A Simple Poverty Scorecard for the Philippines

some timeless patterns but also some random patterns that, due to sampling variation,show up only in the 2004 APIS. Or the scorecard may be overfit in the sense that itbecomes biased as the relationships between indicators and poverty change or when it isapplied to non-nationally representative samples.Overfitting can be mitigated by simplifying the scorecard and by not relying onlyon data but rather also considering experience, judgment, and theory. Of course, thescorecard here does this. Bootstrapping can also mitigate overfitting by reducing (butnot eliminating) dependence on a single sampling instance. Combining scorecards canalso help, at the cost of greater complexity.Most errors in individual households’ likelihoods, however, cancel out in theestimates of groups’ poverty rates (see later sections). Furthermore, much of thedifferences may come from non-scorecard sources such as changes in the relationshipbetween indicators and poverty, sampling variation, changes in poverty lines,inconsistencies in data quality across time, and inconsistencies/imperfections in cost-oflivingadjustments across time and space. These factors can be addressed only byimproving data quantity and quality (which is beyond the scope of the scorecard) or byreducing overfitting (which likely has limited returns, given the scorecard’s parsimony).27