Assignment : 2
Q.3
Scatter Plots: Visualising the relationship between Infant Mortality Rate and Female
Education.
The x-axis represents the represents female education years and the y-axis represents infant
mortality rate. Infant Mortality Rate is a measure of the number of deaths of infants under
one year of age per 1,000 live births in a given year. Female education years indicates the
number of years a female has been educated for.
This scatter plot shows a negative relationship. As the number of education increases the
infant mortality rate reduces and tends to 0. The cluter in the bottom right corner suggests
that with higher level of female education tend to have lower infant mortality rates and closer
to 0.
Scatter Plot: Visualising the relationship between Infant Mortality Rate and GDP per Capita.
The x-axis represents the represents GDP per capita and the y-axis represents infant mortality
rate. Infant Mortality Rate is a measure of the number of deaths of infants under one year of
age per 1,000 live births in a given year. GDP per capita is a measure of a country's economic
output per person.
There seems to be a negative relationship between Infant Mortality Rate and GDP per capita,
(i.e.) as GDP per capita increases, infant mortality rate tends to decrease or is close to zero.
In the graph we can see that the dots are in an L- shape towards the bottom left corner of the
graph which indicates that the countries with lower GDP per capita tend to have bigger range
of infant mortality rates. The countries with low GDP per capita the IMR range anywhere
between 0 to just below 100 in the given data. However, as the GDP per capita increases, the
IMR falls and is always close to 0.
Q. 4. a) Bivariate regressions of the infant mortality rate on female education and interpreting
the coefficient and R2
1. The R-squared value, 0.6925 indicates that approximately 69% of the variation in
infant mortality rate can be explained by the variation in female education years.
2. The coefficient for female education years is -5.189283. This means that for every
additional year of education that women receive, infant mortality rate is expected to
decrease by 5.189283 deaths per 1,000 live births.
This result suggests that there seems to be a strong negative relationship between female
education years and infant mortality rate. Also, that increasing female education is associated
with a decrease in infant mortality.
b) Bivariate regressions of the infant mortality rate on GDP per capita and interpreting the
coefficient and R2
1. The R-squared value 0.2293 indicates that approximately 23% of the variation in
infant mortality rate can be explained by the variation in GDP per capita. This
suggests that a huge part of the infant mortality rate is not explained by the variation
in GDP and there seems to be a weak relationship between the two. This also
indicates that there might be other factors that also play a role in determining infant
mortality rate.
2. The coefficient for GDP per capita is -0.0004914. This means that for every $1
increase in GDP per capita, infant mortality rate is expected to decrease by 0.050%
deaths per 1,000 live births.
Overall, this result suggests that there is a weak negative relationship between GDP per
capita and infant mortality rate. Increasing GDP per capita may be associated with a decrease
in infant mortality rate, but this relationship is not very strong and is weak.
Q.5 Running the regression of IMR on female education and log GDP, and interpreting
the slope coefficients.
The number of observations in the regression are 141
The R2 is 0.7459
The following is the interpretation:
The R-squared value, 0.7459 explains 74.59% of the variation in the infant mortality rate
through the two independent variables female education and GDP per capita.
For every additional year of female education, infant mortality rate decreases by 3.20 per
1,000 live births, holding GDP per capita constant.
For every one-unit increase in log of GDP per capita, infant mortality rate decreases by 5.58
per 1,000 live births, holding female education constant.
Q.6 Running the regression of IMR on female education, log GDP and including basic
sanitation infrastructure, and interpreting the slope coefficients and R2 .
R-squared has increased from 0.7459 to 0.8052. This tells us that the additional variable,
sanitation, improves the model's ability to explain more of the variation in the data and in the
Infant mortality rate than before.
Generally, a high R-squared tells us how good the data fits with the regression and the model
that we have. Including sanitation and an increase in R- squared tells us that sanitation plays a
role in the changes in the infant mortality rate.
Q.7 Running the regression of female labour force share on female education.
The coefficient of labour force should be positive since generally we observe that the share of
labour participation of women goes up when the female population is more educated.
After the regression we see that the coefficient is positive just as we expected it to be.
8. Omitted variable bias occurs when one or more relevant and important variables that are
related to the dependent variable and the independent variable(s) is left out in the regression
model. This might result in biased and inconsistent estimates of the coefficients of the
independent variables that are in the model. It can be positive or negative depending on the
omitted variable’s relationship with the dependent variable and independent variables.
In the previous question, if the economist omitted a measure of fertility, then it could lead to
omitted variable bias because fertility would mostly be related to both female labour force
share and education.
For example, we see that if we females have children they are expected to stay home and look
after their children even if they are educated. Similarly, their education also might be affected
if their fertility rate is high. Therefore, leaving a measure of fertility would lead to a bias.
9. If fertility is negatively related with female labour force participation, the likely sign of the
bias is going to be positive since without the negative female labour force participation the
estimated coefficient on female education would be larger than it would be if fertility was
included.