Informal Insurance in the Presence of Poverty Traps:Evidence from Southern EthiopiaPaulo Santos and Christopher B. BarrettCornell 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…TtU(ct(kt))|}subject to: kt = g( kt-1 + t + ji

t -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.

kct = gc

l(kt-1 + t + jit -ij

t) if i c, kt-1 < = gc

h(kt-1+ t + jit -ij

t) 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

Yes No

Herd size

Yes Poverty traps 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) Gifts Loans

2) Not everyone knows everyone else

3) Doesn’t know Doesn’t give

4) Know (not) Give

Know

GiveYes No

Yes 65 3

No 370 123

Gift

LoanYes No

Yes 425 3

No 10 123

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 whomAlternative 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

(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) -1.188Bold indicates statistical significance at 5% level or lower.

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.

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

(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

Result:

As predicted: transfers related to losses and ex post herd size for those facing multiple equilibria.

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

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 10 20 30 40 50 60

Initial herd size

Exp

ecte

d h

erd

siz

e ch

ang

e af

ter

10 y

rs

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Pr(

her

d s

ize>

30 a

fter

10

yrs)

probability of herd size >30 head10 years after transfer of 1 cattle

expected change in herd size 10 years after transfer of 1 cattle

7 22

Simulated expected herd growth (and long-term herd size)

Who gives to whom

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.

(1) (2) (3) (4) (5)

E (wealth) -0.487 -0.723 -0.023

E (gains) 0.277 0.210 0.418

E (wealth) * Loss

20.724 -15.608

E (gains) * Loss

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

“[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)

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

Use same logit estimation approach, with “know” as dependent variable now.

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.