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INDEX OF SEGREGATION

INDEX OF SEGREGATION. Are Jobs Gender, Race, or Ethnically Blind?. REVIEW. We Have determined the following Under Pure Competition and under the assumption of homogenous workers Firms will hire workers to maximize profits i.e. MR=MC Or equivalently, where w = MRP Where MRP = P*MP L .

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INDEX OF SEGREGATION

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  1. INDEX OF SEGREGATION Are Jobs Gender, Race, or Ethnically Blind?

  2. REVIEW • We Have determined the following • Under Pure Competition and under the assumption of homogenous workers • Firms will hire workers to maximize profits • i.e. MR=MC • Or equivalently, where w = MRP • Where MRP = P*MPL

  3. Discrimination • Hence, if there workers were indeed homogenous and they received different wages then that would imply there was discrimination • However, if workers are not homogenous than different wages alone would not necessarily imply discrimination

  4. Discrimination • If there is disparity in wages • Then the question is why? • There are three sources that may account for wages disparities (or discrimination): • Non-Market Discrimination • Past-Employer Discrimination • Current Employer Discrimination

  5. Non-Market Discrimination • Lower Productivity due to training (schooling, etc) • Geographical (more blacks in the South) • Different preferences in terms of Labor/Leisure • Other

  6. Past-Employer Discrimination • Past Discriminating Hiring Practices • Followed with Mouth to Mouth Hiring Practices

  7. Current Employer Discrimination • Prejudice • Consumer Preferences • Other

  8. First Source: Non-Market Discrimination • Do individuals on average take on different jobs based on personal characteristics such as gender, race, or ethnicity • If so, that may in part explain the difference in wage differentials

  9. WOMEN($) MEN($) WOMEN’S EARNINGS AS PERCENTAGE OF MEN’S EARNINGS ALL 29,215 38,275 76.3 WHITE 29,930 39,834 75.1 BLACK 26,595 31,351 84.8 HISPANIC 21,493 25,083 85.7 ASIA/PACIFIC ISLANDER 30,685 41,853 73.3 U.S. MEDIAN EARNINGS BY GENDER AND RACE/ETHNICITY, YEAR-ROUND FULL-TIME WORKERS, 2001Table 8.1 p. 277

  10. 85% 80% 75% 70% 65% 60% 55% 50% 1960 1970 1980 1990 2000 FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO, U.S. YEAR-ROUND FULL-TIME WORKERSFigure 8.1, p. 278

  11. AGE RANGE WAGE RATIO (%) 1978 1988 1998 18-24 82.4 93.0 94.2 25-34 70.3 82.8 85.0 35-44 58.9 68.7 76.1 45-54 58.2 64.7 71.6 FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEARTable 8.2, p. 280

  12. AGE RANGE WAGE RATIO (%) 1978-1988 1988-1998 ACROSS COHORT 18-24 25-34 35-44 45-54 - - - - 10.6 12.5 9.8 6.6 1.2 2.3 7.4 6.8 WITHIN COHORT 18-24 25-34 35-44 45-54 - - - - -2.4 -1.6 5.8 2.9 -9.2 -6.7 2.9 4.5 FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEARTable 8.2, p. 280

  13. FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO BY EDUCATION LEVEL, 2001Figure 8.2, p. 282

  14. DISTRIBUTION OF ANNUAL EARNINGS BY GENDER, YEAR-ROUND FULL-TIME WORKERS, U.S., 2001Figure 8.3, p. 283

  15. COUNTRY 1979-1981 1989-1990 1994-1998 PERCENTAGE POINT CHANGE IN RATION, 1979-1971 TO 1994-1998 AUSTRALIA 80.0% 81.4% 86.8% 6.8 AUSTRIA 64.9% 67.4% 69.2% 4.3 BELGIUM N.A. 84.0% 90.1% 6.1* CANADA 63.3% 66.3% 69.8% 6.5 FINLAND 73.4% 76.4% 79.9% 6.5 FRANCE 79.9% 84.7% 89.9% 10.0 GERMANY (WEST) 71.7% 73.7% 75.5% 3.8 IRELAND N.A. N.A. 74.5% N.A. *BASED ON CHANGE BETWEEN 1989-1990 AND 1994-1998. FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, 1979-1998Table 8.3, p. 284

  16. COUNTRY 1979-1981 1989-1990 1994-1998 PERCENTAGE POINT CHANGE IN RATION, 1979-1971 TO 1994-1998 ITALY N.A. 80.5% 83.3% 2.8* JAPAN 58.7% 59.0% 63.6% 4.9 NETHERLANDS N.A. 75.0% 76.9% 1.9* NEW ZEALAND 73.4% 75.9% 81.4% 8.0 SPAIN N.A. N.A. 71.1% - SWEDEN 83.8% 78.8% 83.5% -.3 SWITZERLAND N.A. 73.6% 75.2% 1.6* UNITED KINGDOM 62.6% 67.7% 74.9% 12.3 UNITED STATES 62.5% 70.6% 76.3% 13.8 NON-U.S. AVERAGE 71.2% 74.6% 77.8% 6.2 *BASED ON CHANGE BETWEEN 1989-1990 AND 1994-1998. FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, 1979-1998Table 8.3, p. 284

  17. OCUPATION %FEMALE AUTOMOBILE MECHANIC 1.2 ROOFERS 1.5 CARPENTER 1.5 PLUMBERS, PIPEFITTERS, ETC. 1.7 ELECTRICIAN 1.9 CONSTRUCTION TRADES 2.1 BRICKMASONS AND STONEMASONS 2.2 FIREFIGHTERS 2.5 AIRPLANE PILOT AND NAVIGATORS 3.0 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  18. OCUPATION %FEMALE TRUCK DRIVERS 4.3 MECHANICAL ENGINEERS 4.5 MACHINIST 4.8 MECHANICS AND REPAIRERS 4.8 PEST CONTROL 5.6 ELECTRICAL AND ELECTRONIC ENGIREERS 8.8 CIVIL ENGINEERS 9.6 AEROSPACE ENGINEERS 10.7 CLERGY 11.2 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  19. OCUPATION %FEMALE TAXICAB DRIVERS AND CHAUFFEURS 11.7 CHEMICAL ENGINEERS 12.2 FARMING, FORESTRY, AND FISHING 14.9 BUTCHERS AND MEAT CUTTERS 16.4 POLICE AND DETECTIVES 17.5 ATHLETES 20.0 CORRECTIONAL INSTITUTION OFFICERS 21.5 ARCHITECTS 23.7 COMPUTER PROGRAMMERS 27.2 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  20. OCUPATION %FEMALE MAIL CARRIERS, POSTAL SERVICE WORKERS 28.8 MATHEMATICAL AND COMPUTER SCIENTISTS 29.2 JANITORS AND CLEANERS 30.3 SECURITIES AND FINANCIAL SERVICES SALES 32.3 PHYSICIANS 32.6 LAWYERS AND JUDGES 33.7 TEACHERS, COLLEGE AND UNIVERSITY 36.7 BUS DRIVERS 41.3 PHARMACISTS 41.8 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  21. OCUPATION %FEMALE BIOLOGICAL AND LIFE SCIENTISTS 44.5 BAKERS 46.6 BARTENDERS 50.0 REAL ESTATE SALES 51.8 COMPUTER OPERATORS 52.9 INSURANCE SALES 53.1 ECONOMIST 54.2 PHYSICIANS’ ASSISTANT 55.6 TEACHERS, SECONDARY SCHOOL 56.4 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  22. OCUPATION %FEMALE PSYCHOLOGISTS 57.6 PHYSICAL THERAPISTS 61.3 SALES COUNTER CLERKS 64.5 SOCIAL WORKERS 70.3 WAINTERS AND WAITRESSES 71.0 THERAPISTS 71.1 HOTEL CLERKS 75.0 CASHIERS 77.7 TEACHERS, ELEMENTARY SCHOOL 81.5 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  23. OCUPATION %FEMALE LIBRARIANS 83.0 LEGAL ASSISTANTS 84.0 DATA-ENTRY KEYERS 84.6 RECORD CLERKS 84.9 DIETICIANS 87.5 NURSING AIDES, ORDELIES AND ATTENDANTS 89.0 BANK TELLERS 89.1 HAIRDRESSERS AND COSMETOLOGISTS 89.3 FINANCIAL RECORDS PROCESSING 90.6 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  24. OCUPATION %FEMALE REGISTERES NURSES 91.0 SPEECH THERAPISTS 94.0 LICENSED PRACTICAL NURSES 94.4 CLEANERS AND SERVANTS 95.2 DENTAL ASSISTANTS 97.7 RECEPTIONISTS 97.9 TEACHERS, PRE-KINDERGARTEN AND KINDERGARTEN 98.4 CHILD CARE WORKERS 98.5 SECRETARIES 98.6 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002Table 8.2 pp. 286-288

  25. Segregation Index • One way of establishing if jobs are distributed in a gender, race, and ethnic blind form is by looking at whether certain jobs are more likely to have a larger percent of a certain type of employees. • In other words, is this job more likely to be a male or female job • Or, is this job more likely to be held by a minority than a non-hispanic white

  26. Segregation Index • This can be measured thru the use of the Segregation Index • The index attempts to review whether there is a “larger” than expected presence of a certain group in any given job category

  27. Duncan Segregation Index • We will look at two segregation indexes. The First is known as the Duncan Segregation Index

  28. Duncan Segregation Index Where mi and fi represent the percent of males and females working in this job category respectively • Or M and F could represent any other two groups

  29. Duncan Segregation Index • When I = 0 • That implies that there is no segregation in any job category. In other words, Mi= Fi • When I = 1 • That implies that there is complete segregation in all job categories. This can be seen since when Mi>0, the Fi = 0 and vice versa.

  30. Duncan Segregation Index • Mi and Fi are the percentage of the individuals in a given group (M or F) that are working in job category i. • Consequently,

  31. Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women 4 15 81 Men 70 40 7 Duncan Segregation Index: An Example

  32. Duncan Segregation Index: An Example

  33. Duncan Segregation Index: An Example

  34. Duncan Segregation Index: An Example • That means that you need to move 75% of the workers to obtain equal distribution of Employment • That is 75% of women would have to change jobs for the employment distribution be the same

  35. Romance Novelist 130 (74) Hot Dog Venders 74 (55) Mimes 13 (88) Women 56=4+52 34=15+19 6=81-75 Men 70 40 7 Duncan Segregation Index: An Example

  36. Duncan Segregation Index: An Example • Duncan Index therefore states that 75% of women need to change job to obtain evenly distributed workplace • However, one big draw back: the workforce in the different sectors much change • For instance, there would now be 130 romance novelist instead of 74, etc.

  37. IP Segregation Index • The second segregation index is the IP segregation index.

  38. Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women 4 15 81 Men 70 40 7 IP Segregation Index: An Example

  39. IP Segregation Index: An Example

  40. Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women 34 25 41 Men 40 30 47 Duncan Segregation Index: An Example

  41. Duncan Segregation Index

  42. Duncan Segregation Index

  43. Duncan Segregation Index

  44. Duncan Segregation Index

  45. Duncan Segregation Index

  46. Segregation Index • From the previous tables • What can we say occurs when the segregation index is based on more aggregate data as compared to more disaggregate data?

  47. Segregation Index • There is also a hierarchal component to job segregation?

  48. Hierarchal Segregation

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