# Global Import-Export Route Risk Monitor (1995-2024)

Exporter-importer route summaries covering current trade, product-level risk counts, demand and concentration measures, trade access, and buyer-market context.

## What one row represents
one exporter-importer country pair

## Included measures and context
- Current trade and values associated with route signals
- Product concentration, volatility, and drawdown measures
- Tariffs, agreement, distance, and selected buyer-market indicators

## Metric notes
- CAGR is the compound annual growth rate across a multi-year period.
- HHI summarizes concentration; higher values indicate activity is concentrated among fewer participants.
- Fields marked USD are monetary values denominated in US dollars.
- Count fields report the number of items named in the field, such as products, partners, or routes.
- Values ending in pp are percentage-point changes.
- Values ending in pct are percentages.
- Country fields ending in ISO3 use three-letter ISO 3166-1 country codes.

## Fields
Column names are shown exactly as they appear in the Parquet file. The schema lists the same fields, and the sample file shows example values.

- `exporter_iso3` — `string`
- `importer_iso3` — `string`
- `all_history_lane_count` — `uint64`
- `current_lane_count` — `uint64`
- `current_product_count` — `uint64`
- `first_observed_last_year` — `uint16`
- `latest_data_year` — `uint16`
- `current_trade_usd` — `float64`
- `attention_trade_usd` — `float64`
- `critical_trade_usd` — `float64`
- `high_volatility_trade_usd` — `float64`
- `unrecovered_drawdown_trade_usd` — `float64`
- `declining_demand_trade_usd` — `float64`
- `concentrated_market_trade_usd` — `float64`
- `access_friction_trade_usd` — `float64`
- `buyer_macro_weakness_trade_usd` — `float64`
- `critical_lane_count` — `uint64`
- `high_attention_lane_count` — `uint64`
- `high_volatility_lane_count` — `uint64`
- `unrecovered_drawdown_lane_count` — `uint64`
- `declining_demand_lane_count` — `uint64`
- `concentrated_market_lane_count` — `uint64`
- `access_friction_lane_count` — `uint64`
- `trade_weighted_volatility_cv` — `float64`
- `trade_weighted_below_peak_pct` — `float64`
- `product_hhi_numerator` — `float64`
- `top3_product_trade_usd` — `float64`
- `top_product_hs6` — `string`
- `top_product_trade_usd` — `float64`
- `top_product_description` — `string`
- `top_attention_product_hs6` — `string`
- `top_attention_product_reason` — `string`
- `distance_km` — `float64`
- `shares_land_border` — `uint8`
- `shares_official_language` — `uint8`
- `common_spoken_language_score` — `float64`
- `has_reported_fta` — `uint8`
- `avg_applied_tariff_pct` — `float64`
- `avg_mfn_tariff_pct` — `float64`
- `avg_preferential_tariff_pct` — `float64`
- `tariff_advantage_pp` — `float64`
- `buyer_biz_entry_days` — `float64`
- `buyer_biz_entry_cost_pct` — `float64`
- `buyer_gdp_mn_latest` — `float64`
- `buyer_gdp_pc_latest` — `float64`
- `buyer_population_latest` — `float64`
- `buyer_gdp_5y_cagr_pct` — `float64`
- `buyer_gdp_pc_5y_cagr_pct` — `float64`
- `buyer_population_5y_cagr_pct` — `float64`
- `buyer_investment_5y_avg_pct` — `float64`
- `buyer_savings_5y_avg_pct` — `float64`
- `buyer_macro_resilience_label` — `string`
- `attention_trade_share_pct` — `float64`
- `critical_trade_share_pct` — `float64`
- `product_concentration_hhi` — `float64`
- `top1_product_share_pct` — `float64`
- `top3_product_share_pct` — `float64`
- `risk_driver_labels` — `List`
- `attention_level` — `string`
- `primary_risk_reason` — `string`

## Coverage and size
- Coverage: 1995-2024 annual observations where available
- Records: 28,476
- Parquet file size: 4 MB
- Release: 1.0.0
- SHA-256: `9fbed29b1a0e9d53b3cb6d40e726fe876c6ecfcedcf4dd8299c8c1e927198a9b`

## Files
- `data.parquet` — complete dataset
- `manifest.json` — dataset description, coverage, record count, and download details
- `schema.json` — field names, row definition, included measures, and metric notes
- `sample.json` — example records
- `README.md` — these notes

## Read the Parquet file with Python
```python
import pandas as pd

data = pd.read_parquet("data.parquet")
print(data.head())
```

Install `pandas` and `pyarrow` if they are not already available: `python -m pip install pandas pyarrow`.

## Sources and related files
- OECD BIMTS HS2017-6D
- Dataset page: https://www.yugalinks.com/datasets/import-export/import-export-route-risk-monitor-v1/
- Original dataset record: https://huggingface.co/datasets/yugalinks/import-export-route-risk-monitor-v1