Abstract
Policymakers are becoming increasingly adamant that the research they use to evaluate educational interventions, practices, and programs can support statements about cause and effect. An instrumental variables (IV) estimation approach, which has historically been employed by economists, can be utilized to make causal statements regarding a predictor and an outcome when randomized trials are not feasible. In this chapter, we provide an overview of the IV approach as we explore the causal relationship between taking Algebra II in high school and degree attainment in college. We discuss concepts and terminology related to conducting experimental and quasi-experimental work, present the assumptions that should be met by an IV before a researcher can use it to make causal inferences, and demonstrate several IV estimation strategies. Additionally, we provide the reader with annotated Stata code to facilitate the application of this underutilized methodological approach in higher education research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use the term course taking intensity throughout the chapter to refer to the orientation of the courses students take. We use this term to be inclusive of the course taking literature, as researchers operationalize course taking in multiple ways. Examples include the highest level of coursework or number of Carnegie units taken in a particular subject (e.g., Rose & Betts, 2001); participating in curricular “tracks” (e.g., Fletcher & Zirkle, 2009); the number of college preparatory courses taken such as honors, AP, or IB (e.g., Geiser & Santelices, 2004); and indices of course taking that combine several of the aforementioned measures (e.g., Attewell & Domina, 2008).
- 2.
We use the term standard regression in the literature review to refer to nonexperimental studies that employ OLS or nonlinear regression without controls for student self-selection into coursework. Following the introduction of terminology related to causal inference, subsequent sections will employ the term naïve in reference to such studies.
- 3.
- 4.
- 5.
The ELS:02 third follow-up survey is scheduled to occur during Summer 2012.
- 6.
See Holland (1986) for a much more complete discussion of Rubin causal model and the concept of the counterfactual.
- 7.
Murnane and Willett (2011) provide an excellent description and visual representation of a 2SLS framework in Chapter 10 of their Methods Matter text.
- 8.
Schooling is only permitted in period 1 within our model; therefore, \( {h}_{w}^{t}\)only ever takes on the form of \( {h}_{w}^{1}\).
- 9.
Whereas mathematics test scores are not the only potential measure of academic performance, they have been shown to be a strong predictor of postsecondary outcomes (Deke & Haimson, 2006) and are likely to have the highest correlation with our treatment variable. Therefore, the inclusion of this variable is likely to have the greatest impact on omitted variable bias related to academic performance.
- 10.
Stock and Yogo (2005) provide another method for evaluating the strength of instruments through the use of first stage estimates. However, those test statistics are not available in Stata when robust standard errors are used to account for survey design (as in our analysis). See StataCorp (2009, p. 765) for aid in interpreting these statistics.
References
Abdulkadiroglu, A., Angrist, J., Dynarski, S., Kane, T., & Pathak, P. (2011). Accountability and flexibility in public schools: Evidence from Boston’s charters and pilots. Quarterly Journal of Economics, 126(2), 699–748.
Achieve. (2011). Closing the expectations gap: An annual 50-state report on the alignment of high school policies with the demands of college and careers. Retrieved from http://www.achieve.org/ClosingtheExpectationsGap2011
Adelman, C. (1999). Answers in the tool box: Academic intensity, attendance patterns, and bachelor’s degree attainment. Washington, DC: U.S. Department of Education.
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.
Alexander, K. L., Riordan, C., Fennessey, J., & Pallas, A. M. (1982). Social background, academic resources, and college graduation: Recent evidence from the National Longitudinal Survey. American Journal of Education, 90, 315–333.
Altonji, J. G. (1995). The effects of high school curriculum on education and labor market outcomes. Journal of Human Resources, 30(3), 409–438.
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455.
Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106(4), 979–1014.
Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(4), 69–85.
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics. Princeton: Princeton University Press.
Astin, A. W., & Oseguera, L. (2005). Pre-college and institutional influences on degree attainment. In A. Seidman (Ed.), College student retention (pp. 245–276). Westport, CT: American Council on Education.
Attewell, P., & Domina, T. (2008). Raising the bar: Curricular intensity and academic performance. Educational Evaluation and Policy Analysis, 30(1), 51–71.
Baum, C. F., Schaffer, M. E., & Stillman, S. (2003). Instrumental variables and GMM: Estimation and testing. The Stata Journal, 3(1), 1–31.
Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187.
Bettinger, E.P., & Baker, R. (2011). The effects of student coaching in college: An evaluation of a randomized experiment in student mentoring (NBER Working Paper No. 16881). Retrieved from http://www.nber.org.proxy.lib.umich.edu/papers/w16881
Bishop, J. H., & Mane, F. (2004). Educational reform and disadvantaged students: Are they better off or worse off? (CAHRS Working Paper #04-13). Ithaca, NY: Cornell University, School of Industrial and Labor Relations, Center for Advanced Human Resource Studies. Retrieved from http://digitalcommons.ilr.cornell.edu/cahrswp/17
Bishop, J. H., & Mane, F. (2005). Raising academic standards and vocational concentrators: Are they better off or worse off? Education Economics, 13(2), 171–187.
Bozick, R., & Ingels, S. J. (2008). Mathematics course taking and achievement at the end of high school: Evidence from the Education Longitudinal Study of 2002 (ELS: 2002) (NCES 2008-319). Washington, DC: U.S. Department of Education.
Burkam, D. T., & Lee, V. E. (2003). Mathematics, foreign language, and science course taking and the NELS:88 transcript data (NCES 2003-01). Washington, DC: U.S. Department of Education.
Camara, W., & Echternacht, G. (2000). The SAT I and high school grades: Utility in predicting success in college (RN-10). Retrieved from The College Board Office of Research and Development: http://professionals.collegeboard.com/profdownload/pdf/rn10_10755.pdf
Cameron, C. A., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press.
Card, D. (1995). Using geographic variation in college proximity to estimate the return to schooling. In L. Christofides, E. Grant, & R. Swidinsky (Eds.), Aspects of labour market behaviour: Essays in honour of John Vanderkamp (pp. 201–222). Toronto: University of Toronto Press.
Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69(5), 1127–1160.
Carneiro, P., Heckman, J. J., & Vytlacil, E. J. (2011). Estimating marginal returns to education. American Economic Review, 101(6), 2754–2781.
Cellini, S. R. (2008). Causal inference and omitted variable bias in financial aid research: Assessing solutions. The Review of Higher Education, 31(3), 329–354.
Choy, S. P. (2001). Students whose parents did not go to college: Postsecondary access, persistence, and attainment. Washington, DC: U.S. Department of Education.
Cornwell, C., Lee, K., & Mustard, D. (2005). Student responses to merit scholarship retention rules. Journal of Human Resources, 40(4), 895–917.
Cornwell, C., Lee, K., & Mustard, D. (2006).The effects of state-sponsored merit scholarships on course selection and major choice in college (IZA Discussion Paper No. 1953). Retrieved from ftp://ftp.iza.org/dps/dp1953.pdf
Crosnoe, R. (2009). Low-income students and the socioeconomic composition of public high schools. American Sociological Review, 74(5), 709–730.
Dalton, B., Ingels, S. J., Downing, J., &Bozick, R. (2007). Advanced mathematics and science course taking in the spring high school senior classes of 1982, 1992, and 2004. Statistical analysis report (NCES 2007-312). Washington, DC: U.S. Department of Education.
Deke, J., & Haimson, J. (2006). Valuing student competencies: Which ones predict postsecondary educational attainment and earnings, and for whom? Princeton, NJ: Mathematica Policy Research, Inc.
Dynarski, S., Hyman, J. M., & Schanzenbach, D. W. (2011). Experimental evidence on the effect of childhood investments on postsecondary attainment and degree completion (Working paper). Ann Arbor, MI: University of Michigan.
Ehrenberg, R. G., & Marcus, A. J. (1982). Minimum wages and teenagers’ enrollment-employment outcomes: A multinomial logit model. Journal of Human Resources, 17(1), 39–58.
Fletcher, J., & Zirkle, C. (2009). The relationship of high school curriculum tracks to degree attainment and occupational earnings. Career and Technical Education Research, 34(2), 81–102.
Frank, K. A., Muller, C., Schiller, K. S., Riegle-Crumb, C., Mueller, A. S., Crosnoe, R., & Pearson, J. (2008). The social dynamics of mathematics course taking in high school. The American Journal of Sociology, 113(6), 1645–1696.
Gamoran, A. (1987). The stratification of high school learning opportunities. Sociology of Education, 60(3), 135–155.
Geiser, S., & Santelices, V. (2004). The role of advanced placement and honors courses in college admissions. Berkeley, CA: Center for Studies in Higher Education, University of California. Retrieved from http://cshe.berkeley.edu/publications/docs/ROP.Geiser.4.04.pdf#search=%22Saul%20Geiser%22
Goldrick-Rab, S., Carter, D. F., & Wagner, R. W. (2007). What higher education has to say about the transition to college. Teachers College Record, 109(10), 2444–2481.
Goodman, J. (2008). Who merits financial aid? Massachusetts’ Adams scholarship. Journal of Public Economics, 92(10–11), 2121–2131.
Greene, W. H. (2011). Econometric analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Hall, A. R., Rudebusch, G., & Wilcox, D. (1996). Judging instrument relevance in instrumental variables estimation. International Economic Review, 37, 283–298.
Hallinan, M. T. (1994). Tracking: From theory to practice. Sociology of Education, 67(2), 79–84.
Heckman, J., & Vytlacil, E. (2005). Structural equations, treatment effects, and econometric policy evaluation. Econometrica, 73, 669–738.
Heckman, J. J., & Urzua, S. (2009). Comparing IV with structural models: What simple IV can and cannot identify (NBER Working Paper 14706). Retrieved from http://www.nber.org/papers/w14706
Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–970.
Horn, L., & Kojaku, L. (2001).High school academic curriculum and the persistence path through college. Statistical analysis report (NCES 2001-163). Washington, DC: U.S. Department of Education.
Kane, T. J., & Rouse, C. (1993). Labor market returns to two- and four-year colleges: Is a credit a credit, and do degrees matter? (NBER Working Paper #4268). Retrieved from http://en.scientificcommons.org/37629075
Kelly, S. (2009). The Black-White gap in mathematics course taking. Sociology of Education, 82(1), 47–69.
Kim, J., Kim, J., DesJardins, S., & McCall, B. (2012, April). Exploring the relationship between high school math course-taking and college access and success. Paper presented at the meeting of the American Educational Research Association, Vancouver, Canada.
Klopfenstein, K., & Thomas, M. K. (2009). The link between advanced placement experience and early college success. Southern Economic Journal, 75(3), 873.
Lee, V. E., Chow-Hoy, T. K., Burkam, D. T., Geverdt, D., & Smerdon, B. A. (1998). Sector differences in high school course taking: A private school or Catholic school effect? Sociology of Education, 71(4), 314–335.
Lemieux, T., & Card, D. (1998). Education, earnings, and the “Canadian GI Bill” (NBER Working Paper #6718). Retrieved from http://faculty.arts.ubc.ca/tlemieux/papers/w6718.pdf
Levine, P. B., & Zimmerman, D. J. (1995). The benefit of additional high school math and science classes for young women: Evidence from longitudinal data. Journal of Business and Economics Statistics, 13(2), 137–149.
Long, L. H. (1972). The influence of number and ages of children on residential mobility. Demography, 9(3), 371–382.
Long, B. T. (2007). The contributions of economics to the study of college access and success. Teachers College Record, 109(10), 2367–2443.
Long, M. C., Iatarola, P., & Conger, D. (2009). Explaining gaps in readiness for college-level math: The role of high school courses. Education Finance and Policy, 4(1), 1–33.
Ma, X. (2001). Participation in advanced mathematics: Do expectation and influence of students, peers, teachers, and parents matter? Contemporary Educational Psychology, 26(1), 132–146.
McCall, B. P., & Bielby, R. M. (2012). Regression discontinuity design: Recent developments and a guide to practice for researchers in higher education. In J. Smart & M. Paulsen (Eds.), Higher education: Handbook of theory and research (Vol. 27, pp. 249–290). Dordrecht, The Netherlands: Springer.
Michigan Department of Education. (2006).Preparing Michigan students for work and college success. Retrieved from http://www.michigan.gov/mde/0,4615,7-140-37818---S,00.html
Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.
Murray, M. P. (2006). Avoiding invalid instruments and coping with weak instruments. Journal of Economic Perspectives, 20(4), 111–132.
National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. The Elementary School Journal, 84(2), 113–130.
Neumark, D., & Wascher, W. (1995). Minimum wage effects on employment and school enrollment. Journal of Business& Economic Statistics, 13(2), 199–206.
Newhouse, J., & McClellan, M. (1998). Econometrics in outcomes research: The use of instrumental variables. Annual Review of Public Health, 19, 17–34.
Oakes, J., & Guiton, G. (1995). Matchmaking: The dynamics of high school tracking decisions. American Educational Research Journal, 32(1), 3–33.
Pallas, A. M., & Alexander, K. L. (1983). Sex differences in quantitative SAT performance: New evidence on the differential coursework hypothesis. American Educational Research Journal, 20(2), 165–182.
Pike, G. R., Hansen, M. J., & Lin, C. H. (2011). Using instrumental variables to account for selection effects in research on first-year programs. Research in Higher Education, 52, 194–214.
Planty, M., Provasnik, S., & Daniel, B. (2007). High school course taking: Findings from the Condition of Education, 2007 (NCES 2007–065). Washington, DC: U.S. Department of Education.
Porter, S. R. (2012). Using instrumental variables properly to account for selection effects. Unpublished manuscript. Retrieved from http://www.stephenporter.org/papers/Pike_IV.pdf
Rech, J. F., & Harrington, J. (2000). Algebra as a gatekeeper: A descriptive study at an urban university. Journal of African American Studies, 4(4), 63–71.
Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a 2-year institution results in differences in educational attainment. In J. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 24, pp. 47–97). Dordrecht, The Netherlands: Springer.
Rose, H., & Betts, J. R. (2001). Math matters: The links between high school curriculum, college graduation, and earnings. San Francisco: Public Policy Institute of California.
Rose, H., & Betts, J. R. (2004). The effect of high school courses on earnings. The Review of Economics and Statistics, 86(2), 497–513.
Rothenberg, T. J. (1983). Asymptotic properties of some instruments in structural models. In S. Karlin, T. Amemiya, & L. A. Goodman (Eds.), Studies in econometrics, time series, and multivariate Statistics. New York: Academic.
Rubin, D. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701.
Sadler, P. M., & Tai, R. H. (2007). The two high-school pillars supporting college science. Science, 317(5837), 457–458.
Schneider, B., Swanson, C. B., & Riegle-Crumb, C. (1998). Opportunities for learning: Course sequences and positional advantages. Social Psychology of Education, 2, 25–53.
Sells, L. W. (1973). High school mathematics as the critical filter in the job market. In R. T. Thomas (Ed.), Developing opportunities for minorities in graduate education (pp. 37–39). Berkeley, CA: University of California Press.
Simpkins, S., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42, 70–83.
Sovey, A. J., & Green, D. P. (2011). Instrumental variables estimation in political science: A readers’ guide. American Journal of Political Science, 55(1), 188–200.
Staiger, D., & Stock, J. (1997). Instrumental variables regression with weak instruments. Econometrica, 65, 557–586.
StataCorp. (2009). Stata: Release 11. College Station, TX: StataCorpLp.
Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business & Economic Statistics, 20, 518–529.
Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (pp. 80–108). New York: Cambridge University Press.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.
United States Department of Agriculture.(2004). Rural education at a glance (Rural Development Research Report No. 98). Retrieved from http://inpathways.net/rural_education.pdf
United States Department of Education. (2008, December). What Works Clearinghouse, Procedures and standards handbook, version 2. Washington, DC: United States Department of Education.
Useem, E. L. (1992). Middle schools and math groups: Parents’ involvement in children’s placement. Sociology of Education, 65(4), 263–279.
Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: The MIT Press.
Yonezawa, S., Wells, A. S., & Serna, I. (2002). Choosing tracks: “Freedom of choice” in detracking schools. American Educational Research Journal, 39(1), 37–67.
Acknowledgements
The authors would like to thank Brian McCall and Stephen Porter for their helpful suggestions and feedback. All errors and omissions are, however, our own.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: Stata syntax
Appendix: Stata syntax
/*This file will run all analysis for the IV chapter on the ELS data. The file for the NELS data is nearly identical, simply replacing the dependent variable in each model from persistence to bachelor’s degree attainment*/
/*First create global macros for the models. This allows us to insert a large number of variables into our models without having to repeatedly type the variable names.*/
/***Becausethe data used for this analysis are restricted, we will not include actual variable names,but instead will provide alternate names for the variables used***/
/*Exogenous independent variables*/
global exog1 “mathquart2 mathquart3 mathquart4 male black asian_amhisp_amnative_ammixedoth ses_q2 ses_q3 ses_q4 mom_hs mom_att2yr mom_aa mom_att4yr mom_bamom_mamom_phd born_84 born_85 born_86 born_87 unemploy_rate2004 i.hsstate”
/*Endogenous independent variable*/
global endo1 “algebra_2”
/*Instrumental variables*/
global inst1 “unemploy_rate2001 age_16_urate age_16”
/*BIVARIATE CORRELATIONS AMONG ALL VARIABLES. Here we examine the bivariate relationships between each of our variables*/
corpse_att algebra_2 unemploy_rate2001 age_16_urate age_16 mathquart2 mathquart3 mathquart4 male black asian_amhisp_amnative_ammixedoth ses_q2 ses_q3 ses_q4 mom_hs mom_att2yr mom_aa mom_att4yr mom_bamom_mamom_phd unemploy_rate2004
spearmanpse_att algebra_2 unemploy_rate2001 age_16_urate age_16 mathquart2 mathquart3 mathquart4 male black asian_amhisp_amnative_ammixedoth ses_q2 ses_q3 ses_q4 mom_hs mom_att2yr mom_aa mom_att4yr mom_bamom_mamom_phd unemploy_rate2004
/*Multivariate models of first to second-year persistence*/
/***BASELINE OR NAIVE MODEL***/
/*Linear Probability Model (LPM)*/
reg persist $endo1 $exog1, robust
/*We use the ‘eststo’ command to save the estimates from each of our final modelswhich we use later to create a publication-ready table*/
eststo OLSpersist
/***INSTRUMENTAL VARIABLES ESTIMATORS***/
*Two Stage Least Squares (2SLS)
/*First we estimate the model, using the global macros from above*/
ivregress 2sls persist $exog1 ($endo1 = $inst1), vce(robust)
/*Here we examine the model fit statistics from the first stage model in the 2SLS approach. The ‘estatfirststage’ command provides R-squared and Adjusted R-squared statistics along with partial R-squared statistics for and F tests with significance levels for each endogenous variable*/
estat firststage
/*Here we examine the overidentification tests to evaluate the exclusion restriction for our instruments. To confirm our expectation that the variables are properly excluded from the second stage model. If that is the case these test statistics will not be statistically significant*/
estat overid
/*Next we examine tests of the endogeneity of our Algebra II variable. If our variable of interest is in factendogenous, then these statistics will be statistically significant*/
estat endogenous
/*Here we store the estimates from the 2SLS model to be used to create a publication-ready table which will also allow us to compare results across the estimated models.*/
eststo SLSpersist
*Limited Information Maximum Likelihood (LIML)
/*Estimating the model*/
ivregressliml persist $exog1 ($endo1 = $inst1), vce(robust)
/*Evaluatingthe first stage model statistics*/
estatfirststage
/*Overidentification tests*/
estat overid
/*Storing estimates*/
eststo LIMLpersist
*Generalized Method of Moments (GMM)
/*Running the model*/
ivregressgmm persist $exog1 ($endo1 = $inst1), vce(robust)
/*Againexamine the first stage statistics*/
estat firststage
/*Overidentification tests*/
estat overid
/*Tests for endogeneity*/
estat endogenous
/*Storing Estimates*/
eststo GMMpersist
/*Control function with LPM and Bootstrap*/
/*In order to bootstrap the standard errors for these estimates, we need to first write a program that will run the first stage regression, save the residuals, and then include those residuals as controls in the second stage regression */
capture program drop lpmcf
program lpmcf
quietly {
/*estimate the first stage LPM*/
reg algebra_2 $exog1 $inst1, robust
/*just in case we already saved these variables we drop them*/
capture drop alg2_resid_els
/*store the residuals from the first stage */
predict alg2_resid_els, resid
/*estimate the second stage LPM*/
reg persist $endo1 $exog1 alg2_resid_els, robust
}
end
/*Now we estimate a control function model with an LPM second stage on 250 bootstrapped samples and estimate our standard errors from that*/
bootstrap _b, seed(1) r(250): lpmcf
/*Store the estimates*/
eststo CFLPMpersist
*Control function with Logit and Bootstrap
/*This program estimates the first and second stages then we conduct a bootstrap to estimate the proper standard errors*/
capture program drop logitcf
program logitcf
quietly{
/*estimate the first stage*/
reg algebra_2 $exog1 $inst1, robust
capture drop alg2_resid_els_logit
/*store residuals from first stage*/
predict alg2_resid_els_logit, resid
/*estimate the second stage logit*/
logit persist $endo1 $exog1 alg2_resid_els_logit, vce(robust)
}
end
/*Now we estimate a control function model with a logit second stage on 250 bootstrapped samples and produce standard errors from that process*/
bootstrap _b, seed(1) r(250): logitcf
/*Because the coefficients produced through Stata’s logit routine are not directly comparable to those from each of the other models that are linear probability models in the second stage, we use Stata’s ‘margins’ command to convertlogit coefficients into comparable marginal effects*/
margins, dydx(*)
/*Store the results*/
eststo CFLogitpersist
/*Finally we employ the ‘esttab’ routine to create a publication-ready table of marginal effects (b) and significance measures (p)*/
esttabOLSpersistSLSpersistLIMLpersistGMMpersistCFLPMpersistCFLogitpersist, b(3) p(3) not wide plain
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Bielby, R.M., House, E., Flaster, A., DesJardins, S.L. (2013). Instrumental Variables: Conceptual Issues and an Application Considering High School Course Taking. In: Paulsen, M. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5836-0_6
Download citation
DOI: https://doi.org/10.1007/978-94-007-5836-0_6
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5835-3
Online ISBN: 978-94-007-5836-0
eBook Packages: Humanities, Social Sciences and LawEducation (R0)