📊 OECD Statistics Scraper

🚀 Export OECD economic and social statistics in seconds. Pull observations from 1,500+ OECD datasets including Quarterly National Accounts, Economic Outlook, Main Economic Indicators, and the Better Life Index. No sign-up needed.

🕒 Last updated: 2026-05-15 · 📊 Flat row-per-observation · 🌐 1,500+ datasets · 📅 Decades of historical coverage · 🌍 38 OECD members + key partners

The OECD Statistics Scraper pulls flat, row-per-observation data from the official OECD statistics catalogue. Every observation row carries the dataset identifier, series key, full set of dimension codes plus their human-readable labels, the time period, the value, and any series- or observation-level attributes. The underlying catalogue is the gold-standard source for cross-country economic and social comparisons and is cited by central banks, finance ministries, and academic researchers worldwide.

Coverage spans 1,500+ datasets across national accounts, prices, productivity, labour, education, health, environment, R&D, and well-being. Use the catalogue mode to enumerate every dataset (id, agency, version, name) in a single run, then switch to data mode with a dataset id and series key to pull observations. Download as CSV, Excel, JSON, or XML in under three minutes.

🎯 Target Audience	💡 Primary Use Cases
Macro economists, central-bank researchers, finance ministries, ESG analysts, academics, data journalists, policy think tanks	Cross-country GDP comparisons, inflation tracking, productivity benchmarks, well-being index dashboards, time-series forecasting inputs

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📋 What the OECD Statistics Scraper does

Two operating modes share a single input form:

• 📚 Catalogue mode. Enumerate every OECD dataset (id, agency, version, English name) so you can discover what's available before drilling into the data.
• 📈 Data mode. Supply a dataset id (QNA, EO, MEI, KEI, SNA_TABLE1, BLI, EMP_LF, PPP, PRICES_CPI, GBARD, MSTI_PUB and more) plus an optional series key, start time, and end time to pull observations.

Each observation row carries the dataset id, series key, time identifier and label, derived year/quarter/month, the numeric value, plus every dimension as a code+label pair, and any series or observation attributes (status, unit multiplier, base year). One row per observation, ready to load straight into a dataframe.

💡 Why it matters: OECD data is the standard reference for cross-country macro and social comparisons. Building your own pipeline means parsing complex hierarchical envelopes, joining dimension lookups, and refreshing manually. This Actor flattens everything into a plain table on every run.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded OECD time series.

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⚙️ Input

Input	Type	Default	Behavior
maxItems	integer	10	Records to return. Free plan caps at 10, paid plan at 1,000,000.
mode	string	"data"	data = pull observations. catalogue = list every available dataset.
datasetId	string	"QNA"	OECD dataset id (QNA, EO, MEI, KEI, SNA_TABLE1, BLI...). Use catalogue mode to discover.
seriesKey	string	""	Dot-separated dimension values. Empty positions = all. Empty string = entire dataset.
startTime	string	null	Earliest period (e.g. 2020-Q1, 2018, 2020-01).
endTime	string	null	Latest period (e.g. 2024-Q4, 2025, 2025-03).

Example: USA quarterly real GDP from 2020 to 2024.

{
    "maxItems": 100,
    "mode": "data",
    "datasetId": "QNA",
    "seriesKey": "USA.B1_GE.CUR.Q",
    "startTime": "2020-Q1",
    "endTime": "2024-Q4"
}

Example: list every available OECD dataset.

{
    "maxItems": 1000,
    "mode": "catalogue"
}

⚠️ Good to Know: series keys are positional and dot-separated. Each dataset has its own dimension order, so if you're unsure of the structure, run a small unfiltered query first (empty seriesKey) and inspect the dimension code/label fields in the result. Some legacy dataset ids have been retired or renamed in newer OECD releases.

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📊 Output

Each observation is a flat row with dataset metadata, the time period, the value, and full dimension labels. Download the dataset as CSV, Excel, JSON, or XML.

🧾 Schema

Field	Type	Example
🆔 datasetId	string	"QNA"
🔑 seriesKey	string	"0:1:0:0"
📅 time	string	"2023-Q4"
🏷️ timeLabel	string | null	"Q4-2023"
📅 year	number | null	2023
📅 quarter	number | null	4
📊 value	number | null	27360.5
🏳️ LOCATION_code	string	"USA"
🏳️ LOCATION_label	string	"United States"
📋 SUBJECT_code	string	"B1_GE"
📋 SUBJECT_label	string	"Gross domestic product, expenditure approach"
💱 MEASURE_code	string	"CUR"
💱 MEASURE_label	string	"Current prices"
⏱️ FREQUENCY_code	string	"Q"
⏱️ FREQUENCY_label	string	"Quarterly"
🏛️ datasource	string	"OECD"
🔗 url	string	"https://stats.oecd.org/..."
🕒 scrapedAt	ISO 8601	"2026-05-15T00:00:00.000Z"

(Plus dataset-specific dimension and attribute columns. Each dataset emits its own dimension set as <DIMENSION>_code and <DIMENSION>_label pairs.)

📦 Sample records

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✨ Why choose this Actor

	Capability
📚	1,500+ datasets. Every OECD dataset across national accounts, prices, productivity, labour, education, health, environment, and well-being.
🔍	Built-in discovery. Catalogue mode lists every available dataset in a single run so you don't have to guess identifiers.
🧾	Flat output. One row per observation, with every dimension expanded into code+label columns. Ready for any dataframe library.
🌍	Country-level coverage. 38 OECD members plus key partners and observers across most datasets.
⏰	Time-series ready. Year, quarter, and month are derived automatically from the period identifier.
⚡	Fast. Hundreds of observations in seconds, multi-decade pulls in under a minute.
🚫	No authentication. Works against the public OECD statistics catalogue. No login or registration needed.

📊 OECD data is the canonical cross-country macro reference and is cited in every serious comparative-economics study, central-bank report, and finance-ministry briefing.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ OECD Statistics Scraper (this Actor)	$5 free credit, then pay-per-use	1,500+ datasets	Live per run	dataset, series key, time range	⚡ 2 min
Official OECD web portal	Free	All datasets	Manual	Web form, manual export	🐢 Hours per query
Commercial macro-data platforms	$10,000+/year	Multi-source	Daily	Many	⏳ License + onboarding
Manual CSV downloads	Free	One dataset at a time	Manual	Few	🕒 Hours of cleanup

Pick this Actor when you need clean, structured OECD observations on demand without writing a parser.

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🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the OECD Statistics Scraper page on the Apify Store.
3. 🎯 Set input. Pick a mode, supply a dataset id and optional series key plus time range.
4. 🚀 Run it. Click Start and let the Actor collect your observations.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from sign-up to downloaded dataset: 3-5 minutes. No coding required.

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💼 Business use cases

📊 Macro Research & Forecasting Cross-country GDP and inflation comparisons Quarterly nowcasts using OECD leading indicators Productivity and unit-labour-cost benchmarks Long-run growth and convergence studies	💰 Investment & Sovereign Risk Country macro overlays for fixed-income strategy Real-effective-exchange-rate signals Capital-account and trade-balance monitoring ESG country scoring with well-being indicators
🏛️ Policy & Public Sector International benchmarking for finance-ministry briefings Health, education, and environment cross-country dashboards Better Life Index comparisons for civic communications Evidence-based policy briefs for parliamentarians	🎓 Consulting & Advisory Client-facing macro slide decks Industry productivity benchmarks via STAN R&D intensity comparisons via MSTI Pricing-power scans across PRICES_CPI

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🔌 Automating OECD Statistics Scraper

Control the scraper programmatically for scheduled runs and pipeline integrations:

• 🟢 Node.js. Install the apify-client NPM package.
• 🐍 Python. Use the apify-client PyPI package.
• 📚 See the Apify documentation for full details.

The Apify Schedules feature lets you trigger this Actor on any cron interval. Quarterly or monthly refreshes keep downstream macro dashboards in sync automatically.

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🌟 Beyond business use cases

OECD statistics power more than commercial workflows. The same flat observation rows support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical macro and labour-economics papers Cross-country panel datasets for econometrics courses Reproducible studies with cited, versioned dataset pulls Open-data exercises on OECD well-being indicators	🎨 Personal and creative Data-journalism visualisations of cross-country indicators Personal portfolios with macro-time-series dashboards Better Life Index comparisons for blog posts Hobbyist analyses of long-run economic history
🤝 Non-profit and civic NGO research on inequality and well-being Civic transparency around national vs OECD-average benchmarks Investigative journalism on macro and social trends OpenData community contributions	🧪 Experimentation Train ML models on macro-indicator forecasting Validate macro-tech product hypotheses with real data Prototype agent pipelines that resolve OECD dataset ids Test dashboard concepts with live cross-country feeds

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🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

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❓ Frequently Asked Questions

🧩 How does it work?

Pick a mode (data or catalogue), supply a dataset id (and optional dot-separated series key plus time range), click Start, and the Actor returns a flat row per observation with every dimension expanded into code+label columns.

🔍 How do I find the right dataset id?

Run the Actor in catalogue mode. It returns every dataset id, agency, version, and English name in a single run, so you can browse the full catalogue without leaving Apify.

📐 What is a series key?

A dot-separated list of dimension values that filters the data, where each position maps to a specific dataset dimension. For QNA, a key like USA.B1_GE.CUR.Q selects USA, GDP-expenditure-approach, current prices, quarterly. Empty positions (e.g. ..CUR.Q) match every value at that position. Leave the field empty to pull the entire dataset.

📅 What time formats are supported?

Annual (2024), quarterly (2024-Q3), and monthly (2024-09). The Actor derives year, quarter, and month automatically from the time identifier.

🌍 Which countries are covered?

All 38 OECD member countries plus key partners and observers (Brazil, China, India, Indonesia, South Africa, and others) across most datasets. Country lists vary by dataset.

🔁 How often is the dataset refreshed?

The official OECD catalogue updates as releases publish (typically monthly or quarterly per dataset). Every run of this Actor pulls live data, so your dataset reflects the current state at run time.

⏰ Can I schedule regular runs?

Yes. Use Apify Schedules to run this Actor on any cron interval (monthly, quarterly, annually) and keep downstream macro dashboards in sync.

⚖️ Is this data legal to use?

The OECD publishes statistics under a permissive open-data licence that allows free use including for commercial purposes, with attribution. Review the OECD terms for your specific use case.

💳 Do I need a paid Apify plan to use this Actor?

No. The free Apify plan is enough for testing and small runs (10 records per run). A paid plan lifts the limit and gives you access to scheduling, higher concurrency, and larger datasets.

🔁 What happens if a run fails or gets interrupted?

Apify automatically retries transient errors. If a run still fails, you can inspect the log in the Runs tab, fix the input, and re-run. Partial datasets from failed runs are preserved so you never lose progress.

🆘 What if I need help?

Our support team is here to help. Contact us through the Apify platform or use the Tally form linked below.

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🔌 Integrate with any app

OECD Statistics Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• n8n - Self-hosted automation pipelines
• Pipedream - Event-driven workflow automation
• Airbyte - Pipe macro data into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes. Push fresh OECD data into your product backend, or alert your team in Slack.

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🔗 Recommended Actors

• 🌐 UN Comtrade Scraper - Bilateral trade flows by HS commodity code
• 📈 Indexmundi Scraper - Global demographic and economic indicators
• 📊 World Bank Open Data Scraper - Cross-country development indicators
• 🌍 Eurostat Scraper - European Union official statistics
• 🏛️ FRED Economic Data Scraper - U.S. Federal Reserve economic time series

💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

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🆘 Need Help? Open our contact form to request a new scraper, propose a custom data project, or report an issue.

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⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the OECD. All trademarks mentioned are the property of their respective owners. Only publicly available open OECD statistics are collected.