Overview
tradeIndices calculates frequently used international
trade indicators from numeric vectors. It deliberately separates
measurement from data acquisition, allowing researchers to use UN
Comtrade, World Bank, national, or independently verified data.
The numerical data below are illustrative agricultural-trade values
and should not be interpreted as official statistics.
Trade openness
Trade openness is exports plus imports divided by GDP:
\[
TO_t = 100\frac{X_t+M_t}{GDP_t}.
\]
macro <- data.frame(
year = 2020:2024,
exports = c(276, 302, 326, 351, 379),
imports = c(365, 421, 457, 493, 528),
gdp = c(2667, 3150, 3390, 3570, 3810)
)
macro$openness <- with(
macro,
trade_openness(exports, imports, gdp)
)
macro
#> year exports imports gdp openness
#> 1 2020 276 365 2667 24.03450
#> 2 2021 302 421 3150 22.95238
#> 3 2022 326 457 3390 23.09735
#> 4 2023 351 493 3570 23.64146
#> 5 2024 379 528 3810 23.80577
plot(
macro$year, macro$openness,
type = "o", pch = 16, lwd = 2, col = "#1B5E20",
xlab = "Year", ylab = "Trade openness (% of GDP)",
main = "Illustrative trade-openness series"
)
grid()
The monetary units cancel, but exports, imports, and GDP must be
expressed in the same unit and price basis.
Bilateral trade intensity
For reporter country \(i\) and
partner \(j\), conventional export
intensity is
\[
TII_{ij}=\frac{X_{ij}/X_i}{M_j/M_w}.
\]
intensity <- data.frame(
year = 2020:2024,
bilateral_exports = c(14, 16, 19, 21, 25),
reporter_exports = c(180, 195, 210, 225, 245),
world_exports_to_partner = c(310, 335, 360, 390, 420),
world_exports = c(4300, 4650, 4920, 5180, 5490)
)
intensity$tii <- with(
intensity,
trade_intensity(
bilateral_exports,
reporter_exports,
world_exports_to_partner,
world_exports
)
)
intensity
#> year bilateral_exports reporter_exports world_exports_to_partner
#> 1 2020 14 180 310
#> 2 2021 16 195 335
#> 3 2022 19 210 360
#> 4 2023 21 225 390
#> 5 2024 25 245 420
#> world_exports tii
#> 1 4300 1.078853
#> 2 4650 1.138921
#> 3 4920 1.236508
#> 4 5180 1.239658
#> 5 5490 1.333819
Values above one indicate that the reporter directs a larger export
share to the partner than the partner represents in world imports. This
is a relative measure and is not the value or growth rate of bilateral
trade.
Concentration and evenness-based diversification
products <- c(rice = 45, wheat = 25, tea = 20, spices = 10)
c(
HHI = trade_concentration(products),
HHI_10000 = trade_concentration(products, scale = 10000),
One_minus_HHI = trade_diversification(products),
Normalized_HHI_diversity = trade_diversification(
products, method = "normalized_hhi"
),
Shannon = trade_diversification(products, method = "shannon"),
Effective_products = trade_diversification(
products, method = "effective_number"
)
)
#> HHI HHI_10000 One_minus_HHI
#> 0.3150000 3150.0000000 0.6850000
#> Normalized_HHI_diversity Shannon Effective_products
#> 0.9133333 0.9074899 3.5185471
Higher HHI means concentration. Higher one-minus-HHI, normalized HHI
diversity, and normalized Shannon entropy mean a more even distribution.
The effective number converts entropy into the number of equally
important categories that would generate the same entropy.
When indices are compared over time or across countries, the product
classification and included category universe should remain
consistent.
The package includes a small illustrative agricultural-trade file for
reproducible practice:
example_file <- system.file(
"extdata", "agricultural_trade_example.csv",
package = "tradeIndices"
)
ag_trade <- read.csv(example_file)
diversity_by_year <- vapply(
split(ag_trade$country_exports, ag_trade$year),
trade_diversification,
numeric(1),
method = "normalized_hhi"
)
plot(
as.integer(names(diversity_by_year)), diversity_by_year,
type = "o", pch = 16, lwd = 2, col = "#8D6E63",
xlab = "Year", ylab = "Normalized HHI diversification",
main = "Illustrative agricultural export diversification"
)
grid()
Structural diversification relative to a benchmark
The structural diversification index compares a country’s product
shares with a benchmark such as world exports:
\[
DIV_i=\frac{1}{2}\sum_k\left|s_{ik}-s_{wk}\right|.
\]
country_exports <- c(rice = 45, wheat = 25, tea = 20, spices = 10)
world_exports <- c(spices = 30, tea = 15, wheat = 35, rice = 20)
c(
Structural_distance = trade_diversification_index(
country_exports, world_exports
),
Export_similarity = export_similarity(
country_exports, world_exports
)
)
#> Structural_distance Export_similarity
#> 0.3 70.0
Named vectors are automatically aligned by category. A high
structural index means that the country’s export pattern differs from
the benchmark. It does not necessarily mean that the country’s export
basket is more evenly diversified; use
trade_diversification() for that question.
Additional indicators
rca_balassa(
country_product_exports = c(rice = 40, wheat = 20, tea = 10),
country_total_exports = 100,
world_product_exports = c(rice = 300, wheat = 500, tea = 200),
world_total_exports = 5000
)
#> [1] 6.666667 2.000000 2.500000
grubel_lloyd(
exports = c(rice = 40, wheat = 25, tea = 30),
imports = c(rice = 10, wheat = 20, tea = 28),
aggregate = TRUE
)
#> [1] 75.81699
Balassa RCA above one indicates that a product’s export share is
higher in the country than in the world benchmark. A high Grubel-Lloyd
value indicates a larger share of two-way, within-product trade.