# Introduction: This data set includes migration data data broken out by year and country. I aimed to analyze a few different aspects of this data:

  1. Which countries have had the largest inflow of people? Which countries have had the most people leave?
  2. Is there a realtionship between poverty and migration rates?
  3. If we control for population growth, what do the relative migration rates look like?
  4. What countries are leading in absolute migration? Broken out by decade? With these questions in mind, I began working with and manipulating the data to extract these insights.

Load Packages

library(tidyr)
library(dplyr)
library(ggplot2)
library(stringr)
library(ggrepel)

Migration: Read Data Set number 1 and pivot the data

# Read CSV file, skip 3 rows, remove 'X' column
df1 <- read.csv("/Users/williamberritt/Downloads/API_SM/API_SM.POP.NETM_DS2_en_csv_v2_105.csv", header = TRUE, skip = 3) |> subset(select = -c(X))

# Reshape data from wide to long format using pivot_longer
df1 <- df1 |> pivot_longer(cols = (X1960:X2022), names_to = 'year', values_to ='value')

# Display first few rows of the reshaped data frame
head(df1)
## # A tibble: 6 × 6
##   Country.Name Country.Code Indicator.Name Indicator.Code year  value
##   <chr>        <chr>        <chr>          <chr>          <chr> <int>
## 1 Aruba        ABW          Net migration  SM.POP.NETM    X1960     0
## 2 Aruba        ABW          Net migration  SM.POP.NETM    X1961  -569
## 3 Aruba        ABW          Net migration  SM.POP.NETM    X1962  -609
## 4 Aruba        ABW          Net migration  SM.POP.NETM    X1963  -646
## 5 Aruba        ABW          Net migration  SM.POP.NETM    X1964  -684
## 6 Aruba        ABW          Net migration  SM.POP.NETM    X1965  -726

Left Join with list of countries to remove all none country entities like continents, middle class, etc.

# Calculate total migration by country code
migration_by_country_code <- df1 |> 
  group_by(Country.Name) |> 
  summarize(total_migration = sum(value))

# Rename columns
colnames(migration_by_country_code) <- c("country", "total_migration")

# List of country names
country_names <- c("Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burundi", "Cabo Verde", "Cambodia", "Cameroon", "Canada", "Central African Republic", "Chad", "Chile", "China", "Colombia", "Comoros", "Congo", "Costa Rica", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Eswatini", "Ethiopia", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece", "Grenada", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Honduras", "Hungary", "Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kiribati", "Korea, North", "Korea, South", "Kosovo", "Kuwait", "Kyrgyzstan", "Laos", "Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania", "Luxembourg", "Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", "Marshall Islands", "Mauritania", "Mauritius", "Mexico", "Micronesia", "Moldova", "Monaco", "Mongolia", "Montenegro", "Morocco", "Mozambique", "Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "New Zealand", "Nicaragua", "Niger", "Nigeria", "North Macedonia", "Norway", "Oman", "Pakistan", "Palau", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Poland", "Portugal", "Qatar", "Romania", "Russia", "Rwanda", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent and the Grenadines", "Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", "Senegal", "Serbia", "Seychelles", "Sierra Leone", "Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa", "South Sudan", "Spain", "Sri Lanka", "Sudan", "Suriname", "Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania", "Thailand", "Timor-Leste", "Togo", "Tonga", "Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "Uruguay", "Uzbekistan", "Vanuatu", "Vatican City", "Venezuela", "Vietnam", "Yemen", "Zambia", "Zimbabwe")

# Create a data frame with country names
countries_df <- data.frame(country = country_names)

# Merge country names with migration data and remove rows with missing migration data
countries_only <- na.omit(left_join(countries_df, migration_by_country_code, by='country'))

Create data frames that have the countries with most amount of people coming or going

# Extract the top 5 countries with the highest migration influx
top_5_highest_influx <- head(countries_only[order(-countries_only$total_migration),])
top_5_highest_influx
##           country total_migration
## 185 United States        66105084
## 63        Germany        13151498
## 32         Canada         9937374
## 9       Australia         7678338
## 162         Spain         7118049
## 150  Saudi Arabia         6772680
# Extract the top 5 countries with the highest migration exodus
top_5_highest_exodus <- head(countries_only[order(countries_only$total_migration),])
top_5_highest_exodus
##         country total_migration
## 14   Bangladesh       -20194739
## 131    Pakistan       -18321926
## 36        China       -16760013
## 111      Mexico       -14101292
## 75        India       -10760905
## 137 Philippines        -9979653

Read poverty data set

# Read Poverty Data Set
poverty <- read.csv("/Users/williamberritt/Downloads/poverty-rate-by-country-2024.csv")
head(poverty)
##         country PovertyRateValue PovertyRateValueDataYear LessThan10USDPerDay
## 1         India             21.9                     2011                92.1
## 2         China              0.0                     2020                45.8
## 3 United States               NA                       NA                 2.2
## 4     Indonesia              9.5                     2022                79.7
## 5      Pakistan             21.9                     2018                94.5
## 6       Nigeria             40.1                     2018                96.9
##   LessThan365USDPerDay
## 1                 44.8
## 2                  3.0
## 3                  1.2
## 4                 22.4
## 5                 39.8
## 6                 63.5

Merge poverty data with migration numbers to see relationship between migration and poverty

# Merge country names with poverty data
countries_poverty <- left_join(countries_df, poverty, by='country')

# Select relevant columns and remove rows with missing data
countries_poverty <- subset(countries_poverty, select = c('country', 'PovertyRateValue'))
countries_poverty <- na.omit(countries_poverty)

# Merge poverty data with migration data
migration_vs_poverty <- left_join(countries_poverty, migration_by_country_code, by='country')

# Remove rows with missing data
migration_vs_poverty <- na.omit(migration_vs_poverty)

# Extract variables for the plot
x <- migration_vs_poverty$PovertyRateValue
y <- migration_vs_poverty$total_migration

# Create a scatter plot
plot(x, y)

Plot cumulative migration by decade

# Create a copy of the original data frame
df_decade <- df1

# Add a column for the absolute value of migration
df_decade$absolute_value <- abs(df_decade$value)

# Extract the year from the 'year' column
df_decade$year <- str_replace(df_decade$year, 'X', '')
df_decade$year <- as.numeric(df_decade$year)

# Create a new column for the decade
df_decade$decade <- (floor(df_decade$year/10))*10

# Rename columns
colnames(df_decade) <- c("country", "code", 'indicate', 'indic_code', 'year', 'value', 'absolute_value', 'decade')

# Merge country names with the updated data frame
df_decade <- left_join(countries_df, df_decade, by='country')
df_decade <- na.omit(df_decade)

# Calculate total absolute migration by decade
migration_by_decade <- df_decade |> group_by(decade) |> summarize(absolute_migration = sum(absolute_value))

# Adjust the value for the decade 2020
migration_by_decade$adj_value <- ifelse(migration_by_decade$decade == 2020, migration_by_decade$absolute_migration/.3, migration_by_decade$absolute_migration)

# Create a plot
plot(migration_by_decade$decade, migration_by_decade$adj_value)

Adjust migration for population growth and observe differences

# Read population data from CSV file
pop_data <- read.csv('/Users/williamberritt/Documents/pop_data_proj2.csv')

# Select relevant columns
pop_data <- subset(pop_data, select = c('Year', 'World.Population'))

# Create a new column for the decade
pop_data$decade <- (floor(pop_data$Year/10))*10

# Remove commas from population numbers and convert to numeric
pop_data$World.Population <- str_replace_all(pop_data$World.Population, ',', '')
pop_data$World.Population <- as.numeric(pop_data$World.Population)

# Calculate average population by decade
avg_pop_decade <- pop_data |> group_by(decade) |> summarize(avg_pop = mean(World.Population))

# Merge average population and migration data by decade
migration_with_pop <- left_join(avg_pop_decade, migration_by_decade, by='decade')

# Calculate adjusted absolute migration rate per 100 people
migration_with_pop$adj_absolute_migration_rate_per_100 <- (migration_with_pop$adj_value / migration_with_pop$avg_pop) * 100

# Create a plot
plot(migration_with_pop$decade, migration_with_pop$adj_absolute_migration_rate_per_100)

Group data by decade and see which countries had highest absolute movement

# Group data by country and decade, calculating absolute migration
country_grouped_by_decade <- df_decade |> group_by(country, decade) |> summarize(absolute_migration = sum(absolute_value))

# Merge with country names
ONLY_country_grouped_by_decade <- left_join(countries_df, country_grouped_by_decade, by='country')
ONLY_country_grouped_by_decade <- na.omit(ONLY_country_grouped_by_decade)

# Subset data for each decade
decade_analysis_1960 <- subset(ONLY_country_grouped_by_decade, decade == 1960)
decade_analysis_1970 <- subset(ONLY_country_grouped_by_decade, decade == 1970)
decade_analysis_1980 <- subset(ONLY_country_grouped_by_decade, decade == 1980)
decade_analysis_1990 <- subset(ONLY_country_grouped_by_decade, decade == 1990)
decade_analysis_2000 <- subset(ONLY_country_grouped_by_decade, decade == 2000)
decade_analysis_2010 <- subset(ONLY_country_grouped_by_decade, decade == 2010)
decade_analysis_2020 <- subset(ONLY_country_grouped_by_decade, decade == 2020)

# Extract top 5 countries with the highest absolute migration for each decade
decade_analysis_1960 <- head(decade_analysis_1960[order(-decade_analysis_1960$absolute_migration),])
decade_analysis_1970 <- head(decade_analysis_1970[order(-decade_analysis_1970$absolute_migration),])
decade_analysis_1980 <- head(decade_analysis_1980[order(-decade_analysis_1980$absolute_migration),])
decade_analysis_1990 <- head(decade_analysis_1990[order(-decade_analysis_1990$absolute_migration),])
decade_analysis_2000 <- head(decade_analysis_2000[order(-decade_analysis_2000$absolute_migration),])
decade_analysis_2010 <- head(decade_analysis_2010[order(-decade_analysis_2010$absolute_migration),])
decade_analysis_2020 <- head(decade_analysis_2020[order(-decade_analysis_2020$absolute_migration),])

# Display results for 2020 and 1960
decade_analysis_2020
##            country decade absolute_migration
## 1154       Ukraine   2020            6676584
## 894         Poland   2020            3370134
## 1175 United States   2020            2235680
## 845       Pakistan   2020            1226119
## 954   Saudi Arabia   2020            1208176
## 915        Romania   2020             942261
decade_analysis_1960
##            country decade absolute_migration
## 1169 United States   1960            5148652
## 881    Philippines   1960            2305361
## 377         France   1960            1851980
## 399        Germany   1960            1554014
## 234          China   1960            1269564
## 895       Portugal   1960            1235114

Conclusion:

  1. United States has gained the most migrants by a wide margin since 1960. On the other hand, Bangladesh has seen the largest exodus since 1960, closely followed by Pakistan, China and Mexico.
  2. When looking at poverty rates and migration, we don’t see much of a pattern outside of the fact that the countries experiencing the higest level of migration are lower in poverty.
  3. Controlling for population growth when assessing migration rate is critical! The numbers will show that migration has trended positive in the last 60 years but if you factor in population growth, you will see that in the last 20 years migration rates have dropped significantly.
  4. United states seems to always be in the mix for most migration across different decades. Interesting and relevant though, is Ukrain and Poland are leading this decade in absolute migration followed by USA, Pakistan, Saudi Arabia and Romania. Which all seems consistent with world events.