Public Policy Toward Addictive Substances

1. Public Policy Toward Addictive Substances Professor B. Douglas Bernheim Department of Economics Stanford University

2. 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

3. 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

4. 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

5. 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

6. What is addiction? Competing economic perspectives 1. 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

7. What is addiction? Competing economic perspectives 2. 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

8. What is addiction? Competing economic perspectives 2. 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

9. What is addiction? Competing economic perspectives 2. 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

10. Probability Addictive State Simulated choices for a heroin-like substance

11. What is addiction? Competing economic perspectives 3. 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

12. 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) - maxxZ W(x)] • U is conventional utility, W is temptation • Elimination of tempting options improves utility

13. Economic perspectives on addiction: Comments on positive implications Some issues concerning QHD: • Present-bias appears to affect monetary choices • Discounting appears to be domain-specific • Present bias isn’t always operational Some 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

14. 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

15. 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?

16. 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)

17. 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

18. 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.

19. A framework for normative analysis Approach #2: Officiate • Variant A: Only choice data are permitted • Variant B: Non-choice data are also permitted

20. 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.

21. 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) - maxxZ W(x)] • Best policy choice: maximize U(z).

22. 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

23. 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?

24. 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.

25. 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) + jtj-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

26. 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.

27. 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

28. 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.

29. What is addiction?A perspective from neuroscience Standard consumption goods Environmental conditions Basic forecasting mechanism Other cognitive processes Consumption Decision Post-choice experience and reward Learning Learning

30. What is addiction?A perspective from neuroscience Addictive substances Environmental conditions Basic forecasting mechanism Other cognitive processes Direct effect Consumption Decision Post-choice experience and reward Learning Learning

31. 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)

32. 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

33. Implications for valid policy objectives • Protect third parties • Combat misinformation and ignorance • Help consumers avoid mistakes • Moderate consequences of uninsurable risks Conventional Unconventional

34. Canadian policy – graphic warnings