Skip to main content

Table 2 Studies reporting associations between fuel taxation or price and obesity, PA, walking or cycling

From: Obesity-related health impacts of fuel excise taxation- an evidence review and cost-effectiveness study

Study Location/Population Study aim Method Variable of interest (Outcome) Relevant findings QA
Courtemanche 2011 [57] USA
Adults (n = 1,807,266)
To estimate the effect of fuel price on weight and obesity, by looking at its effect on PA, frequency of eating at restaurants and food choices at home. Cross-sectional Fuel price
(BMI (S))
USD2004 $1 increase in fuel price reduces BMI by approx. 0.35 units (s.e. 0.050, p < 0.01).
A permanent USD1.00 increase in fuel prices would, after 7 years, reduce U.S. overweight prevalence by approx. 7% and obesity by approx. 10%.
Rabin et al. 2007 [58] 24 European countries To describe obesity patterns and examine macro-environmental factors associated with obesity prevalence. Ecological, cross-sectional Fuel price
(Prevalence of obesity using BMI (S))
The price of fuel was associated with obesity prevalence for females (b = −0.096, p-value 0.041) and overall (b = −0.095, p-value 0.0542), but not for men. 5
Sun et al. 2015 [59] 47 low-middle income countries To identify CVD risk factors in low-middle income countries. Ecological, cross-sectional Fuel price
(Prevalence of obesity using BMI (S))
The price of fuel was not statistically significantly associated with obesity in either men or women. 5
Physical activity
Hou et al. 2011 [60] Birmingham, Chicago, Minneapolis and Oakland, USA. Young adults 18–30 years at baseline (n = 5115) To investigate longitudinal associations between fuel price and physical activity. Longitudinal cohort Fuel price (Leisure PA - energy units (S)) A hypothetical USD0.25 increase in fuel price significantly associated with increase in energy expenditure (9.9 energy units (EU), 95% UI: 0.8–19.1, p-value 0.03). Equivalent to an increase in walking per week of 17 min. After controlling for all covariates, an USD0.25 increase in fuel price was associated with 1.3 EU increase in walking score (p = 0.2), equivalent to an additional 3 min of walking per week. Results suggest relatively weak association between fuel price and walking. No significant association for cycling. 7
Sen 2012 [61] American adults 15 years plus
(n = 81,957)
Uses data from the time of fuel price rises due to Hurricane Katrina to explore effect on PA. Cross-sectional Fuel price (PA, defined five ways: (1) walking, running, bicycling or rollerblading as part of LTPA, (2) walking or cycling to work or errands, (3) playing with kids, (4) housework of MET > = 3, (5) total time spent on all PA MET > =3. (S)) Higher fuel prices show some association with increases in LTPA (sig. at p < 0.05). Walking and bicycling to work or errands statistically weak and sensitive to model specification. Only one approach resulted in p < 0.05 with OLS estimate 0.74. Changes in participation and time spent in walking or cycling are not large. No association was found between higher fuel prices and PT use, although may be due to lack of accessibility to PT. 8
Sen et al. 2014 [62] American high school students grades 9–12
(n = 58,749)
To examine the relationship between fuel price and driving behaviours in teens. Cross-sectional Fuel price (moderate PA, defined as: (1) whether participates in PA “that did not lead to sweating or breathing hard”, and (2) whether participates more than five times per week or not (S)). Higher fuel prices positively associated with higher levels of moderate PA. Higher fuel prices associated with moderate PA at least 1 day of the week for females (ME = 3.25%, t-stat = |2.90|), males (ME = 2.32%, t-stat = |2.36|), other races (ME = 3.01%, t-test = |2.16|), and teens ages 16 years and younger (ME = 3.98%, t-stat =14.70|). Higher fuel prices were associated with frequent moderate PA for females (ME = 1.92%, t-stat = |2.19|), males (ME = 3.63%, t-stat = |4.16|), non-Hispanic whites (ME = 3.88%, t- stat = 12.511), other races (ME = 3.85%, t-stat = |2.27|), and teens ages 16 years and younger (ME = 3.54%, t-stat = |4.54|). 7
Buehler & Pucher 2012 [63] USA, population of 90 cities To examine the association between levels of cycling and cycle infrastructure. Cross-sectional Fuel price (share of workers commuting by cycling (S)) State fuel prices had a significant positive correlation with cycling levels (correlation coefficient 0.5, sig. at 95%), consistent with the theory that higher fuel prices may lead to more cycling to work. 5
Dill & Carr 2003 [68] USA, population of 35 large cities To explore associations between cycling infrastructure and cycling. Cross-sectional Fuel price (share of workers commuting by bicycle (S)) Although results on fuel price were not explicitly reported, authors state that fuel price was not statistically significant. 4
Pucher & Buehler 2006 [67] USA/Canada
Population of 18 cities
To explore higher cycling rates in Canada than US. Cross-sectional Fuel price (share of workers commuting by cycling (S)) Higher fuel prices are associated with higher rates of cycling to work (coefficient 3.040 (s.e. 1.159, significant at 95% level, adjusted R2 0.596). 6
Rashad 2009 [64] USA, metropolitan area residents
(BRFS n = 146,730
NPTS n = 73,903)
To determine the relationship between cycling and fuel price. Cross-sectional Fuel price (cycling, defined as (1) cycled for pleasure in past month or (2) cycled in a trip yesterday (S)) Increasing fuel price by $1 increased the probability of cycling by between 1.6% (t-stat 3.30, p < 0.01) and 4.7% (t-stat 2.17, p < 0.05) for men. Results for women ranged between 1% (t-stat 5.11, p < 0.01) and 3.5% (t-stat 3.05, p < 0.01). 7
Smith & Kauermann 2011 [65] Residents of Melbourne, Australia To examine the determinants of cycling, including the cross-price elasticity of cycling. Cross-sectional Fuel price (cycling volumes (O)) Substitution into cycling as a mode of transport observed in response to increase in fuel prices, particularly during peak commuting periods and by commuters originating in wealthy and inner city neighbourhoods. Cross-price elasticities vary depending on loop data and method used and time of day, from approximately 0.18 to 0.48 during peak commuting periods, significant at either 5% or after Bonferroni adjustment. 7
Ryley 2008 [66] West Edinburgh, adults living in West Edinburgh who drive (n = 627) To estimate propensity for motorists to walk for short trips, based on changes to fuel price, journey time, parking costs. Cross-sectional, discrete choice modelling, using stated preference data from survey Fuel price (propensity to walk (SP)) Fuel coefficient − 0.4159, significant at 5% (t-value −6.3). Fuel price had lower relative influence than parking costs, or journey time. 4
  1. Table notes: BMI body mass index, DALYs disability adjusted life years, EC elemental carbon, EU energy units, LTPA leisure time physical activity, ME marginal effect, shows the percentage point change in the probability of the outcome being 1, MET metabolic equivalent task, (O) objectively measured, OLS ordinary least squares, PA physical activity, PT public transport, QA quality assessment, (S) self-report, s.e. standard error, (SP) stated preference, UI uncertainty interval, USD United States dollars, VKT vehicle kilometres travelled, VMT vehicle miles travelled