Why is obesity trickling down to lower income classes in China? a longitudinal decomposition
We use the longitudinal China Health and Nutrition Survey (CHNS) to study the reversal of the income-obesity gradient. The CHNS started in 1989, covers about 180 communities, follows the same individuals every two/four years until 2009, and includes anthropometric measures as well as extensive information on socio-economic variables at the individual and household level. In addition to individual level measurements the analyses in this paper make use of the CHNS’s extensive community level information that captures different aspects related to economic development and the environments where people live (i.e. urbanization, food availability, infrastructure etc).
In particular we study (a) the rise of obesity, and (b) the evolution of the income-obesity gradient within an Oaxaca-Blinder decomposition framework (Oaxaca 1973, Blinder 1973). This approach is standard for the mean (Fortin et al. 2011), but has not been applied before to decompose two-dimensional statistics such as the income-obesity gradient. We develop a new decomposition approach for the income-obesity gradient by extending existing decompositions of the concentration index (Wagstaff et al. 2003, Van Ourti et al. 2009, Baeten et al. 2013) such that these (a) account for the panel nature of the data, (b) allow for structural breaks in the coefficients, and (c) for non-linear regression methods and the associated path dependency problem (Fortin et al. 2011, Shorrocks 2013).
Preliminary results confirm a significant rise in overweight levels over the last two decades and a positive and significant relationship between overweight status and the level of economic development. Furthermore the reduction of the socio-economic gradient of overweight indicates that the epidemic is trickling down to poorer parts of the population. The decomposition results suggest that exogenous environmental factors (i.e. urbanization and infrastructure) play an important role in explaining the change in the gradient.