Gender Data Gaps: Structural Equation Modeling Offers an Alternative to Collecting More Data

An enhanced commitment to achieving gender equality, women's rights and women's empowerment in the post-2015 development framework is important. Gender data gaps exist because gender biases have inhibited and altered data collection resulting in policy formation that has insufficiently addressed gender issues. Structural Equation Modelling (SEM) is an effective statistical tool used to estimate correlational relations using limited input data confined to a hypothetical, researcher-specified framework of relationship. This paper highlights the use of SEM to evaluate progress on gender equality and women's empowerment during the period of the first 1,000 days of life (from pregnancy through 2 years of age for infants).