EVALUATION OF CONTINGENT REPAYMENTS IN MICROFINANCE: EVIDENCE FROM A NATURAL DISASTER IN BANGLADESH

EVALUATION OF CONTINGENT REPAYMENTS INMICROFINANCE: EVIDENCE FROM A NATURAL

DISASTER IN BANGLADESH

Masahiro SHOJIFaculty of Economics, Seijo University, Tokyo, Japan

First version received January 2011; final version accepted January 2012

Frequent and strictly scheduled repayments and savings in microfinance often deterio-rate the liquidity of members in the face of negative shocks. Previous articles suggestthe introduction of a contingent repayment system that allows such members to berescheduled, but the unavailability of a suitable dataset makes it difficult to examinehow it would actually work. This study is one of the first to evaluate the impact of thisrepayment system on household livelihood. In employing a unique dataset from Bang-ladesh, I show that rescheduling reduces the possibility of binding credit constraints andborrowing from moneylenders, and may also reduce transitory poverty. However, short-term rescheduling has insignificant effects. Indebted members with less liquid assets aremore likely to be rescheduled.

Keywords: Microfinance; Credit constraint; Natural disasters; Propensity score match-ing with multiple treatments; South Asia; BangladeshJEL classification: O16, G21

I. INTRODUCTION

This study evaluates the impact of contingent repayment systems in Micro-finance Institutions (MFIs). A distinction of the standard MFI loans fromother credit sources is the frequent and strictly scheduled repayment struc-

ture. Ashraf, Karlan, and Yin (2006) show that this plays the role of a commitment

The author is grateful for financial support from the Foundation for Advanced Studies on Interna-tional Development. The author would also like to express his gratitude to the editor and twoanonymous referees of the journal; and Yasuyuki Sawada, Hidehiko Ichimura, and Amy Ickowitz forhelpful and constructive suggestions. The paper also benefited from the comments ofYutaka Arimoto,Koichi Fujita, Masayoshi Honma, Hiro Ishise, Takahiro Ito, Hisaki Kono, Sarah Pearlman, TakeshiSakurai, Chikako Yamauchi, Junfu Zhang, and participants at the Eastern Economic Association2007, the International Atlantic Economic Conference 2007, and seminars at Clark University,Tsukuba University, and the University of Tokyo (Department of Economics and Department ofAgriculture). Any errors and omissions are solely the responsibility of the author.

The Developing Economies 50, no. 2 (June 2012): 116–40

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© 2012 The AuthorThe Developing Economies © 2012 Institute of Developing Economies

doi: 10.1111/j.1746-1049.2012.00161.x

device for borrowers. Other studies also investigate the impact of repaymentfrequency on repayment rates.1 On the other hand, the negative impact of frequentrepayment on household livelihood is also found; it potentially deteriorates bor-rowers’ liquidity and causes credit constraints when they are affected by negativeshocks (Zeller et al. 2001). This could cause them to face further poverty. Forexample, Coleman (1999) finds that MFI members would rather borrow frominformal moneylenders than others.

In the face of this trade-off between the pros and cons of frequent repayment,previous articles suggest the introduction of a contingent repayment system thatallows rescheduling of installments and savings only for, for example, disaster-affected members (Ledgerwood 1999; Norell 2001; Meyer 2002; Park and Ren2001). However, few previous studies empirically examine how the system actu-ally works, partially because of a lack of available data; only a few MFIs introducecontingent repayments.

The goal of this paper, therefore, is to use a unique dataset from Bangladesh toexamine whether contingent repayment mitigates the repayment burden and playsthe role of a safety net during negative shocks. MFIs in Bangladesh have beenintroducing the contingent repayment system since 2002 (Dowla and Barua 2006;Meyer 2002), and this paper uses data collected after a nationwide flood in 2004,the first case in which most MFIs in Bangladesh rescheduled installments andsavings. The data report that 39% of MFI members were rescheduled during theflood.

Rescheduling potentially plays the role of a safety net, but its impact is anempirical one: rescheduling does not change the permanent income of beneficia-ries, only the inter-temporal resource allocation. Thus, the treatment effect ofrescheduling is larger for credit-constrained households. Its impact may alsodepend on the duration of rescheduling. Given this, I examine the following threequestions. First, do MFIs allow rescheduling particularly for members sufferingfrom poor liquidity? Second, how much does rescheduling reduce the possibility ofbinding credit constraints? Third, how much does the impact change with theduration of rescheduling?

To preview the results, I find that rescheduling mitigates the possibility of creditconstraint by 20–27 percentage points. However, short-term rescheduling hasinsignificant effects. At least two weeks of rescheduling was required to ensure asignificant impact, but one-third of rescheduling beneficiaries were allowedrescheduling for only one week. Finally, members indebted and holding less liquidassets were more likely to be rescheduled.

1 See, for example, Basu (2010), Bauer, Chytilová, and Morduch (2010), Field and Pande (2008),Kaboski and Townsend (2005), and McIntosh (2008).

contingent repayment in microfinance 117


In a previous study Shoji (2010) also focuses on evidence from the 2004 floodto show that rescheduling helped affected individuals ensure food availabilityduring the disaster. The present study has three distinctions. First, it uncovers howrescheduling plays the role of a safety net, while Shoji’s (2010) study evaluateshow much it does so. Rescheduling can, first of all, improve welfare by mitigatingcredit constraints and helping MFI members smooth consumption. Although thepresent study focuses on this channel, there could be other channels as well.2 Thesecond distinction is that this study pays attention to the changes in the impact withthe duration of rescheduling. These are important because they are directly relatedto policy implications. Third, this study employs the difference-in-differencesmatching estimator of Heckman, Ichimura, and Todd (1997) and the matchingestimator with multiple treatments of Imbens (2000), while Shoji (2010) uses themaximum likelihood model. The matching estimation is preferable for a number ofreasons in this context.

Furthermore, this study attempts to make three original contributions to theliterature. It is one of the first to examine the contingent repayment system in MFIs.Second, it utilizes a direct indicator of credit constraint following Jappelli (1990)and Boucher, Guirkinger, and Trivelli (2009). The use of this indicator addressesconcerns regarding the approximated indicators used in previous studies, such asZeldes (1989) and Foster (1995). Finally, although some studies find an insignifi-cant poverty reduction effect of MFIs based on the standard repayment system(Coleman 1999), the findings in this study imply that the introduction of contingentrepayment may alleviate transitory poverty by reducing credit constraints.3

This paper is organized as follows. The first part of Section II describes thecontingent repayment structure of Bangladeshi MFIs and floods, and the secondpart describes the dataset. In Section III, the empirical methodology is introduced,while Section IV discusses the findings. Section V examines the changes in therescheduling effect with the duration. Finally, Section VI concludes the paper.

II. CONTINGENT REPAYMENT STRUCTURE IN MFIS ANDDATA DESCRIPTION

A. The 2004 Flood and Rescheduling of Installments in MFIs

One feature of standard loans in Bangladeshi MFIs is the frequent and strictlyscheduled repayment system (Khandker 1998; Mondal 2002): once a MFI member

2 For instance, the introduction of contingent repayment may reduce the probability of binding creditconstraints in the future, which in turn decreases savings for precautionary motives.

3 Reduction of transitory poverty is an important policy goal for developing countries. Jalan andRavallion (1996), for example, find that poverty in rural China could be halved if transitory povertyis solved.

118 the developing economies


borrows from his/her MFI, the amount to be repaid is divided into approximately50 to 60 weekly installments. S/he is required to pay tightly scheduled weeklyinstallments beginning soon after the loan disbursement.

Also, members must deposit money into a savings account of their MFIs everyweek, regardless of whether they are indebted, and borrowers who havedefaulted on their loans are excluded from future access to credit (Armendariz deAghion and Morduch 2005). In the standard system, the joint-liability groupsmake the repayment schedule more flexible because borrowers facing repaymentdifficulties can ask other members in the group to cover their repayment(Townsend 2003). This system, however, does not work during covariate shockssuch as natural disasters.

The nature of this standard repayment structure raises the demand for a safetynet available for MFI members during covariate shocks because Bangladesh is aflood-prone country. Its geographical location, deforestation, and subtropicalmonsoon climate cause yearly floods (Khan and Seeley 2005), the severity ofwhich is hard to predict. The flood in 1998, for example, inundated aroundtwo-thirds of the land, negatively affected the economy, and burdened MFImembers with repayment (del Ninno et al. 2001).

Learning from the 1998 flood, MFIs in Bangladesh have been introducing acontingent repayment structure since 2002. This structure allows rescheduling ofweekly savings and installments during disasters without charging additional inter-est. Indebted MFI members are allowed to reschedule both savings and loaninstallments, while those who do not have debt postpone only savings. MFIs havealso switched loan contracts from joint liability to individual lending (Dowla andBarua 2006).

The first nationwide flood since the introduction of the contingent repaymentoccurred in July 2004, inundating 39 out of 64 districts of the country. Since theflood started during the planting of the main crop in the rainy season, it affected theharvest that was expected in December. Consequently, households became worriedabout earning income persistently, even though the floodwater had receded by theend of September.

MFIs postponed collecting weekly savings and debt installments when the floodstarted. Rescheduling was targeted on members who had difficulty in attending themember meetings, and in paying for weekly savings and installments. However,MFIs did not use any concrete criteria, such as asset holdings, to choose benefi-ciaries of rescheduling. Officers in affected branches visited each member’sresidence and determined whether rescheduling should be applied. Where the flooddamage was severe and it was dangerous for officers to visit, they abandonedefforts to visit the members and allowed them to reschedule (Shoji 2010). Thisapproach makes better use of the limited financial resource of MFIs rather thanrescheduling all loans in affected areas, but it requires officers to visit all affected



MFI members during disasters to assess flood damage, which incurs significantadministrative and monitoring costs.

Rescheduling was important for MFI members, particularly at the beginning ofthe flood. The Bangladesh government also initiated the Vulnerable Group Feeding(VGF) and Gratuitous Relief (GR) programs that aimed to provide victims withfood and agricultural inputs, such as seed and fertilizer. However, mostly they werenot implemented until September and October, two months after the floodingbegan.

B. Data Description

This study uses a unique dataset. A key feature of the data is that it includesinformation on rescheduling collected using MFI members’ bankbooks. The use ofbankbooks alleviates the possibility of recall bias, which is common in retrospec-tive surveys. The second distinction is the availability of data on a direct indicatorof credit constraint following Jappelli (1990) and Boucher, Guirkinger, and Trivelli(2009). The use of this indicator addresses concerns regarding the approximatedindicators used in previous studies such as Zeldes (1989) and Foster (1995).

This dataset is a follow-up survey of a dataset of the International Food PolicyResearch Institute (IFPRI) conducted in 1998, 1999, and 2004 that examined the1998 flood (del Ninno et al. 2001). The IFPRI dataset followed a multistagestratified random sampling methodology for seven districts that were selectedaccording to their economic status and the intensity of the flood in their region:Chadpur, Manikganj, Magura, Barisal, Sunamganj, Narsingdi, and Madaripur. Inthe second stage, IFPRI randomly sampled one Thana from each district and threeunions from each of those Thanas.4 In the next stage, about six villages from eachunion and two clusters from each of the villages were randomly selected. Approxi-mately three households from each cluster were chosen depending on the villagesize.

The data in this paper was collected in December 2005 from three out of theseven IFPRI-survey districts based on flood severity, poverty level, geographicalproperties, and the MFIs’ diffusion: Chadpur, Manikganj, and Magura. This surveysucceeded in interviewing 326 out of the 335 households that IFPRI interviewed inthese three districts in 2004.5 In the December 2005 survey, retrospective infor-mation was collected, based on recall, for four subperiods preceding December2005: mid-January to mid-July 2004, mid-July to mid-November 2004 (during theflood), mid-November 2004 to mid-July 2005, and mid-July to December 2005.6

4 Thanas and unions are administrative units in Bangladesh: a union consists of some villages, andeach Thana includes multiple unions.

5 The attrition is 2.7% mainly because of migration.6 Each period corresponds to the agricultural calendar in Bangladesh.



From this retrospective information, a pseudo-panel dataset was compiled. Thispaper uses only observations that include a MFI member in the household. Thequestionnaire was designed to collect data on flood intensity, demographics, laborand nonlabor income, asset holdings, savings, credit constraints, MFI membership,rescheduling, and food consumption.

In this study, the term “credit constraints” refers to the excess demand forconsumption and investment credit with respect to the overall market, includingformal and informal lenders. Rescheduling is expected to reduce the demand forcredit, mitigating the credit constraints. The questionnaire for credit constraints issummarized in Figure 1. Households were defined as facing credit constraintseither if they borrowed money but could not borrow as much as they wanted, or ifthey did not borrow from any sources because of rejection of credit applications,fear of default, or lack of available credit sources. Households were credit uncon-strained when they borrowed the required amount, or when they did not borrowbecause they did not have to. While such a module is desirable, it is not availablein usual household surveys (Scott 2000). Therefore, previous studies use theamount of landholding or the income–assets ratio to approximate the extent ofcredit constraint (Zeldes 1989; Foster 1995). However, it is unlikely that a singlevariable can sufficiently approximate consumers’ access to credit (Garcia, Lusardi,and Ng 1997).

C. Summary Statistics

Table 1 illustrates the change in livelihood of MFI members through the surveyperiods. First, MFI members in the sample did not drop out from their MFIs afterthe flood, implying no incidences of default. The flood did not severely affect thesolvency of the borrowers. Furthermore, the number of MFI members in thesample households increased after the implementation of rescheduling. Second,labor income during the flood was lower by 25% than during nonflood periods.

Fig. 1. Questionnaire Design for Credit Constraint Module

Did you borrow?

Was it enough? Why not?

Yes No

No need Applied but rejected,Fear of rejection,

No available sources

Yes No

Unconstrained Constrained



TAB

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Food consumption also declined but was relatively smooth when compared toincome fluctuation. Third, more than 70% of members faced binding credit con-straints during the flood, and even more were constrained after the disaster. This isprobably because of the persistent impact of the flood: demand for nonfoodexpenditure such as housing repairs may have increased after the flood, althoughthe dataset does not include the information on nonfood consumption. Fourth,people borrowed from moneylenders during the flood more than during otherperiods, while interest-free informal credit was not remarkably high. This presum-ably reflects the unavailability of credit from sources other than moneylendersduring the flood.7

Table 1 also indicates that 39% of MFI members were allowed to reschedulesavings and installments during the flood. In addition, the average duration andamount of rescheduling were 2.72 weeks and Tk 490, respectively. The durationranged from one to eight installments in the sample areas. The amount of resched-uling was approximately 5.2% of labor income, given that the seasonal laborincome during the flood period was Tk 9,436. Finally, only a few householdsrescheduled at the third and fourth periods, probably because some minor MFIsallowed them to do so.

Table 2 compares household characteristics between rescheduled and nonre-scheduled members. I use the observations obtained during the second and fourthperiods when MFIs implemented rescheduling. Only two households wererescheduled at the third period as the result of a religious festival. These memberswere asked to repay and save double at the next meeting. It appears that therescheduled members were more disaster-affected and poorer in terms of assetholdings and income. The differences between the two groups are statisticallysignificant. It is also reported that 91% of rescheduled members experienced abinding credit constraint, which was significantly higher than the correspondingstatistic for the nonrescheduled members. Finally, rescheduled members borrowedfrom moneylenders less than nonrescheduled members, implying that reschedulinghad an impact on decreasing credit or the low creditworthiness of the rescheduled.

III. ECONOMETRIC METHODOLOGY

This section describes the methodology used to estimate the rescheduling impact oncredit constraints and credit from moneylenders. Credit from moneylenders isconsidered an indicator of high demand for and poor access to credit: moneylenders

7 This paper defines credit from moneylenders as credit from informal sources with interest. This isbecause the term Mohajon, which means professional moneylenders in Bengali, also meansinformal credit contracts with interest. According to the classification, the minimum interest rate ofloans from moneylenders is 10% per year, and the average rate is 71.2%.



charge high interest rates and people borrow from them as a last resort when othercredit sources are unavailable (Khan and Seeley 2005; Shoji 2008).

Given the unavailability of the data on a randomized experiment of reschedulingduring natural disasters, I use the difference-in-differences matching estimator(DIDM), a type of the propensity score-matching (PSM) model, to control forendogeneity of rescheduling (Rosenbaum and Rubin 1983; Heckman, Ichimura,and Todd 1998). The DIDM particularly controls for time-invariant factors usingthe prerescheduling period of panel dataset (Heckman, Ichimura, and Todd 1997;Smith and Todd 2005). Another possible approach is the maximum likelihoodestimator (MLE) with the endogenous rescheduling treatment variable. However,I use the PSM estimator for three reasons. First, the PSM requires a weaker setof assumptions. This is important particularly when the outcome variables are

TABLE 2

Summary Statistics by MFIs Membership and Rescheduling Treatment

Rescheduled Nonrescheduled MeanDifference

Labor income (103 Tk/month) 1.61 (1.55) 3.16 (5.55) ***Food consumption (103 Tk/month) 2.18 (0.73) 2.82 (1.33) ***Dummy if binding credit constraints 0.91 (0.29) 0.79 (0.41) ***Loan from moneylenders (Tk/month) 32.00 (172.74) 114.29 (688.95) *Repayment to MFIs (Tk/month) 107.79 (287.99) 202.81 (447.27) **Amount of rescheduling (Tk/month) 110.58 (114.47)Grain storage (103 Tk) 0.58 (1.03) 1.91 (3.74) ***Jewelry (103 Tk) 2.24 (3.36) 6.02 (10.36) ***Livestock (103 Tk) 3.12 (6.15) 8.25 (12.21) ***Owned house (103 Tk) 20.09 (20.31) 31.44 (45.26) ***Owned field (106 Tk) 80.85 (152.40) 141.94 (298.80) **Other productive assets (103 Tk) 2.31 (4.17) 6.42 (23.94) ***Dummy if own house is inundated 0.12 (0.33) 0.01 (0.09) ***Covariate shock indicator 5.66 (2.51) 7.10 (3.51) ***Males over 16 1.69 (1.01) 2.09 (1.21) ***Females over 16 1.84 (0.84) 2.12 (1.11) **Children under 16 2.12 (1.26) 2.14 (1.58)Age of head 46.72 (10.42) 48.48 (12.14)Female head dummy 0.11 (0.31) 0.09 (0.29)Distance to member meeting 0.22 (0.21) 0.22 (0.22)Distance to river 2.32 (2.44) 1.87 (1.93)Distance to school 0.70 (0.62) 0.67 (0.44)Distance to paved road 0.74 (0.75) 0.62 (0.62)Dummy if evacuation shelter is available 0.19 (0.39) 0.11 (0.31)N 75 252

Notes: 1. The observations of the second and fourth periods are used.2. Standard deviations are in parentheses.

***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively.



censored or binary as in this case (Wooldridge 2002). A second concern regardingthe MLE is the incidental parameter problem (Lancaster 2000). There also existtypes of MLEs that control for fixed effect, such as Honoré (1992). However, I donot use the model because of the endogeneity of the rescheduling treatment.Finally, Shoji (2010), which uses the same dataset to evaluate the reschedulingimpact, employs the MLE. Since the small sample size of this data makes theestimation result unstable, it is important to use a different identification strategy aswell to confirm the robustness of the findings.

The goal of the PSM estimators is to quantify the average treatment effectto the treated (ATT); how the outcomes—credit constraint and credit frommoneylenders—of rescheduled members would have changed if they had not beenrescheduled. It compares the outcome of each rescheduled member to memberswho had “similar” characteristics to the rescheduled member but did not resche-dule. PSM employs similarity in terms of the conditional probability of beingrescheduled given various household characteristics.

Define Ri as a dummy variable which takes the value of 1 if observation i had anopportunity of rescheduling and 0 otherwise, and Yir denotes the outcome ofobservation i when the rescheduling regime is r. Therefore, observation i’sobserved outcome, Yi, is described as Yi = RiYi1 + (1 - Ri)Yi0, because Yi = Yi1 forRi = 1 and Yi = Yi0 for Ri = 0, respectively. Given the notations, ATT is defined asE(Y1 - Y0 | R = 1), where E denotes the expectation operator.

Since Yi0 is not observable from the data when Ri = 1 (the counterfactual), thePSM assumes the selection on observables,

Y R X0 ⊥ , (1)

where X denotes time-variant and invariant observable determinants ofrescheduling. This assumption means that the nonrescheduled outcome Y0 isindependent of rescheduling treatment, R, conditional on X. If this assumption isvalid, it implies that there is no omitted variable bias once X is included in theregression. Another assumption to implement PSM is the overlap assumption:

0 1 1< =( ) <Pr .R X (2)

This assumption ensures that for each rescheduled individual there is anothermatched nonrescheduled individual with a similar X. Under these conditions, theestimated propensity score should satisfy:

R X R X⊥ =( )Pr .1 (3)

This is referred as to the balancing score condition and is used to test the validity ofthe estimation specification (Dehejia andWahba 1999, 2002). Finally, these assump-tions provide the following arrangement of ATT (Rosenbaum and Rubin 1983):



E Y Y R E E Y Y R R X RE E Y R R X

1 0 1 0

1

1 1 1 11 1

− ={ } = − = =( )( ) ={ }= = =( )(

| , Pr |, Pr )){

− = =( )( ) = }E Y R R X R0 0 1 1, Pr .(4)

In particular, this study uses the DIDM model. DIDM controls for observableand hard-to-observe time-invariant factors using the data of the prereschedulingperiod. In this study, MFIs rescheduled installments and savings only during theflood period, but members were not allowed to reschedule even in the face of idio-syncratic negative shocks in other seasons. Indeed, Table 1 shows that MFIsrescheduled mainly in the rainy seasons of the second and fourth periods, but onlytwo members had the opportunity to reschedule during the dry seasons of the firstand third periods.8 Given the nature of rescheduling, I consider the first and thirdperiods as pre-treatment periods, and the second and fourth periods as the treat-ment periods. Therefore, the ATT in DIDM estimator is obtained by the followingspecification:

E Y Y R E E Y Y R R X Rt t t t t t t t tΔ Δ Δ Δ1 0 1 01 1 1 1− ={ } = − = =( )( ) ={ }, Pr , (5)

where, DYt ≡ Yt-Yt-1, and t = 2, 4.As Jalan and Ravallion (2003) mention, an important process in conducting the

PSM is the estimation of Pr(Rt = 1 | Xt). MFI officers allowed rescheduling mainlyfor poor and disaster-affected members who had difficulties in attending membermeetings and paying for installments and savings on time.9 Therefore, this studyconsiders the following determinants as the covariates: flood intensity, povertylevel, distance to the meeting place and other geographic characteristics (approxi-mation of access to the meeting place), income correlation among villagers, debt,and other household characteristics.

1. Flood intensityThe 2004 flood caused various losses to households, such as income and assets.

A concern regarding the PSM estimation assuming the selection on observables isthe possibility of bias caused by unobservable determinants of the treatment(selection on unobservables). Unobservable flood damage would bias estimation.To address this possibility, this study reports the list of self-reported flood damagesobtained from the open-response question in Table 3.

This process alleviates the omitted flood damage because it creates a completelist of the major flood damages the victims suffered. It shows that the main lossesincluded income, houses, and other assets such as livestock, but not health

8 As discussed, rescheduling at the third period was due to a religious festival and therefore we donot consider this a reason for rescheduling.

9 The details of the targeting process are described in Section II.



conditions. Given that the damage level of income and assets could be endogenous,this study controls for flood intensity using a binary variable that takes the value of1 if the house was inundated. Inundation at home causes damage to houses,livestock, and other assets, and these in turn decrease income.

2. Poverty levelThe covariates include six types of asset holdings such as grain storage, jewelry,

owned field, livestock, other productive assets, and housing. I do not add incomelevels or income loss caused by the flood because these could be simultaneouslydetermined with rescheduling. Instead, various types of productive assets and floodintensity variables control for them.

3. Geographical characteristics (access to meeting place)The covariates also include geographical characteristics such as the availability

of evacuation shelters and distance to member meeting places, rivers, schools, andpaved roads. The distance to MFI meeting places and paved roads controls for thedifficulty in attending the member meetings during the flood. Also, the distance torivers and the availability of evacuation shelters approximate the flood intensity.

4. Income correlation among villagersWhere income is highly correlated among the villagers, the risk-sharing arrange-

ment within the village does not work, and therefore MFIs might allowrescheduling more intensively. Therefore, I control for a village-level characteris-tic, Ev[vartv (DIncomeitv)], where Income denotes the labor income and index i, t,and v stand for household, period, and village, respectively. The low value of thisindicator implies high-income correlation among villagers and therefore highdemand for rescheduling. To address the possibility that rescheduling treatment

TABLE 3

Flood Damages Based on Open-Response Questions (Multiple Answers)

MFI Members Nonmembers

Frequency Fraction (%) Frequency Fraction (%)

Income 137 77.0 130 63.7House/utensil 4 2.2 5 2.5Other assets 7 3.9 12 5.9Death of household member 0 0.0 1 0.5Injury/sick member 0 0.0 1 0.5No damage 30 16.9 55 27.0Total 178 100.0 204 100.0



and income-earning activities could be determined simultaneously, I generate thisindicator using the data collected by IFPRI in 1998 and 1999 from the samehouseholds as this dataset.

5. Debt and demographic variablesFinally, I include a binary variable that takes the value of 1 if the observation was

indebted from MFIs as of the beginning of the period. I also control for demo-graphic characteristics such as the headcount of males aged over 16, females over16, children under 16, and the age and sex of household head. These variablesapproximate the availability of risk-coping mechanisms as well as the preferenceshifter: households with more working-age males might have a higher ability tosmooth income by increasing labor participation (Kochar 1999). Older householdheads might have higher social capital, implying better access to risk-sharingarrangements (Coate and Ravallion 1993; Kimball 1988; Kocherlakota 1996).Also, it indicates a determinant of rescheduling such as costs associated withattending member meetings; households with many children might not be able toattend member meetings because of time constraints experienced as a result ofhousehold chores and childcare duties.

IV. RESULTS

A. Estimation of the Propensity Score

Table 4 reports the estimation results of the propensity score using the probitmodel.10 I also estimate the Tobit model, whose dependent variable is the durationof rescheduling, as a robustness check. The first column reports that householdswhose homes were inundated were more likely to be rescheduled by 38.4%, butthis is not robust to the inclusion of period and district fixed effects, shown in thesecond and third columns.

A robust finding from Table 4 is that liquid asset holdings were importantdeterminants of rescheduling with grain storage being the most important; thesecond column shows that a Tk 1,000 increase of grain storage reduces theprobability of rescheduling by 2.3%. This high marginal effect of grain storage islikely because it directly affects food consumption and the subsistence nutritionintake. It therefore is expected to be a more important determinant than otherassets. On the contrary, MFIs did not target those with fewer nonliquid assetsexcept for livestock. The coefficient of other productive assets is negative butinsignificant. That of owned field is counter-intuitively positive and significant in

10 Although some studies use nonparametric approaches to estimate the propensity score, theyrequire large-sample data. See Lee (2005) for examples of methodologies to estimate the propen-sity score.



TAB

LE

4

Det

erm

inan

tsof

Res

ched

ulin

gT

reat

men

t:Pr

open

sity

Scor

eE

stim

atio

n

Prob

itPr

obit

Tobi

t

ME

MSt

d.E

rr.

ME

MSt

d.E

rr.

Coe

f.St

d.E

rr.

Dum

my

ifow

nho

use

isin

unda

ted

0.38

4***

(0.2

04)

0.01

8(0

.061

)0.

76(0

.86)

Gra

inst

orag

e(1

03T

k)-0

.025

*(0

.012

)-0

.023

**(0

.009

)-0

.53*

*(0

.23)

Jew

elry

(103

Tk)

-0.0

08**

(0.0

04)

-0.0

05*

(0.0

03)

-0.1

0*(0

.06)

Liv

esto

ck(1

03T

k)-0

.007

***

(0.0

03)

-0.0

04**

*(0

.002

)-0

.09*

*(0

.04)

Ow

ned

hous

e(1

03T

k)0.

001

(0.0

01)

0.00

05(0

.000

5)0.

01(0

.01)

Ow

ned

field

(106

Tk)

0.11

1(0

.083

)0.

100*

(0.0

56)

2.22

(1.3

9)O

ther

prod

uctiv

eas

sets

(103

Tk)

-0.0

02(0

.003

)-0

.000

4(0

.001

7)-0

.03

(0.0

6)C

ovar

iate

shoc

kin

dica

tor

-0.0

03(0

.005

)-0

.005

(0.0

04)

-0.1

2(0

.10)

Mal

esov

er16

-0.0

30*

(0.0

16)

-0.0

18*

(0.0

12)

-0.1

4(0

.24)

Fem

ales

over

16-0

.019

(0.0

20)

-0.0

15(0

.013

)-0

.21

(0.3

0)C

hild

ren

unde

r16

-0.0

18(0

.012

)-0

.006

(0.0

08)

-0.2

0(0

.20)

Age

ofhe

ad0.

006

(0.0

09)

0.00

4(0

.006

)-0

.03

(0.1

4)Sq

uare

dte

rmof

age

-0.0

23(0

.085

)-0

.021

(0.0

56)

0.34

(1.4

1)Fe

mal

ehe

addu

mm

y-0

.047

(0.0

30)

-0.0

23(0

.019

)-0

.48

(0.7

8)D

umm

yif

inde

bted

0.25

1***

(0.0

52)

0.22

4***

(0.0

62)

3.99

***

(0.6

3)D

ista

nce

tom

embe

rm

eetin

g0.

027

(0.0

65)

0.03

6(0

.042

)1.

32(1

.00)

Dis

tanc

eto

rive

r0.

007

(0.0

06)

0.00

4(0

.004

)0.

10(0

.10)

Dis

tanc

eto

scho

ol0.

010

(0.0

30)

0.00

5(0

.018

)-0

.29

(0.4

1)D

ista

nce

topa

ved

road

0.02

2(0

.024

)0.

005

(0.0

16)

0.35

(0.3

6)D

umm

yif

evac

uatio

nsh

elte

ris

avai

labl

e0.

021

(0.0

49)

0.01

7(0

.038

)-0

.13

(0.7

1)Fl

ood

peri

odfix

edef

fect

0.18

4***

(0.0

61)

3.81

***

(0.5

1)C

hand

pur

fixed

effe

ct-0

.019

(0.0

24)

0.43

(0.7

2)M

agur

afix

edef

fect

-0.0

41**

(0.0

22)

-1.5

2**

(0.6

2)C

onst

ant

-3.6

0(3

.53)

Pseu

doR

20.

200.

520.

32N

327

327

324

Not

es:

1.M

EM

stan

dsfo

rM

argi

nal

Eff

ect

atth

eM

ean.

2.T

heob

serv

atio

nsof

the

seco

ndan

dfo

urth

peri

ods

are

used

.3.

Stan

dard

erro

rsar

ein

pare

nthe

ses.

***,

**,a

nd*

repr

esen

tst

atis

tical

sign

ifica

nce

atth

e1%

,5%

,and

10%

leve

ls,r

espe

ctiv

ely.



the second column, but the statistical significance is marginal and not robust. Theestimated marginal effect is smaller than those of the other assets: a Tk 1,000increase in land holdings increases the probability by only 0.01%.

The indicator of income correlation shows expected signs. MFIs allowedrescheduling for those members whose income was correlated to other householdsin the village, and who were therefore likely to suffer from covariate shocks.However, the estimated coefficients are statistically insignificant. Also, householdswith fewer working-age males were more likely to be rescheduled; a working-agemale in the household decreases the probability of rescheduling by 1.8%. It alsoappears that indebted members were more likely to be rescheduled by 22.4%. Debtalso increases the duration of rescheduling by 3.99 weeks. Regarding the geo-graphic characteristics, the coefficient of distance to the MFI meeting place ispositive but statistically insignificant, unlike the result in Shoji (2010). A possiblereason for the difference is that this study employs data from both the intensiveflood period and nonflood period, while Shoji (2010) investigates only the former.During the intensive flood period, the road to the meeting place is inundated.Therefore, MFI members living far away from the meeting place would havedifficulty in attending the meeting, which in turn increases the possibility of beingrescheduled. In the nonflood period, however, the road is still accessible even in theface of negative events. Thus, distance may not be an important determinant ofrescheduling.

Finally, I test the balancing score (equation 3) for the first and second columns.It is found that only the second column satisfies the condition. Conditional on thepropensity score, household characteristics are not significantly different betweenrescheduled and nonrescheduled groups. Therefore, I use the propensity scoreobtained from the second column to implement the matching estimations.

B. Cross Section and DID Matching Estimation

This subsection implements the matching estimation to evaluate the impact ofrescheduling. Table 5 reports results from eight matching models using only obser-vations of the common support.11 I employ four types of matching methodologies:nearest-neighbor matching, Gaussian kernel matching, stratification matching, andthe radius matching method.12 The first to the fourth columns are the results of theDIDM which controls for time-invariant determinants of rescheduling, while thelast four columns present the results without controlling for them.

The DIDM results show that the estimated ATTs on credit constraint are negativeand statistically significant except for the radius matching; the first three columns

11 Estimations without the restriction of common support show qualitatively similar results (notreported but available on request).

12 I divide the propensity score into 20 equal parts in stratification matching.



TAB

LE

5

Est

imat

edR

esch

edul

ing

Eff

ects

onL

ivel

ihoo

ds

Exp

ecte

dSi

gns

DID

MC

ross

-Sec

tion

PSM

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

NN

Ker

nel

Stra

tifica

tion

Rad

ius

NN

Ker

nel

Stra

tifica

tion

Rad

ius

Cre

dit

cons

trai

nts

–-0

.27*

*-0

.24*

*-0

.20*

-0.0

8-0

.08

0.01

-0.0

10.

03(0

.11)

(0.1

1)(0

.12)

(0.0

6)(0

.06)

(0.0

7)(0

.07)

(0.0

5)C

redi

tfr

omm

oney

lend

ers

–-1

81.6

7-1

99.1

9-1

87.7

7**

-93.

70-1

68.0

0*-1

82.0

2-1

67.1

8*-1

03.2

9**

(118

.25)

(128

.84)

(86.

53)

(62.

85)

(95.

42)

(127

.23)

(84.

62)

(44.

32)

N(r

esch

edul

ed/c

ount

erfa

ctua

l)75

/31

75/9

169

/97

75/9

175

/31

75/9

169

/97

75/9

1

Not

es:

1.St

anda

rder

rors

are

inpa

rent

hese

s.T

hest

anda

rder

rors

ofke

rnel

mat

chin

gan

dst

ratifi

catio

nm

atch

ing

are

estim

ated

usin

gth

ebo

otst

rap

met

hod.

The

band

wid

thof

kern

elm

atch

ing

is0.

06.1

50bo

otst

rap

repl

icat

ions

are

cond

ucte

d.2.

The

radi

usis

0.1

inth

efo

urth

and

eigh

thco

lum

ns.

3.In

the

first

toth

efo

urth

colu

mns

,the

depe

nden

tva

riab

les

are

first

-dif

fere

nced

valu

es.

**an

d*

repr

esen

tst

atis

tical

sign

ifica

nce

atth

e5%

and

10%

leve

ls,r

espe

ctiv

ely.



show that rescheduling reduces the possibility of credit constraint by 20 to 27percentage points. In other words, the standard repayment structure, which doesnot allow rescheduling, means borrowers face credit constraints during negativeshocks. The estimated impacts on credit from moneylenders are also consistentwith consumption smoothing behavior, but are not statistically robust.

As described before, MFI members facing binding credit constraints cope withthe repayment burden by borrowing from other credit sources, such as moneylend-ers, who charge high interest rates. Theoretically, since rescheduling can beconsidered an additional loan disbursement from the MFI, this would reduce thedemand for credit from other sources, while it does not directly change the creditsupply from them. Thus, my finding implies that even a small amount of resche-duling can, by mitigating the repayment burden for the MFI, decrease the excessdemand for credit with respect to the overall market, including formal and informallenders.

One might be concerned about the possibility of selection on unobservables;there might be unobservable determinants of rescheduling such as vulnerabilityand poverty. Omitting these variables would potentially bias estimates of resched-uling toward the positive, because rescheduled members who are poorer and morevulnerable are more likely to be credit constrained and borrow under burdensomeconditions.13 On the contrary, the estimated ATTs are negative. Therefore, thepotential bias caused by these unobservable factors would not affect the resultsqualitatively.

To further discuss the robustness of the results, the fifth to the eighth columnsusing the cross-section PSM are reported. These naïve specifications do not controlfor the household fixed effects, and therefore they should be affected by the sampleselection bias more. Indeed it appears that the estimated results are biased towardthe positive, compared to DIDM. This is consistent with the discussion above;controlling for unobservable determinants such as vulnerability and povertyincreases the absolute value impact of rescheduling. Given that some of theseunobservable determinants may also include time-variant components, the pointestimate shows the lower bound (in terms of absolute value) of the actual ATTs.

However, there might be another concern about the use of the pooled databecause, first of all, the composition of observations in terms of time period maynot be the same between the treatment and control groups. In the treatment group23% of observations are from the fourth period (17 out of 75 observations), whilethe corresponding ratio of the counterfactuals used to calculate ATT is 48% inthe case of nearest-neighbor method (15 out of 31 counterfactuals). The second

13 This tendency is shown in the summary statistics in Table 2. Unless controlling for the endogeneityof rescheduling, the rescheduled members are more likely to face credit constraints than nonre-scheduled members.



potential reason is the persistent effect of rescheduling. If rescheduling has anylong-lasting effects, the treatment effect of rescheduling might depend on theexperience of past rescheduling. There are potential channels through which theimpact of rescheduling persists. The first is through the accumulation of human andphysical capital. Therefore, this study controls for these characteristics in theestimation of propensity score. Second, MFI members may reduce savings forprecautionary motives after the introduction of contingent repayment system.Therefore, I estimate the ATT by using the observations from only the secondperiod, because MFIs allowed rescheduling during the period for the first time. Theresult is reported in Appendix Table 1. While the results are marginally significantbecause of a smaller sample size, it is qualitatively comparable.

V. CHANGES IN RESCHEDULING EFFECT BY THE DURATIONOF RESCHEDULING

This section investigates the changes in rescheduling effect by the duration usingthe DIDM with multiple treatments (Imbens 2000).14 Define Wi as the duration ofrescheduling for observation i. I divide the observations into three levels: Wi = 0(not rescheduled), Wi = 1 (rescheduled only for one week), and Wi = 2 (more thanone week).15 The rearranged ATT in this model is described as follows:

E Y Y W w E E Y Y W w P X W wwt t wt tw w

tΔ Δ Δ Δ− ={ } = − = ( )( ) ={ }0 00, , (6)

where

P X W w X W w X W Xw wt t t t

0 0( ) ≡ =( ) =( ) + =( ){ }Pr Pr Pr . (7)

This approach uses a conditional probability of belonging to the rescheduling levelw given a rescheduling level of w or 0. I employ the multinomial logit model toestimate the conditional probability and the result is reported in Appendix Table 2.

Table 6 shows the change in the impact of rescheduling by the duration. I useonly DIDM estimators in this section. First, there is no significant effect ofshort-term rescheduling. With long-term rescheduling, the estimated impacts aregreater than those of short-term rescheduling and are statistically significant. Theestimation for credit from moneylenders also reports a similar result. It is foundthat rescheduling significantly reduces credit from moneylenders when we focus

14 Lee (2005) and Frolich (2004) summarize the literature of program evaluation with multipletreatments. See also Lechner (2002), Lee (2004), and Behrman, Cheng, and Todd (2004) for othermatching estimation methods with multiple treatments.

15 In the 2004 flood, the duration of rescheduling ranged from one to eight weeks depending on theseverity of the flood; most were one or two weeks.



TAB

LE

6

Cha

nge

inR

esch

edul

ing

Eff

ect

with

Res

ched

ulin

gL

evel

Exp

ecte

dSi

gns

NN

Ker

nel

Stra

tifica

tion

Rad

ius

A.

Cre

dit

cons

trai

nts

Shor

t-te

rmre

sche

dulin

g(o

new

eek)

:E

(DY

1t-

DY0t

|W=

1)–

-0.2

4-0

.16

-0.3

3-0

.05

(0.1

5)(0

.17)

(0.2

0)(0

.08)

N(r

esch

edul

ed/c

ount

erfa

ctua

l)25

/15

25/9

319

/53

25/9

3L

ong-

term

resc

hedu

ling

(mor

eth

anon

ew

eek)

:E

(DY

2t-

DY0t

|W=

2)–

-0.3

6**

-0.4

3***

-0.3

9*-0

.15*

*(0

.16)

(0.1

3)(0

.20)

(0.0

6)N

(res

ched

uled

/cou

nter

fact

ual)

47/1

847

/77

34/7

047

/77

B.

Cre

dit

from

mon

eyle

nder

sSh

ort-

term

resc

hedu

ling

(one

wee

k):

E(D

Y1t

-DY

0t|W

=1)

–-1

96.0

0-1

52.4

6-1

52.6

37.

92(1

34.7

2)(1

82.8

9)(1

39.0

5)(7

7.32

)N

(res

ched

uled

/cou

nter

fact

ual)

25/1

525

/93

19/5

325

/93

Lon

g-te

rmre

sche

dulin

g(m

ore

than

one

wee

k):

E(D

Y2t

-DY

0t|W

=2)

–-2

31.3

8*-2

63.8

7-2

83.1

2*-1

46.5

2*(1

33.4

9)(1

81.2

3)(1

89.9

1)(7

8.75

)N

(res

ched

uled

/cou

nter

fact

ual)

47/1

847

/77

34/7

047

/77

Not

es:

1.St

anda

rder

rors

are

inpa

rent

hese

s.T

hest

anda

rder

rors

ofke

rnel

mat

chin

gan

dst

ratifi

catio

nm

atch

ing

are

estim

ated

usin

gth

ebo

otst

rap

met

hod.

The

band

wid

thof

kern

elm

atch

ing

is0.

06.1

50bo

otst

rap

repl

icat

ions

are

cond

ucte

d.2.

The

radi

usis

0.1

inth

efo

urth

colu

mn.

***,

**,a

nd*

repr

esen

tst

atis

tical

sign

ifica

nce

atth

e1%

,5%

,and

10%

leve

ls,r

espe

ctiv

ely.



on the long term, although the average rescheduling effect is statistically insignifi-cant in Table 5. These results imply that at least two weeks of rescheduling wasrequired during the 2004 flood. However, one-third of rescheduling treatment wasonly for one week.

One might be concerned, however, about the possibility of sample selectionbias: household characteristics might be different between rescheduling levels 1and 2. If the duration was determined according to the extent of credit constraint,it will show a similar result, even if the duration does not matter with the resche-duling impact. It might be straightforward to compare E(DY2 - DY1 | W = 1) andE(DY1 - DY0 | W = 1) to address the issue, but it is impossible because of the smallsample size of the dataset. Instead, I test whether the coefficients of the multino-mial logit model are significantly different between the two groups. It does notreject the null that the coefficients are jointly the same (Appendix Table 2), imply-ing that the difference in household characteristics between short-term and long-term rescheduling groups is insignificant.

VI. CONCLUSION

This study examines the targeting accuracy of rescheduling and its consequenceson the liquidity of households. It is found that rescheduling was targeted to poorand indebted members. Also, rescheduling significantly reduces the possibility thatmembers face binding credit constraints and borrow from moneylenders, which inturn may reduce transitory poverty. However, short-term rescheduling has insig-nificant effects.

These findings have important implications for the literature regarding thepoverty reduction effect of MFIs, because previous studies show that binding creditconstraints remarkably decrease the consumption level of households (Deaton1991; Dercon 2005; Fafchamps 2003; Zeldes 1989). Furthermore, it might affectlivelihood persistently (Banerjee et al. 2010; Carter et al. 2007; Dercon 2004;Hoddinott 2006; Quisumbing 2006).

This study’s findings suggest the importance of further investigations into thenew structure of MFIs. These findings must be interpreted with caution, however,since they hinge on the validity of my identification strategy and the small sampledataset.

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APP

EN

DIX

TAB

LE

1

Mat

chin

gE

stim

atio

nw

ithO

nly

the

Seco

ndPe

riod

(Flo

odPe

riod

)O

bser

vatio

ns

Exp

ecte

dSi

gns

DID

M

(1)

(2)

(3)

(4)

NN

Ker

nel

Stra

tifica

tion

Rad

ius

Cre

dit

cons

trai

nts

–-0

.24

-0.2

5-0

.03

-0.2

4***

(0.1

5)(0

.16)

(0.1

9)(0

.09)

Cre

dit

from

mon

eyle

nder

s–

-211

.21

-190

.69

-138

.80

-177

.22*

*(1

36.4

0)(1

70.4

2)(1

35.5

7)(8

3.94

)N

(res

ched

uled

/cou

nter

fact

ual)

58/1

358

/44

22/8

057

/44

Not

es:

1.St

anda

rder

rors

are

inpa

rent

hese

s.T

hest

anda

rder

rors

ofke

rnel

mat

chin

gan

dst

ratifi

catio

nm

atch

ing

are

estim

ated

usin

gth

ebo

otst

rap

met

hod.

The

band

wid

thof

kern

elm

atch

ing

is0.

06.1

50bo

otst

rap

repl

icat

ions

are

cond

ucte

d.2.

The

radi

usis

0.1

inth

efo

urth

colu

mn.

3.T

hede

pend

ent

vari

able

sar

efir

st-d

iffe

renc

edva

lues

.**

*an

d**

repr

esen

tst

atis

tical

sign

ifica

nce

atth

e1%

and

5%le

vels

,res

pect

ivel

y.



APP

EN

DIX

TAB

LE

2

Prop

ensi

tySc

ore

with

Mul

tiple

Tre

atm

ents

(Mul

tinom

ial

Log

itM

odel

Est

imat

ion)

Shor

t-Te

rm(O

neW

eek)

Lon

g-Te

rm(M

ore

than

One

Wee

k)

Coe

f.S.

E.

ME

MC

oef.

S.E

.M

EM

Dum

my

ifow

nho

use

isin

unda

ted

-0.5

2(1

.59)

-0.0

006

0.68

(1.1

7)1.

51G

rain

stor

age

(103

Tk)

-1.0

7**

(0.5

0)-0

.001

4-0

.42

(0.2

6)-0

.67

Jew

elry

(103

Tk)

-0.1

7(0

.11)

-0.0

002

-0.0

7(0

.07)

-0.1

1L

ives

tock

(103

Tk)

-0.0

9*(0

.05)

-0.0

001

-0.0

6(0

.04)

-0.1

0O

wne

dho

use

(103

Tk)

0.00

(0.0

2)-0

.000

004

0.02

*(0

.01)

0.04

Ow

ned

field

(106

Tk)

4.76

**(1

.94)

0.00

640.

88(1

.59)

1.42

Oth

erpr

oduc

tive

asse

ts(1

03T

k)0.

00(0

.08)

-0.0

0000

4-0

.01

(0.0

2)-0

.01

Cov

aria

tesh

ock

indi

cato

r-0

.28

(0.2

1)-0

.000

4-0

.06

(0.1

2)-0

.10

Mal

esov

er16

-0.3

4(0

.30)

-0.0

004

-0.5

7*(0

.32)

-0.9

1Fe

mal

esov

er16

-0.5

1(0

.42)

-0.0

007

-0.1

2(0

.36)

-0.2

0C

hild

ren

unde

r16

-0.2

9(0

.32)

-0.0

004

-0.1

6(0

.26)

-0.2

5A

geof

head

0.16

(0.2

1)0.

0002

0.01

(0.1

7)0.

01Sq

uare

dte

rmof

age

-1.1

4(2

.06)

-0.0

015

0.30

(1.7

0)0.

48Fe

mal

ehe

addu

mm

y-2

.00

(1.3

7)-0

.001

40.

02(0

.94)

0.03

Dum

my

ifin

debt

ed4.

92**

*(1

.21)

0.01

393.

62**

*(0

.70)

8.79

Dis

tanc

eto

mem

ber

mee

ting

1.83

(1.3

0)0.

0025

-0.4

4(1

.32)

-0.7

1D

ista

nce

tori

ver

-0.0

8(0

.22)

-0.0

001

0.19

(0.1

3)0.

31D

ista

nce

tosc

hool

0.05

(0.8

2)0.

0001

0.22

(0.4

7)0.

35D

ista

nce

topa

ved

road

-0.9

7(0

.78)

-0.0

013

0.48

(0.5

2)0.

78D

umm

yif

evac

uatio

nsh

elte

ris

avai

labl

e-4

4.28

(1.9

1E+0

9)-0

.316

10.

56(0

.87)

1.12

Floo

dpe

riod

fixed

effe

ct2.

20**

*(0

.65)

0.00

334.

61**

*(0

.79)

16.9

5C

hand

pur

fixed

effe

ct-1

.25

(1.1

2)-0

.001

4-0

.09

(0.8

9)-0

.14

Mag

ura

fixed

effe

ct-1

.68*

*(0

.82)

-0.0

018

-1.9

2**

(0.8

3)-2

.41

Con

stan

t-5

.60

(5.4

1)-6

.42

(4.3

3)N

324

Not

es:

1.M

EM

stan

dsfo

rM

argi

nal

Eff

ect

atth

eM

ean.

2.H

0:A

llco

effic

ient

sar

ejo

intly

the

sam

ebe

twee

nsh

ort-

term

and

long

-ter

m:

Chi

2(2

3)=

29.6

3(P

-val

ueis

0.16

).3.

The

obse

rvat

ions

ofth

ese

cond

and

four

thpe

riod

sar

eus

ed.

***,

**,a

nd*

repr

esen

tst

atis

tical

sign

ifica

nce

atth

e1%

,5%

,and

10%

leve

ls,r

espe

ctiv

ely.



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