IDEAS home Printed from https://ideas.repec.org/a/spr/ijlaec/v59y2016i4d10.1007_s41027-017-0074-3.html
   My bibliography  Save this article

Impacts of Vocational Education and Training on Employment and Wages in Indian Manufacturing Industries: Variation across Social Groups—Empirical Evidences from the 68th Round NSSO Data

Author

Listed:
  • Tanima Banerjee

    (University of Calcutta)

Abstract

Vocational education and training (VET) is critical in developing skilled manpower resources in a country. However, in India, where various administrative and institutional factors are key in the determination of employment and wages, people from all social groups may not benefit equally, from VET. This study analyses how the impact of VET on employment and wages varies across social groups in the Indian manufacturing sector. The main data source for this study is the Employment and Unemployment Survey in India (10th Schedule) of the 68th National Sample Survey quinquennial round (2011–2012). To tackle the problem of bias in sample selection, this study uses Heckman’s Sample Selection Model (1979) with the two-steps estimation technique (Heckit). It reveals that VET significantly enhances participation from all social groups in the manufacturing sector and aggregates wages, but is ineffective in certain manufacturing industries. In certain cases, VET variously impacts wages across workers from different castes and ethnicities.

Suggested Citation

  • Tanima Banerjee, 2016. "Impacts of Vocational Education and Training on Employment and Wages in Indian Manufacturing Industries: Variation across Social Groups—Empirical Evidences from the 68th Round NSSO Data," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 59(4), pages 489-509, December.
    • Handle: RePEc:spr:ijlaec:v:59:y:2016:i:4:d:10.1007_s41027-017-0074-3
    DOI: 10.1007/s41027-017-0074-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s41027-017-0074-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s41027-017-0074-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4, National Bureau of Economic Research, Inc.
    2. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, March.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    5. Leontaridi, Marianthi Rannia, 1998. "Segmented Labour Markets: Theory and Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 12(1), pages 63-101, February.
    6. Banerjee, Abhijit & Bertrand, Marianne & Datta, Saugato & Mullainathan, Sendhil, 2009. "Labor market discrimination in Delhi: Evidence from a field experiment," Journal of Comparative Economics, Elsevier, vol. 37(1), pages 14-27, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Au Yong Lyn, Audrey, 2022. "Vocational training and employment outcomes of domestic violence survivors: Evidence from Chihuahua City," International Journal of Educational Development, Elsevier, vol. 89(C).
    2. Jadhav Chakradhar & Arun Kumar Bairwa, 2020. "Employment Probabilities And Workforce Distribution In The Indian Manufacturing Sector: A State-Level Analysis," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 23(1), pages 55-82.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ibrahim Mohamed Abdalla & Mohammed A. Al‐Waqfi & Nasri Harb & Rafiq H. Hijazi & Taoufik Zoubeidi, 2010. "Labour Policy and Determinants of Employment and Wages in a Developing Economy with Labour Shortage," LABOUR, CEIS, vol. 24(2), pages 163-177, June.
    2. Beatriz Muriel Hernández, 2016. "An Analysis of Firm Characteristics as Earnings Determinants: The Urban Bolivia Case," Development Research Working Paper Series 04/2016, Institute for Advanced Development Studies.
    3. Paul W. Miller & Barry R. Chiswick, 2002. "Immigrant earnings: Language skills, linguistic concentrations and the business cycle," Journal of Population Economics, Springer;European Society for Population Economics, vol. 15(1), pages 31-57.
    4. Sandra Nieto & Raúl Ramos, 2013. "Non-Formal Education, Overeducation And Wages," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 21(1), pages 5-28, Spring.
    5. Chloé Duvivier Duvivier & Mary-Françoise Renard & Shi Li, 2012. "Are workers close to cities paid higher non-agricultural wages in rural China?," CERDI Working papers halshs-00673698, HAL.
    6. Boris Hirsch & Marion König & Joachim Möller, 2013. "Is There a Gap in the Gap? Regional Differences in the Gender Pay Gap," Scottish Journal of Political Economy, Scottish Economic Society, vol. 60(4), pages 412-439, September.
    7. Rosen, Harvey S, 1982. "Taxation and On-the-Job Training Decisions," The Review of Economics and Statistics, MIT Press, vol. 64(3), pages 442-449, August.
    8. Joaquin Turmo-Garuz & M.-Teresa Bartual-Figueras & Francisco-Javier Sierra-Martinez, 2019. "Factors Associated with Overeducation Among Recent Graduates During Labour Market Integration: The Case of Catalonia (Spain)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1273-1301, August.
    9. Leonardo Gasparini & Mariana Marchionni & Walter Sosa Escudero, 2000. "Characterization of inequality changes through microeconometric decompositions. The case of Greater Buenos Aires," IIE, Working Papers 025, IIE, Universidad Nacional de La Plata.
    10. Michael Fertig & Katja Görlitz, 2012. "Item Nonresponse in Wages: Testing for Biased Estimates in Wage Equations," Ruhr Economic Papers 0333, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    11. Mar, Don, 2005. "Individual characteristics vs. city structural characteristics: explaining self-employment differences among Chinese, Japanese, and Filipinos in the United States," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 34(3), pages 341-359, May.
    12. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    13. Hans Dietrich & Harald Pfeifer & Felix Wenzelmann, 2016. "The more they spend, the more I earn? Firms' training investments and post-training wages of apprentices," Economics of Education Working Paper Series 0116, University of Zurich, Department of Business Administration (IBW).
    14. repec:zbw:rwirep:0333 is not listed on IDEAS
    15. Lucia Mateos & Ines Murillo & Maria del Mar Salinas, 2014. "Desajuste educativo y competencias cognitivas: efectos sobre los salarios," Hacienda Pública Española / Review of Public Economics, IEF, vol. 210(3), pages 85-108, September.
    16. Angel de la Fuente & Juan Francisco Jimeno, 2004. "The private and fiscal returns to schooling and the effect of public policies on private incentives to invest in education: a general framework and some results for the EU," Working Papers 152, Barcelona School of Economics.
    17. Aleksandra Anić & Gorana Krstić, 2019. "What Lies Behind The Gender Wage Gap In Serbia?," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 64(223), pages 137-170, October –.
    18. Pedro Ortín‐Ángel & Vicente Salas‐fumás, 1998. "Agency‐Theory and Internal‐Labor‐Market Explanations of Bonus Payments: Empirical Evidence from Spanish Firms," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 7(4), pages 573-613, December.
    19. Colm Harmon & Claire Finn & Arnaud Chevalier & Tarja Viitanen, 2006. "The economics of early childhood care and education : technical research paper for the National Economic and Social Forum," Open Access publications 10197/671, School of Economics, University College Dublin.
    20. Muhammad Nauman Malik & Masood Sarwar Awan, 2016. "Analysing Econometric Bias and Non-linearity in Returns to Education of Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 55(4), pages 837-851.
    21. Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022. "Do expert experience and characteristics affect inflation forecasts?," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 205-226.

    More about this item

    Keywords

    Employment; Wage; Vocational education and training; Social groups; Manufacturing; India;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijlaec:v:59:y:2016:i:4:d:10.1007_s41027-017-0074-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.