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Apr 24 Modding Day
data:specsheet_ashehours

ASHE Hours (Average Weekly Hours)

  • Submitted by: Luke Bosworth, l.p.bosworth@warwick.ac.uk
  • Submitted on: 25/04/2016
  • Revision; 3

Data File(s)

This data load contains 1 file, which is as follows:

File Content Size (bytes)
Hours-20160422.asc Data on average weekly hours from ASHE 26,486,689

Source Dataset

These data come from the ASHE (Annual Survey for Hours and Earnings).

Fields and Columns

Hours-20160422.asc

  • year - 2014 only
  • gender - 2 (male & female)
  • status - 1 (full-time employees only at present)
  • industry - 75 standard industries (see classification and aggregation below)
  • occupation - 369 4-digit SOC 2010 categories
  • geography - 12 UK countries and English regions

Two columns have the information needed to calculate the mean hours:

  • Employment - the total employment number that should be used for weighting
  • TotalHours - Total hours worked for the category concerned (Hours*Employment)

Aggregating involves summing these two columns appropriately and dividing the result of the first sum for the aggregate concerned (Total hours) by the second sum (Total employment).

Output

The 'Hours-20160422.asc' file contains predictions for Average Weekly Hours for the 369 SOC 2010 Unit Groups based on ASHE data.

Queries and calculations

Hours Data Specification

The first column is the year.
The second to sixth columns, from 'gender' to 'geography', are the characteristics of people covered by the dataset.
The penultimate column is 'Employment', the last column 'TotalHours' represents the total weekly number of hours for the category with the characteristics in columns two to six.

  1. year
  2. gender
  3. status
  4. industry
  5. occupation
  6. geography
  7. Employment
  8. TotalHours

Description of the data

The estimates are based on published ASHE data but the detailed estimates are predictions based on a simple set of assumptions that differentiates across each of the main dimensions/characteristics. The results are constrained to match the published totals using an iterative RAS process.

There is no hours data available for the 'Armed Forces' occupations. There is, therefore, no employment data for 'Armed Forces' in 'Hours-20160422.asc'.

Classifications and aggregations

Rules for supressing data or raising warning flags

The rules of thumb used are:

  1. If the numbers employed in a particular category / cell (defined by the 12 regions, gender, status, occupation, qualification and industry (75 categories)) are below 1,000 then a query should return “no reliable data available” and offer to go up a level of aggregation across one or more of the main dimensions (e.g. UK rather than region, some aggregation of industries rather than the 75 level, or SOC 2 digit rather than 4 digit). This information is held in the variable 'weight' in the Working Futures employment file ('WFDataOcc4Dig.csv') and in the Pay file ('Pay-20160422.asc') 'Employment' (same in both files).
  2. If the numbers employed in a particular category / cell (defined as in 1.) are between 1,000 and 10,000 then a query should return the number but with a flag to say that this estimate is based on a relatively small sample size and if the user requires more robust estimates they should go up a level of aggregation across one or more of the main dimensions (as in 1).

This is the same as is done for any queries about Employment (including Replacement Demand calculations) and also for Pay.

In the case of Pay and Hours the API needs to use the employment weights included in the relevant 'Pay.asc' or 'Hours.asc' file to do the checks, as in 1.and 2. above, but then to report the corresponding pay or hours values as appropriate.

Rounding of estimates

In order to avoid false impressions of precision the API should round up the estimates before delivering the answer to any query. In the case of ASHE weekly hours any number should be rounded to the nearest hour.

Closing Notes

data/specsheet_ashehours.txt · Last modified: 2016-04-25 12:35 by Luke Bosworth