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Building a Quality of Life Index

May 2017

DOI:10.5772/67821

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In book: Well-Beings and Quality of Life
Publisher: Intech
Editors: Ana Alice Vilas Boas

Authors:

Ryan Yonk

American Institute for Economic Research

Josh T. Smith

Utah State University

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.

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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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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

AQ01

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

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).

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

Observed Value − Minimum Value

__________________________

Maximum Value − Minimum Val 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

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.

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.

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

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

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

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

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

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

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

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

AQ02

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,

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].

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

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.

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.

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

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

Cash transfers and quality of life among households in the Eastern Cape, South Africa: Application of a generalized linear model

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Cash transfers have become an important tool for reducing poverty and improving the well-being of households in many developing countries including South Africa. However, cash transfers' effects on household quality of life are still debatable. This study examined the relationship between cash transfers and quality of life, controlling various household characteristics. The study used the 2021 South African General Household Survey with a sample of 1,499 households selected through a stratified random sampling technique. Data were analyzed using the contingency table technique and a Generalized Linear Model. The results showed that cash transfer families had a greater quality of life than non-transfer households. The results of the GLM analysis revealed that public safety, health, infrastructure, access to remittances and population group were important predictors of quality of life among the households in the research. The results also showed that education, economic environment and gender had no significant effect on the households' quality of life while age group had a minimal effect. The study concludes that cash transfers (both social grants and remittances) have an impact on households' quality of life. The study's findings suggest that policymakers should continue to implement cash transfer programs to improve the well-being of households and improve public safety, health, infrastructure, education and the economic environment in the province.

Multi-perspective quality of life index for urban development analysis, example of the city of Brno, Czech Republic

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Urban system present dynamic systems, where many interactions take place and revealing deeper spatial relations of the urban system and the quality of life is a necessary part of decision-making process in sustainable urban development. The proposed multi-perspective index aims to evaluate the urban quality of life on the basis of five groups of factors and in combination with a cluster analysis it enables a deeper insight of the urban system and its spatial organization. The study is based on quantitative and publicly available data. In total 41 environmental, demographic, social, economic and technical indicators were included. The data used were collected with a clear methodology by official institutions, such as the Czech Statistical Office, the Czech Hydrometeorological Institute, the Ministry of Health and the European Union’s Earth observation programme Copernicus. Using these kinds of data sources ensures the feasibility and transferability of the method to other cities in the Czech Republic but also to other countries, where data on a similar basis are collected. The case study of Brno, Czech Republic, helped to identify disparities in living conditions among the city districts. The influence of historical city development on the quality of life was revealed using cluster analysis.

Re-conceptualising Womanhood and Development in Post-colonial Zimbabwe: A Social Conflict Perspective

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Gender conversations have attracted scholarly attention across disciplines. The pervasiveness of women oppression is attested to in many patriarchal societies, including Zimbabwe where male supremacy is widely accepted and normalised. Such gender constructions influence power relations and provide men with ample opportunities for growth and development while placing constraints on the roles and activities of women. The main thrust of this chapter is to interrogate and problematise gender relations in Zimbabwe in the context of national development narratives. Karl Marx’s insights from social conflict theory are utilised in the discussion and presentation of arguments in the context of womanhood and development in Zimbabwe. The study argues that the conception of woman as second sex, or second class Other positioned at the periphery of society, has impacted on socio-political and economic growth, in general, and female elevation, in particular. It has been realised that monolithic conceptions of woman and womanhood that promote gender differentiation in Zimbabwe not only hamper women’s growth but also limit national development in general. This chapter seeks to mobilise those in positions of cultural, religious and political authority so that they recognise women as active agents of socio-economic transformation that brings about national development. This will enhance their contribution towards the achievement of SDGs 1 (poverty) and 5 (gender equality).

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BRIT FOOD J

Purpose Many studies conducted on green consumer behaviour are related to environmental concerns, the motives behind green consumer behaviour and the impact of demographic characteristics on purchase decisions are not studied yet in the Indian context. Hence, this study aims to analyse the green product purchase decision and factors influencing the decision of Indian consumers along with demographic characteristics. Design/methodology/approach Present study utilise survey-based research method. Primary data were collected from 367 consumers in Hyderabad using a snowball sampling method. A structural equation modelling approach was employed to test the hypothesis. Findings Findings confirm that supporting environmental protection, drive for environmental responsibility, green product experience, environmental friendliness of companies, social appeal and motives behind green product purchase significantly influence the green product purchase decision. Meanwhile, control variables such as gender, qualification and income also have a significant effect on the green product purchase decision. Research limitations/implications Results discussed in this paper will help policymakers to formulate their policies in lieu of variables affecting the purchase decision. Further, the findings of the current study can be utilised by managers to study the buying behaviour of green purchasers. This will help them to remain competitive in the Indian market. Originality/value This research presents a novel conceptual framework for the present study influenced by literature. The present study expanded the framework by adding a new independent variable: motives behind green product purchases and a control variable: demographic characteristics of Indian consumers.

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Eunice Kamaara

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‘Quality of life’ concept in Cohesion Policy evaluation in Poland, 2004–2020

Article

Aug 2022
EVAL PROGRAM PLANN

Quality of life (QoL) is a fashionable concept widely used not only in many scientific fields but also in strategic documents, political speeches, and newspaper headlines. Although QoL is a crucial element of political agendas and a goal of public policies, there is little evidence on how this concept has been incorporated in policy analysis and evaluation. This study presents how the QoL has so far been defined, contextualised, and measured in the European Union Cohesion Policy evaluation in Poland, based on the analysis of 1,431 evaluation reports for the period 2004–2020. The findings show that the interest in the QoL is growing, and almost one-third of reports refer to the term. However, the concept is usually not clearly defined, and the number of evaluations with actual questions concerning QoL is limited. Moreover, the methodological approach to QoL measurement is rudimentary, limited to surveys of beneficiaries or end-users.

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Saim Can BERİTAN

Dünya nüfusunun yarısından fazlasının kentlerde yaşamını sürdürdüğü gerçeği, yaşam kalitesinin neden günümüzde kent çalışmalarının ana tartışma konularından biri olarak görünür olduğu sorusunun anlaşılması bakımından önemlidir. Ayrıca yakın gelecekte her beş insandan birinin nüfusu en az 1 milyon olan kentlerde yaşayacağına yönelik öngörüler, kentsel yaşam kalitesi olgusunun çok daha önemli bir ölçüt olarak referans alınma potansiyelini ortaya koymaktadır. Bununla birlikte öznesi insan olan ve farklı disiplinlerin kendine özgü yöntemlerle ele aldığı yaşam kalitesi konusu, en genel manada, dışsal (nesnel) koşulların onu deneyimleyen insanlar tarafından algılanma (öznel) biçimi ve neticesinde yorumlanması olarak tanımlanabilir. Bu bakış açısından yola çıkarak ekonomik, sosyolojik ve kültürel göstergeler bağlamında Atakum ve İlkadım gibi Samsun’un iki merkez ilçesinin kesişme noktasında bulunan Ondokuz Mayıs Üniversitesi Güzel Sanatlar Kampüsünde öğrenim gören dört farklı fakülteden 136 lisans öğrencisiyle – kentsel yaşam kalitesi üzerine – görüşmeler yapılmıştır. Elde edilen bulgular ise, cinsiyet ve fakülte farklılaşmasına dayalı iki boyutlu değişken üzerinden değerlendirilerek karşılaştırmalı olarak yorumlanmıştır. Sonuç olarak, bu çalışmada nesnel yaşam kalitesi göstergelerinin örneklem grubu tarafından Samsun ölçeğinde nasıl algılandığının anlamlandırılması amaçlanmıştır.

Domestic water quantity, service level and health

Book

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Dec 2020

Sufficient quantities of water for household use, including for drinking, food preparation and hygiene, are needed to protect public health and for well-being and prosperity. This second edition reviews the evidence about the relationships between water quantity, water accessibility and health. The effects of water reliability, continuity and price on water use, are also covered. Updated guidance, including recommended targets, is provided on domestic water supply to ensure beneficial health outcomes.

National Accounts of Subjective Well-Being

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Apr 2015

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).

On the mechanics of economic development

Article

Jul 1988
J MONETARY ECON

Robert E. Lucas

This paper considers the prospects for constructing a neoclassical theory of growth and international trade that is consistent with some of the main features of economic development. Three models are considered and compared to evidence: a model emphasizing physical capital accumulation and technological change, a model emphasizing human capital accumulation through schooling, and a model emphasizing specialized human capital accumulation through learning-by-doing.

Approaches to Economic Development

Article

Jul 1955

On the mechanics of economic development

Article

Jan 1998
J MONETARY ECON

R. Lucas

The correlation between health and schooling

Article

Jan 1975

Michael Grossman

The Relationship between Health and Schooling: What's New?

Article

Dec 2015

Michael Grossman

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.

Social Conditions and Self-Management Are More Powerful Determinants of Health Than Access to Care

Article

Sep 1998

Theodore Pincus

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.

Economic Development: Objectives and Methods.

Article

Jan 1965

The Effect of State and Local Taxes on Economic Development: A Meta-Analysis

Article

Oct 1995

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