What Would You Do With $500? Spending Responses to Gains, Losses, - - PowerPoint PPT Presentation

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What Would You Do With $500? Spending Responses to Gains, Losses, - - PowerPoint PPT Presentation

Dec 13, 2023 248 likes •808 views

What Would You Do With $500? Spending Responses to Gains, Losses, News and Loans Andreas Fuster Greg Kaplan Basit Zafar Reserve Bank of New Zealand December 2017 Another Paper Estimating MPCs ? Approaches to estimating MPCs: 1. Revealed

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SLIDE 1

What Would You Do With $500? Spending Responses to Gains, Losses, News and Loans

Andreas Fuster Greg Kaplan Basit Zafar Reserve Bank of New Zealand December 2017

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Another Paper Estimating MPCs ?

  • Approaches to estimating MPCs:
  • 1. Revealed reference: natural experiments, transitory income
  • 2. Reported preferences: hypothetical surveys
  • Lessons from revealed preference literature:
  • Average quarterly MPC: 15%-30%
  • Heterogenous, bi-modal, correlated with liquid wealth
  • Limited power for developing and distinguishing theories
  • Natural experiments: mostly one-time unanticipated windfalls
  • Correlating with observables: limited to liquid wealth

1 Fuster, Kaplan and Zafar (2017)

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This Paper

  • Use hypothetical survey responses to test and refine existing models
  • f consumption behavior
  • Within-person variation from alternative treatments:
  • GAIN: size effect
  • LOSS: sign asymmetry
  • NEWS about gains and losses
  • LOAN
  • Better hypothetical survey questions than existing literature
  • Compare with theoretical predictions from simple models
  • Implement treatments in calibrated life-cycle models

2 Fuster, Kaplan and Zafar (2017)

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SLIDE 4

Main Findings

  • Responses to GAIN treatment largely consistent with existing literature
  • Insights from additional treatmets:
  • 1. (i) Sign asymmetry: LOSS > GAIN;

(ii) No response to NEWS-GAIN ⇒ importance of low levels of liquid wealth

  • 2. Response to NEWS-LOSS ⇒ not just myopia
  • 3. No response to LOAN ⇒ not short-term credit constraints
  • 4. Size effects ⇒ non-convexities, durables, salience may play a role

3 Fuster, Kaplan and Zafar (2017)

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SLIDE 5

Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

3 Fuster, Kaplan and Zafar (2017)

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SLIDE 6

NY Fed Survey of Consumer Expectations

  • Online survey to elicit expectations about economic variables
  • Rotating panel ∼ 1, 300 household heads
  • Respondents participate for up to 12 months: monthly modules
  • Response rate:
  • First-time invitees: ∼ 55%
  • Repeat respondents: ∼ 85%
  • Four additional modules asked to repeat panelists:

Mar 2016, May 2016, Jan 2017, Mar 2017

  • Panel nature: some respondents answered multiple modules
  • Total of 9,086 responses from 2,586 panelists

4 Fuster, Kaplan and Zafar (2017)

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Sample Characteristics

Demographics Sample ACS 2015 White/Non-Hispanic 0.75 0.69 Age 50.43 51.06 Education BA+ 0.56 0.31 Married 0.64 0.50 Homeowner 0.73 0.59 Midwest 0.25 0.21 Northeast 0.20 0.18 South 0.33 0.38 West 0.22 0.24 Financial Characteristics Sample SCF 2013 Income <= 50k 0.36 0.37 Income 50k-100k 0.36 0.30 Income 100k+ 0.28 0.31 Financial Assets > 20k 0.50 0.35 Non-housing Debt > 20k 0.35 0.23 Net Worth > 200k 0.42 0.34

5 Fuster, Kaplan and Zafar (2017)

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Baseline Survey Instrument

GAIN: Quarterly MPC out of one-time unexpected receipt of $Y

  • Asks respondents how spending behavior would change over

subsequent three months in response to unexpected increase in resources.

  • Three amounts: $500, $2500, $5000
  • Elicit spending response in two stages:
  • 1. Ask whether spending / debt payments / savings would change in

response to windfall

  • 2. Ask about magnitude of change

6 Fuster, Kaplan and Zafar (2017)

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Survey Screen Shot 1

7 Fuster, Kaplan and Zafar (2017)

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Survey Screen Shot 2

8 Fuster, Kaplan and Zafar (2017)

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Survey Screen Shot 3

9 Fuster, Kaplan and Zafar (2017)

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Reported Expenditures in the Literature

  • Shapiro-Slemrod, Parker-Souleles

Thinking about your (family’s) financial situation this year, will XX lead you mostly to increase spending, mostly to increase saving, or mostly to pay off debt?

  • Japelli-Pistaferri (Italy)

Imagine you unexpectedly receive a reimbursement equal to the amount your household earns in a month. How much of it would you save and how much would you spend? Please give the percentage you would save and the percentage you would spend.

  • Graziani-van der Klaauw-Zafar

What are you doing or planning to do with the extra income? Asked to report the share planning to spend / save / pay down debt.

10 Fuster, Kaplan and Zafar (2017)

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Reported Expenditures in the Literature

  • Christelis-Georgarakos-Jappelli-Pistaferri-van Rooij (Netherlands)

Imagine you unexpectedly receive a one-time bonus from the government equal to the amount of net income your household earns in (one-month / three months). In the next 12 months, how would you use this unexpected income transfer? Distribute 100 points over these four possible uses:

  • 1. Save for future expenses [0 …100]
  • 2. Repay debt [0 …100]
  • 3. Purchase within 12 months durable goods (cars, home improvement, furniture, jewelry, other

durable good) that you otherwise would not have purchased or that you would have purchased later [0 …100]

  • 4. Purchase within 12 months non-durable goods and services that do not last in time (food,

clothes, travel, vacation, etc.) [0 …100]

  • Boon-Le Roux-Reinold-Surico (UK)

If your household received an unexpected windfall of [amount] tomorrow, what do you think you would do with this extra money?

  • I would spend all [100%] or more this year
  • I would spend between [75%] and [100%] more this year
  • I would spend between [50%] and [75%] more this year
  • I would spend between [25%] and [50%] more this year
  • I would spend between [1] and [25%] more this year
  • I would not change spending and save the windfall
  • I would not change spending and use the windfall to pay off some of my debt

11 Fuster, Kaplan and Zafar (2017)

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Advantages of Our Questions

  • Explicitly state size of windfall, allowing it to be varied
  • Two-stage approach:
  • Allows more precise estimates of zero MPCs
  • Does not prime respondents toward a non-zero response
  • Explicit about time horizon over which spending response is measured
  • Does not impose that MPC is between 0 and 1
  • Richer information from alternative treatments

12 Fuster, Kaplan and Zafar (2017)

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Treatments

  • GAIN: Quarterly MPC out of one-time unexpected gain of $Y
  • LOSS: Quarterly MPC out of one-time unexpected loss of $Y
  • NEWS-GAIN: Quarterly MPC out of unexpected news about a gain of

$Y to be received Z quarters from from now

  • NEWS-LOSS: Quarterly MPC out of unexpected news about a loss of

$Y to be received Z quarters from from now

  • LOAN: Quarterly MPC out of unexpected interest-free loan of $Y to be

repaid 1 year from from now

  • Respondents exposed to two treatments in each module
  • Order of treatments within a module was randomized

13 Fuster, Kaplan and Zafar (2017)

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Treatments

Mar-16 May-16 Jan-17 Mar-17 GAIN $500 [1,086] [594] $2,500 [543] $5,000 [361] [1,087] [594] NEWS-GAIN $500 in 3 months [362] $5,000 in 3 months [594] LOSS $500 Loss [362] [1,180] NEWS-LOSS $500 in 3 months [594] [590] $500 in 2 years [590] LOAN $5,000 Loan [544]

14 Fuster, Kaplan and Zafar (2017)

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MPC Summary

Share of Respondents Mean with MPC MPC | MPC > 0 Count MPC < 0 = 0 > 0 Mean Median GAIN $500 1638 0.08 0.06 0.75 0.19 0.54 0.50 $2500 540 0.11 0.06 0.66 0.27 0.43 0.40 $5000 1629 0.14 0.06 0.55 0.39 0.36 0.30 LOSS $500 1536 0.30 0.04 0.47 0.49 0.62 0.60 NEWS-GAIN $500, 3 mnth 362

  • 0.00

0.08 0.86 0.06 0.41 0.50 $5000, 3 mnth 594 0.04 0.05 0.81 0.14 0.31 0.30 NEWS-LOSS $500, 3 mnth 975 0.31 0.02 0.46 0.51 0.61 0.55 $500, 2 yr 589 0.14 0.03 0.68 0.29 0.52 0.40 LOAN $5000 541 0.01 0.12 0.79 0.08 0.30 0.20

15 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

15 Fuster, Kaplan and Zafar (2017)

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SLIDE 19

Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

15 Fuster, Kaplan and Zafar (2017)

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Response to Gains

  • Most people do not respond: 75%
  • Conditional on responding, substantial MPC: Mean 54%, Median 50%
  • Majority of response in first month after receipt

Summary 16 Fuster, Kaplan and Zafar (2017)

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Size Effects

  • Extensive: larger gains, more respond: 19% for $500, 39% for $5, 000
  • Intensive: larger gains, smaller responses
  • Extensive slightly dominates: mean MPC from 8% from 14%
  • Fraction on durables increases with size: 24% for $500, 36% for $5, 000

Summary 17 Fuster, Kaplan and Zafar (2017)

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Internal Consistency

$500 $5,000

  • Similar distributions across survey waves
  • Within person consistency over time: 68% in same bin ≤ 0, (0, 1), ≥ 1

Summary 18 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

18 Fuster, Kaplan and Zafar (2017)

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News About Future Gains

  • Far fewer people respond to news about future gains than to

immediate gains: 6% vs 19% for $500, 14% vs 39% for $5, 000

Summary 19 Fuster, Kaplan and Zafar (2017)

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News: Conditional on Responding to Gain

  • No response to news, even among people who respond to actual gains

Summary 20 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

20 Fuster, Kaplan and Zafar (2017)

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Survey Screen Shot Loss

21 Fuster, Kaplan and Zafar (2017)

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Spending Responses to Losses

  • More people respond to $500 loss than gain: 49% vs 19%
  • Larger responses for gains than losses: median 60% vs 50%
  • Overall mean MPC: 30% vs 8%
  • Bi-modality: ≈ 20% fully absorb loss through reduction in spending

Summary 22 Fuster, Kaplan and Zafar (2017)

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Loss MPCs and Liquid Wealth

  • Extensive and intensive margins decline with liquid wealth

Summary 23 Fuster, Kaplan and Zafar (2017)

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Heterogeneity in Sign Asymmetry

  • No reaction to gain: around half also do not react to loss
  • Reaction to gain: more than half react less to loss than gain

Summary 24 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

24 Fuster, Kaplan and Zafar (2017)

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News About Losses

  • Same responses to immediate loss vs loss 3 months in future
  • Opposite to the GAIN vs NEWS-GAIN comparison

Summary 25 Fuster, Kaplan and Zafar (2017)

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News: Conditional on Responding to Loss

  • Among subset who do respond to a loss today, three-quarters would

also respond to a future loss, with similar distribution

  • Over half these households respond to news about loss in 2 years
  • Evidence against extreme myopia.

Summary 26 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

26 Fuster, Kaplan and Zafar (2017)

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Survey Screen Shot Loan

27 Fuster, Kaplan and Zafar (2017)

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Response to a One-Year Interest-Free Loan

  • Very few people increase spending when offered loan (8%)
  • Even among the 39% who do respond to $5, 000 gain
  • Evidence against very short-term borrowing constraints

Summary 28 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

28 Fuster, Kaplan and Zafar (2017)

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Summary

  • 1. Heterogeneity: Most people do not respond to gains, but a set of

people respond substantially

  • 2. Positive size effect: Driven by extensive margin and durables
  • 3. Sign asymmetry: More people respond, and by bigger amounts, to

losses than gains. Correlated with liquid assets

  • 4. No response to news about gains: People do not respond to news

about future gains, even those with large responses to actual gains

  • 5. Response to news about losses: People do respond to news about

future losses, even those with low liquid assets

  • 6. No response to loans: Even for people who respond to gains

29 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

29 Fuster, Kaplan and Zafar (2017)

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Framework and Definitions

  • Budget constraint in quarter t

cit + sit = xit xi,t+1 = yi,t+1 + Rsit,

  • GAIN and LOSS: constraint unexpectedly becomes:

cit + sit = xit + ∆ xi,t+1 = yi,t+1 + Rsit,

  • NEWS-GAIN and NEWS-LOSS: constraint unexpectedly becomes:

cit + sit = xit xi,t+1 = yi,t+1 + Rsit + ∆,

  • LOAN: constraint at t becomes cit + sit = xit + ∆

constraint at t + 4 becomes xi,t+4 = yi,t+4 + R (si,t+3) sit+3 − ∆

  • MPC for treatment T ∈ {G, L, NG, NL, LN}:

MPCT

it = c∆

it −cit

∆

30 Fuster, Kaplan and Zafar (2017)

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SLIDE 41

Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

30 Fuster, Kaplan and Zafar (2017)

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Rule-of-Thumb Consumers

  • Consume all disposable income every period

MPCG = MPCL = MPCLN = 1 MPCNG = MPCNL = 0

  • No size effect
  • No sign asymmetry
  • Not consistent with NEWS-LOSS: people are forward looking

31 Fuster, Kaplan and Zafar (2017)

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PIH Consumers

  • Quadratic utility, fixed interest rate R = β−1, No-Ponzi schemes

MPCL = MPCG = r 1 + r ≈ 0 MPCNL = MPCNG = r (1 + r)2 ≈ 0 MPCLN = r 1 + r − r (1 + r)5 ≈ 0 where approximations hold for small r

  • MPCG = MPCL: small for everyone, no sign asymmetry
  • Similar response to news as to gains / losses
  • MPCG ≈ MPCNG: consistent with NEWS-GAIN
  • MPCL ≈ MPCNL: not consistent NEWS-LOSS

32 Fuster, Kaplan and Zafar (2017)

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Spender-Saver Model

  • Fraction α rule-of-thumb (spenders), fraction 1 − α PIH (savers):

MPCG = MPCL = MPCLN = α MPCNG = MPCNL = 0

  • Simple, popular modeling choice:
  • Heterogenous MPCG
  • Smaller response to news than gains: MPCNG < MPCG
  • MPCL correlated with liquid wealth as in data
  • No size effect
  • No sign asymmetry
  • Not consistent with NEWS-LOSS

33 Fuster, Kaplan and Zafar (2017)

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Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

33 Fuster, Kaplan and Zafar (2017)

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Predictions from Infinite-Horizon Model

V (x) = max

c,s≥0 u (c) + βE [V (x′, y ′)]

subject to c + s = x x′ = Rs + y ′

  • Strict concavity of consumption function c(x): MPCL > MPCG
  • For large x consumption function is approximately linear:

c(x) → [ R (βR)

1 γ − 1

] x

  • In practice, approximation holds well except for low wealth households

near borrowing constraint

34 Fuster, Kaplan and Zafar (2017)

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SLIDE 47

High and Medium Wealth Households

  • No sign asymmetry: MPCG = MPCL
  • No size effect: MPCG = MPCL ≈ r for βR ≈ 1
  • Similar responses to news vs gains / losses

MPCNG = 1 RMPCG MPCNL = 1 RMPCL (differ only because of discounting)

  • For households sufficiently far from constraint MPCLN ≈ 0 (order r 2)

35 Fuster, Kaplan and Zafar (2017)

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SLIDE 48

Very Low Wealth Households

  • Sign asymmetry, size effect:
  • Large response to losses of all sizes: MPCL = 1 for all ∆
  • Responses to gain:MPCG ≤ 1 depending on size of ∆
  • NEWS-GAIN and NEWS-LOSS
  • MPCNG = 0: learn about HtM from MPCG − MPCNG
  • MPCNL: depend on reason for being low wealth
  • LOAN
  • Depends on how long a household expects to be constrained for
  • Very impatient of present biased households: expect to take up

loan, like the rule-of-thumb

36 Fuster, Kaplan and Zafar (2017)

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SLIDE 49

Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

36 Fuster, Kaplan and Zafar (2017)

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SLIDE 50

Adapted from Kaplan and Violante (2010,14)

  • Quarterly lifecycle model: 38 yrs working , 20 yrs retirement
  • Utility over non-durables and housing services:

u(c, s) = ( cφs1−φ)1−γ 1 − γ with γ = 2, φ = 0.85

  • Idiosyncratic income risk: age profile + AR(1), ρ = 0.97
  • Two assets:
  • Liquid asset: r l = 0%
  • Illiquid asset: r a = 5%, housing services = 3%
  • Fixed transaction cost = $2, 000
  • Discount factor β so ratio of av wealth to av. ann. earnings = 3.5
  • One asset models: φ = 1, r l ∈ {0%, 5%}

37 Fuster, Kaplan and Zafar (2017)

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SLIDE 51

Hand-to-Mouth households

1 Asset 1 Asset 2 Asset 1 Asset r = 0% r = 5% (r a, r b) = Low (0%, 5%) Wealth Liquid Wealth: Mean 3.50 3.50 0.05 0.05 Median 1.78 1.68 0.00 0.00 Illiquid Wealth: Mean 3.45 Median 0.93 Overall Hand-to-Mouth: Poor HtM 0.027 0.035 0.148 0.804 Wealthy HtM 0.384 Working Hand-to-Mouth: Poor HtM 0.027 0.035 0.131 0.516 Wealthy HtM, Work 0.263

38 Fuster, Kaplan and Zafar (2017)

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SLIDE 52

MPC Treatments in Calibrated Life-Cycle Models

1 Asset 1 Asset 2 Asset 1 Asset r = 0% r = 5% (r a, r b) = Low (0%, 5%) Wealth GAIN $500 0.05 0.06 0.27 0.52 $2500 0.04 0.05 0.12 0.28 $5000 0.03 0.04 0.05 0.21 LOSS $500 0.10 0.13 0.46 0.89 NEWS-GAIN $500, 3 mnth 0.03 0.03 0.03 0.02 $5000, 3 mnth 0.02 0.03

  • 0.01

0.01 NEWS-LOSS $500, 3 mnth 0.06 0.08 0.23 0.33 $500, 2 yr 0.02 0.03 0.02 0.01 LOAN $5000 0.00 0.01 0.00 0.15

39 Fuster, Kaplan and Zafar (2017)

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SLIDE 53

Outline

  • 1. Data
  • 2. Empirical Results

Baseline: GAIN MPC Treatment: NEWS-GAIN Treatment: LOSS Treatment: NEWS-LOSS Treatment: LOAN Summary

  • 3. Theoretical Implications

Benchmark Models Precautionary Savings Models Quantitative Lifecycle Models

  • 4. Conclusion

39 Fuster, Kaplan and Zafar (2017)

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SLIDE 54

Concluding Thoughts

  • Moving beyond MPC’s for unexpected gains: useful for guiding theory
  • In the absence of natural experiments, suitably designed hypothetical

surveys can offer new insights into consumption behavior

  • Key findings:
  • 1. (i) Size asymmetry: LOSS > GAIN;

(ii) No response to NEWS-GAIN ⇒ importance of low levels of liquid wealth

  • 2. Response to NEWS-LOSS ⇒ not just myopia
  • 3. No response to LOAN: ⇒ not short-term credit constraints
  • 4. Size effects: ⇒ non-convexities, durables or salience
  • To do: explore behavioral phenomena: present-bias, inattention,

mental accounting etc

40 Fuster, Kaplan and Zafar (2017)

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SLIDE 55

News About Losses in 2 years time

All MPC >0 in LOSS

41 Fuster, Kaplan and Zafar (2017)