#################################################################### ## author: Michael L. Wicki ## contact: michael.wicki@istp.ethz.ch, ETH Zurich ## file name: analysisW2.R ## Context: Analysis Neuhausen Single Authored ## started: 2019-02-25 ## summary: Analysis of the Diff-in-Diff effect over the three waves #################################################################### rm(list = ls()) getwd() setwd("\\\\d.ethz.ch\\groups\\baug\\irl\\spur_group\\02_People\\Wicki Michael\\Autonomous_Driving\\analysis_paper") set.seed(42) ################################ load packages ################################ library(dplyr) library(Hmisc) require(memisc) library(corrplot) library(PerformanceAnalytics) library(ggplot2) require(scales) library(reshape2) library(dotwhisker) library(broom) library(car) library(knitr) library(ggthemes) library(tidyr) library(plotrix) library(psych) library(cowplot) library(readxl) ################################ load dataset ################################ load("DiffInDiff_replication.RData") df$ageGr <- ifelse(df$age <=34, "18-34", ifelse( df$age > 34 & df$age <= 44, "35-44", ifelse( df$age > 44 & df$age <= 54, "45-54", ifelse( df$age > 54 & df$age <= 64, "55-64", ifelse( df$age > 64, "65+", NA))))) table(df$munic, df$ageGr, df$male) df$income <- as.factor(ifelse(df$inc1 == "inc1Weiss nicht", "NA", ifelse( is.na(df$inc1), "income unknown", as.character(df$inc1)))) weights <- read_excel("Z:/02_People/Wicki Michael/Autonomous_Driving/analysis_paper/weights.xlsx") df$weight <- ifelse(df$munic=="Neuhausen am Rheinfall" & df$ageGr == "18-34" & df$male == 0, weights[1,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "18-34" & df$male == 0, weights[2,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "18-34" & df$male == 0, weights[3,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "18-34" & df$male == 1, weights[4,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "18-34" & df$male == 1, weights[5,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "18-34" & df$male == 1, weights[6,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "35-44" & df$male == 0, weights[7,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "35-44" & df$male == 0, weights[8,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "35-44" & df$male == 0, weights[9,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "35-44" & df$male == 1, weights[10,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "35-44" & df$male == 1, weights[11,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "35-44" & df$male == 1, weights[12,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "45-54" & df$male == 0, weights[13,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "45-54" & df$male == 0, weights[14,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "45-54" & df$male == 0, weights[15,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "45-54" & df$male == 1, weights[16,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "45-54" & df$male == 1, weights[17,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "45-54" & df$male == 1, weights[18,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "55-64" & df$male == 0, weights[19,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "55-64" & df$male == 0, weights[20,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "55-64" & df$male == 0, weights[21,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "55-64" & df$male == 1, weights[22,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "55-64" & df$male == 1, weights[23,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "55-64" & df$male == 1, weights[24,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "65+" & df$male == 0, weights[25,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "65+" & df$male == 0, weights[26,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "65+" & df$male == 0, weights[27,]$Weight, ifelse( df$munic=="Neuhausen am Rheinfall" & df$ageGr == "65+" & df$male == 1, weights[28,]$Weight, ifelse( df$munic=="Stein am Rhein" & df$ageGr == "65+" & df$male == 1, weights[29,]$Weight, ifelse( df$munic=="Thayngen" & df$ageGr == "65+" & df$male == 1, weights[30,]$Weight, NA )))))))))))))))))))))))))))))) ###### assign weights ######## ##### Prediction ##### ##### Before test ###### m1 <- lm(chgStVO ~ avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct + risk + age + male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m2 <- lm(avNoCrn ~ avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct + risk + age + male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m3 <- lm(avTests ~ avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct + risk + age + male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) summary(m1) summary(m2) summary(m3) library(stargazer) table_beforetest <- stargazer(m1, m2, m3, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitudes before test (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Willingness to take risks", "Age", "Male dummy", "Minimum education", "Secondary education", "Tertiary education", "Test municipality", "Income unknown", "Below CHF 2000", "CHF 2000-4000", "CHF 4001-6000", "CHF 6001-8000", "CHF 8001-10000", "CHF 10001-12000", "CHF 12001-14000", "CHF 14001-16000", "More than CHF 16000", "Intercept"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_beforetest.html') m2plot1 <- dwplot(list(m1, m2, m3), show_intercept = FALSE) %>% relabel_predictors(c(avcSysSc = "System safety concern", avcSysRl = "System reliability concern", avcDrCtr = "Driver control loss concern", avcJoblos = "Job loss concern", avcJoblos = "Job loss concern", avcAcci = "Liability concern", avcPriv = "Privacy concern", avcHack = "Software misuse concern", avCntct = "Self-driving vehicle contact", risk = "Willingness to take risks", age = "Age", male = "Male dummy", educ0 = "Minimum education", educ1 = "Secondary education", educ2 = "Tertiary education", dummyNeu.y = "Test municipality")) + theme_bw() + xlab("Coefficient Estimate") + ylab("") + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) + ggtitle("Predictors of self-driving vehicle attitudes wave 1") + theme(plot.title = element_text(face="bold"), legend.justification = c(0, 0), legend.background = element_rect(colour="grey80")) + scale_colour_manual(name="Dependent Variable", breaks=c("Model 1", "Model 2", "Model 3"), labels=c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), values=c("#e66101","#5e3c99","#b2abd2")) + theme(legend.title=element_text(size=12, family="Sans"), axis.text=element_text(size=12, family="Sans"), axis.title=element_text(size=16, family="Sans"), axis.text.x = element_text(size=12, family="Sans"), panel.grid.minor = element_blank(), legend.text = element_text(size=12, family="Sans"), plot.title = element_text(family="Sans"), legend.position = "bottom", legend.direction = "horizontal") + guides(col=guide_legend(nrow=3,byrow=TRUE)) m2plot1 ggsave(filename = "./plots_weight/predictionBefore.png", m2plot1, width = 8, height = 8) ##### Difference wave 1 - wave 2 ###### df$DchgStV0 <- df$w2chgStVO - df$chgStVO df$DavCncrn <- df$w2avCncrn - df$avNoCrn df$DavTests <- df$w2avTests - df$avTests df$DavcPriv <- df$w2avcPriv - df$avcPriv df$DavcHack <- df$w2avcHack - df$avcHack df$DavcAcci <- df$w2avcAcci - df$avcAcci df$DavcSysSc <- df$w2avcSysSc - df$avcSysSc df$DavcSysRl <- df$w2avcSysRl - df$avcSysRl df$DavcDrCtr <- df$w2avcDrCtr - df$avcDrCtr df$DavcJoblos <- df$w2avcJoblos - df$avcJoblos df$DavCntct <- df$w2avCntct - df$avCntct m4 <- lm(DchgStV0 ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m5 <- lm(DavCncrn ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m6 <- lm(DavTests ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) summary(m4) summary(m5) summary(m6) table_difference <- stargazer(m4, m5, m6, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitude change from wave 1 to wave 2 (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Willingness to take risks", "Age", "Male dummy", "Minimum education", "Secondary education", "Tertiary education", "Test municipality", "Income unknown", "Below CHF 2000", "CHF 2000-4000", "CHF 4001-6000", "CHF 6001-8000", "CHF 8001-10000", "CHF 10001-12000", "CHF 12001-14000", "CHF 14001-16000", "More than CHF 16000", "Intercept"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_differencew1w2.html') m2plot3 <- dwplot(list(m4, m5, m6), show_intercept = FALSE) %>% relabel_predictors(c(DavcSysSc = "System safety concern", DavcSysRl = "System reliability concern", DavcDrCtr = "Driver control loss concern", DavcJoblos = "Job loss concern", DavcAcci = "Liability concern", DavcPriv = "Privacy concern", DavcHack = "Software misuse concern", DavCntct = "Self-driving vehicle contact", risk = "Willingness to take risks", age = "Age", male = "Male dummy", educ0 = "Minimum education", educ1 = "Secondary education", educ2 = "Tertiary education", dummyNeu.y = "Test municipality")) + theme_bw() + xlab("Coefficient Estimate") + ylab("") + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) + ggtitle("Self-driving vehicle Support Prediction Difference (wave 2 - wave 1)") + theme(plot.title = element_text(face="bold"), legend.justification = c(0, 0), legend.background = element_rect(colour="grey80"), legend.position = "bottom", axis.text.x = element_text(size=12, family="Sans"), legend.text = element_text(size=12)) + scale_colour_manual(name="Dependent Variable", breaks=c("Model 1", "Model 2", "Model 3"), labels=c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), values=c("#e66101","#5e3c99","#b2abd2")) ggsave(filename = "./plots_weight/predictionDifferencew1w2.png", m2plot3, width = 10, height = 10) ##### Difference wave 1 - wave 3 ###### df$DchgStV0 <- df$w3chgStVO - df$chgStVO df$DavCncrn <- df$w3avCncrn - df$avNoCrn df$DavTests <- df$w3avTests - df$avTests df$DavcPriv <- df$w3avcPriv - df$avcPriv df$DavcHack <- df$w3avcHack - df$avcHack df$DavcAcci <- df$w3avcAcci - df$avcAcci df$DavcSysSc <- df$w3avcSysSc - df$avcSysSc df$DavcSysRl <- df$w3avcSysRl - df$avcSysRl df$DavcDrCtr <- df$w3avcDrCtr - df$avcDrCtr df$DavcJoblos <- df$w3avcJoblos - df$avcJoblos df$DavCntct <- df$w3avCntct - df$avCntct m7 <- lm(DchgStV0 ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m8 <- lm(DavCncrn ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) m9 <- lm(DavTests ~ DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct + risk + age+ male + educ1 + educ2 + educ0 + dummyNeu.y + inc1, data = df, na.action=na.exclude, weights=weight) summary(m7) summary(m8) summary(m9) table_difference <- stargazer(m7, m8, m9, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitude change from wave 1 to wave 3 (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Willingness to take risks", "Age", "Male dummy", "Minimum education", "Secondary education", "Tertiary education", "Test municipality", "Income unknown", "Below CHF 2000", "CHF 2000-4000", "CHF 4001-6000", "CHF 6001-8000", "CHF 8001-10000", "CHF 10001-12000", "CHF 12001-14000", "CHF 14001-16000", "More than CHF 16000", "Intercept"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_differencew1w3.html') m2plot4 <- dwplot(list(m7, m8, m9), show_intercept = FALSE) %>% relabel_predictors(c(DavcSysSc = "System safety concern", DavcSysRl = "System reliability concern", DavcDrCtr = "Driver control loss concern", DavcJoblos = "Job loss concern", DavcAcci = "Liability concern", DavcPriv = "Privacy concern", DavcHack = "Software misuse concern", DavCntct = "Self-driving vehicle contact", risk = "Willingness to take risks", age = "Age", male = "Male dummy", educ0 = "Minimum education", educ1 = "Secondary education", educ2 = "Tertiary education", dummyNeu.y = "Test municipality")) + theme_bw() + xlab("Coefficient Estimate") + ylab("") + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) + ggtitle("Self-driving vehicle Support Prediction Difference (wave 3 - wave 1)") + theme(plot.title = element_text(face="bold"), legend.justification = c(0, 0), legend.background = element_rect(colour="grey80"), legend.position = "bottom", axis.text.x = element_text(size=12, family="Sans")) + scale_colour_manual(name="Dependent Variable", breaks=c("Model 1", "Model 2", "Model 3"), labels=c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), values=c("#e66101","#5e3c99","#b2abd2")) ggsave(filename = "./plots_weight/predictionDifferencew1w3.png", m2plot3, width = 10, height = 10) #joint plot legend <- get_legend(m2plot3) left <- m2plot1 + theme(legend.position="none") + ggtitle("Before test (wave 1)") + xlab("") middle <- m2plot3 + theme(axis.text.y = element_blank(), legend.position="none") + ggtitle("Change from wave 1 to wave 2") + xlab("Coefficient Estimates") right <- m2plot4 + theme(axis.text.y = element_blank(), legend.position = "none")+ ggtitle("Change from wave 1 to wave 3") + xlab("") fig_joint <- ggdraw() + draw_plot(left, x = 0.0025, y = 0.093, width = 0.45, height = 0.907) + draw_plot(middle, x = 0.45, y = 0.1, width = 0.275, height = 0.9) + draw_plot(right, x = 0.725, y = 0.1, width = 0.275, height = 0.9) + draw_plot(legend, x = 0.2, y = 0.02, width = 1, height = 0.1) fig_joint ggsave(filename = "./plots_weight/fig_joint.png", fig_joint, width = 12, height = 6) ##### Prediction Interactions ##### ##### Before test ###### mm1 <- lm(chgStVO ~ (avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm2 <- lm(avNoCrn ~ (avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm3 <- lm(avTests ~ (avcSysSc + avcSysRl + avcDrCtr + avcJoblos + avcAcci + avcPriv + avcHack + avCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) summary(mm1) summary(mm2) summary(mm3) library(stargazer) table_beforetest_interact <- stargazer(mm1, mm2, mm3, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitudes before test (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Willingness to take risks", "Age", "Male dummy", "Minimum education", "Secondary education", "Tertiary education", "Test municipality", "Income unknown", "Below CHF 2000", "CHF 2000-4000", "CHF 4001-6000", "CHF 6001-8000", "CHF 8001-10000", "CHF 10001-12000", "CHF 12001-14000", "CHF 14001-16000", "More than CHF 16000", "Intercept"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_beforetest_interact.html') ##### Difference wave 1 - wave 2 ###### df$DchgStV0 <- df$w2chgStVO - df$chgStVO df$DavCncrn <- df$w2avCncrn - df$avNoCrn df$DavTests <- df$w2avTests - df$avTests df$DavcPriv <- df$w2avcPriv - df$avcPriv df$DavcHack <- df$w2avcHack - df$avcHack df$DavcAcci <- df$w2avcAcci - df$avcAcci df$DavcSysSc <- df$w2avcSysSc - df$avcSysSc df$DavcSysRl <- df$w2avcSysRl - df$avcSysRl df$DavcDrCtr <- df$w2avcDrCtr - df$avcDrCtr df$DavcJoblos <- df$w2avcJoblos - df$avcJoblos df$DavCntct <- df$w2avCntct - df$avCntct mm4 <- lm(DchgStV0 ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm5 <- lm(DavCncrn ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm6 <- lm(DavTests ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) summary(mm4) summary(mm5) summary(mm6) table_difference_interact <- stargazer(mm4, mm5, mm6, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitude change from wave 1 to wave 2 (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Test municipality", "System safety concern*Test municipality", "System reliability concern*Test municipality", "Driver control loss concern*Test municipality", "Job loss concern*Test municipality", "Liability concern*Test municipality", "Privacy concern*Test municipality", "Software misuse concern*Test municipality", "Self-driving vehicle contact*Test municipality"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_differencew1w2_interact.html') ##### Difference wave 1 - wave 3 ###### df$DchgStV0 <- df$w3chgStVO - df$chgStVO df$DavCncrn <- df$w3avCncrn - df$avNoCrn df$DavTests <- df$w3avTests - df$avTests df$DavcPriv <- df$w3avcPriv - df$avcPriv df$DavcHack <- df$w3avcHack - df$avcHack df$DavcAcci <- df$w3avcAcci - df$avcAcci df$DavcSysSc <- df$w3avcSysSc - df$avcSysSc df$DavcSysRl <- df$w3avcSysRl - df$avcSysRl df$DavcDrCtr <- df$w3avcDrCtr - df$avcDrCtr df$DavcJoblos <- df$w3avcJoblos - df$avcJoblos df$DavCntct <- df$w3avCntct - df$avCntct mm7 <- lm(DchgStV0 ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm8 <- lm(DavCncrn ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) mm9 <- lm(DavTests ~ (DavcSysSc + DavcSysRl + DavcDrCtr + DavcJoblos + DavcAcci + DavcPriv + DavcHack + DavCntct) * dummyNeu.y, data = df, na.action=na.exclude, weights=weight) summary(mm7) summary(mm8) summary(mm9) table_difference_interact <- stargazer(mm7, mm8, mm9, type="html", single.row = TRUE, title="Table A: Predictors of self-driving vehicle attitude change from wave 1 to wave 3 (linear regression)", align=TRUE, star.char = c("+", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), digits = 3, dep.var.labels = c("Legal adaptation support", "Confidence in self-driving vehicles", "Self-driving vehicle test support"), order=c(1,2,3,4,5,6,7,8,9,10,11,12,15,13,14,16,17), covariate.labels = c("System safety concern", "System reliability concern", "Driver control loss concern", "Job loss concern", "Liability concern", "Privacy concern", "Software misuse concern", "Self-driving vehicle contact", "Test municipality", "System safety concern*Test municipality", "System reliability concern*Test municipality", "Driver control loss concern*Test municipality", "Job loss concern*Test municipality", "Liability concern*Test municipality", "Privacy concern*Test municipality", "Software misuse concern*Test municipality", "Self-driving vehicle contact*Test municipality"), object.names = FALSE, notes.append = FALSE, notes.align = "l", notes = "Table entries are ordinary least square coefficient estimates with estimated standard errors. Observations are weighted based on the true distribution of age, gender, and municipality. ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1", out='tabs_weight/table_differencew1w3_interact.html')