Wage effects of employer-mediated transfers Santiago Garriga Dario - - PowerPoint PPT Presentation

Wage effects of employer-mediated transfers

Santiago Garriga Dario Tortarolo

Paris School of Economics UC Berkeley

Labor Lunch, UC Berkeley

April 28, 2020

1 / 31

Motivation

◮ Governments often rely on firms as intermediaries in the

tax-benefit system (e.g., employer-based health insurance; payroll/income

tax withholding; family transfers, etc.)

◮ Sensitive information is revealed in the process and rent

• pportunities arise (wage effects)

◮ Yet, little evidence on the economic incidence/wage effects

• f means-tested transfers

◮ We focus on employer-mediated transfers, which are more

widespread than publicly known

1 / 31

Motivation

Employer-mediated family allowances around the globe:

◮ Latin American countries

◮ Brazil (Sal´

ario Fam´ ılia)

◮ Chile (Asignaci´

• n Familiar)

◮ Paraguay (Asignaci´

• n Familiar)

◮ Per´

u (Asignaci´

• n Familiar)

◮ Developed countries

◮ USA (Advanced Earned Income Tax Credit) 1979-2010 ◮ UK (Working Family Tax Credit) 1999-2003 ◮ Italy (Bonus Renzi 80 Euro) ◮ Switzerland (Familienzulagen) 2 / 31

This project

Does the way means-tested transfers are paid matter? Do employers capture part of the transfer by lowering wages when being the remitter?

◮ Exploit a change in the payment system in Argentina

gradually rolled out in 2003-10:

◮ Before: employers (intermediaries) ◮ After: social security administration (direct deposit)

◮ Event study by switching date comparing employees w/ and

wo/ children within firms

◮ Rich population-wide admin data from Arg (2003-2010)

3 / 31

Contribution to the literature

• 1. Literature on incidence:

◮ Classical PE literature: relative supply and demand elasticities ◮ SSC: Saez et al. QJE’12 and Saez et al. AER’19 ◮ Saliency: taxes [Chetty et al. AER’09]; SSC [Bozio et al. ’19] ◮ Remittance and compliance costs: taxes [Slemrod, NTJ’08;

Kopczuk et al., AEJ-EP’16]

◮ In-work subsidies: EITC [Rothstein AER’10; Leigh ’10; Hoynes-Patel

JHR’18] and WFTC [Azmat, QE’18]

− → We focus on transfers; change payment system holding constant ATR & MTR

• 2. Design of welfare programs and social protection:

− → Unintended consequences of decentralizing key duties

4 / 31

Preview of findings

(1) The way transfers are disbursed matters (affects the final incidence):

◮ Monthly wages ↑ ∼ 2% when the govt’ becomes the remitter ◮ Pass-through: employers were capturing ∼ 10/20% of the

transfer by paying lower wages

(2) Mechanism (preliminary): labor demand Likely, transfer understood as wages under the old system

◮ Driven by new hires rather than incumbents; small firms ◮ Exercises that rule out pay equity concerns (bargaining) 5 / 31

Outline

• 1. Setting: FA scheme, reform and roll out
• 2. Data and empirical strategy
• 3. Results and robustness checks
• 4. Mechanisms (labor demand vs supply)

Family allowance program (FA)

◮ Child benefit for wage earners

◮ Monthly payment that varies by:

(i) Number of kids < 18 years old; (ii) Monthly wage (3 brackets)

◮ Individually-based but only one spouse is entitled

◮ Funding: contributory system based on employer SSC

◮ Specific component devoted to FA funding 7.5%

◮ Adjusted annually due to inflation (period 2003/10)

◮ More on setting 6 / 31

Child benefits in Argentina

Notch 1 Notch 2 Notch 3

$ 40 per child $ 30 per child $ 20 per child

5% 10% 15% 20% 25% 30% 35% 40%

Transfer / Monthly wage (%)

500 1,000 1,500 2,000

Gross Monthly Wage (pesos)

1 kid 2 kids 3 kids

Note: schedule in place from 1996 to 2004. Then updated ≈every year.

7 / 31

The reform (1): a change in the payment system

Old system: Sistema de Fondo Compensador (SFC)

◮ Employers paid child benefits together with the monthly wage

◮ Must appear in pay-slip by law

◮ Deduct benefit amount from employer SSC

New system: Sistema Unico de Asignaciones Familiares (SUAF)

◮ Eliminated the intermediary role played by employers ◮ SSA deposits the benefit directly into workers’ bank account

Motivation 8 / 31

The reform (2)

Old system (SFC) Employees Employers Government SSC − Transfer(τ e) Wage + Transfer(τ e) New system (SUAF) Employees Employers Government SSC Wage Transfer(τ g)

9 / 31

The reform (2)

Old system (SFC) Employees Employers Government SSC−Transfer(τ e) Wage+Transfer(τ e) New system (SUAF) Employees Employers Government SSC Wage Transfer(τ g)

9 / 31

Reform’s roll-out

◮ Gradual roll-out: btw Jun-2003 and Jun-2010 (8 years)

◮ Limited capacity to incorporate millions of beneficiaries at once ◮ Important: # beneficiaries and FA spending don’t ↓

◮ Incorporation: switching date set by the SSA rather than firms

SSA Memo (1)

Incorporation schedule/plan

(about 1-6 months) docs presented and revised firms contacted by SSA SSA Memo (2)

Formal Incorporation

Timeline (within 10 days) form PS.2.61 employers notify workers 10 / 31

Micro roll-out (using E-E microdata)

Jun03 Jul10 Firms Workers 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Share under old system

Dec02 Mar04 Jun05 Sep06 Dec07 Mar09 Jun10 Switching date

Note: gradual transition of firms and workers into the new system.

11 / 31

Roll-out and firm size

[<10] employees [10; 49] employees [50+] employees

0.00 0.20 0.40 0.60 0.80 1.00 Share under old system

Jul03 Oct04 Jan06 Apr07 Jul08 Oct09 Jan11 Switching date

Note: large firms switched first into the new system (size defined in 2003: 86,868 small, 23,159 medium, 5,839 large).

12 / 31

Administrative Data

• 1. Employer-employee (SICOSS) (2003-2010)

◮ E-E panel available since 1995 month by month ◮ Main vars: monthly pre-tax wage, monthly transfer ◮ Other: 4d sector, health insurer code, zip codes

• 2. Family relationships database

◮ Links family members (spouse, children); also DOB

• 3. Other: (a) Monthly financial situation of employers

(Apr’03-Nov’04); (b) Union’s CBA dataset

13 / 31

Empirical strategy: event study

Switch Before (SFC) Transfer paid by employers Gap in mean wage btw T & C G ¯

w f ,t = ¯

w T

f ,t − ¯

w C

f ,t

After (SUAF) Transfer paid by govt Gap in mean wage btw T & C G ¯

w f ,t = ¯

w T

f ,t − ¯

w C

f ,t

• 1
• 2
• 3
• 4
• 5

1 2 3 4 Event window

14 / 31

Empirical strategy: event study

◮ Sample: unbalanced panel of firms for which we observe an

event (old system − → new system)

Details

→ paying FA for at least 6 consecutive months before event → present at least −6/ + 6 months around the event → with eligible (T) and non-eligible (C) workers in the window T: employees w/ children ages [0-17] C: employees wo/ children ages [0-17] → collapse data at the firm-month-year level (f,t)

◮ Run a regular event study specification

G ¯

w f ,t = 12

• j=−13

γj · dj

f ,t + µf + µt + ǫf ,t

(1)

15 / 31

Event frequency

Aug'08 Jun'09 1k 2k 3k 4k 5k 6k 7k Number of firms 2003m7 2005m1 2006m7 2008m1 2009m7 2011m1 Switching date

Note: massive incorporation in Aug’08 (Great Recession), Jun’09, Mar-Jul’10.

Macro context Average size 16 / 31

Event frequency

Aug'08 Jun'09 Jun'10 2k 4k 6k 8k 10k 12k 14k 16k Number of firms 2003m7 2005m1 2006m7 2008m1 2009m7 2011m1 Switching date

Note: massive incorporation in Aug’08 (Great Recession), Jun’09, Mar-Jul’10.

Macro context Average size 16 / 31

First stage (∆ transfer paid by employers)

35 70 105 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: on average, treated workers receive ∼ 90 pesos more in transfer, paid by employers, than the control group (simple mean difference).

17 / 31

Reduced form (∆ in mean wage)

G ¯

w f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: mean wage of workers w/ kids increased by ∼ 9 pesos, relative to workers wo/ kids, after firms switched to new system (pre Aug’08).

Levels T vs C 18 / 31

Reduced form (∆ p25 and p75)

G w

f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

p25 p75

• 10
• 5

5 10 15 20 25 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: increase in wage is larger for those workers located at the bottom of the distribution (p25); likely more treated due to the transfer scheme.

19 / 31

Pass-through

All post periods Last 6 periods Last period [0;11] [6;11] [11] (1) (2) (3) Reduced form ∆ monthly wage 7.71*** 8.74*** 9.23*** (in pesos) (1.25) (1.55) (1.87) First stage ∆ transfer (τ e)

• 90.98***
• 92.17***
• 91.44***

(in pesos) (0.35) (0.37) (0.37) 2sls

∆wage ∆transfer(τ e)

• 0.08***
• 0.09***
• 0.10***

(0.01) (0.02) (0.02) Number of firms 35,787 35,787 35,787 Observations 3,061,870 2,847,148 2,670,757 Avg wage at t-1 868 868 868 Note: Standard errors clustered at the firm level in parentheses. G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t,

where Window is an indicator for the event window.

Robustness Checks 20 / 31

Mechanisms (making sense of the findings)

Labor demand story?

◮ Employers exploit confusion of the old regime and capture

part of the transfer → Result driven by new hires rather than incumbents → Result driven by small and incorporated firms → Anecdotal evidence on perceptions of transfers → Systematic violation of union’s CBA? (in progress) Labor supply story?

◮ Confused employees bargain more aggressively after the event

(pay equity/fairness concerns) → Ruled out by immediate effect at t=0 and new hires → Also effect broken by firm exposure is not U-shaped

21 / 31

All workers vs incumbents

G ¯

w f ,t = 5 j=−6 γj · dj f ,t + µf + µt + ǫf ,t

All workers Incumbents

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 5
• 4
• 3
• 2
• 1

1 2 3 4

Months relative to treatment

Note: incumbents: workers present -7/+7 months around the event.

22 / 31

Mechanisms (making sense of the findings)

Labor demand story?

◮ Employers exploit confusion of the old regime and capture

part of the transfer → Result driven by new hires rather than incumbents → Result driven by small and incorporated firms → Anecdotal evidence on perceptions of transfers → Systematic violation of union’s CBA? (in progress) Labor supply story?

◮ Confused employees bargain more aggressively after the event

(pay equity/fairness concerns) → Ruled out by immediate effect at t=0 and new hires → Also effect broken by firm exposure is not U-shaped

Small vs non-small firms

Dw

f ,t = 24 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Small [<10] Large [10+]

• 10
• 5

5 10 15 20 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: firm size is the average number of employees from t-12 to t-1.

23 / 31

Incorporated vs Unincorporated firms

Dw

f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Incorporated Non-incorporated

• 10
• 5

5 10 15 20 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: first two digits of tax id determine if firm is incorporated or not.

24 / 31

Incorporated firms: small vs non-small

Dw

f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Small [<10] Large [10+]

• 10

10 20 30 40 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: small firms have less than ten employees [< 10] while large more [10+].

25 / 31

Heterogeneities

Incorporated Small Large Non Small Large [< 10] [10+] Incorpo Incorpo [< 10] [10+] (1) (2) (3) (4) (5) (6) Reduced form ∆ monthly wage 9.86*** 4.94*** 0.81 11.37*** 19.27*** 5.73*** (in pesos) (1.96) (1.55) (1.74) (1.72) (3.30) (1.81) First stage ∆ transfer

• 97.02***
• 82.62***
• 96.86***
• 87.52**
• 94.32***

81.97*** (in pesos) (0.54) (1.55) (0.65) (0.41) (0.75) (0.40) 2sls

∆wage ∆transfer(τ e)

• 0.10***
• 0.06***
• 0.01
• 0.13***
• 0.20***
• 0.07***

(0.02) (0.02) (0.02) (0.02) (0.04) (0.02) Number of firms 20,253 15,534 13,029 22,758 9,843 12,915 Observations 1,694,509 1,367,361 1,080,767 1,981,103 833,347 1,080,767

Note: Standard errors clustered at firm level in parenthesis. G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t,

where Window is an indicator for the event window.

26 / 31

Mechanisms (making sense of the findings)

Labor demand story?

◮ Employers exploit confusion of the old regime and capture

part of the transfer → Result driven by new hires rather than incumbents → Result driven by small and incorporated firms → Anecdotal evidence on perceptions of transfers → Systematic violation of union’s CBA? (in progress) Labor supply story?

◮ Confused employees bargain more aggressively after the event

(pay equity/fairness concerns) → Ruled out by immediate effect at t=0 and new hires → Also effect broken by firm exposure is not U-shaped

Anecdotal evidence on recipient’s perception (1)

• 1. Quote from a book on social security:

“... the old system (SFC) blurred the image of the State as responsible for it. (...) The roles are confused. People consider that these benefits integrate their salary and that employers are responsible for them. They even ignore that it is the State that pays for them...”

Note: “Pol´ ıticas de Protecci´

• n familiar, R´

egimen de Asignaciones Familiares y principales planes sociales en la Rep´ ublica Argentina”, CIESS (2007).

27 / 31

Anecdotal evidence on recipient’s perception (2)

• 2. Survey evidence (phone 2018 - SSA)

Who is the responsible of paying the transfer (FA)? Answer type

• A. Government

35.4%

• B. Employer

8.6%

• C. Other

4.0%

• D. Don’t know

52.0%

Source: based on a SSA report (Cruces, 2019).

28 / 31

Mechanisms (making sense of the findings)

Labor demand story?

◮ Employers exploit confusion of the old regime and capture

part of the transfer → Result driven by new hires rather than incumbents → Result driven by small and incorporated firms → Anecdotal evidence on perceptions of transfers → Systematic violation of union’s CBA? (in progress) Labor supply story?

◮ Confused employees bargain more aggressively after the event

(pay equity/fairness concerns) → Ruled out by immediate effect at t=0 and new hires → Also effect broken by firm exposure is not U-shaped

Horizontal equity

Exposure 0.35 0.43 0.48 0.52 0.55 0.60 0.65 0.71

• 0.20
• 0.15
• 0.10
• 0.05

0.00 0.05 2sls coefficient

0-30% Lowest share 10-40% 20-50% 30-60% 40-70% 50-80% 60-90% 70-100% Highest share

Treated workers

Note: each dot refers to a different regression of the type: G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t. Exposure Density 29 / 31

Misc

Long-run wage effects:

• 1. Mean wage
• 2. p25 and p75
• 3. Pure event (levels)

Other real outcomes:

• 4. Composition
• 5. Employment
• 6. Credit score (debt)

30 / 31

Conclusions

◮ ∆ in the remittance system (from employer to govt):

◮ Wages ↑ ∼ 2% after firms switch to the new system

Pass-through: under old payment system employers capture ∼ 10/20% of the transfer by paying lower wages

◮ Welfare improving reform from worker’s point of view

◮ The way transfers are disbursed matters and affects the final

incidence (not captured dollar for dollar by workers)

◮ These results raise questions about the use of employers as

intermediaries to disburse benefits; less salient schemes may lead to capture by employers

31 / 31

Thanks!

Persistent inflation throughout the period

CPI RIPTE

1,000 2,000 3,000 4,000 RIPTE (current Argentinean pesos) 150 200 250 300 350 400 CPI (index)

Jan03 Apr04 Jul05 Oct06 Jan08 Apr09 Jul10 Date

Note: CPI denotes consumer price index while RIPTE to the average salary of registered workers (in current pesos).

Back FA

Evolution of FA brackets and minimum wage

Top bracket 2nd 3rd Minimum Wage 1,000 2,000 3,000 4,000 5,000 Current Argentinian pesos Dec02 Mar04 Jun05 Sep06 Dec07 Mar09 Jun10 Date Note: brackets are updated roughly once per year, but no in fix intervals.

Back FA

Distribution of monthly wage

Back FA

Notch 1 Notch 2 Notch 3

20 40 60 80 Child benefit (in pesos)

10,000 20,000 30,000 40,000

Wage Earners

500 1,000 1,500 2,000 2,500

Gross Monthly Wage (pesos)

Density (left) Transfer (right)

Note: figure corresponds to May’04; employees w/ kids working for 12 months. Notch 1 is located at p40, Notch 2 is located at p70, Notch 3 is located at p80.

1st stage (within firm T-C): beneficiaries

Back FA

• .2
• .15
• .1
• .05

Share receiving transfers (p.p.)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12 Months relative to event (turn 18)

Point estimate 95% C.I.

Note: event study when a kid turns 18 and workers lose eligibility.

1st and 2nd stage (within firm T-C)

Back FA

• 40
• 30
• 20
• 10

10 20 30 40 Constant Pesos 2004

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12 Months relative to event (turn 18)

Child transfer Wage earnings

Note: event study when a kid turns 18 and workers lose eligibility.

Collective bargaining agreements (CBA)

Jun03 Jul10

100 200 300

Number of agreements

Jan03 Apr04 Jul05 Oct06 Jan08 Apr09 Jul10 Date

Note: collective bargaining agreements occur every month. Two-thirds of them are firm level agreements.

Back FA

Average # workers by switching date

Back

Aug'08 20 40 60 80 100 120 140

• Avg. N of employees

2003m7 2005m1 2006m7 2008m1 2009m7 2011m1 Switching date

Explanation of no-results for switchers after July 2008

◮ Drop in economic activity (figure EMAE/GDP growth) ◮ Stabilization of employment growth (figure wage earners)

◮ Interesting if the effect comes from new hires.

◮ Lower ATR? (figure ratio transfer/min W)

◮ This doesn’t seem to be the main raison. Back FA Back Freq

Large drop in economic activity (EMAE)

90 100 110 120 130 140 Monthly economic activity estimator (base =2004) Jan04 Jan05 Jan06 Jan07 Jan08 Jan09 Jan10 Jan11 Date Note: large drop in economic activity from August 2008 onwards.

Back FA Back Freq Back RB

Stabilization of private employment growth

3.00 4.00 5.00 6.00 7.00 Private employees (in millions) 2003Q1 2004Q3 2006Q1 2007Q3 2009Q1 2010Q3 Date Note: stabilization of employment in the third quarter of 2008.

Back FA Back Freq Back RB

Evolution of the ATR (∼ ratio transfer/min wage)

0.00 0.05 0.10 0.15 0.20 Ratio transfer/minimum wage Jul03 Jul04 Jul05 Jul06 Jul07 Jul08 Jul09 Jul10 Date 1st 2nd 3rd bracket Note: ATR remains roughly constant during the period of analysis.

Back FA Back Freq

Transfer saliency in payslip

xxxxPaid by employers (SFC)xxxxxxxxxxxPaid by govt (SUAF)

Go back

Reform’s motivation

According to the Law

◮ Make sure beneficiaries receive the transfer ◮ Transparency and efficiency reasons ◮ Financial relief for firms

Government was pushing for this to happen. − → Identified some cases of fraud. Didn’t find anecdotal evidence on Unions asking for this reform.

Go back

Macro roll-out (official budget information)

0.00 0.20 0.40 0.60 0.80 Share paid through SFC 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year

Note: gradual decline in the share of FA paid through the old system (SFC).

Go back

Total spent and aggregates comparison (SFC+SUAF)

1,000 1,500 2,000 2,500 3,000 3,500 4,000 FA spending (million pesos from 2003) 2003 2004 2005 2006 2007 2008 2009 2010 Year Macro aggregates Microdata

Source of data:

Note: (a) increase in FA payments as time passes, (b) replicate macro aggregates using micro-data.

Go back

Beneficiaries (number of children)

Go back

0.5m 1m 1.5m 2m 2.5m 3m 3.5m Number of children (in millions) 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year

Note: N children receiving the transfer increases (economy booming + formalization).

The reform (3)

Table: Key dimensions under the two payment systems

SFC SUAF (1) (2) Legal liability Employee (¯ τ) Employee (¯ τ) Remittance responsibility Employer (τ e) Government (τ g) Information reporting Form 931 Form 931 Salience to employers High = Employees perception (q) Low ↑ Source of funding Contributory Contributory Employer SSC Employer SSC

Note: no change in timing, transfer’s amount.

Incorporation schedule: memo (1)

Go back

(a) Memo (body text)

Incorporation schedule: memo (1)

Go back

(b) Memo annex (with employer identifiers)

The schedule

◮ We digitized 50+ schedule plans recovering approximately

60K firms with its corresponding “internal deadline”.

◮ Note: not all firms appeared in the schedules (or at least, in

the publicly available ones).

◮ Less than 0.1% of firms appeared in more than one schedule. ◮ Compare deadline with the effective incorporation date.

◮ ∼ 90% of firms were incorporated before deadline.

Scheduled vs effective incorporation date (micro-data)

Deadline to be incorporated (ANSES' schedule) 0.0 0.2 0.4 0.6 0.8 1.0 CDF

• 20
• 15
• 10
• 5

5 10 15 20 Months before/after deadline Note: ∼ 90% of firms were incorporated before government’s deadline.

Go back

Formal approval: memo (2)

Go back

(a) Memo (body text)

Formal approval: memo (2)

Go back

(b) Memo annex (with employer identifiers)

Formal approval: memo (2)

◮ Difficult to track the universe of approval memos ◮ However, it was possible to do a public query so as to check

the formal incorporation date

◮ Random sample of 300 firms

◮ Compare formal incorporation vs effective incorporation date

◮ ∼ 80% firms incorporated in the same date as formal approval

◮ No incentives to delay:

◮ Cannot compensate the money (+ inflation)

◮ Workers’ notification: Within ten days after the switch, firms

should inform their workers about the new payment mechanism and the overall scheme of the FA system

Roll out query

Go back

Formal inclusion and observed incorporation (micro-data)

0.00 0.20 0.40 0.60 0.80 1.00 Cum share

• 20
• 16
• 12
• 8
• 4

4 8 12 16 20 Months Before/after formal incorporation Note: ∼ 80% of firms were incorporated the same date as the formal approval.

Go back

Notification to employees (affidavit)

Go back

Notificación del Régimen de Asignaciones Familiares Sistema Único de Asignaciones Familiares Form. PS.2.61 Frente 1 Versión 1.3 Apellido y Nombre Completo Cuil Domicilio - Calle - Nuemero Localidad Código Postal Piso Teléfono Dirección de Correo Electrónico Depto. Provincia Fecha de Nacimiento Sexo Nacionalidad Estado Civil Tipo y Nº Doc /CUIL RUBRO I – DATOS DEL TRABAJADOR (a completar por todos los trabajadores con o sin cargas de familia) Este Formulario reviste carácter de Declaración Jurada y se debe completar en letra de imprenta, sin tachaduras ni enmiendas Razón Social Dejo constancia, por medio de la presente, que en el día de la fecha, me he notificado de las normas básicas y principales derechos que me asisten con relación al Régimen de Asignaciones Familiares y que surgen del cuadro existente al dorso de la presente, recibiendo copia, en este acto, de la Ley Nº 24.714, sus normas reglamentarias y de la Resolución ANSES Nº 292/08 y sus modificatorias. Asimismo, me notifico que los trámites para solicitar la liquidación y pago de las Asignaciones Familiares que me correspondan deberé realizarlos personalmente o a través de un “Representante” designado por mí para tal fin, dentro de los plazos que surgen del cuadro existente al dorso de la presente, en cualquiera de las Unidades de Atención de ANSES, presentando -cuando corresponda-, debidamente confeccionados, los Formularios respectivos y la documentación que en cada caso se detalla, además de la que adicionalmente me pudiera ser requerida. Tomo conocimiento, además, que cualquier reclamo deberé formularlo personalmente ante ANSES dentro de los plazos de caducidad establecidos por la normativa vigente, presentando el Formulario PS.2.72 “Reclamos Generales para los Sistemas SUAF y UVHI”, debidamente cumplimentado. Dejo constancia también, que asumo el compromiso de notificar a mi empleador toda novedad/modificación que se produzca con relación a mis cargas y relaciones de familia, acompañando la documentación que las acredite, a efectos de que éste las informe a ANSES a través del Programa de Simplificación Registral. Me comprometo a informar a ANSES el medio de pago a través del cual deseo percibir las Asignaciones Familiares. Finalmente me notifico que todos los datos que aporte a ANSES personalmente, a través de un “Representante” o de mi Empleador, para la percepción de las Asignaciones Familiares, tendrán carácter de Declaración Jurada, reconociendo el derecho de ANSES a reclamarme su restitución o compensar automáticamente los importes con

• tras asignaciones en caso de percepción indebida de mi parte, sin necesidad de notificación

previa por parte del citado Organismo. CUIT Domicilio - Calle - Nuemero Localidad Código Postal Piso Teléfono Dirección de Correo Electrónico Depto. Provincia RUBRO I I – DATOS DEL EMPLEADOR Localidad, .................... de .............................……. de ..........…… Firma/Aclaración de Firma del Trabajador Firma/Aclaración de Firma y Sello del Empleador

Sample construction

◮ Panel dimension

• 1. Identify firms belonging to the short panel (unbalanced) i.e.,

exist 12 months around the event (6 before and 6 after).

• 2. Identify firms belonging to the full panel i.e., 96 months

(period 2003-2010).

◮ Event identification

• 1. Identify latest FA payment during the period (2003-2010).
• 2. Look at what happened 6 months before the event and identify

those that paid during all these months. Repeat the analysis with 4, 5, 7 and 8 months.

◮ Checks

• 1. Balanced panel
• 2. Sensitivity FA payments

Go back

First difference model

Back

Outcome variable: monthly wage used as base for employers’ SSC. We get the mean wage for each firm-group-month and take the difference across groups G ¯

w f ,t = ¯

wT

f ,t − ¯

wC

f ,t

Then, for each firm we have a time series of first differences − → run regular event study specification (f: firms, t: month-year) G ¯

w f ,t = α + 12

• j=−13

γj · dj

f ,t + ǫf ,t

(1) ...

First difference model

Back

Outcome variable: monthly wage used as base for employers’ SSC. We get the mean wage for each firm-group-month and take the difference across groups G ¯

w f ,t = ¯

wT

f ,t − ¯

wC

f ,t

Then, for each firm we have a time series of first differences − → run regular event study specification (f: firms, t: month-year) G ¯

w f ,t = α + 12

• j=−13

γj · dj

f ,t + ǫf ,t

(1) ... wmean

i,f ,t

= βTi,f ,t +

12

• j=−13

γj · dj

f ,t · Ti,f ,t + µf ,t + ǫi,f ,t

(2)

Empirical approach: Reduced form

Back

In levels wmean

i,f ,t

= βTi,f ,t +

5

• j=−6

γj · dj

f ,t · Ti,f ,t + µf ,t + ǫi,f ,t

(3) Pooled γs: Alt (1) wmean

i,f ,t

= β1Ti,f ,t+β1Ti,f ,t·Windowf ,t+β2Ti,f ,t·Windowf ,t·Postf ,t+ (4) β3Ti,f ,t · (1 − Windowf ,t) · Postf ,t + µf ,t + ǫi,f ,t Pooled γs: Alt (2) wmean

i,f ,t

= βTi,f ,t + β−6 d−6

f ,t · Ti,f ,t

• d n6

+β5 d5

f ,t · Ti,f ,t

• d 5

(5) βafter d[0;4]

f ,t

· Ti,f ,t

• d after

+µf ,t + ǫi,f ,t

Note: in (3) j = −1 omitted category; in (5) months [-5;-1] are omitted. Window = 1 if months [-5;4].

Empirical approach: Reduced form

Back

First difference G w

f ,t = α + 5

• j=−6

γj · dj

f ,t + ǫf ,t

(6) γ’s should be numerically the same as those estimated in eq.(3) Pooled γs: Average G w before = G w

before = (γ−5 + γ−4 + γ−3 + γ−2 + 0)/5

Average G w after = G w

after = (γ0 + γ1 + γ2 + γ3 + γ4)/5

Average effect = G w

average = G w after − G w before Note: in (6) j = −1 omitted category.

Empirical approach: Reduced form

Back

First difference G w

f ,t = α + 5

• j=−6

γj · dj

f ,t + ǫf ,t

(7) γ’s should be numerically the same as those estimated in eq.(3) Pooled γs: Alt (1) G w

f ,t = α + β1Windowf ,t + β2Windowf ,t · Postf ,t

(8) +β3(1 − Windowf ,t) · Postf ,t + ǫf ,t Pooled γs: Alt (2) G w

f ,t = α + β−6 d−6 f ,t

• d n6

+β5 ·d5

f ,t

• d 5

+βafter d[0;4]

f ,t d after

+ǫf ,t (9)

Note: in (6) j = −1 omitted category; in (8) months [-5;-1] are omitted. Window = 1 if months [-5;4].

Empirical approach: Reduced form

Back

Expected values: Alt (1)

• 1. E(G w/Win = 0, post = 0) = α
• 2. E(G w/Win = 1, post = 0) = α + β1
• 3. E(G w/Win = 1, post = 1) = α + β1 + β2
• 4. E(G w/Win = 0, post = 1) = α + β3

Expected values: Alt (2)

• 1. E(G w/Win = 0, post = 0) = α + β−6
• 2. E(G w/Win = 1, post = 0) = α
• 3. E(G w/Win = 1, post = 1) = α + βafter
• 4. E(G w/Win = 0, post = 1) = α + β5

First difference model

Back

G w Switch

Window = 0 post = 0 Window = 1 post = 0 Window = 1 post = 1 Window = 0 post = 1

• 5
• 1 0

4 Event window Distance

Empirical approach: 2sls

Back

First stage G Transfer

f ,t

= α + δ1Windowf ,t + δ2Windowf ,t · Postf ,t +δ3(1 − Windowf ,t) · Postf ,t + ǫf ,t Reduced form G w

f ,t = α + β1Windowf ,t + β2Windowf ,t · Postf ,t

+β3(1 − Windowf ,t) · Postf ,t + ǫf ,t 2sls (Wald estimator) then we should have − → Θ = β2 δ2

Note: if I drop the binned points I get numerically the same number.

Empirical approach

Back

First difference with firm and time FE G w

f ,t = 5

• j=−6

γj · dj

f ,t + µf + µt + ǫf ,t

(10) Pooled γs: Alt (1) ... closer but not exact G w

f ,t = β1Windowf ,t + β2 · Windowf ,t · Postf ,t

(11) +β3(1 − Windowf ,t) · Postf ,t + µf + µt + ǫf ,t Pooled γs: Alt (2) ... closer but not exact G w

f ,t = β−6 d−6 f ,t

• d n6

+β5 ·d5

f ,t

• d 5

+βafter d[0;4]

f ,t d after

+µf + µt + ǫf ,t (12)

Note: in (10) j = −1 is the omitted category; in (12) months [-5;-1] are the omitted

• nes.Window = 1 if months[-5;4].

Empirical approach

First difference 2sls with firm and time FE Reduced form (z = instrument) G w

f ,t = γ−6 d−6 f ,t

• d n6

+γ5 ·d5

f ,t

• d 5

+γafter d[0;4]

f ,t d after

+ µf + µt + ǫf ,t First stage: G transfer

f ,t

= γ−6 d−6

f ,t

• d n6

+γ5 ·d5

f ,t

• d 5

+γafter d[0;4]

f ,t d after

+ µf + µt + ǫf ,t

Note: the ratio reduced form/first stage is close to the Wald estimator where, again, the difference is due to the controls.

Empirical approach: Heterogeneities

Reduced form G w

f ,t = α + β1Windowf ,t + β2Windowf ,t · Postf ,t

+β3(1 − Windowf ,t) · Postf ,t + +β4Highf + β5Windowf ,t · Highf +β6Windowf ,t·Postf ,t·Highf +β7(1−Windowf ,t)·Postf ,t·Highf +ǫf ,t

If we were to include firm and time FE (µf and µt), then we need to add also the interaction between µt and Highf . β4 disappears as it is absorbed by µf . Specification 2sls with High/low:

Robustness checks

Back

◮ Sample sensitivity

◮ Balanced panel (96 months) ◮ Sensitivity FA payments

◮ Results not driven by modeling choices:

◮ = specs: Simple mean, firm & time FE, firm linear trends

◮ Event window size: 10, 12, 14,... 24 months, etc. ◮ Event by event ◮ Sensitivity to the treatment group definition

◮ Fully treated vs never/partially treated (life events). ◮ Treatment status based on year of birth.

Balanced panel

Dw

f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: results remain unchanged when considering the balanced panel.

Back

Sensitivity FA payments (2sls)

• 0.15
• 0.12
• 0.09
• 0.06
• 0.03

0.00 0.03 0.06 2sls coefficient 4 5 6 7 8 Sensitivity FA payments

Note: each dot refers to a different regression of the type: G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t,

where we vary the sample according to FA payments restriction.

Back

Regression specification (mean)

(1) (2) (3) Reduced Form ∆ monthly wage 6.94*** 7.71*** 5.40*** (in pesos) (0.90) (1.25) (1.27) 2sls

∆wage ∆transfer(τ e)

• 0.08***
• 0.08***
• 0.06***

(0.01) (0.01) (0.01) Simple mean difference

• Firm and time FE
• Firm linear trend
• Observations

3,061,870 3,061,870 3,061,870

Note: Standard errors clustered at firm level in parenthesis.

Go back

Pure event for workers w/ and wo/ kids (mean)

¯ wf ,t = 12

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Treat Control

• 10
• 5

5 10 15 20 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: increase in wage is driven by treated workers.

Go back

Window’s dynamic (mean)

Two years One year around event

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: results remain unchanged for a time window of 6 months before/after.

Back

Effect’s dynamics – 2sls

• 0.12
• 0.11
• 0.10
• 0.09
• 0.08
• 0.07

2sls coefficient

All post periods [0:11] Last-half post periods [6:11] Only last period [11]

Post periods included

Note: each dot refers to a different regression of the type: G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t,

where we vary the post periods included in the event window (W ).

Event by event – 2sls

• 0.150
• 0.100
• 0.050

0.000 0.050 2sls coefficient Jul03 May04 Mar05 Jan06 Nov06 Sep07 Jul08 Excluded month

Note: each dot refers to a different regression of the type: G w

f ,t = β1Windowf ,t +β2·Windowf ,t ·Postf ,t +β3(1−Windowf ,t)·Postf ,t +µf +µt +ǫf ,t,

where we exclude switchers in a given month.

Go back

Rolling window of events (whole roll-out)

10 20 30 40 # of firms (in K)

• 0.16
• 0.12
• 0.08
• 0.04

0.00 0.04 2sls

Jul03- Jun05 May04- Apr06 Mar05- Feb07 Jan06- Dec07 Nov06- Oct08 Sep07- Aug09 Jul08- Jun10

Rolling window of events (2-year window)

Note: each dot refers to a different regression with a rolling window of events.

Go back Go macro context

Alternative treatment group definition (mean)

Workers that have at least one child born in [1992-2002]. This means that these workers are fully treated during the period 2003-2010.

January 2003 Born in (age): 1992 (11) 2002 (1) December 2010 Born in (age): 1992 (18) 2002 (8)

The rest of the workers belong to the control group (never treated and/or partially treated).

Alternative treatment group definition (mean)

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: results hold when using an alternative definition of the treatment group.

Go back

Alternative T group definition (mean) using year of birth

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: results hold when using an alternative definition of the treatment group.

Go back

Whole period

Before crisis After crisis

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: before crisis refers to firms switching into the new system before August 2008; after between August 2008 and July 2010.

Go back

Other potential behavioral responses

• 1. Worker composition
• 2. Employment
• 3. Full vs part-time workers
• 4. Bunching at notches

[1.] Worker composition

G N

f ,t = 12 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 0.75
• 0.50
• 0.25

0.00 0.25 0.50 0.75 Gap in number of workers (Treatment - Control)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: no (short-run) effects on firm’s composition i.e., number of treated minus control workers.

[2.] Employment

Nf ,t = 12

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 2.0
• 1.5
• 1.0
• 0.5

0.0 0.5 1.0 1.5 2.0 Firm size

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: no (short-run) effects on employment i.e., firm size.

[3.] Full vs part-time workers

G SFT

f ,t

= 12

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 0.010
• 0.007
• 0.005
• 0.003

0.000 0.003 0.005 0.007 0.010 Gap in the share of full-time workers (Treatment - Control)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12

Months relative to treatment

Note: no (short-run) effects on full-time job.

[4.]Collusion and bunching at notches

◮ Hypothesis: bunch not to lose transfer. ◮ Result: no visible bunching at notches

Bunch

⇒ some spikes, but not = pattern for workers w/ and w/o children

Kids

◮ No-bunching is not due to poor enforcement. Sharp

discontinuity in transfer’s amount above notches.

◮ Explanation: (i) difficult E-E coordination; (ii) not large

incentives to bunch (change in ATR ∼ 2)

Bunching at AAFF notches

Go back Notch 1 $ 725 Notch 2 $ 1225 Notch 3 $ 2025 MW $ 450 5 10 15 20 25 30

Transfer/ Earnings (%)

5,000 10,000 15,000 20,000 25,000 30,000

Salaried workers

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

• Freq. (left)

ATR 2 kids (right)

By number of children

Go back .005 .01 .015 .02 .025 .03

Density

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

No kids 1 kid 2+ kids

Across months

Go back Notch 1 $ 725 .01 .02 .03 .04

Density

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

Salaries 04-2005 April 2005

Across months

Go back Notch 1 $ 725 .01 .02 .03 .04

Density

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

Salaries 07-2005 April 2005 July 2005

Across months

Go back Notch 1 $ 725 .01 .02 .03 .04

Density

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

Salaries 08-2005 April 2005 August 2005

Across months

Go back Notch 1 $ 725 Notch 1 $ 1200 Update Sept .01 .02 .03 .04

Density

500 1,000 1,500 2,000 2,500

Gross Monthly Earnings (pesos)

Salaries 08-2005 April 2005 November 2005

Median transfer (Aug-2005)

Go back Notch 1 Notch 2 Notch 3

50 100 150 Transfer amount (in Pesos)

10,000 20,000 30,000 40,000 50,000

Salaried Workers

1,000 2,000 3,000 4,000 5,000

Gross Monthly Earnings (pesos)

Density (left) Transfer (right)

Mean transfer (Aug-2005)

Go back Notch 1 Notch 2 Notch 3

50 100 150 Transfer amount (in Pesos)

10,000 20,000 30,000 40,000 50,000

Salaried Workers

1,000 2,000 3,000 4,000 5,000

Gross Monthly Earnings (pesos)

Density (left) Transfer (right)

[1.] Turnover rate

Dw

f ,t = 5 j=−6 γj · dj f ,t + µf + µt + ǫf ,t

High Low

• 5

5 10 15 Constant pesos (base = Jan 2004)

• 5
• 4
• 3
• 2
• 1

1 2 3 4

Months relative to treatment

High: above median turnover rate.

Go back

[2.] Full-time contract

Dw

f ,t = 5 j=−6 γj · dj f ,t + µf + µt + ǫf ,t

Low High

• 5

5 10 15 Constant pesos (base = Jan 2004)

• 5
• 4
• 3
• 2
• 1

1 2 3 4

Months relative to treatment

High: above median share of full-time workers.

Go back

[3.] Transfer discrepancy

Dw

f ,t = 5 j=−6 γj · dj f ,t + µf + µt + ǫf ,t

Low High

• 5

5 10 15 Constant pesos (base = Jan 2004)

• 5
• 4
• 3
• 2
• 1

1 2 3 4

Months relative to treatment

High: above median share of transfer discrepancies.

Go back

Density of Firm Exposure

0.00 0.05 0.10 0.15 0.20 0.25 Fraction 0.00 0.20 0.40 0.60 0.80 1.00 Share treated workers

Note: exposure defined as the within-firm share of workers with children.

Go back

[1.] Mean wage

G w

f ,t = 24 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 10
• 5

5 10 15 Constant pesos (base = Jan 2004)

• 12 -10 -8
• 6
• 4
• 2

2 4 6 8 10 12 14 16 18 20 22 24

Months relative to treatment

Note: the effect stabilizes 12 months after the switch.

Go back

[2.] p25 and p75

Dw

f ,t = 24 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

p25 p75

• 10
• 5

5 10 15 20 25 Constant pesos (base = Jan 2004)

• 12 -10 -8
• 6
• 4
• 2

2 4 6 8 10 12 14 16 18 20 22 24

Months relative to treatment

Note: the effect stabilizes 12 months after the switch.

Go back

[3.] Pure event (mean)

Wf ,t = 24

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Treat Control

• 10
• 5

5 10 15 20 Constant pesos (base = Jan 2004)

• 12 -10 -8
• 6
• 4
• 2

2 4 6 8 10 12 14 16 18 20 22 24

Months relative to treatment

Note: effect driven by the treatment group (workers with children).

Go back

[4.a] Composition

G N

f ,t = 14 j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 1.50
• 1.00
• 0.50

0.00 0.50 1.00 Gap in number of workers (Treatment - Control)

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12 14

Months relative to treatment

Note: firms’ composition seem to change when focusing on a longer time window.

Go back

[4.b] Employment

Nf ,t = 14

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

• 3
• 2
• 1

1 2 3 Firm size

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12 14

Months relative to treatment

Note: firms size increases in the long-run (driven by workers wo/ kids).

Go back

[4.c] Employment by treatment status

Nf ,t = 14

j=−13 γj · dj f ,t + µf + µt + ǫf ,t

Treat Control

• 1.5
• 1.0
• 0.5

0.0 0.5 1.0 1.5 Number of workers

• 12
• 10
• 8
• 6
• 4
• 2

2 4 6 8 10 12 14

Months relative to treatment

Note: firms size increases in the long-run (driven by workers wo/ kids).

Go back

Delinquency rates: debt past due 90+ days

• .04
• .02

.02 .04 Delinquent debt (p.p.)

• 6
• 5
• 4
• 3
• 2
• 1

1 2 3 4 Months relative to event (SFC to SUAF)

Point estimate (10,481 firms) 95% C.I.

Note: firms switching btw Oct’03 and Jul’04 and in 2005 (N=10,481).

Go back

Theoretical framework

Simple model to rationalize our findings (based on Gruber 1997): Ls

i = Ls i ( ˜

wi) = Ls

i (wi(1 + (1 − q)τ e i ))

(13) Ld

i = Ld i (w)

(14) where ˜ w represents the perceived wage as fx of wage (w), perception parameter (q) and transfer by employers (τ e). with τ e = ¯ τ − τ g

◮ perfect perception/knowledge (q=1), the perceived wage is

equal to the true wage ˜ w1 = w

◮ no knowledge (q=0) −

→, the perceived wage includes the transfer ˜ w0 = w(1 + τ e).

Theoretical framework

Totally differentiating supply and demand equations, and rearranging terms we get: dln(wi) dln(1 + te

i )

• ¯

τ=τ e+τ g , ¯ q=q

= ηs

i (1 − q) · [ (1+te

i )

(1+(1−q)τ e

i )]

ηd

i − ηs i

(15) Extreme cases

◮ q=1 −

→ perfect knowledge,

dln(wi) dln(1+te

i ) = 0,

standard incidence result.

◮ q=0 −

→ no knowledge,

dln(wi) dln(1+te

i ) =

ηs

i

ηd

i −ηs i < 0

Change in perception (q)

◮ ∆ in the remittance responsibility −

→ ∆ the information content to employees. − → ∆ scheme’s perception (q), therefore on final incidence. dln(wi) dln(1 + te

i )

• ¯

τ=τ e+τ g =

(1 + η(1−q)

i

) · ηs

i (1 − q) · [ (1+τ e

i )

(1+(1−q)τ e

i )]

ηd

i − ηs i

(16) with η(1−q)

i

= ∂(1−q)

∂τ e

i

·

τ e

i

(1−q) −

→ misperception elasticity i.e., how much (1 − q) changes as the money disbursed by employers increase (>0 elasticity i.e., reinforces the main effect).

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Graphical analysis

l w LS

1 q=1

LS

0 = LS 1 q=1

LD e

w0(1+(1−q)te)

w0 w1

Note:

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Collective bargaining agreement (CBA)

Note: this screenshot presents the first page of a collective agreement, Convenio Colectivo de Trabajo, in Argentina. This is a standard type of agreement where the different articles describe what has been discussed/negotiated.

Digitalization of CBA

Note: summary of the information extracted from a given collective agreement (CCT − 1523 − 2016 − E): it is at firm level (Nivel: Empresa), was celebrated in September 29th 2015 (Celebraci´

• n: 29-09-2015) and involved workers in the oil sector

(Actividad: Petroleros). The main contents discussed are also enumerated (Contenidos discutidos: Adicional tareas de turno; Antiguedad; Aporte Solidario, etc). Finally, firm’s name is available within the extracted information. (Empleador/s: Yel Informatica S.A.).

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First stage (change in τ e)

τe τg

30 60 90 Constant pesos (base = Jan 2004)

• 5
• 4
• 3
• 2
• 1

1 2 3 4

Months relative to treatment

Note: the red series represents the old system where employers paid the transfer (τ e); the blue one simulates the new system where the government directly disburse the money to the beneficiaries (τ g).

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