1. Education and Life time wage potential Chapter 9 Part 2

2. Human Capital Human Capital is similar to Physical Capital but there are some Important Differences –Nonpecuniary (non-monetary) issues The utility derived from attending School The utility of working in an office vs. outdoors

3. Human Capital –It is more difficult to finance human capital than it is to finance physical capital Why?

4. Educational Attainment of the Population by Gender: 1970, 1999, and 2003 (Ages 26-64)* 197019992003 Education Level Male (%) Female (%) Male (%) Female (%) Male (%) Female (%) < 4 yrs of H.S. 39.338.213.612.516.215.6 H. S. 33.542.332.233.831.033.1 Some College 11.910.525.427.524.326.2 >4 yrs of College 15.39.028.826.228.525.1 Total 100.0 * 2003 is for figures 25 or older

5. Educational Attainment of the Population by Gender, Race, and Hispanic Origin 1999 (ages 26-64) Education Level Percent WhitesBlacksHispanic MaleFemaleMaleFemaleMaleFemale < 4 yrs of H.S. 13.011.618.417.541.940.4 H. S. 31.834.139.835.128.227.6 Some College 25.327.526.429.518.920.0 >4 yrs of College 29.826.815.318.011.012.0 Total 100.0

14. Distribution of Employment by Gender/Race by Education Achievement: Male (percent) Total Employed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Male 5832631.16%18.83%8.54%19.73%18.27%3.46% <HS 67765.45%7.04%13.12%27.63%37.03%9.73% HS 1858111.90%16.37%10.20%28.84%28.66%4.03% <CL 1500723.96%25.01%10.38%22.91%15.15%2.60% CL 1796166.81%20.67%3.54%4.69%3.06%1.23% White 4988632.33%18.84%7.47%20.49%17.18%3.68% <HS 57345.72%7.13%11.82%28.99%36.12%10.22% HS 1575712.69%16.47%8.58%30.65%27.36%4.25% <CL 1275625.29%24.84%9.31%23.75%13.90%2.90% CL 1563967.63%20.63%3.25%4.48%2.67%1.34% Black 570218.91%17.38%16.13%15.92%29.48%2.16% <HS 7523.19%5.32%18.35%19.55%46.28%7.31% HS 22066.53%14.51%19.17%18.54%38.85%2.45% <CL 165516.19%24.53%17.10%17.22%24.23%0.73% CL 108859.01%20.68%6.99%6.25%6.80%0.28%

16. Distribution of Employment by Gender/Race by Education Achievement: Male (percent) Total Employed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Male 5832631.16%18.83%8.54%19.73%18.27%3.46% <HS 67765.45%7.04%13.12%27.63%37.03%9.73% HS 1858111.90%16.37%10.20%28.84%28.66%4.03% <CL 1500723.96%25.01%10.38%22.91%15.15%2.60% CL 1796166.81%20.67%3.54%4.69%3.06%1.23% White 4988632.33%18.84%7.47%20.49%17.18%3.68% <HS 57345.72%7.13%11.82%28.99%36.12%10.22% HS 1575712.69%16.47%8.58%30.65%27.36%4.25% <CL 1275625.29%24.84%9.31%23.75%13.90%2.90% CL 1563967.63%20.63%3.25%4.48%2.67%1.34% Black 570218.91%17.38%16.13%15.92%29.48%2.16% <HS 7523.19%5.32%18.35%19.55%46.28%7.31% HS 22066.53%14.51%19.17%18.54%38.85%2.45% <CL 165516.19%24.53%17.10%17.22%24.23%0.73% CL 108859.01%20.68%6.99%6.25%6.80%0.28%

18. Distribution of Employment by Gender/Race by Education Achievement: Male (percent) Total Employed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Male 5832631.16%18.83%8.54%19.73%18.27%3.46% <HS 67765.45%7.04%13.12%27.63%37.03%9.73% HS 1858111.90%16.37%10.20%28.84%28.66%4.03% <CL 1500723.96%25.01%10.38%22.91%15.15%2.60% CL 1796166.81%20.67%3.54%4.69%3.06%1.23% White 4988632.33%18.84%7.47%20.49%17.18%3.68% <HS 57345.72%7.13%11.82%28.99%36.12%10.22% HS 1575712.69%16.47%8.58%30.65%27.36%4.25% <CL 1275625.29%24.84%9.31%23.75%13.90%2.90% CL 1563967.63%20.63%3.25%4.48%2.67%1.34% Black 570218.91%17.38%16.13%15.92%29.48%2.16% <HS 7523.19%5.32%18.35%19.55%46.28%7.31% HS 22066.53%14.51%19.17%18.54%38.85%2.45% <CL 165516.19%24.53%17.10%17.22%24.23%0.73% CL 108859.01%20.68%6.99%6.25%6.80%0.28%

20. Distribution of Employment by Gender/Race by Education Achievement: Female (percent) Total Employ ed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Female4980534.98%38.88%15.45%2.15%7.51%1.03% <HS41295.98%20.71%40.32%4.48%25.50%3.00% HS1617714.46%47.46%21.60%3.12%11.75%1.25% <CL1470429.13%50.18%13.73%1.88%4.30%0.78% CL1479570.94%23.35%3.49%0.71%1.03%0.48% White4109636.50%39.51%13.98%2.04%6.82%1.17% <HS31326.35%22.16%37.77%4.60%25.54%3.58% HS1338315.88%49.65%19.44%2.97%10.64%1.43% <CL1207230.51%50.12%12.86%1.81%3.81%0.90% CL1250971.88%22.76%3.26%0.61%0.95%0.54% Black636025.74%36.32%24.65%2.23%10.79%0.25% <HS7244.42%16.30%53.73%3.45%20.86%1.10% HS22199.60%36.37%33.62%3.02%17.21%0.27% <CL209322.98%50.45%18.01%1.82%6.64%0.10% CL132468.88%24.85%4.31%0.83%1.06%0.08%

21. MGT/PRO TEC/SAL/ADM SERVICE PRE/PROD OP/FAB F/F/F <HS HS <CL CL 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% Percent Job Type Education Level All Females Employment Distribution According To Education Level <HS HS <CL CL

22. Distribution of Employment by Gender/Race by Education Achievement: Female (percent) Total Employ ed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Female4980534.98%38.88%15.45%2.15%7.51%1.03% <HS41295.98%20.71%40.32%4.48%25.50%3.00% HS1617714.46%47.46%21.60%3.12%11.75%1.25% <CL1470429.13%50.18%13.73%1.88%4.30%0.78% CL1479570.94%23.35%3.49%0.71%1.03%0.48% White4109636.50%39.51%13.98%2.04%6.82%1.17% <HS31326.35%22.16%37.77%4.60%25.54%3.58% HS1338315.88%49.65%19.44%2.97%10.64%1.43% <CL1207230.51%50.12%12.86%1.81%3.81%0.90% CL1250971.88%22.76%3.26%0.61%0.95%0.54% Black636025.74%36.32%24.65%2.23%10.79%0.25% <HS7244.42%16.30%53.73%3.45%20.86%1.10% HS22199.60%36.37%33.62%3.02%17.21%0.27% <CL209322.98%50.45%18.01%1.82%6.64%0.10% CL132468.88%24.85%4.31%0.83%1.06%0.08%

24. Distribution of Employment by Gender/Race by Education Achievement: Female (percent) Total Employ ed Mgmt/ Pro. Tech/ Sales/ Admin Service Precision Prod. Oper. Fabric. Farm Forst Fish Female4980534.98%38.88%15.45%2.15%7.51%1.03% <HS41295.98%20.71%40.32%4.48%25.50%3.00% HS1617714.46%47.46%21.60%3.12%11.75%1.25% <CL1470429.13%50.18%13.73%1.88%4.30%0.78% CL1479570.94%23.35%3.49%0.71%1.03%0.48% White4109636.50%39.51%13.98%2.04%6.82%1.17% <HS31326.35%22.16%37.77%4.60%25.54%3.58% HS1338315.88%49.65%19.44%2.97%10.64%1.43% <CL1207230.51%50.12%12.86%1.81%3.81%0.90% CL1250971.88%22.76%3.26%0.61%0.95%0.54% Black636025.74%36.32%24.65%2.23%10.79%0.25% <HS7244.42%16.30%53.73%3.45%20.86%1.10% HS22199.60%36.37%33.62%3.02%17.21%0.27% <CL209322.98%50.45%18.01%1.82%6.64%0.10% CL132468.88%24.85%4.31%0.83%1.06%0.08%

26. Conclusions As compared to the 1970’s, at the end of the XX Century, American women have achieved parity in education attainments A white male college graduate is more likely to achieve managerial or professional status than the black male counterpart

27. Conclusions (continuation) A black male with less than a high school degree is more likely to be an operator or fabricator than the white male counterpart A white male with a high school degree is nearly twice as likely to be in managerial or professional status than the black counterpart

28. Conclusions (continuation) A white female with a high school degree is more likely to be in a technical/sales/administrative job than the black female counterpart. Black female college graduate is about as likely as her white counterpart to be in a managerial or professional job

29. Income Thus it appears that gender, race and education have an impact on the type of jobs and that has an impact on the wages Recall from an earlier handout the following table

30. HOUSEHOLD DATA ANNUAL AVERAGES ANNUAL AVERAGES 39. Median weekly earnings of full-time wage and salary workers by detailed occupation and sex (Numbers in thousands) 2005 Both sexes Men Women Occupation Number Median Number Median Number Median of weekly of weekly of weekly workers earnings workers earnings workers earnings Total, 16 years and over............................................... 103,560 $651 58,406 $722 45,154 $585 Management, professional, and related occupations...................... 36,908 937 18,311 1,113 18,597 813 Management, business, and financial operations occupations........ 14,977 997 8,195 1,167 6,782 847 Professional and related occupations.............................. 21,931 902 10,116 1,058 11,815 792 Service occupations................................................... 14,123 413 7,024 478 7,099 379 Sales and office occupations.......................................... 25,193 575 9,539 690 15,654 520 Sales and related occupations...................................... 10,031 622 5,582 762 4,449 483 Office and administrative support occupations...................... 15,161 550 3,957 605 11,205 533 Natural resources, construction, and maintenance occupations........... 12,086 623 11,569 628 517 486 Farming, fishing, and forestry occupations......................... 755 372 601 388 154 327 Construction and extraction occupations............................ 6,826 604 6,663 606 163 480 Installation, maintenance, and repair occupations.................. 4,504 705 4,305 706 199 691 Production, transportation, and material moving occupations............ 15,251 540 11,963 591 3,288 420 Production occupations............................................ 8,403 538 5,991 608 2,412 423 Transportation and material moving occupations.................... 6,848 543 5,972 574 876 412

31. Marital Status Since financing Human Capital is so expensive and uncertain The marital status of the individual may have some barring on the investment

42. Women Have not Always faired as well Early in the XX Century women were not even allowed to obtain Professional degrees Very few went beyond high school

43. Percentage of Degrees Awarded to Women by Level, 1929-1930 to 2002-2003 Years Percent AssociateBachelorsMaster’sDoctor’s1 st Professional 1929-30 n.a.39.940.415.4n.a. 1960-61 n.a.38.531.710.52.7 1970-71 42.943.440.114.36.3 1980-81 54.749.850.331.126.6 1990-91 58.853.953.637.039.1 1992-93 58.854.354.238.140.1 1996-97 60.855.656.940.842.1 1999-00 60.257.258.044.445.0 2002-03 60.057.558.848.147.8

44. Associate’s Degrees Earned by Race/Ethnicity Race or Ethnicity YEAR 198119902003 Number/Percent 410,174455,102635,912 White 82.782.869.2 Black 8.67.511.9 Hispanic 4.34.710.5 Asian 2.12.95.2 American Indian 0.60.81.2 Non-Resident Alien 1.61.32.1

45. Bachelor’s Degrees Earned by Race/Ethnicity Race or Ethnicity YEAR 198119902003 Number/Percent 934,8001,051,3441,348,503 White 86.484.473.7 Black 6.55.89.2 Hispanic 2.33.16.6 Asian 2.03.76.5 American Indian 0.4 0.7 Non-Resident Alien 2.42.53.2

46. Master’s Degrees Earned by Race/Ethnicity Race or Ethnicity YEAR 198119902003 Number/Percent 294,183324,301512,645 White 82.078.966.7 Black 5.83.98.6 Hispanic 2.21.44.9 Asian 2.12.75.3 American Indian 0.4 0.6 Non-Resident Alien 7.512.814.0

47. Doctor’s Degrees Earned by Race/Ethnicity Race or Ethnicity YEAR 198119902003 Number/Percent 32,83938,37146,024 White 78.968.360.2 Black 3.93.05.5 Hispanic 1.42.03.4 Asian 2.73.25.3 American Indian 0.40.30.4 Non-Resident Alien 12.823.225.3

48. 1 st Professional Degrees Earned by Race/Ethnicity Race or Ethnicity YEAR 198119902003 Number/Percent 71,34070,98880,810 White 90.585.272.6 Black 4.14.87.1 Hispanic 2.23.45.1 Asian 2.04.712.1 American Indian 0.30.40.7 Non-Resident Alien 0.91.52.4

49. But What is The Degree for? Are women selecting specific fields or are they searching for all fields Is there any fields that they avoid and what are the requirements

50. Percentage of Women obtain a Bachelor’s Degree tbl 288 Discipline1965-19661996-1997 Agricultural and Special Resources 2.739.0 Architecture and related Programs 4.035.9 Biological vs. Life Science 28.253.9 Business Administration 8.548.6 Computer and Information Services 13.027.2 Education 75.375.0 Engineering 0.416.6 English and English Literature 66.266.5 Foreign Languages 70.769.7 Health 76.981.5 Home Economics 97.588.4 Mathematics 33.346.1 Physical science and science technology 13.637.4 Psychology 41.073.0 Economics 9.830.9 History 34.638.4 Sociology 39.668.3

55. Relating Education and Wages The Following Data Show the relation between Education and wages