China vs Eswatini: Trading across borders: Time to import (days) (DB06-15 methodology)

China
67.74
in 2014
Eswatini
69.35
in 2014
China rank
109th
Eswatini rank
107th

Trading across borders: Time to import (days) (DB06-15 methodology) over time

  • China
  • Eswatini
0204060200520092014

How they compare

Eswatini currently reports 69.35 against 67.74 in China, a difference of 1.61.

The two have swapped places 1 time across 10 shared years of data; in 2005 it was China ahead.

China ranks 109th and Eswatini ranks 107th of 181 countries.

China has averaged higher in every one of the 2 decades both report.

Head to head by decade

Decade China Eswatini Difference Ahead
2000s 67.1 58.06 9.03 China
2010s 67.74 67.42 0.3226 China

Averages of every year both report within each decade.

Frequently asked questions

Which has higher trading across borders: time to import (days) (db06-15 methodology), China or Eswatini?
Eswatini, at 69.35 against 67.74 in China as of 2014.
What is the difference in trading across borders: time to import (days) (db06-15 methodology) between China and Eswatini?
1.61, with Eswatini ahead.
How many years of comparable data are there for China and Eswatini?
10 years are reported by both, from 2005 to 2014.
How do China and Eswatini rank globally for trading across borders: time to import (days) (db06-15 methodology)?
China ranks 109th and Eswatini ranks 107th of 181 countries.
Where does this data come from?
The World Bank, published as Trading across borders: Time to import (days) (DB06-15 methodology) - Score. Statizoid refreshes it automatically from the source and publishes the full history for both places.

Individual pages

About this data

Indicator
Trading across borders: Time to import (days) (DB06-15 methodology) - Score
Source
World Bank
Licence
CC BY 4.0 (World Bank Open Data)
Coverage
183 places, 1,813 data points, 2005–2014
Last refreshed

The score for the time to import benchmarks economies with respect to the regulatory best practice on the indicator. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance, and is computed based on the methodology in the DB06-15 studies.