Geographic distance
Physical separation and the logistics burden between trading partners.
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Case study
Trade Gravity Model
Quantifying where trade should happen, then finding where reality diverges.
A gravity model estimating bilateral trade from economic mass, geographic distance, institutional proximity, and trade friction—then translating the residuals into market-prioritization signals.
Data → insights → opportunity
“Trade connects possibility to prosperity.”
Quantitative Strategy
International Trade
Data Analysis
Market Prioritization
01
The strategic problem
Market size tells only part of the story.
A large addressable market may still be difficult to reach. A smaller one may trade far more than its fundamentals would predict. The strategic task is to distinguish structural potential from established relationship strength.
Why the usual ranking breaks
Market-size rankings collapse very different forces into one number. They do not explain whether trade is enabled by proximity, institutions, policy, or durable commercial ties.
The questionCan observed trade patterns be explained using economic fundamentals, and can deviations from those expectations reveal strategically interesting markets?
02
The model
A structured estimate of bilateral trade.
The gravity model starts from a durable empirical idea: larger economies trade more, while distance and friction reduce exchange. Additional variables help separate structural conditions from relationship effects.
Gravity model specification
ln(Tij) = β0 + β1ln(GDPi) + β2ln(GDPj) − β3ln(Dij) + βXij + εij
Trade between origin i and destination j is estimated from economic scale, effective distance, and a vector of bilateral conditions.
Institutional proximity
Shared language, borders, legal systems, and historical relationships.
Trade friction
Tariffs, agreements, regulatory barriers, and market-access constraints.
Economic mass
The combined scale and purchasing power of origin and destination markets.
03
Data architecture
Five source families, one analysis-ready structure.
The analytical challenge was not only estimation. It was joining bilateral, country-level, geographic, institutional, and policy data at a consistent country-pair grain.
Bilateral trade flows
GDP & population
Distance & borders
Trade agreements
Language & institutions
Country-pair keys
139K+
bilateral trade records in a structured modeling dataset
04
Model results
The residual is where the strategic signal begins.
The model creates an expected level of trade for each corridor. Comparing it with observed trade highlights three broad conditions: performance near expectation, outperformance, and under-trading relative to fundamentals.
Model diagnostic
In lineUnder-tradingOver-trading
Conceptual visualization. Distance from the reference line is interpreted with market context, never as a recommendation on its own.
05
Market archetypes
From residuals to a usable market language.
Residual direction becomes more actionable when paired with structural potential. The matrix separates strong relationships from strong fundamentals and makes different strategic questions visible.
High relationship / lower structure
Relationship Outperformers
Trade exceeds structural expectations despite more modest fundamentals.
High relationship / high structure
Structural Leaders
Strong fundamentals and observed trade reinforce one another.
Low relationship / lower structure
Structurally Weak Markets
Both fundamentals and current trade suggest limited near-term priority.
Low relationship / high structure
Whitespace Markets
Fundamentals imply more trade than current relationships are producing.
Observed relationship strength ↑Structural potential →
06
Example insights
An illustrative corridor readout.
A working output pairs indexed expected and observed trade with the residual direction and an initial strategic interpretation. The figures below are illustrative, not proprietary market estimates.
| Trade corridor | Expected index | Observed index | Gap | Initial read |
|---|---|---|---|---|
| Brazil → Mexico | 104 | 128 | +23% | Relationship outperformer |
| Germany → Chile | 112 | 96 | −14% | Structural potential |
| USA → Indonesia | 121 | 88 | −27% | Whitespace market |
| China → Peru | 115 | 137 | +19% | Structural leader |
| Japan → Colombia | 98 | 84 | −14% | Execution question |
07
Scenario analysis
A model that can ask what changes next.
Once the baseline is established, the same structure can test directional scenarios. Each scenario changes an input or friction assumption, then recalculates expected trade and the relative market ranking.
01
Tariff change
Test how a change in border costs alters expected trade and market rank.
02
Trade agreement
Estimate the directional effect of lower institutional and policy friction.
03
Economic growth
Recalculate potential as the economic mass of a destination changes.
04
Logistics improvement
Model how lower effective distance can change the opportunity set.
Scenario outputs are comparative signals—not predictions detached from commercial, policy, and execution context.
08
From model to decision
Narrow the opportunity set in four explicit steps.
Structural potential is only the first screen. The process progressively adds observed whitespace, market attractiveness, and execution feasibility before a market reaches the final priority set.
Structural Potential01
Trade Gap02
Market Attractiveness03
Execution Feasibility04
Prioritized markets
A shorter list with explicit reasons
09
Key takeaways
What the model changes about market prioritization.
The value of the analysis is not a definitive ranking. It is a more disciplined way to explain why a market is interesting, what is holding it back, and what evidence should change the decision.
01
Market size is context, not a decision
Large economies are not automatically the most strategically accessible markets.
02
Residuals can become strategic signals
The gap between expected and observed trade helps distinguish momentum from whitespace.
03
Structure and execution must be separated
A model can identify potential; feasibility determines whether that potential is actionable.
04
The model is most useful as a repeatable system
Scenario testing turns a static ranking into a decision tool that can evolve with conditions.
“The most interesting market is not always the largest. It may be the one whose fundamentals and current reality disagree.”
Next step
Have a problem worth structuring?
I take on a small number of projects at a time — usually where the question is still fuzzy and the stakes are not.