precautionary savings to manage common health risks among the poor

2012 Research Conference on Microinsurance

Precautionary Savings to Manage Common Health Risks among the Poor

Jessica PickettUniversity of Pennsylvania

Insurance in combination with other financial services 1

Jessica Pickett

• Poor households struggle with unpredictable out-of-pocket health expenses

• Recent trend towards government, NGO microinsurance• Primarily hospitalization• Pharmaceutical & outpatient expenses rarely covered

• Even minor illness can reduce consumption in inefficient capital markets• Productivity losses from under-treatment• Future losses from high-interest loans or asset sales

• Precautionary savings as an alternative• Constrained by biased beliefs, inattention & inconsistent preferences

Research Problem

12 April 2012 Insurance in Combination with Other Financial Services 2

Jessica Pickett

Insurance

• Catastrophic coverage• Adverse selection &

moral hazard• Regulatory constraints• Oversight, monitoring

& fraud prevention• Most clients will not

benefit

Savings

• Longer time horizon• Contribute while

healthy / productive• Individual

accountability• Must trade current

period consumption for uncertain future benefits

• No protection against worst shocks

Loans

• No uncertainty• High interest rates• Illness may increase

need but decrease ability to repay

Options for Risk Management


Jessica Pickett

• Financial impact of illness is lower in households with access to rural deposit accounts in Indonesia (Gertler, Levine & Moretti, 2009)

• Market women in Kenya with access to individual savings accounts spend less working capital on medical costs & more likely to receive treatment for malaria episodes (Dupas & Robinson, 2010)

• But recent research on health-specific savings technologies with Kenyan ROSCAs (Dupas & Robinson, 2011) and the impact of saving reminders more generally (Karlan, McConnell, Mullainathan, & Zinman, 2011) suggests households systematically underestimate future treatment costs• Financial access is not enough to ensure savings – must also address behavioral

constraints

• Supported by observed demand for health savings & loan products among Indian SHGs and MFI clients in Burkina Faso (Reinsch & Ramirez, 2011)

Savings & Health


Jessica Pickett

• Does outpatient spending affect household consumption?

• Is there demand for insurance and/or precautionary savings for health?

• Why don’t the poor currently save enough for that purpose?

Hypothesis: Insurance should be supplemented with better savings vehicles to overcome biased beliefs, inattention, inconsistent preference & other behavioral constraints to manage common illness

Research Questions


Jessica Pickett

• Permanent income hypothesis predicts households should always opt for remedial treatment for health shocks & thus always prefer full insurance, if actuarially fair

• Predicts positive savings rate when insurance markets are not feasible or loading exceeds household’s willingness to pay• Assuming reasonable levels of risk aversion & interest rates

• But precautionary savings for health still entails significant uncertainty• Exact timing even of frequent illnesses is unpredictable

• Savings goals require accurate information on probability & costs of illness, as well as treatment efficacy

• Households must diligently deposit savings & protect those funds from expropriation• Limited attention, narrow framing & commitment

• Model of healthcare utilization under these forecasting constraints predicts negative savings rate & decreased future consumption due to asset sales, loans or decreased productivity

Theoretical Context


Jessica Pickett

• Wage rate Yi, productive assets At, debt Dt & non-medical consumption Ct

• Insurance at given loading α

• Disability-adjusted labor Ht determined by exogenous probability of illness & depreciation • Superscripts denote realized incidence of illness: 1 indicates a state of the world if sick & 0 if healthy

• Remedial treatment at price pm

Expected Utility Model

Insurance premiumCurrent period earnings Loan paymentsMedical expenses


Jessica Pickett

• Forecast illness with probability) due to:• Inattention or poor cognition• Imperfect information, innumeracy

or biased beliefs• Disutility from considering negative

prospects

Behavioral Model (Savings)

Current period earnings Loan paymentsMedical expenses

Current period utility from illness

Expected forecast of future utility

Current utility of full health


Jessica Pickett

• Model suggests demand for savings products that would allow households to save to smooth consumption against health shocks• Ongoing research proposes the use of reminders, conditions & restrictions targeting individual

accounts• Alternatively, existing community-based savings groups (CBSGs, aka VSLAs) “nudge” members

to save more than simply increasing access to financial services

• CBSG meeting structure and corresponding social dynamics simultaneously serve to maximize trust while mitigating many crucial behavioral constraints: • Mandated savings implicitly represents target savings goal & explicitly reminds members to

save (compounded by peer effects)• Salience of medical costs & other financial risks is amplified by observing other members apply

for loans or receive social fund grants• Reduces “negative” expropriation of funds by others by introducing formal loan structure

where loans are likely to be repaid & member has access to liquid capital during interim• Loan requests subject to approval by the group, serving as a semi-liquid commitment device

protecting against temptation goods or time-inconsistent preferences

• Better way to manage outpatient expenses than ROSCAs or MFIs, where existing health savings pilots observed demand for health-specific accounts• But still subject to information problems

Community-Based Savings Groups


Jessica Pickett

Case Study: Tajikistan


Jessica Pickett

• Poor, land-locked country in post-Soviet Central Asia; rural & mountainous, with a population of 7 million• Few natural resources; economy is heavily dependent on agriculture & remittances• Per capita income $1,860 (PPP); 47% poverty rate; GDP growth of 3.4% per year• High education levels & 100% literacy rate in Tajik (Persian) or Russian• Communicable disease ~ 72% of the overall disease burden, especially TB (MDR)

• Crumbling Soviet health system: ostensibly free network of government-run hospitals (61 beds per 10,000) & salaried doctors (20 per 10,000)• Unsustainable excess institutional capacity

• Vast majority of health costs paid out-of-pocket, mostly for pharmaceuticals, transportation & informal payments to government providers• 64% of households rate it “difficult” or “impossible” to find money for needed treatment

• Aggravated by inefficient capital markets (17% interest rate spread)• Rural households lack access commercial banks, especially for relatively small transactions• MFIs increased access to credit but prohibited from accepting deposits or insurance

products

• Recent, widespread expansion of CBSGs has potential to mitigate these challenges



Jessica Pickett

Tajikis

tan IndiaLao

s

Vietnam

Mexico

Nicarag

uaKen

yaGhan

a

Indonesia

Thaila

nd

United St

ates

Netherl

ands

0%

10%

20%

30%

40%

50%

60%

70%

80%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Out-of-Pocket Health Spending (Sample Countries)

Private out-of-pocket expenditures as % of total health expendituresOut-of-pocket health spending as % of income per capita (PPP)

% T

otal

Hea

lth E

xpen

ditu

res

% P

er C

apita

Inco

me



Jessica Pickett

• Existing: Tajik Living Standards Survey (TLSS) contains data on household health expenditures, consumption & financial access• Nationally representative sample of 4,860 households in 2007 (stratified

by region & population density)• WB Living Standards & Measurement Survey research program

• Subset of panel data from 1503 households (10,069 individuals) in 2009• 3rd wave of data collection began in November 2011

• Proposed: Detailed health & expenditure logs• Sub-sample of TLSS participants via SMS or phone interviews

• “Large T” analysis can dramatically increase power with more frequent observations of a smaller overall sample for health outcomes, household expenditures & other noisy outcomes with low autocorrelation (McKenzie, 2011)

• Also important given recall bias for frequent health spending (Das, Hammer & Sanchez, 2012)

• CBSG expansion as a natural experiment

Data


Jessica Pickett

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

∆ Medical Expenditures -0.480*** -1.604*** -0.127 -0.823* (0.080) (0.449) (0.068) (0.373)

Constant -111.0*** -95.94*** -88.35*** -71.53*** (12.24) (1.077) (11.10) (0.895) Observations 1,451 245 1,451 245R-squared 0.401 0.039 0.262 0.017Number of PSUID 167 118 167 118Community FE Yes Yes Yes YesWealth Decile FE Yes Yes Poorest Quintile Yes YesFood Only Yes Yes

Empirical Test of Full InsuranceEffect of Health Expenditures on Per Capita Non-Medical Consumption

Robust standard errors in parentheses, clustered by strata*** p<0.01, ** p<0.05, * p<0.1


Jessica Pickett

• Households should be willing pay a “risk premium” in excess of expected expenses to insure against illness• Based on variance of losses, income & risk tolerance:

• Use TLSS to estimate maximum % of expected benefits that households would pay to fully insure ambulatory medical care• Typically lower for outpatient care than for hospitalization due to lower

variance (Pauly, Blavin, & Meghan, 2010)

• Compare to plausible levels of administrative loading• ~30% in other developing countries

• Higher regulatory & monitoring costs for informal payments to public providers

• Higher loading costs for outpatient care given greater number of transactions & providers • Also aggravates selection problem (especially with relatively low risk aversion)

Theory of Insurance


Jessica Pickett

Monthly Per Capita Non-Medical Consumption

Mean 185.87

Std. Dev. 83.46

Monthly Per Capita Outpatient Expenditures

Mean 8.68

Std. Dev. 15.68

Coefficient of variation 1.81

Risk Premium 1.32

% of mean 15.2%

Estimated Demand for InsuranceSurvey-Adjusted Risk Premium

(2007/2009 Panel)

* CRRA


Jessica Pickett

• Do outpatient medical expenditures affect household consumption?• TLSS data suggests health expenditures are only partially offset through informal

coping mechanisms & negatively affects current consumption• More pronounced among the poorest households

• Is there demand for insurance and/or precautionary savings for health?• Absence of insurance for ambulatory care appears consistent with low demand

relative to plausible loading• Variance of OOP spending still high enough to affect behavior & policy →

suggests high value of savings & other mechanisms to smooth consumption more cheaply

• Consistent with observed demand for health savings in India, Kenya & Burkina Faso

• Potential for bundling with hospitalization insurance

• Why don’t the poor currently save enough for that purpose?• Inefficient capital markets combined with biased beliefs, inattention, inconsistent

preference & other behavioral constraints

Policy Implications


Jessica Pickett

• Would savings increase utilization and/or out-of-pocket health spending?• Depends on demand elasticity & price discrimination• Preliminary evidence from recent pilots & objective of future

research

• Do savings actually improve health outcomes?• Unknown → mitigated by poor treatment-seeking behaviors on the

part of patients combined with low quality & corruption on the part of providers• More ambiguous demand function (price elasticity & non-price factors),

combined with less obvious outpatient health benefits• Need for patient education plus incentives for high-quality care &

evidence-based treatment

Future Research


precautionary savings to manage common health risks among the poor

Documents

financial access

common expenses

common health risks

common medical expenses

title precautionary

health sector

catastrophic insurance

common illnesses