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1. 1.Read Cunha, Menichini, and Crockett (2015) “The retention effects of high years of service cliff-vesting pension plans,” Economics Letters. 2. 2.Reproduce the 2 tables and the 3 survival curves...

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1. 1.Read Cunha, Menichini, and Crockett (2015) “The retention effects of high years of service cliff-vesting pension plans,” Economics Letters.

2. 2.Reproduce the 2 tables and the 3 survival curves presented in the paper using the Stata dataset “retirement_data.dta” with 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. Do you need to drop any observations? Why? Was there a different way to handle the missing data?

b. In a few sentences, discuss the issues surrounding causal inference in this paper. Do you believe whether the removal of the 20-year cliff vest causes higher attrition? What are some threats to this causal interpretation of the empirical results?

exercise-1-bl3twdyx.docx retirementdata-1-xptwmsmu.dta assignment-1-myoqbh1x.r cunha-menichini-and-crockett-2015-economics-letters-the-retention-effects-of-high-years-of-service-cliff-vesting-pension-plans-zflx1fdx-cm3dqcaj.pdf
Answered 8 days After Jul 21, 2022

Solution

Mohd answered on Jul 29 2022
73 Votes
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2022-07-29
Importing required packages and Data
li
ary(haven)
li
ary(stargazer)
li
ary(dplyr)
li
ary(psych)
li
ary(survival)
li
ary(survminer)
etirementdata_1 <- read_dta("retirementdata-1.dta")
etirementdata_1%>%
count(superfund)
## # A ti
le: 2 x 2
## superfund n
## ## 1 DFRDB 629
## 2 MSBS 5723
etirementdata<-retirementdata_1%>%
mutate(Navy=ifelse(service=="NAVY",1,0))%>%
mutate(Army=ifelse(service=="ARMY",1,0))%>%
mutate(AIR_FORCE=ifelse(service=="AIR FORCE",1,0))%>%
mutate(DFRDB=ifelse(superfund=="DFRDB",1,0))%>%
mutate(MSBS=ifelse(superfund=="MSBS",1,0))%>%
mutate(Female=ifelse(sex=="F",1,0))%>%
mutate(Male=ifelse(sex=="M",1,0))
li
ary(skimr)
skim(retirementdata)
Data summary
    Name
    retirementdata
    Number of rows
    6352
    Number of columns
    14
    _______________________
    
    Column type frequency:
    
    characte
    3
    Date
    3
    numeric
    8
    ________________________
    
    Group variables
    None
Variable type: characte
    skim_variable
    n_missing
    complete_rate
    min
    max
    empty
    n_unique
    whitespace
    sex
    0
    1
    1
    1
    0
    2
    0
    service
    0
    1
    4
    9
    0
    3
    0
    superfund
    0
    1
    4
    5
    0
    2
    0
Variable type: Date
    skim_variable
    n_missing
    complete_rate
    min
    max
    median
    n_unique
    hiredate
    0
    1.00
    1990-09-03
    1992-09-30
    1991-04-09
    268
    birthdate
    0
    1.00
    1955-05-31
    1976-07-01
    1972-06-05
    2916
    firstseparationdate
    1209
    0.81
    1994-06-30
    2013-07-29
    1999-01-17
    2817
Variable type: numeric
    skim_variable
    n_missing
    complete_rate
    mean
    sd
    p0
    p25
    p50
    p75
    p100
    hist
    office
    8
    1
    0.16
    0.37
    0
    0
    0
    0
    1
    ▇▁▁▁▂
    Navy
    0
    1
    0.28
    0.45
    0
    0
    0
    1
    1
    ▇▁▁▁▃
    Army
    0
    1
    0.45
    0.50
    0
    0
    0
    1
    1
    ▇▁▁▁▆
    AIR_FORCE
    0
    1
    0.27
    0.44
    0
    0
    0
    1
    1
    ▇▁▁▁▃
    DFRDB
    0
    1
    0.10
    0.30
    0
    0
    0
    0
    1
    ▇▁▁▁▁
    MSBS
    0
    1
    0.90
    0.30
    0
    1
    1
    1
    1
    ▁▁▁▁▇
    Female
    0
    1
    0.18
    0.39
    0
    0
    0
    0
    1
    ▇▁▁▁▂
    Male
    0
    1
    0.82
    0.39
    0
    1
    1
    1
    1
    ▂▁▁▁▇
etirementdata_df<-na.omit(retirementdata)
1. Reproduce the 2 tables and the 3 survival curves presented in the paper using the Stata dataset “retirement_data.dta” with R Studio. I should be able to run your script without e
ors by only changing the parent directory.
etirementdata<-retirementdata%>%
mutate(Age_enlistemnt=as.numeric(round((hiredate-birthdate)/365,2)))%>%
mutate(Yrs_service=as.numeric(round((firstseparationdate-hiredate)/365,2)))
FY1991_cohort<-retirementdata%>%
filter(hiredate >'1991-10-01' & hiredate<'1992-09-30')
FY1992_cohort<-retirementdata%>%
filter(hiredate <'1991-10-01' | hiredate>'1992-09-30')
#first...
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