Web Appendix for Should I Stay or Should I Go? Career

Choices for Young Workers in Latin America

Graciana Rucci, Fernando Saltiel and Sergio Urzua

August 13, 2019

1 Labor Market Institutions

We follow Almeida and Carneiro (2012) to assess the importance of labor market institutions.

In particular, we incorporate the authors’ preferred measure of enforcement – the number of in-

spections per 100 firms in each municipality in 2002 –, into our analysis. There are 3.2 inspections

per 100 firms in the average Brazilian municipality, and we split the sample at the median, into

’more’ and ’less’ regulated municipalities. We estimate the returns to experience and tenure fol-

lowing our outlined empirical strategy, separately for each sample (the results remain robust to

a linear interaction in the number of inspections per municipality). We control for time-varying

municipality characteristics, including population and income per capita, as well as state-level fixed

effects, effectively exploiting within-state differences in enforcement.

Within our framework, we expect that the returns to both experience and tenure should be

higher in more regulated cities. We present the results in Table R1. While the returns to experience

do not vary across the number of inspections, we find evidence of higher returns to tenure in

municipalities with more inspections, fitting in with our cross-country comparison. As such, this

result provides additional suggestive evidence that firms in more regulated labor markets face

additional incentives to provide specific human capital.

2 Informality

Longitudinal data covering workers’ labor market trajectories across formal and informal sec-

tors rarely exist, thus difficulting the analysis of whether informal sector experience may bias our

estimated returns. Fortunately, while such data sources do not exist for Brazil, we access longitu-

dinal information from Chile. We exploit the sample of individuals interviewed across six waves of

the Encuesta de Proteccin Social (EPS, Social Protection Survey), a longitudinal study tracking

workers across formal and informal employment between 1981 and 2015, thus allowing us to con-

struct measures of experience in both sectors. The EPS is a longitudinal survey conformed by five

1

waves, starting in 2002 through 2015, covering a nationally representative sample of over 14,000

individuals. In the first wave, respondents were required to retroactively report their annual labor

market activities from 1980 through 2002, including the employment status, the number of hours

worked, and critical to our analysis whether they made social security contributions and/or had

employment contracts, which indicate whether a worker was formally employed or not. We thus

exploit the representative sample of 5,966 individuals interviewed in 2002, 2004, 2006, 2009 and

2015 to construct measures of both their formal and informal sector experience.1 We find these

measures have a correlation of -0.2204, confirming our posited argument in page 14.2

We have also replicated the analysis by Lopez Garcia (2015), who used the same data source and

found that experience in the informal sector does not result in wage gains in formal jobs. Within

the EPS sample, we estimate the returns to experience in an OLS regression and present our results

in Table R2. Among workers employed in Chile’s formal sector in 2015, cumulative experience in

the informal sector does not have a sizable impact on workers’ wages in the formal sector, providing

further evidence that informal sector experience is not rewarded in the labor market and of the

trade-off in accumulating experience across these two sectors.3

3 Selection into employment after job displacement

In principle, selection into employment after job displacement might be an underlying factor

affecting our results. To assess this, we control for the period in which the worker has found a new

job and hence estimate the effects within workers that found the new job in a given period.

Tables R3 and R4 present the estimated returns to experience and tenure in both countries

controlling for the year of re-entry for displaced workers, defined as the number of years from

displacement to re-entry. We do not find any significant differences with respect to our main

estimates.

4 Robustness to sample definition

We re-estimated the model including all wage observations in the wage and tenure reduced

form regressions. The reduced form results are presented in Table R5, which shows that age has a

strong positive relationship with experience, participation and tenure in both countries, while the

coefficients on the potential experience interaction are negative. To examine whether the differences

in the reduced form results vis-a-vis those presented in the paper affect the returns to experience

and tenure, we present the estimated returns in Tables R6 and R7. The fourth column presents the

results from the control function approach, and we find that the cumulative returns to five years of

labor market experience in both Brazil and Chile are generally in the same range (45% and 56%,

1For a detailed description of the sample, including a discussion about the sampling scheme and longitudinalsampling weights, see http://www.encuestas.uc.cl/eps/base.html).

2This result follows from 46,657 individual-year observations for which we observe active employment as well asan indicator for social security contributions.

3Our stata database and do files are available upon request.

2

respectively) as in Table 3 in the paper. Moreover, the estimated returns to tenure also remain in

a similar range compared to those in the paper, reaching 9% in Chile and 17.1% in Brazil.

As discussed in our main text, we prefer estimating the reduced form using the displaced sample,

as, for instance, firms may price in workers’ age, due to their expectation that workers may remain

in the firm and thus ensure that wages reflect lifecycle considerations. This issue is less likely to

hold in the displaced sample, and as such, we prefer estimating the reduced form for the displaced

sample. Nonetheless, as shown in Tables R5-R7, the empirical analysis would be largely unchanged

using the full-sample reduced form model, so we are open to changing our approach.

5 Mass layoffs vs. plant closures

We present a separate analysis for the returns to experience and tenure after mass layoffs and

plant closures for robustness. This might help to rule out, for example, that individuals that are

laid off in a mass layoff may still be selected.

We estimated both the OLS and control function regressions for the displaced worker sample

separately for workers displaced in mass layoffs and in firm closure events. We present the estimated

returns to experience and tenure for these samples in Tables R8 and R9, respectively. These results

show there are no discernible differences in the estimated returns to experience and tenure across

layoff types. While the estimated returns to experience and tenure are slightly larger for Chilean

workers displaced in mass layoff events, these differences are not statistically significant through the

fifth year, thus remarking that the estimates presented in the paper remain robust to heterogeneity

across layoff type.

6 References

Almeida, Rita, and Carneiro, Pedro. (2012). Enforcement of labor regulation and informality.

American Economic Journal: Applied Economics, 4(3), 64-89.

Lopez Garcia, Italo. 2015. Human Capital and Labor Informality in Chile: A Life-Cycle

Approach (February 5, 2015). RAND Working Paper Series WR- 1087. Available at SSRN:

https://ssrn.com/abstract=2798909 or http://dx.doi.org/10.2139/ssrn.2798909

3

Figure R1. Total Number of Jobs Held Through 2012 by Labor Market Experience

Notes: Figure 2 shows the total number of jobs held through 2012 for all workers in our sample by their cumulativelabor market experience in Brazil and Chile.

Sources: RAIS and Chilean Unemployment Insurance Database

4

Table R1. Estimated Returns to Experience and Tenure Across Number of Inspections byMunicipality

Less Regulated More Regulated Less Regulated More Regulated

Experience Tenure

1 Year 0.070 0.083 0.030 0.040(0.020)*** (0.020)*** (0.006)*** (0.005)***

2 Years 0.148 0.169 0.061 0.081(0.039)*** (0.040)*** (0.012)*** (0.011)***

3 Years 0.234 0.257 0.093 0.124(0.057)*** (0.058)*** (0.018)*** (0.018)***

4 Years 0.328 0.348 0.125 0.168(0.076)*** (0.077)*** (0.025)*** (0.024)***

5 Years 0.430 0.441 0.159 0.214(0.099)*** (0.099)*** (0.033)*** (0.032)***

Observations 6,799 21,117 14,393 43,116

Note: Standard Errors in Parenthesis. *** p < 0.01, ** p < 0.05, * p < 0.1. The heterogeneous returns areestimated separately for municipalities in each group, following the control function approach described in Section3. We split the sample into ’Less’ and ’More Regulated’ at the median of inspections per 100 firms by municipalityfrom Almeida and Carneiro’s (2012) data. We control for time-varying municipality characteristics, includingpopulation and income per capita, as well as state-level fixed effects, effectively exploiting within-state differences inenforcement.

5

Table R2. Estimated Returns to Formal and Informal Sector Experience in Chile

Formal Sector Workers

Formal Experience 0.0042(0.0021)**

Informal Experience -0.0084(0.0056)

Age 0.0355(0.098)***

Age2 -0.0003(0.0001)

Female -0.2509(0.0359)***

HS Graduate 0.3093(0.0552)***

College 0.7825(0.0596)***

R2 0.2223Observations 1,046

Note: Standard Errors in Parenthesis. *** p < 0.01, ** p < 0.05, * p < 0.1. We estimate the returns to formal andinformal sector experience for workers employed in the formal sector in 2015 using data from Chile’s Encuesta deProteccin Social. We define formal sector experience as all the months in employment with a formal contract, whileinformal experience is acquired when employed without a contract. Both experience measures are defined in years.We restrict the analysis to the 2015 sample as a sizable number of wage observations are missing for earlier years.

6

Table R3.Returns to Experience Controlling for Years to Reentry

Panel A. Brazil

OLS All OLS NJ OLS PD CF PD

1 Year 0.052 0.041 0.075 0.107(0.001)*** (0.002)*** (0.011)*** (0.023)***

2 Years 0.100 0.079 0.140 0.198(0.002)*** (0.003)*** (0.016)*** (0.043)***

3 Years 0.146 0.117 0.201 0.281(0.002)*** (0.003)*** (0.017)*** (0.062)***

4 Years 0.192 0.157 0.261 0.365(0.002)*** (0.003)*** (0.017)*** (0.082)***

5 Years 0.239 0.203 0.327 0.457(0.002)*** (0.003)*** (0.017)*** (0.105)***

Observations 1,526,581 478,936 27,975 27,975Note: Standard Errors in Parenthesis;

*** p < 0.01, ** p < 0.05, * p < 0.1

Panel B. Chile

OLS All OLS NJ OLS PD CF PD

1 Year 0.123 0.136 0.169 0.156(0.001)*** (0.002)*** (0.011)*** (0.025)***

2 Years 0.214 0.239 0.297 0.288(0.002)*** (0.003)*** (0.016)*** (0.041)***

3 Years 0.281 0.319 0.395 0.393(0.002)*** (0.004)*** (0.017)*** (0.056)***

4 Years 0.330 0.382 0.474 0.472(0.002)*** (0.004)*** (0.017)*** (0.075)***

5 Years 0.368 0.438 0.544 0.530(0.003)*** (0.004)*** (0.019)*** (0.103)***

Observations 1,707,803 714,565 43,355 43,145

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to experience represent the cumulative returns a worker experiences after reaching t years ofparticipation in the formal sector. We estimate the results using a polynomial of degree three in experience.OLS All : Estimated returns follow from the full sample of workers in the sample. OLS NJ : Estimated returns arederived from first observation at new jobs, excluding initial firm. OLS PD : Returns to experience are estimatedusing the first observation in the first post-displacement job controlling for years to reentry. Control Function:Returns to experience are estimated using the first observation in the first post-displacement job controlling foryears to reentry.

7

Table R4. Returns to Tenure Controlling for Years to Reentry

Panel A. Brazil

OLS All OLS NJ OLS PD CF PD

1 Year 0.016 0.017 0.010 0.043(0.000)*** (0.000)*** (0.001)*** (0.007)***

2 Years 0.033 0.034 0.021 0.075(0.001)*** (0.001)*** (0.003)*** (0.007)***

3 Years 0.050 0.052 0.031 0.101(0.001)*** (0.001)*** (0.004)*** (0.007)***

4 Years 0.067 0.070 0.042 0.124(0.001)*** (0.001)*** (0.006)*** (0.007)***

5 Years 0.084 0.088 0.053 0.157(0.002)*** (0.002)*** (0.008)*** (0.007)***

Observations 1,526,581 805,051 57,612 57,612Note: Standard Errors in Parenthesis;

*** p < 0.01, ** p < 0.05, * p < 0.1

Panel B. Chile

OLS All OLS NJ OLS PD CF PD

1 Year 0.042 0.009 0.006 0.015(0.000)*** (0.000)*** (0.002)*** (0.006)*

2 Years 0.085 0.018 0.012 0.027(0.001)*** (0.001)*** (0.003)*** (0.006)***

3 Years 0.131 0.027 0.018 0.048(0.001)*** (0.001)*** (0.005)*** (0.006)***

4 Years 0.178 0.036 0.024 0.066(0.002)*** (0.002)*** (0.007)*** (0.006)***

5 Years 0.227 0.045 0.030 0.068(0.003)*** (0.002)*** (0.008)*** (0.006)***

Observations 1,707,803 755,081 79,529 79,175

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to tenure represent the cumulative returns a worker experiences after reaching t years oftenure at the same firm. We estimate the results using a polynomial of degree three in tenure.OLS All : Estimated returns follow from the full sample of workers in the sample. OLS NJ : Returns are calculatedfrom within firm wage growth, as the differences in logs of wages over time. OLS PD : Returns to tenure areestimated using the full spell of the first post-displacement job controlling for years to reentry. Control Function:Returns to tenure are estimated using the full spell of the first post-displacement job controlling for years to reentry.

8

Table R5. Reduced Form Estimation in Brazil and Chile Including All Wage Observations

Brazil Chile

(1) (2) (3) (4) (5) (6)Experience Participation Tenure Experience Participation Tenure

Age 0.951*** 0.420*** 0.259*** 0.381*** 0.405*** 0.132***(0.004) (0.010) (0.004) (0.004) (0.011) (0.003)

c it -0.028*** -0.128*** -0.027*** -0.038*** 0.008* 0.004**(0.002) (0.005) (0.002) (0.001) (0.004) (0.001)

Age=20 0.294*** 0.121 -0.211*** 0.241*** -0.448*** 0.070**(0.025) (0.063) (0.024) (0.027) (0.076) (0.022)

Age=22 -0.291*** 0.001 -0.007 -0.064** 0.392*** 0.023(0.024) (0.060) (0.023) (0.020) (0.056) (0.017)

Age=23 -0.562*** -0.237*** -0.108*** -0.088*** 0.616*** 0.042*(0.025) (0.062) (0.024) (0.020) (0.057) (0.017)

Age=24 -0.859*** -0.551*** -0.339*** -0.121*** 0.851*** 0.058***(0.026) (0.066) (0.025) (0.021) (0.060) (0.018)

Age=25 -1.135*** -0.691*** -0.648*** -0.187*** 0.837*** 0.063***(0.028) (0.070) (0.027) (0.023) (0.064) (0.019)

Age=26 -1.480*** -0.856*** -1.058*** -0.222*** 0.800*** 0.080***(0.031) (0.077) (0.029) (0.025) (0.071) (0.021)

Age=27 -1.582*** -0.422*** -1.358*** -0.181*** 0.651*** 0.119***(0.033) (0.083) (0.032) (0.028) (0.079) (0.023)

Age=28 -0.366*** 0.095 -0.274*** -0.132*** 0.462*** 0.086***(0.035) (0.088) (0.034) (0.031) (0.088) (0.026)

Age=20 × c it 0.012*** 0.011* 0.006** 0.000 -0.009 0.000(0.002) (0.006) (0.002) (0.003) (0.007) (0.002)

Age=22 × c it -0.011*** -0.000 0.003 0.001 0.024*** -0.001(0.002) (0.006) (0.002) (0.002) (0.005) (0.002)

Age=23 × c it -0.020*** 0.011 0.009*** 0.005** 0.022*** -0.001(0.002) (0.006) (0.002) (0.002) (0.005) (0.002)

Age=24 × c it -0.026*** 0.019*** 0.020*** 0.012*** 0.004 -0.003*(0.002) (0.006) (0.002) (0.002) (0.005) (0.001)

Age=25 × c it -0.033*** 0.013* 0.030*** 0.022*** -0.002 -0.005***(0.002) (0.006) (0.002) (0.002) (0.005) (0.001)

Age=26 × c it -0.031*** 0.012* 0.049*** 0.032*** -0.016*** -0.006***(0.002) (0.006) (0.002) (0.002) (0.005) (0.001)

Age=27 × c it -0.047*** -0.026*** 0.057*** 0.038*** -0.021*** -0.010***(0.002) (0.006) (0.002) (0.002) (0.005) (0.001)

Age=28 × c it -0.143*** -0.052*** -0.019*** 0.042*** -0.023*** -0.011***(0.002) (0.005) (0.002) (0.002) (0.005) (0.001)

Age=29 × c it -0.166*** -0.052*** -0.034*** 0.044*** -0.023*** -0.013***(0.002) (0.005) (0.002) (0.002) (0.005) (0.001)

High School Graduate -0.024*** -0.198*** -0.173*** -0.078*** 0.393*** 0.092***(0.005) (0.014) (0.005) (0.004) (0.011) (0.003)

More than High School -0.083*** 0.017 -0.135*** -0.412*** -0.126*** 0.016**(0.009) (0.022) (0.008) (0.006) (0.018) (0.005)

Male Dummy 0.219*** 0.052*** 0.019*** 0.302*** -0.019** -0.066***(0.003) (0.007) (0.003) (0.002) (0.006) (0.002)

Age=29 0.010 0.279** 0.047(0.034) (0.097) (0.028)

Age=30 × c it 0.046*** -0.020*** -0.014***(0.002) (0.006) (0.002)

P-Value 0.000 0.000 0.000 0.000 0.000 0.000Observations 1,530,581 1,530,581 1,530,581 1,707,803 1,707,803 1,707,803R2 0.497 0.021 0.092 0.467 0.071 0.072

Note: Standard Errors in Parenthesis; *** p < 0.01, ** p < 0.05, * p < 0.1

In Table A1, we present the coefficients associated with the excluded age and potential experience instruments, along withtheir interaction terms for the reduced form regressions for experience, participation and tenure. We also include educationdummies (workers with less than high school are the excluded category) and a dummy for gender. We include all workers inour sample for each country.

9

Table R6. Returns to Experience: Reduced Form Includes All Wage Observations

Panel A. Brazil

OLS All OLS NJ OLS PD CF PD

1 Year 0.052 0.041 0.069 0.104(0.001)*** (0.002)*** (0.011)*** (0.023)***

2 Years 0.100 0.079 0.131 0.194(0.002)*** (0.003)*** (0.015)*** (0.045)***

3 Years 0.146 0.117 0.189 0.278(0.002)*** (0.003)*** (0.016)*** (0.068)***

4 Years 0.192 0.157 0.247 0.363(0.002)*** (0.003)*** (0.015)*** (0.094)***

5 Years 0.239 0.203 0.311 0.456(0.002)*** (0.003)*** (0.015)*** (0.124)***

Observations 1,526,581 478,936 27,975 27,975Note: Standard Errors in Parenthesis;

*** p < 0.01, ** p < 0.05, * p < 0.1

Panel B. Chile

OLS All OLS NJ OLS PD CF PD

1 Year 0.123 0.136 0.184 0.158(0.001)*** (0.002)*** (0.011)*** (0.018)***

2 Years 0.214 0.239 0.321 0.286(0.002)*** (0.003)*** (0.016)*** (0.032)***

3 Years 0.281 0.319 0.424 0.393(0.002)*** (0.004)*** (0.017)*** (0.045)***

4 Years 0.330 0.382 0.506 0.483(0.002)*** (0.004)*** (0.017)*** (0.060)***

5 Years 0.368 0.438 0.579 0.563(0.003)*** (0.004)*** (0.019)*** (0.081)***

Observations 1,707,803 714,565 43,355 43,355

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to experience represent the cumulative returns a worker experiences after reaching t years ofparticipation in the formal sector. We estimate the results using a polynomial of degree three in experience.OLS All : Estimated returns follow from the full sample of workers in the sample.OLS NJ : Estimated returns are derived from first observation at new jobs, excluding initial firm.OLS PD : Returns to experience are estimated using the first observation in the first post-displacement job.Control Function: Returns to experience are estimated using the first observation in the first post-displacement job.

10

Table R7. Returns to Tenure: Reduced Form Includes All Wage Observations

Panel A. Brazil

OLS All OLS NJ OLS PD CF PD

1 Year 0.016 0.017 0.010 0.046(0.000)*** (0.000)*** (0.001)*** (0.006)***

2 Years 0.033 0.034 0.019 0.081(0.001)*** (0.001)*** (0.003)*** (0.007)***

3 Years 0.050 0.052 0.029 0.109(0.001)*** (0.001)*** (0.004)*** (0.007)***

4 Years 0.067 0.070 0.039 0.135(0.001)*** (0.001)*** (0.006)*** (0.007)***

5 Years 0.084 0.088 0.049 0.171(0.002)*** (0.002)*** (0.008)*** (0.007)***

Observations 1,526,581 805,051 57,612 57,612Note: Standard Errors in Parenthesis;

*** p < 0.01, ** p < 0.05, * p < 0.1

Panel B. Chile

OLS All OLS NJ OLS PD CF PD

1 Year 0.016 0.017 0.010 0.046(0.000)*** (0.000)*** (0.001)*** (0.006)***

2 Years 0.033 0.034 0.019 0.081(0.001)*** (0.001)*** (0.003)*** (0.007)***

3 Years 0.050 0.052 0.029 0.109(0.001)*** (0.001)*** (0.004)*** (0.007)***

4 Years 0.067 0.070 0.039 0.135(0.001)*** (0.001)*** (0.006)*** (0.007)***

5 Years 0.084 0.088 0.049 0.171(0.002)*** (0.002)*** (0.008)*** (0.007)***

Observations 1,526,581 805,051 57,612 57,612

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to tenure represent the cumulative returns a worker experiences after reaching t years oftenure at the same firm. We estimate the results using a polynomial of degree three in tenure.OLS All : Estimated returns follow from the full sample of workers in the sample.OLS NJ : Returns are calculated from within firm wage growth, as the differences in logs of wages over time.OLS PD : Returns to tenure are estimated using the full spell of the first post-displacement job.Control Function: Returns to tenure are estimated using the full spell of the first post-displacement job.

11

Table R8. Heterogeneous Returns to Experience by Layoff Type

Panel A. Brazil

OLS All OLS NJ OLS PD OLS CFFirm Closure Mass Layoff Firm Closure Mass Layoff

(1) (2) (3) (4) (5) (6)

1 Year 0.052 0.041 0.060 0.052 0.083 0.082(0.001)*** (0.002)*** (0.006)*** (0.006)*** (0.021)*** (0.021)***

2 Years 0.100 0.079 0.122 0.108 0.169 0.167(0.002)*** (0.003)*** (0.010)*** (0.011)*** (0.041)*** (0.041)***

3 Years 0.146 0.117 0.184 0.167 0.257 0.255(0.002)*** (0.003)*** (0.013)*** (0.013)*** (0.060)*** (0.060)***

4 Years 0.192 0.157 0.248 0.229 0.347 0.346(0.002)*** (0.003)*** (0.015)*** (0.015)*** (0.079)*** (0.079)***

5 Years 0.239 0.203 0.313 0.295 0.440 0.440(0.002)*** (0.003)*** (0.016)*** (0.016)*** (0.101)*** (0.102)***

Observations 1,526,581 478,936 11,520 16,516 11,520 16,516

Panel B. Chile

OLS All OLS NJ OLS PD OLS CFFirm Closure Mass Layoff Firm Closure Mass Layoff

(1) (2) (3) (4) (5) (6)

1 Year 0.123 0.136 0.154 0.153 0.127 0.156(0.001)*** (0.002)*** (0.007)*** (0.007)*** (0.020)*** (0.020)***

2 Years 0.214 0.239 0.286 0.288 0.241 0.294(0.002)*** (0.003)*** (0.013)*** (0.012)*** (0.037)*** (0.038)***

3 Years 0.281 0.319 0.395 0.405 0.343 0.413(0.002)*** (0.004)*** (0.016)*** (0.015)*** (0.053)*** (0.055)***

4 Years 0.330 0.382 0.482 0.504 0.433 0.515(0.002)*** (0.004)*** (0.018)*** (0.017)*** (0.072)*** (0.075)***

5 Years 0.368 0.438 0.546 0.584 0.510 0.598(0.003)*** (0.004)*** (0.022)*** (0.021)*** (0.099)*** (0.104)***

Observations 1,707,803 714,565 12,722 30,633 12,722 30,633

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to tenure represent the cumulative returns a worker experiences after reaching t years ofparticipation at the same firm. We estimate the results using a polynomial of degree three in tenure. We split thesample for displaced workers in mass layoffs and firm closures in both the displaced sample OLS and controlfunction estimates.OLS All : Estimated returns follow from the full sample of workers in the sample.OLS NJ : Estimated returns are derived from first observation at new jobs, excluding initial firm.OLS PD : Returns to experience are estimated using the first observation in the first post-displacement job.Control Function: Returns to experience are estimated using the first observation in the first post-displacement job.

12

Table R9. Heterogeneous Returns to Tenure by Layoff Type

Panel A. Brazil

OLS All OLS NJ OLS PD OLS CFFirm Closure Mass Layoff Firm Closure Mass Layoff

(1) (2) (3) (4) (5) (6)

1 Year 0.016 0.017 0.012 0.008 0.055 0.044(0.000)*** (0.000)*** (0.002)*** (0.002)*** (0.008)*** (0.013)***

2 Years 0.033 0.034 0.024 0.016 0.079 0.068(0.001)*** (0.001)*** (0.004)*** (0.004)*** (0.012)*** (0.017)***

3 Years 0.050 0.052 0.036 0.024 0.097 0.086(0.001)*** (0.001)*** (0.006)*** (0.005)*** (0.018)*** (0.023)***

4 Years 0.067 0.070 0.049 0.032 0.099 0.066(0.001)*** (0.001)*** (0.008)*** (0.007)*** (0.025)*** (0.030)***

5 Years 0.084 0.088 0.061 0.040 0.131 0.120(0.002)*** (0.002)*** (0.011)*** (0.009)*** (0.037)*** (0.042)***

Observations 1,526,581 805,051 24,316 33,426 24,316 33,426

Panel B. Chile

OLS All OLS NJ OLS PD OLS CFFirm Closure Mass Layoff Firm Closure Mass Layoff

(1) (2) (3) (4) (5) (6)

1 Year 0.042 0.009 0.012 0.017 0.024 0.067(0.000)*** (0.000)*** (0.002)*** (0.002)*** (0.009)*** (0.014)***

2 Years 0.085 0.018 0.025 0.035 0.023 0.065(0.001)*** (0.001)*** (0.005)*** (0.004)*** (0.013)*** (0.018)***

3 Years 0.131 0.027 0.037 0.052 0.065 0.108(0.001)*** (0.001)*** (0.008)*** (0.006)*** (0.020)*** (0.025)***

4 Years 0.178 0.036 0.050 0.070 0.079 0.117(0.002)*** (0.002)*** (0.010)*** (0.009)*** (0.030)*** (0.035)***

5 Years 0.227 0.045 0.062 0.089 0.075 0.119(0.003)*** (0.002)*** (0.013)*** (0.011)*** (0.048)*** (0.053)***

Observations 1,703,755 755,081 24,512 55,017 24,512 55,017

Note: Standard Errors in Parenthesis;*** p < 0.01, ** p < 0.05, * p < 0.1

The estimated returns to tenure represent the cumulative returns a worker experiences after reaching t years ofparticipation at the same firm. We estimate the results using a polynomial of degree three in tenure. We split thesample for displaced workers in mass layoffs and firm closures in both the displaced sample OLS and controlfunction estimates.OLS All : Estimated returns follow from the full sample of workers in the sample.OLS NJ : Estimated returns are derived from first observation at new jobs, excluding initial firm.OLS PD : Returns to experience are estimated using the first observation in the first post-displacement job.Control Function: Returns to experience are estimated using the first observation in the first post-displacement job.

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