Racial Residential Segregation and Social Control: A Panel Study of the Variation in Police Strength Across U.S Cities, 1980–2010

Despite a great deal of theoretical and empirical attention given to racial residential segregation and its influence on a number of social problems in the United States, few scholars have examined the role that this persistent form of racial inequality plays in shaping the magnitude of formal social control efforts. Our study examines this relationship by assessing the potential influence that the isolation of minorities may have on efforts to control crime in urban centers across the United States. Using a pooled time-series regression technique well suited for the analysis of aggregate, longitudinal data, we assess the potential influence of racial segregation on the size of municipal police departments in 170 U.S. cities between 1980 and 2010. After accounting for minority group size, economic threat, crime, and disorganization, we find that racial residential segregation has a significant non-linear effect on police force size. Cities with the most racially integrated populations have the smallest police presence but at very high levels of segregation, police strength levels off. This finding is consistent with expectations derived from the contact hypothesis. Under such conditions, majority group members appear to be less inclined to demand greater crime control measures such as increased police protection. Period interactions with residential segregation also suggest that this relationship has grown stronger in each decade since 1980. Overall, our study provides strong support for threat theories and the contact hypothesis but offers necessary refinements

SYSTEMIC ANALYSIS OF ILLEGAL MASS MIGRATION IN THE CENTRAL MEDITERRANEAN REGION

This thesis explores the systemic behavior of illegal mass migration in the Central Mediterranean region and proposes strategic approaches to address the problem. We hypothesize that the illegal migration is a complex systemic problem, where parts of the system are interdependent and behavioral change of any element effects the behavior of the whole. This research applies a series of quantitative and qualitative analyses where each reveals different aspects and properties of the migration system as a whole. The systemic analysis highlights the interconnectedness of different parts and their impact of the system’s output. Also, it reveals the cognitive background as a unique aspect of this region: namely, the decision to migrate is based on biased perception and bounded rationality rather than rational choice. In conclusion, we claim that the system’s output (i.e. level of illegal migration) is characterized by the interrelated behavior of parts of the migration system; therefore, strategic planning requires the notion of the dominant feedback loops, self-organization, time delays, limitations, and non-linear relations. Also, we conclude that a skewed perception based on social influence and cognitive biases influences a large number of people in that region to migrate.Captain, Hungarian Defence ForceApproved for public release. Distribution is unlimited

Non-Parametric Causality Detection: An Application to Social Media and Financial Data

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about the statistical relationships among the variables of the study and can effectively control a large number of factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201

Understanding Complex Systems: From Networks to Optimal Higher-Order Models

To better understand the structure and function of complex systems, researchers often represent direct interactions between components in complex systems with networks, assuming that indirect influence between distant components can be modelled by paths. Such network models assume that actual paths are memoryless. That is, the way a path continues as it passes through a node does not depend on where it came from. Recent studies of data on actual paths in complex systems question this assumption and instead indicate that memory in paths does have considerable impact on central methods in network science. A growing research community working with so-called higher-order network models addresses this issue, seeking to take advantage of information that conventional network representations disregard. Here we summarise the progress in this area and outline remaining challenges calling for more research.Comment: 8 pages, 4 figure

Exploring the trend of New Zealand housing prices to support sustainable development

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

A Causal Analysis of Life Expectancy at Birth. Evidence from Spain

Background: From a causal point of view, there exists a set of socioeconomic indicators concerning life expectancy. The objective of this paper is to determine the indicators which exhibit a relation of causality with life expectancy at birth. Methods: Our analysis applies the Granger causality test, more specifically its version by Dumitrescu–Hurlin, starting from the information concerning life expectancy at birth and a set of socioeconomic variables corresponding to 17 Spanish regions, throughout the period 2006–2016. To do this, we used the panel data involving the information provided by the Spanish Ministry of Health, Consumer Affairs and Social Welfare (MHCSW) and the National Institute of Statistics (NIS). Results: Per capita income, and the rate of hospital beds, medical staff and nurses Granger-cause the variable “life expectancy at birth”, according to the Granger causality test applied to panel data (Dumitrescu–Hurlin’s version). Conclusions: Life expectancy at birth has become one of the main indicators able to measure the performance of a country’s health system. This analysis facilitates the identification of those factors which exhibit a unidirectional Granger-causality relationship with life expectancy at birth. Therefore, this paper provides useful information for the management of public health resources from the point of view of the maximization of social benefits

The Spontaneous Emergence of Social Influence in Online Systems

Social influence drives both offline and online human behaviour. It pervades cultural markets, and manifests itself in the adoption of scientific and technical innovations as well as the spread of social practices. Prior empirical work on the diffusion of innovations in spatial regions or social networks has largely focused on the spread of one particular technology among a subset of all potential adopters. It has also been difficult to determine whether the observed collective behaviour is driven by natural influence processes, or whether it follows external signals such as media or marketing campaigns. Here, we choose an online context that allows us to study social influence processes by tracking the popularity of a complete set of applications installed by the user population of a social networking site, thus capturing the behaviour of all individuals who can influence each other in this context. By extending standard fluctuation scaling methods, we analyse the collective behaviour induced by 100 million application installations, and show that two distinct regimes of behaviour emerge in the system. Once applications cross a particular threshold of popularity, social influence processes induce highly correlated adoption behaviour among the users, which propels some of the applications to extraordinary levels of popularity. Below this threshold, the collective effect of social influence appears to vanish almost entirely in a manner that has not been observed in the offline world. Our results demonstrate that even when external signals are absent, social influence can spontaneously assume an on-off nature in a digital environment. It remains to be seen whether a similar outcome could be observed in the offline world if equivalent experimental conditions could be replicated