Labor Market Transitions in Peru

1
Labor Market Transitions in Peru

• Javier Herrera
• David Rosas Shady
• IRD and INEI, E-mail jherrera_at_inei.gob.pe
• IADB, E-mail davidro_at_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

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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

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Figure 1 U rates and macroeconomic fluctuations,
Peru 1980-2000
Source INEI Note Unemployment rates for
Metropolitan Lima
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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)

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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

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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.

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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

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Labor mobility in Peru
Table 2 Flows in the labor market during the
period 1998-1999 ()
Source ENAHO Panel 1997-99, build by the authors

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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
Source ENAHO Panel 1997-99, build by the authors
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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).

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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

to


,
with m I, E, S and
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Table 5 Urban labor market mobility between 1998
and 1999 by individual characteristics in 1997
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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.

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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).

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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.

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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.

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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.