Statistics and Data Analysis

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Statistics and Data Analysis

• Professor William Greene
• Stern School of Business
• IOMS Department
• Department of Economics

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Statistics and Data Analysis
Part 24 Statistical Tests 3
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A Bivariate Latent Class Correlated Generalised
Ordered Probit Model with an Application to
Modelling Observed Obesity Levels

• William Greene
• Stern School of Business, New York University
• With Mark Harris, Bruce Hollingsworth, Pushkar
  Maitra
• Monash University, Melbourne
• Health Econometrics Workshop
• December 4-6, 2008
• University of Milan - Bicocca

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Introduction

• The International Obesity Taskforce
  (http//www.iotf.org) calls obesity one of the
  most important medical and public health problems
  of our time.
• Defined as a condition of excess body fat
  associated with a large number of debilitating
  and life-threatening disorders
• Health experts argue that given an individuals
  height, their weight should lie within a certain
  range
• Most common measure Body Mass Index (BMI)
• Weight (Kg)/height(Meters)2
• WHO guidelines
• BMI lt 18.5 are underweight
• 18.5 lt BMI lt 25 are normal
• 25 lt BMI lt 30 are overweight
• BMI gt 30 are obese
• Around 300 million people worldwide are obese, a
  figure likely to rise

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Costs of Obesity

• In the US more people are obese than the number
  who smoke or use illegal drugs
• Obesity is a major risk factor for
  (non-communicable) diseases like heart problems
  and cancer
• Obesity is also associated with
• lower wages and productivity, and absenteeism
• low self-esteem
• And is costly to society
• USA costs are around 4-8 of all annual health
  care expenditure - US 100 billion
• Canada, 5 France, 1.5-2.5 and New Zealand 2.5

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Background

• It has also been argued that obesity is, in part,
  an economic phenomenon
• Obesity is seen as potentially avoidable
• Behavioural adjustments, such as to diet and
  increased physical activity, can be made if
  perceived benefits exceed costs
• So, it is clearly an enormous public health
  issue, worldwide !
• This is a growing area of research, but to date
  there have been relatively few economic and
  econometric analysis of obesity

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An Ordered Probit Approach

• A Latent Regression Model for True BMI
• BMI ?1x1 ?2x2 ?,
• True BMI a proxy for weight is unobserved
• Observation Mechanism for Weight Type
• WT 0 if BMI lt 0 Normal
• 1 if 0 lt BMI lt ? Overweight
• 2 if BMI gt ? Obese

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Data

• US National Health Interview Survey (2005)
  conducted by the National Centre for Health
  Statistics
• Information on self-reported height and weight
  levels, BMI levels
• Demographic information
• Remove those underweight
• Split sample (30,000) by gender

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BMI Ordered Choice Model

• Here we use, conditional on class membership,
  lifestyle factors
• Marriage comfort factor.only for .normal. class
  women
• Both classes negatively associated with income,
  education and
• Exercise effects similar in magnitude except for
  exercise
• Exercise intensity only important for
  non-normal class
• Home ownership only important for
  .non-normal.class, and negative result of
  differing soci-economic status distributions
  across classes?

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Males

• Marriage comfort factor for both classes higher
  for normal class
• Income positively associated with weight levels
  for normal class
• Home own unlike females, only normal class
  affected and positively
• Education negatively associated, and of a similar
  magnitude
• Exercise - as with females negatively associated
  with weight but now more pronounced in
  non-normal class
• Vigorous exercise only important for .non-normal.
  class

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Effects of Aging on Weight Class
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Effect of Education
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Effect of Income