1. The Econometrics of Habit and Addiction A research proposal Robert Rosenman Washington State University

2. Purpose of the Study The purpose of the study is to explore how to use econometrics to estimate sequential behavior that may be habit or addiction. I want to see how the two behaviors differentially manifest themselves and what implications that has for parameters in a model that might nest both. Also interested in statistical issues of such a model

3. Definitions Habit: A settled or regular tendency, behavior or practice, especially one that is hard to give up. Addiction: Physical or mental dependence on a substance (or activity) to the extent of being unable to stop taking it (or doing it) without incurring adverse effects.

4. Research Question How do we differentiate between habit and addiction in an econometric model of sequential behavior?

5. Contextual Economic Model Habit or addiction infer a sequence of behavioral decisions over time. At each time period, an individual must decide whether she will do the activity or take the substance, or not. Period by period, she will do the activity or take the substance if her utility of doing so is positive.

6. Contextual Economic Model (cont) The sequence allows a person to go into or out of the activity, leaving and returning based on the utility each period. The difference between habit and addiction may be construed as one of degree An addiction is much harder to disrupt, hence we may see a greater likelihood of the activity each period in the sequence. One important issue is how the decisions related over the sequence.

7. Related Econometric Literature Gotz and McCall, “Estimation in Sequential Decision Models,” Eocnomic Letters, 1980 Eckstein and Wolpin, “The specification and estimation of dynamic stochastic discrete choice models: A survey,” J. of Human Resources, 1989. Heckman and Navarro, “Dynamic Discrete Choice and Dynamic Treatment Effects,” J. of Econometrics, 2007 Cunha, Heckman and Navarro, “The Identification and Economic Content of Ordered Choice Models with Stochastic Thresholds,” WP 2007

8. An Extension of the Econometrics Mittelhammer, Rosenman and Tennekoon add structure allowing for agents to leave and reenter the sequence, while updating their decisions from prior experience. They think about the model as a series of sequential decisions in a voluntary treatment program.

9. MRT Framework represents a state vector of anticipated utilities for DM i who is at the point of making her decision regarding the t th treatment in a course of J treatments. At the initial decision point, D i1 =[0,0,…0] T so no treatments have been experienced. If the agent takes treatment k, based on her experience, she updates her anticipated utility for all remaining treatments, that is, for all t>k.

10. Summary of Decisions

11. Econometric Specification

12. Decision Rule Then is follows that Where the function I(A)=1 if A is true.

13. At point t=2 where v i1 is new information the DM learned from attending session 1.

14. And in general for t  2

15. Addiction and Habit Econometric key is contained in the Bs and the c ij which make up a a matrix

16. Habit and Addiction (cont.) Addiction is that Bs are such that X′ ij B j >0 for all j. Quite likely, B j =0 except for contants>0. Habit is that Bs are such that X’ ij B j >0 for most j. In Addiction, expect that c ij  0 so transient effects do not change behavior. In Habit, expect that c ij  0 but “small” so (large) transient effects change behavior but small transient effects do not.

17. Testable hypotheses (Addiction) H0A: When the decision is something like smoking (an addiction) the Bs are such that X’ ij B j >0 for all j. H1A: When the decision is something like smoking (an addiction) the Bs are such that X’ ij B j  0 for some j. H0B: When the decision is something like smoking (an addiction) the c ij  0. H1B: When the decision is something like smoking (an addiction) the c ij >>0.

18. Testable hypotheses (Habit) H0C: When the decision is something like exercising (a habit) the Bs are such that X’ ij B j >0 for most j. H1C: When the decision is something like exercising (a habit) the Bs are such that X’ ij B j  0 for many j. H0D: When the decision is something like exercising (a habit) the c ij  0 but are “small”. H1D: When the decision is something like exercising (a habit) the c ij >>0.

19. Expected Findings Addiction is much less likely to be disrupted by previous experience that habits. Addictive behavior shows predominantly an “all or nothing” behavior (identification problem). Habitual behavior shows more varied experiences, with people moving in and out of the activity.

20. Next Steps Identify necessary data – Need data on different behaviors, like smoking (an addiction) and exercise (a habit). – Need day-to-day, week-to-week or if need be year-to-year data, something that allows for sequential decision making. – Need proper covariates, such as demographics and socio-economic variables. Program estimation routine. Run empirical tests.