First Steps

require(readxl) # may be unneccessary as I'm saving to .csv
#require(dplyr)
#require(tidyr)
require(tidyverse) # i think this gives me dplyr and tidyr
require(janitor) # clean names
require(ggplot2) # data viz
require(ggcorrplot) #cor matrix and plots
require(ivreg) # two stage least squares regression
require(kableExtra) # tables

Data Guide

GuideDF <- read_excel("DataGuide.xlsx") 
GuideDF
NA

Second Steps

Data Exploration

A Table of summary statistics of variables to include: * mean * sd * min-max * dot plots

Iteration 1: X1.csv cleaned data of 13 columns, 357 observations. Create DF with clean names

X1 <- read.csv("X1.csv") %>% 
           clean_names() #piping prevents an umlaut over the i
X1 # output be visible in R notebook; no need to View()
NA

Statistical Summary Table

Now, we make a statistical summary table. Instrumented variables are rounded in the table for mean, min and max. Perhaps they should be rounded in the DF itself.

Descriptive1 <- matrix( 
  round(
    c(
      mean(X1$hur),sd(X1$hur),min(X1$hur),max(X1$hur),
      
      mean(X1$taxcorp),  sd(X1$taxcorp), min(X1$taxcorp),max(X1$taxcorp),
      
      mean(X1$ud), sd(X1$ud),min(X1$ud),max(X1$ud),
      
      mean(X1$pslm),sd(X1$pslm),min(X1$pslm),max(X1$pslm),
      
      round(mean(X1$epl),0), sd(X1$epl), round(min(X1$epl),0),round(max(X1$epl),0),
      
      round(mean(X1$wsc),0), sd(X1$wsc), round(min(X1$wsc),0),round(max(X1$wsc),0),
      
      mean(X1$cbc), sd(X1$cbc),min(X1$cbc),max(X1$cbc),
      
      mean(X1$brr1y), sd(X1$brr1y), min(X1$brr1y),max(X1$brr1y),
      
      mean(X1$ttr), sd(X1$ttr),min(X1$ttr),max(X1$ttr),
      
      mean(X1$inf_gdpd),sd(X1$inf_gdpd),min(X1$inf_gdpd),max(X1$inf_gdpd)
       ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive1) <- c("Mean", "SD","Min","Max")
rownames(Descriptive1) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive1, caption = "Table 2: Variable Summary Statistics, All Observations") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

Now, we need to break down summary statistics by year. We will do this by creating a filtered df per year. These can come in handy in the future as well. This is going to be a fat chunk of code as I’m not going to take the time to learn how to automate. Code is in notebook but echo is false annual_data.R

Table 2.1: Annual Variable Summary Statistics - 2001
Mean SD Min Max
UR 7.17 4.17 3.08 19.46
TAXCORP 2.78 0.91 0.58 3.90
UD 29.58 17.16 12.90 73.90
PSLM 1.65 1.18 0.41 4.02
EPL 2.00 1.02 0.00 4.00
WSC 2.00 1.56 1.00 5.00
CBC 51.37 28.71 14.70 96.00
BRR1Y 48.62 23.42 9.00 86.00
TTR 34.40 5.65 25.53 45.92
Delta_i 3.38 2.86 -1.08 11.05
Table 2.2: Annual Variable Summary Statistics - 2002
Mean SD Min Max
UR 7.45 3.59 3.67 18.81
TAXCORP 3.00 1.00 0.99 4.95
UD 34.63 22.54 12.80 78.00
PSLM 1.79 1.15 0.42 4.05
EPL 2.00 1.14 0.00 5.00
WSC 3.00 1.53 1.00 5.00
CBC 59.66 29.70 14.50 96.00
BRR1Y 54.25 23.03 9.00 86.00
TTR 34.67 6.51 24.50 45.41
Delta_i 2.05 1.53 -1.39 4.19
Table 2.3: Annual Variable Summary Statistics - 2003
Mean SD Min Max
UR 7.08 2.08 4.78 11.49
TAXCORP 2.88 0.89 1.23 4.51
UD 28.58 17.48 12.40 72.40
PSLM 1.85 1.34 0.46 4.30
EPL 2.00 1.24 0.00 4.00
WSC 3.00 1.60 1.00 5.00
CBC 54.07 30.07 14.30 96.00
BRR1Y 52.42 24.92 9.00 86.00
TTR 33.50 6.36 24.14 45.58
Delta_i 1.83 1.38 -1.61 3.93
Table 2.4: Annual Variable Summary Statistics - 2004
Mean SD Min Max
UR 7.81 3.29 4.28 18.36
TAXCORP 3.42 1.78 1.51 9.69
UD 30.68 19.22 10.50 73.50
PSLM 1.74 1.24 0.36 4.23
EPL 2.00 1.06 0.00 4.00
WSC 3.00 1.37 1.00 5.00
CBC 63.09 28.53 13.80 100.00
BRR1Y 47.83 26.67 0.00 86.00
TTR 35.36 6.21 24.76 46.39
Delta_i 2.67 1.79 -1.12 5.84
Table 2.5: Annual Variable Summary Statistics - 2005
Mean SD Min Max
UR 6.55 2.03 3.83 11.28
TAXCORP 3.77 2.23 1.75 11.53
UD 32.84 19.23 12.00 75.70
PSLM 1.60 1.10 0.35 3.81
EPL 2.00 0.91 0.00 4.00
WSC 3.00 1.50 1.00 5.00
CBC 59.44 31.18 13.70 100.00
BRR1Y 46.94 25.95 0.00 86.00
TTR 36.48 6.46 25.83 48.00
Delta_i 2.34 2.12 -1.19 8.75
Table 2.6: Annual Variable Summary Statistics - 2006
Mean SD Min Max
UR 7.03 2.45 3.91 13.47
TAXCORP 3.50 0.98 2.10 6.28
UD 32.84 20.92 11.50 74.30
PSLM 1.57 0.99 0.33 3.26
EPL 2.00 1.08 0.00 4.00
WSC 3.00 1.42 1.00 5.00
CBC 63.69 29.07 13.10 100.00
BRR1Y 48.41 27.29 0.00 85.00
TTR 35.80 6.36 26.62 46.46
Delta_i 2.11 1.43 -0.86 5.12
Table 2.7: Annual Variable Summary Statistics - 2007
Mean SD Min Max
UR 6.27 2.20 3.58 11.23
TAXCORP 3.41 0.81 2.19 4.80
UD 29.56 18.57 11.60 70.80
PSLM 1.30 0.86 0.31 2.73
EPL 2.00 0.93 0.00 3.00
WSC 3.00 1.49 1.00 5.00
CBC 56.04 32.63 13.30 100.00
BRR1Y 43.81 26.13 0.00 84.00
TTR 35.96 5.97 26.73 46.42
Delta_i 2.27 1.61 -1.36 5.34
Table 2.8: Annual Variable Summary Statistics - 2008
Mean SD Min Max
UR 6.02 2.11 2.72 11.27
TAXCORP 3.52 2.02 1.69 11.99
UD 29.46 18.86 10.20 69.90
PSLM 1.28 0.76 0.32 2.66
EPL 2.00 0.92 0.00 4.00
WSC 3.00 1.27 1.00 5.00
CBC 61.02 29.65 13.10 100.00
BRR1Y 49.88 23.92 0.00 83.00
TTR 35.48 6.28 23.58 44.76
Delta_i 3.21 2.14 -0.91 10.41
Table 2.9: Annual Variable Summary Statistics - 2009
Mean SD Min Max
UR 8.57 3.41 3.63 17.87
TAXCORP 2.30 0.60 1.33 3.37
UD 30.63 20.13 7.60 72.50
PSLM 1.88 0.99 0.64 3.78
EPL 2.00 0.98 0.00 4.00
WSC 3.00 1.36 1.00 5.00
CBC 59.03 30.16 12.70 100.00
BRR1Y 46.90 24.73 0.00 83.00
TTR 34.14 6.95 22.56 44.96
Delta_i 0.75 1.95 -4.62 3.61
Table 2.10: Annual Variable Summary Statistics - 2010
Mean SD Min Max
UR 8.24 3.71 3.71 19.88
TAXCORP 2.67 0.99 1.31 5.75
UD 28.04 18.75 8.20 71.40
PSLM 1.71 1.01 0.54 3.99
EPL 2.00 0.87 0.00 4.00
WSC 3.00 1.21 1.00 5.00
CBC 57.42 31.56 12.20 100.00
BRR1Y 50.38 23.68 0.00 84.00
TTR 34.04 6.71 22.38 44.76
Delta_i 1.15 1.49 -1.88 4.65
Table 2.11: Annual Variable Summary Statistics - 2011
Mean SD Min Max
UR 8.37 3.97 3.41 21.41
TAXCORP 2.71 1.01 1.21 4.98
UD 26.50 17.27 7.00 68.70
PSLM 1.41 1.01 0.30 3.79
EPL 2.00 0.85 0.00 4.00
WSC 3.00 1.33 1.00 5.00
CBC 52.15 32.17 12.60 100.00
BRR1Y 46.23 25.11 0.00 83.00
TTR 33.46 6.54 21.12 44.79
Delta_i 1.61 1.55 -1.62 5.47
Table 2.12: Annual Variable Summary Statistics - 2012
Mean SD Min Max
UR 8.13 4.45 3.22 24.79
TAXCORP 2.92 1.92 1.23 10.33
UD 27.32 19.61 6.00 69.20
PSLM 1.39 1.00 0.33 3.81
EPL 2.00 0.77 0.00 3.00
WSC 3.00 1.41 1.00 5.00
CBC 53.26 31.56 12.50 100.00
BRR1Y 45.33 23.29 0.00 84.00
TTR 34.11 7.22 21.33 45.51
Delta_i 1.78 1.21 -0.76 4.07
Table 2.13: Annual Variable Summary Statistics - 2013
Mean SD Min Max
UR 8.26 5.05 3.10 26.12
TAXCORP 2.93 1.51 1.19 8.26
UD 27.18 18.99 10.10 68.80
PSLM 1.58 1.11 0.35 3.56
EPL 2.00 0.86 0.00 3.00
WSC 3.00 1.36 1.00 5.00
CBC 59.32 33.28 12.40 100.00
BRR1Y 48.53 24.95 0.00 84.00
TTR 34.99 8.08 19.86 45.89
Delta_i 1.24 0.71 -0.35 2.56
Table 2.14: Annual Variable Summary Statistics - 2014
Mean SD Min Max
UR 8.21 4.27 3.49 24.45
TAXCORP 2.79 1.20 1.37 6.64
UD 26.35 19.34 5.30 68.50
PSLM 1.41 0.98 0.28 3.43
EPL 2.00 0.76 0.00 3.00
WSC 3.00 1.33 1.00 5.00
CBC 52.70 31.08 8.70 100.00
BRR1Y 42.77 24.88 0.00 84.00
TTR 34.27 7.35 19.61 48.53
Delta_i 1.49 1.40 -0.59 5.91
Table 2.15: Annual Variable Summary Statistics - 2015
Mean SD Min Max
UR 8.15 4.07 3.38 22.08
TAXCORP 2.73 0.89 1.46 4.41
UD 25.16 20.00 4.50 68.20
PSLM 1.35 0.96 0.28 3.31
EPL 2.00 0.81 0.00 3.00
WSC 3.00 1.37 1.00 5.00
CBC 50.94 34.44 8.50 100.00
BRR1Y 43.95 22.39 9.00 84.00
TTR 34.79 7.00 20.39 46.06
Delta_i 1.30 1.24 -0.88 4.95
Table 2.16: Annual Variable Summary Statistics - 2016
Mean SD Min Max
UR 7.12 3.69 3.12 19.65
TAXCORP 3.00 0.91 1.61 4.88
UD 23.84 18.34 7.70 67.40
PSLM 1.28 0.79 0.26 3.14
EPL 2.00 0.83 0.00 3.00
WSC 3.00 1.37 1.00 5.00
CBC 51.90 32.09 8.30 100.00
BRR1Y 44.36 24.45 9.00 85.00
TTR 34.85 6.29 24.75 45.49
Delta_i 0.86 1.00 -1.47 2.48
Table 2.17: Annual Variable Summary Statistics - 2017
Mean SD Min Max
UR 6.40 3.43 2.81 17.23
TAXCORP 3.16 1.10 1.49 5.11
UD 27.45 18.34 7.70 66.10
PSLM 1.28 0.76 0.24 2.55
EPL 2.00 0.84 0.00 3.00
WSC 3.00 1.36 1.00 5.00
CBC 54.26 33.50 8.30 100.00
BRR1Y 47.33 22.43 8.00 84.00
TTR 35.26 6.54 22.58 44.09
Delta_i 1.85 1.15 -0.07 4.24
Table 2.18: Annual Variable Summary Statistics - 2018
Mean SD Min Max
UR 5.34 2.67 2.27 15.27
TAXCORP 3.18 1.45 1.05 6.32
UD 22.02 18.21 5.90 67.50
PSLM 1.17 0.73 0.25 2.87
EPL 2.00 0.81 0.00 3.00
WSC 2.00 1.40 1.00 5.00
CBC 47.20 31.07 6.10 98.00
BRR1Y 43.95 25.10 8.00 84.00
TTR 34.79 6.00 24.89 44.17
Delta_i 2.09 1.24 0.00 4.85
Table 2.19: Annual Variable Summary Statistics - 2019
Mean SD Min Max
UR 4.67 1.34 2.35 6.28
TAXCORP 2.99 1.14 1.34 4.16
UD 21.57 14.13 7.40 49.10
PSLM 1.19 0.71 0.31 2.01
EPL 2.00 1.17 0.00 4.00
WSC 3.00 1.77 1.00 5.00
CBC 48.01 40.39 7.90 98.00
BRR1Y 41.57 24.99 8.00 77.00
TTR 35.17 6.96 24.97 43.87
Delta_i 1.85 0.79 0.64 3.03

We will now create data visualizations to inform our expected specification. Hex codes below for copypasta.

Color HEX Compliment
Blue #320EF1 #CDF10E
Red #F10E5B #0EF1A4
Lt. Green #CDF10E #320EF1
Green #0EF1A4 #F10E5B 
# overall hur vs taxcorp, averages
Trend1 <- read.csv("X1.csv") %>% 
  clean_names()
Trend1 <- Trend1 %>% select(i_time1, location1, hur, taxcorp) %>% 
  group_by(i_time1) %>% 
  drop_na() %>% 
  summarize(mean_ur=mean(hur), mean_taxcorp = mean(taxcorp))
#View(Trend1)
colors <- c("Unemployment Rate" = "#F10E5B", "Corporate Tax Revenue" = "#0EF1A4")
ggplot(Trend1, aes(x=i_time1))+
  geom_line(aes(y=mean_ur,color = "Unemployment Rate"))+
  geom_line(aes(y=mean_taxcorp,color ="Corporate Tax Revenue"))+
  labs(x="Year", y="Mean %", color = "Legend",
       title = "Figure 1: Means by Year, Overall",
       subtitle = "32 OECD Countries from 2001-2019 N=357", caption = "Chart by Josiah Nelson, Data: OECD 2022a, OECD 2022b")+
  scale_color_manual(values = colors)+
  ylim(0,10)
### faceted plots for assignment
#hur vs taxcorp by year
ggplot(data=X1)+
  geom_point(mapping=aes(x=taxcorp,y=hur), color = "#CDF10E" ) + geom_smooth(mapping=aes(x=taxcorp,y=hur), method = lm, se=F, color = "#320EF1")+
  facet_wrap(~i_time1) + labs(x = "TAXCORP", y="UR", title = "Figure 1.1: Unemployment Rate vs Corporate Tax Revenue, By Year")
`geom_smooth()` using formula 'y ~ x'

#hur vs ud
ggplot(data=X1)+
  geom_point(mapping=aes(x=ud,y=hur, color = "#320EF1"))+ geom_smooth(mapping=aes(x=ud,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "UD", y="UR", title = "Figure 1.2: Unemployment Rate vs Union Density")
`geom_smooth()` using formula 'y ~ x'

#hur vs pslm
ggplot(data=X1)+
  geom_point(mapping=aes(x=pslm,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=pslm,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "PSLM", y="UR", title = "Figure 1.3: Unemployment Rate vs Public Spending on Labor Markets")
`geom_smooth()` using formula 'y ~ x'

#hur vs epl
ggplot(data=X1)+
  geom_point(mapping=aes(x=epl,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=epl,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "EPL", y="UR", title = "Figure 1.4: Unemployment Rate vs Employment Protection Legislation")
`geom_smooth()` using formula 'y ~ x'

#hur vs wsc
ggplot(data=X1)+
  geom_point(mapping=aes(x=wsc,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=wsc,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "WSC", y="UR", title = "Figure 1.5: Unemployment Rate vs Wage Setting Coordination")
`geom_smooth()` using formula 'y ~ x'

#hur vs cbc
ggplot(data=X1)+
  geom_point(mapping=aes(x=cbc,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=cbc,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "CBC", y="UR", title = "Figure 1.6: Unemployment Rate vs Collective Bargaining Coverage")
`geom_smooth()` using formula 'y ~ x'

#hur vs brr
ggplot(data=X1)+
  geom_point(mapping=aes(x=brr1y,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=brr1y,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "BRR", y="UR", title = "Figure 1.7: Unemployment Rate vs Benefit Replacement Rate (1yr)")
`geom_smooth()` using formula 'y ~ x'

#hur vs ttr
ggplot(data=X1)+
  geom_point(mapping=aes(x=ttr,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=ttr,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "TTR", y="UR", title = "Figure 1.8: Unemployment Rate vs Total Tax Rate")
`geom_smooth()` using formula 'y ~ x'

#hur vs delta_i
ggplot(data=X1)+
  geom_point(mapping=aes(x=inf_gdpd,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=inf_gdpd,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "delta_i", y="UR", title = "Unemployment Rate vs Change in Inflation")
`geom_smooth()` using formula 'y ~ x'

#hur vs l1_delta_i; lag for science
ggplot(data=X1)+
  geom_point(mapping=aes(x=l1_inf_gdpd,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=l1_inf_gdpd,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "delta_i", y="UR", title = "Unemployment Rate vs Change in Inflation, t-1")
`geom_smooth()` using formula 'y ~ x'

Infographics!

Try to make a heat map in ggplot choropleth map of averages. If all else fails, there is always datawrapper \(\rightarrow\) world map

Correlation Matrix; Visual

CorData = subset(X1, select = -c(i_time1, location1, l1_inf_gdpd))
ggcorrplot(cor(CorData), type = "upper", method = c("circle"),
           colors = c("#320EF1", "white", "#F10E5B")) + 
  labs(title = "Figure 2: Correlation Matrix Infographic")
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.

Regression Analysis

print("Coming Soon")

Clean Environment

rm(list=ls())
---
title: "ECON 145 Data Project Notes"
output:
  html_notebook: default
  always_allow_html: true
---


## First Steps
* required packages 
```{r}
require(readxl) # may be unneccessary as I'm saving to .csv
#require(dplyr)
#require(tidyr)
require(tidyverse) # i think this gives me dplyr and tidyr
require(janitor) # clean names
require(ggplot2) # data viz
require(ggcorrplot) #cor matrix and plots
require(ivreg) # two stage least squares regression
require(kableExtra) # tables
```

* attempt to clean data using R (It's not working out so well); I did it in Excel (Mar 12)
* take variables *TOCP* and *UR_b* from appropriate files and make a dot plot to show correlation

### Data Guide
```{r Guide Table}
GuideDF <- read_excel("DataGuide.xlsx") 
GuideDF

```
## Second Steps
### Data Exploration
A Table of summary statistics of variables to include:
* mean
* sd
* min-max
* dot plots 

#### Iteration 1: X1.csv cleaned data of 13 columns, 357 observations. Create DF with clean names
```{r Read Into R}
X1 <- read.csv("X1.csv") %>% 
           clean_names() #piping prevents an umlaut over the i
X1 # output be visible in R notebook; no need to View()

```
#### Statistical Summary Table
Now, we make a statistical summary table. Instrumented variables are rounded in the table for mean, min and max. Perhaps they should be rounded in the DF itself.
```{r Table 2}
Descriptive1 <- matrix( 
  round(
    c(
      mean(X1$hur),sd(X1$hur),min(X1$hur),max(X1$hur),
      
      mean(X1$taxcorp),  sd(X1$taxcorp), min(X1$taxcorp),max(X1$taxcorp),
      
      mean(X1$ud), sd(X1$ud),min(X1$ud),max(X1$ud),
      
      mean(X1$pslm),sd(X1$pslm),min(X1$pslm),max(X1$pslm),
      
      round(mean(X1$epl),0), sd(X1$epl), round(min(X1$epl),0),round(max(X1$epl),0),
      
      round(mean(X1$wsc),0), sd(X1$wsc), round(min(X1$wsc),0),round(max(X1$wsc),0),
      
      mean(X1$cbc), sd(X1$cbc),min(X1$cbc),max(X1$cbc),
      
      mean(X1$brr1y), sd(X1$brr1y), min(X1$brr1y),max(X1$brr1y),
      
      mean(X1$ttr), sd(X1$ttr),min(X1$ttr),max(X1$ttr),
      
      mean(X1$inf_gdpd),sd(X1$inf_gdpd),min(X1$inf_gdpd),max(X1$inf_gdpd)
       ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive1) <- c("Mean", "SD","Min","Max")
rownames(Descriptive1) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive1, caption = "Table 2: Variable Summary Statistics, All Observations") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)
```
Now, we need to break down summary statistics by year. We will do this by creating a filtered df per year. These can come in handy in the future as well. This is going to be a fat chunk of code as I'm not going to take the time to learn how to automate. Code is in notebook but echo is false **annual_data.R**
```{r Table 2.XX, echo=FALSE}
#make sure all pre-reqs are loaded
F2001X1 <- filter(X1, i_time1==2001); F2002X1 <- filter(X1, i_time1==2002)
F2003X1 <- filter(X1, i_time1==2003); F2004X1 <- filter(X1, i_time1==2004)
F2005X1 <- filter(X1, i_time1==2005); F2006X1 <- filter(X1, i_time1==2006)
F2007X1 <- filter(X1, i_time1==2007); F2008X1 <- filter(X1, i_time1==2008)
F2009X1 <- filter(X1, i_time1==2009); F2010X1 <- filter(X1, i_time1==2010)
F2011X1 <- filter(X1, i_time1==2011); F2012X1 <- filter(X1, i_time1==2012)
F2013X1 <- filter(X1, i_time1==2013); F2014X1 <- filter(X1, i_time1==2014)
F2015X1 <- filter(X1, i_time1==2015); F2016X1 <- filter(X1, i_time1==2016)
F2017X1 <- filter(X1, i_time1==2017); F2018X1 <- filter(X1, i_time1==2018)
F2019X1 <- filter(X1, i_time1==2019)
# 2001
Descriptive2001 <- matrix( 
  round(
    c(
      mean(F2001X1$hur),sd(F2001X1$hur),min(F2001X1$hur),max(F2001X1$hur),
      
      mean(F2001X1$taxcorp),  sd(F2001X1$taxcorp), min(F2001X1$taxcorp),max(F2001X1$taxcorp),
      
      mean(F2001X1$ud), sd(F2001X1$ud),min(F2001X1$ud),max(F2001X1$ud),
      
      mean(F2001X1$pslm),sd(F2001X1$pslm),min(F2001X1$pslm),max(F2001X1$pslm),
      
      round(mean(F2001X1$epl),0), sd(F2001X1$epl), round(min(F2001X1$epl),0),round(max(F2001X1$epl),0),
      
      round(mean(F2001X1$wsc),0), sd(F2001X1$wsc), round(min(F2001X1$wsc),0),round(max(F2001X1$wsc),0),
      
      mean(F2001X1$cbc), sd(F2001X1$cbc),min(F2001X1$cbc),max(F2001X1$cbc),
      
      mean(F2001X1$brr1y), sd(F2001X1$brr1y), min(F2001X1$brr1y),max(F2001X1$brr1y),
      
      mean(F2001X1$ttr), sd(F2001X1$ttr),min(F2001X1$ttr),max(F2001X1$ttr),
      
      mean(F2001X1$inf_gdpd),sd(F2001X1$inf_gdpd),min(F2001X1$inf_gdpd),max(F2001X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2001) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2001) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2001, caption = "Table 2.1: Annual Variable Summary Statistics - 2001") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

# 2002
Descriptive2002 <- matrix( 
  round(
    c(
      mean(F2002X1$hur),sd(F2002X1$hur),min(F2002X1$hur),max(F2002X1$hur),
      
      mean(F2002X1$taxcorp),  sd(F2002X1$taxcorp), min(F2002X1$taxcorp),max(F2002X1$taxcorp),
      
      mean(F2002X1$ud), sd(F2002X1$ud),min(F2002X1$ud),max(F2002X1$ud),
      
      mean(F2002X1$pslm),sd(F2002X1$pslm),min(F2002X1$pslm),max(F2002X1$pslm),
      
      round(mean(F2002X1$epl),0), sd(F2002X1$epl), round(min(F2002X1$epl),0),round(max(F2002X1$epl),0),
      
      round(mean(F2002X1$wsc),0), sd(F2002X1$wsc), round(min(F2002X1$wsc),0),round(max(F2002X1$wsc),0),
      
      mean(F2002X1$cbc), sd(F2002X1$cbc),min(F2002X1$cbc),max(F2002X1$cbc),
      
      mean(F2002X1$brr1y), sd(F2002X1$brr1y), min(F2002X1$brr1y),max(F2002X1$brr1y),
      
      mean(F2002X1$ttr), sd(F2002X1$ttr),min(F2002X1$ttr),max(F2002X1$ttr),
      
      mean(F2002X1$inf_gdpd),sd(F2002X1$inf_gdpd),min(F2002X1$inf_gdpd),max(F2002X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2002) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2002) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2002, caption = "Table 2.2: Annual Variable Summary Statistics - 2002") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2003
Descriptive2003 <- matrix( 
  round(
    c(
      mean(F2003X1$hur),sd(F2003X1$hur),min(F2003X1$hur),max(F2003X1$hur),
      
      mean(F2003X1$taxcorp),  sd(F2003X1$taxcorp), min(F2003X1$taxcorp),max(F2003X1$taxcorp),
      
      mean(F2003X1$ud), sd(F2003X1$ud),min(F2003X1$ud),max(F2003X1$ud),
      
      mean(F2003X1$pslm),sd(F2003X1$pslm),min(F2003X1$pslm),max(F2003X1$pslm),
      
      round(mean(F2003X1$epl),0), sd(F2003X1$epl), round(min(F2003X1$epl),0),round(max(F2003X1$epl),0),
      
      round(mean(F2003X1$wsc),0), sd(F2003X1$wsc), round(min(F2003X1$wsc),0),round(max(F2003X1$wsc),0),
      
      mean(F2003X1$cbc), sd(F2003X1$cbc),min(F2003X1$cbc),max(F2003X1$cbc),
      
      mean(F2003X1$brr1y), sd(F2003X1$brr1y), min(F2003X1$brr1y),max(F2003X1$brr1y),
      
      mean(F2003X1$ttr), sd(F2003X1$ttr),min(F2003X1$ttr),max(F2003X1$ttr),
      
      mean(F2003X1$inf_gdpd),sd(F2003X1$inf_gdpd),min(F2003X1$inf_gdpd),max(F2003X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2003) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2003) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")
kbl(Descriptive2003, caption = "Table 2.3: Annual Variable Summary Statistics - 2003") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)
#2004
Descriptive2004 <- matrix( 
  round(
    c(
      mean(F2004X1$hur),sd(F2004X1$hur),min(F2004X1$hur),max(F2004X1$hur),
      
      mean(F2004X1$taxcorp),  sd(F2004X1$taxcorp), min(F2004X1$taxcorp),max(F2004X1$taxcorp),
      
      mean(F2004X1$ud), sd(F2004X1$ud),min(F2004X1$ud),max(F2004X1$ud),
      
      mean(F2004X1$pslm),sd(F2004X1$pslm),min(F2004X1$pslm),max(F2004X1$pslm),
      
      round(mean(F2004X1$epl),0), sd(F2004X1$epl), round(min(F2004X1$epl),0),round(max(F2004X1$epl),0),
      
      round(mean(F2004X1$wsc),0), sd(F2004X1$wsc), round(min(F2004X1$wsc),0),round(max(F2004X1$wsc),0),
      
      mean(F2004X1$cbc), sd(F2004X1$cbc),min(F2004X1$cbc),max(F2004X1$cbc),
      
      mean(F2004X1$brr1y), sd(F2004X1$brr1y), min(F2004X1$brr1y),max(F2004X1$brr1y),
      
      mean(F2004X1$ttr), sd(F2004X1$ttr),min(F2004X1$ttr),max(F2004X1$ttr),
      
      mean(F2004X1$inf_gdpd),sd(F2004X1$inf_gdpd),min(F2004X1$inf_gdpd),max(F2004X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2004) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2004) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2004, caption = "Table 2.4: Annual Variable Summary Statistics - 2004") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2005
Descriptive2005 <- matrix( 
  round(
    c(
      mean(F2005X1$hur),sd(F2005X1$hur),min(F2005X1$hur),max(F2005X1$hur),
      
      mean(F2005X1$taxcorp),  sd(F2005X1$taxcorp), min(F2005X1$taxcorp),max(F2005X1$taxcorp),
      
      mean(F2005X1$ud), sd(F2005X1$ud),min(F2005X1$ud),max(F2005X1$ud),
      
      mean(F2005X1$pslm),sd(F2005X1$pslm),min(F2005X1$pslm),max(F2005X1$pslm),
      
      round(mean(F2005X1$epl),0), sd(F2005X1$epl), round(min(F2005X1$epl),0),round(max(F2005X1$epl),0),
      
      round(mean(F2005X1$wsc),0), sd(F2005X1$wsc), round(min(F2005X1$wsc),0),round(max(F2005X1$wsc),0),
      
      mean(F2005X1$cbc), sd(F2005X1$cbc),min(F2005X1$cbc),max(F2005X1$cbc),
      
      mean(F2005X1$brr1y), sd(F2005X1$brr1y), min(F2005X1$brr1y),max(F2005X1$brr1y),
      
      mean(F2005X1$ttr), sd(F2005X1$ttr),min(F2005X1$ttr),max(F2005X1$ttr),
      
      mean(F2005X1$inf_gdpd),sd(F2005X1$inf_gdpd),min(F2005X1$inf_gdpd),max(F2005X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2005) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2005) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2005, caption = "Table 2.5: Annual Variable Summary Statistics - 2005") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2006
Descriptive2006 <- matrix( 
  round(
    c(
      mean(F2006X1$hur),sd(F2006X1$hur),min(F2006X1$hur),max(F2006X1$hur),
      
      mean(F2006X1$taxcorp),  sd(F2006X1$taxcorp), min(F2006X1$taxcorp),max(F2006X1$taxcorp),
      
      mean(F2006X1$ud), sd(F2006X1$ud),min(F2006X1$ud),max(F2006X1$ud),
      
      mean(F2006X1$pslm),sd(F2006X1$pslm),min(F2006X1$pslm),max(F2006X1$pslm),
      
      round(mean(F2006X1$epl),0), sd(F2006X1$epl), round(min(F2006X1$epl),0),round(max(F2006X1$epl),0),
      
      round(mean(F2006X1$wsc),0), sd(F2006X1$wsc), round(min(F2006X1$wsc),0),round(max(F2006X1$wsc),0),
      
      mean(F2006X1$cbc), sd(F2006X1$cbc),min(F2006X1$cbc),max(F2006X1$cbc),
      
      mean(F2006X1$brr1y), sd(F2006X1$brr1y), min(F2006X1$brr1y),max(F2006X1$brr1y),
      
      mean(F2006X1$ttr), sd(F2006X1$ttr),min(F2006X1$ttr),max(F2006X1$ttr),
      
      mean(F2006X1$inf_gdpd),sd(F2006X1$inf_gdpd),min(F2006X1$inf_gdpd),max(F2006X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2006) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2006) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2006, caption = "Table 2.6: Annual Variable Summary Statistics - 2006") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2007
Descriptive2007 <- matrix( 
  round(
    c(
      mean(F2007X1$hur),sd(F2007X1$hur),min(F2007X1$hur),max(F2007X1$hur),
      
      mean(F2007X1$taxcorp),  sd(F2007X1$taxcorp), min(F2007X1$taxcorp),max(F2007X1$taxcorp),
      
      mean(F2007X1$ud), sd(F2007X1$ud),min(F2007X1$ud),max(F2007X1$ud),
      
      mean(F2007X1$pslm),sd(F2007X1$pslm),min(F2007X1$pslm),max(F2007X1$pslm),
      
      round(mean(F2007X1$epl),0), sd(F2007X1$epl), round(min(F2007X1$epl),0),round(max(F2007X1$epl),0),
      
      round(mean(F2007X1$wsc),0), sd(F2007X1$wsc), round(min(F2007X1$wsc),0),round(max(F2007X1$wsc),0),
      
      mean(F2007X1$cbc), sd(F2007X1$cbc),min(F2007X1$cbc),max(F2007X1$cbc),
      
      mean(F2007X1$brr1y), sd(F2007X1$brr1y), min(F2007X1$brr1y),max(F2007X1$brr1y),
      
      mean(F2007X1$ttr), sd(F2007X1$ttr),min(F2007X1$ttr),max(F2007X1$ttr),
      
      mean(F2007X1$inf_gdpd),sd(F2007X1$inf_gdpd),min(F2007X1$inf_gdpd),max(F2007X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2007) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2007) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2007, caption = "Table 2.7: Annual Variable Summary Statistics - 2007") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2008
Descriptive2008 <- matrix( 
  round(
    c(
      mean(F2008X1$hur),sd(F2008X1$hur),min(F2008X1$hur),max(F2008X1$hur),
      
      mean(F2008X1$taxcorp),  sd(F2008X1$taxcorp), min(F2008X1$taxcorp),max(F2008X1$taxcorp),
      
      mean(F2008X1$ud), sd(F2008X1$ud),min(F2008X1$ud),max(F2008X1$ud),
      
      mean(F2008X1$pslm),sd(F2008X1$pslm),min(F2008X1$pslm),max(F2008X1$pslm),
      
      round(mean(F2008X1$epl),0), sd(F2008X1$epl), round(min(F2008X1$epl),0),round(max(F2008X1$epl),0),
      
      round(mean(F2008X1$wsc),0), sd(F2008X1$wsc), round(min(F2008X1$wsc),0),round(max(F2008X1$wsc),0),
      
      mean(F2008X1$cbc), sd(F2008X1$cbc),min(F2008X1$cbc),max(F2008X1$cbc),
      
      mean(F2008X1$brr1y), sd(F2008X1$brr1y), min(F2008X1$brr1y),max(F2008X1$brr1y),
      
      mean(F2008X1$ttr), sd(F2008X1$ttr),min(F2008X1$ttr),max(F2008X1$ttr),
      
      mean(F2008X1$inf_gdpd),sd(F2008X1$inf_gdpd),min(F2008X1$inf_gdpd),max(F2008X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2008) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2008) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2008, caption = "Table 2.8: Annual Variable Summary Statistics - 2008") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2009
Descriptive2009 <- matrix( 
  round(
    c(
      mean(F2009X1$hur),sd(F2009X1$hur),min(F2009X1$hur),max(F2009X1$hur),
      
      mean(F2009X1$taxcorp),  sd(F2009X1$taxcorp), min(F2009X1$taxcorp),max(F2009X1$taxcorp),
      
      mean(F2009X1$ud), sd(F2009X1$ud),min(F2009X1$ud),max(F2009X1$ud),
      
      mean(F2009X1$pslm),sd(F2009X1$pslm),min(F2009X1$pslm),max(F2009X1$pslm),
      
      round(mean(F2009X1$epl),0), sd(F2009X1$epl), round(min(F2009X1$epl),0),round(max(F2009X1$epl),0),
      
      round(mean(F2009X1$wsc),0), sd(F2009X1$wsc), round(min(F2009X1$wsc),0),round(max(F2009X1$wsc),0),
      
      mean(F2009X1$cbc), sd(F2009X1$cbc),min(F2009X1$cbc),max(F2009X1$cbc),
      
      mean(F2009X1$brr1y), sd(F2009X1$brr1y), min(F2009X1$brr1y),max(F2009X1$brr1y),
      
      mean(F2009X1$ttr), sd(F2009X1$ttr),min(F2009X1$ttr),max(F2009X1$ttr),
      
      mean(F2009X1$inf_gdpd),sd(F2009X1$inf_gdpd),min(F2009X1$inf_gdpd),max(F2009X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2009) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2009) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2009, caption = "Table 2.9: Annual Variable Summary Statistics - 2009") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2010
Descriptive2010 <- matrix( 
  round(
    c(
      mean(F2010X1$hur),sd(F2010X1$hur),min(F2010X1$hur),max(F2010X1$hur),
      
      mean(F2010X1$taxcorp),  sd(F2010X1$taxcorp), min(F2010X1$taxcorp),max(F2010X1$taxcorp),
      
      mean(F2010X1$ud), sd(F2010X1$ud),min(F2010X1$ud),max(F2010X1$ud),
      
      mean(F2010X1$pslm),sd(F2010X1$pslm),min(F2010X1$pslm),max(F2010X1$pslm),
      
      round(mean(F2010X1$epl),0), sd(F2010X1$epl), round(min(F2010X1$epl),0),round(max(F2010X1$epl),0),
      
      round(mean(F2010X1$wsc),0), sd(F2010X1$wsc), round(min(F2010X1$wsc),0),round(max(F2010X1$wsc),0),
      
      mean(F2010X1$cbc), sd(F2010X1$cbc),min(F2010X1$cbc),max(F2010X1$cbc),
      
      mean(F2010X1$brr1y), sd(F2010X1$brr1y), min(F2010X1$brr1y),max(F2010X1$brr1y),
      
      mean(F2010X1$ttr), sd(F2010X1$ttr),min(F2010X1$ttr),max(F2010X1$ttr),
      
      mean(F2010X1$inf_gdpd),sd(F2010X1$inf_gdpd),min(F2010X1$inf_gdpd),max(F2010X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2010) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2010) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2010, caption = "Table 2.10: Annual Variable Summary Statistics - 2010") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2011
Descriptive2011 <- matrix( 
  round(
    c(
      mean(F2011X1$hur),sd(F2011X1$hur),min(F2011X1$hur),max(F2011X1$hur),
      
      mean(F2011X1$taxcorp),  sd(F2011X1$taxcorp), min(F2011X1$taxcorp),max(F2011X1$taxcorp),
      
      mean(F2011X1$ud), sd(F2011X1$ud),min(F2011X1$ud),max(F2011X1$ud),
      
      mean(F2011X1$pslm),sd(F2011X1$pslm),min(F2011X1$pslm),max(F2011X1$pslm),
      
      round(mean(F2011X1$epl),0), sd(F2011X1$epl), round(min(F2011X1$epl),0),round(max(F2011X1$epl),0),
      
      round(mean(F2011X1$wsc),0), sd(F2011X1$wsc), round(min(F2011X1$wsc),0),round(max(F2011X1$wsc),0),
      
      mean(F2011X1$cbc), sd(F2011X1$cbc),min(F2011X1$cbc),max(F2011X1$cbc),
      
      mean(F2011X1$brr1y), sd(F2011X1$brr1y), min(F2011X1$brr1y),max(F2011X1$brr1y),
      
      mean(F2011X1$ttr), sd(F2011X1$ttr),min(F2011X1$ttr),max(F2011X1$ttr),
      
      mean(F2011X1$inf_gdpd),sd(F2011X1$inf_gdpd),min(F2011X1$inf_gdpd),max(F2011X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2011) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2011) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2011, caption = "Table 2.11: Annual Variable Summary Statistics - 2011") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2012
Descriptive2012 <- matrix( 
  round(
    c(
      mean(F2012X1$hur),sd(F2012X1$hur),min(F2012X1$hur),max(F2012X1$hur),
      
      mean(F2012X1$taxcorp),  sd(F2012X1$taxcorp), min(F2012X1$taxcorp),max(F2012X1$taxcorp),
      
      mean(F2012X1$ud), sd(F2012X1$ud),min(F2012X1$ud),max(F2012X1$ud),
      
      mean(F2012X1$pslm),sd(F2012X1$pslm),min(F2012X1$pslm),max(F2012X1$pslm),
      
      round(mean(F2012X1$epl),0), sd(F2012X1$epl), round(min(F2012X1$epl),0),round(max(F2012X1$epl),0),
      
      round(mean(F2012X1$wsc),0), sd(F2012X1$wsc), round(min(F2012X1$wsc),0),round(max(F2012X1$wsc),0),
      
      mean(F2012X1$cbc), sd(F2012X1$cbc),min(F2012X1$cbc),max(F2012X1$cbc),
      
      mean(F2012X1$brr1y), sd(F2012X1$brr1y), min(F2012X1$brr1y),max(F2012X1$brr1y),
      
      mean(F2012X1$ttr), sd(F2012X1$ttr),min(F2012X1$ttr),max(F2012X1$ttr),
      
      mean(F2012X1$inf_gdpd),sd(F2012X1$inf_gdpd),min(F2012X1$inf_gdpd),max(F2012X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2012) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2012) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2012, caption = "Table 2.12: Annual Variable Summary Statistics - 2012") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2013
Descriptive2013 <- matrix( 
  round(
    c(
      mean(F2013X1$hur),sd(F2013X1$hur),min(F2013X1$hur),max(F2013X1$hur),
      
      mean(F2013X1$taxcorp),  sd(F2013X1$taxcorp), min(F2013X1$taxcorp),max(F2013X1$taxcorp),
      
      mean(F2013X1$ud), sd(F2013X1$ud),min(F2013X1$ud),max(F2013X1$ud),
      
      mean(F2013X1$pslm),sd(F2013X1$pslm),min(F2013X1$pslm),max(F2013X1$pslm),
      
      round(mean(F2013X1$epl),0), sd(F2013X1$epl), round(min(F2013X1$epl),0),round(max(F2013X1$epl),0),
      
      round(mean(F2013X1$wsc),0), sd(F2013X1$wsc), round(min(F2013X1$wsc),0),round(max(F2013X1$wsc),0),
      
      mean(F2013X1$cbc), sd(F2013X1$cbc),min(F2013X1$cbc),max(F2013X1$cbc),
      
      mean(F2013X1$brr1y), sd(F2013X1$brr1y), min(F2013X1$brr1y),max(F2013X1$brr1y),
      
      mean(F2013X1$ttr), sd(F2013X1$ttr),min(F2013X1$ttr),max(F2013X1$ttr),
      
      mean(F2013X1$inf_gdpd),sd(F2013X1$inf_gdpd),min(F2013X1$inf_gdpd),max(F2013X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2013) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2013) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2013, caption = "Table 2.13: Annual Variable Summary Statistics - 2013") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)
#2014
Descriptive2014 <- matrix( 
  round(
    c(
      mean(F2014X1$hur),sd(F2014X1$hur),min(F2014X1$hur),max(F2014X1$hur),
      
      mean(F2014X1$taxcorp),  sd(F2014X1$taxcorp), min(F2014X1$taxcorp),max(F2014X1$taxcorp),
      
      mean(F2014X1$ud), sd(F2014X1$ud),min(F2014X1$ud),max(F2014X1$ud),
      
      mean(F2014X1$pslm),sd(F2014X1$pslm),min(F2014X1$pslm),max(F2014X1$pslm),
      
      round(mean(F2014X1$epl),0), sd(F2014X1$epl), round(min(F2014X1$epl),0),round(max(F2014X1$epl),0),
      
      round(mean(F2014X1$wsc),0), sd(F2014X1$wsc), round(min(F2014X1$wsc),0),round(max(F2014X1$wsc),0),
      
      mean(F2014X1$cbc), sd(F2014X1$cbc),min(F2014X1$cbc),max(F2014X1$cbc),
      
      mean(F2014X1$brr1y), sd(F2014X1$brr1y), min(F2014X1$brr1y),max(F2014X1$brr1y),
      
      mean(F2014X1$ttr), sd(F2014X1$ttr),min(F2014X1$ttr),max(F2014X1$ttr),
      
      mean(F2014X1$inf_gdpd),sd(F2014X1$inf_gdpd),min(F2014X1$inf_gdpd),max(F2014X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2014) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2014) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2014, caption = "Table 2.14: Annual Variable Summary Statistics - 2014") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2015
Descriptive2015 <- matrix( 
  round(
    c(
      mean(F2015X1$hur),sd(F2015X1$hur),min(F2015X1$hur),max(F2015X1$hur),
      
      mean(F2015X1$taxcorp),  sd(F2015X1$taxcorp), min(F2015X1$taxcorp),max(F2015X1$taxcorp),
      
      mean(F2015X1$ud), sd(F2015X1$ud),min(F2015X1$ud),max(F2015X1$ud),
      
      mean(F2015X1$pslm),sd(F2015X1$pslm),min(F2015X1$pslm),max(F2015X1$pslm),
      
      round(mean(F2015X1$epl),0), sd(F2015X1$epl), round(min(F2015X1$epl),0),round(max(F2015X1$epl),0),
      
      round(mean(F2015X1$wsc),0), sd(F2015X1$wsc), round(min(F2015X1$wsc),0),round(max(F2015X1$wsc),0),
      
      mean(F2015X1$cbc), sd(F2015X1$cbc),min(F2015X1$cbc),max(F2015X1$cbc),
      
      mean(F2015X1$brr1y), sd(F2015X1$brr1y), min(F2015X1$brr1y),max(F2015X1$brr1y),
      
      mean(F2015X1$ttr), sd(F2015X1$ttr),min(F2015X1$ttr),max(F2015X1$ttr),
      
      mean(F2015X1$inf_gdpd),sd(F2015X1$inf_gdpd),min(F2015X1$inf_gdpd),max(F2015X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2015) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2015) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2015, caption = "Table 2.15: Annual Variable Summary Statistics - 2015") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2016
Descriptive2016 <- matrix( 
  round(
    c(
      mean(F2016X1$hur),sd(F2016X1$hur),min(F2016X1$hur),max(F2016X1$hur),
      
      mean(F2016X1$taxcorp),  sd(F2016X1$taxcorp), min(F2016X1$taxcorp),max(F2016X1$taxcorp),
      
      mean(F2016X1$ud), sd(F2016X1$ud),min(F2016X1$ud),max(F2016X1$ud),
      
      mean(F2016X1$pslm),sd(F2016X1$pslm),min(F2016X1$pslm),max(F2016X1$pslm),
      
      round(mean(F2016X1$epl),0), sd(F2016X1$epl), round(min(F2016X1$epl),0),round(max(F2016X1$epl),0),
      
      round(mean(F2016X1$wsc),0), sd(F2016X1$wsc), round(min(F2016X1$wsc),0),round(max(F2016X1$wsc),0),
      
      mean(F2016X1$cbc), sd(F2016X1$cbc),min(F2016X1$cbc),max(F2016X1$cbc),
      
      mean(F2016X1$brr1y), sd(F2016X1$brr1y), min(F2016X1$brr1y),max(F2016X1$brr1y),
      
      mean(F2016X1$ttr), sd(F2016X1$ttr),min(F2016X1$ttr),max(F2016X1$ttr),
      
      mean(F2016X1$inf_gdpd),sd(F2016X1$inf_gdpd),min(F2016X1$inf_gdpd),max(F2016X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2016) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2016) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2016, caption = "Table 2.16: Annual Variable Summary Statistics - 2016") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2017
Descriptive2017 <- matrix( 
  round(
    c(
      mean(F2017X1$hur),sd(F2017X1$hur),min(F2017X1$hur),max(F2017X1$hur),
      
      mean(F2017X1$taxcorp),  sd(F2017X1$taxcorp), min(F2017X1$taxcorp),max(F2017X1$taxcorp),
      
      mean(F2017X1$ud), sd(F2017X1$ud),min(F2017X1$ud),max(F2017X1$ud),
      
      mean(F2017X1$pslm),sd(F2017X1$pslm),min(F2017X1$pslm),max(F2017X1$pslm),
      
      round(mean(F2017X1$epl),0), sd(F2017X1$epl), round(min(F2017X1$epl),0),round(max(F2017X1$epl),0),
      
      round(mean(F2017X1$wsc),0), sd(F2017X1$wsc), round(min(F2017X1$wsc),0),round(max(F2017X1$wsc),0),
      
      mean(F2017X1$cbc), sd(F2017X1$cbc),min(F2017X1$cbc),max(F2017X1$cbc),
      
      mean(F2017X1$brr1y), sd(F2017X1$brr1y), min(F2017X1$brr1y),max(F2017X1$brr1y),
      
      mean(F2017X1$ttr), sd(F2017X1$ttr),min(F2017X1$ttr),max(F2017X1$ttr),
      
      mean(F2017X1$inf_gdpd),sd(F2017X1$inf_gdpd),min(F2017X1$inf_gdpd),max(F2017X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2017) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2017) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2017, caption = "Table 2.17: Annual Variable Summary Statistics - 2017") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2018
Descriptive2018 <- matrix( 
  round(
    c(
      mean(F2018X1$hur),sd(F2018X1$hur),min(F2018X1$hur),max(F2018X1$hur),
      
      mean(F2018X1$taxcorp),  sd(F2018X1$taxcorp), min(F2018X1$taxcorp),max(F2018X1$taxcorp),
      
      mean(F2018X1$ud), sd(F2018X1$ud),min(F2018X1$ud),max(F2018X1$ud),
      
      mean(F2018X1$pslm),sd(F2018X1$pslm),min(F2018X1$pslm),max(F2018X1$pslm),
      
      round(mean(F2018X1$epl),0), sd(F2018X1$epl), round(min(F2018X1$epl),0),round(max(F2018X1$epl),0),
      
      round(mean(F2018X1$wsc),0), sd(F2018X1$wsc), round(min(F2018X1$wsc),0),round(max(F2018X1$wsc),0),
      
      mean(F2018X1$cbc), sd(F2018X1$cbc),min(F2018X1$cbc),max(F2018X1$cbc),
      
      mean(F2018X1$brr1y), sd(F2018X1$brr1y), min(F2018X1$brr1y),max(F2018X1$brr1y),
      
      mean(F2018X1$ttr), sd(F2018X1$ttr),min(F2018X1$ttr),max(F2018X1$ttr),
      
      mean(F2018X1$inf_gdpd),sd(F2018X1$inf_gdpd),min(F2018X1$inf_gdpd),max(F2018X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2018) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2018) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2018, caption = "Table 2.18: Annual Variable Summary Statistics - 2018") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)

#2019
Descriptive2019 <- matrix( 
  round(
    c(
      mean(F2019X1$hur),sd(F2019X1$hur),min(F2019X1$hur),max(F2019X1$hur),
      
      mean(F2019X1$taxcorp),  sd(F2019X1$taxcorp), min(F2019X1$taxcorp),max(F2019X1$taxcorp),
      
      mean(F2019X1$ud), sd(F2019X1$ud),min(F2019X1$ud),max(F2019X1$ud),
      
      mean(F2019X1$pslm),sd(F2019X1$pslm),min(F2019X1$pslm),max(F2019X1$pslm),
      
      round(mean(F2019X1$epl),0), sd(F2019X1$epl), round(min(F2019X1$epl),0),round(max(F2019X1$epl),0),
      
      round(mean(F2019X1$wsc),0), sd(F2019X1$wsc), round(min(F2019X1$wsc),0),round(max(F2019X1$wsc),0),
      
      mean(F2019X1$cbc), sd(F2019X1$cbc),min(F2019X1$cbc),max(F2019X1$cbc),
      
      mean(F2019X1$brr1y), sd(F2019X1$brr1y), min(F2019X1$brr1y),max(F2019X1$brr1y),
      
      mean(F2019X1$ttr), sd(F2019X1$ttr),min(F2019X1$ttr),max(F2019X1$ttr),
      
      mean(F2019X1$inf_gdpd),sd(F2019X1$inf_gdpd),min(F2019X1$inf_gdpd),max(F2019X1$inf_gdpd)
    ), 2),
  ncol = 4, byrow = T)

colnames(Descriptive2019) <- c("Mean", "SD","Min","Max")
rownames(Descriptive2019) <- c("UR","TAXCORP","UD","PSLM","EPL","WSC","CBC","BRR1Y","TTR","Delta_i")

kbl(Descriptive2019, caption = "Table 2.19: Annual Variable Summary Statistics - 2019") %>% 
  kable_styling(bootstrap_options = c("striped", "hover","condensed")) %>% 
  column_spec(1, bold = T)
```


We will now create data visualizations to inform our expected specification. Hex codes below for copypasta.
```{r Color Table, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
ColTable <- "
Color     | HEX     | Compliment |
----------|:-------:|-----------:|
Blue      | #320EF1 | #CDF10E    |
Red       | #F10E5B | #0EF1A4    |
Lt. Green | #CDF10E | #320EF1    |
Green     | #0EF1A4 | #F10E5B    |

"
cat(ColTable) # output the table in a format good for HTML/PDF/docx conversion
```
```{r Figure 1}
# overall hur vs taxcorp, averages
Trend1 <- read.csv("X1.csv") %>% 
  clean_names()
Trend1 <- Trend1 %>% select(i_time1, location1, hur, taxcorp) %>% 
  group_by(i_time1) %>% 
  drop_na() %>% 
  summarize(mean_ur=mean(hur), mean_taxcorp = mean(taxcorp))
#View(Trend1)
colors <- c("Unemployment Rate" = "#F10E5B", "Corporate Tax Revenue" = "#0EF1A4")
ggplot(Trend1, aes(x=i_time1))+
  geom_line(aes(y=mean_ur,color = "Unemployment Rate"))+
  geom_line(aes(y=mean_taxcorp,color ="Corporate Tax Revenue"))+
  labs(x="Year", y="Mean %", color = "Legend",
       title = "Figure 1: Means by Year, Overall",
       subtitle = "32 OECD Countries from 2001-2019 N=357", caption = "Chart by Josiah Nelson, Data: OECD 2022a, OECD 2022b")+
  scale_color_manual(values = colors)+
  ylim(0,10)
```
```{r}
### faceted plots for assignment
#hur vs taxcorp by year
ggplot(data=X1)+
  geom_point(mapping=aes(x=taxcorp,y=hur), color = "#CDF10E" ) + geom_smooth(mapping=aes(x=taxcorp,y=hur), method = lm, se=F, color = "#320EF1")+
  facet_wrap(~i_time1) + labs(x = "TAXCORP", y="UR", title = "Figure 1.1: Unemployment Rate vs Corporate Tax Revenue, By Year")
```
```{r Figure 1.XX}
#hur vs ud
ggplot(data=X1)+
  geom_point(mapping=aes(x=ud,y=hur, color = "#320EF1"))+ geom_smooth(mapping=aes(x=ud,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "UD", y="UR", title = "Figure 1.2: Unemployment Rate vs Union Density")
#hur vs pslm
ggplot(data=X1)+
  geom_point(mapping=aes(x=pslm,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=pslm,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "PSLM", y="UR", title = "Figure 1.3: Unemployment Rate vs Public Spending on Labor Markets")
#hur vs epl
ggplot(data=X1)+
  geom_point(mapping=aes(x=epl,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=epl,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "EPL", y="UR", title = "Figure 1.4: Unemployment Rate vs Employment Protection Legislation")
#hur vs wsc
ggplot(data=X1)+
  geom_point(mapping=aes(x=wsc,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=wsc,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "WSC", y="UR", title = "Figure 1.5: Unemployment Rate vs Wage Setting Coordination")
#hur vs cbc
ggplot(data=X1)+
  geom_point(mapping=aes(x=cbc,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=cbc,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "CBC", y="UR", title = "Figure 1.6: Unemployment Rate vs Collective Bargaining Coverage")
#hur vs brr
ggplot(data=X1)+
  geom_point(mapping=aes(x=brr1y,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=brr1y,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "BRR", y="UR", title = "Figure 1.7: Unemployment Rate vs Benefit Replacement Rate (1yr)")
#hur vs ttr
ggplot(data=X1)+
  geom_point(mapping=aes(x=ttr,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=ttr,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "TTR", y="UR", title = "Figure 1.8: Unemployment Rate vs Total Tax Rate")
#hur vs delta_i
ggplot(data=X1)+
  geom_point(mapping=aes(x=inf_gdpd,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=inf_gdpd,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "delta_i", y="UR", title = "Unemployment Rate vs Change in Inflation")
#hur vs l1_delta_i; lag for science
ggplot(data=X1)+
  geom_point(mapping=aes(x=l1_inf_gdpd,y=hur), color = "#320EF1")+geom_smooth(mapping=aes(x=l1_inf_gdpd,y=hur), method = lm, se=F, color = "#CDF10E")+
  facet_wrap(~i_time1)+ labs(x = "delta_i", y="UR", title = "Unemployment Rate vs Change in Inflation, t-1")
```

### Infographics!
Try to make a ~~heat map in ggplot~~ choropleth map of averages. If all else fails, there is always datawrapper $\rightarrow$ [world map](https://datawrapper.dwcdn.net/2Leh5/1/)

#### Correlation Matrix; Visual
```{r Figure 2}
CorData = subset(X1, select = -c(i_time1, location1, l1_inf_gdpd))
ggcorrplot(cor(CorData), type = "upper", method = c("circle"),
           colors = c("#320EF1", "white", "#F10E5B")) + 
  labs(title = "Figure 2: Correlation Matrix Infographic")
```

## Regression Analysis
* Two Stage least squares regression is in the **ivereg** package. An overview can be found [here](https://cran.r-project.org/web/packages/ivreg/vignettes/ivreg.html).
```{r}
print("Coming Soon")
```


### Clean Environment
```{r}
rm(list=ls())
```

