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1. (60p) You will replicate and extend the work reported in Acemoglu, Johnson and Robinson (2001). The authors provided an expanded set of controls when they published their 2012 extension and posted the data on the AER website. This dataset is AJR2001 on the course website.

log(𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎) = 0.5 𝑟𝑖𝑠𝑘 (1) (0.06)

the reduced form regression

̂
𝑟𝑖𝑠𝑘 = −0.61log(𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦)+ 𝑢̂ (2)

(0.13)

and the 2SLS regression:

̂
𝑙𝑜𝑔(𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎) = 0.94 𝑟𝑖𝑠𝑘 (3)

(0.16)

(Which point estimate is different by 0.01 from the reported values? This is a common phenomenon in empirical replication).

  1. (b)  (6p) For the above estimates, calculate both homoskedastic and heteroskedastic-robust standard errors. Which were used by the authors (as reported in (1)-(2)-(3)?)
  2. (c)  (6p) Calculate the 2SLS estimates by the two-stage approach. Are they the same?
  3. (d)  (6p) Calculate the 2SLS estimates by the control variable approach. Are they the same?
  4. (e)  (6p) Acemoglu, Johnson and Robinson (2001) reported many specifications including alternative regressor controls, for example latitude and africa. Estimate by least-squares the equation for logGDP adding latitude and africa as regressors. Does this regression suggest that latitude and africa are predictive of the level of GDP?
  5. (f)  (6p) Now estimate the same equation as in (e) but by 2SLS using log mortality as an instrument for risk. How does the interpretation of the effect of latitude and africa change?
  6. (g)  (6p) Return to our baseline model (without including latitude and africa ). The authors reduced form equation uses log(mortality) as the instrument, rather than, say, the level of mortality. Estimate the reduced form for risk with mortality as the instrument. (This variable is not provided in the dataset, so you need to take the exponential of the mortality variable.) Can you explain why the authors preferred the equation with log(mortality)?

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  1. (h)  (6p) Try an alternative reduced form, including both log(mortality) and the square of log(mortality). Interpret the results. Re-estimate the structural equation by 2SLS using both log(mortality) and its square as instruments. How do the results change?
  2. (i)  (6p) For the estimates in (h), are the instruments strong or weak using relevance test?
  3. (j)  (6p) Calculate and interpret a test for exogeneity of the instruments.

2. (40p) A prospective employer receives two resumes a resume from a white job applicant and a similar resume from a African American applicant. Is the employer more likely to call back the white applicant to arrange an interview? Marianne Bertrand and Sendhil Mullainathan carried out a randomized controlled experiment to answer this question. Because race is not typically included on a resume, they differentiated resumes on the basis of “white-sounding names” (such as Emily Walsh or Gregory Baker) and “African American-sounding names” (such as Lakisha Washington and Jamal Jones). A large collection of fictious resumes was created, and the presupposed “race” (based on the “sound” of the name) was randomly assigned to each resume. These resumes were sent to prospective employers to see which resume generated a phone call (a call back) from the prospective employer.

Data file Names.dta contains the data from the experiment. provided in the file Names_Description.pdf.

(a) (8p) Fill out the following table using OLS: Dependent Variable = call_back

Regressor (1) (2) (3) Black()

High
High×Black
College
College×Black
Intercept () ()

A detailed description of the variables is

(4) (5) ()()

() ()

() ()

()
() () ()

()

(b) (8p) To answer rest of the questions, you can use the table in solution to part (a). Define the call back rate as the fraction of resumes that generate a phone call from the prospective employer. What was the call back rate for high-quality resumes? For low-quality resumes? Discuss if the difference is statistically significant?

(c) (4p) What was the call back rate for African Americans? For whites? Discuss if the differences in each rate is statistically significant?

(d) (6p) What is the call back rate for applicants with college degree? For applicants with no college degree? Discuss if the differences in each rate is statistically significant?

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(e) (6p) What is the high-quality/low-quality difference for white applicants? For black applicants? Is there a significant difference?

(f) (8p)Whatisthewithcollege-degree/nocollege-degreedifferenceforwhiteapplicants?Forblack applicants? Is there a significant difference?

Following questions will not be graded, they are for you to practice and will be discussed at the recitation:

1. [ungraded] SW Empirical Exercise 12.1
2. [ungraded] SW Exercise (not Empirical Exercises) 13.3 3. [ungraded] SW Exercise (not Empirical Exercises) 13.5 4. [ungraded] SW Exercise (not Empirical Exercises) 13.7

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