research frontiers nicolai v. kuminoffnkuminof/lec5.pdf · 2017-06-13 · challenge for future...

RESEARCH FRONTIERS

DEPARTMENT OF ECONOMICS

Nicolai V. Kuminoff

(ASU Economics & NBER)

Copy of slides: www.public.asu.edu/~nkuminof/lec5.pdf

http://www.public.asu.edu/~nkuminof/uce3nk.pdf

1. Spatial dispersion of amenities

2. Strategic regulatory behavior

3. Heterogeneity in information and beliefs

4. Revealed preferences analysis when some choices don’t reveal preferences

5. Can features of spatial equilibria inform the VSL?

TOPICS

Spatial dispersion of air

pollution is non-uniform

This matters for hedonic

estimation

Strategic Regulatory Behavior

Strategic Regulatory Behavior

My Question: Is the projected improvement in

air quality simply caused by diverting auto

traffic away from air quality monitors?

AZDOT Answer: “Siting, operation, and

recording information from monitoring sites are

the responsibility of the Maricopa Air Quality

Department…According to EPA guidance, new

monitors are not necessary to analyze air

quality impacts.”

Broader Evidence of Strategic Behavior:

Aufhammer, Bento and Lowe (JEEM 2009)

Kahn and Mansur (JPUBE 2013)

Grainger, Schreiber and Chang (2016)

If people are fully informed

about amenities, then

information disclosures will

have no effect on prices.

Disclosure increases price

discount by 37 percentage points

1. Strategic behavior on the part of firms and regulators may weaken the

observable assignment of pollution to people

2. There is heterogeneity in information and beliefs

3. Pollution may create heterogeneity in cognition and decision making

Question: what does this mean for revealed preferences and policy analysis?

Summary

Frictions and Nudges

Consumers leave money on the table

• Automobiles: Busse et al. (QJE 2015), Lacetera et al. (AER 2012)

• Home loans: Agarwal & Mazumder (AEJ 2013), Woodward & Hall (AER 2012)

• Health insurance: Ketcham et al. (AER 2012,2014), Handel & Kolstad (AER 2015)

• Retirement savings: Bernheim et al. (AER 2015), Chetty et al. (QJE 2015)

• Energy: Allcott & Taubinsky (AER 2015), Gillingham & Palmer (REEP 2014)

Explanations: costly information, cognitive ability, psychological biases,

latent product quality

Federal policies increasingly manipulate choice architecture

• Decision support: EPA air quality index; CMS Medicare plan finder

• Default options: HHS (2014) proposal for assignment to low cost plans in ACA

• Menu restrictions: CAFE standards, appliance efficiency standards, light bulb ban

The Air Quality Index (AQI)

• Established in 1999, it translates ambient readings of ozone, PM, CO, NO2, and SO2 into an overall index of health risk from outdoor exposure to air pollution.

• States are required to use major media outlets to report daily AQI values to the general public in all metro areas with greater than 350,000 people.

• Covers 65% of the U.S. population

The Air Quality Index (AQI)

The AQI is included in weather reports in the television news, newspapers, radio, internet, and apps for computers, tablets, and smartphones.

Frictions and Nudges

Consumers leave money on the table

• Automobiles: Busse et al. (QJE 2015), Lacetera et al. (AER 2012)

• Home loans: Agarwal & Mazumder (AEJ 2013), Woodward & Hall (AER 2012)

• Health insurance: Ketcham et al. (AER 2012,2014), Handel & Kolstad (AER 2015)

• Retirement savings: Bernheim et al. (AER 2015), Chetty et al. (QJE 2015)

• Energy: Allcott & Taubinsky (AER 2015), Gillingham & Palmer (REEP 2014)

Explanations: costly information, cognitive ability, psychological biases,

latent product quality

Federal policies increasingly manipulate choice architecture

• Decision support: EPA air quality index; CMS Medicare plan finder

• Default options: HHS (2014) proposal for assignment to low cost plans in ACA

• Menu restrictions: CAFE standards, appliance efficiency standards, light bulb ban

Under the guidance of the US Social and Behavioral Sciences Team Federal agencies must:

“…identify programs that offer choices and carefully consider how the presentation and structure of those choices, including the order, number, and arrangement of options, can most effectively promote public welfare, as appropriate, giving particular consideration to the selection of default options.”

—Section 1(b)(iii) of Executive Order #13707

September 15, 2015

EXECUTIVE ORDER 17307

Using Behavioral Science Insights to Better Serve the American People

Challenge for Future Research

Challenge: refine the current revealed preference sorting framework to analyze equity and efficiency of policies that modify choice architecture in markets for differentiated goods.

Catch-22: such a framework is only needed in settings where some people’s choices do not reveal their preferences.

Research question: how?

A Conceptual Approach: Bernheim-Rangel (QJE 2009)

1. Identify choices that we suspect do not reveal consumers’ preferences: “suspect choices”

2. Estimate preference parameters from choices that we believe reveal preferences: “non-suspect choices”

3. Assume that observationally identical people making suspect and non-suspect choices share the same underlying preferences

4. Evaluate welfare for prospective policies targeting choice architecture

• Predict policy’s effect on the choice process

• Step 1: identify non-suspect choices

o info treatment (Allcott-Taubinsky AER 2015)

o knowledge test (Handel-Kolstad AER 2015)

o RP test (Ketcham et al. AER 2016)

o Survey information (Ketcham et al. NBER 2016)

• Step 2: estimate decision parameters for suspect and non-suspect groups

• Step 3: evaluate prospective policies

Estimating the Heterogeneous Welfare Effects of Choice Architecture

DEPARTMENT OF ECONOMICS

Jonathan D. Ketcham* Nicolai V. Kuminoff† Christopher A. Powers‡

* Arizona State University Marketing Department

† Arizona State University Economics Department, and NBER

‡ United States Centers for Medicare and Medicaid Services

March 2017

This research was supported by a grant from the National Institute for Health Care Management (NIHCM) Research and Educational Foundation. All results and products

derived from this research are the responsibility of the research team; the findings do not necessarily represent the views of the NIHCM Research and Educational Foundation.

Our Empirical Setting: Medicare Part D

Prescription drug insurance is a differentiated good

• Average consumer choses among 50 plans offered by 20 insurers

• Plans differ in cost, risk protection, and quality

• Average senior spends about $1,300 per year (6% of income)

• Markets enroll 24 million seniors with annual federal outlays over $65 billion

Proposals to simplify choice architecture

• Restrict insurers to sell no more than two plans (2014 CMS proposal)

• Assign consumers to low-cost default plans (2014 HHS proposal)

• Personalized decision support (Kling et al. QJE 2012)

Prescription Drug Plan (PDP) Choice

• Beliefs about PDPs: 𝑉 𝑐𝑗 , 𝑞𝑗 , 𝐹𝑖; 𝜃𝑖 , 𝑏𝑖 = 𝑉 𝑐 𝑖𝑗 , 𝑞 𝑖𝑗 , 𝐹 𝑖; 𝜃𝑖

• Objective measures of PDPs: 𝑉 𝑐𝑗 , 𝑞𝑗 , 𝐹𝑖; 𝜃𝑖

• Revealed preference analysis: 𝜃 = min𝜃∈Θ

𝑄𝑁 𝑐𝑗 , 𝑞𝑗 , 𝐹𝑖 , 𝜃

• Consistency of an estimator for 𝜃:

As 𝑁 → ∞, 𝜃 → 𝜃 if 𝑐𝑗 = 𝑐 𝑖𝑗 , 𝑞𝑗 = 𝑞 𝑖𝑗, and 𝐹𝑖 = 𝐹 𝑖 ∀ 𝑖

Identifying Suspect Choices

max𝑗∈𝐽

𝑉 𝑐 𝑖𝑗𝑡 , 𝑞 𝑖𝑗𝑡 , 𝐹 𝑖𝑡; 𝜃𝑖

Definition. A choice is suspect if we have reason to believe it does not reveal the consumer’s preferences for PDP attributes because 𝑐 𝑖𝑗𝑡 ≠ 𝑐𝑗𝑡, 𝑞 𝑖𝑗𝑡 ≠ 𝑞𝑗𝑡,

and/or 𝐹 𝑖𝑡 ≠ 𝐹𝑖𝑡.

Nonsuspect choices are assumed to be informed in the sense that the decision maker’s beliefs about plan attributes coincide with the objective measures as we have defined them

Potential indicators grounded in theory

1. Incorrect answer to MCBS knowledge question

2. Choice violates axioms of consumer preference theory

Suspect Choice Indicator #1: Knowledge Test

(true / false) “ Your out-of-pocket costs are the same in all Medicare prescription drug plans ”

• True for people with no drug claims

• False for people with any drug claims

• We code it as correct if the enrollee gave the right answer for year t-1 or t

•1

𝑁𝑇 max

𝑗𝑜𝑜𝑝𝑗𝑡 𝑥𝑖𝑡 − min

𝑗𝑜𝑜𝑝𝑗𝑡 𝑥𝑖𝑡𝑁𝑇 = $1,112

Suspect Choice Indicators, by Year

• We observe if the knowledge question is answered by the beneficiary or a proxy.

• Respondents who answered incorrectly could have saved 16% more by switching, on average, than those who answered correctly.

2006 2007 2008 2009 2010

fails knowledge test 44 37 34 29 28

plan dominated ex post 19 18 18 16 15

fails knowledge test U plan dominated ex post 54 48 45 40 38

Suspect choice indicator

Percent of choices

Suspect Choice Indicator #2: Preference Axiom Test

If preferences satisfy completeness, transitivity, monotonicity, and risk aversion, then a consumer will never choose a plan, j, that is dominated by another, k, in the sense that (i)-(iv) hold simultaneously:

(i) E 𝑐𝑖𝑘𝑡 ≤ E 𝑐𝑖𝑗𝑡

(ii) var 𝑐𝑖𝑘𝑡 ≤ var 𝑐𝑖𝑗𝑡

(iii) 𝑞𝑗𝑡 ≤ 𝑞𝑘𝑡

(iv) At least one inequality is strict

Dominated plan choices are:

• Off the efficiency frontier in attribute space (Lancaster JPE 1966)

• Inconsistent with the axioms of consumer theory; e.g. implying the decision maker must be risk loving or have negative marginal utility of income (Ketcham, Kuminoff and Powers AER 2016)


• Dominated in terms of mean and variance of ex post cost, CMS vertical quality index, and brand.

2006 2007 2008 2009 2010





Percent of choices


2006 2007 2008 2009 2010





Percent of choices

Suspect Choice Definition

Enrollment decisions coded as suspect if any of the following are true:

i. Active choice

a. of a dominated plan

b. while giving wrong answer to MCBS knowledge question

ii. Passive reenrollment in a plan that was

a. dominated when it was actively chosen, or

b. actively chosen in enrollment cycle with wrong answer to knowledge question

• Drop about 4,000 passive reenrollments for which we do not observe the enrollee’s knowledge at the time of their original active enrollment into that plan.

• Drop all enrollment decisions in 2006 due to evidence of learning.

• Final sample has 9,119 PDP choices (42% suspect) made by 3,444 enrollees.

• Reference person: 65-69 year old white

male who does not have a high school

degree but makes decisions on his own,

has not been diagnosed with cognitive

illnesses, and has not searched for CMS

info using internet or 1-800-Medicare

• linear probability model includes

indicators for year and CMS region

• Standard errors are robust and

clustered by enrollee

college graduate -0.058 [0.021]***

income>$25k -0.012 [0.019]

currently working 0.009 [0.026]

married 0.011 [0.020]

has living children -0.064 [0.034]*

uses the internet -0.015 [0.022]

searched for CMS info: internet -0.083 [0.021]***

searched for CMS info: 1-800-Medicare -0.066 [0.020]***

has help making insurance decisions 0.016 [0.018]

number of available plans (standardized) -0.003 [0.016]

female 0.028 [0.019]

nonwhite 0.114 [0.036]***

age: 70-74 0.047 [0.023]**

age: 75-79 0.065 [0.027]**

age: 80-84 0.071 [0.028]**

age: over 84 0.118 [0.030]***

dementia including Alzheimer's 0.040 [0.027]

depression 0.011 [0.023]

number of drug claims (standardized) 0.033 [0.008]***

number of plan choices

number of enrollees

mean of the dependent variable

R-squared

Suspect Choice

9,119

3,444

0.42

0.059

Logit Estimation: Initial Enrollment Decisions

𝑈𝑖𝑗𝑠 = 𝑝𝑗𝑠 + 𝜇𝑖𝑗𝑠 𝛼𝑖𝑠 + 𝜎𝑖𝑗𝑠2 𝛽𝑖𝑠 + 𝑞𝑗𝑠𝛾𝑖𝑠 + 𝜖𝑖𝑗𝑠

𝑝𝑗𝑠 + 𝜇𝑖𝑗𝑠 = plan premium + individual OOP costs

𝜎𝑖𝑗𝑠2 = variance of individual OOP costs

𝑞𝑗𝑠 = CMS quality index, brand dummies

𝜖𝑖𝑗𝑠 = iid type I EV preference shocks

• 𝛼𝑖𝑠 = 𝛼0 + 𝑑𝑖𝑠𝛼1, where 𝑑𝑖𝑠 is a vector of demographics

• Costs of search and enrollment assumed to be constant over plans

• Initial plan choice becomes the default in period 𝑡 = 𝑠 + 1

Logit Estimation: Subsequent Decisions

𝑈𝑖𝑗𝑡 = 𝑝𝑗𝑡 + 𝜇𝑖𝑗𝑡 𝛼𝑖𝑡 + 𝜎𝑖𝑗𝑡2 𝛽𝑖𝑡 + 𝑞𝑗𝑡𝛾𝑖𝑡 + 𝜂𝑖𝑡Δ𝐵𝑖𝑗𝑡 + 𝛿𝑖𝑡Δ𝑃𝑖𝑗𝑡 + 𝜖𝑖𝑗𝑡

𝑝𝑗𝑡 + 𝜇𝑖𝑗𝑡 = plan premium + individual OOP costs

𝜎𝑖𝑗𝑡2 = variance of individual OOP costs

𝑞𝑗𝑡 = CMS quality index, brand dummies

𝜖𝑖𝑗𝑡 = iid type I EV preference shocks

Δ𝐵𝑖𝑗𝑡,Δ𝑃𝑖𝑗𝑡 = dummies for active choice to switch out of

the default brand or plan

Logit Estimation: Subsequent Decisions

𝑈𝑖𝑗𝑡 = 𝑝𝑗𝑡 + 𝜇𝑖𝑗𝑡 𝛼𝑖𝑡 + 𝜎𝑖𝑗𝑡2 𝛽𝑖𝑡 + 𝑞𝑗𝑡𝛾𝑖𝑡 + 𝜂𝑖𝑡Δ𝐵𝑖𝑗𝑡 + 𝛿𝑖𝑡Δ𝑃𝑖𝑗𝑡 + 𝜖𝑖𝑗𝑡

𝑝𝑗𝑡 + 𝜇𝑖𝑗𝑡 = plan premium + individual OOP costs

𝜎𝑖𝑗𝑡2 = variance of individual OOP costs

𝑞𝑗𝑡 = CMS quality index, brand dummies

𝜖𝑖𝑗𝑡 = iid type I EV preference shocks

Δ𝐵𝑖𝑗𝑡,Δ𝑃𝑖𝑗𝑡 = dummies for active choice to switch out of

the default brand or plan

Inertia due to hassle costs, latent preferences, and psychological biases

Multinomial Logit Results: Main Effects

• Reference person: 78 years old, white, no college degree, income below $25k, drug claims in middle tercile

of the distribution, no help making decisions, did not search for CMS info on web or 1-800-Medicare.

• Replicate the prior finding that variance coefficient is small and insignificant

(Abaluck and Gruber AER 2011; Ketcham, Kuminoff and Powers AER 2016)

expected cost -0.283 [0.017]*** -0.377 [0.029]*** -0.197 [0.021]***

variance 0.076 [0.085] -0.433 [0.118]*** 0.621 [0.126]***

quality (CMS index) 0.035 [0.078] 0.056 [0.104] -0.012 [0.124]

within-brand switch -3.307 [0.109]*** -3.239 [0.152]*** -3.396 [0.155]***

between-brand switch -5.181 [0.095]*** -4.923 [0.128]*** -5.591 [0.141]***

pseudo R2

number of enrollment decisions

number of enrollees

All ChoicesNon-Suspect

choicesSuspect choices

3,442 2,175 1,560

0.66 0.64 0.71

9,119 5,248 3,871




• Implied risk premium for NS group in line with prior literature (50-50 bet {1000,892})

(Cohen and Einav AER 2007, Handel AER 2013, Handel and Kolstad AER 2015)

expected cost -0.283 [0.017]*** -0.377 [0.029]*** -0.197 [0.021]***

variance 0.076 [0.085] -0.433 [0.118]*** 0.621 [0.126]***




pseudo R2


number of enrollees



3,442 2,175 1,560

0.66 0.64 0.71

9,119 5,248 3,871




• Implied WTP to avoid switching brands is $2,958 for suspect group and $1,292 for

non-suspect group, consistent with prior evidence on inertia

expected cost -0.283 [0.017]*** -0.377 [0.029]*** -0.197 [0.021]***

variance 0.076 [0.085] -0.433 [0.118]*** 0.621 [0.126]***




pseudo R2


number of enrollees



3,442 2,175 1,560

0.66 0.64 0.71

9,119 5,248 3,871

Welfare Analysis

Non-suspect choices—infer preferences from observed choices

Suspect choices — infer preferences from non-suspect choices

Let 𝜃 = 𝛼, 𝛽, 𝛾, 𝛿, 𝜂

Assumption: 𝜃𝑠 = 𝜃 𝑛and 𝐹𝑠 𝜖𝑖𝑗𝑡 , 𝐹𝑛 𝜖𝑖𝑗𝑡 ~ type I EV

Welfare Effects of Modifying Choice Architecture

𝐸 𝐶𝑆𝑖 =1

𝛼 𝑛 𝑙𝑛 𝑒𝑥𝑝 𝑉 𝑖𝑗𝑗∈𝐽 + 𝐶 + 𝜓𝑖𝑗 𝑉𝑖𝑗𝑛 − 𝑉 𝑖𝑗𝑗∈𝐽

where, 𝑉 𝑖𝑗 is logit estimate for observable part of utility | 𝑖 ∈ 𝑆 or 𝑖 ∈ 𝑁𝑆

𝑉𝑖𝑗𝑛 is logit estimate for observable part of utility | 𝑑𝑖𝑡 , 𝜃

𝑛𝑠 , and

multinomial logit choice probability: 𝜓𝑖𝑗 =𝑒𝑥𝑝 𝑉 𝑖𝑗

𝑒𝑥𝑝 𝑉 𝑖𝑚𝑚∈𝐽






𝑛𝑠 , and



standard expression for consumer surplus derived by Small and Rosen (ECMA 1981)






𝑛𝑠 , and



standard expression for consumer surplus derived by Small and Rosen (ECMA 1981)

Adjustment derived by Leggett (ERE 2002)


∆𝐸 𝐶𝑆𝑖𝑛 =

1

𝛼 𝑛 𝑙𝑛 𝑒𝑥𝑝 𝑉𝑖𝑘

𝑛1𝑘∈𝐾

𝑒𝑥𝑝 𝑉𝑖𝑗𝑛0

𝑗∈𝐽

∆𝐸 𝐶𝑆𝑖𝑠 =

1


𝑠1𝑘∈𝐾

𝑒𝑥𝑝 𝑉𝑖𝑗𝑠0

𝑗∈𝐽

+ 𝜓𝑖𝑘1 𝑉𝑖𝑘

𝑛1 − 𝑉𝑖𝑘𝑠1

𝑘∈𝐾 − 𝜓𝑖𝑗0 𝑉𝑖𝑗

𝑛0 − 𝑉𝑖𝑗𝑠0

𝑗∈𝐽



1


𝑛1𝑘∈𝐾


𝑗∈𝐽


1


𝑠1𝑘∈𝐾


𝑗∈𝐽





𝑗∈𝐽

standard log sum ratio post-policy adjustment pre-policy adjustment


1


𝑛1𝑘∈𝐾


𝑗∈𝐽


1


𝑠1𝑘∈𝐾


𝑗∈𝐽





𝑗∈𝐽

• Consistent with Bernheim & Rangel’s (QJE 2009) proposal to use proxies when ancillary conditions inhibit revealed preference logic.

• Consistent with Kahneman et al.’s (QJE 1997) “hedonic” and “decision” utility.

• Consistent with Camerer et al.’s (UPLR 2003) proposal to evaluate policies based on an “asymmetric paternalism” criterion.




1


𝑛1𝑘∈𝐾


𝑗∈𝐽


1


𝑠1𝑘∈𝐾


𝑗∈𝐽





𝑗∈𝐽

Information needed to evaluate a prospective policy

1. 𝑉𝑖𝑗𝑠0, 𝑉𝑖𝑗

𝑛0 infer from observed behavior

2. 𝑉𝑖𝑘𝑠1, 𝑉𝑖𝑘

𝑛1 predict based on features of the policy



• Step 1: identify non-suspect choices

o info treatment (Allcott-Taubinsky AER 2015)

o knowledge test (Handel-Kolstad AER 2015)

o RP test (Ketcham et al. AER 2016)

o Survey information (Ketcham et al. NBER 2016)

• Step 2: estimate decision parameters for suspect and non-suspect groups

• Step 3: evaluate prospective policies

1. Spatial dispersion of amenities

2. Strategic regulatory behavior

3. Heterogeneity in information and beliefs

4. Revealed preferences analysis when some choices don’t reveal preferences

5. Can features of spatial equilibria inform the VSL?

TOPICS

• EPA regulations account for 44%-56% of all costs and 61%-80% of all benefits.

• Lee and Taylor (2013) estimate that up to 70% of benefits across all rules are due to

mortality reductions valued by VSL

• Also consider

effects on

housing,

agriculture,

forestry, and

recreation

CAAA ⇒ ΔPM2.5 ⇒ 160,000 fewer deaths x $7.4 million = $1.2 trillion in 2010

“ First, the wages of labor vary with the ease

or hardship, the cleanliness or dirtiness, the

honorableness or dishonorableness of the

employment…A journeyman blacksmith,

though an artificer, seldom earns so much in

twelve hours as a collier, who is only a laborer,

does in eight. His work is not quite so dirty, is

less dangerous, and is carried on in day-light,

and above ground. ”

— Adam Smith, Wealth of Nations (1776)

Chapter X, Part I.

• Thaler and Rosen (NBER 1976) formalized

the theory in terms of state-dependent

utility and an equilibrium hedonic wage

function, and reported some early VSL

estimates, building on earlier work by

Jones-Lee (JPE 1974) and Shelling (1968).

Suppose wage is measured as hourly

earnings, risk is measured as annual deaths

per 1,000 workers, and the hedonic wage

equation is linear and additively separable:

𝑉𝑆𝐿 =𝜕𝑤𝑎𝑔𝑒

𝜕𝑟𝑖𝑠𝑘× 𝑎𝑣𝑔 ℎ𝑜𝑢𝑟𝑠 / 𝑦𝑒𝑎𝑟 × 1,000

Industry, occupation

Mining, accountant

Mining, miner

Education, research professor

Education, extension professor

Track job switchers

Implications for Evaluating the Clean Air Act Amendments

CAAA Benefits and Costs in 2020 ($2006 billion)

• $65 billion cost estimate includes some (but not all) costs from Becker and Henderson (JPE 2000) and Greenstone (JPE 2002)

• Excludes $7 billion in foregone wages from Walker (QJE 2013)


Why aren’t health effects of air pollution fully

capitalized into property values?

Hypothesis 1: they are capitalized into wages

Hypothesis 2: homebuyers are uninformed

Hypothesis 3: homebuyers are discounting the future

Hypothesis 4: VSL is overestimated

Hypothesis 5: heterogeneity in willingness to pay to

reduce mortality risk

• Disentangling these hypotheses is crucial for learning how spatial equilibrium can inform benefit transfer and environmental policy


0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

EPA cost EPA cost+ wage loss

(Walker QJE 2013)

SortingBKT (JEEM 2009)

EPA Benefit PM Mortality(VSL = $8.8m)

Housing PCE(NIPA - 2015)










Spatial Migration Tends to Reduce PM2.5 Exposure…

-0.60

-0.55

-0.50

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

66 71 76 81 86 91 96

ann

ual

ch

ange

in m

ean

ho

url

y P

M2

.5(μ

/m3)

age

non-movers

movers


0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

EPA cost EPA cost+ wage loss

(Walker QJE 2013)

SortingBKT (JEEM 2009)

EPA Benefit PM Mortality(VSL = $8.8m)

Housing PCE(NIPA - 2015)










Whose VSL should we be trying to measure?

Thank you!

Further reading:

1. Papers cited on the website and slides

2. References in Kuminoff, Smith and Timmins (JEL 2013)

Questions: [email protected]

mailto:[email protected]

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