IDEAS home Printed from https://ideas.repec.org/p/wvu/wpaper/20-05.html
   My bibliography  Save this paper

State Exit Exams and Graduation Rates: A Hierarchical SLX Modelling Approach

Author

Listed:
  • Joshua C. Hall

    (West Virginia University)

  • Donald J. Lacombe

    (Texas Tech University)

  • Shree B. Pokharel

    (West Virginia Legislature)

Abstract

The literature on high school exit exams has found both positive and negative effects of these high stake exams on high school graduation rates. To this point the literature has not taken into account the embedded nature of school districts within state education systems. We employ a Bayesian Hierarchical SLX model to account for the hierachical nature of education data in the United States. Our approach also allows us to account for spatial spillovers that influence graduation rates across districts and states. Using school district and state-level data for 45 states and 8194 school districts in the United States in 2015, we generally find no statistically significant effect of state exit exams on high school graduation rates. Random effect coefficients, however, point towards high school exit exams being negatively associated with graduation rates in a handful of states.

Suggested Citation

  • Joshua C. Hall & Donald J. Lacombe & Shree B. Pokharel, 2020. "State Exit Exams and Graduation Rates: A Hierarchical SLX Modelling Approach," Working Papers 20-05, Department of Economics, West Virginia University.
  • Handle: RePEc:wvu:wpaper:20-05
    as

    Download full text from publisher

    File URL: https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=1043&context=econ_working-papers
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2009. "New Evidence about Brown v. Board of Education: The Complex Effects of School Racial Composition on Achievement," Journal of Labor Economics, University of Chicago Press, vol. 27(3), pages 349-383, July.
    2. Hoxby, Caroline M., 1999. "The productivity of schools and other local public goods producers," Journal of Public Economics, Elsevier, vol. 74(1), pages 1-30, October.
    3. Joshua C. Hall & Peter T. Leeson, 2010. "Racial Fractionalization and School Performance," American Journal of Economics and Sociology, Wiley Blackwell, vol. 69(2), pages 736-758, April.
    4. Ou, Dongshu, 2010. "To leave or not to leave? A regression discontinuity analysis of the impact of failing the high school exit exam," Economics of Education Review, Elsevier, vol. 29(2), pages 171-186, April.
    5. Donald J. Lacombe & Miguel Flores, 2017. "A hierarchical SLX model application to violent crime in Mexico," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 119-134, January.
    6. James J. Heckman & Paul A. LaFontaine, 2010. "The American High School Graduation Rate: Trends and Levels," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 244-262, May.
    7. Steven W. Hemelt & Dave E. Marcotte, 2013. "High School Exit Exams and Dropout in an Era of Increased Accountability," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 32(2), pages 323-349, March.
    8. James P. LeSage, 2014. "What Regional Scientists Need to Know about Spatial Econometrics," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 13-32, Spring.
    9. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    10. Bishop, John H. & Moriarty, Joan Y. & Mane, Ferran, 2000. "Diplomas for learning, not seat time: the impacts of New York Regents examinations," Economics of Education Review, Elsevier, vol. 19(4), pages 333-349, October.
    11. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-1177, September.
    12. Alan B. Krueger, 2003. "Economic Considerations and Class Size," Economic Journal, Royal Economic Society, vol. 113(485), pages 34-63, February.
    13. Jac C. Heckelman, 2013. "Income convergence among U.S. states: crosssectional and time series evidence," Canadian Journal of Economics, Canadian Economics Association, vol. 46(3), pages 1085-1109, August.
    14. C. Kirabo Jackson & Rucker C. Johnson & Claudia Persico, 2016. "The Effects of School Spending on Educational and Economic Outcomes: Evidence from School Finance Reforms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 157-218.
    15. Caroline M. Hoxby, 1999. "The Productivity of Schools and Other Local Public Goods Providers," NBER Working Papers 6911, National Bureau of Economic Research, Inc.
    16. David Darmofal, 2009. "Bayesian Spatial Survival Models for Political Event Processes," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 241-257, January.
    17. Joshua Hall, 2007. "Local School Finance and Productive Efficiency: Evidence from Ohio," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 35(3), pages 289-301, September.
    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. Amir B. Ferreira Neto & Joshua C. Hall, 2019. "Economies of scale and governance of library systems: evidence from West Virginia," Economics of Governance, Springer, vol. 20(3), pages 237-253, September.
    2. Ludger Wößmann, 2006. "Bildungspolitische Lehren aus den internationalen Schülertests: Wettbewerb, Autonomie und externe Leistungsüberprüfung," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 7(3), pages 417-444, August.
    3. Joshua C. Hall & Donald J. Lacombe & Joylynn Pruitt, 2017. "Collective bargaining and school district test scores: evidence from Ohio bargaining agreements," Applied Economics Letters, Taylor & Francis Journals, vol. 24(1), pages 35-38, January.
    4. Zegarra, Eduardo & Ravina, Renato, 2003. "Teacher Unionization and the Quality of Education in Peru: An Empirical Evaluation Using Survey Data," IDB Publications (Working Papers) 3284, Inter-American Development Bank.
    5. Elke Lüdemann, 2011. "Schooling and the Formation of Cognitive and Non-cognitive Outcomes," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 39.
    6. Steve Bradley & Jim Taylor, 2010. "Diversity, Choice and the Quasi‐market: An Empirical Analysis of Secondary Education Policy in England," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 1-26, February.
    7. Oscar Montes Pineda & Luis Rubalcaba, 2014. "School choice, equity and efficiency: International evidence from PISA-2012," Investigaciones de Economía de la Educación volume 9, in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 31, pages 585-614, Asociación de Economía de la Educación.
    8. Fábio D. Waltenberg, 2010. "Essential educational achievements as the currency of educational justice," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, June.
    9. Zimmerman, Brianne R. & Collins, Alan R. & Lacombe, Don, 2017. "Analyzing the Spatial Distribution of NRCS Conservation Programs in West Virginia," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258378, Agricultural and Applied Economics Association.
    10. Ludger Wößmann, 2003. "Schooling Resources, Educational Institutions and Student Performance: the International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(2), pages 117-170, May.
    11. Torberg Falch & Astrid Marie Jorde Sandsør & Bjarne Strøm, 2017. "Do Smaller Classes Always Improve Students’ Long-run Outcomes?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 654-688, October.
    12. Lee, Kyung-Gon & Polachek, Solomon, 2014. "Do School Budgets Matter? The Effect of Budget Referenda on Student Performance," IZA Discussion Papers 8056, Institute of Labor Economics (IZA).
    13. Wo[ss]mann, Ludger & West, Martin, 2006. "Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS," European Economic Review, Elsevier, vol. 50(3), pages 695-736, April.
    14. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    15. Bourdon, Jean & Frölich, Markus & Michaelowa, Katharina, 2007. "Teacher Shortages, Teacher Contracts and their Impact on Education in Africa," IZA Discussion Papers 2844, Institute of Labor Economics (IZA).
    16. Cécile Bonneau, 2020. "The Concentration of investment in education in the US (1970-2018)," Working Papers halshs-02875965, HAL.
    17. John Bishop & Ludger Wossmann, 2004. "Institutional Effects in a Simple Model of Educational Production," Education Economics, Taylor & Francis Journals, vol. 12(1), pages 17-38.
    18. Cohen-Zada, Danny & Gradstein, Mark & Reuven, Ehud, 2013. "Allocation of students in public schools: Theory and new evidence," Economics of Education Review, Elsevier, vol. 34(C), pages 96-106.
    19. Currie, Janet & Neidell, Matthew, 2007. "Getting inside the "Black Box" of Head Start quality: What matters and what doesn't," Economics of Education Review, Elsevier, vol. 26(1), pages 83-99, February.
    20. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.

    More about this item

    Keywords

    Spatial dependence; Bayesian statistics; hierarchical modelling; state exit exams;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wvu:wpaper:20-05. 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: Feng Yao (email available below). General contact details of provider: https://edirc.repec.org/data/dewvuus.html .

    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.