Limited Insurance Within the Household: Evidence from a
Field Experiment in Western Kenya
Jonathan Robinsony
University of California, Santa Cruz
July 31, 2007
Abstract
This paper presents results from a randomized …eld experiment to test for the importance of limited commitment (due to incomplete contract enforceability) in explaining intrahousehold risk sharing arrangements in Western Kenya. The experiment followed 143 daily
income earners and their spouses for 8 weeks. Every week, each individual had a 50% chance
of receiving a 150 Kenyan shilling (US $2) income shock (equivalent to about 1.5 days’income for men and 1 week’s income for women). This paper has 2 main results. First, since
the experimental payments are random, they allow for a direct test of allocative Pareto e¢ ciency. I reject e¢ ciency, as male private goods expenditures are sensitive to the receipt of
the payment. Second, the experiment was designed to assess the role of limited commitment
in explaining the failure of full insurance. The sample was split into 3 Treatment Groups
with varying levels of intra-household correlation in the experimental payments. I …nd that
women send bigger transfers to their husbands when shocks are independent or negatively
correlated, a result consistent with the presence of limited commitment, which predicts that
transfers are higher when incomes are less correlated. I …nd no di¤erence in transfers for
men between the 3 groups.
JEL Classi…cation: C93, D13, D61, O12
I would like to thank my advisors Orley Ashenfelter, Esther Du‡o, Michael Kremer, and Christina Paxson for
continuous support. I thank Alicia Adsera, David Atkin, David Evans, Jane Fortson, Filippos Papakonstantinou,
Tanya Rosenblat, Laura Schechter, Ethan Yeh, seminar participants at UC Santa Cruz, the University of Houston,
University College London, the University of Pittsburgh, IFPRI and participants in the Princeton development
lunch for helpful comments, and especially Pascaline Dupas for suggestions and for assistance throughout. I am
grateful to Willa Friedman, Anthony Keats, and especially Eva Kaplan for excellent research assistance. This
project would not have been possible if not for the work of Jack Adika, Daniel Egesa, Alice Kalakate, Nduta
Kamui, Nathan Mwandije, Nashon Ngwena, Priscilla Nyamai, Seline Obwora, Isaac Ojino, Anthony Oure, Iddah
Rasanga, and Nathaniel Wamkoya in collecting and entering the data. I thank Aleke Dondo of the K-Rep
Development Agency for hosting this project in Kenya. Financial support for this project was provided by the
Princeton University Industrial Relations Section, the Center for Health and Wellbeing at Princeton University,
and the MIT Poverty Action Lab.
y
Department of Economics, University of California, Santa Cruz, e-mail: jmrtwo@ucsc.edu
1
Introduction
Individuals in developing countries are subject to considerable risk but lack access to formal
mechanisms that would allow them to insure themselves against unexpected income shocks. Instead, households often use informal systems of gifts and loans to pool idiosyncratic risk. While
these informal networks do provide some protection against shocks, they also face substantial
problems of asymmetric information and payment enforceability, and existing evidence suggests
that inter-household risk sharing networks are rarely, if ever, e¢ cient (Townsend, 1994; Udry,
1994; Fafchamps and Lund, 2003).
Individuals may also choose to share risk within their own household.
Whether people
are able to e¢ ciently pool within-household risk is of interest for several reasons. First, since
information and enforcement are presumably better within a single household than between
several households, intra-household insurance might be expected to be more e¢ cient than interhousehold insurance.
Second, given the ine¢ ciency of other risk sharing strategies, spouses
should have a strong incentive to insure each other. Third, in the absence of alternative riskcoping mechanisms, a documented failure of intra-household risk sharing might signal the need
for market intervention, particularly in regards to relatively bigger shocks.
This paper presents results from a …eld experiment in Western Kenya designed to test
whether intra-household risk-sharing arrangements are e¢ cient. The experiment followed 143
married couples for 8 weeks. Every week, each individual had a 50% chance of receiving a 150
Kenyan shilling (US $2) income shock, equivalent to roughly 1.5 days’ income for men and 1
week’s income for women.
As these shocks are, by de…nition, random, transitory, and idio-
syncratic, the experimental design makes it possible to test directly for allocative e¢ ciency, by
comparing the di¤erence in the responsiveness of individual private consumption between weeks
in which an individual receives the shock himself and weeks in which his spouse receives the
shock.
If the household pools risk e¢ ciently, increases in private consumption should be the
same for both types of shocks. However, I …nd that husbands increase their expenditures on
privately consumed goods in weeks in which they receive the shock but do not change their
expenditures in weeks in which their wives receive the shock, a rejection of Pareto e¢ ciency.
Female expenditures on private goods (or on any other expenditure category) do not change
1
with either type of shock.
In the second part of the experiment, I explore the possibility that limited commitment
caused by incomplete contract enforceability is a partial explanation for this ine¢ ciency. Under
limited commitment, an individual cannot be legally forced to make the Pareto e¢ cient transfer
to his insurance partner, even if, ex ante, he had agreed to; instead, insurance arrangements
must be self-enforcing. For this reason, non-zero transfers are sustainable only if partners can
punish each other for failing to make payments. In particular, individuals might punish nonpayment by terminating the insurance relationship entirely. Given such a threat, an individual
balances the current utility loss from making a transfer against the long-term expected utility
gain from insurance (relative to autarky), a constraint which implies that only those transfers
which reduce current utility by less than the expected di¤erence in lifetime utility between
insurance and autarky are feasible. Limited commitment models have been found to explain
both inter-household (Coate and Ravallion, 1993; Ligon, Thomas, and Worrall, 2002) and intrahousehold (Foster and Rosenzweig, 2001; Wahhaj, 2005) behavior better than other models.1
I test for limited commitment by experimentally varying the intra-household correlation in
the random income shocks that were paid out between three, randomly selected Treatment
Groups.
In Group 1, the correlation in the experimental shocks was 0.5; in Group 2, it was
0; and in Group 3, it was -0.5.
Prior to the start of the experiment, these correlations were
explained in lay language to respondents so that they understood the treatment.
The intra-
household correlations mean that the gains from insurance were greatest in Group 3 and smallest
in Group 1, so that limited commitment would predict that transfers should be highest in Group
3 and lowest in Group 1, a prediction which holds true for wives but not for husbands: women
in Groups 2 and 3 transfer 35 and 41 Kenyan shillings (Ksh) more of the 150 Ksh shock,
respectively, than do women in Group 1.
In contrast, transfers from men to women do not
respond to the treatment.
The experimental design employed in this paper has several advantages over the empirical
strategies employed in most existing studies of risk sharing or intra-household resource alloca1
Though it might not be realistic to think that spouses would completely terminate their insurance relationship
due to non-payment, the limited commitment predictions are similar so long as at least some punishment is
available. For instance, an individual may reduce his future transfers rather than stop making transfers entirely.
2
tion. First, the experimental shocks are random so that the test for Pareto e¢ ciency is cleanly
identi…ed. Second, the shocks are purely transitory and do not involve a permanent component.
While this distinction is not important under full insurance in which all risk is completely insured, permanent and transitory shocks might be treated very di¤erently if insurance contracts
are subject to renegotiation. For instance, it might be the case that consumption shares can be
renegotiated through a bargaining process, and that bargaining weights depend on permanent
income. If so, a change in consumption due to a permanent change in relative income (holding
total income …xed) may be consistent with e¢ ciency.
In contrast, purely transitory shocks which are small relative to lifetime income should have
no e¤ect on the bargaining weight, so that an e¢ cient household (as in the collective model
developed by Chiappori and others (Chiappori, 1992; Browning and Chiappori, 1998; Browning
et al., 1994)), should be able to insure such shocks.
Third, since the shocks are experimentally generated, I am able to vary their intra-household
correlation to test for limited commitment in a much simpler and more direct way than has
previously been possible. Since limited commitment arrangements specify a transfer for every
possible state of nature and since sustainable transfers depend on a variety of factors that are
di¢ cult to observe (including preferences, levels of risk aversion, rates of time discounting, and
altruism), most tests have involved dynamic programming solutions that depend on assumptions
about these factors (for instance, Foster and Rosenzweig, 2001; Ligon, Thomas, and Worrall,
2002).
Finally, this study is, to my knowledge, the …rst …eld experiment in risk sharing or intrahousehold resource allocation to observe real-world outcomes. Other studies have instead been
conducted in a laboratory or other controlled setting (for example, Ashraf (2005), Iversen et al.
(2006), Barr (2003), and Charness and Genicot (2004)), which might be less representative of
normal behavior.
2
Background
This paper tests for Pareto e¢ ciency by comparing the responsiveness of private consumption
in weeks in which an individual receives the experimental payment to weeks in which his spouse
3
receives the payment.
Though this test is similar to other intra-household tests, it is more
powerful because it is based on a transitory income shock. With respect to permanent shocks,
many studies have shown that household decisions are sensitive to ostensibly exogenous changes
in relative intra-household incomes.
For instance, Du‡o (2003) uses the expansion of the
government of South Africa’s pension program to show that nutrition improved for girls living
with a female pensioner (relative to girls living with an elderly female not quite age eligible
for the pension), but that there was no e¤ect for boys and that there was no e¤ect for either
boys or girls living in households with a male pensioner. In Brazil, Thomas (1990) shows that
increases in female income induce greater improvements in family health than do increases in
male income.
Lundberg, Pollak, and Wales (1997) show that a policy change in the United
Kingdom that transferred a child allowance from men to women increased household spending
on women’s and children’s clothing and reduced spending on men’s clothing.
Haddad and
Hoddinott (1994) show that male and female agricultural income in the Cote D’Ivoire are spent
in di¤erent ways.
These results are all inconsistent with a unitary or standard income pooling model in which
the household behaves as if it were maximizing a single utility function.
However, since all
of these studies examine changes in outcomes due to permanent changes in relative incomes,
they are not necessarily incompatible with the more general collective model of the household
(Chiappori, 1992; Browning and Chiappori, 1998; Browning et al., 1994).
In the collective
model, household members have separate utility functions and bargain over allocations, but
reach a Pareto e¢ cient outcome. Since each individual’s bargaining weight likely depends on
his non-labor income, it may be e¢ cient to adjust relative consumption shares in response to
changes in permanent relative incomes.
The di¢ culty with the generality of the collective model is that, since preferences and the
bargaining process (which may be quite complex) are unobserved, the model is di¢ cult to
test directly.2 However, whatever the underlying bargaining process, purely transitory income
2
For this reason, some authors have instead focused on other implications of Pareto e¢ ciency, such as whether
the household is able to achieve productive e¢ ciency (for instance, by maximizing agricultural pro…ts).
Udry
(1996) rejects productive e¢ ciency by showing that female-controlled plots in Burkina Faso are less productive
than male-controlled plots.
However, using a di¤erent nationally representative dataset, Akresh (2005) shows
4
shocks that are small relative to the lifetime budget constraint should be insured. Assuming
that these transitory shocks do not a¤ect relative bargaining weights (because they do not a¤ect
lifetime incomes), and assuming that household members are risk averse, failing to insure these
shocks would leave potential gains from trade unexploited. Such a …nding would constitute a
rejection of the collective model (as well as of the unitary model).
Direct tests of intra-household risk sharing are rare, because they require data on individuallevel income and consumption, information which is not usually available.
exist cast some doubt on e¢ ciency.
Those that do
For instance, Goldstein (2004) rejects intra-household
e¢ ciency for a sample of agricultural households in Ghana. Although he …nds that spouses are
able to privately smooth consumption intertemporally, changes in consumption do not comove
between couples.
Furthermore, Goldstein …nds that individuals insure themselves through
networks outside rather than within the household. Using individual consumption data from
the Philippines, Dubois and Ligon (2005) reject the collective model.
Dercon and Krishnan
(2000) show that poor Ethiopian women bear the brunt of negative income shocks, in terms of
reduced body mass. Du‡o and Udry (2004) show that consumption patterns in the Cote D’Ivoire
are sensitive to transitory relative income shocks caused by rainfall, which di¤erentially a¤ect
male and female crops. These studies all echo the results of the vast majority of inter-household
risk-sharing studies, which have consistently rejected e¢ ciency.
There are many possible reasons that intra-household insurance is incomplete, including
moral hazard or other problems of asymmetric information. The experiment described in this
paper, however, focuses on the possibility that households su¤er from a limited commitment
problem caused by an inability to enforce insurance contracts. Under limited commitment, an
individual is not legally compelled to honor his transfer commitment to his partner. If he does
not make the agreed-upon transfer, however, his partner will punish him by terminating the
insurance relationship. Therefore, it must be the case that the utility loss from a transfer must
be less than the expected gain in utility between insurance and autarky.3
Limited commitment schemes can be very complicated. In even the simplest static model
that productive e¢ ciency cannot be rejected in other regions within Burkina Faso.
3
If resorting to autarky is not a credible option between spouses but some reduction in insurance is feasible,
the relative value of insurance is measured against that alternative instead.
5
(Coate and Ravallion, 1993), a transfer is speci…ed for every state of nature, and the agreedupon transfers depend upon a variety of factors including the correlation in incomes and levels
of risk aversion.
In more general dynamic models, transfers may also be history dependent,
so that they may serve a quasi-credit role whereby higher current consumption can be …nanced
by higher future transfer commitments.
As such, …tting models to data depend on various
assumptions and must typically be solved by dynamic programming simulations. By varying
the gains to insurance through the correlation in income shocks, the experiment described in
this paper provides a much more straightforward test of the model than has previously been
feasible.
Though there do exist other experimental studies on risk sharing or intra-household resource
allocation, this paper is, to my knowledge, the …rst …eld experiment to use real-world expenditures as dependent variables. The other experimental or quasi-experimental papers on risk
sharing include Charness and Genicot (2004), who conduct a laboratory experiment among university undergraduates in which they vary the probability that the insurance relationship will
be terminated to show that the continuation value a¤ects the amount of risk that is shared.
They also use their data to explore the impact of reciprocity, inequality, and risk aversion on
risk sharing.
Barr (2003) conducts a one-shot risk sharing game among individuals from 23
Zimbabwean villages.
The experiment divided the villages into 4 treatment groups, between
which levels of payment enforceability and information were varied.4 Barr then compares the
amount of risk shared between the 4 groups, …nding that more risk is pooled with complete
contracts and that, interestingly, less risk is pooled when the decision to break the insurance
contract must be publicly announced.
Two other related experimental papers are Ashraf (2005) and Iversen et al. (2006), though
neither is speci…cally a risk sharing study.
4
Ashraf (2005) gives private payments to married
The speci…c design was as follows. Four treatment groups were formed. In the …rst group, no risk sharing
is allowed.
In the second, people are allowed to form risk sharing groups in which all payo¤s are shared.
In
the third, risk sharing groups may again be formed but information is private, so that people can not verify
the outcomes of others. This allows people to hide the shock they received to avoid contributing to the shared
pool. Finally, in the fourth group, individuals may again opt out of risk sharing agreements but have to publicly
announce their decision.
6
individuals in the Philippines and then asks them to choose between saving the money or taking
it in cash (or in committed consumption).
Three treatment groups were randomly selected,
between which the information available to the spouses of these individuals was varied.5 Ashraf
…nds that men are less likely to deposit the payment into their own account and more likely
to deposit it into their spouse’s account when information is public and negotiation is possible.
Finally, Iversen et al. (2006) conduct several variants of household public goods games among
married couples in Eastern Uganda. In the games, each spouse decides how much to contribute
to a shared investment that is multiplied by a factor of 1.5.
varied the ownership of the investment pro…ts:
The experimental treatment
pro…ts are controlled by the husband in one
treatment, by the wife in another, and are controlled jointly in a third.
The authors reject
surplus maximization (which is also a rejection of Pareto e¢ ciency) and …nd that control of the
investment pro…ts a¤ects the amount that is contributed.
3
Experimental Design
This project was conducted between April and October, 2006 among a sample of 143 couples,
drawn from a group of daily income earners (men who work as bicycle taxi drivers - called
boda bodas in Kiswahili - and women who sell produce and other items in the marketplace) in
the towns of Busia, Sega, and Ugunja in Western Kenya. Daily income earners were targeted
because the project is focused upon transitory shocks to income, which are more commonly
encountered among daily income earners than in a sample of, for instance, farmers.
The towns targeted in this study are semi-urban areas located along a major highway from
Nairobi, Kenya to Kampala, Uganda. Though many people in the area are employed in agriculture, a substantial fraction of people in the towns themselves work in retail or other small
business, or work for wages in bars, restaurants, or other businesses near the highway. Moving
further from the road, the area becomes more rural and the vast majority of people work as
farmers, usually on a family farm. Though all of the individuals recruited for this project work
5
In the …rst treatment group, information was private. In the second, payments were made while both spouses
were in the room.
The third treatment was identical to the second except spouses were allowed to negotiate
before making decisions.
7
in the towns, many of them actually live in villages in the surrounding rural areas: the mean
travel time from home to town for men (by bicycle) was approximately 65 minutes (Table 1,
Panel A); the median travel time was 32.5 minutes.
In the towns, the bodas are arranged in stages (or taxi stands), often at a speci…c landmark
such as a big tree or near public transportation dropo¤ points along the highway. The same
group of bodas will work from the same location every day, returning there after each fare.6
Similarly, the market women in this study sell vegetables and other foodstu¤s from a set location,
often along the road.
To recruit individuals into the study, a trained enumerator approached an individual at his
place of work and asked to meet with him individually for a few minutes.
The enumerator
…rst asked the individual if he was married, and all those that were single were not interviewed
further.7 For those that were married, the enumerator then asked the respondent if he would be
interested in participating in a project that would take approximately 8 weeks to complete, and
that would require the administration of weekly monitoring surveys to both the respondent and
his spouse. In particular, a precondition for participation was that the enumerator be allowed
to visit the spouse at home without the primary respondent’s supervision. Individuals were told
that the weekly monitoring survey would take approximately 1 hour per week to complete, and
that they would be compensated if they agreed to participate. If the individual was interested
in the project, the enumerator took his name and contact information, and told him that we
would return later to begin the project; the spouse’s consent was obtained later, at the …rst
monitoring interview.
Although we did not keep detailed records of those that refused to participate, attrition was
relatively low. However, the sample is not necessarily representative of the population of married
daily income earners in these areas. We were unlikely to …nd individuals that worked from town
only occasionally, and instead were more likely to interview those working there regularly. In
addition, any background characteristic correlated with the decision to agree to participate in
the project (which might include education, for instance) would a¤ect the representativeness of
the sample.
6
7
The standard fare is 10 shillings ($0.14 US) per ride.
Several individuals lied about being married and were later dropped from the study.
8
After enrolling in the study, each spouse was visited by one of ten trained enumerators once
a week for approximately 8 weeks. Each week, the same enumerator visited both spouses and
administered a detailed monitoring survey that included questions on consumption, expenditures, income (and income shocks), and labor supply over the previous 7 days. These surveys
were conducted privately and con…dentially, and information was not shared with the spouse.8
If one of the spouses could not be found on the day of the survey, the enumerator tried again for
the next several days; if this individual was eventually traced, the enumerator asked about the
same time period that was asked of the spouse (the 7 days prior to the scheduled meeting). If
the individual could not be traced that week, I dropped the spouse’s survey, so the analysis to
be presented below includes only those weeks in which information is available for both spouses.
To measure the accuracy of within-household information, each individual was also asked to
estimate all the same values for their spouse, as in Goldstein (2004). At the conclusion of the
project, each individual was administered a background questionnaire which included questions
on access to credit and savings, asset ownership, and related issues.
To test for intra-household Pareto e¢ ciency, it is necessary to identify exogenous, transitory
shocks to relative incomes.
To cleanly identify such shocks, this project randomly provided
150 Kenyan shilling (about US $2) income shocks to participants. The probability of receiving
the shock in a given week was 50% for all participants.
To make the payment of the shocks
as transparent as possible, each enumerator carried with him a black plastic bag containing 56
slips of paper with the numbers 1-56 on them. Each number corresponded to a payment for
both spouses.
For each spouse, the drawing of 28 of the slips resulted in payment, while the
drawing of the other 28 resulted in no payment. The shocks were announced to each spouse,
so that each knew what the other had gotten.
Payments were made privately, however, and
individuals were told that they could spend the money however they chose.
This experimental design has several advantages.
First, the shocks are big relative to
incomes in the area, equivalent to approximately 1.5 days’income for men and 7 days’income
for women (Table 2, Panel A). Second, since the shocks were publicly observable (unlike many
real-world shocks, which are usually only partially observable), any observed ine¢ ciency is not
8
In most cases, the primary respondent was interviewed at work and the spouse at home.
9
attributable to the information available to the spouse, so that comparing the responsiveness
of private consumption to own and spouse’s income shocks represents a direct test of Pareto
e¢ ciency.
Third, through the data collected with the monitoring surveys, it is possible to
compare the experimental results with real world responses to ‡uctuations in weekly labor
income.
One disadvantage of the study, however, is that (for ethical and practical reasons) the income
shocks provided were always positive, unlike real-world shocks which can of course be either
positive or negative.
If individuals treat gains di¤erently than losses, behavior may di¤er
between positive and negative income shocks. In particular, if individuals are risk averse over
gains but risk loving over losses (called the re‡ection e¤ect by Kahneman and Tversky, 1979;
1991), I would observe more risk sharing than would otherwise be the case - the re‡ection e¤ect
would bias my results towards the acceptance of the null hypothesis of e¢ cient risk sharing.
The other primary purpose of the …eld experiment is to test for intra-household limited commitment by experimentally manipulating the continuation value of the insurance relationship.
The experiment is based on the fact that the value of an insurance relationship is the expected
utility gain from insurance, relative to autarky.
sibly negative) transfer
yit
i (ht ).
i (ht )
An insurance arrangement speci…es a (pos-
after history ht , so that post-transfer income for individual i is
With perfect enforcement, transfers can be set at their Pareto e¢ cient levels and full
insurance is achieved. Under limited commitment, however, individuals are not legally bound
by their ex ante insurance agreement, so insurance agreements must be self-enforcing. This can
be accomplished if the partner can credibly threaten to terminate the insurance relationship in
the event of non-payment, so that an individual balances a current utility loss against the lifetime expected utility gain from insurance. Under such a setup, the set of transfers must satisfy
1
P
k
the sustainability constraint ui (yit
ui (yit ) +
E(ui (yik
ui (yik )) Pi ,
i (ht ))
i (hk ))
k=t+1
where Pi is a moral or social cost incurred for reneging on the relationship, and
is the discount
factor. This constraint, which is simply that the one-period utility gain from reneging on the
agreement is less than the sum of the cost Pi and the (expected) lifetime di¤erence in utility
between insurance and autarky, must hold for each partner.9
9
This model will be discussed in greater detail in Section 5.
10
The basis of this experiment is that the expected utility gain from insurance
1
P
k=t+1
i (hk ))
k
E(ui (yik
ui (yik )), is greater the less correlated are partners’incomes. This is because partners
are more likely to be in opposite states of nature, so that transfers (and the resulting utility gain)
are more common, which in turn means that the punishment available to partners is greater,
and that higher transfers are sustainable. Of course, even under complete insurance, average
transfers are higher the less correlated are incomes, simply because it is more likely to observe
partners in opposite states in which transfers are made.
For this reason, the results in this
paper are conditional on the current set of shocks that are received.
To test this implication, the sample was split into 3 groups with varying correlation in the
probability of receiving the 150 Kenyan shilling income shocks.10 In Group 1, the correlation
was 0.5; in group 2, the correlation was 0; and in Group 3, the correlation was -0.5. The shocks
on the payment schedule that the enumerators carried re‡ected these correlations.
For the treatment to be meaningful, individuals must know and understand the correlation
in the shocks, a task made somewhat di¢ cult by the average level of education in this sample,
which is 7.7 years for men and 7.0 years for women (Table 1).
To ensure comprehension,
each enumerator read from a prepared script which made no speci…c mention of correlations
but emphasized instead the probability that both the respondent and his spouse would either
both receive or both not receive the shock.11 These probabilities were presented both as speci…c
percentages or odds (i.e. 75% or 3/4), and as general likelihoods (i.e. "more than half the time").
At the end of the script, individuals answered questions about the various probabilities, and any
misunderstandings were discussed. A shorter script and follow-up comprehension questionnaire
were administered once to approximately half the sample and twice to the other half. In general,
individuals seemed to understand the setup.
10
These groups were randomly picked by computer after individuals expressed interest in the project but before
any data collection began.
11
For instance, individuals in Group 1 were told that, if they received the shock, the probability that their
spouse would also receive the shock was 3/4; if they did not receive the shock, the probability that the spouse
would also not receive the shock was again 3/4.
11
4
Data
4.1
Background Information
Summary statistics from the background survey are reported in Table 1.12 From Panel A, just
over 84% of the men in the sample are bicycle taxi drivers, the rest distributed among various
other jobs, while 55% of women report having no job. The sample is predominantly of the Luo
tribe, with the remainder Luhya.13 The average age in the sample is 27.4 years, the men are 6.5
years older than the women, and the average educational attainment is 7.4 years. The average
couple has 2.6 children, 3.4 dependents (including children), and the average individual has 4.8
siblings. As mentioned in the previous section, the average travel time to the home for men is
about 65 minutes, suggesting that many respondents work in town but live in the surrounding
villages.
Panel B presents statistics on access to savings and credit. Though formal savings accounts
are very rare, 64.0% of men and 44.2% of women participate in Rotating Savings and Credit
Associations (ROSCAs).
For those in ROSCAs, the average amount contributed in the past
year was over 3,060 Kenyan shillings for men and 1,790 shillings for women, a signi…cant amount
given the average labor income in the sample.
Similarly, formal credit is nearly unheard of;
however, the vast majority of both men and women have access to informal credit in the form
of gifts and loans from friends and family. Again, the amounts given and received are relatively
large: the average amount given was 1,502 shillings and the average amount received was 1,929
shillings.
Most of these households do not, however, receive transfers from other individuals
within their household (other than the spouse), as only 4.3% of individuals report receiving
money from another member of the household, and only 9.0% report receiving support from
other members of the household in purchasing shared items.14
12
Table 1 includes information on 136 men and women, out of 143 in the sample. The remainder could not be
traced for this survey.
13
The Luo make up 12% of the Kenyan population, Luhya 15% (CBS, 2004).
14
Note that many of these individuals live in family compounds, in which each adult couple has their own
dwelling but the distinction between di¤erent households in the same compound might not be very sharp.
12
Panel C presents statistics on asset ownership. Though assets are primarily controlled by
the male, females do hold assets as well. On average, men own 0.78 acres of land, compared
to just 0.14 acres for women.15 Similarly, women control a total of a bit less than 900 shillings
worth of animals and other durable goods, compared to more than 5,500 shillings for men.16
Finally, Panel D presents the means of several other variables that might in‡uence intrahousehold bargaining power.
About 58.5% of women and 94.9% of men are able to make
independent …nancial decisions. The next few rows report measures of exposure to and attitudes
towards divorce and separation, which may a¤ect an individual’s willingness or ability to resort
to divorce.17 Just over 50% of the people in this sample know a friend or family member that is
divorced, and a roughly equal number know someone that is separated. The perceived stigma
from separation or divorce is very high, as 75.7% of men and 89.4% of women report the stigma
from divorce to be very negative; similar percentages apply to separation. Finally, one variable
that has been suggested to a¤ect bargaining power is whether the woman can stay at a friend
or relative’s home if she has a problem at home. About 95% of women and 69% of men report
having such an option.
4.2
Overview of Monitoring Data
Table 2 provides some summary information from the data collected during the weekly monitoring visits. Due to some problems with certain enumerators, particularly towards the beginning
of the data collection activities, the database is trimmed of the top and bottom 1% of responses
for individual and household expenditures, as well as savings outliers. This leaves 918 visits for
143 couples. All …gures in the tables are weekly totals.
Panel A presents summary statistics on weekly labor income and hours (not including agricultural income). Income for those selling produce or other items (who are mostly female), is
15
The per acre value of land controlled by women appears to be much lower than that of men. However, as
these …gures are self-reports, they should be taken with some caution, as women may underestimate or men may
overestimate the value of their land.
16
Durable goods include beds, sofas, tables, chairs, cookers, radios, TVs, mobile and landline phones, clocks,
watches, sewing machines, irons, bicycles, and bednets.
17
Rangel (2005) has shown that the introduction of a law that increased female alimony rights in Brazil a¤ected
household outcomes. Women worked less and invested more in children’s education after the passage of the law.
13
calculated as the di¤erence in sales and money spent restocking. Of the couples sampled for
the survey, husbands make about 727 Kenyan shillings a week (just over US $10) and wives
about 142 shillings (about US $2). For men, this income comes primarily from their regular job
(which, for most, is working as a bicycle taxi driver); for women, income comes primarily from
more informal sources. Even women without jobs earn some money: average income for such
women is 62 shillings per week, compared to 221 shillings per week for women with jobs (Table 2
footnote). Panel A also shows that the experimental income shock is relatively large, equivalent
to roughly 1.5 days’income for men and over a week’s income for women. To put this in terms
of a developed country equivalent, for men, the shock is equivalent to roughly $80 for a worker
making $20,000 per year. For women, the shock is much larger, equivalent to roughly $400.
Though I have collected data on both consumption and expenditures, I will focus on expenditures throughout the paper, for several reasons. First, to reduce the length of the monitoring
survey, the consumption questions were asked only at the household level so that I do not have
speci…c measures of individual consumption shares: the only additional information that the
consumption data provides is household in-kind saving or dissaving over periods (which was
in fact small) or household consumption of own-farm produce.
Second, the test of e¢ ciency
employed in this paper concerns the consumption of private goods (alcohol, cigarettes, soda,
clothing and shoes, hairstyling, entertainment, newspapers, own meals in restaurants, transportation and various other items), and expenditures on these items are equal to consumption
in most cases. This is because individual consumption should di¤er from individual expenditures
only if a share of the expenditure was allocated to another household member, or if individuals
saved a portion of the expenditure for future consumption or consumed expenditures that had
been saved in a previous period (for example, by consuming maize that had been purchased the
week before). However, expenditures that were allocated to another individual were recorded
as in-kind transfers in the monitoring survey, so that all individual expenditures should have
eventually been consumed by the same individual.
Also, though certain private items could
in principle be saved for future use (such as cigarettes or bottled beer), in practice these were
consumed immediately.
Panel B of Table 2 presents the expenditure data. The …rst few rows of Panel B show total
14
expenditures, total shared expenditures, and total private expenditures, while the following rows
show speci…c expenditure subcategories. Shared expenditures include all shared food consumed
at home, as well as other shared items such as soap and cleaning supplies, rent, and household
bills such as water, kerosene and …rewood.
Total expenditures include shared and private
expenditures, as well as items for children, medical expenditures, charity, and animal purchases
and construction.
Total household expenditures are roughly 1,250 shillings per week, over two thirds of which is
contributed by the male. The majority of these expenditures are concentrated on shared goods
(taking up about 60% of household expenditures), though total private expenditures average
roughly 26% of total household expenditures, with over 75% of these private expenditures going
to men.
Interestingly, nearly one third of male private expenditure are meals in restaurants,
likely because men tend to eat lunch outside the home when they are working.
Only about
10% of private expenditures appear to be spent on alcohol, soda, or cigarettes, a result which,
anecdotally, appears lower than true expenditures on these items and is likely a result of a
perceived stigma. In total, the amount spent on private items is over 2.5 times the roughly 10%
found by Goldstein (2004), in his study of agricultural couples in Ghana, though this e¤ect is
highly concentrated on the male.
Panel C of Table 2 presents summary statistics on savings and transfers.
Savings, which
are calculated as the sum of individual income (including the experimental shock, if it was
received), transfers and ROSCA and bank ‡ows minus total expenditures, are actually negative
on average, perhaps re‡ective of measurement error or of the very high variance in the savings
measure.
Transfer ‡ows, which are de…ned as positive for out‡ows and negative for in‡ows,
include both cash and in-kind transfers. Thus, if the husband were to purchase items for his
wife, these purchases are recorded as transfers and not as expenditures. In total, women receive
an average of 58 shillings per week from their husbands, the vast majority of which are gifts
rather than loans. The households in the sample received an average of 35 shillings from outside
the household, which seems reasonable since this population is not particularly a- uent.
15
4.3
Information
As part of the monitoring survey, individuals were asked to provide estimates of their spouse’s
income, expenditures, and transfers.
Table 3 compares reports between spouses.
Panel A
presents statistics on female knowledge of male behavior, while Panel B covers male knowledge
of female behavior.
In each table, Column 1 presents the mean own report, Columns 2 and
3 present the percentage of people that did not know their spouse’s total,18 and Columns 4-6
compare reports between spouses for those that were able to make estimates. Finally, Column
7 presents the coe¢ cient from a …xed e¤ect regression of own reports on the spouse’s estimates
(including week controls). The …gures in this Table are important because standard theories of
insurance assume perfect information between parties, and because intra-household risk sharing
has been posited to potentially be more e¢ cient than other arrangements because of household
informational advantages.
Table 3 seems to indicate, however, that within-household information is quite limited (just
as in Goldstein, 2004). A sizeable percentage of people do not know what their spouse is doing.
For women, 45% do not know their husband’s labor income, 37% do not know their husband’s
outside transfers, and 6% do not know their husband’s total expenditures. The percentages for
men are 25%, 32%, and 19%, respectively. Column 3 shows, however, that there are few couples
that report never knowing their spouse’s total: only about 2% of women and just under 5% of
men never know about their spouses. As would be expected, spouses know more about items
that are more publicly observable, and less about private items: 38% of women do not know
about their husband’s private expenditures, but just 7% do not know about their husband’s
shared expenditures. Interestingly, Panel B indicates that men appear to know no more about
shared expenditures than private expenditures, and that men know less about their spouses than
women do: 25% of men cannot estimate even their spouse’s shared expenditures.
Columns 4-6 compare reports between spouses, excluding those that report not knowing.
Even among these couples, there is signi…cant underestimation of spouse’s behavior.19
18
In
As it is di¢ cult to distinguish those that respond "don’t know" from those that respond that their spouse
spent nothing, the "don’t know" category includes both those that directly reported not knowing their spouse’s
total and those that incorrectly responded that their spouse’s total was 0.
19
Some of the ratios are in fact over 1, which would suggest overestimation. However, these are generally for
16
terms of total expenditures, women believe that their husbands spend only 52.3% as much as
they actually do; for men, this …gure is only 41.4%.
for private items than for shared items.
For women, the ratio is generally lower
Men, however, greatly underestimate both types of
expenditures. Men and women do not seem even to know if their spouse is sick, though this
actually might be overreporting of own illness (Panels A and B, row 1).
Column 7 presents estimates from …xed e¤ects regressions of female estimates of male expenditures on actual male expenditures, and vice-versa.
As with the mean estimates, these
coe¢ cients are all less than 1, suggesting that spouses consistently underestimate changes in all
of these values. Nearly all of these coe¢ cients are highly statistically signi…cant, however, so
spouses seem to have some idea about changes in these values from one period to another.
Finally, respondents were asked whether their spouse participated in a Rotating Savings and
Credit Association (ROSCA). The last row of Table 3 shows that individuals do tend to know
about their spouse’s ROSCA participation. Column 2 shows that 90% of both men and women
answered this question correctly.
In sum, Table 3 suggests that intra-household information is quite limited, which could
signi…cantly impede risk sharing. This is doubly important in that many of the variables listed
in Table 3 are commonly assumed to be perfectly observable. However, while these results do
suggest that information is a barrier even within the household, they do not directly impact the
results of the current paper, because the experimental shocks were publicly announced to both
spouses.
5
5.1
Testing for E¢ ciency
Theoretical Framework
In this section, I will …rst lay out a simple one-period model, and then extend it to multiple
periods.
Following Chiappori and others (Chiappori, 1992; Browning and Chiappori, 1998;
Browning et al., 1994), the household’s problem is to a maximize a weighted sum of male and
small expenditure subcategories.
17
female utilities:
max
fci ;Li gi2fm;f g
Vm (cm ; Lm ) + Vf (cf ; Lf )
where ci is the consumption vector, Li is labor, and
(1)
represents the Pareto bargaining weight
of the female in the household’s decision problem. The budget constraint is
p(cm + cf )
wm Lm + wf Lf + Sm + Sf + Am + Af
(2)
where p are prices, wm and wf are the wage rates, Am and Af are non-labor assets for each
spouse, and Sm and Sf are transitory income shocks. Labor is constrained by the time endowment so that Lm ; Lf
L.
For simplicity, it is assumed that the functions Vm () and Vf () are additively separable in
consumption and leisure, so that Vi (ci ; cj ; Li ) = ui (ci ) + hi (Li ).
The …rst order conditions
on consumption of good k (cmk and cf k ) give the well-known result that the ratio of marginal
utilities should equal the bargaining power parameter
u0m (cmk )
=
u0f (cf k )
(3)
If it is further assumed that ui (cik ) displays constant absolute risk aversion (CARA) with
identical parameters for both spouses (so that utility is of the form ui (cik ) =
1
e
cik ),
then it
is the case that
log(cmk )
=
log(cf k )
(4)
or that insurance is complete and the log consumption ratio is constant.
However, complete
insurance has been consistently rejected in the data, either between households (Townsend, 1994;
Udry, 1994) or within a single household (Du‡o and Udry, 2004; Dercon and Krishnan, 2000).
Within a household, the rejection of full insurance can be seen as a rejection of the unitary
model.
Such a rejection is not necessarily, however, Pareto ine¢ cient.
would be that the Pareto weight
One simple explanation
might not be a …xed weight but instead might depend on
wages and non-labor income, so that the weight should be written as
18
= (wm ; wf ; Am ; Af ).
From (3), consumption can be written as ci = ci (wm; wf ; Sm ; Sf ; Am ; Af ; ). Thus the change
in consumption due to a change in non-labor assets Ai or Aj would yield the result that
dci
dAj
dci
dAi
Even if income is pooled so that
the bargaining weight, so that
@
@Aj
=
=
@ci
@Aj
=
6=
@
@Ai
@ci
@ci @
+
@Aj
@ @Aj
@ci
@ci @
+
@Ai
@ @Ai
@ci
@Ai ,
(5)
changes in permanent income will likely a¤ect
6= 0 and
dci
dAj
6=
dci
dAi .
Though such a …nding (that a
change in male non-labor assets a¤ects individual consumptions di¤erently than an equal change
in female non-labor assets) violates full insurance, the result is still Pareto e¢ cient. This makes
it di¢ cult to use permanent changes in income to test for e¢ ciency.
Under the Pareto e¢ cient collective model, however, household members should insure each
other against transitory income risk, because not doing so would leave potential gains from
trade unexploited, and because purely transitory shocks should have no e¤ect on the bargaining
process. To emphasize this point, the one-period case from above can be generalized into an
intertemporal model. Assuming that the insurance contract can be renegotiated in response to
changes in permanent incomes, the intertemporal maximization problem at t can be written as
max
T
P
fci ;Li gi2fm;f g =t
t
(Vm (cm ; Lm ) + Vf (cm ; Lf ))
(6)
where T is the number of periods. Assuming constant prices, the budget constraint becomes
p(cmt + cf t ) + Amt + Af t
wm Lmt + wf Lf t + Smt + Sf t + (1 + r)Am;t
1
+ (1 + r)Af;t
where Ait represent assets which can be invested at the risk-free rate 1 + r.
@
transitory shocks do not a¤ect bargaining power ( @S
=
it
@
@Sjt
1
(7)
Assuming that
= 0) a transitory income shock
would cause a change in consumption equal to
dcikt
@cikt @cikt @
@cikt
=
+
=
dSit
@Sit
@ @Sit
@Sit
Transitory shocks should not a¤ect allocations beyond the income e¤ect
(8)
@cikt
@Sit ,
which forms the
basis of the test in this paper. Since the experimental shock is the same size for both spouses,
19
the income pooling test is that
dcmkt
dSmt
dcf kt
dSf t
=
=
dcmkt
dSf t
dcf kt
dSmt
(9)
However, as these are transitory shocks, the Permanent Income Hypothesis suggests that
households should choose to intertemporally smooth their consumption and save the money (as
has been tested in, for instance, Paxson (1992)).
For this reason, it will only be possible to
test for e¢ ciency to the extent that personal savings do not allow for complete intertemporal
consumption smoothing.
5.2
Empirical Framework
It is di¢ cult to estimate an equation like (9) directly, as it is rare to have data on individual
consumption shares cikt .
Instead, researchers typically make inferences based on changes in
aggregate household consumption cmkt + cf kt on goods that can be assigned to one member
or the other (Browning et al., 1994). In this study, however, I have collected individual panel
data on expenditures and so will be able to perform a direct test. I will …rst test the following
reduced form version of Equation (9):
cikt =
k Sit
+
k Sjt
+
=
k
i
+
t
+ "ikt
(10)
The test of income pooling is that:
k
(11)
A more complete speci…cation is to use the experimental shocks as instruments for changes
in total income (de…ned as the sum of total labor income and the experimental shock, if it was
received).
The …rst stage is therefore to regress income Iit on the experimental shock and
individual and time …xed e¤ects:
Iit = Sit + ei + et + e
"ikt
20
(12)
and then to estimate the following relationship:
cikt =
k Iit
+
k Ijt
+
=
k
0
i
+
0
t
+ "0ikt
(13)
and test that
k
5.3
(14)
Testing the Model: Individual Data
The results from estimating the reduced form speci…cation (10) by …xed e¤ects are presented in
Panels A (for the male) and B (for the female) in Table 4. The dependent variables in this Table
are individual expenditures by each spouse. For ease of interpretation, all coe¢ cients have been
divided by the size of the experimental shock (150 Kenyan shillings), so that the coe¢ cients
in the Table can be interpreted as a percentage of the shock. However, due to small changes
in labor income, the coe¢ cients do not necessarily sum to 1.20
In both Panels, Columns 1-3
present aggregated results for overall total expenditures, total shared expenditures, and total
private expenditures, respectively, while Columns 4-12 present results for various subcategories
(several subcategories are not included).
Column 3, therefore, represents the test of Pareto
e¢ ciency.
For both males and females, total own expenditures appear to increase in weeks in which
the shock is received and to actually decrease in weeks in which the spouse receives the shock,
though neither e¤ect is statistically signi…cant. Similarly, there does not seem to be much of
an e¤ect with respect to shared expenditures.
Of more interest is the test of Pareto e¢ ciency, which is presented in Column 3. Men spend
about 21.1% (31.7 shillings) of their own shock on private items, a result which is statistically
signi…cant at 1%. Meanwhile, male private expenditures actually decrease (insigni…cantly) when
a shock is received by the female. Since the di¤erence in these 2 coe¢ cients is statistically significant (at the 1% level), these results constitute a rejection of the Pareto e¢ cient collective model
of the household.
Though the data lacks power to assign this increase to speci…c categories,
there are increases in spending on clothing, meals in restaurants, and in other private categories
20
The e¤ects of the shocks on hours and labor income are included in Appendix Table A1.
21
(which includes transportation, personal hygiene products, bicycle expenditures, and other private items such as airtime for cell phones). In total, this increase of 31.7 shillings on private
expenditures amounts to approximately a 13% increase in weekly male private expenditures.21
Panel B indicates, however, that women do not spend the experimental shock as men do.
Column 3 of Panel B shows there to be no change in female private expenditures in weeks in
which she receives the shock. The only change in the overall pattern of female expenditures is
an increase in medical expenditures, which may re‡ect female preferences but which may also
represent female contributions to shared household expenses. Finally (and strangely), women
appear to spend more money on animals or construction when a shock is received by her spouse
than when she herself receives a shock, though the e¤ect is small.
Panel C shows the e¤ect of the shock on individual savings and transfers. These results are
not tests of Pareto e¢ ciency but instead provide an accounting of what individuals choose to do
with the income shock.22 One interesting result from Panel C is that the majority of the shock
appears to be saved: men save about 88.6% of the shock, women about 57.7%. Note that the
Permanent Income Hypothesis (PIH) predicts that the entire shock should be saved, so that the
marginal propensity to save out of the shock should be 1. Given the relatively large standard
errors from these estimates, I cannot reject the PIH for either spouse.
Interestingly, men do not seem to share much or any of the shock with their spouse in the
form of transfers.
Men send about 7.7% of the shock to their wife (and 5.6% outside the
household), though both e¤ects are statistically insigni…cant. Women, by contrast, send about
16.2% of the shock (about 24 shillings) to their husband (signi…cant at 1%), and another 8.8%
outside the household.
In sum, men spend about 21.1% of the shock on themselves, and otherwise actually decrease
expenditures by about 5.4% of the shock. They transfer 7.7% to their wife and 5.6% outside
the household, and save the remaining 88.6% (these e¤ects do not sum to 1 due to changes in
labor income). Women do not spend money on themselves but increase total expenditures by
15.8% of the shock. They transfer 16.2% to their husbands and 8.8% outside the household,
21
22
These results are very similar if an interaction term between the 2 shocks is also included in the analysis.
E¢ ciency does require, however, that total household savings should be equal across both types of shocks, a
hypothesis which will be tested in the next subsection.
22
and save the remaining 57.7%.
5.3.1
Instrumental Variables Estimation
Although the reduced form estimates in Table 4 include all the information necessary to test
for e¢ ciency, instrumental variable results are also useful because they directly test the theory,
which relates outcomes to relative incomes.
The results are presented in Table 5.
Panel A
reports the …rst stage …xed e¤ects regression of total income on the experimental shock, while
Panels B, C, and D present results for male and female expenditure, savings, and transfers.
The …rst stage results con…rm that labor income did not adjust signi…cantly to the experimental shock: neither change in income is statistically di¤erent from 150 Kenyan shillings, the
size of the experimental shock (see also Appendix Table A1). In addition, the IV results are
generally in line with the reduced form estimates presented in Table 4.
In particular, male
private expenditures increase with shocks received by the male but not with shocks received by
the female, a rejection of e¢ ciency.
5.3.2
Individual Data on Labor Income
Before leaving the individual data, I present some evidence on the impact of real world income
‡uctuations on expenditure patterns.
Appendix Table A2 presents estimates of the respon-
siveness of various expenditure categories to changes in relative male and female labor income.
Though labor supply and income are clearly not exogenous, this approach is valid if it can be
assumed that permanent income is constant for the 8 weeks in which couples were followed, and
that any deviation between income in a given week and average weekly income is exogenous.23
Each Panel in Table A2 presents the results of …xed e¤ects regressions of the dependent
variable on individual labor income and total household labor income.
The coe¢ cients on
individual labor income can therefore be thought of as the impact of changes in relative labor
income, holding total labor income …xed. As before, Panels A and B present expenditure results
for males and females, respectively, and Panel C presents changes in savings and in transfers.
23
A better approach would be to …nd instruments for weekly income, but the potential instruments in my
dataset are too weak for that.
23
Increases in own income are associated with increases in total expenditures and shared
expenditures, as spouses contribute more towards household expenditures in weeks in which
they make a larger share of household income. The more important result is that male private
expenditures are again increasing in male relative income (Panel A, Column 3), but that female
private expenditures do not respond to female relative income (Panel B, Column 3). The e¤ect
for men seems to originate from meals in restaurants (Column 9)24 and other private items
(Column 10).
The propensity to save out of current labor income is 0.72 for men and 0.82 for women (Panel
C), both of which are statistically distinguishable from 1. If labor income were truly exogenous,
this represents a rejection of the Permanent Income Hypothesis. Interestingly, transfers within
the household do not respond signi…cantly to changes in relative incomes, but transfers outside
the household do. This is similar to the results in Goldstein (2004), who found that agricultural
couples receive insurance from outside the household, but that within-household insurance is
limited.
To the extent that labor income is exogenous, Table A2 seems to concur generally with the
results in Tables 4 and 5: private male expenditures increase with transitory increases in male
relative income, a rejection of Pareto e¢ ciency.
5.4
Household Data
One additional test of Pareto e¢ ciency is that the change in total household savings should
be the same for both types of shocks.
This prediction is tested using an Instrumental Vari-
ables speci…cation in Table 6 (which also presents results for household expenditure categories).
Column 4 shows that the estimated propensity to save is 0.939 out of the male shock and
0.913 out of the female shock. These …gures are statistically indistinguishable from each other,
which suggests that household savings decisions are e¢ cient. In addition, neither propensity is
signi…cantly di¤erent from 1, so the Permanent Income Hypothesis cannot be rejected.
24
Note that the e¤ect of male income on meals in restaurants need not be causal. Since most men in the sample
work away from home, they tend to eat lunch at restaurants when they are working. However, the e¤ect in Table
4 holds even when controlling for hours spent working, so that this does not appear to be the only explanation
for the result.
24
6
Limited Commitment
As the results in the previous section represent a rejection of Pareto e¢ ciency, the remainder
of the paper will test whether limited commitment might serve as a partial explanation for the
results. I will start by laying out the simple limited commitment model.
6.1
Theoretical Framework
In contrast to full insurance models such as Townsend (1994), limited commitment models
assume that Pareto e¢ cient transfers cannot be legally enforced. Ex ante, insurance partners
may agree to pool risk, but they cannot be compelled to make those transfers when the actual
state of nature is revealed, which necessitates that all insurance contracts be self-enforcing.
Once the state is revealed, the individual that is to make the transfer would prefer to simply
transfer nothing and keep the money for himself. He can only be induced to make a transfer
through the threat of punishment, which is commonly thought to come in two forms.25 First,
there might exist a social or moral cost Pi for individual i to reneging on the insurance contract.
This cost might include disutility from a feeling of sel…shness or from community scorn, for
instance.
Second, his partner can punish him by terminating the insurance relationship so that no
future transfers will be made.
The termination decreases lifetime utility by the di¤erence in
expected lifetime utility under insurance and expected lifetime utility in autarky.26 Of course,
it is somewhat unlikely that a spouse would completely terminate all future transfers from a
single non-payment, so that this setup is not completely realistic. On the other hand, spouses
have a variety of other ways of punishing each other that cannot easily be measured.
Limited commitment arrangements can either be static, in which a transfer is speci…ed for
25
For simplicity, I assume that the individual does not display direct altruism towards his partner. In principle,
altruism could either improve e¢ ciency by motivating higher transfers or decrease e¢ ciency by making the
termination threat less credible.
Empirically, altruism appears to improve e¢ ciency (Foster and Rosenzweig,
2001).
26
For simplicity, the model assumes that the only partnership available is with the spouse, so the termination of
the relationship results in autarky. In a more general setup with many partners, the termination results instead
in a smaller insurance network.
25
every state of nature but transfers do not depend on the history of shocks received (Coate
and Ravallion, 1993), or dynamic, in which history dependence is allowed (Foster and Rosenzweig, 2001; Ligon, Thomas, and Worrall, 2002; Wahhaj, 2005).
The important di¤erence
between these two models is that a history-dependent scheme allows transfers to also serve a
"quasi-credit" role, whereby increases in current consumption can be …nanced with higher future transfer commitments. In a dynamic model, a transfer
i (ht )
is speci…ed after history ht
(where ht is a vector of the shocks received in previous periods, so that ht = (s1 ; s2 ; :::; st
Note also that
i (ht )
=
j (ht ).
1 )).
Abstracting from labor supply, lifetime utility for individual
i under the scheme can be written as
Uit (ht ) = ui (yit
i (ht ))
+
1
P
k t
E[ui (yik
i (hk ))]
(15)
k=t+1
However, since transfers cannot be legally enforced, an individual that …nds himself in a
favorable state of nature and his partner in an unfavorable state could choose to keep the
speci…ed transfer, incur the cost Pi , and resort to autarky forever after.
His lifetime utility
from doing this is:
Vit (ht ) = ui (yit ) +
1
P
k t
E[ui (yik )]
Pi
(16)
k=t+1
so that the sustainability constraint for the contract to be self-enforcing is that utility under
(15) be greater than under (16):
ui (yit
i (ht ))
ui (yit ) +
1
P
k t
E(ui (yik
i (hk ))
ui (yik ))
Pi
(17)
k=t+1
Among spouses, it might not be realistic to assume that a spouse will completely terminate
the insurance relationship in the case of non-payment.
Instead, it might be the case that
spouses are bound by some social norm to provide at least some minimal level of insurance to
each other, so that the value of the partnership is weighed against this minimum, rather than
autarky. Though this would reduce the punishment available to spouses, it would not eliminate
punishment altogether, and the basic implication of (17) will still apply.
In any case, for the purposes of this paper the key implications are simply those of a constrained Pareto e¢ cient allocation. These are that, if the constraints (17) are not binding, then
consumption shares are allocated as in (3):
u0m (cmk )
u0f (cf k )
= . If the constraints are binding, then
consumption is set to balance (17), for whichever is the binding constraint.
26
The experimental design of this project is designed around the sustainability constraint (17).
As was discussed in the experimental design section, the sample was split into 3 groups, between
which the intra-household correlation in the experimental shocks was varied. In Group 1, the
correlation was 0.5; in Group 2, it was 0; and in Group 3, it was -0.5 The signi…cance of this is
that, if the probability of a given state s in which incomes are yits is known to be ps , then the
expected utility gain from insurance is
1
P
k t
E(ui (yik
i (hk ))
ui (yik )) =
k=t+1
1 P
S
P
k t
k=t+1 s=1
ps (ui (yiks
ui (yiks ))
i (hk ))
(18)
Transfers will be sent from i to j in those states where i receives a relatively favorable shock
and j receives a relatively unfavorable shock, and vice-versa. But since i and j will tend to be
in opposite states the less correlated are the income shocks, the gains from insurance (the right
hand side of (18)) are greatest in Group 3 and smallest in Group 1. To observe higher transfers,
it must be the case that the constraint is binding in at least one of the treatment groups.
6.2
Empirical Methodology
Ideally, tests of limited insurance would focus on private expenditures. However, expenditures
are measured somewhat imprecisely in my data.
For this reason, I will focus on observed
transfers, though I will also present expenditure results. The basic test will be of the form
it
=
1 Sit
+
2 G2i Sit
+
3 G3i Sit
+
1 Sjt
+
2 G2i Sjt
+
3 G3i Sjt
where G2i and G3i are indicators for Groups 2 and 3, respectively.
that
1
<
2
3
> 0 and that
1
< 0.
+
i
+
t
+ "ikt
(19)
Risk sharing will imply
In addition, under limited commitment,
3
>
2
> 0 and
< 0. Though transfers are the primary outcomes of interest as they are measured with
the least error, I examine also the sum of transfers and shared expenditure items. These give
qualitatively similar results, though with much decreased precision due to noisiness in shared
expenditures.
27
7
Testing for Limited Commitment
7.1
Checking Randomization
As discussed in the experimental design section, the sample was randomly divided into 3 Treatment Groups. However, as the randomization was done before collecting the background data,
it was impossible to condition on background characteristics, so that there might exist di¤erences between groups. Table 7 presents baseline di¤erences between the Treatment Groups,
along with an F-test for joint equality of the 3 means.
The standard errors are clustered by
couple.
There are 33 outcomes in Table 7, several of which are signi…cantly di¤erent from each other
at the 10% level: religion, the number of siblings, whether the individual knows a divorced
friend or family member, and the perceived stigmas from both divorce and separation. Several
of these are also signi…cant at 5%. Though this does suggest some di¤erence between groups,
many of these di¤erences are small and some (notably the stigma from divorce and separation,
in which nobody in Group 2 reported the stigma to be very negative) appear to be a result
of the small sample size.
In addition, all experimental results are estimated by …xed e¤ects,
so that mean di¤erences due to background variables will be di¤erenced out; the coe¢ cients
will be biased only if there are interaction e¤ects between pre-treatment di¤erences and the
experimental shocks that are not captured by the …xed e¤ects.
7.2
Female Private Expenditure Share
Before turning to the …xed e¤ects results, I …rst explore the determinants of female private
expenditure share. In a household bargaining model, the female Pareto weight
might depend
on factors such as non-labor wealth or the threat of divorce or separation. Of course, any such
correlation between private consumption share and other variables is not necessarily causal, and
does not necessarily represent any ine¢ ciency.
Such a relationship would, however, at least
suggest the existence of some sort of underlying bargaining process.
The results from running regressions of individual average female consumption shares on
variables that might conceivably a¤ect the female bargaining parameter (across couples) are
28
presented in Appendix Table A3. In this Table, all independent variables are standardized to
have mean 0 and standard deviation 1. Interestingly, many variables are statistically signi…cant,
in the expected direction. From Panel A, older women tend to get greater consumption shares.
In addition, women in partnerships with smaller age di¤erences between the husband and wife
also get higher shares, an interesting result in that signi…cant age di¤erences between spouses
might be indicative of a power di¤erential. Women that travel less frequently to a nearby city
or are married to men that travel less frequently get smaller shares.
Panel B suggests that attitudes towards divorce also a¤ect private consumption shares.
Women that do not perceive the stigma from divorce as being very negative or that know more
friends or relatives that are divorced receive higher shares; conversely, women receive lower shares
if their husband does not attach a great stigma towards divorce.
in line with, for instance, Rangel (2005).
These results are generally
Panel C shows that women with greater access to
informal credit receive higher shares, while women with husbands with greater access receive
lower shares, a result which is again consistent with a bargaining model. From Panel D, female
shares are also increasing in female wealth, though not necessarily decreasing in male wealth.
Finally, Panel E shows no consistent pattern of female share between couples with better or
worse information about their spouse’s activities.
7.3
Experimental Evidence
Tables 8 (with week …xed e¤ects) and 9 (without week …xed e¤ects) present the empirical tests
of limited commitment for females, using the experimental shocks. Male outcomes with week
e¤ects are presented in Table 10.
In each Table, Panel A shows the e¤ect on expenditures
(total, shared, and private) and on savings, and Panel B shows the e¤ect on transfers. Within
each set of regressions, Columns 1-3 show the e¤ects of the shock in each group, and Column
4-6 report p-values from F-tests comparing the coe¢ cients. The coe¢ cients of primary interest
are transfers, presented in Panel B. As before, all results are divided by the size of the shock.
29
7.3.1
Results for Females
As with the previous results, female private expenditures (Panel A, 2nd row) do not seem to
adjust to the shock. The same appears to generally be true of total expenditures and shared
expenditures. The test of limited commitment comes instead from the e¤ect on transfers, in
Panel B. In weeks in which the shock is received, women in Group 1 do not appear to increase
their transfers to their husbands.
By contrast, women in Group 2 transfer about 23.6% of
the shock (35 shillings) and women in Group 3 transfer about 27.0% of shock (41 shillings).
These e¤ects are statistically distinguishable from Group 1 at 9.8% and 6.9%, respectively. The
same general pattern is obtained in examining the impact on the sum of the transfer and female
contribution to shared household expenses, though noisiness in the expenditure measure causes
the standard errors to increase.
The di¤erence in transfers is signi…cant at 5%, however, when week e¤ects are removed, as in
Table 9. In this speci…cation, the e¤ect for Group 2 increases to 24.9% of the shock (37 shillings)
and the e¤ect for Group 2 increases to 31.6% of the shock (47 shillings). The di¤erence between
Groups 1 and 3 is also now signi…cant at 4.5%, while the di¤erence between Groups 1 and 2 is
signi…cant at only 10.3%.
With or without week …xed e¤ects, transfers appear higher in Groups 2 and 3 than in Group 1,
though the relatively small sample size makes these di¤erences signi…cant at only 10% in certain
speci…cations. I take these di¤erences as suggestive of the presence of limited commitment.
7.3.2
Results for Males
The results for men (with week …xed e¤ects) are included in Table 10. As with the overall results,
men do not signi…cantly change their transfers in weeks in which they receive the experimental
payment, in any of the groups. One possibly suggestive result, however, is the 2nd row of Panel
A, which shows the e¤ect of the shock on male private expenditures. Male private expenditures
appear to be higher in Groups 1 and 2 than in Group 3, though the di¤erences are only signi…cant
at 16.0% and 8.2%, respectively.
30
7.4
Possible Explanations
The experimental evidence presented so far has shown that female transfers appear to be motivated at least partially through limited commitment, but has shown no e¤ect for males.27 One
possible reason for this might be that males are better able to self-insure than are women, perhaps because they have better access to credit and savings (as is suggested by the background
data in Table 1). Though it is di¢ cult to explore this issue adequately given data limitations, I
attempt to do so in Table 11. This Table shows the results of …xed e¤ects regressions of transfers on the experimental shocks, and interactions between the shocks and certain background
variables. The interactions include ROSCA membership, access to informal credit, an indicator variable for whether the respondent can make independent …nancial decisions, and the age
di¤erence between spouses. As the dependent variable is transfers from female to male, female
interactions that are associated with increased transfers should have positive coe¢ cients, while
male interactions that are associated with increased transfers should have negative coe¢ cients.
Though many of the relationships in this Table are weak, some are suggestive. Column 2
shows that female transfers are less responsive to the shock the greater is her access to credit.
However, this is not true of savings, at least as measured by ROSCA membership (Column 1).
Both women and men also transfer less if they are able to make independent …nancial decisions
for themselves (Column 6). Overall, the evidence in this Table is at best speculative, though
it is possible that access to credit explains at least part of the observed di¤erence between men
and women in the experimental section.
7.5
History Dependence
As discussed in the theoretical discussion, limited commitment models may be either static or
dynamic.
The key di¤erence between these two is that a dynamic system allows for history
dependence, whereby static models specify a set of transfers that are state-dependent but not
history-dependent.
I will test for history dependence by running a reduced form …xed e¤ects regression of the
27
That male expenditures on private goods increases with the shock need not be due to limited commitment.
For instance, this could be due to moral hazard or other problems of asymmetric information.
31
form
it
= Sit + Sjt + hikt + hjkt +
i
+
t
+ "ikt
(20)
I will use several speci…cations for the history hikt : the overall sum of shocks received to date
tP1
Si ), the shock from the previous period (Sit 1 ), and the sum of shocks from the
t 1 (
=1
previous 2 periods (Sit
1
+ Sit
2 ).
I will perform the test using both the experimental shocks
and changes in weekly labor income.28
Table 12 presents the reduced form estimates of Equation (20) for women.
As expected,
the coe¢ cients for the receipt of the income shock are of the expected sign and in line with
the previous results. However, the coe¢ cients on the history variables are of the opposite sign
than what would be expected: transfers appear to be increasing in the history of own income
shocks received and decreasing in the history of spouse shocks received, though many of these
coe¢ cients are insigni…cant.29 By contrast, theory would suggest that an individual’s transfers
should be lower if he had previously received a positive shock, as he would have presumably
transferred part of that shock to his spouse and should now be paid back in the form of lower
transfers. However, the p-values for the F-tests of joint signi…cance are 0.075, 0.318, and 0.094,
respectively, so it is not possible to reject the null hypothesis of no history dependence given the
sample size available.
8
Robustness Checks
Though the experimental design and the …xed e¤ects speci…cation employed in this paper likely
address many of the common econometrics issues that might arise, it remains possible that some
of the results presented in this paper are sensitive to functional form assumptions. This section
replicates the previous analysis with dependent variables in logs rather than levels.
Appendix Table A4 presents the e¢ ciency results. Because some of these variables could
28
I have also run these regressions with interactions between the history and the shocks, but those terms are
statistically insigni…cant.
29
The number of observations goes down in (2) and (3) as I drop the 1st observation and then the …rst 2
observations for each individual. Including the …rst week as 0’s and the 2nd week as the previous week’s sum do
not change the coe¢ cients or p-values signi…cantly.
32
take negative or 0 values, the variables were transformed to log(x) for x > 0,
log( x) for x < 0,
and the zeroes were not transformed. The male expenditure results are in line with those found
previously, but the sample size is too small to reject the null hypothesis of Pareto e¢ ciency with
respect to total private expenditures. In the log speci…cation, however, male clothing expenditures increase signi…cantly when the male receives the shock, and actually decline somewhat
when the female receives it (the di¤erence is, however, only signi…cant at 11.3% because of the
sample size).
The log speci…cation rejects e¢ ciency for women as well. As with men, log private clothing
expenditures, in particular, tend to increase with the receipt of the own shock. However, total
log expenditures also increase, a result which is somewhat surprising because, in the levels, female
private expenditures actually decrease somewhat when the wife receive the shock.
Though I
prefer the original speci…cation, these results do suggest that it might not just be the men that
tend to spend the money on themselves.
The results from redoing the limited commitment results in logs are presented in Appendix
Tables A5 (for women) and A6 (for men). As with the e¢ ciency robustness checks, the results
are generally in line with the main …ndings presented earlier.
Of particular interest is Table
A5, Panel B, which presents female transfers. As before, transfers are higher in Groups 2 and
3 than they are in Group 1, though log transfers are actually somewhat higher in Group 2 than
in Group 3. Both are signi…cantly di¤erent from those in Group 1, however (at 5%).
Turning to Table A6, the 2nd set of results in Panel A show the e¤ect of the shock on log
male private expenditures.
In weeks in which the male received the shock, log expenditures
seem to be lower in Group 3 than in the other Groups, though the sample size makes it too
di¢ cult to assign statistical signi…cance to this di¤erence. In general, however, the log results
seem more or less in agreement with previous …ndings.
9
Conclusion
This paper has presented evidence that suggests that intra-household risk-sharing arrangements
in Western Kenya are ine¢ cient and that limited commitment may be a partial explanation for
that ine¢ ciency. Employing the results of a …eld experiment conducted among a sample of 143
33
daily income earners and their spouses, in which each individual received 150 Kenyan shilling
income shocks with 50% probability, I have shown that men increase their private consumption
in response to transitory income shocks, a violation of Pareto e¢ ciency. While this is in line
with a number of other studies that reject the unitary household model, the …nding that male
consumption is responsive to even a transitory income shock is also a rejection of the more general
model of Chiappori and others (Chiappori, 1992; Browning and Chiappori, 1998; Browning et
al., 1994). This result is similar to those found in Dercon and Krishnan (2000) and Du‡o and
Udry (2004).
To test whether limited commitment is a partial explanation for these results, I randomly
split the sample into 3 Treatment Groups, between which the within-couple correlation in experimental payments was varied. As the continuation value of the insurance partnership is greatest
when incomes are least correlated, limited commitment models would predict that transfers
would be highest and risk sharing would be most e¢ cient in the Groups with least correlation.
Indeed, I …nd that women make signi…cantly higher transfers when incomes are less or negatively
correlated, suggesting that limited commitment is a constraint on risk sharing.
In demonstrating the importance of limited commitment, this paper contributes to a substantial intra-household risk-sharing literature. Interest in this …eld is generated partly because
insurance within a single household is likely to su¤er less from the information and enforcement
problems that exist in inter-household insurance arrangements, and because married couples
in developing countries should have an incentive to insure each other because of the lack of
alternative risk-sharing mechanisms. Given this, many have argued that, if e¢ cient insurance
is to be found anywhere, it is to be found within the household. This paper suggests that it
is not to be found anywhere: just like any other insurance system with incomplete contracts,
the self-interest of insurance partners limits the e¤ectiveness of the system.
Married or not,
individuals that …nd themselves in a favorable state relative to their partner always have an
incentive to reduce transfers below their Pareto e¢ cient levels, making full insurance di¢ cult.
34
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37
Table 1. Summary Statistics
(1)
Mean
Overall
(2)
Mean
Males
(3)
Mean
Females
A. Background Information
Occupation:
Bicycle Taxi Driver
Market Stall
Shopkeeper
Housewife / no job
Teacher
Other
0.415
0.178
0.007
0.285
0.004
0.111
0.842
0.045
0.015
0.015
0.008
0.075
0.000
0.307
0.000
0.547
0.000
0.146
Tribe
Embu
Luhya
Luo
0.004
0.087
0.909
0.000
0.085
0.915
0.008
0.089
0.903
Religion
Catholic
Anglican
Other Protestant
Other
0.301
0.074
0.383
0.242
0.343
0.080
0.350
0.226
0.258
0.068
0.417
0.258
27.361
(8.513)
7.409
(2.288)
0.832
(0.374)
0.795
(0.405)
2.590
(2.083)
3.402
(2.599)
4.772
(2.479)
30.613
(8.694)
7.748
(2.433)
0.876
(0.331)
0.861
(0.347)
24.109
(6.970)
7.033
(2.061)
0.786
(0.412)
0.725
(0.448)
4.526
(2.395)
64.904
(75.131)
5.031
(2.548)
0.016
(0.124)
0.640
(0.526)
1.259
(0.587)
3061.06
(4716.94)
0.912
(0.285)
2358.05
(2589.97)
0.880
(0.327)
2133.88
(5085.80)
0.036
(0.188)
0.087
(0.283)
0.008
(0.087)
0.442
(0.499)
1.345
(0.645)
1791.85
(3072.55)
0.910
(0.288)
1503.55
(2051.75)
0.797
(0.404)
876.34
(1403.31)
0.050
(0.219)
0.094
(0.292)
Age
Education
Read Swahili
Write Swahili
# children
# dependents
# siblings
Travel Time to Family Home (minutes)
B. Access to Savings / Informal Credit
Has Savings Account
Participates in ROSCA
# of ROSCAs (for those participating in
at least 1 ROSCA)
Amount contributed to ROSCA
in last year (for those in ROSCAs)
Received gift or loan in last year
Amount received in gifts and
loans
Gave gift or loan in last year
Amount given in gifts and
loans
Receives money from a member
of the household (other than spouse)
Member of household (other than
spouse) contributes to expenses
0.012
(0.107)
0.543
(0.521)
1.294
(0.610)
2508.87
(4120.34)
0.911
(0.286)
1929.26
(2370.17)
0.839
(0.368)
1502.84
(3770.99)
0.043
(0.204)
0.090
(0.287)
(4)
Median
Overall
(5)
Median
Males
(6)
Median
Females
25.0
28.0
22.0
8.0
8.0
7.0
32.5
865.0
1200.0
560.0
1050.0
1520.0
859.0
600.0
775.0
300.0
Observations
274
137
137
Notes: All figures are self-reported. Columns 1-3 report means, Columns 4-6 report medians.
Table report results for 136 men and 136 women, out of 143 total in the project. The rest could
not be traced for this survey. Standard deviations in parentheses.
Table 1. Summary Statistics (cont'd)
(1)
Mean
Overall
(2)
Mean
Males
(3)
Mean
Females
0.458
(1.240)
23731.22
(78916.89)
1708.10
(3534.14)
1498.87
(11037.78)
0.776
(1.630)
43324.22
(106164.60)
2671.62
(4546.77)
2871.38
(15524.04)
0.141
(0.483)
3668.00
(17634.96)
751.51
(1614.60)
136.24
(813.58)
(2)
Mean
Males
(3)
Mean
Females
0.772
(0.421)
0.607
(0.489)
54.71
(367.50)
542.26
(832.51)
94.44
(223.56)
0.496
(0.501)
0.504
(0.501)
0.818
(0.387)
0.949
(0.221)
0.642
(0.481)
40.15
(188.99)
422.87
(692.34)
59.56
(205.11)
0.471
(0.501)
0.526
(0.501)
0.693
(0.463)
0.585
(0.495)
0.569
(0.497)
69.84
(488.74)
681.01
(954.96)
154.39
(242.24)
0.523
(0.501)
0.481
(0.502)
0.947
(0.225)
Perception of Stigma from Separation
Not Negative at all
Somewhat Negative
Very Negative
0.030
0.146
0.825
0.051
0.191
0.757
0.008
0.098
0.894
Perception of Stigma from Divorce
Not Negative at all
Somewhat Negative
Very Negative
0.026
0.108
0.866
0.044
0.154
0.801
0.008
0.061
0.932
C. Asset Ownership
Acres of land owned
Value of land owned
Value of Durable Goods Owned
Value of Animals Owned
(1)
Mean
Overall
D. Other Variables that Might Affect Bargaining Power
Can make financial decisions
independently
Can buy food independently
Days since last visit to town
Days since last visit to mediumsized city 1 hour away
Days since last reading newspaper
Knows a friend or family member
who is separated
Knows a friend or family member
who is divorced
Can stay with friend or relative if
problem at home
(4)
% never
Overall
(5)
% never
Males
(6)
% never
Females
0.02
0.02
0.02
0.10
0.06
0.15
0.33
0.18
0.49
Observations
274
137
137
Notes: All figures, including the value of land, durable goods, and animals are self-reported.
Columns 1-3 report means with standard deviations in parentheses. Columns 4-6 report the percentage of
respondents answering "never" to the question.
Median values of variables in Panel C are not reported because many people could not estimate the value
of land and other asset ownership.
Durable goods include beds, sofas, tables, chairs, cookers, radios, TVs, mobile and landline phones, clocks,
watches, sewing machines, irons, bicycles, and bednets.
The table reports results for 136 men and 136 women, out of 143 total in the project. The rest could
not be traced for this survey.
Table 2. Summary Statistics from Monitoring Surveys
Panel A. Income
(1)
Male
Total Labor Income
727.12
(767.66)
Total Income, Including Experimental
804.43
Shocks
(768.23)
Income from Regular Job
515.57
(434.50)
Income from Other Activities
211.54
(752.04)
Total Hours Worked
55.48
(64.89)
Hours worked in Regular Job
47.29
(42.34)
Hours worked in Other Activities
8.30
(55.93)
Panel B. Expenditures
Total Expenditures
Total Shared
Total Private
Items for Children
Medical
Charity
Animal Purchase and Construction
Shared Categories
Shared Food
Other Shared
Private Categories
Alcohol, Soda, Cigarettes
Own Clothing and Shoes
Hairstyling, Entertainment, &
Newspapers
Restaurant Meals for Self
Other Own
Panel C. Savings and Transfers
Total Savings
(Net) Transfers to Spouse
(Net) Transfers from Outside HH
(2)
Female
142.16
(568.94)
221.85
(574.57)
49.32
(527.93)
92.85
(235.33)
16.67
(32.97)
6.09
(17.92)
10.69
(27.68)
(1)
Male
851.11
(532.84)
499.19
(389.73)
246.46
(195.18)
18.09
(69.32)
42.85
(102.98)
30.50
(78.93)
14.40
(81.79)
(2)
Female
390.46
(404.33)
247.14
(273.11)
74.00
(150.93)
16.73
(54.61)
25.06
(89.81)
22.46
(61.97)
4.86
(34.08)
(3)
Household Total
1251.97
(722.71)
752.97
(508.46)
321.50
(257.06)
35.03
(91.63)
68.37
(152.92)
54.73
(113.94)
19.25
(88.82)
(4)
Share of Household Total
-
383.02
(274.64)
115.73
(228.26)
192.94
(202.29)
54.20
(112.39)
580.61
(338.61)
172.44
(277.50)
0.46
27.75
(51.24)
20.88
(84.76)
12.87
(25.56)
73.32
(79.99)
111.64
(122.31)
4.34
(17.81)
21.54
(76.53)
6.45
(21.82)
5.32
(24.08)
36.34
(113.01)
32.11
(54.41)
42.53
(116.54)
19.47
(34.70)
78.68
(83.99)
148.70
(172.40)
(1)
Male
-93.39
(770.09)
58.46
(146.20)
-18.42
(358.12)
(2)
Female
-93.34
(596.89)
-195.86
(1004.11)
-16.59
(318.30)
-34.99
(475.44)
0.60
0.26
0.03
0.05
0.04
0.02
0.14
0.03
0.03
0.02
0.06
0.12
Observations
918
918
918
Number of IDs
143
143
143
Note: Number of observations slightly different for certain variables.
Total expenditures equal the sum of total shared, total private, items for children, medical,
animals & construction, and charity. Transfers are positive for outflows and negative for inflows.
Average labor income is 221 shillings for women with a job, 62 shillings for women without a job.
Household totals in Panels B and C include totals from other household members.
Savings are calculated as the sum of labor income, the experimental shock (if it was received), ROSCA and
bank flows, and transfers minus total expenditures. Standard deviations in parentheses.
See text or Table 4 for detailed description of expenditure categories.
Table 3. Comparison Between Own and Spouse's Reports (Wife's Knowledge of Husband)
(1)
(2)
(3)
(4)
Average Male % Female % Female
Average Male
Report
that Do Not That Never
Report
All Couples
Know
Know
Excluding
Male Total
"Do not Knows"
Panel A. Wife's Knowledge of Husband's Behavior
Sickness
0.359
(0.480)
Total Labor Income
727.12
(767.662)
Transfers Outside Household
-18.42
(358.122)
General Expenditure Categories
Total Expenditures
851.11
(532.84)
Total Shared
499.19
(389.73)
Total Private
246.46
(195.18)
Items for Children
18.09
(69.32)
Medical
42.85
(102.98)
Charity
30.50
(78.93)
Animal Purchase and Construction
14.40
(81.79)
Specific Shared Expenditure Categories
Shared Food
383.02
(274.64)
Other Shared
115.73
(228.26)
Specific Private Expenditure Categories
Alcohol, Soda, Cigarettes
Clothing
Hair / Entertainment / News
Meals in Restaurants
Other Private
Other Information
ROSCA Participation
27.75
(51.24)
20.88
(84.76)
12.87
(25.56)
73.32
(79.99)
111.64
(122.31)
(1)
Mean
Male
Participation
0.64
-
-
0.45
0.021
0.37
0.021
0.06
0.021
0.07
0.021
0.38
0.021
0.09
0.021
0.21
0.021
0.27
0.023
0.03
0.021
0.08
0.021
0.17
0.021
0.29
0.021
0.07
0.021
0.23
0.021
0.38
0.021
0.39
0.021
(5)
Average Fem.
Estimate of
Male Report
Excluding
"Do not Knows"
(6)
Ratio
(7)
Coef. From
FE Regression
of Male Total
on Female
Estimate
0.359
(0.480)
575.67
(945.401)
14.79
(173.635)
0.235
(0.424)
379.54
(1288.447)
15.78
(237.410)
0.655
0.450
(.037)***
-0.077
(.046)*
0.411
(.085)***
870.88
(527.51)
515.36
(392.12)
263.54
(204.06)
7.05
(45.14)
31.12
(92.10)
24.55
(77.40)
5.46
(45.71)
455.54
(427.21)
352.94
(360.09)
113.98
(143.32)
9.53
(51.24)
28.60
(67.20)
14.31
(75.78)
9.35
(69.55)
0.523
395.74
(275.21)
114.34
(226.64)
282.43
(214.91)
85.41
(300.18)
8.29
(38.46)
3.72
(28.25)
5.14
(18.26)
24.77
(55.90)
86.57
(128.90)
7.39
(27.63)
8.67
(47.71)
5.16
(16.53)
22.05
(49.49)
55.27
(107.74)
0.659
1.067
0.685
0.433
1.351
0.919
0.583
1.712
0.714
0.747
0.891
2.334
1.004
0.890
0.638
0.360
(.036)***
0.252
(.031)***
0.427
(.063)***
0.549
(.045)***
0.758
(.056)***
0.091
(.038)**
0.470
(.039)***
0.384
(.036)***
0.093
(.028)***
0.267
(.073)***
0.259
(.065)***
0.338
(.059)***
0.065
(0.077)
0.466
(.059)***
(2)
% Females
Answer
Correctly
0.90
Observations
918
Number of IDs
143
NOTE: The "total shared" category includes shared food, cleaning supplies, rent, water, other household bills, and other shared household items.
The "total private" category includes alcohol, soda, cigarettes, own clothing, privately consumed food (mostly meals in restaurants),
entertainment, hairstyling, beauty and personal hygiene products, and other private items such as transportation.
Total private does not include ROSCA or private savings payments, which are included in total savings.
Sickness is a dummy variable equal to 1 if the individual reported any of the following: chest congestion, malaria, typhoid, or diarrhea.
Columns 7 and 8 report coefficients from fixed effects regressions of Male reports on Female estimates, with week controls.
Don't know includes those that could not estimate a total for their spouse and that incorrectly estimated that their spouse's total was 0.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Table 3. Comparison Between Own and Spouse's Reports (cont'd: Husband's Knowledge of Wife)
(1)
(2)
(3)
(4)
Average Female
% Male
% Male
Average Female
Report
that Do Not That Never
Report
All Couples
Know
Know
Excluding
Female Total
"Do not Knows"
Panel B. Husband's Knowledge of Wife's Behavior
Sickness
Total Labor Income
Transfers Outside Household
General Expenditure Categories
Total Expenditures
Total Shared
Total Private
Items for Children
Medical
Charity
Animal Purchase and Construction
Specific Shared Expenditure Categories
Shared Food
Other Shared
0.404
(0.491)
142.16
(568.938)
-16.59
(318.302)
-
-
0.25
0.049
0.32
0.049
0.19
0.051
0.25
0.049
0.26
0.049
0.13
0.049
0.22
0.049
0.29
0.054
0.02
0.049
192.94
(202.29)
54.20
(112.39)
0.27
0.049
0.31
0.049
4.34
(17.81)
21.54
(76.53)
6.45
(21.82)
5.32
(24.08)
36.34
(113.01)
0.10
0.049
0.10
0.049
0.09
0.049
0.08
0.049
0.18
0.049
390.46
(404.33)
247.14
(273.11)
74.00
(150.93)
16.73
(54.61)
25.06
(89.81)
22.46
(61.97)
4.86
(34.08)
(5)
Average Male
Estimate of
Female Report
Excluding
"Do not Knows"
(6)
Ratio
(7)
Coef. From
FE Regression
of Female Total
on Male
Estimate
0.404
(0.491)
38.37
(194.985)
5.68
(135.298)
0.274
(0.446)
29.86
(164.240)
9.78
(116.453)
0.679
0.472
(.036)***
0.147
(0.140)
0.887
(.131)***
407.08
(396.97)
246.29
(242.11)
44.81
(114.82)
2.07
(18.75)
7.22
(31.65)
19.76
(71.14)
0.75
(9.75)
168.59
(187.09)
118.88
(140.15)
38.37
(79.39)
4.11
(28.32)
7.07
(27.28)
13.14
(93.90)
1.26
(13.00)
189.22
(192.40)
34.26
(75.37)
105.07
(122.95)
22.58
(49.69)
1.65
(15.44)
3.88
(28.26)
2.29
(14.30)
0.78
(9.23)
15.67
(56.85)
1.95
(13.14)
6.81
(34.95)
3.00
(13.82)
1.40
(13.30)
17.02
(52.67)
0.778
1.723
0.414
0.483
0.856
1.988
0.980
0.665
1.676
0.555
0.659
0.381
(.073)***
0.382
(.072)***
0.592
(.078)***
0.227
(.073)***
0.408
(.132)***
0.135
(.02)***
0.445
(.096)***
0.295
(.064)***
0.298
(.092)***
Specific Private Expenditure Categories
Alcohol, Soda, Cigarettes
Clothing
Hair / Entertainment / News
Meals in Restaurants
Other Private
Other Information
ROSCA Participation
(1)
Mean
Male
Participation
0.44
1.181
1.756
1.311
1.788
1.086
(2)
% Females
Answer
Correctly
0.90
Observations
918
Number of IDs
143
NOTE: The "total shared" category includes shared food, cleaning supplies, rent, water, other household bills, and other shared household items.
The "total private" category includes alcohol, soda, cigarettes, own clothing, privately consumed food (mostly meals in restaurants),
entertainment, hairstyling, beauty and personal hygiene products, and other private items such as transportation.
Total private does not include ROSCA or private savings payments, which are included in total savings.
Sickness is a dummy variable equal to 1 if the individual reported any of the following: chest congestion, malaria, typhoid, or diarrhea.
Columns 7 and 8 report coefficients from fixed effects regressions of Female reports on Male estimates, with week controls.
Don't know includes those that could not estimate a total for their spouse and that incorrectly estimated that their spouse's total was 0.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
0.067
(.04)*
0.440
(.081)***
0.642
(.054)***
0.073
(0.077)
0.642
(.083)***
Table 4. Experimental Shocks on Individual-Level Outcomes (Reduced Form)
Panel A. Expenditures (Male)
Male Received Payment
Female Received Payment
F-test (for private items)
R-squared
Panel B. Expenditures (Female)
Male Received Payment
Female Received Payment
F-test (for private items)
R-squared
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
0.157
-0.134
0.211
(0.194)
(0.140)
(.082)***
-0.150
-0.043
-0.110
(0.192)
(0.139)
(0.081)
.007***
0.09
0.07
0.03
(4)
Shared
Food
-0.021
(0.093)
-0.013
(0.092)
0.08
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
-0.121
-0.004
0.007
0.062
0.035
0.103
0.048
(0.100)
(0.034)
(0.042)
(0.043)
(0.026)
(.054)*
(0.049)
-0.038
-0.014
-0.037
-0.005
-0.042
-0.081
0.058
(0.099)
(0.033)
(0.041)
(0.042)
(0.026)
(0.054)
(0.049)
0.458
0.280
.041**
.018**
0.03
0.02
0.01
0.01
0.04
0.04
0.06
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
-0.039
-0.042
-0.039
(0.153)
(0.096)
(0.071)
0.158
0.093
-0.022
(0.152)
(0.095)
(0.070)
0.863
0.03
0.06
0.01
(4)
Shared
Food
-0.051
(0.073)
0.057
(0.072)
0.06
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
0.010
-0.024
0.038
0.016
-0.006
-0.025
0.014
(0.048)
(0.026)
(.017)**
(0.039)
(0.010)
(0.053)
(0.043)
0.037
0.032
0.006
-0.015
-0.002
-0.005
0.079
(0.048)
(0.026)
(0.017)
(0.038)
(0.010)
(0.053)
(.042)*
0.196
0.567
0.790
0.792
0.03
0.03
0.02
0.03
0.02
0.01
0.03
Panel C. Savings / Transfers
Male Received Payment
Female Received Payment
R-squared
(1)
Total
Savings
0.886
(.35)**
0.142
(0.346)
0.06
Male Outcomes
(2)
(3)
Transfer to
Transfers
Spouse
Outside HH
0.077
0.056
(0.061)
(0.180)
-0.162
-0.135
(.06)***
(0.179)
0.04
0.01
Female Outcomes
(4)
(5)
(6)
Total
Transfer to
Transfers
Savings
Spouse
Outside HH
0.172
-0.077
-0.022
(0.263)
(0.061)
(0.158)
0.577
0.162
0.088
(.261)**
(.06)***
(0.156)
0.16
0.04
0.02
Observations
903
Number of IDs
143
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variables in Panels A and B are individual expenditures by men and women, respectively.
All coefficients are divided by 150 shillings, the size of the experimental shock. The test of Pareto efficiency is Column 3 in Panels A and B, which shows the effect of the shocks on private expenditures.
The dependent variables in Panel C are individual savings and transfers for men (Columns 1-3) and women (Columns 2-4). Transfers are defined as positive for outflows and negative for inflows.
Savings are defined as the sum of total income (including the experimental shock), transfers, and bank and ROSCA flows minus total expenditures.
Total expenditures equal the sum of total shared, total private, items for children, medical, animals & construction, and charity.
The "total shared" category includes Columns 6 and 7: shared food (all food consumed jointly at home), and "other shared," which includes cleaning supplies, rent, water, other household bills,
and other shared household items. The "total private" category includes Columns 8-10 (clothing and shoes, meals in restaurants, and other private), as well as other omitted subcategories,
including alcohol, soda, cigarettes, hairstyling, entertainment, newspapers, and transportation. Column 10 (other private) includes transportation, bicycle expenditures, personal hygiene products,
and other private items such as airtime for mobile phones. Total private does not include ROSCA or private savings payments, which are included in total savings.
See Tables 5 and A1 for the effect of the shocks on labor supply.
(12)
Charity
0.037
(0.038)
0.005
(0.038)
0.01
(12)
Charity
0.014
(0.030)
-0.032
(0.030)
0.03
Table 5. Shocks on Individual-Level Outcomes (Instrumental Variables Regression)
Panel A. First Stage Results
Individual Received Experimental
Shock
Observations
Number of IDs
R-squared
Panel B. IV: Expenditures (Male)
Male Income (instrumented with
experimental shocks)
Female Income (instrumented with
experimental shocks)
Chi-squared test (for private items)
R-squared
Panel C. IV: Expenditures (Female)
Male Income (instrumented with
experimental shocks)
Female Income (instrumented with
experimental shocks)
Chi-squared test (for private items)
R-squared
Panel D. IV: Savings / Transfers
Male Income (instrumented with
experimental shocks)
Female Income (instrumented with
experimental shocks)
R-squared
(1)
Total
Male
Income
174.73
(50.012)***
903
143
0.04
(2)
Total
Female
Income
147.01
(35.233)***
903
143
0.24
----- Aggregate Categories ----(2)
(3)
Total
Total
Total
Expend
Shared
Private
0.14
-0.11
0.19
(0.164)
(0.133)
(.083)**
-0.13
-0.06
-0.08
(0.189)
(0.156)
(0.096)
.028**
0.17
0.04
0.10
(4)
Shared
Food
-0.02
(0.082)
-0.02
(0.094)
0.01
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
-0.10
0.00
0.01
0.05
0.03
0.09
0.04
(0.101)
(0.029)
(0.037)
(0.040)
(0.024)
(.052)*
(0.045)
-0.05
-0.01
-0.04
0.00
-0.04
-0.07
0.06
(0.116)
(0.034)
(0.043)
(0.046)
(0.027)
(0.060)
(0.052)
0.422
0.393
.05*
.04**
0.07
0.01
0.01
0.01
0.07
0.05
0.04
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
-0.04
-0.04
-0.03
(0.132)
(0.084)
(0.063)
0.16
0.09
-0.03
(0.153)
(0.097)
(0.073)
0.944
0.08
0.08
0.00
(4)
Shared
Food
-0.05
(0.064)
0.05
(0.074)
0.03
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
0.01
-0.02
0.03
0.01
-0.01
-0.02
0.01
(0.042)
(0.024)
(.018)*
(0.034)
(0.009)
(0.047)
(0.040)
0.04
0.03
0.01
-0.01
0.00
-0.01
0.08
(0.048)
(0.028)
(0.021)
(0.039)
(0.010)
(0.054)
(.046)*
0.407
0.578
0.873
0.847
0.10
0.01
0.00
0.01
0.00
0.00
0.02
(1)
Total
Savings
0.76
(.227)***
0.26
(0.263)
0.38
Male Outcomes
(2)
(3)
Transfer to
Transfers
Spouse
Outside HH
0.07
0.05
(0.067)
(0.156)
-0.15
-0.13
(.078)**
(0.180)
0.00
0.02
Female Outcomes
(4)
(5)
(6)
Total
Transfer to
Transfers
Savings
Spouse
Outside HH
0.13
-0.07
-0.02
(0.169)
(0.067)
(0.136)
0.60
0.15
0.09
(.195)***
(.078)**
(0.157)
0.44
0.00
0.02
Observations
903
Number of IDs
143
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included.
See Table 4 for definitions of various categories. Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
(12)
Charity
0.03
(0.035)
0.01
(0.040)
0.00
(12)
Charity
0.01
(0.027)
-0.03
(0.031)
0.00
Table 6. Shocks on Household-Level Outcomes (Instrumental Variables)
---------- Aggregate Categories ---------------------------------------- Specific Subcategories -------------------------------(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Total
Total
Total
Total
Total
Shared Other Children Animal / Clothing
Meals in
Other Medical Charity
Expend
Shared
Private Savings Transfers
Food Shared
Construct
Restaurants Private
Total Male Income
0.063
-0.203
0.161
0.939
0.027
-0.078 -0.126
-0.027
0.040
0.068
0.027
0.078
0.047
0.045
(0.238)
(0.190)
(0.105) (.3)***
(0.208)
(0.114) (0.122) (0.039)
(0.041)
(0.053)
(0.025)
(0.071) (0.069) (0.049)
Total Female Income
-0.029
-0.026
-0.115
0.913
-0.040
0.028 -0.055
0.012
-0.026
-0.008
-0.041
-0.083
0.148
-0.021
(0.275)
(0.219)
(0.122) (.347)*** (0.240)
(0.131) (0.141) (0.045)
(0.048)
(0.062)
(0.029)
(0.082) (.08)* (0.057)
Chi-squared Test of Equality
0.796
0.532
.08*
0.953
0.828
0.530
0.698
0.512
0.281
0.339
.071*
0.132
0.328
0.367
Observations
903
903
903
902
903
903
902
903
903
903
903
903
903
903
Number of IDs
143
143
143
143
143
143
143
143
143
143
143
143
143
143
R-squared
0.08
0.03
0.07
0.38
0.02
0.00
0.06
0.00
0.01
0.01
0.05
0.04
0.03
0.00
NOTE: Instrument are experimental payoffs. All regressions estimated by fixed effects. Controls for the week of the interview included. Standard errors in parentheses.
First stage results presented in Table 5.
Total income is the sum of labor income and the experimental shock, if it was received.
The results in Panel B are similar if total household income is included instead of either male or female income.
See Table 4 for definitions of the various categories.
Total private does not include ROSCA or private savings payments, which are included in total savings.
Total transfers are transfers outside of the household.
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 7. Checking for Randomization (Differences in Baseline Means Across 3 Treatment Groups)
(1)
(2)
(3)
(4)
(5)
Group 1
Group 2
Group 3
F-test for
(corr=0.5) (corr=0) (corr=-0.5) Joint Equality Observations
A. Background Information
Occupation:
Bike Taxi Driver
0.424
0.446
0.376
0.215
269
(0.028)
(0.022)
(0.032)
Market Stall
0.163
0.141
0.235
0.33
269
(0.038)
(0.037)
(0.052)
Housewife / no job
0.272
0.304
0.271
0.769
269
(0.038)
(0.036)
(0.043)
Tribe
Luo
0.864
0.922
0.833
0.334
262
(0.044)
(0.039)
(0.050)
Religion
Catholic
0.200
0.363
0.337
.09*
267
(0.051)
(0.063)
(0.068)
Other Protestant
0.511
0.352
0.279
.037**
267
(0.067)
(0.060)
(0.060)
Other
0.233
0.198
0.302
0.459
267
(0.058)
(0.053)
(0.065)
Age
26.351
27.370
28.442
0.353
272
(0.956)
(1.050)
(1.083)
Education
7.368
7.310
7.506
0.861
255
(0.262)
(0.244)
(0.269)
Read Swahili
0.856
0.822
0.814
0.747
266
(0.037)
(0.045)
(0.047)
Write Swahili
0.789
0.800
0.791
0.982
266
(0.041)
(0.046)
(0.044)
# children
2.456
2.644
2.663
0.86
266
(0.304)
(0.304)
(0.267)
# dependents
3.264
3.554
3.342
0.843
249
(0.347)
(0.374)
(0.313)
# siblings
4.711
4.400
5.244
.059*
266
(0.282)
(0.265)
(0.240)
B. Access to Savings / Informal Credit
Participates in ROSCA
0.551
0.584
0.506
0.636
263
(0.061)
(0.058)
(0.059)
# of ROSCAs (for those participating in
1.347
1.267
1.262
0.747
136
at least 1 ROSCA)
(0.087)
(0.104)
(0.083)
Amount contributed to ROSCA
1830.43
1416.50
897.47
0.12
277
in last year (for those in ROSCAs)
(455.96)
(304.67)
(221.73)
Received gift or loan in last year
0.910
0.934
0.894
0.6
265
(0.033)
(0.025)
(0.032)
Amount received in gifts and
1700.97
2269.74
1733.38
0.23
277
loans
(225.37)
(265.58)
(314.79)
Gave gift or loan in last year
0.798
0.899
0.823
0.137
257
(0.041)
(0.034)
(0.046)
Amount given in gifts and
1008.17
1411.30
2065.24
0.12
277
loans
(148.90)
(237.14)
(628.26)
Receives money from a member
0.032
0.022
0.078
0.275
277
of the household (other than spouse)
(0.018)
(0.015)
(0.031)
Member of household (other than
0.064
0.086
0.122
0.448
277
spouse) contributes to expenses
(0.025)
(0.032)
(0.039)
Notes: All figures, including the value of land, durable goods, and animals are self-reported.
Columns 1-3 report means with standard deviations in parentheses. Columns 4-6 report the percentage of
respondents answering "never" to the question.
Table report results for 136 men and women, out of 143 total in the project. The rest could
not be traced for this survey. Standard errors are clustered by household.
(6)
IDs
136
136
136
129
131
131
131
136
121
130
130
130
125
130
128
55
139
131
139
126
139
139
139
Table 7. Checking for Randomization (Differences in Baseline Means Across 3 Treatment Groups - cont'd)
(1)
(2)
(3)
(4)
(5)
Group 1
Group 2
Group 3
F-test of
(corr=0.5)
(corr=0)
(corr=-0.5) Joint Equality Observations
C. Asset Ownership
Acres of land owned
0.447
0.425
0.456
0.983
277
(0.125)
(0.116)
(0.131)
Value of land owned
29720.930 18722.890 20476.190
0.679
253
(11369.210) (5132.081) (6686.440)
Value of Durable Goods Owned
1817.872
1470.462
1619.000
0.804
277
(427.185)
(310.525)
(377.934)
Value of Animals Owned
1514.415
2007.312
735.022
0.558
277
(619.104)
(1828.510) (528.572)
D. Variables that might affect bargaining power
Can make financial decisions
0.778
independently
(0.040)
Can buy food independently
0.600
(0.062)
Days since last visit to town
99.786
(82.529)
Days since last visit to medium634.170
sized city 1 hour away
(123.757)
Days since last reading newspaper
84.558
(28.164)
Knows a friend or family member
0.438
who is separated
(0.055)
Knows a friend or family member
0.409
who is divorced
(0.050)
Can stay with friend or relative if
0.811
problem at home
(0.044)
139
0.638
265
129
0.55
261
128
0.495
239
111
0.896
173
64
0.395
266
131
.048**
264
130
0.385
267
131
.014**
266
131
0.769
266
131
0.425
266
131
0.045
0.000
0.035
.026**
266
(0.022)
(0.000)
(0.020)
Somewhat Negative
0.124
0.088
0.116
0.748
266
(0.032)
(0.036)
(0.037)
Very Negative
0.831
0.912
0.849
0.301
266
(0.042)
(0.036)
(0.046)
Notes: All figures, including the value of land, durable goods, and animals are self-reported.
Columns 1-3 report means with standard deviations in parentheses. Columns 4-6 report the percentage of
respondents answering "never" to the question.
Table report results for 136 men and women, out of 143 total in the project. The rest could
not be traced for this survey. Standard errors are clustered by household.
131
Very Negative
Perception of Stigma from Divorce
Not Negative at all
0.035
(0.020)
0.128
(0.037)
0.837
(0.046)
139
129
Somewhat Negative
0.000
(0.000)
0.143
(0.040)
0.857
(0.040)
125
265
0.056
(0.024)
0.169
(0.042)
0.775
(0.049)
0.753
(0.038)
0.659
(0.064)
39.431
(14.908)
545.422
(92.079)
93.174
(14.188)
0.523
(0.058)
0.593
(0.056)
0.779
(0.048)
139
0.792
Perception of Stigma from Separation
Not Negative at all
0.789
(0.037)
0.578
(0.060)
25.681
(12.008)
459.251
(85.744)
106.281
(36.750)
0.538
(0.058)
0.522
(0.052)
0.857
(0.034)
(6)
Number of
IDs
131
131
Table 8. Testing for Limited Commitment with Experimental Shocks (Females) - Week Fixed Effects
Panel A. Female Expenditures and Savings
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
(1)
Group 1
(2)
Group 2
-0.26
(0.261)
0.340
(0.262)
903
143
0.04
0.426
(0.261)
-0.115
(0.250)
(1)
Group 1
(2)
Group 2
-0.17
(0.121)
-0.056
(0.121)
903
143
0.02
0.154
(0.121)
-0.058
(0.116)
(1)
Group 1
(2)
Group 2
Total Expenditures
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.284
.06*
0.950
.053*
(0.265)
0.232
0.205
0.772
0.345
(0.276)
Total Private
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.049
.055*
0.471
0.234
(0.123)
0.089
0.991
0.402
0.386
(0.128)
(7)
Group 1
(8)
Group 2
Total Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
-0.06
(0.165)
0.157
(0.165)
903
143
0.06
0.171
(0.165)
-0.050
(0.157)
-0.247
(0.167)
0.114
(0.174)
(7)
Group 1
(8)
Group 2
0.15
(0.450)
0.371
(0.452)
903
143
0.16
0.158
(0.450)
0.723
(.431)*
(7)
Group 1
(8)
Group 2
0.326
0.404
.071*
0.359
0.854
0.479
Total Savings
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.292
0.993
0.825
0.832
(0.458)
0.692
0.570
0.620
0.961
(0.476)
Panel B. Female Transfers
Male Received Payment
Transfer to Spouse
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.044
0.996
0.919
0.923
(0.106)
0.270
.098*
.069*
0.814
(.11)**
Transfer + Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.291
0.360
0.475
0.105
(0.180)
0.384
0.906
0.372
0.427
(.187)**
-0.06
-0.058
-0.11
0.113
(0.104)
(0.104)
(0.177)
(0.177)
Female Received Payment
0.000
0.236
0.157
0.186
(0.104)
(.099)**
(0.178)
(0.170)
Observations
903
903
IDs
143
143
R-squared
0.04
0.05
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included. All dependent variables are individual female values.
Coefficients of primary interest are Transfers (Panel B).
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
Table 9. Testing for Limited Commitment with Experimental Shocks (Females) - Without Week Fixed Effects
Panel A. Female Expenditures and Savings
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
(1)
Group 1
(2)
Group 2
-0.42
(0.258)
0.193
(0.257)
903
143
0.01
0.306
(0.259)
-0.244
(0.246)
(1)
Group 1
(2)
Group 2
-0.16
(0.119)
-0.031
(0.118)
903
143
0.01
0.166
(0.119)
-0.058
(0.113)
(1)
Group 1
(2)
Group 2
Total Expenditures
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.500
.049**
0.819
.028**
(.26)*
-0.042
0.220
0.530
0.581
(0.270)
Total Private
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.026
.055*
0.439
0.252
(0.119)
0.090
0.873
0.480
0.381
(0.124)
(7)
Group 1
(8)
Group 2
Total Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
-0.20
(0.165)
0.030
(0.164)
903
143
0.01
0.066
(0.165)
-0.142
(0.157)
-0.439
(.165)***
-0.096
(0.172)
(7)
Group 1
(8)
Group 2
0.12
(0.476)
0.646
(0.474)
903
143
0.01
0.467
(0.476)
0.833
(.454)*
(7)
Group 1
(8)
Group 2
0.263
0.297
.031**
0.449
0.597
0.843
Total Savings
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.166
0.608
0.947
0.656
(0.478)
0.668
0.776
0.974
0.807
(0.498)
Panel B. Female Transfers
Male Received Payment
Transfer to Spouse
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.001
0.916
0.850
0.769
(0.103)
0.316
0.103
.045**
0.643
(.107)***
Transfer + Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.437
0.324
0.387
.064*
(.177)**
0.220
0.810
0.500
0.650
(0.184)
-0.03
-0.041
-0.22
0.025
(0.102)
(0.102)
(0.176)
(0.176)
Female Received Payment
0.018
0.249
0.049
0.107
(0.102)
(.097)**
(0.175)
(0.168)
Observations
903
903
IDs
143
143
R-squared
0.02
0.01
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview are not included. All dependent variables are individual female values.
Coefficients of primary interest are Transfers (Panel B).
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
Table 10. Testing for Limited Commitment with Experimental Shocks (Males)
Panel A. Male Expenditures and Savings
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
(1)
Group 1
(2)
Group 2
0.08
(0.330)
0.186
(0.331)
903
143
0.10
0.551
(.33)*
-0.169
(0.316)
(1)
Group 1
(2)
Group 2
0.26
(.14)*
-0.002
(0.140)
903
143
0.04
0.323
(.14)**
-0.162
(0.134)
(1)
Group 1
(2)
Group 2
Total Expenditures
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.395
0.304
0.307
.042**
(0.336)
-0.702
0.434
.061*
0.251
(.349)**
Total Private
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.020
0.730
0.160
.082*
(0.142)
-0.269
0.405
0.183
0.585
(.148)*
(7)
Group 1
(8)
Group 2
-0.30
(0.240)
0.232
(0.241)
902
143
0.07
0.109
(0.240)
-0.093
(0.229)
(7)
Group 1
(8)
Group 2
0.05
(0.597)
0.417
(0.599)
903
143
0.07
0.855
(0.597)
0.047
(0.571)
(7)
Group 1
(8)
Group 2
Total Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.350
0.219
0.888
0.173
(0.244)
-0.341
0.323
.096*
0.462
(0.253)
Total Savings
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
1.865
0.332
.03**
0.228
(.607)***
0.503
0.651
0.920
0.586
(0.630)
Panel B. Male Transfers
Male Received Payment
Transfer to Spouse
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.044
0.996
0.919
0.923
(0.106)
-0.270
.098*
.069*
0.814
(.11)**
Transfer + Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.312
0.237
0.882
0.186
(0.261)
-0.616
0.116
.023**
0.433
(.271)**
0.06
0.058
-0.26
0.165
(0.104)
(0.104)
(0.256)
(0.256)
Female Received Payment
0.000
-0.236
0.219
-0.334
(0.104)
(.099)**
(0.257)
(0.245)
Observations
903
903
IDs
143
143
R-squared
0.04
0.09
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included. All dependent variables are individual male values.
Coefficients of primary interest are Transfers (Panel B).
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
Table 11. Effect of Various Background Variables on Transfers
Dependent Variable = Female Transfer to Male
--------------------------------------------- Interaction Terms --------------------------------------------(2)
(3)
(4)
(5)
(6)
(7)
Received Loan Amt. Received
Gave Gift
Amt. Given Respondent Can Make Absolute Value
in Past Year
in Loans
in Past Year
in Gifts
Independent Financial
of Age
in Past Year
in Past Year
Decisions
Difference
Male Received Payment
-11.40
-6.13
-11.30
-8.35
-10.13
-30.77
-10.68
(10.179)
(9.702)
(9.606)
(10.188)
(9.515)
(13.62)**
(9.552)
Male Received Payment *
-5.621
11.357
3.823
0.628
-0.985
39.227
-2.944
Male Variable
(9.355)
(9.645)
(8.062)
(9.943)
(6.693)
(19.835)**
(8.938)
Female Received Payment
24.616
23.106
23.027
23.961
20.529
15.568
25.066
(10.105)**
(9.585)**
(9.56)**
(10.06)**
(10.06)**
(10.441)
(9.45)***
Female Received Payment *
5.506
-26.731
-10.260
-12.185
-23.556
-14.274
-4.826
Female Variable
(10.014)
(9.019)***
(10.577)
(9.331)
(19.778)
(8.027)*
(8.743)
Observations
804
804
868
770
868
815
858
R-squared
0.04
0.05
0.04
0.04
0.04
0.05
0.04
NOTE: Dependent variables are transfers from the female to the male.
All regressions estimated by fixed effects. Controls for the week of the interview included.
Columns display the independent variables that are interacted with the experimental shocks.
All independent variables are standardized to have mean 0 and standard deviation 1, so the coefficients represent the effect of a 1 standard
deviation increase in the independent variable.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
(1)
ROSCA
membership
Table 12. Testing for History Dependence (Experimental Shocks)
Female Outcomes
Dependent Variable: Transfers to Spouse
Respondent Received Shock
Spouse Received Shock
Sum of Own Shocks
Sum of Spouse's Shocks
Respondent Received Shock Previous Week
Spouse Received Shock Previous Week
Sum of Respondent Shocks Past 2 Weeks
Sum of Spouse Shocks Past 2 Weeks
(1)
(2)
(3)
30.121
(9.813)***
-17.608
(9.616)*
13.467
(8.319)
-16.600
(8.109)
26.940
(9.607)***
-15.123
(9.857)
35.158
(11.347)***
-17.835
(11.797)
13.174
(9.875)
-8.467
(10.081)
19.759
(9.603)**
-8.719
(9.965)
.094*
608
142
0.02
p-value for joint significance of history variables
.075*
0.318
Observations
903
753
Number of IDs
143
143
R-squared
0.02
0.01
NOTE: Fixed effect regressions. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Appendix Table A1. Effect of Experimental Shocks on Labor Supply
Panel A. Male Labor Supply
Male Received Payment
Female Received Payment
F-test
R-squared
(1)
Hours
Worked
2.67
(4.795)
-5.49
(4.754)
0.238
0.02
(2)
Labor
Income
22.07
(50.100)
-21.43
(49.663)
0.547
0.02
(1)
Hours
Worked
1.46
(2.097)
-4.56
(2.079)**
.046**
0.02
(2)
Labor
Income
5.10
(35.649)
-2.26
(35.338)
0.886
0.22
Panel B. Female Labor Supply
Male Received Payment
Female Received Payment
F-test
R-squared
Observations
903
Number of IDs
143
NOTE: Fixed effects regressions. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
The effect of the shock on total income (labor income + the experimental shock) are
presented in the 1st-stage results in Table 5.
Appendix Table A2. Changes in Labor Income and Individual-Level Outcomes (Reduced Form)
Panel A. Expenditures (Male)
Male Labor Income
Total Household Income
R-squared
Panel B. Expenditures (Female)
Female Labor Income
Total Household Income
R-squared
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
0.16
0.11
0.06
(.034)***
(.025)***
(.015)***
0.01
0.01
-0.01
(0.028)
(0.021)
(0.012)
0.16
0.14
0.05
(4)
Shared
Food
0.05
(.017)***
-0.01
(0.014)
0.09
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
0.06
0.00
0.01
0.00
0.02
0.03
0.00
(.018)***
(0.006)
(0.008)
(0.008)
(.005)***
(.01)***
(0.009)
0.02
0.00
-0.01
0.00
0.00
-0.01
0.01
(0.015)
(0.005)
(0.006)
(0.007)
(0.004)
(0.008)
(0.008)
0.10
0.02
0.01
0.01
0.07
0.06
0.06
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
0.09
0.06
0.02
(.028)***
(.017)***
(0.013)
0.04
0.03
0.01
(.016)**
(.01)***
(0.008)
0.07
0.10
0.02
(4)
Shared
Food
0.04
(.013)***
0.01
(.008)*
0.09
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
0.01
0.01
0.00
0.01
0.00
0.01
0.00
(0.009)
(.005)*
(0.003)
(0.007)
(0.002)
(0.010)
(0.008)
0.01
0.00
0.00
0.01
0.00
0.00
0.00
(.005)***
(0.003)
(0.002)
(.004)*
(0.001)
(0.006)
(0.005)
0.05
0.03
0.02
0.04
0.02
0.02
0.03
Panel C. Savings / Transfers
Own Labor Income
Total Household Income
R-squared
(1)
Total
Savings
0.72
(.047)***
0.00
(0.039)
0.49
Male Outcomes
(2)
(3)
Transfer to
Transfers
Spouse
Outside HH
-0.01
0.12
(0.011)
(.032)***
0.01
-0.02
(0.009)
(0.027)
0.02
0.04
Observations
918
Number of IDs
143
NOTE: Fixed effect regressions. Controls for the week of the interview included
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Female Outcomes
(4)
(5)
(6)
Total
Transfer to
Transfers
Savings
Spouse
Outside HH
0.82
-0.01
0.11
(.033)***
(0.011)
(.029)***
-0.02
0.00
-0.02
(0.020)
(0.007)
(0.017)
0.59
0.02
0.04
(12)
Charity
-0.02
(.007)***
0.02
(.006)***
0.02
(12)
Charity
0.00
(0.005)
0.00
(0.003)
0.02
Appendix Table A3. Determinants of Female (Private) Expenditure Share
Dependent Variable: Average Female Private Expenditure as Share of Total Expenditures
Panel A. Background Variables
Independent Variables
Female Value
Male Value
Observations
R-squared
(1)
135
0.03
Education
0.006
(0.004)
0.000
(0.004)
118
0.02
(4)
Last time to
Nearby City
-0.011
(.004)**
-0.003
(.004)**
105
0.07
(5)
# of
Children
0.003
(0.004)
131
0.00
(6)
# of
Dependents
0.002
(0.004)
126
0.00
(7)
# of
Siblings
0.006
(0.004)
0.006
(0.004)
129
0.04
(2)
Knows
Someone
that is Separated
0.011
(.004)***
-0.004
(.004)***
129
0.06
(3)
# Known
that are
Separated
0.011
(.004)***
-0.006
(.004)***
129
0.06
(4)
Knows
Someone
that is Divorced
0.002
(0.004)
-0.002
(0.004)
127
0.00
(5)
# Known
that are
Divorced
0.009
(.004)**
-0.006
(.004)**
127
0.05
(6)
Stigma from
Separation is
Not Negative
0.012
(.004)***
-0.002
(.004)***
129
0.07
(7)
Stigma from
Divorce is
Not Negative
0.012
(.004)***
-0.001
(.004)***
129
0.07
0.005
(0.004)
-0.005
(0.004)
126
0.02
(2)
Amount
Received in
Loans in Past Yr.
0.012
(.004)***
0.000
(.004)***
137
0.06
(3)
Amount
Received in
Gifts in Past Yr.
0.013
(.004)***
0.002
(.004)***
137
0.08
(4)
Amount
Given in
Loans in Past Yr.
0.013
(.005)***
-0.002
(.004)***
137
0.06
(5)
Amount
Given in
Gifts in Past Yr.
0.009
(.004)**
-0.001
(.004)**
137
0.04
(6)
Amount Received in
Shared Expend from other
HH Members in Past Yr.
0.002
(0.004)
0.001
(0.004)
128
0.00
(7)
Amount Received in
Gifts / Loans from other
HH Members in Past Yr.
0.006
(0.004)
-0.004
(0.004)
133
0.02
(1)
Value of
Animals
Owned
0.001
(0.004)
0.002
(0.004)
137
0.00
(2)
Value of
Durable Goods
Owned
0.007
(0.004)
-0.002
(0.004)
137
0.02
(3)
Value of
Own Land
(4)
Value of
Bridewealth
Received
0.008
(.004)*
131
0.03
(5)
Has Job
(6)
Average
Labor
Income
0.001
(0.004)
0.0038
(0.004)
143
0.01
Age
0.014
(.005)***
-0.007
(.005)***
135
0.06
Panel B. Variables that Might Affect Bargaining Power
Independent Variables
(1)
Can Stay
Somewhere if
Problem at Home
Female Value
0.006
(0.004)
Male Value
-0.003
(0.004)
Observations
130
R-squared
0.02
Panel C. Access to Savings / Credit
Independent Variables
Female Value
Male Value
Observations
R-squared
Panel D. Asset Ownership and Income
Independent Variables
Female Value
Male Value
Observations
R-squared
Panel E. Intrahousehold Information
Independent Variables
(1)
ROSCA
membership
(2)
Abs. Value of
Difference in Age
-0.008
(.004)**
(3)
0.003
(0.004)
0.0005
(0.004)
115
0.01
0.010
(.004)**
0.0021
(.004)**
132
0.05
(1)
(1)
(3)
Ratio between
Ratio between
Ratio between
Own Estimate of
Own Estimate of
Own Estimate of
Spouse's Total Expend
Spouse's Shared Expend
Spouse's Private Expend
Expend & True
Expend & True
Expend & True
Total Expend
Shared Expend
Private Expend
Female Value
0.001
0.001
0.001
(0.004)
(0.004)
(0.004)
Male Value
0.002
0.002
0.002
(0.004)
(0.004)
(0.004)
Observations
137
137
137
R-squared
0.00
0.00
0.00
NOTE: Mean of Dependent variable is 0.054. Regressions are OLS estimates across couples.
All independent variables are standardized to have mean 0 and standard deviation 1, so the coefficients represent the effect of a 1 standard deviation increase in the independent variable.
Appendix Table A4. Experimental Shocks on Log Individual-Level Outcomes (Reduced Form)
Panel A. Expenditures (Male)
Male Received Payment
Female Received Payment
F-test (for private items)
R-squared
Panel B. Expenditures (Female)
Male Received Payment
Female Received Payment
F-test
R-squared
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
0.06
-0.04
0.11
(0.040)
(0.060)
(0.079)
-0.06
-0.14
-0.12
(0.039)
(.059)**
(0.078)
.042**
0.08
0.05
0.04
(4)
Shared
Food
0.04
(0.065)
-0.11
(.064)*
0.04
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
-0.14
-0.02
0.17
0.23
0.02
0.19
0.31
(0.112)
(0.111)
(.09)*
(.114)**
(0.111)
(0.123)
(.141)**
-0.29
-0.19
-0.10
-0.03
-0.15
-0.38
0.15
(.111)***
(.11)*
(0.090)
(0.113)
(0.110)
(.122)***
(0.140)
.042**
0.113
0.286
.002***
0.04
0.03
0.01
0.01
0.02
0.04
0.08
----- Aggregate Categories ----(1)
(2)
(3)
Total
Total
Total
Expend
Shared
Private
-0.08
-0.12
-0.20
(0.072)
(0.088)
(0.155)
0.24
0.08
0.30
(.072)***
(0.087)
(.154)*
.027**
0.04
0.02
0.02
(4)
Shared
Food
-0.11
(0.095)
0.06
(0.094)
0.02
---------------------------------------------- Specific Subcategories ---------------------------------------------(5)
(6)
(7)
(8)
(9)
(10)
(11)
Other
Children
Animal /
Clothing
Meals in
Other
Medical
Shared
Construct
Restaurants
Private
-0.10
0.04
0.15
0.11
-0.05
-0.20
-0.03
(0.116)
(0.114)
(.063)**
(0.123)
(0.058)
(0.134)
(0.123)
0.15
0.22
0.02
0.27
0.00
-0.07
0.26
(0.115)
(.114)*
(0.062)
(.122)**
(0.057)
(0.133)
(.122)**
0.148
0.380
0.547
0.506
0.03
0.03
0.05
0.04
0.02
0.02
0.04
Panel C. Savings / Transfers
Male Received Payment
Female Received Payment
R-squared
(1)
Total
Savings
1.45
(.371)***
0.37
(0.368)
0.11
Male Outcomes
(2)
(3)
Transfer to
Transfers
Spouse
Outside HH
0.20
0.18
(0.156)
(0.289)
-0.75
-0.23
(.155)***
(0.286)
0.05
0.02
Female Outcomes
(4)
(5)
(6)
Total
Transfer to
Transfers
Savings
Spouse
Outside HH
0.35
-0.20
-0.22
(0.344)
(0.156)
(0.246)
1.48
0.75
0.13
(.342)***
(.155)***
(0.244)
0.06
0.05
0.02
Observations
903
Number of IDs
143
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Dependent variables are in logs.
(12)
Charity
0.02
(0.123)
-0.05
(0.122)
0.04
(12)
Charity
0.20
(.109)*
-0.03
(0.108)
0.05
Appendix Table A5. Testing for Limited Commitment with Experimental Shocks (Females): Log Outcomes
Panel A. Female Expenditures and Savings
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
(1)
Group 1
(2)
Group 2
-0.24
(.124)*
0.425
(.125)***
904
143
0.05
0.085
(0.125)
0.082
(0.119)
(1)
Group 1
(2)
Group 2
-0.37
(0.266)
0.413
(0.267)
904
143
0.02
-0.101
(0.266)
0.321
(0.255)
(1)
Group 1
(2)
Group 2
Total Expenditures
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.115
.062*
0.476
0.255
(0.127)
0.234
.046**
0.285
0.385
(.131)*
Total Private
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.118
0.467
0.497
0.964
(0.272)
0.169
0.802
0.521
0.683
(0.280)
(7)
Group 1
(8)
Group 2
Total Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
-0.08
(0.150)
0.217
(0.150)
904
143
0.03
-0.162
(0.150)
-0.136
(0.144)
-0.127
(0.153)
0.153
(0.158)
(7)
Group 1
(8)
Group 2
-0.10
(0.588)
2.176
(.59)***
904
143
0.06
0.389
(0.589)
1.329
(.564)**
(7)
Group 1
(8)
Group 2
-0.20
(0.359)
0.782
(.36)**
904
143
0.05
-0.362
(0.360)
0.727
(.344)**
0.701
0.830
0.868
.087*
0.764
0.170
Total Savings
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.466
0.556
0.497
0.926
(0.601)
1.003
0.294
0.164
0.693
(0.620)
Panel B. Female Transfers
Male Received Payment
Transfer to Spouse
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.172
0.588
0.683
0.896
(0.272)
0.996
.029**
.042**
0.941
(.281)***
-0.02
-0.221
(0.266)
(0.267)
Female Received Payment
0.222
1.024
(0.267)
(.255)***
Observations
904
IDs
143
R-squared
0.06
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Dependent variables are in logs.
Transfer + Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.205
0.748
0.993
0.757
(0.367)
1.253
0.911
0.359
0.297
(.379)***
Appendix Table A6. Testing for Limited Commitment with Experimental Shocks (Males): Log Outcomes
Panel A. Male Expenditures and Savings
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
Male Received Payment
Female Received Payment
Observations
IDs
R-squared
(1)
Group 1
(2)
Group 2
0.07
(0.068)
-0.031
(0.068)
904
143
0.09
0.144
(.068)**
-0.047
(0.065)
(1)
Group 1
(2)
Group 2
0.15
(0.135)
-0.086
(0.136)
904
143
0.05
0.215
(0.135)
-0.077
(0.130)
(1)
Group 1
(2)
Group 2
Total Expenditures
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.065
0.415
0.167
.029**
(0.069)
-0.164
0.856
0.169
0.221
(.072)**
Total Private
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.096
0.720
0.200
0.103
(0.138)
-0.282
0.961
0.313
0.281
(.143)**
(7)
Group 1
(8)
Group 2
-0.14
(0.102)
-0.100
(0.103)
903
143
0.06
0.040
(0.103)
-0.080
(0.098)
(7)
Group 1
(8)
Group 2
0.95
(0.640)
0.317
(0.643)
904
143
0.10
1.515
(.642)**
-0.084
(0.614)
(7)
Group 1
(8)
Group 2
-0.09
(0.121)
0.003
(0.121)
904
143
0.05
0.082
(0.121)
-0.196
(.116)*
Total Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.075
0.216
0.665
0.427
(0.105)
-0.250
0.890
0.308
0.239
(.108)**
Total Savings
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
2.146
0.531
0.185
0.485
(.655)***
1.095
0.650
0.396
0.190
(0.676)
Panel B. Male Transfers
Male Received Payment
Transfer to Spouse
(3)
(4)
(5)
(6)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
0.172
0.588
0.683
0.896
(0.272)
-0.996
.029**
.042**
0.941
(.281)***
0.02
0.221
(0.266)
(0.267)
Female Received Payment
-0.222
-1.024
(0.267)
(.255)***
Observations
904
IDs
143
R-squared
0.06
NOTE: All regressions are estimated by fixed effects. Controls for the week of the interview included.
Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Dependent variables are in logs.
Transfer + Shared
(9)
(10)
(11)
(12)
Group 3 Test 1=2 Test 1=3 Test 2=3
p-value
p-value
p-value
-0.052
0.319
0.837
0.434
(0.123)
-0.416
0.231
.016**
0.194
(.127)***