Welfare Dynamics in Rural Kenya and Madagascar

Christopher B. Barrett, Paswel Marenya, John McPeak, Bart Minten, Festus Murithi, Willis

Oluoch-Kosura, Frank Place, Jean Claude Randrianarisoa, Jhon Rasambainarivo and Justine

Wangila  

November 15, 2004USAID BASIS CRSP Policy ConferenceCombating Persistent Poverty in Africa

Washington, DC

Why is poverty so persistent in rural Africa?

The design of appropriate strategies to combat persistent poverty depend on its origins.

Is poverty something …

… all people naturally grow out of in time (unconditional convergence)? … implies laissez-faire /macro focus.

… some people grow out of in time (conditional convergence)? … implies need

for targeted productivity improvements.

… some people can be trapped in perpetually (poverty traps due to multiple equilibria)? … implies need for safety nets and cargo nets.

Economic Mobility and Poverty Dynamics

Ultra-Poverty Transition MatricesAs measured against $0.50/day per capita income poverty line

Poor in Subsequent Period Non-Poor in Subsequent Period

Poor in Initial Period

2000-2002Dirib Gombo100.0%

70.8%

1989-2002 Madzuu60.7% 1997-2002Fianarantsoa82.8%

2000-2002Dirib Gombo0.0%

11.2%

1989-2002 Madzuu20.2% 1997-2002Fianarantsoa10.3%

2000-2002Ng’ambo86.5%

1997-2002Vakinankaratra58.5%

2000-2002Ng’ambo9.0%

1997-2002Vakinankaratra7.4%

Non-Poor in Initial Period

2000-2002Dirib Gombo0.0%

11.3%

1989-2002 Madzuu10.1%1997-2002Fianarantsoa6.9%

2000-2002Dirib Gombo0.0%

6.8%

1989-2002 Madzuu9.0% 1997-2002Fianarantsoa0.0%

2000-2002Ng’ambo0.0%

1997-2002Vakinankaratra22.3%

2000-2002Ng’ambo4.5%

1997-2002Vakinankaratra11.7%

Kenya rural poverty line ~ $0.53Madagascar poverty line ~ $0.43

Poverty deepest and most persistent where agroecology and markets least favorable (“remote rural areas” or “less favored lands”)

Moving beyond headcount measuresWe want to know the directions and magnitudes of welfare change, not just discrete movements relative to an arbitrary poverty line.

Annual average percent change in income, by site and resurveying interval

-50.0% 0.0% 50.0% 100.0%

0

1

2

3

4

5

6

Annualized percent change in household real per capita income

Ros

enbl

att-

Par

zen

dens

ity

Dirib Gombo (2 years)

Ng'ambo (2 years)

Madzuu (13 years)

Fianarantsoa (5 years)

Vakinankaratra (5 years)

Key point:

Short panels may exaggerate economic mobility. Much year-on-year change is random. When we look at longer-term transitions, a lot of stasis – look at structural determinants

Economic Mobility and Poverty Dynamics

Raw data suggests convergence … But structural component suggests multiple equilibria

Economic Mobility and Poverty Dynamics

Blue (red) dashed lines are structural (stochastic) component of income change

0.00 0.20 0.40 0.60 0.80 1.00

-1.00

-0.50

0.00

0.50

1.00

20

02

-19

97

ch

an

ge

in p

er

cap

ita d

aily

inco

me

(re

al 2

00

2 U

S$

)

1997 Per capita daily income (real 2002 US$)

a) Fianarantsoa

0.00 0.50 1.00 1.50 2.00 2.50

-2.00

-1.00

0.00

1.00

2.00

20

02

-19

97

ch

an

ge

in p

er

cap

ita d

aily

inco

me

(re

al 2

00

2 U

S$

)

1997 Per capita daily income (real 2002 US$)

b) Vakinankaratra

0.00 0.50 1.00 1.50 2.00

-1.00

-0.50

0.00

0.50

1.00

2002

-198

9 ch

ange

in p

er c

apita

dai

ly in

com

e (r

eal 2

002

US

$)

1989 Per capita daily income (real 2002 US$)

c) Madzuu

0.00 0.10 0.20 0.30 0.40 0.50

Income Level

-0.40

-0.20

0.00

0.20

0.40

Qu

art

erl

y In

com

e C

ha

ng

e

Base period per capita daily income (real 2002 US$)

d) Dirib Gombo

0.00 0.50 1.00 1.50

-1.00

-0.50

0.00

0.50

1.00

Base period per capita daily income (real 2002 US$)Q

ua

rte

rly

Inco

me

Ch

an

ge

e) Ng'ambo

Summary of Findings on Economic Mobility and Poverty

Dynamics- Considerable persistence of ultra-poverty

with low rates of net exit from poverty

- Poverty deepest where agroecology and markets least favorable (“remote rural areas” or “less favored lands”)

- Stochastic component of income appears substantial

- Structural component consistent w/existence of multiple equilibria

- Data consistent with both the conditional convergence and poverty traps hypotheses..

Why Economic Immobility?

Explanation 1: Wealth-differentiated risk mgmt

0 5 10 15

TLU per capita

0.0

0.5

1.0

1.5

2.0

2.5

0.00

0.05

0.10

0.15

0.20

0.25

Coe

ffici

ent o

f var

iatio

n

Ros

enbl

att-

Par

zen

den

sity

Expenditures

Income

Asset and consumption smoothing among northern Kenya pastoralists …

Consumption smoothing a luxury enjoyed by the wealthiest third.

0 5 10 15

0

50

100

150

Per

cap

ita

dai

ly in

com

e (K

Sh

)

Household TLU per capitaHousehold TLU per capita

Household TLU per capita

Associated with locally increasingincome returns to herd size.

Why Economic Immobility?

Explanation 2: Locally increasing returns Barriers to entry into higher-return activities

- educational attainment and social network rationing (skilled off-farm employment)- labor and liquidity constraints and SRI

… expected result is nonlinear asset dynamics, with rapid accumulation beyond key thresholds

Marginal Income - ariary

Riceland Area (ares)0 50 100 150 200 250

-75000

0

75000

Marginal Income - ariary

Labor Force Size (number)0 1 2 3 4 5 6 7

-2.0e+06

0

2.0e+06

Marginal return to hh labor supply and rice area, Fianarantsoa

Asset Dynamics with Multiple Equilibria

-1 0 1 2

-1

0

1

2

Fianarantsoa

Vakinankaratra

Madzuu

Su

bse

qu

ent

Per

iod

Sah

n-S

tife

l Ass

et In

dex

Beginning Period Sahn-Stifel Asset Index

Asset Index DynamicsHighland Kenya/Madagascar

Asset dynamics appear consistent in the Kenya sites with multiple equilibria, but low-level conditional convergence seems to fit the Madagascar sites better.

0 5 10 15 20

One Quarter Lagged Herd Size (TLU per capita)

0

5

10

15

20

Her

d S

ize

(TLU

per

cap

ita)

Herd DynamicsNorthern Kenya Rangelands

Conclusions and Policy Implications

1) Sound policy design and programming requires a clear idea of the causal mechanism behind persistent poverty.

2) No support for the unconditional convergence hypothesis.

3) Conditional convergence apparent at community level in both countries. In Madagascar, the evidence points to geographic poverty traps and the need for exogenous productivity improvements to create path out of poverty.

4) Qual-quant evidence most consistent with poverty traps hypothesis in rural Kenya. Also need multi-dimensional safety nets to protect assets to block pathways into poverty (due to health shocks, natural disasters, etc.).

5) Poverty traps seem to exist due to missing financial markets and (i) excessive risk exposure and/or (ii) significant barriers to entry to remunerative livelihoods.

Misaotra! Asante! Thank you!