who are mexico's new formal workers? a decomposition …home.uchicago.edu/~bsamaniego/files/who...

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Who Are Mexico’s New Formal Workers? ADecomposition Using Labor Market Flows

Brenda Samaniego de la Parra

Federal Reserve Bank of St. LouisPost-doctoral Fellow

August 2, 2017

Brenda Samaniego de la Parra Who Are Mexico’s New Formal Workers? August 2, 2017 1 / 20

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Disclaimer

This paper uses confidential data from Mexico’s National System ofStatistical and Geographical Information (Sistema Nacional de InformacionEstadistica y Geografia), accessed through the National Institute ofStatistics and Geography (INEGI) Microdata Laboratory, and from theMinistry of Labor and Social Welfare (STPS) Directory of Firms(Directorio Nacional de Empresas).

All results reported herein, however, are my own and are not part ofINEGI’s or STPS’s official statistics. The views expressed are those of theindividual authors and do not necessarily reflect official positions of theFederal Reserve Bank of St. Louis, the Federal Reserve System, or theBoard of Governors.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Motivation

Since 2013, formal employment growth has outpaced GDP growth inMexico.

-3% -2% -1% 0% 1% 2% 3% 4% 5% 6%

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

AnnualGrowth

RealGDP Wage-EarnersRegisteredwithIMSS

Corr(∆GDPq,∆Formal Lq)

1999-2012 (4 year MA) 2013-2017

0.876 0.608


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Goal of this paper

1 What caused the increase in formal employment?

New firms enter the formal marketExisting firms enter the formal marketExisting formal firms creating new formal jobsExisting formal firms “converting” informal jobs into formal ones

2 Are these newly formalized workers different from the ones in previousperiods?

Compositional changes vs. changes in returns to characteristics

3 Should we care where the jobs are coming from / who is gettingthem?

Evolution and cyclicality of the unemployment rateWage dynamicsFuture GDP / Employment trendsWelfare


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Methodology

1 What caused the increase in formal employment?

6 possible labor market statesDecompose the formal employment variation into changes in inflow andoutflow rates.

2 Are these newly formalized workers different from the ones in previousperiods?

Study the underlying factors of transitions into formal employmentusing a Oaxaca-Blinder decomposition.Disaggregate the change in transition probabilities into compositioneffects and changes in firms’ and workers’ decisions.

3 Should we care?

Suggestive evidence of the effect of informal labor markets onunemployment volatility.Decompose the cyclical variation in the unemployment rate intochanges in inflow and outflow rates.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Results Preview

Almost half of the increase in formal employment is due towithin-firm informal to formal job transitions.

Starting in 2014, the entry of previously informal self-employedworkers also had a significant effect.

In terms of informal-to-formal transition probabilities, returns toeducation decreased and returns to tenure increased after 2012.

Changes in the composition of non-employed workers dampened theincrease in formal employment.

The change in the pool of newly formalized workers can haveimportant effects on the cyclicality of the unemployment rate.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Overview

1 DataDefining Informality

2 What caused the increase in observed formal employment?Inflows and Outflows of Formal EmploymentFormal employment growth decomposition using labor market flows(Elsby et. al., 2009)

3 Who are the newly formalized workers?Compositional changes

4 ImplicationsUnemployment CyclicalityWage DynamicsWelfare

5 Next Steps and Conclusions


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Defining Informality

By position in firm

Self-employed or employerEmployee

By firm’s type (gov. registration /tax payments)

By job type (access to socialsecurity benefits)

1 Informalself-employed/firm

2 Formalself-employed/firm

3 Informal employee atinformal firm

4 Formal employee atformal firm

5 Informal employee atformal firm


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Defining Informality

By position in firm

Self-employed or employer

Employee

By firm’s type (gov. registration /tax payments)

By job type (access to socialsecurity benefits)

1 Informalself-employed/firm

2 Formalself-employed/firm

3 Informal employee atinformal firm

4 Formal employee atformal firm

5 Informal employee atformal firm


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Data

National Employment and Occupation Survey (ENOE) 2005-2016

Quarterly rotating panel of 120,260 households (avg. 420,000individuals)

Self-reported

Focus on 15 yrs olds+, “remunerated, subordinate” employees atformal firms (112,455 workers per quarter)

Complementary data: STPS’s Directory of Firms (DNE) 106,789establishments


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Inflows and Outflows from Formal Employment

Predicted Quarterly Transition Probabilitiesto and from Formal Employment

(2005-2016)

Predicted Job Finding

Probability

Predicted Separation

Probability

2005-2012 2013-2016 2005-2012 2013-2016

Informal Employee at Formal Firm

{Same Firm

Diff. Firm

13.2%

3.4%

19.6%

3.0%

0.3%

5.7%

0.5%

5.8%

Informal Employee at Informal Firm 3.8%

4.0% 1.7% 1.6%

Informal Self-Employed and Employers 3.9%

4.6% 1.9% 1.9%

Formal Self-Employed and Employers 7.0%

6.8% 1.0% 0.9%

Unemployed 4.0%

2.7% 1.5% 2.2%

OLF 2.4%

2.4% 5.7% 5.8%


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.



(2005-2016)


Probability


Probability

2005-2012 2013-2016 2005-2012 2013-2016


{Same Firm

Diff. Firm

13.2%

3.4%

19.6%

3.0%

0.3%

5.7%

0.5%

5.8%

Informal Employee at Informal Firm 3.8% 4.0%

1.7% 1.6%

Informal Self-Employed and Employers 3.9% 4.6%

1.9% 1.9%

Formal Self-Employed and Employers 7.0% 6.8%

1.0% 0.9%

Unemployed 4.0% 2.7%

1.5% 2.2%

OLF 2.4% 2.4%

5.7% 5.8%


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.



(2005-2016)


Probability


Probability

2005-2012 2013-2016 2005-2012 2013-2016


{Same Firm

Diff. Firm

13.2%

3.4%

19.6%

3.0%

0.3%

5.7%

0.5%

5.8%

Informal Employee at Informal Firm

3.8% 4.0%

1.7% 1.6%

Informal Self-Employed and Employers

3.9% 4.6%

1.9% 1.9%

Formal Self-Employed and Employers

7.0% 6.8%

1.0% 0.9%

Unemployed

4.0% 2.7%

1.5% 2.2%

OLF

2.4% 2.4%

5.7% 5.8%


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.



(2005-2016)


Probability


Probability

2005-2012 2013-2016 2005-2012 2013-2016


{Same Firm

Diff. Firm

13.2%

3.4%

19.6%

3.0%

0.3%

5.7%

0.5%

5.8%

Informal Employee at Informal Firm 3.8% 4.0% 1.7% 1.6%

Informal Self-Employed and Employers 3.9% 4.6% 1.9% 1.9%

Formal Self-Employed and Employers 7.0% 6.8% 1.0% 0.9%

Unemployed 4.0% 2.7% 1.5% 2.2%

OLF 2.4% 2.4% 5.7% 5.8%


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Decomposing Changes in Formal Employment(Elsby, Michaels, and Solon (2009))

Decomposing the share of formal employment by inflows:

fet = fe INFatFt + fe INFatINFt + feFSEt + fe INFSEt + feUt + feOLFt

Let λx and δx be the job finding and job separation rates for state x .

Let θx be the share of the labor force in state x .

In the steady state:

λxθx = δx fex

dlog (fex) = dlog (λx)− dlog (δx) + dlog (θx) ∀xLog-differentiating:

dlog (fe) = ωINFatFdlog(fe INFatF

)+ ωINFatINFdlog

(fe INFatINF

)+ ωINFSEdlog

(fe INFSE

)+ ωFSEdlog

(feFSE

)+ ωudlog (feu) + ωOLFdlog

(feOLF

)where ωx is the share of formal employment from previous labor marketstatus x


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.







λxθx = δx fex

dlog (fex) = dlog (λx)− dlog (δx) + dlog (θx) ∀x

Log-differentiating:


)+ ωINFatINFdlog

(fe INFatINF

)+ ωINFSEdlog

(fe INFSE

)+ ωFSEdlog

(feFSE


(feOLF



.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.







λxθx = δx fex

dlog (fex) = dlog (λx)− dlog (δx) + dlog (θx) ∀xLog-differentiating:


)+ ωINFatINFdlog

(fe INFatINF

)+ ωINFSEdlog

(fe INFSE

)+ ωFSEdlog

(feFSE


(feOLF



.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Decomposing Changes in Formal Employment (cont.)


-0.02 -0.01

00.010.020.030.040.05

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

A.Informal Employee Within-Firm Formalization

d(ln(jobfindingrate)) -d(ln(separationrate))

-0.02

-0.01

0

0.01

0.02

0.03

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

B.Informal EmployeeCross-Firm Formalization


-0.01

0

0.01

0.02

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

C.InformalEmployee atInformal Firm


-0.04

-0.02

0

0.02

0.04

0.06

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

D.Informal Self-Employed


-0.02

-0.01

0

0.01

0.02

0.03

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

E.Non-Employed



.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Decomposing Changes in Formal Employment (cont.)


-0.02 -0.01

00.010.020.030.040.05

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

A.Informal Employee Within-Firm Formalization


-0.02

-0.01

0

0.01

0.02

0.03

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

B.Informal EmployeeCross-Firm Formalization


-0.01

0

0.01

0.02

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

C.InformalEmployee atInformal Firm


-0.04

-0.02

0

0.02

0.04

0.06

Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows



-0.02

-0.01

0

0.01

0.02

0.03Jun-13

Sep-13

Dec-13

Mar-14

Jun-14

Sep-14

Dec-14

Mar-15

Jun-15

Sep-15

Dec-15

ChangeinLogFlows

E.Non-Employed



.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Who are the newly formalized workers?

Are there differentials in formalization probabilities across differenttypes of workers?

Have they changed after 2013?

T xi ,t =

{1 if Si ,t−1 = x and Si ,t = FE

0 if Si ,t−1 = x and Si ,t ̸= FE∀x ∈ [NE , INF ]

2 groups: 2005-2012 and 2013-2016

pi ,g = Pr (Ti ,t = 1|Xi ,g ) = βg ,0+K∑

k=1

Xi ,g ,kβg ,k

Control variables Xi ,g : non-employment duration or tenure,education, gender, age, location, industry, occupation, firm size, andtime fixed effects.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Oaxaca-Blinder Decomposition

pi ,g = Pr (Ti ,t = 1|Xi ,g ) = βg ,0+K∑

k=1

Xi ,g ,kβg ,k

p̂i ,1 − p̂i ,0 =K∑

k=1

(X 1,k − X 0,k

)β∗k︸︷︷︸

composition

+(β̂1,0 + β̂0,0

)︸︷︷︸diff. in intercepts

+K∑

k=1

[X 1,k

(β̂1,k − β∗

k

)+ X 0,k

(β∗k − β̂0,k

) ]︸︷︷︸

differences in returns

Sample Means

Estimated Parameters(β̂g

)


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

O-B Decomposition: Inflows from Non-Employment

Inflows from Non-Employment

Contribution (s.e.) % Contribution

Raw Differential -0.0146 (0.0376) 100%

Explained1 (Composition Effect) -0.0492*** (0.0073) 337%

Unexplained1 (Changes in returns) 0.0346 (0.0376) -237%

Explained (Composition Effect)

Search Duration (weeks) -0.002 (0.003) 11.2%

Yrs. of Schooling -0.027*** (0.004) 188.1%

Male -0.006* (0.003) 40.2%

Age Group 25-34 -0.001 (0.001) 9.1%

35-44 -0.002** (0.001) 14.3%

45-54 0.001 (0.001) -8.6%

55-65 0.004* (0.002) -27.7%

Locality Size 15,000-99,999 0.001 (0.001) -4.8%

2,500-14,999 -0.001 (0.001) 3.5%

<2,500 0.000 (0.000) -0.3%

N 6,993,116

1Includes the effects from monthly indicator variables. Disaggregated Unexplained Effect


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

O-B Decomposition: Inflows from Informal EmploymentInflows from Informal Employment


Raw Differential 0.0708 (0.0212) 100%

Explained1 (Composition Effect) -0.0200 (0.0080) -28.2%

Unexplained1 (Changes in returns) 0.0908*** (0.0210) 128.2%

Job Tenure (months) 0.0368*** (0.0122) 51.9%

Yrs. of Schooling -0.1092*** (0.0288) -154.2%

Male 0.0138 (0.0150) 19.6%

Age Group 25-34 -0.0194*** (0.0072) -27.3%

35-44 -0.0162** (0.0078) -23%

45-54 -0.0096 (0.0068) -13.7%

55-65 -0.0022 (0.0052) -3.1%

Locality Size 15,000-99,999 0.0002 (0.0050) 0.3%

2,500-14,999 -0.0138** (0.0056) -19.4%

<2,500 0.0022 (0.0056) 3.2%

Firm Size 2-5 -0.0348** (0.0148) -49.2%

6-10 -0.0114*** (0.0032) -16.2%

11-15 -0.0056*** (0.0016) -8%

16-50 -0.0102*** (0.0024) -14.4%

+51 -0.0082*** (0.0024) -11.7%

N 3,431,230 Disaggregated Composition Effect

1Includes the effects from industry, occupation, and monthly indicator variables. Gov. Actions?


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Implications

Should we care about where these new formal workers are comingfrom / who they are?

Within-firm formalization vs. new job creationWage dynamicsUnemployment volatility


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Unemployment Volatility

λxux = δxe

dlog (ux) = dlog (δx)− dlog (λx) + dlog (e) ∀x

-0.20 -0.15 -0.10 -0.05 0.000.050.100.150.20

Jun-08 Sep-08 Dec-08 Mar-09 Jun-09 Sep-09

ChangeinLogFlows

B.Formal Employees atFormal Firms

d(ln(separationrate)) ⎯d(ln(jobfindingrate))

-0.2 -0.1

0

0.1

0.2

0.3

0.4

0.5


ChangeinLogFlows

A.Informal Employees atFormal Firms


-0.6 -0.5 -0.4 -0.3 -0.2 -0.1

0

0.1


ChangeinLogFlows

C.InformalEmployees atInformal Firms


-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1

0


ChangeinLogFlows



-0.05

0.00

0.05

0.10

0.15

0.20


ChangeinLogFlows

F.NewEntrants(OLF)


-0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00


ChangeinLogFlows

E.Formal Self-Employed



.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Conclusions

Within-firm formalization rates were the main driver behind theincrease in formal employment in 2013.

Afterwards, informal self-employed workers transitioning to formaljobs also had a significant positive effect.

Relative to 2005-2012, tenure mattered more and education less, todetermine who transitioned from an informal to a formal job after2013.

Changes in the education level of the non-employed pool dampenedthe increase in formal employment.

The change in the pool of newly formalized workers can haveimportant effects on the cyclicality of the unemployment rate.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Next Steps

Firm-side composition effects

Compare to other periods of formal employment growth

Model firms’ decisions when government enforcement changes

Analyze the dynamic effects of composition changes in the pool ofworkers

Outflows analysis

Wage and welfare implications


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Summary Statistics by Group Xg

Inflows from Non-Employment Inflows from Informal Employment

2005-2012 2013-2016 2005-2012 2013-2016

Transition Rate 0.064 0.051 0.243 0.312

(0.190) (0.191) (0.407) (0.413)

Tenure/Search Duration 8.888 8.887 88.596 93.242

(months / weeks) (19.721) (20.121) (108.404) (108.392)

Yrs. of Schooling 7.298 7.814 8.143 8.702

(4.142) (4.156) (4.098) (3.999)

Male 0.298 0.311 0.597 0.592

(0.457) (0.463) (0.491) (0.491)

Age 15-24 0.398 0.394 0.245 0.220

(0.490) (0.489) (0.430) (0.414)

25-34 0.179 0.168 0.241 0.233

(0.383) (0.374) (0.428) (0.423)

35-44 0.152 0.152 0.236 0.240

(0.359) (0.359) (0.425) (0.427)

45-54 0.132 0.136 0.173 0.190

(0.339) (0.343) (0.378) (0.392)

55-65 0.139 0.150 0.105 0.118

(0.346) (0.357) (0.306) (0.322)

N 3,496,558 1,189,114 1,715,615 581,812

Back


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Summary Statistics by Group Xg (cont.)

Inflows from Non-Employment Inflows from Informal Employment

2005-2012 2013-2016 2005-2012 2013-2016

Locality Size 100,000+ 0.478 0.477 0.598 0.581

(0.500) (0.499) (0.490) (0.493)

15,000-99,999 0.141 0.142 0.175 0.176

(0.348) (0.349) (0.380) (0.381)

2,500-14,999 0.142 0.143 0.164 0.162

(0.349) (0.350) (0.370) (0.369)

<2,500 0.238 0.238 0.166 0.172

(0.426) (0.426) (0.372) (0.377)

Firm Size 1 employee - - 0.198 0.209

(0.261) (0.406)

2-5 - - 0.509 0.498

- - (0.500) ( 0.500)

6-10 - - 0.079 0.078

- - (0.270) (0.268)

11-15 - - 0.026 0.026

- - (0.159) (0.159)

16-50 - - 0.057 0.055

- - (0.232) (0.227)

51+ - - 0.050 0.048

(0.217) (0.213)

N 3,496,558 1,189,114 1,715,615 581,812

Back


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Determinants of Inflows to Formal Employment fromNon-Employment


2005-2012 2013-2016

β̂0 (s.e.) β̂1 (s.e.)

Search Duration (weeks) -0.0770 (0.0000) -0.0880 (0.0001)

Yrs. of Schooling 0.0498 (0.0001) 0.0516 (0.0002)

Male 0.3128 (0.0011) 0.4661 (0.0017)

Age Group 25-34 0.1878 (0.0012) 0.1810 (0.0021)

35-44 0.1004 (0.0015) 0.2185 (0.0024)

45-54 -0.1241 (0.0018) -0.0594 (0.0029)

55-65 -0.4874 (0.0028) -0.6190 (0.0045)

Locality Size 15,000-99,999 -0.0408 (0.0021) -0.0666 (0.0033)

2,500-14,999 -0.4210 (0.0034) -0.2936 (0.0054)

<2,500 -0.5429 (0.0166) -0.2302 (0.0233)

Constant -2.1939 (0.0026) -2.3678 (0.0042)

N 3,496,558 1,189,114

Includes month fixed effects. Back


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Determinants of Inflows to Formal Employment fromInformal Employment

Inflows from Informal Employment2005-2012 2013-2016

β̂0 (s.e.) β̂1 (s.e.)

Job Tenure (months) -0.0050 (0.0000) -0.0010 (0.0000)

Yrs. of Schooling 0.0519 (0.0001) 0.0672 (0.0001)

Male 0.2572 (0.0005) 0.2295 (0.0010)

Age Group 25-34 0.2429 (0.0006) 0.3433 (0.0012)

35-44 0.2833 (0.0007) 0.3627 (0.0013)

45-54 0.2772 (0.0007) 0.3377 (0.0014)

55-65 0.2104 (0.0009) 0.2333 (0.0018)

Locality Size 15,000-99,999 -0.3037 (0.0006) -0.3053 (0.0011)

2,500-14,999 -0.62 (0.0007) -0.5166 (0.0013)

<2,500 -0.8388 (0.0008) -0.8557 (0.0015)

Firm Size 2-5 0.2894 (0.0006) 0.3750 (0.0012)

6-10 0.9760 (0.0008) 1.1501 (0.0016)

11-15 1.1828 (0.0011) 1.4379 (0.0021)

16-50 1.3946 (0.0009) 1.6171 (0.0016)

+51 1.6286 (0.0009) 1.8356 (0.0018)

Constant -3.4221 (0.002) -3.3920 (0.0072)

N 1,715,615 581,812

Includes industry, occupation, and month fixed effects. Back


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

O-B Decomposition: Inflows from Non-Employment



Raw Differential -0.0146 (0.0376) 100%

Explained1 (Composition Effect) -0.0492*** (0.0073) 337%

Unexplained1 (Changes in returns) 0.0346 (0.0376) -237%

Search Duration (weeks) 0.013 (0.031) -88.9%

Yrs. of Schooling -0.02 (0.072) 135.3%

Male -0.086 (0.039) 588.8%

Age Group 25-34 0.002 (0.017) -12.5%

35-44 -0.02 (0.014) 135.5%

45-54 -0.007 (0.012) 50.9%

55-65 0.008 (0.008) -53.3%

Locality Size 15,000-99,999 0.002 (0.008) -11.8%

2,500-14,999 -0.004 (0.005) 25.5%

<2,500 0.000 (0.000) 3.0%

N 6,993,116

1Includes the effects from monthly indicator variables. Back


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

O-B Decomposition: Inflows from Informal EmploymentInflows from Informal Employment


Raw Differential 0.0708 (0.0212) 100%

Explained1 (Composition Effect) -0.0200 (0.0080) -28.2%

Unexplained1 (Changes in returns) 0.0908*** (0.0210) 128.2%

Explained (Composition Effect)Job Tenure (months) 0.0034*** (0.0006) 4.8%

Yrs. of Schooling -0.0410*** (0.005) -57.9%

Male 0.0006 (0.0008) 0.9%

Age Group 25-34 0.0032**** (0.0008) 4.5%

35-44 -0.0024**** (0.0008) -3.4%

45-54 -0.0072*** (0.0012) -10.1%

55-65 -0.0036*** (0.0006) -5%

Locality Size 15,000-99,999 0.0006 (0.0008) 0.9%

2,500-14,999 -0.0010 (0.0014) -1.4%

<2,500 0.0048*** (0.0018) 6.8%

Firm Size 2-5 0.0036*** (0.001) 5.1%

6-10 0.0028** (0.0014) 4%

11-15 -0.0004 (0.001) -0.6%

16-50 0.0040*** (0.0016) 5.6%

+51 0.0010 (0.0018) 1.3%

N 3,431,230 Back

1Includes the effects from industry, occupation, and monthly indicator variables.


.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Government Enforcement

Inspection Probability and Establishment Distributionby No. of Inspections Received

0%

10%

20%

30%

40%

50%

0%

25%

50%

75%

100%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

ProbabilityofInspection

%ofE

stablishm

entsinDNE

Inspected3orMoreTimes InspectedTwice

InspectedOnce ProbabilityofReceivingatLeastOneInspection- rightaxis

Back


who are mexico's new formal workers? a decomposition …home.uchicago.edu/~bsamaniego/files/who...

Documents