to a 1 percent increase in unemployment. Thus wage elasticity of -.3 implies that an increase of 1 percent (not a percentage point!) in unemployment is associated with a .3 percent decrease in wages. The advantage of elasticity-based measures is that they are unit-free, and thus readily comparable across samples and countries.

While for most of the established market economies the wage elasticity with respect to local unemployment rates is around -0.1 after controlling for worker characteristics, evidence from transition countries is more diverse. Higher than flexibility than in Western countries was found in some former Communist countries, while in others no wage curve was discovered at all. OECD (2003a, Tables A3.1A3.2), using year 2000 LFS data, finds statistically significant wage curves in Estonia and Latvia but not in Lithuania; Hazans et al (2002) compare 1999 and 2000 results. The estimated elasticities in Estonia were –0.24 in 1999 and –0.15 in 2000, and in Latvia were –0.11 in 1999 and –0.05 in 2000. Thus, during the 1999 crisis the impact of unemployment on wages was twice as strong as in 2000. Men’s wages were much more sensitive to local unemployment rates than women's.

Results presented in Appendix Tables 24 and 25 confirm a very significant effect of local unemployment on wages across the 33 NUTS 4 regions<sup>14</sup> of Latvia. We find an elasticity of wages with respect to unemployment of -0.19 on average. Again there are substantial differences between the sexes. For men it is -0.24, while for women just -0.13. Private sector wages are more responsive to local labor market conditions than those in the public sector, with respective elasticities of -0.26 and -0.15 respectively. Finally, earnings of employees with higher education are significantly less sensitive to local unemployment rates than are earnings of less educated individuals. Estimated elasticities are -0.08 with and -0.20 without higher education. These estimates are derived from models (not shown in the tables) with interaction terms.

Numerical values of the elasticity of wages with respect to unemployment are higher than found in the OECD (2003a) study for several reasons. One potential cause of difference is the methodology applied.<sup>15</sup> Still, some of the difference is attributable to the fact that labor market flexibility has somewhat increased since 2000, the year of the OECD (2003a) data. These issues are discussed in more detail below.

**Regional effects.** Earnings functions may also shed light on the degree of geographical segmentation of labor markets due to barriers to mobility.  The inclusion of regional variables representing the capital city, the capital district, port city, small city, and rural area in the earnings function generally have highly significant effects.  These estimates are shown in Appendix Tables 24 and 25.  In the year 2002, an employee working in a rural area earned 10 percent less than an otherwise similar employee in a small city. Employees working in Riga earned 37 percent more than those working in smaller towns (Appendix Table 24, column (2)). Working in Jurmala, Riga district, Ventspils or Liepaja also increases earnings significantly, although less than working in Riga. Employees in Daugavpils, however, earned 11 percent less than in small cities (other things equal). The wage differential associated with working in Riga is very big by international standards. In Estonia, Lithuania and Poland the wage premium associated with working in the capital city is just above 20 percent (see Table A3.1 in OECD (2003a); and Newell (2001)).

A substantial part of the effect of job location on wages can be explained by differences in unemployment rates. When local unemployment (measured by the 33 NUTS 4 regions) is controlled for, the wage advantage of Riga, Riga district and Ventspils is strongly reduced (compare columns (4) and (2) of

> 14  Registered unemployment rates (at the end of previous year) were used because they are more reliable for these small regions; LFS-based rates, however, give similar results.

> 15  Interval regression used here is more likely to give unbiased estimates than wage imputation used by OECD.

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