Abstract
While the causes of obesity have been widely discussed from various perspectives, studies that examine how the physical form of a neighborhood could causally affect obesity remain limited. This study combined individual-level longitudinal data from the Indonesian Family Life Survey and subdistrict-level land cover data to investigate whether a neighborhood’s physical form affects individuals’ obesity status. We controlled for individual and location fixed-effect to account for individuals’ sorting preferences and unobserved heterogeneity at the subdistrict level. Our results suggest that a sprawling neighborhood corresponds to a lower body mass index, particularly among males. We also show that consumption behavior can explain this mechanism.
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Abbreviations
- BMI:
-
Body mass index
- ESA CCI:
-
European space agency climate change initiative
- IFLS:
-
Indonesian family life survey
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Appendices
Appendix 1: Variable definitions
Body mass index: Individual weight divided by height squared (kg/m2). Source: Indonesian Family Life Survey (2000, 2007, 2014).
Obesity status: Equal to 1 if the respondent is obese, and 0 otherwise. An individual is obese if the BMI is calculated at more than 27. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Subdistrict sprawl index: Share of the undeveloped area around the urban settlement in a subdistrict area (%). This value is 0 for the least sprawling area and 100 for the most compact area. Processed from digital maps of land use in 2000, 2007, 2014 from the European Space Agency Climate Change Initiative (ESA-CCI). The digital map of administrative regions of Indonesia is the 2017 version. Source: (ESA-CCI, 2019; Statistics of Indonesia, 2017).
District sprawl index: Share of undeveloped area around the urban settlement in a district area (%). This value is 0 for the least sprawling area and 100 for the most compact area. Processed from digital maps of land use in 2000, 2007, 2014 from the ESA CCI and digital maps of administrative regions of Indonesia in 2017. Source: ESA CCI (2019); Statistics Indonesia (2017).
Physical activities dummy: Equal to 1 if the respondent performs respective physical activities (walking, or moderate/heavy activities) in the past week, or 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Consumption dummy: Equal to 1 if the respondent consumes respective items (vegetables, fast foods, soft drinks, and sweetened foods) in the past week, or 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Feeling of safety: Equal to 1 if the respondent feels safe (in general or during walking outside), or 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Number of household members: Number of individuals residing in the respondent’s residence, including the respondent. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Age: Respondent’s age at the time of the census (year). Source: Indonesian Family Life Survey (2000, 2007, 2014).
Years of schooling: Duration of education that the respondent has received (in years). Source: Indonesian Family Life Survey (2000, 2007, 2014).
Marital status: Equal to 1 if the respondent is married, and 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Urban–rural dummy: Equal to 1 if the respondent is living in an urban area, and 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Gender dummy: Equal to 1 if the respondent is male, and 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Working status dummy: Equal to 1 if the respondent is working, and 0 otherwise. Source: Indonesian Family Life Survey (2000, 2007, 2014).
Appendix 2: Descriptive statistics
All | Relative to sprawl index | |||||
|---|---|---|---|---|---|---|
≤ Median | > Median | |||||
Mean | S.D | Mean | S.D | Mean | S.D | |
Dependent variables | ||||||
Body mass index (BMI) | 23.08 | 4.40 | 23.48 | 4.53 | 22.68 | 4.23 |
Obesity dummy (1 yes, 0 no) | 0.17 | 0.38 | 0.20 | 0.40 | 0.15 | 0.35 |
Walking dummy (1 yes, 0 no) | 0.77 | 0.42 | 0.76 | 0.43 | 0.79 | 0.41 |
Moderate/vigorous activities (1 yes, 0 no) | 0.50 | 0.50 | 0.47 | 0.50 | 0.54 | 0.50 |
Vegetable consumption (1 yes, 0 no) | 0.92 | 0.27 | 0.91 | 0.28 | 0.93 | 0.26 |
Fast-food consumption (1 yes, 0 no) | 0.10 | 0.29 | 0.13 | 0.33 | 0.06 | 0.24 |
Soft drink consumption (1 yes, 0 no) | 0.16 | 0.37 | 0.18 | 0.38 | 0.14 | 0.34 |
Sweetened food consumption (1 yes, 0 no) | 0.48 | 0.50 | 0.51 | 0.50 | 0.45 | 0.50 |
Feeling of being safe (1 yes, 0 no) | 0.97 | 0.18 | 0.95 | 0.21 | 0.98 | 0.15 |
Walk alone (1 yes, 0 no) | 0.90 | 0.31 | 0.88 | 0.33 | 0.91 | 0.28 |
Independent variables | ||||||
Sprawl index | 62.70 | 33.33 | 34.38 | 23.64 | 91.02 | 7.71 |
Age | 43.40 | 14.80 | 42.75 | 14.45 | 44.06 | 15.11 |
Years of schooling | 7.61 | 4.71 | 8.45 | 4.70 | 6.77 | 4.56 |
Number of household members | 4.42 | 1.97 | 4.57 | 2.10 | 4.27 | 1.83 |
Marital status | 0.77 | 0.42 | 0.77 | 0.42 | 0.78 | 0.42 |
Sex (1 male, 0 female) | 0.46 | 0.50 | 0.46 | 0.50 | 0.46 | 0.50 |
Household income (in ln) | 10.88 | 5.63 | 10.91 | 5.90 | 10.85 | 5.35 |
Working status | 0.72 | 0.45 | 0.70 | 0.46 | 0.75 | 0.43 |
Observations | 37,882 | 18,941 | 18,941 | |||
Appendix 3: Detailed steps for urban sprawl index calculation and land classification
The sprawl index we used in this study was built using the digital land cover map provided by the ESA CCI. The ESA CCI site provides worldwide land-use maps with a spatial resolution of 300 × 300 m. To obtain national-level land cover data, the ESA CCI land cover raster map was clipped with a vector map of Indonesian administrative areas. We reclassified the land cover as urban settlement and undeveloped areas.
Steps to calculate the sprawl index are as follows:
-
1.
Reclassify the land cover categories into two classes: developed and undeveloped land.
-
2.
Calculate the percentage of cell categorized as developed land to the total cell in selected radius (1.5, 1.2, and 3.0-km).
-
3.
To obtain a radius of 1.5 km around the urban settlements, we used the menu to select the attributes "urban settlement" and "focal statistics" in the GIS software.
-
4.
Then, we calculated the percentages of developed and undeveloped areas. This percentage is the percentage around urban settlement cells.
-
5.
To obtain the sprawl index at the subdistrict level, the percentages of the undeveloped areas around all urban settlement cells in the same subdistrict are averaged. This process uses the zonal statistics feature.
Reclassification process for raw land use map data, West Java province.
Land classification on the digital ESA CCI map
Appendix 4: Association between urban sprawl, access to road infrastructure, electricity, minimarket, and permanent market
Appendix 5: Robustness and falsification test—likelihood of obesity
Dependent variable: obesity status | Long-panel | Dropping sample | Functional form | Sprawl index measurement | |||
|---|---|---|---|---|---|---|---|
2000–2014 (1) | No marriage status change (2) | Age outliers (3) | Age (20–60 years old) (4) | Quadratic (5) | Subdistrict | ||
1200 m (6) | 3000 m (7) | ||||||
Sprawl index | − 0.0012* | − 0.0010** | − 0.0010** | − 0.0012** | − 0.0033*** | − 0.0008* | − 0.0007* |
(0.0006) | (0.0005) | (0.0005) | (0.0005) | (0.0011) | (0.0005) | (0.0004) | |
(Sprawl index)2 | 0.0000** | ||||||
(0.0000) | |||||||
R-sq | 0.4604 | 0.5590 | 0.5498 | 0.5421 | 0.5527 | 0.5525 | 0.5525 |
Mean of dependent variable | 0.178 | 0.177 | 0.177 | 0.185 | 0.173 | 0.173 | 0.173 |
Observations | 17,684 | 31,567 | 36,654 | 30,227 | 37,882 | 37,882 | 37,882 |
Samples were limited to individuals above 18 years of age. Standard errors clustered at the subdistrict-year level are reported in parentheses. Control variables include age, age squared, years of schooling, marital status, working status, the number of household members, household income, urban–rural dummy, and island-year fixed effects. Column (1) used a sample of respondents that were interviewed in 2000 and 2014. Column (2) used a sample of those who never changed marital status. Column (3) removed 10% of the youngest and oldest respondents. Column (4) used a sample of those aged more than 20 years old in 2000. Column (5) assumed a quadratic relationship. Columns (6) and (7) used 1200- and 3000-m radii for sprawl index calculation, respectively.
*, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
Appendix 6: The effects of sprawl on obesity, different clustering level
(1) | (2) | (3) | (4) | |
|---|---|---|---|---|
Clustering at… level | ||||
District-year | Individual | |||
BMI | Obese | BMI | Obese | |
Sprawl index | − 0.0095* | − 0.0009** | − 0.0095*** | − 0.0009** |
(0.0050) | (0.0004) | (0.0035) | (0.0005) | |
R-sq | 0.7888 | 0.5526 | 0.7868 | 0.5483 |
Mean of dependent variable | 23.078 | 0.173 | 23.078 | 0.173 |
Observations | 37,882 | 37,882 | 37,882 | 37,882 |
Samples were limited to individuals above 18 years of age.Standard errors clustered at the district-year or individual level are reported in parentheses. Control variables include age, age squared, years of schooling, marital status, working status, the number of household members, household income, urban–rural dummy, and island-year fixed effects.
*, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
Appendix 7: Effects of a sprawling neighborhood on obesity—urban and rural subsamples
Body mass index | Obesity status | |
|---|---|---|
(1) | (2) | |
Panel A: Urban | ||
Sprawl index | − 0.0030 | − 0.0002 |
(0.0063) | (0.0007) | |
R2 | 0.7940 | 0.5487 |
Mean of dependent variable | 23.545 | 0.206 |
Observations | 18,186 | 18,186 |
Panel B: Rural | ||
Sprawl index | − 0.0083 | − 0.0011 |
(0.0063) | (0.0008) | |
R2 | 0.7809 | 0.5569 |
Mean of dependent variable | 22.638 | 0.142 |
Observations | 19,329 | 19,329 |
Samples were limited to individuals above 18 years of age. Standard errors clustered at the subdistrict-year level are reported in parentheses. Control variables include age, age squared, years of schooling, marital status, working status, the number of household members, household income, and island-year fixed effects.
*, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
Appendix 8: Effect of a sprawling neighborhood on carbohydrate consumption
(1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
Spending on Carbohydrate per Capita | Uncooked Rice (kg) | Spending on Carbohydrate per Capita (log) | Uncooked Rice (log) | Proportion of Spending on Carbohydrate to Total Food Spending (%) | |
Sprawl | − 68.75 | 0.0101 | − 0.011 | − 0.000836 | − 0.000526 |
(45.28) | (0.0137) | (0.007) | (0.00334) | (0.00045) | |
R-sq | 0.208 | − 0.000791 | 0.217 | 0.223 | 0.23 |
Mean of dependent variable | 10,608.373 | 1.829 | 7.432 | 0.711 | 0.174 |
Observations | 37,812 | 21,378 | 37,812 | 21,378 | 37,682 |
Samples were limited to individuals above 18 years of age. Standard errors clustered at the subdistrict-year level are reported in parentheses. Control variables include age, age squared, years of schooling, marital status, working status, the number of household members, household income, urban–rural dummy, and island-year fixed effects.
*, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
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Yunita, Yudhistira, M.H. & Kurniawan, Y.R. Does a sprawling neighborhood affect obesity? Evidence from Indonesia. Int J Health Econ Manag. (2024). https://doi.org/10.1007/s10754-024-09371-6
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DOI: https://doi.org/10.1007/s10754-024-09371-6