out as important contributors to the
wage differential due to structural differences
(see
For example, higher returns for
full-time employment in the East North Central
account for 30 percent of the structural differen
tial between it and the South Atlantic, and 35 per
cent of the structural differential between the East
table 3a).8
workers, or why returns to experience would be
greater for East North Central workers than for
South Atlantic workers. It could be that the indus
tries that are concentrated in the East North Cen
tral require more experienced, stable, full-time
employees than industries concentrated in the
Southern regions.
Differences in the degrees of racial
discrimination between the North and South also
appear to be a fairly important contributor to the
Wage Rate Equations, 1975
(estimated standard errors in parentheses)
Dependent
variable: In W
East
North Central
East
South Central
South
Atlantic
0.4564
(0.0657)
0.0914
(0.1163)
0.1866
(0.0769)
Education
0.0452
(0.0037)
0.0507
(0.0065)
0.0447
(0.0045)
Experience
0.0137
(0.0027)
0.0169
(0.0050)
(0.0033)
Experience squared
-0.0002
(0.0001)
-0.0002
(0.0001)
-0.0004
(0.0001)
Sex
0.3319
(0.0196)
0.3424
(0.0381)
0.2626
(0.0241)
Race
-0.0283
(0.0290)
0.0919
(0.0463)
0.1197
(0.0279)
Marital status
0.0049
(0.0206)
0.0388
(0.0400)
-0.0390
(0.0275)
Full time
0.1052
(0.0245)
0.0526
(0.0491)
0.0901
(0.0305)
0.1148
(0.0173)
0.2205
(0.0372)
0.2045
(0.0279)
R2
0.5206
0.5425
0.5069
N
2,069
594
Constant
Union member
0.0214
1,299
SOURCE: Data from 1983 and 1975 Current Population Surveys, Department o f Com m erce, Bureau o f the Census.
TABLE
3B
North Central and the East South Central. Differ
ences in returns for each additional year of exper
ience account for 40 percent of the structural dif
ferential between the East North Central and the
South Atlantic, while accounting for only 5 per
cent of the structural differential between the East
North Central and East South Central.
There is no simple explanation for
why returns to full-time workers would be higher
for East North Central workers than for Southern
8
Full regression results are available on request from the author.
structural differentials. The differences in returns
between black and white workers account for 14
percent of the structural differential between the
East North Central and South Atlantic, and for 8
percent of the differential between the East North
Central and East South Central. While differences
in the degrees of racial discrimination between
the North and the South have long been recog
nized, it appears that relative to other variables
and to the unknown portion of the differential,
the contribution of differences in racial discrimi
nation played a small role in the wage structure
component of the differential in 1983.
Another interesting result is that the
wage premium of unionized workers is very simi
lar across the three regions observed. In fact, dif
ferences in the returns to unionized workers show
that in the East North Central, unionized workers
have a slightly smaller wage advantage over nonunionized workers than is true in the two South
ern regions. The wage premium of unionized
workers is about 15 percent in the East North
Central, compared to about 18 percent in the East
South Central and 17 percent in the South Atlan
tic. The slightly smaller union premium in the
East North Central may result partly from the spill
over effects of unions on nonunion wages. This
seems probable, given the high degree of unioni
zation and its associated threat effect in the re
gion. But, as stated before, this spillover effect is
difficult to measure. The similarities in wage pre-
Mean Values for Independent Variables, 1983
(standard deviations from the mean in parentheses)
Dependent
variable: In W
East
North Central
East
South Central
South
Atlantic
Education
12.9880
(2.6067)
12.3549
(2.9317)
12.5144
(2.894)
Experience
21.2579
(11.6783)
21.2350
(11.6274)
21.3799
(11.6998)
Experience squared
588.2824
(567.2277)
586.1243
(578.9908)
593.9879
(582.5812)
0.5570
(0.4967)
0.5476
(0.4977)
0.5346
(0.4988)
0.8967
(0.3044)
( 0.3802)
0.8247
0.8047
(0.3964)
(0.2885)
0.1711
(0.3766)
0.1875
(0.3903)
0.0117
(0.1075)
0.0042
(0.0648)
0.0078
(0.0878)
Spanish origin
0.0165
(0.1274)
0.0032
(0.0562)
0.0355
(0.1850)
Marital status
0.7343
(0.4417)
0.7553
(0.4299)
0.7175
(0.4502)
Full time
0.8635
(0.3433)
0.8899
(0.3131)
(0.3233)
0.8231
( 0 .38 16 )
0.7871
(0.4131)
0.7916
(0.4061)
Federal government
0.0255
(0.1577)
0.0532
(0.2245)
0.0422
0 2010
State government
0.0398
(0.1955)
0.0594
0.0544
(0.2363)
(0.2269)
(0.3145)
0.1057
(0.3074)
0.1118
(0.3151)
0.3426
(0.4746)
0.2217
(0.4154)
0.1700
(0.3756)
Constant
Sex
20
Race:
White
Black
Other
0.0916
0.8814
Class of worker:
Private sector
Local government
Union coverage
0.1116
SOURCE: Data from 1983 and 1975 Current Population Surveys, Department o f Com m erce, Bureau o f the Census.
( .
)
miums to unionized workers across regions may
reflect the relative pay-setting practices of union
ized workers within industries across regions.
As stated earlier, a popular,
although incomplete, measure of unionization’s
effect on the regional wage level is the propor
tionate union/nonunion wage advantage, multi
plied by the proportion of the work force that is
in 1983 between the East North Central and the
South Atlantic (see
In contrast to the
decline in the overall differential in both regional
wage comparisons, the share of the differential
due to wage structural differences was higher in
1983 than in 1975. The portion of the wage dif
ferential between the East North Central and the
East South Central due to wage structure differen-
table 2).
Mean Values for Independent Variables, 1975
(standard deviations from the mean in parentheses)
Dependent
variable: In W
East
North Central
East
South Central
South
Atlantic
Education
12.3245
(2.7458)
11.4895
(3.2228)
11.6821
(3.1300)
Experience
22.8545
(11.7477)
24.6004
(12.8308)
23.0627
(12.0892)
660.3341
(583.2343)
769.8099
(672.9978)
678.0376
(621.6552)
0.6247
(0.4342)
0.5735
(0.4946)
0.5613
(0.4962)
0.9304
(0.2544)
0.8804
(0.3690)
0.8406
(0.3885)
0.8392
(0.3673)
0.8374
(0.3690)
0.8147
(0.3885)
0.88334
(0.3210)
0.8924
(0.3099)
0.8767
(0.3288)
0.3524
(0.4777)
0.2432
(0.4290)
0.1620
(0.3684)
Constant
Experience squared
Sex
Race
Marital status
Full time
Union member
SOURCE: Data from 1983 and 1975 Current Population Surveys, Department o f Com m erce, Bureau o f the Census.
TABLE
4B
table 4a).
unionized (see
Based on this proce
dure, the unionization effect in 1983 was 0.05 in
the East North Central, 0.04 in the East South Cen
tral, and 0.03 in the South Atlantic. Hence, while
the wage premium to unionized workers is
slightly less in the East North Central than in the
Southern regions, the union effect is greater
because of the large concentration of unionized
workers in this region.
VII. Changes in the Decomposition
Ov er Time
The overall wage differential between the East
North Central and each of the two Southern
regions appears to have decreased between 1975
and 1983. The overall wage differential between
the East North Central and the East South Central
went from 20 percent in 1975 to 18 percent in
1983, and from 23 percent in 1975 to 20 percent
ces rose from 66 percent in 1975 to almost 80
percent in 1983. Over the same period, the por
tion of the wage differential between the East
North Central and the South Atlantic due to dif
ferences in wage structures differences rose from
about 60 percent to 70 percent.
When the Southern regions are
used as the base, differences in wage structures
showed similar increases in their contribution to
the overall wage differential. One interesting dif
ference in the results using the Southern bases
was that, in 1975, differences in compositional
mix accounted for almost 50 percent of the wage
differential between the East North Central and
the Southern regions. Regardless of the base
used, differences in compositional mix have
become less important in the overall regional
wage differentials over time.
In 1975, as in 1983, the major por
tion of the structural component of the differen-
21
22
tial is not identified in the wage equation. Again,
the intercept terms raise the wage structure in the
East North Central above that of the Southern re
gions. There were also similarities in the variables
identified in the wage equation that are important
contributors to the structural differential in 1975,
as was the case in 1983- Differences in returns to
full-time workers explain 35 percent of the struc
tural component between the East North Central
and the East South Central in 1975, compared to
30 percent in 1983. Differences in returns to full
time workers explain less than 10 percent of the
structural component between the East North
Central and South Atlantic in 1975, compared to
35 percent in 1983- This result suggests that,
between 1975 and 1983, differences in returns to
full-time employment became a more important
source of the regional wage differential between
the East North Central and South Atlantic.
Differences in degrees of racial dis
crimination were, as one might expect, even
more pronounced in 1975 than in 1983- The de
cline in the role of racial discrimination in ex
plaining wage structure differences may reflect a
decline in discriminatory practices in the Southem regions between the two years considered.
Between 1975 and 1983, differences
in the degree of unionization across regions per
sisted, but returns to unionization became more
similar. In 1975, the difference in the wage advan
tage to unionization across regions was consider
ably greater than it was in 1983 (see
and
But, in 1975, as in 1983, unionized
workers in the South received a greater wage pre
mium than their East North Central counterparts.
The total union effect in 1975 was
smaller in the East North Central (0.04), than it
was in 1983- It was larger in the East South Cen
tral (0.05), and was little changed in the South
Atlantic (0.03). The union effect in the East South
Central was greater than in the East North Central
in 1975 despite the larger share of unionized
workers in the latter region. This is because of
much higher wage premiums to unionized
workers in the East South Central at the time.
Market pressures probably con
tributed to the convergence in regional wage dif
ferentials over the period observed. Between
1975 and 1983, total non-agricultural employment
rose by only 3 percent in the East North Central,
compared to 27 percent in the South Atlantic and
to 13 percent in the East South Central. While
both of these Southern regions experienced
stronger employment growth than the East North
Central, it appears that labor market conditions
were even tighter in the South Atlantic. This is
suggested not only by the exceptionally strong
employment growth in the region, but also by
the region’s relatively low unemployment rates
over the periods considered. For example, in
3b).
tables 3a
1983, the unemployment rate in the South Atlan
tic was 8.5 percent, compared to 12.3 percent in
the East South Central. Because of tighter labor
market conditions in the South Atlantic, one
might expect the regional wage differential to
show greater convergence between the East
North Central and the South Atlantic than that
which exists between the East North Central and
the East South Central. Indeed, this appears to be
the case. The percentage wage differential
between the East North Central and South Atlantic
declined by 13 percent between 1975 and 1983,
while the differential between the East North
Central and the East South Central fell 10 percent.
The portion attributable to wage structure differ
ences, however, rose for both sets of regions, as
was discussed above. The major reason for con
vergence appears to be the growing similarities in
work force composition between the East North
Central and Southern regions.
VIII. Conclusion
This study finds great similarity in the nature of
wage differentials between the East North Central
and the East South Central and South Atlantic
regions. In both 1975 and 1983, structural differ
ences account for most of the wage differential
between the East North Central and the Southern
regions. There are also similarities in the way that
the differential changed between 1975 and 1983For both regional comparisons, the importance of
wage structure differences in the overall regional
wage differentials grew over the time period con
sidered. This wage convergence appears to result
more from growing similarities in the composi
tion of the work force than from returns to
worker characteristics. The characteristics of the
populations in the Southern regions have be
come more similar to those of the East North
Central population, causing the importance of
compositional differences in the overall wage dif
ferential to decline (see
and
The
rise in the importance of the structural compo
nent appears to be solely attributable to the dec
lining importance of compositional differences
across regions.
While major sources of the differ
ential remain unknown, it is clear that wage dif
ferentials continue to exist between the broad
regional groupings observed in this study. Furth
ermore, adjustments for the standard productivity
and skill-related variables, degrees of unioniza
tion, and the existence of race and sex discrimi
nation, only eliminate about one-quarter of the
overall regional wage differentials.
One encouraging result is that the
wage differential between the regions considered
declined between 1975 and 1983. Even if the
decline continues at a rate similar to that expe-
tables 4a
4b).
rienced over the period (although there is no
reason to expect this), nominal regional wage dif
ferentials can be expected to persist for some
time. This suggests that considerable attention
should be given to improving productivity in the
East North Central and in other high-wage
regions, in order to compensate for the region’s
higher, although converging, wages. Greater
attention should also be given to the importance
of nonwage factors that can be affected by
regional policies, such as differences in the provi
sion of public goods and services, in the unex
plained portion of regional wage differentials.
References
Becker, Gary. Human Capital: A Theoretical and
Empirical Analysis with Special Reference to
Education. National Bureau of Economic
Research, New York, NY: Columbia University
Press, 1975.
Denison, Edward F. “Analysis of Interstate Differ
entials: Comment,” in Regional Income.
National Bureau of Economic Research, Prin
ceton: Princeton University Press, 1957, pp.
161-179.
Hanna, FA “Contribution of Manufacturing
Wages to Regional Differences in Per Capita
Income,” Review of Economics and Statistics,
vol. 33 (February 1951) pp. 18-28.
Hanushek, Eric A “Regional Differences in the
Structure of Earnings,” Review of Economics
and Statistics, vol. 55, no. 2 (May 1973), pp.
204-13Johnson, George. “Intermetropolitan Wage Dif
ferentials in the United States,” in Jack E. Tri
plett, ed., The Measurement of Labor Cost.
National Bureau of Economic Research, Chi
cago: The University of Chicago Press, 1983,
pp. 309-32.
Kiefer, Nicholas M., and Sharon P. Smith. “Union
Impact and Wage Discrimination by Region,”
Journal of Human Resources, vol. 12, no. 4
(Fall 1977), pp. 521-34.
Martin, R.L “Wage-Change Interdependence
Amongst Regional Labour Markets: Conceptual
Issues and Some Empirical Evidence for the
United States,” in R.L. Martin, ed., Regional
Wage Inflation and Unemployment. New
York, NY: Methuen, 1981, pp. 96-135.
Moore, W., R. Newman, and J. Cunningham. “The
Effect of the Extent of Unionism on Union and
Nonunion Wages,” Journal of Labor Research,
vol. 6, no. 1 (Winter 1985).
Oaxaca, Ronald. “Male-Female Wage Differentials
in Urban Labor Markets,” International Eco
nomic Review, vol. 14, no. 3 (October 1973),
pp. 693-709.
Roback, Jennifer. “Wages, Rents, and the Quality
of Life,” Journal of Political Economy, vol. 90,
no. 6 (December 1982), pp. 1257-1278.
Sahling, Leonard G., and Sharon P. Smith. “Re
gional Wage Differentials: Has the South Risen
Again?” Review of Economics and Statistics,
vol. 65, no. 1 (February71983), pp. 131-35.
23
Labor Market Conditions
in Ohio Versus the Rest of
the United States:
1973-1984
by James L. Medoff
Jam es L . M edoff is a professor of
economics at Harvard University.
An earlier version of this paper was
presented at a conference on labor
costs sponsored by the Federal
Reserve Bank of Cleveland and by
the Regional Economic Issues
program. The author would like to
thank Nina Mendelson and Martin
VanDenburgh for invaluable
assistance with this paper.
2 4
Introduction
This paper presents evidence that contrasts labor
market conditions in Ohio and the rest of the
United States during the 1973 to 1984 period. The
evidence supports the following four propositions:
1. Whether we focus on the entire
private sector or just on private manufacturing,
O hio’s percentage change in employment was
less than the percentage change in employment
in the United States as a whole from 1973 to
1984. While this was particularly true in the last
five years of the period, it was nearly as true for
the first six.
2. The impact of unions on
Ohio’s relative wages undoubtedly contributed to
the fact that O hio’s employment growth was
below the national average, but the existing evi
dence does not support the belief that the direct
union wage effect was a key factor.
3. While increases in the price of
the U.S. dollar have deservedly received much at
tention of late, changes in exchange rates were
not a significant factor in the
worsening
of Ohio’s employment situation. The appreciation
in the dollar’s price hurt every state in the country,
but did not hurt Ohio by an above-average amount.
4. Netting out the direct wage ef
fects of unions, Ohio’s manufacturing wage rates
for a given quality of labor are substantially above
the national average today, as they were in 1973.
While we do not know exactly why Ohio’s non
union manufacturers pay a great deal more than
comparable employers elsewhere in the country,
this phenomenon is likely to be one reason why
relative
Ohio’s employment growth rate was below the
national average during the past 10 years.
The evidence presented is based
on May Current Population Survey (CPS) micro
data for 1973, 1979, 1983, and 1984. These data
come from surveys of about 60,000 households
conducted by the Bureau of the Census for the
Bureau of Labor Statistics. The CPS surveys collect
information on such things as employment status,
usual hourly earnings, state of residence, union
status, years of education, age, sex, race, occupa
tion, and industry.
I. Findings
gives unemployment rates for the United
States as a whole, for Ohio, for a group of “highgrowth” states, and for five states to which Ohio
frequently compares itself—Michigan, Pennsylva
nia, Indiana, Illinois, and New York. The table
reveals that, in 1973, O hio’s unemployment rate
was slightly below the rate in the United States as
a whole. In 1979, the two rates were identical,
and in 1984, the Ohio rate was substantially
above the national figure. Thus, the unemploy
ment statistics suggest that O hio’s labor market
conditions worsened slightly more than condi
tions elsewhere in the country during the 1973 to
1979 period, and worsened substantially more in
the years between 1979 and 1984.
It is now well known that unem
ployment rates depend greatly on the extent to
which the labor force is affected by the business
cycle and by various structural factors. Thus, many
Table 1
1979.) What these two tables indicate is that the
“union wage effect” has been lower in Ohio than
elsewhere in the United States throughout the
past decade, and that it has become substantially
lower throughout the 1973 to 1984 period. The
ability of unions to raise their members’ wages
above those of comparable nonunion employees
is today much less in Ohio than it is in the vast
majority of states. Furthermore, the fact that the
union/nonunion wage differential is conditioned
by the impact of unions on nonunion wages has
been recognized since measurement of that dif
ferential first began.1
and
provide
estimates of the percentage amount by which
private sector hourly earnings were higher in
Ohio than in comparison states in 1973, 1979,
and 1983-84, respectively;
and
provide analogous estimates for the manufactur
ing sector taken by itself.2 It is instructive to con
sider the first column in
The first figure
in this column indicates that in 1973, usual
hourly earnings were 4.8 percent higher in Ohio
than in the rest of the country. The second figure
in this column indicates that when the compari-
Private Sector Union Percentages in 1973, 1979, and 1984
All sectors
United States
Ohio
High-growth states
Michigan
Pennsylvania
Indiana
Illinois
New York
Manufacturing
1973
1979
1984
1973
1979
1984
24
21
31
17
36
31
34
31
15
34
30
30
27
26
16
22
39
51
26
58
50
60
44
41
35
54
22
54
27
42
16
52
53
53
38
38
43
59
29
26
31
30
13
28
20
28
19
23
Tables 5A 5B,
NOTE: High-growth states include California, Florida, Georgia, Massachusetts,
North Carolina, and Texas.
SOURCE: May C urren t P opulation Survey data for all years.
TABLE
5C
tables 6A, 6B,
3
manufacturing sector taken by itself are given in
(Because the sample used to construct
usual hourly earnings was cut substantially
between the 1979 and 1983 May CPS surveys, the
1983 and 1984 surveys were merged to produce a
sample of roughly the same size as was used in
table 4B.
6C
table 5A
Percentage Amounts by which Union Hourly Earnings Exceeded
Nonunion Hourly Earnings in 1973, 1979, and 1983-84
A. Private Sector as a Whole
B. Private Sector, Manufacturing Only
Same worker,
same industry
Same worker
Same worker,
same industry
Same worker
1973
1979
1983-4
1973
1979
1983-4
1973
1979
1983-4
1973
1979
1983-4
29
(0.6)
26
(0.8)
29
(0.9)
23
(0.6)
21
(0.8)
24
(0.9)
17
(0.8)
18
(1.1)
20
(1.2)
14
(0.8)
14
(1.0)
16
(1.2)
Ohio
25
(2.4)
23
(3.1)
17
(3.9)
18
(2.3)
19
(3.0)
14
(3.7)
14
(2.7)
8.9
(3.5)
5.3
(4.7)
12
(2.7)
4.8
(3.4)
1.5
(4.7)
High-growth
states
30
(1.3)
26
(1.8)
35
(2.0)
25
(1.3)
22
(1.7)
31
(1.9)
16
(1.7)
19
(2.4)
25
(2.9)
13
(1.7)
14
(2.4)
21
(2.9)
Michigan
27
(2.7)
19
(3.5)
22
(4.2)
19
(2.7)
15
(3.4)
16
(4.0)
14
(3.7)
16
(4.3)
18
(5.2)
6.4
(3.6)
13
(4.0)
9.6
(5.1)
25
(2.6)
15
(3.2)
18
(3.4)
18
(2.5)
8.6
9.8
(3.1)
(3.3)
12
(2.8)
2.2
(4.0)
8.2
(4.7)
7.6
(2.8)
-2.2
(3.8)
7.1
(4.9)
29
(3.2)
24
(4.5)
31
(5.3)
22
(3.2)
18
(4.5)
20
(5.0)
14
(3.7)
10
(5.3)
5.2
(5.1)
8.4
(3.7)
5.0
(5.8)
-0.4
(5.1)
23
(2.5)
21
(3.4)
27
(4.1)
17
(2.4)
17
(3.4)
21
(4.1)
7.4
(4.7)
13
(5.5)
10
(3-1)
(3.1)
9.8
(5.1)
14
(5.8)
16
(2.1)
7.2
(2.7)
16
12
(2.0)
5.8
(2.7)
13
(3.1)
7.1
(2.9)
7.0
(4.2)
-1.1
(5.6)
7.7
(3.0)
9.3
(4.4)
1.8
(6.0)
United States
Pennsylvania
Indiana
Illinois
New York
(3.1)
11
NOTES: Numbers in parentheses b e lo w percentages are standard errors. The adjective “sam e” refers to years o f education, age and its
square, race, sex and occupation (o n e o f eight broad categories ). The expression “ same industry” denotes on e o f seven broad categories
(in the case o f table 4A ) and o n e o f 20 two-digit SIC industries in the case o f table 4B. High-growth states include California, Florida,
Georgia, Massachusetts, North Carolina and Texas.
SOURCE: May C urren t P opulation Survey data for all years.
Percentage Amounts by which Private Sector Hourly Earnings
W ere Higher in Ohio than in Comparison States
A.
1973
C om parison states
United States
High-growth
states
Michigan
Pennsylvania
Indiana
Illinois
N ew York
4.8
(1.3)
6.4
(1.4)
-5.5
(1.8)
4.1
(1.7)
3.5
(2.0)
6.1
(1.7)
-8.8
(1.5)
1.9
(1.0)
3.1
(1.1)
-8.1
1.3
(1.5)
-7.7
(1.2)
-8.4
(1.3)
2.8
(1.3)
(1.1)
Same workers, net
of union premium
0.0
(0.9)
-0.3
(1.0)
-7.2
(1.2)
2.6
(1.3)
1.3
(1.4)
-7.1
(1.2)
(1.1)
Same workers,
same industry
1.7
(0.9)
2.7
(1.0)
-7.8
(1.2)
3.3
(1.2)
2.0
(1.4)
-7.5
(1.2)
-8.6
(1.1)
Same workers, same
industry, net of
union premium
0.3
(0.9)
-0.1
(1.0)
-7.2
(1.2)
3.1
(1.2)
1.9
(1.4)
-7.1
(1.1)
-8.3
(1.1)
Total amount
3.5
(1.7)
6.2
(1.8)
-7.4
(2.2)
0.3
(2.2)
5.8
(2.7)
-6.9
(2.2)
-0.6
(2.1)
Same workers
2.0
4.8
(1.4)
-8.8
(1.6)
1.5
(1.7)
5.2
(2.0)
-8.4
(1.6)
-0.2
(1.6)
1.0
(1.4)
-8.6
(1.6)
1.4
(1.7)
4.5
(2.0)
-9.0
(1.5)
-0.3
(1.6)
-8.0
(1.6)
2.5
(1.6)
6.0
(2.0)
-8.0
(1.6)
-0.2
(1.6)
Total amount
Same workers
B.
-8.1
1979
(1.3)
Same workers, net
of union premium
-0.0
(1.3)
Same workers,
same industry
(1.3)
4.7
(1.4)
Same workers, same
industry, net of
union premium
0.1
(1.3)
1.5
(1.3)
-7.9
(1.6)
2.4
(1.6)
5.5
(1.9)
-8.5
(1.5)
-0.3
(1.6)
33
1.6
(1.9)
-3.6
(2.5)
0.8
(2.4)
4.9
(3.0)
-5.3
(2.3)
-2.6
(2.2)
-4.8
(1.9)
-0.4
(1.8)
3-8
(1.3)
-0.2
(1.4)
(2.3)
-6.7
(1.8)
-4.2
(1.7)
Same workers, net
of union premium
-0.8
(1.3)
-2.5
(1.4)
-3-7
(1.9)
-0.2
(1.7)
4.3
(2.2)
-6.9
(1.7)
-36
(1.7)
Same workers,
same industry
1.1
(1.3)
0.2
(1.4)
-5.4
(1.8)
0.1
(1.7)
4.6
(2.2)
-6.4
(1.7)
-3.7
(1.7)
Same workers, same
industry, net of
union premium
-0.2
(1-3)
-1.9
(1.4)
-4.5
(1.8)
0.2
(1.7)
4.9
(2.2)
-6.6
(1.7)
-3.2
(1.7)
C.
2.1
1983-84
Total amount
( 1.8)
Same workers
0.7
NOTES: Numbers in parentheses b e lo w percentages are standard errors. The adjective “ sam e” refers to years o f education, age and its
square, race, sex, and occupation (o n e o f eight broad categories). The expression “ same industry” means on e o f seven broad categories.
High-growth states include California, Florida, Georgia, Massachusetts, North Carolina, and Texas.
SOURCE: May C urren t P opulation Survey data for the given year.
manufacturers pay substantially more for a given
type of worker than do employers elsewhere in
the country. While this may reflect a desire to
“avoid unionization,” the evidence to support this
contention has not yet been forthcoming.
Even if employers in Ohio have to
pay more to attract and retain their workers than
do employers elsewhere in the country, O hio’s
employment situation can improve. A weakening
of the dollar would not help Ohio more than the
average state in the country on the employment
front, but it clearly would increase the number of
jobs in the state. Productivity improvements, on
the other hand, would improve both O hio’s abso
lute and its relative employment situation. In the
political arena, where I believe the trade situation
can ultimately be improved, and at the worksite,
where many productivity-enhancing innovations
can be adopted, labor and management should
be working together toward a common end —
greater competitiveness. I also believe that this
cooperation is much more likely if neither party7
continuously blames the other for today’s prob
lems, especially without solid evidence to sup
port the position. Where one of the parties is
clearly at fault, it must be willing to work with the
other in the name of more and better jobs. Labor
and management must be united, not divided, to
improve labor market conditions in Ohio and in
the rest of the country.
Economic Commentary
Medicaid: Federalism and the Reagan
Budget Proposals
Paul Gary Wyckoff
August 15, 1985
The Dollar in the Eighties
Owen F. Humpage and
Nicholas V. Karamouzis
September 1, 1985
Interstate Banking: Its Impact on Ohio Banks
Thomas M. Buynak
September 15, 1985
The M l Target and Disinflation Policy
William T. Gavin
October 1, 1985
International Trade and the Fourth
District’s Recovery
Robert H. Schnorbus
October 15, 1985
Reserve Borrowings and the Money Market
Richard Mugel
November 1, 1985
Federal Reserve’s Response to the Problems
Experienced by ODGF Thrifts
Karen N. Horn
April 1, 1985
Imports and Domestic Steel
Amy Kerka
April 15, 1985
The Debt Burden, What You Don’t See
John B. Carlson
May 1, 1985
CRR and Monetary Control
Michael R. Pakko
May 15, 1985
Bank Earnings: Comparing the Extremes
Paul R. Watro
November 15, 1985
The Difficulty in Explaining Postwar Stability
KJ. Kowalewski and Eric Kades
December 1, 1985
How Desirable Is Dollar Depreciation?
Gerald H. Anderson
December 15, 1985
A Correct Value for the Dollar?
Owen F. Humpage and
Nicholas V. Karamouzis
January71, 1986
The Financial Distress in American Farming
Michael F. Bryan and Gary Whalen
June 1, 1985
Bank Holding Company Voluntary
Nonbanking Asset Divestitures
Gary Whalen
January 15, 1986
Major Trends in Capital Formation
Robert H. Schnorbus
June 15, 1985
Junk Bonds and Public Policy
Jerome S. Fons
February71, 1986
The Dynamics o f Federal Debt
John B. Carlson and E. J. Stevens
Can We Count on Private Pensions?
James F. Siekmeier
February715, 1986
July 1, 1985
Is Manufacturing Disappearing?
Michael F. Bryan
July 15, 1985
Solutions to the International Debt Problem
Gerald H. Anderson
August 1, 1985
American Automobile Manufacturing:
It’s Turning Japanese
Michael F. Bry7an and Michael W. Dvorak
March 1, 1986
31
@FedFRASER