A Business Case for Financial Education and Microsavings

Promotion: Experimental Evidence from a For-Pro�t Program in

Rural Peru*

Chris M. Boyd�and Sandro Díez-Amigo�

Preliminary version

Abstract

This paper intends to contribute to the mixed body of evidence on the e�ectiveness of �nancial educa-

tion to promote savings among poor rural populations, and to rigorously test the business case for �nancial

education in rural areas provided by private �nancial institutions on a for-pro�t basis. In order to do so

we experimentally evaluate a for-pro�t �nancial education program, paired with savings account market-

ing, targeting poor rural women in the Apurímac region of Peru. We �nd that the program signi�cantly

increased the familiarity and trust in formal �nancial institutions, and that this e�ect was particularly

large for the implementing institution. Moreover, we �nd that the program signi�cantly increased the

probability of saving with the implementing institution. However, we only observe an incipient e�ect on

credit uptake with the implementing institution. Our �ndings suggest that �nancial education programs

can be a successful marketing tool for the promotion of microsavings among the poor even in remote rural

areas, and that their provision by �nancial institutions on a for-pro�t basis can be commercially viable if

their focus is expanded beyond savings, in order to promote and generate cross-selling of credit products

with higher pro�t margins.

*The authors would like to thank Martín Naranjo, Cecilia Marquina and their colleagues at Financiera Con�anza and theBBVA Micro�nance Foundation for their time and support, Claudia Martínez, Yuri Soares, Claudia Gutiérrez, Andrea Reyesand numerous seminar participants for their useful comments, and Cecilia Vargas Yana for her outstanding research assistance.Financial support was generously supplied by the Multilateral Investment Fund of the Inter-American Development Bank,the International Development Research Centre, the Ford Foundation, and Proyecto Capital. Research was conducted whileChris M. Boyd was a�liated to the Instituto de Estudios Peruanos (IEP), and Sandro Díez-Amigo was at the Inter-AmericanDevelopment Bank (IDB).

�University of Minnesota. Corresponding author: e-mail: boydl001@umn.edu; Address: 1994 Buford Ave, St. Paul, MN55108; Phone: (612) 625 5000.

�World Bank Group. e-mail: sdiezamigo@worldbank.org; Address: 1818 H St NW, Washington D.C. 20433; Phone: (202)453 1001

JEL Classi�cation Codes: C93, D04, D14, M21, R51.

Keywords: Financial Education, Financial Inclusion, Micro�nance, Savings, Rural Microsavings, Impact Evaluation,

Business Case.

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1 Introduction

The ProSavings Program, led by the Multilateral Investment Fund of the Inter-American Development Bank,

promotes the development of business strategies to o�er liquid and planned savings services tailored to low-

income populations in Latin America and the Caribbean. As part of this program in 2015 the Peruvian

micro�nance institution Financiera Con�anza, a subsidiary of the BBVA Micro�nance Foundation, started

to implement the Ahorro para Todos (�Savings for All�) �nancial education program. This intervention

targeted the rural population of the Apurímac region in Southern-Central Peru, with a focus on women over

18 years old, and its four training modules were aimed at generating knowledge and trust in the formal

�nancial system and at promoting �nancial best practices. Also, during the program trainers promoted an

associated savings account o�ered by Financiera Con�anza.

The evidence regarding the ability of government- and non-pro�t-sponsored �nancial education programs

to promote savings in the formal �nancial system is mixed. Moreover, �nancial education programs also

bene�t formal �nancial institutions by expanding their pool of clients, allowing them to reach previously

underserved rural populations. Therefore, �nancial education can be thought of not only a public good, but

also as an e�ective marketing mechanism, and succesful private or public-private provision models would be

a win-win-win for the government, rural populations, and formal �nancial institutions. However, �nancial

institutions face challenges which may or may not allow them to provide for-pro�t �nancial education in a

commercially viable manner.

In this context our experimental impact evaluation of the Ahorro para Todos program intends to contribute

to the mixed body of evidence on the e�ectiveness of �nancial education to promote savings among poor

rural populations, and to rigorously test the business case for �nancial education in rural areas provided by

private �nancial institutions on a for-pro�t basis.

We use an optimal random assignment at the community level to generate comparable control and treatment

groups which are statistically indistinguishible on both observable and unobservable characteristics. This

allows us to robustly identify and estimate the impact of the program on a range of dependent outcome

variables. Also, in July 2016 we carried out an endline survey of a random sample 1801 households across

the 89 communities included in the evaluation. Information was gathered on a range of dependent outcome

variables, grouped in six categories: (i) �nancial literacy, (ii) savings, (iii) credit, (iv) income and assets,

(v) consumption, and (vi) female empowerment. Finally, we also have access to administrave information

provided by Financiera Con�anza on transactions and balances between January 2015 and July 2016, with

which we complement the self-reported data.

We �nd that the �nancial education program signi�cantly increased the familiarity and trust in formal

�nancial institutions in poor rural areas of the Andes, and that this e�ect was particularly large for Financiera

Con�anza. Moreover, we also �nd that the program signi�cantly increased the probability of saving with

Financiera Con�anza, even if we �nd no evidence of a signi�cant impact of the program on the probability

of saving at home or with other formal �nancial institutions or cooperatives. However, on the contrary

we observe no evidence of an e�ect of the program on the probability of buying other �nancial products

from Financiera Con�anza or from any other �nancial institution. On the other hand, we estimate that the

program signi�cantly reduced poverty.

3

All the above suggests that �nancial education programs can be a succesful marketing tool for the promotion

of microsavings among the poor even in remote rural areas, and that their provision by �nancial institutions

on a for-pro�t basis can be commercially viable if their focus is expanded beyond savings, in order to promote

and generate cross-selling of credit products with higher pro�t margins. Also, the observed success of Ahorro

para Todos in reducing poverty levels rea�rms the public good nature of �nancial education, and suggests a

rationale for public sector support of this type of private development programs.

The rest of the paper is organized as follows: Section 2 presents the background for the paper; Section

3 provides a description of the intervention; Section 4 outlines the methodology used in the experimental

evaluation; Section 5 presents the main �ndings; Section 6 concludes.

2 Background

Under the presidencies of Alejandro Toledo (2001-2006), Alan García (2006-2011) and Ollanta Humala (2011-

2016) Peru experienced robust economic growth. Poverty levels fell dramatically, from 54.2% in 2002 to

21.8% in 20151, and while a�ected by the 2015 global commodities slump, it continues to be one of the best

performing economies in Latin America.

However, there are great economic disparities among Peruvian regions, and particularly across rural an urban

areas: half of the rural population in Peru (45.2%) was below the poverty line in 2015 , and more than an

eighth (13.9%) was considered extremely poor. This gap was exarcerbated in the Andean regions, were 49%

of the population was below the poverty line, and 16.5% was considered extremely poor [INEI(INEI2016)].

And, as in other contexts, the higher levels of poverty were correlated with lower levels of education, and

high rates of �nancial illiteracy.

Apurímac, an Andean region in Southern-Central Peru with a particularly challenging geography2, was the

poorest region in the country in 2012, contributing 0.4% of its GDP and growing at an average rate of 4.3%

between 2007 and 2014 [INEI(INEI2016)]. These facts made it the target of numerous public and private

development iniatives in subsequent years, such as large mining investments, which succesfully accelerated

regional GDP growth. However, at the same Apurímac struggled to tackle the issue of income inequality

across urban and rural areas, motivated in great part by the lack of access to the formal �nancial system in

the latter.

The ProSavings Program, led by the Multilateral Investment Fund of the Inter-American Development Bank,

promotes the development of business strategies to o�er liquid and planned savings services tailored to low-

income populations in Latin America and the Caribbean. As part of this program the Peruvian micro�-

nance institution Financiera Con�anza, a subsidiary of the BBVA Micro�nance Foundation, implemented

the Ahorro para Todos (�Savings for All�) �nancial education program in the Apurímac region3, with a focus

on its Abancay, Antabamba, Aymaraes and Grau provinces.

Financial education programs and related initiatives to expand access promise rural populations the op-

1See [Herrera(2003)],[INEI(INEI2016)].2Mountains are worshipped as gods in Andean religion, and Apurímac means where the gods speak in Quechua language.3After its initial implementation in Apurímac the program was subsequently expanded to the Cusco region with some

modi�cations. However, here we only study the intervention in Apurímac.

4

portunity to bene�t from the formal �nancial system. However, the evidence regarding the ability of

government- and non-pro�t-sponsored programs in Peru and elsewhere to promote savings in the formal

�nancial system and improving welfare is mixed. For example, [Cole et al.(2009)Cole, Sampson, and Zia]

�nd that a �nancial education program in Indonesia has modest e�ects on the demand for bank accounts,

which is however succesfully estimulated with savings subsidies. However, while in their evaluation of a

savings promotion pilot program in Peru [Boyd and Aldana(2015)] only �nd a small impact on �nancial

literacy, they observe a large signi�cant impact on the probability of saving at a bank (16%). Similarly,

[Ashraf et al.(2010)Ashraf, Karlan, and Yin] �nd that a Philippine commitment savings program combined

with �nancial education focused on the bene�ts of saving had a large signi�cant impact (81%) on the amount

of formal savings 12 months after the intervention, but this e�ect is no longer observed 32 months after the

intervention.

Moreover, �nancial education programs also bene�t formal �nancial institutions by expanding their pool of

clients, allowing them to reach previously underserved rural populations. Therefore, �nancial education can

be thought of not only a public good, but also as an e�ective marketing mechanism, and succesful private or

public-private provision models would be a win-win-win for the government, rural populations, and formal

�nancial institutions. However, �nancial institutions face challenges which may or may not allow them to

provide for-pro�t �nancial education in a commercially viable manner.

In this context our experimental impact evaluation of the Ahorro para Todos program intends to contribute

to the mixed body of evidence on the e�ectiveness of �nancial education to promote savings among poor

rural populations, and to rigorously test the business case for �nancial education in rural areas provided by

private �nancial institutions on a for-pro�t basis.

3 Intervention

The Ahorro para Todos (�Savings for All�) �nancial education program implemented by Financiera Con�anza

from May 2015 to April 2016 targeted the rural population of the Apurímac region in Southern-Central Peru,

with a focus on women over 18 years old. This was motivated by the evidence generated by the pilot

program carried out in early 2015, which as discussed by [Boyd and Aldana(2015)] suggested that focusing

the program on women would improve its e�ectiveness, making it easier to boost savings and providing a

more creditworthy4 potential pool of clients for the �nancial institution.

The treatment of the Ahorro para Todos program consisted on two interacting components:

(i) The �rst component consisted of four modules focused on general �nancial education.

(ii) The second component focused on the promotion of the associated savings account o�ered by Financiera

Con�anza, also called Ahorro para Todos.

The four modules5 of the �nancial education component intended to generate knowledge and trust in the

4Even if the program is focused on savings promotion, creditworthiness remains a key dimension, as it is a stylized industryfact that the taking of deposits is a commercially viable activity only insofar as it leads to the cross-selling of other productswith higher pro�t margins, such as loans.

5The four modules were named (1) �Trusting my savings to Financiera Con�anza�, (2) �If we know, we win�, (3) �Let's control

5

formal �nancial system, by explaining the advantages and disadvantages of the di�erent types of �nancial

products such as savings accounts, loans and insurance policies, as well as by promoting �nancial best

practices such as the creation of a monthly budget for the household.

An important innovation included in this part of the program consisted on the use of the �edutainment�

approach used to impart the four modules, shown to achieve results in other settings6. In order to do so,

each of the four teaching modules were structured as short stories of (made-up) local women, to which the

target population could easily relate. Also, program trainers used custom-designed dolls and portable stages

to support their explanations and maintain the concentration of the audience.

Although the challenging logistics of reaching remote rural communities in a context of limited transportation

options and budgetary contstraints meant that exceptions needed to be made, in general the four �nancial

education modules were delivered at a rate of one per month. Trainers traveled to each community, and

each module was taught in a one-hour session over or immediately before/after a meeting organized by one

of the pre-established groups7. Bene�ciaries could decide whether or not to participate in each �nancial

education session, and while a mentioned the program focused on female bene�ciaries, those men who asked

to participate were allowed to do so.

At the end of each session trainers addressed the questions and doubts of the audience about the contents

taught in the corresponding module, and then spent some time promoting the Ahorro para Todos savings

account8.

This account is a simple savings account, which (a) features no administration or transfer fees, (b) o�ers an

unsbsidized but competitive interest rate of 0.75% (3) requires a minimum opening deposit of 20 Peruvian

soles, or approximately 6 U.S. dollars, (4) provides a complimentary debit card once the amount saved

reaches 50 soles, and (5) provides a life microinsurance policy active if the amount saved is at least 100 soles.

Participants in the �nancial education sessions were explicitly informed that their attendance did not entail

any commitment to contract the associated savings account. Also, in order to open an account and make

deposits prospective clients needed to visit the Financiera Con�anza branch at the regional capital, Abancay.

Although it was originally conceived as a �commitment� savings account with a minimum recurrent deposit,

this requirement proved very unpopular among prospective clients during the pilot phase, and was subse-

quently dropped. However, coinciding with the main holidays Financiera Con�anza ra�ed a (non-perishable)

food basket, valued at 120 soles, among clients who had made a deposit during the previous month. This

ra�e was advertised and used during the promotion of the savings account at the end of each teaching session.

our savings and plan our expenses�, and (4) �I need insurance, and I need credit! Let's get to know �nancial services�.6For example, see [La Ferrara et al.(2012)La Ferrara, Chong, and Duryea] for an analysis of the impact of soap operas on

fertility in Brazil, or [Valdivia and Chong(2013)] for a proposal on how to use an analogous approach to promote savings amongwomen in rural Peru.

7In order to maximize female participation in the program selected groups were generally those with a majority of femaleattendance. Also, the majority of groups were �pre-established� in the sense that they had already been set up by governmentalprograms (e.g. Juntos, Health Center meetings) or another institution working in the community.

8Trainers were hired by Financiera Con�anza as �Savings Promotion Agents�.

6

4 Evaluation Methodology

4.1 Experimental Design

We use random assignment at the community level that maximizes the balance of observable characteristics

prior to the intervention. This allows us to robustly identify and estimate the impact of the program on a

range of dependent variables, as described more in detail in Section 4.3 below.

During the pilot phase of the program a �eld test was conducted by Financiera Con�anza in order to calculate

the average travel time from the regional capital of Abancay, where its nearest branch is located, to each of the

prospective intervention communities. Based on the results of this exercise Financiera Con�anza decided to

limit the intervention to communities at a distance of under six hours of Abancay, since reaching communities

further away was deemed ex-ante commercially unviable. Similarly, Financiera Con�anza determined that

Ahorro para Todos would only be implemented in communities located at least one hour and half away

from Abancay, in order to guarantee that the program maintained its focus on providing �nancial education

and access to formal �nancial services for the rural population. Moreover, some remote communities within

the six hour radius were grouped together and treated as one, in order to better accomodate the logistical

constraints arising from the limited transportation options and challenging geography, and to minimize the

potential for contamination of the control group whenever two communities were very close to each other.

Also, communities with fewer than 15 bene�ciaries of the JUNTOS conditional cash transfer program were

also excluded to ensure that the program focused on communities with a high percentage of poor households.

Similarly, additional communities were excluded because it was determined that they had no contact with the

regional capital, or because they were deemed potentially dangerous for program trainers to visit. Finally,

Financiera Con�anza also agreed to exclude the districts where communities had received or were scheduled

to receive any �nancial education treatment provided by the government or by Proyecto Capital as part of

its paralell activities in the region 9.

After the above described pre-selection process was completed there were 89 eligible communities or groups

of communities left across 22 districts in the Abancay, Antabamba, Aymaraes and Grau provinces of the

Apurímac region.

In order to maximize the balance between treatment and control groups we carried out 3,000 random assign-

ments, and chose the randomization which maximized the balance across all available variables 10. Due to

budgetary restrictions, a relevant baseline survey could not be carried out, thus we used information from

the Peruvian Social Programs Targeting O�ce (SISFOH) in order to carry out this analysis11.

9In particular, we excluded districts which had participated in the governmental Haku Wiñay program, and those which werescheduled to participate in another �nancial inclusion program to be piloted by Proyecto Capital.

10In particular, we tested for di�erences in means across 22 balance variables in each randomization, and selected the ran-domization with the highest minimum p-value.

11We used 19 SISFOH variables at the household level, collected in 2013 from all households in the evaluation districts andcompared their means at the community level to carry out the balance maximization: (i) percentage of men in the household,(ii) percentage of pregnant women in the household, (iii) head of household age, (iv) average age of household members, (v)gender of household head, (vi) bene�ciary status in ESSALUD health insurance program, (vii) bene�ciary status in SIS healthinsurance program, (viii) percentage of household members without health insurance, (ix) participation in the Vaso de Leche

program, (x) bene�ciary status in the school breakfast/lunch program, (xi) bene�ciary status in JUNTOS conditional cashtransfer program, (xii) bene�ciary status in social programs, (xiii) bene�ciary status in Pension 65 program, (xiv) percentage ofhousehold members with Spanish as native tongue, (xv) percentage of household members with Quechua as a native tongue, (xvi)

7

This exercise yielded a balanced ex-ante random assignment of 44 communities in the control group and 45

communities in the treatment group .

After the completion of the evaluation Financiera Con�anza o�ered the Ahorro para Todos program also in

communities assigned to the control group.12

4.2 Data Collection

In July 2016 we carried out an endline survey of a random sample of 1801 rural households across the 89

communities included in the evaluation, which had been randomly assigned to the control and treatment

group prior to the start of the intervention13. Information was gathered on a range of dependent outcome

variables, grouped in six categories: (i) �nancial literacy, (ii) savings, (iii) credit, (iv) income and assets, (v)

consumption, and (vi) female empowerment.

In the absence of a population census the random sampling strategy for the endline survey was as follows:

(a) for each community we calculated the female population above 18 years old according to the information

available from the Peruvian Social Programs Targeting O�ce (SISFOH); (b) for communities with less than

90 females over 18 years old we assigned a maximum target number of 30 surveys to be collected, allowing

for a maximum of two replacements; (c) for communities with more than 90 females over 18 years old we

assigned a minimum target number of 30 surveys to be collected, and then randomly added observations to

each community until reaching the target of 1800 observations split evenly between treatment and control.

In addition, on arrival to each community the surveyors followed a �eld protocol to ensure the random selection

of households to be interviewed: (1) �rst, they looked for the community president, informed her/him about

the study and requested permission to do it; (2) then, with the help of the community president they mapped

the community, noting the main buildings or geographical characteristics, and divided the community into

four quadrants; (3) next, surveyors divided each quadrant in blocks of 3-4 houses and administered the

survey to a randomly selected household in each block, leaving the other 2-3 as replacements; and (4) �nally,

surveyors applied this protocol until the target number of observations for the community was reached,

randomly choosing replacements within each block as necessary.

We supervised the �eld operations in several communities and veri�ed GPS geodata for each household in

order to validate the randomness of the sampling. The results of this validation were positive, however

percentage of household members with a national identity document, (xvii) percentage of household members who know howto read and write, (xviii) household involved in agriculture, and (xix) household involved in the service sector. In addition, wealso looked at 3 additional community level variables for balance: (xx) distance to regional capital, (xxi) number of bene�ciaresof JUNTOS conditional cash transfer program, and (xxii) percentage of JUNTOS recipients receiving the transfer in a bankaccount.

12As of January 2017 Financiera Con�anza had started to implemented the Ahorro para Todos program in some of the controlcommunities located nearer to the regional capital, expanding it to include activities to promote group credit products.

13Note that the 89 communities included four intermediate cities, two in the control group and two in the tratment group.Financiera Con�anza invited people from the urban areas of these cities to training meetings, but none attended the meetings,although a few opened the promoted savings account. Thus, we excluded urban areas from the endline survey to keep the focuson rural population, but this does not seem to a�ect the balance between treatment and control groups . Also, note that a fewrural communities close to each other were regrouped since Financiera Con�anza decided to do one bigger meeting for all ofthem. As a result of these two changes, we have 84 communities (clusters in the following regressions) instead of the designed89, but the targeting was done as originally planned: in all of the 89 communities assigned to the treatment, people were invitedto the �nancial education training meetings, and none in the communities originally assigned to control was invited to themeetings.

8

in some small communities the target number of observations could not be reached, due to migration or

reluctance to take the survey. In these instances the remaining observations were randomly reassigned to

other communities, resulting in the �nal endline survey tally of 907 observations in the control group and

894 observations in the treatment group.

Also, we check the ex-post balance between treatment and control at endline by looking at characteristics

considered to be orthogonal to the intervention, and therefore unlikely to have changed in a systematically

di�erent manner in both groups before and after the program was implemented: (i) distance to regional

capital; (ii) frequency of travel to provincial capital by respondent; (iii) number of household members;

(iv) gender of respondent; (v) age of respondent; (vi) maximum educational level of household; and (vii)

bene�ciary status of JUNTOS conditional cash transfer program.14

As detailed in the appendix, the balance at endline is good, with no signi�cant di�erences in balance variables

between treatment and control at the 1% or 5% level15.

Finally, we also have access to administrave information provided by Financiera Con�anza on transactions

and balances between January 2015 and July 2016, with which we complement the self-reported data obtained

in the endline survey.

4.3 Model Speci�cations

Using the econometric models described below we estimate the e�ect of the program on a range of dependent

outcome variables, which are grouped in six categories: (i) �nancial literacy, (ii) savings, (iii) credit, (iv)

income and assets, (v) consumption, and (vi) female empowerment16.

All reported standard errors are clustered at the community level since this was our randomization unit .

Intent-to-Treat (ITT)

Let Zi ∈ {0, 1}denote the random assignment of household i to a control or treatment area in a causal model

á la Rubin17. Then if Yi is the dependent outcome variable, the intent-to-treat (ITT) estimate of the e�ect

of the program can be represented as:

δITT = E(Yi | Zi = 1)− E(Yi | Zi = 0)

This reduced form estimate captures the relative average e�ect of the program among all those households in

communities randomly assigned to the treatment group compared to households in communities randomly

14Due to SISFOH anonimization of individual households, we could not check ex-post balance in the 22 variables used tomaximmize balance at the communidity level. However, using the Endline survey data, we checked that balance remains amongall the variables used for maximizing balance that were supposed to be time-invariant and orthogonal to the intervention.

15The only signi�cant di�erence detected was only so at the 10% level: sampled treatment households live on average 9minutes further away from the regional capital than sampled control households (2 hours and 57 minutes versus 2 hours and 48minutes).

16For simpli�cation purposes only a subset of most relevant variables are presented in each category.17See [Imbens and Rubin(2010)].

9

assigned to the control group, and can be calculated using an ordinary least squares (OLS) regression model:

yi = α0 + δITTZi + ei (1)

where i indexes households, yi is the relevant outcome variable, and Zi is a dummy variable which takes a

value of 1 if the household belongs to a community assigned to the treatment group which was o�ered to

participate in the program and 0 otherwise.

We subsequently augment this simple di�ference in means OLS regression model to include additional co-

variates:

yi = α′

0 + δ′

ITTZi +

6∑h=1

α′

hxhi+e′

i (2)

where x1i is the distance from the household's community to the regional capital in average hours of travel18,

x2i is the number of household members, x3i is a dummy variable which takes a value of 1 if the household

head is a female, x4i is the maximum educational level among all members of the household in years of

education, x5i is the age of the respondent in years, x6i is a dummy variable which takes a value of 1 if the

household is a bene�ciary of the JUNTOS conditional cash transfer program, and the rest of the notation is

as above.

Finally, we again augment the OLS regression model to include �xed e�ects by district:

yi = α′′

0 + δ′′

ITTZi +

6∑h=1

α′′

hxhi+dj + e′′

i (3)

where j indexes districts, dj are district �xed e�ects, and the rest of the notation is as above.

No additional assumptions beyond random assignment are necessary for the validity of the ITT estimate of

the impact of the program.

Treatment-on-the-Treated (TOT)

Take-up of the program was imperfect in the treatment communities in which it was o�ered, as some house-

holds chose not to participate in it. Similarly, some households from control communities which were not

o�ered the program did participate in it by attending meetings held in treatment communities.Therefore, we

also estimate treatment-on-the-treated (TOT) e�ects of the program.

TOT estimates address imperfect assignment by scaling up the ITT to re�ect how the random assignment in-

�uenced participation rates in treatment and control communities. As proposed by [Angrist et al.(1996)Angrist, Imbens, and Rubin]

18The city of Abancay is the capital of both the Abancay province and the Apurímac region. Also, as mentioned a �eldtest was conducted by Financiera Con�anza in the pilot phase of the program to calculate the average travel time from eachcommunity to Abancay.

10

this is achieved by correcting actual treatment status Ti ∈ {0, 1}, a endogenous variable in the context of

imperfect compliance, by using the original random assignment statusZi as an instrumental variable (IV).

The validity of this approach rests on two assumptions:

(i) (Yi, Ti) ⊥ Zi

(ii) Ti | Zi = 1 ≥ Ti | Zi = 0 ∀i

The �rst exclusion restriction requires that the dependent outcome variableYi and actual treatment Ti are

jointly independent from assigned treatment Zi, so that the fact that households in treatment communities

were assigned to participate in the program only a�ected the outcome indirectly by increasing their probability

to actually participate in it. The second monotonicity assumption requires that being assigned to participate

in the program makes it more likely that household in treatment communities actually participate in it.

Because in the context of our experimental design we randomly assigned communities to control and treat-

ment groups and participation in the program was signi�cantly higher in the latter19, both assumptions

seem resonable IV Wald estimator will consistently estimate the TOT e�ect. However, as discussed by

[Imbens and Angrist(1994)] this estimated e�ect will di�er from the ITT in that it is a local average treat-

ment e�ect (LATE), so that it only captures the e�ect of the program on those households which actually

participated in it because they were randomly assigned to do so, but would not have participated in it other-

wise. This is, the TOT estimate captures the e�ect of the program on the compliers, a subset of the actually

treated for which (D | Z = 1) = 1and (D | Z = 0) = 0).

The TOT e�ect is therefore given by the IV Wald estimate

δTOT =E(Yi | Zi = 1)− E(Yi | Zi = 0)

E(Ti | Zi = 1)− E(Ti | Zi = 0)

which we calculate using a two stage least squares (2SLS) regression model:

yi = β0 + δTOT T̂i + ui (4)

Ti = γ0 + γ1Zi + vi (5)

where Ti is a dummy variable which takes a value of 1 if the household actually participated in the program20

and 0 otherwise, and the rest of the notation is as above.

As before, we subsequently augment this simple di�ference in means 2SLS regression model to include addi-

tional covariates:

19As mentioned before, at endline the take-up in treatment group was estimated at 57.7%, and the contamination in thecontrol group was estimated at 5.1%.

20Participation in the program is de�ned as having attended one or more �nancial education session.

11

yi = β′

0 + δ'TOT

T̂i +

6∑h=1

β′

hxhi + u′

i (6)

Ti = γ′

0 + γ′

1Zi +

6∑h=1

γ′

hxhi + v′

i (7)

where the notation is as above.

Finally, we again augment the 2SLS regression model to include �xed e�ects by district:

yij = β′′

0 + δ′′

TOTT̂i +

6∑h=1

β′′

hxhi + dj + u′′

i (8)

Tij = γ′′

0 + γ′′

1Zi +

6∑h=1

γ′′

hxhi + dj + v′′

i (9)

where the notation is as above.

5 Results

According to endline survey data we estimate take-up of the program in treatment areas at 57.7%. Conversely,

5.1% of surveyed control households participated in the program despite the fact that it was not o�ered in

their communities.21 Therefore, we have a �rst stage with which to calculate the TOT impact of the program,

which given the take-up and contamination rates is generally approximately double the observed ITT e�ect.

Both sets of results are presented in Tables 2 to 8. Below we divide the results into direct and indirect

impacts since we assume that the �nancial education program �rst impacts �nancial literacy and savings�

direct e�ect�, and then through savings other variables�assets, female empowerment�can be impacted by

the �nancial education program.

5.1 Direct Impact

Financial Literacy

As presented in Table 2 we observe a mild but signi�cant e�ect of the program on �nancial knowledge

variables like the familiarity with formal �nancial institutions, and identifying lending and borrowing as roles

of formal �nancial institutions. However, we observe impacts of the program on practical knowledge and

trust in �nancial institutions. Speci�cally, we observe sign�cant impacts of the program on the likelihood of

21Participation in the program is de�ned as having attended one or more teaching sessions.

12

saving in a formal �nancial institution (∼ 7.5 pp ITT, ∼ 15.1 pp ToT) and the awareness about the deposit

guarantee they o�er and the additional governmental supervision to which they are subject (∼ 8.5 pp ITT,

∼ 17.1 pp ToT). Moreover, these results on awareness and trust are magni�ed when looking at familiarity

with and trust in Financiera Con�anza in particular. We estimate a large and signi�cant positive impact

(∼ 4.6 pp ITT, ∼ 9.2 pp ToT) of the program on the probability of being familiar with Financiera Con�anza,

raising familiarity with the institution from a control average of 2.1%. Similarly, we �nd a very large and

signi�cant positive impact (∼ 21.1 pp ITT, ∼ 42.2 pp ToT) of the program on the probability of claiming to

trust Financiera Con�anza, raising trust in the institution from a control average of 5.4% .

However, we only �nd very mild or no evidence of program impact on other dimensions in this category,

including some which received substantial attention during the teaching sessions (e.g. interest in other

�nancial products like insurance, or the correct use of a budget in the household). This suggests the �nancial

education program was succesful in transmitting the target population the �rules of thumb� that allowed

them to save in the formal system, but it was less successful in transmitting more complex concepts such

as �nancial roles of formal �nancial institutions, or insurance. Moreover, this re�ects the priorities in the

design and implementation of the program, which were promoting savings and generating trust in the formal

�nancial system and the implementing micro�nance institution.

Savings

As presented in Table 3, we �nd a signi�cant positive impact of the program on the probability of saving

with Financiera Con�anza. The observed e�ect is moderate (∼ 0.31σ ITT, ∼ 0.61σ TOT) when looking at

self-reported savings with Financiera Con�anza, raising the probability of having saved with the institution

since January 2015 by ∼ 2.3 pp ITT, ∼ 4.5 pp ToT, from a control average of 0.6%. However, when looking

at the administrative information provided by Financiera Con�anza (in Table 4) we �nd that the program

actually had a considerably larger positive e�ect (∼ 1.30σ ITT, ∼ 2.60σ TOT), raising the probability of

having saved with the institution since January 2015 by ∼ 4.3 pp ITT, ∼ 8.6 pp ToT, from the control

average of 0.1%.22

Moreover, we �nd mild to no evidence of a signi�cant impact of the program on the probability of saving at

home or with other formal �nancial institutions or cooperatives, suggesting that the observed positive e�ect

of the program is limited to increasing the probability of saving with Financiera Con�anza.23 Although not

signi�cant, the point estimates for savings amounts in Table 3 suggest savings were moved from cooperatives

(negative point estimates) to homes and the formal �nancial system.24

22The observed underrreporting of savings with Financiera Con�anza at the endline survey could be simply due to recall bias.However, it could also be due to respondents being afraid to report any savings in a non-governmental �nancial institution (e.g.while enforcement of the rule is unclear, since December 2015 bene�ciaries of the JUNTOS program could lose their conditionalcash transfer if they save in an account other than the one provided by the program at Banco de la Nación, or if they obtaincredit from a formal �nancial institution).

23Since we do not have administrative information from other institutions, it is possible that the probability of savings withthem is underreported, as it is the case with Financiera Con�anza.

24Note that the dispersion of these continuous variables is considerably larger than those of the binary probability of savingvariables, so that the statistical power to detect e�ects of the same size is greatly reduced. For example, while only signi�cantwith 90% con�dence, the observed impact on the maximum amount saved with Financiera Con�anza since January 2015 isabout 20 soles, corresponding with the minimum deposit required to open a savings account at the institution. Transactionalinformation provided by Financiera Con�anza suggests that the average number of deposits was similar to that of withdrawals,with a total of PEN 6,245,725 deposited and PEN 5,817,236 withdrawn as of February 2017, for a net increase in deposits ofPEN 428,489.

13

Credit

As presented in Table 5, we �nd little evidence of an e�ect of the program on having a credit with Financiera

Con�anza (less than 1pp increase, signi�cant only at the 10% level), measured with administrative data. At

the same time, we observe a small but positive impact�only for our preferred speci�cation�on obtaining

a credit with a formal �nancial institution and a cooperative through the use of saving, measured with self-

reported data. This suggests there is room for Financiera Con�anza to o�er competitive credit products to

the target population, where cooperatives are present and have had the monopoly of the rural micro�nance

market. Table 5 also shows that other credit-related variables (e.g., considering themselves creditworthy)

were not singi�cantly a�ected by the �nancial education intervention.25

5.2 Indirect Impact

Income and Assets

As presented in Table 6, we do not �nd consistent signi�cant impacts of the program on agricultural activities.

However, the negative point estimates, some of them signi�cant for our preferred speci�cation, suggest an

incipient departure from livestock activities due to the intervention. For instance, the likelihood of performing

veterenary procedures decreased by ∼ 9.3 pp ITT, ∼ 18.6 pp ToT, for our preferred speci�cation. Moreover,

the total number of income-generating activities or the involvement on new activities aside agriculture and

livestock do not seem to have been a�ected by the program.

Household assets as well as agriculture-related assets were not consistently and signi�cantly a�ected by the

program, but the negative point estimates raise concerns about wether investments on assets were made from

savings, or whether savings were increased due to a decrease in assets.

We also measured the impact of the program on the probability of being poor, as measured by the JUNTOS

conditional cash transfer program poverty score, and �nd no signi�cant e�ects although the related point

estimates were always negative.26 This suggests the �nancial education program, in any case, was not harmful

to the target population.

Consumption

As presented in Table 7, we �nd no signi�cant evidence of an e�ect of the program on many di�erent measures

of food consumption. The program did not improve any good consumption habits, neither it changed any

25Note that there are more strict requirements to qualify for a credit product than to open a savings account, and thatwhile the Ahorro para Todos savings account was actively promoted during program activities, that was not the case for creditproducts such as loans. However, in October 2016 Financiera Con�anza linked the Ahorro para Todos savings account withits Palabra de Mujer program o�ering credit to groups of women, resulting in 19 groups created and PEN 51,350 worth ofcredit extended to 140 customers as of February 2017. This anecdotical evidence suggests that with additional re�nements theprogram may indeed have the potential to generate cross-selling opportunities beyond savings.

26This score was created as a targetting instrument by the JUNTOS conditional cash transfer program using National Surveydata from 2001 and 2004. It estimates the probability that a rural household is poor based on its characteristics: (i) percentageof illiterate adult females in the household, (ii) percentage of children under 18 (legal age) attending school, (iii) type of fuelused for cooking, (iv) appliances in the household, (v) services the household has access to, and (vi) dwelling type.

14

tempation goods consumption. This suggests that, at least, the program positive e�ects on savings did not

come from reducing food consumption.

Female Empowerment

As presented in Table 8, we �nd some evidence of a positive impact of the program on female community

participation. In particular, we �nd a modest e�ect ( ∼ 19.5 pp ITT, ∼ 38.9 pp ToT) on the number of

groups or organizations to which women belong. This result probably comes directly from the fact that the

�nancial education was delivered to groups of women. As the very least, this result suggests the provision

of �nancial education in groups made women believe they belong to a new group of peers. However, we �nd

no evidence of a signi�cant impact of the program on a variety of measures of female empowerment at the

household (e.g., women decides freely how to spend her income) nor at the community level (e.g., the number

of positions that women hold in the groups to which they belong).

Overall, it is possible that the lack of indirect impacts of the program is due to the little timethat passed

between the end of the invention and the survey data collection (nearly three months). We recognize that

important social transformation processes, like female empowerment, require more than a few months and

more than one program to occur.

It is worth mentioning that spillovers cannot be estimated with our design. If spillovers are present, they

would be reducing the magnitude of our impacts, not in�ating them. From the follow-up of training sessions,

we know that people treated by the Program actually received the training, since trainers registered all

participants every session, and that knowledge was hardly transmitted outside the sessions or to people

outside the training groups. Thus, we think spillovers may not the biasing our results importantly .

5.3 Heterogeneous e�ects

Heterogeneous e�ects regressions show that knowledge and trust in Financiera Con�anza do not change with

transaction costs. However, the positive impact on savings with Financiera Con�anza (administrative data)

decreases with the distance from the capital Abancay�where the main branch is located�for our preferred

speci�cations (with control variables and district �xed e�ects).

5.4 Robustness Checks

Our results are robust to Bonferroni adjustment. Moreover, alternative measures of the self-reported savings

in Table 3 do not change the main results on savings.

15

6 Conclusion

We �nd that Ahorro para Todos, a for-pro�t �nancial education program, succesfully increased the familiarity

and trust in formal �nancial institutions in poor rural areas of the Andes. This e�ect was particularly

large for Financiera Con�anza, the Peruvian micro�nance institution which implemented the program in

the Apurímac region of Peru from May 2015 to April 2016: we estimate that it increased familiarity with

Financiera Con�anza by approximately 9.2 percentage points with respect to a control average of 1.2%, and

that it increased trust in the institution by aproximately 42 percentage points with respect to a control average

of 5.4% for the target population that e�ectively received at least one module of the �nancial education and

savings promotion program.

Moreover, we �nd that the �nancial education program increased the probability of saving with Financiera

Con�anza by 8.6 percentage points from the control average of 0.06%, for those e�ectively treated. Although

not sign�cant, self-reported data about savings at home and other institutions suggest the target population

might have been moving their savings from (informal) cooperatives to home and formal �nancial institu-

tions.27This very large e�ect, particularly in the context of a very competitive micro�nance market, suggests

that Ahorro para Todos was a very e�ective marketing tool for Financiera Con�anza which did not generate

�free-riding� from other �nancial institutions. 28

Moreover, we observe very incipient evidence of an e�ect of the program on the probability of buying other

�nancial products from Financiera Con�anza (or from any other �nancial institution), but at the same time

the possibility of expansion with these products in rural areas, still captured by cooperatives. Given that

it is a stylized industry fact that the taking of deposits is a pro�table business only insofar as it leads to

the cross-selling of other products with higher pro�t margins (e.g. loans), this suggests that in order to be

commercially viable the �nancial education program should expand its focus beyond savings. It is possible

that we do not �nd a large e�ect on having a credit with Financiera Con�anza due to the brief amount of

time elapsed between the intervention and the endline survey. Note that we are not able to follow up later

with administrative data since Financiera Con�anza intervined in the control communities right after the

endline survey. However, Financiera Con�anza decided to do the expansion linking the Ahorro para Todos

Program to their group credit for women program (Palabra de Mujer) in order to enhance the cross-selling

of savings and credit products.

Regarding the indirect e�ects of the program, we �nd no signi�cant evidence of the program inducing changes

in assets acquisition, either for the household or for income-generating activities. Moreover, we �nd no

evidence that the program signi�cantly reduced poverty, nor that it caused changes in food consumption.

Similar to to the �ndings of [Boyd and Aldana(2015)] we observe that program did not have any e�ects

on female empowerment at the household level or at the community level�as it is usually expected from

programs targeting women�, except for the positive impact on female community participation, suggesting

that additional e�orts are necessary on this front. The overall lack of indirect e�ects con�rms that the

27Cooperatives have been one of the most important providers of credit and savings in the rural areas of Apurimac. However,as they are currently unregulated in Peru, they have defaulted and left with people's savings. This created an initial mistrust inthe Program, which was addressed di�erentiating cooperatives from formal �nancial institutions. However, people in the controlgroup, or people receiving only a few sessions might still be saving at them.

28Note that the impacts on total savings or savings at other institutions were not signi�cant, but had large variances. Aswell, it is possible that the �free-riding� appears later, since the impacts on savings may vanish over time, as it was found by[Ashraf et al.(2010)Ashraf, Karlan, and Yin] for a Philippine commitment savings account.

16

�nancial education intervention achieved its goal of increasing savings among rural people in the formal

�nancial system, specially in the implementing insitution, without harmful side e�ects.

Similarly, we �nd that Ahorro para Todos had no impact on some dimensions of �nancial education which

were featured prominently in the program, such as the use of a budget in the household. This sug-

gests that despite the observed positive impacts on several fronts, additional e�orts are necessary to maxi-

mize the e�ciency of the program, for example by complementing the �edutainment� approach with other

methodologies with potential to improve learning of best practices such as �rules of thumb� proposed by

[Drexler et al.(2014)Drexler, Fischer, and Schoar]. 29

Moreover, when comparing survey and administrative data we �nd that savings levels are signi�cantly un-

derreported. While this could simply be due to recall bias, it could also be due to respondents being afraid

to report any savings in a non-governmental �nancial institution for fear of losing their bene�ciary status

in social programs. In any case, irrespective of the underlying reason this underreporting has important

implications for the interpretation and validity of any results based on self-reported savings data from poor

populations in Peru.

In conclusion, all the above suggests that �nancial education programs can be a succesful marketing tool

for the promotion of microsavings among the poor even in remote rural areas, and that their provision by

�nancial institutions on a for-pro�t basis can be commercially viable if their focus is expanded beyond savings,

in order to promote and generate cross-selling of credit products with higher pro�t margins. However, this

requires the formal �nancial institutions to design �nancial products suitable to the rural population demand

and being competitive in an enviroment dominated by the informal cooperatives.

29Although budget notebooks were designed as part of the program, they were not implemented due to logistic issues. Theywere supposed to reinforce the lessons learnt on the budgeting module, so as to make doing budgets a habit. If we considerbudgeting as a �nancial ability, it is not surprising that the sole fact of having training sessions did not e�ectively promote thepractice of budgeting.

17

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06

58

)(0

.02

70

)(0

.02

70

)

0.0

19

2**

0.0

06

35

0.0

11

8

(0.0

06

71

)(0

.02

87

)(0

.02

70

)

-0.0

14

4*

-0.0

06

94

-0.0

12

6

(0.0

06

39

)(0

.02

76

)(0

.02

61

)

0.0

00

76

4-0

.00

71

9-0

.00

82

4*

(0.0

01

77

)(0

.00

41

7)

(0.0

04

13

)

-0.0

21

9***

-0.0

02

77

-0.0

04

13

(0.0

06

50

)(0

.02

03

)(0

.02

02

)

0.0

28

2***

0.0

18

00

.02

08

(0.0

05

16

)(0

.02

19

)(0

.02

14

)

-0.0

12

2***

-0.0

00

66

7-0

.00

08

93

(0.0

02

83

)(0

.00

51

8)

(0.0

04

52

)

-56

.04

***

5.2

38

0.6

08

(2.6

89

)(1

4.0

2)

(13

.21

)

0.0

29

4-0

.01

08

-0.0

54

2

(0.0

18

3)

(0.1

89

)(0

.19

2)

0.0

60

5***

0.1

55

0.1

56

(0.0

09

41

)(0

.09

48

)(0

.09

43

)

10

89

98

98

4O

bse

rvat

ions

*p

<0

.1;

**p

<0

.05

; ***p

<0

.01

;

Note

s: C

olu

mn (

1)

rep

ort

s th

e b

alan

ce b

etw

een c

ontr

ol

and

tre

atm

ent

com

munit

ies

(T0

- T

1)

for

the

rand

om

izat

ion t

hat

max

imiz

ed b

alan

ced

pri

or

to

the

inte

rven

tio

n,

usi

ng h

ouse

ho

ld l

evel

dat

a fr

om

SIS

FO

H f

or

var

iab

les

i to

xix

. V

aria

ble

s x

x t

o x

xii

wer

e ca

lcula

ted

at

the

com

munit

y l

evel

usi

ng t

he

Junto

s R

oll

of

Use

rs.

Co

lum

n (

2)

rep

ort

s th

e b

alan

ce b

etw

een c

ontr

ol

and

tre

atm

ent

com

munit

ies

usi

ng a

ver

ages

of

each

var

iab

le a

t th

e co

mm

unit

y

level

, fo

r th

e 8

9 c

om

munit

ies

assi

gned

to

tre

atm

ent

or

contr

ol

pri

or

to t

he

inte

rven

tio

n.

Co

lum

n (

3)

rep

ort

s th

e b

alan

ce b

etw

een c

ontr

ol

and

tre

atm

ent

com

munit

ies

usi

ng a

ver

ages

of

each

var

iab

le a

t th

e co

mm

unit

y l

evel

, w

her

e co

mm

unit

ies

are

regro

up

ed a

cco

rdin

g t

o t

he

actu

al i

nte

rven

tio

n (

trai

nin

g

sess

ions)

, an

d t

hey

co

mp

rise

the

bal

ance

of

var

iab

les

at t

he

com

munit

y l

evel

use

d i

n t

he

regre

ssio

ns.

Sta

nd

ard

err

ors

rep

ort

ed i

n p

aren

thes

es.

Sourc

e: S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er 2

01

4.

xvii

. H

ouse

ho

ld m

emb

ers

who

kno

w h

ow

to

rea

d (

%)

xvii

i. H

ouse

ho

ld m

emb

ers

in a

gri

cult

ura

l ac

tivit

ies

(%)

xix

. H

ouse

ho

ld m

emb

ers

in s

ervic

es a

ctiv

itie

s (%

)

xx

. N

um

ber

of

Junto

s C

CT

rec

ipie

nts

in t

he

com

munit

y

xx

i. D

ista

nce

fro

m t

he

com

munit

y t

o t

he

regio

nal

cap

ital

xx

ii.

Junto

s C

CT

rec

ipie

nts

in t

he

com

munit

y r

ecei

ven

g t

he

tran

sfer

at

a

BN

(%

)

xvi.

Ho

use

ho

ld m

emb

ers

wit

h a

Nat

ional

Id

enti

ty D

ocu

men

t (%

)TA

BL

E 1

(C

onti

nued

)

Bal

ance

Bet

wee

n C

ontr

ol

and

Tre

atm

ent

Gro

up

s

xii

i. H

ouse

ho

ld m

emb

ers

no

t re

ceiv

ing a

ny S

oci

al P

rogra

m (

%)

xiv

. H

ouse

ho

ld m

emb

ers

wit

h S

pan

ish a

s fi

rst

tongue

(%)

xv.

Ho

use

ho

ld m

emb

ers

wit

h Q

uec

hua

as f

irst

to

ngue

(%)

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.0

46

50

.06

42

*0

.07

54

**

0.0

88

30

.12

34

*0

.15

12

**

0.2

74

51

,80

1

(0.0

36

7)

(0.0

35

5)

(0.0

31

9)

(0.0

68

9)

(0.0

68

4)

(0.0

65

2)

(0.4

46

5)

0.1

23

80

.14

40

0.1

30

1*

0.2

35

20

.27

67

*0

.26

09

*1

.05

40

1,8

01

(0.1

00

9)

(0.0

87

3)

(0.0

72

4)

(0.1

91

6)

(0.1

66

6)

(0.1

45

5)

(0.8

20

8)

0.0

56

1*

**

0.0

53

0*

**

0.0

45

9*

**

0.1

06

6*

**

0.1

01

9*

**

0.0

92

0*

**

0.0

12

11

,80

1

(0.0

20

4)

(0.0

13

4)

(0.0

14

5)

(0.0

38

3)

(0.0

25

7)

(0.0

27

4)

(0.1

09

5)

0.0

11

0-0

.01

57

-0.0

23

40

.02

09

-0.0

30

3-0

.04

69

0.3

72

71

,80

1

(0.0

57

6)

(0.0

47

5)

(0.0

41

4)

(0.1

08

6)

(0.0

90

2)

(0.0

81

7)

(0.4

83

8)

0.0

70

20

.06

54

**

0.0

85

1*

**

0.1

33

40

.12

57

**

0.1

70

7*

**

0.2

15

01

,80

1

(0.0

45

6)

(0.0

32

4)

(0.0

31

7)

(0.0

84

9)

(0.0

60

7)

(0.0

61

3)

(0.4

11

0)

0.1

85

3*

**

0.1

80

5*

**

0.2

10

7*

**

0.3

52

1*

**

0.3

47

0*

**

0.4

22

4*

**

0.0

54

01

,80

1

(0.0

53

9)

(0.0

35

7)

(0.0

36

4)

(0.0

98

5)

(0.0

71

1)

(0.0

73

2)

(0.2

26

2)

0.0

59

50

.07

48

*0

.07

12

**

0.1

13

10

.14

39

*0

.14

28

**

0.1

72

01

,80

1

(0.0

50

1)

(0.0

40

0)

(0.0

30

9)

(0.0

95

2)

(0.0

76

6)

(0.0

61

0)

(0.3

77

6)

0.0

17

4-0

.01

04

-0.0

15

60

.03

31

-0.0

20

1-0

.03

12

0.5

07

21

,80

1

(0.0

51

6)

(0.0

43

7)

(0.0

43

3)

(0.0

97

1)

(0.0

82

7)

(0.0

84

9)

(0.5

00

2)

0.0

38

80

.03

15

0.0

62

3*

*0

.07

38

0.0

60

50

.12

50

**

0.1

32

31

,80

1

(0.0

26

2)

(0.0

24

0)

(0.0

26

2)

(0.0

50

6)

(0.0

46

4)

(0.0

55

6)

(0.3

39

0)

0.0

27

1-0

.00

85

-0.0

33

00

.05

15

-0.0

16

3-0

.06

62

0.7

08

91

,80

1

(0.0

34

4)

(0.0

32

1)

(0.0

35

7)

(0.0

64

9)

(0.0

61

1)

(0.0

71

7)

(0.4

54

5)

0.0

31

90

.03

25

0.0

48

5*

**

0.0

60

50

.06

25

0.0

97

2*

**

0.1

13

61

,80

1

(0.0

32

1)

(0.0

25

3)

(0.0

17

4)

(0.0

60

0)

(0.0

48

5)

(0.0

35

2)

(0.3

17

5)

0.0

17

30

.02

10

0.0

03

10

.03

29

0.0

40

30

.00

61

0.1

12

51

,80

1

(0.0

22

8)

(0.0

21

3)

(0.0

20

4)

(0.0

42

6)

(0.0

40

3)

(0.0

40

1)

(0.3

16

1)

f. T

rust

s F

inan

cier

a C

on

fian

za (

1 =

Yes

)

g.

Kn

ow

s d

epo

sits

at

form

al f

inan

cial

in

stit

uti

on

s ar

e

gu

aran

teed

in

cas

e o

f b

ankru

ptc

y (

1 =

Yes

)

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a si

ngle

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

)

pre

sent

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m

assi

gnm

ent

as t

he

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in

mea

ns

wit

h a

dd

itio

nal

co

var

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

level

of

ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s an

d d

istr

ict

fixed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er

20

14

.

k.

Mak

es a

bu

dget

(1

= Y

es)

h.

If i

n d

ou

bt

asks

per

son

nel

of

form

al f

inan

cial

in

stit

uti

on

s fo

r

info

rmat

ion

ab

ou

t sa

vin

gs

(1 =

Yes

)

i. H

as r

equ

este

d i

nfo

rmat

ion

ab

ou

t fi

nan

cial

pro

du

cts

fro

m a

ny

form

al f

inan

cial

in

stit

uti

on

(1

= Y

es)

j. I

f s/

he

cou

ld,

s/h

e w

ou

ld b

uy i

nsu

ran

ce f

or

pea

ce o

f m

ind

(1

=Y

es)

a. W

ou

ld s

ave

in a

fin

anci

al i

nst

itu

tio

n (

1 =

Yes

)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

l. M

akes

a b

ud

get

co

rrec

tly (

1 =

Yes

)

b.

Wit

h h

ow

man

y f

orm

al f

inan

cial

in

stit

uti

on

s is

fam

ilia

r

c. I

s fa

mil

iar

wit

h F

inan

cier

a C

on

fian

za (

1 =

Yes

)

Est

imat

ed I

mp

act

of

the

Pro

gra

m o

n F

inan

cial

Lit

erac

y

Co

ntr

ol

Mea

nN

TA

BL

E 2

d.

Iden

tifi

es l

end

ing a

nd

tak

ing d

epo

sits

as

role

s o

f fo

rmal

fin

anci

al i

nst

itu

tio

ns

(1 =

Yes

)

e. K

no

ws

form

al f

inan

cial

in

stit

uti

on

s ar

e su

per

vis

ed b

y t

he

go

ver

nm

ent

(1 =

Yes

)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.0

24

70

.01

38

0.0

22

40

.04

69

0.0

26

60

.04

48

0.5

45

81

,80

1

(0.0

72

8)

(0.0

61

9)

(0.0

23

9)

(0.1

37

5)

(0.1

17

2)

(0.0

46

8)

(0.4

98

2)

0.0

48

10

.03

53

0.0

49

8*

0.0

91

40

.06

79

0.0

99

9*

0.2

30

41

,80

1

(0.0

31

1)

(0.0

28

0)

(0.0

25

2)

(0.0

58

6)

(0.0

53

7)

(0.0

51

6)

(0.4

21

3)

55

.28

39

25

8.0

34

5*

41

4.2

77

1*

*1

05

.03

22

49

6.2

74

4*

83

0.9

60

3*

**

97

4.3

49

71

,79

9

(15

5.4

97

4)

(15

3.3

22

9)

(17

1.6

81

0)

(29

7.4

98

2)

(29

0.0

80

3)

(32

2.2

58

4)

(17

06

.98

20

)

0.0

20

2*

*0

.02

16

**

*0

.02

27

**

*0

.03

84

**

0.0

41

5*

**

0.0

45

4*

**

0.0

05

51

,80

1

(0.0

08

3)

(0.0

07

1)

(0.0

06

2)

(0.0

16

0)

(0.0

13

8)

(0.0

12

4)

(0.0

74

1)

0.1

63

71

.82

01

2.8

23

20

.31

09

3.4

98

65

.66

03

14

.33

30

1,8

01

(11

.62

41

)(8

.71

36

)(7

.84

25

)(2

1.9

43

8)

(16

.54

52

)(1

5.5

47

4)

(28

8.3

18

7)

0.1

02

1*

*0

.10

20

**

*0

.11

48

**

*0

.19

39

**

0.1

96

0*

**

0.2

30

2*

**

0.0

27

91

,80

1

(0.0

45

0)

(0.0

36

2)

(0.0

34

9)

(0.0

86

5)

(0.0

70

6)

(0.0

71

2)

(0.4

88

7)

0.0

19

1*

**

0.0

19

2*

**

0.0

20

8*

**

0.0

36

2*

**

0.0

37

0*

**

0.0

41

7*

**

0.0

03

31

,80

1

(0.0

07

1)

(0.0

06

1)

(0.0

06

3)

(0.0

13

7)

(0.0

11

8)

(0.0

12

5)

(0.0

57

4)

0.0

17

9*

**

0.0

18

0*

**

0.0

18

4*

**

0.0

34

1*

**

0.0

34

6*

**

0.0

36

9*

**

0.0

03

31

,80

1

(0.0

06

7)

(0.0

05

6)

(0.0

05

8)

(0.0

12

8)

(0.0

10

9)

(0.0

11

4)

(0.0

57

4)

0.0

14

6*

*0

.01

37

**

*0

.01

33

**

*0

.02

77

**

0.0

26

4*

**

0.0

26

7*

**

0.0

03

31

,80

1

(0.0

06

8)

(0.0

05

2)

(0.0

04

8)

(0.0

13

0)

(0.0

10

1)

(0.0

09

9)

(0.0

57

4)

0.0

02

60

.00

32

0.0

02

60

.00

50

0.0

06

20

.00

52

0.0

27

61

,80

1

(0.0

09

6)

(0.0

08

0)

(0.0

07

8)

(0.0

18

2)

(0.0

15

2)

(0.0

15

3)

(0.1

63

8)

24

.90

29

41

.18

22

11

6.1

71

4*

47

.30

21

79

.16

08

23

2.9

16

3*

10

7.9

38

31

,80

1

(73

.11

14

)(6

8.3

40

5)

(68

.04

20

)(1

38

.40

03

)(1

30

.08

07

)(1

34

.72

47

)(1

26

7.1

09

0)

-0.0

08

7-0

.01

44

0.0

03

0-0

.01

66

-0.0

27

60

.00

60

0.0

92

61

,80

1

(0.0

19

9)

(0.0

19

9)

(0.0

18

5)

(0.0

37

6)

(0.0

37

6)

(0.0

36

3)

(0.2

90

0)

-23

6.5

20

3-1

10

.67

39

-78

.65

50

-44

9.2

61

5-2

12

.73

84

-15

7.6

98

35

40

.75

96

1,8

01

(19

7.4

44

6)

(14

3.4

20

6)

(17

2.6

07

1)

(37

0.6

64

2)

(27

1.7

94

5)

(34

0.2

10

0)

(42

70

.64

40

)

-25

8.2

77

0-9

4.3

88

75

5.1

69

1-4

90

.69

21

-18

1.5

36

51

10

.65

85

78

6.3

49

21

,79

9

(19

6.4

72

2)

(12

9.2

51

6)

(14

1.8

75

3)

(36

4.6

57

8)

(24

4.7

84

8)

(27

7.4

86

1)

(33

94

.84

80

)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

f.3

. H

as m

ore

th

an S

/.2

0 s

aved

wit

h F

C a

t th

e ti

me

of

the

surv

ey

f.4

. H

as m

ore

th

an S

/.5

0 s

aved

wit

h F

C a

t th

e ti

me

of

the

surv

ey

Est

imat

ed I

mp

act

of

the

pro

gra

m o

n S

avin

gs

(Su

rvey

Dat

a)

Co

ntr

ol

Mea

nN

TA

BL

E 3

d.

Sin

ce J

anu

ary 2

01

5 h

as s

aved

wit

h F

inan

cier

a C

on

fian

za (

1 =

Yes

)

a. C

urr

enty

sav

es (

1 =

Yes

)

b.

Sin

ce t

he

star

t o

f 2

01

5 h

as s

aved

fo

r a

spec

ific

go

al (

1 =

Yes

)

c. M

axim

um

am

ou

nt

saved

at

ho

me

sin

ce t

he

star

t o

f 2

01

5

k.

To

tal

savin

gs

(at

ho

me,

fo

rmal

an

d i

nfo

rmal

fin

anci

al

inst

itu

tio

ns)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

e. M

axim

um

am

ou

nt

saved

wit

h F

inan

cier

a C

on

fian

za s

ince

Jan

uar

y 2

01

5

f.1

. L

og o

f am

ou

nt

saved

wit

h F

C a

t th

e ti

me

of

the

surv

ey

g.

Sin

ce J

anu

ary 2

01

5 h

as s

aved

wit

h o

ther

fo

rmal

fin

anci

al

inst

itu

tio

ns

(1 =

Yes

)

h.

Max

imu

m a

mo

un

t sa

ved

wit

h o

ther

fo

rmal

fin

anci

al i

nst

itu

tio

ns

sin

ce J

anu

ary 2

01

5

i. S

ince

Jan

uar

y 2

01

5 h

as s

aved

wit

h a

fin

anci

al c

oo

per

ativ

e (1

=

Yes

)

j. M

axim

um

am

ou

nt

saved

wit

h a

co

op

erat

ive

sin

ce J

anu

ary 2

01

5

f.2

. H

as m

ore

th

an S

/.5

sav

ed w

ith

FC

at

the

tim

e o

f th

e su

rvey

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.0

55

9*

**

0.0

50

6*

**

0.0

43

0*

**

0.1

06

3*

**

0.0

97

3*

**

0.0

86

2*

**

0.0

01

11

,80

1

(0.0

14

4)

(0.0

10

8)

(0.0

11

1)

(0.0

27

6)

(0.0

21

6)

(0.0

21

2)

(0.0

33

2)

25

.01

17

**

16

.55

33

**

3.5

25

64

7.5

08

8*

*3

1.8

18

9*

*7

.06

86

0.1

26

31

,80

1

(11

.42

50

)(7

.35

53

)(7

.22

20

)(2

1.6

68

6)

(14

.32

51

)(1

4.1

72

8)

(3.8

02

9)

14

.31

42

**

9.8

58

7*

*3

.39

91

27

.18

93

**

18

.95

05

**

6.8

15

00

.02

48

1,8

01

(6.5

44

6)

(4.0

10

0)

(4.1

35

4)

(12

.46

75

)(7

.84

84

)(8

.09

13

)(0

.74

83

)

0.2

55

5*

**

0.2

28

2*

**

0.1

75

0*

**

0.4

85

3*

**

0.4

38

6*

**

0.3

50

9*

**

0.0

04

21

,80

1

(0.0

72

1)

(0.0

50

6)

(0.0

51

4)

(0.1

38

3)

(0.1

01

5)

(0.0

98

8)

(0.1

26

5)

0.0

54

8*

**

0.0

50

4*

**

0.0

43

0*

**

0.1

04

1*

**

0.0

97

0*

**

0.0

86

3*

**

0.0

01

11

,80

1

(0.0

14

5)

(0.0

10

9)

(0.0

11

1)

(0.0

27

8)

(0.0

21

8)

(0.0

21

2)

(0.0

33

2)

0.0

44

8*

**

0.0

41

6*

**

0.0

29

9*

**

0.0

85

0*

**

0.0

80

0*

**

0.0

60

0*

**

0.0

01

11

,80

1

(0.0

13

5)

(0.0

09

9)

(0.0

10

1)

(0.0

25

8)

(0.0

19

7)

(0.0

19

5)

(0.0

33

2)

0.0

25

7*

**

0.0

22

1*

**

0.0

13

1*

*0

.04

89

**

*0

.04

25

**

*0

.02

63

**

0.0

00

01

,80

1

(0.0

08

4)

(0.0

05

4)

(0.0

05

3)

(0.0

16

1)

(0.0

10

7)

(0.0

10

4)

(0.0

00

0)

e. H

as h

ad m

ore

th

an S

/.5

sav

ed w

ith

FC

(=

1)

on

aver

age

f. H

as h

ad m

ore

th

an S

/.2

0 s

aved

wit

h F

C (

=1

) o

n a

ver

age

g.

Has

had

mo

re t

han

S/.

50

sav

ed w

ith

FC

(=

1)

on

aver

age

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a si

ngle

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

)

pre

sent

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m

assi

gnm

ent

as t

he

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in

mea

ns

wit

h a

dd

itio

nal

co

var

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

level

of

ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s an

d d

istr

ict

fixed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er

20

14

.

a. S

ince

Jan

uar

y 2

01

5 h

as s

aved

wit

h F

inan

cier

a C

on

fian

za (

1

= Y

es)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

b.

Max

imu

m a

mo

un

t sa

ved

wit

h F

inan

cier

a C

on

fian

za s

ince

Jan

uar

y 2

01

5

c. A

ver

age

amo

un

t sa

ved

wit

h F

inan

cier

a C

on

fian

za (

Ap

ril

20

14

-Ju

ly 2

01

6)

d.

Lo

g o

f av

erag

e am

ou

nt

saved

wit

h F

inan

cier

a C

on

fian

za

(Ap

ril

20

14

-Ju

ly 2

01

6)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

Est

imat

ed I

mp

act

of

the

pro

gra

m o

n S

avin

gs

at F

inan

cier

a C

on

fian

za (

Ad

min

istr

ativ

e D

ata)

Co

ntr

ol

Mea

nN

TA

BL

E 4

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

-0.0

25

30

.02

10

0.0

17

9-0

.04

80

0.0

40

40

.03

58

0.5

56

41

,80

1

(0.0

45

9)

(0.0

35

8)

(0.0

36

0)

(0.0

86

5)

(0.0

68

3)

(0.0

70

8)

(0.4

97

)

0.0

02

60

.02

20

0.0

36

70

.00

49

0.0

42

40

.07

35

0.1

78

81

,80

1

(0.0

32

9)

(0.0

32

1)

(0.0

34

7)

(0.0

62

1)

(0.0

61

4)

(0.0

69

1)

(0.3

83

3)

0.0

01

60

.02

18

0.0

41

40

.00

31

0.0

41

90

.08

30

0.1

88

21

,80

1

(0.0

36

0)

(0.0

35

4)

(0.0

39

9)

(0.0

68

0)

(0.0

67

7)

(0.0

79

2)

(0.4

14

5)

0.0

03

4*

0.0

02

6*

0.0

02

60

.00

64

*0

.00

49

*0

.00

52

*0

.00

17

1,8

01

(0.0

01

8)

(0.0

01

3)

(0.0

01

6)

(0.0

03

4)

(0.0

02

5)

(0.0

03

1)

(0.0

40

8)

0.0

01

20

.00

47

0.0

01

40

.00

23

0.0

09

10

.00

27

0.0

08

31

,80

1

(0.0

04

8)

(0.0

05

6)

(0.0

05

4)

(0.0

09

1)

(0.0

10

7)

(0.0

10

6)

(0.0

90

9)

-0.0

00

80

.01

25

0.0

26

6*

*-0

.00

15

0.0

24

10

.05

33

**

0.0

28

41

,79

8

(0.0

09

6)

(0.0

09

7)

(0.0

11

0)

(0.0

18

1)

(0.0

18

7)

(0.0

22

9)

(0.1

66

1)

0.0

13

90

.01

94

0.0

32

2*

*0

.02

63

0.0

37

30

.06

47

**

0.0

50

11

,79

7

(0.0

16

4)

(0.0

16

7)

(0.0

15

2)

(0.0

31

1)

(0.0

32

3)

(0.0

31

9)

(0.2

18

2)

e. B

elie

ves

th

at s

avin

g i

n a

fo

rmal

fin

anci

al i

nst

itu

tio

n m

akes

it

easi

er t

o o

bta

in a

lo

an (

1 =

Yes

)

f. I

n t

he

pas

t h

as o

bta

ined

a l

oan

usi

ng s

avin

gs

in a

fo

rmal

fin

anci

al i

nst

itu

tio

n a

s co

llat

eral

(1

= Y

es)

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a si

ngle

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

)

pre

sent

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m

assi

gnm

ent

as t

he

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in

mea

ns

wit

h a

dd

itio

nal

co

var

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

level

of

ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s an

d d

istr

ict

fixed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er

20

14

.

a. C

on

sid

ers

her

/him

self

cre

dit

wo

rth

y (

1 =

Yes

)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

g.

In t

he

pas

t h

as o

bta

ined

a l

oan

usi

ng s

avin

gs

in a

fin

anci

al

coo

per

ativ

e as

co

llat

eral

(1

= Y

es)

b.

Sin

ce J

anu

ary 2

01

5 h

as m

ade

pay

men

ts t

ow

ard

s o

ne

or

mo

re

loan

s (1

= Y

es)

c. N

um

ber

of

loan

s si

nce

Jan

uar

y 2

01

5 i

n a

ny f

inan

cial

inst

itu

tio

n

d.

Has

a l

oan

wit

h F

inan

cier

a C

on

fian

za (

1 =

Yes

)

(Ad

min

istr

ativ

e D

ata)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

Est

imat

ed I

mp

act

of

the

pro

gra

m o

n C

red

it

Co

ntr

ol

Mea

nN

TA

BL

E 5

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.2

03

30

.01

25

-0.0

91

20

.38

61

0.0

24

0-0

.18

29

3.4

65

91

,80

1

(0.1

67

4)

(0.1

07

7)

(0.0

69

1)

(0.3

07

8)

(0.2

04

1)

(0.1

37

1)

(1.1

36

3)

-0.0

55

4*

*0

.00

03

-0.0

02

9-0

.10

52

**

0.0

00

5-0

.00

58

0.5

61

81

,80

1

(0.0

27

2)

(0.0

18

1)

(0.0

16

5)

(0.0

50

9)

(0.0

34

3)

(0.0

32

3)

(0.2

53

7)

-35

9.8

11

66

69

.32

06

16

7.8

07

6-6

84

.89

67

1,2

87

.82

92

33

5.7

45

15

17

6.1

32

01

,78

6

(87

7.6

81

2)

(73

6.8

70

3)

(74

2.9

39

9)

(1,6

57

.06

36

)(1

,41

1.1

32

1)

(1,4

56

.42

11

)(1

09

96

.31

)

-0.0

85

60

.05

85

0.0

21

0-0

.16

25

0.1

12

40

.04

22

0.7

79

61

,79

7

(0.1

31

5)

(0.0

68

1)

(0.0

87

1)

(0.2

44

8)

(0.1

29

8)

(0.1

71

0)

(1.1

56

6)

-0.0

84

0-0

.00

43

0.0

45

2-0

.15

97

-0.0

08

40

.09

08

0.2

65

11

,79

9

(0.0

71

7)

(0.0

30

5)

(0.0

27

8)

(0.1

32

6)

(0.0

57

8)

(0.0

55

7)

(0.4

41

5)

-0.0

22

1-0

.00

20

-0.0

31

9-0

.04

19

-0.0

03

9-0

.06

40

0.6

26

31

,80

1

(0.0

40

4)

(0.0

32

3)

(0.0

42

7)

(0.0

76

4)

(0.0

61

1)

(0.0

83

6)

(0.4

83

9)

0.0

29

6-0

.07

22

-0.0

92

8*

*0

.05

62

-0.1

38

8-0

.18

61

**

*0

.51

19

1,8

01

(0.0

64

6)

(0.0

45

8)

(0.0

36

3)

(0.1

21

3)

(0.0

87

8)

(0.0

71

0)

(0.5

00

0)

-0.0

85

9*

-0.0

52

8-0

.05

50

-0.1

63

1*

-0.1

01

4-0

.11

03

*0

.35

87

1,8

01

(0.0

47

4)

(0.0

42

1)

(0.0

33

6)

(0.0

89

1)

(0.0

79

5)

(0.0

65

3)

(0.4

79

7)

-0.0

54

1-0

.04

84

**

-0.0

28

1-0

.10

28

-0.0

93

1*

*-0

.05

63

0.4

81

41

,80

1

(0.0

38

0)

(0.0

20

7)

(0.0

24

5)

(0.0

71

0)

(0.0

37

8)

(0.0

47

8)

(0.4

99

8)

0.0

03

6-0

.03

36

-0.0

39

8*

0.0

06

8-0

.06

46

-0.0

79

8*

0.2

48

81

,80

1

(0.0

35

7)

(0.0

28

5)

(0.0

23

0)

(0.0

67

4)

(0.0

54

2)

(0.0

45

3)

(0.4

71

7)

-0.0

24

8-0

.00

82

-0.0

05

7-0

.04

72

-0.0

15

8-0

.01

15

0.5

94

71

,80

1

(0.0

21

1)

(0.0

16

0)

(0.0

13

2)

(0.0

40

9)

(0.0

30

5)

(0.0

26

1)

(0.1

80

5)

Est

imat

ed I

mp

act

of

the

pro

gra

m o

n A

sset

s an

d I

nco

me

Co

ntr

ol

Mea

nN

d.

Nu

mb

er o

f m

arket

cro

ps

h.

Co

nst

ruct

ed n

ew a

gri

cult

ura

l, l

ives

tock

or

fore

stry

infr

astr

uct

ure

sin

ce J

anu

ary 2

01

5 (

1 =

Yes

)

g.

Per

form

ed v

eter

inar

y p

roce

du

res

sin

ce J

anu

ary 2

01

5 (

1 =

Yes

)

f. A

dd

ed o

ne

or

mo

re t

yp

es o

f li

ves

tock

sin

ce J

anu

ary 2

01

5 (

1 =

Yes

)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

i. B

ou

gh

t o

ne

or

mo

re e

lect

rica

l ap

pli

ance

s si

nce

Jan

uar

y 2

01

5

TA

BL

E 6

e. U

sed

fer

tili

zer

in t

he

last

agri

cult

ura

l se

aso

n (

1 =

Yes

)

j. N

um

ber

of

inco

me-

gen

erat

ing b

usi

nes

ses

no

t re

late

d t

o

agri

cult

ure

, li

ves

tock

or

fore

stry

sin

ce J

anu

ary 2

01

5

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a

single

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

) p

rese

nt

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m a

ssig

nm

ent

as

the

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

covar

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

lev

el o

f ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s

and

dis

tric

t fi

xed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er 2

01

4.

a. N

um

ber

of

acti

vit

ies

gen

erat

ing i

nco

me

for

the

ho

use

ho

ld

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

k.

JUN

TO

S c

on

dit

ion

al c

ash

tra

nsf

er p

rogra

m p

over

ty s

core

(0

-

1)

b.

Per

cen

tage

of

inco

me

ori

gin

atin

g f

rom

agri

cult

ure

, li

ves

tock

or

fore

stry

sel

f-em

plo

ym

ent

acti

vit

ies

c. L

and

ow

ned

(m

2)

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.0

12

60

.00

44

0.0

12

60

.02

39

0.0

08

40

.02

53

0.9

48

91

,80

1

(0.0

22

5)

(0.0

14

6)

(0.0

13

5)

(0.0

42

5)

(0.0

27

6)

(0.0

26

5)

(0.2

20

2)

0.0

25

60

.02

16

0.0

51

70

.04

86

0.0

41

50

.10

37

0.6

94

11

,80

1

(0.0

54

0)

(0.0

41

6)

(0.0

34

2)

(0.1

02

1)

(0.0

78

9)

(0.0

67

6)

(0.4

60

9)

-0.0

03

0-0

.02

55

-0.0

25

1-0

.00

57

-0.0

49

0-0

.05

03

0.9

47

81

,80

1

(0.0

20

5)

(0.0

24

0)

(0.0

20

2)

(0.0

38

7)

(0.0

45

1)

(0.0

39

0)

(0.2

22

5)

0.0

12

4-0

.04

50

-0.0

65

7*

0.0

23

6-0

.08

64

-0.1

31

7*

0.7

07

41

,80

1

(0.0

45

4)

(0.0

39

2)

(0.0

39

1)

(0.0

85

5)

(0.0

74

1)

(0.0

77

1)

(0.4

55

1)

0.0

40

20

.04

60

0.0

16

10

.07

63

0.0

88

40

.03

23

0.7

04

61

,80

1

(0.0

58

3)

(0.0

40

4)

(0.0

33

6)

(0.1

10

9)

(0.0

76

2)

(0.0

66

6)

(0.4

56

3)

0.0

26

40

.02

01

-0.0

23

10

.05

01

0.0

38

6-0

.04

63

0.4

43

11

,80

1

(0.0

68

9)

(0.0

47

1)

(0.0

39

9)

(0.1

30

4)

(0.0

88

9)

(0.0

78

5)

(0.4

96

9)

0.0

09

5-0

.01

50

0.0

08

00

.01

80

-0.0

28

80

.01

60

0.5

80

21

,80

1

(0.0

54

0)

(0.0

35

8)

(0.0

34

3)

(0.1

02

2)

(0.0

68

1)

(0.0

67

5)

(0.4

93

7)

-0.0

30

5-0

.05

05

-0.0

41

3-0

.05

80

-0.0

97

2-0

.08

29

0.2

69

31

,80

1

(0.0

49

6)

(0.0

35

5)

(0.0

30

1)

(0.0

92

8)

(0.0

69

2)

(0.0

60

5)

(0.4

43

7)

0.0

28

90

.01

63

0.0

04

80

.05

50

0.0

31

40

.00

96

0.2

34

91

,80

1

(0.0

40

5)

(0.0

36

7)

(0.0

27

8)

(0.0

76

1)

(0.0

69

8)

(0.0

54

6)

(0.4

24

0)

0.0

14

60

.02

01

0.0

23

90

.02

77

0.0

38

60

.04

80

0.0

86

61

,80

1

(0.0

17

3)

(0.0

18

8)

(0.0

21

0)

(0.0

32

8)

(0.0

35

9)

(0.0

41

4)

(0.2

81

4)

h.

Ho

use

ho

ld d

rin

ks

sod

a in

a b

ad m

on

th (

1 =

Yes

)

g.

Ho

use

ho

ld d

rin

ks

sod

a in

a g

oo

d m

on

th (

1 =

Yes

)

f. H

ou

seh

old

eat

s o

ut

in a

bad

mo

nth

(1

= Y

es)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

Est

imat

ed I

mp

act

of

the

Pro

gra

m o

n C

on

sum

pti

on

Co

ntr

ol

Mea

nN

i. H

ou

seh

old

dri

nks

alco

ho

l in

a g

oo

d m

on

th (

1 =

Yes

)

TA

BL

E 7

e. H

ou

seh

old

eat

s o

ut

in a

go

od

mo

nth

(1

= Y

es)

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a

single

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

) p

rese

nt

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m a

ssig

nm

ent

as

the

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

covar

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

lev

el o

f ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s

and

dis

tric

t fi

xed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er 2

01

4.

a. H

ou

seh

old

eat

s m

eat

in a

go

od

mo

nth

(1

= Y

es)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

j. H

ou

seh

old

dri

nks

alco

ho

l in

a b

ad m

on

th (

1 =

Yes

)

b.

Ho

use

ho

ld e

ats

mea

t in

a b

ad m

on

th (

1 =

Yes

)

c. H

ou

seh

old

eat

s fr

uit

in

a g

oo

d m

on

th (

1 =

Yes

)

d.

Ho

use

ho

ld e

ats

fru

it i

n a

bad

mo

nth

(1

= Y

es)

(1.1

)(1

.2)

(1.3

)(2

.1)

(2.2

)(2

.3)

0.0

43

50

.02

65

0.0

41

1*

0.0

81

80

.05

05

0.0

81

5*

0.3

78

71

,74

8

(0.0

35

7)

(0.0

33

0)

(0.0

23

8)

(0.0

66

5)

(0.0

61

9)

(0.0

47

6)

(0.4

85

2)

-0.0

21

00

.00

13

-0.0

13

9-0

.03

97

0.0

02

5-0

.02

77

1.5

60

91

,78

5

(0.0

39

0)

(0.0

33

6)

(0.0

33

4)

(0.0

73

3)

(0.0

63

4)

(0.0

65

2)

(0.3

87

0)

0.2

89

2*

0.1

19

80

.19

50

**

*0

.54

71

**

0.2

29

3*

0.3

89

0*

**

1.9

36

71

,78

5

(0.1

48

4)

(0.0

74

6)

(0.0

63

2)

(0.2

66

3)

(0.1

38

5)

(0.1

25

7)

(1.2

78

9)

0.0

14

3-0

.00

61

-0.0

03

10

.02

58

-0.0

11

0-0

.00

58

0.5

42

81

,51

8

(0.0

92

4)

(0.0

74

7)

(0.0

70

1)

(0.1

65

1)

(0.1

32

7)

(0.1

29

9)

(0.8

78

5)

0.0

08

2-0

.00

23

-0.0

00

70

.01

45

-0.0

04

1-0

.00

13

0.6

97

01

,23

9

(0.0

23

9)

(0.0

19

5)

(0.0

19

3)

(0.0

41

8)

(0.0

33

8)

(0.0

34

6)

(0.1

97

6)

0.0

17

60

.00

15

-0.0

02

50

.03

01

0.0

02

6-0

.00

47

0.2

37

01

,37

8

(0.0

17

4)

(0.0

10

6)

(0.0

08

2)

(0.0

29

4)

(0.0

18

6)

(0.0

14

6)

(0.1

44

1)

0.0

08

8-0

.01

22

-0.0

25

80

.01

59

-0.0

22

5-0

.05

00

0.6

83

51

,51

0

(0.0

20

1)

(0.0

14

6)

(0.0

16

1)

(0.0

36

2)

(0.0

26

6)

(0.0

30

6)

(0.1

80

1)

0.0

25

9-0

.00

64

-0.0

11

50

.04

66

-0.0

11

5-0

.02

17

0.6

88

91

,50

2

(0.0

25

5)

(0.0

16

9)

(0.0

17

1)

(0.0

45

0)

(0.0

30

0)

(0.0

31

5)

(0.2

11

9)

TA

BL

E 8

e. I

nte

rper

son

al r

elat

ion

ship

s an

d s

elf-

stee

m i

nd

ex (

0-1

)

Note

s:

This

tab

le r

epo

rts

dif

fere

nce

s fi

nan

cial

lit

erac

y i

n b

etw

een t

he

rand

om

ly a

ssig

ned

tre

atm

ent

and

co

ntr

ol

gro

up

s at

end

line.

Eac

h c

ell

in c

olu

mns

(1.x

) an

d (

2.x

) re

po

rts

resu

lts

for

a

single

reg

ress

ion o

f th

e in

dep

end

ent

var

iab

le i

n t

he

corr

esp

ond

ing r

ow

, w

ith c

lust

ered

sta

nd

ard

err

ors

at

the

com

unit

y l

evel

in p

aren

thes

es b

elo

w e

ach e

stim

ate.

Co

lum

ns

(1.x

) p

rese

nt

OL

S I

nte

nt-

to-T

reat

(IT

T)

esti

mat

es.

Co

lum

ns

(2.x

) p

rese

nt

2S

LS

Tre

atm

ent-

on-t

he-

Tre

ated

(T

OT

) es

tim

ates

, w

ith a

ctual

tre

atm

ent

as t

he

end

ogen

ous

var

iab

le a

nd

ran

do

m a

ssig

nm

ent

as

the

inst

rum

ent.

Co

lum

ns

(x.0

) p

rese

nt

the

dif

fere

nce

in m

eans

bet

wee

n t

reat

men

t an

d c

ontr

ol

wit

ho

ut

add

itio

nal

co

var

iate

s. C

olu

mns

(x.1

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

covar

iate

s: (

i) d

ista

nce

to

reg

ional

cap

ital

; (i

i) n

um

ber

of

ho

use

ho

ld m

emb

ers;

(ii

i) g

end

er o

f ho

use

ho

ld h

ead

; (i

v)

age

of

resp

ond

ent;

(v)

max

imum

ed

uca

tio

nal

lev

el o

f ho

use

ho

ld;

and

(vi)

ben

efic

iary

sta

tus

of

JUN

TO

S c

ond

itio

nal

cas

h t

ransf

er p

rogra

m,

bes

ides

all

bal

ance

var

iab

les

in T

able

1.

Co

lum

ns

(x.2

) p

rese

nt

the

dif

fere

nce

in m

eans

wit

h a

dd

itio

nal

co

var

iate

s

and

dis

tric

t fi

xed

eff

ects

. C

olu

mn (

3)

rep

ort

s th

e m

ean v

alue

of

each

ind

epen

den

t var

iab

le a

t en

dli

ne,

wit

h s

tand

ard

dev

iati

ons

in p

aren

thes

es.

Co

lum

n (

4)

rep

ort

s th

e num

ber

of

ob

serv

atio

ns

in e

ach r

egre

ssio

n. Sourc

e:

End

line

surv

ey o

f 1

80

1 h

ouse

ho

lds

carr

ied

out

in J

uly

20

16

, S

ISF

OH

20

13

and

Junto

s R

oll

of

Use

rs b

y S

epte

mb

er 2

01

4.

a. T

he

wo

man

dec

ides

fre

ely h

ow

to

sp

end

her

in

com

e (1

= Y

es)

*p

<0

.1;

**p

<0

.05

; *

**p

<0

.01

;

h.

Co

mm

un

ity p

arti

cip

atio

n i

nd

ex (

0-1

)

b.

On

a s

cale

of

1 t

o 4

, h

ow

wel

l d

oes

th

e w

om

an f

eel

wit

h h

er

acti

vit

ies,

rel

atio

nsh

ips,

etc

.

c. N

um

ber

of

gro

up

s o

r o

rgan

izat

ion

s to

wh

ich

th

e w

om

an

bel

on

gs

d.

Nu

mb

er o

f p

osi

tio

ns

the

wo

man

has

hel

d i

n t

he

gro

up

s to

wh

ich

sh

e b

elo

ngs

g.

Gen

der

ro

les

ou

tsid

e th

e h

ou

seh

old

in

dex

(0

-1)

Inte

nt-

to-T

reat

(IT

T)

Tre

atm

ent-

on

-th

e-T

reat

ed (

TO

T)

Est

imat

ed I

mp

act

of

the

Pro

gra

m o

n F

emal

e E

mp

ow

erm

ent

Co

ntr

ol

Mea

nN

f. G

end

er r

ole

s in

th

e h

ou

seh

old

in

dex

(0

-1)