Global Macro Shock Case Study
Ashton Desaussure
2026-03-30
ASK
Main Question:
How does the Iran conflict affect the global economy under different levels of severity, and which macroeconomic indicators shift the most?
Indicators we will analyze:
- Oil prices
- Global GDP growth
- Global inflation
- MENA GDP growth
- Shipping cost index
- Fertilizer price index
PREPARE
We create the dataset directly inside R so it is reproducible and self‑contained.
data <- data.frame(
year = c(2023,2024,2025,2026,
2023,2024,2025,2026,
2023,2024,2025,2026),
scenario = c("baseline","baseline","baseline","baseline",
"moderate_shock","moderate_shock","moderate_shock","moderate_shock",
"severe_shock","severe_shock","severe_shock","severe_shock"),
oil_price_brent = c(82,85,88,90,
82,102,108,104,
82,128,142,130),
global_gdp_growth = c(3.0,2.9,2.8,2.9,
3.0,2.5,2.3,2.4,
3.0,1.6,1.2,1.4),
global_inflation = c(6.8,5.4,4.8,4.5,
6.8,6.2,5.8,5.3,
6.8,7.9,7.1,6.5),
mena_gdp_growth = c(1.9,2.1,2.3,2.4,
1.9,1.4,1.6,1.8,
1.9,0.2,0.5,0.9),
shipping_cost_index = c(100,102,103,104,
100,115,118,114,
100,138,145,135),
fertilizer_price_index = c(100,101,102,103,
100,112,115,110,
100,130,138,128),
conflict_severity = c(0,0,0,0,
1,1,1,1,
2,2,2,2)
)
str(data)## 'data.frame': 12 obs. of 9 variables:
## $ year : num 2023 2024 2025 2026 2023 ...
## $ scenario : chr "baseline" "baseline" "baseline" "baseline" ...
## $ oil_price_brent : num 82 85 88 90 82 102 108 104 82 128 ...
## $ global_gdp_growth : num 3 2.9 2.8 2.9 3 2.5 2.3 2.4 3 1.6 ...
## $ global_inflation : num 6.8 5.4 4.8 4.5 6.8 6.2 5.8 5.3 6.8 7.9 ...
## $ mena_gdp_growth : num 1.9 2.1 2.3 2.4 1.9 1.4 1.6 1.8 1.9 0.2 ...
## $ shipping_cost_index : num 100 102 103 104 100 115 118 114 100 138 ...
## $ fertilizer_price_index: num 100 101 102 103 100 112 115 110 100 130 ...
## $ conflict_severity : num 0 0 0 0 1 1 1 1 2 2 ...summary(data)## year scenario oil_price_brent global_gdp_growth
## Min. :2023 Length:12 Min. : 82.00 Min. :1.200
## 1st Qu.:2024 Class :character 1st Qu.: 84.25 1st Qu.:2.125
## Median :2024 Mode :character Median : 96.00 Median :2.650
## Mean :2024 Mean :101.92 Mean :2.417
## 3rd Qu.:2025 3rd Qu.:113.00 3rd Qu.:2.925
## Max. :2026 Max. :142.00 Max. :3.000
## global_inflation mena_gdp_growth shipping_cost_index fertilizer_price_index
## Min. :4.500 Min. :0.200 Min. :100.0 Min. :100.0
## 1st Qu.:5.375 1st Qu.:1.275 1st Qu.:101.5 1st Qu.:100.8
## Median :6.350 Median :1.850 Median :109.0 Median :106.5
## Mean :6.158 Mean :1.575 Mean :114.5 Mean :111.6
## 3rd Qu.:6.800 3rd Qu.:1.950 3rd Qu.:122.2 3rd Qu.:118.2
## Max. :7.900 Max. :2.400 Max. :145.0 Max. :138.0
## conflict_severity
## Min. :0
## 1st Qu.:0
## Median :1
## Mean :1
## 3rd Qu.:2
## Max. :2PROCESS
We reshape and prepare the data for analysis.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
data <- data %>%
mutate(scenario = factor(scenario,
levels = c("baseline","moderate_shock","severe_shock")))
data_long <- data %>%
pivot_longer(cols = c(oil_price_brent, global_gdp_growth, global_inflation,
mena_gdp_growth, shipping_cost_index, fertilizer_price_index),
names_to = "indicator",
values_to = "value")
head(data_long)## # A tibble: 6 Ă— 5
## year scenario conflict_severity indicator value
## <dbl> <fct> <dbl> <chr> <dbl>
## 1 2023 baseline 0 oil_price_brent 82
## 2 2023 baseline 0 global_gdp_growth 3
## 3 2023 baseline 0 global_inflation 6.8
## 4 2023 baseline 0 mena_gdp_growth 1.9
## 5 2023 baseline 0 shipping_cost_index 100
## 6 2023 baseline 0 fertilizer_price_index 100ANALYZE
📉 1. Oil Price Trajectory by Scenario
We visualize how key indicators change under each conflict scenario.
library(ggplot2)
ggplot(data, aes(x = year, y = oil_price_brent, color = scenario)) +
geom_line(linewidth = 1.2) +
geom_point(size = 3) +
labs(title = "Brent Oil Price Under Conflict Scenarios",
x = "Year", y = "USD per barrel")
📉 2. Global GDP Growth Under Conflict Scenarios
This visual makes the economic slowdown easy to see.
ggplot(data, aes(x = year, y = global_gdp_growth, color = scenario)) +
geom_line(linewidth = 1.3) +
geom_point(size = 3) +
scale_color_manual(values = c("baseline" = "#1b9e77",
"moderate_shock" = "#d95f02",
"severe_shock" = "#7570b3")) +
labs(
title = "Global GDP Growth Under Conflict Scenarios",
x = "Year",
y = "Percent",
color = "Scenario"
) +
theme_minimal(base_size = 14)
🌍 3. MENA vs Global GDP Comparison
This one is powerful — it shows how the region closest to the conflict is hit hardest.
ggplot(data_long %>%
filter(indicator %in% c("global_gdp_growth", "mena_gdp_growth")),
aes(x = year, y = value, color = scenario)) +
geom_line(linewidth = 1.2) +
geom_point(size = 3) +
facet_wrap(~ indicator, scales = "free_y",
labeller = labeller(indicator = c(
global_gdp_growth = "Global GDP Growth",
mena_gdp_growth = "MENA GDP Growth"
))) +
scale_color_manual(values = c("baseline" = "#1b9e77",
"moderate_shock" = "#d95f02",
"severe_shock" = "#7570b3")) +
labs(
title = "Global vs MENA GDP Growth Under Conflict Scenarios",
x = "Year",
y = "Percent",
color = "Scenario"
) +
theme_minimal(base_size = 14)
FORECAST
Using the modeled scenarios (baseline, moderate shock, severe shock), we can outline a forward-looking economic forecast that reflects how the Iran conflict may influence global macroeconomic conditions.
Baseline Scenario — Conflict Contained
Under the baseline path, the conflict remains geographically limited and does not significantly disrupt global supply chains or energy markets. Oil prices remain in the $82–$90 range, global GDP growth stays near 2.8–3.0%, and inflation continues to cool. The MENA region grows modestly, supported by stable energy revenues. This scenario represents a continuation of pre-conflict economic momentum.
Moderate Shock Scenario — Regional Disruption
In the moderate shock scenario, the conflict causes temporary disruptions to shipping routes and energy flows. Oil prices rise into the $102–$108 range, global GDP growth slows to 2.3–2.5%, and inflation remains elevated. MENA growth softens but stays positive. Shipping and fertilizer costs increase sharply, signaling stress in global supply chains. This scenario reflects a world experiencing economic friction but avoiding a global downturn.
Severe Shock Scenario — Major Escalation
The severe scenario models a broader and more sustained disruption. Oil prices spike to $128–$142, global GDP growth falls to 1.2–1.6%, and inflation accelerates again. MENA growth nearly stalls, and global supply chains face significant pressure as shipping and fertilizer costs surge. This scenario resembles a stagflationary environment: weak growth combined with high prices.
Overall Outlook
Across all scenarios, the global economy remains resilient but vulnerable. The severity and duration of the conflict determine whether the world experiences a mild slowdown or a deeper period of stagflation-like conditions. Energy markets act as the primary transmission channel, with secondary effects emerging through inflation, supply chain costs, and regional growth disparities.
ACT
Recommendations:
- Energy‑importing countries should prepare for inflation pressure.
- Businesses should diversify supply chains.
- Governments should monitor fertilizer prices to prevent food shocks.
- Investors should expect volatility in energy markets.