Effectiveness of PRIME Interventions in Greater
Rangpur at the Household level and Institutional level:
A Longitudinal Approach
3rd Round Evaluation Report
Prepared for:
Palli Karma-Sahayak Foundation (PKSF)
Prepared by
Institute of Microfinance (InM) July 05, 2011
PRIME 3rd Round Report.
Please do not cite or circulate.
Project Team
Atonu Rabbani
Department of Economics
University of Dhaka
Mohammad Monirul Hasan
Md. Mehadi Hasan
Institute of Microfinance (InM)
Institute of Microfinance (InM)
Tunazzina Choudhury Mithun
Aparna Howlader
Project Officer - PRIME
Project Officer - PRIME
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PRIME 3rd Round Report.
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Contents
List of Tables ..................................................................................................................................... v
List of Figures ....................................................................................................................................vi
Executive Summary ......................................................................................................................... viii
Acknowledgement............................................................................................................................. xi
1.
Introduction .............................................................................................................................. 1
1.1
Background ........................................................................................................................ 1
1.2
Evolution of the Program ................................................................................................... 4
1.3
Outreach of PRIME ............................................................................................................. 5
1.4
Overview of this Report...................................................................................................... 8
2.
Methods .................................................................................................................................. 10
2.1
Sampling .......................................................................................................................... 10
2.2
Data ................................................................................................................................. 11
2.3
Identification.................................................................................................................... 13
2.4
Notes on Analytical Framework........................................................................................ 17
2.5
Discussion and Concluding Remarks on Identification....................................................... 18
3.
Impact of PRIME on Selected Parameters ................................................................................ 19
3.1
Introduction ..................................................................................................................... 19
3.2
Consumption Ordering ..................................................................................................... 19
3.3
Other Outcome Parameters and Age of PRIME Participation ............................................ 24
3.4
Crisis Coping..................................................................................................................... 30
3.5
Health, Perception and Decision Related Aspects ............................................................. 33
3.5.1
Awareness about Health Care................................................................................... 33
3.5.2
Perception regarding Child Education ....................................................................... 35
3.5.3
Participation of Women in Decision Making ............................................................. 35
3.6
Discussion and Concluding Remarks on Impact of PRIME ................................................. 35
4.
Overall Assessment of the Program ......................................................................................... 37
5.
Concluding Remarks ................................................................................................................ 41
A.
Operational Sustainability of the PRIME Branches.................................................................... 43
1.
Preamble ............................................................................................................................. 43
2.
Evaluation of the PRIME branches........................................................................................ 43
3.
Performance Parameters ..................................................................................................... 44
Productivity and Efficiency ....................................................................................................... 45
Financial Spread and Operational Self-Sufficiency .................................................................... 45
4.
Sample................................................................................................................................. 45
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5.
Description of the Data ........................................................................................................ 47
6.
Assessing Financial Performance against Age of the Program ............................................... 49
7.
Dynamics of Branch Performance over the Age of the Program ........................................... 50
8.
Financial Spread................................................................................................................... 52
9.
Operational Self-Sufficiency (OSS) ........................................................................................ 53
10.
Discussion ........................................................................................................................ 54
11.
Concluding Remarks ......................................................................................................... 55
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List of Tables
Table 1: Income Generating Activities (Thousand IGAs) by Major Sub-Categories. ............................. 7
Table 2: Sampling ............................................................................................................................ 10
Table 3: Summary Statistics for Selected Variables from Third Phase of Household Survey. ............. 12
Table 4: Summary Statistics of Selected Household and Village Characteristics by Participation Status.
........................................................................................................................................................ 14
Table 5: Percentage distribution of PRIME membership duration by district .................................... 16
Table 6: Summary statistics of selected household characteristics by duration of PRIME membership
(in years) ......................................................................................................................................... 16
Table 7: Comparisons between “Benchmark” Households and PRIME Participants with less than One
Year in the Program. ........................................................................................................................ 25
Table 8: Land and Non-Land Assets against Age of PRIME Participation. .......................................... 26
Table 9: Asset value and Savings against Age of PRIME Participation. .............................................. 27
Table 10: Income against Age of PRIME Participation. ..................................................................... 28
Table 11: Food and Non-Food Expenditure against Age of PRIME Participation................................ 29
Table 12: Engagement in Agricultural and Non-Agricultural Self-Employment against Age of PRIME
Participation. ................................................................................................................................... 30
Table 13: POs and Coverage ............................................................................................................ 46
Table 14: Summary Statistics for PRIME Branches............................................................................ 48
Table 15: Age of PRIME Implementation.......................................................................................... 49
Table 16: Age of PRIME Implementation over Financial Years 2008-2010. ....................................... 49
Table 17: Operational Self-Sufficiency (OSS) for different categories of PRIME POs. ......................... 55
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List of Figures
Figure 1: Map of Bangladesh showing Rangpur division in red. .......................................................... 1
Figure 2: Loan Outstanding and Total Members, 2009-2010. ............................................................. 3
Figure 3: Average Loan Outstanding Per Borrower............................................................................. 4
Figure 4: Disbursement of Microcredit under PRIME Program. .......................................................... 4
Figure 5: Savings Mobilized under PRIME. ......................................................................................... 5
Figure 6: Income Generating Activities (IGAs) associated with Microcredit under PRIME Program. .... 5
Figure 7: Number of Remittance Services Rendered under PRIME. .................................................... 8
Figure 8: Number of Health Services (in Thousands) Rendered under PRIME. .................................... 8
Figure 9: Distribution of the Households over Consumption Ordering during “Normal” Time........... 20
Figure 10: Distribution of the Households over Consumption Ordering during Monga Time. ........... 20
Figure 11: Cumulative Distribution of Consumption Ordering during “Normal” Time ....................... 21
Figure 12: Cumulative Distribution of Consumption Ordering during Monga Time........................... 21
Figure 13: Fraction (%) of Household Reporting at least Three Meals/Day during “Normal” Time. ... 22
Figure 14: Fraction (%) of Household Reporting at least Three Meals/Day during Monga Time. ....... 22
Figure 15: Marginal Impact of the Program over Age of Participation on Reporting Three Meals/Day
during “Normal” Time. .................................................................................................................... 23
Figure 16: Marginal Impact of the Program over Age of Participation on Reporting Three Meals/Day
during Monga Time. ........................................................................................................................ 23
Figure 17: Fraction of Households Adopting Self-Coping Mechanism. .............................................. 31
Figure 18: Marginal Impact of Program on Reporting Self-Coping Mechanism to Cope The Shock . .. 31
Figure 19: Value of Housing by Duration of PRIME Participation. ..................................................... 32
Figure 20: Participation in Non-PRIME MFI Programs by Duration of PRIME Participation. ............... 32
Figure 19: Fraction (%) of Households Reporting to have Treatment from “Quack”. ........................ 33
Figure 20: Fraction (%) of Households Reporting Interest to Educate Their School-Going Aged
Children........................................................................................................................................... 34
Figure 21: Fraction (%) of Household Reporting Mother’s Participation in the Decision of Children’s
Marriage ......................................................................................................................................... 34
Figure 22: Growth Curve for Number of MFI Branches Implementing PRIME .................................. 46
Figure 23: Borrower Per Field Officer by Age of Branches. ............................................................... 50
Figure 24: Portfolio Outstanding Per Field Officer (Thousand Taka) and Age of Branches. ................ 51
Figure 25: Deposit Outstanding Per Credit Officer (Thousand Taka) and Age of Branches. ............... 51
Figure 26: Salary and Benefit as a % of Average Portfolio Outstanding and Age of Branches. ........... 51
Figure 27: Operating Cost Ratio as a % of Average Portfolio Outstanding and Age of Branches. ....... 51
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Figure 28: Financial Spread and Age of Branches. ............................................................................ 52
Figure 29: Distribution of OSS as 2010 for Branches Established through Implementing PRIME (N =
132)................................................................................................................................................. 53
Figure 30: Distribution of OSS as 2010 for Pre-existing MFI Branches Implementing PRIME (N = 99) 53
Figure 31: Percentage of Branches with OSS above 100%. .............................................................. 54
Figure 32: Operational Self-Sufficiency (OSS, %) and Age of Branch. ................................................ 54
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Executive Summary
Introduction
Palli Karma Sahayak Foundation (PKSF) initiated the project entitled Programmed Initiatives for
Monga Eradication (PRIME) in the greater Rangpur region in 2006 to support ultra-poor households
through both year-round and seasonal microcredit/microfinance services and other monetary (e.g.
cash for work in the earlier period) and non-monetary (e.g. training and health services) instruments.
The program started in 2006 in Lalmonirhat and later expanded to all other districts of the Greater
Rangpur region (namely Gaibandga, Kurigram, Nilphamari and Rangpur). This report attempted to
identify the impacts of the program on different markers of household welfare such as consumption
ordering, food and non-expenditure, income, asset accumulation, self-employment etc.
Findings
One of the big challenges of the evaluation team was to identify a proper comparison group (control
group). We used a sample of households that reported not participating with any NGO/MFI
programs in any one of the three rounds of surveys spanning over 2008 through 2010 calendar
years. This report used these non-participants as a “benchmark” group and compared many of the
outcomes in comparison to this group. One should note that this group was not a proper control
group because this non-participating group differed from the new participants significantly in many
household characteristics. E.g. non-participating households were more likely to be in chars and
female headed. Head of the benchmark households were more likely to older, unmarried and have
less education. However, it was interesting to find that these households did not differ statistically
for many of the outcome parameters. We looked at many of the outcome variables over years of
households’ participation in PRIME.
We found that food security among the sampled households improved on average over the three
years of survey as typified by consumption ordering (i.e. having at least three meals per day). During
the monga season in 2008, 58% of the households in our sample reported having at least three
meals while this reached 75% during the similar season in 2010. This overall secular trend among all
households (benchmark and PRIME participants as well households receiving participating outside
PRIME program) actually made it chellenging to associate the improvement in food security with
PRIME participation.
If we looked over the years of PRIME participation, we found that the new participants reported a
very similar level of food security (44% of the households in their first year of PRIME benefit having
three meals a day) in comparison to the households that did not receive any support from any
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MFI/NGOs (44% having at least three meals). However, the consumption ordering increased over the
age of PRIME participation and after four years or more in participation 60% of the households
reported having at least three meals during the last monga.
Other outcome variables also exhibited a similar pattern. It was interesting to find that most of the
outcome variables were very similar (in level) between the “benchmark” households and the new
participants (the households with less than one year of PRIME participation). Only food and nonfood expenditure, total savings and months engaged in non-agricultural self-employment exhibited
differences that were statistically significant. It is possible to infer that PRIME may have immediate
impacts on expenditure and savings before having significant impacts on assets, income and more
importantly on overall food security (as we found above).
We found that the households reported having 21% more land with three years of participation and
33% more with participating four years or more in compared with the “benchmark” households. The
total number of livestock is also 15% higher for the households with three years and 23% higher with
four or more years of participation. We also found similar pattern when we looked at different types
of livestock and poultry. This suggested the households who participated into the program managed
to accumulate assets over time and the value of their assets also increased over time.
We found a similar “growth curve” over the years of PRIME participations. Total income for the
households who are new to PRIME had 18% higher income compared with the “benchmark”
households. As for households with three years of participation the income was 24% higher while it
was 35% higher for households with four years or more of PRIME participations. Total income
livestock was significantly high only for the households with three years (49% higher) and four years
or more (66% higher) of PRIME participations.
The food and non-food expenditure were also higher for the PRIME participants and this also
exhibited an upward trend over the years of PRIME participations. Households that just entered the
program showed an immediate jump in both types of expenditures. For the households with
different length of participations until the third years showed a somewhat flat growth curve (around
17-18% in comparison with the “benchmark” group) which climbed to 29% for the households with
four years of participation. We also found a similar pattern for the first three years of participation
(the positive differences with the “benchmark” households were around 33-36%) while it went up to
58% for the households with four years or more of PRIME participation.
As expected, self employment in both agriculture and non-agricultural sectors also increased over
the age of participation. There was some evidence for improvement in better use of health care use
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as households showed lesser tendency to avail health services from the informal providers. The
decision to send children or participating in family and/or community level did not vary over the age
of the program participation as such we did not find any significant association with the time
households spent with the program. We also found that total value of housing was higher for
participants compared with the benchmark households and PRIME participants continued to
participate in other non-PRIME MFI programs. We found no significant association between PRIME
participation and migration of any household member.
Concluding Remarks
All these findings suggested that it was important to look at households over a period of time after
they participated into ultra-poor programs such as PRIME because it might take a while for the ultrapoor households to rip benefits from such programs. This report concluded that inclusion of ultrapoor households to financial system (as offered by microfinance industry) along with training and
perhaps monitoring could put such households in a path of development and improvement of their
economic situations.
We also explicitly addressed the cost of delivery and operational sustainability of the MFI branches
implementing PRIME. Here, the picture showed a less optimistic situation. The cost of delivery of
financial services to the hard-to-reach households was higher and the branches established through
PRIME program had smaller loan size per borrower, smaller portfolio managed by the field officer
and higher operating cost in comparison to the branches that were in microfinance operations
before the PRIME was initiated. As such the branches did not show the capacity to be operationally
sustainable even after three years or more into operations. As expected so-called “double bottomline” was especially stringent for these branches and reaching out to ultra-poor households in the
remote areas in a sustainable manner would probably require innovations in financial service
delivery mechanism.
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Acknowledgement
First, I would like to thank the members of my team without whose hard work this report would
never be finished in time. I would specially like to thank Mehadi Hasan for looking over the
household survey and successfully carrying it out in a timely manner. I would like to acknowledge
the effort of Monirul Hasan for looking after the branch survey and also the GIS data collection. The
research assistance from Tunazzina Chowdhury Mithun and Aparna Howalader has also been an
invaluable asset throughout the life of this project.
The research team also benefited immensely consulting with a number of individuals at the PKSF.
Mr. Md. Golam Touhid (General Manager, Operations), Mr. A.Q.M. Golam Mawla (Deupty General
Manager, Operations), Dr. Sharif Ahmed Chowdhury (Project Coordinator, PRIME) and Mr. A. K. M.
Zahirul Haque (Deputy Manager, Operations) at PKSF has always been available to provide the
research team with information and data on PRIME and with useful insights into the programs that
has helped the research team to understand more about the program. The research team would
also like to thank Devnet Ltd. (especially Mr. Abdullah Sumon) for efficiently designing the
questionnaire in ICR format, capturing the data and ensuring completion of the project on time.
The research team would also like to thank Dr. M. A. Baqui Khalily (Professor, Department of Finance
and Banking, University of Dhaka), Dr. Meherun Ahmed (Assistant Professor, Asian University for
Women), Dr. Tanweer Hasan (Professor of Finance, Roosevelt University) and especially Dr. Rashid
Faruqee (Visiting Scholar, Institute of Microfinance) for helpful comments on many aspects of the
project. The current version of the report also reflects some of the comments and suggestions the
research team received during a seminar arranged by the InM. Lastly, the research team would
specially like to thank the Institute of Microfinance (InM) for their continual support for carrying out
this project. Special credit goes to Dr. Mosleh Uddin Sadeque (Interim Executive Director), Dr. M. A.
Latif (Director, Research and Knowledge Management), Mr. Touhid Uz Zaman (Senior Deputy
Director, Research & Knowledge Management), Mr. Md. Abdul Hye Mridha (Senior Deputy Director,
Training & Administration) and Mr. Azahar Ali (Senior Assistant Director, Accounts) who have
extended assistance and helping hand whenever the research team needed. Their support and InM’s
hospitality are greatly acknowledged.
Atonu Rabbani
Team Leader
PRIME Impact Evaluation (3rd Round)
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1. Introduction
1.1
Background
Palli Karma Sahayak Foundation (PKSF) initiated the project entitled Programmed Initiatives for
Monga Eradication (PRIME) in the greater Rangpur region in 2006. Initially it was introduced in five
upazillas (sub-districts) of Lalmonirhat and later PKSF expanded the program expanded to cover all
five districts of the current Rangpur division (Gaibandha, Kurigram, Nilphamari and Rangpur being
the other four districts). Monga is a
situation of income deprivation resulting
Figure 1: Map of Bangladesh showing Rangpur
division in red.
from lack of working opportunity during
the pre-harvest situation during the months
of September through November. Such lack
of work opportunity (many a times
aggravated by floods) in turn results in lack
of food insecurity. As such, the main
objective of the program was to support
ultra-poor households through both yearround
and
seasonal
microcredit/microfinance
and
other
monetary (e.g. cash for work in the earlier
period) and non-monetary (e.g. training
and health services) instruments. As such,
in 2007 and 2008 all 35 upazillas of the
greater
Rangpur
region
(in
the
aforementioned five districts) were covered
under the program to reach an initial
estimate
of
nine
hundred
thousand
households living under both acute and
Source: Internet.
chronic state of deprivations – food, income or otherwise.
One should note the acute underemployment and lack of work opportunities during the pre-harvest
period pose the poor households in this region in an especially vulnerable situation which lead to
severe seasonal food insecurity. This is traditionally known as monga. Having some of the major
rivers (the Brahmaputra) and their feeders (e.g. Tista) also put the ultra poor households in an
ecologically vulnerable state. This accentuates the severity of poverty of the households living in
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chars and low-lying river banks because of floods. Dependence on agriculture, lack of
industrialization and regular havocs caused by floods have caused immense hardship for these
households which have drawn attention of policy-makers, development practitioners and
researchers for a long time. The programs under PRIME epitomize the concerns of these various
groups and resulted in a very comprehensive coverage of the ultra-poor households to mitigate the
impact of monga and alleviate the economic hardship in the short to midterm while eradicating
poverty in this region in the long run.
Institute of Microfinance (InM) has been conducting the evaluation of these programs since 2008
following a sample of households comprising all five districts. The households were chosen in a
stratified random fashion in a hierarchal fashion (see Section 2). Currently, InM in collaboration with
PKSF is implementing third round of evaluation phases. This gives a unique opportunity to follow a
large number of households over a period of three years and draw conclusions on impact of the
program in a rigorous and sophisticated way. While household survey comprises of the largest
component of the evaluation exercise, the PRIME evaluation team has also collected various
supplementary information (e.g. village information, branch information and as a pilot study, GIS
information for a subset of households in the same during the latest round of evaluation).
This report was primary a result of the third phase of the evaluation to document the evolution of
the program over time and identify the impacts of the programs where possible. The project team
followed up the previous studies through a longitudinal dataset of about 6,500 households. The
sample was drawn in such a fashion that researchers could address a number of issues such as
selection, participation as well as identifying the impact of the programs on a number of parameters.
Many of the outcomes continued from the previous studies to understand the dynamics of the
impacts and their sustenance at the household level. Additionally, the research team also looked at
some of the additional issues (as required by the Terms of Reference, see Box 1) dealing with (a) the
operational sustainability of the program from the suppliers’ point-of-view and (b) some gender
specific decision making outcomes at the household level.
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Hence, the bulk of this report would address the
Box 1: Objectives of the Study according to the
Terms of References.
The main objectives of the third round impact study
will be to:
a) assess the impact of the project interventions on
the life and livelihood of the monga affected
households, in both the social and economic
terms;
b) compare the current year (3rd round) impact of
the project intervention with first round and
second round impact.
c) assess the impact on institutional capacity
strengthening initiatives for Partner
Organizations.
d) assess the operational sustainability of prime
branches and suggest possible way to attain
operational sustainability.
outcomes included in the PRIME evaluation
reports of the previous rounds. Additionally, we
would focus on some of the outcomes related with
health seeking behaviors (as we will show PRIME
included so called “credit plus” services in recent
time) and also some decision making outcomes for
women among PRIME beneficiary households.
A major innovation of the present phase of the
report is to address the issue of sustainability of
the program in operational sense. PRIME is
implemented through 16 partner organizations
(POs) of PKSF (for a list of all POs responsible for implementing PRIME see Table 17 in the appendix).
Currently, these POs have 231 branches in the greater Rangpur region implementing the program.1
An important dichotomy between these
branches
was
some
branches
were
established to implement PRIME and these
branches were not likely to be in existence if
PRIME did not induce establishment of these
branches.
Other branches implementing
PRIME were already in operations and chosen
primarily because they were operating in
remote areas where many of the potential
PRIME beneficiaries lived. The research team
at InM collected financial information for all
these branches and addressed the issue of
productivity,
efficiency
and
Figure 2: Loan Outstanding and Total Members,
2009-2010.
900
800
700
600
500
400
300
200
100
0
1
2
3
4
1
2009
2
3
4
2010
Flexible Loan Outstanding (Million Taka)
Total Members (Thousand)
operational
sustainability of these branches in a very
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
comprehensive way.
1
As PRIME has evolved, so have operations of the programs. After the introduction of the program in all
districts of Rangpur there were initially 235 branches. As per requirements, some the branches have merged
and sometimes infused with other branches of the same PO. By design, only one PO was allowed to implement
PRIME in one union. The same PO and other POs (whether or implementing PRIME) often had other ultra-poor
programs in the same area (i.e. union).
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PRIME 3rd Round Report.
1.2
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Evolution of the Program
To facilitate the support for the ultra-poor in the Rangpur region PKSF initiated PRIME2 which was
later supported by Department for International Development (DFID) under Promoting Financial
Services for Poverty Reduction (PROSPER) from July, 2007. The program design has changed
somewhat over the years while retaining many of the core aspects of the initial intervention at
place. Microfinance and support through both microcredit (typically associated with some income
generating activity or IGA) and mobilizing savings from the poor households has been the crux of the
program all along. There were also multiple types of microcredit products covering both flexible and
emergency in nature, albeit emergency loans constituting a small fraction of total loan disbursed.
There were also some ancillary non-credit financial services such as remittance services offered to
the PRIME beneficiaries.
Figure 3: Average Loan Outstanding Per Borrower.
2,700
Figure 4: Disbursement of Microcredit under
PRIME Program.
600
90
80
500
2,500
70
400
2,300
60
50
300
40
200
2,100
30
20
100
10
1,900
0
0
1
1,700
1
2
3
2009
4
1
2
3
4
2010
2
3
2009
4
1
2
3
4
2010
Total Disbursement (Million Taka) [Left Axis]
Loan Outstanding per borrower (Taka)
Total Number of Loans Disbursed (Thousand) [Right
Axis]
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
Initially there were more direct transfers through temporary wage employment or more traditional
Cash for Work (CFW) type interventions. This was implemented in a substantial scale and was later
discontinued because it overlapped with similar programs in the region (generally offered by the
government).3 Member POs implementing PRIME kept records on the IGAs for which microcredit
2
Concurrently, PKSF also started another program called Learning Innovative Fund to Test New Ideas (LIFT) in
the same area.
3
The discontinuation of CFW in the initial period was mostly caused by prevalence of similar programs
initiated and implemented in the same regions by the Government of Bangladesh.
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PRIME 3rd Round Report.
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was provided.4 This was also important because PRIME program included training for the
beneficiaries. Usually MFI branches arranged these training sessions through hiring government
agricultural extension officers.
In recent time MFI practitioners are focusing more and more on credit plus products to incorporate
health and education related services to the beneficiaries. PRIME presupposes these roles of
MFI/NGO by incorporating health services to the poor beneficiaries. PRIME is currently providing
primary health care services to the beneficiary households. Such services include both consultation
and medicines.
Figure 5: Savings Mobilized under PRIME.
Figure 6: Income Generating Activities (IGAs)
associated with Microcredit under PRIME
Program.
20
300
19
250
18
200
Thousand
Million Taka
17
16
15
14
13
150
100
50
12
0
11
3
4
1
2
3
4
1
2
3
4
10
1
2
3
4
1
2
3
4
2008
Agricultural
2009
2010
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
1.3
2009
2010
Livestock Related
Non-farm
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
Outreach of PRIME
In this section, we will focus more on the aggregate outreach of the program and quantify some of
the dynamic aspects of the outreach of the program under PRIME. As mentioned before PRIME is
currently implemented in all 5 districts of Rangpur. As of December, 2010, 329.3 thousand members
4
This is clearly evident from the MIS data both PKSF and POs shared with the research team. The MIS data
included very detailed IGA criteria. While fungibility of money makes it difficult to relate a use with the
availability of credit within the household, the previous analysis using household data generally found that the
bulk of the credit is diverted to many income generating activities.
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PRIME 3rd Round Report.
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were brought under PRIME and cumulatively a total of 829.2 million taka in (flexible) loan was
outstanding by the end of 2010 calendar year (see Error! Reference source not found.).
The maturity of the program is also visible in the size of loan outstanding per borrower as reported
in the administrative data (see Error! Reference source not found.). In the beginning of 2009 (in the
first quarter) the average outstanding loan size was Tk. 1,813 per borrower. The loan size reached
Tk. 2,518 per borrower by the end of 2010 (about 40% growth over the same time).
The increase in the average loan size was partly possible because the disbursement outpaced the
total number of disbursement which one could see in Error! Reference source not found.. The total
number of loan disbursed did not show any visible trend over these two years (2009 and 2010) while
the total disbursed amount in aggregate exhibited an upward trend over the same period. One
should note that there was a clearly seasonal pattern visible in the loan disbursement pattern
(though there was an upward trend in the amount disbursed). This suggested a complex interplay
between employment, agricultural productivity and credit demand stemming from typical
seasonality. As of December, 2010 a total of Tk. 492.4 million was disbursed to about 67 thousand
beneficiaries (see Error! Reference source not found.).
The PRIME program had also been able to mobilize savings from the ultra-poor households (see
Error! Reference source not found.). In the beginning of 2009 calendar year, a total Tk. 11.2 million
was mobilized under the program. By the end of 2010 calendar year, this number reached Tk. 18.6
million (a 65% increase over the two years, reaching a total number of about 329 thousand
beneficiaries in the program area). While it was not shown here, the average savings stayed
surprisingly flat over this period.
From the official data it was also possible to understand and follow the trends in income generating
activities associated with microcredit disbursement toward the program beneficiaries. The official
figures actually revealed that the IGAs pursued by the program beneficiaries could be classified into
three broad categories. The agriculture related IGAs had been one of the major IGAs and as of
December, 2010, a total of 134 thousand agriculture related IGAs were undertaken cumulatively by
the program beneficiaries (see Figure 6). However, it always remained smaller compared with the
other broad IGA categories.
Livestock related IGAs (second broad category) dominated in the recent time and by the end of 2010
calendar year, 268 thousand livestock related IGAs were associated with microcredit borrowing
under the program (see Figure 6 depicting a 22% growth over the entire two and half year period).
Non-farm related IGAs (mostly for small businesses) were the third and most frequent categories
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PRIME 3rd Round Report.
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under which the credits were taken by the borrowers. However, this category experienced the
lowest level of average monthly growth over the entire period. By the end of 2010 calendar year, a
total number of about 223 thousand IGAs were associated with non-farm income activities.
Table 1 showed the some of the major sub-categories of IGAs within all three broad categories.
Within agriculture related IGAs, crop production dominated with about 97 thousand IGAs by the end
of 2010. This was followed by homestead gardening cumulatively which stood at about 19 thousand
IGAs at the end of 2010. Among livestock category, goat rearing garnered the most dominant IGA
category with 77 thousand times with dairy cow and heifer rearing securing second and third most
prominent IGAs under the livestock category with frequencies of about 68 thousand and 60
thousand frequencies. As we mentioned earlier small business dominated under non-farm related
IGAs with rickshaw and van purchase (and maintenance) as the next most important category with a
cumulative number 54 thousand times such IGA taken by the end of 2010.
Table 1: Income Generating Activities (Thousand IGAs) by Major SubCategories.
2008
2009
2010
Agriculture Related IGAs
of which:
Crop Production (as per crop calendar)
Homestead Gardening
38.5
81.2
134.4
23.9
10.0
57.6
14.0
96.7
18.9
Livestock Related IGAs
of which:
Goat rearing
Dairy cow rearing
Heifer rearing
54.8
158.2
268.3
26.7
10.5
6.8
53.6
39.7
29.2
77.0
68.4
59.6
Non-Farm Related IGAs
of which:
Small business
Rickshaw/Van purchase/maintenance
82.1
151.8
222.9
51.2
21.1
95.1
38.4
137.2
54.2
Source: InM Research Team’s Calculations using PKSF Administrative Data from PRIME
Cell. Note: Numbers are in thousands
There were number of other services that PRIME offered to its beneficiaries. It provided remittance
services (i.e. helping the beneficiary households receiving money transfer mostly from domestic
sources where the income earning member of the household is living). Over the life of the program
by the end of 2010 calendar year, the program assisted 8,334 times with such financial transactions.
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PRIME 3rd Round Report.
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Figure 7: Number of Remittance Services Figure 8: Number of Health Services (in
Rendered under PRIME.
Thousands) Rendered under PRIME.
9,000
1,200
8,000
1,000
7,000
6,000
800
5,000
600
4,000
3,000
400
2,000
200
1,000
0
0
3
4
1
2008
2
3
4
2009
1
2
3
4
3
2010
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
4
2008
1
2
3
2009
4
1
2
3
4
2010
Source: InM Research Team’s Calculations using PKSF
Administrative Data from PRIME Cell.
Health services had also become an important and integral part of the PRIME program in the recent
year and we could also track the outreach of health services from the official data. Such data
revealed that by the end of 2010 calendar year, 1.1 million units of health services (defined by
primary care visits and consultations and drug dispensing in the recent time) were disbursed to the
beneficiary households in the program areas. This particular component also experienced an
unprecedented growth rate of more than 50% every month over the two and half years ending in
2010 calendar year.
1.4
Overview of this Report
We addressed the impacts of the program on the household and focused on selected outcome
variables most pertinent to the program and as guided by the Terms of References. As we mentioned
earlier, we also focused on the supply-side factors such as productivity, efficiency and sustainability
of the branches implementing PRIME. As such we would address the issue of sustainability with
PRIME branch implementing PRIME as units of analysis.5
In Section 2, we described the data we would be using to identify the impacts of the program on
selected household outcome variables. As one would see, we exploited the panel structure of the
data set as much as possible to find different synthetic cohorts which differ by the intensity of
5
It is commonly mentioned in the literature that the MFIs face the double bottom-lines as they try to reach to
poor households and provide them with financial services under the constraint that the program itself has to
be self-sustaining in the operational sense. This is really important because this constraint will especially be
binding for programs that try to reach to the ultra-poor households living in remote areas suggesting higher
cost and lower productivity.
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association of PRIME. The intensity was defined by the age of participation into the program
regardless whether the household was a continuing member of the program or not. As our findings
revealed, selection was an issue as the non-participants (precluding the households who were
participating in the other NGO/MFI programs) were different from the PRIME participants (see
Section 2.3). However, later we showed that the outcome variables were more similar on average
(and these means were not different in the statistical sense) between the “benchmark” group
(defined in Section 2) and households that were member into the program for less than one year
(see Section 3.3). This suggested that the determinants of the selection worked more like natural
barriers to participate into the program albeit being similar in many economic outcomes.
In Section 3, we provided evidence on the effectiveness of the program on a number of parameters.
We first looked at consumption ordering. It was evident from the data (by comparing all three
rounds) that the monga situation in the greater Rangpur regions improved over last three years (i.e.
between 2008 and 2010). However, we found a systematic association between age of PRIME
participation and the likelihood of reporting a better food situation. This trend was later found to be
present in many other household characteristics.
In Section 4, we provided an overall assessment of the program bringing in both the impact we
identified in the previous section and the sustainability issue which we addressed separately. The
program was intended for hard-to-reach ultra poor households in the char areas. As such the
branches exhibited lower productivity and were not able to become operationally self-sufficient
after three years in operations. As such positive impacts of the program were especially important to
justify the program. But it was important to realize that positive impacts of such program might take
a long time to surface (two years or more). We mentioned few limitations of the present study in the
conclusion.
In the appendix, we analyzed branch level data we collected to address the issue of sustainability
and other productivity parameters. We found that the branches implementing these programs are
yet to reach a sustainable level of operations. The branches implementing PRIME program were less
productive as their loan outstanding per borrower or borrowers per credit officer were lower than
what it was required for a branch to recoup its operating cost. While the social returns from reaching
out to hard-to-reach ultra-poor households in the char and other remote places probably justified
continuation of the program, to sustain the program on its own, it would require innovative
practices to pull down costs and be more efficient.
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2. Methods
2.1 Sampling
PRIME extended in roughly three stages. In the first stage (during 2006), PRIME was implemented in
5 upazillas of Lalmonirhat district. In 2007-08, the program was extended to selected 18 upazillas of
other 4 districts in the greater Rangpur region (namely Gaibandha, Kurigram, Nilphamari and
Rangpur). First impact evaluation of PRIME program used census data6 of 482,984 poor households
in these 23 upazillas as its population of program upazillas. The census covered a total of 23
upazillas, 209 unions, 2,531 villages and 482,948 households.
Later, in the first phase of impact evaluation study, the research team at InM targeted a total of
5,240 households for a household survey from all five districts which consist of 16 upazillas, 61
unions and 271 villages (using multistage cluster sampling technique). A total of 5,308 households
were surveyed eventually and the first evaluation report was based on this sample (including
information from the baseline census where available).
As the program was later extended in the other 12 upazillas (the third phase of the extension hence
the program came to cover all 35 upazillas of the 5 districts of greater Rangpur region), the second
phase of the impact evaluation targeted the households covered in the first survey along with
additional about 2,500 households in the new area covered under PRIME. The second phase of the
evaluation used information for all these households (a total of 7,212 out of 7,432 households
included in the household survey) and assessed the impact of the program using a much longer
period of data. One of the major findings was that the identification of the impact actually needed
a longer period (usually two years or more) for the effects of the program to surface.
Table 2: Sampling
Phase 1
Phase 2
Phase 3
5
20
71
271
27
298
4,606
702
5,308
5
27
108
362
39
401
6,552
880
7,432
5
26
102
340
6,988
District
Upazilla
Union
Program Village
Control Village
Total village
Household in program village
Household in Non-program village
Total number of household
Source: PRIME Household Surveys, Various Rounds.
6
The census data was primarily collected in order to identify and select households for program delivery. The
information gathered for this purpose was later linked with the later rounds to assess the program in the first
and second rounds.
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By the year 2009, PKSF has extended the PRIME program to all 35 upazillas in 5 districts. So in third
round of program evaluation study, there was no non-program village. In 2009 total number of
villages was 401. Number of households in some villages was very small. Third phase of the impact
evaluation have excluded those villages from the sample where number of households was less than
9. Using this exclusion criteria total number of village selected for survey was 340. Total number of
households in these villages was 6,988. Details of sample size in all phases are summarized in Table 2.
Field survey for the third phase started on December 1, 2010 and ended on January 15, 2011. The
research team trained 50 field enumerators and 5 supervisors one week of extensive training on
household questionnaire. From the list of 6,988 households, enumerators could reach for 6,830
households during the survey time. The field enumerators could not reach rest of the households
because of permanent or temporarily migration or incorrect addresses. Rate of attrition was much
smaller compared to any other years of impact evaluation. There were two possible explanations for
high rate of reaching the households in the list by the enumerators: (i) last survey was conducted in
February-March 2010. So the event of migration (or perhaps deaths) of household member was
supposed to occur less frequently in these eight-nine months; and (ii) this year each enumerator
was paid based on number of questionnaire completed properly. So the incentive to find the
household was high for the enumerators.
2.2 Data
This section describes some characteristics of all households in the sample. We have already
mentioned in Section 2.1 that the sample was drawn from a group of households that were eligible
to participate in PRIME program. The program was initiated targeting the ultra poor households of
that region. So the summary statistics of the household characteristics in this section supposed to
reflect the characteristics of ultra poor households.
Table 3 showed that our sample households spent about 80 percent of their expenditure on food.7
This indicated that our sample households were mostly from the poorer group of the society. Most
of the household head’s primary occupation was daily wage earning. Income from this occupation
was more prone to shocks because of its relation with seasonality.8 Daily wage worker in agriculture
also depend on the seasonality of different crop. In our sample household, those who are engaged in
daily wage worker in agriculture, average working day in Bangla months of Vadro, Ashwin and Katrik
7
It is common to observe that the proportion of expenditure on food to total household expenditure falls as
income of the household increases. This outcome is usually robust, so researchers often use the share of
expenditure on food as the measure of poverty. For example, household that spend more than 60 percent of
their expenditure on food might be considered as poor (see Haughton & Khandker, 2009).
8
For example, opportunity to work in brick-field as daily wage worker stopped in rainy season.
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Table 3: Summary Statistics for Selected Variables from Third Phase of Household Survey.
Mean
Standard
Deviation
12.4%
43.8
1.5
33%
13
2.6
Maximum education in a Household (years)
4.2
3.1
Family size
4.01
Sex Ratio (Number of female/Number of male)
1.2
1.5
0.9
Electricity access
8.8%
28.4%
Access to tube-well or tape water
98.8%
10.7%
Access to sanitary latrine
52.8%
49.9%
53.9%
49.9%
8.6%
28.1%
25.4%
43.6%
22.9%
42.0%
15.4%
36.2%
Total land (decimal)
11.6
47.7
Total livestock (number)
4.5
5.8
Total number of cow (number)
0.6
1.1
Total number of goat (number)
0.3
0.8
Total number of poultry (number)
3.6
5.3
Total asset value including land (Taka)
1,09,330
2,47,608
Total asset value excluding land (Taka)
31,852
41,684
Savings (Taka)
2,459
10,191
Total Income (Taka)
49,104
1,14,294
Total Income from livestock (Taka)
1,513
5,686
Expenditure on food (Taka)
35,542
15,133
Nonfood expenditure (Taka)
8,752
12,991
Household head characteristics
Household head is female
Household head’s age (years)
Household head’s education level (years)
Family characteristics
Infrastructure facilities
Occupation and source of income
Primary occupation of household head wage worker
Primary occupation of household head self employment in
agriculture
Primary occupation of household head self employment in non
agriculture
Any of the household member migrate
Household head migrate
Physical and financial asset
Household income (yearly)
Household expenditure (yearly)
Source: Household survey. The total number of observations is 6,830.
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PRIME 3rd Round Report.
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is about 10 days. Average working days in these three months were much lower than the average
working days of others month in the year. So, the households that depended on the income from
daily wage earning in agriculture faced seasonal income negative “shock” that affected consumption
adversely in those months. Percentage of household heads involved in self-employed agriculture
activities was very low because of little ownership of cultivable land. Average land holding of the
household was very marginal, about 24 percent of the sample households are landless. Because of
the unemployment in the lean season, some household members migrated to other districts of the
country. About 23 percent households in our sample experienced the migration of at least one
member in the last year, and in most of the cases household head was the one who migrated.
Percentage of household having access to hygienic drinking water was high. Use of hygienic toilet
was around the projected national average level but access to electricity was found much below the
national average level. Another interesting feature was that sex ratio was higher than one, which
meant that number of female was higher than male for most of the households. In our sample area
wage rate of female was much lower than wage rate of male, so higher value of sex ratio actually
indicated the lower earnings of the households.
2.3 Identification
It was a big challenge to identify the impact of a particular program using non-random intervention
(even randomization is not sufficient when the impacts take long time to surface). It was important
to address the eligibility criteria of the program first. There were some selection criteria to identify
who were eligible to participate in program. To participate in the PRIME program the household had
to fulfill at least one of the three conditions: (i) Monthly income of household less than 1,500 Tk; (ii)
Primary occupation of household head was daily wage earning; (iii) Land ownership was less than 50
decimal. However, meeting eligibility criteria themselves did not guarantee participation because
from the households’ point of view it is voluntary. Field officer and households (or beneficiaries)
jointly determined an outcome entailing whether participation took place or not. One restriction
regarding the program participation was that households having intervention from programs offered
by any NGO-MFIs (including PRIME POs) were not eligible to participate in the program. Still we
found that there was a group of households who never participated in any sort of program initiated
by any MFI/NGOs during the period covered under all three rounds of household surveys (i.e. 20082010 calendar years). We used these non-participants as a “benchmark” group.9 One should
understand that even this group could not be considered a proper control group because their
9
We have systematically omitted the households that received non-PRIME MFI services during our period of
study as suggested during any of the three rounds of studies. As such we have limited our analysis to 2,948
“benchmark” households and 1,353 PRIME participants only.
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baseline characteristics were observationally different from the new participations (i.e. households
who received the “treatment” within last one year, see Table 4).
Table 4: Summary Statistics of Selected Household and Village Characteristics by Participation Status.
Village characteristics
Household in char
Household head characteristics
Female headed household
Age of household head (years)
Education of household head (years)
Household head currently married
Family characteristics
Maximum education (years)
Family size (number)
Occupation and income source
Household head migrate
Household head's primary occupation wage worker
HH head's primary occupation self-employed agriculture
HH head's primary occupation self-employed non-agriculture
Number of income source
Infrastructural facilities
Household have access to electricity
Household drink from tube-well/tap
Household use sanitary latrine
Outcome variables
Total income
Total land
Total livestock
Source: Household survey.
PRIME participant
(Age of
participation 0 to
1 year)
N=535
Nonparticipant
N=2,948
p-value
10.47%
22.25%
< 0.01
6.54%
40.63
1.76
93.08%
18.66%
45.49
1.32
79.88%
< 0.01
< 0.01
< 0.01
< 0.01
4.52
4.14
3.67
3.68
< 0.01
< 0.01
15.70%
56.45%
8.79%
26.54%
1.83
16.08%
57.60%
8.24%
17.61%
1.66
= 0.41
= 0.31
= 0.34
< 0.01
< 0.01
10.84%
98.32%
55.89%
5.87%
99.05%
47.66%
< 0.01
= 0.06
< 0.01
46,933
10.37
4.33
45,712
11.65
4.25
= 0.43
= 0.32
= 0.38
Table 4 showed means and percentage comparisons between non-participants and new participants
in PRIME. Most of the household characteristics revealed that these two groups differ significantly.
For most of the cases these were the determinant variables that were associated with the decision
whether a household would participate in any MFI/NGO initiated program or not. For example
percentage of non-participant household living in char was higher compared to new participants. In
char areas MFI/NGO activity was usually very limited because of inaccessibility of char area in terms
of communication, so living in char increased the probability of not participating in the program.
Different characteristics of household heads also determined whether a household would
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participate or not. Table 4 shows that female headed households were less likely to participate. In
our sample females were reported as household head not because they were taking decision over
their male counterparts, rather in most of the cases they were either widow or separated. This was
supported by current marital status of non-participant which showed that the percentage of widow
or separated group was higher in non-participant group. Having multiple sources of income was
important for the households to participate in the microfinance/microcredit programs. If a
household participated in program and took a loan then they had to repay the loan in weekly
installment. To avoid loan default the household should have smooth income source which was
possible if the household diversified and had multiple income sources. Diversified income sources
reduced the seasonal income shock which was very much observed in the sample area i.e. in Greater
Rangpur Region. So number of different income source in household could determine the
participation decision of the family. Table 4 also showed that lower age of household head increase
the probability of joining the program. Lower aged people were more capable of generating higher
earnings, which in turn increased the probability of participating in program.
But the above mentioned criteria were not used to select the eligible group for participating in the
program. Already we had mentioned that the key indicators of eligible group selection were income,
land ownership and occupation of household head. The objective of the program was that over the
year the participants would do better in terms of these indicators or outcome variables. Our
assumption was that at the initial stage of participation they would be similar with those households
who had never participated in any program in terms of those variables. Table 4 showed that these
two groups did not significantly differ in terms of total income, land ownership and livestock. Also
the percentage of household where primary occupation of household head was wage worker did not
significantly differ between these two groups. These results from Table 4 indicated that these two
groups were in same platform. So now we could use this non-participant group as our control group
to see whether longer involvement in program had any impact on some selected outcome variable.
As we mentioned in Section 2.1, PRIME was first implemented in 5 upazillas of Lalmonirhat district.
Eventually all 35 upazillas in 5 districts were brought under coverage. Since selection was an issue (as
evident from Table 4), we compared between households who had already been selected into the
program. However, to identify the impact of the program we compared the households who had
been with the program longer with the households who just started into the program. Thus, the age
of participation into the program acted as the main identifier for the impact of the program. One
should understand that the main purpose of this evaluation exercise was to estimate the treatment
of the treated. The treatment in this case was such that it introduced the households to the
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microfinance interventions. As such PRIME created a snowball effect for the participating household
(on average) and established in higher growth curve (in income, asset and status of food security). So
looking at PRIME participants with different length of participation into the program would give us
the intensity of the impact and this was repeatedly identified throughout this report for different
outcome variables. Different households participated in the program at different time. We divided
the PRIME participant groups in four categories – households that were member for less than one
year, households that were member of two years, households that were member of three years and
those who were members for four or more than four years. Table 5 shows the distribution of PRIME
membership duration by districts.
Table 5: Percentage distribution of PRIME membership duration by district
PRIME membership duration
District
0-1
2
3
(N=535)
(N=333)
(N=268)
24.86%
25.23%
20.52%
Gaibandha
4+
(N=217)
14.75%
Kurigram
19.63%
23.12%
22.39%
18.89%
Lalmonirhat
6.73%
17.12%
30.97%
38.71%
Nilphmari
23.18%
14.71%
15.67%
11.52%
Rangpur
25.61%
19.82%
10.45%
16.13%
Source: Household survey.
Table 5 showed that most of the members with duration of membership three or more were from
Lalmonirhat. This was because PRIME was first implemented in Lalmonirhat district. But percentage
of new participant (0-1) gave the indication that the rate of expansion of program in Lalmonirhat in
recent year fell. Some basic summary statistics for these groups were presented in Table 6.
Table 6: Summary statistics of selected household characteristics by duration of PRIME membership (in
years)
Household has electricity
Household has access to tube-well/tap
water
Household has sanitary toilet
Household head is female
Household head’s age: years
Household head’s education: years
Household size
Primary occupation of household head is
wage worker
Source: Household survey.
0-1
(N=535)
10.8%
PRIME membership duration
2
3
(N=333)
(N=268)
8.1%
7.8%
4+
(N=217)
13.8%
98.3%
99.1%
98.9%
99.1%
55.9%
6.5%
40.6
1.8
4.1
56.5%
9.0%
41.7
1.5
4.4
53.7%
9.3%
43.1
1.4
4.3
68.2%
6.9%
44.3
1.3
4.4
56.5%
53.8%
55.6%
48.9%
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Table 6 showed that these four groups were very much similar in different household specific
characteristics. Only in terms of household using sanitary latrine, the households in the earlier
cohort of participants came up with good position (which itself might be result of the program). Also,
they were in comparatively better position in terms of access to electricity. These variables were
kind of outcome variable and this difference in percentage could be treated as the impact of longer
involvement with the program. Table 6 also showed that percentage of household whose primary
occupation was daily wage worker is less among the eldest group. It had been evident that daily
wage worker were the most vulnerable group in this region because of irregularity in income in lean
season. So the households that depended on daily wage earning were subject to consumption shock
in lean season. PRIME encouraged their borrowers to use their loan in different income generating
activities (IGA) like buying a rickshaw, or raising livestock, or meet the agriculture related expenses.
PRIME also provide training on different IGA for their members. The idea was that promoting IGA
might help them to move away from daily wage worker as their primary occupation to some selfsustaining activities. Table 6 showed that longer involvement in program could help them to
change their primary occupation status.
2.4
Notes on Analytical Framework
As evident from the previous section the analytical underpinning of this evaluation exercise relied on
identifying the “treatment-on-the-treated”.10 There are two different ways we intended to do it in
this report. If we denote a particular outcome variable as
, where denotes a household and
denotes receiving in the treatment (e.g. participating in PRIME);
= 0 means non-participation. We intend to identify
(
|
= 1 means participation while
= 1) − ((
|
= 1) which is
defined as treatment on the treated. Unfortunately we do observe outcome for the same household
in the absence of the treatment (
). The source of bias stems from selection as participation
depends on unobservables and they can potentially confound the outcome variables. As such we
used the variation in the length of participation because the selection bias would be smaller for this
group. We showed that the households not participating in any MFIs are different based on
observed characteristics (hence likely based on unobservables as well). However, the differences in
observed outcomes (which we expected would be affected by PRIME participation) are many times
not significant. Hence, we assumed that while there were natural barriers for households to
participate in PRIME they were very similar in terms household welfare (indicated by income and
assets) that PRIME intervention intended to alter. Hence, comparing households with different
10
Please see Angrist, J. D., & Pischke, J.-S. (2008). Mostly Harmless Econometrics: An Empiricist's
Companion. Princeton, NJ: Princeton University Pres.
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duration in PRIME (excluding the households participating in other MFIs exclusively) and also with
the “benchmark” households (i.e. the ones not participating in any NGO/MFI programs over last
three years) would identify the treatment-on-the-treated we intended to identify.
2.5 Discussion and Concluding Remarks on Identification
The main objective of PRIME program was to provide the ultra-poor households in the monga prone
areas with access to finance and other support programs (such as IGA related training and in the
recent time, health services). As such, this program brought households into the program who would
not have access to microfinance otherwise. This resulted in “snowball” effects for the households
that might lead way to a better economic situation. So the year(s) since participating into the
program is an important marker to identify the efficacy and effectiveness of the program. If we
assumed that at the time of first selection into the program, similar households participated into the
program, then the new program participants could be used as a legitimate control group (however
synthetic it might be) and we could compare the outcome of these group(s) with the more seasoned
PRIME participants to identify the impact on the selected outcome variables. It was important to
realize this reasoning would allow us to use households who did not continue to participate into the
program. This was because, through providing the initial access to microcredit/microfinance, the
PRIME program supposedly revealed that the participating households were capable of continuing in
a rural credit program (offered by possibly another microcredit organization). Thus, we identified
the total effects of the program through this comparison because later effects would not be
possible but for PRIME. This was an important conceptual point that would help us interpreting the
results we would turn to in the next section on impact of PRIME on the ultra-poor households.
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PRIME 3rd Round Report.
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3. Impact of PRIME on Selected Parameters
3.1
Introduction
This section would present the impacts on different parameters (as suggested by the Terms of
References) that one could empirically associate with PRIME interventions. Using non-random
assignment for an intervention it was challenging to identify impacts of a program and associate it
with causal implications. On the other hand, as we saw in the previous reports it might take more
than two years for impacts of programs such as PRIME to surface. It was operationally impossible to
restrict a population (or a sample of population) to be refrained from getting microfinance
interventions for three years in a country where penetration of such programs (PRIME or otherwise)
had been very high.
As we argued in the previous section, the age of the participants could (under certain assumptions)
identify the intensity of the program’s impact across different households. A direct corollary of this
conjecture was the positive “impacts” of the programs (e.g. higher consumption ordering or
households’ asset accumulations) would be associated with earlier a household was associated with
PRIME (defined as “age” in this report, see Section 2). In the next couple of sections, we would
systematically show association of changes in the selected parameters with age of PRIME for
surveyed households starting with consumption ordering.
3.2
Consumption Ordering
One of the major problems of identifying impact of a program was to separate out the effect of a
program from the secular changes in the outcome variables even if one had control and treatment
groups. We saw this in the data where we documented the changes in consumption orderings for all
households over all three rounds of household surveys (spanning over about three years of
information) for most of the household.
Figure 9 showed the changes in consumption ordering as reported during the time of the survey
which one could regard as a more “normal” time (as opposed to a period when food deprivation was
more pronounced i.e. during the monga time). We found that the incidence of “occasional
starvation” was not common situation for the households during any of the rounds and it had
become even for uncommon in the last round (i.e. in 2010). The most remarkable change was
households reporting having three full meals increased dramatically (by 20 percentage points)
between the last two rounds of surveys (i.e. between March, 2010 and December, 2010-January,
2011). It seemed that households previously reporting having three half meals were more likely to
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PRIME 3rd Round Report.
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report consuming three full meals during the “normal” periods (see Figure 9) during the 3rd round of
the survey.
We perhaps saw a similar change in consumption ordering during monga for the same set of
households. There was a 12 percentage points increase in households reporting having three full
meals every day and a similar increase for the households reporting a three half meals. The most
dramatic change was evident in the “Occasional Starvation” category which experienced a decline of
19 percentage points between the second and the third rounds of households survey.
Figure 9: Distribution of the Households over Figure 10: Distribution of the Households over
Consumption Ordering during “Normal” Time.
Consumption Ordering during Monga Time.
50%
60%
40%
% of Households
% of Households
50%
40%
30%
20%
30%
20%
10%
10%
0%
0%
Occasional
Two
Three half Three full
Starvation meals/day meals/day meals/day
Round 1 (N = 5,308)
Round 2 (N = 7,090)
Round 3 (N = 6,758)
Source: InM Research Team’s calculations from Household
Surveys (Various Rounds).
Occasional
Two
Three half Three full
Starvation meals/day meals/day meals/day
Round 1 (N = 5,308)
Round 2 (N = 7,090)
Round 3 (N = 6,758)
Source: InM Research Team’s calculations from Household
Surveys (Various Rounds).
Another way to look at the same issue would be to look at cumulative distribution over the same
consumption ordering so looking at the consumption up to a certain level of food intake (measured
by number of meals each day). This also highlighted (see Figure 11) the fact that much of the
improvement in number of meals were within the top two levels of consumption ordering while
there was a significant decrease in people having less than three meals (full or half) as well. As Figure
12 suggested there was a general improvement in food intake (as measured by consumption
ordering) during monga time as well over the three rounds of surveys (i.e. over 2008-10 period).
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Figure 11: Cumulative Distribution of Figure 12: Cumulative Distribution
Consumption Ordering during “Normal” Time
Consumption Ordering during Monga Time
100%
100%
80%
80%
60%
60%
40%
40%
20%
20%
0%
0%
Three full Three half
Two
Occasional
meals/day meals/day meals/day Starvation
Three full Three half
Two
Occasional
meals/day meals/day meals/day Starvation
At Least
At Least
Round 1 (N = 5,308)
Round 2 (N = 7,090)
Round 3 (N = 6,758)
Source: InM Research Team’s calculations from Household
Surveys (Various Rounds).
Round 1 (N = 5,308)
of
Round 2 (N = 7,090)
Round 3 (N = 6,758)
Source: InM Research Team’s calculations from Household
Surveys (Various Rounds).
The analysis of the data using the consumption ordering suggested that it would be important to
separate out the impact of PRIME on consumption ordering from the overall trend in the
improvement in the everyday’s meal patterns. As we argued in the previous section, the age of
participation in the PRIME program would provide us some indications whether the program worked
on the targeted households or not. In the next set of figures we showed this.
We found that the fraction of households reporting at least three meals per day was higher for
households that participated earlier in the PRIME program compared to the households who were in
to the programs for less than a year. The households in the latter category reported 74% of the time
having at least three meals per day while the households who have been in the program for 4 or
more years (mostly the households in the Lalmonirhat district) reported having at least three meals
82% of the time suggesting an increase of about eight percentage points. The “impact” increased
with the age of PRIME participation.
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Figure 13: Fraction (%) of Household Reporting Figure 14: Fraction (%) of Household Reporting
at least Three Meals/Day during “Normal” at least Three Meals/Day during Monga Time.
Time.
Normal Time
Monga Time
65%
84%
82%
60%
82%
60%
80%
80%
55%
52%
78%
50%
76%
76%
50%
46%
74%
45%
74%
72%
40%
0-1
2
3
0-1
4+
2
3
4+
Duration of PRIME Participation (in Years)
Duration of PRIME Participation (in Years)
rd
Source: InM Research Team’s estimates using 3
Round of household surveys. The benchmark
indicates households who did not participate in any
MFI/NGOs over 2008-2010 (N = 2,907). The vertical
axis shows the fraction of household (%) reporting
having at least three meals/day currently (i.e. at the
time of the third round interview).
rd
Source: InM Research Team’s estimates using 3
Round of household surveys. The benchmark
indicates households who did not participate in any
MFI/NGOs over 2008-2010 (N = 2,907). The vertical
axis shows the fraction of household (%) reporting
having at least three meals/day during the last
monga.
We saw the similar pattern for consumption ordering during the time of the monga. While only 46%
of the households reported having at least three meals every day among the PRIME participants with
less than a year in the program, 60% reported having at least three meals that had been in the
program for 4 to 5 years. This suggested that in comparison with the new participants (households
without the influence of the program) the beneficiaries who experienced it earlier enjoyed a
fourteen percentage points higher incidences of a better meal comprising of three meals per day.
One of the important findings of this report was that we identified a group of households who had
been consistently out of the programs offered by any NGO/MFIs. This gave us a cleaner
“benchmark” to compare our results with.11 To qualify this claim we should expect to see that the
benchmark households should behave similar to the households with less than one year of program
participations. Interestingly, both for the “normal” time (basically the time of the survey that the
research carried out between December and January) and monga time (the calendar months of
September through middle of November), we found this to be the case. For the benchmark cases,
11
One should again be careful to interpret these households belonging to a control group because we found
these households to be different from the households participating into the program. See Section 2 for a
comparison between many baseline parameters between these two groups.
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PRIME 3rd Round Report.
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73% of the households reported having at least three meals per day during the “normal” time (as
oppose to 74% for the household with less than one year in the program) while 42% reported having
at least three meals for the “benchmark” households during the last monga (in comparison to 44%
of the households with less than one year in the program) .
We further analyzed the data using the additional (marginal) probability of reporting three meals per
day in linear probability models using the households without any participation with MFI/NGO over
the three years of surveys as the baseline group. For “normal” time (see Figure 15), we found that
the benchmark case and the households with less than one year of program participation were
indistinguishable in the statistical sense when it came to reporting at least three meals per day.
Interestingly, the marginal impact of age or participation (in PRIME) did not become statistically
significant even after two years. However, the households with three or more years of participation
gained significantly (seven and ten percentage points respectively, see Figure 15).
Figure 15: Marginal Impact of the Program over Figure 16: Marginal Impact of the Program over
Age of Participation on Reporting Three Age of Participation on Reporting Three
Meals/Day during “Normal” Time.
Meals/Day during Monga Time.
24%
16%
19%
12%
10%
8%
7%
4%
9%
9%
6%
3%
4%
2%
2%
0%
-1%
-4%
-6%
0-1
16%
14%
2
3
4+
Age of PRIME Participation in Years
0-1
2
3
4+
Age of PRIME Participation in Years
Source: InM Research Team’s estimates using household surveys. The benchmark indicates households who
did not participate in any MFI/NGOs over 2008-2010 (N = 2,907). The vertical axis shows the coefficients for
each category in the horizontal axis. The vertical line for each point shows the 95% confidence intervals.
We saw a similar pattern for the case of consumption ordering during monga as well. However, the
“marginal” impacts (as identified by the coefficients for each category) were little stronger and
statistically significant after two years of participations into the program (as seen in Figure 16).
Again, the “benchmark” households and households with less than one year of participation were
statistically same when reporting at least three meals per day. However, the positive impacts of the
program showed up after two years and the households with four to five years of participations in
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PRIME 3rd Round Report.
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the program would give an additional 16 percentage point higher probability of reporting at least
three meals per day.
Box 2: A Composite Dynamics of Consumption Ordering during Monga Season, 20082010.
60%
50%
0-1
2
40%
3
4+
30%
Never
20%
10%
2008
2009
2010
Rather than looking at one cross-section, we could potentially look at the outcome variables
over last three years. Here we looked at consumption ordering during monga season over
last three years (i.e. 2008, 2009 and 2010). We found that the households with four or more
years of participation had a more impressive growth trajectory. With only exception of
households who participated in PRIME for two years† as reported by the end of 2010, all the
households improved their food security during monga over the calendar years of 20082010. It was also interesting to find that the households who did not report participating in
any NGO/MFI programs and households who participated less than a year in the program
reported very similarly over the study period. One should expect for these households to
diverge in the coming years.
†At this moment the research team did not have a satisfactory explanation for this anomaly. However, the
households with duration of PRIME membership of two years coincide with the expansion of the program to new
areas (13 new upazillas in the Greater Rangpur region) and these households were likely to be different from the
benchmark and other households receiving benefit under PRIME. The severities of floods were higher in 2009
which might explain the deterioration of food security of these households between 2008 and 2009. However, this
justification would require further analysis of the data.
3.3
Other Outcome Parameters and Age of PRIME Participation
We used the same methodological scheme to identify the impact of the program for other outcome
parameters. While we have found that the “never” participants (the households that did not
participate with any NGO/MFIs over the three rounds of households surveys during the period of
2008-2010 calendar years) were observationally different. However, as we claimed (see Section 2)
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PRIME 3rd Round Report.
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these households signified a market with a further barrier to access for the MFIs to overcome and
presented with a reasonable comparison group.
Table 7: Comparisons between “Benchmark” Households and PRIME Participants with less than
One Year in the Program.
Land and Non-Land Assets
Total Land Holdings (in Decimal)
Total Number of Livestock
Total Number of Cow(s)
Total Number of Goat(s)
Total Number of Poultry
Asset Values
Total Asset Values (Thousand
Taka)
Total
Non-Land
Asset
(Thousand Taka)
Total Savings (Taka)
Total Housing Value (Taka)
Income
Total Income (Taka)
Total Income from Livestock
(Taka)
Expenditure
Food Expenditure (Taka)
Non-Food Expenditure (Taka)
Benchmark Households
(Non-Par cipants)
(N = 2,907)
Households with less than
one year in PRIME
(N = 533)
p-value
11.7
4.2
0.54
0.30
3.4
10.4
4.3
0.46
0.29
3.6
= 0.651
= 0.757
= 0.009
= 0.950
= 0.467
103.59
97.57
= 0.661
27,572.69
30,534.31
= 0.110
1,794.74
11,035.81
2,919.62
13,843.62
= 0.060
< 0.001
45,683.82
46,976.39
= 0.857
1,286.47
978.15
= 0.248
32,250.54
7,018.09
36,517.92
8,781.34
< 0.001
= 0.003
Self-Employment
Months Engaged in Non1.93
3.30
< 0.001
Agricultural Self-Employment
Days per Month Engaged in
9.48
10.81
= 0.181
Agricultural Self-Employment
Source: InM Research Team’s estimates using household surveys. The benchmark indicates households who
did not participate in any MFI/NGOs over 2008-2010.
For the case of consumption ordering, we found that some of the baseline characteristics were
different between the new PRIME participants and the “benchmark” households the program
participants. But most of the outcome variables for the “benchmark” households and the
households just entered the program with less than one year of participation were surprisingly
similar (see Table 7). Most of the outcome parameters (such as asset and income) did not exhibit any
between group differences which were statistically significant up to any reasonable levels of
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confidence. Expenditure (both on food and non-food items) were different between the two groups
and for both cases the participants, even with less than one of year of participation, exhibited higher
spending. Interestingly, PRIME participants also spent higher number of months in non-agricultural
self-employment implying incentives the participants received to engage in non-agricultural
enterprises.
Table 8: Land and Non-Land Assets against Age of PRIME Participation.
Benchmark
Level*
[1]
[2]
[3]
[4]
[5]
Log(Total
Land)
Total Number
of Livestock
Total Number of
Cow(s)
Total Number of
Goat(s)
Total Number
of Poultry
11.7
4.2
0.54
0.30
3.41
-0.166*
(-0.330 - -0.003)
-0.067
(-0.264 - 0.130)
0.188+
(-0.014 - 0.390)
0.394**
(0.163 - 0.624)
-0.008
(-0.202 - 0.186)
-0.405**
(-0.683 - -0.126)
0.230+
(-0.015 - 0.476)
0.572**
(0.307 - 0.837)
0.047
(-0.058 - 0.151)
-0.026
(-0.157 - 0.104)
0.133+
(-0.009 - 0.275)
0.170+
(-0.001 - 0.341)
Age of PRIME Participation (in years)
9.5
0.018
0-1
(-1.5 - 20.6)
(-0.084 - 0.121)
9.4
-0.054
2
(-3.3 - 22.0)
(-0.182 - 0.074)
20.5*
0.146*
3
(3.8 - 37.3)
(0.007 - 0.285)
32.6**
0.233**
4+
(13.0 - 52.2)
(0.066 - 0.400)
4,200
4,200
4,200
4,200
4,200
N
The benchmark level corresponds to the set of households (N = 2,907) which did not receive any MFI
support during any of the three rounds of household surveys. Robust 95% Confidence Intervals in
parentheses. ** p<0.01, * p<0.05, + p < 0.1. The baseline group in this case is the benchmark households
who did not participate in any NGO/MFI during 2008-2010 calendar years. For total land (in logs, Column
[1]), we used simple OLS with robust standard errors. For the other variables which are count in nature, we
used GLM models with negative binomial family and log link.
*
We found for the first two years of participation, the households did not gain significantly in
comparison to the benchmark households, while after three years of participations, the households
accumulated 21% higher total land and the households who participated for four years (and possibly
moved on to other programs even before that) accumulated 33% more land assets (see Column [1]
in Table 8)12.
We saw a similar trend in other types of assets as well. The total number of livestock increased by
15% in comparison to the benchmark households after three years since participating into the
program while it increased by 23% for the households after four years and more of participating into
the program. For total number of cows, we again found for the first two years, the households did
12
One should notice that the reference group had only 11 decimals of land (see Table 7), so 21% and 33%
translate into only 2-3 decimals of land for these household.
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PRIME 3rd Round Report.
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not exhibit any significant impact13. However, after three years since participating in to the program
households accumulated 19% higher number of cows (significant at 10% level of confidence)
compared with the “benchmark” case and 39% more after four or five years of participation in to the
program. We saw a similar pattern for the other categories (goats and poultry, albeit a little weaker
for the total number of poultry in the statistical sense) as well suggesting that the PRIME program
established the participating households in a dynamic path of asset accumulation which usually
surfaced after three years since participating in the program. This endorsed findings reported in the
second evaluation report.
Table 9: Asset value and Savings against Age of PRIME Participation.
Log(Total Asset)
Log(Total Asset Value
excluding Land)
Log(Savings)
103.59
27.57
1.79
24.6**
(17.3 - 31.9)
14.4**
(4.4 - 24.4)
23.4**
(13.0 - 33.9)
37.8**
(23.9 - 51.7)
157.8**
(138.0 - 177.5)
162.4**
(139.1 - 185.6)
142.5**
(113.0 - 172.0)
188.0**
(157.0 - 219.1)
*
Benchmark Level
(Thousand Taka)
Age of PRIME Participation (in years)
23.0**
0-1
(12.9 - 33.0)
17.7**
2
(5.1 - 30.4)
30.2**
3
(15.6 - 44.8)
42.1**
4+
(24.2 - 60.0)
4,200
4,200
4,200
N
The benchmark level corresponds to the set of households (N = 2,907) which did not receive any
MFI support during any of the three rounds of household surveys. Robust 95% Confidence
Intervals in parentheses. ** p<0.01, * p<0.05, + p < 0.1.
*
We also looked at the accumulation of assets in value terms.14 Not surprisingly, we found the same
dynamic pattern in asset accumulation in value terms as well. However, there were some nonlinearity in this case. The households who participated into the programs earlier higher asset values
for all three categories of assets we looked into. Total assets grew by 30% after three years and 42%
after four years or more since participating into the program. While exhibiting a U-shape pattern in
non-land asset accumulation the early participants did distinctly better compared with the
“benchmark” households. Having savings an integral part of the microfinance services offered to the
participating households under PRIME, the savings were higher compared with the “benchmark”
households and continued to grow as the years progressed since participating into the program.
13
As a matter of fact, for this particular case, the households who are new participants in to the program
actually were worse off compared with the “benchmark” households.
14
These results are based on cross-sectional data for the same year. So it did not require correcting for price
levels for the same current year. This qualification is applicable to all value or nominal figures in this report.
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PRIME 3rd Round Report.
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Table 10: Income against Age of PRIME Participation.
Log (Total Income)
Log (Total Income
from Livestock)
45.68
1.29
18.1**
(11.6 - 24.6)
17.9**
(10.1 - 25.7)
24.0**
(16.6 - 31.3)
34.7**
(24.9 - 44.5)
-5.8
(-36.5 - 25.0)
-2.8
(-41.7 - 36.1)
49.1*
(3.6 - 94.7)
66.2*
(10.3 - 122.1)
*
Benchmark Level
(Thousand Taka)
Age of PRIME Participation (in
years)
0-1
2
3
4+
4,200
4,196
N
The benchmark level corresponds to the set of households (N = 2,907) which did
not receive any MFI support during any of the three rounds of household surveys.
Robust 95% Confidence Intervals in parentheses. ** p<0.01, * p<0.05, + p < 0.1.
*
We saw a similar trend in total income as well (see Table 10). The participants did distinctly better
compared with the “benchmark” households. Moreover, over the age of program participation the
total income exhibited a pattern of continual increment. The first two years of participation we did
not see much of a trend while after three years the total income increased by 24% and by four years
the total income of the participants increased by 35%. For the first two years, the income from
livestock did not change in any statistically significant way. However, three years after participation
into the program the income from livestock increased by 49% and it continued to grow to 66%
compared with the “benchmark” households.
We also looked at the food and non-food expenditure separately (see Table 11). We found the same
dynamic pattern in expenditure also. In the case of newer participants (such as less than two years
of participation), the households showed a significantly higher levels for both kinds of expenditures
however, there was no significant changes in the growth rates in food and non-food expenditures
during the first three years of participations. However, there was a significant jump in the levels of
expenditure after three years. Food expenditure grew 19% after three years and 29% after four
years or more since first participating into the program. Similarly, non-food expenditure grew by
36% after three years and 58% after four years or more since participating into the program in
comparison with the “benchmark” households.
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PRIME 3rd Round Report.
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Table 11: Food and Non-Food Expenditure against Age of PRIME
Participation.
Log(FoodExpenditure)
Log(Non-Food
Expenditure)
32.25
7.02
17.1**
(13.6 - 20.6)
18.4**
(13.5 - 23.2)
18.6**
(13.4 - 23.9)
29.1**
(22.9 - 35.4)
32.6**
(25.3 - 39.8)
34.2**
(24.9 - 43.4)
35.6**
(26.1 - 45.2)
58.3**
(44.9 - 71.6)
*
Benchmark Level
(Thousand Taka)
Age of PRIME Participation
(in years)
0-1
2
3
4+
4,198
4,087
N
*
The benchmark level corresponds to the set of households (N = 2,907) which
did not receive any MFI support during any of the three rounds of household
surveys. Robust 95% Confidence Intervals in parentheses. ** p<0.01, * p<0.05, + p <
0.1.
We also found a similar dynamic pattern of impact in time spent in both agricultural and nonagricultural self-employment (see Table 12). Here we again had the “marginal impacts” of
participation comparing to the “benchmark” level (i.e. no participation). Months engaged in nonagricultural self-employment increased by 1.4 months/year (about 54% more) engaged in nonagricultural self-employment compared with the “benchmark” level. But, this increases by 1.6
months/year after three years and 1.9 months/year after four years or more since participating into
the program. Days per month engaged in agricultural self-employment increases by 1.3 days after
three years and 4.9 days after four years or more since participating into the program which means
an increase of nearly two months of agricultural employment in a whole year for the older
participants.
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Table 12: Engagement in Agricultural and Non-Agricultural Self-Employment against Age of
PRIME Participation.
[1]
[2]
Non-Agricultural Self-Employment
Month(s)/Year
1.9
(1.8 - 2.0)
Age of PRIME Participation (in years)
1.4***
0-1
(1.0 - 1.7)
1.5***
2
(1.0 - 1.9)
1.6***
3
(1.1 - 2.1)
1.9***
4+
(1.3 - 2.5)
4,200
N
Percent
---
[3]
[4]
Agricultural Self-Employment
Day(s)/Month
9.5
(9.1 - 9.8)
Percent
---
0.535***
(0.428 - 0.641)
0.565***
(0.434 - 0.697)
0.596***
(0.451 - 0.740)
0.684***
(0.511 - 0.856)
1.3**
(0.3 - 2.4)
-0.7
(-1.8 - 0.3)
1.3*
(-0.1 - 2.8)
4.9***
(2.7 - 7.2)
0.132***
(0.035 - 0.228)
-0.081
(-0.202 - 0.040)
0.133**
(0.001 - 0.266)
0.418***
(0.260 - 0.577)
4,200
4,200
4,200
*
The benchmark level corresponds to the set of households (N = 2,907) which did not receive any MFI support during
any of the three rounds of household surveys. Robust 95% Confidence Intervals in parentheses. ** p<0.01, * p<0.05, +
p < 0.1.
3.4
Crisis Coping
In the current household survey, 2,693 households reported going through at least one unexpected
shock in 2010. Total number of reported shocks or crisis was 2,998. About 50% of all shocks was in
the form of accident or illness of household member. Other major shocks were natural disasters like
flood, storm; livestock diseases etc. In 40% of the cases, households did not report any actions to
cope up with these shocks. In rest of the cases, we found that self-insurance was the major coping
mechanism adopted by the household. Other major coping mechanisms were borrowing and
aid/help from different sources. Self coping mechanism included coping up the shock using either
own savings or sell/mortgage of land or sell of livestock or other assets, etc. In the absence of more
formal insurance schemes, a better asset position would allow a household to be in a better position
to deal with negative shocks. We already saw (in Table 8 and Table 9) that PRIME participants were
in a better position in terms of accumulating physical and financial assets. So the households which
participated in PRIME for a longer time were supposed to have a better self-coping mechanism.
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PRIME 3rd Round Report.
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Figure 17: Fraction of Households Adopting Self- Figure 18: Marginal Impact of Program on
Coping Mechanism.
Reporting Self-Coping Mechanism to Cope The
Shock .
20%
PRIME Participants
46%
44.4%
15%
44%
11.7%
42%
10%
40%
37.8%
38%
36%
37.1%
5.0%
5%
35.5%
34%
4.3%
2.7%
0%
32%
0-1
30%
2
3
4+
-5%
0-1
2
3
4+
Age of PRIME Participation in year
Source: InM Research Team’s estimates using
household surveys. The benchmark indicates
households who did not participate in any MFI/NGOs
over 2008-2010 (N = 1,114). The vertical axis shows
the fraction of household (%) reporting adopting selfcoping mechanism to cope the shock.
Source: InM Research Team’s estimates using
household surveys. The benchmark indicates
households who did not participate in any MFI/NGOs
over 2008-2010 (N = 1,114). The vertical axis shows
the coefficients for each category in the horizontal
axis. The vertical line for each point shows the 95%
confidence intervals.
Figure 17 showed that about 35% PRIME participants with less than a year in the program
households reported to cope up with the shock using self-coping mechanism while this percentage is
about 44% for those who had been in the program for 4 to 5 years. Figure 18 showed that up to
participation age of three years, the impact of program on adopting self-coping mechanism was not
that significant. But this marginal impact was statistically significant when the program participation
age was four or more.15
15
Temporary within country migration is probably another mechanism by which households tend to cope up
with negative income shocks. However, it was not ex ante obvious whether higher financial access and
associated larger income and asset position would encourage or deter such migration behavior. So the
research team decided to leave this issue outside the purview of this report.
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PRIME 3rd Round Report.
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Figure 19: Value of Housing by Duration of Figure 20: Participation in Non-PRIME MFI
PRIME Participation.
Programs by Duration of PRIME Participation.
13.3
13.5
79%
74%
11.0
71%
68%
Benchmark
0-2
3+
Value of Housing (Thousand Taka) by
Duration of PRIME Participation, Measured
in Year(s)
Source: InM Research Team’s estimates using
household surveys.
0-1
2
3
4+
Duration of PRIME Membership, Measured in
Year(s)
Source: InM Research Team’s estimates using
household surveys.
We further looked at value of housing by duration of PRIME participation. Compared with the
benchmark group (the households without any NGO/MFI participation), the total value of the
housing of the PRIME participants was about Tk. 2,000 higher on average and the gap was
statistically significant. However, there was no dynamic trend over the duration of membership in
PRIME. We also looked at participation in other NGO/MFI programs. We found that over the
duration of PRIME membership participation in other programs increased and seventy-five to
seventy-nine percent of the participants with three or more years in PRIME retained participation in
other NGO/MFI programs. Thus, PRIME worked as induction into the MFI industry which perhaps
otherwise would not be possible.16
16
We also looked at migration and its association with PRIME membership duration. There was no meaningful
association with duration of PRIME membership and also investigated level of migration (defined as if any of
the household members lived outside home over the last year) for the benchmark households. We did not find
any difference in level of migration between these groups (i.e. the benchmark households and PRIME
participants with different lengths of durations)
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PRIME 3rd Round Report.
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Figure 21: Fraction (%) of Households Reporting to have
Treatment from “Quack”.
65
62.02
60
55
49.61
50
49.05
45.7
45
40
0-1
2
3
4+
Source: InM Research Team’s estimates using Household surveys. The
benchmark indicates Households who did not participate in any MFI/NGOs
over 2008-2010 (N= 2,863). The vertical axis shows the fraction of household
(%) reporting their interest to send their school-going aged children to school.
3.5
Health, Perception and Decision Related Aspects
We also addressed some health case use and decision making capacity by women related
parameters in the report (as required by The Terms of References for the third round of evaluation
exercise). As such, we intended to find the impact of PRIME intervention program on access to
health care and women’s participation at both family and community level decision making
processed as well.
3.5.1
Awareness about Health Care
To do that we restricted the target sample by the households who had at least one member
suffering from any disease over the three months before the time the household was surveyed and
had taken some treatment for the ailment. We classified different health service providers into two
broad categories namely formal and informal sources of treatment. The formal sources included
MBBS doctor, hospital or clinic, upazilla health complex, health worker from NGOs or any other
formal sources. The Informal sources, on the contrary, included all types of informal providers of
health care including quacks, kabiraj, compounder of a pharmacy or any other informal provider.
The households living in the rural areas usually have a much limited access to formal health care so
they are driven to seek health care from informal sources by consulting quacks or the village doctor
in the first place whenever they encounter a disease, which sometimes lead them towards more
sufferings as these village doctors are not qualified enough and often fake people in the name of
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treatment. We tried to see whether the PRIME intervention had any impact on the awareness about
health care among people.
Like the previous sections, we also tried to see whether the age of participation in the PRIME
program provided any indication whether the program improved the awareness about health care
among the targeted households or not.
We found that the fraction of households reporting treatment from a village doctor is lower for the
households participating in the PRIME program earlier compared to the households who were in to
the program for less than a year (Error! Reference source not found.). The latter members reported
62% of the time getting treatment from a quack, whereas the households who have been in the
program for four or more years reported seeing a quack 46% of the time. This suggested a 16
percentage point decrease, implying an “impact” of the PRIME associated with the age of
participation.
Comparing with the benchmark, we found that the newest members of PRIME are somewhat close
to those who were not in to the program yet (including any other NGO/MFIs). The “benchmark”
level implied that about 60% of the non-participant households reported taking treatment from a
village doctor, which is slightly lower than the percentage of household those have been under the
program for 0 to 1 year, which is around 62%. But with the age of participation in PRIME, percentage
of households reporting health care from a quack declined and for the households under the
program for 4 or more years reported taking treatment from quack is the lowest.
Figure 22: Fraction (%) of Households Reporting Figure 23: Fraction (%) of Household Reporting
Interest to Educate Their School-Going Aged Mother’s Participation in the Decision of Children’s
Children.
Marriage
90.0
100.0
99.35%
99.5
89.5
99.25%
89.5%
99.0
89.0
98.5
88.5
98.0
97.89%
89.15%
88.0
97.73%
87.5
97.5
87.55%
87.0
97.0
0-1
2
3
4+
87.08%
86.5
0-1
2
3
4+
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PRIME 3rd Round Report.
Source: InM Research Team’s estimates using Household
surveys. The benchmark indicates Households who did not
participate in any MFI/NGOs over 2008-2010 (N=1,261). The
vertical axis shows the fraction of household (%) reporting their
interest to send their school-going aged children to school.
3.5.2
Please do not cite or circulate.
Source: InM Research Team’s estimates using Household
surveys. The benchmark indicates Households who did not
participate in any MFI/NGOs over 2008-2010 (N= 2,863). The
vertical axis shows the fraction of household (%) reporting their
interest to send their school-going aged children to school.
Perception regarding Child Education
In order to capture the perception about children’s education we targeted the group of households
having at least one child aged between 0 to 7 years. Although the percentage of households showing
willingness to send their children to school was high for the “benchmark” group, yet the fraction was
higher for the program participants. Even the newest members of the program reported 98% of the
times that they were willing to send their children to school, whereas for the benchmark households
this fraction was around 96%. We also found that the probability of such report improved with the
age of participation. The households under PRIME for 4 or more than 4 years reported 99% of time
willingness to educate their children, which was, in fact, a very inspiring figure.
3.5.3
Participation of Women in Decision Making
In the patriarchal society of Bangladesh husband is usually the major decision-maker, especially on
issues relating to family matters. In our analysis, we tried to see whether the PRIME program has any
effect in improving women’s access in some major decisions like their children’s marriage or other
major family issues. The targeted group was those who have children eligible to get married (aged
between 13 to 25 years).
We found that the benchmark households had always been below the PRIME participants in case of
women’s participation in the decision making process of their children’s marriage. The following
figure showed that for the oldest members of PRIME 89.15% of the time they reported that the
decision of their children’s marriage was taken either by the mother alone or both the parents
together. Although the participants aged between 2 and 3 years show a lower percentage value than
the newest and oldest members, yet the fraction was higher than the “benchmark” level.
3.6
Discussion and Concluding Remarks on Impact of PRIME
While it was challenging to identify the impact of the program from a non-random intervention, we
carefully found pattern in the data that suggested systematically that inclusion of ultra-poor
households to financial system (as offered by microfinance industry) along with training and perhaps
monitoring could put such households in a path of development and improvement of their economic
situations. One of the challenges was to find a control group comparable with the households that
received the treatment (i.e. participated in the PRIME). In absence of defining such a group a priori,
we synthetically defined a group that would be less prone to selection. We (as we did in the previous
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2nd phase report) compared the new entrants with the households who participated in to the
program three or more years. People or households that participated first in to the program were
probably similar (in terms of many baseline household characteristics) and selection was probably
much less of an issue between these groups. We also identified and included a group of households
who never participated in any NGO/MFI program. We found many outcome parameters were very
similar between these “benchmark” and new entrants in to the program (i.e. households who were
PRIME participants for less than a year) exhibited very similar levels for many of the outcome
variables.
Our findings endorsed that it was important to look at households over a period of time after they
participated into ultra-poor programs such as PRIME because it might take a while for the ultra-poor
households to rip benefits from such programs. The longer term dynamics of the program’s impact
on the households was evident for most of the parameters. As for consumption ordering, we found
similar results. It was important to notice that there was a general trend of improvement in
consumption situation over last three years. However, when we compared households with
different age of participation we found that households who participated in the program ended up
with a much better consumption ordering compared to households that either were new entrants in
the program or were in the “benchmark” group.
Other outcome variables also exhibited a similar pattern. The households who participated into the
program managed to accumulate assets over time and the value of their assets also increased over
time. We also found total income and income from livestock also increased over the age of the
PRIME participation. As expected, self employment in both agriculture and non-agricultural sectors
also increased over the age of participation.
There were some evidences for improvement in better use of health care use as households showed
lesser tendency to avail health services from the informal providers. PRIME did have health services
in its program design to provide some health consultation and medicines. However, implications for
such services from the data should be followed up in later studies. The decision making by women
showed little variances in the data. The decision to send children or participating in family and/or
community level did not vary over the age of the program participation as such we did not find any
significant association with the time households spent with the program.
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PRIME 3rd Round Report.
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4. Overall Assessment of the Program
It was challenging to cater to the demand for financial services to the low-income households and
this issue typified the microfinance sector from the very beginning. While reaching out to a
particular segment of the rural poor households had largely been successful, extending the breadth
of the market to the segments that had traditionally been ignored posed some special problems.
PRIME was one such extension and reaching out to the ultra-poor households in the monga prone
areas in the greater Rangpur areas probably offered many of the same problems typical of
microfinance serving to clients that are both hard-to-reach because of living in the remote areas
(such as char). It was our assessment that one of the major achievements of the program was to
bring in many households in these areas into the microfinance outreach, especially those who
proved to become viable microcredit/microfinance clients. The participants, while they moved out
of PRIME, continued to procure bigger loans and divert resources to income generating activities in
the long-run.
This issue was important in identifying the impact of program and interpreting the results we
presented in this report. It is our assessment that by exposing and engaging the beneficiaries to
microfinance practices the program led many of the beneficiaries into a higher growth curve in
terms of better food security, higher income, more self-employment and larger asset
accumulation. While one could argue that these sometimes just meant delivery of the intervention,
but a proper discount of the total benefit would probably show that the total value accrued would
far exceed the initial investment on these households. The true economic value of the investment
(since the investment came from a subsidized program) would probably show a better assessment
which was left outside the purview of this evaluation exercise.
One of the main items of interest was food security which has always a burning issue for the poor
households in the region. Our findings suggested that the households that were brought into the
development path because of the program, on average, exhibited a better food security by reporting
having meals at least three times a day during both “normal” (during the time of the survey) and
monga periods. The dynamic pattern we found in the previous edition of the evaluation report
continued this year as well as evident from the data. While overall monga situation improved over
time and this secular trend for all types of households (categorized by MFI participation including
non-participants) we could systematically associate improvement in consumption ordering with
years since an individual household participated in PRIME.
One very interesting facts of the dynamic impact of the program was to find that the households
who never participated in any of the program (the “benchmark” households) and the households
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PRIME 3rd Round Report.
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who were recently selected into the programs (i.e. less than one year into the program) were
surprisingly similar in many of the outcome variables we looked at. This suggested while there were
probably many non-participating households in a similar economic situation (as indicated by the
outcome variables) but because of various natural (e.g. char) and socio-economic constrain could
not participate in the program.
“In spite of having sufficient staff, the PRIME
We ventured into the selection issue and did find
branches are facing higher cost than revenue
some systematic associations between program
earned because of lower average loan size.
participation
Average loan size should be more than BDT
and
some
selected
household
characteristics. For example, the non-participating
households were more likely to be in the chars. As
such, geographic barriers and living in the remote
places
did
provide
some
hindrance
against
8,000. In this regard the supply of money should
be ensured. According to the demand various
kind of loan program can be offered like RMC,
UMC, ME in the PRIME branches.” - A PRIME
coordinator.
participating in the program (PRIME or otherwise).
We also found the non-participating households were more likely to be female-headed, had older
household heads with less education and were also less likely to be currently married. These nonparticipating households also had lesser number of income sources and spent less time in nonagricultural self employment. Similarly, the relative economic deprivation was also evident from
never participating households having lesser access to electricity and sanitary latrine. All these in
combinations suggested that the program could
further reach out to households who perhaps had
“In order to be sustainable branch drop out of
some use of microfinance products however, at the
the borrowers should be stopped. They should
time of the survey did not have any “access” to any
microfinance program.
This led us to look into the institutional and
operational side of the story because PRIME
be offered various kind of non-credit program.
To sustain the borrowers they should be offered
after loan support like IGA implementation
monitoring and other support.” A PRIME
coordinator.
program was implemented under a particular
institutional set-up. While the program kept the cost of fund lower by the whole-sale credit provider
(i.e. PKSF) providing the fund at a subsidized rate, the POs also faced the problem of dealing with a
situation where the branches perhaps would exhibit less productivity because of smaller number of
clients per field officer and also smaller loan size. So the double bottom-lines were more stringent
for the branches under the program and we found this to be largely true in the data.
For example, from a group meeting with the PRIME coordinators from all the POs implementing
PRIME, the research team gained some important insights into the program from an operational
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point of view. The participants of the meeting raised concerns about the financial viability of the
branches implementing PRIME (which we found to be true from the data as well). The main
incentive to run these branches were pressure from PKSF and according to them even the subsidy
itself was not enough to cover the cost of operation for these branches. So, for the POs it was loans
tied with conditionality and they were cross-subsidizing from more profitable operations in other
areas.
One of the major concerns was high turn-over rate and high drop-out rates. It was not that the
dropped out beneficiaries stopped taking loans altogether (rather they moved on to other more
lucrative offers indicating competitive pressure from other MFIs even in such targeted areas). One of
the major reasons for such high turn-over was the cap on loan-size. Many beneficiaries showed
capacity to take larger loans and divert it to useful income generating activity in a productive way.
However, since these beneficiaries were new
comers to the microfinance sector, as a policy
MFIs were reluctant to provide larger loans to
“As PRIME is designed for the very vulnerable
households and the loan management capacity
of these people are low the average loan size
them hence they many-a-times found such loans
becomes low. So it needs minimum three years
not so useful and dropped out. It was important to
to get operationally self-sufficient for a branch.
note larger loan per borrower was also a way to
Big organization can provide subsidy but the
be more efficient and productive. This was one
small MFIs can’t. So to open a branch the
aspect of the program the designer should look
into more.
sufficient amount of money should be ensured at
first.” A PRIME coordinator.
A related issue was offering of non-credit program with the more traditional microfinance services
(credit and/or savings). The coordinators expressed concern that it was common for them to
compete with bigger NGO/MFIs because of the larger one offer so called “credit plus” services (e.g.
BRAC). So the more traditional credit services were facing more and more competitive pressure.
There were ancillary services offered under PRIME however more product diversifications and along
with need-based program along with microcredit/ microfinance might help retain clients. It was not
also obvious (to the researchers) what policies were in place for the graduating clients which the
again the designers should address for the overall welfare for both the clients and MFIs.
Since the program itself was yet to be operationally (hence financially) sustainable, we needed to
focus and highlight the social returns from the program which we found to be largely positive. In
almost all of the parameters we found the dynamic pattern of improvement with some nonlinearity. The PRIME participants continued to gain in terms of asset accumulation both in kind and
in values. The households (under PRIME) found themselves in a higher income growth path and we
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PRIME 3rd Round Report.
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found evident that the income from self-employment (in both agricultural and non-agricultural
sector) and direct income from livestock grew accordingly.
We also looked at few health-related parameters. Health service had been made an integral part of
the program and the team met with medical officer (a registered physician) employed under the
program. The program gave out primary health services (consultation) and some medicines to the
beneficiary households. One limitation of the study was that it did not explicitly look at this aspect of
the program in details. However, the questionnaire did include a specific section on the overall
health care use. We found that taking health care services from the informal sector came down with
the age of the program. However, we feel that health care related issues should be addressed more
closely in the coming rounds because the poor households are especially vulnerable to disease and
many-a-times health related shocks are considered as one of the major reasons for households to
fall back to poverty through loss of income and asset depletion.
We also looked at few parameters which would indicate improvement in decision making by the
women in the beneficiary households. However, we did not find any significant association with the
age of the program. In most cases, there were not much variations in the data to identify the impact.
We found some “soft” association of mother being involved in child’s marriage over the age of the
program. But sociological gain from the program was hard to measure to begin with from a
household survey and perhaps more ethnographic study was required to identify and document
these aspects of the program which was outside the purview of this exercise.
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PRIME 3rd Round Report.
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5. Concluding Remarks
One should not shy away from noticing the limitations of this study. Firstly, the issue of selection was
not embedded in the study design itself. As such the estimates were expected to be biased. While
we were careful enough to construct synthetic cohorts by varying intensity of impact and also a
“benchmark” group, this perhaps still would not replicate identifying the average treatment effect of
the program in a clear way. However, the overall research design would allow a researcher to
comment on the selection issue itself in a more general framework of participation which could
potentially turn out to be strength of the research design. Secondly, like most other evaluation
exercises, this research was based on a household survey asking interviewees to recall past
experience and activities (e.g. consumption). This was bound to introduce some recall bias. While for
few variables (e.g. loans) this error was expected to be minimal (because most households report
these information from written passbook documents), for consumption and days worked
throughout the year was bound to have some measurement errors in them. Thirdly, for some of the
parameters like decision making and women empowerment household survey was probably not the
right research design. A more “anthropological” approach or at least embedding those features in a
questionnaire format remained challenge which should be addressed in the future rounds. Fourthly,
while we explicitly included questions regarding health care use we needed to address the specific
program related health care products and use in the later rounds of the study. Fifthly, the project
appraisal could benefit greatly if there were some intermediate assessments in a higher frequency
especially addressing the monga situation in real time. Sixthly, operational assessment would benefit
from a more systematic reporting. For example, the research team found it difficult to measure
parameters like loan loss provision (and the expense related to it) because of absence of comparable
reporting across different MFIs. The improvement of MIS can reduce the cost (thus increasing the
efficiency and productivity of the branches) of branch management.
Even after these limitations, we made a consistent effort through a systematic data mining exercise
to understand the impacts of the program in a dynamic setting. As we mentioned the concern in the
industry about the “double bottom-lines” was especially salient for this program. The branches dealt
with this issue and a frequent turnover probably sustained a low level of productivity given the
constraint of mainly ultra-poor households as beneficiaries in hard-to-reach remote areas. Investing
in productivity of the branches should be a top priority through a better data management process
and retaining more productive employees. However, the cross-subsidization at the institutional level
should be maintained through proper incentives. These policies are all the more important because
of the long-term positive impact of PRIME program as clear evidence of a sustaining growth path in
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PRIME 3rd Round Report.
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income, asset accumulation and engagement in productive enterprises were exhibited throughout
this evaluation exercise.
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PRIME 3rd Round Report.
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A. Operational Sustainability of the PRIME Branches
1. Preamble
Making financial services available to the poorest people is recognized as important tool for poverty
reduction. While “increasing outreach” has been the catch-cry as long as microfinance as its current
delivery mode came into existence for the last thirty years, the present delivery models were not
quite meeting the challenge, especially when it came to serving communities in remote locations
characterized by low population density. One reasonable conclusion might be that reaching out to
the poor people living in the ecologically vulnerable areas (such as char) would involve subsidized
delivery mechanism in the short run and sustainability of the MFI branches serving such areas
should receive some special attention.
The quest for sustainability and eventual self-sufficiency is widely regarded as a best practice in the
microfinance industry. Self-sufficiency is seen as an appropriate mechanism for achieving the longterm viability of the microfinance sector. First, available resources and subsidies are too small to
provide microfinance to all who might benefit from it. Second, a focus on self-sufficiency can lead to
decreased costs through increased efficiency. Third, leverage is more easily attained by
organizations that generate the means to repay debt. Finally, reliance on subsidies might alter a
firm’s incentive structure in ways that could increase the likelihood of a negative event.
2. Evaluation of the PRIME branches
One novelty of the present evaluation report was to explicitly address the issue of sustainability of
the program from the financial point-of-view. One of the major achievements of the microfinance
programs (subsidized or otherwise) was to reaching the poor though financial instrument (e.g.
microcredit) using a mode of delivery which also addressed the issue of self-sufficiency. This, of
course, put an additional constraint on a MFI’s outreach motive and delivering financial product to
the hard-to-reach clients subject to operational and financial viability received its due attention. The
PRIME program actually addressed this issue. But one should keep in mind that in its current form it
was still subsidized and reaching toward program sustainability would first require the PRIME
branches to show operational self-sufficiency. As a prerequisite to reach the goal of operational selfsufficiency it would also be important to assess the efficiency, productivity and sustainability of the
branches through which PRIME had been initiated and implemented.
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PRIME 3rd Round Report.
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While there were many different aspects for branch performances, we, for the purpose of this
evaluation of the program would focus on a limited set of parameters. As per Terms of Reference,
the research team was required to assess the sustainability of the program. This was the first time
the research team explicitly focused on the sustainability of the program using branch level
information extensively. The collected data (through a structured questionnaire for each branch, see
sections on data and sampling below) allowed the research
team to answer some very specific questions such as:
According to the TOR with
PKSF, dealing with the
institutional level is a part of
PRIME evaluation. At the
institutional
level,
the
objective is to assess:

How productive are the PRIME branches?

How efficiently
performing?
are

Are the PRIME
sufficient?
branches
self
(i) Impact on institutional
capacity
strengthening
initiatives for POs

How the previous measures behaved over the age
of a branch?

What factors are associated with the observed
variations in the above measures?
(ii) Operational sustainability
of prime branches and
suggest possible way to attain
operational sustainability.
the
PRIME
branches
operationally
Answering the above questions would provide guidance how to formulate the future path of the
program so that the program was both operationally and financially viable. Increasing client
outreach provided economies of scale that in turn made the microfinance program more efficient
and therefore more sustainable, at least in the immediate financial terms. However, by design
PRIME worked in areas where household traditionally had little access to financial services. The cost
of delivering such services to ultra-poor households was generally high (because of being at river
islands or “char”). Thus solely focusing on self-sufficiency in financial terms might cause such
programs to move away from the initial aims of supporting the deprived households coping with
monga. Yet the financial status of the programs might shed some light on designing the program
more efficiently for future growth (possibly in other areas with similar situation).
3. Performance Parameters
In order to analyze the sustainability and efficiency of the program, we looked at the individual
PRIME branches. This was reasonable because the branches were the lowest units through which
interface with the program beneficiaries actually took place. New branches were established to
implement the program to reach out to the targeted households while some of the existing
branches, strategically placed to reach the ultra-poor households were brought under the program.
We emphasized on few parameters which can loosely be categorized under the following categories.
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PRIME 3rd Round Report.




Please do not cite or circulate.
Productivity and Efficiency
Expenses
Financial Spread
Operational Self-Sufficiency
Productivity and Efficiency
To measure the efficiency of human resource utilisation or staff productivity ratios – clients per
member of staff and outstanding portfolio per member of staff – were the two key indicators. This
analysis used the client-to-loan officer ratio and portfolio-to-loan officer ratio with some
qualifications. It was difficult to classify staff as loan officers across MFIs because in most of the
cases field officers played multiple roles which might go beyond credit management for borrowers.
Many MFIs gave field officers responsibility for all functions related to microfinance groups. In this
situation the definition of a loan officer was not clear.
Financial Spread and Operational Self-Sufficiency
For measuring the portfolio quality and financial viability, we looked at financial spreads and the
operational self-sufficiency of all PRIME branches. Financial spread showed the ratio of net income
(i.e. total income less financial cost) and average portfolio outstanding. Operational Self-Sufficiency
(OSS) measured the financial viability of an institution. OSS showed the required amount of revenue
required to meet the cost of the branch. OSS is a fraction (expressed in percent) which depicted the
ratio of income and expenses of any institution. As such, a self-sufficient branch (in the operational
sense) would necessarily have OSS of 100% or more. A MFI branch continuing under 100% of OSS is
not considered operationally viable in the long-run. However, most branches (and organizations)
initially exhibited an OSS below the threshold level of 100% in which case the trend in OSS over the
age of the branch implied whether the branch would be operationally self-sufficient over time.
4. Sample
We selected all 231 PRIME branches of five districts of Greater Rangpur currently under
operations.17 These branches belonged to 16 different MFIs all of whom were partner organizations
of PKSF and these were selected because of their prior performance and experience in implementing
ultra-poor households in the program areas. As such we expected to draw a comprehensive
operational and to some extent financial picture of the entire program from the supply-side. One
should keep in mind, many of the MFI branches later started regular microcredit programs and
currently only 41 branches were involved with micro-credit/micro-finance programs targeting only
for ultra-poor households.
17
The branches of the POs implementing PRIME have changed over time in the sense that few branches have
merged. There were 235 branches over the PRIME existence however after the branch mergers there are 231
branches under operation as of now.
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Table 13: POs and Coverage
1
Partner
Organizations
ASOD
2
ESDO
3
RDRS
4
5
6
TMSS
SKS
GUK
SL
7
8
9
10
11
12
13
14
15
16
PMUK
POPI
UDDIPON
SSS
S-SUS
Gonoshastho
GBK
SHARP
JCF
BEES
Distric(s) covered
Rangpur & Lalmonirhat
Nilphamari, Rangpur, Gaibandha
& Lalmonirhat
Nilphamari,Kurigram
&
Lalmonirhat
Rangpur,Kurigram, Gaibandha &
Lalmonirhat
Gaibandha, Rangpur
Gaibandha
Nilphamari,Rangpur, Gaibandha
& Lalmonirhat
Nilphamari, Lalmonirhat
Kurigram, Rangpur
Rangpur & Kurigram
Rangpur
Gaibandha
Rangpur & Saidpur
Nilphamari
Rangpur
Rangpur, Gaibandha
Number of
Upazilas
Covered*
6
Number of
Unions
Covered
20
8
43
29
15
66
53
10
8
2
44
50
7
27
35
4
10
2
6
4
2
4
4
1
1
2
29
12
28
11
12
8
9
4
3
8
12
4
18
8
6
7
5
4
2
6
354
231
All POs
Number of
Branches
11
Source: Construction from PKSF administrative data by the InM Research Team.
*Upazilas covered here are double counted as the different POs are operating in the same upazila. But uniquely the 16 POs
covered the 35 upazilas. Later on some POs had merged some of their branches. So the number of branched may change
overtime. Up to March 2010, it is reported that there are 231 branches are operating in the field.
The research team collected official year-end
financial data for all PRIME branches. The
Figure 24: Growth Curve for Number of MFI
Branches Implementing PRIME
program coordinator and the MIS officer
231
205
responsible especially for PRIME program for
each PO were entrusted with the data
137
collection using a structured questionnaire
prepared
by
the
research
team.
The
questionnaire asked for last three financial
years’ data including number of borrowers,
21
loan outstanding, disbursement and savings.
The
questionnaire
also
included
forms
regarding cost and income of the branch over
2006
2007
2008
2009
Source: PKSF Administrative Data from PRIME Cell. There are few
instances of branch mergers with are not reported here.
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PRIME 3rd Round Report.
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the last three financial years. InM research team conducted a training session in Rangpur and also
conducted a focus group discussion. The day long training basically provided the concern POs how to
fill up the data and what they thought about the entire PRIME program. They also provided insights
on the sustainability issues of the PRIME branches.
5. Description of the Data
We have data for last three financial years of every PRIME branch where available.18 PRIME starting
years varied among branches. Initially 21 branches in 2006 started PRIME program. Later a rapid
expansion took place in 2007 and in 2008. There were 231 branches operating PRIME program in
Greater Rangpur. There were 132 branches (57% of total) started with PRIME and among them some
branches were operating PRIME with some other programs. At the time of writing this report, 41
branches were running only PRIME or ultra-poor microcredit/microfinance program. The other 99
branches existed before PRIME program started and were chosen to implement with PRIME because
of their geographical and programmatic advantage to reach the appropriate clientele. We analyzed
the financial information of these two types of branches separately (see Table 14).
We presented the summery statistics for these two types of branches in Table 14 and one should
note that there were some differences between them exhibiting differences in financial
performances between the two groups. The number of active borrowers per field officer was higher
for the branches established through implementing PRIME (1,282 active borrowers per branch) than
the pre-existing branches (1,478 active borrowers per branch). The portfolio outstanding per field
officer for pre-existing branches (Tk. 1,248 thousand/field officer) was higher than newly initiated
PRIME branches (Tk. 1,059 thousand/field officer). The number of active depositor per field officer
was higher for PRIME initiated branches than the pre-existing branches implementing PRIME. The
deposit outstanding per field officer and savings collected per field officer was higher for existing
branches rather than newly initiated PRIME branches.
The newly established branches also showed a lower level of efficiency (in the operational sense)
compared with the pre-existing branches implementing PRIME. The operating cost as a percentage
of average portfolio outstanding was much lower for the pre-existing branches (16%) than that of
newly initiated branches (31%).
The salary and benefit as a percentage of average portfolio
outstanding was also smaller for pre-existing branches (13%) than the new branches of PRIME (24%).
Loan loss provision was almost same for two groups. Interestingly, financial spread (as measured by
18
Some of the branches were established later so for these branches financial information were not available
in the earlier years.
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PRIME 3rd Round Report.
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financial income less operating cost expressed as percent of average loan outstanding) was higher
for the new branches (17.5%) as opposed to the pre-existing branches (15.7%).
Table 14: Summary Statistics for PRIME Branches.
Productivity Ratios
Number of Active Borrowers
per Branch
Number of Active Borrowers
per Field Officer
Portfolio Outstanding per
Field Officer (Thousand
Taka)
Amount Disbursed per Field
Officer (Thousand Taka)
Number of Active Depositors
per Field Officer (Number)
Deposit Outstanding per Field
Officer (Thousand Taka)
Savings Collected per Field
Officer (Thousand Taka)
Efficiency Ratios
Operating Cost Ratio (% of
Average Portfolio
Outstanding)
Salary and Benefit (% of
Average Portfolio
Outstanding)
Loan Loss Provision (% of
Average Portfolio
Outstanding)
Financial Spread (% of Average
Portfolio Outstanding)
All (N = 231)
Pre-existing MFI
Branches Implementing
PRIME (N = 99)
Branches Established
through Implementing
PRIME (N = 132)
1,366
1,478
1,282
241
232
247
1,140
1,248
1,059
2,207
2,746
1,804
272
260
281
385
476
316
242
250
236
24.5 (N=228)
15.9 (N=97)
30.9 (N=131)
19.1 (N=228)
12.8 (N=97)
23.8 (N=131)
4.5 (N=228)
4.2 (N=97)
4.7 (N=131)
16.7
15.7
17.5
Operational Self-Sufficiency (%)
78.3
93.1
Percentage of Branches with
OSS >= 100%
24.2
40.4
Source: InM PRIME Research Team’s calculations from primary branch level data.
67.1
12.1
Lastly, we looked at operational self-sufficiency (OSS) of all the branches and also by the two types
of branches defined above. One striking feature was that the average OSS over all branches was just
78.3% as of 2010 financial year. While the pre-existing branches were at a higher OSS (about 93%),
the average OSS for branches established for PRIME were at a meager 67%. Even after 5 years into
the program only twelve percent of the branches (16 out of 132 branches included in the analysis)
reached operational self-sufficiency of 100% as oppose to forty percent of pre-existing branches (40
out of 99 branches included in the analysis) reached operational self-sufficiency.
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PRIME 3rd Round Report.
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6. Assessing Financial Performance against Age of the Program
It was not expected that a bank branch (microcredit or otherwise) would reach certain level of
efficiency and self-sufficiency right away. It would usually take time for a branch to be financially
viable. This convexity might exert a fixed cost for a branch start-up and justified investing on branch
set-up in areas where MFIs would not go otherwise. As such we should expect better efficiency over
time and the branch to be more productive, efficient and self-sufficient over the age of the program
which we would address now.
In Table 15, we classified the branches
Table 15: Age of PRIME Implementation.
according to the previous types and
showed the distribution of the branches by
age of the program. One should note that
while for the PRIME initiated branches the
age of the program and age of the branch
coincided, for the pre-existing branches,
this was not true. In these cases, we
continued to classify the branches by the
Years
0
1
2
3+
All
(N =
231)
Pre-existing
MFI Branches
Implementing
PRIME
(N = 99)
26
69
115
21
10
23
60
6
Branches
Established
through
Implementing
PRIME
(N = 132)
16
46
55
15
Source: InM PRIME Research Team’s calculations from primary
branch level data 2010. “Age of branch” measures the number of
years the branch is implementing PRIME.
age of the PRIME program, not by the age
of the branch. The average age of the program for a typical branch was 2.55 years. For the preexisting branch implementing PRIME was 2.62 years while it is 2.52 years for new branch established
for implementing the program.
In this section, we
Table 16: Age of PRIME Implementation over Financial Years 2008-2010.
also exploited the
information
available for all
three
financial
years and the unit
of observation in
this
case
was
branch-year. Over
three
years,
financial
we
had
All
(N = 640)
Pre-existing MFI
Branches
Implementing PRIME
(N = 297)
Branches Established
through
Implementing PRIME
(N = 343)
Years
0
68
43
25
1
210
93
117
2
205
89
116
3+
157
72
85
Source: InM PRIME Research Team’s calculations from primary branch level data
2010. “Age of branch” measures the number of years the branch is implementing
PRIME. N=640 means that a branch is being counted in different years in different age
groups.
unbalanced panel
of 640 observations (not all branches existed all the time hence the unequal number of observations
over the years). Table 16 showed the distribution of age over the branch-years for all three years. To
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PRIME 3rd Round Report.
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have enough observations in each group of age (measured in years), we stratified the sample into 0,
1, 2 and 3 plus years of age.
7. Dynamics of Branch Performance over the Age of the Program
We found that (see Figure 25) the number of
Figure 25: Borrower Per Field Officer by Age of
active borrowers per field officer increased with Branches.
ages for both types of branches (hence in
300
aggregate, see Figure 25). It was much higher
250
for existing branches up to age of 2 years but
200
after 2 years into the program, the PRIME
initiated
branches
caught
up
with
the
preexisting branches in terms of borrowers per
150
100
50
field/loan officers.
0
0
We saw a similar trend in portfolio outstanding
per field officer however the PRIME initiated
branch never reached the same level as the pre-
1
2
3+
All
Pre-existing MFI Branches Implementing PRIME
Branches Established through Implementing PRIME
existing branches and even for the branches
that were in the program for three years or
more had a difference of Tk. 256 thousand in
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
portfolio outstanding per field officer. If we compared this result with the ones we saw for the
number of borrowers per field officer, the field officers at the pre-existing branches are maintaining
a larger loan outstanding per borrower over a similar number of borrowers making them more
efficient.
We also saw a similar result for savings deposit outstanding (see Figure 27). The pre-existing
branches had a much larger savings outstanding per field officer as a result pre-existing branches
show a better capacity for resource mobilization over all (we should reemphasize that these
branches probably had a more diversified portfolio). It was worth noticing that the branches
managed to maintain a larger savings deposit over the age of the program on average.
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Figure 26: Portfolio Outstanding Per Field Figure 27: Deposit Outstanding Per Credit
Officer (Thousand Taka) and Age of Branches.
Officer (Thousand Taka) and Age of Branches.
1,400
600
1,200
500
1,000
400
800
300
600
200
400
200
100
0
0
0
1
2
3+
0
1
2
3+
All
All
Pre-existing MFI Branches Implementing PRIME
Pre-existing MFI Branches Implementing PRIME
Branches Established through Implementing PRIME
Branches Established through Implementing PRIME
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
Figure 28: Salary and Benefit as a % of Average Figure 29: Operating Cost Ratio as a % of
Portfolio Outstanding and Age of Branches.
Average Portfolio Outstanding and Age of
Branches.
30%
60%
50%
40%
20%
30%
20%
10%
10%
0%
0%
0
1
2
0
3+
1
2
3+
All
All
Pre-existing MFI Branches Implementing PRIME
Pre-existing MFI Branches Implementing PRIME
Branches Established through Implementing PRIME
Branches Established through Implementing PRIME
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
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The personnel cost (as measured by salary and benefit) as a fraction of total portfolio outstanding
exhibited a hump shape as the cost (as percent of portfolio outstanding) went up after one year then
came back to the previous level after that (i.e. after two years). We again saw that the pre-existing
branches performed better in this dimension as well.
Since salary and benefits were major part of the operating cost we found the similar trend in overall
operating cost as well. The hump shape dynamics was especially visible among the pre-existing
branches implementing PRIME and after two years the operating cost as percent of loan outstanding
came down to pre-PRIME level. Not surprisingly, the PRIME initiated branches started with a much
higher level of operating cost and this came down after two years of operations. So there were
evidences of efficiency gain over time.
8. Financial Spread
The average financial spread of the branches implementing PRIME showed an improvement over
time. While in the first year of operation the financial spread stood at 7.4% while it reached 17.7%
by the fourth year of operation. Financial
spreads between two groups were somewhat
distinguishable. The financial spreads for the
branches established through PRIME showed a
Figure 30: Financial Spread and Age of Branches.
30%
25%
20%
more consistent dynamic over time. These
branches started with a much lower level of
financial spread but exhibited a secular as they
15%
10%
aged over the years. In the first year of
5%
operation, the average financial spread of
0%
these branches was at 1.6% while it increased
0
1
2
3+
All
to 19.8% by four years. The average financial
spread for the pre-existing branch
Implementing PRIME showed a more erratic
pattern. The average financial spread of the
Pre-existing MFI Branches Implementing PRIME
Branches Established through Implementing PRIME
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME.
pre-existing in the first year was 10.8% and by
the end of fourth year it stood at 15.1%. It was interesting to observe that the by the end of fourth
year (the last possible year of observation) in terms of financial spread the branches established
through PRIME actually performed better.
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9. Operational Self-Sufficiency (OSS)
Operating self-sufficiency is a ration (expressed in percent), which indicates whether or not enough
revenue has been earned to cover a branch’s total cost which constitute operational expenses, loan
loss provisions and financial costs. It was an important measure of sustainability of the lending
operations. The OSS is measured by dividing Operating Income (Loans + Investments) by costs
(Operating Costs + Loan Loss Provisions + Financing Costs).
0
0
.005
Density
.005
Density
.01
.01
.015
Figure 31: Distribution of OSS as 2010 for Figure 32: Distribution of OSS as 2010 for PreBranches Established through Implementing existing MFI Branches Implementing PRIME (N =
PRIME (N = 132)
99)
0
200
400
oss
600
800
Source: InM PRIME Research Team’s calculations from
primary branch level data.
0
100
200
oss
300
400
Source: InM PRIME Research Team’s calculations from
primary branch level data.
We showed the distribution of OSS for two types of branches separately In Figure 31 and Figure 32.
The distribution of OSS for the branches established through PRIME were more concentrated toward
the left side of the support suggesting our previous findings of lower percent of branches exhibiting
operational self-sufficiency compared to the pre-existing branches implementing PRIME.
Figure 33 showed that the pre-existing MFI branches implementing PRIME had a higher proportion
of branches reporting OSS more than 100%. In 2010, 40 percent of the pre-existing branches had
OSS greater than 100% whereas only 12% of the newly initiated PRIME branches had OSS greater
than 100%.
Latly, we looked at the OSS over the age of the program (see Figure 34). The overall average OSS for
all the branches did not show any discernible trend over time. Initially the MFI branches
implementing PRIME had an OSS of 80%. It came down after that before exhibiting an improvement
over time. It reached 81% by the end of four years in implementing PRIME. The branches established
through PRIME started with a low level of OSS of 23% while it increased to 70% by the end of four
years. One should note that by the end of fourth year in operation these branches did not get
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anywhere near self-sufficiency. Also more interesting was to notice that the OSS for pre-existing
branches actually came down after PRIME was initiated through these branches (an average level of
113% in the first year to 95% by the end of the fourth year).
Figure 33: Percentage of Branches with OSS
above 100%.
120
40.4
38.4
Figure 34: Operational Self-Sufficiency (OSS, %)
and Age of Branch.
100
32.3
80
60
12.1
6.3
40
20
6.1
0
2008
2009
0
2010
Pre-existing MFI Branches Implementing PRIME (N = 99)
1
2
3+
All
Pre-existing MFI Branches Implementing PRIME
Branches Established through Implementing PRIME (N =
132)
Source: InM PRIME Research Team’s calculations from
primary branch level data.
Branches Established through Implementing PRIME
Source: InM PRIME Research Team’s calculations from
primary branch level data 2010. “Age of branch” measures
the number of years the branch is implementing PRIME. OSS
is measured as ratio of Operating Income (Loans +
Investments) and costs (Operating Costs + Loan Loss
Provisions + Financing Costs).
We also classified the branches by the size of the MFIs (as reported by PKSF). There were no clear
patterns between average branch OSS under a MFI and its size. Branches of only four MFIs were
above or at least close to operational self-sufficiency (see Table 17). There were some instances for
which the OSS actually declined.
10. Discussion
The findings in this section suggested that operationally it remained a challenge for MFIs to reach
out to ultra-poor households. While the branches on average maintained an average number of
borrowers between 1,200 to 1,300 active borrowers (which is usually considered to be sufficient for
a branch to break even), the branches on average were not able to show the level activities to selfsustain themselves. Low size of loans and high operating costs did not allow the branches to reach a
level which would require to cover the cost of operations through service charges and other financial
and non-financial revenues. So we observed that the PRIME branches increased their client outreach
by increasing membership with small loan size which was reflected in the portfolio outstanding per
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field officer and the amount disbursed per field officer. The loan ceiling of PRIME was much lower
than the non-PRIME program.
Table 17: Operational Self-Sufficiency (OSS) for different categories of
PRIME POs.
Size of MFIs
PRIME POs
Operational Self-Sustainability (OSS, %)
2008
2009
2010
JCF
15.3
72.5
PMUK
28.8
66.6
61.0
RDRS
85.1
79.8
97.2
Large
SSS
25.3
35.8
52.5
TMSS
63.4
73.2
77.4
UDDIPAN
23.8
26.6
58.6
ASOD
84.5
50.2
37.8
BEES
97.1
81.4
94.9
Medium
ESDO
22.4
38.3
53.0
POPI
35.9
47.5
83.0
SKS
135.1
119.5
111.7
GBK
74.1
55.1
57.2
GUK
1.5
21.9
60.1
Small
GSK
36.8
58.9
60.7
S-SUS
152.5
148.8
103.9
SHARP
4.0
43.1
Source: InM PRIME Research Team’s calculations from primary branch level data 2010.
The operational self-sufficiency ratios depended on productivity of the branch and also the
efficiency. We found that the branches established through PRIME typically exhibited lower loan size
and higher cost relatively in comparison with the branches existed before the PRIME was
introduced. However, the ultra-poor programs evidently put some additional constraints on the
performance of the MFI branches implementing PRIME.
It was important to notice the dynamics of the financial performance of the branches under study.
While it is generally expected that MFI branches to become operationally self-sufficient after three
years, the PRIME branches did not indicate to reach this threshold level after three years. So
increasing the productivity and efficiency of the branches should attract renewed attention.
11. Concluding Remarks
Increasing client outreach provided economies of scale that in turn made the microfinance programs
more efficient and therefore more sustainable, at least in immediate financial terms. However,
emphasizing more on the higher operational sustainability (OSS) might eventually cause “mission
drift” for such programs targeted toward special market segments.
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PRIME especially focused on the ultra-poor households who otherwise did not have access to the
financial services. On the other hand, if “increasing outreach” was taken to mean “targeting hard-toreach clients” such as people living in remote areas, then “outreach” and “sustainability” might
effectively turn out to be competing terms. Reaching clients in remote areas was relatively costly,
which made the microfinance program less productive and therefore less sustainable. This was the
real outreach challenge for microfinance programs because it required new, as yet unproven
business models and processes, including technological innovation. These would remain challenges
for the sector in general and special programs such as PRIME in near future.
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