It is not surprising that a measure of women's inclusion, such as the Gender Empowerment Measure (GEM) of the UNDP, should correlate highly with GDP per capita or years of formal education of adult women. As we have seen, income and education are closely correlated and one or the other is almost invariably a key driver in our forecasts of change in governance. It is perhaps more surprising, in the formulation below, that together they both make statistically significant contributions to GEM. The relationship between GDP per capita and the GEM has shifted over time—the advance of global education, even in countries with low levels of income, helps explain that shift and almost certainly helps account for the independent contribution of education to higher levels of female empowerment. Interestingly, women's education does not differ in its statistical contribution from that of men; we nonetheless use that of women in our formulation.

One might expect a strong relationship between total fertility rate and GEM as women who bear fewer children rise in other ways in society. There is, in fact, a strong correlation. Interestingly, however, a stronger one inversely relates the size of the youth bulge to the GEM. The IFs formulation is:

where

GEM=UNDP Gender Empowerment Measure

GDPPCP=GDP per capita at purchasing power parity in thousand dollars

EDYRSAG15=average years of education for females age 15 or older

YTHBULGE=youth bulge, the population aged 15–29 as a portion of the entire adult population

gemm=an exogenous multiplier for scenario analysis

R-squared in 2010=0.66

We experimented with a variation on the above formulation in which GDP per capita enters in a logged term, and found nearly as high an R-squared (0.64). However, a problem in longer-term forecasting with such a variation is that the saturation of the log of GDP per capita nearly stops growth in GEM for more developed countries, often well below parity for women.

A user can control the progression of gender empowerment with a simple multiplier ( gemm ) or via setting a target value for it movement to some number of standard errors above or below a cross-sectionally estimated function ( gemsetar ) across a set number of years ( gemseyrtar ).