“A Cross Sectional Study of the relationship between Housing and Happiness at Metropolitan level in the US”
Background of Happiness Literature
During the last decades, Economic of happiness becomes a brand new issue in research and applied economics. The main concept of economic of happiness is the research of relationships between human wellbeing and different variables. In Diener et al’s (1997) definition, subjective well-being is seen as the consequences of an overall appraisal of life that sums up the good or bad things. There is a rapidly emerging theory that attempting to define, analyze and measure happiness and identify the fundamental factors affecting it. Much of the debates over the happiness or subjective wellbeing evaluate diverse income levels is the mainly factor that can fluctuate subjective wellbeing. In recent work, Stevenson and Wolfers(2008)have found a firm relationship between income and wellbeing. While Stutzer and Frey (2012) also believe wealth is acknowledge as the most important variables that can affect happiness among different variables including wages, unemployment, human capital and health. Similar to above findings Bayer and Juessen (2012) also finds positive correlation of income and happiness. Money seems to be a very important aspects determining the measure of happiness looking from the above mentioned. Many suggestions show that people with lower incomes are less satisfied than those people with higher income. Reasons explained certainly because money can improve the living standard of human beings despite just meeting the basic needs of humans which are water, food and shelter. The recent contributions seek to shed additional light on the ongoing debate over happiness and income and ignore other variables that may have significant effect on happiness. According to my home country Hong Kong, Chinese University conducted a Hong Kong quality of life index (2014) finding the “housing affordability index” reached its bottom point within the last 10 years, even as the unemployment rate and average wages were both improved. Still, the Chinese University’s quality of life index fell to the third-lowest level seen since 2002. Associate professor of economics at Chinese University of Hong Kong Terence Chong said “Although the income of Hong Kong people increases, yet this cannot be a guarantee of improvement in their life quality as the housing prices are definitely out of reach.” Regarding to this case, housing seems to be another major problem to our psychological wellbeing.
My studies tend to focus on a more interesting and less developed variable, housing to the effect of happiness. However, although the happiness research has been expanding in recent years exploring different factors, the relationship between housing and happiness is still largely under researched. Diaz- Serrano (2009) has proofed that home ownership can bring positive benefits to individual wellbeing across European countries. On the other hand, Parker et al. (2011) argues that life satisfaction decreases due to home ownership when owners have to bear heavy mortgages every month or when the house value depreciates. Narrowed empirical evidences on housing are generated mostly across the western developed nations but still remain inconclusive. This study will contribute to this under researched area by exploring the relationship between housing and happiness. Moreover, the existing literatures consider housing price or home ownership status as their only core study factor on housing. For example, Hussaun A. Syed (2016) reviews that housing prices in Canada is the most significant factor as it fluctuates wealth then happiness. F.Hu (2011) shows that the strong relationship of home ownership status between overall happiness and house satisfaction in China. In the explanations of my current investigation, housing not only can refer housing prices and homeownership rates but to many different aspects for example median housing values, housing to wages ratio, homeownership rate and household size. Ideally, data collected for this study should be panel data on a significant number of countries (e.g. 30 countries) and using data from 4 to 5 years. However, due to limited access to data, this study is going to examine whether housing has played a significant role in metropolitan happiness instead of nationally. Whereas most of the existent literatures have stressed on finding factors that affect happiness across nations (Ed Diener & M. Diener 1995; Easterlin & Angekescu 2009; Inglehart & Klingemann 2003; Lynn & Steel 2006; Steel & Ones 2002; Veenhoven 1993; Sivak 2003), the metropolitan level is important since individuals actively seek out locations, identify with their place of residence and derive considerable satisfaction as well as emotional attachment from them. The purpose of this study is to explore the relationship between housing and happiness, using cross sectional from metropolitan regions in the US.
1.2 Research Questions and Aims
Among the past studies (F.Hu 2011; Hussaun A. Syed 2016; Parker et al. 2011; Diaz- Serrano 2009), it has been found that housing could be a significant factor that affects happiness in national-level studies. In response to these findings, the purpose of this research is to examine the relationship between four aspects of housing (median housing values, housing to wages ratio, homeownership rate and household size) and happiness in metropolitan areas. Through using the Gallup Community Well-Being Rankings 2016, the research will also examine different economic and social factors which are thought to effect happiness by previous literature (unemployment, income, age, population density and education) and tested on metropolitan level of happiness. This main aims of this study is to investigate the relationship between housing and happiness in cities as well as factors that could build the happiness of the cities.
Research Questions are structured below
Do Housing indicators (median housing values, housing to wages ratio, homeownership rate and household size) impact Happiness at a metropolitan level?
Research Method– A variety of statistical methods is used in this research. It divides into three parts. First, bivariate Pearson correlation coefficient analyses are going to construct between the happiness index and the independent variables listed below. Scatter graphs are shown to illustrate their relationships. Second, partial correlations are run controlling the effect of the average wages level to improve significance. Third, multivariate ordinary least- squares (OLS) regressions will use to examine the effects of housing on happiness through controlling for average wages and other variables that associated with happiness. OLS is a relevant method as it captures the relationship between dependent variables and independent variables and determines best fitted line. This research is likely to be conducted through the quantitative measure on which SPSS and Stata will be applied after collection of data.
Wellbeing Index- Gallup- Healthway’s 2016 Community Well-Being Rankings
The report examines well-being across the nation, with 189 communities ranked based on their Well-Being Index score. The report analyzes how well-being varies by community, based on the five elements of well-being—purpose, social, financial, community and physical. These data are based on a subset of 354,473 telephone interviews with U.S. adults across all 189 metropolitan cities, conducted from January 2, 2015 to December 30, 2016. Metropolitan Statistical Areas (MSAs) are based on the U.S. Office of Management and Budget definitions. The Well-Being Index is calculated on a scale of 0 to 100, where zero represents the lowest possible well-being and 100 represents the highest possible well-being.
Housing– The central hypothesis is that there is a strong association between housing and happiness at the metropolitan scale. Four measures are indicated for housing.
Housing cost measures– This research uses Median Property Value in 2016 collected in the Livability website and American Community Survey (ACS) which is a nationwide survey designed to provide communities with reliable and timely demographic, social, economic, and housing data for the nation, states, congressional districts, counties, places, and other localities every year. It has an annual sample size of about 3.3 million addresses across the United States and Puerto Rico.
Housing Affordability– It is a ratio of median housing cost to average wages. The median housing cost will be using median property value in 2016 collected in the Livability website and American Community Survey (ACS) and divided by the average wage. The average wage data is gathered from the Bureau of Labor Statistics 2015.
Homeownership rate– It is the percentage of homes that are owned by their occupants. The data source is from the US Census from 2011-2015.
Average Household Size– Household size is the average number of persons per household. The household size data will be collected from Sperling’s BestPlaces which their data source is mostly based on U.S. Census Bureau American Community Survey.
Income-Two measures are used to measure income
Median Household Income– Household income is a measure of the combined incomes of all people sharing a particular household or place of residence. It includes every form of income, e.g., salaries and wages, retirement income, near cash government transfers like food stamps, and investment gains. Data is collected from US Census Bureau and Small Area income and Poverty Estimates (SAIPE) Program.
Average wages– Average wages is a narrower measure and covers total money earnings received for work performed as an employee in the region. This measure includes wages, salary, armed forces pay, commissions, tips, piece-rate payments, and cash bonuses earned before deductions were made for taxes, bonds, pensions, union dues, etc. It is measured on a per worker basis and is gathered from the 2015 US Bureau of Labor Statistics.
Unemployment Rate– It is the percentage of the share of the labor force that is jobless taken form 2015 US Bureau of Labor Statistics.
Education– It is expressed as percentage of people gaining Bachelor’s degree or higher that aged more than 25 years old from 2011-2015. The data source is from US Census Bureau.
Population Density- It is measure of population per square kilometer and taken from US Census Bureau.
Age- It is the median age from US Census 2010.
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