Georgia vs Israel: Trading across borders: Time to import (days) (DB06-15 methodology)

Georgia
90.32
in 2014
Israel
90.32
in 2014
Georgia rank
30th
Israel rank
30th

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

  • Georgia
  • Israel
20406080100200520092014

How they compare

Georgia currently reports 90.32 against 90.32 in Israel, a difference of 0.

The two have swapped places 2 times across 10 shared years of data; in 2005 it was Israel ahead.

Georgia ranks 30th and Israel ranks 30th of 181 countries.

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

Head to head by decade

Decade Georgia Israel Difference Ahead
2000s 72.58 87.1 14.52 Israel
2010s 89.68 90.32 0.6452 Israel

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), Georgia or Israel?
Georgia, at 90.32 against 90.32 in Israel as of 2014.
What is the difference in trading across borders: time to import (days) (db06-15 methodology) between Georgia and Israel?
0, with Georgia ahead.
How many years of comparable data are there for Georgia and Israel?
10 years are reported by both, from 2005 to 2014.
How do Georgia and Israel rank globally for trading across borders: time to import (days) (db06-15 methodology)?
Georgia ranks 30th and Israel ranks 30th 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.