International comparisons on the quality of life are widely studied in management science. The first step in measuring the quality of life, or any subject for that matter, is to determine which categories/sub-categories your study will focus on. Of course the categories/sub-categories chosen should be relevant to the subject at hand. Once these variables are chosen and the data is gathered, it is left up to the individual researcher to determine the rankings of these variables and assign weights to each. This leads to subjective judgement and the likelihood that the gathered results will have large variations. Because of this, it is essential to use management science in order to obtain non-subjective judgement. Therefore, the mathematical approach conducted in our analysis relies on Pareto optimization which uses linear programming to improve efficiencies. What we observed through our analysis in using Pareto optimization is that any (additional) change made to make any country better off is impossible without making another country worse off.
Quality of life is a modern concept in the field of outcome measurement and has been passionately adopted by clinicians, researchers, economists and managers. The term “quality of life” was originally coined in the U.S. during the post-war period to describe the effect of material affluence (evidenced by the possession of cars, houses and other consumer goods) on people’s lives and was subsequently broadened to encompass education, health and welfare, economic and industrial growth, and defense of the “free world”. In socio-medical literature, quality of life has been equated with a variety of terms, including life satisfaction, self esteem, well-being, happiness, health, the value and meaning of life, functional status and adjustment. Although, there are many interpretations to the term, “quality of life”, the closest to any consensus definition is that it is an umbrella concept encompassing health states as well as satisfaction with a broader range of domains such as environment, economic resources, relationships, work and leisure time. Conceptual work in the social sciences has attempted to clarify the definition and has distinguished it from related themes such as life satisfaction, morale, happiness or anomie, but has nevertheless failed to provide an operational definition (Carr, 1996). According to ecological economist Robert Costanza, “While Quality of Life (QOL) has long been an explicit or implicit policy goal, adequate definition and measurement have been elusive. Diverse “objective” and “subjective” indicators across a range of disciplines and scales, and recent work on subjective well-being (SWB) surveys and the psychology of happiness have spurred renewed interest” (Costanza, 1). This renewed interest has sparked the creation of a number of indexes which have been designed to measure and compare the quality of life across countries. One of these new indexes has been developed by the Economist Intelligence Unit, which is the world leader in providing country, industry and management analysis. The EIU’s QOL index is based on exclusive methodology that links the results of subjective life-satisfaction surveys to the objective determinants of quality of life across countries. These subjective life-satisfaction surveys ask people simple questions such as how satisfied they are in their lives in general. Critics have argued that cultural, verbal and psychological differences among countries can distort the responses to these surveys and that these responses reflect the dominant view of life, rather than actual quality of life in a country. Life satisfaction is seen as a judgment that depends on socially and culturally specific frames of reference. But this relativism is disproved by the fact that people in different countries report similar criteria as being important for life satisfaction and that most differences in life satisfaction across countries can be explained by differences in objective circumstances. In addition, it has been found that the responses of immigrants in a country are more closely related to the level of the local population than to that of responses in their motherland. Answers to questions on satisfaction in bilingual countries do not reveal any linguistic bias arising from possibly different meaning and connotations of the words “happiness” and “satisfaction”. Therefore, self-reports of overall life satisfaction can be meaningfully compared across nations. While the results of life-satisfaction surveys are important in developing an index, one should not strictly rely on this information since comparable results for a sufficient amount of countries tend to be out-of-date and data for many nations may not even be able. Therefore, there is a bigger chance of error in assessing QOL between countries if we take a single average life-satisfaction score as opposed to a multi-component index. Most importantly, although most of the inter-country variation in the life-satisfaction surveys can be explained by objective factors, there is still a significant unexplained component which, in addition to measurement error, might be related to specific factors that we would want to net out from an objective QOL index. Therefore, the results of these surveys are used only as a starting point and as a means in deriving weights for the various determinants of quality of life across countries, in order to calculate an objective index. The scores are then averaged and related in a multivariate regression to various factors that have been shown to be associated with life satisfaction in many studies. As many as nine factors survive in the final estimation equation. Together these variables explain more than 80% of the inner-country variation in life-satisfaction scores. Beta coefficients are then used to derive the weights of the various factors with health, material well being and political stability and security being the most important. These were followed by family relations and community life. Next in order of importance were climate, job security, political freedom and finally gender equality. The coefficients in the estimated equation weight automatically the importance of the various factors. The method also means that the original units of measurement of the various indicators can be used. This study has found that “GDP per person explains more than 50% of the inter-country variation in life satisfaction, and that estimated relationship is linear” (Economist, 3). The aim of this paper is to compare the international quality of life among twenty-three developed countries. As discussed previously, there are several variables that help determine the quality of life in a particular country. This paper will focus on the following five variables: economic state, education, environment, health and household demographics.
When assessing quality of life, the gross domestic product of a country would once have been considered the fundamental indicator of a country’s rank. Despite the importance of GDP in ranking a country in relation to quality of life, a broader spectrum of indicators needs to be taken into account. Factors beyond economic state, such as education, environment, health, and household demographics also need to be considered. Gross domestic product “measures output just fine, but as a stand-in gauge for a country’s overall well-being, this super number is less than perfect” (Uchitelle, 1). Robert Kennedy was even aware of this forty years ago when he acknowledged that GDP “measures everything in short, except that which makes life worthwhile” (Uchitelle, 1). Over the past decade, the gross domestic product in the U.S. has continued to rise. Ranking high in this particular subcategory, GDP, however, does not paint a clear picture of the overall quality of life in that particular country. For instance, as noted within the U.S., “while the GDP has continued to rise, wages have stagnated, pensions have shrunk or disappeared and income in reality has increased. Health care is measured by the money spent, not by the improvements in people’s health. Obesity is on the rise, undermining health, but that is not subtracted” (Uchitelle, 1). This demonstrates that analyzing GDP as the sole determinant of quality of life is inadequate. If the economic state were directly correlated to other quality of life indicators such as health, then the U.S. would thus have the highest life expectancy rate and the lowest mortality rate due to the fact that they have highest health rate expenditure. This as a result, proves the necessity to research and analyze other contributing factors to a country’s progress and overall quality of life. Some countries may enjoy a high quality of life in regards to economic state and income, but that does not necessarily influence all the other categories contributing to quality of life. The United Kingdom for example, ranks high in terms of household income, but this in turn has not necessarily favorably affected other aspects of their quality of life. In the United Kingdom, the cost of living is generally higher and thus higher incomes are needed just to combat that. Also, less money is spent on education and health. A citizen of this country was quoted as saying, “we earn substantially more than our European neighbors, but this level of income is needed just to keep a roof over our heads, food on our table, and our houses warm. It’s giving us a decent standard of living, but it’s not helping us achieve the quality of life that people in other country’s enjoy” (Wallop, 2). In 1954, the United Nations began to realize the importance of studying a greater variety of factors affecting social development and living standards in countries internationally. As a result, they appointed the Statistics Division, to gather information and statistics regarding a greater realm of factors affecting quality of life. “As early as 1954, the Division recognized that the development of social statistics involves the arraying of data in such a way as to make possible an analysis of differences among social groups and countries in topical issues, such a housing, health education, conditions of work, and employment” (Demographics & Social Statistics, 2). The first step the Statistics Division took was developing a framework for the compilation of social indicators. This lead to the publication of a document titled, Survey of Social Statistics, and a report called “International definition and measurement of standards and levels of living”. These two documents identified and outlined various social indicators that were important to quality of life analysis. They also determined a list of steps that needed to be executed in order to improve the quality of the statistics that were gathered on these indicators. Since these two publications were released, the Statistics Division has continued to revise indicators and their subsequent statistics. The culmination of all the processes that were started by the Statistics Division in 1954 was the World Summit for Social Development held in Copenhagen, in 1995. The idea that people should be at the center of development was the conclusion reached at this Summit. “On that occasion a general consensus was reached on the fact that development could no longer be equated to mere economic well-being, but encompassed a much wider range of necessities based on the right to live in a physical and political environment consonant with human dignity” (Mikkelson & Menozzi, 202). Once again, the notion that a variety of indicators are needed in determining a country’s social development and quality of life was reinforced. Besides the work of the Statistics Division, there are two indexes that strive to measure indicators that affect quality of life. These indexes include the Physical Quality of life Index (PQLI) and the Human Quality Index (HQI). Quality of life rankings should encompass both the physical and financial attributes of a country. “Notwithstanding the importance of per capita national income in assessing a country’s development, the factors outside the monetary sphere are neglected by such a measure. The ranks of countries according to their per capita national income also conflict with ‘common sense’ ranking their development” (Ray, 2). This is why two different indexes have been used in establishing these rankings. Developed in 1979 by David Morris, the PQLI is a measure of the quality of life or well being of a country. This index measures adult literacy, life expectancy at birth, and infant survival rates. Despite its attempt to include physical variables when assessing the quality of life of a country, it completely ignored the financial aspect. The Human Quality Index, on the other hand, developed by the United Nations Development Program, added to the PQLI by acknowledging the economic welfare of a country. The Human Quality Index takes into account life expectancy at birth, educational attainment, as well as GDP per capita. With continually changing household demographics, economic downturns, and changing environmental factors, even more emphasis is being placed on the evaluation and analysis of various quality of life indicators, including those within the Human Quality Index. Most recently, in 2008, the President of France, Nicolas Sarkozy, launched “The Commission on Measurement of Economic Performance and Social Progress”. This commission has set out to redefine indicators that rank a countries quality of life. “First, they have set out to find composite measures for the quality of life by aggregating objective indicators, based on personal experiences, with respect to good health, proper education, political freedom and oppression, etc. Second, they are finding out how people spend their time and how much enjoyment they derive from the things they do every day. The third approach is to ask people to provide broad qualities of judgments on life as a whole. Finally, the equity asks people to rank (and evaluate) the importance they attach to factors such as income, leisure, health, job security and so on” (Stiglitz, 2). As you can see, this commission is putting emphasis on the overall picture of quality of life and not just focusing on one key factor. While there have been numerous attempts to develop a statistic to measure social and economic well being, there are a number of factors that affect a person’s well being and quality of life; one statistic alone cannot measure this. “The main problem with the measures is a selection bias and arbitrariness in the factors chosen to assess quality of life” (Economist, 1). However in an effort to create a comparison between the countries researched a few measurements were chosen. Economic State:
GDP remains the most accurate measure of this quality assessment for economists. According to the Economist, “it has a clear, substantive meaning and prices are the objective weights for the goods and services that make it up” (Economist, 1). However, this statistic seems to indicate that the quality of life is better than most perceive. GDP is supposed to measure the value of output of goods and services. However, this does not take into account if the funds were spent efficiently or not; which creates a major problem. Historically, the GDP of the U.S. has always grown at a better pace than in Europe, but the underlying issue was that GDP was growing as a result of consumer indebtedness as households continued to increase outstanding credit.
Education was a second measure that was chosen to measure the overall quality of life. Today, education is recognized as a key factor of production. Literacy rates are important in determining a country’s QOL since many policy analysts consider literacy rates as a crucial measure to enhance a country’s wealth. According to the analysis conducted, only one country had a literacy rate below 98.9% which was Portugal with a literacy rate of 94.9% (UN Data, 2007). Tertiary enrollment and tertiary graduate rates are also important in determining a country’s QOL since they show how well educated the adult population is. In our analysis, Poland has the highest literacy and tertiary graduation rate as well as the second highest enrollment rate. It is believed that a better educated population base will equate to better production, which in turn creates more opportunities for the population and a better quality of life. Environment:
Environment was a third measure that was chosen to measure the quality of life. While economic state/wealth, health and education might be the measures that one thinks about first, environment is also an important measure of quality of life. Climate, pollutants emitted, energy consumption and total land mass covered by forests are examples of topics that can be measured to evaluate the overall quality of life of a particular country. While Luxembourg had the highest GDP per capita, its population also emitted the most CO2 and consumed the most energy. Based on the analysis conducted, it is believed that improving energy consumption, improving air quality or preservation of forests could go a long way in improving or sustaining a better quality of life. Health:
While GDP remains the most tangible measure of quality of life, it may not be the most accurate. As mentioned before, if it were the sole measure of quality of life, the U.S. would rank at the top every year, having the highest life expectancy, highest savings rate or highest graduation rate, which it is not. Health is also an important factor in measuring the quality of life. According to the British Journal of Rheumatology, “Technological advances in the medical field have increased the possibilities for influencing the quality of life” (Carr, 275). The instruments used to measure population health allow doctors to identify health needs or how to allocate specific resources so that quality of life in an area can be improved by possibly treating a disease, extending life expectancy rates, or lowering infant mortality rates for example. It should be noted however, that although a country may spend more or allocate more resources to health, it does not correlate to a better quality of life. The U.S. spent over 15% of its GDP on health, however had the third lowest life expectancy rate and highest infant mortality rate. Meanwhile, Luxembourg spent 7% of its GDP on health and had the lowest infant mortality rate and ranked in the middle for average life expectancy (UN Data, 2007). Household Demographics:
The final category that was selected to measure quality of life was household demographics. The category consists of taxes on the average worker as a percentage of a household’s total income, total telephone subscribers per 100 inhabitants, internet users per 100 inhabitants and average savings rate as a percentage of disposable income. Based on our research it was concluded that higher taxes do not correlate to lower savings rates. Of the countries surveyed, it was determined that the countries that possessed a higher savings rates had lower costs of living despite higher taxes. It was also assumed that since most TV/Internet companies offer a telephone service for free, that there could be a direct link between internet users and telephone subscribers. This however is not the case as many of the developed country’s populations have switched over to or have mobile phones and do not require a dedicated phone line. Mobile services provide communication almost anywhere and services that cannot be matched by dedicated phone lines like access to emails, text messaging and unlimited long distance.
An international comparison of the quality of life for twenty-three different countries is being described within a series of five categories. We chose these five categories on the basis that these would give us the greatest possible way of being able to compare an individual’s happiness and well-being. The categories used in this study are as follows: economic state, education, environment, health, and household demographics. Each category has three to four subcategories to make up the measurements for quality of life. We collected our data from several areas which include UN Data: A World of Information and OECD Statistics (UN Data, 2007). As you will see in Table 1, which includes all the information that we collected and put together based on the year 2007. To be able to gauge a comparison between the countries, we needed to select measurements that would keep it fair for evaluation purposes. The statistics that are shown for currency measures are denominated in the USD to keep the measurements the consistent across the board. We chose to use a per capita basis for the countries, especially for GDP, because some countries due to their size will have larger numbers. Therefore, this was done in order to keep these numbers relative for this comparison. The countries chosen for our study are spread out across the world and thus are not centralized in one location. This approach was taken as it will give the study the ability to capture measurements outside of specific regions. All of the countries that we used are considered to be industrialized. We chose measurements for type of data, category and type of measurement to be able to be as accurate as possible in judging the quality of life among these countries. We tried to choose categories that we felt affected the overall well being of an individual on a daily basis. Through our measurements and findings, we have noticed that the categories you choose are the most important in being able to compare the differences in quality of life throughout the world.
We will be discussing the categories and subcategories that we chose for the comparison of quality of life in the following sections. The subcategories that make up economic state are GDP as a percent of capita, unemployment rate based on annual percentage of labor force, consumer price index, and gross capital formation as a percent of GDP. As mentioned previously, we needed to use GDP per capita to make sure that there was equivalent comparison that took the size advantage out of the statistics. The numbers used are also based in USD currency and this allowed us to have the same purchasing powers between the countries. The first subcategory of economic state that was selected was GDP per capita for the year 2007. This is a good starting point because it can be assumed that the greater the GDP per capita, the more the country can spend on goods, which in turn, can show how well the economy is doing across each country as well as be able to look at the economic conditions. The economic condition is usually approximated by disposable personal income, which represents the degree of command exerted by an individual over the market goods and services that determine his/her material standard of living. We chose income instead of personal expenditures, because income is a true means to command resources as required by our notion of welfare (Grasso & Canova, 2007). The second subcategory that was selected was unemployment rate as a percentage of annual labor force. This rate is important because it shows how well the economy is doing from a jobless perspective. It would be believable to say that the higher the unemployment rate, the more people out of work, less income to spend and lower ability to enjoy life. Here are some more examples of how the rate affects welfare: “non-income impacts: loss of freedom, social exclusion and familial instability, loss of skills and cognitive abilities, psychological harm, reduction of motivation and of civil and political participation” (Grasso & Canova, 2007). The unemployment rate gives you a real feel for how well or not the working force has had it in the year under measurement. The third subcategory that was selected was consumer price index. This is an inflationary indicator that measures the change in cost of a fixed basket of goods and services. This measure usually includes electricity, housing, transportation and food. For this study, we started with an index number of 100 for the year 2000 to have a starting point for change in the last seven years. The CPI is used mainly as an inflationary measure, but we believe the change in the price of goods is a better determinant of how much money an individual would have to spend to keep the same quality of life as in years prior. It is also used to indicate economic activity, to deflate other economic series in order to make the data comparable and to adjust dollar values so that one can compare item prices over a span of time. If the CPI has increased, consumers would need to pay more for goods and services, which would lead to less being able to be purchased. The fourth subcategory that was selected was gross capital formation as a percent of gross domestic product. This is an indicator of the amount of capital owned or under one’s control to mobilize capital resources for the purpose of investing. This can also indicate the amount of economic activity expansion. This measure can show you the increase in domestic demand and the fixed capital formation by the firms of that specific country. The gross capital formation contributes to sustainable economic growth not only on the demand-side but also on the supply-side, because an important part of these expenditures are dedicated to the renewal of the firms’ fixed capital (Pavelescu, 2008). All of the statistics for the economic state focus on the income bearing measures that would affect the purchasing power of not only the individuals in a country but also their families. Each measure tries to capture how easy it is to have a job or be able to provide for themselves without struggle. The more purchasing power that a country has, the more happiness and greater quality of life they will have. We believe that all these economic measures give us an unbiased result in which we can use for our objective in this comparison.
The second category we chose to assist us in the international comparison of the quality of life was education. According to the Webster’s dictionary, education is the act or process of imparting or acquiring general knowledge. The variables that we measured under this category consist of literacy rates, tertiary enrollment rates, tertiary graduate rates and government expenditures in education. In today’s globalized information-based society, knowledge in now widely recognized as a key factor of production. Literacy rates are important in determining a country’s QOL since many policy analysts consider literacy rates as a crucial measure to enhance a country’s wealth. This observation was made by the fact that training costs for literate people tend to be lower than that of illiterate people, literate people have higher socio-economic status and enjoy better health and employment opportunities. Policy makers also argue that literacy increases job opportunities since literate people have access to higher education. For example, in Kerala, India, female and child mortality rates declined dramatically in the 1960s, when girls who were schooled according to the education reforms after 1948 began to raise families. Recent researchers argue, however, that such correlations may have more to do with the overall effects of schooling rather than literacy alone. (Wikipedia, 2009). Therefore, this is just one variable we need to consider when looking at the impact education has on the quality of life in a country. In addition to the potential for literacy to increase wealth, wealth may promote literacy, through cultural norms and easier access to schools and tutoring services. Our analysis showed that the majority of the countries included in our study have literacy rates of 99%, with Portugal (94.9%) and Spain (97.9%) having the lowest. It has also been stated that life expectancy rises by as much as two years for every 1% increase in literacy. Being literate is a big advantage in being successful as it’s considered to be the building blocks necessary to further your education. Furthering your education will increase your chances of living a happier and more prosperous life. We next looked at tertiary enrollment and tertiary graduate rates within these countries. Tertiary education, also referred to as third stage, third level, and post-secondary education, is the educational level following the completion of a school providing a secondary education, such as a high school, secondary school, or gymnasium. Tertiary enrollment and tertiary graduate rates are important in determining a country’s QOL since they show how well educated the adult population is. In many countries, tertiary education is now becoming a standard and secondary education is no longer sufficient. Based on our analysis, the U.S. has the highest tertiary enrollment rates with Poland having the highest tertiary graduate rates. In addition to showing us how well educated the adult population is, these attainment levels are necessary for economic growth in a country as successful tertiary education lays the foundation for an individual’s future career. The better one’s career, the more production that is supplied in a given country; therefore justifying that the quality of a country’s exportable services and merchandise is directly correlated with the quality of the country’s education. Therefore, we believe that the higher the tertiary enrollment and graduate rates a country has, the better the quality of life is in that home country since advanced skills and knowledge help to create innovate ideas and technology that better the overall community. In addition, to these three subcategories, it is also important to look at the investment made in the education system by the government. Government investment in education includes professor salaries, teaching curriculum, access to information, physical buildings and much more. Based on our research, the government in Denmark spends the most on educational investment with Luxembourg spending the least as a % of GDP among the twenty-three countries being analyzed. One may think that the more investments made in education, the better achievements made by the students in that educational system. However, according to the PISA 2006 survey, both the government in Korea and the Netherlands spend below the average in educational investment, while their students remain as one of the top performers (Hanushek, 2008). This is an interesting fact because it shows that while government investment in education is important as it gives individuals access to quality education, it’s really up to the individual in determining on whether or not they will succeed. Although education is a significant foundation of a country’s economic well being, preparing individuals for the workforce is only one goal of education. “Of equal importance are enabling individuals to: lead lives of dignity and purpose; construct knowledge and put it to humane ends; and participate as informed citizens in a democratic society” (Education Indicator, 2). The Calvert-Henderson Education Indicator provides summary statistics and offers insight on how well educational systems are meeting these goals. Based on their studies, the education level of the U.S. population has increased tremendously over the past 60 years. “In 1940, almost 75% of the population had less than 12 years of school, while by 2008, the percentage had reversed, with more than 85% of the adult population having completed at least 12 years of school and over a quarter of all adults having completed at least 4 years of college” (Education Indicator, 2). As a result by 2006, the average earnings for U.S. individuals with a high school degree were about 50% higher than for someone without a high school degree. In addition, those who had a bachelor’s degree earned up to 70% more than those with just a high school diploma. This shows that educational attainment has the capability of improving the quality of life for those who are able to achieve it. When we view these four subcategories as a whole, it is quite evident that the more education a country has, the better chance they have for economic growth. In order to stimulate an economy, specialized goods and services are needed. Obtaining a tertiary degree and putting these acquired skills to use is a good way to do this. From the very beginning, the learning environment helps to shape individual’s lives. The more one learns, the more productive they become and the more skills they have to offer for the betterment of society.
The third category that was selected to assist us in the international comparison of the quality of life was environment. The category of environment consists of forested area as a percentage of land area in each country, average temperature in degrees Celsius, annual average CO2 emissions per person based on 2007 population information and energy consumption per capital which is based on kilograms of oil consumed annually by each country’s 2007 population. All numbers used are based on information that was gathered from the UN. The first subcategory assesses the percentage of each country’s land area that is covered by forested area. The main component of this subcategory is influenced by population and climate. It might be assumed that countries with cooler average temperatures have generally smaller populations and a larger percentage of forested area, while the countries with a more temperament climate have larger populations. However, there are a few exceptions to this assumption. Iceland, which had an average temperature of 4.5 degrees Celsius, had ½ of a percent of its land area covered by forests and the U.S., which has an average temperature of 12.1 degrees Celsius, had 33% of its land area covered by forests (UN Data, 2007). The reason why the relationship between forested area and temperature is not consistent with the rest of the countries surveyed is due to the land size of the U.S. compared to the rest of the countries as well as Iceland being the most northerly situated of the countries surveyed. The third and fourth subcategories can also be linked together as their measurements could be used to protect the global climate system from further harm due to emissions of greenhouse gases and help humanity and the natural world adapt to unavoidable climate change (WRI, 2009). The third subcategory indicates the average amount of CO2 pollutants emitted per person annually. This subcategory is important because increases in greenhouse gases into the air can have an effect on overall life expectancy as rises in temperatures will adversely affect human communities and natural systems around it (WRI, 2009). The final subcategory of environment is energy consumption. This particular indicator measures the amount of oil, in Kg’s, that is consumed per capita annually. It can be argued that energy consumption has a direct correlation with CO2 emissions as populations continue to expand, utilize more resources and emit more greenhouse gases. The U.S. is a good example of this. They have one of the largest population growth rates of the countries analyzed and also have the highest CO2 emissions and energy consumption per capita. We selected these four subcategories to make up the main heading of environment because they are an integral part in determining the quality of life for a specific area or country. Spending to improve energy consumption or preservation of forests could go a long way in improving or sustaining a quality of life. For these reasons, we feel that investment in environment is one of the most important factors for a country to be considered to have a good quality of life. We also feel that when combined and used for comparison with the other categories, environment will equally compare the countries selected for the study.
Three subcategories have been chosen to analyze how the category of health contributes to the analysis of the international comparison of the quality of life. The first subcategory of health is infant mortality rate. “Indicators derived from mortality rates provide a good picture of overall population health” (World Health Statistics, 1). This particular indicator analyzes the probability of dying before the age of 1 per 1000 live births in both sexes. These estimates of mortality are collected from death registration data that are reported annually to the World Health Organization. Infant mortality rates are an important factor in assessing the quality of life of a country because the rates are used to compare the health and well being of populations across and within specific countries. These rates are affected by various factors. One of the most important factors affecting mortality rates is the lack of adequate medical and nursing intervention. This can cause a sharp decline in the rates. Nutrition, availability of safe water, medication and immunizations, and disease are also important factors that impact if a newborn lives past its first year of life. One might think that infant mortality rates would be directly related to the next subcategory, health care expenditure. Infant mortality rates, however, can be surprising. The U.S. faces the staggering fact that, “28,000 children under the age of 1 still die every year” (Bakalar, 1). This is despite the U.S’s total expenditure on health as a percent of gross domestic product. Globally, the average expenditure on health was about 8.7% in 2006. The Americas had the highest percentage with 12.8% (World Health Statistics, 2009). This subcategory is important to demonstrate that the amount of money spent on health care does not necessarily affect the quality. In some cases, such as in Poland, health care expenditure seems to be directly correlated with the general health of the country. Poland exhibited one of the lowest life expectancy rates and highest infant mortality rates. This in turn can be related to their lower total expenditure on health as a percent of their GDP. If a positive correlation between total expenditure on health and life expectancy and infant mortality rates were to always hold true, then the U.S. would have the lowest infant mortality rate. A final subcategory of health is life expectancy at birth. This subcategory is important because it shows how long an adult is expected to live in years. Life expectancy at birth is the average number of years a newborn would be expected to live if health conditions were to remain stable and constant throughout their entire life span. Even though this average number may reflect the general health of a country, this rate also does not necessarily correlate to the country’s health care expenditure.
The final category that was chosen to help measure quality of life was household demographics. The subcategories that make up household demographics are taxes on the average worker as a percentage of total income earned, total telephone subscribers per 100 inhabitants, internet users per 100 inhabitants and average savings rate as a percentage of disposable income. As you can see, we needed to have the savings and taxes be represented as a percentage of disposable income and internet and phone subscribers represented as per 100 inhabitants to make sure that there was equivalent comparison that took the overall wealth of a country and size advantage out of the statistics. The first subcategory of household demographics that was selected was taxes on the average worker as a percentage of total income earned. It can also be linked to the fourth subcategory, average savings rate. It was determined that this would be a good starting point as it can be assumed that the greater the taxes, the less people will have to spend to improve their quality of life. However, this is false when we consider the average savings rate as a percentage of disposable income. For example, if we look at the U.S., it ranks towards the top in taxes but ranks towards the bottom in average savings. This is because the cost of living in the U.S. is greater than most of the countries that were surveyed. Germany, on the other hand, had the second highest tax on income earned, yet had one of the highest savings rates as the cost of living there, outside of taxes, is considerably less (Caine, 2009). This was the case for most of the European Countries. Therefore, there is an inverse relationship between taxes on the average worker as a percentage of total income earned and savings rate as a percentage of disposable income. The household demographic subcategories pertaining to technology are important in the analysis of the quality of life. The two subcategories analyzed were total telephone users per 1000 inhabitants and internet users per 100 inhabitants. Economic, cultural, demographic, geographic, and educational factors influence whether or not a household has either or both of these technologies. There is a wide gap between the levels of internet users internationally. Internet users per 100 inhabitants range from a low of 33.4 in Poland to a high of 91.4 in the Netherlands. According to Barry Wellman, member of the Global Consumer Advisory Board, “the distribution of Internet users is extremely uneven around the world. Not all people are experiencing the benefits of the Internet, such as access to friends, jobs and information” (AMD, 1). It is important to study the number of internet users in households because of the access it grants its users. Having access to this technology can thus contribute to a country’s quality of life. Many factors affect whether or not a household has internet users. “The various digital divides are affected by characteristics of a country, such as developmental level, as well as by characteristics of an individual, such as his or her socio-economic status, age and gender” (AMD, 1). The main factor however seems to be economical. Households in a lower income bracket are less likely to have internet access. This could be because they can not afford a computer, let alone an internet connection. Employment rates also tie into the economic issue facing potential users. A final issue affecting the quantity of internet users in a country are a person’s education level and if they know how to use the internet properly. Though it is more common to have telephone access in a home than internet access, the factors contributing to telephone subscriptions are similar to that of internet users with the economic state being the main factor. Telephone subscriptions should be taken into account when looking at a country’s quality of life because telephones in a household promote social connectedness. Households with telephone subscriptions are able to remain in contact with friends and family in the absence of face to face contact. This is especially helpful in the case when there are large distances between people. Telephones are also important in case of emergencies. Without a telephone, a 911 call can not be made in a case of emergency. Therefore, help may not arrive in time.
The next objective that we needed to accomplish was to take all of the data that we collected in Table 1 and be able to have a procedure for modifying the data so we could compare it to one another. We decided to use a system that ranked the statistics in each subcategory from 1 to 10. The number 10 would be the best rank that statistic could receive and the number 1 would be the worst. This ranking scale would allow us to take a deeper look about the quality of life differences among the twenty-three countries. We then did an analysis of the numbers collected are we were able to come up with a scale that would be suitable for comparison. For example, taking one of our subcategories such as government expenditures on education as of percent GDP from our education category, we tried to look at the numbers before assigning rank. For this statistic, we decided to use the 1-10 ranking scale to give the highest number which is close to 10 to the country with greatest percentage spent on education. In comparison, we would give the countries a number close to 1 that had the lowest percentage contributed to education. We converted the statistics to numbers by using an average across all countries to make sure that the scale was consistent. We decided that the 8.3% was 10 because it was the highest and 3.4% was the lowest. We were careful in deciding how the scale should be ranked for each category to make sure that we captured the importance of each number. Some subcategories would be given a better rank for having lower numbers just based on the statistic. An example of this would be the subcategory unemployment rate under the main category economic state. The greater the unemployment %, which means a higher rate, than the worse it is for a country. As you will see on the appendix provided, we show that the higher the ranking is around a 10 and the lower the ranking the number is around a 1. We did have some statistics that were negative, but they were given a number between 1 and 10 to keep all the rankings within the same range. At this point in the comparison, we were able to take the data we collected in Table 1 and convert it into comparable statistics for each subcategory in Table 2. The higher the numbers in Table 2 meant that these were the countries with the better statistics according to our ranking scale from 1-10.
The next step that our group needed to accomplish was to convert Table 2 into Table 3. We did this using linear programming to create another ranking scale. This ranking scale would be based out of 100. The country with the highest score in the category using all of the subcategory statistics from Table 2 would be given a score of 100 and vice a versa, a country with the lowest scores were given a score close to zero. The highest score that could be given to a country is 100 and the lowest would be zero. A country that receives a high score for the main category would be saying that this country has a better quality of life compared to a country with a low score for the purposes of this of this particular category. Our team needed to use an equation to convert the numbers from Table 2 into Table 3. We needed to assume that the model was linear for the comparison of the countries. We needed to maximize the following equation for each category:
Subject to: Sn*Wn+ Sn*Wn…….
Constraints: Cn<= 100
Cn = this is the total final value ranking given to the specific country for each category Sn= the number of measurements under the each category
Wn=this is the weight that was associated with measurement under each category The constraints for the formula are that the final value for each country can not be greater than 100 and the weight for each country must be equal or greater than zero based on our ranking scale. We performed the same measure for each of the five categories with the same steps for measurement as above. The results for the tests performed are all summarized in Table 3. The total scores are shown in the table and all came from the above test while using the following variables with the excel solver function. We used the weights from Table 2 as a consistent basis for all countries because we felt this would give us a more accurate measure for a true ranking. The basic equation above allowed us to view each country’s total score with a base value. The Pareto optimization is a technique that gives you the optimal value based on a set of parameters with multiple objectives. You can change these parameters around to test different scenarios. An allocation is Pareto-optimal if it is impossible to make at least one person better off without making anyone else worse off; a Pareto improvement is a change in an allocation which makes someone better off without making anyone else worse off (Caplan, 15a). The main categories that we selected act as organizations and these will be measured for the countries quality of life ranking. If you were to try to evaluate the ranking score for one country against the others it would not be optimal because it would lead to one category being favored. If you were to add another country to the study or take a country out this could affect the ranking score by changing it. This is why we must treat each country separate to come up with the total ranking. In our evaluation of the statistics from Table 2, we assigned weights to each subcategory that must be maximized to gain the highest possible score. Remember, we stated earlier that we put a constraint on these weights for each category to not allow for the quality of life ranking to exceed 100, which is important to allow for a true comparison. The important area to remember is that we must focus on all the categories and not the categories that would benefit the ranking the most. It would be optimal to proceed with following that mentality, but this may throw off the scores of other countries by putting them at a disadvantage. To have one country’s quality of life be better than others, another country must have a below average quality of life under the Pareto Optimal definition. The last step to our comparison process was creating Table 4, from taking the information we computed in Table 3. This final table would be the overall ranking scores for all twenty-three countries to allow us to determine which of the countries had a better quality of life. We used a simple average to come up with the final numbers for our table. This would allow us to incorporate all the individual countries ranking scores from each of the five main categories.
The true measurement of the quality of life for a country is very hard to determine from just a few calculations. The quality of life for one individual might be completely different for someone else. Countries may have different visions of how they want their people to be happy and how to enjoy life. It is near impossible to really to give an assessment on which country could potentially have a better quality of life over another without looking at every individual and statistic.
When you think about what quality of life really means it is the measure of how someone lives their life with satisfaction on a daily basis. This could be through being healthy, being wealthy, or just having the ability to do certain things. If you were to have another individual do this study they may come out with completely different results based on the information they used or methods for comparison. We used the pareto optimization method because of the simplicity and we were able to use linear programming. The information could be the same, but they could feel that one category has more weight than another. These will lead to large variations and ultimately a different outcome in the end. The above calculations can also show you have effective a country can be when making their citizens happy. It can help countries try to improve areas of the study where they were lacking statistics. As you can see by our table 4 the country that had the highest overall score was Japan with Switzerland coming in a close second. This result would lead one to believe that these two countries have citizens with a very high quality of life. The shocking statistic was the fact that the United States ended finishing right in the middle. The United States has a very low household savings rate and the infant mortality rate is very high. If you were to go through all of the statistics you could identify what needs to be improved upon to strength your countries quality of life.
As the world keeps turning everyday it may be harder to really give a definition to the quality of life measure. The quality of life factors must be stated by the citizens to allow for complete happiness to occur. It may not come down to a comparison but a comparison of quality of life within your own country.
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