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Homework 4
Due on UB Learns 8/13/2024 at 11:59pm
1.) Download the data CPS2015. I have included the data description, but this data set is from the 2015 Current Population Survey. It contains information for full-time, full-year workers, ages 25-34, with either a high school diploma or bachelor’s degree as their highest education. We are interested in exploring the relationship between a worker’s age and their earnings. Theoretically older workers have more experience and higher earnings.
a.) Run a regression of average hourly earnings (AHE) on age, sex, and education. What is the relationship between age and earnings from your results? Which coefficients are significant?
b.) Run a regression of average hourly wages on ln(age), sex, and education. What is the relationship between age and earnings from your results? Which coefficients are significant?
c.) Compare the regressions from 1a and 1b. Which specification explains the data better and why?
d.) Run a regression of the natural logarithm of average hourly wages on age, sex, and education. What is the relationship between age and earnings from your results? Which coefficients are significant?
e.) Run a regression of the natural logarithm of average hourly wages (ln(AHE)) on ln(age), sex, and education. What is the relationship between age and earnings from your results? Which coefficients are significant?
f.) Compare the regressions from 1d and 1e. Which specification explains the data better and why?
g.) Run a regression of the natural logarithm of average hourly wages (ln(AHE)) on age, age2, sex, and education. Is the relationship between age and earnings constant based on your results? How do you know?
h.) Compare the regressions from 1d, 1e, and 1g. Which specification explains the data better and why?
i.) Run the regression of ln(AHE) on age, age2, sex, education, and the interaction between sex and education. What is the relationship between the interaction term and earnings?
j.) Alexis is a 30-year-old female with a bachelor’s degree, what does the regression in 1i predict her natural log of earnings will be?
k.) Jenifer is a 30-year-old female with a high school diploma, what does the regression in 1i predict her natural log of earnings will be?
l.) Bob is a 30-year-old male with a bachelor’s degree, what does the regression in 1i predict his natural log of earnings will be?
m.) Jim is a 30-year-old male with a high school diploma, what does the regression in 1i predict his natural log of earnings will be?
2.) Assume we want to study the effect of a country’s political regime on their real GDP per capita. Give an example of something in the study that would threaten the external validity of the results. Classify this example as differences in sample population or differences in setting.
3.) If the true population relationship between age and earnings is a linear-log model with a squared age term, what problem would occur with the OLS estimates in a linear model of earnings on age?
4.) Assume we want to study the effect of a country’s political regime on their real GDP per capita. Which of the following scenarios will cause bias in our OLS estimates relating to missing data:
i. The data on political regime and real GDP per capita is missing for a random set of 5 countries.
ii. The data on political regime and real GDP is missing for all countries with a dictatorship political regime.
iii. The data on political regime and real GDP per capita is missing for all countries with a real GDP greater than $40,000 (US dollars).
5.) Give an example of a study that would suffer from sample selection bias. Make sure to identify the econometric question of the study, how the sample is selected, and how this would bias the study
6.) Give an example of a study that would suffer from simultaneous causality bias. Make sure to identify the econometric question of the study and explain the causality between the variables both ways.