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Analyzing risk perception and social influence effects on self-restriction behavior in response to the COVID-19 pandemic in Japan: First results

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This presentation has a published article associated with it. Please refer to: Parady, G., Taniguchi, A., Takami, K. (2020) Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing the effects of risk perception and social influence on going-out self-restriction. Transportation Research Interdisciplinary Perspectives (In press) Open Access
The second Bridging Transportation Researchers (BTR) Online Free Conference
11-12 August 2020
http://bridgingtransport.org/
Analyzing risk perception and social influence effects on
self-restriction behavior in response to the COVID-19
pandemic in Japan: First results
Giancarlos Parady
Lecturer
The University of Tokyo
Ayako Taniguchi
Professor
Tsukuba University
Kiyoshi Takami
Associate Professor
The University of Tokyo
Key dates regarding COVID-19 activity restriction requests in Japan
The context
Source: Asahi Shimbun (2020)
Source: BBC (2020) Coronavirus: The world in lockdown in maps and charts.
https://www.bbc.com/news/world-52103747 (Accessed: May 31, 2020)
Date
Action taken
February 26th , 2020 The central government issues a request to cancel or postpone large-scale events
March 2nd , 2020 The central government requests the closure of all elementary, junior high, and high
schools across the country
March 26th , 2020 The Tokyo Metropolis and the four adjacent prefectures in the capital region issue a joint
stay-at-home request
April 7th, 2020
(late afternoon) The central government declares a state of emergency for 7 prefectures across Japan,
including the Tokyo Metropolis, and the prefectures of Chiba, Saitama and Kanagawa
April 9th, 2020 Tokyo governor Koike defines which facilities are requested to stop operations during the
state of emergency
April 11th, 2020 The central government requests a reduction of at least 70% of office commuters for the
regions targeted by the emergency state declaration
April 16th, 2020 The central government expands the target of the emergency state declaration to all
prefectures
Trends in travel behavior changes
Percentage change in visit frequency
to different facilities from the 15th of
February to the 17th of April.
The gray vertical lines mark national
holidays. The red vertical lines mark the
following key events (from left to right):
1) The Tokyo Metropolis and the four
adjacent prefectures in the capital
region issue a joint stay-at-home
request
2) The central government declares a
state of emergency for 7 prefectures
across Japan, including the Greater
Tokyo Area
3) The central government expands
the target of the emergency state
declaration to all prefectures.
Source: Produced by authors using data from:
https://www.google.com/covid19/mobility/
Research question
Given that stay-at-home requests are non-enforceable and non-binding,
what factors affect the observed self-restriction behavior at the individual level?
Web-survey details
Region
Prefecture
Wave 1
Wave 2
Apr.1
st
Apr.8
th
Apr.16
th
rd
All Japan
-
4873
Northern Kanto
Ibaraki
24
77
119
Tochigi
14
15
39
Gunma
19
29
107
Greater Tokyo Area
Saitama
98
209
479
Chiba
164
284
525
Tokyo
527
1203
2457
Kanagawa
120
279
614
Number of COVID-19 infections by prefecture
Target region:
Northern Kanto (Ibaraki, Gunma, Tochigi)
Greater Tokyo Area (Tokyo, Saitama, Chiba, Kanagawa)
Sampling:
Method: Quota sampling
Sample size:
1) First wave: 800
2) Second wave: 722 (Participation rate: 90.2%)
Quota criteria:
1) Region (Northern Kanto, Greater Tokyo Area)
2) Gender (Male, female)
3) Age cohort (20s, 30s, 40s, 50s, and 60s and over)
Survey items:
Basic socio-demographic characteristics
Mobility tools ownership
Measures of social anxiety
Risk perception
Weekly trip frequency by purpose:
1) Before COVID-19 spread (before February 2020)(t0)
2) After COVID-19 spread / before emergency declaration (t1)
3) After COVID-19 spread / after emergency declaration (t2)
Behavioral changes in response to COVID-19
Perception of degree of going-out self-restriction
Social expectations regarding going-out self-restriction
Subjective well-being
Description of key variables: Risk recognition
Comparison of risk perception of different hazards
Risk recognition (Slovic,1987)
We measured risk perception of COVID-19
using three questions related to:
1) Levels of dread:
I think COVID-19 is frightening
2) Familiarity:
I know COVID-19 well
3) Perception of hazard controllability:
I think COVID-19 can be controlled
Each item was measured using a 5-point
Likert scale.
We also measured perception of other
hazards to evaluate the relative risk
perception of COVID-19.
For the behavioral analysis variables were
dummy-codified so that they take value 1 if
the rating was 4 or 5and 0, otherwise.
Description of key variables: Perception of self-restriction
Perception of degree of going-out self-
restriction:
Measured as a 7-point Likert scale to the
question:
to what extent do you think you and other
people are practicing going-out self-
restriction?.
Measured for the respondents themselves,
as well as for relatives, work- and or
schoolmates, neighbours, other friends,
and the general public.
Perception of degree of going-out self-restriction behavior (original scale)
For the behavioral analysis, principal
component analysis was used to extract a
single component of perception of degree
of going-out self-restriction of others.
For ease of understanding, these variables
were normalized so that the possible range
of values falls between 0 and 1.
Description of key variables: Changes in travel behavior
From t0to t1, a large share of the sample reported reductions
in activity frequency.
Large share of no change in behavior for commute, grocery
shopping and other shopping.
From t1to t2a rebound for shopping activities was observed,
suggesting a sort of adjustment process.
Share of decreasing trends ranged from 34% for grocery shopping
to 87% for eating out for business.
Description of key variables: Changes in travel behavior
Modeling behavioral change
Trip frequency:
Grocery shopping
Other shopping
Pooled OLS model
Random Intercept model
First difference model*
Probability of self-restriction:
Eating-out (Alone; in groups)
Leisure (Alone; in groups)
Pooled MNL model
Choices: Go out, stay home
Mixed logit model (error component)
Choices: Go out, stay home
MNL of changes in frequency*
Choices: Increase, decrease, no
change
Variable
Mean
SD
Med.
t
0(before infection spread)
Grocery shopping
2.409
1.774
2
Other shopping
1.140
1.223
1
Eat-out (alone)
0.713
1.237
0
Eat-out (private groups)
0.541
0.843
0.5
Leisure (alone)
0.543
1.032
0
Leisure (groups)
0.562
0.832
0.5
t
1(after spread - before emergency declaration)
Grocery shopping
1.869
1.603
2
Other shopping
0.787
1.083
0.5
Eat-out (alone)
0.327
0.905
0
Eat-out (private groups)
0.227
0.657
0
Leisure (alone)
0.253
0.804
0
Leisure (groups)
0.212
0.597
0
t
2(after spread - after emergency declaration)
Grocery shopping
2.038
1.541
2
Other shopping
0.773
1.118
0.5
Eat-out (alone)
0.295
0.962
0
Eat-out (private groups)
0.122
0.568
0
Leisure (alone)
0.184
0.771
0
Leisure (groups)
0.122
0.530
0
Trip frequencies over time
Covariates: Gender, age, population density (1/sq.km), household size, number of cars in
household, number of infections (city level), commuting frequency.
* Only time-changing covariates included.
Modeling behavioral change: Shopping frequency
Elasticities and marginal effects of key variables for shopping frequency models
Measure of effect Model Grocery shopping Other shopping
Estimate (95% C.I.) Estimate (95% C.I.)
Elasticity of perception of
degree of self-restriction of
others
Pooled model -0.069 -0.109 to -0.029 -0.084 -0.116 to -0.052
Random effect model -0.036 -0.087 to 0.014 -0.046 -0.100 to 0.007
First difference model -0.035 -0.067 to -0.004 -0.028 -0.041 to -0.015
Marginal effect of COVID-19
dread
Pooled model 0.100 -0.095 to 0.295 -0.110 -0.288 to 0.067
Random effect model -0.014 -0.240 to 0.212 -0.089 -0.289 to 0.111
First difference model -0.017 -0.319 to 0.285 -0.083 -0.331 to 0.165
Modeling behavioral change: Reductions in discretionary activities
Model
Measure of effect
Eat out (alone) Eat out (Private) Leisure (alone) Leisure (group)
Est. 95% C.I. Est. 95% C.I. Est. 95% C.I. Est. 95% C.I.
Pooled binary logit
of self
-restriction
behavior
Elasticity of perception of
degree of self
-restriction of
others: stay home
0.169 0.032 to 0.307 0.107 0.015 to 0.200 0.101 -0.020 to 0.222 0.120 0.013 to 0.227
Marginal effect of COVID
-
19 dread: stay home
0.107 0.025 to 0.179 0.092 0.028 to 0.145 0.051 -0.019 to 0.110 0.076 0.004 to 0.134
Error component
mixed binary logit
of self
-restriction
behavior
Elasticity of perception of
degree of self
-restriction of
others: stay home
0.155 0.030 to 0.219 0.096 0.009 to 0.139 0.082 -0.050 to 0.148 0.114 0.004 to 0.159
Marginal effect of COVID
-
19 dread: stay home
0.099 0.019 to 0.169 0.095 0.029 to 0.148 0.062 -0.009 to 0.122 0.048 -0.023 to 0.107
Elasticities and marginal effects of key variables of discrete choice models (1/2)
Modeling behavioral change: Reductions in discretionary activities
Model
Measure of effect
Eat out (alone) Eat out (Private) Leisure (alone) Leisure (group)
Est. 95% C.I. Est. 95% C.I. Est. 95% C.I. Est. 95% C.I.
MNL of
changes in
frequency
Elasticity of perception of
degree of self
-restriction of
others: increase
-0.101 -0.271 to 0.070 -0.120 -0.374 to 0.134 -0.026 -0.219 to 0.167 0.075 -0.234 to 0.384
Elasticity of perception of
degree of self
-restriction of
others: decrease
0.060 -0.023 to 0.142 0.083 0.001 to 0.165 0.080 -0.005 to 0.165 0.131 0.051 to 0.212
Marginal effect of COVID
-19
dread: increase
-0.043 -0.072 to 0.023 -0.009 -0.035 to 0.054 -0.039 -0.067 to 0.025 -0.012 -0.039 to 0.075
Marginal effect of COVID
-19
dread: decrease
0.158 0.027 to 0.277 0.142 0.019 to 0.254 0.157 0.027 to 0.274 0.120 0.004 to 0.228
Elasticities and marginal effects of key variables of discrete choice models (2/2)
A simulation exercise
Simulation of the effects of perception of degree of self-restriction of others, and COVID-19 dread on going-out self-restriction
(“stay home”) choice probability for eating-out and leisure, and comparison between binary logit and mixed logit results
Other covariates are fixed as follows: time period = t1. All continuous variable set to mean values. All categorical variables set to reference categories
A simulation exercise
Simulation of the effects of perception of degree of self-restriction of others, and COVID-19 dread on
changes in activity frequency choice probability for eating-out and leisure
Other covariates are fixed as follows: time period: 1st period (t1-t0) . All continuous variable set to mean values. All categorical variables set to reference categories
Policy implications
In the context of non-binding requests, soft measures such as campaigns to promote a reduction of non-essential travel
might be more effective if they:
Appeal to the group, rather than the individual, emphasizing the behavior (or at least the
perception of behavior) of others.
Identifying the reference group
Persuading agency has the responsibility to guarantee that the fears claimed are legitimate and that coping measures
effective, by relying on sound science and effective science communication.
Properly convey the severity of the threat posed by COVID-19 as well as its coping mechanisms.
Fear arousal, Protection motivation theory
Based on the above
Too strong a fear appeal, might result in discrimination and prejudice (“self-restriction police”)
Need to achieve a difficult balance: Effective threat appraisal while avoiding discrimination and prejudice
Sufficient policy support to minimize the costs of coping behavior.
Take away message
As a measure of social influence, the perception of degree of going-out self-restriction of others was
consistently associated with a small reduction in shopping trip frequencies, and non-negligible increases in the
probability of going-out self-restriction for eating-out and leisure purposes.
As a measure of risk perception, COVID-19 dread was also associated with non-negligible increases in the
probability of going-out self-restriction for eating-out and leisure purposes.
In the context of non-binding going-out self-restriction requests, soft measures such as campaigns to promote a
reduction of non-essential travel might be more effective if they (1) properly convey the severity of the threat
posed by COVID-19 as well as its coping mechanisms and (2) Appeal to the group, rather than the
individual, emphasizing the behavior (or at least the perception of behavior) of others.
Thank you!
Contact: gtroncoso@ut.t.u-tokyo.ac.jp
Parady, G., Taniguchi, A., Takami, K. (2020) Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing
the effects of risk perception and social influence on going-out self-restriction. Transportation Research Interdisciplinary
Perspectives (In press) Open Access
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