When haste makes sense: Cracking down on street markets for illicit drugs

This paper presents an analytical approach to the tactical question: ‘What level of enforcement over time allows one to eliminate a street market for illicit drugs while expanding the least possible total effort?’ The analysis is done in the context of Caulkins' model [6] which predicts the rate of change of dealers as a function of enforcement level and several market parameters. Our analysis suggests that the simple strategy of using the maximum available enforcement intensity until the market has been eliminated minimizes the total enforcement effort required.     

A copula-based time series model for global horizontal irradiation

The increasing importance of solar power for electricity generation leads to increasing demand for probabilistic forecasting of local and aggregated photovoltaic (PV) yields. Based on publicly available irradiation data, this paper uses an indirect modeling approach for hourly medium to long-term local PV yields. We suggest a time series model for global horizontal irradiation that allows for multivariate probabilistic forecasts for arbitrary time horizons. It features several important stylized facts. Sharp time-dependent lower and upper bounds of global horizontal irradiations are estimated. The parameters of the beta distributed marginals of the transformed data are allowed to be time-dependent. A copula-based time series model is introduced for the hourly and daily dependence structure based on simple vine copulas with so-called tail dependence. Evaluation methods based on scoring rules are used to compare the model’s power for multivariate probabilistic forecasting with other models used in the literature showing that our model outperforms other models in many respects.     

A meta-validation model for assessing the score-validity of student teaching evaluations

Virtually every institution of higher education in the US uses some type of student teaching evaluation (STE) instrument as a means of assessing instructors’ instructional performance in courses. Unfortunately, many administrators and faculty misinterpret STE ratings. Therefore, the present article provides a comprehensive critique of STE instruments. In particular, we build on Messick’s (Educational Measurement, MacMillan, pp. 13–103, and Messick (Am. Psychol., 50, 741–749, 1995, 1989) conceptualization of validity to yield what we refer to as a meta-validity model that subdivides content-, criterion-, and construct-related validity into several areas of evidence. We use our meta-validity model to conduct a meta-validity analysis of STEs. Specifically, we assessed the score-validity of STEs based on findings from the extant literature. We conclude that strong evidence has been provided with respect to areas of criterion-related validity; however, for the most part, weak or inadequate evidence has been provided with regard to areas of both content-related and construct-related validity. This seriously calls into question both the score-validity and utility of STEs.     

A mixture model for credit card exposure at default using the GAMLSS framework

The Basel II and III Accords propose estimating the credit conversion factor (CCF) to model exposure at default (EAD) for credit cards and other forms of revolving credit. Alternatively, recent work has suggested it may be beneficial to predict the EAD directly, i.e.modelling the balance as a function of a series of risk drivers. In this paper, we propose a novel approach combining two ideas proposed in the literature and test its effectiveness using a large dataset of credit card defaults not previously used in the EAD literature. We predict EAD by fitting a regression model using the generalised additive model for location, scale, and shape (GAMLSS) framework. We conjecture that the EAD level and risk drivers of its mean and dispersion parameters could substantially differ between the debtors who hit the credit limit (i.e.“maxed out” their cards) prior to default and those who did not, and thus implement a mixture model conditioning on these two respective scenarios. In addition to identifying the most significant explanatory variables for each model component, our analysis suggests that predictive accuracy is improved, both by using GAMLSS (and its ability to incorporate non-linear effects) as well as by introducing the mixture component.     

Efficiency evaluation for higher education institutions in China considering unbalanced regional development: A meta-frontier Super-SBM model

The development of higher education institutions (HEIs) is related to the future overall development of higher education in China. Studying the efficiency of provincial HEIs in China has great significance in deepening the reform of higher education evaluation, promoting coordinated regional development, and building a country powerful in its higher education. Based on the Super-SBM method, this paper creatively integrates meta-frontier theory and an index of international exchange and cooperation into an evaluation system for higher education efficiency. Taking 2011–2020 as the sample period, this study conducts a dynamic assessment of the higher education efficiency in 30 mainland Chinese provinces. It explores the differences in production technology in the eastern, central, and western regions, analyzing the environmental factors that affect the efficiency of higher education. The results show that during the sample period, the overall efficiency of higher education in China improved steadily. Efficiency Change is the main driving force. The three regions have significantly different technology gap ratio, and the structure of resource allocation significantly impacts efficiency. Therefore, this study's main recommendations are that the Chinese government should not only deepen the coordinated development of higher education between regions but also pay more attention to solving the problem of unbalanced development between China's geographical regions. At the same time, educational authorities should expand the number of ordinary colleges and universities in the central and western regions, develop non-financial funding sources, and improve the level of refined management of HEIs.     

A composite indicator of social inclusion for EU based on the inverted BoD model

In this paper, after reviewing the existing modeling approaches for reverse indicators, we opt for the use of the inverted Benefit-of-the-doubt (IBoD) model when all the considered indicators are reverse, as is the case with composite indicators related to social inclusion. Using EU data for 2014 we provide comparative empirical results using the IBoD model and three data transformations employed by previous studies. We also provide a thorough analysis of the performance of EU countries during the period 2011–2020 in terms of the composite indicator obtained from the IBoD model. Our empirical results indicate that relative performance across the EU has neither improved nor deteriorated and there is evidence supporting local instead of global convergence in social inclusion with certain groups of countries.