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Mapping Philippine
Workers at Risk of
Automation in the
Fourth Industrial Revolution
AIM RSN PCC Working Paper 2019-001
ASIAN INSTITUTE OF MANAGEMENT
RIZALINO S. NAVARRO POLICY CENTER FOR COMPETITIVENESS
WORKING PAPER 2019-001
Mapping Philippine Workers at Risk of Automation
in the Fourth Industrial Revolution
Jamil Paolo Francisco
Stephanie Rose Flores
Tristan Canare
Christopher Ed Caboverde
Benjur Emmanuel Borja
Christopher Monterola
Asian Institute of Management
APRIL 2019
2
AIM Rizalino S. Navarro Policy Center for Competitiveness
3/F Eugenio Lopez Foundation Building,
Joseph R. McMicking Campus, Asian Institute of Management,
123 Paseo de Roxas, 1260 Makati City, Philippines
Telephone: +63 2 892 4011 | +63 2 403 9498
Email: policycenter@aim.edu
Cover page photo source: Rodolfo Clix (CC0 License)
This document has been produced with the financial assistance and/or support of Konrad-
Adenauer-Stiftung (KAS) Philippines.
The views expressed herein do not necessarily reflect the views of KAS or the Asian Institute of
Management.
3
Mapping Philippine Workers at Risk of Automation
in the Fourth Industrial Revolution
1
Jamil Paolo Francisco
Tristan Canare
Stephanie Rose Flores
Christopher Ed Caboverde
Benjur Emmanuel Borja
Christopher Monterola
Abstract
The fourth industrial revolution (4IR) is expected to change the nature of jobs and the way people
work. One of its prominent features is the automation of tasks, made possible by remarkable
advancements in computing technology, artificial intelligence, and advanced robotics. While
automation may increase labor productivity, it may also displace workers whose main tasks are
automated. This study maps Philippine workers at different levels of automation risk across eight
categories: economic sector, age, gender, educational attainment, income, type of organization,
nature of employment, and administrative region.
Keywords: Fourth Industrial Revolution, industry 4.0, automation, jobs, Philippines.
Corresponding Author:
Tristan Canare, AIM Rizalino S. Navarro Policy Center for Competitiveness
Tel: +632-892-4011. Email: tcanare@aim.edu
1
We thank Diane Cruz, Luis Lopez and Kaye Ng for their collaboration and invaluable support in the process of
matching Philippine and U.S. occupations.
4
I. INTRODUCTION AND OBJECTIVES
Each industrial revolution had radically changed the way we live and work. The production of
electricity, the mechanisation of agriculture and industry, and the proliferation of computers
and electronic equipment had transformed the nature of jobs and employment during each
period of rapid technological advancement. The Fourth Industrial Revolution (sometimes
referred to as ‘4IR’) is expected to be equally disruptive, or perhaps even more so, given the
unprecedented speed in which new technological breakthroughs are discovered and adopted.
We now face the reality of three-dimensional printing, self-driving vehicles, artificial
intelligence, advanced robotics, data analytics, and the internet of things. While these
technologies may be more accessible to and thus more prevalent in developed countries, the
digital nature of these technologies allow them to spread more quickly to the Developing World
than had many technologies in the past. Whereas electricity and telephones took about 30
years to become widely used around the world, mobile phones and the internet have taken less
than a decade (Williamson et al., 2015).
In each industrial revolution, the adoption of new technologies directly contributed to massive
gains in productivity, boosted economic activity, increased per capita incomes, and lifted
workers’ quality of life (United Nations Department of Economic and Social Affairs, 2017;
International Federation of Robots, 2017). At the center of each industrial application of new
technology was some form of automation (Davenport & Kirby, 2015). In the past, industrial
machines were primarily developed and installed to relieve human workers of burdensome or
dangerous manual labor, allowing workers to shift to higher value occupations. Since the
beginning of the third industrial revolution, the advent of computing technologies began to
augment human capacities in these higher value occupations. What sets apart the kind of
‘digital automation’ that characterises the technologies of the fourth industrial revolution is that
the ever-increasing cognitive and analytic powers of computers have started to replace human
capacities across occupations (Robinson & Bogen, 2017; Tito, 2017)
Typically, this displacement effect has been the biggest cause for concern among policymakers
grappling with the possible impacts of 4IR (ADB, 2018). The possible repercussions in terms of
long-term unemployment, income inequality, transient poverty, and social unrest are
particularly worrisome in developing countries. An ILO study (Chang & Huynh, 2016) estimates
that about 56 percent of jobs in five ASEAN countries—Cambodia, Indonesia, Philippines,
Thailand and Vietnam—are at risk of automation. According to the same study, 49 percent of
jobs in the Philippines can be automated.
We submit that the first step in determining the best course of action for policymakers in
response to the threat of ‘digital automation’ is to identify the specific jobs and sectors in the
5
local economy that are particularly at risk, so as to guide targeted policy measures.
A number of studies (Bresnahan 1999; Autor et al. 2003; Goos & Manning, 2007) had sought to
identify how computer technology could affect jobs and employment in the future, but it is the
seminal work of Frey and Osborne (2017) that provides highly useful information on the specific
level of risk of being automated for each occupation in the United States. In this study, we map
the occupations at particular levels of risk of automation across sectors, income groups, and
geographic regions in the Philippines by matching Frey and Osborne’s output with the
equivalent Philippine occupational classifications, and combining this information with socio-
economic and demographic data from the merged 2015 Family Income and Expenditure Survey
and Q1 2016 Philippine Labor Force Survey.
II. THE IMPACT OF AUTOMATION ON JOBS
In attempting to predict the impact of automation on jobs, two important concepts must be
understood: (1) industrial technologies automate tasks not jobs, and (2) technological feasibility
does not mean economic viability (Kang & Francisco, 2019).
A job consists of a bundle of tasks, some of which are more ‘automatable’ than others.
Automation can relieve workers of certain tasks, usually those that are manual and routine
(Autor et al. 2003), thereby increasing worker productivity and potentially freeing up resources
for use in more valuable tasks. However, when numerous tasks associated with a particular job
are automatable, workers employed in this job face a higher risk of displacement. For example,
the job of a retail sales clerk involves many tasks that are now mechanically or digitally
automatable—receiving merchandise, keeping inventory, stocking shelves, preparing invoices,
operating cash registers, accepting payments and processing transactions. It is unsurprising
therefore that sales clerks and shop assistants are among those recognised as facing the most
serious threat from automation by the ILO (Chang & Huynh, 2016). Robots and artificial
intelligence (AI) may soon make human shop attendants unnecessary, especially as e-commerce
continues to replace many brick-and-mortar operations.
This trend may take place across many other sectors in the near future. One US study found that
from 1990 to 2017, every additional robot displaced six workers, and that increasing robot
density by one new robot per thousand workers reduced workers’ wages by half a percentage
point (Acemoglu & Restrepo, 2017).
The impact of automation on employment and wages in the future may vary widely per sector
and per economy. In China, Japan and Korea, where close to 890,000 robots had been deployed
as of 2015, most robots were deployed in capital-intensive industries, such as electronics,
automotive, metals processing, plastics, and chemicals. These industries generally did not
6
employ as many workers in the labour-intensive industries that prevailed in many developing
Asian economies (Asian Development Bank, 2018). For example, while the automotive industry
accounted for almost 40% of robots deployed in Asia, it only provided about 4% of total
employment, making the impact of automation on jobs relatively low. Fortunately also for
workers, the garment, textile and leather industries, which provided 19% of employment,
accounted for less than 0.1% of robot sales (Figure 1). While automation has begun in a number
of capital-intensive industries across Asia, its impact on employment so far may have been
dampened by the current economic viability of labor-intensive operations.
Figure 1. Robot Sales versus Employment Share in Manufacturing, Selected Industries in Asia
Source: Asian Development Bank, 2018. Asian Development Outlook 2018: How Technology Affects Jobs. Available
at: https://www.adb.org/sites/default/files/publication/411666/ado2018.pdf.
While new technologies may technically displace human labor, actual displacement will depend
on the economic viability of automation vis-a-vis labor-intensive alternatives. In the case of
intermediate goods exporters serving global markets, the cost of installing, programming,
operating and maintaining robots that can technically replace human workers must be low
enough to offset the cost savings that motivated the offshoring or subcontracting of production
in the first place (Kang & Francisco, 2019). In the case of local businesses catering mostly to local
demand, the economic motivation to automate will also depend on relative costs and benefits.
Approximately 85% of jobs in the Philippines serve mainly domestic final demand (Philippine
Statistical Authority, 2017), which makes them less vulnerable to the threat of automation in
7
the short term than those that serve foreign markets. Replacing human workers with expensive
robots may not yet be economically feasible given the relatively low price of labor and high
price of capital. However, this may change as the price of new technology drops and if the gains
in productivity are sufficiently large.
Nevertheless, labor abundant countries must prepare their workforce for future-readiness not
only to maintain or improve their global competitiveness, but also to minimise the harmful
effects of job displacement. Developing countries like the Philippines particularly face higher
risks of (1) long-term unemployment and underemployment, especially among the youth, and
(2) widening income inequality, which may further contribute to (3) persistent
underdevelopment and social unrest (Kang & Francisco, 2019).
An important step towards the development of optimal policies to minimise these risks is
identifying vulnerable sectors. This will allow policymakers to focus strategic interventions on
sectors and groups that may require most urgent support.
III. FRAMEWORK AND METHODOLOGY
III.A. The Frey and Osborne (2017) Framework
Frey and Osborne (2017) performed pioneering work on identifying occupations that were at-
risk due to the automation of tasks. They computed for estimates of the probability of being
automated for 702 occupations listed under the United States Standard Occupation
Classification (US SOC) System.
The Frey and Osborne framework was built on the seminal work of Autor et al. (2003), which
posited that routine manual and routine cognitive tasks could be easily “computerized”, making
jobs that mostly involved these tasks susceptible to automation. An important realization made
by Frey and Osborne was that recent technological advancements in machine learning, artificial
intelligence, computational statistics, and robotics, had made it technologically feasible for
computers to perform more complex functions, making even non-routine cognitive and manual
tasks automatable.
The Frey and Osborne framework assumes a Cobb-Douglas production function of the following
form:
,
where Q is output, LS and LNS are susceptible and non-susceptible labor inputs, respectively, and
C is computer capital. This production function differs from the Autor et al. model in that
susceptible and non-susceptible labor inputs are not limited to routine and non-routine tasks. In
8
addition, computer inputs C are limited by certain engineering bottlenecks. These bottlenecks
are the factors that limit the substitutability between labor and computer inputs. Frey and
Osborne recognise that if there is enough data available for pattern recognition, any task is
automatable as long as these engineering bottlenecks are overcome.
The Frey and Osborne framework posits that there are three task characteristics that serve as
bottlenecks to automation: perception and manipulation, creative intelligence, and social
intelligence. It is difficult to automate tasks that require these skills because robots and
computers have yet to acquire these abilities even with machine learning, artificial intelligence,
and large amount of data.
Depth and breadth perception are skills that computers and robots still do not possess; and
perception is important in tasks that require manipulation. Manipulation is particularly
important in tasks that require handling of irregular objects and an unstructured work
environment. Creative intelligence tasks are those that require the ability to generate novel and
valuable ideas such as concepts, theories, compositions, poems, and jokes. And while novelty is
not necessarily difficult for computers, what is valuable may change across time and could vary
across cultures. This differences in sources of value will be a challenge for computers to
perceive. In addition, creative intelligence tasks require sensible combinations of ideas, which in
turn requires a rich body of knowledge similar to those possessed by humans. Meanwhile,
social intelligence tasks are crucial to occupations requiring negotiation, persuasion, care, and
other forms of human interaction. This is a bottleneck for automation because although
computers can imitate some human interaction, they still cannot recognise and respond
properly to human emotions (Frey and Osborne 2017; Boden 2004).
Frey and Osborne calculated the probabilities of automation for each of the 702 occupations
using the O*Net Center database. This data set describes and characterises about 1,000
2
occupations in terms of worker characteristics, worker requirements, experience requirements,
occupational requirements, workforce characteristics, and occupation-specific information.
Each of these six domains are characterised by several variables. The variables that describe the
three automation bottlenecks fall under worker characteristics. The perception and
manipulation bottleneck was represented by the variables ‘finger dexterity', ‘manual dexterity',
and ‘cramped workspace and awkward positions’. On the other hand, creative intelligence was
represented by ‘originality’ and ‘fine arts’, while social intelligence was represented by ‘social
perceptiveness’, ‘negotiation’, ‘persuasion’, and ‘assisting and caring for others’. These variables
are described in detail in Appendix 1.
2
Since the O*Net database contained more than 1,000 occupations, similar occupations were aggregated to match
those in the US SOC listing.
9
III.B. Methodology
In this study, we adopted the probabilities estimated by Frey and Osborne for each occupation
in the US SOC list, and assigned them to equivalent Philippine occupations. Ideally, Frey and
Osborne’s framework could have been used on local job descriptions, and probabilities of
automation could have been estimated for each local job. However, given that a database of
worker characteristics and occupation-specific information like that of US O*Net was currently
unavailable for Philippines jobs, we simply adopted the probabilities estimated by Frey and
Osborne for US occupations. In doing this, we implicitly assumed that the equivalent Philippine
occupations had similar descriptions.
We used Philippine data from the merged 2015 Family Income and Expenditure Survey (FIES)
and first quarter 2016 Labor Force Survey (LFS). We matched the 409 distinct occupations in
this FIES-LFS dataset with the 702 occupations in the Frey and Osborne list. To automate the
matching process, we created an AI-based Natural Language Processing technique to find the
nearest Frey and Osborne job title equivalent for each job title in the FIES-LFS list. We
represented the job titles such that their semantic similarity and closeness with one another
can be calculated using numerical distance values, with the best matches having the shortest
distances. We used the GloVe word embeddings (Pennington, Socher, & Manning, 2014) to
obtain these numerical representations of the words. GloVe word embeddings maps each word
into vector representations generated based on their co-occurrence within a corpus, or a set of
texts. Words which are often found close to one another within the corpus are considered
related or similar. After generating the vectors for each of the more than 400 unique FIES-LFS
and more than 700 Frey and Osborne occupations, we used a distance metric (cosine similarity)
to find the best matching between the two list.
The FIES-LFS occupations were based on the Philippine Standard Occupation Code (PSOC). The
resulting matches were visually checked for accuracy; and incorrectly-matched occupations
were manually corrected by selecting the most appropriate occupation match from the Frey
and Osborne list. Manual matching was done first by looking at the occupation title and then at
their official definitions based on the PSOC and O*Net databases.
The final occupational matches and their corresponding probability of being automated are
presented in Appendix 2. Not all jobs in the FIES-LFS dataset has a reasonable match with the
Frey and Osborne occupations. Nonetheless, this accounts for a small share of occupations and
observations. Only ten out of the 408 occupations in FIES-LFS have no corresponding match,
accounting for 0.2 percent of total observations. The unmatched occupations are reported in
Appendix 3.
10
Mapping was done across eight variables – sector, educational attainment, class of worker,
gender, age, region, income decile, and nature of employment. The groups within these
variables were compared two ways. First, in terms of the average probability that the
occupation of each member of the group will be automated. For instance, workers can be
grouped according to the tri-sector classification – Agriculture, Industry, and Services. The
average job automation probability of the workers belonging to each of these three sectors
were compared. The second is by comparing the share of workers whose jobs have low,
medium, and high probability of being automated. Low probability of being automated was
arbitrarily defined as zero to 33 percent, medium is from greater than 33 percent to 66 percent,
and high is greater than 66 percent.
IV. RESULTS AND DISCUSSIONS
Overall, jobs in the Philippines were found to have a 67.9 percent average probability of being
automated. Based on Q1 2016 employment data, 64.8 percent of Philippine workers are
employed in high-risk jobs (Figure 2). Disaggregating the observations according to the eight
variables gives a better picture of which group of workers are more at-risk from automation.
Figure 2. Proportion of workers employed in low, medium, and high-risk jobs
Source: Author’s computations
IV.A. By Sector
In the three-sector classification, jobs in Agriculture were found to have the highest average
probability of automation at 90 percent, followed by industry at 71 percent. The services sector,
which accounts for majority of jobs in the country (57%), faces an average probability of
automation of 54 percent (Figure 4).
22%
13%
65%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low Medium High
11
Figure 3. Employment share by sector, in percent, 2007 to 2017.
Source: Philippine Statistical Authority.
When examined according to level of risk, the vulnerability of the agriculture sector becomes
striking as 97 percent of agricultural workers are found to have jobs considered to be at high
risk of automation (Figure 5). The services sector has the greatest diversity in terms of
automation risk with 42 per cent of jobs classified as high risk, and 36 per cent as low risk.
Figure 4. Average probabilities of jobs being automated, by sector
Source: Author’s computations
35.1 35.3 34.4 33.2 33 32.2 31 30.5 29.2 27 25.4
15.3 14.8 14.5 15 14.9 15.3 15.6 16 16.2 17.5 18.3
48.9 49.6 49.9 51.1 51.8 52.1 52.6 53.5 54.7 55.6 56.3
0
10
20
30
40
50
60
70
80
90
100
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Agriculture Industry Services
90%
71%
54%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Agriculture Industry Services
12
Figure 5. Proportion of workers in low, medium, and high-risk jobs, by sector
Source: Author’s computations
Disaggregating into 19 sub-sectors
3
gives a clearer map of the risk distribution. Agriculture,
forestry, and fishery jobs are found to be most susceptible to automation, at 90 percent average
risk (Figure 6). Other sub-sectors that face high risk of automation on average are Finance and
Insurance (79 percent), Mining and Quarrying (78 percent), Construction (76 percent), Water
Supply, Sewerage, Waste Management, and Remediation (74 percent), Accommodation and
Food (72 percent), Transportation and Storage (72 percent), and Real Estate (71 percent). The
sub-sectors with the smallest average probability of job automation are Education (15 percent),
Arts, Entertainment, and Recreation (33 percent), and Human Health and Social Work (33
percent).
Looking at the proportion of high-risk jobs gives a slightly different ranking of susceptibility to
automation (Figure 7). Agriculture jobs remain the most susceptible – 97 percent of which are
classified as high-risk. Construction (89 percent), Real Estate (80 percent), Finance and
Insurance (78 percent), Mining and Quarrying (78 percent), and Accommodation and Food (70
percent) follow. The sub-sectors with the lowest proportion of high-risk jobs are Education (7
percent), Arts, Entertainment, and Recreation (14 percent), and Human Health and Social Work
(23 percent).
3
The industry grouping was patterned after PSIC classification, but two industries were merged with Other Service
Activities: (1) activities of households as employers; undifferentiated goods-and-services-producing activities for
own use; and (2) activities of extraterritorial organizations and bodies.
0.4%
17%
36%
3%
5%
22%
97%
78%
42%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Agriculture Industry Services
Low Medium High
13
Figure 6. Average probabilities of jobs being automated, by sub-sector
Source: Author’s computations
A cursory check of the labor force survey data would suggest which jobs drive the numbers in
Figure 7. For instance, the high-risk occupations in the Agriculture sector are mainly farmhands,
farm laborers, and fishermen. In Construction and Mining, the high-risk occupations are mostly
manual laborers such as construction and mining workers. Meanwhile, the high-risk
occupations in the finance and insurance sub-sector are mostly bank tellers, counter clerks, and
debt collectors. On the other hand, Education and Health are dominated by low-risk
occupations because it is mostly composed of teachers, doctors, and nurses, which cannot yet
be automated.
90%
78%
65%
58%
74%
76%
54%
72%
72%
64%
79%
71%
63%
56%
40%
15%
33%
30%
56%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Agriculture, Forestry and Fishing
Mining and Quarrying
Manufacturing
Electricity, Gas, Steam and Air-conditioning supply
Water supply; Sewerage, Waste Mgt.
Construction
Wholesale and Retail; Repair of vehicles
Transportation and Storage
Accommodation and Food Service Activities
Information and Communication
Financial and Insurance activities
Real Estate activities
Professional, Scientific and Technical Activities
Administrative and Support Service Activities
Public Admin. & Defense; Compulsory Social Secu.
Education
Human Health and Social Work activities
Arts, Entertainment and Recreation
Other service activities
14
Figure 7. Proportion of workers in low, medium, and high-risk jobs, by sub-sector
Source: Author’s computations
IV.B. By Age
All age groups face a high risk of automation, but younger workers (ages 15 to 24) face the
greatest risk at 76 percent. This declines significantly to 68 percent for the 25 to 34 years old;
and the probability did not change much as we move to the older age groups. Similarly, the
share of workers with high-risk jobs are significantly higher in the youngest age group (Figure
9). Seventy-two percent of workers between ages 15 and 24 group are at high risk of being
automated. This decreases to 64 percent in both 25 to 34 and 35 to 44 groups, 63 percent for
the 45 to 54 years old, and to 62 percent for the 55 to 64 and 65 and above groups. In addition,
the share of low-risk jobs increases with age.
This result is important given that majority of the Philippines’ workers are young. The
demographic sweet spot that the country is in may be threatened if the youth have jobs that
are at risk of being automated. In addition, the vulnerability of younger workers is a double-
edged sword, because although younger workers tend to be more trainable and more
adaptable than their older peers, higher levels of youth unemployment may lead to increased
crime and heightened social unrest, which may undermine economic progress.
0.4%
20%
26%
23%
10%
9%
40%
1%
22%
18%
16%
10%
30%
40%
55%
88%
61%
65%
24%
3%
2%
6%
34%
24%
3%
30%
53%
8%
29%
6%
9%
7%
8%
13%
5%
15%
21%
6%
97%
78%
69%
43%
66%
89%
29%
47%
70%
53%
78%
80%
63%
52%
31%
7%
23%
14%
70%
0% 20% 40% 60% 80% 100% 120%
Agriculture, Forestry and Fishing
Mining and Quarrying
Manufacturing
Electricity, Gas, Steam, Air-conditioning
Water supply; Sewerage, Waste Mgt.
Construction
Wholesale and Retail; Repair of vehicles
Transportation and Storage
Accommodation and Food Service
Information and Communication
Financial and Insurance activities
Real Estate activities
Professional, Scientific and Technical
Administrative and Support Services
Public Admin. & Defense; Compulsory Social Secu.
Education
Human Health and Social Work activities
Arts, Entertainment and Recreation
Other service activities
Low Medium High
15
Figure 8. Average probabilities of jobs being automated, by age group
Source: Author’s computations
Figure 9. Proportion of workers in low, medium, and high-risk jobs, by age group
Source: Author’s computations
IV.C. By Gender
Jobs held by men face a higher probability of being automated at an average of 73 percent
compared to 60 percent for jobs held by women (Figure 10). Looking at levels of risk, 71
percent of employed men are in jobs classified as high-risk, higher than 55 percent for women
(Figure 11). In addition, a larger proportion of females are employed in low-risk jobs (33.7
percent) as compared with males (14 percent).
Numbers in Figure 11 can be explained by the types of jobs commonly held by males and
females. For instance, jobs that are common and with high risk of automation such as
farmhands and laborers, rice and coconut farmers, carpenters, and building construction
76%
68% 66% 65% 64% 65%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 and older
10% 21% 24% 26% 28% 27%
18%
15% 12% 11% 11% 11%
72% 64% 64% 63% 62% 62%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 and
older
Low Medium High
16
laborers are dominated by males. On the other hand, low-risk jobs, particularly those in the
teaching profession, are mostly held by females.
Figure 10. Average probabilities of jobs being automated, by gender
Source: Author’s computations
Figure 11. Proportion of workers in low, medium, and high-risk jobs, by gender
Source: Author’s computations
IV.D. By Educational Attainment
Workers who have achieved a higher level of educational attainment appear to face lower risk
of being automated. Occupations of workers who earned a college degree face an average of 46
percent probability of being automated. This notably increased to 63 percent among those who
have some forms of post-secondary education but did not earn a college degree. Among
elementary graduates who received no further education, the probability is 75 percent; and 80
percent for those who did not earn an elementary degree (Figure 12).
73%
60%
0%
20%
40%
60%
80%
100%
Male Female
14%
34%
15%
11%
71% 55%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Male Female
Low Medium High
17
Figure 13 provides further evidence in support of the inverse relationship between educational
attainment and the probability of job automation in general. Thirty-seven percent of college
degree holders have jobs with high risk of being automated. This jumps to 54 percent among
those who have some forms of post-secondary education but did not graduate from college.
This proportion is 63 percent for high school graduates, 76 percent for elementary graduates,
and 83 percent for those with below elementary degree. Similarly, the share of workers with
jobs classified as low-risk increases with educational attainment. In fact, among college degree
holders, the proportion of low-risk workers is greater than the share of high-risk workers. This is
the only group that exhibits this case.
However, our findings also show that while jobs usually filled by workers who had received
higher levels of education, including college degrees, were less likely to be automated than jobs
usually filled by workers with lower levels of education, receiving higher levels of education is
not a guarantee that one’s job would be immune to automation. As much as 37 per cent of jobs
filled by college graduates face a high risk of being automated—these include accountants and
financial analysts.
The differences in proportion of high-risk workers can be explained by the predominance of
certain high-risk occupations among lower-education groups, particularly farmhands and farm
laborers, farmers, and building construction workers. Low-risk occupations, particularly those
that require a college degree and a professional license such as teachers, doctors, and other
professionals, are dominated by high-education workers.
Figure 12. Average probabilities of jobs being automated, by highest educational attainment
Source: Author’s computations
80% 75% 73% 68% 63%
46%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Below
elementary
degree
Elementary
degree
Below high
school degree
High school
degree
Above high
school degree
but below
college degree
College degree
or higher
18
Figure 13. Proportion of workers in low, medium, and high-risk jobs,
by highest educational attainment
Source: Author’s computations
IV.E. By Per Capita Income Decile
Mapping at-risk workers by income decile shows that the average probability of job automation
declines from lower to higher incomes deciles (Figure 14). From 82 percent in the first decile,
the average probability of automation decreases to 46 percent in the tenth. Moreover, the
biggest drop in probability occurs from the ninth to the tenth decile - a nine percentage point
decrease from 55 to 46 percent.
This is also reflected in the share of high-risk workers (Figure 15). Eighty-five percent of workers
in the first decile have jobs classified as high-risk of being automated, constantly decreasing
until it reaches 40 percent in the tenth. Moreover, the share of low-risk workers increases with
income, with the biggest jump occurring from the ninth to the tenth decile. From eight percent
in the poorest decile, it gradually increases to 37 percent until the ninth decile before jumping
to 48 percent in the tenth.
These results, along with that of education, have potentially serious social implications. While
the least-educated and the lowest-income groups face the lowest wages at present, the higher
risk of job displacement in the future worsens their vulnerability.
9% 14% 15% 19% 27%
51%
8% 10% 13%
18%
19%
12%
83% 76% 72% 63% 54%
37%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Below
elementary
degree
Elementary
degree
Below high
school degree
High school
degree
Above high
school degree
but below
college degree
College degree
or higher
Low Medium High
19
Figure 14. Average probabilities of jobs being automated, by decile
Source: Author’s computations
Figure 15. Proportion of workers in low, medium, and high-risk jobs, by decile
Source: Author’s computations
IV.F. By Class of Worker and Nature of Employment
Class of worker refers to the type of organization in which the person is employed. Figure 16
shows that unpaid family workers are the most at-risk, with an average probability of job
automation at 86 percent; followed by paid family workers at 79 percent. Government workers
82% 77% 75% 72% 70% 67% 64% 60% 55%
46%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
First Second Third Fourth Fifth Sixth Seventh Eighth Ninth Tenth
8% 11% 13% 16% 18% 22% 26% 31% 37% 48%
7% 10% 12% 14% 15% 18% 17% 18% 15%
12%
85% 79% 75% 70% 67% 60% 57% 51% 47% 40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
First Second Third Fourth Fifth Sixth Seventh Eighth Ninth Tenth
20
are found to be least at-risk, with an average probability of job automation at 32 percent. This
is due to the high number of public-school teachers, government officials, government health
workers, and the police belonging to this classification.
Aside from unpaid and paid family workers, those who work in private households are also
vulnerable because they have the highest proportion of workers classified as high-risk. These
include domestic helpers, hand launderers, and drivers.
Figure 16. Average probabilities of jobs being automated, by class of worker
Source: Author’s computations
Figure 17. Proportion of workers in low, medium, and high-risk jobs, by class of worker
Source: Author’s computations
64%
75%
32%
63%
65%
79%
86%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Private Household
Private Establishment
Gov't/Gov't Corporation
Self Employed
Employer
With pay (Family owned Business)
Without Pay (Family owned Business)
10%
13%
65%
31%
27%
6%
1%
5%
14%
11%
14%
10%
18%
19%
85%
74%
24%
55%
63%
76%
80%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Private Household
Private Establishment
Gov't/Gov't Corporation
Self Employed
Employer
With pay (Family owned Business)
Without pay (Family owned Business)
Low Medium High
21
A concept similar to class of worker is the nature of employment, which refers to the worker’s
tenure in his or her current occupation and workplace. As seen in Figure 18, workers classified
as working for different employers or customers on a day-to-day or week-to-week basis
(Different Employer) are the most vulnerable to automation with an average probability of job
automation of 87 percent. This is followed by workers engaged in short-term, seasonal, or
contractual jobs that are expected to end in less than a year (Short Te rm Jobs) at 75 percent.
Lastly, workers in Permanent Jobs are the least susceptible to job automation with an average
probability of 65 percent. Workers employed in short-term arrangements thus face a double
whammy of not having job security and being in occupations more likely to be automated.
Those working in Different Employer arrangement also have the largest proportion of high-risk
workers at 96 percent (Figure 19). These are mostly farm hands and farm laborers, construction
workers, carpenters, and hand launderers. Seventy-three percent of those under Short Term
Jobs have occupations that are classified as high-risk; while this proportion is 60 percent for
those under Permanent Job. Similarly, Permanent Job workers have the highest share of low-risk
occupations at 26 percent, followed by those under Short Term Job at 13 percent. Only two
percent of Different Employer workers have jobs classified as low-risk.
Figure 18. Average probabilities of jobs being automated, by nature of employment
Source: Author’s computations
65%
75%
87%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Permanent Job Short term Job Different Employer
22
Figure 19. Proportion of workers in low, medium, and high-risk jobs,
by nature of employment
Source: Author’s computations
IV.G. By Region
Figure 20 shows the average probability of jobs being automated per administrative region. Jobs
in the National Capital Region face the lowest risk at 61 percent, while jobs in the Cagayan
Valley face the highest average probability of automation at 76 percent. The top four regions
with the highest average probabilities of job automation are Cagayan Valley, ARMM (75
percent), Central Mindanao (71 percent) and Cordillera Administrative Region (71 percent). The
top three regions with the lowest average probabilities of job automation are NCR,
CALABARZON (64 percent) and Central Luzon (64 percent).
Figure 20. Average probabilities of jobs being automated, by region
Source: Author’s computations
26% 13% 2%
14%
14%
2%
60% 73%
96%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Permanent Job Short term Job Different Employer
Low Medium High
61%
71% 70% 76%
64% 64% 69% 69% 69% 68% 68% 70% 69% 69% 71% 75%
65%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
23
ARMM (77 percent), Cagayan Valley (76 percent), and CAR (71 percent), are also the regions
with the highest share of high-risk workers (Figure 21), owing to their dependence on the
Agriculture sector for employment. In the LFS dataset, these three regions also have the highest
share of workers in the Agriculture sector. On the other hand, NCR (55 percent), Central Luzon
(55 percent), and CALABARZON (58 percent) have the lowest share of high-risk jobs as the
economy of these regions, particularly NCR, also rely on Industry and Services. Expectedly,
these three regions have the highest share of workers employed under Industry and Services.
Figure 21. Proportion of workers in low, medium, and high-risk jobs, by region
Source: Author’s computations
V. SUMMARY AND CONCLUSION
Although there are convincing arguments for countries to be hopeful that automation should
increase labor productivity and create new jobs resulting in a positive net impact on the
economy in the long term, job displacement and economic disruptions in the short term should
certainly be a major cause for concern among policymakers and business leaders.
Recognizing that the level of risk of automation for a given occupation is determined by how
likely the tasks in that job can be automated, our mapping of workers employed in various
28% 22% 19% 15% 24% 25% 21% 21% 21% 21% 22% 21% 20% 21% 20% 15% 25%
17%
8% 13% 9%
21% 16%
10% 12% 12% 13% 12% 11% 15% 13% 12%
8%
16%
55%
71% 68% 76%
55% 58% 69% 67% 68% 66% 66% 68% 65% 66% 68% 77%
59%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low Medium High
24
occupations that face specific levels of automation risk based on Frey and Osborne's (2017)
estimates reveal the following sectors to be most vulnerable: (1) the youth – i.e. those between
age 15-24, (2) those who have received less education, (3) those who receive lower incomes, (4)
those who are employed in casual or irregular and seasonal jobs, or working without pay, (5)
and those working in agriculture, forestry and fishing. Our findings suggest that workers
employed in jobs that are most likely to be automated in the future also tend to be the most
vulnerable at present given their current economic conditions.
Government and the private sector must act quickly to equip the workforce – especially those
that face the greatest risk of automation – with higher level skills required to thrive in
automated work environments, while providing social safety nets to ensure that disadvantaged
workers do not fall between the cracks. They must also ensure that the rapid adoption of new
technologies does not worsen inequalities that may permanently lock in inefficiencies. A
worsening ‘digital divide’ may not only exacerbate current gaps, but also create new ones,
especially given that the Fourth Industrial Revolution has the potential to accelerate the returns
to resources, skills, and assets that may not be equally available to all. Special attention must
be given to the weakest and least capable sectors—the poor, the youth, the less skilled and less
educated, and those with little or no job security—where disruption is likely to have more
devastating and permanently debilitating effects.
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27
APPENDICES
Appendix 1. Bottlenecks to automation
Variable
Definitions
Perception and manipulation
Finger dexterity
The ability to make coordinated finger movements to grasp and
manipulate small objects
Manual dexterity
The ability to move hands and arms to grasp and manipulate objects
Cramped work space,
awkward positions
Measures how often the occupation is working under these
conditions
Creative intelligence
Originality
The capability to develop unique and clever ideas specific to a
situation, including problem solving
Fine arts
The possession of knowledge on theory and application of music,
dance, visual arts, drama, and sculpture
Social intelligence
Social Perceptiveness
The awareness of a person’s reaction and understanding why he or
she reacted that way
Negotiation
The ability to bring people together and settle their differences
Persuasion
The capability to change another person’s mind
Assisting and caring for others
The ability to provide personal assistance, medical service, and
emotional support to others
Appendix 2. Jobs matching and the corresponding probabilities
FIES-LFS (Philippine) Jobs
Frey and Osborne Jobs
Probability
Senior officials of humanitarian and other
special-interest organizations
Social and community service managers
0.0067
Legislative officials
Political Scientists
0.039
General managers/managing proprietors n.
e. c.
General and operations managers
0.16
Production and operations managers in
transport, storage and communications
Transportation, storage, and distribution
managers
0.59
Road transport service supervisors
Transportation, storage, and distribution
managers
0.59
General managers/managing proprietors in
construction
Construction managers
0.071
Government social benefits officials
Social and community service managers
0.0067
Other specialized managers
Managers, all other
0.25
Buyers
Wholesale and retail buyers, except farm
products
0.29
Production and operations managers in
business services
Business operations specialists, all other
0.23
Calculating machine operators
Accountants and auditors
0.94
28
Statistical, mathematical and related
associate professionals
Statisticians
0.22
Other plant growers
Soil and plant scientists
0.021
Town planners and related professionals
Urban and regional planners
0.13
Religious professionals
Clergy
0.0081
Non-ordained religious associate
professionals
Directors, religious activities and
education
0.025
Teaching associate professionals
Teachers and Instructors, all other
0.0095
Handicraft workers in wood and related
materials
Craft artists
0.035
Handicraft workers in textile, leather and
related materials
Craft artists
0.035
Handicraft workers in chemicals and related
materials
Craft artists
0.035
Other health professionals (except nursing)
Healthcare practitioners and technical
workers, all other
0.055
Other health associate professionals (except
nursing)
Healthcare practitioners and technical
workers, all other
0.055
Midwifery associate professionals
Healthcare practitioners and technical
workers, all other
0.055
Home-based personal care workers
Home health aides
0.39
Dental assistants
Dental assistants
0.51
Veterinary assistants
Veterinary assistants and laboratory
animal caretakers
0.86
Staff officers
First-line supervisors of police and
detectives
0.0044
Service and related workers
Production worker, all others
0.92
Waiters, waitresses and bartenders
Waiters and waitresses
0.94
Drivers of animal-drawn vehicles and
machinery
Animal trainers
0.1
Production and operations managers in
personal care, cleaning and relative services
First-line supervisors of personal service
workers
0.076
Sales supervisors in retail trade
First-line supervisors of retail sales
workers
0.28
Tellers and other counter clerks
Tellers
0.98
Government licensing officials
Court, municipal, and license clerks
0.46
Receptionists and information clerks
Receptionists and information clerks
0.96
Coding, proof-reading and related clerks
Proofreaders and copy markers
0.84
Cattle and dairy farmers
Miscellaneous agricultural workers
0.87
Hog raising farmers
Miscellaneous agricultural workers
0.87
Other livestock farmers
Miscellaneous agricultural workers
0.87
Chicken farmers
Miscellaneous agricultural workers
0.87
Other poultry farmers
Miscellaneous agricultural workers
0.87
29
Fish-farm cultivators (excluding prawns)
Miscellaneous agricultural workers
0.87
Oysters and mussels farm cultivators
Miscellaneous agricultural workers
0.87
Seaweeds cultivators
Miscellaneous agricultural workers
0.87
Other aqua products cultivators
Miscellaneous agricultural workers
0.87
Rice farmers
Miscellaneous agricultural workers
0.87
Corn farmers
Miscellaneous agricultural workers
0.87
Sugarcane farmers
Miscellaneous agricultural workers
0.87
Vegetable farmers
Miscellaneous agricultural workers
0.87
Cotton and fiber crops farmers
Miscellaneous agricultural workers
0.87
Root crops farmers
Miscellaneous agricultural workers
0.87
Field legumes farmers
Miscellaneous agricultural workers
0.87
Other field crop farmers
Miscellaneous agricultural workers
0.87
Coconut farmers
Miscellaneous agricultural workers
0.87
Fruit tree farmers
Miscellaneous agricultural workers
0.87
Tree nut farmers
Miscellaneous agricultural workers
0.87
Coffee and cacao farmers
Miscellaneous agricultural workers
0.87
Other orchard farmers
Miscellaneous agricultural workers
0.87
Farmhands and laborers
Farm labour contractors
0.97
Carpenters and joiners
Carpenters
0.72
Building frame and related trades workers
n. e. c.
Construction labourers
0.88
Construction and maintenance laborers:
roads, dams and similar constructions
Construction labourers
0.88
Building and related electricians
Electricians
0.15
Painters and related workers
Painters, construction and maintenance
0.75
Varnishers and related painters
Painters, construction and maintenance
0.75
Inland and coastal waters fishermen
Fishers and related fishing workers
0.83
Mining-plant operators
Mine cutting and channeling machine
operators
0.59
Assembling laborers
Team assemblers
0.97
Tea, coffee and cocoa processing machine
operators
Food and tobacco roasting, baking, and
drying machine operators and tenders
0.91
Tobacco production machine operators
Food and tobacco roasting, baking, and
drying machine operators and tenders
0.91
Shoemaking and related machine operators
Shoe machine operators and tenders
0.97
Wood processing plant operators
Sawing machine setters, operators, and
tenders, wood
0.86
Chemical-still and reactor operators (except
petroleum and natural gas)
Chemical equipment operators and
tenders
0.76
Brewers and wine and other beverage
machine operators
Separating, Filtering, Clarifying,
Precipitating, and Still Machine Setters,
Operators, and Tenders
0.88
30
Mineral ore and stone-processing plant
operators
Service unit operators, oil, gas, and
mining
0.93
Grain and spice milling machine operators
Crushing, grinding, and polishing machine
setters, operators, and tenders
0.97
Sugar production machine operators
Crushing, grinding, and polishing machine
setters, operators, and tenders
0.97
Cement and other mineral products
machine operators
Mixing and blending machine setters,
operators, and tenders
0.83
Glass makers, cutters, grinders and finishers
Cutters and trimmers, hand
0.64
Charcoal makers and related workers
Furnace, kiln, oven, drier, and kettle
operators and tenders
0.37
Jewelry and precious metal workers
Jewelers and precious stone and metal
workers
0.95
Potters and related clay and abrasive
formers
Molders, shapers, and casters, except
metal and plastic
0.9
Glass, ceramics and related plant operators
n. e. c.
Molders, shapers, and casters, except
metal and plastic
0.9
Papermaking plant operators
Paper goods machine setters, operators,
and tenders
0.67
Dairy products machine operators
Separating, filtering, clarifying,
precipitating, and still machine setters,
operators, and tenders
0.88
Metal, rubber and plastic products
assemblers
Production workers, all other
0.92
Fiber preparers
Production workers, all other
0.92
Ammunition and explosive products
machine operators
Production workers, all other
0.92
Maritime transport service supervisors
First-line supervisors of transportation
and material-moving machine and vehicle
operators
0.029
Travel attendants and travel stewards
Flight attendants
0.35
Hand or pedal vehicle drivers
Taxi drivers and chauffeurs
0.89
Railways transport service supervisors
Railroad conductors and yardmasters
0.83
Crane, hoist and related plant operators
Hoist and winch operators
0.65
Shoe cleaning and other street services
elementary occupations
Janitors and cleaners, except maids and
housekeeping cleaners
0.66
Workers reporting occupations
unidentifiable or inadequately defined
Labourers and freight, stock, and material
movers, hand
0.85
Sweepers and related laborers
Janitors and cleaners, except maids and
housekeeping cleaners
0.66
Government administrators (including
career executive service officers)
Chief executives
0.015
31
Postal service supervisors
First-line supervisors of office and
administrative support workers
0.014
Geodetic engineers and related
professionals
Civil engineers
0.019
Photographers and image and sound
recording equipment operators
Photographers
0.021
Medical equipment operators
Medical and Clinical Laboratory
Technicians
0.47
Ship and aircraft controllers and technicians
Aircraft mechanics and service
technicians
0.71
Travel consultants and organizers
Travel guides
0.057
Auctioneers
Cashiers
0.97
Customs and immigration inspectors
Detectives and criminal investigators
0.34
Other personal services workers, n. e. c.
Personal care aides
0.74
Ornamental plant growers
Farmer, Ranchers, and Other Agricultural
Managers
0.047
Duck raisers
Miscellaneous agricultural workers
0.87
Miners and quarry workers
Rock splitters, quarry
0.96
Shop salespersons and demonstrators
Demonstrators and product promoters
0.51
Professional, technical and related officers
Computer support specialists
0.65
Commanding officers
First-line supervisors of police and
detectives
0.0044
Officers, n. e. c.
Police and sheriff’s patrol officers
0.098
Technician, skilled, semi-skilled workers
Production workers, all other
0.92
Traditional chiefs and heads of villages
Chief executive
0.015
Production and operations managers in
agriculture, hunting, forestry and fishery
First-line supervisors of farming, fishing,
and forestry workers
0.57
Directors and chief executives of
corporations
Chief executives
0.015
Production and operations managers in
manufacturing
Industrial production managers
0.03
Production and operations managers in
wholesale and retail trade
First-line supervisors of retail sales
workers
0.28
Production and operations managers in
construction
Construction managers
0.071
Production and operations managers in
restaurant and hotels
Lodging managers
0.0039
Finance and administration managers
Financial managers
0.069
Personnel and industrial relations managers
Human resources managers
0.0055
Sales and marketing managers
Sales managers
0.013
Advertising and public relations managers
Public relations specialists
0.18
Supply and distribution managers
Transportation, storage, and distribution
managers
0.59
Computing services managers
Computer and information systems
0.035
32
managers
General managers/managing proprietors in
agriculture, hunting, forestry and fishing
First-line supervisors of farming, fishing,
and forestry workers
0.57
General managers/managing proprietors in
manufacturing
Industrial production managers
0.03
General managers/managing proprietors in
wholesale and retail trade
First-line supervisors of retail sales
workers
0.28
General managers/managing proprietors of
restaurants and hotels
Food service managers
0.083
General managers/managing proprietors in
transportation, storage and
communications
Transportation, storage, and distribution
managers
0.59
General managers/managing proprietors of
business services
Administrative services managers
0.73
General managers/managing proprietors in
personal care, cleaning and relative services
First-line supervisors of personal service
workers
0.076
School supervisors and principals
Education administrators, elementary
and secondary school
0.0046
School principals
Education administrators, elementary
and secondary school
0.0046
Air transport service supervisors
Transportation, storage and distribution
managers
0.59
Transport and communications service
supervisors n. e. c.
First-line supervisors of transportation
and material-moving machine and vehicle
operators
0.029
Production supervisors and general
foremen
First-line supervisors of production and
operating workers
0.016
Other sales supervisors
First-line supervisors of retail sales
workers
0.28
Sales supervisors in wholesale trade
First-line supervisors of retail sales
workers
0.28
Other supervisors, n. e. c.
First-line supervisors of office and
administrative support workers
0.014
Chemists
Chemists
0.1
Mathematicians and actuaries
Mathematicians
0.047
Statisticians
Statisticians
0.22
Systems analysts and designers
Computer systems analysts
0.0065
Computer programmers
Computer programmers
0.48
Other computer professionals
Computer support specialists
0.65
Architects
Architects, except landscape and naval
0.018
Electrical Engineers
Electrical engineers
0.1
Civil engineers
Civil engineers
0.019
Electronics and communications engineers
Electronics engineers, except computer
0.025
33
Mechanical engineers
Mechanical engineers
0.011
Chemical engineers
Chemical engineers
0.017
Computer engineers and related
professionals
Computer hardware engineers
0.22
Industrial engineers
Industrial engineers
0.029
Other engineers and related professionals
Engineers, all other
0.014
Biologists, botanists, zoologists and related
scientists
Conservation scientists
0.016
Bacteriologists, pharmacologists,
pathologists and related scientists
Medical scientists, except
epidemiologists
0.0045
Agronomists and related scientists
Biological scientists, all other
0.015
Foresters and related scientists
Biological scientists, all other
0.015
Medical doctors
Physicians and surgeons
0.0042
Dentists
Dentists, general
0.0044
Veterinarians
Veterinarians
0.038
Pharmacists
Pharmacists
0.012
Nutritionists-dietitians
Dietitians and nutritionists
0.0039
Optometrists and opticians
Optometrists
0.14
Medical technologists
Health technologists and technicians, all
other
0.4
Physiotherapists
Physical therapists
0.021
Professional nurses
Healthcare practitioners and technical
workers, all other
0.055
Professional midwives
Healthcare practitioners and technical
workers, all other
0.055
College, university and higher education
teaching professionals
Postsecondary teachers
0.032
Technical and vocational
instructors/trainors
Human resources, training, and labour
relations specialists, all other
0.31
General secondary education teaching
professionals
Adult basic and secondary education and
literacy teachers and instructors
0.19
Science and mathematics teaching
professionals
Middle school teachers, except special
and career/technical education
0.17
Vocational education teaching professionals
Career/technical education teachers,
secondary school
0.0088
General elementary education teaching
professionals
Elementary school teachers, except
special education
0.0044
Science and mathematics elementary
education teaching professionals
Elementary school teachers, except
special education
0.0044
Pre-elementary education teaching
professionals
Kindergarten teachers, except special
education
0.15
Nonformal education teaching professionals
other than technical and vocational
Adult basic and seconary education and
literacy teachers and instructors
0.19
34
trainors/instructors
Teaching professionals for the handicapped
and disabled
Teachers and Instructors, all other
0.0095
Education methods specialists
Educational, guidance, school, and
vocational counselors
0.0085
Other teaching professionals
Teachers and Instructors, all other
0.0095
Personnel and human resource
development professionals
Human resources assistants, except
payroll and timekeeping
0.9
Other business professionals
Business operations specialists, all other
0.23
Accountants and auditors
Accountants and auditors
0.94
Lawyers
Lawyers
0.035
Justices
Judges, magistrate judges, and
magistrates
0.4
Judges
Judges, magistrate judges, and
magistrates
0.4
Librarians, archivists and curators
Librarians
0.65
Economists
Economists
0.43
Philologists, translators and interpreters
Interpreters and translators
0.38
Psychologists
Clinical, counseling, and school
psychologists
0.0047
Social work professionals
Social and human service assistants
0.13
Other social science professionals
Social scientists and related workers, all
other
0.04
Authors, journalists and other writers
Writers and authors
0.038
Sculptors, painters and related artists
Fine artists, including painters, sculptors,
and illustrators
0.042
Composers, musicians and singers
Musicians and singers
0.074
Choreographers and dancers
Choreographers
0.004
Actors and stage directors
Producers and directors
0.022
Other creative or performing artists
Dancers
0.13
Civil engineering technicians
Civil engineering technicians
0.75
Electrical engineering technicians
Electrical and electronics engineering
technicians
0.84
Electronics and communications
engineering technicians
Electrical and electronics engineering
technicians
0.84
Mechanical engineering technicians
Mechanical engineering technicians
0.38
Mining and metallurgical engineering
technicians
Engineering technicians, except drafters,
all other
0.24
Other physical science and engineering
technicians
Geological and petroleum technicians
0.91
Draftsmen
Architectural and civil drafters
0.52
Computer assistants
Computer support specialists
0.65
35
Computer equipment operators
Computer operators
0.78
Broadcasting and telecommunications
equipment operators
Radio operators
0.98
Ships' deck officers and pilots
Captains, mates, and pilots of water
vessels
0.27
Air traffic controllers
Air traffic controllers
0.11
Aircraft pilots, navigators and flight
engineers
Airline pilots, copilots, and flight
engineers
0.18
Air traffic safety technicians
Air traffic controllers
0.11
Building and fire inspectors
Construction and building inspectors
0.63
Life science technicians
Life, physical, and social science
technicians, all other
0.61
Safety, health and quality inspectors
(vehicles, processes and products)
Health and safety engineers, except
mining safety engineers and inspectors
0.028
Farm technicians
Farm equipment mechanics and service
technicians
0.75
Medical assistants
Medical assistants
0.3
Other life science technicians
Life, physical, and social science
technicians, all other
0.61
Pharmaceutical assistants
Pharmacy aides
0.72
Nursing associate professionals
Registered nurses
0.009
Traditional medicine practitioners
Health diagnosing and treating
practitioners, all other
0.02
Faith healers
Health diagnosing and treating
practitioners, all other
0.02
Insurance representatives
Insurance sales agents
0.92
Estate agents
Real estate sales agents
0.86
Technical and commercial sales
representatives
Sales representatives, wholesale and
manufacturing, technical and scientific
products
0.25
Appraisers and valuers
Appraisers and assessors of real estate
0.9
Other finance and sales associate
professionals
Securities, commodities, and financial
services sales agents
0.016
Labor contractors and employment agents
Human resources, training, and labour
relations specialists, all other
0.31
Clearing and forwarding agents
Cargo and freight agents
0.99
Other business services and trade brokers
Business operations specialists, all other
0.23
Administrative secretaries and related
associate professionals
Legal secretaries
0.98
Legal and related business associate
professionals
Paralegals and legal assistants
0.94
Bookkeepers
Bookkeeping, accounting, and auditing
clerks
0.98
36
Other administrative associate
professionals
Secretaries and administrative assistants,
except legal, medical, and executive
0.96
Government tax and excise officials
Tax examiners and collectors, and
revenue agents
0.93
Police inspectors and detectives
Detectives and criminal investigators
0.34
Social work associate professionals
Social and human service assistants
0.13
Decorators and commercial designers
Commercial and industrial designers
0.037
Radio, television and other announcers
Radio and television announcers
0.1
Street, nightclub and related musicians,
singers and dancers
Musicians and singers
0.074
Athletes and related workers
Athletes and sports competitors
0.28
Stenographers and typists
Word processors and typists
0.81
Word processor and related operators
Word processors and typists
0.81
Data entry operators
Data entry keyers
0.99
Secretaries
Legal secretaries
0.98
Accounting and bookkeeping clerks
Bookkeeping, accounting, and auditing
clerks
0.98
Statistical and finance clerks
Statistical assistants
0.66
Stocks clerks
Stock clerks and order fillers
0.64
Transport clerks
Reservation and transportation ticket
agents and travel clerks
0.61
Library and filing clerks
File clerks
0.97
Production clerks
Production, planning, and expediting
clerks
0.88
Mail carriers and sorting clerks
Postal service mail carriers
0.68
Other office clerks
Office clerks, general
0.96
Cashiers and ticket clerks
Cashiers
0.97
Bet bookmakers and croupiers
Gaming managers
0.091
Pawnbrokers and money lenders
Loan officers
0.98
Debt collectors and related workers
Bill and account collectors
0.95
Telephone switchboard operators
Switchboard operators, including
answering service
0.96
Travel agency clerks and related workers
Reservation and transportation ticket
agents and travel clerks
0.61
Transport conductors
Railroad conductors and yardmasters
0.83
Travel guides
Travel guides
0.057
Housekeepers and related workers
Maids and housekeeping cleaners
0.69
Cooks
Cooks, restaurant
0.96
Child care workers
Childcare workers
0.084
Institution-based personal care workers
Healthcare social workers
0.0035
Personal care and related workers, n. e. c.
Personal care aides
0.74
Hairdressers, barbers, beauticians and
Hairdressers, hairstylists, and
0.11
37
related workers
cosmetologists
Companions and valets
Parking lot attendants
0.87
Undertakers and embalmers
Embalmers
0.54
Firefighters
Firefighters
0.17
Police officers
Police and sheriff’s patrol officers
0.098
Fashion and other models
Models
0.98
Protective services workers n. e. c.
Child, family, and school social workers
0.028
Prison guards
Correctional officers and jailers
0.6
Stall and market salespersons
Retail salespersons
0.92
Other animal raisers
Nonfarm animal caretakers
0.82
Forest tree planters
Forest and conservation workers
0.87
Concessionaires and loggers
Logging equipment operators
0.79
Minor forest products gatherers
Forest and conservation workers
0.87
Deep-sea fishermen
Fishers and related fishing workers
0.83
Hunters and trappers
Hunters and trappers
0.77
Fishermen n. e. c.
Fishers and related fishing workers
0.83
Shotfirers and blasters
Explosives workers, ordnance handling
experts, and blasters
0.48
Stone splitters, cutters and carvers
Molders, shapers, and casters, except
metal and plastic
0.9
Builders (traditional materials)
Construction and related workers, all
other
0.71
Masons and related concrete finishers
Cement masons and concrete finishers
0.94
Roofers
Roofers
0.9
Floor layers and tile setters
Tile and marble setters
0.75
Insulation workers
Insulation workers, mechanical
0.64
Glaziers
Glaziers
0.73
Plumbers, pipe fitters and other related
workers
Plumbers, pipefitters, and steamfitters
0.35
Metal molders and coremakers
Molding, coremaking, and casting
machine setters, operators, and tenders,
metal and plastic
0.95
Welders and flamecutters
Welders, cutters, solderers, and brazers
0.94
Sheet-metal workers
Sheet metal workers
0.82
Structural-metal preparers, erectors and
related workers
Structural iron and steel workers
0.83
Wood and related products assemblers
Production workers, all other
0.92
Paperboard, textile and related products
assemblers
Production workers, all other
0.92
Riggers and cable splicers
Telecommunications line installers and
repairers
0.49
38
Blacksmiths, hammersmiths, and forging-
press workers
Forging machine setters, operators and
tenders, metal and plastic
0.93
Tool-makers and related workers
Tool and die makers
0.84
Machine-tool setters and setter operators
Woodworking machine setters,
operators, and tenders, except sawing
0.97
Metal-wheel grinders, polishers and tool
sharpeners
Grinding, lapping, polishing, and buffing
machine tool setters, operators, and
tenders, metal and plastic
0.95
Motor vehicle mechanics and related trades
workers
Motorcycle mechanics
0.79
Aircraft engine mechanics and fitters
Aircraft mechanics and service
technicians
0.71
Marine craft mechanics
Motorboat mechanics and service
technicians
0.66
Agricultural or industrial machinery
mechanics and fitters
Industrial machinery mechanics
0.67
Business machines mechanics and repairers
Computer, automated teller, and office
machine repairers
0.74
Electrical mechanics and fitters
Electric motor, power tool, and related
repairers
0.76
Electronics fitters
Electrical and electronics repairers,
powerhouse, substation, and relay
0.38
Electronics mechanics and servicers
Electrical and electronics installers and
repairers, transportation equipment
0.91
Telecommunication equipment installers
and repairers
Telecommunications equipment
installers and repairers, except line
installers
0.36
Lineman, line installers and cable splicers
Telecommunications line installers and
repairers
0.49
Precision instrument makers and repairers
Machinists
0.65
Musical instrument makers and tuners
Musical instrument repairers and tuners
0.91
Glass, ceramics and related decorative
painters
Painting, coating, and decorating workers
0.92
Compositors, typesetters and related
workers
Printing press operators
0.83
Printing engravers and etchers
Etchers and engravers
0.98
Photographic and related workers
Prepress technicians and workers
0.97
Bookbinders and related workers
Print binding and finishing workers
0.95
Silk-screen, block and textile printers
Printing press operators
0.83
Pressman letterpresses and related workers
Printing press operators
0.83
Butchers, fishmongers and related food
preparers
Food preparation workers
0.87
Bakers, pastry cooks and confectionery
makers
Bakers
0.89
39
Food preservers
Agricultural and food science technicians
0.97
Food and beverage tasters and graders
Graders and sorters, agricultural products
0.41
Tobacco preparers and tobacco products
makers
Food and tobacco roasting, baking, and
drying machine operators and tenders
0.91
Cabinet/furniture makers and related
workers
Woodworking machine setters,
operators, and tenders, except sawing
0.97
Woodworking machine setters and setter-
operators
Woodworking machine setters,
operators, and tenders, except sawing
0.97
Rattan, bamboo and other wicker furniture
makers
Craft artists
0.035
Basketry weavers, brush makers and related
workers
Production workers, all other
0.92
Weavers, knitters and related workers
Textile knitting and weaving machine
setters, operators, and tenders
0.73
Tailors, dressmakers and hatters
Tailors, dressmakers, and custom sewers
0.84
Textile, leather and related patternmakers
and cutters
Shoe and leather workers and repairers
0.52
Sewers, Embroiderers and related workers
Sewers, hand
0.99
Upholsterers and related workers
Upholsterers
0.39
Tanners
Textile bleaching and dyeing machine
operators and tenders
0.97
Shoemakers and related workers
Shoe and leather workers and repairers
0.52
Well drillers and borers and related workers
Earth drillers, except oil and gas
0.85
Ore and metal furnace operators
Metal-refining furnace operators and
tenders
0.88
Metal melters, caster and rolling mill
operators
Milling and planing machine setters,
operators, and tenders, metal and plastic
0.98
Metal drawers and extruders
Extruding and drawing machine setters,
operators, and tenders, metal and plastic
0.91
Glass and ceramics kiln and related machine
operators
Production workers, all other
0.92
Paper pulp plant operators
Production workers, all other
0.92
Crushing, grinding and chemical-mixing
machinery operators
Crushing, grinding, and polishing machine
setters, operators, and tenders
0.97
Chemical heat-treating plant operators
Chemical plant and system operators
0.85
Petroleum and natural gas refining plant
operators
Petroleum pump system operators,
refinery operators, and gaugers
0.71
Chemical processing plant operators n. e. c.
Chemical plant and system operators
0.85
Power production plant operators
Power plant operators
0.85
Incinerator, water treatment and related
plant operators
Water and wastewater treatment plant
and system operators
0.61
Automated assembly-line operators
Team assemblers
0.97
40
Machine tool operators
Multiple machine tool setters, operators,
and tenders, metal and plastic
0.91
Pharmaceutical and toiletry products
machine operators
Chemical plant and system operators
0.85
Metal finishing, plating and coating machine
operators
Plating and coating machine setters,
operators, and tenders, metal and plastic
0.92
Photographic products machine operators
Office machine operators, except
computer
0.92
Rubber products machine operators
Extruding, forming, pressing, and
compacting machine setters, operators,
and tenders
0.93
Chemical products machine operators n. e.
c.
Chemical plant and system operators
0.85
Plastic products machine operators
Plating and coating machine setters,
operators, and tenders, metal and plastic
0.92
Wood products machine operators
Woodworking machine setters,
operators, and tenders, except sawing
0.97
Fiber preparing, spinning and winding
machine operators
Textile winding, twisting, and drawing out
machine setters, operators, and tenders
0.96
Weaving and knitting machine operators
Textile knitting and weaving machine
setters, operators, and tenders
0.73
Sewing machine operators
Sewing machine operators
0.89
Bleaching, dyeing and cleaning machine
operators
Cleaning, washing, and metal pickling
equipment operators and tenders
0.81
Textile and leather products machine
operators n. e. c.
Textile cutting machine setters,
operators, and tenders
0.95
Meat and fish processing machine
operators
Meat, poultry, and fish cutters and
trimmers
0.94
Baked goods and cereal and chocolate
products machine operators
Production workers, all other
0.92
Fruit, vegetable and nut processing machine
operators
Food and tobacco roasting, baking, dand
drying machine operators and tenders
0.91
Mechanical machinery assemblers
Electromechanical equipment assemblers
0.97
Electrical equipment assemblers
Electrical and electronic equipment
assemblers
0.95
Electronic equipment assemblers
Electrical and electronic equipment
assemblers
0.95
Other machine operators and assemblers
Engine and other machine assemblers
0.82
Motorcycle drivers
Taxi drivers and chauffeurs
0.89
Car, taxi and van drivers
Taxi drivers and chauffeurs
0.89
Bus drivers
Bus drivers, school or special client
0.89
Heavy truck and lorry drivers
Heavy and tractor-trailer truck drivers
0.79
Motorized farm and forestry plant
operators
Logging equipment operators
0.79
41
Earth-moving and related plant operators
Excavating and loading machine and
dragline operators
0.94
Lifting truck operators
Industrial truck and tractor operators
0.93
Ship's deck crews and related workers
Sailors and marine oilers
0.83
Market and sidewalk stall vendors
Door-to-door sales workers, news and
street vendors, and related workers
0.94
Street ambulant vendors
Door-to-door sales workers, news and
street vendors, and related workers
0.94
Door-to-door and telephone salespersons
Door-to-door sales workers, news and
street vendors, and related workers
0.94
Domestic helpers and cleaners
Maids and housekeeping cleaners
0.69
Helpers and cleaners in offices, hotels and
other establishments
Janitors and cleaners, except maids and
housekeeping cleaners
0.66
Hand launderers and pressers
Laundry and dry-cleaning workers
0.71
Building caretakers
Janitors and cleaners, except maids and
housekeeping cleaners
0.66
Vehicle, window and related cleaners
Cleaners of vehicles and equipment
0.37
Messengers, package and luggage porters
and deliverers
Couriers and messengers
0.94
Doorkeepers, watchpersons and related
workers
Locker room, coatroom, and dressing
room attendants
0.43
Garbage collectors
Refuse and recyclable material collectors
0.93
Forestry laborers
Forest and conservation workers
0.87
Fishery laborers and helpers
Fishers and related fishing workers
0.83
Mining and quarrying laborers
Rock splitters, quarry
0.96
Building construction laborers
Construction labourers
0.88
Hand packers and other manufacturing
laborers
Labourers and freight, stock, and material
movers, hand
0.85
Freight handlers
Labourers and freight, stock, and material
movers, hand
0.85
Appendix 3. Unmatched occupations.
Other government associate professionals
Enlisted personnel n. e. c.
Combat soldiers
Wood treaters
Trade brokers
Research and development managers
Other optical and electronic equipmen
Fortune tellers, palmists, and related
Dairy products makers
Clowns, magicians, acrobats, and related workers