Industry (including construction), value added (annual % growth)

The indicator 'Industry (including construction), value added (annual % growth)' provides an insightful measurement of economic performance by quantifying the annual growth in value added by the industrial and construction sectors of an economy. This metric is essential as it reflects the overall health of key sectors that significantly contribute to a nation’s GDP. The industrial sector encompasses manufacturing, mining, electricity, and gas, while construction encompasses both residential and non-residential activities. By providing a clear picture of the industrial sector's performance, this indicator assists policymakers, investors, and researchers in making informed decisions about economic strategy and resource allocation.

Understanding the importance of this indicator helps to highlight its relevance in the context of economic trends and development. A positive growth percentage signifies a robust industrial and construction sector, indicative of increasing productivity, job creation, and economic stability. In contrast, negative growth can signal economic distress, loss of employment, and reduced output, which can have cascading effects on other sectors, such as services and agriculture. Given that industry often acts as a backbone for many economies, its health can be instrumental in advancing overall economic resilience.

This indicator is intricately connected to other economic indicators. For instance, it often correlates with GDP growth, labor market dynamics, investment trends, and export performance. A thriving industrial sector typically leads to increased demand for labor, contributing to lower unemployment rates and potentially higher wages. Furthermore, robust industrial growth can drive capital investments, facilitating the development of infrastructure and technology that bolster efficiency in production processes. Additionally, the performance of the industry can be closely linked to consumer confidence, where greater industrial output often translates to higher consumption levels.

Several factors influence the value added by industry and construction. Economic policies play a significant role; favorable government regulations, tax incentives, and support for innovation can stimulate growth in these sectors. Additionally, external factors such as globalization can impact the competitiveness of domestic industries. Fluctuations in global markets or commodity prices can either enhance or detract from the industrial growth of an economy. Infrastructure quality, availability of skilled labor, and access to capital also significantly affect industrial productivity and growth trajectories.

Challenges often arise in improving industrial growth rates. For instance, over-reliance on a single industry, poor infrastructure, and inadequate technology adoption can hinder competitiveness. Furthermore, economic downturns, such as those caused by pandemics or geopolitical tensions, can drastically impact industrial performance. Strategies to bolster the industrial sector might include diversification of industry sectors, investment in R&D and technology, and enhancing workforce skills through education and training programs. Governments may also consider establishing public-private partnerships to foster infrastructure development, which in turn supports industrial expansion.

In analyzing the data for the latest year, 2023 shows a median value of 1.56% for industrial growth across various regions. This figure reveals modest growth overall, suggesting that while some regions are thriving, others are lagging behind. Notably, the top five areas with the highest growth—Libya at 17.77%, Slovakia at 15.03%, Congo - Kinshasa at 14.56%, Sierra Leone at 14.39%, and Andorra also at 14.39%—illustrate extreme variations in industrial development. Libya, for example, might be experiencing a recovery phase post-conflict, seeing increased investments in rebuilding its infrastructure and industries.

On the other hand, the bottom five regions indicating negative growth, such as Timor-Leste at -56.97% and Ireland at -21.06%, represent significant concerns. These declines can be attributed to various factors, including economic instability, lack of investment, or significant contractions in key industries. Timor-Leste’s staggering drop may indicate major structural issues or a lack of diversification, while Ireland’s negative growth could reveal broader global economic pressures impacting its industrial sectors.

Looking at historical data, we see fluctuations in the global trend of industrial value added growth. From 3.32% in 1995 to a notable decline in subsequent years, the fluctuations highlight that global economic conditions are ever-changing. A critical point is the growth in 2021, which surged to 7.1% post-pandemic recovery, only to moderate again in subsequent years. The figures for 2022 and 2023, which stand at 2.18% and 2.26% respectively, reflect a slowly stabilizing but still cautious economic environment.

In conclusion, the 'Industry (including construction), value added (annual % growth)' is a pivotal indicator that encapsulates the economic narrative of nations. It reveals the underlying health of industrial sectors, their interconnection with broader economic indicators, and highlights the significance of strategic investments and policies aimed at fostering sustainable industrial growth. While opportunities abound in particular regions, a cautious approach is necessary to understand and address the flaws and challenges manifested within the global industrial landscape.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'NV.IND.TOTL.KD.ZG'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'NV.IND.TOTL.KD.ZG'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))