Which Form of Safety Net Transfer is Most Beneficial? Impacts on Income, Food Security, and Child...

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


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


10

20

30

40

16.714.7

9.7

36.4

North


Perc

enta

ge in

crea

se in

tot

al e

xpen

ditu

re


10

20

30

40

3.7

10.18.5

25.3

South


Perc

enta

ge in

crea

se in

tot

al e

xpen

ditu

re

Impact of transfers on per capita monthly food expenditure: Percentage change


10

20

30

40

50

19.5

15.2

10.6

47.4North


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


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


5

10

15

20

11.7

13.7

16.0

6.9

North


Perc

enta

ge in

crea

se in

non

-foo

d ex

pend

iture


5

10

15

20

-5.6

7.9

11.8

9.4

South


Perc

enta

ge in

crea

se in

non

-foo

d e

xpen

ditu

re


50

100

150

200

250

300

106120

232

55

North


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)


50

100

150

200

250

300

5 -2

21

151

South


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


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


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


-18

-16

-14

-12

-10

-8

-6

-4

-2

0

-10.2

-11.6

-16.8

-6.6

North


Perc

enta

ge p

oint

s re

ducti

on in

pre

vale

nce

<180

5 kc

al/p

erso

n/da

y


-18

-16

-14

-12

-10

-8

-6

-4

-2

0

-0.7

1.1 0.6

-10.2

South


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


5

10

15

20

5.3

8.7

6.0

17.0

North


Food

con

sum

ption

sco

re


5

10

15

20

3.9

5.8

4.3

12.5

South


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


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


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)


-14

-12

-10

-8

-6

-4

-2

0

-6.6 -6.8

-5.6

-12.2

North


Perc

enta

ge p

oint

s re

ducti

on in

stu

nting

rat

e


-14

-12

-10

-8

-6

-4

-2

0-0.04

-0.98

-2.28

-5.39

South


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


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.


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.


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.


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

Which Form of Safety Net Transfer is Most Beneficial? Impacts on Income, Food Security, and Child...

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