# Presentation: 057614

## Household Borrowing High and Lending Low:Neoclassical v. Behavioral Explanations

- Jonathan Zinman
- Dartmouth College
- October 26, 2006

## Motivation

- Average U.S. credit card holder spends about $100 per year by:
- Borrowing high on credit cards
- Lending low in demand deposit accounts
*Why are households borrowing high and lending low (BHLL)?*

## Motivation:Rise of Behavioral Explanations

- Several recent papers* have argued that:
- BHLL is a puzzle for neoclassical models of consumer choice**
- BHLL requires psychological explanations (mental accounting)
- Explanations: mental accounting as *spending* control device
- Crux of these arguments is that BHLL represents *foregone arbitrage*
- Consumer leaves money on table, ergo he is not *homo economicus*
- * Gross and Souleles (2002); Bertaut and Haliassos (2002); Bogan and Hamammi (2004); Haliassos and Reiter (2005)
- ** Laibson et al (2005) study time-inconsistent preferences as a explanation for BHLL w/r/t *illiquid* assets– different issue since illiquidity actually provides commitment

## Project Plan

- Paper #1 (“...BHLL Under No-Arbitrage”):
- Behavioral argument is fundamentally flawed: BHLL is not foregone arbitrage because demand deposits and credit card debt are (very) different assets
- Expand set of stylized facts by focusing on explicit and implicit costs of BHLL
- Not just the household balance sheet positions
- Show that stylized facts are easy to reconcile with old-fashioned liquidity motives
- Money demand literature
- Corporate finance 101

## Project Plan

- Work-in-progress (joint with Victor Stango, Tuck):
- Theoretical and empirical basis for explaining BHLL with mental-accounting-for-spending-control is very thin
- So let’s test it by studying high-frequency payment and spending decisions:
- Need linked transaction, account, income data
- (currently acquiring)
- Identify average prevalence, importance of MA rules
- Identify individual-specific decision rules
- Explore correlations between decision rules and financial outcomes (spending, BHLL)

## Talk Today

- Paper #1
- (get stylized facts and plausibility of competing explanations straight)
- Set stage for work-in-progress

## Measuring BHLL

- Recall basic notion behind BHLL as a “puzzle”: households should use low-yielding assets to pay down relatively expensive debt. This implies:
**(1)***** Unadjusted***** *****Wedge*****i ****= min[*****Credit Card Debt*****i*****, Demand Deposits*****i****]**
- Where “i” indexes households
- Demand Deposits = “Money” (Mulligan & Sala-i-Martin 2000)
- Then what we really care about is:
**(2) *****Unadjusted Cost*****i***** *****= max[0, *****Unadjusted Wedge******(r****i****c**** – r****t****a****)]**
- Where **r****i****c **is the household-specific (marginal) credit card rate
** ****r****t****a **is the aggregate average return on demand deposits
- Lack microdata on this or other asset yields

## Measuring BHLL

- Data for Paper #1 = SCF
- Great for measuring prevalence
- Good for measuring distribution issues
- A la Massoud et al
- Pretty good for measuring intensity/costs
- Depending on view of credit card reporting accuracy, and how easy to correct for any inaccuracy
- Pretty poor for testing competing explanations for BHLL
- Work-in-progress with new data

## My Argument in Paper #1

- Foregone arbitrage view of BHLL is fundamentally flawed
- Arbitrage as commonly defined and applied refers to *riskless* profit opportunities from the (near-) simultaneous purchase and selling of an *identical* asset
- But credit cards and demand deposits are *not* identical assets (quite the contrary), consequently using liquid assets to pay down credit card debt *does* entail risk

## My Argument:BHLL Involves Different Assets

- Cash is still king as medium of exchange
- Credit cards still can not be used to directly settle most household expenditures
- Demand deposits readily convertible to cash; readily used for payment
- Lines of credit can be converted to cash even when cards not accepted (“cash advances”), but:
- Costly for routine transactions
- Risky (rationed) for contingencies

## My Argument in a Nutshell

- Credit card is (very) imperfect substitute for cash/debitable account as a payment device
- Therefore BHLL is not *foregone* arbitrage
- And we’re done.
- If you have neoclassical priors, there is no “puzzle”.... there must be some rational reason(s) why consumers BHLL
- Even if consumers start off on wrong foot, BHLL is high-frequency decision: opportunities for learning (Lucas 1986)

## Now what?

- What are exactly are neoclassical reasons for BHLL?
- With specific neoclassical explanations in hand, can we say something about whether they actually fit the data?

## BHLL Explanation

- Money demand lit suggests that:
*Liquidity motives *drive BHLL
- Given the friction that credit cards are less widely accepted as a payment device
- Given some demand for credit (consumption smoothing)
- Need to hold liquid assets for transaction purposes

## BHLL Explanation

- Crux of liquidity motives perspective: neoclassical consumer optimizes portfolio w/r/t liquidity needs arising from payments and credit frictions
- Follows intuitively (though not totally formally) from 5 decades of research in finance and money demand
- Money demand: frictions give liquid assets implicit value » hold them even though they are “rate-of-return-dominated”
- Theory and empirics have long focused on high-yielding assets as the opportunity cost margin, but this is changing thanks to Telyukova and Wright (2006); Telyukova (2006)
- Corporate finance analogy: agent facing a liquidity problem and uncertain access to credit in bad state may rely on asset management rather than liability management

## So Why Are Consumers BHLL?

- Very generally, *transaction demand*
- Routine
- Precautionary
- Also, (and more specifically)– several *features of common contracts*:
- Minimum balance checking accounts (Stavins)
- Downpayment requirements (Faig and Shum)
- Overdraft penalties (Bar-Ilan; Fusaro)
- Imperfect enforcement » strategic default (Lehnert and Maki)

## Do Liquidity Motives Fit the Data? 

*Liquidity motives*
- Payment frictions create implicit value for liquid assets » **nominal yield understates true yield**
- But does this set of explanations fit the data?

## Related Work in theLiquidity Motives Tradition

- Telyukova (2006): calibrates stochastic partial equilibrium model with neoclassical agents and a precautionary motive for BHLL.... BHLL consistent with the model
- I reach similar conclusion with less formal approach:
- broader set of motives/incentives
- rationality of BHLL is *easy* to see
- like Tobin (1957) on BHLL w/r/t to installment debt

## Do BHLL Portfolios Pass a Neoclassical Smell Test?

- I explore by estimating BHLL cost distributions
- Prevalent, substantial (implausibly high) BHLL costs symptomatic of deviations from neoclassical models
- Economically insignificant costs clearly consistent with neoclassical models
- I do this in 3 steps....

## Neoclassical Smell Test #1

- Approach #1. Calculate upper bound on true BHLL costs (assume demand deposits have no implicit value)
- Table 1 shows:
- 23-30% of households with credit cards lose > $10 per month
- Distribution of costs for households who do BHLL is very similar to households with credit cards who don’t BHLL (but do hold money instead of higher-yielding, safe assets)

## Slide 20

| Table 1. Upper Bound (Unadjusted) Monthly Costs of Borrowing High and Lending Low  |  |  |  |  |
| --- | --- | --- | --- | --- |
| Unadjusted Wedge =  | min [money, credit card debt]  | min[money, credit card debt*3]  | min[money, credit card debt*3]  | money  |
| Sample  | Credit card holders  | Credit card holders  | Credit card borrowers  | Non-borrowers  |
| Number of households  | 3,476 | 3,476 | 1,461 | 2,015 |
|   | (1) | (2) | (3) | (4) |
| Mean  | 9.46 | 15.38 | 27.38 | 30.87 |
| Median  | 0 | 0 | 11.14 | 9 |
| 75th percentile  | 8.61 | 13.97 | 30.42 | 26.38 |
| 90th percentile  | 26.92 | 40.5 | 68.15 | 68.75 |
| Proportion cost > $0  | 0.49 | 0.49 | 0.86 | 0.95 |
| Proportion cost > $10  | 0.23 | 0.3 | 0.53 | 0.48 |
| Proport. cost/inc > .01  | 0.06 | 0.1 | 0.17 | 0.12 |
| Median Wedge if Wedge>0  | 1,100 | 1,800 | 1,800 | 15,600 |
| Wedge = assets  | 0.52 | 0.64 | 0.64 | 1 |
| Wedge= card debt(*3)  | 0.49 | 0.37 | 0.37 | 0 |

## Neoclassical Smell Test #2

- Approach #2:  Show that cost savings from “pay down and charge up strategy” are small
- Commensurate with time/bandwidth costs

## Table 2. Estimated Cost Savings From An Extra Credit Card Payment Per Month

## Neoclassical Smell Test #3

- Rough accounting for routine & precautionary demands; contract incentives
- Perhaps 7-10% of households incur nontrivial real costs by BHLL

## Table 3. Adjusted BHLL Costs

## Distributional Consequences?

- Remaining concern:
- What if those making substantial BHLL mistakes can ill afford it?
- Does not seem to be the case;  conditional on other demographics, Table 4 shows:
- Lower income hh’s *less* likely to incur big BHLL costs
- Lower education hh’s *less* likely to incur big BHLL costs

## Table 4. Who Borrows High and Lends Low?

## Paper #1 Findings in Brief

- BHLL is not a puzzle *per* *se* for neoclassical models of consumer choice
- BHLL is not foregone arbitrage because credit cards and demand deposit different assets
- In fact viewing BHLL through lenses of rich literatures in money demand and finance suggest several rational motives/incentives
- Apparent wedge between borrowing costs and lending yields need not be a real wedge
- Descriptive evidence is *consistent *with BHLL being a rational response to frictions in payments and credit markets
- Also little reason for concern that those who do pay substantial BHLL costs are vulnerable

## In-Progress Approach: Horserace

- Several papers offer alternative (neoclassical v. behavioral) explanations for BHLL, but which does *better/best* job of fitting the data?
- Can test competing models by:
- Focusing on the right high-frequency decisions
- Getting the right data to test alternative drivers of those decisions

## In-Progress: Key Questions

- Are departures from neoclassical optimization prevalent in high-frequency choice?
- Mental accounting
- Boundedly rational decision rules
- Do any departures have impacts/implications for:
- Borrowing/spending (vs. saving)
- Portfolio choice (BHLL)

## In-progress: Empirical Strategy

- Focus on debit v. credit decision at transaction-level frequency
- Similar attributes as payment devices » relatively easy to identify mental accounting motives with the right data (Zinman, “Debit or Credit” 2004.0)
- MA is plausible, theoretically appealing on this margin (Prelec and Loewenstein 1998, Thaler 1999)
- Aggregate transaction patterns fit an MA model (Reda 2003)
- And of course h/hold portfolios (BHLL) fit MA models
- Boundedly rational decision rules plausible, given potentially low pecuniary stakes of the payment choice *per se*

## The Catch:Enormous Data Demands

- Need linked transaction, account, and spending data
- Options:
- *Consumer panel with all of this (Lightspeed)
- Deposit institution that issues credit cards
- Progress? yes (we think).
- Data? not quite yet.

## To Be Continued...Other Next Steps in Studying BHLL

- **Test whether a neoclassical model or behavioral alternative(s) does a *better* job of explaining behavior
- Can do this with transaction + account-level data
- Other BHLL combos on h/hold balance sheets:
- But stock of liquid assets may bound extent to which can economize on other debts
- Liquidate other assets to pay down debts? Transaction costs should matter.
- Expand study to those without credit cards:
- Relatively poor... relatively incomplete balance sheets in SCF (cash, payday borrowing)?

## Plan for Paper and Talk

- BHLL measurement details
- Elaboration on main argument and findings:
- 2. The absence of arbitrage
- Limited credit card acceptance
- Costly cash advances
- Upper bound on BHLL mistakes
- Adjusting for liquidity motives & incentives
*Who* is BHLL?
- Next steps

## Measuring BHLL

- Why define BHLL w/r/t credit card debt?
- Most expensive among prevalent sources of consumer credit (median rates of 14.4% in 2001; 11.5% in 2004)
- Arguably most important source of marginal credit for U.S. households
- Why define BHLL w/r/t to demand deposits?
- Other financial assets less liquid » estimating wedge w/r/t financial assets requires (more) ad-hoc adjustments for transaction costs, timing considerations
- Explicit returns on demand deposits relatively homogeneous » relying on aggregate average yields less problematic

## Measuring BHLL

- Data source: primarily 2004 Survey of Consumer Finances (SCF)
- Most comprehensive source of microdata on household balance sheets
- Single snapshot
- Analysis sample: credit card holders
- More on this in concluding remarks
- I focus on BHLL *costs* (equation 2), not the $ amount of the *wedge *that could be used to pay down debt (equation 1)
*Wedge *has been focus of most other work on BHLL

## BHLL is notForegone Arbitrage

**Credit card is imperfect substitute for demand deposits as payment device:**
- Only 24% of consumer expenditure paid for by credit card in early 2000s
- Most major, recurring expenditures (loan, rent, utility payments) require cash or debitable accounts (ABA/Dove)
*So households need liquid assets for routine transactions*
- Households can get cash advances, but:
- Expensive: fees, high rates
- May be rationed: limited to fixed amount (per advance or per week), or to proportion of available credit
*So households need liquid assets for contingencies*

## But is BHLL Indicative of Traditional Rationality?

- BHLL is not foregone arbitrage, but....
- This insight should only shift our priors back to agnostic, or neoclassical
- What drives BHLL still an empirical question
- Again, BHLL also consistent with sophisticated (mental accounting) and unsophisticated (mistakes) “behavioral anomalies”
- These more likely manifest in substantial BHLL costs
- Minimal (real) BHLL clearly consistent with neoclassical models
- So: measure distributions of (real) BHLL costs

## Measuring BHLL CostsApproach #1: An Upper Bound

- Get upper bound by calculating (2) without adjusting for any implicit value of liquid assets
- Distribution of these costs suggests that there is a substantial right tail worth worrying about (Table 1)...
- 23%-30% pay more than $10/month by not using money to pay down card debt

## Table 1. Upper Bound (Unadjusted) Monthly Costs of Borrowing High and Lending Low 

## Measuring BHLL Costs, Approach #2: Savings from Aggressive Liquidity Mgmt

- What’s savings from reducing BHLL costs by closely managing the *Unadjusted Wedge*?  E.g.,:
- “Pay Down and Charge up” strategy
- “pay down”: make more the required 1 credit card payment per month
- then “charge up” by using card for payments
-  Additional payment incurs a transaction cost: time and attention if nothing else (Baumol-Tobin)

## Limited Benefits toMore Aggressive Liquidity Management

- What’s the benefit (= BHLL cost savings) to Pay Down and Charge Up?
- Table 2 shows that it is limited to a small fraction of the cost due to:
- Small fraction of chargeable expenditure
- Cost of “borrowing-to-charge”
- 75th percentile of savings only $5/month
- Again, $5 is upper bound: no adjustment for other BHLL motives (precautionary) or incentives
- more on these in a minute...
- <10% of card holders could save >$10/month

## Measuring BHLL Costs, Approach #3: Adjustments for Liquidity Motives

- 3rd approach: adjust *Wedge* for routine and precautionary transaction demands
- Accounting approach: simply subtract a proxy for the demanded amount(s) from the *Wedge*, then recalculate *Cost*
- But how measure household-specific transaction demands?
- Routine: subtract one month’s income, or recurring expenses (SCF has loans, rent)
- Precautionary: use Kennickell-Lusardi (2004) question– “About how much do you think you (and your family) need to have in savings for emergencies and other unexpected things that may come up?”
- Assumes pecking order of precautionary assets

## Other Incentives for BHLL:Financial Contracts

- Table 3 suggests that general transaction demand eliminates nontrivial BHLL for all but perhaps 7%-10% of card holders (or 25% max)
- But even this may overestimate prevalence & size of mistakes....
- Specific types of contracts offer incentives for BHLL:
- Minimum balance checking (Stavins 1999)
- Checking overdraft penalties (Fusaro 2005; Tufano et al 2005)
- Introductory pricing on balance transfers
- Downpayment constraints (Faig and Shum 2002)
- Strategic default under limited enforcement (Lehnert and Maki 2005; Dawsey and Ausubel 2004)

## Who is BHLL?

- Are those paying substantial BHLL costs “vulnerable” (=== poor, uneducated)?
- No...

## That’s All For Now...

- Thanks!

## Use Home Equity to Pay Down Credit Card Debt?

- Some stats from 2004 SCF:
- 13% of households with a credit card balance have a HELOC (vs. 12% of all h/holds)
- Median rate difference for those who have a credit card, and a HELOC balance (only see rates if have HELOC balance): 400bp
- Why not use HELOC to pay down cards?
- Option value of defaulting on unsecured
- Transaction costs of setting up HELOC

## Why BHLL?  Neoclassical v.Behavioral Explanations

- So which priors should we believe?
- “Behavioral”: mental accounting has some intuitive and anecdotal appeal, but:
- Little direct field evidence (especially re: spending control)
- Lab evidence is mixed
- Theory shows that mental accounting may promote, not curtail, (credit card) spending (Prelec and Loewenstein 1998; Thaler 1999)
- Neoclassical: rich money demand lit shows why and when agents hold “rate of return dominated” assets

## So Why are Consumers BHLL?

- So BHLL is not a puzzle *per se*
- Appears be ample motives, incentives for BHLL
- But.... does this mean that consumers are neoclassical?  Not necessarily.  BHLL also consistent with:
- Sophisticated behavioral rules (e.g. mental accounting)
- Mistakes
- Overvaluing liquidity (Campbell 2006)
- Underestimating borrowing costs (Ausubel 1991; Agarwal et al 2005)*
- * In interest of full disclosure, see also the less directly related Bertrand-Karlan-Mullainathan-Shafir-Zinman 2005; Stango-Zinman 2006