Low-wage worker characteristics: implications for children in poverty.

Christopher, Jan E.

I. INTRODUCTION

The labor market in the United States is characterized by wage gaps between skilled and unskilled labor (also known as the dual, two-tiered or segmented labor market). (1) The extant two-tiered labor market fosters inequality and heightens poverty. In previous studies, central city core poverty in the 1980s initially was higher than the poverty rate of the metropolitan area or the nation as a whole (between 20 to 40 percent higher) regardless of economic growth in the metropolitan area; yet over time, as housing segregation mitigated, central city poverty has slowed and presently follows similar growth patterns of the Metropolitan Statistical Area (MSA). (2)

Poverty, the byproduct of economic inequality, has three major components: (1) central city core vs. suburban regional location components, (2) labor market and labor force participation rate components; and (3) household demographics components. The elements of three components serve as the initial characteristics associated with low-wages. The rise in the one-parent family, changes in the labor force participation patterns of households, and the outsourcing of jobs to foreign counties have also contributed to an increase in poverty.

Madden (2000) noted differences between poverty rates in the central cities in comparison to the larger Metropolitan Statistical Areas (MSAs) between 1969 and 1989. She reported that poverty rates for MSAs in general are lower than the national average because of more affluent suburbs. She attributed the wage gap, i.e., the increasing gap between skilled and unskilled wages, deindustrialization, economic growth, regional location, and family demography as perpetrators of the urban--suburban wage gap, household income inequality and wage inequality. She noted that by improving the number and quality of jobs for lower wage workers there would be a decrease in poverty. An earlier paper by Madden and Daniels (1995) noted that policies that allow for exclusionary zoning, subsidize new construction or encourage fragmentation of metropolitan governments should be modified to further reduce poverty across jurisdictions. (3) In reviewing the Madden (2000), Madden and Daniels (1995) and Bernick (2005) research studies, the factors that have contributed to poverty in the United States can be enumerated as follows to answer the question:

What are some of the factors that have contributed to poverty in the United States?

1. Poverty Rate Thresholds: The level of income established by the Federal and/or State governments that help to determine public assistance eligibility.

2. Demographics: Proportion of female-headed households, number of wage-and-salary earners, multiple wage earners in a household, being African-American, being over 65, being an immigrant, being a farm worker, and number of persons per household.

3. Skill Composition: Median years of education (ages 25-64 years old), worker motivation, educational inequality or inaccessibility, English language proficiency, literacy skills, and limited knowledge of advanced processes. (4)

4. Labor Market Characteristics: Wage and salary inequality, employment-to-population ratio, years of work experience, and horizontal and vertical mobility.

5. Job Quality: Hours of classroom instruction or professional development, on-the-job training, working conditions, occupational hazards, workmanship techniques and practices.

6. Personal Traits: Being a long-term welfare recipient, former welfare recipient, or exconvict; mental and health disabilities; and drug addiction or juvenile delinquency.5

7. Other characteristics: Working in the central city, residing in the central city, population growth, per capita income, residing in low-income areas, and residential segregation inequality.

What are some of the characteristics associated with low-wage workers in the U. S.?

Previous research has yielded the following classifications of low-wage worker characteristics: age, education, skills, demographics and ethnicity (Toussaint-Comeau 2007). Other factors characterizing low-wage employment include:

1. Work Environment: Poor working conditions; numerous workplace safety violations; environmental toxins; high rates of sexual harassment; non-unionized work environments; abusive work environments; high workplace injury rates; irregular work shifts; limited number of workplace amenities; number of work hours determined by employer without consulting the employee; mandatory overtime; arbitrary disciplining and frequent firing.

2. Medical Benefits and Paid Leave: No sick leave; no annual leave; no family leave; no employer-provided medical benefits engendering less healthy workers; no on-site child care; no dependent spending accounts; no flextime; no telecommuting; and no job sharing.

3. Lack of Political Power: Inability of workers to challenge management decisions; inability of workers to challenge government policies relating to work rules and taxes; inability of workers to negotiate decent wages; and inability of workers to obtain legal counsel for discrimination and related issues.

4. Networks: Low-wage workers lack informal networks for promotions and advancement; tend to have higher turnover rates and greater job insecurity; and immigrants are often excluding from the "mainstream" because of language or cultural barriers.

Outline of the Study

In this study, three sampling frames will be developed. The three sampling frames presented will consist of a review of low-wage rural counties (The Rural Sampling Frame); low-wage metropolitan counties (The Metropolitan Area Sampling Frame); and central city core counties of major metropolitan areas (The Central City Core Sampling Frame). The three sampling frames will then be combined to yield a representative database of low-wage urban, suburban and rural workers. (6) Demographic and employment characteristics will be provided along with recommendations and implications for children in poverty will be considered.

II. SAMPLING METHODOLOGY

2.1.1 The Rural Sampling Frame

* All Rural Counties in the United States, n=695 or 22.1% of all U.S. counties.

* Low-Wage Rural Counties, n=431, or 62.0% of all rural counties.

2.2.1 The Metropolitan Area Sampling Frame

* 28 Major Metropolitan Areas exist in the United States in which the U.S. Bureau of Labor Statistics (BLS) reports the Consumer Price Index - Urban Consumer (CPI U). (7)

* 210 Metropolitan Counties and Independent Cities comprise the 28 major metropolitan areas based on their 2008 U.S. Department of Housing and Urban Development metropolitan area boundaries, n=210 or 6.7% of all U.S. counties.

2.3.1 Central City Core Sampling Frame

* 81 Central City Core Counties are the economic center of the 28 Major Metropolitan Areas of the United States, n=81 or 2.6% of all U.S. counties make up the central city core areas.

* Low-Wage Central City Core Counties, n=55, or 26.2% of all major metropolitan area counties. (8)

2.1.2 The Rural Sampling Frame

In the rural sampling frame, the 695 counties that have an Urban Influence Code (UIC) defined by the U.S. Department of Agriculture Economic Research Service of 7, 10, 11 and 12 were used to determine rural counties of the United States. (9) Shown in Table 1, the 41.7 percent of the rural counties were in the Midwest Census Region. (10) The mid-western states of Kansas, Nebraska, and South Dakota have a disproportionately large number of rural counties.

In Table 1, the U.S. Census publication of County Business Patterns was used for the years 2005 and 2006 to develop a dataset of rural counties in the United States. Of the 695 rural counties, 684 of these counties had recorded wage-rate data values for 2006. From the 684 rural counties, the median wage rate was $11.63 and the average wage rate was $12.24.

The 2006 weighted average poverty thresholds for a family of five comprised of related children was $12.29 per hour and the weighted average poverty threshold for a family of nine or more was $21.26 per hour. (11) Based on these figures, this study defines wages under $12.30 per hour to be "low-wage" by linking the 2006 poverty level guidelines with the average wage rate for each county. (12) This translates to $13.15 per hour using constant March 2008 dollars as measured by the CPI-U. (13) Central city core counties will be treated differently (See section 2.3.2). A low-wage worker is one who works at least 37 weeks per year and whose total family income falls below the federal poverty level (Toussaint-Comeau 2007). From this definition of low-wage, 431 counties were of low-wage in the rural areas.

Wage differentials were calculated between the rural counties, low-wage rural counties, and the state average wage rate. It was determined that low-wage rural counties had the lowest rate of pay of any geographic area, earning about 36% less than all employees working within the state in general. Rural workers earned on average 25% less than the state average wage.

In Table 2, the wage rates by industry were defined using the 2002 North American Industry Classification System (NAICS). In rural areas, the primary minimum-wage industries included Arts, Entertainment, Recreation (NAICS Code 71) and Accommodation and Food Services (NAICS Code 72). (14) Low-wage rural employment is present in the industries of (1) NAICS Code 72, (2) NAICS Code 71, in addition to the following NAICS 2-digit industry sectors in ranked order from lowest average wage to highest average wage: (3) Other Services (except Public Administration) (NAICS Code 81); (4) Retail Trade (NAICS Code 44); (5) Administration and Support, Waste Management and Remediation Services (NAICS Code 56); (6) Real Estate and Rental and Leasing (NAICS Code 53); (7) Educational Services (NAICS Code 61); (8) Health Care and Social Assistance (NAICS Code 62); (9) Professional, Scientific, and Technical Services (NAICS Code 54); and (10) Construction (NAICS Code 23). More specifically, in Table 3, the 3-digit NAICS codes can be used to further define low-wage employment in rural areas by industry. (15)

2.2.2 The Metropolitan Area Sampling Frame

In the metropolitan area sampling frame, the U.S. Bureau of Labor Statistics (BLS) provides area indexes of inflation for the 28 MSAs in which it calculates the CPI-U for the entire MSA. Such areas include the densely populated major metropolitan areas of Baltimore-Washington, Chicago, Los Angeles, Miami, and New York City.

The challenge of comparing wages and incomes across metropolitan areas has been mitigated over the last decade by the ability of the Bureau of Labor Statistics to calculate the Consumer Price Index for these 28 large metropolitan areas. (16) These MSA and CPI calculations enable researchers to focus on inter-metropolitan variations in costs of living reflected in the wage rates prevalent in self-representing urban core counties, suburban and non-metropolitan urban counties.

In this study, wage rates were calculated for each selected county, MSA, and corresponding state based on the March 2006 Current Population Survey and the 2006 County Business Patterns (released July 2008). Both are publications of the U.S. Bureau of the Census.

From the 210 MSA counties, 46 counties were designated to be "low-wage" counties having average wage rates below $12.30 per hour in 2006, and 164 were designated as "medium wage" for the purposes of this study, with average wage rates for all industries below $21.26 per hour. Moreover, the average wage rate for all 210 large metropolitan area counties was $17.16 per hour (see Table 4). (17)

The self-representing MSAs and the metropolitan counties associated with these MSAs were used to determine wage-and-salary differentials. MSA salaries were more competitive than rural salaries, but were on average, less competitive than the overall state average-wage-per-job. Overall, the 210 urban counties and independent cities in the 28 major metropolitan areas represent about 42 million workers (see Table 4).

From these 210 counties or independent cities, 42 in 2005 and 35 in 2006 were categorized as low-wage counties for the purposes of this research study. Combining the counties for the two years, the urban sampling frame creates a dataset of 46 counties that are urban counties of large metropolitan areas, yet categorized as low-wage.

These low-wage counties were not central city counties, but were instead urban fringe counties. For example, in the State of Colorado, two counties in the Denver MSA (Elbert County and Park County) were low-wage counties according to the definition created for this research paper. But when the Home Mortgage Disclosure Act (HMDA) census statistics were obtained for these counties, (18) the majority of the counties were classified according to the HMDA as middle income instead of low income. Furthermore, when exploring the income status of the various census tracts in these counties, 95.37% of the census tracts were classified as moderate or middle income in 2008. On average, low-wage counties of major metropolitan areas were categorized as middle income areas.

Suburban fringe counties are metropolitan area counties that are not part of the central city core but have been annexed into the MSA as the metropolitan boundary definitions have expanded. Suburban fringe counties provide the labor pool necessary to service the extremely populated central cities of the self-representing large metropolitan areas. These suburban fringe counties are not low-income counties, but are instead counties in which the wage-and-salary employment does not sustain the incomes of the residents. During economic downturns, residents in fringe suburban areas must then resort to entrepreneurship, rental incomes, commuting, etc. to sustain their livelihoods. Out of the 46 low-wage urban counties, 39 urban counties, or 85 percent, were low-wage and contained no upper-income census tracts. These 39 counties will now be labeled as the suburban fringe sampling frame (see Table 2).

Revisiting Table 2, the wage rates by industry were defined for the suburban fringe sampling frame. In suburban areas, the primary minimum-wage industry is Accommodation and Food Services (NAICS Code 72). (19) Low-wage employment is also prevalent in the industries of Retail Trade (NAICS Code 44); Administration and Support, Waste Management and Remediation Services (NAICS Code 56); Educational Services (NAICS Code 61); Arts, Entertainment, and Recreation (NAICS Code 71); and Other Services (except Public Administration) (NAICS Code 81).

2.3.2 The Central City Core Sampling Frame

Urban counties with the highest populations in 2000 were rank ordered in descending order. The top 20 counties in terms of population are: Los Angeles County, CA (Los Angeles); Cook County, IL (Chicago); Harris County, TX (Houston); Maricopa County, AZ (Phoenix); San Diego County, CA (San Diego); Kings County, NY (New York City); Miami-Dade County, FL (Miami); Queens County, NY (New York City); Dallas County, TX (Dallas); Wayne County, MI (Detroit); King County, WA (Seattle); New York County, NY (New York City); Philadelphia County, PA (Philadelphia); Cuyahoga County, OH (Cleveland); Bronx County, NY (New York City); Allegheny County, PA (Pittsburgh); Hennepin County, MN (Minneapolis); St. Louis County, MO (St. Louis); Hillsborough County, FL (Tampa); and Fairfax County, VA (District of Columbia). In total, the 28 major metropolitan areas had 210 urban counties associated with their boundaries and 81 central city core counties. The central city core counties are extremely large with over 200 low-income census tracks existing in the some of the largest inner cities.

Unlike the metropolitan area sampling frame with 46 low-wage counties, this central city core sampling frame has no low-wage counties, (according to the $12.30 criterion) but has numerous low-income census tracts within each county. These low-income census tracts were extracted to provide information on the central city core areas. Furthermore, the definition of low-wage was raised to less than $23 per hour to accommodate the higher wages that exist in the central city core and the corresponding higher living costs. In the final database, there were 55 counties designated as central city core counties with average wage rates of less than $23 per hour.

III. NON-PARAMETRIC STATISTICAL TESTS

In the draft version of this article presented at the Oxford Round Table on Child Poverty: Educational Initiatives and Consequences (July 2008), a random control sample of metropolitan counties was created. This control sample consisted of counties outside of the 28 major metropolitan areas of the United States. All 50 states, the District of Columbia and all metropolitan counties with UIC codes of 1 or 2 were eligible for the control sample. (20) Forty-two (42) counties were selected for the control sample and the following non-parametric tests were conducted.

3.1 The Kruskal-Wallis Test

To determine whether the multivariate statistical model would be feasible, three non-parametric statistical tests were conducted on the rural, metropolitan, and central city core sampling frames. The first statistical test was the Kruskal-Wallis Test (the non-parametric counterpart of the one-way ANOVA test), which was used to test: (1) The average wage, in the 209 urban counties of selected MSAs in 2005 (vs. 210 in 2006), was tested against (2) the average wage in the random control group of counties using all MSAs with the UIC codes of 1 and 2, versus (3) the corresponding state average wages for the random control group. The Kruskal-Wallis K returned an observed value that was greater than the chi-square critical value and therefore, the null hypothesis was rejected. In other words, the wages in the selected urban counties, compared to the random set of urban counties, and then compared to their state average wage were not the same wage rate. It can be concluded that each of the three wages are distinct units of measurement and should be analyzed independently.

3.2. The Mann-Whitney Test

The Mann-Whitney U Test, which is the non-parametric counterpart of the t-test, was used on (1) the random sample of MSAs versus (2) the low-wage urban counties of the large metropolitan areas. The Mann-Whitney U test revealed there was a difference between the wages in the lowwage counties and the average wage rate in the MSAs, in general. The Mann-Whiney U Test also revealed that there is no difference between the average wage in a low-wage rural county and the average wage in a low-wage metropolitan fringe county. In sum, living in a low-wage county regardless of urban or rural, adversely impacts employment prospects, and by extension enhances child poverty.

3.3 Wilcoxon Matched-Pairs Signed Rank Test

The Wilcoxon Matched-Pairs Signed Rank test was used to observe the difference between the MSA wages and the corresponding average wage for the state as a whole. The observed Z of 2.77 was only slightly larger than the critical Z of 2.576. Therefore, the difference between the mean wages of an MSA and the corresponding average wage for the entire state was only slightly statistically significant at the two-tailed 99% statistical level.

Applying this same line of reasoning to the low-wage urban counties adjacent to the most populous major metropolitan areas, it is evident that there is a difference between these low-wage urban counties and the average state wage. This difference is separate and distinct from any popularized urban core theory. By using the entire county instead of the neighborhoods or census tracts within the county, one can reveal the bias towards poor people living in these areas. The Wilcoxon test also revealed a significant difference between the average wage of a low-income rural county and the average wage of the state.

IV. MODELING INTERGENERATIONAL POVERTY

There are a number of challenges involved in comparing poverty-level income and poverty rates across MSAs. One important challenge is that poverty-level income is defined for the nation as a whole and does not reflect any inter-metropolitan variation in the cost of purchasing food or other essentials (except for the States of Hawaii and Alaska). The poverty rate, therefore, overstates the level of need in areas with lower price levels relative to those with higher price levels. (21)

4.1 Data Definitions and Variables List

Extracted from existing literature, a multivariate model of low-wage worker characteristics could include the following variables:

1. The percentage change in the poverty rate.

2. The proportion of female-headed households.

3. The number of wage earners in the household.

4. The ethnicity of the household.

5. The age distribution of the wage earners, particularly if any earner is over age 65.

6. The immigration and citizenship status of the wage earner.

7. The number of persons in the household.

8. Median education of the wage earner(s).

9. The number of years of work experience of the wage earner(s).

10. The employment-to-population ratio.

11. The proportion of workers living in the central city.

12. The proportion of residents in the central city.

13. The population of the metropolitan area, county and urban core.

14. Per capita income within the metropolitan area, county and urban core.

15. The inflation rate of the MSA or state.

16. The land area of the central city.

An additional listing of data definitions of selected variables is provided in Table 5. The rationale for the inclusion of these variables into a model on low-wage worker characteristics is the belief that wages and salaries account for the majority of income accruing to low-wage households.

V. EVIDENCE ON WORKER CHARACTERISTICS AND POVERTY

Using the data reduction method of principal component analysis, the variables listed in Table 5 were reduced to only two principal components: The first principal factor in determining poverty was demographics, in general and the second component was labor market characteristics. These two are instructive in yielding regression results based on the available data.

5.1 Wage Rate Determination

The principal component analysis enabled data reduction of the large number of variables into those appearing in Table 5. Then multivariate regression modeling was developed using the 2006 Wage Rate as the dependent variable. Data from (1) the central city core counties, (2) the low-income urban counties, and (3) all corresponding rural counties (in the states in which counties from the central city core and urban sampling frames appeared) were placed in a larger dataset consisting of 308 counties from the three sampling frames. The best-fit model on Wage Rates is:

Wage = [[beta].sub.0] + [[beta].sub.1]AA + [[beta].sub.2]Adopt + [[beta].sub.3]Est + [[beta].sub.4][Fem.sub.NS] + [[beta].sub.5]Hisp + [[beta].sub.6][HouseAge.sub.Yrs] + [[beta].sub.7][Male.sub.NS] +[[beta].sub.8][Minority.sub.%] + [[beta].sub.9][Pop.sub.18-64] + [[beta].sub.10][Poverty.sub.%]+ [[beta].sub.11][Rent.sub.$] + [[beta].sub.12]WhHHInc + [[epsilon].sub.t]

where,

AA =Black/African American Population

Adopt =Adopted Child in Family Household Population

Est =Number of Establishments

[Fem.sub.NS] =Female Population 25 and Over with No Schooling

Hisp =Hispanic/Latino Population

[HouseAge.sub.Yrs] =Median House Age

[Male.sub.NS] =Male Population 25 and Over with No Schooling

[Minority.sub.%] =Minority Percentage

[Pop.sub.18-64] =Population 18 to 64 Years

[Poverty.sub.%] =Poverty Level Percent

[Rent.sub.$] =Median Gross Rent

WhiteHHInc =White Median Household Income

5.2 Poverty Rate Determination

The principal component analysis enabled data reduction of the large number of variables into those appearing in Table 5. A multivariate regression model was developed using the Poverty Rate as the dependent variable. Data from (1) the central city core counties, (2) the low-income urban counties and (3) all corresponding rural counties (in the states in which counties from the central city core and urban sampling frames appeared) were placed in a larger dataset consisting of 308 counties from the three sampling frames. The model on Poverty Rate can be illustrated as follows:

[Poverty.sub.%] = [[beta].sub.0] + [[beta].sub.1]AA + [[beta].sub.2]AS + [[beta].sub.3]Emp + [[beta].sub.4][Fem.sub.Age] +[[beta].sub.5][FS.sub.50-99] + [[beta].sub.6]Hisp + [[beta].sub.7][HH.sub.PovLevel] + [[beta].sub.8][HH.sub.NoIncome]+ [[beta].sub.9][HH.sub.SelfEmpInc] + [[beta].sub.10][Minority.sub.%] + [[beta].sub.11]Payroll + [[beta].sub.12][Pop.sub.65+]+ [[beta].sub.13][Rent.sub.$] +[epsilon].sub.t]

Where, AA =Black/African American Population

AS =Asian Population

Emp =Total Number of Employees

[Fem.sub.Age] = Female Median Age

[FS.sub.50-99] = Number of Firms with 50 to 99 Employees

Hisp =Hispanic/Latino Population

[HH.sub.PovLevel] =Households with Income Less than Poverty Level

[HH.sub.NoIncome] = Households with No Wage or Salary Income

[HH.sub.SelfEmpInc] = Households with Self Employment Income

[Minority.sub.%] =Minority Percentage

Payroll =Payroll, Gross ($)

[Pop.sub.65+] = Population 65 and Over

[Rent.sub.$] =Median Gross Rent

The results of the regression analysis appear in Tables 6 and 7. Although only 74% to 75% of the variation in the dependent variables (Wage Rate and Poverty Percentage) can be explained by the independent variables, the results go a long way in providing some rationale for modern-day poverty, its causes, consequences and implications for wage determination and economic inequality.

VI. IMPLICATIONS AND CONCLUDING REMARKS

This article addresses the challenges in modeling complex, dynamic relationships with cross-sectional data methods. By stratifying the available data accurately, however, it was discovered that there is no statistically significant difference between a suburban (urban fringe) county and a rural (emerging urban) county in terms of average wage rates. Larger MSAs tend to have higher poverty rates, whereas MSAs with higher population and income growth rates tend to have lower rates of growth of poverty.

Other critical findings include but are not limited to the following: (1) Median education in years enhances skill composition and increases the skill level of the overall population, therefore making the workers in certain areas more attractive to employers. (2) Since labor market participation is the primary source of income for most households, the quantity and quality of jobs in an area enhances the average earnings of all households. (3) It is also assumed that the quantity of jobs available in a jurisdiction is reflected in the labor force participation rates of residents. And (4) Tighter labor markets typically increase the quantity of jobs available in the low-wage sectors relative to the higher-skilled labor sectors.

Based on the level of dedication needed to complete a research study of this type, it is apparent that there is still work to be done in the development of databases and modeling techniques to adequately model the inter-generational relationships among poverty and low-wage worker characteristics.

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(1) United States Economy Overview in the Central Intelligence Agency World Fact Book 2008. https://www.cia. gov/library/publications/the-world-factbook/geos/us.html .

(2) Paul A. Jargowsky, "Stunning Progress, Hidden Problem: The Dramatic Decline of Concentrated Poverty in the 1990s." (2003; repr., Washington, DC: Brookings Institution, 2008), 1.

(3) Janice Fanning Madden and Robert C. Daniels, "Changes in the Distribution of Poverty across and within U.S. Metropolitan Areas: 1979-89," (November 9, 1995).

(4) Michael S. Bernick, Job Training That Gets Results: Ten Principles of Effective Employment Programs (Kalamazoo, MI: W.E. Upjohn Institute for Employment Research, 2005), Chapter 6.

(5) Bernick, Job Training That Gets Results, 14.

(6) Low wage occupations can include: agricultural workers, auto mechanics, business machine repair, car salesmen, carpenters, child care workers, cashiers, clerical skills, doormen, handyman, food preparation, hotel and motel front desk workers, housecleaning, janitorial and housekeeping, laundry and maintenance workers, medical secretary, nursing home aides, nursing, reception, nutritionists, restaurant workers, retail salespersons, room attendants, security guards, telemarketing, television repair, upholstering, waitressing, and welding.

(7) The U.S. Bureau of Labor Statistics (BLS) defines Primary Sampling Units (PSUs) for each state. BLS produces the CPI-U by placing into strata the PSUs and classifying them by population size with a designation of A, B or C. The methodology defines the areas as large metropolitan (A), small metropolitan (B), or non metropolitan urban (C). The PSUs with populations of over 1.5 million persons are also classified as "self-representing" for the purposes of calculating the CPI area indexes. For the purposes of this study, the self-representing large metropolitan areas are: Anchorage, Atlanta, Baltimore, Boston, Chicago, Cincinnati, Cleveland, Dallas, Denver, Detroit, District of Columbia, Honolulu, Houston, Kansas City, Los Angeles, Miami, Milwaukee, Minneapolis, New York, Philadelphia, Phoenix, Pittsburgh, Portland, San Diego, San Francisco, Seattle, St. Louis, and Tampa. For non metropolitan areas, PSUs are defined by county. See also http://www.bls.gov/opub/hom/pdf/homch17.pdf.

(8) Central city core counties of "self-representing" major metropolitan areas have a higher wage rate than all other counties of the United States. For the purposes of this study, any county of a major metropolitan area with an average wage of under $23 per hour is classified as a low-wage central city core county.

(9) The following Urban Influence Codes (UICs) define the extent of "rurality" among counties of the United States. All counties with the codes of 7, 10, 11, and 12 are rural counties. The UIC definitions are changed every 10 years. According to the 2003 UIC definitions, 695 of 3,141 counties were rural.

UIC 7 = Noncore adjacent to small metro area and does not contain a town of at least 2,500 residents. UIC 10 = Noncore adjacent to micro area and does not contain a town of at least 2,500 residents. UIC 11 = Noncore not adjacent to metro or micro area and contains a town of at least 2,500 residents. UIC 12 = Noncore not adjacent to metro or micro area and does not contain a town of at least 2,500 residents. Source: U.S. Department of Agriculture. Economic Research Service.

(10) The Midwest consists of the states of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota and Wisconsin.

(11) U.S. Census Bureau., Housing and Household Economic Statistics Division. Poverty Thresholds for 2006 by Size of Family and Number of Related Children Under 18 Years.

(12) U. S. Bureau of the Census. Poverty Thresholds 2006. Federal Register 71, no. 15 (January 24, 2006) 3848-49.

(13) For the CPI-U calculations use ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt.

(14) Minimum wage rates (of under $6.50 per hour) for rural 3-digit industries in ranked order include: NAICS 512 - Motion Picture and Sound Recording Industries; NAICS 722 - Food Services and Drinking Places; NAICS 721- Accommodation; NAICS 813 - Religious, Grantmaking, Civic, Professional, and Similar Organizations; NAICS 485 - Transit and Ground Passenger Transportation; NAICS 451 - Sporting Goods, Hobby, Book, and Music Stores; NAICS 453 - Miscellaneous Store Retailers; NAICS 448 - Clothing and Clothing Accessories Stores; NAICS 447 - Gasoline Stations; and NAICS 445 - Food and Beverage Stores.

(15) In low-wage rural areas, the industries with above average number of persons employed are Retail Trade, Health Care and Social Insurance, Manufacturing, Accommodation and Food Services, and Construction. More specifically the primary industries for employment in low-wage rural areas, Food Services and Drinking Services, Ambulatory Health Care Services, Gasoline Stations, Specialty Trade Contractors, Accommodation, and Professional, Scientific, and Technical Services provided approximately 39% of total employment in low-wage rural counties throughout the United States. Source: Calculations based on census data provided by the Federal Financial Institutions Examination Council.

(16) The U.S. Department of Commerce Bureau of Economic Analysis (BEA) Regional Economic Accounts also publishes a similar series on "Metropolitan Statistical Areas Average Wage per Job (dollars) [CA34 - Wage and Salary Summary]." The employment estimates in the BEA series are counted according to the job, not the person. In both series, however, if a person holds more than one job then they will be counted for each job held.

(17) For the purposes of this research study, the wage rate was calculated from County Business Patters 2006, a publication of the U.S. Census Bureau. The March 2006 variable amounts for quarterly wages, number of employees, and total payroll were used to calculate the average wage per worker. No measure exists in the CBP database, however, for the corresponding number of years of work experience associated with the County Business Patterns payroll and employment numbers.

(18) FFIEC Home Mortgage Disclosure Act (HMDA) Census Data Products. http://www.ffiec.gov/hmda.

(19) Minimum wage 3-digit industries in the suburban fringe areas include: NAICS 113 - Forestry and Logging; NAICS 315 Apparel Manufacturing; NAICS 337 - Furniture and Related Product Manufacturing; NAICS 447 - Gasoline Stations; NAICS 448 - Clothing and Clothing Accessories Stores; NAICS 451 - Sporting Goods, Hobby, Book, and Music Stores; NAICS 453 Miscellaneous Store Retailers; NAICS 519 - Other Information Services; NAICS 624 - Social Assistance; NAICS 713 Amusement, Gambling, and Recreation Industries; NAICS 721 Accommodation; and NAICS 722 Food Services and Drinking Places.

(20) UIC=1 designates a large metropolitan area of 1+ million residents. UIC=2 designates a small metropolitan area of less than 1 million residents. Source: U.S. Department of Agriculture Economic Research Service 2003 Urban Influence Codes.

(21) Madden and Daniels, "Distribution of Poverty across U.S. Metropolitan Areas 1979-89."

Jan E. Christopher, Associate Professor of Economics, Department of Accounting, Economics and Finance, Delaware State University

Table 1. Comparisons of Rural Wages, Low-Wage Rural Wages, and State
Average Wages by
Census Region and Division, 2006

                                             Low-Wage
                            Rural            Rural
                            Counties         Counties         State
                            Average          Average          Average

Census Region         n     Wage       n     Wage       n     Wage
Northeast             14    $14.95     4     $11.32     14    $19.64
Midwest               285   $ 11.39    207   $ 10.42    289   $ 19.64
South                 241   $ 12.18    148   $ 10.47    243   $ 16.76
West                  144   $ 13.74    72    $ 10.59    149   $ 16.45
Total                 684   $ 12.24    431   $ 10.47    695   $ 16.38

                                             Low-Wage
                            Rural            Rural
                            Counties         Counties         State
                            Average          Average          Average

Census Division       n     Wage       n     Wage       n     Wage
New England           7     $16.02     1     $ 11.96    7     $17.60
Mid Atlantic          7     $ 13.88    3     $ 11.10    7     $ 21.67
East North Central    48    $ 12.88    22    $ 11.04    48    $18.43
West North Central    237   $ 11.09    185   $ 10.35    241   $ 15.37
South Atlantic        86    $ 12.20    53    $ 10.99    86    $ 17.32
East South Central    67    $12.44     39    $ 10.24    67    $ 14.97
West South Central    88    $ 11.96    56    $ 10.14    90    $ 17.56
Mountain              127   $ 13.83    67    $ 10.56    132   $ 16.09
Pacific               17    $ 13.06    5     $ 10.91    17    $ 19.18
Total                 684   $ 12.24    431   $10.47     695   $ 16.38

                      Rural          Low-Wage
                      County         Rural County
                      Wage           Wage
                      Differential   Differential
Census Region         to State       to State
Northeast             -24%           -42%
Midwest               -42%           -47%
South                 -27%           -38%
West                  -16%           -36%
Total                 -25%           -36%

                      Rural          Low-Wage
                      County         Rural County
                      Wage           Wage
                      Differential   Differential
Census Division       to State       to State
New England           -9%            -32%
Mid Atlantic          -36%           -49%
East North Central    -30%           -40%
West North Central    -28%           -33%
South Atlantic        -30%           -37%
East South Central    -17%           -32%
West South Central    -32%           -42%
Mountain              -14%           -34%
Pacific               -32%           -43%
Total                 -25%           -36%

Table 1. Provides average wage calculations by Census Region and
Census Division for rural counties and low-wage rural
counties compared to the average state wage. The results reveal that
rural wages per job are about 25% lower than the state
average wage. Low-wage rural county wages per job are about 36% lower
than their respective average state wage. Source:
Compiled using County Business Patterns: 2006.

Table 2. Comparison of 2-Digit Industry Wages by Rural, Urban and
Suburban Fringe, 2006

                                                            Average
                                                            Hourly
NAICS                                                       Wages
Code    Rural Counties, 2-Digit Industry Title              2006

23      Construction                                        $12.033
44      Retail Trade                                        $8.031
53      Real Estate and Rental and Leasing                  $9.028
54      Professional, Scientific, and Technical Services    $11.589
56      Administrative, Support, Waste Management           $8.932
61      Educational Services                                $9.959
62      Health Care and Social Assistance                   $11.146
71      Arts, Entertainment, and Recreation                 $6.528
72      Accommodation and Food Services                     $4.241
81      Other Services (except Public Administration)       $7.008
99      Miscellaneous Services                              $5.177
n=684 Rural Counties

                                                            Average
                                                            Hourly
NAICS   Urban Counties of Major Metropolitan Areas,         Wages
Code    2-Digit Industry Title                              2006

44      Retail Trade                                        $10.595
56      Administrative, Support, Waste Management           $12.178
61      Educational Services                                $11.602
71      Arts, Entertainment, and Recreation                 $10.080
72      Accommodation and Food Services                     $5.975
81      Other Services (except Public Administration)       $10.641
99      Miscellaneous Services                              $8.215
n=210 Urban Counties of Major Metro Areas

                                                            Average
                                                            Hourly
NAICS   Suburban Fringe Counties of Major                   Wages
Code    Metropolitan Areas, 2-Digit Industry Title          2006

44      Retail Trade                                        $8.882
53      Real Estate and Rental and Leasing                  $9.682
56      Administrative, Support, Waste Management           $8.908
61      Educational Services                                $7.915
62      Health Care and Social Assistance                   $11.496
71      Arts, Entertainment, and Recreation                 $6.416
72      Accommodation and Food Services                     $4.484
81      Other Services (except Public Administration)       $7.679
99      Miscellaneous Services                              $5.891
n=39 Suburban Counties

                                 Average
                      Number     Hourly
NAICS   Standard      of         Wages
Code    Deviation     Counties   ($ July 2008)

23      $6.603        487        $13.992
44      $1.733        623        $9.338
53      $4.588        217        $10.497
54      $5.012        372        $13.475
56      $4.110        214        $10.386
61      $3.055        51         $11.580
62      $2.675        549        $12.960
71      $5.769        123        $7.591
72      $1.908        508        $4.931
81      $2.342        504        $8.149
99      $3.998        39         $6.020
n=684 Rural Counties
                                                    Urban Wage
                                 Average            Advantage
                      Number     Hourly             over Similar
NAICS   Standard      of         Wages              Jobs in
Code    Deviation     Counties   ($ July 2008)      Rural Areas

44      $1.679        206        $11.664            24.91%
56      $3.514        196        $13.407            29.08%
61      $3.779        161        $12.773            10.30%
71      $6.578        160        $11.097            46.18%
72      $1.698        206        $6.577             33.38%
81      $3.369        203        $11.715            43.75%
99      $4.897        81         $9.044             50.23%
n=210 Urban Counties of Major Metro Areas
                      Average    Average            Low-Wage
                      Hourly     Hourly             Suburban to
                      Wages      Wages              Major
NAICS   Number        (July      Major Metro        Metropolitan
Code    of Counties   2008)      Counties           Wages

44      37            $9.778     $10.595            84%
53      26            $10.659    $15.444            63%
56      29            $9.807     $12.178            73%
61      12            $8.713     $11.602            68%
62      36            $12.656    $15.546            74%
71      15            $7.064     $10.080            64%
72      35            $4.936     $5.975             75%
81      34            $8.454     $10.641            72%
99      10            $6.486     $8.215             72%
n=39 Suburban Counties

Table 2. Provides average wage calculations, standard deviations, and
constant dollar wages for 2008 by 2-digit North American
Industry Classification System (NAICS) codes for the rural, urban,
and suburban fringe sampling frames. The results reveal that
urban wages per job are between 10 and 50 percent higher than similar
jobs in rural areas. Residents living in a low-wage, low-
income neighborhoods of suburban fringe counties should expect the
residents in those areas to have wages 16 to 37 percent
lower than the metropolitan area as a whole. Source: Compiled by
Author (July 2008).

Table 3. Rural Wages by 3-Digit Industry, 2006

                       Average Wage   Average Wage by
NAICS    Number        by Industry    Industry
Code     of Counties   ($ 2006)       ($ 2008)

624      78            $6.53          $6.87
713      51            $6.87          $7.22
452      98            $6.87          $7.22
315      1             $6.94          $7.30
812      216           $7.48          $7.87
314      2             $8.02          $8.43
561      88            $8.28          $8.71
442      60            $8.47          $8.91
623      110           $8.66          $9.11
531      105           $8.67          $9.12
711      8             $9.25          $9.73
487      4             $9.26          $9.74
712      6             $9.30          $9.78
611      51            $9.96          $10.47
532      66            $9.99          $10.51
444      391           $10.04         $10.56
811      288           $10.12         $10.64
443      91            $10.22         $10.75
339      10            $10.68         $11.23
236      228           $10.76         $11.32
238      305           $10.83         $11.39
423      24            $10.91         $11.47
515      6             $11.26         $11.84
326      7             $11.30         $11.88
562      33            $11.47         $12.06
323      11            $11.54         $12.14
511      22            $11.56         $12.16
541      372           $11.59         $12.19
446      156           $11.69         $12.29
441      393           $11.96         $12.58
113      36            $12.02         $12.64
454      101           $12.07         $12.69
311      35            $12.17         $12.80

NAICS
Code     3-Digit Industry Title

624      Social Assistance
713      Amusement, Gambling, and Recreation Industries
452      General Merchandise Stores
315      Apparel Manufacturing
812      Personal and Laundry Services
314      Textile Product Mills
561      Administrative and Support Services
442      Furniture and Home Furnishings Stores
623      Nursing and Residential Care Facilities
531      Real Estate
711      Performing Arts, Spectator Sports, and Related Industries
487      Scenic and Sightseeing Transportation
712      Museums, Historical Sites, and Similar Institutions
611      Educational Services
532      Rental and Leasing Services
444      Building Material & Garden Equipment & Supplies Dealers
811      Repair and Maintenance
443      Electronics and Appliance Stores
339      Miscellaneous Manufacturing
236      Construction of Buildings
238      Specialty Trade Contractors
423      Merchant Wholesalers, Durable Goods
515      Broadcasting (except Internet)
326      Plastics and Rubber Products Manufacturing
562      Waste Management and Remediation Services
323      Printing and Related Support Activities
511      Publishing Industries (except Internet)
541      Professional, Scientific, and Technical Services
446      Health and Personal Care Stores
441      Motor Vehicle and Parts Dealers
113      Forestry and Logging
454      Nonstore Retailers
311      Food Manufacturing

Table 3 provides the array of low-wage industries, their average
wages in 2006, and the average wage in constant 2008 dollars
using the 3-digit North American Industrial Classification (NAICS)
codes for the rural sampling frame. The results reveal that
certain industries are minimum wage, whereas a large number of
industries are low-wage.

Source: Compiled using U.S. Bureau of the Census, County Business
Patterns: 2006 and the U.S. Department of Commerce,
Bureau of Economic Analysis, GDP Implicit Price Deflator (Seasonally
Adjusted): 2006-A to 2008-A.

Table 4. Urban Wages for 28 Major Metropolitan Areas, 2006

                                                          Average.
                                                          Hourly
Metropolitan Statistical Area (MSA)         State Code    Wage

1 Anchorage                                 AK            $18.616
2   Atlanta-Sandy Springs-Marietta          GA            $14.865
3 Baltimore-Towson                          MD            $18.109
4 Boston-Quincy                             MA            $24.336
5 Chicago-Naperville-Joliet                 IL            $18.257
6 Cincinnati-Middletown,                    OH-KY-IN      $14.669
7 Cleveland-Elyria-Mentor                   OH            $16.696
8 Dallas-Plano-Irving                       TX            $16.072
9 Denver-Aurora                             CO            $18.082
10 Detroit-Livonia-Dearborn                 MI            $21.729
11 Honolulu                                 HI            $16.540
12   Houston-Sugar Land-Baytown             TX            $16.224
13   Kansas City                            MO-KS         $14.100
14   Los Angeles-Long Beach-Glendale        CA            $18.505
15   Miami-Miami Beach-Kendall              FL            $18.505
16   Milwaukee-Waukesha-West Allis          WI            $18.167
17   Minneapolis-St. Paul-Bloomington       MN-WI         $17.113
18   New York-White Plains-Wayne            NY-NJ         $24.221
19 Philadelphia                             PA            $23.012
20 Phoenix-Mesa-Scottsdale                  AZ            $15.927
21 Pittsburgh                               PA            $14.835
22 Portland-Vancouver-Beaverton             OR-WA         $16.755
23   San Diego-Carlsbad-San Marcos          CA            $20.450
24   San Francisco-San Mateo-Redwood City   CA            $30.048
25 Seattle-Bellevue-Everett                 WA            $21.854
26   St. Louis                              MO-IL         $13.484
27   Tampa-St. Petersburg-Clearwater        FL            $14.952
28 Washington-Arlington-Alexandria          DC-VA-MD-WV   $19.148
Weighted Averages                                         $17.160

                                            Number
                                            of         Number
Metropolitan Statistical Area (MSA)         Counties   of Workers

1 Anchorage                                 2          150,290
2   Atlanta-Sandy Springs-Marietta          28         2,204,005
3 Baltimore-Towson                          7          1,120,924
4 Boston-Quincy                             3          1,054,251
5 Chicago-Naperville-Joliet                 8          3,487,476
6 Cincinnati-Middletown,                    15         927,832
7 Cleveland-Elyria-Mentor                   5          965,951
8 Dallas-Plano-Irving                       8          1,814,250
9 Denver-Aurora                             10         1,089,586
10 Detroit-Livonia-Dearborn                 1          663,804
11 Honolulu                                 1          359,474
12   Houston-Sugar Land-Baytown             10         2,116,579
13   Kansas City                            15         914,103
14   Los Angeles-Long Beach-Glendale        1          3,895,886
15   Miami-Miami Beach-Kendall              1          868,560
16   Milwaukee-Waukesha-West Allis          4          789,858
17   Minneapolis-St. Paul-Bloomington       13         1,660,777
18   New York-White Plains-Wayne            11         4,662,764
19 Philadelphia                             5          1,785,815
20 Phoenix-Mesa-Scottsdale                  2          1,638,331
21 Pittsburgh                               7          1,056,137
22 Portland-Vancouver-Beaverton             7          920,161
23   San Diego-Carlsbad-San Marcos          1          1,205,862
24   San Francisco-San Mateo-Redwood City   3          967,702
25 Seattle-Bellevue-Everett                 2          1,259,559
26   St. Louis                              16         1,262,357
27   Tampa-St. Petersburg-Clearwater        4          1,026,277
28 Washington-Arlington-Alexandria          20         1,919,177
Weighted Averages                           210        41,787,748

                                                      MSA Wage
                                            State     as %
                                            Average   of State
Metropolitan Statistical Area (MSA)         Wage      Wage

1 Anchorage                                 $19.610   95%
2   Atlanta-Sandy Springs-Marietta          $18.241   81%
3 Baltimore-Towson                          $19.959   91%
4 Boston-Quincy                             $23.492   104%
5 Chicago-Naperville-Joliet                 $20.965   87%
6 Cincinnati-Middletown,                    $17.410   84%
7 Cleveland-Elyria-Mentor                   $17.410   96%
8 Dallas-Plano-Irving                       $19.126   84%
9 Denver-Aurora                             $19.303   94%
10 Detroit-Livonia-Dearborn                 $18.830   115%
11 Honolulu                                 $15.855   104%
12   Houston-Sugar Land-Baytown             $19.126   85%
13   Kansas City                            $19.126   74%
14   Los Angeles-Long Beach-Glendale        $21.901   84%
15   Miami-Miami Beach-Kendall              $16.351   113%
16   Milwaukee-Waukesha-West Allis          $16.738   109%
17   Minneapolis-St. Paul-Bloomington       $19.429   88%
18   New York-White Plains-Wayne            $29.640   82%
19 Philadelphia                             $18.487   124%
20 Phoenix-Mesa-Scottsdale                  $17.188   93%
21 Pittsburgh                               $18.487   80%
22 Portland-Vancouver-Beaverton             $17.120   98%
23   San Diego-Carlsbad-San Marcos          $21.901   93%
24   San Francisco-San Mateo-Redwood City   $21.901   137%
25 Seattle-Bellevue-Everett                 $19.769   111%
26   St. Louis                              $16.942   80%
27   Tampa-St. Petersburg-Clearwater        $16.351   91%
28 Washington-Arlington-Alexandria          $29.765   64%
Weighted Averages                           $17.754   94%

Table 4 provides average wage calculations by Metropolitan Statistical
Area for urban counties of the 28 major metropolitan
areas of the United States. Overall, the average wage was $17.16 in
the 210 urban counties where 42 million workers reside. The
average corresponding state wage was $17.75. The results reveal that
some MSA wages are higher than the state average wage
and some are lower. Overall MSA wages are about 94% of the
corresponding average state wage. Source: Author compiled
using County Business Patterns: 2006.

Table 5. Data Definitions and Selected Statistics for Urban Core
Areas, Major Metropolitan
Areas and Selected MSAs, 2008

Data Definitions and Variables List             Low-Income
Age, Ethnicity, Education, Household,           Urban Core
Poverty, and Population Indicators              Census Tracts
Female Median Age                               31
Male Median Age                                 43
Person Median Age                               29
Am. Indian Alaskan Native Med Household Inc.    24,174
American Indian Alaska Native Population        63,674
Asian Median Household Income                   17,510
Asian Population                                312,526
Black Median Household Income                   19,895
Black African American Population               2,926,288
Hispanic Median Household Income                27,664
Hispanic/Latino Population Total                2,113,652
White Median Household Income                   30,161
White Population                                1,577,686
Female Pop 25 And Over with 9th Grade Ed.       102,372
Female Pop 25 And Over with Associates Degree   78,742
Female Pop 25 And Over with Less 1 Yr College   106,237
Female Pop 25 And Over with No Schooling        105,746
Living in Group Quarters--College Dorms         82,809
Male Pop 25 And Over with 9th Grade Ed.         91,948
Male Pop 25 And Over with Associates Degree     55,269
Male Pop 25 And Over with Less 1 Yr College     76,336
Male Pop 25 And Over with No Schooling          91,812
Households With No Wage Or Salary Income        695,563
Households With Other Income                    315,761
Households With Public Assist Income            327,598
Households With Self Employment Income          130,333
Household Count                                 2,220,078
Adopted Child Family Household Population       42,562
Household Income Less than Poverty Level        819,752
Median Gross Rent                               489
Median House Age                                49
Minority Percentage                             70
Poverty Level Percent                           36
Poverty Status Total Households                 2,220,530
Female Population Total                         3,410,073
Female Pop Under 16 Years                       941,008
In Group Quarters, Population                   261,681
Male Population Total                           3,190,314
Population 16 Yrs And Over                      4,678,752
Spanish Speaking Pop 18 To 64, Eng. Not Well    343,652
Population 18 To 64 Years                       3,908,792
Population 5 To 17 Years                        1,524,228
Population 65 And Over                          565,867
Population                                      6,695,340
Urban Population                                6,584,488

Data Definitions and Variables List             Total Major
Age, Ethnicity, Education, Household,           Metropolitan
Poverty, and Population Indicators              Area MSAs
Female Median Age                               36
Male Median Age                                 34
Person Median Age                               35
Am. Indian Alaskan Native Med Household Inc.    23,717
American Indian Alaska Native Population        490,809
Asian Median Household Income                   45,577
Asian Population                                5,458,897
Black Median Household Income                   39,719
Black African American Population               14,565,015
Hispanic Median Household Income                42,992
Hispanic/Latino Population Total                16,187,002
White Median Household Income                   49,858
White Population                                60,480,537
Female Pop 25 And Over with 9th Grade Ed.       705,445
Female Pop 25 And Over with Associates Degree   2,004,557
Female Pop 25 And Over with Less 1 Yr College   2,247,761
Female Pop 25 And Over with No Schooling        572,036
Living in Group Quarters--College Dorms         449,412
Male Pop 25 And Over with 9th Grade Ed.         658,467
Male Pop 25 And Over with Associates Degree     1,589,646
Male Pop 25 And Over with Less 1 Yr College     1,710,989
Male Pop 25 And Over with No Schooling          502,428
Households With No Wage Or Salary Income        6,873,036
Households With Other Income                    3,969,092
Households With Public Assist Income            1,305,565
Households With Self Employment Income          3,816,803
Household Count                                 33,861,215
Adopted Child Family Household Population       657,385
Household Income Less than Poverty Level        3,830,240
Median Gross Rent                               718
Median House Age                                34
Minority Percentage                             38
Poverty Level Percent                           12
Poverty Status Total Households                 33,869,215
Female Population Total                         46,642,984
Female Pop Under 16 Years                       10,288,454
In Group Quarters, Population                   1,936,811
Male Population Total                           44,605,019
Population 16 Yrs And Over                      69,513,442
Spanish Speaking Pop 18 To 64, Eng. Not Well    2,058,384
Population 18 To 64 Years                       57,463,273
Population 5 To 17 Years                        17,135,456
Population 65 And Over                          10,274,927
Population                                      91,248,003
Urban Population                                85,319,932

Data Definitions and Variables List             All MSAs
Age, Ethnicity, Education, Household,           in
Poverty, and Population Indicators              Selected States
Female Median Age                               37
Male Median Age                                 34
Person Median Age                               36
Am. Indian Alaskan Native Med Household Inc.    222,241
American Indian Alaska Native Population        1,709,024
Asian Median Household Income                   41,933
Asian Population                                9,538,839
Black Median Household Income                   35,785
Black African American Population               26,588,323
Hispanic Median Household Income                40,179
Hispanic/Latino Population Total                32,852,663
White Median Household Income                   45,999
White Population                                178,081,533
Female Pop 25 And Over with 9th Grade Ed.       1,915,935
Female Pop 25 And Over with Associates Degree   5,418,058
Female Pop 25 And Over with Less 1 Yr College   6,113,937
Female Pop 25 And Over with No Schooling        1,198,284
Living in Group Quarters--College Dorms         1,602,728
Male Pop 25 And Over with 9th Grade Ed.         1,837,129
Male Pop 25 And Over with Associates Degree     4,261,714
Male Pop 25 And Over with Less 1 Yr College     4,794,722
Male Pop 25 And Over with No Schooling          1,136,093
Households With No Wage Or Salary Income        19,888,359
Households With Other Income                    11,656,890
Households With Public Assist Income            3,098,179
Households With Self Employment Income          10,585,525
Household Count                                 88,392,338
Adopted Child Family Household Population       1,714,866
Household Income Less than Poverty Level        10,187,996
Median Gross Rent                               650
Median House Age                                35
Minority Percentage                             32
Poverty Level Percent                           13
Poverty Status Total Households                 88,392,338
Female Population Total                         120,339,717
Female Pop Under 16 Years                       26,307,821
In Group Quarters, Population                   6,554,619
Male Population Total                           116,218,830
Population 16 Yrs And Over                      182,640,344
Spanish Speaking Pop 18 To 64, Eng. Not Well    3,632,948
Population 18 To 64 Years                       146,298,448
Population 5 To 17 Years                        44,615,577
Population 65 And Over                          29,724,815
Population                                      236,558,547
Urban Population                                188,249,308

Data Definitions and Variables List             Urban Core
Age, Ethnicity, Education, Household,           as %
Poverty, and Population Indicators              Major Metro
Female Median Age                               85%
Male Median Age                                 126%
Person Median Age                               84%
Am. Indian Alaskan Native Med Household Inc.    102%
American Indian Alaska Native Population        13%
Asian Median Household Income                   38%
Asian Population                                6%
Black Median Household Income                   50%
Black African American Population               20%
Hispanic Median Household Income                64%
Hispanic/Latino Population Total                13%
White Median Household Income                   60%
White Population                                3%
Female Pop 25 And Over with 9th Grade Ed.       15%
Female Pop 25 And Over with Associates Degree   4%
Female Pop 25 And Over with Less 1 Yr College   5%
Female Pop 25 And Over with No Schooling        18%
Living in Group Quarters--College Dorms         18%
Male Pop 25 And Over with 9th Grade Ed.         14%
Male Pop 25 And Over with Associates Degree     3%
Male Pop 25 And Over with Less 1 Yr College     4%
Male Pop 25 And Over with No Schooling          18%
Households With No Wage Or Salary Income        10%
Households With Other Income                    8%
Households With Public Assist Income            25%
Households With Self Employment Income          3%
Household Count                                 7%
Adopted Child Family Household Population       6%
Household Income Less than Poverty Level        21%
Median Gross Rent                               68%
Median House Age                                142%
Minority Percentage                             183%
Poverty Level Percent                           292%
Poverty Status Total Households                 7%
Female Population Total                         7%
Female Pop Under 16 Years                       9%
In Group Quarters, Population                   14%
Male Population Total                           7%
Population 16 Yrs And Over                      7%
Spanish Speaking Pop 18 To 64, Eng. Not Well    17%
Population 18 To 64 Years                       7%
Population 5 To 17 Years                        9%
Population 65 And Over                          6%
Population                                      7%
Urban Population                                8%

Data Definitions and Variables List             Major Metro
Age, Ethnicity, Education, Household,           as %
Poverty, and Population Indicators              All MSAs
Female Median Age                               98%
Male Median Age                                 99%
Person Median Age                               98%
Am. Indian Alaskan Native Med Household Inc.    11%
American Indian Alaska Native Population        29%
Asian Median Household Income                   109%
Asian Population                                57%
Black Median Household Income                   111%
Black African American Population               55%
Hispanic Median Household Income                107%
Hispanic/Latino Population Total                49%
White Median Household Income                   108%
White Population                                34%
Female Pop 25 And Over with 9th Grade Ed.       37%
Female Pop 25 And Over with Associates Degree   37%
Female Pop 25 And Over with Less 1 Yr College   37%
Female Pop 25 And Over with No Schooling        48%
Living in Group Quarters--College Dorms         28%
Male Pop 25 And Over with 9th Grade Ed.         36%
Male Pop 25 And Over with Associates Degree     37%
Male Pop 25 And Over with Less 1 Yr College     36%
Male Pop 25 And Over with No Schooling          44%
Households With No Wage Or Salary Income        35%
Households With Other Income                    34%
Households With Public Assist Income            42%
Households With Self Employment Income          36%
Household Count                                 38%
Adopted Child Family Household Population       38%
Household Income Less than Poverty Level        38%
Median Gross Rent                               110%
Median House Age                                99%
Minority Percentage                             119%
Poverty Level Percent                           94%
Poverty Status Total Households                 38%
Female Population Total                         39%
Female Pop Under 16 Years                       39%
In Group Quarters, Population                   30%
Male Population Total                           38%
Population 16 Yrs And Over                      38%
Spanish Speaking Pop 18 To 64, Eng. Not Well    57%
Population 18 To 64 Years                       39%
Population 5 To 17 Years                        38%
Population 65 And Over                          35%
Population                                      39%
Urban Population                                45%

Source: Federal Financial Institutions Examination Council, HMDA
Census Products: 2008.

Table 6. Regression Output for the Wage Rate Model

Dependent Variable:                  Unstandardized
Wage Rate (2006 $)                   Coefficients     Standard
                                     P                Error
(Constant)                           6.436            0.502
Adopted_Child_Fam_Hh_Pop             -0.005           0.001
Black_African_American_Population    0.000            0.000
Female Pop 25 And Ovr No Schooling   0.003            0.001
Hisp_Pop_Total                       0.000            0.000
Male_Pop_25_And_Over_No_Schooling    -0.005           0.001
Median_Gross_Rent                    0.010            0.001
Median_House_Age                     0.007            0.003
Minority_Percentage                  0.018            0.005
Pop_18_To_64_Years                   0.000            0.000
Poverty_Level_Percent                0.046            0.012
Total Establishments                 0.000            0.000
White_Median_Household_Income        0.000            0.000
ANOVA(b)
                                     Sum of
                                     Squares          df
Regression                           1861             12
Residual                             642              295
Total                                2503             307
Model Summary(b)

Model                                R                R Square
                                     0.862            0.743

Dependent Variable:                  Standardized
Wage Rate (2006 $)                   Coefficients
                                     Beta
(Constant)
Adopted_Child_Fam_Hh_Pop             -0.781
Black_African_American_Population    0.281
Female Pop 25 And Ovr No Schooling   1.671
Hisp_Pop_Total                       -0.462
Male_Pop_25_And_Over_No_Schooling    -2.313
Median_Gross_Rent                    0.333
Median_House_Age                     0.073
Minority_Percentage                  0.176
Pop_18_To_64_Years                   1.017
Poverty_Level_Percent                0.176
Total Establishments                 0.902
White_Median_Household_Income        -0.129
ANOVA(b)

                                     Mean Square
Regression                           155.053
Residual                             2.178
Total
Model Summary(b)
                                     Adjusted R
Model                                Square
                                     0.733

Dependent Variable:
Wage Rate (2006 $)
                                     t-statistic              p-value
(Constant)                           12.812                   0.000
Adopted_Child_Fam_Hh_Pop             -5.984                   0.000
Black_African_American_Population    5.029                    0.000
Female Pop 25 And Ovr No Schooling   3.733                    0.000
Hisp_Pop_Total                       -2.468                   0.014
Male_Pop_25_And_Over_No_Schooling    -5.236                   0.000
Median_Gross_Rent                    10.073                   0.000
Median_House_Age                     2.343                    0.020
Minority_Percentage                  3.959                    0.000
Pop_18_To_64_Years                   6.416                    0.000
Poverty_Level_Percent                3.873                    0.000
Total Establishments                 10.157                   0.000
White_Median_Household_Income        -3.934                   0.000
ANOVA(b)

                                     F                        Sig.
Regression                           71.199                   0.000
Residual
Total
Model Summary(b)

Model                                Std. Error of the Estimate
                                     1.476

Table 6. Reveals there is an inverse relationship between adopting a
child or a male having no formal schooling and the wage
rate. Perhaps, being able to adopt a child is a better indicator of
self-employment income and higher wage-and-salary income.
Note that this model explains 74.3% of the factors that influence
wage rates. Source: Author's Compilation (July 2008).

Table 7. Regression Output for Poverty Level (Percent) Model

Dependent Variable:                 Unstandardized   Standard
Poverty_Level_Percent               Coefficients     Error
                                    [beta]

(Constant)                          56.913           4.485
Asian_Population                    0.0002           0.000
Black_African_American_Population   0.000            0.000
Employees, Number of                0.000            0.000
Female_Median_Age                   -0.775           0.086
Hh_Income_Less_than_Poverty Level   0.001            0.001
Hh_With_No_Wage_Or_Salary_Inc       0.002            0.001
Hh_With_Self_Employment_Income      -0.005           0.001
Hispanic_Population_Total           -0.001           0.000
Median_Gross_Rent                   -0.023           0.004
Minority_Percentage                 0.104            0.019
Establishments_w_50_99_Employees    -0.034           0.015
Population_65_and_Over              -0.001           0.001
Quarterly Payroll (March 2006)      0.000            0.000

ANOVA(b)
                                    Sum of
                                    Squares          df
Regression                          27776.601        13
Residual                            9488.959         294
Total                               37265.560        307

Model Summary(b)

Model                               R                R Square
                                    0.863            0.745

Dependent Variable:                 Standardized   t-statistic
Poverty_Level_Percent               Coefficients
                                    Beta

(Constant)                                         12.689
Asian_Population                    0.912          6.379
Black_African_American_Population   -0.822         -6.871
Employees, Number of                4.217          4.227
Female_Median_Age                   -0.404         -9.015
Hh_Income_Less_than_Poverty Level   1.052          2.160
Hh_With_No_Wage_Or_Salary_Inc       2.181          2.663
Hh_With_Self_Employment_Income      -1.088         -7.444
Hispanic_Population_Total           -1.998         -7.026
Median_Gross_Rent                   -0.198         -5.447
Minority_Percentage                 0.262          5.553
Establishments_w_50_99_Employees    -1.647         -2.336
Population_65_and_Over              -1.085         -1.761
Quarterly Payroll (March 2006)      -1.949         -2.521

ANOVA(b)

                                    Mean Square    F
Regression                          2136.662       66.201
Residual                            32.275
Total

Model Summary(b)

                                    Adjusted R
Model                               Square         Std. Error of
                                                   the Estimate
                                    0.734          5.681

Dependent Variable:                 p-value
Poverty_Level_Percent

(Constant)                          0.000
Asian_Population                    0.000
Black_African_American_Population   0.000
Employees, Number of                0.000
Female_Median_Age                   0.000
Hh_Income_Less_than_Poverty Level   0.000
Hh_With_No_Wage_Or_Salary_Inc       0.032
Hh_With_Self_Employment_Income      0.008
Hispanic_Population_Total           0.000
Median_Gross_Rent                   0.000
Minority_Percentage                 0.000
Establishments_w_50_99_Employees    0.020
Population_65_and_Over              0.079
Quarterly Payroll (March 2006)      0.012

ANOVA(b)

                                    Sig.
Regression                          0.000
Residual
Total

Model Summary(b)

Model