📈 DBnomics Economic Series Scraper

🚀 Pull economic time series in seconds. Turn any DBnomics series into clean dated observations with provider, dataset, frequency, unit, and value, ready for your model or dashboard.

🕒 Last updated: 2026-06-04 · 📊 11 fields per record · 93 providers · ECB, IMF, Eurostat, World Bank, OECD, BIS and more

DBnomics aggregates public economic statistics from 93 official providers into one consistent structure. This Actor reads that structure and hands you back one tidy row for every observation, so a 50 year GDP series becomes 50 dated rows you can chart, join, or load straight into a notebook.

Coverage spans exchange rates, inflation, GDP, unemployment, population, central bank policy rates, trade, and thousands of other indicators. Fetch a single known series by its ID, or run a keyword search that resolves matching datasets into their underlying series automatically.

🎯 Target Audience	💡 Primary Use Cases
Economists and analysts	Build and refresh macro indicator panels
Data scientists and quants	Feed time series into forecasting and backtests
Fintech and research teams	Track inflation, rates, and currency moves
Journalists and educators	Pull official figures with full provenance
Dashboard builders	Power live economic charts and reports

📋 What the DBnomics Economic Series Scraper does

This Actor talks to the public DBnomics API and returns economic observations as flat rows. For each series it zips the period array, the start day, and the value array into individual records, each carrying the provider, dataset, series code, series name, frequency, and unit. You can target one exact series, several known series from a curated list, or a free text search that pulls observations from the top matching datasets.

🎬 Full Demo (🚧 Coming soon)

⚙️ Input

Field	Type	Description
seriesId	select	A known series in the form provider/dataset/series_code. Defaults to the ECB USD/EUR exchange rate.
customSeriesId	string	Any DBnomics series as provider/dataset/series_code. Overrides seriesId when set.
searchQuery	string	Keyword search such as inflation, GDP, or unemployment. Resolves matching datasets to their series.
maxItems	integer	Cap on observations returned. Free plan is limited to 10.
maxSeriesPerSearch	integer	How many series to pull from each matched dataset during a search. Default 5.

Example 1, one known series

{
    "seriesId": "IMF/WEO:2024-10/USA.NGDPD.us_dollars",
    "maxItems": 50
}

Example 2, a keyword search

{
    "searchQuery": "inflation",
    "maxSeriesPerSearch": 3,
    "maxItems": 100
}

⚠️ Good to Know: A search query takes priority over the seriesId dropdown, so use customSeriesId when you want a specific series alongside or instead of search. Daily series can be very long, for example the daily ECB USD/EUR rate has 6,742 observations, so set maxItems to keep runs focused.

📊 Output

Each record is a single observation with full series provenance.

Field	Type	Description
🏛 provider	string	Provider code, for example IMF, ECB, OECD.
🗂 dataset	string	Dataset code within the provider.
📚 datasetName	string	Human readable dataset name.
🔖 seriesCode	string	Dot separated series code.
📌 seriesName	string	Human readable series name.
⏱ frequency	string	Series frequency, for example annual, monthly, daily.
📐 unit	string	Unit of the value when the dataset defines one.
🗓 period	string	Raw period label, for example 1989 or 2020-01.
📅 date	string	ISO start date of the period.
🔢 value	number	The observation value.
🕒 scrapedAt	string	ISO timestamp when the row was collected.
❌ error	string	Null on success, message on failure.

Real sample records

{
    "provider": "IMF",
    "dataset": "WEO:2024-10",
    "datasetName": "World Economic Outlook by countries",
    "seriesCode": "USA.NGDPD.us_dollars",
    "seriesName": "United States – Gross domestic product, current prices (NGDPD) – U.S. dollars",
    "frequency": "annual",
    "unit": "us_dollars",
    "period": "1980",
    "date": "1980-01-01",
    "value": 2857.325,
    "scrapedAt": "2026-06-04T19:25:09.671Z",
    "error": null
}

{
    "provider": "IMF",
    "dataset": "WEO:2024-10",
    "datasetName": "World Economic Outlook by countries",
    "seriesCode": "USA.NGDPD.us_dollars",
    "seriesName": "United States – Gross domestic product, current prices (NGDPD) – U.S. dollars",
    "frequency": "annual",
    "unit": "us_dollars",
    "period": "1982",
    "date": "1982-01-01",
    "value": 3343.8,
    "scrapedAt": "2026-06-04T19:25:09.844Z",
    "error": null
}

{
    "provider": "OECD",
    "dataset": "DSD_REG_ECO_ROPI@DF_ECO_ROPI",
    "datasetName": "Economic statistics ROPI-adjusted for inflation - Regions (for 'Developer API')",
    "seriesCode": "A.CTRY.AUS._Z.EMP.A._Z.PS",
    "seriesName": "Annual – Country – Australia – Not applicable – Employment – Agriculture, forestry and fishing – Not applicable – Persons",
    "frequency": "annual",
    "unit": "PS",
    "period": "1989",
    "date": "1989-01-01",
    "value": 425300,
    "scrapedAt": "2026-06-04T19:24:35.558Z",
    "error": null
}

✨ Why choose this Actor

• One row per observation, no nested arrays to unpack.
• Full provenance on every row, so you always know the provider, dataset, and series.
• Works keyless against the public DBnomics API, no account or token to manage on the source side.
• Curated list of known good series plus free text search for discovery.
• Clean numeric values with ISO dates, ready for charts and models.

📈 How it compares to alternatives

Approach	Provenance per row	Search to series	Setup effort
This Actor	Yes, 7 metadata fields	Yes, automatic	Pick a series or type a query
Manual API calls	You build it yourself	You build it yourself	Write and maintain code
Spreadsheet downloads	Often lost	No	Manual and repetitive

🚀 How to use

1. Sign up for a free Apify account using this link.
2. Open the DBnomics Economic Series Scraper in the Apify Console.
3. Pick a series from the dropdown, paste a custom series ID, or type a search query.
4. Set maxItems and, for searches, maxSeriesPerSearch, then click Start.
5. Watch the run finish and open the results to view or pull your observations.

💼 Business use cases

Macro research

Goal	How this helps
Build indicator panels	Pull GDP, inflation, and rate series into one table
Cross country comparison	Collect the same indicator across providers and regions

Quant and modeling

Goal	How this helps
Feature engineering	Turn long history series into dated training rows
Backtesting	Align observations by ISO date across series

Fintech and reporting

Goal	How this helps
Currency tracking	Fetch ECB exchange rate series at chosen frequency
Policy monitoring	Follow central bank and inflation indicators

Content and education

Goal	How this helps
Data journalism	Cite official figures with provider and series names
Teaching	Hand students clean, sourced economic series

🔌 Automating DBnomics Economic Series Scraper

Connect runs to Make, Zapier, Slack, Airbyte, GitHub, or Google Drive through the Apify integrations. Schedule a daily run to refresh an inflation panel, post new observations to a Slack channel, or sync results into a Google Sheet for a live dashboard.

🌟 Beyond business use cases

• Research: assemble reproducible datasets with full provenance for papers.
• Personal: track the indicators you care about in a private dashboard.
• Non-profit: monitor development indicators from the World Bank and IMF.
• Experimentation: prototype economic models without writing API plumbing.

🤖 Ask an AI assistant

Drop your results into ChatGPT, Claude, Perplexity, or Microsoft Copilot and ask it to summarize trends, compute growth rates, or compare series across providers.

❓ Frequently Asked Questions

What is DBnomics? DBnomics is a public aggregator that gathers economic statistics from 93 official providers into one consistent API.

Do I need an API key or account on DBnomics? No. The DBnomics API is public and keyless. You only need an Apify account to run this Actor.

How do I find a series ID? Browse db.nomics.world, open a series, and read its provider, dataset, and code. Combine them as provider/dataset/series_code.

What format is a series ID? Three parts separated by slashes, for example ECB/EXR/A.USD.EUR.SP00.A. The dataset part may include a version tag like WEO:2024-10.

What does a search query return? It finds matching datasets and pulls observations from the top series in each, controlled by maxSeriesPerSearch.

Why was my chosen series skipped during a search? A search query takes priority over the dropdown. Use customSeriesId to force a specific series.

How many observations can I get? Free plans return up to 10. Paid plans can return up to 1,000,000 per run.

What date does the date field use? It uses the ISO start day of each period, for example 1980-01-01 for the year 1980.

Why is the unit field sometimes missing? Not every dataset defines a unit dimension. The field appears only when the source provides one.

Can I pull very long daily series? Yes, though they can be large. The daily ECB USD/EUR rate has 6,742 observations, so use maxItems to focus the run.

Is the value always numeric? Yes. Non numeric and missing entries are skipped so every value is a usable number.

Which providers are covered? All 93 DBnomics providers, including ECB, IMF, Eurostat, World Bank, OECD, BIS, and national statistics offices.

🔌 Integrate with any app

Use the Apify API, webhooks, and scheduler to wire results into your own stack, or trigger downstream jobs whenever a run completes.

🔗 Recommended Actors

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• Commodity Prices Scraper by ParseForge

💡 Pro Tip: browse the complete ParseForge collection.

🆘 Need Help? Open our contact form

⚠️ Disclaimer: independent tool, not affiliated with DBnomics or any data provider. Only publicly available data is collected.