1. Galatasaray University, GIAM
PUBLIC-PRIVATE WAGE DIFFERENTIALS IN TURKEY Ayça Akarçay Gürbüz and Sezgin Polat
Galatasaray University, GIAM
Istanbul – Turkey
BETAM - September,

2. Preliminary remarks on public employment
Overall decline in share
Composition: Education upgraded

3. OVERALL Public formal Private formal Private informal Total 2005
2006
2007
2008
2009
2010
2011
Public formal
Private formal
Private informal
Total
2005
24.80%
44.40%
30.80%
100%
2006
23.40%
46.90%
29.70%
2007
22.00%
50.70%
27.40%
2008
20.90%
55.10%
24.00%
2009
56.00%
23.10%
2010
20.10%
57.30%
22.50%
2011
19.70%
58.10%
22.20%

4. POST-SECONDARY Public formal Private formal Private informal Total
2005
694800
102498
2006
813909
106088
2007
934939
112889
2008
109173
2009
105676
2010
107822
2011
120067

5. POST-SECONDARY Public formal Private formal Private informal 2005
59.00%
35.70%
5.30%
2006
57.30%
37.80%
4.90%
2007
54.90%
40.30%
2008
52.20%
43.50%
4.30%
2009
52.40%
43.60%
4.00%
2010
53.30%
43.00%
3.80%
2011
51.90%
44.40%
3.70%
Public formal
Private formal
Private informal
Total
2005
42.70%
14.40%
3.10%
18.00%
2006
46.60%
15.40%
3.20%
19.00%
2007
49.50%
15.80%
3.50%
20.00%
2008
52.80%
16.70%
3.80%
21.00%
2009
57.00%
17.70%
3.90%
23.00%
2010
60.70%
17.20%
2011
63.10%
18.30%
4.00%
24.00%

6. Questions What is the public-private wage gap?
What are the contributions of the composition and price effects at the mean and across the wage distribution?
How has the public wage premium evolved from 2005 to 2011, and why? …
… alongside the public-private wage gap analysis, we decompose real wage increase within the public and private sector separately in order to compare the respective price effects and see whether the evolution observed in the premia has been the result of a greater price effect in either one of the sectors.
Techniques:
Oaxaca-Blinder decomposition (gap at the mean)
Melly (2005)’s decomposition using quantile regressions.
Compare the quantile decomposition (Melly, 2005) results with the John-Murphy-Pierce (1993) (JMP) decomposition.
total (or raw or unadjusted) wage gap = composition effect (or observed (observable) effects (characteristics)/ control variables) + price effect (or unobserved (unobservable) effect, or adjusted pay gap, or wage premium

7. Literature - Turkey
Budget surveys because the question is not explicitly asked in labor surveys
Tansel (2005) 1994 HICES “Household Income and Consumption Expenditure Survey”
Akhmedjonov and Izgi (2012) 2009 HBS
San and Polat (2012) 1994 HICES and 2008 HBS

8. Literature – Turkey - shortcomings
Methodological (sector, dependent variable etc)
Budget survey + small sample (esp. Public)
Single year
Mean not distribution
Limited controls: especially sector, age and region – improvements in the last release of datasets
Sector: compatible NACE rev 2 (21 to 9 sector agregation – better controlling for public activities)
Age – continuous
Region – NUTS 2 breakdown important to control for regional variations in private wages and public wage compensations

9. Literature - world
We use two recent comparative studies (notwithstanding methodological disparities):
Depalo et al (2013) Austria, Belgium, France, Germany, Greece, Ireland, Italy, Portugal, Slovenia and Spain
Mizala et al (2011) - Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Honduras, Panama, Paraguay, El Salvador and Uruguay – early 1990s to mid 2000s

10. Sample
formal wage-earners (informal wage-earners, self-employed and unpaid family workers whose earnings are unreported are omitted)
“public employee” = wage-earner not working in the private sector
trim 1% at both ends of the distribution
population aged 21 years and above to minimize selection bias at earlier ages
dependent variable: log real hourly wages
= declared monthly wage / (regular hours worked * 4.3)

11. SELECTED SUMMARY STATISTICS
2005
2011
public
private formal
informal
total
mean
Gender (woman)
22.3%
18.6%
20.1%
20.0%
27.8%
20.9%
23.7%
22.9%
Urban
80.5%
88.4%
78.9%
83.7%
79.9%
85.9%
71.5%
81.9%
Tenure
12.5
5.4
5.5
7.4
12.9
4.7
2.9
6.1
Tenure2
2.2
0.6
0.8
1.1
2.5
0.5
0.4
0.9
Age
38.0
32.5
34.9
34.6
38.9
33.7
36.6
35.3
Age2
15.1
11.2
13.2
12.8
15.9
12.0
14.6
13.3
Real hours
43.4
54.8
56.6
52.2
41.3
53.5
53.9
51.1
Number of household members
3.9
4.0
4.2
3.6
4.8
4.1
No education
0.7%
1.4%
9.6%
3.4%
0.3%
2.2%
12.9%
3.8%
Primary and less than primary
24.5%
53.3%
71.8%
50.4%
14.7%
49.3%
66.2%
45.3%
High school
18.3%
16.0%
8.7%
14.6%
11.0%
14.8%
9.1%
Vocational high school
14.5%
15.2%
6.4%
12.6%
11.3%
15.4%
6.9%
Post-secondary
42.0%
14.2%
3.6%
18.8%
62.7%
18.2%
4.8%
25.0%
Agriculture, forestry and fishing
0.9%
0.6%
9.4%
3.0%
0.8%
2.8%
Mining and quarrying and other industry
4.2%
0.5%
1.9%
2.7%
2.1%
Manufacturing
3.7%
44.8%
23.6%
28.0%
1.6%
35.6%
20.5%
25.7%
Construction
1.3%
5.1%
17.5%
7.4%
0.4%
7.9%
16.9%
8.0%
Wholesale and retail trade, transportation and storage, accommodation and food service activities
5.2%
30.7%
32.1%
24.1%
29.6%
29.9%
Information and communication + Financial and insurance activities + Real estate activities
4.3%
1.0%
1.7%
5.3%
2.4%
4.0%
Professional, scientific, technical, administration and support service activities
5.8%
3.9%
11.5%
3.3%
7.6%
Public administration, defence, education, human health and social work activities
75.9%
23.2%
86.6%
4.6%
6.6%
22.1%
Other services
3.5%
9.8%
5.0%
8.8%
Legislators, senior officials and managers
6.0%
4.4%
2.3%
Professionals
29.3%
1.5%
10.8%
35.2%
4.9%
1.2%
10.5%
Technicians and associate professionals
14.4%
9.7%
13.5%
10.0%
9.5%
Clerks
14.3%
11.4%
10.2%
16.6%
12.3%
11.6%
Service workers and shop and market sales workers
12.0%
13.7%
17.7%
11.7%
15.9%
22.7%
16.3%
Skill agricultural and fishery workers + Craft and related workers + Plant and machine operators and assemblers
14.0%
42.5%
44.9%
35.4%
8.6%
36.9%
37.8%
31.2%
Elementary occupations
12.2%
26.1%
6.5%
15.7%
28.5%
16.2%
SELECTED SUMMARY STATISTICS

12. Descriptive Rationalization and specialization Causes:
Overall decline in share
Composition: Education + Sector and Skill – upgraded and specialized
Causes:
Public finance
Quality
Specialization
Generational
Increased supply of post-secondary labor force
Central examination: entry barrier

13. Descriptive KERNEL DENSITIES
increase in real hourly wages, in both sectors for both genders
distributions are more compressed for women than men
private-public gap is a little larger for women
public wages are roughly the same in the public sector for both genders
vs real wages in the private sector for women are lower
the increase in private sector wages from 2005 to 2011 is less for women than men

14. Comparison
Turkey’s size of public sector ( : 24.8 to 19.7%) is
closer to European countries in 2005 (Germany 19.1 and Belgium 37.6%, 26.5% on average)
higher than Latin American countries (Colombia 7.7% and Panama 17.8%, 13% on average).
However, the decreasing trend has been relatively fast, in comparison the public sector in Latin American was 15.8% on average in the first half of the 1990s before decreasing to 13% in the mid-2000s

15. The mean raw gap and premia are positive
2005
2011
MELLY MEN
MELLY WOMEN
JMP MEN
JMP WOMEN
Total
0.648
0.864
0.686
0.878
0.649
0.685
Composition
0.349
0.492
0.5
0.571
0.320
0.472
0.402
0.523
Price
0.299
0.371
0.187
0.307
0.329
0.391
0.283
0.355
Obs.
37904
51165
9220
15024
Contribution of composition at the mean
54%
57%
73%
65%
49%
55%
59%
60%
Mean
The mean raw gap and premia are positive
Increased from 2005 to 2011 for both genders
Corresponding figures are also positive in European (except in Belgium) and Latin American countries, and higher in the latter. Comparatively Turkey is situated closer to Latin American countries having higher premia
The composition effect dominates both years. Observed characteristics explain more than half of the total mean wage gap
The contribution of observed characteristics obtained here lies within a plausible range: in European countries the contribution of the composition effect varies from 85% in Germany to 33% in Spain (overall 56% in average, excluding Belgium where the raw gap is very low and the contribution of the composition effect extremely high).

16. PUBLIC-PRIVATE WAGE DIFFERENTIALS
Melly men 2005
Melly men 2011
Melly women 2005
Melly women 2011
Total
Composition
Price
Price/total
p10
0.588
0.182
0.405
69%
0.836
0.223
0.613
73%
0.675
0.288
0.387
57%
0.390
0.446
53%
p20
0.744
0.192
0.552
74%
0.956
0.262
0.693
0.762
0.357
0.987
0.470
0.517
52%
p30
0.754
0.239
0.515
68%
1.019
0.325
0.875
0.539
0.336
38%
40%
p40
0.783
0.426
54%
1.009
0.500
0.509
50%
0.852
0.629
26%
1.049
0.731
0.318
30%
p50
0.794
0.511
0.283
36%
0.969
0.592
0.377
39%
0.746
0.105
12%
1.050
0.799
0.251
24%
p60
0.575
0.178
0.990
0.730
0.260
0.822
0.829
-0.007
-1%
1.037
0.814
22%
p70
0.720
0.589
0.131
18%
0.900
0.207
23%
0.000 0%
0.924
0.049 5%
p80
0.619
0.580
0.039 6%
0.833
0.140
17%
0.636
0.756
-0.120
-19%
0.811
0.724
0.087
11%
p90
0.457
0.627
-0.170
-37%
0.668
-0.115
-17%
0.462
-0.118
-25%
0.560
-0.069
-12%
Along the distribution line
The price effect dominates the composition effect at the lower end of the distribution.
For men the composition effect exceeds the price effect after the 50th percentile, for women after the 30th percentile
In comparison: “for Spain, Greece, Ireland, Italy and Portugal the explained part of the wage differential exceeds the unexplained part above the 60th percentile of the wage distribution whereas for Germany, France and Belgium this turning point is well below the 40th percentile of the distribution. For Slovenia and Austria this point can be located around the 40th percentile of the wage distribution.” (Depalo et al., 2013)

18. the mean values are very similar with O-B decomposition
2005
2011
MELLY MEN
MELLY WOMEN
JMP MEN
JMP WOMEN
Total
0.648
0.864
0.686
0.878
0.649
0.685
Composition
0.349
0.492
0.5
0.571
0.320
0.472
0.402
0.523
Price
0.299
0.371
0.187
0.307
0.329
0.391
0.283
0.355
10th percentile
0.588
0.836
0.675
0.847
0.182
0.223
0.288
0.390
0.222
0.416
0.405
0.613
0.387
0.446
0.345
0.415
50th percentile
0.794
0.969
0.852
1.050
0.775
0.971
0.848
1.048
0.511
0.592
0.746
0.799
0.389
0.540
0.506
0.640
0.377
0.105
0.251
0.356
0.414
0.328
0.385
90th percentile
0.457
0.668
0.46
0.560
0.487
0.468
0.573
0.627
0.783
0.580
0.629
0.260
0.381
0.303
-0.170
-0.115
-0.12
-0.069
0.258
0.243
Obs.
37904
51165
9220
15024
JMP vs MELLY
the mean values are very similar with O-B decomposition
however values along the distribution do not fit stylized facts: “[Q]uantile regression accounts for heteroscedasticity while others, like the JMP decomposition, assume independent error terms. However, the variance of the residuals expands as a function of education […]This is a composition effect and not an increase in the price of unmeasured skills as concluded traditionally.” (Melly, 2005, p. 579).
Indeed, the JMP decomposition results suggest that the price effect is overestimated (except for the first percentile) and increasingly so along the distribution line, i.e., we observe a stable rather than an increasing gap between the composition and the price effect.

19. Along the distribution
2005
2011
MELLY MEN
MELLY WOMEN
Differences in premia
50-10
-0.122
-0.236
-0.282
-0.195
90-50
-0.453
-0.492
-0.223
-0.320
Along the distribution
The evolution of the level of the premium and the inter-percentile variation confirm a decrease along the distribution line with a penalty at the higher end of the distribution as in other countries.
The differences of premia between the 90th and 10th percentiles are negative for all the countries, the decline is steeper in Latin American countries and Turkey compared to European countries.
The decline of the premium is faster in the second half (90th – 50th percentile); this is verified in all cases except in two European countries, three Latin American countries and in Turkey for women in the year 2005.

20. 2005
2011
MELLY MEN
MELLY WOMEN
Contribution of composition at the mean
54%
57%
73%
65%
Contribution of the price (premium)
10th percentile
69%
53%
50th percentile
36%
39%
12%
24%
90th percentile
-37%
-17%
-25%
-12%
Differences in premia
50-10
-0.122
-0.236
-0.282
-0.195
90-50
-0.453
-0.492
-0.223
-0.320
Differences in contributions of premia
-0.333
-0.344
-0.450
-0.294
-0.729
-0.561
-0.379
-0.363
Besides levels, contributions of price and composition effects to the total gap are important. Depalo et al. (2013) : “A striking regularity in all countries is that the overall wage gap is the result of a combination of the explained part, which increases along the wage distribution, and of decreasing returns. […] the ratio of the premia to the total wage gap decreases in all the countries”
Our results confirm that the contribution of the price effect is decreasing along the distribution line with decreasing returns (faster after the median) for men (2005 and 2011) and women for the year 2011 only (bottom panel of Table 5). As a corollary, the share of composition is increasing along the distribution, with a slower pace after the median.

21. EVOLUTION OF PREMIA 2005 TO 2011 - LEVELS
Levels: increase for all percentiles. More at lower percentiles for men vs second half of the distribution for women --- premia become more like men’s at the higher end.
ISSUE: Increase of premia/ decrease of penalty at the higher of distribution:
“Chile experienced a significant decline in the wage penalty faced by public servants in the highest percentiles of the wage distribution after This could be explained by the implementation of a human resources management reform, aiming to attract and retain highly-skilled workers in the public sector. This is also the case for Uruguay that engaged in a civil service reform in 1997” (Mizala et al, 2011, p. 123).
The explanation suggested for Latin America is public policy, however this may as well be the result of private sector wage policy… within sectors?

22. WITHIN SECTOR WAGE DIFFERENTIALS
WITHIN SECTOR WAGE DIFFERENTIALS
Melly public men
Melly private men
Melly public women
Melly private women
Total
Composition
Price
Price/total public
Price/total private
p10
0.436
0.223
0.213
49%
0.187
0.028
0.159
85%
0.300
0.087
71%
0.139
0.000
100%
p20
0.387
0.095
0.293
76%
0.176
0.324
0.049
0.276
0.099
p30
0.403
0.073
0.330
82%
0.282
0.253
90%
p40
0.346
0.069
0.277
80%
0.120
-0.028
0.148
123%
0.308
0.065
0.243
79%
0.110
p50
0.336
0.034
0.302
0.160
0.290
0.046
84%
0.092
-0.016
0.107
117%
p60
0.335
0.057
0.278
83%
0.307
0.043
0.264
86%
p70
0.289
0.051
0.237
0.109
0.261
0.030
0.232
89%
p80
0.313
0.056
0.257
0.041
0.058
59%
0.270
0.224
0.101
-0.006
-6%
p90
0.255
0.016
0.239
94%
0.010
74%
81%
0.172
0.085

23. EVOLUTION OF PREMIA 2005 TO 2011 – LEVELS - PUBLIC OR PRIVATE?
Men: lower end - public high wage increase vs higher end – private low wage increase
Women: more equal wage increase in the public sector along the distribution vs higher end – private low wage increase

24. EVOLUTION OF PREMIA 2005 TO 2011 – CONTRIBUTION OF PREMIA

25. Concluding remarks Positive premium Increasing composition effect
Decreasing price effect (premium)
2005 to 2011: increased premium

26. Lower wages Lower wage earners’ premia higher:
Public sector protection (premium)
Private sector (also increases although less than public): increased demand vs decreased supply (esp. men) + Institutional wage setting: minimum wage
Note.- For formal sector only – excluded segment in private sector → informal sector (Tansel and Kan, 2012)

27. Higher wages Higher wage earners  increased supply
Public sector smaller + entry barrier  private sector  lower wage increase (demand and supply dynamics)
Also: quality / signalling issue of increased post-educated labor supply
Unemployment
Women: more supply and/or more discrimination?

29. Selection IV  issue of ex-ante variable (prior to selection) ..
PSM  find individuals that match in both sectors according to control variables, who then constitute the non-biased sample used for directly measuring the premium Trade-off between:
having a large number of independent variables which is desirable for controlling observables at best, but..
decreases the number of individuals that fall within a common support region (nb of obs)
The PSM then has two drawbacks:
either the distributions obtained after PSM will contain too few individuals (especially in the public sector), or..
as a number of controls must be left out, the sample will actually continue bearing heterogeneities, or both