Suicide and the South African business cycle: a time series approach, 2006-2015 - Pitot, Amaury (Rhodes University, Faculty of Commerce, Department of Economics and Economic History, 2018)
Suicide is a major public health issue and imposes substantial economic cost on society every year. For example, the World Health Organisation has estimated that there are over one million completed suicides every year, of which about 75% occur in middle and low income countries. In South Africa, suicide is one of the leading causes of non-natural death, but remains under-researched from an economic point of view due to limited data availability. Using monthly data for the period 2006-2015, this study explores whether there is a relationship between suicide and the South African business cycle. This is further broken down to examine how, if at all, this relationship with the business cycle differs across age-, gender-, and racial groups. The primary source of data for suicide and demographic groups were obtained from Statistics South Africa's Mortality and Causes of Death Data from Death Notification released since 2006. The coincident indicator was used as a proxy for the business cycle as it represents the business cycle in real time. Using an autoregressive distributed lagged model (ARDL), a long run relationship was established with suicide being a function of the coincident indicator, divorce and fertility rate. The findings of this paper show that the overall suicide rate moves with the South African business cycle (i.e. pro-cyclical relationship) in the long run. This relationship holds for males, the black population group and the 15-29 and 30-44 age categories. In addition, the divorce rate had a positive and significant relationship with the overall suicide rate, as well as suicide among the black population group and for the 30-44 age category, whereas fertility rates had no significant relationship with suicide.
Keywords: Suicide -- South Africa; Business cycles -- South Africa; Autoregression (Statistics); Divorce -- South Africa; AutoRegressive Distributed Lagged model (ARDL)