Written evidence submitted by Dr Chris Farrell (IGR0038)

Re: The Catapult Scheme

This submission concerns the effectiveness of the Catapult scheme in the regions. To be effective these catapults must fire, and their projectiles must seed, a fertile landscape. That requires a vibrant innovation profession capable of growing those seeds to profusive fruition.

Pages 1 to 4 are introductory. Please turn to page 5 where you will find the title 'Action Requires a Stronger Innovation Profession'.

The UK's Innovation Profession - such as it is - adheres to no body of knowledge from which it certifies its practitioners. It does not offer universal training. It does not hold an annual conference that feeds an Academic Journal. It does not have a quantified-by- result Award System. Such factors are prominent in Exhibit 7 but are missing from Exhibit 8.

These deficiencies are systemic and should be addressed.

Questions Behind UK Productivity

Answers & Actions

Chris Farrell Ph.D. Innovation Professional Technology Matters

Questions abound,

Why did UK Labour Productivity (output per hour) - which had grown steadily at 2.3% per year to 2007 - suddenly stagnate? Why does the gap between what ought to be achieved and what has been achieved continue to rise? It was up from 17% in 2015 to 24% last year.

SPERI Paper No. 28 1

Why is it,

'conceivable that by 2030 economists will have devised a new means of measuring an economy's productivity directly, rather than through its proxy, output per hour'? 2

Why wait until 2030? Isn’t it obvious that what is done in those hours is more important than the hours themselves? Hours spent developing ideas that create innovations are primarily responsible for GDP (proven in8) and those hours are accurately accounted for as current expense.

Idea development expense - iDe - is therefore substituted for hours in the following exhibits.

The results have profound consequences. The situation is more serious than hours reveal. Some UK innovation productivity has been in decline since 2000.

Further details on this shocking result are unfolded in nine Exhibits.

Exhibit 1

When the productivity input is switched from hours to innovation the economy’s growth behaviour is quite different. It includes decline. Whereas the hourly productivity grows and then essentially stagnates, innovation productivity for the whole economy rises to 2006 and then falls. Now the slowdown has become a reversal that should cause alarm.

Divide the economy by sector for more insight. These divisions are Manufacturing and Non-Manufacturing where Manufacturing can be sub-divided into non-durable manufactured goods and durable manufactured goods. Non-durable goods are considered next,

Exhibit 2

Innovation productivity in non-durable goods3 has its own unique pattern. It would be hard to discern an overall trend upward or downward. There are no red dots.

Exhibit 3

Another unique pattern for innovation productivity emerges for durable goods4 Their decline begins after 2010 as shown by the red dots.

Exhibit 4

For non-manufacturing5 a decline begins even earlier. The red dots start in 2001.

These exhibits show that trends in innovation productivity are sector dependent6. Their effect on the economy as a whole is an accretion of these trends. For this reason no specific importance should be attached to an event in a particular year. This unique insight derives directly from redefining productivity. As a consequence of missing pieces8 too much current economic attention has been drawn to the financial crisis.

Exhibit 5

Exhibit 5 separates the output and input values that are divided to find innovation productivity for the whole economy in Exhibit 1. Output to GDP (black solid circles) is enumerated from the left axis. Corresponding input from iDe (blue solid triangles) is enumerated from the right axis.

Portion this plot to discover that

in the left portion the output is growing faster than the input while in the right portion the output is growing slower than input. This dichotomy leads to a diagnosis. The cause of the UK’s Productivity Crisis lies in the process that connects the blue solid triangles to the black solid circles. That process is the Innovation Funnel’s process.

And is the province of the Innovation Profession - Exhibit 6.

The original Innovation Funnel from the 1980s pictures many ideas (open squares, right) that are somehow sorted out into a single successful one (black square, right). This version is too simple for the current purpose.

In the economic version iDe has stages that produce a product or service with innovation metric (p/c) whose numerator drives GDP7. Its successful execution from concepts (very cheap) to commercial success (extremely expensive) requires dedicated teams with very special professional skills. Exhibits 7 and 8 indicate that the UK is not at the leading edge of honing these special skills.

300 Intangibles

1 Tangible

Action Requires a Stronger Innovation Profession

The UK is rightly proud of its Science. It also realizes that the advantages its scientific advances offer has too often been exploited elsewhere. This tension is only resolved when there is a strong innovation profession at work. Otherwise the only impact of the science sector on UK GDP will be publication and education.

The foregoing demonstrates that the UK Productivity crisis arises from within innovation itself. It could be getting harder to execute or have a lowered success rate or the UK innovation profession is internationally weaker than is realized (compare Exhibit 8 to Exhibit 7). These possibilities merit immediate urgent investigation and assistive action.

Exhibit 7 - Innovation Professional Support in the USA

Exhibit 8 – Innovation Professional Support in the UK

Exhibit 9 – The Rise and Fall of Economic Growth (two views)

The Rise and Fall of American Growth (Gordon 2016) can also be explained from 1951 to 2001 by the innovation metric (p/c) driven by iDe (Farrell 2018) (Endnotes8, figure 43) where a transit gap separates basic research, R, from iDe. Crossing this R-iDe gap is an historical ? for the UK since Harold Wilson’s ‘White Heat’ of the 1960s. There is also a current ? on the other side of the gap over UK execution of its innovation funnels. Funnel success requires the prospect of a superior p in conjunction with the lowest eventual c in markets. Output from funnels to GDP requires R-iDe followed by iDe→ p/c (not just R&D).

Chris Farrell Ph.D. is a practitioner and innovation professional with twenty-five years private sector experience developing and managing the creation of new products and their manufacturing technologies.

Products from his many patents have been commercialized and won awards. His contribution to polymer chain dynamics had earlier vitalized an important stream of academic research.

A technology forecast he made for American Can’s corporate technology strategy ignited his interest in the economics of innovation. It was spurred by the discovery of a mutual interest with Robert J. Gordon at Northwestern University in using Sears Catalog data.

In 2007 the US Department of Commerce launched their effort to ‘Track the State of Innovation in the American Economy’ but were foiled by not identifying the missing pieces required to do so.

These were in gestation at the time and were not ready until 2014 for the volume ‘Innovation in Economics: Missing Pieces’. This is an instruction manual on how to think about Economics if you do not have access to innovation professional experience. It rests on a five-decade foundation in commercial data interpreted through those experiences. Its applied physics has unearthed four previously unknown laws of economic growth.

Chris Farrell received his B.A. in Natural Sciences from Cambridge University (Christ’s College) and his Ph.D. in Physics under Professor Andrew Keller FRS. He served on the Board of Directors of the Product Development and Management Association and on the Industry Relations Advisory Board of Northwestern University.

17 January 2025

Endnotes

1. This paper by Richard A.L. Jones from the Sheffield Political Economy Research Institute is an excellent exposition on the topic from 2016.

2. This penetrating and Delphic comment from the Federal Reserve Board’s former Chairman Alan Greenspan comes from his 2007 book ‘The Age of Turbulence’, page 473.

3. iDe data for non-durable goods comes from the Office for National Statistics. Its nearest equivalent is BERD (Business Enterprise Research and Development). The non-durable sector’s BERD was summed from nine series DLBY, DLCT, DLCR, DLCQ, DLCP, DLCE, DLCD,

DLCC and E4BM. Its output to GDP uses the Second Element methodology from Appendix A of ‘Innovation in Economics Missing Pieces’ page 72 and is a sum of UTII and LLJL. Necessary intermediates are estimated by applying ratios from the ‘Combined Use Matrix’ for ‘Intermediate Demand’ in 1997.

4. The BERD for Durable Goods is summed from fifteen series DLCS, DLCO, DLCN, DLCM, DLCL, DLCK, DLCJ, DLCI, DLCH, DLCG,

DLCF, DLCB, DLCA, LADM and DLCU. Its output to GDP is a sum of UTIA, LLJM, LLJN, LLJO, DLWZ, DLXI plus necessary intermediates.

5. The BERD for Non-manufacturing (services and construction) is summed from nine series DLCY, LAEB, DLCZ, DLDE, DLDD, DLDC, DLDB, DLDA and DLCX. Its output to GDP is the value added within a sum of UTIM, NQEP, ABNV, NNAQ, DFDK and DLWS.

3, 4, 5. From comparison with HMRC tax credit statistics the Office for National Statistics has discovered an under-reporting in their BERD data; 3, 4 and 5 have inherited that limitation.

6. Non-manufacturing has the largest productivity and Durable Goods has the smallest. This reflects their relative ease of development.

7. The full economic analysis of the Innovation Funnel (absent from Economics) occupies pages 41 to 44 of ‘Innovation in Economics Missing Pieces’. It also constitutes link 1 of the ‘Innovation Parallelogram’ reproduced from page 66 opposite8. Because data on c (the unit cost of delivery to a market) is not collected in the UK it is challenging to measure UK innovation performance rigorously. What is presented in this booklet is the best that can be done with available data.