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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
Aty
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loym
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and
inco
mes
in E
uro
pe:
mac
ro-
and
mic
roan
alys
is.
LFS
& S
ILC
, 3
rd u
ser
con
fere
nce
, GES
IS,
20
13
.03
.21
-22
Introduction
• Atypical employment Income
• Atypical employment = part-time + job instability
• Relation : micro (individual) or macro (country)
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LFS
& S
ILC
, 3
rd u
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con
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, GES
IS,
20
13
.03
.21
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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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, GES
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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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and
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in E
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pe:
mac
ro-
and
mic
roan
alys
is.
LFS
& S
ILC
, 3
rd u
ser
con
fere
nce
, GES
IS,
20
13
.03
.21
-22
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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LFS
& S
ILC
, 3
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, GES
IS,
20
13
.03
.21
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Germany
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LFS
& S
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, GES
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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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& S
ILC
, 3
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20
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Variables dans l'équationa
A E.S. Wald ddl Sig. Exp(B) Etape 1
b Upper
Secondary 625768.111 2 0.000
Lower secondary or less
.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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mac
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mic
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LFS
& S
ILC
, 3
rd u
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con
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nce
, GES
IS,
20
13
.03
.21
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Variables dans l'équationa
A E.S. Wald ddl Sig. Exp(B) Etape 1
b Upper
Secondary 542844.372 2 0.000
Lower secondary or less
.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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LFS
& S
ILC
, 3
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, GES
IS,
20
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.03
.21
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Variables dans l'équationa
A E.S. Wald ddl Sig. Exp(B) Etape 1
b Upper
Secondary 14700.750 2 0.000
Lower secondary or less
.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
10 20 30 40 50
0.3
00
.35
0.4
00
.45
Relation (macro) between the extend of income inequality and part-time jobs
Proportion of part-time contracts (%)
Gro
ss la
bo
ur
inco
me
in
eq
ua
lity
(G
ini)
AT
BE
BG
CZ
DE
DK
EE
ES
FI
FR
GR
HU
IS
IT
LT
LU
LV
MT
NL
NO
PL PTRO
SESI
SK
UK
Aty
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mac
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and
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LFS
& S
ILC
, 3
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con
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, GES
IS,
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.21
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LFS & SILC databases
Macroanalysis (2)
4 6 8 10 12
0.3
00
.35
0.4
00
.45
Relation (macro) between the extend of income inequality and unstable jobs
Proportion of unstable jobs (%)
Gro
ss la
bo
ur
inco
me
in
eq
ua
lity
(G
ini)
AT
BE
BG
CZ
DE
DK
EE
ES
FI
FR
GR
HU
IS
IT
LT
LU
LV
MT
NL
NO
PLPTRO
SESI
SK
UK
Aty
pic
al e
mp
loym
ent
and
inco
mes
in E
uro
pe:
mac
ro-
and
mic
roan
alys
is.
LFS
& S
ILC
, 3
rd u
ser
con
fere
nce
, GES
IS,
20
13
.03
.21
-22
LFS & SILC databases
Macroanalysis (3)
4 6 8 10 12
10
20
30
40
50
Relation (macro) between the extend of part-time and unstable jobs
Proportion of unstable jobs (%)
Pro
po
rtio
n o
f p
art
-tim
e c
on
tra
cts
(%
)
ATBE
BG
CZ
DE DK
EE
ESFI
FR
GR HU
IS
IT
LT
LU
LV
MT
NL
NO
PL
PTRO
SE
SI
SK
UK
Aty
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al e
mp
loym
ent
and
inco
mes
in E
uro
pe:
mac
ro-
and
mic
roan
alys
is.
LFS
& S
ILC
, 3
rd u
ser
con
fere
nce
, GES
IS,
20
13
.03
.21
-22
LFS & SILC databases
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
Correlation (Pearson's)
.614** 1 -.117
Signification .001 .561
Labour income inequality (Gini)
Correlation (Pearson's)
-.060 -.117 1
Signification .768 .561
Aty
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and
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in E
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LFS
& S
ILC
, 3
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, GES
IS,
20
13
.03
.21
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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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mac
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LFS
& S
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, 3
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20
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20 40 60 80
0.3
00
.35
0.4
00
.45
Relation between labour income inequality and union density
Union density
Gro
ss la
bo
ur
inco
me
in
eq
ua
lity
(G
ini)
AT
BE
BG
CZ
DE
DK
EE
ES
FI
FR
GR
HU
IS
IT
LT
LU
LV
MT
NL
NO
PL PT RO
SESI
SK
UK
Aty
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al e
mp
loym
ent
and
inco
mes
in E
uro
pe:
mac
ro-
and
mic
roan
alys
is.
LFS
& S
ILC
, 3
rd u
ser
con
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nce
, GES
IS,
20
13
.03
.21
-22
SILC & ICTWSS databases