World Governance Indicators

Ty Tuff, ESIIL Data Scientist 2023-05-21

The Worldwide Governance Indicators (WGI) dataset is an influential resource in the realm of political science, economics, and international development. It offers comprehensive and comparable data on various dimensions of governance for over 200 countries and territories around the globe.

Compiled by a network of scholars and practitioners, the WGI dataset provides indicators that reflect perceptions of governance in six key dimensions. These dimensions include voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. Together, these metrics provide a holistic picture of a nation’s governance, taking into account aspects of civil liberties, political processes, institutional quality, and the efficacy of laws and regulations.

The WGI dataset is updated annually, reflecting changes in governance conditions over time. This temporal aspect makes it a valuable tool for studying trends and patterns in global governance. The data’s standardized format allows for comparisons between countries and regions, contributing to its wide use in comparative political and economic analysis.

Data from the WGI is readily available for download in various formats, including Excel and CSV, enabling easy integration into a multitude of data analysis tools and platforms. The WGI data has been instrumental in a variety of scholarly and policy-oriented research, and its open availability continues to facilitate robust debate about the nature of governance around the world.

https://info.worldbank.org/governance/wgi/ https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130

R code:

List of possible indicators

library(httr)
library(jsonlite)

# Define the base URL and parameters
base_url <- "https://api.worldbank.org/v2/indicators"
format <- "json"
per_page <- 1000

# Create the full URL
url <- paste0(base_url, "?format=", format, "&per_page=", per_page)

# Make an API request
response <- GET(url)

# Check the status of the response
if (http_status(response)$category == "Success") {
  # Parse the response
  data <- content(response, "text", encoding = "UTF-8")
  data_json <- fromJSON(data)

  # Extract the relevant data
  indicators <- data.frame(data_json[[2]])

  # Print the first few rows of the data
  kable(head(indicators))
} else {
  cat("Failed to retrieve data:", http_status(response)$message, "\n")
}

Download one of those indicators and flatten into a table.

library(httr)
library(jsonlite)
library(ggplot2)
library(tidyverse)

# Define the base URL and parameters
base_url <- "https://api.worldbank.org/v2/country/all/indicator"
indicator <- "NY.GDP.MKTP.CD" # Example indicator (GDP in current US dollars)
date_range <- "2020:2020" # Filter only for the year 2020
format <- "json"
per_page <- 1000

# Create the full URL
url <- paste0(base_url, "/", indicator, "?date=", date_range, "&format=", format, "&per_page=", per_page)

# Make an API request
response <- GET(url)

# Check the status of the response
if (http_status(response)$category == "Success") {
  # Parse the response
  data <- content(response, "text", encoding = "UTF-8")
  data_json <- fromJSON(data)

  # Extract the relevant data and flatten the data frame
  indicators <- data.frame(data_json[[2]])
  indicators_flat <- indicators %>% 
    unnest(cols = c(country, indicator), names_sep = "_") %>%
    select(countryiso3code, country_value, date, value)

  # Remove rows with missing values
  indicators_filtered <- indicators_flat[!is.na(indicators_flat$value),]



  # Create a plot for GDP in 2020
  ggplot(indicators_filtered, aes(x = reorder(country_value, -value), y = value)) +
    geom_bar(stat = "identity") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    labs(title = "GDP in current US dollars (2020)", x = "Country", y = "GDP (current US$)") +
    coord_flip() +
    scale_y_continuous(labels = scales::comma)
} else {
  cat("Failed to retrieve data:", http_status(response)$message, "\n")
}

library(knitr)
kable(indicators_filtered)

Python code:

import requests
import pandas as pd
import matplotlib.pyplot as plt

# Define the base URL and parameters
base_url = "https://api.worldbank.org/v2/country/all/indicator"
indicator = "NY.GDP.MKTP.CD"  # Example indicator (GDP in current US dollars)
date_range = "2020:2020"  # Filter only for the year 2020
params = {
    "format": "json",
    "date": date_range,
    "per_page": 1000,
}

# Create the full URL
url = f"{base_url}/{indicator}"

# Make an API request
response = requests.get(url, params=params)

# Check the status of the response
if response.status_code == 200:
    # Parse the response
    data_json = response.json()

    # Extract the relevant data and flatten the data frame
    indicators = pd.json_normalize(data_json[1])
    indicators_flat = indicators[["countryiso3code", "country.value", "date", "value"]]

    # Remove rows with missing values
    indicators_filtered = indicators_flat.dropna(subset=["value"])

    # Create a plot for GDP in 2020
    indicators_filtered = indicators_filtered.sort_values(by="value", ascending=False)
    plot = indicators_filtered.plot.bar(
        x="country.value", y="value", figsize=(15, 8), legend=None
    )
    plot.set_title("GDP in current US dollars (2020)")
    plot.set_xlabel("Country")
    plot.set_ylabel("GDP (current US$)")
    plt.xticks(rotation=90)
    plt.show()
else:
    print("Failed to retrieve data:", response.status_code)

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Last update: 2024-05-09