Independent Business Research Analysis
Independent Business Research Analysis Statistical Analysis of Life Expectancy Aliona Tkach Southern States University
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Independent Business Research Analysis
1. Introduction There is an increasing concern in reduction of life expectancy around the world. Hundreds of factors contribute to this process. Among them are the newly-borne diseases, social and economic instabilities, poor diets, education levels etc. Today, however, some of statisticians and scientists are concerned that life expectancy will be greatly influenced by unemployment rate that was to a great extent increased by the economic downturn of 2008 (http://www.worldlifeexpectancy.com/unemployment-reduces-life-expectancy). Though it will take a lot of years before something is scientifically proved, a lot of figures indicate that unemployment is one of the main triggers to life expectancy decline. There are lots of reasons behind it. Firstly, there is an evidence that suicide rates increased by almost twice in 2008 as compared to all the previous years (Hirsch Mental Health, 2008). Secondly, the increase in things like stress, depression, hear-diseases, alcohol consumptions and domestic violence cases are all greatly impacted by unemployment as well. Another important factor influencing life-expectancy is health care expenditures. It is a known fact that the countries with lower disease rates and smaller population use more resources on health care than those with larger population and higher disease rates. Consequently, there is an obvious connection between the amounts spent by various countries on health care and the life expectancy in those countries. 2. Hypothesis The abovementioned facts have triggered me to examine and try to prove the connection between 3 factors, life expectancy, unemployment rate and health care expenditures of various countries.
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Independent Business Research Analysis
In particular, my hypothesis is that life expectancy increases with a decrease in unemployment rate and an increase in health care expenditures. 3. Variables description To find and prove the relationships between those notions, I chose three variables. One dependant variable and two independent ones. The data is cross-sectional, multivariate. The dependant variable is the life expectancy rate (at birth). This rate indicates the number of years an infant is likely to live if the patterns of mortality will stay the same throughout its whole life as they were at its birth. The first independent variable is the health expenditure (per capita). This figure represents a sum (in dollars) of private and public expenditures for health care as a ratio of the whole population of a particular country. The second independent variable is the unemployment rate. This figure indicates the share of labor force that has no job and is not working but is available and looking for work. The figures are expressed in percentage. All the variables are presented in terms of 65 countries and represent the data for year 2009.
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Independent Business Research Analysis
Descriptive statistics Life exp2009 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count
Health 2009 78.80765679 0.703680799 79.84 80.1 5.673255976 32.18583337 8.186065829 -2.31859223 33.372 53.068 86.44 5122.497691 65
Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count
unempl 2009 2091.546589 272.7229402 1004.425498 #N/A 2198.762638 4834557.138 0.434799683 1.181761195 8168.174349 14.68075999 8182.855109 135950.5283 65
Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count
10.08547194 0.770559787 8.077384924 8.077384924 6.212451615 38.59455507 3.670821258 1.813743932 31.82557143 1.13532242 32.96089385 655.5556759 65
Having run the descriptive statistics using the chosen variables, the following conclusions are made. On average, the life expectancy across all 65 countries is around 78 years, with a difference of 6 years, plus or minus. In most cases, people live around 80 years. In 50 percent of countries under consideration the life expectancy exceeds 79 years and in the other 50 percent people live less than that. The expenses paid for health vary to a great extent in different countries. The average health care expenditure per person across 65 countries is $2,092, give or take $2,198. In half of the presented countries the health expense is more than $1,004 while the other half spends less than that. The
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Independent Business Research Analysis
least that some countries pay for health care is $14 per person while the maximum amount is $8,182. As for unemployment rate, it is also very different across the countries. The minimum percentage of people available and looking for job is 1%. The highest rate of unemployed labor force is 32%. On average, 10 % of population has no job. All in all, half of countries under question have less than 8% of unemployment and the other half exceeds that. Correlation Life expectancy 2009 Life expectancy Health expenses unemployment
Health expenses 2009
unemployment
1 0.569688575 -0.588977167
1 -0.395745457
1
The correlation matrix is made with an aim of determining the nature of relationships between the chosen variables. The analysis of this matrix drives us at the following. The positive correlation between life expectancy and health care expenditures tells us that the more money a country spends on the health care, the more population of that country tends to live. One can also say that this relationship is quite strong. The negative correlation between life expectancy and unemployment rate indicates that the smaller are unemployment rates, the more people live.
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Independent Business Research Analysis
Health 2009 and all other X Regression Statistics Multiple R 0.395745457 R Square 0.156614466 Adjusted R Square 0.143227394 Standard Error 2035.218936 Observations 65 VIF 1.185697359
The correlation matrix also presupposes that there is a slight relationship between unemployment rate and health expenditure, which are in my case independent variables. In order to prove that those notions do not bear the same kind of information, and don’t predict one another better than they predict life expectancy, a variance inflation factor (VIF) is run. VIF is 1.2, which, according to some guidelines is not a significant value of multicollinearity. If the VIF is not equal or higher than 5, we can conclude that health expenditures and unemployment rates are not correlated to each other more than each of them correlates to life expectancy. Regression Analysis Table 1.1
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Independent Business Research Analysis
Regression analysis is another statistical model that aims at explaining the connection between the variables. In my case there are three variables, which means I will do the multiple linear regression, which is represented by the formula Y= B0+B1 X1+B2X2, with Y being our life expectancy (dependent variable), X1 being health expenditures and X2 being the unemployment rate (independent variables). B0 will be the intercept (the estimation of Y under condition that X1 and X2 are equal to zero). In our case B0 is equal to 80.62366554 which means the life expectancy is around 80 years if the health expenditures and unemployment rate are zero. B1 and B2 are the coefficients that indicate the change that is expected to occur in the dependent variable under the condition that independent variables are unchanged. Looking into the table 1.1, we can conclude the following about our data. If the health expenditures are increased by 1 unit, the life expectancy will increase by 0.001029785. On the other hand, if the unemployment rate is increased by 1 unit, it will decrease the life expectancy by 0.39362085. ANOVA Table1.2
ANOVA df Regression Residual Total
2 62 64
SS 991.2946547 1068.598681 2059.893336
MS F 495.6473273 28.75741 17.2354626
Significance F 1.45904E-09
ANOVA is another statistical tool, whose aim is to test the constructed model and find its significance level.
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Independent Business Research Analysis
The zero hypothesis is H0: b1=b2=0. Which means we suppose there is no linear relationship between life expectancy, unemployment rate and health expenditures. The hypothesis 1 is H1: b1≠0 or b2≠0. Which means that we suppose at least one of the variables is not equal to zero. In order to reject the null hypothesis, the figure of F significance must be lower than the figure of F value. Based on the table 1.2, our model is significant and we can conclude that there is a linear relationship between life expectancy, unemployment rate and health expenditures, as the figure of F-significance is much lower than that of F value. We can also prove the significance of each of the variables chosen for the analysis. In other words, we can prove that b1≠0 and b2≠0 (which will be our H1). H0 is: b1=0; b2=0 To analyze the significance of separate variables, one must look at the p-values in table 1.2 and see if they are in the rejection regions, that is if they are less than 0.05. In our case both of the pvalues are less than 0.05, consequently we conclude that the variables we have chosen are significant in our model. In other words, there is sufficient evidence that the change in both unemployment rates and health care expenditures has impact on the change in life expectancy rate. Confidence interval This figure shows us what would be change in life expectancy rate given a 1-unit change in unemployment or health care expenditures. According to table 1.1 health care expenditure coefficient is 0.001029785, which falls between the range of 0.000516053 and 0.001543517. That means that we are 95% confident that a 1-unit 8
Independent Business Research Analysis
change in health expenditure will lead to a change in life expectancy within the range of (0.000516053; 0.001543517). We can also conclude a 95% confidence that there is significant relationship between life expectancy and health care expenditures. Based on the same principle, using table 1.1 we are 95 % confident that a 1-unit change in unemployment will lead to a change in life expectancy within the range (5.59E-05; 0.211796521). Plus we are 95% confident the relationship between life expectancy and unemployment rate is also significant.
Regression Statistics Multiple R 0.693711695 R Square 0.481235915 Adjusted R Square 0.46450159 Standard Error 4.151561465 Observations 65
R-square figure tells us the degree of influence of independent variables on the dependent one. In this case we can say that the life expectancy is 45% influenced by unemployment rates and health care expenditures. That drives us to a conclusion that there are other important factors impacting life expectancy which are not accounted for in this research. Using the regression analysis we can make forecasts about how particular changes in unemployment rates and health care expenditures can change the life expectancy. Let’s assume that the health expenditure is equal to $1000 and unemployment is equal to zero. Using the linear regression equation we have the following results.
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Independent Business Research Analysis
Life exp = 80+0.001(1000)+0= 80 +1=81 Which means that with an increase of health care expenditures by a $1000, the life expectancy will equal 81 years (holding the unemployment rate as zero). Now let’s assume, that the unemployment rate is 10, and health care expenditures is 0. Life exp= 80+0-10=70 Consequently the unemployment rate of 10, decreases life expectancy by 10 years, holding the health care expenditures 0. In the third scenario we will assume that unemployment rates are equal to 10 and health care expenditures are equal to $1000. Life expectancy =80+1-10= 71 years. Conclusion Analyzing the information I got from my analysis, I can say that there are sufficient evidence to prove that life expectancy truly depends to certain extent on unemployment rates and health care expenditures. In particular, the higher are the health expenditures in the country, the more people of that country are likely to live. I can also conclude that unemployment factor also has a great impact on the life longevity, the lower it is – the longer is the longevity. I should also mention, however, that there are also other factors that can influence life expectancy, which I didn’t include in my model. So while predicting life expectancy one should take into consideration the education level, disease level, life style etc.
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Independent Business Research Analysis
References: 1. Life expectancy and unemployment (2011). Retrieved from http://www.worldlifeexpectancy.com/unemployment-reduces-life-expectancy 2. Life expectancy depends on health care expenditures (2011). Retrieved from http://www.worldlifeexpectancy.com/unemployment-reduces-life-expectancy 3. World bank (2011) Retrieved from http://data.worldbank.org/
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Independent Business Research Analysis
Appendix. Graphs and Charts Histogram
Frequency
Unemployment 30 25 20 15 10 5 0
Frequency
Bin
Scatter diagram
100
Life expectancy (in years)
90 80 70 60 50 40
Life exp2009
30 20 10 0 0
5
10
15
20
25
30
35
Unemployment level
The scatter diagram shows that there is linear negative correlation between unemployment rates and life expectancy. We can observe some outliers on the diagram which are explained by the countries with very high unemployment rate which in some countries correspond to high life 12
Independent Business Research Analysis
expectancy but in other cases surprisingly high figures of life longevity. That must be explained by other social or economic factors which were not accounted for in this research.
Life exp2009
Health 2009 Line Fit Plot 100 50
Life exp2009 Predicted Life exp2009
0 0
2000
4000
6000
8000 10000
Health 2009
The health line fit plot shows that there is linear positive correlation between life expectancy and health care expenditures. There are also several outliers, which again means that not all possible factors are taken into account while considering life expectancy.
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