No CrossRef data available.
Article contents
Some Modern Morality Plays: Social Science Models and Methods
Published online by Cambridge University Press: 20 November 2018
Abstract
An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Review Essay
- Information
- Copyright
- Copyright © American Bar Foundation, 1984
References
1 Kenneth Laird Haydock, Book Review Sequence, in the Wisconsin Law School 1982 Law Revue (from the script for students' annual spring show, April 1982).Google Scholar
2 Id.Google Scholar
3 Cf. Learner, Edward E., Let's Take the Con Out of Econometrics, 73 Am. Econ. Rev. 31, 40 (1983) (“Methodology, like sex, is better demonstrated than discussed, though often better anticipated than experienced”).Google Scholar
4 See John Barth, The Floating Opera 6–7 (London: Secker & Warburg, 1968).Google Scholar
5 Argued, 43 U.S.L.W. 3582 (Apr. 21, 1975), rev'd and remanded, 428 U.S. 904, 905 (imposition and carrying out of death penalty determined to constitute cruel and unusual punishment, in violation of Eighth and Fourteenth amendments).Google Scholar
6 See, e.g., Fowler v. North Carolina, restored for reargument, 422 U.S. 1039 (June 23, 1975, order for reargument revoked, 424 U.S. 903 (Feb. 23, 1976). Of the large number of death penalty cases decided by the Court in 1976, several were accorded relatively lengthy discussion by the Court. See Gregg v. Georgia, 428 U.S. 153 (1976); Proffitt v. Florida, 428 U.S. 242 (1976); Jurek v. Texas, 428 US. 262 (1976); Woodson v. North Carolina, 428 U.S. 280 (1976); Roberts v. Louisiana, 428 U.S. 325 (1976).Google Scholar
7 See, e.g., Baldus, David C. & Cole, James W. L., A Comparison of the Work of Thorsten Sellin and Isaac Ehrlich on the Deterrent Effect of Capital Punishment, 85 Yale L.J. 170 (1975); Bowers, William J. & Pierce, Glenn L., The Illusion of Deterrence in Isaac Ehrlich's Research on Capital Punishment, 85 Yale L.J. 187 (1975); Ehrlich, Isaac, The Deterrent Effect of Capital Punishment: A Question of Life and Death, 65 Am. Econ. Rev. 397 (1975); cf. Arnold Barnett, Further Standards of Accountability for Deterrence Research, in Methods at 127. See generally Lempert, Richard O., Desert and Deterrence: An Assessment of the Moral Bases of the Case for Capital Punishment, 79 Mich. L. Rev. 1177, 1187–1224 (1981) (discussing deterrence as basis for morality of capital punishment and reviewing social science evidence on deterrent effects). Much of the research on deterrence antedates the 1970s outpourings, and had, indeed, entered into the Supreme Court's 1972 consideration of the constitutionality of the death penalty. See, e.g., Furman v. Georgia, 408 U.S. 238, 245–54 (1972) (Marshall, J., concurring) (reviewing social science evidence on deterrent effect of death penalty). What was different in 1975 was that, for the first time, a researcher had found a statistically significant deterrent effect for the death penalty and obtained this result using sophisticated econometric methods. See Ehrlich, supra, at 409–11 (tables 2,3, & 4 and discussion in text). To Ehrlich's assertion that one execution may deter seven or eight additional homicides per year, id. at 398,414, came a volley of responses. See, e.g., Passell, Peter, The Deterrent Effect of the Death Penalty: A Statistical Test, 28 Stan. L. Rev. 61, 79 (1975) (“there is no evidence that executions act as a deterrent,” and no such conclusion from Ehrlich's research is warranted). Indeed, to the extent that decisions about the death penalty depend on the existence or magnitude of a deterrent effect, the model and methodology employed in researching the question may, in effect, weigh heavily in the determination of social policy. Consequently, where the model or methods used reflect biases of a researcher, those biases may, via the seemingly innocuous technical detail of the research, affect the research outcome and hence the policy decisions. For example, using identical data, but employing various model specifications to span the spectrum of political views, economist Edward Leamer determined that a researcher could find anything from a deterrent effect of a death penalty to a perverse effect: “Thus the right winger can report that the inference from this data set that executions deter murders is not fragile. The rational maximizer similarly finds that conclusion insensitive to choice of model, but the other three priors allow execution actually to encourage murder, possibly by a brutalizing effect on society.” Leamer, supra note 2, at 42. Moreover, “[a]nything between these two extremes can be similarly obtained.” Id. Leamer found that the possible range of values for deterrent effect—that is, the number of murders deterred by one execution—ran from a range of 1 to 23, as determined under the “right-wing” specification, to a range for the so-called bleeding-heart specification, of from 26 murders deterred to 12 additional murders. Id.Google Scholar
8 See, e.g., Gregg v. Georgia, 428 U.S. 153, 184–86 (1976) (results of the debate on death penalty deterrence have been inconclusive; resolution of this “complex factual issue … properly rests with the legislatures,” id. at 186); Proffitt v. Florida, 428 U.S. 242 (1976) (no additional discussion of social science research); Jurek v. Texas, 428 U.S. 262 (1975) (same); Woodson v. North Carolina, 428 U.S. 280 (1976) (statute violates Eighth and Fourteenth amendments; no additional reference to deterrence of death penalty); Roberts v. Louisiana, 428 U.S. 325 (1976) (same); cf. Gregg v. Georgia, supra, at 233–36 (Marshall, J., dissenting) (Ehrlich study “is of little, if any, assistance in assessing the deterrent impact of the death penalty,” id. at 236, and other evidence is convincing that capital punishment is not necessary as a deterrent); Roberts v. Louisiana, supra at 354–55 (White, J., dissenting) (because results of research on deterrence are “conflicting,” Court should “accept the reasonable conclusions of Congress and 35 state legislatures that there are indeed certain circumstances in which the death penalty is the more efficacious deterrent of crime,” id. at 355).Google Scholar
9 This tale is part of the oral tradition of the social sciences, and I have been unable to discern its origin. Its creator deserves better than these thanks to an unknown soldier.Google Scholar
10 See J. Kmenta, Elements of Econometrics 347–49 (New York: Macmillan Publishing Co., 1971).Google Scholar
11 Linda Mathews, Study Saying Executions Deter Murder Stirs Furore, L.A. Times, May 5, 1975, at 1.Google Scholar
12 See, e.g., the following essays in Methods: Joseph C. Fisher & Robert L. Mason, The Analysis of Multicollinear Data in Criminology, at 99; Lee R. McPheters & Don E. Schlagenhauf, Evaluation of Alternative Crime Forecasting Models, at 147; cf. David F. Greenberg & Ronald C. Kessler, Panel Models in Criminology, at 1,2 (on special advantages of using panel data); Paul Maxim, The Analysis of Qualitative Data in Criminology; An Application of Log-Linear Models, at 19, 20 (the use of log-linear models for analysis of categorical data). In fact, most of the essays in both books could properly be included in this footnote.Google Scholar
13 One of my colleagues, Dirk Hartog has told me that John the Baptist was irritating, not boring. Having done a little more research in response to this comment (very little), I am unable to reject the initial hypothesis of the contemporary perception of boringness. Should Professor Hartog be correct, however, I would encourage knowledgeable readers to substitute for John the Baptist their favorite boring prophet.Google Scholar
14 Cf. Lindbloom, Charles E., The Science of “Muddling Through,” 19 Pub. Ad. Rev. 79 (1959). It is reasonable at this point to ask, who is the “we”? Both the authors of Models and Methods and this reviewer have tended to write for other researchers, as if they were the “we.” There is, however, another “we,” namely the people who run the criminal justice institutions and the legislators and citizens who set policy for them. How the efforts of these two groups become integrated—if ever they do—is a question that must be left for another time.Google Scholar
15 It is, of course, true that at times a particular statistical measure will be unhelpful as a description of a collection of data, as, for example when a few outliers pull the weighted average of a sample away from the main cluster of values. This would occur, for example, if a town comprised almost entirely of low-income individuals turned out to have an average income of 50,000 per resident, because of the presence of a multimillionaire. In such a case, the careful researcher may turn to other indicators, such as the median income (that of the person in the middle of the list when the townspeople are ranked by income).Google Scholar
16 Thomas S. Kuhn, The Structure of Scientific Revolutions (2d ed. Chicago: University of Chicago Press, 1970); cf. Michel Foucault, The Order of Things: An Archaeology of the Human Sciences 344–87 (New York: Random House, 1970) (presenting epistomology of the “human sciences”).Google Scholar
17 See, e.g., Ernest Van den Haag, Punishing Criminals: Concerning a Very Old and Painful Question (New York: Basic Books, 1975); cf. Alfred Blumstein & Daniel Nagin, On the Optimum Use of Incarceration for Crime Control, in Models, at 39: The debate has fueled a number of prescriptions for imprisonment policy [examples], but very little analysis of the implications of alternative policies. Authors appear to be for or against incapacitation or deterrence more on the basis of principle alone than on the basis of any consideration of its estimated effects…… . [T]he current debate on imprisonment policy can benefit from some of the insights that have been derived even from the preliminary model presented here. This would introduce an element of rationality into a debate that is more often characterized by polemics. Id. at 58.Google Scholar
18 See, e.g., Stanton Wheeler & Leonard S. Cottrell, Jr., Juvenile Delinquency—Its Prevention and Control 28–29 (New York: Russell Sage Foundation, 1966) (almost all juveniles have engaged in some form of theft or vandalism during adolescence). Marvin E. Wolfgang, Robert M. Figlio, & Thorsten Sellin, Delinquency in a Birth Cohort 54 (Chicago: University of Chicago Press, 1972) (35% of the members of a Philadelphia birth cohort had at least one contact with the police by age 18. Of the non-whites, 50% were labeled delinquent, and of the whites, 29%).Google Scholar
19 See Lawrence R. Zeitlin, A Little Larceny Can Do a Lot for Employee Morale, Psychology Today, June 1971, at 22, 24 (over 75% of employees participate in “merchandise shrinkage”).Google Scholar
20 See, e.g., U.S., National Commission on Marihuana and Drug Abuse, Drug Use in America: Problem in Perspective: Second Report (Washington, D.C.: Government Printing Office, 1973) (commission report to president and congress); U.S., President's Commission on Law Enforcement and Administration of Justice, The Challenge of Crime in a Free Society: A Report (Washington, D.C.: Government Printing Office, 1967).Google Scholar
21 I have been unable to discern a common scheme of notation among the articles, although there may be a few exceptions to this for particular pairs of articles. For example, the Daniel Nagin article, Methodological Issues in Estimating the Deterrent Effect of Sanctions, in Models, at 121, employs some of the same notation as that of Blumstein & Nagin, in Models, at 39. See e.g., Blumstein & Nagin at 44,43 (C represents aggregate crime rate; S is the average time served by imprisoned offenders); Nagin at 124–25 (C is crime rate; S is clearance rate; and T is average sentence served).Google Scholar
22 See, e.g., Russell R. Barton & Bruce W. Turnbull, A Failure Rate Regression Model for the Study of Recidivism, in Models, at 81 (discussing importance of functional form employed): but cf. Thomas Orsagh, A Criminometric Model of the Criminal Justice System, in Models, at 163, 173–84, & 173 n.2 (employing linear empirical model, apparently for tractability, despite absence of “compelling reason [for] adopt[ing] such a formulation”).Google Scholar
23 Blumstein & Nagin at 1; Nagin at 122; Greenberg & Kessler at 1; Hellman & Naroff, Urban Land Use Models: Applications in Criminology, at 141.Google Scholar
24 See, e.g., in Models, Blumstein & Nagin at 39 (excerpts); Nagin at 122 (reprint).Google Scholar
25 See, e.g., in Models, Robert M. Figiio, Delinquency Careers as a Simple Markov Process, at 25; Michael L. Greene & Stephen Stollmack, Estimating the Number of Criminals, at 1; Orsagh at 163.Google Scholar
26 See Leff, Arthur Allen, Law and, 87 Yale L.J. 989 (1978).Google Scholar
27 See, e.g., Tampa Elec. Co. v. Nashville Coal Co., 365 U.S. 320,324,327-29 (1961) (requiring proof of substantial share of relevant market); L.G. Balfour Co. v. FTC,442F.2d 1, 12–14 (7th Cir. 1971) (applying substantial market share test).Google Scholar
28 See, e.g., Hazelwood School Dist. v. United States, 433 U.S. 299, 307–8 (1977) (noting importance of statistics as source of proof in employment discrimination cases); International Bhd. of Teamsters v. United States, 431 U.S. 324, 339–40(1977) (same); Brown v. Board of Educ. 347 U.S. 483,494 n.11 (1954) (citing studies on effects of school segregation on minority children); Muller v. Oregon, 208 U.S. 412, 419 n.1 (1908) (famous “Brandeis brief” noted, citing social science evidence of effects of long work hours on women); EEOC v. American Nat'l Bank, 680 F.2d 965, 966–70 (4th Cir. 1982) (extensive discussion of statistical evidence in employment discrimination cases).Google Scholar
29 See, e.g., death penalty studies, supra notes 6–8, and many of the essays in Models.Google Scholar
30 See, e.g., Fairley, William B., Probabilistic Analysis of Identification Evidence, 2 J. Legal Stud. 493 (1973).Google Scholar
31 Guido Calabresi, The Costs of Accidents: Legal and Economic Analysis (New Haven, Conn.: Yale University Press, 1970).Google Scholar
32 See, e.g., Clark, Robert C., The Interdisciplinary Study of Legal Evolution, 90 Yale L.J. 1238 (1981); Komesar, Neil, Taking Institutions Seriously—Introduction to a Strategy for Constitutional Analysis 51 U. Chi. L. Rev. (forthcoming 1984).Google Scholar
33 Dunn v. Blumstein, 405 U.S. 330, 338–42 (1972).Google Scholar
34 Ballew v. Georgia, 435 U.S. 223,231–32 (1978) (The scholarly writings on jury size “do not draw or identify a bright line below which the number of jurors would not be able to function as required by the standards enunciated in Williams. On the other hand, they raise significant questions about the wisdom and constitutionality of a reduction below six.”)Google Scholar
35 Ballew v. Georgia, 435 US. 223, 230–39 (1978), citing, inter alia, Harry Kalven, Jr. & Hans Zeisel, The American Jury (Chicago: University of Chicago Press, 1971); Michael J. Saks, Jury Verdicts: The Role of Group Size and Social Decision Rule (Lexington, Mass.: Lexington Books, 1977); Lempert, Richard O., Uncovering “Nondiscernible” Differences: Empirical Research and the Jury-Size Cases, 73 Mich. L. Rev. 643 (1975); Zeisel, Hans & Diamond, Shari Seidman, “Convincing Empirical Evidence” on the Six Member Jury, 41 U. Chi. L. Rev. 281 (1974).Google Scholar
36 Julius G. Getman. Stephen B. Goldberg, & Jeanne B. Herman, Union Representation Elections: Law and Reality 111-30 (New York: Russell Sage Foundation, 1976).Google Scholar
37 Shopping Kart Food Mkt., 228 N.L.R.B. 1311 (1977).Google Scholar
38 See, e.g., articles cited in Goldberg, Stephen B., Getman, Julius G., & Brett, Jeanne M., Union Representation Elections: Law and Reality: The Authors Respond to the Critics, 19 Mich. L. Rev. 564, 580–93 (1981).Google Scholar
39 General Knit, Inc., 239 N.L.R.B. 619, 620 (1978) (overruling Shopping Kart).Google Scholar
40 Midland Nat'l Life Ins. Co., 263 N.L.R.B. No. 24, 110 L.R.R.M. 1489 (1982) (overruling General Knit). See also Lachman, Judith A., Freedom of Speech in Union Representation Elections: Employer Campaigning and Employee Response, 1982 A.B.F. Res. J. 755; PaulWeiler, , Promises to Keep: Securing Workers' Rights to Self-Organization Under the NLRA, 96 Harv. L. Rev. 1769 (1983).Google Scholar