The Chicken and Egg Problem: Obesity and the Urban Monocentric Model

Abstract

Recent medical studies have examined ways to offer enhanced spatial planning opportunities to increase a person’s level of physical activity. These studies demonstrate a decreasing prevalence of obesity in denser and less car-oriented communities with mixed land uses. Yet, these studies raise the chicken and egg problem, namely, whether or not prevalence of obesity motivate change of residence, or whether or not people who are more physically active prefer to live in denser and less car-oriented communities? Based on a two-year longitudinal survey of the Israeli Central Bureau of Statistics (CBS), we explore this question using a 3SLS methodology, where the two endogenous variables include: BMI and apartment size. The age of the housing unit in years and the structure type provide the exogenous variables that identify the BMI equation. This empirical model is justified based on the urban monocentric model, which forecasts smaller apartments in multi-story structures with smaller building footprints, where price of land is expensive (at the central cities), which, in turn, give rise to denser and less car-oriented communities. In contrast, single family units with larger lot size, which give rise to more car-oriented communities, are typical of suburbs, where the price of land is relatively cheap. Moreover, the natural evolution theory of suburbanization process explains the correlation between construction age, type, income level, and location in central cities. Results of our study give rise to the possibility that for the Jewish female and male populations, health considerations may influence housing choice, but not vice versa. For other populations (i.e., Arab females and males), and with one exception, no correlation between BMI and housing choice is found. Public policy implications of our study suggest that health-related considerations might be employed to promote return to denser urban areas/central cities (reverse suburbanization) particularly among the Jewish population group.

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Notes

  1. 1.

    World Health Organization (WHO): Global Strategy on Diet, Physical Activity and Health available at: https://www.who.int/dietphysicalactivity/pa/en/ accessed at May 17, 2019.

  2. 2.

    World Health Organization (WHO): Obesity and overweight. Key facts, Available at: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight accessed at May 17, 2019.

  3. 3.

    This suburbanization process itself spurred the creation of urban economics as a separate discipline in economics. Mills and Hamilton (1989), for example, provide a formal proof to the statement that under realistic conditions, high income households live further from the city center than do low-income households. Baum-Snow (2007) estimated that had the US interstate highway systems not been built during the 1950s and 1960s, aggregate central city population would have increased by 8% from 1950 to 1990 – rather than declining by 17% (McDonald and McMillen, 2011; page 318).

  4. 4.

    On the other hand, based on a longitudinal survey carried out among movers from one neighborhood to another, Eid et al. 2008 found no relationship between obesity and urban sprawl.

  5. 5.

    Based on appropriate key words, we found 6685 studies in PubMed that report on gender differences in the context of BMI and obesity.

  6. 6.

    The respective 95% and 99% confidence intervals for females are: (24.63, 24.94) and [24.58, 24.99] and for males are: (26.23,26.48) and [26.19, 26.52]. See Table 1.

  7. 7.

    The 13% frequency is for women whose age is below or equal 62 years (the retirement age for women). This frequency rises to 14% for women whose age is below or equal 67 years (the retirement age for men). See Table 1.

  8. 8.

    Note, that compared to the full sample of 7511 observations×years, information regarding the age of construction is available for only 5067 observations×years. To avoid the information loss of 2444 observations×years associated with inclusion of CONSTRUCT_AGE, in subsequent sections we ran two versions of the empirical model: with and without this variable.

  9. 9.

    Israeli CBS Press Release: Dwelling in Israel - Findings from the Household Expenditure Survey 2016 at: https://www.cbs.gov.il/he/mediarelease/doclib/2017/358/15_17_358b.pdf. Note, that for many years Israel had especially high homeownership rates. According to Alperovich 1997: “Second, the housing market in Israel is overwhelmingly an owners’ market, and not a renters’ market. Official figures based on the recent 1983 Census of Population and Housing show that over 75% of all households own rather than rent their housing.” (page 130)

  10. 10.

    In that context, Johnston and Dinardo (1997) state that: “Many coefficients that are both numerically large and also adjudged statistically significant by tests to be described later may contain no real information. That statistical significance has been achieved does not necessarily imply that a meaningful and useful relationship has been found. The crucial question is, What has caused the observed covariation? If there is a theory about the joint variation of X and Y, the sign and size of the correlation coefficient may lend support to that theory, but if no such theory exists or can be devised, the correlation may be classified as a nonsense correlation.” (page 9). The authors provide three examples of nonsense correlation in subsequent page: “Our favorite spurious, or nonsense, correlation was given in a beautiful 1926 paper by the statistician G. Udny Yule. Yule took annual data from 1866 to 1911 for the death rate in England and Wales and the proportion of all marriages solemnized in the church of England and found the correlation coefficient to be +0.95. However, no British politician proposed closing down the church of England to confer immortality on the electorate. More recently, using annual data from 1897 to 1958, Plosser and Schwert (1978) have found a correlation coefficient of +0.91 between the log of nominal income in the United States and the log of accumulated sunspots. Hendry (1980) has noted a very strong, though somewhat nonlinear, positive relationship between the inflation rate and the accumulation of annual rainfall in the United Kingdom. It would be nice if a British could reduce their inflation rate and, as a bonus, enjoy the inestimable side effect of improved weather, but such happy conjunctions are not to be.” (page 10).

  11. 11.

    Compared to the alternative approach of pooling the sample and using interaction variables incorporated in one equation (e.g., Greene, 2012: 201–202), the advantage of this approach lies in its simplicity in terms of direct interpretation of the coefficients for each ethnic group separately.

  12. 12.

    According to Mieszkowsky and Mills (1993): “The older, smaller, centrally located units, built when average real incomes were lower, filter down to lower income groups. This natural working of the housing market leads to income stratified neighborhoods, and there is a tendency for low income groups to live in central locations and for affluent households to reside in outlying suburban areas. The majority of the middle class apparently prefers larger single family lots in the suburbs to denser multi-family residences in the central city.”

  13. 13.

    Referring to rural and urban South Africa, Prioreschi et al. (2017) states in the discussion section that: “This suggests that although westernisation may have influenced body image satisfaction and eating attitudes in young South African women towards a preference for a normal weight silhouette, traditional desirability of higher BMIs in African populations continue to have greater influence over rural compared to urban perceptions.” (page 10, end of first paragraph).

  14. 14.

    Available at:https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight accessed at March 28, 2019.

  15. 15.

    Available at:https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight accessed at March 28, 2019.

References

  1. Alperovich, G. (1997). Israeli settlement in occupied territories and its impact on housing prices in Israel. Journal of Regional Science, 37(1), 127–144.

    Google Scholar 

  2. Arbel, Y., Fialkoff, C., & Amichai, K. (2019). Determinants of ownership rates among new immigrants to Israel: Ethnic origin, and tenure mode in the Host Country. Journal of Real Estate Literature, 27(2), 1–46 Forthcoming.

    Google Scholar 

  3. Arroyo-Johnson, C., & Mincey, K. D. (2016). Obesity epidemiology worldwide. Gastroenterology Clinics of North America, 45(4), 571–579.

    Google Scholar 

  4. Atalayer, D., Pantazatos, S. P., Gibson, C. D., McOuatt, H., Puma, L., Astbury, N. M., & Geliebter, A. (2014). Sexually dimorphic functional connectivity in response to high vs. low energy-dense food cues in obese humans: An fMRI study. NeuroImage, 100, 405–413.

    Google Scholar 

  5. Barone, A., & Nese, A. (2016). Educational outcomes, gender and body weight: Evidence from Italy. Rivista Internazionale di Scienze Sociali, 124(3–4), 257–282.

    Google Scholar 

  6. Baum-Snow, N. (2007). Did highway cause suburbanization? Quarterly Journal of Economics, 122, 775–805.

    Google Scholar 

  7. Bourassa, S. C. (2000). Ethnicity, endogeneity, and housing tenure choice. The Journal of Real Estate Finance and Economics, 20(3), 323–341.

    Google Scholar 

  8. Creatore, M. I., Glazier, R. H., Moineddin, R., Fazli, G. S., Johns, A., Gozdyra, P., Matheson, F. I., Kaufman-Shriqui, V., Rosella, L. C., Manuel, D. G., & Booth, G. L. (2016). Association of neighborhood walkability with change in overweight, obesity, and diabetes. JAMA, 315(20), 2211–2220.

    Google Scholar 

  9. Deng, Y., & Wu, J. (2014). Economic returns to residential green building investment: The developers’ perspective. Regional Science and Urban Economics, 47, 35–44.

    Google Scholar 

  10. Eid, J., Overman, H. G., Puga, D., & Turner, M. A. (2008). Fat city: Questioning the relationship between urban sprawl and obesity. Journal of Urban Economics, 63, 385–404.

    Google Scholar 

  11. Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., & Raudenbush, S. (2003). Relationship between urban sprawl and physical activity, obesity, and morbidity. American Journal of Health Promotion, 18(1), 47–57. https://doi.org/10.4278/0890-1171-18.1.47.

    Article  Google Scholar 

  12. Ewing, R., Meakins, G., Hamidi, S., & Nelson, A. C. (2014). Relationship between urban sprawl and physical activity, obesity, and morbidity – Update and refinement. Health and Place, 26, 118–126. https://doi.org/10.1016/j.healthplace.2013.12.008.

    Article  Google Scholar 

  13. Furman, D., Hejblum, B. P., Simon, N., Jojic, V., Dekker, C. L., Thiébaut, R., Tibshirani, R. J., & Mark M. D. (2014). Systems Analysis of Sex Differences Reveals an Immunosuppressive Role for Testosterone in the Response to Influenza Vaccination. PNAS, 111(2),869–874.

  14. Frank, L. D., Saelens, B. E., Powell, K. E., & Chapman, J. E. (2007). Stepping towards causation: Do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Social Science and Medicine, 65(9), 1898–1914.

    Google Scholar 

  15. Griffin, B. A., Eibner, C., Bird, C. E., Jewell, A., Margolis, K., Shih, R., Slaughter, M. E., Whitsel, E. A., Allison, M., & Escarce, J. J. (2013). The relationship between urban sprawl and coronary heart disease in women. Health and Place, 20, 51–61.

    Google Scholar 

  16. Goedecke, J. H., George, C., Veras, K., Peer, N., Lombard, C., Victor, H., Steyn, K., & Levitt, N. S. (2016). Sex differences in insulin sensitivity and insulin response with increasing age in black south African men and women. Diabetes Research and Clinical Practice, 122, 207–214.

    Google Scholar 

  17. Greene, W. H. (2012). Econometric analysis (7th ed.). Pearson Education Limited.

  18. Hendry, D. F. (1980). Econometrics – Alchemy or science? Economica, 47, 387–406.

    Google Scholar 

  19. Israeli Central Bureau of Statistics: Longitudinal Survey 2015–2016.

  20. Johnston, J., & Dinardo, J. (1997). Econometric methods (4th ed.). New York: McGraw Hill International Edition.

    Google Scholar 

  21. Lopez, R. (2004). Urban sprawl and risk for being overweight or obese. American Journal of Public Health, 94(9), 1574–1579.

    Google Scholar 

  22. McDonald, J. F., & McMillen, D. (2011). Urban economics and real estate, Theory and Policy (2nd ed.). Hoboken: Wiley Incorporated.

    Google Scholar 

  23. Mieszkowsky, P., & Mills, E. S. (1993). The causes of metropolitan suburbanization. Journal of Economic Perspectives, 7(3), 135–147.

    Google Scholar 

  24. Mills, E. S., & Hamilton, B. W. (1989). Urban economics (4th ed., pp. 425–434). Appendix A: Simplified Mathematical Model of Urban Structure.

  25. Nivola, P. (1998). Fat City: Understanding American urban form from a transatlantic perspective. The Brookings Review, 11(4), 17–20.

    Google Scholar 

  26. OECD/EU. (2016). Health at a glance: Europe 2016 – State of health in the EU cycle. Paris: OECD Publishing. https://doi.org/10.1787/9789264265592-en.

    Book  Google Scholar 

  27. Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K. M. (2006). Prevalence of overweight and obesity in the United States, 1999–2004. Journal of the American Medical Association, 295(13), 1549–1555.

    Google Scholar 

  28. O’Sullivan, A. (2012). Urban economics, Eight Edition. Chapter 6: Urban Land Rent: 127–151. Chapter 7: Urban Sprawl: 181–184.

  29. Painter, G., Gabriel, S., & Myers, D. (2001). Race, immigrant status, and housing tenure choice. Journal of Urban Economics, 49(1), 150–167.

    Google Scholar 

  30. Plosser, C. I., & Schwert, W. (1978). Money, income and sunspots: Measuring economic relationships and the effect of differencing. Journal of Monetary Economics, 4, 637–660.

    Google Scholar 

  31. Prioreschi, A., Wrottesley, S. V., Cohen, E., Reddy, A., Said-Mohamed, R., Twine, R., Tollman, S. M., Kahn, K., Dunger, D. B., & Norris, S. A. (2017). Examining the relationships between body image, eating attitudes, BMI, and physical activity in rural and urban South African young adult females using structural equation modeling. PLOS ONE, 1–16. https://doi.org/10.1371/journal.pone.0187508.

  32. Sabia, J. J., & Rees, D. I. (2015). Body weight, mental health capital, and academic achievement, 2015. Review of Economic of the Household, 13, 653–684.

    Google Scholar 

  33. Sade, M., & Dankner, R. (2014). A green environment in the city and its relation to health: A survey of the current epidemiological research. Ecology and Environment, 7(3), 227–235 (Hebrew).

    Google Scholar 

  34. Saelens, B. E., & Handy, S. L. (2008). Built environment correlates of walking: A review. Medicine and Science in Sports Exercise, 40(7 Suppl), S550–S566.

    Google Scholar 

  35. Sallis, J. F., Cerin, E., Conway, T. L., Adams, M. A., Frank, L. D., Pratt, M., Salvo, D., Schipperijn, J., Smith, G., Cain, K. L., Davey, R., Kerr, J., Lai, P.-C., Mitš, J., Reis, R., Sarmiento, O. L., Schofield, G., Troelsen, J., Dyck, V., Delfien, Bourdeaudhuij, D., Ilse, & Owen, N. (2016). Physical activity in relation to urban environments in 14 cities worldwide: A cross-sectional study. Lancet, 387, 2207–2217.

    Google Scholar 

  36. Scharoun-Lee, M., Adair, L. S., Kaufman, J. S., & Gordon-Larsen, P. (2009). Obesity, race/ethnicity and the multiple dimensions of socioeconomic status during the transition to adulthood: A factor analysis approach. Social Science & Medicine, 68(4), 708–716.

    Google Scholar 

  37. Su, D., Esqueda, O., Li, L., & Pagan, J. (2012). Income inequality and obesity prevalence among OECD countries. Journal of Biosocial Science, 44(4), 417–432. https://doi.org/10.1017/S002193201100071X.

    Article  Google Scholar 

  38. World Health Organization (WHO): Global Strategy on Diet, Physical Activity and Health available at: https://www.who.int/dietphysicalactivity/pa/en/. Accessed 17 May 2019.

  39. World Health Organization (WHO): Obesity and overweight. Key facts, Available at: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 17 May 2019.

  40. Yule, U. G. (1926). Why do we sometimes get nonsense correlations between time series? Journal of the Royal Statistical Society, Series A, General, 89, 1–69.

    Google Scholar 

  41. Zhao, Z., & Kaestner, R. (2010). Effects of urban sprawl on obesity. Journal of Health Economics, 29, 779–787.

    Google Scholar 

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Correspondence to Yuval Arbel.

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The authors are grateful to Israel Social Sciences Data Center (ISDC), the Hebrew University of Jerusalem for provision of project data and to Yifat Arbel, Gil Cohen, Miri Kerner, C. F. Sirmans (the editor), an anonymus reviewer, and the participants of the 2019 management department seminar at the Western Galilee College for helpful comments.

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Arbel, Y., Fialkoff, C. & Kerner, A. The Chicken and Egg Problem: Obesity and the Urban Monocentric Model. J Real Estate Finan Econ 61, 576–606 (2020). https://doi.org/10.1007/s11146-019-09737-5

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JEL Classification

  • H75
  • I12
  • R21
  • R58

Keywords

  • Suburbanization
  • Body mass index
  • Obesity