State-Society-Culture Dynamics

I remember arguing with David about the necessity of normalizing my variables by transforming them using standard procedures such as logarithmic transformation. True to his generation of applied statisticians, David did not see anything wrong with “normalizing” variables which were skewed, truncated, or otherwise not normally distributed. And I, also true to form, always the maverick, argued against it, feeling “instinctively” that information would be lost in the process of transforming raw variables which were distributed in their native form, that is, as originally observed. Our intellectual dispute was rooted in the need to use the General Linear Model (GLM), specifically, multivariate numerical and categorical regressions to model and predict the impact of multiple spatial moves on cumulative fertility of Filipino women. The GLM, while possessing amazing versatility in modeling sociological phenomena like demographic processes, is hostage to the demands of parametric

If only for the spectacle of “effect sizes” of public health policies, there could not be a more appropriate statistic than deaths to mothers per 100,000 live births. This indicator implicates sanitation, hygiene, medical skills and technology, which are all functions of the developmental state. It not only indexes public economic resources, but also to where these resources are put to use. A full length essay on this aspect of the developmental process will have to wait. Meanwhile, I can vouch for the spectacle of Vietnam, recovering from its gruesome war wounds, an erstwhile socialist economy, embarrassing the blatantly capitalist Philippines, also among the oldest “democracies” of Asia. Singapore the winner in the developmental race, is clearly kindest to its mothers, and Malaysia and Thailand are front runners. These three are worthwhile models of public health and reproductive policies, but what about the rest? the year is 2008, and the data are provided by UNESCAP, 201