Transcript Informal Insurance in the presence of Poverty Traps

Informal Insurance in the
Presence of Poverty Traps:
Evidence from Southern Ethiopia
Paulo Santos and Christopher B. Barrett
Cornell University
September 14, 2006 seminar
Michigan State University
Core Question

Models of consumption smoothing and informal
insurance typically rely on the assumption of
stationary income processes.

Our question: what happens when that
assumption does not hold?
Outline
1: What do we know
2: Asset shocks and insurance
3: Data
4: Who gives to whom
5: Who knows whom
6: Conclusions
1: What do we know

Lybbert et al (2004 EJ)

Evidence of multiple
equilibria

Asset risk is largely
idiosyncratic

But asset transfers are
quite small
What do we know

Santos and Barrett (2006)

Asset shocks associated with
adverse rainfall events are the
source of non-linear asset
dynamics (multiple equilibria)

Boran pastoralists perceive this.

Ability matters !
2: Asset shocks and insurance
Poverty trap models emphasize assets and thresholds. So we
focus on asset dynamics, risk and transfers around thresholds.
Basic intertemporal decision model:
Max{ct,ijt} E{t=0…TtU(ct(kt))|}
subject to: kt = g( kt-1 + t + jit -ijt)
cT(kT) = kT
k0 given, ~[-kt,0], t ={0,}
Transfers () and asset shocks () affect asset (k) dynamics,
underlying income generation and consumption (c).
Asset shocks and insurance
Growth dynamics are key to understanding the nature of
the resulting informal insurance arrangements.
k ct
= gcl(kt-1 + t + jit -ijt) if i c, kt-1 < 
= gch(kt-1+ t + jit -ijt) if i c, kt-1  
for clubs c=1,…,C
The most general specification allows for:
1) different clubs w/o thresholds (C>1, =0),
2) unique club w/ threshold (C=1, >0),
3) canonical convergence model (C=1, =0, g(.) concave) that
implicitly underpins the standard consumption smoothing and
informal
insurance literatures
Asset shocks and insurance



Convergence: every match is in insurance pool (standard literature)
Precautionary savings: only capacity to reciprocate (but not actual
losses) matters (McPeak JDE 2006)
Poverty traps due to multiple equilibria:
1) exclude the poorer and those with lower ability (i.e, those at lower level
equilibria) because it is harder to punish them if they don’t reciprocate.
2) privilege those at the threshold (because maximizes gains from transfer).
Losses
Herd
size
Yes
Yes
Poverty traps
No
Precautionary
savings
No
Convergence
?
3: Data



Pastoral Risk Management (PARIMA) project
(USAID GL CRSP)
119 households, 2000-2003
Data on insurance networks

5 Random matches [X] within sample :



Question 1: Do you know [X]?
Question 2: Would you give to [X] if s/he asked?
Advantage/(potential) disadvantages:



no bias because lack of knowledge of one side of the relation
data on links, not transfers: but transfers are small
potential, not real, links: but inference based on this information is
reliable (Santos and Barrett, 2006)
Data
1)
2)
3)
4)
Gifts Loans
Gift
Yes
No
Yes
425
3
No
10
123
Yes
No
Yes
65
3
No
370
123
Loan
Not everyone knows
everyone else
Doesn’t know  Doesn’t
give
Know (not) Give
Know
Give
4: Who gives to whom
lij* = αi+ 1 f(hj)+  Lj+ Σ t=1…4 βt Etj+ δ Xij+ λZi+ εij
Key variables: hj (recipient herd size), Lj (recipient herd loss),
Ej (recipient equilibrium regime)
Xij = (possibly asymmetric) differences between i and j
Zi = characteristics of the respondent
Assumptions on εij:
εij ~ log(0, 2/3)
E (εij,εih) ≠ 0 if j ≠ h
E (εih,εjh) = 0 if i ≠ j
 Logit model, observations clustered on the respondent
Who gives to whom
Alternative assumption:
E (εih , εjh) ≠ 0 if i ≠ j
Ways to check/correct for this possibility:
- Udry & Conley (2005), Fafchamps and Gubert (JDE
forthcoming) use Conley’s estimator to correct for correlated
error structures
- Quadratic Assignment Procedure (QAP): nonparametric
permutation test that gives correct p-values
Ultimately, these more complex error structures matter little
Who gives to whom
Result:
Transfers respond to
losses – i.e., they are
state-contingent
insurance claims – but
also depend on ex post
herd size.
We thus reject the
precautionary transfers
and insurance under
convergence hypotheses
in favor of the insurance
in the presence of
poverty traps.
(1)
(2)
(3)
(4)
hj=0
0.357
0.275
0.140
1.207
hj
-0.014 -0.021
-0.024
-0.020
E2
-0.092
0.275
-0.387
E3
0.203
0.655
0.005
E4
-0.611
-0.019
-0.734
Lj
0.919
Lj * E1
0.465
Lj * E2
1.711
Lj * (hj=0)
-1.188
Bold indicates statistical significance at 5% level or lower.
Who gives to whom
Conclusion: Asset transfers are best understood as
insurance of permanent income, preventing recipients
from falling into persistent poverty and excluding those
who are not expected to be able to reciprocate.
Who gives to whom



Does “ability club” membership matters?
A priori expectation: those with low ability should not
receive gifts, if match’s ability is observed by
respondents.
Approach followed:



Get estimates of efficiency (high, medium, low)
Re-estimate previous model
Bootstrap results to get correct SE
Who gives to whom
Result:
As predicted:
transfers related to
losses and ex post
herd size for those
facing multiple
equilibria.
(1)
(2)
(3)
Low
1.137
0.376
1.334
Medium
2.542
0.435
2.616
E2*low
1.372
-0.248
E2*medium
-1.145
1.588
E2*high
1.607
2.720
Lj* E2* low
Dropped
Lj* E2* medium
2.856
Lj* E2* high
2.500
Who gives to whom

Does the threshold play a role in targeting?



No if transfers are given to those with maximal capacity to
reciprocate
Yes if transfers are intended to maximize expected gains from
transfer
The predictions of the two models diverge for those
herders who suffered losses but are above the threshold




Helped in the 1st model
Not helped in the 2nd model
Problem: no data in the region where the predictions differ (above
the threshold)
Solution: use simulation results on expected gains from transfers
Who gives to whom
Simulated expected herd growth (and long-term herd size)
0.60
expected change in herd size 10 years after
transfer of 1 cattle
1.4
0.50
1.2
0.40
1.0
0.8
0.30
probability of herd size >30 head
10 years after transfer of 1 cattle
0.6
0.20
0.4
0.10
0.2
0.0
0.00
0
7
10
20
22
30
Initial herd size
40
50
60
Pr(herd size>30 after 10 yrs)
Expected herd size change after 10 yrs
1.6
Who gives to whom
(1)
Result:
Transfers seem ex
post insurance that
takes into account
recipient’s expected
gains but not his/her
expected wealth
… a non-monotonic
relation between
recipient’s wealth and
transfers.
E (wealth)
E (gains)
E (wealth)
* Loss
E (gains) *
Loss
(2)
-0.487
(3)
(4)
-0.723
0.277
(5)
-0.023
0.210
20.724
0.418
-15.608
1.524
2.144
Who gives to whom
Conclusions:
1) Transfers are influenced:
By the existence of thresholds
By the existence of ability clubs
2) Asset transfers seem to be best understood as
insurance of the permanent component of income
and driven largely by expected recipient gains
5: Who knows whom:
Social exclusion and poverty traps
Use same logit estimation
approach, with “know” as
dependent variable now.
Coef.
No cattle since 2000
-1.106
E1 since 2000
-0.145
E2 since 2000
-0.127
E3 since 2000
-0.581
E4 since 2000
-1.297
Lost cattle 2000-2003
0.203
More cattle
-0.014
Less cattle
0.040
“[t]o be poor is one thing, but to be
destitute is quite another, since it
means the person so judged is
outside the normal network of social
relations and is consequently
without the possibility of successful
membership in ongoing groups, the
members of which can help him if he
requires it. The Kanuri [in the West
African savannah] say that such a
person is not to be trusted”.
(Iliffe, 1987, The African Poor)
6: Conclusions


Implications for public transfers - is crowding out
really a concern for the poorest? No
Our results:




The poorest are (rationally) not recipients of informal
transfers: no risk of crowding out at very low levels of
assets
Possibility of crowding in (by moving people nearer the
threshold, where private transfers can be triggered … see
Chantarat and Barrett, 2006)
Targeting may be especially difficult: public transfers must
consider [needs * dynamics * ability]
Social invisibility of the poorest makes community based
targeting a challenge
Thank you for your attention …
I welcome your comments and questions.