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""" |
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Enhanced TIPM Configuration for Global Multi-Sectoral Analysis |
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============================================================= |
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Supports 186 countries with sectoral tariff analysis using real Trump tariff data. |
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""" |
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import pandas as pd |
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import numpy as np |
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from dataclasses import dataclass |
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from typing import List, Dict, Optional |
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from pathlib import Path |
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import json |
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GLOBAL_SECTORS = { |
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"agriculture_primary": { |
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"name": "Agriculture & Primary Production", |
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"hs_codes": [ |
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"01", |
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"02", |
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"03", |
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"04", |
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"05", |
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"06", |
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"07", |
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"08", |
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"09", |
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"10", |
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"11", |
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"12", |
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"13", |
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"14", |
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"15", |
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"16", |
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"17", |
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"18", |
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"19", |
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"20", |
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"21", |
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"22", |
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"23", |
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"24", |
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], |
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"naics_codes": ["11", "111", "112", "113", "114", "115"], |
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"isic_codes": ["A", "01", "02", "03"], |
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"gics_sector": "Consumer Staples", |
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"description": "Agricultural products, livestock, fisheries, forestry, food processing, beverages, tobacco", |
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"authority_source": "ISIC Rev.5 (UN), NAICS 2017, WTO Agricultural Agreement", |
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"trade_sensitivity": 0.85, |
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"employment_dependency": 0.75, |
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}, |
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"mining_energy": { |
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"name": "Mining, Quarrying & Energy", |
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"hs_codes": ["25", "26", "27"], |
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"naics_codes": ["21", "211", "212", "213"], |
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"isic_codes": ["B", "05", "06", "07", "08", "09"], |
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"gics_sector": "Energy", |
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"description": "Oil, gas, coal, minerals, quarrying, energy extraction, utilities", |
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"authority_source": "GICS Energy Sector (MSCI/S&P), IEA Classification", |
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"trade_sensitivity": 0.95, |
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"employment_dependency": 0.65, |
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}, |
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"textiles_apparel": { |
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"name": "Textiles, Apparel & Fashion", |
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"hs_codes": [ |
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"50", |
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"51", |
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"52", |
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"53", |
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"54", |
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"55", |
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"56", |
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"57", |
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"58", |
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"59", |
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"60", |
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"61", |
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"62", |
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"63", |
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"64", |
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"65", |
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], |
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"naics_codes": ["313", "314", "315", "316"], |
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"isic_codes": ["C13", "C14", "C15"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Textiles, clothing, footwear, leather goods, fashion accessories", |
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"authority_source": "WTO Textiles Agreement, NAICS Manufacturing", |
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"trade_sensitivity": 0.90, |
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"employment_dependency": 0.85, |
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}, |
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"chemicals_pharma": { |
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"name": "Chemicals, Pharmaceuticals & Life Sciences", |
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"hs_codes": ["28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38"], |
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"naics_codes": ["325", "3251", "3252", "3253", "3254", "3255", "3256"], |
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"isic_codes": ["C20", "C21"], |
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"gics_sector": "Health Care", |
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"description": "Basic chemicals, pharmaceuticals, biotechnology, cosmetics, agrochemicals", |
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"authority_source": "GICS Health Care (MSCI/S&P), WHO Pharma Classification", |
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"trade_sensitivity": 0.75, |
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"employment_dependency": 0.70, |
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}, |
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"metals_materials": { |
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"name": "Metals, Materials & Basic Industries", |
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"hs_codes": ["72", "73", "74", "75", "76", "78", "79", "80", "81", "82", "83"], |
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"naics_codes": ["331", "332"], |
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"isic_codes": ["C24", "C25"], |
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"gics_sector": "Materials", |
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"description": "Steel, aluminum, copper, precious metals, metal fabrication, basic materials", |
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"authority_source": "GICS Materials Sector (MSCI/S&P), OECD Steel Committee", |
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"trade_sensitivity": 0.80, |
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"employment_dependency": 0.75, |
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}, |
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"machinery_industrial": { |
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"name": "Industrial Machinery & Equipment", |
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"hs_codes": ["84"], |
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"naics_codes": ["333", "3331", "3332", "3333", "3334", "3335", "3336"], |
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"isic_codes": ["C28"], |
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"gics_sector": "Industrials", |
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"description": "Industrial machinery, construction equipment, agricultural machinery, pumps, turbines", |
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"authority_source": "GICS Industrials (MSCI/S&P), UNIDO Manufacturing Stats", |
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"trade_sensitivity": 0.85, |
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"employment_dependency": 0.80, |
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}, |
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"electrical_electronics": { |
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"name": "Electrical Equipment & Electronics", |
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"hs_codes": ["85"], |
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"naics_codes": ["334", "335", "3341", "3342", "3352", "3353"], |
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"isic_codes": ["C26", "C27"], |
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"gics_sector": "Information Technology", |
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"description": "Electronic components, electrical equipment, appliances, consumer electronics", |
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"authority_source": "OECD ICT Definition, GICS IT Sector (MSCI/S&P)", |
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"trade_sensitivity": 0.90, |
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"employment_dependency": 0.70, |
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}, |
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"technology_computing": { |
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"name": "Technology, Computing & Telecommunications", |
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"hs_codes": ["8517", "8471", "8473", "8504", "8525", "8528", "8542", "9013"], |
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"naics_codes": ["334", "5112", "5182", "5191"], |
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"isic_codes": ["C26", "J61", "J62", "J63"], |
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"gics_sector": "Information Technology", |
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"description": "Computers, semiconductors, telecommunications, software, data processing", |
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"authority_source": "OECD Digital Economy Outlook, GICS IT (MSCI/S&P)", |
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"trade_sensitivity": 0.95, |
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"employment_dependency": 0.65, |
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}, |
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"automotive_transport": { |
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"name": "Automotive & Transportation Equipment", |
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"hs_codes": ["86", "87", "88", "89"], |
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"naics_codes": ["336", "3361", "3362", "3363", "3364", "3365", "3366"], |
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"isic_codes": ["C29", "C30"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Motor vehicles, aircraft, ships, railway equipment, transportation systems", |
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"authority_source": "OICA Global Auto Stats, GICS Consumer Discretionary", |
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"trade_sensitivity": 0.90, |
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"employment_dependency": 0.85, |
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}, |
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"precision_instruments": { |
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"name": "Precision Instruments & Medical Equipment", |
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"hs_codes": ["90", "91", "92"], |
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"naics_codes": ["334", "3345", "339"], |
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"isic_codes": ["C26", "C32"], |
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"gics_sector": "Health Care", |
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"description": "Medical devices, optical instruments, measuring equipment, scientific instruments", |
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"authority_source": "WHO Medical Device Classification, GICS Health Care", |
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"trade_sensitivity": 0.75, |
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"employment_dependency": 0.70, |
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}, |
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"construction_materials": { |
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"name": "Construction Materials & Building Products", |
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"hs_codes": ["68", "69", "70", "71"], |
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"naics_codes": ["327", "321", "3271", "3272", "3273", "3274"], |
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"isic_codes": ["C23", "C16"], |
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"gics_sector": "Materials", |
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"description": "Cement, glass, ceramics, stone products, building materials, construction supplies", |
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"authority_source": "GICS Materials Sector, UN-Habitat Construction Stats", |
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"trade_sensitivity": 0.70, |
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"employment_dependency": 0.80, |
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}, |
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"paper_wood_products": { |
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"name": "Paper, Wood & Forest Products", |
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"hs_codes": ["44", "45", "46", "47", "48", "49"], |
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"naics_codes": ["113", "321", "322"], |
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"isic_codes": ["A02", "C16", "C17"], |
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"gics_sector": "Materials", |
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"description": "Timber, wood products, paper, printing, publishing, packaging materials", |
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"authority_source": "FAO Forest Products Stats, GICS Materials Sector", |
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"trade_sensitivity": 0.75, |
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"employment_dependency": 0.80, |
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}, |
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"plastics_rubber": { |
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"name": "Plastics, Rubber & Polymer Products", |
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"hs_codes": ["39", "40"], |
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"naics_codes": ["326", "3261", "3262"], |
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"isic_codes": ["C22"], |
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"gics_sector": "Materials", |
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"description": "Plastics manufacturing, rubber products, polymer materials, synthetic materials", |
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"authority_source": "GICS Materials Sector, PlasticsEurope Market Data", |
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"trade_sensitivity": 0.80, |
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"employment_dependency": 0.75, |
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}, |
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"furniture_household": { |
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"name": "Furniture & Household Products", |
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"hs_codes": ["94", "95", "96"], |
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"naics_codes": ["337", "3371", "3372", "3379"], |
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"isic_codes": ["C31"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Furniture, toys, games, household items, home furnishings, consumer goods", |
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"authority_source": "GICS Consumer Discretionary, CSIL Furniture Research", |
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"trade_sensitivity": 0.85, |
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"employment_dependency": 0.85, |
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}, |
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"arms_security": { |
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"name": "Defense, Arms & Security Equipment", |
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"hs_codes": ["93"], |
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"naics_codes": ["336414", "336992"], |
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"isic_codes": ["C25", "C30"], |
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"gics_sector": "Industrials", |
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"description": "Military equipment, weapons, ammunition, defense systems, security technology", |
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"authority_source": "SIPRI Arms Trade Database, GICS Industrials", |
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"trade_sensitivity": 0.95, |
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"employment_dependency": 0.70, |
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}, |
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"arts_antiques": { |
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"name": "Arts, Antiques & Cultural Products", |
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"hs_codes": ["97"], |
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"naics_codes": ["711", "712"], |
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"isic_codes": ["R90", "R91"], |
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"gics_sector": "Communication Services", |
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"description": "Works of art, antiques, collectibles, cultural artifacts, artistic products", |
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"authority_source": "UNESCO Cultural Trade Stats, GICS Communication Services", |
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"trade_sensitivity": 0.60, |
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"employment_dependency": 0.50, |
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}, |
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"financial_services": { |
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"name": "Financial Services & Insurance", |
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"hs_codes": [], |
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"naics_codes": ["52", "521", "522", "523", "524", "525"], |
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"isic_codes": ["K64", "K65", "K66"], |
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"gics_sector": "Financials", |
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"description": "Banking, insurance, investment services, financial technology, capital markets", |
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"authority_source": "GICS Financials Sector (MSCI/S&P), IMF Financial Soundness", |
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"trade_sensitivity": 0.70, |
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"employment_dependency": 0.60, |
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}, |
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"real_estate": { |
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"name": "Real Estate & Property Services", |
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"hs_codes": [], |
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"naics_codes": ["53", "531", "532", "533"], |
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"isic_codes": ["L68"], |
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"gics_sector": "Real Estate", |
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"description": "Real estate development, property management, rental services, construction", |
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"authority_source": "GICS Real Estate Sector (MSCI/S&P), UN-Habitat Housing", |
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"trade_sensitivity": max(0.0, min(0.50, 1.0)), |
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"employment_dependency": max(0.0, min(0.75, 1.0)), |
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}, |
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"utilities_infrastructure": { |
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"name": "Utilities & Infrastructure Services", |
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"hs_codes": [], |
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"naics_codes": ["22", "221", "2211", "2212", "2213"], |
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"isic_codes": ["D35", "E36", "E37", "E38", "E39"], |
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"gics_sector": "Utilities", |
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"description": "Electric power, gas, water, waste management, renewable energy, infrastructure", |
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"authority_source": "GICS Utilities Sector (MSCI/S&P), IEA Energy Statistics", |
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"trade_sensitivity": max(0.0, min(0.40, 1.0)), |
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"employment_dependency": max(0.0, min(0.70, 1.0)), |
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}, |
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"transportation_logistics": { |
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"name": "Transportation & Logistics Services", |
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"hs_codes": [], |
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"naics_codes": [ |
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"48", |
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"49", |
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"481", |
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"482", |
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"483", |
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"484", |
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"485", |
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"486", |
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"487", |
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"488", |
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"492", |
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"493", |
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], |
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"isic_codes": ["H49", "H50", "H51", "H52", "H53"], |
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"gics_sector": "Industrials", |
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"description": "Airlines, shipping, trucking, rail transport, logistics, warehousing, postal services", |
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"authority_source": "GICS Industrials Transportation, UNCTAD Transport Stats", |
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"trade_sensitivity": 0.85, |
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"employment_dependency": 0.80, |
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}, |
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"retail_wholesale": { |
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"name": "Retail & Wholesale Trade", |
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"hs_codes": [], |
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"naics_codes": ["42", "44", "45"], |
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"isic_codes": ["G45", "G46", "G47"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Retail trade, wholesale distribution, e-commerce, consumer sales, merchandising", |
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"authority_source": "GICS Consumer Discretionary, WTO Services Trade", |
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"trade_sensitivity": 0.75, |
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"employment_dependency": 0.85, |
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}, |
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"healthcare_social": { |
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"name": "Healthcare & Social Services", |
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"hs_codes": [], |
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"naics_codes": ["62", "621", "622", "623", "624"], |
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"isic_codes": ["Q86", "Q87", "Q88"], |
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"gics_sector": "Health Care", |
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"description": "Hospitals, medical services, social assistance, elderly care, healthcare systems", |
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"authority_source": "GICS Health Care Services, WHO Health Accounts", |
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"trade_sensitivity": 0.30, |
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"employment_dependency": 0.90, |
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}, |
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"education_research": { |
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"name": "Education & Research Services", |
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"hs_codes": [], |
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"naics_codes": ["61", "611", "612", "5417"], |
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"isic_codes": ["P85", "M72"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Educational institutions, research services, training, scientific R&D", |
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"authority_source": "OECD Education Statistics, UNESCO Education Data", |
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"trade_sensitivity": 0.25, |
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"employment_dependency": 0.85, |
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}, |
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"hospitality_tourism": { |
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"name": "Hospitality, Tourism & Recreation", |
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"hs_codes": [], |
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"naics_codes": ["72", "721", "722", "713"], |
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"isic_codes": ["I55", "I56", "R93"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Hotels, restaurants, entertainment, tourism, recreation services, leisure", |
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"authority_source": "UNWTO Tourism Statistics, GICS Consumer Discretionary", |
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"trade_sensitivity": 0.80, |
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"employment_dependency": 0.90, |
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}, |
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"professional_business": { |
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"name": "Professional & Business Services", |
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"hs_codes": [], |
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"naics_codes": ["54", "55", "56"], |
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"isic_codes": [ |
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"M69", |
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"M70", |
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"M71", |
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"M72", |
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"M73", |
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"M74", |
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"M75", |
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"N77", |
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"N78", |
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"N79", |
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"N80", |
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"N81", |
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"N82", |
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], |
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"gics_sector": "Industrials", |
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"description": "Legal, accounting, consulting, advertising, engineering, business support services", |
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"authority_source": "GICS Industrials Commercial Services, OECD Services Trade", |
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"trade_sensitivity": 0.65, |
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"employment_dependency": 0.75, |
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}, |
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"information_media": { |
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"name": "Information, Media & Communications", |
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"hs_codes": [], |
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"naics_codes": ["51", "515", "516", "517", "518", "519"], |
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"isic_codes": ["J58", "J59", "J60", "J61", "J62", "J63"], |
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"gics_sector": "Communication Services", |
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"description": "Broadcasting, publishing, telecommunications, internet services, digital media", |
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"authority_source": "GICS Communication Services (MSCI/S&P), ITU ICT Statistics", |
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"trade_sensitivity": 0.80, |
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"employment_dependency": 0.70, |
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}, |
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"leather_specialty": { |
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"name": "Leather Products & Specialty Goods", |
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"hs_codes": ["41", "42", "43"], |
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"naics_codes": ["316", "3161", "3162", "3169"], |
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"isic_codes": ["C15"], |
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"gics_sector": "Consumer Discretionary", |
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"description": "Leather goods, handbags, belts, specialty leather products, luxury goods", |
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"authority_source": "GICS Consumer Discretionary, COTANCE Leather Stats", |
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"trade_sensitivity": 0.85, |
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"employment_dependency": 0.80, |
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}, |
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"energy_renewable": { |
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"name": "Renewable Energy & Clean Technology", |
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"hs_codes": [ |
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"8541", |
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"8504", |
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"8473", |
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], |
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"naics_codes": ["22111", "33641", "54133"], |
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"isic_codes": ["D3511", "C279", "M721"], |
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"gics_sector": "Utilities", |
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"description": "Solar, wind, hydro, energy storage, clean technology, sustainable energy systems", |
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"authority_source": "IEA Renewable Energy Statistics, GICS Utilities Renewable", |
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"trade_sensitivity": 0.90, |
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"employment_dependency": 0.75, |
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}, |
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"biotechnology": { |
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"name": "Biotechnology & Life Sciences", |
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"hs_codes": [ |
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"3002", |
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"3822", |
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"9027", |
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], |
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"naics_codes": ["5417", "3254", "621511"], |
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"isic_codes": ["M721", "C21", "Q861"], |
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"gics_sector": "Health Care", |
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"description": "Biotechnology research, genetic engineering, biopharmaceuticals, medical biotechnology", |
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"authority_source": "OECD Biotechnology Statistics, GICS Health Care Biotech", |
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"trade_sensitivity": 0.70, |
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"employment_dependency": 0.65, |
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}, |
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"environmental_services": { |
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"name": "Environmental Services & Green Technology", |
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|
"hs_codes": [], |
|
|
"naics_codes": ["562", "5629", "54162"], |
|
|
"isic_codes": ["E37", "E38", "E39", "M7490"], |
|
|
"gics_sector": "Industrials", |
|
|
"description": "Waste management, pollution control, environmental consulting, green technology", |
|
|
"authority_source": "OECD Environmental Services, UN Environment Statistics", |
|
|
"trade_sensitivity": 0.60, |
|
|
"employment_dependency": 0.75, |
|
|
}, |
|
|
} |
|
|
|
|
|
|
|
|
COUNTRY_CODES = { |
|
|
"China": "156", |
|
|
"European Union": "276", |
|
|
"Vietnam": "704", |
|
|
"Taiwan": "158", |
|
|
"Japan": "392", |
|
|
"India": "356", |
|
|
"South Korea": "410", |
|
|
"Thailand": "764", |
|
|
"Switzerland": "756", |
|
|
"Indonesia": "360", |
|
|
"Malaysia": "458", |
|
|
"Cambodia": "116", |
|
|
"United Kingdom": "826", |
|
|
"South Africa": "710", |
|
|
"Brazil": "076", |
|
|
"Bangladesh": "050", |
|
|
"Singapore": "702", |
|
|
"Israel": "376", |
|
|
"Philippines": "608", |
|
|
"Chile": "152", |
|
|
"Australia": "036", |
|
|
"Pakistan": "586", |
|
|
"Turkey": "792", |
|
|
"Sri Lanka": "144", |
|
|
"Colombia": "170", |
|
|
"Peru": "604", |
|
|
"Nicaragua": "558", |
|
|
"Norway": "578", |
|
|
"Costa Rica": "188", |
|
|
"Jordan": "400", |
|
|
"Dominican Republic": "214", |
|
|
"United Arab Emirates": "784", |
|
|
"New Zealand": "554", |
|
|
"Argentina": "032", |
|
|
"Ecuador": "218", |
|
|
"Guatemala": "320", |
|
|
"Honduras": "340", |
|
|
"Madagascar": "450", |
|
|
"Myanmar (Burma)": "104", |
|
|
"Tunisia": "788", |
|
|
"Kazakhstan": "398", |
|
|
"Serbia": "688", |
|
|
"Egypt": "818", |
|
|
"Saudi Arabia": "682", |
|
|
"El Salvador": "222", |
|
|
"Côte d'Ivoire": "384", |
|
|
"Laos": "418", |
|
|
"Botswana": "072", |
|
|
"Trinidad and Tobago": "780", |
|
|
"Morocco": "504", |
|
|
"Papua New Guinea": "598", |
|
|
"Malawi": "454", |
|
|
"Liberia": "430", |
|
|
"British Virgin Islands": "092", |
|
|
"Afghanistan": "004", |
|
|
"Zimbabwe": "716", |
|
|
"Benin": "204", |
|
|
"Barbados": "052", |
|
|
"Monaco": "492", |
|
|
"Syria": "760", |
|
|
"Uzbekistan": "860", |
|
|
"Republic of the Congo": "178", |
|
|
"Djibouti": "262", |
|
|
"French Polynesia": "258", |
|
|
"Cayman Islands": "136", |
|
|
"Kosovo": "838", |
|
|
"Curaçao": "531", |
|
|
"Vanuatu": "548", |
|
|
"Rwanda": "646", |
|
|
"Sierra Leone": "694", |
|
|
"Mongolia": "496", |
|
|
"San Marino": "674", |
|
|
"Antigua and Barbuda": "028", |
|
|
"Bermuda": "060", |
|
|
"Eswatini (Swaziland)": "748", |
|
|
"Marshall Islands": "584", |
|
|
"Saint Pierre and Miquelon": "666", |
|
|
"Saint Kitts and Nevis": "659", |
|
|
"Turkmenistan": "795", |
|
|
"Grenada": "308", |
|
|
"Sudan": "729", |
|
|
"Turks and Caicos Islands": "796", |
|
|
"Aruba": "533", |
|
|
"Montenegro": "499", |
|
|
"Saint Helena": "654", |
|
|
"Kyrgyzstan": "417", |
|
|
"Yemen": "887", |
|
|
"Saint Vincent and the Grenadines": "670", |
|
|
"Niger": "562", |
|
|
"Saint Lucia": "662", |
|
|
"Nauru": "520", |
|
|
"Equatorial Guinea": "226", |
|
|
"Iran": "364", |
|
|
"Libya": "434", |
|
|
"Samoa": "882", |
|
|
"Guinea": "324", |
|
|
"Timor-Leste": "626", |
|
|
"Montserrat": "500", |
|
|
"Chad": "148", |
|
|
"Mali": "466", |
|
|
"Maldives": "462", |
|
|
"Tajikistan": "762", |
|
|
"Cabo Verde": "132", |
|
|
"Burundi": "108", |
|
|
"Guadeloupe": "312", |
|
|
"Bhutan": "064", |
|
|
"Martinique": "474", |
|
|
"Tonga": "776", |
|
|
"Mauritania": "478", |
|
|
"Dominica": "212", |
|
|
"Micronesia": "583", |
|
|
"Gambia": "270", |
|
|
"French Guiana": "254", |
|
|
"Christmas Island": "162", |
|
|
"Andorra": "020", |
|
|
"Central African Republic": "140", |
|
|
"Solomon Islands": "090", |
|
|
"Mayotte": "175", |
|
|
"Anguilla": "660", |
|
|
"Cocos (Keeling) Islands": "166", |
|
|
"Eritrea": "232", |
|
|
"Cook Islands": "184", |
|
|
"South Sudan": "728", |
|
|
"Comoros": "174", |
|
|
"Kiribati": "296", |
|
|
"Sao Tomé and Principe": "678", |
|
|
"Norfolk Island": "574", |
|
|
"Gibraltar": "292", |
|
|
"Tuvalu": "798", |
|
|
"British Indian Ocean Territory": "086", |
|
|
"Tokelau": "772", |
|
|
"Guinea-Bissau": "624", |
|
|
"Svalbard and Jan Mayen": "744", |
|
|
"Heard and McDonald Islands": "334", |
|
|
"Reunion": "638", |
|
|
"Algeria": "012", |
|
|
"Moldova": "498", |
|
|
"Oman": "512", |
|
|
"Angola": "024", |
|
|
"Uruguay": "858", |
|
|
"Democratic Republic of the Congo": "180", |
|
|
"Bahamas": "044", |
|
|
"Jamaica": "388", |
|
|
"Lesotho": "426", |
|
|
"Mozambique": "508", |
|
|
"Ukraine": "804", |
|
|
"Paraguay": "600", |
|
|
"Bahrain": "048", |
|
|
"Zambia": "894", |
|
|
"Qatar": "634", |
|
|
"Lebanon": "422", |
|
|
"Mauritius": "480", |
|
|
"Tanzania": "834", |
|
|
"Fiji": "242", |
|
|
"Iraq": "368", |
|
|
"Iceland": "352", |
|
|
"Georgia": "268", |
|
|
"Kenya": "404", |
|
|
"Senegal": "686", |
|
|
"Liechtenstein": "438", |
|
|
"Azerbaijan": "031", |
|
|
"Guyana": "328", |
|
|
"Cameroon": "120", |
|
|
"Haiti": "332", |
|
|
"Uganda": "800", |
|
|
"Bosnia and Herzegovina": "070", |
|
|
"Albania": "008", |
|
|
"Nigeria": "566", |
|
|
"Armenia": "051", |
|
|
"Namibia": "516", |
|
|
"Nepal": "524", |
|
|
"Brunei": "096", |
|
|
"Sint Maarten": "534", |
|
|
"Bolivia": "068", |
|
|
"Åland Islands": "248", |
|
|
"Panama": "591", |
|
|
"Gabon": "266", |
|
|
"Venezuela": "862", |
|
|
"Kuwait": "414", |
|
|
"North Macedonia": "807", |
|
|
"Togo": "768", |
|
|
"Ethiopia": "231", |
|
|
"Suriname": "740", |
|
|
"Ghana": "288", |
|
|
"Belize": "084", |
|
|
} |
|
|
|
|
|
|
|
|
@dataclass |
|
|
class EnhancedTariffData: |
|
|
"""Enhanced tariff data structure with sectoral breakdown""" |
|
|
|
|
|
country: str |
|
|
country_code: str |
|
|
tariff_to_usa: float |
|
|
reciprocal_tariff: float |
|
|
sector_impacts: Dict[str, float] |
|
|
trade_volume: Optional[float] = None |
|
|
gdp_impact_factor: Optional[float] = None |
|
|
|
|
|
|
|
|
@dataclass |
|
|
class SectoralAnalysisConfig: |
|
|
"""Configuration for sectoral tariff analysis""" |
|
|
|
|
|
selected_countries: List[str] |
|
|
selected_sectors: List[str] |
|
|
base_year: int = 2023 |
|
|
projection_years: int = 5 |
|
|
tariff_escalation_rate: float = 0.0 |
|
|
include_retaliation: bool = True |
|
|
|
|
|
|
|
|
class EnhancedTariffDataManager: |
|
|
""" |
|
|
Manages comprehensive tariff data for 186 countries with sectoral breakdown |
|
|
""" |
|
|
|
|
|
def __init__( |
|
|
self, |
|
|
data_path: str = "data/trump_tariffs_by_country.csv", |
|
|
use_synthetic: bool = False, |
|
|
): |
|
|
""" |
|
|
Initialize EnhancedTariffDataManager. |
|
|
Args: |
|
|
data_path (str): Path to tariff data CSV. |
|
|
use_synthetic (bool): If True, use synthetic data for testing. |
|
|
""" |
|
|
import logging |
|
|
|
|
|
self.logger = logging.getLogger("TIPM.EnhancedTariffDataManager") |
|
|
self.data_path = Path(data_path) |
|
|
self.tariff_data: Dict[str, EnhancedTariffData] = {} |
|
|
self.sectors = GLOBAL_SECTORS |
|
|
self.country_codes = COUNTRY_CODES |
|
|
self.use_synthetic = use_synthetic |
|
|
self.logger.info( |
|
|
f"Initializing EnhancedTariffDataManager (synthetic={use_synthetic})" |
|
|
) |
|
|
self.load_tariff_data() |
|
|
|
|
|
def load_tariff_data(self): |
|
|
""" |
|
|
Load and process tariff data with sectoral breakdown. |
|
|
Handles missing data gracefully and supports synthetic data for testing. |
|
|
""" |
|
|
import logging |
|
|
|
|
|
try: |
|
|
if self.use_synthetic: |
|
|
self.logger.warning("Using synthetic data for testing.") |
|
|
|
|
|
for country in ["China", "Japan", "Germany", "Brazil"]: |
|
|
country_code = self.country_codes.get(country, "000") |
|
|
sector_impacts = { |
|
|
s: np.random.uniform(0, 2) for s in self.sectors.keys() |
|
|
} |
|
|
self.tariff_data[country] = EnhancedTariffData( |
|
|
country=country, |
|
|
country_code=country_code, |
|
|
tariff_to_usa=np.random.uniform(0, 1), |
|
|
reciprocal_tariff=np.random.uniform(0, 1), |
|
|
sector_impacts=sector_impacts, |
|
|
trade_volume=np.random.uniform(1, 100), |
|
|
gdp_impact_factor=np.random.uniform(0.01, 0.25), |
|
|
) |
|
|
return |
|
|
if not self.data_path.exists(): |
|
|
self.logger.error(f"Tariff data file not found: {self.data_path}") |
|
|
raise FileNotFoundError(f"Tariff data file not found: {self.data_path}") |
|
|
df = pd.read_csv(self.data_path) |
|
|
for _, row in df.iterrows(): |
|
|
country = row.get("Country", None) |
|
|
if not country: |
|
|
self.logger.warning(f"Missing country in row: {row}") |
|
|
continue |
|
|
country_code = self.country_codes.get(country, "000") |
|
|
|
|
|
tariff_to_usa = max( |
|
|
0.0, min(row.get("Tariffs charged to USA", 0.0), 1.0) |
|
|
) |
|
|
reciprocal_tariff = max( |
|
|
0.0, min(row.get("Reciprocal Tariffs", 0.0), 1.0) |
|
|
) |
|
|
sector_impacts = self._generate_sector_impacts( |
|
|
country, tariff_to_usa, reciprocal_tariff |
|
|
) |
|
|
self.tariff_data[country] = EnhancedTariffData( |
|
|
country=country, |
|
|
country_code=country_code, |
|
|
tariff_to_usa=tariff_to_usa, |
|
|
reciprocal_tariff=reciprocal_tariff, |
|
|
sector_impacts=sector_impacts, |
|
|
trade_volume=self._estimate_trade_volume(country), |
|
|
gdp_impact_factor=self._estimate_gdp_impact_factor(country), |
|
|
) |
|
|
self.logger.info( |
|
|
f"Loaded tariff data for {len(self.tariff_data)} countries." |
|
|
) |
|
|
except Exception as e: |
|
|
self.logger.error(f"Error loading tariff data: {e}") |
|
|
raise |
|
|
|
|
|
def _generate_sector_impacts( |
|
|
self, country: str, base_tariff: float, reciprocal: float |
|
|
) -> Dict[str, float]: |
|
|
"""Generate sector-specific tariff impacts based on country characteristics""" |
|
|
|
|
|
|
|
|
sector_weights = self._get_country_sector_weights(country) |
|
|
|
|
|
sector_impacts = {} |
|
|
for sector_id, sector_info in self.sectors.items(): |
|
|
|
|
|
base_impact = base_tariff * sector_weights.get(sector_id, 1.0) |
|
|
|
|
|
|
|
|
sector_modifier = self._get_sector_modifier(sector_id, country) |
|
|
|
|
|
|
|
|
final_impact = base_impact * sector_modifier |
|
|
sector_impacts[sector_id] = min(final_impact, 2.0) |
|
|
|
|
|
return sector_impacts |
|
|
|
|
|
def _get_country_sector_weights(self, country: str) -> Dict[str, float]: |
|
|
"""Get country-specific sector importance weights""" |
|
|
|
|
|
|
|
|
tech_leaders = ["China", "Taiwan", "South Korea", "Japan", "Singapore"] |
|
|
manufacturing_hubs = [ |
|
|
"Vietnam", |
|
|
"Bangladesh", |
|
|
"Cambodia", |
|
|
"Thailand", |
|
|
"Malaysia", |
|
|
] |
|
|
commodity_exporters = [ |
|
|
"Australia", |
|
|
"Brazil", |
|
|
"Chile", |
|
|
"South Africa", |
|
|
"Kazakhstan", |
|
|
] |
|
|
oil_exporters = [ |
|
|
"Saudi Arabia", |
|
|
"United Arab Emirates", |
|
|
"Kuwait", |
|
|
"Qatar", |
|
|
"Algeria", |
|
|
] |
|
|
agricultural_countries = ["Argentina", "Ukraine", "New Zealand", "Uruguay"] |
|
|
|
|
|
if country in tech_leaders: |
|
|
return { |
|
|
"technology": 1.5, |
|
|
"machinery": 1.3, |
|
|
"electronics": 1.4, |
|
|
"automotive": 1.2, |
|
|
"chemicals": 1.1, |
|
|
"textiles": 0.8, |
|
|
} |
|
|
elif country in manufacturing_hubs: |
|
|
return { |
|
|
"textiles": 1.6, |
|
|
"machinery": 1.3, |
|
|
"technology": 1.2, |
|
|
"chemicals": 1.0, |
|
|
"automotive": 0.9, |
|
|
"agriculture": 0.7, |
|
|
} |
|
|
elif country in commodity_exporters: |
|
|
return { |
|
|
"metals": 1.5, |
|
|
"agriculture": 1.3, |
|
|
"chemicals": 1.2, |
|
|
"machinery": 0.9, |
|
|
"textiles": 0.8, |
|
|
"technology": 0.8, |
|
|
} |
|
|
elif country in oil_exporters: |
|
|
return { |
|
|
"chemicals": 1.4, |
|
|
"metals": 1.2, |
|
|
"machinery": 1.1, |
|
|
"technology": 0.9, |
|
|
"textiles": 0.7, |
|
|
"agriculture": 0.8, |
|
|
} |
|
|
elif country in agricultural_countries: |
|
|
return { |
|
|
"agriculture": 1.6, |
|
|
"chemicals": 1.2, |
|
|
"machinery": 1.1, |
|
|
"textiles": 0.9, |
|
|
"technology": 0.8, |
|
|
"automotive": 0.8, |
|
|
} |
|
|
else: |
|
|
|
|
|
return {sector: 1.0 for sector in self.sectors.keys()} |
|
|
|
|
|
def _get_sector_modifier(self, sector_id: str, country: str) -> float: |
|
|
"""Get sector-specific impact modifiers""" |
|
|
|
|
|
|
|
|
if sector_id == "technology": |
|
|
return 1.3 |
|
|
elif sector_id == "automotive": |
|
|
return 1.2 |
|
|
elif sector_id == "agriculture": |
|
|
return 0.9 |
|
|
elif sector_id == "arms": |
|
|
return 0.7 |
|
|
else: |
|
|
return 1.0 |
|
|
|
|
|
def _estimate_trade_volume(self, country: str) -> float: |
|
|
"""Estimate trade volume with US (in billions USD)""" |
|
|
|
|
|
|
|
|
major_traders = { |
|
|
"China": 650, |
|
|
"European Union": 400, |
|
|
"Japan": 200, |
|
|
"South Korea": 180, |
|
|
"United Kingdom": 120, |
|
|
"India": 100, |
|
|
"Taiwan": 90, |
|
|
"Vietnam": 85, |
|
|
"Thailand": 60, |
|
|
"Singapore": 55, |
|
|
"Malaysia": 50, |
|
|
"Indonesia": 45, |
|
|
"Brazil": 40, |
|
|
"Australia": 35, |
|
|
"Switzerland": 30, |
|
|
"Israel": 25, |
|
|
} |
|
|
|
|
|
return major_traders.get(country, np.random.uniform(0.5, 15.0)) |
|
|
|
|
|
def _estimate_gdp_impact_factor(self, country: str) -> float: |
|
|
"""Estimate how much tariffs affect country's GDP (trade dependency)""" |
|
|
|
|
|
|
|
|
high_dependency = ["Singapore", "Taiwan", "South Korea", "Vietnam", "Malaysia"] |
|
|
medium_dependency = ["China", "Thailand", "Japan", "United Kingdom", "India"] |
|
|
|
|
|
if country in high_dependency: |
|
|
return np.random.uniform(0.15, 0.25) |
|
|
elif country in medium_dependency: |
|
|
return np.random.uniform(0.08, 0.15) |
|
|
else: |
|
|
return np.random.uniform(0.02, 0.08) |
|
|
|
|
|
def get_countries_by_tariff_level( |
|
|
self, threshold: float = 0.5 |
|
|
) -> Dict[str, List[str]]: |
|
|
"""Categorize countries by tariff levels""" |
|
|
|
|
|
high_tariff = [] |
|
|
medium_tariff = [] |
|
|
low_tariff = [] |
|
|
|
|
|
for country, data in self.tariff_data.items(): |
|
|
if data.tariff_to_usa >= threshold: |
|
|
high_tariff.append(country) |
|
|
elif data.tariff_to_usa >= 0.3: |
|
|
medium_tariff.append(country) |
|
|
else: |
|
|
low_tariff.append(country) |
|
|
|
|
|
return {"high": high_tariff, "medium": medium_tariff, "low": low_tariff} |
|
|
|
|
|
def get_available_countries(self) -> List[str]: |
|
|
"""Get list of all available countries""" |
|
|
return list(self.tariff_data.keys()) |
|
|
|
|
|
def get_available_sectors(self) -> List[str]: |
|
|
"""Get list of all available sectors""" |
|
|
return list(self.sectors.keys()) |
|
|
|
|
|
def get_sector_analysis(self, countries: List[str], sectors: List[str]) -> Dict: |
|
|
""" |
|
|
Perform detailed sector analysis for selected countries. |
|
|
Returns analysis dict with confidence scores and logging. |
|
|
""" |
|
|
self.logger.info( |
|
|
f"Running sector analysis for {len(countries)} countries, {len(sectors)} sectors." |
|
|
) |
|
|
analysis = { |
|
|
"countries_analyzed": len(countries), |
|
|
"sectors_analyzed": len(sectors), |
|
|
"total_impact": 0.0, |
|
|
"sector_impacts": {}, |
|
|
"country_impacts": {}, |
|
|
"risk_assessment": {}, |
|
|
"summary_statistics": {}, |
|
|
"confidence_score": 0.0, |
|
|
} |
|
|
total_impact_sum = 0.0 |
|
|
valid_countries = 0 |
|
|
for sector in sectors: |
|
|
sector_data = [] |
|
|
sector_impact_sum = 0.0 |
|
|
for country in countries: |
|
|
if country in self.tariff_data: |
|
|
data = self.tariff_data[country] |
|
|
impact = data.sector_impacts.get(sector, 0.0) |
|
|
|
|
|
if data.trade_volume: |
|
|
scaled_impact = impact * min(data.trade_volume / 100.0, 1.5) |
|
|
else: |
|
|
scaled_impact = impact * 0.8 |
|
|
|
|
|
confidence = ( |
|
|
1.0 if data.trade_volume and data.gdp_impact_factor else 0.7 |
|
|
) |
|
|
sector_data.append( |
|
|
{ |
|
|
"country": country, |
|
|
"impact": scaled_impact, |
|
|
"raw_impact": impact, |
|
|
"trade_volume": data.trade_volume or 0.0, |
|
|
"base_tariff": data.tariff_to_usa, |
|
|
"confidence": confidence, |
|
|
} |
|
|
) |
|
|
sector_impact_sum += scaled_impact |
|
|
if sector_data: |
|
|
avg_impact = sector_impact_sum / len(sector_data) |
|
|
max_impact = max([d["impact"] for d in sector_data]) |
|
|
avg_confidence = np.mean([d["confidence"] for d in sector_data]) |
|
|
analysis["sector_impacts"][sector] = { |
|
|
"average_impact": avg_impact, |
|
|
"max_impact": max_impact, |
|
|
"countries_affected": len(sector_data), |
|
|
"sector_total_impact": sector_impact_sum, |
|
|
"details": sector_data, |
|
|
"confidence": avg_confidence, |
|
|
} |
|
|
total_impact_sum += avg_impact |
|
|
for country in countries: |
|
|
if country in self.tariff_data: |
|
|
data = self.tariff_data[country] |
|
|
sector_impacts_list = [] |
|
|
for sector in sectors: |
|
|
if sector in data.sector_impacts: |
|
|
sector_weight = self._get_sector_weight_for_country( |
|
|
country, sector |
|
|
) |
|
|
weighted_impact = data.sector_impacts[sector] * sector_weight |
|
|
sector_impacts_list.append(weighted_impact) |
|
|
if sector_impacts_list: |
|
|
country_impact = sum(sector_impacts_list) / len(sector_impacts_list) |
|
|
valid_countries += 1 |
|
|
else: |
|
|
country_impact = 0.0 |
|
|
gdp_loss = country_impact * (data.gdp_impact_factor or 1.0) * 50 |
|
|
confidence = ( |
|
|
1.0 if data.trade_volume and data.gdp_impact_factor else 0.7 |
|
|
) |
|
|
analysis["country_impacts"][country] = { |
|
|
"average_impact": country_impact, |
|
|
"trade_volume": data.trade_volume or 0.0, |
|
|
"gdp_factor": data.gdp_impact_factor or 1.0, |
|
|
"estimated_gdp_loss": gdp_loss, |
|
|
"base_tariff": data.tariff_to_usa, |
|
|
"reciprocal_tariff": data.reciprocal_tariff, |
|
|
"confidence": confidence, |
|
|
} |
|
|
total_trade_volume = sum( |
|
|
[ |
|
|
(self.tariff_data[country].trade_volume or 0.0) |
|
|
for country in countries |
|
|
if country in self.tariff_data |
|
|
] |
|
|
) |
|
|
if valid_countries > 0: |
|
|
avg_impact = total_impact_sum / len(sectors) if sectors else 0.0 |
|
|
else: |
|
|
avg_impact = 0.0 |
|
|
if avg_impact > 0.5: |
|
|
risk_level = "Very High" |
|
|
elif avg_impact > 0.35: |
|
|
risk_level = "High" |
|
|
elif avg_impact > 0.2: |
|
|
risk_level = "Medium" |
|
|
elif avg_impact > 0.1: |
|
|
risk_level = "Low" |
|
|
else: |
|
|
risk_level = "Very Low" |
|
|
analysis["total_impact"] = avg_impact |
|
|
if analysis["sector_impacts"] and analysis["country_impacts"]: |
|
|
most_affected_sector = max( |
|
|
analysis["sector_impacts"].keys(), |
|
|
key=lambda s: analysis["sector_impacts"][s]["average_impact"], |
|
|
) |
|
|
most_affected_country = max( |
|
|
analysis["country_impacts"].keys(), |
|
|
key=lambda c: analysis["country_impacts"][c]["average_impact"], |
|
|
) |
|
|
analysis["risk_assessment"] = { |
|
|
"overall_risk": risk_level, |
|
|
"total_trade_at_risk": total_trade_volume, |
|
|
"most_affected_sector": most_affected_sector, |
|
|
"most_affected_country": most_affected_country, |
|
|
"risk_score": avg_impact, |
|
|
"countries_at_high_risk": len( |
|
|
[ |
|
|
c |
|
|
for c, data in analysis["country_impacts"].items() |
|
|
if data["average_impact"] > 0.4 |
|
|
] |
|
|
), |
|
|
} |
|
|
else: |
|
|
analysis["risk_assessment"] = { |
|
|
"overall_risk": "Unknown", |
|
|
"total_trade_at_risk": 0.0, |
|
|
"most_affected_sector": "None", |
|
|
"most_affected_country": "None", |
|
|
"risk_score": 0.0, |
|
|
"countries_at_high_risk": 0, |
|
|
} |
|
|
if analysis["country_impacts"]: |
|
|
country_impacts = [ |
|
|
data["average_impact"] for data in analysis["country_impacts"].values() |
|
|
] |
|
|
gdp_losses = [ |
|
|
data["estimated_gdp_loss"] |
|
|
for data in analysis["country_impacts"].values() |
|
|
] |
|
|
confidences = [ |
|
|
data["confidence"] for data in analysis["country_impacts"].values() |
|
|
] |
|
|
analysis["summary_statistics"] = { |
|
|
"mean_country_impact": np.mean(country_impacts), |
|
|
"median_country_impact": np.median(country_impacts), |
|
|
"std_country_impact": np.std(country_impacts), |
|
|
"total_estimated_gdp_loss": sum(gdp_losses), |
|
|
"countries_above_threshold": len( |
|
|
[x for x in country_impacts if x > 0.3] |
|
|
), |
|
|
"mean_confidence": np.mean(confidences), |
|
|
} |
|
|
analysis["confidence_score"] = np.mean(confidences) |
|
|
|
|
|
analysis["visualization"] = ( |
|
|
"See tipm/utils/visualization_utils.py for plotting functions." |
|
|
) |
|
|
self.logger.info( |
|
|
f"Sector analysis complete. Confidence score: {analysis['confidence_score']:.2f}" |
|
|
) |
|
|
return analysis |
|
|
|
|
|
def _get_sector_weight_for_country(self, country: str, sector: str) -> float: |
|
|
"""Get sector importance weight for specific country""" |
|
|
weights = self._get_country_sector_weights(country) |
|
|
return weights.get(sector, 1.0) |
|
|
|
|
|
def export_analysis_data( |
|
|
self, |
|
|
countries: List[str], |
|
|
sectors: List[str], |
|
|
file_path: str = "enhanced_tariff_analysis.json", |
|
|
): |
|
|
"""Export comprehensive analysis data""" |
|
|
|
|
|
analysis = self.get_sector_analysis(countries, sectors) |
|
|
|
|
|
export_data = { |
|
|
"metadata": { |
|
|
"analysis_date": pd.Timestamp.now().isoformat(), |
|
|
"countries_count": len(countries), |
|
|
"sectors_count": len(sectors), |
|
|
"data_source": "Trump Tariffs by Country (Enhanced)", |
|
|
}, |
|
|
"configuration": { |
|
|
"selected_countries": countries, |
|
|
"selected_sectors": sectors, |
|
|
"sector_definitions": { |
|
|
s: self.sectors[s] for s in sectors if s in self.sectors |
|
|
}, |
|
|
}, |
|
|
"analysis_results": analysis, |
|
|
"raw_data": { |
|
|
country: { |
|
|
"tariff_to_usa": data.tariff_to_usa, |
|
|
"reciprocal_tariff": data.reciprocal_tariff, |
|
|
"sector_impacts": { |
|
|
s: data.sector_impacts.get(s, 0.0) for s in sectors |
|
|
}, |
|
|
"trade_volume": data.trade_volume, |
|
|
"gdp_impact_factor": data.gdp_impact_factor, |
|
|
} |
|
|
for country, data in self.tariff_data.items() |
|
|
if country in countries |
|
|
}, |
|
|
} |
|
|
|
|
|
with open(file_path, "w", encoding="utf-8") as f: |
|
|
json.dump(export_data, f, indent=2, default=str) |
|
|
|
|
|
return file_path |
|
|
|
