CIO 100 2011 - Community Learning Information Centres - Loyford Murithi - eGovernmnet
Welfare Dynamics in Rural Kenya and Madagascar Christopher B. Barrett, Paswel Marenya, John McPeak,...
-
date post
21-Dec-2015 -
Category
Documents
-
view
216 -
download
0
Transcript of Welfare Dynamics in Rural Kenya and Madagascar Christopher B. Barrett, Paswel Marenya, John McPeak,...
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!