Understanding Commissions Motivated Advice: Evidence from Indian Life Insurance

1. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Understanding Commissions Motivated Advice:
Evidence from Indian Life Insurance
Shawn Cole (Harvard Business School), Santosh Anagol
(Wharton), Shayak Sarkar (Harvard)
IFPRI
September 27, 2012

2. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Introduction
• Many financial products difficult to value, particularly for
those with limited financial experience (mortgages, life
insurance)
• Little learning for long-horizon products
• May limit usefulness of brokers to build reputations for
providing the right products
• Research Agenda: How do consumers make decisions about
these 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?

3. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Motivation: Why Life Insurance in India?
• Why India?
• Increasing incomes in China, India other fast developing
countries will greatly increase capacity to invest in formal
financial products
• How will these consumers make informed decisions? What role
should government play?
• Important question in the U.S. as well (creation of Consumer
Financial Protection Bureau)
• Why Life Insurance?
• 20 % of Indian household financial savings in life insurance
products
• Easiest product to identify potentially “bad” decisions
• Approximately 2.4 million life insurance agents in India (approx
434,000 in the US)

4. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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 during
that period, or nothing if survive N years.
• Whole: pay Pw per year for N years, receive Pw N + 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 (Pw N )
• Importantly, not compounded
• Whole type products have estimated 60-80% market share

5. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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

6. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Replicating Portfolio
• Fixed Bank Deposits
• Receive equivalent income tax deduction as whole life premia
• Average fixed deposit rates from 1957 to present are 9.69%
• Minimum 5.75%
• Even at this rate, term + savings value at age 80 twice as
large as whole
• Public provident fund
• Guaranteed 8% return
• Commitment features (7 year lock-in, mandatory 500 rupees
per year deposit)
• Life insurance bonuses are not guaranteed
• “Best case” bonus equal to 7%, but sometimes as low as 2%
• Very difficult to construct scenario in which whole dominates

7. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Why Would Anyone Choose Whole?
• Agent receives commission of 35% on whole, 5% on term
• Buyer cannot calculate effective cost
• “Term is throwing money away–if you survive until the end of
the policy, it’s worth nothing”
• People don’t appreciate importance of compounding (Zinman
and 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
• Model of the equilibrium in this kind of market:
• Gabaix and Laibson (2006) - show that firms might not
unshroud socially wasteful products like whole insurance

8. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Empirical Roadmap: Part 1
• Part 1: Quality of Advice
• Theory Papers: Inderst and Ottaviani (2011), Gabaix and
Laibson (2006)
• Empirical Papers:
• Bergstresser et al. (2009): Broker-recommended funds
underperform
• Mullainathan et al (2010): Advisors if anything exaggerate
behavioral biases
• This paper:
• Do agents recommend whole even though dominated by term +
savings? Even to people who explicitly only want risk coverage?
• Do agents cater their advice to customer’s beliefs (potentially
incorrect) or to the needs of the customer?

9. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Empirical Roadmap: Part 2
• Part 2: How do Regulation and Market Structure Affect
Quality of Advice?
• Little empirical work in this area
• Three empirical tests on how advice might improve:
• Do disclosure requirements (that potentially “de-bias”
consumers) change advice?
• Does threat of competition improve quality of advice?
• Does expectation that customer is unbiased change advice?

10. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Audit Study
• Hire 10 auditors, making a total of 1,026 visits to insurance
agents over 12 months
• Field experiment conducted in two major cities in India
• Audit process developed by a former life insurance salesman
from 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 married
man with two kids...”)
• Certain features disguised
• “My salary? Let’s say I earn Rs. 10,000 per month”

11. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Channels of Life Insurance Sales
Distribution Channel
(1)
Individual Agents 79.6
Banks 10.6
Other Corporate Agents 4.30
Brokers 1.38
Direct Selling 4.13
Source: IRDA Annual Report, 2009 - 2010.

12. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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?

13. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Pilot Script: Proportion of Term Recommendations
Recommendation Risk Coverage Script
(1)
Only Term Policy Recommended .09
Any Term Policy Recommended .16
Only Whole Type Policies Recommended .31
Any Whole Type Policy Recommended .64
Any Other Policy Type .18
Observations 60

14. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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’s
a rip-off”

15. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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 care
of 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 will
not be able to live comfortably or meet our financial
obligations. 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 that
further? ”
• “I have heard that term insurance is a really good product. I
think it may be suitable for me. Maybe we can explore that
further? ”

16. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Balance Across Treatments
All Need Term Need Whole Belief Term Belief Whole
LIC 0.76 0.74 0.77 0.76 0.76
State Bank of India 0.06 0.06 0.063 0.07 0.05
All Government 0.83 0.84 0.84 0.84 0.81
Agent Male 0.85 0.88 0.82 0.85 0.85
Observations 473 217 256 243 230
• No statistical differences in treatments across underwriter or agent gender

17. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Data on Agent/Auditor Interactions
Full Sample
Audit Duration (Minutes) 36.7
Agent Asked About:
Work 0.93
Family 0.78
Marriage 0.76
Income 0.68
Dependents 0.56
Health 0.16
Assets 0.06
Tobacco/Alcohol 0.03
Agent’s Response:
Not Interested 0.02
Slightly Interested 0.05
Interested But Not Pushy 0.53
Eager 0.28
Overly Eager 0.12
Observations 512
• Average audit duration 36.7 minutes
• Agents do ask about some relevant characteristics (work/income, family)
• Majority of agents seem interested in interaction

18. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Quality of Advice: Multiple Recommendations
• Most term recommendations come as a part of multiple
recommendations (a package)

19. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Breakdown of Term Recommendations
# of Recs Proportion of Term Recs
(1) (2)
Only Term Recommended 15 .25
Whole + Term Recommended 38 .63
Other + Term 7 .12
Total Term Recommended 60 1.0
• 63% of term recommendations came as a package with a whole recommendation

20. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Quality of Advice: Responding to Needs & Beliefs
• Overall low rate of recommending term insurance - even when auditor says they
want 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

21. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Catering vs. Quality Advice: Term Insurance
Dependent 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 YES
Observations 511 511
Adjusted R-squared 0.10 0.034
Mean 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

22. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Treatment Effects of Risk Coverage Amounts Vs. Premium
Amounts
Variable Dep Var: Ln(Risk Coverage) Dep Var: Ln(Premium)
Belief Term 0.13** -0.01
[0.06] [0.06]
Need Term 0.15** 0.00
[0.08] [0.06]
Belief Term * Need Term 0.02 0.05
[0.12] [0.07]
Government Underwriter -0.21** -0.03
[0.10] [0.05]
Constant 12.8*** 11.0***
[0.3] [0.6]
Observations 473 473
Adjusted R-squared 0.07 0.01
Mean of Dep Var 13.2 10.2
• Stating need for risk coverage increases amount of suggested risk coverage
• Does not increase amount of recommended premium amount

23. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Wide Range of Risk Coverages Recommended
Belief & Need = Term
Risk Cover (U.S.D) Premium (U.S.D)
Whole Life Type Policies 12,997 629
Term 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

24. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Improving Advice: Natural Experiment on Effect of
Disclosure
• Natural experiment on ULIP disclosures
• Prior to July 1, 2010, agents required to inform buyers about
total ULIP costs/charges
• As of July 1, 2010, agents are required to provide separate
breakdown of commission costs
• Allows us to isolate disclosure of agency problems
• Measure agent reaction

25. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Field Experiment Overlaid on Natural Experiment
• Overlay with field experiment
• Agent expresses knowledge of agency problems
• “Can you give me more information about the commission
charges I’ll be paying?”
• Agent does not express knowledge of agency problems
• [No statement]

26. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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 258 258
R-squared 0.35 0.35

27. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Disclosure Results

28. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Improving Advice: Competition?
• Competition and bad advice: does the threat of losing a sale
to another agent make an agent more likely to match needs of
customer?
• 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?

29. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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

30. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Improving Advice: Sophisticated vs. Un-Sophisticated
Customers
• High level of sophistication:
• “In the past, I have spent time shopping for the policies, and
am perhaps surprisingly somewhat familiar with the different
types of policies: ULIPs, term, whole life insurance. However, I
am less familiar with the specific policies that your firm offers,
so I was hoping you can walk me through them and
recommend a policy specific for my situation.”
• Low level of sophistication:
• “I am aware that Life Insurance products are complex, and I
don’t understand them very much. However I am interested in
learning, 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

31. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Sophistication Results: Product Recommendation
(1) (2) (3)
Dependent Variable: Wholelife Endowment Endow/Whole
Sophisticated -0.14** -0.02 -0.08
[0.06] [0.06] [0.07]
Constant 0.06 0.01 0.03
[0.04] [0.03] [0.03]
Observations 196 196 196
• Sophisticated agents 14 percentage points less likely to receive
recommendation of whole
• Small effect: Overall, 80% recommend whole/endowment

32. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Systematic Review of Financial Press
• Where else might one turn for advice?
• Survey leading periodicals in India and US

33. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Systematic Review of Financial Press
Articles Screened 1859
Provide Consumer Information 483
Provide Specific Recommendations 15
Provide Sensible Recommendations 13

34. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
Type of Recommendations Provided
• Term provides insurance cheaply (6)
• Term insurance provides pure protection at very low costs. It allows a person to take the right
amount of insurance easily. In a savings or investment-linked plan the cost of insurance is a lot
higher. This is as there is a dual usage of the money split between the ratio of insurance and
investment.
• Insurance is a complex/expensive way to save (2)
• If you don’t understand what you are buying, don’t buy it, Mr. Daily says. ”Whole life is one of
the most complex financial products consumers are likely to buy.”
• Explicit comparison of returns (5)
• The plan will give a return of Rs 8.87 lakh if a 35-year old invests Rs 43,530 each year for 15 years,
respectively, considering a sum assured of Rs 5 lakh.
Comparison. If you buy a term insurance plan for a sum assured of Rs 10 lakh (at Rs 2,850) and
deposit the remaining amount in the Public Provident Fund, you will receive a corpus of around Rs
11.92 lakh in 15 years
• Non-sensible advice (2)
• ULIPs, as compared to any regular insurance policy, have less overhead charges & the premium
paid by you will be invested back in your investment.

35. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
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

36. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion
The Next Project: De-Biasing Consumers
• This project measured quality of advice, understand the
supply side
• Can consumers be ”de-biased” to choose term instead of
whole?
• Partnering with large Indian information technology firm to
give their employees advice on life insurance
• Advice given in January 2013 through video training program,
as most Indian consumers buy whole life as a way to save on
taxes
• Follow up survey in April 2013 (after tax deadline) will ask
about actual purchases