**Job creation is low in Latgale, Kurzeme and Zemgale** .  For the unemployed in Kurzeme or Zemgale the probability of exiting to employment within a year is lower by 0.10 than in Riga region, while for Latgale differential is even larger: -0.14 (see Appendix Figure 11). This again highlights the serious mismatch problems in the labor market of Latgale. Vidzeme, on the other hand, is not different from the Riga region as far as job search prospects are concerned, but jobseekers in Vidzeme more often end up out of the labor force.  This is an interesting observation because the stock of discouraged individuals in Vidzeme, although higher than in Riga by most definitions, is lower than the national average (see Appendix Table 11). Appendix Table 20 lists districts with highest and lowest estimated job finding probabilities.

**Rural residents are less likely to stay unemployed.** Rural residents have significantly higher probabilities of both finding a job within a year and of leaving the labor force than their urban counterparts (see Appendix Figure 12). Consequently, they are less likely to become long-term (or recurrently) unemployed. It is important to notice that of those who move to employment more than a half find jobs in industries and services rather than agriculture. Many of these jobs are located in cities. As discussed in Section B, the role of commuting in overcoming spatial mismatch of skills in Latvia should not be underestimated.

# **C.  DETERMINANTS OF EXIT FROM EMPLOYMENT**

This section explores factors responsible for labor market transitions in Latvia. The main factors associated with making transitions from jobs to unemployment, and from jobs to nonparticipation, are assessed for individuals who were employed in 2001. Predicted probabilities of each transition type are presented in Figures 20 - 24, and discussed below.

## **Box 2. 1  Estimating Multivariate Models of Labor Force Transitions**

A multinomial logistic model is estimated using data from the Latvian Labor Force Survey 2002. This model is used to predict the probabilities of three outcomes: employment, unemployment or inactivity in 2002, conditional on being employed one year prior to the interview. A sample of 9,659 is included. The estimated model predicts the probability of each of the three outcomes based on the following explanatory variables: education level (6 categories), gender, ethnicity, age, employment status, occupation, ownership type, sector of economic activity (14 categories), and region of residence. Predicted probabilities are displayed in Figure 19 and Appendix Figures 13 - 17. Estimation results are presented in Appendix Table 19.

**Completed higher education clearly provides protection from unemployment** :  Figure 20 shows that the higher educated are relatively unlikely to experience job loss. Other things equal, the probability of making a transition from employment to unemployment within a year is just 0.02 for prime-age workers with higher education, two times smaller than the probability for their counterparts with basic education. The probability of exit to inactivity for persons with higher education is lower than amongst those who are less educated, especially for young and elderly workers.  On the other hand, those with general or technical secondary and postsecondary vocational education do not have significantly different job loss probabilities from those with basic education.  Those who were in their last year of university studies in 2001 have a high estimated probability of being unemployed in 2002. This post-university unemployment, however, is most likely related to labor force entry rather than job losing.

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