Household Mortgage Expenditure for North Sydney and Wagga Wagga - Case Study Example

Demographic Profiles and Distributions and Inequalities of Household Income and Household Mortgage Expenditure Insert Name Insert Name of University Insert Name of Course Insert Name of Professor October 22, 2016 Table of Contents Introduction 2 Wagga wagga City 2 Demographic Profiles 2 Household income distribution and inequality in Wagga Wagga 5 Household mortgage repayment distribution and inequality in the Wagga Wagga 7 Demographic profiles of North Sydney 9 Demographic Profiles 9 Household income distribution and inequality in the North Sydney 12 Household mortgage repayment distribution and inequality in North Sydney 13 References 15 Introduction The demographic composition presents a challenge. A rapid increase in the number of people aged 65 years old and beyond will continue for many years (Datamonitor). The New South Wales still one of the most inequitable societies in the world, with the wealthy people making six times as much money as the poorest quintile (Datamonitor). This inequality is also evident in mortgage expenditure and income distribution, including access to care and insurance coverage.in this paper, demographic Profiles and distributions and Inequalities of Household Income and Household mortgage Expenditure for North Sydney and Wagga Wagga will be explored. Wagga wagga City Demographic Profiles Age groups – the following figure presents the demographic information for residents in terms of age group of Wagga Wagga city of New South Wales . The figure shows that residents from 0 to 18 years old have the largest proportion while 36-50 years old are second largest population. The most interesting part is that people consisting retirement age occupy the third largest percentage. This makes a population not active in producing income for mortgage payment to be 44% (18%+26%) Gender- In the figure presented above, it shows that although female dominate the population of the city, the margin of difference is not highly significant. This may have an implication on other aspects of diversity within the population. Ethnicity - presents that Locals still occupy the largest proportion of population of Wagga Wagga, as may be expected. The percentage of each ethnic grouping also shows a diversified population in terms of proportion of non-locals. Highest qualification achieved- the figure below shows highest qualification of the income earners in in the city. Most income earners were have no qualification in tertiary institution as shown from the graph. A quarter of the whole earning population have vocation training positions. Occupational structure- in terms of occupation, most income earners were mostly Professional followed technical and trades while mangers are third. This is shown in the graph below. 10.65% of income earners occupy laborer positions. Household income distribution and inequality in Wagga Wagga The figure below shows percentage distributions of households in Wagga wagga by income categories. According to information above there exists a drastic change in the socio-economic landscape within the geographical confines of Wagga Wagga city. The findings shows that during 2011 the composition of income was $0-$999 had 44%, $1000-$1999 had 33%, $2000-$2999 had 16%and others have 7%. The share of very low income and middle-income in the neighborhoods to the total geographic area has more than 77% total. In sharp contrast, the share of high and very high-income neighborhoods has increased from 24% to 26%. On the other hand the share of middle-income neighborhoods has drastically fallen from 82% to 53%. These changes in the composition of the localities are even more prominent in the Wagga Wagga city. These few figures alone paint a gloomy picture of the deteriorating social conditions within the Wagga wagga city i.e. people capable to paying for mortgage since low income and the middle class in large numbers. Consequently, the city of Wagga Wagga will have to face a situation in which its residents will be living in a completely unfair society; where the poor socio-economic classes will not have access to the same mortgage payments. This will eventually create a situation in which the society would become divided into different segments; each of which will abhor and detest other social groups. The above analysis may look like a satirical mocking of the situation, but the probability of such a situation is actually quite high. Furthermore the racial polarization between different ethnic and social groups has also been deepened. In simpler terms the whites enjoy higher income as compared with other ethnic minorities and immigrants. In this regard it can be predicted with quite certainty that the share of white population will continue to decrease whereas this ethnic segment will continue to hold and control a majority of the resources within Wagga Wagga city. The data was used to calculate Gini coefficients as follows; HOUSEHOLD INCOME % of household (Y) (X) Cumulative % of payment (σY) Cumulative % of (σX) |X-Y| σYi-1 + σYi (A) σXi-1 – σXi (B) A*B $0-$999 8686 44.21% 17.00% 44.21% 17.00% 0.27206 0.44 0.17 0.07515 $1000-$1999 6,409 32.62% 17.00% 76.82% 34.00% 0.156174 1.21 0.17 0.205749 $2000-$2999 3,131 15.93% 17.00% 92.76% 51.00% 0.010653 1.70 0.17 0.288288 $3000-$3999 1045 5.32% 16.00% 98.08% 67.00% 0.106817 1.91 0.16 0.305335 $4000-$4999 214 1.09% 17.00% 99.17% 84.00% 0.159109 1.97 0.17 0.335311 $5000-$5999 164 0.83% 16.00% 100.00% 100.00% 0.151654 1.99 0.16 0.318665 100.00% 19649 0.856465 1.528497 Dissimilarity Index 0.42823 ID = G31*0.5 Gini's Coefficients 0.52850 G = |1-J31| The calculated Gini's Coefficients is 0.5282. Household mortgage repayment distribution and inequality in the Wagga Wagga The calculation of the percentage distribution of households by household mortgage repayment categories in Wagga Wagga city are presented in the graph below. The chart shows that those who were paying mortgages of $1000-$1999 were majority mortgage takers at 49% followed by those paying $1-$999. However those that were paying less than $3000 accounts for 88% of mortgage takers. Another significant mortgage payment issue is that the segment that paying mortgages of $3000-$3999 is five percentage of the mortgage payment population ranges. Nonetheless, households paying mortgages more than this comprise a negligible proportion of the population. The resources owned by the population differ dramatically, in regard to the earlier lifestyle and occupation of the individuals. The following table indicate the calculation of Gini coefficient households with a mortgage   % of Payment (Y) mortgage (X) Cumulative % of payment (σY) Cumulative % of (σX) |X-Y| σYi-1 + σYi (A) σXi-1 – σXi (B) A*B $1-$999   19.500% 0.14 0.1950 0.14 0.0550 0.1950 0.1400 0.0273 1000-1999   48.600% 0.14 0.6810 0.28 0.3460 0.8760 0.1400 0.1226 $2000-$2999   19.200% 0.14 0.8730 0.42 0.0520 1.5540 0.1400 0.2176 $3000-$3999   4.900% 0.15 0.9220 0.57 0.1010 1.7950 0.1500 0.2693 $4000-$4999   1.400% 0.14 0.9360 0.71 0.1260 1.8580 0.1400 0.2601 $5000 and over   1.200% 0.14 0.9480 0.85 0.1280 1.8840 0.1400 0.2638 Not stated   5.200% 0.15 1.0000 1.00 0.0980 1.9480 0.1500 0.2922     100.00% 1.00     0.90600     1.4528 Dissimilarity Index 0.4530 ID = G31*0.5 Gini's Ratio 0.4528 G = |1-J31| The gini ratio is 0.45283 Demographic profiles of North Sydney Demographic Profiles Age groups – the following figure presents the demographic information for residents in terms of age group of North Sydney. The figure shows that residents from 26 to 35 years old have the largest proportion while 0-18 years old are second largest population. The retirement age and children which is non-working population for mortgage payment is 31% (25%+6%) Gender- the figure below presents proportion of gender in the area, it shows that although female dominate the population of the city, the margin of difference is not highly significant. This may have an implication on other aspects of diversity within the population. Ethnicity – The Chart below shows presents that ethnicity of the communities living in the area. English and Europeans occupy the largest proportion of population of North Sydney while Australians occupy the third position. The percentage of each ethnic grouping also shows a diversified population in terms of proportion of non-locals. Highest qualification achieved- the figure below shows Bachelor or Higher degree consist of almost 50% of working population in the area. Most income earners were having no qualification in tertiary institution or did not state their qualification as shown from the graph. A 8% of the whole earning population have vocation training positions. Occupational structure- in terms of occupation, most income earners were mostly Professional at 43.6% followed managers. It further depicts that 14.1% of income earners occupy clerical positions. Household income distribution and inequality in the North Sydney The figure below shows percentage distributions of households in North Sydney by income categories show that those who earn $2000-$2999 followed by $1000-$1999. The area seems to be occupied by middle income earner. The findings shows that during 2011 the composition of income was $2000-$2999 had 27%, $1000-$1999 had 26%, $0-$999 had 22%and others have25%. The share of high income and middle-income in the neighborhoods to the total geographic area has more than 75% total. These changes in the composition of the localities are even more prominent in the North Sydney. These few figures alone paint a gloomy picture of the deteriorating social conditions within the North Sydney city i.e. people capable to paying for mortgage since low income and the middle class in large numbers. Consequently, the city of North Sydney will have to face a situation in which its residents will be living in a completely unfair society; where the poor socio-economic classes will not have access to the same mortgage payments. This will eventually create a situation in which the society would become divided into different segments; each of which will abhor and detest other social groups. The data was used to calculate Gini coefficients as follows; HOUSEHOLD INCOME % of household (Y) (X) Cumulative % of payment (σY) Cumulative % of (σX) |X-Y| σYi-1 + σYi (A) σXi-1 – σXi (B) A*B $0-$999 5721 21.60% 17.00% 21.60% 17.00% 0.04603 0.22 0.17 0.03672 $1000-$1999 6,773 25.58% 17.00% 47.18% 34.00% 0.08576 0.69 0.17 0.1169 $2000-$2999 7,210 27.23% 17.00% 74.41% 51.00% 0.1023 1.22 0.17 0.20669 $3000-$3999 3561 13.45% 16.00% 87.85% 67.00% 0.02553 1.62 0.16 0.25961 $4000-$4999 1,333 5.03% 17.00% 92.89% 84.00% 0.11966 1.81 0.17 0.30725 $5000-$5999 1,884 7.11% 16.00% 100.0% 100.0% 0.08886 1.93 0.16 0.30862 100.0% 26482 0.46810 1.23583 Dissimilarity Index 0.23405 ID = G31*0.5 Gini's Ratio 0.23583 G = |1-J31| The calculated Gini's Coefficients is 0.2358. Household mortgage repayment distribution and inequality in North Sydney The calculation of the percentage distribution of households by household mortgage repayment categories in North Sydney The chart shows that those who were paying mortgages of $2000-$2999 were majority mortgage takers at 24% followed by those paying $3000-$3999. However those that were paying more than $2000 accounts for73.78% of mortgage takers. Another significant mortgage payment issue is that the segment that paying mortgages of $1000-$1999 is 17% of the mortgage payment population ranges. Nonetheless, households paying mortgages more than this comprise a negligible proportion of the population. The following table indicates the calculation of Gini coefficient. households with a mortgage   % of Payment (Y) mortgage (X) Cumulative % of payment (σY) Cumulative % of (σX) |X-Y| σYi-1 + σYi (A) σXi-1 – σXi (B) A*B $1-$999 638 9.418% 0.14 0.09418 0.14 0.04582 0.09418 0.14000 0.01319 1000-1999 1,196 17.656% 0.14 0.27074 0.28 0.03656 0.36492 0.14000 0.05109 $2000-$2999 1,636 24.151% 0.14 0.51225 0.42 0.10151 0.78299 0.14000 0.10962 $3000-$3999 1,239 18.291% 0.15 0.69516 0.57 0.03291 1.20741 0.15000 0.18111 $4000-$4999 758 11.190% 0.14 0.80706 0.71 0.02810 1.50221 0.14000 0.21031 $5000 and over 980 14.467% 0.14 0.95173 0.85 0.00467 1.75878 0.14000 0.24623 Not stated 327 4.827% 0.15 1.00000 1.00 0.10173 1.95173 0.15000 0.29276   6,774 100.00% 1.00     0.35129     1.10430 Dissimilarity Index 0.17565 ID = G31*0.5 Gini's Ratio 0.10430 G = |1-J31| The aging population will have a substantial repercussion on mortgage payments. The impact of high life expectancy and progressive decline in life expectancy without any impairment have triggered a remarkable evolution of the demographic in most area. The evolution of demographics within the population segment of persons above 50 years of age, influence other issues of the mortgage payment. An increase of life expectancy leads to growth of the older persons, and long life. This will impact directly on mortgage expenditure. References Butler, T., & Watt, P. 2007. Understanding Social Inequality. SAGE. Chomsky, N. 2012. Making the Future: The Unipolar Imperial Moment. City Lights Books. Community profile, 2016. North Sydney Council area. Retrieved October 24, 2016 http://profile.id.com.au/north-sydney/ Community profile, 2016. Wagga Wagga Council area. Retrieved October 24, 2016 http://profile.id.com.au/north-sydney/ Hauser, R., & Becker, I. , 2003. Reporting on Income Distribution and Poverty: Perspectives from a German and a European Point of View. Springer. Hurst, C. E. (2012). Social Inequality: Forms, Causes, and Consequences. Pearson College Division. Munck, R., 2005. Globalization and Social Exclusion: A Transformationalist Perspective. Kumarian Press. Neckerman, K. M. 2004. Social Inequality. New York: Russell Sage Foundation. Sernau, S., 2010. Social Inequality in a Global Age. New York: Pine Forge Press. Read More