Labor Market Transitions in Peru

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1 Labor Market Transtons n Peru Javer Herrera* Davd Rosas Shady** *IRD and INEI, E-mal: jherrera@ne.gob.pe ** IADB, E-mal: davdro@adb.org

2 The Issue U s one of the major ssues n Peru However: - The U rate s only around 10% - The U s characterzed by a weak sensblty to wde macro economc fluctuatons

3 The Issue Possble explanaton: - The net U rate s a statc ndcator of cross secton net U balance and s compatble wth hgh flows n and out of E states - The U would be essentally a frctonal phenomenon - Most of the people leavng E status, voluntary or nvoluntary, go drectly to I

4 Purpose of the paper We want to verfy: If labor moblty s hgh n Peru If permanent U really exsts We want to determne: Who are the most mportant labor transtons Factors determnng labor moblty focusng partcularly on ndvdual characterstcs assocated wth labor market transtons

5 Stylzed facts Durng the 90s: a macro economc stablzaton program and an mportant set of structural reforms Contrasted economc evoluton The performance of the labor market was also affected by the labor lberalzaton reform Labor market flexblty was mproved and the rate of turnover ncreased A fall n the average employment duraton and a large ncrease n labor moblty durng ths perod

6 Fgure 1: U rates and macroeconomc fluctuatons, Peru % 15% 10% 10% 8% 5% U rate (%) 6% 0% GDP g (%) 4% -5% 2% -10% 0% Unemployment Anos rate GDP grow th -15% Source: INEI Note: Unemployment rates for Metropoltan Lma

7 Prevous studes of labor moblty n Peru Labor moblty has been rarely analyzed n Peru - There are 3 mportant studes of labor moblty and all use the quarterly panel of 1996: 1. MTPS (1998) 2. Chacaltana (1999) 3. Daz and Maruyama (2001)

8 Prevous studes of labor moblty n Peru Man results: - The mean duraton of U n Peru s very short - Permanent U seems not to be a very mportant problem - Labor moblty s very mportant n urban Peru - The most mportant labor transtons occur between E and I status, and vce versa - Females and young people are the most affected by transtons

9 Data and varables used The ENAHO surveys and the panel - To analyze labor moblty we need to conduct a dynamc analyss usng Panel Data - We constructed a panel of workng age ndvduals at the natonal level for the perod The panel sample s relatvely large: 6006 ndvduals.

10 Data and varables used The selecton bas ssue - The ndvduals n the panel represents only 38% of ndvduals older than 14 years n We checked the qualty of the panel and we observed lttle dfferences Varables used - 2 knds of explanatory varables were used: ndvdual and household characterstcs - Varables were measured n two ways: the ntal characterstcs n 1997 and the change from 1997 to 1998

11 Labor moblty n Peru Table 2: Flows n the labor market durng the perod (%) Total Urban Rural Immoblty Males Females Males Female s Always employed Always unemployed Always nactve Total mmoblty Moblty Ext employment - to unemployment to nactvty Ext unemployment - to employment to nactvty Ext nactvty - to employment to unemployment Total moblty Source: ENAHO Panel , buld by the authors

12 Labor moblty n Peru We also observed - Labor moblty changed between and 98-99, especally n the urban sector. The economc recesson ncreased transtons from E to I - Labor market n Peru s very complex. For example n the urban sector we observed:

13 Fgure 2 : Entry and ext urban labor market flows Total 1999 not workng 26.4% not workng 20.9% not workng 36.4% workng 5.5% not workng 38.5% not workng 4.4% workng 10.0% workng 5.6% not workng 4.8% not workng 10.8% workng 6.0% workng 63.6% not workng 8.5% workng 61.5% workng 52.9% workng 44.4% Source: ENAHO Panel , buld by the authors

14 The determnants of labor market transtons We consdered the relatve rsks condtonal on the others factors that determne labor market transtons. We estmated the determnng factors of dfferent forms of labor moblty between 98 and 99 usng a multnomal logt model. Values of the dependent varable: - Always Employed (O) - Permanent I or U (I) - Ext out of Employment (S) - Enter nto Employment (E).

15 The Model Ths model predcted the probablty that an ndvdual wth gven characterstcs wll experence one of the four labor market transtons. The multnomal logt s: 1( ) Pr ( y = m x ) = j = O exp( x exp(, I, E, S β x m ) β j ) ( 2) Pr( y = m x ) = exp( x β m ) 1 + exp( x β I, E, S j ) to m O exp( x β m ) ( 3) Pr ( y = m x ) = Pr y = O x ( ) j = I, E, S j = I, E, S exp( 1 exp( wth m= I, E, S and β O = 0. 1 x x β β j j ) ) x β, = exp( ) m

16 Table 5: Urban labor market moblty between 1998 and 1999 by ndvdual characterstcs n 1997 N o m ob lty M ob lty Indvd uals characterstcs O I S E Total Age *** 29.5*** 29.9*** 33.5 Age groups (% ) *** 54.3*** 50.1*** *** 16.8*** *** 10.7*** 13.0*** ** 11.3*** 10.4*** and m ore * Sex (% ) - m ale *** 33.6*** 39.7*** fem ale *** 55.3*** 60.3*** 52.3 H ousehold head (% ) *** 6.8*** 20.9*** Partner (% ) *** Chldren (% ) *** 50.1*** 46.8*** 36.7 O thers relatves * Sze of household ** 5.9** 6.0*** 5.7 M artal Status (% ) - lvng alone *** 59.9*** 57.1*** lvng as a couple *** 40.1*** 42.9*** 52.5 N um ber of chldren w th less *** 0.64*** than 10 years old +++ Educaton (% ) - no educaton ** prm ary educaton ** 20.1** secondary educaton *** unversty and others *** 19.7*** 26.2 Student (% ) *** 41.8*** 32.6*** *** H u m a n c a p ta l o f th e household (rato) Labor m arket stuaton - em ployed *** 18.5*** 52.1*** unem ployed ** 13.8*** 10.4*** nactve *** 67.7*** 37.5*** Sectors of actvty (% ) - prm ary secondary tertary Insttutonal dvson (% ) - publc *** 4.6*** form al nform al * 62.9*** 49.8 S k lls (% ) - sklled ** 15.1*** 18.5*** unsklled *** 84.9* 81.5***

17 Man results In the urban sector: - Sex and age had mportant effects on labor moblty. - For example: the relatve probablty of beng I relatve to beng O ncreased wth age. - Hgher levels of educaton seemed to protect aganst I.

18 Man results - Labor market varables had hgh and sgnfcant effects on labor moblty. - For example: work experence and sklls seemed to protect aganst I. Also, the ndvduals wth hgher probabltes of beng I or E were those who had the worst jobs. - Some varables on change had effects. - For example: havng prevously exted from an economc sector apparently decreased the probablty of beng I but ncreased the probablty of S (relatve to beng O).

19 Man results In the rural sector: - Varables were less sgnfcant but the results and the coeffcents were somewhat dfferent from the varables n the urban sample. - Age affected the probablty of E.

20 Man results - The effects of sex and of beng a student were stronger. - Sklled ndvduals had relatve hgher probabltes of E. - The effects of been prevously nactve and the effect of the level of household human captal were not as strong. - The dwellng qualty ncreased the probablty of beng n E relatve to O.

21 Summary Labor moblty n rural and urban sectors s ndeed relatvely very hgh Permanent unemployment does not really exst. Most of the labor market transtons occur between E and I (and vce versa).

22 Summary Labor market moblty s hgher n the urban sector than n the rural areas and that t does not affect the same people. Some ndvdual characterstcs, labor market characterstcs, household characterstcs, and varables of change seem to be mportant determnants of labor market transtons.

Labor Market Transitions in Peru

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