WHAT IS THE RELATIONSHIP BETWEEN INDUSTRY,

OCCUPATION, AND BODY WEIGHT IN CANADA?

by

Saibiao Peng

Bachelor of Economics, Hunan Normal University, 2011

A Report Submitted in Partial Fulfilment of Requirements for the Degree of Master of Arts

In the Graduate Academic Unit of Economics

Supervisor: Philip Leonard, PhD, Dept. of Economics

Examining Board: Weiqiu Yu, PhD, Dept, of Economics

Paul Peters, PhD, Dept, of Economics

This report is accepted by the Dean of Graduate Studies

THE UNIVERSITY OF NEW BRUNSWICK

January, 2017

©Saibiao Peng, 2017

ii

Abstract Overweight and obesity are well known to be associated with negative health

outcome. Canadians spend a large portion of their walking hours at work and their

level of physical activity (or lack thereof) and eating habits while there likely play a

role in their body weight. This study examines the association between industry and

occupation of work and the likelihood of overweight and obesity. This paper

managed to discover social economic factors and human behavior factors that will

help identify groups that are most at risk of being overweight and obese. Cycle 5 of

NPHS and all 8 cycles of CCHS are used in logit and fixed-effect models to run

regression analysis. Results show that compare to male, female are less likely to

become overweight and obese, age has negative effect on people’s body weight, and

people who live in Ontario, Birth Columbia have the lowest risk of being overweight

and obese. Also the results indicate that the following variables: education,

household-income, physical activity and eating habits are negatively associated with

being overweight and obese. For industry and occupation, the results show: people

who work at public administration and education industry have highest risk to

become obese; Occupation as manager or sales contribute most to people’s risk of

being obese.

iii

Acknowledgement

First of all, I need to extend my sincere gratitude to my supervisor, professor Philip

Leonard, with his patiently encouragement and guidance. And without his support I

could not complete this report and reached its present form. Also, I need to thanks

for professor Weiqiu Yu and professor Paul Peters, thanks for their useful comments

and suggestions on my report.

At last, I need to gratitude my parents and friends, thanks for their support and

encourage.

iv

TABLE OF CONTENTS

Abstract………………………………………………. ii

Acknowledgment………………………………. iii

Table of Contents ………………………………. iv

List of Tables ……………………………………… v

I Introduction …………………………………... 1

II Literature Review …………………………….. 3

2.1 Measurements ………..………………………….. 3

2.2 Prevalence ………………………….…………..... 5

2.3 Effects ……………………………………………. 7

2.4 Potential explanations for Obesity ……………. 10

III Models and data …………..……………………………....... 16

IV Results ……………………………………..... 33

4.1 CCHS Results ……………………………………….. 33

4.2 NPHS Results ……………………………………….. 38

4.3 NPHS Longitudinal Results ………………………… 42

4.4 Link-test ………………………………………….. 44

V Discussion …………………………………….... 45

VI Conclusion ……………………………………………………. 51

References ……………………………………………………….. 89

Appendix ……………………………………………. 93

Curriculum Vitae

v

List of Tables Table 1: Health risk classification according to Body Mass Index…………...3

Table 2: Ethnic-specific values for waist circumference promoted by the WHO and

Canadian Diabetes Association………………………………………………..4

Table 3: Adults who are obese in 2014 by province to territory in Canada…...7

Table 4: Logit model based on overweight as outcome (CCHS, 2007-2014)…55

Table 5: Logit model based on obesity as outcome (CCHS, 2007-2014)……..61

Table 6: Logit model based on overweight as outcome (NPHS, cycle 5)……..67

Table 7: Logit model based on obesity as outcome (NPHS, cycle 5)……….....73

Table 8: Fixed effect logit model based on overweight as outcome (NPHS)….83

Table 9: Fixed effect logit model based on obesity as outcome (NPHS)………62

Table 10: Rank of industry and work occupation based on BMI (for

overweight)…………………………………………………………...50

Table 11: Rank of industry and work occupation based on BMI (for obesity) ….50

Table 12: 4 groups of work occupation category……………………………….93

Table 13: 9 groups of industry category…………………………………………93

vi

List of figures

Figure 1: Percentage of who were overweight or obesity (self-report), by sex,

household population aged 18 and older, Canada, 2003 to 2014, percent……….7

Figure 2: Relationship between BMI and highest level of education (CCHS)…..22

Figure 3: Relationship between BMI and household income (CCHS)…………..23

Figure 4: Relationship between BMI and work occupation (CCHS)…………….24

Figure 5: Relationship between BMI and 9 groups of industries (CCHS)……….24

Figure 6: Relationship between BMI and highest level of education (Cycle 5,

NPHS)…………………………………………………………………………….25

Figure 7: Relationship between BMI and household income (Cycle 5, NPHS)….25

Figure 8: Relationship between BMI and work occupation (Cycle 5, NPHS)…..26

Figure 9: Relationship between BMI and industry (Cycle 5, NPHS)…………….27

1

I Introduction:

According to the World Health Organization (WHO), the global obesity rate more

than doubled between 1980 and 2014. More than 1.9 billion adults （18+）were

overweight, and more than 600 million people were obese in 2014 (WHO, 2016). The

World Health Organization has defined obesity as abnormal or excessive fat

accumulation that may impair health, and described the main reason leading to

overweight and obesity as an energy imbalance between calories consumed and

calories expended (WHO, 2010). Much research has proven that high body mass

index (BMI) is a major risk factor for non-communicable diseases like: cardiovascular

diseases, musculoskeletal disorders and some cancers (Akil & Ahmad, 2011).

Moreover, Hammond and Levine (2010) have done research from four perspectives:

direct medical costs, productivity costs, transportation costs, and human capital costs

to find the relationship between economic impacts and obesity. They found that

people in United States have annual economic costs associated with obesity in excess

of $215 billion (Hammond & Levine, 2010).

There are many papers that have analyzed the specific causes of high body mass

index. Kelly et al. (2012) tried to study the impact of early occupational choice on

health behaviors, by using the American Time Use Survey (ATUS) by following

Grossman’s health demand model. Their findings suggested that initial occupations

described as craft, operative, and service are related to high body mass index and

obesity later in life. People who work in those industries/occupations have higher

2

physical activity involved. One of my critical hypotheses is, as higher body activities

are required in their job, people’s risk of being obese will be greater. Inas and Dhaval

(2012) found that initial blue-collar work is associated with a higher probability of

being obese. Even though physical activity has a positive relationship with health, this

relationship could be explained better by the contribution of leisure physical activities,

other than work-based physical activities. Meanwhile, work stress will influence

people’s body weight in many ways. A recent study by Isabel Diana Fernandez found

that people who were left behind at a downsized company often carry more stress and

higher body weight. That study supports another hypothesis of mine, that the heavier

the stress workers are carrying, the higher the risk for them to become obese. Chaput

et al. (2015) conducted a longitudinal analysis from the Quebec Family Study

(Canada) to estimate the relationship between workplace standing time and the

incidence of obesity and type 2 diabetes. According to their research, long

occupational standing time is not sufficient in and of itself to prevent overweight and

obesity in adults. However, there are limited studies that examined in the relationship

between specific industries, work occupations, and overweight/obesity in Canada.

Therefore, this report will focus on which industries and work occupations are most at

risk of being overweight and obese.

Based on the Canadian Community Health Survey (CCHS) and Canada’s

National Population Health Survey (NPHS), I will use basic logit models and

fixed-effect logit models to identify which industries and work-occupations are

associated with the highest risk of being overweight and obese. Also, household

3

income, education and lifestyle factors have been tested to find whether these

variables could lead to greater probability of being overweight and obese.

The rest of the report is organized as follows: SectionⅡreviews the literature and

introduces the measurement of adults’ obesity, the prevalence of overweight and

obesity in Canada, the effects and causes of overweight and obesity. Section Ⅲ

describes the methodology and variables. Then, in Section Ⅳ, I will show the

relationship between education, household income, work occupation, industry, and the

categories of body mass index (BMI), graphically. Also in this section, I will describe

the data from two surveys. Finally, I will show the results and the conclusion in last

two sections of my paper.

II Literature review

2.1 Measurement:

There are three major ways to measure an adult’s individual obesity; the first one is

BMI defined as the human’s weight in kilograms over the square of the height in

meters; a BMI over 30 is defined as obese and over 25 is defined as overweight.

BMI and health risk have different classifications in various countries, Table 1 show

the BMI classification by WHO and Health Canada.

Table 1: Health Risk Classification according to Body Mass Index (BMI)

Classification BMI Category (kg/𝑚2) Risk of Developing Health

4

Problem

Underweight < 18.5 Increased Risk

Normal Weight 18.5- 24.9 Least Risk

Overweight 25.0- 29.9 Increased Risk

Obese Class I 30.0- 34.9 High Risk

Obese Class II 35.0- 39.0 Very High Risk

Obese Class III >= 40.0 Extremely High Risk

Note 1: For persons 65 years and older the ‘normal’ range may begin slightly above BMI 18.5 and

extend into the ‘overweight’ range.

Note 2: For use with adults over the age of 18, excluding pregnant and lactating women.

The second classification is Waist Circumference (WC) which is an indicator of

health risk associated with excess fat around the waist. The World Health

Organizations recommended measurement method is to let participants stand, feet

apart from 25 to 30 cm, weight evenly distributed. Measurement location is the in

horizontal edge of a former iliac crest and the 12th ribs on the halfway point of the

attachment. Measuring scale should be close to the soft tissue, but cannot oppress

those tissues. Measuring accuracy value to 0.1 cm (WHO.2013). Table 2 shows values

of waist circumference standards below.

Table 2: Ethnic-specific Values for waist circumference promoted by the WHO

and Canadian Diabetes Association.

Country or ethnic group Central obesity as defined by

WC

5

Men- cm

(inches)

Women-cm

(inches)

European, Sub-Saharan African, Eastern

Mediterranean and Middle Eastern (Arab)

94(37.6) or

greater

80(32) or

greater

South Asian, Chinese, Japanese, South and Central

American

90(36) or

greater

80(32) or

greater

Note: For use with adults over the age of 18, excluding pregnant and lactating women.

The obesity standards for men are different between Western Countries and Eastern Countries;

there are 94cm and 90cm respectively. However, the overweight standards for women are the

same cross world. Once the WC is over 80cm, women can be defined as obese.

The third way is called Waist Hip Ratio (WHR). According to Wikipedia, the World

Health Organization’s data gathering protocol, WHR, is the ratio of waist

circumference to hip circumference. Hip circumference is the most outstanding points

around the hip measuring the circumference of the body level. By using waist

circumference over hip circumference, the measurement of obesity for white male is

greater than 0.90 and for white female is greater than 0.85(WHO, 2008). WHR tests

the level of abdominal and adipose accumulation.

For this study I will use National Population Health Survey and Canadian

Community Health Survey, which are collected by Statistics Canada. These surveys

include individuals’ measured height and weight, which are the required variables to

estimate BMI. There are several studies that use Body Mass Index to estimate the

relationship between obesity, insufficient physical activity and household income

level. For example, Jolliffe (2011) studied the relationship between income and body

6

mass index by using the National Health and Nutrition Examination Survey

(NHANES). After using the unconditional quantile regression to analyzed the

differences between poor and non-poor, male and female, he found that there is a

significant relationship between BMI and income level. Moreover, increased income

will reduce the BMI value. Petersen et al. (2016) studied the relationship between

sitting time for non-strenuous workers and their BMI. He used time analysis to prove

that among workers in non-strenuous jobs, every 10 additional hours spent on

working will be associated with an increase in BMI of 0.424 for women and 0.197 for

men, representing an increase of 2.5 and 1.4 pounds, respectively. According to WHO

Diabetes Country Profiles 2016, BMI is related to blood pressure diseases and cancer.

Since only BMI is available in the NPHS and CCHS, Waist Circumference and Waist

Hip Ratio will not be considered in this study. As per the standard for Canada, I use a

BMI cutoff of 30 for obesity and 25 for overweight in all of my analysis.

2.2 Prevalence:

According to Canadian Community Health Survey, the trend of obese adults among

the population is smoothly but steadily increasing from 2003-2014. Obesity rates were

16% for adult men and 14.5% for adult women in 2003; those rates become 21.8%

and 18.7% respectively in 2014. This significant change might be due to

self-reporting bias, since changes of obesity rate should be small during short term

cycle. CCHS is a cross-section survey that collects information related to health status,

7

health care utilization and health determinants for the Canadian population. There

were approximately 130,000 respondents interviewed during the reference periods of

2001, 2003 and 2005; the sample size decreased 65,000 each year since 2007 as the

survey were conducted annually instead of bi-annually. There are therefore gaps in

2004 and 2006, in which no data are available. Figure 1 shows

Figure 1, Percentage of respondents were overweight or obese (self-report), by

sex, household population aged 18 and older, Canada, 2003 to 2014.

Sources: Canada Community Health Survey, overweight and obese adults 2003, 2005, 2007 to

2014.

Based on CCHS 2014, there are regional differences among provinces. As Table 3

shows, compared to the national average, Newfoundland and Labrador, Nova Scotia,

New Brunswick, and Northwest Territories have significantly higher obesity rates

(10.2%, 7.6%, and 6.2% 13.2% respectively). On the other hand, British Columbia

and Quebec have significantly lower obesity rates (4.25% and 2% respectively) than

the national average of 20.2%. Ontario has similar obesity rate as the national

average.

Table 3: Adults who are obese in 2014 by province to territory in Canada:

0

10

20

30

40

50

2003 2005 2007 2008 2009 2010 2011 2012 2013 2014

Overweight men

Overweight women

Obesity men

Obesity women

8

Province or territory Prevalence (%)

National 20.2

Prince Edward Island 24.2

Newfound and Labrador 30.4

Nova Scotia 27.8

New Brunswick 26.4

Quebec 18.2

Ontario 20.4

Manitoba 24.5

Saskatchewan 25.1

British Columbia 16

Yukon 23.2

Northwest Territories 33.7

Nunavut 24.7

Alberta 21.5

Source: Statistic Canada, Canadian Community Health Survey (CCHS), 2014.

2.3 Effects:

Obesity and overweight could cause multiple issues, including health risks and

economic consequences. From a health point of view, the World Health Organization

suggests that overweight and obesity have significant positive relationships with a

9

number of chronic diseases, including diabetes, cardiovascular diseases, and cancer

which occur not only in the developed countries with high income but also in the low

and middle income countries, especially in urban settings (WHO, 2016). Moreover,

some research has analyzed the impact of body fatness on mortality; a J-or U- shaped

relationship between BMI and mortality has been reported in a number of US studies

(Lee & Manson, 1997); that is, mortality is higher at both high and low extremes of

body weight. In 2000, a Canadian study tested the proportion of all deaths among

adults 20-64 years old and found that overweight and obesity grew from 5.1% to 9.3%

between 1985 and 2000 (Public Health Agency of Canada, 2011). Another Canadian

study used the National Population Health Survey (NPHS) which has included 11,326

participants and followed them for 12 years from 1994/1995, and found that the

obesity category classⅡ or Ⅲ had a significantly increased risk of all-cause

mortality (Katzmarzyk et al,. 2004). For better understanding of the health effects of

obesity, the following discusses examples of diseases which are considered to be

related to body fatness and the impact of obesity on the economy.

Mokdad et al. (2003) studied the relationship between diabetes and obesity

among U.S adults in 2001. The self-reported data show a significant rise in obesity

over a one-year period (2000-2001); meanwhile, diabetes' prevalence has risen by

8.2%. Furthermore, a comparison was provided by that study between overweight

adults and normal weight adults. He used a logistic model to generate the odds ratios

(ORs) and their 95% confidence intervals (CIs) for the association of medical and

BMI conditions. Results show that compared to adults with normal weight, adults

10

with a BMI of 40 or higher had an odds ratio (OR) of 7.37 for diagnosed diabetes, 6

for high blood pressure, 1.88 for high cholesterol levels, 2.72 for asthma, 4.41 for

arthritis, and 4.19 for fair or poor health. Both overweight and obese were associated

with diabetes, high blood pressure, high cholesterol levels, asthma, arthritis, and fair

or poor health status significantly.

Stanford Health Care states that the probability of having heart disease is 10

times higher for obese people than normal weight people. Joint problems are more

common in obese people. Obesity has different effects on men and women. Obese

women have a higher risk for breast cancer. Overweight in men could cause colon

cancer and prostate cancers.

From an economic point of view, several studies show that obesity and

overweight increase the burden of public health welfare. INSPQ (2014) published a

report illustrating what economic cost could be generated due to prevalence of obesity

in Canada. First is the direct cost, for example, the cost of hospitalization, medical

consultations in outpatient clinics and the consumption of medications. The other is

indirect cost referring to lost productivity when individuals must temporarily or

permanently leave work for health reasons. Trogdon et al. (2008) pointed out that

indirect costs also include insurance since compared with non-obese workers, obese

workers miss more workdays due to illness, injury, or disability. With higher life

insurance premiums and more workers’ compensation paid for employees who are

obese than for those who are not, the costs for employers become bigger. Meanwhile,

Colditz and Wang (2008) found obesity is associated with lower wages and lower

11

household income. Konnopka et al. (2011) estimated the amount the German

government has to pay for the treatments of obese and overweight and the diseases

that caused by them and found that obesity caused 4,854 million EUR in direct costs

that corresponded to 2.1% of the overall German health expenditures in 2002 and

5,019 million EUR in indirect costs, 43% of direct costs that resulted from

endocrinology diseases like obesity and diabetes itself, followed by cardiovascular

diseases (38%), neoplasms (14%) and digestive diseases (6%). Sixty per- cent of

indirect costs resulted from unpaid work, and 67% of overall indirect costs were due

to mortality.

Another Canadian research program which use the CCHS, NPHS and Economic

Burden of illness in Canada, analyzed the impact of obesity on Canada economic

costs from 2000 to 2008. This study has calculated the effect of inflation on average

incomes and health care costs over that period. The study looked at both the direct

costs to the health system (i.e. the hospital care, pharmaceuticals, physician care and

institutional care) and indirect costs to productivity (i.e. the value of economic output

lost as a result of premature death and short and long term disability), which have

been defined as the economic burden of obesity. This study focused on eight chronic

diseases which have extensively been considered related to obesity, and found that

from 2000 to 2008, the annual economic burden of obesity in Canada increased from

$3.9 to $ 4.6 billion (Public Health Agency of Canada, 2011).

2.4 Potential Explanations for Obesity:

12

Many studies have illustrated various possible economic explanations for the

prevalence of obesity, including income, education, food prices, etc. Meanwhile, some

medical reports also point out the potential physical reasons for obesity, like

decreased sleep, increased consumption of weight control medicine, etc. Most

researchers focus on a small number of the possible causes of obesity. This review

will focus on limited economic reasons, leaving the rest for further discussions.

A widely acknowledged opinion is that there is a negative relationship between

income level and obesity, which means as the income level goes up, the obesity rate

will go down. Jolliffe (2011) examined the impact of income on obesity by using data

from the National Health and Nutrition Examination Survey (NHANES) over 5 time

periods: 1971-1974, 1976-1980, 1988-1944, 1999-2002, and 2003-2006. For the data

setting part, the author grouped the data into two categories, poor and not poor, and

for all analysis in this study, poor was defined as less than or equal to 130% of the

poverty line. The author applied the unconditional quantile regression (UQR) to

analyze the differences between poor and non-poor, male and female，in overweight

and obese respectively. Also, the author used OLS regression to compare with the

UQR to get marginal effect of some explanatory variables on both conditional and

unconditional mean of the dependent variable. The study concluded that for the last

35 years, it was 5.1 to 6.5 percentage points more likely for poor people to become

obese than non-poor people, based on distribution-sensitive measures and that the

UQR estimator showed a strong relationship between income and BMI at the tails of

BMI distribution, while the UQR estimation indicated a negative relationship between

13

an increase in income and BMI values.

Anca and Kurt (2013) used the Kuznets Curve to define the relationship between

income level and obesity. The Kuznets Curve (Akerman & Kuznets, 1955) was first

used to describe a country’s development with the progression of economic inequality.

They used panel data of the Behavioral Risk Factor Surveillance System (BRFSS) and

Current Population Survey (CPS) from 1991 to 2010. Through a difference- in-

differences strategy, they set the obesity prevalence as the dependent variable and

income level as independent variable, also including a fixed effect estimator. The

results suggest, at the first, the trends of obesity and income are the same, and then

obesity decreases with a continued increase in income. The peak of this curvilinear

relationship is $29,744 in total pre-tax income. However, the study included the fact

that the relationship between obesity and income level was limited to white females;

there is less evidence of similar results for white male.

Obesity is not only an issue in the United States but also a general problem in

Europe. The prevalence of obesity has increased by 10% - 40% in most European

countries over the last decade, Garcia and Quintana (2008) studied the relationship

between overweight, obesity and income level for both males and females using a

panel data set from the European Community Household Panel, Eurostat consisting of

eight waves from 1994-2001 years and from nine countries in Europe. Using BMI as

the dependent variable and dummy modality, they run OLS regression, multinomial

logit estimation and quantile regression for males and females respectively. Labor

income and other household income were treated separately. The results show that

14

there was a statistically significant negative relationship between contemporaneous

BMI and own labor earnings for female, but there was limited relationship between

BMI and income for male in these nine European countries.

Using a sample of 56311 individuals from the Russia Longitudinal Monitoring

Survey (RLMS) from 1994 to 2005, Staudigel (2011) explored the relationship

between food prices; body mass and obesity for urban adult Russians-aged 18 and

above. The author used fixed-effect panel models to deal with unobserved individual

heterogeneity in the determinants of BMI. For investigating the probability of being

obese, a logit model was used. The main results showed that there was little evidence

supporting a relationship between food price and overweight and obesity in Russia;

the price of only a few foods, like milk and pork could affect the BMI of male and

female, respectively.

Using four U.S. nationally representative data sets, Mao and Yan (2013) showed

that eating habit plays a big role in obesity. They found that the overweight and obese

individuals consumed less vegetables and fruits, and higher calorie drinks compared

to the normal weight control groups even though the overweight and obese groups

had stronger intention to lose weight.

Brown et al, (2003) studied the relationship between sitting time, physical

activity and BMI in two control groups. Their study included variables age, number of

children, physical activity, sitting time, BMI, gender and work patterns. They ran

logistic regression to explore which factors contribute to obesity most among

participants. The results showed that compared with men in full-time work, women,

15

regardless of work in full-time or part-time, or full-time home duties, were less likely

to be overweight or obese. Participants with high daily level sitting time are

significantly more likely to become obese, compared with participants with low level

sitting time. However, a major limitation of this study is non-random selection of the

participants with self-reported data.

Most recently, Wanner et al, (2016) explored the relationship between physical

activity, sitting time and different measurements of obesity. In their report, they used

Swiss Cohort SAPALDIA (SAP) to study the cross-sectional associations between

domain-specific physical activity, sitting time, and obesity, as well as longitudinal

associations between patterns of change in physical activity and weight ten years later.

They found that leisure time and physical activity have negative relationship with

obesity. However, that relationship only affects body fat significantly, not BMI.

Sarma et al. (2013), by studying the NPHS, finds that leisure-time physical

activities and working – time activities have a negative effect on BMI of Canadian

adults. Overall, cross-sectional and longitudinal results support the relationship

between physical activity and obesity; physical activity could contribute to weight

control.

Baum and Ruhm (2007) studied the relationship between age and obesity; their

results show that, in the U.S, for the populations above age 30, weight significantly

increased with age. Also many studies have showed that those with higher education

levels will have a lower risk of obesity. Rodolfo (2000) found that education has a

positive effect on reducing the probability of obesity. Devaux et al. (2011) studied the

16

relationship between education and obesity among OECD countries. They focused on

adults aged between 25-64 adults in Canada, England, Australia and Korea. After

completing a logistic model, they found a broadly linear relationship between the

numbers of years spent in full-time education and the probability of obesity. They also

found that the more people are educated, the less probability there is for them to be

obese (the only exception being men in Korea). However, this negative relationship

was found to be significantly stronger in females than males, especially among the

candidates from England and Korea. This inverse relationship between education and

obesity exists due to several reasons: first, higher education leads to greater access to

health-related information; second, acknowledgement of risks that related to lift style

choice. However, the major limitation for this report is that BMI was measured in

England and Korea, but self-reported in Canada and Australia, which might cause

errors in estimation.

III Models and Data

3.1 Methods

To analyze the relationship between overweight/obesity, income and other

independent variables I described above, we begin with the following simple linear

equation:

BMI = 𝛽0 + 𝛽1 ∗ 𝑎𝑔𝑒 + 𝛽2 ∗ 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽3 ∗ 𝑖𝑛𝑐𝑜𝑚𝑒 + 𝛽4 ∗ 𝑔𝑒𝑛𝑑𝑒𝑟 + 𝛽5

∗ 𝑤𝑜𝑟𝑘 𝑜𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 + 𝛽6 ∗ industry + 𝛽7 ∗ human behavior + 𝜀𝑖

17

In a linear model, the residuals are assumed to be normally distributed. We could

observe whether those independent variables are related to obesity, but couldn’t

observe which independent variable is the crucial factor of obesity. In that case, it is

necessary to introduce logit regression and fixed-effect regression. Logistic regression

is very similar to linear regression but with binary dependent variable. By using logit

regression, I could identify which variable triggers the highest probability of

overweight. Here is the logit function

Ln (𝑝𝑟𝑜𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝐵𝑀𝐼 > 30

1 − 𝑝𝑟𝑜𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝐵𝑀𝐼 > 30)

= 𝛽0 + 𝛽1 ∗ 𝑎𝑔𝑒 + 𝛽2 ∗ 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽3 ∗ 𝑖𝑛𝑐𝑜𝑚𝑒 + 𝛽4 ∗ 𝑔𝑒𝑛𝑑𝑒𝑟

+ 𝛽5 ∗ 𝑤𝑜𝑟𝑘 𝑜𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 + 𝛽6 ∗ 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝛽7 ∗ ℎ𝑢𝑚𝑎𝑛 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑟

+ 𝜀_𝑖

Since panel-data has been included in my study, if the model above contains all

observed individual effects, then OLS estimator will be consistent and efficient.

However, with unobserved individual effects, or if the unobserved effects are

correlated with the explanatory variables in my model, the estimator will become

inconsistent and biased. The theories of Wooldridge (2010), could explain my

dilemma well. On one side, using panel data would reveal the relationship between

observed individual effects and included independent variables; on the other side,

there are suspicious unobserved individual effects that may be correlated with

independent variables. Since there are so many social economic factors included in

my model, they are generally endogenous. It is necessary to introduce fixed – effects

regression to help me solve my dilemma. If there are omitted variables, and these

18

variables are correlated with the variables in the model, then fixed effects models may

provide a means for controlling for omitted variable bias. Fixed effects models control

for the effects of time-invariant variables with time-invariant effects. This is true

whether the variable is explicitly measured or not.

To better understand the contributions of individual predictors, we need to

examine the regression coefficients. In linear regression models, the changes in each

unit changes predictors, which we use the regression coefficients to represent. In

logistic regression, the regression coefficients imply the change in logit for each unit

change predictor. Therefore, based on the logistic regression model, people will use

odds ratio to quantify the effect size. Odds ratio is the ratio of relative risks that

represents the change in an outcome (y) from a change in variable (x). The following

equation shows the definition of odds ratio.

𝑂𝑑𝑑𝑠 𝑅𝑎𝑡𝑖𝑜 =𝑃(𝑌 = 1|𝑋 + 1)/𝑃(𝑌 = 0|𝑋 + 1)

𝑃(𝑌 = 1|𝑋)/𝑃(𝑌 = 0|𝑋)

(Note: Y=0 means y equal to the based category);

In this paper, I will use odds ratio to represent results for each set of control variables.

Each table contains the results as I have controlled the independent variables like

household income, industry, work occupation and human individual behavior

separately.

In my research, the main purpose is using the logit regression and fixed effect logit

regression to analyze the relationship between obesity and the work occupation under

different industries by using CCHS and NPHS data.

3.2 Data

19

To focus on building the health information system, Canadian Community Health

Survey (CCHS) uses cross-sectional questionnaires to collect health related

information like health status for the Canadian population. Similarly, the National

Population Health Survey (NPHS) uses longitudinal methods to collect information

about how economic and fiscal situation affects health care systems, and the health

status of Canadians.

The differences between those two surveys are 1) The changing of health status

related to many conditions, for example, public policy development and health care

utilization; NPHS contain more details about those conditions compare to CCHS. 2)

Because of a lager sample size, the CCHS is able to act as a data source on small

population and rare characteristics; NPHS consists of separate households, health

institutions, and north components. 3) For the NPHS, the earliest year of data is

1994-95, and the survey was conducted every two years until 2011; CCHS began their

survey in 2001-16. 4) For the sample size, the total sample of CCHS which I used in

my regression is 274351; the first cycle of NPHS contains 17,276 respondents and

keeps declining over the cycles. Both surveys are used in this study

For this paper, two kinds of data-sets have been selected to complete the analysis-

cross sectional and panel. The longitudinal data utilizes Statistics Canada’s National

Population Health survey (NPHS) and the cross sectional data comes from the

Canadian Community Health Survey (CCHS). Both of them collect information

related to health status, health care utilization and health determinants of the Canadian

20

population.

3.2.1 CCHS

The target population of CCHS covers Canadians over 12 years old, who live in the

ten provinces and the three territories. Three groups of people are excluded from the

survey’s coverage: candidates who are living on reserves and other Aboriginal

settlements in the provinces; people who are full-time members of the Canadian

Forces; the institutionalized population and people who live in the Quebec health

regions. Those three groups of people represent less than 3% of the Canadian

population aged 12 and over. The main purposes of the survey are health surveillance,

population health research and convenience in every field of study for researchers

who use this information to improve Canadian’s health.

In 2001, Statistics Canada and Health Canada began the first survey and repeated

it every two years as one cycle until 2005. The Canadian Community Health Survey

changed data collecting from every two years to annually from 2007.

3.2.2 NPHS

The National Population Health Survey (NPHS) was designed to collect health and

related socio-demographic information of Canadian population including household

residents in ten Canadian provinces since 1994/1995. However, in the last cycle the

survey was extended to cover all of Canadian including three territories and people

who moved to the US.

21

The households, the health institution, and the North components are the three

main data resources of NPHS; in this paper I will focus on the household only. In

1994/1995, the NPHS started the first survey and repeated it every two years as a

cycle, until 2010/2011; NPHS contains nine cycles in total. For the first three cycles

(1994/1995, 1996/1997 and 1998/1999), the data are both longitudinal and

cross-sectional with original samples from cycle 1. From cycle 4, NPHS became

strictly longitudinal.

The questionnaire of NPHS was designed by specialists from Statistics Canada,

provincial ministers of health and other researchers in academic field. In order to

choose samples that represent the real situation of all Canadian populations; Statistics

Canada also adjusted to create different longitudinal weights. In this report I use the

longitudinal square weights provided by Statistics Canada

There were 17276 Canadians of all ages chosen as the observations for the

NPHS first cycle. Unlike the calculation methods of other cycles’ response rates, the

response rate of the first cycle was based on 20095 in-scope respondents. The rest of

the cycles’ response rates were based on the 17276 individuals who responded in

Cycle 1. As time passed, the respondents decreased from more than 17276 to 12041;

furthermore, the NPHS lost almost half of applicable results due to incomplete

responses. The response rates are 83.6%, 92.8%, 88.3%, 84.9%, 80.8%, and 77.6%,

77.0%, 70.7% and 69.7% respectively. In my research, data are selected from cycle 1

to cycle 9 and I restricted the age group from 20 to 50 in cycle 1. For the last cycle the

largest age of the samples is 67 and not beyond the retirement age. Therefore, 8401

22

individuals are selected as my regression sample in cycle 1; afterwards, only 4619

observations are left in the last cycle for me to analyze.

3.2.3 Descriptive Statistics

In this paper, I used 8 cycles of data from 2007 to 2014. Given the focus of my study

on the relationship between obesity and occupations, I restricted the age from 25 to 65

years old in each cycle. Meanwhile, I treated the values which are not applicable, not

stated and refused as missing data, and therefore deleted those observations. Also

pregnant women were also removed from the data because their body weight will

affect the true value of their BMI. In the end, there are 274,351 observations left for

the regression. The following four charts represents the tendency of normal weight,

overweight and obese in relation to highest completed level of education, level of

household income, four groups of work occupations and nine types of industries.

Figure 2 shows the relationship between completed highest level of education and

normal weight, overweight and obese respectively. Overall as the completed

education level goes up, obese population becomes lower (25% to 11%) of all, and

normal weigh population grows from 35% to 53%.

Figure 2: Relationship between BMI and highest level of education (CCHS)

23

Figure 3 represents the relationship between household income and body weight,

normal weighted population decreased as household-income increased. Overweight

and household income move in the same direction, which indicates higher income is

associated with higher body weight (overweight population grows from 15% to 35%).

However, the tendency of obesity is a pretty flat line, probably due to low sample

sizes at the very low income level.

Figure 3: Relationship between BMI and household income (CCHS)

Figure 4 shows the population distribution among four work occupation groups1. As

we can see, group 2 and group 3 contain the largest normal weight population, and

1 Occupation 1 contains managers; Occupation 2 includes business, finance and administration occupations,

natural and applied sciences and elated occupations, health and education occupations; Occupation 3 consists of middle management in retail, sales and service; Occupation 4 included middle management in transportations, agriculture and manufacturing.

0102030405060

Normal

Overweight

Obese

0102030405060

Normal

Overweight

Obese

24

group 1 and group 4 contain the largest overweight and obese population.

Figure 4: Relationship between BMI and work occupation (CCHS)

Figure 5 represents the relationship between industries and body weight. The lowest

body weight occurs when the industry is related to health and education, and the

highest body weight occurs when the industry is related to mining and construction.

Figure 5: Relationship between BMI and 9 groups of industries (CCHS)

In the descriptive statistics, I treat cycle 5, 2004/2005 as cross-sectional data, since

data from those two years contain the most individual characteristic variables that I

used in CCHS. That makes it convenient for me to perform comparisons between the

CCHS and NPHS. The following figures come from Cycle 5 only.

From Cycle 5, Figure 6, the relationship between education and obesity is overall

negative. From the starting point, the percentage of normal weight people keeps

0 10 20 30 40 50 60

Occupation 1

Occupation 2

Occupation 3

Occupation 4

Obese

Overweight

Normal

0102030405060

Normal

Overweight

Obese

25

growing as their highest completed education level increases, which indicates a rise

from 25% to 50%. At the same time, the tendency of overweight people and obese

people is towards the opposite direction which decreases from 35% to 25% and 25%

to 9% respectively.

Figure 6: Relationship between BMI and highest level of education (Cycle 5,

NPHS)

Figure 7 shows that: as income increases, people’s body weight index increases as

well. People are less likely to be obese but more likely to be overweight; the

overweight population rises from 20% to 39%.

Figure 7: Relationship between BMI and household income (Cycle 5, NPHS)

0102030405060

Normal

Overweight

Obese

0.0010.0020.0030.0040.0050.0060.00

Normal

Overweight

Obese

26

Figure 8 reflects the relationship between occupation2 and BMI. People who work in

the business and finance sector have the lowest chance of becoming obese, which

forms the biggest portion of the population. This contrast with other occupations like

managers, who spend most of their time sitting behind desks. Workers in sales and

service, transportation and trade department are involved in a lot of physical activities

in their daily work, and have higher chance to become overweight and obese.

Figure 8: Relationship between BMI and work occupation (Cycle 5, NPHS)

Figure 9 shows the relationship between industry and body weight; it turns out that

people who work in education industry and entertainment industry have a lower

chance of being obese. Agriculture, mining, manufacturing and public administration

industry contain the largest obese and overweight population.

Figure 9: Relationship between BMI and industry (Cycle 5, NPHS)

2 Occupation 1 contains managers; Occupation 2 includes business, finance and administration occupations,

natural and applied sciences and elated occupations, health and education occupations; Occupation 3 consists of middle management in retail, sales and service; Occupation 4 included middle management in transportations, agriculture and manufacturing.

0 10 20 30 40 50

Occupation 1

Occupation 2

Occupation 3

Occupation 4

Obese

Overweight

Normal

27

3.3 Variable Description

3.3.1 Dependent Variable

The two dependent variables of the study are dummy variables indicating whether or

not a person is overweight or obese. According to the WHO (2012) classifications and

Health Canada, body mass index of 18.5- 24.9 is normal weight, from 25.0 to 29.9 is

overweight, greater than 30.0 is obese. An individual is considered to be obese when

her or his BMI (a measurement obtained by dividing a person's weight in kilograms

by the square of height of the person in meters) equals or exceeds 30 kg/m (Health

Canada, 2003). The NPHS uses height data and self-reported weight to estimate BMI.

The respondents select the exact height and weight, and then are classified as

underweight, normal weight, overweight or obese. In my study, I will focus on

overweight and obese only.

3.3.2 Independent variables

3.3.2.1 Age

In NPHS, household respondents provide their accurate date of birth, according to

0102030405060

Normal

Overweight

Obese

28

which, age has been calculated. Each sample in cycle one must complete the general

questionnaire. There were 2022 observations selected from cycle one and who

continued to be interviewed from the second cycle until 2011, which means I could

observe the BMI changes over 17 years to determine the relationship between age and

obesity.

3.3.2.2 Education

In questionnaires, the respondents need to tell what is the highest level of education

they have ever attained; they may also answer questions about what level of education

they are attending now, how many years of study they have finished, and what is their

student status (part-time or full -time). Those questions could help me to gather the

education background for respondents and keep tracking their education level.

3.3.2.3 Gender

A lot of research illustrates that compared to men; women are more vulnerable to

weight change. The study of Case and Menendez (2009), reveals that women in South

Africa face a higher risk of obesity; similarly, Burke and Heiland (2008) find that

women from different races face different risks of obesity; however, this is not

standard for men. In my work, gender has been treated as a control variable so I could

study the potential relationship between BMI and gender. I define the variable

“female” equal to 1 if the individual is a female and zero otherwise.

3.3.2.4 Household income

As a critical factor, income has been considered as an important determination of BMI.

There is divergence between many studies. Some of the results support that income

29

could affect BMI, some of them do not. For instance, Cawley (2008) tested whether

income has an effect on BMI of elderly Americans by using data from National

Health Interview Surveys, his empirical results denied the existence of that effect.

However, Grecu and Rotthoff (2013) found that there is an obesity Kuznets curve for

white females, which means, as income increases, BMI will increase then decrease.

NPHS set up a series of detailed questions to help me collect necessary information.

For the household, the respondents were asked to answer what the sources of their

income were from 13 categories, which are wages and salaries, income from

self-employment, dividend and interest, employment insurance; worker’s

compensation, benefits from Canada or Quebec pension plan, retirement pensions; old

age security and guaranteed income supplement, child tax benefit; provincial or

municipal social assistance or welfare, Child support, Alimony and Other. Then, they

were asked to pick one source as their main income source from those choices above,

and give an estimation of their household income for past 12 months. For individual,

the respondents have to classify their income into one range by 12 months; the

minimum is below $5000, and the maximum is above $10, 0000.

3.3.2.5 Work occupation

NPHS and CCHS contain a labor force chapter to ask respondents what work

occupation they currently have, or what work occupation they used to have. For

example, “What kind of work are/were you doing?” From those questions, I collect

the information about respondents’ occupations. Inas and Dhaval (2012) found that

blue-collar work is associated with a higher probability of being obese. In order to

30

observe the influence of work occupation on overweight and obesity based on the

National Occupational Classification, I re-categorize the work occupation into 4

groups with similar production processes and skill requirements. Group 1 contains

managers, group 2 includes business, finance and administration occupations, natural

and applied sciences and related occupations, health occupations, occupations in

education, law and social, community and government services; occupations in art,

culture, recreation and sport. Occupation 3 consists of middle management

occupations in retail, whole-sale trade, customer service, sales and service

occupations. Occupation 4 includes middle management occupations in trades,

transportation, production and utilities; trades, transport and equipment operators,

related occupations; natural resources, agriculture and related production occupations,

occupations in manufacturing and utilities. (Group 1 is reference group.)

3.3.2.6 Industry

NPHS and CCHS ask respondents “What’s the name of your business?” “What kind

of industry is this?” From those questions I collected the information about

respondents’ industries. A Statistics Canada report suggests that workers in trades,

transport are more likely to be obese than workers in financial and administration.

Based on the North American Industry Classification system, I re-categorize the

industries into 9 groups respectively with similar production processes and skill

requirements. For example, industries like public administration require the highest

administrative skills, which limit their daily physical activities during working time.

Manual worker such as constructors and mining workers require the least

31

administrative skills but highest physical activities during their regular working time.

In the control group, agriculture, forestry, fishing and hunting are involved. Mining,

construction group consists of mining, quarrying, oil and gas extraction, utilities and

construction. The third one is manufacturing group. Whole-sale and retail trade group

including whole-sale trade, retail trade, transportation and warehousing. Next is the

public administration group. In the finance and insurance group, information; finance

and insurance; real estate and rental and leasing; professional, scientific and technical

service; management of companies and enterprises, administrative and support and

waste management and remediation service are included. Education service, health

care and social assistance make up the education, health group. The entertainment

group includes arts, entertainment and recreation; accommodation and food services.

In the other services group, other services exclude public administration. In addition,

table 12 and 13 in the appendix show the re-categorization of work occupations and

industries.

3.3.2.7 Eating habits

Eating habits define how people eat and which foods they intake. The CCHS and

NPHS focus on the frequency of respondents eating green salad, fruit, fruit juice and

potatoes. Questions include “Not counting juice, how often do you usually eat fruit?”

“How often do you eat green salad?” The respondents need to describe the frequency

using terms such as per day, per week, per month, per year or never. Through those

questions, I will be able to find the relationship between individual behaviors and

BMI.

32

3.3.2.8 Physical Activity

NPHS has divided physical activity into two sections, leisure-time physical activity

and working-time physical activity. Leisure-time activity includes walking for

exercise, gardening and yard work, swimming; bicycling, popular or social dance,

home exercises, ice hockey, ice-skating, downhill skiing or snowboarding, jogging or

running, golfing, exercise class or aerobics, cross-country skiing, bowling, baseball or

softball, tennis, weight-training, fishing, volleyball, basketball, in-line skating or

roller-blading, yoga or tai-chi, and all other reported activities, Work time activity

includes the usual daily activities or work habits, respondents have to select from the

following four answers: 1,“usually sit during the day and don’t walk around very

much”, 2,“stand or walk quite a lot during the day but don’t have to carry or lift things

very often”, 3, “usually lift or carry light loads, or have to climb stairs or hills often”,

4, “Do heavy work or carry very heavy loads”. The respondents were asked to report

which activities they participated in during leisure-time, how often they did them do

and how long they spend on those activities.

3.3.2.9 Drink type and stress at work

Both NPHS and CCHS data classified the frequency of how often respondents drank

in the past twelve months, from daily drinker, weekly drinker, monthly drinker to

never drink. According to National Obesity Observatory’s (NOO, 2012), alcohol

might become a component of the risk for weight gain. Also, in NPHS and CCHS

data, they classified the stress at work as dummy variables. NPHS focused on whether

respondents were satisfied with their job and CCHS aimed to find out whether they

33

have had stress during respondents’ work. In 2007, Susan (2007) used the longitudinal

studies and proved that stress at work may be causally linked to weight gain.

IV Results

Tables 4 and 5 show the results for overweight and obese, respectively. Both tables

use the cross-sectional CCHS data. Tables 6 and 7 illustrate the results of the

overweight and obese as the outcomes of general logit estimation model by using the

NPHS data (Cycle 5). In addition, Tables 8 and 9 show the fixed effect logit results of

the NPHS data (all Cycles). The data set of each respondent in NPHS could be treated

as panel data since it is longitudinal. If I pick one of the nine cycles, and only focus on

those two years, data in that cycle could be treated as cross-sectional data. In Tables 6

and 7, I treat the cycle 5 of NPHS as cross-sectional data, and used cycle 5 to do the

estimation only for reasons given in the data description part.

4.1 CCHS Result

Table 4, Logit model based on overweight as outcome (CCHS).

The base model of Table 1 shows the coefficient for female is statistically significant

at the 1% level and negatively correlated with overweight. This implies that the

probability of females being overweight is 18.1% less than for males. The coefficient

on age is significant at the 1% level and positively correlated with overweight. This

indicates that as respondents’ age increases, the probability of becoming overweight

34

will increase 1.29% for each additional year. The reason for that may be lack of

physical exercise and eating more. Meanwhile, metabolic generation rate will

decrease and fat content will increase. For the urban variable, it is statistically

significant at 1% level and compared to the people who live in rural areas, the urban

people are less likely to be overweight. A possible explanation is that the fitness

facilities are more complete and convenient in urban areas, and people can receive

health information from multiple channels like advertisements and the internet. The

coefficients of most provinces are statistically significant at the 1% level except

Northwest Territories and Nunavut, which means compared to Newfoundland and

Labrador, people who live in other provinces (not including Northwest Territories and

Nunavut) are less likely to be overweight. For example, people living in New

Brunswick will have a higher risk of being overweight compared to those who live in

British Columbia. Northwest Territories is not statistically significant at the 10% level

and people who live in Nunavut are more likely to be overweight than people who

live in Newfoundland and Labrador. For the education variable, people with less than

9 years of education form the control group. The coefficients for each education

variable are all statistically significant at 1% level. As the education level increases,

the risk of being overweight decreases. That fits my hypothesis very well: with higher

education level, people are likely to play an important role in society, which lead them

to pay attention on their appearance, which force them to consume low calorie food

and go to the gymnasium regularly.

For the second regression of Table 4, I added household income categories to the

35

list of control variables. Coefficients of all other variables changed very little with the

introduction of controls for family income. Except for the control group, household

income equal to zero, the rest of the household income groups are statistically

significant at the 1% level. Individuals with no household income have the lowest

body weight compared with other categories; the highest risk group of being

overweight is household income of 10K-14.999K with odds 45.6% and significant at

the 1% level. However, from the range 10K to 59.9K, the risk of being overweight is

decreasing smoothly.

The results from the third regression show that both industries and work

occupations are statistically significant at 1% level. For the industry category, I treat

agriculture, forestry, fishing and hunting as the base group and the coefficients for the

remaining groups are all positive and the odds show that the people in following

industries are more likely to be overweight: Mining 13.27%, Manufacturing 0.778%,

trade 28.59%, Finance 21.45%, Education, 34.61%, Entertainment 10.88%, Other

services 15.22%, Public administration 35.95%. For the work occupation, compared

with major occupation 1, major occupation 2 and occupation 3 are less likely to be

overweight and statistically significant at 1% level with the odds 5.92% and 5.66%

respectively. The odds ratio of overweight in major occupation 4 is 1.08% higher than

major occupation 1. The trade, finance, education and public administration industries

require are less physical, compared to others. That might be the main reason for their

higher than normal odds to become overweight.

The effects of gender, age, regions and provinces in the third regression are

36

similar that in the second equation. Also the coefficient of household income changes

a lot compared with the second regression, the coefficient of selected groups from less

than 5k to 50 k-59.999 k become negative. As income increases, the risk of being

overweight increases. That might be due to a correlation between household income

and occupation.

In the fourth regression, human individual behavior variables are added to

observe the effect of eating habits and physical activity on overweight and obesity.

The results of male, age, region of residence, province, education, and industry and

work occupation show no obvious differences compared to those in the third

regression. The coefficients for the eating habits, drinking fruit juice, eating fruit,

eating carrots and other vegetables are all statistical significant at the 1% level, which

means eating healthy food daily will decrease risk of being overweight. Physical

activity during leisure time is significant at the 1% level which means people not

involved in leisure time activity will suffer a higher risk of being overweight at

39.06%. Drinking type is statistically significant at the 1% level. Occasionally

drinkers and abstainers are more likely to be overweight compared with regular

drinker. That is against my prediction; the reason for this may be because the question

for this survey is based on the last 12 months, and the sample that already is

overweight may give up drinking to reduce weight. Finally, the results show that

stress work has a higher risk becoming overweight.

Table 5 contains the results of general logit regressions using obesity as outcome

(CCHS).

37

For the base model, the results of female, age and region of residence are quite similar

to the results for overweight reported in Table 4. For the province, compared with the

control group (Newfound land and Labrador), people who live in Nunavut are 24.47%

less likely to be obese statistically significant at the 1% level. Another difference for

the province variable compare to table 4 is that people who live in the Northwest

Territories are less likely to be obese, which is statistically significant at the 1% level.

The relationship between education and obesity is similar to table 1 except people

with trade and college certificate are more likely to become obese.

For the second regression of Table 5, the relationship between income and

obesity is positive. Compare to the result of regression three in Table 4, the results of

regression three in Table 5 shows a U shape, which means income could decrease the

probability of obese but increase that probability after passing some threshold. Results

also show that as income increases the risk of not being obese increases from 21% to

33.1%; after the total household income meet 29.99K, the probability of people not

being obese decreases. For the industry variable, the only difference is that the group

of people who work at art, entertainment, recreation; accommodation and food

services are less likely to become obese compared to the base group. The only

difference for the occupation variable among those two regressions is that results in

Table 5 shows people who work at occupation 2 and occupation 4 are less likely to be

obese.

After adding individual behavior variables in the fourth regression, the gender,

age and region of residence have the similar results compared to Table 4. The only

38

difference in the province variable in the two tables is people who live in Nunavut are

9.2% less likely to become obese. In general, the following variables have similar

results among those two regressions: education, household income, industry, work

occupation, physical activity, drink type and stress of work. The differences in those

tables are eating fruit per year and eating carrot per month. In Table 5, eating fruit per

year and eating carrot per month will decrease the probability of being obese.

4.2 NPHS result:

As I mentioned before, Cycle 5 of NPHS has more individual characteristic variables

that those in CCHS. The results of the base model in Table 6 clearly show that the

variable of age and gender are statistically significant at the 1% level. Compared to

males, the risk of females being overweight is 59.7% less. On the other hand, with

each year of age the odds increased by 3.13% for being overweight per year. For the

province variable, due to insufficient amount of respondents from Yukon and Nunavut,

the regression drops observations from those two territories. People from Ontario,

Quebec, British Columbia and those who moved to the United States have lower risk

of being overweight. Compared with the people whose highest education level is less

than 13 years, the odds of being overweight decrease with increasing education level

and all are significant at the 1% level.

When we add household income into the regression, the situation for the female,

age and province stay the same with the base model in Table 6. And the only

difference for education is for people with secondary education level; the result

39

becomes insignificant. Due to the insufficient sample size of no household-income

groups, I dropped those groups from all regressions, because I focus people who are

employed and have household income, for that I treat household income less than 5K

as control group. Except the household income below 9.9K and above 80K, people

have higher household income have lower risk of overweight. Above household

income of 29.999K, as household income increase people face higher probability to

become overweight. The reason for excluding those groups is because the numbers of

respondents in those two groups are too small.

For the third regression in Table 6, industry and work occupation variables are

added to the regression. Overall, the results of female, age, province and education are

not much difference from those in the second regression. However, the coefficient of

household income in the third regression becomes negative from 15K to 39.999K

although the tendency of income and overweight is same. Compared to the base group,

all industries have negative relationship with overweight which means people who

works at those industries are less likely to become overweight, especially people who

work at entertainment industry have lowest probability of being overweight with odds

lower by 33.84% and significant at the 1% level. The relationship between occupation

and overweight is negative, which means compared to manager jobs, people who have

business and finance; sales and service; trades and transportation job are more likely

to keep their body weight in normal range.

For regression 4, one of the notable differences with regression 3 is the

relationship between education and overweight. The higher the completed education

40

level the less likely people are obese. This result matches my hypothesis. The other is

the relationship between household income and overweight. As I mentioned before, in

regression three the effect of income is not stable, it changes between positive and

negative. However, in regression 4, the overall effect is positive, which means that as

income increases, the probability of not being overweight decreases by 39%, but still

keeps people less likely to become obese.

With respect to eating habits, the more frequently people eat fruit and veggies, is

the lower is the risk for them to become overweight. Compared to those people who

never do physical activities in their leisure time, people who spend time on those

activities achieve their lower risk of being overweight. Compared to monthly drinkers,

weekly drinkers and daily drinkers are less likely to become obese, which is similar to

the results obtained from using the CCHS data. The stress in work could cause big

issues for one’s health; the more people satisfied with their work, the lower risk for

being overweight.

Table 7 shows the results when obesity is used as the dependent variable. The

regression results of the base model are similar to what I got in Table 6 which

overweight as the dependent variable: compared to males, females are less likely to be

obese; meanwhile, age has a positive effect on the likelihood of obesity. People from

Saskatchewan and New Brunswick are more likely to suffer obesity problems among

all provinces except Yukon and Nunavut. Education has a positive influence on

people’s body weight, as the higher level of education you completed, the lower the

41

probability of being obese.

When I controlled for additional variables:

household income, compare to base model, female have lower risk to become

obese; regression results of age and provinces are similar as base model;

even though when people’s highest completed degree are secondary and college

certification which are higher than 9 – 13 grades, they are more likely to become

obese; the overall relationship between obesity and education stay the same;

compare to control group, when household income reach 5K to 9.99K, people

have lowest risk to become obese, for the rest household income ranges, as

household income increased they have better management on their body weight.

compare to the results in table three, household income has significant positive

effect on obesity control.

After adding industries and work occupations as the new independent variables for

model 3, the odds of female, age, province and education are almost the same as in

the second regression, except the p value of province Manitoba become statistically

significant at the 1% level and the p- value of people who have college certifications

changes to insignificant. The overall tendency of the household income variable does

not change, however the odds ratios become bigger for all income ranges. Compare to

regression three in table three, this result fits my hypothesis better. People who work

in mining and construction industries have the highest risk of being obese among all

industries with odds 14.06% and statistically significant at the 1% level. For work

occupation, it is clear that people who work as sales and services will have the highest

42

probability of obesity. Compared to the results in regression three table three, the

overall relationship does not change.

From the fourth regression of Table 6, the coefficient of mining and construction

industries used to be strictly negative and people who work at other services are more

likely to become overweight. However, the results I got in Table 7 appear to be the

opposite: people who work at the mining and construction industries suffer the highest

risk of becoming obese. And the insignificant results from the eating habits variable

are not supportive for my assumptions which might be due to the sample size being

too small. Moreover, the results for the job satisfaction, drink type and physical

activity during leisure time stay the same as the same model in Table 3. Since the odds

of physical activity at leisure time becomes bigger, which means compared to people

who are involved in leisure time physical activities, people who don’t participate

leisure time physical activities will suffer 93.94% higher risk of being obese, and that

suits my prediction very well.

4.3 NPHS (Longitudinal) Results

Since the NPHS is the longitudinal data set, I use fixed effect model in which, time

invariant variables such as gender, highest level of education and province are

excluded from regressions. The first regression controlling for household income

indicates that age has a negative effect for people to manage their body weight, which

means that as age grows, the risk being overweight will increase by 18.02% and

statistically significant at the 1% level. Furthermore, household income has a negative

43

effect for body weight control, as income increase the risk of being overweight

increases.

In the second regression, industry variables are added into my model. The

regression results of age and household income are consistent with those from the

base model. The regression results show that, no matter in which industries people

work, they are all suffering higher risk of being overweight than people work in the

agriculture industry. The third model uses work occupation instead of industry as

control variables. In general, age and household income stay the same as in the second

model. Compared to management occupations, business and finance occupation will

raise the probability of being overweight.

Adding controls for eating and physical activity behaviors makes a substantial

difference to the estimated coefficients. The coefficient of income becomes negative

and the probability of being overweight decreases at first; after the household income

reaches 30k to 39.999k the risk being overweight increases as income grows. The

relationship between being overweight and industry becomes negative, which is

different compared to regression two. Compared to agriculture, people who work at

the other industries have a lower risk of being overweight. Furthermore, the results of

trade and transportation become statistically insignificant, and people who are in sales

and services are more likely to become overweight with the odds 4.54%, significant at

the 1% level. For the eating habit variable, drinking fruit juice regularly will lower

risk of being overweight. On the other hand, the other results are not what I had

expected. To understand the reasons why the regression results in model four changed

44

so rapidly, I have done a variable test with the same observations in model four, and

the results show that the main cause is individual characteristics variables added into

the regression with undersized sample (as opposed to the change in the sample size).

Table 9 shows the results of fixed-effect regressions using obesity as the dependent

variable and the longitudinal NPHS data.

First, from the first regression, we see that the coefficient on age is statistically

significant at the 1% level and positive correlated with obesity. The coefficients of

household income are negative, however, as increasing household income increases

the risk of being obese. For the second regression, after industry is added into the

regression, the results of age and income are almost the same as those in the base

model. The coefficients of industry and obesity are positive, especially for those

people who work at finance and insurance industries; people who work at other

services are more likely to become obese. However, due to small sample size, the

results may be less reliable. For income below $40K in the third regression, increases

in household income could help control body weight; however, when household

income reaches $40K to 49.99k and above $60K, income has a negative effect on

obesity. Compared to managers, all other occupations have positive benefit for

people’s body weight management.

4.4 Link-test and Hausman test

As link-test examines whether a model has been properly specified; I have done the

link-test for CCHS and NPHS regression respectively. However, after having tried

45

many methods the results of the variable _hat and _hatsq still are all statistically

significant, means there still some variables that related to BMI but not have been

included in my regression. The main reason might due to the variables I selected are

all dummy variables.

To check whether the non-observed factors should be dealt with by a random effect

model or fixed effect model, I have done the Hausman test to found out if the null

hypothesis is true or not. In this report, the result after the Hausman test shows that

the null hypothesis was not true, which means the random effect model is inconsistent,

hence, fixed-effect model could be used in my report to deal with the panel data.

V Discussion

The results from using CCHS, NPHS cross-sectional and NPSH panel data are

consistent with most of the variables included and consistent with what has been

found in other research. Jungwee (2009) analyzed the relationship between obesity

and work occupation and found that employed males are more likely to be obese than

females. Similarly, according to CCHS and NPHS data the risk for female being

overweight is 25.7% and 69.9% lower than males respectively. The probability of

females being obese is 59.4% lower than males in the CCHS data regression analysis

and 35.5% based on NPHS data regression estimate. Age as a variable also followed

my expectation: with aging, people are more likely to be overweight and obese. For

the province variable, people living in British Columbia, Quebec and Ontario have the

46

lowest risk of being overweight and obese while people living in lower GDP

provinces will suffer higher risk of being overweight and obese. However, some

territories’ results are omitted in the NPHS regression result because of insufficient

samples.

Education has direct and indirect effects on the likelihood of being overweight

and obese. The direct contribution is: educated individuals make better use of

health-related information than those who are less educated (Speak et al., 2005). Also,

it is possible that highly-educated people have the knowledge to develop healthy

lifestyles and have more awareness of the health risks associated with being obese

(Yoon, 2006). For the indirect effect, education levels could determine the individual’s

social economic conditions, like income and work occupation. In my NPHS

cross-sectional estimation, the results confirm the well-known negative relationship

between highest education levels and probability of being overweight and obese.

Overall, for the CCHS regression results, the tendency is that as higher levels of

education are achieved, the risk of people being obese decreases. However, there are

exceptions at the trade certificate and college levels; people at these levels have a

higher probability of being overweight and obese. A possible explanation for this

result could be that education is correlated with other variables in this model, for

example, work occupation and household income.

Koffi (2014) found that income has a negative impact on both BMI and obesity,

which means increased income will slow down the growth of the population

becoming obese. However, my finding has a contrary result; compared to middle-high

47

income group, zero income groups have the smallest probability of being obese. The

difference might be due to the following reasons: first of all, we use different model to

do the estimation: I use the logit and fixed effect logit model, they used the OLS and

2SLS model. Second, I used a different period of CCHS data, particularly data from

2007 to 2014, while they used data from 2000 to 2009. Moreover, Tjepkema (2006)

used the 2004 CCHS data to analyze the impact of socio-economics on obesity; he

found that, compared to the middle income people, the high income group was less

likely to be obese, which contrasts with my CCHS estimation. In my research, I found

that the highest household income groups suffer higher risk of being overweight and

obese than lower household income samples. As Tjepkema (2006) tested how

different household income levels will affect BMI while each family contains same

number of people, I tested how different level of household income will affect BMI

regardless how many people were in one household This might account for the

difference in findings. The other possible cause of my results could be due to the

household income variable being correlated with work occupation and individual

behavior.

The results from the fourth CCHS data regression show that regular intake of

fruit and vegetables have a positive contribution to control people’s BMI. However,

the regression results from NPHS cross-section and panel data are not significant

enough to support my assumption, which might be due to insufficient sample size.

Still, people should be encouraged to eat more fruits and vegetables. For all three

datasets, physical activity at leisure time is a factor for being overweight and obese.

48

This was noticed when comparing active people to those who have never participated

in physical activity at leisure time in the last 12 months. The job satisfaction variable

in NPHS data is similar to the stress of work variable in CCHS data; both of them

measure the emotional condition of employed respondents. Results from CCHS data

show that people who are satisfied with their work will have higher risk of being

obese. This is contrary to the results from the NPHS panel data, which might be due

to inadequacy of the regression samples. It is common that people regularly drink

alcohol will have better BMI if they already suffer other health issues and which

causes their body weight to decrease, or due to the health issue they could better

control their body weight.

The following two tables summarizes the overall relationship of industry, work

occupation and BMI; I rank industry and work occupation from the highest risk of

being overweight and obese to the lowest risk of being overweight and obese. From

the CCHS data, the individual behavior variable has a minor effect on risk ranking of

all industries. People who work for the public administration and education for last 12

months have the highest risk of being overweight and obese. This might be due to the

good social welfare and sedentary working environment. Mining, finance, trade,

manufacture and other service are in the middle range. People who work in those

industries have a lower risk of being overweight and obese compared to people

working in public administration and education. This is possibly because people

involved in these groups are less sedentary. Agriculture and entertainment industries

are at the lowest range. Here are some possible explanations: First, agriculture

49

industry involves agriculture, forestry, and fishing and hunting, which requiring a lot

of outdoor physical activity. Second, people who work in entertainment industry

should maintain good body shape, which makes them have the lowest risk of being

overweight and obese. On the other hand, the results show in NPHS cross-sectional

data, people who works in the entertainment industry still have the lowest risk of

being overweight and obese. However, mining and agriculture have the highest risk of

being obese, while public administration, education, trade, finance and other service

become the moderate risk of being overweight and obese. The reason for that might

be due to the fact that in mining and agriculture industries, people require more

physical activity during the work time; therefore, at the leisure time those people will

be less likely to maintain their physical activity. Compared to the CCHS data, the

NPHS cross-section data use a different time period and sample size, which might

cause the results to differ. For the NPHS panel data, the results are quite different

from those from the CCHS and NPHS cross-sectional data, which might relate to an

insufficient sample size.

Comparing the regression results, people who work in occupation 1 and

occupation 3 have a higher risk to become obese, whereas people who work in

occupation 2 and occupation 4 are less likely to be obese. This could because the

category has too many different job classifications. The significant factor which could

affect BMI in occupation 1 and occupation 3 is sedentary time. Household income

correlates to occupation. For example, people who fall under the category of

occupation 2 have middle household income which contributes to the lowest risk of

50

being obese. However, the occupation 4 includes trade, transportation, manufacturing

and agriculture that require lots of physical activity.

Table 10: Rank of industry and work occupation based on BMI3

(for

overweight)

Industry

CCHS with HB NPHS with HB Panel with HB

Public administration Other service Agriculture

Education Manufacturing Finance

Trade Public administration Manufacturing

Finance Agriculture Other service

Mining Education Education

other service Mining Public administration

Entertainment Finance Trade

Manufacturing Trade Mining

Agriculture Entertainment Entertainment

Occupation

occupation 4 occupation 1 occupation 2

occupation 1 occupation 3 occupation 3

occupation 3 occupation 4 occupation 1

occupation 2 occupation 2 occupation 4

Note: 1, HB= human behavior.

2, the explanation of the work occupation category shows on the methodology part.

Table 11: Rank of industry and work occupation based on BMI4 (for obesity)

Industry

CCHS with HB NPHS with HB Panel with HB

Public administration Mining Finance

Education Agriculture Manufacturing

Mining Public administration Other service

Trade Trade Trade

Finance Education Entertainment

Other service Other service Mining

Manufacturing Finance Education

3 Occupation 1 contains managers; Occupation 2 includes business, finance and administration occupations,

natural and applied sciences and elated occupations, health and education occupations; Occupation 3 consists of middle management in retail, sales and service; Occupation 4 included middle management in transportations, agriculture and manufacturing. 4 Occupation 1 contains managers; Occupation 2 includes business, finance and administration occupations,

natural and applied sciences and elated occupations, health and education occupations; Occupation 3 consists of middle management in retail, sales and service; Occupation 4 included middle management in transportations, agriculture and manufacturing.

51

Agriculture Manufacturing Agriculture

Entertainment Entertainment Public administration

Occupation

occupation 1 occupation 3 occupation 3

occupation 3 occupation 1 occupation 1

occupation 4 occupation 4 occupation 2

occupation 2 occupation 2 occupation 4

Note: 1, HB= human behavior.

2, the explanation of the work occupation category shows on the methodology part.

VI Conclusion

This report assesses the existence and strength of the potential relationship between

industries, work occupations, and body weight. To do so, CCHS and NPHS data have

been used to do the estimation separately. Logit regression and fixed effect logit

regressions are run to determine which industries and work occupations will have the

higher risk of being overweight and obese. Also, many correlated variables have been

added to the estimation regression based on the literature and information available in

the data.

BMI has been selected to identify whether people are normal weight, overweight

or obese. The samples have been categorized based on the WHO and Health Canada

body mass index classification systems. Four steps of regression estimation have been

utilized to process the results. First, I used age, gender, province, and education as

independent variables for the base model, and then I added the household income at

second step, the industry in the third step and work occupation variables and human

behaviors variables in the last step. In addition, I restricted the age of respondents

from 25 to 65 as these people are likely to be employed. Hence total number of CCHS

52

respondents is 274,351 in the first regression and 186,967 in the fourth step. For the

NPHS (Cycle 5), there were 6267 respondents at beginning and 3680 left. For the

NPHS panel database, a total of 14,080 individuals are included in the first regression,

while only 2226 respondents are left in the last regression.

The regressions indicates that compared to females, males are more likely to be

overweight and obese, which is contrary to some literatures that female have higher

risk of being overweight and obese for Canada. This could due to different variables

included in my models; also, there are factors like human behaviors which might

correlate with gender. The results for the age variable that with aging people will

suffer higher risk of being overweight and obese are what I had expected. For the

province variable, perhaps due to insufficient NPHS data to do the estimation, there

are some differences between the outcome of CCHS and NPHS, but still in general,

provinces with higher GDP will have lower risk of being overweight and obese.

Overall, the tendency of education results are quite similar to other researchers’ results,

with higher education level, people have better body weight control. The exceptions

are when people obtained highest education in the trade certificate and college level;

they have bigger risk to become obese. This might due to some correlated variables

are added in my regression like household income, occupation and human behavior.

Higher income contributes to higher BMI, which is in contrast to most studies

and the result is inconclusive. Results from human behavior variable (eating habits,

drink type, stress at work and physical activity) suggest that undertaking physical

activity during leisure time, eating healthy food and less work stress will benefit

53

people’s body mass index. However, the regression results on this are not consistent

between using CCHS and NPHS data. This might due to the different period of time I

choose and insufficient observations in NPHS panel data.

The main results from CCHS data show: People who work at public

administration and education industry have highest risk to become obese; people who

work at mining, trade, finance, other service and manufacturing industries have

moderate risk to become obese; only people involved in entertainment industry are

less likelihood of obesity and overweight. On the other hand, results from NPHS data

show a different story: people choose to work at mining and agriculture industries are

more likely to become obese; people in entertainment industry have lowest risk to

become obese; other industries are in the middle range. The last data set, NPHS panel

data, contains contrary results compare to NPHS cross-sectional data and there is little

pattern in it. Occupation 1 and 3 contribute most to people’s risk of being obese cross

all three data sets. People in occupations 2 and 4 have smaller chances of being obese.

My results are different from past Canadian studies; the likely main reason is we have

different categories on industry and occupation. Most Canadian studies classify

industries and occupations by how much physical activity involve in people’s daily

work, so there are two occupations left, blue collar and white collar. According to

their studies, blue collar suffers higher risk to become obese compared to white collar.

In my study, all industries and occupations are sorted by professional skills according

to North American Industry Classification System (NAICS) and National

Occupational Classification (NOC).

54

Compared to other’s work, there are limitations in my work. The first limitation

is data. In my report I have used two databases CCHS and NPHS. For both data sets,

most of the answers in these surveys are simply yes or no. Moreover, due to the

serious attrition rates, almost half of respondents omitted at the end of the NPHS

cycle.

In my study I only focus on how industries and work occupations will influence

people’s body weight. However, people’s body weight could also limit their career; I

didn’t test the correlation from this side. Finally, I classified the industry and work

occupation according to the professional skills. There are many factors could

influence people’s body weight, and many of them can’t be observed from my

original data.

As I mentioned before, even though my research filled some research gaps, there are

remains blank space for further research.

55

Table 4; Logit model based on overweight as outcome (CCHS, 2007-2014)

Dependent variable=overweight

Base

model

Control household income Control industry and

occupation

Control Human Behavior

Variables OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es

Female

0.81899

92

-0.29687

77 ***

0.8221

1

-0.195

88 ***

0.7864

6

-0.240

21 ***

0.7431

35

-0.296

88 ***

Age

1.01295

5

0.01066

24 ***

1.0129

83

0.0128

99 ***

1.0120

62

0.0119

89 ***

1.0107

19

0.0106

62 ***

Urban 0.89960

96

-0.14125

87 ***

0.8939

79

-0.112

07 ***

0.8763

44 -0.132 ***

0.8682

65

-0.141

26 ***

Province

P.E.I

0.85545

26

-0.18823

87 ***

0.8551

09

-0.156

53 ***

0.8629

97

-0.147

34 ***

0.8284

17

-0.188

24 ***

NS

0.91076

27

-0.13943

57 ***

0.9071

71

-0.097

42 ***

0.8860

35 -0.121 ***

0.8698

49

-0.139

44 ***

NB

0.91890

13

-0.14360

96 ***

0.9092

6

-0.095

12 ***

0.8917

1

-0.114

61 ***

0.8662

26

-0.143

61 ***

Quebec

0.54372

17

-0.60223

29 ***

0.5443

37

-0.608

19 ***

0.5315

31

-0.631

99 ***

0.5475

88

-0.602

23 ***

ON

0.64321

12

-0.47621

01 ***

0.6385

25

-0.448

59 ***

0.6280

88

-0.465

07 ***

0.6211

33

-0.476

21 ***

Manitoba

0.80183

71

-0.26972

76 ***

0.8000

72

-0.223

05 ***

0.7743

94

-0.255

67 ***

0.7635

87

-0.269

73 ***

Saskatchewan 0.88287 -0.17121 *** 0.8776 -0.130 *** 0.8472 -0.165 *** 0.8426 -0.171 ***

56

26 56 38 52 8 72 4 22

Alberta

0.68410

63

-0.38283

79 ***

0.6804

35

-0.385

02 ***

0.6797

35

-0.386

05 ***

0.6819

23

-0.382

84 ***

BC

0.46053

24

-0.75886

25 ***

0.4624

97

-0.771

12 ***

0.4674

32

-0.760

5 ***

0.4681

99

-0.758

86 ***

Yukon

0.65964

64

-0.40084

19 ***

0.6555

01

-0.422

36 ***

0.6469

42

-0.435

5 ***

0.6697

56

-0.400

84 ***

NT

0.99733

07

-0.00565

56 0.642

0.9995

42

-0.000

46 0.937

0.9590

32

-0.041

83 ***

0.9943

6

-0.005

66 ***

Nunavut

1.03810

5

0.14989

47 ***

1.0333

68

0.0328

24 ***

1.0929

17

0.0888

5 ***

1.1617

12

0.1498

95 ***

Education

Grade 9-13

0.99429

83

#VALU

E! ***

1.0090

41 0.009 ***

1.0117

78

0.0117

09 ***

1.0234

12

0.0231

43 ***

secondary 0.92198

74

-0.08122

37 ***

0.9527

77

-0.048

37 ***

1.0163

66

0.0162

33 ***

1.0414

85

0.0406

48 ***

trades CER 0.93073

21

-0.07178

38 ***

0.9588

65

-0.042

01 ***

1.0092

13

0.0091

71 ***

1.0243

42

0.0240

51 ***

College

0.89335

52

-0.11277

1 ***

0.9205

21

-0.082

82 ***

0.9821

58 -0.018 ***

0.9974

29

-0.002

57 0.462

below bachelor

0.72508

58

-0.32146

53 ***

0.7505

36

-0.286

97 ***

0.8108

29

-0.209

7 ***

0.8245

15

-0.192

96 ***

bachelor degree

0.58914

18

-0.52908

83 ***

0.6065

66

-0.499

94 ***

0.6454

68

-0.437

78 ***

0.6525

66

-0.426

84 ***

Univ, and above

0.42417

72

-0.85760

4 ***

0.4364

48

-0.829

09 ***

0.4532

52

-0.791

31 ***

0.4632

85

-0.769

41 ***

57

Income

<5k

1.1123

48

0.1064

73 ***

0.7329

78

-0.310

64 ***

0.7055

15

-0.348

83 ***

5k-9.99k

1.2999

98

0.2623

63 ***

0.8015

32

-0.221

23 ***

0.7968

82

-0.227

05 ***

10k-14.999k

1.4562

71

0.3758

79 ***

1.1055

14

0.1003

1 ***

1.1581

41

0.1468

16 ***

15k-19.999k

1.3683

34

0.3135

94 ***

0.9035

14

-0.101

46 ***

0.9003

87

-0.104

93 ***

20k-29.999k

1.2854

91

0.2511

41 ***

0.8978

43

-0.107

76 ***

0.8684

48

-0.141

05 ***

30k-39.999k

1.1903

82

0.1742

75 ***

0.9181

13

-0.085

43 ***

0.8931

95

-0.112

95 ***

40k-49.999k

1.1877

78

0.1720

84 ***

0.9437

62

-0.057

88 ***

0.9166

82

-0.086

99 ***

50k-59.999k

1.1661

25

0.1536

87 ***

0.9523

44

-0.048

83 ***

0.9331

84

-0.069

15 ***

60k-79.999k

1.2531

84

0.2256

87 ***

1.0091

4

0.0090

99 0.389 0.9833

-0.016

84 0.119

80k-99.999k

1.2935

96

0.2574

26 ***

1.0552

38

0.0537

66 ***

1.0375

45

0.0368

58 ***

>100k

1.2433

84

0.2178

37 ***

1.0238

33

0.0235

53 0.026

1.0174

04

0.0172

54 0.11

Industry

Mining, Construction

1.1326

96

0.1246

01 ***

1.1717

2

0.1584

73 ***

58

Manufacturing

1.0077

79

0.0077

49 ***

1.0056

96

0.0056

8 ***

Whole, retail TRD

1.2859

48

0.2514

96 ***

1.2719

49

0.2405

5 ***

Finance and

Insurance

1.2144

73

0.1943

1 ***

1.2140

21

0.1939

38 ***

Educational, Health

1.3461

04

0.2972

15 ***

1.3632

43

0.3098

66 ***

Entertainment

1.1087

91

0.1032

7 ***

1.1332

16

0.1250

6 ***

Other Services

1.1521

59

0.1416

38 ***

1.1543

99

0.1435

8 ***

Public

Administration

1.3595

4

0.3071

46 ***

1.3875

33

0.3275

28 ***

Occupation

Occupation 2

0.9408

49

-0.060

97 ***

0.9560

75

-0.044

92 ***

Occupation 3

0.9434

17

-0.058

25 ***

0.9781

5

-0.022

09 ***

Occupation 4

1.0108

25

0.0107

67 ***

1.0398

94

0.0391

19 ***

Drink fruit

per week

1.1881

75

0.1724

18 ***

per month

1.2892

97

0.2540

97 ***

59

per year

1.4613

84

0.3793

84 ***

never

1.3305

63

0.2856

02 ***

Eat fruit

per week

1.1466

48

0.1368

43 ***

per month

1.2366

65

0.2124

18 ***

per year

1.0595

98

0.0578

89 ***

never

1.0988

07

0.0942

25 ***

Eat carrots

per week

1.0375

11

0.0368

25 ***

per month

1.0641

25

0.0621

53 ***

per year

1.0773

29

0.0744

85 ***

never

1.2173

66

0.1966

89 ***

Eat other vegetable

per week

1.0616

79

0.0598

51 ***

per month

1.1352 0.1268 ***

60

14 21

per year

1.2807

7

0.2474

62 ***

never

1.2321

72

0.2087

79 ***

PC (leisure time)

no

1.3906

22

0.3297

52 ***

Smoke type

occasionally

1.1109

02

0.1051

73 ***

not at all

1.4493

74

0.3711

32 ***

Drink type

occasionally

1.5121

01 0.4135 ***

not at all

1.1149

64

0.1088

22 ***

Stress of work

Not very

1.0030

29

0.0030

24 ***

A bit

1.0634

94

0.0615

6 ***

Quite a bit

1.2042

22

0.1858

34 ***

Extremely

1.4550 0.3750 ***

61

61 48

NO of observations 274351

26773

6

19365

1

18696

7

Note: *** statistically significant at the 1% level

Table 5, Logit model based on obesity as outcome (CCHS, 2007-2014).

Dependent variable=obesity

Base

model Control household income Control industry and

occupation

Control Human Behavior

Variables OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es

Female

0.4547

65

-0.7879

8 ***

0.4611

16

-0.7741

1 ***

0.4308

81

-0.8419

2 ***

0.4161

95 -0.8766 ***

Age

0.0228

28

1.0230

91 ***

1.0231

4

0.0228

76 ***

1.0228

72

0.0226

14 ***

1.0223

11

0.0220

66 ***

Urban 0.8945

52

-0.1114

3 ***

0.9021

39

-0.1029

9 ***

0.8946

76

-0.1112

9 ***

0.8871

56

-0.1197

3 ***

Province

P.E.I

0.7216

82

-0.3261

7 ***

0.7302

81

-0.3143

3 ***

0.7347

74

-0.3081

9 ***

0.7062

04

-0.3478

5 ***

NS

0.8148

83

-0.2047

1 ***

0.8240

55

-0.1935

2 ***

0.8126

88

-0.2074

1 ***

0.7959

57

-0.2282

1 ***

NB

0.8363

93

-0.1786

6 ***

0.8340

84

-0.1814

2 ***

0.8118

06

-0.2084

9 ***

0.7933

51

-0.2314

9 ***

62

Quebec

0.5092

74

-0.6747

7 ***

0.5108

91 -0.6716 ***

0.5043

09

-0.6845

7 ***

0.5023

63

-0.6884

3 ***

ON

0.5988

32

-0.5127

8 ***

0.5886

79

-0.5298

7 ***

0.5857

8

-0.5348

1 ***

0.5700

89

-0.5619

6 ***

Manitoba

0.7506

61 -0.2868 ***

0.7375

08

-0.3044

8 ***

0.7293

59

-0.3155

9 ***

0.7039

97

-0.3509

8 ***

Saskatchewan

0.7956

29

-0.2286

2 ***

0.7742

28

-0.2558

9 ***

0.7602

61

-0.2740

9 ***

0.7474

1

-0.2911

4 ***

Alberta

0.6363

49

-0.4520

1 ***

0.6097

17

-0.4947

6 ***

0.5947

25

-0.5196

6 ***

0.5774

95

-0.5490

5 ***

BC

0.4313

92

-0.8407

4 ***

0.4267

97

-0.8514

5 ***

0.4329

14

-0.8372

2 ***

0.4212

68

-0.8644

9 ***

Yukon

0.5856

12 -0.5351 ***

0.5768

28

-0.5502

1 ***

0.5489

36

-0.5997

7 ***

0.5535

62

-0.5913

8 ***

NT

0.9014

53

-0.1037

5 ***

0.8833

73

-0.1240

1 ***

0.8873

98

-0.1194

6 ***

0.9017

4

-0.1034

3 ***

Nunavut

0.7552

66

-0.2806

9 ***

0.7346

04

-0.3084

2 ***

0.8206

46

-0.1976

6 ***

0.9081

43

-0.0963

5 ***

Education

Grade 9-13

0.9556

75

-0.0453

4 ***

0.9286

83

-0.0739

9 ***

1.0849

37

0.0815

22 ***

1.1002

75

0.0955

6 ***

secondary 0.9996

94

-0.0003

1 0.867

0.9252

92

-0.0776

5 ***

1.1020

19

0.0971

44 ***

1.1102

39

0.1045

76 ***

trades CER 1.0516

4

0.0503

51 ***

0.9530

67

-0.0480

7 ***

1.1377

78

0.1290

77 ***

1.1371

86

0.1285

57 ***

College 1.0737 0.0711 *** 0.9517 -0.0494 *** 1.1470 0.1371 *** 1.1350 0.1266 ***

63

09 19 5 5 47 91 67 91

below bachelor

0.8905

82

-0.1158

8 ***

0.7717

09

-0.2591

5 ***

0.9649

3 -0.0357 ***

0.9550

3

-0.0460

1 ***

bachelor degree

0.7417

29

-0.2987

7 ***

0.6379

62

-0.4494

8 ***

0.7843

06

-0.2429

6 ***

0.7617

97

-0.2720

7 ***

Univ, and above

0.5739

95

-0.5551

3 ***

0.4858

66

-0.7218

2 ***

0.5951

42

-0.5189

6 ***

0.5820

36

-0.5412

2 ***

Income

<5k

1.3253

98

0.2817

13 ***

0.7903

57

-0.2352

7 ***

0.7891

73

-0.2367

7 ***

5k-9.99k

1.2335

45

0.2098

92 ***

0.7742

57

-0.2558

5 ***

0.7902

67

-0.2353

8 ***

10k-14.999k

1.4129

76

0.3456

98 ***

0.7367

26

-0.3055

4 ***

0.7601

32

-0.2742

6 ***

15k-19.999k

1.4367

12

0.3623

57 ***

0.7121

07

-0.3395

3 ***

0.7248

1

-0.3218

5 ***

20k-29.999k

1.3108

86

0.2707

03 ***

0.6689

88

-0.4019

9 ***

0.6564

1

-0.4209

7 ***

30k-39.999k

1.4057

25

0.3405

53 ***

0.7498

19

-0.2879

2 ***

0.7378

86

-0.3039

7 ***

40k-49.999k

1.4771

62

0.3901

23 ***

0.7980

09

-0.2256

4 ***

0.7741

27

-0.2560

2 ***

50k-59.999k

1.4311

72

0.3584

94 ***

0.7887

13

-0.2373

5 ***

0.7734

36

-0.2569

1 ***

60k-79.999k

1.6141

9

0.4788

33 ***

0.8830

56

-0.1243

7 ***

0.8629

98

-0.1473

4 ***

64

80k-99.999k

1.7497

88

0.5594

95 ***

0.9707

2

-0.0297

2 ***

0.9536

22

-0.0474

9 ***

>100k

1.7313

58

0.5489

06 ***

0.9492

9

-0.0520

4 ***

0.9268

87

-0.0759

2 ***

Industry

Mining, Construction

1.1302

1

0.1224

03 ***

1.1731

97

0.1597

33 ***

Manufacturing

1.0345

93

0.0340

08 ***

1.0454

45

0.0444

43 ***

Whole, retail TRD

1.1488

2

0.1387

35 ***

1.1496

98

0.1394

99 ***

Finance and Insurance

1.1106

03

0.1049

03 ***

1.1162

21

0.1099

49 ***

Educational, Health

1.1797

93

0.1653

39 ***

1.1821

19

0.1673

09 ***

Entertainment

0.9656

26

-0.0349

8 ***

0.9995

4

-0.0004

6 0.808

Other Services

1.0533

11

0.0519

38 ***

1.0599

2

0.0581

93 ***

Public Administration

1.3065

37

0.2673

8 ***

1.3145

98

0.2735

31 ***

Occupation

Occupation 2

0.8808

15

-0.1269

1 ***

0.8866

12

-0.1203

5 ***

Occupation 3

0.9641

66

-0.0364

9 ***

0.9910

39 -0.009 ***

65

Occupation 4

0.9473

26

-0.0541

1 ***

0.9802

86

-0.0199

1 ***

Drink fruit juice

per week

1.1138

76

0.1078

46 ***

per month

1.2495

24

0.2227

63 ***

per year

1.2481

41

0.2216

55 ***

never

1.1671

79

0.1545

9 ***

Eat fruit

per week

1.1344

63

0.1261

6 ***

per month

1.1270

6

0.1196

12 ***

per year

0.9749

26

-0.0253

9 ***

never

1.0690

57

0.0667

77 ***

Eat carrots

per week

1.0068

31

0.0068

08 ***

per month

0.9828

29

-0.0173

2 ***

per year

1.1325 0.1244 ***

66

29 53

never

1.1960

9

0.1790

58 ***

Eat other vegetable

per week

1.1178

74

0.1114

29 ***

per month

1.2488

16

0.2221

96 ***

per year

1.0460

91

0.0450

6 ***

never

1.0995

14

0.0948

68 ***

PC (leisure time)

no

1.1663

16

0.1538

5 ***

Smoke type

occasionally

1.2881

0.2531

68 ***

not at all

1.4581

31

0.3771

56 ***

Drink type

occasionally

1.2944

85

0.2581

13 ***

not at all

1.0928

58

0.0887

96 ***

Stress of work

67

Not very

1.0009

09

0.0009

09 0.333

A bit

1.0757

98

0.0730

63 ***

Quite a bit

1.1876

63

0.1719

88 ***

Extremely

1.2948

13

0.2583

67 ***

NO of observations 274351

267736

193651

186967

Note: *** Statistically significant at 1% level

Table 6, Logit model based on overweight as outcome (NPHS, cycle 5).

Dependent variable=Overweight

Base

model

Control household income Control industry and

occupation

Control Human Behavior

Variables OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es

Female

0.40327

1

-0.9081

5 ***

0.40631

3

-0.9006

3 ***

0.40603

8

-0.9013

1 *** 0.30173

-1.1982

2 ***

Age

1.03127

1

0.03079

2 ***

1.03056

6

0.03010

8 ***

1.02696

3

0.02660

6 ***

1.03098

4

0.03051

4 ***

Province

PEI 1.06150 0.05968 *** 1.05013 0.04891 *** 1.05551 0.05402 *** 1.19679 0.17964 ***

68

6 9 3 7 2 6 3

NS

0.77838

3

-0.2505

4 ***

0.79917

4

-0.2241

8 ***

0.71660

5

-0.3332

3 ***

0.52654

5

-0.6414

2 ***

NB

1.01627

2

0.01614

2 0.02

0.97306

9 -0.0273 ***

0.93379

4 -0.0685 ***

0.93697

2 -0.0651 ***

QUE

0.48516

2

-0.7232

7 ***

0.47918

7

-0.7356

6 *** 0.47886

-0.7363

5 *** 0.46631

-0.7629

1 ***

ONT

0.64740

7

-0.4347

8 ***

0.64235

7

-0.4426

1 ***

0.59685

8

-0.5160

8 *** 0.60892

-0.4960

7 ***

MAN 0.70694

-0.3468

1 ***

0.68446

3

-0.3791

2 ***

0.69455

4

-0.3644

9 ***

0.67315

3

-0.3957

8 ***

SASK

0.87135

3

-0.1377

1 ***

0.84856

5

-0.1642

1 ***

0.80983

6

-0.2109

2 ***

0.81050

1 -0.2101 ***

ALTA

0.80954

3

-0.2112

9 ***

0.74794

7

-0.2904

2 ***

0.78715

1

-0.2393

4 ***

0.79034

6

-0.2352

8 ***

BC

0.57263

7 -0.5575 ***

0.56675

4

-0.5678

3 ***

0.56500

9

-0.5709

1 ***

0.55717

9

-0.5848

7 ***

YUKON 1 0

1 0

1 0

NT 7.29204

1.98678

3 ***

7.23709

6 1.97922 ***

6.75662

4

1.91052

3 *** 1 0

NUNAVUT 1 0

1 0

1 0

0.49888

7 0

UNITED STATES

0.64128

8

-0.4442

8 ***

0.64457

3

-0.4391

7 ***

0.61302

6

-0.4893

5 ***

-0.6953

8 ***

Education

Secondary 0.97839 -0.0218 *** 1.00127 0.00127 0.837 1.14858 0.13853 *** 0.35812 -1.0268 ***

69

9 4 2 2 4 1 9

College

0.95457

3

-0.0464

9 ***

0.94831

3

-0.0530

7 ***

1.09434

7

0.09015

8 ***

0.32426

1

-1.1262

1 ***

University

0.81258

8

-0.2075

3 ***

0.83411

8

-0.1813

8 ***

0.95319

9

-0.0479

3 ***

0.26739

4

-1.3190

3 ***

Dipl

0.92350

8

-0.0795

8 *** 0.91502

-0.0888

1 ***

1.01966

6

0.01947

5 0.008

0.29049

3

-1.2361

7 ***

Bachelor

0.69445

1

-0.3646

3 ***

0.69278

8

-0.3670

3 ***

0.78473

4

-0.2424

1 ***

0.24024

3

-1.4261

1 ***

Above master

0.44605

9 -0.8073 ***

0.45020

6

-0.7980

5 ***

0.50971

8 -0.6739 ***

0.12948

4 -2.0442 ***

Income

5k-9.99k

0.82274

2

-0.1951

1 ***

0.35400

4

-1.0384

5 ***

0.24586

6

-1.4029

7 ***

10k-14.999k

2.03870

1

0.71231

3 ***

1.30937

5 0.26955 ***

0.83936

4

-0.1751

1 ***

15k-19.999k

1.69926

6

0.53019

6 ***

0.83661

7

-0.1783

9 ***

0.65033

9

-0.4302

6 ***

20k-29.999k

1.28007

1

0.24691

6 ***

0.77575

2

-0.2539

2 ***

0.46066

6

-0.7750

8 ***

30k-39.999k

1.48827

8 0.39762 ***

0.87917

2

-0.1287

7 ***

0.58509

9

-0.5359

7 ***

40k-49.999k

1.74344

3

0.55586

2 ***

1.10897

9 0.10344 ***

0.63249

9

-0.4580

8 ***

50k-59.999k

1.76162

0.56623

4 ***

1.05719

1

0.05561

6 ***

0.68544

7

-0.3776

8 ***

70

60k-79.999k

1.96747

9

0.67675

3 *** 1.23948

0.21469

2 ***

0.79214

2

-0.2330

1 ***

>=80k

1.55677

8

0.44261

8 ***

0.98344

5

-0.0166

9 0.182

0.63893

6

-0.4479

5 ***

Industry

Mining, Construction

0.85358

7

-0.1583

1 ***

0.95757

5

-0.0433

5 ***

Manufacturing

0.95518

9

-0.0458

5 ***

1.00507

9

0.00506

6 0.413

Whole, retail TRD

0.81530

4

-0.2041

9 ***

0.86827

6

-0.1412

5 ***

Finance and Insurance

0.80923

4

-0.2116

7 ***

0.88890

6

-0.1177

6 ***

Educational, Health

0.89899

7

-0.1064

8 *** 0.96131

-0.0394

6 ***

Entertainment

0.66154

1

-0.4131

8 ***

0.83063

7

-0.1855

6 ***

Other Services

0.88400

2 -0.1233 ***

1.16298

9

0.15099

4 ***

Public Administration

0.94077

9

-0.0610

5 *** 1.00396

0.00395

2 0.57

Occupation

Occupation 2

0.75922

5

-0.2754

6 ***

0.68849

5

-0.3732

5 ***

Occupation 3

0.98069

7

-0.0194

9 ***

0.91207

5

-0.0920

3 ***

71

Occupation 4

0.75390

1

-0.2824

9 ***

0.70124

2 -0.3549 ***

Drink fruit juice

per week

1.20058

0.18280

4 ***

per month

1.20290

6 0.18474 ***

per year

3.79190

2

1.33286

8 ***

Eat fruit

per week

1.04285

2 0.04196 ***

per month

0.94981

5

-0.0514

9 ***

per year

2.70707

7 0.99587 ***

Eat carrots

per week

1.07544

1 0.07273 ***

per month

1.13969

4 0.13076 ***

per year

0.60838

1

-0.4969

5 ***

Eat other vegetable

per weekly

0.75389

2

-0.2825

1 ***

72

per month

0.85035

9 -0.1621 ***

per year

0.93391

2

-0.0683

7 ***

PC (leisure time)

no

1.33023

8

0.28535

8 ***

Stress (work)

Agree

1.38644

1 0.32674 ***

disagree

1.19086

7

0.17468

2 ***

Strong Disagree

1.11156

9

0.10577

3 ***

Job satisfaction

Some

1.08068

9

0.07759

9 ***

Not too

1.25801

7

0.22953

7 ***

Not at all

1.30767

9

0.26825

4 ***

Drink type

weekly

0.63410

3

-0.4555

4 ***

dailly

0.54342

1

-0.6098

7 ***

73

NO of observations 6267

5925

5293

3680

Note: *** Statistically significant at 1% level

Table 7, Logit model based on obesity as dependent variable (NPHS, cycle 5).

Dependent variable=Obesity

Base

model

Control household income Control industry and

occupation

Control Human Behavior

Variables OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es OR Coef.

P-valu

es

Female

0.90086

7 -0.1044 ***

0.88443

3

-0.1228

1 ***

0.88456

1

-0.1226

6 ***

0.74503

9

-0.2943

2 ***

Age

1.00917

7

0.00913

5 ***

1.00983

7

0.00978

9 ***

1.00736

6

0.00733

9 ***

1.00205

5

0.00205

3 ***

Province

PEI

0.72464

9

-0.3220

7 ***

0.73519

8

-0.3076

2 ***

0.73339

5

-0.3100

7 ***

0.90747

7

-0.0970

9 ***

NS

0.85995

8

-0.1508

7 ***

0.84407

3

-0.1695

2 ***

0.86924

3

-0.1401

3 ***

0.67507

5

-0.3929

3 ***

NB

1.32059

7

0.27808

4 ***

1.36042

6

0.30779

8 ***

1.43771

5

0.36305

5 *** 1.82219

0.60003

9 ***

QUE

0.53409

8

-0.6271

8 ***

0.54603

6

-0.6050

7 ***

0.56695

5

-0.5674

8 ***

0.76919

9

-0.2624

1 ***

ONT

0.80802

9

-0.2131

6 ***

0.83912

1 -0.1754 ***

0.87026

8

-0.1389

5 ***

1.05875

2

0.05709

1 ***

74

MAN 1.01037

0.01031

7 0.115

1.00861

5

0.00857

8 0.2

1.06358

2

0.06164

3 ***

1.62037

9 0.48266 ***

SASK

1.23028

8

0.20724

9 ***

1.24572

3

0.21971

6 ***

1.29112

3

0.25551

3 ***

1.98282

6

0.68452

3 ***

ALTA

0.79640

5

-0.2276

5 ***

0.82598

1

-0.1911

8 ***

0.84389

7

-0.1697

2 ***

1.00178

3

0.00178

1 0.845

BC

0.66948

9

-0.4012

4 ***

0.67399

5

-0.3945

3 ***

0.67780

5 -0.3889 ***

1.04904

9

0.04788

4 ***

YUKON

NT

23.8211

8

3.17057

5 *** 26.3747

3.27240

5 ***

25.2870

8

3.23029

4 *** 1 0

NUNAVUT 1 0

1 0

1 0

1 0

UNITED STATES

0.37289

4

-0.9864

6 ***

0.44329

1

-0.8135

3 ***

0.57499

5

-0.5533

9 *** 0.89965

-0.1057

5 ***

Education

Secondary

0.78125

5

-0.2468

5 ***

1.03271

1

0.03218

7 ***

0.98127

9 -0.0189 0.019

0.79179

4

-0.2334

5 ***

College 0.79859

-0.2249

1 ***

1.00470

1 0.00469 0.498

1.01031

7

0.01026

4 0.222

0.89539

6

-0.1104

9 ***

University

0.60895

9 -0.496 ***

0.84498

5

-0.1684

4 ***

0.84378

7

-0.1698

6 ***

0.73062

1

-0.3138

6 ***

Dipl 0.62654

-0.4675

4 ***

0.82702

7

-0.1899

2 ***

0.80417

8

-0.2179

3 ***

0.57267

6

-0.5574

4 ***

Bachelor

0.58909

6

-0.5291

7 ***

0.80984

8

-0.2109

1 *** 0.83589

-0.1792

6 ***

0.69220

5

-0.3678

7 ***

Above master 0.2679 -1.3171 *** 0.35242 -1.0429 *** 0.36436 -1.0096 *** 0.31145 -1.1665 ***

75

4 7 1 2 1 5

Income

5k-9.99k

0.39303

1

-0.9338

7 ***

0.09282

9

-2.3769

9 ***

0.01777

3

-4.0300

5 ***

10k-14.999k

0.87594

2

-0.1324

5 ***

0.60591

4

-0.5010

2 ***

0.28572

2

-1.2527

4 ***

15k-19.999k

0.79936

3

-0.2239

4 ***

0.47094

6

-0.7530

1 ***

0.29573

3 -1.2183 ***

20k-29.999k

0.54861

6

-0.6003

6 ***

0.40766

1

-0.8973

2 *** 0.31449 -1.1568 ***

30k-39.999k

0.64096

5

-0.4447

8 ***

0.49138

2

-0.7105

3 ***

0.36969

6

-0.9950

8 ***

40k-49.999k

0.57488

4

-0.5535

9 ***

0.43313

5

-0.8367

1 ***

0.28782

9

-1.2453

9 ***

50k-59.999k

0.48268

7

-0.7283

9 ***

0.36800

3

-0.9996

7 ***

0.22944

3 -1.4721 ***

60k-79.999k

0.60841

3 -0.4969 ***

0.48015

1

-0.7336

5 ***

0.31570

7

-1.1529

4 ***

>=80k

0.49292

4 -0.7074 ***

0.38918

8

-0.9436

9 ***

0.28162

4

-1.2671

8 ***

Industry

Mining, Construction

1.14060

3

0.13155

8 ***

1.36960

3

0.31452

1 ***

Manufacturing

0.62666

3

-0.4673

5 ***

0.58529

1

-0.5356

5 ***

Whole, retail TRD

0.78536 -0.2416 *** 0.82648 -0.1905 ***

76

1 1 8

Finance and Insurance

0.67754

-0.3892

9 ***

0.63555

4

-0.4532

6 ***

Educational, Health

0.79219

9

-0.2329

4 ***

0.81248

7

-0.2076

6 ***

Entertainment

0.53753

4

-0.6207

6 ***

0.56838

7

-0.5649

5 ***

Other Services

0.68959

8

-0.3716

5 ***

0.67916

8

-0.3868

9 ***

Public Administration

0.92163

2

-0.0816

1 ***

0.91080

3

-0.0934

3 ***

Occupation

Occupation 2

0.80993

7 -0.2108 ***

0.64987

7

-0.4309

7 ***

Occupation 3

1.14624

5

0.13649

1 ***

1.02967

5

0.02924

4 ***

Occupation 4

0.79905

9

-0.2243

2 ***

0.66718

5

-0.4046

9 ***

Drink fruit juice

per week

1.24563

9

0.21964

8 ***

per month

1.04476

9

0.04379

5 ***

per year

1.40600

5

0.34075

2 ***

Eat fruit

77

per week

0.82672

4

-0.1902

8 ***

per month

0.92076

3

-0.0825

5 ***

per year

2.92890

7

1.07462

9 ***

Eat carrots

per week

0.87484

2

-0.1118

4 ***

per month

0.70661

0.07105

3 ***

per year

2.15399

0.11818

3 ***

Eat other vegetable

per week

0.87484

2

-0.1337

1 ***

per month

0.70661

-0.3472

8 ***

per year

2.15399

0.76732

2 ***

PC (leisure time)

no

1.93937

2

0.66236

4 ***

Stress (work)

Agree

1.55058

3

0.43863

1 ***

78

disagree

1.22150

7

0.20008

5 ***

Strong Disagree

1.62541

5

0.48576

3 ***

Job satisfaction

Some

1.10511

8

0.09995

2 ***

Not too

1.13770

3

0.12901

1 ***

Not at all

1.17232

3

0.15898

8 ***

Drink type

weekly

0.6177

-0.4817

5 ***

daily

0.78496

2

-0.2421

2 ***

NO of observations 6267

5925

5293

3680

Note: *** Statistically significant at 1% level

Table 8: Fixed effect logit model based on overweight as outcome (NPHS)

Dependent variable=Overweight

Control household income Control Industry Control Occupation Control Human Behavior

Variables OR Coef. P-valu OR Coef. P-valu OR Coef. P-valu OR Coef. P-valu

79

es es es es

Age

1.1801

98

0.1656

82 ***

1.1911

15

0.1748

9 ***

1.1912

14

0.1749

73 ***

1.1401

63

0.1311

71 ***

Income

5k-9.99k

1.2712

43

0.2399

95 ***

1.4505

93

0.3719

73 ***

1.4485

91

0.3705

91 ***

0.7441

67

-0.2954

9 ***

10k-14.999k

1.7199

3

0.5422

84 ***

1.8782

11

0.6303

2 ***

1.8597

6

0.6204

47 ***

0.4898

07

-0.7137

4 ***

15k-19.999k

1.3899

11

0.3292

4 ***

1.8508

97

0.6156

7 ***

1.8673

84

0.6245

39 ***

0.3658

73

-1.0054

7 ***

20k-29.999k

1.3628

82

0.3096

02 ***

1.7569

16

0.5635

6 ***

1.7843

43

0.5790

5 ***

0.4605

3

-0.7753

8 ***

30k-39.999k

1.3999

07

0.3364

06 ***

1.6293

49

0.4881

8 ***

1.6553

42

0.5040

08 ***

0.3272

57

-1.1170

1 ***

40k-49.999k

1.7332

03

0.5499

71 ***

2.0304

57

0.7082

61 ***

2.0792

56

0.7320

1 ***

0.5152

42

-0.6631

2 ***

50k-59.999k

1.5934

91

0.4659

27 ***

1.9963

88

0.6913

4 ***

2.0624

63

0.7239

01 ***

0.5513

48

-0.5953

9 ***

60k-79.999k

2.0030

64

0.6946

78 ***

2.3088

03

0.8367

29 ***

2.3597

27

0.8585

46 ***

0.6653

59

-0.4074

3 ***

>=80k

2.2971

42

0.8316

66 ***

2.6628

82

0.9794

09 ***

2.7185

56

1.0001

01 ***

0.9130

75

-0.0909

4 ***

Industry

Mining, Construction

1.7090

81

0.5359

56 ***

0.4204

11

-0.8665

2 ***

Manufacturing

1.6697 0.5127 ***

0.8065 -0.2149 ***

80

99 03 62 8

Whole, retail TRD

1.3259

96

0.2821

64 ***

0.4472

68 -0.8046 ***

Finance and

Insurance

1.6134

51

0.4783

76 ***

0.9003

91

-0.1049

3 ***

Educational, Health

1.5527

16

0.4400

06 ***

0.5948

48

-0.5194

5 ***

Entertainment

1.2706

84

0.2395

55 ***

0.1594

85

-1.8358

1 ***

Other Services

1.2157

49

0.1953

61 ***

0.6006

41

-0.5097

6 ***

Public Administration

2.0661

96

0.7257

09 ***

0.5662

51

-0.5687

2 ***

Occupation

Occupation 2

1.0184

47

0.0182

79 ***

1.6324

07

0.4900

56 ***

Occupation 3

0.8699

9

-0.1392

7 ***

1.0453

93

0.0443

93 ***

Occupation 4

0.7693

25

-0.2622

4 ***

0.9962

79

-0.0037

3 0.651

Eat potatoes

per week

0.9009

05

-0.1043

6 ***

per month

0.6711

71

-0.3987

3 ***

per year

0.4937 -0.7057 ***

81

62

Eat green salad

per week

1.0550

93

0.0536

29 ***

per month

0.6346

73

-0.4546

4 ***

per year

4.2674

46

1.4510

16 ***

Drink fruit juice

per week

1.0412

18

0.0403

91 ***

per month

1.1315

65

0.1236

01 ***

per year

1.5118

49

0.4133

33 ***

Eat fruit

per week

1.2471

35

0.2208

49 ***

per month

1.1338

7

0.1256

37 ***

per year

0.3654

76

-1.0065

6 ***

Eat carrots

per week

0.9946

8

-0.0053

3 0.2

per month

1.0140 0.0139 ***

82

22 25

per year

0.3087

26 -1.1753 ***

Eat other vegetable

per week

1.1450

47

0.1354

46 ***

per month

1.4393

56

0.3641

96 ***

per year

0.0144

82

-4.2348

7 ***

Physical activity Freq

occasional

1.4670

23

0.3832

35 ***

Infrequent

1.2514

21

0.2242

8 ***

Stress (work)

Agree

0.8356

72

-0.1795

2 ***

disagree

0.9043

83 -0.1005 ***

Strong Disagree

0.7784

58

-0.2504

4 ***

Job satisfaction

Some

1.0069

41

0.0069

18 0.012

Not too

0.5879 -0.5311 ***

83

44 2

Not at all

0.8683

05

-0.1412

1 ***

Drink type

Monthly

1.0181

95

0.0180

32 ***

Weekly

0.9753

43

-0.0249

7 ***

Daily

0.8692

73 -0.1401 ***

NO of Obs 20981

17379

17372

2920

Note: *** Statistically significant at 1% level

Table 9: Fixed effect logit model based on obesity as outcome (NPHS)

Dependent variable=Obesity

Control household income Control Industry Control Occupation Control Human Behavior

Variables OR Coef.

P-value

s OR Coef.

P-value

s OR Coef.

P-value

s OR Coef.

P-value

s

Age

1.18609

7

0.17066

8 ***

1.20003

8

0.18235

3 ***

1.19619

8

0.17914

8 ***

1.23191

5 0.20857 ***

Income

5k-9.99k

0.44574

4

-0.8080

1 ***

0.67007

2

-0.4003

7 ***

0.77489

6

-0.2550

3 ***

0.62534

1

-0.4694

6 ***

10k-14.999k 0.46417 -0.7675 ***

0.60467

8

-0.5030

6 ***

0.61870

8

-0.4801

2 ***

1.43754

8

0.36293

9 ***

84

15k-19.999k

0.43742

7

-0.8268

5 ***

0.52023

4

-0.6534

8 ***

0.55737

8

-0.5845

1 ***

1.25502

9

0.22715

9 ***

20k-29.999k

0.53014

1

-0.6346

1 ***

0.71086

4

-0.3412

7 ***

0.77095

8

-0.2601

2 *** 0.99966

-0.0003

4 0.991

30k-39.999k

0.63227

8

-0.4584

3 ***

0.83684

4

-0.1781

2 ***

0.90492

3

-0.0999

1 ***

1.90408

3

0.64400

1 ***

40k-49.999k

0.64911

3

-0.4321

5 ***

0.74860

2

-0.2895

5 ***

0.82937

8

-0.1870

8 ***

1.74413

6 0.55626 ***

50k-59.999k

0.73302

4

-0.3105

8 ***

0.93046

6

-0.0720

7 *** 1.02478

0.02447

8 0.023

1.61072

5

0.47668

4 ***

60k-79.999k

0.62526

4

-0.4695

8 ***

0.76427

1

-0.2688

3 ***

0.84432

9

-0.1692

1 ***

0.92955

9

-0.0730

4 ***

>=80k

0.80442

6

-0.2176

3 ***

0.99838

8

-0.0016

1 0.881

1.10943

5

0.10385

1 ***

1.55566

7

0.44190

4 ***

Industry

Mining,

Construction

1.10944

6

0.10386

1 ***

1.10099

8

0.09621

7 ***

Manufacturing

1.44515

6

0.36821

7 ***

2.05333

4

0.71946

5 ***

Whole, retail

TRD

1.23997

2

0.21508

9 ***

1.24665

9

0.22046

7 ***

Finance and

Insurance

2.69818

0.99257

8 ***

3.03293

4 1.10953 ***

Educational,

Health

1.93561

9

0.66042

7 ***

1.08473

0.08133

1 ***

Entertainment

1.18018 0.16567 ***

1.21945 0.19840 ***

85

9 5 8 7

Other Services

2.31283

4

0.83847

4 ***

1.42422

9 0.35363 ***

Public

Administration

1.62678

0.48660

3 ***

0.65459

7

-0.4237

4 ***

Occupation

Occupation 2

0.86729

6

-0.1423

8 *** 0.89436

-0.1116

5 ***

Occupation 3

0.90550

7

-0.0992

6 ***

1.05131

5

0.05004

2 ***

Occupation 4

0.59538

2

-0.5185

5 ***

0.29214

2

-1.2305

2 ***

Eat potatoes

per week

0.71414

1

-0.3366

7 ***

per month

0.35253

1

-1.0426

2 ***

per year

0.68517

1

-0.3780

9 ***

Eat green salad

per week

1.70131

7

5.31E-0

1 ***

per month

1.40992

6

0.34353

7 ***

per year

0.16141

9

-1.8237

5 ***

86

Drink fruit juice

per week

1.06309

6

0.06118

5 ***

per month

0.76958

7 -0.2619 ***

per year

0.64002

9

-0.4462

4 ***

Eat fruit

per week

1.01824

2

0.01807

8 ***

per month

1.17070

8

0.15760

9 ***

per year

1.92E+1

0

23.6758

1 0.975

Eat carrots

per week

1.00810

2 0.00807 0.118

per month

1.03952

3

0.03876

2 ***

per year

0.26378

3

-1.3326

3 ***

Eat other

vegetable

per week

1.33485

5

0.28882

2 ***

per month

1.04167 0.04082 ***

87

4 9

per year

8.16E+1

0

25.1245

6 0.985

Physical activity Freq

occasional

1.23997

2

0.21508

9 ***

Infrequent

1.30764

2

0.26822

6 ***

Stress (work)

Agree

1.06467

1

6.27E-0

2 ***

disagree

1.64164

4

0.49569

8 ***

Strong Disagree

2.08873

5

0.73655

9 ***

Job satisfaction

Some

0.93420

3

-0.0680

6 ***

Not too

0.64278

2

-0.4419

5 ***

Not at all

0.75156

8

-0.2855

9 ***

Drink type

Monthly

0.81252

6

-0.2076

1 ***

Weekly

1.05451 0.05308 ***

88

9 4

Daily

2.04984

3

0.71776

3 ***

NO of Obs 14080

11501

11502

2226

Note: *** Statistically significant at 1% level

89

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Appendix

Table 12: 4 groups of work occupation category

Occupation 1 Occupation 2 Occupation 3 Occupation 4

Manager Business Middle management

occupation in

retail and whole

sale trade and

customer service

Middle management

occupations in trades,

transportation,

production and

utilities

Finance

Administration

occupation

Natural and

applied sciences

and related

occupation

Trades, transport and

equipment operators

and related

occupations

Sales and service

occupation

Health

Education

Law and social Natural resources,

agriculture and

related production

occupation

Government service

Art

Culture

Recreation

Sport Occupation in

manufacturing and

utilities

Table 13: 9 groups of industry category

Control group Mining Manufacturing Retail

Agriculture Mining Manufacturing Wholesale trade

Forestry Quarrying

Fishing Oil and gas

extraction

Transportation

Hunting Warehousing

Utilities

Construction

Finance Education Entertainment

Information Education Art Other service

Other service (except

public

administration)

Finance Health care Entertainment

Insurance Social assistance Recreation

Real Estate Accommodation

Scientific Food service

Technical services

Management Public Administration

94

Administrative Public

Administration

Remediation service

Curriculum Vitae

Candidate’s Full Name: Saibiao Peng

University Attended: Bachelor of Economics

Hunan Normal University

Publications: N.A.

Conference Presentations: N.A.