Transcript The Amenity Value of the Italian Climate

The Amenity Value of the Italian
Climate
David Maddison and Andrea Bigano
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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.
In particular, possessing a money metric measure
of the impact of changes in climate, may prove
especially useful given the question about
whether the costs of preventing climate change
are justified by the benefits.
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Introduction: Climate and its Value
•Climate is an important localised imput 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: Hedonic Models
Housing Market
Owner of house i ’s problem:

Max U  u  X ,Q i 
s.t
M  Phi  X  0
Where the house price is
Phi  Ph Qi 
Qi  q1 qk qN 
First order conditions then
imply
u
u
qk
X

 Phi q

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Labour and Housing Market
Max U  u  X , h, qk 
  Pw qk   Ph qk   X 
Where the individual’ endowment is
now
M  P q 
w
k
First order conditions then
imply
u
MRSXq 
k

u
qk
X
 Ph qk  Pw qk 

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Introduction: Hedonic Literature and Climate



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 thoretical 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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Introduction: Why Italy?



This is the first study of this kind outside the U.S.A.;
Economic, climatic, house and labour market data
are availiable 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 has 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. Tyrrenian Coast
7.Calabria and Sicily
8. Sardinia
7
Source: Cantù (Landsberg ed.) (1969-1981)
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Hedonic Literature and 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, a leading
financial newspaper in Italy .
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 labor
income net of housing cost ».
Provincial
Unemployment rate
Labour income
per worker
National after tax
Household Income
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/100m);
.Turin
• Dummy variables were included:
•Years
•Coast Proximity
•Alpine Italy
• Macro-Regions
•Major Cities
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Variables included in the dataset 1
Variable
NETINC
Mean
Std. Dev. Minimum Maximum
Definition
17.5
2.3
9.3
24.6 Expected after tax labor income net of housing costs (M Lira)
JANPRECIP
86
36
32
230 Average precipitation in January (mm)
JULPRECIP
36
31
1
JANTEMP
4.1
3.8
-7.3
10.9 Average mean temperature in January (°C)
JULTEMP
22.7
3
9.3
26.2 Average mean temperature in July (°C)
JANCLEAR
0.38
0.05
0.27
0.5 Average fraction of clear sky in January
JULCLEAR
0.66
0.07
0.46
COAST
0.56
0.5
0
1 Unity if the province borders on the sea, zero otherwise
ALPINE
0.05
0.22
0
1 Unity if the province is in the Alps, zero otherwise
POPDEN
238
325
36
2662 Population density of the province (persons per km2)
4262
263
3650
LAT
149 Average precipitation in July (mm)
0.81 Average fraction of clear sky in July
4600 Latitude (x100)
LONG
1190
258
700
1800 Longitude (x100)
Source. Leemans and Cramer , INPS, Banca d'Italia, and Il Sole 24 Ore del Lunedi.
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Variables included in the dataset 2
Variable
Mean
Std. Dev. Minimum Maximum
Definition
MILAN
0.01
0.1
0
1 Unity if the province is Milan, zero otherwise
ROME
0.01
0.1
0
1 Unity if the province is Rome, zero otherwise
NAPLES
0.01
0.1
0
1 Unity if the province is Naples, zero otherwise
TURIN
0.01
0.1
0
1 Unity if the province is Turin, zero otherwise
CENTRAL
0.25
0.25
0
1 Unity if the province is in Central Italy, zero otherwise
SOUTH
0.18
0.18
0
1 Unity if the province is in Southern Italy, zero otherwise
SARDINIA
0.04
0.04
0
1 Unity if the province is in Sardinia, zero otherwise
SICILY
0.09
0.09
0
1 Unity if the province is in Sicily, zero otherwise
DUM92
0.2
0.4
0
1 Unity if the year is 1992, zero otherwise
DUM93
0.2
0.4
0
1 Unity if the year is 1993, zero otherwise
DUM94
0.2
0.4
0
1 Unity if the year is 1994, zero otherwise
DUM95
0.2
0.4
0
1 Unity if the year is 1995, zero otherwise
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Empirical Analysis: Regression


Sample: Panel ( 95 provincial observations x 5 years).
Functional form:


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
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Non-binary variables are entered as both linear and quadratic
variables (centered to avoid multicollinearity);
Linear, semilog 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, tourned out
to be exogenous and was not instrumented;
2 alternative models were tested ( with or without
longitude and latitude)
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Regression Results
Long. & Lat. inc.
Long. & Lat. excl.
Long. & Lat. inc.
Long. & Lat. excl.
Parameter
Coefficient
Coefficient
Parameter
Coefficient
Coefficient
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
JANCLEAR2
JULCLEAR
JULCLEAR2
LAT
LAT 2
LONG
LONG 2
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ALPINE
ROME
MILAN
NAPLES
TURIN
POPDEN
POPDEN
2
CENTRAL
SOUTH
SARDINIA
SICILY
DUM92
DUM93
DUM94
DUM95
R-Squared
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The welfare impact of marginal changes in climate
Milan
January Temperature (°C) -99.68
(-0.61)
July Temperature (°C)
-334.66
(-2.74)
January Precipitation (mm) -21.69
(-2.24)
July Precipitation (mm)
-22.21
(-1.27)
January Clear Skies (%)
163.72
(-2.25)
July Clear Skies (%)
320.20
(-3.06)
Rome
-199.35
(-1.44)
-367.20
(-2.71)
-17.04
(-2.63)
-29.95
(-1.46)
-228.79
(-0.31)
56.81
(-0.69)
Naples
-207.62
(-1.39)
-367.20
(-2.71)
-13.43
(-3.24)
-30.99
(-1.48)
279.40
(-0.50)
277.85
(-0.33)
Cagliari
-206.58
(-1.40)
-373.91
(-2.69)
-19.11
(-2.44)
-32.54
(-1.50)
-228.79
(-0.31)
-890.89
(-0.83)
Palermo
-199.35
(-1.44)
-380.63
(-2.67)
-15.49
(-2.89)
-32.54
(-1.50)
-736.98
(-0.79)
-1475.52
(-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. Insofar as future
climate change is predicted to result in an increase in
both, it threatens to bring a considerable reduction in
amenity values to Italian households.
Qualifications:




Relevant climate variables not included;
Some arbitrariety in the choice of explanatory variables;
Results are very sensitive to geographical features
Non-marginal changes still to be identified.
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