Gary Mena: Intended and unintended effects of unconditional cash

Motivation Renta Dignidad Data and Methods Effects Summarizing. . .

Intended and unintended effects of unconditional cashtransfers

The case of Bolivia’s Renta Dignidad

Werner Hernani-Limarino Gary Mena

Fundación ARU

International Seminar on Social Protection, Entrepreneurship and LabourMarket Activation

Brasilia - September, 2014


1 Motivation

2 Renta DignidadProgram Design

3 Data and MethodsDataMethods

4 EffectsWelfareInvestments and savingsLabor market outcomes

5 Summarizing. . .


�� Motivation


Motivation

Why should we study the effects of "Renta Dignidad" on labor marketoutcomes and household investments?

1. Important changes in Bolivia during the 2000’s, but high levels of genderinequality in the labor market remainAccording to household surveys,

Inequality (Gini) has decreased from 59 to 46 (Eid and Hernani, 2013).Extreme and moderate poverty rates have decreased in 55 and 32%,respectively (Hernani and Uribe, 2013).Quality of labor market insertion (measured as pc labor income) of womenis half of that of men. (Hernani and Mena, 2014).Gaps in labor market participation and paid employment account for almost90% of the gap in quality of labor market insertion.

According to the census dataImportant demographic changes. Dependency ratio has changed from 77.9(2001) to 66.8% (2012).

2. Not much is known about the effect(s) of RD on labor market outcomesand household investments in Bolivia. (Martínez (2004); Loza, Martínez yMendizábal (2013))


Gender inequalities in the Bolivian labor market (Hernani and Mena 2014)

gaps in labor market outcomes

.2

.4

.6

.8

1

1.2

Pro

port

ion

wom

en/m

en

1999 2001 2003 2005 2007 2009 2011

pc labor income participation paid employmenthours wage

.2

.4

.6

.8

1

1.2

Pro

port

ion

wom

en/m

en

1999 2001 2003 2005 2007 2009 2011


.2

.4

.6

.8

1

1.2

Pro

port

ion

wom

en/m

en

1999 2001 2003 2005 2007 2009 2011


pc labor income gap decomposition

45

46

9

47

50

3

41

51

7

45

51

4

40

53

7

44

48

8

42

49

8

43

49

8

44

49

7

40

49

11

43

49

8

44

46

10

54

36

10

0

20

40

60

80

100

%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012

participation gap paid employment gap income gap

(a) Bolivia

68

15

17

69

15

16

60

19

21

63

20

16

57

28

16

65

17

18

62

22

16

63

21

16

68

17

15

59

24

17

64

21

15

64

22

14

68

17

16

0

20

40

60

80

100

%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012


(b) Urban

19

73

9

20

76

3

19

76

4

21

77

2

18

76

6

18

76

6

17

77

6

18

78

4

14

82

5

16

76

8

18

77

5

17

75

8

31

61

8

0

20

40

60

80

100

%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012


(c) Rural


�� Renta Dignidad


Program Design

Program Design

"Renta Dignidad" RD is a benefit for all the Bolivians that consists of noninheritable lifetime payments in favor of people no younger than 60 and aburial allowance. Not the first of its kind in Bolivia, though:

1997 1998-1999 2001-2002 2003-2007 2008-2012 2013+

Bonosol Bolivida Bonosol Renta DignidadAnnuity Monthly (cumulative)

Beneficiaries +65 +60Contributory pensions $US 248 Suspended $US 120 $US 248 $US 21(248) $US 27 (300)No contributory pensions $US 27 (300) $US 34 (413)

Funding: fixed share of the special direct tax on hydrocarbons (impuestodirecto a los hidrocarburos (IDH)), with contributions from all levels ofgovernment, and dividends from capitalized public enterprises.the current non-contributory pension scheme was included in the newBolivian Constitution (2009, Art. 60) as a right of Bolivian citizens

[. . . ] the state will provide an old-age pension, under theframework of an integral social security system.


�� Data and Methods


Data

Why do we use households survey data

Table : Available data sources in Bolivia to study "Renta Dignidad"

Variables HS ’05-’11 HS 2011 EPAM 2011 ETE 2009-2010

Full sample 114,476 (29,000) 33,821 (8,851) 9,176 (2,478) ??age∈ [55, 60) 3,889 1,242 1,109 ??age∈ [60, 65) 3,132 982 925 ??age∈ [65, 70) 2,581 779 772 ??age∈ [55, 99) 13,792 4,270 3,923 ??

Labor market supplyLM participation X X X Xhours worked (intensity

of supply)X X n.a. X

Sector of employmentFamily Worker X X X XSelf-employed X X X XInformal salaried X X ? XFormal salaried X X n.a. X

Household variablesIncome X X X XConsumption X X X n.a.Savings X X X n.a.Education expenditure X X X n.a.Health expenditure X X ? n.a.Durables expenditure X X n.a. n.a.Dwelling’s investment X X n.a. n.a.

Number of households in parentheses. "n.a."= non available.

HS=Houhsehold surveys; EPAM=Encuesta a hogares con Personas Adultas Mayores; ETE= Encuesta Trimestral de Empleo.


Data

Basic setup 2groups x 2periods case: sample sizes

For individual outcomes we have: G = {0, 1} T = {0, 1}

G0=agei ∈ [55, 60) G1=agei ∈ [60, 64)

T0=2005-2007Bolivia =1,519 Bolivia =1,217Men =716 Men =581

Women =803 Women =636

T1=2008-2011Bolivia =2,364 Bolivia =1,912Men =1,157 Men =944

Women =1,207 Women =968

For household outcomes we have more combinations available. They areclassified according to the age of the oldest family member. We furtherclassify households based on whether all of the individuals in the ageinterval are men, women or if the household has both (in the age interval).

G0=agehhmaxage ∈ [55, 60) G1=agehhmaxage ∈ [60, 64)

T0=2005-2007Bolivia =953 Bolivia =791

only G0 men =434 only elderly men =370only G0 women =336 only elderly women =286

w+m =183 w+m =135

T1=2008-2011Bolivia =1,491 Bolivia =1,289

only G0 men =709 only elderly men =603only G0 women =509 only elderly women =460

w+m =273 w+m =226

Note: observations with 0 or missing pc income or pc consumption where excluded from household sample


Data

Household outcomes (means)

Bolivia Only men IAI Only women IAI Both

[55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DIDpc income (log 2012 Bs. a month)

T=0 6.32 6.19 6.39 6.29 6.32 6.09 6.18 6.12[0.05] [0.05] 0.17 [0.07] [0.08] 0.14 [0.07] [0.09] 0.36 [0.11] [0.13] -0.11

T=1 6.51 6.55 [0.09]** 6.57 6.61 [0.13] 6.46 6.60 [0.14]*** 6.45 6.28 [0.20][0.03] [0.03] [0.05] [0.05] [0.06] [0.05] [0.08] [0.08]

pc consumption (log 2012 Bs. a month)T=0 6.44 6.45 6.39 6.51 6.50 6.43 6.41 6.33

[0.03] [0.03] 0.00 [0.04] [0.04] -0.10 [0.04] [0.05] 0.11 [0.05] [0.06] 0.02T=1 6.65 6.66 [0.05] 6.68 6.70 [0.08] 6.66 6.70 [0.09] 6.55 6.51 [0.11]

[0.02] [0.02] [0.03] [0.03] [0.03] [0.04] [0.05] [0.05]pc labor income (thousands of 2012 Bs. a month)

T=0 0.90 0.75 1.11 0.86 0.69 0.63 0.82 0.74[0.05] [0.05] -0.09 [0.08] [0.08] 0.05 [0.07] [0.08] -0.17 [0.09] [0.11] -0.27

T=1 1.06 0.82 [0.09] 1.18 0.98 [0.16] 0.95 0.71 [0.14] 0.94 0.59 [0.16]*[0.05] [0.04] [0.08] [0.07] [0.07] [0.05] [0.06] [0.05]

pc non-labor income (thousands of 2012 Bs. a month)T=0 0.26 0.31 0.26 0.28 0.30 0.37 0.23 0.25

[0.04] [0.03] 0.17 [0.08] [0.03] 0.13 [0.04] [0.05] 0.22 [0.05] [0.04] 0.17T=1 0.14 0.35 [0.05]*** 0.14 0.31 [0.09] 0.15 0.44 [0.08]*** 0.11 0.30 [0.08]**

[0.01] [0.02] [0.02] [0.03] [0.02] [0.04] [0.02] [0.04]pc intrahousehold transfers (thousands of 2012 Bs. a month)

T=0 0.09 0.10 0.06 0.05 0.13 0.17 0.07 0.06[0.01] [0.02] 0.00 [0.02] [0.01] -0.01 [0.02] [0.04] 0.02 [0.03] [0.02] 0.01

T=1 0.07 0.08 [0.02] 0.06 0.05 [0.02] 0.09 0.15 [0.05] 0.04 0.03 [0.04][0.01] [0.01] [0.01] [0.01] [0.01] [0.03] [0.01] [0.01]

Note: Standard Errors in brackets. "IAI"=in age interval. Deflated with CPI base December 2012


Data

Household outcomes (means)

Bolivia Only men IAI Only women IAI Both

[55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DIDSaving ratey : (y-c)/y

T=0 -1.05 -1.77 -1.05 -1.53 -0.95 -2.20 -1.22 -1.53[0.22] [0.31] 1.10 [0.43] [0.48] 0.99 [0.18] [0.52] 1.69 [0.35] [0.64] 0.17

T=1 -1.08 -0.70 [0.42]*** -1.08 -0.56 [0.68] -1.03 -0.59 [0.60]*** -1.17 -1.31 [0.96][0.15] [0.09] [0.19] [0.09] [0.20] [0.10] [0.51] [0.38]

Saving ratec : (y-c)/cT=0 0.29 0.12 0.46 0.15 0.11 0.07 0.23 0.15

[0.04] [0.03] 0.13 [0.08] [0.05] 0.24 [0.05] [0.06] 0.12 [0.11] [0.07] -0.13T=1 0.21 0.18 [0.07]* 0.28 0.21 [0.12]** 0.11 0.19 [0.10] 0.26 0.05 [0.16]

[0.03] [0.03] [0.06] [0.04] [0.04] [0.05] [0.07] [0.06]Education expenditure (thousands of 2012 Bs. a month)

T=0 0.06 0.06 0.07 0.06 0.05 0.08 0.05 0.03[0.01] [0.01] -0.02 [0.01] [0.01] -0.01 [0.01] [0.03] -0.03 [0.01] [0.01] -0.01

T=1 0.06 0.05 [0.02] 0.06 0.05 [0.02] 0.06 0.05 [0.04] 0.05 0.03 [0.01][0.00] [0.00] [0.01] [0.01] [0.01] [0.01] [0.01] [0.00]

Health expenditure (thousands of 2012 Bs. a month)T=0 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02

[0.00] [0.00] 0.00 [0.00] [0.00] -0.01 [0.00] [0.00] 0.02 [0.00] [0.01] -0.01T=1 0.03 0.03 [0.01] 0.03 0.03 [0.01] 0.03 0.04 [0.01] 0.02 0.02 [0.01]

[0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.00] [0.00]Expenditure on durables (last year) (thousands of 2012 Bs. a month)

T=0 0.04 0.04 0.05 0.04 0.03 0.03 0.05 0.02[0.01] [0.01] -0.01 [0.01] [0.01] -0.01 [0.00] [0.01] -0.01 [0.02] [0.01] -0.00

T=1 0.06 0.05 [0.01] 0.06 0.06 [0.02] 0.05 0.04 [0.02] 0.06 0.03 [0.03][0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] [0.01]

Dwelling investments (thousands of 2012 Bs. of 2012 a month)T=0 0.02 0.03 0.03 0.02 0.01 0.06 0.01 0.01

[0.00] [0.02] -0.02 [0.01] [0.01] -0.01 [0.00] [0.04] -0.04 [0.00] [0.00] -0.02T=1 0.03 0.02 [0.02] 0.03 0.01 [0.02] 0.01 0.02 [0.04] 0.04 0.02 [0.02]

[0.01] [0.00] [0.01] [0.00] [0.00] [0.00] [0.02] [0.01]

Note: Standard Errors in brackets. "IAI"=in age interval. Deflated with CPI base December 2012


Data

Individual outcomes (means)

Bolivia Men Women

[55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DIDParticipationT=0 0.80 0.74 0.92 0.82 0.69 0.66

[0.01] [0.01] -0.04 [0.01] [0.02] 0.02 [0.02] [0.02] -0.10T=1 0.85 0.75 [0.02]* 0.96 0.88 [0.02] 0.74 0.62 [0.03]***

[0.01] [0.01] [0.01] [0.01] [0.01] [0.02]Family workerT=0 0.13 0.16 0.02 0.03 0.23 0.27

[0.01] [0.01] -0.01 [0.01] [0.01] -0.01 [0.02] [0.02] -0.02T=1 0.13 0.14 [0.02] 0.02 0.02 [0.01] 0.23 0.25 [0.03]

[0.01] [0.01] [0.00] [0.00] [0.01] [0.02]Informal n/salariedT=0 0.45 0.45 0.57 0.62 0.34 0.31

[0.01] [0.01] -0.02 [0.02] [0.02] -0.02 [0.02] [0.02] -0.03T=1 0.49 0.47 [0.03] 0.61 0.64 [0.04] 0.37 0.30 [0.03]

[0.01] [0.01] [0.02] [0.02] [0.02] [0.02]Informal salariedT=0 0.10 0.07 0.17 0.10 0.04 0.05

[0.01] [0.01] -0.01 [0.01] [0.01] 0.02 [0.01] [0.01] -0.03T=1 0.10 0.07 [0.01] 0.16 0.11 [0.03] 0.05 0.03 [0.01]**

[0.01] [0.01] [0.01] [0.01] [0.01] [0.01]Formal salariedT=0 0.10 0.04 0.15 0.07 0.06 0.02

[0.01] [0.01] 0.00 [0.01] [0.01] 0.02 [0.01] [0.01] -0.02T=1 0.12 0.07 [0.01] 0.16 0.11 [0.02] 0.09 0.03 [0.02]

[0.01] [0.01] [0.01] [0.01] [0.01] [0.01]

Note: Standard Errors in brackets.


Data

Individual outcomes (means)

Bolivia Men Women

[55, 60) [60, 64) DID [55, 60) [60, 64) DID [55, 60) [60, 64) DIDLabor supply intensity-all jobs (hours p/week)T=0 38.14 34.51 45.65 40.55 31.53 29.03

[0.72] [0.82] -1.43 [0.88] [1.16] 0.27 [1.05] [1.11] -3.07T=1 39.77 34.71 [1.43] 47.42 42.59 [1.86] 32.55 26.98 [2.03]

[0.60] [0.71] [0.70] [0.91] [0.89] [0.99]Labor supply-PA (hours p/week)T=0 36.38 33.07 43.62 38.89 30.03 27.80

[0.70] [0.80] -1.49 [0.87] [1.12] 0.37 [1.01] [1.08] -3.28T=1 38.06 33.26 [1.39] 45.19 40.83 [1.80] 31.32 25.83 [1.97]*

[0.58] [0.68] [0.69] [0.87] [0.87] [0.96]Wage-all jobs (2012 Bs. p/hour)T=0 7.61 5.07 11.57 7.58 4.14 2.80

[0.57] [0.38] -0.23 [1.10] [0.66] 1.32 [0.40] [0.37] -1.65T=1 8.55 5.78 [0.83] 11.60 8.93 [1.48] 5.67 2.68 [0.76]**

[0.34] [0.33] [0.51] [0.55] [0.42] [0.32]Labor income-all jobs (thousands of 2012 Bs. p/month)T=0 1.36 0.88 2.16 1.38 0.65 0.43

[0.08] [0.06] -0.03 [0.14] [0.11] 0.27 [0.06] [0.06] -0.31T=1 1.51 1.01 [0.13] 2.15 1.64 [0.23] 0.92 0.39 [0.12]**

[0.06] [0.06] [0.09] [0.10] [0.08] [0.04]

Note: Standard Errors in brackets.


Methods

Standard difference-in-differences (linear)

Let Gi = 0, 1; Ti = 0, 1 and Ii denote the treatment defined as:

Ii ={

1 if Gi = 1,Ti = 10 otherwise

estimate:

Yi = β0 + β1Gi + β2Ti + τDID Ii + εi (1)

to calculate the impact as:

τDID = [E [Y |G = 1,T = 1]− E [Y |G = 1,T = 0]]− [E [Y |G = 0,T = 1]− E [Y |G = 0,T = 0]]

DID is a valid method of identification, although. . .functional form dependency.heterogeneity in the effect of treatment.not possible to estimate effect of the treatment on the control.


Methods

Changes-in-Changes (Athey and Imbens (2002, 2006a, 2006b))

Baseline model:1. Y (0) = h(U,T ): outcome with no treatment depends on an unknown

function h, unobservables u and time t, hence all differences across groupsare due to different unobservables u and the production function h doesnot vary with group.

2. U ⊥ T |G : distribution of U does not vary over time within a group3. h(u, t) is monotone in u. can be relaxed in the case of discrete (binary)

outcomes4. the support of U|G = 1 is a subset of the support of U|G = 0

analogous DID "common trend" assumption


Methods

. . . then AI show that it is possible to identify the distribution ofY (0)|G = 1,T = 1:

FY (0),11(y) = FY ,10(F−1Y ,00 (FY ,01(y))

)(2)

where FY ,gt(y) denotes the distribution function of Yi given Gi = g ,Ti = t,and FY (0),11(y) represents the counterfactual distribution of the treated inT = 1 in the absence of treatment.Thus, the average treatment effect can be written as

τCIC = E [Y (1)11 − Y (0)11] = E(Y (1)11)− E [F−1Y ,01(FY ,00(Y10))] (3)

if the support assumption does not hold, it is still possible to estimate theeffect of the treatment on the quantile q:

τCICq = F−1Y (1)11(q)− F−1Y (0)11(q) = F−1Y (1)11(q)− F−1Y ,01(FY ,00(F−1Y ,10(q))) (4)


Methods

Changes in Changes transformation

Source: Extracted from Athey and Imbens (2006).


Methods

In practice we use:the empirical cumulative distribution function

FY ,gt(y) =∑Ngt

i=1 I{Ygt,i ≤ y}Ngt

(5)

and the estimator of the inverse distribution function used is:

F−1Y ,gt(q) = inf {y ∈ Ygt : FY ,gt(y) ≥ q} (6)

FY (0)11(y) is estimated according to:

FY (0)11(y) =

0 if y < ymin,01

FY ,10(F−1Y ,00(FY ,01(Y ))) if ymin,01 ≤ y < ymax,01

1 otherwise.

and, τCIC can be (consistently) estimated through:

τCIC =

∑N11i=1 Y11,i

N11−∑N10

i=1 F−101 (F00(Y10,i))

N10(7)

We report bootstrapped standard errors with 1000 draws. (it is alsopossible to derive analytical se)


Methods

For discrete outcomes AI propose upper and lower bounds for thecounterfactual distribution.It is possible to include covariates, that in the case of discrete outcomeswill help to improve point estimates.1. Let Ygt,i = Ygt,i − X ′gt,iβ and define

D = ((1 − T )(1 − G),T (1 − G), (1 − T )G,TG)′

2. Regress (OLS, with no constant) Yi = D′δ + X ′i β + εi3. Obtain the augmented residuals Y = Yi − X ′i β = D′δ + εi , and apply the

CIC estimator.Included X1. Individual controls

Education attainment (years of education)SexEthnicity, multidimensional index based on three indicators: i) speaks anindigenous language, ii) self-reported ethnicity, and iii) native languageDummies if there are individuals in age∈[0,3], [4,6],[7,15],[16,18])Wealth index (5) quantiles (see Rutstein and Johnson (2004))

2. other controlsRuralRegional fixed effects

for continuous variables, zero values where recoded to half the minimumobserved value to work with logs (Meyer, Viscusi, and Durbin (1995,pp330)).


�� Effects


Welfare

Empirical cumulative distribution functionsPc income (log)

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

2 4 6 8 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

2 4 6 8 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men


Welfare

Effect of RD on pc income (log): CIC

CICaverage CICq0.1 CICq0.2 CICq0.5 CICq0.8 CICq0.9

BoliviaτCIC 0.170 0.583 0.288 0.047 0.069 0.172

[0.081]** [0.235]** [0.199] [0.087] [0.088] [0.107]τCIC

w/cov 0.155 0.541 0.436 0.050 -0.106 0.047[0.119] [0.318]* [0.240]* [0.159] [0.139] [0.113]

WomenτCIC 0.371 1.286 0.783 0.219 0.161 0.004

[0.138]*** [0.285]*** [0.421]* [0.109]** [0.117] [0.179]τCIC

w/cov 0.476 1.392 0.970 0.298 0.037 0.171[0.211]** [0.475]*** [0.485]** [0.229] [0.283] [0.269]

MenτCIC 0.128 0.174 0.198 -0.021 0.063 0.268

[0.112] [0.361] [0.248] [0.119] [0.091] [0.120]**τCIC

w/cov 0.046 0.009 0.102 0.037 -0.105 0.115[0.143] [0.436] [0.356] [0.189] [0.215] [0.170]

Bootstrapped standard errors in brackets


Welfare

Empirical cumulative distribution functionsPc labor income (log)

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men


Welfare

Effect of RD on pc labor income (log): CIC


BoliviaτCIC -0.309 -0.469 -0.303 -0.145 -0.064 -0.047

[0.127]** [0.717] [0.209] [0.097] [0.077] [0.112]τCIC

w/cov -0.333 -0.432 -0.535 -0.202 0.038 -0.064[0.156]** [0.805] [0.317]* [0.151] [0.153] [0.191]

WomenτCIC -0.290 -4.837 -0.154 -0.146 -0.019 -0.077

[0.299] [1.446]*** [0.463] [0.167] [0.164] [0.222]τCIC

w/cov -0.237 -2.955 0.307 -0.054 -0.142 0.169[0.320] [1.192]** [0.622] [0.291] [0.167] [0.284]

MenτCIC -0.014 0.288 -0.237 -0.047 0.054 0.149

[0.173] [0.678] [0.320] [0.136] [0.123] [0.162]τCIC

w/cov -0.101 0.142 -0.403 -0.089 0.008 0.105[0.235] [0.868] [0.412] [0.214] [0.170] [0.184]



Welfare

Empirical cumulative distribution functionsPc non-labor income (log)

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men


Welfare

Effect of RD on pc non-labor income (log): CIC


BoliviaτCIC 2.580 0.000 4.556 5.772 0.600 0.393

[0.202]*** [0.000] [0.073]*** [0.035]*** [0.224]*** [0.171]**τCIC

w/cov 2.460 0.887 4.324 4.059 0.660 0.568[0.237]*** [0.231]*** [0.167]*** [0.368]*** [0.283]** [0.281]**

WomenτCIC 3.057 3.709 4.855 5.879 0.914 0.781

[0.275]*** [1.157]*** [0.103]*** [0.523]*** [0.314]*** [0.327]**τCIC

w/cov 2.983 3.640 4.831 4.583 1.116 1.011[0.362]*** [0.855]*** [0.294]*** [0.528]*** [0.474]** [0.530]*

MenτCIC 1.946 0.000 2.457 5.477 0.111 0.174

[0.317]*** [0.000] [1.130]** [1.034]*** [0.343] [0.358]τCIC

w/cov 1.896 0.467 2.156 3.779 0.002 -0.139[0.310]*** [0.214]** [0.704]*** [0.612]*** [0.495] [0.453]



Welfare

Empirical cumulative distribution functionsPc consumption (log)

4 6 8 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

4 6 8 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

4 6 8 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

4 6 8 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

4 6 8 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

4 6 8 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men


Welfare

Effect of RD on pc consumption (log): CIC


BoliviaτCIC -0.007 -0.073 0.104 -0.003 0.060 0.018

[0.057] [0.083] [0.069] [0.048] [0.076] [0.095]τCIC

w/cov -0.017 0.048 0.005 -0.018 0.005 0.066[0.094] [0.139] [0.119] [0.120] [0.143] [0.113]

WomenτCIC 0.089 0.172 0.239 0.118 0.173 -0.050

[0.068] [0.160] [0.102]** [0.081] [0.121] [0.216]τCIC

w/cov 0.148 0.417 0.342 0.104 0.128 -0.165[0.138] [0.230]* [0.167]** [0.171] [0.209] [0.262]

MenτCIC -0.075 -0.172 -0.083 -0.032 0.018 0.064

[0.069] [0.101]* [0.112] [0.059] [0.091] [0.144]τCIC

w/cov -0.126 -0.291 -0.179 -0.160 -0.099 0.101[0.105] [0.217] [0.139] [0.142] [0.173] [0.221]



Investments and savings

Empirical cumulative distribution functionsDwelling investments (log)

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 2 4 6 80

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on dwelling investments (log): CIC


BoliviaτCIC 0.209 0.000 0.000 0.000 0.000 1.268

[0.119]* [0.000] [0.000] [0.000] [0.000] [1.129]τCIC

w/cov 0.214 -0.003 -0.006 0.077 0.126 1.251[0.145] [0.022] [0.024] [0.076] [0.149] [0.991]

WomenτCIC 0.300 0.000 0.000 0.000 0.000 2.049

[0.162]* [0.000] [0.000] [0.000] [0.625] [1.581]τCIC

w/cov 0.341 0.040 0.042 0.182 0.305 1.437[0.184]* [0.041] [0.049] [0.123] [0.510] [1.279]

MenτCIC 0.097 0.000 0.000 0.000 0.000 1.086

[0.200] [0.000] [0.000] [0.000] [0.530] [0.997]τCIC

w/cov 0.064 -0.030 -0.018 -0.007 0.031 0.883[0.186] [0.025] [0.032] [0.095] [0.647] [1.156]




Empirical cumulative distribution functionsSaving ratey

−100 −50 00

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−60 −40 −20 00

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−100 −50 00

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−100 −50 00

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

−60 −40 −20 00

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

−100 −50 00

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on saving ratey : CIC


BoliviaτCIC 1.214 1.951 0.448 0.125 0.006 0.040

[0.666]* [1.095]* [0.268]* [0.066]* [0.037] [0.033]τCIC

w/cov 1.110 1.086 0.483 -0.148 -0.129 -0.004[0.615]* [1.231] [0.346] [0.194] [0.063]** [0.080]

WomenτCIC 2.138 2.309 0.615 0.203 0.066 0.015

[1.143]* [1.836] [0.573] [0.150] [0.065] [0.056]τCIC

w/cov 2.243 3.209 0.557 0.156 0.041 0.169[1.125]** [2.328] [0.796] [0.297] [0.154] [0.121]

MenτCIC 1.212 2.171 0.582 0.184 0.063 0.100

[0.585]** [2.575] [0.451] [0.082]** [0.043] [0.053]*τCIC

w/cov 1.076 0.703 0.592 -0.221 -0.177 -0.023[0.629]* [2.484] [0.575] [0.224] [0.104]* [0.101]




Empirical cumulative distribution functionsSaving ratec

0 5 10 15 200

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 10 15 200

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 10 15 200

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 5 10 15 200

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on saving ratec : CIC


BoliviaτCIC 0.103 0.118 0.093 0.106 0.017 0.188

[0.047]** [0.043]*** [0.045]** [0.051]** [0.090] [0.139]τCIC

w/cov 0.101 0.129 0.157 0.065 -0.003 0.178[0.064] [0.092] [0.092]* [0.062] [0.100] [0.155]

WomenτCIC 0.135 0.127 0.122 0.158 0.180 0.077

[0.100] [0.068]* [0.064]* [0.091]* [0.185] [0.307]τCIC

w/cov 0.170 0.281 0.276 0.167 0.148 0.127[0.098]* [0.130]** [0.137]** [0.087]* [0.132] [0.268]

MenτCIC 0.183 0.141 0.117 0.157 0.163 0.437

[0.070]*** [0.069]** [0.073] [0.062]** [0.139] [0.184]**τCIC

w/cov 0.158 0.040 0.095 0.107 0.084 0.266[0.089]* [0.161] [0.137] [0.086] [0.121] [0.250]



Labor market outcomes

Empirical cumulative distribution functionsLabor market participation

0 0.5 10

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 0.5 10

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 0.5 10

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 0.5 10

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 0.5 10

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 0.5 10

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on labor market participation: CIC

CICdisc CICdisc LB CICdisc UB

BoliviaτCIC -0.06 -0.25 0.01

[0.022]** [0.012]*** [0.016]τCIC

w/cov -0.07 -0.07 -0.07[0.031]** [0.031]** [0.031]**

WomenτCIC -0.10 -0.38 -0.04

[0.032]*** [0.018]*** [0.025]*τCIC

w/cov -0.12 -0.12 -0.12[0.040]*** [0.040]*** [0.040]***

MenτCIC -0.02 -0.12 0.06

[0.024] [0.013]*** [0.022]***τCIC

w/cov -0.01 -0.01 -0.01[0.031] [0.031] [0.031]

Bootstrapped standard errors in brackets. LB= Lower Bound, UB=Upper Bound.



Empirical cumulative distribution functionsLabor supply intensity (log)

0 2 40

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 40

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 40

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 2 40

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 2 40

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 2 40

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on labor supply intensity (log): CIC


BoliviaτCIC -0.226 0.000 0.000 0.000 0.000 0.000

[0.109]** [0.000] [0.811] [0.054] [0.035] [0.015]τCIC

w/cov -0.245 -0.268 -1.462 -0.094 0.096 -0.101[0.128]* [0.143]* [0.660]** [0.092] [0.103] [0.086]

WomenτCIC -0.416 0.000 0.000 -0.357 -0.036 0.105

[0.129]*** [0.000] [0.000] [0.132]*** [0.063] [0.047]**τCIC

w/cov -0.495 -0.102 -0.267 -0.298 -0.179 0.014[0.167]*** [0.089] [0.164] [0.155]* [0.124] [0.081]

MenτCIC -0.087 0.000 0.000 0.000 0.000 -0.028

[0.118] [0.850] [0.223] [0.044] [0.057] [0.035]τCIC

w/cov 0.016 0.183 0.062 0.156 -0.105 0.000[0.167] [0.934] [0.756] [0.103] [0.069] [0.053]




Empirical cumulative distribution functionsWage (log)

−5 0 50

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−5 0 50

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−5 0 50

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

−5 0 50

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

−5 0 50

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

−5 0 50

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on wage (log): CIC


BoliviaτCIC -0.426 0.000 0.000 -0.590 -0.090 -0.153

[0.225]* [0.000] [0.000] [0.268]** [0.090] [0.094]τCIC

w/cov -0.437 0.119 0.045 -0.742 0.074 0.242[0.268] [0.092] [0.145] [0.379]* [0.215] [0.175]

WomenτCIC -0.714 0.000 0.000 0.000 -0.554 -0.310

[0.246]*** [0.000] [0.000] [1.082] [0.173]*** [0.234]τCIC

w/cov -0.689 0.020 0.137 -0.166 -0.828 -0.439[0.302]** [0.061] [0.093] [0.837] [0.266]*** [0.254]*

MenτCIC -0.220 0.000 -0.576 0.015 0.024 0.053

[0.210] [0.913] [0.572] [0.123] [0.133] [0.145]τCIC

w/cov -0.367 -2.807 -0.916 -0.175 0.203 0.034[0.261] [1.128]** [1.118] [0.207] [0.141] [0.201]




Empirical cumulative distribution functionsLabor income (log)

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=0,T=1 G=0,T=0

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(a) Bolivia

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(b) Women

0 5 100

0.2

0.4

0.6

0.8

1

G=1,T=1 G=1,T=0

(c) Men



Effect of RD on labor income (log): CIC


BoliviaτCIC -0.454 0.000 0.000 -0.472 0.026 -0.068

[0.228]** [0.000] [0.000] [0.269]* [0.093] [0.083]τCIC

w/cov -0.461 0.202 0.015 -0.668 0.020 0.116[0.305] [0.093]** [0.184] [0.523] [0.213] [0.162]

WomenτCIC -0.812 0.000 0.000 0.000 -0.614 -0.265

[0.318]** [0.000] [0.000] [1.459] [0.234]*** [0.135]**τCIC

w/cov -0.742 0.056 0.190 -0.266 -0.985 -0.486[0.310]** [0.112] [0.084]** [1.271] [0.230]*** [0.167]***

MenτCIC -0.216 0.000 -0.473 -0.033 -0.076 0.092

[0.261] [1.377] [0.562] [0.154] [0.111] [0.127]τCIC

w/cov -0.394 -4.143 -0.825 -0.116 0.175 0.164[0.291] [1.456]*** [0.702] [0.175] [0.152] [0.121]



�� Summarizing. . .


Evidence of RD effects on welfare, investments and savings

Elderly Women Men τWaverage/τ

Maverage

WelfarePc income ◦ +,q0.2 ◦ 0.066/0.136

(log) +q0.1 +average,q0.1,q0.2 ◦ 0.371/0.128Pc consumption ◦ +q0.2 ◦ −0.056/− 0.077(log) ◦ +q0.2 ◦ 0.089/− 0.075

Pc labor income ◦ ◦ ◦ −0.113/0.030(log) -average -q0.1 ◦ −0.290/− 0.014

Pc non-labor income +avg,q0.2,q0.5,q0.9 all+ +q0.5 0.251/0.110(log) +avg,q0.2,q0.5,q0.8,q0.9 all+ +avg,q0.2,q0.5 3.057/1.946

Pc intra-household transfersand log

◦ ◦ ◦ 0.036/− 0.016

Household investments and savingsEducation expenditure and

log◦ ◦ ◦ 0/− 0.005

Health expenditure and log ◦ ◦ ◦ 0.019/− 0.017Durables expenditure and log ◦ ◦ ◦ −0.012/− 0.001Dwelling’s investment ◦ ◦ ◦ 0.003/− 0.003(log) ◦ +average ◦ 0.300/0.097

Saving ratey +average +average +average 2.138/1.212Saving ratec +q0.2 +q0.1,q0.2,q0.5 +average 0.135/0.183

- = negative effect on; + = positive effect on, and ◦= no evidence of effect. Note: for education, health, durables and intra-household transfers

we show only τ for the variable in levels.


Evidence of RD effects on labor market outcomes

Elderly Women Men τWaverage/τ

Maverage

Labor market participation -average -average ◦ −0.10/− 0.02Family Worker ◦ ◦ ◦ −0.02/− 0.004Informal non-salaried ◦ ◦ ◦ −0.03/− 0.02Informal salaried ◦ ◦ ◦ −0.03/0.02Formal salaried ◦ ◦ ◦ −0.02/0.02Labor supply intensity ◦ -average,q.05 ◦ −3.228/0.557

(log) -average -average,q0.5 ◦ −0.416/− 0.087Wage ◦ ◦ ◦ −1.236/− 0.051(log) -average -average,q0.8 ◦ −0.714/− 0.220

Labor income ◦ ◦ ◦ −0.293/0.121(log) ◦ -average,q0.8,q0.9 ◦ −0.812/− 0.216

- = negative effect on; + = positive effect on, and ◦= no evidence of effect

Gary Mena: Intended and unintended effects of unconditional cash

Government & Nonprofit

Transcript of Gary Mena: Intended and unintended effects of unconditional cash