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Natural Assets: Surfing a wave of economic growth

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OxCarre Research Paper 170
Natural Assets: Surfing a wave of
economic growth
Thomas McGregor
Oxford
&
Samuel Wills
OxCarre
Natural Assets: Surfing a wave of economic growth
Thomas McGregor and Samuel Willsú
February 2016
DRAFT
Abstract
Many natural assets can not be valued at market prices. Non-market valuations
typically focus on the value of an individual asset to an individual user, ignoring
macroeconomic spillovers. We estimate the contribution of a natural asset to ag-
gregate economic activity by exploiting exogenous variation in the quality of surfing
waves around the world, using a global dataset covering over 5,000 locations. Treat-
ing night-time light emissions as a proxy for economic activity we find that high
quality surfing waves boost activity in the local area (<5km), relative to compar-
able locations with low quality waves, by 0.15-0.28 log points from 1992-2013. This
amounts to between US$ 18-22 million (2011 PPP) per wave per year, or $50 billion
globally. The eect is most pronounced in emerging economies. Surfing helps re-
duce extreme rural poverty, by encouraging people to nearby towns. When a wave
is discovered by the international community, economic growth in the area rises by
around 3%.
JEL codes: H41, O13, Q26, Q51, Q56
Key words: Non-market valuation, natural capital, surfing, night-time lights.
úOxford Center for the Analysis of Resource Rich Economies, Department of Economics, University
of Oxford, UK. Samuel Wills would like to thank the Economic and Social Research Council for financial
support [grant number ES/K009303/1]. Corresponding email: thomas.mcgregor@economics.ox.ac.uk
1
Figure 1.1: Wealth breakdown by income group (Jarvis et al., 2011)
1 Introduction
Natural assets are an important part of the world’s capital stock. According to the World
Bank (Jarvis et al., 2011) they account for approximately 5% of global capital, and 30%
of that in developing countries (Figure 1.1). Existing estimates of natural capital focus on
assets that can be valued at market prices, including non-renewable assets like energy and
mineral resources, and renewable assets like cropland, pasture and forests. Many other
forms of natural capital are excluded from estimates because they can not be valued
at market prices. These non-market assets include spectacular mountain ranges, tree-
covered forest paths and, to some eyes, clean six-foot waves peeling seductively down a
point break.
This paper estimates the contribution to aggregate economic activity of a particular non-
market natural asset: surfing waves. We choose surfing waves to exploit a natural exper-
iment: the exogenous distribution of high quality waves around the world; and a unique
dataset: a crowd-sourced online database of wave location, characteristics and quality. In
doing so we estimate the value to individual users as well as macroeconomic spillovers in
the surrounding area.
The quality of surfing waves provides a clean natural experiment. It is entirely predeter-
mined by a specific combination of environmental characteristics that include the shape
of the coastline, the sea-floor, and the direction of prevailing winds and swells. For much
of history this specific combination was of no interest to humans, until surfing became a
popular pastime in the 1960s. Unlike most non-market assets we are able use the exogen-
ous variation in the measured quality of waves to determine the marginal contribution of
these assets to economic activity.1
There are two mechanisms by which non-market assets like waves may contribute to
local economic activity: by stimulating activity within the region, and attracting new
activity to it. Stimulating local activity happens by creating a demand for complementary
goods and services. For surfing this includes manufacturing and retail of surfboards,
wetsuits and other specialised accessories, and services such as board repairs, surfing
lessons and lifeguarding. Attracting new activity happens by drawing new demand to the
1The quality of surfing waves has also been used as a natural experiment to study the emergence of
informal property rights in California (Kane, 2009).
2
1992 2000 2013
5km
10km
Figure 1.2: Example of illumination growth in the 5km and 10km surrounding Anchor
Point, a “World Class” (quality 3) wave in southern Morocco.
area to exploit the scarce resource. For surfing this may include transient and seasonal
demand, like tourism; or more permanent demand like retirees and people relocating for
lifestyle reasons. Surfing is particularly well suited to studying these mechanisms because
waves are a common pool resource and, as such, are liable to over-use (Rider, 1998).2
The historical solution has been for surfers to travel and discover new waves, making
exploration a core part of surfing lore.
We are able to determine how good quality waves contribute to economic activity by com-
bining a unique dataset on the characteristics of 5,151 waves around the world, with two
detailed and geographically disaggregated datasets on night-time lights and population.
The wave data is compiled using Python from the website www.wannasurf.com. WannaS-
urf is an online database of surf spots recording their location, quality, diculty, coastal
geography, best wind, swell and tide conditions and accessibility, amongst other things.
The data on the website is crowd-sourced (like Wikipedia) from a community of 78,000
“WannaSurfers”, on whom data is also available. The second dataset records the amount
of light emitted at night-time around the globe, at a 1km2resolution, which is a useful
proxy for economic activity (Henderson et al., 2011; 2012). The third is from LandScan
and uses a variety of spatial inputs to measure population, also at a 1km2resolution.
We employ a polynomial distributed lag model to determine how waves aect illumination,
and in turn economic activity. Our control group is areas surrounding the lowest quality
waves. This is a relatively high hurdle, as these areas are coastal and of sucient interest
to surfers to appear in our crowd-sourced database. The model measures the marginal
contribution of higher quality waves to economic activity over the course of our sample,
controlling for both wave and time fixed eects.
We find that high quality waves increase economic activity (proxied by lights) in the
surrounding 5km area by 0.15-0.28 log points, or 16%-32%, over 21 years (1992-2013),
relative to places with low quality waves. This amounts to US$18-22 million (2011 PPP)
2Attempts to allocate property rights and charge entry, as at Cloudbreak in Fiji, have been short-lived
due to public outcry.
3
per wave per year in the surrounding 50km, or US$48 billion globally, which is consistent
with existing survey-based estimates.3The eect is highest for 4-star waves (out of 5)
because the highest quality waves tend to be too dicult for the average surfer. Emerging
economies benefit the most, so long as they have a sucient level of political stability and
ease of doing business.
Economic activity increases in aggregate, rather than simply being reallocated from other
areas. It is shared amongst nearby towns, and is particularly pronounced in the closest
town and the largest town within 50km. Activity in unlit-rural areas, which are typically
extremely poor, does not increase. However, surfing does reduce extreme poverty by en-
couraging the rural poor to move to more urban areas. Overall the permanent population
around 4-star waves falls, consistent with tourists driving up rents. When new waves
are discovered, surrounding economic growth can rise by up to 3 percent. These results
are robust to other, non-surf related, characteristics of the coastline. Our estimates are
a lower bound for the utility value of indirect natural assets, because many of the rents
from the asset will not accrue to the local area (such as profits to surfwear companies,
and the travel costs of tourists spent to get to the waves).
This work contributes to the extensive literature on valuing natural assets. Market-
based techniques are the most straightforward, but are only suited to traded assets and
ecosystem services. This can be extended to non-traded assets through securitisation
(Chichilnisky and Heal, 1998). Non-market techniques are based on either stated or
revealed preferences (Freeman, 1993; Kopp and Smith, 1993). While stated preference
methods like contingent valuation are widely used, bias remains an issue. It can be
improved with appropriate incentives (Carson et al., 2014). Our method relies on revealed
preferences, building on a small literature valuing individual surf breaks using travel costs
(Mavericks, California: Coman and Burnett, 2009), and hedonic pricing (housing in
Santa Cruz, California: Scorse et al., 2015). We extend these works by studying a panel
of more than 3000 waves in over 130 countries; and by capturing the macroeconomic
spillovers of the asset to surrounding economic activity beyond those captured by the
user or home-owner.
Academic work on valuing non-market natural assets has not yet been adopted consist-
ently by policymakers. Since the Agenda 21 agreement at the 1992 UN Earth Summit in
Brazil there has been an international movement towards integrating environmental and
economic accounts. The most recent iteration is the System of Environmental Economic
Accounting 2012 (SEEA: UN, 2014). This measures natural assets in both physical and
monetary terms, though the scope of the latter is limited to market or near market as-
sets. The SEEA can encompass the value of non-market assets in land values, though
isolating them is dicult. It has prioritised developing “consistent valuation techniques
beyond the System of National Accounts in the absence of market prices”. The World
Bank also estimates natural capital but excludes most non-market assets due to a lack
of data (Jarvis et al., 2011). However, it acknowledges that “missing ecosystem services”
like recreation and aesthetic views may be important, especially in high-income countries.
This work also contributes to the literature on the geographic determinants of economic
activity. Many papers have used night-time lights to this end (Ghosh et al., 2010; Chen
3Lazarow (2009) uses surveys to find that surfing contributes approximately $113-216 million (US$
2011) in direct expenditure to the economy of the Gold Coast, Australia. This covers 8 high quality
waves but excludes macroeconomic spillovers.
4
and Nordhaus, 2011; Henderson et al. 2012; Michalopoulos and Papaioannou, 2013; Smith
and Wills, 2016). Other work uses satellites to study non-marketed ecosystem services
using data on landcover (Sutton and Costanza, 2002; Costanza et al., 2014). Faber
and Gaubert (2015) study the local eects of tourism in Mexican beach towns, using an
instrument for beach quality based on sand colour and oshore islands. In contrast our
quality instrument comes from direct ratings by users, and covers the entire world.
We hope that valuing surf breaks is useful in two ways. The first is development: by
understanding the benefits of surf breaks to local economies, policymakers might be more
willing to invest in infrastructure needed to access them. This is particularly true in
developing countries, where waves remain under-utilised. The second is conservation: by
assessing the value of surf breaks, better cases can be made to conserve them from coastal
erosion, pollution and rising sea levels.
The paper proceeds as follows. Section 2 provides a background to surfing and the geo-
graphical characteristics that give rise to good waves, which underpins our identification
strategy. Section 3 describes our data. Section 4 presents the methodology. Section 5
presents and discusses our findings, and a range of robustness checks. Section 6 concludes.
2 A primer on surfing
Surfing was originally a central part of Polynesian culture. Europeans first observed this
“very dangerous diversion” (King, 1779) in the 1760s, but it was not until the turn of the
20th century that the sport appeared in North America and Australia. It was in these
areas that, after World War II, the global phenomenon of surfing began. Recent reports
estimate continuing growth in the popularity of surfing, with the global population of
surfers rising from 26 million in 2001 to 35 million in 2011 (The Economist, 2012). This is
expected to continue as highly-populated, wave-rich emerging economies like Brazil and
Indonesia increasingly consume leisure.
The waves where surfers practice their craft are created through wind acting on the surface
of the ocean. These waves propagate along the ocean’s surface for up to thousands
of kilometres until they approach shallow water. Resistance from the sea floor slows
movement at the base of the wave, causing the top to spill over, or “break” (Figure 2.1).
For surfers each wave has three key characteristics: size, shape and length, which are
determined by a broad range of factors. The specific combination of factors that creates
good waves underpins our identification strategy.
The size, or amplitude, of a wave is mainly determined by winds that generate swell,
hundreds or thousands of kilometres away from where it is eventually ridden. Important
are the wind’s strength and direction at its source, the area over which it acts, the length
it blows, and how far away the source is. For example, many of the best surfing waves
in Europe occur in the Basque country of northern Spain and southern France. These
waves typically originate in the North Atlantic and are funnelled into the region by the
deep ocean trench of the Bay of Biscay (Figure 2.2).
The shape of a wave describes whether it spills down the face, pitches over, or surges
when it breaks. This is determined locally by the gradient of the sea floor and local wind
5
Figure 2.1: Waves form through wind acting on the surface of the ocean, and break when
they reach shallow water.
i.
ii.
Figure 2.2: Waves formed in the North Atlantic are funnelled by a deep trench into the
Bay of Biscay, creating good surf breaks.
6
i. Spilling wave ii. Pitching wave iii. Surging wave
Figure 2.3: Surfers tend to prefer pitching waves
Figure 2.4: Length describes how long a wave can potenially be ridden.
conditions. A gradual rise in the sea floor causes white-water to spill down the face of the
wave when it breaks. A steep rise in the sea floor - such as moving from deep ocean to a
reef - causes the breaking wave to pitch, creating a “barrel”. A very steeply rising sea-floor
will create a surging wave, as seen at the base of sea clis. Local winds also aect this:
“oshore” winds blowing from beach to ocean hold the wave up longer, causing it to pitch
more when it eventually breaks. Onshore winds do the opposite.
The length of a wave describes how long it breaks before reaching the shore. This is
determined locally by the shape of the coastline. Waves break for longer when they reach
the coastline at an angle, causing the whitewater to spill continuously to the left or right.
Long waves therefore typically occur along headlands (“point-breaks”), rivermouths or
coral reefs.
Surfers ride these waves as close to the point of breaking as possible, so the nature of the
breaking process matters. High quality waves will be larger, pitching and longer, all else
being equal. Surfers naturally prefer higher quality waves, but only up to a point dictated
by ability. The direction of the breaking wave is also a consideration as surfers prefer to
face the wave as they ride it (which for most is a wave that breaks to the right). The
shape of a surfboard can be tailored to suit particular types of waves, resulting in local
shaping industries.
High quality waves therefore require a very specific combination of global weather, local
weather and bathymetric conditions, small deviations from which will result in lesser qual-
ity waves. Such a specific combination of characteristics are unlikely to aect economic
activity through any mechanism other than surfing. The range of characteristics also
allows for a lot of natural heterogeneity. Two locations on the same coast may receive
exactly the same swell, but have vastly dierent quality waves because of the shape of the
sea-floor and coastline. We exploit this heterogeneity in our identification strategy.
7
Figure 3.1: Overview of WannaSurf wave locations
3Data
3.1 Waves
Wannasurf (www.wannasurf.com) is an online “world surf spot atlas, made by surfers for
surfers”. It records the location, quality, type, accessibility, coastal and oceanic character-
istics of 5,288 surf spots (waves) around the world (Figure 3.1). Of these we drop 137 for
which the data on quality is either missing or rated 0 stars (“choss”), leaving 5,151 surf
spots in our dataset. The data is crowd-sourced from a community of 78,000 website users
who create and edit the information on each surf spot, in a similar way to Wikipedia.4
The geographic coordinates of each wave are given precisely. The waves are distributed
amongst 146 countries, though they are particularly concentrated in Australia (888 waves)
and the US (878), as shown in Figure 3.2. This is to be expected because of the long
coastline and large surfing population in these countries, from whom the data is crowd-
sourced.5
Each wave is assigned one of five quality ratings, ranging from “sloppy” (1-star) to “totally
epic” (5-star).6Most waves fall into the 2 and 3-star ratings, as shown in Figure 3.1. Wave
quality is not evenly distributed across countries, with Namibia, Western Sahara and the
Maldives having the highest average quality, and Ukraine, Qatar and Kuwait the lowest.
There is a large exogenous element to this, because coastal structure and exposure to wind
and swell are important components of quality. There is also some degree of selection.
Wannasurf contributors are more likely to record unremarkable waves in countries with
4Each user has a publicly available profile including information on their age, location and surfing
preferences, which we do not use.
5The website was created in 2004, and mainly codified existing knowledge that was previously available
oine. As such we do not have the “discovery date” of each wave.
6This rating is crowd-sourced. There is also a user poll for wave quality rating, though we don’t use
this as it typically has less than 100 respondants for most locations.
8
Figure 3.2: Our data on waves is distributed around the world, though is particularly
concentrated in Australia and the US.
Star Rating Description Frequency Share
1 Sloppy 384 7.5%
2 Normal 2,027 39.4%
3 Regional Classic 2,129 41.3%
4 World Class 450 8.7%
5 Totally Epic 161 3.1%
Total 5,151 100%
Table 3.1: Breakdown of waves by quality.
large surfing populations, either local or tourist. On the other hand, only good waves
tend to be recorded away from the beaten path. Western Sahara is an example. Of the
four waves recorded, three are 4-star, and one is 5-star.
The characteristics of each wave are also recorded. These include variables on accessibility
(“Distance”, “Easy to find?”, “Public access?”, “Crowd”), diculty (“Experience”), the
type of wave (“Frequency”, “Type”, “Direction”, “Length”, “Bottom”, “Power”) and
oceanic conditions (“Good swell direction”, “Good wind direction”, “Swell size”, “Best
tide”). Of these we make particular use of the “Type” variable, which indicates whether
the shoreline is a beach, a reef, a rivermouth, a headland (point-break) or a breakwater.
Wave quality also varies by Type, as shown in Figure 3.3.
Finally, we also conduct a small event study around the date that waves were discovered.
The date of discovery is taken from two sources. The first is the date of the “Rip Curl
Pro Search” competition, which was an event on the surfing world tour organised by
the Association of Surfing Professionals annually from 2005-2010. It took place in a
dierent location each year, which was previously relatively unknown to the global surfing
community. The second is the “Google Earth Challenge”, which was a competition run
by Surfing Magazine in 2007 to discover a previously unknown wave using Google Earth.
Surfing Magazine is a leading industry publication read by millions worldwide. Table 3.2
shows the seven wave discoveries in our sample.
9
Figure 3.3: Breakdown of wave quality by type.
Wave Country Date of Discovery Quality Source
St Leu Reunion 2005 World Class Rip Curl
La Jolla Mexico 2006 World Class Rip Curl
El Gringo Chile 2007 World Class Rip Curl
Skeleton Bay Namibia 2007 Totally Epic Surfer Mag
Uluwatu Indonesia 2008 Totally Epic Rip Curl
Supertubos Portugal 2009 Totally Epic Rip Curl
Middles Puerto Rico 2010 Regional Classic Rip Curl
Table 3.2: Wave Discoveries
10
Figure 3.4: Night-time light and population data
3.2 Night-time lights
The Defence Meterological Satellite Program’s Operational Linescan System (DMSP-
OLS) uses satellites to record the average annual night-time light intensity around the
world, from 1992-2013 (Figure 3.4). The data is provided at a resolution of 30x30 arc-
seconds (approximately 1 square kilometre near the equator), and ranges from 0 to 63.
The data is constructed by overlaying all daily images over the course of a year, discarding
those that are obfuscated by cloud cover, lightning, aurora, etc. for a given pixel.
There is a strong link at the national level between the growth of GDP and mean light
intensity (Doll et al., 2006; Henderson et al., 2012; Michalopoulos and Papaioannou, 2014).
This is illustrated in Figure 3.5, which plots the log of the sum of light readings by country
against two measures of log PPP-adjusted GDP: based on expenditure and production
(Penn World Tables 8.1). The associated regressions yield an adjusted r-squared of .82
and .80 respectively. We make use of the high spatial resolution of the data to study
economic activity at a sub-national level, as has been done in a number of other studies
(Chen and Nordhaus, 2011; Michalopoulos and Papaioannou, 2013; Hodler and Raschky,
2014; Jedwab and Moradi, 2015; Jedwab et al., 2015).7
7This data is subject to “top-coding”, where economic activity beyond the maximum luminosity rating
11
6 8 10 12 14 16
ln(GDP), Expenditure, 2003
0 5 10 15 20
Ln(Total Lights, 2003)
Figure 3.5: PPP-adjusted GDP vs Night-time lights (in logs), 2003 (see Smith and Wills,
2016).
3.3 Population
The LandScan data set is produced by the Oak Ridge National Laboratory and provides
annual mid-year spatial population counts at a 30x30 arcsecond resolution from 2000-2013
(Figure 3.4).8It reports “ambient” population, which is the average over a 24 hour period,
rather than simply where people sleep. However, it excludes intermittent populations such
as tourists or temporary relief workers, and may not reflect things like seasonal migrations
or refugee movements. The dataset is constructed by distributing known national and sub-
national population counts across the grid according to a likelihood model that uses inputs
including land cover data, roads data, and high resolution satellite imagery, among other
sources.9This data is similar to that from NASA’s Socioeconomic Data and Applications
Center (SEDAC) which also measures population at a 30x30 arcsecond resolution, but is
only available for the years 1990, 1995 and 2000 (see Dell, 2010 and Alesina et al., 2015
amongst others).
The LandScan data pays special attention to coastal features. To account for the dynamics
of coastal change the LandScan model extends all coastal boundaries several kilometres
seaward. This ensures that all shore and small island features are included within an
administrative unit boundary.10
3.4 Urban and rural classification
SEDAC also provides an “Urban Extents Grid”, which uses 1995 population count estim-
ates to classify each square of a 30x30 arcsecond global grid as either urban or non-urban.
of 63 cannot be distinguished. This is mostly an issue in the centre of dense, economically active areas
in developed countries (Michalopoulos and Papaioannou, 2014), and is not a particular concern in our
study of coastal areas. The data is also subject to “overglow”, where lights appear larger over water and
snow. We address this by clipping our dataset to the shoreline.
8This is the same resolution as the lights data, although the pixels are not aligned. We use grid cells
aligned with the lights rasters but not the population rasters. To address this the Zonal Statistics tool
in ArcGIS internally resamples the raster files so that they are aligned.
9For further detail http://web.ornl.gov/sci/landscan/landscan_documentation.shtml)
10For more information see the LandScan documentation http://web.ornl.gov/sci/landscan/landscan_documentation.shtml#01.
12
The classification is based on contiguous lighted squares (as of 1995) and squares known
to hold at least 5000 people.
3.5 Political stability and ease of doing business
The World Bank provides data on political stability in its Worldwide Governance Index,
and on the ease of doing business in its Doing Business Survey. We collect countries into
four groups with similar numbers of waves based on their 2014 scores on each. Table B.2
groups countries by political stability, and table B.1 groups them by the quality of their
business environment.
4 Methodology
4.1 Measuring economic activity
In our analysis we use two measures of economic activity as the dependent variable:
illumination in the immediate vicinity and illumination in nearby towns.
Illumination in the immediate vicinity is measured using luminosity in surrounding circles
of various radii. We draw these circles at 1km, 5km, 10km and 50km around each wave
and take the sum of illumination within each circle for each year. Because waves are
located on the coastline, and there is no economic activity generated out to sea, we clip
these circles so as only to include area covered by land.
We also separate each circle into urban, lit-rural and unlit-rural areas. The distinction
is based on the 1990 SEDAC Urban Extents Grid dataset, which demarcates urban and
rural areas using a combination of population counts (persons), settlement points, and the
presence of night-time lights. Urban areas are those with significant lit cells, a buered
settlement point, or a total population greater than 5,000 persons. Lit-rural areas are
non-urban cells that were lit in 1992. Unlit-rural areas are non-urban cells that were
not lit in 1992 but have a positive population. Studying unlit-rural areas allows us to
determine the impact of natural assets on rural poverty (see Smith and Wills, 2016).
Illumination in nearby towns is measured by endogenously locating towns by their popu-
lation density. A town is defined by a perimeter enclosing cells with a population density
of 300 persons per square kilometre or more.11 Each wave is linked to two towns: the
closest town and the largest town within 50km radius (based on total population).
4.2 Identification
Estimating the economic return to a non-market natural asset presents challenges of
endogeneity and attributability. We address this using a natural experiment that exploits
the exogenous variation in the quality of surfing waves.
11We also use a cut-oof 600 persons per square kilometer as a robustness check.
13
Endogeneity can arise for a variety of reasons. The first, and most obvious is due to
omitted variables: observable and unobservable characteristics that are correlated with
both the location of waves and local economic activity. If these omitted variables are
time-varying then controlling for time fixed-eects will not help. Examples of omitted
variables for areas near waves could include geographic characteristics, political stability
and institutional quality.
Endogeneity can also arise due to reverse causality. The exploitation of surfing waves may
depend largely on the level of economic activity, and associated infrastructure, already
established in the area. We believe this to be less of an issue for surfing, given the strong
history of intrepid exploration by surfers, the rarity of top quality waves and the sheer
isolation of some locations in our data. For example, to access Red Bluin Western
Australia (a “totally epic” quality 4 wave) one must drive for 4.5 hours along a dirt road
from Carnarvon, an isolated town with population less than 5000. Reverse causality
remains a concern.
Attributing changes in economic activity to a specific natural asset requires strong iden-
tification of the asset itself. There may be many other factors, natural or otherwise, that
attract economic activity to the area surrounding a wave specific area. For surfing waves
this may include trade, boating, fishing and the benefits of nearby beaches.
We address the identification challenges of endogeneity and attributability by exploiting
the exogenous variation in wave quality, rather than the existence of waves per se. Whilst
the location of a wave may well be endogenous to local economic activity for the afore-
mentioned reasons, the variation in the quality of a wave’s surfing potential is exogenous.
In other words, the quality of a surfing wave can be treated as a natural experiment.
We also conduct robustness tests to verify the success of our identification strategy. This
includes varying the baseline quality to control for selection of low quality waves into our
database, and testing whether particular coastal features (reefs, rivermouths, etc) drive
our results.
4.3 Estimating Equations
The eect of surfing wave quality on spatial outcomes are estimated using the following
polynomial distributed lag model:
Yi,t =+(t)Qi+(t)+Wi+Zt+i,t (4.1)
where
(t)=1t+2t2+3t3+4t4
(t)=1t+2t2+3t3+4t4
where Yi,t is the outcome of interest for wave iat time t=[0,...,21],Qiis an indicator
equal to zero if the wave is of poor quality (1-star) and one if the wave is of some higher
quality (2-5 stars), Ztis time fixed eects and Wiis wave fixed eects. The polynomial
14
structure is imposed on (t)and (t)to reduce the eects of collinearity in the data. The
standard errors are clustered at the wave level to address the spatial correlation between
observations (surf breaks).
An alternative linear specification is used test the relative significance of wave qualities,12
Yi,t =Õ+
t
ÿ
s=0
Õ(ZsQi)+Zs+Wi+i,t.(4.2)
The dependent variable in our estimating equation, Yi,t, varies between the log of lights
and the log of population, either in the immediate vicinity of waves or in nearby towns,
depending on the outcome of interest.
We drop observations for which there is no GPS data for the location of the wave which
leaves us with 5,288 waves globally. We then drop those observations for which the wave
quality is either missing or rated 0 stars. In total these make up 2.6% of all waves, leaving
5,151 observations in total.
5 Results
This section shows that good quality waves boost economic activity in the surrounding
areas, relative to areas with low quality waves. The eect is most pronounced in emerging
economies. Activity (proxied by night-time illumination) increases overall, rather than
simply being reallocated from nearby areas. However we do find that the permanent
population falls around good waves, which we attribute to tourism. The increase in
activity is broad-based: it occurs in the immediate vicinity and in nearby towns. It
also reduces rural poverty by encouraging the poor to move to areas of higher activity.
These results are robust to a variety of controls, including non-surfing related coastal
characteristics and alternative specifications.
5.1 Good surfing waves boost nearby economic activity
Areas with high quality surfing waves have higher economic activity than those with low
quality waves, peaking with 4-star waves. This is a relatively high hurdle. Our control
group is the area surrounding the lowest quality waves, and so is already on the coast and
suciently known by surfers to appear in WannaSurf. We also control for time and wave
fixed eects, to isolate the marginal eect of good surfing conditions.
Figure 5.1 uses the model in equation 4.1 to show how waves aect economic activity
within a 5km radius. Higher quality waves increased economic activity, with 4-star waves
increasing activity by 0.28 log points relative to 1-star waves over our sample. 5-star
increased relative activity by 0.19 log points, 3-star waves by 0.15 log points, and 2-star
waves by 0.03 log points (which was not significant). The relative eect on activity at
12Results available online.
15
Figure 5.1: Eect of waves of various qualities on economic activity within 5km.
Figure 5.2: Distribution of experience required by wave quality.
1km, 10km and 50km radii display a similar pattern, where 4-star waves have the largest
eect on local economic activity (though slightly less pronounced, see Appendix A).
The economic impact of good quality waves is significantly higher than bad quality waves
after 21 years. We test the significance of wave quality against 1-star waves in the main
specification, and against other quality waves in the linear specification (equation 4.2).
Both 3-star and 4-star waves increase illumination significantly more than 2-star waves
at the 1% or 5% level for most distances (1km - 50km). 4-star waves only significantly
increase illumination more than 3-star waves at the 50km radius.
Wave quality has an inverse U-shaped eect on economic activity. This is because the
highest quality waves require a lot of experience to ride, being disproportionately rated
for “Pros or Kamikazes only” (experience level 3) as illustrated in Figure 5.2. This limits
their appeal.
16
5.2 Emerging economies benefit from surfing the most
High quality waves increase economic activity on average, though the eect is concentrated
in emerging economies. Waves largely generate economic activity through tourism. The
strength of this channel will depend on both supply and demand. The demand for tourism
will depend on the institutional and political characteristics of the country. The supply
of tourism services will depend on the ease with which new businesses can respond to an
inflow of prospective surfers. Using World Bank data on political stability and ease of
doing business we find that waves have the most pronounced eect in countries that score
“low” on both, as they have significant scope to grow.
Figure 5.3 shows how 4-star waves aect economic activity in the surrounding 5km, based
on their country’s political stability and ease of doing business. We omit the USA and
Australia from the analysis as they are large, developed and stable countries who over-
whelmingly dominate our sample. Waves have the largest eect on countries with interme-
diate political and business environments. Countries with “low” “or moderate” political
stability are suciently stable to attract surfers, unlike those countries scoring “very
low”. However, they are unstable enough that their tourism industry still has scope to
grow. Similarly, countries with a “moderate” business environment are able to facilitate
economic activity,allowing tourism to expand to meet demand from surfers, unlike those
scoring “low” or “very low”. However, they will not have such well established tourism
infrastructure that surfers will not add to economic activity.13
5.3 Surfing increases activity overall, rather that redistributing
it from surrounding regions
Economic activity near good waves increases in aggregate, rather than just being drawn
away from other areas. Figure 5.4 shows how waves of dierent quality aect economic
activity in surrounding concentric rings out to a 50km radius, relative to 1-star waves. If
the increase in activity described in Section 5.1 simply reallocated activity from surround-
ing areas, then we would expect higher activity in the closest rings, and lower activity
further out. Instead we find that activity is higher in all rings, and falls the further from
the wave one travels. If anything this suggests that surfing generates positive spillovers
for the surrounding areas.
We find that 4-star waves drive the largest increase in surrounding activity at all distances
over 21 years. Within 5km the eect was 0.28 log points, falling to 0.26 log points in
the 5-10km, and 10-50km rings. The eect of 2-star waves remained insignificant at all
distances, 3-star waves fell from 0.15 to 0.10 log points, and 5-star waves rose from 0.12
to 0.20 log points. This is again consistent with an inverse-U relationship between wave
quality and economic activity.
While surfing does not redistribute economic activity it does redistribute the permanent
population. The permanent population falls in the 5km surrounding 2-5 star waves, rel-
ative to 1-star waves (see Figure 5.5, panel i.). This is most pronounced for 4-star waves,
13To confirm that our results are not being driven by large, developed, wave-rich countries we re-run
the analysis specifically for Australia and the USA, as shown in Figure B.1.
17
i.
ii.
Figure 5.3: Eect of wave quality on local illumination by i. business environment and ii.
political stability
18
i.
ii.
Figure 5.4: The eect of waves of various quality on economic activity in expanding
concentric rings of i. 5-10km and ii. 10-50km.
19
which increase economic activity the most and reduce the local permanent population
by -0.35 log points over our sample. Panel iii. of Figure 5.5, shows that the population
increases at further distances, particularly at 10km-50km. This is consistent with the per-
manent population moving away from the waves because tourists, which are not included
in the LandScan data, drive up property values.
5.4 Surfing benefits areas of existing economic activity, includ-
ing nearby towns
We have seen that surfing waves increase nearby economic activity. Now we investigate
how this happens. First, we break the 5km circle surrounding each wave into urban and
lit-rural areas. Second, we construct our own measure of the closest town, and the largest
town within 50km. We find that good waves increase activity in each. In the next section
we turn our attention to unlit-rural areas, and the role of natural assets in economic
development.
Figure 5.6 shows how the 5km surrounding each type of wave is divided between urban,
lit-rural and unlit-rural areas. Overall the largest proportion of waves are in urban areas
(48%), followed by lit-rural (43%) and unlit-rural areas (9%). As wave quality increases,
so too does the share of waves in rural areas. This is consistent with selection in the
WannaSurf database. While a 1-star wave in an urban area might be surfed suciently
often to warrant entry in the WannaSurf database, the same wave in a rural area might
not. In contrast, surfers might be willing to travel to rural areas to surf a totally epic
(5-star) wave. Selection may aect our results, because our interpretation of faster light
growth near high quality waves might actually be a story of faster light growth in rural
areas. As we will show next, the second interpretation can be discarded because our
results also hold when considering urban, and rural areas individually.
Figure 5.7 investigates how aggregate light growth in the 5km surrounding each wave is
allocated between urban and lit-rural areas. The main results in Section 5.1 is confirmed:
better quality waves increase illumination in the surrounding areas, peaking with 4-star
waves. The eect is larger in lit-rural than urban areas because their initial level of
illumination is lower, allowing for a larger percentage change. As well as understanding the
nature of growth near natural assets, this also provides some robustness for the selection
eects mentioned above.
As well as increasing economic activity in their immediate surroundings, waves also in-
crease economic activity in nearby towns. Figure 5.8 shows how waves aect illumination
in the closest town (defined as having a population density over 300 people per km2),
and in the largest town within 50km. Illumination in the closest town increases by 0.14
log points for 3 and 4-star waves, while there is a small an insignificant eect for 2 and
5-star waves. In contrast illumination in the largest nearby town increases for all wave
qualities, with the largest eect of 0.15 log points for 4 and 5-star waves. The eect on
the largest nearby town is larger than on the closest town for each wave quality. This
suggests that the economic benefits of natural assets like surfing waves tend to accrue to
areas of existing economic activity, where there is the infrastructure needed to support
tourism and other non-recreation activities. The eect is most pronounced for the highest
20
i.
ii.
iii.
Figure 5.5: Eect of waves on population within surrounding i. 5km, ii. 5-10km and iii.
10-50km concentric rings.
21
Figure 5.6: Breakdown of the 5km surrounding waves of each quality
i.
ii.
Figure 5.7: Eect of waves on lights in i. urban and ii. lit-rural areas within a 5km radius.
22
i.
ii.
Figure 5.8: Eect of wave quality on the i. closest town (>300 people per km2) and ii.
largest town within 50km, relative to lowest quality waves
quality waves, which can be attributed to their proportionally greater incidence in rural
areas, and the greater requirements needed to service surfers riding those waves (such as
board repairs, healthcare, etc).
5.5 Surfing also reduces rural poverty
Natural assets account for a large share of the capital stock in developing countries.
There is 365,000km of coastline in the world - nine times the circumference of the earth. A
significant proportion of this lies in developing countries, with Africa’s coastline stretching
for 26,000km, and Asia’s 62,800km. The potential for surfing assets to exist along these
coastlines is huge. This section shows that waves have significant potential for reducing
poverty in their local areas.
To understand the potential for waves to reduce poverty we turn our attention to unlit
rural areas. These are areas within 5km of a wave that were unlit but had a positive
23
Figure 5.9: Population living in unlit rural areas within 5km of surfing waves.
population in 2000 (when our data begins). Smith and Wills (2016) show that the share
of population living in unlit rural areas - the rural poor - is a good proxy for extreme
poverty. When households cross the extreme poverty line they quickly illuminate, due to
the high returns from longer working hours. Figure 5.9 shows that the population living
in unlit rural areas falls in areas near good waves by up to 0.7 log points, relative to 1-star
waves. This suggests that there is significant potential for natural assets like surfing waves
to be harnessed for reducing extreme poverty.
There are two ways that the population living in unlit rural areas can fall: by unlit
areas becoming lit, or by people moving away from unlit areas. Figure 5.10 shows the
proportion of areas that were unlit in 2000 that became lit in subsequent years. Areas near
good waves illuminate slower than areas near bad waves. It suggests that waves do not
cause unlit rural areas to become illuminated. This implies that people move away from
unlit areas. Testing this implication directly produces a statistically insignificant result.
However, we do find evidence that the population in nearby towns increases (Figure 5.11).
In large towns this amounts to a population rise of up to 0.35 log points for 5-star waves
over our sample. Figure 5.11, panel ii.). This is consistent with surfing waves reducing
extreme rural poverty by attracting people from rural areas to nearby towns.
5.6 Surfing waves contribute around US$50 billion to global eco-
nomic activity each year
We find that surfing waves contribute US$51.2 billion (2011 PPP) globally each year to
economic activity in their surrounding 50km. To arrive at this figure we allocate all pixels
of luminosity within 50km of a wave to a particular wave quality (proportionately if within
50km of more than one wave). This accounts for 9.4% of global illumination. We then
aggregate total illumination and total GDP (US$ 94.1 trillion in 2011 PPP , World Bank
WDI) to find an average value for each pixel of luminosity. Using the parameter estimates
from equation 4.1 we deduce the marginal contribution of surfing waves to activity for
24
Figure 5.10: Share of unlit areas in 2000 that became lit in subsequent years, within 5k
of a wave.
i.
ii.
Figure 5.11: Eect of waves on population in nearby towns (quadratic polynomial model)
25
Star rating Annual Contribution per Wave Frequency Annual Contribution Total
3 17.9 2,129 38,109
4 22.3 450 10,035
5 19.0 161 3,059
Total 2,740 51,203
Table 5.1: Annual global contribution of waves to economic activity in surrounding 50km
(US$ million 2011 PPP)
each wave quality, averaged over 22 years. We exclude 2-star waves because their eect
is not statistically significant from zero. The results are reported in Table 5.1.
Each 4-star wave contributes US$22.3 million on average to surrounding economic activity
each year. 5-star waves contribute US$19.0 million on average, and 3-star waves US$17.9
million on average. The largest aggregate contribution comes from 3-star waves which,
due to their prevalence, account for 75% of surfing’s contribution to global GDP.
5.7 Discovering a new wave significantly raises activity in the
local area
To provide further evidence that good quality waves improve local economic activity we
conduct a small event study, using the date that waves were discovered. This is dierent to
the previous analysis because we are exploiting exogenous variation over time, rather than
over space. Using discovery dates from two sources, the “Rip Curl Pro Search” competition
and the “Google Earth Challenge” (see Section 3.1), we find that illumination near waves
grows 4% faster after they are discovered by the international surfing community.
There are two challenges in conducting this type of event study. First, we require a
meaningful definition of a wave being “discovered”. If a wave is only known to a handful
of locals, it is unlikely to generate much local economic activity. What we really mean by
“discovery” is that the global surfing community becomes aware, for the first time, about
a new high-quality wave. Second, we need to define when a discovery takes place. There
is no ocial surfing body or archive that stores and maintains this kind of information.
Discoveries must also take place within our night-time lights sample, from 1992-2013.
To estimate the impact of the discovery of a surfing wave on local economic activity we
fit the following linear model on our sample of seven wave discoveries:
Yi,t =+i,tWiúTt+i,t DiúTt+Zi+Wi+i,t (5.1)
where Yi,t is the log of lights within 50km of each wave i=[0,...,5] at time t=[0,...,21],
Ttis a continuous time variable cantered on zero at the year of discovery, Wiis a wave
fixed eect, Ztis time fixed eects, Diis our discovery indicator equal to 0 before the
year of discovery and 1 after. The coecient i,t measures the linear growth rate of lights
before the wave was discovered, and i,t measures the change in the rate of growth of
lights after the wave was discovered. Eq. 5.1 compares light growth over time pre and
26
post discovery, controlling for changes in light intensity over time that are common to all
waves and dierences in light intensity between each wave.
Our main coecient of interest, i,t, estimates the change in trend light growth after the
wave was discovered: our treatment eect. We find ˜
i,t =0.04 which is significant at
better that the 1% level (p>|t|=0.005).
This result implies that discovering a wave leads to an increase in annual light growth
of around 4% per year, on average across our seven discoveries. Translating our headline
results to annualised growth rates yields increases in annual growth rates of 0.7% for 3-
star waves, 1.2% for 4-star waves and 0.8% for 5-star waves, all relative to 1-star (normal)
waves. This is substantially larger than our headline results for 3-star, 4-star and 5-star
waves.
The following figures plot the results from Eq. 5.1 for each of our seven wave discoveries.
They compare the predicted light growth pre and post discovery with the actual light
growth over time, controlling for changes in light intensity over time that are common to
all waves. The red line in each of the figures denotes the date of discovery for each wave,
centred at zero, whilst the solid and dashed black lines denote the fitted linear model pre
and post discovery respectively for each wave. The results are clear. Discovering a wave
increases trend growth rates in the local area.
5.8 Robustness
To check the robustness of our main results we investigate two alternative explanations.
The first adds to the wave event study of the previous section by investigating whether
our results may be driven by other coastal characteristics unrelated to surfing. The second
tests whether the selection into our database of low quality waves near towns is driving
our results; complementing the town-based analysis in Section 5.4. Our results are robust
to both tests.
Non-surfing coastal characteristics To test whether our identification strategy is
valid we investigate whether other, non-surfing related, coastal characteristics might be
driving our results. If the particular meteorological and bathymetric conditions that
create good surfing waves also give rise to other economic activity, like swimming on
sandy beaches, diving on coral reefs, fishing from rivermouths, or trading from harbours,
then our results may be biased. To test this we exploit data on the type of each wave,
outlined in Table 5.2.
Figure 3.3 shows that the distribution of wave quality varies by type, with rivermouths,
reefs and point-breaks all being better than average. Rivermouths make up only 2.6% of
our observations (2.7% of 4-star and 1.2% of 5-star waves) and so there are not enough
to systematically bias our results. Reefs and point-breaks comprise a greater share, so
we re-run our analysis excluding these observations to see if our results still hold (see
Figure 5.13). Excluding reefs, point-breaks and both produces a similar outcome to the
main results in Section 5.1. 4-star waves increase illumination in the surrounding 5km by
0.20-0.25 log points over our sample.We also get broadly similar results when we exclude
27
i. ii.
iii. iv.
v. vi.
vii.
Figure 5.12: Event study results: five wave discoveries
28
Wave Type Description Frequency Percent
0 Beach-break 2,013 41.6
1Breakwater/jetty 124 2.6
2 Don’t know 12 0.3
3 Point-break 643 13.3
4 Reef-artificial 23 0.5
5Reef-coral 261 7.5
6Reef-rocky 998 20.6
7 Rivermouth 118 2.4
8 Sand-bar 549 11.3
N/A Missing 85 N/A
Total 4,926
Table 5.2: Breakdown of waves by type
reefs and point-breaks from our analysis of closest and largest nearby towns in Section
5.4, as described in Appendix C. For both the closest and the largest nearby towns the
eect is of a similar magnitude to our main results, peaking with 4-star waves for the
closest town and 5-star waves for the largest town.
Selection of low-quality urban waves The WannaSurf database exhibits some se-
lection bias, where the lowest quality waves are more likely to appear in the data when
they are close to towns and cities (54%). This might bias our results if lights in towns
grow slower than in rural areas. To test this we re-run the analysis using 2-star waves as
the baseline, rather than 1-star. A similar share of 2-star waves are located in towns or
cities (49%), as are the 4-star waves that are our focus (48%). Figure 5.14 shows that our
results are broadly the same as with the 1-star baseline, with slightly smaller coecients
(also see Appendix D).
29
i.
ii.
iii.
Figure 5.13: Robustness test excluding i. reefs, ii. points and iii. both.
30
Figure 5.14: Eect of waves on illumination in the surrounding 5km, with quality 1 as
baseline.
6 Conclusion
This paper oers a global panel study on the eect of a non-market natural asset on
economic activity. We combine three high-resolution spatial datasets, on the location of
surfing waves, night-time light emissions and population, to answer the question: how
much do surfing waves contribute to the surrounding economy?
We find that surfing waves contribute approximately US$50 billion to global economic
activity each year. This amounts to an average contribution of US$18-25 million per
wave per year.The eect on activity increases with wave quality, except for the highest
quality waves which require a lot of skill to ride. Emerging economies benefit the most
from surfing, as long as they have a sucient level of political stability and ease of doing
business. Furthermore, the increase in activity does not just represent a reallocation
away from surrounding areas. Waves do, however, cause the permanent population to
move further away - which is consistent with tourists driving up property prices. Surfing
also appears to play a role in reducing extreme poverty, again by encouraging people to
move away from unlit rural areas into nearby towns. These results capture the value of
macroeconomic spillovers from the natural asset, in contrast to most existing methods of
non-market valuation. The results are also robust to a range of tests for endogeneity and
attributability.
While this began as a personal interest project for a couple of sandy-footed economists,
it also has several policy implications. The first is to provide policymakers with an
understanding of the potential benefits of waves for economic development, especially
in developing countries. This is true for both naturally occurring waves and artificially
constructed waves - be they oshore artificial reefs or onshore wave pools. The second is
to highlight the importance of conserving the quality of waves. This involves limiting both
coastal pollution and changes to the characteristics of waves through dredging, coastal
manipulation or rising sea levels.14
14It may also involve protection from sharks, which has been at the forefront of the surfing community’s
31
The paper also suggests a range of extensions. By providing a methodology for valuing
the economic spillovers from non-market natural assets we capture externalities that may
not be included in other methods of non-market valuation. This methodology is relevant
for any natural asset that exogenously varies in quality around the world, including rock-
climbing clis and natural reserves (including UNESCO natural heritage sites).
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Appendix
A Aggregate economic activity
Figure A.1: Eect of waves of various qualities on economic activity within 1km, 10km
and 50km.
34
B Ease of doing business and political stability cat-
egories
Figure B.1: Headline results are not being driven by the USA or Australia
35
WB DB - very low WB DB - low WB DB - moderate WB DB - high
Country Waves Country Waves Country Waves Country Waves
Brazil 291 South Africa 207 Spain 182 France 290
Indonesia 136 Puerto Rico 53 Japan 118 Portugal 162
Ecuador 47 Chile 50 Italy 113 United Kingdom 149
Argentina 34 Costa Rica 50 Mexico 92 New Zealand 114
Venezuela 24 Greece 50 Peru 76 Ireland 58
Nicaragua 20 Morocco 43 Mauritius 14 Canada 38
Senegal 20 Philippines 29 Belgium 12 Netherlands 32
Barbados 19 Panama 27 United Arab Emirates 9 Germany 22
Sri Lanka 18 Israel 25 Bulgaria 8 Taiwan 18
India 13 Uruguay 24 Poland 4 Denmark 15
Micronesia 11 Turkey 18 Croatia 3 Iceland 14
Bahamas 10 Namibia 17 Malaysia 14
Papua New Guinea 10 Dominican Republic 16 Sweden 11
Angola 9 Seychelles 15 South Korea 7
Maldives 9 Russia 14 Hong Kong 5
Verde 9 Thailand 14 Switzerland 5
Ghana 8 El Salvador 12 Lithuania 3
Madagascar 8 China 11 Estonia 2
Egypt 7 Colombia 11 Finland 2
Mozambique 7 Tunisia 11 Latvia 2
Lebanon 6 Samoa 10 Austria 1
Guinea 5 Vietnam 10
Algeria 4 Cyprus 8
Liberia 4 Guatemala 7
Sao Tome And Principe 4 Dominica 6
Tog o 4 Fi j i 6
Cameroon 3 Malta 5
Cote d’Ivoire 3 Oman 4
Gambia 3 Brunei Darussalam 3
Kenya 3 Saint Lucia 3
Myanmar 3 Albania 2
Nigeria 3 Tonga 2
Sierra Leone 3 Vanuatu 2
Tanzania 3 Jamaica 1
Benin 2 Kuwait 1
Gabon 2 Qatar 1
Grenada 2 Trinidad And Tobago 1
Haiti 2 Ukraine 1
Kiribati 2
Palau 2
Rep Congo 2
Bangladesh 1
Belize 1
Cambodia 1
Honduras 1
Iran 1
Saint Kitts And Nevis 1
Saint Vincent And T.. 1
Solomon Islands 1
Timor-Leste 1
Zimbabwe 1
Tot a l 78 5 77 0 63 1 96 4
Table B.1: Countries and wave count by World Bank Doing Business categories.
36
WB WGI Pol Stab - very low WB WGI Pol Stab - low WB WGI Pol Stab - moderate WB WGI Pol Stab - high
Country Waves Country Waves Country Waves Country Waves
South Africa 207 Brazil 291 France 290 Portugal 162
Indonesia 136 Spain 182 United Kingdom 149 Japan 118
Mexico 92 Greece 50 Italy 113 New Zealand 114
Peru 7 6 Ecua dor 4 7 Pu erto R ico 53 Irela nd 58
Morocco 43 Argentina 34 Chile 50 Canada 38
Philippines 29 Panama 27 Costa Rica 50 Netherlands 32
Israel 25 Reunion 21 Namibia 17 Uruguay 24
Venezuela 24 Nicaragua 20 Seychelles 15 Germany 22
Senegal 20 Dominican Republic 16 Malaysia 14 Barbados 19
Sri Lanka 18 Vietnam 10 Belgium 12 Taiwan 18
Turkey 18 Bulgaria 8 Verde 9 Denmark 15
Russia 14 South Korea 7 Cyprus 8 Saint Martin 15
Thailand 14 Sao Tome And Principe 4 Fiji 6 Iceland 14
India 13 Gabon 2 Oman 4 Mauritius 14
El Salvador 12 Benin 2 Croatia 3 Micronesia 11
China 11 Belize 1 Albania 2 Sweden 11
Colombia 11 Trinidad And Tobago 1 French Guiana 2 Bahamas 10
Tunisia 11 Kuwait 1 Kiribati 2 Samoa 10
Papua New Guinea 10 Jamaica 1 Latvia 2 Maldives 9
Angola 9 Cambodia 1 Vanuatu 2 United Arab Emirates 9
Ghana 8 Saint Kitts And Nevis 1 Aruba 7
Madagascar 8 Solomon Islands 1 Dominica 6
Egypt 7 Hong Kong 5
Guatemala 7 Malta 5
Mozambique 7 Switzerland 5
Lebanon 6 Polan d 4
Guinea 5 Virgin Islands, U.S. 4
Algeria 4 Brunei Darussalam 3
Liberia 4 Lithuania 3
Tog o 4 Saint Lucia 3
Cameroon 3 Anguilla 2
Cote d’Ivoire 3 Bermuda 2
Gambia 3 Estonia 2
Kenya 3 Finland 2
Myanmar 3 Grenada 2
Nigeria 3 Tong a 2
Sierra Leone 3 Aust ria 1
Tanzania 3 Qatar 1
Haiti 2 Saint Vincent And T.. 1
Rep Congo 2
Bangladesh 1
Honduras 1
Iran 1
Somalia 1
Timor-Leste 1
Ukraine 1
Zimbabwe 1
Total Waves 888 726 805 783
Table B.2: Countries and wave count by WB Worldwide Governance Indicators, Political Sta-
bility categories
37
C Excluding certain wave types
i.
ii.
iii.
Figure C.1: Robustness test: excluding i. reefs, ii. points and iii. both from our analysis
of illumination in the closest town.
38
i.
ii.
iii.
Figure C.2: Robustness test: excluding i. reefs, ii. points and iii. both from our analysis
of illumination in the largest nearby town.
39
D Alternative baseline
Figure D.1: Eect of waves on illumination within surrounding 1km, 10km and 50km,
with quality 1 as baseline.
40
Figure D.2: Eect of waves on illumination at various distance buckets, quality 1 as
baseline.
41
... Arriving at coastlines, waves break in patterns favorable for surfing-the practice of riding breaking waves-at typically nearshore, generally discrete locations called surf breaks (Butt et al., 2004;Scarfe et al., 2009). Surf breaks have natural and cultural endowments with significant value for stakeholders across economic (Nelsen et al., 2007;Lazarow et al., 2008;Buckley et al., 2014;Ponting and O'Brien, 2015;McGregor and Wills, 2016), cultural (Ford and Brown, 2005; Barbieri and Sotomayor, 2013;Mixon, 2014;Usher and Kerstetter, 2015), historical (Warshaw, 2010;Westwick and Neushul, 2013), and other dimensions (Taylor, 2007;Moore, 2011;Reineman and Ardoin, 2018) at local scales to global scales (see, e.g., Butt et al., 2004;Nelsen et al., 2007;Lazarow et al., 2008;Buckley et al., 2014). ...
... Surf conservation, the resources it seeks to sustain, and the wider ecosystems in which surfing resources are embedded have received less attention in the conservation movement relative to other cultural resources (e.g., shipwrecks) or biological resources that have long been the focus for marine conservation efforts (see, e.g., U.N. Sustainable Development Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development). This is changing as communities, researchers, and practitioners increase understanding and visibility of the value of surf breaks for local and, particularly, developing economies (McGregor and Wills, 2016). At the Uluwatu surf area in Bali, Indonesia, for example-a priority region for coral reef and marine mammal conservation-a survey of surf tourists' expenditures estimated the total actual annual expenditure to be $35.3 ...
... Globally, surf tourism is valued at $31.5 to $64.9 B USD with participants willing to pay more for sustainable surf tourism opportunities (Mach and Ponting, 2021). Such benefits provide the mechanism to accelerate economic growth in communities surrounding surf breaks-an effect magnified in emerging economies (McGregor and Wills, 2016). ...
Article
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Conserving biodiversity and ecosystem services requires diverse models that empower communities to steward and benefit from resources. Here we investigate the potential of surfing resources, a new conservation asset class, and the surfing community, an underutilized conservation constituency, to conserve marine biodiversity. We conducted a spatial analysis of the overlap among Key Biodiversity Areas, Marine Protected Areas (MPAs), and 3,755 surf breaks globally. We find that 62.77% of surf breaks are not within MPAs and that 25.81% of all surf breaks are within 5 km of a Key Biodiversity Area, but are not within a MPA, suggesting that strategic conservation opportunities arise from the co-occurrence of surfing resources and biodiversity priorities. Establishing or extending protections to surfing ecosystems could increase protection for biodiversity at one-quarter of surf breaks. Sustainable management of these resources ensures their ability to provide for the character, economy, and development of coastal communities worldwide.
... Neste período de transição para a Década dos Oceanos (ONU, 2017), incluir os ecossistemas de surfe no planejamento territorial de cidades litorâneas poderá ser uma forte iniciativa para serem alcançadas as metas e os objetivos de desenvolvimento sustentável (VON SCHUCKMANN et al., 2020). Ao considerar-se a surfabilidade das ondas como um ativo natural do território, tal qual um serviço ecossistêmico característico dos ecossistemas de surfe, é possível perceber que as ações direcionadas para a conservação deste seu atributo irão refletir positivamente na proteção de outras funções importantes (diretas e indiretas) também realizadas por estes ecossistemas, contribuindo para a manutenção do equilíbrio ecológico e para o bemestar humano na zona costeira (GROOT; WILSON;BOUMANS, 2002;MCGREGOR;WILLS, 2016;SILVA;SANTOS;DUTRA, 2016;GALLAS et al., 2017;RAMOS et al., 2019). ...
... Neste período de transição para a Década dos Oceanos (ONU, 2017), incluir os ecossistemas de surfe no planejamento territorial de cidades litorâneas poderá ser uma forte iniciativa para serem alcançadas as metas e os objetivos de desenvolvimento sustentável (VON SCHUCKMANN et al., 2020). Ao considerar-se a surfabilidade das ondas como um ativo natural do território, tal qual um serviço ecossistêmico característico dos ecossistemas de surfe, é possível perceber que as ações direcionadas para a conservação deste seu atributo irão refletir positivamente na proteção de outras funções importantes (diretas e indiretas) também realizadas por estes ecossistemas, contribuindo para a manutenção do equilíbrio ecológico e para o bemestar humano na zona costeira (GROOT; WILSON;BOUMANS, 2002;MCGREGOR;WILLS, 2016;SILVA;SANTOS;DUTRA, 2016;GALLAS et al., 2017;RAMOS et al., 2019). ...
Research Proposal
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Projeto de dissertação apresentado para o PPG em Oceanografia da UFSC.
... Therefore the occupancy percentage could be used as a signifier for the occupancy rate of the town. Surf tourism is often the first connection for remote costal areas to the global economy (McGregor and Willis 2016;O'Brien and Ponting 2013;Ponting, McDonald, and Wearing 2005;Krause 2012). Traditionally there are numerous negative effects on the local population and environment, as neo-liberal development practices are often the first to be implemented in these areas (Ibid). ...
Conference Paper
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Globally, the Muslim population is growing and if current trends continue, Muslims will make up 26.4% of the world’s total projected population of 8.3 billion in 2030. This worldwide growth of the Muslim population in the middle class and younger population has meant that Muslim tourists are becoming a significant segment within the global travel and tourism sector. According to the latest statistics total Muslim tourist arrivals were 116 million in 2014 and the Islamic Republic of Iran, despite having 7th rank in terms of Muslim tourist arrivals with 4.1 million total Muslim tourist arrivals, was in the 11th rank in terms of Islamic and Halal tourism receipts with US$ 942.5 million recorded in 2014. Sub-optimal place of Iran in Islamic and Halal tourism market as one of the most important Islamic countries, its low share of this highly profitable market despite possessing maximum Muslim population and an Islamic government with high compatibility of this type of tourism with its socio-cultural and religious values and norms, depicts the importance of this newfound type of tourism as one of the most appropriate options for tourism industry development in Iran and necessity of paying attention to its high potential in this countries tourism planning and policy-making process.
... De acordo com os resultados apresentados por McGregor & Wills (2016), que mapearam os picos de surf no mundo, somente no Brasil foram iden ficados 291 destes que equivalem a 5,9% de um total de 5000. Neste sen do, o Brasil está classificado como o terceiro país com maior número de picos de surf no mundo (figura 3). ...
Research
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O Estado da arte sobre as Reservas de Surf: Uma visão escalar, do global à proposta de um programa nacional.
... For surf tourism industry, waves can mean different things as well. Prior literature could give insights on what waves mean for surf tourism business: resource (Buckley et al., 2017), natural asset (McGregor & Wills, 2016), economic income (Hritz & Franzidis, 2018), and attraction (Orams & Towner, 2012). In front of neoliberal tourism industry, waves become an object of management (Buckley, 2002b), governance (Mach & Ponting, 2018), utilization (O'Brien & Ponting, 2013), and protection (Martin & Assenov, 2014). ...
Article
This article is about the relation between surf tourism and development agenda. The implementation of surf tourism retribution in Mentawai Islands, Indonesia, offers an interesting case study to understand how surf tourism can coalesce with development agenda and policy. In this article, I record and narrate how local people in Siberut, Mentawai Islands, perceive the policy of surf tourism retribution under the guise of development narratives and in relation to local meanings of waves. Based on an ethnographic fieldwork, the research finds that the enforcement of surf tourism retribution has further complexified the notion of waves as resource among Mentawaians, as well as re-interrogated the idea of waves for socio-economic development in Mentawai. The re-interrogation process may hint that there is a disjuncture between rhetoric and practice of ‘waves for development’ policy in the islands. Aside from putting forward the importance of local people’s practices and perceptions in tourism, this article contributes to the body of literature on tourism-development nexus by adding the case study of surf tourism and retribution policy mechanism.
... A widely cited pre-COVID-19 estimate suggested that coral reefs were generating $36 billion a year in economic value from tourism -$19 billion was attributed to direct 'on reef' activities like diving, snorkeling, and wildlife boat tours and $16 billion to "reef adjacent" tourism, which includes activities "afforded by the sheltering effect of adjacent reefs" (Spalding et al., 2017;Brumbaugh, 2017). While paddle boarding was briefly acknowledged as a reefadjacent activity, surfing was not mentioned at all, despite the reality that coral reefs provide the bathymetric foundation for many heavily patronized surf tourism destinations (Dolnicar & Fluker, 2003;McGregor & Wills, 2016) The persistent omission of surfing's contribution to the blue economy warrants attention and perhaps even more so presently because surfers will be some of the first tourists to fan out to remote coastal communities once COVID-19 travel restrictions are lifted. Perhaps even with a gold rush-like fervor to reach certain coveted surf destinations before the crowds return. ...
Article
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This manuscript provides the only empirically derived pre-COVID-19 global estimation of international surf travel spending and the first assessment of sustainable surf tourism attitudes, behaviors, and willingness to pay. It establishes important baselines that can serve as points of comparison as, and after, surf tourism returns, inevitably changed, post-COVID-19. Employing a direct cost method, international surf tourism expenditure was valued between $31.5 to $64.9 billion USD per year and surfers reported being willing to pay between $1.99 and $4.1 billion USD more annually for sustainable surf tourism products. These results suggest surfing tourism deserves a more significant place in funding initiatives, discussions, and research related to fostering sustainable development from ocean resources in the rapidly changing world.
... Es un deporte practicado por millones de personas a nivel mundial (Westwick & Neushul, 2013) que forma parte del programa de los Juegos Olímpicos de Tokio 2020 (Tokyo 2020Organising Committee, 2018. Además, moviliza un sector turístico y económico que tiene un impacto económico mundial de 50 billones de dólares (McGregor & Wills, 2016) y se ha convertido en un campo de conocimiento e investigación en crecimiento (Pérez-Gutiérrez & Cobo-Corrales, 2020). ...
Article
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El objetivo del artículo fue realizar una revisión de los trabajos que estudiaron la práctica del surf dentro de la educación formal para conocer las iniciativas desarrolladas y resumir las orientaciones pedagógicas para su aplicación. Para ello, se realizó una revisión sistemática en las bases de datos Dialnet, Eric, SciELO, Scopus y Web of Science. Se recuperaron 12 documentos, publicados entre 2008 y 2018, desarrollados en cinco países diferentes y centrados en el surf, el bodyboard y el standup paddle. Dichos trabajos concluyeron que debe realizarse una aproximación progresiva al medio acuático y al surf en tres fases (centro educativo, piscina y playa), comenzando con la práctica del bodyboard por ser más fácil y menos peligrosa que el surf de tabla corta. Para las sesiones de piscina y playa, se recomienda utilizar el tiempo del recreo para los desplazamientos, ubicando las sesiones en las últimas horas de la jornada escolar. Los docentes de Educación Física que quieran implementar el surf en sus clases deberían adquirir conocimientos relacionados con la seguridad en el mar, con el equipamiento necesario para la práctica y con la propia técnica del surf. Abstract: the aim of this paper was to carry out a review of documents studying the surfing in formal education for discovering the programs developed and summarizing the pedagogical orientations for its application. Thus, a systematic review was performed in Dialnet, Eric, SciELO, Scopus and Web of Science databases. Twelve documents, published from 2008 to 2018, developed in five different countries and focused on surfing, bodyboarding and standup paddle, were retrieved. These documents concluded that a progressive approach to the aquatic environment and to surfing should be done in three phases (educational centre, swimming pool and beach), starting with the practice of bodyboard since it is easier and safer than shortboard surfing. The recess time should be used for travels in those sessions developed in the swimming pool and the beach. Moreover, these sessions should be scheduled in the last hours of the school day. Physical Education teachers willing to implement surfing in their sessions should gain knowledge about sea safety, surfing equipment and its own techniques.
... Surfing and surfing reserves are currently attracting increasing attention by researchers, including studies on the benefits from combining transdisciplinary systems thinking and adaptive co-management to address environmental issues of surf breaks (Arroyo, Levine, & Espejel, 2019;Skellern, Peryman, Orchard, & Rennie, 2013), surf tourism development and the disconnection of local communities (Doering, 2018), the conservation potential of coastal surfing resources through legislative protection, the awareness and management of surfing sites (Martin & A ssenov, 2015), sustainable surfing tourism development and the empowerment of local people (Ponting, McDonald, & Wearing, 2005), the development and difficulties of protecting remote surfing locations (Orams, 2017), social and economic values of surfing and the negative consequences of developments (Lazarow, 2010;Lazarow, Miller, & Blackwell, 2007, 2009Usher, 2017), surf site discovery and economic growth, particularly for emerging economies (McGregor & Wills, 2016), relationships between informal property rights and surf quality (Kaffine, 2009) and accessing uncrowded surf locations in Africa (Wales, 2012). ...
Chapter
This chapter outlines the role that surfing reserves play in biodiversity conservation in Australia and Brazil. Surfing reserves demonstrate the possibility of untapped opportunities to use recreation (and possibly other cultural values) to secure biodiversity conservation in situations where purely environmental concerns are insufficient to obtain and fund protection. Both countries are megadiverse and surfing reserves are an important innovative opportunity to meet biodiversity targets while helping protect social and cultural capital, and to deliver economic benefits. These two countries provide contrasting examples of how surfing reserves have been implemented in conjunction with broader biodiversity measures, and the effectiveness of the various surfing reserve regimes. These cases identify a new International Union for Conservation of Nature category, ‘low-impact passive recreation (surfing_reserve)’, that has the potential to deliver positive biodiversity outcomes.
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Researchers, using contingent valuation (CV) to value changes in nonmarket goods, typically believe respondents always answer questions truthfully or they answer truthfully only when it is in their interest to do so. The second position, while consistent with economic theory, implies that interpreting survey responses depends critically on the incentive structure provided. We derive simple tests capable of distinguishing the two views. Our theoretical model for examining the incentive structure of a single binary choice relaxes the usual expected utility assumption. We test our theory using a field experiment involving voting to provide a public good. Experimental results are consistent theoretical predictions and cast doubt on the relevance of a large experimental literature using inconsequential questions and non-incentive-compatible mechanisms to make inferences about CV. The framework put forth should help in understanding the role played by theoretical conditions for preference elicitation and lend insight into the hypothetical bias literature.
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In many parts of the world, natural vegetation has been cleared to allow agricultural production. To ensure a long-term flow of ecosystem services without compromising agricultural activities, restoration of the environment requires a balance between public and private benefits and costs. Information about private benefits generated by environmental assets can be utilized in the identification of conservation opportunities on private lands, evaluation of environmental projects, and in the design of effective policy instruments. We use a spatio-temporal hedonic model to estimate private benefits of native vegetation on rural properties in the state of Victoria, Australia. Specifically, we estimate the marginal value of native vegetation on private land and examine how it varies with the extent of vegetation on a property and across a range of property types and sizes. Private benefits of native vegetation are greater per unit area on small and medium-sized properties and smaller on large production-oriented farms. Native vegetation exhibits diminishing marginal benefits as its proportion of a property increases. The current extent of native vegetation cover is lower than the extent that would maximize the amenity value to many landowners. There is a scope for improved targeting of investment in the study region by incorporating private benefits of environmental projects into environmental planning processes. Landowners with high marginal private benefits from revegetation would be more willing to participate in a revegetation program. Targeting those landowners would likely to provide higher value for money, because such projects could be implemented at lower public cost
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We develop a statistical framework to use satellite data on night lights to augment official income growth measures. For countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights. Our estimates differ from official data by up to three percentage points annually. Using lights, empirical analyses of growth need no longer use countries as the unit of analysis; we can measure growth for sub- and supranational regions. We show, for example, that coastal areas in sub-Saharan Africa are growing slower than the hinterland. (JEL E01, E23, O11, 047, 057)
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The growing field of 'surfonomics' attempts to document surfing's economic contribution to local and regional communities, as well as the consumer surplus that surf breaks provide to millions of surfers. To date, no research has examined the extent to which the value of surf breaks is capitalized into home prices. This study uses the hedonic price method with data from three distinct beach-adjacent neighbourhoods in Santa Cruz, CA, USA, to estimate whether proximity to surf breaks leads to higher home values. The authors find that, after controlling for proximity to the beach, ocean views, the specific characteristics of the homes and neighbourhood effects, proximity to surf breaks is a statistically significant contributor to overall home value. A home that is right next to a surf break is valued on average at approximately US$106,000 more than an equivalent home a mile away.
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We investigate the role of deeply-rooted pre-colonial ethnic institutions in shaping comparative regional development within African countries. We combine information on the spatial distribution of ethnicities before colonization with regional variation in contemporary economic performance, as proxied by satellite images of light density at night. We document a strong association between pre-colonial ethnic political centralization and regional development. This pattern is not driven by differences in local geographic features or by other observable ethnic-specific cultural and economic variables. The strong positive association between pre-colonial political complexity and contemporary development also holds within pairs of adjacent ethnic homelands with different legacies of pre-colonial political institutions.
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In this collection of papers-originally prepared for a conference held by Resources for the Future in 1988 but rewritten since then in response to rapidly changing developments-the editors and other contributors examine the complex and evolving interactions among economic research on assessing natural resource damages, legislation (such as Superfund) establishing liability for such damages, and the litigation and regulatory processes affecting implementation of damage assessments. Recent court decisions have suggested that among the injuries to publicly owned natural resources for which liability may be claimed are losses of the nonmarket services of the resources; as Kopp and Smith explain, such injuries are seen to diminish people's valuation of these services, and the diminished value is a measure of the economic damage. The challenge to economists-which is the focus of this book-is how to measure such nonmarket values in the context of litigation, regulations, and the damage assessment provisions of Superfund. Contributors reveal that although existing nonmarket valuation methods, such as contingent valuation, have been used to assess natural resource damages, the damage assessment process itself has dramatically changed the context for applying these methods and has had a major influence on economic research associated with nonmarket valuation of environmental resources. In that context, for example, valuation of nonmarket measures takes place largely in the courtroom rather than in agencies, and the procedure itself changes how the measures are presented, received, and defended.
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We estimated global marketed and non-marketed economic value from two classified satellite images with global coverage at 1 km2 resolution. GDP (a measure of marketed economic output) is correlated with the amount of light energy (LE) emitted by that nation as measured by nighttime satellite images. LE emitted is more spatially explicit than whole country GDP, may (for some nations or regions) be a more accurate indicator of economic activity than GDP itself, can be directly observed, and can be easily updated on an annual basis. As far as we know, this is the first global map of estimated economic activity produced at this high spatial resolution (1 km2). Ecosystem services product (ESP) is an important type of non-marketed value. ESP at 1 km2 resolution was estimated using the IGBP land-cover dataset and unit ecosystem service values estimated by Costanza et al. [Valuing Ecosystem Services with Efficiency, Fairness and Sustainability as Goals. Nature's Services, Island Press, Washington DC, pp. 49–70]. The sum of these two (GDP+ESP)=SEP is a measure of the subtotal ecological–economic product (marketed plus a significant portion of the non-marketed). The ratio: (ESP/SEP)×100=%ESP is a measure of proportion of the SEP from ecosystem services. Both SEP and %ESP were calculated and mapped for each 1 km2 pixel on the earth's surface, and aggregated by country. Results show the detailed spatial patterns of GDP, ESP, and SEP (also available at: http://www.du.edu/∼psutton/esiindexisee/EcolEconESI.htm). Globally, while GDP is concentrated in the northern industrialized countries, ESP is concentrated in tropical regions and in wetlands and other coastal systems. %ESP ranges from 1% for Belgium and Luxembourg to 3% for the Netherlands, 18% for India, 22% for the United States, 49% for Costa Rica, 57% for Chile, 73% for Brazil, and 92% for Russia. While GDP per capita has the usual northern industrialized countries at the top of the list, SEP per capita shows a quite different picture, with a mixture of countries with either high GDP/capita, high ESP/capita, or a combination near the top of the list. Finally, we compare our results with two other indices: (1) The 2001 Environmental Sustainability Index (ESI) derived as an initiative of the Global Leaders of Tomorrow Environment Task Force, World Economic Forum, and (2) Ecological Footprints of Nations: How much Nature do they use? How much Nature do they have? developed by Mathis Wackernagel and others. While both of these indices purport to measure sustainability, the ESI is actually mainly a measure of economic activity (and is correlated with GDP), while the Eco-Footprint index is a measure of environmental impact. The related eco-deficit (national ecological capacity minus national footprint) correlates well with %ESP.