Special Interest Groups and Economic Growth in
the United States
Oguzhan Dincer, Department of Economics, Illinois State University, Normal, IL 61790-4200
Abstract
Using a direct measure of special interest group strength from Thomas and Hrebenar (1999), I analyze the
effects of special interest groups on economic growth across 48 contiguous U.S. states. Thomas and Hrebenar
(1999) categorize the strength of SIGs in each state into 5 categories: dominant, dominant/complementary,
complementary, complementary/subordinate, and subordinate. I find a negative relationship between the
special interest group strength and economic growth supporting Olson (1982). Holding everything else constant,
the growth rate of median income over a decade is almost 4.5 percentage point lower in states in which special
interest groups are dominant than it is in states in which interest groups are complementary/subordinate. The
results are robust to endogeneity between economic growth and special interest group strength.
Introduction
Since Olson’s (1982) influential book, there is growing interest in how special interest groups (SIG) affect
economic growth in the literature. Olson (1982) argues that political stability create an ideal environment for
SIGs to form and develop. As SIGs form and develop, they interfere with policymaking more and more
reducing the economic growth. Although policies leading to economic growth are in the common interest of all
SIGs in the economy, since they are individually small relative to the economy as a whole, they have no
incentive to fight for these policies. It is simply because, while the members of a SIG would enjoy only a small
share of the benefits of economic growth, they would bear all the costs. As Brace and Cohen (1989) argue, these
groups would prefer to fight for increasing their slice of the pie, rather than increasing the whole pie. Olson
(1982) provides quite a few examples on how SIGs make their members better off by reducing economic
efficiency and hence economic growth. They fight for price supports increasing the prices of the goods and
services above their marginal social costs. They also fight to block innovation. Unions, for example, block labor
saving innovations, while firms via trade associations block innovations of their competitors.
Quite a few empirical studies analyze the relationship between SIGs and economic growth using both crosscountry and cross-state data. The most common problem that each of these studies has is the measurement of
interest group strength. Unfortunately, a direct measure of interest group strength is not available. Following
Olson (1982), several studies use length of time of political stability in a country or in a state to measure the
SIG strength. According to Olson (1982), there is a positive relationship between the strength of SIGs and the
length of time they exist. Political instability in the forms of wars and revolutions prevents the existence of
special interest groups. Choi (1983), for example, uses data from U.S. states and measures the SIG strength as
the length of time since statehood, or for Confederate states, since the end of the civil war. Choi (1983) finds a
negative relationship between state age and economic growth supporting Olson (1982). Nevertheless, as Garand
(1992) argues, using a state’s age to measure the interest group strength is quite problematic. Several studies
such as Gray and Lowery (1988) and Garand (1992) find that the relationship between state age and economic
growth is time dependent. Both studies find that the direction and the magnitude of the estimated state age
coefficients change significantly depending on the time period used in the growth regressions. Nardinelli,
Wallace, and Warner (1987), on the other hand, do not find a statistically significant relationship at all. In
addition to cross-state studies there are quite a few cross-country studies using political stability as a measure of
SIG strength. Choi (1983), Weede (1984, 1986), and McCallum and Blais (1987) find a negative relationship
between the age of democracy in a country and economic growth supporting Olson(1982).
A second measure used in the literature is the unionization rate. Dye (1980) and Choi (1983) both find a
negative relationship between the unionization rate and economic growth across U.S states while McCallum
and Blais (1987) find a similar relationship using cross-country data.
The last measure used in the literature is the number of special interest groups. Several studies such as
McCallum and Blais (1987), Heckelman (2000), and Coates and Heckelman (2003) use cross-country data from
Murrell (1984) on the number of interest groups to analyze the relationship between SIG strength and economic
growth. Although McCallum and Blais (1987) do not find a significant relationship between the number of
SIGs and economic growth, Coates and Heckelman (2003) find a non-linear relationship. Heckelman (2000)
finds a negative relationship between the number of special interest groups and economic growth using data
from a small number of politically stable countries. Using data from U.S states, Gray and Lowery (1999), on the
other hand, find a positive relationship.
Perhaps because all of these studies use an indirect measure of SIG strength, their results on the relationship
between special interest group strength and economic growth are inconsistent. In this study, following Shughart,
Tollison, and Yan (2003), I use a more direct measure of SIG strength from Thomas and Hrebenar (1999) for 48
contiguous U.S. states.1 Thomas and Hrebenar (1999) categorize the strength of SIGs in each state into 5
categories: dominant, dominant/complementary, complementary, complementary/subordinate, and subordinate.
Ordinary Least Squares (OLS) estimates suggest that states in which interest groups are dominant grow
significantly slower. Holding everything else constant, the growth rate of median income over a decade is
almost 4.5 percentage point lower in such states than it is in states in which interest groups are
complementary/subordinate. The results are robust to endogeneity between economic growth and special
interest group strength.
The study is organized as follows. I summarize the data on SIG strength, as well as on control variables, in
Section 2. In Section 3, I present the empirical model and the results. Robustness is discussed in Section 4.
Section 5 overviews the contribution of the study.
Data
As Thomas and Hrebenar (1999) argue, while it is possible to evaluate the strength of individual SIGs in their
ability to affect the policy makers, the overall effect of these groups on policy making is a lot more difficult to
evaluate. Thomas and Hrebenar (1999) categorize SIGs based on survey data collected from political
practitioners and political scientists in each state for two time periods, the 1980s and the 1990s. They first ask
each survey respondent to collect as much data as possible on SIGs including lobbyists and lobbying activities
1
Shughart, Tollison, and Yan (2003) analyse the relationship between SIG strength and income inequality.
and lobbying expenditures. They then ask each respondent to identify ways in which his or her state's SIGs vary
from existing theories of SIGs in the studies such as Zeller (1954) and Morehouse (1981). Finally, using all the
data collected they categorize the strength of SIGs in each state into 5 categories: dominant,
dominant/complementary, complementary, complementary/subordinate, and subordinate. Dominant states are
those in which groups affect the policymaking process overwhelmingly and consistently. Groups in
complementary states are generally constrained by other aspects of policy making process. In subordinate states,
groups are consistently subordinated to other aspects of policymaking process. The dominant/complementary
and complementary/subordinate states are those which alternate between the two or which are in the process of
moving from one to another. Following Shughart, Tollison, and Yan (2003) I use 5 dummy variables for each
category Dominant, Dominant/Complementary, Complementary, Complementary/Subordinate, and
Subordinate.2 As Shughart, Tollison, and Yan (2003) argue, Thomas and Hrebenar (1999) measure of SIG
strength has several advantages over alternative measures. First, Thomas and Hrebenar (1999) improve the
measures of Zeller (1954) and Morehouse (1981) which have only three categories of interest group strength:
strong, moderate, and weak. Second, the measures such as the number of SIGs in a state used in previous
studies fail to differentiate, for example, Florida where the AARP is quite strong, from Michigan where AFLCIO is and fail to differentiate the strength of AARP in Florida from the strength of AFL-CIO in Michigan
(Shughart, Tollison, and Yan 2003, 446). In fact, neither Gray and Lowery (1999) nor Shughart, Tollison, and
Yan (2003) find a significant relationship between the number of SIGs and the Thomas and Hrebenar (1999)
measure. Table 1 presents these 5 categories and lists 48 contiguous U.S. states according to the strength of
SIGs in 1980s and 1990s.
One of the most commonly used measures of economic growth in cross-state growth regressions is the growth
rate of median income (Glaeser and Saks 2006). I use growth rate of median income over the two time periods
1980-1989 and 1990-1999 as my dependent variable. The data are from the Census Bureau. I also include a set
of control variables to minimize the omitted variable bias. First, along with the initial values of median income
(Income), I control for education (Education). My measure of education is the percentage of the population age
25 and above with a high school degree or more education given by the Census Bureau. Second, I control for
income inequality (Gini). According to Alesina and Rodrik (1994), for example, inequality leads to
redistribution which reduces economic growth. Using cross-country data, they find a significant negative
relationship between income inequality and economic growth. Panizza (2002) and Glaeser and Saks (2006) find
a similar relationship using data from U.S states. I measure income inequality across states by using the Gini
Index (Gini) for pre-tax household income given by the Census Bureau. Third, following Panizza (2002) and
Glaeser and Saks (2006), I control for urbanization (Urbanization). I measure urbanization as the share of
population that lives in urban areas. Fourth, I control for fiscal decentralization. Several studies such as Akai
and Sakata (2002) and Akai, Nishimura, and Sakata (2007) argue that fiscal decentralization increases
government efficiency which increases economic growth. I measure fiscal decentralization (Decentralization)
as the share of local government expenditure to total (local and state) government expenditure. Both
urbanization and decentralization data are from the Census Bureau. Finally, I control for corruption
(Corruption). Using cross-country data, Mauro (1995) finds that corruption reduces economic growth. Glaeser
and Saks (2006), using data from U.S. states, on the other hand, do not find a significant negative relationship
between corruption and economic growth. I measure corruption as the number of government officials
convicted in a state for crimes related to corruption in a specific year. The data are obtained from the Justice
Department’s “Report to Congress on the Activities and Operations of the Public Integrity Section” and cover a
2
Since there are not any states categorized as subordinate, I actually use 4 dummy variables.
broad range of crimes from election fraud to wire fraud.3 Following Glaeser and Saks (2006), I deflate the
number of convictions by state population. For all control variables except Corruption I use the initial values.
For Corruption I use the decade averages.
Results
I first estimate following standard linear model by Ordinary Least Squares (OLS):
Growthit = Intercept + γ Ln Incomeit + α Lobbyit + β Xit + uit
where Growthit represents the growth rate of median income in state i during period t, i.e., periods 1 and 2.
Lobbyit represents the set of dummy variables for the strength of SIGs in each state and Xit represents the set of
control variables that affect the growth rate of median income including the region dummies and a time
dummy.4 The results of the OLS estimation are given Table 2. The estimated coefficients of the dummy
variables representing the strength of the SIGs are negative and highly significant supporting Olson (1982). The
estimated coefficients of Dominant, Dominant/Complementary, and Complementary are, according to Wald
test, significantly different from each other. According to the results of the OLS estimation, ceteris paribus, the
growth rate of median income decreases by 12 percentage points over a decade in the states in which the special
interest groups are dominant relative to the states in which the interest groups are complementary/subordinate.
Following Shughart et al. (2003), in order to evaluate the relative effects of the interest groups across the U.S.
states, I also estimate the three other possible combinations of the dummy variables representing the interest
group strength in a state. The results are given in Table 3. The last column gives the estimated coefficients from
the original model, the next-to-last column shows the ceteris paribus effects of the interest groups in Dominant,
Dominant/Complementary, and Complementary/Subordinate states and so on. Table 3 gives the following
ordering regarding the negative effects of SIG strength on growth:
Dominant > Dominant/Complementary > Complementary = Complementary/Subordinate.
Ceteris paribus, states in which SIGs are Complementary or Complementary/Subordinate grow faster than the
states in which SIGs are Dominant/Complementary which grow faster than the ones in which SIGs are
Dominant. The growth in Dominant states is estimated to be more than 1 standard deviation slower than the
Complementary/Subordinate states.
The estimated coefficients of control variables are mostly consistent with the earlier studies. The estimated
coefficient of the initial value of median income is negative and significant supporting Solow (1956). The
estimated coefficients Education and Urbanization are both positive and significant (Glaeser and Saks 2006).
Income inequality has a negative and significant effect on the growth rate of median income (Alesina and
Rodrik 1994, Panizza 2002, and Glaeser and Saks 2006). There is an inverse-U shaped relationship both
between Corruption and growth and Decentralization and growth. Mendez and Sepulveda (2006), using data
from a cross-section of democratic countries, find that growth maximizing level of corruption is significantly
positive. Since United States is a democratic country, an inverse-U shaped relationship between Corruption and
3
State convictions data are used to measure corruption in several studies such as Goel and Rich (1989), Fisman and Gatti (2002),
Fredriksson et al. (2003), Glaeser and Saks (2006), Dincer (2008), and Apergis et al. (2010).
4
Since I have data from only two time periods it is not possible to use dynamic panel data estimation. It is not possible to use fixed
effects panel data estimation either. Due to the dynamic nature of the model, state fixed effects are correlated with the error term
(Caselli et al. 1996). Following Barro (2000), I estimate the model with random effects panel data estimation as well. The results are
very similar to the OLS estimation.
growth across states supports their results.5 The effects of Decentralization on growth are still ambiguous.
Several studies find a negative relationship between Decentralization and growth while several others find a
positive relationship. My results support the results of Thiessen (2003) and Akai et al. (2007).
Since the control variables that I use are measured in different units, using standardized coefficients to evaluate
their effects on growth is probably quite helpful.6 A one standard deviation increase in Urbanization causes the
growth rate of median income to increase by almost two thirds of a standard deviation. The standardized
coefficient of Education is almost the same as the standardized coefficient of Gini.
Robustness of the Results
The first robustness issue is the possible presence of spatial autocorrelation. Growth rate of income in a state is
likely to be affected by the growth rate in proximate states (Garrett et al. 2007). Ignoring spatial autocorrelation
in growth causes biased estimates. To control for spatial autocorrelation, I estimate the following spatial
autoregressive (i.e., spatial lag) model by maximum likelihood (ML):
Growthit = Intercept + γ Ln Incomeit + α Lobbyit + ρ W Growthit + β Xit + uit
where W is the spatial-lag weighting matrix and ρ is the coefficient giving the sign and the strength of spatial
autocorrelation.. I adopt a simple weighting scheme of strict state contiguity, such that wij = 1 if i ≠ j and state i
is contiguous to state j and w j = 0 otherwise. W Growthit is nothing but the average growth rate of median
income in state i’s neighboring states at time t. The results of the ML estimation are given in Column 2 of Table
2. According to Wald, and LM tests, spatial autocorrelation is in fact present. Even controlling for spatial
autocorrelation, the estimated coefficients of Dominant, Dominant/Complementary, and Complementary are
negative and highly significant. The ordering regarding the negative effects of SIG strength on growth is the
same as the basic model.
The second robustness issue is the endogeneity of the SIG strength. Bischoff (2003), for example, argues that
the number and hence the strength of special interest groups in Germany and the U.S. increased in 1970s and
1980s as a result of income growth. Using cross-country data both Bischoff (2003) and Coates et al. (2007) find
that positive relationship between income and the number of SIGs. Instrumental variables (IV) estimation helps
address this problem. The choice of the instrument is extremely important. A good instrument is a variable that
is supposed to be uncorrelated with the error term but correlated with the endogenous variable Lobby. I estimate
three different models using two instruments to help correct for endogeneity. The first instrument I use is
Morehouse’s (1981) measure of SIG strength across states for the late 1970s. Morehouse (1981) categorizes the
strength of SIGs in each state into 3 categories: strong, moderate, and weak. In order to be able to estimate the
model I convert both Thomas and Hrebenar (1999) and Morehouse (1981) categorical variables into numerical
variables, Lobby, with a maximum possible value of 4 (3) for dominant (strong) and minimum possible value of
1 for complementary/subordinate (weak). The second instrument I use is the political culture of each state
categorized by Elazar (1984). According to Elazar (1966), politics in the states are affected by their political
subcultures: whether they are moralistic, individualistic, or traditionalistic. Thomas and Hrebenar (1999) argue
5
Glaeser and Saks (2006), using cross-state data, do not find a significant relationship between Corruption and growth. Nevertheless,
they do not test the significance of Corruption2 in their model.
6
Standardized coefficients are simply the coefficient estimates of a regression after standardizing all variables to have a mean of 0 and
a standard deviation of 1.
that SIG strength is less in the states that have moralistic political subcultures such as Maine and Vermont than
states that have individualistic subcultures, such as Nevada and New Mexico. I measure political culture using
Sharkansky’s (1969) 9 point categorization of states based on Elazar (1966), where 1 is purely moralistic, 5 is
purely individualistic and 9 is purely traditionalistic. Values between these pure types are the states with
combinations of these three subcultures. Finally, I estimate a model using both Morehouse’s (1981) SIG
strength and Elazar’s (1966) political culture. The results of the IV estimation are given in Table 4. The
estimated coefficient of Lobby is negative and highly significant in all regressions indicating that our results are
robust to endogeneity. As long as the instruments affect the growth rate of median income through Lobby, the
instruments are theoretically valid. According to the 1st stage F and the Hansen J statistics given in Table 4, they
are empirically valid as well.7
The third robustness issue is the possible measurement error in Trust. However, IVestimation not only helps
correct for the endogeneity but also measurement error. The final robustness issue is the presence of outliers. It
is also possible that outliers in the data are driving the results. To identify outliers I use Hadi’s methodology.
His methodology does identify several states in each period as potential outliers. To ensure that these data
points are not driving the results, I estimate the model with them excluded; the results do not change.8
Conclusion
According Olson (1982), a special interest group serves its members’ interests by getting a larger share of the
society’s production for its members. In other words, an SIG, in principle, serves its members either by making
the pie the society produces larger, so that its members get larger slices even with the same shares as before or
alternatively by getting larger shares or slices of the social pie for its members (Olson 1982, 42). SIGs tend to
favor the latter. Favoring redistribution over efficiency reduces economic growth. In other words, according to
Olson (1982), societies in which SIGs are strong grow slower. Nevertheless, the empirical evidence regarding
the relationship between SIG strength and economic growth is inconsistent. This is partly the result of indirect
measures of SIG strength used in the analyses. Several studies such as McCallum and Blais (1987), Heckelman
(2000), Coates and Heckelman (2003), and Gray and Lowery (1999) use the number of groups in a country or
in a state to measure the SIG strength while others such as Choi (1983), Weede (1984, 1986), and McCallum
and Blais (1987), Nardinelli, Wallace, and Warner (1987), Gray and Lowery (1988), and Garand (1992) use the
length of time a country or a state enjoys political stability as a measure. A few studies use the unionization rate
(Dye 1980, Choi 1983, and McCallum and Blais 1987).
Using a more direct measure from Thomas and Hrebenar (1999) for 48 contiguous U.S. states, I provide new
evidence of a negative relationship between SIG strength and economic growth. Thomas and Hrebenar (1999)
caregorize the strength of SIGs in each state into 5 categories: dominant, dominant/complementary,
complementary, complementary/subordinate, and subordinate. I find that the growth rate of median income
decreases by 12 percentage points over a decade in the states in which the special interest groups are dominant
relative to the states in which the interest groups are complementary/subordinate. To put this finding in
perspective, up to 35 percent of the growth differential between Louisiana and Vermont in 1980s, and up to 40
percent between Minnesota and Connecticut in 1990s are potentially explained by different SIG strengths in
those states.
7
The correlation coefficients between Thomas and Hrebenar (1999) measure and the Morehouse (1981) measure is higher
than 0.6 while it is approximately 0.55 between Thomas and Hrebenar (1999) measure and the Sharkansky (1969) meaure.
8
The results are available upon request.
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Table 1 SIG strength in U.S states in the 1980s and the 1990s*
Dominant
Dominant/
Complementary
Complementary
Complementary/
Subordinate
Alabama
Florida
+Nevada
South Carolina
West Virginia
-Alaska
Arizona
Arkansas
California
++Connecticut
Georgia
Idaho
+Illinois
+Iowa
+Kansas
Kentucky
-Louisiana
+Maryland
-Mississippi
Montana
Nebraska
-New Mexico
Ohio
Oklahoma
Oregon
-Tennessee
Texas
Virginia
+Washington
Wyoming
Colorado
+Delaware
Indiana
-Hawaii
Maine
Massachusetts
Michigan
Missouri
New Hampshire
New Jersey
New York
North Carolina
North Dakota
Pennsylvania
-Utah
Wisconsin
Minnesota
Rhode Island
-South Dakota
Vermont
*
Subordinate
States that changed categories from 1980s to 1990s are indicated with a plus or minus sign. A plus sign means that
SIGs in that state became stronger, moving to the left on the table. A minus sign indicates that they became weaker.
Connecticut moved up two categories and thus has two plus signs.
Source: Thomas and Hrebenar (1999)
Table 2 Summary statistics
Mean
Std. Dev.
Min
Max
Growth Rate of Income
0.076
0.085
-0.191
0.264
Income
36,203
5783
25,878
54431
Education
0.789
0.057
0.643
0.879
Gini
0.414
0.024
0.371
0.476
Urban
0.709
0.146
0.382
0.944
Corruption
0.187
0.192
0.004
1.060
Decentralization
0.507
0.077
0.331
0.676
Table 3 SIG strength and growth
(1)
OLS estimation
-0.614
Ln Income
(0.085)***
-0.119
Dominant
(0.038)***
Dominant/Complementary -0.078
(0.033)***
-0.050
Complementary
(0.031)*
0.700
Education
(0.312)**
-1.774
Gini
(0.689)***
0.389
Urban
(0.092)***
0.274
Corruption
(0.107)***
-0.236
Corruption2
(0.103)**
1.533
Decentralization
(1.041)*
-1.345
Decentralization2
(1.018)*
ρ
Region Dummies
Yes
(2)
ML estimation
-0.378
(0.076)***
-0.099
(0.024)***
-0.059
(0.017)***
-0.031
(0.015)**
0.529
(0.204)***
-0.924
(0.468)**
0.254
(0.065)***
0.162
(0.089)**
-0.147
(0.082)**
1.179
(0.681)**
-1.007
(0.675)*
0.575
(0.078)***
Yes
Time Dummy
Yes
Yes
Inrtercept
6.038
(1.043)***
3.482
(0.864)***
Wald Test of ρ
χ2
53.944
0.000
P-value
LM Test of ρ
χ2
P-value
N
96
41.368
0.000
96
R2/Log Likelihood
0.52
156.596
Robust standard errors in parentheses.
All test one tailed. * significant at 10%; ** significant at 5%; *** significant at 1%
Table 4 Relative effects of SIG strength
Excluded Variables
Included Variables
Dominant
Dominant
Dominant/
Complementary
Complementary Complementary/
Subordinate
-0.040
(0.022)**
-0.069
(0.024)***
-0.119
(0.038)***
-0.028
(0.015)**
-0.078
(0.033)***
Dominant/
Complementary
0.040
(0.022)**
Complementary
0.069
(0.024)***
0.028
(0.016)**
Complementary/
Subordinate
0.119
(0.038)***
0.078
(0.033)***
-0.050
(0.031)*
0.050
(0.031)*
Table 5 SIG strength and growth: IV estimation
Region Dummies
(1)
Instrument:
Morehouse
-0.617
(0.078)***
-0.046
(0.025)**
0.676
(0.300)**
-1.693
(0.674)***
0.382
(0.089)***
0.286
(0.095)***
-0.241
(0.0912)***
1.483
(1.006)*
-1.294
(0.962)*
Yes
(2)
Instrument:
Elazar
-0.615
(0.087)***
-0.076
(0.038)**
0.554
(0.351)*
-1.502
(0.744)**
0.353
(0.085)***
0.310
(0.106)***
-0.240
(0.086)***
1.766
(0.971)**
-1.529
(0.953)*
Yes
(3)
Instrument:
Morehouse & Elazar
-0.616
(0.079)***
-0.052
(0.022)***
0.654
(0.301)**
-1.659
(0.675)***
0.377
(0.086)***
0.291
(0.095)***
-0.241
(0.090)***
1.534
(0.983)*
-1.337
(0.943)*
Yes
Time Dummy
Yes
Yes
Yes
Intercept
6.114
(0.955)***
23.98
0.00
6.111
(1.112)***
5.38
0.02
96
96
6.114
(0.978)***
14.12
0.00
0.454
0.50
96
Ln Income
Lobby
Education
Gini
Urban
Corruption
Corruption2
Decentralization
Decentralization2
First Stage F
P-value
Hansen J
P-value
N
Robust standard errors in parentheses.
All test one tailed. * significant at 10%; ** significant at 5%; *** significant at 1%