Tsukuba Economics Working Papers
No. 2021-001

Impact of Rural Credit on Household Welfare: Evidence
from a Long-Term Panel in Bangladesh
by
MD. Alamgir Hossain
Abdul Malek Mohammad
andZhengfei Yu

March 2021

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

1

Impact of Rural Credit on Household Welfare: Evidence
from a Long-Term Panel in Bangladesh

Authors1
MD. Alamgir Hossain
Accounts and Budgeting Department, Bangladesh Bank, Dhaka, Bangladesh
Abdul Malek Mohammad2
Graduate School of Humanities and Social Science, University of Tsukuba, Japanand
Zhengfei Yu
Graduate School of Humanities and Social Science, University of Tsukuba, Japan

Abstract
Access to rural credit has long been considered a potential solution to ease liquidity constraints andimprove household welfare in Bangladesh. Earlier studies on rural credit mostly focused on theimpact of microfinance; however, the available results could not provide conclusive findings andfailed to suggest how different sources of credit, namely, banks, microfinance institutes, and informal
channels affect household welfare in the long term. This study aims to evaluate the long-term impact
of different rural credit sources on household welfare indicators. To generate evidence, we use fiveround (1988, 2000, 2004, 2008, and 2014) panel datasets of a nationally representative sample survey.
We use a household-level panel fixed-effect model to estimate the impact on different outcome
indicators. The results suggest that access to rural credit from any source has no significant impact on
1

We thank the Economics Professors specially Hisahiro Naito and Yuko Nakano and
Scholars at Program in Economics and Public Policy (PEPP) at University of Tsukuba, Japanfor their helpful comments during PEPP seminars, and ANM Mahfuzur Rahman, Hasib
Reza and Colleagues at Socioconsultant for answering our queries on data preparation.
Finally, we thank late Dr. Mahabub Hossain for granting access to the dataset.
2

Correspondence: Room No. 8A406, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki-305-8571, Japan.
Email: abdul.malek.ft@u.tsukuba.ac.jp

the increase in the household economic welfare in the long term. However, in the short term, access tobank credit increases the access to rented-in land, improves rice yield, and enhances girls’ school
enrollment among rural households. The impact estimates are found to be consistent across different
model specifications, implying the robust internal validity of the study results.
Key words: Long-term impact, panel data, rural credit sources, rural households, economic welfare,
Bangladesh
JEL Codes: 012, Q14, I31 and C33

1. Introduction
In many developing countries, access to rural credit has long been considered apotential solution to the liquidity constraints of households that fail to develop livelihoods
and improve their welfare (Lin et al., 2019).In Bangladesh, rural households often borrowfrom formal, quasi-formal and informal sources. Formal credit sources include commercial
banks and other formal financial intermediaries in rural areas. Microfinance institutions(MFIs) are considered as a quasi-formal source of credit. Friends, relatives and local money
lenders constitute informal credit facilities. If credit can be accessed through any source; suchas banks, MFIs or individuals, credit-constrained rural households may become involved inincome generating activities (IGAs) and improve household welfare. Formal banking services
are yet to cover most rural households for high operating costs and less profit (de Aghion,
Armendáriz, & Morduch 2007).Since the late 1980s, with the advent of MFIs, such as
Grameen Bank and extended agricultural loan support from agricultural banks, rural people’s
credit demand has been mitigated to a certain extent. However, the importance of money
lenders and other informal sources exists in rural households, even with higher interest rates,
due to their convenient accessibility (Manig, 1996; Mallick, 2012). Microcredit, the basis for
the Nobel Peace Prize in 2006,which is also a great innovation in easing the credit access forthe poor people and a pathway to leave poverty, has been a debatable issue in recent years
3because of its differential impact on various welfare outcomes (Banerjee et al., 2015;
Banerjee, Karlan, & Zinman, 2015).
Rural credit, especially microcredit, has been established to be successful in many parts of theworld. Many researchers and scholars globally have examined the causal effect of
microcredit on the welfare indicators of rural households. However, informal credit and othernon-MFI credit sources have largely been unexplored. Thus far, none of the randomized
experiments has determined any significant positive impact of microcredit on householdincome (Angelucci, Karlan, & Zinman,

2015; Augsburg et al., 2012; Banerjee et al., 2015;

Hossain et al., 2019; Karlan & Zinman, 2011; Tarozzi, Desai, & Johnson, 2013). Somestudies have established significant results on borrowers’ solvency and poverty measures(Imai & Azam, 2012; Khandker, 1998 & 2005; Rui & Xi, 2010; Tedeschi, 2008; Zaman,
1999), while others (Diagne & Zeller,2001; Shaw, 2004) fail to locate any impact on poverty.
The impact of microcredit on various household welfare measurements has also been
overestimated (Banerjee et al., 2015).
The use of long-term panel data for impact evaluation has also been suggested by numerous
researchers (Banerjee et al., 2015; Banerjee, Karlan, & Zinman 2015; Islam 2011; Kabeer
2005; Khandker 2005; LaLonde 1986). Most of the previous microfinance studies wereunable to control for fungibility (Hulme 2000; Khalily 2004; Pitt & Khandker 1998), which
may overestimate the results. The failure to consider other close substitutes of MFIs, such asformal and informal credit, has been regarded as one of the challenges in evaluating
microcredit programs (Banerjee et al. 2015; Banerjee 2013). Owing to the lack of longer-termhousehold-level panel data, there has been no evidence of whether rural credit access has
sustainable welfare impacts. In addition to contributing to the continuing debate on microcredit impact, our objective is to evaluate the impact of different credit sources on household
welfare from a long-term (three decades) perspective.

We use a widely used (Kikkawa & Otsuka 2020,
Balagtasa etal 2014, Hossain,

Kikkawa, Matsumoto & Otsuka 2018,

Rahman & Estudillo 2009, Hossain 2007, Nargis & Hossain

2006) long term panel dataset, recently known as the Mahabub Hossain (MH) panel survey
data (Gautam & Faruqee, 2016). This dataset was earlier collectedby the Bangladesh

Institute of Development Studies (BIDS), International Rice Research Institute (IRRI) andBRAC. The data was collected from a nationally representative sample following a multistage (districts-unions-villages-households) random sampling technique from 62 villages in
Bangladesh. The original sample consisted of 1,231 households in 1988, 1,872 in 2000, 1,927
in 2004, 2,010 in 2008, and 2,846 households in 2014.
We use a household-level panel fixed-effect model to estimate the impacts on different
outcome indicators. The results suggest that access to rural credit from any source has no
significant impact on the increase in household economic welfare in the longer term.
However, in the shortrun, access to bank credit increases access to rented-in land, improves
rice yield, and enhances girls’ school enrollment among rural households. As a by-product ofour study, in line with the theory of dynamic incentives for microfinance borrowers (Shapiro,
2015), we find a long-term association between households and MFIs.The impact estimates
are found to be consistent across different model specifications, implying robust internal
validity of the study results.
The remainder of this paper is organized as follows: We present an overview of the rural
credit market in Bangladesh, followed by a description of the sampling design, data and asummary of the statistics and empirical strategies. Finally, the regression’s results with arobustness check are presented, followed by a conclusion and an indication which can beused for future research.

52. Overview of the Rural Credit Market in Bangladesh
Bangladesh’s rural credit market consists of formal, quasi-formal and informal
borrowing sources. Agricultural banks, such as Bangladesh Krishi Bank (BKB), Rajshahi
Krishi Unnoyan Bank (RKUB), state-owned commercial banks and other private scheduledbanks are the main sources of formal credit. In contrast, microcredit from MFIs is known as aquasi-formal source (Hasan & Malek, 2017). Village people also rely on informal sources,
such as moneylenders, landlords, owners of sharecropped land, businesspersons, relatives,and friends. However, the demand for informal loans declines with the spread of MFIs in thevillage credit market (Berg, Emran & Shilpi, 2013).
At present, 60 scheduled banks, five non-scheduled banks and 34 non-bank financial
institutions operate throughout Bangladesh (Bangladesh Bank Website 2021). According to
the Central Bank, the number of rural bank branches should be a minimum of 50% of thetotal of new branches approved in a given calendar year. Currently, the total number of bankbranches is 10588 of which 5452 branches are in urban areas and 5136 are located in rural
areas. Only 10.30% of the total of formal banking loans and advances have been disbursed
through urban branches (Bangladesh Bank, 2020). Most banking activities in rural areas
focus on savings but not on credit accounts (Islam & Mamun, 2011). A comparison of loan
disbursements by the urban and rural branches of formal banking is demonstrated in Annex
Figure 1. Loans and advances in urban branches have expanded at an increasing rate since
2000, with a sudden increase in 2011, due to an increase in government borrowing from thebanking sector, while total credit disbursement by rural branches observed slow growth. The
lack of a large loan demand, a high operating cost per loan and smaller deposits make rural
branches less profitable and borrowing costs higher for clients, that induce banks toconcentrate only on urban areas (de Aghion et al., 2007).According to Figure 1, borrowing

Bank
80

72%

Percent

60
40

10

31%
21%

19%

69%

56%

50%

50

20

Informal

70%

70

30

MFI

20%

24%
10%

9%

24%

18%
7%

0
1988

2000

2004
Survey year

2008

2014

Figure 1. Trend of credit participation by rural households from different sources, 1988-2014
Source: Author’s calculation from five rounds of MH survey datasets

7

from commercial banks indicates a declining trend, while MFI borrowing demonstrates anincreasing trend over the last 25 years.
In 1959, Akhter Hameed Khan, a social scientist, initiated “Comilla Model” for ruraladvancement which failed, due to inefficient control and lack of a donor fund(Berg, Emran &Shilpi, 2013; Alamgir, 2010).They added that the founder of Grameen Bank Muhammad
Yunus and the late Fazle Hasan Abed of BRAC, the largest nongovernmental organization(NGO), which originated in Bangladesh, learnt some lessons from this failure. In addition,
they adopted a more efficient and centralized means of control and a new credit delivery
channel targeting the poor without collateral. Grameen Bank was recognized as anindependent bank in 1983 and received the Nobel Peace Prize together with its founder in2006 for their contribution to social development and the rural economy. Following theapproach and success of Grameen and BRAC, many other MFIs have evolved over the years
and are associated with diversified social programs to connect to the poor in rural areas.
According to the Microcredit Regulatory Authority (MRA), Bangladesh has more than 30
million borrowers of microcredit, which is the highest number in the world after India. A
total of 783 MRA-registered MFIs with 17120 branches operate mostly in rural areas. The
total microcredit disbursement until 2017 by MFIs, including Grameen Bank, Government
projects, Commercial banks, and other members of MRA, was 1313.67 billion Taka (Annex
Figure 2). Approximately 91% of borrowers in the microfinance sector are women in
Bangladesh and 40% of the total loans are provided for agricultural purposes.
In the earlier days, the urgent credit requirements of rural households were mainly served by
Mohajons (money lenders) and landowners, due to the lack of formal sources of credit. These
Mohajons, also called “usurious monopolists,” charged exorbitant rates of interest for lending
money. The lack of the required collateral for formal credit and convenient accessibility of
informal credit resulted in the latter being sustainable over a period of decades, despitecharging a 100-120% interest rate per annum (Berg,Emran&Shilpi 2013). This form ofborrowing mostly occurs without legal paperwork, is sometimes inaccurate and may have a
fraudulent intension; driving people into absolute poverty commitments that sometimes drive
them into acute poverty (Mudahar & Ahmed, 2010). Our estimation suggests that the share of
informal credit has been reduced over the years in rural areas, with an increase in
microfinance program coverage and spread of loans (See Annex Figure 3).

3. Sampling design, data description and a summary of the statistics
As mentioned earlier, we used a five-round panel dataset (1988, 2000, 2004, 2008 and 2014)
from the Mahabub Hossain (MH) survey. The data were earlier collected by the Bangladesh
Institute of Development (BIDS), the International Rice Research Institute (IRRI), andBRAC. The first round survey was conducted

in 1987-1988 as a component of the“Livelihood Systems in Bangladesh” project. Detailed information on farm and non-farmactivities, borrowing, income and expenditure, poverty, resource ownership and otherhousehold and village-level characteristics are collected.
It is a panel-structured survey covering 62 districts out of 64 districts (in total) in the country.
The sample villages and households are selected based on a multistage random sampling
method using socio-economic indicators of each district (Hossain, Rahman, & Estudillo,
2009; Rahman & Hossain, 1995). During the census period, due to administrative problems,
two villages were dropped, and on average, 153 households` data from each of the remaining
villages were collected. Based on the land tenure and ownership from all households in thevillages, households were classified into four groups (rich, solvent, poor, and ultra-poor) for
stratification following the participatory rural appraisal (PRA) approach. From each of the 64
villages, 20 households were randomly selected in the first round in 1988, resulting in a totalof 1280 households being surveyed.
9The same villages were surveyed again in 2000, 2004, 2008 and 2014 to collect data from theoriginal households and their descendants. Over the years, many households were divided
into multiple new households (e.g., for marriage), and some also permanently migrated. To
address sample attrition and keep the sample representative of the population, some newhouseholds were added in each round. In 2000, a second-round survey was conducted on
1880 households. The third-round survey was conducted in 2004 on 1930 households and in
the fourth-round, the sample comprised of 2010 households. In the final round of the survey(in 2014), the total sample size was 2846, including the households present in the first fourwaves and their offshoots.
To conduct the panel data analyses, we first created a balanced panel dataset of 804 identicalhouseholds. We determined that some households moved to and from other villages. To
control this in the second stage, we drop those households, and finally, we are left with 791
households that are present in all five rounds of the survey. To describe the summary of thestatistics, we use only those 791 consistent households, as depicted in Table 1.
Table 1 indicates that over the study period, the average household size, male headships inhouseholds, land owned by households, working members of households and farm size
decreased. In contrast, the household members’ average age, educational level, migration,
and access to electricity improved gradually from 1988 to 2014. This represents the ruraldevelopment in terms of education, migration, life expectancy and the access to electricity.
Due to the scarcity of the land and the nature of the law, the average land ownership and farm
size will be reduced with the increasing number of households.

Table1. Characteristics of Sample Households, 1988-2014
1988

2000

2004

2008

2014

Household size (No of members)

6.13

5.59

5.36

5.29

4.60

Gender of household head (Male=1,(2.89)
0.95(0.20)(2.52)
0.94(0.22)(2.31)
0.93(0.25)(2.35)
0.88(0.31)(2.01)
0.84(0.36)

41.73

45.90

47.85

49.3

48.1

(13.96)
3.17(3.94)(12.41)
3.76(4.21)(12.75)
3.98(4.41)(13.7)
3.95(4.29)(13.5)
4.55(4.43)

0-16)
Highest education by a member of

4.83(4.27)

6.89(3.92)

7.23(3.90)

7.62(3.67)

8.89(4.41)

householdTotal working member of household

1.71(1.08)

1.58(0.90)

1.67(0.90)

1.68(1.02)

1.32(0.68)

Land owned by household (hector)

0.70(1.04)

0.58(1.02)

0.52(0.87)

0.50(0.88)

0.40(0.73)

Farm size (hector)

0.59(0.97)

0.41(0.68)

0.40(0.76)

0.35(0.60)

0.31(0.55)

Migration (if any member

0.10(0.30)

0.50(1.11)

0.21(0.41)

0.25(0.43)

0.35
(047)

5.45
(3.41)

5.45(3.41)

5.44(3.42)

5.45(3.42)

5.48(3.46)

0.25(0.43)

0.49(0.50)

0.63(0.48)

0.80(0.39)

0.88(0.32)

Female=0)Age of household head (years)
Education of head (schooling years)

migrated=1)
Distance to upazila headquarter fromvillage (kilometer)
Electricity Access (village has

electricity=1)
Note: The numbers in parentheses are standard deviations.

11

The main outcome variables that measured household welfare are presented in Table 2. In
rural areas, households with labor forces and bullocks for cultivation try to access more landto cultivate crops. They can take land as rented-in from others. Rented-in land by households
has increased from 1988 (0.13 hector) to 2014 (0.16 hector) as compared to total land owned
by a household. The total land owned by households declined from 0.70 to 0.40% over thesurvey periods (Table 1). Although land ownership decreased, rented-in land increasedslightly. Because of green revolution technologies in Bangladesh, agricultural productivity
increased exponentially and modern rice variety adoption continued to replace traditionalvarieties. In 1988, 47% of the total rice yield (780 kg per household) used the traditionalvarieties, whereas 93% of the rice production (1,652 kg per household) adopted the modern
variety in 2014.
Household income, which is the main indicator of welfare, has been reported for each of thefive waves. Here, all monetary figures have been adjusted based on the CPI index (base year2010), which is obtained from IMF statistics. From the first round, the average total income
continued to increase through 2014, with a slight decrease in 2000 and 2004, rising to
1,47,821 Taka from 1,20,428 Taka in 1988. The CPI adjusted income amounts were reduced
by 2% in 2000 from 1988 and 5% in 2004 from 2000. The average total income perhousehold indicates an increase of approximately 13% in 2008 from 2004 and 16% in 2014
from 2008. Household income from crop cultivation, total agricultural income, and wage
income indicated a downward trend, while business income and remittance inflows haveincreased over the years.

Table 2. Summary of Main Outcome Variables, 1988-2014

Rented-in land (hector)
Total rice yield (kg)
Traditional variety
Modern variety
Total householdincome(BDT/year)
Crop income
Non-crop income
Wage income
Income from business
Income from agriculture
Remittance inflows

1988
0.13
(0.31)
1,657(3,316)780(1,656)877(2,378)120,428(175,389)

2000
0.13
(0.33)
2,065(5,943)
151
(784)
1,913
(5,503)
118,093(174,429)

2004
0.15
(0.33)
1,732(3,017)
253
(846)
1,478
(2,621)
111,569(144,323)

2008
0.12
(0.30)
1,544(2,829)
156
(922)
1,388
(2,722)
126,929(167,736)

2014
0.16
(0.44)
1,652(2,873)
102
(450)
1,550
(2,829)147,821(175,995)

43,002(69,506)
25,796(37,171)
25,205(87,522)
10,701(34,215)
68,798(80,087)

28,577(63,494)
21,983(36,553)
11,044(20,490)
30,456(1,21,574)
50,560(76,812)30,417(58,851)
14,837(33,616)
13,158(23,440)
21,539(63,587)
51,366(71,039)

33,098(70,211)15,023(33,026)
17,796(28,011)
16,668(57,661)
55,937(82,613)28,354(72,163)
24,685(39,207)
20,034(32,176)
24,958(65,207)
53,040(86,536)

9,801(94,526)

17,460(58,992)

16,699(57,747)

27,992(1,00,659)

40,425(1,18,686)

Poverty status (poor=1, non-

0.59
0.47
0.41
0.53
0.38
(0.49)(0.49)(0.49)(0.49)(0.48)
poor=0)enrollmentrate for boys
School
64.49
90.86
92.39
90.90
96.77
(45.26)(27.06)(25.90)(28.37)(17.72)
Schoolenrollmentrate for girls
56.52
91.96
94.86
96.15
98.09
(6-10 years)(47.36)(26.06)(21.45)(18.46)(13.70)Note:
Numbers in parentheses are standard deviations. All monetary figures are adjusted for(6-10 years)
inflation using a Consumer Price Index (CPI)of 28.66, 53.91, 64.60, 87.73 and 136.13 for 1988,
2000, 2004, 2008 and 2014 respectively (base year=2010).

13

During the survey period, rural poverty was reduced by almost half. A poverty statusindicator is calculated using the absolute poverty line income, as per the FAO norm (Annex
Table 1). Households who are unable to incur the minimum required food (2,110 calories offood per head) and non-food expenses (30% for non-foods) are treated as absolute poor orotherwise non-poor.
School enrollment3for an eligible child (more than five years old children) is a mandatory
pre-requirement to develop an educated society. In rural areas, where acute poverty prevails,
households find it difficult to send their children to school because of financial difficulties
and unawareness. Whether a person attends school is the main determinant of school
enrollment. Of the total number of boys and girls, the percentage of students attending schoolis the rate of enrollment for each gender. The school enrollment rate for boys has increased
from 64.5% to 96.7% over the years. Initially, the girls’ school enrollment rate was less thanthat of the boys; however, this trend was reversed in 2014.

4. Empirical Strategies
To measure the welfare effect, we compare the outcomes between credit takers andsimilar non-credit takers. However, a simple comparison is questionable, due to the nonrandom credit disbursement, self-selection bias, fungibility and other unobservable factorsthat affect credit participation and household welfare (Bao Duong & Izumida, 2002; Islam,2011; Khalily 2004; Khandker & Rashidur, 1999; Khandker, 2005; Pitt & Khandaker, 1996
& 1998; Quach, Mullineux, & Murinde, 2005). The household credit participation decision ishighly influenced by its member’s capacity to repay, entrepreneurship skills, latent abilities,
and some other unobserved behaviors. To address these issues, we use a household-levelc

Enrollment rate = (number of children attend to school/total number of kids in household) × 100To calculate
enrollment variables, only children aging from six to 10 years of age have been considered as school enrollment
rate in this age group.

fixed effect model to control for unobserved factors, such as individual-or village-level
heterogeneity that may be correlated with independent variables.
Our first set of regression equations is as follows, with k=0, 1 and 2, respectively:

Where Wit represents the welfare indicators (for example household income) for household i
at time t and Xit is a vector of the household and village level observed characteristics withthe relevant control variables. In addition,

indicates household i’s credit participationfrom any source in the current period if k = 0, previous period if k=1, or two periods ago if
k=2,

and 𝜶 denote the time fixed effect and household fixed effect. The

coefficientmeasures the effect of any credit access (in the current or previous periods) on

the outcome variables andis the error term.

Robust standard errors were clustered at the household level. Household and village level
attributes Xit contain the total land owned by household (hector), farm size (hector), age ofhousehold head, age squared, gender of the head, education of the head (schooling years),
education square, highest educational attainment from household (schooling years),
household size (number of members), total number of workers in the household, migration
status and access to electricity are used as control variables.
We then conduct a more detailed analysis of the effect of the credit sources by replacingwith three indicators for credit sources: Bank credit, MFI credit and informal credit.
This leads to the second set of regression equations:

where

, and

denote household i’s access to the credit

provided by banks, the IMF, and informal channels. The coefficients
capture the effect of each credit source on the household’s outcome.

15

,

, and

The estimates of regression equations (1) and (2) for the different outcome variables arepresented in Tables 3–7. The outcome variables we consider include rented-land, rice yields,household income, poverty reduction and child school enrollment. In each table, columns (1)to (3) correspond to k=0,1 and 2. Panel A reports the estimates ofPanel B presents the estimates of

,

, and

in regressions (1) andin regressions (2).

5. Regression results and discussion
Impact on rented-in land
Rural credit-constrained households are likely to ease their liquidity problems throughformal or informal borrowing. Therefore, credit access can encourage rural households toengage more in farm activities. In the village area, landless or land-deficient households
require financial capital for the permission to cultivate land by way of obtaining land as
rented-in. Farmers usually pay a fixed amount of rent before growing crops on other land.
Table 3 summarizes the impact of credit access on rented-in land estimated from regressions(1) and (2).Column (1) of Panel (A) indicates that current year access to any credit increases
land obtained as rent by around 17% as compared to non-borrowers. Column (1) of Panel (B)further finds that only credit from the formal banking institutions (increases rented-in land byTable 3. Impact of Credit on Rented-in Land Using Panel Fixed Effects
Main dependent variable:
Log of rented-in land (decimals)

(1)

(2)

(3)

Current credit

1st credit lag

2nd credit lag

Panel A: Credit participation dummy from any source(1=yes, 0=no)
Any credit

R-squared

0.169*-0.210**-0.092(0.092)(0.094)(0.088)

0.122

0.123

0.121

Panel B: Differential impact (credit dummy for each credit source)Bank credit

MFI credit

Informal credit

R-squared

0.544***-0.110-0.159(0.210)(0.186)(0.158)0.081-0.122-0.326***(0.109)(0.117)(0.122)0.208-0.375***0.192*(0.132)(0.129)(0.111)

0.127

0.125

0.127

Note: Household and village characteristics, such as age, age squared, education, education squared
and gender of household head, land owned, household size, farm size, total workers in household,migration status and electricity access are controlled. The results are estimated using the panel data for
791 households. Year and household fixed effects are applied in both panels. Robust standard errors in
the parentheses are clustered at household levels, which are presented in parentheses. ***P<0.01,**P<0.05, *P<0.1.

1754%) is the main catalyst. This is probably because the agricultural loan size fromcommercial banks: BKB and RKUB are usually higher than MFIs and other informal sources.
As a result, credit from sources other than banks may not be sufficient for a household to rent
more cultivable land.
Columns (2) and (3) suggest that MFI and informal borrowers tend to lose their rented-inland after the first and second rounds of the survey. One-period-lagged informal credit has anegative impact (rented-in land reduces by 37.5%) and two-period-lagged MFI borrowers
take less land (32%) from the tenancy market as compared to non-borrowers.
The intervention of agricultural microcredit for tenant farmers increases the rented-in land
under leasing or sharing cropping, albeit not significantly (Hossain et al. 2019). This study
determines similar results for the microcredit impact and additionally discovers that in thelong term, it may have negative impacts. The supply of land is limited and many
unobservable factors, except financial liquidity, play an important role in this case. Thistenancy market influences agricultural production; while contrarily, other forms of inflow(profit, income and remittances) influenced by participation may also contribute to thetenancy market.
Impact on rice yieldTo cultivate rice, the main staple food in Bangladesh, approximately 67% of the total
cultivated area is used (Hossain et al., 2018), accounting for 75% of the total crop production(Talukder & Chile 2014), whose production cost is often mobilized from the rural credit
market. Therefore, we used the farm rice yield as a measure of the agricultural productivity.
The impact of current and lagged credit on the total rice production by a household has been
estimated.

Table 4. Impact of Credit Participation on Rice Yield Using Panel Fixed Effects
Main dependent variable:
Log of total rice yield

(1)

(2)

(3)

Current credit

1st credit lag

2nd credit lag

Panel A: Credit participation dummy from any source(1=yes, 0=no)
Any credit

R-squared

0.077-0.229-0.075(0.146)(0.153)(0.146)

0.119

0.120

0.119

Panel B: Differential impact (credit dummy for each credit source)Bank credit

MFI credit

Informal credit

R-squared

0.614**-0.328-0.202(0.287)(0.319)(0.254)0.178-0.146-0.373*(0.170)(0.185)(0.208)-0.065-0.2500.266(0.212)(0.216)(0.177)

0.121

0.120

0.122

Note: Age, age squared, education, education squared and gender of household head, land owned, household
size, farm size, total workers in household, migration status and electricity access are controlled. The results
are estimated using the panel data for 791 households. Year and household fixed effects are applied in bothpanels. Robust standard errors are clustered at household levels, which are presented in parentheses.
***P<0.01, **P<0.05, *P<0.1.

19

Table 4 summarizes the results of the current (latest) and previous credit access on rice yieldcontrolling household and village characteristics, using household fixed effects and year fixed
effects. Access to any credit does not have any significant impact on households’ rice yield,
not only in the latest period but also in earlier periods.
If the credit access is disaggregated into three main sources, then it is established that thesignificant positive impact of the current credit participation on household rice yield is
associated with only commercial bank credit. Bank credit increases the total rice yield by
61%. This result is consistent with Miah et al. (2006), who studied RKUB and GB loans andnoted that farmers use commercial bank loans more than microcredit for rice production andrice yield increased by 1.21, as compared to non-borrowers. However, in the longer term
(using first and second credit lags), the commercial bank credit impact is not sustained and noother credit source could increase the rice yield in the longer term.
In every season, farmers require financial capital to prepare the land and purchase material
inputs. A convenient agricultural loan facility may assist households in increasing their farmrice yield. From the microcredit literature, we learned that borrowing farmers tend to adopt
modern technology, HYV seeds, improved fertilizers, and other inputs, which could increasetechnical efficiency and productivity. The adoption of new technologies in agriculture andincreased crop yield through credit access has been studied by many scholars worldwide,
including experimental studies (Hossain et al., 2019; Abate et al., 2016; Abdulai & Huffman,
2005; Anang, Bäckman, & Sipiläinen, 2016; Bao Duong & Izumida, 2002; Binam et al.,
2003; Chandio et al., 2018; Croppenstedt, Demeke, & Meschi, 2003; Girabi & Mwakaje,
2013; González, 2014;; Isham, 2002; Javed et al., 2006; Miah, Alam & Rahman, 2006;
Rahman, 2011; Islam, Sumelius, & Bäckman,2012). However, using long-term panel data,
we did not locate any evidence that microcredit increases the farm rice yield.

Our results suggest that current year commercial bank credit increases MV rice yield,
although MFI and informal credit participation do not have any impact on traditional or MV
rice yields. However, we do not note any impact of one lagged credit on rice yield except
MFI credit access (30% increase of the traditional variety rice yield at a five percent
significance level) and log of the loan amount (at a 10% level of significance). If is because
the MFI loan might have been used for alternate purposes (working capital for other crops
and small businesses). In addition, for the longer term, we also do not determine any
consistent impact of the credit amount, which supports an increase in the household rice
yield.
Impact on household income
Most of the rural household income sources are not restricted to one income source
but rather multiple income sources, which helps them to smoothly run the consumption andaddress shocks. The major components of household income are crop income, non-crop farmincome, wage income, business income and remittance income. Therefore, any individual
measurement of an income component is likely to lack accuracy, therefore, it is optimal toconsider all the related income sources. The impact of credit access on different components
of household income using a five-round panel dataset was regressed separately, as shown inAnnex Table 2.
Access to any credit source increases the income from crops and businesses but reduces theremittance earnings for the borrower as compared to non-borrowers, as borrowers engage in
agriculture and self-employment activities instead of migrating. Bank and informal credit do
not have any impact on any of the components of household income, whereas solely MFI
credit increases agricultural (crop and non-crop farm income together) and business income.
However, it is also important to estimate the impact on total household income, as presented
in Table 5.

21

Table 5: Impact of Credit on Total Household Income Using Panel Fixed Effects
Main dependent variable:
Log of total household income

(1)

(2)

(3)

Current credit

1st credit lag

2nd credit lag

Panel A: Credit participation dummy from any source(1=yes, 0=no)
Any credit

R-squared

0.039

0.034

-0.036(0.034)(0.032)(0.034)

0.204

0.204

0.204

Panel B: Differential impact (credit dummy for each credit source)Bank credit

MFI credit

Informal credit

R-squared

0.054

0.038

0.030

(0.065)(0.057)(0.064)

0.058

0.057

-0.045(0.037)(0.038)(0.042)-0.0150.007-0.066*(0.050)(0.048)(0.039)

0.205

0.204

0.205

Note: Age, age squared, education, education squared and gender of household head, land owned,
household size, farm size, total workers in household, highest educational level by any member,migration status and electricity access are controlled.The results are estimated using the panel data for
791 households.Year and household fixed effects are applied in both panels. Robust standard errors
are clustered at household levels which are presented in parentheses. ***P<0.01, **P<0.05, *P<0.1.

Using the fixed effect models (1) and (2), we noted consistent results with previous studies
where no experimental study and most other non-experimental studies failed to locate any
significant impact on the increasing household income. Access to any credit for both thecurrent year and lagged credit does not influence the total income. When the credit impact issegregated into bank, MFI, and informal credit, we also do not establish any evidence thatproves that access to any credit source has an impact on the total household income.
In addition to total household income, analyzing different components of income, we findthat in the short term access to any credit increase crop and business income but reduce
remittance income significantly. Differential impact indicates microcredit as the mainunderlying reason for the rise in agriculture and business income. Surprisingly, these impact
estimates are not consistent and also negative in the long term for second credit lag. Thatmeans though MFI credit can be initially helpful in agriculture and business but in the longer
period this does not sustain. Whereas, wage income for the households who access to bankcredit increase significantly in the long term instead of increasing other components ofincome. This indicates improper utilization of agriculture and SME loans by the rural
households.

Impact on poverty reduction
Access to credit facilities for rural credit-constrained households improves
productivity, smooth income and consumption flows, diversifies other income earning
options, generates self-employment and increases other benefits (Khandker 1998; Morduch
2011; Pitt & Khandker 1998; Robinson 2001). As defined earlier, poverty estimates arecorrelated with household income and expenditure, and this is the ultimate outcome ofincreasing household welfare. Table 6 depicts the regression results of the impact of credit on
poverty using household-level panel fixed effects.

23

Table 6. Impact on Poverty Status of Rural Households Using Fixed Effects
Main dependent variable:

(1)

(2)

(3)

Household’s poverty status

Current credit

1st credit lag

2nd credit lag

Panel A: Credit participation dummy from any source(1=yes, 0=no)
Any credit

R-squared-0.029-0.0210.020(0.024)(0.022)(0.022)

0.104

0.103

0.103

Panel B: Differential impact (credit dummy for each credit source)Bank credit

MFI credit

Informal credit

R-squared-0.024-0.0170.001(0.047)(0.041)(0.039)-0.005-0.0370.005(0.028)(0.029)(0.030)-0.044

0.013

0.043

(0.034)(0.033)(0.028)

0.104

0.104

0.104

Note: Age, age squared, education, education squared and gender of household head, land owned,
household size, farm size, total workers in household, highest educational level by any member,migration status and electricity access are controlled. The results are estimated using the panel data for
791 households. Year and household fixed effects are applied in both panels. Robust standard errors
are clustered at household levels which are presented in parentheses. ***P<0.01, **P<0.05, *P<0.1.

During the survey period (1988-2014) the rural poverty status was reduced from 0.60 to 0.38
where poor is given the value of one and non-poor is given the value of 0. Whether credit
access played any role in reducing poverty during the study period is necessary to beanalyzed. Column one of the fixed effect regression demonstrates that the current year accessto any type of credit reduces household poverty, albeit not significantly. In addition, credit
from banks, MFIs, or informal sources (panel B) do not assist rural households in relieving
them of poverty. In columns two and three, the longer-term impact (first and second creditlags) estimates are presented. Similar to household income, credit access in the longer term
does not reduce poverty significantly, not only for any credit but also for banks, MFIs, andinformal sources. For short-and long-term banks, informal credit is not established tosignificantly reduce poverty.
Our findings are consistent with that of the previous experimental studies on microfinance,
which did not determine any positive impact on poverty reduction (Angelucci, Karlan, &Zinman, 2015; Attanasio et al., 2015; Augsburg et al., 2012; . Banerjee et al., 2015; Crépon etal., 2015; Karlan & Zinman 2011). Diagne & Zeller (2001) & Shaw (2004) also indicatedsimilar results using non-experimental data.
Impact on children’s school enrollment
Children’s school attendance is related to household income, distance to school andmany other factors. Due to government and non-government initiatives regarding compulsory
primary education, there has been a significant improvement in children’s schooling
outcomes for both boys and girls. In particular, the girls’ education scenario changed
substantially over the study period. Table 7 summarizes the results of both girls’ and boys’
school enrolment. Access to any credit increases girls’ school enrolment by around five
percent. Boys’ school enrolment decreased by two percent, although the difference was notstatistically significant.

25

Table 7. Impact on Children School Enrollment Using Fixed Effects
Main dependent variable:
Current credit

2nd credit lagst

1 credit lag

School enrollment rate for boys

(5)
(1)

(2)

(3)

(4)

and girls aged 6-10 years

(6)
Girls

Boys

Girls

Boys

Girls

Boys

Panel A: Credit participation dummy from any source (1=yes, 0=no)
Log of total credit

R-squared

4.909**-2.116-1.919-2.905-2.9050.086(2.460)(4.498)(2.389)(4.526)(2.057)(3.549)

0.088

0.091

0.078

0.093

0.081

0.090

Panel B: Differential impact (credit dummy for each credit source)Bank credit (log)

MFI credit (log)

Informal credit (log)R-squared

10.528*-2.473-3.0193.710-3.4670.530(5.396)(5.418)(2.137)(4.876)(4.290)(3.120)3.410-2.068-1.172-4.317-2.995-3.115(3.228)(5.801)(3.241)(6.157)(3.239)(4.943)5.619-0.556-2.744-4.908-1.402

1.799(3.462)(6.265)(3.070)(7.240)(1.954)(5.158)

0.096

0.091

0.079

0.098

0.081

0.093

Note: Household and village characteristics such as age, age squared, education, education squared andgender of household head, land owned, household size, farm size, total workers in household, highest
educational level by any member, migration status and electricity access are controlled. The results are
estimated using the panel data for 791 households. Year and household fixed effects are applied in bothpanels. Robust standard errors are clustered at household levels which are presented in parentheses.
***P<0.01, **P<0.05, *P<0.1.

After the first or second round of the survey, changes in access to credit did not change
school enrollment significantly for both boys and girls.
However, the differential impact of various credit sources (panel B) indicates that only
borrowing from formal banking institutions influences girls’ school enrollment rate (10%),whereas MFI and other informal credit participation do not. This is consistent with previous
microfinance studies (Banerjee et al., 2015; Morduch, 2011),which did not establish any
significant impact. Moreover, this article includes the additional concepts of longer-term
impact of microfinance, banks and informal credit on children’s school enrollment. Usinghousehold-level fixed effects, we observe that changes in credit access from any source did
not change the enrollment rate for both boys and girls.
Dynamic incentives of microfinanceMicrofinance participation has been increasing since its inception and unlike others,
we do not locate any convincing evidence of increasing household welfare. We studied all
major sources of borrowing and noted that commercial banks and informal borrowers are
unstable in progressive credit taking, while microcredit clients adhere to the same source.
When a borrower does not default on the current loan and adheres to the same source toobtain a large loan in the future, it is known as a dynamic incentive. Shapiro (2015) develops
a new model on dynamic incentives of microfinance, where he determines that the borrower’s
expectation of future loans does not assist with loan repayment, and that it may have anegative effect in the case of double-dipping.
Using the five rounds (1988-2014) MH dataset, we estimated the dynamic incentives of MFI
credit. Whether the household’s previous year credit access from microfinance has any
significant impact on next rounds of borrowing is estimated by using cross-sectional datasetsand the following equation:27

whereindicates access to MFI credit (borrowers=1 and non-borrowers=0) for

household i in village j at survey year k. Here, k represents second to fifth rounds (1988, 2000,
2004, 2008, and 2014). Thus,is the previous round that takes access. If k takes thevalue of 2014 (fifth round), then k-1 is the previous round that is 2008 (fourth round). Xij is avectorofvariables,

householdlevel

observedcharacteristics

with

the

relevant

controlmeasure the effect of the last rounds of MFI credit access on the current MFI

credit-taking decisions andis the error term.

In 1988, only a few households (9% of the borrowing households) participated in the
microcredit program, which is the reason for first-round credit access not providing anadequate rationale for the following round. However, households who participated in the MFI
program in 2000 had 29% more probability than others to borrow credit from any MFI in
2004, 19% in 2008 and 12% in 2014. MFI credit access in 2004 and 2008 resulted in thesame significant outcome for the subsequent rounds. This establishes that the progressive
lending concept that borrowers keep borrowing from the same sources and do not default due
to their expectation of obtaining continuous and larger loans in the future.

Table 8. Impact of Previous Microfinance access on Next Round MFI Credit Access
2000

2004

2008

2014

0.00

-0.02

0.03

0.16

(0.07)(0.08)(0.07)(0.11)

0.29***

0.19***

0.12**(0.05)(0.05)(0.05)

0.34***

0.18***(0.04)(0.05)

Independent variables:
MFI credit in 1988

MFI credit in 2000

MFI credit in 2004

0.23***

MFI credit in 2008

(0.04)

Note: Household and village characteristics are controlled. The results are estimated using thepanel data for 791 households. Robust standard errors are clustered at village levels.
***P<0.01, **P<0.05, *P<0.1.

29

Robustness check
The impact of rural credit participation on household welfare outcomes does notdepend solely on previous credit access. Present credit participation from different sources
may also be a significant cause that should be controlled to identify the segregated impact ofboth present and past estimators. For more robust results, we control for present credit access,where time and household fixed effects are also applied. Using the first credit lag (Annex
Table 3) and second credit lag (Annex Table 4), we find no consistent results demonstrating
the long-term impact of any credit participation that may improve household welfare. Thoughrented-in land and rice yield indicate negative coefficients, the first credit lag is used;however, this does not persist when we use the second credit lag.
In addition, we use an unbalanced panel dataset for all rounds (2,885 observations), with thesame household-level fixed effect estimation (Annex Table 5). The results suggest that theprevious year’s access (second credit lag) to any credit does not have an impact on any of thewelfare indicators. These results are consistent with our main estimation. MFI credit appears
to decrease the household income in the long term; however, the results are not consistent
when different lags of credit are used. Therefore, in the longer term, rural credit access cannot
contribute to the livelihoods of rural households by increasing income, reducing poverty, or
any other improvement in the household welfare indicators.
Moreover, in addition to first and second credit lags, we also use third and fourth credit lags
for the longer term impact analysis. Using third lag, in annex table 6, we see results are
consistent with previous analysis except reduction in rented in land and boys’ school
enrolment rate by informal credit participation. Impact estimates of fourth credit lag (Annex
table 7) also do not confirm long term sustainable impact of different credit sources onhousehold welfare. So in the longer term, rural credit access cannot contribute to livelihoods

of the rural households by increasing income, reducing poverty or any other improvement ofhousehold welfare indicators.

6. Summary and Conclusion
The rural credit market of Bangladesh has changed significantly over the past decadewith the central bank’s directive regarding agricultural loans and rural branches, as well as
microfinance innovation, especially for the poor. We study the impact of various credit
sources, including credit accessibility, for the appropriate formal or informal sources on the
different household welfare indicators. The study attempted to contribute to the ongoingdebate on the impact of microcredit on different outcomes and to estimate the long-term
effect of rural credit access from different sources using a true-panel dataset. A five-roundlongitudinal survey (1988-2014) for a period of 25 years has been used. We apply ahousehold-level panel fixed effect to examine the changes in the household welfare indicators
within households, whose credit participation changes over time.
The results of credit access are inconclusive. Overall, credit access does not have any
significant impact on the total household income, poverty status, boy’s school enrollment andrice yield in the short term. The current year access to bank credit increases households
rented-in land, rice yield and girls’ school enrollment; while microfinance may increase theincome from agriculture and business. Using credit access lags (first and second credit lags),
we estimate the long-term impact, which is the main objective of our study. The previous
round of overall credit access does not increase the rural household welfare in the long term.
When different sources of rural credit are used as explanatory variables, we note that there isno significant evidence to improve the livelihoods of rural households. Finally, microfinancetends to have dynamic incentives for borrowers, as long-term associations have been
determined between households and MFIs.
31

Microfinance impacts were established to be consistent with all previous experimental studies(Angelucci, Karlan, & Zinman 2015; Attanasio et al. 2015; Banerjee et al. 2015; Crépon etal.,2015; Hossain et al., 2019; Karlan & Zinman 2011). It is not feasible to conduct long-term experimental studies on microfinance; however, most of the short-term experimental
studies conducted thus far have not found a significant positive impact on total income,poverty, or consumption. In this context, the optimistic expectation of microfinancesupporters regarding the increase in household welfare has been defined as overvalued by (A.
Banerjee, Karlan, & Zinman 2015).
One important indicator of household economic performance is consumption; however,owing to the lack of consistent and detailed expenditure related data for all survey periods,
this study could not locate an impact on consumption. Moreover, we do not determine a timevarying proper instrument, which is the independent variable, to estimate more robust resultsin the dataset. The time gap between survey periods is not consistent, which may contributeto inaccuracy in the average impact. The longest gap was 12 years between the first andsecond rounds, whereas the smallest was four years.
Identifying the credit demand of rural households and institutional credit innovations tomaximize household welfare is required to be a policy concern and it is crucial to identify theunderlying factors for which credit is unable to contribute to in the long term. Certainly, rural
households generate short-term benefits from credit access; however, they probably do not
reinvest for productive purposes or consume the gains, which may be a potential rationale forthe absence of long-term impact.

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Annex Figure 1. Disbursement of Loans through theRural and Urban Branches, 20002017

Number of bank branches

8000
7000
6000
5000
4000
Rural

3000

Urban

2000
1000
2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0
Year

Source: Banking Regulation and Policy Department, and Statistics Department, Bangladesh Bank.

39

Credit disbursement in billion taka

Annex Figure 2. Microcredit Disbursement by MRA Registered Institutions, 2013-2017
1200

1045.781000
782.67

800
600

634
432.28

462

2013

2014

400
200
0
2015
Year

Source: MRA-MIS Database- 2017

2016

2017

Annex Table 1: Estimates of the Poverty Line from 1988 to 2014
Reference year of survey

Estimated income poverty line (current Taka)

1988

4,609

2000

7,023

2004

8,332

2008

15,1942014

24,522

Note: Poverty line estimation figures are taken from Hossain and Bayes (2015, 2018).

41

Annex Table 2: Impact of Credit on Various Components of Household Income Using
Panel Fixed Effects
(1)

(2)

(3)

(4)

(5)

Crop

Non-crop

Wage

Business

RemittancePanel A. Access to any credit (1=yes, 0=no)
Current credit

1st credit lag

2nd credit lag

0.284**

0.178

0.050

0.825***-0.080**(0.127)(0.117)(0.228)(0.212)(0.036)-0.1330.019-0.085-0.1570.055*(0.132)(0.112)(0.220)(0.230)(0.032)-0.156-0.1500.413**-0.392*-0.026(0.123)(0.108)(0.208)(0.214)(0.034)

Differential impact (credit dummy for each credit source)
Panel B: Bank credit dummy (1=yes, 0=no)
Current credit

1st credit lag

2nd credit lag

0.047-0.084-0.2400.376-0.026(0.214)(0.238)(0.459)(0.460)(0.071)

0.254

0.123

0.003

0.157

0.051

(0.204)(0.186)(0.381)(0.397)(0.047)0.095-0.1750.726**

0.257-0.084(0.165)(0.153)(0.336)(0.406)(0.056)

Panel C: MFI credit dummy (1=yes, 0=no)
Current credit

1st credit lag

2nd credit lag

0.297*

0.232*

0.074

1.250***-0.052(0.156)(0.131)(0.266)(0.261)(0.036)-0.1210.030-0.059

0.019

0.041

(0.165)(0.150)(0.272)(0.288)(0.040)-0.394**-0.1360.193-0.507*0.043(0.182)(0.162)(0.295)(0.283)(0.040)Panel D: Informal credit dummy (1=yes, 0=no)
Current credit

1st credit lag

2nd credit lag

0.238

0.068

0.372

0.014

-0.084(0.179)(0.171)(0.323)(0.280)(0.053)-0.252-0.172-0.231-0.346

0.096*(0.192)(0.162)(0.307)(0.343)(0.050)0.062-0.0230.283-0.438-0.028(0.164)(0.131)(0.251)(0.280)(0.044)

Note: Household and village characteristics are controlled. Year and household fixed effects are applied in bothpanels. Robust standard errors are clustered at household levels which are presented in parentheses. ***P<0.01,**P<0.05, *P<0.1. Number of observations for all panels are 2,373.

43

Annex Table 3. Lagged Credit first lag) Impact on Household Welfare Controlling
Current Credit Access
Main household welfare indicators

(1)

(2)

(3)

(4)

Any credit

Bank

MFI

Informal-0.175*0.050-0.122-0.349**(0.098)(0.188)(0.119)(0.137)-0.322**-0.167-0.237-0.414*(0.1616)(0.345)(0.195)(0.231)

Total household income (log),

0.050

0.056

0.077*

0.002

BDT(0.033)(0.060)(0.039)(0.050)

Poverty status (poor=1, non-poor=0)-0.033-0.027-0.040-0.002(0.023)(0.043)(0.030)(0.034)Girls school enrollment rate (6-10-0.342-0.113-0.205-1.207

years)(2.614)(2.808)(3.543)(3.286)

Boys school enrollment rate (6-10-4.412

4.572-5.551-5.857

years)(4.697)(3.497)(6.479)(7.631)

Rented-in land (log), in decimal

Total rice yield (log), in kilogram

Note: Household and village characteristics are used as control variables in all regressions with year
and household fixed effects. Robust standard errors are clustered at household levels. (***P<0.01,**P<0.05, *P<0.1). No. of observations for all outcomes are 2373 except educational outcome (girls
enrollment-772, boys enrollment 570).

Annex Table 4: Lagged Credit (second Lag) Impact on Household Welfare Controlling
Current Credit Access
Main household welfare

(1)

(2)

(3)

(4)

indicators

Any credit

Bank

MFI

Informal-0.066-0.085-0.318**0.232**(0.090)(0.159)(0.126)(0.112)-0.063-0.104-0.3390.266(0.150)(0.256)(0.216)(0.181)-0.031

0.042-0.032-0.071*(0.034)(0.065)(0.043)(0.039)0.015-0.001

0.004

0.038

(0.022)(0.040)(0.031)(0.028)Girls school enrolment rate (6-10-2.236-1.798-2.905-0.743

years)(2.003)(4.060)(3.318)(1.896)

Boys school enrolment rate (6-10

0.203

0.458

-2.924

1.767

years)(3.494)(3.255)(5.155)(5.143)Rented-in land (log)

Total rice yield (log)

Total household income (log)Poverty status (poor=1, non-poor=0)

Note: Household and village characteristics are used as control variables in all regressions with year andhousehold fixed effects. Robust standard errors are clustered at household levels.(***P<0.01, **P<0.05,*P<0.1). No. of observations for all outcomes are 2373 except for educational outcome (girls enrolment-772,
boys enrolment 570).

45

Annex Table 5: Lagged Credit Impact on Welfare Indicators Using Unbalanced Panel
Rented in

Total rice

land

yield

IncomePoverty

School enrolment rate
Girls
Boys

Panel A. Access to any credit (lagged credit)
Any credit-0.0740.018-0.0390.025-2.631-0.104(0.082)(0.139)(0.032)(0.021)(1.916)(3.404)

Observations

2,885

2,885

2,885

2,885

915

685

R-squared

0.119

0.102

0.206

0.109

0.076

0.084

Panel B: Differential impact (lagged credit dummy for each source )Bank credit-0.144

0.008

0.024

0.006

-3.1530.150(0.147)(0.245)(0.060)(0.038)(3.772)(3.035)-0.30***-0.294-0.071*0.017-2.793-3.107(0.116)(0.196)(0.040)(0.029)(3.115)(4.808)0.170*

0.299*-0.0400.034-1.3461.258(0.101)(0.168)(0.037)(0.026)(1.799)(4.796)

Observations

2,885

2,885

2,885

2,885

915

685

R-squared

0.124

0.105

0.206

0.109

0.076

0.086

MFI credit

Informal credit

Note: Household and village characteristics are used as control variables in all regressions with year andhousehold fixed effects. Robust standard errors are clustered at household levels. (***P<0.01, **P<0.05,*P<0.1). Second lag of credit access is used to see longer term impact.

Annex Table 6: Credit Impact on Welfare Indicators Using Third Credit Lag
Rented in

Total rice

land

yield

IncomePoverty

School enrolment rate
Girls
Boys

Panel A. Access to any credit (lagged credit)
Any credit-0.194*-0.194-0.052

0.045

1.696

-7.090(0.107)(0.191)(0.048)(0.031)(3.103)(8.746)

Observations

1,582

1,582

1,582

1,582

509

353

R-squared

0.318

0.216

0.213

0.142

0.125

0.397

Panel B: Differential impact (lagged credit dummy for each source )Bank credit

0.170

0.009

0.034

0.019

-2.0360.560(0.173)(0.362)(0.108)(0.054)(3.280)(14.708)-0.187-0.458-0.038

0.024

5.560

16.310(0.163)(0.284)(0.065)(0.046)(5.039)(18.307)-0.342**-0.081-0.0370.006-0.310-25.343**(0.137)(0.254)(0.056)(0.039)(3.582)(10.311)

Observations

1,582

1,582

1,582

1,582

509

353

R-squared

0.323

0.218

0.212

0.140

0.134

0.504

MFI credit

Informal credit

Note: Household and village characteristics are used as control variables in all regressions with year andhousehold fixed effects. Robust standard errors are clustered at household levels. (***P<0.01, **P<0.05,*P<0.1). Third lag of credit access is used to see longer term impact.

47

Annex Table 7: Credit Impact on Welfare Indicators Using Fourth Credit Lag
Rented in

Total rice

land

yield

IncomePoverty

School enrolment rate
Girls
Boys

Panel A. Access to any credit (lagged credit)
Any credit

0.108-0.107-0.004-0.028-1.5372.596(0.124)(0.243)(0.058)(0.034)(1.722)(3.082)

Observations

791

791

791

791

262

155

R-squared

0.409

0.257

0.382

0.154

0.118

0.105

Panel B: Differential impact (lagged credit dummy for each source )Bank credit-0.173-0.024-0.126-0.026

1.758

3.644

(0.208)(0.407)(0.097)(0.057)(3.129)(4.972)-0.163

0.842

0.089

-0.066-7.189*3.067(0.309)(0.606)(0.145)(0.085)(3.973)(8.396)0.221*-0.2520.041-0.009-0.6001.761(0.134)(0.263)(0.063)(0.037)(1.840)(3.391)

Observations

791

791

791

791

262

155

R-squared

0.411

0.260

0.384

0.154

0.128

0.108

MFI credit

Informal credit

Note: Household and village characteristics are used as control variables in all regressions. (***P<0.01,**P<0.05, *P<0.1).
analysis.

Fourth lag of credit access is used to see impact after 25 years using cross sectional