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Building a Quality of Life Index

Authors:
  • American Institute for Economic Research

Abstract

Scholars of economics, sociology, political science and social psychology have attempted to effectively quantify their definitions of quality of life in order to make meaningful observations of society and formulate policy prescriptions. Milbrath (1979) states that quality of life information is a useful policymaking tool because it can, “[I]dentify predicaments, provide value weightings, infer prospective project impacts, assess project outcomes, …suggest alternate lifestyles, [and] alert leaders to growing disaffection.” Our chapter explores the building and validation of an index measuring quality of life at the county level of the United States, and the policy questions it informs. We create a unique and data-driven approach to calculating quality of life, and focuses on both the data driven aggregate measures, and validates those measures using confirmatory factor analysis and experimental approaches. To our knowledge, no other county level index of quality of life has been developed, nor validated in this way.
Provisional chapter
Building a Quality of Life Index
Ryan M. Yonk, Josh Smith and Arthur Wardle
Additional information is available at the end of the chapter
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Provisional chapter
© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
10.5772/67821
Building a Quality of Life Index
Ryan M. Yonk, Josh Smith and Arthur Wardle
Additional information is available at the end of the chapter
Abstract
This chapter outlines how an index measuring quality of life should be developed and
then applies that work at the county level in the United States. The index we create is a
unique and data‐driven approach to calculating quality of life. To our knowledge, no
other county level index of quality of life has been developed. In the chapter, we explain
the process that leads us to selecting our ve indicators: public safety, health, economic
development, infrastructure, and education. Each indicator breaks apart into subindi
cators. Education, for example, includes several measures of education that together
create a valuable proxy for education. This chapter both theoretically and statistically
veries our chosen indicators and subindicators. First, we develop theoretical arguments
explaining the connections between quality of life and our indicators. Then, we perform
conrmatory factor analyses on our index to empirically verify our theoretical arguments
for why each component should be included in the index. Further, we nally verify our
theory and index using survey results. Given that indexes are often plagued by “gar
bage‐in garbage‐out” methodologies, we use only publicly available data to facilitate
replication by others. The results of our conrmatory factor analysis provide statistical
evidence for our choice of subindicators and indicators in measuring quality of life. Our
ndings indicate that those measuring quality of life must account for the roles of: public
safety, health, economic development, infrastructure, and education. Most importantly,
our results show that our index is a valid measure of quality of life. Our index has been
theoretically and empirically veried and those wishing to measure quality of life can
examine it for themselves and employ it in their own research.
Keywords: quality of life, index, institutions, government, public policy, political
behavior, well‐being, happiness
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© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
One of the central debates in the quality of life literature revolves around whether the indica‐
tors used to measure quality of life are “subjective” or “objective” in nature. Understanding
the division among the literature is a useful starting point for any aempt to create a quality
of life measure, particularly one designed to be used as a metric of success.
If the goal is to measure reections of individual preferences and aggregate those reected
preferences then measures of satisfaction, happiness, or other individually subjective psy
chological phenomenon are the appropriate choice. The problems with this approach are
substantial particularly if the end goal is a connection between particular policy choices and
societal (state, nation, or any relatively large group) level well‐being. Chief among these prob
lems are the transitory nature of life quality evaluations which are closely tied to individual
time sensitive circumstances and can be inuenced by cultural dierences, the survey itself,
or the simple vagaries of human emotions [1].
In short, the debate surrounding the objective and subjective issue focuses on dierences
in what is actually being measured. The objective measures represent environmental indi
cators that most people see as necessary conditions for a high quality of life, but they in
themselves are not sucient. On the other hand, subjective, micro measures only measure
a person's psychological perception of satisfaction and life quality, which may be inde
pendent of environmental conditions. If the overall goal is to create a common metric of
what a high quality of life community and society is then the starting point by necessity
must be identifying circumstances under which individuals thrive and whether common
circumstances, outcomes, and approaches can be identied that are common across indi
vidual transient preferences. Using “objective” measures allows us to build metrics that are
strongly rooted in theory and then test those propositions against subjective measures as a
validation tool.
2. Quality of life in the scholarly literature
In the literature, objective measures are dened as being based on aggregate population data
and have been advocated by such measures as the UNDP [2] in their Human Development
Index and the World Bank [3] in their World Development Indicators. Measures such as life
expectancy, adult literacy rates, student enrollment ratios, and gross domestic product per
capita are used to create the Human Development Index. The reasoning behind using these
measures is that the use of quantiable aggregate measures of economic, social, health or
other indicators is sucient to gauge the quality of life for a given population. Their usage
and ecacy also rest on the assumption that the indicators that are being measured are objec
tive in the sense that they are universally seen as desirable aributes.
On the other hand, subjective measures, such as those advocated by Brooks [4] and Gill [5],
place the measurement of quality of life in the psychological realm of satisfaction and overall
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Well-Being and Quality of Life2
happiness, which is only denable by the individual and thus can only be measured by the
use of surveys to individuals. Instead of measuring what they believe to be the most important
indicators of quality of life like the UNDP and World Bank do, Gill, for example, proposes
using surveys that ask the respondent to mark their level of overall quality of life on a scale
of 0–100 [5]. This allows for the respondents to create their own value weightings for all the
inputs into their lives. These results may be statistically combined to draw conclusions about
the aggregate population.
The literature however suggests the division might be less clearly delineated than a rst
blush might suggest. Costanza et al. assert that so‐called objective measures (of quality of life)
are actually proxies for experience identied through “subjective” associations of decision
makers”; and thus “the distinction between objective and subjective indicators is somewhat
illusory” [6]. Indeed, a recent review of common characteristics among countries with high
subjective well‐being measures looks strikingly similar to the list of indicators used to build
most objective measures [7].
The purpose in building a quality of life index should be to explore the substantive eects
of quality of life. This reality suggests the necessity of including only those indicators with
a theoretic basis for aecting individual citizens’ life quality. In what follows we review the
relevant literature for each of the subindicators, and explore how variation in those indicators
should aect life quality.
2.1. What is quality of life?
Scholars throughout the social sciences have aempted to dene and quantify their deni
tions of quality of life in order to make meaningful observations of society and to formu
late policy prescriptions. The literature on quality of life touches many areas of interest;
unfortunately, most of it has failed to connect the overlapping indicators and methods
from the various elds with each other to achieve a consensus on a denition of quality
of life and how to measure it. We have examined many of the past indexes that had been
created by other researchers. Each researcher found distinct aspects to include in the index,
often based on what the research was intended to study. Lambiri et al., compiled most
of the signicant studies, analyzed their similarities, and grouped them into six dierent
classications:
natural environment (climate, state of natural environment, etc.), built environment (type and state of
building, etc.), socio‐political environment (community life, political participation, etc.), local economic
environment (local income, unemployment, etc.), cultural and leisure environment (museums, restau‐
rants, etc.), public policy environment (safety, health care, education provision, etc.) [8].
We nd these distinctions useful in examining what the dierent studies used to measure the
quality of life. Using this classication system as a model, we examined other indexes and
found ve specic classications and a sixth category of other: public safety, health, infra
structure, education, economic environment, and other (anything included in the index that
did not t within the other four categories).
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Building a Quality of Life Index 3
3. Constructing a quality of life index
We believe that indexes should enable comparability and so should be designed to maximize
variation and comparisons between observations as well as individual observations across
time. We suggest a three‐step procedure to scale data into this index; for each variable we con
verted the actual value to a scale from 0 to 1. To accomplish this scaling, we used the well‐tested
and veried metric of the United Nations Human Development Index. The basic formula is
ObservedValue MinimumValue
__________________________
MaximumValue MinimumVal ue
Using this scaled value allows for direct comparability within the data set without any further
calculations. Because we convert each variable to this scale, we are no longer measuring the
actual results of a particular variable but rather the counties score in relation the maximum
and minimum observed for that value. This becomes important to the next step, where we
aggregate the data into subindicators.
Because the scaled variables now represent a ranking they can be aggregated using simple aver
ages and for each subindicator aggregate those values by taking an average of the county's score
on each of the variables included. The formula we suggest employ uses S as the scaled value
of the individual variable, and X as the total number of variables included in the subindicator.
After taking the average the data is scaled using the above formula to obtain the value of the
subindicator.
Using the value of the subindicators, the value of the overall indicator and quality of life score
can be calculated using the same mechanism.
3.1. Validating the index
The goal of creating a quality of life index (or really any index) must be validity. A critical
intersection for any index's validity is the data collected. Data must be theoretically relevant
to the indicators and uniformly available. Once the data are collected, a valid index must be
able to analyze that data and draw conclusions from it. The data found in quality of life study
indices can be used for a wide variety of purposes. Politicians can use them make beer pub
lic policy choices, businesses can use them for marketing purposes, and academics can use
them for research. If the data does not explain anything, it is of lile use. Thus, the data must
be presented in a way that it is informative. The methods used to construct the quality of life
index must also be easy to understand and replicate.
Any index, including our own, must be viewed skeptically. At the heart of the scientic
method and index building is the need for validation. Indexes can be plagued with measure‐
ment problems that center on whether they are actually measuring what they purport to be
measuring. The prelude to testing whether an index is measuring what it claims to measure
is to validate its methodologies.
This methodology for calculating quality of life scores yields a reliable and repeatable index.
This index can be calculated using commonly available data, where all parts of the index are
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Well-Being and Quality of Life4
separable. As discussed above, meeting these requirements is of paramount importance if the
data is to be used to explain phenomenon in the real world.
While methodological rigor is important, even the most rigorously constructed index can fail
if it does not measure what it purports to. We suggest a three‐prong approach to validating an
index. First, any index that claims to measure a social phenomenon must begin with a strong
theoretical explanation to back up why the data included in the index is in fact a component
of or a proxy for what is being measured. Second, the data included in the index should scale
together using some commonly accepted approach to analysis. Finally, independent tests of
the theoretical links such as secondary data analysis or experimental tests should validate the
construction of the index.
4. Where the rubber meets the road: deciding what to include
Despite the relative consistency, which emerged from the meta‐analysis conducted by Lambiri
and from our own review of the mechanics of the various quality of life indexes, deciding what
to actually include is substantially more complex. The categories which emerged from the lit
erature are Education, Health, Public Safety, Infrastructure, and Economic Development. In
each case, we suggest a two‐fold approach to measuring life quality that focuses on service
availability (potential in the private and/or public market) and outcome measures.
4.1. Public safety
Community‐wide safety and peace are important parts of the quality of life for residents. Crime,
lack of re protection, and deciencies in other services designed to protect security, well‐being,
and property impact citizens negatively. Public safety involves the prevention of and protection
from potential occurrences that could jeopardize the well‐being or security of the general public.
The majority of quality of life indices we examined included public safety measures and most
public safety measures included some element regarding crime. Most found some way of rep
resenting the amount of violent crime in the area: Graves used the number of violent crimes
per 100,000 [9]; Rosen simply uses the total crime rate [10]; Blomquist et al., Ceshire and
Hay, Stover and Leven, Ready, Burger, and Blomquist, Nzaku and Bukenya (even though
they place this measure in an “amenities” category), and Shapiro all use a measure of violent
crime in the area to measure public safety [11–16]. The Economist uses a measure of political
stability and security to measure the public safety between countries in their index [17]. Most
indexes simply include some measure of the frequency of crime, generally specied to be
violent crime, as the standard of measurement for public safety of an area.
To understand public safety, it is important to know the benets of public safety service avail
ability. We focus on two subindicators: the availability of police and re protection in each
area. The available data, dichotomous availability, had no explanatory power when compil
ing the index. Thus, we still believe the availability of these resources important but will only
include the funding eort data, which captures availability, in the nal data analysis.
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Building a Quality of Life Index 5
Fire services throughout the country are signicant in identifying, developing, and promot
ing ways and means of protecting life and property from re‐related perils, such as house,
school, car, and job‐related res, etc. In 2015, 3280 Americans died in 1.35 million res [18].
Shoup and Madema in their book Public Finance discuss the necessity of re service avail
ability for protection to life and property. The authors also specify re service's positive
role in contributing to economic development: “Risk, in the sense of relative dispersion of
possible outcomes of a venture, is reduced for almost any venture by an increment to re
protection service. All in all, re protection is clearly one of the most important stimuli to
economic growth” [19]. Clearly, the availability of local re services in each county is neces
sary in maintaining higher public safety, greater economic growth, and beer quality of life
for county residents.
The availability of police services in rural counties is an important contributor to the preven
tion of various types of property and violent crimes toward its residents. Police persons are
in charge of maintaining order, enforcing the law, and preventing and detecting crime for the
well‐being and safety of the citizens in their area. Mladenka and Hill discuss the importance
of distributing police services evenly among states in order to maintain public safety [20]. In
Gyimah's analysis of police production, he uses the crime rate to measure community safety.
Although somewhat obvious, his reasoning and empirical data simply show that when “the
crime rate is lower in community A than it is in community B, then it is reasonable to pos
tulate that community A is safer than community B” [21]. We can therefore determine that
people will have a higher quality of life with a greater amount of police service protection.
The use of this crime data in the analysis is necessary to arrive at a more accurate measure
of quality of life. It is obvious that the less frequent violent crimes occur in each county, the
greater the public safety will be. Cebula and Vedder did a quality of life study on how crime
aects peoples’ decisions when migrating to new areas. They state that “Higher crime rates
should lower net benets obtainable from migration in a number of ways: loss through theft
of property, higher insurance rates, an increase in fear and tension, etc.” [22]. Thus one can
determine that quality of life is usually lower in counties with higher crime rates.
While it is clear that the presence or absence of police and re protection is important to pub
lic safety in a particular area, it tells only part of the story. The whole story can be understood
only by examining the availability of funds to provide those services. We consider the avail‐
ability of funds for these services by using a measurement of per‐capita expenditures for re
and police services. We use this measure for two reasons. First, while spending of this sort
may be subject to the law of diminishing returns, we believe that as more is spent per person
on re and police services, the higher public safety will likely be. Second, it is clear that even
in areas with higher crime rates, residents perceive additional police spending as contribut
ing positively to public safety. According to Charney, “public [safety] expenditures reect
both the quality and cost of providing public services,” even if “public [safety] expenditures
are not a perfect measure of the quality of public services.” For example, a county with high
public safety expenditures could signify an area that demands more safety spending, “rather
than measuring a high feeling of safety” [23]. Even though this is a dicult measure of public
safety quality, county residents will still have a greater amount of re and police protection if
more money is spent per capita for these public services.
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Well-Being and Quality of Life6
The amount of countywide per‐capita expenditures on re and police services can act as prox
ies for other county spending on public safety, such as ambulance services and correctional
facilities. If the data shows that a county puts a high priority on public safety by spending
more per capita on re and police services than average, it is presumably true that the county
will also spend more per capita on these other public safety services. For example, spending
on ambulance services in rural counties is important for the health and life expectancy of its
residents. The service's role is to help maintain the life of the injured/dying until transported
to the nearest hospital for emergency care. According to Stults et al., communities served by a
basic ambulance service, as opposed to conventional advanced ambulance care, have a lower
survival rate [24]. From this, one can also verify that counties’ public safety will be much
lower if access to ambulance services is scarce.
Public safety is a crucial indicator in determining quality of life. Public safety, as dened ear
lier, involves the prevention of and protection from potential occurrences that could jeopar
dize the well‐being or security of the general public. We believe that the measurement of these
types of services designed to protect the security, well‐being, and property of county resi
dents is necessary in order to have a valuable quality of life index. We conclude that county
residents with greater public safety will also have a greater quality of life.
4.2. Health
It is dicult, or untenable at best, for someone to have a good quality of life if they are living
in unhealthy conditions or do not have access to quality health care. Maslow underscored the
signicance of good health when he placed physiological needs at the base of his hierarchy of
needs in his explanation of human motivation [25].
The measures of health in quality of life indexes were less uniform than the public safety
measurement. Although a common theme was to use mortality rates or life expectancy, this
is certainly not the only way that researchers chose to examine this element of quality of life.
Calvert and Henderson chose to use a composite that includes the infant mortality rate, the life
expectancy rate, and self‐reported health [26]. The Economist uses the life expectancy at birth
in years for the health indicator [17]. Suan simply uses the infant mortality rate [27]. Agostini
and Richardson combine infant mortality, child mortality, and maternal mortality to measure
public health [28]. The majority of the quality of life literature that was reviewed for this study
includes a measure of health as an indicator, and inclusion in our own index was important.
Review in the health measurement literature uncovered some interesting intellectual
debates surrounding the demand for health care. Newhouse and Hitiris and Posne make
the assertion that since per‐capita health expenditures follow GDP fairly closely, health
expenditure consumption is elastic, indeed elastic enough that it is a luxury good since its
income elasticity of demand coecient is greater than 1.0 [29, 30]. This implies there is a lot
of spending in health care that only marginally improves quality of life and an increase in
funding will not necessarily result in an increase in care. The counter to this claim is that
since health care represents a basic human need it must be a necessity and an inelastic good.
Parkin asserts that the claim of its being a luxury good can only be measured as a luxury by
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Building a Quality of Life Index 7
incorrectly applying microeconomic data to a macroeconomic problem [31]. We agree with
portions of both arguments and eventually came to the same conclusion as Geen who
views health care expenditures as both a necessity and a luxury which can vary with the
level of analysis [32]. On the micro level, health care is a necessity at rst because a certain
level of care is essential, and thus inelastic. However, due to diminishing marginal returns
there is a point reached where health expenditures become a luxury, even on the micro
level. While we are not sure where this point of diminishing returns is, there is some level
of health expenditure that is a necessity that must be funded in order to have a good quality
of life. The indicators are designed to capture the aggregate health care system to determine
if it aords individuals at least the necessary level of care needed, if not also desired luxury
health goods.
To capture an aggregate measure of the health system in the test counties, we rst use a mea
sure of the availability of professional health workers. Our measure includes physicians per
1000 and health care workers per 1000 to assess this availability. Originally, we had hoped to
use measures of hospitals per 1000 and hospital beds per 1000 in addition to the number of
professionals, but that data was not available at the county level. However, since health care
requires very specic and well‐practiced skills, we assume that the more of these health care
workers there are in a population, the more likely it is that they will have facilities to work in.
This measure is sucient to furnish a snapshot of the availability of health care facilities that
we believe to be most vital to a good quality of life.
We do, however, acknowledge that there may be other factors that may also be indicators
of the health of a population other than physical facility access. Socioeconomic status, edu
cational aainment, and cultural factors have all been shown in some cases to be the single
greatest determinant of health status [33–35]. The most important of these factors are covered
elsewhere in our index and should therefore not confound nal results.
While having health facilities readily available is important, the existence of the facilities is of
marginal value if people do not have the resources, primarily health insurance, required to be
treated in the facilities. We use a measure of health insurance enrollment to help determine
accessibility. The number of people with health insurance in a community reects a measure
of access to care and is valuable to the study. The measure that we use to show the insurance
rate is taken from the U.S. Census data and includes all forms of insurance including govern
ment programs such as Medicaid and Medicare. While it may be true that there are aws
associated with the insurance system in the country, such as overconsumption as outlined
by Feldstein [36], the level of insurance in a county helps us to determine what portion of the
population is at least having their basic health needs met.
After considering access to health care through availability and insurance, we examine what
health‐related outcomes are being produced from access to that care. There is a debate in the
literature concerning what the most telling measure of health should consist of. Some scholars
argue that today's unique circumstances warrant breaking with traditional measures of health
that have mainly dealt with morbidity and mortality and also take into account “diseases of
civilization” like obesity and depression that have recently appeared as society has become
more developed [37]. It is their belief that even though there might be longer life spans and
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Well-Being and Quality of Life8
less infant deaths in developed societies, that does not mean that the health of the people is
any beer o since they see these new diseases as a drain on quality of life.
However, it is our view that while these may be real threats to the well‐being of individuals,
their inclusion in this measure would be very dicult to achieve since that data is not con
sistently available and they aect individual populations dierentially. While our measure
may not capture a complete picture overall health in a specic area, it does capture a suf
cient portion of the whole system as infant mortality is a particularly telling indicator of
care. It is also easily accessible for every area we looked at and universal in its application,
whereas the inclusion of other subjective indicators would have to be more area specic.
We decided to use a measure of health outcomes that was the most objective possible.
Nearly every study we looked at used infant mortality measures in one form or another,
including the UNDP's Human Development report [2]. Consequentially, we also decided
to use infant mortality as the basis of our health outcomes measures. This indicator is also
one of the most obvious and observable results of a good, accessible health care infrastruc
ture that was measured earlier. Our initial measurements of the availability of physicians
and hospital beds are directly connected to infant mortality and with life expectancy that
we measured in this area. Hospitals and their services are vital to helping mothers give
birth to children and combating chronic sickness that often appear in the later years of
life. While some scholars would argue that a beer measure of health outcomes would be
broader than ours, very few would argue that infant mortality is not one of the most tell
ing individual indicators of health. This measure captures the availability of nonluxury
health care.
Health services that are readily available could still be inadequate to properly serve the
needs of the patients. Health services need adequate funding to be able to function well. We
measure the health services funding eort in order to determine if the services are being
adequately funded and given every chance to succeed. This measure includes the overall
per‐capita health expenditures by government agencies and the total amount spent on pay
roll in health care professions. Funding for health related services is not cheap. Some esti
mates place the total yearly spending in the U.S. around $3 trillion or nearly 20% of GDP.
By capturing this funding information we was able get a beer understanding of the health
services in the targeted areas. This then allows basic health care, which would impact the
health outcome indicators of life expectancy and infant mortality, to be measured. Basic
health care is dened in various ways, but for simplicity purposes we dene it as access to
the services and procedures that sustain life and impact of the health outcome indicators. If
a person has access to basic health care, we assume they would have a greater probability of
surviving birth and living to an older age. As summarized earlier, we realize that the amount
of funding does not guarantee quality since there is a real potential to waste the funds after
they reach the point of diminishing returns. By our reasoning, a higher level of funding indi
cates a higher likelihood that those basic needs will be lled even if there is waste happening
elsewhere. There is good literature that indicates that higher expenditures on health care are
linked to beer health results [38]. Poland et al. also seems to agree that higher expenditures
should produce beer health outcomes [39]. We feel that the measurement of the funding
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Building a Quality of Life Index 9
eort for health services provides the reader with an overview of the system without making
normative judgments.
In sum, we chose to use the measures we did because they are the best way for us to capture
the availability of and access to health services in a given area. It encompasses the causes as
well as the consequences of a good health system and allows us to see its impact on the overall
quality of life in a dened area.
4.3. Infrastructure
Infrastructure that functions eciently and eectively is another positive component to qual
ity of life. Infrastructure is the physical and organizational structures needed for operation of
a societal structure or the services and facilities necessary for an economy to function. Basic
infrastructure facilitates economic transactions, allows access to services such as health and
education, and provides individuals with the ability to realize their preferences for goods and
services across time and space.
There was not a large consensus regarding what data best represent infrastructure. In gen
eral, the indexes aempt to quantify this by examining three things: population character
istics, available utilities, and housing characteristics. Both Rosen and Roback examine the
population size, and the population density, but uniquely include central city population
and population growth rate, respectively [10, 40]. Nzaku and Bukenya use a composite that
includes population density with age of the population, nonwhite population, owner‐occu
pied housing, per‐capita tax rate, distance to metro area, and road density [15]. Still other
indexes include a measure of the available facilities for the treatment of water, sewage, or
landlls [11, 13, 14, 26].
Our metric captures the various types of infrastructures that are necessary for individuals to
maximize the other indicators of the index and their quality of life. To measure infrastruc
ture, we use both service availability and funding eort that is the existence of the infrastruc
ture and the resources devoted to its expansion, maintenance, and replacement. Measured
infrastructure could include a wide variety of public services. We have chosen to use three
indicators that we believe capture what is essential to improving quality of life. Our metric
represents an expansion of earlier work that has primarily focused on the provision of public
or quasi‐public goods such as highways as infrastructure. We assert that a more expansive
denition of infrastructure is necessary. Our metric both recognizes the importance of the
public or quasi‐public goods to infrastructure and adds private or toll goods to the measure of
infrastructure. These indicators—culinary water, grid fuel, and telephone—are measured as
the percentage of households with these services directly available in their homes. This pen
etration metric, which uses end consumer access as a proxy for general service availability,
provides a clear picture of the development of infrastructure and allows for dierentiation
between areas where most residents have access and other areas where most do not.
The systemic availability of culinary water—also known as domestic water, drinking water,
or potable water—is a large contributor to the well‐being of those with the service. Culinary
water is the water suitable for human consumption or use in the preparation of food. The
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Well-Being and Quality of Life10
study measures the percentage of households per county with culinary water access directly
in their homes from a communal source. We conclude that households with culinary water
communally available will have a higher quality of life and that counties with higher per‐
centages of culinary water penetration will aract more residents and more development.
Howard and Bartram support this assertion, and they indicate that signicant benets are
available as culinary water services are more accessible, namely advances in greater public
health and sanitation [41]. The percentage of grid culinary water availability per county is also
a proxy for government involvement and spending in that specic county. Because grid culi
nary water is primarily a government service, we assert that a greater percentage availability
of grid culinary water in a particular county also translates to a greater amount of other gov‐
ernment provided infrastructure in that county. For example, municipal solid waste (MSW)
services and sewer services are not recorded in the data but are highly correlated with grid
culinary water provision, and because culinary water is highly correlated to the provision of
MSW and sewer services, counties with grid culinary water are also likely to provide MSW
and sewer services as well. Sewer systems collect sewage waste from local buildings and are
later used to either dispose of or treat the sewage for sanitary purposes. Having available
sewer systems and MSW services provides greater sanitation and health to the community.
Furthermore, a major source of water used to create culinary water is ground water, and
according to Miranda et al., MSW services are important in reducing groundwater contami
nation as well as reducing other solid and hazardous waste material [42].
The second measure of infrastructure availability is access to grid fuel. Having access to grid
fuel is a signicant measure of a county's development, and unlike the earlier measure of
grid water is likely to be provided by private sources over public ones. Grid fuel is primarily
natural gas, although there are other types of grid fuel used less commonly. Having house
hold access to these fuels is a positive measure of residents’ quality of life. The benets of
household access include the direct inux of fuel for heating or cooking without having to
actively seek the fuel; all the residents must do is pay a monthly bill. Rothfarb et al. argue for
the importance of a well‐organized system in providing natural gas to US households and
business, due to their great “depend[ence] on gas for heating and other essential services.”
The authors discuss the greater availability and reduced cost benets consumers receive with
beer developed and systematized grid fuel systems [43].
Our nal measure of infrastructure service availability is the household penetration of tele
communication. Although this is not as strong of an indicator as the other two used, we
believe it to be a useful measurement nonetheless. Hudson explains very well the quality of
life advantages of telecommunication availability:
Telecommunications is a tool for the conveyance of information, and thus can be critical to the de‐
velopment process. By providing information links between urban and rural areas and among rural
residents, telecommunications can overcome distance barriers, which hamper rural development. Ac‐
cess to information is key to many development activities, including agriculture, industry, shipping,
education, health and social services [44].
Without telecommunications access, it is more difficult for residents to receive and con
vey necessary information for their day‐to‐day transactions. In addition, household
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Building a Quality of Life Index 11
telecommunications availability also presents access to minimum low‐speed internet.
Having at least dial‐up internet available in the home can provide important communi
cation and information access. Strover states the significance of “adequate connections
to advances telecommunications infrastructure and services [for] rural communities…to
be able to fully participate in the emerging information economy” [45].
While it is clear that the presence or absence of our selected proxies and their penetration rates
provides an important picture of the level of development of infrastructure in a particular
county, it tells only part of the story. The rest of the story can be understood only by examin
ing the availability of funds to provide infrastructure. While our rst set of measures speaks
to the level of development of a county's infrastructure, our second set of measures speaks to
the nancial resources available for infrastructure and how those resources are being used.
To capture both the presence and absence of infrastructure, we also analyzed the funding
that is available to each county that could be used to develop infrastructure, measured both
as a function of the total land area in a county and as a per‐capita measure. This distinction is
important as both dierences in size and population create diering infrastructure needs. We
use utility bonding numbers and transportation expenditures as proxies for the larger suite
of infrastructure goods. Using these proxies allows for both a measurement of spending on
immediate needs—transportation—and longer‐term needs—utility bonding. This combina
tion provides evidence for the level of investment in infrastructure. Both measures are popu‐
lation controlled to ensure the opportunity of intercounty comparisons.
We measure the public transportation spending per capita for all US counties. Public trans
portation can include subways, buses, streetcars, light‐rail transit, or the most common form
of highway funding. Higher spending on all types of public transportation provides a higher
quality of life to its residents than do counties with lower per‐capita spending on transporta
tion. Transportation spending has a myriad of benets in facilitating business, recreation, social
and family, emergency health, and education travel, etc. A key element of transportation infra
structure spending in dealing with economic development is the amount of highway spending
allocated by each county. In an economic growth study by Dye, he states that “highway spend
ing emerges as the strongest correlate of economic growth” because of its ability to facilitate
commerce and transportation [46]. A few of the major benets of having a well‐developed
highway system include the “expansion of existing business, araction of new business, and
tourism growth, […] increasing business productivity over time associated with reducing ship
ping costs,” and reduced travel times [47]. Residents’ opportunity for greater productivity and
a higher quality of life are signicantly increased by counties that spend more on highways.
Not measured in the data, yet highly correlated with transportation spending, is the avail
ability of transit and airport services. If more funding is allocated for transportation by a
county, it is very likely that transit services will be oered as well. The availability of local
public transit services is a positive contributor to quality of life. For various reasons, numer
ous county residents might not have access to private transportation or the ability to travel
on their own. Public transportation, whether by bus or rail, is signicant to their well‐being
when traveling to and from home to work, to shop, or to study, etc. Baum‐Snow et al. explain
a number of benets to having public transit accessible in their 2000 article: “…beer transit
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Well-Being and Quality of Life12
may disproportionately improve the quality of life and the quality of job opportunities….
Public transit potentially increases the access of the poor to beer labor market opportunities.
This comes in addition to reduce commuting times for people served by beer transit.” They
also add public transit's contribution to reducing air pollution [48].
The benets of airport services are associated with transportation spending in that counties
with transportation spending as a priority will likely have similar reasoning to provide air‐
port services as well. Counties with airport availability provide advantages to the quality of
life of its residents more than those counties who do not oer the service. The benets of hav
ing a local airport, mentioned by Newkirk and Casavant, “include economic development,
health care and emergency medical services, support of business and commerce, recreation,
community activities, enriched community life…. [These] themes support the strong conclu
sion that rural airports clearly improve the quality of life in rural communities” [49].
The more developed infrastructure accessible to county residents, the more it can achieve the
desired economic development that brings the greatest opportunity to the people within the
county. These advantages include greater access to transportation, communication, house
hold energy, water, activities, etc. A well‐constructed index that purports to measure quality
of life must include a coherent measure of the infrastructure.
4.4. Education
The quality of an education system in a county is a telling indicator of the quality of life in that
area. And since quality of life is connected to education, its quality is an indicator of what the
future will hold for an area. Areas with beer education systems have been shown to have
higher levels of educational aainment, and as a consequence, higher income [50].
Roughly half of the indexes that we examined included some measure of educational quality.
The most common way to represent this was including a measure of the ratio of students to
teachers [11, 13, 14, 51]. Other studies include input‐based measurements like cost‐adjusted
per pupil, and library circulation in number of books [52]. Others look at outputs of education:
percent of children in secondary school [27], or mean year of schooling, number of 16 year
olds enrolled in school, and college and postcollege graduates [28]. Calvert and Henderson
created a composite variable made of educational aainment levels, educational expendi
tures, literacy rates, access to education, distribution, segregation, discrimination, lifelong
learning, and alternative education [26].
In our measure of education as an indicator of overall quality of life, we capture a measure
of the availability of educational services. We look at the services that are oered in public
schools in order to determine if the schools are fullling the educational needs of the largest
number of students possible. One of the programs that we measure is the availability of col‐
lege preparation courses like Advanced Placement, International Baccalaureate, or concurrent
enrollment for college credit while still in high school. This allows us to capture a measure
of the needs fulllment for advanced students that could be held back from reaching their
potential if these courses are not oered and they are kept with the bulk of the students
in classes that don't challenge them. We also capture a measure of the needs fulllment of
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Building a Quality of Life Index 13
the students in a school system that may need extra assistance to succeed. The availability
of a Limited English Procient (LEP) program is measured to account for the ever‐growing
number of students who need extra help with English due to the diversity of home‐spoken
languages. In addition, we measure the availability of special education services to help those
students with special needs.
Also in our measure of service availability for education, we measure the access that people
in a particular county have to higher education. There is a myriad of literature on the benets
of higher education to individuals and society [50] and the citation here of the full literature
would be superuous. We assume and the literature concurs that the proximity and availabil
ity of higher education make taking advantage of its benets easier for the local population
and it is a positive aribute to have access to higher education. As education system becomes
increasingly competitive in aempts to capture previously untapped markets, new technolo
gies and eorts are being made to make higher education available to increasingly isolated
places [53]. We expect to see access to higher education to continue to expand to the benet of
the local citizens in most counties.
The nal measures of availability that we used are of the presence of charter schools in a
county, as well as other education services oered such as private schools. The presence of
charter schools is measured by the annual survey done by the National Center for Education
Statistics and the measure of other education services is obtained from the U.S. Census data.
The presence of either or both of these indicators represents eorts by the local government
and population to oer services that can be invaluable to those that take advantage of them.
While charter and private schools are not designed to be to the benet of everyone, those
who wish to take advantage of their service often feel it is very important and can strongly
inuence their academic performance. It is also claimed by some that the presence of choices
within the education system is healthy as it usually fosters competition [54] and increased
eciency with funding [55].
If an area has a good education system, many studies assert they should have positive out
comes from that system to show for it [50]. In the aempt to determine if an area has these
positive outcomes, we use a number of dierent indicators to measure the education system's
impact. We rst looked at the dropout rate in the local secondary schools. A student is dened
as a dropout if he or she is between the ages of 16 and 19, has not graduated from high school,
and is not enrolled. Those who t this category have either failed the system or been failed by
the system, neither of which tells of a promising quality of life in an area. We expect to see a
lower dropout rate in areas with beer education systems. Another outcome of a good educa
tion system is the number of persons enrolled in higher education. We use U.S. Census data
to get this indicator that measures all the previous year's high school seniors who are enrolled
in higher education and also the number of any others who are enrolled in higher education
in the county. This allows us to see both the level of high schools students going on to aend
college and also the total number of people enrolled in higher education in a given area.
The nal outcome that we captured by this method is the education level of the population in
the given county. Using U.S. Census data, we are able to capture the percent of the population
that has graduated from high school, the percent that has graduated college, and the percent
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Well-Being and Quality of Life14
that has obtained an advanced degree. This allows us to determine the level of education of
the whole community, which is important to understand how much an area values education
and its impacts. We suspect that a higher level of education in the community at large will
correlate with the other indicators of quality of life.
Our measure of educational availability, funding, and outcomes gives an eective and signi
cant measure of the education system. This measure allows us to adequately account for the
education system of an area since, as Lyson notes, education “serves as an important marker
of social and economic viability and vitality” [56].
4.5. Economic environment
Economic development is a necessary indicator when determining quality of life. Economic
development can be dened as eorts that seek to improve the economic well‐being and
quality of life for a community by creating and/or retaining jobs and increasing incomes. It is
the institutional changes made to promote economic beerment and the social organizational
changes made to promote growth in an economy.
Every index we reviewed included some measure of economic conditions but dierent
indexes use dierent indicators to capture this information. The Economist [17] used GDP
per person and percent unemployment; Roback uses the unemployment rate, as does Rosen
although Rosen includes population growth as part of the index [10, 40]. In contrast, Agostini
and Richardson capture the economic environment using the real per‐capita income [28].
We have chosen to use and gather data for three categories that we believe to best determine
the county residents’ quality of life level, namely the availability of services, economic out
comes—such as per‐capita income and the unemployment rate—and availability of private
capital for the rural counties. The following paragraphs will support our argument that the
more economically developed a county is, the higher quality of life its residents have.
How accessible services are in each county aects the quality of life of its residents. To mea
sure service availability, we focus on the total number of employers and the number of new
businesses per year in each county.
Employment is one of the most fundamental measurements of economic development. When
unemployment is high, it creates a downward spiral in a community's economy: the unemployed
residents cannot receive an income, which reduces consumer spending, which in turn reduces
industry earnings, creating fewer jobs, and so on. Thus, a healthy economy arrives close to full
employment, generating more consumer spending and industry growth in the community. We
chose to measure the total number of employers in each county as an economic quality of life
indicator because when more opportunities are available for resident employment, residents
have the ability to receive their desired income with greater facility. Hence, they will be able to
beer satisfy their needs and wants.
By measuring the total number of employers, the number of individual businesses within
the community can be determined. Wennekers and Thurik assert that the positive economic
eects from the number of small rms within a community include: “routes of innovation,
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Building a Quality of Life Index 15
industry dynamics and job generation” as well as “a lower propensity to export employment,
a qualitative change in the demand for capital, and more variety in the supply of products
and services” [57].
The greater number of new businesses established each year is also linked to a higher qual
ity of life for residents in the counties we researched. Buchanan and Ellis list entrepre
neurship, the creation and development of new businesses, as one of the basic factors that
pushes economic development [58]. When more businesses are created, more opportunity
for employment is available for the residents. Business expansion can also be evidence of
more capital availability and greater response to higher consumer demand. We measured
and recorded data on how many new establishments were created in each county per year
to capture the entrepreneurship that is occurring in each of the counties. To calculate this
activity, we take the number of businesses that existed the previous year and subtract the
current year's business count.
Reduced employment opportunities, due to poor business creation and diversication within
a county, create the necessity to travel for employment. We measured data on the number of
county residents who travel for employment by determining the commute time and destina‐
tion. These measures indicate the investment of time people are making for desired employ
ment. To measure destination, we measured the percent of residents employed outside of
a county. From this measurement, we can conclude that a greater percentile of residents
employed outside the county of residence is indicative of a lower level of economic develop‐
ment in that county. Khan et al. explain the eects of commuting on individual economic
growth: “if economic growth elsewhere raises an individual's earning prospects, the indi
vidual will move, but if the individual can exploit economic growth elsewhere by commut
ing, he will not need to move to gain from the expansion” [59]. In other literature, Shields
and Swenson conducted research on 65 Pennsylvania counties to determine how commuters
balance employment and wage opportunities with relation to housing prices and travel costs.
The results suggest that the “proportion of jobs lled by in‐commuters varies by industry”
[60]. This is an important factor because it illustrates why counties should focus on industry
diversity when aracting businesses in order to best capture all types of employment.
In determining the level of economic development of counties, we have chosen three indi
cators: economic diversity, per‐capita income, and unemployment rate. Quantifying these
variables will help us beer measure resident standard of living as well as economic growth
by county.
The more diversied business in a county, the higher the opportunity for the residents to
have a higher quality of life. For example, consider a county with mining as its sole industry.
If resources were exhausted or a natural accident occurred that made it impossible to ours,
the county and its residents’ quality of life would decrease substantially. A book by Phillips
supports this example in stating that economic diversity is vital to sustaining development in
rural areas because of the negative eects of the boom and bust cycles [61]. In this data, we
used Hachman's method to determine the economic diversity score. We therefore conclude
that a county that has employment and business across diverse industries is more economi‐
cally developed and can provide a higher quality of life for its residents [62].
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Well-Being and Quality of Life16
Per‐capita income is one of the most obvious and routinely used indicators of quality of
life. Those who have a higher per‐capita income have more funds to purchase the neces
sities as well as more disposable income to purchase luxuries. Lucas, in his study “On the
Mechanics of Economic Development,” argues that per‐capita income is the best indicator of
economic development [63]. However, Alpert reminds us that that per‐capita income is not
an all‐encompassing indicator when determining the degree of economic development [64].
We agree, and our index reects that conclusion, per‐capita income is important, but not suf
cient in determining economic quality of life.
The unemployment rate is another indicator of how economically developed a county is. This
measurement has been used in many quality of life studies: a lower unemployment rate provides
more opportunities for residents to nd jobs, which leads to higher quality of life. Phillips argues
the unemployment rate is an important indicator in determining economic development. He
states both the need for both “basic and nonbasic employment: basic jobs are those that bring new
money into the economy” whereas “nonbasic jobs are those that recycle money through the local
economy” [61].
The nal indicator seeks to measure the availability of capital in counties. Capital availabil
ity is a vital part of any county's economic development as it represents the potential funds
that can be used to hire workers, develop infrastructure, and power the engine of economic
growth. We used total deposits in commercial banks, manufacturing capital expenditures,
and total annual payroll of all industries as the indicators.
The greater the total deposits in local commercial banks, the greater the funds readily available
for use in entrepreneurial activities, for larger scale business investment, and for private invest
ment on homes/home improvement and automobiles, and so on. Low et al. explain the positive
correlation between bank deposits and entrepreneurial growth, emphasizing the eects of bank
deposits on “creat[ing] loanable funds that could help regional entrepreneurs invest and grow
further” [65].
Although funding availability through deposits in commercial banks is useful in community
economic development, simple capital availability does not necessarily indicate productive
potential use of the capital. Capital has a multiplicative eect when it is invested and put to
use that cannot occur when it is simply held in reserve. The measurement of manufacturing
capital expenditures is a valuable measurement of capital use and availability in economic
development because it illustrates how businesses apply their capital. Measuring manufac
turing capital expenditures is valuable in providing evidence of business growth and produc‐
tivity within distinct communities due to local capital investment.
Our nal subindicator measures the total annual payroll of all industries for each county.
This measure, which indicates the amount of money businesses allocate to paying employees
each year, is evidence of industry growth or decline. Greater payroll indicates an expansion in
the local community because industries have additional funds to pay employees after cover‐
ing their costs and other nancial obligations. Payroll can also indicate the quality of human
capital available in the county: employees with higher degrees and work experience receive
higher wages. With greater payroll provided to employees, greater opportunity for private
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Building a Quality of Life Index 17
capital investment is available as well. The reverse is also true, as noted by Eberts and Fogarty:
“as private investment increases, demand for labor and thus payrolls also increase, expanding
the income of the local economy” [66]. Thus, with more private capital availability, oppor
tunity for growth and development increases, creating a greater quality of life for residents.
As described above, economic development can be dened as eorts that seek to improve
the economic well‐being and quality of life for a community by creating and/or retaining jobs
and increasing incomes. From the three areas discussed above—service availability, economic
outcomes, and private capital availability—I was able to establish the advantages to having an
economically developed county. We can therefore see that residents living in a county with a
more advanced level of economic development will have a beer quality of life than of those
whose county is less economically developed.
4.6. Other indicators
Although many of the indexes examined had variables that t well within these categories,
there were usually a few that did not. Some used a variety of dierent indicators, but there
were a few similar indicators that repeatedly showed throughout the literature. One of the
most prevalent indicators was weather and environment in general. Many indexes examined
the amount of pollution, the type of weather, the location, or other positive aspects of the
natural environment. Many tried to capture a social environment, like Shapiro who mea
sured the number of restaurants in an area [16]. Florida aempts to measure the many uncon
ventional aspects of an area, including the homosexual population, the number of bars and
nightclubs, the amount nonprot art museums and galleries, the number public golf courses
among a host of other factors [67].
The factors that seek to extend the explanation of quality of life beyond our ve included
indexes and the natural environment are not particularly useful and in our opinion should
not be included in quality of life metric as they are not consistently included across studies of
quality of life, and represent idiosyncratic conceptions of what life quality is.
5. Empirical validation
Properly constructing an index requires a bit of a balancing act. While the index must include
enough variables to capture a reasonably complete picture of what is purportedly being mea
sured, adding unnecessary variables introduces noise to the index and dilutes the explanatory
value of other variables. To achieve that balance, strong theoretical justications must exist for
the inclusion of each variable. This was done in the previous section. After constructing a theo
retical basis and collecting data, the resulting index can be statistically and empirically veed
to further establish its validity. First, the collected data should behave as the theory predicts.
Second, the index should mirror how individuals actually comprehend their own life quality.
To conrm that our index behaves as expected, we performed both a conrmatory factor
analysis. To verify that the index reects real people's life quality, we used a survey.
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Well-Being and Quality of Life18
5.1. Conrmatory factor analysis
In building the index, we have aempted to compile a set of indicators that all contribute to
quality of life in predictable directions. Conrmatory factor analysis is a statistical tool used
to ascertain whether a set of observed variables (the indicators, in our case) are commonly cor
related with another unobserved factor (quality of life). To conrm that this index is indeed
measuring quality of life, each of the indicators should return a positive value and, because
we elected not to weight any of the indicators, each should return a similar value. Table 1
reports the results of our conrmatory factor analysis.
In our factor analysis, two factors are retained, factor one oering evidence in support of our
hypothesis. Positive numbers ranging from .42 to .62 suggest that the indicators truly are
related to some common underlying trend, while the high uniqueness values indicate that
each provides unique information about that trend, rather than merely reiterating informa
tion already captured by another variable.
5.2. Survey
To oer empirical evidence that the construction of the index is appropriate, we conducted
a survey of undergraduate students in ve classes, most of which were general credit classes
and all of which covered social science topics. The students in these classes included all stu
dent years (freshman‐senior) and all sorts of majors.
The survey collected demographic, university, and political information about each partici
pant, asked to rank their own situation along each of the index's indicators, and nally to scale
their own quality of life. The middle of the survey also included a distraction, which asked
students about their knowledge of the school, its governance, and whether they would sup
port a fee proposal. For the index to be valid, the ratings oered by survey participants for the
indicators ought to align with the overall quality of life score. After collecting data from the
surveys, we conducted two OLogit regressions, one with the indicator and the other without
it. The results of these regressions are included in Table 2.
The coecients displayed in Table 2 demonstrate that the indicators are indeed associated
with a signicant improvement in quality of life. The Psuedo R Square for the model includ
ing the indicators, as reported above, is .2254, compared to only .0440 in the controls‐only
Variable Factor 1 Factor 2 Uniqueness
Education .5122 −.1826 .7043
Public safety .5326 −.0702 .7114
Infrastructure .6135 .0588 .6202
Health .4294 .2141 .7697
Economic development .6094 .0047 .6286
Table 1. Conrmatory factor analysis.
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Building a Quality of Life Index 19
model. We conclude that the indicators lend signicant explanatory power to the model, and
that the index therefore captures with ample veracity what it claims to capture.
Using both the conrmatory factor analysis and the ordered logit regressions to back up the
theoretical relationships discussed earlier, we have demonstrated that the index is a reliable
and meaningful measure of the quality of life.
6. Conclusion
The initial impetus behind this project was a desire to beer understand the relationship
between public policy outcome and the life quality of individual citizens. Claims about the
impact particular policy decisions have on livability, well‐being, and quality of life are com
monly invoked by policy maker and politicians as justications for particular policy choices.
There has however been lile substantive quantitative work on measuring these somewhat
amorphous concepts.
Our interest in these questions grew in large part form from claims about the impact of
publically owned lands on the quality of life of residents who live and work among them.
Numerous claims that public lands positively impacted the well‐being of citizens both eco
nomically and in nonquantiable ways are replete in these policy discussions. We found these
claims intriguing and warranting a more in‐depth examination.
This exploration led us to the central research question of this chapter, how do you mea
sure life quality? We found that the concept of life quality and its measurement has been
discussed and debated among scholars of various elds for many years, and, while there
are a variety of positions advocated by various disciplines, there appears to be an emerging
consensus regarding its importance, but not its measurement. While the limits of quantitative
measurement of life quality are clear, namely that they are by their very nature an abstraction,
they provide a metric where by claims of policy makers and politicians that particular policy
approaches are beer for life quality can be evaluated. Our approach provides one such tool
for policy evaluation.
3 Coef Standard error P score
Personal safety .3429 .1785 .05*
Infrastructure .2753 .1080 .01**
Economic .1503 .0739 .04*
Health .3505 .1208 .00**
Education .7941 .1246 .00**
N = 258 Pseudo R Square: .2254.
*Control variables excluded from the table.
Table 2. Survey results‐ordered logit. AQ04
AQ03
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Well-Being and Quality of Life20
Acknowledgements
The basis of this chapter grew from previous work conducted on life quality under a grant
from the United States Department of Agriculture at the Institute of Political Economy.
Author details
Ryan M. Yonk*, Josh Smith and Arthur Wardle
Address all correspondence to: ryan.yonk@usu.edu
Utah State University, Logan, UT, USA
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Building a Quality of Life Index 25
... In this work, regional analyses of the quality of life in the eight self-governing regions of Slovakia were used. The input indicators are selected based on the identification of the most important empirically confirmed factors of quality of life [28,29]. We have created our own Quality of Life Index in the regions of Slovakia, since there is no available index on a regional level. ...
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The Corruption Incidence is a measure of perceived level of corruption in the public sector that is annually released by the Transparency International Organization. The corruption is a big factor that can affect the quality of living, national economy, and the local communities. The purpose of this study is to determine the relationship of Corruption Indicence and the Quality of Life of selected countries in Asia that was selected using the Most Different System (MDS) design. This study applied the Pearson Correlation, Analysis of Covariance, and Regression Analysis, through pooled least square and seemingly unrelated regression, to obtain the impact of corruption in the Quality of Life indices. Seemingly unrelated regression is administered as a mean of correction because contemporaneous autocorrelation was detected in the panel regression by executing durbin-watson statistics. The study also tried to prove the concept of Growth Model of Corruption and Public Choice Theory. The results of the analysis revealed that the Corruption Incidence and GDP per capita and Undernourishment has a very strong positive relationship. While there is a strong positive relationship between Corruption Incidence and infant mortality, life expectancy, and tuberculosis incidence. Also, there is a moderate positive relationship between Corruption Incidence and births attended by skilled health personnel and the Corruption Incidence has no significant relationship with primary school survival. Additionally, there is no significant difference in the relationship of Corruption Incidence to Infant Mortality Rate, Life Expectancy at Birth, Undernourishment, Tuberculosis Incidence, and Births Attended by Skilled Health Personnel when grouped according to country type. However, there is a significant difference in the relationship between Corruption Incidence to GDP per capita and Primary school survival. Moreover, the Corruption Incidence and Quality of Life has an inverse relationship. Therefore, as the Corruption Incidence increases, the Quality of Life decreases and as the Corruption Incidence decreases, the Quality of Life increases. Finally, the study proves that the concept of Growth Model of Corruption and Public Choice Theory is true. Keywords: Quality of Life, Corruption Incidence, High-income countries, middle-income countries, GDP per capita, Infant Mortality, Life Expectancy, Undernourishment, Primary school survival, tuberculosis incidence, births attended by skilled health personnel
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Diener (2000) proposed that National Accounts of Well-Being be created to complement existing economic and social indicators that reflect the quality of life in nations. These national accounts can provide valuable information to policymakers and other leaders. Systematic measurement of subjective well-being provides novel information about the quality of life in societies, and it allows for the accumulation of detailed information regarding the circumstances that are associated with high subjective well-being. Thus, accounts of subjective well-being can help decision makers evaluate policies that improve societies beyond economic development. Progress with well-being accounts has been notable: Prestigious scientific and international institutions have recommended the creation of such national accounts, and these recommendations have been adopted in some form in over 40 nations. In addition, increasing research into policy-relevant questions reveals the importance of the accounts for policy. Psychologists can enlarge their role in the formulation and adoption of policies by actively studying and using accounts of subjective well-being to evaluate and support the policies they believe are needed. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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Many studies suggest that years of formal schooling completed is the most important correlate of good health. There is much less consensus as to whether this correlation reflects causality from more schooling to better health. The relationship may be traced in part to reverse causality and may also reflect “omitted third variables” that cause health and schooling to vary in the same direction. The past five years (2010-2014) have witnessed the development of a large literature focusing on the issue just raised. I deal with that literature and what can be learned from it in this paper. Published: Online October 2015. In print December 2015.
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Professional organizations advocate universal access to medical care as a primary approach to improving health in the population. Access to medical services is critical to outcomes of acute processes managed in an inpatient hospital, the setting of most medical education, research, and training, but seems to be limited in its capacity to affect outcomes of outpatient care, the setting of most medical activities. Persistent and widening disparities in health according to socioeconomic status provide evidence of limitations of access to care. First, job classification, a measure of socioeconomic status, was a better predictor of cardiovascular death than cholesterol level, blood pressure, and smoking combined in employed London civil servants with universal access to the National Health Service. Second, disparities in health according to socioeconomic status widened between 1970 and 1980 in the United Kingdom despite universal access (similar trends were seen in the United States). Third, in the United States, noncompletion of high school is a greater risk factor than biological factors for development of many diseases, an association that is explained only in part by age, ethnicity, sex, or smoking status. Fourth, level of formal education predicted cardiovascular mortality better than random assignment to active drug or placebo over 3 years in a clinical trial that provides optimal access to care. Increased recognition of limitations of universal access by physicians and their professional societies may enhance efforts to improve the health of the population.