Memorandum by Dr David Maddison, Senior
Lecturer in Environmental Economics, University College London
THE AMENITY VALUE OF THE CLIMATE
INTRODUCTION
1. Efforts to reduce greenhouse gas emissions
are perceived to be very costly. Little is known, however, about
people's preferences for a particular climate or their willingness
to pay to avoid negative impacts of climate change. Research work
on the economic consequences of climate change has generally focused
on changes in productivity in sectors such as agriculture, energy
and tourism or on the costs of sea level rise. The impact of climate
change on households has been largely ignored although I believe
that it is here that the most profound impacts of climate change
will be felt. In this note I shall attempt to address the following
questions: Do households have preferences for particular types
of climate and if so why? How do households reveal their preferences
for particular types of climate? Will households necessarily be
damaged by climate change or will some gain and if so who will
gain and by how much?
2. Climate is an important input to many
household activities. Climate influences our health and determines
our heating and cooling requirements, clothing and nutritional
needs. Climate also fashions our leisure activities and particular
types of climate are known to promote a psychological sense of
well-being. Climate also has an indirect effect on households'
welfare in that particular types of climate support fauna and
flora over which households may have preferences. It would be
of no surprise to find that households living in climatically
different regions are likely to purchase different patterns of
goods even if they face the same incomes and the same prices:
it is to compensate for or alternatively take advantage of particular
types of climate. Furthermore otherwise identical households living
in different climatic zones are likely to enjoy different levels
of welfare. Finally there can be no presumption that changes in
climate will necessarily be detrimental to households. It may
be more helpful to think of climate change moving household closer
to or further away from an optimal climate.
3. In order to determine how good or bad
climate change is monetary estimates of willingness to pay for
climate amenities are required. A number of methods have employed
in this respect including: Hedonic analysis; Migration analysis;
Household Production Function approach; Hypothetical Equivalence
Scales; and Happiness studies. Before explaining these valuation
techniques in more detail and discussing the evidence that they
yield on the question of to what extent do households value the
climate it is worth pointing out that all of these methodologies
depend on non-testable assumptions. Some of these assumptions
are more plausible than others especially in the context of valuing
climate amenities. There are also significant data problems which
researchers using these techniques have struggled to overcome.
It would therefore be foolish to place too much faith in the results
of any one study. Only when multiple applications of the same
technique in different contexts point in the same direction, and
when different methodologies produce similar results can one feel
more confident about drawing conclusions. I think that we are
nearing that point.
HEDONIC ANALYSIS
4. If at the same time as being free to
adjust patterns of demand individuals are also freely able to
select from differentiated localities then climate itself becomes
a choice variable. The tendency will be for the costs and benefits
associated with particular climates to become capitalised into
property prices and wages. Thus, across different locations there
must generally exist both compensating wage and house price differentials
and in such cases the value of marginal changes in climate can
be discerned from hedonic house and wage price regressions. As
an example, the observation that all else being equal property
prices are higher and wage rates lower in areas characterised
by lower rainfall would be taken as evidence that households view
rainfall as a disamenity. By examining the increase in house prices
and the reduction in wage rates associated with a one millimetre
reduction in rainfall one could derive the amenity value of rainfall
to households.
5. The main advantage of using the hedonic
technique to deduce the value of climate variables is that since
one is in effect using current day analogues for future climate
change it can be presumed that long-run cost minimising adaptation
has already occurred. Apart from its strong theoretical underpinnings
and familiarity it is in this sense that the hedonic technique
possesses particular appeal.
6. Unfortunately the hedonic technique has
some drawbacks when used in this context. Barriers to the movement
of households exist as would prevent them from bidding up the
price of houses in locations with desired climates. This problem
is exacerbated because climate varies only over significant distances.
A further problem is that at any moment in time labour and housing
markets are likely to be in disequilibrium. Many other factors
determine the desirability of a location and climate is unlikely
to be the most important determinant of locational choice. One
therefore has to be concerned about the risk of mistakenly attributing
variations in house prices to climate when in reality they are
best attributed to another variable not included in the model.
7. Notwithstanding these difficulties Maddison
and Bigano (2003) use the hedonic technique to analyse the amenity
value of the climate of Italy. They find that labour incomes net
of housing costs are significantly higher in areas with high July
temperatures, high January rainfall and cloud cover and interpret
this as evidence of a compensating differential. Households are
shown to dislike high levels of precipitation in January and high
temperatures in July. The latter results seem especially plausible
since frequently debilitating July temperatures are normal for
Italy and this is clearly alarming in that climate change is predicted
to increase July temperatures in Italy. The most interesting finding
however is that whereas the Milanese would be willing to pay a
significant amount to increase the fraction of clear skies in
both January and July, people living in other locations would
not. Here there is a direct correspondence between one of the
most unusual features of the Italian climate—the persistent
fogs in the Po Valley—and the results of the analysis. By
contrast the remainder of Italy enjoys relatively clear skies.
The willingness to pay for a one unit change in climate variables
is given in Annex 1. The correct interpretation of these figures
is for example that a household would be willing to pay 648,000
Lira to avoid a 1(ºC) increase in July temperatures. Note
also that the willingness to pay for a one unit change in climate
variables differs with location. This is because the underlying
climate of Milan and Rome is different and it provides evidence
in favour of the existence of an "optimal" climate.
These figures could be combined with actual changes in climate
to compute an overall estimate of the impact of climate change
on Italian households.
8. Rehdanz and Maddison (2005) use the hedonic
approach to measure the amenity value of climate in Germany. The
evidence suggests that households in Germany are compensated for
climate amenities mainly through hedonic housing markets rather
than labour markets. Houses are more expensive in areas with higher
January temperatures which the authors interpret as evidence that
households are willing to pay for an increase in January temperatures.
Households are also willing to pay for a reduction in precipitation
in January and a reduction in July temperatures. The results are
very similar to those obtained from Italy although obviously the
climate of Italy is more varied than that of Germany and somewhat
warmer. The distinguishing feature of this study is that the authors
have an abundance of high-resolution data and control for differences
in the level of over one hundred amenities. The authors show that
the same results pertain of sub-regions of Germany. There is in
this study therefore little chance that the significance of the
climate variables is due to confounding. A number of other studies
exist for the United States.
MIGRATION BASED
ANALYSES
9. Hedonic studies focus on equilibrium
outcomes whilst migration studies focus on the process of equilibration.
Migration decisions are made on the basis of differences in wage
rates, housing costs, employment possibilities. Do regions with
more desirable climate ceteris paribus experience net inward migration?
How much extra income in the form of higher wages and lower property
prices is necessary to encourage a household to stay in an area
with an unpleasant climate? Migration studies attempting to explain
the influx or outflux of individuals from particular regions can
also be used to make inferences about the amenity value of climate.
10. Cragg and Kahn (1999) consider migration
patterns between different states of the US. They consider the
propensity of individuals to move between states as a function
of the characteristics of those states namely wage rates, house
prices and amongst several other things climate variables. The
results indicate that individuals appear to be paying thousands
of dollars to enjoy preferred climates. More specifically the
authors find that individuals are attracted by high wintertime
temperatures, low summertime temperatures, sunshine and low humidity.
Interestingly the authors are able separately to estimate the
willingness to pay for climate variables for individuals at different
stages of the life cycle. More elderly people have far stronger
preferences for climate which obviously accords with expectation.
11. There are many more studies of migration
in which climate has been included despite not being the main
focus of interest. In virtually all of these studies climate variables
have been found to be highly significant (see Cushing, 1987 for
a summary). This approach is an interesting adjunct to the hedonic
approach although Cragg and Kahn demonstrate that the results
can differ between the two approaches. It shares some of the weaknesses
of the hedonic approach namely the assumption that individuals
are willing to move large differences in order to realise the
net advantages afforded by different locations and the risk of
confounding.
THE HOUSEHOLD
PRODUCTION FUNCTION
APPROACH
12. As household expenditures increase household
expenditure shares typically change. The share of household expenditure
spent on some commodities declines whereas the expenditure share
accounted for by other goods tends to increase. Assuming that
the number of children imposes fixed costs on the household how
much money would a family with children have to be given before
it exhibited the same pattern of expenditure shares as a household
without any children? In the Household Production Function approach
the difference in income would be interpreted as the cost of the
children and could be inferred by comparing the expenditure patterns
of households with and without children. In fact such an approach
is regularly used in economics to calculate the needs of households
with differing composition and the extension to climate variables
is straightforward. Analysing household expenditure patterns how
much more income (if any) would a household living in a cold climate
require before it resembled a household living in a warmer climate
in terms of expenditure shares? One might imagine that a household
living in a colder climate would experience greater fixed costs
in terms of its need for heating and warm clothes and would therefore
need extra income before its expenditure shares resembled those
of a household inhabiting warmer climate. These extra costs represent
the amenity value of a warm climate.
13. In order to undertake a Household Production
Function study for climate variables it is necessary to possess
data on expenditures drawn from households living in climatically
different zones. It is also necessary to control for all the other
factors that might explain why households exhibit different commodity
expenditure shares such as different prices, incomes and demographic
variables. The advantage of the approach is that it enables the
researcher to understand why climate is of value to the household
in the sense of what commodity groups are sensitive to differences
in climate. Unlike the hedonic and migration approach it does
not require one to assume that households are willing to move
substantial distances to eliminate the net benefits of different
locations. The weakness of the approach is that it assumes that
the only role played by climate is supplanting the need for marketed
commodities such as heating and warm clothing.
14. Using a sophisticated version of the
Household Production Function approach Maddison (2003) analyses
broad commodity consumption patterns for 88 different countries
by reference to prices, incomes, climate and other demographic
differences. The climate variables that he included (average temperature,
temperature range and temperature deviations and similarly for
precipitation) significantly contributed to explaining the variation
in demand especially for food and drink, clothing and footwear,
and miscellaneous goods. Surprisingly the demand for energy and
housing were not affected. He then calculates the change in the
cost of living imposed by a particular climate change scenario
provided by the UKMO associated with carbon dioxide doubling.
15. According to the simulations all of
Northern Europe would benefit from limited climate change at least
insofar as amenity values are concerned. These gains are particularly
pronounced for the United Kingdom and Ireland. Italy on the other
hand appears largely unaffected by climate change. Indeed, among
the European countries represented in the data set only Greece
is adversely affected by the hypothesised 2.5 ºC increase
in annually averaged global temperatures. Turning to Asia, a completely
different picture emerges with the majority of countries losing
from the predicted climate change scenario. The highly populated
countries of India and Pakistan are particularly hard hit. All
the African countries represented in the data set appear to lose
from predicted global climate change. In North America, the United
States is largely unaffected by climate change. Further north
Canada enjoys a reduction in the cost of living. Mexico on the
other hand suffers a large increase in the cost of living.
16. Although this study is wide in its ambit
a major defect is the fact that each country is assumed to represent
a homogenous climatic zone. This is not unreasonable for a small
country like a Caribbean island but wholly implausible for a country
like the United States. Maddison attempts to overcome this problem
by using a climate index that is weighted according to the population
of the main cities in each country but this is no substitute for
possessing micro-economic data.
HYPOTHETICAL EQUIVALENCE
SCALES
17. In the Hypothetical Equivalence Scales
approach a sample of individuals are asked whether they would
describe an income of $X as good, bad or neither good not bad
for someone in their particular circumstances. Amount $X is varied
across the sample and the results analysed using regression analysis
to determine what factors the respondents implicitly believe mean
that their household requires higher incomes to reach any arbitrarily
defined level of welfare. Individuals' answers seem to reflect
the size and composition of their household. For example, individuals
with a larger number of children tend to declare that they need
higher incomes to reach the same level of welfare.
18. The underlying assumption of this technique
of course is that individuals share a common understanding of
what constitutes a "good" and "bad" standard
of living. Evidence suggests that this is not a wholly robust
assumption. Nonetheless this technique avoids the need to collect
the difficult to obtain data on expenditure patterns required
for the Household Production Function Study. It also deals with
situations in which climate affects welfare in ways other than
through reallocation of expenditure.
19. Friijters and Van Praag (2001) present
hypothetical equivalence scales for Russian households. Amongst
other things they find that implicitly individuals take account
of climate variables in their verbal assessment of particular
income levels. The results indicate that Russians strongly prefer
warmer winters. Put another way, individuals living in areas of
Russia enjoying warmer winters were happy to describe the same
level of income as "good" that someone living in an
area characterised by colder winters would describe as "poor".
The values identified by the Friijters and Van Praag study suggest
that the gains to Russia from higher temperatures could be very
significant.
HAPPINESS ANALYSES
20. Although it may seem bizarre at first
blush psychologists have long invited individuals to state how
happy they are on a 1-5 scale. Recently economists such as Professor
Richard Layard have started to use such studies to examine whether
growth makes people happy and whether inflation and unemployment
make individuals unhappy. Analyses of the economic determinants
of happiness now appear with regularity in leading academic economics
journals such as the American Economic Review, the Review
of Economics and Statistics and the Economic Journal.
In these studies happiness, welfare and utility are taken as being
synonymous with one another.
21. Although it appears very difficult to
explain cross-country differences in average happiness scores,
Rehdanz and Maddison (2005) use cross-country data on self proclaimed
happiness to analyse preferences for climate. Happiness is clearly
potentially affected by a large number of economic variables,
demographic variables, cultural variables, civil liberties as
well as environmental variables like climate. Despite including
a sizeable number of variables in their attempts to explain international
differences in happiness scores only per capita income and climate
are significant. It appears that low winter temperatures and high
summer temperatures make people unhappy—precisely what was
found in the hedonic studies for Italy and Germany.
22. Rehdanz and Maddison use their results
to simulate the change in real GDP per capita necessary to hold
happiness (or welfare) at its current levels in the face of predicted
changes in climate. Countries in high latitudes like Canada, Norway,
Finland, Sweden, Iceland, Denmark, Great Britain and Ireland gain
from climate change. Countries for which precipitation is expected
to increase in previously dry months like Peru, Venezuela or India
are also likely to gain from limited climate change. The full
results are shown in Annex 2 and are of course dependent upon
climate model predictions regarding the expected pattern of climate
change. The proper interpretation of these figures is for example
that an increase in GDP per capita of $138.58 would compensate
Argentinians for the kind of climate change predicted by 2039
under the business as usual emission scenario. A negative sign
as for example for Armenia means that the country would benefit
from climate change.
CONCLUSIONS
23. A large number of methodologies have
now been used to measure the amenity value of climate in monetary
terms. Despite differences in geographical context, the quality
of the data and the plausibility of the underlying assumptions
most studies indicate that climate is rather important to household
welfare. It is nonetheless difficult to say whether different
methodologies produce similar results not least because they characterise
climate in a different way (eg average temperature versus temperature
deviations versus January and July averages).
24. A number of the studies described in
this note merely uncover household preferences for climate and
present their results in terms of willingness to pay per unit
change. But being able to determine households' preferences for
climate change is only part of the story. To obtain the overall
welfare impact it is necessary to combine these preferences with
estimates of the extent of climate change. Some of these approaches
determine the amenity value of climate to today's households.
Future households will be richer and face different prices for
goods and commodities. This will affect the perceived impact of
climate change. It is an interesting question whether future households
will be more or less impacted by climate change. It is also interesting
to speculate whether poorer households are likely to be more affected
by climate change because the commodities on which they tend to
spend the majority of their income are those that can be supplanted
by climate. Valuation studies offer the prospect of resolving
these interesting and important questions.
25. On the basis of the research described
in this note I do not know if the impact of climate change on
global amenity values will be positive or negative. In any case
the amenity values presented in this paper are only part of the
overall impact of climate change: there are the changes in productive
activities like agriculture and forestry as well as the effects
of sea level rise and the loss of biodiversity. I do, however,
feel secure enough to assert that the impact of climate change
will redistribute welfare between countries and that some countries
will gain from limited climate change. Two very different studies
both suggest that high latitude countries will enjoy a significant
increase in amenity values from climate change whereas low latitude
countries will lose.
26. There is abundant scope for more elaborate
attempts to estimate the amenity value of climate to households
here in the UK as elsewhere. For example, the data used to estimate
amenity values used by all of these studies was collected for
another purpose and is generally aggregated over politically defined
regions. Much more accurate results could be obtained by using
higher resolution data collected over climatically homogenous
zones. The valuation techniques described in this paper have been
widely used to value other environmental goods but there are very
few applications to climate. It would be quite possible to conduct
a high quality hedonic house price study for the United Kingdom
as well as a household production function study. But there is
resistance to valuing the impact of climate change in monetary
terms and also to the idea that climate change may be not detrimental
to all countries. More than anything however there is simply limited
awareness of the possibilities afforded by such techniques amongst
researchers and those who fund climate change research.
28 February 2005
REFERENCES
Cragg, MI and Kahn, ME, 1997. New Estimates
of Climate Demand: Evidence from Location Choice. Journal of
Urban Economics, 42:261-284.
Cushing, BJ, 1987. A Note on Specification of
Climate Variables in Models of Population Migration. Journal
of Regional Science, 27:641-649.
Frijters, P and Van Praag, BMS, 1998. The Effects
of Climate on Welfare and Wellbeing in Russia. Climatic Change,
39:61-81.
Maddison, D, 2003. The Amenity Value of Climate:
The Household Production Function Approach Resource and Energy
Economics, 25:155-175.
Maddison, D and Bigano, A, 2003. The Amenity
Value of the Italian Climate. Journal of Environmental Economics
and Management, 45:319-332.
Rehdanz, K and Maddison, D, 2005. Climate and
Happiness. Ecological Economics, 52: 111-125.
Rehdanz, K and Maddison, D, 2005. The Amenity
Value of Climate to Households in Germany. Submitted to the Journal
of Environmental Economics and Management.
Annex 1
THE AMENITY VALUE OF THE CLIMATE TO HOUSEHOLDS
IN ITALY (000s OF LIRA PER HOUSEHOLD PER YEAR IN 1993 PRICES)
| Milan | Rome
|
| January Temperatures (ºC) | -193
| -386 |
| July Temperatures (ºC) | -648**
| -711** |
| January Precipitation (mm) | -42*
| -33** |
| July Precipitation (mm) | -43
| -58 |
| January Clear Skies (%) | 317*
| -443 |
| July Clear Skies (%) | 620**
| 110 |
Source: Maddison and Bigano (2003). Note that ** means significant at the 1 per cent level and * means significant at the 5 per cent level of confidence.
| | |
Annex 2
CONSTANT HAPPINESS CHANGE IN GDP PER CAPITA (1995 USD)
| Country | Year 203
| 9 Year 206 | 9
| |
| Argentina | 138.58 | 233.85
| | |
| Armenia | -75.89 | -135.27
| | |
| Australia | 78.12 | 128.00
| | |
| Austria | 123.35 | 247.42
| | |
| Azerbaijan | -74.63 | -116.75
| | |
| Bangladesh | -17.25 | -79.69
| | |
| Belarus | -64.94 | -116.33
| | |
| Belgium | 15.66 | 32.88
| | |
| Bosnia—Hercegovina | 158.89
| 276.68 | |
|
| Brazil | 58.26 | 114.05
| | |
| Bulgaria | 244.03 | 421.09
| | |
| Canada | -354.08 | -591.61
| | |
| Chile | 102.21 | 163.55
| | |
| China | 1.14 | -0.14
| | |
| Colombia | 12.36 | 29.12
| | |
| Croatia | 219.44 | 380.28
| | |
| Czechia | 89.95 | 151.70
| | |
| Czechoslovakia | 89.95 |
151.70 | | |
| Denmark | -81.79 | -147.89
| | |
| Dom Republic | 65.02 | 103.48
| | |
| Estonia | -31.93 | -65.78
| | |
| Finland | -356.60 | -612.47
| | |
| France | 123.77 | 210.12
| | |
| Georgia | -86.01 | -152.52
| | |
| Germany | 12.70 | 21.61
| | |
| Ghana | 71.36 | 119.54
| | |
| Great Britain | -26.87 |
-53.56 | | |
| Hungary | 158.89 | 273.71
| | |
| Iceland | -188.69 | -323.90
| | |
| India | -28.52 | -43.86
| | |
| Ireland | -28.35 | -53.14
| | |
| Israel | 241.48 | 401.53
| | |
| Italy | 203.77 | 337.37
| | |
| Japan | -81.79 | -142.00
| | |
| Latvia | -88.11 | -155.47
| | |
| Lithuania | -76.73 | -136.53
| | |
| Macedonia | 253.78 | 440.66
| | |
| Mexico | 166.08 | 283.89
| | |
| Moldova | 65.02 | 102.63
| | |
| Montenegro | 272.43 | 470.46
| | |
| Netherlands | -4.73 | -12.02
| | |
| New Zealand | 38.54 | 59.10
| | |
| Nigeria | 180.48 | 304.25
| | |
| Northern Ireland | 17.68 |
34.86 | | |
| Norway | -271.45 | -463.11
| | |
| Peru | -60.30 | -66.20
| | |
| Philippines | 12.53 | 14.47
| | |
| Poland | 26.08 | 36.51
| | |
| Portugal | 218.17 | 368.38
| | |
| Puerto Rico | 37.86 | 60.37
| | |
| Romania | 233.85 | 389.63
| | |
| Russia | -75.89 | -127.69
| | |
| Serbia | 219.44 | 395.15
| | |
| Slovakia | 136.46 | 230.89
| | |
| Slovenia | 189.37 | 328.87
| | |
| South Africa | 158.04 | 272.01
| | |
| South Korea | -95.27 | -160.09
| | |
| Spain | 268.19 | 453.43
| | |
| Sweden | -272.29 | -457.92
| | |
| Switzerland | 146.19 | 258.02
| | |
| Turkey | 298.31 | 500.27
| | |
| USA | 24.60 | 57.42
| | |
| Ukraine | -74.63 | -133.16
| | |
| Uruguay | 147.04 | 260.14
| | |
| Venezuela | -33.07 | -55.25
| | |
| | |
| |
Source: Rehdanz and Maddison (2005).
| 
