On 12 May 2020, the IATF announced the relaxing of community quarantine protocols as we transition from Enhanced Community Quarantine (ECQ) to Modified ECQ (MECQ), General Community Quarantine (GCQ), and Modified GCQ (MGCQ) starting on 16 May. Under the new schemes, the list of economic activities to be allowed are expanded at varying levels of capacity and subject to minimum health standards.
To inform the phasing-in of different sectors and jobs, we present here findings from our risk assessment of various job types. Our analysis showed that:
• As economic activities are opened up, proper phasing in of different sectors and jobs should consider both economic contributions and the health risks involved, with health as the top priority. Understanding disease-related risks associated with different jobs and sectors can help guide management of the COVID-19 pandemic.
• About half of Philippine jobs are categorized as high contributors to the economy for the next 18 months but are high-risk spreaders.
• Half of the jobs are exposed to moderate risk to disease, while 38% are at high risk.
• Manageable interventions like increasing PPE and hygiene practices and/or effectivity, and switching to work from home arrangements can lessen jobs at moderate to high risk by at least 21%.
We also propose simple decision support tools that can be used as guides for the reopening of workplaces.
Potential COVID-19 Spreaders by Job Type
The previous ECQ practically suspended on-site work for all job types, except for our so-called healthcare- and non-healthcare frontliners. This has serious economic implications especially since 816 out of 987 job types (82%) in the Philippines have high importance to sustain the economy in the next 18 months (Figure 1). Our main challenge then is that the majority of these jobs (65%) have high potential to spread the COVID-19 disease as they require close or regular interaction with people. This includes health workers (Category E) who make up 13.8% of jobs.
Aside from those in the healthcare industry, most of the jobs that are both highly important and high-risk spreaders at the same time (Category D) are in the food, agriculture, manufacturing, and service sectors. These sectors are highly interdependent with others, and effects on one sector can have long-lasting effects on another. Outside of healthcare workers, we need to carefully reconsider the costs and benefits of reactivating activities related to this group, and ensure that strict public health measures are followed.
We also need to look into the case of jobs that are relatively not as critical in sustaining the economy for the next 18 months but are high risk disease spreaders. These comprise 16.4% of jobs (Category C), mostly in the education and recreation sectors. These jobs are relatively less interdependent on others. For this sector, a clear intervention would be to explore other modes of job delivery to minimize social interaction, without necessarily completely discontinuing operations, such as through continued work from home and other alternative working arrangements. This way, the potential for spreading the disease through these jobs becomes low.
About one-fifth of jobs (17.7%) are of great importance to sustaining the economy for the next 18 months and are low risk disease spreaders (Category B). Most of these are in the financial, banking and BPO companies. These also are highly interdependent and have long-lasting effects on other sectors. Only less than one percent (0.9%) of jobs are considered part of Category A which contribute less to sustaining the economy for the next 18 months and are low risk disease spreaders. Minimum public health measures should be observed in these two groups (Categories A and B).
COVID-19 Risk to Various Workers
To understand the potential impact of reopening certain economic activities, we analyzed the potential risk of becoming sick and spreading the disease through various jobs in particular sectors and occupations. The risk scores were calculated using their potential exposure to the disease and physical proximity to others. The level of exposure to disease of the different job sectors were based on the number of people they encounter and how long they work, while physical proximity is estimated based on how crowded their workplace is. The risk score also takes into account workers’ protection level, such as use of personal protective equipment (PPE) and hygiene practices.
Assuming a 30% level of workplace protection, our analysis revealed that half of the jobs are exposed to moderate risk to disease, while 38% are at high risk. Aside from health and social workers, high risk industries include workers involved in water supply, sewerage, waste management and remediation activities; education; accommodation and food service activities; transportation and storage; arts, entertainment and recreation; and other service activities.
Not surprisingly, by occupational group, high risk was found among service and sales workers, professionals, but mostly due to health care professionals, and technicians and associate professionals. Breaking these down into sub-major groups, the ones most at risk include the armed forces, personal care workers, and protective services workers.
Job Risk by Income Level
Our analysis further showed that there is no correlation between average monthly salary and risk level. Majority of the jobs have average monthly incomes of around 20,000 to 60,000 pesos, with varying risk scores ranging from low (0.08) to high (4.55). A few high-salary jobs (more than 80,000 pesos) were estimated to have low or moderate risk scores. These include high-ranking army officers, judges, chief executives, and managers (Figure 2).
In any case, while the level of risk is not particularly related to the level of job salary, COVID-19 may have disproportionate impacts as those in the lower income bracket would have a much harder time to cover hospitalization expenses or recover lost income due to suspension of work. We also note that the data we have for the analysis do not account for jobs in the informal sector.
Job Risk by Regions
The impact of the pandemic varies across regions, with the National Capital Region, CALABARZON and Central Visayas the hardest hit in terms of incidence and prevalence of the disease. Cognizant of this, the IATF released risk-based guidelines in deciding whether regions, provinces or highly urbanized cities should be under ECQ, MECQ, GCQ or MGCQ.
To shed light on which sectors can be activated while minimizing risk at the same time, we conducted Binary Integer Goal Programming at the regional level. Our model showed that for regions to produce at least 60% of the Gross Regional Domestic Product, 22.7 million workers can go to work with the average computed risk score of 0.58 +/- 0.77. These workers belong to several sectors, namely, agriculture, hunting and forestry; fishing and aquaculture; manufacturing; electricity, gas, steam, and air conditioning supply; water supply; sewerage, waste management and remediation activities; wholesale and retail trade; repair of motor vehicles and motorcycles; transportation and storage; information and communication; financial and insurance activities; real estate activities; public administration and defense; compulsory social security; and human health and social work activities. Additionally, for this to happen, the Education and Other Services sectors may not need to resume activity under GCQ in all regions.
Effect of Interventions on Job Risks
Protection level plays a key role in the risk level. Decreasing the protection level increases the risk of each job for each industrial sector, occupational group, and UPSE classification. Without any protection, almost two-thirds (63%) of UPSE Category D jobs and one-third (33%) of UPSE Category B jobs will be at high risk of getting infected. Under increased protection level (45%), which assumes perfect use of face masks and strict hand hygiene, 74 out of 505 (15%) UPSE Category D and 11 out of 175 (6%) UPSE Category B jobs are at high risk.
Job risks due to disease spread can be managed through various measures like the use of PPEs, hygienic practices and work from home arrangements to lessen contact. Job risks are lowered when protection levels were increased by 1) general increase in protection levels by 15% assuming increased or more effective use of PPEs; 2) 99% protection level for jobs that can shift to work from home; and, 3) 80% protection levels for certain health workers again assuming increased or more effective use of PPEs. Moderate and high-risk jobs lessened by 8% and 14%, respectively. Low risk jobs increased by 21%. There is ample room for intervention from the work from home setup since currently, around 90% or 887 jobs are normally not work from home, and most of these are moderate and high infection risk jobs.
Decision Support Tools to Guide Workplace Reopening
Out of the 987 jobs we have identified in our analysis, only 100 (10.13%) can be done through a work from home arrangement. The proportion is even smaller for jobs that pay Php18,200 or less per month, where only 28 of 488 jobs (5.74%) can be performed remotely.
In light of the foregoing, the UP COVID-19 Pandemic Response Team has developed simple decision support tools that both public and private organizations can use as we transition to modified community quarantine. Figure 3 below shows a decision tree that institutions can refer to in quickly assessing their readiness to resume operations. It takes into account the basic health requirements of the IATF and DOH.
Another tool that government agencies, companies, or even individuals may find useful in assessing relative risks and interventions is our Job Risk Profiling Tool developed up by the UPLB Biomathematics Team. It provides more information on the analysis presented here, and comes with calculators that can be used to search for specific job risk profiles and specify different configurations of encounters, work shift duration, workplace crowd density, and level of protection. This open calculator can be used to look at potential scenarios of intervention in specific workplace situations.
 Jobs based on the National Center for O*NET Development (Center) (https://www.onetcenter.org/overview.html) which has data on exposure and physical proximity per job.
 Adopted with modifications from the UP School of Economics (UPSE) classification.
 Methods based on the paper by Dy, L., & Rabajante, J. (2020) A COVID-19 Infection Risk Model for Frontline Health Care Workers. doi: 10.1101/2020.03.27.20045336 were used to derive exposure to disease, physical proximity data and the list of job titles from the National Center for O*NET Development. Work Context: Physical Proximity. O*NET OnLine. (Retrieved April 17, 2020, from https://www.onetonline.org/find/descriptor/result/4.C.2.a.3), and National Center for O*NET Development. Work Context: Exposed to Disease or Infections. O*NET OnLine. (Retrieved April 17, 2020, from https://www.onetonline.org/find/descriptor/result/4.C.2.c.1.b?a=1). Risk scores and assessment were calculated using the risk model similar to Gamio, L. (2020). The Workers Who Face the Greatest Coronavirus Risk (Retrieved 17 April 2020, from https://www.nytimes.com/interactive/2020/03/15/business/economy/coronavirus-worker-risk.html)
For questions or clarifications related to the technical or other aspects of this policy note, please send an email to upri.covid19＠up.edu.ph. Scientific reports related to this statement will be posted in the endcov.ph site.