CaçapavaExports (2019): $96.8M, Rnk 332/2197
Top Destination (2019): $32.7M, Argentina
Top Export (2019): $20.9M, Safety Glass
Imports (2019): $99.7M, Rnk 203/2197
Top Origin (2019): $13.9M, China
Top Import (2019): $17.7M, Margarine
Overview: This page contains the latest international trade data for Caçapava, including export and import data.
Exports: In 2019, Brazil's Caçapava exported $96.8M, making it the 332nd largest exporter out of the 2197 exporters in Brazil. In 2019 the top exports of Caçapava were Safety Glass ($20.9M), Vehicle Parts ($18.1M), Locomotive Parts ($14.3M), Baked Goods ($13.1M), and Insulated Wire ($7.69M).
Imports: In 2019, Brazil's Caçapava imported $99.7M, making it the 203rd largest importer out of the 2197 importers in Brazil. In 2019 top imports of Caçapava were Margarine ($17.7M), Seats ($16.8M), Carbonates ($4.72M), Insulated Wire ($4.64M), and Polyacetals ($4.42M).
Yearly Exports
#permalink to sectionTop Destination (2019): Argentina, $32.7M
Top Product (2019): Safety Glass, $20.9M
In 2019 the top export destinations of Caçapava were Argentina ($32.7M), United States ($26.4M), Mexico ($9.66M), Colombia ($7.81M), and Chile ($2.86M).
In 2019 the top exports of Caçapava were Safety Glass ($20.9M), Vehicle Parts ($18.1M), Locomotive Parts ($14.3M), Baked Goods ($13.1M), and Insulated Wire ($7.69M).
Export Dynamics
#permalink to sectionYearly Imports
#permalink to sectionIn 2019 the top import origins of Caçapava were China ($13.9M), Thailand ($12.2M), United States ($12.1M), Uruguay ($9.34M), and Malaysia ($8.41M).
In 2019 the top imports of Caçapava were Margarine ($17.7M), Seats ($16.8M), Carbonates ($4.72M), Insulated Wire ($4.64M), and Polyacetals ($4.42M).
Import Dynamics
#permalink to sectionLatest Data
#permalink to sectionComparison in Time
#permalink to sectionThis section shows differences between total trade from Caçapava throughout time.
Forecasts
#permalink to sectionForecasts
#permalink to sectionThis section shows forecasts for total exports and imports from Caçapava. The forecast is based in a Long Short-Term Memory Model constructed using monthly trade data.
