Revealed preference studies

In a revealed preference study, non-marketed values are estimated through observing data on market behaviour. Examples of revealed preference studies include hedonic pricing and the travel cost method. The theoretical underpinnings of revealed preference studies were outlined in Rosen (1974), which described a model of market behaviour for differentiated goods. Differentiated goods are defined to be goods that are sufficiently similar such that they are regarded as a single commodity in consumers’ minds yet exhibit a variety of characteristics such that in equilibrium, different goods command different prices.

Hedonic pricing has been extensively used to estimate the marginal benefits of noise pollution. In a hedonic pricing study, an implicit price function is estimated from the price changes of a marketed good in response to different levels of noise. Examples of marketed

28

goods which could be used to estimate the marginal benefits of noise pollution include differential pricing of residential properties (Bateman et al. 2001; Navrud 2002), different usage of electricity (Agarwal et al. 2016), and variation in wages (Dean 2017).

Among these marketed goods, most studies have focused on estimating the effect of noise on the price of residential properties (Bennett 2011; Navrud 2002). These studies hypothesise that home-buyers will be willing to pay a lower price for a property if the property experiences higher levels of noise as compared to other properties. A commonly used indicator when estimating hedonic price models is the noise sensitivity depreciation index (NSDI), which indicates the percentage decrease in house prices per decibel increase in noise levels. A review of hedonic studies estimating the NSDI was presented by Bateman et al. (2001). NSDI was found to range between 0.08 to 2.22, although Bateman et al. suggest that the average value is probably at the lower end of this range.

In addition to the housing market, Agarwal et al. (2016) also analysed the effect of construction noise on the consumption of electricity in Singapore. The researchers hypothesised that electricity consumption of residences located near construction sites is higher than for other residences as the residences exposed to construction noise are more likely to close their windows and turn on air-conditioning. The researchers found that electricity consumption by households living close to construction sites increase electricity consumption by 6%. Further, they find persistent effects of construction activity, even after the completion of construction activities.

Dean (2017) sought to investigate the effect of industrial noise on workers in developing countries. The researcher recruited textile workers in Kenya and varied the noise levels in a factory by generating additional noise in the factory. Results of the study indicate that a 10dB increase in noise levels decreases worker productivity by around 5%. This decrease in productivity is mainly due to the increased noise levels impeding the cognitive function of workers.

While a large number of revealed preference studies have been conducted, the majority of these studies have not estimated the marginal benefits of publicly-provided noise abatement. These studies suffer from several shortcomings. First, noise pollution may be correlated with other amenities or disamenities which affect house prices. For instance, houses which experience higher levels of noise pollution as they are located near roads may also experience higher prices due to the connectivity afforded by the road. Further, construction sites and roads

29

may also cause other disamenities beyond noise pollution, such as visual disamenities and higher concentrations of dust. Estimates from the hedonic pricing method may conflate these disamenities with noise pollution.

Second, noise abatement can be provided privately or by the government. However, revealed preference studies may not be able to estimate the marginal benefits of publicly- provided noise abatement. For instance, if electricity was used as the proxy market, then the marginal benefits estimated from the study refer to the benefits associated with the private provision of noise abatement. Use of these estimates in designing publicly-provided noise abatement policies implicitly extrapolate the marginal benefits of privately-provided noise abatement to publicly-provided noise abatement. If privately-provided noise abatement only reduces indoor noise, then the scope of privately-provided noise abatement is smaller than publicly-provided noise abatement since publicly-provided noise abatement can potentially reduce outdoor noise. Consequently, extrapolation of privately-provided noise abatement may understate the willingness-to-pay for publicly-provided noise abatement. Further, if the housing market is used as the proxy market, then the estimated marginal benefits incorporate both publicly- and privately-provided noise abatement. For example, a house located near a road with a noise barrier may also have installed double-glazed windows. In this case, the estimated marginal benefits will overstate the benefits associated with publicly-provided noise abatement, unless the privately-provided noise abatement can be observed. However, administrative data on privately-provided noise abatement measures are not available in Singapore.

Third, implicit prices are sensitive to model specification and different specifications of the functional form of the implicit price function could also lead to differing estimates of the NSDI. Kuminoff, Parmeter & Pope (2010) conducted a Monte Carlo simulation of different functional forms and found that standard linear models can be improved with the use of more flexible functional forms that use a combination of spatial fixed effects, quasi-experimental specification and temporal controls. Nonetheless, Navrud (2002) suggests that hedonic pricing studies still rely on log-normal functional forms. Further, Bateman et al. (2001) found that omitted attributes had a significant effect on NSDI estimates. Specifically, if only the characteristics of the house were included, NSDI was found to be 0.84. NSDI fell to 0.57 and 0.42 with the inclusion of neighbourhood characteristics and accessibility variables, falling further to 0.2 when controlling for visual disamenities.

30

Fourth, the accuracy of the revealed pricing method in eliciting the benefits associated with noise abatement is predicated upon potential home-buyers having information about the level of noise in the prospective home (Navrud 2002). If potential home-buyers are unable to perceive the level of noise in the prospective home, they may not price the external costs associated with noise pollution into their bid for the new home. For instance, prospective home- owners who only inspect homes in the night may not know the noise level over the course of the day.

Fifth, Rosen’s (1974) model of differentiated goods assumes perfect competition in the market for the differentiated good and zero transaction cost. In reality, these assumptions may not be satisfied, particularly in the property market, where government regulations may restrict the sale and purchase of properties. Transaction costs may also be significant in the property market. For instance, when purchasing properties, it is necessary to incur transaction costs to provide conveyancing services.

Sixth, Rosen (1974) highlighted that the implicit price function is not a measure of welfare gain as the price is jointly determined by both the demand and supply of the proxy good. This simultaneous determination of prices by the demand and the supply side leads to endogeneity when estimating the implicit price function for noise pollution. Consequently, Rosen suggests the use of demographic details of both buyers and sellers as exogenous shift variables in a simultaneous system of equations. Hence, the estimation procedure involves a first-stage where the implicit price functions are estimated, with the inclusion of the exogenous shift variables, followed by a second stage, where the simultaneous demand and supply system is solved. However, researchers rarely have access to individual-level data of buyer and sellers in the property market. As such, Parmeter & Pope (2013) observed that most studies examined the implicit price function without estimating the second stage to determine the welfare gains. Nonetheless, research that conducted the second-stage regression found that the recovered preference parameters were heterogeneous among agents. For example, von Graevenitz (2018) examined the preferences for road noise reduction in Copenhagen, Denmark and found that the recovered preference parameters exhibited heterogeneity across households. Controlling for observed demographic characteristics accounts for only 40% of this variation in preferences for a quieter environment.

Finally, Klaiber & Smith (2011) proposed an extension to Rosen’s (1974) model with a general equilibrium model. Klaiber & Smith’s model recognises the mobility of households

31

depend on both changes in environmental factors as well as changes in prices of residences. The effect of price changes on demand for residences is particularly important if home-owners are purchasing properties for the asset value of the property. As the researcher may not observe the motives for home purchase, i.e., whether the home is purchased for occupation or for investment, a naïve model of the NSDI may be an incorrect estimate of the marginal benefits of noise abatement.

In summary, revealed preference studies can only identify the marginal benefits associated with noise abatement if detailed demographic details of the house-buyers and house- sellers are known. Further, in order to estimate the marginal benefits associated with publicly- provided noise abatement in revealed preference studies, information on privately-provided noise abatement is also required. These data sources are not publicly available in Singapore from administrative sources, precluding the use of the revealed preference method to estimate the marginal benefits of noise abatement.