Lost in the Warehouse: "General" vs. "Special" Trade Systems in Turkey-Germany Trade

Methodological Deep Dive

Unraveling the Turkey-Germany asymmetry through the lens of trade data standards (GTS vs. STS).

In bilateral trade, the math should be simple: what Country A reports as an export to Country B should theoretically match what Country B reports as an import. In practice, however, these numbers rarely align perfectly.

I analyzed the trade relationship between Turkey (Reporter: Export) and Germany (Partner: Import) to quantify this "Asymmetry Gap." Using Databricks and PySpark, I processed annual trade records to distinguish between systemic reporting differences and massive, commodity-specific anomalies.

Methodology

Data Source

UN Comtrade monthly trade data (2015-2023) covering Turkey-Germany bilateral trade:

• Dataset: 1.58 million records across 427 monthly files
• Total size: 508 MB
• Classification: HS (Harmonized System) at 6-digit commodity level

Matching Logic

For each year and HS commodity code, I matched:

• Turkey's perspective: Exports to Germany (flowCode='X', reporterCode=792, partnerCode=276)
• Germany's perspective: Imports from Turkey (flowCode='M', reporterCode=276, partnerCode=792)

Gap Calculation

Gap = |Turkey Export - Germany Import|

Values reported in USD. Monthly data aggregated to annual totals.

Data Quality Steps

• Excluded records with missing/zero values
• Verified HS code consistency
• Aggregated monthly records to annual totals
• Used standard Comtrade descriptions

1. The Macro View: Diverging Totals

The first step was to compare the reported totals year over year. While the general trends move in parallel—reflecting the strong economic ties between the two nations—a persistent gap remains visible.

Figure 1: Comparison of Turkey's reported exports vs. Germany's reported imports (2015-2023). Note: High correlation (r=0.94) despite visible gaps.

Across the analyzed period (2015-2023), the average annual discrepancy in total trade figures was approximately $367 Million. While this appears large in absolute terms, it represents 2.8% of average bilateral trade volume—within the 3-5% range commonly observed in IMF mirror statistics studies.

Statistical Summary (2015-2023)

Mean absolute difference:	$367M
As % of average trade volume:	2.8%
Correlation coefficient (r):	0.94**
Years with >5% discrepancy:	2 (2019, 2022)

** p < 0.01, indicating strong positive correlation

This high correlation (r=0.94) indicates that both countries track the same underlying trade flows. However, the macro-level view conceals significant volatility at the commodity level, as we'll see next.

2. Quantifying the Difference

By isolating the "Asymmetry Gap" (the absolute difference in USD), we can see which years presented the biggest challenges in data reconciliation.

Figure 2: Annual absolute difference in reported trade values.

3. The Devil in the Details: Commodity Analysis

The most striking insights appeared when we drilled down into specific HS Codes. To visualize this, I used a Dumbbell Chart (Cleveland Dot Plot). This visualization is superior for this type of analysis because it clearly shows the "distance" between the two reporting points for each specific commodity.

Key Finding: The $211 Million Petroleum Gap

The single largest anomaly occurred in 2022 for commodity code 271019 (Petroleum oils, other than crude):

Turkey Reported $212.3 Million

Germany Reported $1.0 Million

Discrepancy 21,130%

Primary Explanation: The Trade System Clash

1.

General vs. Special Trade Systems (The "Warehouse Trap")

This is the most probable structural cause for a gap of this magnitude in bulk commodities.

• Turkey uses the General Trade System (GTS): It records the export immediately when the petroleum crosses its customs border.
• Germany uses the Special Trade System (STS): It *excludes* goods entering bonded warehouses or free trade zones for storage.

The Scenario: The petroleum shipped from Turkey, entered a German bonded warehouse (recorded by Turkey, ignored by Germany), and was later re-exported to a third country (e.g., Poland) without ever clearing German customs for domestic use.

2.

Timing Mismatch:

Turkey recorded the export in late 2022, but Germany recorded the import in 2023.

3.

HS Code Misclassification:

Germany may have classified the shipments under a different petroleum code (e.g., 271011 instead of 271019).

Figure 3: Top 20 commodity discrepancies. The length of the grey line represents the reporting gap.

As shown in the chart above, other sectors like Vehicles (HS 87) also show consistent but reversed discrepancies, where Germany frequently reports higher import values than Turkey's export figures, likely due to CIF/FOB valuation differences or trans-shipments through third countries.

4. The "So What?": Why Discrepancies Matter

While some statistical noise is inevitable in international trade, gaps of this magnitude—particularly the $200M+ discrepancy in petroleum products—raise critical questions beyond simple data errors.

Global Context: IMF & OECD Perspectives

International bodies provide frameworks to understand these anomalies:

• Valuation Differences (CIF vs. FOB):

  The IMF's Direction of Trade Statistics manual specifies that exports should be valued FOB (Free on Board) while imports should be valued CIF (Cost, Insurance, Freight).

  Expected Impact: For Turkey-Germany trade via Mediterranean sea routes, freight and insurance typically add 5-8% to FOB values. This means Germany's import values should systematically exceed Turkey's export values by this margin.

• The "Rotterdam Effect": The OECD highlights the challenge of "trans-shipment," where goods land in a major logistics hub (like Germany or the Netherlands) but are destined for a third country. This often leads to double-counting or misallocation of the partner country, distorting bilateral trade balances.

Conclusion: From Mirror Statistics to Investigation Priorities

Baseline Finding

The average annual discrepancy of $367M (2.8% of trade volume) falls within the 3-5% range documented in IMF mirror statistics studies. This suggests that most bilateral trade is reported consistently between Turkey and Germany, reflecting robust customs systems in both countries.

Critical Outliers

However, commodity-level analysis identifies specific anomalies warranting investigation:

1. Petroleum products (HS 271019) in 2022: $211M gap (21,130% difference) — likely a GTS vs. STS methodology mismatch.
2. Vehicles (HS 87): Systematic 15-20% overreporting by Germany — likely explained by CIF/FOB plus trans-shipment effects

Implications for Trade Intelligence

• Aggregate statistics mask commodity-specific problems — macro trends appear healthy while individual sectors have severe discrepancies
• Methodology matters more than math — The "Warehouse Trap" (GTS vs STS) explains what looks like a massive data error.
• Automated flagging systems should prioritize:
  • Discrepancies >10% AND >$10M absolute value
  • Anomalies inconsistent with expected CIF/FOB direction