|Clerks|0.140**(0.077)|0.069**(0.034)|0.118*(0.048)|0.106**(0.052)|
|---|---|---|---|---|
|Service,shopand market sales workers|0.079**(0.079)|-0.088**(0.039)|-0.033**(0.049)|0.044**(0.044)|
|Skilled agricultural and fisheryworkers|-0.079**(0.078)|-0.148**(0.033)|0.319*(0.142)|-0.109**(0.129)|
|Craft and related trades workers|-0.263** (0.159)|0.217* (0.097)|0.016** (0.060)|0.114* (0.035)|
|Plant and machine operators and<br>assemblers|-0.105**(0.078)|-0.024**(0.026)|0.161(0.054)|0.143(0.036)|
|Militaryofficers|-0.149**(0.079)|-0.177**(0.036)|.|0.394(0.083)|
|Constant|8.886(0.193)|9.091(0.147)|8.890(0.191)|9.440(0.155)|
|R-squared|0.530|0.362|0.532|0.380|
|Observations|2,152|2,775|2,026|2,572|

Robust standard errors in parentheses; *** p <0.01, ** p<0.05, * p<0.1 Source: Serbia LFS, 2008 and 2009 The estimates from these regressions are used to calculate a threefold and a twofold Oaxaca-Blinder decomposition (Table 9). The wage differentials between men and women decreased from 9.2% in 2008 to 4.6% in 2009. As mentioned earlier, this is likely because male-dominated sectors have been somewhat more affected by the global economic crisis.

The results of a threefold decomposition (based on equation 3 described above) show that women have higher endowments (e.g. higher education levels) than men; however they receive lower returns for these endowments. In fact, if men on average had the same characteristics as women the wage differentials would have been larger. These conclusions are supported by findings from a twofold decomposition (according to equation 4). Women on average have better employment-related characteristics as reflected by the negative sign in “the explained part”), which has an equalizing effect on the overall wage gap. The “unexplained part” (i.e. the factors that cannot be attributed to differences in observed worker characteristics), accounts for the greatest part of the wage differential between men and women and is indicative of discrimination against women in the Serbian labor market.

To understand which variables have the greatest impact on the endowments and coefficients effects, a detailed OaxacaBlinder threefold decomposition was performed and the results are presented in

**Table 9. Oaxaca-Blinder threefold and twofold decomposition of** **<u>monthly wages of male and female employees</u>**

||2008|2009|
|---|---|---|
|Natu|ral Log of Monthly Ear|nings|
|Male|10.062*** (0.011)|10.146*** (0.012)|
|Female|9.970***(0.012)|10.100*** (0.014)|
|Wage Differential|0.092*** (0.016)|0.046* (0.018)|
|**Threefold Decomp**|**osition**||
|Endowments (E)|-0.110*** (0.019)|-0.098***(-0.021)|
|Coefficients (C)|0.144*** (0.015)|0.106***(0.017)|
|Interaction (I)|0.058*** (0.018)|0.038*(0.019)|
|**Twofold Decompos**|**ition**||
|Explained|- 0.064***(0.013)|-0.071***(0.014)|
|Unexplained|0.156***(0.015)|0.117***(0.016)|

Robust standard errors are in parentheses; ***, **, * denote statistical significance at 1%, 5% and 10% respectively Source: Estimates are based on Serbia LFS, 2008 and 2009 surveys

**Table 10. Gender Wage Gap at Quartiles, 2009** **<u>(Log of Monthly Wages)</u>**

||Q1|Q2|Q3|Q4|
|---|---|---|---|---|
||9.533***|10.003***|10.327***|10.800***|
|Male|(0.014)|(0.004)|(0.004)|(0.012)|
||9.523***|9.989***|10.326***|10.786***|
|Female|(0.014)|(0.005)|(0.005)|(0.015)|
|Wage|0.010|0.014**|0.001|0.014|
|Differential|(0.020)|(0.007)|(0.006)|(0.019)|
||**Threef**|**old Decompo**|**sition**||
||-0.009|-0.002|-0.020|0.023|
|Endowments|(0.026)|(0.008)|(0.013)|(0.023)|
||0.009|0.013|0.016*|0.047*|
|Coefficients|(0.024)|(0.008)|(0.009)|(0.023)|
||0.010|0.003|0.005|- 0.056*|
|Interaction|(0.03)|(0.010)|(0.014)|(0.026)|

***, **, * denote statistical significance at 1%, 5% and 10% respectively; robust standard errors in parenthesis Source:  Serbia LFS, 2009

Annex 4. The key finding of this decomposition is that women had higher education levels than men, 16