Testing for Poverty Traps: Asset Smoothing versus Consumption - - PowerPoint PPT Presentation

Testing for Poverty Traps: Asset Smoothing versus Consumption Smoothing in Burkina Faso

(with some thoughts on what to do about it)

Travis Lybbert Michael Carter University of California, Davis

Risk & Why It Matters

• In the presence of full and complete financial

markets, risk presents no particular problem

• But without those market or other forms of

mitigation, risk may make & keep people poor by distorting income generation & asset accumulation strategies

• In this context, notion that individuals can

mitigate risk by low cost asset destabilization/consumption smoothing strategies is especially important

• But how and for whom does consumption

smoothing work?

Canonical Model of Savings & Consumption

• FONC for this problems implies a constrained form of the

Permanent Income Hypothesis:

 

 

1

1 1 ,

max 1

: ( ) ( )(1 )

t i

t it c M t

E u c it it it it it

subjectto c F M M r t M t





        

      

       

1

( ) max{ ( ), [ ( )]}, 1 1

t t t t t t t

u c u x E u c r where and x M y   



              

7% Solution to 10% Problem

• Coefficient of variation of income: 10%
• Coefficient of variation of consumption: 4%
• Smoothing Ratio: 40%(=4/10)

Testing the Efficacy of Consumption Smoothing

• Consumption should be unresponsive to

transitory shocks (under interior solution)

• Alternatively, asset liquidation should follow

shocks

• In Burkina Faso, we might look to see if shocks

to crop income are matched by livestock sales:

Livestock Sales & Shocks

• Fafchamps, Marcel, Chris Udry and Katherine Czukas (1998). ―Drought and saving in West Africa:

are livestock a buffer stock?‖ Journal of Development Economics 55:273-305.

Pooled Regression on Sales

• Fafchamps, Marcel, Chris Udry and Katherine Czukas (1998). ―Drought and saving in West Africa:

are livestock a buffer stock?‖ Journal of Development Economics 55:273-305.

Rethinking Theory of Savings & Consumption

• Problem with empirical literature is that no clear alternative model

against which the consumption smoothing hypothesis is being tested (if coefficient relating shocks to assets does not equal -1, then what?)

• There are some ad hoc empirical approaches in the literature

– Consumption smoothing as ―luxury‖ for non-poor (Jalan & Ravallion) – But this does not tell how to look and further reifies arbitrary (non- behavioral) poverty lines (Kazianga & Udry on Burkina) – Nor does it tell us much about what it means & how to fix it

• So when does consumption smoothing not make sense?

– Deaton-esque answers (AR(1)) apply to everyone and make it hard to explain inconsistent evidence of Fafchamps et al. – Poverty trap models with richer economic environment:

• Multiple assets (productive & buffer)
• Asset price risk, especially with covariant shocks
• Non-convexities  irreversabilities
• Common property of Micawber Threshold

– Let‘s look at contrasting results from Zimmerman & Carter analysis:

• Zimmerman, F. and M. Carter (2003). ―Dynamic Portfolio Management under Risk

and Subsistence Constraints in Developing Countries‖ J of Dev Econ.

20 40 60 80 100 120 140 160 180 200 Time 1000

800 900 2000 3000 4000 5000 6000 7000 8000 9000

Income and Consumption (Log Scale)

Income and Consumption Under Optimal Stable Strategies

Defensive Entrepreneurial

Consumption Income

• Zimmerman, F. and M. Carter (2003). ―Dynamic Portfolio Management under Risk

and Subsistence Constraints in Developing Countries‖ J of Dev Econ.

Threshold Econometrics

• Note that this model inadequate from perspective of

Barrett-Carter-Ikegami model which  conditional

• But need to wait for these econometrics …
• In the meantime, how recover estimates of permanent &

transitory income components?

1 2 3 1 2 3 P T U ivt ivt ivt i ivt ivt ivt P T U ivt ivt ivt i ivt ivt

y y y if L L Net Livestock Sales y y y if L L          

       

                   

z ivt z ivt

β z β z % %

) ( ~

i

L 

Recovering Income Components

• Note that could decompose transitory into (more easily

insured) idiosyncratic & covariant components

ivt v vt i ivt

F         

1 ivt 2 vt ivt

α z +α F X +

P ivt ivt i T ivt ivt v vt U ivt ivt ivt

Permanent Income y Transitory Income y F Unexplained Income y            

1 ivt 2 vt ivt

α z α F X +

Threshold Regression Results

Note cattle costs 30-40,000 CFA  almost perfect c-smoothing for upper regime!

Threshold Regression Results

Note cattle costs 30-40,000 CFA  almost perfect c-smoothing for upper regime!

Conclusions—1

• Whose assets are really smoothed by ―asset

smoothing?‖

– …asset smoothing implies an attempt to preserve assets, but consumption is an input into the formation and maintenance of human capital. [Thus] the distinction between consumption and asset smoothing, while useful as a descriptive tool, may be somewhat misleading. Rather, household responses to adverse shocks are effectively changes in their asset portfolio, with a critical issue being the extent to which the draw down of a given asset has permanent consequences. (Hoddinott 2006)

• Costs of unmitigated risk for asset smoothers can thus

be quite high

• Insurance instruments that mitigate some of this risk

would thus seem to be a promising development policy instrument:

– Sustain human capital investment – Positive moral hazard effects on asset portfolio – Social benefits of reduced indigency

• But can it be done?

Financial Instruments to Mitigate Smallholder Risk

• So can insurance be made to work for smallholder sector?
• Conventional (individual) insurance unlikely to work:

– Transactions costs – Moral hazard/adverse selection

• However, ‗index insurance‘ avoids problems that make individual

insurance unprofitable for small, remote clients: – No transactions costs of measuring individual losses (payouts based on a single index for a location) – Preserves effort incentives (no moral hazard) as no single individual can influence index. – Adverse selection does not matter as payouts do not depend

• n the riskiness of those who buy the insurance
• So let‘s look at how this might work in the case of Burkina Faso

NDVI-based Index for Grains in Burkina

NDVI-based Index for Grains in Burkina

NDVI-based Index for Grains in Burkina

Conclusions—2

• So can this be made to work, will there be uptake, and

will it sufficiently mitigate risk such that costly asset smoothing can be offset?

• Related pilots underway—stay tuned!

Thank you for your time, interest and comments!

NDVI-based Livestock Mortality Index

NDVI February 2009, Dekad 3

High Quality Data

Deviation of NDVI from long-term average February 2009, Dekad 3

One possible index is based on area average livestock mortality predicted by remotely-sensed (satellite) information on vegetative cover (NDVI):

Livestock mortality index

Estimate separate response functions for distinct clusters (Marsabit District)

Geographic Clusters

Index performance

Performance of mortality index in predicting insurance trigger Location Strike Correct decision False positive False negative Chalbi 10% 71% 13% 17% 15% 81% 6% 13% 20% 88% 4% 8% 25% 85% 10% 4% 30% 94% 4% 2% 35% 92% 6% 2% 40% 94% 6% 0% Laisamis 10% 80% 9% 11% 15% 88% 3% 9% 20% 84% 9% 6% 25% 81% 14% 5% 30% 84% 13% 3% 35% 94% 6% 0% 40% 95% 5% 0% Incorrect decision

Index performance

Index predicts large-scale losses very well

How will IBLI work?