Childhood obesity has long-lasting effects in physical, cognitive and socio-emotional skills that are critical for the labor market. From a life-cycle perspective, the costs of overweight and obesity are substantial: decreased quality of life, elevated risk of mortality, chronic physical and mental health conditions, increased health-care consumption, productivity losses and absenteeism, and social stigma. The economic return to mitigate obesity alone is six-fold. Overall, giving children the best conditions for human capital skills building is more effective across any future occupation.
Our solution is founded on exploiting the potential of such routine administrative data at the PK-12 ages. For example, data is periodically collected for all students in specific grades attending public and voucher schools in Chile.
Our toolkit is based on a simple human capital framework that formalize the statistical assumptions needed for a country-wide model that allows to predict the future path for children's human capital accumulation, based on the longitudinal data available from multiple cohorts. Our solution not only can forecast future risks at the local level (e.g. district), but also allow to predict and evaluate the impact of interventions. The toolkit is based on a combination of theoretical premises as well as statistical foundations, using simple Python tools.
Based on our results, we encourage governments to collect similar data as the Chilean case, and take advantage of our proposed toolkit, in order to anticipate future needs for human capital accumulation, in order to create a healthier labor force in the next generation.