Panel 4 Discussion:

Credit, Wealth, and Consumption

Scott Schuh (WVU)

2024 FDIC Consumer Research Symposium

March 15, 2024
Observations common to both papers 

1. About consumption and...

 unique form of wealth (crypto)

 credit availability (limit increases)

2. Fun and enlightening!

 Interesting, well-done applications to important topics in the field

3. Forgot to cite me! 
One more (substantive) common observation

Gentle reminders about sampling theory...

1. Following do not guarantee representativeness:

 Large numbers of observations

 Demographics similar to national shares

 Customer of a very large bank, FI, FinTech, or data aggregator

2. Selection effects are:

 Always prevalent (even if we hope not)

 Usually unobserved and hard to identify

Especially without a representative sample for comparison

Best to concede limitations and argue data is relatively better!
Johnson et al, “Cryptocurrency”

Three things I really like

1. Unified assessment of crypto impact on real economy

2. Innovative use of rich, proprietary transactions data

3. Main result is that crypto is a “normal” asset!

(One thing I hate – I’m working on a very similar paper with a different data set but 

not as far along and don’t have a draft yet...)
(Semi-)Unified assessment – a brief history

Three Phases:

1. Payments – Bitcoin: “A Peer-to-Peer Electronic Cash System”

 “Free,” anonymous, private money (no inflation, rising BTC/$)

2. Blockchain – secure historical record storage

 Many other apps (healthy, assets, etc.); CBs  no payments (exc. CBDC)

3. Speculative investment – new “intrinsically worthless” assets

 Very high return, volatility; many new coins issued

This paper’s contribution: truly insightful linkage of #1 and #3!

 A2A transfers (BTC-to-$) first, then pay for expenditures

 But where are direct crypto payments (like BTC)?...
Innovative data use

1. Source – Large, rich proprietary data (not sole users)

 Great but still imperfect provision of all HH financial info

 Not quite representative (sorry, it’s not...)

 Next slide: representative data, direct measurement of crypto

2. Construction – A2A transaction flows  crypto funds for C, I, A

 BTC-to-DDA = withdrawals for expenditure (C), capital goods (I)

 DDA-to-BTC = deposits for financial investments (A)

3. Regressions – leverage events and heterogeneity

 Massive price appreciations  withdrawals identify certain C, I

 Regional crypto wealth  heterogeneity identifies home purchase, price
Evidence from representative survey (SCPC), p1

Cody Adams and Scott Schuh (2023), “U.S. Consumers Adoption and Use of Bitcoin” (updated version of Scott Schuh and Oz Shy (2016))
Evidence from representative survey (SCPC), p2

Cody Adams and Scott Schuh (2023), “U.S. Consumers Adoption and Use of Bitcoin” (updated version of Scott Schuh and Oz Shy (2016))
Main result: “normal” asset!

1. MPCs – crypto wealth ≈ traditional wealth (house, stocks)

 Apparently, volatility/uncertainty don’t diminish asset value!

2. Best results – my preferred comparison...

 MPC(crypto) = $0.07 (Table A.3, 2SLS, broad index)

 MPC(traditional) = $0.04-0.05 (literature; why can’t you estimate?)

3. Housing markets – crypto wealth effects on ownership, price

 Largely similar to effects of traditional wealth in literature

 Statistically significant but economically small in the aggregate?

QUESTION: Why emphasize differences rather than similarities?
Most important concerns – Johnson 

1. Spending regressions – not quite ideal

 Lagged Y ≠ E(Y) – expected income matters a lot!! (Gilyard-Schuh 2023)

 Omitting non-crypto wealth worries me

 Direct crypto payments missing

2. Crypto flows – clever but...how much measurement error?

 Get/use direct measures of crypto & flow values? – need more info!

3. Housing section – potentially cool but...another paper?

 Regressions lack literature’s standard housing market controls

4. Missed opportunity – learn about HH financial management

 How good are consumers at timing crypto investment (benefitting C, I)?
Yin, “Limit Learning”

Three things I really like

1. New evidence on link between limits and B, C

2. Improved inference from RCT + survey data

3. Thought-provoking theory about info in limit changes
Important new evidence

1. Not enough known! – Why do banks change limits?

 See Fulford and Schuh (2023, 2024) referee reports!...

 Policy proposals to prohibit unsolicited changes (require opt-in)

2. Limits increased – Sample of Chinese bank customers

 Standard tracking of B, C responses (6 mos) with transactions data

3. Extension – Similar to Aydin (2021/AER) experiment in Turkey 

 But investigates the role of information to consumers in bank action

RCT with differential information among customers

 Asks consumers!

 Conduct follow-up survey to elicit hidden info
Improved inference

1. Selection of customers – June 19-23

 Control versus treatment samples

2. Random notification – July 3

 T1 = standard bank limit increase announcement only

 T2 also gets more info: “limit-increase event”; “good credit”; “random”

3. Random survey offer – July 3-12 (~67% response, CNY 15)

 S=yes (half) gets 21 questions online (one mode)

 13 personal finances to enhance RCT data

 5 macro conditions to evaluate E(Y)

 3 hypothetical about limit changes (2), implicit discount rate measurement

 S=no (half)
Thought-provoking theory

1. Agent thoughts – what did they decide?

 BANK “credit-score model suggested higher limits” (p. 16)

 CONSUMER  bank is signaling my E(Yi) increased?

Increased B, C...and E(Y)? Only if bank knows more!

2. Agent actions – what did they do?

 BANK  raised limits of only specific consumers

 CONSUMERS  those with higher limits raised B, C, and allegedly E(Y)

 T1 increase > T2  clearly random notification had some effect

3. Questions – I’m less sure than the author...

 Q1: why would bank and consumer perspectives be so different?

 Q2: what does the typical consumer actually think?

 ANECDOTES -- Not higher E(Y)! “Want me to spend/borrow more, increase profits!”

 Elena Botella, formerly Capital One (2019, New Republic): “...push people into debt who 

would rather avoid it”

Elena Botella (2022), Delinquent: Inside America’s Debt Machine
Most important concerns – Yin 

1. Model too simple – Fulford and Schuh (2024) better suited

 Infinite horizon, RA/single preference, no R vs C, are not frontier

 Need life-cycle consumption with CC debt and CC payments

 Revolving versus convenience use is essential to identify preference (discount) heterogeneity!

 Match utilization (B/L) behavior over longer time 

2. Announcement interpretation – clever but unclear

 Other sensible interpretations of the limit increase?

Attitudes toward banks are negative and suspicious in US

3. Survey improvements – some potential improvements....

 Why not ask consumers how they interpreted limits (after they happened)?! 

 Ask consumers their own expected income (before the limit increase)

 Use multi-mode for those who don’t have/like online?

4. Missed opportunity – see last slide
LC BS models and data

• One-preference model can’t fit 

B and C

• Two-preference model can:

• Impatient (revolving) ββ = 0.885

• Patient (convenience) ββ = 0.965

• Scott Fulford and Scott Schuh (2024), “Credit Cards, 

Credit Utilization, and Consumption,” revise & 

resubmit

• Scott Fulford and Scott Schuh (2023), “Revolving and 

Convenience Use of Credit Cards: Evidence from U.S. 

Credit Bureau Data,” Journal of Money, Credit, and 

Banking, 55(7), 1667-1701.
Credit utilization (CU = B/L) 

• CU is remarkably stable

• Individual, life-cycle, and business 

cycle (not pictured)

• CU almost 100% back to fixed 

effect within 2 years

• Limit and income shocks similar...
Missed opportunity – “Show me the money, Jerry!”

• Plot actual Y data!

• Macro (GDP)

• Individual customers (Y)

• Study errors = Y – E(Y)