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, July.
    3. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    4. Leontaridi, Marianthi Rannia, 1998. "Segmented Labour Markets: Theory and Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 12(1), pages 63-101, February.
    5. 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.
    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, April.

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    8. 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.
    9. 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.
    10. 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.
    11. Mona Said & Fatma El-Hamidi, 2008. "Taking Technical Education Seriously in MENA: Determinants, Labor Market Implications and Policy Lessons," Working Papers 450, Economic Research Forum, revised 09 Jan 2008.
    12. Solomon Polachek, 2003. "Mincer's Overtaking Point and the Life Cycle Earnings Distribution," Review of Economics of the Household, Springer, vol. 1(4), pages 273-304, December.
    13. Arceo-Gómez, Eva O. & Campos-Vázquez, Raymundo M., 2014. "Evolución de la brecha salarial de género en México," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(323), pages .619-653, julio-sep.
    14. repec:lan:wpaper:4408 is not listed on IDEAS
    15. Christina Boll & Andreas Lagemann, 2018. "Does Culture Trump Money? Employment and Childcare Use of Migrant and Non-Migrant Mothers of Pre-School Children in Germany," SOEPpapers on Multidisciplinary Panel Data Research 1015, DIW Berlin, The German Socio-Economic Panel (SOEP).
    16. Carlos Foronda Rojas, 2006. "Evaluación de los efectos de las políticas de exportación y de productividad en la distribución del ingreso a partir de microsimulaciones," Investigación & Desarrollo 0206, Universidad Privada Boliviana, revised Mar 2006.
    17. Thierry Debrand & Sophie Pennec & Anne-Gisèle Privat, 2003. "Un modèle de microsimulation pour la projection des retraites du régime général," Economie & Prévision, La Documentation Française, vol. 0(4), pages 215-230.
    18. Ravi Srivastava, 2019. "Emerging Dynamics of Labour Market Inequality in India: Migration, Informality, Segmentation and Social Discrimination," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 62(2), pages 147-171, June.
    19. Emilia Bedyk & Jacek Liwiński, 2016. "The wage premium from parents’ investments in the education of their children in Poland," Working Papers 2016-14, Faculty of Economic Sciences, University of Warsaw.
    20. Kentaro Shimada & Zeba Khan & Suguru Mizunoya & Ayako Wakano, 2016. "An Update of the Returns to Education in Kenya: Accounting both endogeneity and sample selection biases," Discussion Papers in Economics and Business 16-18, Osaka University, Graduate School of Economics.
    21. Sandra Nieto & Raul Ramos, 2017. "Overeducation, Skills and Wage Penalty: Evidence for Spain Using PIAAC Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(1), pages 219-236, October.

    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.