Benefits of Environmental Protection

Econ 2277

Prof. Richard L. Sweeney

(print this presentation)

Overview

Last class: Economic efficiency requires we set the marginal benefits from environmental protection equal to the marginal costs.

To do this, we need to measure benefits and costs

Outline for next two weeks

  • Review concept of economic benefits
  • Revealed preference methods
  • Stated preference
  • Benefit transfer: Valuing risk of death

What are the benefits of environmental protection?

[list them]

Non-economic description of environmental benefits

  • Human Health
    • Morbidity and Mortality Risks
  • Amenities
    • Visibility, Odors, etc.
  • Ecological Impacts
    • Market Products: food, fuel, timber, etc.
    • Non-Market Recreation & Aesthetics: fishing, boating, hiking,etc.
    • Indirect Ecosystem Services: flood protection, water filtration;
    • Non-Use Values (coming soon)
  • Materials Damage

How would you translate those benefits into dollars?

Private Good Example: Bird Scooters

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What are the benefits of these scooters?

[List them]

How would you place a dollar value on these benefits?

Bird already does this!

  • When someone rents a scooter, they consider the full benefits (fun, transportation, convenience, etc)
  • If willingness to pay (WTP) is higher than the price, they rent

To get a measure of the total benefits, we'd just need the demand curve for scooters

Companies use demand curves to set prices and make investments.

Imagine we had internal projections from Bird:

  • At a base price of 15 cents per minute ($9 per hour) demand in Cambridge is 100,000 hours
  • At a higher price of 20 cents per minute ($12 per hour) demand is estimated drop to 50,000 hours.

This gives us an (inverse) demand curve

Two points (12, 50K) and (9, 100K)

P(Q)=153/(50,000)QP(Q) = 15 - 3/(50,000) Q

  • So at $15, nobody drives one.
  • Max demand (P=0) is 250K

How can we see the benefits of Bird on this graph?

Need to decide if we care about Bird profits or not.

  • Gross benefits = area under demand curve up to base quantity (100K)
  • Net benefits (consumer surplus) = that minus total (private) cost

[If Cambridge cared about Bird profits, they'd use marginal cost not price]

Let's apply that logic to environmental quality

  • What we'd like to know is how the quantity of environmental quality demanded changes as it becomes more or less expensive.

  • Challenge: People don't pay for the environment.

Two Conceptual Strategies

Revealed Preference Methods: Use people’s observed behavior in markets to infer their WTP for environmental goods/services

Intuition: Even if people don't pay for environment, often spend money (incur real costs) to get access to clean environment (or avoid poor environment)

Stated Preference Methods - Design surveys that ask people what they would be WTP or WTA

Two types of environmental benefits

  1. Use Value: The benefits from using a good/service, directly or indirectly

    • For example, benefits of recreational swimming-day or benefits of clean air on your walk to school

Revealed preference methods preferred for measuring use values

  1. Non-Use Value: Utility people gain from environmental goods that do not benefit them directly or indirectly

    • What are some sources of this?

Sources of non-use value

  • Option Value

    • Benefits people receive now from having option to use good/service in the future.
    • For example, benefits (now) of preserving Grand Canyon so that tourists can see it in the future
  • Existence Value

    • Benefits people receive from knowledge of the existence of good/service (they never intend to use).
    • For example, benefits of preserving panda habitat in China

For non-use value, we have no choice – must use stated preference methods (measure total value)

Household Production Models

Household Production Models

– people often combine a private good with an environmental good to produce another good, which is the real source of utility

  • e.g., travel + wilderness area = recreational day

  • Idea: If we know how to value the costly input, then we can infer the value of the environmental good.

[Not most useful method, but good introduction to revealed preference models]

Travel cost method

  • Origin of method (Hotelling-Clawson-Knetsch)
    – Letter in 1954 to six economists from Director, National Park Service
    – Response from Harold Hotelling (UNC)

  • Conceptual Thought Experiment
    – Tuolumne River: to estimate demand function, to ascertain WTP: “Build a fence and charge admission”

  • Collect Data:
    – Travel Cost (including opportunity cost of time)
    – # of visits from various origins (zones),
    – population of zones (for example, treat all zones as having same population)

Intuition: Visiting a park takes time and money.
– Time to drive, gas, etc.
– Plus entrance fees.

Implications:
– The more awesome the park, the more I’m willing to give up to visit it.
– The closer I live to a park, the more likely I am to visit it.

We can use this to trace out a WTP curve for a park
– Number of visitors from towns at different distances away
– Distance to park gives variation in price
– Variation in visitors gives variation in quantity

Example: Valuing Mt. Monadnock

alt text

• 3165 feet high
• Many hiking trails
• Spectacular views of Boston and Eastern Massachusetts
• Policy question: How much is Mount Monadnock worth?

Who visits Mt. Monadnock?

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Who visits Mt. Monadnock?

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Plot inverse demand curve

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Convert to per capita

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Use wage rate to convert to dollars

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[Time valued at the average hourly wage in each city (avg $27)]

To get total value, multiply by population in each city

Issues

  1. Is the wage rate an appropriate way of translating distance to travel cost?

– Hours may be fixed, so the tradeoff is with leisure time.

∗ Need to know the shadow price of leisure time.

– People may have different utility or disutility from traveling vs. working

  1. Other Factors Matter
    – Yes, of course: income, education, age, etc.
    – Not a problem: use multiple regression

  2. Multi-Purpose Trips

    • would this over or understate true value?

Takeaway: Travel Cost Method

– TCM very useful for understanding concept of revealed preference

– Sophisticated versions still used to value recreation sites

– But the method is going to be of limited value for estimating benefits of many environmental policies

Limited applicability: Only recreational sites or environmental quality associated with recreational sites

Public goods

Most economic goods are rival -- if one person consumes something, no one else can.

Many environmental goods are non-rival, meaning if one person uses something, it doesn't impact the consumption value for anyone else:

  • clean air
  • beautiful landscape

Non-rival goods commonly called public goods

Note:

  • A second important of economic goods is whether or not they are excludable
  • If a good is excludable, people can be prevented from using it.
  • Non-rival non-excludable goods are called pure public goods
    • example: air, public radio
  • Non-rival excludable goods are called club goods
    • example: DirectTV

Aggregate demand for a public good

  • benefit estimation typically occurs at the micro level

  • by for policy evaluation, we're often interested in calculating total benefits

    • area under the aggregate demand curve up to the point of the policy
  • to do this, we need to aggregate the demand curves of all affected parties

Adding demand curves: private goods

  • normal private goods are rival
    • example: an apple
  • need to sum demand horizontally!
  • demand typically takes the form of Q(P)
  • however, supply and demand graphs presented as P(Q)
  • need to invert demand first
  • ie we want to add up all Q demanded at a given P, not sum over P at a common Q

Steps:

– solve for qi(P)q_{i}(P)

– sum up over all ii at the same PP to get QQ

– now invert again to get P(Q)P(Q)

Example: Demand for apples

  • two consumers, AA and BB

• A really likes apples: P=20QAP=20-Q_{A}

• B likes them less: P=10QBP=10-Q_{B}

Graph these. What does total (aggregate) demand look like?

  1. Solve for Q
    QA=20PQ_{A}=20-P
    QB=10PQ_{B}=10-P

  2. Add curves if Q > 0
    [Can't have negative demand]
    QT=20PQ_{T}=20-P if P>10P > 10
    QT=302PQ_{T}=30-2P if P10P \le 10

  3. Now invert back to graph:
    P=20QTP=20-Q_{T} if Q<10Q < 10
    P=15QT/2P=15-Q_{T}/2 if Q10Q \ge 10

What if we want demand for a public good?

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Imagine the good is instead a very rare animal

– neither A nor B ever see it, but get utility from its existence

  • Now Q is non rival (and non excludable)

– so QT=QA=QBQ_{T}=Q_{A}=Q_{B}

What does the aggregate demand curve look like now?

Calculating demand for a pure public good

Assume same demand curves:
• A gets utility up to the point where 20 are saved: P=20QAP=20-Q_{A}

• B only cares as long as there are 10: P=10QBP=10-Q_{B}

Now we actually do want to add vertically

P=302QP=30-2Q if Q10Q \le 10

P=20QTP=20-Q_{T} if Q>10Q > 10

Summary on calculating total benefits

  • To get the total consumer benefits of policy, we want the area under the demand curve.

    • If they incur a private cost, subtract that.
    • If the cost is public (for example paid for by the government), remove that from net benefits, but not consumer surplus
  • Often times demand curves are estimated at the individual level

    • To get the aggregate demand for a rival good, need to add horizontally.
    • To get the aggregate demand curve for a non-rival good, need to sum vertically.

[this is useful for the problem set]

Hedonic Pricing Models

Hedonics and the environment

  • goods are bundles of attributes

    • restaurant: food, ambiance, service
    • smartphone: camera, apps, look
  • even if people only buy a bundle, variation in prices across similar bundles can reveal how much people value a particular attribute

    • Example from last class: can get Camry with and without hybrid
    • If consumer buys, values fuel economy $5,000\ge \$5,000
  • this is useful for environmental economists because many prominent purchases contain the environment as a key aspect of the bundle

    • Example: houses

Price and location of houses easily observable

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As is information about local air quality

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Can use this information to figure out how much people value clean air

  • imagine we find two houses that are identical in every way except one is in a more polluted area (lower Air Quality Index)

    • house 1 AQI = 50 ; house 2 AQI = 100
    • you can see Boston's AQI here
  • the difference in sales price provides a hedonic estimate of WTP for better air quality

    • for example, if house 1 sells for $5,000 more, we'd infer people are willing to pay $100 per unit of AQI improvement
  • This is approach referred to as hedonic (property) valuation

    • Has been used to study education, access to hospitals, community safety, etc

Key Assumptions

  • Constant MWTP

    • we know WTP declines as resource improves.
    • in previous example $100 per AQI valid only at relatively clean locales in 50-100 range.
    • important to estimate at different levels of air quality
  • Perfect information

    • What if homebuyers don't know the AQI at each location?
    • Pipeline example
  • Estimated relationship is causal

    • observed price difference due entirely to difference in air quality

What would happen if areas with better air quality also had better schools?

  • In this example, imagine the house with better air quality also has the best school system in the state.

    • How would that change your interpretation?
  • Common solution: Multiple regression

    • Run a regression projecting house price on to air quality and all other important observable factors
  • Challenge is that lots of amenities are correlated (school quality, safety, commute etc), and can't control for everything.

You should think of this complication as the norm in public policy evaluation

  • Ideally we'd observe the same “subject” in two otherwise identical states of the world, one where the policy was in effect and one where it wasn't

    • “Fundamental Problem of Causal Inference”
  • Drug trials get as close to possible to this ideal by randomly giving some patients a placebo

  • Often the best we can do in public policy analysis is to look for "quasi-" experiments, where some external factor alters the exposure of some subjects and not others in an essentially random fashion.

Example 1: Valuing shoreline loss

  • Many sections of the U.S. coastline are severely eroding.

    • The long-term rate of shoreline loss along the New England and mid-Atlantic coasts is 1.6 feet per year
    • In Florida, some segments lose more than 10 feet per year.
  • Climate change is expected to lead to sea level rise

Under current predictions of a 1.1 foot rise in average sea levels by the year 2100, these erosion rates will accelerate, and between 3,000 and 7,000 square miles of dry land could be lost (IPCC, 2007; Titus, 1989).”

  • How costly is this?
    • could decide to build seawalls

Could look at house prices in areas with wide vs narrow beaches

What would be the concern here?

Ranson (2012)

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Paper here

Ranson (2012) results

  • Finds that one-foot beach nourishment adds $42 - $68 to a home's sale price

    • Average project adds $2,927 to $4,760 per household
  • Large, but considerably smaller results from "cross-sectional" comparison

    • ie just looking at homes with longer / shorter beaches
  • Cost of beach nourishment is roughly $1,000,000 per mile

  • Implies would have to be between 210 and 342 beachfront homes per mile in order for the project to generate positive marginal benefits.

Example 2: Hazardous waste

  • Prior to the 1970s, industrial firms often disposed of hazardous waste by burying it in the ground

Still in the news

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Trump administration also recently proposed cutting this program

Love Canal, Niagara Falls, NY

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Love Canal timeline

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Superfund Program

The 1980 Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) gave the EPA the right to place sites that pose an imminent danger on the National Priorities List (NPL)

Can we use the hedonic method to value this program?

What other variables are likely correlated with polluted site locations?

Greenstone & Gallagher (2008) used budget limitations to estimate a causal effect of the program

  • In 1983, funding initially allocated for 400 sites

  • 1500 candidate sites identified, 690 finalists

  • Each finalist was given a Hazardous Ranking System score

– Cutoff: HRS> 28.5 were cleaned up; others weren't

Insight: Sites with scores near cutoff very similar

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Greenstone and Gallagher summary

  • Conventional approach to analyzing Superfund cleanup showed large gains

    • ~3-7% home value
  • Using more credible approach, the estimates become economically small and statistically insignificant

    • Correlations probably reflect fact these areas are undesirable on other dimensions.
  • Compared to the average clean up cost of $43 million, the Superfund program does not appear welfare enhancing

    • People do not appear to be be willing to pay that much themselves.

Causality wrapup

  • Can teach a whole class on causal inference in environmental policy

  • For this class, I mainly want you to understand that correlation isn't causation

    • Also have some intuition for which what bias would go:
      • if unobserved factors positively correlated with the attribute of interest, WTP overestimated (vice versa for negative correlation)
  • Tried to give you some examples of what a more "credible" approach to estimating WTP might look like

    • Most common solution to this problem is to look for something that changed the amenity for reasons external to the affected parties.
    • [you will not be tested on this]

Hedonic property method summary

  • conceptually powerful framework, that has been used to value many public policies (like schools, safety, etc)

  • estimation requires important assumptions

    • people observe and pay attention to attribute of interest
    • no omitted variables (other factors that also affect house prices)

Averting expenditures

Motivating Example: WTP for clean water

[Source: Berck and Helfand]

  • In 1987, residents of Perkasie, Pennsylvania, faced contamination of their drinking water by trichloroethylene (TCE), a toxic chemical.

  • In response, many of those residents bought bottled water or water filters, boiled their water, or hauled water from elsewhere.

  • The costs associated with these activities were estimated to average between $22 and $48 per household.

Can these values be used to provide an approximation of the amount that people in the community, on average, would pay to reduce the risk?

Averting Behavior Method for Estimating WTP

Premise: people may change their behavior to avoid or lessen exposure to externalities

  • What are some other examples?

Intuition: Can infer WTP for risk reduction from expenditures for risk-averting activities

– What information / assumptions would you need?

Example: What is WTP for PM reductions in China?

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Thought experiment

Assume that air filters remove 50% of indoor PM

In Shanghai, where the price is $200, 25% of households have one

In Xian, closer to the factory, the price is $100 and 50% of hh's have one

Natural experiment: Huai River Policy

For decades China gave heavily polluting boilers to communities north of the river

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This significantly reduced life expectancy

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Ito (2018) finds northern residents responded by buying air filters to protect themselves

  • Market share jumps 20% at border.
  • Implies residents willing to pay $5.46 per unit of PM reduction.
    Read more here.

Averting Behavior: Issues & Problems

  1. Difficult to separate risk-reduction benefits from other benefits of the product or activity

– Example: bottled water may taste better, be convenient, etc.

– So we may over-estimate WTP for risk-reduction

  1. Difficult to separate risk-reduction benefits from negative benefits (utility-reducing attributes of averting behavior)

– Example: bicycle helmets are uncomfortable

– We will under-estimate WTP for risk reduction

Averting Behavior: Issues & Problems

In another example, residents of the area around the Nak-Dong River in Korea faced industrial pollutants in the early 1990s. (Source: Berck and Helfand)

• By 1996, water quality was greatly improved and met safety standards

• Yet people still undertook risk avoidance measures.

– People were acting based on their perception of how polluted the water was.

• The estimated willingness to pay to reduce suspended solids was 3X that what would have been predicted based on safety alone

And what about “Cost-of-Illness Method?”

• Many pollutants result in doctor or hospital visits

• Assume we can perfectly observed all medical expenditures

• Can we use these to estimate willingness to pay?

• Chipotle example

And what about “Cost-of-Illness Method?”

• Does not estimate WTP/WTA, but change in explicit market costs resulting from change in incidence of illness:

– Direct health-care costs

– Indirect costs of loss of work time

– What’s left out?

Summary: “Cost-of-Illness Method?”

• May be considered a lower bound on WTP, but empirical evidence from comparisons suggests difference can be very large!

• Sometimes used in health economics to value morbidity changes (and by courts in wrongful death cases)

• Method is not theoretically correct nor empirically reliable, but two advantages:

– Cheaper than better approaches

– Easy to explain to policy makers and general public

• So, it’s inexpensive, easy to explain, and wrong.

Wrapping up

  • Review concept of economic benefits
    • Linked to Willingness to pay
  • Revealed preference methods
    • We often pay to get these benefits: either indirectly with time; as a bundle; or even directly with avoidance goods

Up next:

  • Benefit transfer: Valuing risk of death
    • For large benefits, we sometimes think we "know" the benefit from other settings
    • Most important example is WTP to reduce risk of death
  • Stated preference