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204f58d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | ## Daniel J. Mallinson
## Fitzgerald 2018 Replication Script
rm(list=ls()) #clear workspace
#install.packages(c("readstata13", "tidyverse", "reshape2", "prais", "panelAR")) #uncomment to install necessary packages
library(foreign)
library(readstata13)
library(tidyverse)
library(reshape2)
library(prais)
library(panelAR)
data <- read.dta13("compiled.dta")
hhsize <- read.dta13("hhsize.dta")
epa <- read.dta13("epa.dta")
## Reshape household size (hhsize) from wide to long
hhsize <- melt(hhsize, id.vars=c("State", "state_id_no", "state_fip"))
year <- c(rep(7,50), rep(8,50), rep(9,50), rep(10,50), rep(11,50),
rep(12,50), rep(13,50), rep(14,50), rep(15,50), rep(16,50))
hhsize <- cbind(hhsize, year)
hhsize <- hhsize[c("State", "value", "year")]
names(hhsize)[2] <- "hhsize"
## Merge hhsize with rest of data
data <- merge(data, hhsize, by=c("State", "year"))
data <- merge(data, epa, by=c("State", "year"))
## Calculate Employed Population %
data$emppop_pct <- data$emppop/(data$pop*1000)*100
## Calculate Manufacturing % of GDP
data$manu_gdp <- data$manuf/data$gdp*100
## Log transform continuous variables
data[c("epa", "wrkhrs", "emppop_pct", "laborprod", "pop", "manu_gdp",
"energy", "hhsize", "workpop")] <- log(data[c("epa", "wrkhrs", "emppop_pct", "laborprod", "pop", "manu_gdp",
"energy", "hhsize", "workpop")])
#### Registration Analysis
## Draw sample for analysis set up
states <- unique(data$State)
group_var <- data %>%
group_by(State) %>%
groups %>%
unlist %>%
as.character
group_var
set.seed(42)
random_states <- data %>%
group_by(State) %>%
summarise() %>%
sample_n(5) %>%
mutate(unique_id=1:NROW(.))
random_states
sampledata <- data %>%
group_by(State) %>%
right_join(random_states, by=group_var) %>%
group_by_(group_var)
sampledata <- sampledata[order(sampledata$State, sampledata$year),]
sampledata <- as.data.frame(sampledata)
## Replication models with 5% sample
model1 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=sampledata, panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE)
summary(model1)
## Model with original years
model2 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=sampledata[which(sampledata$year<14),], panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE)
summary(model2)
## Model with only new years
#Does not run, not enough data in sample
model3 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=sampledata[which(sampledata$year>13),], panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE, rho.na.rm=TRUE)
summary(model3)
## Models with full data (Not yet run)
model4 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=data, panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE)
summary(model4)
## Model with original years
model5 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=data[which(data$year<14),], panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE)
summary(model5)
## Model with only new years
model6 <- panelAR(epa ~ wrkhrs + emppop_pct + laborprod + pop + manu_gdp +
energy + hhsize + workpop + State + factor(year), data=data[which(data$year>13),], panelVar='State', timeVar='year', panelCorrMethod='pcse',singular.ok=TRUE, autoCorr="psar1", complete.case=TRUE)
summary(model6)
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