Reforms in technical education sector: evidence from World Bank-assisted Technical Education Quality Improvement Programme in India

Abstract

In this paper, we identify factors which improve the quality of technical education using the data from World Bank’s Technical Education Quality Improvement Programme (TEQIP) in India. We evaluate the success of TEQIP in improving the quality of technical education in the country. Our findings show significant impact of this intervention on the quality of the technical education. The design, strategy, and implementation of TEQIP have crucial lessons for developing countries who want to build their technical education sector for rapid economic growth.

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Change history

  • 21 February 2019

    Tables 9 and 10 in the original article contained a typographical error. The corrected Tables 9 and 10 are now given next page.

Notes

  1. 1.

    Gross Attendance Ratio (for Primary to higher Secondary) =\( \frac{\mathrm{Number}\ \mathrm{of}\ \mathrm{persons}\ \mathrm{attending}\ \mathrm{classes}\ \mathrm{I}\ \mathrm{to}\ \mathrm{XII}}{\mathrm{Estimated}\ \mathrm{Population}\ \mathrm{in}\ \mathrm{the}\ \mathrm{age}\ \mathrm{group}\ \mathrm{of}\ 6-17\ \mathrm{years}}\ast 100 \)

  2. 2.

    Gross Attendance Ratio (Higher Education) =\( \frac{\mathrm{Number}\ \mathrm{of}\ \mathrm{persons}\ \mathrm{attending}\ \mathrm{classes}\ \mathrm{above}\ \mathrm{higher}\ \mathrm{secondary}\ }{\mathrm{Estimated}\ \mathrm{Population}\ \mathrm{in}\ \mathrm{the}\ \mathrm{age}\ \mathrm{group}\ \mathrm{of}\ 18-29\ \mathrm{years}}\ast 100 \)

  3. 3.

    Gross Enrolment Ratio in Higher education in India is calculated for 18–23 years of age group. Total enrolment in higher education, regardless of age, expressed as a percentage to the eligible official population (18–23 years) in a given school year.

  4. 4.

    In India, education is in the concurrent list of the constitution with both Union and State Governments having jurisdictions over it.

  5. 5.

    https://economictimes.indiatimes.com/industry/services/education/india-is-in-the-middle-of-an-engineering-education-crisis/articleshow/63680625.cms

  6. 6.

    http://planningcommission.nic.in/aboutus/committee/strgrp/stgp_scndry.pdf

  7. 7.

    In India, the most of the private colleges take “donations.” Usually they are paid in cash and are either not accounted for or only partly accounted for in the accounts of the institutions. These payments, mostly at the time of admission, are referred to as capitation fees (Agarwal 2007).

  8. 8.

    http://documents.worldbank.org/curated/en/981201507212564957/pdf/Implementation-Completion-Results-ICR-India-TEQIP-10022017.pdf

  9. 9.

    http://projects.worldbank.org/P072123/technicalengineering-education-quality-improvement-project?lang=en&tab=overview

  10. 10.

    http://projects.worldbank.org/P102549/tech-engr-educ-quality-improvement-ii?lang=en

  11. 11.

    Source: NPIU: http://npiu-teqipmis.edu.in. National Project Implementation Unit (NPIU) is a unit of Ministry of Human Resource Development, Government of India for coordination, facilitation, and monitoring of the externally funded projects and to provide guidance to the States/Institutions in all aspects of the projects.

  12. 12.

    The NBA, India is established by AICTE in the year 1994, for periodic evaluations of technical institutions and programs according to specified norms and standards as recommended by AICTE council.

  13. 13.

    Excellent details of methods used are available in Khandker et al. (2010) and Gertler et al. (2016).

  14. 14.

    A description on the rationale for weights in different sub-components will be made available to the readers on request.

References

  1. Agarwal, P. (2007). Higher education in India: growth, concerns and change agenda. Higher Education Quarterly, 61(2), 197–207.

    Article  Google Scholar 

  2. Angrist Joshua, D., & Pischke, J. S. (2009). Mostly harmless econometrics. An empiricist’s companion. Princeton University Press, Princeton.

  3. Becker, G. S. (1962). Investment in human capital: a theoretical analysis. Journal of Political Economy, 70(5, Part 2), 9–49.

    Article  Google Scholar 

  4. Becker, S. O., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. The Stata Journal, 2(4), 358–377.

    Article  Google Scholar 

  5. Benhabib, J., & Spiegel, M. M. (1994). The role of human capital in economic development evidence from aggregate cross-country data. Journal of Monetary Economics, 34(2), 143–173.

    Article  Google Scholar 

  6. Barro, R. J. (1992). Human capital and economic growth (pp. 199–230). Federal Reserve Bank of Kansas City: Proceedings.

    Google Scholar 

  7. Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104, 184–198.

    Article  Google Scholar 

  8. Ben-Porath, Y. (1967). The production of human capital and the life cycle of earnings. Journal of Political Economy, 75(4, Part 1), 352–365.

    Article  Google Scholar 

  9. Borooah, V. K. (2002). Logit and probit: ordered and multinomial models. Thousand Oaks, CA: Sage.

  10. Blom, A., & Saeki, H. (2011). Employability and skill set of newly graduated engineers in India (English). Policy Research working paper; no. WPS 5640. Washington, DC: World Bank.

  11. Cunha, F., & Heckman, J. (2007). The technology of skill formation. American Economic Review, 97(2), 31–47.

    Article  Google Scholar 

  12. Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. (2016). Impact evaluation in practice. World Bank Publications.

  13. Greene, W. H. (2003). Econometric analysis. Pearson Education India.

  14. Heck, R. H., Tabata, L., & Thomas, S. L. (2013). Multilevel and longitudinal modeling with IBM SPSS. New York, NY: Routledge.

  15. Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. The Review of Economic Studies, 64(4), 605–654.

    Article  Google Scholar 

  16. Heckman, J. J., Ichimura, H., & Todd, P. (1998a). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), 261–294.

    Article  Google Scholar 

  17. Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998b). Characterizing selection bias using experimental data. Econometrica, 66(5), 1017–1098.

    Article  Google Scholar 

  18. Hosmer Jr, D. W., Lemeshow, S. (2000). Applied logistic regression (2nd edition). New York, NY: John Wiley & Sons.

  19. Kapur, D., & Mehta, P. B. (2004). Indian higher education reform: from half-baked socialism to half-baked capitalism. Center for International Development Working Paper, 103.

  20. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: quantitative methods and practices. World Bank Publications.

  21. Liu, X. (2016). Applied ordinal logistic regression using Stata: from single-level to multilevel modeling. Sage Publications.

  22. Loeb, S., Kalogrides, D., & Béteille, T. (2012). Effective schools: teacher hiring, assignment, development, and retention. Education Finance and Policy, 7(3), 269–304.

    Article  Google Scholar 

  23. Lucas Jr., R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.

    Article  Google Scholar 

  24. McMahon, W. W. (1999). Education and development: measuring the social benefits. Oxford: Oxford University Press.

  25. McMahon, W. W., & Oketch, M. (2013). Education’s effects on individual life chances and on development: an overview. British Journal of Educational Studies, 61(1), 79–107.

    Article  Google Scholar 

  26. Nussbaum, M. C. (1997). Cultivating humanity: a classical defense of reform in liberal education. Cambridge MA and London: Harvard University Press.

  27. Nye, B., Konstantopoulos, S., & Hedges, L. V. (2004). How large are teacher effects? Educational Evaluation and Policy Analysis, 26(3), 237–257.

    Article  Google Scholar 

  28. Mincer, J. (1984). Human capital and economic growth. Economics of Education Review, 3(3), 195–205.

    Article  Google Scholar 

  29. Ministry of Human Resource Development (MHRD). (2009). Annual Report. India: New Delhi.

    Google Scholar 

  30. Ministry of Human Resource Development (MHRD) (2016). 2015–16, New Delhi. http://aishe.nic.in/aishe/viewDocument.action?documentId=227. Accessed 17 July 2018.

  31. National Sample Survey Office (2016). Education in India (NSSO 71st Round, January–June 2014, Report No. 575(71/25.2/1)), New Delhi.

  32. O’Connell, A. A. (2006). Logistic regression models for ordinal response variables. Thousand Oaks, CA: Sage.

  33. Puukka, J., & Marmolejo, F. (2008). Higher education institutions and regional mission: lessons learnt from the OECD Review Project. Higher Education Policy, 21(2), 217–244.

    Article  Google Scholar 

  34. Rockoff, J. E. (2004). The impact of individual teachers on student achievement: evidence from panel data. American Economic Review, 94(2), 247–252.

    Article  Google Scholar 

  35. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037.

    Article  Google Scholar 

  36. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  37. United Nations (UN), Department of Economic and Social Affairs, Population Division (2015). World population prospects: the 2015 revision, key findings and advance tables. Working Paper No. ESA/P/WP.241.

  38. U. R. Rao Committee (2003). Revitalizing technical education, report of the review committee on AICTE, Ministry of Human Resource Development, Government of India, September, 2003.

  39. Walker, M. (2006). Higher education pedagogies: a capabilities approach. McGraw-Hill Education (UK).

  40. World Bank (2008). Indian road construction industry, capacity issues, constraints & recommendations. World Bank, Washington DC, USA.

  41. World Bank (2009). Implementation completion and results report (IDA-37180) on a credit for a Technical/Engineering Education Quality Improvement Project. World Bank, Washington DC, USA.

Download references

Acknowledgements

This paper is based on a research study funded by Govt. of India for Impact Evaluation of World Bank-assisted “Technical Education Quality Improvement Programme” Phase II (TEQIP-II) in India. Authors are grateful to Mr. R. Subrahmanyam and Ms. Tripti Gurha of Ministry of Human Resource Development, Govt. of India and to Ms. Tara Béteille and Mr. Francisco Marmolejo of the World Bank for all their comments and inputs at various stages of the study. We also thank to two anonymous referees of the paper whose comments significantly improved the quality of the paper. Usual disclaimer applies.

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Correspondence to Amlendu Dubey.

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The original version of this article was revised: Tables 9 and 10 in the original article contained a typographical error. The corrected Tables 9 and 10 are now shown here.

Appendix 1: Development of TEQIP-II impact score

Appendix 1: Development of TEQIP-II impact score

The key deliverables as identified at the initiation of TEQIP-II are noted in Table 11. All the expected outputs of the project are having their clearly defined indicators. These indicators are used to develop a TEQIP-II impact score. The data for developing the scores have been collected from all the 190 participating institutions through a questionnaire survey.

Table 11 Expected outcomes of TEQIP-II and corresponding indicators

Impact scores are calculated separately for 1.1 institutes, 1.2 institutes, and 1.2.1 institutes (1.2 institutions with COEs). The relative weights of different sub-components were identified based on a workshop conducted and in overall consultation with NPIU and World Bank.Footnote 14

The scores were calculated as follows:

$$ TIS\ (1.1)=0.26\times AATS+0.12\times FASS+0.08\times ES+0.10\times PEQS+0.10\times SPRS+0.10\times SPLS+0.12\times ELFS+0.12\times FIS $$
(A1)
$$ TIS\ (1.2)=0.22\times AATS+0.11\times FASS+0.08\times ES+0.09\times PEQS+0.10\times SPRS+0.08\times SPLS+0.09\times ELFS+0.10\times FIS+0.13\times ROS $$
(A2)
$$ TIS\ \left(\mathrm{1.2.1}\right)=0.18\times AATS+0.12\times FASS+0.04\times ES+0.07\times PEQS+0.05\times SPRS+0.05\times SPLS+0.07\times ELFS+0.09\times FIS+0.16\times ROS+0.17\times CCS $$
(A3)
  • TIS (1.1)—TEQIP-II impact score for sub-component 1.1 participating institutes

  • TIS (1.2)—TEQIP-II impact score for sub-component 1.2 participating institutes

  • TIS (1.2.1)—TEQIP-II impact score for sub-component 1.2.1 participating institutes

  • AATS—Autonomy, Accountability and Transparency Score

  • FASS—Fund Availability & Sustainability Score

  • ES—Equity Score

  • PEQS—Program Expansion and Quality Score

  • SPRS—Student Performance Score

  • SPLS—Student Placement Score

  • ELFS—Enhanced Learning Facility Score

  • FIS—Faculty Improvement Score

  • ROS—Research and Outreach Score

  • CCS—COE Component Score

  1. 1.

    Autonomy, Accountability and Transparency Score (AATS):

This score has been developed based on the level of participation and representation of different stake holders namely Students, Faculty, Administrative Staff, Owners/Donors, Alumni, and Private Sector/Industry Representative in the decision making processes of the institute. Participation of a particular stakeholder is high if that stakeholder is involved in the decision making process of the institute as measured by following four processes: defining the goals of the institutions, strategy and planning, budget allocation, and defining and changing academic programs. Representation of a particular stakeholder is high if that stakeholder is represented in any of these three set-ups: Highest decision making body of the institute, Academic Board or equivalent, and Research Council or equivalent.

  • Overall assessment metric is

$$ AATS=0.4\times {\mathrm{AATS}}_P+0.6\times {\mathrm{AATS}}_R $$
(A4)
$$ {\mathrm{AATS}}_P=\kern0.5em \left\{0.15\kern0.5em \sum \frac{S_S}{4}+0.3\sum \frac{S_F}{4}\kern0.5em +0.1\sum \frac{S_{Ad}}{4}+0.05\sum \frac{S_O}{4}+0.2\sum \frac{S_{Al}}{4}+0.2\sum \frac{S_P}{4}\right\} $$
(A5)
$$ {\mathrm{AATS}}_R=\kern0.5em \left\{0.15\kern0.5em \sum \frac{S{\prime}_S}{6}+0.3\sum \frac{S{\prime}_F}{6}\kern0.5em +0.1\sum \frac{S{\prime}_{Ad}}{6}+0.05\sum \frac{S{\prime}_O}{6}+0.2\sum \frac{S{\prime}_{Al}}{6}+0.2\sum \frac{S{\prime}_P}{6}\right\} $$

(A6)

  • Component metrics are based on the following:

AATS Autonomy, Accountability and Transparency Score
AATSP AATS Participation
AATSR AATS Representation
SS Participation score-Students
SF Participation score-Faculty
SAd Participation score-Administrative staff
SO Participation score-Owners / Donors
SAl Participation score-Alumni
SP Participation score-Private sector/Industry representative
S′S Representation score-Students
S′F Representation score-Faculty
S′Ad Representation score-Administrative staff
S′O Representation score-Owners/Donors
S′Al Representation score-Alumni
S′P Representation score-Private sector/Industry representative
  1. 2.

    Fund Availability and Sustainability Score (FASS):

This score has been developed based on the amount of funding received from TEQIP-II and sustainability plan of the institute. As per the TEQIP-II mandate, all the participating institutes were to create four funds; Corpus Fund, Faculty Development Fund, Equipment Replacement Fund, and Maintenance Fund. For the institutions, who had received higher per capita (as measured by the total number of UG students) TEQIP-II funding and who have created and endowed most these funds, this score is higher.

$$ FAS S=0.5\times FAS+0.5\times FSS $$
(A7)
$$ FAS={P}_f\left\{\frac{Amount\ of\ Total\ TEQIP- II\ funding\ to\ the\ Institute}{Total\ Number\ of\ UG\ Students\ }\right\} $$
(A8)
$$ FSS=0.6\ \left(\sum \frac{Number\ of\ Four\ Funds\ Established}{4}\right)+0.4\ {P}_f\left(\frac{Total\ Amount\ of\ Four\ Funds\ Established}{Total\ Number\ of\ UG\ Students\ }\right) $$
(A9)
  • FASS—Fund Availability and Sustainability Score

  • FAS—Fund Availability Score

  • FSS—Fund Sustainability Score

  • Pf—Percentile Fraction

  1. 3.

    Equity Score (ES):

One of the objectives of the TEQIP-II was implementation of an Equity Action Plan. Equity has been defined in terms of improvement in admissions, academic performance, and employability of female students and students with socially disadvantageous backgrounds. It also included an emphasis on enhancement in student soft skills, faculty upgradation, training/internship/placement of academically weak students, grievance redressal mechanisms and student mentors, having counselors and sexual harassment prevention cells on the campus, and having adequate facilities for persons with disability.

$$ ES=\left\{\frac{1}{6}\ast \mathrm{ESI}+\frac{1}{6}\ast \mathrm{ESA}+\frac{1}{6}\ast \mathrm{ESG}+\frac{1}{6}\ast \mathrm{ESPr}+\frac{1}{6}\ast \mathrm{ESPl}+\frac{1}{6}\ast \mathrm{ESPk}\right\} $$
(A10)
  • ES—Equity Score

  • ESI—Equity Score-Equity Plan Implementation

  • ESA—Equity Score-Admission

  • ESG—Equity Score-Graduation

  • ESPr—Equity Score-Performance

  • ESPl—Equity Score-Placement

  • ESPk—Equity Score-Package

$$ ESI=\left\{0.25\kern0.5em \sum \frac{S_{EAP}}{5}+0.25\ \frac{S_C}{4}+0.25\ \frac{S_{\mathrm{S} HC}}{4}+0.25\ \frac{S_{PWD}}{4}\right\} $$
(A11)
  • SEAP—Score obtained with regard to enhancement in student soft skills, faculty upgradation, training/internship/placement of weak students, grievance redressal mechanisms, and student mentors

  • Sc—Score obtained on having counselor on the campus

  • SSHC—Score obtained on having sexual harassment prevention cells on the campus

  • SPWD—Score obtained on having facilities to person with disability

$$ ESA=\frac{1}{3}\ Pf\ \left\{\frac{Total\ Female\ UG\ Students\ Admitted\ in\ Institute}{ Total\ Admitted\ UG\ Students}\right\}+\frac{1}{3}\ Pf\ \left\{\frac{Total\ SC+ ST+ OBC\ UG\ Students\ Admitted\ in\ Institute}{ Total\ Admitted\ UG\ Students}\right\}+\frac{1}{3}\ Pf\ \left\{\frac{Total\ PWD\ UG\ Students\ Admitted\ in\ Institute}{ Total\ Admitted\ UG\ Students}\right\} $$
(A12)
$$ ESG=\left\{\frac{1}{3}\left\{\ Average\ Graduation\ Rate\ Female\ (UG)\ \right\}+\frac{1}{3}\left\{\ \mathrm{A} verage\ Grduation\ Rate\ SC+ ST+ OBC\ (UG)\right\}+\frac{1}{3}\left\{\ Average\ Graduation\ Rate\ PWD\ (UG)\right\}\right\} $$
(A13)
$$ ESPr=\left\{\frac{1}{3}\left\{\ Average\ Graduation\ Rate\ Female\ with\ge 75\% Marks\ (UG)\right\}+\frac{1}{3}\left\{\ Average\ Graduation\ Rate\ SC+ ST+ OBC\ with\ge 75\% Marks\ (UG)\ \right\}+\frac{1}{3}\left\{\ Average\ Graduation\ Rate\ PWD\ with\ge 75\% Marks\ (UG)\ \right\}\right\} $$
(A14)
$$ ESPl=\left\{\frac{1}{3}\left\{\ Average\ Placement\ Rate\ Female\ (UG)\ \right\}\kern0.5em +\frac{1}{3}\left\{\ Average\ Placement\ Rate\ SC+ ST+ OBC\ (UG)\ \right\}+\frac{1}{3}\left\{\ Average\ Placement\ Rate\ PWD\ (UG)\ \right\}\right\} $$
(A15)
$$ ESPk=\left\{\frac{1}{3}{P}_f\left\{ Average\ Annual\ Packge\ of\ Female\ Students\ Placed\ (UG)\right\}+\frac{1}{3}{P}_f\left\{ Average\ Annual\ Packge\ of\ SC+ ST+ OBC\ Students\ Placed\ (UG)\right\}+\frac{1}{3}{P}_f\left\{ Average\ Annual\ Packge\ of\ PWD\ Students\ Placed\ (UG)\right\}\right\} $$
(A16)
  1. 4.

    Program Expansion and Quality Score (PEQS):

This score has been developed based on number of new programs started and the number of programs accredited by National Board of Accreditation (NBA). To ensure quality of the programs offered by the institutions, TEQIP insisted on the accreditation by NBA. The score has been calculated separately for 1.1 and 1.2 institutions as per following equations.

$$ {\mathrm{PEQS}}_{1.1}=0.4\times {P}_f\left\{ Number\ of\ New\ UG\ Programmes\ \mathrm{Started}\right\}+0.4\times \left\{ Share\ of Accredited\ UG\ Programmes\right\}+0.1\times {P}_f\left\{\mathrm{N} umber\ of\ New\ M. Tech\ Programmes\ Started\ \right\}\kern0.5em +0.1\times \left\{ Share\ of\ Accredited\ M. Tech\ Programmes\ \right\} $$
(A17)
$$ {\mathrm{PEQS}}_{1.2}=0.1\times {P}_f\left\{ Number\ of\ New\ UG\ Programmes\ \mathrm{Started}\right\}+0.1\times \left\{ Share\ of Acc\mathrm{r} edited\ UG\ Programmes\right\}+0.4\times {P}_f\left\{ Number\ of\ New\ M. Tech\ Programmes\ Started\ \right\}\kern0.5em +0.4\times \left\{ Share\ of\ Accredited\ M. Tech\ Programmes\kern0.5em \right\} $$
(A18)
  1. 5.

    Student Performance Score (SPRS):

This score has been developed based on number of students admitted, number of students graduated, and the performance of graduated students. The score has been calculated separately for 1.1 and 1.2 institutions as per following equations.

$$ {SPRS}_{1.1}=\frac{1}{3}\left\{0.9\times {P}_f\left\{ Total\ UG\ Students\right\}+0.1\times {P}_f\left\{ Total\ MTech+ PhD\ Students\right\}\right\}+\frac{1}{3}\left\{0.7\times \left\{ Average\ UG\ Graduation\ Rate\right\}+0.3\times \left\{ Average\ M. Tech\ Graduation\ Rate\right\}\right\}+\frac{1}{3}\left\{0.7\times \kern0.5em \left\{ Average\ UG\ Grauation\ Rate\ with\ge 75\% Marks\right\}+0.3\times \kern0.5em \left\{ Avergae\ M. Tech\ Graduation\ Rate\ with\ge 75\% Marks\right\}\right\} $$
(A19)
$$ {SPRS}_{1.2}=\frac{1}{3}\left\{0.1\times {P}_f\left\{ Total\ UG\ Students\right\}+0.9\times {P}_f\left\{ Total\ MTech+ PhD\ Students\right\}\right\}+\frac{1}{3}\left\{0.3\times \kern0.5em \left\{ Average\ UG\ Graduation\ Rate\right\}+0.7\times \kern0.5em \left\{ Average\ M. Tech\ Graduation\ Rate\right\}\right\}+\frac{1}{3}\left\{0.3\times \left\{ Average\ UG\ Grauation\ Rate\ with\ge 75\% Marks\right\}+0.7\times \kern0.5em \left\{ Avergae\ M. Tech\ Graduation\ Rate\ \mathrm{w} ith\ge 75\% Marks\right\}\right\} $$
(A20)
  1. 6.

    Student Placement Score (SPLS):

This score has been developed based on number of companies visited, percentage of students placed on campus, and average annual package offered to students.

$$ SPLS=0.2\times {P}_f\left\{ Number\ of\ Companies\ visited\ campus\right\}+0.4\times \left\{\% Share\ of\ students\ placed\ UG\right\}+0.4\times {P}_f\left\{ Average\ Annual\ Packge\ UG\right\} $$
(A21)
  1. 7.

    Enhanced Learning Facility Score (ELFS):

This score has been developed based on the amount of funds spent in creation/modernization of learning facilities such as laboratory equipment, software, and library facilities.

$$ ELFS={P}_f\left\{\frac{Amount\ spent\ on\ equipment+ Software+ Library+ New\ Labs}{Total\ Amount\ of\ TEQIP\ II\ funds\ Received\ }\right\} $$
(A22)
  1. 8.

    Faculty Improvement Score (FIS):

This score has been developed based on faculty strength, faculty quality, faculty training, and faculty student ratio in the institute. Faculty with Ph.D. is used as a proxy for faculty quality.

$$ FS=\frac{1}{4}\ Pf\left\{\frac{Regular\ Faculty\ Stregnth}{Sacntioned\ Stregnth}\right\}+\frac{1}{4}\ Pf\ \left\{\frac{Faculty\ with\ PhD}{Total\ Faculty\ Strength\ \left(R+C\right)}\right\}+\frac{1}{4}\ Pf\left\{\frac{Average\ N\mathrm{u} mber\ of\ Faculty\ Trained\ with\ TEQIP- II}{ Average\ Faculty\ Strength\ \left(R+C\right)}\right\}+\frac{1}{4}\ Pf\left\{ Faculty\ Student\ Ratio\right\} $$
(A23)
  1. 9.

    Research and Outreach Score (ROS):

This score has been developed based revenue generated through consultancy and sponsored research projects and outreach activities like conferences, workshops, and seminars organized. This score has been calculated only for 1.2 institutes.

$$ ROS=\frac{2}{3}\ Pf\left\{\frac{Revenue\ Generated\ Consulancy+ Sponsored\ RnD}{Total\ TEQIP\ II\ Funds}\right\}+\frac{1}{3}\ Pf\left\{\frac{Number\ of\ Conference, Workshop, Seminar\ etc\ Organised}{Total\ TEQIP\ II\ Funds}\right\} $$
(A24)
  1. 10.

    COE Component Score (CCS):

This score has been developed based on number of departments participating in the COEs, number of PG programs launched under the CEOs, number of faculty members associated with the CEOs, number of publications, number of patents, and number of industrial linkages established under the COEs. This score has been calculated only for 1.2.1 institutes.

$$ {\displaystyle \begin{array}{l} CCS=\\ {}\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of\kern0.17em Department\kern0.17em Participanting/ Number\kern0.17em of\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\kern0.17em of\; COE}\right\}+\\ {}\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of\; PG\; Programmes\kern0.17em Launched/ Number\;o\kern0em f\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\;o\kern0em f\; COE}\right\}+\\ {}\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of Faculty/ Number\kern0.17em of\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\kern0.17em of\; COE}\right\}=+\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of\kern0.17em Publications/ Number\kern0.17em of\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\kern0.17em of\; COE}\right\}+\\ {}\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of Patents/ Number\kern0.17em of\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\kern0.17em of\; COE}\right\}+\frac{1}{6} Pf\left\{\frac{Number\kern0.17em of\kern0.17em Industrial\kern0.17em Linkags/ Number\kern0.17em of\; COE}{Total\kern0.17em TEQIP\; II\; Funds/ Number\kern0.17em of\; COE}\right\}\end{array}} $$
(A25)

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Dubey, A., Mehndiratta, A., Sagar, M. et al. Reforms in technical education sector: evidence from World Bank-assisted Technical Education Quality Improvement Programme in India. High Educ 78, 273–299 (2019). https://doi.org/10.1007/s10734-018-0343-1

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Keywords

  • Technical education
  • Difference-in-differences
  • Propensity score matching
  • Quality improvement
  • Proportional odds model