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Ecuador Macro Deep Dive

Data extraction pipeline for a comprehensive macroeconomic analysis of Ecuador, pulling from three major sources: the IMF, World Bank, and FRED.

Ecuador adopted the US dollar as legal tender in 2000, making it one of the largest dollarised economies in the world. This creates a unique analytical setup — US monetary policy effectively is Ecuador's monetary policy, so understanding the country requires layering macro fundamentals, development indicators, and US financial conditions together.

Data Sources

Script	Source	What it pulls
ecuador_imf.py	IMF DataMapper API	~100+ macro indicators — GDP, government debt, fiscal balances, BOP, capital openness, inflation, WEO forecasts
ecuador_worldbank.py	World Bank v2 API (WDI)	~1,500 development indicators — poverty, inequality, health, education, governance, environment, infrastructure
ecuador_fred.py	FRED API	~300 Ecuador-specific series + 25 US monetary/financial series relevant to dollarisation

Why three sources?

• IMF covers the macro framework: what's happening to GDP, debt, the fiscal position, and the current account. It also provides 5-year forecasts via the World Economic Outlook.
• World Bank covers the structural/development layer: poverty, inequality, governance quality, access to services. Essential context for why the macro numbers look the way they do.
• FRED is an aggregator — much of its international data originates from the IMF and World Bank. But it uniquely provides US monetary policy data (Fed funds rate, Treasury yields, financial conditions indices), oil prices, and EM risk spreads. For a dollarised economy, this is indispensable.

Setup

Requirements

pip install requests fredapi pandas python-dotenv

FRED API Key

Get a free key from FRED and create a .env file in the project root:

FRED_API_KEY=your_key_here

The IMF and World Bank APIs do not require authentication.

Usage

Run all three extractors sequentially with the orchestrator script:

python ecuador_run_all.py

Or run them individually:

python ecuador_imf.py         # ~5 min
python ecuador_worldbank.py   # ~20-30 min (iterates ~1,500 indicators)
python ecuador_fred.py        # ~5-10 min

Output

ecuador_data/
├── imf/
│   ├── ecuador_imf_all_indicators.csv   # Wide format: Year x indicators
│   └── indicator_metadata.csv           # Indicator catalogue with year coverage
├── worldbank/
│   ├── ecuador_wb_all_indicators.csv    # Wide format: Year x indicators
│   └── indicator_metadata.csv           # Indicator catalogue with topics and sources
└── fred/
    ├── _metadata.csv                    # Full metadata (flags original source for overlap audit)
    ├── us_fed_funds_rate.csv            # Individual series files
    ├── wti_crude_oil_price.csv
    └── ...

The FRED metadata CSV includes a source column and notes field so you can identify which series are World Bank/IMF pass-throughs versus genuinely unique FRED data.