IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v23y2016i11p757-760.html
   My bibliography  Save this article

An estimate of the error in self-reported college major

Author

Listed:
  • Daniel Kuehn

Abstract

This letter provides an estimate of the extent of error in self-reports of college major. A student’s field of study is an important determinant of labour market outcomes and of increasing interest to labour economists, but little is known about the reliability of survey data on college major. A unique dataset from the United States with both transcript and survey data on major field of study suggests that the error rate of self-reported college major is almost 20%. Error rates are higher for relatively small or obscure majors, and are lower for larger majors or majors closely associated with licensed professions (e.g. health care). Although these error rates are not trivial, they are comparable to prior estimates of error in reporting educational attainment.

Suggested Citation

  • Daniel Kuehn, 2016. "An estimate of the error in self-reported college major," Applied Economics Letters, Taylor & Francis Journals, vol. 23(11), pages 757-760, July.
    • Handle: RePEc:taf:apeclt:v:23:y:2016:i:11:p:757-760
    DOI: 10.1080/13504851.2015.1105915
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2015.1105915
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2015.1105915?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. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    2. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
    3. Marc Roemer, 2002. "Using Administrative Earnings Records to Assess Wage Data Quality in the March Current Population Survey and the Survey of Income and Program Participation," Longitudinal Employer-Household Dynamics Technical Papers 2002-22, Center for Economic Studies, U.S. Census Bureau.
    4. Black, Dan & Sanders, Seth & Taylor, Lowell, 2003. "Measurement of Higher Education in the Census and Current Population Survey," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 545-554, January.
    5. repec:fth:prinin:419 is not listed on IDEAS
    6. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    7. Emily Isenberg & Liana Christin Landivar & Esther Mezey, 2013. "A Comparison Of Person-Reported Industry To Employer-Reported Industry In Survey And Administrative Data," Working Papers 13-47, Center for Economic Studies, U.S. Census Bureau.
    8. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    9. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    Full references (including those not matched with items on IDEAS)

    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. Engzell, Per, 2017. "What Do Books in the Home Proxy For? A Cautionary Tale," Working Paper Series 1/2016, Stockholm University, Swedish Institute for Social Research.
    2. Yingyao Hu & Geert Ridder, 2012. "Estimation of nonlinear models with mismeasured regressors using marginal information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 347-385, April.
    3. Daniel Wilhelm, 2018. "Testing for the presence of measurement error," CeMMAP working papers CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    5. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    6. Melvin Stephens & Takashi Unayama, 2019. "Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 468-475, July.
    7. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
    8. Geert Ridder & Yingyao Hu, 2004. "Estimation of Nonlinear Models with Measurement Error Using Marginal Information," Econometric Society 2004 North American Summer Meetings 21, Econometric Society.
    9. Jesse Bricker & Gary V. Engelhardt, 2007. "Measurement Error in Earnings Data in the Health and Retirement Study," Working Papers, Center for Retirement Research at Boston College wp2007-16, Center for Retirement Research, revised Oct 2007.
    10. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    11. Abdurrahman Aydemir & George J. Borjas, 2011. "Attenuation Bias in Measuring the Wage Impact of Immigration," Journal of Labor Economics, University of Chicago Press, vol. 29(1), pages 69-113, January.
    12. Steven Haider & Gary Solon, 2006. "Life-Cycle Variation in the Association between Current and Lifetime Earnings," American Economic Review, American Economic Association, vol. 96(4), pages 1308-1320, September.
    13. Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2014. "Misreported schooling, multiple measures and returns to educational qualifications," Journal of Econometrics, Elsevier, vol. 181(2), pages 136-150.
    14. Vitor Possebom, 2021. "Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification," Papers 2106.00536, arXiv.org, revised Jul 2023.
    15. Peter Z. Schochet, 2013. "A Statistical Model for Misreported Binary Outcomes in Clustered RCTs of Education Interventions," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 470-498, October.
    16. Paulus, Alari, 2015. "Tax evasion and measurement error: An econometric analysis of survey data linked with tax records," ISER Working Paper Series 2015-10, Institute for Social and Economic Research.
    17. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    18. García-Suaza, A & Lobo, J & Montoya, S & Ordóñez, J & Oviedo, J. D, 2022. "Impact of the collection mode on labor income data. A study in the times of COVID19," Documentos de Trabajo 20396, Universidad del Rosario.
    19. Kasahara, Hiroyuki & Shimotsu, Katsumi, 2022. "Identification Of Regression Models With A Misclassified And Endogenous Binary Regressor," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1117-1139, December.
    20. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," NBER Working Papers 21676, National Bureau of Economic Research, Inc.

    More about this item

    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:taf:apeclt:v:23:y:2016:i:11:p:757-760. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.