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Gender segregation in the Spanish labor market: An alternative approach Coral del Río and Olga Alonso-Villar. Universidade de Vigo. Background: Overall segregation in the case of two categories. Index of dissimilarity (Duncan and Duncan, 1955):. - PowerPoint PPT Presentation

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  • Gender segregation in the Spanish labor market: An alternative approach

    Coral del Ro and Olga Alonso-Villar

    Universidade de Vigo

  • Background: Overall segregation in the case of two categoriesIndex of dissimilarity (Duncan and Duncan, 1955):fi is the number of female workers in occupation i; mi is the number of male workers.F is the total number of female workers and M is the total number of male workers.

  • Background: Overall segregation in the case of two categoriesGini index (Silber, 1989):KM index (Karmel and Maclachlan, 1988):Hutchens indexes (Hutchens, 2004):Characterized in terms of axioms

  • Moir and Selby Smith (1979) and Lewis (1982):ti is the number of workers in occupation i and T is the total number of workers Background: Local segregation in the case of two categories

  • Background : Segregation in the case of multiple categories

    Silber (1992) Karmel and MaclachlanBoisso et al. (1994) GiniReardon and Firebaugh (2002) inequality measures/ axiomsFrankel and Volij (2007): Established a set of axioms for the measurement of overall segregationCharacterized the Mutual information index

    Alonso-Villar and Del Ro (2008):Established a set of axioms for the measurement of local segregationCharacterized a family of local segregation measuresProposed several local measures consistent with overall measures

    OverallLocal

  • Background: Alonso-Villar and Del Ro (2008):Decomposablej ---> occupationg ----> target group (female workers)

  • Background: Alonso-Villar and Del Ro (2008):BASIC PROPERTIES: Scale invariance Symmetry in groups Movements between groups Insensitivity to proportional divisions

    ADDITIONAL PROPERTY:

    Aggregation

  • Background: Alonso-Villar and Del Ro (2008):

    Silber (1992) extended the M-K index to the multigroup case, which he denoted as

    , this overall index is the weighted mean of our index

    for each population subgroup:

    .

    _1270476811.unknown

    _1273055327.unknown

    _1270476797.unknown

    The Gini index,

    , proposed by Reardon and Firebaugh (2002) to measure overall segregation is the weighted mean of our index

    for each population subgroup:

    .

    _1269772441.unknown

    _1270299490.unknown

    _1269771984.unknown

    The Mutual information index proposed by Frankel and Volij (2007) to measure overall segregation in a multigroup context is the weigthed mean of our index

    for each population subgroup:

    _1273055798.unknown

    _1273055907.unknown

  • Background: Alonso-Villar and Del Ro (2008):Local segregation curve:

    where

    is the proportion of cumulative employment represented by the first j categories (occupations-sectors) lined up in ascending order of the ratio

    (

    ).

    _1255510604.unknown

    _1259486294.unknown

    _1254830630.unknown

  • Background: Alonso-Villar and Del Ro (2008):Local segregation curve:the above axioms

    Theorem 1. Let us consider two vectors

    .

    if and only if

    for any local segregation index

    satisfying axioms 1, 3, 4 and 5.

    _1268571690.unknown

    _1268571704.unknown

    _1268571730.unknown

    _1254927978.unknown

  • The aim of this paper:

    To analyze occupational segregation by gender in Spain

    In doing so, the local and overall indexes mentioned above are used

    In addition, two decompositions of the local segregation curves are proposed:One by groups of occupationsAnother by population subgroups

    These decompositions are used to go further in the empirical analysis

  • Descomposing local segregation curvesi) Decomposition by subgroups of occupations:ii) Decomposition by population subgroups:

  • Occupational segregation in Spain: EPA, 2007, 2nd quarter

  • Young: 16 - 29 -- Middle-aged: 30 - 44 -- Elderly: >= 45 years oldAGE

  • 45%40%

  • Education level

  • Type of contract

  • Type of job

  • 1) Female segregation explains, at least, 50% of overall gender segregation, even though the demographic weight of women in the labor force is 41%.

    2) Within the female group, the young and the elderly are the ones suffering the highest occupational segregation.

    3) Regarding males, segregation is higher for young workers.Conclusions1/2

  • 4) Human capital increases do not necessarily reduce segregation, since for both women and men, the educational group with the lowest segregation level is that with intermediate-education.

    5) The type of contract (permanent versus temporary) is more important to explain male segregation than female segregation.

    6) Part-time jobs have more power to explain female segregation, since women working part-time tend to concentrate in the most feminized occupations of the economy, while for men part-time jobs are more evenly distributed across occupations.Conclusions2/2

  • Occupational segregation in Spain: EPA, 2007, 2nd quarter Figure 1: Local segregation curves of female and male workers.

    Grfico1

    000

    0.000089310.007012110.06591423

    0.001303340.015988610.10562134

    0.001780930.01888970.11645285

    0.00185350.026737290.1356653

    0.002754230.038189330.16254357

    0.003306820.042765480.17304112

    0.005116910.052139780.19376723

    0.006916990.057866650.20603763

    0.007044290.082734060.25606758

    0.010396020.104885350.29585887

    0.010422890.106110070.29805471

    0.010653360.110849720.30636665

    0.013760010.115140870.3122805

    0.014926140.150325720.36064058

    0.016884440.15070710.36114986

    0.017540470.159312070.37252798

    0.022301880.177752030.39645322

    0.026760220.185967790.40693245

    0.028717310.196271070.41979022

    0.029367970.207254120.43330997

    0.0353480.214267250.44082744

    0.041860210.224839280.45186078

    0.054043030.226090980.45316282

    0.068606180.232989280.46021722

    0.077283910.237149130.46441167

    0.083844140.247167480.47446116

    0.08588870.252272210.4793598

    0.090168980.256888250.48372515

    0.112869050.265049280.49131179

    0.117947820.283329720.50790069

    0.12196450.292786910.51645894

    0.126455780.299280710.52222687

    0.139003640.324934730.54495732

    0.15753470.343353540.56096644

    0.162259980.350537250.56704507

    0.169527010.357035710.57252489

    0.183686190.365577820.5796455

    0.190447790.405654710.6125902

    0.194453050.413568090.61901241

    0.199055660.417447940.62213897

    0.209149870.430457050.63250119

    0.213394020.44936170.64720827

    0.220672630.4831230.67308983

    0.222046980.512917640.69565583

    0.233742970.530794840.70886835

    0.241984860.550140630.72272826

    0.25914830.552275020.72425367

    0.275675320.55875430.72887685

    0.28940580.573646290.73948642

    0.321210050.591411310.7519142

    0.336571510.594923660.7542542

    0.337264510.605544440.76131953

    0.40454940.611984110.76559471

    0.41279410.637439640.7818773

    0.426237240.63960.78324555

    0.429827970.639903110.78343528

    0.494957270.702146740.82150717

    0.581130540.704512180.82295398

    0.602800010.744080370.84702052

    0.639831790.806531750.88458212

    0.658834810.828601580.89782036

    0.70787120.871509980.92348701

    0.743408050.875596870.92592626

    0.765630770.903945330.94283532

    0.849479130.990153230.99415811

    111

    Females

    Males

    Equity

    Cumulative employment

    Cumulative target groups .

    Curvas Seg Ocup

    EPA 2 TRIMESTRE DE 2007

    CURVAS DE SEGREGACIN DE MUJERES Y HOMBRES

    POBLACION TOTAL - HOMBRES Y MUJERES

    Empleo TotalSeg MujeresEmpleo TotalSeg HombresEquidad

    00000

    0.005841890.000089310.065914230.007012110.06591423

    0.057164680.001303340.105621340.015988610.10562134

    0.074073740.001780930.116452850.01888970.11645285

    0.076512990.00185350.13566530.026737290.1356653

    0.102179640.002754230.162543570.038189330.16254357

    0.115417880.003306820.173041120.042765480.17304112

    0.152979480.005116910.193767230.052139780.19376723

    0.177046020.006916990.206037630.057866650.20603763

    0.178492830.007044290.256067580.082734060.25606758

    0.216564720.010396020.295858870.104885350.29585887

    0.216754450.010422890.298054710.106110070.29805471

    0.21812270.010653360.306366650.110849720.30636665

    0.234405290.013760010.31228050.115140870.3122805

    0.238680470.014926140.360640580.150325720.36064058

    0.24574580.016884440.361149860.15070710.36114986

    0.24808580.017540470.372527980.159312070.37252798

    0.260513580.022301880.396453220.177752030.39645322

    0.271123150.026760220.406932450.185967790.40693245

    0.275746330.028717310.419790220.196271070.41979022

    0.277271740.029367970.433309970.207254120.43330997

    0.291131650.0353480.440827440.214267250.44082744

    0.304344170.041860210.451860780.224839280.45186078

    0.326910170.054043030.453162820.226090980.45316282

    0.352791730.068606180.460217220.232989280.46021722

    0.367498810.077283910.464411670.237149130.46441167

    0.377861030.083844140.474461160.247167480.47446116DecilasPoblacion totalMujeresHombres

    0.380987590.08588870.47935980.252272210.4793598

    0.38740980.090168980.483725150.256888250.483725150000

    0.42035450.112869050.491311790.265049280.491311790.10.10.002677740.01471781

    0.427475110.117947820.507900690.283329720.507900690.20.20.008937720.05504876

    0.432954930.12196450.516458940.292786910.516458940.30.30.039719040.10721932

    0.439033