PeachCorps代做、代写OLS model、代做c/c++程序、Java/Python代写

日期：2019-03-28 09:02

Chapter 8 and 9 Problems – Homework 4 - DUE Friday, 3/29/19

Email me your answers and your program before class and turn in a hard copy of your answers at the beginning of class. Make sure all team member names are on the assignment.

Names:

1. How do young people respond to economic conditions? Are they more likely to pursue public service when jobs are scarce? To get at this question, we’ll analyze data in PeachCorps.csv, which contains variables on state economies and applications to the Peace Corps. Table 8.9 lists the variables.

a)Before looking at the data, what relationship do you hypothesize between these two variables? Explain your hypothesis.

b)Run a pooled regression of Peace Corps applicants per capita on the state unemployment rate. Describe and critique the results.

c)Plot the relationship between unemployment and Peace Corps applications. What sticks out? How may this impact the estimate on unemployment rate in the pooled regression above? Create a scatterplot without the outlier and comment briefly on the difference.

d)Run the pooled model from above without the outlier. Comment briefly on the results.

e)Run a two-way fixed effect model without the outlier using the hybrid approach. Do your results change from the pooled analysis? Which results are preferable?

2. In 1993 Georgia initiated a HOPE scholarship program to let state residents who had at least a B average in high school attend public attend public college in Georgia for free. The program is not need based. Did the program increase college enrollment? Or did it simply transfer funds to families who would have sent their children to college anyway? Dynarski (2000) used data on young people in Georgia and neighboring states to assess this question. Table 8.11 lists the variables.

(a)Run a basic difference-in-difference model. What is the effect of the program?

(b)Calculate the percentage of people in the sample in college from the following four groups: (i) Before 1993/non-Georgia, (ii) Before 1993/Georgia, (iii) After 1992/non-Georgia, and (iv) After 1992/Georgia. Use the coefficients from the OLS output in part (a).

(c)Estimate a two-way fixed effect model with the difference-in-difference model to control for all year and state effects. Do the results change?

3. Does education reduce crime? If so, spending more on education could be a long-term tool in the fight against crime. The file inmates.csv contains data used by Lochner and Moretti in their 2004 article in The American Economic Review on the effects of education on crime. Table 9.20 describes the variables. (This data set contains over 3 million observations and depending on the speed of your computer, regressions in this problem can take a little longer to run.)

(a)Run a regression with prison as the dependent variable and education, age, and African American as independent variables. Make this a fixed effects model by including dummies for state of residence (state) and year of census data (year). Report and briefly describe the results.

(b)Based on the OLS results, can we causally conclude that increasing education will reduce crime?

(c)Lochner and Moretti use 2SLS to improve their OLS estimates. They use changes in compulsory attendance laws (set by each state) as an instrument. The variable ca9 indicates that compulsory schooling is equal to 9 years, ca10 indicates that compulsory schooling is equal to 10 years, and ca11 is 11 or more years. The control group is 8 or fewer years. Does this set of instruments satisfy the two conditions for good instruments?

(d)Estimate a 2SLS model using the instruments just described and the control variables from the OLS model above (including state and year dummy variables). Briefly explain the results.

(e)2SLS is known for being less precise than OLS meaning it often has a higher variance. Is that true here? Is this a problem for the analysis in this case? Why or why not?