CENTER FOR ECONOMIC STUDIES KATHOLIEKE UNIVERSITEIT LEUVEN The Amenity Value of the Italian Climate...

CENTER FOR ECONOMIC STUDIES

KATHOLIEKEUNIVERSITEIT

LEUVEN

The Amenity Value of the Italian The Amenity Value of the Italian ClimateClimate

David Maddison and Andrea Bigano



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Presentation’s PlanPresentation’s Plan

Introduction Hedonic Literature and Climate Data Empirical Analysis Results Conclusions



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Introduction: Valuing ClimateIntroduction: 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 ValueIntroduction: Climate and its Value

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.

in the Labour Market: Costs and Benefits

associated with particular climates are collaterised into wages: we ask compensation for working in unpleasant conditions.

•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:



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Introduction: Introduction: Hedonic ModelsHedonic Models

Housing Market

Owner of house i ’s problem:

Labour and Housing Market

0XPMts

XuUMax

hi

i

.

,Q

Where the house price is ihhi PP Q

First order conditions then imply

k

hi

k qP

Xu

qu

Nk1i qqq Q

XqPqP

qhXuUMax

khkw

k

,,

Where the individual’ endowment is now

First order conditions then imply

kwkh

kXq

qPqPX

uq

u

MRS

kw qPM



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Introduction: Introduction: Hedonic Literature and ClimateHedonic 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?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 cost-minimising adaptation has already occurred.



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Introduction: The Climatic Regions of ItalyIntroduction: The Climatic Regions of Italy

Source: Cantù (Landsberg ed.) (1969-1981)

1

2

35

8

7

6 4

1. Alpine Italy1. Alpine Italy

2. Po Valley2. Po Valley

3.3. Northern AdriaticNorthern Adriatic

4. Southern Adriatic4. Southern Adriatic

5.5. LiguriaLiguria

6. Tyrrenian Coast6. Tyrrenian Coast

77.Calabria and .Calabria and SicilySicily

8. Sardinia8. Sardinia



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Hedonic Literature and the Amenity Value of ClimateHedonic Literature and the Amenity Value of ClimateStudy Sample Dependent

Variable(s) Climate Explanatory Variables Other

Explanatory Variables Results

Hoch & Drake (1974)

86 SMSAs

Wages Winter and Summer Temperature, Precipitation, Wind Speed, Degree Days, Very Hot Days, Very Cold Days

Regional Dummies Racial Composition Urban Size

Climate variable Significant, but not in the first sub sample when regional dummies are considered.

Nordhaus (1996)

Wages Temperature and Precipitation in January, April, July and October.

Population Density, Latitude and Longitude, Presence of major water bodies. Tax structures and public goods accounted for.

Not conclusive

Robak (1982) 98 SMSAs

Wages Residential Site Prices

Snowfall, Degree Days Cloudy Days, Clear Days

Population Growth, Population Density, Unemployment Rates Crime Rates, Pollution (TSP)

Climate variables are significant with the expected sign in the wages regression, but not in the house prices regression

Smith (1983) 44 SMSAs

Wages Sunshine hours, Highest and

Lowest Temperature, Wind

Speed, Precipitation

Job Specific Characteristics, Site specific Characteristics

Only Sunshine significant

Hoen et al. (1987) Blomquist et al (1988)

285 SMAs

Wages House Prices

Sunshine Hours, Humidity, Heating Degree Days, Cooling Degree Days Wind Speed, Precipitation, Visibility

Coast Proximity Teachers-pupils Ratios, TSP, Crime Rates

Amenity variables significant. Only Sunshine was unambiguously valued.

Clark & Cosgrove

(1990)

564 U.S. Home

owners

House Prices Heating Degree Days, Cooling Degree Days, Rainfall

House characteristics, Amenities, Unemployment, Immigration, Location, Wages.

Hot weather and high levels of rainfall are compensated for by lover prices.

Clark & Cosgrove (1991)

6668 U.S. Movers

Wages Heating Degree Days, Difference between min January and Max July temperature, Rainfall, hours of sunshine

Coast Proximity, Amenities, Type of employment, Population density.

Rainfall is a significant disamenity, but the other climate variables are not significant.



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Data SourcesData Sources

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 IEmpirical Analysis: Variables’ Creation I

Our dependent variable (D.V.) is defined as « expected after tax labor income net of housing cost ».

D.V.

Expected Provincial

after-tax

Labour income

Provincial housing costs for a

dwelling of constant dimensions

--

Provincial

Unemployment rate

Labour income

per worker

National after tax

Household Income

Nationally averaged

Housing Costs

Provincial

property prices /m2



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Empirical Analysis: Variables’ Creation IIEmpirical Analysis: Variables’ Creation II

•Temperatures are adjusted so that they correspond to the average elevation of each province (-0.6° C/100m);

• Dummy variables were included:

•Years

•Coast Proximity

•Alpine Italy

• Macro-Regions

•Major Cities

.Turin



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Variables included in the dataset 1Variables included in the dataset 1

Variable Mean Std. Dev. Minimum Maximum Definition

NETINC 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 1 149 Average precipitation in July (mm)

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 0.81 Average fraction of clear sky in July

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)

LAT 4262 263 3650 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 2Variables 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






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Empirical Analysis: RegressionEmpirical Analysis: Regression

Sample: Panel ( 95 provincial observations x 5 years). Functional form:

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 intra-provincial 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 ResultsRegression ResultsLong. & Lat. inc. Long. & Lat. excl. Long. & Lat. inc. Long. & Lat. excl.

Parameter Coefficient Coefficient Parameter Coefficient Coefficient

109.4359 19.90963 -0.8880134 -0.9882339-3.46 -1.17 (-2.00) (-2.00)0.2825204 0.236183 -5.520199 -5.063282-1.32 -1.01 (-3.62) -2.820.0114957 0.0506542 -1.455395 -1.445131

-0.48 -2.03 (-1.41) (-1.64)0.6355427 0.3408511 -7.880024 -5.697631-2.73 -1.3 (-4.33) (-3.96)0.0210637 0.0080926 -33.67878 -21.13761

-1.01 -0.41 (-3.42) (-2.38)0.0338616 0.02021 -5.535101 -3.958793-2.6 -1.52 (-10.65) (-5.73)-0.0001205 -0.0000623 -0.0067318 -0.0051518

(-1.29) (-0.69) (-3.33) (-2.30)0.0415362 0.0001074 8.07E-06 5.15E-06-1.25 0 -3.38 -2.2-0.0003399 -0.0002748 -1.604921 -1.525399

(-1.44) (-0.99) (-1.44) (-1.91)-11.97623 18.15799 -4.756948 -3.409359(-1.19) -3.07 (-2.88) (-4.21)164.024 90.33523 -9.699294 -3.519012

-1.86 -1.12 (-3.80) (-2.41)-33.67762 -27.34107 -6.327532 -6.209634(-2.14) (-1.46) (-2.75) (-4.40)282.9566 61.30847 0.6742714 0.6742714

-2.9 -0.65 -12.66 -12.72-0.0171869 0.7844456 0.7844456(-2.89) -7.61 -7.64-0.000028 0.9398976 0.9398976(-2.68) -7.95 -7.99-0.007514 1.959178 1.959178(-3.22) -19.06 -19.150.0000109

-2.81 R-Squared 0.64 0.552

DUM92

DUM93

DUM94

DUM95

CENTRAL

SOUTH

SARDINIA

SICILY

NAPLES

TURIN

POPDEN

POPDEN2

COAST

ALPINE

ROME

MILAN

LONG

LONG2

JULCLEAR2

LAT

LAT2

JULPRECIP2

JANCLEAR

JANCLEAR2

JULCLEAR

JULTEMP2

JANPRECIP

JANPRECIP2

JULPRECIP

CONST

JANTEMP

JANTEMP2

JULTEMP



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The welfare impact of marginal changes in climateThe welfare impact of marginal changes in climate

Note: Euro / household / year. T-statistics are in parentheses

Milan Rome Naples Cagliari Palermo

January Temperature (°C) -99.68 -199.35 -207.62 -206.58 -199.35(-0.61) (-1.44) (-1.39) (-1.40) (-1.44)

July Temperature (°C) -334.66 -367.20 -367.20 -373.91 -380.63(-2.74) (-2.71) (-2.71) (-2.69) (-2.67)

January Precipitation (mm) -21.69 -17.04 -13.43 -19.11 -15.49(-2.24) (-2.63) (-3.24) (-2.44) (-2.89)

July Precipitation (mm) -22.21 -29.95 -30.99 -32.54 -32.54(-1.27) (-1.46) (-1.48) (-1.50) (-1.50)

January Clear Skies (%) 163.72 -228.79 279.40 -228.79 -736.98(-2.25) (-0.31) (-0.50) (-0.31) (-0.79)

July Clear Skies (%) 320.20 56.81 277.85 -890.89 -1475.52(-3.06) (-0.69) (-0.33) (-0.83) (-1.21)



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ConclusionsConclusions 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.

CENTER FOR ECONOMIC STUDIES KATHOLIEKE UNIVERSITEIT LEUVEN The Amenity Value of the Italian Climate...

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