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Survey of rural and urban happiness in Indonesia during the corona crisis

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Abstract

The COVID-19 pandemic has drastically changed urban life, and it can be said that the time is at hand when cities and rural areas should promote symbiotic projects. These projects are diverse and include medical conditions, socioeconomic activities, working conditions, information technology, food conditions, culture as well as education. According to previous studies, medical conditions are excellent, but well-being of the mental health of people in developed countries is higher in rural areas than in urban areas. Meanwhile, developing countries tend to have higher levels of well-being in urban life, while rural areas have lower levels of well-being and mental health, because of a focus on lagging economic activities and vulnerability in medical care. Preliminary interviews in Bali, Indonesia, the author's study area, revealed no livelihood change in subsistence farming villages during the COVID-19 disaster indicating no effects by the pandemic. Meanwhile, urban residents faced difficulties obtaining food due to the government curfew and halt in economic activities. Most workers lost their jobs and suffered hardships in the tourism industry. With this situation, the conditions are slightly different from the previous studies in developing countries mentioned above. Previous studies did not reveal any mental health and well-being assessment for life in the rural areas of developing countries during the corona disaster. This study aimed to clarify the reality of urban and rural well-being during the Corona Disaster in a developing country, namely Bali. The hypothesis is that in Bali, Indonesia, a developing country, the level of well-being under the corona disaster is higher for rural residents than for urban residents. Six groups were surveyed with 71 questions from the survey items of previous studies including the World Happiness Report conducted worldwide, WHR2020, AHI and The Oxford Happiness Survey. Face Sheet, Mental Health, Anxiety, Happiness, Good things due to corona, and Corona infection control behaviors were included. The questionnaire was categorical to allow for a quantitative analysis and began in September 2021. I collected 280 samples from two villages, each in rural and urban areas of Bali, and analyzed the results with simple cross-tabulations and a difference of means, factor analysis, multiple regression analysis and structural analysis of covariance. The analyses revealed a tendency toward inward self-loneliness in the urban areas and outward anxiety about one's surroundings in the rural areas. Under the corona disaster, subjects in rural areas stayed optimistic about external stress, in contrast to those in urban areas, who became inwardly oriented and negative. This point does not imply that well-being is higher among rural people, but it suggests that they are more mentally stress-tolerant because they are more likely to positively view the situation. Although the hypothesis was not proven, life in rural areas, where people have optimistic feelings and are not lonely, can be considered relatively humane and mentally healthy. This may indicate that the level of well-being of people living in rural areas is higher than people living in urban areas. The results of this study differ from previous studies in which people in rural areas of developing countries had lower levels of happiness and mental health. However, this study provides new knowledge about the situation of the corona disaster in developing countries by surveying the well-being of both rural and urban residents.
Vol.:(0123456789)
Asia-Pacific Journal of Regional Science
https://doi.org/10.1007/s41685-022-00265-4
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ARTICLE
Survey ofrural andurban happiness inIndonesia
duringthecorona crisis
YokoMayuzumi1
Received: 2 June 2022 / Accepted: 29 September 2022
© The Japan Section of the Regional Science Association International 2022
Abstract
The COVID-19 pandemic has drastically changed urban life, and it can be said that
the time is at hand when cities and rural areas should promote symbiotic projects.
These projects are diverse and include medical conditions, socioeconomic activi-
ties, working conditions, information technology, food conditions, culture as well as
education. According to previous studies, medical conditions are excellent, but well-
being of the mental health of people in developed countries is higher in rural areas
than in urban areas. Meanwhile, developing countries tend to have higher levels of
well-being in urban life, while rural areas have lower levels of well-being and mental
health, because of a focus on lagging economic activities and vulnerability in medi-
cal care. Preliminary interviews in Bali, Indonesia, the author’s study area, revealed
no livelihood change in subsistence farming villages during the COVID-19 disaster
indicating no effects by the pandemic. Meanwhile, urban residents faced difficulties
obtaining food due to the government curfew and halt in economic activities. Most
workers lost their jobs and suffered hardships in the tourism industry. With this situ-
ation, the conditions are slightly different from the previous studies in developing
countries mentioned above. Previous studies did not reveal any mental health and
well-being assessment for life in the rural areas of developing countries during the
corona disaster. This study aimed to clarify the reality of urban and rural well-being
during the Corona Disaster in a developing country, namely Bali. The hypothesis
is that in Bali, Indonesia, a developing country, the level of well-being under the
corona disaster is higher for rural residents than for urban residents. Six groups were
surveyed with 71 questions from the survey items of previous studies including the
World Happiness Report conducted worldwide, WHR2020, AHI and The Oxford
Happiness Survey. Face Sheet, Mental Health, Anxiety, Happiness, Good things due
to corona, and Corona infection control behaviors were included. The questionnaire
was categorical to allow for a quantitative analysis and began in September 2021.
I collected 280 samples from two villages, each in rural and urban areas of Bali,
and analyzed the results with simple cross-tabulations and a difference of means,
factor analysis, multiple regression analysis and structural analysis of covariance.
The analyses revealed a tendency toward inward self-loneliness in the urban areas
Extended author information available on the last page of the article
Asia-Pacific Journal of Regional Science
1 3
and outward anxiety about one’s surroundings in the rural areas. Under the corona
disaster, subjects in rural areas stayed optimistic about external stress, in contrast to
those in urban areas, who became inwardly oriented and negative. This point does
not imply that well-being is higher among rural people, but it suggests that they are
more mentally stress-tolerant because they are more likely to positively view the
situation. Although the hypothesis was not proven, life in rural areas, where peo-
ple have optimistic feelings and are not lonely, can be considered relatively humane
and mentally healthy. This may indicate that the level of well-being of people living
in rural areas is higher than people living in urban areas. The results of this study
differ from previous studies in which people in rural areas of developing countries
had lower levels of happiness and mental health. However, this study provides new
knowledge about the situation of the corona disaster in developing countries by sur-
veying the well-being of both rural and urban residents.
Keywords Happiness· Well-being· COVID-19· Anxiety· Mental health·
Developing country· Indonesia· Bali· Structural equation modeling
JEL Classification I31· N35· Q01· Z10· Z31
1 Introduction
1.1 Research background‑urban–rural health disparities intheCOVID‑19 disaster
The pandemic of COVID-19 has drastically changed urban life, and it can be said
that the time is at hand when cities and rural areas will promote symbiotic projects.
They are diverse and include medical conditions, health and health care, spiritual
life, socioeconomic activities, working conditions, information technology, migra-
tion, agricultural and food conditions, culture, education, and population structure.
In particular, rural residents often comprise the majority of the population at high
risk for serious illness, including the elderly and the poor. In addition, they face
healthcare disparities in the prevention and treatment of infectious diseases in the
healthcare setting, resulting in an environment with limited numbers of viral tests
and recoveries. Charbel (2018) reports that deaths from infectious diseases in the
urban group declined much more rapidly than in the rural group. Qian (2021) pre-
sented four explanatory variables, “location (urban/rural classification),” “existing
social vulnerability (Social Vulnerability Index),” “community resilience (Baseline
Resilience Indicators for Communities),” and “diversity among neighborhoods,”
and found that urban groups had higher levels of resilience than rural groups. This
result is consistent with the earlier findings of Charbel (2018), as well as Cutter etal.
(2016) Qian (2021) report that this correlation has matched in all the U.S. counties.
On the other hand, when focusing on mental health regarding well-being,
there are reports of different aspects of the existence of health disparities due to
inadequate rural health care. It is about the paradox of rural well-being in devel-
oped countries (Denmark is the case here). According to reports on the subject
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in developed countries, rural residents tend to exhibit higher levels of subjective
well-being than urban residents. Higher connective social capital in rural areas
and higher access to natural amenities contribute to the paradox of well-being in
rural Denmark (Sørensen 2014).
As noted above, the COVID-19 pandemic highlights strengths and weaknesses
in urban and rural areas. However, Sørensen (2014) cautions against generalizing
these findings to other regions, especially considering differences in demographic
structure, socioeconomic status, transportation, culture, physical environment,
and scale of intervention.
This study aims to determine the reality of urban and rural subjective well-
being in a corona disaster in a developing country, specifically the Indonesian
island of Bali, as a case study. It will address the following issues in turn and
finally verify the hypothesis: 1. to present an index of “subjective well-being”
based on an objective global case study; 2. to select the survey subjects in Bali
and conduct a questionnaire survey, and 3. to analyze the obtained data and verify
the hypothesis through the results of the analysis.
Aristotle called happiness the final goal of life and defined “well-being” as not
only temporary pleasure and happy feelings but also a life in which people use
their unique human reason and make the best use of their abilities to the fullest
(The previous studies in this field have used both well-being and happiness.). As
the tool for examining a person’s well-being, there is a proposal for a quality of
life assessment scale, the WHO Quality of Life 26 (1997), which can be of com-
mon use across countries, cultures, ages, and genders around the world. This tool
is a 26-item QOL questionnaire routinely used in clinical settings in health care.
The WHO defines QOL as “an individual’s perception of their position in life in
the context of the culture and value systems in which they live and in relation to
their goals, expectations, standards and concerns. And it consists of six domains
(1. physical domain, 2. psychological domain, 3. level of independence, 4. social
relationships, 5. environment, and 6. spirituality/religious/beliefs) (Tasaki et al.
1998). On the other hand, as an indicator of people’s happiness comprehensively
from the viewpoint of consumers and with a qualitative evaluation perspective,
there is a happiness indicator as a theory of people’s subjective well-being, apart
from QOL (Quality of Life: ease of living and satisfaction), which is an indicator
of well-being as a quality of life. Happiness is discussed primarily in the fields
of psychology and economics. A typical example is Pugno’s (2004) “paradox of
happiness.” Research on methods of measuring and quantifying happiness is still
ongoing, and recently, self-reported “subjective well-being (individual evaluation
of positive and negative emotions, happiness, life satisfaction, etc.)” in psychol-
ogy is considered a better indicator. The mainstream view is that direct measure-
ment of “subjective well-being” using questionnaires is effective (Ando 2014).
The following further summarizes previous studies about well-being on the
content generated by the COVID-19 pandemic for urban and rural areas, divided
into developed and developing countries, and on the strengths and weaknesses of
their respective health care.
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2 Literature review onhealth inequalities andhappiness
2.1 Health inequalities andhappiness inurban areas ofdeveloped countries
withcorona disasters, compared torural areas.
Migration from rural to urban areas has been a megatrend ever since 1900 (UN2006,
2019). The reality of urban life provides a favorable environment for health and
health care. They are proximity to medical care and a conducive environment for the
treatment of serious illnesses, the presence of ICUs and physicians with specialized
skills, diversified economic activities and corresponding employment opportunities,
high implementation of telework, and guarantees in case of inability to work due to
illness (OECD 2020). Urban residents have an advantage in diverse living conditions
compared to rural. According to the World Happiness Report (2020), most people
choose to live in urban areas. This fact is because metropolitan areas offer a higher
quality of life, both in terms of employment opportunities and access to amenities
and public services (Faggian etal. 2011; Edward etal. 2016). However, these ben-
efits from cities may be challenging to receive uniformly. Urbanization is generally
associated with higher actual costs of living. The extent to which these costs are
experienced and reflected in measures of well-being depends on the education and
related socioeconomic status of residents (e.g., Morrison 2011; Edward etal. 2016).
Although rural life is associated with more robust connections through social capi-
tal, the number of respondents who said they had relatives and friends they could
rely on when in trouble was lower in rural areas than in urban areas (WHR 2020).
Life in urban areas is more socially satisfied than in rural areas. The percentage of
people who are satisfied with meeting people and making friends is the largest in
high-income countries, with 79% satisfied in urban areas compared to 68% in rural
areas. All different income groups score nearly the same as urban areas in semi-
dense areas (WHR 2020). Tokyo’s population in Japan also continues to grow as
young people leave the rural countryside hinterland searching for better educational
and employment opportunities Julian (2020). Qian (2021) said that the urban–rural
disparity relates to cultural perceptions of health and health care. Rural residents
are more reluctant to seek medical care or engage in preventive health behaviors
than urban residents. For example, rural residents are less likely to use sunscreen to
prevent skin cancer (Zahnd etal. 2018). In addition, the Latino community, which
works in rural industries, such as agriculture, poultry, and meat production, and has
no separate household registration, often lacks health insurance coverage (Dorn
etal. 2020). This example would mean that rural communities are less likely to be
willing to seek and receive health care on their own.
2.2 Health inequalities andhappiness inrural areas ofdeveloped countries
withcorona disasters, compared tourban areas.
The Gallup World Poll to examine urban–rural happiness differentials across the
world in 2020 (Pugno 2004) shows that the desire to live in rural and small-town
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surroundings has increased significantly over the past two years (OECD 2020).
Almost half of U.S. adults said they would prefer to live there in 2020. This result is
an increase of 9% from 2018. Remote work is crucial to be the driving force behind
this change.
Several large U.S. cities reported that in 2020 when many people would be
migrating to many rural areas of the country. And the expectation is that those who
have not escaped urban sprawl will undoubtedly crave greener pastures and wide-
open spaces. Deaton (2015) presents an 11-point Cantril Ladder index of numerical
values for subjective well-being for the 150 countries surveyed. The results showed
that the global average life rating for urban people was 5.48, while the global aver-
age life rating for rural people was 5.07, a difference of 0.41 points. In contrast, in
101 countries (67%), the average life rating of rural people was significantly higher
than the average life rating of urban people. The following countries have lower-
than-average urban life ratings, with average values closer to rural areas. They are
Lebanon (− 0.41), Iceland (− 0.38), the Netherlands (− 0.35), New Zealand (− 0.34),
the United Kingdom (− 0.34), and Egypt (− 0.34). However, there are no countries
in this category in Oceania and North America, and in most Northern and West-
ern Europe, there is no statistically significant difference in life evaluations between
urban and rural populations (WHR 2020).
Social capital and nature have also often been key trump cards to prove the virtues
of rural areas in developed countries. For example, in the introduction to the Organi-
zation for Economic Cooperation and Development’s (OECD 2006) rural policy
document, The New Rural Policy Paradigm, in “Policy and Governance,” attention to
“existing assets, such as location, natural and cultural amenities, and social capital” in
rural areas, is encouraged. Here spatial location satisfaction, social capital, and access
to natural amenities are expected to be positively correlated with subjective well-being.
In rural areas, spatial location satisfaction, social capital, and access to natural ameni-
ties may be higher. Thus, these factors may contribute to understanding the origins of
the paradox of well-being in rural areas, as Requena (2016) points out. Furthermore, an
analysis of surveys in which respondents choose one of five options (1) large cities, (2)
medium cities, (3) small cities, (4) villages, and (5) rural areas shows that satisfaction is
higher in settlements where there is ample space. This point supports previous research
that found that self-reported location satisfaction is positively related to self-reported
life satisfaction (Adam and Rubia 2018). Rural residents in developed countries tend
to report higher levels of subjective happiness than in their urban areas. This point has
proven to be the case in studies of rural vs. urban happiness surveys in counties world-
wide (Berry and Okulicz-Kozaryn 2009; Requena 2016; Easterlin etal. 2011), Survey
in EU (Sørensen 2014; Camilla and Giovanni 2016), Survey in the USA (Campbell
etal. 1976; Berry and Okulicz-Kozaryn 2013; John and Yu 2017), Survey in Australia
(Ida etal. 2018). In addition, Berry and Okulicz-Kozaryn (2009) find that for a sample
of high-income countries, dissatisfaction with life is highest in large cities with popu-
lations over 500,000 and lowest in rural areas with people under 2000. On the other
hand, in their sample of low-income countries, they find that living in a rural area or a
large city has no statistically significant association with life satisfaction. residents in
other regions of the world. For example, Piper (2015) found that residents of the capital
cities of 16 European countries have lower happiness levels than residents outside the
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capital. In addition, studies from the United States (Adam and Rubia 2018), New Zea-
land (Morrison 2011), Sweden (Gerdtham and Johannesson 2001), Romania (Camilla
and Giovanni 2016), several studies of single countries have reported low levels of sub-
jective happiness in the capital and most significant cities. These studies rely on survey
data that include self-reports of personal happiness, life satisfaction, or quality of life.
Furthermore, some studies have reported that for residents of capital cities in devel-
oped countries, subjective happiness in New Zealand was lowest in Auckland, the
largest city in New Zealand. In response to this result, Morrison (2011) named it the
“paradoxical localization of affluence.” Furthermore, Sørensen (2014), using data from
the European Values Study 2008 for 28 EU member states, reveals that life satisfac-
tion is higher in rural areas than in urban areas when controlling for socioeconomic
factors, such as gender, age, education, employment, and income. In response to these
results, Sørensen (2014)) explores the reasons for higher life satisfaction in rural areas
and considers rural–urban differences in levels of anxiety, frames of comparison (in
assessing one’s life satisfaction), and social capital (social interaction). Furthermore,
when the state ensures a relatively generous basic income, soft factors become more
critical parameters of subjective happiness. In Denmark, the soft factors of conjunctive
social capital and access to the natural environment are strong predictors of personal
happiness (Rozaki 2020). According to classical sociologists’ theory (Louis 1938),
social interaction, social cohesion, and solidarity are higher in rural communities than
in urban communities. Louis (1938) explains the rural–urban conflict by the opposi-
tion of rural Gemeinschaft, characterized by high social cohesion and involvement, and
urban Gesellschaft, characterized by weak family relations, friendship ties, division of
labor, and high population heterogeneity.
Furthermore, a case study in Japan reports on agricultural experiences and mental
health linked to rural life: JA and Juntendo University surveyed in November 2018 at
a hands-on farm in Nerima-Ku, Tokyo (n = 40 men and women living in Tokyo). It
asks participants to do fertilizing and harvesting work and compares saliva composi-
tion before and after. The results confirm that cortisol and chromogranin A, hormones
that increase with stress, decreased, and oxytocin, a hormone that expresses happiness,
increased on average, although there are gender and personal differences. Furthermore,
questionnaires measuring mood revealed that negative factors, such as anger, confu-
sion, depression, fatigue, and tension decreased. These results indicate that working on
a hands-on farm contributes to a certain level of stress reduction and increased happi-
ness. Professor Akiro Mizushima of the same university has suggested the possibility
of “Agri-healing (healing through agriculture) effects” (Chiba 2021). This result could
be one factor indicating a higher level of happiness in rural life.
2.3 Health inequalities andhappiness inurban areas ofdeveloping countries
withcorona disasters, compared torural areas.
The World Happiness Report (WHR 2020) shows the high valuation of life in urban
areas regarding positive and negative feelings about the 115 countries in its sample.
More people in urban areas experience fun than in rural areas, and more people in
rural areas experience physical pain and sadness than in urban areas. This gap is
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Asia-Pacific Journal of Regional Science
huge in low-income countries, where 46% of the rural areas say they experienced
physical pain more often yesterday, compared to 43% in towns and semi-dense
regions and 41% in urban areas, which decreases as one moves toward cities. Sad-
ness is also more frequently found in rural areas of low-income countries, with 38%
of respondents reporting experiencing more sadness in a day, compared to only 34%
in urban areas. In low- and middle-income countries, more people in urban areas
experience joy, and fewer experience pain and sadness than in rural areas. In addi-
tion, more people in urban areas than in rural areas feel they can rely on family
and friends, meet people, and make friends. Urban dwellers value their lives more
highly, and it is not surprising that they are more likely to move from rural to urban
areas (WHR 2020).
As noted in 1.3 above, Deaton (2015) presented an 11-point Cantril Ladder index
of numerical values for subjective happiness. The global average life rating for urban
populations is 5.48, while the global average life rating for rural populations is 5.07,
a difference of 0.41 points. The difference between urban and rural populations is
most remarkable in East Asia (0.56) and Sub-Saharan Africa (0.56), followed by
South Asia (0.47), Southern Europe (0.46), Latin America, and the Caribbean
(0.38). These findings suggest that the average happiness of urban residents tends
to be higher than that of rural residents, especially in less economically prosper-
ous countries, such as those found primarily in developing countries in Africa and
Southeast Asia. Furthermore, those who say they can count on family and friends
in times of need are lower in rural areas, at 63%, compared to 68% in urban areas
(WHR 2020). Requena (2016) reports that urban residents in developing countries
have consistently higher levels of subjective happiness than rural residents. He then
examines whether the geographic dispersion of happiness within a nation depends
on the country’s stage of development. Using individual-level data from 29 Euro-
pean countries that participated in the 2012 European Social Survey, two groups
consist of 24 developed countries with a GDP per capita of at least US$20,000 and
five developing countries. The results show that developed countries have the high-
est levels of happiness in the countryside and the lowest in large cities, while the
opposite is true for developing countries. Easterlin (2011) also reports higher levels
of life satisfaction in urban areas in developing countries and the same or higher
levels of life satisfaction in rural areas in developed countries. Rural life satisfaction
exceeds urban life satisfaction in 14 countries, and rural life satisfaction exceeding
urban life satisfaction is most pronounced in developed countries, such as the United
Kingdom, the Netherlands, and Norway. In the remaining countries, life satisfaction
is higher in urban areas, and urban life satisfaction exceeding rural life satisfaction
is most pronounced in the least developed countries, such as Uganda, Tanzania, and
Cambodia.
2.4 Health inequalities andhappiness inrural areas ofdeveloping countries
withcorona disasters, compared tourban areas
In a study of rural–urban migrants in China by Knight and Gunatilaka (2010), rural-
to-urban migrant households settled in urban China has lower average happiness
Asia-Pacific Journal of Regional Science
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than rural households. They attribute this to the migrants’ high expectations of
life in the city before migration. However, it is also possible to explain the rural-
to-urban migrants as having been forced to move to the cities to survive economi-
cally but were willing to remain in their rural homelands if they were free to choose,
regardless of their job status, according to the report. If China is an emerging devel-
oping country, this is a rare report of a case where rural well-being is considered
high. Many studies have reported that happiness tends to be higher in rural areas
in developed countries and lower in developing countries. On the other hand, only
a few studies have examined the possible causes behind the rural happiness para-
dox (higher happiness in rural areas) in developing countries (Sørensen 2014; Piper
2015; Morrison and Weckroth 2018). There are few reports on differences in urban
and rural happiness in developing countries, and it is unclear whether the differences
in urban and rural happiness are by people-based factors, such as social capital, or
place-based factors, such as access to health care. Complicating the issue is that peo-
ple do not value environmental attributes in the same way, and some reports say that
the relationship between place of residence and well-being is heterogeneous (Plaut
etal. 2002).
2.5 COVID‑19 occurrence andthestrength ofrural areas withnoseparation
betweendeveloped anddeveloping countries
Table1 summarizes rural areas emerging from the COVID-19 crisis (OECD 2020).
According to OECD (2020), the outbreak of COVID-19 led to new digital solutions
that connect urban and rural areas in a more integrated way that could spur growth
in firms and jobs. Since many jobs are in large metropolitan areas, remote and
decentralized networks can strengthen rural–urban ties. This concept also reflects
the ongoing shift in work practices from traditional office-based workers to more
flexible methods, including telecommuting, working in multiple time zones, and
nomadic (remote workers who move between different locations). These synergistic
effects may be related to changing social and policy preferences for proximity ser-
vices, increased local consumption, and the recovery of strategic industries. Sealing
off certain areas to prevent the spread of viral infections could reduce pollution and
CO2 emission levels. This measure could positively affect policies that support green
and sustainable growth. In the recovery process, society’s high level of understand-
ing of the path of sustainable development for rural communities will accelerate the
Table 1 Rural opportunities emerging from the crisis of COVID-19 (OECD 2020)
Higher relevance to enhance quality and use of digital tools/broadband in rural regions
Remote distributed work might increase linkages between rural and urban
Shift in consuming habits can favor local products and destinations
Greater awareness to ensure accessibility to quality services (e-health, e-education)
Re-shoring of strategic industries that were once delocalized (i.e., raw materials)
Momentum to accelerate a just transition toward a low-carbon economy for rural communities
Mobilize and strengthen local networks and co-operative structures to face future shocks
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transition to a zero-carbon economy. Rural communities occupy a significant portion
of the land, water, and other natural resources essential for absorbing CO2, providing
ecosystem services, and protecting biodiversity. Supporting countries in building
climate-smart rural economic development pathways is key to COVID-19 recovery.
In addition, the Corona pandemic provides an opportunity for rural communities to
strengthen local networks and collaborations to confront the economic shock. Local
initiatives that temporarily emerge to address the pandemic’s impending economic
and social impact (i.e., community groups transporting health care workers and the
elderly) can become organic mechanisms to promote the well-being and cohesion of
rural communities in the long run.
3 Subject andhypothesis, purpose inthis study
3.1 Subject ofthis study
As mentioned above, previous studies have shown that although the medical situ-
ation is excellent in developed countries, the mental health of the people living in
the countryside has a higher impact on subjective happiness in rural areas than in
urban areas. On the other hand, developing countries tend to have higher levels of
happiness in urban life, while rural areas have lower levels of happiness and mental
health, focusing on lagging economic activity and vulnerability in medical care.
According to preliminary interviews in Bali, Indonesia, a developing country
where the author researches, the author found that rural residents who could main-
tain self-sufficiency did not change their lives much during the Corona disaster and
may not have been disturbed by the pandemic. On the other hand, urban residents
had difficulty procuring food due to the government’s curfew and orders to sus-
pend economic activities, and most of the workers lost their jobs and struggled to
make ends meet because their principal workplace was in the tourism industry. This
situation may be slightly different from the developing countries mentioned above.
The medical condition in Bali is indeed vulnerable, as in the developing countries
mentioned before. However, only a few studies have examined the possible reasons
behind the rural happiness paradox (rural areas have higher levels of happiness) in
developing countries, as indicated before (Sørensen 2014; Piper 2015; Morrison
and Weckroth 2018). Research has not yet clarified the assessment of mental health
and happiness for living in rural areas in developing countries with corona disasters.
Therefore, I am aware of the problem that I should show new findings on the status
of happiness in the corona disaster in developing countries by surveying residents’
consciousness about rural and urban life and happiness in developing countries.
3.2 Hypothesis andpurpose
About many reports that medical conditions are better in urban areas and rural areas
are more vulnerable and have lower levels of happiness, as summarized in previous
studies on the corona disaster in developing countries, it is not possible to determine
Asia-Pacific Journal of Regional Science
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whether this is necessarily a trend in developing countries. I question whether this
is necessarily the case in developing countries, as there are many different situations
there. Furthermore, as in the case of developed countries, the high level of happi-
ness in rural life and the increase in life satisfaction due to connectivity with urban
areas with the development of information technology may also apply to developing
countries, but as far as the author can find, there are no such reports. With this back-
ground, this study aims to compare residents’ urban and rural happiness in develop-
ing countries and show that rural happiness is not the same as the trends found in
previous studies. In this study, the author conducts a consciousness survey of rural
and urban residents regarding their happiness in the COVID-19 disaster, quantita-
tively compares their consciousness, and shows their ways of thinking. Completing
a questionnaire survey in the Corona disaster is extremely difficult due to the condi-
tions of overseas travel and restrictions on the movement of local people. For this
reason, the author conducts the survey as a case study in Bali, Indonesia, where the
author is familiar with the local people and their communication.
Based on the background, subject, and objective of this study, as mentioned,
the hypothesis of this study is as follows. “In Bali, Indonesia, a developing coun-
try, happiness in the COVID-19 disaster is higher for rural residents than for urban
residents.”
4 Research methods
4.1 Detailed content ofthequestionnaire
To quantitatively indicate the level of happiness by surveying residents’ conscious-
ness through questionnaires, we need a generalized index to measure these fac-
tors. Therefore, about happiness, “What should use as the indicator of happiness?”
should refer to the idea that has become a global trend, and I should set the questions
accordingly. As mentioned above, the World Happiness Report (WHR 2020) is the
first reference. Then, the author refers to “happiness,” Peterson’s Authentic Happi-
ness Inventory (AHI) (Peterson 2005), for “general happiness” and “subjective hap-
piness,” Lyubomirsky and Lepper (1999), and for the “Depression Self-Rating Scale
(CES-D),” Radloff (1977) of the National Institutes of Health, and The Oxford Hap-
piness Survey by Peter and Micahel (2002), the Be Happy Index (BHI) by Rob-
ert (2009). The author has referred to three surveys as follows as the indicator for
“Mental Health,” ’Mental Health’ in the Household Pulse Survey by the National
Center for Health Statistics (NCHS) (2021), COVID-19 Health and Well-being Sur-
vey by Ministry of Health, New Zealand (2020), and Survey of Mental Health in
Connecticut, USA (2021).
As a result, it divides into six groups of the questionnaire: Face Sheet, Mental
Health, Worry, Happiness, Good things by COVID-19, and Anti-corona Infection
Behavior, with further questions prepared for each group, for a total of 69 questions
(Table2). For the questionnaire survey, the coordinator of the local environmental
foundation in this study negotiates for acceptance of the questionnaire in Bali. The
staff then visits the rural and urban areas and uses the survey method of reading
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the questions directly to the subjects and asking for their responses. This action is
because literacy rates may be low in rural areas, and we may not obtain accurate
survey data.
4.2 Selection ofsurvey subjects
In selecting the sample for the rural and urban areas, the local environmental foun-
dation with which the author collaborated negotiated the survey implementation, as
foreigners are not allowed to conduct surveys alone in Indonesia. In addition, the
postal method, the Internet, and other communication-based methods are ineffec-
tive for collecting questionnaires from a wide age range in Bali. This situation is
because of the poor postal conditions and the possibility of significantly lower col-
lection rates due to lower literacy rates among older age groups and unfamiliarity
with answering methods by correspondence. In addition, conducting a street survey
requires permission from the municipality and the village self-help organization, as
well as a briefing session for the residents. Unfortunately, we cannot do this under
the Corona Disaster. With these backgrounds, the local environmental foundation,
with which I’m collaborating, negotiated directly with key persons in the target vil-
lages and selected those areas that agreed to accept the survey as the subjects. As a
result of the negotiations, the rural areas are two rice farmer villages located in the
suburbs of Gianyar, an inland province in central Bali famous for its rice terrace
scenery, and the urban areas are the villages in Denpasar, the provincial capital, and
Kuta area, a nearby tourist downtown area. The survey method consists of staff from
the local environmental foundation visiting each house, reading the survey items in
an interview format, and obtaining responses. We explained to the subjects that we
would use the survey results only for academic reporting and obtained their consent.
5 Results anddiscussion
The questionnaire survey begins in September 2021, although it is not easy to pro-
ceed with the field survey due to the curfew in Indonesia. We have the survey in two
rural and two urban areas in Bali and collected 280 samples. The rural area targets
rice farmers in an inland site in the middle of Bali, which is famous for its rice ter-
race landscape. This residential area is a rural village located next to terraced and
flat rice fields. The urban areas are the villages of Denpasar, the provincial capital,
and Kuta, the neighboring tourist downtown area. Statistical analysis of the survey
results will focus first on the overall trends of the total sample of 280, then on the
differences in consciousness between the rural and urban areas, and finally on the
testing of the hypotheses based on these results. For the five-question groups other
than Face Sheet, the response format for each question is a 9-point Likert scale. A
reliability analysis response to these five groups of questions; Cronbach’s Alpha
coefficient was 0.835 for urban areas and 0.934 for rural areas, which is reliable
enough to conduct a statistical analysis.
Asia-Pacific Journal of Regional Science
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Table 2 Detailed content of the questionnaire
Face sheet
1 Area of residence 6 Media obtaining for COVID-19 information
2 Name of Residence 7 Income before COVID-19
3 Gender 8 Income during COVID-19
4 Age Group 9 Teleworking k opportunities under the Corona Disaster
5 Educational background 10 Frequency of smartphone use under the corona disaster
Mental health Happiness
M_1 Nervous H_1 I feel I am a successful person
M_2 Fidgety, restless H_2 I am usually in a good mood
M_3 Troubled H_3 I have a pretty good idea of the purpose of my life
M_4 Troubled by things that normally don’t bother me H_4 I usually get what I want
M_5 Feeling depressed H_5 I have more joy than sorrow in my life
M_6 Feel that everything I did was for nothing H_6 What I do is always of interest to me
M_7 Feel fearful H_7 I feel proud of who I am
M_8 Talk less than usual H_8 My existence has a small but positive impact on the world
M_9 Feel alone H_9 I can do most things well
M_10 Feel that people around me do not like me H_10 I am enthusiastic about things
M_11 Feel irritable H_11 I am optimistic about the future of the world
M_12 Cannot stop worrying H_12 I feel happy to be myself
M_13 Feel physically tired H_13 If I have to score my life, I am better than others
M_14 Feel I am not attractive to others H_14 I experience more joy than suffering
M_15 I can’t do the things I want to do H_15 I have a good life
M_16 I spend time in my day doing things I don’t want to do H_16 I know myself, and I like me
M_17 I want to get out of this routine H_17 I know how to have fun, and I enjoy it
M_18 I feel unmotivated H_18 I take care of my health
Worry Good things about being a COVID-19
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Table 2 (continued)
F_1 I have concerns about infection of myself or family members Y_1 I spend more time with family than before
F_2 I have concerns about changes in relationships with family and
friends
Y_2 I havemore time to sleep than before
F_3 I have anxiety about income for themselves and their family Y_3 I haveless stress in interpersonal relationships than before
F_4 Iworry about shortages of food and daily necessities Y_4 I haveless commuting to and from work and school than before
F_5 I have anxiety about changes in my life due to self-restraint, etc. Y_5 I havemore leisure time and other meaningful activities than before
F_6 I have anxiety about discrimination in response to the region Y_6 Mywork hours have decreased, and work-life balance has improved
F_7 At any rate, I have vague concerns Covid-19 Infection Control Actions
F_8 I eat less food than before COVID-19 A_1 Hand washing and hand sanitizing
F_9 I have less sleep than before COVID-19 A_2 Coughing etiquette
F_10 I have anxiety about the future of my job A_3 Leave as much space as possible between people when going out
F_11 I have anxiety due to information obtained from sources I come
into contact with
A_4 Wear a mask
F_12 I have anxiety that I may not be able to receive medical care A_5 Social distancing (Closed spaces, Crowded places, Close-contact
settings)
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5.1 Results inface sheet
140 males and 137 females, three unknown. The age group between 20 and 64 is
257 persons, which is assumed to be the working-age, accounting for more than
90% of the total number of respondents. The most common educational back-
ground is high school graduation (113), junior or vocational school (49), univer-
sity (36), and master’s degree (25). In terms of income, 153 persons (about 50%)
earned more than 3juta before COVID-19, while under the Corona Disaster, 37
persons made more than 3juta, and 243 persons deserved less 2juta, indicating
that the income of most people has dropped. In addition, 40 people (14%) are tel-
eworking under the Corona Disaster (Table3).
5.1.1 Results ofchi‑square test amongFace sheet questions
Next, it shows the results of Pearson’s chi-square test between the Face Sheet
questions. There is a significant probability of association between education
and income at p < 0.001 for both pre and under Corona. The higher the education
level, the higher the revenue. Regarding education and teleworking, there is an
association with a significant probability of p < 0.005. Again, the higher the edu-
cation level, the more telework is used.
5.2 Results oftheanalysis related totheFace sheet questions andthefive groups
ofquestions onhappiness
We have the Mann–Whitney U test between the gender and age groups and edu-
cational background of the Face sheet and the five groups of questions about hap-
piness to compare means. In this analysis, I use nonparametric tests because the
results obtained from the questionnaire utilize ordinal scales and do not assume
any particular distribution, such as a normal distribution, for the data under con-
sideration. All of the tests on the results adopt nonparametric tests for the same
reason.
5.2.1 Results oftests ofdifferences betweenmen andwomen inthefive‑question
groups onhappiness
To examine differences in the consciousness of men and women in the five groups
of questions (59 questions) on happiness, only two questions (M_2 “Fidgety and
restless” (Average: Men 5.7 Women 5.4) and H_7 “Feel proud of me” (Average:
Men 6.7 Women 6.4)) showed significant probabilities of p > 0.05 and differences
in mean values (Table3). Although there is not so numerical difference in these
results, men are slightly more likely to feel proud of themselves, although less
restless. In the statistical analysis, Gender comparisons analysis is a standard
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Table 3 Result of face sheet (n = 280)
Total Rural area Urban area
Gender Male 140, Female 137, Unknown 3 Male 77, Female 62, Unknown 1 Male 63, Female 75, Unknown 2
Age Group 15–19 yrs old 9, 20–24 yrs old 73,
25–34 yrs old 56, 35–44 yrs old 61,
45–54 yrs old 47, 55–64 yrs old 20,
65–74 yrs old 11, 75 yrs old or more 3
15–19 yrs old 4, 20–24 yrs old 16,
25–34 yrs old 26, 35–44 yrs old 36,
45–54 yrs old 30, 55–64 yrs old 17,
65–74 yrs old 9, 75 yrs old or more 2
15–19 yrs old 5, 20–24 yrs old 57, 25–34
yrs old 30, 35–44 yrs old 25, 45–54 yrs
old 17, 55–64 yrs old 3, 65–74 yrs old 2,
75 yrs old or more 1
Educational background Elementary school 22, junior high
school 34, high school 113, college/
vocational school 49, university 36,
master 25, doctor 1
Elementary school 22, junior high
school 31, high school 59, college/
vocational school 10, university 16,
master 2, doctor 0
Elementary school 0, junior high school
3, high school 54, college/vocational
school 39, university 20, master 23,
doctor 1
Media obtaining for COVID-19 informa-
tion
TV news 124, news sites 120, social
media 14, radio 19, print media 1
TV news 76, news sites 50, social media
0, radio 14, print media 0
TV news 48, news sites 70, social media
14, radio 5, print media 1
Income before COVID-19 Under 1juta 58, 2juta 79, 3juta 57, 4juta
23, 5juta 22, 6juta 15, 7juta and above
26
Under 1juta 48, 2juta 37, 3juta 21, 4juta
11, 5juta 11, 6juta 5, 7juta and above
6,
Under 1juta 10, 2juta 41, 3juta 36, 4juta
12, 5juta 11, 6juta 10, 7juta and above
20
Income during COVID-19 under 1juta 142, 2juta 101, 3juta 27,
4juta 1, 5juta 7, 6juta 1, 7juta and
above 1
under 1juta 113, 2juta 25, 3juta 1, 4juta
0, 5juta 0, 6juta 0, 7juta and above 0
under 1juta 29, 2juta 76, 3juta 26, 4juta 1,
5juta 7, 6juta 1, 7juta and above 1
Teleworking k opportunities under the
Corona Disaster
Did 40, did not do 239, unknown 1 Did 1, did not do 138, unknown 1 Did 39, did not do 101, unknown 0
Frequency of smartphone use under the
corona disaster
Increased 193, decreased 87 Increased 69, decreased 70 Increased 124, decreased 17
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method. However, since these are the only two questions that got differences in
mean values, we will not further analyze and discuss the results by gender for the
280 subjects.
5.2.2 The results ofthetest ofdifference inmeans values andfactor analysis
acrossage groups inthefive‑question groups
To determine the differences in mean values in the consciousness of the five
groups of questions (59 questions) on happiness by age group (8 groups: 9 per-
sons 15–19 years, 73 persons 20–24 years, 56 persons 25–34 years, 61 per-
sons 35–44 years, 47 persons 45–54 years, 20 persons 55–64 years, 11 persons
65–74years, 3 persons 75years and older), I use the Kruskal–Wallis Test of non-
parametric. Due to the variability in the number of subjects in these 8 age groups,
I divide them into 3 groups: 15–19 (n = 9) and 20–24 (n = 73), 25–34 (n = 56) and
35–44 (n = 61), 45–54 (n = 47) and 55–64 (n = 20). As a result, 19 questions (M_3,
M_6, M_11, M_13, M_14, M_15, M_17, F_4, F_8, F_9, H_2, H_5, H_8, H_13,
H_14, Y_3, Y_4, A_2, A_4) obtained differences in means between each group at
p < 0.05 level. Since many of the questions obtained differences, it is necessary to
know the structural consciousness. For this reason, I tried a factor analysis (Max-
imum Likelihood, Rotation Method: Promax) on these 19 questions for the three
groups (Table4). In all age groups, the first factor shows that they have good and
positive confidence in their life. This trend increases with age group. After the
second factor, the negative aspects are more prominent, with a certain number of
respondents in all age groups, just under 20%, suffering from mental instability.
Only age groups 3 (45–54 (n = 47) and 55–164 (n = 20)) shows a strong commitment
to infection control rather than psychological aspects in the third factor. We assume
from these results that the middle-aged and elderly in age group 3 are not negative
but positive psychologically, confident in their lives, and want to take infection con-
trol measures and get back to normal as soon as possible.
5.2.3 Results oftests ofdifferences inmeans values betweenthefive groups
ofquestions onhappiness andeducational history
To examine the difference in means in consciousness between the five groups of
questions on happiness (59 questions) and education (n = 280: 22 elementary
schools, 34 middle schools, 33 national high schools, 80 private high schools,
49 vocational and junior colleges, 36 4-year universities, 25 master’s programs)
(exclude doctoral programs because of one person), I use the Kruskal–Wallis Test.
Table 5 shows the results. There are differences for 38 of the 59 questions, with
p < 0.05 for 12 questions, p < 0.01 for four questions, and p < 0.001 for 22 questions.
There are differences in many of the questions, but the most noticeable trend is in
the mental health group and elementary and secondary school graduates.
About “M_8 I talk less than usual, M_12 I can’t stop worrying, M_13 I feel phys-
ically tired, M_14 I don’t see myself as attractive to others, M_15 I can’t do what I
want to do, M_16 I spend my day doing things I don’t want to do, M_17 I want to
get out of this routine, M_18 I don’t feel motivated,” the elementary and secondary
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Table 4 Consciousness by age group results of factor analysis
Age group 1 Age group 2 Age group 3
15–19 yrs old: 9, 20–24 yrs old: 73 25–34 yrs old: 56, 35–44 yrs old: 61 45–54 yrs old: 47, 55–64 yrs
old: 20
First factor Confidence in my life Confidence in my life Confidence in my life
Factor contribution ratio % 22% 34% 45%
Second factor Mental instability Confusion Strong desire to return to
normal
Factor contribution ratio % 15% 15% 16%
Third factor Irritable Mental instability Infection control behavior
Factor contribution ratio % 10% 11% 10%
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Table 5 Difference in means by educational background Questions that elementary and secondary school graduates’ rate low
* 1: Does not apply at all – 9:Very much apply
* SD Junior School, SMP Junior High School, SMA National High School, SMK Private High School, Diploma Vocational school Junior college, S1 University, S2 Master’s
Course, S3 Doctoral Course
* I exclude one doctoral graduate from the analysis
Q SD SMP SMA SMK Diploma S1 S2 S3 Total mean Kruskal
Wallis test
H_1 I feel I am a successful person 2.3 2.6 3.4 4.4 4.0 5.2 5.3 8.0 4.0 0.000
H_2 I am usually in a good mood 2.5 2.8 3.6 4.7 5.0 5.7 5.4 6.0 4.4 0.000
H_5 I have more joy than sorrow in my life 2.7 2.9 3.5 4.7 4.8 5.5 5.5 8.0 4.4 0.000
H_6 What I do is always of interest to me 2.9 2.9 3.7 3.8 4.1 4.3 5.0 6.0 3.8 0.000
H_13 If I have to score my life, I am better than others 2.2 3.3 3.8 5.1 5.9 5.9 6.1 8.0 4.8 0.000
H_14 I experience more joy than suffering 2.7 3.2 3.6 4.8 4.8 5.8 6.0 2.0 4.5 0.000
Y_2 I havemore time to sleep than before 3.2 3.9 3.8 4.7 5.7 6.2 6.1 4.0 4.9 0.000
A_2 Coughing etiquette 5.1 6.4 6.7 7.0 7.2 7.1 7.3 8.0 6.8 0.000
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school graduates respondents are more likely than the other education groups to say
that they are “not applicable”. In contrast, about “H_9 I can do most things well,
H_13 If I had to score people’s lives, I am better than others, H_14 I experience
more joy than suffering, H_15 My life is good, Y_3 I have less stress in interper-
sonal relationships”, it is more “not applicable” than other education groups. The
results suggest that elementary and secondary school graduates are relatively opti-
mistic about their mental health but tend to rate their happiness pessimistically low.
5.3 Differences inconsciousness betweenrural andurban areas
See Table3 for simple aggregate results for rural and urban areas for each question
on the face sheet. This section shows the results of the statistical analysis.
5.3.1 Results ofmean differences inconsciousness byrural andurban areas inface
sheet
The same questions on the Face sheet compare the means of the rural and urban
responses by a Mann–Whitney U test. Differences with a p < 0.001 probability of
significance are age group, education, income, and frequency of smartphone use.
The age group is higher in rural areas and, concerning education, higher in urban
areas. Revenue is higher in urban areas, both before and under the Corona disaster.
Smartphone use is increasing in urban areas.
5.3.2 Results oftests ofdifferences inmeans byrural andurban areas forthefive
groups ofquestions onhappiness
I performed a Mann–Whiney U test concerning the five groups of questions (59
questions) on happiness for the rural (n = 140) and urban (n = 140) groups. The
results show that 39 of the 59 questions differed between the two groups, with
p < 0.05 for 7 questions (M_1, M_3, M_5, M_18, F_4, F_10, F_12), with p < 0.01
for 7 questions (M_4, M_15, M_16,H_3, H_8, H_12, A_4,) with p < 0.001 for 25
questions (M_6, M_11, M_13, F_1,F_3, F_11,H_1, H_2, H_4, H_5, H_6, H_9,
H_11, H_13, H_14, H_15, H_17, H_18,Y_2, Y_3, Y_4, Y_5, Y_6,A_1, A_2).
There are 20 questions where the mean differs by more than 1 point between rural
and urban areas, shown in Table6. Of these, seven questions have larger means
(more likely to apply) in rural areas, with values ranging from 1: Does not apply at
all to 9: Very much apply, and 13 questions have larger means in urban areas. These
trends can be summarized as follows.
People in urban areas tend to have high happiness levels and are regaining their
own time in the Corona Disaster, but are confusing because they cannot live the life
they want. In other words, they tend to have a high sense of superiority and self-
esteem in their view of life, despite their mental depression and negative psychology
due to various anxieties.
People in rural areas tend to care about their health and are optimistic, but are
exhausted by the uncertainty of income and infection of their families. In other
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Table 6 Results of tests of differences in means by rural and urban areas for five groups of questions on
happiness *20 questions with means differing by more than 1 point
Q Rural
1,urban 2
Mean
Rank
Mean Asymp. Sig.
(2-tailed)
M_6 Feel that everything I did was for nothing 1 113.9 2.9 0.000
2 166.8 4.3
M_11 Feel irritable 1 114.7 3.6 0.000
2 164.8 4.9
M_13 Feel physically tired 1 172.0 6.8 0.000
2 109.4 5.3
F_3 I have anxiety about income for themselves
and their family
1 163.0 7.8 0.000
2 118.4 6.9
H_1 I feel I am a successful person 1 111.2 3.2 0.000
2 169.4 4.9
H_2 I am usually in a good mood 1 101.1 3.4 0.000
2 179.3 5.4
H_5 I have more joy than sorrow in my life 1 98.5 3.3 0.000
2 181.9 5.5
H_6 What I do is always of interest to me 1 115.9 3.3 0.000
2 163.9 4.4
H_9 I can do most things well 1 110.7 3.4 0.000
2 169.8 4.7
H_11 I am optimistic about the future of the world 1 168.5 7.1 0.000
2 111.7 5.9
H_13 If I had to score my life, I am better than
others
1 99.9 3.7 0.000
2 180.5 6.0
H_14 I experience more joy than suffering 1 102.9 3.6 0.000
2 177.5 5.5
H_17 I know how to have fun, and I enjoy it 1 161.9 6.8 0.000
2 118.2 5.9
H_18 I take care of my health 1 167.5 7.4 0.000
2 113.9 6.2
Y_2 I haveore time to sleep than before 1 111.5 4.0 0.000
2 169.1 5.7
Y_3 I have less stress in interpersonal relation-
ships than before
1 102.9 2.5 0.000
2 177.6 4.2
Y_4 I have less commuting to and from work and
school than before
1 192.2 8.0 0.000
2 89.6 5.4
Y_5 I have more leisure time and other meaning-
ful activities than before
1 122.9 4.4 0.000
2 157.9 5.4
Y_6 I work hours have decreased, and work-life
balance has improved
1 111.4 3.1 0.000
2 169.2 4.4
A_1 Hand washing and hand sanitizing 1 170.4 7.6 0.000
2 111.0 6.7
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words, they are not mentally down and optimistic about their daily lives, but they
have lower happiness and confidence in their own lives than urban people, and they
worry about lack of food and income and family illness in terms of their livelihoods.
5.3.3 Results offactor analysis byrural andurban areas infive groups ofquestions
onhappiness
In comparing responses by rural and urban areas in the five groups of questions on
happiness in 5.3.2, it obtains differences in means for many questions. The group
of questions on happiness is the most common (15 out of 18 questions), and it gets
more than half of the questions in each question group. To learn more about the
priorities of each group’s thinking and the overall trend in thought, it conducts a
factor analysis (maximum likelihood method and Promax rotation) for each of the
five groups of questions on happiness (59 questions: mental health group, happiness
group, anxiety group, good things due to corona group, and corona infection control
group). It shows the results of the analysis in Tables7, 8 for results with a “Kai-
ser–Meyer–Olkin Measure of Sampling Adequacy” of 0.5 or greater, a probability
of significance of p < 0.001, a numerical value of Extraction for factor extraction in
the Communalities of 0.5 or greater, and an initial “sum of explained variance” in
the “total variance explained results for which the eigenvalue is greater than or equal
to 1 for the target factor.
First, it compares the mental health and anxiety groups for which it could extract
factors in both rural and urban areas. For the mental health group, rural areas have
trouble and confusion, while urban areas are lonely and apathetic. As for the anxiety
group, the rural group is more worried about not having access to proper medical
care, while the urban group is more worried about life and infection.
Although it could not get the factors for both groups, the “Good things about
being a COVID-19” group has more time with their families in the urban areas, as
for the “infectious behavior” group, people are more in rural areas concerns about
cough dispersal etiquette.
5.3.4 Results ofmultiple regression analysis byrural andurban areas infive groups
ofquestions onhappiness
In the factor analysis results in 5.3.3, some groups get no factors. For this reason, I
use multiple regression analysis (Linear: stepwise method) to analyze them to learn
more about trends in consciousness in another way. The advantage of performing
multiple regression analysis is that it is possible to predict which and to what extent
the dependent variable among several related factors (independent variables) influ-
ences the results when explaining the dependent variable. In the present study, it
is difficult to predict and narrow down the explanatory (independent) variables in
advance because of the limited previous literature on well-being surveys in devel-
oping countries. In other words, this is a not determined case of the independent
variables used in the analysis. For this reason, I perform multiple regression anal-
yses using the stepwise method. The stepwise method automatically selects vari-
ables related to the objective (dependent) variable among the input explanatory
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Table 7 Results of factor analysis by rural areas (n = 140)
* Two rural areas (n = 140)
Mental health Wo rry Happiness Good things
about being a
COVID-19
Covid-19 infection control actions
First factor Unstable and confusion Concerns about medical care and the
surrounding community
Factor extrac-
tion impos-
sible
Factor extrac-
tion impos-
sible
Cough dispersal etiquette
Factor contribution ratio % 56% 48% 37%
Second factor troubled and nervous Anxiety about income and food Avoiding 3 close (Closed spaces,
Crowded places, Close-contact
settings)
Factor contribution ratio % 17% 13% 21%
Third factor Anxiety based on obtained information
Factor contribution ratio % 11%
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Asia-Pacific Journal of Regional Science
Table 8 Results of factor analysis by urban areas (n = 140)
* two urban areas (n = 140)
Mental health Wor ry Happiness Good things about being a COVID-19 Covid-19
infection
control
actions
First factor Feeling lonely and apathetic Worry about
income, food,
and infection
Confidence in one’s own existence Can relax with family Factor
extrac-
tion
impos-
sible
Factor contribution ratio % 30% 37% 41% 37%
Second factor Powerlessness with no way out Discrimina-
tion in policy
responses by
region
I get what I want Work and interpersonal relationships
become easier
Factor contribution ratio % 17% 16% 12% 21%
Third factor Troubling Positive
Factor contribution ratio % 14% 11%
Fourth factor Full attention to my health
Factor contribution ratio % 8%
Asia-Pacific Journal of Regional Science
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(independent) variables and can obtain analysis results with a narrowing of vari-
ables. On the other hand, the forced imputation method, which uses all the imputed
explanatory variables, predicts that the accuracy of the analysis will be low because
of the large number of variables in this survey. In addition, this study will not only
test the hypothesis and conclude from the results of this stepwise multiple regression
analysis alone. It will also conduct a covariance structure analysis while considering
the results of this analysis as material for further consideration. In addition, for mul-
ticollinearity, I check the strength of the association between variables in advance by
the correlation value and analyze by removing values that are not acceptable.
I excluded “Good things about being a COVID-19” group (6 questions) and the
“COVID-19 Infection Control Actions” group (5 questions) because the number of
questions is a few, and I got some results by examining the difference in the means
in 5.3.2.
First, it does a multiple regression analysis about the “happiness” group, which
got no factor in the rural areas. For the two areas, plus the same dependent variable,
it gets no results with a coefficient of determination R2 greater than 0.60, capable of
capturing trends above a certain level. Therefore, for each independent variable, it
shows that a coefficient of determination (coefficient of determination) R2 is 0.60 or
greater. It gets H_1, H_2, H_5, H_13, and H_14 for rural areas and H_8, H_12, and
H_15 for urban areas. A characteristic trend shows among these.
It shows the results for the urban area in Table9 in 5.3.3. The dependent vari-
able of H_8 (My existence has a small but positive impact on the world) shows that
“I feel confident about my existence, even though I feel I am not doing what I am
interested in.”
It shows the results for rural areas in Tables10, 11 in 5.3.3. Regarding the dependent
variable H_2 (I am usually in a good mood), the respondents feel that “I’m successful
Table 9 Happiness about urban area. The result of multiple regression analysis
* Dependent Variable: H_8 My existence has a small but positive impact on the world
* R2 = 0.60 F = 33.05 ANOVA p < 0.001
Urban area Unstandard-
ized coef-
ficients β
Standardized
coefficients
Beta
Sig t-value VIF (Variance
Inflation Fac-
tor)
(Constant) − 0.581 − 1.018
H_15 I have a good life 0.196 0.175 0.020 2.352 1.826
H_7 I feel proud of who
I am
0.256 0.240 0.000 3.589 1.477
H_9 I can do most things
well
0.366 0.323 0.000 4.864 1.453
H_12 I feel happy to be
myself
0.364 0.313 0.000 4.350 1.707
H_6 What I do is always of
interest to me
− 0.272 − 0.233 0.000 − 3.965 1.137
H_17 I know how to have
fun, and I enjoy it
0.168 0.153 0.013 2.525 1.206
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Asia-Pacific Journal of Regional Science
and joyful in life, although I’m not confident about myself,” and regarding the depend-
ent variable H_13 (If I had to score my life, I am better than others), they feel that “I’m
a successful parson who experience much joy.”
The difference in consciousness between urban and rural areas is that urban people
are confident in themselves, and rural people are not convinced. In other words, urban
people are confident and proud of themselves, while rural people do not think so but
consider themselves successful, experiencing much joy.
Next, to learn more about the relationship between feelings of happiness and
anxiety, it conducts a multiple regression analysis for the mental health group, the
anxiety group, and the happiness group in urban and rural areas (Tables12, 13,
14, 15). The results show that in both urban and rural areas, with the two mental
health group questions (M_17, M_18) as dependent variables, the coefficient of
determination R2 is above 0.60. As for the dependent variable “M_17: I want to
get out of this routine,” the results show that in urban areas, the respondents are
unmotivated, lonely, and stagnant, while in rural areas, they are both unmotivated
and in a good mood. As for the dependent variable “M_18: I feel unmotivated”
the results show that in urban areas, the respondents are anxious but competent
and want to get out of this routine, while in rural areas, the respondents are frus-
trated by the uncertainty of their lives but competent and want to get out of this
routine. These results indicate that people in both urban and rural areas com-
monly consider themselves useful to the world. Furthermore, the urban respond-
ents are lonely and anxious, while the rural respondents are optimistic but anx-
ious about life.
5.3.5 The results offactor analysis andstructural analysis ofcovariance (SEM/
structural equation modeling) foreach rural andurban area inthethree
question groups
Finally, it conducts a factor analysis (maximum likelihood method/Promax rotation)
for each urban and rural area on all three groups of questions for the mental health,
anxiety, and happiness groups, and then, using the results as a reference, creates a
path diagram for structural analysis of covariance (SEM/Structural Equation Mode-
ling: Maximum Likelihood Method). In the factor analysis, the Kaiser–Meyer–Olkin
Measure of Sampling Adequacy is above 0.5, the probability of significance
p < 0.001, the value of Extraction after factor extraction in Communalities is above
0.5, and in the “sum of explained variance,” the questions for which the initial value
is above or equal to 1 in the target factor (Table16). It uses the original data with-
out missing values for the structural analysis of covariance. I adopt the best fit with
the goodness of fit coefficients (CMIN/DF (2 or less), AGFI (Adjusted Goodness
of Fit Index; adjusted goodness of fit index), RMR (Root Mean square Residual
(Root Mean Square of Residual), Akaike’s Information Criterion (AIC), RMSEA
(Root Mean Square Error of Approximation), degrees of freedom (d.f.), chi-square
(chi-squared).
For urban areas, it gets the following trends (Fig.1).
Asia-Pacific Journal of Regional Science
1 3
Table 10 Happiness about rural area. The result of multiple regression analysis
* Dependent Variable: H_2 I am usually in a good mood
* R2 = 0.80 F = 90.86 ANOVA p < 0.001
Rural area Unstandardized coef-
ficients β Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) 2.139 2.461
H_1 I feel I am a successful person 0.449 0.440 0.000 7.293 2.471
H_5 I have more joy than sorrow in my life 0.283 0.277 0.000 5.068 2.019
H_7 I feel proud of who I am − 0.234 − 0.111 0.008 − 2.709 1.146
H_11 I am optimistic about the future of the world 0.204 0.122 0.006 2.789 1.300
H_12 I feel happy to be myself − 0.262 − 0.145 0.001 − 3.568 1.122
H_13 If I have to score my life, I am better than others 0.156 0.171 0.007 2.745 2.636
1 3
Asia-Pacific Journal of Regional Science
Table 11 Happiness about rural area. The result of multiple regression analysis
* Dependent Variable: H_13 If I had to score my life, I am better than others
* R2 = 0.78 F = 95.42 ANOVA p < 0.001
Rural area Unstandardized coef-
ficients β
Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) − 3.326 − 4.738
H_14 I experience more joy than suffering 0.601 0.531 0.000 9.384 1.957
H_1 I feel I am a successful person 0.329 0.294 0.000 5.305 1.882
H_18 I take care of my health 0.322 0.175 0.000 4.117 1.106
H_11 I am optimistic about the future of the world 0.168 0.092 0.041 2.067 1.212
H_15 I have a good life 0.038 0.083 0.048 1.993 1.050
Asia-Pacific Journal of Regional Science
1 3
Table 12 Happiness and Worry and Mental Health about urban area. The result of multiple regression analysis
* Dependent Variable: M_17 I want to get out of this routine
* R2 = 0.67 F = 43.69 ANOVA p < 0.001
Urban area Unstandardized coef-
ficients β
Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) 1.333 1.911
M_18 I feel unmotivated 0.503 0.427 0.000 7.991 1.116
M_10 Feel that people around me do not like me 0.324 0.302 0.000 5.356 1.239
M_14 Feel I am not attractive to others 0.292 0.232 0.000 4.032 1.291
H_14 I experience more joy than suffering − 0.325 − 0.228 0.000 − 4.389 1.049
F_9 I have less sleep than before COVID-19 0.183 0.158 0.006 2.784 1.261
M_7 Feel fearful − 0.161 − 0.116 0.030 − 2.196 1.096
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Asia-Pacific Journal of Regional Science
Table 13 Happiness and Worry and Mental Health about rural area. The result of multiple regression analysis
* Dependent Variable: M_17 I want to get out of this routine
* R2 = 0.92 F = 407.89 ANOVA p < 0.001
Rural area Unstandardized
coefficients β
Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) − 0.597 − 3.870
M_18 I feel unmotivated 0.490 0.485 0.000 9.640 4.471
H_2 I am usually in a good mood 0.383 0.266 0.000 5.493 4.123
M_16 I spend time in my day doing things I don’t want to do 0.246 0.181 0.000 3.753 4.114
M_10 Feel that people around me do not like me 0.135 0.110 0.001 3.267 1.998
Asia-Pacific Journal of Regional Science
1 3
Table 14 Happiness and Worry and Mental Health about urban area. The result of multiple regression analysis
* Dependent Variable: M_18 I feel unmotivated
* R2 = 0.63 F = 27.19 ANOVA p < 0.001
Urban area Unstandardized
coefficients β
Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) 1.109 1.397
M_17 I want to get out of this routine 0.434 0.511 0.000 8.307 1.301
M_16 I spend time in my day doing things I don’t want to do 0.250 0.269 0.000 4.638 1.159
F_2 I have concerns about changes in relationships with family and friends − 0.295 − 0.273 0.000 − 4.711 1.153
F_7 At any rate, I have vague concerns 0.335 0.338 0.000 5.333 1.383
F_6 I have anxiety about discrimination in response to the region − 0.191 − 0.200 0.002 − 3.157 1.384
H_3 I have a pretty good idea of the purpose of my life − 0.176 − 0.158 0.009 − 2.661 1.209
H_8 My existence has a small but positive impact on the world 0.291 0.278 0.000 4.358 1.399
M_6 Feel that everything I did was for nothing 0.178 0.187 0.003 3.072 1.275
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Asia-Pacific Journal of Regional Science
Table 15 Happiness and Worry and Mental Health about rural area. The result of multiple regression analysis
* Dependent Variable: M_18 I feel unmotivated
* R2 = 0.90 F = 203.15 ANOVA p < 0.001
Rural area Unstandardized
coefficients β
Standardized coef-
ficients Beta
Sig t-value VIF (Vari-
ance Inflation
Factor)
(Constant) − 0.272 − 0.337
M_17 I want to get out of this routine 0.717 0.724 0.000 13.489 3.889
M_11 Feel irritable 0.187 0.129 0.009 2.659 3.183
F_6 I have anxiety about discrimination in response to the region − 0.200 − 0.129 0.000 − 4.254 1.233
F_5 I have anxiety about changes in my life due to self-restraint, etc. 0.218 0.140 0.000 3.737 1.888
H_18 I take care of my health − 0.191 − 0.080 0.005 − 2.841 1.059
H_8 My existence has a small but positive impact on the world 0.245 0.072 0.011 2.575 1.053
Asia-Pacific Journal of Regional Science
1 3
“I’m confident in my life due to the good life and my competent presence.
I have confidence in my greatness, even though it torments me with endless
lethargy that leaves me with no motivation to do anything.”
Table 16 Consciousness for three groups (mental health, anxiety, happiness) by urban and rural area
The result of factor analysis
Three groups (mental health, anxiety, happiness)
Rural area (n = 140) Urban area (n = 140)
First factor Disappointment that I am superior and
cannot get out of this situation
Confidence in my own existence
Factor contribution ratio % 45% 37%
Second factor Nervousness Confident good mood
Factor contribution ratio % 15% 16%
Third factor Worry about living income Mental instability
Factor contribution ratio % 6% 10%
Fourth factor I have no problem, but anxious about my
surroundings
Apathy with no way out
Factor contribution ratio % 4% 8%
䢰䢶
䢰䢸
䣖䣣䣭䣧䢢䣲䣴䣫䣦䣧
䣫䣰䢢䣯䣻䢢䣮䣫䣨䣧
䣧䢳
䢰䢹
䣷䣵䣧䣨䣷䣮䢢䣶
䣶䣪䣧䢢䣹䣱䣴䣮
䣧䢴
䢰䢶
䣊䣣䣲䣲䣫䣰䣧䣵䣵䢢䣱䣨
䣤䣧䣫䣰䣩䢢䣯䣻䣵䣧䣮䣨
䣧䢵
䢰䢸
䣕䣷䣥䣥䣧䣵䣵䣨䣷䣮
䣲䣧䣴䣵䣱䣰
䣧䢶
䢰䢵
䣃䣮䣹䣣䣻䣵䢢䣫䣰䢢
䣱䣱䣦䢢䣯䣱䣱
䣧䢷
䢰䢴
䣕䣯䣣䣮
䣲䣲䣧䣶䣫䣶䣧 䣧䢸
䢰䢴
䣎䣣䣥䣭䢢䣱
䣵䣮䣧䣧䣧䢹
䢰䢸
䢰䢷
䣱䣱䣦䢢䣮䣫䣨
䣧䢺
䢰䢲
䣮䣱䣸䣧
䣯䣻䣵䣧䣮䣨
䣧䢻
䢰䢲
䢰䢷
䣐䣱
䣯䣱䣶䣫䣸䣣䣶䣫䣱
䣧䢳䢲
䢰䢹䢰䢵
䣹䣣䣰䣶䢢䣶
䣩䣧䣶䢢䣱䣷䣨䢢䣱
䣧䢳䢳
䢰䢺䢰䢹䢰䢸
䣧䢳䢴
䣧䢳䢵
䣧䢳
䢰䢵
䢯䢰䢴䢳
䢰䢴
䢰䢴
䢰䢳
䢰䢶
䢯䢰䢷䢴
䣉䣈䣋䢿䢰䢻䢸䢶
䣃䣉䣈䣋䢿䢰䢻䢳䢺
䣅䣈䣋䢿䢰䢻䢻䢺
䣅䣪䣫䢯䣵䣳䣷䣣䣧䢿䢴䢻䢰䢺䢷䢸
䣦䣨䢿䢴䢻
䣲䢿䢶䢴䢳
䣃䣋䣅䢿䢳䢲䢵䢰䢺䢷
䣔䣏䣔䢿䢰䢳䢴䢴
䣔䣏䣕䣇䣃䢿䢰䢲䢳䢷
䢰䢵
䢰䢷
䢰䢵
䢯䢰䢲䢹 䢰䢴
䢰䢹
䢰䢵
䢰䢺
䢰䢸䢰䢷
䢯䢰䢳䢶
Fig. 1 The result of structural analysis of covariance for urban area
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Asia-Pacific Journal of Regional Science
For rural areas, it gets the following trends (Fig.2).
“I’m not as confident in myself as others, but I can do anything I put my mind
to and am always in a good mood and optimistic. And more, fearful of infec-
tion with corona and the future state of my life, I’m restless and very nervous
and want to escape from this situation. But because of my naturally optimistic
nature, I try to be optimistic about those fears.”
6 Discussion andhypothesis validation
It will discuss the analysis results separately for urban and rural areas and examine
the hypotheses.
First, it considers urban areas. The Face Sheet shows that the average pre-
COVID-19 income in urban areas is 3.6 juta and 2.2 juta under Corona Disaster,
with higher education levels leading to higher revenue and teleworking. Both income
and education are higher in urban areas. Smartphone use is also higher under Corona
Disaster. Although many younger age groups have a good quality of life and posi-
tive confidence in themselves, just under 20% of all age groups suffer from mental
䣙䣱䣴䣴䣫䣧
䣣䣤䣱䣷䣶䢢䣫䣰䣨䣧䣥䣶䣫䣱
䣧䣧䣮䢢䣦䣱䣹
䣕䣷䣥䣧䣵䣵䣨䣷䣮
䣲䣧䣴䣵䣱䣰 䣃䣮䣹䣣䣻䣵䢢䣫
䣣䢢䣩䣱䣱䣦䢢䣯䣱䣱䣦
䣧䢶
䣧䢷 䣧䢸
䣙䣱䣴䣴䣫䣧䣵䢢䣣䣤䣱䣷䣶
䣮䣫䣨䣧䣵䣶䣻䣮䣧䢢䣥䣪䣣䣰䣩䣧䣵
䣧䢳
䣧䢻
䣙䣱䣱䣴
䣣䣤䣱䣷䣶䢢䣫䣰䣥䣱䣯䣧
䣧䢳䢴
䣏䣱䣴䣧䢢䣪䣣䣲䣲䣫䣰䣧䣵䣵
䣶䣪䣣䣰䢢䣵䣣䣦䣰䣧䣵䣵
䣱䣨䢢䣱䣷䣴䢢䣹䣱䣴䣮䣦
䣧䢳䢵
䣙䣱䣴䣴䣻䢢䣣䣤䣱䣷䣶
䣪䣧䢢䣨䣷䣶䣷䣴䣧䢢䣹䣱䣴
䣧䢳䢷
䣐䣧䣱䣸䣱䣷䣵䣰䣧䣵
䣔䣧䣵䣶䣮䣧䣵䣵
䣧䢳䢸
䣧䢳䢹
䣖䣣䣮䣭䢢䣣䢢䣮䣫䣶䣶䣮
䣧䣧䣮䢢䣣䣮䣱䣰䣧
䣧䢳䢻
䣧䢴䢲
䣙䣱䣴䣴䣻䢢䣣䣤䣱䣷䣶
䣵䣯䣣䣮
䣯䣧䣦䣫䣥䣣䣮䢢䣱䣲䣲䣱䣶䣷䣰䣫䣶䣻
䣧䢴䢳
䣄䣧䣶䣶䣧䣴
䣶䣪䣣䣰䢢䣱䣶䣪䣧䣴
䣧䢴
䣄䣧䢢䣵䣶䣴䣷䣥
䣤䣻䢢䣨䣧䣣
䣧䢴
䣧䢴
䣧䢴
䣹䣣䣰䣶䢢䣶
䣧䣵䣥䣣䣲䣧䢢䣨䣴䣱
䣦䣣䣫䣮䣻䣮䣫䣨
䣋䢢䣦䣱䢢䣶䣪䣫䣰䣩
䣋䢢䣦䣱䣰䢩䣶䢢䣹䣣䣰䣶
䣶䣱䢢䣦䣱䢰
䣧䢴䢻
䣧䢵䢲
䣧䢵
䣧䢵䢴
䢰䢸
䣙䣱䣴䣴䣻䢢䣣䣤䣱䣷
䣥䣪䣣䣰䣩䣧䣵䢢䣫䣰
䣴䣧䣮䣣䣶䣫䣱䣰䣵䣪䣱䣲
䣧䢵䢵
䣗䣵䣧䣨䣷䣮
䣲䣧䣴䣵䣱䣰
䣧䢵䢶 䣆䣱䣫䣰䣩䢢䣹䣪䣣䣶
䣋䢩䣯䢢䣫䣰䢢䣫䣰䣶䣧䣴䣧䣵䣶䢢䣫䣰
䣧䢵
䢰䢴
䢰䢴
䢰䢴
䢰䢶
䢰䢸
䢯䢰䢺䢶
䢰䢶
䢯䢰䢵䢶
䢰䢸
䣉䣧䣶䢢䣯䣱䣴䣧
䣪䣣䣲䣲䣫䣰䣧䣵
䣶䣪䣣䣰䢢䣵䣷䣨䣨䣧䣴䣫䣰䣩
䣧䢵䢻
䢰䢶
䣥䣣䣰䢢䣦䣱
䣯䣱䣵䣶䢢䣶䣪䣫䣰䣩
䣹䣧䣮䣮
䣧䢵
䢯䢰䢻䢸
䢰䢻
䢰䢻
䢰䢴
䢰䢷
䢰䢸
䢰䢶
䢯䢰䢳䢵
䢰䢴
䢯䢰䢴䢺
䢰䢻
䢰䢳
䢰䢶
䢰䢳
䢰䢷
䢰䢶
䢯䢰䢶䢶
䢯䢰䢺䢷
䢰䢳
䢰䢵
䢯䢰䢴䢻
䢰䢵
䢰䢵
䢰䢻
䢰䢶
䢰䢺
䢰䢺䢰䢺
䢰䢳
䢰䢴
䢰䢴
䢰䢴
䢰䢷
䢰䢶
䢰䢴
䢰䢹
䢰䢹
䢰䢸
䢰䢴
䢰䢲
䢰䢴
䢯䢰䢴䢹
䢰䢺
䢰䢴
䣉䣈䣋䢿䢰䢺䢹䢸
䣃䣉䣈䣋䢿䢰䢺䢳
䣅䣈䣋䢿䢰䢻䢹䢺
䣅䣪䣫䢯䣵䣳䣷䣣䣴䣧䢿䢴䢴䢻䢰䢹䢲
䣦䣨䢿䢳䢹䢴
䣲䢿䢰䢲䢲䢴
䣃䣋䣅䢿䢵䢻䢳䢰䢹䢲
䣔䣏䣔䢿䢰䢳䢶䢳
䣔䣏䣕䣇䣃䢿䢰䢲䢶䢻
䢰䢶
Fig. 2 The result of structural analysis of covariance for rural area
Asia-Pacific Journal of Regional Science
1 3
instability. Regarding the difference between the means in the five groups of ques-
tions about happiness and mental health, the respondents have a high level of joy and
are regaining their time under the Corona Disaster, but they are confused because
they cannot live their lives as they want to. In other words, urban people tend to have
a higher sense of superiority and self-esteem in their view of life, even though they
are suffering from mental depression and a negative psychological state due to vari-
ous anxieties. In the mental health group results in the factor analysis, subjects are
lonely and apathetic. In the results of the anxiety group, subjects are more anxious
about life and infection. The results of the multiple regression analysis for the mental
health, anxiety, and happiness groups (48 questions) show that they are confident
and proud of themselves, according to the analysis results, with the questions within
the happiness group as the dependent variable. On the other hand, in the analysis
results with the questions within the mental health group as the dependent variable,
they are unmotivated, lonely, and stagnant, and they want to get out of this routine
because they are anxious but competent. Considering these results, it creates a path
diagram using structural analysis of covariance, and the results are as follows. ’It
torments me by endless lethargy with no motivation to do anything, yet I am confi-
dent in my life because of the good life and my competent and great self.’ The point
that urban people are “confident in their greatness, superior to others” may be a fac-
tor in their mental stability. However, concerning the “persistent state of anxiety and
lethargy,” recent studies have pointed out the following.
Therapeutic work with people who experience low levels of life function (This
refers to one’s psychological, occupational, family-related, and social performances
(Altshuler etal. 2002) and happiness should focus on coping, decreasing anxiety,
and the tendency for stress responses. On the other hand, people who functioned
better were happier and had better experiences despite the pandemic and the lock-
down (Cohen-Louck and Levy 2022). And another report suggests a deterioration
in mental health during the course of the COVID-19 pandemic, which emphasizes
the importance to implement targeted health promotions to prevent a further symp-
tom escalation especially in vulnerable groups (Christoph etal. 2022).
The government should take two possible measures based on this report. For anx-
iety, the government should provide opportunities for mental therapy and discuss
future working plans in the workplace. For apathy, the government should provide
opportunities for achieving a sense of well-being. Under the Corona pandemic, In
the case of mental health, there should be opportunities to gain the joy of learn-
ing through online learning lessons to acquire qualifications, opportunities to create
something that one can do on one’s own, and opportunities for family interaction to
generate motivation. For physical health, it would need online dance, exercise, and
yoga programs to improve health and wellness.
Next, it considers rural areas. The Face Sheet shows that pre-COVID-19
income in rural areas averaged 2.8 juta of payment, although most are 1 juta, and
under the Corona Disaster averaged 1.2 juta. In addition, 53 (38%) rural primary
and secondary school graduates compared to 3 (2%) in urban areas. This result
shows that most have incomes at the 1 juta level. This point means that rural
areas have less income and low education than urban areas. The factor analysis
results show that all age groups have good and positive confidence in their lives
1 3
Asia-Pacific Journal of Regional Science
in the first factor, but less than 20% of all age groups suffer from mental instabil-
ity. There are 30 subjects (21%) in the urban area 45years of age or older, com-
pared to 58 (41%) in the rural area. It can suppose that this group does not have
negative psychology but rather positive psychology, confidence in their lives,
and wanting to get back to normal as soon as possible by taking measures to
prevent infection. Regarding the differences in means across the five groups of
questions on happiness and mental health, primary and secondary school gradu-
ates, who are more common in rural areas, are relatively optimistic about their
mental health but tend to rate their happiness pessimistically low. On the other
hand, all subjects are optimistic, although they are concerned about their health.
However, they are tired of worrying about income and infecting their families. In
other words, rural people are less mentally depressed and optimistic about their
daily lives, but they are less happy and less confident about their human lives
than their urban counterparts, and they worry about lack of food and income and
family illness in terms of their livelihood. The factor analysis results show that
subjects are distressed and confused about the mental health group. About the
anxiety group, they are more concerned about not receiving appropriate medi-
cal care. The results of the multiple regression analysis for the three groups (48
questions), the mental health group, the anxiety group, and the happiness group,
show that when selecting the happiness group question as a dependent variable,
the subjects believe that they are not confident or proud of themselves. However,
they experience a lot of joy and success in their lives. When selecting the mental
health group question as a dependent variable, they believe that they are a person
who is helpful in the world and that optimism and life anxiety coexist. The results
of the covariance structure analysis show the following results. ’I am not as confi-
dent in myself as others, but I can do everything myself and am always in a good
mood and optimistic. I am fearful of infection with corona and the future state
of my life, I am restless and very nervous, and I want to escape from this current
situation. However, because of my optimistic nature by nature, I try to take those
fears easy.’
Rural people think “they are optimistic and have a sense of success in their own
lives without comparison to others, although they are concerned about infecting
their families and others around them.” This thinking may be because in rural areas,
despite their low educational background, they are in an environment where they
can live by their skills and belong to a community where the mentality is liberating.
This result may seem consistent with the point shown below in Chapter1. That is,
rural residents in developed countries tend to report higher subjective well-being
than urban residents. The paper tells that higher levels of connective social capital in
rural areas and higher access to natural amenities contribute to the paradox of well-
being in rural Denmark (Sørensen 2014).
Regarding “concern about infecting others around them,” there is no need for the
same mental and physical healing and health promotion measures as those taken for
people in urban areas. However, concerns about the risk of infection among family
members and villagers living as a community may be influenced by a lack of knowl-
edge about virus infection or by excessive fear spread by media reports about post-
infection symptoms. Therefore, correct knowledge provision may be necessary. In
Asia-Pacific Journal of Regional Science
1 3
particular, local governments would need taking on the role of explaining in detail
the risk of infection in sparsely populated rural areas and provide better protection
measures to reduce their fears.
From discussing the results of the analysis of urban and rural areas, I verify the
hypotheses. The hypothesis of this study is as follows:
“In Bali, Indonesia, a developing country, happiness degree under the corona
disaster is higher for rural residents than for urban residents.
Concerning happiness, the two regions have different views of how they perceive
happiness. It assumes that in urban areas, people feel happy that they are confident
that they are better than others, and in rural areas, people feel so glad that they are
successful and optimistic about their own lives, not in comparison to others. Fur-
thermore, when it examines these results together with those of the mental health
and anxiety groups, the results are loneliness and apathy in urban areas and nervous-
ness and anxiety in rural areas. In other words, urban subjects tend to be inwardly
solitary themselves, while rural subjects tend to be outwardly anxious about their
surroundings. The rural subjects may be optimistically spending the Corona Disaster
with outward anxiety, unlike the urban subjects, who are inward-looking and nega-
tive. The findings of this study on happiness under the corona disaster in developing
countries differ from those of previous studies. In other words, it does not neces-
sarily mean that people living in a developing country are happier living in urban
areas, and it is not possible to say whether people are happier in rural or urban areas.
And as in the previous study, the findings that people in rural areas worry about
the people around them are consistent with high levels of conjunctive social capital,
which may contribute to the paradox of happiness in rural areas. In other words,
it is possible that, as previous studies about the case of developed countries state,
the mental health of people in the developing countries targeted by this study has
a higher impact on happiness in rural areas than in urban areas. The results of this
study differ from previous studies reporting that people in rural areas of develop-
ing countries have lower levels of happiness and mental health. Life in rural areas,
where people are optimistic and not tormented by loneliness, is relatively humane
and healthy mental health.
Finally, I hope that the results of this study for developing country will help rural
life and that, together with the future enhancement of agricultural policies and infra-
structure, rural areas will develop as active, sustainable, mentally healthy, and happy
places to live.
Declarations
Conflict of interest The author declares no conflict of interest.
Informed consent The author and the local environmental foundation, Bali Biodiversitas, negotiated the
survey implementation to obtain subjects for this survey, as it is prohibited in Indonesia for foreigners to
conduct surveys on their own. The local environmental foundation, with which the author collaborated,
negotiated the implementation of the study. As a result, in villages that fully understood the purpose of
the survey and accepted its commission, the person in charge of the negotiations selected the subjects and
provided explanations. The foundation visited the indicated subjects and obtained their responses to the
1 3
Asia-Pacific Journal of Regional Science
questionnaire. For those who could not read, they read the questionnaires to them, and the foundation staff
filled in their oral responses.
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Authors and Aliations
YokoMayuzumi1
* Yoko Mayuzumi
mayuzumi@bunkyo.ac.jp
1 Bunkyo University, 5-6-1 Hanahata, Adachi, Tokyo121-0061, Japan
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
and institutional affiliations.
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the
author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article
is solely governed by the terms of such publishing agreement and applicable law.
... For example, Hanesaka and Hirano [3], Henning-Smith [4], and van Beek and Patulny [5] all indicated that people living in rural areas experienced relatively higher levels of loneliness during the pandemic. In contrast, Mayuzumi [6] stated that people who grew up in urban areas experienced loneliness more frequently during the pandemic. Meanwhile, Abshire et al. [7] and Henning-Smith et al. [8] reported no significant differences in loneliness based on geographic location during the pandemic. ...
... Therefore, COVID-19 preventative measures, such as social distancing and lockdowns, vary by region. Stricter measures in urban areas have limited social interaction and community engagement, leading to anxiety and isolation [1,6,25]. Although the use of technology and other communication devices have mitigated such feelings of isolation to some extent [8], they have not completely diminished the pre-existing social isolation and loneliness experienced in rural areas [3,5]. ...
... Furthermore, urban residents who began living alone were lonelier during the pandemic than their rural counterparts, which is consistent with the findings of Beere et al. [1], Mayuzumi [6], and Greteman et al. [44]. This further explains why people living alone tend to experience more disruptions in their daily activities due to COVID-19 restrictions. ...
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Importance Infectious diseases are mostly preventable but still pose a public health threat in the United States, where estimates of infectious diseases mortality are not available at the county level. Objective To estimate age-standardized mortality rates and trends by county from 1980 to 2014 from lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis. Design and Setting This study used deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Validated small-area estimation models were applied to these data to estimate county-level infectious disease mortality rates. Exposures County of residence. Main Outcomes and Measures Age-standardized mortality rates of lower respiratory infections, diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis by county, year, and sex. Results Between 1980 and 2014, there were 4 081 546 deaths due to infectious diseases recorded in the United States. In 2014, a total of 113 650 (95% uncertainty interval [UI], 108 764-117 942) deaths or a rate of 34.10 (95% UI, 32.63-35.38) deaths per 100 000 persons were due to infectious diseases in the United States compared to a total of 72 220 (95% UI, 69 887-74 712) deaths or a rate of 41.95 (95% UI, 40.52-43.42) deaths per 100 000 persons in 1980, an overall decrease of 18.73% (95% UI, 14.95%-23.33%). Lower respiratory infections were the leading cause of infectious diseases mortality in 2014 accounting for 26.87 (95% UI, 25.79-28.05) deaths per 100 000 persons (78.80% of total infectious diseases deaths). There were substantial differences among counties in death rates from all infectious diseases. Lower respiratory infection had the largest absolute mortality inequality among counties (difference between the 10th and 90th percentile of the distribution, 24.5 deaths per 100 000 persons). However, HIV/AIDS had the highest relative mortality inequality between counties (10.0 as the ratio of mortality rate in the 90th and 10th percentile of the distribution). Mortality from meningitis and tuberculosis decreased over the study period in all US counties. However, diarrheal diseases were the only cause of infectious diseases mortality to increase from 2000 to 2014, reaching a rate of 2.41 (95% UI, 0.86-2.67) deaths per 100 000 persons, with many counties of high mortality extending from Missouri to the northeastern region of the United States. Conclusions and Relevance Between 1980 and 2014, there were declines in mortality from most categories of infectious diseases, with large differences among US counties. However, over this time there was an increase in mortality for diarrheal diseases.