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STATE OF ENGINEERING EDUCATION IN KERALA

Study of effectiveness of Engineering from Employment and Income Perspectives – an AIPC Research Initiative

Sudheer Mohan, Dr. Daly Paulose

Abstract CURRENT STATE OF ENGINEERING EDUCATION, CHALLENGES, OPPORTUNITIES AND SUGGESTIONS TO MODERNISE ENGINEERING

EDUCATION TO HELP GENERATE JOBS AT SCALE FOR YOUNG INDIAN ENGINEERING GRADUATES


Executive Summary With the advent of Digital, AI and Machine learning, every industry is undergoing unprecedented transformation. With Data becoming new currency, a lot is changing at dramatic pace in every organization’s decision making. Even political parties are vying to exploit the competitive advantage these new technologies can provide. This is shaking up not only enterprises but also the roles and jobs at these enterprises. The time has come to revamp our education to realign with these emerging trends as many roles will go obsolete and a lot shall happen by itself. Since engineering is one of the leading job generating segment for Indian youth, All India Professionals Congress, Kerala decided to review the current state and plight of engineering education and its effectiveness to help youth build a career. AIPC Kerala conducted an online survey about the quality of engineering education. 2600+ participants responded to the questions which were designed to understand whether people are finding jobs, paid enough or finding deserving career options after completing engineering graduation. Survey also had questions around what should change and what should be included in engineering education. Findings from this survey will be analyzed in detail in this document. It highlights the difficulties faced by an overwhelming number of respondents in finding a suitable job, their resorting to non-‐engineering jobs in a difficult market, and most strikingly, their own feeling of ill-‐preparedness for the jobs of tomorrow. This survey was ideated, designed and conducted by Sudheer Mohan, State Secretary, AIPC Kerala on behalf of AIPC. Dr. Daly Paulose, Assistant Professor – St. Teresa’s College, Ernakulam helped with statistical analysis of the survey data. Dr. Daly Paulose and Sudheer Mohan, sat together to go through the findings and inferences to convert them to logical conclusions. Dr. Muralee Thummarukudy, Head of Natural Disaster Risk Mitigation, United Nations Environment Program (and one of the most followed Malayalee in Facebook) helped with valuable advice and support in social media. Arun Mohan Sukumar helped in proof reading and formatting the document. –

Background It’s no exaggeration to say that a domestic help in Kerala is offered more pay than many engineering graduates. The comparison serves not to highlight the welcome improvement in living conditions of wage labourers and helps, but of the plight of engineering graduates in the state. The situation of graduate engineers won’t be very different in other states as well. Over these years, fresh engineers were trying to take up whatever jobs they could manage to get, ranging from bus conductors to door to door sales. This uncovers the disaster that’s unveiling in our country, destruction of professional education sectors in our country and inability of our economy to generate jobs at scale within the country. Knowledge Industry,


the biggest service sector job generator, is struggling to find quality talent. Number of vacant positions in India’s IT Companies (and the lost revenue due to open positions) will tell the story of ever widening supply and demand gap. This is an ugly contrast – unfulfilled positions on one hand and no jobs for many on the other. Lack of quality talent is forcing IT outsourcing companies to look at other countries and thereby inflicting permanent damage on our economy. All this prompted AIPC to do a deeper investigation of situation in pursuit of what can change the situation. The self-‐financing college boom started in Kerala in the early 90s. Since 1993, there was a steady increase in number of self-‐financing engineering colleges in Kerala. Today, there are around 120 self-‐financing engineering colleges in Kerala. In last few years, there is continuous chatter about vacant engineering seats, falling graduation rates and a steep decline in quality of engineering graduates from Kerala and an even steeper rise in unemployment for engineering graduates. Almost every middle-‐class family in Kerala seems to have an engineering student while affluent families send their wards to medical schools or to better engineering colleges (Private deemed universities). Youngsters who are not able to graduate are commonly found and the tale of young engineering graduates without a job has become one of the most clichéd stories in Kerala. Pass percentage data from our engineering colleges is far from acceptable norms. As per KTU (the university for all engineering colleges in Kerala), only 46% of the students are passing in semester exams. We came across an eye opening analysis of 5th semester result in 2018. According to the data presented, there are 70 colleges in Kerala where the pass percentage is below 40% and 23 colleges where the pass percentage is below 25%. All this points towards the danger that’s looming large. Kerala is becoming a factory of highly incompetent engineers. A situation that shall adversely affect Kerala’s prospects to emerge as an employment hub in knowledge based industries. Even good students shall suffer due to this deteriorating trend on quality of engineering education in kerala. One can encounter engineering graduates in almost all kind of jobs in Kerala. From temporary staff in political party offices to bus conductors to office boys, engineering graduates are seen taking up anything that comes in their partly due to desperation and partly due to incompetency. [During the same time, interestingly, a bunch of engineering graduates and an engineering dropout (from abroad) flourished in Malayalam cinema both on screen and off screen] While some pursued their passion, most are stuck without an option but to find any job that can leave them on their feet. The Gulf, a natural employment oasis for Kerala, has started drying up and our youth are left without many options to choose from. This unpleasant yet unspoken social situation in Kerala prompted us to do something about this situation. We started off with a survey to understand the situation as is rather than letting our intuition and bias to draw inferences and hasty conclusions.


High level Findings Out of the 2600 correspondents, 25% were unemployed. Out of the employed engineering graduates, 66% seem to working on non-‐engineering jobs. This explains the gravity of the situation we need to deal with. Unless we focus on creating jobs and sustaining them, we shall soon go back to our 70s with high unemployment, social unrest and anti-‐institution movements. This time, it will be a lot more destructive with much higher number of unemployed, equipped with social media. Inferences from the survey are in line with widely acknowledged perceptions. From an employment seekers point of view

1. 58% of the respondents are finding it hard to find a suitable job 2. Almost 66% of new graduates are taking up a non-‐engineering job (esp. as first job) 3. Mean Entry level pay for graduate engineers is declining over years 4. Students from Self-‐Financed Engineering colleges are the most affected group 5. Pass percentage of students is at 50% which also reflects on poor intake quality and

inability of academic institutions to help students excel From an employed engineer’s point of view, following emerged as key concerns

1. 80% of the people cited the lack of industry connect as the major concern 2. Out dated syllabus & labs 3. Out dated mode of teaching 4. Mismatch between what is taught and what is in demand at enterprises 5. Lack of exposure to what is in demand at work

What most people wanted are

1. Industry Connect 2. Need to establish internships while at college 3. Need to revamp syllabus in consultation with industries 4. Need for better infrastructure and teaching environment 5. Need for quality teachers 6. Continuous interaction with Industry


Suggestions for Policy making Considering what’s happening in industry and at colleges, AIPC has the following recommendations to revamp engineering education sector

1. Constitute a Ministry for Job creation and Professional Education 2. Create universities exclusively for teachers to improve quality of teaching 3. CSR funds to be utilized to build Centers of Excellence by Corporates for students in

government college campuses 4. Increase number of hours of Engineering Education by adding Saturday as working day 5. Extra hours added should be used towards industry exposure and hands on trainings 6. Create an industry approved curriculum so that what is taught is relevant 7. Split Engineering education into – Foundation Course & Modern Engineering 8. Modern Engineering courses should change in line with future trends 9. Implement mandatory internship of 2 months every year 10. Ensure all industries accept interns all through the year 11. Implement Industry – Institute connect to ensure students are exposed to industry

expectations and skills while at Campus 12. Every industry to assess their immediate and mid-‐term skill demand every year and

share it with Ministry for professional education to make modifications to curriculum


Detailed Analysis Report

By Dr. Daly Paulose – Assistant Professor, St. Teresa’s College, Ernakulam In consultation with Sudheer Mohan – Secretary, AIPC Kerala


INTRODUCTION The analysis presented here uses a combination of Descriptive statistics and Inferential statistics. Descriptive statistics simply describe what the data shows. A research study may have lots of measures or may measure a large number of people on any measure. Each descriptive statistic reduces large datasets into a simpler summary of that variable. Inferential statistics is used here to draw inferences from the sample data to make generalizations to the population. It serves to analyse the data collected from the respondents and describes the level of support for the hypotheses used in the study by way of presenting the results.

1. RESPONDENT PROFILE The study covers a sample of 2646 engineering graduates drawn randomly from the length and breadth of the country. This section tries to profile the respondents based on their demographic characteristics as well as their academic and professional credentials. It tabulates aspects like Employment Status, Occupation, Annual Income, Academic Performance etc. Profile details are listed below in Table 1 .1 through Table 1.3.

1.1. Demographic Profile of Respondents

The demographic profile of the respondents of the study are detailed in Table 1.1 Table 1.1: Table showing the demographic profile of Respondents of the Study Demographic Criteria Categories Number of

respondents Percentage

Employment Status

Employed 1739 66%

Unemployed 895 34%

TOTAL 2634 100%

Annual Income (INR)

Less than INR 1 Lakh 709 29.8%

INR 1 Lakh – 3 Lakh 404 17%

INR 3 Lakh – 6 Lakh 429 18%

INR 6 Lakh – 12 Lakh 351 14.8%

INR 12 Lakh – 20 Lakh 176 7.4%

Greater than INR 20 Lakh 308 13%

TOTAL 2377 100%

Country of residence & employment

In India 2051 78%

Outside India 577 22%


TOTAL 2628 100%

Source: Primary data Majority of the respondents are currently employed (66%) with a good percentage of them (78%) working in India itself. Annual Income of respondents appear to be skewed to the right, with more respondents belonging to the lower income groups. This could be attributable to the fact that most of the respondents are recent graduates from college (around 70% graduated after year 2010).

1.2. Academic Profile of Respondents

The academic profile of the respondents of the study are detailed in Table 1.2 Table1. 2: Table showing the Academic profile of Respondents of the Study Academic Criteria Categories Number of


Place of Study In Kerala 2116 80.2%

Outside Kerala 524 19.8%

TOTAL 2640 100%

Year of Graduation

Before 2010 719 27.3%

Between 2010 and 2014 900 34.2%

2015 287 10.9%

2016 370 14.1%

2017 356 13.5%

TOTAL 2632 100%

Type of College Studied

Govt Engineering College 806 31.1%

Self-‐Financing College 1370 52.8%

University (Indian & Foreign) 105 4.0%

IITs & NITs 71 2.7%

Others 242 9.3%


TOTAL 2594 100%

Branch of Engineering Studied

Mechanical 680 26.1%

Computer Science 344 13.2%

EC 507 19.5%

Civil 392 15%

IT 89 3.4%

Electrical 364 14%

Chemical 31 1.2%

Others 198 7.6%

TOTAL 2605 100%

Grade Achieved

Below 60% 279 11%

60%-‐80% 2005 78.8%

Above 80% 259 10.2%

TOTAL 2543 100%

Nature of First Placement

Campus Placed 624 23.7%

Not Campus Placed 2014 76.3%

TOTAL 2638 100%

Source: Primary data A good majority of the respondents were average academic performers in various colleges within Kerala state itself with 78% respondents scoring between 60-‐80% marks. It is interesting to note that less than half the respondents (24%) in the study got their first jobs through campus placement. Where the branch of study is concerned, majority of the respondents appear to be Mechanical Engineering graduates (26%) followed by Electronics and Communication Engineers (19%).

1.3. Professional Profile of Respondents

The professional profile of the respondents of the study are detailed in Table 1.3 Table 1.3: Table showing the Professional Profile of Respondents Professional Criteria Categories Number of



Job Profile Engineering Job 1346 51.2%

Non-‐Engineering Job 1282 48.8%

TOTAL 2628 100%

Job Category Engineer 1112 42.5%

Entrepreneur 126 4.8%

Bank Officer 72 2.8%

Teacher 124 4.7%

Management Professional 208 8%

Unemployed 650 24.8%

Others 324 12.4%

TOTAL 2616 100%

Source: Primary data It is interesting to note that the respondent data is evenly distributed across those pursuing Engineering and Non-‐engineering jobs at the time of the survey. There is some discrepancy when we compare the Job profile statistics of those doing engineering jobs (51%) with the corresponding entry in Job category description (42.5%) as the totals in both cases do not match exactly. This may be attributed to the missing entries under the Job category segment with only 2616 respondents responding to the question on Job Category as against 2628 responses to the question on Job Profile.

1.4. Profiling based on perception of respondents

The perceptions of the respondents of the study have been summarised for quick reference in Table 1.4(a) and 1.4(b)

Table 1.4(a): Table showing Perception Summary of respondents Perception Attributes Categories Number of


Key challenge in Engineering Education

Difficult to Switch Jobs 43 1.6%

Difficult to find Suitable Jobs 1511 57.6%

Insecurity in Current Job 135 5.1%


Perception Attributes Categories Number of


Poor Quality of Education 782 29.8%

Low Salaries 152 5.8%

TOTAL 2623 100%

Desired Areas for Improvement in Engineering Course

Exposure to Latest Work-‐ related Technology

851 32.6%

Teaching Methods and Syllabus 506 19.4%

Industry Interaction and Mentoring by Industry Leaders

1161 44.5%

Physical Infrastructure and Intellectual Capital

89 3.4%

TOTAL 2607 100%

Suggestion to Improve Engineer Quality

Physical Infrastructure 50 1.9%

Practical Orientation and Industry Exposure 2189 84.2%

Curricular Aspects and Faculty Quality 209 8%

Student Quality & Student Progression Opportunities

151 5.8%

TOTAL 2599 100%

Areas requiring total transformation

Teaching methods and styles 470 18.1%

Insufficient exposure to Seminars, Conferences, Industry Exposure

266 10.2%

Outdated Workshops and Labs 522 20.1%

Industry Disconnect 580 22.3%

Outdated Syllabus 322 12.4%

Assessment System 168 6.5%

Dearth of Industry-‐Institute-‐Interaction, Campus adoption initiatives by corporates

270 10.4%


Perception Attributes Categories Number of


TOTAL 2598 100%

Source: Primary data One of the key challenges in engineering education seems to be with regard to finding suitable jobs with 58% respondents vouching for it. This could explain why many engineers end up working in non-‐engineering profiles. It may be noted that the most popular and equivocal sentiment of the respondents when it comes to improving the quality of engineers is to inculcate practical orientation in engineering students by way of industry exposure. There is no clear consensus among the respondents when it comes to recommending the one area that requires total transformation and overhaul in engineering education with all the aspects being preferred more or less equally across the sample. However, as expected, Industry disconnect and outdated practical exercises lead the way. Table 1.4(b): Table Summarising Overall Suggestion for improvement by Respondents

Parameter Criteria Percentage of responses

Suggestions for improvement in Engineering Education

Exposure to work related technology 17.6%

Education Quality (syllabus, teaching methods)

21.2%

Industry Interaction 51.1%

Student Progression Opportunities 8.6%

Physical Infrastructure and Intellectual Capital

1.5%

TOTAL 100%

Source: Primary data It can be inferred from Table 1.4(b) that 51% of the responses root for Industry Interaction as being the primary area that requires priority. This is followed by suggestions for improvement in the delivery of quality education by way of upgrading syllabi and overhauling outdated teaching methods.


2. TESTS FOR SIGNIFICANCE OF ASSOCIATION The Chi-‐square test of independence is generally deployed to determine whether there is a significant association between two categorical variables from a population. It is a statistical method which assesses the goodness of fit between a set of observed values and those expected theoretically. Here, Chi-‐square is used to ascertain hypothesised relationships pertaining to sample characteristics captured on categorical scales such as the Association between Period of Graduation and Campus Placement opportunities, College Type and Campus Placement opportunities, Branch of Engineering pursued and Current Annual Income of respondents.

1. Association between Period of Graduation and Campus Placement Opportunities

To assess the possibility of campus placements being related to the period of graduation, a cross tabulation of the corresponding variables was done and contingency table generated for the same. Chi-‐square test was run on this dataset to assess if the association (if it exists) is statistically significant enough to be extrapolated to the population. The alternative hypothesis for the analysis is stated below

H1: There is a significant association between period of graduation of respondents and campus placement opportunities.

The contingency table is shown in Table 2.1 (a) and test of association results in Table 2.1 (b). Table 2.1(a): Relationship between period of graduation and campus placement

opportunities.

Campus Placement

Period of Graduation

Total Before

2010

Between 2010 and 2014

2015 2016

2017

Yes

Count

200 215 50 87 68 620

% 27.9% 23.9% 17.7% 23.5%

19.1%

23.6%

No Count

517 684 233 283 288 2005


% 72.1% 76.1% 82.3% 76.5%

80.9%

76.4%

Total

Count

717 899 283 370 356 2625

%

100% 100% 100% 100%

100%

100%

Source: Primary data Table 2.1 (b): Chi-‐Square Test of Independence

Method χ2

Value p value

Pearson Chi-‐square 16.893 0.002

Source: Primary data

From Table 2.1(a), it can be roughly inferred that the campus placement trend is reflective of the period of study of the engineer. As a general trend, we can observe that campus placements have declined over time with the statistic peaking prior to 2010 and then subsequently dropping over time. This could point towards the deteriorating standards of BTech education or laxities in student intake quality. Analysis of the data using Chi-‐square test [Table 2.1(b)] revealed that it can be inferred with 95% confidence that this relationship is statistically significant ( χ2 = 16.893, p<.05).

2. Association between College Category/Type and Campus Placement Opportunities

There is a widespread notion that the pedigree of a professional college actually influences the initial decision of corporates to visit campuses for placements. In order to check if such a relationship exists from the response data of this survey, the following alternative hypothesis is proposed.

H2: There is a significant association between College Category and campus placement opportunities.


Chi-‐square test is used to verify if an association exists between category the college belongs to and campus placement opportunities received by students. The cross-‐tabulation is shown in Table 2.2 (a) and test of association results in Table 2.2 (b). Table 2.2(a): Relationship between college category and campus placement opportunities.

Campus Placement

Category of Engineering College

Total Govt

College

Self-‐Financing College

University

IITs & NITs

Others

Yes

Count

270 213 30 50 46 609

% 33.6% 15.6% 29.1% 70.4%

19%

23.5%

No

Count

534 1153 73 21 196

1977

% 66.4% 84.4% 70.9% 29.6%

81%

76.5%

Total

Count

804 1366 103 71 242

2586

% 100% 100% 100% 100%

100%

100%


Method χ2

Value p value



From Table 2.2(a), we may observe that the campus placement trend is seen to be reflective of the type of college the respondent graduated from. We can observe that campus


placements have been most vibrant in national institutes like the IITs and NITs followed at a distance by Government Engg. Colleges. 70% of the respondents who graduated from the national institutes claim to have been placed through campus on their first jobs followed by 34% respondents from Government colleges. It can be inferred that there is a significant relationship between the campus placement opportunities available and the institute type, when it comes to professional courses. Chi-‐square test values in [Table 2.2(b)] reveal that this relationship is statistically significant at 5% level ( χ2 = 184.17, p<.05) and can be extrapolated to the population.

3. Relationship between branch of Engineering pursued and Current Income

H3: There is a significant relationship between Branch of Engineering pursued by the respondent and Current Income drawn

The relationship is graphically represented in Figure 1 below and the contingency table and Chi-‐square test results presented in Table 2.3(a) and Table 2.3(b) respectively.


Table 2.3 (a): Relationship between engineering branch pursued and Current Income

Branch studied

Annual Income (INR)

Total Less than 3 Lakh

3 Lakh -‐ 6 Lakh

6 Lakh – 12 Lakh

Greater than 12 Lakh

Mechanical

Count

331 89 74 126 620

% 53.4%

14.4%

11.9%

20.3%

100%

Computer Science

Count

115 58 66 76 315

Figure 1: Relationship between Branch of Engineering Studied and Annual Income


Branch studied

Annual Income (INR)

Total Less than 3 Lakh

3 Lakh -‐ 6 Lakh

6 Lakh – 12 Lakh

Greater than 12 Lakh

% 36.5%

18.4%

21% 24.1%

100%

EC

Count

170 93 87 110 460

% 37% 20.2%

18.9%

23.9%

100%

Chemical Count

8 3 3 14 28

% 28.6%

10.7%

10.7%

50% 100%

Civil Count

201 66 29 40 336

% 59.8%

19.6%

8.6% 11.9%

100%

IT Count

36 20 10 13 79

% 45.6%

25.3%

12.7%

16.5%

100%

Electrical Count 147 61 54 64 326

% 45.1%

18.7%

16.6%

19.6%

100%

Total

Count

1008 390 323 443 2164

% 46.6%

18% 14.9%

20.5%

100%



Table 2.3(b): Chi-‐Square Tests of Independence

Methodology Value p value

Pearson Chi-‐Square

102.385 .000

Cramers V 0.318 .000

From Table 2.3(a) we can infer that majority of the respondents in the sample fall in the lowest income bracket (46.6% of the respondents). Of the lot, majority of the Mechanical and Civil Engineers in the sample appear to be part of the lowest income bracket (59% Civil Engineers and 53.4% Mechanical Engineers). People who studied Chemical Engineering followed by EC and Computer Science Engg. form the majority of the highest income group (income in excess of INR 12 Lakh). These relationships observed are seen to be statistically significant because the Pearson Chi Square test values seen in Table 2.3(b) are found to be significant. The correlation is a moderate one as Cramer’s V value is between 0.3 to 0.7 and hence the relationship stands validated.

4. Relationship between Respondent Perception about Challenges in Engg. Education and Branch of Study of respondent

We can visualise the relationship between two categorical variables having more than 2 categories using Correspondence Analysis. The added advantage is that the categories are depicted in the 2D chart for easy interpretation of the relationships. Here, correspondence analysis is used to identify if there is an association between the respondent perception of challenges in engineering education and the branch of study of the respondent. Table 2.4(a) depicts the distribution of customer perception of challenges across respondent’s domain of study in engineering. Of all the perceptions about challenges being ‘difficulty in switching jobs’ and ‘difficulty in finding suitable jobs’, most of the responses (37.2% and 31.2% respectively) came from Mechanical engineering graduates. EC graduates showed higher concern about insecurity on current jobs (28.6%) and lamented about the poor quality of education (22.2%). The key concern for Civil Engineers appears to be issue of low salaries with 36% of all responses in this segment coming from them. Table 2.4(b): Chi-‐Square Test of Goodness of Fit of Model

Table 2.4(a). Engineering Branch-‐wise distribution of Perception of Challenges


Dimension Chi Square Value Significance Proportion of Inertia

Accounted for Cumulative

1 174.450

0.00

0.545 0.545

2 0.347 0.891

3 0.060 0.951

4 0.049 1.000 Here the data has generated four dimensions and the percentage of model variance explained by each dimension is depicted by the Proportion of Inertia values depicted in Table 2.4(b). Here Dimension 1 explains 54% of the variance in the model and Dimension 2 explains 34.7%. The Chi-‐square value is significant at 5% confirming that there is a statistically strong correlation between the dimensions generated for the categorical variables under study. Further on, we can observe the 2D Visualisation diagram in Figure 2 closely to understand the dimensions and the relationships more closely.

5. Relationship between Respondent Perception about Challenges in Engg. Education and Branch of Study

The correspondence analysis can be used to visualise the relationship between two categorical variables when the variables have more than two categories. The categories are eventually depicted

Figure 2: 2D Visualisation of Relationship between Respondents Perception of Challenge and Branch of Engineering Studied


From the cluster diagram of the various points in Figure 2, we can infer that Dimension 1 could be ‘Poor Quality of education’ and Dimension 2 which is the second highest influencer could be ‘Difficulty to find suitable jobs’. We can infer that the salary issue is the key challenge only for Civil Engineers. Current Job Insecurity seems to be a unique concern to IT and Computer Science Graduates. The Mechanical Engineering Graduates share their concern about ‘difficulty in finding and switching jobs with Electrical Engineers. Poor quality of Education is the main concern common to all these graduates with the points being almost equidistance from all variable points. It is interesting to note that of the entire lot, the only group that seems to take a minimalist views about challenges are Chemical Engineering graduates.

5. Relationship between Respondent Perception about Challenges in Engg. Education and Year of Graduation

Using Correspondence Analysis, we can identify if there is an association between the respondent perception of challenges in engineering education and his /her year of graduation. Table 2.5(a) and (b) provide the desired output required for the statistical validation of this analysis.

Table 2.5(a) depicts the distribution of customer perception of challenges in engineering education based on respondent’s year of graduation. It is interesting to note that majority of the responses under all the five challenge perceptions came from those who graduated between Years 2000 and 2014 with the exception of ‘Low salaries’ where Year 2016 and 2017 graduates expressed sufficient concern. Table 2.5 (b): Chi-‐Square Test for Goodness of Fit of Model Dimension Chi Square Value Significance Proportion of Inertia

Accounted for

Cumulative

Table 2.5(a): Distribution of Perception of Challenge against Year of Graduation


1 74.875

0.00

0.915 0.915

2 0.080 0.995

3 0.004 0.999

4 0.001 1.000 Here the data has generated four dimensions and the percentage of model variance explained by each dimension is depicted by the Proportion of Inertia values depicted in Table 2.5(b). Here Dimension 1 explains 91% of the variance in the model and Dimension 2 explains only 8%. The Chi-‐square value is significant at 5% confirming that there is a statistically strong correlation between the dimensions generated and the variables under study.

From the cluster diagram of the various points in Figure 3, we can infer that Dimension 1 could be ‘Difficulty to find suitable job’ as it appears to be equidistant from all most of the independent variable points. This could mean that engineering graduates from the past 20

Figure 3: 2D Visualisation of Relationship between Respondents Perception of Challenge and Year of Graduation


years may have faced issues in finding the suitable job that matches their knowledge/interests/skillsets. We can also infer that low salaries are a key concern only for fresh pass outs. Difficulty in switching jobs seems to assume least relevance as a challenge when considered against the variable Year of Graduation. 6. Relationship between Current Income and Category of College of Study Using Correspondence Analysis, we can try to identify if there is an association between the current income of the respondent and the type of college he/she graduated from.

Table 2.6(a) depicts the distribution of Income earned based on Engineering College category. Here, we can infer that 62% of the low-‐income respondents came from Self Financing Colleges and 44.6% of the highest income respondents came from Government Engg Colleges. Table 2.6(b): Chi Square Test for Goodness of Fit of Model Dimension

Chi Square Value

Significance

Proportion of Inertia

Accounted for

Cumulative

1 152.348

0.00

0.959 0.959

2 0.022 0.981

3 0.019 1.000 Here the data has generated three dimensions and the percentage of model variance explained by each dimension is depicted by the Proportion of Inertia values depicted in Table 2.6(b). Here Dimension 1 explains 95% of the variance in the model and Dimension 2 explains only 2%. The Chi-‐square value is significant at 5% confirming that there is a statistically strong correlation between the dimensions generated and the variables under consideration. Figure 4: 2D Visualisation of Relationship between Respondents Annual Income and Type

of College of Graduation


From the 2 D diagram in Figure 4, we can infer that the most relevant dimension (Dimension 1) could be ‘3 Lakh-‐6 Lakh income group’ as it appears to be equidistant from most of the independent category points. It is evident from the figure that most of the graduates cluster around this income group. We can also infer that the access to the highest income bracket is closely contested by Govt Engg college and National Institute alumni as both seem to be equidistant from the particular income point (Greater than 12 Lakh), albeit the number of respondents in that bracket being small in number. It appears to be the alumni of Self Financing colleges who have been most affected in terms of future income prospects. 7. Relationship between Branch of Study and Perception of Challenges in Engg. Education

for respondents from who studied in Kerala and outside Kerala In analysing the relationship between Branch of Study and Perception about challenges in Engg education separately for people who completed their engineering degree in Kerala and those who studied outside Kerala, the following contingency table (Table 2.7) have been generated. This table will also help assess if there is a difference in the overall challenge perception across different branches of study between respondents with engineering degrees from Kerala and those with degrees outside the state.


Table 2.7: Table showing comparison between Branch of Study and Perception about Challenges based on Place of Study

Branch studied

Place of Study (In Kerala & Outside Kerala)

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

Job Switch Difficult


Find Suitable Job

Find Suitable Job

Job Insecurity

Job Insecurity

Education Quality

Education Quality

Low Pay

Low Pay

Mech %

2.3

2.6

69.8

65.2

3.3

3.2

21.3

23.9

3.3

5.2

CS %

3 1 44.6

40.5

7.1

6.8

40.1

47.3

5.2

4.1


Branch studied


In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala



Find Suitable Job

Find Suitable Job

Job Insecurity

Job Insecurity

Education Quality

Education Quality

Low Pay

Low Pay

EC %

0.5

2.2

56

48.4

7.5

7.5

33.3

35.5

2.7

6.5

Chemical

%

0 0 73.9

66.7

0 0 26.1

33.3

0 0


Branch studied


In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala



Find Suitable Job

Find Suitable Job

Job Insecurity

Job Insecurity

Education Quality

Education Quality

Low Pay

Low Pay

Civil %

3 1.6

56.9

57.4

4.9

3.3

22.3

29.5

15

8.2

IT %

1 9.5

39.4

19

12.1

9.5

39.4

57.1

7.6

4.8


Branch studied


In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala



Find Suitable Job

Find Suitable Job

Job Insecurity

Job Insecurity

Education Quality

Education Quality

Low Pay

Low Pay

Electrical

%

4 1.7

60.9

49.2

2.3

3.4

30.5

39

5 6.8

Others %

2 0 57.8

62.8

6.5

4.7

29.2

20.9

5.2

11.6


Branch studied


In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala

In Kerala

Outside Kerala



Find Suitable Job

Find Suitable Job

Job Insecurity

Job Insecurity

Education Quality

Education Quality

Low Pay

Low Pay

Total %

1.5

2.1

58.6

53.7

5.2

4.9

28.8

33

5.7

6.3

It may be inferred that Mechanical and Chemical Engineers, irrespective of Place of study, cited ‘finding suitable jobs’ as a key challenge. IT engineers who studied both in and outside the state have cited ‘Job insecurity’ as a serious concern. When it comes to citing ‘Quality of education’ as the key challenge, IT and Computer science graduates lead the way. The issue of Low salaries is a prime concern for Civil engineers in Kerala and not so much for those outside Kerala. Overall, the issues of ‘finding a suitable job’ and ‘Quality of education’ seem


to be the prime concern for engineering graduates cutting across state borders. The interesting point to note is that in general, the notion of challenges in engineering education, as observed by engineers within the same branches of study, exhibit high levels of similarity irrespective of the state of study. 8. Relationship between Current Income and Year of Graduation based on type of Job

In analysing if a relationship exists between Current Income and Period of graduation separately for people engaged in Engineering and Non-‐engineering jobs, the following contingency table [Table 2.8(a)] and Table 2.8(b) have been generated. These tables help assess if there is a difference in the overall income levels for those doing engineering jobs and non-‐engineering jobs depending on their period of graduation.

Table 2.8(a): Table relating Current Income and Year of Graduation based on Type of Job

Type of Job (Engineering Job/Non-‐Engineering Job)

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Less than 3 Lakh

Less than 3 Lakh

Between 3L and 6L

Between 3L and 6L

Between 6L and 12L

Between 6L and 12L

Greater than 12 L

Greater than 12 L

Before 2010

%

4.8%

34.7%

12.2%

20%

23.8%

12.9%

59.2%

32.4%

Between 2010

%

26%

62.3%

31.5%

20.8%

26%

12.7%

16.5%

4.2%


Type of Job (Engineering Job/Non-‐Engineering Job)

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Engineering Job

Non-‐Engg. Job

Less than 3 Lakh

Less than 3 Lakh

Between 3L and 6L

Between 3L and 6L

Between 6L and 12L

Between 6L and 12L

Greater than 12 L

Greater than 12 L

and 2014


%

63.4%

91.9%

24.7%

4.7%

7.9%

1.6%

4% 1.8%

Total % 26.8%

72.3%

22.2%

12.8%

20.7%

7.3%

30.3%

7.6%

Table 2.8(b): Chi-‐square test of Independence


Category Chi-‐square Value

p value Cramer’s V

Engineering Job 586.575 0.000 0.470

Non-‐engineering Job 325.189 0.000 0.397

From Table 2.8(a), it may be inferred that respondents doing engineering jobs earn substantially higher income compared to those in non-‐engineering jobs. However, there has been a substantial drop in the average income of those engineers who passed out between 2015 and 2017 as compared to those who graduated between 2010 and 2014. It can be observed that around 64% of fresh recruits in engineering jobs earn less than INR 3 lakh annually and the corresponding value for non-‐engineering jobs is 91.9%. This corresponding figures for those graduated between 2010 and 2014 are 26% and 62% respectively indicating a sharp increase in the low income groups over the last 8 years. From Table 2.8(b), the relationships mentioned here are seen to be significant at 95% confidence level with the Cramers V values confirming moderate strength of the relationships. Even though the data prior to 2010 points to 60% engineers earning more than 12 Lakh per annum, that has not be considered for inference, as the experience and career progression factors would significantly influence the salary variation in that group.

9. Association between Period of Graduation and Type of Job

To assess is the Period of Graduation is linked in any significant way to the type of job of the graduate, a cross tabulation of the corresponding variables was done and contingency table generated for the same. Chi-‐square test was run on this dataset to assess if the association (if it exists) is statistically significant enough to be extrapolated to the population. The alternative hypothesis for the analysis is stated below

H4: There is a significant association between period of graduation of respondents and type of job performed.

The contingency table is shown in Table 2.9 (a) and test of association results in Table 2.9 (b). Table 2.9(a): Relationship between period of graduation and type of job.

Type of Job Period of Graduation


Before 2010



Total

Engineering Count 530 475 334 1339

% 74.1% 53% 33.3% 51.2%

Non-‐Engineering

Count 185 422 668 1275

% 25.9% 47% 66.7% 48.8%

Total Count 715 897 1002 2614

% 100% 100% 100% 100%


Method χ2

Value p value


Cramer’s V 0.497 0.000


From Table 2.9(a), it can be very clearly observed that the type of job received is reflective of the period of study of the engineer. As a general trend, we can observe that the majority of the early graduates (Before 2010) are pursuing engineering jobs (74%). But over the subsequent years, a clear drop in this statistic is observed with the balance finally tilting in favour of non-‐engineering jobs for those graduated between 2015 and 2017 (66% in non-‐engineering jobs and 33% in engineering jobs). From the values of the Chi-‐square test [Table 2.9(b)], it can be inferred with 95% confidence that this relationship is statistically significant ( χ2 = 279.53, p<.05). Cramer’s V value further confirms the strength of the association to be moderate.

10. Association between Period of Graduation and Job Category

Once again, we are going to visualise the relationship between two categorical variables having more than 2 categories using Correspondence Analysis. Here, correspondence analysis is used to identify if there is a significant pattern between the respondents period of


graduation and the popular categories of jobs. Table 2.10(a) and (b) provide the desired output required for the statistical validation of this analysis.

Table 2.10(a): Table showing the distribution of job categories across periods of graduation

Job category Period of Graduation

Before 2010 Between 2010 and 2014


Engineer 0.372 0.350 0.278

Entrepreneur 0.294 0.317 0.389

Bank Officer 0.338 0.451 0.211

Teacher 0.387 0.379 0.234

Management Professional

0.353 0.377 0.271

Unemployed 0.062 0.259 0.679

Others 0.260 0.433 0.307

Source:Primary Data Table 2.10(a) depicts the distribution of popular job categories based on respondent’s year of graduation. It is interesting to note of all engineers, 37.2% graduated before 2010 and less than 30% are fresh graduates. It may also be noted that the largest proportion of unemployed graduates (67.9%) in the respondent sample are fresh graduates belonging to the 2015-‐2017 batch of graduation. Table 2.10 (b): Chi-‐Square Test for Goodness of Fit of Model Dimension Chi Square Value Significance Proportion of Inertia


Accounted for

Cumulative

1 386.43

0.00

0.964 0.964

2 0.036 1.000 Here the data has generated two dimensions and the percentage of model variance explained by each dimension is depicted by the Proportion of Inertia values depicted in Table 2.10(b). Here Dimension 1 explains 96% of the variance in the model and Dimension 2 explains only 3.6 %. The Chi-‐square value is significant at 5% confirming that there is a statistically strong correlation between the dimensions generated and the variables under study.

From the 2 D diagram in Figure 5,it appears that the most popular dimension (Dimension 1) could be either ‘Engineers, Teachers or Management Professionals ’ as all these these points appears to be equidistant for those who graduated before 2014. It is however not easy to identify Dimension 1 given the scattered plot pattern. It is however evident that the issue of

Figure 5: 2D Visualisation of Association between Job Category and Year of Graduation


unemployment appears to be a primary concern with only the 2015-‐2017 graduates and hardly a concern for other groups.

3. TEST OF SIGNIFICANCE FOR TESTS OF DIFFERENCE

3.1.Test of difference between Current Income and Place of Work The Mann Whitney U Test can be used to evaluate the difference between two groups when the independent variable is nominal and dichotomous and the dependent variable is continuous. Here the Mann Whitney U Test is employed to test the difference in annual income based on Place of work of the respondent. The Dependent Variable here is Current Annual Income (which is ordinal scaled) and the independent variable is Place of Work (Options being within India and Outside India). The alternative hypothesis is stated as follows. The findings of the analysis are reflected in Figure 5 below and Table 3.1. H4: There is a significant difference between the medians of the two groups namely Annual Income and Place of work for the respondents in the study.

Figure 5: Output of Independent Samples Mann-‐Whitney U Test


Table 3.1: Independent Samples Mann-‐Whitney U Test of difference Total number of cases 2368

221,405 0.000 Mann Whitney U Value

Significance

Work in India Work outside India

Number of cases 1814 554

Mean Rank 1691 1029 Table 3.1 shows that there is a statistically significant difference between the Annual Income of respondents based on their place of work as the Significance Value of the test is less than 0.05. The mean rank for those working abroad is 1691 and for those working in India is 1029 indicating that the former group earns more than the home country based work group. 3.2.Test of difference between Current Income and Type of Job The Mann Whitney U Test can be used to evaluate the difference between the Dependent Variable namely Current Annual Income of the respondent and the independent variable Type of Job (Options being Engineering Job and Non-‐Engineering Job). The output is tabulated in Figure 6 and Table 3.2 H5: There is a significant difference between the medians of the two groups namely Annual Income and Job Type for the respondents in the study.


Table 3.2: Independent Samples Mann-‐Whitney U Test of difference Total number of cases 2372

1,081,401 0.000 Mann Whitney U Value

Significance

Engineering Job Non-‐engineering Job

Number of cases 1332 1040

Mean Rank 1478 812 Table 3.2 shows that there is a difference between the Annual Income of respondents based on their Job type as the difference is significant at 95% level of confidence. It can be summarised that the median rank for annual income for the respondents doing Engineering

Figure 6: Output of Independent Samples Mann-‐Whitney U Test


jobs is higher than those doing Non-‐Engineering Jobs, indicating that the former group earns more than the latter. 4. PREDICTIVE ANALYSIS USING LOGISTIC REGRESSION Logistic Regression can be used to predict whether a given case will belong to one or the other category of the response variable, when the dependent variable is nominal and dichotomous and the independent variable assumes scale of any order.

4.1.Dependence of Type of Job performed on Academic Grade, Campus Placement and

Place of Study

Here, Logistic Regression is used as a predictive technique to estimate the probability that a respondent will be doing an engineering job in the future based on his past information such as grade acquired in college, whether he got campus placement from college for his first job and his place of study. The probabilities are calculated based on the principal that Probability of success = (p)/(1-‐p). Here DV= Respondent currently in an engineering job (Yes =1/No = 0) IV1 = Academic Grade acquired in college (Below 60% =1, 60%-‐80% =2, Above 80% =3) IV2 = Campus Placed (Yes = 1, No=0) IV3 = Place of Study (in Kerala = 1, Outside Kerala = 0) First of all, the Academic Grade which has three categories is reduced to 2 categories by creating dummy variables to run the regression analysis. The model generated is found to be acceptable as the Model coefficients in the Omnibus Test and the Hosmer and Lemeshow Test used for Goodness of Fit is seen to be statistically significant. Now that the robustness of the model is satisfied, we can observe Table 4.1(a), 4.1(b), 4.1(c) and 4.1(d) to predict the probabilities of remaining in an Engineering Job. Table 4.1(a): Table showing the Regression Coefficients in the Model Variable Names Regression

Coefficient (B) Wald Test Chi-‐Square Value

Significance Anti-‐log of Regression Coefficient Exp(B)

Place of Study -‐0.325 10.477 0.001 0.722

Grade <60% 0.233 1.912 0.167 1.263

Grade 60%-‐80% -‐0.140 1.374 0.041 0.869

Campus Placed 0.752 58.298 0.000 2.121


Constant -‐0.237 1.913 0.167 0.789 From Table 4.1(a), it can be deciphered that the regression coefficients are significant as the Chi square values of the Wald Test are less than 0.05 in all cases except Grade acquired below 60% where p value is 0.167. So it will not be relevant to include this category of the independent variable in subsequent analysis. We know that the Outcome variable (Doing an Engineering Job or not) is a binomial variable with two outcomes (namely Yes and No), where Yes is a success outcome and No is a failure outcome. The anti-‐log Exp(B) depicts the odds of success for each case in the sample depending on the values of the independent variables. Hence the following rule of thumb is applied. Table 4.1(b): Thumb rule for estimating probabilities for Binomial distribution If Exp(B)>1 Subjects in that category have higher odds than subjects in reference category.

If Exp(B)<1 Subjects in that category have lower odds of success compared to reference category.

If Exp(B) =1 The odds of success are the same for subjects in both categories.

From Table 4.1(b), the Exp(B) value for Place of Study is 0.722. This is lesser than 1. Hence the probability of a person who studied outside Kerala has lower chance of being in an engineering job than a person who studied in Kerala. How much lesser? It is 72 times less likely that a person who studied outside Kerala remains in an Engineering job than a person who studied in an engineering college in Kerala. Given that the probability of an event to happen (p) based on odds (o) 𝑝 = #

$%# Hence 𝑝 = &.((

$%&.(( = 0.43 or 43%

That is Probability of a person who studied outside Kerala doing an Engineering job in future is 43%. Probability of a person who studied in Kerala ending up doing an Engineering job is (100-‐43) = 57%. By doing the same calculation for the remaining independent variables we arrive at the following conclusions depicted in Table 4.1(c). Table 4.1(c): Summary of statistical Inference for Probability distribution


Independent Variables Anti-‐log of Regression Coefficient Exp (B)

Statistical Inference Probability of continuing in an Engineering Job

Campus Placed (PN: Here reference Group is ‘Not campus placed’)

2.121 A person who is placed through campus initially has a 212% higher chance of continuing in an engineering job than a person who didn’t get campus placement

For a person placed through campus: 67.9% For a person not got campus placement: 32.1%

Academic Grade between 60% and 80% in college (PN: Here reference Group is ‘Grade above 80%’ )

0.869 A person with an academic performance between 60%-‐80% has an 86.9% lower chance of continuing in an engineering job than a person who scored more.

For a person with an academic performance between 60% and 80% in college: 46.5% For a person with an academic performance above 80% in college: 53.5%

Place of Study (PN: Here reference Group is ‘Studied in Kerala’.)

0.722 A person who studied outside Kerala has 72% lower chance of being in an engineering job than a person who studied in Kerala.

For a person who studied outside Kerala : 43%. For a person who studied in Kerala: 57%.

To evaluate the accuracy of our prediction using the data, we can look at the values generated in the Classification Table [Table 4.1(d)]. Table 4.1(d): Classification Table for Outcome variable (Type of Job) based on Independent Variables


From the Classification Table 4.1(d) above we can see that among the respondents doing an engineering job, 38% were correctly classified. Among those doing non-‐engineering jobs, 83.3% were correctly classified. The overall percentage of correct classification is 60.5%. As the generally accepted cut-‐off for such models is 50%, we can see that the probabilities of college grade, campus placement and place of study impacting respondent’s chances of choosing an engineering job are statistically reliable. To conclude, it can be said that factors like completing their engineering degree from Kerala, scoring high academic grades during the engineering course and getting first employment through campus drives can positively influence the propensity of an engineer to remain in an engineering job. 4.2.Dependence of Current Annual Income on Academic Grade, Campus Placement and

Type of Job Logistic Regression maybe used as a predictor of current respondent income. An effort is made to estimate the probability that respondent’s current annual income is based on his/her past information such as grade acquired in college, type of job currently in (engineering or non-‐engineering) and whether first job was through campus placement. 4.2. (a): Table Showing Regression Coefficients in the Model Parameters Regression

Coefficient (B)

Wald Test (Chi Square Value)

Significance (p value)

Anti-‐log of Regression Coefficient Exp(B)

Probability of Success (p) based on odds(o)

Whether the findings are statistically significant

Doing Engineering Job

1.714 268.895 0.000 5.552 0.85 Significant

Got Campus Placement

0.797 54.356 0.000 2.219 0.69 Significant

Grade (<60%)

0.251 1.532 0.216 1.285 0.56 Not Significant


Parameters Regression Coefficient (B)

Wald Test (Chi Square Value)

Significance (p value)

Anti-‐log of Regression Coefficient Exp(B)

Probability of Success (p) based on odds(o)

Whether the findings are statistically significant

Grade (60%-‐ 80%)

0.225 2.670 0.102 1.253 0.55 Not Significant

Constant -‐2.195 101.556 0.000 0.111 The summary of the inferences pertaining to the probabilities of each category earning higher income later in life is depicted in Table 4.2(a). Here it may be noted that we cannot consider the Academic Grade as a reliable estimator as the Chi square values of the Wald’s Test are not significant (p value>0.05). However it can be safely inferred that the other two parameters namely Job Type and Campus Placement have a significant impact on the Income. The summary of the inferential statistics is depicted in Table 4.2(b) below. Table 4.2(b): Summary of statistical Inference for Probability distribution Independent Variables Anti-‐log of Regression

Coefficient Exp (B)

Statistical Inference Probability of continuing in an Engineering Job

Doing Engineering Job (NB: Here reference Group is ‘Not doing engineering job)

5.552 A person who is doing an engineering job has a higher chance of earning higher income

For a person doing engineering job: 85%

Got employed through Campus Placement (NB: Here ‘never placed through campus’ is reference group’)

2.219 An engineer who got placed through campus has higher chance of earning higher income

For an engineer employed initially through campus: 69%

Academic Grade below 60% in college (PN: Here reference Group is ‘Grade above 80%’ )

1.285 A person with an academic grade below 60% has a higher chance of earning higher income than reference group

For a person with an academic grade below 60% in college: 56%


Academic Grade between 60% and 80% in college (PN: Here reference Group is ‘Grade above 80%’ )

1.253 A person with an academic grade between 60% and 80% has a higher chance of earning higher income than subjects in reference group

For a person with an academic grade between 60% and 80% in college: 55%

In order to confirm the robustness of the regression model and see if it applies to the population of engineers in general, we can look at the Classification Table 4.2(c) which depicts the cross-‐tabulated information between Actual Income levels captured in the sample and Predicted Income Levels based on the Ordinal regression output and try to make sense of the percentage of correctly classified cases in the model. Table 4.2(c): Classification Table for Outcome variable (Annual Income)

From the Classification Table 4.2(c), we can see that among the respondents in the low income group, 90% were correctly classified. Among the high income group, 31% were correctly classified. The overall percentage of correct classification is 69.4%. As the generally accepted cut-‐off for such models is 50%, We can say that the model is robust. That is to say, factors like getting initially employment opportunities through campus placement and deciding to remain in an engineering job can have a long term positive impact on an engineer’s career prospects in terms of salary. The impact of academic grade on annual income could not be statistically validated and hence is not considered.


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State of Engineering Education Final...For!further!questions,[email protected]!(Secretary,!AIPC!Kerala)!!!!! !!!! ! !!!!! !!STATE!OF!ENGINEERING!EDUCATION!IN!