# OECD Data Scraper - Macro & Economic Time Series (`scrapers_lat/oecd-data-scraper`) Actor Extract OECD macroeconomic, social and financial time-series: dataflow, indicator/subject, country, frequency, period, value and unit. One flat row per observation. Export to JSON, CSV or Excel. - **URL**: https://apify.com/scrapers\_lat/oecd-data-scraper.md - **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community) - **Categories:** Business, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.80 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## OECD Data Scraper - Macro & Economic Time Series > Pull OECD macroeconomic, social and financial statistics as clean, flat rows. Pick a dataset, filter by country, indicator and date range, and get one tidy row per observation ready for JSON, CSV or Excel. **๐Ÿ“ฅ [Input](https://apify.com/scrapers_lat/oecd-data-scraper/input-schema) ยท ๐Ÿ“ค [Output](https://apify.com/scrapers_lat/oecd-data-scraper/output-schema) ยท ๐Ÿ’ฐ [Pricing](https://apify.com/scrapers_lat/oecd-data-scraper/pricing) ยท โ–ถ๏ธ [Examples](https://apify.com/scrapers_lat/oecd-data-scraper/examples)** ### What you get Every run returns a flat table of observations. Each row includes: - **dataflow / dataflowName** - the OECD dataset the figure comes from - **subjectCode / subjectName** - the indicator or measure (CPI, GDP, unemployment rate, and more) - **countryCode / countryName** - the ISO3 area and its full name - **frequency** - Annual, Quarterly or Monthly - **period** - the exact time period (2024, 2024-Q1, 2024-01) - **value** - the numeric observation, already parsed - **unit** - unit of measure - **breakdown** - any remaining series dimensions (adjustment, transformation, expenditure, etc.) - **obsStatus** - observation status (normal, estimated, provisional) Empty observations are dropped automatically, so you never pay for blank cells. ### Who is it for - Economists, analysts and researchers building macro dashboards - Fintech, risk and investment teams tracking inflation, GDP, rates and trade - Data journalists and academics who need clean cross-country panels - Anyone who wants OECD figures in a spreadsheet instead of a portal ### How to use it 1. Choose a **Dataset** (CPI, Economic Outlook, GDP, unemployment, interest rates, trade, health, population and more). 2. Optionally list the **countries** (ISO3 codes like USA, DEU, JPN) and **indicator codes** you care about. 3. Set the **start** and **end period** (year, quarter or month). 4. Set **Max Observations** and run. Export the dataset to JSON, CSV or Excel. ### FAQ **Do I need an account or key?** No. The actor works out of the box. **Can I get more than one country at once?** Yes. Leave the country filter empty for every available area, or list several ISO3 codes. **Why are some values missing?** Not every country reports every indicator for every period. Rows with no value are skipped so your output stays clean. **Which periods can I request?** Any year, quarter or month range supported by the chosen dataset. ### Related actors - [World Bank Indicators Scraper](https://apify.com/scrapers_lat/world-bank-indicators-scraper) - [IMF Data Scraper](https://apify.com/scrapers_lat/imf-data-scraper) ### From scrapers.lat Built by [scrapers.lat](https://scrapers.lat) - reliable data extractors with flat, spreadsheet-ready output and pay-per-result pricing. You are only charged for real observations returned; empty runs cost nothing. --- *This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the OECD. It collects publicly available information for research and analysis.* # Actor input Schema ## `dataflow` (type: `string`): OECD dataset to extract. Each option returns a full macro/social/economic time series. ## `countries` (type: `array`): Optional ISO3 country/area codes to keep (e.g. USA, DEU, JPN, MEX, BRA). Leave empty for all available areas. ## `subjects` (type: `array`): Optional indicator/subject codes to keep (varies by dataset, e.g. CPI). Leave empty to keep every indicator in the dataset. ## `startPeriod` (type: `string`): Earliest period to include. Year (2015), quarter (2015-Q1) or month (2015-01). ## `endPeriod` (type: `string`): Latest period to include. Year (2024), quarter (2024-Q4) or month (2024-12). ## `maxObservations` (type: `integer`): Maximum number of observation rows to return. ## Actor input object example ```json { "dataflow": "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0", "countries": [ "USA", "DEU", "JPN", "MEX" ], "startPeriod": "2020", "endPeriod": "2024", "maxObservations": 500 } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "dataflow": "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0", "countries": [ "USA", "DEU", "JPN", "MEX" ], "startPeriod": "2020", "endPeriod": "2024", "maxObservations": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("scrapers_lat/oecd-data-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "dataflow": "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0", "countries": [ "USA", "DEU", "JPN", "MEX", ], "startPeriod": "2020", "endPeriod": "2024", "maxObservations": 500, } # Run the Actor and wait for it to finish run = client.actor("scrapers_lat/oecd-data-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "dataflow": "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0", "countries": [ "USA", "DEU", "JPN", "MEX" ], "startPeriod": "2020", "endPeriod": "2024", "maxObservations": 500 }' | apify call scrapers_lat/oecd-data-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapers_lat/oecd-data-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OECD Data Scraper - Macro & Economic Time Series", "description": "Extract OECD macroeconomic, social and financial time-series: dataflow, indicator/subject, country, frequency, period, value and unit. One flat row per observation. Export to JSON, CSV or Excel.", "version": "0.1", "x-build-id": "gwbcXCNakVub3p1vA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapers_lat~oecd-data-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapers_lat-oecd-data-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapers_lat~oecd-data-scraper/runs": { "post": { "operationId": "runs-sync-scrapers_lat-oecd-data-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapers_lat~oecd-data-scraper/run-sync": { "post": { "operationId": "run-sync-scrapers_lat-oecd-data-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "dataflow": { "title": "Dataset (Dataflow)", "enum": [ "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0", "OECD.SDD.STES,DSD_KEI@DF_KEI,4.0", "OECD.ECO.MAD,DSD_EO@DF_EO,1.2", "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA,1.1", "OECD.SDD.NAD,DSD_NAMAIN10@DF_TABLE1_EXPENDITURE,1.0", "OECD.SDD.TPS,DSD_LFS@DF_IALFS_UNE_M,1.0", "OECD.SDD.STES,DSD_STES@DF_FINMARK,4.0", "OECD.SDD.TPS,DSD_BOP@DF_BOP,1.0", "OECD.ELS.HD,DSD_HEALTH_STAT@DF_HEALTH_STATUS,1.0", "OECD.ELS.SAE,DSD_POPULATION@DF_POP_HIST,1.0" ], "type": "string", "description": "OECD dataset to extract. Each option returns a full macro/social/economic time series.", "default": "OECD.SDD.TPS,DSD_PRICES@DF_PRICES_ALL,1.0" }, "countries": { "title": "Countries / Areas", "type": "array", "description": "Optional ISO3 country/area codes to keep (e.g. USA, DEU, JPN, MEX, BRA). Leave empty for all available areas.", "items": { "type": "string" } }, "subjects": { "title": "Indicator / Subject Codes", "type": "array", "description": "Optional indicator/subject codes to keep (varies by dataset, e.g. CPI). Leave empty to keep every indicator in the dataset.", "items": { "type": "string" } }, "startPeriod": { "title": "Start Period", "type": "string", "description": "Earliest period to include. Year (2015), quarter (2015-Q1) or month (2015-01)." }, "endPeriod": { "title": "End Period", "type": "string", "description": "Latest period to include. Year (2024), quarter (2024-Q4) or month (2024-12)." }, "maxObservations": { "title": "Max Observations To Collect", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Maximum number of observation rows to return." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```