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+# Project Data Dictionary & Processing Guide
+
+## Overview
+This document describes all datasets for the **Global Commodity Shocks & Production Networks** project. Each team member should process their assigned datasets following the specifications below.
+
+---
+
+## 1. COMMODITY PRICES DATA
+
+### **File:** `CMO-Historical-Data-Monthly.xlsx`
+**Location:** `data/raw/commodity_prices/`  
+**Source:** World Bank Pink Sheet  
+**Coverage:** Monthly, 1960-2024  
+
+#### **What It Contains:**
+- **Sheet 1 - Monthly Prices:** Nominal prices (USD) for 70+ commodities
+  - Energy: Crude oil (Brent, WTI, Dubai), Natural gas, Coal
+  - Agriculture: Wheat, Rice, Maize, Soybeans, Sugar, Coffee
+  - Metals: Copper, Aluminum, Iron ore, Gold, Silver
+  
+- **Sheet 2 - Monthly Indices:** Price indices (2010=100 base year)
+  - Aggregate indices by commodity group
+  - Useful for comparing relative price movements
+
+- **Sheet 3 - Index Weights:** Weights used in index construction
+
+#### **Why We Need It:**
+This is our **primary shock variable**. Oil price spikes, wheat price volatility, and metal price crashes are the "shocks" we're studying. Changes in these prices affect India's production network.
+
+#### **Processing Tasks:**
+1. Extract columns: Date, Crude oil (average), Wheat (US HRW), Rice (Thai 5%), Copper, Aluminum
+2. Filter to 2010-2024 only
+3. Calculate log returns: `log(Price_t / Price_{t-1})`
+4. Calculate rolling volatility (3, 6, 12-month windows)
+5. Create shock indicators: binary variable = 1 if price change > 2 standard deviations
+6. Save as: `data/processed/commodity_prices_clean.csv`
+
+#### **Expected Output Columns:**
+```
+date, oil_price, wheat_price, rice_price, copper_price, aluminum_price,
+oil_return, wheat_return, rice_return, copper_return, aluminum_return,
+oil_volatility_3m, oil_volatility_6m, oil_volatility_12m,
+oil_shock_binary, wheat_shock_binary, etc.
+```
+
+---
+
+## 2. CLIMATE DATA (INSTRUMENTAL VARIABLE)
+
+### **File:** `Monthly Oceanic Nino Index (ONI) - Wide.csv`
+**Location:** `data/raw/climate/`  
+**Source:** NOAA Climate Prediction Center  
+**Coverage:** Monthly, 1950-2024  
+
+#### **What It Contains:**
+- **Oceanic Niño Index (ONI):** 3-month running mean of sea surface temperature anomalies in the Niño 3.4 region
+- Values range from -2.5°C (strong La Niña) to +2.5°C (strong El Niño)
+
+#### **Why We Need It:**
+El Niño/La Niña events affect global weather patterns → agricultural production → wheat/rice prices. We use ONI as an **instrumental variable** (IV) for agricultural commodity prices because:
+- ONI affects crop yields (relevant)
+- ONI doesn't directly affect Indian manufacturing output (excludable)
+- This helps us establish **causal** relationships, not just correlations
+
+#### **Processing Tasks:**
+1. Filter to 2010-2024
+2. Classify ENSO phases:
+   - El Niño: ONI ≥ 0.5
+   - La Niña: ONI ≤ -0.5
+   - Neutral: -0.5 < ONI < 0.5
+3. Create lag variables (1, 3, 6 months) - weather affects crops with delay
+4. Save as: `data/processed/climate_oni_clean.csv`
+
+#### **Expected Output Columns:**
+```
+date, oni_index, enso_phase, oni_lag1, oni_lag3, oni_lag6
+```
+
+---
+
+## 3. TRADE DATA
+
+### **File:** `dataset_2025-10-22T07_56_33...csv` (3 GB!)
+**Location:** `data/raw/trade/`  
+**Source:** IMF International Merchandise Trade Statistics (IMTS)  
+**Coverage:** Monthly bilateral trade flows, all countries, 2000-2024  
+
+#### **What It Contains:**
+- **Bilateral trade flows:** Country A → Country B, by product category
+- **Trade values:** Imports/Exports in USD
+- **Product codes:** HS classification (Harmonized System)
+
+#### **Why We Need It:**
+Measures India's trade exposure to different countries and commodities. High dependence on Gulf states for oil = high vulnerability to oil shocks.
+
+#### **Processing Tasks:**
+1. **Filter to India only:** Reporter = India
+2. **Select 8 key partners:** USA, China, Saudi Arabia, UAE, Qatar, Germany, France, Italy
+3. **Aggregate by commodity group:**
+   - Energy (HS 27): Mineral fuels, oils
+   - Food (HS 10, 11): Cereals, grain products
+   - Metals (HS 74, 76): Copper, Aluminum
+4. Calculate trade concentration metrics:
+   - **HHI (Herfindahl Index):** Sum of squared trade shares
+   - Partner diversification score
+5. Save as: `data/processed/trade_india_bilateral.csv`
+
+#### **Expected Output Columns:**
+```
+date, partner_country, commodity_group, 
+import_value_usd, export_value_usd, trade_balance,
+import_share, export_share
+```
+
+#### **Warning:** This file is HUGE (3GB). Use `pd.read_csv(chunksize=100000)` or filter early with SQL/Dask.
+
+---
+
+### **File:** `WITS-Partner.xlsx`
+**Location:** `data/raw/trade/`  
+**Source:** World Bank WITS (World Integrated Trade Solution)  
+**Coverage:** Annual trade data with partner country details  
+
+#### **What It Contains:**
+- Country names, ISO codes, regional classifications
+- Use as a lookup table to map country codes → country names
+
+#### **Processing Tasks:**
+1. Extract mapping: ISO3 code → Country name → Region
+2. Merge with IMF trade data
+3. Save as: `data/processed/country_mapping.csv`
+
+---
+
+## 4. MACROECONOMIC DATA
+
+### **File:** `Index of Industrial Production.xlsx`
+**Location:** `data/raw/macroeconomic/`  
+**Source:** Reserve Bank of India (RBI)  
+**Coverage:** Monthly, 2010-2024, Base year 2011-12  
+
+#### **What It Contains:**
+- **IIP General Index:** Overall industrial production
+- **Sectoral Indices:**
+  - Mining & Quarrying
+  - Manufacturing (15+ sub-sectors: Food, Textiles, Chemicals, Metals, Machinery, etc.)
+  - Electricity
+- **Use-based Classification:**
+  - Basic goods
+  - Capital goods
+  - Intermediate goods
+  - Consumer durables
+  - Consumer non-durables
+
+#### **Why We Need It:**
+This is our **main outcome variable**. We're predicting: "When oil prices spike, which manufacturing sectors see production decline?" IIP measures exactly that.
+
+#### **Processing Tasks:**
+1. Extract all sectoral indices (rows) across time (columns)
+2. Convert from wide to long format:
+   ```
+   date | sector | iip_value
+   ```
+3. Calculate month-over-month growth rates
+4. Calculate year-over-year growth rates
+5. Identify energy-intensive sectors (Manufacturing - Chemicals, Basic Metals, etc.)
+6. Save as: `data/processed/iip_sectoral.csv`
+
+#### **Expected Output Columns:**
+```
+date, sector_name, iip_index, 
+iip_mom_growth, iip_yoy_growth,
+is_energy_intensive
+```
+
+---
+
+### **File:** `Wholesale Price Index - Monthly Data.xlsx`
+**Location:** `data/raw/macroeconomic/`  
+**Source:** Office of Economic Adviser, India  
+**Coverage:** Monthly, 2010-2024  
+
+#### **What It Contains:**
+- WPI for different product categories
+- Inflation measure at wholesale level (before goods reach consumers)
+
+#### **Why We Need It:**
+Commodity price shocks → input cost inflation → affects production decisions. WPI captures cost pressures on manufacturers.
+
+#### **Processing Tasks:**
+1. Extract WPI for: Fuel & Power, Manufactured Products, Food Articles
+2. Calculate inflation rate: `(WPI_t - WPI_{t-12}) / WPI_{t-12} * 100`
+3. Save as: `data/processed/wpi_inflation.csv`
+
+---
+
+### **Files:** GDP Quarterly Estimates (3 files)
+**Location:** `data/raw/macroeconomic/`  
+**Source:** MOSPI National Accounts Statistics  
+
+#### **What They Contain:**
+- Quarterly GDP at constant prices (real GDP)
+- Quarterly GDP at current prices (nominal GDP)
+- Quarterly GVA (Gross Value Added) by sector
+
+#### **Why We Need It:**
+Control variables for macroeconomic conditions. GDP growth affects all sectors simultaneously.
+
+#### **Processing Tasks:**
+1. Merge all three files
+2. Calculate GDP growth rate (YoY)
+3. Resample to monthly frequency (forward-fill)
+4. Save as: `data/processed/gdp_quarterly.csv`
+
+---
+
+### **Files:** OECD Data (2 CSV files)
+**Location:** `data/raw/macroeconomic/`  
+**Source:** OECD Data Explorer  
+
+#### **What They Contain:**
+- **File 1:** G20 GDP growth rates (quarterly)
+- **File 2:** G20 price indices (monthly)
+
+#### **Why We Need It:**
+Global economic conditions affect India through trade channels. US/China/EU slowdowns reduce demand for Indian exports.
+
+#### **Processing Tasks:**
+1. Extract data for: USA, China, Germany, France, Italy (India's trade partners)
+2. Calculate average G20 GDP growth
+3. Merge with India data
+4. Save as: `data/processed/global_macro.csv`
+
+---
+
+## 5. INPUT-OUTPUT TABLE
+
+### **File:** `Input-Output-Transactions-Table-India-2015-16.pdf`
+**Location:** `data/raw/input_output/`  
+**Source:** MOSPI (Ministry of Statistics, India)  
+**Coverage:** 139 sectors, year 2015-16  
+
+#### **What It Contains:**
+- **Use Table:** Shows which sectors use inputs from which other sectors
+  - Rows = industries producing inputs
+  - Columns = industries using inputs
+  - Cell (i,j) = Sector j buys inputs worth ₹X from sector i
+
+- **Make Table:** Shows which sectors produce which outputs
+  - Rows = industries
+  - Columns = products
+  - Cell (i,j) = Sector i produces ₹X worth of product j
+
+#### **Why We Need It:**
+This is the **CORE** of the project. The I-O table shows the **production network**:
+- Oil refining → Chemicals → Plastics → Manufacturing
+- If oil prices spike → refining costs up → chemicals cost up → plastics cost up → manufacturing slows
+
+We model these cascading effects through the network structure.
+
+#### **Processing Tasks:**
+**WARNING:** This is a PDF with large tables. Need careful extraction.
+
+1. **Extract Use Table:**
+   - Use Tabula or pdfplumber to extract tables
+   - Create 139×139 matrix: `A[i,j]` = input from sector i to sector j
+   
+2. **Calculate Technical Coefficients:**
+   - `a[i,j] = A[i,j] / X[j]` where X[j] = total output of sector j
+   - This gives "input per unit of output"
+   
+3. **Calculate Leontief Inverse:**
+   - `L = (I - a)^(-1)` where I is identity matrix
+   - L[i,j] = total output from sector i needed to produce 1 unit of final demand in sector j
+   - This captures **direct + indirect** linkages
+   
+4. **Calculate Forward & Backward Linkages:**
+   - Backward linkage = sum of column in L matrix (how much sector j pulls from others)
+   - Forward linkage = sum of row in L matrix (how much sector i pushes to others)
+   
+5. **Build Network Graph:**
+   - Nodes = 139 sectors
+   - Edge (i→j) with weight = a[i,j] (technical coefficient)
+   - Save edge list as: `data/processed/production_network_edges.csv`
+   - Save node attributes as: `data/processed/production_network_nodes.csv`
+
+#### **Expected Outputs:**
+```
+production_network_edges.csv:
+source_sector, target_sector, input_coefficient, input_value
+
+production_network_nodes.csv:
+sector_id, sector_name, 
+backward_linkage, forward_linkage,
+total_output, is_key_sector
+```
+
+---
+
+## 6. NETWORK METRICS TO CALCULATE
+
+Once production network is built, calculate these for each sector:
+
+### **Centrality Measures:**
+1. **Degree Centrality:**
+   - In-degree: How many sectors provide inputs to this sector
+   - Out-degree: How many sectors does this sector supply to
+   
+2. **Betweenness Centrality:**
+   - How often this sector lies on shortest paths between other sectors
+   - High betweenness = bottleneck sector (e.g., electricity, transport)
+   
+3. **Closeness Centrality:**
+   - Average distance to all other sectors
+   - High closeness = well-connected, quickly affected by shocks
+   
+4. **Eigenvector Centrality:**
+   - Importance based on importance of neighbors
+   - High eigenvector = connected to other important sectors
+   
+5. **PageRank:**
+   - Google's algorithm applied to production network
+   - Measures "influence" in the network
+
+### **Network Topology:**
+1. **Clustering Coefficient:** How interconnected are neighbors
+2. **Path Length:** Average steps between any two sectors
+3. **Network Density:** % of possible connections that exist
+4. **Community Detection:** Groups of tightly connected sectors
+
+**Save as:** `data/processed/network_metrics.csv`
+
+---
+
+## SUMMARY: TEAM TASK ASSIGNMENTS
+
+### **Person 1: Commodity Prices + Climate**
+- Process CMO commodity prices
+- Process NOAA ONI climate data
+- Create shock indicators
+- **Deliverable:** `commodity_prices_clean.csv`, `climate_oni_clean.csv`
+
+### **Person 2: Trade Data**
+- Process IMF IMTS (3GB file - use chunking!)
+- Calculate trade concentration indices
+- Merge with country mapping
+- **Deliverable:** `trade_india_bilateral.csv`, `trade_concentration.csv`
+
+### **Person 3: Macroeconomic Data**
+- Process IIP (most important!)
+- Process WPI, CPI, GDP files
+- Merge OECD global data
+- **Deliverable:** `iip_sectoral.csv`, `macro_controls.csv`
+
+### **Person 4: Input-Output Table + Network**
+- Extract I-O table from PDF (hardest task!)
+- Build production network
+- Calculate Leontief inverse
+- Calculate all network metrics
+- **Deliverable:** `production_network_edges.csv`, `production_network_nodes.csv`, `network_metrics.csv`
+
+---
+
+## FINAL MERGED DATASET
+
+Once all processing is complete, merge into master dataset:
+
+**File:** `data/processed/master_dataset.csv`
+
+**Structure:**
+```
+date, sector_name,
+oil_price, wheat_price, copper_price, aluminum_price,
+oil_shock, wheat_shock,
+oni_index, enso_phase,
+import_value, export_value, trade_hhi,
+iip_index, iip_growth,
+wpi_inflation, gdp_growth,
+degree_centrality, betweenness_centrality, eigenvector_centrality,
+backward_linkage, forward_linkage,
+is_energy_intensive, is_key_sector
+```
+
+**Dimensions:** ~180 months × 139 sectors × 30+ features = ~750,000 rows
+
+This master dataset feeds into:
+- Causal analysis (IV, SCM, VAR)
+- ML models (LSTM, XGBoost, GNN)
+- Scenario simulations
+- Vulnerability index
+
+---
+
+## QUESTIONS?
+
+Contact project lead if:
+- Files don't match descriptions above
+- Data extraction fails (especially I-O PDF)
+- Need clarification on calculations
+- Encounter missing data / data quality issues
+
+**Target completion:** End of Week 1 (Day 5)