Wages, Women, and Workers •••
Wages, Women, and Workers Comparing Populations in High and Low Opportunity Areas of the Baltimore Region Ashley Sampson URSP601 Research Methods Interim Report 1 April 15, 2014 Introduction 1
Wages, Women, and Workers •••
Wages, Women, and Workers Comparing Populations in High and Low Opportunity Areas of the Baltimore Region
Introduction In 2013 the National Center for Smart Growth Research and Education endeavored to map the geography of opportunity in the Baltimore Metropolitan Council (BMC) planning region. The center created a composite index for each census tract incorporating 92 indicators of neighborhood opportunity selected by a committee of local subject matter experts. The resulting map showed that while average opportunity at the county level varied largely along a suburb/city divide, wide variations also existed within each County and Baltimore City at the census tract level (See Figure 1 in the Appendix). Having identified low-opportunity areas, this report seeks to provide a better understanding of the households that live in low-opportunity areas by discerning how they differ from households in high-opportunity neighborhoods. Knowledge of these differences can help to refine and direct policies that are intended to level the playing field between low and high opportunity areas. This report focuses on population characteristics that influence economic outcomes for working-age adults and their families, particularly characteristics that may restrict economic mobility among low-income households.
Variables Although women’s hourly wages have increased by over 50% since 1970, men’s incomes still are most important to increasing a family’s economic status (Pew, 2014). According to an April 2014 report from the Pew Research Center, when education and labor force participation rate are controlled Introduction 2
Wages, Women, and Workers ••• for among couples, the higher wages earned by men are almost twice as important as women’s wages to predicting higher household incomes. Bradford Wilcox of the National Marriage Project points out that family income for the bottom two quintiles has fallen since 1970 while the top three quintiles have gained (2014). Wilcox asserts that these declines are occurring despite the income gains made by women because the gains are being eroded by “declines in marriage among the poor and working class.” Further, the gender wage gap persists at all levels of the economic ladder partly due to the higher likelihood that women take time off from work to have and raise children, work less than full-time, and occupy low-wage jobs at a higher rate than men (Budig and England, 2001; Boraas and Rodgers, 2003). The following variables were selected for this study to potentially provide insight into how these aforementioned trends may be playing out to affect household economic outcomes in the Baltimore region: sex, family income, mean wages and salary, marital status, wage earners per household, and presence of persons under the age of 18 (see Appendix for corresponding data codes).
Data The data used for this analysis comes from the 2007 – 2011 American Community Survey Public Use Microdata Sample (PUMS) for all Public Use Microdata Areas (PUMAs) within the BMC planning region. Data for individual household heads were joined to the data for each household, resulting in a dataset containing information only about the household and the household head and excluding individual-level information on any other members of the household. Each PUMA was designated as either a low opportunity or high opportunity area using the mean opportunity index score for the census tracts in that PUMA as a measure. PUMAs falling below the mean opportunity index score for
Data 3
Wages, Women, and Workers ••• all census tracts are defined as low opportunity, while all other PUMAs are defined as high opportunity. The dataset presents two challenges when interpreting statistical results. First, we must be careful to qualify any data on individual characteristics as describing solely the household heads. For example, the sex variable reflects the gender balance of the household heads only—not the total population—because all other individuals are excluded from the dataset. Secondly, PUMAs are geographic areas with approximately 100,000 residents. Baltimore City has six PUMAs, while all of Carroll County is one PUMA. This is a very large geographic scale at which to examine household differences considering how widely the opportunity index varies within county and city boundaries at the census tract level. Designating areas as high or low opportunity at the PUMA level masks a great deal of this variation, potentially diluting the results of our analysis.
Methodology I use independent samples t-tests to check for statistically significant differences between the means of continuous variables describing households and household heads in low and high opportunity areas. For categorical variables, I use a difference of proportions test to calculate a Z statistic. The p-value for statistical significance is defined as p < 0.05. Variances are assumed to be unequal.
Findings All of the household and individual characteristics studied exhibit statistically significant differences between high and low opportunity areas (see Table 1). Low-opportunity households in the Baltimore region are more likely to be headed by a woman and less likely to have more than one wage
Methodology 4
Wages, Women, and Workers ••• earner. The proportion of female-headed households in low opportunity areas is just over half at 52%, while the proportion for high opportunity areas is 44%. The difference is significant for p < .01. On average low opportunity households have lower family incomes and their household heads have lower salaries and wages. There is a large and statistically significant gap between family income in low opportunity and high opportunity areas, while the gap between household-head salary and wages is less wide (although still statistically significant). This is possibly because households in lower opportunity areas have fewer incomes contributing to the total family income. While 62% of high opportunity households have two or more workers per household, only 54% of low opportunity households have two or more workers. This difference is significant at the p < .01 level. We can also see that there is a large and statistically significant difference between the proportions of household heads in each area that are married. This supports the assertion that there are fewer dual-income households in low opportunity areas, although it is not a direct measure because of cohabitation. Table 1: Selected Characteristics of Households Living in Low and High Opportunity BMC PUMAs
Variable % Household Heads that are Women
High Opportunity PUMA Mean N
Low Opportunity PUMA Mean N
P-Value *
44.41%
28,349
52.03%
26,186
0.000
Mean Family Income
$117,397
18,233
$88,346
15,196
0.000
Mean Household Head Wages and Salary
$46, 522
28,332
$32,870
26,175
0.000
% Households with 2 or More Workers
62.34%
18,233
54.19%
15,196
0.000
% Household Heads that are Married
54.13%
28,349
41.48%
26,186
0.000
% Households With Persons Under Age 18
32.11%
26,161
29.07%
24,352
0.000
* P-value for alternative hypothesis: u1≠u2. T-tests assuming unequal variances used for all continuous variables. Difference of proportions test used for categorical variables.
The difference between the proportions of households with persons under the age of 18 is statistically significant, with 32% of high-opportunity households housing minors, and only 29% of
Findings 5
Wages, Women, and Workers ••• low-opportunity households housing minors. Although relatively small, the difference is somewhat surprising because of the nationally lower rate of childbirth among highly educated, higher-income women (Livingston and Cohn, 2010).
Policy Implications This report has shown that there are statistically significant differences in household and family structure between low opportunity and high opportunity areas. These differences, which include sex of the household head, number of wage earners in the household, marriage status, and family income, have important implications for household economic status. Because of the high proportion of households that are headed by women in low opportunity areas, policies aimed at increasing women’s earnings will have a direct and substantial impact on these household’s economic status and opportunity for upward economic mobility. The policies outlined below should be part of any strategy that aims to reduce the inequality of opportunity in the Baltimore region:
o
Raising the minimum wage will have outsized benefits for families in the Baltimore region because of the high rate of female-headed households and the overrepresentation of women in low-skill service sector jobs. According to analysis from the Economic Policy Institute, women would comprise 55.1% of those affected by raising the minimum wage to $10.00 per hour in Maryland.
o
Policies and legislation supporting wage-parity between the sexes will benefit a majority of households in low-opportunity neighborhoods.
o
Childcare/afterschool programs and mobility-focused transportation policies support a female household head’s ability to work full time, which is associated with higher mean hourly wages (Budig and England, 2001).
Policy Implications 6
Wages, Women, and Workers •••
References Boraas, Stephanie and William M. Rodgers III. 2003. “How Does Gender Play a Role in the Earnings Gap? An Update,” Monthly Labor Review. March: 9–15. http://stats.bls.gov/opub/mlr/2003/03/art2full.pdf, accessed 14 April 2014. Budig, Michelle and Paula England. 2001. “The Wage Penalty for Motherhood,” American Journal of Sociology 66: 204–225. http://www.asanet.org/images/members/docs/pdf/featured/motherwage.pdf, accessed 14 April 2014. Hall, Doug and David Cooper. 2013. “How Raising Maryland’s Minimum Wage Will Benefit Workers And Boost The State’s Economy.” Economic Policy Institute. http://s2.epi.org/files/2013/benefitsraising-minimum-wage-maryland.pdf, accessed 14 April 2014. Livingston, Gretchen and D’Vera Cohn. 2010. “Childlessness Up Among All Women; Down Among Women with Advanced Degrees ” Pew Research Center, Washington, D.C. http://www.pewsocialtrends.org/files/2010/11/758-childless.pdf, accessed 14 April 2014. Wilcox, Bradford. 2014. “Women's Wages Are Rising: Why Are So Many Families Getting Poorer?” The Atlantic. http://www.theatlantic.com/business/archive/2014/04/womens-wages-are-rising-whyare-so-many-families-getting-poorer/359991/, accessed 14 April 2014. “Women’s Work; The Economic Mobility of Women Across a Generation.” 2014. Pew Research Center, Washington, D.C. http://www.pewstates.org/uploadedFiles/PCS/ContentLevel_Pages/Reports/2014/Womens-Work-Report-Economic-Mobility-Across-a-Generation.pdf, accessed 14 April 2014.
References 7
Wages, Women, and Workers •••
Appendix Figure 1: Opportunity Mapping Advisory Panel Composite Opportunity Index
Source: National Center for Smart Growth Research and Education, Technical Memorandum #2 to the Nexus Committee of the Baltimore Regional Sustainable Communities Initiative, 16 September 2013.
Appendix 8
Wages, Women, and Workers •••
Table 2: Variables and Corresponding PUMS Codes Variable
PUMS Code
% Household Heads that are Women
SEX
Mean Family Income
FINCP
Mean Household Head Wages and Salary
WAGP
% Households with 2 or More Workers
WIF
% Household Heads that are Married
MAR
% Households With Persons Under Age 18
R18
Appendix 9