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Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion

Understanding Commissions Motivated Advice:Evidence from Indian Life Insurance

Santosh Anagol (Wharton), Shawn Cole (Harvard BusinessSchool), Shayak Sarkar (Harvard)

Wharton, February, 2012

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Introduction

• Many financial products difficult to value, particularly forthose with limited financial experience (mortgages, lifeinsurance)

• Little learning for long-horizon products

• May limit usefulness of brokers to build reputations forproviding the right products

• Research Agenda: How do consumers make decisions aboutthese complicated financial products?

• Research Questions Today:• What is the quality of advice that commissions motivated

agents provide?• Under what conditions does advice improve?

• Many other inputs into consumers decisions: Press? Friends?Regulations? Government campaigns?


Motivation: Why Life Insurance in India?

• Why India?

• Increasing incomes in China, India other fast developingcountries will greatly increase capacity to invest in formalfinancial products

• How will these consumers make informed decisions? What roleshould government play?

• Important question in the U.S. as well (creation of ConsumerFinancial Protection Bureau)

• Why Life Insurance?

• 20 % of Indian household financial savings in life insuranceproducts

• Easiest product to identify potentially “bad” decisions• Approximately 2.4 million life insurance agents in India (approx

434,000 in the US)


Focus on Term vs. Whole

• Most popular products

• Easy for us to compare/evaluate

• Term : pay PT for N years, receive payout CT at death duringthat period, or nothing if survive N years.

• Whole: pay Pw per year for N years, receive PwN + B at min(year of death, max(40 years after purchase, age 80))

• Surrender value: 30% of premiums paid if paid > 3 years

• How are bonuses (B) determined?

• Discretion of life insurance company• A percentage (typically 3%-5%) of sum assured (PwN)

• Importantly, not compounded

• Whole type products have estimated 60-80% market share


Replicating Portfolio• Consider Rs. 500,000 (ca. $10,000) coverage for 34 year-old

male• Whole life policy costs Rs. 13,574 per year, paying 3% bonus• Term policy for equivalent coverage (Rs. 500,000) and save

remainder• 2,507 per year + 11,067 savings deposit (earning 8%) for 25

years (until 2035)• Savings contribution 13,574 from 2035 until 2056

• Clear violation of law of one price• If you die before 2056: almost surely better off with term +

savings (savings are liquid)• If you survive until 2056

• Whole redemption value: Rs. 1,205,000• Savings balance: Rs. 5,563,378

• Note: no risk of future premium increases for term product(Cochrane (1995))

• Rajagopalan (2010) has similar findings


Why Would Anyone Choose Whole?

• Agent receives commission of 35% on whole, 5% on term

• Paper presents model where a dominated product with highcommissions can exist in competitive equilibrium

• Buyer cannot calculate effective cost

• “Term is throwing money away–if you survive until the end ofthe policy, it’s worth nothing”

• People don’t appreciate importance of compounding (Zinmanand Stango (2009))

• Whole policies pay 3%-5% bonus per year–not compounded!

• Commitment to save• Why does commitment to save have to bundled with

insurance?• Public provident fund is a commitment savings product paying

compound better returns


Audit Study

• Hire 10 auditors, making a total of 1,026 visits to insuranceagents over 12 months

• Field experiment conducted in two major cities in India

• Audit process developed by a former life insurance salesmanfrom major bank

• Agents found on publicly available yellow pages type websites

• Week-long training, practice audits

• Each auditor has personalized (true) script (“I am a marriedman with two kids...”)

• Certain features disguised

• “My salary? Let’s say I earn Rs. 10,000 per month”


Channels of Life Insurance Sales

Distribution Channel(1)

Individual Agents 79.6Banks 10.6Other Corporate Agents 4.30Brokers 1.38Direct Selling 4.13

Source: IRDA Annual Report, 2009 - 2010.


Pilot Script

• Introduce self, express need for life insurance

• Not looking for investment product• Seeking maximum risk cover at minimum cost• If need to save, prefer to save in a bank

• What policy do you recommend?


Pilot Script: Proportion of Term Recommendations

Recommendation Risk Coverage Script(1)

Only Term Policy Recommended .09Any Term Policy Recommended .16Only Whole Type Policies Recommended .31Any Whole Type Policy Recommended .64Any Other Policy Type .18Observations 60


Agents Talk Down Term Insurance

• “You want term: Are you planning on killing yourself?”

• “Term is throwing money away”

• Term is not for:

• Women• Middle class

• Term is only for:

• businessmen• government employees

• Offered endowment policy, calling it a term policy

• Only one instance of explicit debiasing “Don’t buy whole, it’sa rip-off”


Quality of Advice: Multiple Recommendations

• Most term recommendations come as a part of multiplerecommendations (a package)


Quality of Advice: Suitability and Catering• Do agents provide advice based on client’s actual need, or

client’s beliefs about what is the right product?• Important question in context where clients are unlikely to

understand differences in products

• Vary need:• Whole: “I want to save and invest money for the future, and I

also want to make sure my wife and children will be taken careof if I die. I do not have the discipline to save on my own”

• Term: “I am worried that if I die early, my wife and kids willnot be able to live comfortably or meet our financialobligations. I want to cover that risk at an affordable cost.”

• Vary beliefs:• “I have heard that whole insurance is a really good product. I

think it may be suitable for me. Maybe we can explore thatfurther? ”

• “I have heard that term insurance is a really good product. Ithink it may be suitable for me. Maybe we can explore thatfurther? ”


Quality of Advice: Responding to Needs & Beliefs

• Overall low rate of recommending term insurance - even when auditor says theywant risk coverage and have heard term is a good product

• But needing risk coverage does cause about 12% higher probability of receiving

term recommendation

• Even when customer initially believes whole may be better for them

• At least some agents know that term insurance is better for risk coverage


Catering vs. Quality Advice: Term InsuranceDependent Variable Any Term Only Term

(1) (2)

Belief Term 0.10*** 0.02*[0.03] [0.02]

Need Term 0.12*** 0.016[0.04] [0.01]

Belief Term * Need Term .024 .052*[.059] [.031]

Government Underwriter -0.12*** -.01[.041] [.02]

Constant -0.06 -0.01[0.05] [0.01]

Auditor FE YES YESObservations 511 511Adjusted R-squared 0.10 0.034Mean of Dep Var 0.13 0.03

• Agents do cater advice to both customer preferences and need for risk coverage

• Not just whole recommending machines• But catering mainly by adding on a term policy on top of a whole policy

• Following the ”path of least resistance”

• Government underwriters less likely to mention term plans overall


Wide Range of Risk Coverages Recommended

Belief & Need = TermRisk Cover (U.S.D) Premium (U.S.D)

Whole Life Type Policies 12,997 629Term Type Policies 44,494 619

• Only 10% of auditors get a term recommendation

• But the amount of risk coverage they get recommended is approx 4 times larger

• Possible theory: agents cater to premium amount that can be paid


Improving Advice: Natural Experiment on Effect ofDisclosure

• Natural experiment on ULIP disclosures

• Prior to July 1, 2010, agents required to inform buyers abouttotal ULIP costs/charges

• As of July 1, 2010, agents are required to provide separatebreakdown of commission costs

• Allows us to isolate disclosure of agency problems

• Measure agent reaction


Field Experiment Overlaid on Natural Experiment

• Overlay with field experiment

• Agent expresses knowledge of agency problems

• “Can you give me more information about the commissioncharges I’ll be paying?”

• Agent does not express knowledge of agency problems

• [No statement]


Results

Table: 8-Effect of Disclosure on Product Recommendations

Dep Var = Ulip Recommended (1) (2)Post Disclosure Regulation -0.22*** -0.21***

[0.05] [0.08]Disclosure Knowledge -0.01 -0.004

[0.05] [0.07]Agent Home -0.06 -0.06

[0.11] [0.11]Auditor Home -0.13 -0.13

[0.17] [0.17]Agent Office -0.05 -0.05

[0.10] [0.10]Auditor Office -0.04 -0.04

[0.20] [0.20]LIC -0.44*** -0.44***

[0.05] [0.05]Post Disclosure Regulation * Disclosure Knowledge -0.02

[0.10]Observations 258 258R-squared 0.35 0.35


Disclosure Results

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Dramatic reduction in ULIP recommendations Agents instead recommend Whole insurance

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Improving Advice: Competition?

• Competition and bad advice: does the threat of losing a saleto another agent make an agent more likely to match needs ofcustomer?

• Vary level of competition by varying source of beliefs:

• “I have heard from a friend that whole (life)...”• “I have heard from another agent from whom I am considering

purchasing...”

• Does agent try to win business by correcting another agent?


Does Competition Matter for Type of Advice?

• Agents de-bias when advice comes from another agent

• Statistically significant at 5 percent level

• Note that this de-biasing is mainly through recommending term in addition to whole


Improving Advice: Sophisticated vs. Un-SophisticatedCustomers

• High level of sophistication:

• “In the past, I have spent time shopping for the policies, andam perhaps surprisingly somewhat familiar with the differenttypes of policies: ULIPs, term, whole life insurance. However, Iam less familiar with the specific policies that your firm offers,so I was hoping you can walk me through them andrecommend a policy specific for my situation.”

• Low level of sophistication:

• “I am aware that Life Insurance products are complex, and Idon’t understand them very much. However I am interested inlearning, what type of policy may be right for me?”

• Delivered in introduction of auditor to agent

• Remainder of script unchanged

• In particular, stated income held constant

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Sophistication Results: Product Recommendation

(1) (2) (3) (4)Dependent Variable: Any Term Only Term Ln(Coverage) Ln(Premium)Sophisticated 0.10* 0.10 * 0.21* -0.06

[0.06] [0.06] [0.12] [0.10]Government Underwriter -0.08 -0.09 -0.25 0.05

[0.07] [0.06] [0.16] [0.10]Auditor FE YES YES YES YESAudit Location FE YES YES YES YESObservations 217 217 209 209

• Sophisticated agents 10 percentage points less likely to receiverecommendation of any term

• Sophisticated agents also recommend to buy more coverage,but not to pay more premiums - consistent with catering topremium amount story


Conclusion

• Quality of Advice

• Agents mainly recommend whole despite fact that term +savings seems to dominate

• Even to customers who mainly want risk coverage

• Agents will cater to incorrect beliefs• When agents do recommend term, they prefer to do it as a

package (whole + term)

• Improving Advice• When agents forced to disclose information changes advice• Some evidence that agents will compete by providing different

advice• When consumer signals sophistication gets weakly better

advice

Documents

Understanding Commissions Motivated Advice: Evidence from ...siteresources.worldbank.org/EXTABCDE/Resources/... · types of policies: ULIPs, term, whole life insurance. However, I