This analysis examines Brazil’s potential trade policy responses to proposed tariff increases. By integrating U.S. export data by state with Brazilian import statistics, we develop a framework for strategic trade retaliation that maximizes political impact while minimizing economic disruption. We identify products where Trump-supporting states dominate U.S. exports, providing policymakers with evidence-based targeting options.
library(dplyr)
library(comexr)
library(readr)
library(readxl)
library(janitor)
library(tidyr)
library(knitr)
library(gt)# Download Brazilian trade data
comex_download(2025, types="ncm")
# Load Trump vote share by state
trump <- read_csv(here::here("data/trump_vote_share.csv")) %>%
clean_names() %>%
mutate(state = gsub(" ", "_", tolower(state)))
# Load and process US exports by state and HS6
us <- read_excel(here::here("data/State Exports by HS Commodities.xlsx"), sheet=3, skip=2) %>%
slice(-c(1:3)) %>%
clean_names() %>%
pivot_longer(-c(1:3)) %>%
transmute(hs6 = substr(state, 1, 6), state = name, value) %>%
filter(!grepl("[^0-9]", hs6), !is.na(value)) %>%
inner_join(trump) %>%
mutate(trump_exports = (trump_percentage/100) * value) %>%
group_by(hs6, trumpist = trump_percentage > 50) %>%
summarise(trump_exports = sum(trump_exports), exports = sum(value), .groups = "drop") %>%
group_by(hs6) %>%
mutate(p_exports = exports/sum(exports)) %>%
filter(exports > 10e6)
# Identify products dominated by Trump states
target_hs6 <- us %>%
filter(trumpist, p_exports > 0.5, exports > 10e6) %>%
arrange(desc(exports))Our approach employs a three-stage targeting methodology:
Economic Impact Assessment - Focus on high-value products (>$10 million in exports) where target states control >50% of U.S. exports to Brazil.
Strategic Filtering - Exclusion of sensitive goods including pharmaceuticals, medical devices, and food products to minimize humanitarian impact.
Data sources
This analysis utilizes two primary datasets:
U.S. Trade Data: State-level export statistics by HS6 commodity codes from the U.S. Census Bureau’s USA Trade Online database (https://usatrade.census.gov/data/Perspective60/) Data from January-May, 2025.
Brazilian Trade Data: Import statistics from Brazil’s Ministry of Development, Industry and Foreign Trade, from January-June 2025. (https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/estatisticas/base-de-dados-bruta)
# Summary statistics
total_products <- nrow(target_hs6)
total_value <- sum(target_hs6$exports)
avg_trump_share <- mean(target_hs6$p_exports)The analysis identifies 166 HS6 product codes representing $14 billion in U.S. exports to Brazil, with an average Trump state exports share of 85.6%.
Primary Retaliation Targets
Table 1 presents the top strategic retaliation targets identified through our methodology. These products represent high-value Brazilian imports from the United States where production is concentrated in Trump-supporting states.
target_ncm %>%
ungroup() %>%
select(NCM = co_ncm,
Product = no_ncm_ing,
`Import Value (USD)` = fob_usd,
`US Market Share` = p_country,
`Trump State Dominance` = p_exports) %>%
gt() %>%
gt::tab_header(title = "Table 1: Strategic Retaliation Targets") %>%
gt::fmt_currency(`Import Value (USD)`, decimals=0, scale_by=1) %>%
gt::fmt_percent(decimals=0, columns = c(`US Market Share`, `Trump State Dominance`)) %>%
gt::tab_style(
style = cell_fill(color = "#f8f9fa"),
locations = cells_body(columns = `Trump State Dominance`)
) %>%
gt::tab_footnote(footnote = "Import Value (USD): Brazilian imports from the US by NCM in 2025.") %>%
gt::tab_footnote(footnote = "US Market Share: % of Brazilian imports with origin from US.") %>%
gt::tab_footnote(footnote = "Trump State Dominance: % of exports to Brazil (HS6) origining from US states that voted >50% for Trump (2 party vote).") %>%
gt::tab_options(table.font.size = "small")| Table 1: Strategic Retaliation Targets | ||||
| NCM | Product | Import Value (USD) | US Market Share | Trump State Dominance |
|---|---|---|---|---|
| 39269090 | Other articles of plastics | $50,621,210 | 15% | 68% |
| 87032410 | Autom.with motor explosion,cm3>3000,up to 6 passengers | $26,730,057 | 31% | 99% |
| 22083020 | Whiskies, capacity container <= 2 l | $17,289,690 | 18% | 51% |
| 22087000 | Liqueurs | $12,468,019 | 50% | 100% |
| 87038000 | Other vehicles, equipped only with electric motor for propulsion | $10,454,364 | 2% | 98% |
| 87032490 | Autom.with motor explos.cm3>3000,more than 6 passengers | $6,996,572 | 26% | 99% |
| 85258929 | Other digital still cameras and video cameras | $4,389,081 | 7% | 70% |
| 33030010 | Perfumes (extracts) | $3,524,344 | 5% | 87% |
| 85285900 | Other monitors, not incorporating television reception apparatus, not classified under previous codes | $2,314,917 | 18% | 95% |
| 33030020 | Cologne water | $2,083,696 | 4% | 87% |
| 95045000 | Consoles and video game machines (ex. 9504.30) | $1,081,415 | 1% | 85% |
| Import Value (USD): Brazilian imports from the US by NCM in 2025. | ||||
| US Market Share: % of Brazilian imports with origin from US. | ||||
| Trump State Dominance: % of exports to Brazil (HS6) origining from US states that voted >50% for Trump (2 party vote). | ||||
Aggregate Impact Assessment
# Calculate comprehensive impact metrics
total_retaliation_value <- sum(target_ncm$fob_usd)
num_products <- nrow(target_ncm)
avg_us_share <- mean(target_ncm$p_country)
avg_trump_dominance <- mean(target_ncm$p_exports)
# Create summary table
impact_metrics <- tibble(
Metric = c("Total Import Value (USD)",
"Number of Product (NCM) Categories",
"Average U.S. Market Share of Brazilian imports (NCM)",
"Average Trump State Dominance of US exports (HS6)",
"Estimated Political Impact",
"Economic Risk Assessment"),
Value = c(scales::dollar(total_retaliation_value),
as.character(num_products),
scales::percent(avg_us_share, accuracy = 0.1),
scales::percent(avg_trump_dominance, accuracy = 0.1),
"High - Concentrated in states in which Trump won (2024)",
"Moderate - Non-essential imports")
)
impact_metrics %>%
gt() %>%
gt::tab_header(title = "Table 2: Retaliation Impact Assessment") %>%
gt::cols_width(
Metric ~ px(300),
Value ~ px(250)
) %>%
gt::tab_options(table.font.size = "small")| Table 2: Retaliation Impact Assessment | |
| Metric | Value |
|---|---|
| Total Import Value (USD) | $137,953,365 |
| Number of Product (NCM) Categories | 11 |
| Average U.S. Market Share of Brazilian imports (NCM) | 16.0% |
| Average Trump State Dominance of US exports (HS6) | 85.5% |
| Estimated Political Impact | High - Concentrated in states in which Trump won (2024) |
| Economic Risk Assessment | Moderate - Non-essential imports |
Strategic Implications
The analysis reveals significant opportunities for targeted retaliation that could maximize political pressure while minimizing economic disruption to Brazil. The concentration of production in Trump-supporting states for key product categories suggests that strategic tariffs could generate disproportionate political costs relative to their economic impact on Brazilian consumers.
Based on the quantitative analysis, we recommend the initial targeting of high-value, low-sensitivity products identified in Table 1, representing the most politically effective retaliation options.
Data sources: U.S. Census Bureau USA Trade Online (https://usatrade.census.gov/data/Perspective60/) and Brazilian Ministry of Development, Industry and Foreign Trade (https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/estatisticas/base-de-dados-bruta)