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The graph above indicates a strong linear relationship between income inequality and a measure of health and social problems. The two variables have a positive association. This indicates that as income inequality rises, so will the index of health and social problems. As a result, Richard and Kate contend that more disparity creates health concerns. This claim, however, may be invalid. This is due to the fact that correlation does not always imply causation. Although countries with high inequality have a higher level of health and social problems that may not imply that the high inequality caused the high level of health and social problems. Several factors could have contributed to the strong positive correlation between the two variables.

First, the conclusion that greater inequality causes health problems may not be true because the health problems may, in contrast, be the cause of the greater inequality. In this case, the dependent variable may have been interchanged with the independent variable. If this is the case, the conclusion would be misleading because if one changed or manipulated inequality, the resulting effect would not be a change in health problems. Instead, one would change or manipulate inequality by changing health problems.

The other reason that could have led to the high positive correlation between the two variables is the presence of confounding variables. Confounding variables are the variables that are not included in the correlation analysis, and that could be influencing the change in the two variables (Magnusson, 2014). Therefore, the positive correlation between the two variables could be as a result of a common driving factor. For example, we may assume that the confounding variable for this phenomena is the population demographics. In a country where the majority of the citizens are older, there is likely to be higher income inequality due to a lower number of working population. At the same time, the higher number of older people may also be associated with a higher level of health problems. Therefore, the relationship between inequality and health concerns may be a case of association and not causation (James & Terry, 2013).

Part 2

This report will present the findings on the relationships between four social economic factors. The four social economic factors are income inequality, health, income and social capital.

Income inequality

Income inequality is the measure of the relative distribution of income among households or individuals in the economy. It is usually measured in the form of the percentage of income held by a certain proportion of the population. The Gini index is normalized and ranges from zero to one. A Gini index of zero implies perfect income equality. This is the ideal value of the Gini index when all the economic players earn equally. A Gini index of one shows the perfect inequality where very one member in the economy earns the income while the other economic players earn nothing.

Health

Health is the state of well-being of individuals. It is also a measure of the social standard in an economy. It is difficult to measure and determine the level of health in the economy. One of the measures that are used to measure the relative state of health in the economy is the life expectancy. Countries with a higher life expectancy are expected to have a higher level of health status than the countries with lower life expectancy. Life expectancy is the number of years that a person is expected to live. It is highly depended on the standards of living as well as the quality of medical and health care in the economy. In this study, the life expectancy for 2011 for the sample countries will be used.

Income

Income is the money or its equivalent that individuals in an economy receive in exchange for goods or services. Income determines the level of expenditure for an individual. Individuals with higher income have higher standards of living while individuals with lower income have lower standards of living. The best estimator of an individual’s income is the post taxation income. Therefore, in this study, we shall use the household disposable income to represent the income for households.

Social capital

Social capital is the framework and networks build within a society that enables people to relate and interact. Social capital provides the environment for peaceful co-existence within a society. In this study, we shall use the social mobility index to measure social capital. The social mobility index measures the probability of households and individuals especially children of moving up the social strata relative to others in different locations. A lower value of the social mobility index is more desirable.

Methodology

A sample of 36 countries is used in the study. Four continuous quantitative variables are recorded. The variables include LE2011 representing the life expectancy at 2011, INEQ representing the social mobility index for the countries, HNDI representing the household disposable income and Gini representing the Gini coefficient. Both descriptive and inferential statistics will be used to determine certain properties of the data. Correlation analyses will be conducted to determine the relationships between the variables. Further, a regression analysis will be conducted to determine the effect of the three independent variables on health.

Research questions

The aim of this study is to investigate and determine the effect of the three variable on health. The research hypotheses for the study are;

a) Greater income inequality is associated with lower health.

b) Higher income is associated with better health.

c) Lower social capital is associated with lower health.

Income inequality and health

Income inequality is represented by the Gini index while the life expectancy represents health. The graph below shows the relationship between the two variables.

The above graph does not indicate a definite relationship between the two variables. The table below shows the correlation coefficients.

Income and health

The graph below shows the relationship between income and health.

The above scatter plot shows a positive linear correlation between income and health.

Social capital and health

The graph below shows the relationship between social capital and health.

There seems to be a negative correlation between life expectancy and the social mobility index.

Correlation

The table below shows the correlation between the dependent variable and the independent variables.

LE2011

INEQ

HNADI

Gini

LE2011

1

INEQ

-0.4800828

1

HNADI

0.6557185

-0.4646437

1

Gini

-0.4398991

0.96283315

-0.4151621

1

Greater income inequality is associated with lower health

The correlation coefficient for the relationship between health and income inequality is -0.4399. This indicates that there exists an inverse relationship between health and income inequality. The indicator for income inequality is such that countries with higher values of Gini index have higher levels of income inequality while countries with lower values of Gini index have lower levels of income inequality. On the other hand, the indicator for health is measured such that countries with a higher life expectancy have better health than countries with lower life expectancy.

Hence, the negative correlation between health and income inequality shows that an increase in Gini index (increasing inequality) would be associated with a decrease in life expectancy (decreasing health). Similarly, a decrease in Gini index would be associated with an increase in life expectancy.

The correlation coefficient is significant because it is less than – 3. This a weak negative correlation between the two variables. Therefore, we conclude that greater income inequality is associated with lower health.

Higher income is associated with better health

The correlation coefficient for the relationship between income and health is 0.6557. This indicates that there exists a positive correlation between the two variables. Higher values for household disposable income, the indicator for income, indicate higher income while lower values of the household disposable income indicate lower income levels.

Therefore, the positive correlation between the two variables shows that an increase in disposable income is associated with an increase life expectancy. Similarly, a decrease in income is associated with a decrease in life expectancy. Assuming that income has a causal effect on life expectancy, then 65.57% of the changes in life expectancy are attributed to changes in income.

The correlation coefficient is significant because it is greater than 0.3. Therefore, there exists a medium positive correlation between health and income. Hence, we can conclude that higher income is associated with better health.

Lower social capital is associated with lower health

The correlation coefficient for the relationship between social capital and health is negative. This indicates that the relationship between social capital and health is inverse. Higher values of social mobility index are associated with lower life expectancy while lower values of social mobility index are associated with higher life expectancy.

The correlation coefficient shows that 48.01% of the variation in life expectancy is attributed to the variation in social mobility index. Therefore, lower levels of social mobility index are associated with higher life expectancy and higher levels of social mobility index are associated with lower life expectancy.

The correlation between social capital and health is significant because the coefficient is less than -3. Therefore, there exists a weak inverse relationship between social mobility index and life expectancy. However, lower values of social mobility index indicate higher social capital while higher values of social mobility index indicate lower social capital. Hence, we conclude that higher social capital is associated with better health while lower social capital is associated with poorer health.

Regression

We will perform a multiple regression analysis to evaluate the effect of social capital, income and income inequality in determining the level of health. Life expectancy, the indicator for health, is the dependent variable while Gini coefficient, household disposable income, and social mobility index are the independent variables.

The table below shows the results of the regression analysis.

Coefficients

Standard Error

t Stat

P-value

Intercept

76.556581

3.09621054

24.7258964

4.8744E-21

INEQ

-17.798845

21.6606945

-0.8217116

0.41794575

HNADI

0.00025054

6.6196E-05

3.78483071

0.00071521

Gini

0.08728377

0.21879247

0.39893405

0.6928643

We can obtain the following regression coefficient for predicting life expectancy.

Life expectancy = 76.5566 – 17.7988(INEQ) + 0.00025(HNADI) + 0.0873(Gini)

Where INEQ is the social mobility index, HNADI is the household disposable income and Gini is the Gini coefficient.

The coefficients for social mobility index and Gini index are not significant predictors because the p-values, 0.418 and 0.693, are greater than the level of significance, 5%. Household disposable income is the significant predictor variable because the p-value, 0.00072 is less than the level of significance.

The table below shows the result soft the analysis of variance (ANOVA) for the significance of the regression model.

ANOVA

df

SS

MS

F

Significance F

Regression

3

163.674182

54.5580608

9.92083114

0.00011543

Residual

29

159.480969

5.49934376

Total

32

323.155152

The multiple regression model is significant because the p-value, 0.00012 is lower than the level of significance. Therefore, we conclude that the regression model is a significant predictor of the dependent variable.

The table below shows the correlation for the regression model.

Regression Statistics

Multiple R

0.71167965

R Square

0.50648793

Adjusted R Square

0.45543495

Standard Error

2.34506796

Observations

33

The coefficient of determination for the regression model is 0.5065. This shows that the three independent variables explain 50.65% of the variation in life expectancy for the model.

Discussion

One of the objectives of the government and social services in increasing health and medical services is the increase in life expectancy for the citizens. There are various ways that the government and other policy stakeholders can increase life expectancy. First, life expectancy can be increased by reducing income inequality in the country. Secondly, life expectancy can be increased by increasing the disposable income for the citizens. And thirdly, life expectancy can be increased by increasing social capital for the citizens.

Reference List

James, M. T. and Terry, S. (2013). Statistics. Boston: Pearson.

Magnusson, K. (2014). Visualization: Interpreting Correlations. [online] Available at: http://rpsychologist.com/new-d3-js-visualization-interpreting-correlations

World Bank, n.d. World Development Indicators. [online] Available at: http://databank.worldbank.org/data/reports.aspx?source=world-development indicators# [Accessed 19 April 2017].

May 17, 2023

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