HOW TO MEASURE THE LOCAL POVERTY RATE

HOW TO MEASURE THE LOCAL POVERTY RATE

What is the best way to measure poverty at the local level? Should we rely on the traditional Poverty Threshold Basket (PTB), or should we use the more comprehensive Multidimensional Poverty Index (MPI)? Or perhaps, as I often wonder, could we combine both methods to arrive at a more accurate and practical picture of how people are really living?

The PTB, as we know, is based on an “imaginary basket of goods.” It computes the minimum cost of basic food and non-food items such as housing, clothing, utilities, transport, education, and health. It sounds simple enough, but here’s the catch: many of the inclusions are outdated. For example, in some versions, landlines and postage are still there, but cell phone load and internet access are not. Can we still say that the PTB represents the essentials of modern life?

This is where the MPI provides a fresh perspective. Instead of focusing only on income, MPI looks at several dimensions—health, education, and living standards. In practical terms, this means checking if families have electricity, clean water, proper sanitation, and decent housing, or if children are in school and not dropping out because of costs. The MPI reflects what Nobel laureate Amartya Sen called “capability deprivations”—not just lack of money, but lack of real freedoms and opportunities.

Now, what about the Community-Based Monitoring System (CBMS)? This is where things get interesting. Mandated under Republic Act 11315, CBMS requires LGUs to collect household-level data on socio-economic conditions. In other words, it gives us the granular details—from the barangay down to individual households—that can make MPI truly relevant at the local level.

So, can we combine PTB, MPI, and CBMS? Absolutely. PTB gives us the monetary baseline; MPI gives us the multidimensional picture; CBMS gives us the local context. With artificial intelligence now widely available, there is no reason why LGUs cannot integrate and analyze these data sources to build poverty maps in real time. Imagine a dashboard where mayors can see which barangays lack toilets, which sitios have children skipping meals, or which puroks need housing upgrades. That would make decision-making faster and smarter.

There are already success stories. In Pasay City, regression models were used to generate district-level MPI, helping identify pockets of deprivation that income surveys missed. Bohol province combined CBMS and MPI to add local indicators such as disaster vulnerability—very practical in a typhoon-prone island. Quezon City, meanwhile, discovered that housing and sanitation, not just income, were the biggest contributors to urban poverty. And in Libjo, Dinagat Islands, CBMS data allowed MPI to be computed down to the barangay level, resulting in targeted interventions for food security, education, and housing.

If these examples show us anything, it is that poverty measurement is not just a statistical exercise. It has direct policy consequences. The better you measure, the better you target. Which raises an important question: shouldn’t we reward LGUs that can both measure and reduce poverty effectively? After all, the Internal Revenue Allotment (IRA) is now linked to performance indicators. Why not include poverty reduction as one of them?

In fact, some barangays in Pandi, Bulacan, have already shown what is possible. By piloting CBMS as early as the 1990s, they were able to track minimum basic needs and direct resources accordingly. The result? Programs that actually responded to what residents needed—be it livelihood tools, classrooms, or sanitation facilities.

Of course, one persistent problem remains: the poverty line versus the minimum wage. In many areas, minimum wage earners are still below the poverty threshold. This makes us ask—are wages too low, or is the threshold unrealistic? Either way, the poor remain caught in the middle.

My suggestion? Modernize the PTB by including digital access and energy costs. Strengthen MPI indicators by tailoring them to local realities—like aquaculture tools for fishing towns or burial services in remote barangays. And let CBMS be the backbone for all LGU poverty monitoring.

With AI and cloud-based platforms now available at affordable costs, there is no excuse for LGUs not to use them. Poverty measurement should no longer be guesswork. It should be precise, participatory, and transparent.

The bottom line is this: poverty measurement is not about numbers alone. It is about people. It is about whether children go to school, whether families eat three meals a day, whether homes are safe from typhoons, and yes, whether the dead are buried with dignity. Unless we measure these things properly, we cannot hope to solve them.

And so, when we ask how to measure the local poverty rate, perhaps the best answer is this: measure it as if real lives depend on it—because they do.

Ramon Ike V. Seneres, www.facebook.com/ike.seneres
iseneres@yahoo.com, senseneres.blogspot.com

01-30-2026