PEDAKSI: Methodology for Collecting Data about Survey Non-Respondents

The effects of unit non-response on survey errors are of great concern to researchers.However, direct assessment of non-response bias in survey estimates is rarely possible.Attempts are often made to adjust for the effects of non-response by weighting, but thisusually relies on the use of frame data or external population data, which are at bestmodestly correlated with the survey variables. This paper reports the development ofa method to collect limited survey data from non-respondents to personal interviewsurveys and a large-scale field test of the method on the British Crime Survey (BCS).The method is shown to be acceptable and low cost, to provide valid data, and to haveno detrimental effect on the main survey. The use of the resultant data to estimatenon-response bias is illustrated and some substantive conclusions are drawn for the BCS.     

The effect of reporting errors on the cross-country relationship between inequality and crime

This paper analyzes reporting errors in crime data to see how they impact econometric estimates, particularly of the key relationship between inequality and crime. Criminal victimization surveys of 140,000 respondents in 37 industrial, transition and developing countries are used. Comparing the crimes experienced by these respondents with those reported to the police, non-random and mean-reverting errors are apparent. Time-varying factors affect the propensity of victims to report crimes to the police, undermining the use of country-specific fixed effects as a means of dealing with measurement error in official crime data. These errors substantially attenuate both cross-sectional and panel estimates of the partial correlation between inequality and crime.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

论数据权益的刑法保护——民刑交错视角下的法益保护论探讨

关于“数据权益的刑法保护”问题,在理论层面上,可从“刑法解释论”和“刑法立法论”两种路径进行探讨。“刑法解释论”探讨路径以现行《刑法》罪名体系为出发点,主要任务是揭示哪些罪名的犯罪构成之设置是以“数据权益”为规范保护目的;“刑法立法论”探讨路径是在刑法立法层面上探讨立法者为在刑法政策上实现数据权益的刑法保护这一目的,动用刑罚禁止何种行为类型才具正当性的问题。然而,两种探讨路径都将依赖于“数据权益”的内涵确定以及法益格的认定问题,换言之,是刑法法益理论在数据权益刑法保护问题上的具体运用。《民法典》第127条并非是关于数据的确权性规定,且在民法理论界关于数据权益的法律属性存在诸多争议的状况下,“数据权益”内涵不明、法律属性不清导致其并不具备作为刑法保护客体的“法益”格。当前刑法理论界存在的关于“数据权益”的刑法类法益保护模式探讨路径,是对刑法法益保护方式的误读,并未触及“数据权益刑法保护”之规范层面问题的实质。“数据安全”法益观的探讨路径则不仅混淆了现象层面与规范层面上的“数据犯罪”概念的界分,也由于没有论证“数据安全”如何能够构成刑法的保护法益,而导致结论过于武断。在理论探讨上,应该明确作为行为客体的物理层面上的“数据”概念与作为保护客体的法益层面上的“数据权益”概念之间的区别,据此明确“数据权益的刑法保护”在规范层面上是否构成新问题。     

增设金融行业从业人员背信罪的探讨

金融行业从业人员违背信托责任从事非法行为,对金融秩序和公私财产权造成极大损害,而刑法规制条款的缺位则是该类行为屡有发生的重要原因.由此,设置金融行业从业人员背信罪成为防止类似行为发生的重要措施.由于此类行为的特殊性和典型性,罪名的确定、罪状的设置及刑罚的配置都需要具体分析和深度思考.刑法的谦抑性决定了刑法的最后性和补充性,因此,前置制度的完善与对背信行为的堵疏结合才是治本之策.     

论内幕信息的界定

《最高人民法院、最高人民检察院关于执行<中华人民共和国刑法>确定罪名的补充规定(四)》将《刑法》第180条第4款规定为利用未公开信息交易罪。但该款规定的犯罪对象其他未公开的信息属于我国《证券法》规定的内幕信息的一种,即对该公司证券的市场价格有重大影响的尚未公开的信息。这种信息在其他国家与地区也是作为内幕信息认定的。对于违反我国《刑法》第180条第4款规定的行为,我们可以按照内幕交易罪进行处理。     

严查行贿犯罪之实体进路

行贿直接诱发受贿．是腐败产生的温床,严查行贿犯罪是当前我国遏制腐败的必然要求。从实体法上解决查处行贿犯罪面临的“瓶颈”问题是刑法本身的社会性、刑事程序的僵化性、配套法律的欠缺性和国际条约的普适性之必然选择。为此,我们应该围绕严查的标准和需求,检视现行实体法存在的诸多不足,在此基础上,从罪与刑两个方面共同着力,既要严密法网,扩大行为犯罪化,加大追诉力度,又要疏缓刑罚,实现犯罪轻刑化,增加侦破几率．以此切实达到严查行贿犯罪之目的。     

论我国经济犯罪的刑事政策

当前经济犯罪持续高发,在目前的经济犯罪刑事政策收效甚微的情况下,有必要对其进行反思。文中从刑事司法、刑事立法及犯罪预防三个角度对当前的经济犯罪的刑事政策进行了评析,并提出从这三个方面进行的调整及完善。     

我国上市公司高管犯罪公告的市场反应研究

为了研究我国上市公司高管犯罪的经济后果,本文通过收集1998～2010年我国A股市场63个上市公司高管涉案犯罪的公告,将总体样本分为在职公告样本和离职公告样本,运用事件研究法,从实证的角度分析了上市公司高管犯罪公告的市场反应,通过分析事件窗口期[t-5,t+5]的11个交易日内个股的非正常收益,通过单样本均值t检验、配对样本t检验等显著性检验,发现高管犯罪公告当日产生了负的显著性非正常收益,并且在t和t+1日两天的非正常收益率存在显著性差异。