Willingness to Pay to Avoid Water Restrictions in Australia Under a Changing Climate

Abstract

Mandatory water use restrictions have become a common feature of the urban water management landscape in countries like Australia. Water restrictions limit how water can be used and their impacts have often been enumerated by using stated preference techniques, like contingent valuation. Most interest in these studies emerged in times of drought, when the severity of restrictions and their deployment had increased and water managers contemplate supply augmentation measures. A question thus arises as to whether the same estimates can be legitimately deployed to water supply projects undertaken when water is more plentiful. This study sheds light on the impact on estimates of willingness to pay when the climatic backdrop to a contingent valuation experiment is altered. We report the results of a comparison between two surveys, undertaken in 2008 and 2012, using a common multiple-bounded discrete choice contingent valuation design, administered across six cities in Australia, covering metropolitan and regional settings. Using a finite mixture, scaled ordered probit model we investigate changes over time in willingness to pay by city, and also causes of individual heterogeneity in willingness to pay. We find that willingness to pay estimates significantly change over time in most regional centres but this is not the case for the major cities of Sydney and Melbourne, once changes in housing prices are included in the analysis.

Exploring customer heterogeneity with a scale‐extended latent class choice model: Experimental evidence drawn from urban water users

“…Hensher et al (2006) used a choice model to also show that households in Canberra had a low willingness to pay for moderate water restrictions, but their estimates of willingness to pay increased substantially when the restrictions were more severe. Cooper et al (2019) reviewed several studies that focus on customers' willingness to pay to avoid water restrictions and noted variations in estimated willingness to pay. For example, Australian studies in the 2000s and late 1990s report household willingness to pay off between approximately A$150 per year and A$240 per year, while other US studies covering the same time period show willingness to pay estimates ranging from about US$55 to US$410 per year.…”

“…Cooper et al (2019) reviewed several studies that focus on customers' willingness to pay to avoid water restrictions and noted variations in estimated willingness to pay. For example, Australian studies in the 2000s and late 1990s report household willingness to pay off between approximately A$150 per year and A$240 per year, while other US studies covering the same time period show willingness to pay estimates ranging from about US$55 to US$410 per year.…”

“…There are several explanations to the variations witnessed in estimated willingness to pay. First, the climatic setting of water restriction studies varies (drought versus nondrought) and Cooper et al (2019) have shown that climatic context can be important in these types of analyses. Second, the nature of water restrictions themselves can differ across settings.…”

Analysing Irrigation Farmers’ Preferences for Local Governance Using a Discrete Choice Experiment in India and Pakistan

“…Discrete choice experiments are underpinned by random utility theory [23] where the frequency with which a participant selects one alternative over another is linked to the benefits (i.e., utility) assigned to the alternative. Choice modelling is one form of discrete choice and is a widely employed stated preference technique (see, for example, [24,25]). The popularity of the technique is in part due to the notion that it is able to replicate real markets where individuals make choices between products based on various attributes [26].…”

Economic valuation meta-analysis of freshwater improvement in developed and developing countries. Are they different?

“…This effect is reflected in the positive estimated regression coefficients for WTP. Thus, for example, although for Amondo et al (2013) the income variable was not significant, Moffat et al (2011) and Roldán (2016) obtained positive coefficients with statistical significance at the 5% level, and Jalilov (2018), Cooper et al (2018), Wang et al (2015), Meibodi et al (2011), Diamini (2015, Kwak et al (2013)'s results were significant even at the 1% level. However, although many primary studies find this positive correlation between income level and WTP, results may differ when integrated into a meta-analysis because the ceteris paribus of each base study is not homogeneous, which implies that new variables emerge.…”