New evidence reveals that moving suburbs can shift your weight toward the local average, meaning your food access, neighbourhood design, and postcode may influence your body just as much as your personal choices.
Study: Product of our environment? Place effects on Body Mass Index. Image credit: YARUNIV Studio/Shutterstock.com
In a recent study in Social Science & Medicine, researchers examined the influence of where people live, rather than their individual characteristics, on their body weight using a dynamic event study in Australia.
They found that location explained approximately 15.5 % of variations in weight, indicating a robust ‘place effect.’ In some cases, location influenced food-related spending behaviors by up to 50 %.
Why place may matter for body weight
Excess weight is a major global health challenge, linked to higher risks of cardiovascular disease, diabetes, and several cancers. Rates of obesity have risen sharply in the United States, Europe, and Australia over recent decades. In Australia, for example, obesity rates increased from 24.6 % in 2007-08 to 31.7 % in 2022.
However, the burden of excess weight is not evenly spread: some regions have obesity rates below 15 %, while others exceed 40 %. These striking differences raise an important question: are regional weight disparities primarily due to personal factors such as income, diet, or physical activity, or do characteristics of the local environment play a larger role?
Understanding the relative influence of person-level and place-based factors has major policy implications. If individual traits dominate, interventions may focus on behaviors; if place matters more, policymakers may need to address food environments, walkability, or local disadvantage.
Long running dataset reveals location effects on BMI
The authors followed people who moved between areas and tested whether their weight shifts toward the average weight of their new location. They also compared groups of regions defined by body mass index (BMI) levels, disadvantage, food access, and density. Finally, they examined whether place shapes diet and physical activity, given past literature showing strong links between food environments, exercise opportunities, and weight outcomes.
The study uses survey data from a nationally representative longitudinal dataset running annually since 2001. Because BMI was first collected in 2006, and coronavirus disease 2019 (COVID-19) disrupted weight and mobility patterns starting in 2020, the authors restricted the main sample to 2006-2019, yielding 99,801 observations from 15,620 adults.
Individuals with extremely high BMI values, those who were pregnant, or those with missing data across waves were excluded. Movers were defined as people who changed their two-digit postcode once during the study period; those who never moved served as non-movers.
The core method is a dynamic event study, where the ‘event’ is the movement to a new location. The model tracked weight several years before and after relocation to estimate how much of any weight change can be attributed to the characteristics of the destination area.
This design allowed the authors to adjust the analysis for pre-existing weight trends. They also conducted decomposition analyses comparing weight differences across groups of areas. To explore behavioral pathways, they replaced BMI with measures of grocery spending, spending on ready-made food, and frequency of physical activity.
Moving alters weight toward local BMI average
The event study showed a 15.5 % convergence toward the average BMI of a mover’s new location, suggesting that place accounts for about one-sixth of geographic weight differences. Women showed significantly stronger place effects than men, indicating that local environments may have a greater influence on females.
Removing BMI outliers slightly weakened the effect, while excluding very young and very old adults strengthened it. Limiting the time window around relocation showed that place effects are strongest within the first few years after a move rather than immediately upon relocation.
Diagnostic checks confirmed that key assumptions, including linearity, the absence of pre-trends, and stable effects over time, were met. Robustness tests showed consistent results when BMI was logged, used as a category, or calculated with different area definitions, including large statistical regions. Effects also persisted when adjusting covariates or examining samples with long-distance movers or work-related relocations. Only moves shorter than 100 km produced no significant effects.
Decomposition analyses revealed varying contributions of place across area comparisons. Place explained more than 20 % of the differences between high-BMI areas and nearly 30 % of the differences between areas with varying access to fruit and vegetable stores. Pathway analyses showed strong place effects on grocery spending and eating out, but no meaningful influence on physical activity.
Location matters, but personal factors matter more
The study demonstrates a clear place effect on weight, with location accounting for roughly 15 % of BMI variation across Australia. Larger contributions emerged when comparing regions with differing food access or density, reinforcing earlier research linking local environments to weight outcomes. These findings suggest that improving access to healthy foods and enhancing public spaces could complement individual-level strategies for reducing excess weight.
A major strength is the event-study design, which captures weight trajectories before and after moves and thoroughly tests key assumptions. Extensive robustness checks across multiple subsamples and model specifications add confidence to the results.
However, Australia’s unique population distribution, characterized by sparse inland areas and high coastal concentration, may limit generalizability, and some remote regions lack sufficient data.
Overall, while place factors clearly matter, individual characteristics explain most weight variation. Policy efforts may benefit from combining targeted environmental improvements with support for individual behavior change.
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