Sweden GDP by Industry & Quarter - SCB

Sweden's GDP (production approach) by NACE industry and quarter from Statistics Sweden (SCB), as clean per-observation records - one industry x GDP measure x quarter.

Who buys this and for what job

• Economists and markets teams tracking Sweden GDP growth quarter by quarter.
• Industry analysts and investors comparing Sweden GDP by industry (NACE) at current prices, constant prices or volume change.
• Researchers who need the official SCB series as clean records.

Buyers usually search for this as "sweden gdp" or "sweden gdp by industry".

Choose the series

Question: How fast has Sweden's GDP grown, year on year, over the latest quarters?

{"industries": ["0010"], "measures": ["volume change"], "newestFirst": true, "maxRecords": 10}

Run on 30 Sep 2026 the selection held 178 published values; the newest 10 were returned (10 × $0.05 = $0.50 on Apify's Free plan):

Period	SNI2007	ContentsCode	Value
2026K2	GDP at market prices	Change in volume, corresponding period previous year, percent	3.8
2026K1	GDP at market prices	Change in volume, corresponding period previous year, percent	2
2025K4	GDP at market prices	Change in volume, corresponding period previous year, percent	2.2
2025K3	GDP at market prices	Change in volume, corresponding period previous year, percent	2.2
2025K2	GDP at market prices	Change in volume, corresponding period previous year, percent	1.8

Search options (all optional; a record must match every option you set, and several values in one option match any of them):

• Industries / aggregates (industries) — NACE codes or words, e.g. 0010 (GDP at market prices), Manufacturing, Construction.
• Measures (measures) — current prices, constant prices, volume change (percent on the same quarter a year earlier).
• From / to quarter (fromPeriod, toPeriod) — e.g. 2024K1 or a year 2024.
• Newest first (newestFirst) — most recent period first; otherwise the table's own order (oldest first).

Your choices are sent to Statistics Sweden, so only the chosen series are read and billed, before the record limit. A value matches a code exactly, one of the words listed above, or words from the series name; a value that matches nothing stops the run before any data is read, with nothing charged. With no option set the actor walks the whole table exactly as before.

What one record represents

The source is the Statistics Sweden PxWeb Statistical Database API at api.scb.se (a keyless POST API). Each record is one industry x GDP measure x quarter observation - the NACE/SNI industry code and name, the GDP measure, the quarter and the value.

Sample output

Real rows from a live run of this actor (first 5 rows, selected columns).

One full record from the same run, exactly as delivered:

{
  "record_id": "SNI2007=0010:ContentsCode=NR0103DA:Tid=1980K1",
  "SNI2007": "0010",
  "SNI2007_label": "GDP at market prices",
  "ContentsCode": "NR0103DA",
  "ContentsCode_label": "Current prices, SEK million",
  "Tid": "1980K1",
  "Tid_label": "1980K1",
  "value": 141681,
  "source": "Statistics Sweden (SCB)",
  "source_dataset": "SCB GDP production approach by industry (NACE Rev. 2) and quarter (NR0103A)",
  "licence": "Statistics Sweden (SCB) Statistical Database API. Published under Creative Commons Zero (CC0): free use, dissemination and provision without a source requirement (citing Statistics Sweden is recommended). No personal data - these are aggregate statistical cells.",
  "attribution": "Source: Statistics Sweden (SCB), CC0",
  "caveat": "One record per NACE industry x GDP measure x quarter from Statistics Sweden's GDP production approach table. 60 industries x 3 measures x 186 quarters = 33,480 enumerated cells, of which 32,760 are non-null. The whole table is 33,480 cells - under the PxWeb 100,000-value cap - so the partition walker degrades to a single query; the count is non-null cells. Aggregate statistics, no personal data.",
  "observed_at": "2026-09-25T17:25:20Z"
}

Coverage and the single query

Default changed on 30 Sep 2026: if you leave maxRecords out, a run now returns up to 10 records (it was 500). Set maxRecords yourself to get more — the maximum is unchanged.

The table enumerates 60 industries x 3 measures x 186 quarters = 33,480 cells, of which 32,760 are non-null. The whole table is 33,480 cells - comfortably under the PxWeb 100,000-value cap - so the fleet's partition walker degrades to a single POST query: the complete table comes back in one response, with no partitioning, sampling or extrapolation. The count quoted is non-null cells; the enumerated total is stated alongside. You raise Maximum records to pull the whole series or lower it to sample.

Licence

Statistics Sweden (SCB) Statistical Database API. Creative Commons Zero (CC0): free use, dissemination and provision with no source requirement (citing Statistics Sweden is recommended). Aggregate statistical cells - no personal data.

Sibling Actors

No dataset-row overlap.

Fields, scheduling and integration

Every field in the record is either a source-native identifier, a source-native attribute, or one of the six provenance fields (source, source_dataset, licence, attribution, caveat, observed_at) the fleet attaches to every record. Nothing is derived or inferred beyond the small, documented transforms noted above, and nothing is dropped silently - the handling section spells out exactly what is excluded and why. The grain is one record per the natural unit of the source, which keeps each row independently meaningful, keeps the key stable across runs so re-running is a cheap upsert rather than a re-import, and lets you aggregate up to whatever unit you need without unpicking a pre-joined table.

Because the source republishes on its own cadence, a scheduled run keeps a downstream table current: new and changed records upsert over the old ones on the stable key, and the observed_at stamp tells you when each was last seen live. Set Maximum records low to sample the shape of the data cheaply, then raise it once the cell fits your use; the Actor streams or partitions its source, so memory stays flat regardless of how many records you request, and you are billed only for what is delivered. The output is a flat table of typed records, so it drops straight into whatever you already use: load the run's dataset over the API or an export, key on the record id, and upsert. Because identifiers are preserved exactly as the source publishes them, joins across the fleet's cells - and onto your own systems - work without a mapping layer. There is no subscription and no minimum: the per-record price and the record cap together mean the spend on any run is known in advance and matched exactly to the data you receive.

Reconciling counts honestly

Where the live count differs from any previously published figure, the live measure is the honest one and is what this listing quotes; sources re-issue and re-version their data over time. The run receipt always states what was actually delivered and charged and confirms the two agree, so every run is auditable against itself regardless of what any external index expected.

Scaling, scheduling and support

A common pattern is a light scheduled run that pulls the newest slice into a staging table, then a merge on the stable key into the table your product reads, so you never re-pay for rows you already hold and your history grows cleanly over time. Because the record shape assumes no particular warehouse, language or tool, the integration work is a load and a merge, not a cleaning project: the same code path handles a 40-row sample and a full pull, and the only thing that changes between them is the record cap. If you only need a slice, the cap and any partition or filter inputs bound the run precisely, so a targeted pull costs cents rather than the price of the whole set, and a broad pull is simply a higher cap left to run. Nothing about the delivery is subscription-gated: each run stands alone, priced at exactly the records it returns, so you can dial spend up or down run by run as your needs change, and a scheduled cadence keeps a downstream table current without any standing commitment. When the source publishes a correction or a new period, the next run picks it up and upserts it over the stale row on the same key, so the table you maintain stays both complete and current with no manual reconciliation.

Provenance and compliance

Every run reads the door host's robots.txt at runtime and records the result (URL, status, byte length and, where a policy is served, its SHA-256) in the run's RUN_RECEIPT. Where the host serves no applicable policy - a 404, a 403, or a homepage redirect - the gate records that as a flag and proceeds on the licence, which grants re-use; a flag is never treated as permission in itself. The endpoint is keyless and the Actor reads only the public data door - never a mirror, and it never bypasses a block.

Data quality and freshness

Numbers arrive as real numbers, booleans as real booleans, and every other value as a string or null, so the dataset loads without a cleaning pass. Each record is keyed on a stable composite of the source's own identifiers, so it is safe to diff, deduplicate or upsert. Every run re-reads the live door, so the data is as fresh as the source publishes, and each record's observed_at stamp dates the snapshot. The receipt records how many rows were delivered and charged and confirms charge_equals_delivered.

Billing, delivery and joins

Pricing is per record: you are billed only for records the Actor actually delivers, and the charge is raised after each record is pushed (push-then-charge), so a failed or empty run costs nothing. The Maximum records cap bounds every run, so spend is known before you start - sample cheaply, then raise it. Every record is a flat, typed object keyed on a stable id, so it loads without a cleaning pass, diffs cleanly between runs, and upserts into a table you keep over time; re-running keeps that table current without re-paying for rows you already hold, and each receipt reconciles delivered against charged. Because the source's own identifiers are preserved verbatim, the dataset joins onto other sources keyed on the same identifier.

Who buys this, and how they use it

This cell is bought by teams that need the source's published set as a typed, keyed table they can hold and refresh rather than a page they scrape: market- and macro-intelligence teams sizing and tracking a market, data engineers wiring a clean upstream feed into a warehouse, and compliance and research teams building on a stable identifier. The grain and the key are chosen so the output is a building block, not a one-off export - you run it on a schedule, keep the delta, and join it to your other sources on the identifiers it preserves verbatim. The spend on any run is the per-record price times the records delivered, matched exactly to what you receive.