Measuring the Impact of the New York City Calorie Labeling Mandate on Obesity

Tulane Economics Working Paper Series

Information, Choice, and Obesity: Measuring the Impact of the New York City Calorie Labeling Mandate on Obesity

Rodrigo Aranda Balcazar Michael Darden Donald Rose Department of Economics Department of Economics School of Public Health and Tulane University Tulane University Tropical Medicine [email protected] [email protected] Tulane University [email protected]

Working Paper 1611 August 2016

Abstract The New York City Calorie Labeling Mandate of 2008 required fast food restaurants to post calorie information for all standardized items. We estimate the impact of the mandate on the rate of obesity using data from the Selected Metropolitan/Metropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System (SMART-BRFSS) from 2004 to 2010. We show that the mandate plausibly reduced the obesity rate by 2.5 percentage points - a 12% decline. Our results are robust to a variety of sensitivity checks and strengthened by various placebo tests. Using data from the Consumer Expenditure Survey and the American Time Use Survey, we show that our obesity result was not driven by changes in fast food frequency or expenditure but may have been driven by a large increase in the extensive margin of physical activity.

Keywords: Information Asymmetry; Obesity; Calorie Labeling JEL codes: D82; D83; I12; I18 Information, Choice, and Obesity: Measuring the Impact of the New York City Calorie Labeling Mandate on Obesity

Rodrigo Aranda Balcazar, Michael Darden, and Donald Rose∗ Tulane University

June, 2016

Abstract

The New York City Calorie Labeling Mandate of 2008 required fast food restaurants to post calorie information for all standardized items. We estimate the impact of the mandate on the rate of obesity using data from the Selected Metropolitan/Metropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System (SMART-BRFSS) from 2004 to 2010. We show that the mandate plausibly reduced the obesity rate by 2.5 percentage points - a 12% decline. Our results are robust to a variety of sensitivity checks and strengthened by various placebo tests. Using data from the Consumer Expenditure Survey and the American Time Use Survey, we show that our obesity result was not driven by changes in fast food frequency or expenditure but may have been driven by a large increase in the extensive margin of physical activity.

JEL Classiﬁcation: D82; D83; I12; I18 Keywords: Information Asymmetry; Obesity; Calorie Labeling

∗Rodrigo Aranda Balcazar: [email protected]; Michael Darden: [email protected]; Donald Rose: [email protected]

1 1 INTRODUCTION

1 Introduction

The extent of the obesity epidemic in the United States is well known: between 2013 and 2014, 37.9% of adults and 17.2% of children were obese, and important diﬀerences in obesity rates exist by race, education, and income (Ogden et al., 2016;

Flegal et al., 2016). The economic view of the rise in obesity is that, through both the increasingly sedentary nature of the workplace and mechanized distribution of calorie dense food, the price per calorie has declined significantly and the cost of physical activity has significantly increased (Lakdawalla et al., 2005).1 In light of this decline, the rise in obesity is not surprising given evidence that the persistent average daily calorie imbalance (intake vs. expenditure) required to explain the rise in weight is only 220 calories (Hall et al., 2011; Cawley, 2015). However, policies aimed at reducing obesity by changing the relative prices of unhealthy foods have mixed results. For example, Andreyeva et al. (2010), who conduct a review of the price elasticity of demand for different types of food and beverages, suggest that the demand for high calorie meals away from home and soft drinks may be nearly unit elastic. However, Wang(2015) shows that soda taxes raise revenue but are unlikely to significantly reduce soda consumption specifically because of persistent, unobserved consumer tastes. In addition to changing the relative price of unhealthy foods, a focus of policy has been to inform individuals of both the causes and consequences of obesity. For example, the Affordable Care Act (ACA) requires the disclosure of the nutritional content of fast food and vending machine menu items.2 Fast food, and more generally food away from home, has been shown to be higher in calories and generally associated with obesity (Currie et al., 2010). Indeed, Powell et al. (2012) show that, in 2007 and 2008, fast food and food away from home accounted for 24% of adolescent and adult energy intake, and

Burton et al. (2006) and Burton et al. (2014) show that fast food consumers (and nutrition professionals!) systematically underestimate the calorie content of fast food by an average of more than 600 calories. Furthermore, Binkley(2007) shows that both fast food and table service meals have more calories than home cooked meals. If consumers have asymmetric information regarding the nutritional content of fast food, then, depending on consumer preferences, policies that alleviate information asymmetries may be welfare enhancing. In this paper, we study the effect of the New York City Calorie Labeling Mandate (NYC-CLM) of 2008 on the rate of obesity. This New York City health code provision was mandatory for food establishments that belong to a group of 15 or more establishments (locally or nationally) that operate under common ownership or are individually franchised or do business under the same name. The mandate was specific to standardized menu items with respect to portion size, formulation, and ingredients. The mandate went into effect on May 5th, 2008 and was fully enforced on July 18th, 2008. The mandate was followed by an intense three-month advertising campaign called “Read ’em before you eat ’em.” By the end of 2008, compliance with the labeling mandate was 85 percent. The NYC-CLM serves as an interesting precursor to the

1See Cawley(2015) for the most recent survey of the economics of obesity. 2After legal challenges, all chain restaurants with over 20 locations nationwide will be required to post calorie labels as of December 1st, 2016. See http://kﬀ.org/interactive/implementation-timeline/

2 1 INTRODUCTION

ACA fast food labeling mandate, which goes into effect in December 2016. To identify the effect of the mandate on obesity, we examine the conditional obesity trends of adults in New York City relative to other urban areas in the Selected Metropolitan/Micropolitan Area Risk Trends of the Behavioral Risk Factor Surveillance System (SMART-BRFSS) from 2004 to 2010. We find that the labeling mandate reduced the proportion of obese adults by 2.5 percentage points, a roughly 12% decline. This result is confined almost entirely to transitions out of class I obesity, defined as BMI between 30 and 35. We find economically and statistically insignificant effects at higher levels of body mass index. Our main difference-in-differences estimator passes tests for the standard pre-treatment parallel trend assumption, and our results are robust to a variety of alternative specifications and placebo tests. A consumer of fast food may respond to calorie labels in a variety of ways depending on her prior beliefs, preferences, and constraints. Upon learning of the calorie content of fast food, consumers may a.) substitute towards healthier options within the same restaurant or b.) substitute away from fast food restaurants in general. Using data from the Consumer Expenditure Survey (CEX) from 2006 to 2010, we find a statistically significant decline in monthly fast food frequency in New York City after 2008 relative to other cities, but this effect is small in magnitude (3.5% of a standard deviation). While our SMART-BRFSS data do not include measures of fast food consumption, we estimate a selection model of fast food frequency in the Consumer Expenditure Survey and use the model to predict the propensity of SMART-BRFSS respondents to visit fast food restaurants. Using the predicted value of fast food restaurant frequency, we show that frequency was not significantly related to our obesity results. Furthermore, conditional upon visiting a fast food restaurant, the CEX data suggest a statistically significant, but again very small in magnitude, increase in expenditure per visit. This increase may be consistent with substitution towards healthier, more expensive items. Overall, our results on fast food frequency and expenditure are consistent with several studies of the NYC-CLM that have found a negligible effect on purchasing behavior conditional on visiting a fast food restaurant (Dumanovsky et al., 2010; Elbel et al., 2009; Cantor et al., 2015). Even if consumers who learn about fast food calorie content maintain their consumption of fast food, an observed decline in obesity could occur if consumers compensate by becoming more physically active. To investigate, we use American Time Use Survey (ATUS) data from New York City and other metropolitan areas from 2004 to 2010 to estimate a two part model for participation and intensity in “sports, exercise, and recreation activities.” Our results suggest a roughly four percentage point (25%) increase in physical activity on the extensive margin in New York City following the calorie labeling mandate that is statistically significant with at least 99% confidence. This result may be overstated if the NYC-CLM caused systematic misreporting of physical activity or if the assumption of parallel pre-trends fails (although we do not find statistical evidence of this). Nevertheless, the large magnitude of our findings suggests a plausible mechanism behind our obesity results. We do not find a statistically significant increase on the intensive margin of physical activity. Our results provide some clarity to a mixed literature on the effects of calorie labeling. For example, Dumanovsky et al.

3 1 INTRODUCTION

(2010) surveyed fast food consumers one year prior and nine months after the NYC-CLM at 168 randomly chosen locations of the top 11 fast food chains during lunchtime. They find no overall decline in calories purchased, but declines in some restaurants and increases (Subway) in others; 15% of consumers reported using the calorie information. Elbel et al. (2013) studies the effect of the 2010 Philadelphia fast food calorie labeling law on fast food frequency and expenditure using a difference-in-differences estimator relative to changes in Baltimore. Those authors find no effect for a sample of mostly black, high school educated individuals. Elbel et al. (2009) conducted a similar difference-in-differences technique with respect to the

NYC-CLM in low-income, minority New York communities and Newark, New Jersey. 27.7% of consumers reported seeing the calorie labels, but the authors found no change in calories purchased.3 In an important study, Bollinger and Sorensen(2011) estimate a difference-in-differences of individuals in New York City relative to Boston and Philadelphia with internal data from Starbucks on over 100 million transactions. Those authors found that calorie labels reduced calories purchased by 5.8% per transaction, an effect that persisted at least 10 months. Longitudinal analysis on a subset of loyalty customers showed that calories purchased for those usually buying more than 250 calories dropped by 26%. To summarize, Cawley(2015) suggests that the results of Bollinger and Sorensen(2011) indicate that individuals may be inelastic to calorie information when ordering the primary product at the store they have chosen (Starbucks beverages or fast food sandwiches) but may alter their choice of supplemental or side items. Our result of a 2.5 percentage point reduction in obesity in New York City is very large. Indeed, to generate 2.5 percentage point reduction in obesity citywide, the NYC-CLM would have to cause a much larger reduction in obesity in the subset of individuals who both visit fast restaurants and notice the calorie labels. Unfortunately, we cannot separately identify the effect of calorie labels at fast food restaurants, whose effect would largely be observed in fast food customers, from the citywide advertising campaign that coincided with the labeling mandate. However, for several reasons, we suspect that the NYC-CLM lead to a significant reduction in obesity. First, we show that the nearly all of the reduction in obesity is driven by reductions in BMI for those with BMI between 30 and 35. Second, we demonstrate a plausible mechanism – compensating physical activity – that is both unexplored in the previous literature and that may have generated the reduction in obesity. Third, alternative specifications that allow for commuters, alternative definitions of control and treatment groups, and controls for the 2007 trans-fat ban in New York City, all suggest that our results are robust. Fourth, we experimented with a synthetic control method to construct counterfactual obesity trends in the absence of NYC-CLM and found similar magnitudes.4 Fifth, we estimate our preferred model using cigarette smoking as the dependent variable and found statistically insignificant effects.

Finally, our research design is powered to observe small changes as a result of labeling. Our paper proceeds as follows. Section2 provides details on the NYC-CLM policy and describes SMART-BRFSS and CEX data. Section3 provides our main empirical model as well as our triple diﬀerences speciﬁcation. Section4 presents

3Cantor et al. (2015) examines New York City compared to Newark in 2008 and 2013-2014 and also ﬁnds no change in calories purchased. 4See Appendix A.

4 2 DATA our main ﬁndings and provides evidence that our results may plausibly be interpreted as causal. Section5 puts our results in context, exploring potential drivers and mechanisms behind our main results and highlighting limitations of our study.

Finally, Section6 concludes.

2 Data

In 2006, New York City, though its Department of Health and Mental Hygiene Board of Health (DHMH), amended Article 81 of the New York City Health Code to require food service establishments to disclose calorie information for standardized menu items on menu boards and menus next to each menu item. The requirement was implemented for food items that are standardized with regard to portion size, formulation, and ingredients.5 The mandate was required for fast food chains that belong to a group of 15 or more establishments and that operate under common ownership or are individually franchised, whether it is locally or nationally, or they do business under the same name.6 While the calorie labeling mandate became effective on March 31, 2008, implementation was delayed until May 5th, 2008 due to litigation, and full enforcement did not occur until July 18th, 2008. During the final three months of 2008, New York City’s Health Department launched a Calorie Education Campaign called ”Read ’em before you eat ’em”, which consisted of five different advertisements appearing in over 100 New York City train cars. The campaign was designed both to help individuals realize how quickly calories consumed in fast-food establishments can accumulate and to inform individuals of the average calorie recommendation for an adult.7 To the extent that the advertising campaign registered with subway riders, it potentially reached a wider audience than just those that frequent fast-food restaurants. By the end of 2008, compliance with the calorie-posting mandate was 85 percent8. To evaluate the impact of the NYC-CLM, we turn to the Selected Metropolitan/Micropolitan Area Risk Trends subset of the Behavioral Risk Factor Surveillance Survey (SMART-BRFSS). SMART-BRFSS uses cross-sectional telephone survey questions in the BRFSS in select cities to gauge the prevalence of several health behaviors, including cigarette smoking and alcohol abuse, and the basic health status of resident individuals. Included in the survey are self-reported measures of height and weight, from which we are able to construct body mass index (BMI).9 These data are ideal for our setting because we are able to identify individual respondents in New York City, as well as respondents in counties surrounding New York City and other metropolitan and micropolitan statistical areas (MMSAs). The data are representative of the areas in which the surveys are administered.

5Notice of adoption of an amendment (81.50) to article 81 of the New York City Health Code. 6Approximately 10% of restaurants in New York City met this requirement. 7Press Release # 066-08 New York City Health Department. 8New York City Department of Health and Mental Hygiene, Agency Biennial Report 2007-2008 9See Cawley(2015) for an overarching critique of using self-reported height and weight information to study obesity. In the context of our study, misreporting will only cause a bias (beyond traditional measurement bias) if the NYC-CLM caused an individual to report her height/weight diﬀerently.

5 2 DATA

Using repeated cross-sections of the SMART-BRFSS from 2004 to 2010, our data include 1,308,743 observations from the continental United States.10 To arrive at our estimation sample, we drop individuals with a calculated body mass index of less than 10 or greater than 60 (65,307 observations deleted). Next, we restrict our sample to those over the age of 18 (9,001 observations deleted), and we drop pregnant women (8,819 observations deleted). To avoid contaminating our control group with individuals who may plausibly be aﬀected by the NYC-CLM, we exclude individuals who live in counties near New York City. These potential commuters are individuals who work, study, or have other relevant activities in New

York City on a regular basis, and who could also be aﬀected by the policy change. We identify and drop those counties in which more than nine thousand or more individuals work in New York City (57,173 person/year observations deleted).11 We drop individuals from other counties or states that enacted calorie labeling mandates during this period of time (53,977 observations deleted). Finally, we drop individuals with missing values on height or weight, county, time of the survey, age, gender, race, education, no physical activity, and income (145,583 person/year observations deleted).12 Our ﬁnal sample consists of 968,883 observations.

Table 1

Within our final sample, we are able to identify individuals at the county/month/year level. Because the month of interview is recorded in our data, we define the “post” NYC-CLM treatment period to be all observations after July 31st, 2008. Treatment individuals are those living in any New York City county, while our control group consists of all respondents living in metropolitan/micropolitan statistical areas in the 370 other counties surveyed by BRFSS that do not have an existing calorie labeling mandate. Table 1 presents summary statistics of key variables in New York City and control cities before and after the NYC-CLM.13 While New York City’s rate of obesity is statistically lower than the control cities, the rate of change in obesity in New York City is flat whereas it is increasing in the control cities. Table 1 shows the importance of examining trends in outcomes rather than comparing levels: prior to the NYC-CLM policy, New York City has statistically larger fractions of African-Americans, Hispanics, females, and unemployed. To control for unobserved, supply-side heterogeneity, we merged to our SMART-BRFSS data, information on number of fast food restaurants, full service restaurants, and fitness and recreation centers per 1,000 residents from the U.S. Census

10SMART-BRFSS data are available through 2012, but we only consider survey through 2010 because CDC changed the weighting methodology and added cell phone only respondents after 2010, which limit the comparability with previous data. CDC publishes separate trends from 2000-2010 and 2011-2012 11As measured by the Census Bureau for the Residence County to Workplace County Commuting Flows from 2009 to 2013. Our results are not sensitive to this deﬁnition. We conduct sensitivity analysis in which we keep respondents living in counties adjacent to New York City, separately treating these potential commuters in our treatment and control groups. Our results are not sensitive to the way in which we classify commuters. See below. 12Our results are not sensitive to including these individuals in our sample and estimating our preferred model with missing dummy variables. 13All statistics are weighted using SMART-BRFSS weights.

6 3 EMPIRICAL STRATEGY

Bureau County Business Patterns. Currie et al. (2010) show that the density of fast-food restaurants may cause weight gain in young adults.

Much of the literature on calorie labeling mandates has focused on behavioral changes following the display of calorie information. In contrast, our research estimates the effect of a calorie labeling mandate on weight outcomes, which could be generated by changes in a variety of behaviors that influence the balance between the input and output of calories. After we establish the effect on weight outcomes, we turn to plausible mechanisms that could drive our outcome results.

3 Empirical Strategy

To identify the extent to which the NYC-CLM changed BMI and Obesity rates, we compare the conditional trends in weight outcomes in New York City to those in 370 other counties across the United States. Our main estimating equation is given as:

y = δ + γd + φNY C + δNY C d + β0 X + . (1) ict o t c c ∗ t ict ict

Where yict is the weight outcome of individual i living in county c in month/year t, dt is a binary variable for observations recorded after July 18th, 2008, NYCc is a binary variable for New York City residents, and Xict is a vector of exogenous control variables listed in Table 1. Our empirical model also includes county and month/year fixed effects, and we control for supply-side fast-food characteristics such as the number of fast food restaurants, full service restaurants, and fitness and recreation centers per 1,000 residents in a county . The key identifying assumption is that the conditional time trends in our outcome variables would be the same in New York City as those trends in other cities in the absence of the NYC-CLM. That is, the mandate introduced a deviation from the New York City trend. Thus, our parameter of interest is δ, the difference-in-differences parameter on the interaction term between NYCc and dt. To measure the impact on weight outcomes, we estimate equation 1 with the following outcome variables: the logarithm of body mass index and a dichotomous variable that states if individual i is obese or not. To allow for differential effects of the NYC-CLM by year, we modify equation 1 such that our NYC binary variable is interacted with year binary variables from the “post” period. To explore the effect of NYC-CLM at different points in the BMI distribution, we estimate linear probability models for overweight (BMI [25, 30)), class one obesity (BMI [30, 35)), class two obesity (BMI [35, 40)), and ∈ ∈ ∈ class three obesity (BMI> 40). Finally, to explore heterogeneity in the effect of the NYC-CLM on weight outcomes, we estimate triple differences models that allow for differential impacts of the mandate for specific groups of interest. To do this, we modify equation 1 as follows:

y = α + γd + φNY C + ζP + δ NYC d + δ NYC P + δ NYC P d + βX + (2) ict t c i 1 c ∗ t 2 c ∗ i 3 c ∗ i ∗ t ict ict

7 4 RESULTS

Here, Pi, is a binary variable for a specific group (e.g., African-Americans) of interest. The parameter δ3 measures the differential effect of the NYC-CLM for group Pi. We estimate equations 1 and 2 for the 968,883 observations in our data from 2004-2010 for all adults living in the city of New York and control counties/states.

4 Results

Coeﬃcient estimates and robust standard errors from estimation of equation 1 on obesity and the logarithm of BMI are presented in Table 2. Our results suggest that, following the NYC-CLM, obesity was 2.5 percentage points lower in New York city than in the control areas. This represents a roughly 12% decline in obesity at the mean obesity rate in New York

City of 0.21. Our main diﬀerence-in-diﬀerences estimator is consistent between models that include and do not include a variety of control variables. Turning to columns three and four of Table 2, our results on BMI suggest a much smaller impact of a 1.3% decline in BMI. For both obesity and BMI, we fail to reject the null hypothesis of parallel trends prior to the implementation of the mandate (p-values 0.26 and 0.60, respectively).

Table 2

To reconcile our large estimated impact on the prevalence of obesity with our relatively small estimated impact on body mass index, we estimate separate linear probability models for several classes of overweight and obesity. To gauge changes in the New York City distribution of BMI following NYC-CLM, Table 3 presents difference-in-differences coefficient estimates and robust standard errors for each category from our full sample. Table 3 shows that nearly our entire estimated difference- in-differences effect from Table 2 comes out of reductions in class one obesity, (BMI [30, 35)). We find no statistically ∈ significant effect of the mandate at any other point in the distribution. Naturally, given the time frame of our sample, we would not expect to observe large transitions from class two and three obesity into the normal and overweight categories; however, Table 3 suggests that our relatively small BMI estimate in Table 2 may be consistent with our obesity estimate if the types of people who respond to calorie labeling are those on the margin of obesity.

Table 3

The difference-in-differences estimates reported in Tables 2 and 3 represent the average effect of the policy over the treatment period of July 18th, 2008 through December, 2010. To explore the timing of the impact within our post period, we re-estimate equation 1 with interactions between NYC and each year of the post period in place of the post NYC term. ∗ Table 4 demonstrates that the majority of our overall estimates in Table 2 come from effects in 2009. Again the results are robust to the inclusion of sociodemographic characteristics. We find statistically negligible effects in 2008.

8 4 RESULTS

Table 4

Finally, we explore the extent to which the NYC-CLM had heterogeneous eﬀects across observable individual characteristics. We estimate equation 2, a triple diﬀerences model, and we correct the standard errors for multiple hypothesis testing

(Benjamini and Hochberg, 1995; Benjamini et al., 2006; Anderson, 2012). Table 5 presents our results for both obesity and (log) BMI. For both dependent variables, we include controls for other sociodemographic characteristics and county and month/year fixed effects. Aside from significant differences for middle income respondents and those with some college education, results presented in Table 5 do not suggest heterogeneity in the effect of the NYC-CLM on weight outcomes.

Table 5

While we fail to reject the null hypothesis that conditional trends in weight outcomes were parallel prior to the NYC-CLM, we are still concerned that unobserved heterogeneity in New York City may generate differential trends in weight outcomes. To gain confidence in our estimates in Tables 2 through 4, we estimate equation 1 under a variety of robustness and sensitivity checks. Table 6 presents difference-in-differences estimates for obesity and (log) BMI under a number of alternative scenarios.

Table 6

First, rather than considering a national set of control cities, row 1 of Table 6 present results in which we consider only Northeastern states as controls areas. Row 2 presents results in which we further restrict the control group to be only large cities in the Northeast; row 3 treats all respondents who live in commuting areas of New York City, as defined above, in the treatment group. In July of 2007, New York City passed a ban on trans-fat in all restaurants within the city. While there is not a biological reason why this should bias our pre-period trends, if individuals changed behavior, perhaps by substituting more or fewer meals away from home as a result of the trans-fat ban, then our results may be confounded by the trans-fat ban. To test for this, we allow our pre-period to be split before and after July, 2007, and the results of our differences-in-differences are presented in row 4. Row 5 considers the treatment period to be all observations after 2008. Next, we estimate our model with placebo timing in which we redefine our post period to be after January, 2007 and January, 2006, respectively. Finally, we re-estimate our model using the extensive margins of cigarette smoking and asthma, outcomes that are plausibly unrelated to calorie labeling. Results in Table 6 provide further credibility to our results in Tables 2 through 5. In each of rows 1 through 5 of Table 6, we find negative and statistically significant effects on obesity and BMI. In each case, the findings are actually larger in

9 5 MECHANISMS magnitude than our preferred results in Tables 2 through 5. Furthermore, we ﬁnd nothing statistically diﬀerent than zero for placebo policy dates nor for unrelated outcomes.

Results in Tables 2 through 6 suggest the NYC-CLM did reduce obesity rates in New York City; however, especially in light of the results of Cantor et al. (2015), who ﬁnd no eﬀect of NYC-CLM on purchasing behavior in fast food restaurants, our results are surprising. In the next Section, we analyze data from the Consumer Expenditure Survey and the American Time Use Survey to investigate potential mechanisms that may explain our overall estimates in Tables 2 through 4.

5 Mechanisms

Analysis of the mechanisms behind our results in Tables 2 through 4 is motivated by the basic cause of weight gain or loss, an imbalance between energy intake and expenditure. In other words, changes either to the consumption of calories or the expenditure of calories due to the NYC-CLM could drive our results. On changes in the consumption of calories, consumers who observe calorie labels may respond in two important ways. If the average meal at a fast food restaurant is higher in calories than the next best alternative, New York City residents may change either the frequency with which they visit fast food restaurants or, conditional upon visiting a fast food restaurant, their menu selections following the posting of calorie information. To the extent that consumers shift their consumption within a fast food restaurant following calorie labeling, evidence suggests that, because the price per calorie of food is substantially cheaper from energy-dense foods as compared to healthier, less energy-dense foods, we may observe an increase in overall expenditure on fast food following the NYC-CLM (Drenowski and Darmon (2005) and Denowski and Specter (2004)).

To investigate the potential for changes in fast food frequency and expenditure, we analyze data from the Consumer Expenditure Survey (CEX). Although the CEX is not specifically designed to give precise results for states, cities, and counties, New York City can be identified given that the city is used from 2006 to 2013 as a primary sampling unit in the public-use data. The data have information on consumers’ expenditure and income, as well as expenditure on food away from home and specifically at fast food restaurants. The CEX data on fast food expenditure can be further divided by the type of meal, including lunch, dinner, snacks, and breakfast. We focus on two main indicators: household expenditure per fast food visit and the frequency of fast food visits. We estimate

y = α + γd + φNY C + δNY C d + βX + (3) ict t c c ∗ t ict ict

Where yict is the number of visits to a fast food restaurant for person i living in city c during month t, dt is a binary variable for observations recorded after July 18th, 2008, NYCc is a binary variable for respondents from New York City (as identified by the primary sampling units of the survey), and Xict are control variables mentioned above. This model also includes state and month fixed effects. Again, δ captures the deviation from trend for New York City relative to control

10 5 MECHANISMS areas. Conditional on the frequency of fast food visits in a given month being greater than zero, we also estimate Equation 3 for expenditure per visit, both for households and per consumer unit within households.

Aside from New York City, which is identiﬁable as a primary sampling unit, the frequency and expenditure analysis is done at the state level because counties are not identiﬁable in the CEX.14

Table 7

Table 7 presents estimates of δ from Equation 3. Our results indicate positive and significant increases in expenditures of approximately 11% of a standard deviation when we focus on expenditure per fast food visit. We find no evidence of changes in expenditure person consumer. Our results suggest statistically significant declines in the frequency of fast food visits per month of approximately 3.5% of a standard deviation. Unfortunately, the p-value on the null hypothesis that pre-NYC-CLM trends in fast food frequency are parallel between New York City and other areas is 0.000. As with mixed previous results on calorie labeling, we find that the NYC-CLM may have changed fast food frequency and expenditure behavior, though perhaps not enough by itself drive observed changes in obesity. Consistent with the input versus output balance of calories, our weight outcome results could have been generated by changes in the expenditure of calories following the NYC-CLM. For example, upon learning the calorie content of fast food, an individual may adjust her exercise habits while maintaining both her fast food frequency and expenditure. To investigate, we examine data from the American Time Use Survey (ATUS) from 2004 to 2010. Again, we identify individual respondents in New York City, and we compare the trend in both the extensive and intensive margins of physical exercise in New York City to the trend in other counties (as defined by Current Population Survey (CPS) county codes). Specifically, we model respondents’ answer to a question on whether they participated in “Sports, Exercise, and Recreation Activities” in the previous week. Table 8 presents difference-in-differences estimates for both the extensive and intensive margins of our exercise variable.

Our results suggest economically and statistically signiﬁcant increases on the extensive margin of exercise and physical activity following NYC-CLM. Indeed, at the mean of the dependent variable, our results that condition on sociodemographic characteristics suggest a 25% increase in physical activity and exercise in New York City following the NYC-CLM. While the intensive margin does not past standard tests for parallel trends (p-value= 0.000), we fail to reject the null hypothesis of parallel trends on the extensive margin (p-value= 0.433). Our results are consistent with compensating behavior as a result of the NYC-CLM. To our knowledge, no study has explored this mechanism.

14Subsequent years were not used to maintain comparability with the health outcomes results, and because after 2011 posting calories laws were introduced in a higher number of states and counties which could bias results and contaminate the control group defined. Results from using more years are not significantly different.

11 6 CONCLUSION

Results presented in Tables 2 through 8 are suggestive of a reduction in obesity that may be largely driven by changes in physical activity. However, our results come with two important caveats regarding external validity. First, we cannot separately identify the calorie labeling mandate from the advertising campaign that followed. While the calorie labeling mandate could only reasonably be expected to aﬀect those that frequent fast food restaurants, the advertising campaign was visible to all those taking public transportation. The wider breath of coverage of the advertising campaign may to some extent explain the large magnitude of our weight and physical activity outcomes.

Second, the NYC-CLM (and the advertising campaign) coincided with the 2008 financial crisis, and our data do not allow us to separately identify these events. For example, Oddo et al. (2016) find that childhood obesity increased with the increasing unemployment rate following 2008 in California. For adults, Latif(2014) find no effect of the rate of unemployment on the probability of obese or overweight in Canada during this period, and Finkelstein et al. (2005) finds that increases in the unemployment rate actually reduced the prevalence of severe obesity between 1987 and 2000. With the direction of any potential bias from the financial crisis not clear, we argue that our results are suggestive of reductions in class one obesity that are due to the NYC-CLM.

6 Conclusion

We model the rates of obesity and body mass index in New York City before and after the New York City Calorie Labeling Mandate relative to rates in other counties in the United States. We estimate our preferred diﬀerence-in-diﬀerences model with data from the Behavioral Risk Factor Surveillance Survey Selected Metropolitan/Metropolitan Area Risk Trends (BRFSS-

SMART) from 2004 to 2010. Our results indicate a signiﬁcant reduction in class one obesity that may be due to a large reduction in physical inactivity. Results in this paper provide some hope for calorie labeling. While much attention has been paid to modest behavioral responses at fast food restaurants, we argue that calorie labels may have promoted an overall awareness of obesity and its determinants that motivated behavioral changes on both sides of the energy balance equation, consumption and expenditure..

12 REFERENCES REFERENCES

References

Abadie, A., Diamond, A. and Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59 (2), 495–510.

Anderson, M. L. (2012). Multiple inference and gender diﬀerences in the eﬀects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. Journal of the American Statistical Association.

Andreyeva, T., Long, M. and Brownell, K. (2010). The impact of food prices on consumption: A systematic review of

research on the price elasticity of demand for food. American Journal of Public Health, 100 (2), 216–22.

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), pp. 289–300.

—, Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93 (3), 491–507.

Binkley, J. K. (2007). Calorie and gram diﬀerences between meals at fast food and table service restaurants. Review of

Agricultural Economics, 30 (4), 750–763.

Bollinger, P. L., Bryan and Sorensen, A. (2011). Calorie posting in chain restaurants. American Economic Journal: Economic Policy, 3, 91–128.

Burton, S., Creyer, E. H., Kees, J. and Huggins, K. (2006). Attacking the obesity epidemic: the potential health beneﬁts of providing nutrition information in restaurants. American Journal of Public Health, 96 (9), 1669–1675.

—, Tangari, A. H., Howlett, E. and Turri, A. M. (2014). How the Perceived Healthfulness of Restaurant Menu Items

Inﬂuences Sodium and Calorie Misperceptions: Implications for Nutrition Disclosures in Chain Restaurants. Journal of Consumer Aﬀairs, Spring, 62–95.

Cantor, J., Torres, A., Abrams, C. and Elbel, B. (2015). Five years later: Awareness of new york citys calorie labels

declined, with no changes in calories purchased. Health Aﬀairs, 34 (11), 1893–1900.

Cawley, J. (2015). An economy of scales: A selective review of obesitys economic causes, consequences, and solutions. Journal of Health Economics, 43, 244–268.

Currie, J., DellaVigna, S., Moretti, E. and Pathania, V. (2010). The eﬀect of fast food restaurants on obesity and weight gain. American Economic Journal: Economic Policy, 2 (August), 32–63.

13 REFERENCES REFERENCES

Dumanovsky, T., Huang, C. Y., Bassett, M. T. and Silver, L. D. (2010). Consumer awareness of fast-food calorie information in new york city after implementation of a menu labeling regulation. American Journal of Public Health,

100 (12), 2520–2525.

Elbel, B., Kersh, R., Brescoll, V. L. and Dixon, L. B. (2009). Calorie labeling and food choices: a first look at the effects on low-income people in new york city. Health Affairs, 28 (6), w1110–w1121.

—, Mijanovich, T., Dixon, B., Abrams, C., Weitzman, B., Kersh, R., Auchincloss, A. and Ogedegbe, G. (2013). Calorie labeling, fast food purchasing and restaurant visits. Obesity, 21 (11), 2172–2179.

Finkelstein, E. A., Ruhm, C. J. and Kosa, K. M. (2005). Economic causes and consequences of obesity. Annu. Rev.

Public Health, 26, 239–257.

Flegal, K., Kruszon-Moran, D., Carroll, M., Fryar, C. and Ogden, C. (2016). Trends in obesity among adults in the united states, 2005 to 2014. Journal of the American Medical Association, 315 (21), 2284–2291.

Hall, K., Sacks, G., Chandramohan, D., Chow, C., Wang, C., Gortmaker, S. and Swinburn, B. (2011). Quan- tiﬁcation of the eﬀect of energy imbalance on bodyweight. The Lancet, 9793 (378), 826–837.

Lakdawalla, D., Philipson, T. and Bhattacharya, J. (2005). Welfare-Enhancing Technological Change and the Growth

of Obesity. American Economic Review, 95 (2), 253–257.

Latif, E. (2014). The impact of macroeconomic conditions on obesity in canada. Health Economics, 23, 751–759.

Oddo, V., Nicholas, L., Cleich, S. and Jones-Smith, J. (2016). The impact of changing economic conditions on over-

weight risk among children in california from 2008 to 2012. Journal of Epidemiology and Community Health, forthcoming.

Ogden, C. L., Carroll, M. D., Lawman, H., Fryar, C., Kruszon-Moran, D., Kit, B. and Flegal, K. (2016). Trends in obesity prevalence among children and adolescents in the united states, 1988-1994 through 2013-2014. Journal

of the American Medical Association, 315 (21), 2292–2299.

Powell, L. M., Nguyen, B. T. and Han, E. (2012). Energy intake from restaurants: demographics and socioeconomics, 2003–2008. American journal of preventive medicine, 43 (5), 498–504.

Wang, E. (2015). The Impact of Soda Taxes on Consumer Welfare: Implications of Storability and Taste Heterogeneity. Rand Journal of Economics, 46 (2), 409–41.

14 A APPENDIX A: SYNTHETIC CONTROL METHOD

A Appendix A: Synthetic Control Method

As the most populous city in the United States, New York is unique. To explore the robustness of our results in Table 2, we also estimate the impact of the NYC-CLM using the synthetic control method, which provides a systematic way to choose comparison units in comparative case studies (Abadie et al., 2015). Using our SMART-BRFSS sample from 2004-2010, we use the 50 largest metropolitan areas in the United States (excluding cities that had some type of calorie labeling during the period of the analysis) to candidate counterfactual New York City’s based on observable characteristics. We estimate synthetic weights to construct the synthetic control group, and the selected MSAs are Miami-Fort Lauderdale-Pompano

Beach, Florida, Orlando-Kissimmee, Florida, Tucson Arizona, and Bridgeport-Stamford-Norwalk, and Connecticut. The estimated weights are reported in Table 9.

Table 9

Using the weights reported in Table 9, we match trends in observable characteristics before the NYC-CLM. Table 10 presents summary statistics of pre-trend observable characteristics by area.

Table 10

Figure 1 shows trends in the percentage of obese adults for the New York MSA and its synthetic equivalent. Trends are roughly equivalent prior to the NYC-CLM, and they diverge signiﬁcantly after 2008. Figure 1 supports our general ﬁnding of a 2.5 percentage point reduction in obesity.

Figure 1

To assess the robustness of our main ﬁndings, we estimate placebo interventions by reassigning the other 49 MSAs as “treatment” areas. Figure 2 shows that New York is the only MSA that has the minimum variation before the intervention and a consistent impact after 2008. Furthermore, Figure 3 that New York City has the highest ratio of post-period Root Mean Squared Prediction Error (RMSPE) to the pre-period RMSPE, which indicates that the gap induced by the intervention is 11.7 times larger than what it was before labeling. If we select a MSA at random from the donor pool, the chances of obtaining a ratio as high as the one for New York would be 1/50 = 0.02.

Figure 2

15 A APPENDIX A: SYNTHETIC CONTROL METHOD

Figure 3

16 B APPENDIX B: FIGURES

B Appendix B: Figures

Figure A1: Trends in Percentage of Obese Adults: New York MSA versus Synthetic New York MMA

17 B APPENDIX B: FIGURES

Figure A2: Percentage of Obese Adults Gaps in New York MSA and Placebo Gaps in 49 MSAs

18 B APPENDIX B: FIGURES

Figure A3: Ratio of Postperiod RMSPE to Preperiod RMSPE: New York MSA and rest of MSAs

19 C APPENDIX C: TABLES

C Appendix C: Tables

Table 1: Overall Descriptive Statistics

2004-2008 2008-2010 Control Control Control Cities NYC Cities P-value NYC Cities P-value (1) (2) (3) (1) (2) (3)

BMI 26.582 27.133 0.000 26.615 27.428 0.000 Obesity 0.210 0.246 0.000 0.211 0.268 0.000 Female 0.508 0.489 0.022 0.512 0.491 0.057 Ethnicity African-American 0.215 0.113 0.000 0.255 0.113 0.000 Hispanic 0.240 0.166 0.000 0.204 0.167 0.000 Education < High School 0.120 0.099 0.000 0.091 0.091 0.999 High School 0.226 0.245 0.007 0.230 0.235 0.609 Some College 0.212 0.268 0.000 0.221 0.262 0.000 College or More 0.442 0.388 0.000 0.458 0.413 0.000 Age [18,24] 0.083 0.097 0.018 0.093 0.087 0.504 [25,34] 0.218 0.207 0.152 0.195 0.192 0.726 [35,44] 0.228 0.219 0.155 0.211 0.216 0.624 [45,54] 0.182 0.197 0.009 0.195 0.204 0.219 [55,64] 0.140 0.136 0.401 0.141 0.148 0.243 65 or More 0.149 0.144 0.376 0.165 0.153 0.053 Income <10k 0.069 0.047 0.000 0.069 0.051 0.002 [10,15k) 0.064 0.048 0.000 0.049 0.046 0.526 [15,20k) 0.100 0.066 0.000 0.079 0.063 0.004 [20,25k) 0.085 0.081 0.414 0.086 0.078 0.190 [25,35k) 0.121 0.112 0.101 0.093 0.097 0.448 [35,50k) 0.139 0.149 0.096 0.137 0.133 0.602 [50,75k) 0.137 0.174 0.000 0.138 0.158 0.007 >75k 0.285 0.324 0.000 0.350 0.374 0.018 Has Diabetes 0.082 0.085 0.500 0.102 0.095 0.252 Works 0.654 0.658 0.538 0.598 0.614 0.136 Unemployed 5.653 4.937 0.000 8.786 9.063 0.000 Retired 0.127 0.139 0.009 0.135 0.143 0.137 Student 0.041 0.040 0.798 0.059 0.044 0.025 Notes: n=968,883 . Authors calculations using SMART-BRFSS from 2004 to 2010.

20 C APPENDIX C: TABLES (0.004) (0.045) (0.006) (0.002) (0.002) (0.005) (0.002) (0.002) (0.001) (0.001) (0.001) (0.014) (0.034) (0.010) (0.001) (0.004) (0.004) (0.003) (0.003) (0.002) (0.002) (0.001) (0.002) (0.002) (0.003) (0.004) (4) -0.013*** 3.842*** 0.003 -0.043*** 0.074*** 0.042*** -0.095*** -0.013*** 0.016*** 0.031*** 0.043*** 0.000 0.001 0.010 -0.001 0.019*** 0.020*** 0.019*** 0.016*** 0.012*** 0.016*** 0.015*** -0.004 -0.001 -0.038*** -0.609*** .5978 968,883 (0.003) (0.009) (0.006) (0.001) 0.01. < Log(BMI), S.D.=0.189 (3) -0.012*** 3.815*** 0.002 -0.595*** 968,883 0.05, *** p < (0.007) (0.096) (0.015) (0.003) (0.004) (0.007) (0.005) (0.004) (0.003) (0.003) (0.003) (0.032) (0.079) (0.026) (0.001) (0.007) (0.006) (0.006) (0.005) (0.004) (0.004) (0.002) (0.003) (0.004) (0.004) (0.009) 0.10, ** p < (2) -0.025*** 0.250*** -0.018 -0.017*** 0.126*** 0.055*** -0.095*** 0.010** 0.050*** 0.070*** 0.087*** 0.003 -0.011 -0.006 -0.001 0.054*** 0.059*** 0.045*** 0.040*** 0.023*** 0.034*** 0.033*** -0.008** -0.005 -0.079*** -0.055*** .2586 968,883 (0.004) (0.022) (0.015) (0.004) Table 2: Main Results for Obesity and Body Mass Index Obesity, Mean=0.21 (1) 10k [10,15k) [15,20k) [20,25k) [25,35k) [35,50k) [50,75k) Highschool Some College College or More [18,24] [25,34] [35,44] [45,54] [55,64] < Education Constant -0.055*** NYC*PostNYCPostWomen African-American Hispanic Age -0.025*** 0.230*** Full Service Restaurants -0.018 Fitness and Recreation Centers Fast Food Restaurants Unemployment Rate Income Pre-trend P-Value Observations 968,883 Notes: Authorscounties/boroughs. estimations using Control individuals SMART-BRFSSlinear are from probability those models. 2004 living Robust tothat in standard trends 2010. errors any in clustered of the by Treatment 370 conditional county individuals expectation in other parentheses. are are counties parallel The within those p-value prior MMSAs on living to in pre-trends treatment. in is the * with one United p respect States. of to the the Obesity null ﬁve results hypothesis are New from York City

21 C APPENDIX C: TABLES

Table 3: Results by BMI Category

BMI Category [20-25) [25-30) [30-35) [35-40) [>40] (1) (2) (3) (4) (5)

NYC*Post 0.018 0.006 -0.024*** 0.003 -0.003 (0.011) (0.016) (0.007) (0.004) (0.003)

Observations 968,883 968,883 968,883 968,883 968,883 Notes: Authors estimations using SMART-BRFSS from 2004 to 2010. Treatment individuals are those living in one of the ﬁve New York City counties/boroughs. Control individuals are those living in any of 370 other counties within MMSAs in the United States. Results come from linear probability models. Robust standard errors clustered by county in parentheses. p<0.10, ** p<0.05, *** p<0.01.

Table 4: Estimation results by year

(1) (2)

Panel A: Obesity, Mean=0.21 NYC*2008 -0.005 (0.017) -0.006 (0.016) NYC*2009 -0.043*** (0.006) -0.037*** (0.010) NYC*2010 -0.018* (0.010) -0.023* (0.014)

Panel B: Log(BMI), S.D.=0.189 NYC*2008 -0.011 (0.008) -0.011 (0.007) NYC*2009 -0.031*** (0.004) -0.027*** (0.006) NYC*2010 0.004 (0.002) 0.001 (0.004)

County and year-month F.E. Yes Yes County variables No Yes Sociodem. Charact. No Yes

Observations 968,883 968,883

Notes: Authors estimations using SMART-BRFSS from 2004 to 2010. Treatment individuals are those living in one of the ﬁve New York City counties/boroughs. Control individuals are those living in any of 370 other counties within MMSAs in the United States. NYC*2008 uses observations from May 2008 to December of that year. Obesity results are from linear probability models. Robust standard errors clustered by county in parentheses. p<0.10, ** p<0.05, *** p<0.01.

22 C APPENDIX C: TABLES

Table 5: Heterogeneous Eﬀects Results on Obesity and Body Mass Index

Obesity, Mean=0.21 Log(BMI), S.D.=0.189 DDD St. Err. q-value DDD St. Err. q-value (1) (2)

Women 0.042 (0.029) 0.498 0.010 (0.007) 0.741 African-American -0.002 (0.016) 0.999 -0.009 (0.006) 0.741 Hispanic -0.030 (0.027) 0.633 -0.008 (0.007) 0.741 Education < High School -0.007 (0.028) 0.999 -0.001 (0.008) 0.986 High School 0.036 (0.024) 0.498 0.017 (0.018) 0.884 Some College -0.064* (0.024) 0.076 -0.034* (0.014) 0.080 College or More 0.027 (0.024) 0.633 0.015 (0.013) 0.741 Age [18,24] -0.057 (0.027) 0.308 -0.040 (0.024) 0.680 [25,34] 0.063 (0.048) 0.629 0.025 (0.016) 0.680 [35,44] 0.001 (0.028) 0.999 0.003 (0.008) 0.986 [45,54] -0.023 (0.015) 0.498 -0.003 (0.011) 0.986 [55,64] -0.015 (0.020) 0.819 0.008 (0.009) 0.884 65 or More 0.009 (0.014) 0.819 -0.004 (0.004) 0.884 Income <10k 0.050 (0.032) 0.498 0.016 (0.014) 0.741 [10,15k) 0.017 (0.024) 0.819 0.014 (0.020) 0.895 [15,20k) -0.037 (0.062) 0.819 0.005 (0.027) 0.986 [20,25k) -0.011 (0.025) 0.826 -0.007 (0.018) 0.986 [25,35k) -0.066** (0.021) 0.043 -0.045*** (0.011) 0.001 [35,50k) -0.000 (0.024) 0.999 -0.010 (0.009) 0.741 [50,75k) 0.043 (0.029) 0.498 0.029*** (0.008) 0.005 >75k 0.009 (0.016) 0.819 0.005 (0.006) 0.884 Notes: Authors estimations using SMART-BRFSS from 2004 to 2010. Treatment individuals are those living in one of the ﬁve New York City counties/boroughs. Control individuals are those living in any of 370 other counties within MMSAs in the United States. Obesity results are from linear probability models. Robust standard errors clustered by county in parentheses. The q-values are used to control the false discovery rate for independent test statistics, they were obtained using do-ﬁles provided by Anderson 2008:. p<0.10, ** p<0.05, *** p<0.01.

23 C APPENDIX C: TABLES

Table 6: Estimates from Alternative Speciﬁcations and Dependent Variables

Obesity Log(BMI) (1) (2) Alternative Control/Treatment Definitions Northeast States -0.032*** (0.007) -0.016*** (0.003) Northeast Megalopolis -0.027*** (0.007) -0.015*** (0.003) With Commuters -0.024*** (0.005) -0.011*** (0.002) Trans-Fat Control -0.045*** (0.010) -0.019*** (0.006) Without Time to Comply -0.032*** (0.010) -0.015*** (0.005) Placebo Policy Timing Intervention in 2007 0.001 (0.011) -0.003 (0.004) Intervention in 2006 0.006 (0.010) -0.000 (0.005) Unrelated Dependent Variables Smoking 0.011 (0.010) Asthma 0.010 (0.007) Notes: Authors estimations using SMART-BRFSS from 2004 to 2010. Each difference- in-differences estimate is generated from a separate regression. Robust standard errors clustered by county in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Northeast States results use as controls Connecticut, Maine, Massachusetts, New Hamp- shire, Rhode Island, Vermont, New Jersey, New York, and Pennsylvania. Northeast Megalopolis use as controls District of Columbia, Virginia, Maryland, Delaware, Pennsyl- vania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, New Hampshire, and Maine. With Commuters results include commuter counties to NYC as part of the treatment group. Trans-Fat Control results includes a control for the timing and counties who banned cooking with oils that have trans fats. Without Time to Comply results does not include observations from May to December 2008.

24 C APPENDIX C: TABLES

Table 7: Fast Food Behavior in Consumer Expenditure Survey

(1) (2)

Panel A: Expenditure Per Fast Food Visit, S.D.=2.79

NYC*Post 0.279** 0.316*** (0.104) (0.101)

Observations 151,569 151,569 Pre-trend P-Value 0.151 Panel B: Expenditure Per Consumer Unit, S.D.=31.869

NYC*Post -0.167 -0.132 (0.358) (0.315)

Observations 56,876 56,876 Pre-trend P-Value 0.187 Panel C: Frequency of Fast Food Visits, S.D.=5.922

NYC*Post -0.186*** -0.212*** (0.065) (0.063)

Observations 56,876 56,876 Pre-trend P-Value 0.000 State and year-month F.E. Yes Yes Sociodem. Charact. No Yes

Panel D: Probability of Fast Food Visits (DDD)

Dependent Variable Obesity Ln(BMI) NYC*Post*Prob. -0.039 -0.012 (0.058) (0.055)

Observations 968,883 968,883 County and year-month F.E. Yes Yes Sociodem. Charact. Yes Yes

Notes: Panels A to C:Authors estimations using CES from 2006 to 2010. NYC*2008 uses observations from May 2008 to December of that year. Expenditures are in January 2008 prices.Robust standard errors clustered by state in parentheses. Panel D: Authors estimations using SMART-BRFSS from 2004 to 2010. Obesity results are from linear probability models, both coefficients come from a Difference in Difference in Differences estimation using the imputed probability of attending a Fast Food Restaurant from CES. Robust standard errors clustered by county in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

25 C APPENDIX C: TABLES

Table 8: Estimation Results for Physical Activity

Physical Activity, Log(Minutes), Mean=0.165 S.D.=0.789 (1) (2) (3) (4)

NYC*Post 0.041*** 0.039*** 0.081* 0.075 (0.011) (0.012) (0.047) (0.046) NYC -0.062*** -0.041*** 0.067*** 0.074*** (0.003) (0.005) (0.016) (0.015) Post -0.083*** -0.087*** 0.110 0.126 (0.028) (0.028) (0.137) (0.120) Full Service Rest. -0.395** -0.379** 0.980 0.903 (0.184) (0.183) (0.817) (0.789) Fitness and Rec. Centers 0.126 0.247 7.506** 8.208*** (0.823) (0.816) (2.893) (2.744) Fast Food Rest. 0.062 0.030 -1.032* -1.087* (0.119) (0.121) (0.572) (0.545) Age [18,24] 0.015* 0.369*** (0.008) (0.040) [25,34] -0.035*** 0.167*** (0.010) (0.036) [35,44] -0.048*** 0.187*** (0.009) (0.033) [45,54] -0.038*** 0.098*** (0.009) (0.032) [55,64] -0.037*** 0.045 (0.010) (0.045) Women -0.042*** -0.275*** (0.004) (0.029) African-American -0.036*** -0.105*** (0.006) (0.028) Hispanic -0.031*** -0.062 (0.007) (0.040) Education Highschool 0.007 0.031 (0.006) (0.035) Some College 0.027*** 0.021 (0.009) (0.044) College or More 0.097*** -0.077 (0.009) (0.048) Constant 0.913*** 0.881*** 3.133*** 3.179*** (0.200) (0.206) (0.740) (0.718)

Pre-trend P-Value 0.433 0.000 Observations 65,709 65,709 10,959 10,959 Notes: Author calculations using ATUS from 2004 to 2010. Ph. Act. results are from linear probability models that indicate individual participated in Sports, Exercise, and Recreation Activities. Log(min) results are from a linear model for minutes dedicated to Sports, Exercise, and Recreation Activities.Robust standard errors clustered by state in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

26 C APPENDIX C: TABLES

Table 9: Synthetic Weights

Synthetic Control Weight Miami FL MSA 0.77 Orlando FL MSA 0.03 Tucson AZ MSA 0.07 Bridgeport CT MSA 0.13 Note: Authors’ calculations using SMART-BRFSS MMSA from 2004-2010.

Table 10: Observable Characteristics Prior to NYC-CLM

New York Synthetic New York Obesity(2005) 0.19 0.19 Obesity(2007) 0.23 0.23 BMI logarithm(2005) 3.25 3.26 BMI logarithm(2006) 3.26 3.27 Education Some College(2007) 0.21 0.26 Diabetes(2004) 0.09 0.08 Age 45 to 54 0.18 0.19 Note: Authors’ calculations using SMART-BRFSS MMSA from 2004-2010.