Exploring Changes in Poverty in South Carolina During the Great Recession Using a Spatial Durbin Model

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1 Exploring Changes in Poverty in South Carolina During the Great Recession Using a Spatial Durbin Model Willis Lewis, Jr. Winthrop University Christopher Johnson University of North Florida Received: 06/12/2018 Accepted: 07/26/2018 Abstract In this paper, we use a spatial Durbin model (SDM) to analyze poverty in South Carolina - a predominantly rural state with large pockets of persistent poverty counties. This allows for identifying specific factors in surrounding areas that influence local poverty rates, something missing from the existing literature. Earlier research relied on the general spatial model that we now know is not appropriate in applied research. We find poverty rates increased with more retirees and kids, more single moms, and higher average TANF payments in a county. There were no significant effects from income and job growth. Population density was the only variable with a negative effect during the study period 2003 to Introduction The war on poverty has lasted for decades and, unfortunately, will continue as long as there are economic contractions like the Great Recession. According to data from the Census Bureau, the official poverty rate for the United States was 14.9 percent in This is slightly higher than the year before the start of the Great Recession and has remained above 14 percent for the last few years. This is the highest since 1960 when the poverty rate was over 20 percent. In comparison, the poverty rate for the nation was 12.4 percent in Poverty rates for South Carolina was higher in each time period 22.1 in 1960, 14.1 percent in 2000 and 17.6 percent in Unlike the growth of the 1990s, the recent United States economy experienced a major recession with a relative weak recovery. Given the pockets of persistent poverty, the rural South faired much worse than the nation after the Great Recession. Periods of strong economic growth, like the 1960s and 1990s, generally resulted in reduced poverty (Anderson, 1964; Blank, 2000). As a result, government officials often consider job creation as the panacea for reducing poverty. However, as with any input, there is a point of diminishing returns. For example, Anderson (1964) found the impact of growth diminished over time. Hence, economic growth alone may not have the anticipated long-term effect of reducing poverty (Larson, 1989; Johnson et al., 2011). Previous studies focused on explaining the variation in poverty across regions and measuring the effects of economic growth on poverty (Anderson, 1964; Larson, 1989; Triest, 1997; Blank, 2000; Levernier et al., 2000). These studies explained the variation in poverty using job growth, unemployment rates, per capita income, race, educational attainment, and gender of head of household. The growth of spatial econometrics allowed for improved poverty studies to account for the importance of location in explaining the variation in poverty rates across the nation. Crandall and Weber (2004) researched the importance of social capital in reducing poverty using tract-level data. Their study also researched the extent of spatial spillovers in poverty rates. Rupasingha and Goetz (2007) improved the literature by testing for the impact of political competitiveness and social capital on county-level poverty rates. These two studies confirmed the existence of 54

2 spatial dependence in poverty rates. In other words, the poverty rate in one county varies with poverty rates in surrounding counties. Think about the southern Black Belt, the Mississippi Delta, or rural Appalachia. While these studies greatly improved the literature, they have one major pitfall and left one question unanswered. First, they used the combined spatial autoregressive model (SAC) which recent research by Lesage (2014) details why regional scientists should not use this method as the specification may result in contaminated parameter estimates. Policies created using biased or misspecified results may be counterproductive in poverty reduction efforts. Second, the studies did not identify which factors in the surrounding counties are significant in explaining poverty in the local county. Understanding regional spillovers allows for more targeted policies. This research addresses those issues. Our research advances the current poverty literature by employing the spatial Durbin model to separate the direct effect (within a county) of the explanatory variables from the indirect effect (to/from surrounding counties) the spillovers. This will give insight into the regional mechanisms influencing poverty and may help state officials determine the appropriateness of regional policies for combating poverty. Our study restricts the sample to South Carolina, which embodies the southern Black Belt. This is important due to weaker economic growth in the region, which limits the effect of existing poverty reducing policies. In 2014, more than half of the counties in the state had poverty rates above 20 percent. In addition, there are twelve (26.1 percent) persistent poverty counties in the state. Successful strategies in a high poverty region should be applicable to all regions. We also analyze changes in poverty rates during the Great Recession to determine if different approaches are required at different times of the business cycle. The existing research has not fully addressed these issues. The next section of the paper describes the data and the econometric approach employed. Then, we present key results from the model. We conclude with a discussion of the results and policy implications. 2 Data and Econometric Model The data used in this study comes from several government agencies. First, county identifiers were developed from the Beale codes from the United States Department of Agriculture. Employment data was obtained from County Business Patterns provided by the United States Census Bureau. Poverty data was also obtained from the Census Bureau. Regional Economic Information System (REIS) from the Bureau of Economic Analysis provided counties in South datacarolina on per covering capita poverty income rates from 2003 and to Temporary Counties are used Assistance for Needy Families (TANF) payments. Population data was obtained from South Carolina Department of Health and Environmental Control. This study uses all 46 counties in South Carolina covering poverty rates from 2003 to as the spatial unit as counties within a state tend to be more similar than states in distant regions within the nation. Counties are used as the spatial unit as counties within a state tend to be more similar than states in distant regions within the nation. Figure 1 shows the spatial distribution of poverty in South Carolina. This Figure 1 shows the spatial clustering distribution of poverty provides ofa compelling povertyexample in South of the importance Carolina. of controlling Thisfor clustering of poverty provides a compelling example of the importance of controlling for spatial dependence in poverty. spatial dependence in poverty. Figure Poverty Rates Figure 1: 2003 Poverty Rates 55

3 Detailed descriptions of spatial econometric models are presented in Anselin (1988). LeSage and Pace (2009) explains the modeling techniques required to perform spatial analysis. Elhorst (2014) explains the methods used to control for spatial dependence in panel data. J. Paul Elhorst developed the MAT- LAB routines employed in this paper. These and other spatial routines are available from his website They are used in combination with routines from the spatial econometrics toolbox available from spatial-econometrics.com. Crandall and Weber (2004) and Rupasingha and Goetz (2007) found spatial dependence present in poverty rates using the SAC model. The SAC takes the following form: y it = ρw y it + α1 n + X it β + u it (1) u it = λw u it + ɛ it (2) where Y it is a nx1 vector of the dependent variable, X it is the nxk matrix of explanatory variables, and β is the k parameters estimated. In this model, λ is the spatial error autocorrelation parameter, ρ is the spatial autoregressive parameter, W is the nxn spatial weight matrix, and ɛ is the normal OLS error term with the traditional assumption of ɛ it being distributed N(0,σ 2 I n ). Each observation is for spatial unit i for each time period t. Last, 1n is a nx1 vector of ones associated with the intercept parameter α. The two spatial parameters offers increased precision but at a cost Lesage (2014). First, misspecification of one part may result in incorrect estimates of the other. In other words, the spillovers will be bias if the errors are incorrectly specified. Second, the SAC model is erroneously assumed to be a combined model with a spatial lag of the dependent variable (spatial autoregressive model - SAR) and spatial dependence in the errors (spatial error model SEM). LeSage and Pace (2009) provides the derivation of the combined model which results in the spatial Durbin model (SDM) in (3), not the SAC. The SDM and the SLX are the only models that should be considered for applied research Lesage (2014). In addition, it is one of the separable models Pace and Zhu (2012) suggests for unbiased estimates in the presence of misspecification of spatial dependence in the errors. Elhorst (2010) adds that the SDM is state of the art in spatial econometrics and is the only model to use for capturing local and global spillovers. In addition, the SDM produces unbiased estimates regardless of the true spatial component of the data and it allows for testing to see if the model collapses to the SAR or SEM. The SDM takes the form: y it = ρw y it + α1 n + X it β + W X it θ + ɛ it (3) In the SDM, local impacts are captured in X it, which is the standard nxk matrix of explanatory variables. The model includes a spatially lagged dependent variable W y it as an explanatory variable to capture the endogenous influence of poverty changes in surrounding counties. The model also includes spatially lagged explanatory variables W X it to capture the exogenous impacts from surrounding counties. The econometric specification adopted to predict county poverty rate takes the following form: y = f(ec, De, So, W y, W Ec, W De, W So) (4) where Ec represents economic variables, De represents demographic variables, So represents social capital, W y represents poverty rates in surrounding counties, W Ec represents economic activity in surrounding counties, W De represents demographic characteristics of the surrounding counties, and W So represents social capital in surrounding counties. Economic variables include the change in per capita income, job growth, and manufacturing employment share. Demographic variables include black population share, elderly population share, kid population share, single mom share, the average TANF payment, and population density. Last, a social index is calculated to measure social capital. Descriptive statistics are presented in Table 1. Job growth measures the annual change in jobs in the local county. The variable provides a measure for the strength of the local economy. It is hypothesized that job growth represents a stronger economy. Rupasingha and Goetz (2007) found a positive relationship between employment growth and poverty in their model for the rural South. The coefficient on this variable is expected to be negative as more job opportunities should reduce the number of individuals living in poverty. 56

4 Table 1: Descriptive Statistics Variable Mean Minimum Maximum Poverty Percent Income Growth Manufacturing Share Job Growth Black Share TANF Elderly Share Single Mom Share Pop density Kid Share Change in per capita income is also included to capture the strength of the local economy. It is believed that greater incomes exemplify healthy, diversified economies that encourage economic growth which should reduce poverty. Economically robust counties tend to have higher per capita income. These counties also tend to have higher land values, larger tax collections, better school systems, and more diversified economies. Thus, the coefficient on this variable is expected to be negative as poverty is expected to decrease as per capita income increases. Manufacturing employment share measures the ratio of manufacturing employment to total employment in the local county. The variable provides another measure for the strength of the local economy. Rupasingha and Goetz (2007) found a negative relationship between poverty and the manufacturing sector in the rural South. Previous research by Lewis et al. (2012) found manufacturing is the dominant industry in most rural counties and South Carolina in general. The decline of manufacturing in the Southeast has contributed to high unemployment in rural areas. High unemployment results in a larger poverty share. Moreover, manufacturing in rural counties tend to be routine industries with relative low wages while metro counties tend to have more high tech manufacturing with relative high wage jobs. As a result, it is difficult to determine the sign of this coefficient. Black population share is often used in poverty research as a measure of the legacy of poverty and/or as a demographic control variable. The Black Belt in the southeast corresponds to counties with high rates of poverty, unemployment, welfare usage and a large share of blacks. Swanson et al. (1994) found a strong association between poverty and the share of African Americans in 1970 and Saenz (1997) also found higher poverty in areas with larger clusters of African Americans. The coefficient on this variable is expected to be positive. Temporary Assistance to Needy Families (TANF) is a government social welfare program for low-income families. There are high recidivism rates among welfare users. Poverty increases as more individuals join welfare lines. We use the average TANF payment to capture the relative size of the low-income population in the county. The coefficient on this variable is expected to be positive. The elderly share and kid share are commonly used to measure the number of individuals out of the labor force. More elderly and kids tend to increase poverty rates. The coefficients on these variables are expected to be positive. Female head of household is another demographic control variable commonly used in poverty studies. Levernier et al. (2000) and Johnson et al. (2011) found higher poverty in areas with a large number of households with female heads. The data do not allow for extraction of single female heads. We use single mom share of all mothers as a proxy. The coefficient on this variable is expected to be positive. Population density is often used as a measure for the rural/metro scale. Highly dense areas are more metropolitan with greater development while more rural areas are less dense. As such, the coefficient on this variable is expected to be negative. Social index is an index of social capital constructed using principal component analysis as described in Rupasingha et al. (2006). The index includes values for good social capital like bowling alleys and golf courses, civic groups, business associations, bad social capital (crime rates), charitable giving, and religious organizations. The authors found a positive relationship between economic growth and social capital. As a result, the coefficient on this variable is expected to be negative, as greater social capital should increase growth, which will decrease poverty. 57

5 3 Results lost 190 jobs a year, which is not conducive to reducing poverty. Another feature that makes reducing poverty difficult in South Carolina is the fact that 53 percent of all children are born to single mothers. Table 1. Descriptive Statistics Variable Mean Minimum Maximum Poverty Percent Income Growth Manufacturing Share Job Growth Poverty rates ranged fromblack 10 Share to 42 percent in 0.37 the study period Figure shows the poverty rates in Notice the clusters of hightanf poverty counties grew 0.03 larger since On0.13 average, counties in South Carolina Elderly Share lost 190 jobs a year, which Single ismom not Share conducive 0.54 to reducing0.29 poverty. Another 0.83 feature that makes reducing Pop density poverty difficult in South Carolina is the fact that 53 percent of all children are born to single mothers. Kid Share Figure Poverty Rates Figure 2: 2014 Poverty Rates Using the steps outline by Elhorst (2014), we find the spatial Durbin model is appropriate to model poverty rates. Recall, the spatial Durbin model includes a spatially lagged dependent variable and spatially lagged independent variables. We use the Wald test and the LR test to see if the spatial Durbin model simplifies to the spatial lag or the spatial error model. If θ + ρβ = 0 then the spatial Durbin model reduces to the spatial error model Elhorst (2014). Similarly, if θ = 0 then the spatial Durbin model reduces to the spatial lag model. The significance of the Wald test and the LR test in Table 2 concludes that our model cannot be reduced to the spatial error model or the spatial lag model. These findings confirm that the spatial Durbin model is appropriate. Table 2: Tests for the Appropriateness of the SDM Test Test Value p-value Wald Test Spatial Lag LR Test Spatial Lag Wald Test Spatial Error LR Test Spatial Error Using the spatial Durbin model, we calculate the direct, indirect, and total effect of each independent variable. The results of the spatial Durbin model are presented in Table 3. The direct effect measures the impact of changing an independent variable in one county on poverty rates in that county. This change may influence surrounding counties, which, in turn, may feed back into the poverty rate in the initial county. The indirect effect measures the impact of changing an independent variable in a surrounding county on poverty rates in the initial county. First, the significant spatial autocorrelation ρ coefficient confirms local poverty rates grow in direct relationship with contiguous poverty rates. As expected, local poverty rates increase with larger shares of elderly and kids, more single moms, and higher average TANF payments in a county. This is evident from the positive and highly significant total effect for these variables. These groups generally have reduced or low income making poverty more likely. Insignificant effects from income and job growth was unexpected as more jobs and a higher income are often suggested as a requirement for reducing poverty and are commonly used as the foundation of most antipoverty policies. These were the only variables in the model that did not have any significant effects. The positive sign on the index for social capital was not expected. The sign for the indirect and total effect for black population share was not expected as well. The direct effect was positive but the indirect effect was negative. The total effect was the same as the indirect effect 58

6 Table 3: Spatial Durbin Model Results Coefficient t-stat t-prob Direct Income Growth Manufacturing Share *** Job Growth Black Share *** TANF *** Elderly Share Social Index * Single Mom Share *** Pop density ** Kid Share Indirect Income Growth Manufacturing Share Job Growth Black Share *** TANF *** Elderly Share *** Social Index *** Single Mom Share Pop density *** Kid Share *** Total Income Growth Manufacturing Share ** Job Growth Black Share TANF *** Elderly Share *** Social Index *** Single Mom Share ** Pop density *** Kid Share *** Spatial autocorrelation *** R Note: *** Significant at 1% level; **Significant at 5% level; *Significant at 10% level indicating the strength of the regional demographic composition on poverty rates. Oddly, the total effect was insignificant while the direct and indirect effects were significant. As stated earlier, the indirect effect captures the impact of changes in independent variables in surrounding counties on the local poverty rate. The results show local poverty rises with increases in regional TANF payments, elderly population, social capital, and kid population. Only regional population density and black population share had a significant negative relationship with local poverty rates. Population density was the only variable in the model that had a significant negative total effect. First, the significant spatial autocorrelation ρ coefficient confirms local poverty rates grow in direct relationship with contiguous poverty rates. As expected, local poverty rates increase with larger shares of elderly and kids, more single moms, and higher average TANF payments in a county. This is evident from the positive and highly significant total effect for these variables. These groups generally have reduced or low income making poverty more likely. Insignificant effects from income and job growth was unexpected as more jobs and a higher income are often suggested as a requirement for reducing poverty and are commonly used as the foundation of most antipoverty policies. These were the only variables in the model that did not have any significant effects. The positive sign on the index for social capital was not expected. The sign for the indirect and total effect for black population share was not expected as well. The direct effect was positive but the indirect effect was negative. The total effect was the same as the indirect effect indicating the strength of the regional demographic composition on poverty rates. Oddly, the total effect was insignificant while the direct and indirect effects were significant. As stated earlier, the indirect effect captures the impact of changes in independent variables in surrounding counties on the local poverty rate. The results show local poverty rises with increases in regional TANF 59

7 payments, elderly population, social capital, and kid population. Only regional population density and black population share had a significant negative relationship with local poverty rates. Population density was the only variable in the model that had a significant negative total effect. 4 Discussion This study improves our understanding of poverty in a predominantly rural southern state. However, there are limitations to this study. First, the findings of this study are limited to the county level. Caution must be used when generalizing the results to the city or census tract level. Next, we are concerned with the impact of surrounding counties so we use all contiguous counties for the contiguity matrix, which prevents exploration for the optimal weight matrix. Understanding changes in poverty rates should help government officials improve programs designed for poverty reduction. Population density was the only variable in the study that had a significantly negative relationship with poverty rates. This negative relationship applies to the local county, as well as the neighboring counties. In other words, there are lower poverty rates in more densely populated regions, most likely, metro and suburban areas. Figure 3 shows how poverty rates grew over the study period. There are 21 metro counties in South Carolina and most of them are along the I-85 and I-20 corridors. Most economic growth in the state originates in these counties. These are the lightly shaded regions, which had the smallest increase in poverty rates during the study period. Figure 3. Change in Poverty Rates, Figure 3: Change in Poverty Rates, The coefficients for job growth and income growth were positive but not The coefficients for jobsignificant. growththis and was not income expected. However, growth job growth weretends positive to have mixed but results not in significant. This was not expected. However, job growth tends to have mixed results in poverty studies. Rupasingha and Goetz poverty studies. Rupasingha and Goetz (2007) and Crandall and Weber (2004) found a (2007) and Crandall and Weber (2004) found a significant and negative relationship between poverty and job growth. Levernier et al. significant (2000), and negative conversely, relationship found between poverty no significant and job growth. relationship Levernier et al. between poverty and job growth. Income growth was (2000), included conversely, found as an no significant additional relationship measure between poverty for and thejob level growth. of development in the local economy. However, there was no impact on poverty in South Carolina during the Great Recession. This Income growth was included as an additional measure for the level of development in the finding supports earlier studies that concluded economic growth alone might not have the anticipated longterm effect of reducing poverty (Larson, 1989; Johnson et al., 2011). This may explain our findings for South local economy. However, there was no impact on poverty in South Carolina during the Carolina during this study Great period. Recession. This finding supports earlier studies that concluded economic growth Manufacturing share was alone the might only not have significant the anticipated long-term economic effect of variable reducing poverty in(larson our 1989 model. Manufacturing is the dominant industry in most counties in South Carolina. Lewis et al. (2012) found manufacturing employment was higher in rural South Carolina counties with high unemployment rates. As a result, higher poverty is expected in counties with a large share of manufacturing employment. Increasing the share of blacks in the local county increases the poverty rate in that county. However, having a larger share of blacks in neighboring counties decreases the poverty rate in the local county. This 60

8 is surprising as the Black Belt passes through the state where clusters of counties tend to have high poverty combined with a large black population. The direct effect and the indirect effect were both significant but the effects cancel each other resulting in an insignificant total effect. All the demographic control variables had significant positive total effects. However, there were mixed results for the direct and indirect effects. Elderly share and kid share had insignificant direct effects but significant indirect effects. The impact of single moms was the opposite. Social capital has been shown to increase development Rupasingha et al. (2002) and reduce poverty (Crandall and Weber, 2004; Rupasingha and Goetz, 2007). However, having more high poverty neighbors reduces the impact of social capital with respect to reducing poverty Crandall and Weber (2004). We find a positive relationship with poverty and social capital. This is the only variable in the model where the indirect effect is greater than the direct effect. In other words, social capital in surrounding counties has a greater impact on local poverty compared to own-county social capital. This may be due to the layout of South Carolina counties. Most rural counties lie adjacent to metro counties. Most job growth, and social capital, occur in the metro areas that lie along the interstates (see Figure 3). Hence, there would be higher poverty in the rural counties surrounding the metro counties with greater social capital. Our results give an explanation as to why reducing poverty in predominantly rural states is extremely difficult. Population density is the only variable in our model with a significant negative relationship with poverty over the twelve-year period. In addition, the results suggest the typical policy approach job creation has no significant effect on poverty rates. Similarly, government officials actively recruit manufacturing plants for job creation. Our results suggest this practice may help with providing jobs but does little for reducing poverty. Using the spatial Durbin model, we illustrate the importance of factors in surrounding areas in modeling poverty rates. This highlights the importance of controlling for spatial dependence when modeling poverty. This also supports having a regional approach to reducing poverty as oppose to a county-by-county approach. A few questions remain. Future research needs to closely examine the relationship between metro growth and rural poverty. It may be possible to target urban sprawl to specific rural regions. Next, more investigation is needed to understand the mixed results between poverty and job growth. The mixed results in the literature suggest other policy approaches are required for reducing poverty. In addition, future research should seek to understand the relationship with black population share and poverty in the local county relative to surrounding counties. Answering these questions will improve the current understanding of poverty rates. References Anderson, L. (1964). Trickling down: The relationship between economic growth and the extent of poverty among American families. Quarterly Journal of Economics, 78(4): Anselin, L. (1988). Spatial econometrics: Methods and Models. Kluwer Academic Publishers, Boston. Blank, R. (2000). Fighting poverty: Lessons from recent US history. Journal of Economic Perspectives, 14(2):3 19. Crandall, M. S. and Weber, B. A. (2004). Local social and economic conditions, spatial concentrations of poverty, and poverty dynamics. American Journal of Agricultural Economics, 86(5): Elhorst, J. P. (2010). Applied spatial econometrics: Raising the bar. Spatial Economic Analysis, 5(1):9 28. Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer, Heidelberg. Johnson, C., Formby, J., and Kim, H. (2011). Economic growth and poverty: a tale of two decades. Applied Economics, 43(28): Larson, D. K. (1989). Transitions of poverty amidst employment growth: Two nonmetropolitan case studies. Growth and Change, 20(2): Lesage, J. P. (2014). What regional scientists needs to know about spatial econometrics. Review of Regional Studies, 33(3): LeSage, J. P. and Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press, Boca Raton. Levernier, W., Partridge M., and Rickman, D. (2000). The causes of regional variations in US poverty: A cross-country analysis. Journal of Regional Science, 40(3):

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