Download
1 / 11
110 likes | 300 Views
Discussion of Courtemanche , Heutel , McAlvanah 2011 “Impatience, Incentives and Obesity” NBER Working Paper 17483 Joseph Guse Econ 398, Fall 2011. CDC Obesity Trend Movie. http://www.cdc.gov/obesity/data/trends.html. Motivation: What explains This?. Main Question and Finding.
E N D
Discussion of Courtemanche, Heutel, McAlvanah 2011 “Impatience, Incentives and Obesity”NBER Working Paper 17483Joseph GuseEcon 398, Fall 2011
CDC Obesity Trend Movie • http://www.cdc.gov/obesity/data/trends.html
Main Question and Finding • Lots of studies have looked at the effect of food prices on BMI • A handful of studies have looked at the effect of time preferences on BMI • Do these things interact? In other words, should we expect patience to shield people from the effect of cheap food. • Main Finding: Yes fairly strong interaction effect in the “intuitive” direction. • Claim that this helps to explain the change in right tail of the distribution.
Model Summary • Develop a model of food consumption that captures the immediate pleasure and financial cost of eating as well as the future utility of weight gain. • Two period model generates predictions on the price of food, patience and their interaction on weight gain. • Three period model captures time-inconsistent preferences.
Empirical Model • Linear Regression • Dep Var: BMI • Key RHS Vars: DF, FoodPrices, Interaction Term (eqn 17, p. 22) • Also run … • models without foodPrices • Models that distinguish Beta and Delta DFs
Interaction Effects • Y = Ѳ1*x1 + Ѳ2*x2 + Ѳ3*x1x2 • Marginal effect of x1: • dY/dx1 = Ѳ1 + Ѳ3x2 • Suppose for example that Ѳ1 < 0 and Ѳ3 > 0. Then for higher values of x2, the magnitude of marginal effect of x1 will be (typically) reduced. (assuming we are starting from a point where dY/dx1 < 0)
What I liked About this Paper • Question is well-motivated. Public and private cost of both higher BMI and higher BMI variance are nicely documented. We should care about this. • laying out the 3 open questions on the issue of time preferences and obesity. An excellent way of showing how this work fits into the literature. • Falsification tests. Awesome. Intuitive misspecification tests. These things should not affect height, but let’s make sure. (p. 21) • Interaction Term. Carving the i.t. between patience and incentives as a focus is a really nice idea. • BMI distribution simulations based on regression results. (Figures 3,4,5)
Improvement Suggestions (page 1) Patience acts on BMI through many pathways. Several of the control variables are expected to be correlated with patience. (College, net worth, married?) The total effect of patience on BMI, therefore, is understated by the coefficient on the discount factor per se. This should be discussed at more (any) length in your interpretation and conclusions. Calories never default. Measuring the discount factor with a question about a money prize and extrapolating to future weight gain is slightly problematic. In particular, when I eat (or cut back on eating) the future consequences are fairly certain (if vague) while the promise of paying a prize at a later date instead of immediately incorporated default risk. i.e. your “Beta” may not be a “present bias”, it may simply reflect default risk which would not apply to the issue of present calories leading to future weight gain; the calories NEVER default on that promise!
Needs Improvement (page 2) • Variance of BMI is difficult to explain without explicit physiological model. In the introduction, authors say about previous work “agg-level vars help explain the growth in average BMI, but cannot explain increasing variance unless some people respond more strongly to changing economic incentives” (p. 2). This is not obvious to me. Without a physiological model, it is hard for me to say, for example, that a uniform response in food consumption would not lead to higher variance in BMI. In other words it could just be your “g(f)” though you don’t investigate the shape of this proposed function at all.
Needs Improvement (page 3) • Food Index has almost zero sugar! There is strong research in physiology that sugar plays a disproportionate role in obesity. However the food price index is constructed from a list of 19 items which completely ignores soda and sugary snacks. • Idea for future Research: Authors already experiment with removing “healthy” foods from the index as a robustness check, but I would like to see an examination of sugary food prices specifically. (CPI data?)
