Modelling household water demand in Europe.
Date: 2015-08-24
Institutions: Institute for Environment and Sustainability
Subjects: EU Member State, drinking water, econometrics, household, research report, wastewater, water, water consumption, water management, water protection, water resources, water supply

Modelling Household Water Demand in 
Europe 

Insights from a Cross-Country 
Econometric Analysis of EU-28 
countries 

Arnaud Reynaud 

2 0 1 5  

Report EUR 27310 EN 

 
 
 
European Commission 
Joint Research Centre 
Institute for Environment and Sustainability 

Contact information 
Arnaud Reynaud 
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 101, 21027 Ispra (VA), Italy 
E-mail: Arnaud.Reynaud@jrc.ec.europa.eu; areynaud@toulouse.inra.fr 
Tel.: +39 0332 78 6231 

JRC Science Hub  
https://ec.europa.eu/jrc 

Legal Notice 
This publication is a Technical Report by the Joint Research Centre, the European Commission’s in-house science service.  
It aims to provide evidence-based scientific support to the European policy-making process. The scientific output 
expressed does not imply a policy position of the European Commission. Neither the European Commission nor any 
person acting on behalf of the Commission is responsible for the use which might be made of this publication. 

All images © European Union 2015 

JRC96268 

EUR 27310 EN 

ISBN 978-92-79-48998-3 (PDF) 

ISSN 1831-9424 (online) 

doi: 10.2788/95638 

Luxembourg: Publications Office of the European Union, 2015 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

Abstract 

This  document  provides  an  update  of  the  knowledge  on  household  water  use  in  Europe.  We  first  review  the  existing 
scientific literature on household water demand modelling in Europe. Second, we have assembled a new dataset with a 
NUTS 3 resolution and an EU-28 coverage with data on household water consumption and on household water prices. 
The  typical  source  of  data  includes  national  statistical  offices,  regulators  of  water  and  wastewater  services,  national 
associations of water and wastewater services and national associations of municipalities. Having access to this reliable 
and disaggregated data on water use is a prerequisite for designing any efficient water management policy. Third, we 
provide  some  new  estimates  of  household  water  demand  functions  for  all  EU-28  countries  using  consistent  data  and 
econometric  methods.  Primary  data  on  household  water  consumption,  household  water  prices  and  household 
characteristics that are supposed to have an impact on water consumption have been collected, assembled and checked 
for  each  one  of  the  EU-28  countries.  A  household  water  demand  function  has  then  been  estimated  for  each  country 
using aggregate and recent data (typically municipality, water service or NUTS 3 levels covering the period 2005-2012). 
The econometric estimates allow us to identify the  determinants of the household water demand for each country. We 
also provide a new set of price and income elasticities.  

This work  is a  part  of  the  Euro  Freshwaters project  developed  by the  JRC and  led by Ad de Roo  and  Alberto  Pistocchi. 
Lead author is Arnaud Reynaud. Contributing authors are Denis Lanzanova and Vassileos Markantonis. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Executive summary

This report provides an update on the knowledge on household water demand (with a specific focus on economic
modelling) in the European Union. The report is largely based on existing public data sources at the national
and sometimes regional level, consolidated into a coherent way.

Context Water demand modelling has taken on new importance with the need to better understand market
and non-market water use values for evaluation of reallocation and investment benefits and policies. In Europe,
Article 9 of the Water Framework Directive requires the implementation of pricing policies that provide an
incentive to use water efficiently. Among the possible tools which can be used by public authorities, it has
been stressed by European Commission (2012) that pricing is a powerful awareness-raising tool for consumers
which combines environmental and economic benefits while stimulating innovation. However, any change in
water prices will induce some modifications in household behaviour and will also impact on issues such as
water affordability for the poorest. Economic modelling tools are then required to understand ex-ante how
household water consumption may react to changes in price schemes. Economists have developed a great
variety of models to predict water demand for domestic users. Although estimations of a domestic water
demand function have been undertaken for a substantial number of countries all over the world, the level of
knowledge in Europe is still limited or incomplete. In Europe, household water demand functions are available
for several countries, but the recent report from the European Environmental Agency (EEA, 2013) has pointed
out that most available reference studies date back 10 or 20 years.

Methodology We first review the existing scientific literature on household water demand modelling in Europe.
By doing so, we have not been able to find any estimate of the household water demand function for 10 of the
EU-28 countries (Austria, Bulgaria, Croatia, Estonia, Finland, Hungary, Ireland, Latvia, Lithuania and Slovenia).
For the 18 remaining countries, it should be pointed out that existing estimates of the household water demand
function rely on data posterior to 2010 for six of them only (Cyprus, Czech Republic, Germany, Luxembourg,
Romania and the United Kingdom). For Denmark and Sweden, the more recent studies date back to 1990
and 1992, respectively. Despite these limitations, this literature provides some indications on the empirical
determinants of household water use in Europe.

Second, we have assembled a new dataset with a NUTS3 resolution and an EU-28 coverage with data on
household water consumption and household water price. The typical source of data includes national offices of
statistics, regulators of water and wastewater services, national associations of water and wastewater services
and national associations of municipalities. Having access to this reliable and disaggregated data on water use
is a prerequisite for designing any efficient water management policy. Our dataset aims at solving one of the
main limitations faced by policymakers, namely the lack of detailed and up-to-date water use statistics.

Third, we provide some new estimates of household water demand functions for all EU-28 countries using
consistent data and econometric methods. Primary data on household water consumption, household water
prices and household characteristics supposed to have an impact on water consumption have been collected,
assembled and checked for each one of the EU-28 countries. Then, a household water demand function has
been estimated for each country using the most possible disaggregated data (typically municipality-, water
service- or NUTS3- levels) and the most recent data (typically 2005-2012). The econometric estimates allow
us to identify the determinants of the household water demand for each country. We also provide a new set of
price and income elasticities.

i

Main insights
For most of the countries, the estimated price elasticity of the household water demand is
found to be negative. Facing a price increase, EU households will react by reducing their water consumption. It
is then demonstrated that water price may play a role in signalling water scarcity or water cost to households.
In addition, we have found that household water demand functions are typically price-inelastic for most of the
EU-28 countries. This means that the household water consumption decreases by less than 1 % for every
1 % increase in price. The price elasticities typically vary between -0.5 and -0.1 across countries. Facing a
price increase by 10 %, it is then expected that the household water consumption will be reduced by 1 to 5 %.
To achieve more significant reductions of household water consumption, public authorities should complement
their price policies with non-price policies such as education or awareness campaigns.

Household water consumption may be, at least somehow, persistent over time due to household habits or
It is then expected that households
to the time needed to adjust durable equipment (showers, for instance).
may react to a change in the water price in the long term rather than in the short term. Using the panel-data
structure for some countries, we have shown that long-term price elasticity is in general greater (in absolute
value) than short-term price elasticity. From a policy perspective, it means that even if households do not
immediately react to a change in the water price, public authorities may expect them to modify their behaviour
in the long term. So the benefits (in terms of reducing the water consumption) from a water price increase
may be visible only a few years after having implemented the price change. It should be noted that the higher
responsiveness of household water consumption in the long term to change in water price is in line with the
existing literature (Nauges and Thomas 2000, Musolesi and Nosvelli 2007, Hortová and Kristoufek 2014).

Our range of value for income elasticities is much wider. For Austria, Croatia, France, Germany, Greece and
Spain the income elasticity is very low (between 0.00 and 0.25). We do not expect any strong impact of change
of household income on household water consumption per capita for these countries. The income elasticity is
found to be much higher in Bulgaria, Cyprus, Estonia, Finland, Latvia, Lithuania, Portugal and Slovakia (greater
than 0.50). This group of countries includes some Eastern European countries where both household income and
water pricing have significantly changed over the last 10 years. For these countries, the expected trend in water
consumption is a priori difficult to assess since it will be the result of two opposite effects. First, since household
income is expected to increase in these countries (income convergence process), one may expect an increase in
the water consumption per capita (income effect). On the other hand, we may anticipate some price increases
for these countries, in particular due to the implementation of the full cost-recovery principle for water and
wastewater services. This price effect should result in a decreasing impact on water consumption per capita.
The combined impact of the income and price effects on water consumption is then a priori undetermined.
Case-by-case analyses have then to be conducted.

The remainder of this document is organised as follows.

Structure of the report
In the first chapter we
summarise the existing knowledge and gaps in household water demands in the EU-28. We also present our
new dataset and the main results of the cross-country econometric analysis of household water demand for
the EU-28.
In particular we provide in this chapter an updated set of price and income elasticities of the
household water demand function for all Member States. This report also includes a chapter for each country
where in more detail we present the source of data used for the econometric analysis and the estimation of the
household water demand function.

ii

Acknowledgements

This collective work is a part of the euro freshwaters project developed by the Joint Research Centre (JRC) of
the European Commission at Ispra and led by Ad de Roo and Alberto Pistocchi. Lead author is Arnaud Reynaud
and contributing authors are Denis Lanzanova and Vassileos Markantonis. All of them are working at the Water
Resources Unit of the Institute of Environment and Sustainability (JRC-IES).

Several persons and institutions have contributed to this report by providing data, expertise and advices. We

would like to deeply thank all of them and in particular the following ones.

- Anne Adrup (Swedish Water & Wastewater Association, Sweden);

- Mika Rontu (Finnish Water Utilities Association, Finland);

- Jyrki Laitinen (Finnish Environment Institute SYKE, Finland);

- Peter Mooney (Environmental Protection Agency, Ireland);

- Aigars Meˇzals (Public Utilities Commission, Latvia);

- Alexandra Lungu (National Regulatory Authority for Public Utilities Community Services, Romania);

- Stefan Heidler (Kommunalkredit Public Consulting GmbH, Austria);

- Konstantinos Voulgaris (Hellenic Statistical Authority, Greece);

- Lenka Slavikova (University of Economics at Prague, Czech Republic);

- Peeter Peda (University of Turku, Estonia);

- Osiel Davilla (International Centre for Research on the Environment and the Economy, Greece);

- Phoebe Koundouri and Ioannis Souliotis (International Center for Research on the Environment and the

Economy, Athens, Greece);

- Catarina Roseta Palma (Lisbon University Institute, Portugal);

- Beate Werner, Nihat Zal and Stefan Ulrich Speck (European Environmental Agency);

- Tatiana Della Carità (Federconsumatori Nazionale, Italy).

Naturally all errors, omissions and mistakes in this report are only under the responsibility of the author of

this report.

iii

Contents

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1.2 The household water demand approach .
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Price and income elasticities of household water demands
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Specifying the residential water demand function .
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Validation of the household water demand models .
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3.3.2

Flanders .

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3.5 Water demand function estimate .

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Results (Wallonia and Brussels-Capital)
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Results (Flanders)

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3.5.1
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4 Bulgaria

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4.1 Existing literature .
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4.3 Data .
4.4 Empirical analysis of residential water consumption .
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4.5 Water demand function estimate .

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Specifying the residential water demand function .
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4.5.1
4.5.2
4.5.3

5 Croatia

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5.1 Existing literature .
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5.3 Data .
5.4 Empirical analysis of residential water consumption .
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5.5 Water demand function estimate .

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Specifying the residential water demand function .
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Estimation methods .
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Results .

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5.5.1
5.5.2
5.5.3

6 Cyprus

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6.4 Empirical analysis of residential water consumption .
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6.5 Water demand function estimate .

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Estimation methods .
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Results .

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7 Czech Republic

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Specifying the residential water demand function .
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8.4 Empirical analysis of residential water consumption .
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9.4 Empirical analysis of residential water consumption .
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9.5 Water demand function estimate .

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10.5 Water demand function estimate .

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11 France

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11.5 Water demand function estimate .

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12.1 Existing literature .
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12.2 Urban water sector in Germany .
12.3 Data .
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12.4 Empirical analysis of residential water consumption .
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14.4 Empirical analysis of residential water consumption .
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15.4 Empirical analysis of residential water consumption .
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16.3 Empirical analysis of residential water consumption .
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18.4 Empirical analysis of residential water consumption .
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18.5 Water demand function estimate .

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19.4 Empirical analysis of residential water consumption .
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26 Slovenia

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28 Sweden

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29 United Kingdom (England & Wales)
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x

List of Figures

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1.1 Predicted versus observed water consumption 1/2 (m3 per capita)
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1.2 Predicted versus observed water consumption 2/2 (m3 per capita)
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1.3 Plot of the household water demand function 1/2 (m3 per capita per year) .
1.4 Plot of the household water demand function 2/2 (m3 per capita per year) .
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1.5 Use of the water demand functions to predict household water use in Europe .
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1.6 Scenario analysis and use of the water demand functions .

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3.1 Observed versus predicted household water consumption in Belgium (Wallonia and Brussels-
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3.2 Observed versus predicted household water consumption in Belgium (Flanders) .

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4.1 Observed versus predicted household water consumption in Bulgaria .

5.1 Observed versus predicted household water consumption in Croatia .

6.1 Observed versus predicted household water consumption in Cyprus .

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7.1 Observed versus predicted household water consumption in Czech Republic .

8.1 Observed versus predicted household water consumption in Denmark

9.1 Observed versus predicted household water consumption in Estonia .

10.1 Observed versus predicted household water consumption in Finland .

11.1 Observed versus predicted household water consumption in France .

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13.1 Observed versus predicted household water consumption in Greece .

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14.1 Observed versus predicted household water consumption in Hungary .

15.1 Observed versus predicted household water consumption in Ireland .

16.1 Observed versus predicted household water consumption in Italy .

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17.1 Observed versus predicted household water consumption in Latvia .

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18.1 Observed versus predicted household water consumption in Lithuania .

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19.1 Observed versus predicted household water consumption in Luxembourg .

20.1 Observed versus predicted household water consumption in Malta .

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21.1 Observed versus predicted household water consumption in the Netherlands .

22.1 Observed versus predicted household water consumption in Poland .

23.1 Observed versus predicted household water consumption in Portugal

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25.1 Observed versus predicted household water consumption in Slovakia .

26.1 Observed versus predicted household water consumption in Slovenia .

27.1 Observed versus predicted household water consumption in Spain .

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xii

List of Tables

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1.1 Household water demand studies in the EU-28 .
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1.4 Description of data collected for estimating household water demands in the EU-28
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1.5 Household water use and price in the EU-28 .
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1.6 Main determinants of household water demands in the EU-28
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1.7 Estimated price and income elasticities of household water demand in the EU-28 .
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1.8 Estimated sort-term and long-term price elasticities of household water demands in selected
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2.1 Household water consumption and price in Austria .
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2.2 Regional household water consumption and price in Austria .
2.3 Estimation of the household water demand in Austria .
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2.4 Estimation of the household water demand with lagged water consumption in Austria .

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3.4 Regional household water consumption and price in Belgium (Flanders) .
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3.5 Estimation of the household water demand in Belgium (Wallonia and Brussels-Capital)
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3.6 Estimation of the household water demand in Belgium (Flanders) .

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4.3 Estimation of the household water demand in Bulgaria .

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5.1 Household water consumption and price in Croatia .
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5.3 Estimation of the household water demand in Croatia .
5.4 Estimation of the household water demand with lagged water consumption in Croatia .

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6.1 Household water consumption and price in Cyprus .
6.2 Estimation of the household water demand in Cyprus .
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7.4 Estimation of the household water demand with lagged water consumption in Czech Republic .

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8.1 Household water consumption and price in Denmark .
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8.3 Estimation of the household water demand in Denmark .
8.4 Estimation of the household water demand with lagged water consumption in Denmark .

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9.3 Estimation of the household water demand in Estonia .
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10.2 Regional household water consumption and price in Finland .
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10.3 Estimation of the household water demand in Finland .

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11.1 Household water consumption and price in France .
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11.4 Estimation of the household water demand with lagged water consumption in France .

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12.3 Estimation of the household water demand in Germany .
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13.3 Estimation of the household water demand in Greece .

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14.1 Household water consumption and price in Hungary .
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14.3 Estimation of the household water demand in Hungary .
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18.4 Estimation of the household water demand with lagged water consumption in Lithuania .

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22.4 Estimation of the household water demand with lagged water consumption in Poland .

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24.3 Estimation of the household water demand in Romania .
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25.3 Estimation of the household water demand in Slovakia .
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26.3 Estimation of the household water demand in Slovenia .
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27.3 Estimation of the household water demand in Spain .
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xvi

Chapter 1

Updating the knowledge on household
water demand in the EU

1

1.1 Introduction

This report focuses on household water consumption defined as the quantity of water used to cover household
and related utility needs of the population through the water supply industry and self-supply, calculated as a
total and per capita. Household water consumption provides a measure of the pressure on the environment
in terms of water abstraction from different water sources through household use. This type of indicator is
important for defining the level of development of water economy services and the degree of water accessibility
to cover all household needs of the population. The indicator may also help to identify trends in household
water use in a particular country.

There are several reasons that call for a good understanding of household water use. First, most national
allocation regimes define domestic and human needs as the highest priority use (OECD, 2015).1 Second,
most large-scale water assessment models predict some very significant changes in household water use (or
more generally in urban water use) for the next 50 years (Hejazi, 2013). Third, water is an essential good
for households in the sense that water has no good substitute for most indoor water uses (personal hygiene,
cleaning, etc.).

Economists have been working on household water use for a long time. However, water demand modelling
has taken on new importance with the need to better understand the role economic instruments (i.e. water
pricing) might have to induce change in water user behaviours (i.e reduction of water abstraction or polluted
discharges). To this end, economists have developed a great variety of models allowing to predict water
demands for industrial, agricultural and domestic users. For domestic users, the level of knowledge is quite
advanced. Estimations of domestic water demand functions have been undertaken for a substantial number of
countries all over the world, and the existing literature has already been summarised and reviewed by several
authors (Arbues 2003; Worthington, 2008; Tanverakul, 2012) .

In Europe, household water demand functions are available for some countries. However, the recent report
from the European Environmental Agency (EEA, 2013) points out that most available reference studies in Europe
date back 10 or 20 years and that `new case studies with primary data are required to provide fresh and relevant
evidence that accounts for the socio-economic, management and technological changes that have taken place
in the last 20 years'. This is the main objective of this report.

This document therefore provides an update on the knowledge on household water use in Europe.
We first review the existing scientific literature on household water demand modelling in Europe. By
reviewing the existing literature, we have not been able to find any estimate of the household water demand
function for 10 of the EU-28 countries (Austria, Bulgaria, Croatia, Estonia, Finland, Hungary, Ireland, Latvia,
Lithuania and Slovenia). For the 18 remaining countries, it should be pointed out that existing estimates of the
household water demand function rely on data posterior to 2010 for 6 of them only (Cyprus, the Czech Republic,
Germany, Luxembourg, Romania and the United Kingdom). For Denmark and Sweden, the more recent studies
date back to 1990 and 1992, respectively. Despite these limitations, this literature provides some indications
on the empirical determinants of household water use in Europe.

Second, we have assembled a new dataset with a NUTS 3 resolution and an EU-28 coverage with data
on household water consumption and on household water prices. The typical source of data includes national
statistical offices, regulators of water and wastewater services, national associations of water and wastewater
services and national associations of municipalities. Having access to this reliable and disaggregated data on
water use is a prerequisite for designing any efficient water management policy. Our dataset aims at solving
one of the main limitations faced by policymakers: the lack of detailed and up-to-date water use statistics.

Third, we provide some new estimates of household water demand functions for all EU-28 countries using

1Some exceptions include the Netherlands, a small number of Canadian provinces, water uses in Israel, and Peru.

2

consistent data and econometric methods. Primary data on household water consumption, household water
prices and household characteristics that are supposed to have an impact on water consumption have been
collected, assembled and checked for each one of the EU-28 countries. A household water demand function
has then been estimated for each country using the most possible disaggregated data (typically municipality,
water service or NUTS 3 levels) and the most recent data (typically 2005-2012). The econometric estimates
allow us to identify the determinants of the household water demand for each country. We also provide a new
set of price and income elasticities.

The remaining of this document is organised as follows. In Section 1.2 we briefly present the water demand
function approach. In Section 1.3 we summarise the existing knowledge and gaps in household water demands
in the EU-28. Section 1.4 is devoted to presenting our new dataset that allows to conduct a cross-country
analysis of household water demand for the EU-28. In Section 1.5, we present the cross-country econometric
analysis of household water demand in EU-28. In particular, we first provide a new price and income elasticities
list of the household water demand function before presenting country-specific chapters where more detailed
information on data and estimation methods is provided for each country.

1.2 The household water demand approach

Economic water demand modelling began in 1926, when Leonard Metcalf presented a hand-drawn regression
on a double-logarithmic scale of price against water demand (Metcalf, 1926). A few decades later, several
articles were published estimating proper water demand functions (Howe, 1967 etc.). Since these initial studies,
the literature on water demand modelling has produced an abundance of published and unpublished research
papers, primarily focusing on household demand (Gardner, 2010).
In this section, we provide some basic
methodological foundations of the household water demand function approach.

The water demand function approach The water demand function approach relies on standard neoclassical
In particular, it is assumed that for each consumer there exists a continuous utility
economic assumptions.
function that is of the consumed commodity bundle and where the functional form is determined by underlying
consumer preferences. The utility of each consumer is then maximised under a budget constraint and given
the prices of the commodities. Thus the demand for a commodity is assumed to depend on the income of
consumers, on the price of the commodity and on the availability and prices of all other commodities that are
substitutes or complements to the commodity in question, as well as on consumer preferences.

Most scholars have made the assumption of weak separability of water with respect to other goods. Under
this assumption, household water consumption will not depend on the price of other goods consumed. As
discussed in Arbués, Barberán, and Villanúa (2004), there is no logical difficulty in imposing separability of
water with respect to other goods. First, most indoor water uses (personal hygiene, cleaning, etc.) have no good
substitutes. Second, household habits may be considered constant, at least in the short run. Third, complemen-
tary goods related to domestic water consumption are typically durable equipment (washing machines, sanitary
equipment, etc.) that is unlikely to be changed in the short term in reaction to a water price change. Under this
separability assumption, the Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(1.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

3

In this report, we are especially interested in providing empirical evidence on the relationship between water
price and household water consumption, as well as the relationship between household income and household
water consumption. A simple way to measure these relationships is to compute the price elasticity of the water
demand and the income elasticity of the water demand. The price elasticity of the water demand measures
the responsiveness (or elasticity) of the water use to a change in water price, all other things being equal. To
be more precise, it gives the percentage change in household water use in response to a 1 % change in price
(all other things being equal, i.e. holding all the other determinants of demand, such as income, constant). The
price elasticity of the water demand may be written as:

(cid:15)p =

∂y∗(.)
∂p

×

p
y∗ .

(1.2)

Similarly, the income elasticity of the water demand measures the responsiveness (or elasticity) of the water
use to a change in household income, all other things being equal. To be more accurate, it gives the percentage
change in household water use in response to a 1 % change in household income. The income elasticity of the
water demand may be written as:

(cid:15)I =

∂y∗(.)
∂I

×

I
y∗ .

(1.3)

Water demand functions can be used for computing the implications of alternative water pricing policies on
consumer behaviours.
In addition, they provide some tools for developing welfare analysis. For instance, the
welfare effects of an alternative pricing policy on households can be measured (approximated, in fact) using
the household water demand function (consumer surplus).

In reality, however, water that is used by households is a composite good.

Discussion One implicit assumption we have made when writing Equation (1.1) is that water is a homogenous
It consists of the direct use
good.
of water for drinking water (which in general represents a small share of the total water consumption) and the
indirect use for water as a complement to different household activities (washing, cooking, hygiene, gardening,
etc.). Water is therefore a necessity in households in a number of its uses and, as such, has no substitute,
while it is not a necessity in many other of its uses.
In the latter case, the demand for water is likely to be
more affected by price changes. Since it is not usually possible to separate the different types of demand, the
estimated elasticities are usually based on an aggregated household demand for water as depicted in Equation
(1.1). However, alternative specifications, such as a Stone-Geary utility function, which allows to identify a
volume of water covering basic needs, have also been explored (Gaudin, Griffin, and Sickles 2001).

One should also stress that the price and income elasticities of the water demand function presented in
Equation (1.2)-(1.3) provide only a local measure of the responsiveness of the water demand to changes in water
price and household income. For any given household, it is likely that the price elasticity may vary depending
on the level of the water price.

As discussed in Gardner (2011), the existing literature has debated on a large number of methodological and
empirical issues. Methodological issues include the appropriated level and structure of data for estimation (micro
data versus aggregated data), the appropriate price specification (marginal water price versus average water
price) and the appropriate functional form to be used for estimating Equation (1.1). Empirical issues include the
magnitude and variation of estimated price and income elasticities (Espey, Espey, and Shaw 1997, Dalhuisen,
Florax, de Groot, and Nijkamp 2003, Worthington and Hoffman 2008), the existence of seasonal differences,
the long and short-run differences in water consumption, and geographic or income group differences in water
consumption. The literature offers vast coverage. Estimations have been undertaken in several countries
(Gardner 2011). The literature has already been summarised and reviewed by several authors (Arbués, García-
Valiñas, and Marti«ez-Espiñeira 2003, Worthington and Hoffman 2008, Tanverakul and Lee 2012). In addition,

4

four meta-analyses have been conducted, more specifically on the price and income elasticities of the household
water demand (Espey, Espey, and Shaw 1997, Dalhuisen, Florax, de Groot, and Nijkamp 2003, Gardner 2011,
Sebri 2014). The interested reader should also refer to Kelly Gardner's PhD dissertation Gardner (2011) for a
more extensive discussion on methodological and empirical aspects of household water use modelling.

1.3 Existing knowledge and gaps on household water demands in EU-28

In this section we summarise the literature on household water demand modelling that has focused on the
Member States of the European Union.
In what follows, we present the main insights from studies having
estimated a household water demand function in a Member State. A few general and quite robust conclusions
may be drawn from this literature.

1.3.1 Existing studies on household water demands

The scientific references have been selected through systematic searches of the keywords `household water
demand', `residential water demand', `household water use', `residential water use' and `urban water' on various
search engines and on the web sites of major publishers of academic journals (Scopus, Science Direct, Wiley,
Web of knowledge, RepEc, AgEconSearch, etc.). Lastly, the `grey literature' was searched using various search
engines, including Google Scholar. We have also used the references provided by the previous meta-analysis
on residential water use. The list of reviewed studies having estimated a household water demand is provided
in Table 1.1. The selection procedure led us to retain 45 studies; a vast majority of them are peer-reviewed
articles

Although the spatial coverage is vast, we have not been able to find any household water demand function
for 10 Member States, namely Austria, Bulgaria, Croatia, Estonia, Finland, Hungary, Ireland, Latvia, Lithuania
and Slovenia. For the 18 remaining states, it should be pointed out that the estimates of the household water
demand functions rely on data posterior to 2010 for six of them only (Cyprus, the Czech Republic, Germany,
Luxembourg, Romania and the United Kingdom). For Denmark and Sweden, the more recent studies date back
to 1990 and 1992, respectively. Not surprisingly, the three countries for which the highest number of studies on
household water demand function is available are Spain, Italy and France. Since these three countries regularly
face problems of water scarcity, identifying and understanding the drivers of household water use might be of
primary interest for public authorities. In addition, the fact that water services are organised at the municipality
level may facilitate data collection.

One may argue that the results obtained for the 18 countries may be `transferred' to the other 10 countries.
Value transfer, however, raises some methodological issues, but we will not discuss that here. In addition, in his
recent meta-analysis of household water demand functions, Sebri (2014) has shown that estimates strongly
vary across the geographic location, so decision-makers in a given country should not directly rely on the findings
of studies conducted in other countries when formulating their own policies.

Concerning the type of data, a vast majority of published studies have used aggregated data at the
In other words, these studies have estimated a household water demand function for a
municipality level.
representative household using a set of municipalities. A few studies have relied on household-level data, but
they are usually limited in terms of spatial coverage to a specific municipality or a specific region. 2 It is then

2One recent example is Vanhille (2012) who has used household-level data to estimate the residential water demand function in
the Flanders region in Belgium. One reason explaining the scarcity of household-level studies is that household-level data are typically
obtained from water company records. It is then often difficult to match these household-level records with household characteristics,
such as income or age.

5

Table 1.1: Household water demand studies in the EU-28

Country
Austria
Belgium
Bulgaria
Croatia
Cyprus

Czech Republic

Denmark
Estonia
Finland
France

Germany

Greece

Hungary
Ireland
Italy

Latvia
Lithuania
Luxembourg
Malta
Netherlands

Poland
Portugal

Romania
Slovakia
Slovenia
Spain

Sweden

Study
n.a.
Vanhille (2012)
n.a.
n.a.
Polycarpou and Zachariadis (2013)
Hajispyrou et al. (2002)
Grafton et al. (2009)
Slavíková et al. (2013)
Hortová and Kristoufek (2014)
Hansen (1996)
n.a.
n.a.
Nauges and Reynaud (2001)
Nauges and Thomas (2003)
Garcia and Reynaud (2004)
Grafton et al. (2009)
Rinaudo et al. (2013)
Frondel and Messner (2008)
Schleich and Hillenbrand (2009)
Muller (2012)
Athanasiadis et al. (2005)
Bithas and Chrysostomos (2003)
Vagiona and Mylopoulos (2009)
n.a.
n.a.
Mazzanti and Montini (2006)
Statzu and Stazzera (2007)
Musolesi and Nosvelli (2007)
Grafton et al. (2009)
Di Cosmo (2011)
n.a.
n.a.
MECE (2012)
Delia (2004)
Kooreman (1993)
Linderhof (2001)
Grafton et al. (2009)
Bartczak et al. (2009)
Martins and Fortunato (2007)
Monteiro and Roseta-Palma (2011)
Monteiro et al. (2014)
Ciomos et al. (2012)
Dalmas and Reynaud (2005)
n.a.
Martinez-Espineira (2002)
Martinez-Espineira (2003)
Arbues (2004)
Martinez-Espineira and Nauges (2004)
García-Valinas (2005)
Martinez-Espineira (2007)
Martinez-Espineira and García-Valinas (2010)
March and Sauri (2010)
Arbues et al. (2012)
March et al. (2012)
Hanke-de Maré (1982)
Hoglund (1999)
Grafton et al. (2009)

Data*
n.a.
H
n.a.
n.a.
M
H
H
M
R
H
n.a.
n.a.
M
M
M
H
M
H
M
R
H
M
H
n.a.
n.a.
M
M
M
H
H
n.a.
n.a.
M
M
M
H
H
M
M
M
H
M
M
n.a.
M

H
M
H
M
M
M
H

H
M
H
H

Spatial coverage
n.a.
1 Region
n.a.
n.a.
3 Muni
Country
Country
2 Muni
14 Regions
1 Muni
n.a.
n.a.
225 Muni
116 Muni
50 Muni
Country
137 Muni
1 Muni
Country
Country
17 Muni
1 Muni
1 Muni
n.a.
n.a.
1 Region
1 Region
Muni
Country
Country
n.a.
n.a.
Country
Country (av)
60 Muni
Country
Country
Country
5 Muni
Country
10 Muni
1 Muni
Country
n.a.
132 Muni

1 Muni
1 Muni
1 Muni
1 Muni
301 Muni
160 Muni
1 Muni
1 Muni
1 Miuni
Country
Country
1 WU

Time coverage
n.a.
2009
n.a.
n.a.
2008-2009
1997-1998
2008
2004-2009
2000-2011
1981-1990
n.a.
n.a.
1989-1995
1988-1993
1995-1998
2008
2005
1998-2001
2001-2005
2007, 2010
1994-2000
1981-1999
1997-2005
n.a.
n.a.
1998-2001
2000-2005
1998-2001
2008
1999-2005
n.a.
n.a.
2010
1989-1999
1988-1989
1978-1994
2008
2001-2005
1998-2003
1998-2005
1998-2005
2002-2010
1999-2005
n.a.
1993-1999
1995-2003
1996-1998
1991-1999
1991-2000
1991-1999
2005
2003
1996-1998
2003-2007
1971-1978
1980-1992
2008
2007-2010

United Kingdom Gardner (2010)
*: H, M, R for household-, municipal- and regional-level data respectively.

6

difficult to extrapolate such local results at the scale of a country. More generally speaking, even when a large
number of spatial units was used (either municipalities or regions) for estimating the household water demand
function, the national territory was not fully covered.3

1.3.2 Knowledge and gaps on household water demands

In Table 1.2, we summarise the main findings in terms of significant drivers of the household water demand
function in the EU-28. As a word of caution, it should be pointed out that the cross-country comparison is
made difficult by the fact that variables introduced as determinants of the household water demands are
not consistently defined across studies.
In addition, the dependent variable may also differ between studies,
some of them using water consumption per capita while others may refer to water consumption per household.
However, certain general and persistent trends do emerge. In order to organise the discussion, we will analyse
different groups of determinants, including water price and tariffs, household income, population characteristics,
housing characteristics and climate conditions.

Water price and water tariffs Most economists working on domestic water use generally recognise that
domestic water consumption reacts to changes in water prices. It is however usually found that the household
water demand function is price inelastic which means that water consumption decreases by less than 1 % for
every 1 % increase in price, the price-elasticity typically varying between -0.1 and -1.0.4

Among all the studies we have reviewed for the EU-28, only two of them have found a price-elastic
household water demand, Mazzanti and Montini (2006) in Italy and Arbues et al.
(2012) in Spain. As ex-
plained by the former, the high tariffs characterising Emilia Romagna, with respect to other regions in Italy,
In Arbues et al. (2012), the highest price elasticity (in absolute value) is found for
may explain their results.
a single-person household. For a typical household made up of four persons, the price elasticity is equal to -0.27.

Most published studies provide single price elasticity for the household water demand function. However,

some authors have investigated the heterogeneity of price elasticity.

- Price elasticity varies depending on the type of water use. Essential uses such as water for human
consumption or for cooking are found to be very price inelastic, whereas water-related leisure activities
(watering the garden or making use of swimming pools) are usually much more price reactive.

- Price elasticity also varies over time. Demand studies using summer data that appear to exhibit higher

price elasticity in absolute value (Arbués, García-Valiñas, and Marti«ez-Espiñeira 2003).

- Price elasticity is found to vary depending upon some characteristics of households.

It is for instance
documented that elasticity varies with household size (Arbués, Villanúa, and Barberán 2010, Vanhille
2012). In developed countries, it is also usually found that price elasticity varies with household income,
lower income groups being more price-responsive than higher income groups. For Cyprus, Hajispyrou,
Koundouri, and Pashardes (2002) reports price elasticity equal to -0.79 for the lowest income group
compared to -0.39 for the highest income group.
In Belgium, the price elasticity for the lowest income
quintile is estimated to be -0.76 compared to -0.25 for the highest income quintile (Vanhille 2012). Since

3There are a few exceptions. For instance Grafton, Ward, To, and Kompas (2011) has estimated residential water demand functions
for 10 countries using household-level data. They use a representative sample stratified with respect to income, age, gender and region
within each country (approximately 1 000 households per country).

4In his recent meta-analysis, (Sebri 2014) reports a mean and a median price elasticities equal to -0.365 and -0.291, respectively.

7

Table 1.2: Price, income and other determinants of household water demand in the EU-28

Country
Austria
Belgium
Bulgaria
Croatia
Cyprus

Czech Republic

Denmark
Estonia
Finland
France

Germany

Greece

Hungary
Ireland
Italy

Latvia
Lithuania
Luxembourg
Malta
Netherlands

Poland
Portugal

Romania
Slovakia
Slovenia
Spain

Sweden

Study
n.a.
Vanhille (2012)
n.a.
n.a.
Polycarpou and Zachariadis (2013)
Hajispyrou et al. (2002)
Grafton et al. (2009)
Slavíková et al. (2013)
Hortová and Kristoufek (2014)
Hansen (1996)
n.a.
n.a.
Nauges and Reynaud (2001)
Nauges and Thomas (2003)
Garcia and Reynaud (2004)
Grafton et al. (2009)
Rinaudo et al. (2013)
Frondel and Messner (2008)
Schleich and Hillenbrand (2009)
Muller (2012)
Athanasiadis et al. (2005)
Bithas and Chrysostomos (2003)
Vagiona and Mylopoulos (2009)
n.a.
n.a.
Mazzanti and Montini (2006)
Statzu and Stazzera (2007)
Musolesi and Nosvelli (2007)
Grafton et al. (2009)
Di Cosmo (2011)
n.a.
n.a.
MECE (2012)
Delia (2004)
Kooreman (1993)
Linderhof (2001)
Grafton et al. (2009)
Bartczak et al. (2009)
Martins and Fortunato (2007)
Monteiro and Roseta-Palma (2011)
Monteiro et al. (2014)
Ciomos et al. (2012)
Dalmas and Reynaud (2005)
n.a.
Martinez-Espineira (2002)
Martinez-Espineira (2003)
Arbues (2004)
Martinez-Espineira and Nauges
(2004)
García-Valinas (2005)
Martinez-Espineira (2007)
Martinez-Espineira
Valinas (2010)
March and Sauri (2010)
Arbues et al. (2012)
March et al. (2012)
Hanke-de Maré (1982)
Hoglund (1999)
Grafton et al. (2009)

and García-

United Kingdom Gardner (2010)

Price elasticity
n.a.
-0.62
n.a.
n.a.
-0.45 ; -0.25
-0.79 ; -0.39
-0.42
-
-0.20 ;-0.54
-0.10
n.a.
n.a.
-0.22 ; -0.08
-0.40 ; 0.40
-0.25
-0.41
-0.18
-0.49
-0.24
-0.46 ; 0.26
-0.34
-0.10
-0.95
n.a.
n.a.
-1.33 ; -0.99
-0.25
-0.47 ; 0.27
-0.59
-0.36 ; -0.14
n.a.
n.a.
-0.33
-0.37 ; -0.28
-0.19 ; -0.09
-0.07
-0.40
-0.20
-0.56
-0.13 ; -0.05
-0.48
-0.70
-0.50 ; -0.35
n.a.
-0.16 ; -0.12
-0.67 ; -0.37
-0.06 ; -0.03
-0.10

-0.55 ; -0.46
-0.50 ; -0.10
-0.06

n.a.
-1.31 ; -0.26

-0.15
-0.20; -0.10
-0.41
-0.29

Income elasticity
n.a.
0.62
n.a.
n.a.
0.53 ; 0.75
0.22 ; 0.48
0.01
-
0.10
-
n.a.
n.a.
0.01
0.50
0.03
0.01
0.42
0.13 ; 0.30
0.35
-0.01 ; 0.04
0.35
0.72
-
n.a.
n.a.
0.40 ; 0.71
0.43
0.18 ; 0.31
0.01
0.40 ; 0.46
n.a.
n.a.
0.60
0.24
-
0.11
0.01
0.12
0
0.04 ; 0.09
0.25
-
0.26 ; 0.32
n.a.
0.30 ; 0.68
n.a.
0.07 ; 0.21
0.1

0.58
0.00
0.06

0.70
0.00

0.11
0.09 ; 0.12
0.01
0.01

8

Other determinants
n.a.
Population density (+) Size(+) Age (+)
n.a.
n.a.
Temp (+) Rainfall (+) Rationing (-)
-
Dual-flush toilets (-) Age(+) Size(+) Housing size(+)
-
LagCons (+) Rainfall (-)
PriceElectricity (-)
n.a.
n.a.
Housing recent (-)
-
Size (+) Recent Housing (+)
Dual-flush toilets (-) Age(+) Size(+) Housing size(+)
Temp (+) Dry days (+) Secondary homes (+)
Size (+) House(+) Dishwater (+)
Size (-) Age(+) Rain (-) Well (-)
Size (+)
Temp (+) Rainfall (-) Information (-)
Time trend (+)
Temp (+) Rainfall (+)
n.a.
n.a.
Altitude (-)
Size (+) Aridity index (+) Tourist (+) Rationing (-)
Age (+)
Dual-flush toilets (-) Age(+) Size(+) Housing size(+)
-
n.a.
n.a.
-
-
-
Size (+) Age(-) Dishwasher(+) No washing machine(-)
Dual-flush toilets (-) Age(+) Size(+) Housing size(+)
Size (-)
Size (+) Temp (+) Age (-)
Temp (+) Age(-) No bath (-) Non-permanent (-)
Size (+) House(+) Secondary homes (-)
-
Size (-)
n.a.
Temp (+) Rainfall(-) Size (-)

n.a.

Temp (+)
Size (+) Age (-) Rainfall (-) Tourist (+)

Size (+) Age (-) Density (-)
Temp (+) Age (-)

Size (-)
Dual-flush toilets (-) Age(+) Size(+) Housing size(+)
Size (+) Temp (+) rain (-) Summer (+)

households will react in a very different way to water pricing depending on their level of income, any
change in the water price policy will have to address some social and equity issues.

- Price elasticities differ in the short and in the long run. Short-run elasticities are usually found to be
smaller than their long-run counterparts, suggesting that consumers might need time to adjust water-
using capital stocks and to learn about the effects of use on their bills. For Spain, Martínez-Espiñeira
(2007) finds that the price elasticity of demand is around -0.1 in the short run and -0.5 in the long
run. For France the short- and long-run price elasticities reported by Nauges and Thomas (2003) are
respectively -0.26 and -0.40. Using a panel of 101 Italian municipalities, Musolesi and Nosvelli (2007)
find a short-term and long-term elasticity for the Italian household water demand equal to be -0.27 and
-0.47, respectively. For the Czech Republic, the water demand is shown by Hortová and Kristoufek (2014)
to be more elastic in the long run than in the short run, as the price elasticity in the short run is estimated
to be -0.20, while the price elasticity in the long run is -0.54.

- Spatial variations in price elasticities have also been documented. Dalhuisen, Florax, de Groot, and Nijkamp
(2003) reports that price elasticities tend to be smaller in Europe compared to the United States, and price
elasticities within the United States are greater in absolute value in the arid West. These spatial patterns
have also been recently reported in (Sebri 2014).

Household income It is widely accepted and has been empirically demonstrated that domestic water con-
sumption is positively correlated with income. The explanation is quite simple. A high level of income is
associated with high living standards, which could imply a higher quantity of water-consuming appliances and a
higher probability of the presence of high-water demanding outdoor uses such as lawn gardens and swimming
pools.

Population characteristics
The age distribution within the household also affects residential water use even
if the impact of age on water use still needs to be explored. It is usually found that older people, all else being
equal, consume less water than younger people. Nauges and Thomas (2000) supports this finding and observes
that communities with more seniors have lower water consumption, and similar results have been found by
(Martínez-Espiñeira 2002, Martins and Adelino 2007, Musolesi and Nosvelli 2007). By contrast, Schleich and
Hillenbrand (2009) finds the converse, namely that as people get older they consume more water per person,
and proposes three types of explanations. Water use may increase with age because retired people spend more
time at home and gardening, because children use less water for washing and hygiene than adults, or because
health reasons may force older people to use the bathroom more frequently. A variable that has a positive
effect on household water consumption is the number of people in a residence (Hanke and Maré 1982).

Residential characteristics associated with houses and properties have, in some
Housing characteristics
studies, been shown to affect household water consumption. Some authors find a statistically significant
effect between household water consumption increases and house size, and also lawn size. Using French data
from 116 communities, Nauges (2000) finds that, all else being equal, the older the house the more water is
consumed.

Indeed, it is
Climate conditions Climate is one of the most studied drivers of domestic water demand.
considered that household water consumption varies depending on variables, such as temperature and rainfall,
which may influence the amount and/or frequency of activities that involve water-consumption activities,
such as garden watering, swimming pool use and personal hygiene (Romano, Salvati, and Guerrini 2014).

9

The climatic indicators usually considered include rainfalls (annual or in the summer, number of rainy days),
evapotranspiration, temperature (maximal or average) and solar radiation in particular.

With respect to weather conditions, Martínez-Espiñeira (2002) (Northwest Spain) has found that water
In addition, Martínez-Espiñeira (2002) and Schleich and Hillenbrand (2009) (for
use was highest in summer.
In contrast,
Germany) have found that water consumption decreases as the number of rainy days increases.
Arbués and Villanúa (2006) has reported an association between high temperatures and low water consumption
in the city of Zaragoza in Spain, which they suggested was due to consumption levels tapering off in the
summer because of the outflow of residents to holiday destinations. Focusing also on Spain, García-Valiñas
(2005) has observed higher price elasticity in peak periods (summer) than in off-peak periods (all other seasons).
In Portugal, Martins and Adelino (2007) has demonstrated that high temperatures tend to result in an increase
of the demand for water, although rainfall has no significant association with it.

Non-price policies Non-price policies correspond to all non-market-based programmes designed to increase
the efficiency of water use or water conservation. Although non-price policies are by nature very heterogeneous,
they may be classified into three categories: public education, technological improvements and water restrictions.

A number of papers have considered the impact of non-price policies on residential water use. Public
education programmes have been shown to have a limited impact on residential water use, especially in the
short term. Literature suggests that a certain critical mass of educational programmes is necessary to generate
significant benefits Michelsen, McGuckin, and Stumpf (1999).

Somewhat more attention has been paid to understanding the effectiveness of technological changes, es-
pecially indoor retrofitting of water-using devices such as toilets, showerheads and washing machines. Studies
with this focus are frequently based on engineering assumptions of expected reductions (Michelsen, McGuckin,
and Stumpf 1999). One notable exception is provided by Millock and Nauges (2010). Using survey data on
10 OECD countries, the authors of this paper show that the adoption of water-efficient equipment is strongly
affected by housing ownership status, by being water-metered and charged with a volumetric price on water
consumption and by behavioural factors. Environmental attitudes are shown to be strong predictors of the
adoption of water-efficient equipment, with a marginal effect that exceeds ownership status in some cases.
Gilg and Barr (2006) has also focused on attitudinal factors that determine water consumption behaviour (in
particular on environmental preferences, intrinsic motivations and social norms). This study reveals that it is
possible to classify households into relatively homogenous groups based on their water consumption behaviour.
These attitudinal differences should then be taken into account when designing non-price policies.

Lastly, some authors have specifically focused their attention on the efficiency of restrictions in water use.
For Spain, Garcia-Valinas (2006) measures the impact on consumers of rationing policies implemented during
water shortages. The author demonstrates that the restrictions implemented during the drought in Seville have
had an important impact on water demands. Some papers, whose articles generally focus on the comparison
of voluntary programmes versus mandatory programmes, have analysed the effectiveness of outdoor watering
restrictions and. consistently show significant savings from mandatory restrictions (sometimes 30 % or more).
Findings regarding voluntary restrictions are much more variable.

Attitudinal and behavioral drivers Grafton, Ward, To, and Kompas (2011) finds that attitudinal char-
acteristics and environmental concerns increase the likelihood for households of undertaking certain specific
and self-reported water-saving behaviour. Some attitudinal characteristics and environmental concerns also

10

increase the rate of adoption of a low volume/dual-flush toilet that reduces household water consumption.
The Spanish study by Domene and Saurí (2006) is one of the very few to examine the influence of attitudinal
variables on household water consumption, and it finds a significant association.

1.4 A new dataset for analyzing household water demand in the EU-28

We present in this section our new dataset which will be our main material for estimating the household water
demand functions in each EU-28 country. Primary data on household water consumption, on household water
prices and on household characteristics supposed to have an impact on water consumption have been collected,
assembled and checked for each one of the EU-28 countries. The typical source of data includes national
statistical offices, regulators of water and wastewater services, national associations of water and wastewater
services and national associations of municipalities.

1.4.1 Data sources

Estimating a household water demand for a single country can sometimes be difficult due to the lack of
appropriate data. Conducting a cross-country analysis on 28 different countries represents a challenge in terms
of data collection. We describe here the main source of data we have relied on. Our data collection strategy
has been to use official statistics published by each country as much as possible.

We present in Table 1.3 the main source of data for each EU-28 country. We limit our presentation to the
main source of data, noting that data may have been complemented by other sources of information. More
detailed information on data sources is provided in each country-specific chapter of this report.

11

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When collecting data, we have tried to rely on published official statistics (and regularly undated) either
provided directly by national statistical offices or by governmental agencies such as ministries, environmental
agencies or regulators of the water sector.

For all countries, our major source of data (especially for household and housing characteristics) has been
the national statistical offices. Depending on the country, data related to water pricing and to water consumption
of households may come from water service regulators (Bulgaria, Latvia, Portugal and the United Kingdom),
from national environmental agencies (Ireland), from associations of water and wastewater services (Austria,
Denmark, Estonia, Finland and Sweden) or from other sources including central banks or non-governmental
organisations (Greece, Ireland, Italy, Malta and Slovenia).

Most of the data collected are publicly available (either from the web or from published reports). For some

countries, data have been obtained through personal communication and are not publicly available.

1.4.2 Data description

In Table 1.4, we give a brief overview of the type of data collected. In particular we describe in this table both
the spatial and the temporal resolution/coverage.

Table 1.4: Description of data collected for estimating household water demands in the EU-28

Country

Spatial resolution

Number spatial units

Type of data

Time coverage

Mun
Mun
NUTS 3
WS
NUTS 3
NUTS 3
WS
NUTS 3
Mun
Mun
NUTS 3
Other
NUTS 3
Other
Mun
Mun
NUTS 3
Mun
Country
NUTS 3
NUTS 3
Mun
NUTS 3
NUTS 3
NUTS 3
NUTS 2
NUTS 3

72
Austria
249 / 308
Belgium
28
Bulgaria
21
Croatia
1
Cyprus
14
Czech Republic
56
Denmark
15
Estonia
140
Finland
9 000-13 000
France
370
Germany
114
Greece
20
Hungary
34
Ireland
113
Italy
77
Latvia
10
Lithuania
14
Luxembourg
1
Malta
40
Netherlands
66
Poland
232
Portugal
18
Romania
8
Slovakia
12
Slovenia
17
Spain
21
Sweden
United Kingdom (England & Wales) WS
16
Spatial resolution: Mun for municipality, WS for water service. Municipal unit in Greece. Water Service Authority in Ireland.
Type of data: CS for cross-section, TS for time-series.
For Belgium separated data are available for Flanders and for the Walloon Region (including Brussels-Capital region).

2007-2009
2004, 2008, 2011 / 2012-2013
2010
2000-2004
1998-2012
2006-2011
2010-2012
2006-2012
2011
2008-2011
2004, 2007, 2010
2010
2000-2007
2011
2001-2011
2013
2001-2012
2010
2000-2010
2009-2011
2003-2012
2007, 2009
2000-2010
2001-2011
2001-2011
2004-2011
2011
2002-2009

Panel
Panel
CS
Panel
TS
Panel
Panel
Panel
CS
Panel
Panel
CS
Panel
CS
Panel
CS
Panel
CS
TS
Panel
Panel
Panel
Panel
Panel
Panel
Panel
CS
Panel

We have collected aggregated data with various levels of resolution. For the spatial resolution, our strategy

13

has been to collect and compile recent data available at the most disaggregated level.5 There is of course a
trade-off between having access to highly spatially disaggregated data but with a specific and limited area and
using more aggregated data but covering a large area. Since our objective is to estimate a household water
demand function for each EU-28 country, the latter option has always been preferred.

Most of the datasets we have collected have a spatial resolution at least corresponding to NUTS 3. For 12
countries (Bulgaria, the Czech Republic, Estonia, Germany, Hungary, Lithuania, Netherlands, Romania, Slovakia,
Slovenia and Sweden), our resolution matches NUTS 3 exactly.6 We have municipal-level data for seven countries
(Austria, Finland, France, Italy, Luxembourg, Latvia and Portugal) and data at the water service level for three
countries (Croatia, Denmark and the United Kingdom). Greece has the highest spatial resolution (municipal unit
which corresponds to a sub-division of municipalities) but with a relatively low spatial coverage (see the specific
chapter on Greece). For Spain, due to data availability we will work at NUTS 2 level (autonomous communities
and cities).

The number of spatial units in each country varies from one (Cyprus and Malta) to a few thousands, such

as the case of France. In most cases, our data cover a substantial area in each country.

We have collected panel data for 18 countries (Austria, Croatia, the Czech Republic, Denmark, Estonia,
France, Germany, Hungary, Italy, Lithuania, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain
and the United Kingdom). The remaining countries are split across cross-section data (Bulgaria, Finland, Greece,
Ireland, Latvia and Sweden) and time-series data (Cyprus and Malta). A vast majority of datasets collected
refers to the period 2000-2010. The oldest data corresponds to Croatia (2000-2004). For 13 countries we
have data after 2010 (Cyprus, Denmark, Estonia, France, Ireland, Italy, Latvia, Lithuania, Netherlands, Poland,
Slovakia, Slovenia and Sweden).

1.4.3 Measuring household water use and water price

Since household water use patterns and household water prices differ very significantly across water services,
municipalities or regions in any given country, comparing national averages should be done with caution.
Moreover, when comparing water prices across countries one should take into account differences in purchasing
power parity.

Household water use In this report our focus is on understanding and modelling the drivers of household
water use. By reference to the United Nations Economic Commission for Europe, we will define household water
use as the quantity of water used to cover the household and related utility needs of the population through
the water supply industry and self-supply, calculated as a total and per capita. In our report our focus will be on
household water use per capita. We will then propose empirical models to predict the water consumption per
capita in each of the EU-28 countries. Although this definition may appear simple at first, it raises substantial
issues when considering a cross-country analysis since the definition used in each country might differ from the
proposed one.

First, household water use may be mixed with water use for some other types of consumers (typically small
In our work we have tried to identify the water that is really used

industrial or commercial establishments).

5Ideally we would have needed household-level micro data. This type of data is not, however, available in a standardised way for
a vast majority of EU-28 countries. The bias of using aggregated data (compared to using household data) is not clear since the vast
majority of studies has relied on aggregated level.

6For any given country in this case, the number of spatial units included in our analysis might not be equal to the number of NUTS 3

in the considered country due to data availability. For Cyprus and Malta, the NUTS 3 level corresponds to the country-level.

14

by households.7 Second, some countries may not report the water actually used by households but a volume
including distribution network losses. In such cases, the distribution losses have been excluded when computing
household water use. Third, in some countries self-supply is included in the household water use. Finally, for
computing household water use per capita, some countries use the total population whereas others rely on the
population connected to the water network. This issue is not important if almost all households are connected to
the water network, but it may really matter otherwise. Our approach has been to try to get reliable information
on the share of households connected to the water network. We have then focused on household water use per
capita connected to the network.

When compiling data from the EU-28 countries, we have tried as much as possible to comply with our
definition of household water use per capita. As a result, figures presented in Table 1.5 should be viewed as
being relatively homogenous across countries in terms of definition of household water use per capita. Columns
2-5 in this table give the national average for household water use per capita for 2008 through to 2011.

Table 1.5: Household water use and price in the EU-28

Country

Service

Household water use
(m3/capita/year)
2010
2009

2008

Household water price
(e /m3)

Household water price (PPP)
(PPPe /m3)

2011

2008

2009

2010

2011

2008

2009

2010

2011

53

2.1

1.6

2.6

2.0

2.1

1.9

1.4

0.7

4.2

1.3

1.4

2.0

2.2

34.3

40.5

54.3

37.8

63.1

35.9

31.7

36.4

37.7

1.4
3.2

1.3
2.9

0.6
2.0

0.8
3.0

0.7
2.6

0.7
2.2

87
33.6

79.5
34.1

2.0
3.9
1.3

0.8
2.3
2.8
2.2

0.9
3.1
2.0
2.9

53.7
31.9
36.3

49.5
43.7
81.9
35.8

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32.6
44.6
33.8

0.8
2.5
2.9
2.3
3.7
2.1

94.7
32.2
47.6
40.1
66.5
49.1

w
Austria
w
Belgium
w
Bulgaria
w,w
Croatia
w
Cyprus
w,w
Czech Republic
w
Denmark
w,w
Estonia
w
Finland
w
France
w,w
Germany
w,w
Greece
w
Hungary
w,w
Ireland
w,w
Italy
w,w
Latvia
w
Lithuania
w,w
Luxembourg
w
Malta
w
Netherlands
w
Poland
w,w
Portugal
w
Romania
w,w
Slovakia
w,w
Slovenia
w,w
Spain
Sweden
w
United Kingdom w
Source: Author's calculations based on the data sources presented above. Please refer to country-specific chapters for more details.
Column `Service' gives services considered for computing the price. `w' for water supply only and `w,w' for water supply and wastewater.
For Latvia, data refer to 2013. Belgium restricted to Wallonia. United Kingdom restricted to England and Wales.

27.3
48.0
34.8
63.5
57.3
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45.9
54.4

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61.0
33.4
47.9
56.2

2.3
1.3
1.0
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1.7
2.3
2.0
1.4

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1.4
0.6
1.2
1.0
1.7
1.8
1.4

35.9
49.3
64.0
58.6
25.0

55.8
30.1
44.8
52.4
61.1

24.1
51.2
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48.0
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54.4
29.6
44.5
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2.6
2.3
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2.7

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1.5
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1.6
1.6
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3.8
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69.2

27.2

25.4

68.3

66.8

24.3

53.7

53.6

0.7

1.1

1.4

1.2

1.4

1.4

1.0

0.7

1.9

1.8

1.8

1.3

1.1

0.7

0.5

1.9

3.8

0.9
3.4
2.1
3.0
3.0
1.9

1.8
1.5
1.9
1.1

1.3
1.2

2.5
2.3

7For some countries, the definition of a household water consumer is based on a maximum volume of water per year (250 m3 per

year in the case of Belgium for instance).

15

Household water price Since pricing has been shown in most of the existing studies as an important
determinant of household water use, we also present in Table 1.5 some basic statistics on the water price
paid by households for the water service. An inherent difficulty when comparing water prices across countries
is related to the fact that pricing schemes might be very different from one country to another, for instance
since price levels and pricing structures can be decided at different levels (local, regional or national level). In
some countries the water and wastewater services are jointly charged to household, which can make difficult
the identification of the specific part related to the water service.
In some countries, the water bill is not
related to the water consumption, which makes difficult the computation of an average water price. The price
data presented in Table 1.5 refer to the average water price (in EUR per cubic meter) paid by a representative
household either for the water supply service only or for the water supply and wastewater services (the `Service'
column gives the services considered for computing the price for each country). It follows that the water prices
are not directly comparable across countries.

Discussion A first message from Table 1.5 is that there are major differences in household water use per
capita across countries. A first group of countries emerges with a high level of household water use per capita
(more than 60 m3 per inhabitant per year). This group includes Cyprus, Greece, Italy, Portugal and Sweden
(Southern European countries mainly). Contrarily, a low level of household water use per capita (less than 40
m3 per inhabitant per year) is found in Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Poland and
Slovakia. This group then includes mainly Eastern European countries.

There are also major differences across countries in terms of average water price paid by household users.
A first group of countries with a `high' water price includes Belgium, Germany, Luxembourg and Sweden (water
supply service only) and France, Malta and the United Kingdom (water supply and wastewater services). Low
Interpreting this
water prices are however recorded in the Czech Republic, Lithuania, Poland and Romania.
result is very difficult. Differences in prices may be driven by many factors, including the cost and quality of
the water provision service, the application of the cost-recovery principle and the cross-subsidisation across
It is indeed well-known that there is a high level of heterogeneity across countries with regards to the
users.
implementation of the cost-recovery principle through pricing. In many countries, water prices are distorted by
subsidies (or cross-subsidies among types of water users) or due to the fact that the water sector is highly
regulated and non-competitive. For instance, costs are almost fully recovered in Germany: 99 % of drinking
water costs and 96 % of wastewater costs are directly paid for by the consumers. In Austria, the amount paid by
consumers represents 93 % of drinking water costs and 78 % of wastewater costs (BDEW 2015). But in Croatia,
an analysis for four utility companies showed service prices do not reflect real costs, with a cost recovery of
77 % for drinking water supply and 45 % for wastewater (ICPDR 2005). A similar situation is observed in
Romania where an analysis of water and wastewater systems in the Cluj and Salaj counties revealed a recovery
of investment cost equal to 38 % for water and wastewater (ICPDR 2005). In addition, within a given country,
the cost-recovery principle might be applied differently depending on the sector considered. Industrial tariffs are
generally higher than tariffs paid by households, even if it cannot be explained by differences in infrastructure or
operation costs. This cross-financing phenomenon is common in the water sector but variable across countries.
For instance, the ratio of commercial to household tariffs for water supply is 3.3 for Albania, 1.6 for Croatia and
2 for Montenegro (REC 2009).

It is finally interesting to have a look at how water consumption and water price have evolved over time.
In terms of change in water consumption, there is a majority of countries where the per capita consumption
has decreased (either moderately or strongly) over recent years. Cyprus is the only country where a significant
increase in water consumption per capita has been recorded in the recent year. The water price has increased
(either moderately or strongly) over recent years in most countries (16 of 28). The strongest rates of increase

16

for the household water price are found in Cyprus, Estonia, Hungary, Italy, Malta and Slovakia.

1.5 A new set of household water demand functions for the EU-28

Based on the new dataset presented in the previous section, a household water demand function has been
estimated for each country using the most possible disaggregated data (typically municipality, water service or
NUTS 3 levels) and the most recent data (typically 2005-2012).

We summarise in this section the main findings of our cross-country analysis of household water demand

in the EU-28. We focus in particular on price and income elasticities of household water demand.

1.5.1 Specifying the residential water demand function

To estimate Equation (1.1), a wide variety of functional forms have been applied in the water demand literature,
including linear forms, semi or double logarithm forms and more complex forms such as the Stone-Geary
specification and the quadratic almost ideal demand system (Vanhille 2012). The existing literature is however
not very informative concerning the specification which should be preferred. Since the double-log model is the
most common specification in the literature on residential water demand, we have adopted this specification
in order to facilitate the comparison to other studies. Furthermore, the specification implies that coefficient
estimates are also elasticity estimates. With a double-log specification, the residential water demand function
writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(1.4)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand.

Depending on the data available for each country, this equation will be estimated using simple OLS or panel
data methods. The interested reader may refer to each country case study for a presentation of the econometric
method used for each country.

1.5.2 Validation of the household water demand models

A possible way to assess the validity of the model is to compare the water consumption predicted by the demand
function to the observed water consumption, see Figure 1.1 and Figure 1.2. For most of the countries in our
dataset, the estimated water demand functions perform relatively well.

In the country-specific chapters, we have also computed the mean absolute percentage error from the
predicted water consumption per capita. For a vast majority of countries, the mean absolute percentage error
is lower than 20 %.

1.5.3 Main determinants of household water demands

In Table 1.6, we present our results in terms of main significant determinants of household water demand
functions. We focus in particular on the impact of water price, household income, climate condition and some
household characteristics, including the average household size of age structure. These variables have been
previously shown to be the main determinants of household water use.

With the exception of a few countries (Cyprus, Finland, Ireland, Lithuania, Slovenia and Sweden), the water
price is shown to have a significant and negative impact on household water use, which means that facing a
price increase, households in most of the EU-28 countries will adjust their consumption by reducing it. Our

17

(a) Austria

(b) Belgium (Wallonia)

(c) Bulgaria

(d) Croatia

(e) Cyprus

(f) Czech Republic

(g) Denmark

(h) Estonia

(i) Finland

(j) France

(k) Germany

(l) Greece

(m) Hungary

(n) Ireland

(o) Italy

Figure 1.1: Predicted versus observed water consumption 1/2 (m3 per capita)

18

4550556065Wathat020406080100WathatWathat2830323436Wathat20304050WathatWathat253035404550Wathat2030405060WathatWathat406080100120Wathat0100200300400500WathatWathat708090100110Wathat60708090100WathatWathat25303540Wathat253035404550WathatWathat20406080Wathat050100150200WathatWathat1520253035Wathat050100150WathatWathat20406080100Wathat050100150WathatWathat406080100120140Wathat050100150200WathatWathat30405060Wathat20406080WathatWathat60708090100Wathat0100200300400WathatWathat20406080Wathat20406080WathatWathat3040506070Wathat20406080100WathatWathat(a) Latvia

(b) Lithuania

(c) Luxembourg

(d) Malta

(e) Netherlands

(f) Poland

(g) Portugal

(h) Romania

(i) Slovakia

(j) Slovenia

(k) Spain

(l) Sweden

(m) United Kingdom

(n)

(o)

Figure 1.2: Predicted versus observed water consumption 2/2 (m3 per capita)

19

253035404550Wathat020406080100WathatWathat1520253035Wathat152025303540WathatWathat30405060Wathat20406080100WathatWathat2426283032Wathat262830323436WathatWathat404244464850Wathat3540455055WathatWathat20406080100120Wathat050100150200250WathatWathat20406080100120Wathat050100150200250WathatWathat406080100120Wathat0100200300400WathatWathat2030405060Wathat203040506070WathatWathat354045505560Wathat20406080100120WathatWathat5055606570Wathat4050607080WathatWathat5055606570Wathat5060708090100WathatWathat50556065Wathat455055606570WathatWathat  Table 1.6: Main determinants of household water demands in the EU-28

H. size

Pop. age

Other variables of interest

(age,−)***

(density,+)***

Country
Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France

Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland

Price
(−)***
(−)***
(−)***
(−)*
(−)
(−)**
(−)**
(−)*
(−)
(−)***

(−)***
(−)*
(−)***
(−)
(−)***
(−)**
(−)
(−)
(−)**
(−)***
(−)***

Income
(+)
(+)***
(+)***
(−)
(+)***
(+)**
(+)**
(+)**
(+)
(+)***

(+)***
(−)
(+)***
(+)
(+)**
(+)**
(+)**
(+)
(+)
(+)**
(+)***

Climate conditions
(evap,+)***
(rain,+)***
(evap,+)
(rain,−)***
(rain,−)
(rain,−)*
(rain,−)**
(norain,+)
(rain,−)***
(rain,−)***,
(norain,+)***

(rain,+)
(rain,−)
(rain,−)***
(evap,+)
(norain,−)
(norain,−)
(rain,+)
(rain,−)***
(norain,−)
(evap,+)*
(rain,−)***

(−)
(−)***

(−)***

(−)***

(−)**

(−)

(pop1565,+)*
(pop9,+)
(pop70,+)***
(age,−)***

(pop1565,−)***

(pop65,+)*

(pop18,−)
(age,+)

(density,−)***,
(permanentHousing,−)***,
(House,−)***
(density,+)***
(density,+)***

(rationing,−)

(dishwasher,+)***

(pop9,−)***,
(pop70,−)***
(pop14,−)
(pop15,−)
(age,−), (pop14,−)
(pop14,+)
(age,−)***
(age,+)**

(−)

(+)***
(+)
(+)**
(+)**
(+)
(+)
(+)***

(−)*
(−)**
(−)***
(−)
(−)***
(−)
(−)***

(rain,−)***
(norain,+)*
(norain,+)
(rain,+)
(rain,+)
(rain,−)
(rain,−)***

Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United
Kingdom
(England & Wales)
***, **, * for significant at 1 %, 5 % and 10 % respectively.
Price measures the price of water in (EUR per m3) paid by housholds.
Income represents the household income (sometimes proxied by the GDP per capita or by the average salary).
Rain measures rainfalls, norain is the number of day without rain and evap is the evapotranspiration.
H. size is the number of person per household.
Age is the average or median population age, pop9 (resp. pop14 pop18) is the population share below 9 (resp. 14, 18),
pop1565 is the share of the population between 15 and 65, pop65 (resp. pop70) is the population share above 65 (resp. 70).
Density is the population density.

(meter,−)***

(density,−)

20

results are in line with previous works in Europe (and elsewhere in the world) showing that domestic water
consumption reacts to changes in water prices. We will discuss our price elasticity results in more detail in the
following paragraph.

We provide some evidence that water is a normal good for most EU-28 countries. The household water
use tends to increase with household income (except for Greece and Croatia). Our results are consistent with
the previous literature showing that domestic water consumption is positively correlated with income (Arbués,
García-Valiñas, and Marti«ez-Espiñeira 2003). The income effect is however not significant for Austria, Croatia,
Finland, Greece, Ireland, Malta, Romania, Spain and Sweden. Again we will discuss our income elasticity results
in more detail in the following paragraph.

Concerning the climate variables, we obtain mixed evidence. For 13 Member States, at least one climate
variable (rainfalls, evapotranspiration or the number of days without rain) is found to be significant. For these
countries, we expect the household water consumption to be impacted by climate conditions.
It is generally
found that drier and hotter conditions will result in an increase in the household water use. This result is
particularly important in a context of climate change.

Some household characteristics appear to be consistently significant across countries. This is the case, for
instance, for the household size which seems to have a decreasing impact on household water use per capita.
A large household size is associated with lower household water consumption per capita.8 Since household size
is an important determinant of water use, policymakers should need to include it in the design of demand-side
water management measures. This is particularly important both for Western and Eastern European countries
where we observe a decreasing trend in household size.

Population age and age structure of the population have mixed effects on household water use per capita.
It is quite difficult to extract general and robust findings from the country estimates. We still need additional
studies to understand the impact of age on household water use.

1.5.4 Price and income elasticities of household water demands

In Table 1.7, we discuss in more detail our results concerning price and income elasticities of the residential
water demand function for EU-28 countries.

We find that household water demand function is price inelastic for most of the EU-28 countries, which
means that the water consumption decreases by less than 1 % for every 1 % increase in price. The price-
elasticity typically varies between -0.5 and -0.1, which is consistent with the existing literature. Our results have
important policy implications.
Indeed pricing reforms are often cited as the first measure to be implemented
to signal water scarcity and to encourage a reasonable use of water. The effectiveness of any pricing policy
in engaging water consumption depends, however, on the price elasticity of consumption. The larger the price
elasticity, the more effective these policies are at reducing water consumption. Our country-specific price
elasticities allow decision-makers to simulate the impact of change for water price on household water use per
capita.

Our range of value for the income elasticities is much wider. For Austria, Croatia, France, Germany, Greece
and Spain the income elasticity is very low (between 0.00 and 0.25). We do not expect any strong impact
of change in household income on household water consumption per capita for these countries. The income
elasticity is found to be much higher in Bulgaria, Cyprus, Estonia, Finland, Latvia, Lithuania, Portugal and Slovakia
(greater than 0.50). This group of countries includes some Eastern European countries where both household

8An explanation of the negative relationship between per capita water consumption and household size proposed by Schleich and
Hillenbrand (2009) is that several water uses such as washing, gardening or even cooking tend to increase less than proportionally to
the household size.

21

Table 1.7: Estimated price and income elasticities of household water demand in the EU-28

Country

Spatial coverage

Time coverage

Elasticities

Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom (England & Wales) WS (16)

Mun (72)
Mun(249/308)
NUTS 3 (28)
WS (21)
NUTS 3 (1)
NUTS 3 (14)
WS (56)
NUTS 3 (15)
Mun (140)
Mun (9,000-13,000)
NUTS 3 (370)
Other(114)
NUTS 3 (20)
Other(34)
Mun (113)
Mun (77)
NUTS 3 (10)
Mun (14)
Country (1)
NUTS 3 (40)
NUTS 3 (66)
Mun (232)
NUTS 3 (18)
NUTS 3 (8)
NUTS 3 (12)
NUTS 2 (17)
NUTS 3 (21)

Panel (2007-2009)
Panel (2004,2008,2011/2012-2013)
CS (2010)
Panel (2000-2004)
TS (1998-2012)
Panel (2006-2011)
Panel (2010-2012)
Panel (2006-2012)
CS (2011)
Panel (2008-2011)
Panel (2004,2007,2010)
CS (2010)
Panel (2000-2007)
CS (2011)
Panel (2001-2011)
CS (2013)
Panel (2001-2012)
CS (2010)
TS (2000-2010)
Panel (2009-2011)
Panel (2003-2012)
Panel (2007,2009)
Panel (2000-2010)
Panel (2001-2011)
Panel (2001-2011)
Panel (2004-2011)
CS (2011)
Panel (2002-2009)

Price
-0.20 ; -0.18
-0.12 ; -0.04
-0.27 ; -0.26
-0.37 ; -0.14
-0.33
-0.28 ; -0.18
-1.00 ; -0.33
-0.63 ; -0.00
-0.28
-0.43 ; -0.10
-0.45 ; -0.44
-0.10
-0.79 ; -0.07
0.00
-0.58
-0.40
-0.28
-1.20
-0.31
-0.63 ; -0.13
-0.39 ; -0.18
-0.27
-0.58
-0.94 ; -0.66
-0.30 ; -0.11
-0.21 ; -0.00
-0.58 ; -0.28
-0.18 ; -0.20

Income
0.00
0.03 ; 0.12
0.61 ; 0.62
0.00
1.09
0.16 ; 0.46
-0.37 ; -0.50
0.43 ; 1.70
0.56
0.07 ; 0.26
0.08 ; 0.14
0.01
0.13 ; 1.10
0.16 ; 0.33
0.31
0.61
2.01
0.39
0.44
0.06 ; 0.23
0.22 ; 0.55
0.67 ; 0.84
0.26
0.62 ; 1.05
0.38 ; 0.44
-0.30 ; 0.05
0.37 ; 0.40
0.26

22

income and water pricing have significantly changed over the last 10 years.

1.5.5 Long-term price elasticities of household water demands

Water consumption may be at least somehow persistent over time due to household habits or to the time
needed to adjust durable equipment (showers, for instance). With panel or time-series data, one way to take
this time persistence characteristic of household water consumption into account is to introduce the lagged
In our case, since we work with annual
consumption as an additional independent variable in Equation (1.5).
data, the lag water consumption corresponds to the water consumption for the previous year. The dynamic
specification of our equation of interest then becomes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(1.5)

where Lag y represents the lagged water consumption. From this specification it is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(1.6)

In Table 1.8, we provide some estimates of the long-term price elasticities for countries for which a panel
dataset is available. We then compare the short-term price elasticities obtained either with generalised least
squares (GLS) or generalised least squares with instrumental variables (GLSiv) models with the corresponding
long-term price elasticities.

Table 1.8: Estimated sort-term and long-term price elasticities of household water demands in selected European
countries

Country

GLS

GLSiv

1.1

-0.78

Croatia
Cyprus
Czech Republic
Denmark
Estonia
France
Germany
Hungary
Italy
Lithuania
Malta
Poland
Romania
Slovakia
Slovenia
Spain
United Kingdom (England & Wales)
***, **, * for significant at 1 %, 5 % and 10 % respectively.

Short-term Long-term Short-term Long-term
-0.14*
-0.33
-0.20***
-0.33**
-0.17
-0.05***
-0.44***
-0.06**
-0.22***
-0.21**
-0.31*
-0.18***
0.00
-0.66***
-0.11
-0.21***
-0.20***

-1.16
-0.51
0.08**
-1.28***
-0.70*
-0.06***
-0.50***
-0.42***
-0.29***
-0.33**
-0.36*
-0.25*
-0.06
-1.03***
-0.17**
-0.24
-0.28**

-0.61**
-0.90*
-2.08***
4.13***
2.00
-0.69***
-0.25

-0.28**
-1.00**
-0.52*
-0.5
-0.45***
-0.79***
-0.58***

-1.30
-0.05
-1.14***
-0.66**
0.64

-0.34
-0.58**
-0.94***
-0.30
0.14

The household water demand is still price inelastic in the long term. However, we find higher estimated price
elasticities (in absolute values) in the long term compared to short term. From a policy perspective, it means that
even if households do not react immediately to a change in the water price, public authorities may expect that
they will modify their behaviour in the long term. So the benefits (in terms of reducing the water consumption)
from a water price increase may be visible only a few years after having implemented the price change.
It
should be noted that the higher responsiveness of household water consumption in the long term to change in

23

water price is in line with the existing literature (Nauges and Thomas 2000, Musolesi and Nosvelli 2007, Hortová
and Kristoufek 2014).

1.5.6 Household water demand functions

In this paragraph we graphically represent the household water demand function for each country. We plot
the predicted household water use (m3 per capita per year) as a function of the water price (EUR per m3). All
other variables (household income, climate conditions and household characteristics) are fixed at the sample
mean for the year considered. As a result, the shape of the household water demand functions represented in
Figure 1.3 and in Figure 1.4 depends on the estimated price elasticities and on the specific functional form we
have used (log-log form).

1.6 Conclusion

1.6.1 Main message

Economists have been working on household water use for a long time, but water demand modelling has taken
on new importance with the need to better understand the potential role economic instruments might have
to reduce abstraction and polluted discharges, for instance. To this end, economists have developed a great
variety of models in order to predict water demands for industrial, agricultural and domestic users. For the
latter, the level of knowledge is quite advanced. Estimations of domestic water demand functions have been
undertaken for a substantial number of countries all over the world, and the existing literature has already been
summarised and reviewed by several authors (Arbués, García-Valiñas, and Marti«ez-Espiñeira 2003, Worthington
and Hoffman 2008, Tanverakul and Lee 2012). In Europe, the recent report from the European Environmental
Agency (EEA, 2013) has stressed, however, that most available reference studies date back 10 or 20 years
and that new case studies with primary data are required in order to provide fresh and relevant evidence that
accounts for the socio-economic, management and technological changes that have taken place in the last 20
years.

This document has provided an update on the current knowledge on household water use in Europe.
We have first reviewed the existing scientific literature on household water demand modelling in Europe. By
doing so, we have not been able to find any estimate of the household water demand function for 11 of the
EU-28 countries (Austria, Belgium, Bulgaria, Croatia, Estonia, Finland, Hungary, Ireland, Latvia, Lithuania and
Slovenia). For the 17 remaining countries, it should be pointed out that existing estimates of the household
water demand function rely on data posterior to 2010 for six of them only (Cyprus, the Czech Republic, Germany,
Luxembourg, Romania and the United Kingdom). For Denmark and Sweden, the more recent studies date back
to 1990 and 1992, respectively. Despite these limitations, this literature provides some indications on the
empirical determinants of household water use in Europe.

Second, we have assembled a new dataset with a NUTS 3 resolution and an EU-28 coverage with data on
household water consumption and household water price. The typical source of data includes national statistical
offices, regulators of water and wastewater services, national associations of water and wastewater services
and national associations of municipalities.

Third, we have provided some new estimates of household water demand functions for all EU-28 countries
using consistent data and econometric methods. Primary data on household water consumption, on household
water prices and on household characteristics supposed to have an impact on water consumption have been

24

(a) Austria

(b) Belgium (Wallonia)

(c) Bulgaria

(d) Croatia

(e) Cyprus

(f) Czech Republic

(g) Denmark

(h) Estonia

(i) Finland

(j) France

(k) Germany

(l) Greece

(m) Hungary

(n) Ireland

(o) Italy

Figure 1.3: Plot of the household water demand function 1/2 (m3 per capita per year)

25

40455055Water consumption  per capita (m3/year)11.522.53water price (euro/m^3)Austria (2009)30.53131.53232.533Water consumption  per capita (m3/year)22.533.54water price (euro/m^3)Belgium − Wallonia & Brussels (2011)2025303540Water consumption  per capita (m3/year)0.511.52water price (euro/m^3)Bulgaria (2010)708090100Water consumption  per capita (m3/year)0.511.52water price (euro/m^3)Croatia (2004)8090100110Water consumption  per capita (m3/year).4.6.811.21.4water price in EUR/m3)Cyprus (2010)2830323436Water consumption  per capita (m3/year)1.522.533.5water price (euro/m^3)Czech Republic (2010)253035404550Water consumption  per capita (m3/year)2.533.544.55water price (euro/m^3)Denmark (2010)2425262728Water consumption  per capita (m3/year)11.522.53water price (euro/m^3)Estonia (2010)40455055Water consumption  per capita (m3/year)234567water price (euro/m^3)Finland (2011)565758596061Water consumption  per capita (m3/year)1234water price (euro/m^3)France (2010)4045505560Water consumption  per capita (m3/year)22.533.544.5water price (euro/m^3)Germany (2010)70758085Water consumption  per capita (m3/year).511.52water price (euro per m3)Greece (2010)102030405060Water consumption  per capita (m3/year).511.522.5water price (euro/m^3)Hungary (2007)455055Water consumption  per capita (m3/year)11.522.53water price (euro/m^3)Ireland (2011)304050607080Water consumption  per capita (m3/year)11.522.533.5water price (euro/m^3)Italy (2010)(a) Latvia

(b) Lithuania

(c) Luxembourg

(d) Malta

(e) Netherlands

(f) Poland

(g) Portugal

(h) Romania

(i) Slovakia

(j) Slovenia

(k) Spain

(l) Sweden

(m) United Kingdom

(n)

(o)

Figure 1.4: Plot of the household water demand function 2/2 (m3 per capita per year)

26

253035404550Water consumption  per capita (m3/year).511.522.53water price (euro/m^3)Latvia (2013)15202530Water consumption  per capita (m3/year).511.522.53water price (euro/m^3)LITHUANIA (2011)4045505560Water consumption  per capita (m3/year)34567water price (euro/m^3)Luxembourg (2010)25303540Water consumption  per capita (m3/year).511.52water price EUR/m3)Malta (2010)25303540Water consumption  per capita (m3/year)22.533.54water price (euro/m^3)Natherlands (2010)2030405060Water consumption  per capita (m3/year)0.511.52water price (euro/m^3)Poland (2010)405060708090Water consumption  per capita (m3/year)01234water price (euro/m^3)Portugal (2009)406080100Water consumption  per capita (m3/year)0.511.52water price (euro/m^3)Romania (2010)20406080100Water consumption  per capita (m3/year).511.522.5water price (euro/m^3)Slovakia (2010)3540455055Water consumption  per capita (m3/year)11.522.53water price (euro/m^3)Slovenia (2010)4550556065Water consumption  per capita (m3/year).511.522.5water price (euro/m^3)Spain (2010)4050607080Water consumption  per capita (m3/year)23456water price (euro/m^3)Sweden (2010)505560Water consumption  per capita (m3/year)11.522.53water price (euro/m^3)UK (2009)  collected, assembled and checked for each one of the EU-28 countries. Then, a household water demand function
has been estimated for each country using the most possible disaggregated data (typically municipality, water
service or NUTS 3 levels) and the most recent data (typically 2005-2012). The econometric estimates allow us
to identify the determinants of the household water demand for each country.

We also provide a new set of price and income elasticities. For most of the countries, the estimated price
elasticity of the household water demand is found to be negative. Facing a price increase, EU households will
react by reducing their water consumption.
It is then demonstrated that water price may play a role towards
signalling water scarcity or water cost to households. In addition, we have found that household water demand
functions are typically price inelastic for most of the EU-28 countries. This means that the household water
consumption decreases by less than 1 % for every 1 % increase in price. The price-elasticities typically vary
between -0.5 and -0.1 across countries. Facing a price increase by 10 %, it is then expected that the household
water consumption will be reduced by 1 to 5 %. This result has important policy implications.
Indeed, pricing
reforms are often cited as the first measure to be implemented in order to signal water scarcity and to encourage
a reasonable use of water. The effectiveness of any pricing policy in engaging water consumption depends,
however, on the price elasticity of consumption. The larger the price elasticity, the more effective these policies
are at reducing water consumption. Our country-specific price elasticities allow decision-makers to simulate the
impact of change in the water price on household water use per capita. To achieve more significant reductions
of the household water consumption, public authorities should complement their price policies with non-price
policies such as education or awareness campaigns.

Household water consumption may be, at least somehow, persistent over time due to household habits or to
the time needed to adjust durable equipment (showers, for instance). It is then expected that households may
react to a change in the water price in the long term rather than in the short term. Using the panel-data structure
for some countries, we have shown that long-term price elasticity is in general greater (in absolute value) than
short-term price elasticity. From a policy perspective, it means that even if households do not immediately
react to a change in the water price, public authorities may expect them to modify their behaviour in the long
term. So the benefits (in terms of reducing the water consumption) of a water price increase may be visible
only a few years after having implemented the price change. It should be noted that the higher responsiveness
of household water consumption in the long run to change in water price is in line with the existing literature
(Nauges and Thomas 2000, Musolesi and Nosvelli 2007, Hortová and Kristoufek 2014).

Our range of value for income elasticities is much wider. For Austria, Croatia, France, Germany, Greece and
Spain the income elasticity is very low (between 0.00 and 0.25). We do not expect any strong impact of change
of household income on household water consumption per capita for these countries. The income elasticity is
found to be much higher in Bulgaria, Cyprus, Estonia, Finland, Latvia, Lithuania, Portugal and Slovakia (greater
than 0.50). This group of countries includes some Eastern European countries where both household income and
water pricing have significantly changed over the last 10 years. For these countries, the expected trend in water
consumption is a priori difficult to assess since it will be the result of two opposite effects. First, since household
income is expected to increase in these countries (income convergence process), one may expect an increase in
the water consumption per capita (income effect). On the other hand, we may anticipate some price increases
for these countries, in particular due to the implementation of the full cost-recovery principle for water and
wastewater services. This price effect should result in a decreasing impact on water consumption per capita.
The combined impact of the income and price effects on water consumption is then a priori undetermined.

27

1.6.2 The way forward

Mapping of household water use per capita at the EU scale Having access to reliable and disaggregated
data on water use is a prerequisite for designing any efficient water management policy. The main limitation
faced by policymakers is then the lack of detailed and up-to-date water withdrawal statistics. One solution
consists in disaggregating national public water withdrawal statistics (from Eurostat, for example) to a regional
level (NUTS 3) using geographical information system (GIS) techniques (Vandecasteele, Bianchi, Batista e Silva,
Lavalle, and Batelaan 2014). These disaggregation procedures are however subject to caution since they require
making strong assumptions on population distributions and household water consumption behaviour.

In this report we have shown that it is in fact possible to collect data for the EU-28 countries, mainly from
national statistical offices, on household water use per capita at a spatial resolution that corresponds to NUTS 3
at least. It is then possible to map this NUTS 3 data on household water use with an EU-28 coverage, therefore
avoiding to implement any spatial disaggregation procedure as done in Vandecasteele's publication (2014).
Alternatively, one may use the estimated water demand models to predict the water use per capita at regional
level (NUTS 3) for all EU countries.

As an example we provide two maps at EU-level taken from (Bernhard, Reynaud, Lanzanova, and deRoo
2015). In the first map, the water demand functions are used to estimate the water use at regional level (NUTS
3) per capita for all EU countries. In the second map, the water demand models are combined with a detailed
population map and demographic projections. This results in a 5 km grid with the household water use per cell.

Integration of residential water demand functions in hydro-economic modelling tools at EU scale
We have started the process of integrating the estimated household water demand functions into the Lisflood
model, calibrated at the European level.9 The main methodological improvement is to incorporate feedback
between household water use and the rest of the system modelled. For instance, any change to climate
conditions will affect the hydrological part of the model but also household water needs.
This updated
modelling framework may then be used for the evaluation of programmes of measures under the Water
Framework Directive.

Scenario analysis Pricing reforms is often cited as the first measure to be implemented to signal water
scarcity and to encourage a reasonable use of water. The effectiveness of any pricing policy in engaging
water consumption depends, however, on the price elasticity of consumption. The larger the price elasticity, the
more effective these policies are at reducing water consumption. The water demand function can be used for
assessing the impact of change in water price for a specific country or for the whole EU-28 area. Alternatively,
a scenario of economic growth or climate change can be also evaluated.

The water demand models predictions can be combined with scenarios of demographic change, either for
a given country or across Europe. As an example, we provide in figure 1.6 a map showing the change in water
demand by 2030 if water prices grow 10 % faster than gross domestic product, (Bernhard, Reynaud, Lanzanova,
and deRoo 2015) for additional details.

Welfare analysis
The demand functions defined in this report are useful for measuring the consumer (Mar-
shallian) surplus derived from water consumption, which is basically the difference between what a consumer
would have been ready to pay for a good and what he really pays (expressed in monetary terms). As a result

9The Lisflood model is a hydrological rainfall-runoff model that is capable of simulating the hydrological processes that occur in a
catchment. Lisflood has been developed by the floods group of the natural hazards project of the Joint Research Centre of the European
Commission.

28

(a) Predicted household water use per capita in 2014

(b) Predicted aggregated water use in hm3 in 2014

Figure 1.5: Use of the water demand functions to predict household water use in Europe

29

Figure 1.6: Scenario analysis and use of the water demand functions

the water demand functions provide us with tools for realising some applied welfare analysis. We can then
measure the consumer welfare losses due to restricting water consumption, for instance. Alternatively, the loss
of welfare resulting from a price increase can be measured, and eventually for different groups of households
that are segmented based on their income (Reynaud 2015).

The estimated house-
Assessing the impact of full cost recovery of water services on EU households
hold water demand functions can be used to assess the impact of full cost recovery (FCR) of water services
on EU households. This is the work carried out in Reynaud (2015) in which the assessment relies on three
dimensions. First the author of this work measures how household water consumption will react to the price
change induced by implementing the FCR principle. Second, he provides a measure of the resulting welfare
losses for households. Third, he evaluates how water affordability for households is impacted. This assessment,
which relies on a household water demand function approach, has been conducted for eight European countries
(Austria, Bulgaria, the Czech Republic, Estonia, France, Greece, Portugal and Spain). For most of the countries
considered, Reynaud (2015) shows that implementing the FCR principle will not lead to substantial water af-
fordability issues. Bulgaria and Estonia are two exceptions, since households in the first income decile will have
to devote about 3 % of their income to paying their water and wastewater bill. The fact that water affordability
might be an issue under FCR for some countries gives some ground for public authorities to develop specific
policies targeted at poor households.

Extension of spatial coverage The data collection constitutes the main difficulty for conducting the cross-
country analysis of household water use in the EU-28. One may argue that the collection has been made easy
due to the fact that all countries belong to the EU. This is clear that this has helped, but one should mention at
this stage that we have already estimated a set of preliminary household water demand functions using similar
data for countries not belonging to the EU. The set of countries for which we already have some estimates
includes Bosnia and Herzegovina, Kosovo, Moldova, Montenegro, Serbia, Ukraine, Norway and Turkey.

30

Chapter 2

Austria

31

2.1 Existing literature

To our best knowledge, there doesn't exist any published article having estimated a residential water demand
function in Austria.

2.2 Urban water sector in Austria

The Austria water supply sector consists of a very high number of municipal services (about 2,000) and very
small cooperative utilities (in total another 3,500), see (Neunteufel, Perfler, Schwarz, Bachner, and Bednar-
Friedl 2015). The wastewater sector is organized in a similar way.

Water tariff levels are set by municipalities (owners of the utilities) but have to be in accordance with the
legal framework. The ``user pays'' principle and the ``polluter pays'' principle, respectively are implemented.
In general, the annual water bill paid by water users is composed of an annual fixed fee and a single water
tariff rate independent of actual water consumption (i.e. no increasing block water tariff or similar tarification
schemes).

2.3 Data

Our panel data set consists of municipality-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2007--209. Most of the data
come from the annual publication Cities in Figures published by the Statistical Office of Austria. We will then
consider only the largest municipalities in Austria (about 70 municipalities). The data consist of aggregate data
at municipality-level on water consumption per capita and water prices. The data have been combined with
information on household's income, climate conditions, and household characteristics mainly from Statistics
Austria.

Water consumption data Volume of water from the public network per capita from 2007 to 2009 at
municipal level.

Water price data Our main source of data are the annual reports Cities in Figures. As a price indicator, we
use the average price paid by households for the water service (in euros per m3).

Household income We use the average gross domestic product per capita available from Statistics Austria
from 2007 to 2009 at Nuts3.

Other socioeconomic variables Other socio-economic variables from 2007 to 2011 come from Statistics
Austria.

Climate data All meteorological data come from JRC climate database. Grid data (5km × 5km) on rainfalls,
temperatures and evapotranspiration have been aggregated at regional level. We have historical data from
1990 to 2013.

32

2.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Austria.

Table 2.1: Household water consumption and price in Austria

Year

2007
2008
2009
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

54.746
53.649
53.070
53.821

1.358
1.366
1.378
1.367

Table 2.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
In 2009, the average annual consumption
(average water price, in euros m3) in Austria from 2007 to 2009.
per inhabitant amounted to 53.8 m3. There has been a decreasing trend in domestic water consumption since
2007. Over the period 2007-2009, the average water price (only for the water service) has increased from 1.36
euros per m3 to 1.38 euros per m3 .

Table 2.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2009. As it can be seen, there are some differences across regions, both in terms of
water consumption and water prices. With more than 100 m3 per capita per year, the water consumption is
the higher in the Nordburgenland Nuts3. On contrary, a low water consumption is reported in the Unterk¨arnten
county (40.0 m3 per capita per year).

2.5 Water demand function estimate

2.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(2.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (2.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(2.2)

33

Table 2.2: Regional household water consumption and price in Austria

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

1.448
2.067
1.412
1.586
1.363
1.247
1.276
1.237
1.409
0.960
1.140
0.959
0.996
1.450
1.282
1.534
1.122
1.689
1.018
1.711
1.622
1.184
1.358
1.341
1.305
1.375
1.378

Bludenz-Bregenze Wald
Graz
Innsbruck
Innviertel
Klagenfurt-Villach
Linz-Wels
Mostviertel-Eisenwurzen
Nieder¨osterreich
Nordburgenland
Oberk¨arnten
Osttirol
Pinzgau-Pongau
Rheintal-Bodenseegebiet
Salzburg und Umgebung
St. P¨olten
Steyr-Kirchdorf
Tiroler Unterland
Traunviertel
Unterk¨arnten
Waldviertel
Weinviertel
Westliche Obersteiermark
Wien
Wiener Umland-Nordteil
Wiener Umland-S¨udteil
¨Ostliche Obersteiermark
Average

42.979
45.443
66.019
47.871
47.948
50.404
48.185
51.313
103.204
50.602
58.856
45.465
49.023
54.776
61.046
55.845
52.712
51.507
40.931
54.478
47.987
53.929
53.655
61.244
67.320
55.632
53.070

34

Table 2.3: Estimation of the household water demand in Austria

AustriaOLS
b/se

AustriaGLS
b/se

AustriaGLSiv
b/se

ln Water price (in euros per m3)

ln Share of groundwater abstraction

-0.176**
(0.09)
ln Household income (in euros per capita) 0.031
(0.08)
0.027***
(0.01)
0.482***
(0.14)
3.167***
(0.87)

ln summer evapotranspiration

Constant

-0.203**
(0.08)
0.047
(0.13)
0.014
(0.01)
0.376***
(0.14)
3.103**
(1.33)

0.355
(0.41)
0.058
(0.11)

0.412***
(0.16)
2.818**
(1.21)

R-squared
N. of obs.

0.127
187.000

187.000

187.000

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

2.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(2.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

2.5.3 Results

We report here our estimate for the residential water demand function in Austria, based on our municipality
dataset for Austria (2007 to 2009). The first model estimated in Table 2.3 corresponds to simple OLS whereas
in the second model we estimate Equation (2.4) with a random parameter estimator.
In the third model, we
account for potential endogeneity of the price by using an instrumental variable approach.

Table 2.3 present the main significant variables explaining household water consumption in Austria. The
price elasticity varies across models, from --0.18 with the OLS to --0.20 with the GLS (not significant with
GLSiv). Based on the GLS models, a 10% increase in water price results in a 2.0% decrease in short-run water
consumption in Austria. Water demand in Austria is inelastic, i.e. the estimated price elasticity in absolute values
is lower than one.

The income elasticity is not significant.

35

Figure 2.1: Observed versus predicted household water consumption in Austria

Climate conditions play a significant role. The higher is the summer evapotranspiration, the higher with be

the consumption per capita.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 2.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 20.1.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (2.2). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(2.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(2.5)

We provide in Table 2.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the AustriaGLSlag model, the long-term
price elasticity is estimated to be -0.22 but not significantly different from zero. With the AustriaGLSivlag
model, the long-term price elasticity is estimated to be -1.45 but not significantly different from zero.

36

4550556065Wathat020406080100WathatWathatTable 2.4: Estimation of the household water demand with lagged water consumption in Austria

AustriaGLSlag
b/se

AustriaGLSivlag
b/se

ln Water price (in euros per m3)

-0.017
(0.04)
ln Household income (in euros per capita) 0.016
(0.04)
0.922***
(0.03)
0.128
(0.39)

ln Lag of water consumption per capita

Constant

-0.168
(0.16)
0.015
(0.05)
0.887***
(0.04)
0.314
(0.52)

R-squared
N. of obs.

126.000

120.000

37

Chapter 3

Belgium

38

3.1 Existing literature

Vanhille (2012) has recently explored the role of socio-demographic household characteristics in the Flemish
context of water pricing. Using the case of volumetric wastewater charges in Flanders, she employs observed
price heterogeneity between different water pricing areas to model a quadratic almost ideal demand system,
allowing to estimate households' differential price responsiveness. Vanhille (2012) finds that that all households
in Flanders are responsive to prices, regardless of their relative income position or size. The estimated price
elasticity evaluated at the population average is -0.615 and the income elasticity reaches 0.62. She also reports
very different price and income elasticity between household groups. Lower income households and smaller
size households are found to be more responsive to increased prices than higher income households and larger
size households.

3.2 Urban water sector in Belgium

Analysis of the urban water sector in Belgium is made difficult by the fact that there are strong institutional
differences between the three Belgian regions (Flanders , Brussels-Capital region and the Walloon region).

For the Walloon region, there were 51 water distributors of water in 2011 (AQUAVAL 2012). All of Walloon
region's water distributors are 100% state owned. The legal form of the operator varies across distributors:
regional company (société régionale), inter-municipal company (``intercommunale''), municipal water service
(``service communal des eaux''), or municipal water regie (``régie communale des eaux''). Since 2005, a single
rate structure applies to all Wallonia consumers. This structure is based on the notion of the true cost of water
(AQUAVAL 2012). A Water Social Fund fee and 6% VAT are then added get the final price paid by water users.
The supply of drinking water in Flanders is organized at the municipality level. Since each municipality
has different options for organising its supply of drinking water, there is a huge diversity the way water and
wastewater service provision. According to VMM (2012), on 1st January 2012, the public water distribution
network in the 308 Flemish municipalities is managed by 11 operators.

In the Brussels-Capital region, the distribution of the water to households is assured by Hydrobru, an

inter-municipal company owned by the 18 municipalities of the region.

3.3 Data

3.3.1 Wallonia and Brussels-Capital

Our panel data set consists of municipality-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2004, 2008 and 2011. Most
of the data come from the national or regional statistical office (be.STAT, WalStat) and from the Union of public
water cycle operators (Aquawal in Wallonia). The data consist of aggregate data at municipal-level on water
consumption per capita and water prices. The data have been combined with information on income, weather,
and household characteristics mainly from the the National Statistics Institute.

Water consumption data From WalStat, we have at the municipality level for the Walloon and for Brussels-
Capital regions the volume of water distributed per connected household for years 2004, 2008 and 2011 (in
m3 per household).1 Using the average household size per municipality, we have then the water consumption
per capita in each municipality (m3/ inhab.).

1A household is defined as a water user consuming less than 250 m3 per year.

39

Water price data We rely annual report ``Walloon drinking water and waste-water treatment statistics''
published by Aquawal. From these reports we have the total bill for water service corresponding to an annual
consumption of 100 m3. From this information we have computed an average price per cubic meter. This
information is available at the water service level. Each municipality has been matched to the corresponding
water operator. As a price indicator, we use the average price paid by households for the drinking water service
(in euro m3 for an annual water consumption equal to 100 m3).

Household income We use the median taxable income per municipality (euros per households) available
from Statistics Belgium for years 2004, 2008 and 2011.

Other socioeconomic variables Other socio-economic variables for years 2004, 2008 and 2011 comes
from Statistics Belgium.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

3.3.2 Flanders

Our panel data set consists of municipality-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2012 and 2013. Most of the
data come from the national or regional statistical office (be.STAT) and from the Environment Agency in Flanders
(VMM). The data consist of aggregate data at municipal-level on water consumption per capita and water prices.
The data have been combined with information on income, weather, and household characteristics mainly from
the the National Statistics Institute.

Water consumption data We use the annual reports ``Watermeter - Drinking water production and distribu-
tion in figures'' published by the Environment Agency in Flanders (VMM). From these reports, we have the volume
of water distributed per household per year for different type of households (1 to 5 persons per household).
Using the average household size per municipality, we have then the water consumption per capita in each
municipality (m3/ inhab.).

Water price data We use the annual reports ``Watermeter - Drinking water production and distribution in
figures'' published by the Environment Agency in Flanders (VMM). From these reports we have the total bill
for water service corresponding to different type of households. From this information we have computed an
average price per cubic meter. This information is available at the water service level. Each municipality has
been matched to the corresponding water operator. As a price indicator, we use the average price paid by
households for the drinking water service (in euro m3).

Household income We use the median fiscal income per municipality (euros per households) available from
Statistics Belgium for years 2012 and 2013.

Other socioeconomic variables Other socio-economic variables for years 2012 and 2013 come from
Statistics Belgium.

40

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

3.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Belgium.

3.4.1 Wallonia and Brussels-Capital

Table 3.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price for 100 m3, in euros m3) in Belgium (Wallonia and Brussels-Capital) from 2004 to 2011.
In 2011, the average annual consumption per inhabitant amounted to 30.3 m3. The water consumption per
capita slightly decreased from 2004 to 2011. Over the period 2004-2011, the average water price has increased
very quickly from 2.5 euros per m3 in 2004 to 4.2 euros per m3 in 2011.

Table 3.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per Nuts 3 in 2011. As it can be seen, at Nuts3 level, both water consumption and water prices are relatively
homogeneous.

Table 3.1: Household water consumption and price in Belgium (Wallonia and Brussels-Capital)

Year

2004
2008
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

32.548
31.890
30.279
31.713

2.541
3.242
4.165
4.050

3.4.2 Flanders

Table 3.3 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price in euros per m3) in Belgium (Flanders) for 2012 and 2013. In 2012, the average annual
consumption per inhabitant amounted to 38.4 m3. The water consumption per capita has slightly decreased
from 2012 to 2013. Over the same period, the average water price has slightly increased from 1.68 euros per
m3 in 2012 to 1.7 euros per m3 in 2013. The household water price in the Flemish region tends to be lower
than in the Brussels-Capital region and the Walloon region.

Table 3.4 gives the annual residential water consumption (m3 per capita and per year) and the water price
per Nuts 3 in 2012. As it can be seen, at Nuts3 level, both water consumption and water prices are relatively
homogeneous. The average water consumption per capita varies from 35.3 m3 per capita in the Tielt Nuts3 to
45 m3 per capita in the Antwerpen Nuts3.

3.5 Water demand function estimate

41

Table 3.2: Regional household water consumption and price in Belgium (Wallonia and Brussels-Capital)

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Arr. Arlon
Arr. Ath
Arr. Bastogne
Arr. Charleroi
Arr. Dinant
Arr. Huy
Arr. Liége
Arr. Marche-en-Famene
Arr. Mons
Arr. Mouscron
Arr. Namur
Arr. Neufchâteau
Arr. Nivelles
Arr. Philippeville
Arr. Soignies
Arr. Thuin
Arr. Tournai
Arr. Verviers
Arr. Virton
Arr. Waremme
Arr. de Bruxelles-Capitale
Average

34.124
27.009
30.725
30.604
29.240
30.918
33.251
29.814
31.234
27.810
32.530
30.338
32.782
26.870
28.690
28.710
26.498
34.758
31.547
31.758
33.158
31.769

4.009
4.009
4.009
4.009
3.894
4.011
4.112
3.951
4.009
4.009
4.003
4.009
3.789
3.934
4.009
3.944
4.009
3.955
4.009
4.009
6.680
4.671

Table 3.3: Household water consumption and price in Belgium (Flanders)

Year

2012
2013
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

1.681
1.723
1.702

38.397
37.269
37.833

42

Table 3.4: Regional household water consumption and price in Belgium (Flanders)

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

1.901
1.569
1.799
1.889
1.739
1.855
1.903
1.795
1.451
1.785
1.826
1.683
1.414
1.494
1.878
1.883
1.789
1.820
1.713
1.415
1.439
1.589
1.681

Arr. Aalst
Arr. Antwerpen
Arr. Brugge
Arr. Dendermonde
Arr. Diksmuide
Arr. Eeklo
Arr. Gent
Arr. Halle-Vilvo
Arr. Hasselt
Arr. Ieper
Arr. Kortrijk
Arr. Leuven
Arr. Maaseik
Arr. Mechelen
Arr. Oostende
Arr. Oudenaarde
Arr. Roeselare
Arr. Sint-Niklaa
Arr. Tielt
Arr. Tongeren
Arr. Turnhout
Arr. Veurne
Average

37.000
45.054
37.799
36.679
36.222
37.000
36.616
36.727
35.713
36.324
36.848
35.993
35.332
41.605
37.000
36.227
36.374
36.765
35.318
35.355
40.947
36.918
38.397

43

3.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(3.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (3.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(3.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

3.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(3.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

3.5.3 Results (Wallonia and Brussels-Capital)

We report here our estimate for the residential water demand function in Belgium, based on our municipal
dataset (2004, 2008 and 2011). The first model estimated in Table 3.5 corresponds simple OLS whereas in the
second model we estimate Equation (3.3) with a random parameter estimator.

Table 3.5 presents the main significant variables explaining household water consumption in Belgium
(Wallonia and Brussels-Capital). The price elasticity varies across models, from -0.04 with the OLS model to
-0.12 with the GLS (significant at 10% and 1% respectively). Based on the GLS model, a 10% increase in
water price results in a 1.2% decrease in short-run water consumption in Belgium. Water demand in Belgium is
inelastic, i.e. the estimated price elasticity in absolute values is lower than one.

44

Table 3.5: Estimation of the household water demand in Belgium (Wallonia and Brussels-Capital)

BelgiumWBOLS BelgiumWBGLS
b/se

b/se

ln Water price (in euros per m3)

-0.038*
(0.02)

ln Household median income (in euros per household) 0.096***

ln Average population age (years)

ln Population density (inabitants per km2)

ln average of daily rainfall in summer (mm per day)

Constant

R-squared
N. of obs.

(0.02)
-0.262**
(0.11)
0.033***
(0.00)
0.320***
(0.03)
2.982***
(0.44)

0.217
744.000

-0.120***
(0.02)
0.063***
(0.02)
-0.320**
(0.13)
0.034***
(0.01)
0.119***
(0.02)
3.819***
(0.49)

744.000

Figure 3.1: Observed versus predicted household water consumption in Belgium (Wallonia and Brussels-Capital)

45

2830323436Wathat20304050WathatWathatThe income elasticity is quite consistent with all models. It varies between 0.10 and 0.06 and the income
elasticity is significantly different from zero with both models. This suggests that there is an increasing
relationship between water consumption and household income. The positive significant coefficient for income
indicates that, based on the GLS model, a 10% increase in income will result in a 0.6% increase in water
consumption.

We find that the water consumption per capita tends to decrease with the average population age.
Climate conditions play also a significant role but with an unexpected sign. An increase of summer rainfall

by 10% will imply an increase in water consumption by 1.2%.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 3.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 8.35.

3.5.4 Results (Flanders)

We report here our estimate for the residential water demand function in Belgium (Flanders), based on our
municipal dataset (2012 and 2013). The first model estimated in Table 3.6 corresponds to simple OLS whereas
in the second model we estimate Equation (3.3) with GLS (random parameter estimator).

Table 3.6: Estimation of the household water demand in Belgium (Flanders)

BelgiumFLOLS BelgiumFLGLS
b/se

b/se

ln Water price (in euros per m3)

-0.102***
(0.02)

ln Household median income (in euros per household) 0.115***

ln average of daily rainfall in summer (mm per day)

Constant

R-squared
N. of obs.

(0.03)
0.076***
(0.01)
2.468***
(0.28)

0.081
616.000

-0.124***
(0.03)
0.032
(0.04)
0.053***
(0.01)
3.313***
(0.39)

616.000

Table 3.6 presents the main significant variables explaining household water consumption in Belgium

(Flanders).

The price elasticity varies across models, from -0.10 with the OLS model to -0.12 with the GLS (significant
at 1%). Based on the GLS model, a 10% increase in water price results in a 1.2% decrease in short-run water
consumption in Belgium (Flanders). Water demand in Belgium (Flanders) is inelastic, i.e. the estimated price
elasticity in absolute values is lower than one.

The income elasticity varies across models between 0.03 and 0.10. The income elasticity is significantly
different from zero with the GLS model. The positive coefficients for the income variable suggest that there is an
increasing relationship between water consumption and household income. The positive significant coefficient
for income indicates that, based on the GLS model, a 10% increase in income will result in a 0.3% increase in
water consumption.

46

Figure 3.2: Observed versus predicted household water consumption in Belgium (Flanders)

Climate conditions play also a significant role. An increase of summer rainfall by 10% will imply an increase

in water consumption by 0.5%.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 3.2. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 5.99.

47

3536373839Wathat3035404550WathatWathatChapter 4

Bulgaria

48

4.1 Existing literature

To our best knowledge, there doesn't exist any published article having estimated a residential water demand
function in Bulgaria. There are some descriptive papers on household water consumption in Bulgaria. Clark
and Finley (2007) work on household water consumption in Blagoevgrad, Bulgaria. They use household data
(728 questionnaires collected in summer 2003). They employ the theory of planned behavior as a theoretical
framework for evaluating determinants of intention to implement a set of specific water conservation behaviors
among Blagoevgrad residents.

4.2 Urban water sector in Bulgaria

Water availability This paragraph is based on MRD (2014). Bulgaria is characterized by a situation of limited
water stress. The projected total domestic water consumption (3,340 million cubic meters in 2035 excluding
hydro energy and nuclear power plants) is lower than the multi-year average internal water resource (18,547
million cubic meters excluding the Danube River) over the period 1974-2008. Before 1990, Bulgaria was close
to be considered as a water-scarce country. Since then, water abstractions have fallen drastically both for
agricultural and industrial purposes.

Organization of the water sector According to the report WorldBank (2015a) Bulgaria has 64 water and
sanitation service providers, of which 56 are state owned. The 29 regional water companies provide services
to 76% of the population, with the rest of service providers serving single municipalities. Fourteen multi-city
utilities are state-owned water companies. They cover the largest area of the country, and they are managed by
the Ministry of Regional Development. The other utilities are owned by municipalities. The Sofia water service
is the only water concession awarded to a private company for 25 years (WorldBank 2015a). In 2005, a joint
water and energy regulator (State Energy and Water Regulatory Commission-SEWRC) was established. The
Water and Sanitation Services Regulation Act is the basis for the regulation of WSSCs.

Water and wastewater coverage The rate of access to piped water in Bulgaria is high by European
standards with 99 percent of the population of Bulgaria having access to piped water. Only two districts
in Bulgaria have less than full coverage from centralized piped water. At the national level, about 670,000
people living in agglomerations greater than 2,000 person equivalents need to be connected to wastewater
collection and approximately 1.8 millions people need to be connected to a wastewater treatment plant in order
to comply with EU regulations. Bulgaria has to increase both wastewater collection and the connection to urban
wastewater treatment plants from the current coverage levels of 66 percent and 50 percent respectively in
order to comply with the regulations. Non-revenue water is reported to be 60 percent (MRD 2014).

4.3 Data

Our data set consists of Nuts3-level (county) data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for year 2010. Most of the data come
from the National Statistical Office of Bulgaria. The data consist of aggregate data at Nuts3-level on water
consumption per capita and water and sewerage prices. The data have been combined with information on
household's income, climate conditions, and household characteristics mainly from National Statistical Office
Bulgaria.

49

Water consumption data From the Statistical Office of Bulgaria we have the water used by households
from public water supply by statistical region and by district (average per capita) from 2000 to 2010. Our
endogenous variable is then the annual water consumption per capita (in m3 per year per capita).

Water price data Our main source of data is the State Energy and Water Regulatory Commission of Bulgaria.1.
We have the price paid by households for the water service (not for wastewater) in 2010 for 61 water services
in Bulgaria. The price information is then almost exhaustive for Bulgaria. We have then matched each water
service with the corresponding Nuts3.

Household income Our proxy for household income is the average annual salary of employees under labor
contract. This information is available from the National Statistical Office of Bulgaria from 2006 to 2011 at
Nuts3 level.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from the National
Statistical Office of Bulgaria.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

4.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Bulgaria.

Table 4.1: Household water consumption and price in Bulgaria

Year

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

34.979
33.045
35.003
34.393
33.998
35.464
36.892
36.335
36.476
35.864
35.252

.
.
.
.
.
.
.
.
.
0.900
0.900

1The State Energy and Water Regulatory Commission is the regulator of the water and energy sectors. I has been established with a
Decree of the Council of Ministers of the Republic of Bulgaria No 181 of 10 Sep 1999. The main duties of the regulator concerning the
water sector is to regulate the quality of water and wastewater services and to carry out price regulation of the water and wastewater
services.

50

Table 4.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Bulgaria from 2001 to 2010. In 2010, the average annual consumption
per inhabitant amounted to 35.9 m3. It has remained quite stable from 2001. In 2010, the average water price
is 0.9 euros per m3. The report MRD (2014) provide some data on water price in Bulgaria. According to this
report, the combined water and wastewater tariffs in Bulgaria have increased significantly since 2008 ( 0.80,
0.87, 0.92, 0.94 euro per m3 respectively in 2008, 2009, 2010 and 2011) , but continue to be lower than the
combined tariffs in other European countries.

Table 4.2: Regional household water consumption and price in Bulgaria

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Blagoevgrad
Burgas
Dobrich
Gabrovo
Haskovo
Kardzhali
Kyustendil
Lovech
Montana
Pazardzhik
Pernik
Pleven
Plovdiv
Razgrad
Ruse
Shumen
Silistra
Sliven
Smolyan
Sofia
Sofia cap.
Stara Zagora
Targovishte
Varna
Veliko Tarnovo
Vidin
Vratsa
Yambol
Average

38.690
40.515
26.645
30.660
28.835
29.200
30.660
30.660
31.025
32.120
36.865
34.310
35.040
24.090
36.500
27.010
27.740
26.645
27.375
32.850
51.100
28.105
21.170
35.405
32.120
29.565
32.485
30.660
35.864

0.387
0.478
1.145
0.808
0.750
0.660
0.692
0.701
0.360
0.433
0.258
0.739
0.654
1.103
0.803
1.064
0.987
0.869
0.966
0.578
0.532
1.033
0.663
0.997
0.711
0.849
0.931
0.736
0.900

Table 4.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2010. As it can be seen, there are some differences across regions, both in terms of
water consumption and water prices. With more than 50 m3 per capita per year, the water consumption is the

51

higher in the Sofia capital Nuts3. The Sofia capital Nuts3 corresponds to the bulagarian capital Sofia and is the
most densely populated area of Bulgaria. On contrary, a low water consumption is reported in the Targovishte
county (21.2 m3 per capita per year).

4.5 Water demand function estimate

4.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(4.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (4.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(4.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

4.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(4.3)

where (cid:15)i is the usual random term.

4.5.3 Results

We report here our estimate for the residential water demand function in Bulgaria, based on our Nuts3 dataset
for Bulgaria for year 2010. The first model estimated in Table 4.3 corresponds simple OLS whereas in the
second model we estimate Equation (4.3) with an instrumental variable approach.

Table 4.3 present the main significant variables explaining household water consumption in Bulgaria. The
price elasticity is consistent across models, from -0.26 with the OLS to -0.27 with the OLSiv (always significant).

52

Table 4.3: Estimation of the household water demand in Bulgaria

BulgariaOLS BulgariaOLSiv
b/se

b/se

ln Water price (in euros per m3)

-0.256***
(0.06)

ln Household income (in euros per capita) 0.613***

ln Household size

ln summer evapotranspiration

Constant

R-squared
N. of obs.

Source: auto.dta

(0.16)
-0.355
(0.42)
0.040
(0.24)
-1.339
(1.57)

0.611
28.000

-0.275***
(0.07)
0.619***
(0.16)
-0.334
(0.42)
0.061
(0.24)
-1.440
(1.58)

0.610
28.000

Based on the OLSiv models, a 10% increase in water price results in a 2.8% decrease in short-run water
consumption in Bulgaria. Water demand in Bulgaria is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

The income elasticity varies between 0.61 and 0.62 and the income elasticity is significantly different from
zero with the two models. This suggests that there is an increasing relationship between water consumption
and household income. The positive significant coefficient for income indicates that, based on the GLS model,
a 10% increase in income will result in a 6.2% increase in water consumption.

Climate conditions don't seem to play a significant role.
A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 4.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 7.8.

53

Figure 4.1: Observed versus predicted household water consumption in Bulgaria

54

253035404550Wathat2030405060WathatWathatChapter 5

Croatia

55

5.1 Existing literature

To our best knowledge, there is no previous estimate of the residential water demand in Croatia.

5.2 Urban water sector in Croatia

This paragraph is based on RepublicofCroatia (2010) and on WorldBank (2015b).

With around 24,495 m3 of renewable water per capita per year (FAO Aquastat 2015), Croatia is a water-rich
country split between two river basin districts, the Danube basin and the Adriatic basin. Water supply comes
mainly from groundwater (96%). Surface water provides 4% of overall drinking water supply (WorldBank 2015b).
Most rivers flow into the Danube or one of its tributaries.

Local governments are responsible for water and sanitation services and provide them through 156 public
utility companies (140 for water and sanitation service and only 16 for sanitation service). The largest utility
(Zagreb Waterworks) serves 17% of the population. The next 84 largest multi-city companies serve 59% of the
population. The remaining 24% of the population is either served by 55 small municipal providers (5%) or uses
self-provision (19%) or individual water resources. Most utility companies provide both water and sewerage
services, although in larger cities, separate utility companies may exist.

The coverage ratio (share of the population able to connect to the public water supply system) on the level
of the Republic of Croatia is on the average 80-82% (RepublicofCroatia 2010). The connection ratio (share of
the population connected to the public water supply system) is somewhat lower and it is estimated to be on
average equal to 74%. There are significant differences in the level of coverage between regions.

5.3 Data

Our panel data set consists of water service-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2000-2004. Most of the data
related to water come from the International Benchmarking Network for Water and Sanitation Utilities (IBNET).
We use a panel of water utilities made of 21 services for years 2000 to 2004. The water data have been
combined with information at different administrative levels on income, weather, and household characteristics
obtained mainly from the Croatian Bureau of Statistics. Each water utility has been matched with one of the
21 Croatian county (Nuts 3).

Water consumption data From IBNET, we have the residential water consumption (l/person/day) which has
been converted in cubic meters per year and per capita. Our dependent variable is then the water supplied to
households per capita and per year.

Water price data For each water service and each year, we have average revenue for the water and
wastewater services (euros per cubic meter of water sold). We will use this average revenue as a proxy of the
water price paid by households.

Household income The Croatian Bureau of Statistics provides the total gross disposable income of households
at county-level for years 2000 to 2005. Using the data on population per county we have computed the gross
disposable income of households per capita at county-level for years 2000 to 2005.

56

Other socioeconomic variables Other socio-economic variables from 2000 to 2005 come from the Croatian
Bureau of Statistics.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

5.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Croatia
based on the IBNET data.

Table 5.1: Household water consumption and price in Croatia

Year

2000
2001
2002
2003
2004
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

103.259
100.521
99.281
101.738
101.045
101.171

0.613
0.654
0.723
0.840
1.059
0.777

Table 5.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Croatia from 2000 to 2004. In our sample of water utilities taken from
IBNET, household water consumption has slightly fallen from 103 cubic meters to 101 cubic meters per capita
and per year from 2000 to 2004.
It should be mentioned that this figure is much higher than expected.
Indeed according to the Statistical Yearbook 2012 of the Republic of Croatia, the volume of water distributed to
households in 2010 was 189.3 millions of cubic meters. With a population connection rate to the water supply
equal to 74% (as indicated in (RepublicofCroatia 2010)), this translates into a water consumption per capita
equal to 58 cubic meters. Over the period 2010 to 2012, the average water price has increased from 0.6 euros
per m3 to 1.1 euros per m3.

Table 5.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2004. As it can be seen, there are some differences across counties, both in terms of
water consumption and water prices.

5.5 Water demand function estimate

5.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

57

(5.1)

Table 5.2: Regional household water consumption and price in Croatia

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Bjelovarsko-bilogorska zupanija
Brodsko-posavska zupanija
Dubrovacko-neretvanska zupanija
Istarska zupanija
Karlovacka zupanija
Koprivnicko-krizevacka zupanija
Licko-senjska zupanija
Osjecko-baranjska zupanija
Pozesko-slavonska zupanija
Primorsko-goranska zupanija
Sibensko-kninska zupanija
Sisacko-moslavacka zupanija
Splitsko-dalmatinska zupanija
Zagrebacka zupanija
Average

47.815
444.205
55.480
67.708
85.775
50.005
110.230
59.495
44.165
108.952
91.980
201.845
113.515
53.533
101.045

1.867
0.056
1.433
2.656
0.144
1.622
0.700
0.511
0.767
1.578
0.922
0.833
0.517
1.115
1.059

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (5.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(5.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

5.5.2 Estimation methods

Let i = {1, .., I} indexes water services and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(5.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two

58

Table 5.3: Estimation of the household water demand in Croatia

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Household size

ln Average of daily rainfall in summer (mm per day)

Constant

R-squared
N. of obs.

CroatiaOLS
b/se

CroatiaGLS
b/se

CroatiaGLSiv
b/se

-0.370***
(0.07)
-0.076
(0.51)
-2.780***
(0.73)
-0.103
(0.12)
7.929*
(4.38)

0.394
95.000

-0.142*
(0.08)
-0.165
(0.23)
-3.722**
(1.47)
-0.072***
(0.03)
9.777***
(3.06)

1.099
(1.41)
-2.766
(3.03)
-1.222
(3.27)
-0.010
(0.09)
28.765
(23.43)

95.000

95.000

estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

5.5.3 Results

We report here our estimate for the residential water demand function in Croatia, based on our water utility
dataset for Croatia (2000 to 2004). The first model estimated in Table 5.3 corresponds to simple OLS whereas
in the second model we estimate Equation (5.4) with a random parameter estimator.
In the third model, we
account for potential endogeneity of the price by using an instrumental variable approach.

Table 5.3 present the main significant variables explaining household water consumption in Croatia. The
price elasticity varies across models, from -0.37 with the OLS to 1.10 with the GLSiv (not significant with the
GLSiv). Based on the GLS model, a 10% increase in water price results in a 1.4% decrease in short-run water
consumption in Croatia. Water demand in Croatia appears to be elastic, i.e. the estimated price elasticity in
absolute values is greater than one.

The income elasticity is never significant.
Large households tend to have a lower water consumption per capita (OLS and GLS models).
Climate conditions play also a significant role (GLS model). An increase by 10% of rainfall will imply a

decrease in water consumption by 0.7% (GLS model).

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 5.1. For most of the water services the model
performs quite well. The mean absolute percentage error is 33.6.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (5.1). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(5.4)

59

Figure 5.1: Observed versus predicted household water consumption in Croatia

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(5.5)

We provide in Table 5.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the CroatiaGLSlag model, the long-term
price elasticity is estimated to be -1.16 and is significantly different from zero at 1%.

60

406080100120Wathat0100200300400500WathatWathatTable 5.4: Estimation of the household water demand with lagged water consumption in Croatia

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Household size

ln Average of daily rainfall in summer (mm per day)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

Source: auto.dta

CroatiaGLSlag
b/se

CroatiaGLSivlag
b/se

-0.035*
(0.02)
0.051
(0.13)
-0.164
(0.19)
-0.027
(0.03)
0.970***
(0.03)
-0.106
(1.16)

-0.016
(0.09)
-0.027
(0.34)
-0.137
(0.25)
-0.025
(0.03)
0.980***
(0.06)
0.461
(2.49)

76.000

76.000

61

Chapter 6

Cyprus

62

6.1 Existing literature

Hajispyrou, Koundouri, and Pashardes (2002) have estimated a residential water demand function for Cyprus
using individual household data drawn from the Cyprus Family Expenditure Survey (1996-97). They estimate the
price and income elasticities of residential demand for water and evaluate the welfare effects associated with
potential changes in the current water pricing system. Depending upon household income, the price elasticity
varies between -0.79 and -0.39. Income elasticities are found between 0.25 and 0.48.

Polycarpou and Zachariadis (2013) present the results of an econometric analysis of residential water
demand in Cyprus, based on quarterly data for the Nicosia, Limassol and Larnaca water boards and for years
2001 to 2009. The average price elasticity is shown to be significant and equal to -0.25, whereas marginal
price elasticity is estimated at -0.45. Income elasticity varies between 0.53 and 0.75.

6.2 Urban water sector in Cyprus

This paragraph is based on Sofroniou and Bishop (2014). Domestic and irrigation are the two main sectors of
water demand in Cyprus. Based on the latest data from the Water Development Department, the total annual
water demand in the Government controlled areas is 275 Mm3 per year, of which the dominated water use
sector is agriculture (64% of the water use). Domestic water supply to households represents 28.4%, 4.7%
corresponds to the tourist and hotel water demands and industrial suppliers make up the 2.9% water demands.

6.3 Data

Our data set consists of Nuts3-level (which correspond to the whole country for Cyprus) data on household water
consumption, household water price, household's socioeconomic conditions income and on climate conditions
for years 1995 to 2012 (15 years). Most of the data come from the Cyprus Statistical Service. The data
consist of aggregate data at Nuts3-level (national) on water consumption per capita and water prices. The data
have been combined with information on household's income, climate conditions, and household characteristics
mainly from the Cyprus Statistical Service.

Water consumption data From the Cyprus Statistical Service we have the annual volume of water supplied
to households users from the public water network from 1998 to 2012. The volume of water has been divided
by the total population, assuming a 100% connection rate. Our endogenous variable is then the annual water
consumption per capita to the public water supply network (in m3 per year per capita).

Water price data The Cyprus Statistical Service provides the price index for Water supply and other services.
This index has been used for computing an average national price for the water service (the average price of
water for household is 0.7 euros per cubic meters in 2007, the wastewater service is not included ).

Household income We use the GDP per capita from the Cyprus Statistical Service.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from the Cyprus
Statistical Service.

63

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

6.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Cyprus.

Table 6.1: Household water consumption and price in Cyprus

Year

1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

70.210
73.945
66.786
62.877
70.191
69.020
82.680
88.782
91.605
94.643
99.255
98.136
96.337
79.456
87.005
98.987
94.723
91.330
84.221

.
.
.
0.405
0.415
0.420
0.422
0.488
0.497
0.512
0.539
0.563
0.600
0.640
0.686
0.766
0.794
0.896
0.576

Table 6.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Cyprus from 1998 to 2012. In 2012, the average annual consumption per
inhabitant amounted to 91 m3. There has been an increasing trend in domestic water consumption since 1998.
In 2008, Cyprus faced one of the most acute and prolonged droughts (a fourth consecutive drought year)
with the winter season being extremely dry. As a result severe restrictions in the use of water by households
have been implemented in 2008, which might explained the low water consumption per capita for this year.

The average water price in 2012 was 0.9 euros per m3.

It has steadily increased since 1998. The water
price is consistent with Polycarpou and Zachariadis (2013) who reports an average water price equal to 0.58
euros per cubic meter for the period 2001-2009.

6.5 Water demand function estimate

64

6.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(6.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (6.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(6.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

6.5.2 Estimation methods

Let t = {1, .., T } indexes years. Our equation of interest becomes:

ln (yt) = α ln (pt) + β ln (It) + γ ln (Zt)(cid:48) + (cid:15)t

(6.3)

where (cid:15)t is the usual random term.

6.5.3 Results

We report here our estimate for the residential water demand function in Cyprus. The model in Table 6.2
corresponds to simple OLS.

Table 6.2 present the main significant variables explaining household water consumption in Cyprus. The
price elasticity varies is found to be -0.32, and is almost significantly different from zero at 10%. Based on the
OLS model, a 10% increase in water price results in a 1.5% decrease in short-run water consumption in Cyprus.
Water demand in Cyprus is inelastic, i.e. the estimated price elasticity in absolute values is lower than one.

The income elasticity is equal to 1.08 (significant at 1%), a value relatively high compared to the existing
literature. This suggests that there is an increasing relationship between water consumption and household
income. The positive significant coefficient for income indicates that a 10% increase in income will result in a
10.1% increase in water consumption.

The dummy variable for year 2008 is highly significant. Water consumption for this specific year seems to
be 25% lower than what would have prevailed under normal climatic conditions. This result suggests that the

65

Table 6.2: Estimation of the household water demand in Cyprus

ln Water price (in euros per m3)

CyprusOLS
b/se

-0.329
(0.20)

ln Household income (in euros per capita) 1.080***

ln average of daily rainfall (mm per day)

dummy for 2008

Constant

R-squared
N. of obs.

(0.28)
-0.016
(0.10)
-0.306**
(0.11)
-6.261**
(2.80)

0.780
15.000

restriction imposed on households on water consumption have had a significant impact. The average of daily
rainfall is not significant.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 6.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 5.2.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (6.2). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(6.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(6.5)

We provide in Table 6.3 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the CyprusOLSlag model, the long-term
price elasticity is estimated to be -0.51 but no significantly different from zero at 10%.

66

Figure 6.1: Observed versus predicted household water consumption in Cyprus

Table 6.3: Estimation of the household water demand with lagged water consumption in Cyprus

CyprusOLSlag
b/se

ln Water price (in euros per m3)

ln average of daily rainfall (mm per day)

-0.249
(0.16)
ln Household income (in euros per capita) 0.620*
(0.29)
-0.028
(0.08)
-0.288**
(0.09)
0.512**
(0.20)
-3.989
(2.44)

ln Lag of water consumption per capita

dum2008

Constant

R-squared
N. of obs.

Source: auto.dta

0.871
15.000

67

708090100110Wathat60708090100WathatWathatChapter 7

Czech Republic

68

7.1 Existing literature

Hortová and Kristoufek (2014) use a panel of aggregate regional data from 2000 to 2011 to estimate of price
and income elasticities of household water consumption the Czech Republic. The short-run price elasticity is
estimated at --0.20 while the long-run price elasticity at --0.54. This indicates that a 1% increase in water price
results in a 0.20% decrease in short-run water consumption but in the long-run, the same price change causes
water consumption to decrease by 0.54%. The elasticity of water demand with respect to income is estimated
to be 0.10.

7.2 Urban water sector in Czech Republic

Municipalities are responsible bodies for drinking water and wastewater services. The most dominant operating
model is a private concession (46% of the population) in the form of a ``separate model'' based on long-term
operating contracts (WorldBank 2015c). Mixed capital utilities (which provide services to 27% of the population),
refers to utilities that operate and own infrastructure. Municipalities are shareholders in the utility and provide
service to 10% of the population. Village administrations (departments or public services) provide water services
to 11% of the population, and around 6% of the population operate their own wells or water sources. More
than 95% of utilities provide both water and wastewater services (WorldBank 2015c).

At national level, the relevant authorities for the water sector are the Ministry of Agriculture and the Ministry
of Environment. The Ministry of Finance also provides regulation and control over surface water fees and water
and wastewater tariffs.

Water and wastewater market regulation is concentrated in two Acts. First, Act 254/2001 Coll on Water
defines the rights and obligations of all entities whose activities may affect the quantity or quality of water.
Second, Act 274/2001 Coll. on Public Water Supply Systems and Sewerage which establishes the public water
supply and sewerage systems as a special branch network. It defines the owner of the infrastructure, the service
provider and the system user.

It is considered that the population of the Czech Republic has full access to water and sanitation services

(WorldBank 2015c).

7.3 Data

Our panel data set consists of Nuts3-level (county) data on household water consumption, household water
price, household's socioeconomic conditions income and on climate condition for years 2006-2011. Most of
the data come from the Statistical Office of the Czech Republic. The data consist of aggregate data at Nuts3-
level on water consumption per capita and water and sewerage prices. The data have been combined with
information on household's income, climate conditions, and household characteristics mainly from the Czech
Statistical Office.

Water consumption data Our dependent variable is the annual volume of water sold to households per
connected capita to the public water supply network at Nuts 3 from 2006 to 2011 (in m3 per year per capita).
It is obtained by dividing overall Nuts3 water consumption by the number of people supplied with water in this
Nuts3. All data come from the Czech Statistical Office.

Water price data As a price indicator, we use the average price paid by households for the water and
wastewater services (in euros per m3 including VAT). The price information is available at Nuts3 from the Czech

69

Statistical Office for the period 2006 to 2011.

Household income We use the average annual gross wage of employees in each Nuts3 from the Czech
Statistical Office for the period 2006 to 2011.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from the Czech
Statistical Office

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

7.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Czech
Republic.

Table 7.1: Household water consumption and price in Czech Republic

Year

2006
2007
2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

35.344
35.595
34.146
33.597
32.554
32.239
33.904

1.898
1.883
2.037
2.195
2.316
2.451
2.131

Table 7.1 gives the annual residential water consumption (m3 per capita and per year) and the water
price (average water price, in euros m3) in Czech Republic from 2006 to 2011.
In 2011, the average annual
consumption per inhabitant amounted to 32.2 m3. There has been a decreasing trend in domestic water
consumption since 2006. Over the period 2002-2011, the average water and wastewater price has increased
from 1.9 euros per m3 to 2.5 euros per m3.

Table 7.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2010. As it can be seen, there are some differences across regions, both in terms of
water consumption and water prices. With almost 38 m3 per capita per year, the water consumption is the
higher in the CZ010 Nuts3. The CZ010 Nuts3 includes the capital Prague and is the most densely populated
area of Czech Republic. On contrary, a low water consumption is reported in the CZ072 county (28.8 m3 per
capita per year).

7.5 Water demand function estimate

70

Table 7.2: Regional household water consumption and price in Czech Republic

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Hlavnímˇesto Praha
Jihoˇceský kraj
Jihomoravský kraj
Karlovarský kraj
Karlovarsk(cid:1)kraj
Královéhradecký kraj
Liberecký kraj
Moravskoslezský kraj
Olomoucký kraj
Pardubický kraj
Plzeˇnský kraj
Stˇredoˇceský kraj
Zlínský kraj
Ústecký kraj
Average

37.939
31.511
32.835
32.305
29.233
31.603
31.748
34.463
30.750
29.824
33.083
32.681
28.853
30.357
32.554

2.336
2.269
2.269
2.469
2.078
2.357
2.760
2.107
2.078
2.373
1.954
2.373
2.382
2.785
2.316

7.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(7.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (7.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(7.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

71

Table 7.3: Estimation of the household water demand in Czech Republic

CzechOLS
b/se

CzechGLS
b/se

CzechGLSiv
b/se

ln Water price (in euros per m3)

-0.178***
(0.06)

ln Household income (in euros per capita) 0.456***

ln Share population between 15 and 65

ln Average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

(0.06)
3.711***
(0.52)
-0.001
(0.03)
-16.752***
(2.20)

0.706
84.000

-0.201***
(0.07)
0.161**
(0.07)
2.173***
(0.63)
-0.054**
(0.03)
-7.186***
(2.65)

-0.281**
(0.12)
0.255***
(0.08)
1.835*
(0.96)
-0.049*
(0.03)
-6.433*
(3.79)

84.000

84.000

7.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(7.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

7.5.3 Results

We report here our estimate for the residential water demand function in Czech Republic, based on our Nuts3
dataset for Czech Republic (2006 to 2011). The first model estimated in Table 7.3 corresponds simple OLS
whereas in the second model we estimate Equation (7.4) with a random parameter estimator. In the third model,
we account for potential endogeneity of the price by using an instrumental variable approach. As instruments
we have used the population density, the share of the population connected to the public network and the water
loss rate in the distribution network.

Table 7.3 present the main significant variables explaining household water consumption in Czech Republic.
The price elasticity are consistent across models, from -0.18 with the OLS to -0.28 with the GLSiv (always
significant). Based on the GLSiv models, a 10% increase in water price results in a 2.8% decrease in short-run
water consumption in Czech Republic. Water demand in Czech Republic is inelastic, i.e.
the estimated price
elasticity in absolute values is lower than one.

The income elasticity varies between 0.16 and 0.46 and the income elasticity is significantly different from
zero with the three models. This suggests that there is an increasing relationship between water consumption
and household income. The positive significant coefficient for income indicates that, based on the GLS model,
a 10% increase in income will result in a 2.6% increase in water consumption.

72

Figure 7.1: Observed versus predicted household water consumption in Czech Republic

Climate conditions seem to play a significant role. An increase by 10% of average daily rainfall will reduce
water consumption by 0.5% (GLSiv). Water consumption per capita appears to increase with the share of the
population between 15 and 65.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 7.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 5.0.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (7.1). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(7.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(7.5)

We provide in Table 7.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the CzechRepublicGLSivlag model,
the long-term price elasticity is estimated to be -0.61 and is significantly different from zero at 1%.

73

25303540Wathat253035404550WathatWathatTable 7.4: Estimation of the household water demand with lagged water consumption in Czech Republic

CzechRepublicGLSlag
b/se

CzechRepublicGLSivlag
b/se

ln Water price (in euros per m3)

ln Share population between 15 and 65

-2.202**
(1.03)
ln Household income (in euros per capita) 1.409
(1.81)
2.655
(12.79)
-0.667
(0.58)
29.713***
(2.40)
-93.569*
(56.04)

ln average of daily rainfall (mm per day)

ln Lag of water consumption per capita

Constant

-0.098**
(0.05)
0.038
(0.06)
-0.004
(0.38)
-0.009
(0.02)
0.840***
(0.07)
0.283
(1.64)

R-squared
N. of obs.

70.000

70.000

74

Chapter 8

Denmark

75

8.1 Existing literature

Hansen (1996) estimate a residential water demand function on pooled time series data for the municipality of
Copenhagen, Denmark. They report a price elasticity around -0.1. The dataset covers the period 1981-1990.

8.2 Urban water sector in Denmark

In Denmark, drinking-water production is almost exclusively based on groundwater which requires very little
treatment and thus allows for a decentralised structure. This factor in combination with Danish traditions of
self-organisation and user ownership has given rise to over 2,000 utilities supplying 5.5 million consumers
(Sorensen 2010). However, two thirds of the water is supplied by around 80 municipal water utilities. The
remaining utilities are consumer-owned, usually organised as cooperatives or partnerships (Sorensen 2010).

8.3 Data

Our panel data set consists of water service-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2010-2012. Most of the data
related to water come from the DANVA (Danish Association of Water and Wastewater utilities) benchmarking
reports. We use a panel of water utilities made of respectively 56, 59 and 60 services for years 2010, 2011
and 2012 serving more than 50% of the population in Denmark. The water data have been combined with
information at different administrative levels on income, weather, and household characteristics obtained mainly
from the Statistics Denmark. Each water utility has been matched with one of the 99 Danish municipalities.
For water utilities serving more than one municipalities, the water service has been affected to the municipality
with the highest population size.

Water consumption data From the DANVA reports, we have the total annual quantity of water supplied to
all users by each water utility. Additionally, we know the share of water which is used by households at county
level. We use this information to compute the quantity of water delivered to households for each water utility.
We have also in the DANVA reports the population served by each water service. Our dependent variable is then
the water supplied to households per capita and per year.

Water price data For each water service and each year, we have the fixed charge and the marginal price for
the water service. We compute then the average water price for a water consumption equal to 100 m3.

Household income We use the average income for families from Statistics Denmark from 1995 to 2010
(municipality level)

Other socioeconomic variables Other socio-economic variables from 2010 to 2012 come from Statistics
Denmark.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

76

8.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Denmark.

Table 8.1: Household water consumption and price in Denmark

Year

2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

44.557
47.633
46.713
46.261

2.832
2.923
3.107
2.951

According to DANVA, household water consumption has fallen by 13.1% in the past 10 years. Table 8.1
gives the annual residential water consumption (m3 per capita and per year) and the water price (average water
price, in euros m3) in Denmark from 2010 to 2012. In 2012, the average annual consumption per inhabitant
amounted to 46.2 m3. Over the period 2010 to 2012, the average water price has increased from 2.9 euros
per m3 to 3.1 euros per m3.

Table 8.2: Regional household water consumption and price in Denmark

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Byen København
Fyn
Københavns omegn
Nordjylland
Nordsjælland
Sydjylland
Vest-og Sydsjæl
Vestjylland
Østjylland
Østsjælland
Average

59.882
44.450
38.560
45.898
37.585
40.031
56.716
40.879
38.897
44.308
44.557

3.076
2.766
3.302
2.715
2.629
2.658
2.891
2.722
2.828
2.496
2.832

Table 8.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts 3) in 2010. As it can be seen, there are some differences across Nuts3, both in terms of water
consumption and water prices. With more almost 60 m3 per capita per year, the water consumption is the
higher in the Byen København Nuts3. On contrary, a low water consumption is reported in the Nordsjælland
Nuts3 (37 m3 per capita per year).

8.5 Water demand function estimate

77

8.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(8.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (8.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(8.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

8.5.2 Estimation methods

Let i = {1, .., I} indexes water services and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(8.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

8.5.3 Results

We report here our estimate for the residential water demand function in Denmark, based on our Nuts3 dataset
for Denmark (2010 to 2012). The first model estimated in Table 8.3 corresponds to simple OLS whereas in the
second model we estimate Equation (8.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 8.3 present the main significant variables explaining household water consumption in Denmark. The
price elasticity varies across models, from -0.33 with the GLS to -1.00 with the GLSiv (always significant). Based
on the GLSiv model, a 10% increase in water price results in a 10% decrease in short-run water consumption in
Denmark. Water demand in Denmark appears to be elastic, i.e. the estimated price elasticity in absolute values
is greater than one. This result is not robust to the type of econometric model (OLS, GLS).

78

Table 8.3: Estimation of the household water demand in Denmark

DenmarkOLS DenmarkGLS DenmarkGLSiv
b/se

b/se

b/se

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Share population below 9

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

-0.543***
(0.15)
-0.444***
(0.16)
0.282
(0.52)
-0.271***
(0.10)
9.270***
(2.69)

0.114
175.000

-0.327**
(0.16)
-0.374*
(0.21)
0.617
(0.68)
-0.237***
(0.09)
6.842**
(3.46)

-1.003**
(0.41)
-0.502**
(0.20)
0.662
(0.64)
-0.252***
(0.10)
9.076***
(3.30)

175.000

175.000

The income elasticity varies between -0.37 and -0.50. The negative sign makes the interpretation of the

coefficient in terms of income elasticity difficult.

Climate conditions play also a significant role. An increase by 10% of rainfall will imply a decrease in water

consumption by 2.5% (GLSiv model).

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 8.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 23.5.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (8.1). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(8.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(8.5)

We provide in Table 8.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the DenmarkGLSlag model, the long-term
price elasticity is estimated to be -1.27 and is significantly different from zero at 1%. With the DenmarkGLSivlag
model, the long-term price elasticity is estimated to be -1.67 and is significantly different from zero at 1%.

79

Figure 8.1: Observed versus predicted household water consumption in Denmark

Table 8.4: Estimation of the household water demand with lagged water consumption in Denmark

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Share population below 9

ln average of daily rainfall (mm per day)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

Source: auto.dta

DenmarkGLSlag DenmarkGLSivlag
b/se

b/se

-0.649***
(0.23)
-0.338*
(0.20)
-0.272
(0.65)
-0.299***
(0.11)
0.491***
(0.09)
8.286**
(3.58)

-0.895*
(0.51)
-0.397*
(0.23)
-0.257
(0.66)
-0.317***
(0.11)
0.465***
(0.10)
9.369**
(4.11)

104.000

104.000

80

20406080Wathat050100150200WathatWathatChapter 9

Estonia

81

9.1 Existing literature

To our best knowledge, there doesn't exist any published article having estimated a residential water demand
function in Estonia.

9.2 Urban water sector in Estonia

Estonia is a water rich country with 8600 m3 per capita available per year and a water exploitation index
relatively low 4%. In the period 1990-2003, water abstraction (all types of use but excluding mining and cooling
water) has dropped steadily from more than 400 hm3 in 1990 to a little bit less than 100 hm3 in 1991.
In
recent years (2004-2012), water abstraction has stayed around 100 million m3 (Antso and Hermet 2013). In
2011, a total of 35 hm3 of water was used for human consumption, 25.4 hm3 was used for manufacturing
and 4 hm3 for agriculture.

Approximately 84% of the Estonian population is connected to the water supply and 75% to the sewerage
systems (Peda 2012).1 The Estonian water supply sector is highly fragmented. Of the 1235 waterworks oper-
ating in 2007, only 21 produced drinking water volumes greater than 1000 m3 per day, but these nevertheless
supplied 61% of the Estonian population. At the same time, 1099 waterworks or 89% of the total produced
less than 100 m3 a day.

In the mid-1990s, ownership of the former state owned water supply and wastewater facilities was trans-
ferred to local government bodies that became responsible for the provision of water and sewerage services.
After the enactment of the Commercial Code in 1995, most of the municipal water utilities were transformed
into public limited companies owned by local government. Today water services in 90% of Estonian cities
(i.e. regional centres) are also still provided by fully publicly owned water companies. However, in the smaller
towns and rural municipalities, there also exist other provision modes such as specialised water companies with
mixed (public and private) ownership, production delegated to private companies or direct production by local
government agencies (departments). The capital city of Tallinn is a different case since it sold the majority of
shares in its water services company to international partners and later listed its shares on the stock exchange.

9.3 Data

Our panel data set consists of Nuts3-level (county) data on household water consumption, household water
price, household's socioeconomic conditions income and on climate condition for years 2006-2012. Most of
the data come from Statistics Estonia (the National Office of Statistics) and from the Estonian Association of
Waterworks (EVEL). The data consist of aggregate data at Nuts3-level on water consumption per capita and
water and sewerage prices. The data have been combined with information on income, weather, and household
characteristics mainly from Statistics Estonia.

Water consumption data From Statistics Estonia we have the water used by households from 1991 to
2013 at Nuts3 level. Our dependent variable is the annual water consumption per capita connected to the public
water supply network (in m3 per year per capita). We have assumed a connection rate equal to 87% (latest
available figure).

1Most of the following paragraph is based on the Phd dissertation on the relationship between governance and performance in water

services provision in Estonian municipalities by Peda (2012).

82

Water price data We rely on price data published on an annual basis by the Estonian Association of
Waterworks (EVEL). As a price indicator, we use the average price paid by households for the water and
wastewater services (in euros per m3). EVEL provides the price information at the water service level. We have
then matched each service with the corresponding Nuts3.

Household income We use the average annual net salary per worker available from Statistics Estonia from
from 1991 to 2012 at Nuts3.

Other socioeconomic variables Other socio-economic variables from 2006 to 2012 come from Statistics
Estonia.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

9.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis of residential water consumption and water price for Estonia.

Table 9.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
In 2012, the average annual consumption
(average water price, in euros m3) in Estonia from 1991 to 2012.
per inhabitant amounted to 34.4 m3. There has been a decreasing trend in domestic water consumption from
1991 to 2012. Over the period 2007-2012, the average water price has increased from 1.6 euros per m3 to
2.5 euros per m3.

Table 9.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2010. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With almost 55 m3 per capita per year, the water consumption is the higher in
the Ida-Viru Nuts3. On contrary, a low water consumption is reported in the Hiiu county (14 m3 per capita per
year).

9.5 Water demand function estimate

9.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(9.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (9.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which

83

Table 9.1: Household water consumption and price in Estonia

Year

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1.583
1.618
2.000
2.154
2.298
2.503
2.025

78.395
77.146
75.067
71.558
69.826
69.769
62.090
50.355
44.467
41.367
38.595
36.885
35.961
34.635
44.639
35.690
38.004
40.520
34.290
33.808
40.100
34.359
50.194

84

Table 9.2: Regional household water consumption and price in Estonia

Year

Harju
Hiiu
Ida-Viru
J¨arva
J˜ogeva
L¨a¨ane
L¨a¨ane-Viru
P¨arnu
P˜olva
Rapla
Saare
Tartu
Valga
Viljandi
V˜oru
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

38.526
14.054
54.277
24.770
18.959
27.376
20.344
31.040
19.794
17.843
17.040
27.144
27.724
27.146
24.001
33.808

2.361
2.615
1.671
2.280
2.047
2.196
2.219
2.057
1.707
2.347
2.105
1.974
2.546
2.015
2.153
2.154

should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(9.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

9.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(9.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

9.5.3 Results

We report here our estimate for the residential water demand function in Estonia, based on our Nuts3 dataset
for Estonia (2006 to 2012). The first model estimated in Table 9.3 corresponds simple OLS whereas in the

85

Table 9.3: Estimation of the household water demand in Estonia

EstoniaOLS
b/se

EstoniaGLS
b/se

EstoniaGLSiv
b/se

ln Share population below 9

ln Water price (in euros per m3)

ln Household income (in euros per capita)

-0.629***
(0.15)
1.702***
(0.25)
-0.993*
(0.51)
0.629
(0.40)
ln number of summer days without rainfalls 0.347*
(0.20)
-12.918***
(2.45)

ln Share population above 70

Constant

-0.175
(0.11)
0.431*
(0.25)
0.427
(0.47)
0.900**
(0.37)
0.088
(0.11)
1.724
(2.37)

-0.598*
(0.32)
0.936**
(0.44)
0.190
(0.54)
1.297***
(0.49)
0.253
(0.17)
-2.589
(3.94)

R-squared
N. of obs.

0.395
90.000

90.000

90.000

second model we estimate Equation (9.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 9.3 present the main significant variables explaining household water consumption in Estonia. The
price elasticity varies across models, from -0.18 with the GLS to -0.63 with the OLS (not significant with GLS).
Based on the GLSiv models, a 10% increase in water price results in a 6.0% decrease in short-run water
consumption in Estonia. Water demand in Estonia is inelastic, i.e.
the estimated price elasticity in absolute
values is lower than one.

The income elasticity varies between 0.43 and 1.70 and the income elasticity is significantly different from
zero with the three models. This suggests that there is an increasing relationship between water consumption
and household income. The positive significant coefficient for income indicates that, based on the GLS model,
a 10% increase in income will result in a 9.4% increase in water consumption.

Climate conditions play also a significant role, together with some household characteristics. An increase of
the number of days without rainfall by 10% will imply an increase in water consumption by 2.5% (OLS model).
Older people tend to have a water consumption per capita higher than the rest of the population.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 12.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 20.8.

Water consumption may to be at least somehow persistent over time due to household habits or due to
the time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way
to take into this time persistence characteristic of household water consumption is to introduce the lagged
consumption as an additional independent variable in Equation (9.1). The dynamic specification of our equation
of interest becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(9.4)

86

Figure 9.1: Observed versus predicted household water consumption in Estonia

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(9.5)

We provide in Table 9.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the EstoniaGLSlag model, the long-term
price elasticity is estimated to be -0.70 and is significantly different from zero at 10%. With the EstoniaGLSivlag
model, the long-term price elasticity is estimated to be -2.08 and is significantly different from zero at 1%.

87

1520253035Wathat050100150WathatWathatTable 9.4: Estimation of the household water demand with lagged water consumption in Estonia

EstoniaGLSlag
b/se

EstoniaGLSivlag
b/se

ln Share population below 9

ln Water price (in euros per m3)

ln Share population above 70

ln Household income (in euros per capita)

-0.152
(0.10)
0.398**
(0.19)
-0.245
(0.31)
0.289
(0.26)
ln number of summer days without rainfalls 0.110
(0.12)
0.783***
(0.07)
-2.920*
(1.74)

ln Lag of water consumption per capita

Constant

-0.901**
(0.36)
1.059***
(0.39)
-0.494
(0.42)
0.450
(0.35)
0.378*
(0.20)
0.568***
(0.13)
-8.363**
(3.32)

R-squared
N. of obs.

90.000

90.000

88

Chapter 10

Finland

89

10.1 Existing literature

To our best knowledge, there is no previous estimate for a household water demand function in Finland. Rajala
and Katko (2004) describe and analyse the trends and levels of water consumption, especially for households,
in Finland. The study concentrates on the specific household water consumption (SHWC) of various types of
housing, housing ownership, metering and billing arrangements. Based on 185 case studies in various parts
of the country, the study shows that SHWC levels of about 120 l/capita/day, or even less, can be achieved,
In addition to individual metering, proper management
while maintaining a high standard of service levels.
includes introduction of modern water fixtures and in-house piping, raising consumer awareness, and active
follow-up. Explanations for declined consumption are quite site- and case specific. Simultaneously, decreasing
consumption and the resulting fall in water sales are a challenge to water utilities.

10.2 Urban water sector in Finland

This paragraph is based on Heinoa, Takalaa, and Katkoa (2011). According to official statistics, more than 90%
of the population in Finland is connected to public water distribution networks, and more than 80% are served by
public sewer networks and urban wastewater treatment plants. Municipalities own most of the water services,
but there are also numerous consumer-managed utilities, mostly cooperatives, especially in the rural areas.
Approximately half a million people get their drinking water from private wells, and some 350.000 households
rely on on-site sanitation

10.3 Data

Water consumption data The water volume distributed to residential water comes from the annual report
published by the Finnish Water Utilities Association (FIWA)1. From these annual reports, we have extracted
the total volume of water invoiced to residential consumers at water-service level for year 2011. These data
have been matched with population by municipalities obtained from the national office of statistics in Finland
(Statistics Finland). In addition, we have cross-checked water volume data using data obtained from the Finnish
Institute of Environment (SYKE) at water-service level from 2000 to 2011. The average water consumption
(based on 265 water services) is estimated to be 51.5 cubic meters per capita per year in 2011 (141 liters per
day per capita).2

Water price data The water price for residential consumers comes also from the annual report published by
the Finnish Water Utilities Association (FIWA). It includes the water and the sewerage services and is available
at water service level. The unit price for an annual water consumption of 180 cubic meters is 4.67 euros per
cubic meters. In some services, it reaches almost 9 euros per cubic meters.

Household income We use the annual average disposable household income at municipal-level obtained
directly from Finland Statistics over the period 2000-2011. In 2011, the average disposable household income

1FIWA is the co-operation and member association of the Finnish water and wastewater utilities, established in 1956. FIWA's

membership includes about 300 Finnish water utilities which cover about 90% of water services in Finland.

2The Helsinki water

service indicates

consume an average of 158 litres of water per day
(http://www.hsy.fi/en/waterservices/domestic_water_issues/Pages/water_consumption.aspx). Some variations in water consumption are
observed among households. For instance, it is reported that 123 litres of water is consumed by each resident every day in detached
houses, whereas in terraced houses the consumption is 149 litres per person per day.

customers

that

90

at municipal-level is 33,391 euros per households.

Other socioeconomic variables We collected household socioeconomic characteristics at municipal-level
from Finland Statistics (household size, population density, average age of population, etc.).

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

10.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Finland.

Table 10.1: Household water consumption and price in Finland

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

2011

66.457

3.739

Table 10.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
In 2011, the average annual consumption per

(average water price, in euros m3) in Finland for year 2011.
inhabitant amounted to 66.5 m3. For the same year, the average water price was 3.74 euros per m3 .

Table 10.2 gives the annual residential water consumption (m3 per capita and per year) and the water
price per Nuts3 in 2011. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With 95.9 m3 per capita per year, the water consumption is the higher in the
Uusimaa Nuts3 (Helsinki area). On contrary, a low water consumption is reported in the Etel¨a-Savo Nuts3 (40.8
m3 per capita per year).

10.5 Water demand function estimate

10.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(10.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (10.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water

91

Table 10.2: Regional household water consumption and price in Finland

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Etel¨a-Karjala
Etel¨a-Pohjanmaa
Etel¨a-Savo
It¨a-Uusimaa
Kainuu
Kanta-H¨ame
Keski-Pohjanmaa
Keski-Suomi
Kymenlaakso
Lappi
Pirkanmaa
Pohjanmaa
Pohjois-Karjala
Pohjois-Pohjanma
Pohjois-Savo
P¨aij¨at-H¨ame
Satakunta
Uusimaa
Varsinais-Suomi
Average

3.728
4.185
4.053
3.812
3.505
4.044
3.300
4.499
3.848
4.182
3.761
3.637
3.760
3.588
3.981
3.850
3.655
3.299
4.128
3.739

52.004
53.776
40.807
59.990
52.240
68.061
45.454
52.542
46.400
51.945
59.499
58.080
49.163
54.703
56.571
59.577
59.279
95.860
64.226
66.457

92

Table 10.3: Estimation of the household water demand in Finland

Water price (in logs)

Household income (in logs)

Number of persons per household (in logs)

Average population age (in logs)

ln average of daily rainfall (mm per day)

Number of days without rain (in logs)

Constant

R-squared
N. of obs.

Source: auto.dta

finland
b/se

-0.278
(0.19)
0.555
(0.44)
-2.469***
(0.56)
-1.882***
(0.60)
-0.993***
(0.36)
-0.238
(0.69)
9.280
(6.50)

0.194
139.000

demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(10.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

10.5.2 Estimation methods

Let i = {1, .., I} indexes water service. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(10.3)

where (cid:15)i is the usual random term.

10.5.3 Results

We report here our estimate for the residential water demand function in Finland, based on CS dataset made
of municipal-level observations in 2011. We use a OLS estimator.

In Table 10.3, we report our estimates of the household water demand function for Finland using an OLS
approach. Price and income are not significant, although the estimated coefficients exhibit the expected sign.

93

Figure 10.1: Observed versus predicted household water consumption in Finland

Although not significantly different from zero, the price and the income elasticities are -0.278 and 0.555
respectively. We notice a significant family size effect. The water consumption per capita tends to be smaller
in larger households. Age of the population is also a significant determinant, older person having a lower water
consumption per capita. Lastly, water consumption per capita is impacted by climate conditions. An increase in
daily rainfalls by 10% will result in a decrease by 9.9% of residential water consumption. This may be related
to rainwater harvesting or outdoor water use.

To assess the validity of the model, we have compared the water consumption predicted by the demand
function to the observed water consumption, see Figure 10.1. For most of the municipalities the model performs
relatively well with a mean absolute relative prediction error equal to 26.20%.

94

20406080100Wathat050100150WathatWathatChapter 11

France

95

11.1 Existing literature

There are some studies having estimated a household water demand function in France. The existing studies
have however been conducted at the local scale (French region). We provide here the first estimate for France
using a national sample of French municipalities. Compared to the existing studies which have relied on small
sample of municipality-level data (<300), our unbalanced panel dataset of municipalities varies from a little bit
more than 9000 to 13000 observations, depending upon the year.

Nauges and Thomas (2000) have estimated a water demand function on a panel of French municipalities.
The impact of average household income, housing, and system features such as metering is investigated
both on municipal consumption and price. They find evidence that the water price depends on municipality's
characteristics, causing endogeneity of price in the demand equation. Estimated price and income elasticities
are significant and close to previous household level studies.

Nauges and Reynaud (2001) use municipality-level panel data to estimate the residential water demand
function in two French departments (Moselle and Gironde). Socio-economic and meteorologic variables have
thus been introduced in the demand model, in addition to water consumption and price of water, in the demand
function. The results demonstrate a statistically significant price-elasticity in the two departments, estimated at
-0.22 and -0.08. The income elasticity is significant in one of the two departments (Moselle), and is evaluated
at 0.01.

Nauges and Thomas (2003) have proposed a dynamic model of water consumption. Their estimation is
conducted on a sample of French communities observed during the 1988-93 period. They find a long-run (on a
six-year period) price elasticity equal to -0.40 higher, in absolute value, than the short-run price elasticity -0.26.
Rinaudo, Neverre, and Montginoul (2012) estimate the residential water demand on a cross section dataset
of 300 municipalities in SouthWest of France (Languedoc Roussillon region). They report a price elasticity equal
to -0.18 and an income elasticity equal to 0.42, both being significant at 1 percent.

11.2 Urban water sector in France

In France, all water and sanitation services fall within the responsibility of municipal authorities. The municipali-
ties are free to manage the water services by themselves (through a local, public water authority) or to delegate
their operation to a private company. Whatever the method chosen, the public authority remains responsible
for the quality, smooth operation and sustainability of its service.

In France 36,600 municipal authorities and 4,500 intermunicipal bodies thus manage more than 31,000

public collective sanitation or water services.

11.3 Data

Our panel data set consists of municipality-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2008-2011. Most of data
related to water come from the French observatory on public water and sanitation services (SISPEA) initiated in
November 2009 by the French National Agency for Water and Aquatic Environments (Onema). Our unbalanced
panel dataset of municipalities varies from a little bit more than 9000 to 13000 observations, depending
upon the year. The observatory is backed up by a national database that brings together information on
the performance of public water and sanitation services. Data collected concern the features of the service
(management method, type of water resources, billing details, pricing terms, etc.) and offer a technical and

96

economic description (economic indicators, number of inhabitants supplied with drinking water, connected to a
wastewater collection system or non-collective sanitation system, etc.)

Water consumption data We rely on municipal data from the SISPEA observatory. We have the annual
volume of water sold to households for each municipality in France for year 2008-2011. To get the annual
household water consumption per capita, we have divided this volume by the population connected to the
public network in each municipality. The household water consumption in then measure in m3 of water sold to
household per capita and per year.

Water price data We rely on municipal data from the SISPEA observatory. As a price indicator, we use the
average price paid by households for the drinking water service (in euro m3 including VAT). According to SISPEA,
in 2009 the average price in France France 1.90 euros per m3, which translates to an annual bill of 228 euros
on the basis of annual consumption of 120 m3.

Household income We use the average taxable income (in euros per taxable income) obtained directly from
the French National Institute of Statistics and Economic Studies (INSEE) at municipal-level for years 2008-2011

Other socioeconomic variables Other socio-economic variables for 2008-2011 comes from the Institut
National de la Statistique et des Etudes Economiques (INSEE). Some variables describing housing characteristics
are obtained from the French census.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

11.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for France.

Table 11.1: Household water consumption and price in France

Year

2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

63.126
54.275
49.513
49.071
53.210

2.118
2.155
2.022
2.064
2.084

Table 11.1 gives the annual residential water consumption (m3 per capita and per year) and the water
price (retail water price for cold water in euros per m3) in France from 2008 to 2011.
In 2009, the average
annual consumption per inhabitant amounted to 54.3 m3. There has been a decreasing trend in domestic
water consumption which started some ten years ago. There are several reasons to explain this continuous
fall including increases in the price of water, eco-citizen behaviour and technical progress and technological
innovation reducing water consumption of household appliances. There are strong geographic disparities in

97

particular due to climate conditions, the strong presence of individual homes and gardens or high local tourist
activity. Minimum average consumption is observed in Loire Atlantique with 31 m3 per inhabitant per year
whereas the maximum consumption is found on the Reunion Island with 91.3 m3 per inhabitant per year. From
2008 to 2011, the average water price has not significantly changed.

Table 11.2: Regional household water consumption and price in France

Year

11
21
22
23
24
25
26
31
41
42
43
52
53
54
72
73
74
82
83
91
93
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

52.006
51.909
44.795
51.041
62.512
60.094
62.983
38.096
55.568
55.191
57.275
64.243
61.315
46.979
57.058
55.179
50.494
55.234
57.699
71.119
66.242
49.513

1.997
1.678
2.073
2.242
1.910
2.139
2.195
1.975
2.014
1.926
2.002
2.178
2.429
2.022
2.114
1.881
2.539
2.055
2.197
1.646
1.818
2.022

Table 11.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) per region (Nuts 2) in 2010. As it can be seen, there are some
differences across regions, both in terms of water consumption and water prices.

11.5 Water demand function estimate

11.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(11.1)

98

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (11.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(11.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

11.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities (between 9000 and 13000 depending upon the year) and t = {1, .., T }
years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(11.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

11.5.3 Results

We report here our estimate for the residential water demand function in France, based on our municipal dataset
for France (2008 to 2011). The first model estimated in Table 11.3 corresponds simple OLS whereas in the
second model we estimate Equation (11.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 11.3 present the main significant variables explaining household water consumption in France. The
price elasticity is estimated to be -0.05 with the GLS model, and the estimated coefficient is significantly
different from zero. The price elasticity varies across models, from -0.10 with the OLS to -0.43 with the
instrumental variable approach (not significant in this last case). Based on the GLS model, a 10% increase in
water price results in a 0.5% decrease in short-run water consumption in France. Water demand in France is
inelastic, i.e. the estimated price elasticity in absolute values is lower than one.

The income elasticity is also quite consistent with the two models.

It varies between 0.07 and 0.26 and
the income elasticity is significantly different from zero with the three models. This suggests that there is an
increasing relationship between water consumption and household income. The positive significant coefficient
for income indicates that, based on the GLS model, a 10% increase in income will result in a 1.3% increase in
water consumption.

99

Table 11.3: Estimation of the household water demand in France

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Population density

ln number of summer days without rainfalls

ln average of daily rainfall in summer (mm per day)

Share of permanent housings

Share of house

Constant

R-squared
N. of obs.

FranceOLS
b/se

FranceGLS
b/se

FranceGLSiv
b/se

-0.099***
(0.01)
0.065***
(0.01)
-0.056***
(0.00)
0.058***
(0.01)
-0.063***
(0.01)
-0.467***
(0.01)
-0.348***
(0.02)
4.316***
(0.07)

-0.049***
(0.01)
0.133***
(0.01)
-0.058***
(0.00)
0.098***
(0.01)
-0.047***
(0.01)
-0.460***
(0.02)
-0.354***
(0.02)
3.536***
(0.09)

-0.431
(0.64)
0.256**
(0.12)
-0.063***
(0.01)
0.100***
(0.02)
-0.018
(0.02)
-0.506***
(0.14)
-0.245**
(0.11)
2.572***
(0.62)

0.087
56041.000 56040.000 49110.000

100

Figure 11.1: Observed versus predicted household water consumption in France

In
Some characteristics of the population matters for explaining residential water consumption in France.
particular, the water consumption per capita will by higher in areas with a high population density. Climate
conditions play also a significant role, together with the housing characteristics.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 11.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 25.8.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (11.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(11.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(11.5)

We provide in Table 11.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the FranceGLSlag model, the long-term
price elasticity is estimated to be -0.06 significantly different from zero at 1%. With the FranceGLSivlag model,
the long-term price elasticity is estimated to be 4.2.

101

406080100120140Wathat050100150200WathatWathatTable 11.4: Estimation of the household water demand with lagged water consumption in France

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Population density

ln number of summer days without rainfalls

ln average of daily rainfall in summer (mm per day)

Share of permanent housings

Share of house

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

FranceGLSlag
b/se

FranceGLSivlag
b/se

-0.022***
(0.01)
0.016*
(0.01)
-0.026***
(0.00)
-0.040***
(0.01)
-0.070***
(0.01)
-0.207***
(0.02)
-0.139***
(0.02)
0.532***
(0.00)
2.326***
(0.09)

0.985***
(0.06)
0.063***
(0.01)
-0.011***
(0.00)
0.047***
(0.01)
-0.044***
(0.01)
0.125***
(0.02)
-0.145***
(0.02)
0.766***
(0.01)
-0.370**
(0.15)

32909.000

29606.000

102

Chapter 12

Germany

103

12.1 Existing literature

Frondel and Messner (2008) use a household panel in the municipal area of Leipzig, Germany, covering the
years 1998 through 2001. Based on an endogenous switching regression model, they provide an estimate
of the water price elasticity of the residential water demand. They show that consumers' response to prices
is significant but only for consumers who at least have a rough idea about average water prices. Depending
upon the model they consider, the price elasticity varies between -0.35 (OLS) and -0.49 (switching regression).
Income is found to be a significant driver of residential water consumption, the income elasticity varying around
0.3.

Schleich and Hillenbrand (2009) have estimated a residential water demand function for about 600 water
supply areas in Germany. Besides prices, income and household size, they consider the effects of population
age, the share of wells, housing patterns, precipitation and temperature. They use an instrumental variable
(IV) approach but find no evidence that prices are endogenous. Their estimation results suggest that the price
elasticity of water demand in Germany is around -0.24. The income elasticity is positive, decreases with higher
income levels and is at least three times higher in the new federal states than in the old federal states. Current
differences in prices and income levels explain about one third of the gap in residential water use between the
two regions. Household size and the share of wells have a negative impact on per capita water demand, and
water use increases with age. Finally, the findings provide some evidence that rainfall patterns rather than total
rainfall affect water consumption, while temperature appears to have no impact at all.

Müller (2012) have estimated a residential water demand function based on a cross-sectional dataset of
counties in Germany. Depending upon the model they consider, the price elasticity varies between -0.26 (OLS)
and -0.46 (IV). Income effects do not seem to be important in their study (income elasticity varies between
-0.01 and 0.04).

12.2 Urban water sector in Germany

With available water resources of 188 billion m3 Germany is a water-rich country. In 2007 around 32.0 billion m3
of water was abstracted from groundwater and surface waters by industry and for supplying private households
(BMU 2011). This is less than 20% of the potential water supply. With 19.7 billion m3 abstracted for cooling
water for energy production, thermal power plants represents the largest share of abstracted water followed
by other industrial purposes (7.2 billion m3) and drinking water for populations (5.1 billion m3). According to
BMU (2011), water withdrawals for agriculture only plays a minor role in Germany.

In Germany, water supply is a prerogative of the public authorities. Responsibility lies with the municipalities,
which can use a range of organisational and legal forms to comply with this duty (in 2011 more than 6200 water
services in Germany). They can supply water themselves, can establish water and special-purpose associations
within the framework of municipal cooperation or can commission a third party with the task while retaining
municipal supervision. The number of privately organised companies has increased in recent years, and they
now represent more than 40% of water companies. They supply over 60% of the water volume.
In contrast,
almost all sewage companies in Germany are public, since German water law considers the treatment of waste
water to be a sovereign task (Schleich and Hillenbrand 2009).

12.3 Data

Our panel data set consists of regional-level (Nuts3 or Kreise in German) data on household water consumption,
household water price, household's socioeconomic conditions income and on climate condition for years 2004,

104

2010 and 2010. Most of the data come from Regionalstatistik (http://www.regionalstatistik.de/), the official
portal of Federal and State Statistical Offices for municipal-, county-, and regional-level data.

Water consumption data We rely on regional data from the National Statistical Office. We have the annual
volume of water distributed to households per region (Nuts3) in Germany for year 1998, 2001, 2004, 2007,
2010. To get the annual household water consumption per capita, we have divided this volume by the population
connected to the public network in each region.

Water price data We use lander-level (nuts2) unit water price (water and wastewater service in euros per
m3) available from 2004 to 2011 from the German Statistical Office.

Household income We use the annual disposable income per capita (at regional level) obtained directly from
the German Statistical Office over the period 2004 to 2011.

Other socioeconomic variables We have collected regional household socioeconomic characteristics using
the German Statistical Office (median population age, population density, etc.).

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

12.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Germany.

Table 12.1: Household water consumption and price in Germany

Year

2004
2007
2010
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

44.666
43.938
43.682
44.635

3.632
3.730
3.862
3.741

Residential water consumption in Germany has changed substantially over the last two decades. From 1991
to 2004 it has decreased by about 13% to reach approximatively 45 m3 per capita and per year (Schleich and
Hillenbrand 2009). Table 12.1 gives the annual residential water consumption (m3 per capita and per year) and
the water price (retail water price for cold water in euros per m3) in Germany from 2004 to 2010. The annual
residential water consumption per capita has decreased from 44.6 m3 per capita in 2004 to reach 43.7 m3 per
capita in 2.012. Over the same period, the water price has increased from 3.6 euros per m3 in 2004 to 3.9
euros per m3 in 2012.

Table 12.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) by lander (Nuts 2) in 2010. As it can be seen, there are some
differences across landers, both in terms of water consumption and water prices.

105

Table 12.2: Regional household water consumption and price in Germany

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Schleswig-Holstein
Niedersachsen
Bremen
Nordrhein-Westfalen
Hessen
Rheinland-Pfalz
Baden-WUrttemberg
Bayern
Saarland
Brandenburg
Mecklenburg-Vorpommern
Sachsen
Sachsen-Anhalt
Thuringen
Average

49.350
45.856
43.599
48.451
43.616
43.882
41.885
46.637
39.099
37.909
39.478
29.441
33.126
31.660
43.675

3.450
3.500
4.960
4.090
4.650
3.580
4.160
3.190
5.070
4.870
4.280
4.410
4.760
4.170
3.862

12.5 Water demand function estimate

12.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(12.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (12.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(12.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

106

Table 12.3: Estimation of the household water demand in Germany

GermanyOLS GermanyGLS GermanyGLSIV
b/se

b/se

b/se

ln Water price (in euros per m3)

-0.433***
(0.02)

ln Household income (in euros per capita) 0.135***

Share of female in total population

share of population between 15 and 65

ln Population density

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

Source: auto.dta

(0.03)
0.082
(0.64)
-1.632***
(0.22)
0.068***
(0.00)
0.030
(0.02)
3.643***
(0.49)

0.454
1110.000

-0.452***
(0.03)
0.084***
(0.03)
1.103
(0.92)
-0.076
(0.20)
0.059***
(0.01)
-0.001
(0.01)
2.697***
(0.61)

-0.439***
(0.08)
0.133***
(0.04)
0.067
(0.66)
-1.627***
(0.23)
0.068***
(0.00)
0.030
(0.02)
3.674***
(0.62)

1110.000

1110.000

12.5.2 Estimation methods

Let i = {1, .., I} indexes regions (373 Nuts3 in Germany) and t = {1, .., T } years. Our equation of interest
becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(12.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

12.5.3 Results

We report here our estimate for the residential water demand function in Germany, based on our regional
dataset for Germany (2004, 2007, 2010). The first model estimated in Table 12.3 corresponds simple OLS
whereas in the second model we estimate Equation (12.3) with a random parameter estimator.
In the third
model, we account for potential endogeneity of the price by using an instrumental variable approach.

Table 12.3 present the main significant variables explaining household water consumption in Germany. The
short-term price elasticity is estimated to be -0.44, and the estimated coefficient is significantly different from
zero. The price elasticity is highly consistent across models, varying from -0.43 to -0.45. This indicates that
a 10% increase in water price results in a 4.4% decrease in short-run water consumption in Germany. Water
demand in Germany is inelastic, i.e. the estimated price elasticity in absolute values is lower than one.

The income elasticity is also quite consistent with the two models.

It varies between 0.08 and 0.14 and

107

Figure 12.1: Observed versus predicted household water consumption in Germany

the income elasticity is significantly different from zero. This suggests that there is an increasing relationship
between water consumption and household income. The positive significant coefficient for income indicates
that a 10% increase in income will result in a 0.8% to 1.3% increase in water consumption.

Some characteristics of the population matter for explaining residential water consumption in Germany. In
particular, the water consumption per capita will by higher in areas with a high population density. Climate
conditions play only a very minor role.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 12.1. For most of the regions the model
performs quite well. The mean absolute percentage error is 9.1.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (12.1). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(12.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(12.5)

We provide in Table 12.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the GermanyGLSlag model, the
long-term price elasticity is estimated to be -0.51 and is significantly different from zero at 1%. With the
GermanyGLSivlag model, the long-term price elasticity is estimated to be 2.0 but not significantly different from
0.

108

30405060Wathat20406080WathatWathatTable 12.4: Estimation of the household water demand with lagged water consumption in Germany

GermanyGLSlag GermanyGLSivlag
b/se

b/se

ln Water price (in euros per m3)

Share of female in total population

share of population between 15 and 65

-0.090***
(0.01)
ln Household income (in euros per capita) 0.007
(0.02)
0.323
(0.33)
-0.010
(0.12)
0.008***
(0.00)
0.001
(0.01)
0.822***
(0.01)
0.512**
(0.26)

ln average of daily rainfall (mm per day)

ln Lag of water consumption per capita

ln Population density

Constant

0.138**
(0.06)
0.048**
(0.02)
0.823**
(0.39)
0.065
(0.13)
-0.005
(0.00)
0.002
(0.01)
0.931***
(0.03)
-0.832*
(0.43)

R-squared
N. of obs.

1101.000

1101.000

109

Chapter 13

Greece

110

13.1 Existing literature

Athanasiadis, Mentes, Mitkas, and Mylopoulos (2005) have estimated a household water demand function on a
sample of 1356 households in the urban area of Thessaloniki, Greece. there analys is based on 17 municipalities
and it covers the period January 1994 until the first 4 months of 2000 (19 time-series observations in total).
They report a price elasticity equal to -0.34. The income elasticity is found to be 0.35.

Bithas and Chrysostomos (2006) examines domestic water use in the greater Athens area. Their study is
based on annual aggregated time series on residential water demand for the metropolitan Athens area and
water prices obtained from the Athens Water Company for the period 1981-1999. They report a price elasticity
equal to -0.10. The income elasticity is found to be 0.72.

Vagiona and Mylopoulos (2009) have estimated a water demand function on a sample of 100 households
In the city of Volos, Greece. They use 3-month period water consumption records from years 1997 to 2005.
They report a price elasticity equal to -0.96.

13.2 Urban water sector in Greece

In Greece water supply
This paragraph relies on Koundouri, Papandreou, Remoundou, and Kountouris (2014).
is considered as a public service and it is mainly the municipalities which are responsible for water supply,
In the largest cities of the country (Athens and Thessaloniki),
waste water collection, treatment and disposal.
In the other
some owned companies (non profit making corporations) own and operate the treatment plants.
cities of more than 10,000 inhabitants water supply is managed by municipal companies - operating as private
enterprises DEYA (Municipal Enterprise for Water Supply and Sewerage) but owned by the municipalities. The
pricing policy is determined by each DEYA on the basis of their cost and is approved by the Municipal Council.

13.3 Data

We work at the municipal unit level which corresponds to a subdivision of municipalities in Greece.1 Our data
related to water come from the river basin district management plans in Greece.2 The data have been combined
with information on household's income, climate conditions, and household characteristics mainly from the
National Statistical Office of Greece.

Water consumption data We use data for five river basin districts for which relevant data could be extracted
form the river basin district management plans: Western Sterea Ellada (GR04), Epirus (GR05), Thessaly (GR08),
East Macedonia (GR11) and Thrace (GR12). According to the last figure from the Hellenic Statistical authority,
the total water use by household for these 5 river basin district has represented 262 million cubic meters. This
is around 30% of the total water use by household in Greece (excluding the river districts of Crete and of Aegean
Islands for which no data is available). We have the population served and the volume of water consumed by
households (excluding network losses) for 114 municipal units belonging to these five river basin districts in
2010 (all municipal units in GR11 and GR12 and the most important in terms of population for GR04, GR05 and

1In 2011, Greece is divided in 7 decentralized administrations, 13 regions, 75 regional units, 325 municipalities and 947 municipal

units.

2Greece has 14 river basin districts, out of which 5 are international sharing water courses with Albania, FYROM and Bulgaria to the
north and Turkey to the east. The river basin district management plans have been adopted in 12 basin districts. They are are available
at http://wfd.ypeka.gr/.

111

GR08). Our endogenous variable is then the annual water consumption per capita in 2010 (in m3 per year per
capita) for each municipal units.

Water price data From the river basin district management plans, we have the unit price paid by households
for the water service and for the wastewater service (euros per m3).

Household income As a proxy of disposable household income, we use the average GDP per capita (euros
per year per capita) available from 2000 to 2011 at Nuts3 from the Statistical Office of Greece. Notice that
Bithas and Chrysostomos (2006) also use GDP per capita as a proxy of household income.

Other socioeconomic variables Other socio-economic variables come from the Statistical Office of Greece.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

13.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Greece.

Table 13.1: Household water consumption and price in Greece

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

2010

81.908

1.270

Table 13.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Greece in 2010. In 2010, the average annual consumption per inhabitant
amounted to 82 m3 (this volume does not account for the water losses which account for approximately 33%
of the volume produced). The average water price in 2010 was 1.3 euros per m3.

Table 13.2: Regional household water consumption and price in Greece

Year

GR04
GR05
GR08
GR11
GR12
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

78.352
93.557
74.676
88.781
73.085
81.908

1.101
1.709
1.435
0.897
1.484
1.270

Table 13.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
aggregated at the river basin district in 2010. As it can be seen, there are some differences across river bassin
districts , both in terms of water consumption and water prices. With 94 m3 per capita per year, the water

112

consumption is the highest in the Epirus river basin. On contrary, a low water consumption is reported in the
Thrace river district (73.0 m3 per capita per year).

13.5 Water demand function estimate

13.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(13.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (13.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(13.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

13.5.2 Estimation methods

Let i = {1, .., I} indexes municipal units. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(13.3)

where (cid:15)t is the usual random term.

13.5.3 Results

We report here our estimate for the residential water demand function in Greece in 2010, based on our municipal
unit level dataset for Greece (we have 114 municipal units). The model estimated in Table 13.3 corresponds
simple OLS.

Table 13.3 present the main significant variables explaining household water consumption in Greece. The
price elasticity vis found to be -0.10. A 10% increase in water price results in a 1.0% decrease in short-run
water consumption in Greece. Water demand in Greece is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

113

Table 13.3: Estimation of the household water demand in Greece

GreeceOLS
b/se

ln Water price (in euros per m3)

Share households with at least 5 persons

-0.095*
(0.05)
ln Household income (in euros per capita) 0.008
(0.19)
-2.460***
(0.93)
-0.029
(0.12)
4.509**
(1.80)

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

0.086
114.000

The share of households with at least 5 persons has a negative and significant impact on water consumption

per capita. Large household tend to have a lower water consumption per capita.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 13.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 18.7.

114

Figure 13.1: Observed versus predicted household water consumption in Greece

115

60708090100Wathat0100200300400WathatWathatChapter 14

Hungary

116

14.1 Existing literature

To our best knowledge, there doesn't exist any estimate of the residential water demand function in Hungary.

14.2 Urban water sector in Hungary

The total water abstraction in 2012 is about 6,000 million m3/year, 75% of which is for cooling water use
(HITA 2013). Within the remaining segment, the public water supply is the major user with 40%, industry
represents one quarter and agriculture uses the rest (irrigation 15%, fishponds 5% and animal breeding 15%).
Municipalities and the central government are responsible for service provision in Hungary (WorldBank
2015d). Depending on the ownership of assets, there is a mixed responsibility for service provision between the
central government and municipalities. Before 2012, there were around 400 water utilities but the 33 largest
water utilities used to represent 84% of the total consumer equivalent in the country. By 2017, the minimum
required size to obtain a license for operation will be 150,000 population equivalent.

Hungarian utilities provide almost full access to drinking water services, and there is a declining gap between
water and sewer connections. According to WorldBank (2015d), 97% of the population is connected to drinking
water networks. The connection rate to the sewer system increased from 46% in 1990 to 74% in 2012.

Since 2012, the Hungarian Energy and Public Utility Regulatory Authority (HEA) has been the key institution

that regulates and oversees the water sector.

14.3 Data

Our panel data set consists of Nuts3-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2000-2007. Most of the data
come from the Hungarian Central Statistical Office. The data consist of aggregate data at Nuts3-level on water
consumption per capita and water prices. The data have been combined with information on income, climate,
and household characteristics mainly from the Central Statistical office of Hungary.

Water consumption data We have used Nuts3-level annual water consumption per inhabitant connected to
the water supply system (m3 per capita) available from 2003 to 2012 from the Hungarian Central Statistical
Office.

Water price data Water prices come from the International Benchmarking Network for Water and Sanitation
Utilities (IBNET) and are available at the water utility-level from 2000 to 2007. Water services have been
matched to their respective Nuts3 (Nuts3 without a water service in the IBNET database are given a water price
corresponding to the national average). As a price indicator, we use the average price (euros per m3) paid by
households for the water service (based on a household annual water consumption equal to 72 m3).

Household income We use the average annual net earnings per employee at Nuts3-level from 2000 to 2012
from the Central Statistical Office of Hungary.

Other socioeconomic variables Other socio-economic variables from 2000 to 2007 come from the Central
Statistical Office of Hungary.

117

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

14.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Hungary.

Table 14.1: Household water consumption and price in Hungary

Year

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

41.030
39.198
40.073
41.589
39.045
38.958
38.829
39.281
37.832
37.645
35.766
35.861
38.769

0.343
0.368
0.439
0.536
0.641
0.780
0.696
0.894
.
.
.
.
0.586

According to HITA (2013), water consumption decreased by about 50% compared to the late 1980s. Today
consumption remains stable with an average annual use per person of about 36-40 m3 of water on a national
level. Table 14.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Hungary from 2000 to 2011. In 2011, the average annual consumption
per inhabitant amounted to 35.8 m3. There has been a decreasing trend in domestic water consumption from
2000 to 2011. Over the period 2000 to 2007, the average water price has increased from 0.3 euros per m3 to
0.9 euros per m3.

Table 14.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts 3) in 2007. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With almost 60 m3 per capita per year, the water consumption is the higher in
the Budapest Nuts3. On contrary, a low water consumption is reported in the Nógrád region (24 m3 per capita
per year).

14.5 Water demand function estimate

14.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the

118

Table 14.2: Regional household water consumption and price in Hungary

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

1.169
0.943
0.577
0.954
0.954
0.932
0.954
1.054
0.543
1.571
0.954
1.195
0.954
0.954
0.577
0.954
0.958
0.865
0.843
1.174
0.894

Baranya
Borsod-Abaúj-Zemplén
Budapest
Bács-Kiskun
Békés
Csongrád
Fejér
Gyr-Moson-Sopron
Hajdú-Bihar
Heves
Jász-Nagykun-Szolnok
Komárom-Esztergom
Nógrád
Pest
Somogy
Szabolcs-Szatmár
Tolna
Vas
Veszprém
Zala
Average

29.851
28.848
59.356
39.255
35.704
40.045
37.712
35.687
32.753
33.912
34.346
29.626
24.454
41.557
35.217
34.591
33.624
35.089
35.310
36.785
39.281

119

Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(14.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (14.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(14.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

14.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(14.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

14.5.3 Results

We report here our estimate for the residential water demand function in Hungary, based on our Nuts3 dataset
for Hungary (2000 to 2007). The first model estimated in Table 14.3 corresponds simple OLS whereas in the
second model we estimate Equation (14.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 14.3 present the main significant variables explaining household water consumption in Hungary. The
price elasticity varies across models, from -0.07 with the GLS to -0.80 with the GLSiv (always significant).
Based on the GLSiv , a 10% increase in water price results in a 8% decrease in short-run water consumption
in Hungary. Water demand in Hungary is inelastic, i.e. the estimated price elasticity in absolute values is lower
than one.

The income elasticity varies between 0.13 and 1.1 and and is significantly different from zero with the
three models. This suggests that there is an increasing relationship between water consumption and household

120

Table 14.3: Estimation of the household water demand in Hungary

HungaryOLS HungaryGLS HungaryGLSiv
b/se

b/se

b/se

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Population density

ln Average of daily rainfall in summer (mm per day)

Share of population connected to water supply

Constant

R-squared
N. of obs.

-0.267***
(0.04)
0.419***
(0.07)
0.096***
(0.01)
-0.105***
(0.03)
0.001
(0.00)
-0.522
(0.51)

0.649
160.000

-0.066***
(0.02)
0.126***
(0.03)
0.141***
(0.03)
-0.061***
(0.01)
-0.002
(0.00)
2.034***
(0.32)

-0.793***
(0.30)
1.092***
(0.39)
0.005
(0.06)
-0.081***
(0.03)

-5.748*
(3.11)

160.000

160.000

Figure 14.1: Observed versus predicted household water consumption in Hungary

121

20406080Wathat20406080WathatWathatTable 14.4: Estimation of the household water demand with lagged water consumption in Hungary

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Population density

ln Average of daily rainfall in summer (mm per day)

Share of population connected to water supply

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

HungaryGLSlag HungaryGLSivlag
b/se

b/se

-0.058***
(0.02)
0.127***
(0.04)
-0.002
(0.01)
-0.053***
(0.01)
0.000
(0.00)
0.864***
(0.03)
-0.549**
(0.27)

-0.122
(0.08)
0.225*
(0.12)
-0.008
(0.01)
-0.050***
(0.01)

0.821***
(0.06)
-1.196
(0.81)

140.000

140.000

income. The positive significant coefficient for income indicates that, based on the GLSiv model, a 10% increase
in income will result in a 10.1% increase in water consumption.

Climate conditions play also a significant role, together with the housing characteristics. An increase of

summer rainfall by 10% will imply a reduction in water consumption by 0.8% (GLSiv model).

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 14.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 12.6.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (14.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(14.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(14.5)

We provide in Table 14.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the HungaryGLSlag model, the long-term
price elasticity is estimated to be -0.42 significantly different from zero at 1%. With the HungaryGLSivlag
model, the long-term price elasticity is estimated to be -0.68 significantly different from zero at 1%.

122

Chapter 15

Ireland

123

15.1 Existing literature

To our best knowledge there is no estimate of the water demand function for households in Ireland. This might
be due to the fact that, until 2014, most of the Irish households were not facing a formal price for water and
wastewater services.

15.2 Urban water sector in Ireland

Ireland's water sector is characterised by a high level of fragmentation (around 3,500 water supply schemes in
2011 according to the Irish Environmental Agency). Indeed, there are numerous different ways in which water
is supplied to the domestic and non-domestic sectors, depending on local circumstances and authorities. These
include public water supplies (84.8%), public group water schemes (2.6% by a contractor on behalf of a local
authority), private group water schemes (5.2%), small private supplies (0.7%), and exempted supplies (6.7%),
see Zhao and Crosbie (2012).

In Ireland, average consumption per capita can only be roughly estimated at present due to the fact that
the majority of households are not metered (Brady and Gray 2013). According to Zhao and Crosbie (2012),
household water consumption was between 45-50 cubic meters per year and per person in 2000. More recent
data suggest a water consumption per capita around 55 cubic meters per year and per person. Working of a
sample of 1650 Irish households, Edgar (2014) find an average water consumption around 45 cubic meters per
year and per person. This study is however only based on metered households and it covers only three months
of observations. As a result, household water consumption could be underestimated.

One specificity of Ireland is that, until 2014, most of the Irish households were not facing a formal price
for water and wastewater services. Domestic water charges were firstly abolished in Ireland in 1977, but were
reintroduced with little success under the 1983 Local Government (Financial Provisions) Act. Thereafter, water
charges were abolished again in 1997 and integrated into central taxation (more specifically through a road
tax) after a general election in 1996, Zhao and Crosbie (2012).

Lastly, it is importance to notice that a significant portion (between 30-40%) of treated water in Ireland is

unaccounted for that is lost through leakage in the distribution system before it reaches the customer.

15.3 Data

There are 34 local authorities (Water Services Authorities, WSA) responsible for local management and strategic
planning of water and sewerage services in Ireland. Our data set consists then of WSA-level data on household
water consumption, household water price, household's socioeconomic conditions income and on climate condi-
tion for year 2011. Data on water supplied come from the Environmental Protection Agency (SAFER-Data). The
final data consist of aggregate data at WSA-level on water consumption per capita and water and sewerage
prices. The data have been combined with information on household's income, climate conditions, and household
characteristics mainly from the Central Statistical Office (CSO) of Ireland.

Water consumption data From Environmental Protection Agency, we have the water population supplied
and the volume distributed for each supply scheme and each year from 2003 to 2013. The volume of water
and the population have been aggregated at the WSA level (34 WSA). For each WSA we have the volume of
water distributed to households (in m3 per capita).

124

Water price data As mentioned above, until 2014, most of the Irish households were not facing a formal
price for water and wastewater services. As a result it is not possible to define a household water price for
Ireland. We have decided to consider the commercial water price as a proxy of the household water price. The
rational for this assumption is that commercial water price reflect the cost of the water service. As a result,
we expect the household water price to be highly correlated to the commercial water price. For each WSA from
2007 to 2013, we have the average water and wastewater commercial charges for the period 2007 to 2013
from the publication IBEC (2013).

Household income We use the average disposable household income (euros per year per capita) available
from 2000 to 2011 at county level from the Central Statistical Office (CSO) of Ireland. Irish counties have been
matched to WSAs.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come the Central
Statistical Office (CSO) of Ireland.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

15.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Ireland.
We limit our analysis to year 2011.

Table 15.1: Household water consumption and price in Ireland

Year

2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

49.255
49.255

2.212
2.212

Table 15.1 gives the annual residential water consumption (m3 per capita and per year) and the water
commercial price (average water and wastewater price, in euros m3) in Ireland in 2011.1 In 2010, the average
annual consumption per inhabitant amounted to 49 m3. The average commercial water price in 2011 was 2.2
euros per m3.

Table 15.2 gives the annual residential water consumption (m3 per capita and per year) and the water
price per WSA in 2010. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With 85 m3 per capita per year, the water consumption is the higher in the
Limerick City Council WSA. On contrary, a low water consumption is reported in the Westmeath County Council
WSA (20 m3 per capita per year).

1As explained above, the commercial water price will serve a proxy of the household water price.

125

Table 15.2: Regional household water consumption and price in Ireland

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Carlow County Council
Cavan County Council
Clare County Council
Cork City Council
Cork County Council
Donegal County Council
Dublin City Council
Dun Laoghaire-Rathdown
Fingal County Council
Galway City Council
Galway County Council
Kerry County Council
Kildare County Council
Kilkenny County
Laois County Council
Leitrim County Council
Limerick City Council
Limerick County Council
Longford County Council
Louth County Council
Mayo County Council
Meath County Council
Monaghan County Council
North Tipperary County Council
Offaly County Council
Roscommon County Council
Sligo County Council
South Dublin Council
South Tipperary County Council
Waterford City Council
Waterford County Council
Westmeath County Council
Wexford County Council
Wicklow County Council
Average

40.334
63.858
68.675
43.652
35.430
46.789
53.038
33.162
43.727
54.444
56.466
50.807
62.632
46.763
42.530
52.939
85.151
48.523
19.805
65.418
57.833
41.639
25.526
50.333
54.738
58.208
61.677
43.854
68.405
44.971
54.412
20.042
22.644
37.710
49.255

126

2.300
2.510
2.890
2.350
2.130
2.310
1.800
2.300
1.790
1.750
2.280
1.930
1.490
2.890
2.450
2.280
2.600
2.600
2.400
1.900
2.400
2.650
2.110
2.400
2.400
2.770
2.420
1.880
2.100
2.350
2.660
2.300
2.560
3.040
2.212

15.5 Water demand function estimate

15.5.1 Specifying the residential water demand function

Important remark Until 2014 most of the Irish households were not facing a formal price for water and
wastewater services. As a result the concept of water demand function might not be adapted to model house-
hold water use. Interpretation of the estimates should be done with caution.

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(15.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (15.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(15.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

15.5.2 Estimation methods

Let i = {1, .., I} indexes Water Services Authorities. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(15.3)

where (cid:15)t is the usual random term.

15.5.3 Results

We report here our estimate for the residential water demand function in Ireland, based on our Council dataset
for Ireland (2010). Both models estimated in Table 15.3 rely on simple OLS. In the first model the average
commercial water price is introduced as a possible determinant whereas in the second model we estimate
Equation (15.3) without any water price variable. Based on the above discussion related to the absence of water
pricing for household in Ireland, the interpretation of the price coefficient should be done with caution.

127

Table 15.3: Estimation of the household water demand in Ireland

IrelandWithPrice
b/se

IrelandNoPrice
b/se

ln Water price (in euros per m3)

ln Share population above 65

-0.215
(0.44)
ln Household income (in euros per capita) 0.155
(1.34)
0.818*
(0.40)
0.024
(0.14)
1.820
(1.80)
-0.395
(13.48)

ln Share detached houses

ln evapotranspiration

Constant

R-squared
N. of obs.

0.146
34.000

0.327
(1.27)
0.785*
(0.39)
0.018
(0.14)
1.759
(1.77)
-2.135
(12.82)

0.139
34.000

Table 15.3 presents the main significant variables explaining household water consumption in Ireland. The
price elasticity is -0.22 in the first model, but not statistically different from zero. This is not a surprising results
based on the fact that households don't face a formal water price in Ireland.

The income elasticity varies between 0.16 and 0.33 but is never significantly different from zero. The water

consumption per capita increases with the share of the population above 65.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 15.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 25.0.

128

Figure 15.1: Observed versus predicted household water consumption in Ireland

129

3040506070Wathat20406080100WathatWathatChapter 16

Italy

130

16.1 Existing literature

Some papers have estimated a residential water demand function for Italy but based on regional or local data
(Mazzanti and Montini 2006, Musolesi and Nosvelli 2007, Statzu and Strazzera 2011). Mazzanti and Montini
(2006) use a panel dataset consisting of 125 municipalities in the Emilia-Romagna region observed over four
years (1998-2001). The estimated water demand price elasticity is negative, showing values between -0.99
and -1.33. Income elasticity is significant within in a range between 0.40 and 0.71. The other socio-economic
variables play only a very limited role in explaining residential water consumption: the number of inhabitants in
the municipality, population density, household size, the shares of both old and young population do not present
significant effects. Altitude seems to negatively influence water demand in a significant way. Musolesi and
Nosvelli (2007) have studied household water demand in the Cremona Province. Statzu and Strazzera (2011)
analyze a data set on residential water consumption in a Sardinia, for a period of six years (2000-2005), using
aggregated data on municipalities. Statzu and Strazzera (2011) report a significant effect of price, with a
price elasticity value of -0.146 together with a significant impact of income, the income elasticity being 0.105.
Household size is significant, and its coefficient indicates that water consumption increases a little more than
proportionally following an increase in the household size. The altitude variable has a significant negative effect
on water consumption.

Recently, Romano, Salvati, and Guerrini (2014) have published the first estimate of a residential water
demand using a national coverage. Using a linear mixed-effects model, Romano, Salvati, and Guerrini (2014)
have estimated the determinants of residential water demand for the 103 provincial Italian capitals, over the
period 2007-2009. Their results confirmed that the price has a negative effect on residential water consumption
(price elasticy equal to -0.18) and that it is a relevant driver of domestic water consumption. Moreover, income
per capita has a positive expected effect on water consumption. Among the other explanatory variables
precipitation and altitude exert a strongly significant negative effect on water consumption, while temperature
does not influence water demand.

16.2 Data

Water consumption data We rely on data from the Italian National Institute of Statistics (Istat). We use the
average annual consumption of drinking water for domestic use in the 113 chief towns of each Italian province
(i.e., meter per capita per year) from 2001 to 2011. On average, the annual consumption of drinking water for
domestic use has decreased from 76.4 m3 per capita per year in 2001 to 64.0 m3 per capita per year in 2011.
In addition, Istat indicates for each year if a restriction of water use has been implemented in a municipality.

Water price data We have used information on household expenses for the water and waste water services,
reported in the annual surveys of the Federconsumatori (the main Italian consumer organization). Expenses
reported include fees for water, wastewater and sewerage services, the fixed component, and value added tax
(VAT). We have computed an average price for two levels of residential household use (100 and 200 cubic
meters of water per year) for the chief town of each Italian province from 2002 to 2011 (missing data for year
2006 and 2007). The water price has increased from 1.01 euro per cubic meter in 2000 to reach 1.66 euros
per cubic meter in 2011.

Household income We use the annual average households income (at regional level) obtained directly from
Istat over the period 2002-2011. It varies from 25,287 euros in 2002 on average for Italy to 29,956 in 2011.

131

Other socioeconomic variables We collected household socioeconomic characteristics data using recent
Istat data (household size, population density, etc.).

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

16.3 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Italy.

Table 16.1: Household water consumption and price in Italy

Year

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

77.552
78.498
76.413
74.917
73.367
72.543
71.951
69.940
69.219
68.465
66.830
64.245
71.898

.
.
0.918
0.951
1.107
1.164
.
.
.
1.300
1.406
1.515
1.222

Table 16.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Italy from 2000 to 2011. In 2011, the average annual consumption per
inhabitant amounted to 64.2 m3. There has been a decreasing trend in domestic water consumption since
2000. Over the period 2002-2011, the average water price (only for the water service) has increased from 0.92
euros per m3 to 1.52 euros per m3 .

Table 16.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per Italian region in 2010. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With more than 83 m3 per capita per year, the water consumption is the higher
in the Lazio region. On contrary, a low water consumption is reported in the Basilicata region (50.6 m3 per
capita per year).

16.4 Water demand function estimate

132

Table 16.2: Regional household water consumption and price in Italy

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Abruzzo
Basilicata
Calabria
Campania
Emilia-Romagna
Friuli-Venezia Giulia
Lazio
Liguria
Lombardia
Marche
Molise
Piemonte
Provincia Autonoma Bolzano / Bozen
Puglia
Sardegna
Sicilia
Toscana
Umbria
Valle d'Aosta
Veneto
Average

63.579
50.577
69.914
60.594
58.716
63.235
83.387
62.594
78.128
55.624
53.634
71.690
59.152
52.111
57.735
64.266
53.647
52.582
65.746
62.160
66.830

1.142
1.422
.
1.186
1.715
0.980
1.080
1.762
0.754
1.694
.
1.337
1.131
2.086
1.407
1.641
2.241
1.874
1.051
1.287
1.406

133

16.4.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(16.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (16.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(16.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

16.4.2 Estimation methods

Let i = {1, .., I} indexes the 113 chief towns of each Italian and t = {1, .., T } years. Our equation of interest
becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(16.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

16.4.3 Results

We report here our estimate for the residential water demand function in Italy, based on our panel dataset
made of municipal-level observations from 2002 to 2011. The first model estimated in Table 16.3 corresponds
to simple OLS whereas in the second model we estimate Equation (16.4) with a random parameter estimator. In
the third model, we account for potential endogeneity of the price by using an instrumental variable approach.
A specific estimation issue arises due to the use of the average water price as an explanatory variable.
Stated simply, the average price a consumer faces depends on his level of consumption but this level of
consumption is also affected by the average price. This simultaneity violates standard assumptions regarding
independence of the error term from explanatory variables. OLS are in such case inconsistent and a specific

134

Table 16.3: Estimation of the household water demand in Italy

Water price (in logs)

Household income (in logs)

Population density (in logs)

Number of persons per household (in logs)

Dummy = 1 if water rationing

Number of days without rain (in logs)

Constant

R-squared
N. of obs.

ItalyOLS
b/se

-0.282***
(0.02)
0.062
(0.08)
0.033***
(0.01)
-0.605***
(0.11)
-0.010
(0.03)
0.093
(0.07)
3.355***
(1.03)

0.517
332.000

ItalyGLS
b/se

ItalyGLSiv
b/se

(0.15)

-0.587***
(0.09)

-0.219***
(0.03)
-0.258*** 0.313**
(0.08)
0.051***
(0.01)
-0.536***
(0.13)
-0.028
(0.02)
0.088*
(0.05)
6.468***
(0.91)

-0.399**
(0.17)
0.025
(0.04)
-0.080
(0.08)
1.811
(1.54)

332.000

332.000

Figure 16.1: Observed versus predicted household water consumption in Italy

135

estimation procedure is required. This simultaneity problem can be addressed by using instrumental variables
(IV) techniques such as Two Stage Least Square (2SLS) regression.

In Table 16.3, we report our estimates of the household water demand function for Italy using an IV
approach. The population density and the share of water volume lost in the distribution system have been used
as instruments. To assess the validity of the model, we have compared the water consumption predicted by the
demand function to the observed water consumption, see Figure 16.1. For most of the municipalities the model
performs quite well with a mean absolute relative prediction error equal to 13.20%.

Table 16.3 gives the main significant variables explaining household water consumption in Italy.
The price elasticity varies across models, from -0.28 with the OLS to -0.59 with the GLSiv (not significant
with GLSiv). The price elasticities belong to a range of values in line with the existing literature and the previous
estimates available for Italy. Based on the GLS models, a 10% increase in water price results in a 5.9% decrease
in short-run water consumption in Italy. Water demand in italy is inelastic, i.e. the estimated price elasticity in
absolute values is lower than one.

The income elasticity is 0.311 with GLSiv model which is also consistent with previous results showing that
there is an increasing relationship between water consumption and household income. Water rationing does
not impact on water consumption, a result in line with what has been found previously by Statzu and Strazzera
(2011) for Sardinia. Water consumption per capita decreases with household's size but is not impacted by
climate conditions.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (16.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(16.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(16.5)

We provide in Table 16.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the ItalyGLSlag model, the long-term
price elasticity is estimated to be -0.29 significantly different from zero at 1%. With the ItalyGLSivlag model,
the long-term price elasticity is estimated to be -0.25 but not significantly different from zero at 10%.

136

Table 16.4: Estimation of the household water demand with lagged water consumption in Italy

Water price (in logs)

Household income (in logs)

Population density (in logs)

Number of persons per household (in logs)

Dummy = 1 if water rationing

Number of days without rain (in logs)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

ItalyGLSlag
b/se

ItalyGLSivlag
b/se

-0.063***
(0.02)
0.010
(0.05)
0.005
(0.01)
-0.122*
(0.07)
-0.003
(0.02)
0.041
(0.04)
0.784***
(0.03)
0.643
(0.63)

-0.037
(0.08)
0.005
(0.07)

-0.087
(0.06)
-0.004
(0.02)
0.041
(0.04)
0.854***
(0.09)
0.398
(0.56)

328.000

328.000

137

Chapter 17

Latvia

138

17.1 Existing literature

To our best knowledge, no previous estimate for the residential water demand function is available for Latvia.

17.2 Urban water sector in Latvia

Until November 2009, the water management sector was regulated by 16 independent municipal regulators. As
of November 1, 2009 the water management sector became a responsibility of the Public Utilities Commission
(PUC) of Latvia which has in charge to issue water licenses and to approve water tariffs.

17.3 Data

Water consumption data We used the national statistics report ``2-Water. An overview of the use of water
resources'' published annually by the Latvian Environment, Geology and Meteorology Centre. These annual
reports include information on the water delivered to domestic users at municipality level (novads), and also at
parish level (pagasts) from 2008 to 2013. The annual consumption per capita has been computed using the
resident population by statistical region, city and county obtained from the Latvian Central Statistical Bureau
and assuming a population connection rate to the public water network equal to 75% (source Latvian water and
wastewater works).

Water price data Data on water tariff have been obtained directly from the PUC of Latvia. The water price
is the price per cubic meter paid by residential water consumers both for the water and the sewerage services.
This price information is available at municipality level (novads), and also at parish level (pagasts) for year
2014. The average price in 2014 is 1.70 euros per cubic meters, varying from 0.66 to 2.73 euros per cubic
meters depending upon the municipality considered.

Household income It has not been possible to obtain a measure of household disposable income in Latvia at
municipality level. As a proxy of household income, we use the annual gross wages in municipalities and county
obtain from the Latvian Central Statistical Bureau. The average annual gross wages is 5,141 euro per worker
per year. It varies from 3,540 to 8,076 euros per worker depending upon the municipality considered.

Other socioeconomic variables Other household socioeconomic characteristics (share of the population
below 18 years, population density) come from the Latvian Central Statistical Bureau.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

17.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Latvia.

Table 17.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Latvia for year 2013. In 2013, the weighted average annual consumption

139

Table 17.1: Household water consumption and price in Latvia

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

2013

58.618

1.420

per inhabitant amounted to 58.6 m3. For the same year, the average water price (only for the water service) is
1.42 euros per m3.

Table 17.2: Regional household water consumption and price in Latvia

Year

Kurzeme
Latgale
Pieriga
Riga
Vidzeme
Zemgale
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

45.483
46.672
38.158
92.264
37.257
35.687
58.618

1.512
1.559
1.869
1.047
1.733
1.724
1.420

Table 17.2 gives the annual residential water consumption (m3 per capita and per year) and the water
price per Nuts3 in 2013. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With more than 92 m3 per capita per year, the water consumption is the higher
in the Riga Nuts3. On contrary, a low water consumption is reported in the Zemgale, Vidzeme and Pieriga Nuts3
(less than 40.0 m3 per capita per year).

17.5 Water demand function estimate

17.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(17.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (17.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,

140

the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(17.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

17.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(17.3)

where (cid:15)i is the usual random term.

17.5.3 Results

In Table 17.3, we report our estimate for the residential water demand function in Latvia, based on our CS
dataset made of municipal-level observations for year 2013. We use a OLS estimator.

Table 17.3: Estimation of the household water demand in Latvia

Water price (in logs)

Household income (in logs)

Number of days without rain in summer (in logs)

Share of the population below 18 years (in logs)

Constant

R-squared
N. of obs.

Latvia
b/se

-0.402**
(0.16)
0.613**
(0.30)
-0.290
(2.47)
-0.202
(0.49)
-1.150
(6.75)

0.121
77.000

Table 17.3 gives the main significant variables explaining household water consumption in Latvia. The
price elasticity is estimated to be --0.40, a figure in line with the existing literature and the previous estimates
available for Latvia. The income elasticity is 0.61 which is also consistent with previous results showing that
there is an increasing relationship between water consumption and household income. None of the other
variables introduced into the model (share of the population below 18 years, number of days without rain in
summer) appears to be significant.

141

Figure 17.1: Observed versus predicted household water consumption in Latvia

To assess the validity of the model, we have compared the water consumption predicted by the demand
function to the observed water consumption, see Figure 17.1. The mean absolute relative prediction error equal
to 33.20%.

142

253035404550Wathat020406080100WathatWathatChapter 18

Lithuania

143

18.1 Existing literature

To our best knowledge, no previous estimate for the residential water demand function is available for Lithuania.

18.2 Urban water sector in Lithuania

Municipalities and the central government are responsible for service provision in Lithuania. The National
Commission for Energy Control and Prices (NCC) is an independent national regulatory in charge of regulating
the water sector (approval of water prices, issuing of licenses).
In 2012, there were 273 water suppliers in
Lithuania.

18.3 Data

Our data set consists of annual county-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition. Most of the data come from Statistics
Lithuania, the national statistical office.

Water consumption data We rely on data from the Statistics Lithuania, the national statistical office. We
have used the volume total of water distributed to households per municipality in Lithuania from 1995 to 2012
(county-level). To get the household water consumption per capita, we have divided this volume by the total
population within each county.

Water price data We use county-level retail water price available from 2000 to 2012 from the Statistics
Lithuania.

Household income We use the annual disposable income per capita (at county level) obtained directly from
Statistics Lithuania over the period 2004-2013.

Other socioeconomic variables We collected household socioeconomic characteristics data using Statistics
Lithuania data at municipal of at county levels (median population age, population density, etc.).

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

18.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Lithuania.
Table 18.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) in Lithuania. The annual residential water consumption per
capita has decreased from 27.3 m3 per capita in 2001 to reach 24.9 m3 per capita in 2.012. Over the same
period, the water price has increased from 0.57 euros per m3 in 2001 to 0.78 euros per m3 in 2012.

Table 18.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) by county (Nuts 3) in 2010. As it can be seen, there are huge

144

Table 18.1: Household water consumption and price in Lithuania

Year

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

27.395
25.553
25.366
26.005
26.483
28.171
25.814
25.354
24.324
24.134
24.974
24.847
25.702

0.571
0.579
0.588
0.594
0.599
0.603
0.622
0.651
0.697
0.728
0.729
0.777
0.645

Table 18.2: Regional household water consumption and price in Lithuania

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

0.578
0.695
0.633
0.710
0.591
0.861
0.915
0.773
0.833
0.694
0.728

Alytus county
Kaunas county
Klaipeda county
Marijampole coun
Panevezys county
Siauliai county
Taurage county
Telsiai county
Utena county
Vilnius county
Average

23.285
29.999
27.584
25.427
19.544
16.799
17.950
17.753
23.015
39.986
24.134

145

differences across counties. The annual residential water consumption is the lowest in the Siauliai county (16.8
m3 per capita and per year). On the other extreme, the annual residential water consumption is the highest in
the Vilnius county with 39.986 m3 per capita and per year. The heterogeneity across counties is lower for what
concerns the residential water price.

18.5 Water demand function estimate

18.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(18.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (18.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(18.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

18.5.2 Estimation methods

Let i = {1, .., I} indexes counties (10 counties in Lithuania) and t = {1, .., T } years. Our equation of interest
becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(18.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

18.5.3 Results

Table 18.3 present the main significant variables explaining household water consumption in Lithuania. The
first model estimated in Table 18.3 uses simple OLS whereas in the second model we estimate Equation (18.4)
with a random parameter estimator.

146

Table 18.3: Estimation of the household water demand in Lithuania

LithuaniaOLS
b/se

LithuaniaGLS
b/se

ln Water price (in euros per m3)

-0.368***
(0.08)

ln Household income (in euros per capita) 0.822***

ln evapotranspiration

ln Median age of the population

ln Household size

Share of groundwater abstracted

ln Population density

Constant

R-squared
N. of obs.

R-squared
N. of obs.

(0.26)
0.757***
(0.28)
-0.206
(0.32)
0.196
(0.27)
-0.145***
(0.05)
0.127
(0.08)
-3.857**
(1.70)

0.687
119.000

0.606
119.000

-0.216**
(0.10)
0.426*
(0.23)
0.496**
(0.25)
-0.078
(0.33)
0.045
(0.34)
-0.155**
(0.07)
0.243***
(0.09)
-1.198
(1.63)

119.000

119.000

Figure 18.1: Observed versus predicted household water consumption in Lithuania

147

1520253035Wathat152025303540WathatWathatThe short-term price elasticity is estimated to be -0.22 with the LithuaniaGLS model, and the estimated
coefficient is significantly different from zero at 5%. This indicates that a 10% increase in water price results
in 0.22% decrease in short-run water consumption in Lithuania. Water demand in Lithuania is inelastic both in
the short-run, i.e. the estimated elasticities in absolute values are lower than one.

.

The income elasticity varies between 0.43 and 0.82 depending upon the model considered.

the income
elasticity is significantly different from zero. This suggests that there is an increasing relationship between
water consumption and household income. The positive significant coefficient for income indicates that a 10%
increase in income will result in a 4.3% to 8.2% increase in water consumption.

Evapotranspiration is significant. Residential water consumption depends upon climate conditions.
A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 18.1. For most of the counties the model
performs quite well. The mean absolute percentage error is 10.48.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (18.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(18.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(18.5)

We provide in Table 18.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the LithuaniaGLSlag model, the
long-term price elasticity is estimated to be -0.33 significantly different from zero at 1%.

148

Table 18.4: Estimation of the household water demand with lagged water consumption in Lithuania

LithuaniaGLSlag
b/se

ln Household size

ln evapotranspiration

ln Water price (in euros per m3)

ln Median age of the population

-0.136**
(0.06)
ln Household income (in euros per capita) 0.449*
(0.23)
0.388
(0.26)
-0.023
(0.24)
0.015
(0.20)
-0.039
(0.04)
0.052
(0.07)
0.588***
(0.07)
-2.718*
(1.53)

ln Lag of water consumption per capita

Share of groundwater abstracted

ln Population density

Constant

R-squared
N. of obs.

Source: auto.dta

108.000

149

Chapter 19

Luxembourg

150

19.1 Existing literature

MECE (2012) have estimated a residential water demand function for Luxembourg using a sample of 77
municipalities. The price elasticity is estimated to be -0.33 and the income elasticity is 0.59.

19.2 Urban water sector in Luxembourg

Water is generally provided by the Service des Eaux of the Commune. However, in some areas of the country,
the water supply is subcontracted to a private supplier.

19.3 Data

Our data set consists of municipality-level (below Nuts3) data on household water consumption, household
water price, household's socioeconomic conditions income and on climate condition for year 2010. Most of the
data come from the Luxembourg Statistical Service. The data consist of aggregate data at municipality-level on
water consumption per capita and water and sewerage prices. The data have been combined with information on
household's income, climate conditions, and household characteristics mainly from the Luxembourg Statistical
Service.

Water consumption data We use the annual reports (``Rapport sur la qualité de l'eau potable''). We have by
municipality the annual volume of water supplied to all users and the population served. In Luxembourg, 56%
of the volume distributed by the public network is used by households (national average). We have applied this
percentage to all municipalities in our sample. Our endogenous variable is then the annual water consumption
per capita to the public water supply network (in m3 per year per capita).

Water price data Following MECE (2012) we use the average price for water and wastewater (source is
Administration de la gestion de l'eau, circulaire number 2821).

Household income Our proxy for the household income is the housing rental price per squared meters
obtained from the Luxembourg Statistical Service at municipal level..

Other socioeconomic variables Other socio-economic variables come from the Luxembourg Statistical
Service.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

19.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis of residential water consumption and water price for Luxembourg.
Table 19.1 gives the annual residential water consumption (m3 per capita and per year) and the water
price (average water price, in euros m3) in Luxembourg in 2010. In 2010, the average annual consumption per
inhabitant was 51 m3 per capita.

151

Table 19.1: Household water consumption and price in Luxembourg

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

51.186

5.697

Table 19.2: Regional household water consumption and price in Luxembourg

Region

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

6.530
5.880
6.470
6.330
6.620
5.720
5.230
5.490
5.490
6.150
5.690
6.630
4.560
6.280
5.697

Bascharage
Bettembourg
Diekirch
Differdange
Ettelbruck
Hesperange
Luxembourg
Mamer
Mersch
Mondercange
Pétange
Sanem
Schifflange
Wiltz
Average

36.196
36.574
37.256
30.366
63.717
42.434
66.340
35.561
42.339
46.828
39.408
32.633
49.474
89.401
51.186

152

Table 19.2 provides some statistics for our municipal sample. Notice that we have only a sample made of
14 municipalities. However, these municipalities supply water services to a little bit less than 40% of the total
population in the Luxembourg.

19.5 Water demand function estimate

19.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(19.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (19.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(19.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

19.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(19.3)

where (cid:15)i is the usual random term.

19.5.3 Results

We report here our estimate for the residential water demand function in Luxembourg. The model in Table 19.3
corresponds to simple OLS.

The price elasticity varies is found to be -1.3, but is not significantly different from zero.
The income elasticity is equal to 0.39 but is not significantly different from zero.
Climate conditions (summer precipitations) have a significant impact on household water consumption.

153

Table 19.3: Estimation of the household water demand

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Average age of the population

ln Average of daily rainfall in summer (mm per day)

Constant

R-squared
N. of obs.

LuxembourgOLS
b/se

-1.289
(0.84)
0.392
(0.91)
-0.020
(2.37)
-8.670**
(3.36)
54.299**
(18.40)

0.467
14.000

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 19.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 17.3.

154

Figure 19.1: Observed versus predicted household water consumption in Luxembourg

155

30405060Wathat20406080100WathatWathatChapter 20

Malta

156

20.1 Existing literature

Delia (2004) presents the results of an econometric analysis of residential water demand in Malta, based on
annual data for years 1989 to 1999 (country-level time series). The price elasticity is shown to be significant
and equal to -0.36 whereas the income elasticity is estimated to be 0.25.

20.2 Urban water sector in Malta

The Water Services Corporation (WSC) is a public utility body responsible for the supply, production and distri-
bution of water in the Maltese Islands. It was set up by the Maltese Parliament in the Water Services Act XXIII
1991. The WSC operates desalination plants and it manages the municipal water distribution network.

20.3 Data

We use time-series national data on household water consumption, household water price, household's socioe-
conomic conditions income and on climate condition for years 2000 to 2010 (11 years). Most of the data come
from the National Statistics Office of Malta. The data consist of aggregate national data on water consumption
per capita and water and sewerage prices. The data have been combined with information on household's
income, climate conditions, and household characteristics mainly from the Malta Statistical Service.

Water consumption data We rely on difference publication of the National Statistics Office of Malta (Envi-
ronment Statistics report, Malta in Figures). From the Malta Statistical Service we have the annual volume of
water billed to households users from the public water network from 1995 to 2010. The volume of water has
been divided by the total population (around 100% of the population is connection to the public water supply
in Malta). Our endogenous variable is then the annual water consumption per capita to the public water supply
network (in m3 per year per capita).

Water price data Data come from the Central Bank of Malta which provides the price index for Water
services. This index has been used for computing an average national price for water (the average price of
water for household was 0.32 Lira per cubic meters in 2000).

Household income We use the household disposable income per capita available from the household bud-
getary surveys conducted by the National Statistics Office of Malta.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from the Malta
Statistical Service.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

20.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Malta.

157

Table 20.1: Household water consumption and price in Malta

Year

1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

32.204
34.794
36.875
27.975
29.217
29.621
28.988
30.989
31.560
31.887
27.605
26.564
27.413
27.234
27.347
25.825
29.756

0.711
0.711
0.712
0.701
0.701
0.746
0.743
0.781
0.781
0.781
0.891
1.102
1.147
1.119
1.769
1.832
0.952

Table 20.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Malta from 1995 to 2010.
In 2010, the average annual consumption
per inhabitant amounted to 25.8 m3. There has been a decreasing trend in domestic water consumption since
2003. The average water price in 2012 was 1.8 euros per m3. It has steadily increased since 1995.

20.5 Water demand function estimate

20.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(20.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (20.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water

158

demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(20.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

20.5.2 Estimation methods

Let t = {1, .., T } indexes years. Our equation of interest becomes:

ln (yt) = α ln (pt) + β ln (It) + γ ln (Zt)(cid:48) + (cid:15)t

(20.3)

where (cid:15)t is the usual random term.

20.5.3 Results

We report here our estimate for the residential water demand function in Malta. The model in Table 20.2
corresponds to simple OLS.

Table 20.2: Estimation of the household water demand in Malta

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln annual number of days without rainfalls

Constant

R-squared
N. of obs.

MaltaOLS
b/se

-0.311**
(0.12)
0.436
(0.41)
-0.309
(0.26)
0.759
(3.74)

0.686
11.000

Table 20.2 present the main significant variables explaining household water consumption in Malta. The
price elasticity varies is found to be -0.31, and is almost significantly different from zero at 10%. Based on the
OLS model, a 10% increase in water price results in a 3.1% decrease in short-run water consumption in Malta.
Water demand in Malta is inelastic, i.e. the estimated price elasticity in absolute values is lower than one.

The income elasticity is equal to 0.43, but is not significantly different from zero.
The annual number of days without rainfalls is not significant.
A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 20.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 3.4.

159

Figure 20.1: Observed versus predicted household water consumption in Malta

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (20.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(20.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(20.5)

We provide in Table 20.3 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the MaltaOLSlag model, the long-term
price elasticity is estimated to be -0.36 significantly different from zero at 10%.

160

2426283032Wathat262830323436WathatWathatTable 20.3: Estimation of the household water demand with lagged water consumption in Malta

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln annual number of days without rainfalls

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

Source: auto.dta

MaltaOLSlag
b/se

-0.269*
(0.14)
0.464
(0.43)
-0.237
(0.29)
0.252
(0.39)
-0.762
(4.57)

0.706
11.000

161

Chapter 21

Netherlands

162

21.1 Existing literature

There are some studies having estimated a residential water demand function in Netherlands, but no recent
one. Kooreman (1993) has estimated a water demand function on a panel of 60 municipalities in Netherlands
(1998-1989). The estimated (short-run) price elasticity of domestic water use in the Netherlands is estimated
to be -0.10. Linderhof (2001) uses a pool of 17 cross-sectional Netherlands Expenditure Survey data from
1978 to 1994 to estimate a residential water demand function. The sample used by Linderhof (2001) contains
28,210 households all over the Netherlands. The price and income elasticities of the residential water demand
in the Netherlands are estimated to be -0.07 and 0.11, respectively.

21.2 Urban water sector in Netherlands

In Netherlands, there are 10 water distribution companies operating at the end of 2010. The water companies
are public limited companies. Shareholders are the municipalities and provinces.

According to Vevin (2012), Dutch drinking water sales have showed a considerable increase over the previous
century: from around 300 million m3 in 1950 to around 870 million m3 in 1970 and subsequently to 1,236
million m3 in 1990. Between 1990 and 1995 sales levels remained more or less stable. Between 1995 and
2004 the volume has started to decline to the current level of approximately 1147 million m3 per year (in
2010).

21.3 Data

Our panel data set consists of Nuts3-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2009-2011. Most of data
related to water come from the annual reports published by the Association of Dutch Water Companies (Vewin).
Socio-economic variables at regional-level come from Statistics Netherlands, the Dutch National Office of
Statistics.

Water consumption data We rely on water utility data from the Vewin for year 2009-2011. We have the
annual volume of water sold to households for each water utility in Netherlands for year 2009-2011. To get the
annual household water consumption per capita, we have divided this volume by the population connected to
the public network in each water utility. The household water consumption in then measure in m3 of water sold
to households per capita and per year. The report Vevin (2012) indicates also which municipalities are served
by the 10 water services. We have used this information to compute annual household water consumption at
Nuts3 for year 2009-2011.

Water price data We rely on water utility data from the Vewin for year 2009-2011. As a price indicator, we
use the average price paid by households for the drinking water service (in euro m3 including VAT).

Household income We use the average disposable income (in euros per capita) obtained directly from
Statistics Netherlands for years 2009-2011.

Other socioeconomic variables Other socio-economic variables for 2009-2011 comes from Statistics
Netherlands.

163

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

21.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Netherlands.

Table 21.1: Household water consumption and price in Netherlands

Year

2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

48.093
47.955
47.503
47.849

1.365
1.396
1.406
1.389

Table 21.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) in Netherlands from 2009 to 2011.
In 2009, the average
annual consumption per inhabitant amounted to 48.09 m3. There has been a decreasing trend in domestic
water consumption which started some ten years ago. From 2009 to 2011, the average water price has slightly
increased form 1.36 euros per m3 in 2009 to 1.41 euros per m3 in 2011.

Table 21.2: Regional household water consumption and price in Netherlands

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Brabant Water
Dunea
Evides Drinkwater
Oasen
PWN aterleidingbedrijf Noord-Holland
Vitens
WML
Waterbedrijf Groningen
Waterleidingmaat-schappij Drenthe
Waternet
Average

41.237
38.305
50.197
41.169
49.220
49.434
48.214
40.541
56.881
50.976
47.955

1.380
1.680
1.570
1.780
1.670
1.310
1.570
1.230
1.300
1.690
1.396

Table 21.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
(retail water price for cold water in euros per m3) per water utility in 2010. As it can be seen, there are some
differences across water utilities, both in terms of water consumption and water prices.

21.5 Water demand function estimate

164

21.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(21.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (21.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(21.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

21.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 (40) and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(21.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

21.5.3 Results

We report here our estimate for the residential water demand function in Netherlands, based on our Nuts3
dataset for Netherlands (2009 to 2011). The first model estimated in Table 21.3 corresponds simple OLS
whereas in the second model we estimate Equation (21.3) with a random parameter estimator.
In the third
model, we account for potential endogeneity of the price by using an instrumental variable approach.

Table 21.3 present the main significant variables explaining household water consumption in Netherlands.
The price elasticity is estimated to be -0.13 with the GLS model, and the estimated coefficient is significantly
different from zero. The price elasticity varies across models, from -0.28 with the OLS to -0.63 with the
instrumental variable approach. Based on the NetherlandsGLSiv model, a 10% increase in water price results

165

Table 21.3: Estimation of the household water demand in the Netherlands

NetherlandsOLS NetherlandsGLS NetherlandsGLSiv
b/se

b/se

b/se

ln Household size

ln Water price (in euros per m3)

-0.275**
(0.12)
ln Household income (in euros per capita) 0.201
(0.19)
0.013
(0.09)
-0.023
(0.14)
2.001
(1.88)

ln summer evapotranspiration

Constant

-0.129**
(0.06)
0.064*
(0.03)
-0.029
(0.09)
0.102***
(0.02)
3.165***
(0.34)

-0.626***
(0.14)
0.230***
(0.08)
-0.176
(0.11)
0.075*
(0.04)
1.898***
(0.72)

R-squared
N. of obs.

0.069
120.000

120.000

120.000

in a 6.3% decrease in short-run water consumption in Netherlands. Water demand in Netherlands is inelastic,
i.e. the estimated price elasticity in absolute values is lower than one.

The income elasticity is also quite consistent with the three models.

It varies between 0.06 and 0.23 and
the income elasticity is significantly different from zero with the GLS and the IV estimators. This suggests that
there is an increasing relationship between water consumption and household income. The positive significant
coefficient for income indicates that, based on the NetherlandsGLSiv model, a 10% increase in income will
result in a 2.3% increase in water consumption.

Climate conditions play also a significant role. According to the NetherlandsGLSiv estimate, increasing the

summer evapotranspiration by 10% will result in increasing the residential water consumption by 0.8%.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 21.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 9.6.

166

Figure 21.1: Observed versus predicted household water consumption in the Netherlands

167

404244464850Wathat3540455055WathatWathatChapter 22

Poland

168

22.1 Existing literature

Bartczak, Kopanska, and Raczka (2009) have estimated the demand for water by households in Poland, using
panel data from 39 municipal districts over the period 2001-2005. Their survey concentrates on medium- and
large-sized cities with population more than 50.000. They found that water demand in urban areas in Poland
is rather inelastic. The estimated price elasticity is around -0.2.

22.2 Urban water sector in Poland

Before 1989, water utilities in Poland were organized and managed in a decentralized system (based on county
level) but with only a limited autonomy from the central state (Kantor and Horváth 2012). The adoption of
the Act on Local Self-Government in March 1990 started a process of decentralization to local authorities.
Nowadays, municipalities (gmina, the smallest administrative unit in Poland, are the responsible authority for
water supply and sanitation connected to public services. The market is highly fragmented with about 2500
gminas (Kantor and Horváth 2012).

Residential water consumption in Poland has changed substantially over the last two decades. Daily water
consumption per capita in the beginning of the Nineties was about 140 litres. However by 2005, this figure
dropped by nearly 30% (Bartczak, Kopanska, and Raczka 2009).

22.3 Data

Our panel data set consists of Nuts3-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2003-2012. Most of the
data come from the Central Statistical Office of Poland (the National Office of Statistics). The data consist of
aggregate data at Nuts3-level on water consumption per capita and water and sewerage prices. The data have
been combined with information on income, weather, and household characteristics mainly from the Central
Statistical office of Poland.

Water consumption data We have used the municipal water consumption per inhabitant (m3 per capita)
available year year from 2003 to 2012 from the Central Statistical Office of Poland.

Water price data We rely on price data published by the the Central Statistical Office of Poland . As a price
indicator, we use the average price paid by households for cold water (only drinking water service).

Household income We use the average annual available income per capita available from Statistics Poland
from from 2003 to 2012 from the Central Statistical Office of Poland.

Other socioeconomic variables Other socio-economic variables from 2003 to 2012 come from the Central
Statistical Office of Poland.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

169

22.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Poland.

Table 22.1: Household water consumption and price in Poland

Year

2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

36.908
35.775
35.509
35.598
34.983
35.318
34.781
34.546
34.655
34.618
34.380
35.186

0.367
0.397
0.422
0.430
0.456
0.508
0.575
0.622
0.691
0.760
.
0.523

Table 22.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Poland from 2003 to 2012. In 2012, the average annual consumption per
inhabitant amounted to 34.7 m3. There has been a decreasing trend in domestic water consumption from 2003
to 2013. Over the same period, the average water price has increased from 0.4 euros per m3 to 0.8 euros per
m3.

Table 22.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts 2) in 2010. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With almost 40 m3 per capita per year, the water consumption is the higher in
the Mazowieckie region. On contrary, a low water consumption is reported in the Podkarpackie region (24 m3
per capita per year).

22.5 Water demand function estimate

22.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(22.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

170

Table 22.2: Regional household water consumption and price in Poland

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Dolnoslaskie
Kujawsko-Pomorsk
Lodzkie
Lubelskie
Lubuskie
Malopolskie
Mazowieckie
Opolskie
Podkarpackie
Podlaskie
Pomorskie
Slaskie
Swietokrzyskie
Warminsko-Mazurs
Wielkopolskie
Zachodniopomorsk
Average

35.499
36.189
37.842
29.165
33.371
29.695
39.834
32.530
24.466
34.180
36.503
33.718
27.172
34.267
38.804
37.536
34.546

0.671
0.551
0.553
0.482
0.692
0.643
0.586
0.578
0.659
0.519
0.551
0.869
0.600
0.523
0.592
0.580
0.622

To estimate Equation (22.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(22.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

22.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(22.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

171

Table 22.3: Estimation of the household water demand in Poland

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Average of daily rainfall in summer (mm per day)

ln Share population below 9

ln Share population above 70

Percentage of population with dishwasher

Constant

R-squared
N. of obs.

22.5.3 Results

PolandOLS
b/se

PolandGLS
b/se

PolandGLSiv
b/se

-0.393***
(0.04)
0.555***
(0.04)
-0.259***
(0.02)
-1.276***
(0.07)
-0.671***
(0.05)
0.024***
(0.01)
-5.954***
(0.47)

0.568
660.000

-0.182***
(0.03)
0.222***
(0.04)
-0.025***
(0.01)
-0.405***
(0.05)
-0.515***
(0.05)
0.009***
(0.00)
-0.697*
(0.41)

-0.339
(0.40)
0.385
(0.40)
-0.024*
(0.01)
-0.293***
(0.11)
-0.615***
(0.15)
0.007***
(0.00)
-2.199
(3.87)

660.000

660.000

We report here our estimate for the residential water demand function in Poland, based on our Nuts3 dataset
for Poland (2003 to 2012). The first model estimated in Table 22.3 corresponds simple OLS whereas in the
second model we estimate Equation (22.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 22.3 present the main significant variables explaining household water consumption in Poland. The
price elasticity varies across models, from -0.40 with the OLS to -0.18 with the GLS (not significant with GLSiv).
Based on the OLS and GLS models, a 10% increase in water price results in a 1.8% to 4.0% decrease in short-run
water consumption in Poland. Water demand in Poland is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

The income elasticity varies between 0.22 and 0.56 and the income elasticity is significantly different from
zero with the OLS and the GLS models. This suggests that there is an increasing relationship between water
consumption and household income. The positive significant coefficient for income indicates that, based on the
GLS model, a 10% increase in income will result in a 2.2% increase in water consumption.

Climate conditions play also a significant role, together with the housing characteristics. An increase of

summer rainfall by 10% will imply a reduction in water consumption by 2.6% (OLS model).

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 22.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 14.7.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption

172

Figure 22.1: Observed versus predicted household water consumption in Poland

as an additional independent variable in Equation (22.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(22.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(22.5)

We provide in Table 22.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the PolandGLSlag model, the long-term
price elasticity is estimated to be -0.25 almost significantly different from zero at 10%. With the PolandGLSivlag
model, the long-term price elasticity is estimated to be -1.30 but is not significantly different from zero at 10%.

173

20406080100120Wathat050100150200250WathatWathatTable 22.4: Estimation of the household water demand with lagged water consumption in Poland

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Average of daily rainfall in summer (mm per day)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

PolandGLSlag
b/se

PolandGLSivlag
b/se

-0.013
(0.01)
0.038***
(0.01)
-0.029***
(0.01)
0.946***
(0.01)
-0.128
(0.08)

-0.511
(0.47)
0.499
(0.45)
-0.037**
(0.02)
0.609***
(0.06)
-3.297
(4.02)

594.000

594.000

174

Chapter 23

Portugal

175

23.1 Existing literature

Martins and Adelino (2007) have used a panel data sample of 5 municipalities located in the Centre Region of
Portugal with monthly observations from January 1998 to December 2003. They report a price elasticity equal
to -0.558. The air temperature variable is statistically significant with a positive coefficient, suggesting that
high temperatures increase the demand for water.

Monteiro and Roseta-Palma (2011) have estimated a residential water demand function based on annual
data for the years 1998, 2000, 2002, and 2005 on water consumption and water and wastewater tariffs
for 278 municipalities in mainland Portugal. Depending upon the model they consider, the price elasticity of
the Portugese water demand varies between -0.133 and -0.051. The income elasticity is not found to be
significantly different from zero. Climate condition matter. Water demand increases with temperature and
decreases with precipitation.

Recently Monteiro, Cardoso, and Figueiredo (2014) have estimated a residential water demand function
using a sample of 383 Portuguese households from 10 municipalities spread across mainland Portugal. On
average, they report a price elasticity equal to -0.48 and an income elasticity equal to 0.254.

23.2 Urban water sector in Portugal

There are 21 utilities operating in the Portuguese wholesale market. Three of them provide drinking water, six
just wastewater services and 12 drinking water and wastewater services together (Cruz, Marques, Romano, and
Guerrini 2012). One of these entities is a private concessionaire while the other 20 are public. At retail level,
this sector is fairly fragmented. According to ERSAR (2013), it is composed by a high number of operators, 380,
generally small and focused on providing the service inside each municipality. More than half the population is
served by retail services under direct management in 70% of the municipalities.
In this sector, municipalities
are the most common submodel, providing this service to nearly 3.1 million inhabitants in 191 administrative
territories. A relatively high number (113) of very small operators is still active, namely parishes and consumers'
associations, and serve 77 thousand inhabitants.

23.3 Data

Our panel data set consists of municipality-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2007 and 2009. Most of the
data come from Statistics Portugal (the National Office of Statistics) and from the Water and Waste Services
Regulation Authority (ERSAR) for what concerns water and sewerage prices. The data consist of aggregate
data at municipal-level on water consumption per capita and water and sewerage prices. The data have been
combined with information on income, weather, and household characteristics mainly from the the National
Statistics Institute.

Water consumption data We have used the municipal water consumption per inhabitant (m3/ inhab.)
available for years 2007 and 2009 from Statistics Portugal. Data source is the Inventário Nacional de Sistemas
de Abastecimento de Água e de Águas Residuais.

Water price data We rely on price data published by the Water and Waste Services Regulation Authority
(ERSAR). As a price indicator, we use the average price paid by households for the drinking water service and
the wastewater service (in euro m3 for an annual water consumption equal to 120 m3).

176

Household income We use the average annual earning (euros per capita) available from Statistics Portugal
from 2006 to 2009.

Other socioeconomic variables Other socio-economic variables for 2008-2011 comes from Statistics
Portugal.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

23.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Portugal.

Table 23.1: Household water consumption and price in Portugal

Year

2006
2007
2008
2009
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

50.431
54.355
59.693
63.523
56.672

.
1.123
.
1.229
1.174

Table 23.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
In 2009,
(average water and wasterwater price for 120 m3, in euros m3) in Portugal from 2006 to 2009.
the average annual consumption per inhabitant amounted to 63.5 m3. There has been an increasing trend in
domestic water consumption from 2006 to 2009. Over the same period, the average water price has increased
from 1.12 euros per m3 to 123 euros per m3.

Table 23.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts 2) in 2010. As it can be seen, there are some differences across regions, both in terms of water
consumption and water prices. With almost 138 m3 per capita per year, the water consumption is the higher in
Algarve. On contrary, a low water consumption is reported in the Ave region.

23.5 Water demand function estimate

23.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(23.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,

177

Table 23.2: Regional household water consumption and price in Portugal

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

0.986
0.908
1.164
0.791
0.963
1.263
0.885
1.197
1.279
1.419
1.604
1.847
1.122
1.223
1.008
1.548
1.164
1.395
0.848
1.329
1.314
1.840
1.159
0.866
0.714
1.374
1.335
1.249
1.229

Alentejo Central
Alentejo Litoral
Algarve
Alto Alentejo
Alto Trás-os-Montes
Ave
Baixo Alentejo
Baixo Mondego
Baixo Vouga
Beira Interior Norte
Beira Interior Sul
Cova da Beira
Cávado
Douro
Dâo-Lafôes
Entre Douro e Vouga
Grande Lisboa
Grande Porto
LezÃ ria do Tejo
Minho-Lima
Médio Tejo
Oeste
Península de Setúbal
Pinhal Interior Norte
Pinhal Interior Sul
Pinhal Litoral
Serra da Estrela
Tâmega
Average

76.749
92.112
137.764
62.297
59.346
26.216
63.083
100.824
69.185
65.957
72.173
44.554
56.803
51.355
42.688
23.589
58.253
67.754
75.085
45.591
59.073
59.730
89.870
45.997
46.234
74.588
32.366
32.150
63.523

178

respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (23.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(23.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

23.5.2 Estimation methods

Let i = {1, .., I} indexes municipalities and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(23.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

23.5.3 Results

We report here our estimate for the residential water demand function in Portugal, based on our municipal
dataset for Portugal (2007 and 2011). The first model estimated in Table 23.3 corresponds simple OLS
whereas in the second model we estimate Equation (23.3) with a random parameter estimator.
In the third
model, we account for potential endogeneity of the price by using an instrumental variable approach.

The price elasticity is estimated to be -0.02 with the GLS model, and the estimated coefficient is significantly

different from zero.

Table 23.3 present the main significant variables explaining household water consumption in Portugal. The
price elasticity varies across models, from -0.269 with the GLSiv to -0.00 with the OLS (not significant with OLS
or GLS). Based on the GLSiv model, a 10% increase in water price results in a 2.7% decrease in short-run water
consumption in Portugal. Water demand in Portugal is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

The income elasticity is quite consistent with all models. It varies between 0.67 and 0.84 and the income
elasticity is significantly different from zero with the three models. This suggests that there is an increasing
relationship between water consumption and household income. The positive significant coefficient for income
indicates that, based on the GLSiv model, a 10% increase in income will result in a 8.4% increase in water
consumption.

Climate conditions play also a significant role, together with the housing characteristics. An increase of

summer rainfall by 10% will imply a reduction in water consumption by 2%.

179

Table 23.3: Estimation of the household water demand in Portugal

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Average of summer rainfall (in mm per day)

ln Share population below 14

Constant

R-squared
N. of obs.

PortugalOLS
b/se

PortugalGLS
b/se

PortugalGLSiv
b/se

-0.007
(0.04)
0.673***
(0.14)
-0.217***
(0.03)
-0.261**
(0.11)
-1.112
(1.00)

0.209
419.000

-0.020
(0.05)
0.690***
(0.16)
-0.154***
(0.03)
-0.240*
(0.13)
-1.170
(1.16)

-0.269*
(0.15)
0.840***
(0.17)
-0.200***
(0.04)
-0.110
(0.15)
-1.947*
(1.14)

419.000

419.000

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 23.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 36.0.

180

Figure 23.1: Observed versus predicted household water consumption in Portugal

181

20406080100120Wathat050100150200250WathatWathatChapter 24

Romania

182

24.1 Existing literature

Ciomos, Ciataras, and Alina (2012) estimate a residential water demand function using time-series data (9
years from 2002 to 2010) for the municipality of Cluj-Napoca, Romania. They report a price elasticity equal to
of -0.70.

24.2 Urban water sector in Romania

Before 1990, water utilities in Romania operated as public services at county level (42 multi-utilities at county),
without any central authority or ministry who coordinated their activities (Vinke-de Kruijf, Dinica, and Augustijn
Infrastructure was owned by county councils who had integrated public companies taking care of
2009).
most public services (e.g. water, waste and energy). After 1990, the organization of water services changed.
Romania returned to the local autonomy principle and major responsibilities have been transferred to the local
communities (more than 800 services). After 2000, a process of regrouping utilities at regional level started.

According to the National Regulatory Authority for Municipal Services (ANRSC), there are in 2011 a little
bit more than 1000 water supply and sewage in Romania including, 42 regional operators, 2 large municipal
utilities and 200 local utilities (serving 10% of the population). Frone and Frone (2013) indicate that in 2009 the
situation of the development of water and wastewater infrastructures was still critical in Romania, especially
in rural areas. The main problematic issues were inadequate water treatment, poor sewerage network and low
access to water and wastewater systems. According to ANRSC, the percentage of the population connected to
the water supply network was 56.5% in 2011, 60% in 2012 and approximately 64% in 2013. Most of the water
consumption is metered (90%).

24.3 Data

Our panel data set consists of Nuts3-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2000-2012. Most of the data
come from the National Institute of Statistics of Romania. The data consist of aggregate data at Nuts3-level on
water consumption per capita and water and sewerage prices. The data have been combined with information
on income, weather, and household characteristics mainly from the National Institute of Statistics of Romania.

Water consumption data We have the quantity of drinking water supplied to household by Nuts3 from
2000 to 2012, together with the population connected to public water supply for each county and year (National
Institute of Statistics of Romania). We have then computed the annual household water consumption per
connected inhabitant (m3 per capita) in each county from 2003 to 2012.

Water price data Water prices come from the International Benchmarking Network for Water and Sanitation
Utilities (IBNET) and are available at the water utility-level from 2000 to 2010. Water services have been
matched to their respective Nuts3 (Nuts3 without a water service in the IBNET database are given a water price
corresponding to the national average). As a price indicator, we use the average price (euros per m3) paid by
households for the water service (based on a household annual water consumption equal to 72 m3).

Household income We use the GDP per capita available from the National Institute of Statistics of Romania
from 1995 to 2010 (Nuts2-level)

183

Other socioeconomic variables Other socio-economic variables from 2000 to 2012 come from the National
Institute of Statistics of Romania.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

24.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Romania.

Table 24.1: Household water consumption and price in Romania

Year

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

105.047
94.234
77.763
68.632
66.883
60.157
60.866
61.151
60.987
57.285
55.803
54.371
55.253
67.802

0.240
0.202
0.220
0.525
0.511
0.701
0.729
0.885
1.267
0.966
0.807
.
.
0.809

Table 24.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Romania from 2003 to 2012. In 2012, the average annual consumption
per inhabitant amounted to 55.2 m3. There has been a decreasing trend in domestic water consumption from
2000 to 2012. The water consumption per capita has been almost divided by two. Over the period 2000 to
2010, the average water price has increased from 0.2 euros per m3 to 0.8 euros per m3.

Table 24.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts 3) in 2010. As it can be seen, there are some differences across Nuts3, both in terms of water
consumption and water prices. With more than 100 m3 per capita per year, the water consumption is the higher
in the Dolj Nuts3. On contrary, a low water consumption is reported in the Botosani district (29 m3 per capita
per year).

24.5 Water demand function estimate

184

Table 24.2: Regional household water consumption and price in Romania

Alba
Arad
Arges
Bacau
Bihor
Bistrita-Nasaud
Botosani
Braila
Brasov
Bucharest Municipality
Buzau
Calarasi
Caras-Severin
Cluj
Constanta
Covasna
Dambovita
Dolj
Galati
Giurgiu
Gorj
Harghita
Hunedoara
Ialomita
Iasi
Ilfov
Maramures
Mehedinti
Mures
Neamt
Olt
Prahova
Salaj
Satu Mare
Sibiu
Suceava
Teleorman
Timis
Tulcea
Valcea
Vaslui
Vrancea
Average

Water consumption
(m3 per capita per year )
41.516
45.771
47.626
44.217
46.871
46.715
29.527
30.276
54.782
96.363
47.501
53.465
47.200
59.950
57.600
59.182
37.106
101.360
50.231
44.825
56.756
49.272
43.370
49.994
58.339
85.093
53.931
50.884
38.292
35.803
71.798
35.266
52.898
38.984
79.000
45.184
77.356
55.331
46.276
48.567
45.256
51.658
55.803

Water price
(euros per m3)
0.588
1.205
0.342
.
.
0.829
.
0.652
0.989
.
0.790
.
.
0.617
1.106
.
.
.
.
.
.
0.617
.
.
1.049
.
0.691
0.718
0.812
.
.
.
.
0.827
0.684
.
.
0.635
.
.
.
1.245
0.807

185

24.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(24.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (24.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(24.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

24.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(24.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

24.5.3 Results

We report here our estimate for the residential water demand function in Romania, based on our Nuts3 dataset
for Romania (2000 to 2010). The first model estimated in Table 24.3 corresponds simple OLS whereas in the
second model we estimate Equation (24.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

Table 24.3 present the main significant variables explaining household water consumption in Romania. The
price elasticity varies across models, from -0.58 with the GLSiv to 0.00 with the GLS (not significant with OLS
and GLS). Based on the GLSiv model, a 10% increase in water price results in a 5.8% decrease in short-run water
consumption in Romania. Water demand in Romania is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

186

Table 24.3: Estimation of the household water demand in Romania

RomaniaOLS
b/se

RomaniaGLS
b/se

RomaniaGLSiv
b/se

ln Water price (in euros per m3)

ln Household income (in euros per capita)

-0.097
(0.07)
-0.130
(0.09)
ln annual number of days without rainfalls 0.126
(0.18)
-0.100
(2.48)
4.566**
(1.75)

Share population below 15

Constant

0.000
(0.05)
-0.205***
(0.07)
0.455***
(0.15)
2.706
(2.40)
3.246**
(1.30)

-0.580**
(0.27)
0.255
(0.23)
0.467*
(0.28)
1.837
(3.15)
-1.327
(3.75)

R-squared
N. of obs.

0.128
124.000

124.000

124.000

Figure 24.1: Observed versus predicted household water consumption in Romania

187

406080100120Wathat0100200300400WathatWathatTable 24.4: Estimation of the household water demand with lagged water consumption in Romania

RomaniaGLSlag
b/se

RomaniaGLSivlag
b/se

ln Water price (in euros per m3)

ln Household income (in euros per capita)

-0.037
(0.05)
-0.072
(0.06)
ln annual number of days without rainfalls 0.256*
(0.15)
0.402***
(0.07)
1.716
(1.14)

ln Lag of water consumption per capita

Constant

-0.125
(0.23)
0.046
(0.18)
0.232
(0.22)
0.531***
(0.08)
0.161
(2.69)

R-squared
N. of obs.

Source: auto.dta

124.000

124.000

The income elasticity varies between 0.20 and 0.26 and the income elasticity is significantly different from

zero only with the GLS models.

Climate conditions play also a significant role. An increase by 10% of the number of days without any

rainfall will imply a increase in water consumption by 4.7% (GLSiv model).

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 24.1. For most of the regions the model
performs quite well. The mean absolute percentage error is 25.9.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (24.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(24.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(24.5)

We provide in Table 24.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the RomaniaGLSlag model, the
long-term price elasticity is estimated to be -0.06 but not significantly different from zero at 10%. With the
RomaniaGLSivlag model, the long-term price elasticity is estimated to be -0.27 again not significantly different
from zero at 10%.

188

Chapter 25

Slovakia

189

25.1 Existing literature

Dalmas and Reynaud (2005) investigates the residential water demand in the Slovak Republic. The demand
model is estimated on a sample of 71 municipalities observed from 1999 to 2001. Three different functional
forms for water demand have been estimated and compared: a lin-lin specification, a log-log form and a Stone-
Geary function. Dalmas and Reynaud (2005) find that the residential water demand in the Slovak Republic
appears to be inelastic, but imperfectly, with the three econometric specifications. Using the Stone-Geary
specification for instance, they get a short-term price elasticity varying from -0.35 to -0.50. Slovak consumers
are price reactive and the water price can be used as an economic tool to indicate resource scarcity. Second,
the price sensitivity threshold using the Stone-Geary specification is estimated at 31.5 cubic meters per person
and per year, a level still lower than the average water consumption per person in 2001, 41.5 cubic meters.

25.2 Urban water sector in Slovakia

Municipalities as asset owners are responsible for water services provision in the Slovak Republic (WorldBank
2015e). The most dominant model is the mixed-capital operating company in which municipalities own the
majority of shares. Ten mixed capital companies provide services to 60% of the population. Only 3 privately
owned companies (separate model) provide service to 20% of the population, on the basis of long-term
concession contracts. Another 4 purely municipal companies render services to 2% of the population, and 400
village administrations provide water to 5% of inhabitants living in rural areas. The remaining inhabitants rely
on self-provision (13%). All utilities provide both water and wastewater services (WorldBank 2015e).

According to MinistryEnvironment (2011), 86.56% out of the population of the Slovak Republic was connected
to the public water supply network. In 2010 the number of municipalities with public water supply network was
2,297 which is 79.5% of the total number of municipalities of the Slovak Republic. The level of public water
supply network is not the same in all regions (picture on the page 35). The highest share of supplied inhabitants
is in Bratislava region.

25.3 Data

Our panel data set consists of Nuts3-level (county) data on household water consumption, household water
price, household's socioeconomic conditions income and on climate condition for years 2001-2011. Most of the
data come from the Statistical Office of the Slovak Republic. The data consist of aggregate data at Nuts3-level
on water consumption per capita and water and sewerage prices. The data have been combined with information
on household's income, climate conditions, and household characteristics mainly from Statistics Slovakia.

Water consumption data From the Statistical Office of Slovakia we have the annual volume of water
supplied to urban users from the public water network from 2002 to 2012 at Nuts3 level (this volume can be
invoiced or not invoiced). We have used the annual national percentage of water used by households to get the
annual volume of water supplied to household from the public water network from 2002 to 2012 at Nuts3 level.
The volume of water has been divided by the population connected to the water network in each Nuts 3 (total
population multiplied by the share of the population served by the water network). Our dependent variable is
then the annual water consumption per connected capita to the public water supply network (in m3 per year per
capita).

190

Water price data Data are annually published by the Ministry of Agriculture, Water and Soil Management
(Green Reports on Water Management in the Slovak Republic). The price includes a 10 percent VAT on water
services. The water price corresponds to an average price (euros per m3) for the drinking water supply service
and the wastewater water service. The water price is available only at the national level.

Household income We use the average disposable household income (euros per year per capita) available
from 2000 to 2011 at Nuts3 from the Statistical Office of the Slovak Republic.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from the Statis-
tical Office of the Slovak Republic.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

25.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Slovakia.

Table 25.1: Household water consumption and price in Slovakia

Year

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

42.536
41.746
39.617
35.900
35.450
32.832
33.801
33.407
30.899
30.128
29.592
35.070

0.618
0.641
0.824
0.900
1.065
1.242
1.490
1.560
1.680
1.770
1.820
1.239

Table 25.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Slovakia from 2001 to 2011. In 2011, the average annual consumption
per inhabitant amounted to 29.6 m3. There has been a decreasing trend in domestic water consumption since
2001. Over the period 2002-2011, the average water price has been multiplied by 3. It has increased from 0.6
euros per m3 to 1.8 euros per m3.

Table 25.2 gives the annual residential water consumption (m3 per capita and per year) and the water
price per county (Nuts 3) in 2010.1 As it can be seen, there are some differences across regions in terms

1For the water and wastewater price, we have only a national average.

191

Table 25.2: Regional household water consumption and price in Slovakia

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Region of Banská Bystrica
Region of Bratislava
Region of Kosice
Region of Nitra
Region of Presov
Region of Trenèí
Region of Trnava
Region of Zilina
Average

29.421
45.611
27.956
26.826
24.939
28.642
31.741
28.592
30.128

1.770
1.770
1.770
1.770
1.770
1.770
1.770
1.770
1.770

of water consumption. With almost 46 m3 per capita per year, the water consumption is the higher in the
Brastislava Nuts3. The Brastislava Nuts3 includes the capital Brastislava and is the most densely populated
area of Slovakia. On contrary, a low water consumption is reported in the PreÅ¡ov county (25.0 m3 per capita
per year).

25.5 Water demand function estimate

25.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(25.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (25.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(25.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

192

Table 25.3: Estimation of the household water demand in Slovakia

SlovakOLS
b/se

SlovakGLS
b/se

SlovakGLSiv
b/se

ln Average population age

ln Water price (in euros per m3)

ln Household income (in euros per capita)

-0.659***
(0.08)
0.625***
(0.16)
-0.233
(1.58)
-0.341
(0.44)
ln number of summer days without rainfalls 0.057
(0.05)
-1.379
(5.20)

ln Share population below 14

Constant

-0.659***
(0.08)
0.625***
(0.16)
-0.233
(1.58)
-0.341
(0.44)
0.057
(0.05)
-1.379
(5.20)

-0.940***
(0.20)
1.048**
(0.51)
-1.795
(5.84)
-0.860
(1.77)
0.157
(0.12)
-0.342
(20.73)

R-squared
N. of obs.

88.000

88.000

48.000

25.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(25.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

25.5.3 Results

We report here our estimate for the residential water demand function in Slovakia, based on our Nuts3 dataset
for Slovakia (2002 to 2011). The first model estimated in Table 25.3 corresponds simple OLS whereas in the
second model we estimate Equation (25.4) with a random parameter estimator. In the third model, we account
for potential endogeneity of the price by using an instrumental variable approach.

In the GLSiv model we have considered two instruments namely the share on non-revenue water and the
average price of water for non-household users. According to (MinistryEnvironment 2011), the amount of
non-revenue water used to represent 32.1% of water intended for use in 2010. Out of this number the loss in
pipe network represents 85.8%.

Table 25.3 present the main significant variables explaining household water consumption in Slovakia. The
price elasticity varies across models, from -0.66 with the OLS to -0.94 with the GLSiv (always significant). Based
on the GLSiv models, a 10% increase in water price results in a 9.4% decrease in short-run water consumption
in Slovakia. Water demand in Slovakia is inelastic, i.e. the estimated price elasticity in absolute values is lower
than one.

193

Figure 25.1: Observed versus predicted household water consumption in Slovakia

The income elasticity varies between 0.63 and 1.0 and the income elasticity is significantly different from
zero with the three models. This suggests that there is an increasing relationship between water consumption
and household income. The positive significant coefficient for income indicates that, based on the GLS model,
a 10% increase in income will result in a 10% increase in water consumption.

Climate conditions don't seem to play a significant role.
A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 25.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 10.25.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (25.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(25.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(25.5)

We provide in Table 25.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the SlovakiaGLSlag model, the long-term
price elasticity is estimated to be -1.00 significantly different from zero at 1%. With the SlovakiaGLSivlag
model, the long-term price elasticity is estimated to be -1.14 significantly different from zero at 1%.

194

2030405060Wathat203040506070WathatWathatTable 25.4: Estimation of the household water demand with lagged water consumption in Slovakia

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Average population age

ln Share population below 14

ln number of summer days without rainfalls

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

Source: auto.dta

SlovakiaGLSlag
b/se

SlovakiaGLSivlag
b/se

-0.325***
(0.10)
0.508***
(0.15)
-0.838
(1.16)
-0.219
(0.37)
-0.008
(0.05)
0.683***
(0.08)
-0.331
(3.91)

-0.586**
(0.28)
0.916*
(0.50)
-1.394
(3.29)
-0.309
(1.05)
-0.019
(0.11)
0.488**
(0.23)
-1.018
(12.03)

80.000

40.000

195

Chapter 26

Slovenia

196

26.1 Existing literature

To our best knowledge, there doesn't exist any published article having estimated a residential water demand
function in Slovenia.

26.2 Urban water sector in Slovenia

This paragraph is based on Globevnik (2013). Slovenia has sufficient water resources to provide continuous
drinking water supply. Groundwater represents the most important source of drinking water in Slovenia,
supplying about 97% of the population. Some areas suffer from periodic deficits particularly in the eastern part
It is considered by Globevnik (2013) that the expected growth of drinking water demand can
of the country.
become a limiting factor for development in the near future, not so much due to the lack of resources (quantity),
but rather due to bad management and weak investments dynamics.

According to the Environment Protection Act, drinking water supply is under the responsibility of local
communities and is performed as a mandatory public service. The facilities and infrastructure needed to perform
public service are the property of local communities. Local communities should carry out a management plan
for drinking water supply that has to be certified by state authority. The monitoring data show that more than
91% of the population receive water in compliance with the current regulations. The water supply system is
quite fragmented since most of the systems in Slovenia supply drinking water to 20,000 to 50,000 persons.

26.3 Data

Our panel data set consists of Nuts3-level (county) data on household water consumption, household water
price, household's socioeconomic conditions income and on climate condition for years 2001-2011. Most of
the data come from the Statistical Office of Slovenia. The data consist of aggregate data at Nuts3-level on
water consumption per capita and water and sewerage prices. The data have been combined with information
on houshol's income, climate conditions, and household characteristics mainly from Statistics Slovenia.

Water consumption data From the Statistical Office of Slovenia we have the annual volume of water
supplied from the public water network to households from 2002 to 2012 at Nuts3 level. The volume of water
has been divided by the population connected to the water network in each Nuts 3 (total population multiplied
by the share of the population served by the water network). Our endogenous variable is then the annual water
consumption per connected capita to the public water supply network (in m3 per year per capita).

Water price data Our main source of data is the report LMVeritas (2012) which describe the price for the
water and the wastewater service for more than 200 municipalities in Slovenia. This report covers the period
2000 to 2011. As a price indicator, we use the average price paid by households for the water and wastewater
services (in euros per m3). LMVeritas (2012) provides the price information at the municipality level. We have
then matched each municipality with the corresponding Nuts3.

Household income We use the average gross domestic product per capita available from Statistics Slovenia
from from 2000 to 2011 at Nuts3 as a proxy of household's income.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from Statistics
Slovenia.

197

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

26.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis of residential water consumption and water price for Slovenia.

Table 26.1: Household water consumption and price in Slovenia

Year

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

48.179
50.360
47.437
46.352
46.933
47.973
47.929
45.912
44.809
44.505
45.139
46.851

1.121
1.440
1.447
1.519
1.526
1.575
1.603
1.861
1.972
1.993
.
1.670

Table 26.1 gives the annual residential water consumption (m3 per capita and per year) and the water
price (average price for water and wastewater services, in euros m3) in Slovenia from 2002 to 2012. In 2012,
the average annual consumption per inhabitant amounted to 45.1 m3. There has been a decreasing trend in
domestic water consumption since 2007. Over the period 2002-2011, the average water price has increased
from 1.1 euros per m3 to 1.9 euros per m3.

Table 26.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2010. As it can be seen, there are some differences across regions, both in terms of
water consumption and water prices. With almost 64 m3 per capita per year, the water consumption is the
higher in the Osrednjeslovensk Nuts3. The Osrednjeslovensk Nuts3 includes the Slovene capital Ljubljana and is
the most densely populated area of Slovenia. On contrary, a low water consumption is reported in the Zasavska
county (21.4 m3 per capita per year).

26.5 Water demand function estimate

26.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

198

(26.1)

Table 26.2: Regional household water consumption and price in Slovenia

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Gorenjska
Goriska
Jugovzhodna Slovenija
Koroska
Notranjsko-kraska
Obalno-kraska
Osrednjeslovenska
Podravska
Pomurska
Savinjska
Spodnjeposavska
Zasavska
Average

43.866
41.054
35.583
41.215
38.747
41.354
64.647
40.048
32.895
33.477
35.686
21.365
44.809

1.677
2.668
1.937
2.245
2.313
2.656
2.087
1.535
1.884
1.906
1.932
1.509
1.972

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (26.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(26.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

26.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(26.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

199

26.5.3 Results

Table 26.3: Estimation of the household water demand in Slovenia

SloveniaOLS
b/se

SloveniaGLS
b/se

SloveniaGLSiv
b/se

ln Water price (in euros per m3)

-0.100
(0.08)

ln Household income (in euros per capita) 0.409***

ln Share population below 14

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

(0.08)
0.057***
(0.02)
0.040
(0.07)
-0.959
(0.82)

0.359
89.000

-0.108
(0.09)
0.381***
(0.12)
0.049*
(0.03)
-0.018
(0.08)
-0.519
(1.20)

-0.299
(0.38)
0.443**
(0.20)
0.030
(0.03)
0.036
(0.09)
-0.792
(1.66)

89.000

78.000

Figure 26.1: Observed versus predicted household water consumption in Slovenia

We report here our estimate for the residential water demand function in Slovenia, based on our Nuts3
dataset for Slovenia (2002 to 2011). The first model estimated in Table 26.3 corresponds simple OLS whereas
in the second model we estimate Equation (26.4) with a random parameter estimator. In the third model, we
account for potential endogeneity of the price by using an instrumental variable approach.

Table 26.3 present the main significant variables explaining household water consumption in Slovenia. The
price elasticity varies across models, from -0.10 with the OLS to -0.30 with the GLSiv (not significant with
GLSiv). Based on the GLSiv models, a 10% increase in water price results in a 3.0% decrease in short-run water
consumption in Slovenia. Water demand in Slovenia is inelastic, i.e. the estimated price elasticity in absolute
values is lower than one.

The income elasticity varies between 0.38 and 0.44 and the income elasticity is significantly different from

200

354045505560Wathat20406080100120WathatWathatTable 26.4: Estimation of the household water demand with lagged water consumption in Slovenia

SloveniaGLSlag
b/se

SloveniaGLSivlag
b/se

ln Water price (in euros per m3)

-0.127*
(0.07)

ln Household income (in euros per capita) 0.298***

ln Share population below 14

ln average of daily rainfall (mm per day)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

Source: auto.dta

(0.08)
0.052***
(0.02)
-0.038
(0.06)
0.234**
(0.09)
-0.584
(0.68)

-0.617**
(0.31)
0.466***
(0.16)
0.010
(0.03)
0.049
(0.08)
0.067
(0.14)
-0.825
(1.02)

86.000

75.000

zero with the three models. This suggests that there is an increasing relationship between water consumption
and household income. The positive significant coefficient for income indicates that, based on the GLS model,
a 10% increase in income will result in a 4.44% increase in water consumption.

Climate conditions don't seem to play a significant role. Water consumption per capita appears to increase

with the share of the population below 14.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 26.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 11.5.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (26.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(26.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(26.5)

We provide in Table 26.4 our estimates for the household water demand function where the lagged consump-
tion has been introduced as an additional independent variable. With the SloveniaGLSlag model, the long-term
price elasticity is estimated to be -0.17 significantly different from zero at 5%. With the SloveniaGLSivlag
model, the long-term price elasticity is estimated to be -0.66 significantly different from zero at 5%.

201

Chapter 27

Spain

202

27.1 Existing literature

Martínez-Espiñeira (2002) estimates a domestic water demand functions using unbalance panel of monthly
aggregate data from 132 municipalities located in the Northwest of Spain (1993-1999). Overall marginal price
elasticity estimates lie between -0.12 and -0.17. Summer-only elasticities and elasticities associated with uses
beyond the effectively free allowances seem significantly higher. Climatic variables significantly affect monthly
use, although probably less than in other wealthier and drier areas. Domestic water use appears to be a normal
good.

Arbués, Barberán, and Villanúa (2004) use household-level data (1,596 users of the domestic water supply)
for estimating the residential water demand function for the city of Zaragoza in Spain. Their analysis cover the
years 1996-1998. They report a price elasticity ranging from -0.029 to -0.058. The average income elasticity
ranges from 0.074 to 0.208. Arbués and Villanúa (2006) presents an empirical study to estimate the urban
residential water demand in Zaragoza, Spain. The empirical application suggests that domestic demands are
inelastic with respect to price.

García-Valiñas, Martínez-Espiñeira, and González-Gómez (2010) use a sample of 301 Andalusian munici-
palities for year 2005. Using a Stone-Geary utility function, the minimum water threshold is estimated to be
128 m3 per household per year.

27.2 Urban water sector in Spain

In Spain, urban water supply, sanitation and wastewater treatment services are under municipal jurisdiction
(García-Rubio, Ruiz-Villaverde, and González-Gómez 2015). These services can be managed directly or indirectly
but regardless of the form of management, tariffs must be approved by the public administration. The most
frequently employed tariff is the two-part tariff composed of fixed and variable fees. However, there is wide
variability among municipalities.

27.3 Data

Our panel data set consists of Nuts2-level (Autonomous Community) data on household water consumption,
household water price, household's socioeconomic conditions income and on climate condition for years 2004-
2011. Most of the data come from the National Statistics Institute (INE) of Spain. The data consist of
aggregate data at Nuts2-level on water consumption per capita and water and sewerage prices. The data have
been combined with information on income, climate, and household characteristics mainly from the National
Statistics Institute of Spain.

Water consumption data Water consumption data at Nuts2-level (Autonomous Community) comes INE
indicators on water. We have the volume of water registered and distributed to household per capita by Nuts2
from 2004 to 2011 We have then computed the annual household water consumption per capita (m3 per capita)
in each Nuts2 from 2004 to 2011.

Water price data Water prices come from the the National Statistics Institute (INE) of Spain and are available
at the Nuts2-level from 1996 to 2011. As a price indicator, we use the average price (euros per m3) paid by
households for the water and sewerage services.

203

Household income We use the average net annual income per household by Autonomous Community from
2003 to 2011.

Other socioeconomic variables Other socio-economic variables come from National Statistics Institute
(INE) of Spain.

Climate data All meteorological data come from JRC climate database. Grid data (5km × 5km) on rainfalls,
temperatures and evapotranspiration have been aggregated at regional level. We have historical data from
1990 to 2013.

27.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Spain.

Table 27.1: Household water consumption and price in Spain

Year

2004
2005
2006
2007
2008
2009
2010
2011
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

63.824
62.187
59.785
57.196
56.182
54.377
52.442
51.629
57.079

0.947
1.008
1.087
1.264
1.316
1.438
1.520
.
1.231

Table 27.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Spain from 2004 to 2011. In 2011, the average annual consumption per
inhabitant amounted to 51.3 m3. There has been a decreasing trend in domestic water consumption from 2004
to 2011. Over the period 2004 to 2010, the average water price has increased from 0.95 euros per m3 to 1.52
euros per m3 (by more than 50%).

Table 27.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per region (Nuts2) in 2010. As it can be seen, there are some differences across Nuts2, both in terms of water
consumption and water prices. With more than 63 m3 per capita per year, the water consumption is the higher
in the Cantabria Autonomous Community. On contrary, a low water consumption is reported in the Baleares
Islands of in the La Rioja Autonomous Community (44 m3 per capita per year).

27.5 Water demand function estimate

27.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the

204

Table 27.2: Regional household water consumption and price in Spain

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Andalucía
Aragón
Canarias
Cantabria
Castilla y León
Castilla-La Mancha
Catalu˜na
Ciudad Autónoma de Ceuta
Comunidad Foral de NAvarra
Comunidad Valenciana
Comunidad de Madrid
Extremadura
Galicia
Illes Balears
La Rioja
País Vasco
Principado de As
Región de Murcia
Average

52.195
52.560
54.385
63.145
60.955
55.480
48.545
59.495
46.720
57.305
51.100
58.400
48.180
44.165
44.530
44.530
58.035
57.670
52.442

1.360
1.340
1.900
1.140
0.980
1.310
1.830
1.330
1.360
1.690
1.640
1.230
1.010
2.690
0.910
1.140
1.070
2.170
1.520

205

Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(27.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (27.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(27.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

27.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts2 and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(27.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

27.5.3 Results

We report here our estimate for the residential water demand function in Spain, based on our Nuts2 dataset for
Spain (2004 to 2011). The first model estimated in Table 27.3 corresponds simple OLS whereas in the second
In the third model, we account for
model we estimate Equation (27.4) with a random parameter estimator.
potential endogeneity of the price by using an instrumental variable approach.

Table 27.3 present the main significant variables explaining household water consumption in Spain. The
price elasticity varies across models, from -0.21 with the GLSiv to -0.10 with the OLS (not significant with
GLSiv). Based on the GLS model, a 10% increase in water price results in a 2.1% decrease in short-run water
consumption in Spain. Water demand in Spain is inelastic, i.e. the estimated price elasticity in absolute values
is lower than one.

The income elasticity varies between -0.30 and 0.051 and the income elasticity is significantly different

from zero only with the OLS model.

Climate conditions don't seem to play a significant role.

206

Table 27.3: Estimation of the household water demand in Spain

SpainOLS
b/se

SpainGLS
b/se

SpainGLSiv
b/se

-0.209***
(0.04)
0.051
(0.11)
-0.475
(0.43)
-1.979***
(0.70)
-0.061
(0.04)
4.038
(2.58)
0.000
(0.00)

-0.100*
(0.05)
-0.295***
(0.10)
0.082
(0.41)
-0.858*
(0.51)
-0.022
(0.02)
3.494**
(1.43)
-0.001
(0.00)
12.615*** 14.364*** 19.216***
(2.97)

0.138
(0.11)
-0.032
(0.14)
0.696
(0.64)
-2.161***
(0.84)
-0.139***
(0.05)
10.164***
(3.47)
-0.001
(0.00)

(5.25)

(4.19)

0.417
119.000

119.000

119.000

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Household size

ln Age median

ln Population density

ln Share of female

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

207

Figure 27.1: Observed versus predicted household water consumption in Spain

Some household's characteristics matter. The water consumption per capita decreases significantly with

the median age of the population and it increases with the share of women in the total population.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 27.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 8.00.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (27.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(27.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

(27.5)

We provide in Table 27.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the SpainGLSlag model, the long-term
price elasticity is estimated to be -0.24 but not significantly different from zero. With the SpainGLSivlag model,
the long-term price elasticity is estimated to be 0.64 but not significantly different from zero.

208

5055606570Wathat4050607080WathatWathatTable 27.4: Estimation of the household water demand with lagged water consumption in Spain

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln Household size

ln Age median

ln Population density

ln Share of female

ln average of daily rainfall (mm per day)

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

SpainGLSlag
b/se

SpainGLSivlag
b/se

-0.024
(0.03)
-0.013
(0.06)
-0.038
(0.26)
-0.148
(0.32)
-0.008
(0.01)
0.365
(0.90)
0.000
(0.00)
0.898***
(0.06)
1.395
(2.01)

0.054
(0.05)
-0.031
(0.06)
0.247
(0.29)
0.214
(0.37)
-0.009
(0.01)
0.347
(0.93)
0.000
(0.00)
0.916***
(0.06)
-0.132
(2.18)

102.000

102.000

209

Chapter 28

Sweden

210

28.1 Existing literature

A few studies have estimated a household water demand function in Sweden, but all of them a quite old.

Hanke and Maré (1982) use a pooled cross-section series data on 1971-1978 for a sample of 69 single
family houses in the town of Malm¨o. The dependent variable used is quantity of metered water per house,
and the explanatory variables are real marginal price of water, real gross income per house, number of adults
per house, number of children per house, rainfall, and a dummy variable for the age of the house. The price
elasticity is estimated to be -0.15, and the income elasticity is estimated to be 0.11.

H¨oglund (1999) estimates a household demand function for water is using community level data for 282
Swedish communities studied annually over the period 1980-1992. Static and dynamic demand functions are
estimated using panel data methods. The results show a long-run price elasticity of -0.10 in marginal price
models and -0.20 in average price models. The income elasticity is estimated to be between 0.07 and 0.13.

28.2 Urban water sector in Sweden

This paragraph is based on Mattisson and Anna (2010).
In Sweden the municipalities are responsible for the
provision of water and sewage services and for the management of stormwater (Water and Sewage Act, SFS
2006:412). Both services are usually conducted by the same entity. The municipalities have the ability to decide
how to arrange the provision of public services. As a consequence there are differences between municipalities in
Sweden in terms of how the provision of services is organized. In-house or municipally owned corporations used
to be the most common solution for organizing water and sewage services in Swedish municipalities. However, as
the pressure on technological and environmental improvements and financial restraints has increased, different
kinds of inter-municipal co-operations became more common. Sweden has slightly over 2000 publicly owned
water works.

28.3 Data

Our data set consists of Nuts3-level (county) data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2011. Most of the data come
from the Statistical Office of Sweden. The data consist of aggregate data at Nuts3-level on water consumption
per capita and water and sewerage prices. The data have been combined with information on household's
income, climate conditions, and household characteristics mainly from Statistics Sweden.

Water consumption data From the Statistical Office of Sweden we have the annual volume of water
supplied to households users from the public water network for 1995, 2000, 2005 and 2010 at Nuts3 level.
The volume of water has been divided by the population in each Nuts 3, assuming a 100% connection rate.
Our endogenous variable is then the annual water consumption per connected capita to the public water supply
network (in m3 per year per capita).

Water price data Data are annually published by the Swedish Water & Wastewater Association for a subset of
approximately 300 municipalities (almost 100% of the Swedish population) As water price, we have considered
the user charge for a normal house (inclusive of VAT).1. The municipalities have been matched with their

1 A normal house is a detached family house with 5 rooms, bathroom with toilets, laundry room, extra toilet room and a garage.
Floor area is 150 m2 including garage 15 m2, garden area 800 m2. The annual water consumption is 150 m3 of water. The property
is assumed to be connected to water, wastewater and stormwater.

211

respective Nust3 and a Nuts3 average water price has been computed (weighted by the population served in
each municipality).

Household income We use the average disposable household income (euros per year per capita) available
from 2000 to 2011 at Nuts3 from Sweden Statistics.

Other socioeconomic variables Other socio-economic variables from 2000 to 2011 come from Sweden
Statistics.

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

28.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price for Sweden.

Table 28.1: Household water consumption and price in Sweden

Year

1995
2000
2005
2010
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

69.751
69.608
62.038
61.137
65.546

.
.
.
3.225
3.225

Table 28.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in Sweden from 1995 to 2010. In 2010, the average annual consumption
per inhabitant amounted to 61 m3. There has been a decreasing trend in domestic water consumption since
1995. The average water price in 2010 was 3.2 euros per m3.

Table 28.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per county (Nuts 3) in 2010. As it can be seen, there are some differences across regions, both in terms of
water consumption and water prices. With 73 m3 per capita per year, the water consumption is the higher in
the JÃ¤mtlands county. On contrary, a low water consumption is reported in the Kalmar county (50.8 m3 per
capita per year).

28.5 Water demand function estimate

28.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

212

(28.1)

Table 28.2: Regional household water consumption and price in Sweden

Year

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

4.381
3.602
4.283
3.635
3.075
2.999
3.037
4.015
4.413
3.602
2.790
2.329
3.477
2.880
3.996
3.184
4.311
3.398
3.434
2.902
3.455
3.225

Blekinge l¨an
Dalarnas l¨an
Gotlands l¨an
G¨avleborgs l¨an
Hallands l¨an
J¨amtlands l¨an
J¨onk¨opings l¨an
Kalmar l¨an
Kronobergs l¨an
Norrbottens l¨an
Skåne l¨an
Stockholms l¨an
S¨odermanlands l¨an
Uppsala l¨an
V¨armlands l¨an
V¨asterbottens l¨an
V¨asternorrlands
V¨astmanlands l¨an
V¨astra G¨otalands
¨Orebro l¨an
¨Osterg¨otlands l¨an
Average

53.552
64.855
59.299
69.408
60.721
73.289
62.466
50.833
64.136
65.794
58.945
66.971
54.684
61.090
60.351
61.719
62.401
53.517
59.009
57.824
51.482
61.137

213

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

To estimate Equation (28.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(28.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

28.5.2 Estimation methods

Let i = {1, .., I} indexes Nuts3. Our equation of interest becomes:

ln (yi) = α ln (pi) + β ln (Ii) + γ ln (Zi)(cid:48) + (cid:15)i

(28.3)

where (cid:15)t is the usual random term.

28.5.3 Results

We report here our estimate for the residential water demand function in Sweden, based on our Nuts3 dataset
for Sweden (2010). The first model estimated in Table 28.3 corresponds simple OLS whereas in the second
model we estimate Equation (28.3) with an instrumental variable approach.

In the OLSiv model we have considered two instruments namely the share water used by non-household

users and the logarithm of the average size of municipalities.

Table 28.3 presents the main significant variables explaining household water consumption in Sweden. The
price elasticity varies across models, from -0.28 with the OLS to -0.58 with the OLSiv (always significant). Based
on the OLSiv models, a 10% increase in water price results in a 5.8% decrease in short-run water consumption
in Sweden. Water demand in Sweden is inelastic, i.e. the estimated price elasticity in absolute values is lower
than one.

The income elasticity varies between 0.37 and 0.40 but is never significantly different from zero. The water

consumption per capita increases with age.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 28.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 7.5.

214

Table 28.3: Estimation of the household water demand in Sweden

SwedenOLS
b/se

SwedenOLSiv
b/se

ln Average age

ln Water price (in euros per m3)

-0.280*
(0.14)
ln Household income (in euros per capita) 0.407
(0.57)
2.021*
(1.04)
-0.126
(0.15)
-6.985
(8.46)

ln average of daily rainfall (mm per day)

Constant

R-squared
N. of obs.

0.265
21.000

-0.577**
(0.24)
0.370
(0.64)
2.851**
(1.28)
-0.180
(0.17)
-9.340
(9.69)

0.057
21.000

Figure 28.1: Observed versus predicted household water consumption in Sweden

215

5055606570Wathat5060708090100WathatWathatChapter 29

United Kingdom (England & Wales)

216

The structure of the water industry in England and Wales differs from Scotland and
Note to the reader
In Scotland and Northern Ireland, government owned companies are subject to economic
Northern Ireland.
regulation (by the Water Industry Commission and the Utility Regulator respectively).
In England and Wales,
privately owned companies are subject to economic regulation (by Ofwat). Due to data availability, this chapter
will only focus on England and Wales.

29.1 Existing literature

In a review paper, Parker and Wilby (2013) provides a brief history of household water demand management
in the UK. They also summarize the existing literature in UK on water demand estimation and forecasting
over the short- and long-term. They conclude that there is surprisingly little literature on UK household water
demand estimation and forecasting, especially taking into account climate change. This probably reflects wider
difficulties in recording, understanding and predicting the complexities of household water use.

Gardner (2011) provides the most detailed econometric analysis of a residential water demand function in
UK, using data on a sample of 200 households (all metered) served by Veolia Water South East water utility
for year 2008. The range of UK price elasticity estimates (from -0.177 to -0.286) suggests that price based
demand management policies have scope to reduce residential water demand in UK. The log-log marginal price
model gives a price elasticity of -0.286. UK summer demand was identified as being less price responsive than
year round demand in a majority of (statistically significant) models.

29.2 Urban water sector in UK

Water resources and water use Globally, England and Wales are not under water stress. On average about
10 per cent of the freshwater resources are abstracted (excluding abstraction to support power production,
which is often returned directly to the environment). There are however some regional disparities. South East
and Eastern England can be classified as an area under stress from water abstraction, with more than 22 per
cent of freshwater resources abstracted.

Water industry The water industry was privatised in 1989 when the water supply and sewerage functions of
10 publicly-owned regional water authorities were transferred to limited companies. There are now 19 licensed
water companies across England and Wales: 10 water and sewerage companies and 9 water-only companies.
Each company is appointed by the regulator (Ofwat) and holds a regional monopoly for providing water and
sewerage services in a particular geographical area.

29.3 Data

Our panel data set consists of water service-level data on household water consumption, household water price,
household's socioeconomic conditions income and on climate condition for years 2002-2009. Most of the data
related to water come from reports published by Ofwat (in particular the annual reports ``Security of supply,
leakage and the efficient use of water'' and ``Water and sewerage charges''). We use a panel of water utilities
made of 16 services (we cover most of the UK population) for years 2002 to 2009. The water data have been
combined with information at different administrative levels on income, weather, and household characteristics
obtained mainly from the Office for National Statistics.

217

Water consumption data From the Ofwat annual reports ``Security of supply, leakage and the efficient use
of water'', we have the residential water consumption (l/person/day) which has been converted in cubic meters
per year and per capita. Our dependent variable is then the water supplied to households per capita and per
year. We have also the share of residential water consumption which is metered.

Water price data For each water service and each year, we have from the Ofwat annual reports ``Water
and sewerage charges'' the water volumetric charges for metered households and the standing charges for
household customers taking metered water supplies. An average price for the water service has then be
computed using the fixed and variable part of the price scheme and the observed water consumption. The unit
water price has been then expressed in euros per cubic meter.

Household income The Office for National Statistics provides the regional gross disposable household income
annually at Nuts3 level. Regional gross disposable household income have been averaged for each water utility
based on population distribution.

Other socioeconomic variables Other socio-economic variables from 2002 to 2009 come from the Office
for National Statistics

Climate data All meteorological data come from the JRC climate database. Grid data (5km × 5km) on
rainfalls, temperatures and evapotranspiration have been aggregated at regional level. We have historical data
from 1990 to 2013.

29.4 Empirical analysis of residential water consumption

In this section, we provide a statistical analysis or residential water consumption and water price in England and
Wales.

Table 29.1: Household water consumption and price in the United Kingdom

Year

2002
2003
2004
2005
2006
2007
2008
2009
Average

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

55.044
57.138
55.692
55.156
54.620
53.954
53.632
53.767
54.875

1.189
1.219
1.294
1.493
1.608
1.733
1.845
1.932
1.539

Table 29.1 gives the annual residential water consumption (m3 per capita and per year) and the water price
(average water price, in euros m3) in UK from 2002 to 2009. In our sample of water utilities, household water
consumption has slightly diminished from 55.0 cubic meters to 53.8 cubic meters per capita and per year from
2002 to 2009. Changes in water pricing might be an explanation. Indeed, over the period 2002 to 2009, the

218

average water price has increased from 1.2 euros per m3 to 1.9 euros per m3. Another driver factor could be
metering. Indeed, the percentage of households with a meter in England and Wales has increased steadily in
the last 10 years.

Table 29.2: Regional household water consumption and price in the United Kingdom

Water consumption
(m3 per capita per year )

Water price
(euros per m3)

Anglian
Bournemouth & W Hampshire
Bristol
Dwr Cymru
Dee Valley
Northumbrian North
Northumbrian South
Severn Trent
South East
South Staffordshire
South West
Sutton & East Surrey
Thames
United Utilities
Wessex
Yorkshire
Average

52.815
56.028
52.889
54.531
52.377
52.122
57.378
45.187
62.597
50.224
50.297
60.882
59.495
49.129
52.377
51.939
53.767

2.043
1.606
1.936
2.220
1.671
2.012
1.655
2.073
1.740
1.630
2.648
1.628
1.782
2.250
2.313
1.701
1.932

Table 29.2 gives the annual residential water consumption (m3 per capita and per year) and the water price
per water utility in 2009. As it can be seen, there are some differences across water utilities, both in terms of
water consumption and water prices. The water consumption per capita is the highest for the South East water
utility (2.1 million customers in Kent, Sussex, Surrey, Hampshire and Berkshire). This is consistent with figures
provided by the Environmental Agency in UK showing that there are considerable pressures on water resources
in South East and Eastern England.

29.5 Water demand function estimate

29.5.1 Specifying the residential water demand function

The residential water demand function can be derived from solving the utility optimization problem of a
consumer. Under the assumption of a weak separability of water with respect to other goods consumed, the
Marshallian demand in water can be written as:

y = y∗(p, I, Z)

(29.1)

where y is the water consumption either per capita or per household, p and I denote the unit water price
(representing both water supply and the sewage treatment services) and the representative household income,
respectively. Z is a vector of exogenous variables assumed to influence water consumption (climate conditions,
household and housing characteristics, etc.).

219

To estimate Equation (29.1), a wide variety of functional forms have been applied in the water demand
literature, including linear forms, semi or double logarithm forms and more complex forms such as the Stone-
Geary specification. The existing literature is however not very informative concerning the specification which
should be preferred. Since the double-log model is the most common specification in the residential water
demand literature, we have adopted this model in order to facilitate comparison to other studies. Furthermore,
the specification implies that coefficient estimates are also elasticity estimates. With a double-log specification,
the residential water demand function writes:

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48)

(29.2)

and the coefficients α and β can be directly interpreted as the price and the income elasticities of the water
demand. Specification tests for the functional form will be conducted in the empirical part but we don't expect
the choice of the particular form to have a fundamental influence on the estimation of the parameters of
interest.

29.5.2 Estimation methods

Let i = {1, .., I} indexes water services and t = {1, .., T } years. Our equation of interest becomes:

ln (yit) = α ln (pit) + β ln (Iit) + γ ln (Zit)(cid:48) + (cid:15)it

(29.3)

where (cid:15)it is the usual random term. Two panel data estimators may be used namely the fixed effects (or
dummy variable) model and the random effects (or error components) model. We propose to use these two
estimators and to conduct some specification tests (Hausman test) to decide which is the most appropriate to
our data.

29.5.3 Results

We report here our estimate for the residential water demand function in UK (England and Wales), based on
our water utility dataset for UK (2002 to 2009). The first model estimated in Table 29.3 corresponds to simple
OLS whereas in the second model we estimate Equation (29.4) with a random parameter estimator.

Figure 29.1: Observed versus predicted household water consumption in the United Kingdom

220

50556065Wathat455055606570WathatWathatTable 29.3: Estimation of the household water demand in the United Kingdom

UKOLS

UKGLS

b/se

b/se

ln Water price (in euros per m3)

ln average of daily rainfall (mm per day)

ln Household income (in euros per capita)

-0.181***
(0.02)
0.274***
(0.04)
0.016
(0.03)
Share of household metered water consumption 0.093
(0.09)
1.340***
(0.37)

Constant

R-squared
N. of obs.

0.456
128.000

-0.199***
(0.03)
0.266***
(0.07)
-0.052***
(0.02)
-0.120**
(0.06)
1.663**
(0.67)

128.000

Table 29.3 present the main significant variables explaining household water consumption in UK. The price
elasticity varies across models, from -0.18 with the OLS to -0.20 with the GLS (all estimates are significant).
Based on the GLS model, a 10% increase in water price results in a 2.0% decrease in short-run water consumption
in UK. Water demand in UK appears to be inelastic, i.e. the estimated price elasticity in absolute values is smaller
than one. Our price elasticity is consistent with previous findings in UK (Gardner 2011).

The income elasticity is significant with a positive sign. The income elasticity is highly consistent with the
two models.
It varies between 0.26 and 0.27. This suggests that there is an increasing relationship between
water consumption and household income in UK. The positive significant coefficient for income indicates that,
based on the GLS model, a 10% increase in income will result in a 2.7% increase in water consumption.

Climate conditions play also a significant but moderate role (GLS model). An increase by 10% of rainfall

will imply a decrease in water consumption by 0.05% (GLS model).

Lastly, the share of household metered water consumption has an expected negative sign with the GLS
In

model. Metered households tend to have a water consumption per capital smaller than unmetered ones.
terms of policy recommendations, this calls for intensifying the use of water meters.

A possible way to assess the validity of the model is to compare the water consumption predicted by the
demand function to the observed water consumption, see Figure 29.1. For most of the municipalities the model
performs quite well. The mean absolute percentage error is 4.2.

Water consumption may to be at least somehow persistent over time due to household habits or due to the
time needed to adjust durable equipments (shower for instance). With panel or time-series data, one way to take
into this time persistence characteristic of household water consumption is to introduce the lagged consumption
as an additional independent variable in Equation (29.2). The dynamic specification of our equation of interest
becomes then :

ln (y) = α ln (p) + β ln (I) + γ ln (Z)(cid:48) + µ ln (Lag y)

(29.4)

where Lag y represents the lagged water consumption. From this specification is is then possible to derive the
long-run price elasticity of the household water demand function which simply writes:

(cid:15)LT
p =

α
1 − µ

.

221

(29.5)

Table 29.4: Estimation of the household water demand with lagged water consumption in United Kingdom

ln Water price (in euros per m3)

ln Household income (in euros per capita)

ln average of daily rainfall (mm per day)

Share of household metered water consumption

ln Lag of water consumption per capita

Constant

R-squared
N. of obs.

UKGLSlag
b/se

-0.042**
(0.02)
0.032
(0.03)
-0.023
(0.02)
-0.036
(0.05)
0.848***
(0.05)
0.361
(0.23)

112.000

We provide in Table 29.4 our estimates for the household water demand function where the lagged con-
sumption has been introduced as an additional independent variable. With the UKGLSlag model, the long-term
price elasticity is estimated to be -0.28 significantly different from zero at 5%.

222

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