EDB and interrupted time series models
Andrew Heiss
November 11, 2016
library(dplyr)
library(tidyr)
library(purrr)
library(broom)
library(readr)
library(ggplot2)
library(ggstance)
library(gridExtra)
library(haven)
library(stargazer)
library(lubridate)
library(lazyeval)
library(sandwich)
library(lmtest)
library(broom)
library(pander)
library(countrycode)
knitr::opts_chunk$set(cache=FALSE, fig.retina=2,
tidy.opts=list(width.cutoff=120), # For code
options(width=120), # For output
warning=FALSE)
countries.with.edb.bureau <- read_csv(file.path(PROJHOME, "data/countries_with_edb_bureau.csv"))
edb.its <- read_dta("~/Dropbox/Andrew/EDB/MasterWBMarch16_15.dta") %>%
filter(year > 1999) %>%
rename(p_edb_rank = p_ebd_rank) %>%
select(ccode, year,
sb_proced, sb_days, sb_capital, sb_cost, con_proced, con_days,
gdp, gdpcap, pop, gdpgrowth, polity = polity2, ibrd,
p_edb_rank) %>%
mutate_each(funs(ln = log1p(.)),
starts_with("sb"), starts_with("con"), gdp, gdpcap, pop) %>%
mutate(year.centered.2005 = year - 2005,
year.centered.2006 = year - 2006,
ranked.2005 = year.centered.2005 >= 0,
ranked.2006 = year.centered.2006 >= 0) %>%
group_by(ccode) %>%
mutate(loan_ln = log1p(sum(ibrd, na.rm=TRUE))) %>%
mutate_each(funs(lag = lag(.))) %>%
ungroup()
edb.its.constrained.countries <- edb.its %>%
mutate(in.report.in.2004 = year == 2004 & !is.na(sb_days),
in.report.in.2001 = year == 2001 & !is.na(sb_days)) %>%
group_by(ccode) %>%
summarise(in.2004 = sum(in.report.in.2004),
in.2001 = sum(in.report.in.2001))
edb.its <- edb.its %>%
left_join(edb.its.constrained.countries, by="ccode") %>%
filter(in.2004 == 1)
edb.its.2001 <- edb.its %>%
filter(in.2001 == 1)
edb.its.committee <- edb.its %>%
filter(ccode %in% countries.with.edb.bureau$cowcode)
edb.its.2001.committee <- edb.its %>%
filter(in.2001 == 1) %>%
filter(ccode %in% countries.with.edb.bureau$cowcode)
edb.its.2001.nocommittee <- edb.its %>%
filter(in.2001 == 1) %>%
filter(!(ccode %in% countries.with.edb.bureau$cowcode))
edb.its.cap.constrained <- filter(edb.its, year >= 2003)
edb.its.2001.cap.constrained <- filter(edb.its.2001, year >= 2003)
edb.its.committee.cap.constrained <- filter(edb.its.committee, year >= 2003)
edb.its.2001.committee.cap.constrained <- filter(edb.its.2001.committee, year >= 2003)
edb.its.2001.nocommittee.cap.constrained <- filter(edb.its.2001.nocommittee, year >= 2003)
theme_edb <- function(base_size=9, base_family="Clear Sans Light") {
update_geom_defaults("label", list(family="Clear Sans Light"))
update_geom_defaults("text", list(family="Clear Sans Light"))
ret <- theme_bw(base_size, base_family) +
theme(panel.background = element_rect(fill="#ffffff", colour=NA),
axis.title.y = element_text(margin = margin(r = 10)),
axis.title.x = element_text(margin = margin(t = 10)),
title=element_text(vjust=1.2, family="Clear Sans", face="bold"),
plot.subtitle=element_text(family="Clear Sans Light"),
plot.caption=element_text(family="Clear Sans Light",
size=rel(0.8), colour="grey70"),
panel.border = element_blank(),
axis.line=element_line(colour="grey50", size=0.2),
#panel.grid=element_blank(),
axis.ticks=element_blank(),
legend.position="bottom",
legend.title=element_text(size=rel(0.8)),
axis.title=element_text(size=rel(0.8), family="Clear Sans", face="bold"),
strip.text=element_text(size=rel(1), family="Clear Sans", face="bold"),
strip.background=element_rect(fill="#ffffff", colour=NA),
panel.margin.y=unit(1.5, "lines"),
legend.key=element_blank(),
legend.margin=unit(0.2, "lines"))
ret
}
plot.its <- function(model, var.name, var.title, y.title, plot.year = 2005) {
# summary_dots <- list(
# n = ~ n(),
# variable = interp(~ mean(val, na.rm = T), val=as.name(var.name)),
# stdev = interp(~ sd(val, na.rm = T), val = as.name(var.name))
# )
if (plot.year == 2005) {
plot.data <- edb.its %>%
group_by(year.centered.2005) %>%
summarise_(variable = interp(~mean(var, na.rm=TRUE), var=as.name(var.name)))
newdata <- data_frame(year.centered.2005 = seq(min(edb.its$year.centered.2005),
max(edb.its$year.centered.2005), by=1),
ranked.2005 = year.centered.2005 >= 0,
gdpcap_ln_lag = mean(edb.its$gdpcap_ln_lag, na.rm=TRUE),
gdpgrowth_lag = mean(edb.its$gdpgrowth_lag, na.rm=TRUE),
pop_ln_lag = mean(edb.its$pop_ln_lag, na.rm=TRUE))
plot.predict <- augment(model, newdata=newdata) %>%
rename(variable = .fitted,
year.centered = year.centered.2005,
ranked = ranked.2005)
} else {
plot.data <- edb.its %>%
group_by(year.centered.2006) %>%
summarise_(variable = interp(~mean(var, na.rm=TRUE), var=as.name(var.name)))
newdata <- data_frame(year.centered.2006 = seq(min(edb.its$year.centered.2006),
max(edb.its$year.centered.2006), by=1),
ranked.2006 = year.centered.2006 >= 0,
gdpcap_ln_lag = mean(edb.its$gdpcap_ln_lag, na.rm=TRUE),
gdpgrowth_lag = mean(edb.its$gdpgrowth_lag, na.rm=TRUE),
pop_ln_lag = mean(edb.its$pop_ln_lag, na.rm=TRUE))
plot.predict <- augment(model, newdata=newdata) %>%
rename(variable = .fitted,
year.centered = year.centered.2006,
ranked = ranked.2006)
}
ggplot(plot.predict, aes(x=year.centered, y=variable)) +
geom_line() +
geom_line(data=plot.predict, aes(colour=ranked), size=0.75) +
geom_vline(xintercept=0) +
scale_colour_manual(values=c("#0073D9", "#CC3340"),
labels=c("Not ranked ", "Ranked"),
name=NULL) +
labs(title=var.title, y=y.title, x=paste("Years since", plot.year)) +
theme_edb()
}
# Calculate clustered robust standard errors
robust.clusterify <- function(model, dat, cluster) {
attach(dat, warn.conflicts = F)
not <- attr(model$model,"na.action")
if( ! is.null(not)) { # only drop the NA values if there are any left
cluster <- cluster[-not]
dat <- dat[-not,]
}
with(dat, {
M <- length(unique(cluster))
N <- length(cluster)
K <- model$rank
dfc <- (M/(M-1))*((N-1)/(N-K))
uj <- apply(estfun(model),2, function(x) tapply(x, cluster, sum));
vcovCL <- dfc*sandwich(model, meat=crossprod(uj)/N)
coefs <- coeftest(model, vcovCL, type="HC1") # HC1 or HC0 are close to Stata
return(list(clcov=vcovCL, coefs=coefs))
})
}Starting a business variables over time
Countries in 2004 report
plot.edb <- edb.its %>%
select(year, sb_days, sb_proced, sb_cost, sb_capital, con_days, con_proced) %>%
gather(variable, value, -year) %>%
group_by(year, variable) %>%
summarise(avg = mean(value, na.rm=TRUE)) %>%
filter(!is.nan(avg))
plot.interventions <- data_frame(year = 2005:2006,
intervention = c("2005", "2006"))ggplot(plot.edb, aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
scale_color_manual(values=c("red", "blue"), name=NULL) +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
labs(x=NULL, y=NULL, title="Average values of sb variables over time",
subtitle="Only countries included in 2004 report") +
facet_wrap(~ variable, scales="free_y") +
theme_edb()
Countries in 2001 report
plot.edb.2001 <- edb.its.2001 %>%
select(year, sb_days, sb_proced, sb_cost, sb_capital, con_days, con_proced) %>%
gather(variable, value, -year) %>%
group_by(year, variable) %>%
summarise(avg = mean(value, na.rm=TRUE)) %>%
filter(!is.nan(avg))
plot.interventions <- data_frame(year = 2005:2006,
intervention = c("2005", "2006"))ggplot(plot.edb.2001, aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
scale_color_manual(values=c("red", "blue"), name=NULL) +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
labs(x=NULL, y=NULL, title="Average values of sb variables over time",
subtitle="Only countries included in 2001 report") +
facet_wrap(~ variable, scales="free_y") +
theme_edb()
Countries with reform committees in 2015
plot.edb.committee <- edb.its.2001.committee %>%
select(year, sb_days, sb_proced, sb_cost, sb_capital, con_days, con_proced) %>%
gather(variable, value, -year) %>%
group_by(year, variable) %>%
summarise(avg = mean(value, na.rm=TRUE)) %>%
filter(!is.nan(avg))
plot.interventions <- data_frame(year = 2005:2006,
intervention = c("2005", "2006"))# ggplot(plot.edb.committee, aes(x=year, y=avg)) +
# geom_vline(data=plot.interventions, aes(xintercept=year,
# colour=intervention),
# linetype="dashed", size=0.5) +
# geom_line() +
# scale_color_manual(values=c("red", "blue"), name=NULL) +
# scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
# coord_cartesian(xlim=c(2000, 2015)) +
# labs(x=NULL, y=NULL, title="Average values of sb variables over time",
# subtitle="Only countries that were in the report by 2011 and have EDB reform committees") +
# facet_wrap(~ variable, scales="free_y") +
# theme_edb()Countries in report in 2001, with/without reform committees
plot.edb.committee.2001 <- edb.its.2001 %>%
mutate(has.committee = factor(ccode %in% countries.with.edb.bureau$cowcode,
levels=c(FALSE, TRUE),
labels=c("No committee", "Committee"),
ordered=TRUE)) %>%
select(year, has.committee, sb_days, sb_proced, sb_cost, sb_capital) %>%
# select(year, has.committee, sb_days, sb_proced, sb_cost, sb_capital, con_days, con_proced) %>%
gather(variable, value, -year, -has.committee) %>%
group_by(year, variable, has.committee) %>%
summarise(avg = mean(value, na.rm=TRUE)) %>%
filter(!is.nan(avg)) %>%
ungroup() %>%
mutate(variable = factor(variable, levels=c("sb_proced", "sb_days",
"sb_cost", "sb_capital"),
labels=c("Procedures", "Days", "Cost", "Capital"),
ordered=TRUE))
plot.interventions <- data_frame(year = 2005:2006,
intervention = c("2005", "2006"))
# plot.con_days <- ggplot(filter(plot.edb.committee.2001, variable=="con_days"),
# aes(x=year, y=avg)) +
# geom_vline(data=plot.interventions, aes(xintercept=year,
# colour=intervention),
# linetype="dashed", size=0.5) +
# geom_line() +
# labs(x=NULL, y="Days") +
# scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
# coord_cartesian(xlim=c(2000, 2015)) +
# scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
# facet_wrap(~ variable + has.committee) +
# theme_edb()
#
# plot.con_proced <- ggplot(filter(plot.edb.committee.2001, variable=="con_proced"),
# aes(x=year, y=avg)) +
# geom_vline(data=plot.interventions, aes(xintercept=year,
# colour=intervention),
# linetype="dashed", size=0.5) +
# geom_line() +
# labs(x=NULL, y="Procedures") +
# scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
# coord_cartesian(xlim=c(2000, 2015)) +
# scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
# facet_wrap(~ variable + has.committee) +
# theme_edb()
plot.sb_capital <- ggplot(filter(plot.edb.committee.2001, variable=="Capital"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y="Percent") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable + has.committee) +
theme_edb() + theme(axis.text.x = element_text(angle=45, hjust=1))
plot.sb_cost <- ggplot(filter(plot.edb.committee.2001, variable=="Cost"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y="Percent") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable + has.committee) +
theme_edb() + theme(axis.text.x = element_text(angle=45, hjust=1))
plot.sb_days <- ggplot(filter(plot.edb.committee.2001, variable=="Days"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y="Days") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable + has.committee) +
theme_edb() + theme(axis.text.x = element_text(angle=45, hjust=1))
plot.sb_proced <- ggplot(filter(plot.edb.committee.2001, variable=="Procedures"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y="Procedures") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable + has.committee) +
theme_edb() + theme(axis.text.x = element_text(angle=45, hjust=1))
plot.with.committees <- ggplot(filter(plot.edb.committee.2001, has.committee=="Committee"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y=NULL, title="Countries with reform committees") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable, scales="free_y") +
theme_edb()# + theme(axis.text.x = element_text(angle=45, hjust=1))
plot.without.committees <- ggplot(filter(plot.edb.committee.2001, has.committee=="No committee"),
aes(x=year, y=avg)) +
geom_vline(data=plot.interventions, aes(xintercept=year,
colour=intervention),
linetype="dashed", size=0.5) +
geom_line() +
labs(x=NULL, y=NULL, title="Countries without reform committees") +
scale_x_continuous(limits=c(2000, 2015), breaks=seq(2000, 2015, 5)) +
coord_cartesian(xlim=c(2000, 2015)) +
scale_color_manual(values=c("red", "blue"), name=NULL, guide=FALSE) +
facet_wrap(~ variable, scales="free_y") +
theme_edb()# + theme(axis.text.x = element_text(angle=45, hjust=1))plot.without.committees
plot.with.committees
grid.arrange(plot.without.committees, plot.with.committees, ncol=2)
Model results
Original models from spring version of paper
These are the models from the first column of the paper from April/May, just for the sake of comparison. There are only tiny differences in coefficients, since data from 1999 is now omitted (it wasn’t in the original models).
model.proced.orig <- lm(sb_proced ~ sb_proced_lag + ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.proced.orig.se <- robust.clusterify(model.proced.orig, edb.its, edb.its$ccode)
model.days.orig <- lm(sb_days ~ sb_days_lag + ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.days.orig.se <- robust.clusterify(model.days.orig, edb.its, edb.its$ccode)
model.days_ln.orig <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.days_ln.orig.se <- robust.clusterify(model.days_ln.orig, edb.its, edb.its$ccode)
model.cost_ln.orig <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.cost_ln.orig.se <- robust.clusterify(model.cost_ln.orig, edb.its, edb.its$ccode)
model.capital_ln.orig <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.capital_ln.orig.se <- robust.clusterify(model.capital_ln.orig, edb.its, edb.its$ccode)
model.proced.orig.nolag <- lm(sb_proced ~ ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.proced.orig.nolag.se <- robust.clusterify(model.proced.orig.nolag, edb.its, edb.its$ccode)
model.days.orig.nolag <- lm(sb_days ~ ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.days.orig.nolag.se <- robust.clusterify(model.days.orig.nolag, edb.its, edb.its$ccode)
model.days_ln.orig.nolag <- lm(sb_days_ln ~ ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.days_ln.orig.nolag.se <- robust.clusterify(model.days_ln.orig.nolag, edb.its, edb.its$ccode)
model.cost_ln.orig.nolag <- lm(sb_cost_ln ~ ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.cost_ln.orig.nolag.se <- robust.clusterify(model.cost_ln.orig.nolag, edb.its, edb.its$ccode)
model.capital_ln.orig.nolag <- lm(sb_capital_ln ~ ranked.2005_lag + gdpcap_ln_lag +
gdpgrowth_lag + polity_lag + pop_ln_lag,
data=edb.its)
model.capital_ln.orig.nolag.se <- robust.clusterify(model.capital_ln.orig.nolag, edb.its, edb.its$ccode)stargazer(model.proced.orig, model.proced.orig.nolag,
model.days.orig, model.days.orig.nolag,
model.days_ln.orig, model.days_ln.orig.nolag,
model.cost_ln.orig, model.cost_ln.orig.nolag,
model.capital_ln.orig, model.capital_ln.orig.nolag,
se=list(model.proced.orig.se$coefs[,2], model.proced.orig.nolag.se$coefs[,2],
model.days.orig.se$coefs[,2], model.days.orig.nolag.se$coefs[,2],
model.days_ln.orig.se$coefs[,2], model.days_ln.orig.nolag.se$coefs[,2],
model.cost_ln.orig.se$coefs[,2], model.cost_ln.orig.nolag.se$coefs[,2],
model.capital_ln.orig.se$coefs[,2], model.capital_ln.orig.nolag.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 10))),
notes="Robust standard errors clustered by country")| EDB outcomes | ||||||||||
| sb_proced | sb_days | sb_days_ln | sb_cost_ln | sb_capital_ln | ||||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | |
| Constant | 0.073 | 8.097*** | 6.687 | 100.262*** | 0.180 | 5.024*** | 0.253** | 8.681*** | 0.302 | 5.022** |
| (0.405) | (2.253) | (4.439) | (30.711) | (0.110) | (0.709) | (0.124) | (0.908) | (0.210) | (2.334) | |
| sb_proced_lag | 0.920*** | |||||||||
| (0.014) | ||||||||||
| sb_days_lag | 0.866*** | |||||||||
| (0.024) | ||||||||||
| sb_days_ln_lag | 0.931*** | |||||||||
| (0.012) | ||||||||||
| sb_cost_ln_lag | 0.947*** | |||||||||
| (0.009) | ||||||||||
| sb_capital_ln_lag | 0.924*** | |||||||||
| (0.010) | ||||||||||
| ranked.2005_lag | -0.257*** | -1.773*** | -0.165 | -19.570*** | -0.036* | -0.548*** | -0.082*** | -0.191*** | -0.034 | -0.988*** |
| (0.066) | (0.233) | (0.828) | (2.354) | (0.019) | (0.060) | (0.017) | (0.064) | (0.047) | (0.148) | |
| gdpcap_ln_lag | -0.041* | -0.718*** | -0.420** | -6.527*** | -0.014** | -0.213*** | -0.035*** | -0.716*** | -0.018 | -0.081 |
| (0.022) | (0.142) | (0.202) | (1.502) | (0.006) | (0.038) | (0.009) | (0.048) | (0.012) | (0.121) | |
| gdpgrowth_lag | -0.020*** | 0.006 | -0.276*** | -0.407 | -0.007*** | -0.0004 | -0.013*** | -0.039*** | -0.008* | -0.029 |
| (0.006) | (0.019) | (0.071) | (0.302) | (0.002) | (0.006) | (0.002) | (0.010) | (0.005) | (0.022) | |
| polity_lag | -0.006 | -0.082** | -0.086 | -0.340 | -0.002 | -0.010 | 0.0002 | 0.003 | 0.004 | -0.067** |
| (0.006) | (0.035) | (0.056) | (0.343) | (0.001) | (0.009) | (0.001) | (0.014) | (0.004) | (0.030) | |
| pop_ln_lag | 0.057** | 0.480*** | -0.016 | 0.380 | 0.008 | 0.023 | 0.011** | 0.019 | -0.007 | -0.065 |
| (0.022) | (0.130) | (0.208) | (1.632) | (0.005) | (0.038) | (0.005) | (0.048) | (0.011) | (0.132) | |
| Year fixed effects | No | No | No | No | No | No | No | No | No | No |
| Observations | 1,660 | 1,794 | 1,660 | 1,794 | 1,660 | 1,794 | 1,660 | 1,794 | 1,559 | 1,692 |
| R2 | 0.909 | 0.288 | 0.881 | 0.191 | 0.902 | 0.285 | 0.963 | 0.595 | 0.904 | 0.084 |
| Adjusted R2 | 0.909 | 0.286 | 0.881 | 0.189 | 0.902 | 0.283 | 0.963 | 0.594 | 0.903 | 0.082 |
| Residual Std. Error | 1.053 (df = 1653) | 2.998 (df = 1788) | 11.859 (df = 1653) | 32.874 (df = 1788) | 0.283 (df = 1653) | 0.776 (df = 1788) | 0.291 (df = 1653) | 0.965 (df = 1788) | 0.712 (df = 1552) | 2.244 (df = 1686) |
| F Statistic | 2,752.381*** (df = 6; 1653) | 144.569*** (df = 5; 1788) | 2,046.473*** (df = 6; 1653) | 84.327*** (df = 5; 1788) | 2,540.216*** (df = 6; 1653) | 142.812*** (df = 5; 1788) | 7,182.407*** (df = 6; 1653) | 525.510*** (df = 5; 1788) | 2,429.318*** (df = 6; 1552) | 31.116*** (df = 5; 1686) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||||
| Robust standard errors clustered by country | ||||||||||
Simple interrupted time series
How to interpret coefficients:
- β0 = Constant: pre-period intercept - baseline pre intervention
- β1 =
year.centered: pre-period slope - baseline time trend - level of increase prior to intervention - β2 =
ranked: immediate effect of the event - change in intercept at point of experiment - β3 =
year.centered:ranked: change in slope after the experiment - what happens after
model.proced.2005 <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.proced.2005.se <- robust.clusterify(model.proced.2005, edb.its, edb.its$ccode)
model.days_ln.2005 <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.days_ln.2005.se <- robust.clusterify(model.days_ln.2005, edb.its, edb.its$ccode)
model.cost_ln.2005 <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.cost_ln.2005.se <- robust.clusterify(model.cost_ln.2005, edb.its, edb.its$ccode)
model.capital_ln.2005 <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.cap.constrained)
model.capital_ln.2005.se <- robust.clusterify(model.capital_ln.2005, edb.its.cap.constrained, edb.its.cap.constrained$ccode)
model.proced.2006 <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its)
model.proced.2006.se <- robust.clusterify(model.proced.2006, edb.its, edb.its$ccode)
model.days_ln.2006 <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its)
model.days_ln.2006.se <- robust.clusterify(model.days_ln.2006, edb.its, edb.its$ccode)
model.cost_ln.2006 <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its)
model.cost_ln.2006.se <- robust.clusterify(model.cost_ln.2006, edb.its, edb.its$ccode)
model.capital_ln.2006 <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.cap.constrained)
model.capital_ln.2006.se <- robust.clusterify(model.capital_ln.2006, edb.its.cap.constrained, edb.its.cap.constrained$ccode)stargazer(model.proced.2005, model.days_ln.2005,
model.cost_ln.2005, model.capital_ln.2005,
model.proced.2006, model.days_ln.2006,
model.cost_ln.2006, model.capital_ln.2006,
se=list(model.proced.2005.se$coefs[,2], model.days_ln.2005.se$coefs[,2],
model.cost_ln.2005.se$coefs[,2], model.capital_ln.2005.se$coefs[,2],
model.proced.2006.se$coefs[,2], model.days_ln.2006.se$coefs[,2],
model.cost_ln.2006.se$coefs[,2], model.capital_ln.2006.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 9.274*** | 3.527*** | 3.502*** | 2.594*** | 9.151*** | 3.458*** | 3.423*** | 2.613*** |
| (0.302) | (0.073) | (0.143) | (0.217) | (0.301) | (0.073) | (0.136) | (0.208) | |
| year.centered.2005 | -0.304*** | -0.090*** | 0.045 | -0.156** | ||||
| (0.095) | (0.022) | (0.031) | (0.069) | |||||
| ranked.2005 | 0.118 | -0.021 | -0.220*** | 0.058 | ||||
| (0.146) | (0.043) | (0.053) | (0.087) | |||||
| year.centered.2005:ranked.2005 | -0.020 | -0.005 | -0.150*** | 0.004 | ||||
| (0.100) | (0.024) | (0.034) | (0.072) | |||||
| year.centered.2006 | -0.257*** | -0.084*** | 0.013 | -0.090** | ||||
| (0.072) | (0.017) | (0.022) | (0.040) | |||||
| ranked.2006 | -0.128 | -0.068 | -0.276*** | -0.144 | ||||
| (0.158) | (0.042) | (0.051) | (0.090) | |||||
| year.centered.2006:ranked.2006 | -0.059 | -0.007 | -0.112*** | -0.056 | ||||
| (0.079) | (0.020) | (0.025) | (0.045) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 2,075 | 2,075 | 2,075 | 1,836 | 2,075 | 2,075 | 2,075 | 1,836 |
| R2 | 0.123 | 0.173 | 0.061 | 0.050 | 0.123 | 0.173 | 0.061 | 0.050 |
| Adjusted R2 | 0.122 | 0.171 | 0.060 | 0.049 | 0.122 | 0.172 | 0.060 | 0.049 |
| Residual Std. Error | 3.262 (df = 2071) | 0.825 (df = 2071) | 1.434 (df = 2071) | 2.229 (df = 1832) | 3.262 (df = 2071) | 0.825 (df = 2071) | 1.434 (df = 2071) | 2.229 (df = 1832) |
| F Statistic | 96.756*** (df = 3; 2071) | 144.085*** (df = 3; 2071) | 45.031*** (df = 3; 2071) | 32.236*** (df = 3; 1832) | 96.739*** (df = 3; 2071) | 144.359*** (df = 3; 2071) | 44.994*** (df = 3; 2071) | 32.291*** (df = 3; 1832) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Simple: In since 2001
model.proced.2005.2001 <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.proced.2005.2001.se <- robust.clusterify(model.proced.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.days_ln.2005.2001 <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.days_ln.2005.2001.se <- robust.clusterify(model.days_ln.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.cost_ln.2005.2001 <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.cost_ln.2005.2001.se <- robust.clusterify(model.cost_ln.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.capital_ln.2005.2001 <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.cap.constrained)
model.capital_ln.2005.2001.se <- robust.clusterify(model.capital_ln.2005.2001, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)
model.proced.2006.2001 <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001)
model.proced.2006.2001.se <- robust.clusterify(model.proced.2006.2001, edb.its.2001, edb.its.2001$ccode)
model.days_ln.2006.2001 <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001)
model.days_ln.2006.2001.se <- robust.clusterify(model.days_ln.2006.2001, edb.its.2001, edb.its.2001$ccode)
model.cost_ln.2006.2001 <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001)
model.cost_ln.2006.2001.se <- robust.clusterify(model.cost_ln.2006.2001, edb.its.2001, edb.its.2001$ccode)
model.capital_ln.2006.2001 <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.cap.constrained)
model.capital_ln.2006.2001.se <- robust.clusterify(model.capital_ln.2006.2001, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)stargazer(model.proced.2005.2001, model.days_ln.2005.2001,
model.cost_ln.2005.2001, model.capital_ln.2005.2001,
model.proced.2006.2001, model.days_ln.2006.2001,
model.cost_ln.2006.2001, model.capital_ln.2006.2001,
se=list(model.proced.2005.2001.se$coefs[,2], model.days_ln.2005.2001.se$coefs[,2],
model.cost_ln.2005.2001.se$coefs[,2], model.capital_ln.2005.2001.se$coefs[,2],
model.proced.2006.2001.se$coefs[,2], model.days_ln.2006.2001.se$coefs[,2],
model.cost_ln.2006.2001.se$coefs[,2], model.capital_ln.2006.2001.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes, only countries in 2001 report",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes, only countries in 2001 report | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 9.350*** | 3.419*** | 3.101*** | 2.701*** | 9.203*** | 3.336*** | 3.020*** | 2.678*** |
| (0.345) | (0.078) | (0.140) | (0.235) | (0.349) | (0.079) | (0.138) | (0.232) | |
| year.centered.2005 | -0.256*** | -0.108*** | -0.037* | -0.105*** | ||||
| (0.085) | (0.019) | (0.022) | (0.037) | |||||
| ranked.2005 | 0.010 | -0.036 | -0.135*** | 0.051 | ||||
| (0.142) | (0.045) | (0.044) | (0.074) | |||||
| year.centered.2005:ranked.2005 | -0.088 | 0.009 | -0.059** | -0.085* | ||||
| (0.092) | (0.022) | (0.024) | (0.044) | |||||
| year.centered.2006 | -0.228*** | -0.101*** | -0.048*** | -0.074** | ||||
| (0.068) | (0.016) | (0.017) | (0.029) | |||||
| ranked.2006 | -0.242 | -0.082* | -0.185*** | -0.120 | ||||
| (0.162) | (0.048) | (0.051) | (0.090) | |||||
| year.centered.2006:ranked.2006 | -0.105 | 0.008 | -0.041** | -0.114*** | ||||
| (0.077) | (0.019) | (0.019) | (0.039) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 1,538 | 1,538 | 1,538 | 1,319 | 1,538 | 1,538 | 1,538 | 1,319 |
| R2 | 0.130 | 0.235 | 0.076 | 0.077 | 0.130 | 0.236 | 0.077 | 0.077 |
| Adjusted R2 | 0.128 | 0.233 | 0.074 | 0.075 | 0.128 | 0.234 | 0.075 | 0.075 |
| Residual Std. Error | 3.366 (df = 1534) | 0.758 (df = 1534) | 1.315 (df = 1534) | 2.127 (df = 1315) | 3.366 (df = 1534) | 0.757 (df = 1534) | 1.315 (df = 1534) | 2.127 (df = 1315) |
| F Statistic | 76.341*** (df = 3; 1534) | 157.050*** (df = 3; 1534) | 42.168*** (df = 3; 1534) | 36.476*** (df = 3; 1315) | 76.403*** (df = 3; 1534) | 157.591*** (df = 3; 1534) | 42.358*** (df = 3; 1534) | 36.471*** (df = 3; 1315) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Simple: Only those with reform committees
model.proced.2005.committee <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.proced.2005.committee.se <- robust.clusterify(model.proced.2005.committee, edb.its.committee, edb.its.committee$ccode)
model.days_ln.2005.committee <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.days_ln.2005.committee.se <- robust.clusterify(model.days_ln.2005.committee, edb.its.committee, edb.its.committee$ccode)
model.cost_ln.2005.committee <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.cost_ln.2005.committee.se <- robust.clusterify(model.cost_ln.2005.committee, edb.its.committee, edb.its.committee$ccode)
model.capital_ln.2005.committee <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee.cap.constrained)
model.capital_ln.2005.committee.se <- robust.clusterify(model.capital_ln.2005.committee, edb.its.committee.cap.constrained, edb.its.committee.cap.constrained$ccode)
model.proced.2006.committee <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.committee)
model.proced.2006.committee.se <- robust.clusterify(model.proced.2006.committee, edb.its.committee, edb.its.committee$ccode)
model.days_ln.2006.committee <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.committee)
model.days_ln.2006.committee.se <- robust.clusterify(model.days_ln.2006.committee, edb.its.committee, edb.its.committee$ccode)
model.cost_ln.2006.committee <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.committee)
model.cost_ln.2006.committee.se <- robust.clusterify(model.cost_ln.2006.committee, edb.its.committee, edb.its.committee$ccode)
model.capital_ln.2006.committee <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.committee.cap.constrained)
model.capital_ln.2006.committee.se <- robust.clusterify(model.capital_ln.2006.committee, edb.its.committee.cap.constrained, edb.its.committee.cap.constrained$ccode)stargazer(model.proced.2005.committee, model.days_ln.2005.committee,
model.cost_ln.2005.committee, model.capital_ln.2005.committee,
model.proced.2006.committee, model.days_ln.2006.committee,
model.cost_ln.2006.committee, model.capital_ln.2006.committee,
se=list(model.proced.2005.committee.se$coefs[,2], model.days_ln.2005.committee.se$coefs[,2],
model.cost_ln.2005.committee.se$coefs[,2], model.capital_ln.2005.committee.se$coefs[,2],
model.proced.2006.committee.se$coefs[,2], model.days_ln.2006.committee.se$coefs[,2],
model.cost_ln.2006.committee.se$coefs[,2], model.capital_ln.2006.committee.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes, only countries with EDB committees",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes, only countries with EDB committees | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 10.399*** | 3.566*** | 3.820*** | 2.519*** | 10.275*** | 3.538*** | 3.686*** | 2.438*** |
| (0.478) | (0.104) | (0.275) | (0.373) | (0.489) | (0.104) | (0.263) | (0.366) | |
| year.centered.2005 | -0.253 | -0.112*** | 0.090 | -0.036*** | ||||
| (0.188) | (0.026) | (0.059) | (0.009) | |||||
| ranked.2005 | 0.205 | 0.088* | -0.247*** | -0.114 | ||||
| (0.268) | (0.051) | (0.086) | (0.112) | |||||
| year.centered.2005:ranked.2005 | -0.184 | 0.0003 | -0.213*** | -0.084** | ||||
| (0.202) | (0.031) | (0.066) | (0.037) | |||||
| year.centered.2006 | -0.220 | -0.090*** | 0.033 | -0.052* | ||||
| (0.144) | (0.019) | (0.040) | (0.029) | |||||
| ranked.2006 | -0.087 | -0.003 | -0.223*** | -0.189 | ||||
| (0.299) | (0.068) | (0.074) | (0.152) | |||||
| year.centered.2006:ranked.2006 | -0.221 | -0.020 | -0.158*** | -0.061 | ||||
| (0.160) | (0.025) | (0.048) | (0.046) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 617 | 617 | 617 | 540 | 617 | 617 | 617 | 540 |
| R2 | 0.229 | 0.274 | 0.080 | 0.032 | 0.228 | 0.274 | 0.079 | 0.032 |
| Adjusted R2 | 0.225 | 0.270 | 0.076 | 0.026 | 0.225 | 0.270 | 0.075 | 0.026 |
| Residual Std. Error | 2.784 (df = 613) | 0.676 (df = 613) | 1.361 (df = 613) | 2.275 (df = 536) | 2.784 (df = 613) | 0.677 (df = 613) | 1.362 (df = 613) | 2.274 (df = 536) |
| F Statistic | 60.525*** (df = 3; 613) | 77.126*** (df = 3; 613) | 17.843*** (df = 3; 613) | 5.844*** (df = 3; 536) | 60.509*** (df = 3; 613) | 76.956*** (df = 3; 613) | 17.595*** (df = 3; 613) | 5.873*** (df = 3; 536) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Simple: In since 2001 and with reform committees
model.proced.2005.2001.committee <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.proced.2005.2001.committee.se <- robust.clusterify(model.proced.2005.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.2005.2001.committee <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.days_ln.2005.2001.committee.se <- robust.clusterify(model.days_ln.2005.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.2005.2001.committee <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.cost_ln.2005.2001.committee.se <- robust.clusterify(model.cost_ln.2005.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.2005.2001.committee <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.2005.2001.committee.se <- robust.clusterify(model.capital_ln.2005.2001.committee, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)
model.proced.2006.2001.committee <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.proced.2006.2001.committee.se <- robust.clusterify(model.proced.2006.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.2006.2001.committee <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.days_ln.2006.2001.committee.se <- robust.clusterify(model.days_ln.2006.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.2006.2001.committee <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.cost_ln.2006.2001.committee.se <- robust.clusterify(model.cost_ln.2006.2001.committee, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.2006.2001.committee <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.2006.2001.committee.se <- robust.clusterify(model.capital_ln.2006.2001.committee, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)stargazer(model.proced.2005.2001.committee, model.days_ln.2005.2001.committee,
model.cost_ln.2005.2001.committee, model.capital_ln.2005.2001.committee,
model.proced.2006.2001.committee, model.days_ln.2006.2001.committee,
model.cost_ln.2006.2001.committee, model.capital_ln.2006.2001.committee,
se=list(model.proced.2005.2001.committee.se$coefs[,2], model.days_ln.2005.2001.committee.se$coefs[,2],
model.cost_ln.2005.2001.committee.se$coefs[,2], model.capital_ln.2005.2001.committee.se$coefs[,2],
model.proced.2006.2001.committee.se$coefs[,2], model.days_ln.2006.2001.committee.se$coefs[,2],
model.cost_ln.2006.2001.committee.se$coefs[,2], model.capital_ln.2006.2001.committee.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes, only countries in 2001 report with EDB committees",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes, only countries in 2001 report with EDB committees | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 10.200*** | 3.612*** | 3.252*** | 2.280*** | 10.031*** | 3.580*** | 3.152*** | 2.149*** |
| (0.543) | (0.109) | (0.179) | (0.398) | (0.564) | (0.113) | (0.183) | (0.377) | |
| year.centered.2005 | -0.306 | -0.096*** | -0.020 | -0.048*** | ||||
| (0.194) | (0.023) | (0.035) | (0.010) | |||||
| ranked.2005 | 0.162 | 0.028 | -0.094 | -0.075 | ||||
| (0.280) | (0.051) | (0.057) | (0.132) | |||||
| year.centered.2005:ranked.2005 | -0.103 | -0.019 | -0.084** | -0.098** | ||||
| (0.212) | (0.029) | (0.036) | (0.042) | |||||
| year.centered.2006 | -0.271* | -0.080*** | -0.040 | -0.079** | ||||
| (0.156) | (0.018) | (0.026) | (0.035) | |||||
| ranked.2006 | -0.083 | -0.078 | -0.096* | -0.096 | ||||
| (0.320) | (0.070) | (0.058) | (0.165) | |||||
| year.centered.2006:ranked.2006 | -0.137 | -0.031 | -0.065** | -0.066 | ||||
| (0.174) | (0.025) | (0.028) | (0.051) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 490 | 490 | 490 | 420 | 490 | 490 | 490 | 420 |
| R2 | 0.222 | 0.337 | 0.120 | 0.057 | 0.222 | 0.337 | 0.120 | 0.057 |
| Adjusted R2 | 0.218 | 0.333 | 0.115 | 0.050 | 0.217 | 0.333 | 0.114 | 0.050 |
| Residual Std. Error | 2.810 (df = 486) | 0.612 (df = 486) | 1.020 (df = 486) | 1.986 (df = 416) | 2.810 (df = 486) | 0.612 (df = 486) | 1.020 (df = 486) | 1.986 (df = 416) |
| F Statistic | 46.308*** (df = 3; 486) | 82.245*** (df = 3; 486) | 22.103*** (df = 3; 486) | 8.384*** (df = 3; 416) | 46.285*** (df = 3; 486) | 82.317*** (df = 3; 486) | 22.053*** (df = 3; 486) | 8.383*** (df = 3; 416) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Simple ITS controlling for lags
model.proced.2005_lag <- lm(sb_proced ~ sb_proced_lag +
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.proced.2005_lag.se <- robust.clusterify(model.proced.2005_lag, edb.its, edb.its$ccode)
model.days_ln.2005_lag <- lm(sb_days_ln ~ sb_days_ln_lag +
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.days_ln.2005_lag.se <- robust.clusterify(model.days_ln.2005_lag, edb.its, edb.its$ccode)
model.cost_ln.2005_lag <- lm(sb_cost_ln ~ sb_cost_ln_lag +
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its)
model.cost_ln.2005_lag.se <- robust.clusterify(model.cost_ln.2005_lag, edb.its, edb.its$ccode)
model.capital_ln.2005_lag <- lm(sb_capital_ln ~ sb_capital_ln_lag +
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.cap.constrained)
model.capital_ln.2005_lag.se <- robust.clusterify(model.capital_ln.2005_lag, edb.its.cap.constrained, edb.its.cap.constrained$ccode)Simple ITS models included as models 1, 3, 5, and 7 for comparison with versions that control for lagged DV.
stargazer(model.proced.2005, model.proced.2005_lag,
model.days_ln.2005, model.days_ln.2005_lag,
model.cost_ln.2005, model.cost_ln.2005_lag,
model.capital_ln.2005, model.capital_ln.2005_lag,
se=list(model.proced.2005.se$coefs[,2], model.proced.2005_lag.se$coefs[,2],
model.days_ln.2005.se$coefs[,2], model.days_ln.2005_lag.se$coefs[,2],
model.cost_ln.2005.se$coefs[,2], model.cost_ln.2005_lag.se$coefs[,2],
model.capital_ln.2005.se$coefs[,2], model.capital_ln.2005_lag.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes with lagged DV as control",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes with lagged DV as control | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 9.274*** | 0.367* | 3.527*** | 0.153*** | 3.502*** | -0.025 | 2.594*** | 0.370*** |
| (0.302) | (0.193) | (0.073) | (0.047) | (0.143) | (0.041) | (0.217) | (0.128) | |
| sb_proced_lag | 0.934*** | |||||||
| (0.012) | ||||||||
| sb_days_ln_lag | 0.941*** | |||||||
| (0.009) | ||||||||
| sb_cost_ln_lag | 0.975*** | |||||||
| (0.004) | ||||||||
| sb_capital_ln_lag | 0.921*** | |||||||
| (0.010) | ||||||||
| year.centered.2005 | -0.304*** | -0.031 | -0.090*** | 0.019 | 0.045 | -0.044* | -0.156** | 0.264** |
| (0.095) | (0.087) | (0.022) | (0.022) | (0.031) | (0.025) | (0.069) | (0.104) | |
| ranked.2005 | 0.118 | -0.029 | -0.021 | -0.048 | -0.220*** | -0.010 | 0.058 | -0.291** |
| (0.146) | (0.168) | (0.043) | (0.043) | (0.053) | (0.045) | (0.087) | (0.127) | |
| year.centered.2005:ranked.2005 | -0.020 | 0.012 | -0.005 | -0.021 | -0.150*** | 0.045* | 0.004 | -0.276*** |
| (0.100) | (0.088) | (0.024) | (0.023) | (0.034) | (0.025) | (0.072) | (0.104) | |
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 2,075 | 1,920 | 2,075 | 1,920 | 2,075 | 1,920 | 1,836 | 1,811 |
| R2 | 0.123 | 0.908 | 0.173 | 0.899 | 0.061 | 0.959 | 0.050 | 0.900 |
| Adjusted R2 | 0.122 | 0.908 | 0.171 | 0.898 | 0.060 | 0.959 | 0.049 | 0.899 |
| Residual Std. Error | 3.262 (df = 2071) | 1.038 (df = 1915) | 0.825 (df = 2071) | 0.285 (df = 1915) | 1.434 (df = 2071) | 0.299 (df = 1915) | 2.229 (df = 1832) | 0.725 (df = 1806) |
| F Statistic | 96.756*** (df = 3; 2071) | 4,724.921*** (df = 4; 1915) | 144.085*** (df = 3; 2071) | 4,247.043*** (df = 4; 1915) | 45.031*** (df = 3; 2071) | 11,245.700*** (df = 4; 1915) | 32.236*** (df = 3; 1832) | 4,043.473*** (df = 4; 1806) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Simple ITS, constrained to original 2001 countries
model.proced.2005.2001 <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.proced.2005.2001.se <- robust.clusterify(model.proced.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.days_ln.2005.2001 <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.days_ln.2005.2001.se <- robust.clusterify(model.days_ln.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.cost_ln.2005.2001 <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001)
model.cost_ln.2005.2001.se <- robust.clusterify(model.cost_ln.2005.2001, edb.its.2001, edb.its.2001$ccode)
model.capital_ln.2005.2001 <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.cap.constrained)
model.capital_ln.2005.2001.se <- robust.clusterify(model.capital_ln.2005.2001, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)
model.capital_ln_controls.2005.2001 <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 +
year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag +
pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its.2001.cap.constrained)
model.capital_ln_controls.2005.2001.se <- robust.clusterify(model.capital_ln_controls.2005.2001, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)Models 2, 4, 6, and 8 are constrained to countries that appeared in the original 2001 EDB report.
stargazer(model.proced.2005, model.proced.2005.2001,
model.days_ln.2005, model.days_ln.2005.2001,
model.cost_ln.2005, model.cost_ln.2005.2001,
model.capital_ln.2005, model.capital_ln.2005.2001,
model.capital_ln_controls.2005.2001,
se=list(model.proced.2005.se$coefs[,2], model.proced.2005.2001.se$coefs[,2],
model.days_ln.2005.se$coefs[,2], model.days_ln.2005.2001.se$coefs[,2],
model.cost_ln.2005.se$coefs[,2], model.cost_ln.2005.2001.se$coefs[,2],
model.capital_ln.2005.se$coefs[,2], model.capital_ln.2005.2001.se$coefs[,2],
model.capital_ln_controls.2005.2001.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes, limited to countries in 2001 report",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", c(rep("No", 8), "Yes"))),
notes="Robust standard errors clustered by country")| EDB outcomes, limited to countries in 2001 report | |||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | ||||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| Constant | 9.274*** | 9.350*** | 3.527*** | 3.419*** | 3.502*** | 3.101*** | 2.594*** | 2.701*** | 17.755 |
| (0.302) | (0.345) | (0.073) | (0.078) | (0.143) | (0.140) | (0.217) | (0.235) | (51.978) | |
| year.centered.2005 | -0.304*** | -0.256*** | -0.090*** | -0.108*** | 0.045 | -0.037* | -0.156** | -0.105*** | -0.055 |
| (0.095) | (0.085) | (0.022) | (0.019) | (0.031) | (0.022) | (0.069) | (0.037) | (0.071) | |
| ranked.2005 | 0.118 | 0.010 | -0.021 | -0.036 | -0.220*** | -0.135*** | 0.058 | 0.051 | 0.097 |
| (0.146) | (0.142) | (0.043) | (0.045) | (0.053) | (0.044) | (0.087) | (0.074) | (0.084) | |
| gdpcap_ln_lag | -0.471 | ||||||||
| (0.375) | |||||||||
| gdpgrowth_lag | 0.011 | ||||||||
| (0.013) | |||||||||
| pop_ln_lag | -0.647 | ||||||||
| (2.673) | |||||||||
| polity_lag | 0.031 | ||||||||
| (0.035) | |||||||||
| year.centered.2005:ranked.2005 | -0.020 | -0.088 | -0.005 | 0.009 | -0.150*** | -0.059** | 0.004 | -0.085* | -0.074 |
| (0.100) | (0.092) | (0.024) | (0.022) | (0.034) | (0.024) | (0.072) | (0.044) | (0.057) | |
| Year fixed effects | No | No | No | No | No | No | No | No | Yes |
| Observations | 2,075 | 1,538 | 2,075 | 1,538 | 2,075 | 1,538 | 1,836 | 1,319 | 1,210 |
| R2 | 0.123 | 0.130 | 0.173 | 0.235 | 0.061 | 0.076 | 0.050 | 0.077 | 0.783 |
| Adjusted R2 | 0.122 | 0.128 | 0.171 | 0.233 | 0.060 | 0.074 | 0.049 | 0.075 | 0.761 |
| Residual Std. Error | 3.262 (df = 2071) | 3.366 (df = 1534) | 0.825 (df = 2071) | 0.758 (df = 1534) | 1.434 (df = 2071) | 1.315 (df = 1534) | 2.229 (df = 1832) | 2.127 (df = 1315) | 1.062 (df = 1100) |
| F Statistic | 96.756*** (df = 3; 2071) | 76.341*** (df = 3; 1534) | 144.085*** (df = 3; 2071) | 157.050*** (df = 3; 1534) | 45.031*** (df = 3; 2071) | 42.168*** (df = 3; 1534) | 32.236*** (df = 3; 1832) | 36.476*** (df = 3; 1315) | 36.345*** (df = 109; 1100) |
| Note: | p<0.1; p<0.05; p<0.01 | ||||||||
| Robust standard errors clustered by country | |||||||||
Simple ITS in countries with reform committees by 2015
model.proced.committee <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.proced.committee.se <- robust.clusterify(model.proced.committee, edb.its.committee, edb.its.committee$ccode)
model.days_ln.committee <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.days_ln.committee.se <- robust.clusterify(model.days_ln.committee, edb.its.committee, edb.its.committee$ccode)
model.cost_ln.committee <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee)
model.cost_ln.committee.se <- robust.clusterify(model.cost_ln.committee, edb.its.committee, edb.its.committee$ccode)
model.capital_ln.committee <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.committee.cap.constrained)
model.capital_ln.committee.se <- robust.clusterify(model.capital_ln.committee, edb.its.committee.cap.constrained, edb.its.committee.cap.constrained$ccode)
model.capital_ln_controls.committee <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 +
year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag +
pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its.committee.cap.constrained)
model.capital_ln_controls.committee.se <- robust.clusterify(model.capital_ln_controls.committee, edb.its.committee.cap.constrained, edb.its.committee.cap.constrained$ccode)stargazer(model.proced.committee,
model.days_ln.committee,
model.cost_ln.committee,
model.capital_ln.committee,
model.capital_ln_controls.committee,
se=list(model.proced.committee.se$coefs[,2],
model.days_ln.committee.se$coefs[,2],
model.cost_ln.committee.se$coefs[,2],
model.capital_ln.committee.se$coefs[,2],
model.capital_ln_controls.committee.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes, limited to countries with EDB reform committees",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", c(rep("No", 4), "Yes"))),
notes="Robust standard errors clustered by country")| EDB outcomes, limited to countries with EDB reform committees | |||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | ||
| (1) | (2) | (3) | (4) | (5) | |
| Constant | 10.399*** | 3.566*** | 3.820*** | 2.519*** | 26.665 |
| (0.478) | (0.104) | (0.275) | (0.373) | (53.078) | |
| year.centered.2005 | -0.253 | -0.112*** | 0.090 | -0.036*** | -0.010 |
| (0.188) | (0.026) | (0.059) | (0.009) | (0.079) | |
| ranked.2005 | 0.205 | 0.088* | -0.247*** | -0.114 | -0.126 |
| (0.268) | (0.051) | (0.086) | (0.112) | (0.131) | |
| gdpcap_ln_lag | -0.039 | ||||
| (0.424) | |||||
| gdpgrowth_lag | 0.007 | ||||
| (0.015) | |||||
| pop_ln_lag | -1.532 | ||||
| (3.288) | |||||
| polity_lag | 0.045 | ||||
| (0.057) | |||||
| year.centered.2005:ranked.2005 | -0.184 | 0.0003 | -0.213*** | -0.084** | -0.078* |
| (0.202) | (0.031) | (0.066) | (0.037) | (0.045) | |
| Year fixed effects | No | No | No | No | Yes |
| Observations | 617 | 617 | 617 | 540 | 538 |
| R2 | 0.229 | 0.274 | 0.080 | 0.032 | 0.793 |
| Adjusted R2 | 0.225 | 0.270 | 0.076 | 0.026 | 0.771 |
| Residual Std. Error | 2.784 (df = 613) | 0.676 (df = 613) | 1.361 (df = 613) | 2.275 (df = 536) | 1.103 (df = 486) |
| F Statistic | 60.525*** (df = 3; 613) | 77.126*** (df = 3; 613) | 17.843*** (df = 3; 613) | 5.844*** (df = 3; 536) | 36.511*** (df = 51; 486) |
| Note: | p<0.1; p<0.05; p<0.01 | ||||
| Robust standard errors clustered by country | |||||
Simple ITS + loans + country fixed effects
model.proced_loan_fe.2005 <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
loan_ln + as.factor(ccode),
data=edb.its)
model.proced_loan_fe.2005.se <- robust.clusterify(model.proced_loan_fe.2005, edb.its, edb.its$ccode)
model.days_ln_loan_fe.2005 <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
loan_ln + as.factor(ccode),
data=edb.its)
model.days_ln_loan_fe.2005.se <- robust.clusterify(model.days_ln_loan_fe.2005, edb.its, edb.its$ccode)
model.cost_ln_loan_fe.2005 <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
loan_ln + as.factor(ccode),
data=edb.its)
model.cost_ln_loan_fe.2005.se <- robust.clusterify(model.cost_ln_loan_fe.2005, edb.its, edb.its$ccode)
model.capital_ln_loan_fe.2005<- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
loan_ln + as.factor(ccode),
data=edb.its.cap.constrained)
model.capital_ln_loan_fe.2005.se <- robust.clusterify(model.capital_ln_loan_fe.2005, edb.its.cap.constrained, edb.its.cap.constrained$ccode)
model.proced_loan_fe.2006 <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
loan_ln + as.factor(ccode),
data=edb.its)
model.proced_loan_fe.2006.se <- robust.clusterify(model.proced_loan_fe.2006, edb.its, edb.its$ccode)
model.days_ln_loan_fe.2006 <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
loan_ln + as.factor(ccode),
data=edb.its)
model.days_ln_loan_fe.2006.se <- robust.clusterify(model.days_ln_loan_fe.2006, edb.its, edb.its$ccode)
model.cost_ln_loan_fe.2006 <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
loan_ln + as.factor(ccode),
data=edb.its)
model.cost_ln_loan_fe.2006.se <- robust.clusterify(model.cost_ln_loan_fe.2006, edb.its, edb.its$ccode)
model.capital_ln_loan_fe.2006 <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
loan_ln + as.factor(ccode),
data=edb.its.cap.constrained)
model.capital_ln_loan_fe.2006.se <- robust.clusterify(model.capital_ln_loan_fe.2006, edb.its.cap.constrained, edb.its.cap.constrained$ccode)stargazer(model.proced_loan_fe.2005, model.days_ln_loan_fe.2005,
model.cost_ln_loan_fe.2005, model.capital_ln_loan_fe.2005,
model.proced_loan_fe.2006, model.days_ln_loan_fe.2006,
model.cost_ln_loan_fe.2006, model.capital_ln_loan_fe.2006,
se=list(model.proced_loan_fe.2005.se$coefs[,2], model.days_ln_loan_fe.2005.se$coefs[,2],
model.cost_ln_loan_fe.2005.se$coefs[,2], model.capital_ln_loan_fe.2005.se$coefs[,2],
model.proced_loan_fe.2006.se$coefs[,2], model.days_ln_loan_fe.2006.se$coefs[,2],
model.cost_ln_loan_fe.2006.se$coefs[,2], model.capital_ln_loan_fe.2006.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes (with loans and country fixed effects)",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("Yes", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes (with loans and country fixed effects) | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 6.570*** | 2.050*** | 1.037*** | 0.475*** | 6.433*** | 1.977*** | 0.951*** | 0.461*** |
| (0.181) | (0.051) | (0.052) | (0.103) | (0.177) | (0.048) | (0.049) | (0.097) | |
| year.centered.2005 | -0.315*** | -0.124*** | -0.033 | -0.167*** | ||||
| (0.090) | (0.020) | (0.022) | (0.048) | |||||
| ranked.2005 | 0.137 | 0.024 | -0.117*** | 0.058 | ||||
| (0.139) | (0.043) | (0.041) | (0.072) | |||||
| year.centered.2006 | -0.269*** | -0.111*** | -0.047*** | -0.109*** | ||||
| (0.068) | (0.015) | (0.016) | (0.029) | |||||
| ranked.2006 | -0.081 | -0.013 | -0.158*** | -0.103 | ||||
| (0.151) | (0.044) | (0.045) | (0.080) | |||||
| loan_ln | 0.026*** | 0.060*** | 0.124*** | -0.000 | 0.026*** | 0.060*** | 0.124*** | 0.000 |
| (0.002) | (0.0004) | (0.0004) | (0.000) | (0.002) | (0.0004) | (0.0004) | (0.000) | |
| year.centered.2005:ranked.2005 | -0.007 | 0.030 | -0.070*** | 0.016 | ||||
| (0.095) | (0.023) | (0.025) | (0.053) | |||||
| year.centered.2006:ranked.2006 | -0.046 | 0.020 | -0.052*** | -0.037 | ||||
| (0.075) | (0.019) | (0.019) | (0.037) | |||||
| Year fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 2,075 | 2,075 | 2,075 | 1,836 | 2,075 | 2,075 | 2,075 | 1,836 |
| R2 | 0.791 | 0.793 | 0.925 | 0.809 | 0.791 | 0.793 | 0.925 | 0.809 |
| Adjusted R2 | 0.774 | 0.776 | 0.918 | 0.791 | 0.774 | 0.777 | 0.919 | 0.791 |
| Residual Std. Error | 1.654 (df = 1918) | 0.428 (df = 1918) | 0.423 (df = 1918) | 1.044 (df = 1679) | 1.654 (df = 1918) | 0.428 (df = 1918) | 0.422 (df = 1918) | 1.044 (df = 1679) |
| F Statistic | 46.592*** (df = 156; 1918) | 47.174*** (df = 156; 1918) | 150.631*** (df = 156; 1918) | 45.636*** (df = 156; 1679) | 46.577*** (df = 156; 1918) | 47.191*** (df = 156; 1918) | 150.833*** (df = 156; 1918) | 45.649*** (df = 156; 1679) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
ITS models + basic controls + country fixed effects
model.proced_controls_fe.2005 <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.proced_controls_fe.2005.se <- robust.clusterify(model.proced_controls_fe.2005, edb.its, edb.its$ccode)
model.days_ln_controls_fe.2005 <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.days_ln_controls_fe.2005.se <- robust.clusterify(model.days_ln_controls_fe.2005, edb.its, edb.its$ccode)
model.cost_ln_controls_fe.2005 <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.cost_ln_controls_fe.2005.se <- robust.clusterify(model.cost_ln_controls_fe.2005, edb.its, edb.its$ccode)
model.capital_ln_controls_fe.2005 <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its.cap.constrained)
model.capital_ln_controls_fe.2005.se <- robust.clusterify(model.capital_ln_controls_fe.2005, edb.its.cap.constrained, edb.its.cap.constrained$ccode)
model.proced_controls_fe.2006 <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.proced_controls_fe.2006.se <- robust.clusterify(model.proced_controls_fe.2006, edb.its, edb.its$ccode)
model.days_ln_controls_fe.2006 <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.days_ln_controls_fe.2006.se <- robust.clusterify(model.days_ln_controls_fe.2006, edb.its, edb.its$ccode)
model.cost_ln_controls_fe.2006 <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its)
model.cost_ln_controls_fe.2006.se <- robust.clusterify(model.cost_ln_controls_fe.2006, edb.its, edb.its$ccode)
model.capital_ln_controls_fe.2006 <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006 +
gdpcap_ln_lag + gdpgrowth_lag + pop_ln_lag + polity_lag +
as.factor(ccode),
data=edb.its.cap.constrained)
model.capital_ln_controls_fe.2006.se <- robust.clusterify(model.capital_ln_controls_fe.2006, edb.its.cap.constrained, edb.its.cap.constrained$ccode)stargazer(model.proced_controls_fe.2005, model.days_ln_controls_fe.2005,
model.cost_ln_controls_fe.2005, model.capital_ln_controls_fe.2005,
model.proced_controls_fe.2006, model.days_ln_controls_fe.2006,
model.cost_ln_controls_fe.2006, model.capital_ln_controls_fe.2006,
se=list(model.proced_controls_fe.2005.se$coefs[,2], model.days_ln_controls_fe.2005.se$coefs[,2],
model.cost_ln_controls_fe.2005.se$coefs[,2], model.capital_ln_controls_fe.2005.se$coefs[,2],
model.proced_controls_fe.2006.se$coefs[,2], model.days_ln_controls_fe.2006.se$coefs[,2],
model.cost_ln_controls_fe.2006.se$coefs[,2], model.capital_ln_controls_fe.2006.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes (with controls and country fixed effects)",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("Yes", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes (with controls and country fixed effects) | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 26.433 | -9.626 | 15.169* | -6.750 | 25.640 | -10.103 | 15.094* | -6.936 |
| (34.183) | (9.738) | (7.829) | (48.810) | (34.144) | (9.751) | (7.845) | (48.763) | |
| year.centered.2005 | -0.212** | -0.114*** | 0.014 | -0.086 | ||||
| (0.096) | (0.025) | (0.024) | (0.059) | |||||
| ranked.2005 | 0.261 | 0.037 | -0.032 | 0.037 | ||||
| (0.180) | (0.054) | (0.050) | (0.073) | |||||
| year.centered.2006 | -0.156* | -0.100*** | 0.010 | -0.056 | ||||
| (0.082) | (0.022) | (0.020) | (0.053) | |||||
| ranked.2006 | 0.011 | -0.023 | -0.097* | -0.099 | ||||
| (0.182) | (0.054) | (0.053) | (0.084) | |||||
| gdpcap_ln_lag | -1.335** | -0.179 | -0.573*** | -0.384 | -1.306** | -0.162 | -0.569*** | -0.375 |
| (0.523) | (0.140) | (0.097) | (0.319) | (0.524) | (0.143) | (0.100) | (0.326) | |
| gdpgrowth_lag | 0.024* | -0.001 | 0.001 | 0.010 | 0.025* | -0.001 | 0.001 | 0.010 |
| (0.013) | (0.004) | (0.003) | (0.009) | (0.013) | (0.004) | (0.003) | (0.009) | |
| pop_ln_lag | -0.292 | 0.698 | -0.411 | 0.566 | -0.266 | 0.711 | -0.409 | 0.570 |
| (1.748) | (0.486) | (0.385) | (2.549) | (1.746) | (0.485) | (0.385) | (2.546) | |
| polity_lag | -0.024 | -0.003 | -0.015 | 0.024 | -0.024 | -0.003 | -0.015 | 0.024 |
| (0.059) | (0.012) | (0.011) | (0.030) | (0.059) | (0.012) | (0.011) | (0.030) | |
| year.centered.2005:ranked.2005 | -0.007 | 0.022 | -0.068*** | -0.043 | ||||
| (0.102) | (0.024) | (0.024) | (0.048) | |||||
| year.centered.2006:ranked.2006 | -0.063 | 0.010 | -0.064*** | -0.071* | ||||
| (0.081) | (0.020) | (0.018) | (0.041) | |||||
| Year fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 1,794 | 1,794 | 1,794 | 1,571 | 1,794 | 1,794 | 1,794 | 1,571 |
| R2 | 0.797 | 0.800 | 0.935 | 0.822 | 0.797 | 0.800 | 0.935 | 0.822 |
| Adjusted R2 | 0.780 | 0.783 | 0.930 | 0.804 | 0.780 | 0.783 | 0.930 | 0.804 |
| Residual Std. Error | 1.665 (df = 1652) | 0.427 (df = 1652) | 0.401 (df = 1652) | 1.017 (df = 1429) | 1.666 (df = 1652) | 0.427 (df = 1652) | 0.401 (df = 1652) | 1.017 (df = 1429) |
| F Statistic | 46.022*** (df = 141; 1652) | 46.864*** (df = 141; 1652) | 169.556*** (df = 141; 1652) | 46.671*** (df = 141; 1429) | 45.974*** (df = 141; 1652) | 46.870*** (df = 141; 1652) | 169.593*** (df = 141; 1652) | 46.672*** (df = 141; 1429) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Seth’s ideas: difference-ish models
\[ y_t = \beta_0 + \beta_1 y_{t-1} + \beta_2 T + \epsilon \]
Models for all countries in 2001 report
model.proced.lag.2005.all <- lm(sb_proced ~ sb_proced_lag + ranked.2005,
data=edb.its.2001)
model.proced.lag.2005.all.se <- robust.clusterify(model.proced.lag.2005.all, edb.its.2001, edb.its.2001$ccode)
model.days_ln.lag.2005.all <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2005,
data=edb.its.2001)
model.days_ln.lag.2005.all.se <- robust.clusterify(model.days_ln.lag.2005.all, edb.its.2001, edb.its.2001$ccode)
model.cost_ln.lag.2005.all <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2005,
data=edb.its.2001)
model.cost_ln.lag.2005.all.se <- robust.clusterify(model.cost_ln.lag.2005.all, edb.its.2001, edb.its.2001$ccode)
model.capital_ln.lag.2005.all <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2005,
data=edb.its.2001.cap.constrained)
model.capital_ln.lag.2005.all.se <- robust.clusterify(model.capital_ln.lag.2005.all, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)
model.proced.lag.2006.all <- lm(sb_proced ~ sb_proced_lag + ranked.2006,
data=edb.its.2001)
model.proced.lag.2006.all.se <- robust.clusterify(model.proced.lag.2006.all, edb.its.2001, edb.its.2001$ccode)
model.days_ln.lag.2006.all <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2006,
data=edb.its.2001)
model.days_ln.lag.2006.all.se <- robust.clusterify(model.days_ln.lag.2006.all, edb.its.2001, edb.its.2001$ccode)
model.cost_ln.lag.2006.all <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2006,
data=edb.its.2001)
model.cost_ln.lag.2006.all.se <- robust.clusterify(model.cost_ln.lag.2006.all, edb.its.2001, edb.its.2001$ccode)
model.capital_ln.lag.2006.all <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2006,
data=edb.its.2001.cap.constrained)
model.capital_ln.lag.2006.all.se <- robust.clusterify(model.capital_ln.lag.2006.all, edb.its.2001.cap.constrained, edb.its.2001.cap.constrained$ccode)
#stargazer(model.proced.lag.2005.all, model.days_ln.lag.2005.all,
model.cost_ln.lag.2005.all, model.capital_ln.lag.2005.all,
model.proced.lag.2006.all, model.days_ln.lag.2006.all,
model.cost_ln.lag.2006.all, model.capital_ln.lag.2006.all,
se=list(model.proced.lag.2005.all.se$coefs[,2], model.days_ln.lag.2005.all.se$coefs[,2],
model.cost_ln.lag.2005.all.se$coefs[,2], model.capital_ln.lag.2005.all.se$coefs[,2],
model.proced.lag.2006.all.se$coefs[,2], model.days_ln.lag.2006.all.se$coefs[,2],
model.cost_ln.lag.2006.all.se$coefs[,2], model.capital_ln.lag.2006.all.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes (all countries from 2001 report)",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8))),
notes="Robust standard errors clustered by country")| EDB outcomes (all countries from 2001 report) | ||||||||
| sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | sb_proced | sb_days_ln | sb_cost_ln | sb_capital_ln | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 0.365*** | 0.180*** | 0.053** | 0.032 | 0.424*** | 0.193*** | 0.041* | 0.087* |
| (0.110) | (0.048) | (0.025) | (0.057) | (0.113) | (0.048) | (0.022) | (0.049) | |
| sb_proced_lag | 0.939*** | 0.935*** | ||||||
| (0.013) | (0.013) | |||||||
| sb_days_ln_lag | 0.922*** | 0.919*** | ||||||
| (0.012) | (0.012) | |||||||
| sb_cost_ln_lag | 0.973*** | 0.973*** | ||||||
| (0.004) | (0.004) | |||||||
| sb_capital_ln_lag | 0.915*** | 0.913*** | ||||||
| (0.011) | (0.011) | |||||||
| ranked.2005 | -0.176** | -0.040* | -0.081*** | -0.030 | ||||
| (0.078) | (0.024) | (0.022) | (0.058) | |||||
| ranked.2006 | -0.240*** | -0.049** | -0.071*** | -0.097* | ||||
| (0.068) | (0.022) | (0.019) | (0.052) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| Observations | 1,427 | 1,427 | 1,427 | 1,318 | 1,427 | 1,427 | 1,427 | 1,318 |
| R2 | 0.909 | 0.887 | 0.953 | 0.895 | 0.910 | 0.887 | 0.952 | 0.895 |
| Adjusted R2 | 0.909 | 0.887 | 0.952 | 0.895 | 0.909 | 0.887 | 0.952 | 0.895 |
| Residual Std. Error | 1.067 (df = 1424) | 0.284 (df = 1424) | 0.298 (df = 1424) | 0.718 (df = 1315) | 1.064 (df = 1424) | 0.284 (df = 1424) | 0.298 (df = 1424) | 0.717 (df = 1315) |
| F Statistic | 7,119.196*** (df = 2; 1424) | 5,573.003*** (df = 2; 1424) | 14,277.920*** (df = 2; 1424) | 5,586.313*** (df = 2; 1315) | 7,162.367*** (df = 2; 1424) | 5,587.572*** (df = 2; 1424) | 14,261.280*** (df = 2; 1424) | 5,605.466*** (df = 2; 1315) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Models for countries with/without reform committees
model.proced.lag.2005.nocom <- lm(sb_proced ~ sb_proced_lag + ranked.2005,
data=edb.its.2001.nocommittee)
model.proced.lag.2005.nocom.se <- robust.clusterify(model.proced.lag.2005.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.days_ln.lag.2005.nocom <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2005,
data=edb.its.2001.nocommittee)
model.days_ln.lag.2005.nocom.se <- robust.clusterify(model.days_ln.lag.2005.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.cost_ln.lag.2005.nocom <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2005,
data=edb.its.2001.nocommittee)
model.cost_ln.lag.2005.nocom.se <- robust.clusterify(model.cost_ln.lag.2005.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.capital_ln.lag.2005.nocom <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2005,
data=edb.its.2001.nocommittee.cap.constrained)
model.capital_ln.lag.2005.nocom.se <- robust.clusterify(model.capital_ln.lag.2005.nocom, edb.its.2001.nocommittee.cap.constrained, edb.its.2001.nocommittee.cap.constrained$ccode)
model.proced.lag.2006.nocom <- lm(sb_proced ~ sb_proced_lag + ranked.2006,
data=edb.its.2001.nocommittee)
model.proced.lag.2006.nocom.se <- robust.clusterify(model.proced.lag.2006.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.days_ln.lag.2006.nocom <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2006,
data=edb.its.2001.nocommittee)
model.days_ln.lag.2006.nocom.se <- robust.clusterify(model.days_ln.lag.2006.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.cost_ln.lag.2006.nocom <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2006,
data=edb.its.2001.nocommittee)
model.cost_ln.lag.2006.nocom.se <- robust.clusterify(model.cost_ln.lag.2006.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.capital_ln.lag.2006.nocom <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2006,
data=edb.its.2001.nocommittee.cap.constrained)
model.capital_ln.lag.2006.nocom.se <- robust.clusterify(model.capital_ln.lag.2006.nocom, edb.its.2001.nocommittee.cap.constrained, edb.its.2001.nocommittee.cap.constrained$ccode)
model.proced.lag.2005.com <- lm(sb_proced ~ sb_proced_lag + ranked.2005,
data=edb.its.2001.committee)
model.proced.lag.2005.com.se <- robust.clusterify(model.proced.lag.2005.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.lag.2005.com <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2005,
data=edb.its.2001.committee)
model.days_ln.lag.2005.com.se <- robust.clusterify(model.days_ln.lag.2005.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.lag.2005.com <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2005,
data=edb.its.2001.committee)
model.cost_ln.lag.2005.com.se <- robust.clusterify(model.cost_ln.lag.2005.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.lag.2005.com <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2005,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.lag.2005.com.se <- robust.clusterify(model.capital_ln.lag.2005.com, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)
model.proced.lag.2006.com <- lm(sb_proced ~ sb_proced_lag + ranked.2006,
data=edb.its.2001.committee)
model.proced.lag.2006.com.se <- robust.clusterify(model.proced.lag.2006.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.lag.2006.com <- lm(sb_days_ln ~ sb_days_ln_lag + ranked.2006,
data=edb.its.2001.committee)
model.days_ln.lag.2006.com.se <- robust.clusterify(model.days_ln.lag.2006.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.lag.2006.com <- lm(sb_cost_ln ~ sb_cost_ln_lag + ranked.2006,
data=edb.its.2001.committee)
model.cost_ln.lag.2006.com.se <- robust.clusterify(model.cost_ln.lag.2006.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.lag.2006.com <- lm(sb_capital_ln ~ sb_capital_ln_lag + ranked.2006,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.lag.2006.com.se <- robust.clusterify(model.capital_ln.lag.2006.com, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)stargazer(model.proced.lag.2005.nocom, model.proced.lag.2005.com,
model.days_ln.lag.2005.nocom, model.days_ln.lag.2005.com,
model.proced.lag.2006.nocom, model.proced.lag.2006.com,
model.days_ln.lag.2006.nocom, model.days_ln.lag.2006.com,
se=list(model.proced.lag.2005.nocom$coefs[,2], model.proced.lag.2005.com$coefs[,2],
model.days_ln.lag.2005.nocom$coefs[,2], model.days_ln.lag.2005.com$coefs[,2],
model.proced.lag.2006.nocom$coefs[,2], model.proced.lag.2006.com$coefs[,2],
model.days_ln.lag.2006.nocom$coefs[,2], model.days_ln.lag.2006.com$coefs[,2]),
type="html", dep.var.caption="EDB outcomes",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8)),
c("EDB reform committee", rep(c("No", "Yes"), 4))),
notes="Robust standard errors clustered by country")| EDB outcomes | ||||||||
| sb_proced | sb_days_ln | sb_proced | sb_days_ln | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 0.229** | 0.866*** | 0.194*** | 0.134* | 0.262*** | 1.004*** | 0.192*** | 0.193*** |
| (0.105) | (0.249) | (0.047) | (0.071) | (0.100) | (0.240) | (0.045) | (0.070) | |
| sb_proced_lag | 0.955*** | 0.890*** | 0.953*** | 0.882*** | ||||
| (0.009) | (0.020) | (0.009) | (0.020) | |||||
| sb_days_ln_lag | 0.915*** | 0.941*** | 0.914*** | 0.929*** | ||||
| (0.011) | (0.017) | (0.012) | (0.017) | |||||
| ranked.2005 | -0.161** | -0.254* | -0.036 | -0.049 | ||||
| (0.076) | (0.145) | (0.024) | (0.029) | |||||
| ranked.2006 | -0.201*** | -0.373*** | -0.034 | -0.082*** | ||||
| (0.070) | (0.133) | (0.023) | (0.027) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| EDB reform committee | No | Yes | No | Yes | No | Yes | No | Yes |
| Observations | 972 | 455 | 972 | 455 | 972 | 455 | 972 | 455 |
| R2 | 0.931 | 0.838 | 0.883 | 0.890 | 0.931 | 0.839 | 0.883 | 0.892 |
| Adjusted R2 | 0.930 | 0.837 | 0.882 | 0.890 | 0.931 | 0.839 | 0.882 | 0.891 |
| Residual Std. Error | 0.972 (df = 969) | 1.239 (df = 452) | 0.301 (df = 969) | 0.244 (df = 452) | 0.970 (df = 969) | 1.233 (df = 452) | 0.301 (df = 969) | 0.242 (df = 452) |
| F Statistic | 6,497.481*** (df = 2; 969) | 1,165.519*** (df = 2; 452) | 3,642.762*** (df = 2; 969) | 1,828.536*** (df = 2; 452) | 6,525.349*** (df = 2; 969) | 1,180.211*** (df = 2; 452) | 3,643.106*** (df = 2; 969) | 1,857.591*** (df = 2; 452) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
stargazer(model.cost_ln.lag.2005.nocom, model.cost_ln.lag.2005.com,
model.capital_ln.lag.2005.nocom, model.capital_ln.lag.2005.com,
model.cost_ln.lag.2006.nocom, model.cost_ln.lag.2006.com,
model.capital_ln.lag.2006.nocom, model.capital_ln.lag.2006.com,
se=list(model.cost_ln.lag.2005.nocom$coefs[,2], model.cost_ln.lag.2005.com$coefs[,2],
model.capital_ln.lag.2005.nocom$coefs[,2], model.capital_ln.lag.2005.com$coefs[,2],
model.cost_ln.lag.2006.nocom$coefs[,2], model.cost_ln.lag.2006.com$coefs[,2],
model.capital_ln.lag.2006.nocom$coefs[,2], model.capital_ln.lag.2006.com$coefs[,2]),
type="html", dep.var.caption="EDB outcomes",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8)),
c("EDB reform committee", rep(c("No", "Yes"), 4))),
notes="Robust standard errors clustered by country")| EDB outcomes | ||||||||
| sb_cost_ln | sb_capital_ln | sb_cost_ln | sb_capital_ln | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 0.052* | 0.044 | 0.093 | -0.084 | 0.041 | 0.031 | 0.137** | -0.007 |
| (0.030) | (0.049) | (0.067) | (0.099) | (0.028) | (0.048) | (0.058) | (0.084) | |
| sb_cost_ln_lag | 0.972*** | 0.980*** | 0.971*** | 0.979*** | ||||
| (0.007) | (0.012) | (0.007) | (0.013) | |||||
| sb_capital_ln_lag | 0.920*** | 0.898*** | 0.917*** | 0.896*** | ||||
| (0.010) | (0.017) | (0.010) | (0.017) | |||||
| ranked.2005 | -0.077*** | -0.089*** | -0.096 | 0.105 | ||||
| (0.024) | (0.032) | (0.064) | (0.098) | |||||
| ranked.2006 | -0.068*** | -0.078*** | -0.155*** | 0.020 | ||||
| (0.022) | (0.029) | (0.056) | (0.084) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| EDB reform committee | No | Yes | No | Yes | No | Yes | No | Yes |
| Observations | 972 | 455 | 898 | 420 | 972 | 455 | 898 | 420 |
| R2 | 0.956 | 0.936 | 0.902 | 0.871 | 0.956 | 0.936 | 0.903 | 0.870 |
| Adjusted R2 | 0.956 | 0.936 | 0.902 | 0.870 | 0.956 | 0.935 | 0.903 | 0.870 |
| Residual Std. Error | 0.308 (df = 969) | 0.277 (df = 452) | 0.709 (df = 895) | 0.734 (df = 417) | 0.308 (df = 969) | 0.277 (df = 452) | 0.707 (df = 895) | 0.735 (df = 417) |
| F Statistic | 10,646.780*** (df = 2; 969) | 3,293.692*** (df = 2; 452) | 4,141.166*** (df = 2; 895) | 1,404.478*** (df = 2; 417) | 10,637.210*** (df = 2; 969) | 3,287.103*** (df = 2; 452) | 4,169.469*** (df = 2; 895) | 1,400.255*** (df = 2; 417) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
ITS for countries in 2001 report, with and without reform committees
model.proced.lag.2005.its.nocom <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.nocommittee)
model.proced.lag.2005.its.nocom.se <- robust.clusterify(model.proced.lag.2005.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.days_ln.lag.2005.its.nocom <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.nocommittee)
model.days_ln.lag.2005.its.nocom.se <- robust.clusterify(model.days_ln.lag.2005.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.cost_ln.lag.2005.its.nocom <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.nocommittee)
model.cost_ln.lag.2005.its.nocom.se <- robust.clusterify(model.cost_ln.lag.2005.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.capital_ln.lag.2005.its.nocom <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.nocommittee.cap.constrained)
model.capital_ln.lag.2005.its.nocom.se <- robust.clusterify(model.capital_ln.lag.2005.its.nocom, edb.its.2001.nocommittee.cap.constrained, edb.its.2001.nocommittee.cap.constrained$ccode)
model.proced.lag.2006.its.nocom <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.nocommittee)
model.proced.lag.2006.its.nocom.se <- robust.clusterify(model.proced.lag.2006.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.days_ln.lag.2006.its.nocom <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.nocommittee)
model.days_ln.lag.2006.its.nocom.se <- robust.clusterify(model.days_ln.lag.2006.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.cost_ln.lag.2006.its.nocom <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.nocommittee)
model.cost_ln.lag.2006.its.nocom.se <- robust.clusterify(model.cost_ln.lag.2006.its.nocom, edb.its.2001.nocommittee, edb.its.2001.nocommittee$ccode)
model.capital_ln.lag.2006.its.nocom <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.nocommittee.cap.constrained)
model.capital_ln.lag.2006.its.nocom.se <- robust.clusterify(model.capital_ln.lag.2006.its.nocom, edb.its.2001.nocommittee.cap.constrained, edb.its.2001.nocommittee.cap.constrained$ccode)
model.proced.lag.2005.its.com <- lm(sb_proced ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.proced.lag.2005.its.com.se <- robust.clusterify(model.proced.lag.2005.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.lag.2005.its.com <- lm(sb_days_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.days_ln.lag.2005.its.com.se <- robust.clusterify(model.days_ln.lag.2005.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.lag.2005.its.com <- lm(sb_cost_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee)
model.cost_ln.lag.2005.its.com.se <- robust.clusterify(model.cost_ln.lag.2005.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.lag.2005.its.com <- lm(sb_capital_ln ~
year.centered.2005 + ranked.2005 + year.centered.2005 * ranked.2005,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.lag.2005.its.com.se <- robust.clusterify(model.capital_ln.lag.2005.its.com, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)
model.proced.lag.2006.its.com <- lm(sb_proced ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.proced.lag.2006.its.com.se <- robust.clusterify(model.proced.lag.2006.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.days_ln.lag.2006.its.com <- lm(sb_days_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.days_ln.lag.2006.its.com.se <- robust.clusterify(model.days_ln.lag.2006.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.cost_ln.lag.2006.its.com <- lm(sb_cost_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee)
model.cost_ln.lag.2006.its.com.se <- robust.clusterify(model.cost_ln.lag.2006.its.com, edb.its.2001.committee, edb.its.2001.committee$ccode)
model.capital_ln.lag.2006.its.com <- lm(sb_capital_ln ~
year.centered.2006 + ranked.2006 + year.centered.2006 * ranked.2006,
data=edb.its.2001.committee.cap.constrained)
model.capital_ln.lag.2006.its.com.se <- robust.clusterify(model.capital_ln.lag.2006.its.com, edb.its.2001.committee.cap.constrained, edb.its.2001.committee.cap.constrained$ccode)stargazer(model.proced.lag.2005.its.nocom, model.proced.lag.2005.its.com,
model.days_ln.lag.2005.its.nocom, model.days_ln.lag.2005.its.com,
model.proced.lag.2006.its.nocom, model.proced.lag.2006.its.com,
model.days_ln.lag.2006.its.nocom, model.days_ln.lag.2006.its.com,
se=list(model.proced.lag.2005.its.nocom.se$coefs[,2], model.proced.lag.2005.its.com.se$coefs[,2],
model.days_ln.lag.2005.its.nocom.se$coefs[,2], model.days_ln.lag.2005.its.com.se$coefs[,2],
model.proced.lag.2006.its.nocom.se$coefs[,2], model.proced.lag.2006.its.com.se$coefs[,2],
model.days_ln.lag.2006.its.nocom.se$coefs[,2], model.days_ln.lag.2006.its.com.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes (ITS, with/without reform committees)",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8)),
c("EDB reform committee", rep(c("No", "Yes"), 4))),
notes="Robust standard errors clustered by country")| EDB outcomes (ITS, with/without reform committees) | ||||||||
| sb_proced | sb_days_ln | sb_proced | sb_days_ln | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 8.953*** | 10.200*** | 3.329*** | 3.612*** | 8.813*** | 10.031*** | 3.222*** | 3.580*** |
| (0.433) | (0.543) | (0.101) | (0.109) | (0.434) | (0.564) | (0.102) | (0.113) | |
| year.centered.2005 | -0.232*** | -0.306 | -0.113*** | -0.096*** | ||||
| (0.086) | (0.194) | (0.026) | (0.023) | |||||
| ranked.2005 | -0.062 | 0.162 | -0.066 | 0.028 | ||||
| (0.163) | (0.280) | (0.061) | (0.051) | |||||
| year.centered.2005:ranked.2005 | -0.082 | -0.103 | 0.022 | -0.019 | ||||
| (0.092) | (0.212) | (0.029) | (0.029) | |||||
| year.centered.2006 | -0.209*** | -0.271* | -0.112*** | -0.080*** | ||||
| (0.069) | (0.156) | (0.021) | (0.018) | |||||
| ranked.2006 | -0.312* | -0.083 | -0.083 | -0.078 | ||||
| (0.184) | (0.320) | (0.062) | (0.070) | |||||
| year.centered.2006:ranked.2006 | -0.090 | -0.137 | 0.027 | -0.031 | ||||
| (0.079) | (0.174) | (0.026) | (0.025) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| EDB reform committee | No | Yes | No | Yes | No | Yes | No | Yes |
| Observations | 1,048 | 490 | 1,048 | 490 | 1,048 | 490 | 1,048 | 490 |
| R2 | 0.104 | 0.222 | 0.210 | 0.337 | 0.105 | 0.222 | 0.210 | 0.337 |
| Adjusted R2 | 0.102 | 0.218 | 0.207 | 0.333 | 0.102 | 0.217 | 0.208 | 0.333 |
| Residual Std. Error | 3.538 (df = 1044) | 2.810 (df = 486) | 0.804 (df = 1044) | 0.612 (df = 486) | 3.537 (df = 1044) | 2.810 (df = 486) | 0.803 (df = 1044) | 0.612 (df = 486) |
| F Statistic | 40.604*** (df = 3; 1044) | 46.308*** (df = 3; 486) | 92.371*** (df = 3; 1044) | 82.245*** (df = 3; 486) | 40.697*** (df = 3; 1044) | 46.285*** (df = 3; 486) | 92.771*** (df = 3; 1044) | 82.317*** (df = 3; 486) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
stargazer(model.cost_ln.lag.2005.its.nocom, model.cost_ln.lag.2005.its.com,
model.capital_ln.lag.2005.its.nocom, model.capital_ln.lag.2005.its.com,
model.cost_ln.lag.2006.its.nocom, model.cost_ln.lag.2006.its.com,
model.capital_ln.lag.2006.its.nocom, model.capital_ln.lag.2006.its.com,
se=list(model.cost_ln.lag.2005.its.nocom.se$coefs[,2], model.cost_ln.lag.2005.its.com.se$coefs[,2],
model.capital_ln.lag.2005.its.nocom.se$coefs[,2], model.capital_ln.lag.2005.its.com.se$coefs[,2],
model.cost_ln.lag.2006.its.nocom.se$coefs[,2], model.cost_ln.lag.2006.its.com.se$coefs[,2],
model.capital_ln.lag.2006.its.nocom.se$coefs[,2], model.capital_ln.lag.2006.its.com.se$coefs[,2]),
type="html", dep.var.caption="EDB outcomes (ITS, with/without reform committees)",
intercept.bottom=FALSE,
omit="\\.factor",
add.lines=list(c("Year fixed effects", rep("No", 8)),
c("EDB reform committee", rep(c("No", "Yes"), 4))),
notes="Robust standard errors clustered by country")| EDB outcomes (ITS, with/without reform committees) | ||||||||
| sb_cost_ln | sb_capital_ln | sb_cost_ln | sb_capital_ln | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| Constant | 3.031*** | 3.252*** | 2.897*** | 2.280*** | 2.958*** | 3.152*** | 2.929*** | 2.149*** |
| (0.187) | (0.179) | (0.290) | (0.398) | (0.184) | (0.183) | (0.289) | (0.377) | |
| year.centered.2005 | -0.044 | -0.020 | -0.131** | -0.048*** | ||||
| (0.027) | (0.035) | (0.054) | (0.010) | |||||
| ranked.2005 | -0.155*** | -0.094 | 0.111 | -0.075 | ||||
| (0.058) | (0.057) | (0.089) | (0.132) | |||||
| year.centered.2005:ranked.2005 | -0.047 | -0.084** | -0.079 | -0.098** | ||||
| (0.031) | (0.036) | (0.062) | (0.042) | |||||
| year.centered.2006 | -0.051** | -0.040 | -0.070* | -0.079** | ||||
| (0.021) | (0.026) | (0.041) | (0.035) | |||||
| ranked.2006 | -0.225*** | -0.096* | -0.136 | -0.096 | ||||
| (0.069) | (0.058) | (0.110) | (0.165) | |||||
| year.centered.2006:ranked.2006 | -0.030 | -0.065** | -0.139*** | -0.066 | ||||
| (0.025) | (0.028) | (0.052) | (0.051) | |||||
| Year fixed effects | No | No | No | No | No | No | No | No |
| EDB reform committee | No | Yes | No | Yes | No | Yes | No | Yes |
| Observations | 1,048 | 490 | 899 | 420 | 1,048 | 490 | 899 | 420 |
| R2 | 0.066 | 0.120 | 0.088 | 0.057 | 0.067 | 0.120 | 0.088 | 0.057 |
| Adjusted R2 | 0.063 | 0.115 | 0.085 | 0.050 | 0.064 | 0.114 | 0.085 | 0.050 |
| Residual Std. Error | 1.430 (df = 1044) | 1.020 (df = 486) | 2.169 (df = 895) | 1.986 (df = 416) | 1.429 (df = 1044) | 1.020 (df = 486) | 2.169 (df = 895) | 1.986 (df = 416) |
| F Statistic | 24.541*** (df = 3; 1044) | 22.103*** (df = 3; 486) | 28.789*** (df = 3; 895) | 8.384*** (df = 3; 416) | 24.811*** (df = 3; 1044) | 22.053*** (df = 3; 486) | 28.775*** (df = 3; 895) | 8.383*** (df = 3; 416) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
| Robust standard errors clustered by country | ||||||||
Flexible ITS (with year²)
# make t more flexible in ITS by squaring itList of countries in initial 2001 report
* indicates country has an EDB reform committee by 2015
country.names <- edb.its.2001 %>%
group_by(ccode) %>%
summarise(cow = unique(ccode)) %>%
ungroup() %>%
mutate(has.committee = ifelse(ccode %in% countries.with.edb.bureau$cowcode, "\\*", ""),
Country = countrycode(cow, "cown", "country.name")) %>%
mutate(Country = case_when(
.$cow == 1001 ~ "Serbia",
.$cow == 1005 ~ "Hong Kong",
TRUE ~ .$Country
)) %>%
arrange(Country) %>%
mutate(Country = paste0(Country, has.committee)) %>%
select(Country)pandoc.table(matrix(c(country.names$Country, rep("", 2)), ncol=4),
split.tables=Inf)| Albania | Ecuador | Lithuania | Serbia |
| Algeria* | Egypt | Madagascar | Singapore |
| Argentina | Ethiopia | Malawi* | Slovakia |
| Armenia | Finland | Malaysia* | Slovenia |
| Australia | France | Mali* | South Africa |
| Austria | Georgia* | Mexico* | Spain |
| Azerbaijan* | Germany | Moldova, Republic of* | Sri Lanka* |
| Bangladesh | Ghana | Mongolia | Sweden |
| Belarus | Greece | Morocco* | Switzerland |
| Belgium | Guatemala* | Mozambique | Syrian Arab Republic |
| Benin | Honduras | Nepal | Taiwan, Province of China |
| Bhutan | Hong Kong | Netherlands | Tanzania, United Republic of |
| Bolivia, Plurinational State of | Hungary | New Zealand | Thailand |
| Bosnia and Herzegovina | India | Nicaragua | Tunisia |
| Botswana* | Indonesia* | Niger | Turkey |
| Bulgaria | Iran, Islamic Republic of | Nigeria* | Uganda |
| Burkina Faso | Ireland | Norway | Ukraine* |
| Cameroon | Israel | Pakistan | United Arab Emirates* |
| Canada | Italy | Panama* | United Kingdom* |
| Chile* | Jamaica | Peru* | United States |
| China | Japan | Philippines* | Uruguay |
| Colombia* | Jordan | Poland* | Uzbekistan* |
| Costa Rica* | Kazakhstan* | Portugal | Venezuela, Bolivarian Republic of |
| Cote d’Ivoire* | Kenya* | Republic of Vietnam | Yemen |
| Croatia* | Korea | Romania | Zambia* |
| Czech Republic* | Kyrgyzstan* | Russian Federation* | Zimbabwe |
| Denmark | Latvia | Saudi Arabia* | |
| Dominican Republic* | Lebanon | Senegal |
Figures
# #' ### Coefficient plots
# all.coefs <- bind_rows(mutate(tidy(model.proced, conf.int=TRUE), model="Simple", variable="sb_proced"),
# mutate(tidy(model.days, conf.int=TRUE), model="Simple", variable="sb_days"),
# mutate(tidy(model.days_ln, conf.int=TRUE), model="Simple", variable="sb_days_ln"),
# mutate(tidy(model.cost_ln, conf.int=TRUE), model="Simple", variable="sb_cost_ln"),
# mutate(tidy(model.capital_ln, conf.int=TRUE), model="Simple", variable="sb_capital_ln"),
# mutate(tidy(model.proced_con, conf.int=TRUE), model="Simple", variable="con_proced"),
# mutate(tidy(model.days_con, conf.int=TRUE), model="Simple", variable="con_days"),
# mutate(tidy(model.proced_con_ln, conf.int=TRUE), model="Simple", variable="con_proced_ln"),
# mutate(tidy(model.days_con_ln, conf.int=TRUE), model="Simple", variable="con_days_ln"),
# mutate(tidy(model.proced_fe, conf.int=TRUE), model="Simple + FE", variable="sb_proced"),
# mutate(tidy(model.days_fe, conf.int=TRUE), model="Simple + FE", variable="sb_days"),
# mutate(tidy(model.days_ln_fe, conf.int=TRUE), model="Simple + FE", variable="sb_days_ln"),
# mutate(tidy(model.cost_ln_fe, conf.int=TRUE), model="Simple + FE", variable="sb_cost_ln"),
# mutate(tidy(model.capital_ln_fe, conf.int=TRUE), model="Simple + FE", variable="sb_capital_ln"),
# mutate(tidy(model.proced_con_fe, conf.int=TRUE), model="Simple + FE", variable="con_proced"),
# mutate(tidy(model.days_con_fe, conf.int=TRUE), model="Simple + FE", variable="con_days"),
# mutate(tidy(model.proced_con_ln_fe, conf.int=TRUE), model="Simple + FE", variable="con_proced_ln"),
# mutate(tidy(model.days_con_ln_fe, conf.int=TRUE), model="Simple + FE", variable="con_days_ln"),
# mutate(tidy(model.proced_loan, conf.int=TRUE), model="Simple + loans", variable="sb_proced"),
# mutate(tidy(model.days_loan, conf.int=TRUE), model="Simple + loans", variable="sb_days"),
# mutate(tidy(model.days_ln_loan, conf.int=TRUE), model="Simple + loans", variable="sb_days_ln"),
# mutate(tidy(model.cost_ln_loan, conf.int=TRUE), model="Simple + loans", variable="sb_cost_ln"),
# mutate(tidy(model.capital_ln_loan, conf.int=TRUE), model="Simple + loans", variable="sb_capital_ln"),
# mutate(tidy(model.proced_con_loan, conf.int=TRUE), model="Simple + loans", variable="con_proced"),
# mutate(tidy(model.days_con_loan, conf.int=TRUE), model="Simple + loans", variable="con_days"),
# mutate(tidy(model.proced_con_ln_loan, conf.int=TRUE), model="Simple + loans", variable="con_proced_ln"),
# mutate(tidy(model.days_con_ln_loan, conf.int=TRUE), model="Simple + loans", variable="con_days_ln"),
# mutate(tidy(model.proced_controls, conf.int=TRUE), model="Controls", variable="sb_proced"),
# mutate(tidy(model.days_controls, conf.int=TRUE), model="Controls", variable="sb_days"),
# mutate(tidy(model.days_ln_controls, conf.int=TRUE), model="Controls", variable="sb_days_ln"),
# mutate(tidy(model.cost_ln_controls, conf.int=TRUE), model="Controls", variable="sb_cost_ln"),
# mutate(tidy(model.capital_ln_controls, conf.int=TRUE), model="Controls", variable="sb_capital_ln"),
# mutate(tidy(model.proced_con_controls, conf.int=TRUE), model="Controls", variable="con_proced"),
# mutate(tidy(model.days_con_controls, conf.int=TRUE), model="Controls", variable="con_days"),
# mutate(tidy(model.proced_con_ln_controls, conf.int=TRUE), model="Controls", variable="con_proced_ln"),
# mutate(tidy(model.days_con_ln_controls, conf.int=TRUE), model="Controls", variable="con_days_ln"),
# mutate(tidy(model.proced_controls_fe, conf.int=TRUE), model="Controls + FE", variable="sb_proced"),
# mutate(tidy(model.days_controls_fe, conf.int=TRUE), model="Controls + FE", variable="sb_days"),
# mutate(tidy(model.days_ln_controls_fe, conf.int=TRUE), model="Controls + FE", variable="sb_days_ln"),
# mutate(tidy(model.cost_ln_controls_fe, conf.int=TRUE), model="Controls + FE", variable="sb_cost_ln"),
# mutate(tidy(model.capital_ln_controls_fe, conf.int=TRUE), model="Controls + FE", variable="sb_capital_ln"),
# mutate(tidy(model.proced_con_controls_fe, conf.int=TRUE), model="Controls + FE", variable="con_proced"),
# mutate(tidy(model.days_con_controls_fe, conf.int=TRUE), model="Controls + FE", variable="con_days"),
# mutate(tidy(model.proced_con_ln_controls_fe, conf.int=TRUE), model="Controls + FE", variable="con_proced_ln"),
# mutate(tidy(model.days_con_ln_controls_fe, conf.int=TRUE), model="Controls + FE", variable="con_days_ln")) %>%
# mutate(low = estimate - std.error,
# high = estimate + std.error) %>%
# mutate(model = factor(model, levels=rev(c("Simple", "Simple + FE", "Simple + loans", "Controls", "Controls + FE")), ordered=TRUE))
# plot.data <- all.coefs %>%
# filter(term == "year.centered:rankedTRUE")
# #+ fig.width=6.5, fig.height=5
# ggplot(plot.data, aes(x=estimate, y=model, xmin=low, xmax=high, colour=model)) +
# geom_vline(xintercept=0, colour="#8C2318", alpha=0.6, size=1) +
# geom_pointrangeh(position=position_dodge(width=.7)) +
# # scale_color_manual(values=c("#FF851C", "#85144A", "#001F40", "#2ECC40", "#ABABAB")) +
# guides(colour="none") +
# facet_wrap(~ variable, scales="free_x") +
# theme_edb() + theme(panel.background = element_rect(fill="grey95", colour=NA))Simple models
plot.its(model=model.proced.2005, var.name="sb_proced",
var.title="Procedures to open a business (2005)",
y.title="Number of procedures", plot.year=2005)
plot.its(model=model.proced.2006, var.name="sb_proced",
var.title="Procedures to open a business (2006)",
y.title="Number of procedures", plot.year=2006)
plot.its(model=model.days_ln.2005, var.name="sb_days_ln",
var.title="Days to open a business (logged) (2005)",
y.title="Days (logged)", plot.year=2005)
plot.its(model=model.days_ln.2006, var.name="sb_days_ln",
var.title="Days to open a business (logged) (2006)",
y.title="Days (logged)", plot.year=2006)
plot.its(model=model.cost_ln.2005, var.name="sb_cost_ln",
var.title="Cost to open a business (logged) (2005)",
y.title="Dollars (logged)", plot.year=2005)
plot.its(model=model.cost_ln.2006, var.name="sb_cost_ln",
var.title="Cost to open a business (logged) (2006)",
y.title="Dollars (logged)", plot.year=2006)
plot.its(model=model.capital_ln.2005, var.name="sb_capital_ln",
var.title="Capital to open a business (logged) (2005)",
y.title="Dollars (logged)", plot.year=2005)
plot.its(model=model.capital_ln.2006, var.name="sb_capital_ln",
var.title="Capital to open a business (logged) (2006)",
y.title="Dollars (logged)", plot.year=2006)