Services, value added (annual % growth)

The indicator of "Services, value added (annual % growth)" is a vital metric that reflects the growth rate of the service sector within an economy. This indicator shows the contribution of services to the overall economic growth, which includes financial services, trade, hospitality, and various other sectors responsible for providing non-material goods. Understanding this indicator is essential for policymakers, investors, and analysts as it provides insights into economic health, labor market trends, and potential areas for investment or development.

The importance of measuring growth in value-added services lies in its correlation with overall GDP growth. In many economies, especially those classified as developing or emerging markets, the service sector is a leading driver of economic activity. As the global economy shifts from agriculture and manufacturing to services, tracking this growth becomes indispensable for understanding national and regional economic trajectories. For instance, a robust growth rate signals increased consumer confidence and spending, driving more investments into the sector. Conversely, stagnation or decline can indicate recessionary trends, prompting government intervention.

This indicator is inherently linked to several other economic metrics. For instance, improvements in value-added service growth can lead to job creation, which influences employment rates and wage levels in an economy. Higher service growth often correlates with improved productivity levels, innovation, and an increase in private sector health. Additionally, this growth can impact other sectors, such as construction, manufacturing, and agriculture, particularly when services provide critical inputs or support for these industries.

Several factors affect the growth of value-added services. Economic conditions are paramount; during periods of economic expansion, service industries thrive due to increased consumer demand. Technological advancements also play a crucial role by improving service delivery and efficiency, enabling companies to scale up operations. Regulatory frameworks and government policies, such as tax incentives or support for entrepreneurship, can stimulate growth. Social factors such as demographic shifts, urbanization, and consumer preferences also drive changes in service demand.

When developing strategies to enhance service sector growth, a multi-faceted approach may yield the best results. Governments and institutions should invest in educational initiatives that increase the skill levels of the workforce, particularly in areas like hospitality, IT, and finance. Encouraging entrepreneurship and fostering an environment that promotes innovation through tax incentives or grants will also bolster growth. Additionally, it is imperative to focus on infrastructural development to support service delivery, particularly in transportation and communications.

Despite its significance, this indicator is not without flaws. For instance, it can sometimes present a misleading picture of economic vitality if not evaluated alongside other indicators. High growth in services could occur in sectors that do not derive substantial value, such as low-paying jobs within the hospitality industry. Furthermore, reliance on the service industry could expose economies to vulnerabilities, especially those tied to external factors like tourism, which can be heavily affected by global economic conditions or crises like pandemics.

Examining the latest data for 2023 reveals a median growth value of 3.71%. This figure suggests steady service sector expansion but may not fully capture the disparities present in various regions. The top five areas for service value-added growth reflect remarkable achievements—Macao SAR China leads by an astounding 76.4%, indicating a booming tourism-driven economy. Fiji, at 13.9%, and countries like Armenia and Rwanda demonstrate significant leaps, driven by various local economic reforms and improvements in governance.

In contrast, the bottom five areas showcase alarming declines, with North Macedonia seeing a drastic -100.0%, representing an economic implosion rather than a usual cycle of growth. Beneficial reforms and environmental stability must be pursued in countries like Sudan and the Central African Republic to overcome their negative growth barriers. These varied results highlight the necessity for tailored economic strategies that address specific challenges facing each region.

The historical context of the world values from 1998 to 2022 illustrates fluctuating growth in the service sector, showcasing both resilience and vulnerability. The data reveals a general upward trend, peaking in the 2000s and facing setbacks during global recessions, such as in 2009 and 2020. However, the recent uptick in 2021 and 2022 signals a potential recovery, underscoring the sector's adaptability. However, these rebounds must be sustained to secure long-term stability.

In summary, the "Services, value added (annual % growth)" indicator serves as a crucial measure of economic health and development. While offering insights into service sector dynamics, its interpretation should be conducted cautiously, recognizing the interconnectedness with other economic factors and the potential underlying challenges. By crafting informed strategies and solutions, economies can strive to enhance their service sector, addressing both current growth aspirations and future vulnerabilities.

                    
# 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.SRV.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.SRV.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))