Manufacturing, value added (annual % growth)

The indicator "Manufacturing, value added (annual % growth)" measures the increase in the value generated by the manufacturing sector of an economy over a specified year. It serves as a crucial gauge of economic health, reflecting the efficiency and productivity of manufacturing activities. As one of the key sectors contributing to Gross Domestic Product (GDP), the growth rate in value-added manufacturing provides insight into industrial performance, employment prospects, and overall economic trends.

The importance of this indicator cannot be overstated. It serves as a bellwether for economic stability and growth. A rise in manufacturing value added suggests that an economy is thriving, attracting investments, and producing goods at an efficient rate. Conversely, a declining value can signal economic challenges, such as reduced consumer demand, inflationary pressures, or ineffective policies. This indicator also correlates with jobs in manufacturing, which remain pivotal in many economies, providing opportunities for skilled labor and contributing to overall employment levels.

Among its various relationships, the manufacturing growth rate often links closely to other economic indicators like GDP growth, employment rates, and export performance. For instance, a robust manufacturing sector typically drives GDP upwards, enhances job creation, and can boost export levels as countries produce more goods for international markets. This relationship underscores the manufacturing sector's role as a cornerstone of economic policy, often driving governmental initiatives, labor relations, and trade agreements.

Several factors can significantly influence the growth rate of manufacturing value-added. Technological advancements frequently enhance productivity, enabling firms to produce more efficiently and effectively, which can lead to higher value addition. Similarly, access to raw materials and supply chain efficiency can impact manufacturing output. Economic policies, including taxation, regulation, and trade agreements, shape the competitive landscape and influence firms' decisions to invest or scale operations. Labor market conditions also play a vital role; a skilled workforce can drive innovation and improve manufacturing processes, contributing positively to growth.

Given the significant importance of manufacturing in the global economy, devising effective strategies to boost manufacturing value-added growth is imperative for policymakers and business leaders alike. Encouraging investment in technology and innovation is one such strategy. By promoting research and development, governments can facilitate a more dynamic manufacturing environment where efficiency and competitive advantage thrive. Additionally, improving infrastructure, including transportation and logistics, can enhance supply chain management and enable manufacturers to operate at peak efficiency.

Solutions to encourage manufacturing growth may also involve enhancing workforce training programs. By aligning educational institutions with industry needs, governments can ensure a continuous supply of skilled labor, thereby fostering innovation and productivity within the manufacturing sector. Promoting sustainable practices and green technologies can further advance the manufacturing sector's growth while simultaneously addressing environmental concerns, a critical consideration in today's economy.

However, this indicator is not without its flaws. One primary challenge lies in its broad measurement scope. Manufacturing sectors can vary significantly between countries and regions, making direct comparisons less meaningful. Additionally, factors such as inflation or currency misalignment can distort growth figures, complicating the interpretation of trends over time. It’s also essential to consider that manufacturing growth does not always equate to overall economic well-being. In some cases, value added may result from cost-cutting measures rather than genuine productivity improvements, leading to employment losses or wage stagnation.

In 2023, the global median value for manufacturing, value added, is 0.91%. This figure indicates moderate growth on average, but nuanced perspectives arise from individual country performances. The top five areas showcasing remarkable growth rates include Gambia at an impressive 21.64%, Slovakia with 19.64%, Ukraine at 13.81%, St. Lucia with 11.9%, and Denmark at 11.3%. Such high growth rates suggest that these countries are likely experiencing robust industrial activity, spurred by favorable economic conditions, competitive industries, and strategic government policies that promote manufacturing.

On the other end of the spectrum, the bottom five areas are marked by alarming declines; Ireland reports a staggering -23.93%, followed closely by Timor-Leste at -23.05%, Kuwait at -17.27%, Lesotho at -14.16%, and the Central African Republic at -10.28%. These figures signal potential economic distress, where factors like reduced investment, loss of skilled labor, or external shocks may be severely impacting manufacturing capabilities. Such declines pose serious challenges for these economies, needing swift interventions to stabilize and revitalize their manufacturing sectors.

In conclusion, the "Manufacturing, value added (annual % growth)" indicator is a vital metric for understanding the economic landscape. By examining its importance, relationships to other indicators, and the various factors influencing growth, stakeholders can better navigate the complexities of the manufacturing sector. The diverse performances observed in different regions reinforce the need for tailored strategies to enhance manufacturing growth, ensuring long-term economic vitality and stability.

                    
# 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.MANF.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.MANF.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))