Still Handcuffing the Cops? a Review of Fifty Years of Empirical Evidence of Miranda's Harmful Effects on Law Enforcement

By Cassell, Paul G.; Fowles, Richard | Boston University Law Review, May 1, 2017 | Go to article overview

Still Handcuffing the Cops? a Review of Fifty Years of Empirical Evidence of Miranda's Harmful Effects on Law Enforcement


Cassell, Paul G., Fowles, Richard, Boston University Law Review


"I believe the decision of the Court . . . entails harmful consequences for the country at large. How serious these consequences may prove to be only time can tell. . . . The social costs of crime are too great to call the new rules anything but a hazardous experimentation."

-Miranda v. Arizona, 384 U.S. 436, 504, 517 (1966) (Harlan, J., dissenting).

"When we get a little distant, some things get clearer."

-THE INDIGO GIRLS, It's Alright, on SHAMING OF THE SUN (Epic Records 1997).

INTRODUCTION

The fiftieth anniversary of Miranda v. Arizona1 offers a chance to assess how the decision has played out in the real world and, in particular, to determine whether it has harmed law enforcement. On the day the Supreme Court handed down its decision, four dissenters predicted that its price would be reduced police effectiveness in solving crimes. In dissent, Justice Harlan warned that the decision would produce social costs, the size of which "only time can tell."2 Justice White, also dissenting, predicted that "[i]n some unknown number of cases the Court's rule will return a killer, a rapist or other criminal to the streets and to the environment which produced him, to repeat his crime whenever it pleases him."3

Since then, the Miranda warnings and the associated procedures have "become part of our national culture."4 But what effect have they actually had on law enforcement effectiveness? In this Article, we take advantage of the time since the Miranda decision-now a little more than fifty years-to see whether it has produced the predicted harmful consequences. In particular, we survey the available empirical evidence. We collect confession rate data, both from the time of Miranda and since, to assess whether Miranda caused confession rates to fall. We also review the Federal Bureau of Investigation ("FBI")'s nationwide data on crime clearance rates to shed light on any changes in the ability of police to solve crimes. Building on research we first published in 1998,5 we capitalize on additional data and new statistical techniques to more fully assess whether Miranda "handcuffed the cops."

Our focus in this Article is a quantitative one. Many academic commentators have offered their qualitative assessments regarding Miranda's effects on law enforcement based on their understandings of doctrinal developments since Miranda. These qualitative views have generally been that Miranda has not harmed law enforcement.6 But this question is, ultimately, a quantitative one that is best assessed, if possible, quantitatively.

Our Article proceeds in eight parts. In Part I, we describe different approaches to gauging Miranda's effect on law enforcement. Ideally, the issue would be approached by evaluating whether confession rates fell after the decision. The limited evidence available suggests that they did. But because only limited confession rate data exist, particularly for recent years, other measures of Miranda's effects need to be examined.

In Part II, we explain why crime clearance rate data become the inevitable second-best measure for evaluating Miranda's long-term effects. Specifically, we report the results of regression equations on crime clearance rates from 1950 to 2012, controlling for factors apart from Miranda that might be responsible for changes in clearance rates. Even controlling for these factors, we find statistically significant reductions in crime clearance rates after Miranda for violent and property crimes, as well as for robbery, larceny, and vehicle theft. We also quantify the number of lost clearances that appear to be due to Miranda.

In Part III, we take advantage of recent advances in statistical modeling to respond to the critique (advanced by John Donohue,7 among others) that discovering a "MIRANDA effect" depends on the variables that a researcher includes or excludes from regression models. Using Bayesian model averaging ("BMA"), we conclude that our findings are not generally subject to model specification problems but rather are extremely robust. …

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