Thursday, 29 August 2019

Dirty Density: Air Quality and the Density of American Cities


Air pollution tends to be worst in large cities and their urban cores. As a result, it is urban air pollution that makes the headlines when the media report on pollution and its effects (see, for example, this, this and this).  Since it is mainly an urban problem, air pollution exposure is shaped by urban planning and policy. In particular, it can be affected by population density, the defining feature of urbanization that distinguishes cities from smaller towns and villages.
In a recent paper we study how air pollution - as measured by fine particulate matter (PM2.5) concentration - is shaped by cities’ population density in the United States. In particular, we want to find out whether residents in dense urban areas are exposed to worse air quality. For this purpose, we use new data on satellite-derived measures of PM2.5 concentration at a fine spatial scale and demographic information from the US census.
Intuitively, our analysis is conducted by comparing cities of different densities and their average concentration of PM2.5. The resulting scatter plot is provided in Figure 1 and serves to illustrate our main result: denser cities tend to have worse air quality. But this naïve approach cannot give us a definitive (causal) answer on whether population density affects air pollution. Why? Because many confounding factors can bias the associated estimates. For example, people decide on where to locate based on various factors including local amenities and employment opportunities. Given that many productive activities (e.g. factories) generate pollution; if people move into areas close to these activities, a naïve estimation which ignores these confounders will overstate the true effect of density on pollution. 

Figure 1 – PM2.5 Concentration and Population Density

Note: The vertical axis represents PM2.5 average residential exposure (in μg /m3), as obtained from the satellite-derived measures. The horizontal axis represents the natural logarithm of population density. The points represent 933 CBSAs (metro and micropolitan areas). The black line is estimated by OLS using the underlying data.

To deal with this and other endogeneity issues, we use data on the geological characteristics at and around US cities as instrumental variables for density. Intuitively, we use these variables to generate variation in density that is not shaped by pollution or other confounders.[1] Our instrumental variable estimates confirm the message in Figure 1: denser locations are associated with higher concentrations of PM2.5. How much higher? Quite a bit. According to our estimates, doubling density – say, increasing the density in Houston to match that of Chicago – increases PM2.5 concentrations by 0.73 μg/m3, which is roughly 10% of the average pollution across cities. Using well-established dose-response functions that map pollution concentration to mortality rates, in conjunction with official mortality costs estimates from the US Environmental Protection Agency, we find that doubling densities would lead to annual mortality costs of as much as USD 630 per capita.
So denser cities lead to higher pollutant concentration. But was it not the case that denser cities are also greener? A prolific strand of research has emphasized the environmental advantages of denser cities (see Kahn and Walsh 2015 for a review). In dense cities, households enjoy shorter commutes when driving to work (Duranton and Turner 2017) and may even switch to other transport modes when these are available (Cervero and Guerra 2011). In a world in which a significant amount of emissions is generated by transport – especially driving – this observation has led to the conclusion that denser cities are also greener (see Glaeser and Kahn 2010). But our results indicate that this should not be interpreted as meaning that high density leads to better air quality. Yes, denser cities are associated with lower emissions, and this is important for reducing global greenhouse gas concentrations. However, if we are concerned about local air quality, having lower emissions does not suffice. Even if emissions are low in denser cities, it is the concentration of pollutants that determines local pollution levels. Our paper shows that pollution exposure is higher in denser cities, making the environmental quality in these cities lower than in other locations.[2]
What should be done?
For decades now, the compact city urban planning approach has been promoting urban densification as a way to contain sprawl, reduce car use and promote some of the beneficial agglomeration forces normally associated with density. There are many things to enjoy about compact cities, including shorter commutes and better access to commercial and recreational activities. Yet our results indicate that the purposed environmental advantages of compactness may be limited to reductions in global pollutants. When it comes to our lungs and hearts, denser cities create a more polluted, harmful environment. Urban planners should take note of these trade-offs when designing the cities of the future.

References:
Cervero, R., & Guerra, E. (2011). Urban densities and transit: A multi-dimensional perspective. Institute of Transportation Studies, University of California, Berkeley.
Combes, P. P., Duranton, G., Gobillon, L., & Roux, S. (2010). Estimating Agglomeration Economies with History, Geology, and Worker Effects. Agglomeration Economics, 15.
Combes, P. P., & Gobillon, L. (2015). The empirics of agglomeration economies. In HHandbook of regional and urban economics (Vol. 5, pp. 247-348). Elsevier.
Duranton, G., & Turner, M. A. (2018). Urban form and driving: Evidence from US cities. Journal of Urban Economics, 108, 170-191.
Glaeser, E. L., & Kahn, M. E. (2010). The greenness of cities: carbon dioxide emissions and urban development. Journal of Urban Economics, 67(3), 404-418.
Kahn, M. E., & Walsh, R. (2015). Cities and the Environment. In Handbook of regional and urban economics (Vol. 5, pp. 405-465). Elsevier.




[1] This strategy was initially developed in the agglomeration literature trying to estimate the productive advantages of cities (see Combes et al. 2010, Combes and Gobillon 2015). In our case, we use it to study one of the congestion forces that constrain city growth.
[2] Complementary results in the paper show that the observed increase in PM2.5 is not driven by a different sectoral composition of production in larger cities or by differences in total city population. See details in the paper.

Monday, 19 August 2019

Why banning the construction of second homes in St. Ives and elsewhere has been a bad idea and what to do instead

By Christian Hilber, LSE Department of Geography and Environment

c.hilber@lse.ac.uk
In May 2016 the local residents of St. Ives approved a referendum that stops newly built houses in town from being used as a second home. A few other Cornish towns have followed suit. And tourist destinations in other parts of the country are contemplating similar policies. The Economist, the Times and the BBC recently  pointed to unintended consequences of these policies: higher prices for existing homes, less construction of newly built homes and an adverse effect on the local economy—mainly tourist and construction businesses.
In recent research (here and here for the academic piece) we explored the economic impacts of banning the construction of new second homes in the touristy parts of Switzerland. The Swiss Second Home Initiative was approved in March 2012 and banned the construction of new second homes in municipalities with more than 20% of such homes.
There is one crucial difference between the Swiss Alps and St. Ives. In the Swiss Alps, primary and second homes are very different; think of wooden chalets near ski lifts as second homes and stone or brick buildings near schools and stores as primary homes. In St. Ives and other towns in Cornwall, primary and second homes tend to be rather similar—they are close ‘substitutes’. This has important implications.
When the ban was introduced in Switzerland, demand of second home investors shifted elsewhere, perhaps to the French or Austrian Alps. Unemployment rates started rising and the price of primary homes started falling relative to the unaffected areas. And because already built second homes became dearer (no new construction allowed!), the price of these rose with the unintended consequence of financially benefiting the owners.
In St. Ives, where the typical primary and second home tend to be rather similar, demand of investors shifted from newly built to existing homes, increasing the price of existing homes and reducing the price of newly built ones. The emerging gap between the two prices is the so called ‘conversion option’ of existing homes—the monetary value of the option to convert a primary into a second home. Newly built homes no longer possess such an option.
So it seems the bans in Switzerland and Cornwall backfired. In the case of St. Ives, existing housing has become even less affordable for young would-be buyers who want to get their feet on the owner-occupied housing ladder, and, there is less new construction of affordable housing. But also local firms, particularly construction and tourism businesses and, importantly, their workforce, lose out. If the ban intended to help young local residents who struggle to find decent jobs and affordable homes, then it backfired spectacularly.
The ban in St. Ives will likely not even succeed in improving the local community ‘character’. One particular concern in tourist destinations like St. Ives is that they are seasonal and thus, for much of the year, resemble ghost towns. The trouble with the ban is that it does encourage second home investors to buy up existing homes from local residents. Over time, St. Ives is thus set to become more—not less—like a ghost town. Exactly what the ban intended to avoid.
The only potential beneficiaries of the ban are already existing owners of housing in St. Ives—owners of existing primary and second homes. They financially benefit because their assets are higher in demand and thus become more valuable.
So what can and should be done to address the legitimate concerns of local residents in touristy places?
First and foremost, local policy makers and local residents have to ask themselves whether they are really willing to accept and bear the long-run adverse consequences associated with keeping second home investors out, namely, an adverse effect on the local construction and tourism businesses. If (big if) the answer is ‘yes’, then local authorities should consider alternative policies to a ban.
A much better policy would be a sizeable annual local tax on the current value of second homes. Compared to a ban on the construction of second homes, such a tax has important advantages. First, it generates revenue for the local authority and this may be used to provide or improve local public services for permanent residents—think of local schools, libraries or social services. A ban, in contrast, generates zero revenue and moreover limits the potential of local authorities to benefit from Section 106 agreements—private agreements between local authorities and developers attached to a planning permission to make development, that would otherwise be unacceptable, palatable to local authorities. Second, since the proposed tax has to be paid every year, it discourages buying property for investment purposes. It makes the investment less attractive financially. This will help with the affordability of existing homes. A sizeable local annual tax will most effectively repel those investors who consider second homes as pure investment and not as consumption. The second home investors who still buy, mainly for consumption motives, can be expected to be around more often. Seasonal tourist locations will look and feel less like ‘ghost towns’.  
But why not just a tax on the transfer of properties? The trouble is that the Stamp Duty does not encourage second home investors to use the property more intensively. In fact, the longer the investor holds the property, the less important, is the Stamp Duty relative to the capital gain at point of sale. The same argument applies to potential new second home investors. A rise in Stamp Duty will lead to a small one-time downward adjustment in the price (reflecting the increased anticipated tax burden). Once prices adjust, new second home investors may still mainly consider expected capital gains and not the presumed consumption value of the property. And it is important that the tax is local because otherwise it does not generate local tax revenue, benefiting local residents.
Allowing local authorities to charge a multiple of the Council Tax to second home investors may be a sensible ‘second best policy’ that is clearly preferable to a ban. The trouble is, that the Council Tax is highly regressive. It thus won’t much discourage wealthy investors from buying large underutilised properties.
How could the proposed policy work in practice? One could just take the last sale price of a house (from the Land Registry) and the corresponding local house price index to adjust the price to the current market price. The local authority could set a tax rate on the so assessed current price. A high (low) tax would reduce house prices significantly (moderately) but also strongly (only weakly) adversely affect the local economy.
The political backlash against second home investors is not confined to Cornwall or Switzerland. It is a worldwide phenomenon. There has been a staggering amount of wealth accumulation among a growing cohort of high earners that has led to a dramatic increase in second home investments in the more desirable seasonal tourist areas worldwide (and in ‘superstar cities’ such as London). The ensuing political backlash has been spreading quickly around the world.
Second home investors are a popular scapegoat—In Britain mainly for the ongoing housing affordability crisis. However, the nation-wide crisis has little to do with second home investors. The underlying causes are mainly a dysfunctional planning system and a lack of fiscal incentives for local authorities to permit residential development (see here or here). If national policy makers are serious about addressing the national housing crisis, they should focus on the underlying causes, otherwise, like the ban in St. Ives, their policies are likely to backfire as well.

Friday, 26 April 2019

Financial innovation in mortgage products spurred the rapid increase in credit and house price growth during the last housing boom


The dominance of the 30-year fixed rate mortgage is a defining feature of the United States’ housing market. For a brief period in the mid-2000s, however, this dominance was challenged by the popularity of non-traditional mortgage products that allowed borrowers easier access to credit through variable interest rates with teaser periods, extended terms, and interest only or negatively amortizing repayment schedules. In effect, borrowers could obtain a mortgage with lower monthly payments in the short-term than were available through the 30-year fixed rate mortgage.


As Figure 1 shows, the share of mortgages with at least one non-traditional feature grew sharply during the last decade. At their peak in 2005, about sixty percent of all purchase loans in the United States included at least one non-traditional feature. Their coincidence with rapid house price appreciation during the housing boom led many to conclude that they were used to speculate on the housing market and, thus, partly to blame for the boom. However, given that incomes rose little duringthat period, borrowers may have instead flocked to non-traditional mortgage products to maintain affordability in a time of increasing home prices.

Figure 1 – Share of mortgages with non-traditional features and national house prices

In a new CEP Discussion Paper, Affordability, Financial Innovation and the Start of the Housing Boom my coauthors and I study the relationship between the start of the housing boom and the use of non-traditional mortgages.

To do so, we first identify the starts of housing booms in individual US counties. Figure 2 shows substantial variation in the timing and size of local house price booms. Next, we use this variability to systematically track the use of non-traditional mortgage products around the local housing boom starts. To illustrate, the house price index for Clark County (containing Las Vegas) is shown in Figure 3(a). Our methodology estimates that the Clark County housing boom began in February 2004. In the figure, there is a clear difference in the house price path and appreciation on either side of the estimated housing boom start. In Clark County, we find that the rapid adoption of non-traditional mortgage products began earlier than the estimated start of the local housing boom (shown in Figure 3(b)).

Figure 2 – Distribution of House Price Breaks
Figure 3a - Clark County Estimated House Price Break and Non-traditional Mortgages, Estimated House Price Break
Figure 3b - Clark County Estimated House Price Break and Non-traditional Mortgages, Use of Alternative Mortgage Products

Like in the experience of Clark County, we find that the increased use of non-traditional mortgage products preceded the accelerated rise of house prices in markets with house price booms that began after the year 2000. In the year before, the share of new purchase mortgages with any non-traditional feature increased 5 percentage points on average, primarily due to the use of variable rate and interest-only mortgage products. Moreover, in late-booming markets, we find results consistent with other findings, that lenders altered their loan supply along dimensions that would be less frequently and consistently reported to investors, such as denial rates and the share of loans classified as subprime. Overall, these findings support the view that financial innovation in the 2000s contributed to rising house prices by reducing payment constraints.   

A key question regarding the county-level patterns we document is: what mechanism drove the expansion of non-traditional mortgage products in markets after 2000? To answer this question, we exploit the dramatic increase in Treasury rates in mid-2003 for plausibly externally influenced variation in lenders' financing constraints. Earlier work shows that in response to the higher Treasury rates and concurrent collapse of mortgage refinancings, lenders rapidly expanded their use of the private mortgage backed securities to finance new mortgage originations. This led to a sharper increase in mortgage supply and house prices in markets with higher shares of lenders connected to the private secondary market -- that is, those lenders that relied more on non-core deposits (funding outside deposits from retail customers) prior to the increase in Treasury rates.

In Figure 4, we show that, in addition to higher prices (panel (a)), counties exposed to a higher share of non-deposit lenders also experienced an immediate increase in the use of non-traditional mortgage products following the 2003 increase (panel (b)). Notably, while the growth in non-traditional mortgage products is instantaneous in mid-2003, house prices appreciate most sharply in the top three quartiles of the non-core deposit distribution of counties, but only after 6--12 months. In addition, across each quartile, growth in alternative mortgage products increased monotonically with the share of non-deposit lenders. This pattern suggests that an important mechanism during the 2000s was an external shock to lenders' cost of capital that drove them toward the secondary market, where non-traditional products flourished.

Figure 4a - Appreciation and Alternative Finance by Non-Core Lending Quartile, House Price Appreciation
Figure 4b - Appreciation and Alternative Finance by Non-Core Lending Quartile, Use of non-traditional mortgage products

However, we uncover a stark difference in the systematic relationship across markets with a house price boom that started before 2000.  In these markets, non-traditional mortgage products emerge as unlikely contributors to county-specific starts in booms. In addition, we find little evidence of credit supply growth in the years immediately before or after the start of these booms. We also find evidence that incomes in these markets grew faster than the national trend in the years before a local boom. These results suggest that the impetus for the housing booms of the late 1990s were driven more by economic fundamentals and that borrowers turned to these products to maintain affordability.

In the wake of the crisis, regulators have placed significant limitations on a number of non-traditional mortgage features by excluding them from the definition of a “Qualified Mortgage” for regulatory purposes. As laid out by the Consumer Financial Protection Bureau in their January 2013 report, lenders will no longer be able to underwrite a qualified mortgage loan based on a “teaser” rate in determining the ability to pay. Moreover, mortgages cannot contain interest-only, have lower payments than the interest due, or extended term features if lenders want to meet the legal presumption of complying with the qualified mortgage regulation. 

As we have shown, whether restricting these contract features will affect the formation and magnitude of housing price cycles in the future depends crucially on the context of the use of these features. Although these rules have only recently come into effect, the consequences of limiting these products is also likely to reduce credit access for some households, while guiding others into more standard contracts.
Additional info:

This article is based on the CEP Discussion Paper, ‘Affordability, Financial Innovation and the Start of the Housing Boom

About the author

Lindsay Relihan – LSE Geography and Environment

Lindsay Relihan is an Assistant Professor of Real Estate Economics and Finance at the London School of Economics. She is an applied microeconomist with interests in urban economics, household finance, housing, and real estate. Her research agenda is focused on understanding how the spatial relationships between firms and consumers shape outcomes in consumer credit markets.




Tuesday, 2 April 2019

Valuing the environmental benefits of canals using house prices

Britain has an extensive canal and navigable river network, which played a vital role in transporting goods from the Industrial Revolution through the 18th, 19th and early part of the 20th Century. Their use for transporting freight had all but disappeared by the mid-20th Century and many had fallen into disrepair or been abandoned. Since then, the canal and waterway network has been restored and developed into a potentially valuable environmental and recreational amenity, providing the venue for extensive range of tourism and leisure activities and a habitat for wildlife. Canals also provide transport corridors for walkers and cyclists along the towpaths formerly used by horses for drawing boats. Features of the canals are an attraction to those interested in industrial heritage and canal-side properties can have distinctive character with an outlook over green space and water.

Our recent research investigates the value of this resource to local residents in England and Wales, using house prices. Analysis of house prices is a well-established method within urban and environmental economics for establishing the value of amenities – such as good schools, transport, low crime or low pollution. This value is expressed as the monetary value of other types of consumption that people have to sacrifice in order to pay more for housing close to a desirable amenity (or away from an undesirable one). Part of our analysis looks prices close to canals across the whole of England and Wales, and part looks specifically at the change in prices induced by the restoration of the Droitwich Canals in the West Midlands after 2007.

We find that there is a quite a large house price premium for living close to a canal, but this is very localised (see Figure). On average, a buyer can expect to pay around 3-4% more for a property within 100m of a canal relative to prices elsewhere (in 2016 prices), but this premium falls to zero beyond 100m. The implication is that the price effect is driven predominantly by canal-side properties and others with a direct outlook on the canals or immediate access. There is no premium for living near a canal other than right up close to it. The premium is higher in dense urban areas, as we would expect if people are willing to pay more in housing markets where green space is scarce. We also find evidence that canal-side locations have been attractive for developers, with a much higher proportion of new-build sales within 100m of canals relative to elsewhere - a 5.9 percentage point increase on an 7.8% baseline.


Price premium  estimates and 95% confidence intervals for properties close to canals and waterways, 2002-2016 data. Distance scale in 100m. Vertical scale is prices in log points relative to properties between 1000m and 1500m from canals and waterways (0.01 = 1%).

Interestingly, the premium fell suddenly at the time of the last recession, from over 8% in 2007 to 4.4% in 2008. This step change suggests there was a structural shift in the demand for this environmental amenity at the time of the recession and the premium has not recovered since (up to the end of our data in 2016). A possible explanation is that demand shifted away from luxury aspects of property, including canal-side locations, as incomes fell and uncertainty about the housing market increased. 

Some back-of-the-envelope calculations indicate that the environmental benefits provide an uplift to land values within 100metres of canals in England and Wales that amounts to around around £0.8-£0.9 billion in 2016.

How we did the analysis

Although the idea of using house prices to value amenities is conceptually simple, there are challenges. The basic method is to use statistical techniques to estimate the average price difference between houses with a high level of an amenity (or dis-amenity) and similar houses with a lower level. Clearly, a key requirement is data on some variable that represents this exposure, in our setting, the indicators of distance from a property to its nearest canal. There are, however, potentially many ‘confounding factors’ which vary with distance to a canal and also affect the price directly – the physical characteristics of the housing, other amenities like distance to employment or distance to transport. Estimation methods must take account of these confounding factors so we are comparing houses on a like-for-like basis. Failure to do so might lead us to attribute differences in prices to proximity to canals, when in reality the price differences are caused by something else. For example, if canals in urban areas are predominantly in old industrial areas, and these industrial areas have older smaller houses and industrial buildings that are less attractive to residents, it might appear that proximity to canals reduces prices when in fact it is the average size of the houses or the industrial character of the environment which reduce prices. 

To avoid this type of bias, we adopt two strategies in our study. First, we use standard multiple regression techniques to estimate the association between canal proximity and housing prices, while adjusting for a rich set of structural housing characteristics and local area attributes on which we can obtain data (‘control variables). We control for a wide range of land use indicators, distance to geographical features, employment and demographic variables, and in our preferred versions of these specifications, we further control for ‘fixed effects’ at a small geographical scale – either Middle Layer Super Output Areas (MSOAS) or Lower Layer Super Output Areas (LSOAs) – and for differing price trends at Local Authority District level. This means we estimate the price effects from variation in the distance to canals, and associated variation in house prices, that occurs within these small geographical areas. Confounding factors that vary at a higher geographical level between LSOAs/MSOAs – such as access to labour markets – are eliminated.

Our second strategy focusses on a specific canal regeneration project, which restored an abandoned canal – the Droitwich Canal in the West Midlands of England. The Droitwich Canals were closed in 1939 and in the early 2000s were mostly overgrown, drained of water, non-navigable or completely destroyed. They underwent a major restoration from 2007 onwards and were re-opened in 2011. The restoration reopened them for boat navigation and recreation, improved the general environment and provided a habitat for aquatic life. In this case, we compare the price changes occurring in a ‘treatment group’ of properties close to the canal when the canal is restored, with price changes occurring at the same time in appropriate ‘control groups’. The assumption behind this method is that prices would have evolved in the treatment group close to the Droitwich canals in much the same way as in the control group, if the Droitwich canals had not been restored. As control groups, we use places further away from the Droitwich canal, and places close to an existing neighbouring canal – the Worcester and Birmingham canal – that has remained in continuous use, and where we would not expect to say any environmental amenity-related price changes at this time. These comparisons allow us to estimate the value of the restoration and the enhanced recreational and environmental amenities it provides, in so far as this value shows up in different price changes in the treatment and control groups. This type of ‘difference-in-difference’ estimator is widely used for estimating the impact of policies on economic outcomes in the policy evaluation literature. Both methods give similar findings, with a sharp increase in prices close to canals, with larger but less precisely measure effects from the analysis of the Droitwich canals restoration.

Disclosure: the research was funded by the Canal and River Trust, but carried out independently by researchers at the Centre for Economic Performance and Department of Geography and Environment.

Tuesday, 11 December 2018

Do foreign migrants ‘grease the wheels’ of the labour market?

[by Michael Amior]

Recent political developments in the US and Europe have led to renewed interest in the large and persistent regional disparities which plague our societies. These disparities have been partly driven by a secular decline in manufacturing employment, whose impact has been heavily concentrated geographically. In principle, these disparities should be eliminated by regional mobility. But at least in the US, fewer people are making long-distance moves than in the past.

In the face of these challenges, it has famously been argued that foreign migration can "grease the wheels" of the labour market. Given that new immigrants have already incurred the (fixed) costs of moving, they are very responsive to regional differences in economic opportunity - and therefore accelerate local population adjustment. If foreign migrants do indeed settle quickly in those regions where they are most needed, forcibly dispersing them within receiving countries (as several European countries do with refugees) may hurt natives as well as the migrants themselves.

In a recent CEP discussion paper I revisit this question using US census data spanning five decades (1960-2010) and 722 commuting zones. Remarkably, I find that new foreign migrants account for 30 to 60 percent of the average population response to local changes in labor demand. However, population is no more responsive in locations better supplied by new migrants: a bigger response from foreign workers is almost entirely offset by a reduced one from internal mobility. This is fundamentally a story of “crowding out”: I estimate that new foreign migrants to a commuting zone crowd out existing US residents one-for-one. This is entirely due to a reduction of internal moves in to the affected areas, rather than larger moves out.

The magnitude of the crowding out effect is puzzling. If Americans take time to adjust geographically to local declines in manufacturing employment, why do they appear so responsive to the location decisions of new immigrants? One plausible explanation is under-coverage of unauthorized migrants in the US census. This would overstate the crowding out effect (and also imply an even larger foreign contribution to local adjustment).

Even in the extreme case of complete crowd-out, a regionally flexible migrant workforce can save natives from having to incur potentially steep moving costs themselves. It is also worth stressing that the US population is generally considered to be relatively mobile. One might expect that foreign migration "greases the wheels" more effectively in parts of Europe where internal population adjustment is more sluggish.

Monday, 19 November 2018

The Economic Impacts of Constraining Second Home Investments

[by Christian Hilber]

Investment in second homes has been surging around the world. This surge has triggered a serious political backlash in many countries, especially in tourist areas and superstar cities. The backlash has at least in part been driven by legitimate concerns, such as ever more unaffordable housing, destruction of areas of natural beauty or creation of ghost towns during large parts of the year.

The crucial question is how politically to address these concerns. Some countries, such as the UK, and cities, such as Vancouver, have introduced substantive transaction taxes on the purchase of second homes.

Another policy that has become increasingly popular are constraints or outright bans on the construction of new second homes. The latest example in the UK is the Cornish seaside town of St. Ives. Other local communities in Cornwall and across the rest of the country have signalled interest in including similar policies in their own Neighbourhood Plans.

What are the economic impacts of such bans on local housing and labour markets? This is the question that my co-author, Olivier Schöni, and I explore in a recent CEP study.

In our empirical analysis, we exploit a unique quasi-natural experiment, the Swiss Second Home Initiative (SHI), to test theoretical predictions and identify causal effects of a ban on the construction of new second homes.

The SHI requested that construction of new second homes be banned in municipalities where such homes represent more than 20% of the total housing stock. The SHI was approved by the narrowest of margins – 50.6% of votes and 13.5 of 26 cantons – in March 2012. It came into force in January 2013.

Voters in tourist municipalities with very high shares of second homes were heavily opposed, presumably due to fears about adverse effects on the local economy. This contrasts with voters in the larger Swiss cities who favoured the initiative.

So what were the effects of banning the construction of new second homes in desirable Swiss tourist locations? The ban on the construction of new second homes lowered the price of primary homes, adversely affecting local homeowners, but increased the price of second homes, further raising the wealth of existing – typically already wealthy – second homeowners. We also find that the policy increased unemployment rates, thus harming the local labour force.

All in all, our findings suggest that the local economy effect (affecting primary house prices negatively) dominated the amenity-preservation effect (affecting primary house prices positively), resulting in an overall fall of the price of primary homes. They also suggest that, at least in the Swiss context, constraining the construction of new second homes reinforces rather than reduces wealth inequality.

Banning the construction of new second homes or imposing transaction taxes on second home purchases may be politically popular policies in the short run. But our research suggests that they may not do anything to cure the underlying causes of the problems.

[If you’d like to learn more about second homes, and the theoretical and empirical work we’re doing to look at the impact of constraints on investment then take a look at our longer piece in the latest edition of Centre Piece]

Tuesday, 13 November 2018

Measuring Urban Economic Density

[By Dzhamilya Nigmatulina]

What makes cities productive? For a long time, researchers have known that city size matters a lot – people who live in larger cities tend to earn more and be more productive. People (and firms) benefit from proximity to each other and these benefits are manifested in cheaper trade, improved hiring and learning, and a wider choice of inputs and products.

But does city size always generate proximity gains? A city that occupies a large area may also have very low density and low proximity, compared to a more compact city with the same population. In the same way, even a city’s high average density may not mean high proximity. Can we test if the shape of city density has economic implications? If so, how can we capture the meaningful dimensions of the city density?

In our recent CEP discussion paper we try to answer these questions using data from cities of sub-Saharan Africa.

A real world example

Cities with poor planning and land institutions may face issues of low proximity. In 1994 the urban scholar Lusugga Kironde living in Dar-es-Salaam noted about his city in his thesis:

"[…] Dar-es-Salaam is not systematically structured either in form of functional zones, or of income levels, or of types of developments. It does not reflect a structure responding to either a city well ordered by government, or, to the niceties of the market theory. It is this kind of unpatterned, irregular, and hotch potchy development, inconsistent with good land use planning or with the economic theories of land use structure, which engendered interest leading to this study."

Figure 1. Dar-es-Salaam, according to 1992 aerial photograph
Source: Kithakye 2009
 

Twenty years later and not much changed in Dar-es-Salaam. Even though the residential population distribution on Figure 3 tells us nothing about the location of firms and workers, it is evident that people reside far from each other: the highest density settlements are informal, and large residential areas are far away from the coastal centre of the city. The story of Dar-es-Salaam is not a unique one in Sub-Saharan Africa. Colonial history, imperfect transport systems, and lack of formal land has impelled cities to grow haphazardly, leaving many crammed in informal sector areas with poor access to the economic and social opportunities of urban life.

Figure 2. Dar-es-Salaam population distribution according to the 2012 population census.
Source: Tanzania National Bureau of Statistics, Population census 2012 and author’s calculations


Does shape matter?

In our study we use Landscan - a dataset the captures the distribution of ambient population, i.e. the average number of people in each square kilometre of the city over 24 hours. This measure captures not only residents, but also indirectly the location of firms. Understanding the actual proximity of people or workers within a city also requires a measure of the city extent, which defines a self-contained labor market. We choose population density to define city extents and use 1,500 people per km^2 as a simple cutoff.

Living Standard Measurement Study (LSMS) surveys from the World Bank give us income and wages for cities in six sub-Saharan Africa countries.

African cities are surprisingly dense and clustered

Comparing 599 large cities in Africa to other continents reveals that the African cities are denser, and more clustered, than those in the developed world. This is surprising, since the example of Dar-es-Salaam is often seen as representative of a more general problem of weaker land institutions in Africa (which would lead to lower clustering). If we look at differences of the cities in sub-Saharan Africa and the cities in other developing countries, we find that sub-Saharan Africa, Asia and North Africa are similar in terms of density and all clustering measures. It is Latin America that is less clustered.

Looking across African cities, density correlates with individual and household wages in that city, but clustering does not help explain differences in wages. This is puzzling. Especially when we see that for detailed firm-level data in Kampala higher worker density does matter for firm value added.

Conclusion

What explains the higher than expected clustering of African cities? One possible explanation is that our measure of “ambient” population captures the residential population more than the worker population. If higher density, or “cramming”, compensates for difficult commutes then the pattern of residential locations swamps the distribution of employment that matters in explaining productivity.

This could also explain why our clustering measures fail to explain wage premiums even though the Marshallian theories (and some additional findings for worker density in Kampala) highlight the benefits of proximity. Landscan only provides a proxy (e.g. built cover, building heights and other undisclosed data) measure for the things we care about. Comparing and contrasting novel spatial datasets, such as Landscan, with traditional survey and economic census data would help improve our understanding.

Adding transport costs can also paint a better picture on the true proximity between workers. See Akbar et al. (2017) for steps in this direction.

These questions and data limitations provide lots of opportunities for future research. As better data become within reach, we will be able to calculate our clustering indices and better test our proximity theories.