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Plantation Economics, Violence, andSocial Well-being: The Lin gering Effectsof Racialized Group Oppression onContemporary Human Development inthe American SouthJeremy R. Porter a

a the City University of New York, Brooklyn College, New York,USA

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To cite this article: Jeremy R. Porter (2011): Plantation Economics, Violence, and Social Well-being: The Lingering Effects of Racialized Group Oppression on Contemporary Human Developmentin the American South, Journal of Human Development and Capabilities: A Multi-DisciplinaryJournal for People-Centered Development, 12:3, 339-366

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Plantation Economics, Violence, and Social Well-being: The Lingering Effects of Racialized Group Oppression on Contemporary Human Development in the American South

JEREMY R. PORTER Jeremy R. Porter is based at the City University of New York, BrooklynCollege, New York, USA

Abstract Historic patterns of racialized oppression, discrimination, andprejudice have been linked to contemporary levels of racialized inequality.Such patterns are thought to be created and maintained through a series of institutions aimed at limiting access to resources for some while openingdoors for others. It is expected that patterns of historical racialized inequality

are the by-product of a historical lack of investment in the cultural capital of the local community, which later manifests itself in the form of low levels of human development, both in relational and absolute terms. In order to testthis pattern in the American South, this link is tested using historical and con-temporary data from the US Census Bureau, the National Institute for Literacy,the Bureau of Economic Analysis, the Center for Disease Control, the Histori-cal American Lynching Project, and the Negro Participation Survey. Spatially-centered nested regression models provide support for this thesis through theidentication of links to persistent patterns of underdevelopment in counties with a history of low levels of non-white education, school desegregation,racialized group mobilization, agricultural means of production, and ahistory of oppression through lynchings.

Key words: Human development, South, Lynching, Discrimination,Historical

Introduction Historic patterns of racialized oppression, discrimination, and prejudice havebeen linked to contemporary levels of racialized inequality (Morrison, 2006).

Such patterns are thought to be created and maintained through a series of institutions aimed at limiting access to resources for some while opening

Journal of Human Development and Capabilities Vol. 12, No. 3, August 2011

ISSN 1945-2829 print/ISSN 1945-2837 online/11/030339-28 # 2011 United Nations Development ProgrammeDOI: 10.1080/19452829.2011.576659

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doors for others (Lucas, 2001). Examining the effects of such inequality inrespect to the ecological context in which it has been developed, maintained,and thrived over a given period of time is important in order to better under-stand the sub-cultural structure of smaller portions of our larger society and

the inter-workings of the cultural orientations within. These cultural orien-tations are to be understood as deeply rooted and embedded ‘ways-of-life’that are generationally passed and directly affect an individual’s ideological view of the world in which they live (Messner et al. , 2005). While it hasbeen documented that movement beyond such patterns is possible over varying temporal periods, and in fact examplars do exist, trends haveshown that these patterns often linger through links to unseen forces tiedto local communities and deeply entrenched ways of life (Morrison, 2006).

This article takes aim at understanding such lingering effects in relation toaggregate measures of well-being, which is conceptualized andoperationalizedas human development as measured by the sub-national Human DevelopmentIndex (HDI) introduced by Porter and Purser (2008). The index is a closeapproximation of the United Nations’ Human Development Index, scaleddown to more local sub-national levels. Here an emphasis is placed on under-standing the unequal spatial distribution of human development across the American South and the historical circumstances that not only brought abouttheir existence, but have also allowed them to persist over a half century, which has been marked by progress and innovation at a rate never beforeseen. Where high levels of inequality and group domination were historically present, one would expect a limiting effect on overall social progress and ulti-mately a lower level of human development for the aggregate population.

As introduced above, recent research has been undertaken to understandthe spatial distribution of ‘relative human development’ within the USA at thesub-national level through application of the HDI (Porter and Purser, 2008).The index itself was created in 1989 when the United Nations sponsored aproject to develop such a multifaceted measure of human development,resulting in the original Human Development Index. The primary aim of this index was to rank countries of the world on the composite score of mul-tiple components as underdeveloped, developing, and highly developed. Thescale uses a multi-component approach in hope of incorporating more thansimple economic indicators, which have been previously employed as the

primary indicators of human development in its seminal stages (Hicks,1940, 1958; Little, 1950; Samuelson, 1950, 1956). In conjunction with aneconomic component the HDI also includes a biological measure of lifeexpectancy and a knowledge-based measure of the education characteristicsof a nation.

Porter and Purser (2008) replicated the United Nations scale at thecounty level and found extreme variations by both census region and metrostatus. Furthermore, a number of large signicant spatial clusters were ident-ied reporting highly developed regions within the Midwest and Northeastand underdeveloped regions in the American Southeast. While the research-ers were able to, both spatially and statistically, describe the HDI within the

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context of US counties, very little was done to understand the social correlatesof development. Furthermore, nothing was done to understand potentialexplanations or predictors of county status within the HDI.

The current article aims to move beyond the simple description of the

HDI by replicating the index and explaining the identied variations inhopes of better understanding the potential determinants of development.Of particular interest here is the large coverage of the identied ‘underdeve-loped’ region, and its accompanying statistically signicant spatial cluster of low development, across much of the American South. Understanding thatprevious research which has identied a linkage to social ills in parts of the American South to historical circumstance’s (Morrison, 2006), we buildupon a line of reasoning from work in social demography concerningunequal spatial distributions of social processes and ‘cultural orientations’, which serve as the back bone of the local communities and guide decision-making that is often directed at maintaining (and even strengthening) the dis-advantageous stance taken in relation to progressive attitudes. Ultimately, theresults support this stance and provide evidence of both an unequal spatialdistribution of development in the American South and a signicant link toeconomic roots, segregation practices, and inequality.

Historical circumstance and persistent inequality Cultural orientations provide a framework through which it is possible tounderstand the transmission of past policies, procedures, and general ‘waysof life’ on the contemporary populations. While this relationship is notdirectly evident, recent research has shown such a relationship has a detri-mental effect on future populations at different points spatially and geographi-cally (Morrison, 2006). Morrison (2006) goes on to show that often theseinjustices are remedied over time; however, still there are a number of instances in which historical circumstance continues to play out in amodern-day series of observable patterns of group-based inequality, both within and between communities in the USA.

Interestingly, these historical circumstances tend to manifest themselvesas both direct and derivative effects. The former referring to actual linkages of how past circumstance has molded the contemporary relations through a

series of observable patterns, while the latter refers to a much more abstracteffect focusing on social structures that transmit the effects inter-generation-ally (Morrison, 2006). The current examination is primarily interested in thedirect effects of historical racial inequality and the roots of economicproduction on patterns of aggregate development, but makes use of existingliterature in order to develop a theoretically grounded framework from which to better understand potential derivative effects through which these culturalorientations are passed from one generation to another.

Morrison (2006) goes on to point out that these lingering effects oftensurvive through a spatial dimension that restricts the uidity of cultural orien-tations in the more abstract sense and populations in the physical sense. For

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instance, the author points out restrictive covenants that did not allow certainracial groups the opportunity to relocate to many desired locations through the process of home buying. This is a well-known phenomenon that hasplayed itself out contemporarily in observable patterns of segregation,

which in turn limit access to valuable resources. It is expected here that his-torical spatial patterns of racialized group oppression in the American South will yield lingering effects in a series of characteristically similar countiesthat all exhibit modern-day low levels of human development. These patternsof oppression (operationalized through various forms of economic pro-duction, segregation, and observable inequalities) are hypothesized to havefostered a culture of hate and intolerance in which progress and overall well-being are expected to be retarded in their ability to take advantage of extra-local patterns of development.

Categorization and inequalityThe manifestations of these cultural orientations that have been highlighted inthe previous section come in the form of racialized inequality. Following the work of Tilly (1998), the patterns of stratication are effectively maintainedthrough the institutionalization of inequality-producing mechanisms. Tilly (1998) makes the point that these patterns of inequality are linked to the cat-egorization of individuals to any number of identifying groups. This develop-ment of in-groups and out-groups then results in the ‘unequal exclusion of each network from resources controlled by the other’ (Tilly, 1998, p. 8).Thus, the ability to exploit the other group while hoarding opportunitiesfor one’s own group becomes the major goal, leading to durable inequality (Tilly 1998). This idea of durable inequality is very closely linked to the ideaof effectively maintaining inequality. In fact, Lucas’ (2001) idea that theschool system is a valuable good in society that groups aspire to controlshould be considered an example of Tilly’s overall argument of the durability of inequality.

Tilly goes on to point out that the categorization of individuals intogroups leads to emulation and adaptation, which are mechanisms thatreinforce the distinction of the groups, thus reinforcing the inequality that per-sists between the groups. Furthermore, these categorical distinction gain

strength at the local level due to the lower social and nancial costs associated with their maintenance (Tilly, 1998). Therefore, variations across the geo-graphic landscape are expected to develop given the unique spatial contextin which these categorizations were both developed and the degree to which they have been successfully maintained. Finally, where these categoriz-ations are most strong, one would expect the mechanisms of opportunity hoarding and exploitation to limit the degree to which the dominant groupsof the American South (whites) are likely to reinvest in the local community.Instead, it is expected that the group is likely to invest more privately, thus lim-iting the development of the larger community through the restrictions of funds for local needs concerning education, health, and other social goods.

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Racialized group domination and segregation in the South The American South has a long and well-known history of racialized inequal-ity, segregation, and underdevelopment. The spatial variation of these socialills is not random and follows distinct geographic patterns related to historicalcircumstances surrounding group oppression and a slave-based economy (Porter and Purser, 2008). However, examinations of these geographic pat-terns have yielded results that illustrate a largely unequal distribution of wealth, income, crime, and other correlates of social well-being (Porter andPurser, 2008; Walters et al. , 1990; Woods, 1998; Messner et al. , 2005; andothers). It is expected that this unequal distribution of well-being (i.e.human development) can nd its roots in a series of interconnected historicalcircumstances ranging from the earliest of economic roots in the oppressiveplantation economies of the Old South, a continued focus on agriculture asthe primary means of production throughout the nineteenth and twentieth

centuries, the eventual formation of racialized mobilization / countermobiliza-tion groups for the purposes of fear and intimidation, the local receptions toracialized desegregation during the Civil Rights era, and the general absoluteand relative standings of non-Whites.

Plantation economies

Well prior to the Civil Rights era, public investments were determined by stateand local government policies as well as the social organization of production(see James, 1988; Walters et al. , 1990; Walters, 1992). In the South, whereplantation agriculture was the dominant form of economic production, theinterests of powerful plantation owners were served by restricting opportu-nities of a largely black tenant class. These interests became institutionalizedthrough local and state policies that imposed constraints on the availability of education resources (schools, teachers, funds) and generated a signicantreduction in black enrollments in counties dominated by plantation agricul-ture relative to other areas (Walters e t al ., 1990).

In subsequent years, although the organizational form of plantations gave way to other types of production (e.g. mid-sized farms with paid labor andtechnological capacity; see Gardner, 2002; Ruef, 2004), most of the elite

planter class remained in place for generations in the face of the well-known and long-term black out-migration (Cobb, 1990; Woods, 1998;Fossett and Siebert, 1997). Thus while plantations as organizational unitsdeclined by historical denitions, the white planter class and the ‘rules of the game’ it established remain powerful regulative forces that continue toinuence educational outcomes in the South today (see James, 1988,p. 194; Woods, 1998; Walters, 2001).

Out of the plantation system a ‘sense of group position’ may have alsoformed. As described by Blumer (1958), this sense refers to a historically-derived collective judgment about where one’s own group should stand in thesocial order vis-a -vis another and reects, in part, feelings of superiority and

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proprietary claim to certain rights and privileges. The fear or suspicion that thesubordinate group harbors designs on these perceived entitlements strengthensracialprejudices not becauseofdirect threats tomaterialadvantages theyprovide,but because such challenges threaten the dominant group’s sense of position vis-

a` -vis the subordinate group. In contexts where this sense of group position setsthe foundation for the prevailing social order, as we argue is the case in former

plantation economies, the more rmly ‘entrenched’ and ‘tenacious’ it becomes(Blumer, 1958, p. 7). Moreover, we suggest that in such contexts, rules deningracial interaction are comprehensive and encompassing of social life.

Legacy of lynching

The use of extreme violence in the past is also expected to vary with rates of human development. This hypothesis follows scholars who highlight the lega-cies of racial violence as a means to social control (Reed, 1970; Soule, 1992;Brundage, 1997; Tolnay and Beck, 1992, 1995; Bobo, 1988; Bobo and Hutch-ings, 1996). In their classic exploration of spatial variation in lynchingsacross the South, Tolnay et al. (1996) nd their occurrence more likely inareas that had larger black populations, were more dependent on cotton,and in which whites felt economic threat from black neighbors (see alsoCorzine et al. , 1988; Soule, 1992). These conditions created a social atmos-phere conducive to the use of violence as a means to maintain racially-dened caste boundaries (Beck and Tolnay, 1990; Olzak, 1990). The atmos-phere created through the historic use of racial violence in a community may have a ripple effect on race relations today.

In a study of interracial homicide in the contemporary South, Messner et al.(2005)theorizethatlocalized ‘cultural supports’rooted in historical formsof pro-duction serve as causal agents that legitimize lynching of blacks by whites. Areasinwhichlynchingstook place areposited tohave cultural orientationsconduciveto the continued use of lethal violenceas a meansof racial control. The empiricalresults generally support their thesis. Within the South, local communities oper-ationalized as counties (or county clusters) that hosted a highernumber of lynch-ing also witnessed higher rates of deadly violence in the present, particularly homicides involving black victims. Their research highlights the long-lastingimplications of previous racial violence on contemporary race relations; a

context they and others emphasize varies greatly within the South (see also Jacobs et al. , 2005; Cunningham and Phillips, 2008). Interpretation of their nd-ings leads one to hypothesize that lower levels of human development will besustained where there exists a ‘legacy of lynching’ as it is in these contexts thecultural orientations that legitimize policies of violent and, we argue, less phys-ically violent forms of racial animus predominate.

Cultural orientations and social closureUltimately, cultural orientations do not act overtly, in an independent fashion,as a pathway for the maintenance of lingering social ills (Morrison, 2006).

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Instead, there are series mediums through which these societal characteristicsmanifest themselves. One such manifestation is through the continued limit-ing of access to resources to the subordinated group, or group-based socialclosure. Often this social closure plays out in society in a much less overt

fashion temporally. For instance, while Blacks in the American South werelynched and forced to work as slaves in up through the Emancipation Procla-mation, a series of laws, statuettes, and organizational inuences continued toallow for the group domination of Blacks by their White counterparts (i.e. JimCrowe Laws, white-ight private schooling following forced desegregation,and continued residential segregation). While the overt nature of theseactions continue to weaken in time, they are no less powerful—and it may even be hypothesized that this form of subvert symbolic violence is much more damaging to progress than the physical violence employed by earlier generations because of its ‘hidden’ nature. Furthermore, both have onebasic commonality, social closure.

Social closure according to Weber (1946) is the process by which socialgroups try to maximize the potential rewards involved in a process by limitingthe access to resources seen as essential to obtaining positions where accessto those rewards is possible. This exclusionary process becomes not only atool used by those hoping to maintain their place within society, but alsobecomes accepted by those who are being excluded as a response to their place as outsiders. Parkin (1979) argues that social closure is a principlemeans for mobilizing power in an effort to preserve or change the distributionof resources and rewards in a society, and therefore plays a very relevant rolein the forms of structured inequality that exist in a society.

The overriding force behind the exploitation associated with socialclosure is the use of power that is dened as implementing one’s will over another even when the other is in opposition. Parkin (1979) metaphorically calls social closure the use of power in a downward direction, by which hemeans that those in the upper and middle class exert exclusionary practiceson those below them in the social class hierarchy in order to maintain thestructured and institutionalized class system that exists at that time. In a capi-talist society the two main exclusionary processes are property andcredentialism.

Pierre Bourdieu (1977) examined the role of the education system in the

maintenance and perpetuation of class in his book Reproduction in Education, Society, and Culture. According to Bourdieu (1977), theeducation system is a not an indifferent component of the greater society aimed at the educating and credentialing of individuals; it is instead a struc-tural mechanism for both the differentiating of individuals by backgroundand a means for the perpetuation of the culture of those most likely to beable to be successful in the educational system (those in the dominantculture). Central, then, to Bourdieu’s theory is the importance of culturalcapital in terms of the exposure to the dominant culture one may bring with them into the educational arena. As a result those in the lower classes

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build a decit of cultural capital and begin to accept the dominant culture assuperior to their own.

In a recent study examining the role of both social closure and the edu-cational system in society, Samuel Lucas’ (2001) ‘Effectively Maintained

Inequality’ shows that the simple completion of high school is not as impor-tant as the perceived quality of education one gets in high school. He positsthat socioeconomically advantaged actors secure for themselves and their chil-dren some degree of advantage wherever advantages are commonly possible(Lucas, 2001). Within the educational system, social background leads to anuneven starting point, which ultimately determines the type of educationone receives. This relates very closely to Bourdieu’s ideas of cultural capitalas the primary weapon in the ‘battleelds’ of the educational arena.

In sum, this review of the literature has identied the historic patterns of economic relations and the use of lethal violence for social control, respect-ively, as factors affecting black access to resources and victimizationthrough contemporary homicides. We interpret the ndings of these twostudies in terms of the ‘cultural orientations’ thesis: the plantation-classform of agricultural production lent itself to white beliefs that the local com-munity was ‘theirs,’ and that the subordination of blacks by legal (Jim Crow laws) and extra-legal (lynching) means, if necessary, as a means to reach ashared (at least among those in power) and desired social goal. All the while, these patterns of inequality and ‘cultural orientations’ are believed tolimit the emphasis put on the continual development of the local community and ultimately result in low levels of human development, as measured by thesub-national HDI. Thus, the focus of oppression by those in power ultimately leads to lower levels of development for all in the community.

Methodology

Sources of data

Data for this study were obtained from the National Institute for Literacy, theBureau of Economic Analysis, the Center for Disease Control, the Historical American Lynching Project (Project HAL), the Negro Participation Survey,the American Communities Project, and Summary Files 1 and 3 from the US

Census Bureau.Indicators used to compute the HDI were obtained from the NationalInstitute for Literacy (county literacy estimates), 1 the Bureau of Economic Analysis (per-capita income), the Center for Disease Control (life expectancy),and the US Census Bureau (percentage college graduates). Data from theNegro Participation Survey, collected by Mathews and Prothro circa 1960, were included as indicators of the historical dominant means of productionin the county, the existence of a race-based ‘mobilized’ organization, absoluteminority socioeconomic well-being, and historical levels of racial inequality.Next, following work by Walters et al. (1990), data were obtained fromDavid James and replicated in order to identify counties identied as having

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a dominant plantation economy in 1910. Next, data pertaining to the histori-cal existence of forced desegregation lawsuits within a county were obtainedfrom the American Communities Project and, lastly, data on the historicaloccurrence of black lynchings by county were obtained from the Project

HAL website on the Historical American Lynching Data Collection Project.

Measuring well-being using the United Nations’ Human Development Index

Social well-being and human development, as indicators of interest to socialscientists’, have been important topics in the study of international develop-ment and affairs for many years. Many correlates have been linked to low levels, including: income inequality, education, race, spatial location, unem-ployment, poverty and dysfunctional families (Murray et al. , 2005; Low et al. , 2005; Crepaz and Crepaz, 2004; Lynch et al. , 2005; Potter, 1991;Gorman and Read, 2006; Arah et al. , 2005; Buttner and Spengler, 2003;Hogan, 1978). Furthermore, many indices have been developed in hopes of better understanding and measuring levels development across geographicentities. Measuring such indicators at the sub-national level has previously relied on one-sided scales that focused solely on economic indicators(Hicks, 1940, 1958; Little, 1950; Samuelson, 1950, 1956). However, recentattempts to employ multi-item measures of development have allowed for amore well-rounded understanding (Raphael, 2004). Subsequently, many scales have developed a multi-component approach in hopes of incorporatingmore than these simple economic indicators, through the introduction of indi-cators of social and biological well-being.

The HDI was created with the hopes of diversifying the determinants of social well-being beyond just economic indicators as a measure of develop-ment. The index is constructed using three basic concepts: health, educationand command of resources (Anand and Harris, 1994). The health indicator ismeasured via the average life expectancy at birth of a specied area and isquantied as life expectancy at birth, meaning that stillborn babies are notincluded. Next, the education indicator is included in the HDI as a measureof knowledge, necessary for full participation in society and contains both a

measure of literacy and enrollment for a given area (United Nations Develop-ment Programme, 1995). For the purposes of the sub-national HDI, the de-nition was adjusted slightly following recent work by Porter and Purser (2008). The authors point out here that, within the USA, variations in enroll-ment are extremely low and should be replaced with the more variable indi-cator of the percentage in the county with a college degree (Porter and Purser,2008).

Finally, the third and nal component of the HDI is related to one’scommand over resources, which is measured via the Gross DomesticProduct (GDP) per capita of a specied country. In the context of the HDI,the GDP allows for the inclusion of an economic component as a part of

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the index as opposed to practices prior to the development of the HDI that were solely concerned with the GDP as a measure of development. Again,given the unit of measurement, the economic indicator was adjusted accord-ingly due to the fact that a county level GDP is not readily available (Porter and

Purser, 2008). In its place a measure of per-capita income was substituted as it was shown to have a very strong association with GDP at the state level with a0.999 correlation (Porter and Purser, 2008). This nal composite index allowsfor the examination of biological (life expectancy), knowledge-based (lit-eracy / education) and economic (income per capita) indicators of humandevelopment, giving a much more complete picture of overall well-being. Itis, in effect, the measurement of human capital development, as opposedto the measure of economic production.

Computing the United Nations’ HDI is rather simplistic as each component is equally weighted as one-third of the nal index. Each county’s score on the HDI is scaled between one and zero by subtractingeach case by the minimum in the category and dividing that by the range of the indicator in order to create a range from zero to one, where a score of one is reective of a high ranking (i.e. high life expectancy, high literacy,high GDP per capita, etc.) (United Nations Development Programme,1995). Finally, these three indicators were added together for a potentialHDI score ranging from zero on the low end to three on the high end. For illustrative purposes this is presented in the following equation:

HDI i = Health i + Education i + Economic i (1)

where:

Health i = [ Z Avg . Life Expect ]

Economic i = [ Z Income per Captia ]

Education i = ([ Z Literacy Rate ]∗2 + [ Z % College Grad ])/ 3

Within the context of the current article, it is expected that counties withinthe American South, which participated in the racialized oppression of Blacks through physical punishment in the form of lynching and structurally by being identied as part of the oppressive plantation economy, are mostlikely to exhibit lower levels of human development in contemporary times.This retardation of development is hypothesized to be theoretically connectedto ‘cultural orientations’ that privilege racialized group oppression, subordina-tion, and violence. In comparison, it is expected that counties which reportlow levels of historical violence and a means of production that nds itself in manufacturing is most likely to have signicantly higher development

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levels based on more positive orientations. Table 1 contains the descriptivestatistics concerning the sub-national relative HDI and the determinants of interest in this analysis, including information on the mean, standard devi-ations, minimum, maximum, and percentage of cases. The table reports

that the average HDI score of counties in the American South is 1.201 on ascale ranging from zero to three.

Determinants of lingering well-being patterns

From the Negro Participation survey, a series of variables (all pertaining tocirca 1960) were computed to measure the historically dominant means of economic production in the county, local levels of racial inequality, absolute

T ABL E 1. Descriptive statistics

Mean (%) Minimum MaximumStandarddeviation

Dependent variableSub-national HDI 1.201 0.55 2.33 0.2571

Independent variableContemporary controls

Total population 60 636 1915 1 937 094 128 324Population density 79.51 3.59 4104.06 209.33Percentage Black 23.19 0 86.12 18.63Unemployment rate 0.08 0.02 0.34 0.03Female labor force participation (%) 48 28 70 0.07Black / White spatial segregation 0.53 0 0.96 0.19Metro County (1 ¼ yes) 29 0 1 –

Ratios of racial equality Black to White education 0.47 0 4.96 1.43Black to White unemployment 2.15 0 3.35 1.41Black to White income 0.53 0 2.39 0.37

Historical equality and minority statusNon-White median years of schooling 1960 4.67 0 8.00 1.09Non-White median income 1960 8,059.60 200.00 2,200.00 2,914.09Non-White to White education 1960 0.57 0.09 1.41 0.16Non-White to White income 1960 0.53 0.17 1.46 0.16

Historical racially segregation communities

Schools desegregated 1968 (%) 25 0 100 –School faculties desegregated 1968 (%) 17 0 100 –Forced desegregation suit 1966–1975 59 0 1 –Black race organization 1960 69 0 1 – White race organization 1960 26 0 1 –Both Black and White race organizations 1960 16 0 1 –

Plantation economics and physical violenceLabor force in agriculture 1950 (%) 35 0 79 –Farms operated by tenants 1950 (%) 28 0 90 –Plantation county status 1910 34 0 1 –Total number of lynchings (historically) 3.01 0 35.00 3.94

Note : Valid n (listwise) ¼ 772.

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levels of minority socioeconomic status, and the local existence of race-based‘mobilized’ organizations. The dominant means of production in the county are measured via an indicator of the percentage of jobs in agriculture in thelate 1950s–early 1960s. The historical levels of racial inequality are measured

via two ratios, one measuring non-White to White income and the other non- White to White median years in schooling. Absolute levels of minority socio-economic status are measured via the median income and years of schoolingof non-Whites in the county. Finally, the existence of a ‘mobilized’ race-basedorganization was measured via a set of three variables indicating the existenceof a Black organization, a White organization, and an interaction measuringthe existence of both Black and White organizations within the same county.

Following Walters et al. (1990), a dummy variable reecting the county’sinvolvement a plantation economy based upon a special Census Bureausurvey for 1910 was developed using geographical information system (GIS)procedures. Counties in 1990 that overlapped with the underlying plantationareas in 1910 were classied as being ‘plantation’ counties. In 1910, theCensus Bureau conducted a survey of plantations and produced a carto-graphic display of the contours of these locales. Following procedures usedby David James (personal communication) for the Walters et al. (1990)study, we rst optically scanned this paper map and georectied it to 1990county boundaries. Second, if a 1990 county’s boundaries overlapped these‘plantation’ zones for 1910, it was classied as having its land area as reectingorigins in the plantation economy, circa 1910. We also experimented with thesame variable set as Walters et al. (1990), since our theoretical perspective is very comparable with theirs. These variables included data obtained from theCensus of Agriculture on the percentage of current sharecropping farmers,percentage of cash tenant farmers, the percentage of all farmland in cottonand tobacco, and the percentage of the total population of the county involved in farming in 1990. However, it appeared that the simple dummy variable reected the county’s history being rooted in a plantation economy a century ago. The resulting dummy variable simply reected plantationcounty ( ¼ 1) versus non-plantation county ( ¼ 0) in 1910.

The occurrence of black lynching within the county was also dummy-coded in order to identify counties with a history of white violence againstblacks through public displays of lynching (see Tolnay and Beck, 1995).

Data from Project Hal were attached to county GIS boundaries for each relevant year (1882–1932) using the Historical United States County Boundary Files (Earle et al. , 1999). Each year was merged to the 1990 county boundariesusing a proportional-allocation method as applied to the counts of black lynch-ing. From here the nal variable used as a determinant of human developmentis a sum of the total number of lynchings in the county.

The Federal desegregation orders for public school districts were linkedto the county level using GIS procedures, with the resulting counts in each county converted into two dummy variables. The rst identied those coun-ties that had desegregation orders prior to 1966, the beginning of the period.The second dummy variable identied counties where desegregation orders

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took place during the study period (1966–1975). In order to test for the exist-ence of ‘contagion’ or ‘scare’ effects of districts in neighboring countiesreceiving a Federal court order to desegregate (see Bullock and Rogers,1976), spatial lag versions of these dummy variables were also created. The

spatial lag variables were computed via spatial statistical tools using Geodasoftware and reect a Queen’s adjacency matrix specication for the neigh-borhood matrix. See Anselin (2003) for details.

Finally, a set of contemporary measures (2000 Census) were included inthe analysis in order to control for residential segregation (Dissimilarity Index,ranging from zero to one), inequality ratios of Black to White median income /percentage with a bachelor’s degree / and unemployment, shifts in inequality via the change in the three ratios from 1990 to 2000, total population, popu-lation density, the percentage Black, the change in the percentage Black from1900 to 2000, the change in the percentage Black in poverty from 1990 to2000, median income of the entire county, and the percentage college gradu-ates in the county.

Analytic techniques

This analysis was undertaken using a multi-phasic approach. First, data wereexamined in univariate and bivariate form in order to ensure that all sub-sequent modeling assumptions were met and to examine isolated relation-ships among the primary variables of interest. 2 This initial descriptive stagealso involved exploratory spatial data analysis (ESDA) given the known high levels of spatial dependence in aggregate Census data. The indicator of spatial autocorrelation employed here is the Moran’s I statistic, which ranges from perfect negative autocorrelation ( 2 1) to perfect positive autocor-relation ( + 1). 3 The Moran’s I statistic is specied in Equation 2 as:

I = (1S 2

)

N

i = 1

N

j = 1v ij (Y i − Y )(Y j − Y )

N

i = 1

N

j = 1v ij

(2)

where:

S 2 =1

N

N

i = 1(Y i − Y )^ 2

In the above equation the measure of spatial dependence is equal to ameasure of variation in the area unit specic rate and the overall mean rate( s2 ) is multiplied by the neighbor weight indicator ( v ij ) times the productof each unit ( i ) minus the overall mean and each neighborhood ( j ) minus

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the overall mean then divided again by the weight indicator and summedacross all units ( i ) and across all neighborhoods ( j ) (Waller and Gotway,2004). The statistic is very similar to Pearson’s correlation coefcient in thatit measures an association between N observed values associated with two

random variables, X i and Y i (Waller and Gotway, 2004). In this case the only difference is replacing the X i variable with the Y j neighborhood variableand introducing the weight matrix ( v ij ).

This equation produces is a statistic in which each unit’s ( i ) interaction with another is taken to account and when neighboring units (indicated by one as the v ij , as opposed to zero for non-neighboring units) are statistically signicant and similar the Moran’s I statistic is positive, meaning areas of closer proximity tend to be more alike than those far apart (Waller andGotway, 2004). In this instance you would have spatial clustering. In order to place a signicance value on the observed Moran’s I statistic, a permu-tations-based tested will be implemented to test the null hypothesis: ‘Nospatial association’ or ‘spatial randomness.’ The test employs a permu-tations-based approach to test the global index on randomly assignedlocations in order to approximate the distribution of the global index under the null assumption (Waller and Gotway, 2004). This examination willimplement a 999 permutations test with a reject region equal to a 0.05 signi-cance level.

So that we can identify statistically signicant independent clusters of contiguous ‘zones’ among counties, we use Anselin’s (1995) local indicator of spatial association (LISA) statistic, which is based upon the globalMoran’s I coefcient decomposed into a ‘local’ level, which is the county inthis study. This local examination repeats the spatial clustering procedurefor each neighborhood, which is equivalent to the reproduction of the pro-cedure i times (once for each county accompanied by all of its identiedneighbors; Waller and Gotway, 2004). This procedure ultimately produces acategorical outcome based on the relationship of county i to the remainder of the counties within the jth neighborhood, producing a result that indicatespositive spatial clustering (county is signicantly like neighbors), negativespatial clustering (county is signicantly unlike neighbors), or spatialrandom distribution (county is not signicantly like or unlike its neighbors)(Anselin 1995). For the purpose of this paper, the general LISA statistic will

be employed. It is specied in Equation 3 as:

I i = N

j = 1v ij (Y i − Y )(Y j − Y ) (3)

From this equation one can see that the random variable (LISA) I i is equalto the weight indicator multiplied by the product of the local unit value ( Y i )minus the global mean ( ∗∗ ∗ ) and the neighborhood average value of thesame variable ( Y

j ) minus the global mean ( ∗∗ ∗ ). Simply put, the LISA value

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for a given location is simply equal to the relationship between the two vari-ables of interest (correlation) multiplied by the weight indicator matrix (one if considered a neighbor, zero if otherwise).

Following the identication of spatial dependence, phase two involves

the specication of a series of nested spatial regression models using a weighted modeling approach. Based on the ndings from the ESDA in thedescriptive portion of the analysis, the explanatory analysis will identify theappropriate technique to control for the existence or absence of spatial auto-correlation across all models in the analysis. 4 In the event of obvious spatialautocorrelation, as expected via the literature review, spatial dependencediagnostics are examined in Geoda in order to select the appropriate spatially weighted model.

These regression models are implemented and presented in a nestedfashion ultimately resulting in a set of six models, including a baseline demo-graphic model and a nal full model. The initial fully reduced model isdesigned to introduce the baseline controls, which includes measures of population, density, percentage minority (black in the American South), thelocal unemployment rate, female labor force participation, residential segre-gation as a control of the contemporary race relations, and the identicationof the county’s metropolitan status. Model 2 is designed to introduce ratios of contemporary racial equality through a series of equality measures concerningblack to white education, unemployment, and income.

Model 3 introduces historical levels of minority status relative to the restof the American South through measures of non-white median years of school-ing, non-white median income, ratio of non-white to white education, and aratio of non-white to white income. 5 Model 4 introduces another series of his-torical measure pertaining to a historical linkage of segregation includingmeasures of the percentage of schools desegregated in 1968, the percentageof school faculties segregated in 1968, the existence of a forced desegregationsuit during the years 1966–1975 (height of school segregation in the Ameri-can South), and the existence of race-based organizations in 1960. The nalisolated model (Model 5) introduces indicators of plantation economics andphysical violence as links to a cultural orientation related to racializedgroup oppression and subordination. This model includes measures of the his-torical percentage of the population working in agriculture, the percentage of

farms operated by tenants, the plantation county status of the county in 1910,and the total number of lynchings recorded in the community. Finally, Model 6tests the consistency of these relationships controlling for all other indicatorsin the analysis in a full model.

Results

Variations in human development in the American South

As with any of the development indices, one of the primary purposes is togauge development at a level of geography for comparison between units.

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The top and bottom 10 counties in the American South are compared in Table2 based on their HDI score. There is signicant variation between the ‘haves’and ‘have-nots’, in terms of human development, as the top 10 counties’ HDIrange varies from 2.33 to 1.93 and the bottom 10 range from 0.55 to 0.64.

Since this is an additive index made up of three components each with a poss-ible contribution of one, it is easy to discern that each of the counties on thispage consistently scored high or low across the board. A comparison of theHDI components by each of the counties allows for a better understandingof the weights each indicator has on the nal index.

The most developed county in the American South, according to thisindex, is Arlington County California, which lies in the Washington, DC Metro-politan Area. This county has an average life-expectancy of 80 years, has only 17% of the population at the lowest functional literacy level, has 52% of thepopulation 25 years or older with a bachelor’s degree, and averages almost$35 000 in personal income per capita. All indicators are signicantly better than can be expected when comparing them with the US averages at thebottom of the table. Some notable points in relation to the top 10 countiesin the American South are that Collier County, Florida has the highest life-expectancy of the group, Loudon County, Virginia and Fayette County,Georgia have the lowest rate of individuals within the lowest literacy level(level 1), and Arlington County, Virginia has the highest percentage of individ-uals with BA degrees and the highest personal income per capita.

In comparison, the least developed county in the American South is Jef-ferson County, Mississippi, which lies in the southwestern part of the stateentirely within the Mississippi Delta Region. This county has an average lifeexpectancy of 72 years (lowest in the bottom 10), has 52% of the populationat the lowest functional literacy level, has only 10% of the population 25 yearsor older with a bachelor’s degree, and averages $9126 in personal income per capita. The bottom 10 are signicantly worse off in comparison with the USaverages. Some notable county stats include Jefferson and Holmes Countiesof Mississippi both having 52% of their population in the level 1 literacy group; Marlboro County, South Carolina and Tallahatchie County, Mississippiboth have the lowest percentage of the population with a bachelor’s degree;and Jefferson County, Mississippi also has the lowest personal income per capita.

Metropolitan trends also seem to be very important in this table. For example, all 10 of the top 10 counties are classied as metropolitan area. Incontrast, all of the bottom 10 counties in the South region are classied asnon-metropolitan counties, with only two of those counties being spatially adjacent to a metropolitan county and the other eight being classied asnon-adjacent to a metropolitan county. Only one state again appears morethan once with six of the bottom 10 counties located in the state of Missis-sippi, while Virginia and Florida have the most counties in the top 10.

For comparative purposes, it is interesting to contrast two counties within the same state on opposite ends of the HDI continuum. The most dis-tinct comparison can be made in the state of Florida, between Collier County,

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T ABL E 2. HDI components and metropolitan status of top 10 and bottom 10 ranked counties HDI in the American South

Lifeexpectancy Literacy

B.A.degree Income

Metrostatus

Top 10 counties(HDI score)1 Va., Arlington

County (2.33)80.1 17 52.3 34 970 Metro

2 Tex, Collin County (2.15)

79.7 11 39.1 28,568 Metro

3 Va., LoudounCounty (2.09)

79.1 10 32.7 29 114 Metro

4 Fl., Collier County (2.05)

81.0 20 22.3 30 601 Metro

5 Va., Fairfax County (2.05)

80.9 13 49.0 17 603 Metro

6 Tenn., WilliamsonCounty (1.94)

78.8 13 34.2 29 038 Metro

7 Ga., Fayette County (1.94)

78.9 10 25.8 24 619 Metro

8 Fl., Palm Beach County (1.94)

79.1 22 22.1 32 070 Metro

9 N.C., OrangeCounty (1.94)

77.9 15 46.1 24 365 Metro

10 Fl., Martin County (1.93)

79.6 21 20.3 30 435 Metro

Bottom 10counties (HDIscore)1 Jefferson county,

Miss. (0.55)71.9 52 10.3 9126 Non-

adjacent2 Tunica County,

Miss. (0.59)71.2 50 8.5 12 951 Adjacent

3 Holmes County,Miss. (0.60)

72.1 52 9.7 10 985 Adjacent

4 Phillips County, Ariz. (0.60)

69.8 42 9.2 13 354 Non-adjacent

5 Marlboro County,S.C. (0.62)

69.6 37 7.9 12 412 Non-adjacent

6 Quitman County,Miss. (0.63)

71.2 45 9.0 11 833 Non-adjacent

7 Sunower County,Miss. (0.63) 71.1 45 12.4 11 063 Non-adjacent8 Coahoma County,

Miss. (0.63)70.1 46 14.7 14 101 Non-

adjacent9 Wilcox County, Al.

(0.63)71.7 49 10.3 12 007 Non-

adjacent10 Tallahatchie County,

Miss. (0.64)71.2 43 7.9 11 315 Non-

adjacentUS average 76.3 19.52 13.4 17 107

Source : Data obtained from the National Center for Health Statistics, National Institute for Literacy, USCensus Bureau and the Bureau of Economic Analysis.

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which is located at the southern tip of the state in the Miami Metropolitanarea, and Union County, which is located in the northeast of the state near the Georgia state line. Collier County is ranked fourth from the top, whileUnion County is ranked 17th from the bottom, both in respect to the 772

counties in the American South. The comparisons are quite distinct, as resi-dents of Collier County, on average, live to be about 81 years old, have only a 20% probability of being in the lowest functional literacy group, have a22% probability of being college educated, and report a per-capita incomeof nearly $31 000. On the other hand, residents of Union County are, onaverage, expected to die 10 years earlier (71 years of age), have a 10%higher probability of being in the lowest functional literacy group (30%),have a 16% lower probability of being college educated (8%), and onaverage make about $21 000 less ($9900) than residents in Collier County.

Exploratory spatial data analysisFrom the ESDA, it is evident that there is a signicant degree of spatial cluster-ing that cannot be ignored concerning this examination. The results from thetop panel of Figure 1 represent, geographically, the spatial distribution of theHDI in the American South. The spatial distribution immediately draws one’sattention to the well-known ‘have-not’ regions of the Mississippi Delta and theBlack belt. This initial nding is not that surprising given what we know aboutthese two areas. Also of interest in this top panel is the relatively high devel-opment patterns throughout Florida ( sans most of the pan handle), North Georgia (Atlanta Metro Area), Central North Carolina, Northern Virginia,Central Tennessee and Northwest Arkansas. In relation, much of Mississippi,Louisiana, Eastern Arkansas, Southern Alabama, Southern Georgia, South Carolina, and Eastern North Carolina are classied as underdeveloped onthe relative index. In the bottom panel of Figure 1, the signicant spatial pat-terns validate what is observed with the naked eye. Here one can see signi-cant spatial clusters of high levels of human development in Central toSouthern Florida, Northern Georgia, Western North Carolina, Central Tennes-see, Northwestern Arkansas, and Northeastern Virginia. Likewise, signicantspatial clusters of low human development can be found along the MississippiDelta, Southern Alabama, and in South Carolina.

Figure 2 reports the Moran’s I statistic results. The positive 0.2280 indi-cates that there is a positive association between a county’s local HDI scoreand its identied neighborhood average score—meaning that places closer together are more likely to be similar than places far apart. Given the signi-cant patterns of spatial dependence in the HDI, the subsequent regressionmodels were all appropriately weighted in order to correct for potential mod-eling assumption violations. Introducing a weight for spatial weight into amodel entails the correcting for non-random error term correlation by adding the spatial weight to the error of the equation; also known as a simul-taneous autoregressive model (SAR), The standardized coefcient estimatesreported from here on out were obtained from the following model

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FIGURE 1. Spatial distribution and statistically signicant spatial clusters of human development in the American South.

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specications:

Y i = x ij b + 1 ij (4)

where:

1 ij = lv 1 + y

From Equation 4 one can see that the SAR model consists of a basicregression model, with the only weighting taking place in the error term ( 1 ij ). Here the correlated error terms are controlled through theintroduction of a spatially weighted adjacency vector ( lv 1 ) and anadded vector of independently and identically distributed errors ( n ). Thisspatial weight, along with the introduction of the uncorrelated error terms, allows for the regression coefcients to be estimated without violat-ing the assumptions inherent in spatially correlated data (Cressie, 1993; Waller and Gotway, 2004).

FIGURE 2. Scatter plot of HDI score by average neighborhood HDI score. Note : The x axis represents the HDI value of the i th county, with the y axis representing the average j th HDI value of the contiguous neighborhood. Signicant positive slopes in all three cases indicate that counties

tend to be like there contiguous neighbors in terms of human development.

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Explaining spatial uneven spatial patterns of human development

The results from the spatial regression models are presented in tabular form inTable 3. The results from Model 1 report that an increase in the total popu-lation, population density, the female labor force, and being a classied as ametropolitan county all are positively associated with higher scores on theHuman Development Index. Also, a lower percentage of the populationthat is Black and unemployed are also associated with higher HDI scores.Interestingly, the level of Black / White segregation in the county has no signi-cant effect on the overall human development of the area. The effects of thesecontrols prove to be consistent across all models, in signicance classicationand direction, with the exception of the effect of population density that looksto be wiped out by the introduction of the plantation economics and physical violence determinants in Model 5. Furthermore, given that these are standar-dized variables, it must be should be noted that comparisons in magnitude can

be made. From the results, it is apparent that the strongest indicators of human development are the percentage black and the percentage of thefemales in the labor force.

In Model 2, the effects of the ratios indicating racial equality, concerningsocioeconomic status, are examined. The results show that only the ratio of Black to White unemployment is signicantly related to levels of localhuman development, with a more even ratio (approaching one) indicatinghigher levels of development. Unexpectedly, in the nal model (Model 6)the effects of all three ratios are signicant, with the effects of Black to White education and income both being negative. This indicates that as the

ratio begins to approach one (equality), in both the case of income and edu-cation, the area reports a lower human development score while the effect of the Black-to-White unemployment ratio remains consistent and positive.

Model 3 introduces the effects of historical patterns of inequality and theabsolute levels of socioeconomic status among non-Whites. Here again, asBlacks and Whites near equality, in 1960, the local areas report a signicantly lower HDI score. In relation to the absolute measures of non-White socioeco-nomic status, areas in which non-Whites have a higher absolute level of edu-cation in 1960 report a current-day higher level of human development.However, the absolute level of non-White income illustrates a signicantand negative effect on current day patterns of human development. Further-more, all signicant and directional effects remain constant into the fullmodel. While the effect of the absolute historical level of education washypothesized, the effects of both the absolute levels of income and the ratioof non-White to white education are puzzling.

In relation, Model 4 introduces the historical effects of segregation in thecommunity on contemporary patterns of human development. The modelreports that only the percentage of the schools that were segregated in1968 and the existence of a Black race-based organization in the county have a signicant effect in both the reduced and full models. The relationshipbetween school desegregation and human development is positive, indicating

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T ABL E 3. Standardized spatial regression coefcients explaining social well-being via the HDI

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Contemporary controlsTotal population 0.129 ∗∗∗ 0.126 ∗∗∗ 0.087 ∗∗ 0.121 ∗∗∗ 0.124 ∗∗∗ 0.091 ∗∗

Population density 0.092∗∗

0.094∗∗

0.071∗

0.088∗

0.057 0.060Percentage Black 2 0.471 ∗∗∗ 2 0.484 ∗∗∗ 2 0.511 ∗∗∗ 2 0.452 ∗∗∗ 2 0.403 ∗∗∗ 2 0.473 ∗∗∗

Unemployment rate 2 0.109 ∗∗∗ 2 0.117 ∗∗∗ 2 0.102 ∗∗∗ 2 0.111 ∗∗∗ 2 0.105 ∗∗∗ 2 0.109 ∗∗∗

Female labor forceparticipation

0.281 ∗∗∗ 0.276 ∗∗∗ 0.282 ∗∗∗ 0.268 ∗∗∗ 0.278 ∗∗∗ 0.267 ∗∗∗

Black / White spatialsegregation

2 0.001 0.002 2 0.001 2 0.004 2 0.012 0.001

Metro County (1 ¼

yes)0.182 ∗∗∗ 0.181 ∗∗∗ 0.171 ∗∗∗ 0.189 ∗∗∗ 0.177 ∗∗∗ 0.178 ∗∗∗

Ratios of racial equality Black to Whiteeducation

– 2 0.021 – – – 2 0.065 ∗

Black to Whiteunemployment

– 0.043 ∗∗ – – – 0.068∗

Black to White income – 2 0.021 – – – 2 0.058 ∗

Historical equality andminority statusNon-White median years of schooling1960

– – 0.247∗∗∗ – – 0.206∗∗∗

Non-White medianincome 1960

– – 2 0.001 ∗∗ – – 2 0.072 ∗

Non-White to Whiteeducation 1960

– – 2 0.243 ∗∗∗ – – 2 0.051 ∗∗∗

Non-White to Whiteincome 1960

– – 0.021 – – 0.046

Historical racially segregationcommunitiesSchools desegregated1968 (%)

– – – 0.063∗ – 0.069 ∗∗

School facultiesdesegregated 1968 (%)

– – – 2 0.014 – 2 0.017

Forced desegregationsuit 1966–1975

– – – 2 0.022 – 2 0.019

Black Raceorganization 1960

– – – 2 0.072 ∗∗ – 2 0.057 ∗

White Raceorganization 1960 – – – 2

0.038 – 2

0.041

Both Black and WhiteRace organizations1960

– – – 0.046 – 0.039

Plantation economicsand physical violenceLabor force inagriculture 1950 (%)

– – – – 2 0.059 ∗ 2 0.061 ∗

Farms operated by tenants 1950 (%)

– – – – 2 0.118 ∗∗∗ 2 0.108 ∗∗∗

( Continues )

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that areas that were early to segregate may be reaping the rewards of theprogressive attitudes in relation to their current levels of human development.However, counties that reported the existence of a Black race-based organiz-ation report lower levels of development, perhaps indicating that Blacks may have been forced to mobilize in areas which lacked the progressive orien-tations of areas where they were not forces to do so.

Finally, Model 5 introduces the effects of the historical mode of production in reference to plantation economics and the account of historicallynchings in the county. The results show that, indeed being a county whonds its roots in plantation economics in the 1950s does drive down thecurrent-day level of development in the county. Also, the results show thatcounties with a history of physical violence in the form of lynchings continueto exhibit indications of low levels of human development contemporarily. All variables from the model remain consistent as indicators of human develop-ment when included in the full model. Ultimately, the full model (Model 6)shows that the largest standardized effect on the human development of acounty is the percentage black. This is followed by female labor force partici-pation and the early indicators of progressive attitudes given the number of years of schooling of non-Whites in the South in the 1960s.

This nding is very interesting given the high spatial correlation ident-ied in the ESDA portion of the analysis. Essentially, the results of the fullmodels show that the effect of race ( 2 0.473) and place (0.409) are equally as important in predicting the spatial variations that exist across the American

South in relation to the geographic distribution of the HDI scale. Furthermore,these affects are found to be independent of one another as the introductionof the two into the modeling of the HDI produces no colinearity issues andconsistently reports equally strong effects for both indicators. This is not tosay that you cannot link race to place, but instead to say that when linkingrace to place it is important to understand that, in relation to human develop-ment, the racial makeup of the community is important but its relationship tospatial context is unique. This study shows that while having a higher percen-tage minority in your population is detrimental to your HDI score, the relation-ship is intensied given the specic spatial context and the culturalorientations associated with that context.

T ABL E 3. (Continued)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Plantation county status 1910

– – – – 0.006 0.012

Total number of lynchings (historically)

– – – – 2 0.091 ∗ 2 0.061 ∗

Spatial parameter ( e ) 0.479∗∗∗ 0.478 ∗∗∗ 0.475 ∗∗∗ 0.474 ∗∗∗ 0.416 ∗∗∗ 0.409 ∗∗∗

AIC 1,214.2 1,209.4 1,225.3 1,181.4 1,209.8 1,006.7 R 2 0.766 0.769 0.777 0.770 0.773 0.801

Note : ∗ p , 0.05, ∗∗ p , 0.10, ∗∗∗ p , 0.001. e ¼ spatial error model (spatial weight applied to thegeographically correlated error terms).

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In relation to the model t, it should be noted that the R 2 statistic is not areliable source of interpretation for model t for spatial models. This is due tothe fact that, where signicant spatial autocorrelation is present, the R 2 stat-istic will be inated due to the effect of the spatial parameter. Furthermore,

changes in the R 2

statistic are then biased as the initial baseline is inatedand any adjustment due to the introduction of determinants is less impressivethan its true contribution to the study. Instead a nested indicator, the Aiaikeinformation criteria (AIC) is more appropriate. When examining the AIC stat-istic, a better model t is illustrated by a decrease in the coefcient. In relationto this study, we see that the baseline model reports a coefcient of 1214.2.Only Models 4 and 6 report a signicant reduction in the AIC, with the nalmodel reporting an AIC of 1006.7. 6

Discussion and conclusions

The main causal imagery that is invoked in this study relates to the unob-served, long-term ‘cultural orientations’ (Messner et al. , 2005) involving white beliefs about race relations and the social good in local communities(Alexander, 1997). As did Messner et al. (2005) involving the historicalconnections within counties (or county clusters) of black lynching and con-temporary homicides, we specied that ‘local cultures’ arose out of a regionalhistory of the plantation economy and the subordination of blacks in theSouth. Furthermore, incorporating a spatially-centered analytic schemeallows us to better understand these issues and directly incorporate thisidea of ‘local culture’ within which we may examine ‘cultural orientations,’

We nd that where a lack of rapid development, modernization, andpopulation in-migration helped maintain these intergenerational social net- works, these communities may have more readily institutionalized oppressiveand divisive mechanisms within the local community, making them a cross-generational ‘tradition’ without much overt avocation of their originalpurpose. It must be noted that some of the identied relationships goagainst one’s initial hypotheses. For instance, one would expect that aslocal levels of past and present inequalities approached equality (ratio of one), current-day levels human development would be higher. However,the analyses here show the opposite to be true. Further research should be

implemented to address this and other interesting issues, but one couldposit that where one would most probably nd the highest levels of equality (especially in the Civil Rights era) would be in the rural / underdevelopedcounties where Whites were often not as wealthy as their urban counterparts.

Furthermore, it is interesting that counties which reported the existenceof a Black race-based mobilization group in the 1960s are also less developed. Again, one would most probably hypothesize that these areas were most likely to develop progressive attitudes given the spirit and ‘taking it to the streets’action of the oppressed group. However, what we nd is that these counties were more than likely unwilling to progress with much of the country inadopting and following the Civil Rights amendments. Instead, it seems that,

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in these counties, Blacks were forced to mobilize in order to attain the samerights and freedoms experienced by their counterparts in more forward-thinking communities.

It seems that local communities which invested in and maintained

oppressive practices aimed at the subordination of minority populationsthrough plantation economics, racialized segregation, and physical violencecurrently reap the disadvantage of retarded levels of human development.In some cases it can be argued that this may be due to unequal origins;however, such an argument neglects to understand that the focus of this analy-sis is simply to understand these persistent patterns and localized orien-tations. Therefore, it is worth our time as social scientists to continually explore contextual and historical explanations for the non-random patternsof development experienced both within the American South and acrossthe larger USA. Policy implications could potentially be vast and, while previous work simply identied these patterns, better understanding their histori-cal roots and contemporary determinants may help to focus efforts for thealleviation of such issues. As is shown here, it is in the best interest of theaggregate to promote development and equality among all groups withinsociety.

AcknowledgementsI wish to express gratitude to Dr Frank M. Howell, Dr Lynn M. Hempel, andSusannah M. Brown for their helpful commentary in the development of the current manuscript. However, all errors of fact or interpretation arethose of the author.

Notes1 Literacy estimates are in relation to the population in the county that is aged 18 + .2 All variables in the analysis were examined for issues of normality and colinearity in ancillary

ordinary least squares models. The results provided evidence that all variables met thesebasic modeling assumptions.

3 The most common case is that of positive autocorrelation in which the local unit’s ( i ) valueon a variable of interest is signicantly, and positively, correlated with the average neighbor-hood ( j ) value. Less frequently, negative autocorrelation refers to an instance when a localunit’s ( i ) value pertaining to a specic variable is signicantly in opposition to the neighbor-hood’s ( j ) average value.

4 It is expected that the sub-national HDI will spatially cluster in the South given the earlier work of Porter and Purser (2008) as well as the propensity of census data to be spatially dependent.

5 In the American South, and especially in the 10-state study region used in this analysis, min-ority or non-white status is and has been equivalent to being Black.

6 Ancillary analyses testing signicant reductions in the goodness-of-t statistic (AIC) providedevidence that Models 4 and 6 signicantly reduce the coefcient, indicating a signicantimprovement in model t in relation to Model 1.

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Power, Pride, Prejudice, and Poverty

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