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Data_Cleaning_Code_Prins_et_al.Rmd
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Data_Cleaning_Code_Prins_et_al.Rmd
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---
title: '“The Serpent of their Agonies”: Exploitation As Structural Determinant of
Mental Illness'
author: "Seth J. Prins, Sarah McKetta, Jonathan Platt, Carles Muntaner, Katherine M. Keyes, Lisa M. Bates"
date: "September, 2020"
output:
html_document:
theme: cosmo
toc: TRUE
always_allow_html: yes
subtitle: PSID Data Processing Notebook
---
```{r, setup}
library(tidyverse)
```
# Notes
- "Head" refers to "head of household"
- Only heads of household were administered the K6 mental health screen, so we do not include spouses except for select variables
# Import CSV
The raw variables from the PSID are listed in each of the renaming steps below.
```{r, eval=FALSE}
RAW <- read_csv("xxx.csv")
```
# Create ID variable
The combination of the 1968 ID and the person number uniquely identify each individual.
To identify an individual across waves use the 1968 ID and Person Number (Summary Variables ER30001 and ER30002). Though you can combine them uniquely in many ways we find that many researchers use the following method:
(ER30001 * 1000) + ER30002
(1968 ID multiplied by 1000) plus Person Number
https://psidonline.isr.umich.edu/Guide/FAQ.aspx?Type=1 (See "How is a person uniquely identified?")
```{r, eval=FALSE}
df <- RAW
df$ID <- df$ER30001 * 1000 + df$ER30002
```
# RENAME VARIABLES
## Sample weight longitudinal core and immigrant
```{r, eval=FALSE}
SampWeight2015 <- df %>% select(one_of(c("ER33430", "ER33546", "ER33637", "ER33740", "ER33848", "ER33950", "ER34045", "ER34154", "ER34268", "ER34413", "ER34650")))
names(SampWeight2015) <- mapply(paste, "SampWeight2015", seq(1997, 2017, 2), sep = "_")
```
## Sample weight longitudinal core
```{r, eval=FALSE}
SampWeight1996 <- df %>% select(one_of(c("ER30019", "ER30042", "ER30066", "ER30090", "ER30116", "ER30137", "ER30159", "ER30187", "ER30216", "ER30245", "ER30282", "ER30312", "ER30342", "ER30372", "ER30398", "ER30428", "ER30462", "ER30497", "ER30534", "ER30569", "ER30605", "ER30641", "ER30686", "ER30730", "ER30803")))
names(SampWeight1996) <- mapply(paste, "SampWeight1996", c(1968:1992), sep = "_")
```
## Sample weight longitudinal core 1993 revised
```{r, eval=FALSE}
SampWeight9396Rev <- df %>% select(one_of(c("ER30864", "ER33119", "ER33275", "ER33318")))
names(SampWeight9396Rev) <- mapply(paste, "SampWeight9396Rev", c(1993:1996), sep = "_")
```
## ID Interview number
```{r, eval = FALSE}
InterviewNum <- df %>% select(one_of(c("ER30001", "ER30020", "ER30043", "ER30067", "ER30091", "ER30117", "ER30138", "ER30160", "ER30188", "ER30217", "ER30246", "ER30283", "ER30313", "ER30343", "ER30373", "ER30399", "ER30429", "ER30463", "ER30498", "ER30535", "ER30570", "ER30606", "ER30642", "ER30689", "ER30733", "ER30806", "ER33101", "ER33201", "ER33301", "ER33401", "ER33501", "ER33601", "ER33701", "ER33801", "ER33901", "ER34001", "ER34101", "ER34201", "ER34301", "ER34501")))
names(InterviewNum) <- mapply(paste, "InterviewNum", c(1968:1997, seq(1999, 2017, 2)), sep = "_")
```
## Person number
```{r, eval=FALSE}
df$PersonNum <- df$ER30002
```
## Relation to head
```{r, eval=FALSE}
RelationToHead <- df %>% select(one_of(c("ER30003", "ER30022", "ER30045", "ER30069", "ER30093", "ER30119", "ER30140", "ER30162", "ER30190", "ER30219", "ER30248", "ER30285", "ER30315", "ER30345", "ER30375", "ER30401", "ER30431", "ER30465", "ER30500", "ER30537", "ER30572", "ER30608", "ER30644", "ER30691", "ER30735", "ER30808", "ER33103", "ER33203", "ER33303", "ER33403", "ER33503", "ER33603", "ER33703", "ER33803", "ER33903", "ER34003", "ER34103", "ER34203", "ER34303", "ER34503")))
names(RelationToHead) <- mapply(paste, "RelationToHead", c(1968:1997, seq(1999, 2017, 2)), sep = "_")
```
## Sampling error cluster
```{r, eval=FALSE}
df$SampErrClust <- df$ER31997
```
## Sampling error stratum
```{r, eval=FALSE}
df$SampErrStrat <- df$ER31996
```
## Individual weight
```{r, eval=FALSE}
Individ.Weight <- df %>% select(ER30019, ER30042, ER30066, ER30090, ER30116, ER30137, ER30159, ER30187, ER30216, ER30245, ER30282, ER30312, ER30342, ER30372, ER30398, ER30428, ER30462, ER30497, ER30534, ER30569, ER30605, ER30641, ER30688, ER30732, ER30805, ER30866, ER33121, ER33277, ER33318, ER33430, ER33546, ER33637, ER33740, ER33848, ER33950, ER34045, ER34154, ER34268, ER34413, ER34650)
names(Individ.Weight) <- mapply(paste, "Individ.Weight", c(1968:1996, seq(1997, 2017, 2)), sep = "_")
```
## Age Head
```{r, eval=FALSE}
Age.Head <- df %>% select(one_of(c("V117", "V1008", "V1239", "V1942", "V2542", "V3095", "V3508", "V3921", "V4436", "V5350", "V5850", "V6462", "V7067", "V7658", "V8352", "V8961", "V10419", "V11606", "V13011", "V14114", "V15130", "V16631", "V18049", "V19349", "V20651", "V22406", "ER2007", "ER5006", "ER7006", "ER10009", "ER13010", "ER17013", "ER21017", "ER25017", "ER36017", "ER42017", "ER47317", "ER53017", "ER60017", "ER66017")))
names(Age.Head) <- mapply(paste, "Age.Head", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Race Head
```{r, eval=FALSE}
Race.Head <- df %>% select(one_of(c("V181", "V801", "V1490", "V2202", "V2828", "V3300", "V3720", "V4204", "V5096", "V5662", "V6209", "V6802", "V7447", "V8099", "V8723", "V9408", "V11055", "V11938", "V13565", "V14612", "V16086", "V17483", "V18814", "V20114", "V21420", "V23276", "ER3944", "ER6814", "ER9060", "ER11848", "ER15928", "ER19989", "ER23426", "ER27393", "ER40565", "ER46543", "ER51904", "ER57659", "ER64810", "ER70882")))
names(Race.Head) <- mapply(paste, "Race.Head", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Alcohol use frequency Head
```{r, eval=FALSE}
AlcFreq.Head <- df %>% select(one_of(c("ER27106", "ER38317", "ER44290", "ER49628", "ER55376", "ER62498", "ER68563")))
names(AlcFreq.Head) <- mapply(paste, "AlcFreq.Head", seq(2005, 2017, 2), sep = "_")
```
## Ever smoked Head
```{r, eval=FALSE}
EverSmoked.Head <- df %>% select(one_of(c("V13444", "ER15546", "ER19711", "ER23126", "ER27101", "ER38312", "ER44285", "ER49623", "ER55371", "ER62493", "ER68558")))
names(EverSmoked.Head) <- mapply(paste, "EverSmoked.Head", c(1986, seq(1999, 2017, 2)), sep = "_")
```
## Depression before age 17 Head
```{r, eval=FALSE}
Depressed17.Head <- df %>% select(one_of(c("ER38341", "ER44314", "ER49654", "ER55402", "ER62524", "ER68589")))
names(Depressed17.Head) <- mapply(paste, "Depressed17.Head", seq(2007, 2017, 2), sep = "_")
```
## Drug or alc problem before 17 Head
```{r, eval=FALSE}
DrugProb17.Head <- df %>% select(one_of(c("ER38342", "ER44315", "ER49655", "ER55403", "ER62525", "ER68590")))
names(DrugProb17.Head) <- mapply(paste, "DrugProb17.Head", seq(2007, 2017, 2), sep = "_")
```
## K6
```{r, eval=FALSE}
K6.Head <- df %>% select(one_of(c("ER19833A", "ER23268", "ER40402", "ER46375", "ER51736", "ER57482", "ER64604", "ER70680")))
names(K6.Head) <- mapply(paste, "K6.Head", c(2001, 2003, seq(2007, 2017, 2)), sep = "_")
```
## Housing status own or rent
```{r, eval=FALSE}
OwnRent <- df %>% select(one_of(c("V103", "V593", "V1264", "V1967", "V2566", "V3108", "V3522", "V3939", "V4450", "V5364", "V5864", "V6479", "V7084", "V7675", "V8364", "V8974", "V10437", "V11618", "V13023", "V14126", "V15140", "V16641", "V18072", "V19372", "V20672", "V22427", "ER2032", "ER5031", "ER7031", "ER10035", "ER13040", "ER17043", "ER21042", "ER25028", "ER36028", "ER42029", "ER47329", "ER53029", "ER60030", "ER66030")))
names(OwnRent) <- mapply(paste, "OwnRent", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Income from interest Head
```{r, eval=FALSE}
InterestIncome.Head <- df %>% select(one_of(c("V21861", "ER27936", "ER40926", "ER46834", "ER52242", "ER58043", "ER65221", "ER71298")))
names(InterestIncome.Head) <- mapply(paste, "InterestIncome.Head", c(1993, seq(2005, 2017, 2)), sep = "_")
```
## Income from interest Spouse
```{r, eval=FALSE}
InterestIncome.Spouse <- df %>% select(one_of(c("V21925", "ER27949", "ER40939", "ER46847", "ER52255", "ER58056", "ER65249", "ER71326")))
names(InterestIncome.Spouse) <- mapply(paste, "InterestIncome.Spouse", c(1993, seq(2005, 2017, 2)), sep = "_")
```
## Total income from labor Head
```{r, eval=FALSE}
TotalLaborIncome.Head <- df %>% select(one_of(c("V74", "V514", "V1196", "V1897", "V2498", "V3051", "V3463", "V3863", "V5031", "V5627", "V6174", "V6767", "V7413", "V8066", "V8690", "V9376", "V11023", "V12372", "V13624", "V14671", "V16145", "V17534", "V18878", "V20178", "V21484", "V23323", "ER4140", "ER6980", "ER9231", "ER12080", "ER16463", "ER20443", "ER24116", "ER27931", "ER40921", "ER46829", "ER52237", "ER58038", "ER65216", "ER71293")))
names(TotalLaborIncome.Head) <- mapply(paste, "TotalLaborIncome.Head", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Wages and salary Head
```{r, eval=FALSE}
WageSal.Head <- df %>% select(one_of(c("V251", "V699", "V1191", "V1892", "V2493", "V3046", "V3458", "V3858", "V4373", "V5283", "V5782", "V6391", "V6981", "V7573", "V8265", "V8873", "V10256", "V11397", "V12796", "V13898", "V14913", "V16413", "V17829", "V19129", "V20429", "V21739", "ER4122", "ER6962", "ER9213", "ER12196", "ER16493", "ER20425", "ER24117", "ER27913", "ER40903", "ER46811", "ER52219", "ER58020", "ER65200", "ER71277")))
names(WageSal.Head) <- mapply(paste, "WageSal.Head", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Wife's money income from work to 1993
```{r, eval=FALSE}
TotalLaborIncome1.Spouse <- df %>% select(one_of(c("V75", "V516", "V1198", "V1899", "V2500", "V3053", "V3465", "V3865", "V4379", "V5289", "V5788", "V6398", "V6988", "V7580", "V8273", "V8881", "V10263", "V11404", "V12803", "V13905", "V14920", "V16420", "V17836", "V19136", "V20436", "V23324")))
names(TotalLaborIncome1.Spouse) <- mapply(paste, "TotalLaborIncome1.Spouse", seq(1968, 1993, 1), sep = "_")
```
## Wife's money income from work from 1993
```{r, eval=FALSE}
TotalLaborIncome2.Spouse <- df %>% select(one_of(c("V21807", "ER4144", "ER6984", "ER9235", "ER12082", "ER16465", "ER20447", "ER24135", "ER27943", "ER40933", "ER46841", "ER52249", "ER58050", "ER65244", "ER71321")))
names(TotalLaborIncome2.Spouse) <- mapply(paste, "TotalLaborIncome2.Spouse", c(seq(1993, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Years worked since 18 Head
```{r, eval=FALSE}
YearsWorked.Head <- df %>% select(one_of(c("V3620", "V4141", "V4630", "V5604", "V6153", "V6750", "V7383", "V8035", "V8659", "V9345", "V10992", "V11739", "V13605", "V14652", "V16126", "V17523", "V18854", "V20154", "V21460", "V23316", "ER3985", "ER6855", "ER9101", "ER11897", "ER15979", "ER20040", "ER23476", "ER27444", "ER40616", "ER46594", "ER51955", "ER57711", "ER64871", "ER70943")))
names(YearsWorked.Head) <- mapply(paste, "YearsWorked.Head", c(seq(1974, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Employment status Head - ever had a job
```{r, eval=FALSE}
EverJob.Head <- df %>% select(one_of(c("V4552", "V5454", "V5971", "V6556", "V7158", "V7806", "V8467", "V9104", "V10604", "V11751", "V13147", "V14245", "V15318", "V16833", "V18257", "V19557", "V20857", "V22644", "ER2324", "ER5323", "ER7419", "ER10337", "ER13476", "ER17515", "ER21357", "ER25346", "ER36351", "ER42376", "ER47689", "ER53389", "ER60404", "ER66413")))
names(EverJob.Head) <- mapply(paste, "EverJob.Head", c(seq(1976, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Employment status working now 1? Head
```{r, eval=FALSE}
EmployedNow1.Head <- df %>% select(one_of(c("V196", "V639", "V1278", "V1983", "V2581", "V3114", "V3528", "V3967", "V4458", "V5373", "V5872", "V6492", "V7095", "V7706", "V8374", "V9005", "V10453", "V11637", "V13046", "V14146", "V15154", "V16655", "V18093", "V19393", "V20693", "V22448", "ER2068", "ER5067", "ER7163")))
names(EmployedNow1.Head) <- mapply(paste, "EmployedNow1.Head", seq(1968, 1996, 1), sep = "_")
```
## Employment status working now 2 Head
```{r, eval=FALSE}
EmployedNow2.Head <- df %>% select(one_of(c("ER2069", "ER5068", "ER7164", "ER10081", "ER13205", "ER17216", "ER21123", "ER25104", "ER36109", "ER42140", "ER47448", "ER53148", "ER60163", "ER66164")))
names(EmployedNow2.Head) <- mapply(paste, "EmployedNow2.Head", c(seq(1994, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Head's annual work hours
```{r, eval=FALSE}
WorkHrs.Head <- df %>% select(one_of(c("V47", "V465", "V1138", "V1839", "V2439", "V3027", "V3423", "V3823", "V4332", "V5232", "V5731", "V6336", "V6934", "V7530", "V8228", "V8830", "V10037", "V11146", "V12545", "V13745", "V14835", "V16335", "V17744", "V19044", "V20344", "V21634", "ER4096", "ER6936", "ER9187", "ER12174", "ER16471", "ER20399", "ER24080", "ER27886", "ER40876", "ER46767", "ER52175", "ER57976", "ER65156", "ER71233")))
names(WorkHrs.Head) <- mapply(paste, "WorkHrs.Head", c(seq(1968, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Industry of Head - 1970 code
```{r, eval=FALSE}
Industry1980.Head <- df %>% select(one_of(c("V197_B", "V640_B", "V1279_B", "V1985_A", "V2583_A", "V3116_A", "V3531_A", "V3969_A", "V4460_A", "V5375_A", "V5874_A", "V6498_A", "V7101_A")))
names(Industry1980.Head) <- mapply(paste, "Industry1980.Head", seq(1968, 1980, 1), sep = "_")
```
## Industry of Head - 1970 code
```{r, eval=FALSE}
Industry2001.Head <- df %>% select(one_of(c("V7713", "V8381", "V9012", "V10461", "V11652", "V13055", "V14155", "V15163", "V16664", "V18102", "V19402", "V20702", "V22457", "ER4018", "ER6858", "ER9109", "ER12086", "ER13216", "ER17227")))
names(Industry2001.Head) <- mapply(paste, "Industry2001.Head", c(seq(1981, 1997, 1), 1999, 2001), sep = "_")
```
## Industry of Head - 2000 code
```{r, eval=FALSE}
Industry2015.Head <- df %>% select(one_of(c("ER21146", "ER25128", "ER36133", "ER42168", "ER47480", "ER53180", "ER60195", "ER66196")))
names(Industry2015.Head) <- mapply(paste, "Industry2017.Head", seq(2003, 2017, 2), sep = "_")
```
## Industry of Head
```{r, eval=FALSE}
Industry.Head <- df %>% select(one_of(c("V197_B", "V640_B", "V1279_B", "V1985_A", "V2583_A", "V3116_A", "V3531_A", "V3969_A", "V4460_A", "V5375_A", "V5874_A", "V6498_A", "V7101_A", "V7713", "V8381", "V9012", "V10461", "V11652", "V13055", "V14155", "V15163", "V16664", "V18102", "V19402", "V20702", "V22457", "ER4018", "ER6858", "ER9109", "ER12086", "ER13216", "ER17227", "ER21146", "ER25128", "ER36133", "ER42168", "ER47480", "ER53180", "ER60195", "ER66196")))
names(Industry.Head) <- mapply(paste, "Industry.Head", c(seq(1968, 1980, 1), seq(1981, 1997, 1), 1999, 2001, seq(2003, 2017, 2)), sep = "_")
```
## Head occupation --1970 code
```{r, eval=FALSE}
Occupation1980.Head <- df %>% select(one_of(c("V197_A", "V640_A", "V1279_A", "V1984_A", "V2582_A", "V3115_A", "V3530_A", "V3968_A", "V4459_A", "V5374_A", "V5873_A", "V6497_A", "V7100_A")))
names(Occupation1980.Head) <- mapply(paste, "Occupation1980.Head", seq(1968, 1980, 1), sep = "_")
```
## Head occupation 1970 code
```{r, eval=FALSE}
Occupation2001.Head <- df %>% select(one_of(c("V3529", "V7712", "V8380", "V9011", "V10460", "V11651", "V13054", "V14154", "V15162", "V16663", "V18101", "V19401", "V20701", "V22456", "ER4017", "ER6857", "ER9108", "ER12085", "ER13215", "ER17226")))
names(Occupation2001.Head) <- mapply(paste, "Occupation2001.Head", c(1974, seq(1981, 1997, 1), 1999, 2001), sep = "_")
```
## Head occupation -- 2000 and 2010 code
```{r, eval=FALSE}
Occupation2017.Head <- df %>% select(one_of(c("ER21145", "ER25127", "ER36132", "ER42167", "ER47479", "ER53179", "ER60194", "ER66195")))
names(Occupation2017.Head) <- mapply(paste, "Occupation2017.Head", seq(2003, 2017, 2), sep = "_")
```
## Head self-employed to 2001
```{r, eval=FALSE}
SelfEmp2001.Head <- df %>% select(one_of(c("V198", "V641", "V1280", "V1986", "V2584", "V3117", "V3532", "V3970", "V4461", "V5376", "V5875", "V6493", "V7096", "V7707", "V8375", "V9006", "V10456", "V11640", "V13049", "V14149", "V15157", "V16658", "V18096", "V19396", "V20696", "V22451", "ER2074", "ER5073", "ER7169", "ER10086", "ER13210", "ER17221")))
names(SelfEmp2001.Head) <- mapply(paste, "SelEmp2001.Head", c(seq(1968, 1997, 1), 1999, 2001), sep = "_")
```
## Head self employed 2003-2017
```{r, eval=FALSE}
SelfEmp2017.Head <- df %>% select(one_of(c("ER21147", "ER25129", "ER36134", "ER42169", "ER47482", "ER53182", "ER60197", "ER66198")))
names(SelfEmp2017.Head) <- mapply(paste, "SelfEmp2017.Head", seq(2003, 2017, 2), sep = "_")
```
## Occupation of Head
```{r, eval=FALSE}
Occupation.Head <- df %>% select(one_of(c("V197_A", "V640_A", "V1279_A", "V1984_A", "V2582_A", "V3115_A", "V3530_A", "V3968_A", "V4459_A", "V5374_A", "V5873_A", "V6497_A", "V7100_A", "V3529", "V7712", "V8380", "V9011", "V10460", "V11651", "V13054", "V14154", "V15162", "V16663", "V18101", "V19401", "V20701", "V22456", "ER4017", "ER6857", "ER9108", "ER12085", "ER13215", "ER17226", "ER21145", "ER25127", "ER36132", "ER42167", "ER47479", "ER53179", "ER60194", "ER66195")))
names(Occupation.Head) <- mapply(paste, "Occupation.Head", c(seq(1968, 1980, 1), 1974, seq(1981, 1997, 1), 1999, 2001, seq(2003, 2017, 2)), sep = "_")
```
## Overtime and extra job earnings
### Head extra job income
```{r, eval=FALSE}
Extra.Job.Income2 <- df %>% select(c(V21801, ER4138, ER6978, ER9229, ER12212, ER16509, ER20441, ER24131, ER27927, ER40917, ER46825, ER52233, ER58034, ER65212, ER71289))
names(Extra.Job.Income2) <- mapply(paste, "Extra.Job.Income2", c(seq(1993, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
### Annual overtime hours
The values for this variable represent the annual overtime hours worked on all jobs if reported separately from regular work hours. All missing data were assigned.
```{r, eval=FALSE}
Annual.OT.Hrs.All.Jobs <- df %>% select(c(V11142, V12541, V13741, V14831, V16331, V17740, V19040, V20340, V21630, ER4094, ER6934, ER9185, ER12172, ER16469, ER20397, ER24079, ER27885, ER40875, ER46765, ER52173, ER57974, ER65154, ER71231))
names(Annual.OT.Hrs.All.Jobs) <- mapply(paste, "Annual.OT.Hrs.All.Jobs", c(seq(1985, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
### Overtime income of Head
```{r, eval=FALSE}
OT.Income <- df %>% select(c(V21743, ER4126, ER6966, ER9217, ER12200, ER16497, ER20429, ER24121, ER27917, ER40907, ER46815, ER52223, ER58024, ER65204, ER71281))
names(OT.Income) <- mapply(paste, "OT.Income", c(seq(1993, 1997, 1), seq(1999, 2017, 2)), sep = "_")
```
## Extra job questions - asked about reported hours and amount of overtime for up to 4 jobs, all reported individually
E.g., job 1 (main job):
### How many hours did that overtime amount to in (that period during) 2002?
```{r, eval=FALSE}
OT.Hrs.Main.Job <- df %>% select(c(ER21429, ER25426, ER36431, ER42458, ER47776, ER53482, ER60497, ER66512))
names(OT.Hrs.Main.Job) <- mapply(paste, "OT.Hrs.Main.Job", seq(2003, 2017, 2), sep = "_")
```
### Did you have any extra jobs or other ways of making money in addition to your main job in 1982? Yes/no
Asked of HOH who are currently employed:
```{r, eval=FALSE}
Extra.Job <- df %>% select(c(V9037, V10564, V11708, V13108, V14206, V15260, V16761, V18199, V19499, V20799, V22580, ER2228, ER5227, ER7323, ER10235, ER13366, ER17398))
names(Extra.Job) <- mapply(paste, "Extra.Job", c(seq(1983, 1997, 1), 1999, 2001), sep = "_")
```
### Actual number of hours per week Head worked on his/her extra job. If Head had more than one extra job, the value here represents a weighted average of hours spent on all extra jobs.
```{r, eval=FALSE}
Hrs.Extra.Jobs <- df %>% select(c(V9042, V10569, V11713, V13113, V14211, V15267, V16768, V18206, V19506, V20806, V22589, ER2233, ER5232, ER7328, ER10240, ER13373, ER17406))
names(Hrs.Extra.Jobs) <- mapply(paste, "Hrs.Extra.Jobs", c(seq(1983, 1997, 1), 1999, 2001), sep = "_")
```
## Hourly/salary
```{r, eval=FALSE}
Hrly.Slry <- df %>% select(V4002, V4509, V5420, V5907, V6518, V7121, V7714, V8382, V9013, V10462, V11653, V13056, V14156, V15164, V16665, V18103, V19403, V20703, V22463, ER2081, ER5080, ER7176, ER10091, ER13217, ER17228, ER21152, ER25141, ER36146, ER42181, ER47494, ER53194, ER60209, ER66210)
names(Hrly.Slry) <- mapply(paste, "Hrly.Slry", c(1975:1997, seq(1999, 2017, 2)), sep = "_")
```
## Head covered by union
```{r, eval=FALSE}
UnionCovered.Head <- df %>% select(one_of(c("V4478", "V5382", "V5877", "V6495", "V7098", "V7709", "V8377", "V9008", "V10458", "V11649", "V13052", "V14152", "V15160", "V16661", "V18099", "V19399", "V20699", "V22454", "ER2079", "ER5078", "ER7174", "ER10089", "ER13213", "ER17224", "ER21150", "ER25138", "ER36143", "ER42178", "ER47491", "ER53191", "ER60206", "ER66207")))
names(UnionCovered.Head) <- mapply(paste, "UnionCovered.Head", c(1976:1997, seq(1999, 2017, 2)), sep = "_")
```
## Spouse covered by union
```{r, eval=FALSE}
UnionCovered.Spouse <- df %>% select(one_of(c("V4861", "V6594", "V7196", "V7882", "V8541", "V9191", "V10676", "V12012", "V13231", "V14327", "V15462", "V16980", "V18401", "V19701", "V21001", "V22807", "ER2573", "ER5572", "ER7668", "ER10571", "ER13725", "ER17794", "ER21400", "ER25396", "ER36401", "ER42430", "ER47748", "ER53454", "ER60469", "ER66482")))
names(UnionCovered.Spouse) <- mapply(paste, "UnionCovered.Spouse", c(1976, 1979:1997, seq(1999, 2017, 2)), sep = "_")
```
## Age
```{r, eval=FALSE}
Age.Head <- df %>% select(one_of(c("ER30004", "ER30023", "ER30046", "ER30070", "ER30094", "ER30120", "ER30141", "ER30163", "ER30191", "ER30220", "ER30249", "ER30286", "ER30316", "ER30346", "ER30376", "ER30402", "ER30432", "ER30466", "ER30501", "ER30538", "ER30573", "ER30609", "ER30645", "ER30692", "ER30736", "ER30809", "ER33104", "ER33204", "ER33304", "ER33404", "ER33504", "ER33604", "ER33704", "ER33804", "ER33904", "ER34004", "ER34104", "ER34204", "ER34305", "ER66017")))
names(Age.Head) <- mapply(paste, "Age.Head", c(1968:1997, seq(1999, 2017, 2)), sep = "_")
```
## Sex
```{r, eval=FALSE}
df$Sex <- df$ER32000
```
## Grades completed
```{r, eval=FALSE}
GradesCompl <- df %>% select(one_of(c("ER30010", "ER30052", "ER30076", "ER30100", "ER30126", "ER30147", "ER30169", "ER30197", "ER30226", "ER30255", "ER30296", "ER30326", "ER30356", "ER30384", "ER30413", "ER30443", "ER30478", "ER30513", "ER30549", "ER30584", "ER30620", "ER30657", "ER30703", "ER30748", "ER30820", "ER33115", "ER33215", "ER33315", "ER33415", "ER33516", "ER33616", "ER33716", "ER33817", "ER33917", "ER34020", "ER34119", "ER34230", "ER34349", "ER34548")))
names(GradesCompl) <- mapply(paste, "GradesCompl", c(1968, 1970:1997, seq(1999, 2017, 2)), sep = "_")
```
# Combine everything
This function may need to be modified depending on other objects in your environment
```{r, eval=FALSE}
colgroups <- ls()
CleanData_Wide <- cbind(df[, c("ID", "SampErrClust", "SampErrStrat", "Sex")], bind_cols(mget(colgroups[!(colgroups %in% c("df", "RAW", "farm"))])))
# save(CleanData_Wide, file = "/CleanData_Wide_5-1-19.Rdata")
```
# Wide to long
```{r, eval=FALSE}
library(tidyverse)
# load("/CleanData_Wide_5-1-19.Rdata")
library(beepr)
CleanData <- CleanData_Wide %>%
gather(key = var, value = val, -ID, -SampErrClust, -SampErrStrat, -Sex) %>%
separate(var, c("Variable", "Year"), "_")
CleanData <- CleanData %>% spread(Variable, val)
# save(CleanData, file = "/CleanData_5-1-19.Rdata")
beep()
```
# Change names of spouse income variables
```{r, eval = FALSE}
CleanData$SpouseIncome1 <- CleanData$TotalLaborIncome1.Spouse
CleanData$SpouseIncome2 <- CleanData$TotalLaborIncome2.Spouse
```
# Restrict data to Heads of household
```{r, eval = FALSE}
df <- CleanData %>%
filter(RelationToHead == 10)
```
# For every respondent from 2003-2015 (when K6 asked), go back as far as their first year of participation (it's 1983)
```{r, eval = FALSE}
UniqueIDs <- df %>% filter(Year %in% c("2003", "2005", "2007", "2009", "2011", "2013", "2015", "2017"))
UniqueIDs <- unique(UniqueIDs$ID)
df <- df %>% filter(ID %in% UniqueIDs)
df$ID <- as.factor(df$ID)
# df %>% group_by(Year) %>% summarize(max(Age.Head, na.rm=TRUE))
```
#Check variable value options for Qs asked every year
```{r, eval = FALSE}
df$Year <- as.numeric(df$Year)
df <- df %>% mutate(Age = ifelse(Year %in% c(1983:1993) & Age.Head > 98, NA, ifelse(Year %in% c(1994:1996) &
Age.Head %in% c(0, 98, 99), NA, ifelse(Year %in% c(1997:2017) & Age.Head > 120, NA,
Age.Head
))))
df <- select(df, -Age.Head)
```
# Recode industries and occupations into standard groups, 1970s codes and 2000s codes, collapse into one variable
## Create categortical industry variable
```{r, eval = FALSE}
df <- df %>% mutate(
Industry_Cat = case_when(
(.$Year %in% c(1983:2001) & .$Industry.Head %in% c(407:479)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(57:69)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(607:639)) ~ "Transport/Comm/PubUtil",
(.$Year %in% c(1983:2001) & .$Industry.Head %in% c(507:698)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(407:459)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(467:579)) ~ "Whole/Retail Trade",
(.$Year %in% c(1983:2001) & .$Industry.Head %in% c(707:718)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(687:699)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(707:719)) ~ "Finance/Insur/RealEstate",
.$Year %in% c(1983:2001) & .$Industry.Head %in% c(727:759) ~ "BusinessRepairServices",
.$Year %in% c(1983:2001) & .$Industry.Head %in% c(769:798) ~ "PersonalServices",
(.$Year %in% c(1983:2001) & .$Industry.Head %in% c(807:809)) | (.$Year %in% c(2003:2017) & .$Industry.Head %in% c(856:859)) ~ "Entertain/Rec Services",
.$Year %in% c(1983:2001) & .$Industry.Head %in% c(999, 0, 998, NA) ~ "Unknown",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(17:29)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(17:28)) ~ "Agra/Forest/Fish",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(37:49)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(47:57)) ~ "Mining",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(77)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(67:77)) ~ "Construction",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(107:399)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(107:398)) ~ "Manufacturing",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(647:679) ~ "Information",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(727:749)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(828:897)) ~ "Pro/Sci/Tech Services",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(757:779) ~ "Mgmt/Admin/Waste Services",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(786:789) ~ "Educ Services",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(797:847) ~ "Healthcare/Social Services",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(866:869) ~ "Accom/Food Services",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(877:929) ~ "Other Services",
(.$Year %in% c(2003:2017) & .$Industry.Head %in% c(937:987)) | (.$Year %in% c(1983:2001) & .$Industry.Head %in% c(907:937)) ~ "Public Admin/Military",
.$Year %in% c(2003:2017) & .$Industry.Head %in% c(848, 999, 0, NA) ~ "Unknown"
),
Industry_Cat = replace_na(Industry_Cat, "Unknown")
)
df <- df %>% select(-c(Industry.Head, Industry1980.Head, Industry2001.Head, Industry2017.Head))
```
## Create change in industry variable
```{r, eval = FALSE}
df <- df %>%
group_by(ID) %>%
mutate(Industry_Change_83_01 = if_else(Year <= 2003 & Industry_Cat != lag(Industry_Cat), 1, 0)) %>%
group_by(ID) %>%
mutate(N_Change_Industry_Base = cumsum(replace_na(Industry_Change_83_01, 0)))
df <- select(df, -Industry_Change_83_01)
```
## Create modal industry prior to 2003 variable
```{r, eval = FALSE}
Modal_Industry_Base <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(Industry_Cat) %>%
dplyr::slice(which.max(n)) %>%
transmute(Industry_Base = Industry_Cat)
df <- left_join(df, Modal_Industry_Base, by = "ID")
df <- df %>% mutate(Industry_Base = replace_na(Industry_Base, "None"))
df <- ungroup(df)
rm(Modal_Industry_Base)
```
## Create categorical occupation variable
### Fix 2017 data that switched to 2010 census codes
```{r, eval = FALSE}
df <- df %>%
mutate(Occupation.Head = ifelse(Year == 2017, Occupation.Head/10, Occupation.Head))
```
```{r, eval = FALSE}
df <- df %>% mutate(Occupation_Cat = case_when(
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(1:95) ~ "Pro/Tech/Kindred",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(401:600) ~ "Crafts/Kindred",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(601:695) ~ "Operatives",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(740:785) ~ "Laborers",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(821:824) ~ "Farm Laborers/Foremen",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(901:965) ~ "Service Workers",
.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(980:984) ~ "PrivateHousehold Workers",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(1:43)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(201:245)) ~ "Management",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(50:74) ~ "Business Ops Specialists",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(80:95) ~ "Financial Specialists",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(100:124) ~ "Computer/Mathematical",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(130:156) ~ "Architect/Engineering",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(160:197) ~ "Life/Phys/Soc Sciences",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(200:206) ~ "Community/Social Services",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(210:216) ~ "Legal",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(220:255) ~ "Educ/Training/Library",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(260:296) ~ "Arts/Media/Sports/Entertainment",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(300:366) ~ "Healthcare",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(370:396) ~ "Protective Services",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(400:416) ~ "Food Prep/Serving",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(420:425) ~ "Building/Grounds/Maintenance",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(430:465) ~ "Personal Care",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(470:497)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(260:285)) ~ "Sales",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(500:594)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(301:395)) ~ "Office/Admin Support",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(600:613)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(801:802)) ~ "Farm/Fish/Forest",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(620:694)) | (.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(620:676)) ~ "Construction/Extraction",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(700:763) ~ "Install/Maint/Repair",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(770:897) ~ "Production",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(900:975)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(701:715)) ~ "Transportation/Material Moving",
.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(980:983) ~ "Military",
(.$Year %in% c(2003:2017) & .$Occupation.Head %in% c(615, 999, 0, 998, 992, NA)) | (.$Year %in% c(1983:2001) & .$Occupation.Head %in% c(0, 999, 998, 810, NA)) ~ "Unknown"
))
df <- df %>% mutate(Occupation_Cat = replace_na(Occupation_Cat, "Unknown"))
#df <- df %>% select(-c(Occupation.Head, Occupation1980.Head, Occupation2001.Head, Occupation2017.Head))
```
## Create change in occupation variable
```{r, eval = FALSE}
df <- df %>%
group_by(ID) %>%
mutate(Occupation_Change_83_01 = if_else(Year <= 2003 & Occupation_Cat != lag(Occupation_Cat), 1, 0)) %>%
group_by(ID) %>%
mutate(N_Change_Occupation_Base = cumsum(replace_na(Occupation_Change_83_01, 0)))
df <- df %>% select(-Occupation_Change_83_01)
```
## Create modal occupation prior to 2003 variable
```{r, eval = FALSE}
Modal_Occupation_Base <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(Occupation_Cat) %>%
dplyr::slice(which.max(n)) %>%
transmute(Occupation_Base = Occupation_Cat)
df <- left_join(df, Modal_Occupation_Base, by = "ID")
df <- df %>% mutate(Occupation_Base = replace_na(Occupation_Base, "None"))
df <- ungroup(df)
rm(Modal_Occupation_Base)
```
# Recode race variable
```{r, eval = FALSE}
df <- df %>% mutate(Race_cat = case_when(
Race.Head == 1 ~ "White",
Race.Head %in% c(2:5) ~ "POC",
Race.Head == 7 ~ "Other",
Race.Head %in% c(0, 9, NA) ~ "Unknown"
))
df <- select(df, -Race.Head)
df$Race_cat <- factor(df$Race_cat, levels = c("White", "POC", "Other", "Unknown"))
```
# CREATE BASELINE VARS, CLEAN TIME VARYING AND FIXED VARS
## Depression, drugs before 17
```{r, eval=FALSE}
df <- df %>% mutate(
Depressed17 = case_when(
Depressed17.Head == 5 ~ "No",
Depressed17.Head == 1 ~ "Yes",
Depressed17.Head %in% c(0, 8, 9, NA) ~ "Unknown"
),
DrugProb17 = case_when(
DrugProb17.Head == 5 ~ "No",
DrugProb17.Head == 1 ~ "Yes",
DrugProb17.Head %in% c(0, 8, 9, NA) ~ "Unknown"
)
)
df <- df %>%
group_by(ID) %>%
mutate(
Depressed.Base = if_else(any(Depressed17 == "Yes"), "Yes", if_else(any(Depressed17 == "No"), "No", "Unknown")),
DrugProb.Base = if_else(any(DrugProb17 == "Yes"), "Yes", if_else(any(DrugProb17 == "No"), "No", "Unknown"))
)
df <- df %>% select(-c(Depressed17.Head, DrugProb17.Head, Depressed17, DrugProb17))
df <- ungroup(df)
```
## Alcohol frequency
```{r, eval = FALSE}
df <- df %>%
mutate(
AlcFreq_TV = as.factor(case_when(
AlcFreq.Head == 0 ~ "Never",
AlcFreq.Head == 1 ~ "<1perMnth",
AlcFreq.Head == 2 ~ "1perMnth",
AlcFreq.Head == 3 ~ ">1perMnth",
AlcFreq.Head == 4 ~ "1perWeek",
AlcFreq.Head == 5 ~ ">1perWeek",
AlcFreq.Head == 6 ~ "EveryDay",
AlcFreq.Head %in% c(8, 9, NA) ~ "Unknown"
))
)
df <- df %>% select(-c(AlcFreq.Head))
```
## Employment status
```{r, eval = FALSE}
df <- df %>%
mutate(EmploymentStatus_TV = case_when(
EmployedNow2.Head == 1 ~ "Working",
EmployedNow2.Head == 2 ~ "TempNotWork",
EmployedNow2.Head == 3 ~ "Looking/Unempl",
EmployedNow2.Head == 4 ~ "Retired",
EmployedNow2.Head == 5 ~ "Disabled",
EmployedNow2.Head == 6 ~ "Keeping house",
EmployedNow2.Head == 7 ~ "Student",
EmployedNow2.Head == 8 ~ "Other",
EmployedNow2.Head %in% c(0, 9, 98, 99, NA) ~ "Unknown"
))
df <- df %>% mutate(EmploymentStatus_TV = replace_na(EmploymentStatus_TV, "Unknown"))
df <- df %>% select(-EmployedNow2.Head)
```
## Change in employement status prior to 2003
```{r, eval = FALSE}
df <- df %>%
group_by(ID) %>%
mutate(EmployStatusChange_to_01 = if_else(Year <= 2003 & EmploymentStatus_TV != lag(EmploymentStatus_TV), 1, 0)) %>%
group_by(ID) %>%
mutate(N_Change_Employ_Base = cumsum(replace_na(EmployStatusChange_to_01, 0)))
df <- select(df, -EmployStatusChange_to_01)
df <- ungroup(df)
```
## Create modal base employment status
```{r, eval = FALSE}
Modal_EmployStatus_Base <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(EmploymentStatus_TV) %>%
dplyr::slice(which.max(n)) %>%
transmute(EmploymentStatus.Base = EmploymentStatus_TV)
df <- left_join(df, Modal_EmployStatus_Base, by = "ID")
df <- df %>% mutate(EmploymentStatus.Base = replace_na(EmploymentStatus.Base, "None"))
df <- ungroup(df)
rm(Modal_EmployStatus_Base)
```
## Grades completed
```{r, eval = FALSE}
# lt high school, 12=high school, 13-15 = some college, 16=college, 17=more than college
df <- df %>% mutate(GradesCompl_TV = case_when(
GradesCompl %in% c(1:11) ~ "<HS",
GradesCompl == 12 ~ "HS",
GradesCompl %in% c(13:15) ~ "Some College",
GradesCompl == 16 ~ "College",
GradesCompl == 17 ~ ">College",
GradesCompl %in% c(0, 99, NA) ~ "Unknown"
))
df <- df %>% select(-GradesCompl)
```
## Baseline and time-varying interest income
```{r, eval = FALSE}
df$InterestIncome_TV <- ifelse(is.na(df$InterestIncome.Head), 0, df$InterestIncome.Head)
## Base
df$InterestIncome.2003 <- ifelse(df$Year <= 2003, df$InterestIncome.Head, NA)
df.interest <- df %>%
group_by(ID) %>%
dplyr::summarize(InterestIncome.Base = mean(InterestIncome.2003, na.rm = TRUE))
# merge back
df <- left_join(df, df.interest, by = "ID")
df <- df %>% mutate(InterestIncome.Base = replace_na(InterestIncome.Base, 0))
df <- select(df, -c(InterestIncome.Head, InterestIncome.2003))
rm(df.interest)
```
## Own or rent, and modal base own/rent
```{r, eval = FALSE}
df <- df %>% mutate(OwnRent_TV = case_when(
OwnRent == 1 ~ "Owns",
OwnRent == 5 ~ "Rents",
OwnRent == 8 ~ "Neither",
OwnRent %in% c(0, 9, NA) ~ "Unknown"
))
Modal_OwnRent_Base <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(OwnRent_TV) %>%
dplyr::slice(which.max(n)) %>%
transmute(OwnRent.Base = OwnRent_TV)
df <- left_join(df, Modal_OwnRent_Base, by = "ID")
df <- df %>% mutate(OwnRent.Base = replace_na(OwnRent.Base, "none"))
df <- ungroup(df)
df <- select(df, -OwnRent)
rm(Modal_OwnRent_Base)
```
## Self-employed
```{r, eval = FALSE}
df <- df %>% mutate(SelfEmployed_TV = case_when(
SelEmp2001.Head == 1 ~ "SomeoneElse",
SelEmp2001.Head == 2 ~ "Both",
SelEmp2001.Head == 3 ~ "Self",
SelEmp2001.Head %in% c(0, 9, NA) ~ "Unknown"
))
Years_Self_Empl <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(SelfEmployed_TV) %>%
filter(SelfEmployed_TV == "Self") %>%
rename(Yrs_Self_Emp_Base = n) %>%
select(-SelfEmployed_TV)
df <- left_join(df, Years_Self_Empl, by = "ID")
df <- df %>% mutate(Yrs_Self_Emp_Base = replace_na(Yrs_Self_Emp_Base, 0))
df <- select(df, -SelEmp2001.Head, -SelfEmp2017.Head)
rm(Years_Self_Empl)
```
## Total labor income
```{r, eval = FALSE}
df$TotalLaborIncome.2003 <- ifelse(df$Year <= 2003, df$TotalLaborIncome.Head, NA)
df.labor <- df %>%
group_by(ID) %>%
dplyr::summarize(TotalLaborIncome.Base = mean(TotalLaborIncome.2003, na.rm = TRUE))
# merge back
df <- left_join(df, df.labor, by = "ID")
df$TotalLaborIncome_TV <- df$TotalLaborIncome.Head
df <- select(df, -c(TotalLaborIncome.Head, TotalLaborIncome.2003))
rm(df.labor)
```
## Salary or hourly
```{r, eval=FALSE}
df <- df %>% mutate(Hrly.Slry_TV = case_when(
Hrly.Slry == 1 ~ "Salary",
Hrly.Slry == 3 ~ "Hourly",
Hrly.Slry == 7 ~ "Other",
Hrly.Slry %in% c(0, 9, NA) ~ "Unknown",
Hrly.Slry == 0 ~ "Inap"
))
Hrly.Slry.Base <- df %>%
group_by(ID) %>%
filter(Year <= 2003) %>%
count(Hrly.Slry_TV) %>%
dplyr::slice(which.max(n)) %>%
mutate(Hrly.Slry.Base = Hrly.Slry_TV) %>%
select(ID, Hrly.Slry.Base)
df <- left_join(df, Hrly.Slry.Base, by = "ID")
df <- df %>% mutate(Hrly.Slry.Base = replace_na(Hrly.Slry.Base, "Inap"))
rm(Hrly.Slry.Base)
```
## Spouse labor income
```{r, eval = FALSE}
# table(df$SpouseIncome1, df$Year) # thru 1993
# table(df$SpouseIncome2, df$Year) #1994 and up
df$SpouseIncome <- ifelse(df$Year < 1994, df$SpouseIncome1, ifelse(df$Year %in% c(1994:2017), df$SpouseIncome2, NA))
## Base - average
df$SpouseIncome.2003 <- ifelse(df$Year <= 2003, df$SpouseIncome, NA)
df.spouseincome <- df %>%
group_by(ID) %>%
dplyr::summarize(SpouseIncome.Base = mean(SpouseIncome.2003, na.rm = TRUE))
# merge back
df <- left_join(df, df.spouseincome, by = "ID")
df <- df %>% mutate(
SpouseIncome_TV = replace_na(SpouseIncome, 0),
SpouseIncome.Base = replace_na(SpouseIncome.Base, 0)
)
df <- select(df, -SpouseIncome1, -SpouseIncome2, -SpouseIncome.2003)
rm(df.spouseincome)