Whose voice matters? An examination of gender bias in intra-household decision making

This version: September 2015

Abstract

It has been suggested that offering microcredit to women to empower them may be ineffective as women borrowers hand over the control of loans to their husbands. We thus conduct a lab-in-the-field experiment to examine whether gender bias exists in intra-household decision making in rural Bangladesh. The experiment mimics a real-life scenario where microcredit was offered to either the wife or the husband in a household and the borrower could decide whether to make his/her own investment choice or to transfer the decision-making to the spouse. We find that women are more likely to let their spouses make decision, compared with their male counterparts. Different treatments in the experiment also allow us to test and quantify the underlying causes of the bias. Our findings show women’s decision to transfer the decision-making is driven both by their lower decision-making power and their belief that their spouses are more capable of making financial decisions. We also look at subjects’ control over use of earnings from the investment and find offering credit to women did not improve their control, irrespective of whether or not they let their spouses make the investment decision.

Keywords: Gender bias, intra-household bargaining, field experiment.

JEL Classification: C91, C93, D13, O12

1. Introduction

Microfinance programs in developing countries have been commonly targeted at

lending to women, with the twofold objective of sustainable repayment rate and

women empowerment. According to Duflo (2012), there is a two-way relationship

between economic development and women empowerment. In one direction,

economic development can reduce gender inequality and in the other direction,

empowering women can stimulate economic growth. In developing areas like rural

Bangladesh, women in most poor households remain inactive, not participating in any

productive activities. This seems a paradox in the context of Bangladesh, where there

is no significant gender gap in education and many women are even better educated

than their spouses, thanks to the various programs targeting girls in rural areas such as

female stipend program to secondary school girls in rural areas (Begum, Islam and

Smyth 2014). The fact that women’s income-earning potential has not been realized is

usually attributed to social gender bias that restricts women from access to economic

resources and opportunities. Microfinance is thus claimed to provide women with

better access to economic resources, so they can participate in income-earning

activities and contribute to household’s income. At the same time, microfinance also

aims to empower women to improve their mobility, bargaining power and decision

making, so reducing the social gender inequality.

Whether those objectives of microfinance can be achieved in reality remains a big

question. The findings from empirical studies are mixed. Some provided positive

outcomes of women empowerment, such as increase in women’s participation in

decision making, ownership of household assets, freedom and mobility, political

awareness (Hashemi, Schuler, & Riley, 1996; Pitt & Khandker, 1996; Pitt, Khandker,

& Cartwright, 2006). However, other findings show no positive outcomes, even

negative outcomes, for example, increase in family violence, passive control of loan,

no change in management of cash within households. Among studies that offer

evidence on negative impact, Goetz & Gupta (1996) used an index of women’s

managerial control over loans as an indicator of empowerment and found women

exercised little or no control over their loans and thus giving credit to women has

negative implication on empowerment, especially with an intensification of tensions

within the household. Another paper by Montgomery, Bhattacharya, Hulme, &

Mosley (1996) also found that access to loans did not change the management of cash

within the household for either female or male loanees. Kabeer (2001) attributed the

conflicts in empirical findings to different understandings of intra-household power

relations which these studies draw on. One main reason is due to difference in

methodologies where some studies relied largely on quantitative data and statistical

testing while others on qualitative and anecdotal evidences. Another source of conflict

comes from how indicators of empowerment are generated. Most negative findings

“focused on processes of loan use while the positive ones focused on outcomes

associated with, and attributed to, access to loans” (p.66, (Kabeer, 2001). However,

all these studies based on self-reported answers to survey questions, thus may not

fully capture the dynamics of intra-household relations. Empirical studies are also

exposed to methodological complication and potential selection bias due to non-

random placement of microfinance programs and non-random program participation.

The central issue to microfinance’s promise to empower women is who actually takes

control of credit. Kabeer (2001) reviewed a number of anecdotes that reported

microcredit loans given to women were mainly controlled by household male

members. This would not only confine households to limited economic opportunities

but also negate the empowerment objective of microfinance.

Our paper thus aspires to offer an experimental approach that examines the

underlying factors in intra-household decision making of investment and allocation of

resources that hinder women from benefiting from microfinance. In particular, we aim

to investigate whether the gender bias exists in the intra-household decision making

and if so by which factors the bias is driven. The household bargaining problem and

its implication on household’s choices have been studied in a number of field

experiments, with a focus on women’s decision to participate in different credit

organizations (including microfinance in Southern Mexico as in Allen, Armendáriz,

Karlan, & Mullainathan, 2010 and roscas in Kenya as in Anderson & Baland, 2002),

household’s fertility choice (Ashraf, Field, & Lee, 2010) and household’s savings

(Schaner, 2011). Among the few lab experiments, Ashraf (2009) and Mani (2011)

examined the effect of asymmetric information and control over money between

spouses on their uses of money and investment. However, all these studies take the

household structure of decision making as given, without explaining the underlining

forces behind the structure. Our paper aims to shed light on this matter. To our best

knowledge the questions of which spouse makes the final decision and which factors

affect the decision making in use of loans and earnings from investment have not

been examined in the literature. In the context of microfinance, the answer to this

question is key to women empowerment since women are not empowered if they are

unable to make their own decision on how the loan is used and how the earnings from

the loan are spent. Our experiment is centred around the microfinance framework, in

which a household is offered a loan and an opportunity to invest in a project that

involves risk. According to Ngo & Wahhaj (2012), “while women may readily keep

control over cash benefits transferred to them, by contrast, loans enter a complex

decision-making process with perplexing impacts on the outcomes of the bargaining

process”. Risk has also been absent in the existing experiment literature of intra-

household decision making. Our paper also contributes to the scarce literature of

behavioural economics in the context of microfinance (including Abbink, Irlenbusch,

& Renner, 2006; Allen, et. al., 2010; Giné, Jakiela, Karlan, & Morduch, 2010).

We conducted a lab-in-the-field experiment with married-couple subjects in the rural

villages of Bangladesh. The subjects played in pairs an investment game, in which

they were given an opportunity to invest in either a safe or a risky project. In each pair

there is a “borrower” and a “spender”. The subjects were randomly allocated to be the

borrower or the spender and into three different treatments: secret, no-secret, and

random-couples. The borrower has the “transfer” option, which is to either make

his/her own decision on which project to invest in or to let his/her partner (the

spender) make the decision. In the secret and no-secret treatments, partners in each

pair are real married couples while in the random-couples treatment partners are in

opposite gender and randomly matched. Information on each partner’s options and

decisions are fully disclosed to their respective partner in the no-secret treatment but

are fully private in the secret and random-couples treatment. These conditions are

used to isolate the different factors that potentially cause the gender difference in

spouses’ decision making. We particularly look at subjects’ control over loan use, or

the transfer decision in the game and control over earnings from the investment,

which is reflected by subjects’ responses to survey questions on how they planned to

use earnings from the game and how they actually used the earnings (which was

asked two weeks after the experiment).

We find that women were more likely to let their spouses make investment decision,

compared with their male counterparts. The difference is more pronounced in the no-

secret treatment than in the secret treatment. The findings suggest the gender bias in

intra-household decision making is driven both by intra-household imbalance of

decision-making power, which prevented female borrowers from making their own

decision under full information disclosure condition, and by intra-household

imbalance of competence, due to which women voluntarily let their spouses make

decision even under asymmetric information condition. We find no evidence for the

effect of strategic behaviours such as women’s compromise over control of loans for

control of expenditures or differences in gender nature such as risk-taking behaviour

and self-confidence. To examine the effect of offering microcredit to women in their

control over household expenditures, we look at the difference between female

borrowers and female spenders in their intentional and actual uses of earnings from

the investment project. We find no significant difference in both intentional and actual

uses, which suggests no effect of offering microcredit to women in improving their

control over household expenditures. We also find among female borrowers those

who made their own decision on the investment were not different from those who let

their spouses make decision in terms of their control over expenditures. This implies

there is no direct correlation between women’s control over loan uses and their

control over household expenditures.

2. Conceptual framework & hypothesis development

We design the experiment following a household model where household decisions

are outcome of the bargaining process between spouses, taking into account the effect

of asymmetric bargaining power and asymmetric information between them. The

model has been introduced in Ashraf (2009) and Ashraf, Field, & Lee (2014) as

opposed to the standard unitary and collective models of the household, which either

treat household decisions as of a single decision maker or assume household decisions

are Pareto efficient. The standard unitary and collective models would imply no

difference in household outcomes between offering credit to wife and offering credit

to husband. On contrast, in Ashraf (2009) and Ashraf et. al. (2014) household

outcomes like use of money and fertility are strongly influenced by which spouse is

given money or access to contraceptives and the degree of asymmetric information

between spouses.

We model a household decision-making process where a poor household in rural

Bangladesh is offered microcredit to invest in a profitable project of its choice. In our

model, either one of the spouses receives the opportunity to make investment. The

spouse could either make his/her own decision on which project to invest in or let

his/her spouse make the decision. We examine which factors could affect the spouse’s

decision on whether to keep or to transfer the control of the investment. The gender

bias that defines the role of men and women in a household and affects the household

outcomes, especially in patrilineal and Muslim-dominant societies is well known in

the economics literature (for examples, see Duflo (2012) and Kabeer (2005)) and the

literature of microfinance in particular (Kabeer, 2001; Armendáriz & Morduch,

2010). In the societies where women’s perceived role is mainly in the domestic

domain, women are expected to have lower bargaining and decision-making power

compared to their spouses. Muslim women are also guided under the Quran on their

roles, duties and rights. For example, verse 4.34 says: “Men are the maintainers of

women because Allah has made some of them to excel others and because they spend

out of their property; the good women are therefore obedient, guarding the unseen as

Allah has guarded; and (as to) those on whose part you fear desertion [committing a

religious sin], admonish them, and leave them alone in the sleeping-places and beat

them; then if they obey you, do not seek a way against them; surely Allah is High,

Great.” Thus Muslim women are taught to be obedient to their husbands. Other than

social norms and religious rules household characteristics including individual

employment (Anderson, 2009, Rahman & Rao, 2004), income (Anderson & Eswaran,

2009), and ownership of assets (Agarwal, 1994, Folbre, 1984, Kabeer, 1999) also

dictates the inferior position of women in the decision-making. In the context of rural

Bangladesh societies women are mostly confined by social and cultural norms to

limited choices of occupation within their households. Specifically, the institution of

purdah promotes the seclusion of women and enforces their exclusion from public

spaces, thus preventing them from employment opportunities outside their households

(Amin, 1997, Kabeer, 2001). Moreover, women in Bangladesh commonly possess

less unearned assets than men. In Bangladesh Islamic law specifies daughters have the

right to inherit half a son’s share of the father’s property (Cain, 1978). However, in

practice daughters receive substantially less than what they are entitled to, mostly in

the form of jewellery from their dowries while sons mostly inherit land (Anderson &

Eswaran, 2009). These factors are thus expected to reinforce the inferior role of

women in intra-household decision making. We thus test the following hypothesis:

H1: Women feel obliged to transfer the control of their loan to their spouses due to

their lower decision-making power. Women thus transferred the decision-making

when there is no asymmetric information between them.

However, other anecdotes and theoretical studies in the literature also suggest

women’s decision to transfer the control of the loan to their spouses is voluntary.

Kabeer (2001) presented testimonies from women who consider conformity with

purdah as “a voluntary adherence to status norms rather than as a direct manifestation

of male control” and women who chose to transfer control over their loans to male

household heads in recognition of their responsibility for the collective welfare of the

household. The paper also pointed to the effect of financial and entrepreneurial

competence in women’s decision-making. Montgomery et. al. (1996) and Goetz &

Gupta (1996) argued that women transferred control of loans to men to have greater

expenditures on their own or consumption needs of the children and the whole family.

A number of theoretical studies showed women concede control of loan to their

spouses for strategic reasons or for the welfare of the household. For example, Tassel

(2004) developed a dynamic bargaining model where loan repayment is required for

continuing borrowing in the next period and borrowers have two investment options

with same expected payoff but different risk profile. In this model, female members

are faced with limited income generating opportunities, thus always choose safer

investment projects, in order to improve their bargaining power in the household. On

the contrary, male members prefer riskier project to protect their bargaining power

against their spouses, even when aggregate household consumption is expected to

increase by the same amount in both investment options. However, in the equilibrium

women would transfer control of their loan to their husband to ensure that he would

help with loan repayment and credit access is not terminated. This transfer of control

actually benefits them even if the investment ends up not being her first choice.

Similarly, but the model by Ligon (2011) based on the assumption of risk averse

agents who aimed at consumption smoothing instead of risk neutrality in Tassel

(2004). To explain the stylized fact that women usually relinquish their microcredit

loans to their husbands, the model shows that women can benefit from letting their

husbands undertake most of the risk from investing and repaying loans. A more recent

paper by Ngo & Wahhaj (2012) introduced a sphere of joint production and

household public good and found heterogeneous impacts across households. Access

to credit may not improve women’s bargaining power if they have limited skills to

engage in an autonomous productive activities, or if they have sufficient skills to do

so but their husband wants to appropriate the loan to maintain their own bargaining

power. We are motivated by these findings to develop the following hypotheses to

test under our experimental design:

H2: Women transferred the control of their loans to their spouses voluntarily. Women

thus transferred the decision-making even under asymmetric information, where they

could hide their options and decisions from their spouses.

H2a: Women transferred the control of their loans to their spouses voluntarily due to

their perceived inferior financial competence.

H2b: Women transferred the control of their loans to their spouses voluntarily to have

greater expenditures on their own or on children and common uses.

In addition to intra-household dynamics as discussed, the literature on gender

difference provides an alternative explanation for women’s transfer of control over

loans. Gender differences have been widely studied in lab experiments, among which

most related to our studies include those that examined differences in risk-taking

behaviour1 and self-confidence2. Women are generally found to be more risk averse

and less self-confident. In our context where women are given an opportunity to make

an investment decision that involves risk, women’s avoidance of risk and lower self-

                                                                                                                         1 See Eckel & Grossman (2002) for a review of literature on gender difference in risk-taking behavior. 2 See Niederle & Vesterlund (2011) for a review of literature on gender difference in competiveness and self-confidence.

confidence could make women inclined to transfer the control of loans to men in

general but not necessarily their spouses. This motivates our next hypothesis:

H3: Women transferred the control of their loans to their spouses voluntarily due to

their risk avoidance and lower self-confidence. Women thus transferred the decision-

making to any men but not necessarily their spouses.

Aside from women’s control over their loans, another aspect of women empowerment

through microfinance is the improvement of women’s control over household

expenditures. Pitt & Khandker (1996) and Hashemi, Schuler, & Riley (1996) are

among studies that provide empirical evidence on positive impact of offering credit to

women on women’s purchasing power. However, these findings remain questionable

due to the potential selection bias related to microfinance program placement and

participation choice. We thus develop the following hypothesis to test whether

offering credit to women improve their decision-making in household’s spending,

irrespective of their decision-making in loan uses.

H4: Women who are offered credit to invest are more likely to keep control of

household expenditure rather than concede the control to their spouses, compared

with those who are not offered credit.

We are also interested to look at whether there is any correlation between women’s

control over loan uses and their control over household expenditures. As Kabeer

(2001) suggested, conflicting conclusions about the impact of credit in empowering in

the literature reflect differences in the questions asked by different studies,

particularly between those that focused on processes of loan uses and those that

focused on outcomes associated with access to loans. We thus test the following

hypothesis:

H5: Women who are offered credit to invest and make their own investment decision

are more likely to keep control of household expenditure rather than concede the

control to their spouses, compared with those who are offered credit but transferred

the control of investment to their spouses. As a result, household expenditures are

spent more towards women’s consumption or children and household common uses.

3. Experimental Design

3.1. Experimental setting

The experiment was conducted with 826 married couples of 18-55 years old in 26

rural villages in three upazilas (Assasuni, Koyra, and Paikgacha) of the Khulna

district in June-July 2014. The map of the villages is shown in Figure 1. Recruiters

randomly went door to door and invited 832 respondents and their spouses3 to a study

on the understanding of financial matters. Each subject would receive a 100 taka

(approximately USD1.5 and an adult’s average daily wage) fee for show up and have

the opportunity to earn more money. The recruiters also conducted a household-level

survey that gathers information on general household characteristics.

In each village we selected a local school as the experiment venue. Only one

experiment session was conducted in each village, to avoid any contamination of the

experiment through information leakage. We conducted the experiment on two

treatment groups (of six treatments in total) at the same time in each session. The

treatment groups were randomly assigned across villages. Each session consisted of

31-32 couples, so each treatment group in each village has 15-16 couples. An

individual-level survey was conducted for each spouse privately and separately after

the experiment was completed so that the survey questions did not prime subjects

                                                                                                                         3 There were six couples who were invited and surveyed but did not show up on the experiment day. Since the attrition rate is only 0.7%, we see no need to perform any attrition test.

about the purpose of the experiment. The survey questions include subjects’

awareness and mobility, earnings and assets, household finance and decision-making

matters, understanding of risk, and other individual preferences.

3.2. Experiment procedure

The game involves each subject being matched with another subject of the opposite

gender to be his/her game partner and both having to make separate investment

choices for the chance of earning money for both himself/herself and his/her partner.

The game procedure is illustrated in the game tree in Figure 2. Each subject was

endowed with 300 taka at the beginning of the game. In each pair one partner played

the role of the “borrower” and the other partner the “spender”. The borrower was

given the opportunity to invest his/her own 300 taka and his/her partner’s 300 taka.

They could either invest in a Safe lottery or a Risky lottery4. The lotteries’ payoffs

and risk are presented in table 1. The lotteries have the same set of events: TRIPLE,

KEEP, and LOSE, in order to ensure any outcome from choosing a lottery would be

possible to be obtained by choosing the other lottery. This feature is critical for the

design of our treatments, which is discussed in section 3.3. TRIPLE means the payoff

triples the initial investment, so each player would receive 900 taka. KEEP means the

payoff remains the same as the initial investment, which is 300 taka. LOSE means the

player would receive zero, losing their initial investment. The risky lottery has higher

expected payoff but also higher risk. Given the lotteries are relatively complicated to

our subject pool, we provided visual demonstration and elaborate training and

practices before the subjects made decision. We explained the lotteries by showing

                                                                                                                         4 In the real experiment, we called the Safe lottery Paan and the Risky lottery Supari. Paan and Supari are the betel leaf and betel nut that are usually chewed together by the locals in Bangladesh (and other South Asian countries) for stimulant effect. Since they are generally consumed together, using their names as the lottery names could prevent subjects from having preference for one lottery over the other irrespective of their payoffs and risk profiles.

see-through bottles that contained balls in three different colours to reflect three

different payoffs.

After making decision on which lottery to invest in, the borrower was asked to make

decision on whether he/she wants to use his/her own lottery choice regardless of what

his/her partner chose or to use the partner’s choice if the partner’s choice was

different from his/her own choice. The borrower had to make this transfer decision

without discussing with the spender or knowing the spender’s choice, therefore we

would know for sure by whom the final decision was made. Since intra-household

interaction and communication between spouses are extremely complex and generally

unobservable, this knowledge would be impossible to obtain outside our controlled

experiment environment. These instructions were literally the same as asking them to

choose between using their own lottery choice or using their partners’ choice

regardless of what their partners chose. However, we intentionally phrased it that way

to highlight the potential conflict where the partners made different lottery choices

from each other. Subjects might not take the decision to transfer the investment

seriously if they were not made aware of the potential conflict, expecting the spouses

would make the same decision as theirs.

At the same time the spender was also asked which lottery he/she would choose if

he/she was to invest his/her endowment and his/her partner’s endowment. The final

payoff, which was based on the actual result of the lotteries, was determined

according to these choices from both partners and distributed equally between them.

The post-game survey included a question that asks on which purpose the subjects

planned to spend the game payoff. The responding options are: (1) to keep for

him/herself for later use, (2) to give to his/her spouse, (3) to buy something for

him/herself, (4) to buy something for his/her spouse, (5) to buy something for children

or common use, and (6) others. We also did home visit to all subjects’ two weeks

after the experiment day and asked them on which purpose they actually spent the

game payoff. The options are: (1) keeping for him/herself for later use, (2) gave to

his/her spouse, (3) bought something for him/herself, (4) bought something for his/her

spouse, (5) bought something for children or common use, and (6) others.

3.3. Experiment treatments

The recruited subjects were randomly assigned into one of six treatment groups,

which are different in two dimensions. In the first dimension, the treatments differ by

the roles of each gender: (1) the female partner plays the role of the borrower and the

male partner the role of the spender or (2) the female partner plays the role of the

spender and the male partner the role of the borrower.

There are three conditions in the second dimension. In the No secret condition,

subjects and their spouses were in the same room but were separated: all men were on

one side and all women were on the other side of the room. Therefore, instructions for

each partner were fully disclosed to the other. Subjects were told that their spouses

would be their game partners. They were also informed that all the choices they made

would be revealed to their spouses after they all made decision. However, men and

women were strictly prohibited from talking to each other, thus no discussion was

allowed between spouses and spouses made decision without knowing the choice(s)

of each other. In the Secret condition, men were in one room and women were in

another room. The instructors gave different instructions to men and women

separately. The subjects were not informed of any instructions given to their spouses

and any decisions their spouses made. They were also told that all their options and

their decisions would be kept confidential and private from their spouses. Even

though it might be impossible to hide the money received after the game finished in

the household context, borrower in the Secret condition could always hide his/her

choices from the spender due to two features of our experiment: (1) the lotteries have

the same possible payoffs and only differ in the possibility of each payoff and (2) the

lotteries were carried out separately to determine each couple’s payoff by the

instructors without the presence of the couple.

To examine whether women are more likely than men to pass the control of the

investment to their spouse, we compare the female borrower’s decision on whose

lottery choice to use with that of the male borrower under the ‘no secret’ condition

and the ‘secret’ condition, separately. Moving from the secret condition to the no-

secret condition can show us the net effect of imbalance in decision-making power

between wife and husband. If one has a lower decision-making power than his/her

spouse and fears of the spouse being aware of him/her disregarding the spouse’s

choice, he/she would be more likely to transfer the control of the investment under the

no-secret condition than under the secret condition. However, if the borrower

transferred the control of the investment even under the secret condition, he/she must

have believed that his/her spouse would make better investment decision and thus

would have been willing to transfer the control. Therefore, any gender difference in

the borrower’s decision to transfer the control of the investment could suggest the

effect of imbalance in financial capacity between spouses.

While the ‘secret’ and ‘no secret’ conditions allow us to isolate the effect of

imbalance in decision-making power and imbalance in financial capacity, we could

not determine whether the bias is between-spouse specific or generally between men

and women. Men and women might be naturally different and in our game context

one may expect a subject of the other gender can make better investment decision. Or

social norm could build the expectation that making such important decisions as

where money should be invested is the role of one gender but not of the other. We

thus introduced another condition where each subject was randomly and anonymously

matched to another subject of the opposite gender that was not his/her real-life

spouse. The subjects were made fully aware of this matching condition. Other than

this condition the additional treatments have the same conditions as the secret

treatments. This treatment therefore can control for any effect of the general gender

bias that exists outside the household.

4. Descriptive statistics

Table 2 summarizes the demographic characteristics among secret, no-secret, and

random-couple treatments. The treatments are generally balanced in most of the

characteristics, except for a slight difference in the number of household members

between the secret and no-secret treatments. The households are relatively poor

compared with the average Bangladesh rural population, since we target on

households that are targeted by microfinance programs. The annual income per capita

in the three treatments range between 15,000 and 16,000 taka, compared with the

national level of 25,560 taka (HIES Survey Report, 2010). The schooling gap between

husband and wife is relatively small.5

In table 3, we report a number of indicators of subjects’ social capital and mobility by

gender. All of the indicators show a significant gender gap. Women seem less

exposed to social media and social gatherings than men. Women are less frequent at

public places and places outside their villages and are also more likely to seek

                                                                                                                         5 This is partly attributed to the Female Secondary School Stipend Program, which was introduced nationwide in 1994 (see, for example, Begum, Islam and Smyth 2014), and other programs targeting girls in rural Bangladesh to address the gender imbalance in schooling at that time.

 

permission of their spouses to go to these places. While most women (82%) earn

income from home activities, only 13% of them do any income-earning works outside

their home in the last year. On the contrary 65% of men have income from work

outside home. Women also earned much less than men.

Figure 3 shows who is/are the main decision maker in different family matters, based

on subjects’ responses to our survey questions. Although the questions are asked to

wife and husband individually and separately, there is generally not much discrepancy

between their answers. The decision-making role seems to be dominated by men in all

aspects, except in the spending for poultry. As poultry farming is commonly women’s

main and only income-earning activity, this could be the only area where women can

have more control than their spouse. Women do not have much say even in the use of

their own earnings, in which only 16% of women can make their own decision.

5. Results

5.1. Decision making in investment

We first look at the likelihood to transfer the decision making of the lottery choice by

gender in the three treatments. Table 4 reports the transfer rate by gender and

treatment at the village level. The male (female) transfer rate is defined as the number

of male (female) subjects in the village who played as ‘the borrower’ and chose to

transfer the decision making of the lottery choice to their partners divided by the total

number of participating couples in that village. The female-male transfer rate is the

difference between the female’s and the male’s transfer rate. We run the Fisher two-

sample randomization test to compare the transfer rates between treatments. We find

the female transfer rate is significantly higher than the male transfer rate in both the

non-secret and secret treatment but not in the random-couple treatment. The average

female transfer rate is relatively high at 65.3% in the non-secret treatment and 43% in

the secret treatment, compared with the male transfer rates of 21.3% and 25.2%

respectively. There is no statistically significant difference between males and

females in the random-couple treatment and both transfer rates are less than 10%. The

female transfer rate and the female-male transfer rate both are significantly higher in

the non-secret treatment than in the secret treatment while the male transfer rate is not

different between the two treatments.

Table 5 shows the probabilities of transferring the decision making at the individual

level. We perform Fisher exact test to compare the difference between treatments.

The probabilities and the one-sided p-values from the test reflect the same results as

the village-level findings. In table 6, we report results from the probit6 regressions that

control for various household characteristics and district fixed effect. We run the

following regression:

𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟!" = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒! + 𝛾𝑁𝑜𝑆𝑒𝑐𝑟𝑒𝑡! + 𝛿𝑅𝑎𝑛𝑑𝑜𝑚! + 𝜃𝐹𝑒𝑚𝑎𝑙𝑒! ∗

𝑁𝑜𝑆𝑒𝑐𝑟𝑒𝑡! + 𝜇𝐹𝑒𝑚𝑎𝑙𝑒! ∗ 𝑅𝑎𝑛𝑑𝑜𝑚! + 𝜋𝑋! + 𝑣! + 𝜀! (1)

where 𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟!" is a dummy variable which equals to one if subject i transferred the

decision making of the lottery choice to his/her partner, and equals to zero otherwise.

𝐹𝑒𝑚𝑎𝑙𝑒! is a dummy variable that indicates the gender of subject i. 𝑁𝑜𝑆𝑒𝑐𝑟𝑒𝑡! and

𝑅𝑎𝑛𝑑𝑜𝑚! are dummy variables that indicate whether subject i was in the no-secret

treatment and the random-couple treatment respectively. The secret treatment is the

base value and thus the coefficient 𝛽  captures the gender effect on decision-making

transferring in the secret treatment. The coefficients 𝜃 and 𝜇 show the difference in

the gender effect of the no-secret treatment versus the secret treatment and the

random-couple treatment versus the secret treatment respectively. 𝑋! is a vector of

subject i’s household characteristics, which are wife’s age, husband’s age, wife’s                                                                                                                          6  We also run OLS regressions and the results are similar to those from the probit regressions.    

schooling, husband’s schooling, religion, number of household members, and

household annual income per capita. 𝑣! captures the village fixed effect.

The regression results are similar across different specifications and offer the same

implication as the results from the Fisher tests. Women in the secret treatment were

14-15% more likely to transfer decision-making than the male counterparts. In the

secret treatment where each spouse’s decision is not disclosed to the other spouse, we

expect almost no effect of the power imbalance (or at least less than in the no-secret

treatment), thus women who let their spouses decide on the lottery choice in the secret

treatment most likely were willing to do so in the absence of any threat. This finding

is consistent with H2. The gender gap is wider in the no-secret treatment than in the

secret treatment by 21%. There is no difference between men in the secret treatment

and those in the non-secret treatment, thus the condition of information between

spouses only affects decision to transfer made by the wife but not the husband. The

wife was more likely to let her husband make the investment decision once all her

options and decisions would be revealed to her husband. This suggests the effect of

the decision-making power imbalance, in which women, but not men, feel threatened

by their spouses being aware of them making own decision. This thus supports H1.

Both men and women were less likely to transfer decision-making in the random-

couple treatment than in the secret treatment. And the gender gap was absent in the

random-couple treatment as shown by the coefficient 𝜇. This proves the difference in

the transfer decision between the wife and the husband is not driven by the belief that

men are generally better than women in making investment decision but most likely

due to household-specific factors. H3 is thus rejected.

To test the hypothesis that women’s willingness to transfer decision making to their

spouses is driven by their belief in the capacity imbalance between themselves and

their spouses, we run the following regression in the secret treatment and the non-

secret treatment separately.

𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟!" = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒! + 𝛾𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦! + 𝑣! + 𝜀! (2)

where 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦! is the measure of the capacity gap between the wife and the

husband. We have four different measures of the capacity gap: (1) subject i has higher

education level than his/her spouse, (2) subject i thinks he/she is better than his/her

spouse in making financial decision, (3) subject i thinks people of his/her gender is

better than those of the opposite gender in making financial decision, and (4) subject i

performed better than his/her spouse in the test of risk understanding. Variables (2)

and (3) are constructed from our survey questions that asks about subject’s own

judgement and variable (4) is from what subjects scored in answering our four

questions that test subjects’ understanding of risk. Thus the former variables are to

capture the effect of subjects’ belief in their comparative capacity with respect to their

spouse and the opposite gender while the latter reflects the difference in their actual

capacity. The survey and the test questions are included in our appendix. We include

each of these variables individually in equation (2). Tables 7A and 7B report the

result in the non-secret treatment and the secret treatment respectively. While having

a higher education level reduced subject’s probability to transfer decision-making to

his/her spouse by 10% in the non-secret treatment and 13% in the secret treatment,

controlling for the education gap only reduces the magnitude of the gender variable

slightly in both treatments (Table 7, column 2). Thus the education gap could not

fully explain the gender gap in transfer decision in the secret treatment. Among the

three measures of financial capacity gap, only the variable that indicates subject’s

belief on his/her better capacity relative to his/her spouse has a statistically significant

effect on the transfer decision. Subjects were 15-17% less likely to transfer the

investment decision-making to their spouses if they believed they were better than

their spouses in making financial decision. Adding this variable also reduces the

magnitude of the gender effect in both the secret and non-secret treatments. The

gender effect is no longer statistically significant in the secret treatment. This thus

provides evidence on the effect of one’s subjective belief in his/her relative financial

capacity versus his/her spouse on his/her transfer decision. This provides evidence to

support H2a. We however find no effect of the subjective comparison between male

and female financial capacity, we again can argue that the general difference in

gender nature plays no role in the intra-household decision making. We also find no

significant effect of the difference in spouses’ test scores. This may suggest the actual

imbalance in spouses’ relative financial capacity does not matter as much as their

subjective comparisons. However, as the variable is constructed based on only

subjects’ responses to our four knowledge questions, it might not fully reflect their

true financial capacity.

To test whether women transferred the control of their loans to their spouses

voluntarily to have greater expenditures on their own or on children and common

uses, we compare the intentional and actual uses of earnings from the investment

between female borrowers who transferred the control of their loans (called passive

female borrowers hereafter) and female spenders, who were not given the opportunity

to make investment decision. The statistics are reported in table 8. There is no

statistical difference in the general distribution of intentional uses and actual uses

between these two groups of women. We find passive female borrowers were more

likely to tentatively keep or use the earnings for self than female spenders. However,

passive female borrowers were less likely to tentatively spend on children or

household common uses compared with female spenders. There is also no difference

in their probability of giving the earnings to their spouses. In terms of actual uses,

there is only significant difference in their probability of keeping the earnings or

purchasing for self but the direction of difference is reverse. We thus do not find

sufficient evidence to support H2b.

We also examine whether there is any correlation between subject’s decision to

transfer the investment control and household characteristics, including the social

capital gap between spouses, earnings gap between spouses, and whether husband’s

mother stays in the household. We run the following regressions:

𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟!" = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒! + 𝜇𝐺𝑎𝑝! + 𝑣! + 𝜀! (3)

and

𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟!" = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒! + 𝜌𝐹𝑒𝑚𝑎𝑙𝑒! ∗𝑀𝑜𝑡ℎ𝑒𝑟𝑖𝑛𝑙𝑎𝑤! + 𝑣! + 𝜀!

(4)

where 𝐺𝑎𝑝! is either the measure of social capital gap between spouses or the

earnings gap between them. The social capital variable is a measure of how much the

subject is exposed to social media and social network and is based on subject’s

responses to our three survey questions that ask whether the subject (1) reads the

newspaper at least once a week, (2) listens to the radio or watch TV at least once a

week, and (3) participates in any club/committee/meeting group at least once a month.

Each positive response adds one-third to the social capital measure, thus the measure

can take the value between zero and one. The social capital gap variable is the

difference between subject’s social capital measure and that of his/her spouse. The

social capital gap between the husband and his wife has a mean of 0.15 and a standard

deviation of 0.4. Similarly, the earnings gap variable is the difference between

subject’s annual earnings and that of his/her spouse. The earnings gap between the

husband and his wife has a mean of 61,500 taka and a standard deviation of 33,900

taka. Thus in general husbands earn more and have a higher level of social capital

than their wives.  𝑀𝑜𝑡ℎ𝑒𝑟𝑖𝑛𝑙𝑎𝑤! is a dummy variable that equals to one if the

husband’s mother stays with the couple and equals to zero otherwise. In

approximately 24% of the households in our experiment the husband’s mother lives

with the couple. We interact this dummy variable with the gender dummy variable

since we expect no similar effect of the wife’s mother (or father) staying with the

couple and the wife’s parent(s) stays with the couple in only less than 1% of the

households.

Tables 9A and 9B report the results from regressions (3) and (4) in the no-secret

treatment and the secret treatment separately. There is a significant correlation

between the social capital gap and the transfer decision in the no-secret treatment but

not in the secret treatment. Thus the social capital gap is correlated more with the

intra-household imbalance in decision-making power than with the intra-household

imbalance in capability. In particular, an increase of one-point in the social capital gap

between the subject and his/her spouse reduced the subject’s probability to let his/her

spouse make the lottery decision by 2.3%-points. This means a decrease of one

standard deviation in the husband-wife social capital gap could reduce the wife’s

transfer probability by 2.7%-points. Since this effect is relatively small and adding the

social capital gap variable does not change the coefficient on the gender variable

considerably, the higher social capital of the husband relative to the wife does not

explain much the gender difference in the transfer decision. We also find a significant

correlation between the earnings gap and the transfer decision in the no-secret

treatment. A decrease of one standard deviation in the husband-wife earnings gap

could reduce the wife’s transfer probability by 6.8%-points. Adding the earnings gap

variable also reduced the gender effect drastically in magnitude and statistical

significance. This supports a high correlation between the earnings gap and the

transfer decision in the no-secret treatment but not in secret treatment, thus suggesting

the earnings gap can fully explain the gap in decision-making power but not the gap

in capacity. The coefficient of the mother-in-law variable is significant in both the

secret and no-secret treatments. Having the mother-in-law live in the same household

increased the probability the wife let her spouse decide on the lottery option by 13%

in the secret treatment and 14% in the no-secret treatment. Since the coefficient is not

statistically larger in the no-secret treatment than in the secret treatment, we may

claim that the mother-in-law affects the wife’s decision-making mostly through her

belief on her capacity relatively to her spouse’s capacity. However, this may not

reflect a causal relationship but simply a correlation where households in which the

mother-in-law lives with the couple are also households in which the wife has lower

financial capacity.

5.2. Risk taking and Decision making in investment

We examine whether there is gender difference in risk taking, using subjects’ choice

between the safe lottery and the risky lottery. Women are commonly found to be more

risk averse than men in the literature. We however find men and women are not

statistically different in the choice of lottery in the whole sample (table 10). We only

find gender difference in the secret treatment: women were about 17% less risk taking

than men. The percentage of women choosing the risky lottery is higher than that of

men in the no-secret treatment, however the difference is not statistically significant.

This suggests the effect of spousal observability on subject’s risk-taking. Since

subjects in the no-secret treatment expected their choices to be disclosed to their

spouses, they might not reveal their true preference of lottery but instead choose what

they think their spouse would choose. Meanwhile subjects in the secret treatment do

not have the same incentive to hide their true preference. Assuming the lottery choice

in the secret treatment reflects subject’s own risk-taking preference, we find on

average women expect their spouses to be more risk taking and men expect their

spouses to be less risk taking than themselves (statistically significant at 1% and 5%

respectively). We also find women in the no-secret treatment are significantly more

likely to choose the risky lottery than those in the secret treatment and women’s guess

of their spouse’s lottery choice is not significantly different from their own choice.

Although the percentage of men taking the risky lottery is lower in the no-secret

treatment than in the secret treatment, the difference is not statistically significant.

This suggests the effect of spousal observability on risk-taking applies stronger to

women than to men. Men and women in the random-couple treatment were not

different in risk taking and both were less likely to choose the risky lottery than in the

real-couple treatments.

We then examine whether subject’s risk-taking behaviour affects his/her decision to

transfer the decision-making of the lottery choice. In table 11 we report the

percentage of men and the percentage of women transferring the decision-making to

their partners by their risk-taking behaviour in each treatment. In the secret and the

no-secret treatment risk-loving men were less likely to let their spouses make decision

than risk-averse men. As previously discussed men expected their spouses to be less

risk-taking than themselves, men who were risk lovers were thus more driven to stick

to their own decision rather than to let their spouse decide. We also ran the Fisher

exact one-sided test to test whether men’s choice of lottery and their guess on their

spouse’s choice are less likely to be the same for risk-loving men than for risk-averse

men. The test p-value confirms the hypothesis at 1% confidence level. We do not find

similar effect among women. There is no difference in women’s transfer decision

between risk-loving and risk-averse women.

5.3. Decision making in spending of earnings

We first examine whether giving credit to women instead of their spouses would

change women’s control of earnings. This analysis is only conducted for the secret

treatment and no-secret treatment but not the random-couple treatment. We report the

results using the combined sample of the secret treatment and the no-secret treatment

in table 12. Table 12A reports how subjects intended to use their earnings from the

game. We classify subjects by player types: the ‘borrower’ vs. the ‘spender’, and

gender. The most common use of the earnings is to spend on purchasing for children

or household’s common purposes. We run the Fisher exact text to compare the

difference in each usage type between male borrowers and male spenders, female

borrowers and female spenders, female borrowers and male borrowers, and female

spenders and male spenders. We also run the two-sample Kolmogorov-Smirnov (K-S)

test to test the difference in distribution of all usages. The K-S test is useful in this

case where there are more than two usage types. There is no significant different

between female borrowers and female spender, in all usages separately and in the

whole distribution. This suggests giving credit to women may not affect their intended

use of the earnings, particularly whether they planned to control the earnings by

themselves or let their spouses take control. There is a significant difference in the

distribution of earning usages between female borrowers and male borrowers. The

Fisher exact test shows the difference lies mostly in the ‘keep/purchase for self’ and

the ‘give to spouse’ categories. The female borrowers were more likely to give their

spouses their earnings and less likely to keep or purchase for themselves. In another

word, the female borrowers were more likely to let their spouses control their

earnings rather than make their own decision. However, we find no difference in the

distribution of earning usages between female spenders and male spenders. We only

find they are different in the probability of giving the earnings to spouses and the

probability of purchasing for spouses. The female spenders were more likely to give

the earnings to their spouses rather than purchasing for their spouses, relatively to the

male spenders. These are consistent with the findings that male spenders are different

from male borrowers. The male spenders were more likely to purchase for children or

common uses or purchasing for their spouses than to keep or purchase for themselves.

While women’s tentative usages of earnings were not affected by which spouse was

given the opportunity to invest, men were more likely to be less ‘selfish’ (tend to

purchase for other household members or household common uses than to keep or

purchase for themselves) when they were not directly given the investing opportunity.

This suggests men relate the control of earnings to the source of investment while

women do not.

Table 12B show what subjects actually did to their earnings from the game. The

information was collected two weeks after the day the experiment was conducted.

There is no significant difference between male borrowers and male spenders and

female borrowers and female spenders, in all usages separately and in the whole

distribution of usages. Thus the actual decision on usages of the earnings from the

game was not affected by who received the opportunity to invest. There is a

significant gender difference among both borrowers and spenders. The K-S test p-

value is below 5% for both the difference between female borrowers versus male

borrowers and the difference between female spenders versus male spenders. The

main difference lies in the ‘give to spouse’ and ‘purchase for children/common use’

categories. Women were more likely to give the earnings to their spouses while men

were more likely to use the earnings to purchase for children or household common

uses. This suggests women irrespective of their role in the game were more likely to

let their spouses control their earnings. We thus reject H4.

Tables 13 report the borrowers’ intentional uses and actual uses by gender and

whether they transferred the decision making to their spouses. Female borrowers are

not different in the distribution of intended uses of earnings, although the Fisher exact

test p-value shows women who did not transferred the investment control had a lower

probability of intentionally giving earnings to spouse and a higher probability of

intentionally purchasing for children or common uses, compared to those who

transferred the control. This might suggest women who make their own decision on

investment can have more control in spending. However, there is no significant

difference in the actual uses of earnings between female borrowers who transferred

the investment control and those who did not. Female borrowers irrespective of their

transfer decision were more likely to give their earnings to spouses and less likely to

purchase for children or common uses, compared to male borrowers. Therefore, H5 is

rejected. We also do the same analysis on the restricted sample that exclude female

borrowers who did not transfer the decision making and lose money from the

investment (final earnings were zero) to look at the difference between women who

were given opportunity to and actually earned money and those who were not given

the opportunity. The results are similar to those of the unrestricted sample.

The transitional matrices in appendix tables A1-A4 show how subjects changed

between intentional uses and actual uses of their earning from the game. In general

these matrices suggest the effect of commitment behaviour and unforeseen post-

experiment interactions. There is a large shift towards actually keeping the earnings or

purchasing for self from other intentional uses among women. The shift is not

pronounced among men. This could be explained by the local context where women

have more options of indulgence goods (for example, clothes & jewelleries) than

men, who were restrained by religion and social norms from bad indulgences like

drinking and gambling. Another notable shift is women who planned to use the

earnings in children or household common uses turned out giving the earnings to

spouses. On the contrary the percentage of men giving the earnings to spouse was not

much different from that of men planning to do so. These two main shifts led to a

higher percentage of women keeping the earnings or purchasing for self, a higher

percentage of those giving the earnings to spouse and a lower percentage of those

spending on children or common uses, compared with the percentages of women who

planned to do so.

We also analyse the uses of earnings for the secret treatment and the no-secret

treatment separately. Since the ‘borrowers’ in the secret treatment could hide both

their choices and available options from their spouses, the ‘spenders’ may not be

aware of the difference in the role of the ‘borrowers’ and the ‘spenders’. Therefore,

we may not find any effect of offering credit to women in the secret treatment even if

the effect exists. We however find the results for the secret treatment and the no-

secret treatment separately are similar to each other and to those of the combined

sample. In general, there is no difference between female spenders and female

borrowers and between those who transferred the decision-making of the lottery

choice and those who did not.

5.4. Robustness check on microfinance membership

Our subject pool is drawn from relatively low-income households, who are targeted

clients by microfinance institutions. However, to make sure our findings apply to

microfinance borrowers we run a robustness check on a smaller sample of households

who are currently microfinance members and households who reported to have plan

of getting microcredit loan in near future. We call this sample ‘potential microfinance

members’ hereafter 7 . Appendix table A5 show statistics of basic household

characteristics for potential microfinance members and those who are not separately.

The t-test p-values show no statistical difference between these two samples, except

for the number of household members and household annual income per capita. The

potential microfinance members are relatively poorer and have more household

members.

We reproduce table 3 using the smaller sample of potential microfinance members

and the results are reported in appendix table A6. The findings are similar to those of

the full sample. The Fisher randomization test p-values show significant gender

difference in the village-level transfer rate in the secret and no-secret treatments but                                                                                                                          7 To address the concern that our estimates of the gender effect might capture the effect of being a microfinance member, we also run another robustness check on the sample of ‘potential microfinance members’ but excluding microfinance members whose memberships are longer than one year.

not in the random-couple treatment. There is also a significant difference in the

female’s transfer rate and female-male transfer rate between the secret and the no-

secret treatment.

6. Conclusion

The present paper analyses findings from a lab-in-the-experiment that was designed to

study the intra-household decision-making process between spouses in rural villages

in Bangladesh. We focus on the control over loan use and household expenditures in

the context of microfinance where a microcredit loan is offered to either spouse and

they have the opportunity to invest in a risky and profitable project. We are motivated

by anecdotes in the literature to explain the common phenomenon that female

borrowers concede control over loan use to their husbands. We find evidence that

gender difference exists in intra-household decision making, which prevents women

from taking control of their own loans. Women were more likely to let their spouses

make decision on which project to invest in and their decision was driven by both

voluntary and involuntary reasons. Women voluntarily transferred the control over

loan use due to their belief that their spouses were more capable of making financial

decision, thus they conceded the control even under asymmetric information

condition. However, when information were fully disclosed between spouses

women’s tendency to transfer the decision making increased, suggesting women felt

obliged to let their spouses make decision, due to their relatively lower decision-

making power. We however do not find evidence for other explanations that have

been suggested in the literature such as women’s strategic behaviour for gaining more

control over household expenditures or differences in gender nature like risk taking

and self-confidence. We also find women’s decision to transfer the control over loan

use is significantly correlated with the difference in social capital and earnings

between spouses and the presence of husband’s mother in the household.

These findings have affirmed that offering credit to women is not directly translated

into improving women’s access to economic resources and stimulating their

participation in household’s income-earning activities and decision-making process.

More importantly, the findings have shed light on the driving factors that hinder

women from taking control over their own opportunity. Most of the literature has

been focused on the imbalance in bargaining power between spouses that underpins

the household decision-making process and suggested the key role of asymmetric

information between spouses in determining household outcomes (for example,

Anderson & Eswaran, 2009; Ashraf, 2009; Ashraf et. al., 2014). Our findings have

provided important evidence for the effect of intra-household imbalance in financial

capacity in household’s financial decision making. We thus propose policies that

target household outcomes in general and microfinance policies in particular take into

account of both the gender difference in decision-making power and the difference in

capacity, with financial capacity as one example. However, this is deemed to be not a

simple task, especially in the context of rural Bangladesh where social norms and

religion strongly dictate the role of women in the family, making these two factors

even intertwined. Since women are obliged to obey their husbands, restricted on

mobility, and confined to household domain, they are restrained from acquiring

knowledge and skills necessary for financial management. On the other direction,

women’s lower level of knowledge and skills would reinforce their lower bargaining

power in the household. Field, Jayachandran, & Pande (2010) conducted a

randomized evaluation of a business training program in India and found positive

impact on business income for Upper Caste women, but not for the least restricted

group of Lower Caste women or the most restricted group of Muslim women. They

explained this finding by the non-monotonic effect of social norms on women’s

ability to acquire and apply business knowledge: Upper Caste women might have had

more to learn than Lower Caste women due to their prior more restricted mobility but

Muslim women had too little mobility to put the knowledge to use. There are however

positive evidences that providing necessary skills and information to women could

improve women’s participation in income-earning activities. Bandiera, Burgess,

Goldstein, Buehren, Gulesci, Rasul, & Sulaiman (2014) found simultaneously

providing vocational training and information on sex, reproduction and marriage

significantly improved both the economic and social position of adolescent girls in

Uganda. They were 72% more likely to engage in income generating activities and

their monthly consumptione expenditures increased by 41%. In the context of

Bangladesh, Bandiera, Burgess, Das, Gulesci, Rasul, & Sulaiman (2013) found

transfers of assets and skills had permanent and positive impact on the occupational

choice and earnings of the poorest women.

The paper also offers an important insight into intra-household decision making in

allocating household expenditures. Our analysis of intentional use and actual use of

the earnings from the loan investment shows women were more likely to let their

spouses take control over household expenditures, irrespective of which spouse was

offered the opportunity to invest and irrespective of whether or not they transferred

the control of loan use. This thus brings reservation to the argument that increasing

women’s earned income could improve their control over household expenditures, at

least in the short time frame and the local context as in our experiment setting.

Offering credit to women does not necessarily translate into improvement in women’s

control over household expenditures, and even more disappointedly women’s control

over loan use does not guarantee their control over earnings from using the loan.

Table 2: Demographic characteristics across treatments Mean t-test p-value

No secret Secret Random No secret-Secret

Secret-Random

No secret-Random

no. couples 319 316 191 muslim 0.80 (0.40) 0.78 (0.41) 0.75 (0.43) 0.65 0.47 0.26 no. household members

4.69 (1.37) 4.50 (1.18) 4.66 (1.46) 0.06* 0.16 0.87

annual income per capital (taka)

15,376 (8,277) 16,235 (10,864) 16,038 (7,533) 0.26 0.83 0.37

no. years of marriage

16.96 (7.22) 16.78 (7.86) 16.61 (7.79) 0.77 0.81 0.61

wife's age 34.05 (6.83) 34.85 (7.66) 34.26 (7.15) 0.16 0.39 0.74 husband's age 40.13 (7.38) 41.03 (7.93) 40.69 (7.19) 0.14 0.62 0.41 wife's schooling 4.44 (3.79) 4.28 (4.23) 4.68 (3.14) 0.62 0.26 0.46 husband's schooling 4.89 (4.25) 4.44 (4.72) 4.80 (3.83) 0.20 0.37 0.81 age gap 6.08 (2.49) 6.18 (2.24) 6.43 (2.48) 0.61 0.25 0.13 schooling gap 0.45 (3.16) 0.16 (2.98) 0.12 (3.03) 0.23 0.88 0.24

Table 1: Lottery payoffs and risk Lottery Event Possibility Payoff (taka) Expected

payoff (taka) Risk (taka)

Safe TRIPLE 1/10 900 330 66.41 KEEP 8/10 300 LOSE 1/10 0 Risky TRIPLE 4/10 900 420 128.69 KEEP 2/10 300 LOSE 4/10 0 Note: Risk is calculated as the standard deviation from the expected payoff.

Table 4: Male and female transfer rate at village level Treatment Mean Std Fisher randomization test one-sided p-value

Non secret-Secret

Secret-Random

Male Non Secret 0.213 0.107 0.827 Secret 0.252 0.098 0.017 Random 0.085 0.077 Female Non Secret 0.653 0.190 0.028 Secret 0.430 0.283 0.0002 Random 0.073 0.047 Female-male Non Secret 0.440 0.241 0.001 0.019 Secret 0.179 0.277 0.038 0.0003 Random -0.012 0.047 0.875

Table 3: Social capital and mobility %/Mean(Std)

Men Women Do you read the newspaper at least once a week? 26.27 10.90 Do you listen to the radio or watch TV at least once a week? 50.97 34.02 Do you participate in any club/ committee/ meeting group at least once a month?

37.89 27.00

Do you go on your own at least once a month to: public places in your village 79.78 48.55

places outside your village 71.31 36.44 Do you need to seek permission of your spouse to go to: public places in your village 14.16 89.71 places outside your village 14.89 87.17 Did you do any income-earning work outside home in the last one year?

65.01 12.59

Did you do any income-earning work on your own at home in the last one year?

63.32 81.84

How much is your annual earnings in the last one year? (taka) 69,831 (33,446) 8,340 (27,218)

Table 5: Male and female transfer rate at individual level Treatment Mean SE/Std Fisher exact test one-sided p-value Non secret-

Secret Secret-Random

Male Non Secret 0.213 0.410 0.244 Secret 0.252 0.435 0.001 Random 0.0842 0.261 Female Non Secret 0.654 0.477 0.000 Secret 0.427 0.496 0.000 Random 0.073 0.261 Female-male Non Secret 0.442 0.050 0.000 Secret 0.175 0.053 0.001 Random 0.011 0.039 0.491

Table 7: Probit Regressions: determinants of decision-making, using different measures of capacity gap

A. No-secret treatment

(1) (2) (3) (4) (5) female 0.39*** 0.38*** 0.32*** 0.39*** 0.39*** (0.050) (0.047) (0.057) (0.050) (0.050) higher education -0.099** (0.045) better than spouse -0.17*** (0.063) better than opp. gender 0.086 (0.091) better than spouse (real assessment) -0.011 (0.051) N 319 319 319 319 319 B. Secret treatment (1) (2) (3) (4) (5) female 0.17** 0.16** 0.12 0.17** 0.17** (0.082) (0.080) (0.076) (0.082) (0.082) higher education -0.13* (0.068) better than spouse -0.15** (0.059) better than opp. gender -0.16 (0.11) better than spouse (real assessment) -0.016 (0.031)

Table 6: Probit Regressions: determinants of decision making

(1) (2) (3) (4) (5) (6) (7)

female 0.22*** 0.14*** 0.15*** 0.14*** 0.15*** 0.15** 0.15** (0.027) (0.043) (0.043) (0.043) (0.043) (0.064) (0.070) no-secret 0.078** -0.038 -0.043 -0.038 -0.045 -0.045 -0.045 (0.032) (0.046) (0.046) (0.047) (0.047) (0.047) (0.045) random -0.32*** -0.21*** -0.21*** -0.22*** -0.22*** -0.22*** -0.22*** (0.044) (0.063) (0.062) (0.063) (0.063) (0.060) (0.058) female*no-secret 0.21*** 0.21*** 0.21*** 0.21*** 0.21** 0.21** (0.061) (0.062) (0.061) (0.061) (0.082) (0.081) female*random -0.17* -0.18** -0.16* -0.17* -0.17** -0.17* (0.090) (0.089) (0.090) (0.089) (0.078) (0.093) +demographics controls x x x x + upazila (sub-district) FE x x x x + corrected standard error for village cluster

x

+ corrected standard error for session cluster

x

N 826 826 826 826 826 826 826 This table shows results from probit regressions where the dependent variable is the probability of transfering the decision making of lottery choice in the game. Demographic controls are: wife’s age, husband’s age, wife’s schooling, husband’s schooling, religion, number of household members, annual income per capita. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

N 316 316 316 316 316 This table shows results from probit regressions where the dependent variable is the probability of transfering the decision making of lottery choice in the game. All specifications control for upazila fixed effect and correct standard error for village cluster but do not include demographic controls. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 8: Women’s intentional and actual uses of game payoff in the secret treatment

Intentional uses Actual uses Female

borrower, transfering

Female spender

Fisher exact test one-sided p-value

Female borrower, transfering

Female spender

Fisher exact test one-sided p-value

keep/purchase for self

25.00% 14.29% 0.070 11.76% 26.40% 0.024

give to spouse 13.46% 8.73% 0.244 29.41% 19.20% 0.102 purchase for spouse

1.92% 3.97% 0.434 1.96% 0.80% 0.497

children/common use

59.62% 73.02% 0.058 56.86% 53.60% 0.411

Kolmogorov-Smirnov test

0.303 0.374

Table 9: Probit Regressions: determinants of decision-making

A. No-secret treatment (1) (2) (3) (4) female 0.39*** 0.37*** 0.14 0.36*** (0.050) (0.053) (0.096) (0.037) social capital gap -0.068* (0.041) earnings gap -0.0020*** (0.00074) female*husband’s mother 0.14* (0.084) N 319 319 319 319 B. Secret treatment (1) (2) (3) (4) female 0.17** 0.18** 0.12* 0.14* (0.082) (0.088) (0.072) (0.074) social capital gap 0.017 (0.048) earnings gap -0.00043 (0.00067) female*husband’s mother 0.13*** (0.044) N 316 316 316 316 This table shows results from probit regressions where the dependent variable is the probability of transfering the decision making of lottery choice in the game. All specifications control for upazila fixed effect and correct standard error for village cluster but do not include demographic controls. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 10: Probability of choosing riskier lottery (%) Men Women Fisher exact

test one-sided p-value

All 31.36 31.11 0.479 No-secret 32.92 37.62 0.123 Secret 36.39 31.01 0.089 Random 20.42 20.42 0.550 Secret vs. no-secret 0.201 0.047 Secret vs. random 0.000 0.006 No-secret vs. random 0.001 0.000

Table 11: Male and female transfer rate by risk-taking behaviour Treatment Risk adverse Risk lover Fisher exact test

one-sided p-value Male Non Secret 0.257 (0.044) 0.136 (0.045) 0.051* Secret 0.300 (0.044) 0.143 (0.051) 0.025** Random 0.091 (0.033) 0.056 (0.056) 0.529 Female Non Secret 0.686 (0.046) 0.593 (0.067) 0.160 Secret 0.404 (0.047) 0.479 (0.073) 0.240 Random 0.053 (0.026) 0.143 (0.078) 0.175

Table 12: Intentional and actual uses of game payoff by gender and partner types

Percentage Fisher exact test one-sided p-value Male

borrower Male spender

Female borrower

Female spender

Male borrower- Male spender

Female borrower- Female spender

Female borrower-Male borrower

Female spender-Male spender

A. Intentional uses keep/purchase for self

30.04% 19.37% 17.00% 16.21% 0.004 0.452 0.000 0.208

give to spouse 5.14% 2.37% 12.25% 11.07% 0.080 0.391 0.003 0.000 purchase for spouse

1.58% 7.91% 3.16% 4.35% 0.001 0.321 0.191 0.068

children/common use

63.24% 70.36% 67.59% 68.38% 0.054 0.462 0.175 0.350

Kolmogorov-Smirnov test

0.016 1.000 0.021 0.832

B. Actual uses keep/purchase for self

27.89% 26.72% 21.91% 24.21% 0.424 0.307 0.074 0.294

give to spouse 6.37% 7.29% 25.90% 25.79% 0.411 0.530 0.000 0.000 purchase for spouse

3.98% 4.45% 3.59% 1.59% 0.485 0.129 0.500 0.052

children/common use

61.75% 61.54% 48.61% 48.41% 0.517 0.518 0.002 0.002

Kolmogorov- 1.000 1.000 0.016 0.003

Smirnov test

Table 13: Borrowers’ intentional and actual uses of game payoff by gender and transfer decision Percentage Fisher exact test one-sided p-value Male

not transferig

Male transfering

Female not transfering

Female transfering

Male r transfering-not transfering

Female transfering- not transfering

Female not transfering-Male not transfering

Female A transfering-Male A transfering

A. Intentional uses keep/purchase for self

29.84% 30.65% 17.95% 16.18% 0.511 0.417 0.013 0.018

give to spouse 5.76% 3.23% 5.98% 17.65% 0.342 0.004 0.560 0.003 purchase for spouse

1.57% 1.61% 2.56% 3.68% 0.678 0.447 0.414 0.389

children/common use

62.83% 64.52% 73.50% 62.50% 0.468 0.041 0.035 0.457

Kolmogorov-Smirnov test

1.000 0.394 0.232 0.297

B. Actual uses keep/purchase for self

42.11% 29.17% 30.34% 26.17% 0.182 0.370 0.140 0.453

give to spouse 6.35% 6.45% 23.28% 28.15% 0.590 0.232 0.000 0.000 purchase for spouse

3.70% 4.84% 3.45% 3.70% 0.467 0.594 0.588 0.487

children/common use

60.32% 66.13% 50.00% 47.41% 0.254 0.389 0.050 0.011

Kolmogorov-Smirnov test

0.958 1.000 0.365 0.059

Figure 1:

Figure 2: Game tree

Figure 3: Intra-household decision making

Appendices

Survey questions

G.  Understanding  of  risk  

G1   There  is  a  lottery  where  the  chance  of  winning  a  1,000  taka  prize  is  1  in  100.  If  1000  people  buy  the  lottery  tickets  and  each  buys  only  1  ticket,  how  many  people  will  win  the  1,000  taka  prize?  

 

G2   Suppose  you  are  offered  either  1  of  2  lottery  bags:  The  Paan  bag  has  2  balls  with  500  taka  value  each  and  2  balls  with  100  taka  value  each.  The  Supari  bag  has  2  balls  with  400  taka  value  each  and  2  balls  with  100  taka  value  each.  You  will  pick  randomly  1  ball  from  the  bag  you  choose  and  that  will  be  your  prize.  Which  bag  would  you  choose?  

Paan  (1)   Supari  (2)  

Either  Paan  or  Supari  (they  are  the  same  to  me)    (3)  

Don’t  know  (4)  

G3   Suppose  you  are  offered  either  1  of  2  lottery  bags:  The  Paan  bag  has  2  balls  with  500  taka  value  each  and  2  balls  with  100  taka  value  each.  The  Supari  bag  has  3  balls  with  500  taka  value  each  and  2  balls  with  100  taka  value  each.  You  will  pick  randomly  1  ball  from  the  bag  you  choose  and  that  will  be  your  prize.  Which  bag  would  you  choose?  

Paan  (1)     Supari  (2)  

Either  Paan  or  Supari  (they  are  the  same  to  me)  (3)  

Don’t  know  (4)  

G4   Suppose  you  are  offered  either  1  of  2  lottery  bags:  The  Paan  bag  has  2  balls  with  500  taka  value  each  and  2  balls  with  100  taka  value  each.  The  Supari  bag  has  5  balls  with  500  taka  value  each  and  5  balls  with  100  taka  value  each.  You  will  pick  randomly  1  ball  from  the  bag  you  choose  and  that  will  be  your  prize.  Which  bag  would  you  choose?  

Paan  (1)   Supari  (2)  

Either  Paan  or  Supari  (they  are  the  same  to  me)  (3)  

Don’t  know  (4)  

H.  Personal  opinions:    H6   In  general  are  men  or  women  better  in  working  

with  numbers  and  making  financial  decisions?  Men  (1)  

Women  (2)   They  are  the  same  (3)  

Don’t  know  (4)  

H7   Are  you  or  your  wife/husband  better  in  working  with  numbers  and  making  financial  decisions?  

You  (1)  

Your  wife/husband  (2)  

We  are  the  same  (3)  

Don’t  know  (4)  

Table A1: Transitional matrix from intentional to actual uses for female borrowers

Intentional uses Actual uses

keep/purchase for self

give to spouse purchase for spouse

children/ common use

Total

keep/purchase for self 11.63% 20.93% 4.65% 62.79% 100.00% give to spouse 20.00% 30.00% 10.00% 40.00% 100.00% purchase for spouse 50.00% 12.50% 25.00% 12.50% 100.00% children/common use 23.53% 27.06% 1.18% 48.24% 100.00% Total 21.91% 25.90% 3.59% 48.61% 100.00%

Table A2: Transitional matrix from intentional to actual uses for male borrowers

Intentional uses Actual uses keep/purchase

for self give to spouse purchase for

spouse children/ common use

Total

keep/purchase for self 25.33% 4.00% 4.00% 66.67% 100.00% give to spouse 38.46% 0.00% 0.00% 61.54% 100.00% purchase for spouse 75.00% 0.00% 0.00% 25.00% 100.00% children/common use 27.04% 8.18% 4.40% 60.38% 100.00% Total 27.89% 6.37% 3.98% 61.75% 100.00%

Table A3: Transitional matrix from intentional to actual uses for female spenders

Intentional uses Actual uses keep/purchase

for self give to spouse purchase for

spouse children/ common use

Total

keep/purchase for self 41.46% 21.95% 0.00% 36.59% 100.00% give to spouse 21.43% 53.57% 0.00% 25.00% 100.00% purchase for spouse 30.00% 30.00% 0.00% 40.00% 100.00% children/common use 20.23% 21.97% 2.31% 55.49% 100.00% Total 24.21% 25.79% 1.59% 48.41% 100.00%

Table A4: Transitional matrix from intentional to actual uses for male spenders

Intentional uses Actual uses keep/purchase give to spouse purchase for children/ Total

for self spouse common use keep/purchase for self 41.67% 10.42% 4.17% 43.75% 100.00% give to spouse 20.00% 20.00% 20.00% 40.00% 100.00% purchase for spouse 15.00% 10.00% 10.00% 65.00% 100.00% children/common use 24.14% 5.75% 3.45% 66.67% 100.00% Total 26.72% 7.29% 4.45% 61.54% 100.00%

Table A5: Demographic characteristics: microfinance members vs. non members Mean (std) t-test p-value

Non microfinance members

Microfinance members

no. couples 246 580 muslim 0.77 0.42 0.78 0.41 0.70 no. household members 4.48 1.25 4.66 1.35 0.07* annual income per capital (taka) 16,815 11,546 15,452 7,982 0.06* no. years of marriage 16.25 8.09 17.05 7.37 0.17 wife's age 34.33 7.73 34.44 7.01 0.85 husband's age 40.70 8.19 40.57 7.27 0.81 wife's schooling 4.27 3.78 4.50 3.85 0.43 husband's schooling 4.40 4.30 4.82 4.36 0.20 age gap 6.37 2.75 6.13 2.22 0.19 schooling gap 0.13 3.13 0.32 3.03 0.41

Table A6: Male and female transfer rate at village level – Microfinance members Treatment Mean Std Fisher randomization test one-sided p-value

Non secret-Secret

Secret-Random

Male Non Secret 0.233 (0.126) 0.636 Secret 0.250 (0.092) 0.009 Random 0.113 (0.113) Female Non Secret 0.659 (0.200) 0.061 Secret 0.464 (0.322) 0.002 Random 0.063 (0.063) Female-male Non Secret 0.426 (0.275) 0.002 0.055 Secret 0.214 (0.287) 0.023 0.021 Random -0.049 (-0.049) 0.875

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