examine the causal effect of requiring unemployed individuals to enroll in job programs.

examine the causal effect of requiring unemployed individuals to enroll in job programs.

Problem 1. I would like to examine the causal effect of requiring unemployed individuals to enroll in job

programs. I have a sample of 3,109 counties in the U.S. For each county I have the number of

unemployed adults, the number that asked to take part in training courses on how to find a job, and

the number of those that were employed one year later. I build a model in which the change in the

unemployment rate is affected by the proportion of those who undertook job programs.
a. Why is this model likely to suffer from omitted variable bias? Which variables would you add to the

regression to control for omitted variables?
b. Please use your answer in a. and equation 6.1 of the text to determine the sign of

the bias (in other words, will the coefficient tend to be too high or too low?)
Problem 2. Let’s assume that {Yi, X1i, X2i} satisfy Key Concept 6.4. We’re interested in β1, the causal effect of

X1i on Yi. Suppose X2i has no effect on Yi. We regress Yi on X1i. Does the coefficient on

X1isuffer from omitted variable bias? Please explain.
Problem 3. Use the Birthweight_Smoking data set a Regress Birthweight on Smoker. What is the estimated

effect of smoking on birth weight? b Regress Birthweight on Smoker, Alcohol, and Nprevist. i. Using the two

conditions in Key Concept 6.1, explain why the exclusion of Alcohol and Nprevist could lead to omitted

variable bias in the regression estimated in (a). ii. Is the estimated effect of smoking on birth

weight substantially different from the regression that excludes Alcohol and Nprevist? Does the regression in

(a) seem to suffer from omitted variable bias? iii. Jane smoked during pregnancy, did not drink alcohol, and

had 8 prenatal care visits. Use the regression to predict the birth weight of Jane’s child.
c. Estimate the coefficient on Smoking for the multiple regression model in (b), using the three step

process in Appendix (6.3) (the Frisch-Waugh theorem). Verify that the three step process yields the same estimated

coefficient for Smoking as that obtained in (b).
Problem 4. Using the data set Growth, but excluding the data for Malta, carry out the following excercises.

a. Construct a table that shows the sample mean, standard deviation, and minimum and maximum values for the series

Growth, Trade-Share, YearSchool, Oil, Rev_Coups, Assassinations, and RGDP60. Include the appropriate units for all entries.

b. Run the regression of Growth on TradeShare, YearSchool, Oil, Rev_Coups, Assassinations, and RGDP60. What is the

value of the coefficient on Rev-Coups? Interpret the value of this coefficient. Is it large or small

in a real-world sense?
c. Use the regression to predict the average annual growth rate for a country that has average values for all

regressors. d. Repeat (c) but now assume that the country’s value for TradeShare is one standard

deviation above the mean.

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