CRDCN Webinar SeriesDo School Junk Food Bans Improve Young Canadians’

Health? Evidence from Canadawith Phil Leonard, University of New Brunswick

October 12, 2017

1

New Brunswick RDC – made available CCHS data

Maritime SPOR Support Unit (MSSU) – funding support

Large increases in overweight and obesity since 1980s◦ Trend is well-established in US Ogden et. al. 2002, 2012◦ Similar trend in Canada Shields 2006, Roberts et. al. 2012

Associated with many negative health outcomes including diabetes, heart disease

21.00

21.50

22.00

22.50

23.00

23.50

24.00

2000 2003 2005 2007 2008 2009 2010 2011 2012 2013

Females

Males

17.00

18.00

19.00

20.00

21.00

22.00

23.00

24.00

25.00

26.00

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Females

Males

19.00

19.20

19.40

19.60

19.80

20.00

20.20

20.40

20.60

20.80

21.00

21.20

2000 2003 2005 2007 2008 2009 2010 2011 2012 2013

Females

Males

21.00

21.50

22.00

22.50

23.00

23.50

2000 2003 2005 2007 2008 2009 2010 2011 2012 2013

Females

Males

21.50

22.00

22.50

23.00

23.50

24.00

24.50

25.00

25.50

2000 2003 2005 2007 2008 2009 2010 2011 2012 2013

Females

Males

Varying findings on what is to blame◦ Sedentary work / computers / video games◦ Prevalence of high calorie (fat, salt) foods – Rashad

et. al. (2006) Decline of cigarette smoking - Baum & Chou (2011)

Policies that have been enacted / considered◦ Policies at/around schools School healthy eating policies (what is sold at school) Unhealthy food bans

Laws banning fast food restaurants locating near schools

Increased time devoted to physical education

◦ General policies Sugar / soda taxes Labelling policies Restrictions on advertising to children

Foods divided into three categories◦ Maximum, moderate and minimum nutrition value◦ Minimum nutrition foods: “provide few nutrients and are generally high in fat,

sugar, and/or salt” (NB Policy 711) Typically include: chocolate bars, candy, chips, soft

drinks, deep fried foods

Typical provincial ban bans foods from minimum nutrition list◦ May provide guidelines on percentage offerings

from maximum and moderate categories

Province Ban Date New Brunswick October 2005PEI* 2005 (English Boards), 2006 (French)Nova Scotia* January 2007 (phased in from 2004)Quebec January 2008Ontario September 2011British Columbia Jan 2008 (Elem.); Sept 2008 (HS)

• No ban – Newfoundland, Manitoba, Saskatchewan, Alberta*, N.W.T., Yukon, Nunavut

• Board-wide ban in Edmonton (2011) (others since my data)

Canada Community Health Survey (CCHS)◦ Years – 2000, 2003, 2005, 2007-2013◦ Keep all youth aged 12 to 26 – sample >150,000◦ Stratified sampling by province, health region Use sampling weights for descriptive analysis, but not

regressions (see Solon, Haider & Wooldridge, 2013)

Intervention “large” enough to expect to see results◦ All elementary and high-school students within a

province “treated”◦ Treatment is for full school day, every school day◦ Students treated for as many as 8 years

Variation in treatment intensity helps with identification (years of ban)◦ Variation within and across provinces and years

Dataset with large number of observations◦ Multiple years of pre- and post- intervention data

Height and weight data are self-assessed◦ Measurement error Likely biased downward

Cohort study◦ Ideal would be panel – re-observe the same

students before and after

But…Dif-in-dif methodology should help with both issues◦ Comparison of age-sex specific cohorts over time

Dif-in-dif exploits difference in timing of junk food bans◦ Compare BMI among age-sex cells in provinces before

and after junk food ban

BMIipy = β1 + β2 Ban_yearsi + ∑𝑎𝑎=14 β2+𝑎𝑎𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎 + β7Female + β8-19Prov/Terr dummies + β20-28Year dummies

Also allow for non-linear policy effect by grouping years of ban: one or less years, 1.5-4.5 years, and 5+ years

Separate males and females for some regressions (and drop female dummy)

Betrand, Duflo & Mullainathan 2004 – How much should we trust dif-in-dif estimates?◦ Document over-rejection of null when using dif-in-dif

methodology: serial correlation within clusters (states/ provinces) causes standard errors to be biased towards 0

Cluster-robust standard errors get part-way there◦ But assumption is that number of clusters is large◦ I have 13 clusters (10 provinces, 3 territories)

Solution – Wild bootstrap procedure suggested by Cameron, Gelbach & Miller (2008)

Age / CCHS Year 2000 2003 2005 2007 2008 2009 2010 2011 2012 2013

26 0 0 0 0 0 0 0 0 0 025 0 0 0 0 0 0 0 0 0 124 0 0 0 0 0 0 0 0 1 223 0 0 0 0 0 0 0 1 2 322 0 0 0 0 0 0 1 2 3 421 0 0 0 0 0 1 2 3 4 520 0 0 0 0 1 2 3 4 5 619 0 0 0 1 2 3 4 5 6 718 0 0 0 2 3 4 5 6 7 817 0 0 0 2 3 4 5 6 7 816 0 0 0 2 3 4 5 6 7 815 0 0 0 2 3 4 5 6 7 814 0 0 0 2 3 4 5 6 7 813 0 0 0 2 3 4 5 6 7 812 0 0 0 2 3 4 5 6 7 7

N Treated N Mean Years of Ban

Mean YOB if YOB>0 Max

NF 4880 0 0 0 0PE 2700 980 2.21 4.29 8NS 5730 1960 1.37 3.11 6.5NB 5820 2150 2.04 4.19 8QU 31090 8440 0.98 2.60 5.5ON 52870 4120 0.13 1.45 2MA 9680 0 0 0 0SA 9920 0 0 0 0AL 18100 0 0 0 0BC 18940 4420 0.86 2.89 5.5YU 1360 0 0 0 0NW 1950 0 0 0 0Nu 1660 0 0 0 0

Full Sample Females MalesBan Years Coef. -0.0462** -0.0597** -0.0332Clustered St. Err. (0.0183) (0.0263) (0.0208)Clustered P-value .027 .042 .136Bootstrapped P-value .078 .088 .208N 153229 76036 77193

Full Sample Females Males<=1 Year of Ban Dummy -0.0537 -0.0991 -0.0110Clustered St. Err. (0.0618) (0.113) (0.0475)Bootstrapped P-value .474 .504 .776

1.5-4.5 Year Dummy -0.0979 -0.191 -0.00169Clustered St. Err. (0.0791) (0.114) (0.0996) Bootstrapped P-value .282 .252 .936

5+ Ban Years Dummy -0.346*** -0.391** -0.308**Clustered St. Err. (0.107) (0.155) (0.104)Bootstrapped P-value .040 .056 .090

N 153229 76036 77193

Negative coefficient on linear “ban years” variable weakly significant◦ Not significant for boys

Focussing on those who have had 5 or more years of exposure ◦ Strongly significant overall and for females; weakly

significant for males◦ Decline of 0.3 BMI represents about 2 pounds for

an individual 5’6

Age 12-15 Age 16-20 Age 21-26Ban Years Coef. -0.0609*** -0.0121 -0.0313Clustered St. Err. (0.0175) (0.0166) (0.0500) Bootstrapped P-value .006 .494 .686N 41285 58191 53753

Age 12-15 Age 16-20 Age 21-26<=1 Year of Ban Dummy -0.118 -0.0119 0.00747Clustered St. Err. (0.0960) (0.0547) (0.157)Bootstrapped P-value .370 .882 .906

1.5-4.5 Year Dummy -0.217*** 0.0174 -0.0706Clustered St. Err. (0.0635) (0.0787) (0.109)Bootstrapped P-value .020 .904 .616

5+ Ban Years Dummy -0.342*** -0.137 -0.808Clustered St. Err. (0.120) (0.0883) (0.475)Bootstrapped P-value .012 .154 .292

N 41285 58191 53753

Results driven by youngest individuals (in my sample)◦ Still in school at time of measurement no relapse once in post-secondary education◦ Mostly in elementary school Less easy to leave school for fast food, snacks

Results strongly significant when focussing on those having 1.5 years or more of policy exposure

Falsification tests◦ Check that policy has no effect on height – it

doesn’t◦ “Years to ban” policy variable is insignificant

Results robust to inclusion of health region dummy variables and visible minority controls

Evidence is supportive that junk food bans having a positive effect on student BMI◦ Greater impact on females ◦ Greater impact on younger students

Presents average result for all provincial programs in Canada◦ Average for students in program for 5 or more

years is decline of 0.3 BMI (roughly 2 pounds)

Follow-up with longitudinal study using NLSCY◦ Smaller sample sizes

Examine whether healthier eating can have positive effect on educational outcomes◦ Students may be more alert, less distractible

Longer term◦ Examine effect of fast food / convenience store

proximity

Leonard, Philip, S.J. 2017. "Do School Junk Food Bans Improve Student Health? Evidence from Canada". Canadian Public Policy. 43 (2), 105-119. DOI: 10.3138/cpp.2016-090 http://www.utpjournals.press/doi/abs/10.3138/cpp.2016-090◦ (Open access downloads)