Wages, Competition, and the Decline of Union
Membership in the US Manufacturing Industry
Simona Aksman
New York University, Department of Economics
Undergraduate Honors Thesis
Adviser : Katarina Borovickova
Final Draft
May 4, 2015
Abstract
This paper examines the empirical application of the hypothesis that union
membership has declined in the US manufacturing industry due to an increasingly
competitive environment among firms, despite the fact that union wages have remained higher than nonunion wages. I construct an industry-level model for union
membership as well as union wages that has competitive indicators, including inward foreign direct investment, the capital-to-labor ratio, terms of trade, and a
ratio of export to import trade volume. I combine the theory of union-firm negotiations and the findings of my empirical study to make the argument that union
wage increases drive union membership downwards through competition. As the
manufacturing industry faces increasing competition from imports abroad as well
as capital’s replacement of labor, firm profits diminish, and the ultimate result is
that unions have declining membership. The key finding of my paper is that the
underlying cause of the union membership decline is an increasing union wage,
which triggers competitive forces to drive down union membership.
1
Introduction and Motivation
For the past several decades, union membership in the United States has been steadily
declining. According to a 2015 report by the Bureau of Labor Statistics (BLS), it declined
from 35.5% to 11.1% between 1945 and 2014. For private industry, the 2015 BLS report
stated that it declined even further, to 6.6% in 2014. Nevertheless, in 2014 union members
had median usual weekly earnings of $970 while their nonunion counterparts had median
earnings of $763, such that a positive union-nonunion wage differential exists. It is important to note that these wages are reflective of variations in individual demographic and
industry characteristics and are therefore likely to be biased estimates. Still, according
to the literature, this positive union-nonunion differential is a significant argument for
the existence of unions. Given the assumption that this positive wage differential has
persisted, I’d like to gain insight into the decline in union membership.
1
In particular, the manufacturing industry is of interest for study because it presents
a case of a significant union membership decline in the past few decades. Historically,
the manufacturing industry was highly unionized due to its concentration of production
occupations (Griswold 2010).1 But then, between 1973 and 2006, the share of total manufacturing jobs to all private industry wage and salary jobs fell by 17.9% (Hirsch 2008).
However, this drop occurred primarily in the unionized sector of manufacturing while
the share of nonunion manufacturing jobs actually slightly increased between 1973 and
2006. The result is that 6 million fewer union members were employed in manufacturing
in 2006 compared to in 1973. This trend is also reflected by the job growth of private
industry in the US overall: while union sector jobs have declined, nonunion sector jobs
have remained relatively flat in the past few decades.
According to the literature, there exist three main categories of explanations for the
decline in union membership: structural, competitive, and institutional (Hirsch 2008).
The structural explanation captures significant movements in employment within occupations, industries, and regions where union density has been traditionally high. The
competitive explanation captures the effects of unions on firms in a competitive environment, as well as an environment of globalization and technological change. The
institutional explanation captures all other environmental factors that affect union organizing, including the legal environment, government regulations, worker preferences, and
managerial opposition.
I’d like to explore the competitive explanation for the manufacturing industry’s decline. The basis of this explanation is the assumption that as a market becomes more
competitive, profits diminish for each firm. In addition, union rents can be thought of as
taxes on company profits (Hirsch 2008). This taxation effect is not conducive to increasingly competitive industries that exhibit diminishing profits. Past studies and anecdotal
evidence point to globalization as a primary driver of increasing competition (Slaughter
2007). In the past couple decades, US industries, and in particular the manufacturing industry, have become increasingly globalized: many mechanized processes that were once
done on US soil have been moved abroad. In the short span of time between 1997 and
2002, the share of foreign-sourced goods in total manufactured inputs nearly doubled for
the US manufacturing industry (Burke et al. 2004).
My objective in conducting this research is to construct an empirical model for union
membership’s decline, built with the intuitions noted above. In particular, I’m interested
to see whether competitive changes in the manufacturing industry have had a negative
effect on union membership over the long-run, which I’ve defined as the period between
1993 and 2012. In addition, I would like to test whether the union-nonunion wage differential is actually a significant determinant of union membership as the theory suggests.
To test the competitive hypothesis, I use a collection of competition indicators that
are supported by the literature and theory. In particular, foreign direct investment and
international trade measures, such as terms of trade and export to import trade ratio,
are suggested by Slaughter (2007; 2001). Capital-to-labor ratio is another measure I
utilize, as suggested by Hirsch and Berger (1984). I give a more in-depth explanation
of how these factors relate to union membership in section three. Additionally, due to
1
Production occupations include occupations such as precision production, craft, repair work; machine
operation; transportation and materials moving; as well as equipment handling and construction (Caves
and Krepps 1993).
2
the similar nature of these measures to one another, I select a combination of the best
indicators for each of the manufacturing sub-industries using an algorithm that removes
multicollinearity.2 I explain this further in section six where I describe results.
In the following section, I give background on the relevant literature on union membership models and the effects of competition. In section three, I discuss a theoretical
model for how unions and firms interact given profit and utility constraints. In section
four, I discuss the methodology for my econometric analysis of the competitive determinants of union membership. In section five, I review the data I work with and how I used
them within my econometric model. In section six, I give an overview of the results of
my econometric analysis. The last section provides my conclusions.
2
Literature Review
A key subset in the relevant literature examines the relationship between union membership and market competition, and particularly of interest, the effect of market competition on union membership. As I mentioned in the introduction, Hirsch (2008) argues
that competition inhibits union rent-seeking activities, because union rent-seeking can be
thought of as a tax on firm profits. An increase in competition eats away at these profits
available to unions. In particular, he points to three main factors that explain competition in product markets: international trade, product market deregulation, and the entry
of low-cost competitors. In a study of union membership and competition, Slaughter
(2007) examines manufacturing industries, particularly those with a significant international presence, and finds evidence of union decline. He concludes that union membership
declines as a result of competition arising from increased globalization, which he measures using inward foreign direct investment (FDI). Slaughter explains that FDI reflects
an increase in capital mobility on the global scale, which has the effect of increasing labor
demand elasticity. Specifically, unions are threatened by the relocation of production
abroad that FDI indicates, and they are threatened by this effect even if domestic firms
are not doing so. This theory that unions are adversely affected by indirect signals of
globalization presents a case of the "demonstration effect", and has support from several
other studies, such as Rodrik (1997) and Scheve and Slaughter (2004).
Slaughter (2001; 2007) also explores the effects of international trade on union membership, and provides a similar theory to the link between FDI and union membership:
increasing trade leads to increasing labor demand elasticity, resulting in lower union
membership. However, though the theory provides a clear story of a negative impact of
international trade on union membership, in studies, international trade indicators have
been shown to have mixed effects on union membership, with sometimes an unexpected
sign on the coefficient of the indicator. In his 2007 study, Slaughter finds that measures
such as exports, imports, net exports, and transportation costs do not have a significant
correlation with union membership or a consistently correct sign for the coefficient. In
my study, I use some of the global competition indicators used by Slaughter in his 2001
and 2007 studies, such as FDI, exports, and imports.
Griswold (2010) suggests an interesting theory of why international trade may actually
2
Multicollinearity is limited when the Variance Inflation Factor (VIF) for each indicator with respect
to the other indicators is less than 5.
3
increase the union labor share instead of decreasing it. If workers are aware of international trade negatively affecting worker security within an industry, they may value union
protection more, thereby actually increasing union membership. Griswold also criticizes
the notion that much of the decline in union membership is the result of competition,
and points to structural and institutional factors as important additional explanations.
Baldwin (2003) finds that most of the decline in union membership is not the result of
international trade, but rather, other factors such as employer opposition, unfavorable legislation, and declining worker demand for unionism. Additionally, Griswold discusses the
literature and theories of the effects of competition on union membership. He points out
that a common theoretical explanation for the decline in union membership is that competition makes changes in the union labor share more sensitive to changes in union wages.
This concept is useful for understanding empirically how competition affects unions: the
underlying trigger for a union membership change is a wage change. Competition has
the effect of increasing the negative effects of wage increases on union membership, but
competition alone is not responsible for these negative effects. This is an idea I explore
further in the theoretical analysis section of this paper, and also has implications for the
interpretation of my results.
There have been several studies on union membership determination, though most
have focused on individual rather than industry-level characteristics. In one of the earliest
of these individual-level studies, Lee (1978) modeled the effects of a union-nonunion wage
differential on union membership using microdata for the manufacturing industry. Lee
concluded that a higher union-nonunion wage differential has a positive correlation with
the union membership of an individual. The methodology proposed by Lee is the basis
of my econometric model, as explained in the methodology section of this paper.
This model was later adopted by Hirsch and Berger (1984), though the focus in this
adopted model was instead a mix of individual and industry effects in the manufacturing
industry using a cross-section of Current Population Survey (CPS) microdata for one
year. Industry effects used by Hirsch and Berger included the capital-to-labor ratio and
the Herfindahl-Hirschman Index (HHI), a measure of market concentration. The team
concluded that the capital-to-labor ratio and HHI are statistically-significant determinants of union membership for the manufacturing industry. The union-nonunion wage
differential, however, was found to have no significant effect on union membership. I
use the capital-to-labor ratio and union-nonunion wage differential in my model of union
membership determination as well.
Farber (2001) points out that the union membership determination model by Lee
(1978), which he calls the worker-choice model, ignores the issue of workers who are
interested in union jobs but are not able to find them. This is because unions are costly
to organize, and it is often the case that although union job seekers outnumber union jobs,
job seekers are not willing to take on the cost of organizing a new union. This results in a
queue for existing union jobs. This theory is the basis of a queuing model established by
Farber (1983): the union status of a worker is not only based on decisions of the worker,
but also the employers, who choose whether or not to unionize. There is also the issue
of skill level, which biases whether or not a worker will be in a union. Unions set a wage
based on the median worker along the skill distribution. Therefore, the workers that are
the most skilled will not have an incentive to be in the union, and the those that are
the least skilled will not be chosen by the unionized employer. Given that I have chosen
4
to work with the union membership determination model established by Lee (1978), I
take the above criticisms into account when constructing my own model. The change I
make to my model is to model union membership on the industry level rather than on
the individual level, such that the dependent variable is union membership as a share of
total employment for an industry rather than as a binary choice for an individual.
3
Theoretical Analysis
Union contracts, and in particular, wages and employment, are determined within a
union-firm bargaining process. It is assumed that the goal of the union is to maximize a
utility function U (w, L), where w refers to wages and L refers to employment, subject to a
constraint. There are two classes of models that have been used to solve this maximization
problem: the labor demand curve model and the contract curve model (Farber 2001).
The labor demand curve model assumes that the level of employment is fixed by the firm
and the union therefore only aims to maximize its member’s wages. The contract curve
model, on the other hand, assumes that both wages and employment are maximized by
the union in the bargaining process, and the union’s utility is determined on a union-firm
contract curve rather than the firm’s labor demand curve. This model has two cases:
the weakly efficient case, where unions maximize wages and employment subject to the
firm’s minimum profit constraint, and the strongly efficient case, where unions maximize
wages and employment subject to the opportunity wage of workers. Based on the results
of MaCurdy and Pencavel (1986), I assume that the weakly efficient case of the contract
curve model best describes the maximization problem that unions and firms face.
In this case, the union-firm bargaining process can be characterized from the union’s
perspective as given below,
max U (w, L) s.t. fi(w, L) Ø fi
w,L
(1)
where U (w, L) is the utility of the union in terms of wage and labor and fi(w, L) Ø fi refers
to the minimum profit constraint of the firm. This maximization objective can also be
written in its dual form to express the firm’s perspective as: max
fi(w, L) s.t. U (w, L) Ø U
fi
where fi(w, L) is the profit of the firm in terms of wage and labor, and U (w, L) Ø U refers
to the minimum utility constraint of the union.
Further, I assume that the union will set its utility exactly equal to the level at which
the firm will make an agreement:
U (w, L) = U
(2)
where U is the union’s minimum utility level. At this level, the firm and union make an
agreement that satisfies the other party’s bargaining constraint. Given that the union’s
utility will be set at this point, the following first order condition holds true:
ˆU (·)/ˆL
w ≠ M RL
=
ˆU (·)/ˆw
L
(3)
where M RL is the marginal revenue product of labor, ˆU (·)/ˆL refers to the partial
derivative of utility with respect to labor, and ˆU (·)/ˆw refers to the partial derivative of
utility with respect to wages. Equation (3) shows that the marginal rate of substitution
5
between wage and employment will be equal for the firm and union. This equation can
be re-written to solve for M RL , as given below:
M RL = w(1 + ‘)
(4)
ˆU (·)/ˆL
where ‘ = ˆU
ú wL which can be simplified to ‘ = ˆw
ú wL , the expression for the
(·)/ˆw
ˆL
elasticity of labor demand. Elasticity of labor demand is defined as the sum of the
substitution effect and the scale effect. According to the substitution effect, when wages
rise, a firm will substitute capital for labor. The scale effect, on the other hand, refers
to the overall cost of production: as wages rise, the overall cost of production rises, such
that the quantity of output declines and the amount of labor required declines.
Increased competition can raise elasticity of labor demand via both effects. FDI is
capable of raising elasticity through both substitution and scale effects, and Slaughter
(2007) gives an example of how FDI raises the elasticity via the substitution effect. If
a vertically integrated multinational firm moves some of it’s manufacturing production
stages abroad, it gains access to a greater pool of input resources. Then, given a wage
rise, it is able to substitute capital for labor more easily, and ‘ rises.
Since ‘ is assumed to be negative, as ‘ rises, M RL will diminish given the relationship
between the variables established in Equation (4). As M RL diminishes, marginal profits
will diminish since marginal profit can be decomposed into
M fiL = M RL ≠ M CL
(5)
where M fiL is the marginal profit of labor and M CL is the marginal cost of labor. For a
competitive market in the long-run, M fiL = 0, a condition equivalent to M RL = M CL .
When this occurs, the minimum profit threshold fi will become a stricter boundary for
unions, and union contract sets will reach lower levels of L, union membership, than
would be the case if profits were not diminishing. An important note to make here is
that w is increasing throughout, such that only L will decrease in the U(w,L) set.
Given that the condition of U (w, L) = U from Equation (2) holds true, one can map
out the union’s indifference curves, since U is an equilibrium outcome in union-firm negotiations. Figure 1 shows these equilibrium outcomes, and the data appears to affirm the
theory that L diminishes as w increases with the various observed equilibrium outcomes
for the years of my study, 1993 - 2012. I further explore the hypothesis that increasing union wages correspond to decreasing levels of union membership in the econometric
study I conduct later in this paper.
Other indicators, such as the capital-to-labor ratio, have a more direct link to the two
effects of elasticity of labor demand. The capital-to-labor ratio provides a strong indicator
of the relationship between the mechanization of the labor force and firm profitability.
Given a wage rise, when firms substitute capital for labor, the substitution effect leads
directly to an increase in the capital-to-labor ratio. Once the capital-to-labor ratio rises,
‘ rises and M fiL diminish, as shown previously in Equations (4) and (5).
6
7
Figure 1: Mean equilibrium outcomes of wage and labor for the manufacturing industry (9 sub-industries included). Figure (1a)
and Figure (1b) show that there is a similar wage-labor trend overall with both the union wage and the union-nonunion wage
differential. Source: Mean union wages are predicted using the 2SLS procedure explained in the methodology section of this paper,
and mean union labor shares are based on data tables collected from unionstats.gsu.edu.
Similarly, an increase in international trade is also theorized to increase ‘ via the
substitution and scale effects, and thus diminish M fiL for the firm. Slaughter (2001)
suggests that international trade can lead to a greater pool of resources, and thus more
substitution possibilities, leading to a substitution effect. Given a wage increase, when
import trade volumes increase, there are more resources with which the substitution and
scale effects can occur. In terms of the export/import trade volume ratio (Exp/Imp), an
increase in import trade volume corresponds to a decline in Exp/Imp.
In addition, as a market becomes increasingly competitive, firms will become increasingly price sensitive and mark-ups shrink. This mark-up rule can be written as the
following equation
P ≠ M CL
≠1
=
(6)
P
‘
where LI is the Lerner Index, and P is price. Given the inverse relationship between
mark-ups and ‘ in Equation (6), as firm-level ‘ rises, mark-ups shrink.
When firm-level ‘ rises, the entire market will experience a rise in ‘. For example, one
international indicator I use in this study is terms of trade (TOT), a ratio of export to
import price indices. When production abroad becomes cheaper than domestic production, TOT will increase. An increase in TOT can therefore be reflective of an increasingly
competitive trade environment and corresponds to increased price sensitivity, declining
mark-ups, and an increase in firm-level ‘ as given in Equation (6).
The union-firm bargaining process model given above reveals how unions and firms
make their decisions based on profit constraints. When profits are diminishing, as is the
case for an increasingly global and competitive market, fi becomes a scarcer quantity, and
therefore, a stricter boundary in the union-firm bargaining process. The result is that
unions see their equilibrium outcomes diminishing in labor opportunities over time.
LI =
4
Methodology
The model of union membership I estimate is based on a model originally presented by
Lee (1978) and modified by Hirsch and Berger (1984).
Based on the assumptions of my union-firm bargaining framework, workers determine
whether or not to join a union based on wage preferences. For a given manufacturing
worker i, Wui and Wni are his union and nonunion wages, respectively. Given that this
worker has a reservation union-nonunion wage differential fli , he will choose to join a
union given the following condition,
Wui ≠ Wni
> fli
Wni
(7)
such that the percentage union-nonunion wage differential exceeds his percentage reservation wage differential, which then represents the worker’s preference for union membership, and can be positive or negative. fli can be described as a function of person’s
individual characteristics and costs associated with union membership, given by the equation
fli = –Xi + —Ci + Á1i
(8)
8
where Xi is a vector of individual characteristics, Ci is the cost of union membership,
2
and Á1i is the error term, assumed to be distributed N(0, ‡1e
). Since the cost of union
membership is not necessarily encompassed by an explicit cost, such as a yearly fee, Ci
is split into two variables: the observable (explicit) and unobservable (implicit) cost, the
latter of which can be assumed to be a residual and uncorrelated with Xi . The observable
cost can be written as
Ci = “1 + “2 Xi + “3 Zi + Á2i
(9)
where Xi is a vector of individual characteristics, Zi is a vector of industry characteristics,
2
and Á2i is the error term, assumed to be distributed N(0, ‡2e
). The individual worker will
choose to join a union if the following condition is met
Wui ≠ Wni
> (– + —“2 )Xi + —“1 + —“3 Zi + Á1i + —Á2i
Wui
(10)
i.e. their percentage union-nonunion wage differential exceeds the cost of joining a union.
This can be rewritten to represent the union membership decision. The model of union
membership takes a probit form, since union membership is a qualitative binary variable.
Uiú > 0 if worker i is in a union and 0 otherwise. This can be written as
Uiú
= ”0 + ”1
3
4
Wui ≠ Wni
+ ”2 Xi + ”3 Zi ≠ Ái
Wui
(11)
where Xi is a vector of individual characteristics, Zi is a vector of industry characteristics,
and the error term is assumed to be distributed N(0, ‡i2 ). An individual’s union status
is thus determined by his or her percentage union-nonunion wage differential, individual
characteristics, as well as his or her industry’s characteristics.
In order to estimate the union-nonunion wage differential, a simultaneous model as
derived by Lee (1978) is introduced. Log forms of the union and nonunion wages are
substituted in to simplify the functional form, since the percentage union-nonunion wage
ni
differential WuiW≠W
can be approximately written as ln(Wui ) ≠ ln(Wni ). The equations
ni
are as given below,
ln(Wui ) = ◊u0 + ◊u1 Xui + ◊u2 Zui + Áui
(12)
ln(Wni ) = ◊n0 + ◊n1 Xni + ◊n2 Zni + Áni
(13)
where ln(Wui ) and ln(Wni ) are the log union and nonunion wage rates for an individual
i, Xui and Xni are vectors of individual characteristics for union and nonunion workers,
and Zui and Zni are vectors of industry characteristics for union and nonunion workers.
2
The error terms for the union and nonunion industries are distributed N(0, ‡ui
) and N(0,
2
‡ni ), respectively.
Some consideration of union bias is required in order to properly estimate ln(Wui )
and ln(Wni ). The issue is that samples of union and nonunion workers are not randomly
drawn from the population, and therefore, wages can be biased by union status. As
a result, the error terms of the union and nonunion wages cannot be assumed to be
independent of union status. This can be expressed as
E(Áui |Ui = 1) ”= 0 and E(Áni |Ui = 0) ”= 0
(14)
Given the above condition, it is necessary to include an instrumental variable as well as
a modified least squares procedure when determining the union and nonunion wages in
9
Equations (11) and (12). This instrumental variable will be an expression for the means
of the wage equations, E(Áui |Ui = 1) and E(Áni |Ui = 0), that adjust the error terms to
remove the dependency on union status.
The following expressions are instrumental variables for mean values that can be used
to adjust the error terms:
E(Áui |Uiú Ø 0) = (‡ui /‡i )[≠f (Uiú )/F (Uiú )]
(15)
E(Áni |Uiú < 0) = (‡ni /‡i )[f (Uiú )/1 ≠ F (Uiú )]
(16)
ln(Ŵui ) = ◊u0 + ◊u1 Xi + ◊u2 Zi + ◊u3 (‡ui /‡i )[≠f (Ûi )/F (Ûi )] + ÁÕui
(17)
ln(Ŵni ) = ◊n0 + ◊n1 Xi + ◊n2 Zi + ◊n3 (‡ni /‡i )[f (Ûi )/1 ≠ F (Ûi )] + ÁÕni
(18)
where ‡ui and ‡ni are the covariances between Áui , Áni and Ái , f (Uiú ) is the standard normal density function, and F (Uiú ) is the cumulative distribution function of the standard
normal distribution. The sample selection bias is controlled by estimating the expressions
for expected value, (‡ui /‡i )[≠f (Uiú )/F (Uiú )] and (‡ni /‡i )[f (Uiú )/1 ≠ F (Uiú )], and inserting these expressions into the union and nonunion wage equations to generate unbiased
estimates:
where E(ÁÕui |Ûi = 1) = 0 and E(ÁÕni |Ûi = 0) = 0. Equations (17) and (18) above express
the union and nonunion wages conditional on individual and industry characteristics as
well as the adjusted mean value terms, which control for wage bias from union status.
These new estimated values of the union and nonunion wages are used to calculate the
wages for each individual in the data set. The final modified least square procedure is
a 2 stage least squares (2SLS) procedure: in stage 1, it produces an estimate of Ûi with
a probit model, and then in stage 2, it produces an estimate of ln(Ŵui ) and ln(Ŵni )
with an OLS model that includes the instrumental mean values that correct for sample
selection bias.
Below are the individual-level features used for the determination of both union status
and the union and nonunion wages:
Xui1 = Xni1 = Xi1 = North-central regional dummy3
Xui2 = Xni2 = Xi2 = South regional dummy
Xui3 = Xni3 = Xi3 = West regional dummy
Xui4 = Xni4 = Xi4 = In metropolitan area dummy
Xui5 = Xni5 = Xi5 = Is married dummy
Xui6 = Xni6 = Xi6 = Is white dummy
Xui7 = Xni7 = Xi7 = Is male dummy
Xui8 = Xni8 = Xi8 = Is production worker dummy4
Xui9 = Xni9 = Xi9 = Years of schooling
Xui10 = Xni10 = Xi10 = Experience
3
For all features marked as dummy variables, the value is 1 if the dummy variable condition is met
and 0 otherwise.
4
As explained previously, production worker status encompasses blue-collar professions such as precision production, craft, repair work; machine operation; transportation and materials moving; as well as
equipment handling and construction. Non-production worker status encompasses executive, administrative, and managerial work; professional specialty; technicians and support; sales; administrative support
and clerical work; as well as service occupations (Caves and Krepps 1993).
10
Xui11 = Xni11 = Xi11 = Experience squared
Xui12 = Xni12 = Xi12 = Number of children
Xui13,14,15,..,32 = Xni13,14,15,...,32 = Xi13,14,15,...,32 = Year dummy (for each year 1994 through
2012)
Each of the simultaneous equations is run on the sub-industry level, such that subindustry characteristics, Zui , Zni , and Zi are implied in the data.
After the 2SLS procedure, all union-nonunion wage differential data is aggregated on
the sub-industry level. Then, a linear regression model for union membership is created
that is similar to Equation (10). Unlike that model, however, this model is a panel, with
both industry and time dimensions. The dependent variable is union membership as a
percentage share for each manufacturing sub-industry across time.
This model is written below as:
F DIjt
K
EXP
) + ⁄3 ( )jt + ⁄4 (
)jt + ⁄5 T OTjt + Ájt
CP It
L
IM P
(19)
where Ujt is the share of unionized workers to total workers in a given sub-industry,
ln(Ŵujt ) ≠ ln(Ŵnjt ) is the estimated mean union-nonunion wage differential, and the rest
DIjt
of the variables are industry characteristics: FCP
is real inward foreign direct investment
It
K
in terms of the CPI at time t, ( L )jt is the capital to labor ratio, ( EXP
) is the ratio of
IM P jt
the export volume to import volume, and T OTjt is the terms of trade, a ratio of export
prices to import prices. All variables are across time periods t = 1993,...2012, and subindustries j = 1,...9. These sub-industries are specified in the following section. The error
2
term is assumed to be distributed N(0, ‡jt
). Sub-industry fixed effects are utilized when
necessary, and are coded in Table 4.
In addition, given the theoretical intuition established in section 3 of the union’s utility
trade-off between labor and wages, I run an additional linear regression model where the
union wage is now the dependent variable.5
Ujt = ⁄0 + ⁄1 [ln(Ŵujt ) ≠ ln(Ŵnjt )] + ⁄2 (
ln(
Ŵujt
K
EXP
) = ⁄0 + ⁄1 Ujt + ⁄2 F DIjt + ⁄3 ( )jt + ⁄4 (
)jt + ⁄5 T OTjt + ÁjtÕ
CP It
L
IM P
(20)
Ŵujt
where ln( CP
) is the estimated real union wage in terms of the CPI at time t, and all
It
independent variables are the same as those given in Equation (19). The error term is
2
assumed to be distributed N(0, ‡jt
Õ ). I use the estimated union wage instead of the unionnonunion wage differential as the dependent variable in Equation (20) given that I am
interested in determining if competitive effects are tied to the union wage as suggested by
the theory. As was the case for the previous model, sub-industry fixed effects are utilized
when necessary. These fixed effects are coded in Table 5.
By running regressions of both labor and wage, I consider both dimensions of the
union’s equilibrium utility outcome U (w, L). This is a more comprehensive approach to
modeling the effects of competition with respect to the union’s utility.
5
See Figure 1 for a visualization of the union’s trade-off between wages and labor.
11
5
Data
In order to construct the union-nonunion wage differentials using the 2SLS procedure,
I needed individual-level microdata to derive the fitted union membership values and
individual characteristics that bias the wage differentials. I collected samples of this data
from the University of Minnesota’s Integrated Public Use Microdata Series (IPUMS)
from the Current Population Survey (CPS). The variables used from this data source were
available for March of each year from 1993 though 2012. I used the March sample because
it is the only month of the year that has a complete data set for both weekly earnings
and all individual characteristics I wanted to include, which I based on a combination
of what Lee (1978) as well as Hirsch and Berger (1984) included in their studies. Each
year’s CPS data is a representative sample. The samples I worked with were restricted to
wage-earning workers between the ages of 16 and 64, employed in both production and
non-production manufacturing occupations. The sub-industries included in this study
are: Food, Beverages & Tobacco, Paper, Chemicals, Plastics & Rubbers, Nonmetallic
Minerals, Primary & Fabricated Metals, Machinery, and Transportation.
The individual-level variables used are: year, region (northwest, north central, south,
west), metropolitan area, marital status, production worker status (blue-collar), number
of children, gender, race, years of schooling, experience, and experience squared. Categorical variables, such as year, marital status, number of children, race, and gender, were
re-coded into dummy variables. Experience was computed using the equation: experience = age - years of schooling - 6. Experience squared was included given the intuition
that an individual’s earnings over the course of their lives often exhibit a quadratic path
rather than a linear one.
To construct the union-nonunion wage differentials for each sub-industry, I computed
the log of the predicted average weekly union wage minus the log of the predicted average
weekly nonunion wage. The predicted wages were the output of the regressions given in
Equation (17) and Equation (18) for each sub-industry in each year from 1993 though
2012.
As for the industry level variables, I utilized a variety of data resources. For inward
FDI, I collected data from the U.S Department of Commerce’s Bureau of Economic
Analysis for the manufacturing sub-industry noted above for the period of 1993 - 2012.
Given that the inward FDI data provided on the site was in nominal terms, I computed
real FDI by dividing the nominal FDI by the average CPI for each year.
Export and import trade volume data was collected from several resources. For the
period of 1993 - 2005, NAICS-converted data was collected from the Yale Social Sciences
Library webpage (Schott 2010; Brambilla et al. 2010). I used customs value-basis imports
to determine import volume, and summed up both imports and exports for each year
and across the relevant sub-industries. From 2005 - 2014, I used the US Census Bureau’s
Industry Statistics Portal to collect values of export and import volume, which were
already computed for each sub-industry based on NAICS values.
The capital-to-labor ratio was collected from the BLS Productivity database for the
period of 1993 - 2012.6 Terms of trade was also collected from the BLS Productivity
database. I computed terms of trade by dividing the US price index for exports by the
US price index for imports. Both capital-to-labor and terms of trade measures were
6
Note that in the BLS database, it is called the capital-to-hours ratio.
12
Union Labor Share (%), 1993 - 2012
Manufacturing Sub-Industry
Mean
Food
18.73
Beverages & Tobacco
20.09
Paper
25.56
Chemicals
9.02
Plastics & Rubbers
17.29
Nonmetallic Minerals
17.64
Primary Fabricated Metals
19.89
Machinery
13.38
Transportation
23.65
Nondurable Goods
18.14
Durable Goods
18.64
All
18.36
St. Deviation
3.82
5.60
3.82
5.53
2.93
4.63
4.77
5.25
4.36
6.95
5.86
6.48
Minimum
12.97
11.80
18.57
4.37
7.23
10.26
11.15
7.11
17.53
4.37
7.11
4.37
Maximum
26.88
31.60
37.33
14.48
24.03
28.08
28.29
21.74
29.82
37.33
29.82
37.33
Table 1: Source: Union membership share data collected directly from unionstats.gsu.edu.
Predicted Wage Diff. (%), 1993 - 2012
Manufacturing Sub-Industry
Mean St. Deviation Minimum Maximum
Food
1.29
1.29
-1.00
4.09
Beverages & Tobacco
0.13
3.29
-6.13
5.29
Paper
2.16
1.21
0.22
5.77
Chemicals
1.03
1.56
-0.83
4.91
Plastics & Rubbers
1.76
1.61
-1.21
5.06
Nonmetallic Minerals
1.06
1.74
-2.09
5.17
Primary Fabricated Metals
1.04
1.03
-0.60
2.87
Machinery
0.76
1.49
-1.18
4.17
Transportation
0.90
1.59
-2.27
4.03
Nondurable Goods
1.27
2.03
-6.13
5.77
Durable Goods
0.94
1.47
-2.27
5.17
All
1.13
1.81
-6.13
5.77
Table 2: Source: These predicted union-nonunion wage differentials were constructed via
the 2SLS procedure as explained in section 4 of this paper, with original data from the
CPS.
13
Competitive Indicators, 1993 - 2012
FDI (Real $)
Mean St. Deviation
Nondurable Goods
502.20 656.51
Durable Goods
553.57 347.74
All
525.0 540.74
EXP
TOT ( Index
)
IndexIM P
Nondurable Goods
0.99
0.07
Durable Goods
1.00
0.06
All
0.99
0.07
$EXP
Exp/Imp ( $IM P )
Nondurable Goods
0.98
0.36
Durable Goods
0.76
0.24
All
0.88
0.33
K/L (Index Base = 100)
Nondurable Goods
80.98 16.88
Durable Goods
73.34 15.14
All
77.58 16.53
Minimum Maximum
48.70
3148.90
67.62
1471.68
48.70
3148.9
0.77
0.86
0.77
1.18
1.19
1.19
0.26
0.39
0.26
1.85
1.33
1.85
36.93
45.02
36.93
109.00
102.48
109.00
Table 3: Source: FDI data from the U.S. Department of Commerce, TOT and K/L
data from the BLS, and Exp/Imp data from the Yale Social Science Library and the US
Census Bureau.
available at the sub-industry level and were relatively consistent with the NAICS industry classifications I used, though averaging and imputation was sometimes required to
construct some measures. Where imputation was required, I used an average value of the
previous and next values as a proxy for the missing value.
Finally, sub-industry level union shares came from a data set derived from CPS reports
by Hirsch and Macpherson (Hirsch and Macpherson 2009).7
Table 1 presents the descriptive statistics for union membership over the period of this
study. In addition, Figure 2 in the Appendix shows the trend over time for union membership. One can see here that union membership has had a consistent decline across the
entire manufacturing industry. The most unionized sub-industry of manufacturing was
Paper, which had a mean union share of 25.56% as well as the lowest standard deviation
in union share. The Chemicals sub-industry, another Nondurable Goods industry, had
the lowest mean union share at 9.02%, a little over one third of the Paper industry’s mean
union share. These numbers suggest that while the union share across the manufacturing
industries varies considerably, it is generally low, with the maximum of all industries at
37.33% and the mean at 18.36%. The decline in union membership is not a new trend,
with the beginnings of it going back to the 1970s (Hirsch 2008). The union membership
data in the time period of my study reflects a long-run decline in union membership that
spans the whole manufacturing industry.
Similarly, descriptive statistics for the predicted wage differentials have been given in
Table 2. This table shows that, for most industries, the predicted union wage is 1-2%
above the nonunion wage. Figure 2 in the Appendix portrays what’s happened over time,
7
Data set available online at unionstats.gsu.edu.
14
and it shows that the union-nonunion wage differential has increased across all industry
segments, which corresponds with the wage trend visible in Figure 1.
Finally, Table 3 presents descriptive statistics for all competitive effects. Figures 2
and 3 reveal that the competitive effects, with the exception of TOT, follow a clear trend
over time. Figure 3 shows that TOT has two opposing trends for the Nondurable and
Durable Goods segments over time, which results in an unclear trend overall. Perhaps
this is reflective of vastly different export and import trade pricing for the Durable and
Nondurable Goods industries. This lack of consistency in the TOT trend across industries
is also reflected in its performance in my econometric study, where TOT has unexpected
signs on its coefficients.
Exp/Imp, on the other hand, has trends for the Nondurable Goods and Durable
Goods segments that appear to move together. For manufacturing overall, it has a
mean value of 0.88, meaning that import volume exceeds export volume. This suggests
that the US manufacturing industry is at a comparative disadvantage in the long run,
particularly among the Durable Goods industries, where Exp/Imp has a mean value
of 0.76. Though the comparative disadvantage may be greater for the Durable Goods
segment than the Nondurable Goods segment, where the mean Exp/Imp is close to 1,
the standard deviation for the Nondurable Goods segment is higher than for the Durable
Goods segment. Figure 3 also reflects this difference in variability of the two segments:
while the Durable Goods segment has low, but relatively consistent Exp/Imp values of
less than 1 for all twenty years of the study, the Nondurable Goods segment shows a
rather steep descent in Exp/Imp over time. This is especially visible between 1995 and
2005, a period during which there was a 44% decline in Exp/Imp. For both segments,
the trends seem to reverse between 2005 and 2009, and then again decline after 2009, this
time perhaps as a result of the 2008 financial crisis. This decline around the financial
crisis may also be visible in the trends of other competitive indicators as well: Figure 2
shows that FDI and K/L have clear upward trends until there is a decline in both around
the period of the financial crisis. Though I do not take the financial crisis into account
in my econometric study, it could potentially explain some of the residual.
6
Results
Tables 2 and 3 present the results of the final regressions of union labor share (Ujt ) and
Ŵujt
wages (ln( CP
)) for each sub-industry of manufacturing between 1993 and 2012. In the
It
tables, coefficients and standard errors for each explanatory variable are presented. In
addition, given that for each sub-industry I ran separate regression models, each model
has an R2 value and F-statistic to provide insight into how well each model performed
overall. Variables that are marked with a dash in the tables were not included in the
model for that specific sub-industry. This is the result of my modeling procedure, by
which I removed variables with high multicollinearity using a stepwise algorithm that
calculated Variance Inflation Factors for each combination of variables in a model.8
8
For
more
details
on
how
this
algorithm
works,
see
https://beckmw.wordpress.com/2013/02/05/collinearity-and-stepwise-vif-selection/
15
this
webpage:
Labor Share (Ujt ) Regression
Manufacturing Sub-Industry
Food
Beverages & Tobacco
Paper
Chemicals
Plastics & Rubbers
Nonmetallic Minerals
Primary Fabricated Metals
Machinery
16
Transportation
Nondurable Goods
Durable Goods
All
Wage Diff.
-40.375
(56.495)
-87.301**
(30.575)
-132.757
(91.565)
-61.472
(31.441)
1.497
(38.627)
-11.419
(36.773)
-83.853
58.414
-251.814***
(47.270)
-91.229
(54.278)
-80.988***
(18.798)
-107.251***
(29.034)
-56.703***
(15.897)
FDI
-0.002
(0.005)
-0.002
(0.006)
-0.067
(0.037)
-0.013***
(0.003)
-
TOT
-7.739
(8.106)
32.312**
(14.387)
-46.188**
(18.771)
-1.826
(21.044)
-9.123
(12.953)
32.936
(17.437)
22.735***
(5.982)
18.546
(18.883)
3.873
(46.387)
-24.358***
(5.675)
23.553***
(5.669)
7.674
(4.071)
Exp/Imp
8.997**
(3.134)
-6.154
(11.408)
8.280**
(3.253)
24.749***
(6.520)
26.574**
(9.314)
8.194
(4.955)
2.939
(6.170)
-2.317
(6.737)
8.014***
(1.451)
3.067**
(1.206)
K/L
-
D1
-
D2
-
D3
-
D4
-
D5
-
D6
-
D7
-
D8
-
R2
0.704
F-stat
8.923
-
-
-
-
-
-
-
-
-
0.656
10.16
-
-
-
-
-
-
-
-
-
0.611
5.885
-
-
-
-
-
-
-
-
-
0.732
14.57
-
-
-
-
-
-
-
-
-
0.707
12.89
-
-
-
-
-
-
-
-
-
0.872
25.5
-0.250***
(0.057)
-
-
-
-
-
-
-
-
-
0.904
35.36
-
-
-
-
-
-
-
-
0.654
10.1
-
-
-
-
-
-
-
-
0.706
9.034
-0.413
(1.313)
-
6.738***
(1.454)
-
10.684***
(1.162)
-
-9.951***
(1.077)
-
-
-
-
-
0.810
55.59
-
4.324***
(0.992)
-15.609***
(1.085)
-5.072***
(1.048)
-3.977***
(0.905)
-2.774***
(0.936)
-9.451***
(0.859)
-10.941***
(1.058)
51.33
-0.604
(1.018)
-3.774***
(0.893)
-4.288***
(0.963)
0.808
-3.315**
(1.305)
0.814
60.93
-0.127
(0.067)
-0.144***
(0.028)
-0.174***
(0.018)
Table 4: Significance codes: 0.01: ***, 0.05: **, Note: for each sub-industry’s attributes, the first row indicates the coefficient
and the second row indicates the standard error. Nondurable Goods refers to Food (D1), Beverages & Tobacco (D2), Paper (D3),
Chemicals (D4), and Plastics & Rubbers (D5). Durable Goods refers to Nonmetallic Minerals (D6), Primary Fabricated Metals
(D7), Machinery (D8), and Transportation (No dummy). Codes D1 - D8 refers to the dummy variables that control for industry
fixed effects included in the regressions for Nondurable Goods, Durable Goods, and All.
Ŵujt
Wage (ln( CP I )) Regression
t
Manufacturing Sub-Industry
Food
Union Share
-0.005
(0.005)
-0.025**
(0.009)
-0.006
(0.004)
-0.010
(0.007)
-0.002
(0.0140)
-0.018**
(0.007)
-
FDI
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000***
(0.000)
-
-
Nondurable Goods
-0.019***
(0.004)
-0.010**
(0.004)
-
Durable Goods
-
-
All
-
-
Beverages & Tobacco
Paper
Chemicals
Plastics & Rubbers
Nonmetallic Minerals
Primary Fabricated Metals
Machinery
17
Transportation
-
-
TOT
0.481
( 0.184)
0.150
(0.788)
-0.143
(0.397)
-0.147**
(0.629)
0.533
(0.734)
1.358
(1.039)
0.077
(0.188)
1.272***
(0.011)
-0.203
(0.810)
-0.151
(0.230)
0.305
(0.157)
-0.022
(0.132)
Exp/Imp
-0.184**
(0.043)
-
K/L
-
D1
-
D2
-
D3
-
D4
-
D5
-
D6
-
D7
-
D8
-
R2
0.779
F-stat
13.23
-
-
-
-
-
-
-
-
-
0.428
3.988
0.217
(0.187)
-
-
-
-
-
-
-
-
-
-
0.338
1.916
-
-
-
-
-
-
-
-
-
0.665
10.6
-0.202
(0.493)
0.500
(0.627)
-0.412***
(0.147)
-0.018
(0.154)
0.055
(0.645)
-0.140**
(0.055)
-0.132***
(0.038
-
-
-
-
-
-
-
-
-
0.112
0.674
-
-
-
-
-
-
-
-
-
0.357
2.967
0.005**
(0.001)
-
-
-
-
-
-
-
-
-
0.565
6.926
-
-
-
-
-
-
-
-
0.744
15.51
-
-
-
-
-
-
-
-
0.827
17.91
-0.287***
(0.049)
-
-0.114**
(0.046)
-
-0.132***
(0.039)
-
0.193***
(0.036)
-
-
-
-
-
0.722
34.2
-
-0.277***
(0.032)
0.064
(0.035)
-0.124***
(0.034)
-0.279***
(0.025)
-0.268***
(0.030)
-0.114***
(0.024)
-0.062
(0.034)
53.01
-0.267***
(0.031)
-0.278***
(0.025)
-0.320***
(0.031)
0.782
-0.438***
(0.042)
0.741
43.74
0.003**
(0.001)
0.004***
(0.001)
0.005***
(0.000)
0.004***
(0.000)
Table 5: Significance codes: 0.01: ***, 0.05: **, Note: for each sub-industry’s attributes, the first row indicates the coefficient
and the second row indicates the standard error. Nondurable Goods refers to Food (D1), Beverages & Tobacco (D2), Paper (D3),
Chemicals (D4), and Plastics & Rubbers (D5). Durable Goods refers to Nonmetallic Minerals (D6), Primary Fabricated Metals
(D7), Machinery (D8), and Transportation (No dummy). Codes D1 - D8 refers to the dummy variables that control for industry
fixed effects included in the regressions for Nondurable Goods, Durable Goods, and All.
Variance Inflation Factors (VIF) can be calculated using the following equation:
V IFk =
1
1 ≠ Rk2
(21)
where the VIF for each explanatory variable k is the reciprocal of the inverse of the R2
from the regression. The algorithm I used calculates the VIF for each k with respect to
all other variables in the model and removes variables that present a VIF Ø 5, a condition
equivalent to an R2 Ø 0.8. A variable that has an R2 that is 0.8 shows strong evidence
of multicollinearity and should be removed in order to preserve the OLS assumption of
linear independence. VIF thresholds are normally between 5 and 10; I chose to use a
less strict threshold of 5 given the small number of observations in the data set for each
regression. All variables left have a VIF < 5. Finally, the regressions for Nondurable
Goods, Durable Goods, and the overall manufacturing industry include dummy variables
that control for industry fixed effects. The notes beneath Tables 4 and 5 provide the
dummy variable codes.
The results in Table 4 and 5 show that union wages and labor share are inversely correlated: wage increases correspond to labor share decreases, while labor share decreases
also correspond to wage increases, though not as often. The labor share regression results in Table 4 indicate that for the manufacturing industry overall, the union-nonunion
wage differential was a statistically significant explanatory variable. Though these results
present correlated variables and not necessarily causal relationships, the theory of how
these variables interact discussed in section 3 supports the conclusion that increasing
union wages have a negative effect on union labor share. A union wage increase due to
a union labor share decline, on the other hand, does not have the same basis in the theory I’ve discussed. Additionally, for the overall manufacturing industry labor share and
wage regressions, Exp/Imp and K/L are the only indicators that are both statistically
significant and have the correct signs on the coefficients.
The significance of K/L is supported by the findings of Hirsch and Berger (1986), while
the significance of international trade indicators such as Exp/Imp is not supported by the
literature, specifically Baldwin (2004) and Slaughter (2007). There are many reasons for
why my results may differ from those of previous studies, but one possibility is that my
study uses more data than these previous studies. Slaughter’s study used a panel for 10
years, 1983 to 1994, while Baldwin’s study used data from only three years: 1977, 1987,
and 1997. Given that my study utilizes a panel for the entire period between 1993 and
2012, my results are perhaps more indicative of long-term international trade effects than
these previous studies. Another reason why my results may differ is that the time period
of my study is more recent, so perhaps my results reflect that these international trade
trends have become more prominent in recent years as the US manufacturing industry
has become more globalized. In any case, I consider Exp/Imp to be a strong indicator of
international trade trends. Both the wage and labor share regression results show that
Exp/Imp never has a wrong sign on the coefficient when it is statistically significant.
Based on the theory as established in section 3, when there is a wage increase, an influx
of imports, which corresponds to an Exp/Imp decline, leads to a greater pool of resources
and more possibilities for capital to substitute labor. The result is that a substitution
effect leads the elasticity of labor demand to increase. This brings down marginal profits
and creates a stricter profit boundary, which in turns leads to a decline in the union labor
18
share.
In general, the union wage results in Table 5 reveal an opposite trend to the labor
share results in Table 4, which was expected. Given the hypothesis that union wage and
labor share have an inverse relationship, for the wage regression I expected all competitive
effects to also have signs on the coefficients opposite of what they had for the labor share
regression. This is true for Exp/Imp and K/L, but not TOT, which has a negative sign
for both the labor share and wage regressions, while I expected a negative sign for the
labor share regression and a positive sign for the wage regression. It is important to note
that the inconsistency of TOT’s coefficient sign does actually fall in line with the findings
of Baldwin (2004) and Slaughter (2007) for international trade indicators.
Despite the inconsistency of TOT’s coefficient sign, there is a case when it proves to
be a useful indicator. Table 4 shows that the statistically significant negative TOT effect
that appears for the Nondurable Goods segment is primarily driven by one sub-industry,
Paper. I did additional research to find out if this effect was supported by empirical
findings. According to the NGO World Growth, the Paper industry faced an import price
crisis in the 2000’s that led to import sanctions in 2009 (Shapiro 2011). A World Growth
report mentions a survey conducted by the International Trade Commission (ITC), which
concluded that lower import prices have increasingly led US businesses to purchase paper
imports, in particular from China and Indonesia. According to the ITC, price was the
only factor for which US manufacturers were inferior to manufacturers abroad, but it was
often the most determining factor when purchasing decisions were made. The theory is
that low import prices decreased the profit margin of the Paper industry, allowing for
the substitution and scale effects to increase the elasticity of labor demand. Given a
wage increase, firms were more likely to substitute in capital and cheaper labor for the
union labor, diminishing the elasticity of labor demand. As elasticity of labor demand
diminished, profits in the Paper industry fell, and as a result, unions in the Paper industry
brought down the union labor share to meet firm profit constraints.
Another notable trend is that FDI and K/L are never present in the same regression
because they are highly multicollinear. Unlike the results of Slaughter (2007), my union
labor share regression results do not show FDI to be a statistically significant indicator
of the decline in labor share for the manufacturing industry overall, though FDI is a
significant indicator for some Nondurable Goods industries. Meanwhile, Table 5 shows
that K/L is a statistically significant indicator for labor share and wage. Though my
results do not support the results of Slaughter (2007), they support Slaughter’s underlying
theory that declines in the union labor share are explained by resource substitution that
occurs when wages rise. The results in Table 5 show that increases in the union wage
are in part explained by increases in K/L, an indicator that can also be directly linked
to the substitution effect. In particular, there is a 0.4% increase in the union wage for
every 1-unit increase in K/L. However, the theory as outlined by Griswold (2010) and
Slaughter (2007) suggests that the causal relationship goes in the opposite direction given a 0.4% increase in the union wage, there is 1-unit increase in K/L. This increase in
K/L leads to an increase in the elasticity of labor demand via the substitution effect, as
is the case for FDI.
Additionally, the interchangeability of FDI and K/L suggests that capital replaces
labor domestically and internationally in the same way for the manufacturing industry,
since FDI is an indicator of international capital substitution, and K/L is an indicator
19
of domestic capital substitution, though possibly also international capital substitution.
Given the existence of multinational corporations in many of these manufacturing subindustries, it is perhaps the case that K/L accounts for international capital substitution
as well.
7
Conclusions
The aim of this paper was to determine whether competitive changes have had a significant effect on the decline of union membership over time despite the persistence of a
positive union-nonunion wage differential. My regression results show that an increasingly
positive wage differential is a significant determinant of union membership’s decline. In
addition, union utility models indicate that union wages and labor share are determined
when unions and firms bargain subject to the firm’s profit constraints, and the union
therefore faces a wage-labor trade-off that is subject to the conditions that affect firm
profits. One of the forces that drives firm profits is competition.
For the manufacturing industry overall, my results support the theory that competition, in the form of low cost and high volume imports as well as foreign and domestic
capital substitution, has the effect of diminishing union membership. However, these
competitive effects alone do not diminish union membership - instead, the theory suggests that the underlying cause of this competitive effect is a wage increase. Given a wage
increase, competition causes an increase in the elasticity of labor, and thus, the sensitivity
of union labor to replacement by less costly resources. For both the Durable Goods and
Nondurable Goods industries, it is the combination of a wage rise, high import volume,
and substitution of domestic (and possibly international) capital for union labor that diminishes union membership. In summary, the push for higher union wages often initiated
by unions brings with it a trade-off in union membership. A wage increase in competitive
conditions leads to an erosion of firm profits, and when this occurs, firms tighten profit
constraints in union-firm negotiations, leaving less room for unions to maximize union
membership. The end result is that the amount of union membership that unions and
firms agree to diminishes.
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22
A
Appendix
23
Figure 2: Trends in union membership, wage differentials, capital-to-labor ratio, and FDI from 1993 - 2012.
24
Figure 3: Trends in terms of trade and the export-import volume ratio from 1993 - 2012.