ABSTRACT
 Top of page
 ABSTRACT
 1. INTRODUCTION
 2. DATA AND METHODOLOGY
 3. ESTIMATION AND RESULTS
 4. CONCLUSION
 REFERENCES
This note examines the correlation between income inequality and economic growth using a panel of income distribution data for 3,109 counties of the U.S. Using the System Generalized Method of Moments (GMM) approach, we find that for the entire sample of 3,109 counties, an increase in a county's level of inequality has a significant negative relationship with future economic growth. In reality, however, the magnitude, sign, and significance of this relationship is not necessarily uniform across all regions of the U.S., motivating the need to examine regional differentials in the relationship between inequality and growth. Consequently, we split our dataset into metropolitan and nonmetropolitan counties, into the eight Bureau of Economic Analysis regions, and into regions of different political affiliation and economicdependence typology. Our results show considerable heterogeneity in the relationship between inequality and growth across these regions.
1. INTRODUCTION
 Top of page
 ABSTRACT
 1. INTRODUCTION
 2. DATA AND METHODOLOGY
 3. ESTIMATION AND RESULTS
 4. CONCLUSION
 REFERENCES
The connection between income inequality and growth has captured the attention of economists for over 50 years, since the seminal work of Kuznets (1955). Different models and methods of analysis, and different data sets have yielded different results, sometimes sharply different, sometimes modestly. Panizza (2002) notes that a possible reason for these conflicting results is that many studies on the inequalitygrowth nexus typically use crosscountry data. A wellknown problem with crosscountry studies is the quality and comparability of the crosscountry inequality data. To sidestep this problem, Partridge (1997), and Hasanov and Izraeli (2011), among others, examine the inequalitygrowth relationship using data on the 48 contiguous U.S. states. Fallah and Partridge (2007) and Atems (2013) investigate the countylevel determinants of inequality on growth; Glaeser, Resseger, and Tobio (2009) study inequality in cities, while Wheeler and La Jeunesse (2008) examine trends in neighborhood income inequality in the U.S.
The aforementioned studies, however, do not consider a detailed examination of heterogeneity in the relationship between growth and inequality. Fallah and Partridge (2007) note that spatial differences in agglomeration economies and economic incentives can produce different channels through which inequality affects growth. One such mechanism, suggested by Galor and Zeira (1993) is that high inequality makes it difficult for individuals at the lower end of the income distribution to invest in their human capital, thereby decreasing growth. To the extent that credit barriers to the poor exist, capital markets may be unable to uplift low income individuals from their cycle of low human capital, further weakening long run growth. Due to the immense spatial variation in rates of educational attainment, and hence human capital across U.S. counties, it is necessary to examine heterogeneity in the inequalitygrowth relationship. Furthermore, Hasanov and Izraeli (2011), using U.S. statelevel data document nonlinearity in the relationship between inequality and growth, while using a spatial econometric approach, Atems (2013) examines the spatial dynamics of growth and inequality using U.S. countylevel data. To the extent that the inequalitygrowth relationship may be nonlinear, and that there exist a spatial dimension to it suggests that there is need to consider geographic heterogeneity or regional differentials in the relationship between inequality and growth.
Another channel through which inequality affects growth, emphasized by Alesina and Rodrik (1994) and Alesina and Perotti (1996) is the role of political (in)stability on investment and growth. Alesina and Perotti (1996) hypothesize that high inequality in income distribution may lead to widespread discontent among the impoverished population, which may translate into civil unrest. Economic agents respond to such civil unrest by decreasing the scope of their economic activities to minimize risks, thereby stifling the rate of growth. Persson and Tabellini (1992), and Bertola (1993) discuss several other political economy channels. While such political economy channels are much less prevalent for advanced economies like the U.S., the rise of the Occupy Wall Street movement and similar movements have made it clear that the role of political economy channels, and the inextricable link between politics, inequality and growth need to be considered. Volscho and Kelly (2012) document that congressional shifts to the Republican Party in the U.S. that diminished union membership and lowered top tax rates played a strong role in the rise of the “superrich.” Consequently, we examine the inequalitygrowth nexus across counties of different political affiliation. That is, we split our sample of 3,109 counties into three subsamples: Counties in Republicanleaning (Red) states, Democraticleaning (Blue) states, and Independent (Purple) states, and examine the inequalitygrowth link across counties in these political regions.
Furthermore, because regions' economic and social characteristics are important determinants of their income distribution and growth dynamics, it is necessary to examine the inequality–growth relationship across regions of varying economicdependence typology. At the crossnational level, the manufacturing sector and industrial development have had important roles in the growth of countries such as China, South Korea, Taiwan, and Indonesia. Some of these countries have managed to achieve growth with equity, while other countries, particularly in agriculturedependent SubSaharan Africa, are witnessing slow growth and high inequality (Kniivila, 2007). To the extent that the inequalitygrowth linkage varies in countries with varying economic dependence typology, it may also vary across U.S. counties with similar differences in economicdependence typology. Consequently, we examine the link between growth and inequality in counties in six nonoverlapping economicdependence classes: farmingdependent, manufacturingdependent, miningdependent, federal and state governmentdependent, servicesdependent, and nonspecialized counties.
To further motivate the need to consider regional differentials in the inequalitygrowth nexus, consider Figure 1 which shows the spatial variation in average annual per capita real income growth rates of U.S. counties in the 1970s and 2000s. A glimpse of Figure 1 shows that in the 1970s, real per capita income grew approximately 3 percent annually (between 0.02 and 0.06 in most regions). A particularly striking feature in panel A of Figure 1 is that counties in the Great Plains experienced relatively low growth, with many experiencing negative growth. Several counties on the East and West coasts recorded positive growth. Three decades later (panel B), while some counties in the Great Plains still had low growth rates, others had progressed in terms of growth. In the Great Plains, except for counties in the central Midwestern states, many northern and southern counties experienced positive growth (between 0.02 and 0.06). The East and West Coasts, which had recorded positive growth in the 1970s witnessed reduced growth rates (0.0–0.02) in the 2000s. Equally noteworthy are counties in the Gulf Coast which saw declining per capita growth in the 2000s.
The growth patterns described above have been accompanied by a reduction in the share of income earned by the bottom 90 percent of households. Figure 2 shows the spatial variation in inequality in U.S. counties in 1970 and 2000 measured by the Gini coefficient. The figure shows that in the 1970s (panel A), inequality was generally low (less than 0.38) in many U.S. counties. High inequality (over 0.38), however, is present in counties in southern and Gulf states such as Arizona, New Mexico, and Texas. Patches of high inequality are also apparent in the Dakotas, particularly in Dewey (0.54) and Shannon (0.52) counties of South Dakota and Emmons (0.45) county in North Dakota. Panel B shows the distribution of family income 30 years later. By 2000, inequality had risen across many counties, even in the hitherto lowinequality counties. The West and Northeastern coasts, which in the 1970s recorded low inequality, were, by 2000, experiencing severe income inequality (over 0.44). Counties in the southern states, as well as those in the Gulf coast which were already experiencing high inequality in 1970, saw higher inequality in 2000.
Figures 1 and 2 show significant regional variations in growth and inequality. Levernier, Partridge, and Rickman (1995, 1998, 2000) document similar variations in U.S. statelevel and countylevel income inequality, while Fallah and Partridge (2007) point out that the relationship between inequality and growth may differ between more and less populated counties. Splitting their sample into metropolitan and nonmetropolitan counties, Fallah and Partridge (2007) find a significant positive relationship for urban counties, and a significant negative relationship in rural counties. These regional variations in growth and inequality shown in Figures 1 and 2 and further documented by Levernier et al. (1995, 1998, 2000), and that the relationship between inequality and growth may vary between urban and rural counties suggests that it may also vary across major geographical regions of the U.S. Scatter plots of growth against inequality seem to support this idea. Panel A of Figure 3 shows a simple scatter plot of the average annual real per capita growth rate against inequality for metropolitan and nonmetropolitan counties, and the eight Bureau of Economic Analysis (BEA) regions of the U.S., namely the Far West, Great Lakes, Mideast, New England, Plains, Rocky Mountains, Southeast, and Southwest. Panel B shows a scatter plot of the relationship between changes in the two variables. Both panels of Figure 3 seem to suggest that regional differentials do exist in the relationship between (changes in) growth and (changes in) inequality. Consequently, there is need to examine such regional heterogeneity in the relationship between inequality and growth in greater detail.
The purpose of this note is to explicitly examine regional heterogeneity in the relationship between growth and inequality using a panel of income distribution data for 3,109 counties or countyequivalent administrative units of the U.S. One advantage of using countylevel data is that much of the measurement errors that sometimes plague crosscountry studies, such as intertemporal and international comparability of data are reduced, since these data are collected using the same techniques for the same time periods within the U.S. In addition, as pointed out by Ravallion (1998), using aggregate data might in fact conceal the effects of inequality on growth. Therefore, the use of countylevel data provides a more comprehensive analysis of the link between inequality and growth, and pushes this inquiry one step closer toward more tenable results. Unlike previous countylevel inequalitygrowth studies (e.g., Fallah and Partridge, 2007) that use crosssectional (between) data, our analysis employs panel data to estimate the dynamic relationship between inequality and growth using the System GMM (within) approach. Therefore, it should be noted that the results presented hereinafter are not directly comparable to previous countylevel studies. As noted by Partridge (2005), crosssectional (or between) approaches focus on the longrun relationship between inequality and growth, while the within approach employed in this paper focuses on the shortrun relationship between changes in inequality and changes in growth.
Using various model specifications, our results indicate that for the full sample of 3,109 counties, increases in inequality are significantly related to decreases in future growth. Following Barro (2000) and Hasanov and Izraeli (2011), we test for nonlinearity in the inequalitygrowth relationship, but find no significant evidence in favor the nonlinear specifications. We however, find significant evidence of regional differences. When we split the data into metropolitan and nonmetropolitan counties, and into the eight BEA regions, our results show negative and significant relationships for both metro and nonmetro counties, and counties in the Far West, Plains, Southeast, and the Southwest. Insignificant positive relationships are found for the Great Lakes and New England, while for the Rocky Mountains, and the Mideast, the relationship is positive and significant. In addition, we find significant positive relationships for counties in blue and purple states, but significant negative relationships for counties in red states. Furthermore, when we split the sample into the six economicdependence classes mentioned earlier, the coefficient on inequality is positive and significant for federal/state governmentdependent, servicesdependent and nonspecialized counties; positive but insignificant for farmingdependent counties; and negative and significant for mining and manufacturingdependent counties. Finally, when we aggregate the data to the U.S. state level, the coefficient on inequality remains negative, but turns insignificant.
2. DATA AND METHODOLOGY
 Top of page
 ABSTRACT
 1. INTRODUCTION
 2. DATA AND METHODOLOGY
 3. ESTIMATION AND RESULTS
 4. CONCLUSION
 REFERENCES
The primary sources of data for the analysis are the U.S. Census Bureau and the Bureau of Economic Analysis Regional Economic Information System (BEAREIS). The data set contains observations for 3,109 U.S. counties from 1970 to 2007. The growth model estimated considers the determinants of the average annual real per capita income growth over 10year horizons (except for the 2000–2007 period where the average annual growth rate is over a sevenyear period) at the county level.
Data on per capita personal incomes and the ensuing growth rates come from the BEAREIS. Following the convention in most economic growth and inequality studies, the average annual real per capita income growth over 10year periods is used as the dependent variable (see Partridge, 1997; Barro, 2000). On the one hand, while this longterm perspective is constrained by data limitations, on the other hand, such a longterm perspective follows the norm in the growth literature that strives to explain long rather than shortrun variations and reduces annual serial correlation from business cycle fluctuations.
The choice of independent variables that affect growth are well grounded in the literature, and their inclusion in the model shown hereinafter rests on the availability, and reliability of data for the counties of interest. These independent variables are dated at the start of each decade. By so doing, there should not be any direct endogeneity issues. By dating the variables at the start of each decade, the model also follows the convention in the literature that posits that economic growth converges to an equilibrium path based on initial conditions (Durlauf and Quah, 1999). To this end, the level of per capita income at the start of each decade is included as a regressor to account for convergence across counties. Data on other control variables come from a variety of sources. Countylevel income inequality data come from Nielsen (2002). Four education variables—the percentage of individuals with (i) less than high school education, (ii) high school, (iii) some college, and (iv) college education—are included to adequately proxy for the effects of human capital on growth. Other control variables used include the average wage per county per job, the county population density, the employmenttopopulation ratio, total nonfarm earnings per county, and the unionization rate per state.1 We begin by specifying a base growth model typical in this literature:
 (1)
where is the natural log of real per capita income of county i in period . The left hand side is the average annual growth rate of real per capita income of county i in period t. is the Gini coefficient at time . contains the other period variables that affect growth mentioned above. denotes the unobservable time effects, , the timeinvariant county effects and, is the disturbance term. Table 1 provides the descriptive statistics, definitions and sources.
Table 1. Summary Statistics, Variable Transformations, and Data Sources  Mean  Standard Deviation  Minimum  Maximum 


y: Real per capita personal income (log)a  9.796  0.385  8.421  11.457 
ineq: Gini coefficient of inequalityb  0.389  0.048  0.013  0.605 
lhsc: Less than high school educationc  0.373  0.166  0.030  0.884 
hsc: High school completion ratec  0.330  0.075  0.058  0.543 
assoc: Some college educationc  0.175  0.085  0.059  0.449 
coll: College completion ratec  0.122  0.070  0.011  0.637 
density: Population density per county (log)d  3.652  1.658  −3.317  12.031 
empratio: Employmenttopopulation ratioa  0.469  0.277  0.049  15.957 
wage: Average wage per job (log)a  10.045  0.267  8.999  11.315 
union: State union membership ratese  0.134  0.073  0.029  0.349 
earnings: Total nonfarm eranings (log)a  11.820  1.818  5.829  19.311 
4. CONCLUSION
 Top of page
 ABSTRACT
 1. INTRODUCTION
 2. DATA AND METHODOLOGY
 3. ESTIMATION AND RESULTS
 4. CONCLUSION
 REFERENCES
This note reconsiders the correlation between growth and inequality using U.S. countylevel data covering the decades of 1960–2000. Using the System GMM approach, we first examine the relationship between growth and income inequality for the entire sample of 3,109 counties, and find a significant negative relationship between changes inequality and changes in subsequent periods.
The robust negative relationship between changes in inequality and changes in growth does not necessarily imply that the magnitude and/or sign of this relationship is the same across all regions of the U.S. Because studies by Levernier et al. (1995, 1998, 2000), and Fallah and Partridge (2007) document significant variations in U.S. statelevel and countylevel income inequality, we investigate whether such regional variations affect the relationship between growth and inequality. We split our sample into the eight major regions of the U.S.—the Far West, the Great Lakes, the Mideast, New England, Plains, Rocky Mountain, the Southeast, and the Southwest—and also into metropolitan and nonmetropolitan counties. We find negative and significant coefficients on inequality for the Far West, Plains, the Southeast, the Southwest, metropolitan, and nonmetropolitan counties; positive and significant coefficients for counties in the Mideast and the Rocky Mountains; and positive but insignificant relationships between growth and inequality for counties in the Great Lakes and New England. We further examine the relationship between growth and inequality in counties in six nonoverlapping economicdependence classes, as well as counties in states of different political affiliation. We find significant growthenhancing effects of inequality in Federal and state governmentdependent, servicesdependent, and nonspecialized counties, as well as in counties in blue and purple states; and inverse relationships between growth and inequality for counties in red states, and manufacturing and miningdependent counties. These findings suggest that different transmission mechanisms of economic incentives, opportunities and obstacles, and agglomeration economies exist across regions of various geographic, political, and economicdependence typology.
A key policy implication of the results presented in this paper is that because of geographic, political, and economic differentials in the relationship between growth and inequality, a onesizefitsall incomeequalizing policy may be detrimental to some regions, while beneficial to others. For the Rocky Mountain, Mideast, servicesdependent, federal and state governmentdependent, and nonspecialized counties, our findings imply that redistribution policies aimed at decreasing income inequality might also lead to a reduction in growth, whereas these same policies might be growthaugmenting for the Far West, Plains, the Southeast, the Southwest, mining and manufacturingdependent counties. Therefore, perhaps policies aimed at decreasing income inequality ought to be more decentralized and/or localized.