Gravity Datasets

Contents

• About
• Acknowledgements
• Motivation
• Sources
• Tables
• Differences with the original sources
• Usage
• Installing the database locally
• Additional configurations
• Contributing
• Collaborating
• License

About

This repository aims at gathering in a single place a set of adapted variables that could be useful to researchers or practitioners that use the structural gravity model of trade. I provide trade flows, as well as geographic, cultural, trade facilitation and macroeconomic variables.

The tables provided here were reshaped under Cobb's Normal Form, and in some cases contain corrections that I explain below. The specific sources are the Gravity database from the Center for Prospective Studies and International Information (CEPII), the International Trade and Production Database for Estimation (ITPD-E) and the Dynamic Gravity Database (DGD) from the US International Trade Commission (USITC), and the Structural Gravity Database (SGD) from the World Trade Organization (WTO).

This is a sub-product, and perhaps the biggest contribution I made during my MA thesis, "NAFTA is the Worst Trade Deal in the History of Trade Deals, Maybe Ever". Of course, that title took inspiration from a US president. I used the gravity model of trade to simulate the effects of NAFTA on the Canadian, American and Mexican economy (ordered from north to south) at sectoral level (i.e., Agriculture, Mining and Energy, and Manufacturing). I needed to correct some inconsistencies in the data, and I thought that it would be useful to share the results with the community.

Acknowledgments

My MA thesis supervisor, Dr. Victor Falkenheim, was highly supportive and understood that, in order to conduct a reasonable General Equilibrium simulation of NAFTA effects by sectors, I had to care about the internal data consistency. I really thank him for his wise advice and support.

I also had indirect but valuable inputs from Dr. Mark Manger and Dr. Leonardo Baccini when discussing the distributional consequences of free trade. Even when they didn't tell me to use SQL or fight with the data or Cobb's third formal norm, their emails demanded the need to reshape my datasets to be able to answer some questions coming from their end, such as "when a few industries concentrate trade gains and others experience losses in a positive overall result scenario, does the distribution of gains introduces a political justification to oppose trade at the expense of economic efficiency?"

Motivation

The motivation to explore this topic follows from the observed change in US Republicans’ speech. Some clear examples can be obtained from what Republican presidents have said about trade in different decades:

“We should beware of the demagogues who are ready to declare a trade war against our friends — weakening our economy, our national security, and the entire free world— all while cynically waving the American flag.” Ronald Reagan, 40th president of the USA.

“. . . .I am a Tariff Man. When people or countries come in to raid the great wealth of our Nation, I want them to pay for the privilege of doing so. It will always be the best way to max out our economic power. We are right now taking in $billions in Tariffs. MAKE AMERICA RICH AGAIN” Donald J. Trump, 45th president of the USA. (https://twitter.com/realdonaldtrump/status/1069970500535902208)

Sources

Conte, M., Cotterlaz, P. & Mayer, T. (2021). The CEPII Gravity Database. CEPII Working Paper 2022-05.

Borchert, Ingo & Larch, Mario & Shikher, Serge & Yotov, Yoto, 2020. The International Trade and Production Database for Estimation (ITPD-E). School of Economics Working Paper Series 2020-5, LeBow College of Business, Drexel University.

Gurevich, Tamara & Herman, Peter, 2018. The Dynamic Gravity Dataset: 1948-2016. USITC Working Paper 2018-02-A.

Larch, Mario & Monteiro, José-Antonio & Piermartini, Roberta & Yotov, Yoto, 2019. On the Effects of GATT/WTO Membership on Trade: They are Positive and Large After All. WTO Staff Working Papers ERSD-2019-09, World Trade Organization (WTO), Economic Research and Statistics Division.

Tables

Database diagram

See here.

CEPII Country-Level Information

Name: cepii_country_information

Source: CEPII, adapted from the World Bank and other sources

Format: A table with 257 rows and 8 columns

variable	description
country_id	Combines a country's ISO3 code with a number identifying potential territorial transformations of the country
iso3	ISO3 alphabetic
iso3num	ISO3 numeric
country	Country name
countrylong	Country official name
first_year	First year of territorial existence
last_year	Last year of territorial existence
countrygroup_iso3	Country group (ISO3 alphabetic)
countrygroup_iso3num	Country group (ISO3 numeric)
iso2	ISO2 alphabetic
heg_iso3_2020	Hegemon (ISO3 alphabetic) if country is a dependency in 2020
heg_iso3num_2020	Hegemon (ISO3 numeric) if country is a dependency in 2020

Description: Allows for a full identification of each country included in the gravity dataset and, if relevant, for a tracking of its territorial changes (splits and merges). Includes one observation for each territorial configuration, mapping the full set of territorial changes that are accounted for in gravity. For example, this dataset includes one observation for West Germany, one for East Germany and one for the unified Germany.

Details: There are differences with respect to the original Stata version. ISO3 alphabetic codes of length zero were converted to NAs and the attributes (i.e., column descriptions), when missing, were added after reading the original documentation. The dynamic codes were added to the dataset and follow from USITC's DGD codes.

CEPII Population Source

Name: cepii_population_source

Source: CEPII

Format: A table with 3 rows and 2 columns

variable	description
pop_source_id	Population source ID
pop_source_description	Population source description

CEPII GDP Source

Name: cepii_gdp_source

Source: CEPII

Format: A table with 3 rows and 2 columns

variable	description
gdp_source_id	GDP source ID
gdp_source_description	GDP source description

CEPII Gravity

Name: cepii_gravity

Source: CEPII, adapted from the World Bank and other sources

Format: A table with 4,428,288 rows and 79 columns for the period 1948-2019:

variable	description
year	Year
country_id_o	Origin country ID
country_id_d	Destination country ID
iso3_o	Origin ISO3 alphabetic
iso3_d	Destination ISO3 alphabetic
iso3num_o	Origin ISO3 numeric
iso3num_d	Destination ISO3 numeric
country_exists_o	1 = Origin country exists
country_exists_d	1 = Destination country exists
gmt_offset_2020_o	Origin GMT offset (hours)
gmt_offset_2020_d	Destination GMT offset (hours)
distw_harmonic	Population-weighted distance between most populated cities (harmonic mean)
distw_arithmetic	Population-weighted distance between most populated cities (arithmetic mean)
distw_harmonic_jh	Population-weighted distance between most populated cities (harmonic mean) by Julian Hinz
distw_arithmetic_jh	Population-weighted distance between most populated cities (arithmetic mean) by Julian Hinz
dist	Distance between most populated cities, in km
main_city_source_o	Source of origin's most populated city
main_city_source_d	Source of destination's most populated city
distcap	Distance between capitals, in km
contig	1 = Contiguity
diplo_disagreement	UN diplomatic disagreement score
scaled_sci_2021	Social connectedness index in 2021
comlang_off	1 = Common official or primary language
comlang_ethno	1 = Language is spoken by at least 9% of the population
comcol	1 = Common colonizer post 1945
col45	1 = Pair in colonial relationship post 1945
legal_old_o	Origin legal system before transition
legal_old_d	Destination legal system before transition
legal_new_o	Origin legal system after transition
legal_new_d	Destination legal system after transition
comleg_pretrans	1 = Common legal origins before transition
comleg_posttrans	1 = Common legal origins after transition
transition_legalchange	1 = Common legal origin changed since transition
comrelig	Common religion index
heg_o	1 = Origin is current or former hegemon of destination
heg_d	1 = Destination is current or former hegemon of origin
col_dep_ever	1 = Pair ever in colonial or dependency relationship
col_dep	1 = Pair currently in colonial or dependency relationship
col_dep_end_year	Independence date, if col_dep = 1
col_dep_end_conflict	1 = Independence involved conflict, if col_dep_ever = 1
empire	Hegemon if sibling = 1 and year < sever_year
sibling_ever	1 = Pair ever in sibling relationship
sibling	1 = Pair currently in sibling relationship
sever_year	Severance year for pairs if sibling == 1
sib_conflict	1 = Pair ever in sibling relationship and conflict with hegemon
pop_o	Origin Population, total in thousands
pop_d	Destination Population, total in thousands
gdp_o	Origin GDP (current thousands US$)
gdp_d	Destination GDP (current thousands US$)
gdpcap_o	Origin GDP per cap (current thousands US$)
gdpcap_d	Destination GDP per cap (current thousands US$)
pop_source_o	Origin Population source
pop_source_d	Destination Population source
gdp_source_o	Origin GDP source
gdp_source_d	Destination GDP source
gdp_ppp_o	Origin GDP, PPP (current thousands international $)
gdp_ppp_d	Destination GDP, PPP (current thousands international $)
gdpcap_ppp_o	Origin GDP per cap, PPP (current thousands international $)
gdpcap_ppp_d	Destination GDP per cap, PPP (current thousands international $)
pop_pwt_o	Origin Population, total in thousands (PWT)
pop_pwt_d	Destination Population, total in thousands (PWT)
gdp_ppp_pwt_o	Origin GDP, current PPP (2011 thousands US$) (PWT)
gdp_ppp_pwt_d	Destination GDP, current PPP (2011 thousands US$) (PWT)
gatt_o	Origin GATT membership
gatt_d	Destination GATT membership
wto_o	Origin WTO membership
wto_d	Destination WTO membership
eu_o	1 = Origin is a EU member
eu_d	1 = Destination is a EU member
fta_wto	1 = The country pair is engaged in a regional trade agreement (source: WTO, supplemented by T. Mayer)
fta_wto_raw	1 = The country pair is engaged in a regional trade agreement (source: WTO)
rta_coverage	Coverage of RTA (source: WTO)
rta_type	Type of RTA (source: WTO)
entry_cost_o	Origin Cost of business start-up procedures (% of GNI per capita)
entry_cost_d	Destination Cost of business start-up procedures (% of GNI per capita)
entry_proc_o	Origin Start-up procedures to register a business (number)
entry_proc_d	Destination Start-up procedures to register a business (number)
entry_time_o	Origin Time required to start a business (days)
entry_time_d	Destination Time required to start a business (days)
entry_tp_o	Origin Days + procedures to start a business
entry_tp_d	Destination Days + procedures to start a business
tradeflow_comtrade_o	Trade flows as reported by the origin, 1000 Current USD (source: UNSD)
tradeflow_comtrade_d	Trade flows as reported by the destination, 1000 Current USD (source: UNSD)
tradeflow_baci	Trade flow, 1000 USD (source: BACI)
manuf_tradeflow_baci	Trade flow of manufactured goods, 1000 USD (source: BACI)
tradeflow_imf_o	Trade flows as reported by the origin, 1000 Current USD (source: IMF)
tradeflow_imf_d	Trade flows as reported by the destination, 1000 Current USD (source: IMF)

Description: Each observation is uniquely identified by the combination of the ISO-3 code of the origin country, the ISO-3 code of the destination country and the year. Country pair appears every year, even if one of the countries actually does not exist. However, based on the territorial changes tracked in the countries dataset, we set to missing all variables for country pairs in which at least one of the countries does not exist in a given year. Furthermore, we provide two dummy variables indicating whether the origin and the destination countries exist. These dummies allow users wishing drop non-existing country pairs from the dataset. A few caveats on the identification of countries through country_id must be noted. Firstly, when countries merge, it is the new country or territorial configuration that exists during transition year but not the old country or territorial configuration. As an example DEU.1 (West Germany) has 1989 as last year, not 1990, while DEU.2 (the unified Germany) has 1990 as first year. This is consistent with the construction of underlying variables that varies over time, such as GDP, population, trade. Secondly, since the dataset is square in terms of country_id, there exist cases in which two configurations of the same alphabetic ISO3 code appear bilaterally, e.g. DEU.1 and DEU.2. While DEU.1 and DEU.2 never existed simultaneously, we still keep these null observations to ensure that the final dataset is square.

Details: The details are the same as for the countries dataset.

USITC Sector-Level Trade

Name: usitc_trade

Source: USITC, adapted from UN COMTRADE and other sources

Format: A table with 72,534,869 rows and 10 columns for the period 1986-2020

Variable name	Variable description
exporter_iso3	ISO 3-letter alpha code of the exporter
exporter_dynamic_code	DGD's dynamic code of the exporter
exporter_name	Name of the exporter
importer_iso3	ISO 3-letter alpha code of the importer
importer_dynamic_code	DGD's dynamic code of the importer
importer_name	Name of the importer
year	Year
industry_id	ITPD industry code
industry_descr	ITPD industry description
broad_sector	Broad sector
trade	Trade flows in million of current US dollars
flag_mirror	Flag indicator, 1 if trade mirror value is used
flag_zero	Flag indicator: p if positive trade, r if the raw data contained zero and `u`` missing (unknown, assigned zero)

Description: The data goes back to 1986 for Agriculture, and to 1988 for Mining & Energy and Manufacturing. Due to administrative data limitations, the data for Services is not available before to the year 2000. @details There are differences with respect to the original CSV file. This version provides a more compact representation of the data, with the following changes:

• The exporter_name and importer_name columns are provided in the country_names table as country_name and can be joined by using the _iso3 and _dynamic columns.
• The industry_descr column is provided in the industry names table.
• The broad_sector column is provided in the sector names table and broad_sector_id was created for this version of the table.

USITC Gravity Variables

Name: usitc_gravity

Source: USITC, adapted from WTO, UN COMTRADE, National Geographic and other sources, with corrections made for the release

Format: A table with 1,940,681 rows and 67 columns for the period 1986-2020

Variable name	Variable description
year	Year of observation
iso3_o	3-digit ISO code of origin country
dynamic_code_o	Year appropriate 3-digit code of origin country
iso3_d	3-digit ISO code of destination country
dynamic_code_d	Year appropriate 3-digit code of destination country
colony_of_destination_ever	Origin country was ever a colony of the destination country
colony_of_origin_ever	Destination country was ever a colony of the origin country
colony_ever	Country pair has been in a colonial relationship
common_colonizer	Country pair has been colonized by a common colonizer
common_legal_origin	Country pair shares common legal origin
contiguity	Country pair shares a common border
distance	Population weighted distance between country pair
member_gatt_o	Origin country is a General Agreement on Tariffs and Trade member
member_wto_o	Origin country is a World Trade Organization member
member_eu_o	Origin country is a European Union member
member_gatt_d	Destination country is a General Agreement on Tariffs and Trade member
member_wto_d	Destination country is a World Trade Organization member
member_eu_d	Destination country is a European Union member
member_gatt_joint	Country pair are both members of the General Agreement on Tariffs and Trade
member_wto_joint	Country pair are both members of the World Trade Organization
member_eu_joint	Country pair are both members of the European Union
lat_o	Latitude coordinate of origin country
lng_o	Longitude coordinate of origin country
lat_d	Latitude coordinate of destination country
lng_d	Longitude coordinate of destination country
landlocked_o	Origin country is landlocked
island_o	Origin country is an island
region_id_o	Geographic region of origin country
landlocked_d	Destination country is landlocked
island_d	Destination country is an island
region_id_d	Geographic region of destination country
agree_pta_goods	Country pair is in at least one active preferential trade agreement covering goods
agree_pta_services	Country pair is in at least one active preferential trade agreement covering services
agree_fta	Country pair is in at least one free trade agreement
agree_eia	Country pair is in at least one economic integration agreement
agree_cu	Country pair is in at least one customs union
agree_psa	Country pair is in at least one partial scope agreement
agree_fta_eia	Country pair is in at least one free trade agreement and at least one economic integration agreement
agree_cu_eia	Country pair is in at least one customs union and at least one economic integration agreement
agree_pta	Country pair is in at least one active preferential trade agreement covering goods
capital_const_d	Capital stock at constant prices of destination country
capital_const_o	Capital stock at constant prices of origin country
capital_cur_d	Capital stock at current PPP of destination country
capital_cur_o	Capital stock at current PPP of origin country
gdp_pwt_const_d	Real, inflation-adjusted, PPP-adjusted GDP of destination country (PWT)
gdp_pwt_const_o	Real, inflation-adjusted, PPP-adjusted GDP of origin country (PWT)
gdp_pwt_cur_d	Real, current, PPP-adjusted GDP of destination country (PWT)
gdp_pwt_cur_o	Real, current, PPP-adjusted GDP of origin country (PWT)
pop_d	Population of destination country
pop_o	Population of origin country
hostility_level_o	Level of the origin/destination country’s hostility toward the destination country
hostility_level_d	Level of the origin/destination country’s hostility toward the origin country
common_language	Residents of country pair speak at least one common language
polity_o	Polity (political stability) score of origin country
polity_d	Polity (political stability) score of destination country
sanction_threat	There exists a threat of sanction between one country in a record towards the other
sanction_threat_trade	There exists a threat of trade sanction between one country in a record towards the other
sanction_imposition	There exists a sanction between one country in a record towards the other
sanction_imposition_trade	There exists a trade sanction between one country in a record towards the other
gdp_wdi_cur_o	Nominal GDP of origin country (WDI)
gdp_wdi_cap_cur_o	Nominal GDP per capita of origin country (WDI)
gdp_wdi_const_o	Real, current, PPP-adjusted GDP of origin country (PWT)
gdp_wdi_cap_const_o	Real GDP per capita of origin country (WDI)
gdp_wdi_cur_d	Nominal GDP of destination country (WDI)
gdp_wdi_cap_cur_d	Nominal GDP per capita of destination country (WDI)
gdp_wdi_const_d	Real, current, PPP-adjusted GDP of destination country (PWT)
gdp_wdi_cap_const_d	Real GDP per capita of destination country (WDI)

Description: The data goes back to 1986 and is suited to join with the trade table.

Details: There are differences with respect to the original CSV files. This version provides a more compact representation of the data, with the following changes:

• Starts in 1986 instead of 1948.
• Is limited to ISO codes contained in the trade table.
• The region_origin and region_destination columns are provided in the region names table as region_name and can be joined by using the region_id_ columns.

USITC-Derived Country Names

Name: usitc_country_names

Source: Own creation, adapted from the original DGD

Format: A table with 267 rows and 3 columns

Variable name	Variable description
country_iso3	ISO 3-letter alpha code of the country
country_dynamic_code	DGD's dynamic country code of the country
country_name	Name of the country

USITC-Derived Industry Names

Name: usitc_industry_names

Source: Own creation, adapted from the original ITPD-E

Format: A table with 170 rows and 2 columns

Variable name	Variable description
industry_id	ITPD industry code
industry_descr	ITPD industry description

USTIC-Derived Sector Names

Name: usitc_sector_names

Source: Own creation, adapted from the original ITPD-E

Format: A table with 4 rows and 2 columns

Variable name	Variable description
broad_sector_id	Broad sector code
broad_sector	Broad sector

USITC-Derived Region Names

Name usitc_region_names

Source Own creation, adapted from the original DGD

Format: A table with 15 rows and 2 columns

Variable name	Variable description
region_id	Region code
region	Region name

WTO Country Names

Name wto_country_names

Source: Own creation, adapted from the original SGD

Format: A table with 232 rows and 2 columns

variable	description
country_iso3	Country ISO3 alphabetic
country_name	Country name

WTO Trade

Name: wto_trade

Source: WTO

Format: A table with 972,692 rows and 5 columns for the period 1980-2016

variable	description
pair_id	Exporter-Importer index
year	Year of observation
exporter_iso3	Exporter ISO3 alphabetic
importer_iso3	Importer ISO3 alphabetic
trade	Trade flows in current US dollars

Differences with the original sources.

All the codes to implement the changes are in the code/ folder.

CEPII

The key differences are:

• Fixes inconsistent indexing in the legal origins columns in the cepii_gravity table to match with the cepii_legal_origin table correctly.
• Includes explanations for the abbreviations (i.e., Customs Union (CU) instead of just CU for the RTA types)
• Removes Malaysia+Singapore in the country information table because it has no ISO-3 code and there is no trade or gravity information for it.
• Includes a dynamic ISO-3 code to be able to create primary keys, this is similar to the USITC dataset buw we added the code ANT.X for the Netherland Antilles + Aruba up to 1986.

Some changes that applied to Gravity V202102 but not to V202211 because those were corrected upstream:

• The RTA types equal to 8 were replaced by NAs, because those do not provide a description, are not aligned with the WTO classification, and do not allow to create a foreign key.

USITC

The key differences are:

• Fixes duplicated ISO3 code + Dynamic code for Cambodia and West Samoa in the usitc_country_names table
• Fixes duplicated label "south_east_asia" vs "suth_east_asia" in the usitc_region_names table
• Fixes inconsistencies in the usitc_gravity table

The last point deserves an example. See the differences in the common_colonizer variable for Argentina-Chile-Peru and Spain. This variable should be the same, for example, for ARG-CHL or CHL-ARG, but it's not in the original dataset.

> tbl(con, "usitc_gravity") %>% 
+   filter(iso3_o == "ARG", iso3_d %in% c("CHL", "ESP", "PER"), year == 2015) %>%
+   select(iso3_o, iso3_d, colony_of_origin_ever, colony_of_destination_ever, colony_ever, common_colonizer)
# Source:   SQL [3 x 6]
# Database: postgres  [pacha@localhost:5432/gravitydatasets]
  iso3_o iso3_d colony_of_origin_ever colony_of_destination_ever colony_ever common_colonizer
  <chr>  <chr>                  <int>                      <int>       <int>            <int>
1 ARG    CHL                        0                          0           0                0
2 ARG    ESP                        0                          1           1                0
3 ARG    PER                        0                          0           0                0

> tbl(con, "usitc_gravity") %>% 
+   filter(iso3_d == "ARG", iso3_o %in% c("CHL", "ESP", "PER"), year == 2015) %>%
+   select(iso3_o, iso3_d, colony_of_origin_ever, colony_of_destination_ever, colony_ever, common_colonizer)
# Source:   SQL [3 x 6]
# Database: postgres  [pacha@localhost:5432/gravitydatasets]
  iso3_o iso3_d colony_of_origin_ever colony_of_destination_ever colony_ever common_colonizer
  <chr>  <chr>                  <int>                      <int>       <int>            <int>
1 CHL    ARG                        0                          0           0                1
2 ESP    ARG                        1                          0           1                0
3 PER    ARG                        0                          0           0                1

I corrected this by using the gravity table itself in two ways:

• By binding rows on a pairwise basis for all symmetrical variables (i.e., common_colonizer) and obtained the maximum for each pair.
• By obtaining a full join on a pairwise basis for all non-symmetrical variables (i.e., colony_of_origin_ever) and obtained the maximum for each pair.

To check this I ran some queries to verify, for example, that the populations for CHL-ESP are around c(20,40) and not c(40,40) (i.e., Spain doubles the population of Chile), and that for the same pair colony_of_origin_ever = 0 but for ESP-CHL colony_of_origin_ever = 1.

WTO

No modification besides adding the full country names in a separate table from trade.

Usage

Estimating the gravity model of trade with exporter/importer time fixed effects for 4 sectors:

# install_github("r-dbi/RPostgres") # optional: use the cpp11-based version

library(RPostgres)
library(dplyr)
library(purrr)
library(fixest)

con <- dbConnect(
  Postgres(),
  user = Sys.getenv("LOCAL_SQL_USR"),
  password = Sys.getenv("LOCAL_SQL_PWD"),
  dbname = "gravitydatasets",
  host = "localhost"
)

# run one model per sector
models <- map(
  tbl(con, "usitc_sector_names") %>% pull(broad_sector_id),
  function(s) {
    message(s)
    
    yrs <- seq(2005, 2015, by = 5)
    
    d <- tbl(con, "usitc_trade") %>% 
      filter(year %in% yrs, broad_sector_id == s) %>% 
      group_by(year, exporter_iso3, importer_iso3, broad_sector_id) %>% 
      summarise(trade = sum(trade, na.rm = T)) %>% 
      inner_join(
        tbl(con, "usitc_gravity") %>% 
          filter(year %in% yrs) %>% 
          select(iso3_o, iso3_d, contiguity, common_language, colony_ever, distance),
        by = c("exporter_iso3" = "iso3_o", "importer_iso3" = "iso3_d")
      ) %>% 
      collect()
    
    d <- d %>% 
      mutate(
        etfe = paste(exporter_iso3, year, sep = "_"),
        itfe = paste(importer_iso3, year, sep = "_")
      )
    
    feglm(trade ~ contiguity + common_language + colony_ever + 
            log(distance) | etfe + itfe,
          family = quasipoisson(),
          data = d)
  }
)

dbDisconnect(con)

print(models)

[[1]]
GLM estimation, family = quasipoisson, Dep. Var.: trade
Observations: 243,259 
Fixed-effects: etfe: 666,  itfe: 685
Standard-errors: Clustered (etfe) 
                 Estimate Std. Error   t value   Pr(>|t|)    
contiguity      -1.100653   0.095092 -11.57462  < 2.2e-16 ***
common_language  0.909522   0.093396   9.73833  < 2.2e-16 ***
colony_ever     -0.580546   0.123115  -4.71548 2.9394e-06 ***
log(distance)   -2.290567   0.049593 -46.18709  < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                                           
  Squared Cor.: 0.995063                   

[[2]]
GLM estimation, family = quasipoisson, Dep. Var.: trade
Observations: 392,413 
Fixed-effects: etfe: 699,  itfe: 699
Standard-errors: Clustered (etfe) 
                 Estimate Std. Error   t value   Pr(>|t|)    
contiguity      -0.607763   0.066653  -9.11825  < 2.2e-16 ***
common_language  1.017510   0.122499   8.30624 5.1286e-16 ***
colony_ever     -0.349214   0.095491  -3.65703 2.7435e-04 ***
log(distance)   -1.232183   0.079635 -15.47282  < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                                           
  Squared Cor.: 0.993544                   

[[3]]
GLM estimation, family = quasipoisson, Dep. Var.: trade
Observations: 183,063 
Fixed-effects: etfe: 658,  itfe: 682
Standard-errors: Clustered (etfe) 
                 Estimate Std. Error   t value  Pr(>|t|)    
contiguity      -1.288772   0.104207 -12.36742 < 2.2e-16 ***
common_language  1.346403   0.132482  10.16290 < 2.2e-16 ***
colony_ever     -0.288555   0.222484  -1.29697    0.1951    
log(distance)   -2.298211   0.074100 -31.01505 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                                           
  Squared Cor.: 0.992104                   

[[4]]
GLM estimation, family = quasipoisson, Dep. Var.: trade
Observations: 46,359 
Fixed-effects: etfe: 578,  itfe: 579
Standard-errors: Clustered (etfe) 
                 Estimate Std. Error    t value  Pr(>|t|)    
contiguity      -2.671951   0.183679 -14.546876 < 2.2e-16 ***
common_language  1.612855   0.127482  12.651606 < 2.2e-16 ***
colony_ever      0.139054   0.170712   0.814552   0.41566    
log(distance)   -2.267021   0.081151 -27.935858 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                                           
  Squared Cor.: 0.998325

Installing the database locally

You need PostgreSQL. An excellent guide is provided by DigitalOcean.

Download the current version, and then from the command line create a SQL database and restore the downloaded dump.

createdb gravitydatasets
pg_restore gravitydatasets.sql -d gravitydatasets

Additional configurations

In order to maximize PostgreSQL performance, I obtained this configuration from pgtune.leopard.in.ua:

# DB Version: 14
# OS Type: linux
# DB Type: desktop
# Total Memory (RAM): 16 GB
# CPUs num: 8
# Connections num: 20
# Data Storage: ssd

ALTER SYSTEM SET
 max_connections = '20';
ALTER SYSTEM SET
 shared_buffers = '1GB';
ALTER SYSTEM SET
 effective_cache_size = '4GB';
ALTER SYSTEM SET
 maintenance_work_mem = '1GB';
ALTER SYSTEM SET
 checkpoint_completion_target = '0.9';
ALTER SYSTEM SET
 wal_buffers = '16MB';
ALTER SYSTEM SET
 default_statistics_target = '100';
ALTER SYSTEM SET
 random_page_cost = '1.1';
ALTER SYSTEM SET
 effective_io_concurrency = '200';
ALTER SYSTEM SET
 work_mem = '10922kB';
ALTER SYSTEM SET
 min_wal_size = '100MB';
ALTER SYSTEM SET
 max_wal_size = '2GB';
ALTER SYSTEM SET
 max_worker_processes = '8';
ALTER SYSTEM SET
 max_parallel_workers_per_gather = '4';
ALTER SYSTEM SET
 max_parallel_workers = '8';
ALTER SYSTEM SET
 max_parallel_maintenance_workers = '4';

This must be inserted by using the postgres user (i.e., sudo -i -u postgres && psql).

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

This is run as an independent project, if this was useful for you, please consider sponsoring or donating.

Collaborating

I am really interested in the gravity model of trade. If you are working on this topic, please reach out to me, I would love to collaborate.

License

CC-BY-NC-SA-4.0