Title: CEPII's GeoDist Datasets
Version: 0.1
Description: Provides data on countries and their main city or agglomeration and the different distance measures and dummy variables indicating whether two countries are contiguous, share a common language or a colonial relationship. The reference article for these datasets is Mayer and Zignago (2011) http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877.
License: CC0
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (≥ 2.10)
URL: https://pacha.dev/cepiigeodist/
BugReports: https://github.com/pachamaltese/cepiigeodist/issues/
Suggests: gravity
NeedsCompilation: no
Packaged: 2020-09-11 14:34:18 UTC; pacha
Author: Mauricio Vargas ORCID iD [aut, cre], Centre d'études prospectives et d'informations internationales (CEPII) [dtc]
Maintainer: Mauricio Vargas <mvargas@dcc.uchile.cl>
Repository: CRAN
Date/Publication: 2020-09-18 12:20:07 UTC

Data on pairs of countries including distance measures and dummy variables indicating common attributes

Description

Provides different distance measures and dummy variables indicating whether the two countries are contiguous, share a common language or a colonial relationship. There are two kinds of distance measures: simple distances, for which only one city is necessary to calculate international distances; and weighted distances, for which we need data on principal cities in each country. The simple distances are calculated following the great circle formula, which uses latitudes and longitudes of the most important city (in terms of population) or of its official capital. These two variables incorporate internal distances based on areas provided in the ‘geo_cepii' dataset. The two weighted distance measures use city-level data to assess the geographic distribution of population inside each nation. The idea is to calculate distance between two countries based on bilateral distances between the largest cities of those two countries, those inter-city distances being weighted by the share of the city in the overall country’s population. The distance formula used is a generalized mean of city-to-city bilateral distances developed by Head and Mayer (2002), which takes the arithmetic mean and the harmonic means as special cases.

Format

A data frame with 50176 observations on the following 14 variables.

iso_o

Country of origin as ISO codes in three characters.

iso_d

Country of destination as ISO codes in three characters.

contig

Variable coded as 1 when the two countries are next to each other and 0 otherwise.

comlang_off

Variable coded as 1 when the two countries share the same official language.

comlang_ethno

Variable coded as 1 when the two countries have at least 9% of their population speaking the same language.

colony

Variable coded as 1 when the country in 'iso_o' was ever a colony of the country in 'iso_d'.

comcol

Variable coded as 1 when the two country share the same colonizer after 1945.

curcol

Variable coded as 1 when the country in 'iso_o' is a colony of the country in 'iso_d'.

col45

Variable coded as 1 when the country in 'iso_o' is a colony of the country in 'iso_d' after 1945.

smctry

Variable coded as 1 when the two countries were or are the same country.

dist

Simple distance (most populated cities, km)

distcap

Simple distance between capitals (capitals, km)

distw

Weighted distance (pop-wt, km) with theta=1 (theta measures the sensitivity of trade flows to bilateral distance dkl)

distwces

Weighted distance (pop-wt, km) theta=-1.

Source

http://www.cepii.fr/CEPII/en/bdd_modele/download.asp?id=6

References

Mayer, T. & Zignago, S. (2011) Notes on CEPII's distances measures: the GeoDist Database CEPII Working Paper 2011-25

Head, K. & Mayer, T. (2002) Illusory Border Effects: Distance Mismeasurement In-flates Estimates of Home Bias in Trade CEPII Working Paper 2002-01

Examples

# filter countries that share borders
dist_cepii[dist_cepii$contig == 1, ]

Data on countries and their main city or agglomeration

Description

There are firstly three identification codes of the country according to the ISO classification, the country's area in square kilometers, used to calculate in particular its internal distance. Variables indicating whether the country is landlocked and which continent it is part of are also included.

Format

A data frame with 238 observations on the following 34 variables.

iso2

ISO codes in two characters.

iso3

ISO codes in three characters.

cnum

ISO codes in three numbers.

country

Name of country in English.

pays

Name of country in French.

area

Country's area in km2.

dis_int

Internal distance of country i, dii=.67*sqrt(area/pi) (an often used measure of average distance between producers and consumers in a country). See Head and Mayer, 2002 for more on this topic.

landlocked

Dummy variable set equal to 1 for landlocked countries.

continent

Continent to which the country is belonging.

city_en

Names of capitals or main cities of the country in English.

city_fr

Names of capitals or main cities of the country in French.

lat

Latitude of the city.

lon

Longitude of the city.

cap

Variable equals to 1 if the city is the capital of the country, to 0 if the city is the most populated city (maincity equals to 1) but not the capital, and to 2 in the cases of two capitals, if the city is the most populated but the "second" capital or the previous capital.

maincity

Variable coded as 1 when the city is the most populated of the country and as 2 otherwise.

citynum

Number of cities for each country used to calculate the weighted distances described in Mayer and Zignago, 2011.

langoff_1

Official or national languages and languages spoken by at least 20% of the population of the country (and spoken in another country of the world) following the same logic than the "open-circuit languages" in Mélitz (2002).

langoff_2

Same as langoff_1.

langoff_3

Same as langoff_1.

lang20_1

Languages (mother tongue, lingua francas or second languages) spoken by at least 20% of the population of the country.

lang20_2

Same as lang20_1.

lang20_3

Same as lang20_1.

lang20_4

Same as lang20_1.

lang9_1

Languages (mother tongue, lingua francas or second languages) spoken by between 9% amd 20% of the population of the country.

lang9_2

Same as lang9_1.

lang9_3

Same as lang9_1.

lang9_4

Same as lang9_1.

colonizer1

Colonizers of the country for a relatively long period of time and with asubstantial participation in the governance of the colonized country.

colonizer2

Same as colonizer1.

colonizer3

Same as colonizer1.

colonizer4

Same as colonizer1.

short_colonizer1

Colonizers of the country for a relatively short period of time orwith only low involvement in the governance of the colonized country.

short_colonizer2

Same as short_colonizer1.

short_colonizer3

Same as short_colonizer1.

Source

http://www.cepii.fr/CEPII/en/bdd_modele/download.asp?id=6

References

Mayer, T. & Zignago, S. (2011) Notes on CEPII's distances measures: the GeoDist Database CEPII Working Paper 2011-25

Head, K. & Mayer, T. (2002) Illusory Border Effects: Distance Mismeasurement In-flates Estimates of Home Bias in Trade CEPII Working Paper 2002-01

Examples

# filter to avoid multiple records for the same country
geo_cepii[geo_cepii$cap == 1 & geo_cepii$maincity == 1, ]