Vouchers, Public School Response and the Role of Incentives: Evidence from Florida

In this paper, I analyze the behavior of public schools facing vouchers. The literature on the effect of voucher programs on public schools typically focuses on student and mean school scores. This paper tries to go inside the black box to investigate some of the ways in which schools facing the threat of vouchers in Florida behaved. Florida schools getting an 'F' grade are exposed to the threat of vouchers, while vouchers are implemented if they get another 'F' grade in the next three years. Exploiting the institutional details of the 1999 program, I analyze the incentives built into the system and investigate whether the threatened public schools behaved strategically to respond to incentives. There is strong evidence that they did respond to incentives. Using highly disaggregated school level data, a difference- in-differences estimation strategy as well as a regression discontinuity analysis, I find that the threatened schools tended to focus more on students below the minimum criteria cutoffs rather than equally on all, but interestingly, this improvement did not come at the expense of higher performing students. Second, consistent with incentives, they focused mostly on writing rather than reading and math. Finally, consistent with substantial costs associated with such reclassification during that period, there is not much evidence of relative reclassification of low performing students in to special education categories exempt from the calculation of grades. These results are robust to controlling for differential pre-program trends, changes in demographic compositions, mean reversion and sorting. These findings have important policy implications and subsequent grading rule changes in Florida suggest that these policy changes have been a response to public school behavior.Vouchers, Incentives, Strategic Behavior, Regression Discontinuity, Mean Reversion

Evolutionary Optimization of ZIP60: A Controlled Explosion in Hyperspace

The “ZIP” adaptive trading algorithm has been demonstrated to out-perform human traders in experimental studies of continuous double auction (CDA) markets. The original ZIP algorithm requires the values of eight control parameters to be set correctly. A new extension of the ZIP algorithm, called ZIP60, requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results than the original ZIP (called “ZIP8” hereafter), for negligable additional computational costs. A genetic algorithm (GA) is used to search the 60-dimensional ZIP60 parameter space, and it finds parameter vectors that yield ZIP60 traders with mean scores significantly better than those of ZIP8s. This paper shows that the optimizing evolutionary search works best when the GA itself controls the dimensionality of the search-space, so that the search commences in an 8-d space and thereafter the dimensionality of the search-space is gradually increased by the GA until it is exploring a 60-d space. Furthermore, the results from ZIP60 cast some doubt on prior ZIP8 results concerning the evolution of new ‘hybrid’ auction mechanisms that appeared to be better than the CDA

The Arizona Kith and Kin Project Evaluation, Brief #3

Professional Development with Family, Friend, and Neighbor Providers: Implications for Dual Language Learners. Indigo Cultural Center, for the Association for Supportive Child Care, with support from First Things First and Valley of the Sun United Way

Iowa Department of Corrections Performance Plan, FY 2013

Agency Performance Plan, Iowa Department of Correction

Reverse-engineering of polynomial dynamical systems

Multivariate polynomial dynamical systems over finite fields have been studied in several contexts, including engineering and mathematical biology. An important problem is to construct models of such systems from a partial specification of dynamic properties, e.g., from a collection of state transition measurements. Here, we consider static models, which are directed graphs that represent the causal relationships between system variables, so-called wiring diagrams. This paper contains an algorithm which computes all possible minimal wiring diagrams for a given set of state transition measurements. The paper also contains several statistical measures for model selection. The algorithm uses primary decomposition of monomial ideals as the principal tool. An application to the reverse-engineering of a gene regulatory network is included. The algorithm and the statistical measures are implemented in Macaulay2 and are available from the authors

Neonatal abstinence syndrome: Pharmacologic strategies for the mother and infant.

Opioid use in pregnancy has increased dramatically over the past decade. Since prenatal opioid use is associated with numerous obstetrical and neonatal complications, this now has become a major public health problem. In particular, in utero opioid exposure can result in neonatal abstinence syndrome (NAS) which is a serious condition characterized by central nervous system hyperirritability and autonomic nervous system dysfunction. The present review seeks to define current practices regarding the approach to the pregnant mother and neonate with prenatal opiate exposure. Although the cornerstone of prenatal management of opioid dependence is opioid maintenance therapy, the ideal agent has yet to be definitively established. Pharmacologic management of NAS is also highly variable and may include an opioid, barbiturate, and/or α-agonist. Genetic factors appear to be associated with the incidence and severity of NAS. Establishing pharmacogenetic risk factors for the development of NAS has the potential for creating opportunities for personalized genomic medicine and novel, individualized therapeutic interventions

App-based feedback on safety to novice drivers: learning and monetary incentives

An over-proportionally large number of car crashes is caused by novice drivers. In a field experiment, we investigated whether and how car drivers who had recently obtained their driving license reacted to app-based feedback on their safety-relevant driving behavior (speeding, phone usage, cornering, acceleration and braking). Participants went through a pre-measurement phase during which they did not receive app-based feedback but driving behavior was recorded, a treatment phase during which they received app-based feedback, and a post-measurement phase during which they did not receive app-based feedback but driving behavior was recorded. Before the start of the treatment phase, we randomly assigned participants to two possible treatment groups. In addition to receiving app-based feedback, the participants of one group received monetary incentives to improve their safety-relevant driving behavior, while the participants of the other group did not. At the beginning and at the end of experiment, each participant had to fill out a questionnaire to elicit socio-economic and attitudinal information. We conducted regression analyses to identify socio-economic, attitudinal, and driving-behavior-related variables that explain safety-relevant driving behavior during the pre-measurement phase and the self-chosen intensity of app usage during the treatment phase. For the main objective of our study, we applied regression analyses to identify those variables that explain the potential effect of providing app-based feedback during the treatment phase on safety-relevant driving behavior. Last, we applied statistical tests of differences to identify self-selection and attrition biases in our field experiment. For a sample of 130 novice Austrian drivers, we found moderate improvements in safety-relevant driving skills due to app-based feedback. The improvements were more pronounced under the treatment with monetary incentives, and for participants choosing higher feedback intensities. Moreover, drivers who drove relatively safer before receiving app-based feedback used the app more intensely and, ceteris paribus, higher app use intensity led to improvements in safety-related driving skills. Last, we provide empirical evidence for both self-selection and attrition biases