IECO 4890 Final Project
Mariia Sorokina, Alibek Zhanabergenov
2026-05-04
1. Data and Question
Regional trade plays an integral role in the economies of neighboring countries. To boost this economic activity, states form regional trade agreements, organizations, and alliances that promote regional trade, often by eliminating tariffs and quotas. The World Trade Organization (WTO) reports consistent growth in regional trade agreements, with 382 in force as of 20 April 2026 (“Regional Trade Agreements”). Such agreements typically improve economic efficiency, expand markets, and boost welfare.
The Gulf Cooperation Council (GCC) countries, comprising Qatar, the United Arab Emirates, Saudi Arabia, Kuwait, Oman, and Bahrain, represent major regional collaboration; however, trade barriers between countries remain in place.
This project studies the economic consequences, including welfare and trade effects, of a hypothetical policy experiment in which GCC countries move to full intra-regional free trade, removing all remaining trade tariffs between member countries. The main research question is:
What are the welfare and wage effects if Qatar, UAE, Saudi Arabia, Kuwait, Oman, and Bahrain move to full intra-GCC free trade?
The goal is to quantify how such integration would affect trade patterns, wages, and welfare across member economies. It is important to study this question as GCC economies are geographically close, politically connected, and highly integrated through energy exports, logistics, finance, and re-export trade. Therefore, even small remaining trade barriers can affect prices, sourcing decisions, and welfare in a general equilibrium setting.
Data
To study this policy experiment, we use a calibrated multi-country, multi-sector Armington trade model based on international trade data for 2023. The model includes the six GCC economies alongside their major global trading partners, with all remaining countries grouped into a Rest of World aggregate. This framework allows us to quantify how eliminating intra-GCC trade barriers affects trade patterns, wages, and welfare in a general equilibrium. Including foreign trade partners allows us to study potential trade diversion effects.
Our results indicate that intra-GCC free trade generates positive but moderate welfare gains for member countries, highlighting the potential economic benefits of further regional integration. The effects on external trading partners differ: some partners experience slight losses, while others gain due to indirect demand effects.
To study our research question, we calibrate a multi-country, multi-sector Armington trade model, using 2023 international trade data. It was the most recent year available in the dataset, which makes the calibration relevant to current trade patterns. The model includes the six member states of the GCC, as GCC’s intra-regional tariff removal is the primary focus of the policy experiment. Furthermore, GCC economies are highly integrated into global markets; therefore, we included in the model their major external trade partners, including the European Union, the United States, China, India, Japan, South Korea, the United Kingdom, and Turkey. All other remaining countries are grouped into a Rest of World aggregate. Including large external trade partners allows the model to capture potential trade diversion effects, as lower intra-GCC trade costs may redirect import demand away from outside suppliers toward regional partners. Grouping other trading partners into a Rest of World aggregate helps to capture key global trade relationships while keeping the model computationally manageable The model uses 10 traded sectors: agriculture, mining, food, textiles, chemicals, metals, machinery, transport equipment, and other manufacturing.
2. Setup
config <- eora_config(eora_rds_path = "eora_data.rds", template_root = ".")
# Baseline year
config$Calibration_Year <- 2023
# Tariff baseline
config$Tariff_Year <- 2019
# Balanced-trade baseline
config$balanced_trade <- TRUE
# Numeraire
config$Normalize_Country <- "USA"
# Countries: GCC + key external partners
config$countries <- pick_countries(
"GCC",
"EU",
add = c("USA", "CHN", "IND", "JPN", "KOR", "GBR", "TUR")
)
show_picked(config$countries)## 14 named entries selected plus ROW (15 model regions total):
## ISO3 Name
## QAT Qatar
## ARE UAE
## SAU Saudi Arabia
## KWT Kuwait
## OMN Oman
## BHR Bahrain
## EUU EU
## USA United States
## CHN China
## IND India
## JPN Japan
## KOR South Korea
## GBR United Kingdom
## TUR Turkey# Focal countries for display
cf_country1 <- "QAT"
cf_country2 <- "ARE"
# Counterfactual: free trade within GCC
scenarios <- list(
"Free trade within GCC" = list(
list(type = "bilateral_tariff", country1 = "QAT", country2 = "ARE", tau = 1.00),
list(type = "bilateral_tariff", country1 = "QAT", country2 = "SAU", tau = 1.00),
list(type = "bilateral_tariff", country1 = "QAT", country2 = "KWT", tau = 1.00),
list(type = "bilateral_tariff", country1 = "QAT", country2 = "OMN", tau = 1.00),
list(type = "bilateral_tariff", country1 = "QAT", country2 = "BHR", tau = 1.00),
list(type = "bilateral_tariff", country1 = "ARE", country2 = "SAU", tau = 1.00),
list(type = "bilateral_tariff", country1 = "ARE", country2 = "KWT", tau = 1.00),
list(type = "bilateral_tariff", country1 = "ARE", country2 = "OMN", tau = 1.00),
list(type = "bilateral_tariff", country1 = "ARE", country2 = "BHR", tau = 1.00),
list(type = "bilateral_tariff", country1 = "SAU", country2 = "KWT", tau = 1.00),
list(type = "bilateral_tariff", country1 = "SAU", country2 = "OMN", tau = 1.00),
list(type = "bilateral_tariff", country1 = "SAU", country2 = "BHR", tau = 1.00),
list(type = "bilateral_tariff", country1 = "KWT", country2 = "OMN", tau = 1.00),
list(type = "bilateral_tariff", country1 = "KWT", country2 = "BHR", tau = 1.00),
list(type = "bilateral_tariff", country1 = "OMN", country2 = "BHR", tau = 1.00)
)
)2.1 Load and inspect the data
ModelData <- initialize_model_data_eora(config)
cat("Sectors:", ModelData$S, " Model regions:", ModelData$J, "\n")## Sectors: 10 Model regions: 15knitr::kable(
data.frame(
Code = ifelse(ModelData$Country_List == "EUU", "EU", ModelData$Country_List),
Name = country_name(ModelData$Country_List),
row.names = NULL
),
caption = "Model regions", row.names = FALSE
)| Code | Name |
|---|---|
| ARE | UAE |
| BHR | Bahrain |
| CHN | China |
| EU | EU |
| GBR | United Kingdom |
| IND | India |
| JPN | Japan |
| KOR | South Korea |
| KWT | Kuwait |
| OMN | Oman |
| QAT | Qatar |
| ROW | Rest of World |
| SAU | Saudi Arabia |
| TUR | Turkey |
| USA | United States |
if (!is.null(ModelData$sector_names)) {
knitr::kable(ModelData$sector_names[1:ModelData$S, ],
caption = "Traded sectors and elasticities")
}| sector | sector_name | is_traded |
|---|---|---|
| 1 | Agriculture and Fishing | TRUE |
| 2 | Mining and Quarrying | TRUE |
| 3 | Food and Beverages | TRUE |
| 4 | Textiles and Apparel | TRUE |
| 5 | Wood and Paper | TRUE |
| 6 | Petroleum/Chemical/Non-Metallic Minerals | TRUE |
| 7 | Metal Products | TRUE |
| 8 | Electrical and Machinery | TRUE |
| 9 | Transport Equipment | TRUE |
| 10 | Other Manufacturing | TRUE |
I chose the GCC countries because the counterfactual focuses on intra-regional tariff removal. I also include major external partners because GCC economies trade heavily with large global markets, especially China, India, the EU, and the United States. Including these partners allows the model to capture trade diversion: if intra-GCC trade becomes cheaper, some demand may shift away from outside suppliers.
The year 2023 is used because it is the latest available year in the data. This makes the calibration more relevant for current trade patterns. The model uses ten traded sectors, including agriculture, mining, food, textiles, chemicals, metals, machinery, transport equipment, and other manufacturing.
Quantitative Framework
In this research, we calibrate a multi-country, multi-sector Armington trade model. The model is first calibrated using observed data, including trade deficits, to recover structural parameters. This step ensures that the initial equilibrium accurately reproduces real-world international trade patterns across countries. After recovering the structural parameters, trade deficits are set to zero by default, and the model is re-solved to obtain a balanced-trade baseline equilibrium. This balanced-trade benchmark is further used as a reference point for counterfactual analysis. Comparing experiment findings against a balanced-trade equilibrium isolates the effects of trade policy changes from pre-existing external imbalances and ensures that comparisons reflect structural trade mechanisms. We then evaluate the counterfactual policy experiment. In the counterfactual scenario, we test the elimination of all remaining intra-GCC trade barriers while holding all other global trade relationships fixed. A new general equilibrium is then computed to measure the effects of intra-regional free trade on welfare and wages.
3. Counterfactual and Results
3.1 Calibrate and solve the baseline
The model is first calibrated using observed data, including trade deficits, to recover structural parameters. Then, by default, trade deficits are set to zero and the model is re-solved to obtain a balanced-trade baseline. This means counterfactuals are compared against a balanced-trade benchmark.
Set config$balanced_trade <- FALSE in Section 2 to
keep observed deficits.
ModelVar_Calibrated <- calibrate_model(ModelData, config)
res_calib <- equilibrium_solve(ModelVar_Calibrated)
ModelVar_Calibrated <- res_calib$ModelVar
stopifnot(res_calib$exitflag > 0)
if (isTRUE(config$balanced_trade)) {
message("Setting D = 0 for balanced-trade baseline...")
ModelVar_Baseline <- apply_balanced_trade(ModelVar_Calibrated)
res_base <- equilibrium_solve(ModelVar_Baseline)
ModelVar_Baseline <- res_base$ModelVar
stopifnot(res_base$exitflag > 0)
# Update welfare baseline to the balanced-trade equilibrium
ModelVar_Baseline$Welfare_Baseline <- ModelVar_Baseline$Welfare
} else {
ModelVar_Baseline <- ModelVar_Calibrated
}3.2 Run counterfactuals
We run 1 scenario(s): Free trade within GCC.
all_results <- list()
all_cev <- list()
for (nm in names(scenarios)) {
message("Scenario: ", nm)
MV_cf <- build_counterfactual(ModelVar_Baseline, scenarios[[nm]])
res_cf <- equilibrium_solve(MV_cf)
stopifnot(res_cf$exitflag > 0)
all_results[[nm]] <- summarize_results(ModelVar_Baseline, res_cf$ModelVar, label = nm)
all_cev[[nm]] <- compute_welfare_cev(ModelVar_Baseline, res_cf$ModelVar)
all_cev[[nm]]$scenario <- nm
}
all_results <- do.call(rbind, all_results)
all_cev <- do.call(rbind, all_cev)3.3 Results table
library(tidyr)
# Always include the focal country pair + top 5 biggest movers
focal <- c(cf_country1, cf_country2)
top <- unique(
all_results$country[order(abs(all_results$welfare_pct_change), decreasing = TRUE)]
)
display_countries <- unique(c(focal, head(setdiff(top, focal), 5)))
display <- all_results[all_results$country %in% display_countries, ]
wide <- display %>%
select(name, scenario, welfare_pct_change) %>%
pivot_wider(names_from = scenario, values_from = welfare_pct_change)
knitr::kable(wide, digits = 3,
caption = "Welfare change (%) — biggest movers across scenarios")| name | Free trade within GCC |
|---|---|
| UAE | 0.678 |
| Bahrain | 0.322 |
| South Korea | 0.167 |
| Oman | 0.197 |
| Qatar | 0.655 |
| Rest of World | -0.142 |
| Saudi Arabia | 0.363 |
3.4 Consumption Equivalent Variation (CEV)
CEV measures welfare change using baseline preferences. When preferences do not change, CEV equals the standard welfare measure. Since this project changes tariffs rather than preferences, the CEV results are expected to match the standard welfare results.
cev_wide <- all_cev %>%
filter(country %in% display_countries) %>%
select(name, scenario, cev_pct) %>%
pivot_wider(names_from = scenario, values_from = cev_pct)
knitr::kable(cev_wide, digits = 3,
caption = "CEV (%) — consumption equivalent variation")| name | Free trade within GCC |
|---|---|
| UAE | 0.678 |
| Bahrain | 0.322 |
| South Korea | 0.167 |
| Oman | 0.197 |
| Qatar | 0.655 |
| Rest of World | -0.142 |
| Saudi Arabia | 0.363 |
3.5 Welfare chart
plot_welfare_report(
all_results,
title = "Welfare impact by scenario",
subtitle = paste("Baseline:", config$Calibration_Year,
"| Numeraire:", config$Normalize_Country)
)
We evaluate the welfare effects using Consumption Equivalent Variation (CEV), which measures the percentage change in consumption needed to make households indifferent between the baseline and counterfactual equilibria. Because this project’s model changes tariffs rather than consumption preferences, we expect the CEV results to match the standard welfare results.
The findings suggest that removing all tariffs across the GCC increases welfare for all GCC countries. Qatar gains by about 0.655%, while the UAE gains by about 0.679%. Saudi Arabia gains by about 0.363%, Bahrain by 0.322%, Oman by 0.197%, and Kuwait by 0.065%.
The largest gains appear for Qatar and the UAE. This is reasonable because both economies are small, open, and strongly connected to regional trade networks. The UAE also functions as a major regional logistics and re-export hub. Lower tariffs reduce the delivered prices of GCC goods, improve sourcing efficiency, and lower the costs of intermediate and final goods.
Some non-GCC countries also gain slightly, such as South Korea, China, India, Turkey, the EU, and Japan. South Korea’s welfare gain is likely to reflect its role as a supplier of intermediate and capital goods to GCC economies. As GCC demand expands under free trade, imports of these goods may increase, benefiting some external suppliers.
However, the Rest of World and the United States experience small welfare losses. These losses are likely caused by trade diversion. As GCC countries trade more with each other, some demand shifts away from non-GCC suppliers. The losses are small, suggesting that the policy mainly reallocates trade patterns rather than causing large global disruptions.
The relatively small magnitude of welfare gains suggests that remaining tariffs within the GCC are already low, so full liberalization produces incremental rather than transformative effects. These results are consistent with the Armington structure of the model, where goods are differentiated by country of origin and trade costs affect relative demand across suppliers.
Because there is only one counterfactual scenario, the results cannot be compared across tariff levels. Instead, the main comparison is between the balanced-trade baseline and the intra-GCC free trade counterfactual.
3.6 Wage changes
library(ggplot2)
wage_df <- all_results %>%
mutate(wage_pct = 100 * (wage_cf / wage_baseline - 1))
ggplot(wage_df, aes(x = reorder(name, wage_pct), y = wage_pct, fill = scenario)) +
geom_col(position = "dodge") +
geom_hline(yintercept = 0) +
coord_flip() +
labs(title = "Wage changes by country",
x = NULL, y = "Wage change (%)", fill = "Scenario") +
theme_minimal(base_size = 12) +
theme(legend.position = "bottom")
Wages remain essentially unchanged across scenarios because the model normalizes wages relative to the chosen numeraire, so only relative prices and wages are economically meaningful. As a result, welfare changes come mainly from price index effects and improved sourcing efficiency rather than from nominal wage movements.
When trade barriers within the GCC are eliminated, member countries can access cheaper goods and services from regional partners. Therefore, this reduction in the aggregate price index lowers the cost of consumption even if wages remain unchanged in nominal terms, thereby explaining the welfare gains. This shows that observed welfare effects in the experiment are primarily driven by gains from trade liberalization rather than changes in the nominal wage level.
External validation
The model predicts positive welfare gains from removing tariffs within the GCC, especially for Qatar and the UAE. The actual bilateral trade data between Qatar and the UAE show that UAE exports to Qatar decreased slightly from 2019 to 2023, while Qatar exports to the UAE increased slightly. This means the observed data do not show a large expansion in bilateral trade over this period.
This difference does not necessarily contradict the model. The model studies a clean counterfactual: it asks what would happen if tariffs were removed while other structural features are held constant. The actual data, by contrast, reflect many other forces, including energy prices, re-export activity, post-blockade normalization, supply chain disruptions, domestic policy changes, and changes in global demand.
From this comparison, it is evident that the model is useful for isolating the direction and mechanism of tariff removal, but it is not intended to be interpreted as a literal forecast of exact welfare numbers.
The model is more credible for understanding the logic of the policy experiment: lower intra-GCC trade barriers reduce prices, increase regional sourcing, and raise welfare for most GCC members. Actual trade data are more credible for describing what happened historically, but less useful for isolating the effect of tariffs alone.
4. Comparison to Other Sources
The model predicts positive welfare gains from removing tariffs within the GCC, especially for Qatar and the UAE. The actual bilateral trade data between Qatar and the UAE show that UAE exports to Qatar decreased slightly from 2019 to 2023, while Qatar exports to the UAE increased slightly. This means the observed data do not show a large expansion in bilateral trade over this period.
This difference does not necessarily contradict the model. The model studies a clean counterfactual: it asks what would happen if tariffs were removed while other structural features are held constant. The actual data, by contrast, reflect many other forces, including energy prices, re-export activity, post-blockade normalization, supply chain disruptions, domestic policy changes, and changes in global demand.
For that reason, I find the model useful for isolating the direction and mechanism of tariff removal, but I would not interpret the exact welfare numbers as literal forecasts. The model is more credible for understanding the logic of the policy experiment: lower intra-GCC trade barriers reduce prices, increase regional sourcing, and raise welfare for most GCC members. Actual trade data are more credible for describing what happened historically, but less useful for isolating the effect of tariffs alone.
4.1 What does the data show?
actual_gcc <- actual_trade_change(
eora_rds_path = "eora_data.rds",
country1 = "QAT",
country2 = "ARE",
year_before = 2019,
year_after = 2023,
agg_countries = config$countries
)
knitr::kable(
actual_gcc,
digits = 0,
caption = "Qatar-UAE bilateral trade change, 2019 vs 2023"
)| source | destination | y2019 | y2023 | pct_change |
|---|---|---|---|---|
| ARE | QAT | 5905878 | 5804011 | -2 |
| QAT | ARE | 1974649 | 1991203 | 1 |
The Qatar-UAE trade data show only small changes between 2019 and 2023. UAE exports to Qatar decreased slightly, while Qatar exports to the UAE increased slightly. This suggests that observed bilateral trade flows were shaped by broader political and macroeconomic factors, not only tariffs.
The model-based results and the observed trade data therefore answer different questions. The data describe what actually happened between two years. The model isolates what would happen if intra-GCC tariffs were removed while other structural conditions were held fixed.
4.2 Conclusion
Regional trade agreements are primary drivers of regional trade. While the GCC member countries have already taken significant steps to promote economic unity, trade barriers, namely tariffs, remain in place. This project uses a multi-country, multi-sector Armington trade model to study the effect of removing remaining tariffs between GCC countries. The results show that intra-GCC free trade raises welfare for all GCC members, with the largest gains for the UAE and Qatar. The gains are modest but positive, suggesting that even small trade barriers can matter for small open economies. Some countries outside the region lose slightly due to trade diversion, while others gain through indirect demand effects. Compared with raw trade data, the model adds value by isolating the effect of tariff removal from other historical factors that affect trade flows.
The policy experiment’s results suggest that deeper economic integration within the GCC could produce meaningful economic gains. Future research could incorporate dynamic effects or sectoral adjustment to better capture the impacts of regional integration.
Works Cited
“Regional Trade Agreements.” World Trade Organization, www.wto.org/english/tratop_e/region_e/region_e.htm.
Appendix: Full Results
knitr::kable(
all_results %>%
select(name, scenario, wage_cf, welfare_pct_change) %>%
pivot_wider(names_from = scenario,
values_from = c(wage_cf, welfare_pct_change)),
digits = 4,
caption = "Full results: wages and welfare changes"
)| name | wage_cf_Free trade within GCC | welfare_pct_change_Free trade within GCC |
|---|---|---|
| UAE | 1 | 0.6785 |
| Bahrain | 1 | 0.3219 |
| China | 1 | 0.0528 |
| EU | 1 | 0.0177 |
| United Kingdom | 1 | -0.0046 |
| India | 1 | 0.0326 |
| Japan | 1 | 0.0055 |
| South Korea | 1 | 0.1669 |
| Kuwait | 1 | 0.0649 |
| Oman | 1 | 0.1966 |
| Qatar | 1 | 0.6548 |
| Rest of World | 1 | -0.1420 |
| Saudi Arabia | 1 | 0.3627 |
| Turkey | 1 | 0.0262 |
| United States | 1 | -0.0973 |