The Joint Effect of Restaurant Trans Fat Bans and Menu Labeling Laws on the Prevalence of Hypertension and Coronary Heart Disease Pubblico

Chen, YuHua (2014)

Permanent URL: https://etd.library.emory.edu/concern/etds/wh246s349?locale=it
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Abstract

Starting in 2007, several city and state governments have implemented restaurant related dietary policies to the overall health outcomes of their constituents. This paper intends to estimate the average treatment effect of the joint treatment of trans fat bans and
menu labeling law on both aggregate measure health outcomes and
individual-level health outcomes. To overcome the problem of policy overlapping and hidden selection bias, I apply propensity score
method for the first stage estimation and inverse probability weighting estimator for average treatment effect on the aggregate prevalence
of hypertension and coronary heart disease. We find trans fat ban decreases the prevalence of coronary heart disease by 2 percentage point.
The average treatment effect of multiple overlapping policies slightly
decreases the prevalence of coronary heart disease by 1.4 percentage
point. The average treatment effect on the treated of multiple overlapping policies decreases the prevalence of coronary heart disease by
1.59 percentage point. I also use individual-level data to estimate the
effect of Baltimore trans fat ban on the prevalence of hypertension.
I apply difference-in-differences method and improve the reliability of
inference by wild bootstrap procedures, I find Baltimore trans fat ban
decreases the prevalence of hypertension among the elderly by 4.65
percentage point in linear probability model

Table of Contents

List of Tables
1 List of U.S cities with trans fat ban and menu labeling law . . 11
2 Summary Statistics by treatment categories for aggregate data 19
3 Multinomial Logit Model for Propensity Score . . . . . . . . . 22
4 Inverse Probability Weighted Treatment Effect of trans fat ban
and menu labeling law on prevalence of hypertension and CHD 24
5 Summary Statistics for Individual-level data . . . . . . . . . . 28
6 Regression result of Baltimore trans fat ban . . . . . . . . . . 33
7 Poisson Estimation for count response of health outcome . . . 39
8 Regression result of NYC trans fat ban:Model 1 . . . . . . . . 52
9 Regression result of NYC trans fat ban . . . . . . . . . . . . . 53

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