Public Policy Toward Addictive Substances

Professor B. Douglas Bernheim

Department of Economics

Stanford University

Some statistics on addictive substances

25 million adults have a history of alcohol dependence

5 million adults are “hard core” chronic drug users

More than 500,000 deaths each year are attributed to alcohol and cigarettes

Total direct and indirect expenses (including health care) exceed 5 percent of GDP

Current policy isn’t working well

Despite criminalization of many substances, use is widespread and health costs are high

Black markets promote organized crime, enrich criminals, and contribute to a culture of violence.

More than 600,000 citizens are incarcerated for drug-related offenses – disproportionately poor and black

What is addiction?A clinical perspective

Substance addiction occurs when, after significant exposure, users find themselves engaging in compulsive, repeated, and unwanted use despite clearly harmful consequences, and often despite a strong desire to quit unconditionally

What is addiction?A behavioral perspective

Unsuccessful attempts to quit Cue-triggered recidivism Self-described mistakes Self-control through precommitment Self-control through behavioral and cognitive

therapies

What is addiction? Competing economic perspectives1. The theory of “rational addiction” Becker and Murphy, and variants thereof Dynamically consistent decision-maker with

coherent lifetime preferences Intertemporal complementarities Critical components include the “high,”

tolerance, and withdrawal Has difficulty accounting for behavioral

patterns on previous slide

What is addiction? Competing economic perspectives2. Cue-triggered decision processes Bernheim and Rangel Use among addicts is sometimes a mistake

-- substances undermine a cue-triggered forecasting process

Experience sensitizes an individual to randomly occurring environmental cues that trigger mistaken forecasts, and hence mistaken usage

Addicts understand and manage their susceptibilities

What is addiction? Competing economic perspectives2. Cue-triggered decision processes, cont’d Formalized as a dynamic programming

problem 2 types of choices: “lifestyle” and use Lifestyle choices affect exposure to randomly

occurring environmental cues Use sensitizes individual to cues, setting

triggers for forecasting malfunctions Solution involves standard tools Use can either be rational or irrational/cue-

triggered

What is addiction? Competing economic perspectives2. Cue-triggered decision processes, cont’d Sharp comparative statics Produces a variety of observed behavioral

patterns including: Cycles of binging/abstention Precommitment (e.g., lock-up rehab) Recidivism Intentional recidivism Resignation

Simulated choices for a heroin-like substance

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Addictive State

What is addiction? Competing economic perspectives3. Quasi-hyperbolic discounting Gruber and Koszegi Dynamically inconsistent preferences (of the

- variety) Imports the critical components of the

“rational addiction” framework (the “high,” tolerance, withdrawal)

Accounts for additional patterns, e.g. precommitment

What is addiction? Competing economic perspectives

4. Temptation preferences Gul and Pesendorfer Preferences defined over objects of the form

(Z,z), where Z is the set from which choice is made, and z is the choice

Choice axioms lead to precise characterization and utility representation:

U(z) + [W(z) - maxxZ W(x)]

U is conventional utility, W is temptation Elimination of tempting options improves utility

Economic perspectives on addiction: Comments on positive implicationsSome issues concerning QHD: Present-bias appears to affect monetary

choices Discounting appears to be domain-specific Present bias isn’t always operationalSome issues concerning temptation

preferences: Implication is that rehab is chosen to avoid

cravings, and not to constrain behavior when cravings occur.

BUT… models can be tweaked

A framework for normative analysis

Critical for policy analysis Sharply conflicting positions

Gul-Pesendorfer, “The Case for Mindless Economics”

Bernheim-Rangel, “Beyond Revealed Preference: Toward Foundations for Behavioral Welfare Economics”

Others weighing in

A framework for normative analysis

A canonical problem Time t, DM must choose either A or B Facts about choice

At time t, DM chooses A over B At time t-1, DM chooses B over A

What do we make of this from a welfare perspective? That is, if the government must choose between A and B for the DM, which choice should it respect?

A framework for normative analysis

Possible approaches

1. Accept choices at face value (literalistic revealed preference)

2. Try to “officiate” between apparently conflicting choices

3. Use an alternative to revealed preference

- Paternalism

- Measurement of satisfaction (happiness)

- Non-outcome-based measures (e.g., opportunity)

A framework for normative analysis

Approach #1: Literalistic revealed preference Standard welfare economics has libertarian

underpinnings – Government should make the choice that people would make for themselves

There is no utility – there are no preferences – there is only choice

Normative analysis is then simply an extension of positive analysis – all we need is a good predictive model

Possible agenda for behavioral welfare economics: No attempt to resolve inconsistencies – government should mimic inconsistent choices

A framework for normative analysis

Approach #1: Literalistic revealed preference, cont’d

Apparent implication here – government should pick A if choosing at t, and B if choosing at t-1

Ambiguities concerning frame: is a delegated choice at t more like a non-delegated choice at t, or a non-delegated choice at t-1?

Unlimited frame-dependence can become unwieldy

“Officiating” appears unavoidable.

A framework for normative analysis

Approach #2: Officiate Variant A: Only choice data are permitted Variant B: Non-choice data are also permitted

A framework for normative analysis

Approach 2A: Officiating based only on choice data

Theme: Ultimately, have to rely on assumptions that cannot be validated through choice data alone.

A framework for normative analysis

Approach 2A: Officiate based on choice data – coherent-self approach

Gul-Pesendorfer & temptation preferences Rhetoric invokes literalistic revealed

preference, but substance involves officiating between apparently conflicting choices

Recall preferences:

U(z) + [W(z) - maxxZ W(x)] Best policy choice: maximize U(z).

A framework for normative analysis

Approach 2A: Officiate based on choice data – coherent-self approach, cont’d

In canonical problem, officiates between choice of B at t-1, and choice of A at t. Implies that choice of B at t-1 is the only one relevant for policy.

But how can this be resolved based on choice data alone? Where does the rabbit go into the hat?

Answer: There is an implicit assumption that is ultimately untestable with choice data

A framework for normative analysis

Approach 2A: Officiate based on choice data – coherent-self approach, cont’d

Key assumption: when choosing z, an object, from Z, a set of objects, well-being is influenced by temptation.

But what if the object itself is a set (e.g., a constraint set) chosen from a set of sets (e.g., possible constraint sets)? GP assume no temptation is experienced

Canonical problem: Is choice of A at time t the result of temptation? Or is choice of constraint for t at t-1 (that is, B) the result of temptation?

A framework for normative analysis

Approach 2A: Officiate based on choice data – coherent-self approach, cont’d

Ambiguity cannot be resolved by choice data. With second-level choice data and second-level

temptation preferences, no welfare question is resolvable.

General version of theorem: With N-th level choice data and N-th level temptation preferences, no welfare question is resolvable.

Without an assumption unsubstantiated by choice data, no amount of choice data can resolve welfare questions.

A framework for normative analysis

Approach 2A: Officiate based on choice data – multi-self approach

One alternative: ignore (use “long run” prefs) A formal justification

Preferences at each moment in time t:

u(ct) + jtj-tu(cj) Use any well-behaved social aggregator As time periods become “short,” welfare criterion

converges to long-run preferences Note: key assumption not testable w/ choice data Similar issue with Pareto criterion

A framework for normative analysis

Approach 2B: Officiate allowing for non-choice data (selectively revealed pref)

As long as reliance on something other than choice data is inevitable, let’s be systematic

There are clearly situations in which actions do not reveal preferences American pedestrians in London

Within the context of standard economics, can understand this as an information constraint, except that the constraint is internal – not observable as part of the environment.

A framework for normative analysis

Approach 2B: Selective revealed preference allowing for non-choice data, cont’d

Our agenda: Incorporate evidence on internal information processing, and then use standard positive and normative economic tools Attention Memory Forecasting Learning

Principle: preferences are more reliably revealed in circumstances where attention is focused, pertinent events are recalled, and forecasting process are working properly

A framework for normative analysis

Approach 2B: Selective revealed preference allowing for non-choice data, cont’d

This agenda defines a clear normative role for non-choice evidence (e.g., from neuroscience):

Identify – at least qualitatively – limitations and malfunctions of information processing within the brain to establish circumstances in which choices provide a reliable guide to preferences, and circumstances in which they do not.

What is addiction?A perspective from neuroscience

Basic forecasting mechanism

Standard consumption goods

Other cognitive

processes

Post-choiceexperience

and reward

ConsumptionDecision

Learning Learning

Environmentalconditions

What is addiction?A perspective from neuroscience

Basic forecasting mechanism

Addictive substances

Other cognitive

processes

Post-choiceexperience

and reward

ConsumptionDecision

Learning Learning

Environmentalconditions

Direct effect

Some details on the neurological evidence

Addictive substances interfere with a primitive dopamine-based forecasting system Anticipatory dopamine elevation measures the

experienced correlation between environmental cues and action-contingent rewards (Schultz et. al.)

Addictive substances share an ability to activate dopamine firing directly (Malenka and others)

The dopamine-based forecasting system creates powerful impulses to act, independent of the true reward The dopamine process is associated with “wanting,”

which appears to be completely distinct from “liking” (Berridge and Robinson)

Implications for specific policies

Criminalization Public education Taxation of addictive substances Subsidization of rehabilitation Other harm-reducing policies Selective legalization with controlled

distribution Policies affecting environmental cues Regulation of use

Implications for valid policy objectives

1. Protect third parties

2. Combat misinformation and ignorance

3. Help consumers avoid mistakes

4. Moderate consequences of uninsurable risks

Conventional

Unconventional

Canadian policy – graphic warnings