# Load required libraries
library(haven) # For reading .dta files
library(dplyr) # For data manipulation
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr) # For data reshaping
library(ggplot2) # For plotting
library(zoo) # For rolling mean
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
# Load the dataset
pwt1001 <- read_dta("pwt1001.dta")
# Define analysis parameters
countries <- c("United States", "United Kingdom", "Germany", "France", "Canada", "Italy")
start_year <- 1960
n_years <- 10 # Number of years for annualized growth rate calculations
# Data Preprocessing
# Filter data for selected countries and variables, and arrange it by country and year
selected_countries <- pwt1001 %>%
filter(country %in% countries) %>%
select(year, country, rgdpna, rkna, emp, labsh, hc) %>%
mutate(year = as.numeric(year)) %>%
filter(year >= start_year) %>%
arrange(country, year)
# Calculate Growth Rates
# Compute 10-year annualized growth rates for GDP per capita
data <- selected_countries %>%
group_by(country) %>%
mutate(
y_pc = rgdpna / emp, # GDP per capita
lagged_y_pc = lag(y_pc, n_years),
lagged_GDP_growth = (((y_pc / lagged_y_pc)^(1 / n_years)) - 1) * 100
)
# PLOT 1: Labour Productivity Growth
ggplot(data[data$year >= 1985, ], aes(x = year, y = lagged_GDP_growth, color = country)) +
geom_line() +
geom_vline(xintercept = 2007, linetype = "dashed", color = "black", size = 0.5) +
labs(
title = paste(n_years, "Year Annualized Labour Productivity Growth Rate"),
x = "Year",
y = "Labour Productivity Growth (%)"
) +
facet_wrap(~ country, scales = "free_y") + # Separate plots for each country
theme_minimal() +
theme(legend.position = "none") # Hide the legend
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Calculate Contributions to Growth
annualized_data <- selected_countries %>%
group_by(country) %>%
mutate(
y_pc = rgdpna / emp, # GDP per capita
k_pc = rkna / emp, # Capital per capita
lagged_y_pc = lag(y_pc, n_years),
lagged_k_pc = lag(k_pc, n_years),
lagged_hc = lag(hc, n_years),
lagged_labsch = rollmean(labsh, k = n_years, align = "right", na.pad = TRUE),
lagged_GDP_growth = (((y_pc / lagged_y_pc)^(1 / n_years)) - 1)*100,
lagged_capital_growth = (((k_pc / lagged_k_pc)^(1 / n_years)) - 1)*100,
lagged_human_capital_growth = (((hc / lagged_hc)^(1 / n_years)) - 1)*100,
physical_capital_deepening = (1 - lagged_labsch) * lagged_capital_growth,
human_capital_deepening = lagged_labsch * lagged_human_capital_growth,
lagged_TFP_growth = lagged_GDP_growth - physical_capital_deepening - human_capital_deepening
) %>%
na.omit()
# Analyze Growth Slowdown
growth_analysis <- annualized_data %>%
mutate(year = as.numeric(year)) %>%
mutate(period = ifelse(year <= 2007, "1985_2007", "2007_2020")) %>%
group_by(country, period) %>%
summarize(
avg_growth_rate = mean(lagged_GDP_growth, na.rm = TRUE),
avg_capital_growth = mean(lagged_capital_growth, na.rm = TRUE),
avg_hc_growth = mean(lagged_human_capital_growth, na.rm = TRUE),
avg_TFP_growth = mean(lagged_TFP_growth, na.rm = TRUE),
avg_human_capital_contribution = mean(human_capital_deepening, na.rm = TRUE),
avg_physical_capital_contribution = mean(physical_capital_deepening, na.rm = TRUE),
avg_tfp_contribution = mean(lagged_TFP_growth, na.rm = TRUE),
mean_labsch = mean(lagged_labsch, na.rm = TRUE)
) %>%
pivot_wider(
names_from = period,
values_from = c(avg_growth_rate, avg_capital_growth, avg_hc_growth, avg_TFP_growth, avg_human_capital_contribution, avg_physical_capital_contribution, avg_tfp_contribution, mean_labsch)
)
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
View(growth_analysis)
growth_analysis2 <- growth_analysis %>%
mutate(
productivity_slowdown = avg_growth_rate_2007_2020 - avg_growth_rate_1985_2007,
human_capital_slowdown = avg_hc_growth_2007_2020 - avg_hc_growth_1985_2007,
physical_capital_slowdown = avg_capital_growth_2007_2020 - avg_capital_growth_1985_2007,
tfp_slowdown = avg_TFP_growth_2007_2020 - avg_TFP_growth_1985_2007,
human_capital_contribution_slowdown = avg_human_capital_contribution_2007_2020 - avg_human_capital_contribution_1985_2007,
physical_capital_contribution_slowdown = avg_physical_capital_contribution_2007_2020 - avg_physical_capital_contribution_1985_2007,
tfp_contribution_slowdown = avg_tfp_contribution_2007_2020 - avg_tfp_contribution_1985_2007,
hc_share = human_capital_contribution_slowdown / productivity_slowdown,
pc_share = physical_capital_contribution_slowdown / productivity_slowdown,
tfp_share = tfp_contribution_slowdown / productivity_slowdown
)
View(growth_analysis2)
# Slowdown Table
library(gt)
library(dplyr)
# Preprocess the data to multiply shares by 100
growth_analysis2 <- growth_analysis2 %>%
mutate(
hc_share = hc_share * 100,
pc_share = pc_share * 100,
tfp_share = tfp_share * 100
)
# Create the table
growth_analysis2 %>%
ungroup() %>% # Remove grouping by country
arrange(desc(productivity_slowdown)) %>% # Sort by highest productivity slowdown
select(
country,
productivity_slowdown,
human_capital_slowdown,
physical_capital_slowdown,
tfp_slowdown,
hc_share,
pc_share,
tfp_share
) %>%
gt() %>%
tab_header(
title = "Accounting for the Slowdown in Labour Productivity after the 2008 Financial Crisis",
subtitle = "Changes in Growth Rates Between 1985-2007, and 2008-2019; and Percentage Contributions to the Slowdown"
) %>%
fmt_number(
columns = c(
productivity_slowdown,
human_capital_slowdown,
physical_capital_slowdown,
tfp_slowdown,
hc_share,
pc_share,
tfp_share
),
n_sigfig = 3
) %>%
cols_label(
country = "Country",
productivity_slowdown = "Productivity Slowdown",
human_capital_slowdown = "Human Capital Slowdown",
physical_capital_slowdown = "Physical Capital Slowdown",
tfp_slowdown = "TFP Slowdown",
hc_share = "Human Capital Contribution (%)",
pc_share = "Physical Capital Contribution (%)",
tfp_share = "TFP Contribution (%)"
) %>%
tab_style(
style = cell_borders(
sides = "right",
color = "black",
weight = px(2)
),
locations = list(
cells_body(columns = c(country)),
cells_column_labels(columns = c(country))
)
) %>%
tab_style(
style = cell_borders(
sides = "right",
color = "black",
weight = px(2)
),
locations = list(
cells_body(columns = c(productivity_slowdown)),
cells_column_labels(columns = c(productivity_slowdown))
)
) %>%
tab_style(
style = cell_borders(
sides = "right",
color = "black",
weight = px(2)
),
locations = list(
cells_body(columns = c(tfp_slowdown)),
cells_column_labels(columns = c(tfp_slowdown))
)
) %>%
tab_style(
style = list(cell_text(weight = "bold")),
locations = cells_column_labels(everything())
)
| Accounting for the Slowdown in Labour Productivity after the 2008 Financial Crisis |
| Changes in Growth Rates Between 1985-2007, and 2008-2019; and Percentage Contributions to the Slowdown |
| Country |
Productivity Slowdown |
Human Capital Slowdown |
Physical Capital Slowdown |
TFP Slowdown |
Human Capital Contribution (%) |
Physical Capital Contribution (%) |
TFP Contribution (%) |
| United States |
−0.387 |
−0.321 |
−0.178 |
−0.146 |
53.5 |
8.68 |
37.8 |
| Canada |
−0.722 |
−0.352 |
−0.564 |
−0.416 |
37.8 |
4.54 |
57.6 |
| United Kingdom |
−1.65 |
−0.364 |
−2.00 |
−0.530 |
11.3 |
56.5 |
32.1 |
| France |
−1.78 |
−0.186 |
−1.85 |
−1.06 |
8.11 |
32.1 |
59.8 |
| Germany |
−1.94 |
−0.519 |
−3.03 |
−0.616 |
18.2 |
50.1 |
31.7 |
| Italy |
−3.06 |
−0.231 |
−2.85 |
−1.77 |
5.60 |
36.6 |
57.8 |
# Accounting Table
# Load necessary library
library(gt)
growth_analysis %>%
select(
country,
starts_with("avg_growth_rate"),
starts_with("avg_capital_growth"),
starts_with("avg_hc_growth"),
starts_with("avg_TFP_growth"),
starts_with("mean_labsch"),
starts_with("avg_human_capital_contribution"),
starts_with("avg_physical_capital_contribution"),
starts_with("avg_tfp_contribution")
) %>%
pivot_longer(
cols = -country,
names_to = c(".value", "period"),
names_pattern = "(.*)_(1985_2007|2007_2020)"
) %>%
mutate(period = recode(period,
"1985_2007" = "1985-2007",
"2007_2020" = "2007-2020")) %>%
gt() %>%
tab_header(
title = "Growth Analysis by Country and Period",
subtitle = "Detailed Breakdown of Growth Metrics Across Periods"
) %>%
fmt_number(
columns = everything(),
decimals = 2
) %>%
cols_label(
avg_growth_rate = "Growth Rate",
avg_capital_growth = "Capital Growth",
avg_hc_growth = "Human Capital Growth",
avg_TFP_growth = "TFP Growth",
mean_labsch = "Labour Share",
avg_human_capital_contribution = "Human Capital Contribution",
avg_physical_capital_contribution = "Physical Capital Contribution",
avg_tfp_contribution = "TFP Contribution",
period = "" # Hide period column label
) %>%
tab_spanner(
label = "1985-2007",
columns = contains("1985-2007")
) %>%
tab_spanner(
label = "2007-2020",
columns = contains("2007-2020")
) %>%
tab_style(
style = list(cell_text(weight = "bold")),
locations = cells_column_labels(everything())
)
| Growth Analysis by Country and Period |
| Detailed Breakdown of Growth Metrics Across Periods |
|
Growth Rate |
Capital Growth |
Human Capital Growth |
TFP Growth |
Labour Share |
Human Capital Contribution |
Physical Capital Contribution |
TFP Contribution |
| Canada |
| 1985-2007 |
1.30 |
2.50 |
0.70 |
0.09 |
0.71 |
0.50 |
0.71 |
0.09 |
| 2007-2020 |
0.58 |
1.93 |
0.34 |
−0.32 |
0.65 |
0.22 |
0.68 |
−0.32 |
| France |
| 1985-2007 |
2.46 |
3.50 |
0.76 |
0.76 |
0.65 |
0.50 |
1.20 |
0.76 |
| 2007-2020 |
0.68 |
1.65 |
0.57 |
−0.31 |
0.62 |
0.35 |
0.63 |
−0.31 |
| Germany |
| 1985-2007 |
2.48 |
3.90 |
0.70 |
0.71 |
0.66 |
0.46 |
1.31 |
0.71 |
| 2007-2020 |
0.54 |
0.87 |
0.18 |
0.10 |
0.62 |
0.11 |
0.33 |
0.10 |
| Italy |
| 1985-2007 |
2.70 |
4.02 |
0.94 |
0.47 |
0.57 |
0.54 |
1.69 |
0.47 |
| 2007-2020 |
−0.36 |
1.17 |
0.71 |
−1.30 |
0.52 |
0.37 |
0.57 |
−1.30 |
| United Kingdom |
| 1985-2007 |
2.24 |
3.22 |
0.79 |
0.37 |
0.56 |
0.44 |
1.43 |
0.37 |
| 2007-2020 |
0.59 |
1.23 |
0.42 |
−0.16 |
0.59 |
0.25 |
0.50 |
−0.16 |
| United States |
| 1985-2007 |
1.66 |
2.13 |
0.60 |
0.48 |
0.62 |
0.37 |
0.81 |
0.48 |
| 2007-2020 |
1.28 |
1.95 |
0.28 |
0.34 |
0.60 |
0.17 |
0.77 |
0.34 |