Final Flashcards
Get a 78 or higher
Benefits and costs are the product of:
dX: The real impact of the policy
How much does the quantity of goods (or “bads”) change as the result of the policy?
P: The valuation (price) of the impacted goods
For each good, how much value do we place on each unit gained/lost?
A market price may provide the marginal social benefit and marginal social cost of a good
Simplest case: the policy changes the quantity of a good that is currently consumed in an undistorted (i.e., efficient) market
MSB = P = MSC
Are there distortions in the market?
Taxes or subsidies result in two prices (one paid by consumers, one received by producers); which to use?
Prices in (unregulated) monopolized markets overstate the true costs (monopolists charge P > MC)
Negative externality: e.g., the “price” of pollution (in the absence of regulation) is equal to 0, but the marginal social cost is not
Positive externality: price captures marginal private benefit but understates marginal social benefit
Are “accounting” and “economic” measures the same?
E.g., data gathered from a non-profit could understate the true (opportunity) cost of volunteer’s time
If distortions exist, you need a measure of the “shadow price” (different from the market price)
Shadow price: what the market price would be if the good was traded in a market in which
demand = marginal social benefit, and
supply = marginal social cost
Two general methods for obtaining shadow prices
- Indirect market methods (BGVW Ch. 14)
- Contingent valuation methods (BGVW Ch. 15)
Indirect market methods
E.g., trade-off method, hedonic pricing, travel cost methods
Rely on “revealed” (as opposed to stated) preferences
Contingent valuation methods
Obtain values from surveys (stated preferences)
Recommend to avoid if possible
Meta-analysis
Meta-analysis is every bit as useful for determining the proper valuation of policy impacts as it is for determining the likely impacts themselves
Value of life
We previously addressed policies that affect life/death/health in cost-effectiveness section ($ per life saved)
When is CEA useful?
Fixed budget.
Fixed goal.
Ranking of alternatives.
But CEA doesn’t tell you whether the life-saving is a “good idea” per say.
Better to implement a policy that costs $1 million per life saved vs. $10 million per life saved.
But is $1 million option worth doing?
Value of (statistical) life
In general, policies subject to CBA or other analysis of this nature do not change the probability of a particular person’s death from 1 (certain death) to 0 (no chance of death) or vice-versa; they deal with small changes in the risk of death
Discussion of the “value of life” actually concerns the Value of a Statistical Life (VSL), which is a probabilistic measure
If we say that a policy will save 1 statistical life, this means that the risk of death for some group of people is reduced so that, on average, one person’s life will be saved
A policy that decreases the probability of death by 100% for 1 person saves one life
A policy that decreases the probability of death by 0.0001% for 1 million people saves one statistical life
How would you expect willingness to pay (or accept) to differ between saving a life vs. saving a statistical life?
A lot of anti-CBA rhetoric revolves around the immorality of monetizing life.
That word “statistical” is often missing and critics don’t know better.
What if we called it “value of a risk reduction” instead?
Rationale for estimating VSL
Estimating the “value of life” (VSL) does not make a philosophical statement about how much we feel a life is worth: we are simply evaluating what people’s behavior says about their willingness to pay (accept) for small decreases (increases) in the risk of death
-Private behavior indicates that people place finite value on changes to the risk of death, as they make decisions that increase the probability of death (e.g., drive cars, play contact sports, take risky jobs).
-It would be inconsistent to value risk changes infinitely in a public policy context
-If lives had infinite social value, then we should undertake any policy that has any chance of saving any number of lives, regardless of the cost (e.g., ban non-emergency use of cars?)
-By implication, we should not undertake any non-life-saving policies (e.g., public funding for education?) until we have exhausted all potentially life-saving policies (In practice, we’d run out of resources first
)
-If we want to be able to make decisions between policies that save lives and policies that do not, we need to assign value to expected lives saved
-Remember two of the basic foundations of economics: scarcity of resources and opportunity cost
Life-saving policies are funded by taxes or reduced expenditures on other policies, which may indirectly impact the probability of death
E.g., higher taxes ->less $ for healthy food, medical care, newer/safer cars, etc. -> increased risk of death
Suppose someone is willing to pay $5 to reduce the risk of a fatal accident from 2 in a million to 1 in a million
The decrease in risk is: ∆p = 0.000002 – 0.000001 = 0.000001 In this case the implied value of a statistical life is: VSL = WTP / ∆p = $5 / 0.000001 VSL = $5,000,000
Trade-off method:
determine the trade-off people are willing to make between the probability of death (or any other impact we’d like to value) and something else that is more easily monetized
Estimating VSL: wage premiums
This approach makes use of the additional wages that people require in order to accept jobs with a higher probability of death (“wage premium” or “compensating differential”)
If you require a wage premium of $400/year to accept a job with an additional 1 in 10,000 annual risk of death (otherwise identical to the best alternative job), then your implied VSL is $4,000,000
∆p * VSL = ∆wages
VSL = ∆wages / ∆p = $400 / 0.0001
Estimating VSL: consumption choices
How much will consumers pay for goods or services that reduce the probability of death?
E.g., smoke detectors, car airbags, medication
Studies of medical goods/services complicated by insurance
Related method: how does consumer behavior change in response to new information about risks?
E.g., new information comes out about how calcium supplements affect heart attack risk; how does behavior change?
What about discounting lives saved in the future?
Should be discounted; money spent to save lives today could be invested so that there would be more money available to save lives in the future (opportunity cost!)
Differences in economic status?
E.g., income effects yield different WTP for a given reduction in risk
Most analyses use an average WTP across population
Adjustments for age / life expectancy?
E.g., should we differentiate the benefit of a heart transplant for a 20 year old vs. a 85 year old?
Could, for example, determine the value of saving an additional year of statistical life, multiply by (discounted) number of expected life years saved
No conclusive theoretical basis for requiring constant (age-independent) value of a year of statistical life
Some empirical evidence that age is a factor in people’s preferences (e.g., would rather save a 30 year old than a 60 year old); valuation of prevented death by age commonly found to have inverted U shape
Voluntary vs. involuntary risks
Studies have found that involuntary risks should be valued at 1.2–1.6 times the value of voluntary risks
What do studies tell us about VSL?
Wide range of estimates
BGVW Table 16-1: best estimate $5.5 million, suggest sensitivity analysis from $2.4 million – $7.2 million
Ashenfelter and Greenstone (2004) literature review: typical average VSL range $1 million – $5 million
Robinson et al. (2010) meta-analysis: best estimate $6.5 million, 95% confidence interval from $5.1 million to $8.2 million
Office of Management and Budget (OMB): research indicates rough range of $1 million to $10 million
Asset valuation method
If a property is located in/near a known environmental hazard (trash dump, high smog area, etc.) or environmental amenity (beach, park, etc.), then we can estimate the feature’s value by comparing against the prices of otherwise similar properties not near these features
The values of these amenities (or, more generally, of government policies or projects that impact the well-being of the property owners) are said to be “capitalized” into the market value of the properties
Example: If a new park is built in a neighborhood, and the value of the park to local residents is $5,000 per household, then home prices should increase by $5,000 to reflect this added value(true even if this $5,000 is the NPV of a stream of future benefits rather than a benefit that is enjoyed instantaneously)
Problem: in practice, you can’t always find properties that differ only in this particular respect, so you need to control for any differences in other relevant characteristics
Solution: regress price on the set of all relevant characteristics, including the variable of interest
-Resulting estimate for the variable of interest describes the relationship between this variable and price, with all other relevant attributes held constant
-Referred to as hedonic pricing or hedonic regression
Note: We won’t be running regressions in this course; focus on conceptual understanding (we can’t find an “otherwise identical” comparison group, but we can use statistical techniques to allow “all else equal” comparisons)
Hedonic pricing
Health context: hedonic pricing could be used to estimate a premium for fatality risk in wage rates
Health context: hedonic pricing could be used to estimate a premium for fatality risk in wage rates
Some problems with hedonic pricing
- Assumes that decision-makers are fully informed about the relevant attributes (level of job fatality risk, pollution exposure levels in house’s location, etc.)
- Assumes a sufficient variety of options for individuals to be able to choose an optimal combination of attributes
- Assumes that all relevant variables are included in the regression (or uncorrelated with the variable of interest)
Travel cost method
-The travel cost method is typically used to estimate the value of recreational sites (e.g., national parks)
-Even if everyone pays the same price for admission (potentially zero), people visiting from different geographic areas face different opportunity costs
Example: total cost of traveling to Yosemite National Park is lower for someone living in Los Angeles than for someone living in New York
-We can explore people’s willingness to pay for a visit to a recreational site by estimating the relationship between the total cost of a visit (including opportunity cost of travel time, cost of lodging, admission fees, etc.) and the quantity of visits
Travel cost method
In practice, it is typically most feasible to collect data from actual visitors to a particular site (rather than surveying the general population of potential visitors)
In this case, the level of analysis is visitors’ geographic zone of origin (“zonal travel cost method”) rather than the individual or household
Person-visit
one person, visiting once (if you go 3 times, that’s 3 person-visits; if a family of 3 goes once, that’s also 3 person-visits)
Zone creation is subjective, but households within a zone should have similar travel costs and similar values of other relevant variables
visit rate
The visit rate (average visits per person) can then be regressed on total cost per person-visit and other explanatory variables to estimate the demand curve for a “representative” individual
This gives the willingness-to-pay curve for a representative visitor; net benefits by zone are then given by consumer surplus for someone with that zone’s particular total cost per visit
Some obstacles for travel cost method
-Provides estimates of willingness to pay for an entire site, but the issue under consideration often involves changes to specific features of the site
-ifficult to measure cost of visiting a site
Opportunity cost (value) of time may differ across zones
Opportunity cost of time spent at site is also relevant, but can be complicated
If constant across zones, can be ignored without biasing consumer surplus estimates (affects WTP and total cost equally), but this is not necessarily the case
-People may derive benefits from the time spent traveling, or have multi-purpose trips (do other things on the way) – what portion of the cost should be allocated to visiting recreational site?
-Travel cost may be a result of preferences regarding recreational sites (proximity to various rec. sites may play a role in deciding where to live)
-Sample only includes those who actually visit the site
Can be dealt with econometrically, just shouldn’t use simple OLS regression
Contingent valuation methods
In general, it is preferable to estimate individuals’ valuations of goods and services on the basis of their observed behavior, as such behavior credibly reveals their preferences
In some cases, however, there are no good behavioral data available
In these cases, the only viable method may be the use of surveys designed to elicit information about individuals’ preferences
Such surveys are known as contingent valuation surveys (or hypothetical valuation surveys) because respondents do not actually face the decisions under consideration and are not actually required to pay their reported valuations
General outline of contingent valuation methods:
Identify the population (those who are affected and have standing) and construct a sample
Recall from week 1: those who have standing are those whose costs or benefits “count” (should be included)
Survey respondents about their valuations of the good
Estimate respondents’ valuations (willingness to pay) from survey responses
Make inferences about the population’s valuation based on the results from the sample
CVM: specific methods
- Open-ended willingness-to-pay method
- Closed-ended iterative bidding method
- Contingent ranking method
