Transcript The Amenity Value of the Italian Climate

The Amenity Value of the Italian
Climate
David Maddison and Andrea Bigano
Journal of Environmental Economics and Management (2003) 45: 319-332.
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Presentation’s Plan
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Introduction
Hedonic Literature and Climate
Data
Empirical Analysis
Results
Conclusions
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Introduction: Valuing Climate



Climate Change is currently a hot and much
debated global issue.
There is disagreement about ‘When’, ‘Where’ and
‘How’ Climate Change will affect us. This paper is
a contribution to the ‘How’ part of the issue.
A money metric measure of the impact of
changes in climate can be useful when evaluating
whether the benefits of preventing climate
change justify the costs.
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Introduction: Climate and its Value
•Climate is an important localised input to many households activities.
•It influences health conditions, heating and cooling requirements,
nutritional needs, leisure activities.
•Households are attracted to regions offering preferred combinations of
local amenities, hence:

in the House Market:

Costs and Benefits
associated with particular
climates are capitalised
into property prices: we
are prepared to pay more
for living in a nice area.
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in the Labour Market:

Costs and Benefits
associated with particular
climates are collaterised
into wages: we ask
compensation for working in
unpleasant conditions.
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Introduction: Why Italy?
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This is the first study of this kind outside the U.S.A.;
Economic, climatic, house and labour market data
are available at a good level of disaggregation;
Italy’s geographic characteristics lead to a marked
variation in climate within a rather limited area.
This allows us to use current day analogues for future
climate changes, presuming that long run costminimising adaptation as already occurred.
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Introduction: The Climatic Regions of Italy
1. Alpine Italy
1
2. Po Valley
2
5
3. Northern Adriatic
3
4. Southern Adriatic
5. Liguria
4
6
8
6. Tyrrenic Coast
7. Sicily
8. Sardinia
7
Source: Cantù (Landsberg ed.) (1969-1981)
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Hedonic Literature and Climate
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If individuals are freely able to select from differentiated
localities then the tendency will be for the costs and
benefits associated with disamenities to become
capitalised into house prices and wage rates.
Across different regions there must exist both
compensating wage and house price differentials and the
value of marginal changes can be discerned from hedonic
house and wage price regressions.
Assumptions of the theoretical model:
 existence of equilibrium in the hedonic markets,
 perfect information,
 absence of relocation costs,
 existence of smoothly continuous trade-off possibilities
among all characteristics (internal solutions),
 existence of a unified market for land and labour.
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Studies in the Amenity Value of Climate
Study
Sample
Hoch & Drake
(1974)
86
SMSAs
Nordhaus
(1996)
Robak (1982)
Dependent
Variable(s)
Wages
Wages
98
SMSAs
Smith (1983)
Climate Explanatory Variables
Winter and Summer Temperature,
Precipitation, Wind Speed,
Degree Days, Very Hot Days,
Very Cold Days
Temperature and Precipitation in
January, April, July and October.
Wages
Residential
Site Prices
Snowfall, Degree Days
Cloudy Days, Clear Days
Wages
Sunshine hours, Highest and
Lowest Temperature, Wind
Speed, Precipitation
Sunshine Hours, Humidity,
Heating Degree Days, Cooling
Degree Days
Wind Speed, Precipitation,
Visibility
Heating Degree Days, Cooling
Degree Days, Rainfall
44
SMSAs
Hoen et al.
(1987)
Blomquist et al
(1988)
285
SMAs
Wages
House Prices
Clark &
Cosgrove
(1990)
564 U.S.
Home
owners
House Prices
Clark &
Cosgrove
(1991)
6668 U.S.
Movers
Wages
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Heating Degree Days,
Difference between min January
and Max July temperature,
Rainfall, hours of sunshine
Other
Explanatory Variables
Regional Dummies
Racial Composition
Urban Size
Results
Population Density, Latitude
and Longitude, Presence of
major water bodies. Tax
structures and public goods
accounted for.
Population Growth,
Population Density,
Unemployment Rates
Crime Rates, Pollution (TSP)
Job Specific Characteristics,
Site specific Characteristics
Not conclusive
Coast Proximity
Teachers-pupils Ratios,
TSP, Crime Rates
Amenity variables significant.
Only Sunshine was
unambiguously valued.
House characteristics,
Amenities, Unemployment,
Immigration, Location,
Wages.
Coast Proximity, Amenities,
Type of employment,
Population density.
Hot weather and high levels of
rainfall are compensated for by
lover prices.
Climate variable Significant, but
not in the first sub sample when
regional dummies are considered.
Climate variables are significant
with the expected sign in the
wages regression, but not in the
house prices regression
Only Sunshine significant
Rainfall is a significant
disamenity, but the other
climate variables are not
significant.
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Data Sources
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Istituto Nazionale Previdenza Sociale provided
Provincial data on expected labour income per
worker;
Banca d’Italia provided provincial data on
national averaged after tax labour income and
nationally averaged annual housing costs;
A survey of provincial property prices per square
meter is taken from Il Sole 24 Ore.
The climate data is taken from Leemans and
Cramer. This database merges records drawn
from a variety of published sources to create a
terrestrial grid at the 0.5° level of resolution.
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Empirical Analysis: Variables’ Creation I

Our dependent variable (D.V.) is defined as «expected after tax labour
income net of housing cost».
Provincial
Unemployment rate
National after tax
Household Income
Labour income
per worker
Expected Provincial
after-tax
Labour income
-
Provincial
property prices /m2
Nationally averaged
Housing Costs
Provincial housing costs for a
dwelling of constant dimensions
D.V.
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Empirical Analysis: Variables’ Creation II
•Temperatures are adjusted so that
they correspond to the average
elevation of each province (-0.6°
C/100 m);
.Turin
• Dummy variables were included:
•Years
•Coast Proximity
•Alpine Italy
• Macro-Regions
•Major Cities
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Variables included in the dataset
Variable
Mean
Std. Dev.
Minimum Maximum
Definition
NETINC
JANPRECIP
JULPRECIP
JANTEMP
JULTEMP
JANCLEAR
JULCLEAR
17.5
86
36
4.1
22.7
0.38
0.66
2.3
36
31
3.8
3
0.05
0.07
9.3
32
1
-7.3
9.3
0.27
0.46
24.6
230
149
10.9
26.2
0.5
0.81
Expected after tax labor income net of housing costs (M Lira)
Average precipitation in January (mm)
Average precipitation in July (mm)
Average mean temperature in January (°C)
Average mean temperature in July (°C)
Average fraction of clear sky in January
Average fraction of clear sky in July
COAST
ALPINE
POPDEN
LAT
LONG
MILAN
ROME
NAPLES
TURIN
CENTRAL
SOUTH
SARDINIA
SICILY
DUM92
DUM93
DUM94
DUM95
0.56
0.05
238
4262
1190
0.01
0.01
0.01
0.01
0.25
0.18
0.04
0.09
0.2
0.2
0.2
0.2
0.5
0.22
325
263
258
0.1
0.1
0.1
0.1
0.25
0.18
0.04
0.09
0.4
0.4
0.4
0.4
0
0
36
3650
700
0
0
0
0
0
0
0
0
0
0
0
0
1
1
2662
4600
1800
1
1
1
1
1
1
1
1
1
1
1
1
Unity if the province borders on the sea, zero otherwise
Unity if the province is in the Alps, zero otherwise
Population density of the province (persons per km2)
Latitude (x100)
Longitude (x100)
Unity if the province is Milan, zero otherwise
Unity if the province is Rome, zero otherwise
Unity if the province is Naples, zero otherwise
Unity if the province is Turin, zero otherwise
Unity if the province is in Central Italy, zero otherwise
Unity if the province is in Southern Italy, zero otherwise
Unity if the province is in Sardinia, zero otherwise
Unity if the province is in Sicily, zero otherwise
Unity if the year is 1992, zero otherwise
Unity if the year is 1993, zero otherwise
Unity if the year is 1994, zero otherwise
Unity if the year is 1995, zero otherwise
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Empirical Analysis: Regression
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
Sample: Panel (95 provincial observations x 5 years).
Functional form:
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Non-binary variables are entered as both linear and quadratic
variables;
Linear, semi-log and inverse specification were tested
(Maddala). The linear model was the most likely to have
generated the data;
Standard errors were corrected to account for likely intraprovincial correlation and are robust to heteroskedasticity;
Population density, likely to be endogenous, turned out to
be exogenous and was not instrumented;
Two alternative models were tested (with or without
longitude and latitude)
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Regression Results
Long. & La t. inc.
Long. & La t. e x cl.
Long. & La t. inc.
Long. & La t. e x cl.
Parame te r
Coe fficie nt
Coe fficie nt
Parame te r
Coe fficie nt
Coe fficie nt
CONST
109.4359
-3.46
0.2825204
-1.32
0.0114957
19.90963
-1.17
0.236183
-1.01
0.0506542
COAST
-0.8880134
(-2.00)
-5.520199
(-3.62)
-1.455395
-0.9882339
(-2.00)
-5.063282
-2.82
-1.445131
-0.48
0.6355427
-2.73
0.0210637
-2.03
0.3408511
-1.3
0.0080926
(-1.41)
-7.880024
(-4.33)
-33.67878
(-1.64)
-5.697631
(-3.96)
-21.13761
-1.01
0.0338616
-2.6
-0.0001205
-0.41
0.02021
-1.52
-0.0000623
(-3.42)
-5.535101
(-10.65)
-0.0067318
(-2.38)
-3.958793
(-5.73)
-0.0051518
(-1.29)
0.0415362
-1.25
-0.0003399
(-0.69)
0.0001074
0
-0.0002748
(-3.33)
8.07E-06
-3.38
-1.604921
(-2.30)
5.15E-06
-2.2
-1.525399
(-1.44)
-11.97623
(-1.19)
164.024
(-0.99)
18.15799
-3.07
90.33523
(-1.44)
-4.756948
(-2.88)
-9.699294
(-1.91)
-3.409359
(-4.21)
-3.519012
-1.86
-33.67762
(-2.14)
282.9566
-1.12
-27.34107
(-1.46)
61.30847
(-3.80)
-6.327532
(-2.75)
0.6742714
(-2.41)
-6.209634
(-4.40)
0.6742714
-2.9
-0.0171869
(-2.89)
-0.000028
(-2.68)
-0.007514
(-3.22)
0.0000109
-2.81
-0.65
-12.66
0.7844456
-7.61
0.9398976
-7.95
1.959178
-19.06
-12.72
0.7844456
-7.64
0.9398976
-7.99
1.959178
-19.15
0.64
0.552
JANTEMP
JANTEMP2
JULTEMP
JULTEMP2
JANPRECIP
JANPRECIP2
JULPRECIP
JULPRECIP2
JANCLEAR
JANCLEAR 2
JULCLEAR
JULCLEAR2
LAT
LAT2
LONG
LONG2
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ALPINE
ROME
MILAN
NAPLES
TURIN
POPDEN
POPDEN
2
CENTRAL
SOUTH
SARDINIA
SICILY
DUM92
DUM93
DUM94
DUM95
R-Square d
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The welfare impact of marginal changes in climate
Milan
January Temperature (°C) -99.67618152
(-0.61)
July Temperature (°C)
-334.6640706
(-2.74)
January Precipitation (mm) -21.69118976
(-2.24)
July Precipitation (mm)
-22.20764666
(-1.27)
January Clear Skies (%) 163.716837
(-2.25)
July Clear Skies (%)
320.2032774
(-3.06)
Rome
-199.352363
(-1.44)
-367.2008553
(-2.71)
-17.04307767
(-2.63)
-29.95450015
(-1.46)
-228.7904063
(-0.31)
56.8102589
(-0.69)
Naples
-207.6156734
(-1.39)
-367.2008553
(-2.71)
-13.42787938
(-3.24)
-30.98741395
(-1.48)
279.4031824
(-0.50)
277.8538117
(-0.33)
Cagliari
-206.5827596
(-1.40)
-373.9147949
(-2.69)
-19.10890527
(-2.44)
-32.53678464
(-1.50)
-228.7904063
(-0.31)
-890.8881509
(-0.83)
Palermo
-199.352363
(-1.44)
-380.6287346
(-2.67)
-15.49370697
(-2.89)
-32.53678464
(-1.50)
-736.983995
(-0.79)
-1475.517361
(-1.21)
Note: Euro / household / year. T-statistics are in parentheses
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Conclusions




There is considerable empirical support for the hypothesis
that information on the amenity value of climate is
contained in the market for housing and labour in Italy;
It appears that Italians regard the high July temperatures
that they experience as a disamenity and similarly for
high levels of precipitation in January.
Climate change is predicted to result in an increase in
both. Hence it threatens to bring a considerable reduction
in amenity values to Italian households.
Qualifications:




Some arbitrariness in the choice of explanatory variables;
Relevant climate variables not included;
Results are very sensitive to geographical features;
Non-marginal changes still to be identified.
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