Education and Crime across America: Inequity’s Cost

Much of current research on crime and education has focused on the effect of minimum dropout age on rates of crime. Combining the FBI’s uniform crime reporting database and district finance data, we study the longitudinal relationship between crime in every town/city (whose police department has reported crime statistics) and its school district spending in years 2003 to 2018. We combine over 213 datasets to control for population, density, wealth, education, employment, cost-of-living, race, law enforcement, and voting history. Additionally, we also look at teacher salary, teacher engagement, and student chronic absenteeism. Using linear mixed-effect modeling, we find an overall average of 2.35% percent decrease in property crime for every $1000 more a school district spends per pupil on education. Moreover, a $1000 increase in education spending decreased property crime nearly four times as much as a 10 percent increase in per capita income. We also looked at the range in district spending in towns/cities and counties whose students attend multiple districts. We find that for every $1000 difference in district spending within a city, property crime increases by an average of 3%; interestingly, violent crime decreases by 3%. When we lag variables of education quality, allowing these effects to playout, we also find that for every 10 percentage-point increase in chronic absenteeism among students, violent crime increases by 4%. Importantly, we find no such effect for property crime, suggesting a distinct mechanism of education on violent crime. Additionally, both law enforcement and unemployment explain little variance in crime. Our results demonstrate a robust relationship between education funding and reduced crime across America with regard to amount spent per student as well as equity in spending.

Corporate risk-taking with long-term, short-term and subordinate credit ratings

PurposeThis study empirically examines, from the first quarter of 1981 until the fourth quarter of 2017, the relations across customary domestic issuer credit ratings (long-term, short-term and subordinate) and three popular corporate risk-taking measurements (the variability of operating profitability, net profitability, and research and development expenses).Design/methodology/approachThe author deploys categorical regressions and robustness tests with control variables, interaction terms, fixed effect variables, lag variables and delta variables.FindingsThe author documents that both short-term and subordinate domestic credit ratings are key determinants of the volatility of operating profitability. The author also identifies long-term credit ratings as secondary factors, yet they do affect broader corporate risk-taking behavioral features (along all three measurements). Furthermore, the author finds that the higher (lower) the credit ratings assigned, i.e. the superior (inferior) the credit quality externally judged, the more (less) overall risk firms tend to undertake.Originality/valueIt is the first research to examine both the inclusive influence and the granular effects of credit ratings on corporate risk-taking (CRT) behavior. It is also the only enquiry to inspect the specific relationships along three types of domestic issuer credit ratings: long-term, short-term and subordinate ratings.

The connectedness of house price affordability (HPA) and rental price affordability (RPA) measures

Purpose The purpose of this paper is to examine the relationship between house price affordability (HPA) and rental price affordability (RPA) in New Zealand. The cointegration of HPA and RPA is of particular focus given rising house prices and rising rents. Design/methodology/approach The study examines the lead-lad correlation between HPA and RPA. The method uses a generalised least square technique and the development of an ordinary least squares model. Findings The study shows that there is an existence of cointegration and unidirectional statistical causality effects between HPA and RPA across 11 regions in New Zealand. Furthermore, Auckland, Wellington and Canterbury are the three regions in which the results detect the most extreme effects amongst HPA and RPA compared to other places in the country. Extended empirical work shows interesting results that there are lead-lag effects of HPA and RPA on each other and on mortgage rates at the national scale. These effects are consistent for both methods but are changed at individual lead-lag variables and amongst different regions. Originality/value The study empirically provides useful insight for both academia and practitioners. Particularly in examining the long-run effects, cointegration and forecasting of the volatile interactions between HPA and RPA.

Geometrical Framework Application Directions in Identification Systems. Review

The approaches review of the framework application in identification problems is fulfilled. It is showed that this concept can have different interpretations of identification problems. In particular, the framework is understood as a frame, structure, system, platform, concept, and basis. Two directions of this concept application are allocated: 1) the framework integrating the number of methods, approaches or procedures; b) the mapping describing in the generalized view processes and properties in a system. We give the review of approaches that are the basis of the second direction. They are based on the analysis of virtual geometric structures. These mappings (frameworks) differ in the theory of chaos, accidents, and the qualitative theory of dynamic systems. Introduced mappings (frameworks) are not set a priori, and they are determined based of the experimental data processing. The main directions analysis of geometrical frameworks application is fulfilled in structural identification problems of systems. The review includes following directions: i) structural identification of nonlinear systems; ii) an estimation of Lyapunov exponents; iii) structural identifiability of nonlinear systems; iv) the system structure choice with lag variables; v) system attractor reconstruction.

Financial instability as a sustained export growth limiting factor of the Russian far east regions

The paper analyses the export dynamics of the Russian Far East regions in the context of cyclical business activity. A multi-regression model with lag variables is formed during correlation-regression analysis, since a tendency for a lagging export reaction in response to fluctuations in financial instability indicators was revealed. The analysis showed that exports are influenced by factors of economic activity, fluctuations in real exchange rates, oil prices and levels of external debt relative to GDP. During the crisis, there was a short-term pattern of growth, but in the subsequent period, the decline in the output of exporters, the shortage of financial resources, the increase in the cost of materials and other costs led to a reduction in the sales of products for export. Financial instability is becoming the main source of financial risk for exporters, requiring regional government intervention and business support mechanisms.

Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland

Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they affect its ability to predict? We focused on NO2 and NOx concentrations, and conducted this research using hourly data from the city of Wrocław (western Poland) from 2015 to 2017; we used multi-objective optimization to determine the optimal delays. It turned out that for both NO2 and NOx, the past values for traffic flow, wind speed, and sunshine duration are more important than the current ones. We built random forest models on each of the pollutants for both the current and past values and discovered that including a lagged variable increases the resulting R2 from 0.51 to 0.56 for NO2 and from 0.46 to 0.52 for NOx. We also analyzed the feature importance in each model, and found that for NO2, a wind speed delay of more than three hours causes a significant decrease, while the importance of relative humidity increases with a seven-hour delay; likewise, wind speed increases the importance for NOx prediction with a two-hour delay. We concluded that, in pollutant concentration modeling, the possibility of a delayed effect of the independent variables should always be considered, because it can significantly increase the performance of the model and suggest unexpected relationships or dependencies.

Lag Variables in Air Pollution Modeling Based on Traffic Flow and Meteorological Factors

In order to refine the research on the impact of environmental factors on the concentration of pollutants in the air, in this paper, we present a mathematical model that allows the possibility of taking into account the past values of factors (explanatory variables) when modeling the current concentration of pollution. We conducted numerical analyzes based on hourly data from meteorological, traffic and air quality monitoring stations in Wrocław (Poland, Central Europe) from 2015–2017. In order to determine the optimal delay of each explanatory variable, we used a multi-objective optimization model (MO). It turned out that for the concentration of nitrogen oxides, delayed traffic flow, wind speed and sunshine duration time are more important than current ones. Then we built two random forest models: an actual model of current values of explanatory variables and a lag model with delayed variables determined by the MO method. Taking into account variables with an optimal delay (lag model) results in an increase in model accuracy for NO2 with R2 = 0.51 to 0.56 and for NOx from 0.46 to 0.52. We deduced that in pollutant concentrations modeling, the possibility of greater influence of variables with delay should always be considered because it can significantly increase the accuracy of the model and indicate additional relationships or dependencies.

Do financial difficulties affect health? A study among older adults in Switzerland

Background In perspective of health promotion strategies among older adults, it is particularly important to identify socioeconomic determinants associated with the dynamic of health. In this study, we want to assess the role of financial difficulties on health among older adults. Methods We use cohort data coming from the Lausanne cohort 65+. Participants were community-dwelling older adults representative of the general population aged 65 to 70 years in 2004 and living in Lausanne (Switzerland). This cohort is a longitudinal survey and was designed as an annual survey where same individuals (N = 1564) are re-interviewed in successive waves. Wave 1 and wave 2 are considered as baselines. We perform our analysis on the 2006-2016 period (11 waves). We consider the health dynamic by estimating system GMM estimators. Our main explanatory variable is a proxy of financial difficulties: “Have you been confronted to financial difficulties in the last 12 months?”. We study the effect of this variable on several health indicators (self-rated health, number of medical conditions, depression, difficulties in activities in daily living) and evaluate if this effect is similar for these different dimensions of health. Results Our econometric analysis underlines the importance of using dynamic approach: health lag variables have significant effects (p < 0.01) and to control for endogeneity bias. We find no evidence of a causal link from having financial difficulties to health. The effect was non-significant for all health variables. Conclusions In terms of public policy, our results provide insight that policies allowing to limit short term financial difficulties are not essential to protect elderly against health deterioration. Key messages There is no evidence of a causal link from having financial difficulties to health. Policies allowing to limit short term financial difficulties are not essential to protect older adults against health deterioration.

Jurisprudence and demand for halal meat in OIC

Purpose The purpose of this paper is to estimate the import demand function for halal meat in member countries of the Organization of Islamic Cooperation (OIC) and to suggest some policy recommendations for OIC members that can enhance intra-OIC halal meat trade. Design/methodology/approach By using an augmented gravity model, this study empirically estimates the major determinants of halal meat import demand in OIC member countries. Moreover, a major determinant is the difference in Islamic jurisprudence (fiqh). Findings The results of this study show that the variation in Islamic jurisprudence is one of the primary determinants of intra-regional trade of halal meat import demand in OIC member countries. Research limitations/implications Although trade flows are set up in several years and lag variables are well capable to examine trade flows, this study only includes the static nature of halal meat trade flows toward selected top 20 OIC member countries. Practical implications This study suggests that developing a common halal meat market and one halal certification body under the OIC can enhance intra-OIC halal meat trade, this may be a challenge given the five diverse interpretations of halal meat within Islamic jurisprudence among OIC member countries. Originality/value This paper identifies the role of Islamic jurisprudence (fiqh) in determining the import demand of halal meat in OIC countries, which has not been addressed in empirical literature. It also provides some policy implications to ameliorate the declining trend of intra-OIC trade flows of halal meat.