A Panel Data Estimation of Domestic Water Demand with IRT Tariff Structure: The Case of the City of Valencia (Spain)

Abstract

In urban water provisioning, prices can improve efficiency, contributing to the achievement of the environmental objective. However, household responses to price changes differ widely based on the household characteristics. Analyses performed at the aggregate level ignore the implications of water demand incentives at the individual household level. A large data sample at the household level enables estimation of econometric models of water demand, capturing the heterogeneity in domestic consumption. This study estimated the domestic water demand in the city of Valencia and its elasticity, along with the demands of its different districts and neighbourhoods (intra-urban scale analysis). Water price structure in Valencia is completely different from that of other Spanish cities: it is a price structure of increasing volume (increasing rate tariffs, IRT). For this estimation, from a microdata panel at the household level, the demand function with average prices for the period 2008–2011 was estimated using panel data techniques including a fixed effect for each neighbourhood. The domestic water demand elasticity at the average price in Valencia was estimated at −0.88 (which is higher than that estimated for other Spanish cities). This value indicates an inelastic demand at the average price of the previous period, which can cause consumers to overestimate the price and react more strongly to changes.

Evaluating the Efficiency of Water Distribution Network Sectors Using the DEA-Weight Russell Directional Distance Model: The Case of the City of Valencia (Spain)

“…Furthermore, the analysis of efficiency by variables reveals the same pattern as that observed for general efficiency. A possible explanation of this trend in the value of inefficiency could be the presence of seasonality in water consumption (in the city of Valencia, in summer, water consumption is reduced by between 5% and 10% compared to the annual average [1]) or the temperature in these quarters (the higher the temperature, the more pipe breaks that increase leakage, which in turn leads to an increase in turbidity and a decrease in free chlorine residual [100][101][102][103]).…”

“…Specifically, Valencia constitutes a large, important urban area (it is the third most populous city in Spain) within the Jucar River Basin, in which agricultural demands represent a very important percentage (about 80%). For this reason, the urban area of Valencia, with a growing population and highly seasonal service activities, must compete for water resource use with an important agricultural area and a natural park, la Albufera [1]. This competition forces an efficient use of available water.…”

Heterogeneity in Domestic Water Demand: An Application of Multilevel Analysis to the City of Valencia (Spain)

“…From the aggregation of the information obtained from the three sources, a raw database was obtained with all the information available at the household level. Subsequently, the database was subjected to a filtering process that reduced the number of observations, eliminating some households and some neighbourhoods for which not all the information was available or those in which the available information did not correspond to the object of study, or were not representative (more details on the sample construction and cleaning in Maldonado and Almenar [53]).…”

“…The latter two variables have been commonly used in the literature [27,30,76,77]. To be more specific about the studies that have made use of the chosen variables in the literature: average price [17,19,28,47,53,78,79], household surface area [19,21,[31][32][33]37,39,40,42,44,47,53] and number of persons per dwelling [17,29,32,33,37,42,43,47,53,79,80].…”

“…Therefore, with the application of a mixed effects analysis, which allows for the incorporation of temporal and spatial scale effects in a hierarchical or multilevel manner, the analysis aimed at capturing heterogeneity within the city building on earlier analysis using panel techniques and the introduction of a fixed effect for each neighbourhood [53].…”