1. 1.Read Ahn, T, J Niven, and A Veilleux (2021), How long have you been waiting? Explaining the role of irrelevant information in the promotion of US Navy officers, Economics Bulletin 2. 2.Reproduce...

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1. 1.Read Ahn, T, J Niven, and A Veilleux (2021), How long have you been waiting? Explaining the role of irrelevant information in the promotion of US Navy officers,
Economics Bulletin

2. 2.Reproduce Table 4 in the paper using the Stata dataset “replication_promotion.dta” Use R Studio. I should be able to run your script without errors by only changing the parent directory.

3. 3.Additional questions to answer:

a. Who do the authors just run LPM? What is lost or gained by doing this instead of logit/probit?

b. In the text, the authors use In-Zone group as the control group since the authors claim they are not impacted by the policy change, yet they write that this is
technically
incorrect. Explain their reasoning for why the IZ group is not exactly a control group and why they treat it as a control group anyway.

c. What other variables or information would be useful to have to answer the research question more fully?

4. Submit (1) do-file; (2) replication of Table 4 (*.rtf or *.xls). Include your answers to “Additional questions” in the *.rtf or *.doc file, or as a block comment at the top of the do-file.

Helpful hints:

· The file “replication_promotion.dta” has the following variables:

o mastid: unique index number of anonymized officer

o promoted: dummy variable, with officer promoted=1 and otherwise=0

o belowzone: dummy variable, with officer currently BZ=1 and otherwise=0

o inzone: dummy variable, with officer currently IZ=1 and otherwise=0

o abovezone: dummy variable, with officer currently AZ=1 and otherwise=0

o female: dummy variable, with female=1 and male=0

o nonwhite: dummy variable, with non-white=1 and white=0

o married: dummy variable, with married=1 and single/divorced/widowed=0

o morethan3kids: dummy variable, with 4+ kids=1 and 3 or fewer kids=0

o priorenlisted: dummy variable, with ascension source via enlisted=1 and otherwise=0

o md_jag: dummy variable, with doctor/lawyer=1 and otherwise=0

o ma: dummy variable, with a MA/MS degree=1 and otherwise=0

o LCDR: dummy variable, with current rank at LCDR=1 and otherwise=0

o CDR: dummy variable, with current rank at CDR=1 and otherwise=0

o totalsuffixcodes: Total # of suffix codes held (proxy for ability) year: calendar year

ahnnivenveilleuxeb-1-1tfea4sa.pdf replication-exercise-2-rueyfs4j.docx replicationpromotion-yovm0ads.dta assignment-2-2w4iiyiz.r

Answered 3 days AfterAug 15, 2022

Answer To: 1. 1.Read Ahn, T, J Niven, and A Veilleux (2021), How long have you been waiting? Explaining the...

Mohd answered on Aug 18 2022

69 Votes

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2022-08-18
library(haven)
## Warning: package 'haven' was built under R version 4.1.1
read_dta(file.choose())
## # A tibble: 20,022 x 16
## mastid promoted belowzone inzone abovezone female nonwhite married
##
## 1 1 0 0 0 1 0 1 0
## 2 1 0 0 0 1 0 1 0
## 3 1 0 0 0 1 0 1 0
## 4 2 0 0 0 1 0 0 0
## 5 3 0 1 0 0 0 0 0
## 6 3 0 1 0 0 0 0 0
## 7 3 1 0 1 0 0 0 0
## 8 3 0 1 0 0 0 0 0
## 9 3 0 1 0 0 0 0 0
## 10 4 0 1 0 0 0 0 0
## # ... with 20,012 more rows, and 8 more variables: morethan3kids ,
## # priorenlisted , md_jag , ma , LCDR , CDR ,
## # totalsuffixcodes , year
retirementdata_1 = read_dta(file.choose())
#Primary model
mod<-lm(promoted~belowzone+inzone+abovezone,data = retirementdata_1)
summary(mod)
##
## Call:
## lm(formula = promoted ~ belowzone + inzone + abovezone, data = retirementdata_1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64836 -0.12910 -0.00668 -0.00668 0.99332
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.129102 0.004872 26.50<2e-16 ***
## belowzone -0.122424 0.005644 -21.69<2e-16 ***
## inzone 0.519259 0.006285 82.62 <2e-16 ***
## abovezone NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2959 on 20019 degrees of freedom
## Multiple R-squared: 0.4673, Adjusted R-squared: 0.4672
## F-statistic: 8779 on 2 and 20019 DF, p-value: < 2.2e-16
corr<-cor(retirementdata_1)
corr[,2]
## mastid promoted belowzone inzone
## 0.009536100 1.000000000 -0.534423027 0.674342997
## abovezone female nonwhite married
## -0.091568137 0.005847389 -0.016660738 -0.005151570
## morethan3kids priorenlisted md_jag ma
## 0.006684904 -0.003799933 0.022428448 0.044623399
## LCDR CDR totalsuffixcodes year
## -0.023671509 -0.104411499 0.032829648 0.015398299
library(skimr)
## Warning: package 'skimr' was built under R version 4.1.1
skim(retirementdata_1)
Data summary
    Name
    retirementdata_1
    Number of rows
    20022
    Number of columns
    16
    _______________________

    Column type frequency:

    numeric
    16
    ________________________

    Group variables
    None
Variable type:...

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1. 1.Read Ahn, T, J Niven, and A Veilleux (2021), How long have you been waiting? Explaining the role of irrelevant information in the promotion of US Navy officers, Economics Bulletin 2. 2.Reproduce...

Answer To: 1. 1.Read Ahn, T, J Niven, and A Veilleux (2021), How long have you been waiting? Explaining the...

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