County Poverty Rates Research Articles

County Poverty: The Case Of Sacramento

<p class="MsoBlockText" style="margin: 0in 0.5in 0pt;"><span style="mso-bidi-font-style: italic;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">This study serves two purposes.<span style="mso-spacerun: yes;">  </span>First, it demonstrates a method of estimating and projecting annual poverty at sub-national levels.<span style="mso-spacerun: yes;">  </span>Data are obtained from decennial censuses to form the benchmarks from which poverty is estimated and projected for various demographic groups.<span style="mso-spacerun: yes;">  </span>Projections are based upon historical curvilinear trends for each group.<span style="mso-spacerun: yes;">  </span>The methodology can be easily applied in a variety of jurisdictional settings and levels.</span></span></span></p><p class="MsoBodyTextIndent" style="text-indent: 0in; margin: 0in 0.5in 0pt;"><span style="font-size: 9pt; mso-bidi-font-style: italic;"><span style="font-family: Times New Roman;"> </span></span></p><p class="MsoBodyTextIndent" style="text-indent: 0in; margin: 0in 0.5in 0pt;"><span style="mso-bidi-font-style: italic;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">The second objective is to provide a specific portrait of poverty by demographic group within the County of Sacramento in the State of California.<span style="mso-spacerun: yes;">  </span>The evidence indicates that, by the end of the decade, the County poverty rate will rise to 16% and the number of poor persons will expand to 216 thousand.<span style="mso-spacerun: yes;">  </span>There will be large differences among the various demographic groups in their rates of change.</span></span></span></p>

School Resource Officers in Financial Crisis: Which Programs Get Cut and Why

In recent years, K–12 education programs throughout the United States have been impacted by budget reductions, particularly in areas that are outside of traditional academic programs. Anecdotal evidence suggests that one of the areas receiving the greatest reductions may be school safety programs. In this article, we use data from school resource officers (SROs) in Kentucky to examine the types of SRO activities that are impacted by budget reductions and the school- and county-level factors associated with those budget reductions. The findings presented here suggest that SRO training and equipment are most impacted by budget cuts; SROs are more likely to serve more than one school in times of budget reductions as well. Results from multivariate logistic regression models suggest a web of economic factors that impact which SRO programs receive budget reductions, including a county's poverty rate, median household income, and population change. Implications for policy and future research are discussed.

Adolescent smoking risk increases with wider income gaps between rich and poor

Data from a state-wide survey of California middle and high school students (N=20,203) were used to assess whether county income inequality and poverty rates were associated with adolescent smoking. Greater county income inequality, but not poverty rates, was associated with higher established smoking risk (p=0.0019). The association was stronger in males than females, whites than other ethnic groups, and urban than rural settings. Neither county income inequality nor poverty rates were associated with experimental smoking. The findings suggest that it may be important to consider and address economic inequality in the prevention and control of adolescent tobacco use.

The geography of American poverty: is there a need for place-based policies?

Partridge and Rickman explore the underlying spatial, demographic, and economic contributors to poverty rates and examine the spatial variation of state and county poverty rates and their trends over time. They conclude that a unique combination of place-based and person-based policies is needed to help defeat poverty in the most distressed American central cities and remote high-poverty rural communities.

County child poverty rates in the US: a spatial regression approach

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re- examine earlier published results (Friedman and Lichter (Popul Res Policy Rev 17:91-109, 1998)). We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of ''wrong sign'' parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place- influences and family-influences on child poverty.

The Relationship of Individual, Family, and Community Characteristics With Physical Health: An Adult Study in 27 Rural Minnesota Counties

This study; investigates associations between the proportion of farm families in rural counties and the health of rural individuals, independent of county poverty rate and individual and family socioeconomic factors. This study also examines relationships of these socioeconomic factors and prevalence of specific diseases. Multilevel analysis of 10,485 responses to a mailed health survey in 27 nonmetropolitan Minnesota counties. Individual- and family-level variables such as age, gender, lower income, and unemployment were significantly associated with adverse physical health. The percentage of farm families in counties was significantly associated with adverse physical health of adults, independent of individual and family characteristics, county poverty rate, ethnic composition, and population density.

An Analysis of Poverty in the American South: How Are Metropolitan Areas Different from Nonmetropolitan Areas?

This article examines the causes of poverty in the American South and identifies differences in the causes that exist between metropolitan and nonmetropolitan counties. Using a multiple linear regression model, a county's poverty rate is regressed on a vector of variables related to characteristics of its population, educational attainment, and economy. This study finds that most of these characteristics affect a county's poverty rate, but it also finds that several of the characteristics have a significantly different effect on the poverty rate of metropolitan counties than on the poverty rate of nonmetropolitan counties. These differences suggests that policy makers involved in fighting poverty in metropolitan counties may need to focus their efforts on changing different characteristics of the county than those engaged in fighting poverty in nonmetropolitan counties. (JEL I30, R10)

Race, Poverty, and American Tort Awards: Evidence from Three Data Sets

We investigate the impact of the race and income of the jury pool on trial awards. The average tort award increases as black and Hispanic county population rates increase and especially as black and Hispanic county poverty rates increase. An increase in the black county poverty rate of 1 percentage point tends to raise the average personal injury tort award by 3–10 percent. An increase in the Hispanic county poverty rate of 1 percentage point tends to raise awards by as much as 7 percent, although this effect is less well estimated. Forum shopping for high‐poverty minority counties could raise awards by hundreds of thousands of dollars. Average awards decrease with increases in white (nonblack, non‐Hispanic) poverty rates in two of our data sets, making these findings even more surprising. Awards increase with black and Hispanic county poverty rates even after controlling for a wide variety of other potential causes.

Changing Economic Opportunities, Family Structure, and Poverty in Rural Areas1

Abstract Instrustrial restructuring in the 1980s ushered in a new pattern of growing economic diversity over geographic space. The objective of this study is to examine the extent and etiology of changing spatial inequality between and within metropolitan (metro) and nonmetropolitan (nonmetro) areas, as measured by increasing or decreasing county poverty rates. Results based on data from the 1980 and 1990 census summary tape files suggest several conclusions. First, poverty rates increased more rapidly in nonmetro than metro counties during the 1980s; historical patterns of metro‐nonmetro economic convergence slowed over the past decade. Second, poverty rates tended to decline in nonmetro counties with traditionally high rates of poverty, thus providing counter‐evidence to arguments suggesting that the gap between traditionally poor and nonpoor nonmetro counties has widened. Third, spatial differences in poverty rates and relative increases in county poverty rates over the 1980s were most strongly associated with women's employment and headship status. The results raise questions about the extent to which traditional rural economic development strategies address the potentially deleterious economic effects of rising percentages of poor female‐headed families.

Migration and the Spatial Concentration of Poverty1

Current Population Survey data are used to estimate the effects of migration of the poor and nonpoor on the spatial concentration of poverty among five categories of counties defined by county poverty rates and, separately, among nonmetropolitan high‐poverty areas, central city high‐poverty areas, and other areas. During the 1981–1984 period studied, migration patterns of both the poor and nonpoor consistently reinforced pre‐existing poverty concentrations. High migration rates of the poor into and out of high poverty counties suggests an equilibrium condition. Implications for theory, research, and policy are discussed.