Women, Work, and Wages Revisited

Women + Girls Research Alliance

Women, Work, and Wages Revisited

Women, Work, and Wages Revisited Prepared by: Harrison S. Campbell, Jr. Associate Professor of Geography University of North Carolina at Charlotte Charlotte, NC 28223

With research assistance from the UNC Charlotte Urban Institute and funding from the Women + Girls Research Alliance

July 2013

Table of Contents Executive Summary………………………………………………………………………………………………………….1 I. Introduction .......................................................................................................................................................................... 3 Setting the Stage ................................................................................................................................................................ 3 A Final Note.......................................................................................................................................................................... 5 II. Women’s Earnings ........................................................................................................................................................... 6 III. Industry and Occupations .......................................................................................................................................... 9 IV. Women’s Labor Force Participation .................................................................................................................. 11 V. The Wage Gap Again .................................................................................................................................................... 13 VI. Educational Attainment............................................................................................................................................ 15 VII. Poverty and Education ............................................................................................................................................ 18 VIII. Poverty, Work and Children ............................................................................................................................... 21 IX. Summary and Conclusions...................................................................................................................................... 25

iv Figures and Tables Figures Figure 1: Median Earnings for Full-time Workers .....................................................................................................6 Figure 2: Women’s Median Full-time Earnings ...........................................................................................................7 Figure 3: Earnings Gap ..............................................................................................................................................................8 Figure 4: Women’s Median Full-time Earnings, Mecklenburg ............................................................................8 Figure 5: Mecklenburg Women in Managerial Positions ....................................................................................10 Figure 6: Women’s Occupational Concentration .....................................................................................................11 Figure 7: Labor Force Participation ................................................................................................................................12 Figure 8: Women’s Labor Force Participation ..........................................................................................................13 Figure 9: Mecklenburg Women’s Labor Force Participation by Race ..........................................................13 Figure 10: Population Age 25+ with a Bachelor’s Degree or Higher ............................................................16 Figure 11: Women with a Bachelor’s Degree or Higher ......................................................................................16 Figure 12: Mecklenburg College Educated Women Age 25+ ............................................................................18 Figure 13: Poverty Rate of the Population ..................................................................................................................19 Figure 14: Women in Poverty.............................................................................................................................................20 Figure 15: Mecklenburg Poverty Rate by Educational Attainment ...............................................................21 Figure 16: Cumulative Commute Times, Mecklenburg 2011 ...........................................................................24 Tables Table 1: Average Monthly Earnings by Selected Industry, Charlotte MSA, 2Q2011 ...............................9 Table 2: The Gender Wage Gap in Mecklenburg County .....................................................................................14 Table 3: Educational Attainment of the Population Age 25+ by Sex, Mecklenburg (%) ....................17 Table 4: Poverty Rate of the Population (%)..............................................................................................................19 Table 5: Work Status of Women in Poverty, 2005 and 2011 (%) ..................................................................22 Table 6: Children in Poverty, 2005 and 2011 ............................................................................................................23 Table 7: Population with No Health Insurance Coverage, 2011 (%) ............................................................25

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Women, Work, and Wages Revisited Executive Summary This report serves three purposes: updates a similar report produced for the Women’s Summit in 2007 with the most recent data available from the U.S. Census American Community Survey, (2) serves as a companion piece to a report prepared by the UNC Charlotte Urban Institute in 2011 describing gender-based differences in labor market outcomes for women and men during the Great Recession, and (3) highlights women’s ongoing struggles and workforce disparities in the aftermath of the recession.

The overall findings can be summarized as follows:  Compared with women nationally and across North Carolina, Mecklenburg women fare favorably in terms of earnings, labor force participation, and educational and occupational attainment.  Within Mecklenburg County, significant workforce disparities exist between men and women, and among women of different races and ethnicities.  Men generally fared worse during and after the recession in terms of absolute job growth, but women fared worse in terms of poverty. Further, the median earnings gap between women and men was virtually unchanged by 2011.  Poverty increased markedly between 2005 and 2011, especially among children.  Some of the largest workforce gaps appear not between women and men, per se, but among women of different races and ethnicities. Hispanic and Black/African American women face the greatest challenges in the post-recession era.

After setting the stage by discussing major trends before, during, and after the Great Recession, this report delves into a more detailed discussion of changes in women’s economic prospects since 2005. Although some rates of change are striking by themselves, they are even more poignant when viewed in the context of a growing population. Between 2005 and 2011, Mecklenburg County added more than 137,000 people, bringing its population to 940,000.

Major highlights include:  In 2011, the typical Mecklenburg woman working full time earned $38,823. Her male counterpart earned $51,068. The wage gap of 24 percent was essentially unchanged from 2005 and slightly greater than the national wage gap of 21 percent.  After adjustment for inflation, real median earnings declined for both women and men since 2005. Women’s earnings declined 1.4 percent while men’s earnings declined by 1.6 percent.  If Mecklenburg women had the same median earnings as men, their total aggregate earnings would be $1.9 billion higher.  The median wage of White women is nearly twice that of Hispanic women and 25 percent greater than Black/African American women.  The gender gap in labor force participation narrowed between 2005 and 2011. However, the change was largely due to falling rates among men.

 Black/African American women have high rates of labor force participation, but remuneration from their work is 25 percent lower than their White female counterparts and 32 percent lower than men’s wages.  Indirect evidence suggests that many Black/African American women have significant need (as evidenced by high labor force participation) but insufficient means (from low educational attainment) to put their earnings on par with White women, much less White men.  Poverty among men nearly doubled to 15.7 percent by 2011 while the percentage of women in poverty increased to 18.4. Poverty struck more than one in four Black/African American women and more than one in three Hispanic women.  Dropping out of high school is disastrous for the earnings potential of men and women in Mecklenburg. Simply finishing high school reduces the chances of living in poverty by about 50 percent.  Today, women are more likely to both attend and graduate from college—causing the education gap to narrow over time.  More than half of White women had earned at least a bachelor’s degree by 2011 while only 24.7 percent of Black/African American women held a college-level degree.  Poverty among White women increased from 5.4 percent to 7.5 percent while poverty rates for Black/African American women increased from 18.9 percent to 25.6 percent. By 2011, onethird of Hispanic women lived in poverty.  On average, those women who earned bachelor’s or graduate degrees faced poverty rates of only 4.3 percent—poverty rates that are four times lower than the average for women overall.  Between 2005 and 2011, the poverty rate among children increased from 15.8 percent to 23.4 percent. Thus, Children have suffered as “collateral damage” in the wake of the Great Recession.  Children of single mothers are twice as likely to experience poverty as those in two-parent households and seven times more likely to live in poverty than children of single men.  More than 16 percent of women in Mecklenburg County had no health insurance in 2011—a higher proportion than the state and nation.

In the years covered by this report, the returns from work declined; economic security of men, women, and children became more tenuous; and poverty became more prevalent. As a result, women experienced rising levels of poverty and little-to-no progress in closing the wage gap relative to men. Ironically, women accounted for more than half of the job growth since 2005. On the bright side, women are making substantial gains in educational attainment, both in absolute terms and relative to men. However, to paint this picture with a broad brush is misleading. The experience of women in the labor force varies significantly by race and ethnicity. Such a wide variety of experiences can only be summarized as complex, highly differentiated, and nuanced. Improving employment and earnings prospects of minority women will achieve the greatest and most equitable progress toward eliminating gender gaps in Mecklenburg County.

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Women, Work, and Wages Revisited1 I. Introduction This report updates a previous a document prepared in 2007 for the Charlotte-Mecklenburg Women’s Summit.2 That report assessed gender-based differences in workforce experience, attachment, and outcomes across a variety of workforce dimensions as of 2005. The report also estimated the economic impact neutralizing gender-based workforce differentials. In 2011, the UNC Charlotte Urban Institute produced a similar report covering the 2007–2010 period.3 Both reports highlighted gender-based and race/ethnicity-based differences in the workforce with special attention to women’s employment, wages, and labor force participation. The 2011 report was especially timely, revealing how men and women were differentially impacted by the Great Recession.

This report provides yet another update, mostly relying on data from 2011, the most recent data available from the U.S. Census American Community Survey. Below, some of the major patterns, trends, and issues from 2005– 201 are revisited. To provide complete context for the discussion below, interested readers are encouraged to review both previous reports. The 2011 report, in particular, provides a good analysis of the interim years covered in this report. In many ways, this report echoes some of the major findings of the 2011 report. Thus, this report serves three purposes: 1. Updates the 2007 report with the most recent data available from the American Community Survey.

2. Serves as a companion piece to the 2011 report.

3. Highlights the ongoing struggles and workforce disparities in the aftermath of the Great Recession.

Setting the Stage The Great Recession, which technically started in December of 2007 and ended in June 2009, has impacted most every person and household. As of this writing, many men, women, and children, still in the recession’s wake, have not fully recovered. They often experience job loss, extended periods of unemployment or underemployment, and subsequent labor force detachment. The following information provides some context about the lasting effects of the Great Recession to consider:  Between the first quarter of 2008 and the second quarter of 2009, U.S. Gross Domestic Product fell 4.3 percent.4  Over the same period, the number of nonfarm jobs in the United States declined by 7.5 million, or 5.4 percent.5  As of May 2013, U.S. nonfarm employment had yet to regain all the jobs lost since the onset of the Great Recession. Current employment levels are more than 2.4 million below their 2007 levels.6 1 The research assistance of the UNC Charlotte Urban Institute, especially that of Jonathan Kozar, is gratefully

acknowledged.

2 Campbell, H.S., Jr. Women, Work, and Wages in Mecklenburg County: An Economic Impact Assessment, UNC Charlotte Urban Institute and Charlotte-Mecklenburg Women’s Summit, August 2007.

3 Working Women and the Great Recession, UNC Charlotte Urban Institute, July 2011.

4 Bureau of Economic Analysis. Retrieved June 21, 2013, from: http://bea.gov/national/index.htm#gdp.

5 U.S. Bureau of Labor Statistics. Retrieved June 21, 2013, from: http://www.bls.gov/data/#employment.

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 Mecklenburg County unemployment stood at 4.6 percent in 2007. In July 2010, the unemployment rate peaked at 11.8 percent and has gradually declined to 8.5 percent as of April 2013, still well above its pre-recession levels.7  Unlike the United States, Mecklenburg County has regained jobs lost during the Great Recession.

Although the depths of the recession hit Mecklenburg in 2009, jobs lost during the Great Recession were regained by the end of 2012.8 Although the Great Recession has been declared over for four years, its many-faceted forms of devastation are still with us. Yet, behind the “big ticket” numbers is a more subtle and differentiated story about how the recession has affected different segments of the population, both nationally and locally.

At the national level, some people were hit harder by the recession than others. Consider the following:  The early stages of the Great Recession were sometimes called the “mancession” because a disproportionate share of job loss (and subsequent unemployment) occurred in construction and manufacturing, sectors that are male dominated.  Mattingly et al. (2011) note that “men, single parents, and young people with less education” were especially hard hit.9  Further, Mattingly et al. report that “regardless of age, education, or race/ethnicity, single parents face higher odds of being unemployed than married people without children [and that] single parents are disproportionately represented in construction and manufacturing, two industries that experienced major declines during the recession, and in wholesale/retail sales, an industry known for its irregular hours and inconsistent scheduling.”  Although the onset of the recession disproportionately affected men, women have not fared as well during its recovery.  Wartenburg (2012) reports that “at the beginning of the recession, approximately 70 percent of jobs lost were by men. The situation, however, has changed considerably, with women now suffering more job loss and unemployment.” 10  For example, “Women have regained only 18.8 percent of all jobs they lost during the recession.

Men, however, have regained 39 percent of all jobs they lost during that same time period.”

(Wartenburg, 2012, p. ii)

Locally, recessionary impacts have been uneven as well but have not always followed the national trend. The 2011 report notes, for example:

6 U.S. Bureau of Labor Statistics. Retrieved June 21, 2013, fro: http://www.bls.gov/data/#employment.

7 North Carolina Division of Employment Security. Retrieved June 21, 2013,

from: http://www.ncesc1.com.

8 Campbell, H.S., Jr. “Job Growth Dominates 2012,” Report issued February 18, 2013. Available online at

www.charlottechamber.com.

9 Mattingly, M., Smith, K., & Bean, J. “Unemployment in the Great Recession,” University of New Hampshire, Carsey Institute, Issue Brief No. 35, Summer, 2011. Available from: http://www.carseyinstitute.unh.edu/publications/IB-BeanUnemployment.pdf.

10 Wartenburg, J. “The Continuing Effects of the Great Recession on Women and Families,” The Global Woman’s Project, Center for Concern, GWP Briefing Paper 14, May 2012. Available from: https://www.coc.org/gwp/continuing-effects-greatrecession-women-and-families.

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 Unemployment among men rose sharply during the recession such that by 2009, unemployment among men in Mecklenburg County reached 12.2 percent, compared with 9.1 percent for women.11 “  Data through the 2nd quarter of 2010 [indicate] job creation increased since the official end of the recession and women fared better in newly created jobs than men in the post-recession period.”12 Women’s job creation was 78.9 percent, compared with 32.1 percent for men between 2009 and 2010.

The summary below provides some of the uneven gender- and race/ethnicity-based impacts since the end of the Great Recession by comparing data from 2005 and 2011. Complementing earlier reports, 2005 and 2011 represent an interesting comparison because 2005 was a pre-recession year of aggregate prosperity in Mecklenburg County while 2011 was a post-recession year, though still in the wake of recessionary turbulence.

By 2011, labor force-related experience varied by gender and race/ethnicity.  Compared with women nationally and across the state, Mecklenburg women fare favorably in terms of earnings, labor force participation, and occupational attainment.  Within Mecklenburg County, significant workforce disparities exist between men and women, and among women of different races and ethnicities.  Men generally fared worse during and after the recession in terms of absolute job growth and women fared worse in terms of poverty. After adjustment for inflation, real median earnings declined from 2005 to 2011 for both women and men working full-time.  In 2011, women working full time earned 76 percent of what men earned. This wage gap has not changed since 2005. Similarly, female labor force participation was largely unchanged between 2005 and 2011 while men’s participation in the labor force declined 4.3 percentage points.  Between 2005 and 2011, the poverty rate among women increased sharply, especially among women of different races/ethnicities and those with low levels of educational attainment.  Children often suffered as “collateral damage” between 2005 and 2011.

A Final Note Throughout this report, many descriptions of the workforce, wages, educational attainment and poverty are discussed in terms of their percent of the population. Indeed, some striking changes have occurred in these data.

Changes also have occurred in Mecklenburg’s population between 2005 and 2011. the population grew by 17.1 percent, or more than 137,000 persons. The female population grew faster than the male population (18.0 percent compared with 16.1 percent). By 2011, Mecklenburg was home to an estimated 940,056 people.13 Thus, several indicators represent growing percentages of a growing population. In many cases the absolute change in workforce-related data is even greater than percentage changes would suggest.

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Working Women and the Great Recession, UNC Charlotte Urban Institute, July 2011.

12 Ibid., p. 10.

13 North Carolina State Office of Budget and Management. Available from:

http://www.osbm.state.nc.us/ncosbm/facts_and_figures/socioeconomic_data/population_estimates/county_estimates.sht m.

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The analysis in this report does not control for gender or race/ethnicity-based attributes known to affect earnings or labor force participation. Some portion of earnings, labor force participation, and the resulting wage gap between women and men is due to differences in education, accumulated human capital, age, industry, occupation, and workforce experience.

II. Women’s Earnings Figure 1 shows median full-time earnings for women and men in 2005 and 2011.14 Immediately obvious is that local earnings are higher than state or national averages, but a significant wage gap exists at all levels. Upon closer inspection, however, the wage gap in Mecklenburg was virtually unchanged during this period. In 2005, median earnings for women working full time and living in Mecklenburg County were $34,171 while men earned $45,048. Consequently, earnings for the typical woman working full time were 75.9 percent of men’s earnings (i.e. 24.1 percent lower), very close to the national average. By 2011, women’s median earnings in Mecklenburg grew to $38,823 while men’s earnings grew to $51,068. In other words, in 2011, women working full-time earned 76.0 percent as much as men, producing a wage gap of 24 percent that was not significantly different from 2005.

The earnings data in Figure 1 were not adjusted for inflation. After adjustment for inflation (see Table 2 on page 13), real median wages actually declined. For example, in Mecklenburg County, inflation-adjusted wages for men declined 1.6 percent. For women the median wage declined by 1.4 percent. At the national level, women’s wages did not keep pace with inflation although men’s wages did. Across the state, men’s wages outpaced inflation while women’s wages were essentially unchanged in real terms since 2005. In both cases, the wage gap was less severe and narrowed slightly over the period.

14 This study focuses on full-time workers

because very little gender-based difference exists among part-time workers.

According to the Bureau of Labor Statistics, for example, the bulk of the part-time labor force are age 25 and above. On average, men earn about $255 per week, compared with $253 for women. Bureau of Labor Statistics, Median weekly earnings of part-time wage and salary workers by selected characteristics. Available from http://www.bls.gov/cps/cpsaat38.pdf.

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Compared with other counties, women in Mecklenburg receive relatively high earnings from full-time work (Figure 2).15 A clear pattern to emerge from this map is that earnings in urban areas tend to be higher than nonurban earnings, especially those nonurban areas in the most remote western parts of the state and the state’s Inner Coastal Plain near Virginia’s Tidewater. Hyde and Chowan counties, for example, have the state’s lowest full-time earnings for women at just $24,484 and $25,651, respectively. Similarly, women’s full-time earnings are relatively low in some of the state’s agricultural counties like Sampson, Duplin and Robeson. As shown elsewhere,16 income in many of these lower wage counties is disproportionately derived from

government transfers such as Social Security and disability payments. On the other hand, women’s median fulltime earnings in the urbanized areas of Charlotte, Greensboro, and Raleigh-Durham are high relative to state averages.

Higher median earnings for women do not always translate into smaller disparities between women and men, as shown in Figure 3. Although total earnings for women are relatively high in the state’s urban areas, they are higher still for men, exacerbating the earnings differential in larger counties such as Guilford, Wake, and Mecklenburg. Earnings appear more equal in some of the state’s lower wage counties such as Onslow, where a high proportion of all workers are found in the retail sector. In two cases, Tyrell and Clay counties, women employed full time actually earned more than men by 7.5 percent and 8.3 percent, respectively.

15 Note: Data in Figure 2 are the 2007–2011 five-year estimates from the American Community Survey expressed in 2011

price levels.

16 Campbell, H. S., Jr. (2003). “Unearned Income and Local Employment Growth in North Carolina: An Economic Base Analysis,” Southeastern Geographer, 43(1), 89–103.

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Figure 4 illustrates women’s earnings by race and ethnicity in Mecklenburg County for 2005 and 2011, adjusted for inflation to 2011 price levels. Clearly, non-White women realize significantly lower wages. In 2011, the greatest difference was between White, non-Hispanic women (hereafter referred to as White) and their Hispanic counterparts. On average, White women earned almost twice the full-time wage as Hispanic women in 2011. Further, Hispanic women experienced significant declines in their real, inflation-adjusted earnings, as their median earnings declined by 12.7 percent. Asian women experienced a real increase in their wages (2.0 percent), although by 2011 their median wages were only two-thirds that of White women.

Similarly Black/African American women, who earn 25 percent less than White women, saw their real wages increase by 2.9 percent. These differences by race and ethnicity underscore the complex, nuanced and differentiated experience of women in the local workforce. Not only do women’s wages differ from those of men, but there are dramatic differences among women as well.

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III. Industry and Occupations As noted in the 2007 report, women’s earnings are, in part, related to the industries and occupations in which they work. Men are overrepresented in construction and manufacturing, while women are overrepresented in various “helping” sectors such as social services, certain branches of health Care, and education, especially grades K-12. However, even when women are employed in high-wage industries and occupations, their earnings do not always equate to those of men.

Table 1, derived from the North Carolina Division of Employment Security, shows average monthly earnings for women and men by selected industries in the Charlotte metropolitan area during the second quarter of 2011.

Table 1 also shows the female-male wage ratio for 2006 and 2011. Although not directly comparable to the earnings data presented above, these data show that the industry in which women find work directly affects their earnings. For example, women receive their highest average monthly earnings in the finance and insurance industry ($4,691) and their lowest earnings in the retail sector ($1,926) with average monthly earnings of $3,211 across all sectors. However, even in the high-wage sectors of finance and Insurance or health care and social assistance, women’s earnings are barely half (or less) that of men’s. Only in public administration, a prominent source of work for women, do women’s wages even approximate those of men although still 12 percent lower. According to this data source, women’s overall average weekly earnings are only 64 percent of men’s, a marginal improvement from 2006.

Table 1. Average Monthly Earnings by Selected Industry, Charlotte MSA (NC Part), 2Q2011 Monthly Earnings ($) Industry

Women

Men

W/M Ratio 2Q2006

2Q2011

Total

3,211

5,045

0.62

0.64

Manufacturing

3,502

5,094

0.69

0.69

Retail

1,926

3,205

0.60

0.60

Finance and Insurance

4,691

8,616

0.51

0.54

Professional, Scientific, and Technical Services

4,541

7,265

0.59

0.63

Health Care and Social Assistance

3,799

9,921

0.40

0.38

Public Administration

3,161

3,605

0.88

0.88

Source: US Census Bureau, Local Employment Dynamics (LED), QWI Online, accessed 08/07 and 06/13 See http://lehd.did.census.gov/led/datatools/quiapp.html

Industry sectors are comprised of a wide range of occupations. Managers, technicians, accountants, administrative assistants, and manual laborers are represented in every sector of the economy. Women, however, are not evenly distributed across all occupations. According to one source, 99 percent of all secretaries 9

and 97 percent of all childcare workers are women, while 88 percent of nonphysician health care workers and 66 percent of food services workers are women, patterns thought to contribute to the “feminization� of poverty.

17 Collectively, the occupational characteristics of Mecklenburg women are somewhat more encouraging. In 2011, for example, 42.7 percent of employed Mecklenburg women held management, professional and related occupations which compares favorably to state and national averages (Figure 5).

A slightly more detailed look at this occupational category is shown in Figure 6 that displays the concentration among women holding top executive, operations specialist, and financial management positions. The national average is indexed to 1.00, such that a local concentration ratio of, say, 1.50 indicates the area has 50 percent more than its prorated share of women in the occupations shown; ratios less than 1.00 indicate lower-thanaverage representation. In the examination of the 2011 concentration of women in some of the top managerial positions, we see that women in Mecklenburg are twice as likely to hold positions as operations specialists and financial managers relative to national averages (their concentration ratios are 2.01 and 2.02, respectively), a substantial gain relative to both the state and nation since 2005. Among top executives, however, there has been little change, with concentration ratios of 0.65 and 0.63 in 2005 and 2011. Among top executives, Mecklenburg women are underepresented by 37 percent compared to the national average.

17 Harrington, J. W. , & Warf, B. (1995). Industrial Location: Principles, Practice & Policy, London: Routledge.

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Thus far, a somewhat mixed picture of women’s equity is revealed in the local economy. Generally, women’s full-time wages compare favorably to those at the state and national level. As real (inflation-adjusted) wages declined for both men and women, the overall wage gap remained unchanged. Although earnings were less in absolute terms, real wages grew for Black/African American and Asian women but declined sharply for Hispanic women in the county. These differing patterns reveal both the nuanced and differentiated experience of women in the workforce and indicate some of the factors affecting their earnings. Clearly, the rewards from work affect one’s labor force participation, the next subject.

IV. Women’s Labor Force Participation Labor force participation and the labor force participation rate are important indicators of labor force “attachment.” To be considered part of the labor force, one must be (a) age 16 or older and (b) employed, or unemployed and actively seeking work. Those who are not employed and not seeking work (e.g. retirees, athome caregivers, the severely disabled, or those discouraged from work) are not counted as part of the labor force. The number of workforce “eligibles” are those age 16 years or older. Thus, the labor force participation rate is simply the percentage of work force eligibles that are part of the active labor force.

Women generally have lower rates of labor force participation than men, but the difference has narrowed steadily in the last 50 years. Schunk and Teel (2005) indicate that in 1948 “…the male participation rate was 86.6 percent while the female participation rate was 32.7 percent. By 2004, the male participation rate had fallen to 73.3 percent while the female rate had risen to 59.2 percent.”18 Many reasons exist for the increase in women’s labor force participation including rising levels of educational attainment, smaller average family sizes, and better employment prospects overall.

18 Schunk, D. L., & Teel, S. J. (2005). The Status

Carolina, p. 36.

of South Carolina’s Women, Moore School of Business, University of South

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As shown in Figure 7, labor force participation in Mecklenburg (for both men and women) is higher than state and national averages although men’s participation in the labor force declined more significantly than women’s since the recession’s end. Between 2005 and 2011, labor force participation among Mecklenburg women was virtually unchanged (66.9 percent compared with 66.6 percent). Men, on the other hand, were subject to a “discouraged worker” effect—dropping out of the labor force typically as a consequence

of long-term unemployment. As men’s labor force participation in Mecklenburg declined by 4.3 percentage points (4.0 points nationally), women increasingly became the primary “bread winner” among married couples and domestic partners. Given their lower wages overall, the loss of men in the labor force meant that men, and some women and children, had to do more with less. Although women’s labor force participation is generally high, there are significant losses to family incomes partly due to job loss and higher unemployment rates, and partly due to declining real wages during the period. The gender gap in labor force participation narrowed between 2005 and 2011. However, change was largely due to falling rates among men.

Figure 8 shows a rather distinctive urban-rural pattern in female participation rates. In urban counties where populations are younger and the returns to work are greater, labor force participation among women is stronger. Still, in no county does the labor force participation rate of women exceed that of men. Participation rates in Mecklenburg County also vary by race and ethnicity, as shown in Figure 9. White women comprise the largest segment of the local labor force, and their rates of participation edged up slightly from 63.5 percent to 64.6 percent. Black/African American women, on the other hand, show the highest rates of participation although theirs declined from 74.9 to 72.1 percent. There is a cruel irony behind these numbers, of course: Black/African American women have high rates of labor force participation, but remuneration from their work is 25 percent lower than their White counterparts and 32 percent lower than men’s earnings in the county, despite experiencing real, but modest, gains in their wage from 2005 to 2011. Thus, the overall welfare of women not only depends on their (expected) earnings from work but on their propensity to engage in the formal labor market. Such variations by sex and race/ethnicity have important implications for women’s economic welfare.

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What do these differences mean for Mecklenburg County as whole? From the information presented above, the aggregate wage gap can be measured, taking into account both differences in employment levels that result from labor force participation and the differences between men and women’s earnings.

V. The Wage Gap Again Significant differences exist between men and women in terms of their earnings and labor force participation.

These variables differ among women and have changed over time. Further, all these variables interact in complex, nuanced, and differentiated ways. One might reasonably ask, What is the “big picture” here? The big picture, or how all these variables interact and change over time in the aggregate, is presented in Table 2. The information in Table 2 draws from the all the information presented above—information about earnings and 13

employment for Mecklenburg men and women in 2005 and 2011—to calculate the aggregate wage gap. All 2005 dollar values associated with earnings have been adjusted for inflation and converted to 2011 price levels so the real change in the aggregate wage gap can be calculated. Put another way, the data in Table 2 show how much more in earnings Mecklenburg women would have realized if their earnings were identical to those of men. How might women’s earnings have changed between 2005 and 2011? The answers to these questions give us a sense of the extent to which Mecklenburg women have made progress in both employment and earnings relative to men over the last six years for which data are available. The table is a bit complicated, so the discussion below focuses on one section at a time.

The top portion of Table 2 shows full-time median earnings and employment for men and women in 2005 and 2011. As noted above, men’s real median earnings declined over the period from $51,885 to $51,068 (-1.6 percent). Women’s real earnings also declined from $39,357 to $38,823 (-1.4 percent). The median wage gap, however, was virtually Table 2. The Gender Wage Gap in Mecklenburg County Men

Women

Full-time Median Annual Earnings 2005

$51,885

$39,357

Full-time Median Annual Earnings 2011

$51,068

$38,823

Full-time Employment 2005

159,632

117,054

Full-time Employment 2011

182,342

144,344

Total Full-time Earnings 2005

$8,282,434,042

$4,606,875,380

Total Full-time Earnings 2011

$9,311,841,256

$5,603,867,112

Total Full-time Earnings 2005 @ $51,885

$8,282,434,042

$6,073,293,790

Total Full-time Earnings 2011 @ $51,068

$9,311,841,256

$7,371,359,392

Aggregate Earnings Gap 2005

$2,209,140,252

Aggregate Earnings Gap 2011

$1,940,481,864

Change in Aggregate Earnings Gap

-$268,658,388

Source: Author calculations based on U.S. Census Bureau, 2005 and 2011 American Community Survey, Table B17004.

Note: 2005 earnings data expressed at 2011 price levels.

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unchanged: in 2005 women earned 75.9 percent that of men (or 24.1 percent less); in 2011 the wage gap was 76.0 percent (or 24 percent less). Total employment for men grew from 159,632 to 182,342, or 22,710 jobs (14.2 percent). For women the same job figures are 117,054 and 144,344, an increase of 27,290 jobs or 23.3 percent.

In the second section of Table 2, we simply multiply median earnings by full-time employment for men and women in both years. Total full-time earnings for men and women in 2005 were about $8.3 billion and $4.6 billion, a difference of about 56 percent. By 2011, the difference shrunk as women’s wage and employment growth outpaced men’s such that women’s aggregate earnings were 60 percent that of men’s (men earned a total of $9.3 billion vs. $5.6 for women). Thus, although the median wage gap did not change between 2005 and 2011, women, in the aggregate, began to close the gap in terms of their total earnings.

In the third section of the table, employment levels are held constant and show how much more women would have earned had their median earnings been equal to men. Obviously, men’s earnings do not change in this scenario, but women’s earnings would have increased to nearly $6.1 billion in 2005 and $7.4 billion in 2011 if they had the same median earnings as men in each of the years.

The fourth section simply subtracts the annual total full-time earnings differentials between men and women from section three, which allows the calculation of the change in the aggregate earnings gap. If Mecklenburg women had the same median earnings as men in 2005, they would have earned $2.209 billion more than they actually did. By 2011, they would have earned only $1.940 billion more. Thus, although the median wage gap did not change between 2005 and 2011 (see Figure 1), the aggregate earnings gap actually narrowed by nearly $269 million. Women’s full-time employment grew faster than men’s, and declines in women real wages were not as severe as those of men.

Once again a nuanced difference can be noted when thinking about the wage gap. If the gap is couched in terms of the typical (i.e., median) worker, the wage gap has not changed since 2005. If changes in wages are considered, labor force participation and the sheer number of men and women in the Mecklenburg workforce in the aggregate, the wage gap has actually narrowed by nearly $269 million since 2005.

Participation in the labor force is partly conditioned on expected earnings, and earnings are known to increase with educational attainment, the subject of the next section.

VI. Educational Attainment The educational attainment of the population is directly related to work, earnings, and labor force participation.

As a basic form of human capital acquisition, and an important ingredient to economic growth, educational attainment is among the top priorities of policymakers in virtually every state. Just as gender differences in labor force participation have narrowed, so too have differences in educational attainment. Today, women are more likely to both attend and graduate from college, causing the education gap to narrow over time.

For decades, educational attainment throughout North Carolina has lagged national levels. As shown in Figure 10, the state still lags national averages, but Mecklenburg County does not. At all levels of geography, educational attainment has been rising, and the gender gap in education has been closing. In 2011, the proportion of Mecklenburg men with at least a college-level education was 42.1 percent, up 1.1 percentage points from 2005. In 2011, 39.3 percent of Mecklenburg women had earned a bachelor’s degree or higher, an increase of 2.1 percentage points.

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Thus, the education gap between men and women is narrowing—Mecklenburg has a growing percentage (for both men and women) of a growing population with higher levels of education. By 2011, the female-male education gap was reduced to 2.8 percentage points. At the state level, women surpassed men in educational attainment (27.3 compared with 26.5 percent), though still behind the national average of 28.7 and 28.3 percent, respectively, for men and women. Thus, although the education gap in Mecklenburg is higher than state or national averages, both men and women in Mecklenburg have, on average, achieved substantially higher levels of educational attainment.

Just as there are gender-based differences in educational attainment, urban-rural differences exist (Figure 11).

Generally, the highest rates of educational attainment among women are found in urban counties with substantial high-order service, government, and education sectors. Counties fitting this profile include Durham, Mecklenburg, Orange, Wake, and Watauga.

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A slightly more detailed look at educational attainment in Mecklenburg County is provided in Table 3. Although high school dropout rates have declined and high school graduation rates have increased, Table 3 also reflects a higher propensity for males to drop out before completing high school, and an elevated tendency for women to attain an associate’s degrees from community colleges (included in “College 1-3”). Although the education gap is narrowing, men, on average, still are more likely to attain the highest levels of education (bachelor’s degrees and advanced, professional degrees). As recent trends in college matriculation appear to work in women’s favor, the present population of women is overrepresented at the associate’s degree level and somewhat underrepresented at higher levels.

Table 3. Educational Attainment of the Population Age 25+ by Sex, Mecklenburg (% ) Female

Male

2005

2011

2005

2011

Less than high school

10.6

10.2

11.7

11.4

High school graduate

22.1

19.7

21.2

20.8

College 1-3

30.1

30.8

26.2

25.7

Bachelor’s

27.1

27.6

28.1

29.3

Advanced

10.1

11.8

12.8

12.8

Source: Author calculations based on U.S. Census Bureau, 2005 and 2011 American Community Survey, Table B15002. Note: “College 1-3” includes those with some college and those with associate’s degrees “Advanced” includes master’s, professional, and PhD degrees.

These patterns are especially pronounced when accounting for race and ethnicity (Figure 12). By 2011, more than half (51.1 percent) of White women had earned at least a bachelor’s degree, while only 24.7 percent of Black/African American women had done so. Black/African Americans also have the highest labor force participation rate among women and relatively modest full-time earnings. This indirect evidence suggests that many Black/African American women have significant need (as evidenced by high labor force participation) but insufficient means (from low educational attainment) to put their earnings on par with White women, much less men. The picture is equally bleak for Hispanic women, because their levels of educational attainment actually fell 2.2 percentage points between 2005 and 2011. Educational attainment among Asian women, on the other hand, jumped 10.7 percentage points to 49.5 percent, the greatest increase among women of any race and ethnicity, and higher than men’s overall levels of educational achievement during the same time.

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VII. Poverty and Education Despite overall gains in educational attainment, the Great Recession has taken its toll on men and women. In few areas is this more obvious than the growth in the number and percentage of persons living in poverty. For example, as shown in Figure 13, in the years leading up to the recession, 8.2 percent of men and 11.2 percent of women in Mecklenburg County were living beneath the poverty line, somewhat lower than either the state or nation in 2005. By 2011, two years after the end of the recession, poverty rates in Mecklenburg County had risen markedly. Poverty among men nearly doubled to 15.7 percent; women in poverty increased to 18.4 percent in Mecklenburg. Although lower than the statewide average, by 2011, Mecklenburg’s poverty rate exceeded the national rate.

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The disparities in poverty by race and ethnicity shown in Table 4 are especially stark. Poverty among White women increased from 5.4 percent to 7.5 percent between 2005 and 2011; poverty rates for Black/African American women increased from 18.9 percent to 25.6 percent. Those women hardest hit by the recession were Hispanic; their poverty rates nearly doubled over the period. By 2011, fully one-third of Hispanic women lived in poverty. The local increase in poverty among Hispanic women far exceeded similar changes at the national level. Once again, it is important to recognize that these are growing percentages of a growing population.

Clearly, then, the Great Recession still is taking its toll on men and women alike, and is particularly devastating for Black/African American and Hispanic women in Mecklenburg County.

Table 4. Poverty Rate of the Population (%) US

NC

Mecklenburg

2005

2011

2005

2011

2005

2011

9.7

14.7

10.7

16.4

8.2

15.7

13.6

17.2

15.6

19.2

11.2

18.4

9.0

11.0

9.8

12.1

5.4

7.5

Black/African American

24.9

28.1

26.3

28.0

18.9

25.6

Asian

11.7

12.8

14.6

14.0

NA

16.5

Other

23.3

28.5

36.5

38.2

NA

33.5

Hispanic

22.7

25.8

31.8

34.9

17.3

33.2

Men Women White Non-Hispanic

Source: Author calculations based on US Census Bureau, 2005, 2011 American Community Survey, Table B17001A-I.

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Looking at poverty across the state does not provide much comfort. Figure 14 shows the 2007–2011 five-year estimates of women in poverty by county. Although Mecklenburg, like other metro areas, appears to compare favorably, there are large pockets of concentrated female poverty in the state’s northeastern region near Virginia’s Tidewater, stretching down through the Inner Coastal Plain, and in some of the state’s agricultural areas around Robeson, Scotland, and Bladen counties. The 2007–2011 poverty rate estimate for Mecklenburg women was 14.9 percent. By comparison, Robeson, Scotland, and Vance counties all had female poverty rates in excess of 30 percent.

The connection between poverty and education is made explicit in Figure 15. Dropping out of high school has always been disastrous for the earnings potential of men and women. In Mecklenburg County, simply finishing high school reduces the chances of living in poverty by about 50 percent. Among women who dropped out of high school, the rate of poverty jumped from 25.5 percent to 42.4 percent between 2005 and 2011. For those with only a high school education, poverty increased to 20.3 percent. Similar patterns of poverty exist for men, though typically at lower levels.

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Also revealed in Figure 15 is evidence of workforce-related educational upgrading with respect to poverty. In 2005, women realized major reductions in poverty by attaining an associate’s degree or attending some college.

By 2011, major reductions in poverty appear for those who had completed a college-level education. For women in Mecklenburg, attaining a bachelor’s degree or higher reduced their chances of living in poverty more than threefold; a similar pattern also holds for men. With a bachelor’s degree or higher, poverty fell to 3.3 percent and 4.3 percent for men and women, respectively.

The relationship between educational attainment and poverty is among the most significant results of this study. Since the end of the Great Recession, a community college education or simply attending a four-year college (without graduating) is no longer enough to assure finding work, much less living above the poverty line.

Attaining at least a bachelor’s degree is now the necessary condition for improving the economic status of women in Mecklenburg County. It is not enough to just finish high school. It is not enough to just attend community college. Even modest levels of economic security require a college education. On average, those women obtaining bachelor’s or graduate degrees faced poverty rates of only 4.3 percent—poverty rates that are four times lower than the average for women overall.

VIII. Poverty, Work and Children It comes as no surprise that women who work full time, year round earn more each year and are less prone to poverty. However, even working full time does not render women immune to poverty. In 2005, 9.4 percent of women in Mecklenburg County who worked full time lived in poverty; by 2011 the percentage grew to 10.5 (Table 5). Poverty among women who worked part time or part of the year actually declined from 45.8 percent to 36.8. This rate, however, is a byproduct of growth in the percentage of women in poverty who did not work at all—their rates of poverty rose to 52.7 percent. Thus, women whose economic security rested on that of a domestic partner were especially vulnerable during and after the Great Recession. As noted in the introduction, this, too, is a byproduct of higher unemployment among men in the recession’s aftermath, thus resulting in higher rates of poverty for some women. In other words, women who do not work are extraordinarily vulnerable to living in poverty.

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Table 5. Work Status of Women in Poverty, 2005 and 2011 (%) US

NC

Mecklenburg

2005

2011

2005

2011

2005

2011

Worked full time, year-round

6.4

6.9

7.5

7.5

9.4

10.5

Worked part-time or part-year

36.6

31.5

39.1

32.8

45.8

36.8

Did not work

57.0

61.6

53.4

59.7

44.8

52.7

Source: Author calculations based on US Census Bureau, 2005 and 2011 American Community Survey, Table B17004

The impact of poverty among men and women also is felt among their children. As shown in Table 6, a higher proportion of children live in poverty than the population as a whole. Both the number and percent of children in poverty grew rapidly between 2005 and 2011. By 2011, Mecklenburg children were slightly more likely to live in poverty than the national average (23.4 percent compared with 22.2 percent). More striking is that the rate of poverty among Mecklenburg children grew by nearly 50 percent during the 2005–2011 period. Children have, thus, suffered as “collateral damage” in the wake of the Great Recession—casualties of economic fallout stemming from their parents’ struggles for economic security.

Further, the impact of family structure and marital status is clear. Both nationally and locally, the percent of children in poverty who lived in two-parent households barely changed between 2005 and 2011. By 2011, however, 61 percent of Mecklenburg children in female-headed households with no husband present lived in poverty. Although the number of children in poverty also rose among single male-headed households, the impact of marital status on children’s economic welfare is clear: children of single mothers (“female householder, no husband present”) are twice as likely to experience poverty as those in two-parent households and seven times more likely to live in poverty than children of single men. Single mothers are not insulated from poverty by the earnings of a spouse, frequently work part time, or find themselves underemployed. Ironically, in attempting to minimize risk to their families, some women face lose-lose situations: Work longer hours to raise the family out of poverty and risk neglecting their children; or reduce work hours and earnings to provide the care and guidance their children require and risk living in poverty.

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Table 6. Children in Poverty, 2005 and 2011 2005

US

Number

438,097

32,769

33.5

28.9

29.9

7.4

6.7

6.2

59.1

64.3

63.9

18.2

21.0

15.8

Male householder, no wife present (%) Female householder, no husband present (%)

Overall Child Poverty Rate (%)

US

Number

In married-couple family (%) Male householder, no wife present (%) Female householder, no husband present (%)

Overall Child Poverty Rate (%)

Mecklenburg

13,008,489

In married-couple family (%)

2011

NC

NC

Mecklenburg

16,087,074

572,095

55,401

34.0

29.9

30.6

9.1

10.0

8.4

56.9

60.1

61.0

22.2

25.3

23.4

Source: Author calculations based on US Census Bureau, 2005 and 2011 American Community Survey, Table B17006

How women minimize risk varies, but women’s travel time from home to work suggests some of the conditions they face. Figure 16 shows cumulative commuting times for all men and women working outside the home in Mecklenburg County in 2011. The steeper slope of the curve describing women’s travel time to work reflects their shorter drive times overall. As is now well known, shorter commute times do not reflect their great fortune to work close to home. Rather, they reflect the necessity for many women to be near home, school, and aging parents, for example, and take care of domestic responsibilities.19 Indeed, shorter commute times for women reflect constraints on their activities including their workplace—constraints imposed by their traditional role as the primary caregiver to their children.

19 Susan Hanson and Geraldine Pratt (1988). “Reconceptualizing the Links Between Home and Work in Urban Geography,”

Economic Geography, 64: 299-321.

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With falling real wages and limited job growth, the Great Recession also has inflicted differential age-based impacts on the population. New labor force entrants, along with their more mature counterparts (who have, in some cases, experienced extended periods of unemployment) lack economic security. All too frequently, they also lack various support resources that come with gainful, full-time employment. Indeed, many employers, uncertain about the recovery and the economic environment generally, have

either been slow to rehire workers or have resorted to part-time employees as a way to reduce the costs of both hiring and job separation. One result has been a growing number of people who lack health insurance (see Table 7). As of 2011, the only year for which consistent data are available, nearly 18 percent of men and 16.1 percent of women in Mecklenburg County had no health insurance, slightly higher than the national average. Among all those ages 25 and above, 19.3 percent in Mecklenburg were without health insurance, compared with 16.3 percent nationally. Hardest hit are those young adults, ages 18 to 24, who are either entering the workforce or attending higher education part time.20 Similar to the state and nation, 26 percent of the population ages 18 to 24 was without health insurance in Mecklenburg County.

20 Most colleges and universities require some form of health insurance for full-time students.

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Table 7. Population with No Health Insurance Coverage, 2011 (%) US

NC

Men

16.7

17.7

17.9

Women

13.7

15.0

16.1

7.5

7.6

7.5

18 to 24 years

25.8

26.7

26.0

25 years and older

16.3

18.0

19.3

Under 18 years

Mecklenburg

Source: Author calculations based on U.S. Census Bureau, 2011 American Community Survey, Table B27001.

IX. Summary and Conclusions The data and information presented above paint a picture of women’s changing workforce experience and status from 2005 to 2011, years prior to and following the Great Recession—a recession that began in December 2007 and ended in June 2009. In many ways, the picture is not a pretty one. In the years covered by this report, the returns to workers have declined; economic security of men, women, and children have become more tenuous; and poverty has more prevalent. As a result, women have experienced rising levels of poverty and little-to-no progress in closing the wage gap relative to men. Ironically, women accounted for more than half of the job growth since 2005. On the bright side, women are making substantial gains in educational attainment, both in absolute terms and relative to men.

To paint this picture with a broad brush is misleading, however. In the wake of the Great Recession, the experience of women in the labor force varied significantly according to race or ethnicity. Different women have had different experiences. Such a wide variety of experiences can only be summarized as complex, highly differentiated, and nuanced. For example, although their median earnings are lower than those of men, the labor market experience of White women is more closely aligned to men than are the experiences of Black/African American, Hispanic, or Asian women. Hispanic women, in particular, faced substantial losses in real, inflationadjusted earnings while Black/African American and Asian women realized modest gains. Unlike men, Black/African American women, in particular, maintained a strong attachment to the labor force, even though their wages were 32 percent lower.

Although the educational attainment of women generally increased over the study period and Asian women posted remarkable gains, educational attainment among Hispanic women fell. Poverty increased in all groups.

However, poverty rates among Asian, Black/African American and Hispanic women were double or triple those of White women. Just as troubling is the fact that the number of children living below the poverty line in Mecklenburg County increased by 22,632.

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It should be clear, given such varied experiences and wide disparities, that no “silver bullet,” no single set of policies or small group of actions will ameliorate gender-based differences in the work force. To elaborating on observations in 2007, the following points are noted:  The gender gap does not have to close all at once. Gradual incremental changes, coupled with human capital investments, could markedly improve the economic status of women over time.  Intercounty (and intracounty) commuting is prevalent in this region. Improved mobility strategies might alleviate some labor market disparities by improving access to job sites in adjacent counties and less accessible parts of Mecklenburg County.  Increasing training options in higher wage occupations and industries, especially those STEM fields (science, technology, engineering, mathematics) where women are underrepresented, could improve women’s labor market outcomes markedly.  In all fields of study, including STEM fields, women’s rates of educational enrollment, graduation, and persistence have become higher than men’s. Because they are underrepresented in several, traditionally male occupations and fields of study (e.g., chemists, material scientists, biological technicians, information scientists, and engineers of all types— biomedical, civil, chemical, electrical, and computer software and applications) the need is urgent to break gender-based stereotypes and encourage more women, especially young women, to pursue some of these emerging high-wage fields. This change, too, would help reduce gender gaps in the labor market.  Who might provide this encouragement? Obviously, families need to be intimately involved. But beyond the immediate family, the Charlotte region’s public and private sectors have a vested interest in increasing the supply of well-trained, qualified workers. Significant educational outreach efforts on the part of public, private and nonprofit organizations would seem to be a high priority, starting in the mid- to late elementary grades and persisting through high school years.  Among older workers, some training could be provided by the public sector. Much of it could be provided on-the-job by current employers in the private sector to facilitate upward occupational mobility. Indeed, private sector employers would seem especially well-positioned as they claim to face constant shortages of well-trained employees in technical fields. Further, employers might find such a strategy beneficial to their “bottom line.” Recruiting and hiring new workers is expensive. Investments in their current workforce would not only reduce costs but increase productivity and, most likely, engender loyalty among their workforce, thereby lessening worker turnover and enhancing workforce continuity.  Employers can make better use of “family friendly” strategies, including onsite or near-site day care facilities (employers could pool their resources for near-site day care); raise the limit on before-tax contributions to flexible spending accounts that can be used for child care (employers could even provide a partial monetary match); implement more flexible work schedules to decrease labor force detachment and increase workforce continuity; and encourage more men to use family leave.

Still relevant, those recommendations emphasized involvement of both the public and private sectors, and place some responsibility on individuals.

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Times have changed, however. Employers are, for a variety of reasons, hesitant to expand payrolls, much less increase benefit levels. Further, when employers do expand payrolls, they are more likely to fill those positions with part-time employees or use the services of temporary employment services,21 thereby sidestepping the benefits issue altogether. However, most any employer would admit that their most valuable assets rest in their employees. Thus, educational outreach and enhancing the skills of their current employees as just discussed makes good economic sense.

To complicate matters, however, the State of North Carolina just eliminated the federal portion of unemployment compensation, effectively reducing the maximum unemployment benefit by 34.6 percent, from $535 per week to $350 per week, immediately affecting about 7,000 Mecklenburg residents.22 A voluntary action on the part of the state to help repay its debt to federal government, it is the only state in the nation to do so.

Given the sluggish pace of job creation and wage growth in the post-recession years, it is natural to wonder if the effects of the recession are cyclical, passing, and thus a short-term deviation from a long-term path, or if they represent a permanent long-run shift (coined the “new normal”) in our social and economic systems. The answers are still far from clear. As discussed above, gender-based rates of labor force participation have been converging over the long run, but since the recession, Joyce Jacobsen notes that female labor force participation has slowly declined causing her to wonder if this is part of a lasting trend where labor force participation rates plateau, or if participation rates will again rise after recessionary effects have run their course.23 She and others note the following:24  “During the recession, it appeared that more cases arose where women may have become the higher earner due to the husband’s job loss or reduced earnings, though this is a longer-run trend…In 2008, women earned more than their spouse in 27 percent of households where they both worked; this percentage was only 18 percent in 1988.”25  “Rates of marriage, divorce, and childbearing all tend to drop during recessions: all require money, which is in shorter supply during recessions.”26  “But lower rates of marriage and childbearing may also mean fewer large households form in the longer run, which has countering effects of both less income sharing, but also potentially fewer dependents relying on income.”

Jacobsen acknowledges the trends in higher education previously discussed, noting that women currently account for 57 percent of college enrollment and graduation, and about 59 percent of graduate school enrollment. However, she also notes that the post-recessionary period might have troubling characteristics:  “This path [of increasing female educational attainment] appears somewhat more problematic in the immediate post-recessionary period, however, with the aftermath of the recession 21 Florida, R. “Is the U.S. Turning Into a Nation of Temps? Depends on Where You Live,” June, 27, 2013. Available from:

http://www.theatlanticcities.com/jobs-and-economy/2013/06/us-turning-nation-temps-depends-where-you-live/5997/.

22 Burley, D., & and Portillo, E. “Bracing for Impact,” The Charlotte Observer, June 23, 2013, p. D1.

23 Jacobsen, J.P. “The Great Recession’s Impact on Women, Wesleyan University, June, 2012. Available from: http://repec.wesleyan.edu/pdf/jjacobsen/2012010_jacobsen.pdf.

24 The New York Times. Available from: http://economix.blogs.nytimes.com/2012/03/21/marital-status-and-therecession/. Russell Sage Foundation. Available from: http://www.russellsage.org/research/chartbook/great-recession.

25 Jacobsen, p. 5-6.

26 Ibid., p. 6

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including rising tuition levels at most universities, increased student loan debt levels for many students, and uncertain job prospects for many college students.”  “Thus the damage wrought by the recession may have lasting effects through changing human capital investment decisions and outcomes for many young persons whose college years happened to coincide with the recession.”27 Jacobsen does not discuss how these trends vary by race and ethnicity, but given the discussion above, they can be expected to be especially severe for Black/African American and Hispanic women if such a long-term trend among the current college-age cohort comes to fruition.

Demographic shifts and their resulting economic implications might be especially critical for Mecklenburg County and its knowledge-based economy specializing in banking, health care, energy, and a variety of business services. Women represent a growing portion of our labor force, yet their wages lag, their poverty rates are higher, and their children are more likely to be raised in poverty if their mothers curtail investment in their human capital. In an era where educational upgrading has become the norm, and job prospects are dim, women, especially minority women, are increasingly vulnerable.

In some ways, the economic gender gap in Mecklenburg County has not changed since 2005 (e.g., the wage gap); in other ways gender gaps have been reduced (e.g., educational attainment and women in managerial positions) although poverty has increased substantially for women, men, and children. Yet, some of the largest gaps facing women are not based in gender; they are based in differences by race and ethnicity among women. Thus, echoing conclusions from our 2007 report, it appears that whether through targeted public/private policies or educational outreach, improving the employment and earnings prospects of minority women will achieve the greatest, most equitable progress toward eliminating gender gaps in Mecklenburg County.

27 Ibid p. 7.

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