Indian industry has emerged from a slowdown and there has been a surge in manpower needs. Whereas the retail, hospitality and banking industries continue to hire in large numbers, ones such as information technology (IT) and IT-enabled services (ITeS), which have a major global clientele, have also recovered. This gives both companies and job-seeking students reasons for optimism. However, a continuing concern is where to find employable candidates.
The employability figure for engineering students in India has been put at 25% by a McKinsey report in 2005. A study conducted earlier this year by Aspiring Minds shows that 17.8% engineers are employable in IT services firms, whereas only 4.2% have the required skills to get a job in an IT product company. This is because higher education institutions in the country are lagging behind in teaching industry-suited curriculum, especially in computer science and programming concepts.
Low employability causes scarcity of talent. This is a well-known problem. However, it creates a second issue: the inability to filter employable candidates out of a large pool of students. For instance, consider IT product companies: How do they identify 4.2% employable candidates across the numerous colleges, cities and states in the country? There is no direct way to identify an employable student apart from an elaborate and expensive recruitment process. Similar to a library, where a book misplaced is a book lost, in the talent ecosystem, an unidentifiable employable student is equivalent to an unemployable student.
This problem arises precisely due to lack of signalling. Signalling in the present context means explicit information in the biodata of the candidate, which would tell whether he or she is employable or not. In older times, there were fewer higher educational institutions. The quality of education was standardized and better. Being a graduate was in itself a signal that the person was employable. Kenneth J. Arrow, a Stanford professor of economics and Nobel Prize winner, discussed the signalling value of higher education for job selection in Western countries in his paper Higher Education as a Filter in 1973.
Today, the situation is different. There are some 300 universities, 20,000 colleges and north of three million candidates graduating every year. The quality of student intake and of education varies drastically. Whereas a person with an MSc in mathematics from a top tier institution could be a quantitative analyst at a multinational firm, a person with the same degree from another institution may not even qualify to become a math teacher. Both have the degrees, both may even have high percentages, but these are no signal to the relative employability of these candidates. Sectors such as the retail industry, which hire candidates with class X and XII qualifications, have a larger pool to select from and a bigger problem.
In the absence of concrete signalling, companies use pseudo methods to find the right kind of candidate; for example—the quality of colleges as projected in the media. Most IT product companies provide job opportunities to candidates from the top 100 engineering campuses. Within this pool, the percentage of employable candidates is higher (10.7% compared with 3.47% in the rest for IT product companies), thus making the search problem easier. However, this is a very dangerous signal to use. The Aspiring Minds study shows that 70% of employable students reside in colleges other than top 100. Thus, using this signalling mechanism shrinks the employable pool further. All the candidates in so-called tier II and tier III campuses are unidentifiable, and thus unemployable, according to the adopted signalling mechanism.
Another method used by companies is class X, XII and college percentages. Only students with at least, say, 60% marks would be eligible to apply for the job. Again, this is a signal used by companies to make the search problem easier. But this is also an invalid signal, because different standards of education in different universities means 70% in one could be considered poor, while in another it could be par excellence.
The above examples of signalling mechanisms clearly show that having a small pool of employable students is one problem, but the inability to identify them shrinks the pool further and is a bigger problem. This creates disillusionment among capable candidates, and erodes the incentives for individuals to invest in education.
A few efforts recently in identifying employable students have been through assessments in large job fairs. The government has entered into partnerships with private firms to facilitate skill assessment of people applying to employment exchanges and match them to jobs. Some other companies are undertaking nationwide examinations to identify employable candidates and present them to potential employers. Many more such efforts are required. Together with improving the size of the employable pool, we need to make sure that the current pool gets equal opportunities.
Varun Aggarwal is founder and director, Aspiring Minds
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