-
Does a Picture Paint a Thousand Words? Evidencefrom a
Microcredit Marketing Experiment
Xavier Giné*, Ghazala Mansuri, and Mario Picón
Female entrepreneurship is low in many developing economies
partly due to con-straints on women’s time and mobility, often
reinforced by social norms. We analyzea marketing experiment
designed to encourage female uptake of a new microcreditproduct. A
brochure with two different covers was randomly distributed among
maleand female borrowing groups. One cover featured 5 businesses
run by men while theother had identical businesses run by women. We
find that both men and womenrespond to psychological cues. Men who
are not themselves business owners, havelower measured ability and
whose wives are less educated respond more negatively tothe female
brochure, as do women business owners with low autonomy within
thehousehold. Women with relatively high levels of autonomy shown
the male brochurehave a similar negative response, while there is
no effect on female business ownerswith autonomy shown the female
brochure. Overall, these results suggest thatwomen’s response to
psychological cues, such as positive role models, may bemediated by
their autonomy and that more disadvantaged women may require
moreintensive interventions. JEL codes: G21, D24, D83, O12
Women in developing countries face numerous barriers to
participation ineconomic life. In addition to constraints on time
and limited access to capital,women’s exposure to markets and
business networks is often also limited by
* Giné (Corresponding author): Development Research Group, The
World Bank (e-mail:xgine@
worldbank.org). Mansuri: Development Research Group, The World
Bank (e-mail: gmansuri@
worldbank.org). Picón: Development Research Group, The World
Bank (e-mail: mpicon@worldbank.
org). We are indebted to three anonymous referees for extremely
insightful comments which have
substantially improved the paper. We also thank Jonathan Zinman
and Tahir Waqar for valuable
discussions. We are especially grateful to the following for
their help and support in organizing the
experiment: Shahnaz Kapadia, at ECI Islamabad, for her help with
designing the brochure; Irfan Ahmad
at RCONs, Lahore, for managing all field operations and data
collection; Dr Rashid Bajwa, Agha Javad,
Tahir Waqar and the field staff at NRSP for implementing the
intervention; Qazi Azmat Isa, Kevin
Crockford and Imtiaz Alvi at the World Bank Office in Islamabad,
and Kamran Akbar at the Pakistan
Poverty Alleviation Program (PPAF) in Islamabad for their
support and encouragement. This project was
jointly funded by the World Bank (Development Research Group,
South Asia Region, Poverty Reduction
and Equity Network, Gender Group), the PPAF and the Kaufmann
Foundation. Santhosh Srinivasan
provided outstanding research assistance. The views expressed
herein are those of the authors and should
not be attributed to the World Bank, its executive directors, or
the countries they represent.
THE WORLD BANK ECONOMIC REVIEW, VOL. 25, NO. 3, pp. 508–542
doi:10.1093/wber/lhr026Advance Access Publication July 10, 2011#
The Author 2011. Published by Oxford University Press on behalf of
the International Bankfor Reconstruction and Development / THE
WORLD BANK. All rights reserved. For permissions,please e-mail:
[email protected]
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low mobility and lack of education. Weak decision-making power
within thehousehold often reinforces these disadvantages, further
limiting women’sability to secure time or resources for their own
productive activities. Onemanifestation of this is the
comparatively low prevalence of female-ownedbusinesses. Even as
entrepreneurs, however, women operate businesses that arefar
smaller in scale and profitability than male businesses.
The recognition that women may therefore be most in need of
credit forsmall businesses has played an important role in the
woman-centered microcre-dit movement of the last two decades
(Yunus, 1999). In Pakistan, however,microfinance as well as other
support for micro-entrepreneurship has focusedprimarily on men
while women remain at the margins of economic life.
This paper reports on an experiment designed to encourage female
uptakeof a new microcredit product that allowed eligible borrowers
the opportunityto borrow up to four times the typical loan size.1
In cooperation with themicrofinance institution, we designed two
different brochures that providedinformation about the
characteristics of the loan and described the applicationprocess.
The brochures were identical, except for the cover page: one
featured5 different businesses with men operating them, while the
other had the exactsame five businesses, but with women
entrepreneurs, instead. Groups of bor-rowers targeted to be offered
the new product were randomized to receiveeither the brochure with
the female or the male pictures.
We find that this form of marketing affects both male and female
clients butquite differently. In the full sample of clients, the
brochure has little impact onloan demand among either men or women.
When we focus on businessowners, however, we find that exposure to
the female brochure substantiallydecreases demand for the larger
loan among women clients but has no impacton male clients.
Importantly, this is not an artifact of business scale. Whilewomen
typically operate much smaller businesses, we show that women
whooperate businesses which are comparable in scale to male
businesses also reactnegatively to the female brochure. However,
once we allow the response to thebrochure picture to vary by
individual characteristics, we find that this negativeeffect is
concentrated among women business owners with low decisionmaking
power. For this group of women, we conjecture that it is the
maledecision maker’s reaction to the female brochure that matters.
We find thatmen also react negatively to the female brochure, but
only amongnon-entrepreneurs, individuals with poor digit span
recall (correlated with edu-cation and entrepreneurship) or with
wives who are relatively poorly educated.This could be interpreted
as evidence of affinity (Evans, 1963; Mobius andRosenblat, 2006),
but it is also consistent with men’s low regard for females
asbusiness owners (see Beaman et al. 2009 who study perceptions
about female
1. While supply constraints may also be an important determinant
of lack of credit to small firms
(Banerjee and Duflo, 2008; de Mel et al. 2010), our sample
consists of individuals that are already
microfinance clients.
Giné, Mansuri, and Picón 509
-
politicians). Interestingly, female business owners with high
decision makingautonomy shown the male brochure also react
negatively by roughly the samemagnitude, while there is no effect
on female business owners with autonomyshown the female
brochure.
These findings contribute to the relatively limited evidence on
the impact ofmarketing on behavior change and in this sense,
Bertrand et al. (2010) is thepaper closest to ours. They analyze a
direct mail field experiment in urbanSouth Africa that randomized
advertising content, loan price, and loan offerdeadlines
simultaneously. Their subjects, like ours, were existing
microfinanceclients. Bertrand et al. find that advertising content
matters, especially amongmen, but it is mostly treatments that
appeal to intuition (such as a picture) asopposed to reason (like a
comparison of interest rates across lenders) that influ-ence
behavior. The reason why reflexive cues are less relevant is
perhapsbecause individuals that received the loan offers had
rationally decided not toborrow, and so were already familiar with
the terms of the loan made salientby reflexive treatments
(Kahneman, 2003).
In Bertrand et al (2010) men respond positively to the image of
womencredit officers in a context where this creates no threat to
relative male auth-ority within the household or within the
community. In our experiment,however, the picture of a female
entrepreneur on a brochure advertising alarger loan challenges
local norms of relative power. In this sense, the paper isrelevant
to the growing literature on social norms which uses traditional
mar-keting techniques to alter attitudes and behavior by changing
individual per-ceptions (see, for example, La Ferrara et al., 2008;
Jensen and Oster, 2007 andPaluck, 2009).
The remainder of the paper is structured as follows. Section I
describes thecontext in Pakistan and the marketing experiment.
Section II discusses thedata, Section III describes the empirical
strategy and Section IV reports theresults of the experiment.
Section V discusses the policy implementations of theresults and
concludes.
I . S E T T I N G A N D I N T E R V E N T I O N
Pakistan has a population of over 162 million, with over 60%
living in ruralareas. Although the agricultural sector continues to
be important for overallgrowth, 45 percent of the rural poor rely
on non-farm activities as importantsources of income.2
Pakistan’s financial system has grown significantly in the past
few years duein part to the successful implementation of various
financial sector reforms,including the granting of banking licenses
to a number of new private banks inthe early 1990s, the
modernization of the governance and regulatory
2. Source: World Development Indicators database, April 2009 and
World Development Report
2008.
510 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
framework of the banking sector in the late 1990s, and the
privatization ofmajor public sector banks since the early to
mid-2000s.
Despite these recent achievements, access to financial services
is still quitelimited, especially in rural areas. According to the
Access to Finance dataset(Nenova et al. 2009), only 14 percent of
households interviewed reportedusing a financial product or service
(including savings, credit, insurance, pay-ments, and remittance
services) from a formal financial institution.3 Wheninformal
financial access is taken into account, however, this figure rises
to justover 50 percent.
Overall, rural firms and households account for about 7 percent
of totalcredit disbursement (about Rs 130.7 billion) and the bulk
of this is for agricul-tural finance (Rs 108.7 billion), including
both farm and nonfarm credit (seeAkhtar, 2008). While microcredit
volumes are skewed towards rural areas,microcredit currently
accounts for only 17 percent of total rural credit andserves some
1.7 million clients. Comparative rates of microfinance
penetrationin the South Asian region are 35 percent in Bangladesh,
25 percent in India,and 29 percent in Sri Lanka.
Among microfinance providers, Khushali Bank is active in 86
districts;National Rural Support Program (NRSP) comes second with a
presence in 51districts while Kashf Foundation has some presence in
24 districts.4 Thesethree microfinance entities account for
approximately 70 percent of the sector’sactive clients (MicroWatch,
2008).
Unlike most other countries, the microfinance sector has focused
primarilyon men rather than women on the grounds that there is less
demand for creditfrom women give their low mobility levels and
cultural norms around womenas economic actors. Consistent with
this, Pakistan continues to under-performon a range of social
indicators relative to other countries at similar levels of
percapita income and rural development. According to the 1998
HumanDevelopment Report, for example, Pakistan ranked 138 out of
174 on theHuman Development Index, 131 out of 163 on the Gender
Development Index(GDI), and 100 out of 102 on the Gender
Empowerment Measure (GEM).
National Rural Support Program
Established in 1991, NRSP is the largest of the Rural Support
Programs in thecountry in terms of outreach, staff and development
activities. It is modeledafter the Aga Khan Rural Support Program,
established in the early 1980s as anot-for-profit rural development
organization. During the early 1990s, NRSPremained small, but the
establishment of the Pakistan Poverty Alleviation Fundin 2000, a
second-tier funding and capacity-building apex, provided
critical
3. In comparison, 32 percent of the population has access to the
formal financial system in
Bangladesh, and this figure amounts to 48 percent in India and
59 percent in Sri Lanka (World Bank,
2008).
4. Both NRSP and Kashf obtain a large fraction of their loan
funds from the Pakistan Poverty
Alleviation Fund (PPAF) which supported this work.
Giné, Mansuri, and Picón 511
-
funding that fueled NRSP and other partner NGOs’ growth. As part
of itsgrowth strategy, NRSP applied for a microfinance bank license
in 2008 andbecame a microfinance bank in early 2010, falling under
the supervision of theState Bank of Pakistan. Microfinance banks
now account for almost half of theoutreach of the microfinance
sector.
NRSP makes loans largely to members of a community organization.
Itsstaff supports the creation of community organizations (CO) by a
process ofsocial mobilization which includes the creation of a
community co-financedand co-managed infrastructure project and
skill and group management train-ing. Members of a CO typically
live close to each other and meet regularly.Most also contribute
towards individual and group savings. Up to date, NRSPhas organized
more than a million poor households into a network of morethan
100,000 COs across the country. Roughly one-half to two-thirds of
COmembers are also active borrowers and group meetings serve as the
venue forthe receipt and repayment of loans for most members.
NRSP has three main credit products: a single installment loan
for agricul-tural inputs (fertilizer, seeds, etc) with maturity of
6 to 12 months; enterpriseloans and loans for livestock that have
12 monthly installments each. Themaximum amount that can be
borrowed depends on the number of loans suc-cessfully repaid (loan
cycle). A new borrower starts with a small loan limit ofRs 10,000
(USD 117)5 which can increase in intervals of up to Rs 5,000
perloan cycle. As a point of comparison, a cow costs around Rs
60,000. All loanshave joint liability at the CO level although new
loans are issued even if someCO members are overdue.6
Besides credit, NRSP offers training in various vocational
skills and providesup to 80 percent financing for infrastructure
projects in the village.
The Experiment and Marketing Intervention
The study was conducted in five branches in the districts of
Bahawalpur,Hyderabad, and Attock, spanning different agro-climatic
regions of Pakistan.7
Figure 1 shows the location of the study districts.NRSP staff
conducted a complete listing of the occupation of CO members
in the study branches to identify those who were engaged in a
non-farmactivity. After the listing, a baseline survey was
conducted in November 2006in a sample of 747 COs, selected so that
their membership was between 5 and26 members. The original sampling
framework included all CO members thataccording to the listing
exercise had a non-farm business and five othermembers selected at
random from each CO. In practice, enumerators ended up
5. Currency converter accessed online on July 2nd, 2010.
6. Borrowers are required to find two guarantors, who can be
members of the same CO. NRSP staff
uses guarantors as a means of exerting peer pressure, rather
than enforcing repayment from them.
7. These branches are as follows: Matiari and Tando Muhammad
Khan in Hyderabad, Attock in
Attock and Bahawalpur (rural and urban) in Bahawalpur.
512 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
interviewing everyone that attended a special CO meeting that
was called toconduct the baseline survey. The resulting sample
consisted of a total of 4,162members interviewed, and 2,284 members
(54.9%) that were in good standing.The timeline of the experiment
is presented in Figure 2.
Using data from the listing exercise, COs were randomly
allocated into twogroups, one of which was assigned to receive
business training. Training ses-sions were held, from February to
May 2007. Each session lasted for 6 to 8days (see Giné and Mansuri
(2011a) for more details about the business train-ing
intervention).
After completing the business training sessions, members from
all study COswere invited to an orientation meeting that introduced
the possibility of bor-rowing a larger loan amount. Most
orientation sessions took place in regularlyscheduled CO meetings
and lasted for about an hour and a half. Attendance atthese
sessions was high, with more than 90 percent of members
attending.Message consistency during the orientation was maintained
by providing
FIGURE 1. Pakistan Study Districts
Giné, Mansuri, and Picón 513
-
training to all NRSP credit officers and other staff who were in
charge of deli-vering the orientations.8
During the orientation meeting, members who were in good
standing i.e.,those who had successfully repaid at least one loan
on time received one oftwo versions of a marketing brochure.
Orientations occurred successfully in596 COs. In the remaining 151
COs orientation meetings could not be heldbecause the CO had either
disbanded or was newly formed so that none of itsmembers was
eligible for the lottery.9 The brochure was identical in all
respectsexcept one. In one version, the entrepreneurs manning the
business were malewhile in the other they were female. To ensure
that only the gender of the busi-nessperson differed between both
versions of the brochure, the exact samebusiness was photographed
twice, the first time with a man as owner and thesecond with a
woman. Figure 3 shows the front and back of the brochurealong with
the picture of the businesses first with men (Male brochure)
andthen with women (Female Brochure). The businesses in the
brochure werechosen to be representative of the type of businesses
typically run by NRSPmicroentrepreneurs. The brochure thus
contained two agribusinesses, tworetail businesses and one
tailoring business. According to our baseline data,49.41 percent of
male businesses were agribusiness, 26.87 percent were inretail and
8.53 percent were involved in handicrafts and tailoring,
thusaccounting for almost 85 percent of all male businesses. Among
femalebusinesses, 19.88 percent were agribusinesses, 17.60 percent
were retailbusinesses and 56.90 were in handicrafts and tailoring,
accounting for almost95 percent of all female businesses. All
members of a CO were given one of thetwo brochures, which were
randomly allocated across COs.
The goal of the brochure was to explain how to apply for a
larger loan via alottery. Appendix A provides the translated text
of the brochure. According tothis, all eligible members could make
a loan request of up to Rs. 100,000. Therequest was subject to all
the usual technical and social reviews conducted by
FIGURE 2. Timeline
8. There were 12 teams of two NRSP staff each in Attock, 29 in
Bahawalpur and 7 in Hyderabad.
9. First time borrowers were not eligible to participate in the
lottery. NRSP felt it did not have
sufficient credit history for this group to allow them access to
the much larger loans available to lottery
winners.
514 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
NRSP credit officers, who could also determine the loan amount
they werewilling to approve for each borrower. Approved loans which
were larger thanthe usual limit of Rs. 30,000, were to be forwarded
to headquarters, where theresult of the lottery were maintained.10
Lottery winners could borrow theapproved amount, while those who
lost the lottery could borrow up to theirregular loan size.
Although the brochure encouraged members to borrow forproductive
purposes, in practice there were no restrictions on the use of
theloan. In addition, qualifying members who already had an
outstanding loanwith NRSP were allowed to apply for the larger
loan, subject to the conditionthat part of the new loan would be
used to pay off the outstanding debt.
Eligible CO members had seven months, from November 2007 to
June2008, to apply for the larger loan. Of the 2,284 eligible CO
members, 713(31.2 percent) applied. NRSP approved 532 loans (74.6
percent). Most appli-cants had their loan amounts reduced. Credit
officers reported that this wasdue to concerns that borrowers would
not be able to make the requiredmonthly installments. Of the
customers approved, 254 were assigned to winthe lottery (47.7
percent) and 211 ended up borrowing (83 percent). Amongthe 278 loan
applicants that lost the lottery, only 161 borrowed (58
percent).Among the reasons cited for changing their mind are time
elapsed from request
FIGURE 3. Brochures
10. The lottery was designed so that the chance of winning was
50 percent. See Giné and Mansuri
2011 for more details.
Giné, Mansuri, and Picón 515
-
to approval (average time was 2 months), and for losers the fact
that the newloan size was not too different than the loan they
currently had.
Figure 4 shows the distribution of loans by disbursement date
and gender.The vertical axis measures number of loans applied for
in each disbursementmonth. It is clear that women did the bulk of
the borrowing in the first threeto four months after the lottery
started. Males, on the other hand, waited forthe months of May and
June to ask for loans, coinciding with the agriculturalseason.
A follow-up survey was conducted in December 2008. This was
sixmonths after the loan lottery concluded and about 13 months
after the loanorientation meetings. In the follow up, some 45
percent of eligible COmembers recalled attending the loan lottery
orientation meeting. Amongthose who recalled attendance, about 70
percent recalled receiving a bro-chure and of these about half were
able to correctly recall the picture theywere shown.
Of the 211 lottery winners who took the larger loan, 125
reported loan usein the follow up. Table 1 reports the share of
loans used for different purposesby gender, along with the p-value
of a test of differences by gender. Onaverage, about 48 percent of
the loan was used for working capital, with mensignificantly more
likely to use loans for this purpose (50 percent as comparedto 36
percent for women). In contrast only 6 percent of loan proceeds
wereused for purchasing business equipment, but here women were 3
times morelikely to report this use (12 percent compared to about 4
percent for men). Forother uses, men and women look roughly
similar, though women use loans
FIGURE 4. Distribution of Loans by Date of Disbursement
(Source: Administrative records, NRSP)
516 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
more frequently for consumer durables (8 percent of loan
proceeds on averageversus about 3 percent for men), while men use
loan proceeds morefrequently for housing improvements (8 percent
versus about 3 percent forwomen).
I I . D A T A
Baseline data collected in November 2006, prior to the business
training andloan lottery orientations, included questions on the CO
member, the member’shousehold and the business. Besides the usual
set of variables, such as age, edu-cation, marital status etc,
individual characteristics include measures of entre-preneurship,
digit span recall, risk preferences and decision making
autonomyacross a range of household and business outcomes.
Household characteristicsinclude information on the income
generating activities of all householdmembers, total household
assets including livestock and past and current bor-rowing and
saving of household members. Business characteristics,
includingage, location and type of business activity, as well as
the scale of the businessas measured by its assets, hired workers
and monthly sales.
Summary statistics from the baseline survey are presented in
Table 2, andvariable definitions are provided in Appendix B. CO
members are about38 years old, have 4.1 years of education, own
some 3.5 acres of land and haveaverage household expenditures of
about Rs 5,200 per month (roughly61 USD). This places them
significantly above the bottom half of the popu-lation of the
villages in which they reside (see Mansuri, 2011). Women are
TA B L E 1. Reported Larger Loan Use
All Male Female P-val of t-test (2)-(3)(1) (2) (3) (4)
Household Durables 0.038 0.027 0.080 0.161Food consumption 0.041
0.041 0.041 0.999School Supplies 0.003 0.003 0.005 0.584Festivals
and Ceremonies 0.010 0.007 0.022 0.250Household Repairs 0.070 0.081
0.027 0.293Previous Loan Repayment 0.059 0.057 0.068 0.787Savings
0.041 0.036 0.063 0.438Inventories/ raw materials for
main business0.475 0.505 0.355 0.110
Equipment for main business 0.055 0.038 0.124 0.064Inventories
and Equipment for
other household businesses0.061 0.053 0.096 0.361
Other uses 0.146 0.153 0.118 0.606Number of Obs. 125 100 25
Note: Authors’ analysis based on data from the follow-up survey
conducted in December2008. Columns report the average fraction of
loans used for each purpose.
Giné, Mansuri, and Picón 517
-
TA
BL
E2
.Sum
mary
Sta
tist
ics
All
mem
ber
sP-v
alu
eof
t-te
st(4
)-(5
)
Busi
nes
sO
wner
sP-v
alu
eof
t-te
st(7
)-(8
)
Mat
ched
Busi
nes
sO
wner
s
P-v
alu
eof
t-te
st(1
0)-
(11)
N.
Obs
All
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Take-
up
Show
nFem
ale
bro
chure
(Yes¼
1)
3,4
51
0.5
20.4
90.5
60.0
00.4
70.5
70.0
20.4
50.5
80.0
2E
ligib
lefo
rlo
anlo
ttte
ry(Y
es¼
1)
3,4
51
0.5
50.6
20.6
20.0
00.6
90.6
70.0
00.7
10.6
80.0
1O
ffer
edB
usi
nes
sT
rain
ing
(Yes¼
1)
3,4
51
0.5
40.5
20.5
30.0
00.5
50.5
30.0
00.5
40.5
60.0
4A
pplied
for
loan
(1¼
Yes
)2,1
49
0.3
10.4
20.1
80.0
00.4
50.1
70.0
00.4
40.2
20.0
0A
ppro
ved,
condit
ional
on
apply
ing
(1¼
Yes
)664
0.7
60.7
60.7
60.0
10.7
80.7
50.0
50.8
10.7
40.3
5B
orr
ow
ed,
condit
ional
on
bei
ng
appro
ved
(1¼
Yes
)503
0.6
90.7
50.5
20.6
20.7
50.5
50.8
50.7
80.6
30.8
7
Am
ount
borr
ow
ed(‘
000
Rs)
445
33.0
033.8
330.0
00.0
634.5
929.5
50.0
236.0
432.5
00.4
3B
ase
line
Chara
cter
isti
csIn
div
idu
al
Chara
cter
isti
csFem
ale
(Yes¼
1)
3,4
51
0.4
62
22
22
22
22
Age
3,4
51
37.8
838.1
837.5
10.0
137.5
36.9
70.2
938.4
939.0
20.9
8Y
ears
of
Educa
tion
(0-1
6)
3,4
51
4.0
95.3
12.6
30.0
05.5
32.7
30.0
05.2
92.6
50.0
0M
arr
ied
(Yes¼
1)
3,4
51
0.8
30.8
20.8
40.1
20.8
20.8
60.1
30.8
40.8
70.0
9D
igit
Span
Rec
all
(0-8
)3,4
51
3.3
13.8
42.6
80.0
04.0
32.7
40.0
03.8
72.4
80.0
0M
ember
of
aM
ixed
Gro
up
(Yes¼
1)
3,4
51
0.0
60.0
40.0
90.0
00.0
50.0
90.0
00.0
30.1
30.0
0
Index
of
Fem
ale
Mobilit
y1,5
71
0.0
62
0.0
62
20.1
22
20.1
52
Index
of
No
Purd
ah
1,5
71
0.1
52
0.1
52
20.1
32
20.3
72
Busi
nes
sO
wner
(Yes¼
1)
3,4
51
0.6
00.6
10.5
80.0
12
22
22
2R
isk
Tole
rance
(0¼
Ris
kA
vers
e;10¼
Ris
kL
ove
r)3,4
51
3.6
13.8
13.3
70.0
03.8
43.3
90.0
04.0
83.5
90.0
0
Month
sas
Mem
ber
3,4
51
26.5
524.5
724.5
60.0
026.9
222.1
10.0
028.3
825.3
50.0
1H
ou
sehold
Chara
cter
isti
csY
ears
of
Educa
tion,
Spouse
(0-1
6)
3,4
51
3.3
12.1
24.7
50.0
02.2
44.6
60.0
02.0
64.5
30.0
0
(Conti
nued
)
518 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E2.
Conti
nued
All
mem
ber
sP-v
alu
eof
t-te
st(4
)-(5
)
Busi
nes
sO
wner
sP-v
alu
eof
t-te
st(7
)-(8
)
Mat
ched
Busi
nes
sO
wner
s
P-v
alu
eof
t-te
st(1
0)-
(11)
N.
Obs
All
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Tota
lH
ouse
hold
Inco
me
(log)
3,4
51
11.5
311.6
211.4
40.0
011.7
111.4
50.0
011.5
311.6
10.9
9E
xpen
dit
ure
s(l
og)
3,4
51
8.2
88.2
98.2
70.0
08.3
28.2
90.0
08.1
98.3
90.4
0N
um
ber
of
Childre
nunder
93,4
51
2.6
52.8
92.3
70.0
02.8
92.4
30.0
02.7
72.3
10.0
1L
and
(are
a)
3,4
51
4.4
85.9
42.7
40.0
05.8
42.5
70.0
14.9
93.4
80.0
1C
redit
const
rain
ts(Y
es¼
1)
3,4
51
0.1
40.1
20.1
60.0
00.1
20.1
20.0
00.1
20.1
30.0
1Fam
ily
ever
inbusi
nes
s(Y
es¼
1)
3,4
51
0.6
10.6
10.6
10.3
10.7
50.7
0.0
60.7
40.6
90.1
4D
ecis
ion
Makin
g(0
-8)
3,4
51
2.6
53.3
41.8
40.0
03.3
51.5
90.0
03.2
01.7
90.0
0Sourc
esof
Cre
dit
%borr
ow
ing
Form
al
Sec
tor
2006-0
82,9
31
0.0
50.0
70.0
30.0
10.0
80.0
30.0
20.0
90.0
10.0
1A
mount
borr
ow
ed2006
(‘000s)
2,9
31
5.9
49.6
71.3
70.0
811.5
70.8
50.1
913.3
50.1
50.3
1%
borr
ow
ing
Mic
rofinance
Inst
ituti
ons
/M
icro
finan
ceB
anks
2006-0
82,9
31
0.8
20.7
90.8
60.0
00.8
60.9
30.0
00.8
70.9
10.0
0
Am
ount
borr
ow
ed2006
(‘000s)
2,9
31
31.1
734.2
327.4
20.0
037.6
728.7
90.0
039.6
231.5
40.1
3%
borr
ow
ing
Fri
ends
and
Fam
ily
(oth
erth
an
CO
mem
ber
s)2006-0
82,9
31
0.2
20.2
10.2
30.1
10.2
0.2
20.3
00.1
90.2
20.4
7
Am
ount
borr
ow
ed2006
(‘000s)
2,9
31
5.5
76.1
94.8
10.0
06.1
94.7
80.0
16.0
44.6
10.1
3%
borr
ow
ing
Info
rmal
Len
der
s2006-0
82,9
31
0.4
80.5
00.4
70.1
60.4
70.4
20.2
70.4
90.4
50.2
0D
ebt
toin
form
al
lender
s2006
(‘000s)
2,9
31
15.0
723.2
15.0
60.0
026.4
54.3
20.0
018.3
84.7
30.0
2B
usi
nes
ses
Agri
busi
nes
s,D
air
y,L
ives
tock
(Yes¼
1)
1,9
62
0.4
00.5
40.2
20.0
00.5
40.2
20.0
00.5
60.5
62
Ret
ail
and
Food
Ser
vic
es(s
hopkee
pin
g)
(Yes¼
1)
1,9
62
0.2
40.2
80.1
90.0
00.2
80.1
90.0
00.2
90.2
92
Handic
raft
,T
ailori
ng,
Voca
tional
Tra
de
(Yes¼
1)
1,9
62
0.2
90.0
90.5
50.0
00.0
80.5
50.0
00.0
50.0
52
Oth
er(Y
es¼
1)
1,9
62
0.0
70.0
90.0
40.0
00.0
90.0
30.0
00.0
90.0
92
Sale
s(‘
000
Rs)
2,0
65
14.5
122.3
4.7
40.0
122.3
4.7
40.0
07.0
67.0
10.4
8
Note
:A
llvari
able
sare
from
the
base
line
surv
ey(N
ove
mber
2006),
exce
pt
for
Sourc
esof
Cre
dit
info
rmat
ion,
whic
his
from
the
follow
up
and
reca
lls
cred
itfo
rth
e2006-2
008
per
iod.
See
Appen
dix
Bfo
rdefi
nit
ion
of
vari
able
s.C
olu
mns
(2)-
(4),
(6)-
(7)
and
(9)-
(10)
report
mea
ns.
Giné, Mansuri, and Picón 519
-
only slightly less likely than men to report owning a business
(61 percent ofsample men and 58 percent of sample women owned a
business at baseline).11
However, there are significant differences between male and
female COmembers in almost all other dimensions, and these
differences are sustainedwhen we focus only on those who owned a
business at baseline. Women tendto have less education, perform
significantly below men on digit span recalland are less risk
tolerant (on a 0 to 10 scale). Women members are also morelikely to
come from households that have less land wealth, as compared to
thehouseholds of male CO members. This indicates some selection of
women COmembers by wealth and is consistent with more stringent
female seclusion prac-tices among landed rural households (see
Jacoby and Mansuri, 2011).Importantly, women members also report
far less decision making autonomythan men do on a range of
household, individual and business outcomes.Among business owners,
women report having complete autonomy overroughly 1.5 decisions out
of a total of 8, whereas men claim to have completeautonomy over
more than 3 decisions.12
Women are also more likely than men to belong to a mixed gender
CO inour sample (about 63 percent of members in mixed COs are
women). Whilethis is a small sample overall, given that only 7
percent of COs have both menand women, there are some interesting
differences between women in mixedand single gender COs. In
particular, the former seem to have less ‘voice’ inthe COs they
belong to. While the odds of holding office in the single genderCOs
are about the same for men and women, at 20 and 18 percent
respect-ively, 48 percent of men and only 7 percent of women in
mixed COs reportholding any office. Women in mixed COs are also
significantly more likely toobserve purdah, even though mixed COs
are more than twice as likely to becomposed of members of the same
zaat (caste).13 They are also younger andhave more young children
under age 9. They are also less risk tolerant, signifi-cantly less
optimistic and have less trust in the formal institutions.
However,they do about as well as other women on digit span recall
and the index ofentrepreneurial literacy. They are also about as
well educated as women infemale only COs and come from households
that are, on average, wealthierthan those of other women in the
sample (Appendix Table A1). Bothe menand women in mixed COs are
also far less likely to be eligible for the largerloan, and this is
even more the case for men, perhaps because they have beenCO
members for a much shorter time, on average.
Entrepreneurs are also different from other CO members. They are
morelikely to be older, more educated and to report a family
history of businessownership. In addition, business owners have
better digit span recall, have a
11. This is not surprising given that the men and women in our
sample are all microfinance clients.
12. Giné and Mansuri (2011a) also find that women members are
often not the effective managers
of businesses they claim to own.
13. Seclusion practices are much less stringent within
zaat/biradari (caste) groups. See Jacoby and
Mansuri (2011).
520 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
better outlook on life and not surprisingly, also score higher
on a businessknowledge test (see Giné and Mansuri, 2011a for
details). They are also morelikely to be risk tolerant. Among
women, those that have a business are lesslikely to report mobility
constraints, but are somewhat more likely to observepurdah, perhaps
because they are relatively wealthier.
Table 2 also suggests that the scale at which male and female
businessesoperate is quite different (see also de Mel et al. 2009).
Men’s businesses yieldmonthly sales that are more than 4 times as
large as monthly sales for women.Women also tend to operate more
businesses from home. While there is con-siderable overlap in the
type of businesses owned by men and women, men arefar more likely
to be in the agribusiness sector, with much greater contact
withwider markets, while women are concentrated in small scale
manufacturing,handicrafts and tailoring in particular.
Table 3 shows the main sources of credit for sample households
at baseline.Only 5 percent of members have any loans from formal
financial sources,mainly commercial banks. In contrast, 34 percent
report borrowing from infor-mal lenders, mainly shopkeepers, and 21
percent report borrowing from rela-tives.14 Not surprisingly, most
borrow from an MFI, including NRSP. Averageloan sizes vary greatly
by source. While the average loan from a commercialbank is around
100,250 Rs (1,172 USD), the average MFI loan is only 12,000
TA B L E 3. Percentage Borrowing and Reasons for not Borrowing
by CreditSource
Credit Source
CommercialBank MFI
InformalLenders
Relatives andFriends
Percent borrowing from [source] in2006
5.07 83.43 33.77 21.12
The main [reason] for not borrowingfrom [source]?Do not
like/need to borrow 52.55 81.39 70.81 74.69Inadequate collateral
18.50 1.98 11.94 6.77Lender’s procedures are toocumbersome
14.49 9.31 6.70 5.82
Lender’s loan terms areunfavorable
5.48 0.59 3.35 0.91
Lender is too far away 3.18 0.40 0.78 1.35Need to pay bribes
2.02 0.79 2.18 0.08Past default with lender 1.96 0.79 0.89
1.15Members not willing to lend to me 1.25 4.55 2.51 6.53Bad credit
history 0.56 0.20 0.84 2.69
Notes: Data come from the baseline survey (November 2006)
14. Informal lenders also include traders and wholesalers and,
to a smaller extent, professional
moneylenders and landlords.
Giné, Mansuri, and Picón 521
-
Rs (USD 140) and loans from informal lenders are typically in
about the samerange. There is little variation in the relative
share of lenders by gender,however, though female CO members tend
to borrow less overall.
Table 3 also reports reasons for not borrowing for those who
reported notusing a credit source. Interestingly, most respondents
without loans report anunwillingness to borrow – either because
they have no need for loans orbecause they dislike borrowing – as
the main reason for not taking a loan andthis varies little across
lenders. Lack of collateral and cumbersome loan appli-cation
procedures come in next, and are particularly important when
dealingwith a formal lender.
Table 4 presents the means of baseline variables for the sample.
Columns 2,3 and 4 report the means for CO members who were exposed
to the male andfemale brochure, respectively and the p-value of the
test that the difference inmeans is significant. We also report the
means and p-value of the same test forthe sample of males (columns
5 to 8) and females (columns 9-12) separately.Overall, we find
balance between the two groups. The difference in means formembers
receiving the male and female brochure is significant at
conventionallevels for only two of the 16 baseline variables we
consider. Study participantswho received the male brochure borrow
more from informal sources and areless likely to be members of a
mixed group (both significant at the 10% level).For the sample of
males, the difference in means in the two groups is again
sig-nificant for only two of the 16 variables considered. Male
participants whoreceived the male brochure are somewhat less likely
to belong to a mixed CO(significant at the 10% level)15 and are
somewhat less likely to borrow fromfriends and relatives
(significant at the 5% level). For the sample of females
thedifference is only significant for own education. Women who
received the malebrochure appear to have higher formal education by
about one third of a year.Table 3 also reports the p-value of an
F-test that all baseline characteristics arejointly insignificant.
We cannot reject this hypothesis in any of the threesamples (lowest
p-value is 0.58).
I I I . E M P I R I C A L S T R A T E G Y
Because the type of brochure is assigned randomly at the CO
level, its impactcan be estimated via the following regression
equation:
Yij ¼ aþ bFBj þ gXij þ eij ð1Þ
where Yij is the outcome of interest for individual i in CO j
(for e.g., and indi-cator variable that takes the value 1 if the
respondent had applied for a largerloan, and 0 otherwise), FBj is
an indicator variable that take the value 1 if the
15. Since brochure-type was assigned at the CO level, the lower
representation of men in mixed
COs likely explains this.
522 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E4
.V
erifi
cati
on
of
Random
izat
ion
All
mem
ber
sM
ale
Fem
ale
N.
Obs.
Mea
ns
P-v
al
of
t-te
st(1
)-(2
)N
.O
bs.
Mea
ns
P-v
al
of
t-te
st(4
)-(5
)N
.O
bs.
Mea
ns
P-v
al
of
t-te
st(7
)-(8
)M
ale
Bro
chure
Fem
ale
Bro
chure
Male
Bro
chure
Fem
ale
Bro
chure
Male
Bro
chure
Fem
ale
Bro
chure
(1)
(1)
(2)
(3)
(5)
(4)
(5)
(6)
(9)
(7)
(8)
(9)
Chara
cter
isti
csat
Base
line
Age
3,4
51
37.9
537.8
10.7
41,8
80
37.9
838.4
00.8
91,5
71
37.9
137.2
00.8
5N
um
ber
of
chil
dre
nunder
93,4
51
1.7
31.8
30.7
71,8
80
1.9
21.9
90.5
31,5
71
1.4
51.4
00.1
7
Busi
nes
sO
wner
(Yes¼
1)
3,4
51
0.6
00.5
90.5
71,8
80
0.6
30.5
90.7
51,5
71
0.5
70.6
00.7
9
Dig
itSpan
Rec
all
3,4
51
3.3
63.2
80.8
51,8
80
3.8
23.8
80.8
31,5
71
2.7
22.6
50.8
2R
isk
Tole
rance
(0¼
Ris
kA
vers
e;10¼
Ris
kL
ove
r)
3,4
51
3.7
13.5
20.2
51,8
80
3.8
13.8
10.5
31,5
71
3.5
73.2
20.4
9
Land
3,4
51
4.3
84.5
80.8
21,8
80
5.8
36.0
50.7
71,5
71
2.3
63.0
50.5
5Y
ears
of
Educa
tion
of
Spouse
3,4
51
3.6
93.7
10.6
91,8
80
2.7
22.7
30.8
61,5
71
5.0
54.7
50.5
5
Mem
ber
of
aM
ixed
Gro
up
(Yes¼
1)
3,4
51
0.0
40.0
80.1
01,8
80
0.0
20.0
60.0
61,5
71
0.0
70.1
00.8
9
Ow
nE
duca
tion
3,4
51
4.2
63.9
50.1
41,8
80
5.2
75.3
60.7
81,5
71
2.8
42.4
70.0
7D
ecis
ion
Makin
g3,4
51
2.7
42.5
70.3
51,8
80
3.4
13.2
60.7
71,5
71
1.8
21.8
50.6
1N
.O
bs.
1,6
62
1,7
89
967
913
695
876
Pct
.B
orr
ow
ing
from
...a
tth
eti
me
of
Base
line*
(Conti
nued
)
Giné, Mansuri, and Picón 523
-
TA
BL
E4.
Conti
nued
All
mem
ber
sM
ale
Fem
ale
N.
Obs.
Mea
ns
P-v
al
of
t-te
st(1
)-(2
)N
.O
bs.
Mea
ns
P-v
al
of
t-te
st(4
)-(5
)N
.O
bs.
Mea
ns
P-v
al
of
t-te
st(7
)-(8
)M
ale
Bro
chure
Fem
ale
Bro
chure
Male
Bro
chure
Fem
ale
Bro
chure
Male
Bro
chure
Fem
ale
Bro
chure
(1)
(1)
(2)
(3)
(5)
(4)
(5)
(6)
(9)
(7)
(8)
(9)
Com
mer
cial
Bank
2,9
31
2.3
81.7
0.6
41,6
16
3.6
92.7
10.6
01,3
15
0.5
10.6
90.7
4M
icro
finance
Inst
ituti
on
2,9
31
71.3
467.6
20.3
01,6
16
69.4
463.7
40.9
81,3
15
74.0
671.7
40.4
6
Fri
ends
and
Rel
ativ
es2,9
31
7.7
16.5
80.4
81,6
16
7.8
55.1
60.0
51,3
15
7.5
8.0
90.1
4In
form
al
lender
s2,9
31
0.7
0.2
0.0
71,6
16
0.8
30.2
60.1
91,3
15
0.5
10.1
30.2
3N
.O
bs.
1,4
27
1,5
04
841
775
586
729
Pct
.O
ffer
edB
usi
nes
s
Tra
inin
g
3,4
51
52.4
751.8
20.8
31,8
80
51.2
952.0
30.9
31,5
71
54.1
51.6
0.4
8
Mem
ber
isel
igib
lefo
r
loan
lott
ery
(Yes
51)
3,4
51
63.7
269.0
00.5
41,8
80
64.6
359.6
90.8
21,5
71
62.4
562.2
10.6
6
P-v
al
of
F-t
est
that
all
base
line
chara
cter
isti
csare
join
tly
insi
gnifi
cant
0.6
50.5
80.6
5
Note
s:*
den
ote
svari
able
mea
sure
dat
foll
ow
-up,
conduct
edin
Dec
ember
2008.
Oth
ervari
able
sare
from
base
line
conduct
edin
nove
mber
2006.
Pct
.O
ffer
edB
usi
nes
sT
rain
ing
and
Mem
ber
elig
ibil
ity
com
efr
om
adm
inis
trat
ive
dat
afr
om
NR
SP.
524 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
respondent was shown the female brochure, Xij is a vector of
individual charac-teristics collected at baseline and e ij is a
mean-zero error term. Standard errorsare clustered at the CO level
throughout since the CO level treatment assign-ment creates spatial
correlation among members of the same CO (Moulton,1986). The vector
Xij includes the following baseline characteristics reported
inTable 2: eligibility, being offered business training, a dummy if
decision-making power is above the median in the same gender
sample, own education,digit span recall, spouse education,
landholdings, membership in a mixedgroup, age, number of children
and risk tolerance. It also includes field unit(branch) dummies,
our stratification variable. This set of variables
includescharacteristics for which there is imbalance as well as
variables that are likelyto affect the decision to borrow. 16 The
coefficient b captures the impact ofbeing shown a brochure with
pictures of female entrepreneurs on the cover andis the coefficient
of interest.
We also examine interactions between the type of brochure shown
and base-line characteristics which could proxy for attitudes
towards women
Yij ¼ aþ rðFB�jZijÞ þ bFBj þ gXij þ eij ð2Þ
Zij is a subset of individual characteristics included in the
vector Xij that couldrepresent an individual’s attitudes towards
women. The coefficient r on theinteraction term FBj * Zij reveals
the extent to which the impact of the femalepicture (FB) on loan
uptake varies according to the members attitudes towardswomen.
I V. E M P I R I C A L R E S U L T S
This section presents evidence on the impact of the female
brochure on twomain outcomes: the decision to borrow and the loan
amount requested.
Table 5 presents regression results from the estimation of
equation (1) forthe decision to borrow. Columns (1)-(3) present
results for the full sample,combined and disaggregated by gender.
On average, only 14.6 percent ofmembers, roughly 31 percent of the
eligible, applied for a larger loan. This issomewhat higher for
men, at 20 percent, and correspondingly lower forwomen, among whom
only 8 percent of the eligible applied. Given that thesample
consists of seasoned microfinance clients, this number appears
low.When asked anecdotally, many borrowers report either that the
monthlyinstallment amount for the larger loan was too high or that
the maturityperiod was too short. Both of these indicate that the
clients of NRSP are not,by and large, constrained by the current
loan size, but that some could benefitfrom larger loans. Among men,
older members and business owners were more
16. The regression results to come are not substantially
affected by the exclusion of this set of
control variables.
Giné, Mansuri, and Picón 525
-
TA
BL
E5
.U
pta
ke
of
Larg
erL
oan
OL
S
Sam
ple
:
All
mem
ber
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
s
All
Male
sFem
ale
sA
llM
ale
sFem
ale
sA
llM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Fem
ale
Bro
chure
20.0
26
20.0
33
20.0
15
20.0
29
20.0
17
20.0
51**
20.0
29
20.0
07
20.0
43
(0.0
21)
(0.0
34)
(0.0
19)
(0.0
27)
(0.0
44)
(0.0
23)
(0.0
35)
(0.0
58)
(0.0
39)
Off
ered
Busi
nes
sT
rain
ing
0.0
48**
0.0
46
0.0
46**
0.0
58**
0.0
67
0.0
37
0.0
71**
0.0
56
0.0
95**
(0.0
21)
(0.0
35)
(0.0
19)
(0.0
27)
(0.0
44)
(0.0
24)
(0.0
35)
(0.0
55)
(0.0
47)
Fem
ale
20.0
16
20.0
34
20.0
29
(0.0
21)
(0.0
31)
(0.0
46)
Age
0.0
06***
0.0
08**
0.0
00
0.0
06
0.0
09
20.0
03
0.0
09
0.0
11
20.0
03
(0.0
02)
(0.0
04)
(0.0
04)
(0.0
04)
(0.0
06)
(0.0
06)
(0.0
06)
(0.0
08)
(0.0
11)
Age^2
20.0
00**
20.0
00**
0.0
00
0.0
00
0.0
00
0.0
00
20.0
00*
0.0
00
0.0
00
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Hig
hD
ecis
ion-m
akin
gpow
er(1¼
yes)
0.0
01
20.0
03
20.0
22
0.0
02
0.0
09
20.0
16
20.0
02
20.0
37
20.0
39
(0.0
02)
(0.0
20)
(0.0
17)
(0.0
03)
(0.0
25)
(0.0
22)
(0.0
05)
(0.0
51)
(0.0
36)
Ow
nE
duca
tion
0.0
02
0.0
05
0.0
00
0.0
03
0.0
04
0.0
01
0.0
04
0.0
04
0.0
07
(0.0
02)
(0.0
03)
(0.0
03)
(0.0
03)
(0.0
04)
(0.0
04)
(0.0
05)
(0.0
07)
(0.0
06)
Dig
itSpan
Rec
all
0.0
03
0.0
02
0.0
01
20.0
01
20.0
02
20.0
02
0.0
02
20.0
01
20.0
01
(0.0
03)
(0.0
05)
(0.0
04)
(0.0
05)
(0.0
08)
(0.0
05)
(0.0
08)
(0.0
13)
(0.0
11)
Ris
kT
ole
rance
0.0
01
20.0
01
0.0
02
0.0
01
20.0
01
0.0
01
0.0
04
20.0
02
0.0
06
(0.0
02)
(0.0
03)
(0.0
03)
(0.0
03)
(0.0
04)
(0.0
03)
(0.0
05)
(0.0
08)
(0.0
06)
Yrs
.O
fE
duca
tion
of
Spouse
0.0
00
20.0
05
0.0
02
20.0
01
20.0
03
0.0
00
20.0
04
20.0
11*
20.0
01
(0.0
02)
(0.0
03)
(0.0
02)
(0.0
02)
(0.0
04)
(0.0
03)
(0.0
03)
(0.0
06)
(0.0
04)
(Conti
nued
)
526 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E5.
Conti
nued
OL
S
Sam
ple
:
All
mem
ber
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
s
All
Male
sFem
ale
sA
llM
ale
sFem
ale
sA
llM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Num
ber
of
childre
nunder
90.0
03
0.0
03
00.0
07
0.0
07
0.0
06
0.0
10.0
17
0.0
01
(0.0
03)
(0.0
04)
(0.0
04)
(0.0
04)
(0.0
06)
(0.0
07)
(0.0
08)
(0.0
11)
(0.0
11)
Busi
nes
sO
wner
(Yes¼
1)
0.0
28**
0.0
40*
0.0
1
(0.0
14)
(0.0
22)
(0.0
18)
Land
0.0
00
0.0
01
0.0
00
0.0
00
0.0
01
0.0
00
-0.0
01
0.0
00
-0.0
02
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
01)
(0.0
01)
(0.0
02)
Mem
ber
of
aM
ixed
Gro
up
(Yes¼
1)
20.0
92***
20.0
86*
20.1
05**
20.1
50***
20.1
21*
20.1
96***
20.1
66**
0.0
21
20.2
72***
(0.0
32)
(0.0
47)
(0.0
48)
(0.0
43)
(0.0
66)
(0.0
70)
(0.0
73)
(0.2
36)
(0.1
00)
Eligib
ilit
y0.1
88***
0.2
46***
0.1
30***
0.2
03***
0.2
99***
0.1
11***
0.2
19***
0.3
04***
0.1
48***
(0.0
15)
(0.0
26)
(0.0
16)
(0.0
19)
(0.0
33)
(0.0
18)
(0.0
24)
(0.0
40)
(0.0
32)
Mea
nD
epen
den
tV
ari
able
0.1
50.2
00.0
80.1
50.2
00.0
80.1
80.2
50.1
1
N.
Obse
rvat
ions
3,4
51
1,8
80
1,5
71
2,0
65
1,1
49
916
726
363
363
R-s
quare
d0.1
70.1
80.1
10.1
80.1
70.1
30.1
70.1
70.1
6
Note
:A
uth
ors
’analy
sis
base
don
surv
eyand
adm
inis
trat
ive
reco
rds
dat
a.
The
dep
enden
tvari
able
takes
valu
e1
ifth
em
ember
applied
for
ala
rger
loan.
All
regre
ssio
ns
incl
ude
bra
nch
fixed
effe
cts
and
are
esti
mat
edusi
ng
OL
Sm
ethods.
Sta
ndard
erro
rsare
clust
ered
atth
eborr
ow
ergro
up
leve
l.T
he
follow
ing
sym
bols
*,
**
and
***
den
ote
signifi
cance
at10,
5and
1per
cent
leve
l,re
spec
tive
ly.
See
Appen
dix
Bfo
rvari
able
defi
nit
ion.
Giné, Mansuri, and Picón 527
-
likely to apply, while among women, being offered business
training increasesloan demand. Membership in a mixed CO dampens
loan demand for bothmen and women even after controlling for
eligibility, perhaps because groupdynamics provide disincentives to
borrow, since only half of the members ofmixed COs are eligible to
borrow (see Appendix Table A1). Exposure to thefemale brochure does
not seem to have any effect, however.
In columns (4)-(6) we focus on all CO members who had a business
at base-line. We find, perhaps surprisingly, that the female
brochure impacts negativelythe uptake by women entrepreneurs. The
effect is not small. From a base of8.3 percent, the point estimate
indicates a reduction of 39 percent the prob-ability of applying
for the larger loan. In contrast, businessmen appear indiffer-ent
to the picture in the brochure and, as before, being offered
businesstraining increases loan demand while membership in a mixed
CO depresses it,but there are no differences by gender in these or
any other characteristics.Taken face value, the result among women
appears counterintuitive: if any-thing, one might expect that a
woman who owns a business would be attractedto a loan product that
makes salient her identity as a business woman. Butsince women
operate businesses that are much smaller than those of men, it
ispossible that the negative impact is an artifact of the business
scale.Specifically, women may find the scale of the business
pictured in the brochuretoo large and they may be discouraged from
applying.17 To explore thishypothesis further, we restrict
attention to female business owners who operateat a scale
comparable with men.18 Specifically, we construct a sample
ofmatched male and female businesses by sector.19 Given differences
in businessscale by gender, this matched sample necessarily
consists of businesses in theupper (lower) tail of the distribution
of female (male) businesses. In particular,female businesses in the
matched sample have on average Rs 4,016 higher salescompared to the
sample of all female businesses, while male businesses in
thematched sample have Rs 8,129 lower sales, on average. Sample
size is alsoconsiderably reduced. Only 363 female businesses can be
matched with acorresponding male business. Columns (7)–(9) report
these results. The coeffi-cient for female brochure remains
negative but we lose precision. Thecoefficient falls by about 16
percent while the standard error increases by
17. We have some anecdotal evidence that businesswomen felt that
some of the businesses featured
in the brochure were run on a larger scale than the typical
female business and that this fact
discouraged them from borrowing. As mentioned, many women
operated each of the three business
types featured in the brochure, which together account for 95
percent of all businesses run by women.
In addition, the brochure clearly stated that the loan could be
used for any purpose and for any
business, but nonetheless, the picture may have triggered a more
deliberative response (Kahneman,
2003).
18. We are grateful to an anonymous referee for suggesting this
analysis.
19. The matched sample is generated as follows. For each female
business, we compute the absolute
difference in sales with each male business in the same sector
and then pick the business with the
smallest difference. A male business is matched only once with a
female business. In the final sample we
keep only those female businesses where the absolute difference
in sales is less than Rs 1000.
528 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
about 70 percent. In sum, we cannot conclusively rule out a
negative effect ofexposure to the female brochure even among women
business owners who runlarger businesses. Note, however, that in
this matched group, being offeredbusiness training increases the
probability of a woman applying for a largerloan by 86 percent from
a base of 11 percent and appears to be driving theoverall increase
in loan applications due to the offer of business training. Onthe
other hand, decision making power does not appear to encourage
loanapplications from women in any of these samples.
Table 6 reports the regression results from specification (2),
for males(columns 1-3) and females (columns 4-6) on all three
samples. All regressionsinclude the baseline controls used in Table
5. The first striking fact is that asubset of men does respond to
the psychological content of the brochure.Specifically, business
owners with low scores on the digit span recall question,a proxy
for ability, and those whose wives are poorly educated (p-value of
FBx Years Education of Spouse is 0.11), respond negatively to the
female bro-chure. In this group, loan demand falls by about 13
percentage points (morethan a 50% decline over the base
demand).
While our experimental design does not allow us to distinguish
betweenlack of affinity towards women and an outright distaste for
female-runbusinesses as possible explanations for the negative
response, it doessuggest one channel through which exposure to the
female brochure amongfemale business owners could depress loan
demand. Specifically, womenwho have low decision making power may
turn to their husbands for per-mission to borrow and may face a
negative response from them whenshown the female brochure.
We do find some evidence in support of this. Females in mixed
groups,which as mentioned before appear to have less ‘voice’, as
well as businesswomen with low decision making power in their
household react negatively tothe female brochure. Interestingly,
female business owners with high decisionmaking autonomy shown the
male brochure also react negatively by roughlythe same magnitude,
while there is no effect on female business owners withautonomy
shown the female brochure (p-value is 0.28).20 This suggests
thatwomen with decision-making autonomy react negatively to the
brochure ofopposite sex by about as much as men without business,
with low digit spanand poorly educated wives. Given the
disadvantaged position of women inrural Pakistan, we conjecture
that men may have less respect for femalebusinesses whereas women
may feel more alienated when shown the malebrochure.
20. More formally we test whether the coefficient on the female
brochure plus the coefficient on
decision-making power above median plus the coefficient on the
interaction between the female
brochure and decision-making power above median is different
from zero, that is b þ gþ r ¼0following the notation of Equation
(2) .
Giné, Mansuri, and Picón 529
-
TA
BL
E6
.H
eter
ogen
eous
Eff
ects
of
Larg
erL
oan
Upta
ke
OL
S
Male
Fem
ale
All
Mem
ber
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
sA
llM
ember
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
s(1
)(2
)(3
)(4
)(5
)(6
)
Fem
ale
Bro
chure
(FB
)2
0.1
29**
20.1
31*
20.1
57
0.0
01
20.0
62
20.0
53
(0.0
51)
(0.0
72)
(0.1
14)
(0.0
34)
(0.0
38)
(0.0
52)
Busi
nes
sT
rain
ing
0.0
64
0.1
01*
0.0
72
0.0
29
0.0
18
0.0
44
(0.0
45)
(0.0
54)
(0.0
69)
(0.0
25)
(0.0
32)
(0.0
50)
FB
xB
usi
nes
sT
rain
ing
20.0
45
20.0
65
0.0
07
0.0
39
0.0
35
0.0
82
(0.0
67)
(0.0
82)
(0.1
10)
(0.0
36)
(0.0
45)
(0.0
74)
Hig
hD
ecis
ion
2m
akin
gpow
er(1¼
yes)
20.0
16
20.0
08
20.0
38
20.0
21
20.0
51*
20.0
95*
(0.0
25)
(0.0
31)
(0.0
68)
(0.0
24)
(0.0
29)
(0.0
54)
FB
xH
igh
Dec
isio
nm
akin
gpow
er0.0
07
0.0
07
0.0
06
0.0
24
0.0
79**
0.1
37**
(0.0
05)
(0.0
07)
(0.0
14)
(0.0
30)
(0.0
39)
(0.0
68)
Dig
itSpan
Rec
all
20.0
05
20.0
14
20.0
16
0.0
10*
0.0
09
0.0
17
(0.0
06)
(0.0
10)
(0.0
17)
(0.0
05)
(0.0
07)
(0.0
13)
FB
xD
igit
Span
Rec
all
0.0
16**
0.0
23*
0.0
23
20.0
12
0.0
15
20.0
23
(0.0
08)
(0.0
12)
(0.0
21)
(0.0
06)
(0.0
09)
(0.0
16)
Yrs
.E
duca
tion
of
Spouse
20.0
39
20.0
65*
20.1
11
20.0
11
20.0
12
0.0
31
(0.0
28)
(0.0
38)
(0.0
69)
(0.0
25)
(0.0
33)
(0.0
65)
FB
xY
rs.
Educa
tion
of
Spouse
0.0
38
0.0
90.1
24
0.0
44
0.0
43
20.0
16
(0.0
40)
(0.0
55)
(0.1
01)
(0.0
32)
(0.0
39)
(0.0
72)
Mem
ber
of
aM
ixed
Gro
up
(Yes¼
1)
20.1
00*
20.1
13
0.0
58
20.0
47
20.1
25***
20.1
52***
(0.0
53)
(0.0
69)
(0.2
42)
(0.0
34)
(0.0
39)
(0.0
51)
FB
xM
ixed
Gro
up
0.0
53
0.0
09
20.0
87
20.1
35**
20.1
68**
20.2
42***
(Conti
nued
)
530 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E6.
Conti
nued
OL
S
Male
Fem
ale
All
Mem
ber
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
sA
llM
ember
sA
llB
usi
nes
sO
wner
sM
atch
edB
usi
nes
sO
wner
s(1
)(2
)(3
)(4
)(5
)(6
)
(0.0
73)
(0.0
91)
(0.2
15)
(0.0
55)
(0.0
67)
(0.0
79)
Busi
nes
sow
ner
(Yes¼
1)
0.0
31
0.0
46**
(0.0
30)
(0.0
22)
FB
xB
usi
nes
sow
ner
0.0
31
20.0
59*
(0.0
42)
(0.0
31)
Mea
nD
epen
den
tV
ari
able
0.2
00.2
40.2
50.0
80.0
80.1
1N
.O
bs.
1880
1149
363
1,5
71
916
363
R2
square
d0.1
90.1
90.1
90.1
20.1
30.1
9
Note
:T
he
dep
enden
tvari
able
takes
valu
e1
ifth
em
ember
applied
for
ala
rger
loan.
All
regre
ssio
ns
are
esti
mat
edusi
ng
OL
Sm
ethods
and
contr
ol
for
elig
ibilit
y,ow
ned
uca
tion,
landhold
ings,
mem
ber
ship
ina
mix
edgro
up,
age,
num
ber
of
childre
n,
risk
tole
rance
,as
wel
las
bra
nch
fixed
effe
cts.
Sta
ndard
erro
rsare
clust
ered
atth
eborr
ow
ergro
up
leve
l.T
he
follow
ing
sym
bols
*,
**
and
**
*den
ote
signifi
cance
atth
e10,
5,
and
1per
cent
leve
l,re
spec
tive
ly.
See
Appen
dix
Bfo
rdefi
nit
ion
of
vari
able
s.
Giné, Mansuri, and Picón 531
-
In the matched sample (column 6), the results for female
business ownerswith high decision making autonomy become stronger
(the point estimateincreases in absolute value).
Table 7A reports the impact of the female brochure and baseline
character-istics on the loan amount requested (columns 1-4) and
approved (columns 5-8)among loan applicants. Table 7B reports the
same regressions among loanapplicants with a business at baseline.
In both tables we find a positive and sig-nificant effect of the
picture on the loan amount requested among males butnot females.
The result in both tables is driven primarily by selection
becausemen without a business and low digit span recall tend to
borrow less anddecide not to borrow when shown a female
brochure.
V. C O N C L U S I O N S
We designed a marketing experiment to test whether exposure to
positive rolemodels could encourage women’s uptake of a new credit
product in a contextwhere women face barriers to participation in
economic life. Brochures adver-tising the new product, a much
larger loan, were varied such that the coverpage featured either
men or women as entrepreneurs running five otherwiseidentical
businesses. The brochures were randomly assigned to existing
clientsin good standing.
The results suggest that both men and women respond to
psychological cuesembedded in this type of social norms marketing.
However, men’s response isconditioned by relative economic status
and ability while women’s response isconditioned by relative status
within the household. In particular, men who arenot themselves
business owners, have lower measured ability and whose wivesare
less educated respond more negatively to the female brochure, as
dowomen business owners who have low autonomy within the
household.Women with relatively high levels of autonomy shown the
male brochure havea similar negative response. In contrast, the
reaction of high autonomy femalebusiness owners shown the female
brochure is no different from that of lowautonomy women shown the
male brochure.
We conjecture that loan demand for low autonomy women is
mediatedthrough men who respond positively to the male brochure.
This suggests thatsocial norms marketing can often be more salient
for the more disadvantaged(Paluck and Ball, 2010) but, as our
results suggest, this could generate perverseresponses in some
contexts.
Finally, our results also suggest that exposing women to
positive role modelsor information that challenges prevailing norms
may meet different levels ofsuccess depending on the level of
autonomy enjoyed by women. In particular,women with low levels of
autonomy may require more intensive interventions,consistent with
other work which has used information campaigns to
changestereotypes. Giné and Mansuri (2011b), for example, find
that the response toa voter awareness campaign directed at rural
women in Pakistan was most
532 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E7
A.
Loan
Siz
e,A
llM
ember
s
OL
S
Log
of
Am
ount
Req
ues
ted
Log
of
Am
ount
Appro
ved
All
Mem
ber
sM
ale
sFem
ale
sA
llM
ember
sM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Fem
ale
Bro
chure
0.0
88
0.0
85*
0.1
61***
20.0
35
0.0
036
20.0
06
0.0
08
20.0
98
(0.0
54)
(0.0
49)
(0.0
52)
(0.1
04)
(0.0
29)
(0.0
30)
(0.0
30)
(0.0
69)
Off
ered
Busi
nes
sT
rain
ing
20.0
41
20.0
32
20.0
92
0.0
04
20.0
10.0
07
(0.0
42)
(0.0
46)
(0.1
04)
(0.0
29)
(0.0
31)
(0.0
68)
Fem
ale
0.0
78
20.0
54
(0.0
79)
(0.0
55)
Age
0.0
10
0.0
15*
20.0
07
0.0
10**
0.0
13**
0.0
02
(0.0
07)
(0.0
09)
(0.0
18)
(0.0
05)
(0.0
05)
(0.0
17)
Age^
20.0
00
20.0
00*
0.0
00
20.0
00*
20.0
00**
0.0
00
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Hig
hD
ecis
ion
2m
akin
gpow
er(Y
es¼
1)
0.0
15***
0.0
87***
0.0
24
0.0
08*
0.0
21
0.1
(0.0
05)
(0.0
28)
(0.0
71)
(0.0
05)
(0.0
26)
(0.0
64)
Ow
nE
duca
tion
0.0
18***
0.0
16***
0.0
16
0.0
16***
0.0
14***
0.0
16
^
(0.0
05)
(0.0
04)
(0.0
10)
(0.0
03)
(0.0
03)
(0.0
10)
Dig
itSpan
Rec
all
20.0
12
20.0
18**
0.0
24
20.0
13**
20.0
12*
20.0
01
(0.0
09)
(0.0
08)
(0.0
24)
(0.0
06)
(0.0
06)
(0.0
16)
Ris
kT
ole
rance
0.0
03
0.0
01
0.0
05
20.0
02
20.0
04
20.0
01
(0.0
04)
(0.0
04)
(0.0
13)
(0.0
04)
(0.0
05)
(0.0
08)
Yrs
.O
fE
duca
tion
of
Spouse
0.0
02
0.0
03
0.0
02
0.0
04
20.0
04
0.0
18**
(0.0
04)
(0.0
05)
(0.0
10)
(0.0
05)
(0.0
05)
(0.0
08)
Num
ber
of
chil
dre
nunder
90.0
04
0.0
04
0.0
07
0.0
04
0.0
03
20.0
17
(0.0
09)
(0.0
09)
(0.0
29)
(0.0
06)
(0.0
06)
(0.0
22)
Busi
nes
sO
wner
(Yes¼
1)
0.0
23
20.0
06
0.0
64
0.0
50
0.0
58
0.0
27
(Conti
nued
)
Giné, Mansuri, and Picón 533
-
TA
BL
E7A
.C
onti
nued
OL
S
Log
of
Am
ount
Req
ues
ted
Log
of
Am
ount
Appro
ved
All
Mem
ber
sM
ale
sFem
ale
sA
llM
ember
sM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
(0.0
32)
(0.0
36)
(0.0
69)
(0.0
35)
(0.0
42)
(0.0
61)
Land
0.0
06***
0.0
05***
0.0
07
0.0
01
0.0
00
0.0
05**
(0.0
01)
(0.0
01)
(0.0
05)
(0.0
01)
(0.0
01)
(0.0
02)
Mem
ber
of
aM
ixed
Gro
up
(Yes¼
1)
0.0
96
0.3
77**
20.2
33
0.0
25
20.0
60.0
76
(0.1
53)
(0.1
70)
(0.1
67)
(0.1
76)
(0.1
76)
(0.1
97)
Mea
nD
epen
den
tV
ari
able
10.9
310.9
310.9
210.9
710.6
710.6
710.7
210.5
2N
.O
bse
rvat
ions
664
664
491
173
503
503
372
131
R2
square
d0.2
10.2
70.3
20.3
30.2
70.3
40.2
60.4
4
Note
:T
he
dep
enden
tvari
able
islo
gof
loan
am
ount
reques
ted
(colu
mns
1-4
)and
log
of
loan
am
ount
appro
ved
(colu
mns
5-8
).T
he
sam
ple
incl
udes
loan
applica
nts
.A
llre
gre
ssio
ns
incl
ude
bra
nch
fixed
effe
cts
and
are
esti
mat
edusi
ng
OL
Sm
ethods.
Sta
ndard
erro
rsare
clust
ered
atth
eborr
ow
ergro
up
leve
l.T
he
follow
ing
sym
bols
*,
**
and
**
*den
ote
signifi
cance
atth
e10,
5,
and
1per
cent
leve
l,re
spec
tive
ly.
See
Appen
dix
Bfo
rdefi
nit
ion
of
vari
able
s.
534 T H E W O R L D B A N K E C O N O M I C R E V I E W
-
TA
BL
E7
B.
Loan
Siz
e,B
usi
nes
sO
wner
s
OL
S
Log
of
Am
ount
Req
ues
ted
Log
of
Am
ount
Appro
ved
Busi
nes
sO
wner
sM
ale
sFem
ale
sB
usi
nes
sO
wner
sM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Fem
ale
Bro
chure
0.0
93
0.0
80.1
35**
-0.0
61
0.0
27
0.0
11
0.0
02
-0.0
68
(0.0
57)
(0.0
50)
(0.0
52)
(0.1
29)
(0.0
32)
(0.0
33)
(0.0
30)
(0.0
81)
Off
ered
Busi
nes
sT
rain
ing
-0.0
32
-0.0
58
-0.0
21
0.0
54**
0.0
26
0.0
32
(0.0
46)
(0.0
47)
(0.1
45)
(0.0
27)
(0.0
26)
(0.0
98)
Fem
ale
0.0
89
-0.0
94
(0.0
82)
(0.0
64)
Age
0.0
13
0.0
17
-0.0
24
0.0
07
0.0
15**
-0.0
06
(0.0
09)
(0.0
11)
(0.0
21)
(0.0
05)
(0.0
06)
(0.0
23)
Age^
2-0
.000*
-0.0
00*
0.0
00
0.0
00
-0.0
00**
0.0
00
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Hig
hD
ecis
ion-m
akin
gpow
er(Y
es¼
1)
0.0
25
0.0
95**
-0.0
22
0.0
29
-0.0
07
0.0
94
(0.0
38)
(0.0
38)
(0.0
91)
(0.0
30)
(0.0
24)
(0.0
70)
Ow
nE
duca
tion
0.0
22***
0.0
20***
0.0
27
0.0
12***
0.0
13***
0.0
12
(0.0
05)
(0.0
05)
(0.0
17)
(0.0
04)
(0.0
04)
(0.0
10)
(Conti
nued
)
Giné, Mansuri, and Picón 535
-
TA
BL
E7B
.C
onti
nued
OL
S
Log
of
Am
ount
Req
ues
ted
Log
of
Am
ount
Appro
ved
Busi
nes
sO
wner
sM
ale
sFem
ale
sB
usi
nes
sO
wner
sM
ale
sFem
ale
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Dig
itSpan
Rec
all
-0.0
14
-0.0
19*
0.0
03
-0.0
09
-0.0
09
0.0
01
(0.0
11)
(0.0
10)
(0.0
34)
(0.0
06)
(0.0
07)
(0.0
20)
Ris
kT
ole
rance
0.0
02
0.0
01
0.0
14
-0.0
01
0.0
00
-0.0
03
(0.0
05)
(0.0
05)
(0.0
17)
(0.0
05)
(0.0
06)
(0.0
11)
Yrs
.O
fE
duca
tion
of
Spouse
0.0
01
0.0
07
-0.0
07
0.0
02
-0.0
05
0.0
13
(0.0
05)
(0.0
05)
(0.0
