Recent developments in school social control

In the U.S., decisions regarding social control are increasingly modeled on two dominant institutions: the criminal justice and medical/healthcare systems. Sociologists and other scholars refer to this adoption of legal and/or medical terminology and technologies as criminalization and medicalization. These models of social control are particular evident in how America defines and manages child behavior. Public schools borrow from both the criminal justice and medical systems as part of the routine educational setting. In this article, I provide the first synthesis and review of the school criminalization and medicalization literatures. In doing so, I argue that criminalized school social controls provide harsh, repressive responses to student misbehavior, while medicalized school social controls provide rehabilitative and restitutive responses. Given these fundamentally different approaches to student behavior, I argue that the disproportionate use of criminalized and medicalized social control across racial/ethnic groups and children from different socioeconomic backgrounds entrenches inequalities and functions to channel racial/ethnic minorities and poor children into the school‐to‐prison pipeline while keeping socially advantaged children in school and away from the problems associated with criminalized social control.     

Policy-induced Innovation in Sub-Saharan African Agriculture: A Multilateral Malmquist Productivity Index Approach

To meet the [African] crisis one must turn to agricultural-led growth. But, based on historical experience, an agricultural-led strategy must be framed in no less than a twenty-year horizon and must entail a combination of technological innovation, policy reform, and institutional restructuring because each, by itself, is limited. (Eicher, 1989)     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

Solution Procedures for the Lot-Streaming Problem

Lot streaming is the process of splitting a job into sublots so its operations can be overlapped and its progress accelerated. We present a computationally efficient procedure for solving the m-machine, two-sublot problem, and we discuss the bottleneck insights that emerge from the analysis. We also examine heuristic approaches for more than two sublots and discuss computational results for these procedures.     

Bayesian estimation of the efficient frontier

In this paper, we consider the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope parameter, from a Bayesian perspective. Their posterior distribution is derived by assigning the diffuse and the conjugate priors to the mean vector and the covariance matrix of the asset returns and is presented in terms of a stochastic representation. Furthermore, Bayesian estimates together with the standard uncertainties for all three parameters are provided, and their asymptotic distributions are established. All obtained findings are applied to real data, consisting of the returns on assets included into the S&P 500. The empirical properties of the efficient frontier are then examined in detail.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...     

ROUND GIVING: A FIELD EXPERIMENT ON SUGGESTED DONATION AMOUNTS IN PUBLIC‐TELEVISION FUNDRAISING

Direct‐mail fundraisers commonly provide a set of suggested donation amounts to potential donors, in addition to a write‐in option. Standard economic models of charitable fundraising do not predict an impact of suggested amounts on charitable giving. However, our field experiments on direct‐mail solicitations to over 10,000 members of a public television station tell a different story. We find that changing one of the suggested amounts in an ask string from $100 to $95 reduces the number of gifts greater than or equal to $90 by more than 30%. This contrasts with our finding that in three independent comparisons, increasing the entire vector of suggested amounts by 20%–40% reduces the probability of giving by approximately 15%, with little effect on the average size of the gift. Both manipulations lead to a larger proportion of write‐in donations, even as they reduce the number of total gifts. We propose a simple behavioral theory to explain the data: many donors prefer to give round numbers, and donors incur a cognitive cost when choosing to give a nonsuggested amount. (JEL C9, H4)     

Legitimacy without Mobilization? How Social Justice Organizations Defend their Democratic Credentials

Qualitative Sociology - This article examines how and with what consequences social justice organizations defend their legitimacy as democratic representatives, especially in circumstances where...