4. The appendix to chapter one will be very useful in answering this question, if you need a refresher or introduction to regression analysis. The following equation is the regression results of a study on infant mortality rates. There were 20 countries in the study and each country had six consecutive years of data collected. All of the variables have been converted to logarithms so the coefficients can be treated as elasticities. Below each coefficient is its t-statistic. Use the results of this regression equation to answer the following questions. The appendix to chapter one would be very useful in answering this question.
IMR = 3.9 - .06 TIME - .8 RGDP - .5 PHYS + .7 URBAN - .004 FLFPR - .1 ED
(2.60) (1.112) (6.83) (6.89) (4.21) (1.21) (2.34)
Adjusted R2 = .954 N = 110
Variables Defined
IMR = infant mortality rate for each country for each year
TIME = the year in each country, such as 2018. This is a time trend to capture changes in technology and knowledge.
RGDP = real gross domestic product per capita in each country per year
URBAN = percentage of population in urban areas for each county for each year
FLFPR = female labor force participation rate for each country for each year
ED = level of education for each country for each year
Based upon the information provided above, answer the following questions.
A. What percentage of the variation in infant mortality rates is explained by the variables? Justify or explain your answer.
B. An increase in which one variable will increase infant mortality rates? Justify or explain your answer.
C. Using health production theory provide a hypothesis or theory about the relationship (direct or inverse) between the first three independent variables ( TIME, RGDP and URBAN) and infant mortality rates.
D. Are the three hypotheses or theories you stated above supported by the regression results above? Explain your answers.
E. Based upon the findings above, would you expect infant mortality rates to be higher in Japan or Mexico? Why?