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GST_BAS_CLASS

GST_BAS_CLASS contains a static list of BAS (Business Activity Statement) classifications supported by the MMS.

GST_BAS_CLASS data is public to all participants.

Column Schema

BAS_CLASS

The BAS classification

SQL Type: VARCHAR2(30) Polars Type: str

This column is mandatory, and a primary key.

Examples:

  • ACQUISITIONS_WITH_NO_GST
  • CREDITABLE_ACQUISITIONS
  • GST_FREE_SUPPLIES
  • INPUT_TAXED_SUPPLIES
  • NOT_SUBJECT_TO_GST
  • TAXABLE_SUPPLIES

DESCRIPTION

Description of the BAS classification

SQL Type: VARCHAR2(100) Polars Type: str

This column is not mandatory.

Examples:

  • Acquisition with no GST in the price
  • Creditable Acquisitions
  • GST Free Supplies
  • Input Taxed Supplies
  • Not subject to GST
  • Taxable Supplies

LASTCHANGED

Last date and time the record changed

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2000/07/05 15:03:20

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast({"BAS_CLASS": str, "DESCRIPTION": str})
    # Deduplicate
    # We want one row per ['BAS_CLASS']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["BAS_CLASS"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_GST_BAS_CLASS_FILE01_202609010000.zip

BAS_CLASSDESCRIPTIONLASTCHANGED
TAXABLE_SUPPLIESTaxable Supplies2000/10/11 11:34:21
INPUT_TAXED_SUPPLIESInput Taxed Supplies2000/07/05 15:03:20
CREDITABLE_ACQUISITIONSCreditable Acquisitions2000/10/11 11:34:16
ACQUISITIONS_WITH_NO_GSTAcquisition with no GST in the price2000/07/05 15:03:20
NOT_SUBJECT_TO_GSTNot subject to GST2000/07/05 15:03:20
GST_FREE_SUPPLIESGST Free Supplies2000/07/05 15:03:20

CSETP.WORLDDVD_GST_BAS_CLASSAEMOPUBLIC2026/10/0915:37:00001791524220263MONTHLY_ARCHIVE001791524220263
IBILLING_CONFIGGST_BAS_CLASS1BAS_CLASSDESCRIPTIONLASTCHANGED
DBILLING_CONFIGGST_BAS_CLASS1TAXABLE_SUPPLIESTaxable Supplies2000/10/11 11:34:21
DBILLING_CONFIGGST_BAS_CLASS1INPUT_TAXED_SUPPLIESInput Taxed Supplies2000/07/05 15:03:20
DBILLING_CONFIGGST_BAS_CLASS1CREDITABLE_ACQUISITIONSCreditable Acquisitions2000/10/11 11:34:16
DBILLING_CONFIGGST_BAS_CLASS1ACQUISITIONS_WITH_NO_GSTAcquisition with no GST in the price2000/07/05 15:03:20
DBILLING_CONFIGGST_BAS_CLASS1NOT_SUBJECT_TO_GSTNot subject to GST2000/07/05 15:03:20
DBILLING_CONFIGGST_BAS_CLASS1GST_FREE_SUPPLIESGST Free Supplies2000/07/05 15:03:20
CEND OF REPORT9

BAS_CLASS,DESCRIPTION,LASTCHANGED
TAXABLE_SUPPLIES,"Taxable Supplies","2000/10/11 11:34:21"
INPUT_TAXED_SUPPLIES,"Input Taxed Supplies","2000/07/05 15:03:20"
CREDITABLE_ACQUISITIONS,"Creditable Acquisitions","2000/10/11 11:34:16"
ACQUISITIONS_WITH_NO_GST,"Acquisition with no GST in the price","2000/07/05 15:03:20"
NOT_SUBJECT_TO_GST,"Not subject to GST","2000/07/05 15:03:20"
GST_FREE_SUPPLIES,"GST Free Supplies","2000/07/05 15:03:20"

C,SETP.WORLD,DVD_GST_BAS_CLASS,AEMO,PUBLIC,2026/10/09,15:37:00,001791524220263,MONTHLY_ARCHIVE,001791524220263
I,BILLING_CONFIG,GST_BAS_CLASS,1,BAS_CLASS,DESCRIPTION,LASTCHANGED
D,BILLING_CONFIG,GST_BAS_CLASS,1,TAXABLE_SUPPLIES,"Taxable Supplies","2000/10/11 11:34:21"
D,BILLING_CONFIG,GST_BAS_CLASS,1,INPUT_TAXED_SUPPLIES,"Input Taxed Supplies","2000/07/05 15:03:20"
D,BILLING_CONFIG,GST_BAS_CLASS,1,CREDITABLE_ACQUISITIONS,"Creditable Acquisitions","2000/10/11 11:34:16"
D,BILLING_CONFIG,GST_BAS_CLASS,1,ACQUISITIONS_WITH_NO_GST,"Acquisition with no GST in the price","2000/07/05 15:03:20"
D,BILLING_CONFIG,GST_BAS_CLASS,1,NOT_SUBJECT_TO_GST,"Not subject to GST","2000/07/05 15:03:20"
D,BILLING_CONFIG,GST_BAS_CLASS,1,GST_FREE_SUPPLIES,"GST Free Supplies","2000/07/05 15:03:20"
C,"END OF REPORT",9

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 1 of 2015. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 7 KB when zipped, 69 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.

PDR Loader configuration is:

Report Name Report Subtype Version
BILLING_CONFIG GST_BAS_CLASS 1

This means that the CSV files start with metadata columns:

I,BILLING_CONFIG,GST_BAS_CLASS,1,...
D,BILLING_CONFIG,GST_BAS_CLASS,1,...

Indexes

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: BILLING_CONFIG