The Philippine Institute for Development Studies (PIDS) recently said that Filipinos classified as “non-poor” may actually be living in poverty, as current measurement methods fail to capture how resources are shared within households, and poverty data based on household averages can mask real conditions, where not all members benefit equally from income.
“Official figures assume that every household member receives an equal slice of income. This design makes intra-household inequality invisible, and systematically misses the gender gap,” PIDS supervising research specialist Deanne Lorraine Cabalfin said.
Using household survey data in a study entitled “Measuring Poverty within Filipino Households: Examining Resources Sharing and Economic Scale,” the researchers found that women only receive 25 to 43 percent of household resources, while children may receive as little as seven to 19 percent each, especially in larger families.
“Many children that may be living in non-poor households may, in fact, be considered poor,” Cabalfin said.
The findings suggest that individuals, particularly women and children, may experience deprivation even if their households are not classified as poor in official statistics.
PIDS senior research fellow Jose Ramon Albert said that many families who are not officially classified as poor today could still fall into poverty, particularly among low-income households, rural communities, and even segments of the middle class. “Our point here is that we don’t just need to reduce poverty, but we need to prevent households from becoming poor in the future,” he said.
The Department of Social Welfare and Development said the findings highlight the need to strengthen social protection platforms.
As our government works to lift Filipinos out of poverty, we will need to look beyond the measurement methods that most likely no longer provide an accurate picture of the situation on the ground. As long as those methods are deemed inaccurate, the effort has to be doubled to ensure that more are raised from poverty, rather than left behind. This is a case where until we are sure that our metrics are accurate, it is always better to err on the side of caution.*
