While the _ceteris paribus_ gender age ratio presented in the Table 7 compares men and women with similar human capital and job characteristics, a more adequate approach to measuring overall gender inequality in earnings must account for the fact that females’ average characteristics are indeed different from males’. Such an approach was developed by Oaxaca (1973) and Oaxaca and Ransom (1988) and has been widely used since in the literature. As noted before, Latvian women are, on average, better educated than men; they also have longer tenure and total work experience (see Appendix Table 27). Moreover, returns to education are higher for women (see Appendix Tables 24 and 25, columns (5) and (6)). Taking this into account, Table 8 shows that in 2002 about 60 percent of entire wage gap in Latvia has to be ascribed to discrimination or unobserved characteristics<sup>12</sup> . Another 15 percent is due to occupational segregation within major occupational groups. Productivity differential absorbs just a quarter of the gender wage gap. In Estonia and Lithuania (2000), however, almost the whole pay gap remains unexplained. In this respect, Lithuania and Estonia are similar to the US, UK and Germany (see Kunze (2000)), while the Latvian situation is closer to that of Central European countries. According to Pailhé (2000) and Juraida (2001) productivity differences between men and women explain from onethird to one-half of the total pay gap in Central European countries.

**Table 8.  Gross male – female wage differentials**<sup>**a**</sup> **and productivity differentials**<sup>**b**</sup> **.**

**_<u>Percent</u>_**

||**Gross wage**<br>**differential**|**Productivity differential**<br>**(without occupation controls)**<br>|**Productivity differential**<br>**(with occupation**<br>**controls)**|
|---|---|---|---|
|Estonia, 2000|24|3|5|
|Latvia, 2000|19|7|10|
|Latvia, 2002|31|8|12|
|Lithuania, 2000|14|-1|0|

_Notes:_<sup>a</sup> These differentials, according to conventional methodology of decomposition, are calculated as _exp(d)_ – 1, where _d_ is the difference between mean log net monthly wages of males and females; b Differential in (geometric) mean predicted wages of males and females, using earnings function estimated over pooled sample without gender dummy; known also as explained gender pay gap.<sup>c</sup> Sector of economic activity is controlled for. Males’ and females’ earnings functions used to derive estimates in columns 2 and 3 are partly presented in Appendix Tables 24 and 25. _Sources:_ Calculations based on LFS data.

**<u>Table 9.  Patterns of Industrial Segregation by Ethnicity in the Baltic States, 2000</u>**

||<br>**Estonia**|<br>**Latvia**|<br>**Lithuania**|
|---|---|---|---|
|||**Public administration**||
|Sectors where<br>**minorities**||Education<br>Agriculture||
|are**under-represented**|**Finance**||**Finance**|
|||**Transport, storage and communications**||
|Sectors where||Manufacturing||
|**minorities**<br>**are over-represented**|**Energy**||**Energy**|

Notes: Sectors shown in bold pay significantly higher than average wages, while wages in Education, Agriculture and Manufacturing are lower than average. _Source_ : Hazans, Earle and Eamets (2002).

> 12  Controlling also for hours worked would narrow the unexplained gap by only 1 percentage point.

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