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UK Productivity User Forum

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At our annual productivity forum we will be discussing our key developments and core priorities for the future. The event will include presentations from the Productivity team at the ONS, as well as key users of the labour productivity statistics.

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UK Productivity User Forum

  1. 1. UK Productivity User Forum Richard Heys Deputy Director Productivity, Innovation, and Economic Research
  2. 2. Agenda Time Article Speaker(s) Organisation 9.30 – 10.00 Coffee and Registration 10.00 – 10.15 Welcome Richard Heys Office for National Statistics 10.15 – 10.30 Expanding our measurement of labour productivity and progress on the Productivity Development Plan Katherine Kent Office for National Statistics 10.30 – 12.00 Labour Productivity Developments (including break) Marianthi Dunn Office for National Statistics Labour Productivity Flash by Industry Todd Bailey Office for National Statistics Regional Labour Productivity Richard Prothero Joshua Abramsky Donavan Ward Office for National Statistics 12.00 – 12.25 International Productivity Gaps: Are labour inputs measures comparable? Ashley Ward Organisation of Economic Cooperation and Development 12.25 – 12.45 Improving estimates of labour productivity and international comparisons Marianthi Dunn Office for National Statistics 12.45 – 13.45 Lunch 13.45 – 14.14 Winners and losers in the knowledge economy: Evidence from linked employer – employee data Rebecca Riley National Institute of Economic and Social Research Economic Statistic Centre of Excellence 14.15 – 14.45 Transforming Labour Market Statistics, David Freeman, ONS David Freeman Office for National Statistics 14.45 – 15.00 Break 15.00 – 15.30 Developments in Multi-factor productivity Rikka Korhonen Office for National Statistics 15.30 – 15.50 Public service productivity Leah Harris Office for National Statistics 15.50 – 16.00 Close Katherine Kent Office for National Statistics
  3. 3. Upcoming Release Calendar Area / Date April 2019 May 2019 June 2019 July 2019 August 2019 September 2019 October 2019 November 2019 December 2019 January 2019 February 2019 March 2019 Labour Productivity †Е ‡ †Е ‡ †Е ‡ †Е ‡Е Productivity Analysis E ? ? E Regional Labour Productivity Theme Day Е Public Service Productivity † † † † Multifactor Productivity † † † † Intangibles and Infrastructure † † † † † = Quarterly UK Productivity Release, National Statistic ‡ = UK Productivity Flash Estimate, Experimental Statistic Е = Experimental Statistic ? = To be decided National Statistics and Experimental Statistics will sometimes be published together in tandem
  4. 4. ONS productivity forum 2019: recent successes and future developments Katherine Kent
  5. 5. Overview • Past and Present • Productivity puzzle • What we produce • Recent Successes • Quarterly multi-factor productivity • Microdata analysis • Future developments • Productivity development plan This Photo by Unknown Author is licensed under CC BY-SA-NC
  6. 6. Why is productivity important? The ultimate driver of higher incomes Key determinant of living standards Efficient allocation of resources Correlated with dynamic & competitive markets “Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.” - Paul Krugman, OECD, 2006
  7. 7. Labour Productivity and the Productivity Puzzle • Current growth is much lower than the long period of average productivity growth prior to the economic downturn, and represents a continuation of the UK's “productivity puzzle”. • This sustained stagnation contrasts with patterns following previous UK economic downturns, when productivity initially fell, but subsequently recovered to the previous trend rate of growth.
  8. 8. Headlines
  9. 9. Regular Publications Title Frequency/Timeline ss National statistics status Additional info Productivity economic commentary Quarterly; t+95 days N/A Summary of all outputs released on the productivity theme day Labour Productivity Quarterly; t+95 days National Statistic Also covers region, industry and unit labour costs Flash estimate of labour productivity Quarterly; t+45 days Experimental Whole economy Quarterly Public Service Productivity Quarterly; t+95 days Experimental Public service Productivity: total Annual t+24months National Statistic Multi-Factor Productivity Quarterly; t+95 days Experimental Includes QALI and VICS Sub-regional productivity Annual +13 months Experimental
  10. 10. New productivity statistics and analysis 10
  11. 11. Quarterly Multi-factor Productivity • Since October 2018 we have published experimental estimates of quarterly multi- factor productivity (MFP) for the UK market sector on the same timetable as our regular labour productivity estimates • a new simple guide to MFP is also available • Multi factor productivity is still below level in 2008
  12. 12. New productivity statistics and analysis continued • Incorporating two new quality adjustment into our annual public service productivity estimates covering the criminal justice system and adult social care • Published ground-breaking research on the relationship between trade and productivity • Developing a management practices survey • Publication of a world-first ‘calculator’ for firms to understand their own productivity relative to their peers
  13. 13. Future Developments 13
  14. 14. Productivity development plan • In July 2018 we published our productivity development plan • The development plan builds on recent improvements to productivity statistics. In line with external recommendations we intend to keep up the pace of developments over the next two years. • We have set out a plan for introducing new outputs, further improving our productivity statistics and consolidating our improvements to date. • Labour Productivity System Improvements • Labour productivity excluding imputed rent • Measuring Intangibles • Industry granularity for MFP • National Statistics designation for experimental statistics • Public service productivity quality adjustments
  15. 15. Questions Katherine.Kent@ons.gov.uk @KatKent_ONS #ukproductivity Productivity@ons.gov.uk
  16. 16. Marianthi Dunn Head of Labour Productivity Branch Developments in labour productivity
  17. 17. Content • Summary of labour productivity statistics we produce • Recent improvements to Labour Productivity stats • Improving the presentation of Labour Productivity release • Core developments (for 2019 and beyond) • Consulting users
  18. 18. Summary of Labour Prod stats we produce Release over 3200 data series every quarter • 500 National Statistics • 2700 Experimental statistics Cover approx. 80 industry groups: • Standard Industry Classification 2007 (SIC 2007) • Statistical classification of economic activities in the European Community (NACE Rev.2) UK is 1 of small number of countries to produce regional labour prod • 12 UK regions NUTS1 • Region by 17 industry groups
  19. 19. Summary of Labour Prod stats we produce What we publish? • Labour productivity flash estimate every t+45 days • Labour productivity quarterly release around t+95 days • Analytical articles
  20. 20. Improvements to Labour Prod stats Improvements to the latest Labour productivity bulletin • Improved mapping of industry classifications prior to 2009 • Consistency across higher and lower levels of industry aggregation • Additional data that improve the coverage of labour hours • Consistent treatment of Labour Force Survey hours and jobs in mining of metal ores • Application of a seasonal adjustment review
  21. 21. Analytical articles Estimating the income of the self-employed, Sept 2018 • Defined the labour share (LS) in the macro- economic accounting framework • Presented the approaches used to adjust LS for mixed income • Described the current sources and explained their constraints • International comparisons
  22. 22. Analytical articles Improving estimates of labour productivity and international comparisons, Jan 2019 • Informed users in Oct 2018 suspended publication due to ongoing review of the methodology and sources • Discussed common sources used and adjustments to estimate labour inputs internationally • Methods used to estimate hours worked in light of OECD recommendations • Discussed proposals for improving ICP estimates • Wider impact on UK labour productivity inputs & the National Statistics
  23. 23. Improvements to Labour Prod stats Working closely with the UKSA to badge 5 experimental datasets into National Statistics (over 2700 series) 1. Output per hour and productivity hours by 2-digits SIC 2. Output per hour, output per job, productivity hours and productivity jobs by industry – Regional combinations 3. Contributions to labour productivity 4. Sectional unit labour costs 5. Quarterly productivity jobs and productivity hours for NUTS 1 regions
  24. 24. Improvements to Labour Prod stats • Expanded commentary in Labour productivity release to include sectional unit labour costs
  25. 25. Improvements to Labour Prod stats • Expanded commentary on regional labour productivity
  26. 26. Improving the presentation of Labour Productivity release • We want to provide more commentary on our datasets • Currently we publish 1 quarterly release, with over 3200 series which cut across multiple themes. • We need to re-think the scope of our current publication and consider how we can expand our commentary to evolve with our users’ needs.
  27. 27. Improving the presentation of Labour Productivity release Quarterly Labour Productivity, UK • LPROD01 • LPROD02 • Revisions triangles • LP by industry • Contributions to LP • Sectional ULC • Quarterly regional LP hours, jobs Quarterly Labour Productivity • Labour Productivity • Revisions triangles for Lprod • Lprod datasets (incl. industry breakdowns) • Contributions to growth • Quarterly regional LProd Quarterly Unit Labour Costs (ULC) • LPROD01 • ULC, UWC, UWCm (2016=100) • Revisions: ULC, UWCm • Revisions triangles: ULC, UWCm • Sectional ULC Annual Regional Labour Productivity • LPROD01 Table 9 Output per hour, nominal GVA (UK=100) • Regional LP • Regional Industry by region LP
  28. 28. • Core developments in 2019 1. Systems developments Improve our production system and harmonise our methodology across common variables. 2. Labour Productivity Flash estimate Complete a feasibility study to explore whether we can include an industry breakdown to the whole economy estimates in our flash estimate of labour productivity
  29. 29. • Core developments in 2019 3. Regional labour productivity Replace GVA(I) data series with the recent National Statistic GVA(B) in our regional labour prod dataset. • Consistent with the regional accounts GVA headline estimate • Introduce better coherence with the regional GVA headline estimate and the sub-regional experimental data sets.
  30. 30. • Core developments 4. International comparisons of labour productivity • Review our labour inputs, in light of recent OECD recommendations • Explore alternative data sources that could encourage coherency and comparability of the data series across countries
  31. 31. Long-term developments 1. Develop a regular set of labour productivity data for the real estate industry, excluding imputed rental. 2. With the introduction of regional short-term output indicators, we can explore the possibility of producing a region-by-industry series from these data, providing a more timely set of these data. 3. Examine the possibility of extending historical estimates of output per hour series back to 1970s.
  32. 32. Consulting users We would like to hear your views on: 1. Separating the quarterly Labour Productivity release into 3 new publications focusing on core productivity themes - Labour Productivity (quarterly) - Unit Labour Costs (quarterly) - Regional productivity (annual) 2. Prioritising our core and long-term developments which support users’ needs
  33. 33. Consulting users We would like to hear your views on these proposals and core developments. Please email your responses to productivity@ons.gov.uk by end of March 2019.
  34. 34. Proposal Todd Bailey, ONS productivity@ons.gov.uk Improvements to productivity flash estimate
  35. 35. Productivity flash estimate Oct-Dec Jan-MarQ4 Flash estimate 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑒𝑟 𝐻𝑜𝑢𝑟 (𝑂𝑃𝐻) = 𝐺𝑉𝐴 𝑇𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑒𝑑 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑒𝑟 𝑊𝑜𝑟𝑘𝑒𝑟 (𝑂𝑃𝑊) = 𝐺𝑉𝐴 𝑇𝑜𝑡𝑎𝑙 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 T+45
  36. 36. Possible industry breakdown
  37. 37. +90 +75 +45 +40 Flash by industry – Potential data sources Oct-Dec Jan-MarQ4 Flash estimate T+45 Labour productivity bulletin T+95 GDP QNA STES Jobs by industry LFS Hours GDP 1st est 𝑃𝑟𝑜𝑑 ℎ𝑜𝑢𝑟𝑠 = 𝐽𝑜𝑏𝑠 × 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 ℎ𝑜𝑢𝑟𝑠
  38. 38. +90 +75 +45 +40 Flash by industry – Potential data sources Oct-Dec Jan-MarQ4 Flash estimate T+45 GDP QNA STES Jobs by industry LFS Hours GDP 1st est Labour productivity bulletin T+95 Month 3 +75 Month 1 +15 Month 2 +45 MWSS Jobs by industry 𝑃𝑟𝑜𝑑 ℎ𝑜𝑢𝑟𝑠 = 𝐽𝑜𝑏𝑠 × 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 ℎ𝑜𝑢𝑟𝑠
  39. 39. Issues yet to be resolved How to estimate quarter from partial data? • Middle month (i.e. Month 2)? • Average of first two months? How to combine MWSS data from big and small firms? How many industries? • Coverage of input data • Precedents from other countries • Acceptance criteria
  40. 40. -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2016 2017 2018 Percent T+45 % Change on previous quarter, flash estimates T+95 % Change on previous quarter, National Statistic Past productivity flash estimates Average revision = 0.05 %points Average absolute revision = 0.07 %points
  41. 41. Acceptance criteria Output per hour and per worker Retrospective flash estimates Evaluate quarter-on-quarter growth • Revisions flash (T+45) vs published (T+95) For each measure and industry: • Average revision -0.1 to 0.1 %points • Average absolute revision < 0.20 %points
  42. 42. What do users think? • What level of revisions would be acceptable for greater granularity (more industry splits)? • How could productivity flash estimates be more useful to you? Your ideas will help shape the future of productivity flash estimates. Please share your thoughts with us by the end of March 2019. Contact: productivity@ons.gov.uk
  43. 43. Regional Labour Productivity Richard Prothero, ONS Joshua Abramsky, ONS Donovan Ward, ONS
  44. 44. Contents • Introduction • Sources • Current Price GVA • Results
  45. 45. Introduction What we publish: • NUTS1 output per hour and output per job, current price (CP) levels – National Statistics • NUTS1 and industry by NUTS1 Region output per hour and output per job, chained volume measures (CVM) indices – experimental statistics • NUTS1 quarterly productivity hours and productivity jobs – experimental statistics ‘Sub-regional’ (NUTS2 and NUTS3) labour productivity is produced by a separate team
  46. 46. Contents • Introduction • Sources • Current Price GVA • Results
  47. 47. Sources (Regional Headline) GVA Jobs AvgHours Regional Accts •GVA(I) – CP •GVA(B) - CVM Employees •STES •MoD Strength by Region Gov’t Supported Training (GST) •STES Self-employed, Unpaid Family Workers •LFS Employees •LFS microdata •Fixed (MoD) Self-employed, GST, Unpaid Family Workers •LFS 𝑂𝑝𝐻 𝑅 = 𝐺𝑉𝐴 𝑅 𝑇𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑒𝑑 𝑅
  48. 48. Sources (Industry by Region) GVA Jobs AvgHours Regional Accts •GVA(I) – CP •GVA(B) - CVM Employees •Workforce Jobs •MoD Strength by Region •Gov’t Support and Training (GST) LFS data Self-employed, Unpaid Farm Workers • STES benchmarked to APS Employees •ASHE paid usual hours, adjusted by using LFS microdata to model actual hours from usual hours •Fixed (MoD) Self-employed, Unpaid Farm Workers •APS 𝑂𝑝𝐻𝐼,𝑅 = 𝐺𝑉𝐴𝐼,𝑅 𝑇𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑒𝑑𝐼,𝑅
  49. 49. Contents • Introduction • Sources • Current Price GVA • Results
  50. 50. Current Price GVA • New Approach: Balanced (Income & Production) – GVA(B) • GVA(B) estimates gained National Statistics status in December 2018 • Already used for Sub-regional Labour Productivity and Regional CVM Labour Productivity • As noted in the last Regional Labour Productivity publication, we therefore want to use them in the Regional Labour Productivity National Statistics
  51. 51. Current Price GVA Gross Value Added New Approach: Balanced (Income & Production) Production approach mainly uses ABS Public Sector data comes from PS employees by region * average earnings of PS in area Regional estimates of bank and building society fees and commission income, and financial intermediation services indirectly measured (FISIM) from HMRC Treasury are used to allocate the GVA of the banking industry (the majority of industry K)
  52. 52. Current Price GVA Gross Value Added What is GVA(B)? GVA(B) is calculated separately by another team, but this is a broad overview at the methodology of how it is produced. • It consists of weights and quality metrics being added to components of both approaches. • Through separate multiplication of both the weights and quality metrics, quality metrics for both approaches are produced, then manually adjusted for anomalous results and re-inserted back into detailed industry and component breakdown.
  53. 53. Current Price GVA • Why have Regional Accounts introduced GVA(B), and what are the potential benefits for Regional Labour Productivity? • As noted in the Regional Accounts GVA(B) methodology paper (see link in final slide) GVA(B) includes more information in the regional estimate, primarily regional ABS, but only to the extent that the new data is of better quality • GVA(B) based estimates of Regional CP Labour Productivity will also be consistent with Sub-regional CP Labour Productivity • These will be the benefits in the short to medium term for Regional Labour Productivity
  54. 54. Contents • Introduction • Sources • Current Price GVA • Results
  55. 55. Chained Volume GVA – Contribution to Growth, Labour Productivity Output per Hour, Industry by Region, 2016 to 2017 -3 -2 -1 0 1 2 3 4 UK North East North West Yorkshire and the Humber East Midlands West Midlands East London South East South West Wales Scotland Northern Ireland % ABDE: Non-Manufacturing Production & Agriculture C: Manufacturing F: Construction K: Finance and Insurance G-J & L-T: Services (excluding Finance) Allocation Effect All Industries
  56. 56. Results – Current Price Regional Output per Hour Relative to the UK Average, GVA (I) to GVA(B), 2017 0.0% 1.4% -1.3% -0.5% -0.4% -1.7% 3.1% 1.6% -1.8% 0.8% -0.8% -0.3% -40% -30% -20% -10% 0% 10% 20% 30% 40% North East North West Yorkshire and the Humber East Midlands West Midlands East London South East South West Wales Scotland Northern Ireland GVA I GVA B Variance
  57. 57. Results - Current Price Regional Output per Hour GVA (I) to GVA (B), 1998 to 2008 & 2008 to 2017 -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 4.0% UK North East North West Yorkshire and the Humber East Midlands West Midlands East London South East South West Wales Scotland Northern Ireland 1998 to 2008 Average 2008 to 2017 Average
  58. 58. Results - Current Price Regional Output per Hour GVA (I) to GVA (B), 1998 to 2017 -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% UK North East North West Yorkshire and the Humber East Midlands West Midlands East London South East South West Wales Scotland Northern Ireland
  59. 59. Results - Current Price Regional Output per Hour GVA (I) to GVA (B) Revisions, 1998 to 2017, Industry K Finance -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% UK North East North West Yorkshire and the Humber East Midlands West Midlands East London South East South West Wales Scotland Northern Ireland
  60. 60. End & Link • Development of a balanced measure of regional gross value added by Trevor Fenton and Bethan West: https://consultations.ons.gov.uk/national-accounts/consultation-on-balanced- estimates-of-regional- gva/supporting_documents/Development%20of%20a%20balanced%20measure%20of %20regional%20gross%20value%20added.pdf
  61. 61. Regional and Subregional Productivity • Regional and sub-regional productivity in the UK published on 6th Feb. • Experimental NUTS2, NUTS3, LEPs and City Region data. • Utilises the newly published GVA(B) data. • Attaches labour market denominator (hours worked or jobs filled), which is constrained to match NUTS 1 data. • Nominal Data in £, and on UK=100 basis. • CVM ‘real’ measure on 2016=100 basis. • Also includes estimates calculated excluding imputed rentals
  62. 62. ‘Real’ Subregional Productivity Data. The constant price GVA(B) data have been derived by deflating the current price estimates for 112 industries using national industry deflators obtained from the UK gross domestic product (output) system. These deflators are consistent with the UK National Accounts, Blue Book 2018 and they are used because in most cases, regional price indices are currently not available. The Eurostat Manual on Regional Accounts (2013) recommends that in the absence of regional prices the use of national deflators is acceptable, provided that deflation occurs at a minimum level of 38 industries.
  63. 63. Real gross value added growth compared with hours worked growth for NUTS2 areas, 2010 to 2017 Changes in Real GVA(B) and Productivity Hours Productivity increased in 27 out of the 41 NUTS2 regions in the 2010 to 2017 period. This underlying data illustrates some very different outcomes in terms of GVA growth and hours worked between regions over this period.
  64. 64. ‘Nominal’ Subregional Productivity Indexed Data. GVA per hour worked for Local Enterprise Partnerships, Highest & Lowest Ranked, UK=100
  65. 65. Productivity as a recommended measure of Regional Economic Performance. ONS BLOG: Mind the gap: why the UK might not be the most regionally unequal country November 23, 2018 Commuting can distort the picture if we measure economic output (GVA) per resident. Instead of using GVA per head, we instead recommend using metrics that directly measure productivity (GVA per hour worked) or household incomes (gross disposable household income per head). Also important to consider the geography, for example relative size or type of area. See also: Regional and subregional productivity comparisons, UK and selected EU countries: 2014
  66. 66. Future Developments • Local Authority Data. • Calculating this at the moment. Will look to publish in the coming months. • At this smaller geographical level, the data will require some caveats around quality and suitability for different types of analysis. • Productivity by industry for NUTS2, LEPs, City Regions etc • We are considering feasibility and quality issues. • Strong demand given policy requirement for production of Local Industrial Strategies, and for these to be focused on productivity.
  67. 67. Regional Microdata Productivity Analysis. • 3 subnational publications over the past couple of years • Most recent subnational report was part of April 2018 Economic Review • These outputs have proved useful for local policymakers to understand the productivity of firms within their areas. • For example, see the ‘Audit of Productivity’ published as part of the evidence base underlying the Greater Manchester Independent Prosperity Review. • Intending to publish a further summary document detailing the findings in May. • To act as an introduction to local productivity issues.
  68. 68. Example charts from the firm level productivity analysis Distribution of firm-level (local plant) productivity (gross value added (GVA) per worker), Distribution of local plant productivity (gross value added (GVA) per worker) by industry groups
  69. 69. INTERNATIONAL PRODUCTIVITY GAPS: ARE LABOUR INPUT MEASURES COMPARABLE? Ashley Ward Statistics and Data Directorate (SDD) OECD UK Productivity Forum – 13-03-2019
  70. 70. • Average hours show very large differences across countries • These differences reflect structural, institutional and cultural differences... • ...but also variation in data sources and methods used 74 Introduction • …and these differences contribute to longstanding productivity gaps
  71. 71. 75 Hours worked and labour productivity
  72. 72. 76 Hours worked and labour productivity
  73. 73. 77 Hours worked and labour productivity
  74. 74. 78 Hours worked and labour productivity
  75. 75. 79 The paper • Discusses the measurement of labour input – employment and hours worked – within the national accounts framework • Presents the results of the OECD/Eurostat 2018 labour input measurement survey • Details an alternate measure of hours worked and explores the impact of measurement on cross- country productivity gaps • Makes a series of recommendations for the measurement of labour input for productivity analysis
  76. 76. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 80 Outline
  77. 77. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 81 Outline
  78. 78. • Employment is most commonly measured in terms of persons and/or jobs • Neither measure accounts for changes in patterns of working time (part-time working etc) • Countries use either or both in their national accounts 82 Measuring employment
  79. 79. Total number of hours actually worked by all persons engaged in production Usual/paid hours Persons employed Employees Job-counts Full-time equivalent 83 Measuring labour input for productivity analysis
  80. 80. Total hours actually worked include hours spent: • directly carrying out tasks and duties of a job; • on related activities (e.g. training time, on call duty, travelling between work locations); time between these hours when available for work; and short resting time. Conversely, hours actually worked exclude: • all types of leave (i.e. annual, public holidays, sick leave, maternity etc.); • commuting time when no productive activity is performed; meal breaks; and educational activities other than on-the-job training time. 84 Measuring total hours actually worked (ILO recommendations)
  81. 81. 85 Adjusting for national accounts concepts Defining those employed in domestic production (persons/jobs) Measuring hours worked for those persons/jobs • Strikes and temporary layoffs • Economic territory • Holidays and annual leave • Sickness leave • Unobserved economy • Jobs to persons (or vice versa) • Overtime • Military / conscripts • Residents working outside the economic territory • Territories not covered • Other collective households • Non-residents working inside the economic territory
  82. 82. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 86 Outline
  83. 83. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 87 Outline
  84. 84. 88 Survey results: Employment sources Note: A main source constitutes the basis of the estimates upon which adjustments might be made. Secondary data sources are used to make adjustments, or to supplement the main data source. FTE stands for full-time equivalent. Source: OECD-Eurostat 2018 labour input survey. • Most countries use a combination of sources, including BS and AS to produce figures for employees. • Most countries use LFS for self- employed, business sources often do not include figures for the self employed. LFS PC BS AS/Other LFS PC BS AS/Other Canada Jobs Main Secondary Secondary Secondary Main Secondary - Secondary France Persons and FTE Secondary - Secondary Main & Secondary & Estimates Secondary - - Main & Secondary & Estimates Germany Persons Secondary - Main* Main & Secondary & Estimates Main - - - Italy Persons and jobs Main - Main* Main & Secondary Main - Main* Main & Secondary Japan Jobs Main Main - Secondary Main Main - Secondary United Kingdom Persons Main - - - Main - - - United Kingdom (PS) Jobs Main - Secondary Secondary Main - - - United States Persons and jobs - - Main Secondary Main - - - United States (PS) Jobs - - Main Secondary Main - - - Self-employed Country Unit Employees
  85. 85. 89 Survey results: Hours worked sources LFS BS AS/Other LFS BS AS/Other Canada Job DM, with adj Main - Secondary DM, with adj Main - Secondary France FTE CM Secondary Main Secondary CM Secondary - Main & Secondary Germany Person CM Secondary Secondary Main & Secondary CM Main - Secondary Italy Person CM Main Secondary Main & Secondary CM Main Secondary Main & Secondary Japan Job CM Secondary Main - CM N/A N/A N/A United Kingdom Persons DM Main - - DM Main - - United Kingdom (PS)1 Job DM Main - Secondary DM Main - - United States Persons and jobs Ratio emp Secondary Main & Secondary - DM Main - - United States (PS)1 Job Ratio emp Secondary Main & Secondary - DM Main - - Country Unit (hours worked per) Method Employees Self-employed Method Note: A main source constitutes the basis of the estimates upon which adjustments might be made. Secondary data sources are used to make adjustments, or to supplement the main data source. FTE stands for full-time equivalent. Source: OECD-Eurostat 2018 labour input survey. • Again, like employment, most countries use some combination of sources, many making use of their administrative data • Most countries use a component method for both employees and self-employed.
  86. 86. Main source Total (%) Persons- jobs (%) Economic territory (%) Unobserved Economy (%) Other adjustments (%) Canada 2016 LFS 10 X X - X France 2015 AS 0.3 n.a. X - X Germany 2016 BS/AS 6.3 n.a. X X X Italy 2011 LFS/BS/AS 9.9 X X X X Japan 2010 LFS/PC 3.3 X X - X United Kingdom LFS 0 - - - - United Kingdom (PS) 2017 LFS X - X - - United States 2016 BS up X X X X United States (PS) 2017 Q4 BS 2.6 - X - X Main source Total (%) Persons- jobs (%) Economic territory (%) Unobserved Economy (%) Other adjustments (%) Canada 2016 LFS -41 X X - X France 2015 AS 0.1 n.a. X X X Germany 2016 LFS 0.9 n.a. - X - Italy LFS/BS/AS Japan 2010 LFS/PC 8.6 X - - X United Kingdom LFS 0 - - - - United Kingdom (PS) LFS United States 2016 LFS - n.a. - - - United States (PS) 2017 Q4 LFS - - - - - Country Period Employees Self-employed Country Period 90 Survey results: Employment adjustments Note: “X”: adjustment made but not quantified; “n.a.”: adjustment not applicable given the original data source; “X(c.)”: adjustment made but confidential; “-” : adjustment not made; “no reply”: no information provided. 1. Information correspond to both employees and self-employed. Source: OECD-Eurostat 2018 labour input survey. 1 1
  87. 87. 91 Survey results: Hours worked adjustments Note: “X”: adjustment made but not quantified; “n.a.”: adjustment not applicable given the original data source; “X(c.)”: adjustment made but confidential; “-” : adjustment not made; “no reply”: no information provided. Information for Bulgaria, Denmark, Finland, France and Sweden refers to adjustments made on total hours worked number and not on average hours worked per person/job as for other countries in the table. 1. Information correspond to both employees and self-employed. Source: OECD-Eurostat 2018 labour input survey. Main source and method % change in average hours worked Holidays & annual leave Sick-ness leave Strikes & temporary lay-offs Paid but unreported overtime Unpaid overtime Under or over- reporting Jobs to persons or vice versa Unobserved economy Other adjustments Canada 2016 LFS, DM with adj X X - X - X - X - X France 2015 BS, CM -18.8 X X X - - - n.a. X X Germany 2016 AS, CM -12.4 X X X X X - n.a. X X Italy no reply LFS/AS, CM no reply X* X - - - X - X X Japan 2010 BS, CM 1 - - - - - - - - X United Kingdom LFS, DM 0 - - - - - - - - United Kingdom (PS) 2016 LFS, DM 0 - - - - - - - - X United States 2016 BS, Ratio of paid to worked hours X X X - - - X - - X United States (PS) 2017 Q4 BS, Ratio of paid to worked hours X X X - - - X - - X Main source and method % change in average hours worked Holidays & annual leave Sick-ness leave Strikes & temporary lay-offs Paid but unreported overtime Unpaid overtime Under or over- reporting Jobs to persons or vice versa Unobserved economy Other adjustments Canada 2016 LFS, DM with adj X X - - - - - X - X France 2015 Other, CM 53.5 X X - - - - n.a. X X Germany 2016 LFS, CM -6.4 X X - - - - n.a. - X Italy no reply LFS/AS, CM no reply X* X - - - X - X X Japan N/A N/A N/A N/A N/A N/A - N/A N/A N/A N/A N/A United Kingdom LFS, DM 0 - - - - - - - - - United Kingdom (PS) 2 2016 LFS, DM 0 - - - - - - - - - United States 2016 LFS, DM, total economy only X - - - - - - - - X United States (PS) 2017 Q4 LFS, DM X - - - - - - - - X Country Period Self-employed Country Period Employees
  88. 88. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 92 Outline
  89. 89. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 93 Outline
  90. 90. 94 An upward ‘bias’ in estimates directly extracted from the LFS, 2016 Source: OECD National Accounts Statistics (database), April 2018. Unpublished OECD estimates based on European Labour Force Surveys results and Eurofound (2015) for statutory leave for European countries, and the Current Population Survey (CPS) microdata and an estimated 15 days of annual paid annual leave and public holidays (Ray et al., 2007) for the United States. Direct method – Average of hours actually worked directly collected (self-reported by the interviewees), generally in the LFS. Component method – Departs from usual hours (or normal hours, paid hours, contractual hours) which are then adjusted for extraordinary work time (paid or not) and absences (vacation, sickness, maternity).
  91. 91. 95 The OECD simple component method Usual weekly hours of work in the main job Holidays and vacation weeks Extra hours on main job = Overtime + variable hours + others Full & part-week absences, non- holiday reasons Hours on additional jobs Average weekly hours on all jobs Annual weeks worked Sickness & maternity2 Annual hours of work11) See Annex 1.A1 of OECD Employment Outlook 2004 for a succinct explanation of the method used by the OECD Secretariat to estimate annual actual hours worked per person in employment. 2) These weeks are already included in full and part-week absences, but are included a second time in order to correct for an assumed 50% under-reporting, an estimate based on analysis of national health surveys (see Annex A1.1 of OECD Employment Outlook 2006). Note: This simplified component method uses statutory leave for each country to measure leave taken, this is only a proxy for actual leave taken. 52
  92. 92. • The quality of LFS data, especially important when using a direct method, is subject to various issues: – proxy responses; – self-reporting; and – recall problems, which can vary significantly across countries. • The direct estimation method is not necessarily inferior to component based approaches, but the evidence points towards upwards bias in the estimation of hours worked using the direct method (and downwards bias in the estimation of labour productivity). • Improved comparability, and likely improved quality, could be achieved if countries currently using a direct method with no adjustments adopted component based approaches. 96 Direct method vs. component method
  93. 93. 97 Changes to average hours worked, 2016 Note: Data for all countries is for 2016, apart from Germany which presents data for 2013. The Netherlands did not respond to the 2018 survey, hence the method used by this country is reported as ‘unknown’ in this chart. Source: OECD National Accounts Statistics (database), April 2018. Unpublished OECD estimates based on European Labour Force Surveys results and Eurofound (2015) for statutory leave for European countries, and the Current Population Survey (CPS) microdata and an estimated 15 days of annual paid annual leave and public holidays (Ray et al., 2007) for the United States. Estimates first presented in OECD (2004). UK average actual annual hours per person shrinks from 1673 to 1515 (90.5% of that suggested by current national accounts figures).
  94. 94. 98 International productivity gaps, 2016 Note: The national accounts series is calculated from the OECD’s Productivity Database using all national accounts data. The counterfactual series is calculated only for those countries using an unadjusted direct method and in exactly the same way as the national accounts series with the exception of average annual hours, which are based on the simplified component method previously discussed. Source: National accounts estimates from OECD Productivity Statistics (database), April 2018. Unpublished OECD estimates based on European Labour Force Surveys results and Eurofound (2015) for statutory leave for European countries, and the Current Population Survey (CPS) microdata and an estimated 15 days of annual paid annual leave and public holidays (Ray et al., 2007) for the United States. Estimates first presented in OECD (2004). UK productivity relative to the US increases from 75.7% to 83.6%.
  95. 95. 99 International comparisons of UK productivity, previous OECD estimates, 2016
  96. 96. 100 International comparisons of UK productivity, OECD recommended approach, 2016
  97. 97. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 101 Outline
  98. 98. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 102 Outline
  99. 99. • Countries are encouraged to work to improve the measurement of labour input (employment and hours worked) in national accounts, by: – aligning with national accounting principles; and – exploiting all available data sources, including administrative sources. 103 Recommendation 1: Produce national accounts consistent labour input measures
  100. 100. • Countries adopting the direct method without any additional refinements are strongly encouraged to adopt a component method. • OECD have adopted this approach in international comparisons for: Austria, Estonia, Finland, Greece, Latvia, Lithuania, Poland, Portugal, Sweden and the United Kingdom. 104 Recommendation 2: Use the OECD simplified component method as an interim solution
  101. 101. • The quality of LFS can vary substantially across countries, especially where business surveys are preferred and LFS plays a secondary role, cross-country differences exist in: – sample selection and population coverage; – proxy responses, self-reporting and recall problems; and – the size of the phenomena for which adjustments may be needed. • This impacts direct approaches, but also the quality of the adjustments made in the simplified component method • Some countries have longstanding experience and knowledge of measuring hours worked in their own economy, with access to data sources to make specific adjustments to, for example, the unobserved economy and actual leave taken • Where countries have more robust sources for the estimation of the components of working time, these should always be preferred 105 Why haven’t we applied the alternate estimates for all available countries?
  102. 102. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 106 Outline
  103. 103. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 107 Outline
  104. 104. • Continuing engagement with country NSOs to improve current labour input measures, particularly those currently using a direct method • Industry level estimates, – which may present even greater challenges, especially given known difficulties with self-reported industries in LFS 108 Further work
  105. 105. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 109 Outline
  106. 106. • Measuring labour input • 2018 labour input survey results • Productivity counterfactual • Recommendations • Further work • Questions 110 Outline
  107. 107. 111 Questions? Ashley Ward Statistics and Data Directorate (SDD) Trade and Competitiveness Statistics (TCS) ashley.ward@oecd.org Ward, A., M. Zinni and P. Marianna (2018), “International productivity gaps: Are labour input measures comparable?”, OECD Statistics Working Papers, 2018/12, OECD Publishing, Paris. http://dx.doi.org/10.1787/5b43c728-en
  108. 108. 112 ANNEX
  109. 109. 113 Survey results: Data availability Industrial classification system Level of industry detail Industrial classification system Level of industry detail Canada NAICS Mix of 3 to 6 digits NAICS Mix of 3 to 6 digits France NACE Rev 2 Partial 2-digit NACE Rev 2 Partial 2-digit Germany NACE Rev 2 Partial 2-digit NACE Rev 2 Partial 2-digit Italy NACE Rev 2 Partial 2-digit NACE Rev 2 Partial 2-digit Japan ISIC Rev 4 1-digit, except manufacturing (2- digit) ISIC Rev 4 (employees only) 1-digit, except manufacturing (2- digit) (employees only) United Kingdom NACE Rev 2 Partial 2-digit NACE Rev 2 Partial 2-digit United States ISIC Rev 4 Partial 2-digit ISIC Rev 4 (employees only) Partial 2-digit (employees only) United States (PS) 2007 NAICS 3 to 4 digit NAICS 3 to 4 digit Hours worked Country Employment (persons or jobs) Most countries produce employment and hours worked data on a NACE Rev 2 basis, at the A64 breakdown (as required by Eurostat), with non-EU countries using ISIC Rev 4 or NAICS. Notes: For all EU + EFTA countries the information in this table reflects the data available in the Eurostat database. For Chile, Israel, Mexico (only for hours worked), New Zealand and the United States, the information corresponds to the data available in the OECD Annual National Accounts Statistics (database). For all other countries, the table reflects the information provided through the survey by NSOs. 1. New Zealand, the United Kingdom and the United States replied to the survey providing methodological information on the construction of labour input measures in their productivity statistics releases. This information is provided for these three countries indicated as PS (Productivity Statistics). Information on labour input in national accounts for New Zealand and the United Kingdom are sourced from exchanges between their NSOs and the OECD. Information on labour input measures in the national accounts for the United States was provided directly through the survey. Source: OECD-Eurostat 2018 labour input survey, OECD National Accounts Statistics (database) and Eurostat database, August 2018.
  110. 110. • New Zealand, the United Kingdom and the United States produce two sets of labour input statistics • Both presented in the paper • These should be the same, with output and labour input calculated in the national accounts and combined to produce coherent and comparable productivity statistics 114 Separation of National Accounts and Productivity Statistics Measures
  111. 111. User Productivity Forum , March 2019 Marianthi Dunn Head of Labour Productivity Branch Improving estimates of labour productivity and international comparisons
  112. 112. Content • Background issues with ICP estimates • Key points to consider when estimating labour inputs & output concepts • Proposals for improving ICP estimates • Discuss wider issues in measuring labour inputs • Consulting users
  113. 113. Background issues with ICP estimates • The ICP estimates showed significant differences in productivity between the UK and other G7 countries. • Was the UK’s productivity significantly lower or was there an underlying issue of coherence with the data?
  114. 114. Background issues with ICP estimates • To compile ICP, ONS used a number of data series from OECD: Persons, Hours, GDP (CP, CVM, PPPs) • How comparable were these variables across countries? • ONS discussed the scope of (and partly funded) the research which led to the OECD working paper: “International Productivity Gaps: Are Labour Input Measures Comparable?” • Second issue concerned missing source from OECD ALFS (PS) – discontinued in 2014 – used in ONS ICP
  115. 115. Background issues with ICP estimates • Temporary approach – ONS used Eurostat (for European countries) and an alternative OECD employment series (for non-European countries) to project the data. • As are a result in Oct 2018 the ONS suspended the publication due to ongoing review of the methodology and sources.
  116. 116. Key points to consider – Labour inputs & output concepts When estimating labour productivity it is important to align the labour inputs with the national accounts concepts of output 1. the unit of measurement used in employment (persons employed or jobs) 2. the distinction between actual hours worked and other concepts of hours worked 3. capture the national accounts production boundary.
  117. 117. Key points to consider – Labour inputs & output concepts • Comparisons of actual hours worked data across countries are significantly affected by the method used to estimate labour input. • LFS hours worked are systematically higher than the Component Method (CM) • Countries producing nat accs estimates based on the CM show strong exhaustive coverage of adjustments needed to bridge contractual hours and actual hours
  118. 118. Proposals for improving ICP estimates The OECD are proposing to revise the country data presented in their productivity database using the following criteria: 1. For countries that already apply the CM to take the Nat Accs values transmitted to Eurostat 2. For countries that apply the DM and make some NA adjustments to take the Nat Accs values 3. For countries that apply the DM and make no adjustments, OECD will derive an estimate “simple component method” using data from the EULFS
  119. 119. Proposals for improving ICP estimates • However this approach incorporates heterogeneous adjustment and coverage issues. • For countries already applying the CM, their labour inputs will be the Nat Accs value. This value will include adjustments for: •actual hours worked + coverage adjustments. • For countries applying the DM, the OECD’s derived CM will ONLY include adjustments for “actual hours”, sourced from the EULFS.
  120. 120. Proposals for improving ICP estimates Does this matter? • Even for the countries that already apply the CM, by simply using a different base source (i.e. admin data instead of EU LFS) we note some differences. • Germany already apply the CM with admin data as their main source. When we tested the EU LFS simple CM the difference resulted in each person working approx. 1.4 to 3.3 hours more per week.
  121. 121. Proposals for improving ICP estimates • To produce ICP estimates we need a coherent source and a systematic application of adjustments across the countries, in order to make them comparable. • For a more coherent coverage ONS propose to investigate the EULFS as a base source across countries and apply the same adjustments discussed in the OECD’s paper to estimate “actual hours worked”.
  122. 122. Proposals for improving ICP estimates • For the UK by deriving a CM from the EULFS, can reduce average weekly hours by approx. 2-3 hrs/pp • This approach is the most comparable methodology across the G7 countries. • Can extend countries to include IE, NL etc. • We recognise these labour input series will differ from those published nationally by countries, but we can provide clear metadata to users
  123. 123. Revisions to HW by country EU LFS CM
  124. 124. GDP per hour worked 2007=100 index
  125. 125. GDP per hour worked 2007=100 index
  126. 126. Proposals for improving ICP estimates If ONS cannot secure long term access of the EULFS data we may need to consider how meaningful ICP estimates will be, given the heterogeneous application of the adjustments and data sources across the countries.
  127. 127. Wider issues for labour inputs • The UK use the DM to estimate labour inputs. • OECD recognise the DM is not necessarily inferior to the CM, though without the use of additional adjustments, it could potentially overestimate the actual hours worked. • OECD recognize that countries compile their methodology depending on the sources available nationally. Although they recommend moving towards the CM, they recognize not all countries may be able to capitalize on administrative sources.
  128. 128. Wider issues for labour inputs We estimate UK productivity using the DM because it has historically been the best method given the data available in the UK. On a national level it would not be possible to apply the CM in the short term. However, as work on administrative data is evolving, it may be possible to consider applying the component method in the long run.
  129. 129. Consulting users In the interim we propose to conduct a scoping study to: 1. Investigate the suggestion that UK hours could be overstated: - Whether LFS respondents understate their leave or sickness absence - If the way we account for non-respondents is biasing the figures upwards
  130. 130. Consulting users 2. We will review wider dependencies with labour market statistics as administrative sources become available over time, that will enable us to improve estimates of “actual hours” worked. 3. Explore the use of possible sustainable sources to capture: - conceptual and exhaustiveness adjustments on the economic territory, - the unobserved economy and any other adjustments that are consistent with the production boundary
  131. 131. Consulting users 4. Consider using concepts of the tabular approach of the Gross National Income (GNI) process table to align labour inputs to the production boundary.
  132. 132. Consulting users We would like to hear your views on these proposals. Please email your responses to productivity@ons.gov.uk by end of March 2019.
  133. 133. Questions?
  134. 134. Transforming Labour Market Statistics Head of Labour Market and Households David Freeman 12 March 2019
  135. 135. Current labour market process
  136. 136. Transformation.. Data Sources Admin data first Collection Digital by default Publication Accessible
  137. 137. Data sources Data Sources Admin data first Collection Digital by default Publication Accessible
  138. 138. PAYE Real Time information - Employees Source: HMRC
  139. 139. PAYE Real Time information - Earnings Source: HMRC
  140. 140. PAYE Real Time Information - Earnings Source: HMRC
  141. 141. Data sources Data Sources Admin data first Collection Digital by default Publication Accessible
  142. 142. Data collection Business Surveys Paper returns Online collection Household Surveys Interviews Mixed mode
  143. 143. Future model Labour market statistics Administrative data Third party data Web scraping Surveys
  144. 144. Data sources Data Sources Admin data first Collection Digital by default Publication Accessible
  145. 145. Annual Survey of Hours and Earnings Annual Survey of Hours and Earnings Employee earnings in the UK Gender pay gap in the UK Low and high pay in the UK • Moved from single release to three specific releases • 50% increase in views compared to 2017
  146. 146. Labour Market release – March 2019 Labour Market Statistics Labour Market overview Employment in the UK Earnings Jobs & vacancies • Move from single release to overview and three supporting pages • Planned for release on 19 March 2019 • All current data tables available • Further details contact labour.market@ons.gov.uk
  147. 147. Next steps Focus on user needs Engage and listen to feedback New outputs from 2021..
  148. 148. Quarterly estimates of Multi- factor Productivity (MFP) Productivity Forum 13th of March 2019 Riikka Korhonen Assistant Economist, Growth Accounting Office for National Statistics Twitter: @ONS #ukproductivity
  149. 149. Outline • Motivation • The Economy • MFP in a nutshell • Components of MFP • Quarterly MFP • Results from January publication • Data development and issues • Next steps
  150. 150. Motivation “Academic researchers and policymakers frequently want high-frequency measures of technology. These measures might be fed into models, or used to understand the effects of technology shocks on the economy, or to assess trends in potential output. Relatively crude measures of the Solow residual are easy to construct. But more careful quarterly measures that better correspond to theoretical concepts are more difficult to construct.” (Fernald, 2014) “Eleven of the 17 NISOs and the ONS produce annual multi-factor productivity estimates (none produce such productivity data on a quarterly basis). [..…] The delay with which the ONS headline multi-factor productivity data are released is the longest among this group.” (DIW/London Economics ‘Review of international best practice in the production of productivity statistics’, December 2017 “We will improve the quality and scope of productivity statistics to deliver a world-class set of statistics to support users attempting to address the “productivity puzzle”. This year we aim to launch: quarterly multi-factor productivity estimates at more granular industry detail new capital productivity statistics” (ONS Economic Statistics and Analysis Strategy, 2017)
  151. 151. The Economy
  152. 152. MFP in a nutshell Decomposition of growth in Market Sector GVA Decomposition of growth in Market Sector GVA/hour
  153. 153. Components of MFP: labour Quality Adjusted Labour Input (QALI) constitutes the labour side of MFP. QALI is a weighted index that takes into account differences between different types of labour through income shares.
  154. 154. Components of MFP: capital Volume Index of Capital Services (VICS) is weighted index that takes into account the differences in assets types through rental prices. 2016 constant prices, £m 2016=100
  155. 155. Components of MFP: GVA Gross Value Added (GVA) is the output measure used for MFP. GVA is calculated by deducting the cost of intermediate inputs of goods and services consumed in the production process. This Photo by Unknown Author is licensed under CC BY-SA-NC
  156. 156. Quarterly Multi-Factor Productivity
  157. 157. MFP Multi-Factor Productivity can be seen as the residual that is left after accounting for the contributions made by labour and capital.
  158. 158. Quarterly MFP • First experimental quarterly MFP data was published in April 2018. Before that MFP estimates were published annually. Before the quarterly MFP publication timetable we also published stand alone VICS and QALI estimates. • Publication in October 2018 marked the start of the new MFP publication timetable in line with the quarterly labour productivity publication. This new timetable made ONS the first National Statistics Office to publish quarterly MFP. • At the moment we publish: • Experimental quarterly estimates from 1994Q1 for 10 component industries. • Experimental annual MFP estimates from 1970 for 19 component industries.
  159. 159. What have we published? • Quarterly indices and growth rates for : • Quarterly factor income weights • Contributions to GVA growth (Hours worked, Labour composition, Capital services) • Contributions to Labour Productivity growth (Capital deepening =Capital services/Hours worked) • Industry contributions to cumulative MFP growth • Additionally can compute • Quality-adjusted labour input (QALI = d(Hours worked)+d(labour composition)) • Capital productivity (GVA/capital services) • Labour-capital ratios (QALI/capital services) • Annual only: implied prices of capital, labour and combined inputs, defined as factor incomes divided by capital services, QALI and combined inputs respectively GVA Hours worked Labour composition Capital services Combined inputs (weighted labour and capital) MFP (GVA/Combined inputs) Labour productivity (GVA/hours worked)
  160. 160. Latest publication, January 2019 𝑙𝑛 𝑡 = 𝐼 𝐼 𝑙𝑛 𝑡 𝑙𝑛𝐴(𝑡) change in real GVA 2-period average labour share x change in QALI (1-2-period average labour share) x change in VICS change in MFP
  161. 161. Latest publication, January 2019 𝑙𝑛 (𝑡) 𝐻(𝑡) = 𝐼 𝑡 𝑡 𝑙𝑛𝐻 𝑡 𝐼 𝑡 𝑙𝑛 𝑡 𝑙𝑛𝐻 𝑡 𝑙𝑛𝐴(𝑡) change in MFP changein GVA per hour 2-period averagelabour sharex changein labour composition (changein QALI- changein hours) (1-2period averagelabour share) x changein capital deepening (change in VICS- change in hours worked)
  162. 162. Latest publication, January 2019
  163. 163. Issues and Next Steps
  164. 164. Data development and issues • National accounts sectorised only in current prices. • Labour market stats not sectorised. • Labour composition source data are thin for small industries. • Limited data on key parameters for capital services, eg asset lives, rates of return. • Quarterly series back to only 1994 Q1. • High rates of return: prima facie evidence of mismatch between asset coverage and returns to capital – land? Inventories? Missing intangibles?
  165. 165. Next steps Hours worked and labour composition Further work to expand the level of industry granularity. We are planning to publish some more granular data for manufacturing in April. Capital services Evaluate sensitivity of results to input parameters where there is uncertainty (asset lives, decay functional form, holding gains, endogenous/exogenous rates of return). Address issue of mismatch between asset coverage and returns to capital (test: convergence of endogenous industry level rates of return). Publish contributions of different asset classes. MFP Expand industry granularity. Investigate methods for estimating MFP in the non-market sector. “TFP” Implement double-deflated National Accounts from Blue Book19.
  166. 166. Thank you for listening Feedback: productivity@ons.gov.uk Find us on Twitter for latest updates: @ONS #ukproductivity @KatKent_ONS For more detailed example, have a look at our publication: Simple Guide to Multi-Factor Productivity
  167. 167. Appendix: MFP • Annual MFP uses Törnqvist factor income weights (average of t, t- 1). Using the same weights gives a break between Q4 and Q1. Using quarterly Törnqvist weights iterates away from the annual MFP estimates. • Published estimates benchmark quarterly series to annuals. This is common across ONS. ‘Tail’ quarters (2018Q2 and 2018Q3) not benchmarked. • Capital services and labour composition are seasonally adjusted where necessary (GVA and hours worked source data are SA). Factor income shares are not seasonally adjusted.
  168. 168. Appendix: GVA • Estimates of quarterly MS GVA by detailed industry are available from the ONS National Accounts systems • These are consistent (i) with the published MS GVA aggregate (L48H), (ii) with published component level GVA estimates for wholly MS industries • Industry detail supports both 19-industry and 64-industry breakdowns • Unpublished chained aggregates follow standard ONS process to bench quarterly series to chained annuals • Sectoral GVA weights are not wholly explicit, updated infrequently, occasionally quite simplistic (see published GDP(O) sources catalogue) • Impact of sectoral reclassifications not always fully synchronised between GVA and labour market sources • National Accounts systems go back only to 1997Q1. Earlier estimates use a mix of ONS, BOE and KLEMS source data
  169. 169. Appendix: hours worked • Bottom-up estimates, based primarily on sectoral markers in labour market sources (LFS and ASHE) • 10 industries with some xMS component, one of which (O: public admin) is wholly xMS • Apply filtering, eg to drop observations where no xMS weight in GVA data (construction, manufacturing), and to drop self- employment in industry O • Aggregate MS hours worked differs slightly from top-down estimates used in Labour Productivity release • Hours worked in wholly MS industries are aligned with those in the Labour Productivity system • Under development: 64-industry breakdown, 51 of which are wholly MS
  170. 170. Appendix: labour composition • Stratify labour by industry, sex, age-group and level of education, 684 cells in total. Generate weights for each cell using hourly labour costs (see below) • Apply benchmarks: (i) to ASHE hourly earnings at lowest available level, (ii) to (MS) labour income by industry in the National Accounts • Measure labour composition as difference between growth of weighted index minus growth of unweighted hours • Under development: 64-industry breakdown. This is 2304 cells. Currently exploring ways to deal with missing/volatile data
  171. 171. Appendix: capital • Capital services are analogous to quality adjusted hours worked: growth of stratified components (asset type, industry, vintage) weighted by ‘user costs’. • Growth is a lag function of current and prior investment (GFCF), in some cases 100s of quarterly estimates. • User costs are analogous to income weights in QALI. Give more weight to components that give up their services more rapidly in production (eg software, IT hardware) than to long-lived assets such as buildings. • User costs made up of 3 components: depreciation, holding gains and a rate of return, and are adjusted for different tax treatment of different assets.
  172. 172. Public Service Productivity Assistant Economist Office for National Statistics Leah Harris 13th March 2019
  173. 173. What service areas do we measure? 1. Healthcare 2. Other 3. Education 4. Defence 5. Adult social care 6. Police 7. Public order and safety 8. Children’s social care 9. Social security administration Expenditure weights by service area, 2016, UK Source: Office for National Statistics 13th March 2019
  174. 174. What estimates do we produce? • Inputs • Output (includes some quality adjustment) • Productivity  As indices that show the change relative to a base year, and growth rates 13th March 2019
  175. 175. Measuring public sector output Labour productivity is measured by output (GVA) per hour Multi-factor productivity is measured as a residual to output growth But what is public service productivity output? Pre 1998, output was assumed to be equal to inputs  no productivity measure Post 1998, output began to be measured with administrative data European System of Accounts 1995: “In the absence of secondary market output by other non-market producers (local Kind of Activity Units), other non-market output is to be valued at the costs of production” 13th March 2019
  176. 176. The Atkinson Review Principle B in the Review: “The output of the government sector should in principle be measured in a way that is adjusted for quality, taking account of the attributable incremental contribution of the service to the outcome.” 13th March 2019
  177. 177. Output and outcomes Output is what a public service provides Outcomes are the end effects or aims of the service The attribution consideration – how far can we attribute a successful outcome to the public service provided, and not other factors? 13th March 2019
  178. 178. Output types Output-type share by service area, 2016, UK Source: Office for National Statistics 13th March 2019
  179. 179. Effect of quality adjustment Total public service productivity index, quality adjusted and non-quality adjusted, 1997 to 2016, UK Source: Office for National Statistics 13th March 2019 90 92 94 96 98 100 102 104 106 108 110 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Index, 1997 = 100 Quality adjusted productivity index Non-quality adjusted productivity index
  180. 180. Example: Public Order and Safety 13th March 2019 Inputs: • Labour • IC • Capital 1. Fire protection 2. Courts 3. Prisons Output: 1. Fire protection 2. Courts 3. Prisons 4. Probation Courts is further broken down into:  Magistrates Courts  County Courts  Crown Courts  Crown Prosecution Service  Legal Aid Quality adjustment: Fire protection services and County Courts are not quality adjusted… 1. Recidivism (severity weighted index of re- offending 2. Prison safety 3. Custody escapes 4. Courts’ timeliness
  181. 181. Example: Public Order and Safety 13th March 2019 Component Recidivism (applied from 2000) Prison safety (1997) Custody escapes (1997) Courts’ timeliness (2011) Prisons 29.2% 37.5% 33.3% Probation 100% Magistrates Courts 50% 50% Crown Courts 50% 50% Crown Prosecution Service 100% Legal Aid 100%
  182. 182. 13th March 2019 POS inputs, output and productivity growth rates, 1998 to 2016, UK Source: Office for National Statistics
  183. 183. What do we publish? Annual total public service productivity, covering 1997 to 2016 (latest release)  two year time lag Quarterly experimental total public service productivity, the latest release being in Jan 2019 for Q3 2018  two quarter time lag  timelier estimate Service area specific articles Methodology and quality articles 13th March 2019
  184. 184. Latest data 13th March 2019
  185. 185. Total UK public service productivity, 1997 to Quarter 3 (July to Sept) 2018 Source: Office for National Statistics 13th March 2019
  186. 186. Total public service inputs, output and productivity growth rates, 1998 to 2016, UK Source: Office for National Statistics 13th March 2019
  187. 187. Contributions to growth of total public service productivity by service area, 1998 to 2016, UK Source: Office for National Statistics 13th March 2019
  188. 188. Total public service productivity indices including and excluding indirectly measured service areas, 1997 to 2016, UK Source: Office for National Statistics 13th March 2019
  189. 189. Users • Departments within UK government • Press and general public • Academic institutions and research bodies • International statistical bodies 13th March 2019
  190. 190. GDP and welfare https://www.escoe.ac.uk/escoe-research-seminar-12-february- 2019/ • How effective is GDP at capturing welfare? • How to account for goods and services that are free at the point of use? • The output and outcomes debate  our quality adjustment accounts for outcomes as well as output… 13th March 2019
  191. 191. GDP and welfare 13th March 2019 Exploratory estimate of ‘Atkinson-consistent’ GDP, 1997-2016, UK Source: Office for National Statistics
  192. 192. Other ONS work Collaboration with other teams • National Accounts • Efficiency Measurement Unit 13th March 2019
  193. 193. Questions? Productivity@ons.gov.uk Leah Harris 13th March 2019

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