bolster.data_sources.nisra.quarterly_employment_survey

NISRA Quarterly Employment Survey (QES) for Northern Ireland.

Provides quarterly employee job estimates for Northern Ireland by broad industry sector, both seasonally adjusted and unadjusted. The QES is used by the ONS as the primary source for NI estimates within UK-wide quarterly workforce statistics.

Sectors covered: - Manufacturing - Construction - Services - Other Industries - All Industries (total)

Data Source:

Statistics page: https://www.nisra.gov.uk/statistics/work-pay-and-benefits/quarterly-employment-survey The module scrapes this page to find the latest quarterly supplementary tables, then downloads the Excel file.

Update Frequency: Quarterly (published ~2 weeks after reference quarter) Geographic Coverage: Northern Ireland Time Series: Q1 2005 to present (seasonally adjusted); Q1 2005 unadjusted

Example

>>> from bolster.data_sources.nisra import quarterly_employment_survey as qes
>>> df = qes.get_latest_qes()
>>> 'total_jobs' in df.columns
True
>>> growth = qes.get_qes_growth(df)
>>> 'total_yoy' in growth.columns
True

Attributes

logger

QES_STATS_URL

QES_BASE_URL

Functions

get_latest_qes_publication_url()

Scrape the QES statistics page to find the latest supplementary tables Excel.

parse_qes_file(file_path[, adjusted])

Parse QES supplementary tables Excel file into long-format DataFrame.

get_latest_qes([force_refresh, adjusted])

Get the latest NI Quarterly Employment Survey data.

validate_qes_data(df)

Validate QES DataFrame for basic integrity.

get_qes_by_year(df, year)

Filter QES data to a specific year.

get_qes_growth(df)

Calculate quarter-on-quarter and year-on-year growth for total employee jobs.

get_sector_shares(df)

Calculate each sector's share of total employee jobs.

Module Contents

bolster.data_sources.nisra.quarterly_employment_survey.logger[source]
bolster.data_sources.nisra.quarterly_employment_survey.QES_STATS_URL = 'https://www.nisra.gov.uk/statistics/work-pay-and-benefits/quarterly-employment-survey'[source]
bolster.data_sources.nisra.quarterly_employment_survey.QES_BASE_URL = 'https://www.nisra.gov.uk'[source]
bolster.data_sources.nisra.quarterly_employment_survey.get_latest_qes_publication_url()[source]

Scrape the QES statistics page to find the latest supplementary tables Excel.

Returns:

URL of the latest QES supplementary tables Excel file

Raises:

NISRADataNotFoundError – If publication cannot be found

Return type:

str

bolster.data_sources.nisra.quarterly_employment_survey.parse_qes_file(file_path, adjusted=True)[source]

Parse QES supplementary tables Excel file into long-format DataFrame.

Reads Table 5.2 (seasonally adjusted) or Table 5.3 (unadjusted), which contain quarterly employee job counts by broad industry sector.

Parameters:

  • file_path (str | pathlib.Path) – Path to downloaded QES supplementary tables Excel file

  • adjusted (bool) – If True, read Table 5.2 (seasonally adjusted). If False, read Table 5.3 (unadjusted).

Returns:

  • date: Timestamp (first day of quarter)

  • year: int

  • quarter: int (1-4)

  • quarter_label: str (e.g. “Q1 2025”)

  • manufacturing_jobs: int

  • construction_jobs: int

  • services_jobs: int

  • other_jobs: int

  • total_jobs: int

  • adjusted: bool (True if seasonally adjusted)

Return type:

DataFrame with columns

Raises:

NISRAValidationError – If file structure is unexpected

bolster.data_sources.nisra.quarterly_employment_survey.get_latest_qes(force_refresh=False, adjusted=True)[source]

Get the latest NI Quarterly Employment Survey data.

Parameters:

  • force_refresh (bool) – If True, bypass cache and download fresh data

  • adjusted (bool) – If True (default), return seasonally adjusted series (Table 5.2). If False, return unadjusted series (Table 5.3).

Returns:

date, year, quarter, quarter_label, manufacturing_jobs, construction_jobs, services_jobs, other_jobs, total_jobs, adjusted

Return type:

DataFrame with columns

Raises:

  • NISRADataNotFoundError – If latest publication cannot be found

  • NISRAValidationError – If file structure is unexpected

Example

>>> df = get_latest_qes()
>>> 'total_jobs' in df.columns
True
bolster.data_sources.nisra.quarterly_employment_survey.validate_qes_data(df)[source]

Validate QES DataFrame for basic integrity.

Parameters:

df (pandas.DataFrame) – DataFrame from get_latest_qes()

Returns:

True if validation passes

Raises:

NISRAValidationError – If validation fails

Return type:

bool

bolster.data_sources.nisra.quarterly_employment_survey.get_qes_by_year(df, year)[source]

Filter QES data to a specific year.

Parameters:
Returns:

Filtered DataFrame (up to 4 quarters)

Return type:

pandas.DataFrame

Example

>>> df = get_latest_qes()
>>> df_2024 = get_qes_by_year(df, 2024)
>>> len(df_2024) <= 4
True
bolster.data_sources.nisra.quarterly_employment_survey.get_qes_growth(df)[source]

Calculate quarter-on-quarter and year-on-year growth for total employee jobs.

Parameters:

df (pandas.DataFrame) – DataFrame from get_latest_qes()

Returns:

  • total_qoq: Total jobs quarter-on-quarter change (absolute)

  • total_yoy: Total jobs year-on-year % change

  • services_yoy: Services jobs year-on-year % change

  • manufacturing_yoy: Manufacturing jobs year-on-year % change

Return type:

DataFrame with additional columns

Example

>>> df = get_latest_qes()
>>> growth = get_qes_growth(df)
>>> 'total_yoy' in growth.columns
True
bolster.data_sources.nisra.quarterly_employment_survey.get_sector_shares(df)[source]

Calculate each sector’s share of total employee jobs.

Parameters:

df (pandas.DataFrame) – DataFrame from get_latest_qes()

Returns:

  • manufacturing_share: Manufacturing % of total

  • construction_share: Construction % of total

  • services_share: Services % of total

  • other_share: Other industries % of total

Return type:

DataFrame with additional columns

Example

>>> df = get_latest_qes()
>>> shares = get_sector_shares(df)
>>> 'services_share' in shares.columns
True