Lead time to export, median case (days)

Lead time to export, measured in days, is a crucial indicator that reflects the duration taken to complete all the necessary steps to export goods from one country to another. This metric provides insight not only into the efficiency of the logistics and customs processes but also into the overall business environment in a given country. Understanding lead time to export is vital for governments, businesses, and policymakers, as it directly influences trade competitiveness and economic performance.

The significance of this indicator cannot be understated. A shorter lead time typically indicates a streamlined process, one that can enhance a country's attractiveness to foreign investors and trading partners. Conversely, longer lead times may deter trade, increase costs for businesses, and lead to reduced economic growth. In an increasingly global market, countries are aware that logistics capabilities can serve as a competitive advantage. That emphasizes the importance of improving lead time to export.

Lead time to export is often related to other critical indicators, such as logistics performance, customs efficiency, and overall infrastructure quality. For instance, countries with advanced logistics systems and efficient customs procedures tend to have shorter lead times. Additionally, the quality and reliability of transport services are integral to this metric. As supply chain management grows more complex with globalization, the interconnectedness of these indicators is becoming increasingly apparent. Understanding how they interplay allows countries to make informed decisions on where improvements are needed.

Several factors can affect lead time to export. Administrative procedures, regulatory framework, and infrastructure quality play significant roles. Countries facing bureaucratic hurdles or inefficient customs processes may encounter longer lead times, primarily stemming from cumbersome paperwork, lack of digital solutions, and delays in approvals. Infrastructure, such as ports, roads, and transportation systems, has a direct corridor effect on lead times; a congested port or poorly maintained road can extend the timeframe drastically.

In the latest available data from 2018, the median lead time to export was recorded at 2.5 days, which marks an intriguing snapshot of global trade efficiency at that time. However, this figure alone does not tell the full story, as there are stark contrasts across different regions. For instance, Ethiopia stood out with a notably high lead time of 60 days, indicating significant logistical bottlenecks and deeper systemic issues that can hinder trade. Other areas with extended lead times included Gabon at 25 days, and both Burundi and Congo-Brazzaville, each at 18 days. These countries need to focus on improving their trade logistics and clearing processes to facilitate exports better.

On the other end of the spectrum, countries like Australia, Brunei, Ghana, Latvia, and Madagascar recorded a remarkably low lead time of just 1 day. Such efficiency in these nations could be attributed to effective regulatory frameworks, comprehensive logistics infrastructures, and proactive policies that enhance the export process. This stark variation emphasizes how specific steps can create a more competitive export environment.

When examining world values over the years, there are fluctuations worth noting. In 2007, the average lead time was approximately 4.05 days, which showed a notable decrease to 3.78 days in 2010, followed by fluctuations until it reached 4.39 days in 2018. Not only does this indicate evolving trade processes, but it also suggests varying economic conditions, technological advancements, and changing regulatory environments across the globe influencing these times.

To mitigate challenges related to lead time to export, governments and enterprises can adopt several strategies. Streamlining customs procedures through digital solutions can drastically reduce times, while investing in necessary transport infrastructure can improve overall efficiency. Training customs personnel and enhancing collaboration between relevant agencies can lay the groundwork for better practices and streamlined processes.

Moreover, fostering a culture of continuous improvement among businesses and government agencies can lead to sustained advancements in export efficiencies. Engaging with stakeholders throughout the supply chain helps identify bottlenecks and areas for growth, ensuring that everyone plays a role in refining lead times.

Nevertheless, it’s essential to recognize the flaws that can accompany efforts to improve lead time to export. Over-reliance on technology might lead to neglecting critical human elements or regional specifics that require tailored strategies. Imposing measures that are too rigorous might stifle local processes, creating inefficiencies instead of the desired streamlined operations. Hence, while the aspiration for shorter lead times is valid, caution and adaptability remain crucial to find balanced solutions.

In conclusion, the lead time to export is an essential indicator of a country's trade efficiency and an influential factor in international trade competitiveness. By understanding its importance, underlying relationships with other indicators, influencing factors, and potential solutions for improvement, stakeholders can take active steps towards enhancing this critical metric. With effective strategies in place, countries can work towards reducing lead times, fostering better trade relationships, and ultimately driving economic growth.

                    
# 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.EXP.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.EXP.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))