saving lives versus saving livelihoods: can big data ...€¦ · israel the shield 1m singapore...
TRANSCRIPT
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Saving Lives versus Saving Livelihoods: Can Big DataTechnology Solve the Pandemic Dilemma?
Kairong Xiao
Columbia Business School
COVID-19 and Economics: China, Asia and BeyondMay 1, 2020
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Motivation
COVID-19: an impossible choice between saving lives and saving livelihoods
Population movement restrictions are deemed necessary to contain pandemics
But such restrictions inflict steep economic costs
U.S. GDP is foretasted to decline at a 37% annual rate from April to June(WSJ, April 29)
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How do we solve the pandemic dilemma?
Table 1: Top 10 Most Downloaded Contact-Tracing Apps
Country App Name DownloadsIndia Aarogya Setu 50MCzech Republic Mapy.cz 1MColombia CoronApp 1MSouth Korea Corona 100m 1MIsrael The Shield 1MSingapore TraceTogether 0.5MIndia (Punjab) COVA Punjab 0.5MSpain (Catalonia) STOP COVID19 CAT 0.5MNorway Infection Stop 0.1M
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Big Data Technology
Advocates
I Detect potential carriers and allow the mass population to resume work
I Successful experience in China and South Korea
Critics
I Inconclusive evidence: unsuccessful experience in Singapore
I Privacy infringement and government surveillance
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This paper
Exploit the staggered adoption of contact-tracing apps in 322 Chinese cities
Use high-frequency measures of economic activities
I Within-city population movements
I Emission of greenhouse gas
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Findings
Cities that adopt contact-tracing apps experience a significant increase ineconomic activities without suffering from higher infection rates
Contact-tracing apps create an economic value of 0.5%-0.75% of GDPduring the COVID-19 outbreak
The economic benefits seem to outweigh the cost of privacy
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Institutional Background
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Contact-Tracing Apps: Health Code
Green: no restriction
Yellow: isolation for 7 days (then it turns green)
Red: isolation for 14 days (then it turns green)
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Contact-Tracing Apps: Health Code
First implemented in Hangzhou on Feb 11, 2020
Implemented by other cities in a staggered manner
The implementation is uncoordinated by the central government
Cities often have different versions of health code
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Data
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Data
Implementation dates of health code for 322 cities in China
Within-city population movements
Greenhouse gas level of each city
Daily COVID-19 infection counts for 322 cities in China
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Adoption of Health Code in Chinese Cities
Wuhan Lockdown
Hangzhou Health Code
010
020
030
0C
umm
ulat
ive
# of
citi
es a
dopt
ing
heal
th c
ode
010
2030
# of
citi
es a
dopt
ing
heal
th c
ode
01jan2020 01feb2020 01mar2020 01apr2020Date
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Adoption of Health Code in Chinese Cities
DateFeb 15Feb 29Mar 15Mar 31Data not available
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Data
Implementation dates of health code for 322 cities in China
Within-city population movements
Greenhouse gas level of each city
Daily COVID-19 infection counts for 322 cities in China
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Economic Activities of Chinese Cities
Wuhan Lockdown
Hangzhou Health Code
020
4060
8010
012
0Ec
onom
ic a
ctiv
ities
01jan2020 01feb2020 01mar2020 01apr2020Date
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Data
Implementation dates of health code for 322 cities in China
Within-city population movements
Greenhouse gas level of each city
Daily COVID-19 infection counts for 322 cities in China
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Nitrogen Dioxide Level of Chinese Cities
Wuhan Lockdown
Hangzhou Health Code
020
4060
8010
0N
O2
01jan2020 01feb2020 01mar2020 01apr2020Date
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Nitrogen Dioxide Level of Chinese Cities from NASA
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Data
Implementation dates of health code for 322 cities in China
Within-city population movements
Greenhouse gas level of each city
Daily COVID-19 infection counts for 322 cities in China
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Nitrogen Dioxide Level of Chinese Cities
Wuhan Lockdown
Hangzhou Health Code
020
000
4000
060
000
8000
0#
of c
ases
01jan2020 01feb2020 01mar2020 01apr2020Date
Confirmed CuredDeaths Active
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Summary Statistics
N mean sd p5 p25 p50 p75 p95
Within-city movements 28658 78 26 32 56 84 99 109
NO2 24742 63 30 23 40 58 81 119
PM2.5 24742 78 51 20 43 68 102 171
Infection rate 28658 2 5 0 0 0 0 20
Confirmed cases 28658 144 1986 0 0 8 31 213
Cured cases 28658 83 1252 0 0 3 18 140
Deaths 28658 5 91 0 0 0 0 3
Emergency level 28658 2 1 0 0 2 3 3
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Results
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Effect of Health Code on Within-city Movement
Regression model
EconomicActivityi,t = βHealthCodei,t + γXi,t + εi,t
(1) (2) (3) (4)Movement Movement Movement Movement
Health code 2.859∗∗∗ 2.687∗∗∗ 2.552∗∗∗ 3.118∗∗∗
[0.410] [0.345] [0.437] [0.399]Control Yes Yes Yes YesCity F.E. Yes Yes Yes YesTime F.E. Yes Yes Yes YesEmergency F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 28,658 27,145 28,658 28,658Adj. R-squared 0.852 0.862 0.867 0.852
The introduction of health code leads to around 2-3% increase in within-citymovement.
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Dynamic Effect of Health Code on Economic Activities
-20
24
6Tr
eatm
ent e
ffect
-20-19-18-17-16-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time since treatment
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Effect of Health Code on Greenhouse Gas
Regression model
EconomicActivityi,t = βHealthCodei,t + γXi,t + εi,t
(1) (2) (3) (4)NO2 NO2 NO2 NO2
Health code 1.792∗ 1.973∗ 1.417 1.980∗∗
[1.006] [1.057] [1.106] [0.990]Control Yes Yes Yes YesCity F.E. Yes Yes Yes YesTime F.E. Yes Yes Yes YesEmergency F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 24,742 23,674 24,742 24,742Adj. R-squared 0.541 0.534 0.555 0.541
The introduction of health code leads to around 2% increase in NO2 level.
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Effect of Health Code on Greenhouse Gas
Regression model
EconomicActivityi,t = βHealthCodei,t + γXi,t + εi,t
(1) (2) (3) (4)PM2.5 PM2.5 PM2.5 PM2.5
Health code 4.989∗∗∗ 4.830∗∗∗ 4.514∗ 5.152∗∗∗
[1.671] [1.767] [2.287] [1.626]Control Yes Yes Yes YesCity F.E. Yes Yes Yes YesTime F.E. Yes Yes Yes YesEmergency F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 24,742 23,674 24,742 24,742Adj. R-squared 0.358 0.352 0.378 0.360
The introduction of health code leads to around 4% increase in PM2.5 level.
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Effect of Health Code on Between-city Migration
Regression model
Inflowi,j,t = βDestinationHealthCodej,t + γXi,j,t + εi,t ,
(1) (2) (3) (4)Inflow Inflow Inflow Inflow
Health Code (destn) 12.694∗∗∗ 13.185∗∗∗ 11.183∗∗∗ 12.557∗∗∗
[1.700] [1.696] [1.709] [1.687]Control Yes Yes Yes YesCity pair F.E. Yes Yes Yes YesSource-time F.E. Yes Yes Yes YesEmergency level F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 1,888,652 1,798,439 1,888,652 1,888,652Adj. R-squared 0.857 0.859 0.863 0.858
The introduction of health code increases inflows to a city.
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Effect of Health Code on Between-city Migration: Inflows
Ningbo
Hangzhou (with health code)
Shanghai (w/o health code)
13%↑
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Effect of Health Code on Between-city Migration
Regression model
Outflowi,j,t = βSourceHealthCodei,t + γXi,j,t + εi,t
(1) (2) (3) (4)Outflow Outflow Outflow Outflow
Health Code (source) -14.840∗∗∗ -14.647∗∗∗ -13.681∗∗∗ -14.549∗∗∗
[1.778] [1.744] [1.612] [1.749]Control Yes Yes Yes YesCity pair F.E. Yes Yes Yes YesDestination-time F.E. Yes Yes Yes YesEmergency level F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 1,887,544 1,834,631 1,887,544 1,887,544Adj. R-squared 0.860 0.862 0.861 0.860
The introduction of health code in the source city decrease flows from a city.
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Effect of Health Code on Between-city Migration: Outflows
Ningbo
Hangzhou (with health code)
Shanghai (w/o health code)
15%↓
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Effect of Health Code on Infection Rates
Regression model
InfectionRatei,t+7 = βHealthCodei,t + γXi,t + εi,t
(1) (2) (3) (4)Infection rate Infection rate Infection rate Infection rate
Health code 0.024 0.028 -0.000 0.027[0.078] [0.081] [0.092] [0.082]
Control Yes Yes Yes YesCity F.E. Yes Yes Yes YesTime F.E. Yes Yes Yes YesEmergency F.E. Yes Yes Yes YesSample Full sample Excl. Hubei Match by cases Match by act.Observations 26,404 25,010 26,404 26,404Adj. R-squared 0.411 0.388 0.412 0.408
The introduction of health code does not lead to a significant increase ininfection rates.
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Dynamic Effect of Health Code on Infection Rates
-2-1
01
2Tr
eatm
ent e
ffect
-20-19-18-17-16-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time since treatment
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Saving Lives vs. Saving livelihoodRegression model
InfectionRatei,t+7 = β1HealthCodei,t × EconomicActivityi,t + γXi,t + εi,t
11.
051.
11.
15In
fect
ion
rate
0 10 20 30 40 50 60 70 80 90 100Economic activities
With Health Code Without Health Code
Without health code, economic activities have to decrease by 19% to reducethe daily infection rate by 1% from its average level.
With health code, economic activities only need to decrease by 17% toachieve the same amount of reduction in infection rate.
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Do Benefits Justify Costs?
2-3% increase in economic activities
The outbreak lasted for a quarter in China
Health code creates an economic value of 0.5%-0.75% GDP
Chinese GDP per capita: $10,000
Economic value per person: $50 − $75
Value of privacy: $33 (Huang, 2019)
Caveat 1: culture differences in value of privacy (Athey et al, 2017)
Caveat 2: data anonymization
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Why Is Big Data Effective in Fighting Pandemics?
The key amplification mechanism: incomplete information
Because of the hidden virus, people are afraid of going out, which bringseconomy actitivities to a standstill
Governments have to impose quarantines on the whole population just tostop a few hidden carriers
Big data technology can effectively alleviate information frictions
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Conclusion
Cities adopt contact-tracing apps experience a significant increase ineconomic activities without suffering from higher infection rates
Contact-tracing apps create an economic value of 0.5%-0.75% of GDPduring the COVID-19 outbreak
The economic benefits seem to outweigh the cost of privacy
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