Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

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Preventing Crime: What Works, What Doesn't, What's Promising. Research in Brief. National Institute of Justice.

Crime fell sharply in the United States in the 1990s, in all categories of crime and all parts of the nation. Homicide rates plunged 43 percent from the peak in 1991 to 2001, reaching the lowest levels in 35 years. The Federal Bureau of Investigation’ s (FBI) violent and property crime indexes fell 34 and 29 percent, respectively, over that same period. These declines occurred essentially without warning: leading experts were predicting an explosion in crime in the early and mid-1990s, precisely the point when crime rates began to plunge. Although experts failed to anticipate the decline, there has been no shortage of hypotheses to explain the drop in crime after the fact. Table 1 presents a tally of a Lexis-Nexis search of the most frequently cited reasons for the crime decline in articles in major newspapers over the period 1991‐ 2001. The single most frequent explanation given is the innovative policing strategies put into place. The crime decline is also frequently attributed to increased imprisonment, changes in the market for crack cocaine, the aging of the population, tougher gun control laws, the strong economy and increases in the number of police. In this paper, I attempt to sort out why crime declined in the 1990s. I begin with a review of the facts. I then analyze the leading explanations for why crime fell, looking at possible determinants that changed in some substantial way in the 1990s. Most of the supposed explanations listed in Table 1 actually played little direct role in the crime decline, including the strong economy of the 1990s, changing demographics, better policing strategies, gun control laws, concealed weapons laws and increased use of the death penalty. Four factors, however, can account for virtually

/pdf/understanding-why-crime-fell-in-the-1990s-four-factors-that-ek5935ldqf.pdf

Understanding Why Crime Fell in the 1990s: Four Factors that Explain the Decline and Six that Do Not

In 1996, a Federal law required the U.S. Attorney General to provide Congress with an independent review of the Many crime prevention programs work. Others don’t. Most programs have not yet been evaluated with enough scientific evidence to draw conclusions. Enough evidence is available, however, to create provisional lists of what works, what doesn’t, and what’s promising. Those lists will grow more quickly if the Nation invests more resources in scientific evaluations to hold all crime prevention programs accountable for their results. Issues and Findings

Preventing Crime: What Works, What Doesn't, What's Promising

How does access to this work benefit you? Let us know! Follow this and additional works at: http://academicworks.cuny.edu/jj_pubs Part of the Courts Commons, Criminal Law Commons, Criminal Procedure Commons, Criminology Commons, Judges Commons, Law and Politics Commons, Law and Society Commons, Law Enforcement and Corrections Commons, Legislation Commons, Politics and Social Change Commons, and the Race and Ethnicity Commons

/pdf/the-growth-of-incarceration-in-the-united-states-exploring-212x90j52g.pdf

The growth of incarceration in the United States: exploring causes and consequences

We address several issues concerning standard error bias in pooled time-series cross-section regressions. These include autocorrelation, problems with unit root tests, nonstationarity in levels regressions, and problems with clustered standard errors. We conduct unit root test for crimes and other variables. We use Monte Carlo procedures to illustrate the standard error biases caused by the above issues in pooled studies. We replicate prior research that uses clustered standard errors with difference-in-differences regressions and only a small number of policy changes. Standard error biases in the presence of autocorrelation are substantial when standard errors are not clustered. Importantly, clustering greatly mitigates bias resulting from the use of nonstationary variables in levels regressions, although in some circumstances clustering can fail to correct for standard error biases due to other reasons. The “small number of policy changes” problem can cause extreme standard error bias, but this can be corrected using “placebo laws”. Other biases are caused by weighting regressions, having too few units, and having dissimilar autocorrelation coefficients across units. With clustering, researchers can usually conduct regressions in levels even with nonstationary variables. They should, however, be leery of pitfalls caused by clustering, especially when conducting difference-in-differences analyses.

Clustering and Standard Error Bias in Fixed Effects Panel Data Regressions

The Local Law Enforcement Block Grants (LLEBG) Program is clearly an important attempt by the U.S. government to reduce crime. Whether it accomplished this goal is a major policy issue, and Worrall’s (2008, this issue) study is potentially among the most important criminology policy studies. Moreover, Worrall’s finding that the LLEBG Program reduced crime is evidence that federal involvement in crime prevention is a worthwhile addition to state and local efforts. For criminology, an equally important underlying policy concern is as follows: The implications of research have minimal policy importance if they do not influence government decisions. Why would a policy maker give this study credibility, other than to support preconceived positions? Should it prompt criminologists to advise policy makers to reinstate the LLEBG Program? The problem, according to Austin (2003), is the poor quality of criminology research. The traditional quality control mechanisms—peer review and replication—are imperfect. Errors can elude referees, and their opinions of articles published are often mixed. Replications in criminology can be difficult and, when done, often reach inconsistent results. The most common replications are reanalyses of the original data. Researchers should make data and regression programs available, preferably on the Internet; otherwise, a study has little credibility because it cannot be easily checked. Broader replications, using new data sets, are especially valuable but time consuming. Austin (2003) argues that research findings should not have policy implications unless replicated. But this argument is partly inconsistent with his argument that research is generally poor; the failure

Can and should criminology research influence policy? suggestions for time-series cross-section studies*

This is a critical review of the literature concerning the impact of prison populations on crime. It summarizes 44 time series studies that use prison population in the crime equation, emphasizing problems of simultaneity and disaggregation bias. It briefly reviews studies that estimate the incapacation impact of prisons by using criminals' individual crime rates, emphasizing problems caused by skewness of the crime rates and their relationship with arrest rates. Almost all the numerous problems with prior research bias results towards finding that prisons have limited impacts, and once the problems are addressed the best estimate of the elasticity of prison populations on crime is about 1.0.

Prison Population and Crime

"Right-to-Carry" (RTC) concealed-handgun laws mandate that authorities issue concealed handgun permits to qualified applicants. A driving force behind these laws is that allowing more citizens to legally carry guns in public places can reduce violent crime by deterring prospective criminals afraid of encountering armed civilians. Critics of the laws argue that violent altercations are more likely to turn deadly when more people carry guns. Whether the laws cause violent crime to increase or to decrease has become an important public policy question, as 33 states have now adopted such legislation. The present study evaluates Florida's 1987 RTC law, which has served as a model for other states adopting RTC laws, and prior research suggests plays a key role in the RTC debate. Specifically, we use panel data for 58 Florida counties from 1980 to 2000 to directly examine the effects on violent crime from increases in the number of people with concealed-carry permits, rather than the binary dummy variable used in prior research. We also address many of the methodological problems encountered in earlier RTC studies. We present numerous model specifications, and find little evidence that the law reduces or increases violent crime.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID321820_code020901500.pdf?abstractid=321820&mirid=5&type=2

Right-to-Carry Concealed Handguns and Violent Crime: Crime Control Through Gun Decontrol?

Although studies examining the relationship between prison populations and homicide rates find significant negative impacts, no published studies have examined the impact of prison releases on homicide. The issue here is whether release rates directly affect crime, independently of any impact they might have through their impact on prison populations. We examine this question by regressing homicide rates on prison release rates, prison population, and numerous control variables using panel data for 46 states from 1975 to 1999. The results provide no evidence of a significant positive relationship between prison releases and homicide. Similar to prior studies, however, we found that prison population growth has greatly reduced homicide rates. The main policy implication of these findings is that those leaving prison have no greater propensity to commit homicide than those entering or remaining in prison.

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Thomas B. Marvell

Papers

Clustering and Standard Error Bias in Fixed Effects Panel Data Regressions

Can and should criminology research influence policy? suggestions for time-series cross-section studies*

Prison Population and Crime

Right-to-Carry Concealed Handguns and Violent Crime: Crime Control Through Gun Decontrol?

When Prisoners Get Out: The Impact of Prison Releases on Homicide Rates, 1975-1999