Search results

CSF Buxmont Academy operates eight school/day treatment programs that use restorative practices, which includes a culture in which restorative characterizes staff interaction with students, and staff-to-staff and student-to-student relationships as well. This chapter presents analyses of the outcome experiences from two waves of discharge cohorts: 919 students during school years 1999–2000 and 2000–2001 and 858 during 2001–2002 and 2002–2003. Outcome measures include program completion rates, changes in self-esteem and anti-social attitudes, and the relationship between the length of program participation and post-release recidivism rates after controlling for individual risk factors. Recidivism rates were significantly related to length of program participation.

We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes, the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, that is, the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm's sequential method, controlling the familywise error rate, the Benjamini–Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real-time forecasting exercise, assessing the predictions of eight macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low-order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high-order components.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

Purpose: This study explores the perspectives of data experts (DXs) and refugees on the algorithms used by law enforcement officers and focuses on emerging insecurities. The authors take police risk-scoring algorithms (PRSA) as a proxy to examine perceptions on algorithms that make/assist sensitive decisions affecting people’s lives.

Methodology/approach: In-depth interviews were conducted with DXs (24) in Estonia and refugees (19) in Estonia and Turkey. Using projective techniques, the interviewees were provided a simple definition of PRSA and a photo to encourage them to share their perspectives. The authors applied thematic analysis to the data combining manual and computer-aided techniques using the Maxqda software.

Findings: The study revealed that the perspectives on PRSA may change depending on the individual’s position relative to the double security paradox surrounding refugees. The use of algorithms for a sensitive matter such as security raises concerns about potential social outcomes, intentions of authorities and fairness of the algorithms. The algorithms are perceived to construct further social borders in society and justify extant ideas about marginalized groups.

Research limitations: The study made use of a small population sample and aimed at exploring perspectives of refugees and DXs by taking PRSA as the case without targeting representativeness.

Originality/value: The study is based on a double security paradox where refugees who escape their homelands due to security concerns are also considered to be national security threats. DXs, on the other hand, represent a group that takes an active role in decisions about who is at risk and who is risky. The study provides insights on two groups of people who are engaged with algorithms in different ways.

We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size distribution allows for conditional heteroskedasticity. Model evaluation focuses on the dynamics of the conditional distribution of returns using density and variance forecasts. Predictive likelihoods provide a period-by-period comparison of the performance of our heterogeneous jump model relative to conventional SV and GARCH models. Furthermore, in contrast to previous studies on the importance of jumps, we utilize realized volatility to assess out-of-sample variance forecasts.

This study focuses on assistant principals, the “forgotten future workforce” of educational leadership. We explored the current landscape of assistant principalship within the context of work performance, including both task and discretionary performance, and the future career aspirations of assistant principals from a cross-national perspective. Specifically, the study aimed to fulfill the following objectives: (a) to identify the factors affecting the task and discretionary performance of assistant principals, (b) to identify the factors affecting three future career aspirations of assistant principals, and (c) to determine whether the influences of these factors differ by national origin. Personal initiative and perceived organizational support (POS) were the independent variables. This study also examined the demographic attributes of the participants and their schools. Two randomly selected samples, which composed of 227 Turkish and 144 American assistant principals were the participants. The data-gathering instrument incorporated the revised versions of the Personal Initiative Scale (Fay & Frese, 2001), the Perceived Organizational Support Scale (Eisenberger, Huntington, Hutchison, & Sowa, 1986), and the School Organizational Citizenship Behavior Scale (DiPaola & Tschannen-Moran, 2001). The findings of the study showed that personal initiative and POS significantly predicted the task performance, discretionary performance, and certain future career aspirations of assistant principals. National origin appeared to be a significantly differentiating factor of the assistant principals' task performances, discretionary performances, and future career aspirations. We drew conclusions and provided suggestions for future research.

Both consumer and corporate credit ratings agencies played a major role in the US subprime mortgage crisis. Equifax, Experian, and TransUnion deployed a formalized scoring system to assess individuals in mortgage origination, mortgage pools then were assessed for securitization by Moody's, S&P, and Fitch relying on expert judgment aided by formal models. What can we learn about the limits of formalization from the crisis? We discuss five problems responsible for the rating failures – reactivity, endogeneity, learning, correlated outcomes, and conflict of interest – and compare the way consumer and corporate rating agencies tackled these difficulties. We conclude with some policy lessons.

Using a national data set from the Early Childhood Longitudinal Study – Birth Cohort, we examined factors associated with approximately 700 young African American boys’ pre-academic skills. The factors examined included (a) family characteristics, behaviors, and beliefs; (b) nonparental care literacy activities; and (c) child health, aggression, and approaches to learning (e.g., curiosity, independence, and persistence). High achieving boys are contrasted with other boys, along the following dimensions: familial, early childhood program, child characteristics and practices and their pre-academic skills, and whether the association was moderated by achievement status. Regression analyses indicated that some aspects of family, preschool, and child characteristics were associated with African American boys’ early outcomes, especially parental caretaking (e.g., bathing and brushing teeth) and approaches to learning (e.g., persistence and attention). Recommendations for educational practices and policies were offered.