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This book is about how public funds and human resources can be allocated to optimize the control of crime in a modern democratic society. The authors build a model of crime generation, and control - through the imposition of sanctions - that provides insight into alternatives for social policy-makers. Econometric techniques are used to analyze policy issues such as: establishing control policies; determining monetary measures of the seriousness of crime; discerning community priorities for fighting crime; choosing between alternative drug-control programs; and extracting useful information from crime data. These techniques are also used to determine: the effect of economic opportunities for youth on crime rates; the influence of rising crime rates on police effectiveness; the cost of police effectiveness; and the possibility for deterring violence. Additional issues examined are: the effect of handgun control on homicide rates; the relative merits of jail and probation; the rate of police manpower growth needed to keep pace with crime rates; and the necessary data needed for planning an optimum level of public safety. The analysis starts with single-equation estimations and builds to system and multi-equation models. The statistical results are based on several data sets with the earlier studies using time series from the 1950's and 1960's. The estimation of the more complex model is based on cross-sectional data from the 1960 and 1970 census for the counties of California.
This title presents a survey of the crime problem in Latin America, which takes a very broad and appropriately reductionist approach to analyse the determinants of the high crime levels, focusing on the negative social conditions in the region, including inequality and poverty, and poor policy design, such as relatively low police presence. The chapters illustrate three channels through which crime might generate poverty, that is, by reducing investment, by introducing assets losses, and by reducing the value of assets remaining in the control of households.
This book deals with the widespread economic and financial crime issues of corruption, the shadow economy and money laundering. It investigates both the theoretical and practical aspects of these crimes, identifying their effects on economic, social and political life. This book presents these causes and effects with a state of the art review and with recent empirical research. It compares the international and transnational aspects of these economic and financial crimes through discussion and critical analysis. This volume will be of interest to researchers and policy makers working to study and prevent economic and financial crime, white collar crime, and organized crime.
This innovative collection of original essays showcases the use of social networks in the analysis and understanding of various forms of crime. More than any other past research endeavor, the seventeen chapters in this book apply to criminology the many conceptual and methodological options from social network analysis. Crime and Networks is the only book of its kind that looks at the use of networks in understanding crime, and can be used for advanced undergraduate and beginner’s graduate level courses in criminal justice and criminology.
Wide ranging and accessible, this is the most up-to-date textbook in this area, taking current economic research and making it accessible to undergraduates and other interested readers.
Finite mixture distributions are a weighted average of a finite number of distributions. The latter are usually called the mixture components. The weights are usually described by a multinomial distribution and are sometimes called mixing proportions. The mixture components may be the same type of distributions with di®erent parameter values but they may also be completely different distributions. Therefore, finite mixture distributions are very °exible for modeling data. They are frequently used as a building block within many modern econometric models. The specification of the mixture distribution depends on the modeling problem at hand. In this thesis, we introduce new applications of finite mixtures to deal with several di®erent modeling issues. Each chapter of the thesis focusses on a specific modeling issue. The parameters of some of the resulting models can be estimated using standard techniques but for some of the chapters we need to develop new estimation and inference methods. To illustrate how the methods can be applied, we analyze at least one empirical data set for each approach. These data sets cover a wide range of research fields, such as macroeconomics, marketing, and political science. We show the usefulness of the methods and, in some cases, the improvement over previous methods in the literature.