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PANORAMA OF THE MARKET DEMAND FOR MECHANICAL ENGINEERS IN SOUTH AMERICAN COUNTRIES
Ronald A. Cardenas
Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera
Lima, Peru [email protected]
Kevin S. Bello Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera
Lima, Peru [email protected]
Alexander R. Valle
Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera
Lima, Peru [email protected]
Elizabeth R. Villota Section of Mechanical Engineering, Pontificia Universidad Catlica del
Peru Lima, Peru
Alberto M. Coronado Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera
Lima, Peru [email protected]
ABSTRACT
Many educational institutions employ surveys in order to
identify what majors to offer or what competencies to
emphasize in their curricula. Different from a survey, we
present an analysis of the labor market needs based on data
collected from job ads available in the Internet. Tools of
natural language processing (NLP) and statistical techniques
have been employed to handle the job ads. For Peru, Chile and
Colombia, a detailed panorama of the market demand has
been depicted: mechanical engineering appears among the top
most demanded engineering majors and maintenance is its
most frequent technical requirement; management (project,
quality and operations) related requirements also rank high,
together with a working knowledge of English. By using
diverse visualization techniques we can also show the social network of a major, where friendship is defined by the amount of job ads shared by any two majors.
1 INTRODUCTION During the past decade, many South American countries
have benefited from soaring commodity prices. Along with
their economic growth, their required working forces have
also grown in complexity and specialization. Many local
employers are having a hard time finding qualified workers,
especially in technical fields [1]. Educational institutions are
also having difficulty to supply the demanded labor.
Particularly, for these educational institutions, the challenge of
providing talented human labor to their local economies is
now bigger than ever, as commodity prices are down and
rising productivity is imperative, if sustained growth is desired
[2].
For educational institutions around the world, and South
America is not the exception, it is not always clear what
majors they should offer or what competencies they should
emphasize in their curricula; and it is also not self-evident
what the relative importance should be among foundational,
professional and emerging (entrepreneurial, innovation) skills
[3,4,5,6]. Some institutions have adopted the ABET program
outcomes and guide their curricula design towards meeting
those outcomes; however, to what extent can a developing
country use those ABET recommendations? and what about
incorporating their own economies needs into their
curriculum? or how much effort do they have to devote to
innovation? Different countries have different needs,
resources, cultures, and hence require different solutions [7].
Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition IMECE2015
November 13-19, 2015, Houston, Texas
IMECE2015-51557
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Considering the specific case of mechanical engineering,
Wolfe [8] performed a survey among mechanical engineering
alumni of MIT. It was found that topics closely related to the
core undergraduate curriculum in mechanical engineering
were identified as the least used in their daily jobs. On the
other hand, topics such as, communications, teamwork,
independent thinking, professional skills and attitudes, and
personal skills and attitudes, were identified among the most
commonly used. Interestingly, the topics just mentioned also
were reported as being learnt mostly outside MIT. In addition,
three important aspects were stressed: 1) MIT graduates
pursued a wide variety of careers, 2) mechanical engineers
needed to learn more than just knowledge about the physical
world, and 3) engineers needed to have strong business skills,
such as communication.
An essential body of knowledge (BOK) for mechanical
engineering students was proposed by Jarosz & Busch-
Vishniac [9]. The authors dissected current curricula in
mechanical engineering, at nine US institutions, into a list of
required topics. Among the 64 topics deemed essential, 45%
correspond to engineering, 18% to mathematics, 15% to
physics, 12% to chemistry, 4% to computer science, 2% to
communication, 2% to statistics, 1% to economics, 1% to
ethics, and 0% to biology. The BOK obtained represents
what is currently taught, not necessarily what should be taught. In addition, it was observed that many of these topics reflect mechanical engineering in the seventeenth and
eighteenth centuries, not necessarily what is currently needed.
At present, universities across the world are engaged in a
race to increase their position in the academic rankings
currently available. This practice is now under increasing
scrutiny. Hill et al. [10] discuss the idea that the structure and
makeup of chemical engineering faculties in the US, and how
hiring and promotion is carried out, may affect the training of
undergraduate students regarding industrial applications. In
the same vein, Xue [11] analyses if there is in fact a STEM crisis or a STEM surplus in the US. It was reported that depending on the major, and sector, it can be one or another.
For example, in the academic job market, there was no
noticeable shortage of any discipline. On the other hand, in the
private sector, there was high demand for software developers,
petroleum engineers, data scientists and skilled trades.
Educational institutions tend to appeal to surveys to
employers, as well as alumni, in order to obtain a glimpse of
the labor market. Different from a survey, whose results are
tightly related to the quality and scope of questions performed,
in this study, we present an analysis of the labor market needs
based on data collected from job ads (tens of thousands)
available in the internet. Working with this data, we are able to
depict a detailed panorama of the market demand in three
South American countries: Peru, Chile and Colombia. The job
ads are handled using tools of natural language processing
(NLP), information retrieval and diverse statistical and
visualization techniques. Uni-, bi- and tri-gram-based
language models were used to extract the most common job
requirements, which can be readily examined as Wordclouds.
The remainder of this article is organized as follows. The
next section presents a brief discussion on the techniques used
to obtain, process and analyze natural language data.
Following, we present the results of our analysis. We start by
showing the market demand by country for diverse
engineering majors. Then we discuss the relationship among
mechanical engineering and other engineering majors taking
into account how often these majors share job ads. Next, we
present Wordclouds with the most common requirements for
mechanical engineers. The final section presents the
conclusions.
2 NATURAL LANGUAGE PROCESSING
2.1 Reference Corpus The corpus used in this work was built by extracting job
ads from several popular job search websites from Peru, Chile
and Colombia. Since the same job ad can be published in more
than one website, we consider it as repeated if the description
of the position is found within the last fifteen days in the
database. The time periods in which the data were extracted
are mentioned in Section 3.
This corpus consists of more than 250000 job ads for
Peru, 180000 for Colombia and 50000 for Chile. It contains
job ads from all areas in the market as published in the
websites.
2.2. Pre-processing Phase There is no standard format for job ads in Latin America.
The writing style is not formal and the text can surely contain
orthographic errors. Therefore, the data must be cleaned
before being classified. Techniques from natural language
processing (NLP) such as regular expressions (regex) and
tokenization were used to eliminate special symbols, HTML
tags, multiple spaces and so on. Furthermore, low frequency
words were mapped to a small set of tags, such as Number,
Date, LongWord, Url, PhoneNumber, among others.
2.3. Majors Classification The classifier was based on regular expressions. It
searches for major names surrounded by a pre-established set
of phrases, such as ...professional with major in... or ...student with major in.... The expression to match is constructed joining up to four sets of commonly seen phrases,
before and after the possible major name, in order to obtain as
much as possible relationships.
The 30 most popular engineering majors were accounted
for graduates from the three countries considered here. The
classifier relies on multi-word identifiers for each major
considered, in order to perform multi-classification. Each
major identifier set was tuned for each country.
The evaluation of the classifier was conducted in a semi-
automatic manner, since no annotated data was available. The
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evaluation corpus consisted of 1000 randomly sampled job
ads. The accuracy for the top five most demanded engineering
majors in Peru can be appreciated in Table 1.
TABLE 1. Classifier accuracy for the top five most
demanded engineering majors in Peru
Engineering major Accuracy (%)
Industrial 94.01
Systems 89.49
Informatics 89.19
Electrical 88.02
Mechanical 88.10
2.4. Market Demand Visualization Two ways of visualizing the market demand were
selected. The first one, known as Treemap, shows the demand
per major in a space-constrained manner. Each engineering
major is represented as a rectangle with area proportional to its
demand.
The second one, known as Circleplot, allows us to
visualize interconnectedness among engineering majors taking
into account how often these majors share job ads. Each pair
of interconnected majors is linked by an arch. The arch
thickness is proportional to the interconnectedness between
majors, expressed as the number of shared job ads.
These graphics are shown in Section 3 for each country.
Interactive versions are available online for Peru [12,13],
Chile [14,15] and Colombia [16,17].
2.5. Requirements Ranking and Visualization Job requirements were modeled as keywords and ranked
using statistical scores. Potential keywords consisted of uni-
grams, bi-grams and tri-grams, filtered by a list of more than
3000 corpus tuned stopword phrases. This stopword set was
built combining standard Spanish stopwords, manually tuned
stopwords and smart stopwords. The smart stopwords were extracted with statistical techniques mentioned by Rose et al
[18].
Three ranking scores were compared. The first one was
term frequency tft, frequency of the term t in the entire corpus.
The main source of noise in this score was the fact that a
phrase can be mentioned multiple times, but only once as a
requirement. In order to deal with the ambiguity of uni-gram
terms, we perform a correction in uni-gram counts before
calculating their scores -only for terms in common with the
tri-gram list. This correction consisted in adding up the counts
of the tri-gram phrases in which the uni-gram term being
analyzed has the same meaning. The semantic relationship
was manually determined.
The second one was document frequency dfw, the number
of ads in the corpus that contains the word w. For this score,
there was no need for correction in uni-gram counts because
this score removes the noise from phrases mentioned in
different contexts, as was the case with the first scoring
method.
The third score compared was a discretized version of TF-
IDF score as implemented by Gutwin and Nevil-Manning
[19]. For the case of the TF score, we used the same procedure
to tackle the ambiguity of uni-gram terms as in the first
scoring method.
The second scoring method yielded better results for our
corpus. The main reason the third one did not performed well
was that it is biased by the length of the documents. In our
case, the document (job ad) body can contain detailed job
functions as well as description of benefits and working
conditions, resulting in ad lengths ranging from one to
hundreds of sentences. Given that requirements are usually
mentioned only once in the ad, the third score gave lower rank
to important requirements in large ads than in the short ones.
The requirement ranking was visualized as Wordclouds,
plotting the phrase font size proportional to its ranking score.
Wordclouds for mechanical engineering for the three countries
are presented in Section 4.
3 MARKET DEMAND BY COUNTRY The job ads obtained from the internet correspond to the
period June-September 2014, plus January-March 2015, for
the three countries considered here. It is important to observe
that different sources were used for obtaining the job ads for
the three countries, so we cannot conclude with these results
that there is more demand for engineering majors in one
country than another. In Fig. 1 we present the Treemap
corresponding to the Peruvian labor market demand
considering only engineering majors. There are several
features worth discussing. For example, it is very common for
a job ad to include more than one engineering major among its
requirements. Any prospective job seeker needs to have
majored in any of the specialties mentioned in the job ad. As a
consequence, if we add 19502 (Industrial Eng) + 8164
(Systems Eng) + 7296 (Informatics) + we will obtain 68682, which is larger than the total amount of job ads for
engineering majors (45368), since many job ads may be
counted more than once.
Also, it is important to observe that in Peru and some other
South American countries, Informatics and Systems
Engineering are closely related majors and most of the times it
is difficult to tell them apart. If we consider them as one, then
we have that mechanical engineering majors are currently the
fourth most demanded in Peru.
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FIGURE 1: Peruvian labor market demand for engineering majors
In Fig. 2 we present the Treemap corresponding to the Chilean
labor market demand considering only engineering majors. In
this case, due to the sources we used to obtain the data, there is
a significant reduction in the number of job ads analyzed. As a
consequence, we may expect that this data corresponds to only
a rough description of the Chilean job market. However, the
most demanded engineering majors still present a great
resemblance with the Peruvian case, with a few differences in
the ranks.
In Fig. 3 we present the Treemap corresponding to the
Colombian labor market demand considering again only
engineering majors. Regarding the top majors, it can be seen
that the ranking presents a very close resemblance to the
Peruvian and Chilean cases.
FIGURE 2: Chilean labor market demand for engineering majors
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FIGURE 3: Colombian labor market demand for engineering majors
As discussed above, it is very common to find more than one
engineering major as requirement in job ads. This information
can help us to build a social network kind of graph, in order to visualize the degree of connectedness among these diverse
technical disciplines. On the left side of Fig. 4, it is shown that
indeed there is a great degree of relationship among
engineering fields considering their common mention in job
ads, for the Peruvian job market case. For example, the two
most common connections of industrial engineering are
systems and mechanical engineering. On the right side of Fig.
4, it is shown the connections that are exclusive to mechanical
engineering. In this case, the majors that share more job ads
with mechanical engineering are: industrial (31%), electrical
(26%) and civil (10%) engineering. Mechanical engineering is
mentioned exclusively in only 10% of its job ads.
FIGURE 4: Relationship among Peruvian engineering majors. Left: all connections, right: mechanical engineering connections
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FIGURE 5: Relationship among Chilean engineering majors. Left: all connections, right: mechanical engineering connections
FIGURE 6: Relationship among Colombian engineering majors. Left: all connections, right: mechanical engineering connections
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Next, we analyze the Chilean job market case. In Fig. 5, on the
left side, we find again that there is a great level of
connectivity among diverse engineering majors. However,
since the sample for this country is relatively small, many of
the least frequent connections (corresponding to the thinner
links) are missing. On the right side of Fig. 5, we observe that
the most common majors related to mechanical engineering
are: electrical (33%), industrial (15%) and electronics (13%)
engineering.
In the case of the Colombian job market we obtained the
following results. On the right side of Fig. 6, we also observe a
great degree of connectivity among diverse majors. On the
right hand side of Fig. 6, we see that mechanical engineering
shares most of its job ads with: industrial (23%), electrical
(22%) and electronics (14%) engineering.
4 MECHANICAL ENGINEERING REQUIREMENTS During the time intervals considered in our analysis, in
the Peruvian labor market, mechanical engineering was
mentioned in 4388 job ads. Almost half of these ads (2141)
cited maintenance as one of its requirements (see Fig. 7). In
order to disambiguate the context in which this term is
employed, it is very useful to look at the related bi- and tri-
grams. For maintenance we found requirements such as: preventive maintenance, maintenance activities, maintenance management, maintenance plan, among others. Next, we have the requirement control, which can be traced to: quality control, control of projects, process control, among others. In the third place we find processes, which is related to: productive processes, industrial processes, process control, among others.
In the case of the Chilean labor market, in our sampling, we
obtained 424 job ads citing mechanical engineering as
requirement. Among them, almost half (232) mention
maintenance as a requirement (see Fig. 8). In this case, we find the following related mentions: preventive maintenance, industrial maintenance, maintenance activities, among others. Next, we have control, which is related to quality control, process control, among others. In third place, we find processes, which is traced to: production processes, manufacturing process, quality processes, among others.
FIGURE 7: Wordcloud for Peruvian mechanical engineering labor market requirements
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FIGURE 8: Wordcloud for Chilean mechanical engineering labor market requirements
Finally, considering the case of the Colombian labor market,
we obtained that maintenance is once again the most common requirement with 910 job ads, out of a total of 1951
job ads asking for mechanical engineers (see Fig. 9). This first
requirement is associated to: preventive maintenance,
industrial maintenance, equipment maintenance, among others. Following, we find the requirement personal, which is mostly related to interpersonal skills. In third place, we find processes, which is linked to: process control, process quality, industrial process, among others.
FIGURE 9: Wordcloud for Colombian mechanical engineering labor market requirements
CONCLUSIONS From our analysis, we have identified that mechanical
engineering is among the most demanded engineering majors
in Peru, Chile and Colombia. An interesting feature we have
unveiled is the friendship between any two majors, defined
by the amount of job ads shared, which readily permits to
define something like a social network for majors. In this setting, mechanical engineering appears closely related to
industrial and electrical engineering majors, among others.
When analyzing the Wordclouds for mechanical engineering,
the technical requirement that is the most frequent is
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maintenance. Management (project, quality and operations)
related requirements also rank high, together with a working
knowledge of English.
Our results can be used as a complementary tool to what
educational institutions already employ in order to dimension
their offer (labor supply) and to define their curriculum
design, i.e. surveys. It is up to each educational institution to
define the combination of skills that best supports its
educational objectives. Some institutions may lean towards
more technical skills (such as CAD, FEA, etc.), while others
may give more weight to soft and managerial skills.
This study represents the use of data analytics as a data-
driven transparency mechanism to align educational
institutions to the labor market. By providing this alignment,
our work can help to ensure conditions for steady economic
growth, better dimensioned university programs, satisfaction
for employers and graduates able to work. Overall, our study
can contribute to improve productivity by helping to provide
the talented work force South American countries require.
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