Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

L1022: Project 100% for A1 2022-23

Maximum project length: 3,000 words

A Cross-Country Analysis of the Determinants of Life Expectancy

Life expectancy is an important measure of human development. If an infant is born today, how many years do we expect them to live? The answer to this question will depend on where they are born. In the United Kingdom, the current life expectancy for women is 82.9 years, and for men it is 79 years. However, in poorer countries life expectancy can be much lower. The purpose of this project is to determine the patterns and determinants of life expectancy across poorer countries.

The World Bank uses income per capita to divide countries into the following groups: “high income countries”, “upper middle income countries”, “lower middle income countries”, and “low income countries”.

You will receive data on a random sample of countries from the latter two groups (lower middle and low income countries) with the following variables:

· life_exp_female (LEF): life expectancy of a female infant born today (in years).

· life_exp_male (LEM): life expectancy of a male infant born today (in years).

· gdp_pc (GDPPC): gross domestic product per capita (in US$), which is a proxy for income in the country.

· water_access (WA): the percentage of people in the country that have access to at least basic water services, which is a proxy for the availability of clean drinking water.

· health_spending (HS): government spending on health per capita (in US$), which is a proxy for public health care provision.

For your data analysis, perform the following steps, and write up the results. (Use the marking scheme document to help you with the write-up.)

1. Describe the data, using summary statistics and graphs, as appropriate.

2. Test whether there is a difference between the average life expectancy of men and women.

3. Test whether average female life expectancy is higher in “Lower middle income countries” than in “Low income countries”.

4. Calculate the Pearson correlation coefficients between female life expectancy and each of the following:  GDP p.c., Water Access, and Health Spending. Comment on the results.

5. Calculate the Pearson correlation coefficients between GDP p.c. and Water Access, as well as between GDP p.c. and Health Spending. Test the statistical significance of each correlation coefficient.

6. Estimate a regression model of the form:

where the i subscript corresponds to country i. Interpret the coefficient that you obtain, and comment on the economic and statistical significance.

7. Interpret the R-squared from this regression and test its statistical significance.

8. Estimate a regression model of the form:

where the i subscript corresponds to country i. Interpret the coefficients that you obtain, and comment on their economic and statistical significance. Compare the results to those you obtained in part 6. Which model is better?

9. Predict female life expectancy for a country where GDP p.c. is $1500, 70% of the population have access to water services, and the government spends $80 per person on health care.