Para analisar um mercado qualquer é necessário conhecer um pouco sobre o comércio internacional do mesmo, de modo a identificar os principais países exportadores e importadores, e a origem e o destino das importações e exportações que o seu país faz.
Para responder as perguntas como esta: Q: Quais são os países que mais exportaram/importaram CERVEJA ano X?
Inicialmente, você necessita conhecer o(s) códigos que este produto é identificado nas bases de dados de comércio internacional. No caso da cerveja, a classe 2203 contempla todos os tipos de cerveja e o código 220300 é o da cerveja com malte. Além disto, você pode conhecer o comércio internacional de um dos principais ingredientes da cerveja: o malte, que é identificado pelo código 1107 e lúpulo (130213)
A principal base de dados estatísticos sobre comércio internacional de produtos é a (UN Comtrade), que é um banco de dados mantido pela Divisão de Estatísticas das Nações Unidas. As informações são padronizadas e constantemente atualizadas. O UN Comtrade é considerado o banco de dados mais abrangente disponível de comércio internacional, com mais de 1 bilhão de registros, contendo estatísticas detalhadas sobre importações e exportações relatadas pelas autoridades estatísticas de cerca de 200 países ou áreas.
Abaixo é apresentada a estratégia de busca realizada
Outra opção é o site da OEC também é possível obter estatísticas de comercio internacional para alguns produtos. Neste link https://oec.world/pt/profile/hs92/2203/ estão as informações sobre cerveja
Comércio Internacional da Cerveja
In 2017, the world exports of "Beer made from malt" exceeded $14.3 billion (according to external trade statistics of 128 countries). It was $13.1 billion in the previous year (according to merchandise trade statistics of 136 countries).
In 2017, the world imports of "Beer made from malt" exceeded $14.5 billion (according to external trade statistics of 141 countries). It was $13.6 billion in the previous year (according to merchandise trade statistics of 146 countries).
Em 2017, os principais exportadores de Cerveja são o México($3,76 Bilhão), a Holanda ($1,91 Bilhão), Bélgica-Luxemburgo ($1,65 Bilhão), a Alemanha ($1,92 Bilhão) e o Estados Unidos ($685 Milhão). Os principais importadores são o Estados Unidos ($5,32 Bilhão), a França ($830 Milhão), a China ($750 Milhão), o Reino Unido ($628 Milhão) e a Itália ($585 Milhão).
| Reporter | 220300. Beer made from malt | |||||
| Exports | Imports | |||||
| 2017 | 2017 | |||||
| Value (US$) | World Rank | World Share, % | Value (US$) | World Rank | World Share, % | |
| USA | 685,509,295 | 5 | 4.78 | 5,326,262,702 | 1 | 36.53 |
| France | 405,046,145 | 7 | 2.82 | 830,242,681 | 2 | 5.69 |
| China | 227,581,935 | 11 | 1.58 | 750,403,526 | 3 | 5.14 |
| United Kingdom | 677,188,748 | 6 | 4.73 | 628,191,269 | 4 | 4.3 |
| Italy | 197,492,412 | 13 | 1.37 | 585,475,932 | 5 | 4.01 |
| Canada | 116,121,155 | 18 | 0.81 | 571,908,583 | 6 | 3.92 |
| Germany | 1,291,303,869 | 4 | 9.02 | 496,896,170 | 7 | 3.4 |
| Netherlands | 1,917,391,352 | 2 | 13.39 | 405,098,935 | 8 | 2.77 |
| Australia | 31,512,323 | 39 | 0.22 | 313,206,788 | 9 | 2.14 |
| Spain | 205,860,262 | 12 | 1.43 | 308,826,323 | 10 | 2.11 |
| Korea | 112,447,901 | 20 | 0.78 | 263,091,011 | 11 | 1.8 |
| Chile | 1,312,696 | 85 | 0 | 205,113,780 | 12 | 1.4 |
| Belgium | 1,651,388,261 | 3 | 11.53 | 201,763,915 | 13 | 1.38 |
| Russia | 129,087,752 | 17 | 0.9 | 195,143,134 | 14 | 1.33 |
| Other Asia, nes | 7,997,425 | 56 | 0.05 | 191,740,521 | 15 | 1.31 |
Fonte: UNCOMTRADE(2019)
Top exporters of Beer made from malt in 2017
The world's largest exporters of this commodity group in 2017 were
- Mexico - 26% of the world exports ($3.76 billion)
- Netherlands - 13.3% ($1.91 billion)
- Belgium - 11.5% ($1.65 billion)
- Germany - 9.02% ($1.29 billion)
- USA - 4.78% ($685 million)
«Beer made from malt» accounted for a substantial share of total exports of
- Saint Vincent and the Grenadines - 8.35% of Saint Vincent and the Grenadines's total exports in 2017 ($3.53 million of $42 million)
- Saint Lucia - 7.53% ($10.6 million of $141 million)
- Burundi - 4.51% ($6.74 million of $149 million)
- Samoa - 4.34% ($1.92 million of $44 million)
- Saint Kitts and Nevis - 3.48% ($1.15 million of $33 million)
- Jamaica - 2.65% ($34 million of $1.3 billion)
- Timor-Leste - 2.55% ($15 million of $588 million)
- Palau - 2.02% ($3.19 million of $157 million)
- Solomon Islands - 1.64% ($9.38 million of $571 million)
- Togo - 1.22% ($9.2 million of $749 million)
According to statistics provided by the major exporters, the largest flows of exports of «Beer made from malt» in 2017 were
- Exports from Belgium to France: (3.61% of the world exports, $517 million according to external trade statistics of Belgium)
- Exports from Belgium to Netherlands: (1.61% of the world exports, $231 million according to external trade statistics of Belgium)
- Exports from Belgium to USA: (2.58% of the world exports, $369 million according to external trade statistics of Belgium)
- Exports from Germany to Italy: (2.05% of the world exports, $294 million according to external trade statistics of Germany)
- Exports from Mexico to USA: (22% of the world exports, $3.25 billion according to external trade statistics of Mexico)
- Exports from Netherlands to France: (1.18% of the world exports, $169 million according to external trade statistics of Netherlands)
- Exports from Netherlands to USA: (5.37% of the world exports, $769 million according to external trade statistics of Netherlands)
- Exports from United Kingdom to USA: (1.25% of the world exports, $179 million according to external trade statistics of United Kingdom)
- Exports from USA to Canada: (1.17% of the world exports, $168 million according to external trade statistics of USA)
- Exports from USA to Mexico: (1.03% of the world exports, $147 million according to external trade statistics of USA)
Top importers of Beer made from malt in 2017
The world's largest importers of this commodity group in 2017 were
- USA - 36% of the world imports ($5.32 billion)
- France - 5.69% ($830 million)
- China - 5.14% ($750 million)
- United Kingdom - 4.3% ($628 million)
- Italy - 4.01% ($585 million)
- Canada - 3.92% ($571 million)
«Beer made from malt» accounted for a substantial share of total imports of
- Timor-Leste - 2.55% of Timor-Leste's total imports in 2017 ($15 million of $588 million)
- Palau - 2.02% ($3.19 million of $157 million)
- Solomon Islands - 1.64% ($9.38 million of $571 million)
- Togo - 1.22% ($9.2 million of $749 million)
- Namibia - 1.22% ($64 million of $5.22 billion)
- Cabo Verde - 1.2% ($9.57 million of $793 million)
- Montenegro - 1.17% ($4.96 million of $420 million)
- Paraguay - 1.17% ($139 million of $11.8 billion)
- Saint Lucia - 1.08% ($7.19 million of $663 million)
- Mexico - 0.92% ($3.76 billion of $409 billion)
According to statistics provided by the major importers, the largest flows of imports of «Beer made from malt» in 2017 were
- Imports to China from Germany (1.43% of the world imports, $209 million according to external trade statistics of China)
- Imports to France from Belgium (3.32% of the world imports, $484 million according to external trade statistics of France)
- Imports to Germany from Denmark (1.52% of the world imports, $222 million according to external trade statistics of Germany)
- Imports to Italy from Germany (1.25% of the world imports, $182 million according to external trade statistics of Italy)
- Imports to Netherlands from Belgium (1.47% of the world imports, $215 million according to external trade statistics of Netherlands)
- Imports to USA from Belgium (2.31% of the world imports, $337 million according to external trade statistics of USA)
- Imports to USA from Germany (1.31% of the world imports, $192 million according to external trade statistics of USA)
- Imports to USA from Ireland (1.52% of the world imports, $222 million according to external trade statistics of USA)
- Imports to USA from Mexico (23% of the world imports, $3.4 billion according to external trade statistics of USA)
- Imports to USA from Netherlands (5.73% of the world imports, $836 million according to external trade statistics of USA)
BRASIL
Entre 146 países, o Brasil ocupa o 21º lugar no ranking dos países exportadores de cerveja e 52º colocação no ranking das importações.
 |
| Fonte: Marajo Health Economics, a partir de dados do UNCOMTRADE |
Além disto, você pode utilizar o Rstudio para obter dados sobre o comércio internacional. Desenvolvi a rotina abaixo para a captar dados da cerveja
#Rotina em R
#definindo diretorio
getwd()
setwd("~/financas em R")
# limpando a memoria
rm(list=ls())
#carregando os pacotes necessarios
my.pkgs <- c('comtradr', 'concordance',
'igraph', 'ITNr', 'scales', 'rjson', 'jsonlite', 'tradestatistics', 'WDI')
install.packages(c (my.pkgs), dependencies = TRUE)
devtools::install_github("r-spatial/mapview", dependencies = TRUE)
library(tradestatistics)
library(comtradr)
library(ggplot2)
library(dplyr)
library(ITNr)
library(rjson)
library(jsonlite)
library(dplyr)
library(purrr)
library(concordance)
library(magrittr)
library(httr)
library(RCurl)
library(rvest)
library(data.table)
library(tidyverse)
library(WDI)
#lista de produtos HS6
products <- fromJSON("https://api.tradestatistics.io/products")
view(products)
#escolhendo as mercadorias
ct_commodity_lookup('beer')
#obtendo os códigos HS
commodity_codes <- ct_commodity_lookup("beer",
return_code = TRUE,
return_char = TRUE)
#exportacao e importacao de cerveja - Brasil
exp_imp_br_cer <- ct_search(
reporters = "Brazil",
partners = "World",
trade_direction = c("exports", "imports"),
commod_codes = "220300",
freq = "annual" )
#fazendo grafico
exp_imp_br_cer %>%
ggplot(aes(x = year, y = trade_value_usd/1e9,
color = trade_flow)) +
geom_line() +
geom_point(size = 2) +
labs(
title = "Exportações e Importações de Cerveja - Brasil - 1990 a 2018",
x = "Ano",
y = "US$ Bilhões"
) +
hrbrthemes::theme_ipsum_rc(
plot_title_size = 10,
base_size = 10
) +
hrbrthemes::scale_color_ipsum(
"Fluxo",
labels = c("Exportações", "Importações")
) +
theme(
legend.position = "bottom"
)
###EXPORTACAO MUNDO
#identificando os países exportadores de todos os tipos de cerveja
df <- ct_search(
reporters = "All",
partners = c("World"),
trade_direction = c("export"),
freq = "annual",
start_date = 2014,
end_date = 2018,
commod_codes =c('220291', '220299', '2203', '220300')
)
#identificando os maiores exportadores para determinado codigo - cerveja com malte
exportadores <- ct_search(
reporters = "All",
partners = "World",
trade_direction = c("export"),
freq = "annual",
start_date = 2014,
end_date = 2018,
commod_codes = "220300"
)
dexp_world <- ct_search(
reporters = "All",
partners = "World",
trade_direction = c("exports"),
freq = "annual",
start_date = 2014,
end_date = 2018,
commod_codes = "220300"
)
# Apply polished col headers.
#dexp_world <- ct_use_pretty_cols(dexp_world)
# Create country specific "trade value " dataframe for plotting.
plotdexp_world <- dexp_world %>%
group_by_(.dots = c("reporter", "year")) %>%
summarise(kg = as.numeric(sum(`netweight_kg`, na.rm = TRUE))) %>%
#summarise(usd= as.numeric(sum("Trade Value usd", na.rm = TRUE))) %>%
as_data_frame()
# Get vector of the top 8 exporters countries/areas by total weight shipped
# across all years, then subset plotdexp2 to only include observations related
# to those countries/areas.
#
top82 <- dexp_world %>%
group_by(`reporter`) %>%
summarise(kg = as.numeric(sum(`netweight_kg`, na.rm = TRUE))) %>%
arrange(desc(kg)) %>%
top_n(8, kg) %>%
.[['reporter']]
view(top82)
plotdexp_world <-plotdexp_world %>% filter(`reporter` %in% top82)
# Create plots (y-axis is not fixed across panels, this will allow us to identify
# trends over time within each country/area individually).
qplot(year, kg, data = plotdexp_world) +
geom_line(data = plotdexp_world[plotdexp_world$`reporter` %in% names(which(table(plotdexp_world$'`reporter') > 1)), ]) +
xlim(min(plotdexp_world$year), max(plotdexp_world$year)) +
labs(title = "Litres of Beer Exports, by Top 8 Countries , 2014 - 2018") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
axis.text = element_text(size = 7)) +
facet_wrap(~factor(`reporter`, levels = top82), scales = "free", nrow = 2, ncol= 4)
# trends over time within each country/area individually).
##RRASIL IMPORTAÇÕES E EXPORTACOES
# IMPORTACOES
#identificando as origens das importações de cerveja com malte - Brasil
dimp_br <- ct_search(
reporters = "All",
partners = "Brazil",
trade_direction = c("import"),
freq = "annual",
start_date = 2014,
end_date = 2018,
commod_codes = "220300"
)
#salvando em tabela excel
write.table(dimp_br, "data_imp.xls", na = "NA", row.names = FALSE, col.names = TRUE, sep=",")
#importações do Brasil de paises selecionados
q_imp_br <- ct_search(reporters = "Brazil",
partners = c("Germany", "France", "Japan", "USA", "Mexico"),
commod_codes = "220300",
trade_direction = "imports")
# EXPORTACOES BRASIL
#identificando os países de destinos de cerveja com malte - Brasil
dexp_br <- ct_search(
reporters = "Brazil",
partners = "All",
trade_direction = c("exports"),
freq = "annual",
start_date = 2014,
end_date = 2018,
commod_codes = "220300"
)
# Apply polished col headers.
dexp_br <- ct_use_pretty_cols(dexp_br)
# Create country specific "total weight per year" dataframe for plotting.
plotdexp_br <- dexp_br %>%
group_by_(.dots = c("`Partner Country`", "Year")) %>%
summarise(kg = as.numeric(sum(`Net Weight kg`, na.rm = TRUE))) %>%
as_data_frame()
# Get vector of the top 8 destination countries/areas by total weight shipped
# across all years, then subset plotdexp to only include observations related
# to those countries/areas.
top8 <- plotdexp_br %>%
group_by(`Partner Country`) %>%
summarise(kg = as.numeric(sum(kg, na.rm = TRUE))) %>%
top_n(8, kg) %>%
arrange(desc(kg)) %>%
.[["Partner Country"]]
plotdexp_br <- plotdexp_br %>% filter(`Partner Country` %in% top8)
# Create plots (y-axis is not fixed across panels, this will allow us to identify
# trends over time within each country/area individually).
qplot(Year, kg, data = plotdexp_br) +
geom_line(data = plotdexp_br[plotdexp_br$`Partner Country` %in% names(which(table(plotdexp_br$'`Partner Country') > 1)), ]) +
xlim(min(plotdexp_br$Year), max(plotdexp_br$Year)) +
labs(title = "Litres of Brazilian Beer Exports, by Destination, 2014 - 2018") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
axis.text = element_text(size = 7)) +
facet_wrap(~factor(`Partner Country`, levels = top8), scales = "free", nrow = 2, ncol= 4)
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