DBnomics Economic Series Scraper

DBnomics Economic Series Scraper

Scrape economic time series from DBnomics by series ID, dataset, or keyword search, up to a million series per run. Every observation comes with its period, value, and metadata. No API key or registration. Export to CSV, JSON, Excel, or XML.

DBnomics aggregates millions of economic series from providers like the ECB, IMF, World Bank, Eurostat, and OECD, but its API requires you to know provider and dataset codes. This Actor resolves series by ID or search term and returns every observation in one flat row. You can pull a single exchange rate, a whole dataset, or every series matching a keyword like inflation.

Who uses it	What they scrape DBnomics for
Economists	Pull the exact series they need for a model without writing API code
Financial analysts	Monitor exchange rates, inflation, or GDP across providers in one dataset
Data journalists	Gather comparable economic indicators for a story or visualization
Researchers	Build a panel of macroeconomic variables from multiple sources

What it does

This Actor collects economic time series from DBnomics by series ID, custom series ID, or keyword search, and returns each observation as a flat row with period, value, and series metadata.

• 🔎 Keyword search: type a term like inflation or unemployment and the Actor resolves matching datasets to their series and observations.
• 🆔 Series ID or custom ID: fetch any series with a provider/dataset/series_code, or paste a custom code from db.nomics.world.
• 📊 Flat observation rows: each period and value becomes one row, ready for pivot tables or time-series models.
• 🌐 Multi-provider coverage: ECB, IMF, World Bank, Eurostat, OECD, and hundreds more through one interface.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with DBnomics data

📈 Track inflation across countries.

An economist runs a search for inflation, sets maxSeriesPerSearch to 10, and gets monthly HICP and CPI series from Eurostat, IMF, and national sources to compare trends.

💱 Monitor exchange rates daily.

A financial analyst schedules the Actor with the ECB USD/EUR series ID and receives the latest monthly or annual average in a CSV for their dashboard.

🌍 Build a macro panel for research.

A PhD student pulls GDP, population, and unemployment series from World Bank and OECD by custom IDs and merges them into one dataset for a regression.

📰 Fact-check economic claims.

A data journalist searches for GDP growth and collects the IMF WEO series to verify a politician's statement with the official numbers.

Why choose this scraper

	What you get
No API key	DBnomics is open, and this Actor reads the public feeds directly
One flat schema	Every observation has the same columns regardless of provider
Search without codes	Find series by keyword and let the Actor resolve the IDs
Scale to a million series	Set maxItems up to 1,000,000 for bulk collection

How it compares

No other Store actor targets DBnomics the same way, so the honest comparison is with the alternatives teams actually weigh.

	DBnomics Economic Series Scraper	Build it in-house	By hand
Setup	Run it now, zero config	Days of engineering	None, but hours per pull
When DBnomics changes	Maintained for you	You fix it	You re-learn the page
Proxies, retries, anti-bot	Built in	Your problem	Browser only
Output	Fixed JSON schema, CSV/Excel export	Whatever you build	Copy-paste
Cost	Pay per result	Engineering time	Analyst hours

Configure the run

Drive the Actor from a known series ID, a custom series ID, or a keyword search, alone or together, and set limits on how many series and observations to collect. The Input tab lists every parameter.

A first run with the defaults:

{
  "maxItems": 10,
  "maxSeriesPerSearch": 5
}

A larger pull:

{
  "maxItems": 200,
  "maxSeriesPerSearch": 5
}

Pricing

Pay-per-result: $0.011 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$1.10
1,000 results	$11.00
10,000 results	$110.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the DBnomics Economic Series Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to DBnomics through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/dbnomics-economic-series-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that your series ID is in the correct provider/dataset/series_code format. If using searchQuery, try a broader term like 'GDP' instead of a specific phrase. Also ensure maxItems is at least 1.

The run returns an error about an invalid series ID.

Verify the series ID on db.nomics.world. Some datasets have version suffixes like IMF/WEO:2024-10. Copy the exact code from the series page.

I get too many observations and the run is slow.

Lower maxSeriesPerSearch or maxItems. If you only need one series, use seriesId or customSeriesId without searchQuery.

The data seems outdated.

DBnomics updates at different frequencies depending on the provider. Check the provider's release calendar and schedule the Actor to run after updates.

Search returns irrelevant series.

Use more specific keywords or combine terms. You can also find the exact series ID on db.nomics.world and use customSeriesId instead of search.

FAQ

Question	Answer
Do I need a DBnomics API key?	No. DBnomics is an open data platform, and this Actor reads the public feeds without authentication.
How do I find a series ID?	Go to db.nomics.world, browse or search for a series, and copy the provider/dataset/series_code from the URL or the series page. You can also use the searchQuery input to find series by keyword.
What is the difference between seriesId and customSeriesId?	seriesId is a dropdown of common series for quick selection. customSeriesId accepts any valid DBnomics series code and overrides seriesId when both are set.
Can I collect multiple series in one run?	Yes. Use searchQuery to pull many series from matched datasets, and set maxItems and maxSeriesPerSearch to control the total.
What providers are covered?	DBnomics aggregates over 100 providers, including ECB, IMF, World Bank, Eurostat, OECD, and many national statistical offices. Any series on db.nomics.world can be fetched.
What format is the output?	The Actor returns a flat dataset where each row is one observation with period, value, and series metadata. You can export to CSV, JSON, Excel, or XML from the Apify platform.
How many observations can I get?	The maxItems input controls the number of series, up to 1,000,000. Each series can have thousands of observations, so the total rows can be very large.
Does the Actor handle revisions or updates?	It fetches the latest data available on DBnomics at the time of the run. Schedule the Actor to run daily or weekly to keep your dataset current.
Can I search in languages other than English?	The searchQuery matches DBnomics metadata, which is primarily in English, but some providers include native-language titles. Try keywords in the provider's language if needed.
Is there a limit on search results?	maxSeriesPerSearch limits how many series are pulled from each matched dataset, and maxItems limits the total series per run. Set them according to your needs.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by CEPREMAP. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.