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GST_RATE

GST_RATE maintains the GST rates on a BAS (Business Activity Statement) class basis.

GST_RATE 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

EFFECTIVEDATE

The effective date of the data set

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

This column is mandatory, and a primary key.

Example: 2000/07/01 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

VERSIONNO

The version number of the data set

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

Example: 1

GST_RATE

The GST rate that applies to this BAS classification

SQL Type: NUMBER(8,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0, 10

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: 2000/05/05 14:46:49

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"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

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",
    )
    .rename(
        {
            # These columns have names that conflict with metadata columns
            "GST_RATE_duplicated_0": "GST_RATE"
        }
    )
    .with_columns(
        pl.col("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "BAS_CLASS": str,
            "EFFECTIVEDATE": pl.Date(),
            "VERSIONNO": int,  # or pl.UInt16
            "GST_RATE": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BAS_CLASS', 'EFFECTIVEDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["BAS_CLASS", "EFFECTIVEDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_GST_RATE_FILE01_202609010000.zip

EFFECTIVEDATEVERSIONNOBAS_CLASSGST_RATELASTCHANGED
2000/07/01 00:00:001ACQUISITIONS_WITH_NO_GST02000/05/05 14:46:49
2000/07/01 00:00:001CREDITABLE_ACQUISITIONS102000/05/05 14:46:49
2000/07/01 00:00:001GST_FREE_SUPPLIES02000/05/05 14:46:49
2000/07/01 00:00:001INPUT_TAXED_SUPPLIES02000/05/05 14:46:49
2000/07/01 00:00:001NOT_SUBJECT_TO_GST02000/05/05 14:46:49
2000/07/01 00:00:001TAXABLE_SUPPLIES102000/05/05 14:46:49

CSETP.WORLDDVD_GST_RATEAEMOPUBLIC2026/10/0915:37:00001791524220695MONTHLY_ARCHIVE001791524220695
IBILLING_CONFIGGST_RATE1EFFECTIVEDATEVERSIONNOBAS_CLASSGST_RATELASTCHANGED
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001ACQUISITIONS_WITH_NO_GST02000/05/05 14:46:49
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001CREDITABLE_ACQUISITIONS102000/05/05 14:46:49
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001GST_FREE_SUPPLIES02000/05/05 14:46:49
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001INPUT_TAXED_SUPPLIES02000/05/05 14:46:49
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001NOT_SUBJECT_TO_GST02000/05/05 14:46:49
DBILLING_CONFIGGST_RATE12000/07/01 00:00:001TAXABLE_SUPPLIES102000/05/05 14:46:49
CEND OF REPORT9

EFFECTIVEDATE,VERSIONNO,BAS_CLASS,GST_RATE,LASTCHANGED
"2000/07/01 00:00:00",1,ACQUISITIONS_WITH_NO_GST,0,"2000/05/05 14:46:49"
"2000/07/01 00:00:00",1,CREDITABLE_ACQUISITIONS,10,"2000/05/05 14:46:49"
"2000/07/01 00:00:00",1,GST_FREE_SUPPLIES,0,"2000/05/05 14:46:49"
"2000/07/01 00:00:00",1,INPUT_TAXED_SUPPLIES,0,"2000/05/05 14:46:49"
"2000/07/01 00:00:00",1,NOT_SUBJECT_TO_GST,0,"2000/05/05 14:46:49"
"2000/07/01 00:00:00",1,TAXABLE_SUPPLIES,10,"2000/05/05 14:46:49"

C,SETP.WORLD,DVD_GST_RATE,AEMO,PUBLIC,2026/10/09,15:37:00,001791524220695,MONTHLY_ARCHIVE,001791524220695
I,BILLING_CONFIG,GST_RATE,1,EFFECTIVEDATE,VERSIONNO,BAS_CLASS,GST_RATE,LASTCHANGED
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,ACQUISITIONS_WITH_NO_GST,0,"2000/05/05 14:46:49"
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,CREDITABLE_ACQUISITIONS,10,"2000/05/05 14:46:49"
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,GST_FREE_SUPPLIES,0,"2000/05/05 14:46:49"
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,INPUT_TAXED_SUPPLIES,0,"2000/05/05 14:46:49"
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,NOT_SUBJECT_TO_GST,0,"2000/05/05 14:46:49"
D,BILLING_CONFIG,GST_RATE,1,"2000/07/01 00:00:00",1,TAXABLE_SUPPLIES,10,"2000/05/05 14:46:49"
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.)

Warning

The REPORT_SUB_TYPE metadata column has the same column name as an actual columns GST_RATE. So the raw CSVs have duplicate column names. You will need to be careful when reading this CSV.

Data Size

Each year of data is approximately 6 KB when zipped, 57 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_RATE 1

This means that the CSV files start with metadata columns:

I,BILLING_CONFIG,GST_RATE,1,...
D,BILLING_CONFIG,GST_RATE,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