Public–private partnerships in Social Impact Bonds: facilitating competition or hindering transparency?

ABSTRACT

The use of Social Impact Bonds (SIBs), which introduces the potential for investor profit in public service provision, has been widely discussed. Some argue that SIBs might promote government transparency because outcome data collection and evaluation are part of contractual terms. On the other hand, some argue that SIBs might hinder government transparency because more contractual parties might lead to more uncertain data ownership and because the profit motive transforms information into a competitive advantage. This paper looks at SIBs in five countries, examining how transparency differed between SIB and non-SIB financed programmes at the same social service provider. On the positive side, SIBs led to more and longer collection of outcome data and the publication of evaluations. On the negative side, it was found that SIBs tend to generate significant obstacles to the release of data to academic researchers and that sponsored evaluations do not measure impacts.     

Creating financial statements using FRx within Microsoft Dynamics GP: Learning vs. teaching

This project requires you to create financial statements using FRx within Microsoft Dynamics GP, an enterprise system. The project emphasizes the learner-centered paradigm rather than a teacher-centered educational paradigm. Researchers have found great importance in the learner-centered approach in educating students, especially in information systems (Landry, Saulnier, Wagner, & Longenecker, 2008; Saulnier, Landry, & Wagner, 2008). Major differences exist between the two approaches; for example, the professor gives information and evaluates, and emphasizes the right answer in a teacher-centered approach. The professor coaches and facilitates, and emphasizes that students generate good questions and learn from mistakes in a learner-centered approach. This project utilizes the learner-centered approach, an effective approach for you to use in today’s environment.     

Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

Learning,large deviations and rare events

We examine the role of generalized stochastic gradient constant gain (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat-tailed distribution even though the model is driven by thin-tailed exogenous stochastic processes. We characterize these large deviations, driven by sequences of consistently low or consistently high shocks and then apply our model to the canonical asset pricing framework. We demonstrate that the tails of the stationary distribution of the price–dividend ratio will follow a power law.     

Do constraints created by economic growth targets benefit sustainable development? Evidence from China

An economic growth target is a declaration by policy authorities of commitment to that target and the corresponding allocation of resources. The constraints created by economic growth target are an important economic management method in China, which has helped China's economy to achieve remarkable achievements. However, it has also brought about serious environmental problems, threatening China's sustainable development. Using the data about economic growth targets in the work reports of 30 Chinese provincial governments from 2006 to 2017, this paper constructs several spatial measurement methods, such as the spatial Durbin model, to examine the constraints created by economic growth target's impacts on air pollution. The main conclusions are as follows. First, a significant “U-shaped” relationship exists between the constraints created by economic growth targets and air pollution. Second, the spatial Durbin model analysis revealed that PM_2.5 across China's provinces display significant positive spatial spillover effects and spatial agglomeration characteristics. Third, the direct, indirect, and total effects of constraints created by economic growth targets on air pollution are all statistically significant and depict a “U shape.” Finally, the constraints created by economic growth targets have an apparent threshold effect on air pollution, and the inhibiting effects increase with human capital and industrial restructuring. However, with the increase in foreign direct investments, constraints created by economic growth targets may increase air pollution. The conclusions of this paper are of great significance for improving the management of local government economic growth targets and sustainable development.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

基于目标管理的团队建设研究

越来越多的组织正在用团队的形式进行生产运营活动,如何建设高效优秀的团队已经被越来越多的企业关注,本文在团队定义的基础上,以目标管理为切入点,详述了组织如何将目标管理运用于团队建设中,讨论了组织在打造高效团队需要遵循的几点原则。     

关于对高职学生现用教材使用情况问卷调查的分析报告

本文分别从问卷调查的基本情况论述了高职学生现用教材使用情况问卷调查的分析报告。     

探究数字化时代对师范生信息素养的要求

本文从数字化时代发展的角度出发,指出数字化学习不仅是数字时代学习的重要方式,更是课程整合的核心,同时提高教师的信息素养是数字化学习的关键。随着信息社会的发展和中小学信息技术课的开展,对师范生的信息素养提出了更高的要求。目前师范生就业形式,信息素养状况怎样,存在哪些问题,怎样去解决这些问题提高自身的竞争力?这都是我们师范院校的学生所思考的问题。针对上述问题,本文提出了自己的观点和看法。