Pandemics According to HANK - Benjamin Moll · 2020-05-12 ·...

Pandemics According to HANK

preliminary

Greg KaplanBen Moll

Gianluca ViolanteSlides at https://benjaminmoll.com/HANK_pandemic/

LSE, May 4, 2020

May 12, 2020

https://benjaminmoll.com/HANK_pandemic/

Questions

1. How save most possible lives while minimizing economic damage?

2. How much foregone prosperity? Is there a tradeoff?

3. Depending on health policies, which mix of economic policies?

• Besides macroeconomy, should worry about distributional effectsand incomplete consumption insurance

• some households hit much harder than others• true both for virus & policies, e.g. lockdown→ occupations

• Expect answers to questions 1.-3. to depend on these effects

1

What We Do

Develop integrated framework to study1. Dynamics of epidemic2. Macroeconomic and distributional effects

under different health, monetary, fiscal & social insurance policies

A HANK + SIR model with• Different occupations, sectors• Two-way feedback: infection risk↔ economic behavior

Document key moments in data: workers in vulnerable occup’ns have1. lower labor incomes2. lower liquid wealth (even rel. to labor income)

Calibrate model to match these, examine counterfactuals• So far: do nothing, lockdowns• Working on: more targeted health/econ policies, e.g. test & trace 2

What We Find

• Epidemic, lockdown policies hit poor households the most(consistent with AdamsPrassl-Boneva-Golin-Rauh, Mongey-Pilossoph-Weinberg,...)

• Different policies trace out a “Pandemic Possibility Frontier”

3

What We Find• Epidemic, lockdown policies hit poor households the most

(consistent with AdamsPrassl-Boneva-Golin-Rauh, Mongey-Pilossoph-Weinberg,...)

• Different policies trace out a “Pandemic Possibility Frontier”

• Advantage: sidesteps VSL debate (but not tradeoffs within axes) 3

What We Find

• Epidemic, lockdown policies hit poor households the most(consistent with AdamsPrassl-Boneva-Golin-Rauh, Mongey-Pilossoph-Weinberg,...)

• Perhaps no tradeoff? ⇔ Is frontier upward-sloping in some parts?

3

What We Find

Today’s results: do nothing vs lockdowns of different duration

• There is a tradeoff but it varies considerably by time horizon

• Short run: do-nothing recession almost as bad as lockdown

• Long run: larger tradeoff

Problem with lockdowns-only strategy is duration

• short lockdown⇒ 2nd wave, large recession and many deaths

• to avoid 2nd wave, need to wait until reach “herd imm threshold”

• this takes a very long time – think 500 days

Duration + dist effects⇒ pure lockdowns won’t work, need alternatives3

Where we are going next

• What policies expand the “Pandemic Possibility Frontier”?

• Candidates: test-trace-isolate, other more targeted policies

3

Dimensions we are not considering today

1. Firm balance sheets, liquidity provision to firms(Buera-FattalJaef-Neumeyer-Shin, Elenev-Landvoigt-VanNieuwerburgh,...)

2. Costly destruction of employment relationships(firm-specific human and organizational capital)

3. Differential impacts across age groups(Glover-Heathcote-Krueger-RiosRull, Bairoliya-Imrohoroglu, Brotherhood-Kircher-Santos-Tertilt,...)

4. Differential impacts across gender(Alon-Doepke-OlmsteadRumsey-Tertilt,...)

5. Input-output linkages(Baqaee-Farhi,...)

6. Economic prosperity→ deaths(cuts to public/private health spending, “deaths of despair”)

7. ... 4

Model

5

Model overviewEpidemiology• SEIR model with added critically ill (ICU) state• Infection rate depends on economic behavior

Households• Face uninsured idiosyncratic labor income risk and health risk• Hold 2 assets: liquid and illiquid• Consume 3 goods: regular, social, home production• Supply 3 types of labor: workplace, remote, home production

Occupations• Remote work = poor substitute for some⇒ labor supply effect• Some primarily employed in social sector⇒ labor demand effect• Essential vs non-essential

Remaining model ingredients: standard NK model 6

SEIR model with ICU state, feedback from behavior• St : susceptible• Et : exposed = infected but not yet infectious• It : infectious• Ct : critical = in ICU and may ultimately die• Rt : recoveredStEtItCtRt

=−βt ItNt βt

ItNt 0 0 0

0 −λE λE 0 0

0 0 −λI λIχ λI(1− χ)0 0 0 −λC λC(1− ∆C)min

{ΘCt , 1

}λR 0 0 0 −λR

T StEtItCtRt

• βt = β(Cst , Lwt): infections depend on social C, workplace L• Death probability of Ct ’s depends on Ct ≷ max ICU capacity Θ• Deaths: Dt = λC

(∆C1{Ct≤Θ} + (∆CΘ+ Ct −Θ)1{Ct>Θ}

)• Population size: Nt = St + Et + It + Ct +Rt with N0 = 1 7

Some Vocabulary, Lockdowns in SIR Models1. Basic reproduction number R0 := β0/λI2. Effective reproduction number Ret := R0 × St/Nt3. Herd immunity threshold S∗/N := 1/R0 or R∗/N := 1− 1/R04. Final size of disease S∞ or R∞ +D∞, solves S∞ = e−R0(1−S∞)

Two key features of SIR models:1. Infections ↑ if Ret > 1 or S > S∗ and ↓ otherwise

(follows from I + E =(R0SN − 1

)λII = (Re − 1)λII )

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

8

Households

Period utility

U[c, υs(I)s, h]− V [υw (I)ℓw , ℓr , h] with I = βIN

• c : regular consumption • s: social consumption• ℓw : workplace hours • ℓr : remote hours• h: home production • υs , υℓ: disutility of infection risk

Health status: healthy or sick = C = hospitalized in ICU

Budget constraint of healthy household working in occupation jb = (1− τ)w jz

(ℓw + ϕ

jℓr)+ rbb + T − c − pss − d − χ(d, a)

a = r aa + d

• b: liquid assets • a: illiquid assets• ϕj ≤ 1: flexibility of occupation j • χ: transaction cost

Sick households cannot produce, gov’t provides c and s 9

Occupations differ in three dimensions

1. Flexibility = substitutability of remote workEffective labor supply = Ljw + ϕjLjr

e.g. ϕj = 0⇔ occupation cannot be performed remotely2. Employment intensities in social and regular sectors, (ξjs , ξjc)

Yi = ZiNαii K

1−αii , Ni =

J∑j=1

(ξji

) 1σ(N ji

) σ−1σ

σσ−1

, i ∈ {s, c}

3. Whether essential = continue working during lockdown

Current calibration: J = 5 occupationsHigh Flexibility Low Flexibility

High intensity in C Software engineer, architect Car mechanic, minerHigh intensity in S Event planner, social scientist Waiter, shop assistant+ Essential, e.g. nurse, supermarket clerk, dentist

10

Lockdowns

1. Constrain activity of S sectorModel as mandated decrease in capital utilitization

Ys = Zs(κsKs)αsN1−αss , κs < 1

2. Constrain workplace hoursModel as mandated maximum share of workplace hours

ℓw ≤ κℓ(ℓw + ℓr ), κℓ < 1

• Example: complete lockdown means κs = κℓ = 0

• Such lockdowns reduce infections because βt = β(Cst , Lwt)

• Also note: lockdowns affect same behavioral margins as epidemic11

Remaining model ingredients

Firms• Monopolistic intermediate-good producers→ final s,c goods• Quadratic price adjustment costs à la Rotemberg (1982)

Investment Fund• Illiquid assets = shares in investment fund• Owns K and intermediate producers in c, s sectors

Government• Issues liquid debt (Bg), spends (G), taxes and transfers (T )

Monetary Authority• Sets nominal rate on liquid assets based on a Taylor rule

12

Summary of market clearing conditions

• Regular goods marketYc = Cc + I + G + χ

• Social goods marketYs = Cs

• Labor market for each occupation

N jc + Njs =

∫z(ℓjw (h, a, b, z) + ϕ

jℓjr (h, a, b, z))dµ, j = 1, ..., J

• Liquid asset marketBh + Bg = 0

• Illiquid asset marketAh = V f (K,S), K = Kc +Ks 13

Parameterization

14

Three key aspects of parameterization

1. Epidemiological model

2. Occupational parameters

• avg. earnings, flex. measures by occ. from ATUS, ONET• sectoral employment intensities in C and S sectors• liquid wealth by occupation from SIPP

3. Two-way feedback: infection risk↔ economic behavior

• infections→ behavior: drop in mobility before lockdown• infections← behavior: drop in R0 after lockdown

• Production side: standard calibration of NK models15

Epidemiological parameters from epi literatureDescription Parameter ValueBasic reproduction number R0 2.5

Avg. duration of Exposure (incubation) TE ⇒ λE = 1/TE 5.2 daysAvg. duration of Infectious TI ⇒ λI = 1/TI 4.3 daysTransmission coefficient β0 = R0/TI 0.58

Case fatality rate CFR = χ∆C 0.01

Daily growth rate of infections at start of epidemic (when S ≈ N ):

I + E = β IN S − λII ≈ (β − λI)I =R0 − 1TI

I = 35%× I

Covid-19 dynamics: do nothing, no behavioral feedback, no ICU constraint 16

Epidemiological parameters from epi literatureDescription Parameter ValueBasic reproduction number R0 2.5

Avg. duration of Exposure (incubation) TE ⇒ λE = 1/TE 5.2 daysAvg. duration of Infectious TI ⇒ λI = 1/TI 4.3 daysTransmission coefficient β0 = R0/TI 0.58

Case fatality rate CFR = χ∆C 0.01

Daily growth rate of infections at start of epidemic (when S ≈ N ):

I + E = β IN S − λII ≈ (β − λI)I =R0 − 1TI

I = 35%× I

Covid-19 dynamics: do nothing, no behavioral feedback, max ICU capacity = 60,000250 mio 16

Occupations: measures of flexibility• ONET: 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

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)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

O*N

ET

(share

of te

lew

ork

able

jobs)

• Model: two flexibility levels• Define high flexibility occupation if ONET share > 0.5.

17

Occupations: employment intensities in S,C sectors

First classify 3-digit NAICS industries as “regular” (C) or “social” (S)

Sector C (VA share: 0.74) Sector S (VA share: 0.26)Agriculture, fishing, and hunting Retail tradeMining Air, rail, and water transportationUtilities Educational servicesConstruction Health care and social assist. servicesManufacturing Rental and leasing servicesWholesale trade Arts, entertainment, accomm. servicesPostal, pipeline and truck transp. Food servicesWarehousing and storage Personal servicesInformationFinance and insuranceReal estateProfessional and business servicesSource: BEA

18

Summary of occupational parameters

Example ϕj ξjc ξjs Empl Share Earnings Liq WealthSoftware Eng 0.95 0.51 0.14 0.23 $74K $4,150Event Planner 0.95 0.04 0.19 0.10 $53K $2,100Car Mechanic 0.15 0.21 0.05 0.16 $44K $890Waiter 0.15 0.03 0.26 0.21 $33K $548Nurse 0.15 0.21 0.35 0.30 $46K $1,200Source: OES, SIPP

1. Start from 3-digit occupations

2. Identify essential ones (Source: Brookings)

3. Rank non-essential ones by flexibility (high, low)

4. Then rank them independently by rel. intensity in S vs C (high, low)

19

Workers in rigid occupations have less liquidity

-1000 0 1000 2000 3000 4000 5000

Median liquid wealth ($)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ON

ET

(sh

are

of

tele

wo

rka

ble

jo

bs)

Correlation between Flexibility and Median Liquid Wealth Across Occupations

Weighted Correlation: 0.51

Source: SIPP

Liquid wealth: checking, saving, MM accounts + directly-held MFs,stocks & bonds, net of CC debt

20

Feedback from economic activity to epidemic

βt = β0

(Cst

Cs

)νsβ (LwtLw

)νwβ.

• Real time estimates of Rt : drop from 2.5 to 0.8 after lockdown• Decline in workplace and social consumption around 50%• It implies νsβ = νwβ = 0.8

Google COVID-19 Community Mobility Data

Feb 29 Mar 14 Mar 28 Apr 11 Apr 25

Date 2020

-60

-50

-40

-30

-20

-10

0

10

Work

pla

ce M

obili

ty Index

Over 90% of population

in lockdown

Feb 15 Feb 29 Mar 14 Mar 28 Apr 11 Apr 25 May 09

Date 2020

-70

-60

-50

-40

-30

-20

-10

0

10

20

Reta

il M

obili

ty Index

Over 90% of population

in lockdown

Workplace mobility Retail & recreation mobility 21

Feedback from epidemic to economic activity

• Parameterize utility shifters:

υs(It) = exp(−νsuIt

), υw (It) = exp

(νwu It

)• Drop in economic activity before government interventions

Feb 15 Feb 22 Feb 29 Mar 07 Mar 14 Mar 21 Mar 28 Apr 04 Apr 11

Date 2020

-70

-60

-50

-40

-30

-20

-10

0

10

% d

iffe

rence fro

m b

aselin

e,w

ork

pla

ces

New York

Business closure

Stay-at-home

School closure

Dining room ban

School closure

Dining room banDining room ban

School closure

Feb 15 Feb 22 Feb 29 Mar 07 Mar 14 Mar 21 Mar 28 Apr 04 Apr 11

Date 2020

-70

-60

-50

-40

-30

-20

-10

0

10

% d

iffe

rence fro

m b

aselin

e,r

eta

il and r

ecre

ation

New York

Business closure

Stay-at-home

School closure

Dining room ban

School closure

Dining room banDining room ban

School closure

• Set νsu = 0.5 and νwu = 0.5 ( = 0 but conservative)

22

Pandemic Scenarios

23

Pandemic scenarios through lens of model1. Do nothing2. Lockdowns (indiscriminate)3. Working on: more targeted policies

• health: testing & targeted quarantines, testing by occupation• econ: targeted exit strategies & social insurance policies

• Goal: plot all of these in “Pandemic Possibility Frontier” space

24

Do Nothing: Covid-19 dynamics without feedbackfrom behavior

• Next: same shock in our HANK+SIR model

• Notes: for now

• 2-week time steps⇒ time paths less smooth• Cobb-Douglas across occ’s⇒ similar labor inc ↓ in rigid vs flex

25

Do Nothing: Epidemic and Macro Aggregates

26

Do Nothing: Distributional Effects by Occupation

• Labor inc ↓ also for essential bc primarily employed in S (dentists)

• Next: relax Cobb-Douglas occ’s⇒ rigid occ labor inc’s ↓ by more27

Short Lockdown: Epidemic and Macro Aggregates

28

Short Lockdown: Distributional Effects by Occupation



Long Lockdown: Epidemic and Macro Aggregates

30

Long Lockdown: Distributional Effects by Occupation



Pandemic Possibility Frontier, 3 Month Horizon

Legend: do nothing, short lockdown, long lockdown

32



33



34

Some Unpleasant Lockdown Arithmetic

• If lockdown only option, how long does effective one need to last?

• Key: need to reach herd immunity. So: how long to reach that?

• Optimistically assume perm immunity, R0 ↓ from 2.5 to 2 (hygiene..)herd immunity threshold = 1− 1/R0 = 50%

• Simple back of envelope calculation for U.S. Assumptions:1. 10% have had disease⇒ need additional 40% ≈ 100 million2. lockdown suppresses Re to 1 (close to current US, Farboodi-Jarosch-Shimer)

3. 200,000 new infections per day (current official count ≈ 30,000)

• ⇒ need some sort of lockdown / control for100 million200, 000

= 500 days

• Note: optimistic calculation assuming low R0, permanent immunity35

Which policies expand Pandemic Possibility Frontier?

Natural candidate: test, trace and isolate36

Next on to-do list: test, trace and isolate

3-months heavy lockdown, then test & trace with R0 ↓ by 50% to 1.25Source: Ferretti et al (Science, 2020) and https://ncase.me/covid-19/

Potential promise: eliminate 2nd wave without damaging economy

Other targeted policies to evaluate• health: testing by occupation,...• econ: targeted exit strategies & social insurance policies

37

https://ncase.me/covid-19/

Conclusion

A HANK+SIR model to study

1. Dynamics of epidemic

2. Macroeconomic and distributional effectsunder different health, monetary, fiscal & social insurance policies

Main takeaways so far:

1. Liquidity matters: most exposed households also have lowestliquidity, may not be able to survive for long without financial help

2. This could significantly affect policy tradeoffs

3. “Pandemic Possibility Frontier” = useful way of structuring thinking38

Pandemics According to HANK - Benjamin Moll · 2020-05-12 ·...

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