SaquaremaMunicipality in Rio de Janeiro State in Brazil
Exports (2019): $38.8k, Rnk 1953/2197
Top Destination (2019): $36.8k, Italy
Top Export (2019): $36.8k, Plastic Lids
Imports (2019): $1.84M, Rnk 912/2197
Top Origin (2019): $1.12M, India
Top Import (2019): $1.1M, Other Plastic Sheetings
Exports: In 2019, Brazil's Saquarema exported $38.8k, making it the 1953rd largest exporter out of the 2197 exporters in Brazil. In 2019 the top exports of Saquarema were Plastic Lids ($36.8k) and Plastic Housewares ($2.02k).
Imports: In 2019, Brazil's Saquarema imported $1.84M, making it the 912th largest importer out of the 2197 importers in Brazil. In 2019 top imports of Saquarema were Other Plastic Sheetings ($1.1M), Kaolin Coated Paper ($300k), Self-adhesive Plastics ($291k), Cellulose Fibers Paper ($48.6k), and Other Paper Machinery ($39.2k).
Top Destination (2019): Italy, $36.8k
Top Product (2019): Plastic Lids, $36.8k
In 2019 the top export destinations of Saquarema were Italy ($36.8k) and Argentina ($2.02k).
In 2019 the top exports of Saquarema were Plastic Lids ($36.8k) and Plastic Housewares ($2.02k).
Top Origin (2019): India, $1.12M
Top Import (2019): Other Plastic Sheetings, $1.1M
In 2019 the top import origins of Saquarema were India ($1.12M), China ($408k), Finland ($304k), Japan ($3.94k), and Angola ($17).
In 2019 the top imports of Saquarema were Other Plastic Sheetings ($1.1M), Kaolin Coated Paper ($300k), Self-adhesive Plastics ($291k), Cellulose Fibers Paper ($48.6k), and Other Paper Machinery ($39.2k).
This section shows differences between total trade from Saquarema throughout time.
The municipalities of Brazil with the highest exports in 2019 were Rio De Janeiro ($11.3B), Duque De Caxias ($10.4B), Parauapebas ($7.02B), Canaã Dos Carajás ($4.93B), and São Paulo ($4.14B)
The municipalities of Brazil with the highest imports in 2019 were Manaus ($10.1B), São Paulo ($10.1B), Rio De Janeiro ($9.48B), Itajaí ($7.3B), and Macaé ($5.31B)
This section shows forecasts for total exports and imports from Saquarema. The forecast is based in a Long Short-Term Memory Model constructed using monthly trade data.
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