**L1022: Assessed Coursework Project**

**Project 1**

**The Relationship between Infant Mortality, Income and Public Expenditures in Sri Lanka 1951 to 1981**

Infant mortality rates have fallen sharply in most countries, rich and poor, over the second half of the twentieth century. Different hypotheses have been proposed to explain this unprecedented trend over the broad sweep of history. Income per capita increased steadily in large parts of the world during this period. Also expenditure on healthcare and education is now significantly higher than in the 1950s. The objective of this project is to establish whether GDP, health and educational expenditures per capita are determinants of the infant mortality rate in one particular country: Sri Lanka.

The data below contains information on the infant mortality rate (IMR), real Gross Domestic Product per capita in rupees (GDPPC), as well as educational and health expenditures per capita in rupees (EDUCPC and HEXPPC) for Sri Lanka, covering the period 1951 to 1981.

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

2. Calculate the pair-wise correlation coefficients between IMR and each of the other variables. Test the statistical significance of each correlation coefficient.

3. Estimate a regression model of the form:

**IMR**_{t }**=α + β**_{1}**GDPPC**_{t}** + β**_{2}**HEXPPC**_{t}** +u**_{t}

where the t subscript corresponds to year t, Interpret the coefficients that you obtain, and comment on their economic and statistical significance..

4. Interpret the R^{2} statistic from the regression and test whether it is statistically significant.

5. Predict the IMR for Sri Lanka at a GDP per capita level of 750 rupees, assuming HEXPPC is at its mean value.

6. Re-estimate the model including the EDUCPC variable and comment on any changes to the results and goodness of fit:

**IMR**_{t }**=α + β**_{1}**GDPPC**_{t}** + β**_{2}**HEXPPC**_{t}** + β**_{3}**EDUCPC**_{t}** +u**_{t}

Explain how the omission of EDUCPC in part 3 may have biased the results. (Note: it is sufficient to discuss the changes, without explicitly showing the testing procedure).

7. What conclusions do you draw from your analysis?

Copy and paste the data into Excel and conduct all the analysis in Excel.

where:

** Year ** = the year of observation;

** IMR ** = Infant Mortality Rate per 1000 live births

** GDPPC ** = Real GDP per capita in rupees

** EDUCPC** = Real Educational Expenditures per capita in rupees

** HEXPPC** = Real Health Expenditures per capita in rupees

**L1022: Assessed Coursework Project**

**Project 2**

**A Cross-Country Analysis of the Determinants of Fertility Rates**

Demographers and economists (rightly or wrongly) have been concerned about world population growth. There has been much investigation of the socioeconomic determinants of fertility levels across countries. The total fertility rate provides a demographic measure of the average number of children born to a woman. It is generally argued that income and health are important determinants of female fertility, while the position of women in society is also hypothesized to play a role.

The purpose of this project is to examine the determinants of female fertility across 64 countries. The variables listed below relate to the early 1980s and include the total fertility rate (TFR), per capita GNP in dollars as a proxy for the wealth of a country (GNPPC), child mortality (CM) as a proxy for the health of a country, the percentage of the female population that are literate (FL) as a rough proxy for the position of women in society, and an index (in percentage terms) of the strength of family planning programmes (FP); the higher the index the greater is the strength of family planning.

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

2. Calculate the pair-wise correlation coefficients between TFR and each of the other variables. Test the statistical significance of each correlation coefficient.

3. Estimate a regression model of the form

**TFR**_{i}** =α + β**_{1}**GNPPC**_{i}** + β**_{2}**CM**_{i}** +β**_{3}**FL**_{i}** + β**_{4}**FP**_{i}** + u**_{i}

where the * i* subscript corresponds to country

*, Interpret the coefficients that you obtain, and comment on their economic and statistical significance.*

*i*4. Interpret the R^{2} statistic from the regression and test whether it is statistically significant.

5. Predict the fertility rate at a per capita income level of $1000, assuming the other independent variables are at their mean.

6. A fellow researcher comments that the low correlation coefficient between GNPPC and TFR (and results in part 3 regarding GNPPC) could be driven by the presence of two outliers in the sample. In particular, observations 30 and 33, which reflect countries that are rich and middle-income, respectively. In contrast, the remaining observations reflect low-income countries.

Drop these two observations from the sample and re-estimate the regression model. How do the results change? Interpret your findings.

7. What conclusions do you draw from the analysis?

Copy and paste the data into Excel and do all the analysis in Excel.

where:

** OBS **= observation

** TFR ** = the total fertility rate

** GNPPC** = Gross National Product per capita in US Dollars

** FL ** = the percentage of the female population who are literate.

** CM** = the child mortality rate per 1000 of live births.

** FP ** = an index of the strength of family planning in the country.