IDEAS home Printed from https://ideas.repec.org/a/eee/ecoedu/v82y2021ics0272775721000376.html
   My bibliography  Save this article

(Em)Powered by Science? Estimating the Relative Labor Market Returns to Majoring in Science in High School in India

Author

Listed:
  • Roychowdhury, Punarjit

Abstract

Despite widespread belief that majoring in science in high school has a greater payoff in the Indian labor market than majoring in business/humanities, there is no hard evidence to substantiate this thought. Here I provide the first evidence of the causal effect of majoring in science on individuals’ labor market outcomes relative to majoring in business/humanities using microdata from India. Estimating the causal effect, however, is a formidable task since selection into high school major is nonrandom and exclusion restrictions are unavailable. I circumvent these difficulties by employing an econometric technique that does not rely on valid exclusion restriction for identification. I find that majoring in science has a negative causal effect on individuals’ employment probability. Conditional on being employed, however, majoring in science has a positive causal effect on individuals’ earnings and probability of being engaged in a professional occupation. These findings suggest, in contrast to conventional wisdom, the labor market effects of majoring in science in high school in India is not a plain tale of ‘science premium’ - while majoring in science might lead to relatively better labor market outcomes for those who are able to find employment, finding employment itself is more difficult for science majors.

Suggested Citation

  • Roychowdhury, Punarjit, 2021. "(Em)Powered by Science? Estimating the Relative Labor Market Returns to Majoring in Science in High School in India," Economics of Education Review, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:ecoedu:v:82:y:2021:i:c:s0272775721000376
    DOI: 10.1016/j.econedurev.2021.102118
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0272775721000376
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econedurev.2021.102118?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. Ian K. McDonough & Constant I. Tra, 2017. "The impact of computer-based tutorials on high school math proficiency," Empirical Economics, Springer, vol. 52(3), pages 1041-1063, May.
    2. George Psacharopoulos, 1985. "Returns to Education: A Further International Update and Implications," Journal of Human Resources, University of Wisconsin Press, vol. 20(4), pages 583-604.
    3. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    4. Levine, Phillip B & Zimmerman, David J, 1995. "The Benefit of Additional High-School Math and Science Classes for Young Men and Women," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 137-149, April.
    5. Daniel L. Millimet & Rusty Tchernis, 2013. "Estimation Of Treatment Effects Without An Exclusion Restriction: With An Application To The Analysis Of The School Breakfast Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 982-1017, September.
    6. George Psacharopoulos & Harry Anthony Patrinos, 2004. "Returns to investment in education: a further update," Education Economics, Taylor & Francis Journals, vol. 12(2), pages 111-134.
    7. Emily Oster, 2019. "Unobservable Selection and Coefficient Stability: Theory and Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 187-204, April.
    8. Joseph G. Altonji, 1995. "The Effects of High School Curriculum on Education and Labor Market Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 30(3), pages 409-438.
    9. Berg Claudia & Emran M. Shahe, 2020. "Microfinance and Vulnerability to Seasonal Famine in a Rural Economy: Evidence from Monga in Bangladesh," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 20(3), pages 1-36, July.
    10. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    11. Arcidiacono, Peter, 2004. "Ability sorting and the returns to college major," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 343-375.
    12. Davide Cantoni & Yuyu Chen & David Y. Yang & Noam Yuchtman & Y. Jane Zhang, 2017. "Curriculum and Ideology," Journal of Political Economy, University of Chicago Press, vol. 125(2), pages 338-392.
    13. Joseph G. Altonji & Erica Blom & Costas Meghir, 2012. "Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and Careers," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 185-223, July.
    14. Fulford, Scott, 2014. "Returns to Education in India," World Development, Elsevier, vol. 59(C), pages 434-450.
    15. Diva Dhar & Tarun Jain & Seema Jayachandran, 2022. "Reshaping Adolescents' Gender Attitudes: Evidence from a School-Based Experiment in India," American Economic Review, American Economic Association, vol. 112(3), pages 899-927, March.
    16. Juanna Schrøter Joensen & Helena Skyt Nielsen, 2009. "Is there a Causal Effect of High School Math on Labor Market Outcomes?," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    17. Stephan Klasen & Janneke Pieters, 2015. "What Explains the Stagnation of Female Labor Force Participation in Urban India?," The World Bank Economic Review, World Bank, vol. 29(3), pages 449-478.
    18. Heather Rose & Julian R. Betts, 2004. "The Effect of High School Courses on Earnings," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 497-513, May.
    19. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    20. Pannenberg, Markus, 2007. "Risk Aversion and Reservation Wages," IZA Discussion Papers 2806, Institute of Labor Economics (IZA).
    21. Feinberg, Robert M, 1977. "Risk Aversion, Risk, and the Duration of Unemployment," The Review of Economics and Statistics, MIT Press, vol. 59(3), pages 264-271, August.
    22. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
    23. Psacharopoulos, George, 1994. "Returns to investment in education: A global update," World Development, Elsevier, vol. 22(9), pages 1325-1343, September.
    24. Alan Fernihough, 2017. "Human capital and the quantity–quality trade-off during the demographic transition," Journal of Economic Growth, Springer, vol. 22(1), pages 35-65, March.
    25. Peet, Evan D. & Fink, Günther & Fawzi, Wafaie, 2015. "Returns to education in developing countries: Evidence from the living standards and measurement study surveys," Economics of Education Review, Elsevier, vol. 49(C), pages 69-90.
    26. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    27. Joshua Goodman, 2019. "The Labor of Division: Returns to Compulsory High School Math Coursework," Journal of Labor Economics, University of Chicago Press, vol. 37(4), pages 1141-1182.
    28. Jain, Tarun & Mukhopadhyay, Abhiroop & Prakash, Nishith & Rakesh, Raghav, 2018. "Labor Market Effects of High School Science Majors in a High STEM Economy," IZA Discussion Papers 11934, Institute of Labor Economics (IZA).
    29. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    30. Lee, Lung-Fei, 1984. "Tests for the Bivariate Normal Distribution in Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 52(4), pages 843-863, July.
    31. Pissarides, Christopher A, 1974. "Risk, Job Search, and Income Distribution," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1255-1267, Nov.-Dec..
    32. Psacharopoulos, George, 1989. "Time trends of the returns to education: Cross-national evidence," Economics of Education Review, Elsevier, vol. 8(3), pages 225-231, June.
    33. Jain, Tarun, 2017. "Common Tongue: The Impact of Language on Educational Outcomes," The Journal of Economic History, Cambridge University Press, vol. 77(2), pages 473-510, June.
    34. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    35. Kaivan Munshi & Mark Rosenzweig, 2006. "Traditional Institutions Meet the Modern World: Caste, Gender, and Schooling Choice in a Globalizing Economy," American Economic Review, American Economic Association, vol. 96(4), pages 1225-1252, September.
    36. Davide Cantoni & Yuyu Chen & David Y. Yang & Noam Yuchtman & Y. Jane Zhang, 2017. "Curriculum and Ideology," Journal of Political Economy, University of Chicago Press, vol. 125(2), pages 338-392.
    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. Sugata Marjit & Rashmi Ahuja & Abhilasha Pandey, 2021. "Education, Lack of Complementary Investment and Underemployment In an Open Economy," CESifo Working Paper Series 9278, CESifo.
    2. Roychowdhury, Punarjit, 2021. "Too unwell to trust? The effect of mental health on social trust in Europe," Economics & Human Biology, Elsevier, vol. 42(C).
    3. Kumar, Anand & Sahoo, Soham, 2021. "Social Identity and STEM Choice: Evidence from Higher Secondary Schooling in India," GLO Discussion Paper Series 900, Global Labor Organization (GLO).

    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. Tarun Jain & Abhiroop Mukhopadhyay & Nishith Prakash & Raghav Rakesh, 2022. "Science education and labor market outcomes in a developing economy," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 741-763, April.
    2. Jain, Tarun & Mukhopadhyay, Abhiroop & Prakash, Nishith & Rakesh, Raghav, 2018. "Labor Market Effects of High School Science Majors in a High STEM Economy," IZA Discussion Papers 11908, Institute of Labor Economics (IZA).
    3. Roychowdhury, Punarjit, 2021. "Too unwell to trust? The effect of mental health on social trust in Europe," Economics & Human Biology, Elsevier, vol. 42(C).
    4. Han Yu & Naci Mocan, 2019. "The Impact of High School Curriculum on Confidence, Academic Success, and Mental and Physical Well-Being of University Students," Journal of Labor Research, Springer, vol. 40(4), pages 428-462, December.
    5. Daniel Kreisman & Kevin Stange, 2020. "Vocational and Career Tech Education in American High Schools: The Value of Depth Over Breadth," Education Finance and Policy, MIT Press, vol. 15(1), pages 11-44, Winter.
    6. Lenka Fiala & John Eric Humphries & Juanna Schrøter Joensen & Uditi Karna & John A. List & Gregory F. Veramendi, 2022. "How Early Adolescent Skills and Preferences Shape Economics Education Choices," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 609-613, May.
    7. Jia, Ning, 2021. "Do stricter high school math requirements raise college STEM attainment?," Economics of Education Review, Elsevier, vol. 83(C).
    8. Gordon Dahl & Dan-Olof Rooth & Anders Stenberg, 2020. "High School Majors, Comparative (Dis)Advantage, and Future Earnings," NBER Working Papers 27524, National Bureau of Economic Research, Inc.
    9. Han Yu & Naci Mocan, 2018. "The Impact of High School Curriculum on Confidence, Academic Success, and Mental and Physical Well-Being of University Students," NBER Working Papers 24573, National Bureau of Economic Research, Inc.
    10. Biewen, Martin & Schwerter, Jakob, 2019. "Does More Math in High School Increase the Share of Female STEM Workers? Evidence from a Curriculum Reform," IZA Discussion Papers 12236, Institute of Labor Economics (IZA).
    11. De Philippis, Marta, 2016. "STEM graduates and secondary school curriculum: does early exposure to science matter?," LSE Research Online Documents on Economics 67679, London School of Economics and Political Science, LSE Library.
    12. Guy Tchuente, 2016. "High School Human Capital Portfolio and College Outcomes," Journal of Human Capital, University of Chicago Press, vol. 10(3), pages 267-302.
    13. Lisha Agarwal & Giorgio Brunello & Lorenzo Rocco, 2021. "The Pathways to College," Journal of Human Capital, University of Chicago Press, vol. 15(4), pages 554-595.
    14. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    15. Katja Görlitz & Christina Gravert, 2018. "The effects of a high school curriculum reform on university enrollment and the choice of college major," Education Economics, Taylor & Francis Journals, vol. 26(3), pages 321-336, May.
    16. Dahl, Gordon B. & Rooth, Dan-Olof & Stenberg, Anders, 2020. "Long-Run Returns to Field of Study in Secondary School," IZA Discussion Papers 13508, Institute of Labor Economics (IZA).
    17. Falch, Torberg & Nyhus, Ole Henning & Strøm, Bjarne, 2014. "Causal effects of mathematics," Labour Economics, Elsevier, vol. 31(C), pages 174-187.
    18. Joshua Goodman, 2019. "The Labor of Division: Returns to Compulsory High School Math Coursework," Journal of Labor Economics, University of Chicago Press, vol. 37(4), pages 1141-1182.
    19. Black, Sandra E. & Muller, Chandra & Spitz-Oener, Alexandra & He, Ziwei & Hung, Koit & Warren, John Robert, 2021. "The importance of STEM: High school knowledge, skills and occupations in an era of growing inequality," Research Policy, Elsevier, vol. 50(7).
    20. Zullo, Matteo, 2022. "(No) Trade-off between numeracy and verbal reasoning development: PISA evidence from Italy's academic tracking," Intelligence, Elsevier, vol. 95(C).

    More about this item

    Keywords

    High School Major; India; Labor Market Outcomes; Science; Selection;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • 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
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

    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:eee:ecoedu:v:82:y:2021:i:c:s0272775721000376. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/econedurev .

    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.