The effect of spatiotemporal resolution on predictive policing model performance

Being able to anticipate crime such that new crime events can be dealt with effectively or prevented entirely, leads police forces worldwide to look at applying predictive policing, which provides predictions of times and places at risk for crime, such that proactive preventative measures can be taken. Ideally, predictive policing models predict crime at a high spatio-temporal level, while also providing optimal prediction performance. The main objective of this paper is therefore to evaluate the impact of varying grid resolution, temporal resolution and historical time frame on prediction performance. To investigate this, we analyse home burglary data from a large city in Belgium and predict new crime events using a range of parameter values, comparing the resulting prediction performances. Given the potential prediction performance costs associated with prediction at a high spatio-temporal resolution, consideration should be given to balance practical requirements with performance requirements.     

Green marketing as an environmental practice: The impact on green satisfaction and green loyalty in a business-to-business context

Many companies have developed a green marketing strategy, aimed at promoting and selling green environmental products. While the majority of articles on this topic report on studies in a business-to-consumer setting, this research focusses on the impact of green marketing strategies on the satisfaction and loyalty of professional buyers in a business-to-business setting. Hypotheses were tested with survey data from 148 Dutch professional purchasers in the cleaning industry. The results emphasize the impact and importance of product quality, product price and corporate image. The most notable and strong impact on satisfaction and loyalty was found for the salesperson expertise.     

Self‐similarity in long‐horizon returns

Asset returns incorporate new information via the effects of independent and possibly identically distributed random shocks. They may also incorporate long memory effects related to the concept of self‐similarity. The two approaches are here combined. In addition, methods are proposed for estimating the contribution of each component and evidence supporting the presence of both components in both the physical and risk‐neutral distributions is presented. Furthermore, it is shown that long‐horizon returns may be nonnormal when there is a self‐similar component. The presence of a self‐similar component also questions positive equity biases over the longer term.     

A Meta‐Analysis of the Investment‐Uncertainty Relationship

In this article we use meta‐analysis to investigate the investment‐uncertainty relationship. We focus on the direction and statistical significance of empirical estimates. Specifically, we estimate an ordered probit model and transform the estimated coefficients into marginal effects to reflect the changes in the probability of finding a significantly negative estimate, an insignificant estimate, or a significantly positive estimate. Exploratory data analysis shows that there is little empirical evidence for a positive relationship. The regression results suggest that the source of uncertainty, the level of data aggregation, the underlying model specification, and differences between short‐ and long‐run effects are important sources of variation in study outcomes. These findings are, by and large, robust to the introduction of a trend variable to capture publication trends in the literature. The probability of finding a significantly negative relationship is higher in more recently published studies.     

Optimal Product Variety and Economic Growth: The Trade-off between Internal and External Economies of Scale

Received July 17, 2000; revised version received February 1, 2001     

A state‐level analysis of the economic impacts of medical tourism in Malaysia

In Malaysia, a country that ranks among the world's most recognised medical tourism destinations, medical tourism is identified as a potential economic growth engine for both medical and non‐medical sectors. A state‐level analysis of economic impacts is important, given differences between states in economic profiles and numbers, origins, and expenditure of medical tourists. We applied input–output (I–O) analysis, based on state‐specific I–O data and disaggregated foreign patient data. The analysis includes nine of Malaysia's states. In 2007, these states were visited by 341,288 foreign patients, who generated MYR1,313.4 m ($372.3 m) output, MYR468.6 m ($132.8 m) in value added, and over 19,000 jobs. Impacts related to non‐medical expenditure are more substantial than impacts related to medical expenditure, and indirect impacts are a substantial part of total impacts. We discuss management and policy responses and formulate recommendations for data collection.     

Artificial intelligence: neither Utopian nor apocalyptic impacts soon

ABSTRACT

After a number of AI-winters, AI is back with a boom. There are concerns that it will disrupt society. The immediate concern is whether labor can win a ‘race against the robots’ and the longer-term concern is whether an artificial general intelligence (super-intelligence) can be controlled. This paper describes the nature and context of these concerns, reviews the current state of the empirical and theoretical literature in economics on the impact of AI on jobs and inequality, and discusses the challenge of AI arms races. It is concluded that despite the media hype neither massive jobs losses nor a ‘Singularity’ is imminent. In part, this is because current AI, based on deep learning, is expensive and difficult for most businesses to adopt, not only displaces but in fact also create jobs, and may not be the route to a super-intelligence. Thus AI is unlikely to have either Utopian or apocalyptic impacts soon. Considering Amara's Law, one should however be wary not to underestimate the long-run impacts of AI.     

绩效奖励的陷阱

物质性奖励并非多多益善。实际上，绩效奖励的负效应与其使用强度相应成比例。     

Cycles in basic innovations

Basic innovations are believed to be one of the drivers of economic growth. In this paper we examine if cycle periods found for economic data correspond with cycles in basic innovations. For an annual time series of count data on innovations covering 1764-1976, we fit a harmonic Poisson regression model. We find the presence of multiple cycles with periods 5, 13, 24, 34 and 61, and these show a remarkable resemblance with commonly found economic cycles.     

Ex Ante Impact Assessment under Imperfect Information: Biotechnology in New Member States of the EU

Some argue that the lack of modern agricultural development in the former socialist countries of Central and Eastern Europe has made cutting‐edge biotechnology attractive. However, enthusiasm for planting genetically modified (GM) crops varies greatly in the enlarged European Union (EU) and especially among the New Member States (NMS); the Czech GM maize area is progressively growing whereas Hungary imposed a de facto ban on GM crops. Remarkably, the Hungarian ban was not supported by any cost–benefit assessment. In the literature, ex ante impact assessments of monopolistically priced technologies are often based on cross‐sectional comparisons of average cropping budgets. Such assessments ignore heterogeneity of farmers and underestimate the true impact of these technologies because of homogeneity bias. Therefore, we propose an improved method by explicitly modelling farmer heterogeneity under imperfect information, and assess the potential value and benefit sharing of GM crops in the two NMS using a stochastic partial equilibrium model. The total potential value of GM crops is estimated at €82 million for both countries, of which €60 million (73%) accrues to farmers and €22 million (27%) to the gene developers and the seed industry. This is in line with the literature on global benefit sharing of first‐generation GM technologies.