Untitled-1
UDC: 37.035.3(437) JEL: I20
ORIGINAL SCIENTIFIC RESEARCH PAPER
Slovak Women Wage Structure:
Does Education Matter?
Struktura zarada
obrazovanje
á Viera*, á Ivana,
A B S T R A C T
The aim of the article is to analyze changes in the returns to education for
women between 2005 and 2009 in the Slovak Republic. A Mincer equation is
estimated along the entire wage distribution using quantile and OLS regressions.
Data used for the analysis are individual data from harmonized EU SILC statistical
survey. The results indicate tree points. Firstly, education affects women's earnings
positively. The return to an additional year of schooling is close to 5 %. Secondly,
returns to education for women did not change significantly from 2005 to 2009.
Thirdly, the influence of education on the women´s earnings is more significant than
of work experience.
KEW WORDS: mincer equation, returns to education, Slovak Republic
Introduction
The wage rewards of schooling are a
central concern to both labor economics and econometrics. There are
numbers of empirical studies documenting the rise in returns to education
in transitional economies of Central and Eastern European countries,
*
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 46
especially among men. The literature on early-transition returns to
education for men includes Krueger and Pischke (1995) and Bird,
Schwarze and Wagner (1994) for East Germany; Rutkowski (1996, 1997)
for Poland; Halpern and Körosi (1997) for Hungary; Orazem and
Vodopivec (1997) for Slovenia; Lubyová and Sabirianova (2001) for
Slovakia and Russia; Jones and Ilayperuma (1994) for Bulgaria; Flanagan
(1995) and Chase (1997) for the Czech Republic. However, there is a
dearth of descriptive evidence on returns to education for women. This
paper fills the gap for the Slovak Republic by estimating private wage
returns to education for women using an EU SILC data in 2005-2009.
Two studies have investigated changes in returns to education for
women in Slovak Republic. They dealt with changes in the benefits of
education between the final years of communism system and the early
years of market economies. Chase (1997) find an increase in annual returns
to schooling for women during period of 1984-1993 in the Slovak
Republic. He reports that returns to a year of education increased from 4.4
percent for women in 1984 to 5.4 percent in 1993 in the Slovak Republic.
Filer, Jurajda and Planovsky (1999) have estimated the development
of returns to education for women in Slovakia, using the enterprise survey
data (Information System on the Cost of Labour). There was the finding
that returns to education in Slovakia increased substantially between 1995
and 1997, when they reached a level two times higher than in 1984. The
rate of increase was approximately equal both for men and women.
Table 1 presents the increase in women's earnings for each additional
year of education in the Slovak Republic for 1984, 1993, 1995, 1996 and
1997.
Table 1: Trends in coefficients on education for women over time in
Slovakia
1984 1993 1995 1996 1997
Years of schooling
OLS 0.044 0.054 0.063 0.074 0.081
Number of observations 2 131 1 776 23 849 53 717 49 984
Source: Chase, 1997; Filer, Jurajda and Planovsky, 1999
Slovak Women, JWE (2011, No. 3-4, 45-61) 47
In this paper, we extend the existing evidence by covering the
situation for women after the end of the first transition decade. We provide
an estimation of the basic earnings equations for calculating private rates
of returns to education and returns to labor market experience for women.
The paper is organized as follows. Section 2 is devoted to the
conceptual framework and the methodology used in the paper. Section 3
describes the data set and shows descriptive statistics and changes in wage
structure between 2005 and 2009. Estimation results are presented in
section 4. Section 5 gives concluding remarks.
Methodology
In this study the conceptual framework used is the human capital
model of earnings determination. This framework is developed by Mincer
(1958 and 1974) and Becker (1975). According to this model wage
differences among individuals are the result of the differences in their
schooling, training and work experience. Accordingly, log hourly wages
are postulated to depend on schooling, experience and other exogenous
socio-economic factors. (Tansel, 2008).
Traditionally the rates of return to education are measured on the
base of standard Mincer earnings function of the log-linear form. Mincer
(1958, 1974) was one of the firsts who applied human capital concepts
directly to the personal distribution of earnings and used the standards
earnings function for estimation of rates of return to education and
experience.
We use earnings equation in the form (Heckman, Lochner, and Todd,
2003):
(1)
where is log of wage at schooling level and work experience
; is coefficient on education variable, which is
schooling levels) and is a mean zero residual with .
The estimation of Mincer earnings function enables us to find the
returns to schooling and experience. It is realized using Ordinary Least
Squares Method and Quantile Regression based on the following authors:
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 48
Koenker (2006), Koenker a Bassett (1978), Koenker a Hallock (2001),
Koenker a Hallock (2008) and Yu, Lu, Sander (2003).
Data and Descriptive Statistics
The results of the official statistical Survey on Income and Living
Conditions (EU SILC) provided by the Slovak Statistical Office is used to
estimate the benefits of education in 2005-2009. EU SILC data clearly
provide the basis for detailed analysis of the standard human capital model
developed by Mincer (see section Methodology). The principal variable in
this model is earnings of log form. For EU SILC data, the earnings are
measured on annual basis.
The following figure (Figure 1) and table (Table 2) and figure
present the descriptive statistics8 of the annual wages within the analyzed
period and box-plot of the wage distribution.
Figure 1: Box-plot of the wage (EUR) distribution in the period 2005-2009
2005 2006 2007 2008 2009
0
5000
10000
15000
20000
25000
30000
Source: authors
8 For more descriptive statistics, see Appendix A.
Slovak Women, JWE (2011, No. 3-4, 45-61) 49
Table 2: Mean, median and probability distribution of women wages
(EUR) in the period of 2005-2009
Mean Median
Year 2005 4 953.14 (149 218.4 SKK) 4 461.28 (134 400.5 SKK)
Year 2006 5 443.96 (164 004.7 SKK) 4 902.08 (147 680.0 SKK)
Year 2007 5 605.71 (168 877.5 SKK) 5 121.52 (154 291.0 SKK)
Year 2008 6 054.40 (182 395.0 SKK) 5 642.97 (170 000.0 SKK)
Year 2009 6 660.22 (200 645.8 SKK) 6 306.85 (190 000.0 SKK)
Source: authors
The positive skewness typical for the distribution of income is
visible. Moreover, the values of mean and median of wages, although
constantly rising over the years and thus corresponding to the economic
growth, are not equal. In each case, mean has exceeded median relatively
significantly. This evokes the conclusion, that more than 50 % of the
sample participants earn less than mean. Therefore, some authors
emphasise the importance of the median as more appropriate measure of
the average income.
The other main variables in the standard Mincer model include years
of schooling and labor market experience. The variable years of schooling
accounts for years of schooling adjusted for actual level of education.
Based on the information on the highest level of education attained, we
impute years of schooling9. This allows us to estimate returns to education
in terms of the increase in income per additional year of schooling. The
variable labo
experience.
Estimation Results
In this section, Mincerian log-wage regressions were estimated.
Earnings equation was applied in the conditions of the Slovak Republic
within the period of 2005 2009. As mentioned earlier, we modeled the
variability of the population wages using both the quantile regression (QR)
and the ordinary least squares method (OLS). Concerning the former one,
9 see Appendix A for details
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 50
we examined the returns to education and experience at different quantiles
of the wage distribution. The analysis has been realized for 5th, 10th, 25th,
50th, 75th, 90th and 95th quantiles, thus provides complex view of the
wages of female employees in the Slovak Republic.
Table 3 reports parameter estimations from log-wage regression
equations. The results imply that wages of female employees in the Slovak
Republic increase by about 5 percent with each additional year of
schooling. Table 3 compares the results of the classical (OLS) and the
median (MR) regression10. The differences are not substantial. However,
further analysis will be based on the quantile regression, as the linear
model requires several conditions which are in case of Mincer equation not
fulfilled.11 Firstly, non-normality of residuals is caused also by the right
skewed distribution of an income. Secondly, expected multicollinearity
(cor>0.9) between covariables exp and exp2 is present, as one variable is
expressed as squared value of another one. Thirdly, in 2005, 2006 and
2008 the assumption of homoscedasticity (constant variance of residuals)
of residuals is violated.
Table 3: Estimated Mincerian returns to education, 2005-2009
Intercept Education (educ)
Work
experience
(exp)
Work
experience
squared
(exp2)
Year 2005 OLS 10.8400 *** 0.0523 *** 0.0179 *** -0.00020 **
MR 10.8832 *** 0.0521 *** 0.0193 *** -0.00030 ***
Year 2006
OLS 10.8300 *** 0.0551 *** 0.0186 *** -0.00023 *
MR 10.8982 *** 0.0555 *** 0.0176 *** -0.00023 ***
Year 2007 OLS 10.8800 *** 0.0613 *** 0.0170 *** -0.00024 **
MR 11.0622 *** 0.0567 *** 0.0128 *** -0.00022 **
Year 2008 OLS 11.0100 *** 0.0566 *** 0.0209 *** -0.00035 ***
MR 11.1724 *** 0.0557 *** 0.0110 *** -0.00017 *
Year 2009
OLS 7.7841 *** 0.0533 *** 0.0141 *** -0.00019 **
MR 7.8002 *** 0.0548 *** 0.0123 *** -0.00013 **
Significant at: *** <0.1%, ** 0.1%, * 1%
Source: authors
10 For more detailed results of the quantile regression, see Appendix D.
11 The results of tested OLS model are in Appendix C.
Slovak Women, JWE (2011, No. 3-4, 45-61) 51
Table 4 provides the graphical analysis of the quantile regression
results for the years 2005-2009. We can observe the effects of the length of
schooling period and working period on the income value of an individual
woman.
The first important conclusion is the positive relationship between
each covariable and the independent variable. The longer the period of
education (work experience) is, the higher the wage is. In most cases, the
education influences the level of the salary more significantly than the
work experience does. Moreover, the differences between the effects of
these two covariables rise with the increasing earnings.
Considering the education more detailed, no eminent rising tendency
of the effect of the covariable is visible, except for the year 2005. The
regression coefficients for the lowest quantiles (5th, 10th) reach relatively
high levels and afterwards they are slightly decreasing to be returned back
to bigger values for the last quantiles.
There are several explanations for this development: Higher effect of
the education on the low wages can be due to the young graduated people
with the tertiary education having their first job, who are often willing to
work for the minimum wage for a certain time merely to gain some
experience. Moreover, considerably high unemployment in the Slovak
Republic forces the unemployed tertiary educated women to look for the
irregular temporary jobs, often for the period of several weeks or months.
No constant increasing of the education effect on the wages can also
be connected with the large number of the female students in the study
programmes, such as Pedagogics, Philosophy or Administration. These are
in the Slovak Republic insufficiently paid.
Observing the second variable, the length of the work experience of
a woman, its effect on the wages is gradually decreasing. In certain cases,
the length of the work experience has appeared as an insignificant factor
with even no influence on the wages (95th quantile in 2007, 2008, 90th and
95th quantile in 2009). One possible explanation of the falling trend is
lower wages in the low qualified jobs. In these cases, the work experience
plays more important role than the university education. Analogically, the
best paid professions are highly qualified (IT sphere, Banking, Finance,
...), thus require tertiary education. Obviously, secondary and tertiary
school graduates usually gain less work experience, as they prefer to spend
several years studying to working.
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 52
Table 4: Coefficients on education an experience for woman in the period
2005-2009
Effect of the number of years of education
on wages
Effect of the number of years of work
experience on wages
Y
ea
r
20
05
Y
ea
r
20
06
Y
ea
r
20
07
Y
ea
r
20
08
Slovak Women, JWE (2011, No. 3-4, 45-61) 53
Y
ea
r
20
09
Source: authors
Conclusion
In this paper returns of the education for women estimated in
Slovakia are provided for the years 2005-2009. These estimates are
provided by using both the OLS and the regression methods.
There are three main conclusions. Firstly, the results indicate that
education has significant and positive influence on the women's earnings.
education are higher for lowest quantiles (5th, 10th) and the last quantiles
(95th) than for other quantiles. Possible reasons are young graduates
looking for their first job or insufficiently paid study programmes, which
are popular among women.
Secondly, the results indicate that the returns estimates for women
did not change dramatically during the period 2005-2009.
The third conclusion emphasises more significant effect of education
on women´s earnings than of work experience.
Consequently, the investment into education as the human capital is
convenient investment.
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 54
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A P S T R A K T
Cilj ovog rada je da analizira promene
. nom
menjalo u periodu od 2005. do 2009.
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 56
Appendix A: Summary statistics
Table A1: 2005
Variable Annual wage (SKK)
Years of
education
Years of work
experience
Number of
observations 2 742 2 742 2 742
Mean 149 218.4 13.384019 19.68162
Median 134 400.5 12.140000 12
Standard deviation 116 995.5 2.919788 10.53392
Variance 136 879.5e+5 8.525163 110.96354
Minimum 1 875 8.5 1
Maximum 3 568 502 21.64 24
Table A2: 2006
Variable Annual wage (SKK)
Years of
education
Years of work
experience
Number of
observations 2 599 2 599 2 599
Mean 164 004.7 13.454205 20.10812
Median 147 680 12.140000 21
Standard deviation 476 495.8 2.875321 10.69927
Variance 227 048.2e+6 8.267470 114.47445
Minimum 1 000 8.500000 1
Maximum 24 000 010 21.640000 47
Table A3: 2007
Variable Annual wage (SKK)
Years of
education
Years of work
experience
Number of
observations 2 700 2 700 2 700
Mean 168 877.5 13.464393 20.57481
Median 154 291.0 12.14 22
Standard deviation 82 743.7 2.904774 10.79359
Variance 684 651.9e+04 8.437709 116.50163
Minimum 300 4 1
Maximum 1 369 005 21.64 49
Slovak Women, JWE (2011, No. 3-4, 45-61) 57
Table A4: 2008
Variable Annual wage (SKK)
Years of
education
Years of work
experience
Number of
observations 3 042 3 042 3 042
Mean 182 395.0 13.479014 19.68540
Median 170 000 12.14 21
Standard deviation 83 061.55 2.844124 10.93297
Variance 689 922.0e+04 8.089044 119.52974
Minimum 2 000 8.5 1
Maximum 1 012 000 21.64 49
Table A5: 2009
Variable Annual wage (EUR)
Years of
education
Years of work
experience
Number of
observations 2 965 2 965 2 965
Mean 6 660.221 13.705470 20.33929
Median 6 306.845 12.14 22
Standard deviation 3 512.574 2.922420 10.96219
Variance 123 381.8e+02 8.540538 120.16959
Minimum 6.638784 8.5 1
Maximum 79 200.03 21.64 47
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 58
Appendix B: Imputation of Years of Schooling
Table B: Classification ISCED 97 and Years of Schooling
ISCED 1997
Years of schooling
Code Name
0 ISCED 0 0
1 ISCED 1 4
2 ISCED 2 8.5
3 ISCED 3 12.5
4 ISCED 4 14
5 ISCED 5 18
6 ISCED 6 21
Appendix C: OLS Tests
Table C1: Results of the Jarque-Bera Normality Test for the OLS Method
Year 2005 Year 2006 Year 2007 Year 2008 Year 2009
p value < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16
Table C2: Results of Durbin-Watson Autocorrelation Test
Year 2005 Year 2006 Year 2007 Year 2008 Year 2009
DW statistics 1.9808 1.9423 1.9517 1.9665 1.8853
Table C3: Results of Breusch-Pagan Heteroscedasticity Test
Year 2005 Year 2006 Year 2007 Year 2008 Year 2009
p value 0.0001846 0.000591 0.08392 0.006211 0.2296
Table C4: Correlation between independent variables - Multicollinearity Test
Cor. coeff. Year 2005 Year 2006 Year 2007 Year 2008 Year 2009
educ/exp -0.1397742 -0.1825382 -0.1549748 -0.1861927 -0.1798790
educ/exp2 -0.1429126 -0.1780933 -0.1501855 -0.1887246 -0.1729618
exp/exp2 0.9663851 0.9648797 0.9651804 0.9638727 0.9656287
Slovak Women, JWE (2011, No. 3-4, 45-61) 59
Appendix D: Regression coefficients and p values for OLS and QR
Table D1: OLS and Quantile Regressions (Women 2005)
Quantiles 5th 10th 25th 50th 75th 90th 95th
Intercept 9.95094 10.25555 10.54630 10.88321 11.11603 11.22325 11.32620
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
education
(educ)
0.04395 0.04838 0.05804 0.05217 0.05550 0.06839 0.07182
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
work
experience
(exp)
0.04429 0.03557 0.02364 0.01933 0.00839 0.00597 0.00680
p value 0.00067 0.00017 0.00000 0.00000 0.00000 0.00000 0.00018
Years of
work
experience
squared
(exp2)
-
0.00064 -0.00059 -0.00043 -0.00030 - - -
p value 0.00067 0.00343 0,00000 0.00006 - - -
Table D2: OLS and Quantile Regressions (Women 2006)
Quantiles 5th 10th 25th 50th 75th 90th 95th
Intercept 9.38776 10.01424 10.67198 10.89823 11.21520 11.32808 11.44963
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
education
(educ)
0.06370 0.05039 0.05695 0.05554 0.05450 0.06217 0.06523
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
work
experience
(exp)
0.05974 0.04944 0.01546 0.01760 0.00724 0.01110 0.00536
p value 0.00035 0.00002 0.00049 0.00000 0.00000 0.00047 0.00882
Years of
work
experience
squared
(exp2)
-
0.00080 -0.00077 -0.00024 -0.00023 - -0.00016 -
p value 0.03050 0.00142 0.02138 0.00023 - 0.01421 -
Journal of Women's Entrepreneurship and Education (2011, No. 3-4, 45-61) 60
Table D3: OLS and Quantile Regressions (Women 2007)
Quantiles 5th 10th 25th 50th 75th 90th 95th
Intercept 9.38892 10.13396 10.65692 11.06222 11.26624 11.42448 11.70814
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
education
(educ)
0.07982 0.06561 0.06428 0.05675 0.05852 0.06674 0.06263
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
work
experience
(exp)
0.05719 0.03766 0.01506 0.01287 0.00627 0.00313 -
p value 0.00000 0.00001 0.00003 0.00004 0.00000 0.00248 -
Years of
work
experience
squared
(exp2)
-
0.00081 -0.00061 -0.00020 -0.00022 - - -
p value 0.00072 0.00013 0.02402 0.00222 - - -
Table D4: OLS and Quantile Regressions (Women 2008)
Quantiles 5th 10th 25th 50th 75th 90th 95th
Intercept 9.68350 10.13497 10.70025 11.17242 11.42279 11.61445 11.76372
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
education
(educ)
0.06589 0.06802 0.06290 0.05577 0.05097 0.05761 0.06297
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
work
experience
(exp)
0.05829 0.04213 0.02579 0.01101 0.01304 0.00356 -
p value 0.00011 0.00000 0.00000 0.00000 0.00002 0.00203 -
Years of
work
experience
squared
(exp2)
-
0.00084 -0.00073 -0.00044 -0.00017 -0.00020 - -
p value 0.00562 0.00019 0,00000 0.01370 0.00422 - -
Slovak Women, JWE (2011, No. 3-4, 45-61) 61
Table D5: OLS and Quantile Regressions (Women 2009)
Quantiles 5th 10th 25th 50th 75th 90th 95th
Intercept 6.80588 7.22748 7.53422 7.80024 8.21925 8.53262 8.55386
p value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
education
(educ)
0.05272 0.04988 0.05351 0.05483 0.04691 0.04680 0.05373
p value 0.00007 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Years of
work
experience
(exp)
0.04612 0.02766 0.02415 0.01233 0.00358 - -
p value 0.00024 0.00020 0.00000 0.00000 0.00000 - -
Years of
work
experience
squared
(exp2)
-
0.00080
-
0.00043
-
0.00044
-
0.00013 - - -
p value 0.00270 0.01712 0.00000 0.00600 - - -
Article history: Received: 15 May 2011
Accepted: 5 September 2011
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/TileHeight 256
/Quality 15
>>
/AntiAliasGrayImages false
/CropGrayImages true
/GrayImageMinResolution 150
/GrayImageMinResolutionPolicy /OK
/DownsampleGrayImages true
/GrayImageDownsampleType /Bicubic
/GrayImageResolution 300
/GrayImageDepth -1
/GrayImageMinDownsampleDepth 2
/GrayImageDownsampleThreshold 1.50000
/EncodeGrayImages true
/GrayImageFilter /DCTEncode
/AutoFilterGrayImages true
/GrayImageAutoFilterStrategy /JPEG
/GrayACSImageDict <<
/QFactor 0.15
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/GrayImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000GrayACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 15
>>
/JPEG2000GrayImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 15
>>
/AntiAliasMonoImages false
/CropMonoImages true
/MonoImageMinResolution 1200
/MonoImageMinResolutionPolicy /OK
/DownsampleMonoImages true
/MonoImageDownsampleType /Bicubic
/MonoImageResolution 1200
/MonoImageDepth -1
/MonoImageDownsampleThreshold 1.50000
/EncodeMonoImages true
/MonoImageFilter /CCITTFaxEncode
/MonoImageDict <<
/K -1
>>
/AllowPSXObjects false
/CheckCompliance [
/None
]
/PDFX1aCheck false
/PDFX3Check false
/PDFXCompliantPDFOnly false
/PDFXNoTrimBoxError true
/PDFXTrimBoxToMediaBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXSetBleedBoxToMediaBox true
/PDFXBleedBoxToTrimBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXOutputIntentProfile ()
/PDFXOutputConditionIdentifier ()
/PDFXOutputCondition ()
/PDFXRegistryName ()
/PDFXTrapped /False
/Description <<
/ENU ()
>>
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [2069.185 1927.460]
>> setpagedevice