chooseCRANmirror(graphics = TRUE, ind=1)
knitr::opts_chunk$set(echo = TRUE)
setwd("~/Desktop")
pr=read.csv("productivity.csv")
wage=read.csv("wage.csv")
tech=read.csv("tech.csv")
cap=read.csv("cap.csv")
tr=read.csv("trade.csv")
install.packages("dplyr")
##
## The downloaded binary packages are in
## /var/folders/tg/ndf9pfq972n0nl0yrwftf9rh0000gn/T//Rtmpbc6uyU/downloaded_packages
library(dplyr)
install.packages("car")
##
## The downloaded binary packages are in
## /var/folders/tg/ndf9pfq972n0nl0yrwftf9rh0000gn/T//Rtmpbc6uyU/downloaded_packages
library(car)
pr=pr%>%inner_join(wage,by="year")
pr=pr%>%inner_join(tech,by="year")
pr=pr%>%inner_join(cap,by="year")
pr=pr%>%inner_join(tr,by="year")
install.packages("ggplot2")
##
## The downloaded binary packages are in
## /var/folders/tg/ndf9pfq972n0nl0yrwftf9rh0000gn/T//Rtmpbc6uyU/downloaded_packages
library(ggplot2)
# Short-run Regressions
r1=lm(GDP_per_hour_worked~real_wage, pr)
summary(r1)
##
## Call:
## lm(formula = GDP_per_hour_worked ~ real_wage, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6827 -1.9640 -0.7272 1.9728 9.7771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7286 10.0406 -0.172 0.865
## real_wage 1.0355 0.1124 9.214 1.13e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.329 on 26 degrees of freedom
## Multiple R-squared: 0.7655, Adjusted R-squared: 0.7565
## F-statistic: 84.89 on 1 and 26 DF, p-value: 1.134e-09
r2=lm(real_wage~GDP_per_hour_worked, pr)
summary(r2)
##
## Call:
## lm(formula = real_wage ~ GDP_per_hour_worked, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.2697 -2.2425 0.2673 2.6193 6.6973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.15522 7.29251 3.038 0.00536 **
## GDP_per_hour_worked 0.73930 0.08024 9.214 1.13e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.658 on 26 degrees of freedom
## Multiple R-squared: 0.7655, Adjusted R-squared: 0.7565
## F-statistic: 84.89 on 1 and 26 DF, p-value: 1.134e-09
# Real wage over years
ggplot(pr, aes(x=year, y=real_wage))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("Real wage over years")
# Labour productivity over years
ggplot(pr, aes(x=year, y=GDP_per_hour_worked))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("Labour productivity over years")
# higher real wage increases labour productivity?
ggplot(pr, aes(x=real_wage, y=GDP_per_hour_worked))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("labour productivity against real wage")
# higher labour productivity demands higher real wage?
ggplot(pr, aes(x=GDP_per_hour_worked, y=real_wage))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("real wage against labour productivity")
# Long-run regressions
# Productivity and real wage and other exogenous factors
r3=lm(GDP_per_hour_worked~real_wage+R_D+Capital+trade_union, pr)
summary(r3)
##
## Call:
## lm(formula = GDP_per_hour_worked ~ real_wage + R_D + Capital +
## trade_union, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2832 -0.5430 0.2327 0.7664 1.6049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.045e+02 7.458e+00 14.016 9.40e-13 ***
## real_wage -1.130e-02 8.164e-02 -0.138 0.89109
## R_D 6.484e+00 2.003e+00 3.237 0.00364 **
## Capital -1.733e-04 3.153e-03 -0.055 0.95664
## trade_union -1.281e+00 9.114e-02 -14.051 8.92e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.284 on 23 degrees of freedom
## Multiple R-squared: 0.9818, Adjusted R-squared: 0.9786
## F-statistic: 309.5 on 4 and 23 DF, p-value: < 2.2e-16
# Potential factors influencing real wage in the LR
r4=lm(real_wage~trade_union,pr)
summary(r4)
##
## Call:
## lm(formula = real_wage ~ trade_union, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0524 -2.1550 -0.5727 2.4166 8.6726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 115.2220 3.2556 35.391 < 2e-16 ***
## trade_union -1.1398 0.1379 -8.263 9.59e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.967 on 26 degrees of freedom
## Multiple R-squared: 0.7242, Adjusted R-squared: 0.7136
## F-statistic: 68.28 on 1 and 26 DF, p-value: 9.595e-09
r5=lm(real_wage~R_D,pr)
summary(r5)
##
## Call:
## lm(formula = real_wage ~ R_D, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.8319 -1.7022 0.3466 2.4314 4.7486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.408 5.591 5.976 2.62e-06 ***
## R_D 21.905 2.186 10.019 2.04e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.427 on 26 degrees of freedom
## Multiple R-squared: 0.7943, Adjusted R-squared: 0.7864
## F-statistic: 100.4 on 1 and 26 DF, p-value: 2.038e-10
r6=lm(real_wage~Capital,pr)
summary(r6)
##
## Call:
## lm(formula = real_wage ~ Capital, data = pr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9619 -3.0435 0.4398 3.4379 8.6539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.501512 5.253536 11.897 5.07e-12 ***
## Capital 0.042918 0.008338 5.147 2.28e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.317 on 26 degrees of freedom
## Multiple R-squared: 0.5047, Adjusted R-squared: 0.4857
## F-statistic: 26.49 on 1 and 26 DF, p-value: 2.279e-05
# Capital investment over years
ggplot(pr, aes(x=year, y=Capital))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("Capital investment over years")
# Trade Union influence over years
ggplot(pr, aes(x=year, y=trade_union))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("Trade union impact over years")
# R&D spending over years
ggplot(pr, aes(x=year, y=R_D))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("technology over years")
# Trade union affects wage setting?
ggplot(pr, aes(x=trade_union, y=real_wage))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("Real wage against trade union")
# technology leads to higher productivity?
ggplot(pr, aes(x=R_D, y=GDP_per_hour_worked))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("labour productivity against technology")
# Capital investment over years
ggplot(pr, aes(x=year, y=Capital))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("capital investment over years")
# Capital investment leads to higher productivity?
ggplot(pr, aes(x=Capital, y=GDP_per_hour_worked))+geom_point(shape=20,size=2,color='light blue')+geom_smooth(method=lm,color=' blue')+theme_minimal()+ggtitle("labour productivity against capital investment")
