### EU-SILC Graphs and Analysis for JESP Paper 30/03/20 library(tidyverse) setwd("~/OneDrive - Nexus365/WEALTHPOL_RESEARCH/Data/Crossnational/EU_SILC/Cross") silc_averages <- read_csv("silc_averages.csv") silc_averages_20 <- read_csv("silc_averages_20.csv") silc_averages_50 <- read_csv("silc_averages_50.csv") silc_averages_r <- read_csv("silc_averages_r.csv") silc_averages_r_20 <- read_csv("silc_averages_r_20.csv") silc_averages_r_50 <- read_csv("silc_averages_r_50.csv") silc_averages_co <- read_csv("silc_averages_co.csv") silc_averages_c_20 <- read_csv("silc_averages_c_20.csv") silc_averages_c_50 <- read_csv("silc_averages_c_50.csv") silc_averages_r_50<-silc_averages_r_50 %>% mutate(renters_aff = 12*hcost_renters_av/gross_inc_renters, owners_aff = 12*hcost_owners_av/gross_inc_owners) silc_averages_c_50<-silc_averages_c_50 %>% mutate(renters_aff = 12*hcost_renters_av/gross_inc_renters, owners_aff = 12*hcost_owners_av/gross_inc_owners) # Regression Analyses silc_averages_r %>% lm(data = ., wealth_tax ~ hcost_av) %>% summary(.) # Graphs # Owners and Housing Burden silc_averages_r_50 %>% filter(hcost_av<2000) %>% ggplot(aes(x = hcost_owners_av, y = burden_owners, label = DB040)) + geom_text()+ theme_classic()+ xlab("Monthly Housing Costs in Euros")+ylab("Financial Burden of Housing Cost - No Burden to Heavy Burden ") # Renters and Housing Burden silc_averages_r_50 %>% filter(hcost_av<2000) %>% ggplot(aes(x = hcost_renters_av, y = burden_renters, label = DB040)) + geom_text()+ theme_classic()+ xlab("Monthly Housing Costs in Euros")+ylab("Financial Burden of Housing Cost - No Burden to Heavy Burden ") # Both p1<- silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(DB010==2006) %>% ggplot(aes(x = hcost_renters_av, y = burden_renters, label = DB040)) + geom_point(alpha = 0.7, color = "grey50")+ geom_smooth(aes(x = hcost_renters_av, y = burden_renters), method = "lm", color = "grey70", se=FALSE)+ geom_text(data = subset(silc_averages_c_50, DB010==2006), aes(x = hcost_renters_av, y = burden_renters, label = DB020), color = "grey50", nudge_x = 35)+ geom_point(aes(x = hcost_owners_av, y = burden_owners, label = DB040), color = "black", alpha = 0.7)+ geom_smooth(aes(x = hcost_owners_av, y = burden_owners, label = DB040), method = "lm", se=FALSE, color = "black")+ geom_text(data = subset(silc_averages_c_50, DB010==2006), aes(x = hcost_owners_av, y = burden_owners, label = DB020), color = "black", nudge_x = 35)+ theme_classic()+ xlab("Monthly Housing Costs in Euros in 2006")+ylab("Financial Burden of Housing Cost in 2006")+ labs(caption = "Renters in Grey. Owners in Black") p1 ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_burden_06.pdf", width = 6, height = 6) p2<- silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(DB010==2009) %>% ggplot(aes(x = hcost_renters_av, y = burden_renters, label = DB040)) + geom_point(alpha = 0.7, color = "grey50")+ geom_smooth(aes(x = hcost_renters_av, y = burden_renters), method = "lm", color = "grey70", se=FALSE)+ geom_text(data = subset(silc_averages_c_50, DB010==2009), aes(x = hcost_renters_av, y = burden_renters, label = DB020), color = "grey50", nudge_x = 35)+ geom_point(aes(x = hcost_owners_av, y = burden_owners, label = DB040), color = "black", alpha = 0.7)+ geom_smooth(aes(x = hcost_owners_av, y = burden_owners, label = DB040), method = "lm", se=FALSE, color = "black")+ geom_text(data = subset(silc_averages_c_50, DB010==2009), aes(x = hcost_owners_av, y = burden_owners, label = DB020), color = "black", nudge_x = 35)+ theme_classic()+ xlab("Monthly Housing Costs in Euros in 2009")+ylab("Financial Burden of Housing Cost in 2009")+ labs(caption = "Renters in Grey. Owners in Black") p2 ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_burden_09.pdf", width = 6, height = 6) p3<- silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(DB010==2016) %>% ggplot(aes(x = hcost_renters_av, y = burden_renters, label = DB040)) + geom_point(alpha = 0.7, color = "grey50")+ geom_smooth(aes(x = hcost_renters_av, y = burden_renters), method = "lm", color = "grey70", se=FALSE)+ geom_text(data = subset(silc_averages_c_50, DB010==2016), aes(x = hcost_renters_av, y = burden_renters, label = DB020), color = "grey50", nudge_x = 50)+ geom_point(aes(x = hcost_owners_av, y = burden_owners, label = DB040), color = "black", alpha = 0.7)+ geom_smooth(aes(x = hcost_owners_av, y = burden_owners, label = DB040), method = "lm", se=FALSE, color = "black")+ geom_text(data = subset(silc_averages_c_50, DB010==2016), aes(x = hcost_owners_av, y = burden_owners, label = DB020), color = "black", nudge_x = 50)+ theme_classic()+ xlab("Monthly Housing Costs in Euros in 2016")+ylab("Financial Burden of Housing Cost in 2016")+ labs(caption = "Renters in Grey. Owners in Black") p3 ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_burden_16.pdf", width = 6, height = 6) p4<- silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(DB010==2009) %>% ggplot(aes(x = renters_aff, y = burden_renters, label = DB040)) + geom_point(alpha = 0.4, color = "grey50")+ #geom_smooth(aes(x = renters_aff, y = burden_renters), method = "lm", color = "grey70", se=FALSE)+ geom_text(data = subset(silc_averages_c_50, DB010==2009), aes(x = renters_aff, y = burden_renters, label = DB020), color = "grey50", nudge_x = 0.008)+ geom_point(aes(x = owners_aff, y = burden_owners, label = DB040), color = "black", alpha = 0.4)+ #geom_smooth(aes(x = owners_aff, y = burden_owners, label = DB040), method = "lm", se=FALSE, color = "black")+ geom_text(data = subset(silc_averages_c_50, DB010==2009), aes(x = owners_aff, y = burden_owners, label = DB020), color = "black", nudge_x = 0.008)+ theme_classic()+ xlab("Housing Costs to Gross Income")+ylab("Financial Burden of Housing Cost")+ labs(caption = "Renters in Grey. Owners in Black") p4 ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_aff_09.pdf", width = 6, height = 6) p5<- silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(DB010==2016) %>% ggplot(aes(x = renters_aff, y = burden_renters, label = DB040)) + geom_point(alpha = 0.4, color = "grey50")+ #geom_smooth(aes(x = renters_aff, y = burden_renters), method = "lm", color = "grey70", se=FALSE)+ geom_text(data = subset(silc_averages_c_50, DB010==2016), aes(x = renters_aff, y = burden_renters, label = DB020), color = "grey50", nudge_x = 0.008)+ geom_point(aes(x = owners_aff, y = burden_owners, label = DB040), color = "black", alpha = 0.4)+ #geom_smooth(aes(x = owners_aff, y = burden_owners, label = DB040), method = "lm", se=FALSE, color = "black")+ geom_text(data = subset(silc_averages_c_50, DB010==2016), aes(x = owners_aff, y = burden_owners, label = DB020), color = "black", nudge_x = 0.008)+ theme_classic()+ xlab("Housing Costs to Gross Income")+ylab("Financial Burden of Housing Cost")+ labs(caption = "Renters in Grey. Owners in Black") p5 ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_aff_16.pdf", width = 6, height = 6) library(cowplot) plot_grid(p1, p2, p4, p5, labels = c("", "" ,"2009", "2016"), label_x = 0.11, label_y = 1.01, label_size = 12) ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_aff_0916.pdf", width = 8, height = 4) plot_grid(p1, p2, p4, p5, labels = c("", "" ,"2009", "2016"), label_x = 0.11, label_y = 1.01, label_size = 12) # Wealth Taxation silc_averages_50 %>% filter(hcost_av<2000) %>% ggplot(aes(x = hcost_av, y = wealth_tax, label = DB040)) + geom_text()+ theme_classic()+ xlab("Monthly Housing Costs in Euros")+ylab("Annual Wealth Tax in Euros") silc_averages_r_50 %>% filter(hcost_av<2000) %>% ggplot(aes(x = hcost_av, y = wealth_tax, label = DB040)) + geom_text()+ theme_classic()+ xlab("Monthly Housing Costs in Euros")+ylab("Annual Wealth Tax in Euros") silc_averages_c_50 %>% filter(hcost_av<2000) %>% ggplot(aes(x = hcost_av, y = wealth_tax, label = DB020)) + geom_text()+ theme_classic()+ xlab("Monthly Housing Costs in Euros")+ylab("Annual Wealth Tax in Euros") # Housing Costs for Owners versus Renters silc_averages_50 %>% filter(hcost_av<2000) %>% filter(n_cases>=50) %>% ggplot(aes(x = hcost_owners_av, y = hcost_renters_av, label = DB040, alpha = DB010)) + geom_text()+ geom_abline(slope = 1, intercept = 0, linetype="dashed")+ xlim(c(0, 1600))+ylim(c(0, 1600))+ theme_classic()+ theme(legend.position = "none") + xlab("Monthly Housing Costs for Owners")+ylab("Monthly Housing Costs for Renters") silc_averages_r_50 %>% filter(hcost_av<2000) %>% filter(n_cases>=50) %>% ggplot(aes(x = hcost_owners_av, y = hcost_renters_av, label = DB040, alpha = DB010)) + geom_text()+ geom_abline(slope = 1, intercept = 0, linetype="dashed")+ xlim(c(0, 1600))+ylim(c(0, 1600))+ theme_classic()+ theme(legend.position = "none") + xlab("Monthly Housing Costs for Owners")+ylab("Monthly Housing Costs for Renters") ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_regional.pdf", width = 6, height = 6) silc_averages_c_50 %>% filter(hcost_av<2000) %>% filter(n_cases>=50) %>% ggplot(aes(x = hcost_owners_av, y = hcost_renters_av, label = DB020, alpha = DB010)) + geom_text()+ geom_abline(slope = 1, intercept = 0, linetype="dashed")+ xlim(c(0, 1600))+ylim(c(0, 1600))+ theme_classic()+ theme(legend.position = "none") + xlab("Monthly Housing Costs for Owners")+ylab("Monthly Housing Costs for Renters") ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/owners_renters_country.pdf", width = 6, height = 6) ## Housing Inequality silc_averages_50 %>% filter(hcost_av<2000) %>% filter(n_cases>=50) %>% ggplot(aes(x = hcost_av, y = hcost_90/hcost_10, label = DB020, group = DB040)) + geom_text(aes(alpha=0.1))+ ylim(c(0, 20))+ theme_classic()+ theme(legend.position = "none") + xlab("Average Montly Housing Costs")+ylab("90:10 House Cost Ratio") ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/avcosts_9010.pdf", width = 6, height = 6) ## Housing as Percent of Gross Income library(scales) n1<-silc_averages_r_50 %>% mutate(h_burden_renters = 12*hcost_renters_av/gross_inc_renters, h_burden_owners = 12 * hcost_owners_av/gross_inc_owners) %>% filter(h_burden_renters<.75) %>% pivot_longer(cols=h_burden_renters:h_burden_owners, names_to = "owners", values_to = "hcost") %>% filter(DB010!=2005 & DB020 !="NO") %>% ggplot(aes(x = DB010, y = (hcost), group = owners, color = owners, fill=owners, label=DB020)) + geom_point()+ geom_smooth(method = "lm")+ xlab("Year")+ ylab("Housing Costs as a Proportion of Gross Income")+ theme_classic()+ scale_fill_discrete(guide=FALSE)+ scale_color_discrete(name = "", breaks = c( "h_burden_renters", "h_burden_owners"), labels = c("Renters", "Owners"))+ scale_x_continuous(breaks= pretty_breaks())+ggtitle("Regions")+ theme(plot.title = element_text(hjust = 0.5))+ theme(legend.position = c(0.2, 1))+ guides(color=guide_legend(override.aes=list(fill=NA))) ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/year_owner_renter_reg.pdf", width = 6, height = 6) n2<-silc_averages_c_50 %>% mutate(h_burden_renters = 12*hcost_renters_av/gross_inc_renters, h_burden_owners = 12 * hcost_owners_av/gross_inc_owners) %>% pivot_longer(cols=h_burden_renters:h_burden_owners, names_to = "owners", values_to = "hcost") %>% filter(DB010!=2005 & DB020 !="NO") %>% ggplot(aes(x = DB010, y = (hcost), group = owners, color = owners, fill=owners, label=DB020)) + geom_text()+ geom_smooth(method = "lm", aes(fill=owners))+ xlab("Year")+ ylab("Housing Costs as a Proportion of Gross Income")+ theme_classic()+ scale_fill_discrete(guide=FALSE)+ scale_color_discrete(name = "", breaks = c( "h_burden_renters", "h_burden_owners"), labels = c("Renters", "Owners"))+ scale_x_continuous(breaks= pretty_breaks())+ggtitle("Countries")+ theme(plot.title = element_text(hjust = 0.5))+ theme(legend.position = c(0.2, 1))+ guides(color=guide_legend(override.aes=list(fill=NA))) ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/year_owner_renter_co.pdf", width = 6, height = 6) library(patchwork) n1+n2 +plot_layout(nrow =2) ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/year_owner_renter_both.pdf", width =6, height = 11) plot_grid(n1, n2, p4, p5, labels = c("", "" ,"2009", "2016"), label_x = 0.11, label_y = 1.01, label_size = 12) ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/combined_one_two.pdf", width = 8.5, height = 11) silc_averages_co %>% ggplot(aes(x = DB010, y = mortgage_arrears, group = DB020, label = DB020, color=DB020)) + geom_text()+ geom_line(aes(alpha = 0.5)) + geom_abline(slope = 1, intercept = 0, linetype="dashed")+ theme_classic() silc_averages_co %>% ggplot(aes(x = DB010, y = mortgage_arrears, group = DB020, label = DB020, color=DB020)) + geom_text()+ geom_line(aes(alpha = 0.5)) + geom_abline(slope = 1, intercept = 0, linetype="dashed")+ theme_classic() silc_averages_co %>% ggplot(aes(x = DB010, y = mortgage_arrears, group = DB020, label = DB020, color=DB020)) + geom_text()+ geom_line(aes(alpha = 0.5)) + geom_abline(slope = 1, intercept = 0, linetype="dashed")+ scale_color_discrete(guide=FALSE)+ scale_alpha(guide=FALSE)+ xlab("Year")+ylab("Has the Household been in Mortgage or Rent Arrears?")+ scale_x_continuous(breaks= pretty_breaks())+ theme_classic() ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/year_arrears.pdf", width = 6, height = 6) silc_averages_co %>% ggplot(aes(x = DB010, y = housing_burden, group = DB020, label = DB020, color=DB020)) + geom_text()+ geom_line(aes(alpha = 0.5)) + geom_abline(slope = 1, intercept = 0, linetype="dashed")+ scale_color_discrete(guide=FALSE)+ scale_alpha(guide=FALSE)+ xlab("Year")+ylab("Financial Burden of Housing Cost - No Burden to Heavy Burden ")+ scale_x_continuous(breaks= pretty_breaks())+ theme_classic() ggsave("~/Dropbox/WEALTHPOL_Research/Papers/JESP/Draft/Graphs/year_burden.pdf", width = 6, height = 6)