The Great Lockdown and the Big Stimulus: Tracing the Pandemic Possibility Frontier for the U.S.

Greg Kaplan Ben Moll Gianluca Violante

Mean Field in Economics EIEF September 2020

Slides at http://benjaminmoll.com/PPF_MFG_slides/ Today’s talk: what you can do with MFGs

• An epi-macro MFG to analyze COVID-19 crisis, health & economic policy responses to it • background: entire emerging epi-macro field, e.g. Alvarez-Argente-Lippi (≠ MFG)

• Hybrid of

1. SIR model from epidemiology – see e.g. https://benjaminmoll.com/SIR_notes/

2. heterogeneous agent model from macro = MFG of income & wealth dist’n (Aiyagari) – see e.g. https://benjaminmoll.com/HACT/

• Paper: rich, complicated model – see https://benjaminmoll.com/PPF/

• Today: • MFG system of very stripped down version to give you gist of things • But show you results from full model

1 Kaplan, Moll and Violante (2020) Objectives

• US policy response to COVID-19:

• Lockdown: workplace and social sector • Fiscal stimulus: CARES Act

• Goal: quantify trade-offs

• Aggregate: Lives versus livelihoods • Distributional: Who bears the economic costs?

• Approach: distributional Pandemic Possibility Frontier (PPF) → PPF

• Name in analogy to “Production Possibility Frontier” = key economic concept • Compare policies without taking stand on economic value of life • Seek policies that flatten and shift the frontier

2 Kaplan, Moll and Violante (2020) Distributional Pandemic Possibility Frontier

10 Mean w/o Fiscal Support p10-p90 w/o Fiscal Support Mean w/ Fiscal Support p10-p90 w/ Fiscal Support 17-Month 8 Lockdown

12-Month 6 Lockdown

4 US Lockdown

US Policy Laissez-faire

2

Economic Welfare Cost (Multiples of Monthly Income) 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Deaths (% of Population) → back to intro

3 Kaplan, Moll and Violante (2020) Simplified Model

4 Kaplan, Moll and Violante (2020) Epidemiological Model: SIR Model

• • St : susceptible It : infectious • • Rt : recovered Nt : population = St + It + Rt

     I I  S˙ S −β t β t 0 t t t Nt t Nt   T     I˙t = A It , A = 0 −γ γ(1 − δ) R˙ t Rt 0 0 0

• Deaths (flow) D˙ t = δγIt , cumulative deaths Dt .

Key feature: βt = β(Ct ): infections depend on aggregate consumption Ct

5 Kaplan, Moll and Violante (2020) Economic Model: Households

• ... differ in their wealth a, income y, and health state h ∈ {S, I, R} • household state x = (a, y, h) • value function v(x, t) – households’ problem on next slide • joint distribution g(x, t)

• Note non-standard notation relative to MFG literature: (v, g) instead of (u, m)

• To simplify notation a bit: • ignore mass points (which do typically arise) & work with density ∫ • write some equations as if h ∈ {S, I, R} were a continuous state, e.g. g(x, t)dx

• Accounting: ∫ ∫

Nt = g(x, t)dx, St = g(a, y, S, t)dady, ∫ ∫

It = g(a, y, I, t)dady, Rt = g(a, y, R, t)dady

6 Kaplan, Moll and Violante (2020) Economic Model: Households solve

∫ ∞ −ρt max E0 e u(ct , It )dt s.t. {ct }t≥0 0

dat = (yt + rat + τt − ct )dt

dyt = µ(yt )dt + σ(yt )dWt

at ≥ a • c : consumption t √ • u: utility function, e.g. u(c, I) = c × e−I . Dependence on infections I = “fear factor” • ρ: discount rate • E0: expectations operator, takes into account death rate δγ > 0 in I state (like higher ρ) • r : fixed interest rate • Wt : Wiener process, independent across households • yt ∈ [y, y¯]: income, reflected at boundaries if it ever reaches them • τt : transfers = fiscal stimulus – set τ = 0 on next slides to simplify notation • a > −∞: borrowing limit e.g. if a = 0, can only save

More general y-processes also possible, e.g. jumps7 = unemployment Kaplan, Moll and Violante (2020) Comment: source of MFG coupling

• Usually, in Aiyagari-type heterogeneous agent models, interest rate rt determined in equilibrium by condition like ∫ 0 = ag(x, t)dx all t ≥ 0

• Interpretation: for everyone who borrows (a < 0) there is someone who lends (a > 0)

• Econ terminology: “bonds are in zero net supply” • and usually = source of MFG coupling (in Galo’s words: “coupling through prices”)

• Here instead:

• interest rate r = fixed scalar • coupling through epi variables

8 Kaplan, Moll and Violante (2020) MFG System in absence of lockdowns Functions v and g on (a, ∞) × (y, y¯) × {S, I, R} × (0, ∞) satisfy σ2(y) ρv = max u(c, It ) + (y + ra − c)∂av + µ(y)∂y v + ∂yy v + Av + ∂t v (HJB) c 2

with state constraint a ≥ a and 0 = ∂y v(a, y, h, t) = ∂y v(a, y,¯ h, t) all a, h, t 1 ∂ g = − ∂ (sg) − ∂ (µ(y)g) + ∂ (σ2(y)g) + ATg (FP) t a y 2 yy

−1 s :=y + ra − c, c = (uc ) (∂av, I),   It It ∫ −β(Ct ) N β(Ct ) N 0  t t  A = 0 −γ γ(1 − δ) ,Ct = c(x, t)g(x, t)dx (EPI) 0 0 0 ∫

It = g(a, y, I, t)dady (I)

g(x, 0) = g0(x) Note two-way feedback: 1. epi → econ through (HJB) and (I) 2. econ → epi through (EPI) 9 Kaplan, Moll and Violante (2020) Lockdowns

• Policy that requires c(x, t) ≤ c¯ • Lockdowns reduce virus transmissions because   It It ∫ −β(Ct ) N β(Ct ) N 0  t t  A = 0 −γ γ(1 − δ) ,Ct = c(x, t)g(x, t)dx 0 0 0

• Full model: different consumption goods, lockdowns only restrict “social” goods

10 Kaplan, Moll and Violante (2020) Full Model

11 Kaplan, Moll and Violante (2020) Full Model

• Integrated SIR + Heterogeneous Agent model with necessary ingredients • Goods: (i) regular; (ii) social; (iii) home production • Types of labor: (i) workplace; (ii) remote; (iii) home production • Occupation heterogeneity: (i) flexibility; (ii) social intensity; (iii) essentiality • Two-way feedback: between virus and economic activity

• Economic exposure to pandemic correlated with financial vulnerability

• Calibrate model to U.S. economy and examine counterfactuals • Laissez-faire vs lockdown vs fiscal policy (CARES Act) • Smarter policies: (i) targeted lockdowns; (ii) Pigouvian taxes

12 Kaplan, Moll and Violante (2020) Main Findings in Full Model

1. Economic welfare costs of pandemic: large and heterogeneous, regardless of lockdown • Large welfare costs for rigid, social-intensive occupations • Large welfare costs for middle of earnings distribution: fiscal stimulus

2. Slope of PPF varies with length lockdown • Driven by hospital constraint and eventual arrival of vaccine • Reconcile conflicting views on extent of trade-off

3. U.S. CARES Act: • Reduced economic cost by 20% on average, highly redistributive • Explains rapid recovery in consumption of poor households

4. Taxation-based alternatives to lockdown: favorable mean trade-off but more dispersion

5. Model prediction for fall/winter: with current U.S. policy, 2nd wave & double-dip recession

→ dimensions not considering today

13 Kaplan, Moll and Violante (2020) Outline

1. Full model: some more details

2. Parameterization

3. Results

4. Conclusions

5. Linked Slides

13 Kaplan, Moll and Violante (2020) Full Model: Add Different Occupations

• Full model features 5 occupation classes

• These differ in “flexibility” = remote-work ability, and sector they are employed in

High Flexibility Low Flexibility High intensity in C Software engineer, architect Car mechanic, miner High intensity in S Event planner, social scientist Waiter, shop assistant Essential Police, nurse, supermarket clerk

14 Kaplan, Moll and Violante (2020) Outline

1. Full model: some more details

2. Parameterization

3. Results

4. Conclusions

5. Linked Slides

14 Kaplan, Moll and Violante (2020) Key Aspects of Parameterization

1. Epidemiological block

• SEIR parameters: epidemiological and clinical studies → SEIR parameters

2. Occupational parameters

• Flexibility measures by occupation: O*NET, ATUS → occupation flexibility details

• Sectoral employment intensities in C and S: OES, CPS → occupation sectoral details

• Earnings and liquid wealth by occupation: SIPP, CPS, SCF → exposure vs vulneability

3. Two-way feedback: virus ↔ economic activity

• → → Economic activity → virus: drop in R0 after lockdown feedback: activity virus

• Virus → economic activity: VSL literature → → feedback: virus → activity

15 Kaplan, Moll and Violante (2020) Outline

1. Full model: some more details

2. Parameterization

3. Results

4. Conclusions

5. Linked Slides

15 Kaplan, Moll and Violante (2020) Laissez-faire vs Lockdown Dynamics

(a) Monthly Death Rate (%) (c) Labor Income (CF: C-intensive, Flexible) (%) (e) Labor Income (SF: S-intensive, Flexible) (%) 0.12 0 0 Laissez-faire

0.10 Full Lockdown 10 10

0.08 20 20 0.06

30 30 0.04

40 40 0.02 Laissez-faire Laissez-faire Full Lockdown Full Lockdown 0.00 50 50 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month (b) Output (%) (d) Labor Income (CR: C-intensive, Rigid) (%) (f) Labor Income (SR: S-intensive, Rigid) (%) 0 0 0

5 10 10

10 20 20

15 30 30

20 40 40 Laissez-faire Laissez-faire Laissez-faire Full Lockdown Full Lockdown Full Lockdown 25 50 50 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month

• Lockdown → second wave, but fewer deaths • Lockdown → longer, deeper contraction and W -shaped recovery • Large drop in income for S-intensive occupations even in laissez faire • Lockdown → further drop in income for C-intensive occupations 16 Kaplan, Moll and Violante (2020)

→ lockdown path → laissez-faire dynamics → lockdown dynamics → lockdown decomposition Pandemic Possibility Frontier (PPF)

Mean 10 p25-p75 p10-p90 p5-p95

17-Month 8 Lockdown

12-Month Lockdown 6

4 US Lockdown Laissez-faire

2 Economic Welfare Cost (Multiples of Monthly Income) 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 Deaths (% of Population)

• Large average economic costs with very non-linear trade-off: ICU constraint vs vaccine • Heterogeneity in economic costs exacerbated with longer lockdowns → ppf by occupation

17 Kaplan, Moll and Violante (2020) Distribution of Economic Welfare Costs

Earnings Decile

• Largest economic costs in middle of distribution: labor income versus transfer income

→ welfare cost distribution

18 Kaplan, Moll and Violante (2020) CARES Act Shifts the PPF

10 Mean w/o Fiscal Support p10-p90 w/o Fiscal Support Mean w/ Fiscal Support p10-p90 w/ Fiscal Support 17-Month 8 Lockdown

12-Month 6 Lockdown

4 US Lockdown

US Policy Laissez-faire

2

Economic Welfare Cost (Multiples of Monthly Income) 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Deaths (% of Population)

• CARES Act: stimulus checks, pandemic UI, PPP, retirement withdrawals → CARES Act details

→ CARES Act dynamics → components of CARES Act → CARES Act by income quartile → components of CARES Act by income quartile

19 Kaplan, Moll and Violante (2020) Consumption Dynamics

(a) Labor Income (%) (b) Consumption (%)

0 0

5

10 10

20 Bottom Quartile Bottom Quartile 15 2nd Quartile 2nd Quartile 3rd Quartile 3rd Quartile 30 20 Top Quartile Top Quartile

Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month

• US Data: biggest income drops, but fastest consumption recovery at bottom → US data • CARES Act redistributed toward low-income households with high MPC → components of CARES Act by income quartile

20 Kaplan, Moll and Violante (2020) Smarter Alternative Polices (a) Comparison (b) Lockdown w/ C Sector Exempted 10 10 Mean: US Lockdown Mean Mean: Lockdown w/ C Sector Exempted p10-p90 Mean: Social Consumption Tax p25-p75 8 Mean: Workplace Hours Tax 8

21-Month 6 6 17-Month 12-Month

4 4 2-Month Laissez-faire 2 2

0 0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 (c) Social Consumption Tax (d) Workplace Hours Tax 10 10 Mean Mean p10-p90 p10-p90 p25-p75 p25-p75 8 8

6 21-Month 6 17-Month 12-Month 21-Month 17-Month 4 4 12-Month 2-Month Laissez-faire Laissez-faire 2 2 2-Month

0 0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Deaths (% of Population) Deaths (% of Population) 21 Kaplan, Moll and Violante (2020) Outline

1. Full model: some more details

2. Parameterization

3. Results

4. Conclusions

5. Linked Slides

21 Kaplan, Moll and Violante (2020) Messages

1. Economic costs of pandemic: large and heterogeneous, regardless of lockdowns 2. Distributional Production Possibility Frontier useful for quantifying trade-offs: • Aggregate tradeoff between lives vs livelihoods • Distributional tradeoff over who bears economic burden 3. Non-linear PPF: reconciles conflicting views on aggregate tradeoff 4. Exposure correlated with vulnerability: rigid, social occupations ⇒ scope for fiscal policy 5. US CARES Act: • Shifts PPF inward: reduces economic costs without increasing deaths • Faster recovery of spending for low income households 6. Taxation-based alternatives to lockdowns improve aggregate trade-off 7. Model prediction for fall/winter: with current U.S. policy, 2nd wave & double-dip recession

Further reading on economics of Covid-19 crisis: this report we wrote for UK Royal Society https://rs-delve.github.io/reports/2020/08/14/economic-aspects-of-the-covid19-crisis-in-the-uk.html

22 Kaplan, Moll and Violante (2020) Outline

1. Full model: some more details

2. Parameterization

3. Results

4. Conclusions

5. Linked Slides

22 Kaplan, Moll and Violante (2020) Some Dimensions we Abstract From

1. Differential impact of the epidemic across age groups (Glover-Heathcote-Krueger-RiosRull, Bairoliya-Imrohoroglu, Brotherhood-Kircher-Santos-Tertilt, ...)

2. Differential impacts of the epidemic across gender (Alon-Doepke-Olmstead Rumsey-Tertilt, ...)

3. Impact of the epidemic on deaths from other causes

4. Input-output linkages in production (Baqaee-Farhi, ...)

5. Firm balance sheets, liquidity provision to firms (Buera-Fattal Jaef-Neumeyer-Shin, Elenev-Landvoigt-VanNieuwerburgh, ...)

6. Costly destruction of viable employment relationships

7. ...

→ model ingredients

23 Kaplan, Moll and Violante (2020) Background on Lockdowns in SIR Models

• See https://benjaminmoll.com/SIR_notes/ for more details • Some vocabulary: 1. Basic reproduction number: R0 := β0/λI e × S N 2. Effective reproduction number: Rt := R0 t / t ∗ ∗ 3. Herd immunity threshold: S /N := 1/R0 or R /N := 1 − 1/R0 −R (1−S∞) 4. Final size of disease: S∞ or R∞ + D∞, solves S∞ = e 0 • Two key features of SIR models: ↑ e S S∗ ↓ 1. Infections if Rt > 1 or > and otherwise ∗ 2. Epidemic “overshoot”: total infections > herd immunity, S∞ > S • Results on lockdowns := R0 ↓ • Even temporary lockdowns reduce total number of infections • But total number of infections ≥ herd immunity threshold • Best lockdowns-only can do is eliminate epidemic “overshoot” • If lockdown too short or too tight, get 2nd wave → back

24 Kaplan, Moll and Violante (2020) Market Clearing Conditions

• Regular goods market Yc = Cc + I + G + χ • Social goods market Ys = Cs • Labor market for each occupation ∫ j j j j j Nc + Ns = z(ℓw (h, a, b, z) + ϕ ℓr (h, a, b, z))dµ, j = 1, ..., 5

• Liquid asset market Bh = Bg • Illiquid asset market A = Vfund(K, Θc , Θs ),K = Kc + Ks

→ model ingredients

25 Kaplan, Moll and Violante (2020) Epidemiological Parameters

Description Parameter Value init init Initial basic reproduction number R0 = β0 /λI 2.5 end end Final basic reproduction number R0 = β0 /λI 2 Avg. duration of Infectious TI ⇒ λI = 1/TI 4.3 days Avg. duration of Exposure (latency) TE ⇒ λE = 1/TE 5.0 days Infection fatality rate IFR = χδC 0.02 × 0.33 = 0.066

• Time trend in transmissions (masks,...): R˜t = (1 − ωt )R0 + ωt R,¯ R¯ = 1.5, ωt = logistic • − init ⇒ − end Herd immunity threshold: 1 1/R0 = 60% 1 1/R0 = 50% • Vaccine arrival after 18 months → back to parameterization

26 Kaplan, Moll and Violante (2020) Occupations: Flexibility • ONET: Share of tasks that can be performed at home (Dingel-Neiman)

• ATUS Q: As part of your (main) job, can you work at home?

• Systematic variation across 3-digit SOC occupations

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

O*NET (share of teleworkable jobs) 0.2

0.1

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ATUS (share of individuals who can work from home)

• Two flexibility levels: high flexibility occupation if ONET share > 0.5.

→ back to parameterization

27 Kaplan, Moll and Violante (2020) Occupations: Social vs Regular Intensity

→ back to parameterization

28 Kaplan, Moll and Violante (2020) Occupations: Exposure vs Vulnerability

j j j Occupation ϕ ξc ξs Empl Share Earnings Liq Wealth Essential 0.1 0.19 0.35 0.31 $45K $1, 300 CF: C-intensive, Flexible 1 0.57 0.12 0.21 $79K $18, 400 SF: S-intensive, Flexible 1 0.03 0.19 0.10 $51K $8, 900 CR: C-intensive, Rigid 0.1 0.19 0.04 0.13 $45K $1, 000 SR: S-intensive, Rigid 0.1 0.04 0.29 0.24 $32K $900 Source: O*NET, OES, SIPP

• Estimate stochastic processes for household wage dynamics by occupation from PSID

• To match liquid wealth we add occupational-specific wedge on liquid rate

→ back to parameterization

29 Kaplan, Moll and Violante (2020) Feedback: Economic Activity to Virus

• Transmission rate for infections: ( ) ( ) νs νw Cst β Lwt β βt = β˜t C¯s L¯w

• Google COVID-19 Community Mobility Data:

20 20

10 10

0 0

-10 -10

Over 90% of population Over 90% of population -20 -20 in lockdown in lockdown -30 -30 Retail Mobility Index

Workplace Mobility Index -40 -40

-50 -50

-60 -60 Feb 18 Mar 03 Mar 17 Mar 31 Apr 14 Apr 28 Feb 18 Mar 03 Mar 17 Mar 31 Apr 14 Apr 28 Date 2020 Date 2020 • Estimates of Rt drop from 2.5 to 0.8 after lockdown • Drop in acrivity of 50% ⇒ elasticities: νs = νw = 0.8 β β → back to parameterization

30 Kaplan, Moll and Violante (2020) Feedback: Virus to Economic Activity

• Parameterize utility shifters as: ( ) ( ) 1 1 D˙ − 0D˙ νℓ D˙ − 0D˙ νs υℓ( ) = exp νℓ , υs ( ) = exp νs

• Maps into VSL calculations: optimality condition for hours worked is ( ) ν1 0 0D˙ ℓ 1 log wit = γℓ νℓ t + γℓ Xit

• Used to estimate monetary compensation for fatality risk: increasing and concave in risk Greenstone et al. (2014), Lavetti (2020)

• Target VSL between $4-10M for fatality rates between 1/1,000 and 1/10,000 per quarter (relevant magnitude for COVID-19)

→ back to parameterization

31 Kaplan, Moll and Violante (2020) Lockdown Scenario

(a) Workplace Lockdown (%) (b) Social Sector Lockdown (%)

60

80 50

40 60

30 40

20

20 10

0 0

Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month

• Calibrated to obtain decline in workplace and retail activity (Google)

→ back • Assumption: no future lockdown in case of 2nd wave

32 Kaplan, Moll and Violante (2020) Aggregates Dynamics: Laissez-Faire

(a) Reproduction Number (b) Infectious (% of Population) (c) Monthly Death Rate (%) 2.5 1.8 0.200

R0 Actual 1.6 0.175 Effective R No ICU Constraint 2.0 1.4 0.150 1.2 0.125 1.5 1.0 0.100 0.8 1.0 0.075 0.6 0.050 0.5 0.4 0.2 0.025

0.0 0.0 0.000 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

(d) Output (%) (e) Consumption (%) (f) Investment and Share Price (%) 10 10 0 Investment 0 5 Share Price 10

10 0 20 20 5 30 30 10 40 Y 40 C Regular Y Regular C 15 50 Social Y 50 Social C 20 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

Government Debt (g) Hours (%) (h) Labor (%) (i) Quarterly GDP 400 2.70 60 20 Labor Productivity 2.65 40 Labor Income 200 10 20 2.60

0 0 0 2.55

20 2.50 200 10 40 2.45

60 20 400 2.40 80 Workplace Hrs. Home Hrs. Remote Hrs. 30 2.35 100 600 → back Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

33 Kaplan, Moll and Violante (2020) Aggregates Dynamics: Lockdown

(a) Reproduction Number (b) Infectious (% of Population) (c) Monthly Death Rate (%) 2.5 1.8 0.200

R0 Actual 1.6 0.175 Effective R No ICU Constraint 2.0 1.4 0.150 1.2 0.125 1.5 1.0 0.100 0.8 1.0 0.075 0.6 0.050 0.5 0.4 0.2 0.025

0.0 0.0 0.000 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

(d) Output (%) (e) Consumption (%) (f) Investment and Share Price (%) 10 10 0 Investment 0 5 Share Price 10

10 0 20 20 5 30 30 10 40 Y 40 C Regular Y Regular C 15 50 Social Y 50 Social C 20 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

Government Debt (g) Hours (%) (h) Labor (%) (i) Quarterly GDP 400 2.70 60 20 Labor Productivity 2.65 40 Labor Income 200 10 20 2.60

0 0 0 2.55

20 2.50 200 10 40 2.45

60 20 400 2.40 80 Workplace Hrs. Home Hrs. Remote Hrs. 30 2.35 100 600 → back Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

34 Kaplan, Moll and Violante (2020) Lockdown Decomposition

(a) Monthly Death Rate (%) (b) Labor Income (CF: C-intensive, Flexible) (%) (c) Labor Income (SF: S-intensive, Flexible) (%) 0.12 0 0 Laissez-faire

0.10 Full Lockdown Social Sector Lockdown 10 10 Workplace Lockdown 0.08 20 20

0.06

30 30 0.04 Laissez-faire Laissez-faire Full Lockdown Full Lockdown 40 40 0.02 Social Sector Lockdown Social Sector Lockdown Workplace Lockdown Workplace Lockdown 0.00 50 50 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul (d) Output (%) (e) Labor Income (CR: C-intensive, Rigid) (%) (f) Labor Income (SR: S-intensive, Rigid) (%) 0 0 0

5 10 10

10 20 20

15 30 30 Laissez-faire Laissez-faire Laissez-faire Full Lockdown Full Lockdown Full Lockdown 20 40 40 Social Sector Lockdown Social Sector Lockdown Social Sector Lockdown Workplace Lockdown Workplace Lockdown Workplace Lockdown 25 50 50 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul → back Month Month Month

35 Kaplan, Moll and Violante (2020) Economic Welfare Cost Distribution

(a) Economic Welfare Cost (b) Lockdown - Laissez-faire 0.20 4 0.15

3 0.10

2 Laissez-faire 0.05 US Policy US lockdown 1 0.00 1 2 3 4 5 6 7 8 9 10 4 3 2 1 0 1 2 Earnings Deciles Economic Welfare Cost (c) Fiscal - Lockdown (d) Fiscal - Laissez-faire 0.4

0.3 0.3

0.2 0.2

0.1 0.1

0.0 0.0 4 3 2 1 0 1 2 4 3 2 1 0 1 2 → Economic Welfare Cost Economic Welfare Cost back

36 Kaplan, Moll and Violante (2020) Production Possibility Frontier by Occupation

• C-intensive, rigid occupations (green line) hurt most by longer lockdowns

→ back

37 Kaplan, Moll and Violante (2020) Modeling CARES Act

(a) Transfers (% of Quarterly GDP) (b) PPP Subsidies (%) (c) Withdrawal Cost (%)

Check Wage 0 3.0 UI Profit 50

5 2.5 40

2.0 10 30 1.5 15 20 1.0

20 10 0.5

0.0 0 25

Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

• Stimulus checks: unconditional transfer of $1,900 to everyone

• Pandemic UI: replacement earnings loss by decile (Ganong-Vavra)

• Paycheck Protection Program: part wage & profit subsidies (half each of total budget)

• 401(k) withdrawals up to $100,000: reduction in withdrawal cost → back

38 Kaplan, Moll and Violante (2020) Aggregates Dynamics: Lockdown + CARES Act

(a) Reproduction Number (b) Infectious (% of Population) (c) Monthly Death Rate (%) 2.5 1.8 0.200

R0 Actual 1.6 0.175 Effective R No ICU Constraint 2.0 1.4 0.150 1.2 0.125 1.5 1.0 0.100 0.8 1.0 0.075 0.6 0.050 0.5 0.4 0.2 0.025

0.0 0.0 0.000 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

(d) Output (%) (e) Consumption (%) (f) Investment and Share Price (%) 10 10 0 Investment 0 5 Share Price 10

10 0 20 20 5 30 30 10 40 Y 40 C Regular Y Regular C 15 50 Social Y 50 Social C 20 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

Government Debt (g) Hours (%) (h) Labor (%) (i) Quarterly GDP 400 2.70 60 20 Labor Productivity 2.65 40 Labor Income 200 10 2.60 20

2.55 0 0 0

20 2.50 200 10 40 2.45

60 20 400 2.40 Workplace Hrs. 80 2.35 Home Hrs. Remote Hrs. 30 100 600 → back Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month

39 Kaplan, Moll and Violante (2020) Decomposition of CARES Act Components

(a) Effective R (b) Labor Income (%) (c) Workplace Hours (%) 2.5 No CARES 5 0 No CARES Check + UI Check + UI 0 10 2.0 Full CARES Full CARES 5 20 1.5 10 30 1.0 15 40 20 No CARES 0.5 50 25 Check + UI Full CARES 60 0.0 30 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul (d) Monthly Death Rate (%) (e) Consumption (%) (f) Share Price (%) 0.030 No CARES 1 No CARES 0 0.025 Check + UI Check + UI Full CARES 0 Full CARES 5 0.020

1 0.015 10

0.010 15 2 No CARES 0.005 20 Check + UI 3 Full CARES 0.000 25 4 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Month Month Month → back

40 Kaplan, Moll and Violante (2020) CARES Act by Income Quartile

(a) Labor Income (%) (b) Total Income (%) (c) Consumption (%) 5 100 10 Bottom Quartile Bottom Quartile Bottom Quartile Top Quartile Top Quartile Top Quartile 80 0 0 60 5

10 40 10

Full CARES 20 20 15

0 30 20 20 40 25 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul (d) Labor Income (%) (e) Total Income (%) (f) Consumption (%) 5 0 10 Bottom Quartile Bottom Quartile Top Quartile Top Quartile 0 5 0 5 10 10 10 15 No CARES 20 15 20 30 Bottom Quartile 20 25 Top Quartile 40 25 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul → back Month Month Month

41 Kaplan, Moll and Violante (2020) Decomposition of CARES Act Components by Income Quartile

(a) Labor Income (%) (b) Total Income (%) (c) Consumption (%) 5 100 0 None Check 0 80 UI 10 Withdraw 60 PPP 5 Full 40 20 10

20 15 Bottom Quartile 30 0 20 40 20

25 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul (d) Labor Income (%) (e) Total Income (%) (f) Consumption (%) 5

5 5 0 0 0

5 5 5

10 10 10

Top Quartile 15 15 15

20 20 20 25 25 25 Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul Apr Jul Oct 2021 Apr Jul → back Month Month Month

42 Kaplan, Moll and Violante (2020) Figure 6: Credit card spending by income quartiles

(a) Percent Change (b) Levels

1 Introduction The Covid-19 pandemic led to a large and immediate decline in U.S. aggregate spending and an in- crease in aggregate private savings. In this paper, we use anonymized bank account information on millions of JPMorgan customers to measure the microeconomic dynamics underlying these ag- gregate patterns. Specifically, we use our household level account data to explore how spending and savings over the initial months of the pandemic vary with household-specific demographic character- istics, such as pre-pandemicConsumption income and industry of Dynamics employment. by Income Quartile: USFigure Data 7: Debit card spending by income quartiles

Figure 1: Aggregate Consumption and Savings (a) Percent Change (b) Levels

30

20

10

0

−10 Year over Percent Change

−20 2019m4 2019m7 2019m10 2020m1 2020m4 Personal Consumption Expenditures Personal Savings Rate

Figure shows the year-over-year growth of Personal Consumption Expenditures and the Personal Savings rate, calculated from monthly Bureau of Economic Analysis data. • Source: Cox-Ganong-Noel-Vavra-Wong-Farrell-Greig Measuring and understanding the link between income, spending, and savings is useful for under-To differentiate the role of income from the→ back role of physical location, we look at the relationship • Consumption of poor recovers faster than consumption of rich standing the causes and dynamics of this recession. For instance, the relationship between individualbetween income and spending over the pandemic within narrow geographic areas. In particular, we income, spending and savings can shed light on the role of supply factors (such as shut-downs and re- 43 compute the following regression Kaplan, Moll and Violante (2020) ducing activities with high infection risk) versus demand factors (such as Keynesian spill-overs across sectors as unemployed workers reduce spending). Understanding these factors can be informative c c about the effectiveness of different stimulus policies for targeting different households and businesses. 2020,z,q 2019,z,q = Quartileq + ZIPz + #z,q Many data sets have already been used to study the dynamics of geographic level spending during the c2019,¯ q pandemic, but aggregated relationships may or may not be identical to those at the individual - where ct,z,q is average spending per customer with t as the year (for the time period April 15-May hold level at which economic behavior is ultimately determined.1 Our paper provides an initial step in analyzing these household-level dynamics.2 28), z is zip code and q is the income quartile. We take two steps to minimize the influence of outliers.

1See e.g. Chetty et al. (2020a). First, note that the denominator is c2019,¯ q which uses everyone in the income quartile. This prevents 2To be clear, our current analysis does not run regressions at the household level, but it does crucially rely on individual household data to define groups and outcomes of interest. We also focus for now on sorting households by pre-pandemic 12 1