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data:specsheet_ashehours

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data:specsheet_ashehours [2016-04-25 12:35]
Luke Bosworth
data:specsheet_ashehours [2019-09-03 15:21] (current)
Luke Bosworth [Rules for supressing data or raising warning flags]
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 ^ File ^ Content ^ Size (bytes) ^ ^ File ^ Content ^ Size (bytes) ^
-| Hours-20160422.asc | Data on average weekly hours from ASHE | 26,486,689 |+| Hours.csv | Data on average weekly hours from ASHE | 25,799,453 |
 ===== Source Dataset ===== ===== Source Dataset =====
  
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 ===== Fields and Columns ===== ===== Fields and Columns =====
  
-==== Hours-20160422.asc ==== +==== Hours.csv ==== 
-  * year - 2014 only+  * year - 2018 only
   * gender - 2 (male & female)   * gender - 2 (male & female)
   * status - 1 (full-time employees only at present)   * status - 1 (full-time employees only at present)
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 ====== Output ====== ====== Output ======
  
-The 'Hours-20160422.asc' file contains predictions for Average Weekly Hours for the 369 SOC 2010 Unit Groups based on ASHE data.+The '​Hours.csv' file contains predictions for Average Weekly Hours for the 369 SOC 2010 Unit Groups based on ASHE data.
 ===== Queries and calculations ===== ===== Queries and calculations =====
  
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 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. 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'.+There is no hours data available for the 'Armed Forces'​ occupations. There is, therefore, no employment data for 'Armed Forces'​ in '​Hours.csv'.
 ==== Classifications and aggregations ==== ==== Classifications and aggregations ====
 [[data:​specsheet_working_futures#​classifications_and_aggregations|As for employment]] [[data:​specsheet_working_futures#​classifications_and_aggregations|As for employment]]
  
-====Rules for supressing ​data or raising warning flags====+====Rules for suppressing ​data or raising warning flags====
 The rules of thumb used are: The rules of thumb used are:
  
-  - 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).+  - 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 ('ashe_pay_main.csv') '​Employment'​ (same in both files).
   - 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).   - 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. 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.+In the case of Pay and Hours the API needs to use the employment weights included in the relevant 'ashe_pay_main.csv' or '​Hours.csv' 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=== ===Rounding of estimates===
data/specsheet_ashehours.1461587753.txt.gz · Last modified: 2016-04-25 12:35 by Luke Bosworth