The costs of traffic noise

Traffic noise is an example of a negative externality - the impact of an action on a third party (a 'bystander') who has not consented to or played any role in the carrying out of that action. In this case, traffic noise caused by drivers on the road negatively affects people who live nearby, and those people haven't played any role in creating the noise.

To the extent that traffic noise negatively affects people living nearby, it should be reflected in property prices. Under hedonic demand theory, when someone buys a property they are really buying a bundle of characteristics of the property, one of which is the presence of traffic noise. Since traffic noise is a negative characteristic, we would expect properties that are exposed to more traffic noise to have lower prices, ceteris paribus (holding all else constant).

That means that the cost of traffic noise can be evaluated by carefully looking at the relationship between property values and traffic noise. That is what this 2025 NBER Working Paper (ungated here) by Enrico Moretti (University of California, Berkeley) and Harrison Wheeler (University of Toronto), sets out to do. They start by looking at the effect of roadside noise barriers on property prices, noting that these barriers provide an exogenous source of variation in traffic noise. They compare houses that are close to the barrier (within 500 metres directly away from the barrier) with those that are further away (between 500 and 1500 metres), using a difference-in-differences (DiD) strategy. That involves comparing the change in property prices between before and after the barrier was erected, between properties close to and those further away.

Moretti and Wheeler primarily use data from Florida, which provides details about the noise barriers that were completed, but also about barriers that were proposed but not completed. That also allows them to use a 'triple-differences' strategy, by matching areas that had a barrier erected, with those that didn't (but where one was proposed). Now, it turns out that the results from the triple-differences model are similar to the more standard DiD, but that should provide some further comfort with the robustness of the DiD results. The data on house prices and other characteristics comes from CoreLogic and covers the period from 1990 to 2022 (house prices) or 2006 to 2022 (property characteristics). Their final dataset includes nearly 600,000 home sales within 1500m of a noise barrier (and on the same side of the road as the barrier).

Focusing on their results that include parcel fixed-effects (so that time-invariant property characteristics are controlled for), Moretti and Wheeler find that:

For houses within 100 m of the barrier, the estimated effect increases to 8.59%. For houses 100–200 and 200–300 m from the barrier, the estimated effects increase to 5.79% and 4.41%, respectively. The effect on properties 300–400 m from the barrier is marginally statistically significant.

So, reducing traffic noise increases property values, and the effect is largest for properties closest to the road generating the noise. Beyond about 300 metres, the effect is statistically indistinguishable from zero, but within 300 metres, the construction of a noise barrier increases property values by between 4.41 and 8.59 percent. Moretti and Wheeler then note that:

Since our data report the construction cost of each barrier, we can compute the marginal value of public funds (MVPF), defined as the property value appreciation over costs... The average MVPF for barriers that were built amounts to 1.7, while the MVPF for barriers proposed but not built is 1.4. This is to be considered as a back-of-the-envelope calculation that ignores property taxes. Property taxes would reduce both the social benefits (since some of the home value increase gets taxed), and the social costs (since property taxes end up in local government coffers).

So, on this measure at least, noise barriers appear to be a good use of public funds, since the increases in property values exceed the costs of erecting the barriers (although noting that there may be other uses of public funds with even higher MVPF values).

Next, Moretti and Wheeler change their model in order to allow the change in price to vary based on the expected decibel reduction. They use a model where the effect is non-linear in noise reduction, and find that:

The effect plateaus at 10 dB of reduction, which represents the 96th percentile in our sample. The effect is estimated to be zero when noise reductions are 4.9 dB...

The average barrier in our sample reduces noise by 7.15 decibels. At this level of noise reduction, our estimates imply that the average price of a decibel is 0.94%...

That is the result that Moretti and Wheeler use later in their paper to estimate the economic cost of the externality. However, first they need to rule out some competing explanations for their effects. They show that air quality is somewhat better near to the barriers, but the effect is small and not statistically significant. They show that the results do not change when accounting for tree canopy or the presence of other buildings, meaning that noise barriers blocking views of the road is unlikely to explain the results. And, they show that the construction of new homes (with higher unobserved quality) does not explain the results.

Moretti and Wheeler next use their results to estimate the economic cost of the externality for each census tract in the US. From that, they find that there is:

...a negative correlation between the cost of the externality and median family incomes. The slope is -0.10 (0.01), indicating that a 10% lower income is associated with a 1% higher per capita cost. The correlations with the share of residents who are Black and the poverty rate are positive. The slopes are 0.08 (0.01) and 0.63 (0.05), respectively, indicating that a 10 percentage point higher share of Blacks or a 1 percentage point higher poverty rate are associated with 0.8% and 0.6% higher per-capita costs...

That means that the externality cost is regressive. That is, the cost of the externality is a larger proportion of income for low-income families than for higher-income families (taking into account the location of low-income and higher-income families and the property values where they live and the traffic noise they face).

In total, Moretti and Wheeler estimate the cost of traffic noise to be US$7.0 billion in Florida, and $109.75 billion for the US as a whole. Looking across cities, they find that per-capita traffic noise costs increase with urban share of the population and population density, which they suggest is because cities with greater urban share or those that are denser have both higher noise exposure, and higher property values.

Finally, Moretti and Wheeler estimate that a one-off Pigovian tax equivalent to US$974 per car would be equal to the marginal external cost of the traffic noise externality (noting that the optimal Pigovian tax is one that is equal to the marginal external cost). They also estimate the potential benefit of a move to 100 percent electric vehicles (which have lower engine noise) at US$5.39 billion for Florida, and $77.28 billion for the US as a whole.

This research tells us that traffic noise is a costly negative externality. Those costs are capitalised into property values and are borne disproportionately by lower-income households. Fortunately, the research also suggests that there are worthwhile ways of reducing those costs, including erecting noise barriers and rolling out more electric vehicles, and the benefits of those solutions may be substantial.

[HT: Marginal Revolution, last year]

Climate change and the tragedy of the commons

My ECONS102 class covered externalities and common resources this past week. In the final slide of content in my lectures, I talked about the challenges of getting global agreement on climate change, because the atmosphere's limited capacity to absorb emissions without causing harmful climate change provides a special case of the tragedy of the commons.

Why is climate change a common resource problem? Common resources are rival and non-excludable. The atmosphere's capacity to absorb emissions is both rival (one country's emissions reduce the capacity available for other countries) and non-excludable (if the capacity is reduced for one country, it is reduced for all countries). The social incentive is for all countries to reduce emissions to the point where the marginal social cost of emissions is equal to the marginal social benefit. The private incentive for each country is to reduce emissions only to where marginal private cost of emissions is equal to marginal private benefit for that country. Each country’s emissions impose a cost on other countries, meaning that each country doesn’t face the full cost of their emissions (so the marginal social cost of emissions is greater than the marginal private cost for each country), so they will emit too much. And since all countries have the same incentive, there are too many emissions relative to the socially optimal quantity.

Within a country, we might be able to solve a common resource problem like this by relying on the government to assign some form of property rights. However, there is no supra-national government to perform this role, so that means we need to arrive at a 'private solution' (albeit one where the private actors in the negotiation are countries).

The 2009 Nobel Prize winner Elinor Ostrom noted that users of a common resource may be able to solve the problem by working together (a common governance approach). A number of things would likely be necessary for such common governance to work. Ostrom noted a number of principles for common governance, one of which was that the boundary of the common resource and the group of users must be well-defined. In the case of climate change, the boundary is the environment, and the group of users is all countries. So, that principle would be no problem, provided all countries agreed to be involved (and that may be a challenge).

For common governance to be successful, the user community must also be relatively homogeneous, so that they can trust each other and develop common goals (and norms or customs) for protecting and allocating the resource. Here is where the challenge lies. The user community (countries) are not homogeneous at all. Countries at different levels of development have different goals and aspirations, and see the role of emissions in contributing to those goals and aspirations differently. And it seems unlikely that countries will really trust each other to do the right thing in relation to any climate agreement.

Ostrom also noted that protecting the resource would be best achieved through persuasion rather than coercion, since this would maintain trust within the user community. Persuading other sovereign countries to do something that makes them individually worse off is obviously a challenge. And so climate change remains one of humanity's greatest challenges. This isn't to say that it isn't an important challenge to solve, only that there are good reasons why, nearly 50 years on from the First World Climate Conference in Geneva in 1979, we are still looking for an effective agreement to protect the climate.

Don't just take my word for it though. This 2012 article by Niggol Seo (University of Sydney), published in the journal Economic Affairs (sorry, I don't see an ungated version online), outlines the case, supported by some estimates of the globally optimal policy (as it would have been at the time). Seo uses the model results to outline the incentives that each of thirteen world regions face in global negotiations over climate change. Seo compares a business-as-usual (BAU) scenario with a scenario based on the globally optimal policy (GOP). Focusing on the GOP scenario, the net costs of addressing climate change in that scenario for seven of the largest world regions are shown by Figure 2 from the paper:

Notice the large costs that China (green) and the US (blue) would face over the entire period up to the end of the century. It should be little wonder, then, that those two countries in particular would have less incentive to agree to emissions restrictions to address climate change. In contrast, the EU, India, Africa, Latin America, and Russia face more modest costs initially, and by 2075 (or 2065 in the case of Africa), the GOP scenario actually shows net benefits for those regions. Again, it should be little wonder that they have greater incentive to support of climate change agreements. Seo concludes that:

...some countries have a strong incentive to push for global regulation due to the expected reduction in climate related damages. The optimal regulation saves these countries hundreds of billions dollars annually by the century’s end. However, it would cause additional costs to China, Russia, Canada and the USA.

To that, I would observe that it particularly impacts China and the US. Climate change is a challenging problem. For an efficient global agreement that would adequately address this challenge, we need all countries to participate. However, the incentives do not necessarily help us to achieve cooperation.  Countries that face relatively low net costs from addressing climate change may need to offer transfers, concessions, technology, or some other form of compensation to countries that face high net costs, in order to change their incentives and get them on board. We may not think that outcome is fair. However, achieving an efficient and effective agreement may require some compromise on fairness, if that is what is needed to ensure that the incentives encourage all countries to participate.

Read more:

• Reason to be surprised, or not, about the Paris Agreement on climate change coming into force

This week in research #145

Here's what caught my eye in research over the past week:

• Guzman, McGuinness, and Turner investigate 16 'mega-universities' in the US, and report that students at these mega-universities have lower average completion rates and leave with higher average student loan debt compared to students at other schools
• Melo et al. (open access) find little evidence that higher minimum wages increase job search for low-skilled jobs, and more evidence that higher minimum wages decrease the number of workers seeking employment in these jobs (file this under surprising results on the supply side of the labour market)
• Malesky et al. (with ungated earlier version here) find that cohorts exposed to the university expansion in Vietnam are 87% more likely to have a university education, but that education increases the incidence of bribe requests and perceptions of corruption

Minimum wages and the adoption of robots

Manufacturing firms typically have a choice of various production technologies. Some production technologies involve more labour. Others involve more automation (robots, as in this post). If labour becomes relatively more expensive compared with robots, firms have a greater incentive to adopt robots. That suggests that higher minimum wages, which make labour relatively more expensive for firms, may not only decrease employment (see the links at the end of this post for more on that point), but may increase the adoption of robots.

The extent to which firms adopt robots in the face of increasing minimum wages is the subject of this recent NBER Working Paper by Erik Brynjolfsson (Stanford University) and co-authors (ungated version here). They look at this question in two ways. First, Brynjolfsson et al. create a state-level measure of exposure to robots, which captures the extent to which robots are over- or under-adopted in each state, given the state's mix of industries and employment. They then correlate changes in that measure with changes in the state-level minimum wage over the period from 2003 to 2015. That correlation is illustrated in Figure 1(c) from the paper:

The regression line in the figure implies that a 10 percent increase in the minimum wage is associated with an increase in robot exposure equivalent to about 8 percent of the sample mean level of robot exposure.

Second, Brynjolfsson et al. use microdata from the US Census Bureau, including the Longitudinal Business Database and Longitudinal Firm Trade Transactions Database (LFTTD), to construct a panel dataset of robot adoption among US manufacturing firms from 1992 to 2021. They use the LFTTD data to identify which firms imported industrial robots. They then compare robot adoption between firms in adjacent counties on opposite sides of state borders, which face different state-level minimum wages but are likely to share many local economic conditions. Their measure of robot adoption in this analysis is simply whether a given firm adopted a robot in a given year, or not. In this second analysis, they find that:

...a 10% increase in minimum wage leads to an 8.4% rise in robot adoption relative to the sample average...

Notice how similar in magnitude the effects from their two analyses are, despite being quite different in nature, as well as covering different time periods. Both analyses suggest that higher minimum wages are associated with greater levels of robot adoption. The state-level relationship in the first analysis is clearly correlational, rather than causal. However, the comparison between firms in adjacent counties provides some plausibly causal effects (at least, there are plenty of other research papers that use a similar approach to estimate the causal effects of minimum wages). More generally, this research provides another example of how firms may respond to minimum wages on margins other than employment (see the links at the end of this post for more). When the relative price of labour increases, firms may change not only how many workers they employ, but also the production technology they use.

[HT: Marginal Revolution, back in February] 

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance
• Higher minimum wages and poverty revisited
• Minimum wages and health
• Effects of the minimum wage on the nonprofit sector
• 15 years of US research on the minimum wage elasticity of employment
• Local minimum wages and low-quality housing rents in Japan
• Minimum wages and student summer employment