Abstract
Poverty in Bangladesh has been decreasing steadily over the last 25 years. For example, during the period 1991–2010, moderate poverty dropped by 25 percentage points (from 57% to 32%), and extreme poverty by 23 percentage points (from 41% to 18%) (World Bank 2013). The corresponding drops in rural areas, where more than 70% of the country’s 150 million people live, show similar trends—24 percentage points and 23 percentage points, respectively. While part of this decline in poverty is due to an economic growth rate of more than 6% over the last decade (World Bank 2013), there are other factors that have contributed to poverty reduction. One such factor is microcredit growth—the topic of this chapter. There has been a remarkable growth in the coverage of microfinance institutions (MFIs) and their members over the last two decades in Bangladesh, and it can be hypothesized that the scale of microfinance operations has had some effect on the economy. This chapter attempts to answer the question of whether microfinance has contributed at all to poverty reduction, and if so, how much of the recent poverty reduction in Bangladesh can be attributed to the increasing use of microfinance.
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Notes
- 1.
While the interest rates of MFIs are higher than those of formal lenders, they are much lower than informal lending rates (Faruqee and Khalily 2011).
- 2.
This study found that the rate of return on microcredit-supported activities is higher than the average interest rate charged by MFIs.
- 3.
An upazila is an administrative unit at the rural sub-district level and consists of several villages.
- 4.
Established by the government of Bangladesh in 1994, PKSF is a wholesale agency that lends government and donor-funded money to its partner organizations (POs) for on-lending as microcredit disbursements. Up to March 2010, PKSF had lent Tk. 88.5 billion to over 250 POs, who in turn had disbursed about Tk. 525 billion to about 10 million microcredit members.
- 5.
Membership does not necessarily imply borrowing. Microfinance programs have members who have savings accounts with the program but do not borrow. In addition, there are members who are relatively new and have not started borrowing yet. Both types can count as non-borrowing members at a given time. In reality, however, borrowers constitute a large share of members.
- 6.
Most SME loans (generally over Tk. 100,000) are disbursed by BRAC. In the third round of the panel survey (2010/11), these loans constituted about 6% of the total number of loans disbursed by BRAC. In comparison, the same type of loans made up only about 1% of the total disbursed by the Grameen Bank.
- 7.
Overall, however, BRAC provides more loans to women than to men as per its annual portfolio review. For example, in 2013, BRAC gave loans to 3.7 million women, but only to 480,000 men. In terms of loan portfolios, women accounted for $799.4 million, and men for $128.4 million, indicating that women received 86% of loans disbursed in that year.
- 8.
The various borrowing purposes in the 1991/92 survey are not compatible with those in the 1998/99 and 2010/11 surveys, so 1991/92 data were not used in this analysis.
- 9.
The miscellaneous category, reported as “Other agricultural expenses,” includes items such as purchase or repair of agricultural equipment, purchase of fishing nets, other fishing equipment or fishing inputs, livestock farming, poultry, and sericulture.
- 10.
Purchase of milch cows and cow-fattening activities are included in the non-farm category because they are not pure farm activities in the sense of livestock raising. They are carried out mostly to sell milk or fatten a cow to sell later.
- 11.
- 12.
The time-invariant characteristics may also include village or community unobserved characteristics; however, sweeping away unobserved village characteristics also takes away the role of unobserved community characteristics. Hence, they are subsumed within the household unobserved characteristics.
- 13.
The HIES data set is more suitable than long panel data for estimating program placement effects, because the former is more representative of the actual program placement in rural Bangladesh. On the other hand, the long panel data were collected mostly from areas where microcredit programs were concentrated, and thus over-represent the program villages. In fact, in the 1998/99 and 2010/11 data of the long panel, all sample villages have microcredit programs.
- 14.
These findings are consistent with that of another recent study that uses HIES data to examine poverty reduction impacts of microcredit in rural Bangladesh (Khandker and Samad 2014a).
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Khandker, S.R., Samad, H.A. (2018). Bangladesh’s Achievement in Poverty Reduction: The Role of Microfinance Revisited. In: Sawada, Y., Mahmud, M., Kitano, N. (eds) Economic and Social Development of Bangladesh. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-63838-6_9
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