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Rural Credit Survey 2014
Bangladesh, 2014
BANGLADESH BUREAU OF STATISTICS
Identification
idno
BGD-BBS-RCS-2014-v01
Title
Rural Credit Survey 2014
Country
| Name | Country code |
|---|---|
| Bangladesh | BGD |
Abstract
The Bangladesh economy has been shifting to urban concentrating economic activities, while the rural economy is still playing a significant role for poverty reduction and economic development through employment generation and continuous contribution to the economy. Agriculture is a dominant sector of Bangladesh economy, which covers almost all of the rural economic activities and contributing around 17 percent of our Gross Domestic Product (GDP). Around 45% of the labor force of the country are engaged in the agricultural sector (BBS, 2011). Development of agriculture and the people in this sector are intensely interwoven with the food security and employment of the vast population of Bangladesh (Bangladesh Bank, 2014). In this view, the government has set priorities for the agricultural sector to ensure food and nutritional security, poverty alleviation and enhanced employment opportunities. It is noteworthy to mention that the country is enjoying the food sufficiency due to the appropriate policy measures. The government and the people of Bangladesh are striving for sustainable agricultural development with continued efforts. Over 70% of the total population lives in rural areas, most of them are engaged in agriculture and rural economic activities including cottage industries, poultry rearing, animal farming, fishery, milk production, different service activities etc. for their livelihood. The base of the national economy can be safeguarded by ensuring stronger and sustainable rural development.
Moreover, in order to achieve the Millennium Development Goal (MDG)-1, it is important to stress upon the rural development of the country. Rural credit is considered as an important scheme to develop the agriculture and other rural economic sectors in Bangladesh. Increasing and persistent demand of rural credit is creating a scope as well as gap to expand the rural economy. To fill up the gap and scope created through increasing demand of loan, all commercial banks and NGOs along with public and specialized banks have been playing an important role in the disbursement of rural credit during the recent years under various schemes. Different government agencies are also contributing a significant proportion to the disbursement of rural credit.
The first Rural Credit Survey was conducted by Bangladesh Bureau of Statistics (BBS) in 1987. According to the result of Rural Credit Survey 1987, the rural credit market of Bangladesh had been dominated mainly (71.6%) by professional moneylenders (mahajans), landowners, friends, relatives and others (BBS, 1989). Some selected key findings have been shown in Annex-VII. From the results of above mentioned survey, it is observed that small-scale farmers are facing difficulties in gaining access to institutional loans which are directing them to seek non-institutional loans.
The policies of the governments in many developing countries are to develop an intentional approach to remove credit constraints in rural areas. Institutional channels were supposed to save the farmers from the exploitation of the ‘usurious monopolist’ cum moneylenders. This has provided some kind of awakening, especially in the context of Bangladesh, the government established the first-ever institution- Agricultural Development Bank (Krishi Bank) in the 1960s- to provide rural credit. Later, the government also encouraged the establishment of rural branches of the nationalized commercial banks in the early 1970s and set up a special agricultural credit programme called Matir Dak in 1977.
Subsequently, in 1986, Rajshahi Krishi Unnayan Bank (RAKUB) was established by a Presidential Ordinance to provide intensive care to the agriculture of Rajshahi and Rangpur Divisions through financial support to the farmers as the region is full of agricultural potentials and characterized by their surplus food grain production, popularly known as the ‘granary of the country’ (RUKAB, 2012).
All of these developments contributed to an increase in the branches of banks as well as in institutional credit inflow to rural areas. However, it has been alleged that from the very beginning these institutions primarily favored the large and medium farmers at the cost of small and marginal ones. This could possibly be due to the weak position of the latter groups in the socioeconomic power structure prevailing in the rural areas. Further, large-scale defaults on agricultural loans provided limit to the loan-recycling capacity of the rural financial institutions in the late 1980s. Quite obviously, and since then, stagnation in credit supply developed in rural areas. Another important aspect of rural financial market developed over the years - and not so much viewed academically - is the growing interactions between formal and informal credit markets. More than two-thirds of the credit of the formal channel very quickly finds its way to the informal channel. In the context of Bangladesh, another source of rural credit is the NGOs. Following the success of the Grameen Bank, a large number of NGOs stepped in to supplying rural credit in remote areas as part of their social development programmes. These programmes targeted mostly women from landless and poor households. The total number of borrowers in the microfinance sector as on December 2013 was found 26.73 million, which was 26.90 million in 2012. The total share of borrower of MF-NGOs was found to be the highest (70.8%) in 2013 serving over 18.93 million borrowers (CDF, 2014).
The sources of rural credit can be classified into two groups. One is a formal or institutional source largely comprised of banks and NGOs. The other source is generally called informal or noninstitutional- mostly moneylenders, friends/relatives, traders, landowners etc.
Rural credit in the country is characterized by a small-organized sector. Unorganized credit transactions carried out by professional moneylenders and by friends and relatives have been quite large in terms of both quantum of loan provided and number of client households served. Banks and co-operative societies in the past few years have promoted efforts towards strengthening the organized credit market. Government and semi-government agencies and non-government organizations have been providing credit for various kinds of rural economic activities. Even in such context and development, organized rural credit sector could not substantially reduce the role still being played by the unorganized sector. The structure of the rural capital market is changing during the recent years. Bangladesh Bank through its agriculture and rural credit policy started to guide the private and foreign commercial banks to disburse agriculture and rural credits from FY 2008-09 as optional. Later from FY 2011-12, disbursement of rural credit at a certain percent of their total credit has been made mandatory for the private and commercial banks along with public and specialized banks, which stimulates largely in expand of rural credit structure in Bangladesh (Bangladesh Bank, 2013). Banks are spreading out credit operations to institutionalize the market. More and more rural households are having access to organized credit agencies for not merely agricultural production support, but also support for nonagricultural income earning ventures. Many rural households, however, are still outside the credit market, especially outside the organized credit market.
Current data on structure and state of rural credit are not available for formulating realistic policies and rationalizing the rural credit system. Due to inadequate information on demand for, and supply of, rural credit, it becomes difficult to formulate appropriate policy and planning. Considering the huge demand for data related to rural credit as well as micro-credit, BBS undertook the Rural Credit Survey 2014. The first Rural Credit Survey was conducted by Bangladesh Bureau of Statistics (BBS) in 1987. After a long gap, to meet the rising demand, this is the second comprehensive survey of its kind, conducted by BBS in January 2014. This survey provides updated data for planners, policy makers, researchers, development partners and other agencies for the management of effective and efficient use of rural credits.
Objectives
The survey was designed to provide current data on the following aspects of the country’s rural credit system:
# to identify the agricultural and non-agricultural borrowing households;
# to identify the sources of credit facilities and their roles in the rural economy;
# to identify amounts of loan advanced to rural households and their classification by purposes of utilization in agricultural and non-agricultural activities;
# to identify the actual utilization of loans by borrowing households;
# to identify the characteristics of outstanding debts of borrowing households and reasons for non-repayment of loans in time;
# to identify economic characteristics of borrowing households and distinction between borrower and non-borrowing household groups by household characteristics.
Moreover, in order to achieve the Millennium Development Goal (MDG)-1, it is important to stress upon the rural development of the country. Rural credit is considered as an important scheme to develop the agriculture and other rural economic sectors in Bangladesh. Increasing and persistent demand of rural credit is creating a scope as well as gap to expand the rural economy. To fill up the gap and scope created through increasing demand of loan, all commercial banks and NGOs along with public and specialized banks have been playing an important role in the disbursement of rural credit during the recent years under various schemes. Different government agencies are also contributing a significant proportion to the disbursement of rural credit.
The first Rural Credit Survey was conducted by Bangladesh Bureau of Statistics (BBS) in 1987. According to the result of Rural Credit Survey 1987, the rural credit market of Bangladesh had been dominated mainly (71.6%) by professional moneylenders (mahajans), landowners, friends, relatives and others (BBS, 1989). Some selected key findings have been shown in Annex-VII. From the results of above mentioned survey, it is observed that small-scale farmers are facing difficulties in gaining access to institutional loans which are directing them to seek non-institutional loans.
The policies of the governments in many developing countries are to develop an intentional approach to remove credit constraints in rural areas. Institutional channels were supposed to save the farmers from the exploitation of the ‘usurious monopolist’ cum moneylenders. This has provided some kind of awakening, especially in the context of Bangladesh, the government established the first-ever institution- Agricultural Development Bank (Krishi Bank) in the 1960s- to provide rural credit. Later, the government also encouraged the establishment of rural branches of the nationalized commercial banks in the early 1970s and set up a special agricultural credit programme called Matir Dak in 1977.
Subsequently, in 1986, Rajshahi Krishi Unnayan Bank (RAKUB) was established by a Presidential Ordinance to provide intensive care to the agriculture of Rajshahi and Rangpur Divisions through financial support to the farmers as the region is full of agricultural potentials and characterized by their surplus food grain production, popularly known as the ‘granary of the country’ (RUKAB, 2012).
All of these developments contributed to an increase in the branches of banks as well as in institutional credit inflow to rural areas. However, it has been alleged that from the very beginning these institutions primarily favored the large and medium farmers at the cost of small and marginal ones. This could possibly be due to the weak position of the latter groups in the socioeconomic power structure prevailing in the rural areas. Further, large-scale defaults on agricultural loans provided limit to the loan-recycling capacity of the rural financial institutions in the late 1980s. Quite obviously, and since then, stagnation in credit supply developed in rural areas. Another important aspect of rural financial market developed over the years - and not so much viewed academically - is the growing interactions between formal and informal credit markets. More than two-thirds of the credit of the formal channel very quickly finds its way to the informal channel. In the context of Bangladesh, another source of rural credit is the NGOs. Following the success of the Grameen Bank, a large number of NGOs stepped in to supplying rural credit in remote areas as part of their social development programmes. These programmes targeted mostly women from landless and poor households. The total number of borrowers in the microfinance sector as on December 2013 was found 26.73 million, which was 26.90 million in 2012. The total share of borrower of MF-NGOs was found to be the highest (70.8%) in 2013 serving over 18.93 million borrowers (CDF, 2014).
The sources of rural credit can be classified into two groups. One is a formal or institutional source largely comprised of banks and NGOs. The other source is generally called informal or noninstitutional- mostly moneylenders, friends/relatives, traders, landowners etc.
Rural credit in the country is characterized by a small-organized sector. Unorganized credit transactions carried out by professional moneylenders and by friends and relatives have been quite large in terms of both quantum of loan provided and number of client households served. Banks and co-operative societies in the past few years have promoted efforts towards strengthening the organized credit market. Government and semi-government agencies and non-government organizations have been providing credit for various kinds of rural economic activities. Even in such context and development, organized rural credit sector could not substantially reduce the role still being played by the unorganized sector. The structure of the rural capital market is changing during the recent years. Bangladesh Bank through its agriculture and rural credit policy started to guide the private and foreign commercial banks to disburse agriculture and rural credits from FY 2008-09 as optional. Later from FY 2011-12, disbursement of rural credit at a certain percent of their total credit has been made mandatory for the private and commercial banks along with public and specialized banks, which stimulates largely in expand of rural credit structure in Bangladesh (Bangladesh Bank, 2013). Banks are spreading out credit operations to institutionalize the market. More and more rural households are having access to organized credit agencies for not merely agricultural production support, but also support for nonagricultural income earning ventures. Many rural households, however, are still outside the credit market, especially outside the organized credit market.
Current data on structure and state of rural credit are not available for formulating realistic policies and rationalizing the rural credit system. Due to inadequate information on demand for, and supply of, rural credit, it becomes difficult to formulate appropriate policy and planning. Considering the huge demand for data related to rural credit as well as micro-credit, BBS undertook the Rural Credit Survey 2014. The first Rural Credit Survey was conducted by Bangladesh Bureau of Statistics (BBS) in 1987. After a long gap, to meet the rising demand, this is the second comprehensive survey of its kind, conducted by BBS in January 2014. This survey provides updated data for planners, policy makers, researchers, development partners and other agencies for the management of effective and efficient use of rural credits.
Objectives
The survey was designed to provide current data on the following aspects of the country’s rural credit system:
# to identify the agricultural and non-agricultural borrowing households;
# to identify the sources of credit facilities and their roles in the rural economy;
# to identify amounts of loan advanced to rural households and their classification by purposes of utilization in agricultural and non-agricultural activities;
# to identify the actual utilization of loans by borrowing households;
# to identify the characteristics of outstanding debts of borrowing households and reasons for non-repayment of loans in time;
# to identify economic characteristics of borrowing households and distinction between borrower and non-borrowing household groups by household characteristics.
Kind of Data
Sample survey data [ssd]
Version
Version Date
2014-12
Coverage
Geographic Coverage
The survey fieldwork was completed over 15 days from 26 January through 09 February 2014. The field enumeration was done by 423 enumerators. All enumerators were the permanent staff of Bangladesh Bureau of Statistics engaged for the short period of the survey. Almost all of those staffs were employees who previously worked for the Bureau in field activities of the population census, agriculture census and economic census. Therefore, it became possible to organize a field force of enumerators exposed to Bureau’s field works of censuses.
During enumeration, field supervision work was performed by 64 District level Officials. Senior Officials of Statistics and Informatics Division and Bangladesh Bureau of Statistics also visited the field levels to check the consistency of the data collected by the enumerators. The entire fieldwork was done under overall supervision and guidance of the Secretary, Statistics and Informatics Division and Director General of Bangladesh Bureau of Statistics.
During enumeration, field supervision work was performed by 64 District level Officials. Senior Officials of Statistics and Informatics Division and Bangladesh Bureau of Statistics also visited the field levels to check the consistency of the data collected by the enumerators. The entire fieldwork was done under overall supervision and guidance of the Secretary, Statistics and Informatics Division and Director General of Bangladesh Bureau of Statistics.
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| BANGLADESH BUREAU OF STATISTICS | Statistics and Informatics Division, Ministry of Planning |
Producers
| Name |
|---|
| GOVERNMENT OF THE PEOPLE’S REPUBLIC OF BANGLADESH |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| Statistics and Informatics Division | SID |
Sampling
Sampling Procedure
Sampling Frame
The Population and Housing Census 2011 served as the frame for the design of the rural credit sample, which was carried out in two phases. Only the rural Enumeration Areas (EAs) had been considered under the sampling frame.
Stratification
Since the study only covered rural regions, there is no need for any regional stratification under urban/rural/city corporation domains. However, since district estimates were required, the districts were considered as independent study domains. It was decided that the adjusted (by splitting larger EAs into several segments) Population and Housing Census 2011 Enumeration Areas (EAs) be used as the PSU and then they were selected with PPS. Each PSU consists of clear boundaries, which can be easily located in the field and measured of size data that were used for selecting PSUs were available for each one of them. PSUs were as manageably small as possible, but large enough to have adequate number of ultimate sampling units.
Sample Selection Procedures
The first phase was a PPS selection of the PSUs with the measure of the size being the census count of households. Selection of the PSUs had been performed as a computer operation, while the second phase entailed creation of HH listing within the selected PSUs. Since EAs were considered as PSUs, but in some cases, they were too small and they had to be linked to adjacent EAs in order to qualify satisfactorily as PSUs, a task that was thought to be much too tedious, costly and time consuming.
Considering the distribution of HHs and the average number of HH per EA in the census findings, it was decided to have EAs with at least 40 households. Approximately 4 percent of the EAs (8,876 EAs) in rural areas had less than 40 households. The way of handling the problem with the large number of small EAs of less than 40 households was to select the sample PSUs first and then collapse the sampled small PSUs (EAs) with the adjacent small PSUs within the same mauza or small PSUs of the neighboring mauza. It reduced large number of small PSUs. The PSU selection had been done before collapsing the small PSUs.
Determination of Sample Size
The sample size is usually determined at the domain level from which separate estimates will be derived. From general theory, the minimum required sample size usually is determined by the sample size determination formula for estimating proportion, which is given by
?? =
(1-??)
?? [??(???/2)]2 × ?? (1)
where, p is an a priori proportion of the required characteristics in the population, z (a/2), the value of the standard normal variate allowing a probability of bad samples, d, the allowable margin of error, r, the relative rate of allowable margin of error, computed as a portion of the assumed true proportion p, and f is the design effect used for complex surveys using multi-stage cluster sampling. Conventionally a can be taken as 0.05 and f can be taken as 1.5 - 2.0 for most socioeconomic surveys in Bangladesh. Theoretically, an a priori p = 0.5 gives the safest sample size since p (1-p) takes the highest value for p = 0.5. In this particular study under interest, the minimum number of households required in each domain (district) had been focused and since the number of households in each of the defined strata was fairly larger than 8,000, no adjustment for population size had been suggested.
Now, if the choice of a = 0.05 giving z (a/2) = 1.96 is considered, then to allow a maximum error of ±10% (r = 0.1) in estimating a common characteristic with p = 0.5, the minimum required sample size using equation (1) can be obtained to be approximately 580 HH per domain. Since at the second stage 25 HH from each of the selected PSUs was planned a minimum of 580
25
˜ 24 EAs had been
required from each of the domains which results 2,240 EAs across the country at yield 56,000 HHs.
Note that this sample size would only be appropriate for providing estimates of common characteristics. In the next section, the allocation of the PSUs among division is described considering this minimum requirement.
The Population and Housing Census 2011 served as the frame for the design of the rural credit sample, which was carried out in two phases. Only the rural Enumeration Areas (EAs) had been considered under the sampling frame.
Stratification
Since the study only covered rural regions, there is no need for any regional stratification under urban/rural/city corporation domains. However, since district estimates were required, the districts were considered as independent study domains. It was decided that the adjusted (by splitting larger EAs into several segments) Population and Housing Census 2011 Enumeration Areas (EAs) be used as the PSU and then they were selected with PPS. Each PSU consists of clear boundaries, which can be easily located in the field and measured of size data that were used for selecting PSUs were available for each one of them. PSUs were as manageably small as possible, but large enough to have adequate number of ultimate sampling units.
Sample Selection Procedures
The first phase was a PPS selection of the PSUs with the measure of the size being the census count of households. Selection of the PSUs had been performed as a computer operation, while the second phase entailed creation of HH listing within the selected PSUs. Since EAs were considered as PSUs, but in some cases, they were too small and they had to be linked to adjacent EAs in order to qualify satisfactorily as PSUs, a task that was thought to be much too tedious, costly and time consuming.
Considering the distribution of HHs and the average number of HH per EA in the census findings, it was decided to have EAs with at least 40 households. Approximately 4 percent of the EAs (8,876 EAs) in rural areas had less than 40 households. The way of handling the problem with the large number of small EAs of less than 40 households was to select the sample PSUs first and then collapse the sampled small PSUs (EAs) with the adjacent small PSUs within the same mauza or small PSUs of the neighboring mauza. It reduced large number of small PSUs. The PSU selection had been done before collapsing the small PSUs.
Determination of Sample Size
The sample size is usually determined at the domain level from which separate estimates will be derived. From general theory, the minimum required sample size usually is determined by the sample size determination formula for estimating proportion, which is given by
?? =
(1-??)
?? [??(???/2)]2 × ?? (1)
where, p is an a priori proportion of the required characteristics in the population, z (a/2), the value of the standard normal variate allowing a probability of bad samples, d, the allowable margin of error, r, the relative rate of allowable margin of error, computed as a portion of the assumed true proportion p, and f is the design effect used for complex surveys using multi-stage cluster sampling. Conventionally a can be taken as 0.05 and f can be taken as 1.5 - 2.0 for most socioeconomic surveys in Bangladesh. Theoretically, an a priori p = 0.5 gives the safest sample size since p (1-p) takes the highest value for p = 0.5. In this particular study under interest, the minimum number of households required in each domain (district) had been focused and since the number of households in each of the defined strata was fairly larger than 8,000, no adjustment for population size had been suggested.
Now, if the choice of a = 0.05 giving z (a/2) = 1.96 is considered, then to allow a maximum error of ±10% (r = 0.1) in estimating a common characteristic with p = 0.5, the minimum required sample size using equation (1) can be obtained to be approximately 580 HH per domain. Since at the second stage 25 HH from each of the selected PSUs was planned a minimum of 580
25
˜ 24 EAs had been
required from each of the domains which results 2,240 EAs across the country at yield 56,000 HHs.
Note that this sample size would only be appropriate for providing estimates of common characteristics. In the next section, the allocation of the PSUs among division is described considering this minimum requirement.
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2014-01-26 | 2014-02-09 |
Mode of data collection
Face-to-face [f2f]
Type of Research Instrument
A questionnaire with the aim of providing data required planning and programming for rural credit was canvassed in the survey. The contents of the questionnaire were worked out keeping in view of the survey objectives and users demand for data. Views and recommendations of such outside agencies as Bangladesh Bank, Nationalized Commercial Banks, PKSF, BRAC, ASA, Department of Social Services, Department of Youth Development, Specialized Banks etc. were taken into account in developing the questionnaire. There are 75 questions in the questionnaire. Modules incorporated into the questionnaire format are as follows:
Module-1: Identification and Basic Characteristics of Households
Module-2: Information on Loan
Module-3: Expenditure on Different Heads during 2013
Module-4: Annual Income of Household
Module-5: If not Institutional Borrowers
Module-1: Identification and Basic Characteristics of Households
Module-2: Information on Loan
Module-3: Expenditure on Different Heads during 2013
Module-4: Annual Income of Household
Module-5: If not Institutional Borrowers
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Statistics and Informatics Division | SID | Ministry of Planning |
Data access
Contact
| Name | Affiliation | URI |
|---|---|---|
| BANGLADESH BUREAU OF STATISTICS | BBS | www.bbs.gov.bd |
Metadata production
Document ID
DDI-BGD-BBS-RCS-2014-v01
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| BANGLADESH BUREAU OF STATISTICS | BBS | Statistics and Informatics Division, Ministry of Planning | Documentation of the study |