knitr::opts_chunk$set(cache = FALSE, fig.retina = 2,
                      tidy.opts = list(width.cutoff = 120),  # For code
                      width = 120)  # For output

library(tidyverse)
library(ggstance)
library(broom)
library(countrycode)
library(pander)
library(stargazer)
library(skimr)
library(here)

# By default, R uses polynomial contrasts for ordered factors in linear models
# options("contrasts") 
# So make ordered factors use treatment contrasts instead
options(contrasts = rep("contr.treatment", 2))
# Or do it on a single variable:
# contrasts(df$x) <- "contr.treatment"

# Load pre-cleaned data
edb_clean <- read_rds(file.path(here(), "output", "data", "edb_clean.rds"))
edb_reforms <- read_rds(file.path(here(), "output", "data", "edb_reforms.rds"))

# Load helpful functions
source(file.path(here(), "lib", "model_stuff.R"))
source(file.path(here(), "lib", "graphics_stuff.R"))

Variable descriptions

edb_summary_base <- read_csv(file.path(here(), "data_raw", "variable-summary-base.csv"))

edb_var_descriptions <- edb_summary_base %>%
  mutate(Variable = paste0("`", Variable, "`"))

caption <- "Description of variables used in analysis"

tbl_var_descriptions <- pandoc.table.return(edb_var_descriptions, 
                                            caption = caption, 
                                            split.tables = Inf,
                                            justify = "lll", missing = "")

cat(tbl_var_descriptions)

Description of variables used in analysis
Variable	Definition	Source
`sb_days`	Number of days required to start a business	World Bank
`sb_cost`	Cost (% of income per capita) of starting a business	World Bank
`sb_capital`	Paid-in minimum capital (% of income per capita) required to start a business	World Bank
`sb_proced`	Number of procedures required for an entrepreneur to legally operate a business	World Bank
`con_proced`	Number of procedures required to resolve a dispute	World Bank
`con_days`	Number of days required for the process of dispute resolution	World Bank
`gdp`	GDP (constant 2005 USD)	World Development Indicators
`gdpcap`	GDP per capita	World Development Indicators
`gdpgrowth`	GDP growth	World Development Indicators
`pop_ln`	Log of population	World Development Indicators
`polity`	Polity IV score	Polity IV Project
`inttot`	Magnitude of international conflict	Center for Systemic Peace
`civtot`	Magnitude of civil conflict	Center for Systemic Peace
`loan_ln`	Log of the sum of all loans from the IBRD since 2005, constant for all years

cat(tbl_var_descriptions, 
    file = file.path(here(), "output", "tables", "app_table_var_desc.md"))

Variable summaries

The small inline histograms in this table will only display correctly when they use a font that supports block elements, such as DejaVu Sans or Arial.

edb_summary_stats <- edb_clean %>%
  select(one_of(edb_summary_base$Variable)) %>%
  gather(Variable, value) %>%
  filter(!is.na(value)) %>%
  group_by(Variable) %>%
  summarize(N = n(),
            Mean = mean(value),
            `Std. Dev` = sd(value),
            Min = min(value),
            Max = max(value),
            Distribution = inline_hist(value)) 

edb_summary <- edb_summary_base %>%
  left_join(edb_summary_stats, by = "Variable") %>%
  mutate(Variable = paste0("`", Variable, "`")) %>%
  select(-Definition, -Source)

caption <- "Summary statistics of variables used in analysis"

tbl_edb_summary <- pandoc.table.return(edb_summary, caption = caption, 
                                       big.mark = ",", split.tables = Inf,
                                       justify = "lcccccc")

cat(tbl_edb_summary)

Summary statistics of variables used in analysis
Variable	N	Mean	Std. Dev	Min	Max	Distribution
`sb_days`	2,368	39.55	52.8	0.5	697	▇▁▁▁▁▁▁▁
`sb_cost`	2,368	57.27	123.1	0	1,540	▇▁▁▁▁▁▁▁
`sb_capital`	2,258	114.2	434.5	0	7,445	▇▁▁▁▁▁▁▁
`sb_proced`	2,368	8.55	3.474	1	20	▂▃▇▅▃▂▁▁
`con_proced`	2,339	36.77	8.006	0	62	▁▁▁▃▇▇▂▁
`con_days`	2,338	590.8	313.3	7	1,800	▂▆▇▃▁▁▁▁
`gdp`	2,468	264,872,882,270	1,122,476,616,993	97,813,220	14,450,329,106,512	▇▁▁▁▁▁▁▁
`gdpcap`	2,480	10,591	16,385	108	113,739	▇▁▁▁▁▁▁▁
`gdpgrowth`	2,468	2.476	5.478	-62.47	102.8	▁▁▂▇▁▁▁▁
`pop_ln`	2,512	15.56	2.068	9.861	21.03	▁▂▂▆▇▅▂▁
`polity`	2,138	3.918	6.238	-10	10	▁▂▁▂▁▂▃▇
`inttot`	2,182	0.06279	0.4875	0	6	▇▁▁▁▁▁▁▁
`civtot`	2,182	0.4216	1.279	0	9	▇▁▁▁▁▁▁▁
`loan_ln`	3,016	13.97	10.78	0	26.46	▇▁▁▁▁▂▇▃

cat(tbl_edb_summary, 
    file = file.path(here(), "output", "tables", "app_table_var_summary.md"))

List of countries in initial 2001 report

* indicates country has an EDB reform committee by 2015

edb_bureaus <- read_csv(file.path(here(), "output", "data", "edb_bureaus.csv"))

country_names <- edb_clean %>%
  filter(in_2001 == 1) %>%
  group_by(ccode) %>% 
  summarize(Country = first(country_name)) %>% 
  ungroup() %>% 
  mutate(has_committee = ifelse(ccode %in% edb_bureaus$cowcode, "\\*", "")) %>%
  arrange(Country) %>%
  mutate(Country = paste0(Country, has_committee)) %>%
  select(Country)

caption <- "Countries in 2001 report"

tbl_countries <- pandoc.table.return(matrix(c(country_names$Country, rep(NA, 2)), 
                                            ncol = 4), caption = caption,
                                     split.tables = Inf, missing = "", justify = "llll")

cat(tbl_countries)

Countries in 2001 report
Albania	Ecuador	Madagascar	Slovakia
Algeria*	Egypt	Malawi*	Slovenia
Argentina	Ethiopia	Malaysia*	South Africa
Armenia	Finland	Mali*	South Korea*
Australia	France	Mexico*	Spain
Austria	Georgia*	Moldova*	Sri Lanka*
Azerbaijan*	Germany	Mongolia	Sweden
Bangladesh	Ghana	Morocco*	Switzerland
Belarus	Greece	Mozambique	Syria
Belgium	Guatemala*	Nepal	Taiwan
Benin	Honduras	Netherlands	Tanzania
Bhutan	Hong Kong SAR China	New Zealand	Thailand
Bolivia	Hungary	Nicaragua	Tunisia
Bosnia & Herzegovina	India	Niger	Turkey
Botswana*	Indonesia*	Nigeria*	Uganda
Bulgaria	Iran	Norway	Ukraine*
Burkina Faso	Ireland	Pakistan	United Arab Emirates*
Cameroon	Israel	Panama*	United Kingdom*
Canada	Italy	Peru*	United States
Chile*	Jamaica	Philippines*	Uruguay
China	Japan	Poland*	Uzbekistan*
Colombia*	Jordan	Portugal	Venezuela
Costa Rica*	Kazakhstan*	Romania	Vietnam
Côte d’Ivoire*	Kenya*	Russia*	Yemen
Croatia*	Kyrgyzstan*	Saudi Arabia*	Zambia*
Czechia*	Latvia	Senegal	Zimbabwe
Denmark	Lebanon	Serbia
Dominican Republic*	Lithuania	Singapore

cat(tbl_countries, 
    file = file.path(here(), "output", "tables", "app_table_countries.md"))

How control variables relate to being in the sample and to the outcomes

Model A1

edb_in_2001 <- edb_clean %>%
  filter(year > 2000) %>%
  group_by(ccode) %>%
  mutate(in_2001_lead = lead(in_2001),
         in_2001_lead = factor(in_2001_lead, levels = 0:1, 
                               labels = c("Not in 2001", "In 2001")))

model_in_2001 <- glm(in_2001_lead ~ gdpcap_ln + gdpgrowth + polity + pop_ln + 
                        inttot + civtot + loan_ln, 
                      data = edb_in_2001, family = binomial(link = "logit")) 

model_in_2001_robust <- robust_clusterify(model_in_2001, edb_in_2001, "ccode") %>%
  magrittr::use_series(coefs) %>% tidy()

model_in_2001_out <- stargazer(model_in_2001, type = "html", dep.var.caption = "",
                               dep.var.labels = "in\\_2001\\_lead",
                               se = list(model_in_2001_robust$std.error),
                               notes = c("Logistic regression model. Robust standard errors clustered by country.", 
                                         "Models include countries present in the 2001 EDB report."))

cat(paste(escape_stars(model_in_2001_out), collapse = "\n"), "\n")


	in_2001_lead

gdpcap_ln	0.688^**
	(0.268)

gdpgrowth	0.012
	(0.024)

polity	0.124^***
	(0.040)

pop_ln	1.635^***
	(0.479)

inttot	-0.527^*
	(0.307)

civtot	-0.586^**
	(0.251)

loan_ln	0.037
	(0.035)

Constant	-31.163^***
	(9.261)


Observations	1,921
Log Likelihood	-678.778
Akaike Inf. Crit.	1,373.555

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	Logistic regression model. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(model_in_2001_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_model_A1.html"))

Models A2–A5

# This function generates an R formula based on a given outcome and its lead. 
# Given "sb_days_ln", it will create and run "sb_days_ln_lead ~ sb_days_ln + ..."
run_leaded_ols_check <- function(outcome, df) {
  outcome_lead <- paste0(outcome, "_lead")
  
  form <- as.formula(paste0(outcome_lead, " ~ ", outcome, 
                          " + gdpcap_ln + gdpgrowth + polity + ", 
                          "pop_ln + inttot + civtot + loan_ln"))
  
  lm(form, data = df)
}

# Define all the models that need to be run
models_to_run <- data_frame(outcome = c("sb_days_ln", "sb_proced",
                                        "sb_cost_ln", "sb_capital_ln"),
                            grouping = 1)  # Temporary variable for joining

edb_checks <- edb_clean %>%
  filter(year > 2000, in_2001 == 1) %>%
  mutate(grouping = 1) %>%
  group_by(ccode) %>%
  mutate_at(vars(sb_days_ln, sb_proced, sb_cost_ln, sb_capital_ln),
            funs(lead = lead(.))) %>%
  group_by(grouping) %>%
  nest() %>%
  right_join(models_to_run, by = "grouping")

# Run all the models within the data frame
edb_checks_models <- edb_checks %>%
  mutate(model = pmap(.l = list(outcome, data), run_leaded_ols_check),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

edb_checks_models_out <- stargazer(edb_checks_models$model, type = "html", 
                                   dep.var.caption = "",
                                   se = edb_checks_models$ses_only,
                                   notes = c("OLS models. Robust standard errors clustered by country.",
                                             "Models include countries present in the 2001 EDB report."),
                                   keep.stat = c("n", "rsq", "adj.rsq"))

cat(paste(escape_stars(edb_checks_models_out), collapse = "\n"), "\n")


	sb_days_ln_lead	sb_proced_lead	sb_cost_ln_lead	sb_capital_ln_lead
	(1)	(2)	(3)	(4)

sb_days_ln	0.911^***
	(0.014)

sb_proced		0.929^***
		(0.015)

sb_cost_ln			0.941^***
			(0.010)

sb_capital_ln				0.915^***
				(0.011)

gdpcap_ln	-0.025^**	-0.045	-0.051^***	-0.042^*
	(0.011)	(0.044)	(0.013)	(0.023)

gdpgrowth	-0.007^***	-0.020^***	-0.014^***	-0.004
	(0.002)	(0.007)	(0.002)	(0.006)

polity	0.0002	0.003	0.003^*	0.012^***
	(0.001)	(0.006)	(0.001)	(0.004)

pop_ln	0.011	0.074^***	0.011^*	0.007
	(0.007)	(0.025)	(0.006)	(0.014)

inttot	-0.006	0.039	0.011	-0.003
	(0.015)	(0.062)	(0.010)	(0.027)

civtot	0.006	0.021	0.003	-0.004
	(0.005)	(0.030)	(0.007)	(0.017)

loan_ln	-0.001	0.002	-0.001	-0.003
	(0.001)	(0.006)	(0.001)	(0.003)

Constant	0.247	-0.533	0.359^**	0.232
	(0.168)	(0.537)	(0.166)	(0.319)


Observations	1,310	1,310	1,310	1,209
R²	0.887	0.912	0.957	0.895
Adjusted R²	0.887	0.911	0.957	0.894

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(edb_checks_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_A2-5.html"))

Starting a Business indicators

Not generated with this script.

Enforcing Contracts indicators

Not generated with this script.

Policy reform: OLS models

# Define all the models that need to be run
models_to_run <- expand.grid(year = 2003:2013,
                             outcome = c("sb_proced", "sb_days_ln",
                                         "sb_cost_ln", "sb_capital_ln"),
                             grouping = c("All countries", 
                                          "No EDB reform committee", 
                                          "Special EDB reform committee"),
                             stringsAsFactors = FALSE)

# As in the paper analysis, this function generates an R model formula based on
# the name of the dependent variable and the year provided. i.e., given
# "sb_proced" and "2005", it will create the formula "sb_proced ~ sb_proced_lag
# + ranked_2005" and run the model
run_lagged_ols_model <- function(outcome, year, df) {
  outcome_lag <- paste0(outcome, "_lag")
  year_variable <- paste0("ranked_", year)

  form <- as.formula(paste0(outcome, " ~ ", outcome_lag, " + ", year_variable))

  lm(form, data = df)
}

dfs_split <- edb_clean %>%
  filter(in_2001 == 1) %>%
  group_by(has_bureau) %>%
  nest() %>%
  mutate(has_bureau = as.character(has_bureau)) %>%
  rename(grouping = has_bureau)

df_all <- edb_clean %>%
  filter(in_2001 == 1) %>%
  mutate(grouping = "All countries") %>%
  group_by(grouping) %>%
  nest()

models_to_run_full <- bind_rows(dfs_split, df_all) %>%
  right_join(models_to_run, by = "grouping")

# Run all the models within the data frame
ols_models_lagged <- models_to_run_full %>%
  mutate(model = pmap(.l = list(outcome, year, data), run_lagged_ols_model),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Extract the ranking coefficients from all models
ols_coefs <- ols_models_lagged %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  # Only look at the ranked* coefficients
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates, labels, and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         term = str_replace(term, "\\.\\d+TRUE", "")) %>%
  # Get rid of extra columns
  select(-c(estimate, std.error, statistic, p.value)) %>%
  spread(outcome, value) %>%
  # Give table clean column names
  select(Subset = grouping, Year = year,
         Procedures = sb_proced, `Cost (log)` = sb_cost_ln,
         `Days (log)` = sb_days_ln, `Capital (log)` = sb_capital_ln)

Table 3 from the paper: summary of coefficients for 8 OLS models

Full models in tables B1–3 below.

tbl_ols_paper <- ols_coefs %>%
  filter(Subset == "All countries", Year %in% c(2005, 2006))

caption <- 'Summary of β~2~ coefficients (i.e. "ranked_200x") for difference models'

tbl_ols <- pandoc.table.return(tbl_ols_paper, caption = caption)

cat(tbl_ols)

Summary of β₂ coefficients (i.e. “ranked_200x”) for difference models
Subset	Year	Procedures	Cost (log)	Days (log)	Capital (log)
All countries	2005	-0.176**	-0.081***	-0.040*	-0.030
All countries	2006	-0.240***	-0.071***	-0.049**	-0.097*

cat(tbl_ols, file = file.path(here(), "output", "tables", "app_table_ols.md"))

Table B1: 2005 analysis, full OLS models

tbl_ols_2005 <- ols_models_lagged %>%
  filter(grouping == "All countries", year == 2005)

tbl_ols_2005_out <- stargazer(tbl_ols_2005$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_ols_2005$ses_only,
                              notes = c("OLS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

tbl_ols_2006 <- ols_models_lagged %>%
  filter(grouping == "All countries", year == 2006)

tbl_ols_2006_out <- stargazer(tbl_ols_2006$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_ols_2006$ses_only,
                              notes = c("OLS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

cat(paste(escape_stars(tbl_ols_2005_out), collapse = "\n"), "\n")


	sb_proced	sb_days_ln	sb_cost_ln	sb_capital_ln
	(1)	(2)	(3)	(4)

sb_proced_lag	0.939^***
	(0.013)

sb_days_ln_lag		0.922^***
		(0.012)

sb_cost_ln_lag			0.973^***
			(0.004)

sb_capital_ln_lag				0.915^***
				(0.011)

ranked_2005	-0.176^**	-0.040^*	-0.081^***	-0.030
	(0.078)	(0.024)	(0.022)	(0.058)

Constant	0.365^***	0.180^***	0.053^**	0.032
	(0.110)	(0.048)	(0.025)	(0.057)


Observations	1,427	1,427	1,427	1,318
R²	0.909	0.887	0.953	0.895
Adjusted R²	0.909	0.887	0.952	0.895

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(tbl_ols_2005_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_B1.html"))

Table B2: 2006 analysis, full OLS models

cat(paste(escape_stars(tbl_ols_2006_out), collapse = "\n"), "\n")


	sb_proced	sb_days_ln	sb_cost_ln	sb_capital_ln
	(1)	(2)	(3)	(4)

sb_proced_lag	0.935^***
	(0.013)

sb_days_ln_lag		0.919^***
		(0.012)

sb_cost_ln_lag			0.973^***
			(0.004)

sb_capital_ln_lag				0.913^***
				(0.011)

ranked_2006	-0.240^***	-0.049^**	-0.071^***	-0.097^*
	(0.068)	(0.022)	(0.019)	(0.052)

Constant	0.424^***	0.193^***	0.041^*	0.087^*
	(0.113)	(0.048)	(0.022)	(0.049)


Observations	1,427	1,427	1,427	1,318
R²	0.910	0.887	0.952	0.895
Adjusted R²	0.909	0.887	0.952	0.895

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(tbl_ols_2006_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_B2.html"))

Table B3: Cutpoints at every possible year, OLS models

caption <- 'Summary of β~2~ coefficients (i.e. "ranked.200x") for difference models for all years'

tbl_ols_all <- pandoc.table.return(ols_coefs, caption = caption,
                                     split.tables = Inf, missing = "—")

cat(tbl_ols_all)

Summary of β₂ coefficients (i.e. “ranked.200x”) for difference models for all years
Subset	Year	Procedures	Cost (log)	Days (log)	Capital (log)
All countries	2003	-0.074	-0.114**	0.002	—
All countries	2004	-0.261***	-0.107***	-0.040	0.096
All countries	2005	-0.176**	-0.081***	-0.040*	-0.030
All countries	2006	-0.240***	-0.071***	-0.049**	-0.097*
All countries	2007	-0.239***	-0.046***	-0.051**	-0.126**
All countries	2008	-0.209***	-0.034**	-0.040**	-0.133***
All countries	2009	-0.110*	-0.008	-0.007	-0.101**
All countries	2010	-0.060	0.002	0.005	-0.035
All countries	2011	-0.093	-0.024	-0.002	-0.073*
All countries	2012	-0.027	-0.015	0.008	-0.073*
All countries	2013	-0.108	-0.028	-0.014	-0.090*
No EDB reform committee	2003	-0.203	-0.112*	0.003	—
No EDB reform committee	2004	-0.246***	-0.097**	-0.049	-0.045
No EDB reform committee	2005	-0.164**	-0.079***	-0.038	-0.085
No EDB reform committee	2006	-0.201***	-0.069***	-0.035	-0.144**
No EDB reform committee	2007	-0.181***	-0.031	-0.040	-0.162***
No EDB reform committee	2008	-0.159**	-0.014	-0.030	-0.178***
No EDB reform committee	2009	-0.031	0.007	0.006	-0.106**
No EDB reform committee	2010	0.031	0.012	0.019	-0.048
No EDB reform committee	2011	-0.005	-0.014	0.012	-0.087
No EDB reform committee	2012	0.049	0.000	0.023	-0.059
No EDB reform committee	2013	-0.010	-0.011	-0.006	-0.081
Special EDB reform committee	2003	0.147	-0.118*	0.002	—
Special EDB reform committee	2004	-0.334	-0.125***	-0.019	0.371*
Special EDB reform committee	2005	-0.244	-0.085**	-0.042	0.076
Special EDB reform committee	2006	-0.370***	-0.074***	-0.077***	-0.010
Special EDB reform committee	2007	-0.416***	-0.074***	-0.071***	-0.060
Special EDB reform committee	2008	-0.375***	-0.072***	-0.059**	-0.047
Special EDB reform committee	2009	-0.339**	-0.038	-0.031	-0.098
Special EDB reform committee	2010	-0.316**	-0.016	-0.022	-0.014
Special EDB reform committee	2011	-0.343**	-0.045*	-0.029	-0.048
Special EDB reform committee	2012	-0.249*	-0.044	-0.016	-0.105
Special EDB reform committee	2013	-0.377**	-0.061	-0.025	-0.112

cat(tbl_ols_all, 
    file = file.path(here(), "output", "tables", "app_models_B3_all.md"))

Policy reform: ITS models

run_its_model <- function(outcome, year, df) {
  year_variable <- paste0("ranked_", year)
  year_centered <- paste0("year_centered_", year)
  
  form <- as.formula(paste0(outcome, " ~ ", year_centered, " + ", year_variable,
                            " + ", year_centered, " * ", year_variable))
  
  lm(form, data = df)
}

# Run all the models within the data frame
its_models_lagged <- models_to_run_full %>%
  mutate(model = pmap(.l = list(outcome, year, data), run_its_model),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Extract the ranking coefficients from all models
its_coefs <- its_models_lagged %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  # Only look at the coefficients from interaction terms (they have ":" in their names)
  filter(str_detect(term, ":")) %>%
  # Clean up the estimates, labels, and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         term = str_replace(term, "(.+)\\.\\d+:(.+)\\.\\d+TRUE", "\\1 × \\2")) %>%
  # Get rid of extra columns
  select(-c(estimate, std.error, statistic, p.value)) %>%
  spread(outcome, value) %>%
  # Give table clean column names
  select(Subset = grouping, Year = year,
         Procedures = sb_proced, `Cost (log)` = sb_cost_ln,
         `Days (log)` = sb_days_ln, `Capital (log)` = sb_capital_ln)

Table 4 from the paper: summary of coefficients for 8 ITS models

Full models in tables C1–3 below.

tbl_its_paper <- its_coefs %>%
  filter(Subset == "All countries", Year %in% c(2005, 2006))

caption <- 'Summary of β~3~ coefficients (i.e. "year.centered.200x × ranked.200x") for ITS models'

tbl_its <- pandoc.table.return(tbl_its_paper, caption = caption)

cat(tbl_its)

Summary of β₃ coefficients (i.e. “year.centered.200x × ranked.200x”) for ITS models
Subset	Year	Procedures	Cost (log)	Days (log)	Capital (log)
All countries	2005	-0.088	-0.059**	0.009	0.054
All countries	2006	-0.105	-0.041**	0.008	-0.019

cat(tbl_its, file = file.path(here(), "output", "tables", "app_table_its.md"))

Table C1: 2005 analysis, full ITS models

tbl_its_2005 <- its_models_lagged %>%
  filter(grouping == "All countries", year == 2005)

tbl_its_2005_out <- stargazer(tbl_its_2005$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_its_2005$ses_only,
                              notes = c("ITS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

tbl_its_2006 <- its_models_lagged %>%
  filter(grouping == "All countries", year == 2006)

tbl_its_2006_out <- stargazer(tbl_its_2006$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_its_2006$ses_only,
                              notes = c("ITS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

cat(paste(escape_stars(tbl_its_2005_out), collapse = "\n"), "\n")


	sb_proced	sb_days_ln	sb_cost_ln	sb_capital_ln
	(1)	(2)	(3)	(4)

year_centered_2005	-0.256^***	-0.108^***	-0.037^*	-0.244^***
	(0.085)	(0.019)	(0.022)	(0.052)

ranked_2005	0.010	-0.036	-0.135^***	0.236^***
	(0.142)	(0.045)	(0.044)	(0.089)

year_centered_2005:ranked_2005	-0.088	0.009	-0.059^**	0.054
	(0.092)	(0.022)	(0.024)	(0.059)

Constant	9.350^***	3.419^***	3.101^***	2.515^***
	(0.345)	(0.078)	(0.140)	(0.244)


Observations	1,538	1,538	1,538	1,428
R²	0.130	0.235	0.076	0.088
Adjusted R²	0.128	0.233	0.074	0.087

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	ITS models. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(tbl_its_2005_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_C1.html"))

Table C2: 2006 analysis, full ITS models

cat(paste(escape_stars(tbl_its_2006_out), collapse = "\n"), "\n")


	sb_proced	sb_days_ln	sb_cost_ln	sb_capital_ln
	(1)	(2)	(3)	(4)

year_centered_2006	-0.228^***	-0.101^***	-0.048^***	-0.170^***
	(0.068)	(0.016)	(0.017)	(0.037)

ranked_2006	-0.242	-0.082^*	-0.185^***	0.040
	(0.162)	(0.048)	(0.051)	(0.101)

year_centered_2006:ranked_2006	-0.105	0.008	-0.041^**	-0.019
	(0.077)	(0.019)	(0.019)	(0.046)

Constant	9.203^***	3.336^***	3.020^***	2.518^***
	(0.349)	(0.079)	(0.138)	(0.239)


Observations	1,538	1,538	1,538	1,428
R²	0.130	0.236	0.077	0.088
Adjusted R²	0.128	0.234	0.075	0.086

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	ITS models. Robust standard errors clustered by country.
	Models include countries present in the 2001 EDB report.

cat(paste(tbl_its_2006_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_C2.html"))

Table C3: Cutpoints at every possible year, ITS models

caption <- 'Summary of β~3~ coefficients (i.e. "year.centered.200x × ranked.200x") for ITS models for all years'

tbl_its_all <- pandoc.table.return(its_coefs, caption = caption,
                                   split.tables = Inf, missing = "—")

cat(tbl_its_all)

Summary of β₃ coefficients (i.e. “year.centered.200x × ranked.200x”) for ITS models for all years
Subset	Year	Procedures	Cost (log)	Days (log)	Capital (log)
All countries	2003	0.072	-0.094*	0.053	—
All countries	2004	-0.124	-0.085***	0.011	0.200*
All countries	2005	-0.088	-0.059**	0.009	0.054
All countries	2006	-0.105	-0.041**	0.008	-0.019
All countries	2007	-0.077	-0.017	0.017	-0.033
All countries	2008	-0.025	0.002	0.032**	-0.019
All countries	2009	0.028	0.014	0.047***	0.012
All countries	2010	0.049	0.010	0.052***	0.019
All countries	2011	0.081	0.011	0.055***	0.019
All countries	2012	0.045	0.001	0.041**	0.002
All countries	2013	0.158**	-0.006	0.043**	-0.038
No EDB reform committee	2003	-0.042	-0.081	0.068	—
No EDB reform committee	2004	-0.127	-0.069	0.015	0.057
No EDB reform committee	2005	-0.084	-0.048	0.020	-0.010
No EDB reform committee	2006	-0.087	-0.031	0.027	-0.070
No EDB reform committee	2007	-0.045	-0.000	0.036	-0.070
No EDB reform committee	2008	0.017	0.021	0.053**	-0.046
No EDB reform committee	2009	0.082	0.031*	0.068***	-0.003
No EDB reform committee	2010	0.108	0.027	0.073***	0.011
No EDB reform committee	2011	0.140*	0.031	0.075***	0.028
No EDB reform committee	2012	0.110	0.020	0.054**	0.015
No EDB reform committee	2013	0.137	-0.002	0.042	0.002
Special EDB reform committee	2003	0.318	-0.120*	0.025	—
Special EDB reform committee	2004	-0.117	-0.116***	0.003	0.495**
Special EDB reform committee	2005	-0.097	-0.081**	-0.015	0.188
Special EDB reform committee	2006	-0.141	-0.062**	-0.029	0.085
Special EDB reform committee	2007	-0.143	-0.050**	-0.023	0.043
Special EDB reform committee	2008	-0.113	-0.035*	-0.009	0.037
Special EDB reform committee	2009	-0.085	-0.021	0.002	0.044
Special EDB reform committee	2010	-0.074	-0.025	0.007	0.036
Special EDB reform committee	2011	-0.040	-0.028	0.014	0.001
Special EDB reform committee	2012	-0.091	-0.039	0.014	-0.023
Special EDB reform committee	2013	0.202*	-0.015	0.046*	-0.120

cat(tbl_its_all, 
    file = file.path(here(), "output", "tables", "app_models_C3_all.md"))

Country fixed effects for OLS models

Table D1: “Ranked” coefficients with and without country fixed effects

# All the FE models to be run
country_fe_models <- expand.grid(outcome = c("sb_proced", "sb_days_ln",
                                             "sb_cost_ln", "sb_capital_ln",
                                             "con_proced", "con_days"),
                                 controls = c("Main", 
                                              "Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`", 
                                              "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`", 
                                              "Main + fixed effects",
                                              "Main - 2001", "Main - 2001 & 2002"),
                                 stringsAsFactors = FALSE) %>%
  mutate(country_fe = controls %in% c("Main + fixed effects", "Main - 2001", "Main - 2001 & 2002"),
         df_to_use = case_when(
           controls == "Main - 2001" ~ "Drop 2001",
           controls == "Main - 2001 & 2002" ~ "Drop 2002",
           TRUE ~ "Full" 
         ))

# Nest filtered data into a data frame
data_to_use <- tribble(
  ~df_to_use,  ~data,
  "Full",      edb_clean %>% filter(year > 2000, in_2004 == 1),
  "Drop 2001", edb_clean %>% filter(year > 2001, in_2004 == 1),
  "Drop 2002", edb_clean %>% filter(year > 2002, in_2004 == 1)
)

# Combine filtered data with all model parameters
country_fe_models_full <- data_to_use %>%
  right_join(country_fe_models, by = "df_to_use") %>%
  select(-df_to_use)

# Build and run a bunch of different models based on parameters that determine
# what controls to add and whether or not to include country fixed effects
run_country_fe_models <- function(outcome, controls, country_fe, df) {
  main_controls <- "gdpcap_ln_lag + gdpgrowth_lag + polity_lag + pop_ln_lag"
  
  if (str_detect(controls, "loan_ln_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_ln_lag"
  } else if (str_detect(controls, "loan_bin_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_bin_lag"
  } else {
    additional_controls <- ""
  }
  
  if (country_fe) {
    country <- " + as.factor(ccode)"
  } else {
    country <- ""
  }

  form <- paste0(outcome, " ~ ", paste0(outcome, "_lag"), " + ranked_lag + ", 
                 main_controls, additional_controls, country) %>%
    as.formula()

  lm(form, data = df)
}

# Run all the models within the data frame
country_fe_models_all <- country_fe_models_full %>%
  mutate(model = pmap(.l = list(outcome, controls, country_fe, data), 
                      run_country_fe_models),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Make tiny data frame indicating if model had country fixed effects
fixed_effects_country <- country_fe_models %>%
  select(controls, country_fe) %>%
  distinct() %>%
  mutate(country_fe = ifelse(country_fe, "Yes", "No")) %>%
  spread(controls, country_fe) %>%
  mutate(Outcome = "Fixed country effects")

# Display all the ranked coefficients
ranked_coefs_country <- country_fe_models_all %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         outcome = factor(outcome, levels = unique(country_fe_models$outcome),
                          labels = paste0("`", unique(country_fe_models$outcome), "`"),
                          ordered = TRUE),
         controls = factor(controls, levels = unique(country_fe_models$controls),
                           ordered = TRUE)) %>%
  # Get rid of extra columns
  select(Outcome = outcome, controls, value) %>%
  spread(controls, value) %>%
  bind_rows(fixed_effects_country)

caption <- 'Summary of coefficients for the lagged "Ranked" variable with and without country fixed effects'

tbl_country_fe <- pandoc.table.return(ranked_coefs_country, caption = caption,
                                      split.tables = Inf)

cat(tbl_country_fe)

Summary of coefficients for the lagged “Ranked” variable with and without country fixed effects
Outcome	Main	Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`	Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`	Main + fixed effects	Main - 2001	Main - 2001 & 2002
`sb_proced`	-0.233***	-0.247***	-0.251***	-0.237**	-0.237**	-0.225**
`sb_days_ln`	-0.029	-0.027	-0.029	-0.061**	-0.061**	-0.057**
`sb_cost_ln`	-0.074***	-0.078***	-0.079***	-0.064**	-0.064**	-0.046*
`sb_capital_ln`	-0.003	0.005	0.003	-0.032	-0.032	-0.032
`con_proced`	-1.339***	-1.293***	-1.279***	-0.045	-0.045	-0.204***
`con_days`	-67.590***	-67.839***	-67.357***	-9.040*	-9.040*	-14.108**
Fixed country effects	No	No	No	Yes	Yes	Yes

cat(tbl_country_fe, 
    file = file.path(here(), "output", "tables", "app_table_D1.md"))

Note: Main controls are gdpcap_ln_lag, gdpgrowth_lag, polity_lag, and pop_ln_lag

Table D2: Sub-indicators for “Starting a Business” with and without country fixed effects

sb_fe <- country_fe_models_all %>%
  filter(str_detect(outcome, "sb_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

sb_country_fe_models_out <- stargazer(sb_fe$model, type = "html", 
                                      dep.var.caption = "",
                                      se = sb_fe$ses_only,
                                      notes = c("OLS models. Robust standard errors clustered by country.",
                                                "Models include countries present in the 2004 EDB report."),
                                      keep.stat = c("n", "rsq", "adj.rsq"),
                                      add.lines = list(c("Country fixed effects", 
                                                         rep(c("No", "Yes"), 4))),
                                      omit = "ccode")

cat(paste(escape_stars(sb_country_fe_models_out), collapse = "\n"), "\n")


	sb_proced		sb_days_ln		sb_cost_ln		sb_capital_ln
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)

sb_proced_lag	0.920^***	0.741^***
	(0.013)	(0.026)

sb_days_ln_lag			0.931^***	0.751^***
			(0.011)	(0.022)

sb_cost_ln_lag					0.948^***	0.695^***
					(0.009)	(0.041)

sb_capital_ln_lag							0.924^***	0.742^***
							(0.009)	(0.025)

ranked_lag	-0.251^***	-0.237^**	-0.029	-0.061^**	-0.079^***	-0.064^**	0.003	-0.032
	(0.069)	(0.096)	(0.019)	(0.029)	(0.018)	(0.029)	(0.045)	(0.063)

gdpcap_ln_lag	-0.017	-0.420^***	-0.015	-0.125^***	-0.027^**	-0.232^***	-0.030^*	-0.224^**
	(0.037)	(0.152)	(0.009)	(0.043)	(0.012)	(0.044)	(0.018)	(0.106)

gdpgrowth_lag	-0.021^***	-0.008	-0.007^***	-0.005^***	-0.013^***	-0.007^***	-0.008	0.002
	(0.006)	(0.007)	(0.002)	(0.002)	(0.002)	(0.002)	(0.005)	(0.006)

polity_lag	-0.005	-0.020	-0.002	-0.005	0.0004	-0.011^*	0.004	0.018
	(0.006)	(0.018)	(0.001)	(0.005)	(0.001)	(0.006)	(0.004)	(0.019)

pop_ln_lag	0.041^*	-1.244^**	0.006	-0.172	0.008	-0.416^***	-0.009	-0.294
	(0.024)	(0.540)	(0.006)	(0.116)	(0.005)	(0.158)	(0.012)	(0.688)

civtot_lag	0.028		0.004		0.004		0.014
	(0.026)		(0.005)		(0.006)		(0.014)

inttot_lag	0.076^*		-0.004		0.031^**		-0.087
	(0.041)		(0.016)		(0.012)		(0.118)

loan_bin_lag	0.103		-0.006		0.036		-0.063
	(0.119)		(0.029)		(0.032)		(0.058)

Constant	0.038	30.747^***	0.204	5.258^**	0.213	10.979^***	0.432^*	7.993
	(0.457)	(10.189)	(0.137)	(2.340)	(0.159)	(3.055)	(0.258)	(12.806)


Country fixed effects	No	Yes	No	Yes	No	Yes	No	Yes
Observations	1,659	1,660	1,659	1,660	1,659	1,660	1,558	1,559
R²	0.909	0.926	0.902	0.919	0.963	0.970	0.904	0.918
Adjusted R²	0.909	0.919	0.902	0.911	0.963	0.968	0.903	0.910

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2004 EDB report.

cat(paste(sb_country_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_D2.html"))

Table D3: Sub-indicators for “Enforcing Contracts” with and without country fixed effects

con_fe <- country_fe_models_all %>%
  filter(str_detect(outcome, "con_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

con_country_fe_models_out <- stargazer(con_fe$model, type = "html", 
                                       dep.var.caption = "",
                                       se = con_fe$ses_only,
                                       notes = c("OLS models. Robust standard errors clustered by country.",
                                                 "Models include countries present in the 2004 EDB report."),
                                       keep.stat = c("n", "rsq", "adj.rsq"),
                                       add.lines = list(c("Country fixed effects", 
                                                          rep(c("No", "Yes"), 4))),
                                       omit = "ccode")

cat(paste(escape_stars(con_country_fe_models_out), collapse = "\n"), "\n")


	con_proced		con_days
	(1)	(2)	(3)	(4)

con_proced_lag	0.708^***	0.379^***
	(0.023)	(0.027)

con_days_lag			0.903^***	0.451^***
			(0.015)	(0.035)

ranked_lag	-1.279^***	-0.045	-67.357^***	-9.040^*
	(0.258)	(0.122)	(5.427)	(4.990)

gdpcap_ln_lag	-0.383^***	0.200	-5.263	3.433
	(0.145)	(0.269)	(3.237)	(7.930)

gdpgrowth_lag	-0.038	0.011	-2.627^***	-0.920
	(0.024)	(0.015)	(0.676)	(0.589)

polity_lag	-0.112^***	0.044	1.219^**	-0.785
	(0.039)	(0.048)	(0.525)	(1.765)

pop_ln_lag	0.023	-0.361	2.358	-48.999
	(0.109)	(1.008)	(2.561)	(33.737)

civtot_lag	0.208^**		9.935^**
	(0.101)		(4.295)

inttot_lag	0.292		-2.168
	(0.393)		(3.915)

loan_bin_lag	-0.371		3.842
	(0.487)		(11.148)

Constant	15.702^***	24.570	124.001^***	1,127.757^*
	(2.421)	(18.769)	(42.007)	(645.682)


Country fixed effects	No	Yes	No	Yes
Observations	1,634	1,635	1,633	1,634
R²	0.725	0.830	0.868	0.924
Adjusted R²	0.723	0.814	0.867	0.917

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2004 EDB report.

cat(paste(con_country_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_D3.html"))

Year fixed effects for OLS models

Table E1: “Ranked” coefficients with and without year fixed effects

# All the FE models to be run
year_fe_models <- expand.grid(outcome = c("sb_proced", "sb_days_ln",
                                          "sb_cost_ln", "sb_capital_ln",
                                          "con_proced", "con_days"),
                              controls = c("Main", 
                                           "Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`", 
                                           "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`", 
                                           "Main + fixed effects",
                                           "Main - 2001", "Main - 2001 & 2002"),
                              stringsAsFactors = FALSE) %>%
  mutate(year_fe = controls %in% c("Main + fixed effects", "Main - 2001", "Main - 2001 & 2002"),
         df_to_use = case_when(
           controls == "Main - 2001" ~ "Drop 2001",
           controls == "Main - 2001 & 2002" ~ "Drop 2002",
           TRUE ~ "Full" 
         ))

# Nest filtered data into a data frame
data_to_use <- tribble(
  ~df_to_use,  ~data,
  "Full",      edb_clean %>% filter(year > 2000, in_2004 == 1),
  "Drop 2001", edb_clean %>% filter(year > 2001, in_2004 == 1),
  "Drop 2002", edb_clean %>% filter(year > 2002, in_2004 == 1)
)

# Combine filtered data with all model parameters
year_fe_models_full <- data_to_use %>%
  right_join(year_fe_models, by = "df_to_use") %>%
  select(-df_to_use)

# Build and run a bunch of different models based on parameters that determine
# what controls to add and whether or not to include country fixed effects
run_year_fe_models <- function(outcome, controls, year_fe, df) {
  main_controls <- "gdpcap_ln_lag + gdpgrowth_lag + polity_lag + pop_ln_lag"
  
  if (str_detect(controls, "loan_ln_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_ln_lag"
  } else if (str_detect(controls, "loan_bin_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_bin_lag"
  } else {
    additional_controls <- ""
  }
  
  if (year_fe) {
    year <- " + as.factor(year)"
  } else {
    year <- ""
  }

  form <- paste0(outcome, " ~ ", paste0(outcome, "_lag"), " + ranked_lag + ", 
                 main_controls, additional_controls, year) %>%
    as.formula()

  lm(form, data = df)
}

# Run all the models within the data frame
year_fe_models_all <- year_fe_models_full %>%
  mutate(model = pmap(.l = list(outcome, controls, year_fe, data), 
                      run_year_fe_models),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Make tiny data frame indicating if model had country fixed effects
fixed_effects_year <- year_fe_models %>%
  select(controls, year_fe) %>%
  distinct() %>%
  mutate(year_fe = ifelse(year_fe, "Yes", "No")) %>%
  spread(controls, year_fe) %>%
  mutate(Outcome = "Fixed year effects")

# Display all the ranked coefficients
ranked_coefs_year <- year_fe_models_all %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         outcome = factor(outcome, levels = unique(year_fe_models$outcome),
                          labels = paste0("`", unique(year_fe_models$outcome), "`"),
                          ordered = TRUE),
         controls = factor(controls, levels = unique(year_fe_models$controls),
                           ordered = TRUE)) %>%
  # Get rid of extra columns
  select(Outcome = outcome, controls, value) %>%
  spread(controls, value) %>%
  bind_rows(fixed_effects_year)

caption <- 'Summary of coefficients for the lagged "Ranked" variable with and without country fixed effects'

tbl_year_fe <- pandoc.table.return(ranked_coefs_year, caption = caption,
                                   split.tables = Inf)

cat(tbl_year_fe)

Summary of coefficients for the lagged “Ranked” variable with and without country fixed effects
Outcome	Main	Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`	Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`	Main + fixed effects	Main - 2001	Main - 2001 & 2002
`sb_proced`	-0.233***	-0.247***	-0.251***	0.102	0.102	0.109
`sb_days_ln`	-0.029	-0.027	-0.029	0.053	0.053	0.058
`sb_cost_ln`	-0.074***	-0.078***	-0.079***	0.033	0.033	0.034
`sb_capital_ln`	-0.003	0.005	0.003	0.320	0.320	0.320
`con_proced`	-1.339***	-1.293***	-1.279***	0.348	0.348	0.395
`con_days`	-67.590***	-67.839***	-67.357***	-1.605	-1.605	-1.585
Fixed year effects	No	No	No	Yes	Yes	Yes

cat(tbl_year_fe, 
    file = file.path(here(), "output", "tables", "app_table_E1.md"))

Note: Main controls are gdpcap_ln_lag, gdpgrowth_lag, polity_lag, and pop_ln_lag

Table E2: Sub-indicators for “Starting a Business” with and without year fixed effects

sb_fe_year <- year_fe_models_all %>%
  filter(str_detect(outcome, "sb_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

sb_year_fe_models_out <- stargazer(sb_fe_year$model, type = "html", 
                                   dep.var.caption = "",
                                   se = sb_fe_year$ses_only,
                                   notes = c("OLS models. Robust standard errors clustered by country.",
                                             "Models include countries present in the 2004 EDB report."),
                                   keep.stat = c("n", "rsq", "adj.rsq"),
                                   add.lines = list(c("Year fixed effects", 
                                                      rep(c("No", "Yes"), 4))),
                                   omit = "year")

cat(paste(escape_stars(sb_year_fe_models_out), collapse = "\n"), "\n")


	sb_proced		sb_days_ln		sb_cost_ln		sb_capital_ln
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)

sb_proced_lag	0.920^***	0.920^***
	(0.013)	(0.015)

sb_days_ln_lag			0.931^***	0.932^***
			(0.011)	(0.012)

sb_cost_ln_lag					0.948^***	0.950^***
					(0.009)	(0.009)

sb_capital_ln_lag							0.924^***	0.926^***
							(0.009)	(0.009)

ranked_lag	-0.251^***	0.102	-0.029	0.053	-0.079^***	0.033	0.003	0.320
	(0.069)	(0.335)	(0.019)	(0.083)	(0.018)	(0.052)	(0.045)	(0.281)

gdpcap_ln_lag	-0.017	-0.038^*	-0.015	-0.014^**	-0.027^**	-0.032^***	-0.030^*	-0.017
	(0.037)	(0.022)	(0.009)	(0.006)	(0.012)	(0.009)	(0.018)	(0.012)

gdpgrowth_lag	-0.021^***	-0.020^***	-0.007^***	-0.007^***	-0.013^***	-0.011^***	-0.008	-0.009^*
	(0.006)	(0.006)	(0.002)	(0.002)	(0.002)	(0.002)	(0.005)	(0.005)

polity_lag	-0.005	-0.006	-0.002	-0.002^*	0.0004	0.0001	0.004	0.004
	(0.006)	(0.006)	(0.001)	(0.001)	(0.001)	(0.001)	(0.004)	(0.004)

pop_ln_lag	0.041^*	0.058^**	0.006	0.008^*	0.008	0.011^**	-0.009	-0.003
	(0.024)	(0.023)	(0.006)	(0.005)	(0.005)	(0.005)	(0.012)	(0.011)

civtot_lag	0.028		0.004		0.004		0.014
	(0.026)		(0.005)		(0.006)		(0.014)

inttot_lag	0.076^*		-0.004		0.031^**		-0.087
	(0.041)		(0.016)		(0.012)		(0.118)

loan_bin_lag	0.103		-0.006		0.036		-0.063
	(0.119)		(0.029)		(0.032)		(0.058)

Constant	0.038	-0.095	0.204	0.112	0.213	0.257^*	0.432^*	-0.004
	(0.457)	(0.426)	(0.137)	(0.121)	(0.159)	(0.140)	(0.258)	(0.238)


Year fixed effects	No	Yes	No	Yes	No	Yes	No	Yes
Observations	1,659	1,660	1,659	1,660	1,659	1,660	1,558	1,559
R²	0.909	0.910	0.902	0.903	0.963	0.963	0.904	0.906
Adjusted R²	0.909	0.909	0.902	0.902	0.963	0.963	0.903	0.905

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2004 EDB report.

cat(paste(sb_year_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_E2.html"))

Table E3: Sub-indicators for “Enforcing Contracts” with and without year fixed effects

con_fe_year <- year_fe_models_all %>%
  filter(str_detect(outcome, "con_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

con_year_fe_models_out <- stargazer(con_fe_year$model, type = "html", 
                                    dep.var.caption = "",
                                    se = con_fe_year$ses_only,
                                    notes = c("OLS models. Robust standard errors clustered by country.",
                                              "Models include countries present in the 2004 EDB report."),
                                    keep.stat = c("n", "rsq", "adj.rsq"),
                                    add.lines = list(c("Year fixed effects", 
                                                       rep(c("No", "Yes"), 4))),
                                    omit = "year")

cat(paste(escape_stars(con_year_fe_models_out), collapse = "\n"), "\n")


	con_proced		con_days
	(1)	(2)	(3)	(4)

con_proced_lag	0.708^***	0.780^***
	(0.023)	(0.032)

con_days_lag			0.903^***	0.971^***
			(0.015)	(0.008)

ranked_lag	-1.279^***	0.348	-67.357^***	-1.605
	(0.258)	(0.506)	(5.427)	(3.386)

gdpcap_ln_lag	-0.383^***	-0.167	-5.263	-3.550^**
	(0.145)	(0.104)	(3.237)	(1.559)

gdpgrowth_lag	-0.038	-0.016	-2.627^***	-1.292^***
	(0.024)	(0.025)	(0.676)	(0.463)

polity_lag	-0.112^***	-0.083^**	1.219^**	0.573
	(0.039)	(0.032)	(0.525)	(0.376)

pop_ln_lag	0.023	0.124	2.358	3.673^**
	(0.109)	(0.087)	(2.561)	(1.839)

civtot_lag	0.208^**		9.935^**
	(0.101)		(4.295)

inttot_lag	0.292		-2.168
	(0.393)		(3.915)

loan_bin_lag	-0.371		3.842
	(0.487)		(11.148)

Constant	15.702^***	4.800^***	124.001^***	17.250
	(2.421)	(1.757)	(42.007)	(35.766)


Year fixed effects	No	Yes	No	Yes
Observations	1,634	1,635	1,633	1,634
R²	0.725	0.836	0.868	0.923
Adjusted R²	0.723	0.834	0.867	0.922

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Robust standard errors clustered by country.
	Models include countries present in the 2004 EDB report.

cat(paste(con_year_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_E3.html"))

Description of Reform Coding

Not generated with this script.

What explains reform committees?

Table F1: Correlates of reform committee presence

model_committee = glm(has_bureau ~ p_edb_rank + icrg_index + 
                        gdpcap + gdpgrowth + fdi_inper + trade + log1p(ibrd) +
                        polity + yrsoffc,
                      data = filter(edb_clean, year == 2008),
                      family = binomial(link = "logit"))

model_committee_robust <- robust_clusterify(model_committee, 
                                            filter(edb_clean, year == 2008), "ccode") %>%
  magrittr::use_series(coefs) %>% tidy()

model_committee_out <- stargazer(model_committee, type = "html", 
                                 dep.var.caption = "",
                                 dep.var.labels = "has\\_bureau",
                                 se = list(model_committee_robust$std.error),
                                 notes = "Logsitic regression model. Year restricted to 2008. Robust standard errors clustered by country.")

cat(paste(escape_stars(model_committee_out), collapse = "\n"), "\n")


	has_bureau

p_edb_rank	0.010
	(0.009)

icrg_index	-0.003
	(0.041)

gdpcap	0.00005
	(0.0001)

gdpgrowth	-0.008
	(0.067)

fdi_inper	-0.029
	(0.043)

trade	0.003
	(0.011)

log1p(ibrd)	-0.125
	(0.185)

polity	-0.046
	(0.071)

yrsoffc	-0.107^**
	(0.053)

Constant	2.241
	(5.395)


Observations	68
Log Likelihood	-43.531
Akaike Inf. Crit.	107.063

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	Logsitic regression model. Year restricted to 2008. Robust standard errors clustered by country.

cat(paste(model_committee_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_F1.html"))

Figure F1: Correlates of reform committee presence

Coefficients from Table F1

committee_tidy_robust <- model_committee_robust %>%
  filter(term != "(Intercept)") %>%
  mutate(conf.low = estimate - std.error * qnorm(0.975),
         conf.high = estimate + std.error * qnorm(0.975)) %>%
  mutate_at(vars(estimate, conf.low, conf.high),
            funs(exp = exp(.))) %>%
  mutate(term = fct_inorder(term, ordered = TRUE))

plot_model_committee <- ggplot(committee_tidy_robust, 
                               aes(x = estimate_exp, y = fct_rev(term))) +
  geom_vline(xintercept = 1, colour = "black") +
  geom_pointrangeh(aes(xmin = conf.low_exp, xmax = conf.high_exp), size = 0.5) + 
  labs(x = "Odds ratio", y = NULL) +
  theme_edb()
plot_model_committee

ggsave(plot_model_committee, 
       filename = file.path(here(), "output", "figures", "app_committee_presence.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_model_committee, 
       filename = file.path(here(), "output", "figures", "app_committee_presence.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)

Table F2: Correlates of control variables from Models A1–A5

edb_checks_a2_a5 <- edb_checks_models %>%
  unnest(tidy_robust) %>% select(-grouping)

edb_checks_a1 <- model_in_2001_robust %>% mutate(outcome = "in_2001")

model_names <- tribble(
  ~model, ~outcome,
  "A",    "in_2001",
  "B",    "sb_days_ln",
  "C",    "sb_proced",
  "D",    "sb_cost_ln",
  "E",    "sb_capital_ln"
) %>%
  mutate(name_clean = paste0("Model ", model, "<br>", "`", outcome, "`"))

edb_checks_significance <- bind_rows(edb_checks_a1, edb_checks_a2_a5) %>%
  left_join(model_names, by = "outcome") %>%
  filter(term != "(Intercept)", !str_detect(term, "sb_")) %>%
  mutate(significance = case_when(
    p.value < 0.10 & sign(estimate) == 1 ~ "+",
    p.value < 0.10 & sign(estimate) == -1 ~ "−",
    TRUE ~ ""
  )) %>%
  mutate(term = paste0("`", term, "`"),
         term = fct_inorder(term, ordered = TRUE)) %>%
  select(` ` = term, significance, name_clean) %>%
  spread(name_clean, significance)

caption <- "Correlates of relevant variables with selection into 2001 sample and with outcomes"

tbl_edb_checks_sig <- pandoc.table.return(edb_checks_significance, 
                                          caption = caption, 
                                          split.tables = Inf,
                                          justify = "lccccc", missing = "", 
                                          style = "multiline",
                                          keep.line.breaks = TRUE)

cat(tbl_edb_checks_sig)

Correlates of relevant variables with selection into 2001 sample and with outcomes
	Model A `in_2001`	Model B `sb_days_ln`	Model C `sb_proced`	Model D `sb_cost_ln`	Model E `sb_capital_ln`
`gdpcap_ln`	+	−		−	−
`gdpgrowth`		−	−	−
`polity`	+			+	+
`pop_ln`	+		+	+
`inttot`	−
`civtot`	−
`loan_ln`

cat(tbl_edb_checks_sig, 
    file = file.path(here(), "output", "tables", "app_table_F2.md"))

Note: A negative coefficient for the outcome variables indicates improvements.

For models and measures, see section “How control variables relate to being in the sample and to the outcomes” above.

Table F3: Rankings in 2005 and 2014

edb_rankings <- edb_clean %>%
  select(ccode, year, p_edb_rank, has_bureau) %>%
  filter(year %in% c(2005, 2014)) %>%
  spread(year, p_edb_rank) %>%
  mutate(change_in_ranking = `2014` - `2005`)

model_rankings <- lm(`2014` ~ `2005` + has_bureau,
                     data = edb_rankings)

model_rankings_interaction <- lm(`2014` ~ `2005` + has_bureau + `2005` * has_bureau,
                                 data = edb_rankings)

model_rankings_change <- lm(change_in_ranking ~ has_bureau,
                            data = edb_rankings)

var_labs <- c("2005 ranking", "Has reform committee",
              "2005 ranking × has reform committee")

model_rankings_out <- stargazer(model_rankings, model_rankings_interaction, 
                                model_rankings_change,
                                type = "html",
                                dep.var.caption = "",
                                dep.var.labels = c("2014", "Change in ranking"),
                                covariate.labels = var_labs,
                                notes = "OLS models. Non-robust standard errors.",
                                keep.stat = c("n", "rsq", "adj.rsq"))

cat(paste(escape_stars(model_rankings_out), collapse = "\n"), "\n")


	2014		Change in ranking
	(1)	(2)	(3)

2005 ranking	1.041^***	1.069^***
	(0.062)	(0.071)

Has reform committee	-20.647^***	-10.934	-19.737^***
	(5.963)	(13.842)	(5.790)

2005 ranking × has reform committee		-0.112
		(0.143)

Constant	14.496^***	12.555^**	17.374^***
	(5.409)	(5.964)	(3.212)


Observations	143	143	143
R²	0.670	0.671	0.076
Adjusted R²	0.665	0.664	0.070

Note:	^p<0.1; ^p<0.05; ^**p<0.01
	OLS models. Non-robust standard errors.

cat(paste(model_rankings_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_F3.html"))

India experiment survey text

Not generated with this script.

Investor experiment survey text

Not generated with this script.

---
title: "Appendix analysis"
author: "Andrew Heiss"
date: "Last run: `r format(Sys.time(), '%B %e, %Y')`"
output:
  html_document:
    code_folding: hide
editor_options:
  chunk_output_type: console
---

```{r setup, message=FALSE}
knitr::opts_chunk$set(cache = FALSE, fig.retina = 2,
                      tidy.opts = list(width.cutoff = 120),  # For code
                      width = 120)  # For output
```

```{r load-libraries-data-functions, message=FALSE, warning=FALSE}
library(tidyverse)
library(ggstance)
library(broom)
library(countrycode)
library(pander)
library(stargazer)
library(skimr)
library(here)

# By default, R uses polynomial contrasts for ordered factors in linear models
# options("contrasts") 
# So make ordered factors use treatment contrasts instead
options(contrasts = rep("contr.treatment", 2))
# Or do it on a single variable:
# contrasts(df$x) <- "contr.treatment"

# Load pre-cleaned data
edb_clean <- read_rds(file.path(here(), "output", "data", "edb_clean.rds"))
edb_reforms <- read_rds(file.path(here(), "output", "data", "edb_reforms.rds"))

# Load helpful functions
source(file.path(here(), "lib", "model_stuff.R"))
source(file.path(here(), "lib", "graphics_stuff.R"))
```


# Variable descriptions

```{r tbl-vars-description, results="asis", warning=FALSE, message=FALSE}
edb_summary_base <- read_csv(file.path(here(), "data_raw", "variable-summary-base.csv"))

edb_var_descriptions <- edb_summary_base %>%
  mutate(Variable = paste0("`", Variable, "`"))

caption <- "Description of variables used in analysis"

tbl_var_descriptions <- pandoc.table.return(edb_var_descriptions, 
                                            caption = caption, 
                                            split.tables = Inf,
                                            justify = "lll", missing = "")

cat(tbl_var_descriptions)
cat(tbl_var_descriptions, 
    file = file.path(here(), "output", "tables", "app_table_var_desc.md"))
```


# Variable summaries

The small inline histograms in this table will only display correctly when they use a font that supports block elements, such as [DejaVu Sans](https://dejavu-fonts.github.io/) or Arial.

```{r tbl-vars-summary, results="asis", warning=FALSE, message=FALSE}
edb_summary_stats <- edb_clean %>%
  select(one_of(edb_summary_base$Variable)) %>%
  gather(Variable, value) %>%
  filter(!is.na(value)) %>%
  group_by(Variable) %>%
  summarize(N = n(),
            Mean = mean(value),
            `Std. Dev` = sd(value),
            Min = min(value),
            Max = max(value),
            Distribution = inline_hist(value)) 

edb_summary <- edb_summary_base %>%
  left_join(edb_summary_stats, by = "Variable") %>%
  mutate(Variable = paste0("`", Variable, "`")) %>%
  select(-Definition, -Source)

caption <- "Summary statistics of variables used in analysis"

tbl_edb_summary <- pandoc.table.return(edb_summary, caption = caption, 
                                       big.mark = ",", split.tables = Inf,
                                       justify = "lcccccc")

cat(tbl_edb_summary)
cat(tbl_edb_summary, 
    file = file.path(here(), "output", "tables", "app_table_var_summary.md"))
```


# List of countries in initial 2001 report

\* indicates country has an EDB reform committee by 2015

```{r tbl-countries, warning=FALSE, message=FALSE, results="asis"}
edb_bureaus <- read_csv(file.path(here(), "output", "data", "edb_bureaus.csv"))

country_names <- edb_clean %>%
  filter(in_2001 == 1) %>%
  group_by(ccode) %>% 
  summarize(Country = first(country_name)) %>% 
  ungroup() %>% 
  mutate(has_committee = ifelse(ccode %in% edb_bureaus$cowcode, "\\*", "")) %>%
  arrange(Country) %>%
  mutate(Country = paste0(Country, has_committee)) %>%
  select(Country)

caption <- "Countries in 2001 report"

tbl_countries <- pandoc.table.return(matrix(c(country_names$Country, rep(NA, 2)), 
                                            ncol = 4), caption = caption,
                                     split.tables = Inf, missing = "", justify = "llll")

cat(tbl_countries)
cat(tbl_countries, 
    file = file.path(here(), "output", "tables", "app_table_countries.md"))
```


# How control variables relate to being in the sample and to the outcomes

## Model A1

```{r model-in-2001, results="hide"}
edb_in_2001 <- edb_clean %>%
  filter(year > 2000) %>%
  group_by(ccode) %>%
  mutate(in_2001_lead = lead(in_2001),
         in_2001_lead = factor(in_2001_lead, levels = 0:1, 
                               labels = c("Not in 2001", "In 2001")))

model_in_2001 <- glm(in_2001_lead ~ gdpcap_ln + gdpgrowth + polity + pop_ln + 
                        inttot + civtot + loan_ln, 
                      data = edb_in_2001, family = binomial(link = "logit")) 

model_in_2001_robust <- robust_clusterify(model_in_2001, edb_in_2001, "ccode") %>%
  magrittr::use_series(coefs) %>% tidy()

model_in_2001_out <- stargazer(model_in_2001, type = "html", dep.var.caption = "",
                               dep.var.labels = "in\\_2001\\_lead",
                               se = list(model_in_2001_robust$std.error),
                               notes = c("Logistic regression model. Robust standard errors clustered by country.", 
                                         "Models include countries present in the 2001 EDB report."))
```

```{r tbl-model-in-2001, results="asis"}
cat(paste(escape_stars(model_in_2001_out), collapse = "\n"), "\n")
cat(paste(model_in_2001_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_model_A1.html"))
```

## Models A2–A5

```{r model-check-sb, results="hide"}
# This function generates an R formula based on a given outcome and its lead. 
# Given "sb_days_ln", it will create and run "sb_days_ln_lead ~ sb_days_ln + ..."
run_leaded_ols_check <- function(outcome, df) {
  outcome_lead <- paste0(outcome, "_lead")
  
  form <- as.formula(paste0(outcome_lead, " ~ ", outcome, 
                          " + gdpcap_ln + gdpgrowth + polity + ", 
                          "pop_ln + inttot + civtot + loan_ln"))
  
  lm(form, data = df)
}

# Define all the models that need to be run
models_to_run <- data_frame(outcome = c("sb_days_ln", "sb_proced",
                                        "sb_cost_ln", "sb_capital_ln"),
                            grouping = 1)  # Temporary variable for joining

edb_checks <- edb_clean %>%
  filter(year > 2000, in_2001 == 1) %>%
  mutate(grouping = 1) %>%
  group_by(ccode) %>%
  mutate_at(vars(sb_days_ln, sb_proced, sb_cost_ln, sb_capital_ln),
            funs(lead = lead(.))) %>%
  group_by(grouping) %>%
  nest() %>%
  right_join(models_to_run, by = "grouping")

# Run all the models within the data frame
edb_checks_models <- edb_checks %>%
  mutate(model = pmap(.l = list(outcome, data), run_leaded_ols_check),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

edb_checks_models_out <- stargazer(edb_checks_models$model, type = "html", 
                                   dep.var.caption = "",
                                   se = edb_checks_models$ses_only,
                                   notes = c("OLS models. Robust standard errors clustered by country.",
                                             "Models include countries present in the 2001 EDB report."),
                                   keep.stat = c("n", "rsq", "adj.rsq"))
```

```{r tbl-model-check-sb, results="asis"}
cat(paste(escape_stars(edb_checks_models_out), collapse = "\n"), "\n")
cat(paste(edb_checks_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_A2-5.html"))
```


# Starting a Business indicators

*Not generated with this script.*


# Enforcing Contracts indicators

*Not generated with this script.*


# Policy reform: OLS models

```{r models-ols-all}
# Define all the models that need to be run
models_to_run <- expand.grid(year = 2003:2013,
                             outcome = c("sb_proced", "sb_days_ln",
                                         "sb_cost_ln", "sb_capital_ln"),
                             grouping = c("All countries", 
                                          "No EDB reform committee", 
                                          "Special EDB reform committee"),
                             stringsAsFactors = FALSE)

# As in the paper analysis, this function generates an R model formula based on
# the name of the dependent variable and the year provided. i.e., given
# "sb_proced" and "2005", it will create the formula "sb_proced ~ sb_proced_lag
# + ranked_2005" and run the model
run_lagged_ols_model <- function(outcome, year, df) {
  outcome_lag <- paste0(outcome, "_lag")
  year_variable <- paste0("ranked_", year)

  form <- as.formula(paste0(outcome, " ~ ", outcome_lag, " + ", year_variable))

  lm(form, data = df)
}

dfs_split <- edb_clean %>%
  filter(in_2001 == 1) %>%
  group_by(has_bureau) %>%
  nest() %>%
  mutate(has_bureau = as.character(has_bureau)) %>%
  rename(grouping = has_bureau)

df_all <- edb_clean %>%
  filter(in_2001 == 1) %>%
  mutate(grouping = "All countries") %>%
  group_by(grouping) %>%
  nest()

models_to_run_full <- bind_rows(dfs_split, df_all) %>%
  right_join(models_to_run, by = "grouping")

# Run all the models within the data frame
ols_models_lagged <- models_to_run_full %>%
  mutate(model = pmap(.l = list(outcome, year, data), run_lagged_ols_model),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Extract the ranking coefficients from all models
ols_coefs <- ols_models_lagged %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  # Only look at the ranked* coefficients
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates, labels, and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         term = str_replace(term, "\\.\\d+TRUE", "")) %>%
  # Get rid of extra columns
  select(-c(estimate, std.error, statistic, p.value)) %>%
  spread(outcome, value) %>%
  # Give table clean column names
  select(Subset = grouping, Year = year,
         Procedures = sb_proced, `Cost (log)` = sb_cost_ln,
         `Days (log)` = sb_days_ln, `Capital (log)` = sb_capital_ln)
```


## Table 3 from the paper: summary of coefficients for 8 OLS models

Full models in tables B1–3 below.

```{r tbl-ols-in-paper, results="asis"}
tbl_ols_paper <- ols_coefs %>%
  filter(Subset == "All countries", Year %in% c(2005, 2006))

caption <- 'Summary of β~2~ coefficients (i.e. "ranked_200x") for difference models'

tbl_ols <- pandoc.table.return(tbl_ols_paper, caption = caption)

cat(tbl_ols)
cat(tbl_ols, file = file.path(here(), "output", "tables", "app_table_ols.md"))
```

## Table B1: 2005 analysis, full OLS models

```{r tbl-ols-2005-2006, results="hide"}
tbl_ols_2005 <- ols_models_lagged %>%
  filter(grouping == "All countries", year == 2005)

tbl_ols_2005_out <- stargazer(tbl_ols_2005$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_ols_2005$ses_only,
                              notes = c("OLS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

tbl_ols_2006 <- ols_models_lagged %>%
  filter(grouping == "All countries", year == 2006)

tbl_ols_2006_out <- stargazer(tbl_ols_2006$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_ols_2006$ses_only,
                              notes = c("OLS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))
```

```{r tbl-ols-2005, results="asis"}
cat(paste(escape_stars(tbl_ols_2005_out), collapse = "\n"), "\n")
cat(paste(tbl_ols_2005_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_B1.html"))
```

## Table B2: 2006 analysis, full OLS models

```{r tbl-ols-2006, results="asis"}
cat(paste(escape_stars(tbl_ols_2006_out), collapse = "\n"), "\n")
cat(paste(tbl_ols_2006_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_B2.html"))
```

## Table B3: Cutpoints at every possible year, OLS models

```{r tbl-ols-all, results="asis"}
caption <- 'Summary of β~2~ coefficients (i.e. "ranked.200x") for difference models for all years'

tbl_ols_all <- pandoc.table.return(ols_coefs, caption = caption,
                                     split.tables = Inf, missing = "—")

cat(tbl_ols_all)
cat(tbl_ols_all, 
    file = file.path(here(), "output", "tables", "app_models_B3_all.md"))
```


# Policy reform: ITS models

```{r models-its-all}
run_its_model <- function(outcome, year, df) {
  year_variable <- paste0("ranked_", year)
  year_centered <- paste0("year_centered_", year)
  
  form <- as.formula(paste0(outcome, " ~ ", year_centered, " + ", year_variable,
                            " + ", year_centered, " * ", year_variable))
  
  lm(form, data = df)
}

# Run all the models within the data frame
its_models_lagged <- models_to_run_full %>%
  mutate(model = pmap(.l = list(outcome, year, data), run_its_model),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Extract the ranking coefficients from all models
its_coefs <- its_models_lagged %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  # Only look at the coefficients from interaction terms (they have ":" in their names)
  filter(str_detect(term, ":")) %>%
  # Clean up the estimates, labels, and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         term = str_replace(term, "(.+)\\.\\d+:(.+)\\.\\d+TRUE", "\\1 × \\2")) %>%
  # Get rid of extra columns
  select(-c(estimate, std.error, statistic, p.value)) %>%
  spread(outcome, value) %>%
  # Give table clean column names
  select(Subset = grouping, Year = year,
         Procedures = sb_proced, `Cost (log)` = sb_cost_ln,
         `Days (log)` = sb_days_ln, `Capital (log)` = sb_capital_ln)
```

## Table 4 from the paper: summary of coefficients for 8 ITS models

Full models in tables C1–3 below.

```{r tbl-its-in-paper, results="asis"}
tbl_its_paper <- its_coefs %>%
  filter(Subset == "All countries", Year %in% c(2005, 2006))

caption <- 'Summary of β~3~ coefficients (i.e. "year.centered.200x × ranked.200x") for ITS models'

tbl_its <- pandoc.table.return(tbl_its_paper, caption = caption)

cat(tbl_its)
cat(tbl_its, file = file.path(here(), "output", "tables", "app_table_its.md"))
```

## Table C1: 2005 analysis, full ITS models

```{r tbl-its-2005-2006, results="hide"}
tbl_its_2005 <- its_models_lagged %>%
  filter(grouping == "All countries", year == 2005)

tbl_its_2005_out <- stargazer(tbl_its_2005$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_its_2005$ses_only,
                              notes = c("ITS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))

tbl_its_2006 <- its_models_lagged %>%
  filter(grouping == "All countries", year == 2006)

tbl_its_2006_out <- stargazer(tbl_its_2006$model, type = "html", 
                              dep.var.caption = "",
                              se = tbl_its_2006$ses_only,
                              notes = c("ITS models. Robust standard errors clustered by country.", 
                                        "Models include countries present in the 2001 EDB report."),
                              keep.stat = c("n", "rsq", "adj.rsq"))
```

```{r tbl-its-2005, results="asis"}
cat(paste(escape_stars(tbl_its_2005_out), collapse = "\n"), "\n")
cat(paste(tbl_its_2005_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_C1.html"))
```

## Table C2: 2006 analysis, full ITS models

```{r tbl-its-2006, results="asis"}
cat(paste(escape_stars(tbl_its_2006_out), collapse = "\n"), "\n")
cat(paste(tbl_its_2006_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_models_C2.html"))
```

## Table C3: Cutpoints at every possible year, ITS models

```{r tbl-its-all, results="asis"}
caption <- 'Summary of β~3~ coefficients (i.e. "year.centered.200x × ranked.200x") for ITS models for all years'

tbl_its_all <- pandoc.table.return(its_coefs, caption = caption,
                                   split.tables = Inf, missing = "—")

cat(tbl_its_all)
cat(tbl_its_all, 
    file = file.path(here(), "output", "tables", "app_models_C3_all.md"))
```


# Country fixed effects for OLS models

## Table D1: "Ranked" coefficients with and without country fixed effects

```{r ranked-country-fe, warning=FALSE}
# All the FE models to be run
country_fe_models <- expand.grid(outcome = c("sb_proced", "sb_days_ln",
                                             "sb_cost_ln", "sb_capital_ln",
                                             "con_proced", "con_days"),
                                 controls = c("Main", 
                                              "Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`", 
                                              "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`", 
                                              "Main + fixed effects",
                                              "Main - 2001", "Main - 2001 & 2002"),
                                 stringsAsFactors = FALSE) %>%
  mutate(country_fe = controls %in% c("Main + fixed effects", "Main - 2001", "Main - 2001 & 2002"),
         df_to_use = case_when(
           controls == "Main - 2001" ~ "Drop 2001",
           controls == "Main - 2001 & 2002" ~ "Drop 2002",
           TRUE ~ "Full" 
         ))

# Nest filtered data into a data frame
data_to_use <- tribble(
  ~df_to_use,  ~data,
  "Full",      edb_clean %>% filter(year > 2000, in_2004 == 1),
  "Drop 2001", edb_clean %>% filter(year > 2001, in_2004 == 1),
  "Drop 2002", edb_clean %>% filter(year > 2002, in_2004 == 1)
)

# Combine filtered data with all model parameters
country_fe_models_full <- data_to_use %>%
  right_join(country_fe_models, by = "df_to_use") %>%
  select(-df_to_use)

# Build and run a bunch of different models based on parameters that determine
# what controls to add and whether or not to include country fixed effects
run_country_fe_models <- function(outcome, controls, country_fe, df) {
  main_controls <- "gdpcap_ln_lag + gdpgrowth_lag + polity_lag + pop_ln_lag"
  
  if (str_detect(controls, "loan_ln_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_ln_lag"
  } else if (str_detect(controls, "loan_bin_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_bin_lag"
  } else {
    additional_controls <- ""
  }
  
  if (country_fe) {
    country <- " + as.factor(ccode)"
  } else {
    country <- ""
  }

  form <- paste0(outcome, " ~ ", paste0(outcome, "_lag"), " + ranked_lag + ", 
                 main_controls, additional_controls, country) %>%
    as.formula()

  lm(form, data = df)
}

# Run all the models within the data frame
country_fe_models_all <- country_fe_models_full %>%
  mutate(model = pmap(.l = list(outcome, controls, country_fe, data), 
                      run_country_fe_models),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Make tiny data frame indicating if model had country fixed effects
fixed_effects_country <- country_fe_models %>%
  select(controls, country_fe) %>%
  distinct() %>%
  mutate(country_fe = ifelse(country_fe, "Yes", "No")) %>%
  spread(controls, country_fe) %>%
  mutate(Outcome = "Fixed country effects")

# Display all the ranked coefficients
ranked_coefs_country <- country_fe_models_all %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         outcome = factor(outcome, levels = unique(country_fe_models$outcome),
                          labels = paste0("`", unique(country_fe_models$outcome), "`"),
                          ordered = TRUE),
         controls = factor(controls, levels = unique(country_fe_models$controls),
                           ordered = TRUE)) %>%
  # Get rid of extra columns
  select(Outcome = outcome, controls, value) %>%
  spread(controls, value) %>%
  bind_rows(fixed_effects_country)
```

```{r tbl-country-fe-ranked, results="asis"}
caption <- 'Summary of coefficients for the lagged "Ranked" variable with and without country fixed effects'

tbl_country_fe <- pandoc.table.return(ranked_coefs_country, caption = caption,
                                      split.tables = Inf)

cat(tbl_country_fe)
cat(tbl_country_fe, 
    file = file.path(here(), "output", "tables", "app_table_D1.md"))
```

*Note*: Main controls are `gdpcap_ln_lag`, `gdpgrowth_lag`, `polity_lag`, and `pop_ln_lag`


## Table D2: Sub-indicators for "Starting a Business" with and without country fixed effects

```{r sb-country-fe, results="hide"}
sb_fe <- country_fe_models_all %>%
  filter(str_detect(outcome, "sb_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

sb_country_fe_models_out <- stargazer(sb_fe$model, type = "html", 
                                      dep.var.caption = "",
                                      se = sb_fe$ses_only,
                                      notes = c("OLS models. Robust standard errors clustered by country.",
                                                "Models include countries present in the 2004 EDB report."),
                                      keep.stat = c("n", "rsq", "adj.rsq"),
                                      add.lines = list(c("Country fixed effects", 
                                                         rep(c("No", "Yes"), 4))),
                                      omit = "ccode")
```

```{r tbl-sb-country-fe, results="asis"}
cat(paste(escape_stars(sb_country_fe_models_out), collapse = "\n"), "\n")
cat(paste(sb_country_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_D2.html"))
```

## Table D3: Sub-indicators for "Enforcing Contracts" with and without country fixed effects

```{r con-country-fe, results="hide"}
con_fe <- country_fe_models_all %>%
  filter(str_detect(outcome, "con_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

con_country_fe_models_out <- stargazer(con_fe$model, type = "html", 
                                       dep.var.caption = "",
                                       se = con_fe$ses_only,
                                       notes = c("OLS models. Robust standard errors clustered by country.",
                                                 "Models include countries present in the 2004 EDB report."),
                                       keep.stat = c("n", "rsq", "adj.rsq"),
                                       add.lines = list(c("Country fixed effects", 
                                                          rep(c("No", "Yes"), 4))),
                                       omit = "ccode")
```

```{r tbl-con-country-fe, results="asis"}
cat(paste(escape_stars(con_country_fe_models_out), collapse = "\n"), "\n")
cat(paste(con_country_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_D3.html"))
```


# Year fixed effects for OLS models

## Table E1: "Ranked" coefficients with and without year fixed effects

```{r ranked-year-fe, warning=FALSE}
# All the FE models to be run
year_fe_models <- expand.grid(outcome = c("sb_proced", "sb_days_ln",
                                          "sb_cost_ln", "sb_capital_ln",
                                          "con_proced", "con_days"),
                              controls = c("Main", 
                                           "Main + `civtot_lag` + `inttot_lag` + `loan_ln_lag`", 
                                           "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`", 
                                           "Main + fixed effects",
                                           "Main - 2001", "Main - 2001 & 2002"),
                              stringsAsFactors = FALSE) %>%
  mutate(year_fe = controls %in% c("Main + fixed effects", "Main - 2001", "Main - 2001 & 2002"),
         df_to_use = case_when(
           controls == "Main - 2001" ~ "Drop 2001",
           controls == "Main - 2001 & 2002" ~ "Drop 2002",
           TRUE ~ "Full" 
         ))

# Nest filtered data into a data frame
data_to_use <- tribble(
  ~df_to_use,  ~data,
  "Full",      edb_clean %>% filter(year > 2000, in_2004 == 1),
  "Drop 2001", edb_clean %>% filter(year > 2001, in_2004 == 1),
  "Drop 2002", edb_clean %>% filter(year > 2002, in_2004 == 1)
)

# Combine filtered data with all model parameters
year_fe_models_full <- data_to_use %>%
  right_join(year_fe_models, by = "df_to_use") %>%
  select(-df_to_use)

# Build and run a bunch of different models based on parameters that determine
# what controls to add and whether or not to include country fixed effects
run_year_fe_models <- function(outcome, controls, year_fe, df) {
  main_controls <- "gdpcap_ln_lag + gdpgrowth_lag + polity_lag + pop_ln_lag"
  
  if (str_detect(controls, "loan_ln_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_ln_lag"
  } else if (str_detect(controls, "loan_bin_lag")) {
    additional_controls <- " + civtot_lag + inttot_lag + loan_bin_lag"
  } else {
    additional_controls <- ""
  }
  
  if (year_fe) {
    year <- " + as.factor(year)"
  } else {
    year <- ""
  }

  form <- paste0(outcome, " ~ ", paste0(outcome, "_lag"), " + ranked_lag + ", 
                 main_controls, additional_controls, year) %>%
    as.formula()

  lm(form, data = df)
}

# Run all the models within the data frame
year_fe_models_all <- year_fe_models_full %>%
  mutate(model = pmap(.l = list(outcome, controls, year_fe, data), 
                      run_year_fe_models),
         # Add robust clustered SEs
         robust_se = pmap(.l = list(model, data, "ccode"), robust_clusterify),
         # Add model summary statistics
         glance = model %>% map(glance),
         # Add a data frame of model parameters with correct SEs
         tidy_robust = robust_se %>% map(~ tidy(.$coef)),
         ses_only = tidy_robust %>% map(~ .$std.error))

# Make tiny data frame indicating if model had country fixed effects
fixed_effects_year <- year_fe_models %>%
  select(controls, year_fe) %>%
  distinct() %>%
  mutate(year_fe = ifelse(year_fe, "Yes", "No")) %>%
  spread(controls, year_fe) %>%
  mutate(Outcome = "Fixed year effects")

# Display all the ranked coefficients
ranked_coefs_year <- year_fe_models_all %>%
  # Spread out the model results
  unnest(tidy_robust) %>%
  filter(str_detect(term, "ranked")) %>%
  # Clean up the estimates and add stars
  mutate(value = paste0(sprintf("%.3f", round(estimate, 3)), p_stars(p.value)),
         outcome = factor(outcome, levels = unique(year_fe_models$outcome),
                          labels = paste0("`", unique(year_fe_models$outcome), "`"),
                          ordered = TRUE),
         controls = factor(controls, levels = unique(year_fe_models$controls),
                           ordered = TRUE)) %>%
  # Get rid of extra columns
  select(Outcome = outcome, controls, value) %>%
  spread(controls, value) %>%
  bind_rows(fixed_effects_year)
```

```{r tbl-year-fe-ranked, results="asis"}
caption <- 'Summary of coefficients for the lagged "Ranked" variable with and without country fixed effects'

tbl_year_fe <- pandoc.table.return(ranked_coefs_year, caption = caption,
                                   split.tables = Inf)

cat(tbl_year_fe)
cat(tbl_year_fe, 
    file = file.path(here(), "output", "tables", "app_table_E1.md"))
```

*Note*: Main controls are `gdpcap_ln_lag`, `gdpgrowth_lag`, `polity_lag`, and `pop_ln_lag`

## Table E2: Sub-indicators for "Starting a Business" with and without year fixed effects

```{r sb-year-fe, results="hide"}
sb_fe_year <- year_fe_models_all %>%
  filter(str_detect(outcome, "sb_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

sb_year_fe_models_out <- stargazer(sb_fe_year$model, type = "html", 
                                   dep.var.caption = "",
                                   se = sb_fe_year$ses_only,
                                   notes = c("OLS models. Robust standard errors clustered by country.",
                                             "Models include countries present in the 2004 EDB report."),
                                   keep.stat = c("n", "rsq", "adj.rsq"),
                                   add.lines = list(c("Year fixed effects", 
                                                      rep(c("No", "Yes"), 4))),
                                   omit = "year")
```

```{r tbl-sb-year-fe, results="asis"}
cat(paste(escape_stars(sb_year_fe_models_out), collapse = "\n"), "\n")
cat(paste(sb_year_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_E2.html"))
```

## Table E3: Sub-indicators for "Enforcing Contracts" with and without year fixed effects

```{r con-year-fe, results="hide"}
con_fe_year <- year_fe_models_all %>%
  filter(str_detect(outcome, "con_"),
         controls %in% c("Main + fixed effects", 
                         "Main + `civtot_lag` + `inttot_lag` + `loan_bin_lag`")) %>%
  mutate(outcome = fct_inorder(outcome, ordered = TRUE)) %>%
  arrange(outcome)

con_year_fe_models_out <- stargazer(con_fe_year$model, type = "html", 
                                    dep.var.caption = "",
                                    se = con_fe_year$ses_only,
                                    notes = c("OLS models. Robust standard errors clustered by country.",
                                              "Models include countries present in the 2004 EDB report."),
                                    keep.stat = c("n", "rsq", "adj.rsq"),
                                    add.lines = list(c("Year fixed effects", 
                                                       rep(c("No", "Yes"), 4))),
                                    omit = "year")
```

```{r tbl-con-year-fe, results="asis"}
cat(paste(escape_stars(con_year_fe_models_out), collapse = "\n"), "\n")
cat(paste(con_year_fe_models_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_E3.html"))
```


# Description of Reform Coding

*Not generated with this script.*


# What explains reform committees?

## Table F1: Correlates of reform committee presence

```{r reform-committee-presence, results="hide"}
model_committee = glm(has_bureau ~ p_edb_rank + icrg_index + 
                        gdpcap + gdpgrowth + fdi_inper + trade + log1p(ibrd) +
                        polity + yrsoffc,
                      data = filter(edb_clean, year == 2008),
                      family = binomial(link = "logit"))

model_committee_robust <- robust_clusterify(model_committee, 
                                            filter(edb_clean, year == 2008), "ccode") %>%
  magrittr::use_series(coefs) %>% tidy()

model_committee_out <- stargazer(model_committee, type = "html", 
                                 dep.var.caption = "",
                                 dep.var.labels = "has\\_bureau",
                                 se = list(model_committee_robust$std.error),
                                 notes = "Logsitic regression model. Year restricted to 2008. Robust standard errors clustered by country.")
```

```{r tbl-reform-committee-presence, results="asis"}
cat(paste(escape_stars(model_committee_out), collapse = "\n"), "\n")
cat(paste(model_committee_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_F1.html"))
```

## Figure F1: Correlates of reform committee presence

Coefficients from Table F1

```{r fig-reform-committee-presence, fig.width=6, fig.height=4}
committee_tidy_robust <- model_committee_robust %>%
  filter(term != "(Intercept)") %>%
  mutate(conf.low = estimate - std.error * qnorm(0.975),
         conf.high = estimate + std.error * qnorm(0.975)) %>%
  mutate_at(vars(estimate, conf.low, conf.high),
            funs(exp = exp(.))) %>%
  mutate(term = fct_inorder(term, ordered = TRUE))

plot_model_committee <- ggplot(committee_tidy_robust, 
                               aes(x = estimate_exp, y = fct_rev(term))) +
  geom_vline(xintercept = 1, colour = "black") +
  geom_pointrangeh(aes(xmin = conf.low_exp, xmax = conf.high_exp), size = 0.5) + 
  labs(x = "Odds ratio", y = NULL) +
  theme_edb()
plot_model_committee

ggsave(plot_model_committee, 
       filename = file.path(here(), "output", "figures", "app_committee_presence.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_model_committee, 
       filename = file.path(here(), "output", "figures", "app_committee_presence.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)
```

## Table F2: Correlates of control variables from Models A1–A5

```{r tbl-sig-summary-a1-a5, results="asis"}
edb_checks_a2_a5 <- edb_checks_models %>%
  unnest(tidy_robust) %>% select(-grouping)

edb_checks_a1 <- model_in_2001_robust %>% mutate(outcome = "in_2001")

model_names <- tribble(
  ~model, ~outcome,
  "A",    "in_2001",
  "B",    "sb_days_ln",
  "C",    "sb_proced",
  "D",    "sb_cost_ln",
  "E",    "sb_capital_ln"
) %>%
  mutate(name_clean = paste0("Model ", model, "<br>", "`", outcome, "`"))

edb_checks_significance <- bind_rows(edb_checks_a1, edb_checks_a2_a5) %>%
  left_join(model_names, by = "outcome") %>%
  filter(term != "(Intercept)", !str_detect(term, "sb_")) %>%
  mutate(significance = case_when(
    p.value < 0.10 & sign(estimate) == 1 ~ "+",
    p.value < 0.10 & sign(estimate) == -1 ~ "−",
    TRUE ~ ""
  )) %>%
  mutate(term = paste0("`", term, "`"),
         term = fct_inorder(term, ordered = TRUE)) %>%
  select(` ` = term, significance, name_clean) %>%
  spread(name_clean, significance)

caption <- "Correlates of relevant variables with selection into 2001 sample and with outcomes"

tbl_edb_checks_sig <- pandoc.table.return(edb_checks_significance, 
                                          caption = caption, 
                                          split.tables = Inf,
                                          justify = "lccccc", missing = "", 
                                          style = "multiline",
                                          keep.line.breaks = TRUE)

cat(tbl_edb_checks_sig)
cat(tbl_edb_checks_sig, 
    file = file.path(here(), "output", "tables", "app_table_F2.md"))
```

*Note*: A negative coefficient for the outcome variables indicates improvements.

For models and measures, see section "How control variables relate to being in the sample and to the outcomes" above.


## Table F3: Rankings in 2005 and 2014

```{r reform-committee-2005-2014, results="hide"}
edb_rankings <- edb_clean %>%
  select(ccode, year, p_edb_rank, has_bureau) %>%
  filter(year %in% c(2005, 2014)) %>%
  spread(year, p_edb_rank) %>%
  mutate(change_in_ranking = `2014` - `2005`)

model_rankings <- lm(`2014` ~ `2005` + has_bureau,
                     data = edb_rankings)

model_rankings_interaction <- lm(`2014` ~ `2005` + has_bureau + `2005` * has_bureau,
                                 data = edb_rankings)

model_rankings_change <- lm(change_in_ranking ~ has_bureau,
                            data = edb_rankings)

var_labs <- c("2005 ranking", "Has reform committee",
              "2005 ranking × has reform committee")

model_rankings_out <- stargazer(model_rankings, model_rankings_interaction, 
                                model_rankings_change,
                                type = "html",
                                dep.var.caption = "",
                                dep.var.labels = c("2014", "Change in ranking"),
                                covariate.labels = var_labs,
                                notes = "OLS models. Non-robust standard errors.",
                                keep.stat = c("n", "rsq", "adj.rsq"))
```

```{r tbl-reform-committee-2005-2014, results="asis"}
cat(paste(escape_stars(model_rankings_out), collapse = "\n"), "\n")
cat(paste(model_rankings_out, collapse = "\n"), "\n",
    file = file.path(here(), "output", "tables", "app_table_F3.html"))
```


# India experiment survey text

*Not generated with this script.*


# Investor experiment survey text

*Not generated with this script.*


Appendix analysis

Andrew Heiss

Last run: July 6, 2018

Variable descriptions

Variable summaries

List of countries in initial 2001 report

How control variables relate to being in the sample and to the outcomes

Model A1

Models A2–A5

Starting a Business indicators

Enforcing Contracts indicators

Policy reform: OLS models

Table 3 from the paper: summary of coefficients for 8 OLS models

Table B1: 2005 analysis, full OLS models

Table B2: 2006 analysis, full OLS models

Table B3: Cutpoints at every possible year, OLS models

Policy reform: ITS models

Table 4 from the paper: summary of coefficients for 8 ITS models

Table C1: 2005 analysis, full ITS models

Table C2: 2006 analysis, full ITS models

Table C3: Cutpoints at every possible year, ITS models

Country fixed effects for OLS models

Table D1: “Ranked” coefficients with and without country fixed effects

Table D2: Sub-indicators for “Starting a Business” with and without country fixed effects

Table D3: Sub-indicators for “Enforcing Contracts” with and without country fixed effects

Year fixed effects for OLS models

Table E1: “Ranked” coefficients with and without year fixed effects

Table E2: Sub-indicators for “Starting a Business” with and without year fixed effects

Table E3: Sub-indicators for “Enforcing Contracts” with and without year fixed effects

Description of Reform Coding

What explains reform committees?

Table F1: Correlates of reform committee presence

Figure F1: Correlates of reform committee presence

Table F2: Correlates of control variables from Models A1–A5

Table F3: Rankings in 2005 and 2014

India experiment survey text

Investor experiment survey text