this 10 pages essay, the writing requirements will be in the pdf chapter 11, and i all also i…
Life Expectancy at Birth
Introduction
Life expectancy estimates the average duration a person lives based on a number of factors. It is an important measure of the productivity of an economy as it determines. An economy with a short life expectancy loses its human resource at their prime ages. The supply of labor is adversely affected in those economies thus retarding the growth of the economy. This study looks at the factors affecting life expectancy at birth and the manner in which those factors affect life expectancy. It examines the variables that can increase or decrease life expectancy at birth. In this research, life expectancy at birth is the dependent variable. The study also aims at developing a model for estimating the life expectancy at birth as well as offering valuable advice to the policymakers on ways to improve life expectancy at birth.
Literature Review
The International Conventions and Agreements
The Alma Ata Declaration of the 1978 set a life expectancy target to be greater than 60 years by the year 2000. This is a goal that was further confirmed and concluded by the World Summit on Social Development (WSSD). It was specified by the ICPD Program of Action that life expectancy should be greater than 65 years by 2005 for those countries that are currently having the highest mortality levels. It is expected to be about 70 by the year 2015 in those countries. ICPD Program of Action also expects the life expectancy to be between 70 and 75 years life expectancy in countries with lower mortality rates (ICPD Program of Action 178-181).
The longevity gains
Life expectancy at birth has been in a continuous remarkable increase in the OECD nations, showing sharp reductions in the rate of mortality across all ages. These longevity gains can be accredited to various factors that include the rise in living standards, better education, improved access to quality healthcare services, and improved lifestyle. Apart from these, other factors that include better sanitation, housing, and nutrition further play a significant role, especially in countries experiencing an era of emerging economies (OECD 204).
Averagely, across the OECD nations, the life expectancy at birth for the entire population hit 79.5 years in the year 2009, depicting a gain of more than 11 years from the data of the year 1960. Japan takes the lead with a figure of among almost the 34 OECD countries to have a life expectancy at birth to be 80 years and more. The following group includes United States, Portugal and various eastern and central European nations having a life expectancy ranging between 75-80 years. The lowest life expectancy among the OECD countries was shown from Hungary and Turkey. Nevertheless, while there has been a modest increase in the life expectancy in Hungry since 1960, it has rapidly grown in Turkey. The increase is quick to an extent it approaches the OECD average (OECD and World Bank 328).
Explanation of the model
The model seeks to explain the relationship between life expectancy at birth and a number of independent variables. In this research, independent variables include the happy index, HIV rate, GDP per capita, percentage health expenditure per capita, stroke rate per 100,000 persons and the number of doctors per 1000 persons. The expected relationships are as shown below.
| Dependent variable | Independent variables | Expected sign |
|
Life expectancy at birth |
Happy index | + |
| HIV rate per 100,000 persons | (-) | |
| GDP per capita | + | |
| % Health expenditure per capita | + | |
| Stroke rate per 100,000 persons | (-) | |
| Number of doctors per 1,000 persons | + |
The Happy index is a measure of the level of happiness of the population in an economy. A higher Happy Index implies that the population is living a better quality life. When the population is living happily, people are expected to live for longer years. Therefore, there is a direct positive association between happy life index and life expectancy at birth. I expect the slope of this variable to be positive.
HIV has a negative impact on life expectancy. Persons infected with HIV are not expected to live for longer years. In situations where the virus is not controlled, the victim dies after about ten years. Therefore, HIV infection rate per 100,000 persons is inversely related to the life expectancy at birth. Where the HIV rate is high, the life expectancy is expected to be short. The slope coefficient for this variable is expected to be negative indicating that an increase in HIV rate will cause a reduction in life expectancy at birth.
GDP per capita is the average gross domestic product per person in an economy. When other factors such as income distribution, inflation, among other factors, are held constant, GDP per capita can be considered as a measure of welfare. Therefore, a higher GDP per capita implies a better welfare of the population. This translated to a longer life hence life expectancy at birth and GDP per capital are positively related hence the coefficient of slope for GDP per capita is expected to be positive.
The well-being of a population is influenced by the quality and availability of health care services. In most countries, the healthcare sector is largely controlled by the government. If the government spends more on health care, the services will be of high quality and will be accessible to most people. Better healthcare lengthens the lives of individuals hence life expectancy at birth has a positive relationship with the health expenditure per capita. The coefficient of the slope for the variable is expected to be positive.
Stroke is one of the most killer diseases. Victims of stroke have a limited probability of survival in addition to the high cost of treatment and managing the disease. Therefore, stroke rate per 100,000 persons is negatively related to the life expectancy at birth hence a negative coefficient of the slope is expected.
The number of doctors per 1,000 persons shows how accessible healthcare services are in the economy. The ease of access to medical services improves the well-being of the population thus increasing the life expectancy at birth. The positive relationship means that the expected slope coefficient is positive.
Description of data
I obtained data on the dependent variable as well as the six independent variables for 105 different countries including the US, China, UK, among other countries. The data on the happy index was obtained from the 2012 Happy Planet Index rankings released by the New Economics Foundation. The data on GDP per capita, health expenditure per capita and the number of physicians were obtained from the World Bank. Statistics of the HIV rate per 100,000 persons and stroke rate per 100,000 persons were gotten from the World Health Organization (WHO), as well as the WorldLifeExpectancy Organization.
Regression results
The wrong model
| Coefficients | Standard Error | t Stat | P-value | |
| Intercept | 50.88400409 | 3.710008973 | 13.71533181 | 1.93109E-24 |
| Happy Index | 0.36050673 | 0.05956825 | 6.051994675 | 2.69252E-08 |
| HIV rate per 100000 | -0.006498442 | 0.004587118 | -1.416672152 | 0.159782417 |
| GDP per capita $ | 0.000176212 | 3.65839E-05 | 4.816648411 | 5.38194E-06 |
| % Health expenditure per capita $ | 28.67071527 | 17.84839529 | 1.606346946 | 0.11144835 |
| Stroke rate per 100000 | -0.032792309 | 0.011593182 | -2.828585646 | 0.005681158 |
| number of doctor per 1000 | 0.63775455 | 0.430071508 | 1.482903511 | 0.141341879 |
| cigarette consumption per capita | 0.002520987 | 0.000787129 | 3.202762258 | 0.001841634 |
Problems with the wrong model
The t-Statistic for HIV rate per 100,000 persons is less than 2. This implies that the variable has a less than significant impact on life expectancy. This also the same case for the percentage healthcare expenditure per capita variable. The value of t-Statistic implies that healthcare spending has an insignificant impact on life expectancy at birth hence it violates the economic theory or assumptions made above. In addition, the model shows an unrealistic relationship between life expectancy and cigarette consumption per capita. The positive coefficient indicates that an increase in cigarette consumption increases life expectancy. The second model below corrects the above limitations of the first model.
The second model
| Coefficients | Standard Error | t Stat | P-value | |
| Intercept | 51.96798861 | 3.865105858 | 13.4454244 | 5.61374E-24 |
| Happy Index | 0.335001062 | 0.061758945 | 5.424332677 | 4.19687E-07 |
| HIV rate per 100000 | -0.009556224 | 0.004693799 | -2.035925061 | 0.044456458 |
| GDP per capita $ | 0.000166594 | 3.81438E-05 | 4.367529266 | 3.12224E-05 |
| % Health expenditure per capita $ | 38.07065661 | 18.41823036 | 2.067009472 | 0.041369661 |
| Stroke rate per 100000 | -0.025390146 | 0.011884955 | -2.136326625 | 0.0351434 |
| number of doctor per 1000 | 1.380764953 | 0.378854046 | 3.644582836 | 0.000430727 |
From the above results, the regression model for life expectancy at birth can be given by the following equation:
LE = 51.968 + 0.335(HI) – 0.009556(HIV) + 0.0001666 (GDP) + 38.07 (HE) – 0.02539 (S) + 1.3808 (DOC)
Where LE is the life expectancy at birth, HI is the Happy Index, HIV is the HIV rate per 100,000 persons, GDP is the GDP per capita; HE is the percentage health expenditure per capita, S is the stroke rate per 100,000 persons, and DOC is the number of doctors per 1,000 persons.
Given the values of each of the six independent variables, one can use the above regression equation to estimate the life expectancy at birth.
Analysis of regression results
Test of significance of the model and coefficients
The Adjusted R Square for the equation is 0.7255. This implies that changes in the six independent variables explain 72.55% of the changes in the dependent variable (Asteriou and Hall 247). The value is more than 50% hence the model is reliable in estimating the life expectancy at birth. Only 27.45% of the changes in life expectancy at birth are due to changes in factors that are not included in the model.
Tests for the intercept and coefficients
The t Stat for the intercept is 13.4454244 with a P-value of 5.61374E-24. The probability of the intercept being statistically insignificant is less than 0.05 hence the intercept is a reliable measure at 95% significance level.
The coefficient for Happy Index is also statistically significant since its P-value (4.19687E-07) is less than 0.05. In addition, the coefficient is positive hence it is in line with the theoretical expectation since Happy Index and life expectancy are positively related.
The t-Stat for HIV rate per 100,000 is negative with a P-value of 0.04. The P-value is below 0.05 thus the coefficient of the variable is a reliable measure of the variation in life expectancy per unit change in HIV rate per 100,000 persons. The negative value of the coefficient and the t-Stat indicates a negative relationship between life expectancy and HIV rate. This confirms the theoretical relationship between the variables. The t-Stat for Stroke rate per 100,000 persons is also negative indicating that an increase in the variable causes a decline in life expectancy (Newbold, Carlson and Thorne 976). The P-value for the coefficient of stroke rate is less than 0.05 hence the coefficient is statistically significant.
The coefficients for GDP per capita, % healthcare expenditure per capita and the number of doctors per 1,000 persons are all positive hence they are in line with economic theory. In addition, the P-values are less than 0.05 hence the coefficients are statistically significant.
The above tests indicate that the model as a whole and the individual coefficients, as well as the intercept, are statistically significant hence the model is reliable for estimating the life expectancy at birth in a country. However, the standard error of 3.82 indicates that estimated life expectancy may deviate from the actual by about four years. In addition, the model only captures 72.55% of the changes in life expectancy. These limitations can be reduced by adding more variables such as the level of education, drug abuse, state of security, among others, to the model.
Impact analysis of the coefficients
Impact analysis indicates the effect of a change in any of the variables on life expectancy at birth. In this case, the analysis explores the impact of increasing the positive variables by 10% and reducing the negative variables by 10%. The table below shows the results of the analysis.
Summary statistics
| Life expentancy at birth | Happy Index | HIV rate per 100000 | GDP per capita $ | % Health expenditure per capita $ | Stroke rate per 100000 | number of doctor per 1000 | ||
| Mean | 73.08 | 46.25 | 31.39 | 18802.15 | 0.057 | 81.07 | 1.93 | |
| Standard Error | 0.711 | 0.66 | 9.094 | 1573.03 | 0.0027 | 4.62 | 0.14 | |
| Median | 74.1 | 45.8 | 2.4 | 13788 | 0.053 | 74 | 1.8 | |
| Mode | 74 | 46 | 0 | 4666 | #N/A | 43.5 | 0.2 | |
| Standard Deviation | 7.31 | 6.80 | 93.19 | 16118.78 | 0.03 | 47.33 | 1.45 | |
| Sample Variance | 53.37 | 6.20 | 8683.49 | 259814965 | 0.0008 | 2240.15 | 2.105 | |
| Range | 34.7 | 27.4 | 555.7 | 78014 | 0.149 | 187.2 | 6.18 | |
| Minimum | 48.7 | 36.6 | 0 | 748 | 0.0061 | 19.7 | 0.02 | |
| Maximum | 83.4 | 64 | 555.7 | 78762 | 0.155 | 206.9 | 6.2 | |
| Sum | 7673.7 | 4856 | 3295.9 | 1974226 | 6.03 | 8512.7 | 202.49 | |
| Count | 105 | 105 | 105 | 105 | 105 | 105 | 105 | |
Impact analysis
| Change | Life expectancy | Intercept | Happy Index | HIV rate | GDP | Health expenditure | Stroke rate | Number of doctors |
| Coefficients | 51.97 | 0.335 | -0.00956 | 0.000167 | 38.07 | -0.0254 | 1.381 | |
| Mean | 1 | 46.25 | 31.39 | 18802.15 | 0.06 | 81.0733 | 1.93 | |
| 73.08 | 51.97 | 15.4938 | -0.3001 | 3.131 | 2.185 | -2.0590 | 2.663 | |
| 10% change in variables | ||||||||
| Happy Index(10% increase) | 1 | 50.875 | 31.39 | 18802.15 | 0.0574 | 81.0733 | 1.93 | |
| 1.55 | 74.63 | 51.97 | 17.04 | -0.3001 | 3.131 | 2.1850 | -2.0590 | 2.663 |
| HIV rate(10% decrease) | 1 | 46.25 | 28.25 | 18802.15 | 0.0574 | 81.0733 | 1.93 | |
| 0.03 | 73.11 | 51.97 | 15.49 | -0.2701 | 3.131 | 2.1850 | -2.0590 | 2.663 |
| GDP per capita(10% increase) | 1 | 46.25 | 31.39 | 20682.2 | 0.0574 | 81.0700 | 1.93 | |
| 0.32 | 73.4 | 51.97 | 15.49 | -0.3001 | 3.444 | 2.1850 | -2.0590 | 2.663 |
| Health expenditure(10% increase) | 1 | 46.25 | 31.39 | 18802.15 | 0.0631 | 89.1770 | 2.12 | |
| 0.28 | 73.36 | 51.97 | 15.49 | -0.3001 | 3.1305 | 2.4037 | -2.2651 | 2.9288 |
| Stroke rate(10% decrease) | 1 | 46.25 | 31.39 | 18802.15 | 0.0574 | 89.1770 | 2.12 | |
| 0.06 | 73.14 | 51.97 | 15.49 | -0.3001 | 3.1305 | 2.1852 | -2.2651 | 2.9288 |
| Number of doctors(10% increase) | 1 | 46.25 | 31.39 | 18802 | 0.0574 | 81.0700 | 2.12 | |
| 0.27 | 73.35 | 51.97 | 15.49 | -0.3001 | 3.1305 | 2.1852 | -2.0592 | 2.9288 |
The average life expectancy was 73.08 years. An increase in Happy Planet Index by 10% leads to an increase in life expectancy from 73.08 to 74.63 years. A 10% reduction in HIV rate per 100,000 persons will result in an increase in life expectancy from 73.08 to 73.11 years. A 10% growth in GDP per capita will lead to a rise in life expectancy to 74.63 years from the initial average. If the government increases healthcare expenditure per capita by 10%, life expectancy in the country will increase to 73.36 years. A decline in stroke rate per 100,000 persons will also cause an increase in life expectancy from 73.08 to 73.14 years. Finally, increasing the number of doctors per 1,000 persons by 10% will cause a surge in life expectancy to 73.35 years.
The above analysis shows that the happy index has the greatest impact on life expectancy at birth of all the six variables. It is followed by GDP per capita, health care expenditure per capita and number of doctors per 1000 persons in that order. HIV rate and stroke rate have the least impact on life expectancy at birth.
Summary and Conclusion
Life expectancy at birth is an important economic measure. The study has shown that it is influenced by the happy index, GDP per capita, expenditure on healthcare, number of doctors per 1,000 persons, HIV infection rates, and stroke rates among the population. The regression results and the economic theory indicate that HIV and stroke rates are negatively related to life expectancy while the other variables are positively related to the independent variable.
Macroeconomic policymakers should, therefore, increase budget allocations to healthcare. It will enhance the quality of medical care thus increasing life expectancy. In addition, they should establish programs to ensure training of adequate doctors in order to increase the number of doctors per 1,000 persons. This may involve offering scholarships for students willing to take medicine courses as well as offering better pay to attract more doctors to work in the country. In addition, the government should fund programs of controlling and managing HIV infections and stroke. These efforts will reduce the cases of HIV and stroke thus increasing life expectancy at birth. This will improve the economy as the population will be available to work in productive activities for a longer period than when the life expectancy is shorter. Furthermore, policymakers should put measures that improve economic growth thus increasing GDP per capita. This would require the application of several macroeconomic policies. In addition, the government should provide recreational facilities such as entertainment as well enhancing social groupings. It should also offer or promote the access to counseling services and stress management. These will enhance the level of happiness of the population thus increasing the happy index.
Works Cited
Asteriou, Dimitrios, and S. G Hall. Applied Econometrics. Basingstoke [England]: Palgrave Macmillan, 2011. Print.
Bank, OECD. Harmonization, Alignment, Results. S.l.: World Bank, 2007. Print.
ICDP. The European Community’s Response to the Challenges of the International Conference on Population and Development: ICPD 5, a Five Year Review, 1994-1998. Luxembourg: Office for Official Publications of the European Communities, 2000. Print.
Newbold, Paul, William L Carlson, and Betty M Thorne. Statistics For Business And Economics. Boston: Pearson Education, 2010. Print.
OECD Annual Report 2004. 2004 ed. S.l.: OECD Pub., 2004. Print.
