Assessing Stability and Change in Criminal Offending: A Comparison of Random Effects, Semiparametric, and Fixed Effects Modeling Strategies

An important theoretical problem for criminologists is an explanation forthe robust positive correlation between prior and future criminaloffending. Nagin and Paternoster (1991) have suggested that the correlationcould be due to time-stable population differences in the underlyingproneness to commit crimes (population heterogeneity) and/or thecriminogenic effect that crime has on social bonds, conventionalattachments, and the like (state dependence). Because of data andmeasurement limitations, the disentangling of population heterogeneityand state dependence requires that researchers control for unmeasuredpersistent heterogeneity. Frequently, random effects probit models havebeen employed, which, while user-friendly, make a strong parametricassumption that the unobserved heterogeneity in the population follows anormal distribution. Although semiparametric alternatives to the randomeffects probit model have recently appeared in the literature to avoid thisproblem, in this paper we return to reconsider the fully parametric model. Viasimulation evidence, we first show that the random effects probit modelproduces biased estimates as the departure of heterogeneity from normalitybecomes more substantial. Using the 1958 Philadelphia cohort data, we thencompare the results from a random effects probit model with a semiparametricprobit model and a fixed effects logit model that makes no assumptions aboutthe distribution of unobserved heterogeneity. We found that with this dataset all three models converged on the same substantive result—evenafter controlling for unobserved persistent heterogeneity, with models thattreat the unobserved heterogeneity very differently, prior conduct had apronounced effect on subsequent offending. These results are inconsistentwith a model that attributes all of the positive correlation between priorand future offending to differences in criminal propensity. Sinceresearchers will often be completely blind with respect to the tenabilityof the normality assumption, we conclude that different estimationstrategies should be brought to bear on the data.     

Mixed effects modelling for glass category estimation from glass refractive indices

520 Glass fragments were taken from 105 glass items. Each item was either a container, a window, or glass from an automobile. Each of these three classes of use are defined as glass categories. Refractive indexes were measured both before, and after a programme of re-annealing. Because the refractive index of each fragment could not in itself be observed before and after re-annealing, a model based approach was used to estimate the change in refractive index for each glass category. It was found that less complex estimation methods would be equivalent to the full model, and were subsequently used. The change in refractive index was then used to calculate a measure of the evidential value for each item belonging to each glass category. The distributions of refractive index change were considered for each glass category, and it was found that, possibly due to small samples, members of the normal family would not adequately model the refractive index changes within two of the use types considered here. Two alternative approaches to modelling the change in refractive index were used, one employed more established kernel density estimates, the other a newer approach called log-concave estimation. Either method when applied to the change in refractive index was found to give good estimates of glass category, however, on all performance metrics kernel density estimates were found to be slightly better than log-concave estimates, although the estimates from log-concave estimation prossessed properties which had some qualitative appeal not encapsulated in the selected measures of performance. These results and implications of these two methods of estimating probability densities for glass refractive indexes are discussed.     

How empirical uncertainties influence the stability of climate coalitions

International climate agreements are negotiated in the face of uncertainties concerning the costs and benefits of abatement and in the presence of incentives for free-riding. Numerical climate coalition models provide estimates of the challenges affecting cooperation, but often resort to assuming certainty with respect to the values of model parameters. We study the impact of uncertainty on the stability of coalitions in the Model of International Climate Agreements using the technique of Monte Carlo analysis. We extend the existing literature by (1) calibrating parametric uncertainty about damages and abatement costs to estimates from meta-studies and by (2) explicitly considering uncertainty in the curvature of the damage function. We find that stability is more sensitive to uncertainty in damages than in abatement costs and most sensitive to uncertainty about the regional distribution of damages. Our calculations suggest that heterogeneity can increase stability of coalitions; however, this depends on the availability of transfers.     

Do Harsher Prison Conditions Reduce Recidivism? A Discontinuity-based Approach

We estimate the causal effect of prison conditions on recidivismrates by exploiting a discontinuity in the assignment of federalprisoners to security levels. Inmates housed in higher securitylevels are no less likely to recidivate than those housed inminimum security; if anything, our estimates suggest that harsherprison conditions lead to more post-release crime. Though smallsample sizes limit the precision of our estimates, we arguethat our findings may have important implications for prisonpolicy, and that our methodology is likely to be applicablebeyond the particular context we study.     

Racial and Ethnic Heterogeneity,Economic Disadvantage,and Gangs: A Macro‐Level Study of Gang Membership in Urban America

There is a lack of macro‐level gang research. The present study addresses this shortcoming by providing a theoretically informed analysis of gang membership in large US cities. More specifically, our goal is to determine whether racial and ethnic heterogeneity conditions the relationship between economic disadvantage and gang membership. Three separate sources of data are used in this study: U.S. Census 2000, Law Enforcement Management and Administrative Services 2000, and National Youth Gang Survey 2002–2006. A series of weighted least‐squares regression models are estimated, finding that both economic disadvantage and racial and ethnic heterogeneity exhibit independent and additive effects on gang membership. In addition, the results show that racial and ethnic heterogeneity has a conditioning relationship with economic disadvantage. Furthermore, our expanded operationalization of the Blau heterogeneity measure indicates that prior research may have underestimated the effects of heterogeneity. The authors discuss these findings in the context of existing gang research and offer directions for future research.     

MERCOSUR enlargement: predicting the effects on trade in primary agricultural commodities

This article estimates the effects of a hypothetical enlargement of the Mercado Común del Sur (MERCOSUR), depicted by granting MERCOSUR associate member countries a full membership status, on trade in primary agricultural commodities. The empirical investigation is implemented through the multiplicative form of the gravity model. The estimated gravity parameters are used to carry out scenarios that predict the effects of MERCOSUR enlargement on trade in primary agricultural commodities. The predictions reveal that MERCOSUR enlargement would generate significant increases in exports from the full members to the associate members. The predictions also show relatively smaller increases in exports from the associate members to the full members (in value terms), but some important increases in trade among the associate members. Trade diversion effects of MERCOSUR enlargement, which influence trade between full members and from non-member countries to full and associate members, are found to be limited.     

Dynamics of Polarizing Rhetoric in Congressional Tweets

Affective polarization is pervasive in modern US politics, and can be intensified by strategic messaging from members of Congress. But there are gaps in our knowledge of the dynamics of polarizing appeals from elected representatives on social media. We explore the usage of polarizing rhetoric by members of Congress on Twitter using the 4.9 million tweets sent by members of Congress from 2009 to 2020, coded for the presence of polarizing rhetoric via a novel and highly accurate application of supervised machine learning methods. Fitting with our expectations, we find that more ideologically extreme members, those from safer districts, and those who are not in the president’s party are more likely to send polarizing tweets, and that polarizing tweets garner more engagement, increasing campaign funding for more polarizing members.     

A Meta-Analysis of Race and Sentencing Research: Explaining the Inconsistencies

Numerous studies have addressed the question: Are African-Americans treated more harshly than similarly situated whites? This research employs meta-analysis to synthesize this body of research. One-hundred-sixteen statistically independent contrasts were coded from 71 published and unpublished studies. Coded study and contextual features are used to explain variation in research findings. Analyses indicate that African-Americans generally are sentenced more harshly than whites; the magnitude of this race effect is statistically significant but small and highly variable. Larger estimates of unwarranted disparity are found in contrasts that examine drug offenses, imprisonment or discretionary decisions, do not pool cases from several smaller jurisdictions, utilize imprecise measures, or omit key variables. Yet, even when consideration is confined to those contrasts employing key controls and precise measures of key variables, unwarranted racial disparities persists. Further, a substantial proportion of variability in study results is explained by study factors, particularly methodological factors.     

Group Size, Group Heterogeneity, and Voting Rules: An Application of the Buchanan-Tullock Model to the European Union

In The Calculus of Consent, Buchanan and Tullock develop a theory of voting rules in which the optimal rule is determined by minimizing the sum of voters' external and decision costs. Other researchers have extended the Buchanan-Tullock model to include the effects of group size and heterogeneity on external and decision costs and, subsequently, on the optimal voting rule. Despite the prominence of the Buchanan-Tullock model in the constitutional, legal, and public-choice literature, their theory has not (to our knowledge) been tested. In this paper we test the Buchanan-Tullock model by examining the establishment and evolution of voting rules in the European Union. Over the past four decades, the European Union has experienced significant changes in number and heterogeneity, and we interpret the general movement towards, and call for, less inclusive voting rules as support for Buchanan and Tullock's original theory.     

试论入境发展黑社会组织成员罪

入境发展黑社会组织成员犯罪是黑社会性质组织犯罪中的一种重要类型，刑法第二百九十四条第二款规定了“入境发展黑社会组织成员罪”的罪名。该罪所侵犯的客体是我国正常的经济、社会生活秩序；在客观方面表现为行为人到中华人民共和国境内发展境外黑社会组织成员的行为；其犯罪主体只能是境外黑社会组织的人员；在主观方面表现为直接故意。广东省地处改革开放的前沿，是全国受境外黑社会组织渗透影响最早和最严重的地区，鉴于入境发展黑社会组织成员犯罪危害的严重性，必须完善对该犯罪行为的立法，加强打击的准确性和力度。     

DO CELLMATES MATTER? A CAUSAL TEST OF THE SCHOOLS OF CRIME HYPOTHESIS WITH IMPLICATIONS FOR DIFFERENTIAL ASSOCIATION AND DETERRENCE THEORIES

In the schools of crime hypothesis, social interactions between inmates are assumed to produce criminogenic rather than deterrent prison peer effects, thus implicating them in the persistence of high recidivism rates and null or criminogenic prison effects. We assess the validity of the schools of crime hypothesis by estimating prison peer effects that result from differential cellmate associations in a male, first‐time release cohort from the Pennsylvania Department of Corrections. To isolate causal prison peer effects in the presence of essential heterogeneity, we use a semiparametric local instrumental variables estimation strategy. Our results do not support the school of crime hypothesis. In our sample, prison peer effects produced in interaction with more criminally experienced cellmates are always null or deterrent rather than criminogenic. Although we do not explicitly test for the operant conditioning mechanisms theorized to underlie social influence in the context of differential association, we argue that, under the assumption that the differential association context relates positively to the direction of peer influence, our universally noncriminogenic estimates exclude direct reinforcement, vicarious reinforcement, and direct punishment as potential drivers of prison peer effects produced in interaction with more criminally experienced cellmates. Our results support the assertion that operant conditioning mechanisms connect differential association and deterrence theories.     

Modeling the Deviant Y in Criminology: An Examination of the Assumptions of Censored Normal Regression and Potential Alternatives

Many dependent variables of criminological interest have censored distributions. Investigations that use such variables increasingly have turned to the Tobit model, a censored regression technique that is specified based on a latent dependent variable. When used under suitable circumstances, this model provides appropriate estimates. This paper discusses key assumptions of the Tobit model. It then highlights the risk of violating these assumptions and reviews alternative flexible parametric and semiparametric modeling techniques, currently used sparingly in criminology, which researchers may find helpful when assumptions regarding the error terms are untenable. By using an empirical example focused on sentencing outcomes and comparing estimates across analytic methods, this study illustrates the potential utility of simultaneously estimating the Tobit model along with some alternatives.

Diversity and heterogeneity in mitochondrial DNA of North American populations

Variation in the mitochondrial DNA (mtDNA) control region as detected by sequence-specific oligonucleotide (SSO) probes is described for 2282 individuals from African-American, European-American, and Hispanic subpopulations from five broadly defined regions of North America (Northeast, Southeast, Central, Northwest, Southwest). Population diversity estimates were uniformly high for all subpopulations and for each major ethnic group. Only the Pennsylvania Hispanic group was remarkable with respect to its mitochondrial DNA types, having both six low frequency population specific types (ranging from 1.2-8.6%) and three high frequency shared types (10-20% each). There was no statistically significant subpopulation heterogeneity present within any of the three major groups at either the subpopulation level or the regional level (p > 0.01). However, statistically significant heterogeneity was measured when comparing the three major groups to each other, with the variance component attributable to this large division accounting for 18.60% of the total variance (p < 0.001). Overall mtDNA is a satisfactory forensic typing locus within broadly defined African-American, European-American, and Hispanic groups from North America, based on the high diversity estimates and absence of heterogeneity, as characterized by SSO typing.     

Plea Discounts or Trial Penalties? Making Sense of the Trial-Plea Sentence Disparities

Abstract

There is a consensus that defendants who plead guilty generally receive less harsh sentences than similarly-situated defendants convicted at trial. However, there is less consensus on how to characterize this disparity in the sentence. Some researchers refer to the disparities as “trial penalties,” whereas others refer to them as “plea discounts.” We contend that the two terms have different theoretical backgrounds and underlying assumptions. As a result, the theories require different modeling strategies, and can lead to different predictions on the relationship between the disparity and some key case characteristics.

We start by differentiating the two perspectives theoretically. We then present an empirical analysis on defendants in New York State to substantiate our theoretical arguments. We demonstrate that the estimates of the trial-plea disparities depend on the assumption of the default, as estimates of the trial penalties differed considerably from the estimates of the plea discounts.     

Agenda Control in Congress: Evidence from Cutpoint Estimates and Ideal Point Uncertainty

This article develops two new tests of partisan and nonpartisan theories of lawmaking based on cutpoint estimates and measures of uncertainty about ideal point estimates. Theories of congressional organization make explicit predictions about the absence of cutpoints in certain intervals of the policy space. We test these theories with new cutpoint estimates and exploit the fact that the ideal points of members located far from the density of cutpoints are necessarily estimated with less precision. We validate our empirical approach through simulations, and we test three models of congressional organization using House roll call data from the 86th through the 110th Congresses (1959–2008). We find strong evidence of partisan agenda control. Our findings exhibit modest differences from the results predicted by Cox and McCubbins's party cartel theory: negative agenda control increases over time and is negatively correlated with the size of the blockout region.     

Using the Arrestee Drug Abuse Monitoring (ADAM) program to further understand the relationship between drug use and gang membership

In this paper, we examine the relationship between drug use and gang membership using data from the Arizona Arrestee Drug Abuse Monitoring (ADAM) program, which collects both self‐report and hard measures (i.e., urinalysis) of drug use. Our analyses revealed that self‐reported recent drug use (i.e., drug use in the past three days) and urinalysis outcomes were similarly associated with the gang‐membership variables. These findings suggest that self‐reported data obtained from gang members is a particularly robust method for gathering information on their recent behavior. Additionally, our results were supportive of the social facilitation model, showing that current gang members were significantly more likely to use marijuana and cocaine compared with former gang members. The implications for policy and future research are discussed.     

Emerging Economies,Turnover Rates and Inflation Variability: A Comparison of Generalized Maximum Likelihood and SUR Models

Theoretical exposition and empirical evidence in central bank independence (CBI) literature confirm an inverse relationship between inflation and measures of CBI mostly in developed economies. Based on this ex ante information on CBI-inflation tradeoff, this paper proposes two functional forms for the diagonal and off diagonal elements in the residual covariance matrix in the estimation process. The proposed functional forms are used in a generalized maximum likelihood and then in a generalized least squares (GLS) (with the restricted covariance matrix) framework for the empirical test. The results are compared to the outcome of an SUR model (unrestricted). The tests involve 14 emerging economies and covers the period 1960–1990. Compared to SUR, majority of results of GLS model in samples with and without outliers provide stronger and more significant evidence confirming the CBI-inflation tradeoff. Notably, the standard errors of the GLS estimates are lower than that of the SUR estimates. Without outliers, the GLS estimates show even lower standard errors as compared to the outcome of the SUR model. Low standard errors provide baseline indication of more accurate estimates. This research has benefited from comments from David Tufte, and Gerald Whitney and contributions by participants at the colloquia series at the Montclair State University and the University of New Orleans.     

Prison gang integration and inmate violence

Purpose

Gang membership has been linked in a number of prior studies with inmate misconduct; known gang members are more prone than non-gang members to act violently behind bars. Theories of intergroup conflict suggest, however, there is reason to expect that broader within-prison gang dynamics, not just gang membership alone, are associated with the incidence of violence.

Methods

We collected data on inmates from a large southern state and estimated multilevel models of inmate-on-inmate violence. Included in our models were a variety of common individual-level correlates of violent misconduct, among them gang membership. Substantive prison-level correlates included the percentage of gang members and “gang integration,” the latter being a measure of gang heterogeneity.

Results

We found a modest association between both gang variables and inmate-on-inmate violence, with gang integration being the most significant of the two.

Conclusions

Gang membership is an important correlate of inmate violence, but attention to broader prison gang dynamics is clearly necessary. We discuss the implications of this finding for theories of inmate violence.     

How management team composition affects academic spin-offs’ entrepreneurial orientation: the mediating role of conflict

This research has been designed with the purpose of analysing the influence of both the composition of academic spin-offs’ management teams and the potential conflict emerged within such teams on the degree of entrepreneurial orientation exhibited by academic spin-offs. To this end, we have used the upper echelon theory as theoretical background and have analysed the impact of the proportion of non-academic managers within management teams, the heterogeneity of such teams with respect to the age and main educational area of their managers, as well as the potential mediating role of the conflict emerged. Drawing on a database of 167 Spanish academic spin-offs, results show, on the one hand, that the presence of non-academic managers within management teams is a key factor in the academic spin-offs' exhibitions of higher levels of entrepreneurial orientation and, on the other hand, that conflict fully mediates the relationship between management teams' age heterogeneity and entrepreneurial orientation.