Monday, 15 October 2018

How much will households pay to avoid council tax?

[By Ted Pinchbeck and Hans Koster]

Are you paying attention? In many situations research suggests not. For example, when we buy things online, on average we respond more to the purchase price than to the shipping cost. As the total comprises the sum of both costs, this doesn’t make sense. A series of recent tests look at whether we take full account of the future when we buy things that are durable, like cars or fridges. Much of this evidence suggests that for these types of purchases we tend to undervalue the future, in some cases by quite large margins. That is when we buy a new car we focus too much on the up front price, and not enough on future savings that come from better fuel economy. This matters from a policy perspective because it means we end up with too many gas guzzlers.

In a recent paper, we apply these insights to the valuation of future Council taxes in home purchases. The basic idea behind our paper is to work out how much we pay upfront to reduce our Council Tax by one Pound every year (by looking at similar homes either side of Local Authority boundaries with different tax rates).

As with the work on cars, we test to see if people pay attention to the discounted costs resulting from future property taxes. The bottom line is that on average it looks like they do: our figures suggest that £30 or so will buy you an annual reduction in Council tax of £1 per year. If we can assume this saving is indefinite, your £30 investment yields a return of between 3 and 4%. This seems a reasonable return as it sits roughly comparable to average mortgage rates over the period.

Why do we care about this? Well for one it tells us that where we find some evidence that people are inattentive to the future in some places they appear to be pretty rational when it comes to home purchases (or at least in valuing the tax elements). Clearly one size does not fit all.

However, we also find that beyond 2008 that discount rates implied by taxes remain flat, and as such become detached from prevailing real terms market rates. Although we are not yet in a position to fully explain why this is (that’s research, folks!), it is clearly interesting because we’d expect changes in borrowing and lending rates to feed through to how people value the future. Our paper suggests that this is not always the case.

Monday, 1 October 2018

Incubators, accelerators and local economic development

Posted by Max Nathan, Birmingham University and CEP

I’ve written a new CEP Discussion Paper on co-working, incubators, accelerators and what they mean for local economic development policy (co-authored with Margarida Madaleno, Henry Overman and Sevrin Waights). It builds on two toolkits for the What Works Centre for Local Economic Growth.

Accelerators and incubators are business support programmes that provide co-working-based packages of support to young firms to help them grow. Widely used in the tech sector, they are now increasingly applied in other industries - including retail, fashion and design and household goods - even in the Bank of England.

Why should we look at these interventions? In a nutshell, they’re now a very visible part of the UK urban landscape. As of April last year, there were 771 incubators, accelerators and co-working spaces in Britain. NESTA have done some great work mapping their spread (see image).

In particular, there’s been an explosion of co-working, incubator and accelerator provision in London: in 2014 there were at least 132 programmes, 50% of which had arrived since 2012, and in 2017 the capital had 171, more than the next ten cities combined. Together with pop-ups, co-working and evolving high streets, these flexible spaces and practices are - arguably - starting to change the wider urban fabric.

It’s also important to look at these co-working models because of what they might achieve. In theory, incubators/accelerators can make entrepreneurs more effective, and help firms/founders to innovate. That feeds into long-term economic growth. In cities, they could also deepen clusters. They may also help groups - like some BME or female founders - facing structural barriers in ‘regular’ economic space.

Given that over half of UK providers now have some public funding behind them, it’s particularly important to understand whether these programmes actually work. For the more selective business models, like accelerators, providers might just be picking founders and firms who would do well anyway – what economists call a selection problem.


We define accelerators and incubators in Table 1, which is adapted from the Harvard Business Review. Put crudely, accelerators (like YCombinator or Entrepreneur First) offer short-term, intensive support to a competitively selected group of firms; while incubators (like TechHub) offer less-intensive, more ad-hoc support to firms on a rolling basis. Accelerators don't charge, and may take equity stakes; incubators typically charge rent. These are 'ideal-types'; in practice we see spaces (such as Second Home or The Trampery) which combine features of both.

It’s also helpful to put these newer programmes in context: there’s a long history of co-working models (Figure 1).  

In the paper we set out four – linked - ways to think about what incubators and accelerators actually do. Urban economists would think of them as cities in miniature, offering matching, sharing and (especially) learning effects over and above what economic actors would encounter at urban or neighbourhood scale.  In addition, economic geographers might focus on how programmes enable different kinds of proximity, and the pros and cons of that; management scholars would frame programmes as de-risking the process of entrepreneurship; and economic sociologists would think about economic communities of practice, where founders can develop an entrepreneurial identity. All of these analytical lenses are helpful, and all suggest important questions for evaluators.


So what does the evaluation evidence tell us? We found fourteen high quality impact evaluations looking at incubators, accelerators or both. We also found eight further studies that look at researchers – often academics – in science parks, lab co-location and in ‘temporary co-location’ settings such as conferences.

These provide good evidence that accelerators increase employment for firms who take part, compared to losing applicants (or similar non-participants). One of these also looks at firm sales, again finding a positive effect. The evidence for incubators is also positive, though less clear-cut.

We find strong evidence that accelerators help cohort firms to raise external finance post-programme, typically angel or VC money. For incubators, we didn't - surprisingly - find any studies that tested this.

Strikingly, both types of programme have a pretty mixed impact on firm survival: of the four accelerator studies that test this, for example, we find one positive, one mixed and two negative results. What's going on here? The most plausible explanation is that accelerators help participants to quickly gauge the quality of their ideas (e.g. via investor / peer feedback on demo days) and encourage those with weak propositions to quit early. That is, the programmes help kill bad ideas: one provider we spoke to told us they run 'startup funerals' to commemorate their passing, as founders move on to new things.

It's rather harder to figure out just how accelerators and incubators achieve these effects - and thus, how to design programmes that reliably get to these outcomes. In part this is because fewer evaluations have explored these issues - so the following results need more caution.

For example, we find no clear differences in outcomes when comparing public and privately-run accelerator programmes, although among the latter group, top programmes in the US (like YCombinator or TechStars) do seem to achieve better outcomes. The evidence for incubators is similarly inconclusive.

We find that more specialist programmes (single industry) help survival compared to more generalist programmes. For incubators, training seems to be more effective than networking, although neither has much impact.

Significantly, what goes on outside the building also seems to matter. For incubators, having university involvement is helpful (although this doesn't apply when individual academics step in). Two accelerator studies find that programmes in regions with denser entrepreneurial networks and high property values achieve better employment and funding effects. Not surprisingly, firms in these programmes are more likely to get funding from local investors.

We also look at co-location of researchers, often academics. Results complement the findings for firms. Very close co-location seems to raise the quality/quantity of collaborations. Spillovers are biggest in closely related fields. It’s striking that both permanent and temporary co-location can help drive up these outcomes.


Overall, we were impressed by how much high quality evidence already exists for accelerator and incubator impacts. We hope local policymakers will be able to work productively with providers to fill in some of the remaining gaps. Many of these are around how programmes achieve their overall effects, and how to consistently replicate this. More broadly, we also need to test accelerators against incubators, and against traditional business support. We also need a clearer sense of programmes' cost-effectiveness. We're not able to find cost data for incubators in the available studies - but in the toolkits we provide some back-of-the-envelope numbers suggesting that accelerators are pretty expensive to run.

There are also some broader academic questions about how these very micro-scale interventions affect larger-scale urban processes. For instance, we know that clusters are characterised by positive and negative feedback loops. Productivity effects grow with cluster size, as the set of knowledge spillovers gets larger and richer; at the same time, growing clusters become progressively more crowded and expensive, often displacing smaller or newer firms.

Co-working-based interventions can - in theory - simultaneously increase cluster productivity for a given size (by enabling innovation and entrepreneurship) and flatten the cost curve (by more densely co-locating firms in physical space).What might be the effect size of such provision, at what scale, and how might such interventions shape cluster trajectories?  There is a big and fascinating agenda to explore here.

Monday, 24 September 2018

There is a housing crisis: there are not enough houses being built and they are becoming ever less affordable; and they are getting smaller…

[By Prof Paul Cheshire]

As a species we are very well honed not to face unpleasant facts. We mock the ostrich but humans have head-burying down to an art. Worried you might have cancer? Don’t worry the doctor – she might confirm it.

But we can do better than deny the danger: we can create for ourselves an alternative story to reassure. Climate change is a major threat? Roll out Nigel Lawson to paint an alternative reality. Brexit is likely to be seriously damaging to our prosperity? Roll out Boris Johnson to imagine a rosier world: oh – or, of course, the all-purpose danger denier – Nigel Lawson.

And so it is with housing. A long term and endemic lack of housing supply? Ian Mulheirn will explain why that is not so. And now houses are getting too small and there isn’t room to swing a cat? No worries: Dr Chris Foye, from the newly funded Collaborative Centre for Housing Evidence can resolve your worries. Contrary to what other researchers and agencies have been saying he can reassure you not only are houses really getting bigger but we do not want more space in houses anyway. So that is both ends covered: we do not have to worry about shrinking living space.

We dealt with Ian Mulheirn’s ‘no shortage’ claims in a recent blog. So what about this most recent claim? All the authorities and surveys of which we are aware show significant falls in the size of new houses built in Britain (or England depending on source); and at the same time they show how much smaller new build houses are in Britain/England than in other comparable countries: 38% smaller than in Germany and 40% smaller than in the land-strapped Netherlands in 2005.

It is true that there is a wealth of folk legend in measuring house sizes and some factoids of dubious provenance (here's an example of how a number of 76 metre squared for the average size of a UK house or new build came to be built in to ‘knowledge’.) We may legitimately have concerns as to the reliability of comparative house size data but the pattern does seem to be consistent. The most recent official publication giving EU wide comparisons shows English houses were small by rich EU country standards and getting smaller: the average size of existing houses was given as 87m2 while that of more recently built houses was 83m2 with only Romania and Italy having smaller new build houses; and Denmark having new homes averaging 132m2.

Dr Foye however, claims new build houses are getting bigger in England – from 88m2 in 2004 to 90m2 in 2016. Examining the details of the source he cites for this however reveals it is spurious. It relates to the size of homes sold: very different from new homes. As the ONS helpfully explain:
… floor space [has] seen small increases over the period with the biggest shift seen between 2008 and 2009. During this period there was an increase in the proportion of detached properties purchased … and a respective fall in the proportion of flats. As flats tend to be smaller than houses this contributed to the growth seen in floor space … between 2008 and 2009. Since 2012, while the proportion of detached properties has remained broadly consistent, the proportion of flats purchased has increased. This has reduced the average … floor space slightly, but it is still above 2004 levels.

In other words, what Dr Foye is talking about is not the size of new build houses but the size of houses currently being sold. And that will, of course, be strongly influenced by the composition of sales. The claimed increase in new house sizes is an artefact of more detached properties being sold during the financial crisis years; not evidence that house sizes are increasing.
A longer term but less well documented source is here. This is not peer reviewed research but was based on samples of houses available on popular property websites and using their dates of construction and details to derive average size of rooms and total floor area by decade built. This, too, could be subject to composition bias but it is less likely to have been a problem with this measure since that would require there to have been systematic differences in the composition of sales varying by year of construction. The conclusion was that houses were biggest if built in the 1970s when they averaged 83m2. Floor area then fell pretty steadily decade by decade to post 2010 when the size was just 68m2 – smaller than those built in the 1930s.

One possible reason house sizes could be getting smaller is that family sizes are falling. But at the same time over the 50 years since 1970 incomes have risen and we know there is a strong income elasticity of demand for space implying house sizes should have increased. And they did in almost all other countries with rising real incomes over time. So falling real incomes since the financial crisis might explain a post-2010 size reduction in new build sizes but over the longer term the expectation would be – if supply was able to respond to the changing structure of demand – new build houses would have got a lot bigger between 1970 and 2015. Indeed there is even some evidence that, independently of income, the demand for space in houses increases with age. And as we know the population of England has been aging since 1970 – this would again suggest that if supply was not constrained in some relevant way, houses would have got bigger.

To be fair to Dr Foye his claim that we really do not want bigger houses anyway seems to be more a subeditor pitching for a good headline than it reflects the contents of his research. There may be an argument to be had - what does one mean by ‘want’? But it is my judgement that at least in terms of effective demand, the evidence is overwhelmingly that as people get richer they demand more space in homes and that the UK planning system frustrates this demand by constraining land supply.

Tuesday, 7 August 2018

The Beeching Axe: the consequences of massive rail disinvestment

By Steve Gibbons, Stephan Heblich and Ted Pinchbeck

Did pulling up nearly half of Britain’s railways in the 1950s, 60s and 70s affect where people chose to live? Did it bring about the demise of rural communities and the ascendance of metropolitan areas? While some argue that it did, an alternative view is that these changes in population patterns were already occurring and that the cuts to rail lines and stations were simply an inevitable response to unprofitability in the face of changing patterns of demand. Our new research (Gibbons, Heblich and Pinchbeck, 2018, Centre for Economic Performance Discussion Paper 1563), on the consequences of the removal of the railways –the ‘Beeching Axe’, as the cuts in the 1960s came to be known – provides some answers to this long standing question.

The headline answer is that, populations in places losing their rail access declined relative to those where rail access was retained. While it is true that rail lines and stations were preserved in places where population was already growing before the 1950s, it is the loss of rail access, rather than any pre-existing population trends that explain subsequent population patterns in affected areas. These patterns had emerged by the 1980s and have persisted. If the cuts hadn’t happened, populations would have been considerably more evenly distributed across Britain at the end of the 20th Century. The London commuting area would have had 9% less population in 2001, but so would other major cities such as Birmingham, Manchester and Glasgow. Much of North West and central England gained population because of the rail cuts in the 1950s and 60s relative to areas in the South West, Wales and Scotland. The map below illustrates the what the populations of Britain’s travel to work areas would have looked like if the cuts hadn’t happened. The white and light grey areas indicate places where the population would be lower than it is now, the darker grey areas where it would be higher.

Figure 1: Counterfactual log population changes, without rail cuts, Travel to Work Areas, 2001
So what happened to rail in the mid-20th Century, and how did this affect places’ accessibility? There had been rapid and largely unregulated growth in the supply of rail infrastructure during the 19th Century, and by the mid-20th Century, with increasing use of roads, the rail network was losing money. Unprofitable lines began to close, a process accelerated by the infamous ‘Beeching report’  - The Reshaping of British Railways 1963  - which proposed a radical programme of cuts.

Figure 2 below illustrates the patterns of 1951-1981 changes in a rail accessibility index, at parish level – parish being the units on which historical census data is available. To measure rail access, we use a ‘closeness centrality’ index (sometimes called a market access index) which adds up how many destinations can be reached from an origin, with less weight on destinations with longer journey times. Bigger negative numbers mean a bigger cut. We have overlaid lines showing the rail lines that were cut from the network. The cuts were brutal. Around 40% of the lines in Britain went over this period. Even where lines were retained, many stations were closed - about 60% of them in all. Evidently, these cuts had the biggest effects in areas outside the cities, particularly outside London and the South East (the north of Scotland doesn’t lose much either, because it had little coverage to begin with).

Figure 2: Rail lines cut and changes in centrality/accessibility at parish level, 1951-198
It is these changes that we see played out in terms of population patterns in Figure 1 above, according to our statistical analysis. We also find changes in other demographic characteristics – populations in parishes losing rail access became less skilled and older. To carry out these analyses we link historical population census data at parish level to the centrality changes shown in Figure 1. We then estimate the statistical association between the change in centrality and the change in rail access, taking careful account of pre-existing population trends, and alternative explanations for subsequent population changes – like rural/urban differences, planning of new towns, and motorways. We also check that we don’t find comparable effects for stations that were proposed for closure in the infamous ‘Beeching report’, but were never actually closed.

One might think that the construction of the motorways and faster car journey would have compensated for these rail cuts. We look at this, finding that populations shifted with the construction of the motorway network too. Places that experienced improvements in accessibility through the motorway network were less affected by the rail cuts. Unfortunately, the places losing rail access were not those targeted by improvements in road access - the changes in rail and road centrality are unrelated to each other - so the motorway network in Britain would have done little to compensate the places worst affected by loss of rail.

So what are the lessons for today from these historical facts? The most general point is that transport shapes the spatial economy. This probably seems obvious, but empirical evidence on the way the spatial organisation of economic activity evolves in relation to changes in transport networks is only recently beginning to emerge. This evidence adds to that picture. The unique perspective it brings is that not only does activity reorganise when transport is improved, as shown by previous evidence, but it also reorganises when transport is destroyed.

Do the results mean that the opening of HS2 will cause everyone to move to the north, or would re-opening large swathes of disused railway relax the pressure on densely populated areas? This is probably far-fetched, given the shape of the modern day economy, the dominance of road and the promise of new transport technology. Though there has been a resurgence in rail use in the 21st century so the demand for rail is evidently there and, as our evidence shows, populations will shift to places with better transport access.

Wednesday, 11 July 2018

Turning Brownfields Around: the Role of Demand in Land Re-Development

Land demand is high in cities. Cities have access to good jobs, people and specialized consumption services. High demand and the limited supply of urban land translate into high land prices. In turn, high prices promote high density land uses. If a parcel in the square mile costs hundreds of millions, expect to find a tall office building there rather than a bucolic bungalow. Despite all this, it is common to find vacant or severely underused land in cities. Old factories, derelict dwellings, abandoned car parks; all are common images from urban life. But, for an economist, this is quite surprising. Is it that some land is simply unresponsive to demand conditions? 

Having a good answer to this question is important. Unused land makes our cities larger and our commutes longer. In stark distinction with parks, or undeveloped green-field land, abandoned previously-developed parcels are usually quite unpleasant (in econ-talk, they generate strong negative externalities). Planning authorities and local governments often have specific policies to promote land re-development, by offering tax breaks, grants or financing options to developers of infill land. Understanding how demand affects re-development helps us to anticipate whether these policies will be successful. Moreover, it also gives us a hint on whether or not market forces can themselves achieve the goal of re-developing these sites.

In a recent Centre for Economic Performance, Urban and Spatial Programme discussion paper, I address this question by using data on brownfields across England. As you may know, in the UK brownfield is the name usually given to land which was previously developed but is currently underused or vacant. Policies fostering redevelopment of brownfield land in priority over green-field (“brownfield-first”) have been a feature of the planning system at least since the 1990s and have seen some resurgence of interest in recent years. Whether these policies are actually effective in encouraging brownfield re-development in response to housing demand remains unclear.  In this paper, I use data on brownfield sites to test whether the prevalence of empty land in cities is indeed sensitive to demand conditions. In essence, I want to test whether high demand (or high price) areas are more likely to be re-developed than otherwise identical areas where demand is lower. The resulting estimate measures the price sensitiveness of brownfield re-development. If you are versed in economics, this is like estimating a price elasticity of land supply. There are several challenges when trying to estimate these parameters properly. I briefly discuss them below in case you are interested.

My findings indicate that brownfield re-development is indeed affected by local demand conditions. In the long run, re-development follows demand. Quantitatively, a 1% increase in prices leads to a 0.07% decrease in the probability of having brownfield land in a given hectare. This number looks very small but the baseline probability of finding brownfield land in my sample is also low, standing at roughly 1.5%. Hence, I conclude that price differences can induce substantial variation in the presence of empty land in our cities. Importantly, this is the case despite the tight planning restrictions that characterize the UK. This is not to say that planning restrictions cannot influence re-development. In my paper, I show that re-development of parcels in areas with tougher planning (such as the South East) is less sensitive to demand conditions.

To sum up, my results show that market forces do affect brownfield development in the long run.[1] According to my estimates, a large but plausible increase of prices of roughly 20% would lead to re-development of a large fraction of brownfields in English cities. It also suggests incentive based policies such as grants or tax breaks could, at least in principle, achieve substantial long-run re-development. Finally, the paper shows that equilibrium land supply is neither fixed nor determined exclusively by sprawl; it can also be shaped by development within the cities’ footprint. 

Technical Details
My analysis is based on data for previously-developed land sites in England, as recorded in the National Land Use Database of Previously Developed Land (NLUD-PDL). I use the 2007 version of this dataset, but newer versions can be found online. Data on prices are obtained from the land registry and data for land use changes are obtained from the Land Use Change Survey. 

 There are a few technical hurdles we have to overcome before we can obtain credible estimates of the price sensitivity of re-development. On the first place, because brownfield sites tend to generate negative externalities on nearby residents, they themselves affect prices. A growing literature has made this case over the last decade.[2] As a result, there is substantial scope for reverse causality, from vacant sites to prices. In addition, re-development costs may be correlated with amenities that affect land prices directly. For example, in a monocentric city, low amenity areas away from the city centre may be harder to re-develop because of reduced accessibility. Other confounders, such as differences in the type of sites (e.g. residential vs. industrial) in different areas could also bias na├»ve estimates.

In order to overcome this problem I need a demand shifter, a variable that affects demand without affecting supply conditions. In this study, I use school quality as my demand shifter. A long literature has documented price effects of school quality in residential markets and the UK is not an exception (see for example Gibbons, Machin and Silva, 2013). Combining data from school quality with detailed maps, I implement a two-stage boundary discontinuity design which uses school quality as an instrument in a regression of brownfield location variables on prices. The boundary discontinuity design uses school admission boundaries to ensure I am comparing locations which are very close to one another but have access to primary education of different quality. This method is what allows me to obtain credible estimates of the price sensitivity of re-development. 

Note: See Figure description in the text.

An illustration of the result is provided in the figure above. The horizontal axis represents distance to a school-admission (county) boundary, with negative distances corresponding to the side with low average school quality and positive distances corresponding to the side with high average school quality. The vertical axis measures the probability that a given hectare contains brownfield land. Fourth degree polynomials fitted on the raw data represented in solid lines. The discontinuity observed in the middle of the figure shows that brownfield land is less common in high demand locations. More details can be found in the paper.

Gamper-Rabindran, S., & Timmins, C. (2013). Does cleanup of hazardous waste sites raise housing values? Evidence of spatially localized benefits. Journal of Environmental Economics and Management, 65(3), 345-360.
Gibbons, S., Machin, S., & Silva, O. (2013). Valuing school quality using boundary discontinuities. Journal of Urban Economics, 75, 15-28.
Greenstone, M., & Gallagher, J. (2008). Does hazardous waste matter? Evidence from the housing market and the superfund program. The Quarterly Journal of Economics, 123(3), 951-1003.

[1] My estimates however, are based on very long run changes. Anecdotal evidence shows that re-development can take a long time. It took 50 years to re-develop the areas around the London Royal Docks or Battersea Power station.
[2] See for example the contributions in Greenstone and Gallagher (2008) and Gamper-Rabindran and Timmins (2013).

Thursday, 14 June 2018

Did the Blitz enhance London’s economy?

By Gerard Dericks & Hans Koster
The Blitz lasted from Sept 1940 to May 1941, during which the Luftwaffe dropped 18,291 tons of high explosives and countless incendiaries across Greater London. Although these attacks have now largely faded from living memory, our recent CEP Urban and Spatial Programme discussion paper ”The Billion Pound Drop” shows that the impact of the Blitz remains evident to this day in London in both its physical landscape and economy.

Figure 1: Greater and Inner London Blitz Bomb Density

Using recently digitised National Archive records on the locations of all bombs dropped during the Blitz (see Figure 1 above), we compare the locations of Blitz bomb strikes with local differences in London’s modern-day building heights, employment levels, and office rental prices. After controlling for the central concentration of bombs, we find that local areas which were more heavily bombed during the Blitz have more permissive development restrictions, more office space, and consequently higher worker densities today. For example, as Figure 2 below illustrates, the eastern core of the City of London was particularly heavily bombed, and is today one of the few areas in the City of London where tall buildings are permitted.

Figure 2: City of London Bomb Strikes and 10 Tallest Buildings

Consistent with considerable empirical evidence from other cities, the consequence of this higher worker density in London has been greater worker productivity (which we proxy with office rents). What is new about this research, however, is the magnitude of measured effects. Whereas previous research has primarily sampled secondary cities and has generally found that a doubling in worker density raises productivity by only about 5% (as measured by wages), even after extensive sensitivity tests our paper shows an increase in London of 25%. We argue that this difference is largely due to London’s unique position as perhaps the world’s foremost financial and commercial centre, and that the benefits of greater worker density here are likely to be exceptionally large.

City planners are tasked with controlling development in order to separate incompatible land uses and mitigate costs of congestion such as traffic. However, these restrictions (especially building height limits) entail various costs, for example, higher property prices and greater price volatility, but equally significant is the fact that constraining worker density damages the productivity of the economy. For many historical reasons London has one of the most restrictive planning regimes in the developed world. Based on back-of-the-envelope calculations, we estimate that the value of the Blitz to London in having reduced the restrictiveness of its planning regime is £4.5bn annually, equivalent to 1.2% of London’s GDP (assuming that, without the Blitz, London's density would have been constrained to what it is in non-bombed areas of the city).

Ideally, planners would calibrate the stringency of development controls to ensure that society makes the best trade-off between the costs and benefits of greater worker densities. However, in order to make this judgement, planners require accurate information on both these costs and benefits. What our research now shows is that for the case of London, and perhaps other global cities such as New York and Tokyo, the benefits of greater worker density appear to be much larger than anyone had previously surmised. Consequently, if welfare maximization is indeed city-planners’ primary goal, then, at least in those cities, planners should now be reviewing the stringency of their height restrictions and new development controls more generally.

The Blitz was a tragic episode in London’s history, the likes of which one only hopes will never be repeated. However, by locally relaxing the restrictive planning regime put in place after the war, for all its human cost, the Blitz has subsequently had an extremely positive effect on London’s present day economy. Furthermore, this lasting influence has now provided us with unique insights into our understanding of urban economics, and spotlights the exceptional dynamism of this enduring city.