LET’S SEPARATE DATA FOR MISEMPLOYMENT

LET’S SEPARATE DATA FOR MISEMPLOYMENT

In our public discourse and in labor policy discussions, we often reference terms like “underemployment” and “skills mismatch” as if they were interchangeable. But I’d like to propose a fresh—albeit uncomfortable—distinction: the concept of misemployment. It’s time we pull it out of the shadows and talk about it.

What is misemployment, anyway?
In simplest terms, misemployment happens when a person’s skills, time or resources are put to work in ways that are inefficient, inappropriate, or even harmful to the individual or to society. It’s broader than underemployment (which tends to mean “needs more work” or “less hours than desired”) and includes the mis­use of talent and the misallocation of labor.

For example: a licensed teacher working as a sales clerk. A professional engineer doing mundane clerical work for years. A barangay health worker assigned to tasks that have little to do with health services. These are mis­employments—a misuse of human capital that rarely makes headlines but steadily erodes potential.

Why does the distinction matter?
In the Philippines our main labor institutions—Department of Labor and Employment (DOLE) and the Philippine Statistics Authority (PSA)—do not track misemployment as a separate statistic. Nor does any other country (to my knowledge) officially publish a “misemployment rate”. Yet we track unemployment, underemployment, joblessness and hours worked. Why skip misemployment?

My late brother, Roy Seneres—former Ambassador and NLRC Chairman—was the first to make me aware of the flawed way we define underemployment in our country. Under our current definition: working fewer than eight hours a day qualifies. But Roy observed: “What about that teacher who becomes a sales-girl? She’s working eight hours a day, even more—but she is under‐utilized, her value wasted.” That is misemployment.

Here’s the problem:

• We need to know how many people are underemployed (for instance, working less than eight hours a day or wanting more work).
  
  

• We also need to know how many people are misemployed (working full hours but in roles well below their qualifications).
  
  

• But which is the bigger driver of the “job-mismatch” we often talk about? I’d argue it’s misemployment. People have jobs—but they’re not the right jobs.
  
  

What do the numbers say?
Officially, for September 2025, PSA data show: an unemployment rate of 3.8 % (about 1.96 million Filipinos) and an employment rate of 96.2 %. Underemployment is at 11.1 % (equivalent to roughly 5.52 million people). 

Notice: We don’t see any figure for misemployment. Because it’s not being measured separately. So it remains hidden—yet likely significant.

So where does the problem lie?
Is it in the educational system that produces graduates ill-matched to the job market? Is it in hiring practices that fail to utilize the right talent? Is it structural—when institutions assign people to the wrong posts or allow talent to go wasted? Might this be one reason why so many Filipinos go abroad, looking for jobs “right” for their skills? Possibly yes.

What can be done?
We need a database solution—perhaps backed by blockchain or AI-enhanced matching—to track skills, roles and assignments. At home, in barangays, in LGUs, we must know who we have, what they’re qualified for, and where they are deployed. For misemployment to be addressed, we first need to measure it.

Proposed modular data categories:

• Skill Mismatch: Individuals working in jobs far below education/training.
  
  

• Role Misallocation: Staff assigned outside mandates (e.g., health worker doing clerical duties).
  
  

• Time Misuse: Staff with few tasks despite full-time status.
  
  

• Resource Misemployment: Equipment or funds allocated for mis-directed jobs.
  
  

• Cultural/Gender Misemployment: Talents of indigenous knowledge-holders or women sidelined in decision-making roles.
  
  

Suggested collection tools:

• Barangay-level surveys that could align occupation, education and task.
  
  

• Focus-group discussions with those misemployed.
  
  

• Stakeholder mapping to identify gaps between assignment and actual work.
  
  

• Policy audits comparing stated mandates with actual roles.
  
  

Why act now?
Because misemployment is silently draining our human capital. It is a form of labor market inefficiency which echoes across sectors: wasted potential, frustrated workers, slower innovation and stagnated productivity. When someone qualified for higher value work is doing lesser value work, society loses twice—what they could have contributed, and the cost of running something less suited.

What I suggest we do:

1. Lobby PSA/DOLE to add a misemployment indicator in their surveys, separate from underemployment.
   
   

2. Conduct pilot audits in selected barangays with the modular framework.
   
   

3. Use technology—smart matching platforms, AI algorithms—to bridge the gap between talent and role.
   
   

4. Integrate misemployment diagnostics into community-restoration, circular-governance models. Remap human capital just like we map physical infrastructure.
   
   

Final thoughts:
We all agree that jobs matter—but what if the job is wrong for you? That doesn’t just affect the individual; it ripples through households, communities and the economy. By lumping misemployment under “underemployment” or ignoring it altogether, we deny ourselves the chance to fix it. So: let’s separate the data, sharpen our focus and aim for not just more jobs—but right jobs.

We owe it to the millions of Filipinos who are working—and yet waiting, or are not earning right.

RAMON IKE V. SENERES

www.facebook.com/ike.seneres iseneres@yahoo.com senseneres.blogspot.com 09088877282/06-14-2026

HOW DO WE TRACK DOWN DISASTER VICTIMS IN REMOTE AREAS?

HOW DO WE TRACK DOWN DISASTER VICTIMS IN REMOTE AREAS?

In our urban centers, disaster-victims can often be found with relative speed: the roads, addresses and communications are more or less in place, and rescue teams know where to look. In remote areas, however ­– mountain villages, far-flung barangays, communities cut off after storms and landslides ­– the challenge is far greater. And that leads to the question: How do we track down disaster victims in these remote, hard-to-reach zones?

The promise of technology

Today, a host of modern tools are available: remote sensing, GIS, GPS, and increasingly, AI-powered localization systems. Put simply:

• Remote sensing: satellites and drones can scan large swathes of land, detect terrain changes, collapsed structures, heat signatures or other signs of human presence.
  
  

• GIS (Geographic Information Systems): integrates spatial data (maps, terrain, infrastructure) with reports and sensor inputs so that responders can visualize where victims might be stranded.
  
  

• GPS: tracks the location of mobile phones or GPS-enabled devices; rescue teams use it to coordinate and pinpoint distress signals.
  
  

• Advanced techniques: for example, RSSI-based localization (using signal strength from mobile/wearable devices corrected by machine learning), sensor networks/IoT devices in the field, and AI that fuses thermal imaging, acoustic sensors and mobile signals to prioritise search zones.
  
  

All of these raise the possibility of finding survivors even when roads are gone, towers have collapsed or communication is down.

But are we really using them?

I ask because the tools may well exist within our government – via the military, the police, and our disaster-response units. Yet having them is only half the battle. The bigger question is: How do we harness them? How do we mobilize the people who have access to these tools? Because what good is a satellite scan if we don’t know who we’re looking for, or where they exactly are?

The crucial missing piece: local data

This is why, in my view, we need robust barangay-based databases. We need to always know:

• Who lives in every barangay (names, numbers, vulnerable households)
  
  

• Where the households are (addresses, GPS coordinates if possible)
  
  

• Which households already live in known danger zones – storm-prone, landslide-prone, flood-prone.
  
  

It may well be that government agencies already hold many of these datasets. But whether they’re consolidated, up-to-date and integrated into the search-and-rescue frameworks is another question. Because in a calamity, what you need is data + technology + coordination.

How it all comes together

Imagine this workflow: After a typhoon sweeps through a remote region, drones fly over the area and produce imagery; GIS maps are updated to show collapsed bridges, flooded terrain, cut-off roads. At the same time, pre-existing barangay databases show, for example, 120 households in Barangay X with 10 tagged as “high-risk (elderly, mobility-impaired)”. Mobile phones or wearable devices register no movement. Search teams, using GPS coordinates and RSSI logic, are dispatched to likely zones. Locals with CB/VHF/UHF radios coordinate communications where cell towers are down. The result: faster, more targeted rescue.

Mobilizing radio operators

Speaking of radios: when cell infrastructure is destroyed, CB/VHF/UHF radio owners become critical. They work without internet or cell service, can connect barangay-to-barangay, and many already have the skills and networks. Here’s how we might bring them into the disaster-response fold:

• Map all active radio operators through ham clubs, LGU registries, civic associations.
  
  

• Provide licensing support, training, and incentives (e.g., fuel stipends, gear upgrades) to those who commit to disaster roles.
  
  

• Conduct joint drills involving LGUs, uniformed services and NGOs; assign roles (relay stations, mobile scouts, shelter communicators).
  
  

• Develop SOPs: fallback frequencies, designated call signs, message formats for emergencies.
  
  

• Equip barangays with solar-powered base stations, handheld radios, mesh Wi-Fi or satellite backups for redundancy.
  
  

Volunteers: the heartbeat of the response

The good news: We already have the legal and institutional frameworks for volunteer mobilization. For example, the Philippine Disaster Risk Reduction and Management Act of 2010 recognizes the participation of civil society, volunteers and local communities in disaster risk reduction. 

Studies show that LGUs and volunteers cooperate — but the relationship requires structure, support and coordination. What we need to ask ourselves: Are we fully tapping volunteers, especially in remote barangays? Are they integrated into the tech-driven systems and databases?

My suggestions

Here are some steps I believe we must take:

1. Audit the tools: Confirm which technology (satellite imagery, drones, GPS trackers, sensor-networks) is already available to which agencies (military, police, DRRM offices).
   
   

2. Build the database backbone: At barangay level create/verify registries of residents, their location, special-needs profiles, hazard-exposure status.
   
   

3. Link the data to the tech: Ensure that the databases feed into GIS platforms, drone flight planning, rescue-deployment software.
   
   

4. Empower local networks: Train radio-operators, map them, integrate them into the communication chain when digital networks fail.
   
   

5. Strengthen coordination: Ensure all government agencies, LGUs, volunteers and civic organizations operate under shared SOPs, interoperable systems and clear roles.
   
   

6. Drill and refine: Conduct regular exercises in realistic remote-area scenarios, test the tech, test the volunteers, test the communication backup. After each exercise, debrief and update the system.
   
   

Final thoughts: the human factor

Technology is only as good as the people who use it and the data that feeds it. You could have the most advanced satellite, drone and AI system – but if you don’t know who you’re looking for, or where, or the local radio-operator doesn’t know the protocol, then you may still fail to reach victims in time. And in remote terrain, every minute counts.

In the end: tracking down disaster victims in remote areas isn’t just a tech problem—it’s a data-problem, a coordination-problem and a community-engagement problem. As we face more intense storms, landslides and infrastructure-failures, we must ensure that our systems, our volunteers and our technologies are ready—and working together.

So I’ll leave you with the question: Are we truly ready? The tools may exist, the laws may be in place—but are all the gears turning in sync?

Let’s hope we are—but let’s also keep pushing until we are.

RAMON IKE V. SENERES

www.facebook.com/ike.seneres iseneres@yahoo.com senseneres.blogspot.com 09088877282/06-13-2026

CAN THE CONSPIRACY THEORY APPLY TO CORRUPTION CASES?

 CAN THE CONSPIRACY THEORY APPLY TO CORRUPTION CASES?

Let me begin by saying this: I am not a lawyer. I write not with legal training, but with common sense—something every citizen is entitled to use. If what I say sounds too simplistic, that’s because I am a simple man trying to understand a complicated system that seems designed to make corruption look like an art form.

That said, I believe the conspiracy theory—at least in its legal sense—can and does apply to corruption cases. It is the principle that allows prosecutors to go after not just one thief, but an entire network of thieves working together.

So, who are these conspirators?

Some say the real masterminds are those inside the Department of Public Works and Highways (DPWH) who have “mastered” the system and know exactly how to bring out the money. But first, they need access to the source of the funds. And where is that? Congress, of course.

It begins with appropriations—officially labeled as infrastructure funds, often for flood control or local projects. When that doesn’t work, there are insertions—those mysterious add-ons to the national budget that appear after the hearings are done.

Once that money is earmarked, the Department of Budget and Management (DBM) enters the picture. Somehow, some way, someone there has to release the funds. Then come the Terms of Reference (TOR), the Bids and Awards Committee (BAC), and the Notice to Proceed (NTP). Each step requires signatures—each signature, a potential collaborator.

From the Head of the Procuring Entity (HOPE) who signs off, to the User Acceptance Test (UAT) committee that certifies the completion of projects, to the accountants who prepare the vouchers, all the way to the banks that clear suspicious transactions—somebody, somewhere, must have looked the other way.

If this sounds like a conspiracy theory, that’s because it is—but one that’s entirely plausible.

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What the Law Says

In Philippine law, conspiracy means two or more persons agreeing to commit a crime and taking steps to execute it. There need not be a written agreement—just evidence of coordinated actions toward an illegal goal.

In corruption cases, that can mean:

• Officials and contractors colluding to rig bids or inflate costs.
  
  

• Coordinated kickback arrangements.
  
  

• Ghost projects and fabricated payrolls.
  
  

When proven, all conspirators are equally liable, even if only one signed the check or issued the approval. The Supreme Court has long ruled that active cooperation—not mere presence—constitutes conspiracy.

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Why This Matters

Applying conspiracy theory to corruption is not just legal jargon—it's a justice strategy. It helps dismantle entire networks instead of stopping at small fish.

Think about the ongoing flood control scam, now under investigation. As of late 2025, at least 25 individuals, including DPWH engineers and private contractors, face non-bailable charges for graft, corruption, and malversation. Investigators say syndicates siphoned off as much as 30% of project funds, with ghost projects uncovered in Bulacan and other provinces.

DPWH Secretary Vince Dizon called it a “massive theft of people’s money,” and he’s right. Documents were falsified, signatures forged, and funds released for projects that never existed. The Department of Justice (DOJ) and the Ombudsman are now reviewing whistleblower testimonies to prove a coordinated conspiracy—from lawmakers who initiated funding, to regional officers who processed papers, down to private contractors who “executed” nothing.

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Collusion and Conspiracy—Two Faces of the Same Crime

In plain language, collusion is how conspiracy works. Collusion means secret cooperation for fraud or deceit; conspiracy means criminal cooperation to commit a crime. In corruption cases, they often walk hand in hand.

When a contractor and a public official agree to rig a bid, that’s collusion. When they act together to make it happen, that’s conspiracy. The courts can infer conspiracy from patterns of coordinated approvals, synchronized signatures, or shared benefits—even without direct evidence of an agreement.

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Why It’s So Hard to Prosecute

Proving conspiracy is tricky. Prosecutors must show a “meeting of the minds” and coordinated acts—not just parallel misconduct. As Prosecutor General Benedicto Malcontento noted, more affidavits and clearer testimonies are needed to build strong conspiracy cases. Politics also complicates matters, since some accused are high-ranking officials.

Still, when proven, conspiracy charges are powerful. They break the “silo defense” (“I was only following orders”) and hold everyone accountable for the collective theft.

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A Citizen’s View

To my simple mind, corruption in the Philippines rarely happens alone. It thrives in company—in boardrooms, corridors, and backrooms where conspirators meet over coffee and contracts. That’s why I say yes: the conspiracy theory applies, and it should be used aggressively.

We must stop treating corruption as isolated acts of greed. It is organized crime in barong Tagalog—executed through paperwork, not pistols.

Perhaps the real challenge is not in defining conspiracy, but in proving the courage to confront it.

So here’s my question: if conspiracy exists in theory, can we make it work in court? Because until we do, the conspirators will keep laughing all the way to the bank—ours, not theirs.

RAMON IKE V. SENERES

www.facebook.com/ike.seneres iseneres@yahoo.com senseneres.blogspot.com 09088877282/06-12-2026