09.27.2012 - Santosh Anagol

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Understanding Commissions Motivated Advice: Evidence from Indian Life Insurance

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09.27.2012 - Santosh Anagol

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Motivation and Context Quality of Advice Improving Advice Objective Advice ConclusionTreatment 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. 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. 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. 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. 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. 27. Motivation and Context Quality of Advice Improving Advice Objective Advice Conclusion Disclosure Results
  28. 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. 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. 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. 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. 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. 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. 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. 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. 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

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