Grant vs. Credit Plus Approach to Poverty Reduction: An Evaluation of BRAC's Experience with Ultra Poor

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Grant vs. Credit Plus Approach to Poverty Reduction: An
Evaluation of BRAC’s Experience with Ultra Poor
Narayan C Das
Sibbir Ahmad
Anindita Bhattacharjee
Jinnat Ara
Abdul Bayes
October 2016
CFPR Working Paper No. 24
BRAC
Research and Evaluation Division

CFPR Working Paper No. 24
Copyright  2016 BRAC
October 2016
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Cover design
Md. Abdur Razzaque
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LIST OF ACRONYMS
ATT Average effect of Treatment on the Treated
BBS Bangladesh Bureau of Statistics
CFPR-TUP Challenging the Frontiers of Poverty Reduction-Targeting the Ultra Poor
DiD Difference-in-Difference
HH Household
HIES Household Income and Expenditure Survey
ITT Intention-to Treat Effect
NGO Non-Government Organization
OTUP Other Targeted Ultra Poor
PWR Participatory Wealth Ranking
STUP Specially Targeted Ultra Poor
WFP World Food Programme

Grant vs. credit plus approach to poverty reduction
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TABLE OF CONTENTS
Acknowledgements v
Abstract vii
1. Introduction 1
2. An overview of BRAC’s CFPR-TUP programme 2
3. Evaluation design and data collection 4
4. Descriptive statistics 6
5. Analytical technique 13
6. Results and discussion 14
7. Conclusion 22
References 23
Annex 25

Grant vs. credit plus approach to poverty reduction
v
ACKNOWLEDGEMENTS
We would like to express our deepest gratitude to “Challenging the Frontiers of Poverty Reduction:
Targeting the Ultra Poor (CFPR-TUP)” programme for giving us the opportunity to be a part of the CFPR
team through research. We would like to thank the CFPR-TUP staff for giving us all sort of cooperation,
especially, the CFPR field staff without whose support and assistance it would not have been possible to
undertake the baseline and the follow-up surveys. We would particularly like to thank Mr. Shameran Abed,
Director, CFPR-TUP Programme and Microfinance Programme; Ms. Anna Minj, Former Director, CFPR-
TUP Programme and Director, Community Empowerment Programme (CEP), BRAC; and Mr. Arunava
Saha, Programme Head, CFPR-TUP Programme for their continued supports and valuable suggestions
at different stages of the study. We are also indebted to the survey respondents for giving their time and
useful data for the study without which this report could not be produced.
The field management and the data management teams of RED, BRAC also deserve special thanks for
their strenuous job. Thanks are also due to Dr. GH Rabbani, Consultant (Editor), Knowledge Management
Unit, RED, BRAC for carefully editing this report. Sincere thanks to Mr Altamas Pasha for copy editing and
final proofing of the manuscript. Mr Akram Hossain and Mr Md Abdur Razzaque also deserve thanks for
formatting and cover design.
We acknowledge the generous financial support of the Department of Foreign Affairs and Trade (DFAT) of
the Australian Government and UK Department for International Development (DFID), the donors of the
CFPR-TUP programme, through the BRAC/DFID/DFAT Strategic Partnership Arrangement (SPA).
However, the views expressed as well as any error or omission in the study remain solely ours.

Grant vs. credit plus approach to poverty reduction
vii
Grant vs. Credit Plus Approach to Poverty Reduction: An
Evaluation of BRAC’s Experience with Ultra Poor
Narayan C Das, Sibbir Ahmad, Anindita Bhattacharjee
Jinnat Ara and Abdul Bayes
ABSTRACT
Challenging the Frontiers of Poverty Reduction- Targeting the Ultra Poor (CFPR-TUP) programme of BRAC
implements two interventions for the ultra-poor: a grant-based support package for specially targeted ultra
poor (henceforth STUP support package), and a credit plus grant support package for other targeted ultra
poor (henceforth OTUP support package). The target group of the OTUP support package is drawn from
relatively well-off households than the STUP ones. Further, the STUP support package is more expensive
compared to the OTUP. An attempt has been made in this paper to evaluate these alternative approaches
to poverty alleviation - OTUP and STUP support packages. Using non-experimental evaluation design, it
was observed that both the STUP and OTUP support packages increase self-employment, total labour
supply, per capita income, consumption of high value food products, and productive asset-base of the
ultra poor. There is also evidence that these supports lead to some additional non-food improvements
such as increased clothing and reduction in domestic violence against women.

Grant vs. credit plus approach to poverty reduction
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1. INTRODUCTION
Recent studies on anti-poverty programmes provide two important lessons. First, transfers for generating
self-employment in both farming and non-farming sectors have long-lasting effects on the livelihoods of
the very poor (Banerjee et al. 2015a; Bandiera et al. 2013; Blattman et al. 2016).
1
Similarly, cash transfer
programme has been found to be an effective tool for poverty reduction (Blattman et al. 2014). Second,
although access to microfinance has been considered as an anti-poverty tool, the evidence of its
effectiveness on poverty reduction is unequivocal. For example, Pitt and Khandker (1998) found positive
effects of microfinance on consumption but, using the same data, Morduch (1998) finds no significant
impacts. Recently, reviewing six articles on randomised evaluations of microfinance, Banerjee et al. (2015b)
conclude that, while microfinance sometimes leads to an increase in business activity, the effect on
average business profit is much more limited; there is no effect on consumption over a one- to three-year
time period. Moreover, several studies claim that microfinance impacts are largely heterogeneous with less
effect on the bottom layer of the poor clients (Hulme and Mosley, 1996; Mosley and Rock, 2004;
Chowdhury, Mosley and Simanowitz, 2004); hence, poorer clients of microfinance need some additional
support such as training to effectively use the credit (Karlan and Valdivia, 2006).
Given the evidences that microfinance is less effective for poorer clients and transfer programmes have
large positive effects, we are left with the question: would a combined policy perform better than a mono-
policy? In other words, whether an intervention that combines elements of microfinance and grant can be
an effective tool for extreme poverty reduction, rather than only grant or only credit. With this hypothesis,
we evaluate BRAC’s anti-poverty programme titled “Challenging the Frontiers of Poverty Reduction-
Targeting the Ultra Poor (CFPR-TUP).
The CFPR-TUP programme implements two intervention packages: (1) asset transfers as grants,
consumption subsidy and training for specially targeted ultra poor (STUP) and (2) credit plus grants in the
form of consumption subsidy, training and some inputs to maintain the income generating activities
subsumed under ‘credit plus’ approach for other targeted ultra poor (OTUP).
2
We estimate the effects of
both these support packages with a sole contribution that, while studies on impact assessments of the
grant-based asset transfer support package run galore (Raza et al. 2012, Krishna et al. 2012, Bandiera et
al., 2013), there seems to be no study on the evaluation of the credit plus grant component (i.e. OTUP
support package) of the CFPR-TUP programme.
3
To the best of our knowledge, study on the effectiveness of credit plus approach in general, is largely
lacking; however, several studies document the effects of flexible repayment system in microfinance. For
example, a recent study by Shonchoy and Kuroshaki (2014) shows that seasonality adjusted repayment
increases consumption, although this has no effect on repayment and overdue. Again, Field et al. (2012)
show that clients repaying on a monthly basis, as compared to those paying on a weekly one, are less
likely to report feeling of being “worried, tense, or anxious’, and more likely to report a feeling of confidence
in repaying. However, it is not quite clear whether these flexibilities help the very poor.
4
1
Banerjee et al. (2015a) and Bandiera et al. (2013) studied transfer programme, also known as graduation programme, originally
developed by BRAC, the largest NGO headquartered in Bangladesh.
2
The consumption subsidy and other inputs are also provided under the grant-based package.
3
Das et al. (2009) reports some descriptive evidences on the effectiveness of this support package. Their study uses data collected
after one year of intervention. We estimate the impact of this support package after two years of intervention.
4
Morduch (1999), for instance, shows that the poorest are less likely to be served by microfinance.

Grant vs. credit plus approach to poverty reduction
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2. AN OVERVIEW OF BRAC’s CFPR-TUP PROGRAMME
BRAC has been implementing the CFPR-TUP programme since 2002. The programme was piloted in few
northern districts of Bangladesh, subsequently scaled up across the country, and later replicated in 20
poorest countries around the globe. In the first phase of its implementation (2002-2006), the programme
covered 1,00,000 ultra poor households from rural Bangladesh. The targeted households were provided
with single-shot grants (mostly in the form of livestock and poultry), weekly allowance, training and some
supervisory support. Based on programmatic and in-house research learning, BRAC later introduced
diversity in support packages. Thus, two different support packages emerged in 2007: (a) a grant-based
support package for specially targeted ultra poor (referred to as STUP package) and (b) a credit plus grant-
based support package for other targeted ultra poor (referred to as OTUP package). Notably, the OTUP
package generally targets relatively less vulnerable ultra poor than the STUP package (which goes to the
most vulnerable ones). In 2012, BRAC started the third phase of the CFPR-TUP programme for a period
of five years (2012-2016) covering ultra poor through both the STUP and OTUP support packages.
The STUP support package comprises of: (1) enterprise development and life skill training; (2) asset
transfer - mostly livestock and poultry; (3) weekly subsistence allowance (BDT 210 for 2012 cohort); (3)
health subsidy, and (4) community mobilisation support. The OTUP support package, on the other hand,
includes: (1) enterprise development training (mostly on livestock and poultry rearing) and life skill training;
(2) soft loans
5
from BRAC microfinance; (3) weekly subsistence allowance (BDT 210 for 2012 cohort); (4)
input supplies (such as vaccine and medicine for livestock and poultry rearing, and fertiliser and seeds for
vegetable cultivation) and (5) health subsidy (BRAC bears health expenses and provides micronutrient
sachet). Given the modus operandi, it is thus no wonder that the OTUP support package effectively stands
out to be a credit plus approach.
The participants of the OTUP support package are initially provided with hands-on training on income
generating activities such as cattle rearing and cow fattening, after which they receive BRAC loans,
conditioned upon investing in the kind of enterprise on which they are trained. In general, the main
difference between the STUP and OTUP support packages is that while the participants of the STUP
package receive assets (e.g. livestock, poultry) as grants, participants of the OTUP package receive soft
loan conditional on using the loan for buying almost similar type of productive assets. Hence, the STUP
support package is costlier than the OTUP one.
Selection Criteria
While both the STUP and OTUP support packages intend to support ultra poor households, the subtle
difference lies in the intensity of ultra poverty addressed by the packages. For example, the participants of
the STUP support package are likely to be drawn from more vulnerable segments than those targeted by
the OTUP support package. BRAC has set out specific targeting criteria for selecting participants for the
STUP and OTUP support packages.
The criteria for selecting eligible households (HH) for the STUP support package are as follows.
1. Has ≤10 decimals of land;
2. Children of school-going age (5-14 years) are engaged in Income Generating Activities (IGA);
3. Has no productive asset;
5
Interest rate is 25% and repayment starts after two months of taking the loan. The size of the loans ranges from BDT 10,000 t o
BDT 20,000.

Grant vs. credit plus approach to poverty reduction
3
4. Mainly dependent on irregular earning (from working as housemaid, day labourer, begging, etc.) of
female member, and
5. Has no male member capable of working for livelihood.
On the other hand, the targeting criteria for selecting households for the OTUP support package are as
follows.
1. Has ≤30 decimals of land;
2. Unable to bear children’s education expenses beyond the primary level;
3. Mainly dependent on irregular labour income;
4. (If any), history of failure to use NGO support in the past
5. Failure to avail either fish or meat or eggs in the last three consecutive days
In addition to these inclusion criteria, the programme also uses two exclusion criteria. Households with no
adult women capable of working are excluded as the programme provides support only to women.
Participants of microfinance and/or recipients of Govt./NGO supports are excluded to avoid duplications.
A household has to meet at least three out of the five respective inclusion criteria and none of the exclusion
criteria to be eligible for the STUP/OTUP support package.
The targeting criteria indicate that the participants of the OTUP support package have some experience
of participating in microfinance; but they could not effectively utilise it. Further, the participants of the STUP
support package are less likely to have working age male members in their households compared to those
targeted by the OTUP support package.
Selection Process
To select ultra poor households, a targeting methodology is followed which combines geographical,
participatory, and proxy means test. The selection process relies heavily on working closely with
communities to identify the poorest areas and the poorest within areas. Initially, based on the poverty
mapping of World Food Programme (WFP), BRAC selects the poorest sub-districts from rural areas of
Bangladesh with the advantage that the organisation has local offices almost all over the country. In the
selected sub-districts, communities that have a high concentration of poverty are identified based on own
knowledge of programme staff or discussion with other BRAC programme managers engaged in
microfinance, health, education, etc. As we show in the descriptive analysis section of this report, areas
that are selected for programme support are indeed poorer than those not selected. In the selected
villages, a participatory wealth ranking (PWR) exercise is carried out at the beginning. In the PWR,
households of the community are ranked into several wealth groups, such as very poor, poor, middle -
class, non-poor. Afterwards, the households from bottom three wealth ranks are visited by programme
staff to verify the specific eligibility criteria, as mentioned earlier.
Household visit by programme staff to check the eligibility criteria proceeds as follows: (a) households from
bottom two wealth ranks of the PWR are first checked to see if they are eligible for the STUP support
package, (b) if not, then they are checked for eligibility for the OTUP support package, and (c) households
from bottom 3rd rank are also checked with eligibility criteria for the OTUP support package.

Grant vs. credit plus approach to poverty reduction
4
3. EVALUATION DESIGN AND DATA COLLECTION
Evaluation Design
As already mentioned, BRAC started the third phase of the CFPR-TUP programme in 2012. The focus of
this study is on this 2012 cohort of the programme. For evaluation purpose, in the first stage, 30 branch
offices were randomly selected from the total list of branches planned for intervention in the year 2012.
For each of these 30 branch offices, a mapping of all nearby branch offices which were not covered by
the programme was conducted. Then, considering the geographical proximity, 30 branch offices were
purposively selected where the CFPR-TUP has never been implemented.
6
In the second stage, 10
communities/villages were randomly selected from each of the treated and non-treated branch offices,
comprising a total of 600 villages (10*(30+30)).
7
It is to be noted that, in the intervention branch offices, BRAC programme staff carried out selection of
ultra poor households for programme support using the PWR exercise and proxy means of verification, as
discussed earlier. Such rigorous selection, however, was not conducted in the non-treated branch offices.
In lieu of that the research team carried out a small census both in the treated and non -treated branch
offices. The census collected information on targeting criteria (mentioned earlier). Based on the census
information, the research team identified eligible households from each village. Sampling of households for
household survey was done based on eligibility of the households for programme support. The idea behind
using the process of sampling just discussed was to use the same process in determining eligible
households from intervention and non-intervention branch offices. To reiterate, the selection process of
ultra poor for programme support based on the census information collected by the research team is not
so rigorous as the one used by the programme (PWR followed by a household visit and a final round of
verification); but it allowed us to have same selection process in both areas so that we could have a suitable
comparison group for assessing the programme effects.
After identifying potential participants for the STUP and OTUP support packages, from each
community/village, nine (9) eligible households for the STUP support package and another nine (9) for the
OTUP support package were randomly selected for the survey. Additionally, four (4) non-eligible
households from each community were also surveyed to allow estimating spillover effects of the
programme (if any), such as translating knowledge of entrepreneur skills to non-participants in the same
community, labour market effects on non-participants through general equilibrium effects.
8
Data Collection
A baseline survey was conducted in May-July, 2012, covering 3,957 households eligible for the STUP
support package and 4,840 households eligible for the OTUP support package. Among the eligible
households for the STUP, 2,197 were from intervention areas and the remaining 1,760 belonged to non-
intervention areas. On the other hand, out of the 4,842 households eligible for the OTUP support package,
2,484 were from intervention areas and the rest 2,356 were from non-intervention areas. A follow-up
survey was conducted in May-July, 2014 when 3,600 eligible households for the STUP support package
6
Research team also requested CFPR-TUP programme mangers not to implement in these areas until 2015. BRAC did not
implement CFPR-TUP programme in these areas until 2012 because these areas have less concentration of poverty compared to
those already covered by the programme. It seems that these areas are likely to have less concentration of poverty. Indeed, as
shown in descriptive analysis, we find this.
7
From the treated brances, we selected 10 communities because programme selection is carried out at the community level
covering about 80-120 households. If a village contained more than 120 households, the programme usually divided it into several
communities, and carried out selection in each community. From the non-treated branches, we randomly selected 10 villages and
then took one community from each with around 120 households.
8
However, measuring spillover effect is beyond the scope of this study. Spillover effects, if any, are unlikely to bias our results as
comparison group is from different communities.

Grant vs. credit plus approach to poverty reduction
5
were successfully revisited (1,981 households from intervention areas and 1,619 households from non-
intervention areas). In case of the OTUP support package, 4,542 households were successfully revisited
during the follow-up survey (2,310 from intervention areas and 2,232 from non-intervention areas). Overall,
the attrition rates are 9% and 6% for STUP and OTUP, respectively (Annex Table A1).
A semi-structured
9
questionnaire was used to collect information and the respondent was the main female
member of the household. The questions were related to demographics, human capital, employment and
income generation, crisis coping mechanism, borrowing and lending, savings, food and non-food
expenditures, food consumption, endowments of the productive and non-productive asset, etc.
BRAC started providing the programme support after completion of baseline survey. The non-intervention
branch offices did not receive any support from the programme until 2014. Among the surveyed 1,981
households eligible for the STUP support package, 1,044 got the programme support. On the other hand,
out of the surveyed 2,310 households eligible for the OTUP support package, 490 were covered by the
programme. The rest remained untreated perhaps because they were not eligible as per selection carried
out by programme staff or were not interested in the programme.
9
Few questions were open-ended.

Grant vs. credit plus approach to poverty reduction
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4. DESCRIPTIVE STATISTICS
This section presents descriptive statistics of all the outcome variables of interest. We present the statistics
separately for the eligible households of the STUP and OTUP support packages. Standard errors of the
differences are clustered at the branch office level. Table 1 shows the means of savings, outstanding loans,
and key physical assets. It appears that at baseline, eligible households of both the STUP and OTUP
support packages from intervention areas, as compared to non-intervention or control areas, had lower
amount of savings, outstanding loan, land, livestock, poultry, key durable asset items, clothing, etc. It is
worth mentioning that some of the differences in means of those variables between intervention and non-
intervention areas are also statistically significant.
In general, the findings from Table 1 indicate that at baseline, households from non-intervention areas were
better-off than their counterparts from intervention areas. This is possibly because the CFPR-TUP
programme usually selects the poorest geographical areas.
In the follow-up survey conducted in 2014, it could be observed that households from both intervention
and non-intervention areas increased most of the asset items. Not surprisingly perhaps, the magnitude of
increase was higher for the intervention areas compared to non-intervention ones. In the intervention areas,
the amount of savings of the eligible households for the STUP support package increased by BDT 1096
between 2012 and 2014, while for the same period- an increase of BDT 755 was found for eligible
households in non-intervention areas. Similar trend is observed for eligible households for OTUP support
package. Again, eligible households for the STUP support package from intervention and non- intervention
areas both posited more land endowment – tentatively amounting to a 56% increase for the intervention
areas against 34% increase for non-intervention areas. For the amount of land holding of the households
eligible for the OTUP package, a similar trend is observed. Looking at other assets, it could be observed
that, in the base period, the households from non-intervention areas of both the STUP and OTUP were
ahead of those from intervention areas in the possession of key assets. In fact, some of the differences
between households from the two areas are statistically significant. In the follow-up survey, however, the
differences seems to have dissipated to some extent – indicating that the increase in the amount of these
assets during 2012-2014 was larger for households from intervention areas.
Table 2 reports the total time devoted to different economic pursuits by working age members (15-65
years), and per capita annual income
10
pertaining to eligible households for the STUP and the OTUP
support packages. The following pertinent observations need mention. First, compared to intervention
areas, the working age males and females from non-intervention areas devoted more time to agricultural
self-employment
11
, and the difference in the mean of this variable between them is found be statistically
significant for both the STUP and OTUP. In the follow-up survey conducted in 2014, the corresponding
differences have increased (even positive for STUP). However, the results are found to be statistically
insignificant. This findings indicate that males and females from intervention areas were more likely to
increase time devoted to agricultural self-employment during the comparable periods (2012 to 2014).
Second, information contained in Table 2 also shows, as far as baseline is concerned, intervention areas
lagged behind non-intervention areas in per capita annual incomes but, in follow-up period in 2014, almost
equalized. This possibly indicates that the per capita income increase in the intervention areas was higher
during the comparable periods.
According to HIES 2010, per capita annual income of the extreme poor households in Bangladesh (at the
national level using lower poverty line) was BDT 13,224 in 2010. At 2010 constant price, baseline per
capita incomes of the sample eligible households for the STUP and OTUP support packages from
10
Per capita income is converted to 2012 prices using rural consumer price index.
11
Note that agricultural self-employment includes livestock rearing.

Grant vs. credit plus approach to poverty reduction
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intervention areas were BDT 9,702 and BDT 10,713, respectively,
12
indicating that, on average, the
sample eligible households are positioned below the national ultra poverty line.
12
Deflating per capita incomes of 11,676 and 12,893 by general price index

Grant vs. credit plus approach to poverty reduction
8
Table 1. Asset holding of the surveyed households
Indicators
STUP -2012
STUP -2014
OTUP -2012
OTUP- 2014
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
(1)
(2)
(3=1-2)
(4)
(5)
(6=4-5)
(7)
(8)
(9=7-8)
(10)
(11)
(12=10-11)
Financial assets and land
Savings (BDT)
212
681
-469***
1308
1436
-128
608
1491
-883***
1718.50
2332
-613***
Outstanding loan (BDT)
5,426
8,844
-3,418**
8,235
12,195
-3,960***
9,428
11,754
-2,326**
13,441
15,407
-1,966
Total land owned (decimal)
1.82
2.39
-0.57***
2.85
3.21
-0.36
4.66
4.99
-0.33
5.78
5.59
0.19
Cultivable land owned
(decimal)
0.03
0.13
-0.09
0.41
0.51
-0.1
1.16
1.5
-0.34
1.45
1.69
-0.24
Homestead land owned
(decimal)
1.76
2.21
-0.45***
2.2
2.61
-0.41**
3.27
3.22
0.06
3.8
3.61
0.2
Rented-in land (decimal)
1.06
2.85
-1.78***
5.49
5.32
0.18
8.03
9.66
-1.63
14.63
14.69
-.06
Other productive asset
No. of cow
0.03
0.08
-0.05
0.45
0.24
0.22***
0.29
0.49
-0.20***
0.46
0.55
-0.09
No. of goat/sheep
0.15
0.23
-0.08
0.49
0.31
0.17**
0.30
0.48
-0.17**
0.36
0.42
-0.06
No. of poultry birds
1.12
1.63
-0.51***
3.69
2.72
0.98**
2.24
2.76
-0.52**
3.88
3.80
0.08
Value of all productive
assets (BDT)
1,171.50
2,053
-881.50***
9,128
5,551
3,577***
5,765
9,192
-3,427***
11,126.40
11,976
-849.60
Non-productive asset
No. of television
0.01
0.04
-0.03
0.03
0.07
-0.03
0.06
0.12
-0.06
0.11
0.15
-0.05
No. of mobile phones
0.22
0.33
-0.11**
0.49
0.53
-0.05
0.58
0.70
-0.13***
0.90
0.93
-0.02
No. of chair
0.33
0.43
-0.10*
0.62
0.60
0.02
0.85
0.99
-0.14*
1.19
1.20
-0.02
No. of table
0.21
0.23
-0.02
0.37
0.36
0.01
0.50
0.55
-0.06
0.65
0.67
-0.02
No. of choki13
0.97
1.05
-0.08
1.23
1.28
-0.06
1.32
1.39
-0.08
1.598
1.55
0.04
No. of mosquito net
1.05
1.15
-0.10
1.31
1.33
-0.02
1.33
1.40
-0.07
1.60
1.56
0.04
No. of sharee14
0.74
0.93
-0.19*
1.27
1.10
0.17
1.23
1.59
-0.36***
1.66
1.64
0.02
No. of lungi15
0.71
0.72
-0.02
0.87
0.88
-0.01
1.66
1.75
-0.09
1.93
1.83
0.09
Value of non-productive
asset (BDT)
2,301
3,028
-726.90***
4,135
4,633
-498
6,054
6,912
-858.10*
8,255
8,654
-399
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively.
13
A basic wooden cot.
14
Sharee is a typical traditional attire of rural Bangladeshi women.
15
Lungi is a typical traditional attire of rural Bangladeshi men.

Grant vs. credit plus approach to poverty reduction
9
Table 2. Employment and income of the surveyed households
Indicators
STUP- 2012
STUP- 2014
OTUP -2012
OTUP- 2014
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
(1)
(2)
(3=1-2)
(4)
(5)
(6=4-5)
(7)
(8)
(9=7-8)
(10)
(11)
(12=10-11)
Working hours (male)
Self-employment in agriculture (hours)
52.34
146.90
-94.56***
200.41
180.40
20.01
179.62
249.70
-70.08**
271.81
279.40
-7.60
Self-employment in non-agriculture
(hours)
139.81
128.50
11.31
259.96
192.80
67.16
163.85
157.80
6.05
227.20
182.10
45.10
Wage employment in agriculture
(hours)
583.83
574.20
9.63
420.24
466.50
-46.26
721.17
696.10
25.07
491.20
538.40
-47.20
Wage employment in non-agriculture
(hours)
562.15
558.80
3.35
647.30
652.70
-5.40
675.50
758.50
-83
705
747.80
-42.80
Salaried employment# (hours)
102.44
105.20
-2.76
99.91
113
-13.09
108.66
72.36
36.30
111.97
91.19
20.78
Working hours (female)
Self-employment in agriculture (hours)
182.16
242.90
-60.74**
186.98
176.10
10.88
377.61
445.10
-67.49*
150.43
168.20
-17.77
Self-employment in non-agriculture
(hours)
30.08
28.40
1.68
14.53
5.48
9.05
10.34
9.58
0.76
11.44
5.91
5.53
Wage employment in agriculture
(hours)
227.80
250.20
-22.40
30.08
51.46
-21.38
74.20
39.06
35.14
16.49
17.32
-0.83
Wage employment in non-agriculture
(hours)
115.73
126.70
-10.97
75.15
73.21
1.94
35.46
21.35
14.11
14.85
15.43
-0.58
Salaried employment# (hours)
39.52
52.53
-13.01
161.45
137.80
23.65
20.75
26.83
-6.08
95.07
99.84
-4.78
Per capita annual income (BDT, at
2012 constant price)
11,676
13,165
-1,489**
14,354
14,235
119
12,893
14,770
-1877***
14,512
15,733
-1,221**
Note: ***, ** and * denote statistical significance at 1%, 5% and 10% respectively.
Time in total hours worked in the last one year.
# Salaried employment refers to non-casual wage employment.

Grant vs. credit plus approach to poverty reduction
10
Table 3 reports changes accruing to non-income sides such as per capita daily food expenditure, and
amounts of key food items consumed.
During baseline and follow-up surveys respectively in 2012 and 2014, the per capita daily food expenditure
(at 2012 constant price) of the eligible households for both the STUP and OTUP support packages from
intervention areas grew faster than non-intervention areas (29% against 21%). With regard to specific food
items, for example in 2012, it appears that eligible households from intervention areas used to consume
relatively less of fish, meat and leafy vegetables than their counterparts. But the follow-up survey in 2014
shows that the pendulum has swung with households from intervention areas reporting higher level of
consumption of these items than their counterparts from non-intervention areas.
Away from income and non-income gains, Table 4 reports the proportion of women (respondent women)
16
that faced different types of violence within the households. The survey asked seven specific questions
related to facing domestic violence (with answer choices being Yes/No). The statistics show that the
proportion of women facing violence was very low at the beginning, and is statistically insignificant. But at
follow-up, some of the differences appeared statistically significant. For instance, for the indicator
“prevented from going outside for work”, mean difference between intervention and non-intervention areas
(for both STUP and OTUP) is negative and statistically significant although baseline difference was
statistically insignificant. The descriptive statistics thus indicates that the programme is likely to reduce
domestic violence against women.
As already mentioned, not all the households determined eligible by the research team based on the
census information from treated areas got the programme support. It may be that these non-participants
were found to be ineligible as per selection carried out by the programme staff. This is evident from the
fact that the participants are indeed poorer than non-participants as shown in Annex Table A2. Information
in this table shows that, at baseline, the participants of the STUP support package were poorer than that
of the OTUP. For example, at baseline, only 0.71% and 1.19% of the STUP participants owned cultivable
lands and cow, key productive assets in rural Bangladesh, respectively while the corresponding
proportions among the participants of the OTUP support package are 8.66% and 12.14%.
16
Respondent of the survey was the main female member of the household, who is basically household head or main decision
maker after head if the household is male-headed. For households that received programme support, the main female (respondent)
was the female that received programme support because in the CFPR-TUP programme, all supports are channelled through the
main female member of the selected household.

Grant vs. credit plus approach to poverty reduction
11
Table 3. Per capita food expenditure and consumption
Indicators
STUP 2012
STUP 2014
OTUP 2012
OTUP 2014
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
Intervention
Non-intervention
Difference
(1)
(2)
(3=1-2)
(4)
(5)
(6=4-5)
(7)
(8)
(9=7-8)
(10)
(11)
(12=10-11)
Per capita daily food
expenditure (BDT, at
2012 constant price)
28.23
28.14
0.09
35.52
34.26
1.26
28.14
29.27
-1.13
33.51
33.73
-0.22
Per capita consumption of food items (in gram)
Rice
523.92
534.80
-10.88
506.06
495.20
10.86
543.08
562.80
-19.72
519.96
512.50
7.46
Pulse & Legumes
10.28
8.65
1.62
12.59
10.56
2.03
8.96
10.74
-1.78
11.42
11.11
0.31
Potato
78.63
77.90
0.73
77.50
78.74
-1.24
80.91
75.18
5.73
68.27
72.90
-4.63
Leafy Vegetables
28.06
46.79
-18.73***
42.46
42.10
0.36
18.98
38.76
-19.78***
37.01
30.71
6.30
Fish
24.54
39.70
-15.16***
51.54
51.04
0.50
37.73
56.65
-18.92***
55.33
58.53
-3.20
Meat
3.88
9.03
-5.15**
11.70
11.54
0.16
10.60
11.01
-0.41
15.56
15.56
0.00
Egg
1.94
1.65
0.28
3.08
2.53
0.55
1.85
2.76
-0.91*
3.19
3.25
-0.07
Milk & Milk Products
4.91
3.92
0.98
9.50
6.65
2.84
12.11
12.53
-0.42
12.71
13.22
-0.51
Note: ***, ** and * denote statistical significance at 1%, 5% and 10% respectively.

Grant vs. credit plus approach to poverty reduction
12
Table 4. Violence against women
Indicators
STUP 2012
STUP 2014
OTUP 2012
OTUP 2014
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
Intervention
Non-
intervention
Difference
(1)
(2)
(3=1-2)
(4)
(5)
(6=4-5)
(7)
(8)
(9=7-8)
(10)
(11)
(12=10-11)
Husband:
Takes away money forcibly
(Yes=1, No=0)
0.02
0.02
0.01
0.04
0.03
0.00
0.03
0.02
0.01
0.07
0.09
-0.02
Takes away personal asset
forcibly (Yes=1, No=0)
0.01
0.00
0.00
0.00
0.01
-0.01
0.01
0.01
-0.00
0.01
0.04
-0.03**
Prevents from visiting parental
home (Yes=1, No=0)
0.02
0.03
-0.00
0.02
0.02
-0.01
0.06
0.05
0.01
0.05
0.08
-0.03
Prevents from going outside
for work (Yes=1, No=0)
0.02
0.02
0.01
0.01
0.04
-0.03**
0.13
0.14
-0.00
0.04
0.10
-0.06**
Assaults physically
(Yes=1, No=0)
0.05
0.06
-0.01
0.07
0.10
-0.04
0.09
0.11
-0.02
0.18
0.21
-0.04
Threats to divorce
(Yes=1, No=0)
0.02
0.01
0.01
0.01
0.02
-0.01
0.03
0.03
0.00
0.05
0.05
-0.00
Threats to second marriage
(Yes=1, No=0)
0.02
0.01
0.01
0.01
0.02
-0.01
0.03
0.03
-0.00
0.03
0.05
-0.02
Note: ***, ** and * denote statistical significance at 1%, 5% and 10% level, respectively.

Grant vs. credit plus approach to poverty reduction
13
5. ANALYTICAL TECHNIQUE
As we have already shown, at baseline, there is large and statistically significant difference in some of the
outcome variables between intervention and non-intervention areas. Taking the advantage of panel data,
we use difference-in-difference (DiD) method to identify the causal effect of the intervention. If the common
trend assumption - that is, participant and non-participant households have a common trend in the
outcome variables in the absence of intervention- holds, then DiD method identifies the causal effects of
the intervention. We estimate the following equation:
𝑦𝑖𝑏𝑡 = 𝑎1+ 𝑎2𝐼𝑁𝑇𝑉
𝑏+ 𝑎3𝑌𝐸𝐴𝑅𝑡+ 𝑎4𝐼𝑁𝑇𝑉
𝑏∗ 𝑌𝐸𝐴𝑅𝑡+ 𝜖𝑖𝑏𝑡………….(1)
Where 𝑦𝑖𝑏𝑡 is the outcome variable of interest for household i in branch office b and year t where t=baseline
and follow up. 𝐼𝑁𝑇𝑉
𝑏 is a binary variable taking the value of 1 if branch b is under intervention, 0 if not.
𝑌𝐸𝐴𝑅𝑡 is a dummy variable taking the value of 1 if t=follow up, and 0 if t=baseline. 𝑎4 is the key parameter
of interest. It identifies the causal effect of the programme assuming that the error term is uncorrelated
with 𝐼𝑁𝑇𝑉
𝑏. Since sampling was clustered at the branch office level, we estimate standard errors at the
branch office level.
𝑎4 in equation (1) is biased if the common trend assumption violates. As we do not have panel data for the
pre-programme period, we cannot verify whether this assumption does hold.
17
A possible avenue for
making the violation of common trend assumption is through correlation of time invariant characteristics
with the intervention. Hence, we also estimate difference-in-difference technique controlling for household
level fixed effects:
𝑦𝑖𝑏𝑡 = 𝛽1+ 𝛽2𝐼𝑁𝑇𝑉
𝑏+ 𝛽3𝑌𝐸𝐴𝑅𝑡+ 𝛽4𝐼𝑁𝑇𝑉
𝑏∗ 𝑌𝐸𝐴𝑅𝑡+ 𝑓
𝑖+ 𝑒𝑖𝑏𝑡………….(2)
Where 𝑓
𝑖 is household level fixed effects. 𝛽4 identifies the causal effect of the intervention assuming that
after controlling for time-invariant household level characteristics, the error term is uncorrelated with 𝐼𝑁𝑇𝑉
𝑏.
If time-invariant individual characteristics are not correlated with 𝐼𝑁𝑇𝑉
𝑏, it is likely that point estimate of 𝛽4
is very close to that of 𝑎4.
As mentioned earlier, not all the eligible households (as per the selection carried out by research team)
participated in the programme. The participation rate is 21% for OTUP and 53% for STUP. Hence the 𝑎4
and 𝛽4 estimate are something similar to ITT (Intention to Treat effect); but they are not exactly ITT because
it may be that not all the eligible households as determined by research team (based on census information)
were offered the intervention. However, we do not have detailed information to verify this possibility.
17
Violation of common trend assumption indicates that- without intervention- either the growth in outcomes for eligible households
from non-treated areas is higher than that from treated areas, or the opposite. We speculate that the former may be the case
because as descriptive statistics show, eligible households from non-treated areas were well-off at baseline. If so, then 𝑎4
underestimates the programme effects. That is, 𝑎4 is the lower bound of true effect.

Grant vs. credit plus approach to poverty reduction
14
6. RESULTS AND DISCUSSION
Impact on asset accumulation
Columns 1 and 2 of Table 5 report the effects (Intention-to-Treat or ITT effects) of the OTUP support
package on the values of productive and non-productive assets, and the physical units of key asset items.
Considering both the impact estimates using DiD with and without fixed effects, we find that the OTUP
support package increases productive assets. Specifically, the values of productive assets as well as the
number of cows, goats and poultry birds each increased due to the intervention (OTUP support package).
The ITT point estimate of the effect on the value of productive assets is BDT 2,646 (column 1 of Table 5).
As programme participation rate of the sample eligible households for the OTUP support package (as per
selection conducted by the research team) is 21%, the average treatment effect (ATT) of this support
package on productive asset is likely to be four times the ITT (i.e. ATT is around BDT 10,500), indicating
that programme increased asset value by about BDT 10,500.
As mentioned earlier, the participants of the OTUP support package receive loans from BRAC for buying
productive assets – predominantly livestock and poultry. Hence, the increase in livestock and poultry
ownership of these households may be associated with an increase in their debt (outstanding loans). But,
we do not observe statistically significant effect of the OTUP support package on outstanding loans
although the point estimate is positive. Hence, the increased asset value of the participants of the OTUP
support package as documented in Table 5 can be attributed to programme effect. We also document
positive effect of the credit plus intervention on savings; however, it is not statistically significant.
Table 5. Impact on asset accumulation
Impact of OTUP support package
Impact of STUP support package
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without
fixed effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without
fixed effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
Value of productive
asset (BDT)
2,646***
(821.4)
2,577***
(804)
5765
4,447***
(412.5)
4,458***
(695)
1171.5
Value of non-productive
asset (BDT)
414.4
(636)
459.2
(619.4)
6054
196.1
(193.9)
228.8
(403.5)
2301
Savings (BDT)
269.5
(167)
269.5
(169)
608
326.5***
(90.3)
341.6***
(126)
212
Outstanding loans
(BDT)
1,611
(1,367)
360.1
(1,172)
9428
-901.7
(1,386)
-542.2
(1,549)
5426
Physical units of key productive assets
Cow
0.116**
(0.0498)
0.116**
(0.0498)
0.287
0.263***
(0.025)
0.263***
(0.0473)
0.032
Goat
0.114***
(0.0388)
0.114***
(0.0388)
0.304
0.252***
(0.0391)
0.252***
(0.0635)
0.152
Poultry birds
0.603**
(0.266)
0.603**
(0.266)
2.235
1.484***
(0.339)
1.484***
(0.383)
1.124
Note: ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively. Figures in the parenthesis are standard
errors clustered at the branch office level. ITT effects are reported.
Results presented in columns 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in
columns 2 and 5 have been estimated using equation 1 presented in Section 5.

Grant vs. credit plus approach to poverty reduction
15
Columns 4 and 5 of Table 5 report the estimated effects of the STUP support package on the values of
productive and non-productive assets, and physical units of key asset items. Column 4 presents the results
using DiD with fixed effects while column 5 presents that using DiD without fixed effects. Like the OTUP
support package, we document positive effects of the STUP support package on the value of productive
assets. The STUP support package has been also found to have positive effects on the number of cows,
goat and poultry birds. Further, the grant-based support package also increases savings of its participants.
The ITT point estimate of the effect on the value of productive assets is BDT 4,447 (column 4 of Table 5).
As programme participation rate of the sample eligible households for the STUP support package (as per
selection conducted by the research team) is 53%, the average treatment effect (ATT) of this support
package on productive asset is likely to be twice the ITT (i.e. ATT is around BDT 10,000). Survey
information shows that the amount of transfer towards productive assets to the participants of the STUP
support package averaged BDT 10,452. This information together with the impact estimates on productive
asset values and savings is likely to indicate that the participants of the STUP support package did not eat
away the assets provided by the programme.
Table 6 presents the effects on durable asset holding. We see that the number of mobile phones, tables,
and chairs each increased due to programme intervention with most of the impact estimates being
statistically significant. Since income effect on luxurious goods (like the ones presented here) is generally
positive, the positive effects on these household durables and communication technologies are expected.
Table 6. Impact on durable assets
Impact of OTUP support package
Impact of STUP support package
Household
durable assets
(numbers)
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
Television
0.0144
(0.011)
0.015
(0.011)
0.058
0.00102
(0.00672)
0.000445
(0.00985)
0.008
Mobile phone
0.103***
(0.032)
0.103***
(0.032)
0.576
0.0666***
(0.0216)
0.0663**
(0.0309)
0.223
Chair
0.123**
(0.050)
0.123**
(0.050)
0.851
0.120***
(0.0317
0.119***
(0.0346)
0.326
Table
0.0419
(0.036)
0.042
(0.036)
0.497
0.0298
(0.0183)
0.0294
(0.0266)
0.212
Choki
0.119***
(0.040)
0.119***
(0.040)
1.317
0.0283
(0.0247)
0.0279
(0.0404)
0.969
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parenthesis are standard errors
clustered at the branch office level. ITT effects are reported.
Results presented in column 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in column
2 and 5 have been estimated using equation 1 presented in Section 5.
The productive asset items shown in Table 5 do not include land holding. Hence, a separate analysis is
conducted for land holding (Table 7). Since land is very expensive in Bangladesh, it is beyond the ability of
the ultra poor households to purchase land. Nonetheless, since tenure system is very widespread in
Bangladesh, they may get access to land through tenure system.
18
Our findings show that the effects of
the interventions on almost all kinds of land holdings are positive; but statistically significant effect using
both methodologies (DiD with/without fixed effects) is documented only for rented-in-land for the STUP
18
Hossain et al. (2014), for example, show that almost 40% of the operated lands in Bangladesh is cultivated under the tenure
system.

Grant vs. credit plus approach to poverty reduction
16
support package. In rural Bangladesh land cultivation is the predominant source of income and access to
land is likely to decrease poverty (Chirwa 2004, Adhikari and Bjørndal 2009, IFAD 2015), indicating that
the CFPR-TUP programme helps participants create sustainable graduation pathways out of ultra poverty
through access to land.
Table 7. Impact on land holding
Impact of OTUP support package
Impact of STUP support package
Land type
(decimal):
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable of
eligible households
from intervention
areas
(1)
(2)
(3)
(4)
(5)
(6)
Total land owned
0.506
(0.312)
0.521*
(0.311)
4.66
0.209
(0.154)
0.215
(0.216)
1.82
Cultivable land
owned
0.128
(0.18)
0.101
(0.191)
1.16
-0.00408
(0.0916)
-0.00471
(0.119)
0.03
Homestead land
owned
0.169
(0.183)
0.142
(0.185)
3.27
0.0374
(0.0878)
0.0478
(0.127)
1.76
Rented-in land
1.44
(1.525)
1.567
(1.565)
8.033
1.952**
(0.832)
1.965**
(0.833)
1.062
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parentheses are standard errors
clustered at the branch office level. ITT effects are reported.
Results presented in columns 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in
columns 2 and 5 have been estimated using equation 1 presented in Section 5.
Impact on employment and income
Capital market imperfection decreases self-employment and increases wage employment (Banerjee and
Newman 1993). There is substantial empirical evidence that the very poor in Bangladesh are capital
constrained. Hossain and Bayes (2009), for instance, show that only about 2% of households owning less
than 0.2 hectares of land had access to bank loans in 2008, while the corresponding proportion among
those owning 2.0 hectare of land was 20%. There is also evidence that representation of the ultra poor in
microfinance is less (Morduch 1999). It is thus likely that the ultra poor in Bangladesh would devote less
time to self-employment due to their capital constraints. The single shot asset transfer to the participants
of the STUP support package significantly affected their productive asset-base, as we have documented
in Table 5. The participants of the OTUP support package received loans towards buying productive
assets, and results show that the programme increased their productive asset-base. It is thus likely that
the interventions would increase self-employment and decrease wage employment.
Table 8 presents the estimated effects on time devoted to various activities of the working age male and
female members. The analysis is done at the individual level. Since the same individual may not appear in
both baseline and follow up, the panel is unbalanced at the individual level. Hence, it is not possible to
estimate the effects controlling for individual-level fixed effects. We have thus estimated the effects using
DiD without fixed effects (i.e. using equation (1))
19
. From Table 8 we can see that the OTUP support
package increased time devoted to agricultural self-employment of both working age male and female
members. Since the participants of the OTUP support package usually invest the loans taken from BRAC
to livestock rearing activities (because training is provided on those activities), an increase in time devoted
to these activities is expected. They also increased time devoted to non-agricultural self-employment.
These effects are statistically significant at the 10% level for males only. The findings also show that the
OTUP support package is likely to increase the total labour supply of both males and females.
19
A similar analysis is conducted at the household level controlling for household level fixed effects. Please see Table A3 in Annex
for the results.

Grant vs. credit plus approach to poverty reduction
17
As can be seen from the results presented in columns 5-8 of Table 8, the STUP support package increased
the working age male and female members’ time devoted to agricultural self-employment, such as
livestock and poultry rearing. These effects are statistically significant. Non-agricultural self-employment
has also increased but the effect is not statistically significant. Findings also indicate that the total labour
supply of the males and females of the households receiving the STUP support package has increased.
Table 8. Impact on employment of working age males and females (using DiD without fixed
effects)
Impact of OTUP support package
Impact of STUP support package
Effects on males
Baseline mean of
outcome variable of eligible
households
from intervention areas
Effects on females
Baseline mean of
outcome variable of eligible
households
from intervention areas
Effects on males
Baseline mean of
outcome variable of eligible
households
from intervention areas p
Effects on females
Baseline mean of
outcome variable of eligible
households
from intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Self-employment
in agriculture
62.49*
(35.99)
179.62
49.72
(36.39)
377.61
114.6***
(26.16)
52.34
71.63**
(32.37)
182.16
Self-employment
in non-agriculture
39.05*
(22.26)
163.85
4.771
(8.73)
10.338
55.84
(43.5)
139.81
7.373
(11.88)
30.08
Wage employment
in agriculture
-72.27
(48.77)
721.17
-35.98
(22.26)
74.2
-55.9
(53.92)
583.83
1.022
(55.39)
227.8
Wage employment
in non-agriculture
40.19
(57.17)
675.5
-14.69
(14.22)
35.46
-8.75
(67)
562.1
12.9
(35.5)
115.73
Salaried
employment#
-15.52
(18.81)
108.66
1.3
(33.91)
20.75
-10.32
(35.86)
102.44
36.65
(47.26)
39.52
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parenthesis are standard errors
clustered at the branch office level. ITT effects are reported. Time in total hours worked in the last one year.
# Salaried employment refers to non-casual wage employment.
Table 9 reports the estimated effect of the interventions on per capita income. The survey collected income
information for the last one year prior to the survey. For each of the activities the household member(s)
were involved in, yearly income was recorded. Per capita income was obtained dividing the total household
income by household size. It is expressed at 2012 constant price using rural consumer price index. As we
have already shown, programme participants increased savings, asset accumulation, and labor supply. A
priori reasoning suggests that programme participation would increase per capita income. Consistent with
the intuition, we find that the effects both of the OTUP and STUP support packages on per capita income
are positive. The effects are statistically significant at 1% level for STUP and 5% level for OTUP. The point
estimates of the effects using DiD with fixed effects are almost close to those using DiD without fixed
effects. The ITT point estimates of the effects of the OTUP support package (using fixed effects) on per
capita income is equivalent to 13% of baseline per capita income. The corresponding proportion for the
STUP support package is 22%. But these findings do not necessarily indicate that the effect is larger for
STUP because these estimates are ITT effects, and programme participation rates among analytical
sample households are different for STUP and OTUP (53% for STUP and 21% for OTUP). Although we
are unable to estimate ATT, the information on ITT point estimates of the effects on income and
programme participation rates indicate that the effects of the OTUP support package is perhaps not less
than that of the STUP.

Grant vs. credit plus approach to poverty reduction
18
Table 9. Impact on per capita income
Impact of OTUP support package
Impact of STUP support package
Impact
estimates
using DiD with
fixed effects
Impact
estimates using
DiD without fixed
effects
Impact
estimates using
DiD with fixed
effects
Impact
estimates using
DiD without
fixed effects
(1)
(2)
(3)
(4)
Per capita annual income (BDT, at
2012 constant price)
1,709***
(357.4)
1,668***
(349)
2,666***
(406.5)
2,560***
(418.3)
Baseline mean of outcome variable of
eligible households from intervention
areas
12,893
12,893
11,676
11,676
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parenthesis are standard errors
clustered at the branch office level. ITT effects are reported. Per capita income was converted to constant price using consumer
price index.
Results presented in column 1 and 3 have been estimated using equation 2 presented in Section 5; and results presented in column
2 and 4 have been estimated using equation 1 presented in Section 5.
Impact on household welfare
Nutrition-based poverty trap literature points out that people are poor because they consume less food
than required, and hence suffer from under-nutrition. In fact, under-nutrition is both a cause and a
consequence of poverty as well as low productivity (UNICEF 2009, Chong et al. 2013). The CFPR-TUP
programme provides ultra poor with consumption subsidy. A priori, this subsidy should lead to an increase
in food consumption. Further, as already shown, the intervention increases income, which may be
translated into an increase in food consumption of the participant households.
Table 10 reports the impacts on food consumption using DiD with/without fixed effects. We report the
impacts on the quantities of the key food items and the total per capita food expenditure (at 2012 constant
price). Results show that the interventions increased per capita food expenditures but the effects are not
statistically significant. Evaluating the STUP support package, Bandiera et al. (2013) also find that the effect
on food expenditure is low in the short-run (after two years); but it becomes larger in the long-run.
Nevertheless, our findings show that programme participation increased the consumption of fish and
vegetables substantially, and these effects are statistically significant. Literature on nutrition shows that
under-nutrition is a consequence of low level of vitamin A and iron intake (WHO 2002, IFPRI 2014). Fish,
meat and leafy vegetables are rich in vitamin A and iron. Since the interventions increased fish and leafy
vegetable consumption, it is likely that this would ultimately improve the nutritional status of the members
of the participant households. Findings also show that the interventions increased rice consumption
substantially.

Grant vs. credit plus approach to poverty reduction
19
Table 10. Impact on food consumption
Impact of OTUP support package
Impact of STUP support package
Indicators
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
Per capita daily food
expenditure (BDT,
2012 constant price)
0.69
(1.242)
0.91
(1.234)
28.14
0.582
(1.328)
1.174
(1.301)
28.23
Per capita daily consumption of key food items (in gram)
Rice
26.71**
(11.16)
27.18**
(11.32)
543.1
16.27
(12.2)
21.74*
(12.44)
523.9
Pulses and Legumes
1.76
(1.318)
2.095
(1.295)
9.0
-0.362
(1.674)
0.406
(1.683)
10.3
Potato
-8.813
(7.855)
-10.36
(8.078)
80.9
-2.082
(9.848)
-1.971
(9.646)
78.6
Leafy vegetables
25.44***
(5.646)
26.09***
(5.668)
19.0
17.89**
(8.228)
19.09**
(8.251)
28.1
Fish
15.71***
(4.769)
15.72***
(4.819)
37.7
13.12***
(4.779)
15.65***
(4.915)
24.5
Meat
-0.434
(3.387)
0.408
(3.367)
10.6
4.386
(3.606)
5.305
(3.524)
3.9
Egg
1.007
(0.648)
0.84
(0.62)
2.8
0.0201
(0.903)
0.268
(0.852)
1.9
Milk and Milk
Products
0.657
(3.183)
-0.0909
(3.111)
12.1
1.941
(2.442)
1.861
(2.324)
4.9
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parenthesis are standard errors
clustered at the branch office level. ITT effects are reported.
Results presented in column 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in column
2 and 5 have been estimated using equation 1 presented in Section 5.
Table 11 reports the estimated effects of the STUP and OTUP support packages on clothing and mosquito
net use. We find that the intervention has positive effects on clothing and mosquito net use. The ITT point
estimates indicate that the number of mosquito nets used by the participants of the STUP and OTUP
support packages increased by 0.07 and 0.11, respectively. These effects are statistically significant. The
interventions also increased the number of sharees and lungis. But, the effect on lungis is not statistically
significant. These findings indicate that the interventions have improved the welfare of ultra poor.

Grant vs. credit plus approach to poverty reduction
20
Table 11. Impact on clothing and mosquito-net use
Impact of OTUP support package
Impact of STUP support package
Indicators
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without fixed
effects
Baseline mean of
outcome variable of
eligible households from
intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
No. of mosquito-
net
0.111***
(0.037)
0.111***
(0.037)
1.333
0.0798***
(0.0306)
0.0794*
(0.047)
1.049
No. of sharee
0.379***
(0.113)
0.379***
(0.113)
1.229
0.361***
(0.0476)
0.361***
(0.106)
0.738
No. of lungi
0.179
(0.178)
0.178
(0.178)
1.662
0.00724
(0.0342)
0.00655
(0.113)
0.706
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parentheses are standard errors
clustered at the branch office level. ITT effects are reported.
Results presented in column 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in column
2 and 5 have been estimated using equation 1 presented in Section 5.
Impact on domestic violence against women
In the CFPR-TUP programme, all supports are provided to female members of the selected households.
So, it is likely that the participant women would become economically empowered. Banerjee et al. (2015a)
show that asset transfer programme indeed empowers the participant women.
20
A natural consequence
of the increased empowerment may be that domestic violence against programme participant woman
would decrease. To test this hypothesis, the survey for the current study covered a module on violence
against woman (main female member of the household) within the household. Information was collected
on seven specific indicators related to physical and verbal assault of the females by their husbands.
21
Table 12 reports the effects of the intervention on these indicators. For each of the indicators, the point
estimate of the effect is negative, although not all are statistically significant. Specifically, the likelihood of
taking away female’s asset forcibly by her husband has declined as a result of the programme intervention.
The effect is statistically significant for both the STUP and OTUP support packages. Similarly, threat to
divorce and second marriage by the husband has also decreased as a result of the pr ogramme
intervention, although this effect is statistically significant only for the STUP support package. In addition,
the possibility of being prevented by the husband from going outside for work has also declined. Again
this effect is statistically significant only for the STUP support package. One reason for this reduced
violence against women might be that their husbands care for the assets owned by them and therefore
has lessened inflicting harm/violence on them.
20
Banerjee et al. (2015a) measured empowerment using indicators related to women’s influence over daily food and non-food
expenditures.
21
The respondent of the survey was the main female member of the household. The sample for this section was restricted to married
(i.e. living with husband) respondent only.

Grant vs. credit plus approach to poverty reduction
21
Table 12. Impact on domestic violence against women
Impact of OTUP support package
Impact of STUP support package
Indicators
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without
fixed effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
Impact
estimates
using DiD
with fixed
effects
Impact
estimates
using DiD
without
fixed effects
Baseline mean of
outcome variable
of eligible
households from
intervention areas
(1)
(2)
(3)
(4)
(5)
(6)
Husband:
takes away money forcibly
(Yes=1, No=0)
-0.0318
(0.0218)
-0.0318
(0.0218)
0.029
-0.00125
(0.00743)
-0.00125
(0.014)
0.021
takes away personal asset
forcibly
(Yes=1, No=0)
-0.0274**
(0.0118)
-0.0274**
(0.0118)
0.005
-0.0123***
(0.00357)
-0.0123**
(0.00612)
0.006
prevents from visiting
parental home
(Yes=1, No=0)
-0.0326
(0.0228)
-0.0326
(0.0228)
0.056
-0.00246
(0.00675)
-0.00246
(0.0125)
0.023
prevents from going
outside for work
(Yes=1, No=0)
-0.0589
(0.054)
-0.0589
(0.054)
0.133
-0.0354***
(0.00665)
-0.0354***
(0.0111)
0.023
assaults physically
(Yes=1, No=0)
-0.0186
(0.0408)
-0.0186
(0.0408)
0.091
-0.0244**
(0.0109)
-0.0244
(0.0258)
0.046
threats to divorce
(Yes=1, No=0)
-0.00193
(0.0136)
-0.00193
(0.0136)
0.028
-0.0205***
(0.00628)
-0.0205**
(0.00913)
0.024
threats to second marriage
(Yes=1, No=0)
-0.0142
(0.015)
-0.0142
(0.013)
0.025
-0.0157***
(0.002)
-0.0157*
(0.0089)
0.021
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parenthesis are standard errors
clustered at the branch office level. ITT effects are reported.
Results presented in column 1 and 4 have been estimated using equation 2 presented in Section 5; and results presented in column
2 and 5 have been estimated using equation 1 presented in Section 5.

Grant vs. credit plus approach to poverty reduction
22
7. CONCLUSION
Since 2002, BRAC has been implementing an anti-poverty programme titled “Challenging the Frontiers of
Poverty Reduction-Targeting the Ultra Poor” or CFPR-TUP. Originally, the programme was developed to
transfer productive assets, skills and consumption subsidy to asset-less ultra poor in Bangladesh, whose
livelihoods are heavily dependent on females’ domestic work, begging, and casual wage employment (Das
2009). Considering heterogeneity among the ultra poor themselves, in 2007, the CFPR-TUP programme
introduced two intervention packages: (1) a grant-based support package for specially targeted ultra poor
or STUP support package, and (2) a credit plus grant support package for other targeted ultra poor or
OTUP support package. While both the support packages target ultra poor households, the target group
of the latter is relatively well-off than those of the former. Further, the STUP support package is costlier
compared to the OTUP. This paper is an initiative to evaluate the OTUP and STUP support packages of
the CFPR-TUP programme by using non-experimental evaluation design. Descriptive statistics shows that
at baseline, the participants of both the STUP and OTUP support packages were asset-poor and heavily
dependent on casual wage employments but the participants of the later w ere relatively well-off than that
of the former. For example, at baseline, only a miniscule proportion of the participants of the STUP support
package owned cultivable land and cow, while 8.66% and 12.14% of the participants of the OTUP support
package owned these assets, respectively.
We find that the STUP support package increases self-employment, total labour supply, per capita income,
consumption of meat, fish, etc., and productive asset-base of ultra poor. The findings on the effectiveness
of the STUP support package seem to echo the positive results obtained by other studies that
experimented similar intervention (Banerjee et al., 2015a; Bandiera et al. 2013). The effect of the less-
costly OTUP support package of the CFPR-TUP programme on the livelihoods of ultra poor has also been
found to be positive. The magnitude of the effect of this support package on productive asset holding is
large. We also find positive effects of this support package on self-employment, per capita income and
consumption of rice, fish, vegetables, etc. There is also evidence that both the support packages increase
the clothing of the ultra poor women and men alike.
Main limitation of this study, however, hovers around the methodological issue: first, we have used non-
experimental evaluation design; nevertheless, we have tried to address the limitation using panel data that
allows us to control for household-level fixed effects. Second, the target group for the OTUP support
package is different from that of the STUP package. Hence, it is difficult to compare the effects of the two
support packages. The ideal could have been experimenting STUP and OTUP packages for the same
target group allowing us to investigate their relative effectiveness. We leave this issue for further research.
But the general message of this study is that a credit plus approach (OTUP support package) can be an
effective intervention at least for the ultra poor that are close to the edge of the ultra poverty line. Notably,
Bangladesh has already achieved the status of lower middle income country; but yet, as per estimates of
BBS (2011), 17% of the population live in ultra poverty, many of whom can perhaps fall in the target group
of the OTUP support package (as per its selection criteria). Since the OTUP package (which is less costly
than STUP) has been found to be an effecitve model, at least in the short-run, this model can be scaled
up to help the ultra poor in Bangladesh.

Grant vs. credit plus approach to poverty reduction
23
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Grant vs. credit plus approach to poverty reduction
25
ANNEX
Table A1. Sample size and attrition rate
Year
STUP
OTUP
Total
Intervention
Non-intervention
Total
Intervention
Non-intervention
Baseline
(2012)
3,957
2,197
1,760
4,840
2,484
2,356
Follow-up
2014
3,600
1,981
1,619
4,542
2,310
2,232
Attrition rate (%)
9.02
9.83
8.01
6.16
7.00
5.26
Table A2. Baseline characteristics of programme participants and eligible non-participants
STUP
OTUP
Indicators
Participant
Non-
participant
Difference
Participant
Non-
participant
Difference
(1)
(2)
(3=1-2)
(4)
(5)
(6=4-5)
% of households have:
Savings
15.62
19.93
-4.31
33.62
34.13
-0.51
Loan
28.26
27.84
0.43
40.94
44.28
-3.34
Cultivable Land
0.71
0.97
-0.25
8.66
9.12
-0.45
Homestead land
58.98
60.50
-1.52
72.85
71.36
1.49
Rent-in land
5.23
5.58
-0.35
21.78
21.26
0.53
Cow
1.19
4.76
-3.57***
12.14
20.18
-8.03**
Goat/sheep
5.67
11.41
-5.75***
15.01
17.43
-2.43
Poultry
26.87
37.35
-10.5***
55.95
50.55
5.40
Television
0.55
1.57
-1.02*
5.19
6.19
-1.00
Mobile
26.60
31.34
-4.75*
55.34
54.87
0.47
Chair
26.79
28.73
-1.94
53.08
52.18
0.90
Table
22.67
28.37
-5.70**
50.34
47.35
2.99
Choki
82.92
81.22
1.70
94.02
92.14
1.88
Mosquito net
87.63
87.33
0.30
95.79
92.44
3.35**
Sharee
52.60
53.67
-1.07
73.15
69.57
3.58
Lungi
55.17
50.20
4.98
89.81
84.56
5.25***
Per capita income (BDT)
12,080.84
11,399.05
681.8*
12,385.12
13,017.40
-632.3**
Per capita food expenditure (BDT)
27.81
28.55
-0.75
27.36
28.30
-0.94
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively and parentheses show the robust standard
errors.

Grant vs. credit plus approach to poverty reduction
26
Table A3. Impact on time devoted to earning achieving by working age (15-65 years) males and females
(individual fixed efforts).
Impact of OTUP support package
Impact of STUP support package
Impact estimates
using DiD with
fixed effects
Impact estimates
using DiD without
fixed effects
Impact estimates
using DiD with
fixed effects
Impact estimates
using DiD without
fixed effects
(1)
(2)
(3)
(4)
Panel A: Males
Self-employment in
agriculture
87.25*
(45.38)
83.54*
(44.64)
154.6***
(36.88)
136.6***
(32.54)
Self-employment in non-
agriculture
61.99**
(29.38)
55.51*
(29.02)
79.05
(50.86)
73.24
(56.35)
Wage employment in
agriculture
-77.77
(59.52)
-72.52
(58.74)
-38.39
(66.17)
-61.23
(62.88)
Wage employment in non-
agriculture
60.82
(72.06)
68.69
(72.26)
-7.673
(86.44)
-1.68
(81.17)
Salaried employment#
-16.28
(22.14)
-15.22
(23.29)
-16.53
(42.76)
-11.55
(43.64)
Panel B: Females
Self-employment in
agriculture
88.27*
(49.57)
64.07
(41.59)
74.81
(50.15)
90.85**
(38.61)
Self-employment in non-
agriculture
5.278
(13.57)
6.035
(9.967)
15.15
(18.79)
8.946
(14.05)
Wage employment in
agriculture
0.693
(30.88)
-44.69*
(25.94)
71.65
(78.19)
7.129
(64.53)
Wage employment in non-
agriculture
-14.46
(24.73)
-18.16
(15.82)
10.92
(67.21)
19.26
(41.3)
Salaried employment#
45.75
(40.5)
2.731
(39.31)
29.93
(53.75)
44.68
(55.67)
Note: ***, ** and * denote statistical significance at 1%, 5% and 10%, respectively. Figures in the parentheses are standard errors,
clustered at the branch office level. Results presented in column 1 and 3 have been estimated using equation 2 presented in Section
5; and results presented in column 2 and 4 have been estimated using equation 1 presented in Section 5.
# Salaried employment refers to non-casual wage employment.