Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

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Transportation Research Interdisciplinary Perspectives
journal homepage: https://www.journals.elsevier.com/transportation-researchinterdisciplinary-perspectives

Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing
the effects of risk perception and social inﬂuence on going-out self-restriction⁎

Giancarlos Parady a, , Ayako Taniguchi b, Kiyoshi Takami aab

The University of Tokyo, Tokyo, Japan
Tsukuba University, Tsukuba, JapanA R T I C L E

I N F O

Article history:
Received 13 June 2020
Received in revised form 20 July 2020
Accepted 20 July 2020
Available online 1 August 2020
Keywords:
COVID-19
Self-restriction
Social inﬂuence
Risk perception
Travel behavior

A B S T R A C T

This article analyzes factors affecting travel behavior changes at the individual level in light of the COVID-19 pandemic
in Japan, in the context of non-binding self-restriction requests. In particular, this study focuses on the effects of risk
perception and social inﬂuence. A panel web-survey was conducted targeting residents of the Kanto Region, including
the Tokyo Metropolis. In addition to describing the observed patterns in behavioral change, we modeled behavioral
changes of four key, non-work-related activities: (i) grocery shopping, (ii) other types of shopping, (iii) eating out,and (iv) leisure. For eating-out and leisure the distinction was made between going alone or in groups. Based on theobserved distributions of these activities, regression models of going-out frequency were estimated for shopping,while for eating-out and leisure, which showed considerably smaller frequencies, a discrete choice approach wasused. Findings showed that as a measure of social inﬂuence, the perception of degree of self-restriction of others
was associated with small reductions in shopping frequencies, and moderate yet non-negligible increases in goingout self-restriction probability for eating-out and leisure activities. Risk perception, measured as COVID-19 dread,
was also associated with higher probabilities of going-out self-restriction for eating-out and leisure. These ﬁndings suggest that in the context of non-binding requests, soft measures such as campaigns to promote a reduction of nonessential travel might be more effective if they (i) properly convey the severity of the threat posed by COVID-19 as
well as its coping mechanisms, and (ii) appeal to the group, rather than the individual, emphasizing the behavior(or at least the perception of behavior) of others.

1. Introduction
The COVID-19 pandemic has had an unprecedented effect on people's
mobility across the globe. By the end of March 2020, more than a hundred
countries had implemented some form mobility restriction, ranging fromfull or partial mandatory quarantines (usually referred to as lockdowns)
to non-binding requests for activity restrictions, such as stay-at-home requests, closing certain types of businesses, canceling events, etc. (BBC,2020).
The case of Japan falls under the latter category. Starting on February
26th with the central government request to cancel or postpone large
events and gatherings, and culminating with a declaration of state of emergency on April 7th, ﬁrst in seven prefectures including the Tokyo area, andlater expanded nationwide on April 16th (Table 1).
Since these measures are not mandatory or legally enforceable underJapanese law, they revolve around self-restriction, and their success largely
dependent on compliance by the population. In this regard, using data fromtelecommunications and IT ﬁrms, the Cabinet Ofﬁce reported signiﬁcantdrops in activity in large employment and commercial hubs of the Greater

Tokyo Area (deﬁned in this article as the Tokyo Metropolis and the prefectures of Chiba, Saitama and Kanagawa) both before and after the state of
emergency declaration, as summarized in Table 2.
Independently, Google used the location history data from their map application to measure changes in visit frequency to different facilities (seeFig. 1). A decreasing trend in visit frequencies to retail and recreation facilities, workplaces and transit stations, and a consequent increase in activities
in residential areas can be observed after the issuing of the joint stay-athome request by the governors of Tokyo and adjacent prefectures.
Certainly, aggregate results show a change in behavioral patterns. The
question that this article then raises is, given that stay-at-home requests
are non-enforceable and non-binding, what factors affect the observed
self-restriction behavior at the individual level. The objective of this study
is thus to quantify the effect of factors that affect behavioral changes in response to the COVID-19 pandemic, in the context of Japan. To do so, we use
data from an original panel survey targeting residents of the Kanto region,
more speciﬁcally, the Tokyo Metropolis and the prefectures of Chiba,
Saitama and Kanagawa (hereinafter the Greater Tokyo Area) and the prefectures of Ibaraki, Gunma and Utsunomiya (hereinafter Northern Kanto).

⁎ Corresponding author.
E-mail addresses: gtroncoso@ut.t.u-tokyo.ac.jp, (G. Parady), taniguchi@risk.tsukuba.ac.jp, (A. Taniguchi), takami@ut.t.u-tokyo.ac.jp. (K. Takami).

http://dx.doi.org/10.1016/j.trip.2020.100181
2590-1982/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.
0/).

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Table 1Key dates regarding COVID-19 activity restriction requests in Japan.
Sources: Asahi Shimbun (2020).
Date

Action taken

February 26th, 2020
March 2nd, 2020
March 26th, 2020
April 7th, 2020
(late afternoon)
April 9th, 2020
April 11th, 2020
April 16th, 2020

The central government issues a request to cancel or postpone large-scale events
The central government requests the closure of all elementary, junior high, and high schools across the country
The Tokyo Metropolis and the four adjacent prefectures in the capital region issue a joint stay-at-home request
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
Tokyo governor Koike deﬁnes which facilities are requested to stop operations during the state of emergency
The central government requests a reduction of at least 70% of ofﬁce commuters for the regions targeted by the emergency state declaration
The central government expands the target of the emergency state declaration to all prefectureschanges in extraordinary situations, such as the COVID-19 pandemic, anissue we aim to evaluate in this study.
Another aspect that might be relevant to behavioral change, is the perception of the risk posed by COVID-19. According to the protection motivation theory, the evaluation of the severity of a threat is one of the cognitive
processes behind the decision to engage in protective behavior (Rogers,1983). In addition, research on risk perception has shown that there is a
gap between actual risks and the perception of risks by individuals(Slovic, 1987). Slovic developed a simple scale of risk measurement to assess the perception of risk relative to other hazards, and identiﬁed three
key factors associated with risk perception: (i) dread, (ii) unknown (unfamiliarity) and (iii) number of people involved.
Based on data collected through an online survey, Wise et al. (2020)
evaluated changes in risk perception in the US during the ﬁrst week ofthe pandemic, and found that as risk awareness grew so did the report frequency of engagement in protective behavior. They also reported that practicing physical distancing was better predicted by the perceived likelihoodof personally being infected, rather than the likelihood of transmission or
severity of potential infections.
Finally, an individual trait that might affect behavioral response is social
anxiety. For example, Spielberger et al. (1983) proposed the State-Trait
Anxiety Inventory (STAI), where state anxiety refers to reactions related
to adverse situations in a speciﬁc moment, while trait anxiety refers to amore stable tendency to experience anxiety in a myriad of situations. As
such, individuals with high scores in social anxiety traits might exhibit particular behavioral responses. At present, we have not found any study
attempting to evaluate the relation between social anxiety and behavioral
responses to COVID-19.

The collected data is then used to model behavioral changes of four key,
non-work-related activities: (i) grocery shopping, (ii) other types of shopping, (iii) eating out, and (iv) leisure.
The rest of this article is structured as follows. Section 2 brieﬂy summarizes ﬁndings from the literature. Section 3 describes the survey design andexecution details. Section 4 describes the data characteristics. Section 5 presents the model results, while Section 6 discusses ﬁndings and limitations of
this study.
2. Literature review
Since at the time of writing the COVID-19 pandemic is still an ongoingcrisis, the literature on the subject is still limited. As such, we present only abrief review with a general overview of ﬁndings, in order to frame the analysis presented in this article.
Given the way of transmission of the SARS-CoV-2 virus, physical distance has emerged as a key mitigation strategy. This translates in someform of mobility restrictions. In the case of Wuhan, China, where the ﬁrst
cases were reported, control measures were drastic, but it has been shownthat these measures substantially mitigated the spread of COVID-19(Kraemer et al., 2020; Zhang et al., 2020). However, such implementations
are not necessarily possible in many countries. In the case of Japan, where
the government cannot legally enforce lockdowns on citizens and residents,
strategies have relied largely on requests for voluntary self-restriction
which include avoiding unnecessary travel, teleworking, etc. (Shaw et al.,2020). As such, in the context of non-enforceable self-restriction requests,
understanding what factors affect behavioral change at the individuallevel is of importance.
Although not speciﬁc to pandemics, human behavior is inﬂuenced bysocial norms, that is, what they perceive that others are doing or what
they think others approve of (Van Bavel et al., 2020). As such, researchers
have suggested applying the principles of behavioral change to “nudge”
people into desirable behaviors to help control the spread of COVID-19(West et al., 2020). Stay-at-home requests, which require changing one's
travel patterns and practicing self-restriction for non-essential activities,
fall squarely into the realm of travel behavior analysis. In the ﬁeld, there
is a small but growing number of articles addressing the issue of social inﬂuence on travel behavior, albeit on ordinary circumstances (Kim et al.,2018). These studies are motivated by the idea that the behavioral patternsof individuals vary with the behavior of the reference group (Manski,1993). In addition, the frequency of discretionary activities such as leisure,
have also been found to be associated with social network characteristics(Carrasco and Miller, 2006; Parady et al., 2019; Parady et al., 2020). As
such, social inﬂuence might also be an important factor affecting behavioral

3. Survey design and execution
A panel data web-survey was conducted through Rakuten Insight, amarket research ﬁrm that maintains an internet panel of around 2.2 million
monitors in Japan. The target area was the Kanto region, speciﬁcally, theGreater Tokyo Area and Northern Kanto.
The ﬁrst wave of the survey was conducted on April 8th, one day after
the emergency state declaration, but well after the spread of the virus(more than 4000 cases had been reported nationwide. See Table A1 in
the Appendix). The sample size was 800 persons. The sampling method
was quota sample, with an equal number of samples for each group. The
group segmentation criteria were region (Greater Tokyo Area or Northern
Kanto), gender (male or female) and age cohort (20s, 30s, 40s, 50s, and60s and over). This resulted in 20 groups of 40 respondents each.

Table 2
Changes in number of people in different commercial districts of the Greater Tokyo Area for April 21st relative to key dates (compiled from DOCOMO mobile spatial statistics).
Source: Cabinet Secretariat (2020).
Average changes compared to/area

Shinjuku (Tokyo) Yokohama station (Kanagawa) Chiba station (Chiba) Omiya station (Saitama)

January 18 – February 14 (before the widespread of the virus)−69.7%
April 6–7 (before the of emergency state declaration in the Greater Tokyo Area) −55.4%
April 15–16 (before the nationwide emergency state declaration)
−4.8%

2

−64.7%−53.1%
0.1%−57.5%−47.2%−4.2%−59.3%−45.9%−4.9%

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Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Fig. 1. 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 four adjacent prefectures issue a joint stay-at-home request, 2) The central government declares a stateof 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/3

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Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

restriction?”. This was measured for the respondents themselves, aswell as for relatives, work- and/or schoolmates, neighbours, otherfriends, and the general public.
9. Social expectations regarding going-out self-restriction behavior: Measured using a 5-point Likert scale to the questions “do you think otherpeople are expecting you to practice going-out self-restriction?” and “doyou think other people would approve of you practicing going-out selfrestriction?”. These were measured for relatives, work- and/or schoolmates, neighbours, other friends, and the general public.
10. Subjective well-being: Measured using a 10-point scale to the question“How happy would you say you are?”3.1. Survey items
The following data were collected:
1. Basic socio-demographic characteristics, including employment type
and working style.
2. Mobility tools ownership.
3. Measures of social anxiety: following Spielberger et al. (1983), we measured two dimensions of social anxiety, namely, state anxiety and trait
anxiety. Although each of these scales involve measuring 20 indicatorvariables, in order to reduce the response burden, we used the three indicators that according to research in the context of Japan (Iwamoto
et al., 1989) had the highest factor loading:a) State anxiety:

The second wave was conducted 2 weeks after the ﬁrst wave, which wasafter the declaration of the state of emergency in the Greater Tokyo Area.
Items 3 to 10 were collected again. Participation rate for the second wave
was 90.2% (722 respondents).

1. “I feel anxious”.
2. “I feel nervous”.
3. “I am worried”.

4. Basic data characteristics and processingb) Trait anxiety:
1. “I feel that difﬁculties are piling up so that I cannot overcome them”.
2. “I am inclined to take things hard”.
3. “Some unimportant thought runs through my mind and bothers me”.

4.1. Social anxiety measures
Principal component analysis was used to reduce the dimensionality ofthe data to one variable per construct. First principal components explained
78% and 80% of the variance in the data for state anxiety and trait anxiety
indicators, respectively. Note that since state anxiety levels are likely tochange in particularly adverse moments, and given that we did not observestate anxiety levels before the spread of COVID-19, in the behavioral analysis reported in the next section, only trait anxiety, considered more stable
over time is used.

4. Risk perception: following Slovic (1987) we measured risk perceptionof COVID-19 using three questions related to (i) levels of dread (“I think
COVID-19 is frightening”), (ii) familiarity (“I know COVID-19 well”) and(iii) perception of controllability of the hazard (“I think COVID-19 canbe controlled”). Each item was measured using a 5-point Likert scale.
In this case too, a reduced number of indicators was used to reduce
the response burden. We also measured perception of other hazards
to evaluate the relative risk perception of COVID-19.
5. Weekly trip frequency by purpose before the spread of COVID-19 (Before February 2020). Frequency was measured on the following scale:“don't do that activity”, “less than weekly”, “1/week”, “2/week”, “3/week”, “4/week”, “5/week”, “6/week”, “7/week”, “few times per day”
6. Weekly trip frequency by purpose after the spread of COVID-19 and before the emergency state declaration, that is, until April 7th, 2020. This
correspond to the behavior up to the day before the ﬁrst wave of thesurvey was conducted.
7. Behavioral changes in response to COVID-19, including hygiene, eating etiquette, shopping, going-out self-restriction, and substitution of
face-to-face interactions with ICT interactions.
8. Perception of degree of going-out self-restriction (“gaishutsu jishuku” inJapanese): Measured using a 7-point scale to the question “to what extent do you think you and other people are practicing going-out self-

4.2. Risk perception
Following Slovic (1987), Fig. 2 plots the means of risk perception interms of dread, unknown and controllability for different hazards to get
an idea of the average level of perceived risk of COVID-19. As the left
panel shows, COVID-19 is second only to earthquakes in terms of dread
and is also perceived to be the least controllable of all hazards inquired.
For the behavioral analysis presented in the next section, risk perception
variables were dummy-codiﬁed so that they take value 1 if the rating was
4 or 5 and 0, otherwise. For the time period before the infection spread(t0), all risk perception variables were set to zero. As an aside, the case ofelderly driving is of particular interest as it shows the effect of availability
on risk perception. With a series of cases where elderly drivers lost controlof the vehicle resulting in fatal accidents reported often in the media, thedread perception of elder driving is high relative to the actual risk.

Fig. 2. Comparison of risk perception of different hazards (ﬁrst wave results).
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4.3. Perception of degree of going-out self-restriction

5. Modeling behavioral change

Fig. 3 illustrates the perception of the degree of going-out selfrestriction of oneself and others and its changes over time. It can be seenthat with the exception of the “general public” case the mean values(marked by the rhomboids) increased. Median values also increased, for“family”, “friends” and “work/school friends”.
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. To guarantee that principal component scores were on thesame scale, the PCA coefﬁcients were calculated using data from time period t1 (after spread - before emergency declaration) and predicted for thetime period t2 (after spread - after emergency declaration). In addition,for ease of understanding, these variables were normalized so that the possible range of values falls between 0 and 1. For time period t0 (before theinfection spread) all values were set to zero.
Tables A2 and A3 in the Appendix summarize the rest of the descriptive
statistics for time invariant and time-varying covariates.

As stated in the introduction, the question that this article seeks to answer is what factors affect the observed self-restriction behavior at the individual level, given that stay-at-home requests are non-enforceable and nonbinding. Since self-restriction decisions regarding commuting and workrelated trips are not completely in the control of individuals, we will narrow
down the scope of the analysis to shopping, eating out and leisure, and exclude work- and business-related trips. Furthermore, based on the observed
distributions of these activities, for shopping variables, which exhibit relatively higher frequencies, regression models of going-out frequency are estimated. On the other hand, given that eating out and leisure frequencies
are not that high to being with, their distributions are not suitable for linear
regression analysis, as such, a discrete choice approach is used.
Based on the discussion in the previous sections, the key variables in thisanalysis are the principal component of perception of degree of self-restriction of others and COVID-19 dread as a measure of risk perception, and to alesser extent the principal component of trait anxiety. Although a set of control variables such as population density at residential location, changes incommuting frequency, mean cumulative number of infections by prefecture(for the seven days up to the survey day), and basic socio-demographic
characteristics are included as control variables in the models, the discussion of results will focus on these three variables.

4.4. Changes in travel behavior
Regarding changes in travel behavior, Figs. 4 and 5 illustrate changes
over time of different activity frequencies. In Fig. 4, for the top plot, theblue bars (no change from zero) show the cases where the activity was
not conducted even before the spread of the infection, for the bottom
plot, it include those that stopped an activity altogether at t1 and kept
doing so at t2. From t0 to t1, a considerable share of the sample reported decreases in frequency for most activities. Interestingly enough, from t1 to t2 a
rebound for shopping activities was observed, suggesting a sort of adjustment process. It can also be seen that for activities such as eating-out andleisure, the reductions in frequency persisted, as shown by the shares of“no change (from zero)” and “decrease” in the bottom plot. These changes
are also a result of changes in the supply side. That is, many eating-out andleisure establishments closed down or shortened their business hours in response to the non-binding request of the government, in contrast to most
shopping facilities which provided more essentials services.
The overall trends across all time periods are shown in Fig. 5. In the top
ﬁgure, the blue bars show those who did not conduct a given activity at any
time period. The bottom ﬁgure is perhaps more enlightening, showing thechanges across all periods when excluding individuals with constant zerofrequencies. It can be seen that for this group, the share of decreasing trends
ranged from 34% for grocery shopping to 87% for eating out for business.
For the behavioral analysis described in the following section, the alternative “few times per day” was truncated at 7, and the alternative “less thanweekly” was coded to 0.5. See Table A4 in the Appendix for the descriptive
statistics of trip frequencies over time, after this transformation.

5.1. Shopping frequency models
To model shopping frequency, instead of depending on a single modelwith a single set of assumptions, we rely on three different types of models,each with different assumptions, thus providing more robustness to theanalysis. The ﬁrst two models are a pooled OLS and a random effectmodel. In these models the dependent variables are the shopping trip frequencies for each time period pooled together. The random effect model estimates random intercepts for each respondent in order to capture
heterogeneity across individuals. These two models however require thatunobserved individual ﬁxed effects be uncorrelated with the explanatory
variables. The third model is a ﬁrst-difference regression, where both dependent and independent variables are differenced over time (Δy = yt −yt−1; Δx = xt − xt−1). Given this transformation, unobserved individualﬁxed effects are allowed to be correlated with explanatory variables,
since these effects are differenced out of the equation. However, as a result,time-constant variables cannot be included. In this case, this is not a problem since the two key variables of this analysis are time varying. The regression models were estimated in R 3.6.3. with the plm (Croissant and Millo,2008) and lme4 (Bates et al., 2014) packages. Robust errors (cluster errors
at the individual level) are estimated for all models, to account for thepanel nature of the data.

Fig. 3. Perception of degree of going-out self-restriction (original scale).
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Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

of around 0.04% to 0.07%, and of around 0.03% to 0.08% for othertypes of shopping.
Although we expected a negative association between COVID-19 dreadand shopping frequency, the effects of COVID-19 dread were estimatedwith very high uncertainty, so no inference can be made about effect direction or magnitude. Finally, in terms of trait anxiety effects, elasticities for allmodels are essentially zero, suggesting no treat anxiety effect on shopping
frequency.
Goodness of ﬁt statistics and out of sample validation statistics
suggest that for grocery shopping, the ﬁrst-difference model (Adj.R2:

Table 3 summarizes the elasticities and marginal effects of key variables
for shopping frequency models. Elasticities are estimated at mean values ofx and y. Full estimation results are reported in the Appendix (Tables A5 andA6).
Regarding the effect of the perception of degree of self-restrictionof others on shopping frequencies, as expected, a negative association
was observed. However, the elasticity estimates are rather small,especially for the ﬁrst-difference regression estimates. Across allmodels, a 1% increase in the perception of self-restriction principal
component score results in a decrease in grocery shopping frequency

Fig. 4. Changes in travel behavior over time.
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Fig. 5. Trend of changes of travel behavior across all time periods.

0.087, correlation1: 0.28) is slightly superior to both the pooled model(Adj.R 2: 0.076, correlation: 0.24) and the random effect model (R 2:
0.074, correlation: 0.24). For other kinds of shopping, when looking at
goodness of ﬁt statistics only, it cannot be said that the ﬁrst-difference
model (R2: 0.045) outperforms the other models (R2: 0.048 (pooled),
0.053(random effect)). However, when looking at out-of-sample validation statistics, the ﬁrst-difference model is marginally superior (correlation:

0.20, against 0.18 for the other models). It must be noted that the R-squarestatistics for both shopping models are low, so the large share of the variance in the data remains unexplained.
5.2. Eating out and leisure models
For eating out and leisure, three types of discrete choice models are estimated. The ﬁrst two models are models of going-out self-restriction. In
these models the dependent variables are the choices to “stay at home” or“go out” for each particular purpose. Data from all time periods are pooled

1
Refers to average correlation between observed and predicted outcomes in out-of-sample
validation.

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Table 3
Elasticities and marginal effects of key variables of shopping frequency models.
Model

Elasticity of perception of degree ofself-restriction of others
Elasticity of trait anxiety

Marginal effect of COVID-19 dread

Grocery shopping

Pooled model
Random effect modelFirst difference model
Pooled model
Random effect modelFirst difference model
Pooled model
Random effect modelFirst difference model

Other shopping

Estimate(95% C.I.)

Estimate(95% C.I.)−0.069−0.036−0.0350.00010–0.100−0.014−0.017−0.109 to −0.029−0.087 to 0.014−0.067 to −0.004
0.0001 to −0.0001
0.0002 to −0.0001–−0.095 to 0.295−0.240 to 0.212−0.319 to 0.285−0.084−0.046−0.028−0.0001−0.0001–−0.110−0.089−0.083−0.116 to −0.052−0.100 to 0.007−0.041 to −0.015
0.0000 to −0.0001
0.0000 to −0.0002–−0.288 to 0.067−0.289 to 0.111−0.331 to 0.165

Elasticities estimated at mean values of x and y.
Bold font indicates coefﬁcients with p-value <0.1.

together. Here, “stay at home” refers to zero-valued weekly activity frequencies, while “go out” implies non-zero-valued weekly activity. The ﬁrst model
is a simple pooled binary logit model. The second model is an error component mixed binary logit model. This model captures individual-speciﬁc effects that cause correlation across successive choices by the same decision
maker, by adding i.i.d. N(0,1) error components that vary across respondents (not choice situations) to each alternative, and estimating an additional parameter θ for the individual errors (Hess et al., 2008). The thirdmodel is a multinomial logit model of changes in trip frequency (decrease,no change or increase) deﬁned based on the time differencing of trip frequencies, as explained in the previous section. For this model, all independent variables are also ﬁrst-differenced when included in the model. Robust
errors (cluster errors at the individual level) are estimated for all models, toaccount for the panel nature of the data. Since we are interested in evaluating what factors affect behavioral change, and given the large percentage ofthe sample that did not engage in eating-out and leisure activities even before the spread of the infection (see Fig. 5), we subset the data to focus on
individuals who reported non-zero frequencies at t0. The discrete choicemodels were estimated in R 3.6.3. with the Apollo package (Hess andPalma, 2019).
Table 4 summarizes the elasticities and marginal effects of key variables. For the full estimation results see Tables A7–A9 in the Appendix.
Elasticities are calculated analytically for the logit models. Elasticities forthe mixed logit models and the marginal effects for categorical variables
are estimated via simulation.
The perception of degree of self-restriction of others is consistently associated with increases in the probability to stay home and the probability to
reduce trip frequencies, suggesting the possible existence of a social inﬂuence mechanism for activities such as eating-out and leisure. For example,

the mixed logit elasticity estimate for eat-out alone is around 0.16, whichsuggest that a 1% increase in the principal component score of selfrestriction perception will result approximately in a 0.16% (C.I. 0.03% to0.22%) increase in the probability of staying home.
COVID-19 dread is also consistently associated with increases in theprobability to stay home and the probability to reduce trip frequencies.
Since COVID-19 dread is a binary variable, marginal effects were estimated.
These indicate the absolute change in choice probability given a change inthe explanatory variable. For example, the probability of reducing leisure(group) frequency will increase on average by 0.12 (C.I. 0.004 to 0.228)
for a person going from not considering COVID-19 dreadful to doing so.
Regarding the trait anxiety coefﬁcients, the mixed logit models including these covariates resulted in non-negative-semideﬁnite Hessian matrices, hence these variables were excluded from the ﬁnal models. As such,these effects could only be evaluated with the pooled logit models. However, as Table 4 shows, the elasticities of trait anxiety were essentially zero.
In addition to the elasticities and marginal effects presented in Table 4,
and to further illustrate the magnitude of the effects of social inﬂuence andrisk perception on behavioral changes, we conducted a simulation exerciseof changes in choice probabilities given changes in 1) the principal component of perception of degree of self-restriction of others and 2) the dread
perception of COVID-19, holding other variables constant. Fig. 6 shows
the simulation results of the binary and mixed logits, while Fig. 7 shows
the results of the changes in trip frequency models. It can be clearly seenthat the magnitudes of these effects are not negligible.
Goodness of ﬁt statistics suggest all models perform rather well, withconsiderable improvements in log-likelihood compared to the constant
only models. Furthermore, out-of-sample mean percentage of correct predictions range from 79.6% to 84.1% for the pooled and mixed binary

Table 4
Elasticities and marginal effects of key variables of discrete choice models.
Eat out (alone)

Eat out (Private)

Estimate

95% C.I.

Estimate

95% C.I.

Leisure (alone)
Estimate

Leisure (group)

95% C.I.

Estimate

95% C.I.

Pooled binary logit of self-restriction behavior
Elasticity of perception of degree of self-restriction of others:
stay home
Elasticity of trait anxiety: stay home
Marginal effect of COVID-19 dread: 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

0.000
0.107

−0.001 to 0.000 0.000
0.025 to 0.179
0.092

0.000 to 0.000
0.028 to 0.145

0.001
0.051

−0.001 to 0.004−0.019 to 0.110

0.000
0.076

0.000 to 0.000
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
Elasticity of trait anxiety: stay home
Marginal effect of COVID-19 dread: 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

–
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

MNL of changes in frequency
Elasticity of perception of degree of self-restriction of others: increase
Elasticity of perception of degree of self-restriction of others: decrease
Marginal effect of COVID-19 dread: increase
Marginal effect of COVID-19 dread: decrease−0.1010.060−0.0430.158−0.271 to 0.070−0.023 to 0.142−0.072 to 0.023
0.027 to 0.277−0.1200.083−0.0090.142−0.374 to 0.134
0.001 to 0.165−0.035 to 0.054
0.019 to 0.254−0.0260.080−0.0390.157−0.219 to 0.167−0.005 to 0.165−0.067 to 0.025
0.027 to 0.274−0.234 to 0.384
0.051 to 0.212−0.039 to 0.075
0.004 to 0.228

Bold font indicates coefﬁcients with p-value <0.1.
8

0.075
0.131
−0.0120.120

G. Parady et al.

Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Fig. 6. 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 activities. Other covariates are ﬁxed as follows: time
period = t1. All continuous variable set to mean values. All categorical variables
set to reference categories.

Fig. 7. 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 eatingout and leisure activities. 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.

logit models, and 66.6% to 72.1% for the frequency change models. It isalso worth pointing out that while the mixed logit model outperformed
the pooled binary logit model in terms of goodness of ﬁt, the binary logit
was superior, albeit very slightly, in terms of out-of-sample validation.

aggregate results reported by IT ﬁrms, a signiﬁcant drop in activity levels
was observed. We showed that as a measure of social inﬂuence, the perception of degree of self-restriction of others was consistently associated with
reductions in activity levels across all activity types. Since the dependent
variables of the estimated models are different, only a rough comparisonof effect magnitudes is possible, but for shopping frequency, effects wererather small, while in terms of probability of self-restriction (stayinghome) and probability of trip frequency reduction for eating-out and leisureactivities, effects were moderate, yet non-negligible. This can be explainedby the fact that grocery shopping, and to some extent other types of

6. Discussion and conclusion
In this article we analyzed factors affecting travel behavior changes at
the individual level during the COVID-19 pandemic in Japan using datafrom a panel web-survey conducted in the Kanto Region. In line with9

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Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

prejudice, as well as undesirable behavior, such as what has come to be
known in Japan as the “self-restriction police” where private individuals aggressively call-out or “force” individuals and businesses that do not respondto voluntary requests of self-restriction to comply. This underscores theneed to achieve a difﬁcult balance where the severity of the threat is effectively communicated, without encouraging discrimination and prejudiceagainst individuals who don't comply with the government requests.
Sufﬁcient policy support is also necessary to minimize the cost of coping
behavior by compensating individuals and businesses for loss of incomethat might result from self-restricting behavior.
Finally, this study has two important limitations. The ﬁrst one is thatgiven the non-probabilistic sampling method, the generalization potential of ﬁndings is limited. The main reason to do so was that we wanted
to capture behavior as close to real time as possible. Based on past experience, using the basic resident register (the most comprehensive sampling frame available in Japan) for a probabilistic sample would havetaken months, and capturing behavior retrospectively might have resulted in a different bias due to difﬁculties associated with accurately
remembering past behavior, and in particular, behavioral changes. As
such, additional studies are necessary to validate the results presented
here. Additional studies are also needed to evaluate to what extent
the ﬁndings reported here replicate in different cities and/or sociocultural contexts.
The second limitation is that due to the nature of the survey instrument,
behavior was measured as trip frequency and trip chaining effects could not
be captured. Regardless, the ﬁndings presented in this article, while pending further validation, provide some insight on the effects of social inﬂuence and risk perception on behavioral changes during the COVID-19
pandemic in Japan.

shopping (excluding of course, leisure and luxury shopping) fall under the
category of essential activities, and consequently they are less susceptible
to self-restriction social effects. On the other hand, activities like eatingout or leisure are not, at least in the short term, absolutely essential for subsistence and thus likely to be more susceptible to self-restriction social
effects.
A similar argument can be made for risk perception, which, measured as
COVID-19 dread, was also found to be associated with non-negligible increases in the probability to stay home and the probability to reduce trip
frequencies. Since shopping activities are necessary for subsistence, theymust be conducted with some regularity in spite of the perceived risk. As
such, it is plausible that individuals would opt to take protective measures
such as wearing masks, physical distancing when in stores, etc. instead of
reducing shopping frequency.
Regarding social anxiety measures, effects were essentially zero, for all
types of activities. Although this might be a result of the use of a limited
measure of trait anxiety (as opposed to using the full STAI scale), further research is necessary to evaluate this.
These ﬁndings have important policy implications for Japan, where
there are no legal mechanisms to enforce a “city lockdown.” Our ﬁndings
suggest that 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 (i) properly convey the severity of the threat posed by COVID19 as well as its coping mechanisms, and (ii) appeal to the group, rather
than the individual, emphasizing the behavior (or at least the perceptionof behavior) of others.
Fear arousal has long been a subject of study in social psychology as amean to promote attitudinal or behavioral change. The protection motivation theory by Rogers (1983), for example, posits two cognitive processes
behind the decision to engage in protective behavior against a threat. The
ﬁrst one is the evaluation of the severity of the threat (threat appraisal),
where fear arousal plays a role by affecting the cognitive appraisal of the severity of the threat. The second one is the evaluation of the ability to copewith such threat (coping appraisal), which takes into consideration not
only the efﬁcacy of the response but also its cost.
As a policy issue, the agency doing the persuasion has the responsibility
to guarantee that the fears claimed are legitimate and the coping measureseffective, by relying on sound science and effective science communication.
Too strong a fear appeal, however, might result in discrimination and

Declaration of competing interest
The authors report no conﬂict of interest.
Acknowledgements
This study was funded by kakenhi Grants-in-Aid for Scientiﬁc Research
20H02266 and 17K18947 from the Japan Society for the Promotion of
Science.

Appendix A
Table A1
Cumulative number of COVID-19 infections by prefecture.
RegionAll Japan
Northern Kanto

Greater Tokyo Area

Prefecture

Wave 1–
IbarakiTochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa

Wave 2

Apr.1stApr.8thApr.16th

Apr.23rd

2107
24
14
19
98
164
527
120

4168
77
15
29
209
284
1203
279

8442
119
39
107
479
525
2457
614

11,172
151
52
131
714
725
3452
841

As reported by the Asahi Shimbun based on documents from the Ministry of Health, Labour and Welfare.
Table A2
Descriptive statistics of time ﬁxed covariates.
Variable

n

Mean

Male
Worker
Age
20–39
40–59
Over 60
Household size

722
722

0.508
0.723

722
722
722

0.377
0.422
0.201

10

SD

Med.

Min.

Max.

G. Parady et al.

Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Table A2 (continued)
Variablen

Mean

1 person
2 persons
3 persons or larger
Marital status
SingleMarriedDivorced
Widower
Other
Prefecture

722
722
722

0.215
0.309
0.476

722
722
722
722
722

0.274
0.623
0.086
0.015
0.001

722
722
722
722
722
722
722
722
722
722
722

0.154
0.195
0.150
0.086
0.127
0.068
0.220
0.737
7.505
0.014
−0.004

Northern
Kanto
Greater Tokyo
Area

Gunma
IbarakiTochigi
Chiba
Kanagawa
SaitamaTokyo

Car available in household
Population density at home location 1000/sq.km
Principal component of state anxiety
Principal component of trait anxiety

SD

Med.

Min.

Max.

7.448
1.520
1.515

4.576
0.412
0.045

0.000
−3.664−2.98434.092
2.487
3.073

SD

Med.

Min.

Max.

0
0
0

0
0
0

0
0
0

0
0
0

0.219
0.223
0.226

0.531
0.589
0.750

0
0
0

1
1
1

0.212
0.228
0.234

0.570
0.645
0.750

0
0
0

1
1
1

Table A3
Descriptive statistics of time-varying covariates.
Variablet0 (before infection spread)
Risk perception
Unknown (1 if value is 4 to 5, 0 otherwise)
Dread (1 if value is 4 to 5, 0 otherwise)
Controllability (1 if value is 4 to 5, 0 otherwise)
Social inﬂuence
PC of self-restriction perception by others
PC of self-restriction expectation by others
PC of self-restriction approval by others
Wt1 (after spread - before emergency declaration)
Risk perception
Unknown (1 if value is 4 to 5, 0 otherwise)
Dread (1 if value is 4 to 5, 0 otherwise)
Controllability (1 if value is 4 to 5, 0 otherwise)
Social inﬂuence
PC of self-restriction perception by others
PC of self-restriction expectation by others
PC of self-restriction approval by otherst2 (after spread - after emergency declaration)
Risk perception
Unknown (1 if value is 4 to 5, 0 otherwise)
Dread (1 if value is 4 to 5, 0 otherwise)
Controllability (1 if value is 4 to 5, 0 otherwise)
Social inﬂuence
PC of self-restriction perception by others
PC of self-restriction expectation by others
PC of self-restriction approval by others

n

Mean

722
722
722

0
0
0

722
722
722

0
0
0

722
722
722

0.229
0.871
0.217

722
722
722

0.527
0.602
0.678

722
722
722

0.251
0.893
0.215

722
722
722

0.563
0.641
0.705

Table A4
Descriptive statistics of trip frequencies over time.
Variable

n

Mean

SD

Med.

Min.

Max.

t0 (before infection spread)
Trip frequencies
CommuteBusiness/errands
Grocery shopping
Other shopping
Eat-out (alone)
Eat-out (work-related)
Eat-out (private groups)
Leisure (alone)
Leisure (groups)
t1 (after spread - before emergency declaration)
Trip frequencies

559
722
722
722
722
722
722
722
722

4.403
1.332
2.409
1.140
0.713
0.346
0.541
0.543
0.562

1.873
2.141
1.774
1.223
1.237
0.709
0.843
1.032
0.832

5
0
2
1
0
0
0.5
0
0.5

0
0
0
0
0
0
0
0
0

7
7
7
7
7
7
7
7
7

(continued on next page)11

G. Parady et al.

Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Table A4 (continued)
Variable

n

Mean

SD

Med.

Min.

Max.

CommuteBusiness/errands
Grocery shopping
Other shopping
Eat-out (alone)
Eat-out (work-related)
Eat-out (private groups)
Leisure (alone)
Leisure (groups)
t2 (after spread - after emergency declaration)
Trip frequencies
CommuteBusiness/errands
Grocery shopping
Other shopping
Eat-out (alone)
Eat-out (work-related)
Eat-out (private groups)
Leisure (alone)
Leisure (groups)

559
722
722
722
722
722
722
722
722

3.581
0.945
1.869
0.787
0.327
0.130
0.227
0.253
0.212

2.257
1.859
1.603
1.083
0.905
0.543
0.657
0.804
0.597

5
0
2
0.5
0
0
0
0
0

0
0
0
0
0
0
0
0
0

7
7
7
7
7
7
7
7
7

710
722
722
722
722
722
722
722
722

2.102
0.896
2.038
0.773
0.295
0.083
0.122
0.184
0.122

2.358
1.812
1.541
1.118
0.962
0.549
0.568
0.771
0.530

0.75
0
2
0.5
0
0
0
0
0

0
0
0
0
0
0
0
0
0

7
7
7
7
7
7
7
7
7

Model estimation results
Table A5
Grocery shopping frequency models.
Variable

Grocery shopping frequency model
Pooled model

Random effect modelFirst-difference model

Est.

t valueEst.

t valueEst.

t value(Intercept)
Period 2 – Period 1 (ﬁrst-differenced)
Period 1 (after spread - after emergency declaration)
Period 2 (after spread - before emergency declaration)
Male
Age: 20–39
Age: 40–59
Household size: 1 person
Household size: 2 persons
Car in household
Population density 1/sq.km.
Commuting frequency
Mean cumulative number of infections/100
PC of perception of degree of self-restriction of others
COVID-19 dread
PC of trait anxiety

2.392–−0.380−0.157−0.458−0.488−0.171
0.129
0.025
0.016
0.034
0.059
*1
−0.3980.100−0.022

12.052–−4.289−1.466−2.547−3.074−1.232
0.976
0.258
0.112
4.865
3.639
*1
−3.376
1.007−0.7872.376–−0.372−0.137−0.460−0.490−0.171
0.133
0.028
0.021
0.035
0.060
−0.003−0.210−0.014−0.0209.625–−3.043−0.946−4.326−2.922−0.991
0.810
0.225
0.135
3.712
2.544
−0.532−1.401−0.120−0.582−0.3700.605−2.131
3.568

Sample details
Number of observations
Number of individuals
Time periods

2166
722
3

2166
722
3

Time-invariant

0.067−0.002−0.204−0.017
Time-invariant

3.277−4.379−2.179−0.1091444
722
2

Goodness of ﬁt statistics
R-square0.0761

Adjusted R-square

Intra-class correlation (individual)

0.070513.64(d.f.:13, 2152, p < 0.01)–

Out-of-sample validation statistic(Repeated learning test, r = 500, calibration sample = 90%)
Average correlation
RMSE2

0.240
1.468

F-statistic (df., p-value)

Marginal: 0.074
Conditional: 0.669
–

0.64

0.235
1.477

0.283
1.032

–

Robust standard errors (cluster errors at the individual level) are used to account for the panel nature of the data.
Bold font indicates coefﬁcients with p-value <0.1.
1
Robust standard error could not be computed, hence excluded from model.
2
RMSE not directly comparable for ﬁrst-difference model and the remaining two as the dependent variable is different.
12

0.087
0.083
27.21
(df.:5, 1438, p < 0.01)–

G. Parady et al.

Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Table A6
Other shopping frequency models.
Variable

Other shopping frequency model
Pooled model

Random effect modelFirst-difference model

Est.

t valueEst.

t valueEst.

t value(Intercept)
Period 2 – Period 1 (ﬁrst-differenced)
Period 1 (after spread - after emergency declaration)
Period 2 (after spread - before emergency declaration)
Male
Age: 20–39
Age: 40–59
Household size: 1 person
Household size: 2 persons
Car in household
Population density 1/sq.km.
Commuting frequency
Mean cumulative number of infections/100
PC of perception of degree of self-restriction of others
COVID-19 dread
PC of trait anxiety

1.087
–
0.051
0.136
0.013
−0.126−0.133−0.184−0.093−0.001
0.005
0.052−0.007−0.487−0.110
0.043

9.183
–
0.543
1.198
0.125
−1.279−1.460−2.057−1.320−0.0091.295
3.864−4.673−5.210−1.221
2.867

1.048
–
−0.0710.014−0.001−0.143−0.161−0.186−0.089−0.0100.005
0.074−0.006−0.269−0.089
0.043

6.455
–
−0.5040.084−0.016−1.253−1.427−2.026−1.057−0.1100.857
4.243−1.444−1.708−0.8752.007−0.1380.194−1.471
1.854

Sample details
Number of observations
Number of individuals
Time periods

2166
722
3

Time-invariant

0.089
*1
−0.162−0.083Time-invariant

2166
722
3

4.599
*1
−4.101−0.6581444
722
2

Goodness of ﬁt statistics
Marginal: 0.053
Conditional: 0.477–

R-square0.048

Adjusted R-square–

Intra-class correlation (individual)

0.042
7.70
(d.f.:14, 2151, p < 0.01)
–

0.45

0.042
16.94
(d.f.: 4, 1439, p < 0.01)–

Out-of-sample validation statistics(Repeated learning test, r = 500, calibration sample = 90%)
Average correlation
RMSE2

0.179
1.139

0.181
1.144

0.198
0.979

F-statistic (df., p-value)

0.045

Robust standard errors (cluster errors at the individual level) are used to account for the panel nature of the data.
Bold font indicates coefﬁcients with p-value <0.1.
1
Robust standard error could not be computed, hence excluded from model.
2
RMSE not directly comparable for ﬁrst-difference model and the remaining to as the dependent variable is different.
Table A7
Pooled binary logit models of going-out self-restriction behavior for eating-out and leisure activities.
Variable

ASC go out (ref)ASC stay home
Period 1: stay home
Period 2: stay home
Male: stay home*1
Age 20–39: stay home*1
Age 40–59: stay home*1
Household size =2: stay home*1
Household size =1: stay home*1
Car in household: stay home*1
Commuting frequency: stay home
Population density (1/sq.km): stay home*1
Mean cumulative number of infections/100: stay home*1
PC of perception of degree of self-restriction of others: stay home
COVID-19 dread: stay home
PC of trait anxiety: stay home*1
Sample details
Number of observations
Number of individuals
Number of choice situations
Observed choice ratio
Go outStay home

Eat-out (alone)

Eat-out (groups)

Leisure (alone)

Leisure (groups)

Est.

t valueEst.

t valueEst.

t valueEst.

t value0−12.887
12.92813.592−0.994−0.2730.085−0.133−0.7730.085−0.049−0.014
0.019
0.997
0.766
−0.046−84.138
22.43522.862−4.722−0.8480.275−0.558−2.9700.321−1.179−0.753
1.101
2.410
2.521
−0.6560−12.405
12.407
13.583−0.240−0.6280.064−0.395−0.015−0.169−0.027−0.025
0.066
0.921
0.779
0.088

−82.977
23.05624.372−1.217−2.0980.207−1.719−0.052−0.568−0.675−1.374
2.961
2.266
2.717
1.256

0
−12.660
13.17614.218−0.899−0.3130.144−0.253−0.565−0.359−0.043
0.028
0.020
0.722
0.405
0.079

−70.09923.23624.111−4.310−1.0820.503−1.090−2.149−1.372−1.017
1.650
1.170
1.629
1.456
1.248

0
−12.52212.95114.058−0.615−0.678−0.302−0.427−0.242−0.259
0.009
0.005
0.026
0.939
0.615
0.014

−74.488
20.64921.504−3.131−2.174−0.973−1.911−0.864−0.957
0.230
0.281
1.426
2.195
2.071
0.221

1080
360
3

1239
413
3

1038
346
3

1287
429
3

0.42
0.58

0.50
0.50

0.46
0.54

0.49
0.51

Goodness of ﬁt statistics(continued on next page)13

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Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Table A7 (continued)
Variable

Eat-out (alone)
Est.

Eat-out (groups)

t valueEst.

t value

Leisure (alone)Est.

t value

Leisure (groups)
Est.

t value

LL(0)
LL(C)
LL(β)
rho-square (β)
Adjusted rho-square (β)−748.60−736.10−418.020.44
0.42−858.81−858.80−401.290.53
0.52−719.49−716.40−371.290.48
0.46−892.08−891.80−442.43
0.50
0.49

Out-of-sample validation statistics(Repeated learning test, r = 100, calibration sample = 90%)
Mean percentage correct
Mean RMSE

80.2%
0.001484.8%
0.000981.2%
0.001283.5%
0.0010Robust standard errors (cluster errors at the individual level) are used to account for the panel nature of the data.
Bold font indicates coefﬁcients with p-value <0.1.
1
All time-invariant coefﬁcients are estimated for the post-infection spread periods only (t1, t2).
Table A8
Error component mixed binary logit models of going-out self-restriction behavior for eating-out and leisure activities.
Variable

Eat-out (alone)
Est.

ASC go out (ref)ASC stay home
Period 1: stay home
Period 2: stay home
Male: stay home*1
Age 20–39: stay home*1
Age 40–59: stay home*1
Household size =2: stay home*1
Household size =1: stay home*1
Car in household: stay home*1
Commuting frequency: stay home
Population density (1/sq.km): stay home*1
Mean cumulative number of infections/100: stay home*1
PC of perception of degree of self-restriction of others: stay home
COVID-19 dread: stay home
PC of trait anxiety: stay home*1θ0−12.669
12.91214.062−1.592−0.5710.016−1.212−0.2320.173−0.075−0.016
0.019
1.490
1.136
*2
2.025

Eat-out (groups)

t valueEst.

−20.153
10.85411.037−4.506−1.1360.032−2.828−0.6110.404−1.154−0.540
0.797
2.317
2.403
*
6.562

0
−20.000
19.95421.725−0.341−0.7420.163−0.069−0.574−0.229−0.042−0.027
0.071
1.207
1.152
*
1.728

Leisure (alone)t valueEst.

−58.258
37.46332.522−1.186−1.7430.367−0.168−1.720−0.535−0.706−1.018
2.480
2.114
2.763
*
5.418

0
−19.275
19.90421.325−1.224−0.3210.244−0.758−0.360−0.468−0.048
0.039
0.022
0.778
0.648
*
1.429

Leisure (groups)

t valueEst.

t value−32.99922.40120.067−4.195−0.8440.649−2.123−1.160−1.334−0.855
1.677
1.018
1.335
1.726
*
5.150

0
−17.879
19.02220.935−1.035−1.214−0.585−0.375−0.752−0.345−0.003
0.011
0.033
1.547
0.682
*
2.293

−20.658
13.16713.076−2.975−2.273−1.102−0.785−1.908−0.748−0.051
0.378
1.272
2.017
1.365
*
6.361

Sample details
Number of observations
Number of individuals
Number of choice situations
Observed choice ratio
Go outStay home

1080
360
3

1239
413
3

1038
346
3

1287
429
3

0.42
0.58

0.50
0.50

0.46
0.54

0.49
0.51

Goodness of ﬁt statistics
LL(0)
LL(C)
LL(β)
rho-square (β)
Adjusted rho-square (β)−748.60−736.10−397.130.47
0.45−858.81−858.80−391.300.54
0.53−719.49−716.40−364.08
0.49
0.47−892.08−891.80−418.51
0.53
0.51

Out-of-sample validation statistics(Repeated learning test, r = 100, calibration sample = 90%)
Mean percentage correct
Mean RMSE

79.6%
0.001484.1%
0.000980.9%0.001283.4%
0.0010Robust standard errors (cluster errors at the individual level) are used to account for the panel nature of the data.
Bold font indicates coefﬁcients with p-value <0.1.
1
All time-invariant coefﬁcients are estimated for the post-infection spread periods only (t1, t2).
2
Parameters resulted in resulted in non-negative-semideﬁnite Hessian matrices, hence excluded from ﬁnal model.
Table A9Discrete choice models of changes in frequency of eating-out and leisure activities.
Variable

ASC increase
ASC no change (ref.)ASC decrease
Period 2: increase
Period 2: decrease

Eat-out (alone)

Eat-out (groups)

Leisure (alone)

Leisure (groups)

Est.

t valueEst.

t valueEst.

t valueEst.

t value−1.4140.000
0.148−0.303−1.022−2.160−1.6230.000
0.146−1.012−1.096−2.767−1.4250.000
0.160−0.209−0.792−2.552−2.2370.000
0.000−0.017−1.084−2.4610.439−0.427−2.91014

0.428−1.559−3.0650.440−0.361−2.0420.000−0.018−3.164

G. Parady et al.

Transportation Research Interdisciplinary Perspectives 7 (2020) 100181

Table A9 (continued)
Variable

Eat-out (alone)

Eat-out (groups)

Leisure (alone)

Leisure (groups)

Est.

t valueEst.

t valueEst.

t valueEst.

t value

Mean cumulative number of infections/100: increase
Mean cumulative number of infections/100: decrease
Commuting frequency increase: increase
Commuting frequency decrease: decrease
PC of perception of degree of self-restriction of others: increase
PC of perception of degree of self-restriction of others: decrease
COVID-19 dread: increase
COVID-19 dread: decrease

0.009
0.011
1.086
0.325
−0.8280.589−0.361
0.667

0.393
0.933
2.531
1.878
−1.1601.418−0.7362.189−0.020
0.004
1.551
0.382
−0.534
0.880
0.150
0.676

−0.737
0.372
2.947
2.337
−0.927
1.985
0.281
2.315

−0.027−0.0100.813
0.169−0.1820.803−0.3000.663−1.150−0.9171.770
1.028−0.2621.838−0.6192.220−0.045−0.005
1.099
0.377
0.441
1.389
0.005
0.590

−1.515−0.487
2.285
2.379
0.474
3.209
0.008
2.037

Sample details
Number of observations
Number of individuals
Number of choice situations

720
360
2

826
413
2

692
346
2

858
429
2

Observed choice ratio
Increase
No change
Decrease

0.08
0.40
0.52

0.04
0.42
0.53

0.07
0.40
0.53

0.04
0.43
0.52

Goodness of ﬁt statistics
LL(0)
LL(C)
LL(β)
rho-square (β)
Adjusted rho-square (β)−791.00−651.18−569.020.28
0.27−907.45−690.21−586.720.35
0.34−760.24−617.02−545.660.28
0.27−942.61−720.45−593.79
0.37
0.36

66.6%
66.6%
33.4%

69.7%
69.7%
30.3%

64.9%
64.9%
35.1%

72.1%
72.1%
27.9%

Out-of-sample validation statistics(Repeated learning test, r = 100, calibration sample = 90%)
Mean percentage correct
Mean percentage clearly right (t = 0.5)
Mean percentage clearly wrong (t = 0.5)

Robust standard errors (cluster errors at the individual level) are used to account for the panel nature of the data.
Bold font indicates coefﬁcients with p-value <0.1.
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