Welfare Dynamics in Rural Kenya and Madagascar: Preliminary Quantitative Findings

Welfare Dynamics in Rural Kenya and Madagascar:

Preliminary Quantitative Findings

Chris BarrettCornell University

March 15, 2004

BASIS CRSP Project Annual Team MeetingNyeri, Kenya

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 cargo nets.

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

Outline

I. Theory and Its ImplicationsII. Economic Mobility and Poverty

DynamicsIII. Why Economic Immobility?IV. Conclusions and Policy Implications

Pov.line

W2

W2

Well-beingt+1

Well-beingt

Brief theoretical background:

The slow convergence possibility

Welfare Dynamics With Unconditional Convergence

Key: unique, common path dynamics with a single stable dynamic equilibrium

Welfare Dynamics With Conditional Convergence

Low group

High groupChronic poverty region

`

Transitory poverty region

Welfare Dynamics With Multiple Dynamic Equilibria

Key: unique path dynamics with a single stable dynamic equilibrium for distinct groups or individuals

Key: nonlinear path dynamics with multiple stable dynamic equilibria and at least one unstable dynamic equilibrium (threshold effect)

Why bother with the theory?These three alternative theoretical foundations for understanding persistent poverty carry very different policy implications.

- need for/design of safety nets for asset protection- need for/methods of targeting cargo nets- need for patience

So need to get a firmer handle on (i) the nature of persistent poverty. (ii) what causes observed poverty traps?(iii) how can we move thresholds and/or path dynamics?

Those are the objectives of this project.

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%


2000-2002Ng’ambo86.5%

1997-2002Vakinankaratra58.5%

2000-2002Ng’ambo9.0%


Non-Poor in Initial Period


11.3%

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


6.8%


2000-2002Ng’ambo0.0%


2000-2002Ng’ambo4.5%


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

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

Estimated annual gross (net) poverty exit rates

Estimate using mobility transition probability: PRt = mt PR0

Site Gross NetDirib Gombo: 0.0% (0.0%)Madzuu: 2.2% (1.0%)Fianarantsoa: 2.3% (0.7%)Vakinankaratra: 2.4% (-4.2%)Ng’ambo: 5.2% (4.1%)

Considerable persistence of ultra-poverty with low rates of net exit from poverty


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.


Filtered vs. unfiltered income change regressionsUnfiltered:

Y = A`[r + εR] + U + εT + εM (2)

dY = dA `[r + εR] + A`[dr +dεR]+ dεT + dεM (4)

includes measurement error … negative bias

Filtered:

E{Y} = A`r + U (3)

E{dY} = E{dA}`r + A`E{dr} (5)

omits true stochastic component of income … positive bias

Regress dY on Y, E{dY} on E{Y}, or both to bracket?


0.00 0.10 0.20 0.30 0.40 0.50

-0.40

-0.20

0.00

0.20

0.40

Qua

rter

ly In

com

e C

hang

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

1.50

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

Qu

art

erl

y In

com

e C

ha

ng

e

e) Ng'ambo

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$)

c) Vakinankaratra

Site-specific filtered and unfiltered income change regressions:It clearly makes a difference

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$

)


b) Fianarantsoa

0.00 0.50 1.00 1.50 2.00 2.50

-2.00

-1.00

0.00

1.00

2.00

2002

-198

9 ch

ange

in p

er c

apita

dai

ly in

com

e (r

eal 2

002

US

$)


a) Madzuu


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

- Not at all clear whether the conditional convergence or poverty traps hypotheses, or both, best explain these data.

Why Economic Immobility?

Explanation 1: Risk-taking and asset/consumption smoothing

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

dens

ity

Expenditures

Income

Wealth-dependent risk management among northern Kenya pastoralists

Consumption smoothing a luxury enjoyed by the wealthiest third.

If income variability increases with wealth, so should returns on assets. Indeed, the income-herd size relation exhibits increasing returns, consistent with risk-based poverty traps:

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



Explanation 2: Barriers to entry into higher-return activities

- educational attainment and rationing (social networks)- lack of credit and liquid savings (negligible credit access) … limited capacity to enter higher-return businesses or even to buy livestock - pastoralist mobility depends on herd size

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


Herd Dynamics in Southern Ethiopia Asset Index Dynamics

Highland Kenya/Madagascar

-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

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


Same with the income data. Multi-modal income distribution in Madzuu.

0.00 0.50 1.00 1.50 2.00 2.50 3.00

2002 real per capita income (US$/day)

0.0

0.2

0.4

0.6

0.8

1.0

Ro

sen

bla

tt-P

arze

n d

ensi

ty

2002 Income Distribution in Madzuu

Consistent with qualitative evidence:

- Importance of non-farm salaried employment, incl. to agricultural intensification

- Fragility of non-poor status, esp. to health shocks


But unimodal distribution in Madagascar reflective more of conditional convergence with significant geographic grouping.

Implied dynamic real income equilibria:

Vakinankaratra ~ $0.61

Fianarantsoa ~ $0.33

Latter seems a geographic poverty trap

0.00 0.50 1.00 1.500

1

2

3

Per capita daily income (2002 US$), 1996 (dashed) and 2002 (solid)

Per capita daily incomes (2002 US$), 1996 (dotted) and 2002 (solid)

Ro

sen

bla

tt-P

arze

n d

en

sity

Vakinankaratra

Fianarantsoa

Real Income Distributions for Madagascar Sites, 1997 and 2002

Real Per Capita Income (2002 US$). Solid line = 2002, dotted line = 1997

Conclusions and Policy Implications

1) Reject the unconditional convergence hypothesis.

2) Qualitative and quantitative evidence most consistent with poverty traps hypothesis in rural Kenya. Need safety nets for asset protection critical for (i) risk management and (ii) to prevent collapse into poverty (for health shocks, natural disasters such as drought/floods, etc.).

3) 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.

4) Conditional convergence apparent at community level in both countries. Cargo nets needed for asset building among poor and for remote communities (i.e., indicator and geographic targeting).

5) Transition technologies, improved market access, etc. key.

Misaotra! Asante! Thank you!

Documents

Welfare Dynamics in Rural Kenya and Madagascar: Preliminary Quantitative Findings