Net migration

Net migration is a demographic indicator that measures the difference between the number of immigrants and emigrants in a given area over a specific period. This metric can provide significant insights into population trends, social dynamics, and economic conditions in various regions globally. Understanding net migration is crucial as it not only reflects the movement of people but also encapsulates broader issues such as economic opportunity, social stability, and environmental factors.

The importance of net migration cannot be overstated. In regions experiencing population decline, positive net migration can serve as a lifeline, bringing in fresh talent, labor, and cultural diversity. On the other hand, areas with negative net migration often face challenges such as labor shortages, declining consumer bases, and potential economic stagnation. The relationship of net migration to other indicators, such as GDP growth, unemployment rates, and birth/death rates, is complex but essential. Positive net migration typically aligns with higher economic growth and lower unemployment, as newcomers often fill critical jobs that support local economies.

Factors affecting net migration are multifaceted and can vary significantly depending on the region. Economic opportunities, political stability, educational prospects, and the quality of life in a destination country can attract migrants. Conversely, conflict, persecution, and economic downturns in a home country can drive people to seek refuge elsewhere. For example, the data from the latest year, 2023, reflects a median net migration value of -318.0, indicating a net loss in the global population when averaged. This suggests a transition in migration trends, possibly influenced by worsening conditions in countries typically associated with emigration.

In examining the top five areas for net migration, the data reveals that the United States leads with a net migration of 1,322,668 individuals, followed by Syria (757,309), Germany (609,553), Chad (496,830), and South Sudan (455,000). The United States has long been a destination for migrants due to its diverse economy, educational opportunities, and established immigrant communities. Meanwhile, Syria’s high net migration number may be attributed to ongoing conflicts that have forced many to flee in search of safety. Germany's strong economy and social welfare system attract international talent, while Chad and South Sudan's figures may reflect regional dynamics and humanitarian crises prompting migration.

On the contrary, the bottom five areas indicate countries that are experiencing significant net emigration. Pakistan (-1,619,557), Sudan (-1,349,998), India (-979,179), China (-567,724), and Bangladesh (-549,918) are all facing challenges that encourage outflow. Pakistan and Sudan are grappling with political instability and economic issues, while India’s significant net migration could be tied to a combination of economic opportunities abroad and educational aspirations. China's figure may reflect a trend where individuals seek better job opportunities or living conditions outside of their home country. Similarly, economic migration patterns in Bangladesh reveal a struggle for many to overcome poverty and lack of opportunities at home.

The global trend observed since 1960, with a striking value of zero for net migration annually until 2023, illustrates a time of relative stability in global population movement compared to the recent surges of migration driven by various crises. This stark contrast indicates a significant shift in how migration is perceived and addressed globally. Strategies for managing and enhancing positive net migration often include policies aimed at attracting skilled labor, creating inclusive immigration laws, and fostering a welcoming environment for newcomers. Addressing the underlying issues that lead to emigration, such as conflict resolution and economic development, is also critical in maintaining healthy population levels.

Despite the advantages of net migration, flaws do exist. One of the foremost issues is the potential for social tension and conflict arising from rapid demographic changes. Host communities may face challenges in integrating large influxes of migrants, leading to cultural friction and resource strain. Moreover, overly restrictive immigration policies can exacerbate the problem by forcing individuals to undertake dangerous journeys or fall victim to trafficking and exploitation.

In conclusion, net migration is a crucial indicator that intertwines with various social, economic, and political factors, impacting individual lives and shaping nations worldwide. The global landscape of migration is ever-changing and requires thoughtful consideration of the driving forces behind it, the consequences for local and global economies, and the strategies needed to support both migrants and host communities effectively. Only through comprehensive understanding and proactive policies can the benefits of migration be maximized while minimizing potential downsides.

                    
# 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 = 'SM.POP.NETM'

# 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 <- 'SM.POP.NETM'

# 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))