economic and productive impacts of social protection in africa · •work in context of multiple...

Economic and productive impacts of social protection in Africa

Benjamin Davis and Silvio Daidone Food and Agriculture Organization,

the From Protection to Production Project, and

the Transfer Project

Social Protection Cooperating Partners Group

Lusaka

August 21, 2014

Why do livelihoods matter for social protection?

• Most beneficiaries in Sub Saharan Africa are rural, engaged in agriculture and work for themselves – Zimbabwe: 88% produce crops; 75% have livestock – Kenya: 80% produce crops; 75% have livestock – Lesotho: 80% produce crops; 60% have livestock – Zambia: 80% produce crops; 50% have livestock

• Most grow local staples, using traditional technology and low levels of modern inputs – Most production consumed on farm

• Most have low levels of productive assets – .5 -2 hectares of agricultural land, a few animals, basic agricultural

tools, few years of education

• Engaged on farm, non farm business, casual wage labour (ganyu/maricho)

• Large share of children work on the family farm – 50% in Zambia

Reaching social goals requires sustainable livelihoods

• Work in context of multiple market failures in credit, insurance, etc – Constrain economic decisions in investment, production, labor

allocation, risk taking • Short time horizon—imperative of meeting immediate needs • Lack of liquidity, difficult to manage risk

– Decisions about production and consumption linked

• “non separability” of production and consumption means that social objectives are conditioned by livelihoods—and vice versa – Labor needs (adults and children), including domestic chores – Investment in schooling and health – Food consumption, dietary diversity and nutrition – Intra household decision making

• Dynamic between men and women, old and young

• Ultimately, reaching social goals requires sustainable livelihoods

Policy makers are concerned about

Dependency

Social cash transfers targeted to poorest of the poor can have productive impacts

• Long term effects of improved human capital – Nutritional and health status; educational attainment – Labor productivity and employability

• Transfers can relax some of constraints brought on by market failure (lack of access to credit, insurance) – Helping households manage risk

– Providing households with liquidity

• Transfers can reduce burden on social networks and informal insurance mechanisms

• Infusion of cash can lead to multiplier effects in local village economy

• Malawi – Mchinji pilot, 2008-2009 – SCT Expansion, 2013-2015

• Kenya – CT OVC, 2007-2011

• Zambia – Child Grant, 2010-2014

• Ethiopia

– Tigray SPP, 2012-2014

• Ghana

– LEAP, 2010-2012

• Lesotho

– CGP, 2011-2013

• Zimbabwe

– HSCT, 2013-2014

• Tanzania

– TASAF Pilot, 2009-2012

Countries/evaluations

included in this review

Still waiting for household

level analysis from:

Mixed method approach

• Household and individual level

impacts via econometric methods (experimental and non experimental)

• Perceptions on household economy and decision making, social networks, local community dynamics and operations via qualitative methods

• Local economy effects via LEWIE (GE) modeling

• Zimbabwe (end 2014)

• Ethiopia (end 2014)

• Malawi (early 2015)

• Zambia three year follow up (end

2014)

Households invest in livelihood activities— though impact varies by country

Zambia Malawi Kenya Lesotho Ghana Tanz

Agricultural inputs +++ - - - ++ +++

Agricultural tools +++ +++ NS NS NS

Agricultural production +++(1) NS ++(2) NS

Home production of food

NS +++ +++ NS NS

Livestock ownership All types All types Small ++(3) NS small

Non farm enterprise (NFE)

+++ NS +FHH NS NS

1) Values of production 2) Maize and garden

plot vegetables 3) Pigs Stronger impact Mixed impact Less impact

Shift from casual wage labor to on farm and family productive activities

adults Zambia Kenya Malawi Lesotho Ghana Tanz

Agricultural/casual wage labor

- - - - - - - - - - - NS

Family farm + ++ +++ ++ (1) +++

Non farm business (NFE) +++ +++ NS NS

Non agricultural wage labor

+++ NS NS NS NS

children

Wage labor NS NS - - - NS NS (3)

Family farm NS - - - (2) +++ - - NS (3)

1) Elderly females 2) Particularly older boys 3) No impact on time use;

labor not reported Shift from casual wage labour to family business—consistently reported in qualitative fieldwork

No clear picture on child labor (but positive impacts on schooling)

Improved ability to manage risk Zambia Kenya Malawi Ghana Lesotho Tanz

Negative risk coping - - - - - -

Pay off debt +++ +++ NS

Borrowing - - - NS - - - NS NS

Purchase on credit NS NS NS

Savings +++ +++ +++ NS ++ poorest

Give informal transfers NS +++ +++

Receive informal transfers NS +++

Remittances - - - NS - - - NS (1)

Trust (towards leaders) ++

Strengthened social networks • In all countries, re-engagement with

social networks of reciprocity—informal safety net

• Allow households to participate, to “mingle” again

• Reduction in negative risk coping strategies

• Increase in savings, paying off debt and credit worthiness—risk aversion

• Some instances of crowding out

1) Mixes remittances and informal transfers

Impact on the local economy

• Transfer raises purchasing power of beneficiary households • As cash spent, impacts spread to others inside and outside

treated villages, setting in motion income multipliers • Purchases outside village shift income effects to non-treated

villages, potentially unleashing income multipliers there. • As program scaled up, transfers has direct and indirect

(general equilibrium) effects throughout region. • Three possible extremes:

– Local supply expands to meet all this demand • Big local multiplier

– Everything comes from outside the local economy • No local multiplier at all: 1:1

– Local supply unable to expand to meet demand, and no imports • Inflation

• Have to follow the money – Surveys and LEWIE model designed to do this

CGP beneficiaries spend most of transfer locally—over 95 percent

Includes village and nearby villages and town

0

0.1

0.2

0.3

0.4

0.5

0.6

crop livestock services manuf retail outside

Bu

dge

t sh

are

Expediture Category

CGP household items purchased in village, inputs in town

Outs ide

(incl .

gov.)

Item Purchased

Reta i l i tems purchased by

households 0.545 0.172 0.281 0.002

Purchased input for crop

production 0.117 0.095 0.535 0.252

Reta i l inputs purchased

by bus inesses 0.172 0.095 0.444 0.289

Animal products

purchased by households 0.82 0.131 0.049 0

Vi l lage Nearby

Vi l lage

Town

These production activities buy inputs from each other, pay wages, and make profits

Payments to factors Payments to factors

Local

Purchases Leakage

Leakage

These expenditures start a new round of

income increases

Large local content

Less local content

Data from Ghana

Simulated income multiplier of the CGP programme

Base model

Income multiplier

Nominal 1.79

(CI) (1.73- 1.85)

Real 1.34

(CI) (1.29- 1.39)

Every 1 Kwacha transferred can generate 1.79 Kwacha of income

Production constraints can limit local supply response, which may lead to higher prices and a lower multiplier

When constraints are binding, every 1 Kwacha transferred can generate 1.34 Kwacha of income

MAX

MIN

Nearly all the spillover goes to non-beneficiary households

0

0.5

1

1.5

2

Total Beneficiary household

Non beneficiary households

Nominal

Spillover

Transfer

0

0.5

1

1.5

2

Total Beneficiary household Non beneficiary households

Real

Cash transfers lead to income multipliers across the region

0

0.5

1

1.5

2

2.5

3

Kenya(Nyanza)

Ethiopia (Abi-Adi)

Zimbabwe Zambia Kenya(Garissa)

Lesotho Ghana Ethiopia(Hintalo)

Nominal multiplier Real multiplier

Every 1 Birr transferred can generate 2.52 Birr of income

If constraints are binding, may be as low as 1.84

Income multiplier is greater than 1 in every country

What explains differences in impact across countries?

Crop Livestock NFE Productive labor

Social Network

Zambia yes yes yes yes

Malawi yes yes no yes small

Kenya no small yes yes

Lesotho yes small no no yes

Ghana no no no small yes

Tanzania small

Predictability of payment

Regular and predictable transfers facilitate planning, consumption smoothing and investment

0

1

# o

f p

aym

en

ts

Zambia CGP

0

1

2

3

4

5

6

# o

f p

aym

en

ts

Ghana LEAP

Regular and predictable Lumpy and irregular

Bigger transfer means more impact

0

5

10

15

20

25

30

35

40

GhanaLEAP(old)

KenyaCT-OVC

(big)

Burkina KenyaCT-OVC

RSACSG

LesothoCGP

(base)

GhanaLEAP

(current)

KenyaCT-OVC(small)

Zim(HSCT)

ZambiaCGP

ZambiaMCP

MalawiSCT

Widespread impact

Selective impact

% o

r p

er c

apit

a in

com

e o

f p

oo

r

Demographic profile of beneficiaries

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

85 to 89

Over 90

1000 500 500 1000 population

Males Females

Ghana LEAP

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

85 to 89

Over 90

2000 500 500 2000 population

Males Females

Zambia CGP

More able-bodied More labour-constrained

Economic context matters

• Vibrant and dynamic local economy?

• Opportunities awaiting if only a bit more liquidity?

Programme messaging matters

• Messaging in unconditional programmes, and conditions in CCTs, affects how households spend the transfer

• Lesotho: CGP transfer combined with Food Emergency Grant – Instructed to spend on children (shoes and uniforms) – Instructed to spend on agricultural inputs – And they did!!

Size of income multiplier varies by country and context—Why?

0

0.5

1

1.5

2

2.5

3

Kenya(Nyanza)

Ethiopia (Abi-Adi)

Zimbabwe Zambia Kenya(Garissa)

Lesotho Ghana Ethiopia(Hintalo)

Nominal multiplier Real multiplier

Beneficiaries are hard working and are responsible for their own income generation and food security

How can cash transfers be better linked to livelihoods?

1. Ensure regular and predictable payments

2. Link cash transfers to livelihood interventions

3. Consider messaging—it’s ok to spend on economic activities

4. Consider expanding targeting to include households with higher potential to sustainably achieve self-reliance

– including able-bodied labour

But keeping in mind potential conflicts and synergies with social objectives

Agriculture, livelihood interventions play important part in social protection systems

• Reaching social objectives and reducing vulnerability require sustainable livelihoods

• Almost three quarters of economically active rural population are smallholders, most producing own food

• Small holder agriculture as key for rural poverty reduction and food security in Sub Saharan Africa – Relies on increased productivity, profitability and sustainability

of small holder farming

• Social protection and agriculture need to be articulated as part of strategy of rural development – Link to graduation strategies

Our websites

From Protection to Production Project

http://www.fao.org/economic/PtoP/en/

The Transfer Project

http://www.cpc.unc.edu/projects/transfer


http://www.fao.org/economic/p2p/en/


Large increase in proportion of households with crop input expenditures

Bold <5% significant, underlined <10%

Impact Baseline Impact Baseline Impact Baseline

crop expenses 0.177 0.225 0.223 0.213 0.134 0.236

seeds 0.100 0.131 0.135 0.12 0.067 0.143

hired labour 0.054 0.029 0.072 0.024 0.038 0.034

fertilizers 0.032 0.009 0.034 0.007 0.029 0.012

other exp 0.151 0.104 0.153 0.105 0.150 0.103

N 4,596 2,336 2,260

≤5 HH members ≥6 HH membersAll

22% at base

Large increase in proportion of households with crop input expenditures

Bold <5% significant, underlined <10%

Greater impacts for

smaller HHs


crop expenses 0.177 0.225 0.223 0.213 0.134 0.236

seeds 0.100 0.131 0.135 0.12 0.067 0.143

hired labour 0.054 0.029 0.072 0.024 0.038 0.034

fertilizers 0.032 0.009 0.034 0.007 0.029 0.012

other exp 0.151 0.104 0.153 0.105 0.150 0.103

N 4,596 2,336 2,260

≤5 HH members ≥6 HH membersAll

Increase in the intensity of crop input use

Bold <5% significant, underlined <10%. Expenses in Zambian Kwacha

Big impact for seeds and

fertilizers


operated land (ha) 0.179 0.496 0.162 0.43 0.197 0.563

crop expenses 31,174 20,817 42,856 13,331 18,394 28,545

seeds 9,860 6,187 11,092 4,578 8,618 7,848

hired labour 8,417 7,093 14,682 2,845 1,155 11,479

fertilizers 7,606 1,413 8,924 721 6,499 2,127

other exp 5,226 6,092 7,967 5,124 2,092 7,091

N 4,596 2,336 2,260

All ≤5 HH members ≥6 HH members

Increase in cropped area



operated land (ha) 0.179 0.496 0.162 0.43 0.197 0.563

crop expenses 31,174 20,817 42,856 13,331 18,394 28,545

seeds 9,860 6,187 11,092 4,578 8,618 7,848

hired labour 8,417 7,093 14,682 2,845 1,155 11,479

fertilizers 7,606 1,413 8,924 721 6,499 2,127

other exp 5,226 6,092 7,967 5,124 2,092 7,091

N 4,596 2,336 2,260


30% increase in land use, but still

small average size

Increase in the intensity of crop input use



operated land (ha) 0.179 0.496 0.162 0.43 0.197 0.563

crop expenses 31,174 20,817 42,856 13,331 18,394 28,545

seeds 9,860 6,187 11,092 4,578 8,618 7,848

hired labour 8,417 7,093 14,682 2,845 1,155 11,479

fertilizers 7,606 1,413 8,924 721 6,499 2,127

other exp 5,226 6,092 7,967 5,124 2,092 7,091

N 4,596 2,336 2,260


Much bigger

for smaller HHs

Moderate increase in maize and rice production….

Bold <5% significant, underlined <10%. Production in KGs.


maize 49.5 148.2 35.1 117.8 63.8 179.5

cassava -68.1 146.6 -17.0 103 -129.2 191.7

rice 20.4 78.9 39.4 78.1 2.7 79.7

N 4,596 2,336 2,260


…….decrease in cassava production



maize 49.5 148.2 35.1 117.8 63.8 179.5

cassava -68.1 146.6 -17.0 103 -129.2 191.7

rice 20.4 78.9 39.4 78.1 2.7 79.7

N 4,596 2,336 2,260


Switching out of cassava production?

Drop in cassava coincides with consumption results

Increase in market participation


All

% selling crops 0.120 0.226 0.144 0.210 0.092 0.242

% consuming crops at home 0.059 0.761 0.063 0.732 0.057 0.790

N 4,596 2,336 2,260

≤5 HH members ≥6 HH members


Food security primarily achieved with

food purchases!

Explicit goal of CGP: ”Increase the number of households owning assets

such as livestock”

Impact Baseline Impact Baseline

Proportion Number

milk cows 0.033 0.053 -0.061 0.196

other cattle 0.084 0.094 0.263 0.417

chickens 0.154 0.404 1.234 1.949

goats 0.036 0.023 0.142 0.057

ducks 0.030 0.032 0.198 0.129

total 0.209 0.480 0.138 0.347

N 4,596 4,596

Bold <5% significant, underlined <10%.

Labour activities: Cross-section Impact Follow-up Impact Follow-up Impact Follow-up

All Males Females

paritcipation of HH members in

wage labour -0.091 0.497 -0.049 0.439 -0.136 0.405

paid agriculture -0.145 0.337 -0.081 0.261 -0.174 0.292

paid non-agriculture 0.037 0.189 0.040 0.181 0.032 0.112

non-farm enterprise 0.171 0.378 0.120 0.178 0.155 0.327

intensity of (days in)




N 2,296 1,764 2,282

Decrease in wage

employment driven by

agricultural labour …



All Males Females


wage labour -0.091 0.497 -0.049 0.439 -0.136 0.405








N 2,296 1,764 2,282


… especially female HH

members


All Males Females


wage labour -0.091 0.497 -0.049 0.439 -0.136 0.405








N 2,296 1,764 2,282


Significant also on the intensity

of labour


All Males Females


wage labour -0.091 0.497 -0.049 0.439 -0.136 0.405








N 2,296 1,764 2,282


So, what are these people now doing? They are

spending more time on farm and running an off-

farm business!

economic and productive impacts of social protection in africa · •work in context of multiple...

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