Labor Market Transitions in Peru

1. Labor Market Transitions in Peru Javier Herrera* David Rosas Shady** *IRD and INEI, E-mail: jherrera@inei.gob.pe ** IADB, E-mail: davidro@iadb.org

2. The Issue U is one of the major issues in Peru However: The U rate is only around 10% The U is characterized by a weak sensibility to wide macro economic fluctuations

3. The Issue Possible explanation: The net U rate is a static indicator of cross section net U balance and is compatible with high flows in and out of E states The U would be essentially a frictional phenomenon Most of the people leaving E status, voluntary or involuntary, go directly to I

4. Purpose of the paper We want to verify: If labor mobility is high in Peru If permanent U really exists We want to determine: Who are the most important labor transitions Factors determining labor mobility focusing particularly on individual characteristics associated with labor market transitions

5. Stylized facts During the 90s: a macro economic stabilization program and an important set of structural reforms Contrasted economic evolution The performance of the labor market was also affected by the labor liberalization reform Labor market flexibility was improved and the rate of turnover increased A fall in the average employment duration and a large increase in labor mobility during this period

6. Figure 1: U rates and macroeconomic fluctuations, Peru 1980-2000

7. Previous studies of labor mobility in Peru Labor mobility has been rarely analyzed in Peru There are 3 important studies of labor mobility and all use the quarterly panel of 1996: MTPS (1998) Chacaltana (1999) Diaz and Maruyama (2001)

8. Previous studies of labor mobility in Peru Main results: The mean duration of U in Peru is very short Permanent U seems not to be a very important problem Labor mobility is very important in urban Peru The most important labor transitions occur between E and I status, and vice versa Females and young people are the most affected by transitions

9. Data and variables used The ENAHO surveys and the 1997-99 panel To analyze labor mobility we need to conduct a dynamic analysis using Panel Data We constructed a panel of working age individuals at the national level for the period 1997-99. The panel sample is relatively large: 6006 individuals.

10. Data and variables used The selection bias issue The individuals in the panel represents only 38% of individuals older than 14 years in 1997 We checked the quality of the panel and we observed little differences Variables used 2 kinds of explanatory variables were used: individual and household characteristics Variables were measured in two ways: the initial characteristics in 1997 and the change from 1997 to 1998

11. Labor mobility in Peru

12. Labor mobility in Peru We also observed Labor mobility changed between 97-98 and 98-99, especially in the urban sector. The economic recession increased transitions from E to I Labor market in Peru is very complex. For example in the urban sector we observed:

13. Figure 2 : Entry and exit urban labor market flows 1997-1999

14. The determinants of labor market transitions We considered the relative risks conditional on the others factors that determine labor market transitions. We estimated the determining factors of different forms of labor mobility between 98 and 99 using a multinomial logit model. Values of the dependent variable: “Always” Employed (O) “Permanent” I or U (I) Exit out of Employment (S) Enter into Employment (E).

15. The Model This model predicted the probability that an individual with given characteristics will experience one of the four labor market transitions. The multinomial logit is:

16. Table 5: Urban labor market mobility between 1998 and 1999 by individual characteristics in 1997

17. Main results In the urban sector: Sex and age had important effects on labor mobility. For example: the relative probability of being I relative to being O increased with age. Higher levels of education seemed to protect against I.

18. Main results Labor market variables had high and significant effects on labor mobility. For example: work experience and skills seemed to protect against I. Also, the individuals with higher probabilities of being I or E were those who had the “worst” jobs. Some variables on change had effects. For example: having previously exited from an economic sector apparently decreased the probability of being I but increased the probability of S (relative to being O).

19. Main results In the rural sector: Variables were less significant but the results and the coefficients were somewhat different from the variables in the urban sample. Age affected the probability of E.

20. Main results The effects of sex and of being a student were stronger. Skilled individuals had relative higher probabilities of E. The effects of been previously inactive and the effect of the level of household human capital were not as strong. The dwelling quality increased the probability of being in E relative to O.

21. Summary Labor mobility in rural and urban sectors is indeed relatively very high Permanent unemployment does not really exist. Most of the labor market transitions occur between E and I (and vice versa).

22. Summary Labor market mobility is higher in the urban sector than in the rural areas and that it does not affect the same people. Some individual characteristics, labor market characteristics, household characteristics, and variables of change seem to be important determinants of labor market transitions.