
Short Communication


Labor Unions and Income Inequality: Evidence from US States

P. Chintrakarn


ABSTRACT

The United States witnessed two interesting distinctions. Labor union rates fell sharply in the 1980s while income inequality has been increasing since the 1980s. Understanding the underlying causes contributing to the marked increase in income inequality in U.S. is an important research and policy question. To analyze these phenomena, the paper employed statelevel panel data on 48 states from 19882003 to estimate the impact of labor unions on U.S. states’ income inequality. The results from using various econometrics models suggested that U.S. states’ labor unionization rates defined as percent of employed workers covered by a collective bargaining agreement had negative and statistically significant effects on U.S. States’ income inequality measure. The findings indicated that increased labor unions played a significant role in making income distribution more equal. 




Received:
July 12, 2011; Accepted: October 14, 2011;
Published: December 12, 2011 

INTRODUCTION
The rising in income inequality has garnered a vast amount of attention from
scholars as well as from policymakers as inequality has increased in both developed
and developing countries over the past two decades. Systematic investigations
on the determinants of income inequality suggested several factors that were
found to be related to income inequality in the literature. At the beginning,
most studies investigated the impact of international trade in which the results
implied increases in international trade accompanied by rising in income inequality
in most developing countries (Goldberg and Pavcnik, 2007)
for a survey of countryspecific evidence). While nations gradually became more
financially integrated, concern over the impact of foreign direct investment
on income inequality attracted considerable attention from scholars as well
as policy makers. However, the empirical evidence yielded no consensus on the
results (Choi, 2006; Driffield and
Taylor, 2000; Feenstra and Hanson, 1997; Jensen
and Rosas, 2007; Taylor and Driffield, 2005). The
role of technology on income inequality became under intense scrutiny as industries
and firms that emphasized more on research and development tended to employ
relatively more highskilled workers and spend a relatively larger share of
their payrolls on the technology (Autor et al.,
1998; Bartel and Sicherman, 1999; Berndt
and Morrison, 1995; Berman et al., 1994).
The United States witnessed two interesting distinctions. Labor union rates
fell sharply in the 1980s while income inequality has been increasing since
the 1980s (Frank, 2009). Understanding the underlying
causes contributing to the marked increasing in income inequality in U.S. is
an important research and policy question (Farber, 2005;
Freeman, 1980; Johnson, 1975;
Western and Rosenfeld, 2011). Reducing income inequality
is not only a fundamental goal of longterm economic development but a means
to achieving the other development goals relating to poverty reduction as well.
As emphasized in Western and Rosenfeld (2011), research
on labor unions and inequality emphasized on two main effects. First, labor
union increased wages among lesseducated and bluecollar workers. Therefore,
unions helped reduce educational and occupational inequality. Second, collective
bargaining power of labor unions helped standardize wages within firms and industries.
Hence, unions mitigated the inequality of wages among union members with similar
characteristics. All together, these effects of unions implied the decline in
union was associated with the increase in wage inequality.
The objective of this study was to reexamine the relationship between these
two factors using novel and new data set. Building on this previous study, this
study revisited the role of labor unions in affecting income inequality of U.S.
states using panel data on 48 states from 19882003. The study aimed to contribute
to the literature along several new dimensions. First crosscountry comparisons
of inequality were generally plagued by problems of poor reliability and inconsistent
methodology. Goldberg and Pavcnik (2007) provided a
nice discussion on problem often encountered in crosscountry comparisons of
inequality. To cast light on this central issue, this study exploited newly
available data on income inequality that produced greater methodological consistency
in inequality measurements. The primary innovation of this data was to use IRS
income tax filing data to construct a comprehensive statelevel panel of annual
income inequality measures.
Second, analyzing data on U.S. states helped alleviate the heterogeneity and
data comparability problems often encountered in crosscountry studies as a
panel of U.S. states was more homogenous than most a panel of crosscountries
data. As emphasized by Frank (2009), the greater homogeneity
of statelevel data helped mitigate the difficulty in adequately capturing structural
differences across international panels of earlier studies such as Forbes
(2000) and Barro and Lee (2001). Finally, this paper
examined the impact of unions on income inequality by utilizing a comprehensive
panel data which was relatively large in both crosssections and timeseries
observations whereas the existing literature thus far has generally relied on
few data sets that lacked of both coverage.
MATERIALS AND METHODS
Statistical analysis: To assess the effects of unions on U.S. states’
income inequality, the study employed the following twoway fixed effects model
which allows for the correlation between unobserved effects and each explanatory
variable (Wooldridge, 2006):
Y_{it} = β_{0}+β_{1}unions_{it}+control
factors_{it}+α_{i}+γ_{t}+timed trend+ε_{it},
I = 1,..., 48; t = 1988, 1989..., 2003 
(1) 
where, y (U.S. state’s income inequality) is subscripted with i (U.S.
state) and t (year). The model included series of dummy variables capturing
both unobserved time invariant statespecific component, α_{i}
and yearspecific component, γ_{t}. In addition, the model also
included time trend to control trend effect. The data covered 48 states observed
for the period 19882003.
Equation 1 was estimated by using Ordinary Least Squared Method (OLS). The parameter of interest is β_{1} which measures the impact of labor unions on income inequality. The hypothesis test on β_{1} was based on the statistical significance at 10 and 5% levels. Under a strict exogeneity assumption on the explanatory variables, the fixed effects estimator is unbiased: Roughly, the idiosyncratic error γ_{it} should be uncorrelated with each explanatory variable across all time periods. The fixed effects estimator allows for arbitrary correlation between α_{i} and the explanatory variables in any time period. The other assumptions needed for a straight OLS analysis to be valid are that the errors ε_{it} are homoskedastic and serially uncorrelated (across t).
Data: For the dependent variable, Gini coefficient as measure of U.S.
states’ income inequality was obtained from (Frank,
2009). This income inequality measure was derived from tax data reported
in Statistics of Income published by the Internal Revenue Service (IRS). The
independent variable of interest was U.S. states’ labor unionization rates
defined as percent of employed workers who are covered by a collective bargaining
agreement obtained from unionstats.com database constructed by Hirsch
and Macpherson (2003). Several control factors which were considered to
affect income inequality in the literature were included in the model. For example,
per capita GSP (Gross State Product) and the squared of per capita GSP were
obtained and calculated using data from Bureau of Economic Analysis (BEA). Data
on U.S. states’ international trade openness (Trade) defined as state export
in percent of GSP and inward FDIrelated employment intensity (FDI) which is
the number of employees in foreign affiliates as percentage of statelevel total
employment were obtained from Statistical Abstract of the United States (various
issues).
For technology capital stock, the Perpetual Inventory Method was applied to
data on statelevel new research and development expenditure (as share of GSP)
obtained from Science and Engineering Indicators (various issues). Research
and development expenditure included Research and development performed by federal
agencies, business, universities, other nonprofit organizations and state agencies.
The data on statelevel research and development for years 1988, 1990, 1992,
1994, 1996, 2001 and 2003 are not available. To circumvent the problem, the
statelevel mean values as the proxy for missing data were used. Education measured
by the proportion of the population with at least a collage degree were obtained
from Frank (2009). Unemployment rates (%) for each state
were obtained through U.S. Bureau of Labor Statistics. Financial development
variables measured by share of Finance and Insurance (FIN) in US states in percent
of GSP and net loans and leases (Loan) of Federal Deposit Insurance Corporation
(FDIC)insured commercial banks, balances at year end, in percent of GSP were
obtained from BEA and FDIC.
RESULTS AND DISCUSSION
Summary statistics were provided in Table 1. Table
2 reported the estimation results. In term of control variables, the estimation
of the model showed that FDIrelated employment intensity had positive and statistically
significant impacts on U.S. states’ income inequality measure while regarding
trade and technology, estimates were not statistically significant.
Table 1: 
Summary statistics 

Notes: Author's calculation 
Table 2: 
Estimation results 

^{1}State, year and trend effects are not reported.*and
**and denote statistical significance at the 10 % and 5% levels, respectively.
tstatistics are reported under the estimates. We employ the robust estimator
of variance 
To be specific, the results suggested that one percent increase in FDIrelated
employment intensity was associated with an increase in Gini coefficient by
0.566 holding others constant. In term of the effects of economic development,
coefficient estimates of per capita GSP and per capita GSP squared appear to
be statistically significant with negative and positive signs, respectively.
The results appeared to be in line with longrun positive relationship between
inequality and growth found by Frank (2009). The estimation
result also showed that education had negative and statistically significant
impacts on U.S. states’ income inequality measure. To be specific, the
results suggested that one percent increase in proportion of the population
with at least a collage degree was associated with a decrease in Gini coefficient
by 0.000598 holding others constant. As for financial development, the results
found no evidence of significant impact on U.S. states’ income inequality
measure. The results also indicated that unemployment had no significant effect
on income distribution. Turning to the variable of interest, labor unions, the
estimation result showed that labor unions had negative and statistically significant
impacts on U.S. states’ income inequality measure. To be specific, the
results suggested that one percent increase in labor union rate was associated
with a decrease in Gini coefficient by 0.000624.
That being said it was important to check the sensitivity of the finding. To
check the robustness of findings, the random effects model was employed by assuming
that the unobserved effect, α_{i}, in Eq. 1 is
uncorrelated with each explanatory variables (Wooldridge,
2006). The results from using the random effects model were also reported
in Table 2. The findings confirm the results found with the
twoway fixed effects model. Labor unions had negative and statistically significant
impacts on U.S. states’ income inequality measure. To be specific, the
results suggested that one percent increase in labor union rate was associated
with a decrease in Gini coefficient by 0.000402.
CONCLUSION This study presented empirical evidence on how labor unions were related to income distribution in a panel data set of US states from 19882003. The results from using various econometrics models suggested that U.S. states’ labor unionization rates defined as percent of employed workers covered by a collective bargaining agreement had negative and statistically significant effects on U.S. States’ income inequality measure. The findings indicated that increased labor unions played a significant role in making income distribution more equal.

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