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147
Bayesian Model Averaging for Linear Regression Models
 Journal of the American Statistical Association
, 1997
"... We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem in ..."
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Cited by 259 (14 self)
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We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e., combinations of predictors) when making inferences about quantities of
The Economic Theory of Public Enforcement of Law
, 1998
"... This article surveys the theory of the public enforcement of law — the use of public agents (inspectors, tax auditors, police, prosecutors) to detect and to sanction violators of legal rules. We first present the basic elements of the theory, focusing on the probability of imposition of sanctions, t ..."
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Cited by 185 (12 self)
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This article surveys the theory of the public enforcement of law — the use of public agents (inspectors, tax auditors, police, prosecutors) to detect and to sanction violators of legal rules. We first present the basic elements of the theory, focusing on the probability of imposition of sanctions, the magnitude and form of sanctions, and the rule of liability. We then examine a variety of extensions of the central theory, concerning accidental harms, costs of imposing fines, errors, general enforcement, marginal deterrence, the principalagent relationship, settlements, selfreporting, repeat offenders, imperfect knowledge about the probability and magnitude of fines, and incapacitation.
Samurai accountant: A theory of auditing and plunder
 Review of Economic Studies
, 1987
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Cited by 91 (1 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.
Income Tax Compliance in a PrincipalAgent Framework
 Journal of Public Economics
, 1985
"... Previous analyses have modeled income tax evasion as a ‘portfolio problem’, deriving the optimal consumption of the ‘risky asset ’ (unreported income) assuming a fixed probability of detection. We compare an alternative audit policy to the standard random audit policy. We focus on an ‘audit cut08 po ..."
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Cited by 83 (0 self)
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Previous analyses have modeled income tax evasion as a ‘portfolio problem’, deriving the optimal consumption of the ‘risky asset ’ (unreported income) assuming a fixed probability of detection. We compare an alternative audit policy to the standard random audit policy. We focus on an ‘audit cut08 policy, in which an agent triggers an audit if reported income is ‘too low’, and is not audited if reported income is ‘sufficiently high’. We find that random audit rules are weakly dominated by audit cutoff rules. Given lumpsum taxes and tines, audit cutoff rules are the leastcost policies which induce truthful reporting of income. 1.
Racial Bias in Motor Vehicle Searches: Theory and Evidence
, 1999
"... African American motorists in the United States are more likely than white motorists to have their cars searched by police checking for illegal drugs and other contraband. The courts are faced with the task of deciding on the basis of trafficstop data whether police are basing their decisions to st ..."
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Cited by 81 (2 self)
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African American motorists in the United States are more likely than white motorists to have their cars searched by police checking for illegal drugs and other contraband. The courts are faced with the task of deciding on the basis of trafficstop data whether police are basing their decisions to stop cars on the race of the driver. We develop a model of law enforcement for a population with two racial types who also differ along other dimensions relevant to criminal behavior. We discuss why a simple test commonly applied by the courts is inadequate when the econometrician observes only a subset of the characteristics observed by the policemen. Next, we show how to construct a test for whether di¤erential treatment is motivated purely out of e¢ciency grounds, i.e. to maximize the number of arrests, or re‡ects racial prejudice. The test is valid even when the set of characteristics observed by the policemen are only partially observable by the econometrician. We apply the tests for discrimination to traffic stop data from Maryland. Finally, we present a
Model Selection and Accounting for Model Uncertainty in Linear Regression Models
, 1993
"... We consider the problems of variable selection and accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. The complete B ..."
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Cited by 50 (6 self)
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We consider the problems of variable selection and accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. The complete Bayesian solution to this problem involves averaging over all possible models when making inferences about quantities of interest. This approach is often not practical. In this paper we offer two alternative approaches. First we describe a Bayesian model selection algorithm called "Occam's "Window" which involves averaging over a reduced set of models. Second, we describe a Markov chain Monte Carlo approach which directly approximates the exact solution. Both these model averaging procedures provide better predictive performance than any single model which might reasonably have been selected. In the extreme case where there are many candidate predictors but there is no relationship between any of them and the response, standard variable selection procedures often choose some subset of variables that yields a high R² and a highly significant overall F value. We refer to this unfortunate phenomenon as "Freedman's Paradox" (Freedman, 1983). In this situation, Occam's vVindow usually indicates the null model as the only one to be considered, or else a small number of models including the null model, thus largely resolving the paradox.
Employee Crime and the Monitoring Puzzle
 Journal of Labor Economics
, 1989
"... (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ..."
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Cited by 42 (1 self)
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(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.
Enforcement Costs and the Optimal Magnitude and Probability of Fines
 J. Law Econ., Apr.1992
"... Some of the costs of enforcing laws are fixed " in the sense that they do not depend on the number of individuals who commit harmful actswhile other costs are "variable"they rise with the number of such individuals. This article analyzes the effects of fixed and variable enforce ..."
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Cited by 27 (4 self)
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Some of the costs of enforcing laws are fixed " in the sense that they do not depend on the number of individuals who commit harmful actswhile other costs are "variable"they rise with the number of such individuals. This article analyzes the effects of fixed and variable enforcement costs on the optimal fine and the optimal probability of detection. It is shown that the optimal fine rises to reflect variable enforcement costs; that the optimal fine is not directly affected by fixed enforcement costs; and that the optimal probability depends on both types of enforcement costs.
Renegotiation of concession contracts in Latin America
 J.L., A. Kartacheva and L. Quesada (2000), “Contract Renegotiations and Concessions in the Latin America and Caribbean Region: An Economic Análisis and Empirical Implications”, The World
"... We construct a regulation model in which renegotiation occurs due to the imperfect enforcement of concession contracts. This enables us to provide theoretical predictions for the impact, on the probability of renegotiation of a concession, of regulatory institutions, institutional features, economic ..."
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Cited by 26 (1 self)
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We construct a regulation model in which renegotiation occurs due to the imperfect enforcement of concession contracts. This enables us to provide theoretical predictions for the impact, on the probability of renegotiation of a concession, of regulatory institutions, institutional features, economic shocks and of the characteristics of the concession contracts themselves. Then we use a data set of nearly 1000 concessions awarded in Latin America and the Caribbean countries from 1989 to 2000, covering the sectors of telecommunications, energy, transport and water, to test these predictions. Finally, we derive some policy implications of our theoretical and empirical work.
The Deterrence Effect of Prison: Dynamic Theory and Evidence,” July 2009. Unpublished manuscript
"... Using administrative, longitudinal data on felony arrests in Florida, we exploit the discontinuous increase in the punitiveness of criminal sanctions at 18 to estimate the deterrence effect of incarceration. Our analysis suggests a 2 percent decline in the logodds of offending at 18, with standard ..."
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Cited by 23 (4 self)
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Using administrative, longitudinal data on felony arrests in Florida, we exploit the discontinuous increase in the punitiveness of criminal sanctions at 18 to estimate the deterrence effect of incarceration. Our analysis suggests a 2 percent decline in the logodds of offending at 18, with standard errors ruling out declines of 11 percent or more. We interpret these magnitudes using a stochastic dynamic extension of Becker’s (1968) model of criminal behavior. Calibrating the model to match key empirical moments, we conclude that deterrence elasticities with respect to sentence lengths are no more negative than0.13 for young offenders.