INDEX OF SEGREGATION

Information about INDEX OF SEGREGATION

Published on December 7, 2007

Author: Charlie

Source: authorstream.com

Content

INDEX OF SEGREGATION:  INDEX OF SEGREGATION Are Jobs Gender, Race, or Ethnically Blind? REVIEW:  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 Discrimination:  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:  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:  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-Employer Discrimination Past Discriminating Hiring Practices Followed with Mouth to Mouth Hiring Practices Current Employer Discrimination:  Current Employer Discrimination Prejudice Consumer Preferences Other First Source: Non-Market Discrimination:  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:  U.S. MEDIAN EARNINGS BY GENDER AND RACE/ETHNICITY, YEAR-ROUND FULL-TIME WORKERS, 2001 Table 8.1 p. 277 FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO, U.S. YEAR-ROUND FULL-TIME WORKERS Figure 8.1, p. 278 :  FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO, U.S. YEAR-ROUND FULL-TIME WORKERS Figure 8.1, p. 278 FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280:  FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280 FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280:  FEMALE/MALE HOURLY WAGE RATIOSBY AGE GROUP AND YEAR Table 8.2, p. 280 FEMALE/MALE MEDIAN ANNUAL EARNINGS RATIO BY EDUCATION LEVEL, 2001 Figure 8.2, p. 282:  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:  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, 1979-1998 Table 8.3, p. 284:  FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, 1979-1998 Table 8.3, p. 284 FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, 1979-1998 Table 8.3, p. 284:  FEMALE/MALE EARNINGS RATIOS, MEDIAN WEEKLY EARNINGS OF FULL-TIME WORKERS, SELECTED DEVELOPED COUNTRIES, 1979-1998 Table 8.3, p. 284 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288:  PROPORTION FEMALE FOR SELECTED OCCUPATIONS, UNITED STATES, 2002 Table 8.2 pp. 286-288 Segregation Index:  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:  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:  Duncan Segregation Index We will look at two segregation indexes. The First is known as the Duncan Segregation Index Duncan Segregation Index:  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 Duncan Segregation Index:  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. Duncan Segregation Index:  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, Duncan Segregation Index: An Example:  Duncan Segregation Index: An Example Duncan Segregation Index: An Example:  Duncan Segregation Index: An Example Duncan Segregation Index: An Example:  Duncan Segregation Index: An Example Duncan Segregation Index: An Example:  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:  Duncan Segregation Index: An Example Duncan Segregation Index: An Example:  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. IP Segregation Index:  IP Segregation Index The second segregation index is the IP segregation index. IP Segregation Index: An Example:  IP Segregation Index: An Example IP Segregation Index: An Example:  IP Segregation Index: An Example Duncan Segregation Index: An Example:  Duncan Segregation Index: An Example Duncan Segregation Index:  Duncan Segregation Index Duncan Segregation Index:  Duncan Segregation Index Duncan Segregation Index:  Duncan Segregation Index Duncan Segregation Index:  Duncan Segregation Index Duncan Segregation Index:  Duncan Segregation Index 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:  Segregation Index There is also a hierarchal component to job segregation? Hierarchal Segregation:  Hierarchal Segregation Segregation Index:  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) Slide56:  HOUSEHOLD DATA 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 Duncan Index Across Years and Countries:  Duncan Index Across Years and Countries The Duncan Index can also be used to compare Segregation over time And Segregation across Countries Slide60:  GENDER DUNCAN INDEX OF SEGRAGATION

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