∎

11institutetext: P. Maas 22institutetext: Menlo Park, CA
22email<EMAIL_ADDRESS>33institutetext: Z. Almquist 44institutetext: Seattle,
WA
44email<EMAIL_ADDRESS>
# Using social media to measure demographic responses to natural disaster:
Insights from a large-scale Facebook survey following the 2019 Australia
Bushfires

Paige Maas  Zack Almquist  Eugenia Giraudy  JW Schneider

###### Abstract

In this paper we explore a novel method for collecting survey data following a
natural disaster and then combine this data with device-derived mobility
information to explore demographic outcomes. Using social media as a survey
platform for measuring demographic outcomes, especially those that are
challenging or expensive to field for, is increasingly of interest to the
demographic community. Recent work by Schneider and Harknett (2019) explores
the use of Facebook targeted advertisements to collect data on low-income
shift workers in the United States. Other work has addressed immigrant
assimilation (Stewart et al, 2019), world fertility (Ribeiro et al, 2020), and
world migration stocks (Zagheni et al, 2017). We build on this work by
introducing a rapid-response survey of post-disaster demographic and economic
outcomes fielded through the Facebook app itself. We use these survey
responses to augment app-derived mobility data that comprises Facebook
Displacement Maps to assess the validity of and drivers underlying those
observed behavioral trends. This survey was deployed following the 2019
Australia bushfires to better understand how these events displaced residents.
In doing so we are able to test a number of key hypotheses around displacement
and demographics. In particular, we uncover several gender differences in key
areas, including in displacement decision-making and timing, and in access to
protective equipment such as smoke masks. We conclude with a brief discussion
of research and policy implications.

###### Keywords:

disasters, facebook, displacement maps, displaced populations

††journal:

##  1 Introduction

Demographers have long been interested in large-scale natural disasters and
their effects on regional populations (Frankenberg et al, 2014). This topic
has only increased in importance as climate change and rising population
densities increased the observed frequency of natural disasters which impact
human settlements (Bouwer, 2011).

Researchers have used mobility data from cell phones and other electronic
devices to characterize both short-term and long-term displacement after a
disaster (Gething and Tatem, 2011); however, this technique has often been
limited by an inability to pair mobility data with key additional information.
In particular, many potential hypotheses could be explored if these methods
also gave information about who is displaced, the household structure of
displaced persons, and the nature of locations to which displaced persons
migrate (e.g. identification as a home of relatives) (Frankenberg et al,
2014).

Migration decisions are complex and depend on not only the severity of a given
disaster, but also the demographic composition of those in the area
(Frankenberg et al, 2014). A displacement event can last anywhere from days to
years, with many choosing to permanently relocate. Prior work has compared
mobility strategies among individuals living in communities which sustained
different degrees of damage due to the 2004 Indian Ocean Tsunami (Frankenberg
et al, 2014). Drawing from a recent example of a severe disaster, we observe
that in 2010 Port-au-Prince, Haiti lost 23% of its permanent residents
following a magnitude 7.0 earthquake struck near that city (Frankenberg et al,
2014). Understanding demographic heterogeneity among affected populations can
help us better understand these disparities, and can better inform policy
response to such events in the future.

Prior large-scale post-disaster surveys are discussed in the literature, but
these studies are expensive to conduct and rare precisely due to the
difficulty of reaching displaced populations (Frankenberg et al, 2014). One
recent attempt utilized surveys conducted on the Facebook ad system in 2018
following the Hurricane Maria disaster in Puerto Rico (Alexander et al, 2019).
We extend this work by surveying directly on the Facebook platform rather than
through advertisements, which gives us finer control over sampling, more
ability to perform bias correction, and further lowers the cost of this work.

In this study we partner with Facebook’s Data for Good program to address the
above limitations of post-disaster displacement surveys. The Data for Good
program has already used mobility data to develop an approach that both
identifies migration and relocation after disasters (Maas et al, 2019) and
provides real-time information about displacement to non-governmental
organizations (NGOs). In this study we extend their work by incorporating
additional demographic and contextual information collected via a rapid-
deployment survey following a disaster. Rapid surveys on a platform such as
Facebook provide a novel way of reaching individuals in situations that would
otherwise render them prohibitively expensive or impractical to reach, and
allow for high-quality demographic estimates in otherwise adverse
circumstances.

From September 2019 to March 2020, Australia experienced one of the worst
bushfires in modern history (Kganyago and Shikwambana, 2020). Over this period
it is estimated that 186,000 square kilometres were burned and 5,900 building
were destroyed. This included more than three thousand homes (Filkov et al,
2020) and resulted in the displacement of tens of thousands of
Australians.111https://www.directrelief.org/2020/01/australian-bushfires-
mapping-population-dynamics/ To better understand the economic and demographic
effects of this disaster in Australia, we conducted a survey of Facebook users
who could have been directly affected by the bushfires. This, when combined
with mobility estimates provided by the Data for Good program, allows us to
refine estimates derived from mobility data (Maas et al, 2019) and to address
hypotheses around how displacement affected people and households in the
region – information that cannot be obtained from observational migration data
alone.222Facebook’s Data for Good program (Maas et al, 2019) makes available
Displacement maps for use by humanitarian partners and this project is larger
part of that effort.

This research provides two major contributions to the literature on demography
and disasters: (1) we demonstrate the viability of using a rapid-deployment
on-platform Facebook survey to collect demographic data following a natural
disaster; and (2) we characterize the demographics displaced persons following
2019 Australia bushfires. Specifically, this survey allowed us to characterize
the impact of displacement from the Australia bushfires on a representative
sample of Australian Facebook users and test key hypotheses in short term
migration around disaster-induced displacement. To these ends, we surveyed
95,649 Facebook users who were over the age of 18 and predicted to be in the
area affected by two major bushfires: the _Green Wattle Creek Fire_ in Eastern
New South Wales and the _Cudlee Creek Fire_ in Adelaide Hills, South
Australia. Both of these fires started on or about December 18, 2019.

This article is laid out in the following manner: (1) Literature review; (2)
Research hypotheses; (3) Data and methods; (4) Survey descriptive statistics;
(5) Analysis; and finally (6) Discussion and limitations.

##  2 Literature Review

Demographers have studied the population consequences of disasters for a
number of specific outcomes. Typically these include issues such as fertility
(Lin, 2010), mortality (Finlay, 2009) and migration (Belasen and Polachek,
2013; Frankenberg et al, 2014). This work primarily focuses on understanding
the latter with an emphasis on demographics of age, gender and household
mobility. We build on the rich literature on the sociology of disasters (Dynes
et al, 1987) with an emphasis on demography and disasters. Here, we employ
Kreps’s definition of “disaster” as used in Smelser et al (2001), which
defines disasters as “non-routine events in societies that involve
conjunctions of physical conditions with social definitions of human harm and
social disruption.”

This section is laid out as follows, first we review the literature on
migration and disasters, then we cover the basics of Facebook’s Data for Good
Displacement map; next, we review briefly the automated displacement
measurements compiled from cellular phone data and apps, and finally we review
the literature of surveys being conducted on Facebook for demographic
estimates and detailed information and generalizability to offline
populations.

###  2.1 Disasters and Demography

The literature on the demography of disasters can be laid-out in five core
themes: (1) fertility, (2) mortality, (3) health, (3) migration and (4) data
collection (Frankenberg et al, 2013). This topic is important, having
warranted a special issue in _Population and Environment_ (Frey and Singer,
2010; Gutmann and Field, 2010; Stringfield, 2010; Hori and Schafer, 2010;
Davies and Hemmeter, 2010; Czajkowski and Kennedy, 2010; Plyer et al, 2010),
which focused on the demographic impacts of Hurricane Katrina in the United
States.

Disasters, in general, have the potential to displace people through either
preemptive effects (e.g. early evacuation) or direct damage property and
economic livelihood. Both voluntary and involuntary displacement can effect
demographic change in a local area (Frankenberg et al, 2014). These
demographic changes can have material consequences for affected areas
(Frankenberg et al, 2013). For example, Fussell et al (2010) shows that
Hurricane Katrina affected the racial and ethnic composition of New Orleans
through disproportionate housing damage experienced by Black residents.
Another example, which used a large-scale survey, includes Gray et al (2014)
which compared mobility strategies among individuals living in small villages.
Smith and McCarty (1996) studied the effects of Hurricane Andrew. This
disaster resulted in the displacement of more than 350,000 individuals, around
40,000 of which were estimated to have never returned. Other significant
studies in the migration of people due to disasters includes Raker (2020);
Schultz and Elliott (2013); James and Paton (2015); and Donner and Rodríguez
(2008).

###  2.2 Facebook’s Gender-Stratified Displacement Map

Facebook’s Data for Good program is a broad initiative designed to provide
data to humanitarian organizations to facilitate their important work in many
fields, including disaster response and disease prevention. One such dataset
is the Gender-Stratified Displacement Map, which aims to quantify the
magnitude of displacement following disasters and describe where the displaced
population has migrated, and enables study of these population trends by
gender. As climate change increases the frequency and severity of natural
disasters, it becomes more important for response organizations to utilize
novel data sources in understanding how many people are displaced, where they
have been displaced, and when they might be able to return home. Facebook is
uniquely positioned to aid organizations in answering these questions, helping
increase the effectiveness of humanitarian response of food, medical and
housing aid while preserving user privacy.

Briefly, Facebook’s Displacement Map dataset estimates how many people were
displaced by a given disaster and where those people are in the period
following it at an aggregate level. Specifically, the models identify a person
as displaced if their typical nighttime location patterns are disrupted after
the event compared to that person’s pre-disaster patterns. These patterns are
obtained from a user’s Location History, which is an optional setting on the
Facebook app users can enable that provides precise locations
333https://www.facebook.com/help/278928889350358. The individual data is
aggregated into a city-level transition matrix showing how many people are
displaced from one city to another for all source cities in the disaster
affected region for each day for a period following the event 444This work as
been published (Maas et al, 2019) and made accessible at
https://dataforgood.fb.com/tools/disaster-maps/. These aggregate trends,
stratified by gender, have revealed gender differences in the patterns of
displacement and return that we seek to investigate further through the lens
of this survey. We articulate specific hypotheses generated by these observed
displacement trends below in Section 4.

####  2.2.1 Other Methods for Automatically Measuring Displaced Populations

There is a growing literature around using cell-phone and app-based data for
research around location and migration, which includes short-term displacement
due to disasters. For example, Lu et al (2012) involved the use of 1.9 million
phone users’ location data subsequent to and for up to one year after the 2010
Haitian earthquake. This work was able to uncover very detailed mobility
patterns among the studied population. Deville et al (2014) follows up on this
work and demonstrates the cost effectiveness of using mobile phone network
data for accurate and detailed maps of populations after a disaster. This is
an active area of the literature, which is also being used to estimate the
population of countries that have historically had weak or non-existent
demographic data (Tatem, 2017).

###  2.3 Facebook for Demographic Surveys

There is much work looking into the viability of using Facebook as survey
platform for demographic data. Schneider and Harknett (2019) explore the use
of Facebook targeted advertisements to collect data on low-income shift
workers in the United States. Blondel et al (2015) uses Facebook ads system to
recruit survey participants to answer issues of mobility and geographic
partitioning. Alexander et al (2019) has employed Facebook ads system to
survey out-migration following a hurricane in Puerto Rico in 2017. Surveys
administered via Facebook ads system has also been used to measure immigrant
assimilation (Stewart et al, 2019), world fertility (Ribeiro et al, 2020), and
world migration stocks (Zagheni et al, 2017). There is a growing literature on
how to re-adjust surveys administered on Facebook ads (Zagheni et al, 2017)
platform or survey system (Feehan and Cobb, 2019) for estimation of both
online and offline populations.

##  3 Australia Bushfires, 2019-2020

The most recent census data for Australia places the country’s total
population at around 26 million,
(2016)555https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/036
and estimates of the economic impact of bushfires ranging from 1.8 billion to
4.4 billion (AUD).666http://aicd.companydirectors.com.au/membership/company-
director-magazine/2020-back-editions/march/the-ongoing-impact-of-bushfires-on-
the-australian-economy and https://www.theguardian.com/australia-
news/2020/jan/08/economic-impact-of-australias-bushfires-set-to-exceed-44bn-
cost-of-black-saturday In this work we focus on the _Green Wattle Creek Fire_
in Eastern New South Wales and the _Cudlee Creek Fire_ in Adelaide Hills,
South Australia (Figure 1), both of which started on or about December 18th,
2020. The Green Wattle Creek fire was extinguished by rainfall in February
2020 and is estimated to have destroyed 467,000 acres of
land777https://wildfiretoday.com/2019/12/21/fires-west-of-sydney-burn-
over-2-million-acres. The Cudlee Creek Fire was put out by Australian
firefighters and damage was estimated at 57,000 acres
888https://www.winespectator.com/articles/australian-wildfires-scorch-vines-
in-adelaide-hills.

##  4 Research hypotheses

Data limitations have long hampered the study of policy and population
responses to natural disasters. The Facebook survey employed in this study
allows us to address decision-making and policy-focused questions around
disaster displacement. In this work we focus on five important hypotheses
around displacement, gender and age.

In this survey we asked about duration of displacement (operationalized as 3
categories: not displaced, displaced 1 to 3 nights, and displaced more than 3
nights).999“More than 3 nights” is the most informative definition for
displaced prediction algorithms like Displacement Maps, and asking about
higher length displacements does not support meaningful additional inferences
Maas et al (2019). While there is some research characterizing populations
displaced by disasters, this research either leverages on-the-ground survey
modes (e.g. Frankenberg et al (2014)), which are both expensive and difficult
to execute in affected regions, or leverage data provided by cell phones and
other tracking apps. The latter lacks identifying demographic information and
limits insights into why people left a given disaster area. This work provides
two significant advances in researching demographic responses to disasters:
(1) it demonstrates the cost effectiveness and ease of implementation of an
in-app survey following a disaster and (2) it allows us to ask demographic,
migration and economic questions to hypotheses that are rarely tested in the
disaster and demography literature Frankenberg et al (2014). Below are a
series of hypotheses we explored.

Our research hypotheses focused on six key themes related to demographic and
policy decision making for disaster relief. First we focused on _who evacuates
and how_ ; second we looked into _who made decisions to evacuate_ ; third we
tested _where people went when they evacuated_ ; fourth we looked into _what
circumstances households became separated during displacement_ ; fifth, we
explored _job disruption due to the disaster_ ; and finally we tested for
inequalities in _access to and knowledge of protective equipment_ (masks)
during a disaster.

Question 1

Who evacuates and how?

Null Hypothesis 1

There will not be differences in who evacuates or how they evacuate by
demographic characteristics.

Alternative 1

There will be differences in who evacuates, when they evacuate, or how they
evacuate by demographic characteristics. Based on trends in Facebook’s
Displacement Maps, we have reason to believe that women will evacuate
differently, on average, than men. We will assess whether survey responses are
consistent with these behavioral signals.

Survey Questions

We test this hypothesis with questions on evacuation timing and displacement
duration, as well as distance of displacement relative to home, in conjunction
with questions measuring demographic characteristics.

Question 2

Who makes the decision to evacuate?

Null Hypothesis 2

There will be no differences in attribution of the evacuation decision by
demographic characteristics.

Alternative 2

There will be differences in attribution of the evacuation decision by
demographic characteristics. For example, men will be more likely to take
ownership of the decision for their household to evacuate.

Survey Questions

We test this hypothesis with a question on who made the decision to evacuate.

Question 3

Where do people go when they evacuate?

Null Hypothesis 3

People will evacuate primarily to nearby towns and areas which are not
affected by the disaster.

Alternative 3a

People will relocate equally to nearby and distant locations from the
disaster.

Alternative 3b

People will relocate primarily to locations far from the disaster.

Survey Questions

Among people who evacuated, we ask respondents where they evacuated relative
to their home (e.g. same city).

Question 4

Under what circumstances do households separate during displacement?

Null Hypothesis 4

Household separations will not meaningfully depend on demographic
characteristics.

Alternative 4

Household separations will meaningfully depend on demographic characteristics.
For example, women may evacuate earlier than the rest of their household and
men may return from displacement before the rest of their household.

Survey Questions

We asked about household separation upon leaving the disaster area and also
when returning to home.

Question 5

Whose work is disrupted?

Null Hypothesis 5

Work disruption will not meaningfully depend on demographic characteristics
after controlling for economic situation.

Alternative 5

Work disruption will meaningfully depend on demographic characteristics after
controlling for economic situation. For example, women may be more impacted by
work disruption.

Survey Questions

We ask the respondent if their work was disrupted due to the bushfires.

Question 6

Who has access to masks and the knowledge to use them effectively?

Null Hypothesis 6

An individual’s knowledge of or access to masks during a bushfire will not
depend meaningfully on demographics.

Alternative 6

An individual’s knowledge of or access to masks during a bushfire will depend
meaningfully on demographics. For example Thomas et al (2015) found that in
the United States men had odds ratio of 2.5 times the knowledge and access to
key equipment for disaster response.

Survey Questions

We ask about access and knowledge of using an N95 mask.101010It is important
to note this question was asked before the global emergence of COVID-19. We
assume mask usage, knowledge, and access are substantially different in the
period of this survey, which was fielded before the pandemic, than in the
world today.

### Summary

We will explore each of these hypotheses with the survey data (See Appendix
for full descriptive statistics). In this work we will employ inferential
statistics and regression analysis to fully review each hypotheses and
question set.

##  5 Data and Methods

###  5.1 Survey Population

We surveyed an effectively random sample of 95,649 Facebook users over the age
of 18. Each of these users answered an array of questions on demographics,
smoke masks, and whether they were in the region at the time of the fires. Of
those surveyed, 24,486 Facebook users were in the area of the two major
bushfires under consideration in this article. Here, we are focusing on two
bushfires that started on or about December 18, 2019 – the _Green Wattle Creek
Fire_ in Eastern New South Wales and the _Cudlee Creek Fire_ in Adelaide
Hills, South Australia (Figure 1). Of those who said they were in the area,
7,073 users said they were affected by the fires (Q 2: loc_q). These users
were then asked a detailed set of questions on displacement outcomes. To
guarantee we only consider the target population, we limit our displacement-
related analysis to these 7,073 users. This is, effectively, a form of
rejection sampling and will maintain our sample properties (Gilks and Wild,
1992). The survey was in the field for two weeks, from February 20, 2020 to
March 5, 2020, approximately two months after the bushfires began. This timing
strikes a balance between decaying respondent recall and allowing the
consequences of a displacement to play out more fully.

###  5.2 Sample Design

The sampling design is built to capture a representative sample of Facebook
users who were 18 or older and predicted to be in the disaster region of the
_Green Wattle Creek_ and _Cudlee Creek_ fires (Figure 1). Through a
combination of app-based user targeting (e.g. in Australia) and survey
gatekeeping (e.g. Q2) we are able to isolate these respondents. Specifically,
we take all Facebook users who have been identified to be in the region based
on their city location and age. We also include users as identified by their
Location History as reported in Facebook’s Data for Good program _Displacement
Map_. We are then able employ rejection sampling to limit our final respondent
set to only included the target population. This method is very general and
will provide a random sample of users in the two regions (Gilks and Wild,
1992). We are able to adjust for survey non-response through the application
of survey weights (see Section 5.3). All users were asked for informed consent
to survey and all questions allowed the respondent to not respond (See
Question 1). No participants were compensated in any way for their
participation.

Though we are able to predict with some accuracy whether a respondent was in
the area of a bushfire, we still take steps in the survey to validate this.
All respondents must self-identify in the survey as having been in the area of
one of the two bushfires under study in December, 2019. Again, though we are
able to predict with some accuracy the length of displacement for a number of
our respondents, we verify in the survey whether they were displaced and for
how long. An example survey question can be seen in Figure 2.

Figure 1: Map of the bounding box survey locations: _Green Wattle Creek Fire_
in Eastern New South Wales and the _Cudlee Creek Fire_ in Adelaide Hills South
Australia. Figure 2: Example survey question from the Facebook Australia
Bushfire survey.

###  5.3 Sample Weights for Non-Response Bias

We employ calibration (e.g raking) weights to adjust the sample to be
representative of the over 18 Facebook population for the two regions under
study. To do this we weight on _age, gender, engagement_ (number of days
active in a month), _region_ , and if the user has _location history_ turned
on. This improves the representativeness of our results among both core
demographics and other key Facebook user categories. All statistics discussed
in this article are re-weighted and employ Horvitz-Thompson estimator for
computing the weighted mean with standard errors computed using the delta
method (Overton and Stehman, 1995).

##  6 Survey Descriptive Statistics

In Table LABEL:demographics we provide respondent counts and basic demographic
summary statistics. Further, based on the Australian demographics from 2016
(most recently available
data111111https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/036)
we have added the most recent census population proportions for comparison.
Our sample is more female than expected by about 3%. Age skews young with more
people in the 20-39 age group than we expect by population and few in 40-70+.
We have a more educated population with 31.9% bachelors degree holders or
higher compared to 22% in the general population (not in table because the
Australian census breaks down education differently than in our survey). For
income our sample contained 18.9% of people with less than $3,200 (Australian
dollars) total household monthly income compared to an expectation of around
20%. The single household distribution (household size of 1) is slightly less
than expected at 12.3% compared to 15.8% in the broader population. Overall we
collected sufficient information to allow for proper controlling for age and
gender in our analysis despite these sample biases. For descriptive statistics
we adjust using the weights discussed in Section 5.3.

Table 1: Distribution of Sample Demographics |  |  |  | Australia Census
---|---|---|---|---
Demographic |  Response | N | Percent in Sample | Pop. Percentiles
Reported Gender |  |  |  |
|  Female | 42230 | 54.2 (53.6, 54.7) | 50.7
|  Male | 35717 | 45.8 (45.3, 46.4) | 49.3
Reported Age |  |  |  |
|  $<$20 | 6171 | 7.7 ( 7.4, 8.1) | 6.0
|  20-29 | 17984 | 22.6 (22.2, 23.0) | 13.4
|  30-39 | 16200 | 20.3 (19.9, 20.8) | 15.0
|  40-49 | 12971 | 16.3 (15.9, 16.7) | 13.6
|  50-59 | 10692 | 13.4 (13.0, 13.8) | 12.7
|  60-69 | 7785 | 9.8 ( 9.4, 10.1) | 10.7
|  70+ | 4643 | 5.8 ( 5.5, 6.1) | 10.7
|  Prefer Not to Say | 3200 | 4.0 ( 3.8, 4.3) |
Education |  |  |  |
|  Elementary | 663 | 2.0 ( 1.7, 2.4) |
|  Junior High | 1291 | 4.0 ( 3.6, 4.4) |
|  High School | 9417 | 29.1 (28.3, 29.9) |
|  Community College | 6996 | 21.6 (20.9, 22.3) |
|  University | 5554 | 17.2 (16.5, 17.8) |
|  Graduate School | 4750 | 14.7 (14.1, 15.3) |
|  Prefer Not to Say | 3711 | 11.5 (10.9, 12.1) |
Employment Type |  |  |  |
|  Managing a Business | 5506 | 8.0 ( 7.7, 8.3) |
|  Employed by Business | 25350 | 36.8 (36.2, 37.3) |
|  Employed, not by Business | 2686 | 3.9 ( 3.7, 4.1) |
|  Government Work | 5983 | 8.7 ( 8.4, 9.0) |
|  Student | 5329 | 7.7 ( 7.4, 8.1) |
|  Retired | 7898 | 11.5 (11.0, 11.9) |
|  Not Working | 7689 | 11.2 (10.7, 11.6) |
|  Prefer Not to Say | 8484 | 12.3 (11.9, 12.7) |
Income |  |  |  |
|  $<$ $3,200 | 5892 | 18.9 (18.1, 19.7) |
|  $3,200-$5,800 | 4263 | 13.7 (13.1, 14.3) |
|  $5,800-$9,100 | 2877 | 9.2 ( 8.8, 9.7) |
|  $9,100-$14,000 | 1547 | 5.0 ( 4.6, 5.3) |
|  $>$ $14,000 | 1624 | 5.2 ( 4.8, 5.6) |
|  Prefer Not to Say | 14918 | 47.9 (47.0, 48.8) |
Household Head |  |  |  |
|  Yes | 24727 | 34.9 (34.3, 35.4) |
|  No | 31150 | 43.9 (43.3, 44.5) |
|  Prefer not to say | 15062 | 21.2 (20.7, 21.7) |
Household Size |  |  |  |
|  1 | 8964 | 12.3 (11.8, 12.7) | 15.8
|  2 | 20319 | 27.8 (27.3, 28.3) |
|  3 | 14392 | 19.7 (19.2, 20.1) |
|  4 | 14910 | 20.4 (19.9, 20.8) |
|  5 | 8197 | 11.2 (10.8, 11.6) |
|  6 or more | 6360 | 8.7 ( 8.4, 9.0) |
Household Child Count |  |  |  |
|  0 | 42914 | 58.7 (58.1, 59.2) |
|  1 | 12010 | 16.4 (16.0, 16.8) |
|  2 | 10701 | 14.6 (14.2, 15.0) |
|  3 | 4424 | 6.0 ( 5.8, 6.3) |
|  4 | 1653 | 2.3 ( 2.1, 2.4) |
|  5 or more | 1453 | 2.0 ( 1.8, 2.2) |
Household Partner |  |  |  |
|  Yes | 37940 | 53.2 (52.6, 53.8) |
|  No | 26162 | 36.7 (36.1, 37.3) |
|  Prefer not to say | 7163 | 10.1 ( 9.7, 10.4) |

##  7 Results and Analysis

###  7.1 Results on Evacuation and Return

####  7.1.1 Who evacuates and how?

Among people in the affected areas, 25.5%, or about 1,500, of our respondents
report being displaced more than one night, with no difference in the rate by
gender. However, in a regression model adjusted for age, gender, categorical
groups for income, and head of household, we see that lower incomes were
associated with higher odds of reporting displacement (Figure 3).

Figure 3: Odds ratios for not being displaced for more than one night, as
compared to ”Yes” displaced more than one night, adjusted for age, gender,
income, and head of household. People less likely to be displaced include
higher income groups, people 50-59, and not head of household. Values
presented in Appendix Table
LABEL:Total_weights_q11_one_night_regression_no_interaction_with_income.

Of those displaced more than one night, 63.4% report displacement of more than
three nights. This suggests a substantial disruption to most people’s lives
caused by their displacement, with implications for policy response. For
example, knowing that such a high share of respondents experienced longer
displacements would suggest remedies and mitigations focusing on housing and
including food and water access.

In Australia, people did not report taking advantage of government-provided
transportation, though we do have difficulty assessing the availability of
such assistance. Of those displaced, only 5.5% of people reported using
government-provided transportation. In fact, the vast majority of people who
evacuated report leaving by car, at 89%. That private transit was a key tool
for evacuation again has clear policy response implications for future
disasters.

####  7.1.2 Who makes the decision to evacuate?

Asked “Whose decision first led to you leaving your home?,” the plurality
responded that it was their own decision (47.7%), while 24.3% and 23.6%
attributed the decision to a family member or the government, respectively.
Men were significantly more likely to say it was their own decision, while
women were more likely to say it was a government decision (Figure 4).

This result held up in regression models adjusted for gender, age, and
education as collected through the survey instrument, and whether the person
was the head of their household, with an odds ratio of 2.3 for women reporting
government as compared to their own decision (Appendix Figure 17, Appendix
Table LABEL:Total_weights_q29_evacuation_decision_regression_no_interaction).
In these models, people who were not head of household had twice the odds of
attributing the evacuation decision to a family member. We should note that
these results reflect what people report to us; it’s entirely possible that
the same sequence of events - say the government advises a person to evacuate
and they do - can result in different attributions for the decision, depending
on whether the person views following the advice as ultimately their own
decision, or as stemming from the government’s decision. These biases are,
however, known to the survey literature and the survey is designed to minimize
this to the extent possible.

Figure 4: Men are significantly more likely to say that the decision to
evacuate was their own, while women are more likely to attribute it to the
government.

####  7.1.3 Where do people go when they evacuate?

Of people who evacuated, the majority (63.4%) evacuated to a different city in
their region, with a much smaller fraction going somewhere else in their same
city (30%) or to a different state in Australia (5.4%). In our survey data,
women were significantly more likely to report that they evacuated within
their home city (Figure 5). This is particularly interesting, as we see this
consistently reflected in the behavioral data we have from aggregate trends in
Displacement maps for numerous disasters around the world.

Figure 5: Women are more likely to evacuate within the same city as their
home. This corresponds with trends in the Displacement Map data across many
disasters and geographies.

####  7.1.4 Under what circumstances do households split up during
displacement?

To understand the extent to which households were splitting up during
displacement, we asked two questions:

  * •

When you were displaced for more than three nights did anyone in your
household stay behind?

  * •

Did you return home at least one day before other members of your household?

Overall, we found that a substantial portion of households do split up either
at the beginning (28%) or end (31.2%) of their displacement. Surprisingly, the
responses to these two questions are uncorrelated, which is to say that people
with a household split during departure are no more likely to split up on
return, compared to households that did not split when leaving.

The vast majority of displaced people (86%) had returned home by the time we
surveyed two months after the fires. Among displaced people, 42% were
displaced for less than seven days, 33% for 7 to 14 days, 13% for 14 to 30
days, and 9% longer than a month. Those who had not returned home cited a
range of reasons including: unsafe (57.5%), new opportunities (22.2%), being
unable or unwilling (9.7%), or not allowed (8%).

While we did not see gender differences for households splitting upon
departure, we found that men return home sooner than their household more
frequently than women do (Figure 6). This is also consistent with the
behavioral data we have for Displacement Maps outputs in many countries,
including Australia.

Figure 6: Men are significantly more likely to return home ahead of the other
members of their household. This is consistent with behavior trends observed
in the Displacement Map.

In a regression model adjusted for age, gender, education, head of household,
and any children in the household: men (OR = 2.1), people aged 50-59 (OR = 2),
heads of household (OR = 2.4) and people with any children (OR = 1.6) are the
groups more likely to return home ahead of their households (Figure 7). People
without a university degree are significantly less likely to return home
ahead.

Figure 7: Odds ratio for returning home ahead of the rest of the household, as
compared to not, adjusted for age, gender, education, household head, and any
children. Values presented in Appendix Table
LABEL:Total_weights_q26_household_split_return_no_ref_regression_no_interaction_with_child_count_reformulated.

####  7.1.5 Whose work is disrupted?

Among displaced people asked: “Did leaving your home prevent you from working
as much as you normally do?”, 54.7% of people said “yes.” These responses did
not significantly differ by gender. It is perhaps unsurprising that in a model
adjusted for age, gender, and education, people who were displaced for more
than three nights were significantly more likely (OR = 1.8) to report that
they were prevented from working, as compared to people displaced fewer nights
(Appendix Table
LABEL:Total_weights_q21_prevent_working_regression_no_interaction_with_income_displacement_reformulated).
Nonetheless, this underscores the importance of duration of displacement on
the degree of disruption in people’s lives.

###  7.2 Gender Differences in Mask Access and Knowledge

####  7.2.1 Mask Access

When asked “Do you have access to a face mask in case of excessive wildfire
smoke?” 38% of the more than 61,000 people who responded said “Yes.” However,
there were significant gender differences (Figure 88(a)). Men were
significantly more likely to report mask access. In a regression model
adjusted for age, gender, education, and head of household, women were
significantly more likely (OR = 1.4) to say that they did not have mask access
(Appendix Figure 18, Table
LABEL:Total_weights_q28_mask_access_regression_no_interaction).

(a) Mask Access by Gender

(b) Mask Knowledge by Gender

Figure 8: Men are significantly more likely to report that they had access to
N95 smoke masks during the Australian bushfires in December 2019, and that
they can tell if a mask is effective.

####  7.2.2 Mask Knowledge

In addition to less mask access, women were also significantly more likely (OR
= 1.6) to report that they do not know how to tell if a face mask is effective
against wildfire smoke, compared to men (Figures 88(b), 9). This is after
already adjusting for the difference in mask access, in addition to age,
education, and head of household.

In the model, people without mask access had five times the odds of not
knowing whether a mask was effective (OR = 5.4), compared to people who did
(Figure 9). People who reported that they were not the head of household were
also significantly more likely (OR = 1.2) to not know whether a mask was
effective.

Figure 9: Odds ratio for not knowing whether a mask is effective, compared to
knowing, adjusted for age, gender, income, mask access, and duration
displaced. People without mask access have 5.5 times the odds of not knowing
whether a mask is effective; controlling for that, women have 1.6 times the
odds of not knowing. Values presented in Appendix Table
LABEL:Total_weights_q29_mask_knowledge_regression_no_interaction_with_mask_access_income_displacement.

##  8 Discussion

###  8.1 Implications

We find that the combination of survey data and location data can provide a
rich set of conclusions stronger than the strict sum of their parts. Mobility
data alone fails to provide proper context for many findings. We would not,
for example, understand the reasons for observed differences in displacement
by gender and would be unable to use this information to inform policy
response based solely on mobility data. Conversely, survey data alone, without
mobility data, gives no reliable indication of distance of displacement or
direction of displacement. Mobility data also greatly increases the efficiency
of collecting survey data by identifying populations of interest without the
need to perform extensive on-the-ground survey preparatory work. Conversely,
survey data can be used to improve and validate models based entirely mobility
data, providing a source of self-reported truth to expose biases or validate
successes of model-based approaches.

These studies can also help better inform policy responses to disasters.
Understanding characteristics of affected persons can inform the degree to
which responses are fiscal, material, or organizational in nature. Social
support can be better designed to account for likely patterns of household
displacement and separation. In this case we reach conclusions that can be
used to inform precisely these responses for future disasters.

Finally, this study provides a case example of the effects of fire on
displacement, offering an investigation into heterogeneity in responses by
demographic characteristics. We find significant differences by gender and
income on a variety of outcomes including evacuation timing, agency in
evacuation decisions, and household separation. This work reinforces the idea
that disasters have highly heterogeneous effects on the affected population.

###  8.2 Limitations

In 2020, it was estimated that 60% of Australians were on Facebook or
approximately 25 million
individuals.121212https://www.socialmedianews.com.au/social-media-statistics-
australia-january-2020 Surveys always have limitations due to sampling frame
(address, phone, social media), non-response and measurement Groves and Lyberg
(2010). We have carefully reweighted the data for non-response and have no
reason to expect this population to not be Representative of Facebook users.
While this sampling frame does not cover all of Australia the general
composition of Facebook users is not radically different than the full
population (e.g., the age distribution looks comparable to full age
distribution131313https://www.socialmedianews.com.au/social-media-statistics-
australia-january-2020).

Interpretation of the household hypothesis could be influenced by the
asymmetry of the questions, as the departure question asks about “anyone”
staying behind whereas the return question refers to “you” the respondent. In
future surveys we will update these questions to be consistent with one
another.

Head of household is based on the respondent’s answer to the question: “Do you
earn the most money among people in your household?” We acknowledge that this
is a limited viewpoint on who leads a household. This is likely a poor
definition in cases where earners travel away from their household for
employment and send back financial support to a partner who, while not the
primary income earner, is primarily responsible for day-to-day decision making
and leadership within the family. In future surveys we can design a question
more directly targeted to assessing this head of household designation.

###  8.3 Conclusion

Trends we had previously observed in Displacement Map data, such as women
evacuating closer to home and men returning home from displacement sooner,
were supported by this survey data. In addition to recognizing these
qualitative similarities, we intend to use this data for a detailed assessment
of the methodology underlying the Displacement map and make adjustments there
if needed.

Gender differences remain an important area of research for us, as men and
women face different challenges and needs when displaced. If men are returning
faster than women, as both our displacement maps and surveys have shown, our
humanitarian partners could consider using this information to plan their
operations differently. Additionally, governments may want to rethink how they
design and communicate evacuation orders in order to empower women to make
evacuation decisions independently. Last, while our survey questions about
mask access and knowledge were in the context of wildfire smoke, in a time of
global pandemic where masks play a pivotal role in preventing spread of
disease, governments and humanitarian organizations should make sure that
there are no gender disparities in both the access and correct use of masks.

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## Appendix

### Tables for Regression Models

### Prevent Working Regression: Model With Income and Displacement Duration,
No Interactions

Table 2: Prevent working, compared to “no”; relative to men, aged 30-39, income less than $3,200 head of household, not displaced based on model with main effects for age, gender, income, head of household, and duration displaced with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
Yes | Gender: Male | 1.00 |  |  |
Yes | Gender: Female | 0.91 | (0.68, 1.2) | 0.542 |
Yes | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | NaN |
Yes | Age: 20-29 | 1.42 | (0.96, 2.1) | 0.082 | .
Yes | Age: 30-39 | 1.00 |  |  |
Yes | Age: 40-49 | 1.05 | (0.69, 1.6) | 0.826 |
Yes | Age: 50-59 | 1.07 | (0.64, 1.8) | 0.802 |
Yes | Age: 60-69 | 0.27 | (0.14, 0.5) | 0.000 | ***
Yes | Age: 70+ | 0.58 | (0.25, 1.3) | 0.184 |
Yes | Age: Prefer Not to Say | 1.55 | (0.52, 4.6) | 0.437 |
Yes | Income: $<$ $3,200 | 1.00 |  |  |
Yes | Income: $3,200-$5,800 | 1.66 | (0.86, 3.2) | 0.131 |
Yes | Income: $5,800-$9,100 | 0.68 | (0.34, 1.4) | 0.276 |
Yes | Income: $9,100-$14,000 | 2.20 | (0.69, 7.0) | 0.183 |
Yes | Income: $>$ $14,000 | 0.88 | (0.32, 2.4) | 0.798 |
Yes | Income: Prefer Not to Say | 0.47 | (0.28, 0.8) | 0.006 | **
Yes | Household_head: Yes | 1.00 |  |  |
Yes | Household_head: No | 1.21 | (0.88, 1.7) | 0.242 |
Yes | Displaced_status: Displaced 1-3 nights | 1.00 |  |  |
Yes | Displaced_status: Displaced more than 3 nights | 1.79 | (1.35, 2.4) | 0.000 | ***

### More than One Night Regression: Model With Income, No Interactions

Table 3: Displaced more than one night, compared to “yes”; relative to men, aged 30-39, income less than $3,200 head of household based on model with main effects for age, gender, income, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 0.92 | (0.80, 1.1) | 0.266 |
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | NaN |
No | Age: 20-29 | 0.82 | (0.68, 1.0) | 0.043 | *
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.95 | (0.77, 1.2) | 0.601 |
No | Age: 50-59 | 1.52 | (1.19, 2.0) | 0.001 | ***
No | Age: 60-69 | 1.30 | (0.98, 1.7) | 0.069 | .
No | Age: 70+ | 1.07 | (0.73, 1.6) | 0.737 |
No | Age: Prefer Not to Say | 0.76 | (0.50, 1.2) | 0.216 |
No | Income: $<$ $3,200 | 1.00 |  |  |
No | Income: $3,200-$5,800 | 1.43 | (1.04, 2.0) | 0.026 | *
No | Income: $5,800-$9,100 | 1.87 | (1.30, 2.7) | 0.001 | ***
No | Income: $9,100-$14,000 | 1.86 | (1.13, 3.1) | 0.015 | *
No | Income: $>$ $14,000 | 2.16 | (1.30, 3.6) | 0.003 | **
No | Income: Prefer Not to Say | 1.90 | (1.46, 2.5) | 0.000 | ***
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 1.32 | (1.12, 1.5) | 0.001 | ***

### Household Split Return Regression: Model With Child Count, No Interactions

Table 4: Return home sooner, compared to “no”; relative to university educated women, aged 30-39, not head of household, with no children based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
Yes | Gender: Male | 2.07 | (1.51, 2.9) | 0.000 | ***
Yes | Gender: Female | 1.00 |  |  |
Yes | Gender: Prefer Not to Say | 1.00 | (NaN, NaN) | NaN |
Yes | Age: 20-29 | 1.19 | (0.75, 1.9) | 0.462 |
Yes | Age: 30-39 | 1.00 |  |  |
Yes | Age: 40-49 | 1.36 | (0.87, 2.1) | 0.182 |
Yes | Age: 50-59 | 2.10 | (1.19, 3.7) | 0.011 | *
Yes | Age: 60-69 | 0.62 | (0.28, 1.4) | 0.246 |
Yes | Age: 70+ | 2.50 | (1.07, 5.9) | 0.035 | *
Yes | Age: Prefer Not to Say | 1.80 | (0.73, 4.4) | 0.202 |
Yes | Education: Elementary | 0.48 | (0.04, 5.7) | 0.564 |
Yes | Education: Junior High | 0.48 | (0.15, 1.5) | 0.216 |
Yes | Education: High School | 0.39 | (0.21, 0.7) | 0.004 | **
Yes | Education: Community College | 0.34 | (0.18, 0.7) | 0.001 | **
Yes | Education: University | 1.00 |  |  |
Yes | Education: Graduate School | 0.09 | (0.03, 0.2) | 0.000 | ***
Yes | Education: Prefer Not to Say | 0.51 | (0.20, 1.3) | 0.149 |
Yes | Household_head: Yes | 2.38 | (1.66, 3.4) | 0.000 | ***
Yes | Household_head: No | 1.00 |  |  |
Yes | Any_children: FALSE | 1.00 |  |  |
Yes | Any_children: TRUE | 1.65 | (1.17, 2.3) | 0.004 | **

### Mask Knowledge Regression: Model With Mask Access, Income, and
Displacement Duration, No Interactions

Table 5: Mask knowledge, compared to “yes”; relative to men, aged 30-39, income less than $3,200, head of household, with mask access, not displaced based on model with main effects for age, gender, income, head of household, and duration displaced with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.56 | (1.50, 1.6) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 1.05 | (1.00, 1.1) | 0.068 | .
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.90 | (0.84, 0.9) | 0.000 | ***
No | Age: 50-59 | 0.84 | (0.79, 0.9) | 0.000 | ***
No | Age: 60-69 | 0.97 | (0.91, 1.0) | 0.425 |
No | Age: 70+ | 1.42 | (1.30, 1.6) | 0.000 | ***
No | Age: Prefer Not to Say | 1.22 | (1.07, 1.4) | 0.003 | **
No | Income: $<$ $3,200 | 1.00 |  |  |
No | Income: $3,200-$5,800 | 0.94 | (0.86, 1.0) | 0.201 |
No | Income: $5,800-$9,100 | 0.79 | (0.72, 0.9) | 0.000 | ***
No | Income: $9,100-$14,000 | 0.75 | (0.66, 0.8) | 0.000 | ***
No | Income: $>$ $14,000 | 0.81 | (0.71, 0.9) | 0.001 | ***
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 1.22 | (1.17, 1.3) | 0.000 | ***
No | Mask_access: Yes | 1.00 |  |  |
No | Mask_access: No | 5.48 | (5.27, 5.7) | 0.000 | ***
No | Displaced_status: Not Displaced | 1.00 |  |  |
No | Displaced_status: Displaced 1-3 nights | 0.75 | (0.59, 0.9) | 0.013 | *
No | Displaced_status: Displaced more than 3 nights | 0.77 | (0.65, 0.9) | 0.003 | **

### Evacuation Decision Regression: Model with No Interactions

Table 6: Evacuation decision made by someone else, compared to “my decision”; relative to university educated men, aged 30-39, head of household based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
Family Member | Gender: Male | 1.00 |  |  |
Family Member | Gender: Female | 1.28 | (0.91, 1.8) | 0.149 |
Family Member | Age: 20-29 | 2.28 | (1.48, 3.5) | 0.000 | ***
Family Member | Age: 30-39 | 1.00 |  |  |
Family Member | Age: 40-49 | 1.57 | (0.95, 2.6) | 0.077 | .
Family Member | Age: 50-59 | 0.71 | (0.36, 1.4) | 0.314 |
Family Member | Age: 60-69 | 0.33 | (0.11, 1.0) | 0.052 | .
Family Member | Age: 70+ | 1.74 | (0.67, 4.5) | 0.252 |
Family Member | Education: Elementary | 2.58 | (0.48, 13.7) | 0.267 |
Family Member | Education: Junior High | 2.25 | (0.65, 7.8) | 0.201 |
Family Member | Education: High School | 1.58 | (0.78, 3.2) | 0.204 |
Family Member | Education: Community College | 0.79 | (0.35, 1.8) | 0.567 |
Family Member | Education: University | 1.00 |  |  |
Family Member | Education: Graduate School | 2.01 | (0.93, 4.4) | 0.077 | .
Family Member | Household_head: Yes | 1.00 |  |  |
Family Member | Household_head: No | 2.03 | (1.40, 2.9) | 0.000 | ***
Government | Gender: Male | 1.00 |  |  |
Government | Gender: Female | 2.34 | (1.68, 3.3) | 0.000 | ***
Government | Age: 20-29 | 1.77 | (1.13, 2.8) | 0.013 | *
Government | Age: 30-39 | 1.00 |  |  |
Government | Age: 40-49 | 1.92 | (1.19, 3.1) | 0.007 | **
Government | Age: 50-59 | 0.94 | (0.51, 1.7) | 0.832 |
Government | Age: 60-69 | 1.87 | (1.01, 3.5) | 0.048 | *
Government | Age: 70+ | 3.21 | (1.41, 7.3) | 0.005 | **
Government | Education: Elementary | 2.23 | (0.49, 10.2) | 0.301 |
Government | Education: Junior High | 1.51 | (0.41, 5.5) | 0.536 |
Government | Education: High School | 0.55 | (0.26, 1.2) | 0.115 |
Government | Education: Community College | 1.57 | (0.81, 3.0) | 0.178 |
Government | Education: University | 1.00 |  |  |
Government | Education: Graduate School | 0.78 | (0.34, 1.8) | 0.545 |
Government | Household_head: Yes | 1.00 |  |  |
Government | Household_head: No | 0.65 | (0.45, 0.9) | 0.022 | *

### Mask Access Regression: Model with No Interactions

Table 7: Mask access, compared to “yes”; relative to university educated men, aged 30-39, head of household based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.37 | (1.33, 1.4) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | NaN |
No | Age: 20-29 | 0.88 | (0.83, 0.9) | 0.000 | ***
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 1.00 | (0.94, 1.1) | 1.000 |
No | Age: 50-59 | 1.02 | (0.96, 1.1) | 0.504 |
No | Age: 60-69 | 0.97 | (0.91, 1.0) | 0.393 |
No | Age: 70+ | 1.17 | (1.07, 1.3) | 0.000 | ***
No | Age: Prefer Not to Say | 0.82 | (0.73, 0.9) | 0.002 | **
No | Education: Elementary | 2.37 | (1.92, 2.9) | 0.000 | ***
No | Education: Junior High | 1.56 | (1.35, 1.8) | 0.000 | ***
No | Education: High School | 1.27 | (1.18, 1.4) | 0.000 | ***
No | Education: Community College | 0.97 | (0.89, 1.0) | 0.366 |
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 0.80 | (0.74, 0.9) | 0.000 | ***
No | Education: Prefer Not to Say | 1.07 | (0.97, 1.2) | 0.185 |
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 0.96 | (0.92, 1.0) | 0.047 | *

### Supplementary Analysis

(a) Displaced More than One Night Overall

(b) Displaced More than One Night by Gender

Figure 10:  The percent of people displaced for more than one night, among
people who reported being in the affected region at the time of the fire.

(a) Displaced More than Three Nights Overall

(b) Displaced More than Three Nights by Gender

Figure 11:  The percent of people displaced for more than three nights, among
people who reported being displaced more than one night.

Figure 12: Percentage of people displaced, overall and by gender; estimated on
aggregated and de-identified data from people who are using Facebook on their
devices and have opted in to Location History.

https://research.fb.com/blog/2020/03/introducing-facebooks-gender-
disaggregated-displacement-maps/

(a) Left home more than three nights before the fire, by gender

(b) Left home before the fire, by gender

Figure 13:  Women report leaving their homes ahead of the fire more often, and
earlier than men do.

(a) Prevented from working, by overall

(b) Prevented from working, by gender

Figure 14:  Proportion of people who responded that leaving home prevented
them from working as much as usual. Figure 15: People who are displaced more
than three nights are much more likely to say they were prevented from
working. Values presented in Appendix Table
LABEL:Total_weights_q21_prevent_working_regression_no_interaction_with_income_displacement_reformulated.

(a) Why Not Returned Home, Overall

(b) Why Not Returned Home, by Gender

Figure 16:  The primary reason that people do not return home is that it is
still unsafe; however, a substantial fraction have found new opportunities.

Figure 17: Evacuation decision made by someone else, compared to “my
decision”; relative to university educated men, aged 30-39, head of household
based on model with main effects for age, gender, education, and head of
household with no interactions. Values presented in Appendix Table
LABEL:Total_weights_q29_evacuation_decision_regression_no_interaction.

Figure 18: Mask access, compared to “yes”; relative to university educated
men, aged 30-39, head of household based on model with main effects for age,
gender, education, and head of household with no interactions. Values
presented in Appendix Table
LABEL:Total_weights_q28_mask_access_regression_no_interaction.

## The Survey Instrument

This section includes a diagram of the survey flow (Figure 19), and the survey
instrument itself (Page 37).

Figure 19: Diagram of the survey flow. Numbers give the approximate number of
people who answered the question. These will not match the later counts
exactly.

See pages - of au_bushfire_survey.pdf

## Summaries by Question

### Reported Gender - What is your gender?

Response | N
---|---
Female | 42230
Male | 35717
Table 8: Reported Gender: Weighted N, Overall and by Gender Response | Percent
---|---
Female | 54.2 (53.6, 54.7)
Male | 45.8 (45.3, 46.4)
Table 9: Reported Gender: Percent, Overall and by Gender Figure 20: Reported
Gender Overall

### Reported Age - How old are you?

Response | Overall | Female | Male
---|---|---|---
$<$20 | 6171 | 3271 | 2712
20-29 | 17984 | 9384 | 8240
30-39 | 16200 | 8368 | 7583
40-49 | 12971 | 6714 | 5995
50-59 | 10692 | 5952 | 4607
60-69 | 7785 | 4277 | 3366
70+ | 4643 | 2508 | 1921
Prefer Not to Say | 3200 | 1040 | 476
Table 10: Reported Age: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
$<$20 | 7.7 ( 7.4, 8.1) | 7.9 ( 7.4, 8.3) | 7.8 ( 7.3, 8.3)
20-29 | 22.6 (22.2, 23.0) | 22.6 (22.0, 23.2) | 23.6 (22.9, 24.3)
30-39 | 20.3 (19.9, 20.8) | 20.2 (19.6, 20.7) | 21.7 (21.0, 22.4)
40-49 | 16.3 (15.9, 16.7) | 16.2 (15.6, 16.7) | 17.2 (16.5, 17.9)
50-59 | 13.4 (13.0, 13.8) | 14.3 (13.8, 14.8) | 13.2 (12.6, 13.8)
60-69 | 9.8 ( 9.4, 10.1) | 10.3 ( 9.8, 10.8) | 9.6 ( 9.1, 10.2)
70+ | 5.8 ( 5.5, 6.1) | 6.0 ( 5.7, 6.4) | 5.5 ( 5.0, 6.0)
Prefer Not to Say | 4.0 ( 3.8, 4.3) | 2.5 ( 2.2, 2.8) | 1.4 ( 1.1, 1.6)
Table 11: Reported Age: Percent, Overall and by Gender

(a) Reported Age Overall

(b) Reported Age by Gender

Figure 21: How old are you?

### Education - What is your highest level of completed education?

Response | Overall | Female | Male
---|---|---|---
Elementary | 663 | 246 | 363
Junior High | 1291 | 674 | 572
High School | 9417 | 4990 | 4255
Community College | 6996 | 3603 | 3282
University | 5554 | 3125 | 2343
Graduate School | 4750 | 2469 | 2114
Prefer Not to Say | 3711 | 1762 | 1526
Table 12: Education: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Elementary | 2.0 ( 1.7, 2.4) | 1.5 ( 1.0, 1.9) | 2.5 ( 1.9, 3.1)
Junior High | 4.0 ( 3.6, 4.4) | 4.0 ( 3.5, 4.5) | 4.0 ( 3.4, 4.5)
High School | 29.1 (28.3, 29.9) | 29.6 (28.5, 30.6) | 29.4 (28.2, 30.7)
Community College | 21.6 (20.9, 22.3) | 21.4 (20.5, 22.2) | 22.7 (21.6, 23.8)
University | 17.2 (16.5, 17.8) | 18.5 (17.6, 19.4) | 16.2 (15.2, 17.2)
Graduate School | 14.7 (14.1, 15.3) | 14.6 (13.9, 15.4) | 14.6 (13.7, 15.6)
Prefer Not to Say | 11.5 (10.9, 12.1) | 10.4 ( 9.7, 11.1) | 10.6 ( 9.6, 11.5)
Table 13: Education: Percent, Overall and by Gender

(a) Education Overall

(b) Education by Gender

Figure 22: What is your highest level of completed education?

### Employment Type - Which of the following best describes your main
employment situation as of Dec. 18, 2019 when the bushfire started?

Response | Overall | Female | Male
---|---|---|---
Managing a Business | 5506 | 2204 | 3184
Employed by Business | 25350 | 12493 | 12484
Employed, not by Business | 2686 | 1465 | 1167
Government Work | 5983 | 3649 | 2206
Student | 5329 | 2917 | 2251
Retired | 7898 | 4377 | 3318
Not Working | 7689 | 4785 | 2713
Prefer Not to Say | 8484 | 4616 | 2932
Table 14: Employment Type: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Managing a Business | 8.0 ( 7.7, 8.3) | 6.0 ( 5.7, 6.4) | 10.5 (10.0, 11.1)
Employed by Business | 36.8 (36.2, 37.3) | 34.2 (33.5, 34.9) | 41.3 (40.4, 42.2)
Employed, not by Business | 3.9 ( 3.7, 4.1) | 4.0 ( 3.7, 4.3) | 3.9 ( 3.5, 4.2)
Government Work | 8.7 ( 8.4, 9.0) | 10.0 ( 9.6, 10.4) | 7.3 ( 6.8, 7.8)
Student | 7.7 ( 7.4, 8.1) | 8.0 ( 7.5, 8.4) | 7.4 ( 6.9, 7.9)
Retired | 11.5 (11.0, 11.9) | 12.0 (11.4, 12.5) | 11.0 (10.3, 11.6)
Not Working | 11.2 (10.7, 11.6) | 13.1 (12.5, 13.7) | 9.0 ( 8.3, 9.6)
Prefer Not to Say | 12.3 (11.9, 12.7) | 12.6 (12.1, 13.2) | 9.7 ( 9.1, 10.3)
Table 15: Employment Type: Percent, Overall and by Gender

(a) Employment Type Overall

(b) Employment Type by Gender

Figure 23: Which of the following best describes your main employment
situation as of Dec. 18, 2019 when the bushfire started?

### Income - What was your total household income last month?

Response | Overall | Female | Male
---|---|---|---
$<$ $3,200 | 5892 | 3083 | 2668
$3,200-$5,800 | 4263 | 2185 | 2032
$5,800-$9,100 | 2877 | 1419 | 1421
$9,100-$14,000 | 1547 | 697 | 827
$>$ $14,000 | 1624 | 711 | 860
Prefer Not to Say | 14918 | 8464 | 5775
Table 16: Income: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
$<$ $3,200 | 18.9 (18.1, 19.7) | 18.6 (17.6, 19.6) | 19.6 (18.4, 20.9)
$3,200-$5,800 | 13.7 (13.1, 14.3) | 13.2 (12.4, 14.0) | 15.0 (14.0, 15.9)
$5,800-$9,100 | 9.2 ( 8.8, 9.7) | 8.6 ( 8.0, 9.1) | 10.5 ( 9.7, 11.2)
$9,100-$14,000 | 5.0 ( 4.6, 5.3) | 4.2 ( 3.8, 4.6) | 6.1 ( 5.5, 6.7)
$>$ $14,000 | 5.2 ( 4.8, 5.6) | 4.3 ( 3.8, 4.8) | 6.3 ( 5.7, 7.0)
Prefer Not to Say | 47.9 (47.0, 48.8) | 51.1 (49.9, 52.3) | 42.5 (41.1, 43.9)
Table 17: Income: Percent, Overall and by Gender

(a) Income Overall

(b) Income by Gender

Figure 24: What was your total household income last month?

### Country Location - Which area were you in on December 18, 2019 when the
Australian bushfires began?

Response | Overall | Female | Male
---|---|---|---
The Green Wattle Creek Fire across Eastern New South Wales | 4581 | 1901 | 1923
The Cudlee Creek Fire in Adelaide Hills, South Australia | 1740 | 772 | 662
Table 18: Country Location: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
The Green Wattle Creek Fire across Eastern New South Wales | 72.5 (70.8, 74.1) | 71.1 (68.6, 73.6) | 74.4 (71.7, 77.0)
The Cudlee Creek Fire in Adelaide Hills, South Australia | 27.5 (25.9, 29.2) | 28.9 (26.4, 31.4) | 25.6 (23.0, 28.3)
Table 19: Country Location: Percent, Overall and by Gender

(a) Country Location Overall

(b) Country Location by Gender

Figure 25: Which area were you in on December 18, 2019 when the Australian
bushfires began?

### Three Nights Before Fires - Did you leave more than 3 nights BEFORE the
bushfire on December 18, 2019?

Response | Overall | Female | Male
---|---|---|---
Yes | 104 | 49 | 30
No | 176 | 49 | 94
Don’t Know | 29 | 22 | 3
Table 20: Three Nights Before Fires: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 33.5 (25.3, 41.7) | 41.2 (26.5, 55.9) | 23.5 (13.5, 33.5)
No | 57.0 (47.5, 66.5) | 40.5 (25.2, 55.9) | 73.9 (63.4, 84.4)
Don’t Know | 9.5 ( 0.8, 18.2) | 18.3 (-1.6, 38.2) | 2.5 (-0.6, 5.7)
Table 21: Three Nights Before Fires: Percent, Overall and by Gender

(a) Three Nights Before Fires Overall

(b) Three Nights Before Fires by Gender

Figure 26: Did you leave more than 3 nights BEFORE the bushfire on December
18, 2019?

### Before Fires Evacuation - Did you leave your home BEFORE the bushfire on
December 18, 2019?

Response | Overall | Female | Male
---|---|---|---
Yes | 339 | 133 | 132
No | 991 | 426 | 402
Don’t Know | 45 | 12 | 13
Table 22: Before Fires Evacuation: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 24.7 (20.8, 28.6) | 23.3 (16.6, 30.0) | 24.1 (18.3, 30.0)
No | 72.1 (68.1, 76.0) | 74.6 (67.9, 81.4) | 73.4 (67.5, 79.3)
Don’t Know | 3.3 ( 2.2, 4.4) | 2.1 ( 0.7, 3.5) | 2.4 ( 1.1, 3.7)
Table 23: Before Fires Evacuation: Percent, Overall and by Gender

(a) Before Fires Evacuation Overall

(b) Before Fires Evacuation by Gender

Figure 27: Did you leave your home BEFORE the bushfire on December 18, 2019?

### Early Evacuation - Were you evacuated BEFORE the bushfire on December 18,
2019 by your local, state, or national government?

Response | Overall | Female | Male
---|---|---|---
Yes | 339 | 133 | 132
No | 991 | 426 | 402
Don’t Know | 45 | 12 | 13
Table 24: Early Evacuation: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 24.7 (20.8, 28.6) | 23.3 (16.6, 30.0) | 24.1 (18.3, 30.0)
No | 72.1 (68.1, 76.0) | 74.6 (67.9, 81.4) | 73.4 (67.5, 79.3)
Don’t Know | 3.3 ( 2.2, 4.4) | 2.1 ( 0.7, 3.5) | 2.4 ( 1.1, 3.7)
Table 25: Early Evacuation: Percent, Overall and by Gender

(a) Early Evacuation Overall

(b) Early Evacuation by Gender

Figure 28: Were you evacuated BEFORE the bushfire on December 18, 2019 by your
local, state, or national government?

### When Did You Leave Your Home - When did you leave your home?

Response | Overall | Female | Male
---|---|---|---
$<$7 | 486 | 209 | 218
7-14 | 229 | 112 | 94
$>$14 | 145 | 68 | 56
Don’t Know | 129 | 50 | 47
Table 26: When Did You Leave Your Home: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
$<$7 | 49.2 (44.4, 54.0) | 47.7 (40.2, 55.2) | 52.5 (44.9, 60.0)
7-14 | 23.1 (18.9, 27.4) | 25.5 (18.3, 32.6) | 22.7 (16.7, 28.8)
$>$14 | 14.6 (11.7, 17.6) | 15.5 (10.6, 20.3) | 13.5 ( 9.3, 17.7)
Don’t Know | 13.0 (10.4, 15.6) | 11.4 ( 7.7, 15.0) | 11.3 ( 7.5, 15.1)
Table 27: When Did You Leave Your Home: Percent, Overall and by Gender

(a) When Did You Leave Your Home Overall

(b) When Did You Leave Your Home by Gender

Figure 29: When did you leave your home?

### One Night - Did you leave your home for more than one night as a result of
the bushfires?

Response | Overall | Female | Male
---|---|---|---
Yes | 1566 | 572 | 548
No | 4517 | 2099 | 2026
Don’t Know | 65 | 18 | 23
Table 28: One Night: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 25.5 (23.8, 27.2) | 21.3 (18.7, 23.8) | 21.1 (18.6, 23.6)
No | 73.5 (71.7, 75.2) | 78.1 (75.5, 80.6) | 78.0 (75.5, 80.6)
Don’t Know | 1.1 ( 0.6, 1.5) | 0.7 ( 0.3, 1.1) | 0.9 ( 0.5, 1.3)
Table 29: One Night: Percent, Overall and by Gender

(a) One Night Overall

(b) One Night by Gender

Figure 30: Did you leave your home for more than one night as a result of the
bushfires?

### Three Nights - Did you leave your home for more than three nights as a
result of the bushfires?

Response | Overall | Female | Male
---|---|---|---
Yes | 942 | 339 | 355
No | 522 | 207 | 175
Don’t Know | 21 | 4 | 0
Table 30: Three Nights: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 63.4 (59.7, 67.1) | 61.6 (54.7, 68.5) | 66.9 (61.2, 72.6)
No | 35.2 (31.5, 38.8) | 37.6 (30.8, 44.5) | 33.0 (27.3, 38.7)
Don’t Know | 1.4 ( 0.0, 2.8) | 0.8 ( 0.0, 1.6) | 0.1 (-0.1, 0.2)
Table 31: Three Nights: Percent, Overall and by Gender

(a) Three Nights Overall

(b) Three Nights by Gender

Figure 31: Did you leave your home for more than three nights as a result of
the bushfires?

### Evacuation Decision - Whose decision first led you to leaving your home?

Response | Overall | Female | Male
---|---|---|---
Mine | 606 | 212 | 294
Family Member | 310 | 138 | 130
Government | 301 | 172 | 109
Other | 56 | 37 | 12
Table 32: Evacuation Decision: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Mine | 47.7 (43.3, 52.0) | 37.9 (31.9, 43.9) | 54.0 (47.2, 60.7)
Family Member | 24.3 (20.6, 28.0) | 24.7 (18.6, 30.7) | 23.8 (18.3, 29.3)
Government | 23.6 (19.4, 27.9) | 30.8 (23.5, 38.1) | 20.0 (14.3, 25.7)
Other | 4.4 ( 3.1, 5.7) | 6.6 ( 4.1, 9.1) | 2.2 ( 1.0, 3.5)
Table 33: Evacuation Decision: Percent, Overall and by Gender

(a) Evacuation Decision Overall

(b) Evacuation Decision by Gender

Figure 32: Whose decision first led you to leaving your home?

### Government Transportation - Did the government help you with
transportation to leave your home?

Response | Overall | Female | Male
---|---|---|---
Yes | 75 | 32 | 24
No | 1262 | 490 | 496
Don’t Know | 28 | 14 | 4
Table 34: Government Transportation: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 5.5 ( 3.0, 7.9) | 6.0 ( 0.5, 11.5) | 4.6 ( 2.5, 6.8)
No | 92.5 (90.0, 95.0) | 91.4 (85.8, 97.0) | 94.6 (92.3, 96.8)
Don’t Know | 2.0 ( 1.2, 2.9) | 2.6 ( 0.9, 4.3) | 0.8 ( 0.1, 1.5)
Table 35: Government Transportation: Percent, Overall and by Gender

(a) Government Transportation Overall

(b) Government Transportation by Gender

Figure 33: Did the government help you with transportation to leave your home?

### Mode Transportation - What mode of transportation did you take when you
left your home for more than one night as a result of the bushfires?

Response | Overall | Female | Male
---|---|---|---
Car | 1268 | 526 | 491
Walking | 50 | 7 | 24
Bus or Train | 55 | 21 | 11
Bike | 37 | 10 | 15
Other | 15 | 3 | 5
Table 36: Mode Transportation: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Car | 89.0 (86.1, 91.8) | 92.7 (87.7, 97.7) | 89.9 (86.8, 93.0)
Walking | 3.5 ( 2.4, 4.7) | 1.3 ( 0.2, 2.3) | 4.4 ( 2.3, 6.5)
Bus or Train | 3.9 ( 1.6, 6.1) | 3.6 (-0.5, 7.8) | 2.1 ( 0.6, 3.5)
Bike | 2.6 ( 1.2, 4.0) | 1.8 (-1.0, 4.6) | 2.7 ( 1.1, 4.3)
Other | 1.1 ( 0.5, 1.7) | 0.6 (-0.1, 1.3) | 1.0 ( 0.2, 1.8)
Table 37: Mode Transportation: Percent, Overall and by Gender

(a) Mode Transporation Overall

(b) Mode Transporation by Gender

Figure 34: What mode of transportation did you take when you left your home
for more than one night as a result of the bushfires?

### Leave Home Where Did Go - Where did you go when you left your home?

Response | Overall | Female | Male
---|---|---|---
Same City As Home | 383 | 185 | 141
Different City in My Region | 810 | 344 | 361
Different State | 69 | 27 | 28
Other | 15 | 0 | 10
Table 38: Leave Home Where Did Go: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Same City As Home | 30.0 (26.3, 33.8) | 33.2 (27.0, 39.4) | 26.1 (20.7, 31.5)
Different City in My Region | 63.4 (59.4, 67.5) | 61.8 (55.2, 68.5) | 66.9 (61.1, 72.8)
Different State | 5.4 ( 3.3, 7.4) | 4.9 ( 1.1, 8.8) | 5.2 ( 2.9, 7.4)
Other | 1.2 ( 0.5, 1.9) | 0.0 ( 0.0, 0.1) | 1.8 ( 0.4, 3.1)
Table 39: Leave Home Where Did Go: Percent, Overall and by Gender

(a) Leave Home Where Did Go Overall

(b) Leave Home Where Did Go by Gender

Figure 35: Where did you go when you left your home?

### Connectivity - While you were away from your home during the bushfires,
did you consistently have access to mobile data or wifi on the main mobile
device that you use?

Response | Overall | Female | Male
---|---|---|---
Yes | 726 | 364 | 332
No | 395 | 176 | 187
Don’t Know | 34 | 12 | 15
Table 40: Connectivity: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 62.8 (58.5, 67.2) | 66.0 (59.7, 72.3) | 62.2 (55.8, 68.6)
No | 34.2 (29.9, 38.5) | 31.8 (25.6, 38.0) | 34.9 (28.6, 41.3)
Don’t Know | 2.9 ( 1.8, 4.1) | 2.2 ( 0.8, 3.5) | 2.9 ( 1.3, 4.5)
Table 41: Connectivity: Percent, Overall and by Gender

(a) Connectivity Overall

(b) Connectivity by Gender

Figure 36: While you were away from your home during the bushfires, did you
consistently have access to mobile data or wifi on the main mobile device that
you use?

### How Long Gone In Total - How long, in total, were you away from your home
as a result of the bushfires?

Response | Overall | Female | Male
---|---|---|---
$<$7 | 388 | 136 | 148
7-14 | 304 | 123 | 109
14-30 | 117 | 39 | 63
$>$30 | 84 | 34 | 28
Don’t Know | 31 | 7 | 8
Table 42: How Long Gone In Total: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
$<$7 | 42.0 (37.0, 47.0) | 40.1 (31.8, 48.5) | 41.8 (33.1, 50.5)
7-14 | 32.9 (27.8, 38.0) | 36.3 (26.6, 45.9) | 30.6 (22.9, 38.2)
14-30 | 12.6 ( 8.0, 17.3) | 11.6 ( 4.4, 18.8) | 17.6 ( 8.3, 27.0)
$>$30 | 9.1 ( 5.8, 12.4) | 10.1 ( 2.7, 17.6) | 7.9 ( 4.3, 11.4)
Don’t Know | 3.3 ( 2.0, 4.7) | 1.9 ( 0.3, 3.6) | 2.1 ( 0.3, 4.0)
Table 43: How Long Gone In Total: Percent, Overall and by Gender

(a) How Long Gone In Total Overall

(b) How Long Gone In Total by Gender

Figure 37: How long, in total, were you away from your home as a result of the
bushfires?

### Been Home - Since you first left as a result of the bushfires have you
been home for at least a total of 3 nights?

Response | Overall | Female | Male
---|---|---|---
Yes | 1062 | 504 | 466
No | 144 | 46 | 67
Don’t Know | 28 | 8 | 8
Table 44: Been Home: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 86.1 (83.0, 89.1) | 90.3 (85.5, 95.1) | 86.1 (82.0, 90.2)
No | 11.7 ( 8.7, 14.6) | 8.2 ( 3.5, 13.0) | 12.4 ( 8.4, 16.3)
Don’t Know | 2.3 ( 1.3, 3.2) | 1.5 ( 0.3, 2.7) | 1.5 ( 0.4, 2.7)
Table 45: Been Home: Percent, Overall and by Gender

(a) Been Home Overall

(b) Been Home by Gender

Figure 38: Since you first left as a result of the bushfires have you been
home for at least a total of 3 nights?

### Why Not Return Home - Why have you not permanently returned to your home?

Response | Overall | Female | Male
---|---|---|---
Unsafe | 72 | 29 | 32
New Opportunities | 28 | 7 | 19
Unable or Unwilling | 12 | 3 | 6
Not Allowed | 10 | 1 | 8
Other | 3 | 3 | 0
Table 46: Why Not Return Home: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Unsafe | 57.5 (43.4, 71.5) | 65.7 (41.3, 90.2) | 48.0 (31.3, 64.7)
New Opportunities | 22.2 (10.4, 34.1) | 17.1 ( 1.9, 32.3) | 29.2 (11.3, 47.1)
Unable or Unwilling | 9.7 ( 3.3, 16.1) | 7.6 (-2.1, 17.3) | 9.7 ( 1.2, 18.2)
Not Allowed | 8.0 ( 1.8, 14.3) | 3.3 (-3.3, 9.8) | 12.4 ( 2.0, 22.8)
Other | 2.6 (-0.6, 5.7) | 6.3 (-2.7, 15.4) | 0.6 (-0.6, 1.9)
Table 47: Why Not Return Home: Percent, Overall and by Gender

(a) Why Not Return Home Overall

(b) Why Not Return Home by Gender

Figure 39: Why have you not permanently returned to your home?

### Prevent Working - Did leaving your home prevent you from working as much
as you normally do?

Response | Overall | Female | Male
---|---|---|---
Yes | 579 | 270 | 285
No | 391 | 198 | 181
Prefer not to say | 88 | 56 | 24
Table 48: Prevent Working: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 54.7 (49.7, 59.8) | 51.5 (44.0, 59.0) | 58.0 (50.8, 65.3)
No | 37.0 (32.0, 41.9) | 37.9 (30.6, 45.1) | 37.0 (29.8, 44.2)
Prefer not to say | 8.3 ( 5.1, 11.5) | 10.7 ( 5.1, 16.2) | 5.0 ( 1.6, 8.4)
Table 49: Prevent Working: Percent, Overall and by Gender

(a) Prevent Working Overall

(b) Prevent Working by Gender

Figure 40: Did leaving your home prevent you from working as much as you
normally do?

### Household Head - Do you earn the most money among people in your
household?

Response | Overall | Female | Male
---|---|---|---
Yes | 24727 | 10262 | 14117
No | 31150 | 19504 | 11037
Prefer not to say | 15062 | 7798 | 5974
Table 50: Household Head: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 34.9 (34.3, 35.4) | 27.3 (26.6, 28.0) | 45.4 (44.4, 46.3)
No | 43.9 (43.3, 44.5) | 51.9 (51.2, 52.7) | 35.5 (34.6, 36.3)
Prefer not to say | 21.2 (20.7, 21.7) | 20.8 (20.1, 21.4) | 19.2 (18.5, 19.9)
Table 51: Household Head: Percent, Overall and by Gender

(a) Household Head Overall

(b) Household Head by Gender

Figure 41: Do you earn the most money among people in your household?

### Household Size - How many people live in your household (including
yourself)?

Response | Overall | Female | Male
---|---|---|---
1 | 8964 | 4412 | 4244
2 | 20319 | 11211 | 8642
3 | 14392 | 7721 | 6313
4 | 14910 | 7928 | 6613
5 | 8197 | 4243 | 3748
6 or more | 6360 | 3228 | 2621
Table 52: Household Size: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
1 | 12.3 (11.8, 12.7) | 11.4 (10.8, 11.9) | 13.2 (12.5, 13.9)
2 | 27.8 (27.3, 28.3) | 28.9 (28.3, 29.6) | 26.9 (26.1, 27.7)
3 | 19.7 (19.2, 20.1) | 19.9 (19.3, 20.5) | 19.6 (18.9, 20.3)
4 | 20.4 (19.9, 20.8) | 20.5 (19.9, 21.0) | 20.5 (19.8, 21.2)
5 | 11.2 (10.8, 11.6) | 11.0 (10.5, 11.4) | 11.6 (11.1, 12.2)
6 or more | 8.7 ( 8.4, 9.0) | 8.3 ( 7.9, 8.8) | 8.1 ( 7.6, 8.6)
Table 53: Household Size: Percent, Overall and by Gender

(a) Household Size Overall

(b) Household Size by Gender

Figure 42: How many people live in your household (including yourself)?

### Household Child Count - How many children 18 years or younger live in your
household?

Response | Overall | Female | Male
---|---|---|---
0 | 42914 | 21908 | 19753
1 | 12010 | 6780 | 4931
2 | 10701 | 6127 | 4308
3 | 4424 | 2469 | 1845
4 | 1653 | 903 | 673
5 or more | 1453 | 574 | 569
Table 54: Household Child Count: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
0 | 58.7 (58.1, 59.2) | 56.5 (55.8, 57.3) | 61.6 (60.7, 62.5)
1 | 16.4 (16.0, 16.8) | 17.5 (16.9, 18.1) | 15.4 (14.7, 16.0)
2 | 14.6 (14.2, 15.0) | 15.8 (15.3, 16.3) | 13.4 (12.8, 14.0)
3 | 6.0 ( 5.8, 6.3) | 6.4 ( 6.0, 6.7) | 5.8 ( 5.3, 6.2)
4 | 2.3 ( 2.1, 2.4) | 2.3 ( 2.1, 2.6) | 2.1 ( 1.8, 2.4)
5 or more | 2.0 ( 1.8, 2.2) | 1.5 ( 1.3, 1.7) | 1.8 ( 1.5, 2.0)
Table 55: Household Child Count: Percent, Overall and by Gender

(a) Household Child Count Overall

(b) Household Child Count by Gender

Figure 43: How many children 18 years or younger live in your household?

### Household Partner - Do you have a spouse or long-term partner?

Response | Overall | Female | Male
---|---|---|---
Yes | 37940 | 20615 | 16658
No | 26162 | 13643 | 11975
Prefer not to say | 7163 | 3507 | 2586
Table 56: Household Partner: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 53.2 (52.6, 53.8) | 54.6 (53.8, 55.4) | 53.4 (52.4, 54.3)
No | 36.7 (36.1, 37.3) | 36.1 (35.4, 36.9) | 38.4 (37.4, 39.3)
Prefer not to say | 10.1 ( 9.7, 10.4) | 9.3 ( 8.8, 9.8) | 8.3 ( 7.7, 8.8)
Table 57: Household Partner: Percent, Overall and by Gender

(a) Household Partner Overall

(b) Household Partner by Gender

Figure 44: Do you have a spouse or long-term partner?

### Household Split Return - Did you return to your home at least one day
before other members of your household?

Response | Overall | Female | Male
---|---|---|---
Yes | 323 | 110 | 182
No | 664 | 371 | 260
Prefer not to say | 49 | 19 | 19
Table 58: Household Split Return: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 31.2 (26.5, 35.8) | 22.0 (16.2, 27.8) | 39.5 (32.0, 46.9)
No | 64.1 (59.3, 68.8) | 74.3 (68.3, 80.3) | 56.4 (48.9, 63.9)
Prefer not to say | 4.7 ( 3.3, 6.2) | 3.7 ( 1.9, 5.6) | 4.1 ( 2.0, 6.2)
Table 59: Household Split Return: Percent, Overall and by Gender

(a) Household Split Return Overall

(b) Household Split Return by Gender

Figure 45: Did you return to your home at least one day before other members
of your household?

### Household Left Behind - When you were displaced for more than 3 nights did
anyone in your household stay behind?

Response | Overall | Female | Male
---|---|---|---
Yes | 252 | 95 | 98
No | 646 | 244 | 253
Table 60: Household Left Behind: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 28.0 (23.4, 32.6) | 28.1 (20.5, 35.8) | 27.9 (19.9, 35.8)
No | 72.0 (67.4, 76.6) | 71.9 (64.2, 79.5) | 72.1 (64.2, 80.1)
Table 61: Household Left Behind: Percent, Overall and by Gender

(a) Household Left Behind Overall

(b) Household Left Behind by Gender

Figure 46: When you were displaced for more than 3 nights did anyone in your
household stay behind?

### Mask Access - Do you have access to a face mask in case of excessive
wildfire smoke?

Response | Overall | Female | Male
---|---|---|---
Yes | 19261 | 9174 | 9431
No | 42320 | 23356 | 17708
Table 62: Mask Access: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 31.3 (30.7, 31.9) | 28.2 (27.5, 28.9) | 34.8 (33.8, 35.7)
No | 68.7 (68.1, 69.3) | 71.8 (71.1, 72.5) | 65.2 (64.3, 66.2)
Table 63: Mask Access: Percent, Overall and by Gender

(a) Mask Access Overall

(b) Mask Access by Gender

Figure 47: Do you have access to a face mask in case of excessive wildfire
smoke?

### Mask Knowledge - Do you know how to tell if a face mask is effective
against wildfire smoke?

Response | Overall | Female | Male
---|---|---|---
Yes | 23209 | 10423 | 11978
No | 37944 | 21926 | 14930
Table 64: Mask Knowledge: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 38.0 (37.3, 38.6) | 32.2 (31.5, 33.0) | 44.5 (43.5, 45.5)
No | 62.0 (61.4, 62.7) | 67.8 (67.0, 68.5) | 55.5 (54.5, 56.5)
Table 65: Mask Knowledge: Percent, Overall and by Gender

(a) Mask Knowledge Overall

(b) Mask Knowledge by Gender

Figure 48: Do you know how to tell if a face mask is effective against
wildfire smoke?

### On Vacation - Were you on holiday in this area on December 18, 2019?

Response | Overall | Female | Male
---|---|---|---
Yes | 3179 | 1130 | 1503
No | 15636 | 7249 | 6705
Table 66: On Vacation: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 16.9 (16.0, 17.8) | 13.5 (12.3, 14.7) | 18.3 (16.8, 19.9)
No | 83.1 (82.2, 84.0) | 86.5 (85.3, 87.7) | 81.7 (80.1, 83.2)
Table 67: On Vacation: Percent, Overall and by Gender

(a) On Vacation Overall

(b) On Vacation by Gender

Figure 49: Were you on holiday in this area on December 18, 2019?

### Travel Check - Did you go on holiday or work travel away from your home
for more than one night between Dec. 18, 2019 and Jan. 1, 2020?

Response | Overall | Female | Male
---|---|---|---
Yes | 26695 | 12845 | 12453
No | 53794 | 28108 | 21923
I don’t know | 1623 | 454 | 570
Table 68: Travel Check: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 32.5 (32.0, 33.0) | 31.0 (30.3, 31.7) | 35.6 (34.8, 36.5)
No | 65.5 (65.0, 66.0) | 67.9 (67.2, 68.6) | 62.7 (61.9, 63.6)
I don’t know | 2.0 ( 1.8, 2.2) | 1.1 ( 0.9, 1.3) | 1.6 ( 1.4, 1.9)
Table 69: Travel Check: Percent, Overall and by Gender

(a) Travel Check Overall

(b) Travel Check by Gender

Figure 50: Did you go on holiday or work travel away from your home for more
than one night between Dec. 18, 2019 and Jan. 1, 2020?

### In Area Gatekeeper - Have you been in an area directly affected by the
Australian bushfires that began on December 18, 2019?

Response | Overall | Female | Male
---|---|---|---
Yes | 26579 | 8629 | 8462
No | 66084 | 32317 | 26175
I don’t know | 2867 | 1184 | 977
Table 70: In Area Gatekeeper: Weighted N, Overall and by Gender Response | Overall | Female | Male
---|---|---|---
Yes | 27.8 (27.4, 28.3) | 20.5 (19.9, 21.1) | 23.8 (23.0, 24.5)
No | 69.2 (68.7, 69.7) | 76.7 (76.1, 77.3) | 73.5 (72.7, 74.3)
I don’t know | 3.0 ( 2.8, 3.2) | 2.8 ( 2.5, 3.1) | 2.7 ( 2.4, 3.1)
Table 71: In Area Gatekeeper: Percent, Overall and by Gender

(a) In Area Gatekeeper Overall

(b) In Area Gatekeeper by Gender

Figure 51: Have you been in an area directly affected by the Australian
bushfires that began on December 18, 2019?

## Appendix 2

## Supplementary Regression Models

This section includes extra regression models we ran in the course of
analyzing the survey. Each subsection presents one model, first as a plot
followed by the corresponding table. Captions describe the model and the
reference group for the odds ratios.

### Evacuation Decision Regression: Model with Age and Gender Interaction
Effects

Figure 52: Evacuation decision made by someone else, compared to “my
decision”; relative to university educated men, aged 30-39, head of household
based on model with age and gender interactions, and education and head of
household main effects

Table 72: Evacuation decision made by someone else, compared to “my decision”; relative to university educated men, aged 30-39, head of household based on model with age and gender interactions, and education and head of household main effects Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
Family Member | Gender: Male | 1.00 |  |  |
Family Member | Gender: Female | 0.72 | (0.36, 1.4) | 0.357 |
Family Member | Age: 20-29 | 2.05 | (1.13, 3.7) | 0.018 | *
Family Member | Age: 30-39 | 1.00 |  |  |
Family Member | Age: 40-49 | 0.84 | (0.40, 1.8) | 0.658 |
Family Member | Age: 50-59 | 0.10 | (0.02, 0.5) | 0.006 | **
Family Member | Age: 60-69 | 0.03 | (0.00, 0.9) | 0.043 | *
Family Member | Age: 70+ | 0.78 | (0.17, 3.6) | 0.746 |
Family Member | Education: Elementary | 1.97 | (0.33, 11.8) | 0.459 |
Family Member | Education: Junior High | 2.21 | (0.62, 7.8) | 0.219 |
Family Member | Education: High School | 1.45 | (0.71, 3.0) | 0.306 |
Family Member | Education: Community College | 0.69 | (0.30, 1.6) | 0.378 |
Family Member | Education: University | 1.00 |  |  |
Family Member | Education: Graduate School | 2.01 | (0.91, 4.4) | 0.086 | .
Family Member | Household_head: Yes | 1.00 |  |  |
Family Member | Household_head: No | 2.10 | (1.43, 3.1) | 0.000 | ***
Government | Gender: Male | 1.00 |  |  |
Government | Gender: Female | 1.88 | (0.94, 3.7) | 0.073 | .
Government | Age: 20-29 | 1.40 | (0.70, 2.8) | 0.337 |
Government | Age: 30-39 | 1.00 |  |  |
Government | Age: 40-49 | 1.76 | (0.85, 3.6) | 0.126 |
Government | Age: 50-59 | 0.77 | (0.31, 1.9) | 0.581 |
Government | Age: 60-69 | 0.48 | (0.13, 1.7) | 0.259 |
Government | Age: 70+ | 3.74 | (1.25, 11.2) | 0.018 | *
Government | Education: Elementary | 2.57 | (0.54, 12.1) | 0.234 |
Government | Education: Junior High | 1.49 | (0.40, 5.5) | 0.555 |
Government | Education: High School | 0.57 | (0.27, 1.2) | 0.149 |
Government | Education: Community College | 1.40 | (0.71, 2.8) | 0.338 |
Government | Education: University | 1.00 |  |  |
Government | Education: Graduate School | 0.75 | (0.32, 1.7) | 0.497 |
Government | Household_head: Yes | 1.00 |  |  |
Government | Household_head: No | 0.66 | (0.45, 1.0) | 0.033 | *

### Household Split Return Regression: Model With No Interactions

Figure 53: Return home sooner, compared to “yes”; relative to university
educated men, aged 30-39, head of household based on model with main effects
for age, gender, education, and head of household with no interactions

Table 73: Return home sooner, compared to “yes”; relative to university educated men, aged 30-39, head of household based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.96 | (1.43, 2.7) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 1.01 | (0.66, 1.6) | 0.953 |
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.79 | (0.51, 1.2) | 0.295 |
No | Age: 50-59 | 0.62 | (0.36, 1.1) | 0.080 | .
No | Age: 60-69 | 2.12 | (1.00, 4.5) | 0.049 | *
No | Age: 70+ | 0.60 | (0.26, 1.4) | 0.215 |
No | Age: Prefer Not to Say | 0.69 | (0.28, 1.7) | 0.425 |
No | Education: Elementary | 1.97 | (0.18, 22.0) | 0.581 |
No | Education: Junior High | 1.80 | (0.58, 5.5) | 0.307 |
No | Education: High School | 2.30 | (1.22, 4.4) | 0.010 | *
No | Education: Community College | 2.56 | (1.35, 4.9) | 0.004 | **
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 10.14 | (4.02, 25.6) | 0.000 | ***
No | Education: Prefer Not to Say | 1.80 | (0.73, 4.5) | 0.204 |
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 2.45 | (1.73, 3.5) | 0.000 | ***
Prefer not to say | Gender: Male | 1.00 |  |  |
Prefer not to say | Gender: Female | 1.49 | (0.71, 3.1) | 0.290 |
Prefer not to say | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
Prefer not to say | Age: 20-29 | 1.23 | (0.48, 3.1) | 0.663 |
Prefer not to say | Age: 30-39 | 1.00 |  |  |
Prefer not to say | Age: 40-49 | 0.40 | (0.11, 1.4) | 0.149 |
Prefer not to say | Age: 50-59 | 0.63 | (0.17, 2.4) | 0.494 |
Prefer not to say | Age: 60-69 | 0.00 | (0.00, 0.0) | 0.000 | ***
Prefer not to say | Age: 70+ | 1.69 | (0.39, 7.3) | 0.487 |
Prefer not to say | Age: Prefer Not to Say | 0.74 | (0.14, 4.0) | 0.730 |
Prefer not to say | Education: Elementary | 30.15 | (1.59, 571.8) | 0.023 | *
Prefer not to say | Education: Junior High | 2.13 | (0.15, 30.1) | 0.577 |
Prefer not to say | Education: High School | 2.99 | (0.64, 14.0) | 0.165 |
Prefer not to say | Education: Community College | 0.22 | (0.01, 6.5) | 0.379 |
Prefer not to say | Education: University | 1.00 |  |  |
Prefer not to say | Education: Graduate School | 12.32 | (2.20, 69.0) | 0.004 | **
Prefer not to say | Education: Prefer Not to Say | 7.55 | (1.37, 41.7) | 0.020 | *
Prefer not to say | Household_head: Yes | 1.00 |  |  |
Prefer not to say | Household_head: No | 1.63 | (0.75, 3.5) | 0.222 |

### Mask Knowledge Regression: Model With No Interactions

Figure 54: Mask knowledge, compared to “yes”; relative to university educated
men, aged 30-39, head of household based on model with main effects for age,
gender, education, and head of household with no interactions

Table 74: Mask knowledge, compared to “yes”; relative to university educated men, aged 30-39, head of household based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.65 | (1.59, 1.7) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 1.00 | (0.95, 1.1) | 0.968 |
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.91 | (0.86, 1.0) | 0.000 | ***
No | Age: 50-59 | 0.87 | (0.82, 0.9) | 0.000 | ***
No | Age: 60-69 | 0.97 | (0.91, 1.0) | 0.348 |
No | Age: 70+ | 1.43 | (1.32, 1.6) | 0.000 | ***
No | Age: Prefer Not to Say | 1.12 | (1.00, 1.3) | 0.059 | .
No | Education: Elementary | 1.57 | (1.30, 1.9) | 0.000 | ***
No | Education: Junior High | 1.66 | (1.45, 1.9) | 0.000 | ***
No | Education: High School | 1.27 | (1.18, 1.4) | 0.000 | ***
No | Education: Community College | 1.01 | (0.94, 1.1) | 0.800 |
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 0.92 | (0.84, 1.0) | 0.032 | *
No | Education: Prefer Not to Say | 1.18 | (1.08, 1.3) | 0.000 | ***
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 1.17 | (1.12, 1.2) | 0.000 | ***

### More than One Night Regression: Model With No Interactions

Figure 55: Not displaced more than one night, compared to “yes”; relative to
university educated men, aged 30-39, head of household based on model with
main effects for age, gender, education, and head of household with no
interactions

Table 75: Displaced more than one night, compared to “yes”; relative to university educated men, aged 30-39, head of household based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 0.93 | (0.81, 1.1) | 0.320 |
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 0.80 | (0.66, 1.0) | 0.021 | *
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.96 | (0.78, 1.2) | 0.673 |
No | Age: 50-59 | 1.52 | (1.18, 1.9) | 0.001 | **
No | Age: 60-69 | 1.32 | (0.99, 1.7) | 0.056 | .
No | Age: 70+ | 1.02 | (0.70, 1.5) | 0.917 |
No | Age: Prefer Not to Say | 0.79 | (0.51, 1.2) | 0.273 |
No | Education: Elementary | 0.26 | (0.12, 0.5) | 0.000 | ***
No | Education: Junior High | 0.92 | (0.54, 1.6) | 0.751 |
No | Education: High School | 0.82 | (0.60, 1.1) | 0.187 |
No | Education: Community College | 0.87 | (0.64, 1.2) | 0.359 |
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 1.01 | (0.71, 1.4) | 0.976 |
No | Education: Prefer Not to Say | 0.86 | (0.59, 1.3) | 0.452 |
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 1.33 | (1.13, 1.6) | 0.000 | ***

### Mask Knowledge Regression: Model with Mask Access, No Interactions

Figure 56: Mask knowledge, compared to “yes”; relative to university educated
men, aged 30-39, head of household, with mask access based on model with main
effects for age, gender, education, and head of household with no interactions

Table 76: Mask knowledge, compared to “yes”; relative to university educated men, aged 30-39, head of household, with mask access based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.57 | (1.51, 1.6) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (NaN, NaN) | NaN |
No | Age: 20-29 | 1.06 | (1.00, 1.1) | 0.057 | .
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.89 | (0.84, 0.9) | 0.000 | ***
No | Age: 50-59 | 0.84 | (0.79, 0.9) | 0.000 | ***
No | Age: 60-69 | 0.97 | (0.91, 1.0) | 0.464 |
No | Age: 70+ | 1.43 | (1.31, 1.6) | 0.000 | ***
No | Age: Prefer Not to Say | 1.21 | (1.06, 1.4) | 0.004 | **
No | Education: Elementary | 1.21 | (1.00, 1.5) | 0.056 | .
No | Education: Junior High | 1.51 | (1.30, 1.7) | 0.000 | ***
No | Education: High School | 1.19 | (1.10, 1.3) | 0.000 | ***
No | Education: Community College | 1.02 | (0.95, 1.1) | 0.550 |
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 0.99 | (0.90, 1.1) | 0.764 |
No | Education: Prefer Not to Say | 1.18 | (1.07, 1.3) | 0.001 | ***
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 1.21 | (1.16, 1.3) | 0.000 | ***
No | Mask_access: Yes | 1.00 |  |  |
No | Mask_access: No | 5.45 | (5.24, 5.7) | 0.000 | ***

### Mask Access Regression: Model With Income Displacement, No Interactions

Figure 57: Mask access, compared to “yes”; relative to men, aged 30-39, income
less than $3,200 head of household, not displaced based on model with main
effects for age, gender, income, head of household, and duration displaced
with no interactions

Table 77: Mask access, compared to “yes”; relative to men, aged 30-39, income less than $3,200 head of household, not displaced based on model with main effects for age, gender, income, head of household, and duration displaced with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.36 | (1.32, 1.4) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 0.89 | (0.84, 0.9) | 0.000 | ***
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 1.00 | (0.95, 1.1) | 0.875 |
No | Age: 50-59 | 1.04 | (0.98, 1.1) | 0.227 |
No | Age: 60-69 | 0.98 | (0.92, 1.1) | 0.641 |
No | Age: 70+ | 1.19 | (1.10, 1.3) | 0.000 | ***
No | Age: Prefer Not to Say | 0.85 | (0.75, 1.0) | 0.007 | **
No | Income: $<$ $3,200 | 1.00 |  |  |
No | Income: $3,200-$5,800 | 0.82 | (0.75, 0.9) | 0.000 | ***
No | Income: $5,800-$9,100 | 0.83 | (0.75, 0.9) | 0.000 | ***
No | Income: $9,100-$14,000 | 0.79 | (0.70, 0.9) | 0.000 | ***
No | Income: $>$ $14,000 | 0.62 | (0.55, 0.7) | 0.000 | ***
No | Income: Prefer Not to Say | 0.80 | (0.74, 0.9) | 0.000 | ***
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 0.98 | (0.94, 1.0) | 0.279 |
No | Displaced_status: Displaced 1-3 nights | 0.80 | (0.64, 1.0) | 0.045 | *
No | Displaced_status: Displaced more than 3 nights | 1.16 | (0.98, 1.4) | 0.088 | .

### Household Split Return Regression: Model With Child Count, No Interactions

Figure 58: Return home sooner, compared to “yes”; relative to university
educated men, aged 30-39, head of household, with no children based on model
with main effects for age, gender, education, and head of household with no
interactions

Table 78: Return home sooner, compared to “yes”; relative to university educated men, aged 30-39, head of household, with no children based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 2.07 | (1.51, 2.9) | 0.000 | ***
No | Gender: Prefer Not to Say | 1.00 | (NaN, NaN) | NaN |
No | Age: 20-29 | 0.81 | (0.51, 1.3) | 0.382 |
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.69 | (0.44, 1.1) | 0.120 |
No | Age: 50-59 | 0.48 | (0.27, 0.9) | 0.014 | *
No | Age: 60-69 | 1.54 | (0.69, 3.5) | 0.294 |
No | Age: 70+ | 0.44 | (0.19, 1.0) | 0.059 | .
No | Age: Prefer Not to Say | 0.60 | (0.24, 1.5) | 0.274 |
No | Education: Elementary | 3.06 | (0.27, 34.5) | 0.366 |
No | Education: Junior High | 2.04 | (0.63, 6.6) | 0.236 |
No | Education: High School | 2.57 | (1.35, 4.9) | 0.004 | **
No | Education: Community College | 3.05 | (1.58, 5.9) | 0.001 | ***
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 11.68 | (4.57, 29.8) | 0.000 | ***
No | Education: Prefer Not to Say | 1.98 | (0.79, 5.0) | 0.146 |
No | Household_head: Yes | 1.00 |  |  |
No | Household_head: No | 2.41 | (1.68, 3.5) | 0.000 | ***
No | Household_child_count: 0 | 1.00 |  |  |
No | Household_child_count: 1 | 0.52 | (0.34, 0.8) | 0.003 | **
No | Household_child_count: 2 | 0.86 | (0.55, 1.3) | 0.515 |
No | Household_child_count: 3 | 0.35 | (0.20, 0.6) | 0.000 | ***
No | Household_child_count: 4 | 0.89 | (0.41, 2.0) | 0.775 |
No | Household_child_count: 5 or more | 0.74 | (0.29, 1.9) | 0.518 |
Prefer not to say | Gender: Male | 1.00 |  |  |
Prefer not to say | Gender: Female | 1.63 | (0.77, 3.4) | 0.198 |
Prefer not to say | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
Prefer not to say | Age: 20-29 | 0.86 | (0.32, 2.3) | 0.770 |
Prefer not to say | Age: 30-39 | 1.00 |  |  |
Prefer not to say | Age: 40-49 | 0.34 | (0.10, 1.2) | 0.095 | .
Prefer not to say | Age: 50-59 | 0.46 | (0.12, 1.8) | 0.269 |
Prefer not to say | Age: 60-69 | 0.00 | (0.00, Inf) | 0.972 |
Prefer not to say | Age: 70+ | 1.13 | (0.25, 5.2) | 0.873 |
Prefer not to say | Age: Prefer Not to Say | 0.72 | (0.13, 3.8) | 0.695 |
Prefer not to say | Education: Elementary | 61.01 | (2.78, 1338.4) | 0.009 | **
Prefer not to say | Education: Junior High | 2.39 | (0.16, 36.1) | 0.528 |
Prefer not to say | Education: High School | 3.36 | (0.70, 16.1) | 0.129 |
Prefer not to say | Education: Community College | 0.28 | (0.01, 8.6) | 0.466 |
Prefer not to say | Education: University | 1.00 |  |  |
Prefer not to say | Education: Graduate School | 14.84 | (2.59, 85.1) | 0.002 | **
Prefer not to say | Education: Prefer Not to Say | 8.41 | (1.50, 47.3) | 0.016 | *
Prefer not to say | Household_head: Yes | 1.00 |  |  |
Prefer not to say | Household_head: No | 1.73 | (0.78, 3.8) | 0.175 |
Prefer not to say | Household_child_count: 0 | 1.00 |  |  |
Prefer not to say | Household_child_count: 1 | 0.34 | (0.12, 0.9) | 0.039 | *
Prefer not to say | Household_child_count: 2 | 0.77 | (0.31, 1.9) | 0.584 |
Prefer not to say | Household_child_count: 3 | 0.20 | (0.05, 0.8) | 0.023 | *
Prefer not to say | Household_child_count: 4 | 0.18 | (0.01, 4.8) | 0.302 |
Prefer not to say | Household_child_count: 5 or more | 0.59 | (0.09, 3.8) | 0.580 |

### Prevent Working Regression: Model With Education, No Interactions

Figure 59: Prevent working, compared to “yes”; relative to university educated
men, aged 30-39, head of household, displaced more than three nights based on
model with main effects for age, gender, education, and head of household with
no interactions

Table 79: Prevent working, compared to “yes”; relative to university educated men, aged 30-39, head of household, displaced more than three nights based on model with main effects for age, gender, education, and head of household with no interactions Response | Covariate | Odds Ratio | 95% CI | P Value | Significance
---|---|---|---|---|---
No | Gender: Male | 1.00 |  |  |
No | Gender: Female | 1.04 | (0.79, 1.4) | 0.783 |
No | Gender: Prefer Not to Say | 1.00 | (1.00, 1.0) | 1.000 |
No | Age: 20-29 | 0.67 | (0.45, 1.0) | 0.038 | *
No | Age: 30-39 | 1.00 |  |  |
No | Age: 40-49 | 0.93 | (0.62, 1.4) | 0.730 |
No | Age: 50-59 | 1.03 | (0.62, 1.7) | 0.919 |
No | Age: 60-69 | 3.44 | (1.90, 6.2) | 0.000 | ***
No | Age: 70+ | 1.45 | (0.65, 3.2) | 0.363 |
No | Age: Prefer Not to Say | 0.67 | (0.22, 2.0) | 0.477 |
No | Education: Elementary | 0.62 | (0.13, 3.0) | 0.545 |
No | Education: Junior High | 0.41 | (0.15, 1.1) | 0.089 | .
No | Education: High School | 1.09 | (0.61, 1.9) | 0.762 |
No | Education: Community College | 0.61 | (0.34, 1.1) | 0.105 |
No | Education: University | 1.00 |  |  |
No | Education: Graduate School | 0.54 | (0.28, 1.1) | 0.072 | .
No | Education: Prefer Not to Say | 0.52 | (0.22, 1.2) | 0.137 |
No | More than One night: Yes | 1.00 |  |  |
No | More than One night: No | 1.00 | (1.00, 1.0) | 1.000 |
No | More than Three Nights: Yes | 1.00 |  |  |
No | More than Three Nights: No | 1.86 | (1.40, 2.5) | 0.000 | ***
Prefer not to say | Gender: Male | 1.00 |  |  |
Prefer not to say | Gender: Female | 2.55 | (1.40, 4.6) | 0.002 | **
Prefer not to say | Gender: Prefer Not to Say | 1.00 | (NaN, NaN) | NaN |
Prefer not to say | Age: 20-29 | 1.27 | (0.55, 2.9) | 0.576 |
Prefer not to say | Age: 30-39 | 1.00 |  |  |
Prefer not to say | Age: 40-49 | 0.71 | (0.24, 2.1) | 0.541 |
Prefer not to say | Age: 50-59 | 0.57 | (0.14, 2.2) | 0.417 |
Prefer not to say | Age: 60-69 | 1.25 | (0.25, 6.2) | 0.786 |
Prefer not to say | Age: 70+ | 7.54 | (2.13, 26.7) | 0.002 | **
Prefer not to say | Age: Prefer Not to Say | 27.94 | (8.35, 93.5) | 0.000 | ***
Prefer not to say | Education: Elementary | 4.88 | (0.98, 24.3) | 0.053 | .
Prefer not to say | Education: Junior High | 0.00 | (0.00, 0.0) | 0.000 | ***
Prefer not to say | Education: High School | 0.45 | (0.15, 1.3) | 0.147 |
Prefer not to say | Education: Community College | 0.26 | (0.07, 1.0) | 0.055 | .
Prefer not to say | Education: University | 1.00 |  |  |
Prefer not to say | Education: Graduate School | 0.03 | (0.00, 0.7) | 0.030 | *
Prefer not to say | Education: Prefer Not to Say | 3.41 | (1.16, 10.0) | 0.025 | *
Prefer not to say | More than One night: Yes | 1.00 |  |  |
Prefer not to say | More than One night: No | 1.00 | (1.00, 1.0) | NaN |
Prefer not to say | More than Three nights: Yes | 1.00 |  |  |
Prefer not to say | More than Three nights: No | 0.97 | (0.54, 1.7) | 0.920 |