Setting development goals using stochastic dynamical system models

PLoS One. 2017 Feb 27;12(2):e0171560. doi: 10.1371/journal.pone.0171560. eCollection 2017.

Abstract

The Millennium Development Goals (MDG) programme was an ambitious attempt to encourage a globalised solution to important but often-overlooked development problems. The programme led to wide-ranging development but it has also been criticised for unrealistic and arbitrary targets. In this paper, we show how country-specific development targets can be set using stochastic, dynamical system models built from historical data. In particular, we show that the MDG target of two-thirds reduction of child mortality from 1990 levels was infeasible for most countries, especially in sub-Saharan Africa. At the same time, the MDG targets were not ambitious enough for fast-developing countries such as Brazil and China. We suggest that model-based setting of country-specific targets is essential for the success of global development programmes such as the Sustainable Development Goals (SDG). This approach should provide clear, quantifiable targets for policymakers.

MeSH terms

  • Africa South of the Sahara
  • Bayes Theorem
  • Child
  • Child Mortality*
  • Developing Countries*
  • Geography
  • Global Health
  • Goals
  • Health Policy
  • Humans
  • Organizational Objectives
  • Probability
  • Stochastic Processes

Grants and funding

This work was funded by Swedish Research Council grant D049040. SCN acknowledges financial support from the “back to Belgium” grant by the Belgian Science Policy Office.