Instantaneous Gratification: Behavior, Models, and Retirement Savings Policy David Laibson Harvard University and NBER July 16, 2008.

Instantaneous Gratification: Behavior, Models, and Retirement Savings Policy David Laibson Harvard University and NBER July 16, 2008

1. Motivating Experiments A Thought Experiment Would you like to have A)15 minute massage now or B) 20 minute massage in an hour Would you like to have C) 15 minute massage in a week or D) 20 minute massage in a week and an hour

Read and van Leeuwen (1998) Time Choosing TodayEating Next Week If you were deciding today, would you choose fruit or chocolate for next week?

Patient choices for the future: Time Choosing TodayEating Next Week Today, subjects typically choose fruit for next week. 74% choose fruit

Impatient choices for today: Time Choosing and Eating Simultaneously If you were deciding today, would you choose fruit or chocolate for today?

Time Inconsistent Preferences: Time Choosing and Eating Simultaneously 70% choose chocolate

Read, Loewenstein & Kalyanaraman (1999) Choose among 24 movie videos Some are “low brow”: Four Weddings and a Funeral Some are “high brow”: Schindler’s List Picking for tonight: 66% of subjects choose low brow. Picking for next Wednesday: 37% choose low brow. Picking for second Wednesday: 29% choose low brow. Tonight I want to have fun… next week I want things that are good for me.

In-class experiment with undergraduates Using incentive compatible revelation mechanism I measure… 12% discount rate between day 0 and day 5 4% discount rate between day 20 and day 25 Subjects choose relatively impatiently in the short-run. Strotz (1950s), Herrnstein (1960s), and Ainslie (1970s), were the first to understand this phenomenon.

Extremely thirsty subjects McClure, Ericson, Laibson, Loewenstein and Cohen (2007) Choosing between, juice now or 2x juice in 5 minutes 60% of subjects choose first option. Choosing between juice in 20 minutes or 2x juice in 25 minutes 30% of subjects choose first option. We estimate that the 5-minute discount rate is 50% and the “long-run” discount rate is 0%. Ramsey (1930s), Strotz (1950s), & Herrnstein (1960s) were the first to understand that discount rates are higher in the short run than in the long run.

Conceptual Outline People are not internally consistent decision-makers Internal conflicts can be modeled and measured Scalable, inexpensive policies can transform behavior

Outline 1.Motivating experimental evidence 2.Theoretical framework 3.Field evidence 4.Neuroscience foundations 5.Neuroimaging evidence –Study 1: Amazon gift certificates –Study 2: chips and juice 6.Policy analysis

2. Theoretical Framework Classical functional form: exponential functions. D(t) =  t D(t) = 1,      U t = u t +  u t+1   u t+2   u t+3  But exponential function does not show instant gratification effect. Discount function declines at a constant rate. Discount function does not decline more quickly in the short-run than in the long-run.

Constant rate of decline -D'(t)/D(t) = rate of decline of a discount function

Rapid rate of decline in short run Slow rate of decline in long run

An exponential discounting paradox. Suppose people discount at least 1% between today and tomorrow. Suppose their discount functions were exponential. Then 100 utils in t years are worth 100*e (-0.01)*365*t utils today. What is 100 today worth today? 100.00 What is 100 in a year worth today? 2.55 What is 100 in two years worth today? 0.07 What is 100 in three years worth today? 0.00

An Alternative Functional Form Quasi-hyperbolic discounting (Phelps and Pollak 1968, Laibson 1997) D(t) = 1,      U t = u t +  u t+1   u t+2   u t+3  U t = u t +  u t+1   u t+2   u t+3   uniformly discounts all future periods.  exponentially discounts all future periods.

Building intuition To build intuition, assume that  = ½ and  = 1. Discounted utility function becomes U t = u t + ½  u t+1  u t+2  u t+3  Discounted utility from the perspective of time t+1. U t+1 = u t+1 + ½  u t+2  u t+3  Discount function reflects dynamic inconsistency: preferences held at date t do not agree with preferences held at date t+1.

Exercise Assume that  = ½ and  = 1. Suppose exercise (current effort 6) generates delayed benefits (health improvement 8). Will you exercise? Exercise Today: -6 + ½ [8] = -2 Exercise Tomorrow: 0 + ½ [-6 + 8] = +1 Agent would like to relax today and exercise tomorrow. Agent won’t follow through without commitment.

3. Field Evidence Della Vigna and Malmendier (2004) Average cost of gym membership: $75 per month Average number of visits: 4 Average cost per vist: $19 Cost of “pay per visit”: $10

Choi, Laibson, Madrian, Metrick (2002) (unreliable?) self-reports about undersaving. Survey –Mailed to 590 employees (random sample) –195 usable responses –Matched to administrative data on actual savings behavior Consider a population of 100 respondents –68 report saving too little –24 of 68 plan to raise 401(k) contribution in next 2 months –Only 3 of 24 actually do so in the next 4 months

Financial education Choi, Laibson, Madrian, Metrick (2004) Seminars presented by professional financial advisors Curriculum: Setting savings goals, asset allocation, managing credit and debt, insurance against financial risks Seminars offered throughout 2000 Linked data on individual employees’ seminar attendance to administrative data on actual savings behavior before and after seminar

Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

$100 bills on the sidewalk Choi, Laibson, Madrian (2004) Employer match is an instantaneous, riskless return on investment Particularly appealing if you are over 59½ years old – Have the most experience, so should be savvy – Retirement is close, so should be thinking about saving – Can withdraw money from 401(k) without penalty We study seven companies and find that on average, half of employees over 59½ years old are not fully exploiting their employer match – Average loss is 1.6% of salary per year Educational intervention has no effect

Financial education effects are small Seminar attendees have good intentions to change their 401(k) savings behavior, but most do not follow through Financial education alone will not dramatically improve the quality of 401(k) savings outcomes Choi et al (2005) study the effect of the Enron, Worldcom, and Global Crossing scandals on employer stock holding No net sales of employer stock in reaction to these news stories These scandals did not affect the asset allocation decisions of new hires. These hires did not affect the asset allocation decisions of new hires at other Houston firms.

Laibson, Repetto, and Tobacman (2007) Use MSM to estimate discounting parameters: –Substantial illiquid retirement wealth: W/Y = 3.9. –Extensive credit card borrowing: 68% didn’t pay their credit card in full last month Average credit card interest rate is 14% Credit card debt averages 13% of annual income –Consumption-income comovement: Marginal Propensity to Consume = 0.23 (i.e. consumption tracks income)

LRT Simulation Model Stochastic Income Lifecycle variation in labor supply (e.g. retirement) Social Security system Life-cycle variation in household dependents Bequests Illiquid asset Liquid asset Credit card debt Numerical solution (backwards induction) of 90 period lifecycle problem.

LRT Results: U t = u t +  u t+1   u t+2   u t+3   = 0.70 (s.e. 0.11)  = 0.96 (s.e. 0.01) Null hypothesis of  = 1 rejected (t-stat of 3). Specification test accepted. Moments: Empirical Simulated (Hyperbolic) %Visa: 68%63% Visa/Y: 13%17% MPC: 23%31% f(W/Y): 2.62.7

4. Neuroscience Foundations What is the underlying mechanism? Why are our preferences inconsistent? Is it adaptive? How should it be modeled? Does it arise from a single time preference mechanism (e.g., Herrnstein’s reward per unit time)? Or is it the resulting of multiple systems interacting (Shefrin and Thaler 1981, Bernheim and Rangel 2004, O’Donoghue and Loewenstein 2004, Fudenberg and Levine 2004)?

Shiv and Fedorikhin (1999) Cognitive burden/load is manipulated by having subjects keep a 2-digit or 7-digit number in mind as they walk from one room to another On the way, subjects are given a choice between a piece of cake or a fruit-salad Processing burden% choosing cake Low (remember only 2 digits)41% High (remember 7 digits)63%

Meso-limbic dopamine system vs. Fronto-Parietal System Mesolimbic dopamine system Frontal cortex Parietal cortex

Relationship to quasi-hyperbolic model Hypothesize that mesolimbic dopamine system is impatient. Hypothesize that the fronto-parietal system is patient Here’s one implementation of this idea: U t = u t +  u t+1   u t+2   u t+3  (1/  )U t = (1/  )u t +  u t+1   u t+2   u t+3  (1/  )U t =(1/  )u t + [   u t +   u t+1   u t+2   u t+3   limbic fronto-parietal cortex

Overview of candidate discount functions, D(t), and value functions, V(t). hyperbolic LimbicCortical quasi-hyperbolic affine transformation of quasi-hyperbolic two system generalization two system: double exponential

Hypothesis: Limbic system discounts reward at a higher rate than does the prefrontal cortex. time discount value prefrontal cortex mesolimbic system 0.0 1.0

5. Neuroimaging Evidence McClure, Laibson, Loewenstein, and Cohen Science (2004) Do agents think differently about immediate rewards and delayed rewards? Does immediacy have a special emotional drive/reward component? Does emotional (mesolimbic) brain discount delayed rewards more rapidly than the analytic (fronto-parietal cortex) brain?

Methods Subjects choose between two rewards: –$15 gift certificate at d or $20 gift certificate at d`>d Identify regions that show elevated activation only when immediacy is an option (i.e., d=0 v. d>0): “  regions.” Identify regions that show elevated activation when making any intertemporal decision relative to benchmark of resting state: “  regions.” Hypothesize that  regions are limbic and para-limbic. Hypothesize that  regions are fronto-parietal.

Choices involving Amazon gift certificates: delay d>0 d’ Reward R R’ Hypothesis: fronto-parietal cortex. delay d=0 d’ Reward R R’ Hypothesis: fronto-parietal cortex and limbic. Time

2 s 12 s Free Response Methods Subjects given a series of choices between ($R at d) and ($R' at d') where R<R' and d<d'. d d'-d (R'-R)/R  { Today, 2 weeks, 1 month }  { 2 weeks, 1 month }  {1%, 3%, 5%, 10%, 15%, 25%, 35%, 50%}

y = 8mm x = -4mm z = -4mm 0 7 T 13 ventral striatum posterior cingulate cortex medial PFC medial OFC hippocampus medial OFC ventral striatum Q: Which regions only show elevated activation when a subject considers an immediate reward? A: Limbic systems and para-limbic cortex.

y = 8mmx = -4mmz = -4mm 0 7 T 13 d = Today d = 2 weeksd = 1 month 0.2% 2s VStr MOFCMPFC PCC  Areas respond “only” to immediate rewards BOLD SignalSeconds

Emotional system responds only to immediate rewards y = 8mmx = -4mmz = -4mm 0 7 T 13 d = Earliest reward available today d = Earliest reward available in 2 weeks d = Earliest reward available in 1 month VStr MOFCMPFC PCC Neural activity Seconds McClure, Laibson, Loewenstein, and Cohen Science (2004) 0.4% 2s

XYZMax Tn Medial OFC-848-45.03416 Ventral striatum 68-44.3695 L Posterior hippocampus -26-38-84.5827 Medial PFC044126.79374 Posterior cingulate cortex -8-28325.35421 All voxels significant at p < 0.001  Analysis Summary of Significant Voxels

x = 44mm x = 0mm d = Today d = 2 weeksd = 1 month 0 15 T 13 VCtx 0.4% 2s PMARPar DLPFCVLPFCLOFC  Areas respond equally to all rewards

x = 44mm x = 0mm 0 15 T 13 VCtx 0.4% 2s RPar DLPFCVLPFCLOFC Analytic brain responds equally to all rewards PMA d = Earliest reward available in 2 weeks d = Earliest reward available in 1 month d = Earliest reward available today

XYZMax Tn Visual cortex-4-80414.7871093 PMA012565.27715 SMA430404.98710 R Posterior parietal cortex 40-60447.364191 L Posterior parietal cortex -32-60529.56873 R DLPFC44 167.42259 R VLPFC4020-87.59639 R Lateral OFC2450-125.5095  Analysis: Summary of Significant Voxels All voxels significant at p < 0.001

0% 25% 50% 75% 100% 1-3%5-25% 35-50% P(choose early) Difficult Easy 0.4% 2s VCtxRPar DLPFCVLPFC LOFC PMA 2.5 3.0 3.5 4.0 4.5 Difficult Easy Effect of Difficulty Response Time (sec) (R’-R)/R 1-3% 35-50% 5-25% (R’-R)/R Sec BOLD Signal

0.0 -0.05 0.05 Choose Smaller Immediate Reward Choose Larger Delayed Reward Emotional System Frontal system Brain Activity Brain Activity in the Frontal System and Emotional System Predict Behavior (Data for choices with an immediate option.)

0.0 -0.05 0.05 Choose Immediate Reward Choose Delayed Reward  areas  areas Hypothesized brain regions predict choice (Trials with immediate reward as an option.) Z-score

Conclusions of Amazon study Time discounting results from the combined influence of two neural systems: Mesolimbic dopamine system is impatient. Fronto-parietal system is patient. These two systems are separately implicated in ‘emotional’ and ‘analytic’ brain processes. When subjects select delayed rewards over immediately available alternatives, analytic cortical areas show enhanced changes in activity.

Overview: Emotional brain doesn’t see the future (myopic as in Shefrin & Thaler, Bernheim & Rangel, O’Donoghue and Loewenstein, Fudenberg and Levine) Fronto-Parietal/Analytic brain treats the future the same way that it treats the present.

Open questions  New experiment on primary rewards: Juice McClure, Ericson, Laibson, Loewenstein, Cohen (Journal of Neuroscience, 2007) 1.What is now and what is later? Our “immediate” option (Amazon gift certificate) did not generate immediate “consumption.” Also, we did not control the time of consumption. 2.How does the limbic signal decay as rewards are delayed? 3.Would our results replicate with a different reward domain? 4.Would our results replicate over a different time horizon?

Subjects water deprived for 3hr prior to experiment (subject scheduled for 6:00)

Free (10s max.)2sFree (1.5s Max) Variable Duration 15s (i) Decision Period(ii) Choice Made(iii) Pause(iv) Reward Delivery 15s10s5s iv. Juice/Water squirt (1s ) … Time iiiiii A B Figure 1

d d'-d (R,R')  { This minute, 10 minutes, 20 minutes }  { 1 minute, 5 minutes }  {(1,2), (1,3), (2,3)} Experiment Design d = This minute d'-d = 5 minutes (R,R') = (2,3)

This minute 10 minutes 20 Minutes P(choose early) Delay to early reward (d) Behavioral evidence for non-exponential discounting 0 0.2 0.4 0.6 0.8

This minute 10 minutes 20 Minutes P(choose early) 0 0.2 0.4 0.6 0.8 d’-d = 5 min d’-d = 1 min Delay to early reward (d) Behavioral evidence for non-exponential discounting 0 0.2 0.4 0.6 0.8 This minute 10 minutes 20 minutes Delay to early reward (d)

Discount functions fit to behavioral data LimbicCortical β = 0.53 (se = 0.041) δ = 0.98 (se = 0.014)  = 0.47 (se = 0.101)  = 1.02 (se = 0.018) Evidence for two-system model Can reject restriction to a single exponential: t-stat > 5 Double exponential generalization fits data best

y = 12mm VStr SMA Ins 0 11 T 2s 0.2% JuiceWater Juice and Water treated equally (both behavioral and neurally) Time (2 second increments)

Figure 4 x = -12mmx = -2mmx = -8mm z = -10mm NAcc MOFC/SGC ACCPCu PCC NAcc ACC SGC PCu x = 0mm x = 40mmx = -48mm PCC SMA/PMA Vis Ctx PPar BA10 Ant Ins BA9/44 BA46 0 11 T Areas that respond primarily to immediate rewards Areas that show little discounting Neuroimaging data

Figure 5 x = 0mmx = -48mm x = 0mmy = 8mm Juice only Amazon only Both Patient areas (p<0.001) Impatient areas (p<0.001) x = 0mmx = -48mmx = -4mmy = 12mm Patient areas (p<0.01) Impatient areas (p<0.01) Comparison with Amazon experiment:

Measuring discount functions using neuroimaging data Impatient voxels are in the emotional (mesolimbic) reward system Patient voxels are in the analytic (prefrontal and parietal) cortex Average (exponential) discount rate in the impatient regions is 4% per minute. Average (exponential) discount rate in the patient regions is 1% per minute.

AntInsL0.9908(0.007) AntInsR0.9871(0.006) BA10L1.0047(0.011) BA10R0.9953(0.010) BA460.9870(0.008) BA9440.9913(0.006) PCC0.9926(0.006) PParL0.9970(0.005) PParR0.9959(0.005) SMAPMA0.9870(0.006) VisCtx0.9939(0.005) Fitting discount functions using neuroimaging data Discount factor StdErr ACC0.1099(0.132) MPFC0.0000NA NAc0.9592(0.014) PCC0.9437(0.014) PCu0.9547(0.011) β = 0.963 (0.004) 0.963 25 = 0.39 δ= 0.990 (0.003) 0.990 25 = 0.78 N(t) = β d R + β d' R' + X(t)·θ + ε(t) N(t) = δ d R + δ d' R' + X(t)·θ + ε(t) β systems  systems

Fitting modular exponential discount functions using neuroimaging data. Discount factorStdErr ACC0.1099(0.132) MPFC0.0000NA NAc0.9592(0.014) PCC0.9437(0.014) PCu0.9547(0.011) AntInsL0.9908(0.007) AntInsR0.9871(0.006) BA10L1.0047(0.011) BA10R0.9953(0.010) BA460.9870(0.008) BA9440.9913(0.006) PCC0.9926(0.006) PParL0.9970(0.005) PParR0.9959(0.005) SMAPMA0.9870(0.006) VisCtx0.9939(0.005)  systems  systems

What determines immediacy? Is mesolimbic reward activation associated with relatively “early” (or earliest) options? or Do juice and money have different discount functions? or Does thirst invoke more intense discounting?

Summary of neuroimaging evidence One system associated with midbrain dopamine neurons (mesolimbic dopamine system) discounts at a high rate. Second system associated with lateral prefrontal and posterior parietal cortex discounts at a low rate. Combined function of these two systems accounts for decision making consistently across choice domains, including non-exponential discounting regularities.

Outline 1.Experimental evidence for dynamic inconsistency. 2.Theoretical framework: quasi-hyperbolic discounting. 3.Field evidence: dynamic decisions. 4.Neuroscience: –Mesolimbic Dopamine System (emotional, impatient) –Fronto-Parietal Cortex (analytic, patient) 5.Neuroimaging evidence –Study 1: Amazon gift certificates –Study 2: juice squirts 6.Policy 7.The Age of Reason

6. Policy Defaults in the savings domain Welcome to the company If you don’t do anything – You are automatically enrolled in the 401(k) – You save 2% of your pay – Your contributions go into a default fund Call this phone number to opt out of enrollment or change your investment allocations

Madrian and Shea (2001) Choi, Laibson, Madrian, Metrick (2004) Automatic enrollment Standard enrollment

Employees enrolled under automatic enrollment cluster at default contribution rate. Fraction of Participants at different contribution rates: Default contribution rate under automatic enrollment

Participants stay at the automatic enrollment defaults for a long time. Fraction of Participants Hired Under Automatic Enrollment who are still at both Default Contribution Rate and Asset Allocation Company B Company C Company D Fraction of Participants Tenure at Company (Months)

Survey given to workers who were subject to automatic enrollment: “You are glad your company offers automatic enrollment.” Agree? Disagree? Enrolled employees: 98% agree Non-enrolled employees:79% agree All employees:97% agree Do people like a little paternalism? Source: Harris Interactive Inc.

The power of deadlines: Active decisions Carroll, Choi, Laibson, Madrian, Metrick (2004) Active decision mechanisms require employees to make an active choice about 401(k) participation. Welcome to the company You are required to submit this form within 30 days of hire, regardless of your 401(k) participation choice If you don’t want to participate, indicate that decision If you want to participate, indicate your contribution rate and asset allocation Being passive is not an option

Active Decision Cohort Standard enrollment cohort

2003 2004 2005 Simplified enrollment raises participation Beshears, Choi, Laibson, Madrian (2006)

Consumer policy Savings interventions have several key features –Effective (large behavior changes) –Scalable and inexpensive –Well-received by public Can we find similar types of policies for consumer goods? Problems with consumer policy –Employer can’t play such a central role –Consumer preferences are highly heterogeneous –People don’t like delegating most choices

Consumer Policy: Two reasons for optimism Most people say that they “want” and “plan” to live healthfully (nutrition, diet, sleep, smoking, etc…) Firms and workers have aligned incentives vis-à-vis health outcomes, so firms may be interested in greasing the rails of health behavior change

Possible directions: Change defaults –Make default portions smaller and more healthful (e.g. don’t bundle sandwich with potato chips) Make good behaviors easier –Put healthy snacks in vending machines (dried fruit/nuts replace candy; diet vs. sugar soda) –Design inviting (sunlit) stairways in office buildings Choose in advance (long-run preference is patient) –Employees place electronic lunch orders before 10 AM –Gyms encourage appointments or pre-commitments –Early decision regulations (e.g. tobacco)

Early Decision Regulations Beshears, Choi, Laibson, Madrian (2006) Regulations can tip the behavioral balance in favor of the (patient) long-run perspective and away from the (impatient) short-run perspective. If people followed their own long-run plans they would: –eat more fruit –smoke fewer cigarettes –drink less alcohol –make fewer wagers How would such regulations work? What are the costs and benefits?

General implications: In quasi-hyperbolic model, decisions made today for tomorrow implement patient preferences –β does not play a role, since β multiplies all future periods. –U t = u t +  u t+1   u t+2   u t+3  If society wants people to be patient, then it is optimal to allow consumers to choose future consumption (partially) in advance. We call this an “early decision.” If consumers have different but predictable preferences, “early decision” regulation may complement sin taxes. Can regulation implement early decisions?

A regulatory example: To buy cigarettes you must have a free “tobacco sticker,” available to adults who apply for one. Smokers who want to quit can destroy their sticker (it takes 4 wks to get a new one). Smokers who want to quit can fail to apply for a new sticker when their old one expires. System creates limited ability to pre-commit. If you don’t have a sticker it’s hard to smoke on impulse (need to find an enabling friend).

Conceptual linkages: Request sticker today so you can smoke in the future. Happy smokers will get sticker. Some ambivalent smokers won’t. System enables happy smokers to keep on smoking, and helps ambivalent smokers quit. Differential impact. Early Decision regulations primarily affect consumers whose long-run goals contradict their short-run impulses.

Other Early Decision regulations: Location-based regulations: –Restrict density of vendors Time-based regulations: –Restrict hours/days of availability Delay-based regulations: –Eliminate immediacy (e.g. mail-order) Self-regulation: –Consumers can constrain themselves (e.g. cigarette sticker or web-based self-rationing)

All work on the same principle: Restricting continuous availability forces consumers to decide for the future. So early perspective is given an advantage. If consumers want to smoke they can. But, if early preference is to quit, that preference can be (partly) enforced.

The path to hell... Unintended costs of early decisions. Encourages black market –But sin taxes may be an even greater inducement for black markets Reduces consumer autonomy/agency –Really a transfer of agency to early perspective from immediate perspective May induce perverse demand effects Creates inefficient nuisance costs for some consumers

Early decision regulations Long-run preferences tend to be patient. Short-run preferences tend to be impatient. Early Decision regulations give an edge to our long- run preferences. Early Decision regulations help consumers follow through on their long-run goals, by making it harder to impulsively reverse long-run plans. Need small scale testing. “The proof’s in the field experiment.” The UK government has recently developed a plan that matches our proposal.

Implementation? Sweden already has a (weak) form of Early Decision regulation in the alcohol market. Extensions to other markets (cigarettes)? Try on small scale first. –E.g. a few small, isolated towns Need experimentation to determine which Early Decision regulations work (i.e. benefits outweigh costs).

7. The Age of Reason Agarwal, Driscoll, Gabaix, Laibson (2008)

(1,2) Home Equity Loans and Home Equity Credit Lines Proprietary data from large financial institutions 75,000 contracts for home equity loans and lines of credit, from March-December 2002 (all prime borrowers) We observe: –Contract terms: APR and loan amount –Borrower demographic information: age, employment status, years on the job, home tenure, home state location –Borrower financial information: income, debt-to-income ratio –Borrower risk characteristics: FICO (credit) score, loan-to- value (LTV) ratio

Home Equity Regressions We regress APRs for home equity loans and credit lines on: –Risk controls: FICO score and Loan to Value (LTV) –Financial controls: Income and debt-to-income ratio –Demographic controls: state dummies, home tenure, employment status –Age spline: piecewise linear function of borrower age with knots at age 30, 40, 50, 60 and 70. Next slide plots fitted values on age splines

What is the Channel for the Age Effect? Banks offer different APRs when the loan-to- value (LTV) ratio is: –less than 80 percent –between 80 and 90 percent –over 90 percent Borrowers estimate their LTV by estimating their house value Banks form their own LTV estimates “Rate-Changing Mistake”: when borrower and bank LTVs straddle two of these categories –E.g., borrower LTV 80.

Rate Changing Mistakes generate two sources of disadvantage for the customer: –If I underestimate my LTV (Loan-to-Value ratio), the bank can penalize me by deviating from its normal offer sheet. –If I overestimate my LTV (i.e., underestimate the value of my house), the bank will penalize me by not correcting my mistake and allowing me to borrow at too high a rate.

A Rate-Changing Mistake costs 125 to 150 basis points. Next slides plot: –Rate-Changing Mistakes by age –APRs for borrowers who do NOT make a Rate-Changing Mistake

For consumers who don’t make a Rate- Changing Mistake, age effect is small All the action is due to consumers who make a Rate-Changing Mistake –That is, consumers who over- or under-estimate their house values (relative to bank model) The propensity to make the mistake is U- shaped with age Hence, the final APR is U-shaped with age

Two channels by which RCM raise interest payments Direct channel: old and young borrowers may have a higher ex-ante likelihood of making a RCM Indirect channel: old and young borrowers may have a higher ex-poste likelihood of accepting the high interest rates they receive after they make a RCM (instead of shopping around)

(3) “Eureka”: Learning to Avoid Interest Charges on Balance Transfer Offers Balance transfer offers: borrowers pay lower APRs on balances transferred from other cards for a six-to- nine-month period New purchases on card have higher APRs Payments go towards balance transferred first, then towards new purchases Optimal strategy: make no new purchases on card to which balance has been transferred

Eureka: Predictions Borrowers may not initially understand / be informed about card terms Borrowers may learn about terms by observing interest charges on purchases, or talking to friends –We should see “eureka” moments: new purchases on balance-transfer cards should drop to zero (in the month after borrowers “figure out” the card terms) Study: 14,798 accounts which accepted such offers over the period January 2000 to December 2002

Seven other examples Three kinds of credit card fees: –Late payment –Over limit –Cash advance Credit card APRs Mortgage APRs Auto loan APRs Small business credit card APRs

U-shape for prices paid in 10 examples –Home equity loans –Home equity lines of credit –Eureka moments for balance transfers –Late payment fees –Over credit limit fees –Cash advance fees –Auto loans –Credit cards –Small business credit cards –Mortgages

Salthouse Studies – Memory and Analytic Tasks Source: Salthouse (forth.)

Dementia Ferri et al 2005 Prevalence of dementia: 60-64:0.8% 65-69:1.7% 70-74:3.3% 75-79:6.5% 80-84: 12.8% 85+: 30.1%

Outline 1.Motivating experimental evidence 2.Theoretical framework 3.Field evidence 4.Neuroscience foundations 5.Neuroimaging evidence 6.Policy discussion Defaults Deadlines Simplicity (make it easy) A copy of these slides will soon be available on my Harvard website.

Should Defaults Influence Economic Outcomes? Standard neoclassical theory: If transactions costs are small and stakes are large, defaults should not influence rational consumers. In practice, defaults make an enormous difference: –Organ donation –Car insurance –Car purchase options –Consent to receive e-mail marketing –Savings –Asset allocation

Overview of defaults 1.Defaults affect all saving and asset allocation outcomes 2.Four psychological factors jointly contribute to the default effect 3.How can we identify optimal defaults 4.Alternative interventions (education) is much less effective

1. Defaults Affect Saving and Asset Allocation i. Participation ii. Contribution rates iii. Asset allocation iv. Pre-retirement distributions v. Decumulation / annuitization

Participation, Contribution rates, and Asset Allocation Automatic Enrollment in a US 401(k) plan Welcome to the company If you don’t do anything… – You are automatically enrolled in the 401(k) – You save 2% of your pay – Your contributions go into a money market fund Call this phone number to opt out of enrollment or change your investment allocations

Madrian and Shea (2001) Choi, Laibson, Madrian, Metrick (2004)

Employees enrolled under auto-enrollment cluster at the default contribution rate. Default contribution rate under automatic enrollment

Participants stay at the automatic enrollment defaults for a long time.

Automatic enrollment Participants hired under automatic enrollment tend to stay at the automatic enrollment defaults (about 75%) –Default saving rates –Default asset allocation Automatic enrollment results suggest that employees are passive

Automatic enrollment: Conclusions Automatic enrollment dramatically increases 401(k) participation Participants hired under automatic enrollment tend to stay at the automatic enrollment defaults Similar default effects are observed for –cash distributions at separation –saving rates at match thresholds

Additional evidence on Asset Allocation Private account component of Swedish Social Security system (Cronqvist and Thaler, 2004) –At inception, one-third of assets are invested in the default fund –Subsequent enrollees invest 90% of assets in the default fund Company match in employer stock (Choi, Laibson and Madrian, 2005b, 2007)

The Flypaper Effect in Individual Investor Asset Allocation (Choi, Laibson, Madrian 2007) Studied a firm that used several different match systems in their 401(k) plan. I’ll discuss two of those regimes today: Match allocated to employer stock and workers can reallocate –Call this “default” case (default is employer stock) Match allocated to an asset actively chosen by workers; workers required to make an active designation. –Call this “no default” case (workers must choose) Economically, these two systems are identical. They both allow workers to do whatever the worker wants.

Consequences of the two regimes Default for Match is Employe r StockNo-Default Own Contributions to Employer Stock 23%20% Matching Contributions to Employer Stock 95%27% Total Contributions to Employer Stock 56%23%

Cash Distributions What happens to savings plan balances when employees leave their jobs? Employees can request a cash distribution or roll balances over into another account –Balances >$5000: default leaves balances with former employer –Balances <$5000: default distributes balances as cash transfer Vast majority of employees accept default (Choi et al. 2002, 2004a and 2004b) When employees receive small cash distributions, balances typically consumed (Poterba, Venti and Wise 1998)

Post-Retirement Distributions Social Security –Joint and survivor annuity (reduced benefits) Defined benefit pension –Annuity –Lump sum payout if offered Defined contribution savings plan –Lump sum payout –Annuity if offered

Defined Benefit Pension Annuitization Annuity income and economic welfare of the elderly –Social Security replacement rate relatively low on average –17% of women fall into poverty after the death of their spouse (Holden and Zick 2000) For married individuals, three distinct annuitization regimes –Pre-1974: no regulation –ERISA I (1974): default joint-and-survivor annuity with option to opt-out –ERISA II (1984 amendment): default joint-and- survivor annuity, opting out required notarized permission of spouse

Defined Benefit Pension Annuitization Effect of joint-and-survivor default on annuitization –Pre-1974: Less than half of married men have joint-and-survivor annuity –Post-ERISA (I + II): joint-and-survivor annuitization increases 25 percentage points (Holden and Nicholson 1998) –Post-1984 amendments: joint-and-survivor annuitization increases 5 to 10 percentage points (Saku 2001)

i.Financial illiteracy ii.Endorsement iii.Complexity iv.Present-bias Four psychological factors contribute to the default effect

i. Financial illiteracy John Hancock Financial Services Defined Contribution Plan Survey (2002) 38% of respondents report that they have little or no financial knowledge 40% of respondents believe that a money market fund contains stocks Two-thirds of respondents don’t know that it is possible to lose money in government bonds Respondents on average believe that employer stock is less risky than a stock mutual fund Two-thirds report that they would be better off working with an investment advisor than managing investments solo

Subjects allocate $10,000 among four funds Randomly choose two subjects to receive any positive portfolio return during the subsequent year Eliminate variation in pre-fee returns –Choose among S&P 500 index funds Unbundle services from returns –Experimenters pay out portfolio returns, so no access to investment company services Financial illiteracy among Wharton MBA’s Choi, Laibson, Madrian (2006)

One year of index fund fees on a $10,000 investment

Experimental conditions Control –Subjects receive only four prospectuses –Prospectuses are often the only information investors receive from companies Fees transparency treatment –Eliminate search costs by also distributing fee summary sheet (repeats information in prospectus) Returns treatment –Highlight extraneous information by distributing summary of funds’ annualized returns since inception (repeats information in prospectus)

Fees paid by control groups (prospectus only) Minimum Possible Fee Maximum Possible Fee t-test: p=0.5086 N = 83 N = 30 $443: average fee with random fund allocation 0% of College Controls put all funds in minimum-fee fund 6% of MBA Controls put all funds in minimum-fee fund

Ranking of factor importance MBA controls 1.Fees 2.1-year performance 3.Performance since inception 4.Investment objectives 5.Desire to diversify among funds 6.Brand recognition 7.Performance over different horizon 8.Past experience with fund companies 9.Quality of prospectus 10.Customer service of fund 11.Minimum opening balance College controls 1.1-year performance 2.Performance since inception 3.Desire to diversify among funds 4.Investment objectives 5.Quality of prospectus 6.Performance over different horizon 7.Brand recognition 8.Fees 9.Customer service of fund 10.Minimum opening balance 11.Past experience with fund companies

Effect of fee treatment (prospectus plus 1-page sheet highlighting fees) t-tests: MBA: p=0.0000 College: p=0.1451 N = 83 N = 30N = 29N = 85 ** 10% of College treatment put all funds in minimum-fee fund 19% of MBA treatment put all funds in minimum-fee fund

Ranking of factor importance MBA fee treatment 1.Fees 2.1-year performance 3.Performance since inception MBA controls 1.Fees 2.1-year performance 3.Performance since inception College fee treatment 1.Fees 2.1-year performance 3.Performance since inception College controls 1.1-year performance 2.Performance since inception 3.Desire to diversify among funds

Returns treatment effect on average returns since inception N = 83 N = 30N = 28N = 84 t-tests MBA: p=0.0055 College: p=0.0000 **

Returns treatment effect on fees N = 83 N = 30N = 28N = 84 t-tests MBA: p=0.0813 College: p=0.0008 **

Ranking of factor importance MBA return treatment 1.1-year performance 2.Performance since inception 3.Fees MBA controls 1.Fees 2.1-year performance 3.Performance since inception College return treatment 1.Performance since inception 2.1-year performance 3.Desire to diversify among funds College controls 1.1-year performance 2.Performance since inception 3.Desire to diversify among funds

Lack of confidence and fees N = 64N = 46N = 36N = 136 t-tests: MBA 1 vs. 2, p=0.2013; MBA 1 vs. 3, p=0.0479; College 1 vs. 2, p=0.2864; College 1 vs. 3, p=0.3335 N = 50N = 5 *

We conducted a similar experiment with Harvard staff as subjects In this new version we have 400 subjects (administrators, faculty assistants, technical personal, but not faculty) We give every one of our subjects $10,000 and rewarded them with any gains on their investment –$4,000,000 short position in stock market

Data from Harvard Staff Control Treatment Fee Treatment 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund 9% of Harvard staff in Fee Treatment put all $$$ in low-cost fund $494 $518 Fees from random allocation $431

ii. Endorsement A non-zero default is perceived as advice Evidence –Elective employer stock allocation in firms that do and do not match in employer stock (Benartzi 2001, Holden and Vanderhei 2001, and Brown, Liang and Weisbenner 2006) –Asset allocation of employees hired before automatic enrollment (Choi, Laibson, Madrian 2006)

Asset Allocation Outcomes of Employees Who are Not Subject to Automatic Enrollment Any balances in default fund All balances in default fund Company D Hired before, participated before AE 13%2% Choi, Laibson, and Madrian (2007)

Asset Allocation Outcomes of Employees Who are Not Subject to Automatic Enrollment Any balances in default fund All balances in default fund Company D Hired before, participated before AE 13%2% Hired before, participated after AE 29%16% Choi, Laibson, and Madrian (2007)

Automatic Enrollment and Asset Allocation Outcomes Any balances in default fund All balances in default fund Company A Hired before AE9.8%1.4% Hired after AE: non- default Company D Hired before AE18.2%5.2% Hired after AE: non- default

Automatic Enrollment and Asset Allocation Outcomes Any balances in default fund All balances in default fund Company A Hired before AE9.8%1.4% Hired after AE: non- default 86.1%61.1% Company D Hired before AE18.2%5.2% Hired after AE: non- default 71.3%30.8%

iii. Complexity Complexity  delay Psychology literature (Tversky and Shafir 1992, Shafir, Simonson and Tversky 1993, Dhar and Knowlis 1999, Iyengar and Lepper 2000 ) Savings literature: each additional 10 funds produces a 1.5 to 2.0 percentage point decline in participation (Iyengar, Huberman and Jiang 2004) Also results on complexity generating more conservative asset allocation (Iyengar and Kamenica 2007). Quick enrollment experiments

Complexity and Quick Enrollment Conceptual Idea –Simplify the savings plan enrollment decision by giving employees an easy way to elect a pre- selected contribution rate and asset allocation bundle Implementation at Company D –New hires at employee orientation: 2% contribution rate invested 50% money market / 50% stable value Implementation at Company E –Existing non-participants: 3% contribution rate invested 100% in money market fund

Complexity and Quick Enrollment Conceptual Idea –Simplify the savings plan enrollment decision by giving employees an easy way to elect a pre- selected contribution rate and asset allocation bundle Implementation at Company D –New hires at employee orientation: 2% contribution rate invested 50% money market / 50% stable value –Existing non-participants: employee selects contribution rate invested 50% money market / 50% stable value Implementation at Company E –Existing non-participants: 3% contribution rate invested 100% in money market fund

iv. Present-Biased Preferences Self control and savings outcomes: Why do today what you can put off until tomorrow? (Laibson 1997; O’Donoghue and Rabin 1999; Diamond and Koszegi 2003) Framework: exponential discounting with an additional factor, β<1, that uniformly down- weights the future. U t = u t +  u t+1   u t+2   u t+3 

Laibson, Repetto, and Tobacman (2004) Use MSM to estimate discounting parameters: –Substantial illiquid retirement wealth: W/Y = 3.9. –Extensive credit card borrowing: 68% didn’t pay their credit card in full last month Average credit card interest rate is 14% Credit card debt averages 13% of annual income –Consumption-income comovement: Marginal Propensity to Consume = 0.25 (i.e. consumption tracks income)

LRT Simulation Model Stochastic Income Lifecycle variation in labor supply (e.g. retirement) Social Security system Life-cycle variation in household dependents Bequests Illiquid asset Liquid asset Credit card debt Numerical solution (backwards induction) of 90 period lifecycle problem.

LRT Results: U t = u t +  u t+1   u t+2   u t+3   = 0.70 (s.e. 0.11)  = 0.96 (s.e. 0.01) Null hypothesis of  = 1 rejected (t-stat of 3). Specification test accepted. Moments: Empirical Simulated (Hyperbolic) %Visa: 68%63% Visa/Y: 13%17% MPC: 23%31% f(W/Y): 2.62.7

Procrastination (assume  ½,  = 1). Suppose you can join the plan today (effort cost $50) to gain delayed benefits $20,000 (e.g. value of match) Every period you delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: -50 + ½ [0] = -50 Join t+1: 0 + ½ [-50 - 10] = -30 Join t+2: 0 + ½ [-50 - 20] = -35 Join t+3: 0 + ½ [-50 - 30] = -40

Interaction with financial illiteracy Consider someone with a high level of financial literacy, so effort cost is only $10 (not $50) As before, every period of delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: -10 + ½ [0] = -10 Join t+1: 0 + ½ [-10 - 10] = -10 Join t+2: 0 + ½ [-10 - 20] = -15 Join t+3: 0 + ½ [-10 - 30] = -20

Interaction with endorsement and complexity Consider a plan with a simple form, or an endorsed form, so the effort cost is again only $10 (not $50) As before, every period of delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: -10 + ½ [0] = -10 Join t+1: 0 + ½ [-10 - 10] = -10 Join t+2: 0 + ½ [-10 - 20] = -15 Join t+3: 0 + ½ [-10 - 30] = -20

Procrastination in retirement savings Choi, Laibson, Madrian, Metrick (2002) Survey – Mailed to 590 employees (random sample) – 195 usable responses – Matched to administrative data on actual savings behavior Consider a population of 100 employees – 68 report saving too little – 24 of 68 plan to raise 401(k) contribution in next 2 months – Only 3 of 24 actually do so in the next 4 months

3. Optimal Defaults – public policy Mechanism design problem in which policy makers set a default for agents with present bias (Carrol, Choi, Laibson, Madrian and Metrick 2007) Defaults are sticky for three reasons –Cost of opting-out of the default –Cost varies over time  option value of waiting –Present-biased preferences  procrastination

Basic set-up of problem Specify behavioral model of households –Flow cost of staying at the default –Effort cost of opting-out of the default –Effort cost varies over time  option value of waiting to leave the default –Present-biased preferences  procrastination Specify (dynamically consistent) social welfare function of planner (e.g., set β=1) Planner picks default to optimize social welfare function

Optimal ‘Defaults’ Two classes of optimal defaults –Automatic enrollment Optimal when employees have relatively homogeneous savings preferences (e.g. match threshold) and relatively little propensity to procrastinate –“Active Decision” — require individuals to make a decision (eliminate the option to passively accept a default) Optimal when employees have relatively heterogeneous savings preferences and relatively strong tendency to procrastinate Key point: sometimes the best default is no default.

1 0 Beta Active Decision Center Default Offset Default 30% 0% Low Heterogeneity High Heterogeneity

Lessons from theoretical analysis of defaults –Defaults should be set to maximize average well-being, which is not the same as saying that the default should be equal to the average preference. –Endogenous opting out should be taken into account when calculating the optimal default. –The default has two roles: causing some people to opt out of the default (which generates costs and benefits) implicitly setting savings policies for everyone who sticks with the default

The power of deadlines: Active decisions Choi, Laibson, Madrian, Metrick (2007) Active decision mechanisms require employees to make an active choice about 401(k) participation. Welcome to the company You are required to submit this form within 30 days of hire, regardless of your 401(k) participation choice If you don’t want to participate, indicate that decision If you want to participate, indicate your contribution rate and asset allocation Being passive is not an option

Active decisions: conclusions Active decision raises 401(k) participation. Active decision raises average savings rate by 50 percent. Active decision doesn’t induce choice clustering. Under active decision, employees choose savings rates that they otherwise would have taken three years to achieve. (Average level as well as the entire multivariate covariance structure.)

New directions for defaults Defaults for savings rate escalation Defaults with high savings rates Defaults for lifecycle rebalancing Defaults for annual rebalancing Defaults for employer stock Defaults at separation Defaults for annuitization Individualized defaults (savings rate and asset allocation) Defaults for employees not covered by DB/DC plans Defaults for investment of tax refunds

Public Policy and Defaults: Annuitization Interesting aspects of the joint-and-survivor Social Security annuity default discussed earlier –Differentiated default: singles vs. marrieds –Annuity election irrevocable –Implicit deadline—must either accept or opt-out of the default before receiving pension payments Note –Largely homogenous preferences –Similarities to active decision approach –Reduced scope for procrastination –Those who do opt-out of joint-and-survivor annuity appear to have economically sound reasons for doing so (Johnson, Uccello and Goldwyn 2003)

Public Policy and Defaults: Annuitization Thinking more generally about retirement income annuitization and defaults in a defined contribution world –Understanding annuitization options is complicated for financial novices  strong endorsement effect likely –Taking a lump-sum is the only way to preserve option value –BUT, lump-sums  potential self-control problems Annuitization and defined contribution savings plans –Required annuitization up to $200,000? –Default annuitization up to $200,000? –Active decision? (Due to irreversibility.)

Public Policy and Defaults: Pre-Retirement Cash Distributions Cash distribution default for balances of less than $5000  leakage from retirement savings Response: for balances of $1000-$5000 –Employers can maintain these balances, or –Employers can roll them over into an IRA Default asset allocation for IRA rollover must preserve principal

Public Policy and Defaults: Match in Employer Stock Employer stock in defined contribution savings plan is only weakly regulated Employer stock in defined benefit pension plan is capped at 10% of total assets Strong evidence that employees misperceive the risks of employer stock (familiarity bias) Policy alternatives –Preclude employers from defaulting matching contributions into employer stock (e.g., preclude companies from choosing a single life annuity as a default for married individuals) –Create annual default rebalancing out of company stock

4. Alternative Policies Paying employees to save Educating employees

$100 bills on the sidewalk Choi, Laibson, Madrian (2004) Employer match is an instantaneous, riskless return on investment Particularly appealing if you are over 59½ years old – Have the most experience, so should be savvy – Retirement is close, so should be thinking about saving – Can withdraw money from 401(k) without penalty We study seven companies and find that on average, half of employees over 59½ years old are not fully exploiting their employer match – Average loss is 1.6% of salary per year Educational intervention has no effect

Financial education Choi, Laibson, Madrian, Metrick (2004) Seminars presented by professional financial advisors Curriculum: Setting savings goals, asset allocation, managing credit and debt, insurance against financial risks Seminars offered throughout 2000 Linked data on individual employees’ seminar attendance to administrative data on actual savings behavior before and after seminar

Effect of education is positive but small Seminar attendeesNon- attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate 28%8%5% Change fund selection47%15%10% Change asset allocation 36%10%6%

Financial education effects are small Seminar attendees have good intentions to change their 401(k) savings behavior, but most do not follow through Financial education alone will not dramatically improve the quality of 401(k) savings outcomes Choi et al (2005) study the effect of the Enron, Worldcom, and Global Crossing scandals on employer stock holding No net sales of employer stock in reaction to these news stories These scandals did not affect the asset allocation decisions of new hires. These hires did not affect the asset allocation decisions of new hires at other Houston firms.

Conclusion Defaults are not neutral for four reasons: –Investors are not financially literate –Investors display an endorsement effect –Investors respond adversely to complexity –Investors are prone to procrastinate Employers/institutions will influence savings outcomes through the choice of defaults (whether the institution wants to do this or not) We should devote more effort to the analysis of how we pick defaults.

Instantaneous Gratification: Behavior, Models, and Retirement Savings Policy David Laibson Harvard University and NBER July 16, 2008.

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Instantaneous Gratification: Behavior, Models, and Retirement Savings Policy David Laibson Harvard University and NBER July 16, 2008.

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