TPI Productivity Lab
Director: Raquel Ortega-Argilés,
Members: Nathan McKeogh,
Olga Menukhin, William Sarsfield, Alfonso Silva-Ruiz, Ruby Watson
Associates: Fatima Garcia-Elena,
TPI Productivity Lab, The Productivity Institute
THE PRODUCTIVITY LAB
The TPI’s data
science centre of
excellence, the
“engine room” for
data-related
activities.
A scientific platform for
collecting, disseminating,
and producing productivity
data, and experimenting
with different analytical
methods rooted in
econometrics and data
science.
2
• Blog articles with custom TPI branded
data analysis and visualisations
• Commissioning blog articles on
productivity data-related topics to
external researchers
• Interactive policy tools
• Harmonised productivity metrics e.g.
regional scorecards
• Creating a webpage on TPI site to
disseminate information on Lab
activities, incl. blogs and data links
• Creating an interactive web
platform for data-related research,
incl. greater functionality
(visualisations, download options,
tools, data reference lists, deposited
TPI data
• Organising and participating in data
and metrics- related events.
• Liaising and collaborating with different
data-related productivity research
groups and institutions
• Receiving advice and contrasting our
work with TPI Lab’s Expert Group
members and TPI experienced
researchers
• Creating reference lists of
productivity data sets, data tools,
data sources.
• Creating a formal repository for
research data (Figshare)
• TPI data-related support facility
(researchers, RPFs, stakeholders)
• Fellowship/visiting Programme Being a
Data Hub
Create
Partnerships
Produce
Insights
Disseminate
data-specific
productivity
research
TPI PRODUCTIVITY LAB
MAIN AREAS OF ACTIVITY
THE UK TPI SCORECARDS
3
TPI PRODUCTIVITY LAB WEB
PLATFORM
The new platform has the following
objectives:
1. Productivity data hub, referencing TPI
and external datasets, with easy search
options
2. Hosting TPI research data (through
Figshare)
3. Provide insights through blogs and
interactive productivity tools
4. Collaboration platform, support for TPI
researchers, also for cooperation with
RPFs, and institutes like ONS – in
development
A very skewed regional productivity distribution
Source: Gouma, Menukhin and Ortega-Argiles (2023). TPI UK ITL3 Scorecards. University of Manchester. https://doi.org/10.48420/23791680
TPI UK ITL1 and ITL3 Scorecards:
Productivity Indicators and Drivers
The TPI Regional Productivity Scorecards
• Data for 12 aggregate ITL1 regions
• Data for 2019, 2020, 2021 and 2022
• Three indicators of (relative) productivity, 4
categories with 12 drivers of productivity
• Enables (inter) regional comparisons of productivity
performance and drivers, linking them to policy
objectives
• Regional experts request data at a more granular
level to allow for meaningful productivity analysis
• ITL1 level data hides a lot of intra-regional
heterogeneity in productivity
• Coverage of 179 regions according to the ITL3
definitions, consistent with European NUTS 3
7
TPI UK ITL1 Productivity Scorecards:
Regional Productivity Indicators and Drivers
8
TPI UK ITL3 Scorecards:
Productivity Indicators and Drivers
The TPI Regional Productivity Scorecards
• Data for 12 aggregate ITL1 regions
• Data for 2019, 2020, and 2021
• Three indicators of (relative) productivity, 4
categories with 12 drivers of productivity
• Enables (inter) regional comparisons of productivity
performance and drivers, linking them to policy
objectives
• Regional experts request data at a more granular
level to allow for meaningful productivity analysis
• ITL1 level data hides a lot of intra-regional
heterogeneity in productivity
• Coverage of 179 regions according to the ITL3
definitions, consistent with European NUTS 3
10
THE TPI UK PRODUCTIVITY DASHBOARDS
13 Productivity Dashboards covering: UK based one with ITL1 regional detail and 12 ITL1 ones with ITL3
detail.
In total, the UK, 12 regional areas and 179 sub-regional areas in the UK for the years 2019-2021.
SUBNATIONAL PRODUCTIVITY GROWTH
DATA TOOL
National
Productivity
Council
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
12
TPI LAB COLLABORATION ACTIVITIES: The
Productivity Measurement Analysis Series
Research collaboration with Martin Fleming (Varicent, MIT) and Klaas de Dries (The Conference Board)
- Analysing quarterly productivity data
releases across the World.
- Commentaries on the US, the EU and the
UK.
INTERNATIONAL QUARTERLY
PRODUCTIVITY DATA TOOL - MOTIVATION
National
Productivity
Council
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
•The TPI Productivity Lab publishes quarterly blogs analysing productivity trends
in the EU, UK, and US.
•The quarterly data tool was developed to complement these blogs by providing a
set of interactive and customisable visualisations for international comparisons.
INTERNATIONAL QUARTERLY
PRODUCTIVITY DATA TOOL - FEATURES
National
Productivity
Council
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
•Visualises quarterly and yearly
productivity data across:
• The US
• Major European countries
• Euro Zone & European Union
aggregates
•Supports filtering by:
• Country
• Productivity metric
• Industrial sector
• Time period
INTERNATIONAL QUARTERLY
PRODUCTIVITY DATA TOOL - FEATURES
National
Productivity
Council
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
REGIONAL TYPOLOGIES
SCORECARDS AND DASHBOARDS
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
Scorecards and dashboards series illustrating comparisons on productivity and
drivers of productivity performance in urban, intermediate and rural areas.
REGIONAL TYPOLOGY SCORECARDS AND
DASHBOARDS
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
REGIONAL TYPOLOGY SCORECARDS AND
DASHBOARDS
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
REGIONAL TYPOLOGY SCORECARDS AND
DASHBOARDS
Eight
Regional
Forums
Eight Research
Themes & new
Nexus Themes
Data Visualisation Observatory Collaboration
National
Productivity
Council
Eight
Regional
Forums
The Data City Collaboration and Fellowship
National
Productivity
Council
The Data City (TDC) is a
Leeds-based company that
creates novel datasets
using machine learning and
web-text crawling.
They classify companies
into sectors not covered by
Standard Industrial
Classification codes.
The resulting data assets
are Real-Time Industrial
Classifications (RTICs)
Summary statistics for
companies classified for
the RTIC Artificial
Intelligence
Technologies and
Applications
The Data City Collaboration & Fellowship
National
Productivity
Council
Selected outputs from the collaboration:
• Digitalisation and Innovation Indicators:
metrics on innovation and digitalisation
practices (MCAs and Sectors).
• Industrial Strategy Sector Mapping: mapping
of SIC (RSIC) and RTIC codes to identify 8
growth sectors from UK Industrial Strategy
Green Paper. 308 views and 150 downloads
• DvO: Implementing TDC data in the DvO
facility. Currently focusing on the Life Science
industry in Manchester but aiming to expand to
other regions/themes
Smart Data Foundry – Research Collaboration
Smart Data Foundry (SDF) is a not-for-profit
based at the University of Edinburgh. They enable
secure, ethical access to financial and
administrative data to tackle societal challenges.
Our collaboration
• Signed collaboration agreement
We work with SDF to:
• Enrich financial capital lens with highly granular
data (individual level)
• Support evidence-based, local productivity
strategies
TPI Investment in Places Productive Campaign
TPI is working with Local Authorities
(municipalities) and regional
stakeholders to enhance prosperity and
productivity across the UK
• Our mixed-methods approach
explores how the 7 Capitals Framework
(UK Levelling Up policy strategy)
operates in practice.
• Our multi-disciplinary lens highlights key
challenges and opportunities for
place-based policy.
• We are also developing a Data Tool to
analyse productive capitals at both
Local Authority and regional levels.
1 Rochdale, Fermanagh & Omagh, London Upper Lea Valley, Newport, South Tyneside, Great Yarmouth, Cumberland, Walsall
8 Local Authorities across two cohorts1
IMPORTANT TAKEAWAYS
• Evidence-based decision making does require a rigorous commitment to use data to
underpin decisions and challenge common wisdom.
• At the same time, one should not just rely on dry data. They are part of a broader narrative
on what drives regional economic development
• Easy access, timeliness, user-friendliness and transparency really matter to increase
trust in data as part of the decision making process.
• Co-creation in data collection is key: only the users can tell you what the “gaps” in the
understanding of your data are.
• Exploring the use of non-official data adds a lot of richness to the data, though often at the
cost of data consistency across space or over time
• Much room for methodological improvement (machine learning, forecasting, and
experimentation) to support evidence-based policy implementation
• Drivers of productivity may be operative at a (geographical) level different from the legislative
or administrative body
26
Contact us at:
TPIproductivitylab@manchester.ac.uk
THANK YOU FOR YOUR ATTENTION
Follow us at
https://www.lab.productivity.ac.uk

The TPI Productivity Lab - Ortega-Argiles

  • 1.
    TPI Productivity Lab Director:Raquel Ortega-Argilés, Members: Nathan McKeogh, Olga Menukhin, William Sarsfield, Alfonso Silva-Ruiz, Ruby Watson Associates: Fatima Garcia-Elena, TPI Productivity Lab, The Productivity Institute
  • 2.
    THE PRODUCTIVITY LAB TheTPI’s data science centre of excellence, the “engine room” for data-related activities. A scientific platform for collecting, disseminating, and producing productivity data, and experimenting with different analytical methods rooted in econometrics and data science. 2
  • 3.
    • Blog articleswith custom TPI branded data analysis and visualisations • Commissioning blog articles on productivity data-related topics to external researchers • Interactive policy tools • Harmonised productivity metrics e.g. regional scorecards • Creating a webpage on TPI site to disseminate information on Lab activities, incl. blogs and data links • Creating an interactive web platform for data-related research, incl. greater functionality (visualisations, download options, tools, data reference lists, deposited TPI data • Organising and participating in data and metrics- related events. • Liaising and collaborating with different data-related productivity research groups and institutions • Receiving advice and contrasting our work with TPI Lab’s Expert Group members and TPI experienced researchers • Creating reference lists of productivity data sets, data tools, data sources. • Creating a formal repository for research data (Figshare) • TPI data-related support facility (researchers, RPFs, stakeholders) • Fellowship/visiting Programme Being a Data Hub Create Partnerships Produce Insights Disseminate data-specific productivity research TPI PRODUCTIVITY LAB MAIN AREAS OF ACTIVITY THE UK TPI SCORECARDS 3
  • 4.
    TPI PRODUCTIVITY LABWEB PLATFORM The new platform has the following objectives: 1. Productivity data hub, referencing TPI and external datasets, with easy search options 2. Hosting TPI research data (through Figshare) 3. Provide insights through blogs and interactive productivity tools 4. Collaboration platform, support for TPI researchers, also for cooperation with RPFs, and institutes like ONS – in development
  • 5.
    A very skewedregional productivity distribution
  • 6.
    Source: Gouma, Menukhinand Ortega-Argiles (2023). TPI UK ITL3 Scorecards. University of Manchester. https://doi.org/10.48420/23791680 TPI UK ITL1 and ITL3 Scorecards: Productivity Indicators and Drivers The TPI Regional Productivity Scorecards • Data for 12 aggregate ITL1 regions • Data for 2019, 2020, 2021 and 2022 • Three indicators of (relative) productivity, 4 categories with 12 drivers of productivity • Enables (inter) regional comparisons of productivity performance and drivers, linking them to policy objectives • Regional experts request data at a more granular level to allow for meaningful productivity analysis • ITL1 level data hides a lot of intra-regional heterogeneity in productivity • Coverage of 179 regions according to the ITL3 definitions, consistent with European NUTS 3
  • 7.
    7 TPI UK ITL1Productivity Scorecards: Regional Productivity Indicators and Drivers
  • 8.
  • 9.
    TPI UK ITL3Scorecards: Productivity Indicators and Drivers The TPI Regional Productivity Scorecards • Data for 12 aggregate ITL1 regions • Data for 2019, 2020, and 2021 • Three indicators of (relative) productivity, 4 categories with 12 drivers of productivity • Enables (inter) regional comparisons of productivity performance and drivers, linking them to policy objectives • Regional experts request data at a more granular level to allow for meaningful productivity analysis • ITL1 level data hides a lot of intra-regional heterogeneity in productivity • Coverage of 179 regions according to the ITL3 definitions, consistent with European NUTS 3
  • 10.
    10 THE TPI UKPRODUCTIVITY DASHBOARDS 13 Productivity Dashboards covering: UK based one with ITL1 regional detail and 12 ITL1 ones with ITL3 detail. In total, the UK, 12 regional areas and 179 sub-regional areas in the UK for the years 2019-2021.
  • 11.
    SUBNATIONAL PRODUCTIVITY GROWTH DATATOOL National Productivity Council Eight Regional Forums Eight Research Themes & new Nexus Themes
  • 12.
    12 TPI LAB COLLABORATIONACTIVITIES: The Productivity Measurement Analysis Series Research collaboration with Martin Fleming (Varicent, MIT) and Klaas de Dries (The Conference Board) - Analysing quarterly productivity data releases across the World. - Commentaries on the US, the EU and the UK.
  • 13.
    INTERNATIONAL QUARTERLY PRODUCTIVITY DATATOOL - MOTIVATION National Productivity Council Eight Regional Forums Eight Research Themes & new Nexus Themes •The TPI Productivity Lab publishes quarterly blogs analysing productivity trends in the EU, UK, and US. •The quarterly data tool was developed to complement these blogs by providing a set of interactive and customisable visualisations for international comparisons.
  • 14.
    INTERNATIONAL QUARTERLY PRODUCTIVITY DATATOOL - FEATURES National Productivity Council Eight Regional Forums Eight Research Themes & new Nexus Themes •Visualises quarterly and yearly productivity data across: • The US • Major European countries • Euro Zone & European Union aggregates •Supports filtering by: • Country • Productivity metric • Industrial sector • Time period
  • 15.
    INTERNATIONAL QUARTERLY PRODUCTIVITY DATATOOL - FEATURES National Productivity Council Eight Regional Forums Eight Research Themes & new Nexus Themes
  • 16.
    REGIONAL TYPOLOGIES SCORECARDS ANDDASHBOARDS Eight Regional Forums Eight Research Themes & new Nexus Themes Scorecards and dashboards series illustrating comparisons on productivity and drivers of productivity performance in urban, intermediate and rural areas.
  • 17.
    REGIONAL TYPOLOGY SCORECARDSAND DASHBOARDS Eight Regional Forums Eight Research Themes & new Nexus Themes
  • 18.
    REGIONAL TYPOLOGY SCORECARDSAND DASHBOARDS Eight Regional Forums Eight Research Themes & new Nexus Themes
  • 19.
    REGIONAL TYPOLOGY SCORECARDSAND DASHBOARDS Eight Regional Forums Eight Research Themes & new Nexus Themes
  • 20.
    Data Visualisation ObservatoryCollaboration National Productivity Council Eight Regional Forums
  • 21.
    The Data CityCollaboration and Fellowship National Productivity Council The Data City (TDC) is a Leeds-based company that creates novel datasets using machine learning and web-text crawling. They classify companies into sectors not covered by Standard Industrial Classification codes. The resulting data assets are Real-Time Industrial Classifications (RTICs) Summary statistics for companies classified for the RTIC Artificial Intelligence Technologies and Applications
  • 22.
    The Data CityCollaboration & Fellowship National Productivity Council Selected outputs from the collaboration: • Digitalisation and Innovation Indicators: metrics on innovation and digitalisation practices (MCAs and Sectors). • Industrial Strategy Sector Mapping: mapping of SIC (RSIC) and RTIC codes to identify 8 growth sectors from UK Industrial Strategy Green Paper. 308 views and 150 downloads • DvO: Implementing TDC data in the DvO facility. Currently focusing on the Life Science industry in Manchester but aiming to expand to other regions/themes
  • 23.
    Smart Data Foundry– Research Collaboration Smart Data Foundry (SDF) is a not-for-profit based at the University of Edinburgh. They enable secure, ethical access to financial and administrative data to tackle societal challenges. Our collaboration • Signed collaboration agreement We work with SDF to: • Enrich financial capital lens with highly granular data (individual level) • Support evidence-based, local productivity strategies
  • 24.
    TPI Investment inPlaces Productive Campaign TPI is working with Local Authorities (municipalities) and regional stakeholders to enhance prosperity and productivity across the UK • Our mixed-methods approach explores how the 7 Capitals Framework (UK Levelling Up policy strategy) operates in practice. • Our multi-disciplinary lens highlights key challenges and opportunities for place-based policy. • We are also developing a Data Tool to analyse productive capitals at both Local Authority and regional levels. 1 Rochdale, Fermanagh & Omagh, London Upper Lea Valley, Newport, South Tyneside, Great Yarmouth, Cumberland, Walsall 8 Local Authorities across two cohorts1
  • 25.
    IMPORTANT TAKEAWAYS • Evidence-baseddecision making does require a rigorous commitment to use data to underpin decisions and challenge common wisdom. • At the same time, one should not just rely on dry data. They are part of a broader narrative on what drives regional economic development • Easy access, timeliness, user-friendliness and transparency really matter to increase trust in data as part of the decision making process. • Co-creation in data collection is key: only the users can tell you what the “gaps” in the understanding of your data are. • Exploring the use of non-official data adds a lot of richness to the data, though often at the cost of data consistency across space or over time • Much room for methodological improvement (machine learning, forecasting, and experimentation) to support evidence-based policy implementation • Drivers of productivity may be operative at a (geographical) level different from the legislative or administrative body
  • 26.
    26 Contact us at: TPIproductivitylab@manchester.ac.uk THANKYOU FOR YOUR ATTENTION Follow us at https://www.lab.productivity.ac.uk