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Which Form of Safety Net Transfer is Most Beneficial? Impacts on Income, Food Security, and Child Nutrition
Akhter Ahmed, John Hoddinott, Wahid Quabili, Shalini Roy, Fiona Shaba, and Esha SraboniInternational Food Policy Research Institute
Stakeholder Workshop3 December 2013, Dhaka
TMRI Objectives
The overall objective of the Transfer Modality Research Initiative is to provide evidence that can be used to streamline the social safety net system in Bangladesh.
The research will inform policymakers which type of program can best improve the income status and food and nutrition security of the poor and thus be a valuable tool to the government as it prepares its social protection strategy.
The research has the following specific objectives:1. Measure the impact and cost-effectiveness of transfer methods on
these key outcomes:– household income– household food security – child nutrition
2. Evaluate the process of delivering benefits (that is, transfers and nutrition knowledge) at the operational level and solicit feedback from program participants
Evaluating Impacts
IFPRI designed a rigorous impact evaluation of the Transfer Modality Research Initiative (TMRI) in the north and the south: 1. Only cash (north & south)2. Only food (north & south)3. Food + cash (north & south)4. Nutrition behavior change communication (BCC) + cash (north)5. Nutrition BCC + food (south)
We developed a randomized controlled trial (RCT) design to evaluate the impact of the 5 transfer modalities
Randomization is often termed as the “gold standard” for impact evaluation because it is the most powerful way to construct a valid counterfactual of what might have happened without the program
We used RCT with “before-and-after” and “with-and-without” differences for estimating the impact of transfers
We used the analysis of covariance (ANCOVA) method of estimating impact
RCT impact estimate with difference-in-differences
Baseline(Before)
Follow-up(After)
PA
CA
Program
Control
Impact = (PA - CA) - (PB - CB)
PB = CB
Outcome
RCT impact estimate using Analysis of Covariance (ANCOVA) regression
The ANCOVA regression model that we used to estimate impact is the following (example for the north):
With difference-in-differences:
Impact = ((Yttreat – Yt-1
treat) - (Ytcontrol – Yt-1
control))
With ANCOVA regression:
Impact = ((Yttreat – αYt-1
treat) - (Ytcontrol – αYt-1
control))
ANCOVA is “more flexible” in the sense that the ANCOVA estimate is equivalent to the diff-in-diff estimate if α = 1. But ANCOVA allows estimating the autocorrelation rather than imposing it to be 1 ANCOVA estimates are preferred over diff-in-diff estimates, given the high variability and low autocorrelation of the data at baseline and follow-up (McKenzie 2012, Journal of Development Economics)
Household surveys for impact evaluation
The required quantitative data for impact evaluation come from three household surveys
The first household survey, carried out in April 2012 (just before the start of transfers), provides the information needed for the baseline study
A first follow-up survey was conducted in June 2013, just after 12 months of transfer distributions were completed
A second follow-up or endline survey will be conducted in June 2014, after 24 months of transfer distribution
The surveys include TMRI participants and non-participant control households
Cash only
Food only
Cash+Fo
od
Cash+BCC
Control
0
200
400
600
800
1,000
1,200
1,400
1,600
1,267 1,2621,209 1,229 1,267
North
Per
capi
ta to
tal e
xpen
ditu
re (
Tk/m
onth
)
Baseline per capita monthly expenditures (proxy for income): 19% higher average income in the south
CashFo
od
Cash+Fo
od
Food+BCC
Control
0
200
400
600
800
1000
1200
1400
16001,466 1,458
1,5441,499
1,439
South
Per
capi
ta e
xpen
ditu
re (
Tk/m
onth
)
Cash only Food only Cash+Food Cash+BCC0
50
100
150
200
250
300
350
400
450
500
211186
120
454
North
Statistically significant Not significant
Incr
ease
in t
otal
exp
endi
ture
(Tk/
capi
ta/m
onth
)
Impact of transfers on per capita monthly expenditure (proxy for income): Absolute change (taka)
Cash Food Cash+Food Food+BCC0
50
100
150
200
250
300
350
400
450
500
53
146126
371
South
Statistically significant Not significant
Incr
ease
in t
otal
exp
endi
ture
(Tk/
capi
ta/m
onth
)
Impact of transfers on per capita monthly expenditure (proxy for income): Percentage change
Cash only Food only Cash+Food Cash+BCC0
10
20
30
40
16.714.7
9.7
36.4
North
Statistically significant Not significant
Perc
enta
ge in
crea
se in
tot
al e
xpen
ditu
re
Cash Food Cash+Food Food+BCC0
10
20
30
40
3.7
10.18.5
25.3
South
Statistically significant Not significant
Perc
enta
ge in
crea
se in
tot
al e
xpen
ditu
re
Impact of transfers on per capita monthly food expenditure: Percentage change
Cash only Food only Cash+Food Cash+BCC0
10
20
30
40
50
19.5
15.2
10.6
47.4North
Statistically significant Not significant
Perc
enta
ge in
crea
se in
foo
d ex
pend
iture
Cash only Food only Cash+Food Food+BCC0
10
20
30
40
50
6.69.2 10.0
35.0
South
Statistically significant Not significant
Perc
enta
ge in
crea
se in
foo
d e
xpen
ditu
re
Impact of transfers on per capita monthly non-food expenditure: Percentage change
Cash only Food only Cash+Food Cash+BCC0
5
10
15
20
11.7
13.7
16.0
6.9
North
Statistically significant Not significant
Perc
enta
ge in
crea
se in
non
-foo
d ex
pend
iture
Cash only Food only Cash+Food Food+BCC0
5
10
15
20
-5.6
7.9
11.8
9.4
South
Statistically significant Not significant
Perc
enta
ge in
crea
se in
non
-foo
d e
xpen
ditu
re
Cash only Food only Cash+Food Cash+BCC0
50
100
150
200
250
300
106120
232
55
North
Statistically significant Not significant
Incr
ease
in c
alor
ie (k
cal/
pers
on/d
ay)
Impact of transfers on per capita daily food energy (calorie) acquisition: Absolute change (kcal)
Cash Food Cash+Food Food+BCC0
50
100
150
200
250
300
5 -2
21
151
South
Statistically significant Not significant
Incr
ease
in c
alor
ie (k
cal/
pers
on/d
ay)
Impact of transfer on food poverty: Percentage points reduction in prevalence of <2,122 kcal/person/day
Cash only Food only Cash+Food Cash+BCC
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
-7.0
-11.3
-19.1
-5.6
North
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
pre
vale
nce
<212
2 kc
al/p
erso
n/da
y
Cash only Food only Cash+Food Food+BCC
-14
-12
-10
-8
-6
-4
-2
00.5 0.5
-1.6
-11.0
South
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
pre
vale
nce
<212
2 kc
al/p
erso
n/da
y
Impact of transfer on hard-core food poverty: Percentage points reduction in prevalence of <1,805 kcal/person/day
Cash only Food only Cash+Food Cash+BCC
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
-10.2
-11.6
-16.8
-6.6
North
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
pre
vale
nce
<180
5 kc
al/p
erso
n/da
y
Cash only Food only Cash+Food Food+BCC
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
-0.7
1.1 0.6
-10.2
South
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
pre
vale
nce
<180
5 kc
al/p
erso
n/da
y
Aggregate food groups and weights to calculate the Food Consumption Score (Source: WFP)
Group Food items Food group
Weight
1 Rice and other cereals Staples 2
2Beans, lentils, peas and nuts Pulses 3
3 Vegetables and fruitsVegetables and fruits 1
4Beef, goat, poultry, eggs, and fish
Meat, eggs and fish 4
5Milk, yogurt, and other dairies Milk 4
6Sugar, sugar products, and honey Sugar 0.5
7 Oils, fats, and butter Oil 0.5
Impact of transfer modalities on diet quality: Absolute change in Food consumption score
Cash only Food only Cash+Food Cash+BCC0
5
10
15
20
5.3
8.7
6.0
17.0
North
Statistically significant Not significant
Food
con
sum
ption
sco
re
Cash only Food only Cash+Food Food+BCC0
5
10
15
20
3.9
5.8
4.3
12.5
South
Statistically significant Not significant
Food
con
sum
ption
sco
re
North: Baseline vs. Endline
Cash+BCC
South: EndlineFood vs. Food+BCC
Kernel density functions of FCS: Examples 0
.01
.02
.03
20 40 60 80 100FCS
Density fn: Baseline Density fn: Endline
Food Consumption Score, Cash+BCC group, North
0.0
05.0
1.0
15.0
2.0
2520 40 60 80 100
FCS
Density fn: Endline food Density fn: Endline food+bcc
Impact of transfer modalities on diet quality: Change in dietary diversity (number of food consumed out of 12 food groups)
Cash only Food only Cash+Food Cash+BCC0.0
0.5
1.0
1.5
0.5
0.4 0.4
1.3
North
Statistically significant Not significant
Num
ber
of fo
od g
roup
s co
nsum
ed
Cash only Food only Cash+Food Food+BCC0.0
0.5
1.0
1.5
0.40.3 0.4
1.0
South
Statistically significant Not significant
Num
ber
of fo
od g
roup
s co
nsum
ed
Impact of transfer on child nutritional status: Percentage points reduction in prevalence of stunting (children 6-59 months <-2 height-for-age Z-score)
Cash only Food only Cash+Food Cash+BCC
-14
-12
-10
-8
-6
-4
-2
0
-6.6 -6.8
-5.6
-12.2
North
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
stu
nting
rat
e
Cash only Food only Cash+Food Food+BCC
-14
-12
-10
-8
-6
-4
-2
0-0.04
-0.98
-2.28
-5.39
South
Statistically significant Not significant
Perc
enta
ge p
oint
s re
ducti
on in
stu
nting
rat
e
Summary and conclusions
• Our estimation strategy relies on the randomized design, which eliminates systematic differences between participants and non-participants and minimizes the risk of “selection bias”
• As a result, average differences in outcomes across the groups after the intervention can be interpreted as being truly caused by, rather than simply correlated with, the receipt of transfers and transfers with nutrition education
• Moreover, we take advantage of the baseline survey and estimate the treatment effect using Analysis of Covariance (ANCOVA) regression, which is our preferred method over difference-in-difference estimates
Summary and conclusions
Differences in the size of impact as revealed from the F-tests: Income in the north: “Only cash” has
statistically significant higher impact than cash+food. There are no statistically significant differences between “only cash” and “only food”. “BCC+cash” has significantly higher impact than those of the other 3 treatment arms.
Income in the south: “BCC+food” has significantly larger impact than those of the other 3 treatments. No statistically significant difference between other treatment arms.
Summary and conclusions
Differences in the size of impact as revealed from the F-tests: Calories in the north: “BCC+cash” has
significantly larger impact than those of the other 3 treatments. No statistically significant difference between other treatment arms.
Diet quality (FCS) in the north and the south: “Only food” has significantly higher impact than “only cash” and “cash+food”. “BCC+cash” and “BCC+food” have significantly larger impact than those of the other 3 treatment arms.
Stunting in the north: “BCC+cash” has significantly larger impact than those of the other 3 treatments. No statistically significant difference between other treatment arms.
Summary and conclusions
In the north, the poorest region, we found statistically significant positive impacts of all 4 modalities on (1) income, (2) food expenditure, (3) non-food expenditure, (4) calorie acquisition, (2) food poverty, (5) diet quality, and (6) child stunting, with cash+BCC having the biggest size of impacts on all 6 indicators.
However, in the south, which is a disaster prone, but higher income region than the north, “cash only” has statistically significant impact only on diet quality. “Food only” has significant impact on income and food and non-food expenditures, and diet quality. “Cash+food” has significant impacts on income, food expenditures and diet quality. “Food+BCC” has significant impacts on income, food and non-food expenditure, calories, and diet quality, but not on stunting.
Summary and conclusions
It is intriguing to find that food and cash transfers have by far the leading impact when they are combined with nutrition BCC. Why does BCC have the largest impact even though the BCC training curriculum does not include non-nutrition livelihoods attributes? Does participation in BCC activities raise women’s status/empower them? We will probe into this question in early 2014 through an in-depth qualitative study.
Why do patterns in the north and the south differ? In the south, participants of “cash only”, “food only” and “cash+food” improved their diet quality rather than quantity. Only “food+BCC” group shows improvements in both diet quantity and quality.
Our survey results indicate that, the greater the risk of disaster, the less likely a household is to immediately “consume” a transfer – and, for example, more likely to use it for precautionary savings given the risk of future bad shocks, or to use it to repair/improve houses that were damaged in a bad shock. But BCC may result in overcoming some of that.
Interim policy options
Integrate nutrition into social safety nets Increase the size of transfers of safety nets to
generate sizable impacts – The size of transfer relative to household income is
tremendously important when trying to achieve sustainable improvements in the food security and livelihoods of the poor
– There are numerous safety net programs currently operating in Bangladesh. However, most of these programs have limited coverage, are uncoordinated, and are not adequately funded. Consolidate and simplify programs and phase out high-cost, ineffective programs.
– Improve the targeting performance of existing safety nets