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Kat Sloan

Potential Productivity Premiums Locked in Spatial Organization of Cities

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Featured Image: Photo by NASA on Unsplash

by Hadi ArbabiDepartment of Civil & Structural Engineering, The University of Sheffield

Our current understanding of and approaches to devising and selecting infrastructural and ultimately land-use interventions for better urban economic performance are often of limited capacity in providing long-term ‘place-based’ blueprints. Best framed by Sir David Higgins, the former Chair of HS2 Limited, what we need is an overall national transport strategy for and against which individual interventions can be constructed and appraised.1 In our Productivity Insights Network project, we attempted to quantifying a sense of productivity premium that may be locked in and associated with the spatial organization and connectivity of small-area neighborhoods and their demographic profiles within cities.

Cities as geographic networks

Emerging studies on patterns of urban scaling across systems of cities have sought to formalize and explain long-observed size-related agglomerations in and across different countries by developing explicit and implicit geographically embedded network models of cities’ inhabitants and mobility infrastructure.2 These works make explicit the assumption within the agglomeration-related work that average-aggregated urban economic output is a function of the number of human interactions fostered and facilitated by cities. Within these spatially explicit frameworks, long-term blueprints of infrastructure can then be thought of in terms of spatial layouts of the cities’ inhabitants that increase or maximize the number of interactions between individuals across the city and hence its economic output. Particularly, if given a certain labor and skills profile, the relative number of interactions between a city as is it exists in space and how it could potentially be (re)arranged provides a gage for the magnitude of potential productivity premium related to the city’s spatial organization.

What we have seen so far

In the rest of this post, I briefly summarize our main observations based on our optimization of inter-neighborhood distances in Sheffield to maximize its inhabitants’ interactions. I should mention in advance that our findings are mostly intuitive in themselves and might appear trivial. What is of importance, however, is that they are independently reaffirmed by our approach which can be seen to be more general in its assumptions especially avoiding strong ones regarding individual behavior.

  1. Denser cities perform better especially if long-range connectivity is more difficult.

This one is too obvious. It is easy to see that a trivial solution to optimizing inter-neighborhood distances, for maximizing individual interactions for a geographically embedded social network on a flat plane, is to stack individuals vertically to maximize density and interactions!

We have estimated Sheffield’s potential productivity premiums as the number of inhabitants’ interactions, under a varying influence of distance on interactions, relative to those of the city’s existing geography. When we assume a mobility infrastructure that assists in formation of long-range interactions already exists, there is virtually no difference in productivity of an optimized layout and only about a 12% advantage for an extremely densified layout. However, as long-range interaction formation becomes more difficult, ie, we assume a more realistic provision of mobility anywhere outside London, rearrangements of the city’s neighborhood layout can unlock a 15% increase in output with extreme densification signaling a near sevenfold increase in output.

  1. Homogenous deployment of mixed-used planning across the city is beneficial.

For most cities, the neighborhoods with the largest number of inter-neighborhood interactions comprise the city center and often exist within the city’s ring road. City centers, therefore, house a high-density mix of residential use, commercial activities, and crucially employment. This combined effect of their density and mixed use transforms them to the foci of both inter- and intra-neighborhood interactions. Meanwhile, the further away one gets from the city center, the more the prominence of suburban commuter-belt residential use and fewer the opportunities for local interactions.

Delving into the inter-neighborhood distances after optimization shows that increasing the number of interactions in the city requires a change in the land use. In essence, city-center type activities need to be more easily accessible city-wide. As such, the long-term optimal spatial layout of cities can be seen as one more resembling a chess board pattern of residential/commercial use that both maximizes overall city-wide interactions and facilitates walkability.3

Choropleth showing indicative mean percentage change of inter-neighborhood distance for each neighborhood. Color scheme from green to blue corresponds to an average decrease of distances to an average increase in distances after optimization; and as such, whether the neighborhood as a whole needs to become more central to facilitate more interactions.
  1. Rearrangement of neighborhoods unlocks the highest potential in relatively lower-income lower-education neighborhoods

Mean increase in neighborhoods’ interactions correlates systematically with their population-weighted levels of education. While, the magnitude of these effects may be due to the particularly segregated organization of neighborhoods in Sheffield, the core reasoning remains the same as that underlying the need for mixed-use planning. Lower-income lower-education neighborhoods are inherently more likely to be vulnerable to effects of low long-range accessibility while simultaneously less likely to feature adequate employment opportunities. The break-up of core city-center type functionality to be more evenly distributed across the city cultivates more interactions in these neighborhoods.

Mean percentage increase in neighborhood interactions against population weighted neighborhood average education levels. It is worth mentioning that the top 10% of the largest increases in interaction count because of our optimization of the layout involve neighborhood pairs with one area of low average income.

Connectivity beyond physical mobility

As a final note, we should perhaps mention that interpretations of urban scaling models of cities need not be constrained to purely physical aspects of mobility. Our approach has been motivated by and particularly focused on the physical aspect of the spatial organization of cities and the provision of mobility within them and we have, simplistically, quantified interactions and the possibility of their existence over distances in a city. With particular reference to the ongoing COVID-19 pandemic, the prevalence of home-working has forced a dramatic drop in physical intra-city mobility.4 However, for those sectors that have remained economically active despite lockdown measures, the underlying individual interactions involved in creating cities’ output have not completely vanished. Portions of these interactions are now simply forced to be made remotely. The spatial patterns of interaction that we have identified, ostensibly based on road distances between neighborhoods, can, therefore, be interpreted as priorities for provision of alternative and/or digital connectivity infrastructure.

Notes

1 – Economic Affairs Committee. The Economics of High Speed 2, House of Lords Economic Affairs Committee 1st Report of Session 2014‒15. https://publications.parliament.uk/pa/ld201415/ldselect/ldeconaf/134/134.pdf (2015).

2 – Bettencourt, L. M. A. The Origins of Scaling in Cities. Science 340, 1438–1441 (2013).

3- Hamiduddin, I. Journey to Work Travel Outcomes from ‘City of Short Distances’ Compact City Planning in Tübingen, Germany. Planning Practice & Research 33, 372–391 (2018).

4 – Google. United Kingdom COVID-19 Community Mobility Report. https://www.gstatic.com/covid19/mobility/2020-06-27_GB_Mobility_Report_en-GB.pdf (2020).

Firm Creation in the UK During the Covid-19 Lockdown (June 2020)

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By Anthony Savagar, Alfred Duncan, and Miguel León-Ledesma, University of Kent.

In support of their research, Anthony Savagar {Senior Lecturer, School of Economics, University of Kent and Centre for Macroeconomics), Alfred Duncan (Lecturer, School of Economics, University of Kent), and Miguel León-Ledesma (Professor, School of Economics, University of Kent and CEPR) have published the following blog ‘Firm Creation in the UK During the Covid-19 Lockdown (June 2020)’.

Read the blog in full here.

Anthony previewed some of the findings within our responsive Covid19 webinar series in the online discussion; ‘Market Concentration in the UK and the effect of COVID-19 on Business Creation‘ in which Dr Anthony Savager presented on the topic summarised below:

In many advanced economies, product market power and market concentration appears to be rising. Anthony Savagar will present results from a small PIN-funded project on the level of market concentration in the UK and its relationship to productivity. Anthony will finish the presentation with a look at how COVID-19 has affected business creation in the UK across regions and industrial sectors. Absence of business creation can lead to long-run consolidation of market power.

A recording of the webinar is also available to watch the event again.

Small Firm Public Sector Tendering Capabilities – A Regional Dashboard of Priorities for LEP Business Eco-systems (£1 in £3)

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Featured Image: Photo by Danielle MacInnes on Unsplash

By Dr Paula Turner, Co-Founder, The Centre for Tendering

info@centrefortendering.com       www.centrefortendering.com

How well do Local Enterprise Partnerships (LEPs) provide a strong value proposition in their business support ecosystem to prepare small firms for a tendering capability learning journey? A productive economy in part depends on SMEs developing a capability to tender for public sector contracts and the enterprise ecosystems that support that capability. In the Covid-19 context, enabling small firms to permeate public sector procurement markets – and ensuring that procurement is used to fulfil economic and social goals so we can “build back better” – is an even higher priority.

The UK Government’s SME Action Plan[i] sets out a modern, ambitious strategy that includes an ambition to spend £1 in every £3 of public sector procurement on smaller businesses by 2022. Achieving the Government’s laudable objectives will demand both a reform of procurement practices to streamline the capabilities demanded by tendering and investment to build the capability to tender in small firms.

An enterprise ecosystem needs to know how to support small firms to strategically commit (or not) to tendering, build the capability needed to be tender-ready and continuously develop tendering capability through tendering experience. This ecosystem will not only supply information via learning opportunities but enhance social capital: an entrepreneur’s ability to get resource – in this case information and sensemaking about tendering – out of networks. The support will, therefore, draw them into a relationship with procurers, expert business support providers and peers and encourage information sharing, reflection and mutual sensemaking. In other words, if an ecosystem is a multi-lateral arrangement of actors and organisations that creates a value proposition[ii] then it requires a coordinated infrastructure to ensure that £1 of £3 of public sector procurement can be spent on small firms that have capacity to tender.

Outside of the retail sector, business founding is usually focused on delivering goods and services to customers, rather than being ready to sell. Selling via tendering can be particularly alien to small firm leaders because most have product or service specific expertise and lack any experience of tendering. For many, their previous employment may have provided face-to-face customer negotiation skills but not ability to strategically situate a business in a tendering environment, decipher tender invitations and write a technical and competitive document.

Small firm learning tends to be problem-situated and small businesses tend to be focused on the short-term problem of survival. Typically, they are not likely to spontaneously develop capability to tender prior to an individual tender invitation. There is an argument, then, for aligning learning to the process of responding to a specific tender invitation. Yet, the Centre for Tendering’s Capability Model for Tendering depicts top-performing capabilities that suggest being successful at tendering demands some pre-existing capability as well as the ability to learn fast about efficient and competitive means of selecting tender invitations, writing tenders and reviewing outcomes. When a small firm approaches tendering without any awareness of its demands, and very low tendering capability, they are likely to waste resources on failure or an aborted attempt to tender, particularly when they have failed to ‘triage’ the opportunity to realise that they are trying to tender for a contract they cannot win or do not want.

For some firms, public sector tendering is not the right strategic choice and learning to avoid this market is sensible. Of more concern are discouraged tenderers, with the potential to thrive through public sector contracting but low optimism or access to support to build capability. And, repeat failures that make multiple unsuccessful attempts to tender but do not learn from these or make a strategic choice to properly invest in building capability or to avoid this market.

There is a puzzle here: how to motivate and support small firms to strategically invest in developing a core set of capabilities so they become tender-ready as well as supporting them to use experience to hone and adapt tendering capability so they build and sustain their competitive edge.

Join The Centre for Tendering free webinar on Tuesday 30th June 1-2pm https://us02web.zoom.us/webinar/register/8815918639780/WN_hu_4r4zuRka3ruOw-9A0Cw to learn how regional enterprise ecosystems can comprehensively support small firms: Enhancing Business Support for SME Tendering: A Dashboard of Priorities for Regions.

Finally, small firms are open to innovation at a time of crisis and we know they often turn to their customers or suppliers to find collaborators for innovation[iii]. It would be unproductive for procurement to become a ‘flat process’ of administration rather than collaborative means of developing economies and communities. To achieve this, LEPS must commission enterprise ecosystem actors that can invest in developing small firm capability to tender and procurement must become more open to input from small businesses.

References

[i]  Department of Business, Enterprise and Industrial Strategy (2019), SME action plan, Crown Copyright, https://www.gov.uk/government/publications/beis-small-and-medium-enterprises-sme-action-plan

 

[ii] Adner, R. (2017) ‘Ecosystem as structure: An actionable construct for strategy’, Journal of Management, 43, (1), pp.39-58

 

[iii] Roper, S. (2020) ‘R&D and innovation after Covid-19: What can we expect? A review of trends after the financial crisis’, Published: 7 May 2020, https://www.enterpriseresearch.ac.uk/people/stephen-roper-3/

A New Green Shovel? Options for the transport stimulus package

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Featured image: Photo by Frank Chou on Unsplash as per the original blog post. 

Iain Docherty (Dean of the Institute for Advanced Studies at the University of Stirling and infrastructure theme leader for the Productivity Insights Network) has a new blog post with the CREDS network which outlines five sets of investment options which will define whether shovel ready schemes are genuinely transformative and green or simply dig us into a bigger hole. The blog with Greg Marsden (Professor of Transport Governance at the University of Leeds and Director of the DecarboN8 Network) and Jillian Anable (Professor of Transport and Energy at the University of Leeds and is the mobility theme leader at CREDS) is available now.

Read it in full here:

A New Green Shovel? Options for the transport stimulus package

What can we learn from previous recessions about the COVID-19 economic crisis?

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Featured Image: Photo by Ben Garratt on Unsplash

By Richard Lewney 
Cambridge Econometrics

Is this time different?

Tolstoy famously began Anna Karenina with the sentence: ‘All happy families are alike; each unhappy family is unhappy in its own way.’ Is that true of periods of economic prosperity and recessions? Is today’s recession so different in character that past experience is irrelevant as a guide to how the present crisis may play out?

Figure 1: UK macroeconomic and sectoral indicators in past recessions

Source: ONS

Recessions have different triggers…

Figure 1 shows the experience of previous recessions. The 1990 recession began with the bursting of the bubble by a sharp policy reversal when the interest rate brake was applied sharply from mid-1988. The first casualties were over-extended borrowers, especially in the property market. The 2008 recession was triggered by a global banking crisis: bank lending and credit markets seized up in the face of the threat of insolvency. GDP fell much more sharply than in 1990, both because the recession was global and because of the credit crunch. The start of the COVID-19 recession has been different again. It is global, but it was not triggered by a banking crisis and nor was there much of a speculative bubble to burst. Instead, production and consumption in activities that require social proximity were shut off by lockdown policies.

… but a similar self-reinforcing pattern

Initially, therefore, the character of the recession reflected the nature of the particular trigger. But past experience shows that once a recession takes hold, some typical reactions produce common consequences. Households experience a fall in wage and dividend incomes and then move quickly to restore their financial wealth, reining in spending sharply.  Economists who assume that households are well-informed and forward-looking emphasise the role of imperfect capital markets in preventing consumption smoothing: some (poor) households lack access to credit.  But it’s not just about imperfect markets: fundamental uncertainty has a critical impact on the decisions of households and banks.

The chart in the top left corner of Figure 1 shows how, in aggregate, households increased their ‘net lending’ during the recession, meaning that collectively they spent (whether on consumer products or housing) less than their income.  While they may run up debts in the very short term, poorer households are unable to access extended credit and richer households act in a precautionary way to rebuild financial wealth.  This shows up in large reductions in spending on discretionary items: housing, holiday travel, appliances, entertainment and private healthcare.  Business also cuts its discretionary spending, chiefly investment.  Together, the result is large reductions in output in the industries that supply investment products, consumer durables and income-elastic consumer services.

We manage uncertainty by deferring spending

In the COVID-19 recession, the lockdown has seen large falls in sales of social consumption items, including some that are not normally particularly income-elastic such as haircuts. But we are also seeing output cuts in sectors linked to discretionary spending, such as cars, which may not bounce back to ‘normal’ as showrooms reopen. Even with substantial government support for incomes, it is likely that households will, collectively, rebuild wealth as typically happens in a recession rather than return spending quickly to pre-recession levels. How many are requesting refunds rather than rebooking holidays?

How productivity will recover

The chart in the bottom left corner of Figure 1 shows the strong cyclical effects on labour productivity in a recession: employers respond with a lag to the fall and then recovery in output growth so that labour productivity slumps and then recovers. The difference in the 2008 recession was that the scale of job shedding was surprisingly modest and, in the recovery, productivity growth resumed the long-term slowdown seen since the mid-1990s. In the COVID-19 recession, we should not be concerned about the heavy cyclical fall in labour productivity: the loss of output during lockdown was unavoidable and the government’s wage subsidy has the goal of limiting job shedding. The question is whether the subsequent recovery in productivity will be achieved by a recovery in consumer and investment spending or by sluggish GDP growth and cuts in job

The Usefulness of Applying Macro-Sector Results to Regional Levels

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Featured Image: Photo by Robert Bye on Unsplash

By Ben Gardiner
Cambridge Econometrics

Context – the lack of timely economic statistics recording local impacts of Covid-19, and the desire to fill that gap

The spatial economic impact of Covid-19 is still emerging, and will do for some time to come. Early indicators such as the unemployment rate are not perfect due to distortions caused by the government’s furlough scheme uptake and other measurement issues, while in most other cases it is still too early to register local impacts.

This has led to a number of attempts (Centre for Cities[1], Centre for Progressive Policy[2], etc) to ‘localise’ impacts on the basis of macro-sector results or assumptions, by taking sector results (such as those produced by the OBR or a macroeconomic model) at national level and assuming that activities in each region behave the same way, with the same growth rates, and then aggregating the results to give an effective average (top-down) average set of results.

But how useful is this method, or to put it another way, how accurate are top-down assumptions when predicting regional and local outcomes in a recession?

Clearly, we don’t yet even know national-sector outcomes, let along local ones, but what if there were a way to assess how accurate the former were in predicting the latter, with perfect foresight?

Method – learning from the outcomes of the last (Great) recession

There is a way, of sorts. If we look back to the past (the Great Recession) and in particular the period of negative national GDP growth during 2008 and 2009, we know what the outcome was for macro-sector growth of GVA, employment, and productivity, and their sub-national equivalents at all spatial levels.

If we take these macro-sector results and apply them in the same top-down way as others are doing for the current Covid-induced crisis, what do we observe?

What happened during 2008-09 at the national-sector level?

Firstly, the chart below shows output (real GVA) and employment growth over the two-year period, 2008 and 2009. Areas of productivity increase or decrease are also highlighted (where output increases are relatively larger than employment) and the 45-sector disaggregation being used[3] is grouped across broad aggregates. We observe the following:

– A wide spread of sector outcomes, although as it is a recessionary period most sectors are clustered in negative quadrants.

– Of the larger sectors, those within manufacturing were among the hardest hit (four of the largest negative GVA growth rates are all in this activity), while non-market services (and some business services) are among those sectors that managed to maintain positive growth.

– For employment, the outturn broadly followed that of output but not completely (the correlation between the two sets of growth rates is around 0.5). There are some quite major differences, e.g. Pharmaceuticals, Electricity & Gas, and Architecture and Engineering Services, where output and employment moved in opposite directions.

What happens when you apply these results to different spatial levels?

To find this out, we took the same 45-sector structure at three regional aggregations:

(i) NUTS1 (old RDA regions – 11 for Britain)

(ii) NUTS2 (36 British regions, either counties or small groups thereof)

(iii) Local Authority Districts (371 areas in England, Scotland and Wales)

We then applied the above-mentioned growth rates and compared this with what actually happened to output, employment, and productivity. The results are shown in the charts below for the first two indicators.

NUTS1 Regions

NUTS2 Regions

LADs

Conclusions – beware of top-down sector-based predictions!

The main findings are that, even with perfect foresight, when applying a top-down approach:

– Given the increasingly similar sectoral structure of places around Britain (due to de-industrialisation and the rise of the service-sector economy) there is not much variation in the projected changes.

– The smaller the spatial level you are disaggregating to (i.e. the more local you get) the more likely it is that specific characteristics and heterogeneity will play an important role in determining outcomes, so the top-down predictions will be less accurate. For example, the tasks and functions undertaken within a sector will also play an important role in which areas do better or worse than the national average[4], as would other characteristics such as the degree of urbanisation[5].

– It is more difficult to predict employment outcomes than it is those for output. Indeed, below NUTS1 level there is really no association between prediction and outcome at all. This implies that local characteristics are dominant in determining employment outcomes than they are for output, as well that the types of job themselves are important determining factors.

– For productivity, whatever the spatial scale, there is no association between actual and projected growth. In other words, using a top-down method to predict what would have happened, even with perfect knowledge of the macro-sector outcomes, would be of no use at all.

We know the coming recession will be different in terms of the speed of its onset, the sectors which are affected (due to the nature of the shock) and the duration. No shock is exactly alike in its cause and effect. And perhaps this time, with wide-spread sectors so badly affected such as tourism, a top-down sectoral approach may be more accurate as, to some extent, everywhere is being affected in similar ways.

However, what we can learn from the past is that this isn’t usually the case. The devil is in the detail, and a place-based approach is really needed to understand the likely reaction, response, and effects that will, eventually, be observed.

[1] https://www.centreforcities.org/blog/what-does-the-covid-19-crisis-mean-for-the-economies-of-british-cities-and-large-towns/

[2] https://www.ft.com/content/4b2c47fc-c195-4109-9432-87cbc9e0ace5

[3] Based on Cambridge Econometrics’ local area database, which is itself based on official ONS data.

[4] For example, see other analysis undertaken by Cambridge Econometrics on this topic – http://www.camecon.com/wp-content/uploads/2020/06/2020-04-16_COVID-19_Local-slide-pack.pptx

[5] Such investigations are beyond the remit of this blog, but would present an interesting research topic.

Why Cleantech Investment Should be a High Priority Now and after COVID-19

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Featured image: Photo by Jack Sloop on Unsplash

By Dr Robyn Owen (Middlesex University London) &
Theresia Harrer at the Centre for Enterprise and Economic Development Research (CEEDR) and Centre for Understanding Sustainable Prosperity (CUSP).   

The COVID-19 crisis threatens all of our lives. Understandably, it is currently the central focus of government policy globally. Yet history tells us that post-crisis economic reconstruction is most successful where investment is greatest in new emerging sectors. It is crucial, therefore, that investment in the UK is directed towards globally leading innovations for environmentally sustainable development, rather than simply to become more efficient at producing and selling more of the same.

Prior to the COVID crisis, progression to Net Zero carbon neutral emissions was rising to the top of the policy agenda in many countries. There was a widespread declaration of the climate crisis and climate war and a proliferation of Green New Deals – overarching policies for an integrated government-led approaches to delivering reduction in carbon use and emissions.

We have argued that an essential element of climate change policies is a recognition that investing in early stage SME Cleantech innovators is crucial. These are companies developing technologies that lower carbon use and which are key to reaching the ambitious goals of an at least 40% reduction in greenhouse gas emissions set by the UNFCCC Paris Agreement of 2015. However, the costs and risks of investments in the cleantech sectors such as renewable energy, transport, building and communications infrastructure are high. Government interventions have been necessary to attract a greater quantum of private investment more rapidly into the sector through inducements such as co-investments, niche loans, grants, tax incentives and green tariff payments.

However, a great deal of this government intervention has been directed at headline large infrastructural schemes such as UK Green Investment Bank investments into Wind Farms and Global Climate Partnership Fund renewable energy projects. An examination of the UK Green Finance Strategy  (2019) underpins this work with commitments to spending billions on further, very necessary, infrastructural works to create low carbon environmental efficiency.

It is a concern, however, that relatively little attention has been given to creating an effective financing system to support the role of the innovative Cleantech SMEs. In fact, it is barely mentioned in the UK Green Finance Strategy. This is worrying, since these companies are acknowledged as one of the key leaders in delivering the global low carbon solutions that can win the war on climate change.

A caveat here is the aforementioned high cost and risk of investments in the Cleantech sector which is most acute in terms of the poor risk reward balance during the so-called ‘valley of death’ period of R&D and early commercialisation, where funding so frequently runs out. The notorious 2016 MIT study of US cleantech failure during the Global Financial Crisis period suggested that venture capital (VC) alone is not the solution, whilst more recent studies suggest an improving Cleantech investment market, prior to COVID. Here it is important to recognise that Cleantech investment is not just about quick win returns from digital technologies (such as smart metres and software Apps), but the willingness to finance sufficiently for the long horizon engineering and bio-science, more capital intensive Cleantech innovations.

The familiar danger signs of another post economic crisis collapse of VC investment are here. This could put back the fight to tackle climate change, since we are now very aware that economic recessions reduce investment liquidity and increase the timelines on successful innovation investment exits by several years.

Now, at this time of deep crisis, we must not lose focus on the fundamental need for a better funded systematic government-led Green Deal approach to early stage Cleantech funding. Such an approach requires government financial intervention policy and targeted programmes to support and encourage environmental impact investing and to ensure that the right types of Cleantechs are properly assisted and fully funded for success, rather than the current disjointed and under-funded efforts which in the UK have led to a culture of funding for failure and sub-optimal trade sale exits – often to foreign investors.

So, even in this crisis period, it is vital for the UK government not to lose sight of the bigger, longer term picture, and the need to improve early stage Cleantech SME innovation finance and support to tackle climate change.

A current study for the ESRC funded UK Productivity Insights Network (PIN) by Middlesex University, Centre for Understanding Sustainable Prosperity (CUSP) researchers, alongside partners from SQW, St John’s Innovation Centre and the UK Green Angel Syndicate is exploring the relationship between early stage Cleantech investing in the UK and its potential environmental sustainability impacts. The aim is to radically disrupt current thinking about the role of innovation in economic productivity measures and demonstrate the crucial importance of early stage impact investing into Cleantech innovation. Early findings demonstrate a paucity of useful data, due to the lack of standard industrial classification sector matching for Cleantech activities. They further demonstrate that, despite years of advice, the development of an effective, efficient UK Cleantech investment funding escalator -which progresses early innovation Cleantechs through for example grants for early proof of concept to significant Series A+ VC rounds and to successful commercialisation – is far from in place.

We welcome feedback and collaboration with our research team from Cleantech investors, SMEs, support services and policymakers. Together we can improve the system and put the UK at the forefront of the globally important Cleantech sector, which can be a key driver for future economic and environmental well-being.

For further information on the Middlesex University PIN project please contact Dr Robyn Owen, or Theresia Harrer at the Centre for Enterprise and Economic Development Research (CEEDR) and Centre for Understanding Sustainable Prosperity (CUSP).

Does productivity still matter?

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Featured Image: Photo by Ravi Roshan on Unsplash

By Vania Sena (University of Sheffield)

As we start the seventh week of lockdown, it is probably a good time to take stock of the impact of the COVID-19 outbreak on the economy. So, what has happened so far? The outbreak has generated a contraction of the aggregate demand worldwide which according to almost all commentators will be even larger than the one experienced during the 2008-2009 financial crisis. Initial estimates from the OBR suggest that, in the UK social distancing measures may result in up to 35 percent drop in economic activity during the second quarter and a yearly contraction of anything between 7% and 15%, depending on the speed of recovery. Revenues have disappeared from one day to the other in a number of industries and reserves have been dried up to pay salaries, many of which are being covered by government schemes.

Unsurprisingly, the priority for the government has been to stabilize the economy where possible. However, the question more people are now asking is: what are the implications of COVID-19 for the long-term growth prospects of the economy? The UK economy will need to grow at a rate that allows it to address the challenges associated with the management of large government debt while improving the performance of the labour market. This would require a strong recovery in productivity growth to a level that predates the 2008 financial crisis. In addition, the characteristics of COVID-19 (i.e. the fact that the current outbreak is not a one-off but several outbreaks are expected for the next eighteen months) implies that building up the foundations for strong productivity growth is intertwined with building up resilience in the economy. In other words, building up resilience in the economic system has to become one of long-term objectives of economic policy along with strong productivity growth.

But what is resilience exactly? According to the definition from a dictionary, resilience refers to the ability of individuals or organizations to recover quickly from shocks. However, in the context of COVID-19, it has become clear there is no consensus on what resilience implies in practice. We all agree that public services need to build up excess capacity to be able to cope with future outbreaks; at the same time, it has been argued that excess capacity will be artificially generating inefficiencies that may eventually hamper productivity growth. Is this really the case?

Let us start from the fact that productivity may grow because of technological progress and of changes in efficiency; in turn these can be spurred by changes in the scale and scope of operations as well as variations in the usage of inputs. Importantly, organizations do not need to experience all of them to be able to increase their efficiency: for instance, for a given level of inputs’ usage, efficiency can be improved by changing the scale of the operations or its operations. In other words, developing excess capacity to be able to cope with future outbreaks implies rethinking the size and scope of the organizations. Increases in the scale of operations in such a way that organizations may find themselves exploiting increasing returns to scale may be helpful to increase efficiency while smart diversification or consolidation of activities increases the conditions to exploit economies of scope which can translate into efficiency gains, for a given level of capacity (whether in excess or not). So, large organizations with excess capacity can be better positioned to generate the productivity growth that the economy needs as well as to cope with the costs associated to excess capacity.

What are the implications of all this for economic policy? On the one hand, we will see an increase in concentration in some industries as large and productive firms will end up dominating a number of industries (where economies of scale and scope will permit it). Clearly, there is scope for regulators to monitor the process to ensure that building up resilience does not translate into inefficient outcomes for consumers and suppliers. On the other hand, there is a need to develop a fresh approach to business support. Clearly a model where large and productive firms are surrounded by micro firms (whether suppliers or competitors) which cannot cope with major shocks is not sustainable in an economy where resilience is a key objective. In this case, the sustainable scaling up of micro firms is the policy priority: all the standard tools of industrial policies (from finance to business support programmes) need to be geared towards companies that have the potential for scaling up and thrive so that industries can be dominated by a mix of firms which can be productive and resilient at the same time.

The bottom line is that productivity growth as a policy goal still matters but more importantly, aiming for productivity is not at odds with creating a resilient economy which can cope with major economic shocks.

Small Business in the Time of Covid-19

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Featured Image: Photo by Kelly Sikkema on Unsplash

By Andrew Henley (Cardiff University) and Tim Vorley (University of Sheffield)

Across the world, as the Covid-19 pandemic is raging, the disruptive impact on small businesses has been without precedent. Immediate commentary documenting both scale and specific examples of this impact is not in short supply. This blog post offers some preliminary thoughts and reflections on performance and productivity for small businesses. While it is too soon to provide any hard assessment of the impact of the current crisis on business performance, so our purpose here is comment on some emerging themes based on current knowledge and ongoing responsive research.

The economic impacts of the crisis are likely to be multidimensional. These include differences between short-term and medium-to-long term impacts, variations across different sectors, the differential spatial impacts of the crisis. At the same time the resilience of small businesses and their ability to survive the crisis has demanded business models be redesigned. Many of the issues that small businesses face and their capacity to respond are determined by their response at the onset of the crisis – and so the immediate decisions of owner managers will have longer term implications.

In the short-term it is inevitable that productivity will take a further significant hit – a conclusion that is particularly unwelcome given the ever-widening UK productivity gap. This is because in a recession businesses generally attempt to retain employees with skills, especially if those skills are scarce or central to the firm. Employee absence due to illness or quarantining is hitting many SMEs, especially small and micro-businesses, very hard, since business-critical skills coverage is not “de-risked” across a large employee pool.

Labour hoarding sees the productivity numerator (turnover or value-added) fall more quickly than the denominator (labour input). If firms have flexibility to reduce hours, then the adverse impact on productivity per hour worked may be less severe than on productivity per employee. Recent emergency policy announcements on wage subsidies under the job retention scheme are specifically designed to maintain employee attachment and therefore indirectly to lower productivity.

These initiatives are likely to be more crucial to business survival for less well-capitalised, and less profitable SMEs, and speedy policy implementation here is of the essence. Survey data collected in mid-March 2020 by the University of Sheffield with Small Business Britain bears this out – almost three quarters of SMEs surveyed expected revenues to fall by over half.[1] One emerging recommendation relates to the urgency of making relief available: roll the policy out and worry about deadweight losses later, which might be partially recoverable through business and self-assessment tax systems.

Early research indicates that since the Government announced the measures available that there is considerable local variation in the administration and access to support. These difficulties are compounded by the eligibility, or perceived eligibility, meaning that not all micro businesses will benefit as intended and as entitled. This reflects another major perennial challenge for small businesses policy, which is encouraging the take-up of government programmes, as 82% of firms surveyed have not previously sought business support.

Some firms simply won’t survive. In the medium-longer-term, if the crisis persists beyond two quarters into late summer and autumn, the least resilient SMEs will shut down, that is those least well capitalised or those seeing the biggest falls in revenue. As recovery returns this shake out process could then see a productivity bounce, although this will have been at a very high price in terms of firm failures.

In the UK the most immediate distress has been seen amongst those businesses which provide non-essential face-to-face physical service delivery in leisure, hospitality, transport services and non-food retailing. In advance of the shutdown and lockdown, these sectors had already been earmarked for support by the Chancellor in the Budget on 15th March 2020. As well as these sectors, places such as historic city centres and coastal resort towns have been hit hard, as the demand shock of ‘social distancing’ is seeing some consumers deferring discretionary spending on non-essential items. The societal importance of supporting these firms is important for safeguarding the efforts and livelihoods of small businesses through this crisis, and will help ensure the future vitality of these hard hit places.

However, despite the early and enhanced support for these sectors many are in the so-called ‘long tail’[2], and while making an important contribution to localities are unlikely to be the engines of recovery and future growth. Naturally, there are some firms are adapting to new realities by shifting their business models with and without the help of government support. The study highlighted that as well as those businesses already working online, many of whom have been less affected, many other owner managers have sought to take their businesses online. The shift from bricks to clicks presents an opportunity for productivity gains as revenue streams shift on-line, with online sales are twice as productive as offline[3] .

Even a cursory glance at the business pages of the news media reveals some significant changes to working practices and business models. At this stage, however, it is unclear to what extent these changes to working practices and business models represent temporary ‘work-arounds’ or whether they are genuinely new-to-business innovation. Where effective these reforms have the potential to promote future resilience and enhance productivity, especially if they result in significant and permanent reductions in cost associated with activities such as business travel etc.

At the other end of the spectrum knowledge based and services businesses, for example software development, are likely to be more resilient and may even see some productivity gains from costs savings associated with remote working and the of closing physical office space. What is as yet unknown is the extent to which SMEs are at risk from collapse in the supply chains in which they sit, especially in sectors such as manufacturing , construction or business-to-business service provision. This potentially threatens the so-called ‘tradeable’ as well as ‘non-tradable’ businesses.

Recent analysis by Brookings highlights the likely differential spatial impact of the crisis, arising from the uneven spatial distribution of SMEs by sector.[4] In the UK the South West, in particular, is exposed to distress in the accommodation, food and drinks sectors, accounting for almost 10% of private sector employment, although this sector is also relatively larger in London. Retailing is another low productivity sector that also accounts for a large employment share in the North and in Wales compared to the Midlands and London. On the other hand knowledge based and services businesses, which might be more resilient to disruption, account for less than 3% of private sector employment in the North East, North West, Yorkshire and Humber, East and West Midlands and Wales, but nearly 8% in London.[5]

So, to conclude, the short-term damage to productivity looks likely to be significant, and in the context of the UK’s persistent and widening productivity gap this is highly unwelcome. However, where micro businesses have the ability to transform and adapt there is the potential for enhance resilience and productivity gains over the longer term where they are able to implement permanent changes to ways of doing business.