How has labor productivity in different sectors evolved over the years?

Global Productivity: Trends, Drivers, and Policies

The COVID-19 pandemic struck the global economy after a decade that featured a broad-based slowdown in productivity growth. Global Productivity: Trends, Drivers, and Policies presents the first comprehensive analysis of the evolution and drivers of productivity growth, examines the effects of COVID-19 on productivity, and discusses a wide-range of policies needed to rekindle productivity growth. The book also provides a far-reaching dataset of multiple measures of productivity for up to 164 advanced economies and emerging market and developing economies and introduces a new sectoral database of productivity.

Labor productivity Real: Economic indicator that measures how much output is produced per unit of labor by dividing real output by hours worked.

Labor productivity PPP: Economic indicator that measures the output per unit of labor, adjusted for PPP (purchasing power parity).

Data Preparation

#Load data
global_prod <- read_excel("GlobalProductivitySectoralDatabase.xlsx")
# Display the first few rows of the data set
head(global_prod)

## # A tibble: 6 × 8
##   country sector           year Value_added_nominal Value_added_real Employment
##   <chr>   <chr>           <dbl>               <dbl>            <dbl>      <dbl>
## 1 Angola  Total            2002             664207.         4033341.     5471. 
## 2 Angola  1.Agriculture    2002              38855.          261770.     2022. 
## 3 Angola  2.Mining         2002             310205.         1558314.       60.8
## 4 Angola  3.Manufacturing  2002              24365.          199228.      168. 
## 5 Angola  4.Utilities      2002               2581.           16758.       17.6
## 6 Angola  5.Construction   2002              35969.          250220.      251. 
## # ℹ 2 more variables: Labor_productivity_real <dbl>,
## #   Labor_productivity_PPP <dbl>

How has labor productivity in different sectors evolved over the years?

#Summarize data
summary_data <- global_prod %>%
  group_by(year, sector) %>%
  summarize(avg_labor_productivity_real = mean(Labor_productivity_real, na.rm = TRUE), avg_labor_productivity_PPP = mean(Labor_productivity_PPP, na.rm = TRUE)
            )

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

#Display summarize data
head(summary_data)

## # A tibble: 6 × 4
## # Groups:   year [1]
##    year sector           avg_labor_productivity_real avg_labor_productivity_PPP
##   <dbl> <chr>                                  <dbl>                      <dbl>
## 1  1950 1.Agriculture                          1118.                       9.49
## 2  1950 2.Mining                              27891.                      53.2 
## 3  1950 3.Manufacturing                        4691.                      21.9 
## 4  1950 4.Utilities                            6003.                      25.6 
## 5  1950 5.Construction                         8099.                      15.9 
## 6  1950 6.Trade services                       8952.                      26.2

Key Insights

1. Average labor productivity (Real) over the years

# Summarize the data for real labor productivity
real_productivity_summary <- global_prod %>%
  group_by(year) %>%
  summarize(
    avg_labor_productivity_real = mean(Labor_productivity_real, na.rm = TRUE)
  )

# Display the summarized data
real_productivity_summary

## # A tibble: 68 × 2
##     year avg_labor_productivity_real
##    <dbl>                       <dbl>
##  1  1950                       7567.
##  2  1951                       8310.
##  3  1952                       7470.
##  4  1953                       7851.
##  5  1954                       8338.
##  6  1955                       8502.
##  7  1956                       8399.
##  8  1957                       8551.
##  9  1958                       8875.
## 10  1959                       9088.
## # ℹ 58 more rows

2. Average labor productivity (PPP) over the years

# Summarize the data for PPP labor productivity
ppp_productivity_summary <- global_prod %>%
  group_by(year) %>%
  summarize(
    avg_labor_productivity_PPP = mean(Labor_productivity_PPP, na.rm = TRUE)
  )

# Display the summarized data
ppp_productivity_summary

## # A tibble: 68 × 2
##     year avg_labor_productivity_PPP
##    <dbl>                      <dbl>
##  1  1950                       31.5
##  2  1951                       32.6
##  3  1952                       30.6
##  4  1953                       31.1
##  5  1954                       32.3
##  6  1955                       33.0
##  7  1956                       33.3
##  8  1957                       34.2
##  9  1958                       34.3
## 10  1959                       34.6
## # ℹ 58 more rows

Visualization

1. Visualization of average labor productivity (Real) over the years

ggplot(real_productivity_summary, aes(x = year, y = avg_labor_productivity_real)) +
  geom_line() +
  geom_point() +
  labs(
    title = "Average labor productivity (Real) over the years",
    x = "Year",
    y = "Average labor productivity (Real)"
  ) +
  theme_minimal()

2. Visualization of labor productivity (Real) over the years by sector

# Assuming your data is in a data frame called 'data'
# Filter out the 'Total' sector
filtered_global_prod <- global_prod %>%
  filter(sector != "Total")

# Create the stacked bar chart
ggplot(filtered_global_prod, aes(x = year, y = Labor_productivity_real, fill = sector)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Labor productivity (Real) by sector over the years",
    x = "Year",
    y = "Labor productivity (Real)"
  ) +
  theme_minimal()

## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_bar()`).