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

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

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

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

Past-Employer Discrimination Past Discriminating Hiring Practices Followed with Mouth to Mouth Hiring Practices

Current Employer Discrimination Prejudice Consumer Preferences Other

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

U.S. MEDIAN EARNINGS BY GENDER AND RACE/ETHNICITY, YEAR-ROUND FULL-TIME WORKERS, 2001 Table 8.1 p. 277 WOMEN($)MEN($) WOMEN’S EARNINGS AS PERCENTAGE OF MEN’S EARNINGS ALL29,21538, WHITE29,93039, BLACK26,59531, HISPANIC21,49325, ASIA/PACIFIC ISLANDER30,68541,

FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO, U.S. YEAR-ROUND FULL-TIME WORKERS Figure 8.1, p % 80% 75% 70% 65% 60% 55% 50%

FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280 AGE RANGE WAGE RATIO (%)

FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280 AGE RANGEWAGE RATIO (%) ACROSS COHORT WITHIN COHORT

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

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

FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, Table 8.3, p. 284 COUNTRY PERCENTAGE POINT CHANGE IN RATION, TO AUSTRALIA 80.0%81.4%86.8%6.8 AUSTRIA64.9%67.4%69.2%4.3 BELGIUMN.A.84.0%90.1%6.1* CANADA63.3%66.3%69.8%6.5 FINLAND73.4%76.4%79.9%6.5 FRANCE79.9%84.7%89.9%10.0 GERMANY (WEST) 71.7%73.7%75.5%3.8 IRELANDN.A. 74.5%N.A. *BASED ON CHANGE BETWEEN AND

FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, Table 8.3, p. 284 COUNTRY PERCENTAGE POINT CHANGE IN RATION, TO ITALYN.A.80.5%83.3%2.8* JAPAN58.7%59.0%63.6%4.9 NETHERLANDSN.A.75.0%76.9%1.9* NEW ZEALAND73.4%75.9%81.4%8.0 SPAINN.A. 71.1%- SWEDEN83.8%78.8%83.5%-.3 SWITZERLANDN.A.73.6%75.2%1.6* UNITED KINGDOM62.6%67.7%74.9%12.3 UNITED STATES62.5%70.6%76.3%13.8 NON-U.S. AVERAGE71.2%74.6%77.8%6.2 *BASED ON CHANGE BETWEEN AND

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION%FEMALE AUTOMOBILE MECHANIC1.2 ROOFERS1.5 CARPENTER1.5 PLUMBERS, PIPEFITTERS, ETC.1.7 ELECTRICIAN1.9 CONSTRUCTION TRADES2.1 BRICKMASONS AND STONEMASONS2.2 FIREFIGHTERS2.5 AIRPLANE PILOT AND NAVIGATORS3.0

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION%FEMALE TRUCK DRIVERS4.3 MECHANICAL ENGINEERS4.5 MACHINIST4.8 MECHANICS AND REPAIRERS4.8 PEST CONTROL5.6 ELECTRICAL AND ELECTRONIC ENGIREERS8.8 CIVIL ENGINEERS9.6 AEROSPACE ENGINEERS10.7 CLERGY11.2

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE TAXICAB DRIVERS AND CHAUFFEURS11.7 CHEMICAL ENGINEERS12.2 FARMING, FORESTRY, AND FISHING14.9 BUTCHERS AND MEAT CUTTERS16.4 POLICE AND DETECTIVES17.5 ATHLETES20.0 CORRECTIONAL INSTITUTION OFFICERS21.5 ARCHITECTS23.7 COMPUTER PROGRAMMERS27.2

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE MAIL CARRIERS, POSTAL SERVICE WORKERS28.8 MATHEMATICAL AND COMPUTER SCIENTISTS29.2 JANITORS AND CLEANERS30.3 SECURITIES AND FINANCIAL SERVICES SALES32.3 PHYSICIANS32.6 LAWYERS AND JUDGES33.7 TEACHERS, COLLEGE AND UNIVERSITY36.7 BUS DRIVERS41.3 PHARMACISTS41.8

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE BIOLOGICAL AND LIFE SCIENTISTS44.5 BAKERS46.6 BARTENDERS50.0 REAL ESTATE SALES51.8 COMPUTER OPERATORS52.9 INSURANCE SALES53.1 ECONOMIST54.2 PHYSICIANS’ ASSISTANT55.6 TEACHERS, SECONDARY SCHOOL56.4

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE PSYCHOLOGISTS57.6 PHYSICAL THERAPISTS61.3 SALES COUNTER CLERKS64.5 SOCIAL WORKERS70.3 WAINTERS AND WAITRESSES71.0 THERAPISTS71.1 HOTEL CLERKS75.0 CASHIERS77.7 TEACHERS, ELEMENTARY SCHOOL81.5

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE LIBRARIANS83.0 LEGAL ASSISTANTS84.0 DATA-ENTRY KEYERS84.6 RECORD CLERKS84.9 DIETICIANS87.5 NURSING AIDES, ORDELIES AND ATTENDANTS 89.0 BANK TELLERS89.1 HAIRDRESSERS AND COSMETOLOGISTS89.3 FINANCIAL RECORDS PROCESSING90.6

PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp OCUPATION %FEMALE REGISTERES NURSES91.0 SPEECH THERAPISTS94.0 LICENSED PRACTICAL NURSES94.4 CLEANERS AND SERVANTS95.2 DENTAL ASSISTANTS97.7 RECEPTIONISTS97.9 TEACHERS, PRE-KINDERGARTEN AND KINDERGARTEN 98.4 CHILD CARE WORKERS98.5 SECRETARIES98.6

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

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

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

Duncan Segregation Index Where m i and f i represent the percent of males and females working in this job category respectively Or M and F could represent any other two groups

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

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

Duncan Segregation Index: An Example Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women41581 Men70407

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

Duncan Segregation Index: An Example Romance Novelist 130 (74) Hot Dog Venders 74 (55) Mimes 13 (88) Women56=4+5234=15+196=81-75 Men70407

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.

I P Segregation Index The second segregation index is the I P segregation index.

I P Segregation Index: An Example Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women41581 Men70407

I P Segregation Index: An Example

Duncan Segregation Index: An Example Romance Novelist 74 Hot Dog Venders 55 Mimes 88 Women Men403047

Duncan Segregation Index

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?

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

Hierarchal Segregation Percent Female of Faculty in Institutions of Higher Education by Academic Rank, , , , Academic Rank Professor Associate Professor Assistant Professor

Segregation Index The segregation is likely to have a large impact on wages For instance, jobs that have generally more women are likely to have lower wages –(will discuss this more when we look at models of discrimination)

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 ,560 $651 58,406 $722 45,154 $585 Management, professional, and related occupations , ,311 1,113 18, Management, business, and financial operations occupations , ,195 1,167 6, Professional and related occupations , ,116 1,058 11, Service occupations , , , Sales and office occupations , , , Sales and related occupations , , , Office and administrative support occupations , , , Natural resources, construction, and maintenance occupations , , Farming, fishing, and forestry occupations Construction and extraction occupations , , Installation, maintenance, and repair occupations , , Production, transportation, and material moving occupations , , , Production occupations , , , Transportation and material moving occupations , ,

Duncan Index Across Years and Countries The Duncan Index can also be used to compare Segregation over time And Segregation across Countries

GENDER DUNCAN INDEX OF SEGRAGATION