The Structure of Unemployment Compensation, Labor Market ... · Unemployment in Brazil PME Data Econometric Model Results Conclusion The Brazilian Labor Market I High Labor Turnover

Unemployment in Brazil PME Data Econometric Model Results Conclusion

The Structure of UnemploymentCompensation, Labor Market Signaling and

Informal Sector Work

David N. MARGOLIS1

Paris School of Economics

May 4, 2009

David N. MARGOLIS

The Structure of Unemployment Compensation, Labor Market Signaling and Informal Sector Work


Unemployment in Brazil

The Pesquisa Mensal de Emprego (PME) data

The Econometric SpecificationData-Induced Econometric IssuesThe Modeling Strategy

Empirical ResultsFormal versus Informal, UI versus FGTSOther Covariates

ConclusionCaveatsPolicy Implications

David N. MARGOLIS



The Brazilian Labor Market

I High Labor TurnoverI Annual job reallocation averages 33% over the 1991-98

periodI Average yearly job creation rate 17.3%I Average yearly job destruction rate 15.5%.

I Large Informal Sector (50 % of Labor Force)I Self employedI Workers sem carteira de trabalho assinadaI Ineligible for Unemployment Compensation

David N. MARGOLIS



Income Insurance in BrazilI Unemployment Insurance (seguro desemprego)

I Covered 43% of dismissals from formal sector by 1990I Average monthly benefit depends on the average wage of

the last three months prior to unemployment (averagebenefit level was ∼ 1.36 times minimum wage in 2005)

I Duration depends on previous formal sector experience

I FGTS (Fundo de Garantia do Tempo de Serviço)I Employer contributes 8% of the individual’s monthly wage

(roughly 1 month/year worked)I Worker can access his entire fund in case of dismissalI Fund augmented by a fine imposed on the employer in

proportion to the FGTS accumulated in the job (increasedin 2000 to reach a rate of 50%)

I For many workers, the amount received by FGTS is higherthan that received from unemployment insurance.

David N. MARGOLIS



The Pesquisa Mensal de Emprego (PME) data

Basic Structure:

I Monthly household surveyI Covers São Paulo, Rio de Janeiro, Salvador, Belo

Horizonte, Porto Alegre & RecifeI Follows CPS structureI Provides information on many variables including

education, age, gender and labor market status.I The sample used covers the period from March 2002 to

August 2007.

David N. MARGOLIS



Construction of Data SetI PME provides information on the job duration and the

formal or informal status if unemployed ≤ 12 monthsI No direct information about UI benefits, can be simulatedI Maximum observable completed spell is 27 monthsI Exclude all first-time labor market entrants and individuals

with missing values for covariatesI Of the 319,402 initial unemployment spell observations:

I Lost 115,189: unable to determine UI eligibilityI Lost 10,969: stock > 12 months at first survey dateI 50,274 are eliminated as first-time labor market entrantsI 5,068 lost due to missing data

I Analysis data set contains 137,902 observations,corresponding to 72,897 unemployment spells (16,445spells not right censored)

David N. MARGOLIS



Descriptive Statistics for Unemployment Spells

Mean Std. Dev.

Total Observed Duration 5.251 4.756

Demographic Characteristics

Male 0.532 0.499

Age 29.772 10.912

Previous Training 0.228 0.420

Currently in School 0.190 0.392

1-3 Years of School 0.060 0.237



11 or More Years of School 0.394 0.489

David N. MARGOLIS



Descriptive Statistics for Unemployment Spells (cont.)

Mean Std. Dev.

Previous Job Characteristics

Formal Sector 0.425 0.494

Eligible for 3 Months UI 0.081 0.273



Salaried 0.786 0.410

Indefinite Term Contract 0.381 0.486

Metropolitan Labor Market Conditions

Unemployment Rate 11.192 2.708

Average Real Wage 1028.096 170.835

Participation Rate 56.307 3.113

David N. MARGOLIS



Raw Transition Rates by UI Eligibility

Formal InformalEx-Informal Sector 17.42% 82.58%

FGTS Only 52.28% 47.72%3 months UI 51.60% 48.40%4 months UI 52.82% 47.18%5 months UI 53.87% 46.13%

David N. MARGOLIS



Data-Induced Econometric Issues

The model needs to account for:

I Two possible destinations, formal and informal sectoremployment =⇒ competing risk model with correlatedrisks.

I Determinants of unemployment duration change with time=⇒ allow for time-varying covariates.

I Several unemployment spells possible within 16 monthwindow =⇒ accommodate multiple spells.

I Short panelI Only flow sample =⇒ too small sampleI Allow for stock sampling

David N. MARGOLIS



Data-Induced Econometric Issues

The model also needs to account for:

I No info on unemployment insurance or FGTS receipt indata =⇒ assume UI administration applies rules correctly& full takeup

I Only know info about spell of employment preceding theunemployment spell, only if unemployed ≤ 12 months

I There is the potential for selection biasI The duration models in the competing risk framework

provide the selection equationI Stock sampling correction serves two roles

1. Eliminate stock sampling bias2. Eliminate observability/selection bias

David N. MARGOLIS



The Modeling Strategy

Caveats with Respect to Standard Job Search Models1. FGTS often larger than UI

I Disincentive effects primarily associated with FGTSI Marginal contribution of UI to job search disincentives may

be small2. Large informal sector

I If more productive workers are disproportionately found inthe formal sector, then ex-formal sector workers will receivemore job offers than ex-informal sector workers

I Since only ex-formal sector gets unemployment benefits,net effect on duration is ambiguous

3. Sensitivity of job offer arrival rates to job search intensitymay be different when looking for formal versus informalsector jobs.

David N. MARGOLIS




Implications for the Econometric Model1. Estimate separate effects for ex-informal sector workers,

those who can access their FGTS accounts but have no UIeligibility and those who have both FGTS and UI eligibility(broken down by the number of months of UI eligibility).

2. Allow for separate destinations (formal and informal sectorjobs)

3. Allow unobserved heterogeneity to influence the speed ofreturn to employment differently for formal and informalsector jobs.

4. Allow the effects of FGTS and UI receipt to differ by thesort of job found (formal or informal).

5. Allow the unobserved determinants that affect the speed oftransition to each type of job to be correlated.

David N. MARGOLIS




Competing risk, proportional hazards model with correlatedunobserved heterogeneity

Compare specifications for baseline hazard:I Common baseline hazard, shifted by ex-formal sector

indicatorI Common baseline hazard, shifted differently by eligibility

I Only FGTSI 3 months of UI (6-11 months previous work)I 4 months of UI (12-23 months previous work)I 5 months of UI (24+ months previous work)

I Separate baseline hazard for ex-formal sector, shifted bytype and length of benefit eligibility

I Separate baseline hazards for each type and length ofbenefit eligibility.

David N. MARGOLIS



Unemployment Compensation Eligibility

4

5

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Figure 1: Relative Speed of Exit from Unemployment by Worker Type and Sector of Job Found

Ex-Informal: Transition to Formal Sector

Ex-Formal: Transition to Formal Sector

Ex-Informal: Transition to Informal Sector

Ex-Formal: Transition to Informal Sector

0

1

2

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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Months of Unemployment

David N. MARGOLIS




5.5

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Figure 2: Hazard Rates for Transitions to Formal Sector Jobs

Ex-Informal

FGTS Only

3 Months UI

4 Months UI

5 Months UI

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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David N. MARGOLIS




3

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Figure 3: Hazard Rates for Transitions to Informal Sector Jobs

-2

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Ex-Informal

FGTS Only

3 Months UI

4 Months UI

5 Months UI

David N. MARGOLIS



Other Covariates

Demographic Characteristics

I Men return to work faster than women, as do heads ofhouseholds.

I Transition rate to both destinations decreases with ageI More highly educated workers transit slower to informal

sector jobsI Still in school transits slower to both destinationsI Previous professional training transits faster to both

destinationsI Previously salaried employees transit faster to formal

sector jobs but slower to informal sector ones.

David N. MARGOLIS



Other Covariates

Macroeconomic Variables

I High local unemployment rate pushes people towardinformal sector jobs

I Higher average real wages in the metropolitan area reducethe speed of return to employment in either sector.

I High local labor force participation rates slow transitions toboth sectors.

David N. MARGOLIS



Other Covariates

Unobserved Heterogeneity

Table: Joint Distribution of Unobserved Heterogeneity Terms

Formal \ Informal 0 δI

0 0.888 0.089δF 0.005 0.018

cov(δF , δI) = 0.116

David N. MARGOLIS



Caveats

Only FGTS or UI Eligibility, Not Receipt, Available

I Fundamental non-identifiability implicit in theunemployment safety net

I The same situations that make an individual eligible forFGTS or UI can also generate other effects

I No natural experiments or regression discontinuity possible

David N. MARGOLIS



Caveats

Distribution of unobserved heterogeneity is commonto workers coming from the formal and informal sector

I Separate distributions not nonparametrically identifiableI Implies ex-formal sector workers can not experience

disproportionately fast (or slow) transitions to formal (orinformal) sector jobs on the basis of unobservedcharacteristics

I Can say that being eligible for UI slows the exit rate afterconditioning on the marginal distribution of unobservedcharacteristics

David N. MARGOLIS



Caveats

Only have information on the job held immediatelyprior to the unemployment spell in progress

I 120 day filing window =⇒ short undeclared informal jobpossible but invisible

I May underestimate formal job seniority if multiple formalspell in 24 months before enrollment

I Of the 30950 unemployment spells that are preceded byformal sector employment in our data, 11765 werepreceded by jobs of at least 24 months

I Others have potential undermeasurementI May explain non-monotonically increasing (in absolute

value) coefficients on the impact of the length of UI benefitson transitions to informal sector employment

David N. MARGOLIS



Policy Implications

Result: Unemployment safety net affects transitions

Stylized facts:

I Slows transitions to informal sectorI FGTS speeds transitions to formal sector, slows transitions

to informal sectorI UI only slows transitions to informal sector

David N. MARGOLIS



Policy Implications

Possible Implications

I Capital Constraints Slow Expansion of Informal SectorI FGTS provides some startup capitalI Longer wait on UI =⇒ more savings available

I Employer selection is important: dual labor markets

I Individuals prefer formal sector and unemployment safetynet allows some individuals to put off transition to informalwork for subsistence

David N. MARGOLIS



Policy Implications

Thank you

David N. MARGOLIS


Documents

The Structure of Unemployment Compensation, Labor Market ... · Unemployment in Brazil PME Data Econometric Model Results Conclusion The Brazilian Labor Market I High Labor Turnover