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Statistics and Data Analysis. Professor William Greene Stern School of Business IOMS Department Department of Economics. Statistics and Data Analysis. Part 24 – Statistical Tests: 3.
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Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics
Statistics and Data Analysis Part 24 – Statistical Tests: 3
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
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 individual’s height, their weight should lie within a certain range • Most common measure = Body Mass Index (BMI): • Weight (Kg)/height(Meters)2 • WHO guidelines: • BMI < 18.5 are underweight • 18.5 < BMI < 25 are normal • 25 < BMI < 30 are overweight • BMI > 30 are obese • Around 300 million people worldwide are obese, a figure likely to rise
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%
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
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* < 0 Normal 1 if 0 < BMI* < Overweight 2 if BMI* > Obese
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
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?
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
