Outflows from industry lead mostly to non-employment, but there are also significant flows from industry to service jobs. Flows from industry to services are twice as large as flows in the other direction. Given the size and diversity of the service sector, it is not surprising that this sector has the lowest outflow rates. Recorded outflows from the service sector are almost exclusively to non-employment.

**Changes between 1990 and 2000.** Appendix Table 16 presents changes in the labor market status of  the Latvian population between 1990 and 2000. The most mobile labor market sector appears to be agriculture. Indeed, farmers recorded both the highest probability of making transitions to unemployment and making transitions out of the labor force. Moreover, among those employed in farming in 1990, less than 40 percent were employed ten years later, and only 13.8 percent were found in the same activity.

Among those who worked in industry or services in 1990, the proportion still employed in 2000 is generally a little higher than it was for those farming in 1990. The majority of those employed in both years stayed in the same main sector. According to this measure of mobility, the least mobile group is public service employees.

Empirical evidence shows that there was substantial movement between sectors over the first decade of transition in Latvia. Employment declines in agriculture and industry have given rise to considerable unemployment. Job losers from these sectors frequently find it difficult to compete for jobs with younger labor market entrants. On average, persons, who were in the service sector already in 1990, experienced the best outcomes, while those who were employed in agriculture at that time faced the greatest difficulties.

# **B. JOB-FINDING PATTERNS**

**What makes a successful jobseeker?** We now examine factors with the greatest impact on job-finding prospects of the unemployed. For this purpose, multinomial regression analysis is applied to a sample of 1,676 respondents from the LFS 2002 who reported themselves to be unemployed one year prior to the survey. The estimated model predicts the probability of each of three outcomes (employment, unemployment or nonparticipation in 2002) based on the following explanatory variables: education level (6 categories), field of education, gender, ethnicity, age, and region of residence.  Results are presented in Appendix Table 17, Figures 16 to 18, and Appendix Figures 11 and 12.

**Education matters most.** While the importance of education for job search is hardly surprising, the magnitude of the effect is impressive. As was shown in the previous section, the mean probability of finding a job in 2001-2002 was 0.31 amongst those unemployed in 2001.  For jobseekers with higher or post-secondary education this probability was by 0.20 larger than for an unemployed with basic education of the same age, gender, ethnicity and region of residence (see Figure 16).

**Problems with vocational training in Jurmala, Rezekne and Liepaja.** The worst outcomes were experienced by a small  but statistically significant group of respondents who completed technical secondary education followed by one year of studies leading to a certificate of general secondary education (ISCED<sup>7</sup> 4A). This group represented 4.4 percent of those unemployed in the 2001 sample. A more detailed scrutiny shows, however, that it is not general education which must be blamed; rather it is a mismatch between skills provided by technical secondary (ISCED 3C) education in three of the main cities – Jurmala, Liepaja and Rezekne-and market demand. Indeed, a substantial proportion (22%) of those with ISCED 4A in the 2001 unemployed sample live in these cities. None of the ISCED 4A

> 7  International Standard Classification of Education

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