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 • t 1 max E u c 0 it 1 c , M i 0 t 1 0 t subjectto : c F ( ) ( M M )(1 r ) t it it it 1 it M 0 t it FONC for this problems implies a constrained form of the Permanent Income Hypothesis: u c ( ) max{ ( ), u x E u c [ ( )]}, t t t t 1 1 r where and x M y t t t 1
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:
Income and Consumption Under Optimal Stable Strategies 9000 8000 Entrepreneurial 7000 6000 Income and Consumption (Log Scale) 5000 4000 3000 Consumption Income 2000 Defensive 1000 900 800 0 20 40 60 80 100 120 140 160 180 200 Time • Zimmerman, F. and M. Carter (2003). ―Dynamic Portfolio Management under Risk and Subsistence Constraints in Developing Countries‖ J of Dev Econ.
• Zimmerman, F. and M. Carter (2003). ―Dynamic Portfolio Management under Risk and Subsistence Constraints in Developing Countries‖ J of Dev Econ.
Threshold Econometrics % β z P T U y y y if L L 1 ivt 2 ivt 3 ivt z ivt i ivt ivt Net Livestock Sales ivt % β z P T U y y y if L L 1 ivt 2 ivt 3 ivt z ivt i ivt ivt • Note that this model inadequate from perspective of ~ L Barrett-Carter-Ikegami model which conditional ( ) i • But need to wait for these econometrics … • In the meantime, how recover estimates of permanent & transitory income components?
Recovering Income Components α z +α F X + F ivt 1 ivt 2 vt ivt v vt i ivt α z P Permanent Income y ivt ivt 1 ivt i α F X + T Transitory Income y F ivt ivt 2 vt ivt v vt U Unexplained Income y ivt ivt ivt • Note that could decompose transitory into (more easily insured) idiosyncratic & covariant components
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 on 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!
High Quality Data NDVI-based Livestock Mortality Index Deviation of NDVI from long-term average NDVI February 2009, Dekad 3 February 2009, Dekad 3
Livestock mortality index One possible index is based on area average livestock mortality predicted by remotely-sensed (satellite) information on vegetative cover (NDVI):
Geographic Clusters Estimate separate response functions for distinct clusters (Marsabit District)
Index performance
Index performance Index predicts large-scale losses very well Performance of mortality index in predicting insurance trigger Location Strike Correct Incorrect decision 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%
How will IBLI work?
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