Lead time to import, median case (days)

The indicator 'Lead time to import, median case (days)' serves as a vital metric for evaluating the efficiency of a country's import processes. This indicator reflects the median time it takes for goods to arrive at a destination after the import procedure has commenced. In the recent data from 2018, the median lead time to import stood at 3.0 days, signifying a relatively streamlined process for many economies. However, as with any statistical measure, it is essential to analyze both the context and implications of these figures, particularly through comparison with historical data and the examination of outliers.

The importance of the median import lead time cannot be overstated. Short lead times are generally associated with enhanced trade efficiency, significantly benefiting businesses that depend on timely deliveries. Faster imports can lead to reduced inventory costs, improved cash flow, and the ability to rapidly respond to market demands. Conversely, prolonged lead times can equate to increased costs for businesses, potential dissatisfaction among consumers, and a less competitive market position. Companies may find themselves unable to meet demands due to delays, ultimately resulting in lost revenue or a tarnished reputation.

The 2018 median value of 3.0 days is notably shorter compared to previous world values, demonstrating the progress made in many regions. Specifically, the data shows a fluctuating trend in lead times over the years: starting at 5.5 days in 2007, dropping to 4.62 days in 2010, and subsequently increasing to 5.89 days in 2012 before declining to 3.1 days in 2014 and slightly rising again to 4.02 days in 2016. The observed decline in 2018 to 3.0 days suggests that nations are increasingly optimizing their customs processes, infrastructure, and overall trade policies.

Analyzing the data further, we observe a stark contrast between the top five areas with the longest import lead times and the bottom five areas with the shortest. Gabon leads the list with an import lead time of 25.0 days, followed by Albania, Malawi, and Uganda, all at 14.0 days, and Venezuela at 12.0 days. Such figures highlight significant inefficiencies in these countries' import systems, which can stem from various factors, including bureaucratic impediments, inadequate infrastructure, or unstable political environments that hinder trade operations.

In contrast, the bottom five areas—Australia, Brunei, Ghana, Latvia, and Madagascar—all posted a commendable lead time of just 1.0 day. These countries exemplify effective governance, advanced logistical frameworks, and reliable customs procedures that facilitate quick and efficient import processes. Such efficiency contributes not only to their economic stability but also enhances their attractiveness as trade partners.

When analyzing the relationships of lead time to import with other indicators, it becomes evident that this metric correlates strongly with factors such as ease of doing business, logistics performance, and regulatory quality. Countries that exhibit high efficiency in lead time typically also perform well in other areas of trade facilitation, resulting in a more robust economic environment. For instance, nations with streamlined import procedures often enjoy better access to international markets and can capitalize on global trade opportunities more effectively.

Several factors can significantly influence lead time to import, including infrastructure quality, customs practices, and the complexity of regulatory procedures. Improved transport networks, such as ports and roads, enhance the capacity to move goods swiftly. The digitization of customs processes can also expedite clearance times, reducing paperwork processing and the potential for errors. Furthermore, the professional training of customs officials and logistics staff can lead to improved efficiency, minimizing delays caused by inexperience.

Strategies to improve lead time to import may encompass policy reforms aimed at simplifying customs procedures, investment in infrastructure upgrades, and the adoption of technology, such as e-customs systems, which facilitate faster processing. Additionally, public-private partnerships can play a key role in enhancing logistical capabilities. Stakeholder collaboration can ensure that various aspects of the import process are aligned, resulting in increased efficiency and reduced costs.

However, despite the strides made in many regions, flaws still exist within the import processes that can hinder potential improvements. Bureaucratic hurdles, lack of effective coordination among various government agencies, and outdated technology can all contribute to inefficiencies. Countries must continually strive to identify and address these weaknesses and to remain adaptable in a global trade landscape that is constantly evolving.

In conclusion, the median lead time to import, as indicated by the 2018 figure of 3.0 days, is a significant benchmark that highlights the efficiency of international trade processes. The disparity between different regions calls attention to the need for targeted reforms and investment to further streamline import practices. As nations focus on enhancing their performance in this area, the overall benefits to economic growth, trade competitiveness, and consumer satisfaction can be profound.

                    
# 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 = 'LP.IMP.DURS.MD'

# 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 <- 'LP.IMP.DURS.MD'

# 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))