Presentation Wage Gap L

Post Secondary Enrolment and The Wage Gap Elisa Barnes, Lauren Minuk, and Aliece Robert, Ice Chan

Concept: GENDER Issue:  There

is a great disparity between men’s and women’s wages, and this goes against the trend that is expected, since more women graduate with post secondary degrees than men.

How will we address these issues?  We

will explore the evidence and statistics regarding the wage gap issue by looking at three major explanations: Women’s

participation of going in & out of the work force Skill sets and “productivity characteristics” The types of majors women choose

SOCIOLOGICAL ISSUE  Affirmative

Action programs have been implemented throughout Canada and yet a gendered wage gap continues to exist  Despite feminist movements that promote equality the “glass ceiling” continues to predicate women’s wages

How is our issue related to lifelong learning? Participation

in lifelong learning in the form of post-secondary education (specifically university and college) SHOULD predict wage patterns in the workforce, but the opposite seems to be true

Women’s Participation in the Work Force  In

1900, 20% of women were in the labor force, 34% in 1950, 61% in 2000 (men’s being 75%)

 Married

women’s participation went from 18% in 1950 to 71% in 2000 The Gender Gap in Wages, Circa 2000

Women’s Participation in the Work Force  However,

even with the rise in women’s participation in the work force, there was not a match in the gender gap in pay.

 Between 1950-1980 the

female-to-male ratio of median annual earnings of full-time workers was about 60%

 This

reached 69% by 1989 and 74% by the mid 1990’s and has now leveled off

 In average

hourly wage rates, the gap is smaller, having a ratio of 66% in 1979 to 80% in 1993 and then stabilizes

The Gender Gap in Wages, Circa 2000

The Gender Gap in Wages, Circa 200

Women’s Participation in the Work Force “One

can argue whether the source of these gender role differences is a form of discrimination rather than an outcome of biological and other deeply rooted psychological and cultural factors.” (page 3, The Gender Gap in Wages, Circa 2000)

Women’s Participation in the Work Force  Children,

especially young children affect the participation of women in work force dramatically In 2001,

women aged 25-44, 34% with children under the age of six were out of the labor force, compared to 16% of women without children 30% of employed mother worked part-time, compared to 11% of women with no children

The Gender Gap in Wages, Circa 200

Women’s Participation in the Work Force  Men’s

participation is much different

 Only

4% of men with children under the age of six (2001) were out of the labor force, and among employed fathers only 2% work part-time

 The

fact that women withdraw from the workforce more often greatly affects the type of occupation that women pursue  These

adaptive occupational choices will tend to lower the market earnings of women relative to men

The Gender Gap in Wages, Circa 200

Participation in the Work Force  "The

relative increase in women's yearsof-work experience accounts for 25% of the narrowing, while changes in the structure of the economy explains 20%." (Young, 1)

 "A woman's

reluctance to move in search of better job opportunities is linked to her marital status." (Young, 468)

Findings from the Current Population Survey (CPS) The Gender Gap in Wages, Circa 2000  According

to the Current Population Survey, major changes that have occurred during the 1979-2001 period in the gender differential in earnings-related characteristics are as follows:  Women

continue to be much more likely than men to work part-time (19 percent versus 5 percent in 2001), although that difference narrowed.  With respect to education, women gained relative to men at the college level. By 2001 they were somewhat more likely than men to be college graduates and were almost as likely to receive a higher degree.

CPS (Continued)  Women have

also been entering occupations requiring more job-specific skills, as measured by SVP (specific vocational preparation), the time required to attain the average level of proficiency in an occupation.



The gender gap in SVP declined by almost half between 1984 and 1994 and has since declined further, but at a slower rate.

CPS (Continued)  However,

despite these changes, women and men remain in occupations that are disproportionately female or male. In 2001

women, on average, worked in occupations in which the percentage of female employees was close to 68 percent; men worked in occupations that were only 30-percent female.

Skills Sets: Nord (1987) 

Nord’s 1987 study uses women with 1-5 years work experience (in 1975) as a reference group, expecting that those women have a superior skill set when compared to the skill sets (productivity characteristics) of their older counterparts (51)



Nord found that the older groups of women would experience a wage increase if they were equipped with the same productivity characteristics as the 1-5 years experience sample group(57)



Nord finds that going to college explains 19.57% of the wage gap for whites (59) and 65.21% for blacks (61)

Skills Sets: O’Neill (2---) (1 of 2)  Some

occupations require more investment in skills (such as aerospace engineer, surgeon, etc.) and due to women entering and exiting the workforce more frequently, their skills would depreciate much more quickly in these occupations, therefore these occupations tend to have disproportionately fewer women

 Women (particularly

mothers) will enter occupations such as nursing and teaching because they give applicable life skills practiced in a variety of different settings and not occupation specific

Skills Sets: O’Neill (2---) (2 of 2) 

Women also choose occupations that are less demanding and allow them to raise a family  



This is why part-time work becomes a much more credible option for women with families to raise Even if women don’t choose part-time work, they will choose occupations that allow more flexibility and shorter work weeks 

Both work attachment and the choice of occupation are expected to be important determinants of women's earnings and important factors underlying the gender wage gap

Average Hourly Wage Ratios (White women/White men) 1.4

1.2

Average Hourly Wage Ratio

1

0.8 Series1 Series2 Series3 0.6

0.4

0.2

0 A

B

C

D

Years of Work Experience

E

F

Choice of a college major/ fields of study  The

most persuasive educational explanation of gender income inequality is that women major in fields that lead to jobs that are not rewarded with higher incomes (Zeher, 2007).  College majors are quite gender segregated as females major in mathematics, technology and sciences less often than males and have a tendency to concentrate in fields such as humanities, social sciences and education, these fields which do not attract many men. (Ayalon, 2003)

Table 2. EGLS Regression Coefficients for Female and Percentage of Gender Income Gap Explained with Alternate Models, including Educ   Model Percentage o Number Model Description Gap Explained ____________________________________________________________________ 1 Female — 2 3 4 5 6 7 8 9

Female, background Female, background, field of college major Female, background, percentage female of college major Female, background, SAT/ACT score Female, background, undergraduate GPA Female, background, graduate degree Female, background, college selectivity Female, background, percentage female of college major, SAT/ACT score, graduate degree, college selectivity

4.2 21.9 38.8 10.0 0 1.8 4.4

  ___________________________________________________________________ a Background factors are controls for race and parental SES. (Zeher, 2007)

36.1

Gender Differences in Cognitive Skills 

Another education related explanation for income inequalities concerns gender differences in cognitive skills which is thought to directly and indirectly though the choice of college majors affect women’s access to jobs (Zeher, 2007).



Since the research by Zeher is done in the U.S., it explains that since 1970s the U.S economy has transformed: math and science abilities have become more predictive of salaries, and math skills translates into higher earnings for all types of workers (Zeher, 2007).



Findings do show that the gender gap in income disappears among professional men and women with the highest math skills but going back to one’s choice of major women compared to men are less likely to choose math, technology and sciences as fields of study to concentrate in which results in women lacking skills that are highly needed in the labour market (Zeher 2007).

Feminization of majors/ fields of study  Another

factor is that certain majors such humanities, social sciences and education have been labeled by researchers as “feminine” fields of study due to the high concentration of females within these programs. Even though “feminine” field of study have their own merits in terms of personal expression, informed citizenship and the acquisition of cultural capital, but within the labour market higher value is still being placed on skills related with math, technology and sciences (Ayalon, 2007).

 As

stated previously by Zeher, since the U.S economy has been changing and skills linked with math and sciences are more marketable in the labour market. This means that “masculine” labeled majors are more rewarding economically and will get males into higher paying jobs compared to females once they have finished college. This helps partially explain the why females are in lower paying jobs compared to males.

Selectivity of college 

In addition to choice of college major/ field of study selectivity of college is tied closely with to explaining the gender gap inequality for income. It shows that females’ attendance at less selective schools contributes to their disadvantaged position in the labour market.



It has been found that females compared to males are significantly more likely to attend selective postsecondary institutions. The reason is a result of institutional bias favoring men, more selective schools tending not to offer traditionally female dominated programs. Lastly parental choices also help explain the gender gap in income because some parents are found to invest more, financially, in sons compared to daughters. (Zeher, 2007).

Other Information to consider  Human

Capital Theory suggests that education and on the job training are complimentary. (Young, 458)

 "Men

have less education on average than women do." (Young, 462)

 "Earnings

also increase significantly with additional hours worked per week for all groups. Marriage is associated with higher earnings for men. The lower earnings for married white women may be due to less job attachment." (Young, 465)

Other Information to consider 

"White women would need to attend 15 years of school (a 21% increase over the average), or increase their weeks of work experience to 380 weeks (a 105% increase over the average) to earn the same as white men.  Black women would need to increase their education to 19 years (1 50% increase over the average), or to increase weeks worked to 430 (a 154% increase) to bring their average earnings in live with those of the average white male." (Young, 466)



"Women, entering the labour market in the 1980s, may have invested in more education with the intention of pursuing further training on-the-job, but market discrimination may prevent them from experiencing the same wage growth experienced by men." (Young, 468)

Our Conclusions  The

wage gap seems to be narrowing

Questions to Think about  "For

example, in analyzing family job relocation decisions Bielby and Bielby (1992) find that a husband's potential loss from a move deters a wife from pursuing and realizing opportunities at a new location.  However, a wife's potential loss does not have the same effect on a husband's decision. Relocation plays an important role in upward career mobility. Reluctance to move may deprive highly educated women of jobs with steeper wage profiles." (Young, 468)

References 

Duncan, K. 1996. Gender Differences in the Effect of Education on the Slope of experience-Earnings Profiles: National Longitudinal Survey of Youth, 1979-1988. American Journal of Economics and Sociology, 55:4, 457-471.



Nord, S. (1987). Productivity and the role of college in narrowing the malefemale wage differential in the USA in 1980. Applied Economics, 19, 51-67



O’Neill, June. The Gender Gap in Wages, Circa 2000. The American Economic Review, Vol. 93, No. 2, Papers and Proceedings of the One Hundred Fifteenth Annual Meeting of the American Economic Association, Washington, DC, January 3-5, 2003. (May, 03), p. 309314.