GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.26(The Excellent Paper of The Third Applied Econometrics Conference)

Does Contingent Repayment in Microfinance Help the Poor
During Natural Disasters?

Masahiro Shoji

January 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Does Contingent Repayment in Microfinance Help the Poor During Natural Disasters?
Masahiro Shoji*
Department of Economics, University of Tokyo
Abstract
Microfinances in Bangladesh introduced a contingent repayment system
beginning in 2002, which allowed rescheduling of savings and installments
during natural disasters for affected members. This paper is one of the firstattempts to evaluate the system employing a unique dataset. In using evidence
from a flood in 2004, I find that rescheduling plays the role of a safety net bydecreasing the probability that people skip meals during negative shocks by
5.1%. This effect is even higher on the landless and females. This study
attempts to contribute to the issue regarding the poverty reduction effect ofMicrofinances.
JEL Codes: O16, O22, O53, G21
Keywords: Microfinance, Safety net, Natural disasters, Bangladesh*

Email address: ee47013@mail.ecc.u-tokyo.ac.jp, mshoji@clarku.edu,
Address: 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan1

1. Introduction
This study evaluates a newly introduced contingent repayment system in MicrofinanceInstitutions (MFIs) using a unique dataset.

The standard repayment system of MFIs is

frequent and strict, and negative shocks occurring after loan disbursements sometimes
causes a repayment burden upon borrowers (Zeller et al. 1999).

Therefore, there haslong been a concern about how flexible loans combined with insurance can work as asafety net for the poor.

To overcome this problem, most MFIs in Bangladesh havebeen introducing a contingent repayment system since 2002, which allows reschedulingof repayment during natural disasters for disaster-affected members (Dowla and Barua
2006).
The goal of this paper is to use a unique dataset to examine the determinants andconsequences of rescheduling.

A number of articles describe the importance ofrescheduling in MFIs and its efficient implementation (Ledgerwood 1998; Norell 2001;
Meyer 2002), but no previous studies examine the issue because of a lack of available
data.

This study is one of the first attempts to evaluate the contingent repayment

system.
This study also fills a gap in the literature by investigating the poverty reduction
effect of MFIs during natural disasters. 1

The existing literature on the issue is limited,despite its practical importance in designing a poverty reduction policy.
employs a dataset collected after a nation-wide flood in 2004.
MFIs being rescheduled simultaneously for the first time.

This paper

That resulted in many

The dataset shows that 39%

of MFI members were allowed to reschedule during the flood.

However, this study is not the first study to examine the performance of MFIs during
natural disasters. For further discussion regarding the issue, see Khandker (2007)
and Khan and Kurosaki (2007).

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This paper finds that rescheduling helps flood victims ensure food consumption.
Rescheduling is found to decrease the probability that people skip meals by 5.1% during
negative shocks.

Furthermore, the impact is larger on the poor than on the rich, andlarger on females than on males.

In the absence of rescheduling, the repayment burden

increases the probability that females skip meals by 5.5%, while the corresponding
change in probability is only 3.5% for males.

Thus, although MFI memberships may

improve the intra-household bargaining power of women (Van Tassel 2004; Pitt et al.
2006), they still suffer from the burden of repayment more than men during negative
shocks.
It is important to study the heterogeneous rescheduling impact within a household.
Previous studies find that females and children are more likely to experience decreases
in nutrition intake than working-age males during negative shocks (Behrman 1988;
Behrman and Deolalikar 1990).

However, Pitt et al. (2003) show that credit provided

to females by MFIs improves the nutrition intake of children.

It is also theoreticallyplausible that food consumption of productive and/or favored members becomes moresensitive to income fluctuations than the other members (Mangyo 2007).
This paper attempts to contribute to the literature regarding the poverty reduction
effects of MFIs particularly by smoothing consumption.

A number of previous

empirical studies examine the impacts of MFI membership on capital accumulation,
consumption, nutrition intake and income. 2

Also, Morduch (1998) and Chemin (2008)

use a case study of Bangladeshi MFIs before introducing the contingent repayment

For example, Pitt and Khandker (1998), Pitt et al. (2003), and Tedeschi (2008) findpositive impacts of MFI participation on households’ outcomes. Coleman (1999),
however, shows that MFI membership does not necessarily improve members’
livelihoods, and it even increases the demand for moneylenders possibly because of therepayment burden to MFIs.

2

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system to show that MFI membership can mitigate fluctuations in consumption overtime.

However, Armendariz de Aghion and Morduch (2005: 222) claim that there isno consensus on whether MFIs with the standard repayment system actually reducepoverty, despite a number of previous attempts.

This is attributed to the difficulty ofcontrolling for the endogeneity of self-selection into MFI membership.

While thisstudy does not examine the long-term impact of MFI membership, it finds that thecontingent repayment system, compared to the frequent repayment system, plays therole of a safety net during natural disasters.
This paper begins with the first part of Section 2, which describes features ofBangladeshi floods and the repayment system of MFIs, while the second part describes
the dataset. In Section 3, the empirical strategies are discussed and Section 4 shows
the results. Section 5 is the conclusion of this paper.

2. Background and Data
The 2004 Flood and the Contingent Repayment Structure in MFIs
MFIs receive attention from academic and non-academic circles because of their highrepayment rates and their unique repayment structure. 3

One feature of standard MFIloans is frequent repayment: once a MFI member borrows from his/her MFI, theamount to be repaid is divided into approximately 40 to 50 weekly installments.
He/She is required to pay tightly scheduled weekly installments beginning soon after
the loan disbursement.

Also, members must attend member meetings and deposit

money into a saving account at their MFIs every week, regardless of whether they havedebt.

All transactions, such as loan disbursements, payments of installments, andFor a comprehensive discussion of MFIs and their repayment structures, see
Armendariz de Aghion and Morduch (2005).

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4

deposits, must be implemented at the member meeting place rather than the borrowers’
home (Armendariz de Aghion and Morduch 2005: 137).

Borrowers who default on

their loans are excluded from future access to credit. If borrowers cannot repay theirinstallments on time, they have to ask the other members in the joint-liability group to
reimburse the repayment. 4

Therefore, the joint liability system can play the role of astate contingent repayment system (Townsend 2003), but it does not work duringcovariate shocks such as natural disasters (Khan and Kurosaki 2007).
Bangladesh is one of the most disaster-prone countries in the world and floods after
the planting seasons are clearly the largest risk to household income.

In recent decades

severe floods have inundated the country in 1974, 1988, 1998, 2004, and 2007.

The

flood in 1998, inundating 68% of the country, was particularly severe and negatively
affected income and assets of MFI members, causing repayment burdens.
Learning from the 1998 flood, most MFIs in Bangladesh have been introducing acontingent repayment structure since 2002.

This new repayment structure allows

rescheduling of weekly repayments and deposits during disasters without charging
additional interest. 5

Indebted MFI members are allowed to reschedule both savingsand loan installments, while those who do not have debt postpone only saving deposits.
MFIs have also switched loan contracts from joint liability to individual lending (Dowla
and Barua 2006).
The first nation-wide flood since the introduction of the contingent repayment
structure occurred in July 2004, affecting 39 out of 64 districts of the country. MFIs
A number of studies theoretically explain that this joint liability structure results inhigh repayment rates (Besley and Coate 1995, Ghatak and Guinnane 1999). On thecontrary, Gine et al. (2006) find that the joint liability would even increase risky
investments. Also, Kono (2006) finds that the joint liability increases the default rates.
5 Natural disasters might affect borrowers’ solvency as well as their liquidity.
However, the new repayment system allows only rescheduling.
4

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postponed collecting weekly saving deposits and debt installments when the flood
started.

The government of Bangladesh also initiated the Vulnerable Group Feeding(VGF) and Gratuitous Relief (GR) programs that aimed at providing victims with foodand agricultural inputs, such as seed and fertilizer.

However, most of them started in

September and October: two months after the flooding began. Also, since floods are atype of covariate shocks, it is difficult to cope with the shocks by risk sharing among thevillagers, increasing the importance of rescheduling.

Therefore, a small amount ofrescheduling can potentially have large consequences particularly at the beginning of
the flood.
The rescheduling was targeted to members who had difficulty in 1) attending themember meetings, and 2) paying for savings and installments.
beneficiaries of rescheduling on an individual or group basis.

MFIs choose

This approach makes

better use of the limited financial resource of MFIs than rescheduling all loans inaffected areas. It requires, however, officers to visit all affected MFI members during
disasters and observe the members’ flood damage, causing significant administrativeand monitoring costs.

This tradeoff between the two targeting approaches is acommon problem in the poverty targeting literature (Coady et al. 2004).

The

following section provides summary statistics regarding the flood damage andrescheduling.

Data Description
This study employs a unique dataset collected from 326 Bangladeshi households
including both MFI members and non-members that has some distinct characteristics
from other household surveys.

First, it includes data on rescheduling treatments

6collected using MFI members’ bankbooks. The use of the bankbooks alleviates thepossibility of recall bias, which is common in retrospective surveys.

Second, thesurvey was conducted one year after the 2004 flood, the first flood in which most MFIs
allowed members to reschedule. 6This dataset is a follow-up survey of the International Food Policy ResearchInstitute (IFPRI).

IFPRI collected data from 757 households in 1998, 1999 and 2004

to examine the 1998 and the 2004 flood (Del Ninno et al. 2001).

The IFPRI dataset

followed the multistage stratified random sampling methodology for seven districts thatwere selected depending on their economic status and the flood intensity: Chandpur,
Manikganj, Magura, Barisal, Sunamganj, Narsingdi, and Madaripur. In the second
stage, IFPRI randomly sampled one Thana from each district and three unions fromeach of those Thanas. 7 In the next stage, about six villages from each union and two
clusters from each of the villages were randomly selected.

Approximately threehouseholds from each cluster were chosen depending on the village size.
The data in this paper was collected only once in December 2005 from three out of
the seven IFPRI-survey districts, Chandpur, Manikganj, and Magura, that were selectedbased on flood severity, poverty level, geographical characteristics, and diffusion of
MFIs. According to Centre for Policy Dialogue (2005: pp16), the flood intensity was
categorized into five levels: “very severe” including Sunamganj, “severe” including
Narsingdi, Manikganj, and Chandpur, “moderate” including Madaripur, “normal” suchas Barisal, and “not flooded” including Magura. Since Sunamganj is too different
from the other districts in terms of geography, economy, and flooding patterns, I
Only a few MFIs allowed rescheduling of installments during the previous flood in1998.
7 Thanas and unions are administration units of Bangladesh; a union consists of somevillages, and each Thana includes multiple unions.
67

dropped the area from my study sites.

Out of the three “severely affected” districts, I

chose Chandpur and Manikganj because Narsingdi is quite similar to Manikganj interms of poverty level, agricultural patters, and geography. Finally, I chose Magura asa control group. 8This survey succeeded in interviewing 326 out of 335 households

that IFPRI surveyed in 2004. 9

My questionnaire covered the magnitude of flood

damage, basic demographic characteristics, labor and non-labor incomes, asset holdings,
savings, MFI memberships, rescheduling treatments, and food consumption fromJanuary 2004 until December 2005.
In the December 2005 survey, retrospective information was collected, based onrecall, for four sub-periods preceding December 2005: mid-January to mid-July 2004,
mid-July to mid-November 2004 (during the flood), mid-November 2004 to mid-July
2005, and mid-July to December 2005.
pseudo-panel dataset was compiled.

From this retrospective information, aEach period corresponds to the agricultural

calendar in Bangladesh. However, the data of the dependent variable -- individual
meal frequencies -- was collected only before and during the flood periods.

Also,

since the focus of this paper is to examine rescheduled and non-rescheduled MFImembers, this paper uses only 148 households that were MFI members during the flood
period.

Therefore, the empirical analysis uses a two-period balanced panel dataconsisting of 148 households, of which 58 were rescheduled during the flood. The
household-level observations include 737 observations of individuals over the age of
Chandpur lies downstream of three major rivers so the flood affected this area themost severely of the three. Manikganj is also adjacent to the major rivers, and damage
in this area was also severe. Out of the sample households, more than 60% of thehouseholds were involved in MFIs. Magura is relatively poor compared to the otherstudy areas, but the damage from the flood was moderate since it is far from the majorrivers. More information on the study sites including summary statistics is available
from the author upon request.
9 The attrition is 2.7% and this is mainly because of migration.
88

two years old.
Table 1 presents the changes in consumption and income level through time toillustrate the impact of the 2004 flood.

The monthly labor income during the flood

period was approximately 25% lower than the average of non-flood periods.
Correspondingly, 39% of MFI members rescheduled during the flood period, while
none of the member households were rescheduled during the pre-flood period. The
average duration of rescheduling was 2.72 weeks and the average amount was 490 Taka
(Tk).

Given that the seasonal labor income during the flood period was 9436 Tk, theamount of rescheduling was equal to approximately 5.2% of labor income. 10

The table

also reports that around 20% of people reduced their meal frequency during the periodof the flood.

In particular, working-age females tended to skip meals more often thanother individuals.
Table 2 compares household characteristics during the flood between therescheduled and non-rescheduled members.

The rescheduled members were more

flood-affected and poorer than the non-rescheduled ones in terms of income and asset
holdings. Also, 30% of people reduced meal frequency in the rescheduled households,
while the corresponding statistics for the non-rescheduled households was only 11%.
These distinctions in household and individual characteristics are statisticallysignificant.
Figure 1 depicts the fractions of individuals skipping meals during the flood by age.
Since the dataset includes few observations aged over 50, I mainly discuss individuals
under 50. For both males and females, those around the age of 41 to 45 skipped meals
the most frequently. It also shows that a gender gap does not appear in the young

10

Average monthly labor income is 2359Tk and the flood period includes four months.
9

generation. The gap becomes the most prominent in their late 20’s and early 30’s,
while it then becomes moderate in elder generations again.

3. Estimation Methodology
This section describes the empirical approach measuring the determinants and impactsof rescheduling.

Although rescheduling may impact on households in various ways,

this study focuses on the individual meal frequency. The use of consumption as anindicator of livelihood is straightforward, and the individual meal frequency isparticularly useful in examining the heterogeneous rescheduling impacts within thehousehold.

Another advantage with using this variable is that it alleviates thepossibility of measurement error caused by the recall bias; it is more accurate to recall
the individual meal frequency than the value of individual food consumption (Appleton
and Collier 1995: 557).
Considering the absence of a randomized experiment of rescheduling treatment
during natural disasters, this paper employs the Recursive Bivariate Probit model which
jointly estimates the determinants of rescheduling and the rescheduling impact on
individual meal frequency. 11

More specifically, I estimate the following model:[

ΔCij = 1 ΔRiα + X i β 0 + I ij β1 + u ij > 0
ΔRi = 1[Z i γ 0 + X i γ 1 + ei > 0]

]

(1)
(2)

where ΔCij takes unity if individual j in household i took meals less than three times a
day during the flood period (the second period), while he/she could take three meals

One might be concerned why this study does not use the amount of rescheduling as
the treatment variable. Since higher amount of rescheduling implies higher amount of
past loan disbursement, estimating the effect of 1Tk of rescheduling might include bothrescheduling effect and past loan disbursement effect.
1110

during the pre-flood period (the first period). 12 ΔRi takes unity if household i wasallowed to reschedule at least one saving and/or installment during the flood period; Xidenotes a vector of household characteristics such as asset holdings, flood damage andother control variables; Iij a vector of individual characteristics; Zi instrumental
variables; 13 and finally uij and ei indicate residuals of the equations, which follow

[ ]

[ ]

[

]

E u ij = E [ei ] = 0 , var u ij = var[ei ] = 1 , and cov u ij , ei = ρ , respectively.

The

first-differencing of the endogenous variables – the meal frequency and rescheduling –controls for observable and unobservable fixed effects.
A major issue of this specification is endogeneity caused by the fact that MFI
officers allowed rescheduling mainly for disaster-affected and poor members. If thereis flood damage and/or other time-varying rescheduling determinants that areunobservable to econometricians but observable to MFI officers, it would cause biased
results despite the use of first-differencing.
To address the issue, this study takes two approaches. I first present the list ofself-reported flood damages obtained from an open-response question in Table 3. 14
This process alleviates the omitted flood damage, because it creates a complete list of
the major flood damage the victims suffered. It shows that the main damage includes
income, houses, and other assets such as livestock, but not health conditions. Given
that the damage to income and assets could be endogenous, this study controls for flood
damage by adding the duration of inundation at home in the covariate set X. 15 This12

In other words, Cijt takes unity if he/she has less than three meals a day, andΔC ij = C ijt − C ijt −1 . In the dataset, nobody reported more frequent meals during theflood than the pre-flood period.
13 The covariates X, I, and Z take the values as of the beginning of the flood period.
14 The survey team let respondents freely answer what kinds of damage they suffered.
15 The dataset also includes data of the height of inundation, but I use the duration
because it has higher variance. Low variation of variables can cause the11affects the damage to houses, livestock, and other assets, and these in turn will decrease
income.
I also employ the instrumental variables method in order to mitigate the bias causedby other unobservable determinants of rescheduling. Suitable instrumental variables
must be determinants of rescheduling, and must affect the individual meal frequency
only through the change in the possibility of rescheduling.
For the instrument, this study utilizes the distance -- from the member’s residence tothe MFI meeting place -- and its quadratic term. According to my field interviews,
MFI officers allowed rescheduling mainly for members who had difficulties in attending
the meetings and paying for installments and savings. However, MFIs did not use any
concrete criteria, such as asset holdings, to choose beneficiaries of rescheduling.
When the flood started, head offices of MFIs picked out affected districts as the firststep. At the second step, officers in affected branches visited each member’s residence
and determined whether rescheduling should be applied. However, where the flood
damage was severe and it was dangerous for officers to visit, they abandoned efforts tovisit the members and allowed them to reschedule.
The idea is that the further the meeting place was located from members’ residences,the more difficult it was for members to attend the meetings during the flood because
people were up to their waists in flood water. It was also impossible for the officers tovisit members’ residences in such a situation. This would, therefore, increase thepossibility of rescheduling, but would not affect the livelihoods of households directly.
However, these instrumental variables might be correlated to unobservable poverty
factors and flood intensity, given a possibility that the locations of meeting places are
multicolinearity and make estimation results unstable, particularly in nonlinear
specifications such as the Bivariate Probit model.
12not randomly determined. For instance, Zeller et al. (1999) find that branches of MFIs
tend to be constructed where access to transportation and electricity infrastructure isgood.

If this tendency is applicable to the location choice of member meetings,

members living far away from the meeting place might presumably live where access tothe infrastructure is poor, such as a riverside. Also, such marginal areas might be more
flood-affected, and more importantly, might suffer from poor access to food markets,
causing upward bias. To address the concerns, I include geographical characteristics
of residences, such as the number of markets and self-employed shops in the union
community, distance to rivers, and distance to paved roads into the covariates. 16
The distance to meeting places might also play a role in the screening of MFI
memberships. Members who attend the meetings from far away might be young andphysically healthy enough to walk the long distance, and might not have to stay at home
for housekeeping or childcare. To deal with these potential issues, I also control forthe household head and demographic characteristics. 17
A third potential issue with the instruments is that the distance might be correlated
to characteristics of the particular MFIs. Members commuting a long distance might
be members of small local MFIs that have only a few members, and therefore eachmember has to commute long distances to attend the meetings. Another possibility isthat such members join MFIs whose group size is large. If MFI members form a large
group compared to the number of total members in the village, the total number of

16 Recall that a union is an administration unit in Bangladesh.
Each union includes
multiple villages. Since data on access to markets and self-employed shops is notincluded in my data, I employ the data from the IFPRI survey.
17 Also, one might be concerned that members commuting the long distance might bemore likely to be Hindu and/or male, given that Muslim law prohibits unmarried
women to walk alone. However, this possibility is negligible because most MFI
members in Bangladesh are married women.

13available groups in the village will be small, and therefore each member might have toattend the meetings from far away. These MFI characteristics might be correlated to
the level of social capital which may affect food security in the face of negative shocks.
It is, however, inadequate to address these possibilities by adding MFI groupcharacteristics into the covariates, since these would be endogenously determined byhouseholds. Instead, I compare the average distance to meeting places [1] between
major and local MFIs, and [2] between MFIs forming large and small groups. Table 4reports that neither of them rejects the null that the average distance is the same between
the two groups, implying that this issue is negligible.
Finally, if members attend the meetings from far away during the flood, it mightcause them to be sick, affecting their meal frequency and causing upward bias. Table 3
is helpful in addressing the issue again.

None of the MFI households report anincidence of ill-health/injury as a major shock during the flood. The individual-level
data also report few incidences of ill-health (not reported in tables). This study further
discusses the adequacy of the instrumental variables in the appendix.

Anotherpotential concern regarding this dataset is its small sample size, which is common for
uniquely collected datasets. This study also addresses this concern in the appendix.

4. Estimation Results
Determinants of Rescheduling

Table 5 presents the results of the equation (2) examining the determinants ofrescheduling. The estimation has 629 observations for individuals aged 2 and over. 18

Observations of individuals less than two years of age were dropped, since their meal
frequencies are irregular. Also, some observations are dropped because of the missing
values in the data on the number of markets and self-employed shops.
18

14

The covariates include the valid instruments of distance to the meeting place and its
quadratic term, the duration of inundation, asset holdings, demographics, household
head and geographical characteristics, and district-level fixed effects.

I employhousehold-level cluster-adjusted robust standard errors to address the possible
correlation of residuals within a household, given that the rescheduling is ahousehold-level treatment.
Table 5 shows that members living far away from the meeting place are more likely
to have the opportunity to reschedule, which is consistent with the discussion in Section
3. Individuals living 100m away from the meeting place were 6.6% more likely toreschedule on average.

The bottom row of the table rejects the null that thecoefficients of instrumental variables are jointly zero, rejecting the possibility of weak
instruments. A similar result is shown in the coefficient of distance to paved roads: a
100m increase in the distance raises the probability of rescheduling by 1.54%. 19 The
transportation cost to attend member meetings increases the possibility of rescheduling.
It is also found that rescheduling is targeted to those who have less grain storage.
A 1000 Tk of grain storage holding reduces the probability by 15.5%. 20

Thissignificant and high marginal effect is likely because it directly affects foodconsumption and the subsistence nutrition intake. It therefore is expected to be an
important determinant. However, the coefficient of the duration of inundation at homeand non-land productive assets – such as livestock, rickshaws, and fishing equipments –One might be concerned that the distance to paved roads would be a more important
determinant of rescheduling and a better option of the instruments. However, thedistance to roads could directly affect the meal frequency through, for example, theoccupation choice. Therefore, it is less likely to satisfy the exclusion restriction.
20 The coefficient of grain storage potentially suffers from a possibility of attenuation
caused by recall bias. Unlike the other asset holdings, the amount of grain storage
fluctuates through time and this might cause a recall bias. Therefore, it would bebetter to interpret the marginal effect of grain storage as being at least 15.5%.
19

15

show expected signs but are statistically insignificant.
Finally, the table also shows some counter-intuitive findings. For instance, thecoefficient of owned field is positive and statistically significant, but the significance ismarginal and the estimated marginal effect is only 0.05% per 1000Tk of land holdings.
Also, households with poor access to the food markets do not intensively benefit fromthe rescheduling.

Rescheduling Effects on Individual Meal Frequency

The first column of Table 6 presents the result of equation (1) evaluating the impact ofrescheduling on individual meal frequency. The main finding of this table is that
rescheduling significantly reduces people skipping meals during the flood.

The

repayment burden in the absence of rescheduling increases the probability that people
will skip meals by 5.06 %. It is also found that inundation at home significantly
reduces meal frequency.
The table also shows that physical and human assets help people ensure foodconsumption.

Coefficients of grain, owned field, non-land productive assets, andhead’s education show expected signs and three of them are statistically significant.
However, the results show diversified marginal effects on meal frequency across asset
categories.

As expected, grain storage plays an important role in ensuring foodconsumption. A 1000 Tk of grain storage reduces the probability of skipping meals by
1.11%, while a 1000Tk increase of non-land productive assets reduces the probability
by only 0.25%. Also, the impact of owned field is statistically insignificant. Thesefindings are consistent to Fafchamps et al. (1998) and Kazianga and Udry (2006), whostate that people mainly utilize grain storage to ensure their consumption.

16

The second column focuses on landless households. Since they are poorer andmore vulnerable to risks (Foster 1995; Kurosaki 2006), the demand for rescheduling andthe expected marginal effect should be high. The table confirms that the reschedulingeffect is larger for landless households than for the entire sample. The coefficients ofphysical assets and heads’ education also show the same signs as those of the entire
sample.
Finally, the table shows that the correlations between the residuals of two equations
are statistically insignificant in both columns, rejecting the necessity of using the
instrumental variables.

However, the estimation uses the Wald test to test thesignificance of the correlation. The performance of the Wald test is poor when asmall-sample dataset is used.

Therefore, I mainly discuss the results from theBivariate Probit models. I also report results from the single Probit models in the
appendix.

Heterogeneous Rescheduling Effects across Gender

Given the fact that females skip meals more frequently than males (Table 1 and Figure1), I divide the individual observations according to gender, and estimate therescheduling impact separately. Table 7 shows the heterogeneous rescheduling effectacross gender.

The absence of rescheduling brings the burden of repayment andsignificantly reduces the frequency of meals for both males and females. However,females are more likely to sacrifice meals; the marginal effect on females, -5.46 %, isapproximately 1.6 times as much as that of males, -3.47%. This finding is consistent
with Behrman (1988) and Behrman and Deolalikar (1990). The higher marginal effecton females implies that they might benefit more than males from rescheduling in17

ensuring the subsistence level of food consumption and nutrition-intake.
A similar tendency is found in the coefficients of physical and human assets.
Households with educated head and more physical assets are less likely to reduce themeal frequency, and the marginal effects on females are larger than those on males.
For instance, a 1000Tk of decline in grain storage increases the probability for females
by 1.34%, while it increases the probability for males by only 0.76%. However, thecoefficient of inundation is counter-intuitively significant only in the first column.
Finally, one might be interested in the rescheduling effects on the young generations.
Malnutrition of children in the face of negative shocks is also an important issue indeveloping countries. It can cause persistent damage in the process of human capital
accumulation, compared to shocks in adulthoods. This study, however, does not tackle
the issue because of the limitation of data: the number of observations of children issmall, and estimations using such a dataset do not show a stable result.

5. Conclusion
MFIs in Bangladesh introduced a contingent repayment system beginning in 2002 thatallowed rescheduling of weekly saving and installments during natural disasters.
Since there is no datasets on randomized rescheduling treatment during natural disasters,
I employ the instrumental variable estimation, finding that rescheduling plays the role ofa safety net during negative shocks. In particular, the benefits from rescheduling are
higher for vulnerable households than for the rest of the population, and higher for
females than for males. It is also found that the determinants of rescheduling include
the transportation cost to attend member meetings, and financial ability to pay forsavings and installments.

18

These findings have an implication regarding the poverty reduction effect of MFIs.
The impact of MFI membership could be diverse including average consumption,
investment, and intra-households bargaining power of members. Morduch (1998) andChemin (2008), for instance, show that MFI membership can reduce poverty by
mitigating the fluctuation of consumption through time. While this study does not
examine the impacts of MFI memberships, it is found that the contingent repayment
system could work as a safety net during negative shocks compared to the standard
system imposing the frequent repayments. This alleviates members temporarily facing
further poverty caused by a repayment burden.
Furthermore, the contingent repayments might have the potential to improve theperformance of MFIs in different ways as well. For instance, vulnerable households
are less likely to join MFIs (Amin et al. 2003), but the introduction of the reschedulingsystem may improve the outreach of MFIs because it mitigates the risk for them to facea repayment burden. 21 Also, this may, in turn, improve the sustainability of MFI
management. A significant amount of arrears caused by excessive rescheduling could
obstruct the financial sustainability of MFIs (Norell 2001), but the strictly scheduled
standard repayment structure sometimes burdens members’ livelihoods and may cause
dropout (Hulme 1999). The introduction of the contingent repayments might be able
to improve the management of MFIs depending on this tradeoff. However, this study
does not tackle these questions because of the limitation of data. More work will be
required to examine the issues.
MFIs have undergone dramatic recent changes. However, most previous studies
regarding MFIs examine the earlier system.

This study’s findings suggest thePearlman (2007) also reveals a theoretical possibility that the vulnerable choose notto participate in MFIs, and empirically finds a consistent result.
21

19

importance of further investigations into new structures of MFIs.

Acknowledgements
The author would like to express gratitude to the editor and two anonymous referees of
the journal, Yasuyuki Sawada and Hidehiko Ichimura for helpful and constructive
suggestions. This paper also benefited from the comments of Yutaka Arimoto, Koichi
Fujita, Yuji Horioka, Amy Ickowitz, Hiro Ishise, Takahiro Ito, Hisaki Kono, Takashi
Kurosaki, Craig McIntosh, Sarah Pearlman, Takeshi Sakurai, Bruce Wydick, Chikako
Yamauchi, Junfu Zhang, and seminar participants at the Northeastern Universities
Development Consortium Conference 2008 (NEUDC 2008), the Pacific Conference forDevelopment Economics 2008, the 11th East Asian Economic Association, the Eastern
Economic Association 2007, the Japanese Economic Association, the International
Atlantic Economic Conference 2007, Yokohama National University, Osaka University,and the University of Tokyo (Department of Economics and Department of Agriculture).
The author is also grateful to financial support from Foundation for Advanced Studies
on International Development (FASID).

Any errors and omissions are theresponsibility of the author.

Appendix

A1: Further Discussion of the Instrumental Variables (Table A1)

Given that the instrumental variable (distance to the meeting) is time-invariant, if the
instrument affects the frequency of meals only through the likelihood of rescheduling, itshould be uncorrelated to the meal frequency before the rescheduling treatment. Table20

A1 reports the household characteristics before the flood relative to the distance to themeeting, and confirms that this condition is satisfied.

It also appears that the

instrumental variable is uncorrelated to asset holdings. The significant correlations ofsome characteristics such as the duration of inundation, although the statisticalsignificance is marginal, imply the importance of controlling for them in the covariates.

A2: Sample Selection Bias Caused by the Missing Observations (Table A2)

Since the dataset does not include the data on the number of markets and self-employed
shops, I additionally use the variables collected in the IFPRI survey in 1998. Whileusing the IFPRI data addresses the possibility of an omitted variable bias, someobservations are dropped because of missing values in the variables. This process
might cause a sample selection bias if the estimation omits observations with particularcharacteristics.
Table A2 compares characteristics of households for which I have data on the twovariables to those for which I do not have the data.

The table shows that thedifferences in the instrumental variable, rescheduling, and the frequency of meals are
statistically insignificant between the two groups.

However, only one variable,

distance to rivers, are significantly different.

A3: Robustness Check I: Small Sample Data (Table A3 and A4)

A potential concern regarding this dataset is its small sample size, which is common
for uniquely collected datasets. Estimation results of nonlinear specifications such as
the Bivariate Probit model using a small-sample data could be unstable. To address
this concern, this study takes two approaches. First, I employ the pooled Probit model

21

rather than the first difference as follows;[

C ijt = 1 Rit α + X it β 0 + I ijt β1 + u ijt > 0
Rit = 1[Z it γ 0 + X it γ 1 + eit > 0]

]

(3)
(4)

where Cijt takes the value of one if individual j in household i took meals less than threetimes a day during the period t, and Rit takes the value of one if household i is allowed
to reschedule at least one saving and/or installment during the period t and zerootherwise. This specification doubles the number of observations, while it does notcontrol for the time-invariant unobservables.
Table A3 reports similar results to Table 5 and Table 6. The first column of thetable shows that the households living far away from the meeting place are more likely
to be rescheduled, and rejects the possibility of weak instruments. The second column
reports the significant rescheduling impact on ensuring food consumption of flood
victims. A difference from the main estimation is that the coefficient of inundation issignificant in the first column: the flood affected members were more likely to berescheduled.
In my second approach to addressing the small sample size, I also attempt thefirst-difference linear probability model with an endogenous rescheduling treatment.
Since the estimation methodology is Linear Two Stage Least Squares rather than therelatively data-hungry Bivariate Probit, this approach addresses the concern of small
sample size, while the estimation result is not efficient. Table A4 shows that the result
from the first stage estimation is similar to previous estimations. However, while eachcoefficient of instrument is statistically significant, the joint test of the two coefficients
does not reject the null. Also, the predicted probabilities of more than 20% of theobservations do not range between zero and one in the second column. As a result of
22

these issues, the coefficient of rescheduling in the second stage equation is insignificant.

A4: Robustness Check II: The Single Probit Model (Table A5)

Table 6 shows that the correlation of the residuals, rho, is statistically insignificant.
Therefore, I also estimate the single Probit model assuming that the correlation is zero,and the result is reported in Table A5. The first column shows the determinants ofrescheduling, which is coherent with the result in Table 5. Again, the distance tomeeting place has positive and significant impact on rescheduling.
The second column reports that the absolute value of rescheduling effect is smaller
than the result from Table 6, and the statistical significance is marginal (the p-value is0.142). The result is consistent with the discussion in Section 3: the estimated impactof rescheduling in the single Probit model might be affected by endogeneity that poor
households were more likely to be rescheduled. Since such households are more likely
to skip meals during the flood, this type of endogeneity should cause upward bias in theestimated rescheduling impact. The table indeed shows this expected result. Given
that the result of the single Probit model is biased toward the positive, it should beconsidered as a lower bound of rescheduling impact.

A5: Robustness Check III: Specification of the Instrumental Variables (Table A6)

The main estimation employs the distance to the meeting place (km) and its
quadratic term as instruments. Although this specification addresses the problem ofnonlinearity between the probability of rescheduling and distance to the meeting place,the strong correlation between these two variables can cause the multicolinearity.
Therefore, I also attempt another specification of the instrument: log (Distance to the23meeting place (meters) +1). Table A6 shows a robust result that rescheduling plays animportant role in ensuring the meal frequency.

A6: Robustness Check IV: Outcomes at the Household Level (Table A7 and A8)

Given that rescheduling is a household-level treatment, I also estimate the impact on
two household-level outcomes. The first outcome is a binary variable which takes
unity if at least one of household members reduces the meal frequency compared to thepre-flood period.

The second outcome variable is the value of monthly foodconsumption at the household level. Since it is a continuous variable, I estimate thefollowing specification;
ΔFi = ΔRiα + X i β 0 + u i(5)ΔRi = 1[Z i γ 0 + X i γ 1 + ei > 0](6)where F is the value of monthly food consumption at the household level.
The results from these models are reported at Table A7 and A8. They show similar
results to the main specifications in Table 5 and Table 6. The distance to the meeting
place is a significant determinant of rescheduling, and rescheduling increases foodconsumption in terms of both meal frequency and the value of food consumption.

24

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27

Table 1: Changes in Income and Consumption during the Flood
Jul to Nov 2004
Nov to Jul 2005
Jul to Dec 2005
Jan to Jul 2004
(Flood period)
Mean
S. D.
Mean
S. D.
Mean
S. D.
Mean
S. D.
Monthly labor income (Tk)3123
(4779)
2359(4757)
3044(4761)
3206(5522)
Monthly food consumption (Tk)
2583
(1193)
2438(1184)
2616(1243)
2804(1244)
Dummy =1 if rescheduled at least one
0.000(0.000)0.392(0.490)0.020(0.141)
0.083(0.277)
installment or saving
N (MFI Households)
148
148
148
148
Only for rescheduled members
Amount of rescheduled installments and489.53 (496.78) 311.50 (270.82) 263.00 (180.56)
savings (Tk)
Duration of rescheduling (week)
2.72(1.78)
1.00(0.00)
1.20(0.41)N (Rescheduled MFI Households)
0
57#
2
15
Individual-level observations
Dummy =1 if a male aged 18 and over skips
0.034(0.181)
0.184(0.388)meals
N(Adult Males)
207
207
Dummy =1 if a female aged 18 and over skips
0.032(0.177)0.230(0.422)meals
N (Adult Females)
217
217
Dummy =1 if a boy aged 1 to 17 skips meals
0.018(0.132)0.140(0.348)N(Boys)171
171
Dummy =1 if a girl aged 1 to 17 skips meals
0.028(0.167)0.141(0.349)N(Girls)
142
142
Standard deviations are in parentheses.
#: 58 households were allowed to reschedule during the flood period, but one of them did not remember how long or how much she
rescheduled because she did not have her bankbook with her. Therefore, the statistics represent only 57 rescheduled households that
remembered the duration and the value of rescheduling.

28

Table 2: Characteristics of Rescheduled and Non-Rescheduled Households
Mean
During FloodRescheduled
Non-Rescheduled Difference
Mean
S.D.
Mean
S.D.
Household Characteristics
Monthly labor income (Tk)1624
(1642)
2832(5921)*
2.36(6.97)
0.50(3.52)
Duration of inundation at home*
(Days)
Grain storage (Tk)643
(1125)
2332(3692)***
Owned field (Tk)
72371 (117513) 156603 (312711)
**
#
6091
(8276)
14877(29896)
Non-land productive assets (Tk)***Distance to rivers (km)2.48
(2.62)
1.68(1.66)**
Distance to paved roads (km)0.77
(0.78)
0.58(0.53)The number of markets
1.71(0.83)
1.43(0.85)
*
233
(162)
287
(139)
The number of self-employed*
shops
Males over 161.43(0.75)
1.74(1.01)**
Females over 16
1.55(0.71)
1.70(0.88)
Children under 16
2.05(1.23)
1.97(1.52)
Age of head
46.48
(10.69)48.92(12.20)
Dummy if educated year of
0.64(0.48)
0.60(0.49)
head=0Distance to group meeting (km)0.23
(0.22)
0.19(0.21)
Observations
58
90
Individual Characteristics
Dummy=1 if skips meals
0.30(0.46)
0.11(0.31)
***
Age
25.23
(17.70)27.34(18.82)
Dummy=1 if female
0.49(0.50)
0.48(0.50)
Educated years
2.66(3.17)
3.01(3.41)
Observations
277
460Standard deviations are in parentheses.
*** 1% significant, ** 5% significant, * 10% significant, respectively.
Non-land productive assets include, for example, livestock, rickshaws, sewing machines, andfishing equipment.

29

Table 3: Flood Damages Based on Open-Response Question (Multiple answers)
MFI members
Non members
Frequency Fraction Frequency Fraction
Income
137
77.0%
130
63.7%
House/utensil
4
2.2%
5
2.5%
Other assets
7
3.9%
12
5.9%
Death of household member
0
0.0%
1
0.5%
Injury/sickness of member
0
0.0%
1
0.5%
No damage
Total

30
178

16.9%
100.0%

55
204

27.0%
100.0%

30

Table 4: Distance to Group Meeting and MFI Characteristics
Whether member of major MFIs (Grameen Bank, ASA, BRAC)
Major MFIs
Local MFIs
Mean Difference
p-value
0.220
0.189
0.030
0.144
(0.219)(0.207)
Whether the number of MFI group member is 10 and larger
Large Group
Small Group
Mean Difference
p-value
0.212
0.213-0.0010.960(0.231)(0.211)
Standard deviations are in parentheses.

31

Table 5: Determinants of Rescheduling [Equation (2)]Instrumental Variables
Distance to group meeting (km)Quadratic term
Flood Damage and Assets
Duration of inundation (days)
Grain (Tk x103)
Owned field (Tk x106)
Non-land productive assets (Tk x103)
Geographic Variables
Distance to rivers (km)Distance to paved roads (km)The number of markets
The number of self-employed shops
Demographics
Males over 16
Females over 16Children under 16
Log (age of head)
Dummy if educated year of head=0
District Fixed Effects
Chandpur
MaguraConstant
Observations
H0: Coefficients of IVs are zero

Coef.

S.E.

Marginal
Effect3.65**-3.89*

1.57
2.09

116%
-124%

0.01
-0.49***
1.50*-0.002

0.02
0.18
0.87
0.005

0.18%
-15.50%47.77%-0.07%

0.04
0.48**
0.64***
0.002

0.10
0.19
0.21
0.002

1.27%
15.44%
20.29%
0.07%

0.12
-0.21
-0.18
-0.17
-0.07

0.17
0.17
0.11
0.77
0.30

3.85%
-6.60%-5.74%-5.54%-2.29%

1.42***
-1.00

0.54
0.66

47.98%-28.00%

-1.04
629
5.85*

2.82

Dependent variable takes unity if the household rescheduled during the flood.
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

32

Table 6: Rescheduling Effect on Individual Consumption [Equation (1)]All
Landless
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffect#Dummy=1 if rescheduled-1.11*
0.65-5.06% -1.48*** 0.50 -23.00%
Flood Damage and Assets
Duration of inundation (days) 0.04*** 0.01
0.18%
0.04
0.03
0.71%
Grain (Tk x103)-0.23**
0.11-1.11% -0.34*** 0.10 -5.85%
Owned field (Tk x106)
-0.28
0.76
-1.39%
Non-land productive assets-0.05*** 0.02
-0.25%-0.02
0.02 -0.39%(Tk x103)
Geographic Variables
Distance to rivers (km)
0.03
0.05
0.16%
0.04
0.07
0.72%
Distance to paved roads (km)
0.32*
0.18
1.55%
0.29
0.21
4.92%
The number of markets
-0.11
0.25
-0.56%
0.06
0.30
0.94%
The number of self-employed -0.006* 0.003 -0.03%-0.003 0.004 -0.06%
shops
Demographics
Males over 16
0.15
0.12
0.72%
0.08
0.20
1.41%
Females over 16-0.29**
0.13-1.43%-0.24
0.22 -4.15%
Children under 16
-0.16
0.12
-0.78%-0.16
0.13 -2.78%
Log (age of head)
-0.31
0.64
-1.53%-0.84
0.90 -14.24%
Dummy if educated year of
0.74*** 0.26
3.50%
0.59
0.40
8.94%
head=0District Fixed Effects
Chandpur
0.98
0.67
7.13%
1.74*
1.01 32.46%
Magura
0.85
0.80
5.91%
1.11
0.97 24.12%
Individual Characteristics
Female dummy
0.16
0.13
0.77%
0.18
0.14
3.14%
Log (age)
0.71*** 0.15
3.49%
0.69** 0.34 11.73%
Educated year
0.05*
0.02
0.23%
0.05
0.04
0.86%
Constant
Observations
Rho
H0: Coefficients of IVs are
zero

-0.69
629
0.42
5.85*

2.36
0.35

0.43
322
0.87
5.52*

2.75
0.27

Dependent variable takes unity if the individual took meals less than three times a day during
the flood, while he/she could take three meals during the pre-flood period.
#: Endogenous variable
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

33

Table 7: Heterogeneous Rescheduling Effect across Gender
Males
FemalesMarginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffectDummy=1 if rescheduled#-1.18*
0.63-3.47%-1.18** 0.60 -5.46%
Flood Damage and Assets
Duration of inundation (days)
0.05*** 0.01
0.16%
0.02
0.02
0.11%
3
Grain (Tk x10 )-0.24** 0.10
-0.76%-0.27** 0.13 -1.34%
Owned field (Tk x106)
-1.63
1.04
-5.11%
0.31
0.82
1.52%
Non-land productive assets-0.05** 0.02
-0.16%-0.06*** 0.02 -0.28%(Tk x103)
Geographic Variables
Distance to rivers (km)
0.07
0.06
0.22%
-0.002 0.048 -0.01%
Distance to paved roads (km)
0.37*
0.21
1.16%
0.27
0.18
1.34%
The number of markets
-0.01
0.25
-0.03%-0.09
0.29 -0.42%
The number of self-employed-0.004 0.003-0.01%-0.007* 0.004 -0.04%
shops
Demographics
Males over 16
0.21
0.13
0.66%
0.12
0.15
0.58%
Females over 16-0.28*
0.16-0.88%-0.38** 0.15 -1.84%
Children under 16-0.24*
0.14-0.76%-0.08
0.13 -0.41%
Log (age of head)
-0.77
0.64
-2.42%
0.32
0.76
1.57%
Dummy if educated year of
0.87**
0.37
2.62%
0.63** 0.29
3.01%
head=0District Fixed Effects
Chandpur
0.75
0.73
3.42%
1.14
0.70
8.66%
Magura
0.21
0.80
0.70%
1.50
0.94 14.41%
Individual Characteristics
Log (age)
0.60*** 0.19
1.90%
0.95*** 0.22
4.66%
Educated year
0.02
0.04
0.06%
0.09*** 0.04
0.46%
Constant
Observations
Rho
H0: Coefficients of IVs are
zero

1.08
318
0.48
4.7*

2.59
0.31

-3.37
311
0.56
8.08**

2.56
0.39

Dependent variable takes unity if the individual took meals less than three times a day during theflood, while he/she could take three meals during the pre-flood period.
#: Endogenous variable
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

34

Table A1: Household Characteristics by Distance to the Member Meeting
Distance>=0.2km
Distance<0.2km Mean
Diff.
Mean
S.D.
Mean
S.D.
Pre-Flood Period
Dummy =1 If anybody in the
0.03
0.16
0.06
0.24
household skips meals
Labor income (Tk/month)
3068
2184
3185
6626
Grain
2710
4428
1879
4348
Owned field
119725 231343 128384 285886
Non-land productive assets
10638
13149
10880
31913
Males over 16
1.71
0.96
1.52
0.88
Females over 16
1.70
0.82
1.58
0.81
Children under 16
2.19
1.41
1.78
1.40
*
Log (age of head)
3.86
0.24
3.82
0.26
Dummy if educated year of
0.57
0.50
0.67
0.47
head=0Distance to river (km)
1.80
2.15
2.21
2.08
Distance to paved road (km)
0.58
0.52
0.75
0.76
The number of markets
1.35
0.84
1.73
0.83
**
The number of self-employed
280.00 151.06 249.00 150.15
shops
Flood Period
Dummy =1 If anybody in thehousehold skips meals
Labor income (Tk/month)
Dummy=1 if rescheduled
Duration of inundation (Days)
Observations

0.24

0.43

0.35

0.48

1757
0.42
1.92
79

2888
0.50
6.87

3048
0.36
0.43
69

6200
0.48
1.85

*
*

Standard deviations are in parentheses.
*** 1% significant, ** 5% significant, * 10% significant, respectively.

35

Table A2: Sample Selection by Missing Values of IFPRI data
Observed
Pre-Flood Period
dummy =1 if anybody in thehousehold skips meals
Flood Period
dummy =1 if anybody in thehousehold skips meals
Dummy=1 if rescheduled
Labor income (Tk/month)
Duration of inundation (Days)
Grain
Owned fieldNon-land productive assets
Males over 16
Females over 16Children under 16
Log (age of head)
Dummy if educated year ofhead=0Distance to rivers (km)Distance to paved roads (km)Distance to meeting place (km)Observations

Missing

Mean

S.D.

Mean

S.D.

0.05

0.21

0.00

0.00

0.29

0.46

0.15

0.37

0.37
2486.41.34
1522.611224.6
120916.4
1.63
1.67
2.04
3.83
0.59

0.49
5062.95.53
2935.025020.9270734.4
0.90
0.78
1.42
0.25
0.49

2.13
0.63
0.19
128

2.22
0.65
0.22

Mean
Diff.

0.35
0.49
1543.31709.10.50
2.24
2616.03779.712773.0 18671.0
140725.0 149560.7
1.55
1.15
1.45
1.00
1.75
1.37
3.89
0.24
0.75
0.44
1.06
0.85
0.24
20

0.79
0.59
0.19

**

Standard deviations are in parentheses.
*** 1% significant, ** 5% significant, * 10% significant, respectively.

36

Table A3: Bivariate Probit Model with the Pooled Data [Equation (3) and (4)]Dependent Variables
Rescheduling
Meal Frequency
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffectDummy=1 if rescheduled#-1.04*** 0.17 -4.84%
Instrumental Variables
Distance to group meeting
2.74*** 0.96 47.14%(km)
Quadratic term
-3.07** 1.34 -52.92%
Flood Damage and Assets
Duration of inundation (days) 0.05*** 0.01
0.88%
0.06*** 0.01
0.42%
Grain (Tk x103)-0.29*** 0.08 -4.99% -0.22*** 0.05 -1.61%
Owned field (Tk x106)
0.65
0.46 11.14%-0.69
0.65 -5.16%
Non-land productive assets-0.002 0.003 -0.03% -0.05*** 0.01 -0.40%(Tk x103)
Geographic Variables
Distance to rivers (km)0.004 0.049 0.07%
0.08** 0.04
0.56%
Distance to paved roads (km)0.41*** 0.10
7.01%
0.08
0.11
0.58%
The number of markets
0.46*** 0.14
7.98%
-0.14
0.15 -1.02%
The number of self-employed0.002 0.002 0.04%-0.001 0.002 -0.01%
shops
Demographics
Males over 16
0.08
0.11
1.42%
0.07
0.12
0.54%
Females over 16-0.20*
0.10 -3.43%-0.10
0.11 -0.76%
Children under 16-0.09
0.07 -1.51% -0.16** 0.07 -1.16%
Log (age of head)-0.11
0.47 -1.93%
0.01
0.40
0.10%
Dummy if educated year of-0.10
0.19 -1.65%
0.34*
0.20
2.47%
head=0District Fixed Effects
Chandpur
0.68*** 0.40 13.46%
0.68*
0.35
6.33%
Magura-1.10** 0.48 -15.76%
0.05
0.46
0.40%
Individual Characteristics
Female dummy
0.04
0.07
0.33%
Log (age)
0.30*** 0.09
2.27%
Educated year
0.003 0.012 0.03%
Constant
-1.45
1.76
-1.19
1.52
Rho
0.95*** 0.06
Observations
1235
H0: Coefficients of IVs are
9.22***
zero#: Endogenous variable
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

37

Table A4: First-Differenced Linear Probability Model [Equation (1) and (2)]All
Rescheduling
Meal Frequency
Coef.
S.E.
Coef.
S.E.
#
-0.1010.231
Dummy=1 if rescheduled
Instrumental Variables
Distance to group meeting (km)0.854**
0.403
Quadratic term
-0.974**0.489
Flood Damage and Assets
Duration of inundation (days)
0.006
0.006
0.013***
0.003
Grain (Tk x103)-0.036***0.013-0.0120.010
Owned field (Tk x106)
0.122
0.130
0.024
0.067
Non-land productive assets (Tk x103)-0.0010.001-0.00070.0005
Geographic Variables
Distance to rivers (km)
0.004
0.021
0.004
0.010
Distance to paved roads (km)0.151***
0.051
0.063*
0.038
The number of markets
0.151***
0.055
0.002
0.040
The number of self-employed shops
0.001
0.001
-0.0004
0.0004
Demographics
Males over 16-0.008
0.043
0.006
0.020
Females over 16-0.075*0.039-0.041
0.025
Children under 16-0.0390.026-0.011
0.017
Log (age of head)
0.129
0.208
0.021
0.106
Dummy if educated year of head=0
0.033
0.077
0.068*
0.036
District Fixed Effects
Chandpur
0.394***
0.151
0.206
0.158
Magura-0.180
0.173
0.029
0.098
Individual Characteristics
Female dummy
0.017
0.021
Log (age)
0.088***
0.019
Educated year
0.00040.0041
Constant-0.4260.777-0.1180.397
Observations
629
H0: Coefficients of IVs are zero
2.30
Observations with the predicted581
490
probability between 0 and 1#: Endogenous variable
Cluster-adjusted robust standard errors are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

38

Table A5: The Single Probit Models [Equation (1) and (2)]Dependent Variables
Rescheduling
Meal Frequency
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffectDummy=1 if rescheduled-0.45
0.30 -1.91%
Instrumental Variables
Distance to group meeting
3.67** 1.57
119%
(km)
Quadratic term
-3.93*
2.10-127%
Flood Damage and Assets
Duration of inundation (days)
0.01
0.02
0.18%
0.03*** 0.01
0.16%
Grain (Tk x103)-0.46*** 0.17 -14.99%-0.16
0.10 -0.74%
Owned field (Tk x106)
1.40*
0.81 45.45%-0.44
0.80 -2.01%
Non-land productive assets (Tk-0.002 0.005 -0.08% -0.05*** 0.02 -0.22%
x103)
Geographic Variables
Distance to rivers (km)
0.04
0.10
1.14%
0.03
0.04
0.13%
Distance to paved roads (km)0.48** 0.19 15.55%
0.24
0.17
1.10%
The number of markets
0.62*** 0.21 20.14%-0.23
0.22 -1.08%
The number of self-employed0.002 0.002 0.07% -0.006** 0.003 -0.03%
shops
Demographics
Males over 16
0.11
0.17
3.56%
0.15
0.13
0.68%
Females over 16-0.22
0.17 -7.24%-0.25*
0.13 -1.13%
Children under 16-0.18
0.12 -5.76%-0.14
0.12 -0.65%
Log (age of head)-0.13
0.75 -4.07%-0.42
0.62 -1.95%
Dummy if educated year of-0.04
0.28 -1.33% 0.76*** 0.26
3.40%
head=0District Fixed Effects
Chandpur1.41*** 0.52 48.25%
0.74
0.61
4.59%
Magura-0.96
0.66 -27.55%
1.07
0.73
7.75%
Individual Characteristics
Female dummy
0.17
0.14
0.81%
Log (age)
0.75*** 0.14
3.43%
Educated year
0.05*
0.03
0.22%
Constant
-1.16
2.78
-0.53
2.28
Observations
629
629
Pseudo-R2
0.37450.4222Cluster-adjusted robust standard errors are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

39

Table A6: A Different Specification of the Instrument [Equation (1) and (2)]Dependent Variables
Rescheduling
Meal Frequency
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffectDummy=1 if rescheduled#-1.19*
0.72 -5.64%
Instrumental Variables
Log (Distance (meters) +1)
0.13** 0.06
4.00%
Flood Damage and Assets
Duration of inundation (days)-0.004 0.022 -0.12% 0.04*** 0.01
0.18%
3Grain (Tk x10 )-0.53*** 0.19 -16.68% -0.24** 0.11 -1.20%
Owned field (Tk x106)
1.78*
0.92 56.04%-0.27
0.75 -1.39%
Non-land productive assets (Tk-0.002 0.005 -0.06% -0.05*** 0.02 -0.25%
x103)
Geographic Variables
Distance to rivers (km)
0.05
0.10
1.47%
0.04
0.05
0.18%
Distance to paved roads (km)0.48** 0.19 15.27%
0.32*
0.18
1.62%
The number of markets
0.60*** 0.21 18.95%-0.11
0.26 -0.55%
The number of self-employed0.002 0.002 0.06%-0.005* 0.003 -0.03%
shops
Demographics
Males over 16
0.08
0.17
2.57%
0.15
0.12
0.75%
Females over 16-0.18
0.17 -5.68% -0.29** 0.13 -1.48%
Children under 16-0.19
0.12 -5.87%-0.16
0.12 -0.81%
Log (age of head)-0.27
0.77 -8.60%-0.30
0.63 -1.54%
Dummy if educated year of-0.13
0.29 -4.06% 0.72*** 0.27
3.55%
head=0District Fixed Effects
Chandpur1.36** 0.56 45.79%
0.98
0.68
7.44%
Magura-0.98
0.66 -27.01%
0.83
0.82
5.85%
Individual Characteristics
Female dummy
0.16
0.13
0.81%
Log (age)
0.70*** 0.15
3.57%
Educated year
0.05*
0.02
0.24%
Constant
-0.53
2.80
-0.65
2.37
Rho
0.47
0.40
Observations
629
H0: Coefficients of IVs are zero
5.27**#: Endogenous variable
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

40Methods
Dependent Variables

Table A7: Household Level Outcomes
Bivariate Probit
Regression with Endogenous DummyRescheduling
Meal Frequency
Rescheduling Food Consumption
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
EffectEffect-1.54*** 0.30 -23.80%
298.63*** 107.87

Dummy=1 if rescheduled#
Instrumental Variables
Distance to group meeting (km)
2.64
1.63
87.56%
Quadratic term
-2.23
2.10
-73.96%
Flood Damage and Assets
Duration of inundation (days)-0.0020.024-0.08%
0.10*
0.06
Grain (Tk x103)-0.55***
0.16-18.11% -0.29*** 0.11
Owned field (Tk x106)
1.83**
0.82
60.73%-0.08
0.78
3
Non-land productive assets (Tk x10 )-0.0050.012-0.16% -0.07** 0.03
Geographic Variables
Distance to rivers (km)
0.07
0.07
2.30%
-0.03
0.08
Distance to paved roads (km)
0.40*
0.23
13.26%
0.51** 0.24
The number of markets
0.54***
0.20
17.94%
0.01
0.21
The number of self-employed shops
0.002
0.003
0.06%
-0.006 0.004
Demographics
Males over 16
0.10
0.19
3.41%
0.16
0.28
Females over 16
0.12
0.21
4.01%
-0.29
0.28
Children under 16
-0.14
0.12
-4.64%-0.28
0.17
Log (age of head)
-0.95
0.71
-31.61%0.42
0.89
Dummy if educated year of head=0
-0.23
0.32
-7.56%
0.72*
0.40
District Fixed Effects
Chandpur1.51**
0.60
52.21% 1.90*** 0.72
Magura-0.66
0.65-20.14%1.38
0.93
Constant
1.51
2.73
-0.64
3.04
Rho
1.00*** 1.83E-11
Observations
119
H0: Coefficients of IVs are zero
4.31#: Endogenous variable. Robust standard errors and marginal effects at the mean are reported.
significant, respectively

2.70*
-2.00

1.50
1.66

1.71%
-4.99%-1.39%-1.21%-0.003-0.39**
1.07-0.03*

0.021
0.18
1.36
0.02

0.14
13.24
247.97
1.32

3.28
19.06
245.940.96-0.60%
8.82%
0.09%-0.10%

0.02
0.07
0.54*** 0.20
0.58*** 0.20
0.001 0.003

-2.88
30.98
25.56
-0.04

8.85
37.99
45.35
0.65

2.79%
-4.96%-4.81%
7.35%
11.81%

0.02
0.23
-0.14
-0.62
-0.23

25.21
-74.99*16.50-74.35-103.68*

46.21
40.58
19.02
146.43
59.40

0.24
0.21
0.12
0.81
0.30

46.64%
31.56%

1.41*** 0.52 -204.36** 101.77
-0.34
1.03
-35.46
180.66
0.42
2.80
83.84
495.27-0.77*** 0.14
117
5.65*
*** 1% significant, ** 5% significant, * 10%41

Table A8: Household Level Outcomes: [Instrument = Log (Distance)]
Bivariate Probit
Regression with Endogenous DummyRescheduling
Meal Frequency
Rescheduling
Food Consumption
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
EffectEffect-1.47*** 0.32 -22.28%
284.02*** 110.09

Dummy=1 if rescheduled#
Instrumental Variables
Log (Distance (meters) +1)
0.13**
0.05
4.39%
0.13**
0.05
Flood Damage and Assets
Duration of inundation (days)
-0.01
0.02
-0.22%
0.09*
0.06
1.59%
-0.004
0.021
0.20
3.27
3
Grain (Tk x10 )-0.58***
0.17-19.24% -0.28** 0.12 -4.75% -0.41**
0.20
12.69
19.21
Owned field (Tk x106)
2.03**
0.84
67.08%-0.04
0.79 -0.63%
1.37
1.37
248.79245.67
Non-land productive assets (Tk x103)-0.0040.013-0.14% -0.07** 0.03 -1.26% -0.03**
0.01
1.30
0.96
Geographic Variables
Distance to rivers (km)
0.08
0.08
2.53%
-0.03
0.07 -0.58%
0.02
0.08
-2.78
8.67
Distance to paved roads (km)
0.41*
0.23
13.43%
0.51** 0.24
8.75%
0.59*** 0.21
32.77
38.28
The number of markets
0.54***
0.20
17.96%
0.01
0.22
0.09%
0.58*** 0.20
27.52
46.06
The number of self-employed shops
0.001
0.003
0.03%
-0.005 0.004 -0.09%
0.0004 0.0033
-0.02
0.64
Demographics
Males over 16
0.09
0.19
2.88%
0.18
0.27
3.06%
0.01
0.23
25.50
45.75
Females over 16
0.18
0.21
5.94%
-0.31
0.27 -5.22%
0.24
0.21
-74.92*
40.17
Children under 16
-0.19
0.13
-6.17%-0.23
0.17 -3.95%
-0.16
0.12
16.33
18.90
Log (age of head)
-1.14
0.75
-37.73%
0.47
1.09
7.97%
-0.79
0.82
-75.24144.59Dummy if educated year of head=0
-0.25
0.29
-8.22%
0.72*
0.41 11.62%
-0.24
0.30
-103.90* 59.00
District Fixed Effects
Chandpur
1.40**
0.63
48.65% 1.91*** 0.74 46.57% 1.39*** 0.49
-197.71* 101.31
Magura-0.51
0.65-15.73%1.29
0.92 28.63%
-0.32
1.05
-39.37178.29Constant
2.30
2.98
-1.01
4.08
1.01
2.84
85.15
487.32Rho
1.00*** 3.03E-09-0.75*** 0.16
Observations
119
117
H0: Coefficients of IVs are zero
6.04**
5.35**#: Endogenous variable. Robust standard errors and marginal effects at the mean are reported. *** 1% significant, ** 5% significant, * 10%
significant, respectively
42

Table A9: Determinants of Rescheduling: Sub-Sample Estimations
Landless
Males
Marginal
Marginal
Coef.
S.E.
Coef.
S.E.
EffectEffectInstrumental Variables
Distance to group meeting (km)4.77** 2.39
159%
3.26*
1.67
103%
Quadratic term
-3.80
2.73
-127%
-3.12
2.16
-99%
Flood Damage and Assets
Duration of inundation (days)-0.02
0.03 -0.72%-0.01
0.02 -0.18%
Grain (Tk x103)-0.37** 0.18 -12.37% -0.51*** 0.18 -15.98%
Owned field (Tk x106)
1.50*
0.81 47.23%
3
Non-land productive assets (Tk x10 )0.003 0.006 0.09%-0.001 0.005 -0.04%
Geographic Variables
Distance to rivers (km)-0.09
0.09 -3.12%
0.08
0.09
2.62%
Distance to paved roads (km)0.95*** 0.29 31.79%
0.41*
0.22 13.06%
The number of markets
0.53*
0.29 17.80% 0.63*** 0.22 20.02%
The number of self-employed shops-0.004 0.006 -0.13%
0.002 0.002 0.07%
Demographics
Males over 160.33
0.24 10.92%
0.15
0.17
4.64%
Females over 16-0.86*** 0.32 -28.66%-0.24
0.17 -7.57%
Children under 16-0.10
0.15 -3.39%-0.15
0.11 -4.71%
Log (age of head)-0.55
0.91 -18.52%-0.12
0.77 -3.86%
Dummy if educated year of head=0-0.40
0.48 -13.79%
0.03
0.31
0.79%
District Fixed Effects
Chandpur
1.99*
1.18 60.75%
1.35** 0.56 45.91%
Magura1.25
1.45 43.92%-1.15*
0.68 -31.48%
Constant
1.33
3.31
-1.36
2.92
Observations
322
318
H0: Coefficients of IVs are zero
5.52*
4.7*
Dependent variable takes unity if the household rescheduled.
Cluster-adjusted robust standard errors and marginal effects at the mean are reported.
*** 1% significant, ** 5% significant, * 10% significant, respectively

Females
Coef.

S.E.

Marginal
Effect4.47***-5.40**

1.58
2.16

142%
-171%

0.03
0.02
0.84%
-0.50*** 0.18 -15.88%
1.60*
0.94 50.83%-0.003 0.005 -0.10%
0.002 0.100
0.57*** 0.19
0.66*** 0.22
0.002 0.003

0.06%
17.94%
21.00%
0.06%

0.13
-0.20
-0.21*
-0.24
-0.20

0.17
0.17
0.12
0.80
0.30

4.02%
-6.47%-6.52%-7.67%-6.33%

1.49***
-0.94
-0.74
311
8.08**

0.56
0.69
2.86

49.67%-25.99%43

Probability of Skipping Meals

0.5
0.4
0.3
0.2
0.1
0
2-5

6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61Age
Males

FemalesFigure 1: Probability of Skipping Meals during the 2004 Flood by Age

44