Atypical employment and incomes in Europe: macro- and … · 2015. 5. 26. · Atypical employment and incomes in Europe: macro- and microanalysis. François Ghesquière (Aspirant

Atypical employment and incomes in Europe: macro- and microanalysis.

François Ghesquière (Aspirant FNRS) & Wels Jacques (FRFC), METICES, Université libre de Bruxelles ULB

LFS & SILC, 3rd user conference, GESIS, Manheim, March 2013

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Introduction

• Atypical employment Income

• Atypical employment = part-time + job instability

• Relation : micro (individual) or macro (country)

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Methods

• Data : SILC & LFS 2010, weighted

• Part-time : self-defined current status

• Instability : change of job since last year.

• Incomes : individual, direct from labour, gross

• Micro : working poor (< 60% of median labour income)

• Macro : Gini’s coefficient

• Among workers

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Microanalysis Proportion of working poor according the atypical employment in Europeans countries Country Full-time Part-time Stable job Instable job

Autria 13.8% 47.7% 20.3% 41.2%

Belgium 10.6% 43.2% 18.0% 37.0%

Bulgaria 16.8% 77.8% No Data No Data

Czech Republic 15.2% 73.7% 16.0% 35.5%

Germany 15.6% 62.2% 25.3% 53.6%

Denmark 14.1% 31.8% 14.9% 31.1%

Estonia 20.9% 65.5% 23.7% 39.9%

Spain 22.2% 69.9% 24.4% 53.0%

Finland 16.3% 62.9% No Data No Data

France 13.0% 53.4% 15.2% 36.0%

Greece 21.7% 82.2% 26.8% 48.6%

Hungary 14.2% 72.2% 13.7% 64.8%

Iceland 16.5% 62.5% No Data No Data

Italy 15.6% 61.7% 19.3% 35.8%

Lithuania 24.3% 70.8% No Data No Data

Luxemburg 19.5% 58.2% 25.1% 53.3%

Latvia 24.9% 67.5% 25.3% 57.5%

Malta 14.4% 78.4% 19.9% 28.3%

Netherlands 8.5% 39.1% 18.7% 30.1%

Norway 14.3% 57.4% No Data No Data

Poland 23.3% 71.5% 25.1% 49.9%

Portugal 22.9% 87.4% 25.6% 53.8%

Romania 24.0% 94.3% 29.3% 40.4%

Sweden 16.4% 46.2% No Data No Data

Slovenia 16.2% 52.7% 16.7% 39.2%

Slovak Republic 14.5% 72.9% 14.1% 43.2%

United Kingdom 13.2% 67.0% 26.5% 30.4%

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Microanalysis (2)

• Binary Logistic Regression :

• Dependent variable =

Poor (1) / Not Poor (0)

• Independent variables =

- Age (reference: 25-34 y)

- Education Level (reference: upper secondary)

- Sex (reference: male)

- Part Time (reference: full time)

- Job instability (reference: change of job during the last 12 months)

Database: SILC

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Germany

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Variables dans l'équationa

A E.S. Wald ddl Sig. Exp(B) Etape 1

b Upper

Secondary 1186737.591 2 0.000

Lower secondary or less

1.160 .001 756637.894 1 0.000 3.190

tertiary education

-.567 .001 218189.190 1 0.000 .567

Sex .558 .001 263520.932 1 0.000 1.747

Age 35-44 3476930.723 5 0.000

Age 15-24 3.119 .002 2741408.339 1 0.000 22.630

Age 25-34 .683 .001 225822.490 1 0.000 1.980

Age 45-54 -.198 .001 22661.590 1 0.000 .821

Age 55-64 .138 .002 8439.667 1 0.000 1.148

Age 65-74 .655 .006 13079.350 1 0.000 1.925

Part-time 2.570 .001 4884696.467 1 0.000 13.065

Stable job -1.374 .002 672904.264 1 0.000 .253

Constant -1.157 .002 376420.796 1 0.000 .314

a. Country = Deutschland

United Kingdom

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b Upper

Secondary 625768.111 2 0.000


.272 .002 25690.254 1 0.000 1.312

tertiary education

-.911 .001 489712.951 1 0.000 .402

Sex .280 .001 49834.388 1 0.000 1.323

Age 35-44 592475.472 5 0.000

Age 15-24 1.253 .002 387543.457 1 0.000 3.501

Age 25-34 .253 .002 21111.683 1 0.000 1.288

Age 45-54 -.126 .002 5740.798 1 0.000 .882

Age 55-64 .086 .002 2240.677 1 0.000 1.090

Age 65-74 .951 .003 92652.847 1 0.000 2.588

Part-time 2.683 .001 4316649.296 1 0.000 14.632

Stable job -.251 .002 24681.480 1 0.000 .778

Constant -1.832 .002 831796.247 1 0.000 .160

a. Country = United Kingdom

France

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b Upper

Secondary 542844.372 2 0.000


.661 .002 165923.691 1 0.000 1.937

tertiary education

-.777 .002 206858.818 1 0.000 .460

Sex .320 .001 46489.147 1 0.000 1.376

Age 35-44 393380.270 5 0.000

Age 15-24 1.456 .003 302946.376 1 0.000 4.290

Age 25-34 .226 .002 13299.927 1 0.000 1.253

Age 45-54 -.131 .002 4910.498 1 0.000 .877

Age 55-64 .197 .002 8285.842 1 0.000 1.218

Age 65-74 .838 .007 14001.957 1 0.000 2.313

Part-time 2.240 .002 2083585.086 1 0.000 9.395

Stable job -1.228 .003 236441.341 1 0.000 .293

Constant -1.392 .003 235300.567 1 0.000 .249

a. Country = France

Denmark

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b Upper

Secondary 14700.750 2 0.000


.570 .006 9197.253 1 0.000 1.768

tertiary education

-.215 .006 1415.402 1 0.000 .806

Sex -.153 .005 953.176 1 .000 .858

Age 35-44 80984.426 5 0.000

Age 15-24 2.212 .010 52080.318 1 0.000 9.133

Age 25-34 .581 .007 7305.791 1 0.000 1.787

Age 45-54 -.018 .007 6.324 1 .012 .983

Age 55-64 -.155 .007 437.827 1 .000 .857

Age 65-74 1.593 .014 13915.168 1 0.000 4.920

Part-time 1.208 .006 41490.559 1 0.000 3.347

Stable job -.588 .008 5604.680 1 0.000 .556

Constant -1.455 .009 26185.199 1 0.000 .233

a. Country = Danmark

Macroanalysis

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Relation (macro) between the extend of income inequality and part-time jobs

Proportion of part-time contracts (%)

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LFS & SILC databases

Macroanalysis (2)

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Relation (macro) between the extend of income inequality and unstable jobs

Proportion of unstable jobs (%)

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Macroanalysis (3)

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Relation (macro) between the extend of part-time and unstable jobs

Proportion of unstable jobs (%)

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Macroanalysis (4)

Pearson's correlation coeficient between the proportion of part-time contracts, the proportion of unstable jobs and the extend of labour incomes inequality

Proportion of part-time contracts

Proportion of unstable jobs

Labour income inequality (Gini)

Proportion of part-time contracts

Correlation (Pearson's)

1 .614** -.060

Signification .001 .768

Proportion of unstable jobs


.614** 1 -.117


Labour income inequality (Gini)


-.060 -.117 1


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Conclusion

• Micro and macro relations differ from each other

• Micro : atypical employment, sex, age, qualification.

• Macro : Industrial relations?

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Relation between labour income inequality and union density

Union density

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SILC & ICTWSS databases

Documents

Atypical employment and incomes in Europe: macro- and … · 2015. 5. 26. · Atypical employment and incomes in Europe: macro- and microanalysis. François Ghesquière (Aspirant