Foreclosure,vacancy and crime

This paper examines the impact of residential foreclosures and vacancies on violent and property crime. To overcome confounding factors, a difference-in-difference research design is applied to a unique data set containing geocoded foreclosure and crime data from Pittsburgh, Pennsylvania. Results indicate that while foreclosure alone has no effect on crime, violent crime rates increase by roughly 19% once the foreclosed home becomes vacant – an effect that increases with length of vacancy. We find weak evidence suggesting a potential vacancy effect for property crime that is much lower in magnitude.     

论网络犯罪及其防治对策

网络犯罪是现代社会的一种新型犯罪,它包括以网络为对象的犯罪和以网络为犯罪现场的犯罪。它具有技术含量高、危害程度重、涉及面广、隐蔽性强等特征。因此,打击该种犯罪,要完善法律法规,加强网络犯罪立法的国际协调与合作。     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Poverty and crime in 19th century Germany

We estimate the impact of poverty on crime in 19th century Bavaria, Germany. Rainfall is used as an instrumental variable for grain (rye) prices to address econometric identification problems in the existing literature. The rye price was a major determinant of living standards during this period. The rye price has a positive effect on property crime: a one standard deviation increased property crime by 8%. OLS estimates are twice as large as instrumental variable estimates, highlighting the value of our empirical approach. Higher rye prices lead to significantly less violent crime, though, and we argue that higher beer prices, caused by the higher rye prices, are a likely explanation.     

MODEL AVERAGING IN ECONOMICS: AN OVERVIEW

Standard practice in empirical research is based on two steps: first, researchers select a model from the space of all possible models; second, they proceed as if the selected model had generated the data. Therefore, uncertainty in the model selection step is typically ignored. Alternatively, model averaging accounts for this model uncertainty. In this paper, I review the literature on model averaging with special emphasis on its applications to economics. Finally, as an empirical illustration, I consider model averaging to examine the deterrent effect of capital punishment across states in the USA.     

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。     

会计职务犯罪的心理学分析

近年来有的财会人员经不住种种诱惑,采取非法手段,侵害国家、集体和人民的利益,其突出表现为做假帐。本文从心理学的角度分析了会计职务犯罪产生的因素,会计职务犯罪对社会的危害。最后,对如何预防会计职务犯罪,运用制度经济学原理从宏观和微观两方面提出了建议。     

Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions

This study performs the challenging task of examining the forecastability behavior of the stock market returns for the Dow Jones Islamic Market (DJIM) and the Dow Jones Industrial Average (DJIA) indices, using non-parametric regressions. These indices represent different markets in terms of their institutional and balance sheet characteristics. The empirical results posit that stock market indices are generally difficult to predict accurately. However, our results reveal some point forecasting capacity for a 15-week horizon at the 95 per cent confidence level for the DJIA index, and for nine-week horizon at the 99 per cent confidence for the DJIM index, using the non-parametric regressions. On the other hand, the ratio of the correctly predicted signs (the success ratio) shows a percentage above 60 per cent for both indices which is evidence of predictability for those indices. This predictability is however statistically significant only four-weeks ahead for the DJIM case, and twelve weeks ahead for the DJIA as their respective success ratios differ significantly from the 50 percent, the expected percentage for an unpredictable time series. In sum, it seems that the forecastability of DJIM is slightly better than that of DJIA. This result on the forecastability of DJIM adds to its other findings in the literature that cast doubts on its suitability in hedging and asset allocation in portfolios that contain conventional stocks.     

Blowing it up and knocking it down: The local and city-wide effects of demolishing high concentration public housing on crime

This paper estimates the effect that the closure and demolition of roughly 20,000 units of geographically concentrated high-rise public housing had on crime in Chicago. We estimate local effects of closures on crime in the neighborhoods where high-rises stood and in proximate neighborhoods. We also estimate the impact that households displaced from high-rises had on crime in the neighborhoods to which they moved and neighborhoods close to those. Overall, reductions in violent crime in and near the areas where high-rises were demolished greatly outweighed increases in violent crime associated with the arrival of displaced residents in new neighborhoods.     

Marked point process hotspot maps for homicide and gun crime prediction in Chicago

Crime hotspot maps are a widely used and successful method of displaying spatial crime patterns and allocating police resources. However, hotspot maps are often created over a single timescale using only one crime type. In the case of short-term hotspot maps that utilize several weeks of crime data, risk estimates suffer from a high variance, especially for low frequency crimes such as homicide. Long-term hotspot maps that utilize several years of data fail to take into account near-repeat effects and emerging hotspot trends. In this paper we show how point process models of crime can be extended to include leading indicator crime types, while capturing both short-term and long-term patterns of risk, through a marked point process approach. Several years of data and many different crime types are systematically combined to yield accurate hotspot maps that can be used for the purpose of predictive policing of gun-related crime. We apply the methodology to a large, open source data set which has been made available to the general public online by the Chicago Police Department.     

Fear of crime and housing prices: Household reactions to sex offender registries

Megan's Law requires public dissemination of information from sex offender registries. Opponents to this controversial law have questioned whether households misinterpret or even use this information. One concern was that the information might simply induce a “fear of crime.” This study finds evidence for both use and misinterpretation of the publicly available information on sex offenders. Using a unique dataset that tracks sex offenders in Hillsborough County, Florida, the results indicate that after a sex offender moves into a neighborhood, nearby housing prices fall by 2.3% ($3500 on average). However, once a sex offender moves out of a neighborhood, housing prices appear to immediately rebound. Surprisingly, these price impacts do not appear to differ in areas near high risk offenders labeled as “predators.”     

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).     

Modelling and predicting recorded property crime trends in England and Wales—a retrospective

In 1999 the Home Office published, for the first time ever, 3-year ahead projections of property crime in England and Wales. The projections covered the period 1999–2001 and indicated strong upward pressure after five full years of falling crime. This pressure was generated by three factors: the number of young men in the general population, the state of the economy and the fact that property crime appeared to be well below its underlying trend level. The projections received a mixed response, with some agreeing that crime was set to rise while questioning the scale of any increase, to others who doubted the value of this type of econometric modelling. In fact, property crime did increase in 1999, although not at the rate suggested by the models—and indeed levels of burglary continued to fall. This paper addresses some of the reasons for this disparity as well as considering various criticisms of the Home Office approach.     

Understanding the perpetration of employee computer crime in the organisational context

While hackers and viruses fuel the IS security concerns for organisations, the problems posed by employee computer crime should not be underestimated. Indeed, a number of IS security researchers have turned their attention to the ‘insider’ threat. Of this group, several focus on the offender, either in terms of a series of attributes required for perpetration, or with reference to forms of safeguards aimed at negating such behaviour. These studies are complemented by those texts which examine the organisational context in which rogue employees commit computer crime. Currently, however, there has been a lack of insight into the relationship between the offender and the context, during the commission process. To address this deficiency, two criminological theories are advanced. This paper illustrates how the theories, entitled the Rational Choice Perspective and Situational Crime Prevention, can be applied to the IS domain, thereby offering a theoretical basis by which to analyse the offender/context relationship during perpetration. By so doing, practitioners may use these insights to inform and enhance the selection of safeguards in a bid to improve prevention programmes. Furthermore, the importation of the Rational Choice Perspective and Situational Crime Prevention into the IS field opens up potentially new areas for future research.     

Tradable and non-tradable expenditure and aggregate demand for imports in an emerging market economy

Using an assumption of non-separability of non-tradable expenditure from imports, this paper examines the impact of the structural transformation undertaken after 2001 on imports and, in turn, the current account stance of Turkey. In this regard, an import demand function is derived under the assumption of non-separability, and is estimated using quarterly time series data from Turkey. The empirical results show that the assumption of non-separability cannot be rejected in the case of Turkey and the relative prices of non-tradable and tradable goods must be among the determinants of import demand in addition to the relative price of imports and real GDP. This result accordingly implies that recent increases in import expenditure are, to some extent, due to changes in the relative price structure in favour of non-tradable goods.     

Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility

Using monthly data from 1973 through 2020, we explore whether it is possible to improve the accuracy of one-month ahead log-aggregate equity return realized volatility point forecasts by conditioning on various nonlinear crude oil price measures widely relied on in the literature. When evaluating the evidence of unconditional relative equal predictive ability as specified in Diebold and Mariano (1995), we observe that similar to well-known economic variables, such as the dividend yield, the default yield spread and the rate of inflation, we rarely observe evidence of statistical gains in relative point forecast accuracy in favor of the crude oil price-based models. However, when evaluating the evidence of conditionalrelative equal predictive ability as specified in Giacomini and White (2006), we observe that contrary to well-known economic predictors, certain nonlinear crude oil price variables, such as the one-year net crude oil price increase suggested in Hamilton (1996) offer sizable point forecast accuracy gains relative to the benchmark. These statistical gains can also be translated into economic gains.