Non-Pharmaceutical Interventions,
Teleworkability, and Involuntary JobSeparations

K. Hoshi1

H. Kasahara1

R. Makioka2

M. Suzuki3

S. Tanaka4

1 Vancouver School of Economics, UBC
2 Research Institute of Economy, Trade and Industry (RIETI)
3 Economic and Social Research Institute (ESRI), Cabinet Office and Tohoku Universitya
4 School of Economics, University of QueenslandaThe views expressed in this paper are those of the authors and do not necessarily represent the official views ofthe Economic and Social Research Institute, the Cabinet Office, the Government of Japan or the institutions theauthors are affiliated with.

1Issues• What is the impact of the “lockdown” policies andpeople’s movement on job losses and the spread ofCOVID-19 in Japan?
• The role of teleworkablity?
• What is a trade-off between job losses and the spreadof COVID-19?

2

Case Growth and Mobility

Weekly Case Growth (Solid Line)−10−200−30−40−1

Avg. Google Mobility Measure (Moving Average) (%) (Dotted Line)1

Prefecture
Chiba
Kanagawa
OsakaSaitama
Tokyo−50
04 2020

05 2020

06 2020

07 2020

08 2020

09 2020

Date

3

Prefecture Government Policies in Japan
State of Emergency

School Closure

Close Bar (Moving Average)

0.75

0.75

0.75

0.50

0.50

0.25

0.25

0.00

0.00
Mar

Apr

May

Jun

Jul

Aug

Indicator

1.00

Feb

0.00

Feb

Mar

Apr

May

Jun

JulAugFeb

Movie Closure

Close Commercial Facility (Moving Average)

0.50

0.25

0.00

0.00
Apr

May

Jun

Jul

Aug

Indicator

0.75

Indicator

0.75

0.25

Jul

Aug

0.50

0.25

0.00

Feb

Mar

Apr

Date

May

Jun

Jul

Aug

Date

Nightclub Closure

Feb

Mar

Apr

May

Jun

Jul

Aug

Date

Policy Index

Close Nightclub (Moving Average)

Mean Policies
1.00

0.75

0.75

Indicator

1.00

0.50

0.25

0.50

0.25

0.00
Feb

Jun

Close Museum (Moving Average)
1.00

0.75

0.50

May

Museum Closure

Close Movie Theater (Moving Average)
1.00

MarApr

Date

1.00

Feb

Mar

Date

Commercial Closure
Indicator

0.50

0.25

Date

Indicator

Bar Closures

Close School (Moving Average)
1.00

Indicator

Indicator

State of Emergency (Moving Average)
1.00

40.00
Mar

Apr

May

Date

Jun

Jul

Aug

Feb

Mar

Apr

May

Date

Jun

Jul

Aug

Unemployed
Leave
Non labor force
Unemployed
Leave

00
909

09
20

2000
8.2

ar

M

b.

Fe

800
8.2
Jan.

ec

D.200

08

20.2

ov

20202020

020
22020-100-100

YOY difference (10 thousand)0
100 200 300 400 500

YOY difference (10 thousand)0
100 200 300 400 500

COVID-19Nct

Op.

Se

l.

Jun.

Juay

M

002

20202020.2r.

AparM

b.

Fen.

Ja

YOY difference in unemployed, non-labor force, leave

Great Recession

Non labor force

Source: Labor Force Survey

5Employer
IncomeRetire8Employer
IncomeRetire

09

920
09

208

ar.200

M

b.

Fen.

Ja

ec.200D80820ov.200

ct.

l.

-2020202020

02020

YOY difference (10 thousand)020406080

YOY difference (10 thousand)020406080-20

COVID-19NO

p.
200Se

Other
Graduate
VoluntaryJuJun.

ay

M020.220202020r.
202Ap

arM

b.

FeJan.

YOY difference in involuntary job separations

Great Recession

Other
Graduate
Voluntary

Source: Labor Force Survey

6

YOY difference in employment: regular vs. non-regular

Jul.

20

202020n.
JuM

ay2020r.
ApRegular
Family/Self

Source: Labor Force Survey

2020002.2
arM

b.
FeJan.

20202020-150

YOY difference (10 thousand)-100-50050

YOY Diff in Employment: Regular vs. Non-Regular

Non-regular7

What is the impact of the “lockdown”
policies and people’s movement on joblosses?

8Did policies and voluntary restraint lead to job losses?

lnYi,j,2020

Yi,j,2019= β1 Zi,j + β2 Zi,j × Teli + γTeli + Xi0 α + ij

for prefecture i on month j = 2, 3..., 7.
• Yi,j : The number of job losses due to employer• Zi,j : Policy index or Mobility index• Teli : Teleworkability index• Xi : the log of GDP per capita, poverty rate, elderly rate,
and population density
9Prefecture-level Monthly Panel Data

• Monthly panel data from Feb to July of 2020.
• “Involuntary job separations due to employer” from Monthly
Report on Employment Insurance Programs, MHLW
(Koyou-Hoken-Jigyou-Geppou)• Policy Index = Average of 7 policy dummy variables• Mobility Index = (Workplaces+Retail+Grocery+Transit)/410

Policy Index and Google Mobility Index

Google Mobility Index

Policy Index
0.0

Google Mobility Index

Policy Index

0.75

0.50

0.25

0.950.75
0.5
0.25
0.05

0.00
2020−022020−032020−042020−05

month2020−06

2020−07

0.95
0.75
0.5
0.25
0.05

−0.1

−0.2

−0.3

−0.4
2020−022020−032020−042020−052020−062020−07month11

Involuntary Job Separations

YOY log diff in # job losses due to employer

Involuntary Job Separations

1.0

0.95
0.5

0.75
0.5
0.25

0.0

0.05
−0.5

2020−01 2020−02 2020−03 2020−04 2020−05 2020−06 2020−07

month12Teleworkability

Four different teleworkabilty indexes:
1. Indexes based on occupational task descriptions• Dingel-Neiman’s measure mapped into Japanese
occupational classifications.
• DN’s measure based on JONET.

2. Indexes based on actual telework hours data
• Persol (April 12-13 2020, regular workers only).
• Line (April 10-12 2020, office workers only).

13Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
SagaNagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

index

Persol Teleworkability
0.5

0.4

0.3

0.2

0.1

0.0

prefecture14

Did policies and voluntary restraint lead to job loss?

Dependent variable: Involuntary Job Separations

Mobilityi,j

(1)

(2)

−0.775∗(0.460)−2.719∗∗(1.079)

Policyi,j−0.556∗∗(0.277)

Persoli
Mobilityi,j × Persoli−0.233(0.257)3.143∗∗∗(1.205)

(3)

(4)

0.174
(0.120)−0.504∗∗(0.257)0.579∗∗(0.292)−0.375(0.247)−0.842∗(0.469)

Policyi,j × Persoli
Observations
R2
Note:

282
0.485

282
0.497

282
0.485

282
0.494

Weighted by Population; Includes prefecture controls and month dummies.

15

Different teleworkability measures

Mobilityi,jLine

Dependent variable: Involuntary Job Separations
DN
DN-JONET
Line
DN

(1)

(2)

(3)

−2.743∗∗(1.098)−7.192∗∗∗(2.493)−3.972∗(2.069)

Policyi,jTeli
Mobilityi,j × Teli

−0.283(0.234)2.761∗∗(1.109)−0.751(1.395)21.260∗∗∗(7.373)−0.024(1.049)8.468∗∗(4.226)

Policyi,j × TeliObservations
R2Note:

282
0.501

282
0.499

282
0.479

DN-JONET

(4)

(5)

(6)

0.507∗(0.278)−0.381∗(0.225)1.606∗∗(0.675)−1.679(1.234)0.898∗(0.535)−0.284(0.972)−0.676(0.417)−5.271∗∗(2.273)−2.190∗(1.278)

282
0.496

282
0.493

282
0.478

Weighted by Population; Includes prefecture controls and month dummies.

16

IV regression — use Policy as an IV for Mobility

Dependent variable: Involuntary Job Separations
PersolLine
DN
DN-JONET

Mobilityi,jMobilityi,j × Teli
TeliObservations
R2(1)

(2)

(3)

(4)

−7.384∗∗(3.730)7.182∗∗(3.446)−0.163(0.260)−6.568∗(3.621)5.724∗(3.004)−0.262(0.217)−10.896∗∗(4.351)31.196∗∗∗(11.662)−0.196(1.250)−8.723(5.651)16.356∗(9.714)−0.188(1.006)

282
0.451

282
0.468

282
0.493

282
0.462

Weighted by Population; Includes prefecture controls and month dummies.

17

Summary of Involuntary Job Loss Regression• “Lockdown” Policies, Mobility ↓⇒ Involuntary Job Separations ↑.
• Higher teleworkability weakens these relationships.
(e.g., Tokyo suffers less from the state of emergency thanother prefectures).

18

What is the impact of the “lockdown”
policies and people’s movement on thespread of COVID-19?

19

Causal Diagram: Chernozhukov, Kasahara, and Schrimpf(2020)
Infoi,t−14

Pi,t−14

Infoi,t−14

Xit∆ log Cit

Mi,t−14• ∆ log Cit : the growth rate of cases• Pi,t−14 : the lagged policies (e.g., state of emergency)• Mi,t−14 : the lagged behavior variables (Google mobility)• Infoi,t−14 : past cases• Xit : confounders (prefecture characteristics, month dummies)

20

Dynamic feedback

Infoi,t−14SEM(t+14)

SEM(t)

SEM(t-14)∆ log Cit

Infoi,t+14Infoit∆ log Ci,t+14

Infoi,t−14 = ∆ log Ci,t−14 , log Ci,t−14∆ log Ci,t+28

0= (lagged case growth, lagged cases)
21

Case Growth Regression Analysis∆ log Cit = αMi,t−14 + µ0 Infoi,t−14 + δY0 Xit + εyit
Mi,t−14 = βPi,t−14 + γ 0 Infoi,t−14 + δB0 Xit + εbit• ∆log∆Cit : the growth rate of cases• Mi,t−14 : Mobility Index lagged by 14 days
• Pi,t−14 : Policy Index lagged by 14 days
• Infoi,t−14 : the log of lagged cases, lagged case growth• Xit : prefecture-level characteristics and month dummies.
22

Daily Panel Data for COVID-19• Data Period: from March 29 to August 31.
• Daily cases: https://gis.jag-japan.com/covid19jp/• The number of tests: Toyo Keizai• Policy Index = Average of 7 policy variables• Mobility Index = (Workplaces+Retail+Grocery+Transit)/4We use 7 days moving averages of all variables23

Case Growth and Mobility

Weekly Case Growth (Solid Line)−10−200−30−40−1

Avg. Google Mobility Measure (Moving Average) (%) (Dotted Line)

1Prefecture
Chiba
Kanagawa
Osaka
Saitama
Tokyo−5004 2020

05 2020

06 2020

07 2020

08 2020

09 2020

Date

24

Case growth and mobility
42

prefecture
Aichi

Weekly Case Growth

Chiba
Fukuoka
0Hokkaido
Hyogo
Kanagawa
Kyoto−2

Okinawa
Osaka
Saitama
Tokyo−4−6−50−40−30−20−100

Lagged Average Google Mobility (%)

25

The Effect of Mobility and Information on Cases

Dependent variable.: ∆log∆CitMobility∆log∆Ci,t−14log∆Ci,t−14
Observations
R21.213∗∗(0.478)0.131∗∗∗(0.043)-0.244∗∗∗(0.047)
2,270
0.426

Note: Weighted by Population; prefecture controls, monthly dummies, Golden week
dummy, and the log of test included.

26

The Effect of Policy on Mobility
Dependent variable: Mobilityit

Policyit(1)(2)−0.144∗∗∗0.013∗∗∗(0.002)−0.011∗∗∗(0.001)−0.085∗∗∗(0.012)−0.272∗(0.141)−0.194∗∗∗(0.031)0.012∗∗∗(0.002)−0.008∗∗∗(0.001)

2,480
0.927

2,480
0.931

(0.007)
Teleworkability
Policyit × Teleworkabilityi∆log∆Cit
log∆Cit
Observations
R2

Note: Weighted by Population; prefecture controls, monthly dummies, and Golden
week dummy included.
27

Fit and Prediction of Weekly Case Growth in Tokyo
Estimate of Case Growth

Case Growth1

type
estimate
observed

0−1

Mar

AprMayJunJulAug

Sepdate28

Trade-off between job losses and thespread of COVID-19?

29

People’s movement as an input for production function

The production function:
Yt = min{Lt , ϕ(Mt )},⇒Yt = Lt = ϕ(Mt ),where• Lt : employment• Mt : people’s movement• ϕ0 (Mt ) captures teleworkability.

30

Social planner’s problem with SIRD modelv0 = max
{Lt }

∞
X

δ t (1 − Dt )u(yt )

t=0s.t. yt :=Lt,
1 − DtMt = ϕ−1 (Lt ),St+1 = St − βt (Mt )St It(Susceptible)

It+1 = (1 − γ)It + βt (Mt )St It(Infectious)

Rt+1 = Rt + πr It+1(Recoverd)

Dt+1 = Dt + πd It+1(Dead)

Trade-off between jobs and deaths
Lt ↑ ⇒ Mt ↑ ⇒ It+1 ↑ ⇒ Dt+1 ↑31

Optimality condition:vt (St , It , Dt ) = max (1 − Dt )u(yt ) + δvt+1 (St+1 , It+1 , Dt+1 ),Lt⇒
0u (yt ) = δ



∂vt+1
∂vt+1
∂vt+1−+ πd
∂It+1
∂St+1
∂Dt+1

∂It+1
∂Lt

where
∂vt+1
< 0, infectious ↑ ⇒ future infections/deaths ↑∂It+1∂vt+1−> 0, susceptible ↓ ⇒ future infections ↓∂St+1∂vt+1πd< 0, disutility from deaths.
∂Dt+132

Motivations for Regression Analysis• Case Growth Regression:
It+1 − It= −γ + βt (Mt ),It⇒∆ log Cit = α0 Mi,t−14 + µ0 Infi,t−14 + δY0 Xit + εyit

• Job Loss Regression:
1 − Lt = 1 − ϕ−1 (Mt ).
⇒ln

Yi,j2020

Yi,j,2019= β1 Mi,j + β2 Mi,j × Teli + γTeli + Xi0 α + ij

33

Short-run trade off between job loss and infection in July
Trade off between Cases and Involuntary Job Loss (Tokyo)
12.5

Cummulative Cases per 10,000, July

10.0

Teleworkability
Aichi's teleworkability
Observed
Tokyo's teleworkability

7.5

Level of Mobility
0.0
−0.1
−0.2
5.0−0.3−0.4−0.5

2.51012141618

Involuntary Job Loss per 10,000, July

It+1 = (1 − γ + βt (ϕ−1 (Lt )))Itgiven It .
34

“Mid-run” trade off: July-September
Trade off between Cases and Involuntary Job Loss (Tokyo)

Cummulative Cases per 10,000, July−September

200

Level of Mobility
0.0
−0.1

150

−0.2
−0.3
−0.4
−0.5

100

Teleworkability
Aichi's teleworkability
Tokyo's teleworkability
50060

80

100120Involuntary Job Loss per 10,000, July−September

TX
s=1It+s =TX(1 − γ + βt−1+s (ϕ−1 (Lt−1+s )))s It .

s=135

Discussion• Short-run trade-off in July in Tokyo:• The short-run trade-off is→saving 1 job loss requires 7 more cases.

• What if Tokyo is like Aichi?
→

saving 1 job loss requires 1 more case.

• Mid-run trade-off in July-Sept:→saving 1 job loss requires 25 more cases.

• Keeping low job losses for a longer period requires a larger
number of cumulative cases because of “exponentialgrowth”.
36Conclusion• “Lockdown” policies ⇒ Mobility ↓.
• Mobility ↓ ⇒ Involuntary Job Loss ↑ and Case Growth ↓.
• High teleworkability weakens these relationships.
(e.g., Tokyo suffers less from the state of emergency).
• Quantifying short-run vs. long-run trade-off between
infections and job losses.

37

Limitations and caveats• Limited economic outcomes.
• Limited health outcomes.
• Google mobility measures are useful but limited.
• Trade-offs are different across different individuals.
• Job loss risk depends on regular vs. non-regular,occupations/industries.
• Health risk depends on young vs. old, underlying medicalconditions.

⇒ Negative externality under heterogenous risks.

38