Before and After the Drought: Evidence on the Impact of Index Insurance on Small Farm Investment and...

Before and After the DroughtEvidence on the Impact of Index Insurance on Small Farm

Investment and Social Protection

Michael R Carter*

(Collaborative work with Ghada Elabed & Sarah Janzen)*University of California, Davis, NBER & the Giannini Foundation

OECD, Paris

September 15, 2015

M.R. Carter Before and After the Drought

Logic of Insurance as a Development Tool

Decades of evidence that risk

Makes people poor by reducing incomes & destroying assets;and,Keeps people poor, by discouraging investment & distortingpatterns of asset accumulation)

The development impacts of risk reduction through insuranceshould therefore be significant:

By protecting households against the worst consequences ofadverse climatic shocks, index insurance should in principalallow households to prudentially invest more in risky, but highreturning agricultural activities.That is, if insurance has ex post protection effects, then itshould also have ex ante investment effects


Today’s Talk

Evidence on the ex ante and ex post effects is just nowemergingReview today impact results from two randomized controlledtrials of index insurance

Index-based Livestock Insurance (IBLI) protected consumption(for poorer households) and assets (for less poor households)after a drought in Northern KenyaTwo-trigger area yield contract in Mali induced substantial (exante) increases in borrowing and cotton plantings bysmall-scale farmers

Synergies between the ex post and ex ante effects of insurancehas important implications for design of effective insurancepolicyDiscuss these implications in the context of specificgovernment programs in Kenya and Peru


Risk & Coping Absent Financial Markets

Households in developing rural economies are exposed to amultitude of risksFor those facing borrowing constraints & missing insurancemarkets, coping strategies reduce to two:

Reduce assets to protect or smooth consumptionReduce consumption to protect or smooth assets

Both strategies can have prolonged economic consequencesExpanding the array of, or access to financial instrumentswould seem likely to have large short & long run impactsThis paper specifically explores the impact of a novel indexinsurance contract on coping after a drought


The IBLI Experiment in Northern Kenya

The arid pastoral regions of Nothern Kenya are an archetypeof risk & coping in the absence of financial markets

Shocks are severeFinancial instruments are sparseHouseholds are extremely poorTraditional reactive emergency response appears exhausted

Index-based Livestock Insurance (IBLI) was rolled out as anRCT in this environment in 20092011 drought created the opportunity to evaluate IBLI’seffectivenessOn average, we find that after the drought insurance leads to:

A 36%-point decrease in livestock salesA 25%-point decrease in meal reduction


Looking Beyond the Averages

However, average impacts can obscure as well as illuminatePoverty trap theory suggests that coping strategies maydifferentiate within communities:

Conventional consumption smoothers(Less conventional) asset smoothers

Guided by these theoretical insights, use thresholdeconometrics to split the sample along the asset continuum &find that consistent with poverty trap theory:

Households above a critical threshold consumption smoothwithout insurance; Insurance leads to a 64%-point drop inasset sales for these less poor householdsHouseholds below that threshold “asset smooth”; Insurance toa 43%-point reduction meal reduction as a coping strategy forthese poorer households


Coexistence of Asset & Consumption Smoothing


Index-based livestock insurance (IBLI)

Drought insurance for livestock launched in January 2010International Livestock Research Institute, Cornell University,Syracuse University and the BASIS Research Program at UC Davis.“Index-based": uses satellite-based NDVI (normalized differencevegetation index) measures of available vegetative cover to predictlivestock mortality


Timeline of Events

Survey 924 Households in October 2011.All households had access to insurance.25% of households purchased insurance.


Descriptive Statistics


Econometric Identification Strategy

IBLI had a random spatial rollout, but this analysis restricted totreated areas & identification through an encouragement designIn each period 60% of surveyed households were randomly selectedto receive coupons offering a 10-60% discount on the first 15 TLUinsured.

Ii = Zi δ + Xi θ + vi (1)

yQi = β Ii + Xi φ+ εi (2)


Threshold Model

Use Hansen’s threshold estimator (2000) to predict critical assetthreshold, L∗, at which households switch betweenconsumption/asset smoothing.Estimate impact of insurance separately for “asset poor" and “assetrich" households:

yQi =

{βlow Ii + Xi φlow + εi,low if Li ≤ L∗

βhigh Ii + Xi φhigh + εi,high if Li > L∗(3)


Quarter 4 Threshold


Quarter 4 Estimated Impacts


Impacts of Anticipated Payoffs

Steady deterioration of rangeland over 2011 made it quitelikely that a payout was comingIBLI regularly posts index condition with a TSA-like colorschemeSee in descriptive statistics that in quarter 3, householdsalready deploying asset & consumption smoothing strategiesMight anticipated payouts influence this behavior (after thedrought, but before payout)?

For asset smoothers, maybe (if trust in insurance)For consumption smoothers, no (unless could borrow againstfuture payout)


Quarter 3 Threshold


Quarter 3 Estimated Impacts


Summary of IBLI Impact Analysis

Data reveal ample evidence of costly, differentiated copingstrategies:

Households below critical threshold much more likely to assetsmooth, with predictable consequences on future humancapital (see paper with Sarah Janzen)Households above critical threshold more conventionallyconsumption smooth, with predictable consequences onlivestock prices & own future well-being

Insurance has large impacts on both strategies:

Cuts in half asset smoothing (or its severity)Similar reduction in asset sales


Index Insurance in Mali (& Burkina Faso)

Farmers pursue a diversified production strategy of growingtheir own food plus some cottonValue chain credit via group loans, but consequences of defaultare substantial (informal collateral)Joint liability itself discourages investment as the more afarmer produces, the more likely that some of his output willbe ’taxed’ away to pay for others in the groupFarmers report growing less cotton then they otherwise would,or by reducing financial risk exposure by investing less in thecropResult is that risk keeps these farmers poorer than they needbe given the economic opportunities available to them


Two Trigger Area Yield Contract

Area yield contracts can in principle offer strong insurancevalue (compared to weather-based insurance)But over what geographic should yields be calculated?

A small area (e.g., the individual farmer’s field in the extreme)creates a moral hazard problemA too large area (e.g„ average yields for an entire departmentor even country) lessens the quality of the insurance

So might two triggers be better than one?

Primary trigger set a small area (e.g., village)A higher level “audit” trigger can control moral hazard

Results are encouraging:


Two Trigger Area Yield Contract


Descriptive Statistics

Original Reclassified

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

Obs Control Treatment Control Treatment

Post Intervention

Area (ha) 954 2.61 2.82 2.53 2.92*

Harvest (kg) 942 2,630 2,729 2,567 2790

Loans (CFA) 967 321,571 332,645 301,740 348,661

Seed (CFA) 952 13,453 16,327 12,749 17,338**

Fertilizer (CFA) 952 135,253 145,301 133,044 148,538

Offered insurance? 25.00% 75.00% 25.00% 84.00%

Are you insured? % yes 0.00% 27.00% 5.00% 29.00%

Baseline

Cotton area (ha) 586 2.20 2.41 2.19 2.44

Cotton harvest (kg) 584 2,339 2,288 2,316 2,291

Age household head 971 55.85 55.33 55.19 55.71

Education head 948 3.50 3.98 3.89 3.77


Impact Results

Standard Instrumental Variable LATE Identification strategyLook at results using original and reclassified householdsSee similar results if look at insurance perception asendogenous variable as opposed to purchase insuranceAlso similar results if control for baseline productioncharacteristics (using smaller sample on which have fullbaseline information)


Impact Results

Reclassified Original

Loans Area Harvest Other crops Loans Area

(kCFA) (ha) (kg) (ha) (kCFA) (ha)

Insured 114.462* 1.389** 951.931 -0.511 60.387 1.106*

Constant 94.203 0.270 171.284 3.929** 95.911 0.279

Obs 949 938 925 952 949 938

R2 (adj) 0.112 0.097 0.156 0.087 0.145 0.129

First stage

Treated 0.24*** 0.254***

Penalty -.0004** -0.00038*

Constant 0.092 .0198

Obs 952 938

R2 0.351 0.449

F-stat 10.11 12.61


Designing Comprehensive to Promote Mobility via the RiskReduction Dividend

Have seen that:

Index insurance can provide real protection to consumption &assetsRisk reduction dividend works in Mali

This “risk reduction dividend” corroborated by a few otherrecent studies (Karlan et al., Emmerick et al.)So how do we design, or not design, index insurance to reapthis dividend?However, policy thinking is often segmented–insurance iseither thought of for social protection for the “poor,subsistence” households or for economic advancement for“commercial” farmers

PeruKenya


Designing Comprehensive to Promote Mobility via the RiskReduction Dividend

But class positions are not fixedIf do not design comprehensive program, may miss opportunityto use insurance as tool for upward mobility, movinghouseholds from subsistence to commercialNeed to work on more comprehensive designs that allowpoorer households to insure marginal risks that might take onwhen insured


Conclusions

Seen that index insurance can workI have not discussed substantial design, pricing and demandissues for insuranceComprehensive insurance suggests new economic logic forcost-sharing via smart subsidies


Before and After the Drought: Evidence on the Impact of Index Insurance on Small Farm Investment and...

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