The Foreign Policy Index (FPI) provides an annual measurement of Brazil’s bilateral engagement with nations around the world, bringing together in a single indicator different dimensions of the country’s international interaction.
The FPI aims to contribute to the evaluation of Brazilian foreign policy, establishing an objective and replicable empirical instrument, allowing the monitoring of Brazil’s level of bilateral engagement over time.
The data used to construct the FPI cover imports and exports, bilateral agreements, presidential trips, congruence in regional blocs and international organizations, covering a substantial part of the country’s bilateral relations in the world. The time series runs from 1998 to 2024.
The FPI updates the work of Rodrigues, Urdinez e Oliveira (2019), who constructed the foreign policy index for the first time for the years 1998 to 2014. The table below describes the index variables.
Table 1. Foreign Policy Index (FPI) Variables
Variable | Description | Source |
Exports and Imports | Annual exports and imports by country (U.S.$) FOB
| Comex Stat MDIC |
Bilateral Agreements | Number of Bilateral Agreements per year | Concórdia Itamaraty |
Convergence in International Financial Institutions | Measure of support for Brazil’s representation on the executive boards of the IMF, World Bank and co-participation in coalitions in the WTO. Ranges from 0 to 1. | International Monetary Fund, World Bank and World Trade Organization. |
Presidential Trip | Number of official visits by the Brazilian head of state to the country per year | Library of the Brazilian Presidency of the Republic |
Joint participation in regional arrangements | Number of regional arrangements that the country co-participates with Brazil per year | Brazilian Foreign Relations Ministry |
Factor analysis was used to produce the FPI by aggregating all the variables highlighted above. This technique allows the reduction of the data dimensionality into a few components, enabling the creation of a single index. The first three dimensions resulting from the factor analysis were maintained. These dimensions represent, in order of relevance, commercial insertion (exports and imports), diplomatic insertion (presidential trips and bilateral agreements), and regional and international organizations insertions (regional congruence and in international financial institutions). After generating the factors and rotating the resulting matrix, the explained variation of each of the three components was used as a weight in the estimation of the mean to combine the factors into a single variable, resulting in the foreign policy index (FPI). After this process, the values represented in the FPI were standardized to vary from 0 to 1, with 0 being the lowest possible international engagement and 1 its maximum value.
Below, we report some relevant indicators of the FPI estimation. Table 2 below shows the communalities of the factor analysis. This indicator returns the proportion of variances associated with the factors estimated in the factor analysis by variable. The closer to “one”, the better represented the variables will be in the common factor space. Variables with an extraction indicator greater than 0.5 should be included in the final index. It is important to mention that the variables presence of an embassy, convergence in the UN General Assembly and participation in extra-regional arrangements did not survive this criterion and were excluded from the composition of the index, despite being part of the original index by Rodrigues, Urdinez and Oliveira (2019). These variables are available in the second database below, called “Disaggregated Foreign Policy Index”, in addition to other data to facilitate the analysis. As we can see in Table 2 below, the variables inserted survive the estimation criterion.
Table 2. Communalities – Factor Analysis
Variable | Extraction |
Exports | .957 |
Imports | .957 |
Bilateral Agreements | .730 |
International Trips | .718 |
Regional Congruence | .718 |
International Financial Institutions Congruence | .714 |
We also show in Table 3, below, the explained variance of the three dimensions estimated in the factor analysis. We used varimax rotation in the estimation. The first dimension denotes trade, the second denotes diplomacy, while the third alludes to regional arrangements and countries support for Brazil at the international financial institutions. As the table shows, the three dimensions have similar weights (% of variance), with a slight advantage for the trade dimension. With the three dimensions, approximately 80% of the total variance is explained, indicating good adequacy of the estimation. It is also important to mention the performance of robustness check tests. The first was the Kaiser-Meyer-Olkin sample adequacy test, with a value of 0.5677. The ideal are values above 0.6, a value very close to that achieved, but still at the lower limit. The second test was Bartlett’s sphericity test, rejecting the null hypothesis that the correlation matrix is an identity matrix. The indicator is significant and demonstrates adequacy. After presenting the three tables relating to the index estimation, you will find two databases to manipulate the index by country between 1998 and 2024, as well as an interactive map to explore below.
Table 3. Explained Variance – Factor Analysis
Dimension | Initial Eigen Values | Sum of Square Rotations | ||||
Total | % Variance | Acumulated % | Total | % Variance | Acumulated % | |
1(Trade) | 2,296 | 38,2 | 38,2 | 1,927 | 32,1 | 32,1 |
2(Diplomacy) | 1,435 | 23,9 | 62,1 | 1,448 | 24,1 | 58,2 |
3(Regional) | 1,063 | 17,7 | 79,9 | 1,420 | 23,6 | 79,9 |
Foreign Policy Index Interactive Map
Foreign Policy Index
Use the “country” and “fpi” filters to manipulate the map above. The other filters change the data in the table below
wdt_ID | wdt_created_by | wdt_created_at | wdt_last_edited_by | wdt_last_edited_at | country | iso3 | year | fpi | export | import | acordos_total | international_trip | regional | finantial_congruence |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34951 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2024 | 0.14 | 3,098,112 | 1,018,765 | 0 | 0 | 0.00 | 0.00 |
34952 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2023 | 0.14 | 4,025,760 | 298,651 | 0 | 0 | 0.00 | 0.00 |
34953 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2022 | 0.12 | 2,751,417 | 303,499 | 0 | 0 | 0.00 | 0.00 |
34954 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2021 | 0.21 | 15,834,475 | 621,595 | 0 | 0 | 0.00 | 0.00 |
34955 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2020 | 0.25 | 37,429,851 | 218,701 | 0 | 0 | 0.00 | 0.00 |
34956 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2019 | 0.24 | 32,459,656 | 454,393 | 0 | 0 | 0.00 | 0.00 |
34957 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2018 | 0.21 | 16,994,642 | 419,589 | 0 | 0 | 0.00 | 0.00 |
34958 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2017 | 0.18 | 9,276,428 | 219,001 | 0 | 0 | 0.00 | 0.00 |
34959 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2016 | 0.17 | 8,782,814 | 152,864 | 0 | 0 | 0.00 | 0.00 |
34960 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2015 | 0.16 | 6,706,901 | 280,870 | 0 | 0 | 0.00 | 0.00 |
34961 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2014 | 0.19 | 11,721,368 | 909,706 | 0 | 0 | 0.00 | 0.00 |
34962 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2013 | 0.19 | 12,468,229 | 243,322 | 0 | 0 | 0.00 | 0.00 |
34963 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2012 | 0.60 | 8,366,059 | 121,977 | 2 | 0 | 0.00 | 0.00 |
34964 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2011 | 0.19 | 10,833,987 | 692,078 | 0 | 0 | 0.00 | 0.00 |
34965 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2010 | 0.17 | 8,350,508 | 35,990 | 0 | 0 | 0.00 | 0.00 |
34966 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2009 | 0.17 | 8,764,299 | 97,822 | 0 | 0 | 0.00 | 0.00 |
34967 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2008 | 0.16 | 7,670,243 | 168,576 | 0 | 0 | 0.00 | 0.00 |
34968 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2007 | 0.13 | 3,207,691 | 579,778 | 0 | 0 | 0.00 | 0.00 |
34969 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2006 | 0.38 | 2,683,261 | 293,094 | 1 | 0 | 0.00 | 0.00 |
34970 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2005 | 0.10 | 1,452,278 | 321,099 | 0 | 0 | 0.00 | 0.00 |
34971 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2004 | 0.06 | 172,025 | 326,752 | 0 | 0 | 0.00 | 0.00 |
34972 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2003 | 0.06 | 336,773 | 207,271 | 0 | 0 | 0.00 | 0.00 |
34973 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2002 | 0.09 | 1,847 | 934,455 | 0 | 0 | 0.00 | 0.00 |
34974 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2001 | 0.10 | 16,652 | 1,489,628 | 0 | 0 | 0.00 | 0.00 |
34975 | pedrofeliu | 27/05/2025 12:06 | pedrofeliu | 27/05/2025 12:06 | Afghanistan | AFG | 2000 | 0.09 | 8,656 | 1,120,562 | 0 | 0 | 0.00 | 0.00 |
Note: Version updated on 05/27/2025. Some modifications were made in relation to the first published version
wdt_ID | wdt_created_by | wdt_created_at | wdt_last_edited_by | wdt_last_edited_at | Name of the Variable | Description | Source | External Link |
---|---|---|---|---|---|---|---|---|
52 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | country | Country name in English | ||
53 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | iso3 | Country ISOA3 code | ISO 3166 international standard | https://www.iban.com/country-codes |
54 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | year | Year | ||
55 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | fpi | Foreign Policy Index. Ranges from 0 to 1. See description at the beginning of this page | POLEN (2024) | |
56 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | export | Annual exports by country in US$ FOB | Comex Stat MDIC | https://comexstat.mdic.gov.br/pt/geral |
57 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | import | Annual imports by country in US$ FOB | Comex Stat MDIC | https://comexstat.mdic.gov.br/pt/geral |
58 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | acordos_total | Total number of bilateral agreements per year | Concórdia Itamaraty | https://concordia.itamaraty.gov.br/ |
59 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | international_trip | Number of official presidential trips per year | Library of the Presidency of the Republic | http://www.biblioteca.presidencia.gov.br/presidencia |
60 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | regional | Average participation of countries in the same regional blocs as Brazil | Ministry of Foreign Affairs (MRE) | https://www.gov.br/mre/pt-br/assuntos/mecanismos-internacionais/mecanismos-de-integracao-regional |
61 | pedrofeliu | 27/05/2025 19:18 | pedrofeliu | 27/05/2025 19:18 | finantial_congruence | Support and joint participation in International Financial Organizations | IMF, World Bank, World Trade Organization | https://www.imf.org; https://www.worldbank.org; https://www.wto.org |
Feliu Ribeiro, P. (2024). POLEN (v1.01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14015246. Avaiable at: polen.iri.usp.br
Foreign Policy Index by country and year (1998-2024)

Disaggregated Foreign Policy Index Data Base
Use the filters below to manipulate the information. The database below displays all the variables used in estimating the Foreign Policy Index, as well as a series of data to facilitate aggregated analysis and graphical visualization
wdt_ID | country | year | iso3 | fpi | trade_dim | diplomatic_dim | regional_dim | export | import | acordos_total | international_trip | brazilagree | interregion | brics | ibas | asa | aspa | cplp | regional | prosul | unasul | otca | aladi | celac | imf | imf_board | imf_g20 | wto_congruence | wto_mercosul | wb_ibrd | wb_miga | wb_ida | wb_ifc | wb_coalition | wto_cairns | wto_g20 | wto_nama11 | wto_fan | wto_w52 | finantial_congruence | mercosul | embassy | global_south | global_north | eu_mem | oecd | region_pol | region_n | income |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
wdt_ID | country | year | iso3 | fpi | trade_dim | diplomatic_dim | regional_dim | export | import | acordos_total | international_trip | brazilagree | interregion | brics | ibas | asa | aspa | cplp | regional | prosul | unasul | otca | aladi | celac | imf | imf_board | imf_g20 | wto_congruence | wto_mercosul | wb_ibrd | wb_miga | wb_ida | wb_ifc | wb_coalition | wto_cairns | wto_g20 | wto_nama11 | wto_fan | wto_w52 | finantial_congruence | mercosul | embassy | global_south | global_north | eu_mem | oecd | region_pol | region_n | income |
1 | Afghanistan | 2024 | AFG | 0.1 | 0.3 | 0.6 | 0.3 | 3,098,112 | 1,018,765 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
2 | Afghanistan | 2023 | AFG | 0.1 | 0.3 | 0.6 | 0.3 | 4,025,760 | 298,651 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 1 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
3 | Afghanistan | 2022 | AFG | 0.1 | 0.3 | 0.6 | 0.3 | 2,751,417 | 303,499 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
4 | Afghanistan | 2021 | AFG | 0.2 | 0.4 | 0.5 | 0.3 | 15,834,475 | 621,595 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
5 | Afghanistan | 2020 | AFG | 0.2 | 0.4 | 0.5 | 0.2 | 37,429,851 | 218,701 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
6 | Afghanistan | 2019 | AFG | 0.2 | 0.4 | 0.5 | 0.2 | 32,459,656 | 454,393 | 0 | 0 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
7 | Afghanistan | 2018 | AFG | 0.2 | 0.4 | 0.5 | 0.3 | 16,994,642 | 419,589 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
8 | Afghanistan | 2017 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 9,276,428 | 219,001 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
9 | Afghanistan | 2016 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 8,782,814 | 152,864 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
10 | Afghanistan | 2015 | AFG | 0.2 | 0.3 | 0.6 | 0.3 | 6,706,901 | 280,870 | 0 | 0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
11 | Afghanistan | 2014 | AFG | 0.2 | 0.4 | 0.5 | 0.3 | 11,721,368 | 909,706 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
12 | Afghanistan | 2013 | AFG | 0.2 | 0.4 | 0.5 | 0.3 | 12,468,229 | 243,322 | 0 | 0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
13 | Afghanistan | 2012 | AFG | 0.6 | 0.1 | 0.9 | 0.0 | 8,366,059 | 121,977 | 2 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
14 | Afghanistan | 2011 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 10,833,987 | 692,078 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
15 | Afghanistan | 2010 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 8,350,508 | 35,990 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
16 | Afghanistan | 2009 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 8,764,299 | 97,822 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
17 | Afghanistan | 2008 | AFG | 0.2 | 0.4 | 0.6 | 0.3 | 7,670,243 | 168,576 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
18 | Afghanistan | 2007 | AFG | 0.1 | 0.3 | 0.6 | 0.3 | 3,207,691 | 579,778 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
19 | Afghanistan | 2006 | AFG | 0.4 | 0.1 | 0.9 | 0.1 | 2,683,261 | 293,094 | 1 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
20 | Afghanistan | 2005 | AFG | 0.1 | 0.3 | 0.6 | 0.4 | 1,452,278 | 321,099 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
21 | Afghanistan | 2004 | AFG | 0.1 | 0.3 | 0.7 | 0.4 | 172,025 | 326,752 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
22 | Afghanistan | 2003 | AFG | 0.1 | 0.3 | 0.7 | 0.4 | 336,773 | 207,271 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
23 | Afghanistan | 2002 | AFG | 0.1 | 0.3 | 0.7 | 0.4 | 1,847 | 934,455 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
24 | Afghanistan | 2001 | AFG | 0.1 | 0.3 | 0.7 | 0.3 | 16,652 | 1,489,628 | 0 | 0 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
25 | Afghanistan | 2000 | AFG | 0.1 | 0.3 | 0.7 | 0.3 | 8,656 | 1,120,562 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 | 0 | South Asia | 2 | Low income |
wdt_ID | wdt_created_by | wdt_created_at | wdt_last_edited_by | wdt_last_edited_at | Variable Name | Description | Source |
---|---|---|---|---|---|---|---|
1 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | country | Country name in English | POLEN (2024) |
2 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | year | Year | POLEN (2024) |
3 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | iso3 | ISOA3 country code – ISO 3166 international standard | https://www.iban.com/country-codes |
4 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | fpi | Foreign Policy Index. Ranges from 0 to 1. See description at the beginning of this page | POLEN (2024) |
5 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | trade_dim | Scores of the first dimension of the factor analysis – see information at the top of this page | POLEN (2024) |
6 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | diplomatic_dim | Scores of the second dimension of the factor analysis – see information at the top of this page | POLEN (2024) |
7 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | regional_dim | Scores of the third dimension of the factor analysis – see information at the top of this page | POLEN (2024) |
8 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | export | Annual exports by country in US$ FOB | Comex Stat MDIC – https://comexstat.mdic.gov.br/pt/geral |
9 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | import | Annual imports by country in US$ FOB | Comex Stat MDIC – https://comexstat.mdic.gov.br/pt/geral |
10 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | acordos_total | Total number of bilateral agreements per year | Concórdia Itamaraty – https://concordia.itamaraty.gov.br/ |
11 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | international_trip | Number of official presidential trips per year | Library of the Presidency of the Republic – http://www.biblioteca.presidencia.gov.br/presidencia |
12 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | brazilagree | Distance of ideal points between Brazil and the country. Ideal points estimated with votes in the UN General Assembly | Voeten, Erik, Anton Strezhnev, and Michael Bailey. 2009. “United Nations General Assembly Voting Data.” Harvard Dataverse. https://doi.org/doi:10.7910/DVN/LEJUQZ. |
13 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | interregion | Average weighted by the participation of countries with a weight of 2 for the CPLP and the BRICS and 1 for the others | POLEN (2024) |
14 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | brics | Participates in BRICS (1- yes, 0-no) | BRICS – https://brics.br |
15 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | ibas | Participates in IBAS (1- yes, 0-no) | IBAS – https://www.ibsa-trilateral.org/pt/index.html |
16 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | asa | Participates in ASA (1- yes, 0-no) | ASA – https://www.asa-international.com |
17 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | aspa | Participates in ASPA (1- yes, 0-no) | ASPA – https://www.un.org/unispal/document-source/summit-of-south-american-arab-countries-aspa/ |
18 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | cplp | Participates in CPLP (1- yes, 0-no) | CPLP – https://www.cplp.org |
19 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | regional | Average participation of countries in the same regional blocs as Brazil | POLEN (2024) |
20 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | prosul | Participates in PROSUL (1- yes, 0-no) | PROSUR – https://prosur.org/ |
21 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | unasul | Participates in UNASUL (1- yes, 0-no) | UNASUL – http://www.unasursg.org |
22 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | otca | Participates in OTCA (1- yes, 0-no) | OTCA – https://otca.org |
23 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | aladi | Participates in ALADI (1- yes, 0-no) | ALADI – https://www.aladi.org |
24 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | celac | Participates in CELAC (1- yes, 0-no) | CELAC – https://celacinternational.org/celac-2-2/ |
25 | pedrofeliu | 27/05/2025 19:43 | pedrofeliu | 27/05/2025 19:43 | imf | Composed of the proportion of support on the IMF executive board (weight 2) and participation in the G20 (weight 1) | POLEN (2024) |
Feliu Ribeiro, P. (2024). POLEN (v1.01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14015246. Avaiable at: polen.iri.usp.br