Increasing obesity-related problems and rising healthcare expenditures have led governments in developed countries to consider the introduction of soda taxes. We study a recent such tax, implemented in Portugal, using extremely detailed panel data from one of the two largest retailers in the country, covering the period between February 2015 and January 2018. We take advantage of the tax breakdown by sugar levels to examine how soda prices and quantities purchased reacted. For identification, we rely on difference-indifferences models with various vectors of fixed effects, comparing each group of products to water. For drinks with more than 80 grams of sugar per liter, results indicate almost full price pass-through to the consumer. For drinks with less than 80 grams of sugar per liter, price pass-through surpassed 100%. Regarding consumption, our findings suggest stockpiling behavior in the quarter when the tax was approved and before it was actually implemented. In the implementation period, there are no significant changes in quantities purchased for most beverages vis-à-vis water, with the exception of soda drinks with comparatively low levels of sugar. This suggests that benefits of the soda tax in terms of reducing sugar intake are mainly due to reformulation, as producers reduced the sugar content of some drinks to fall below the 80 grams per liter threshold.

No abstract

Population aging and policies to redirect long-term care toward home- and community-based services have led to increases in Medicaid home care spending in most states. Changes in state Medicaid home care policy generosity may result from changes in the number of persons served (i.e., Participation) and/or changes in quantities of services covered (i.e., Intensity). This study measures state Medicaid home care Participation and Intensity comprehensively using latent variables, and uses those latent variables to describe changes in Medicaid home care policy generosity over time and across states. Yearly state-level data from the Medicaid Statistical Information System (1999-2012) are analyzed using exploratory and confirmatory factor analyses. Between 1999 and 2012, 29 states expanded both Participation and Intensity, whereas six states reduced both. In the remaining states, a trade-off occurred. Distinguishing between Medicaid home care Participation and Intensity deserves attention, as expansions along these two dimensions represent potentially different implications for beneficiaries.

IntroductionGovernments across Europe want to promote healthy and active aging, as a matter of both public health and economic sustainability. Designing policies focused on the most vulnerable groups requires information at the individual level. However, a measure of healthy and active aging at the individual level does not yet exist.ObjectivesThis paper develops the Selfie Aging Index (SAI), an individual-level index of healthy and active aging. The SAI is developed thinking about a tool that would allow each person to take a selfie of her aging status. Therefore, it is based entirely on self-assessed indicators. This paper also illustrates how the SAI may look like in practice.MethodsThe SAI is based on the Biopsychosocial Assessment Model (MAB), a tool for the multidimensional assessment of older adults along three domains: biological, psychological, and social. Indicators are selected and their weights determined based on an ordered probit model that relates the MAB indicators to self-assessed health, which proxies healthy and active aging. The ordered probit model predicts the SAI based on the estimated parameters. Finally, predictions are rescaled to the 0–1 interval. Data for the SAI development come from the Study of the Aging Profiles of the Portuguese Population and the Survey of Health, Aging, and Retirement in Europe.ResultsThe selected indicators are BMI, having difficulties moving around indoors and performing the activities of daily living, feeling depressed, feeling nervous, lacking energy, time awareness score, marital status, having someone to confide in, education, type of job, exercise, and smoking status. The model also determines their weights.ConclusionResults shed light on various factors that contribute significantly to healthy and active aging. Two examples are mental health and exercise, which deserve more attention from individuals themselves, health-care professionals, and public health policy. The SAI has the potential to put the individual at the center of the healthy and active aging discussion, contribute to patient empowerment, and promote patient-centered care. It can become a useful instrument to monitor healthy and active aging for different actors, including individuals themselves, health-care professionals, and policy makers.