Firm entries and exits in Estonian urban municipalities: urban hinterlands and rural peripheries, 2005–2012

This paper studies firm entries and exits in Estonian urban areas, urban hinterlands, and rural peripheries following EU accession, and it analyzes the effects of changes in population density, employee income level, unemployment, and economic climate on firm entries and exits. It concludes that the firm entry rate exceeded the exit rate in all of the years between 2005 and 2012. The urban hinterland is characterized by the highest level of both entries and exits. The fixed-effect regression models show that income and recession, which are the most significant factors to impact on local entrepreneurship, have a similar effect on different municipality types.     

县域金融发展与产业集聚区经济增长——以浙江为例

本文以浙江为例,在经济增长理论的基础上,建立了县域金融发展与产业集聚区经济增长之间相互作用的内生模型,基于浙江省22县（市）的县级数据,运用面板数据模型对县域金融发展与产业集聚区经济增长的互动关系进行了实证检验,比较了各产业集聚区经济增长的影响因素。研究发现：浙江县域金融发展规模、固定资产投资、实际利用外资与产业集聚区经济增长存在高度正相关关系;金融发展效率对产业集聚区经济增长的检验效果不显著;实际利率对产业集聚区经济增长的影响不显著,提出在执行利率政策时,不能采用金融发展理论中提高利率、解除金融抑制的政策手段。     

A model for adoption of mobile banking services using classification and regression trees

The analysis and improvements of bank performance is a challenging issue for bank managers. Among various banking services, mobile banking is one of the most risky fields that is prone to customer resistance. Mobile banking is a form of banking transaction carried out via a mobile phone. Mobile banking allows bank customers to check their account balances and perform credit card transaction, as well as provide information on the latest transaction made by customers. This paper summarizes experiences and results of productively using knowledge discovery and data mining techniques in a large detail bank data. Data mining is the process of extracting patterns from data. We review prior literature on mobile payments, analyze the various factors that impact mobile payment services markets, and suggest directions for future research in this risk management field. Our research work demonstrates the effectiveness and efficiency of data mining in analysis of retailing bank performance. We use classification and regression tree learning algorithm for proposing a customer adoption model of mobile banking according to some personal characteristics of customers.     

The Danish legislators database

Elections determine the composition of legislatures, but the study of legislative representation has long been hampered by limited individual-level data on legislators. Recent innovations have vastly increased opportunities for studying legislators, but often have limited historical coverage. We introduce the Danish Legislators Database (DLD), a database of members of Denmark’s parliament, Folketinget, for every electoral term since its inception in 1849. Relative to most existing databases, the DLD is rare in covering the entire history of a parliamentary body going back more than 170 years. The DLD thus enables analyses of parliamentary representation with full temporal coverage. We describe the development and content of the DLD and present a set of analyses illustrating the potential uses of the database in the study of representation.     

An empirical investigation of determinants of corporate social performance using multi-dimensional panel data analysis

An assessment of the determinants of corporate social performance (CSP) in emerging economies is still too fractured. This article contributes to general management literature by developing an empirical model based on the existing theoretical models rooted in neo-institutional theory (legitimacy approach), stakeholders management theory, agency theory, the resource-based view of the firm, slack resources argument, and managerial control theory. A robust, multidimensional, unweighted disclosure index was used to measure CSP. This article provides a methodologically and empirically more rigorous assessment of determinants of CSP compared to previous studies by performing panel data regression analysis on 307 firms for 10 years. The results reveal that the presence of a legal framework, board attributes (board size, board diversity, board interlocking), women on board, ownership pattern, financial performance, firm attributes (size, age, leverage), and industry characteristics affect CSP significantly. These findings provide very important clues to design pragmatic strategies to improve CSP.     

Beyond the Z-score: A novel measure of bank stability for effective policymaking

This article looks beyond Z-score and proposes a novel methodological framework to build an all-encompassing indicator of bank stability for individual banks using the optimisation-based ‘benefit-of-the-doubt (BoD)’ approach. Unlike other available approaches, this approach is totally data-driven and generates endogenous weights to aggregate sub-indicators of bank stability and dimensions. Further, the final outcomes are not limited to a scalar measure of bank stability. The unique optimal weights offer valuable policy-relevant insights and highlight the most precarious areas of stability, which demand the immediate attention of the bank's management and the industry regulators for both micro-and macro-level policymaking. Using the data of Indian public sector banks operating in the year 2018, the study illustrates the proposed framework to obtain a holistic indicator of bank stability, defined on 14 ratio sub-indicators and 5 broad dimensions (soundness, asset quality, profitability, management efficiency, and liquidity) of bank stability.     

China's new regulatory regime tailored for the sharing economy: The case of Uber under Chinese local government regulation in comparison to the EU,US, and the UK

Although Uber's arrival in China has resulted in disruptive competition for incumbent taxi companies, it offers an attractive alternative in China's supply-demand-imbalanced urban passenger transport system. China's regulatory regime for Uber has evolved in three stages: from the regulatory vacuum prior to 2015 to its official legalization in 2015–2016, and the enactment of numerous local regulations in 2016, with specific and more demanding requirements for Uber. This policy is a part of the Chinese approach to the gradual liberalization of the urban passenger transport market. Policymakers should consider ‘fair competition’ as the guiding principle to balance the interests of sharing firms and incumbent service providers, as well as between different sharing firms. The core value of this principle lies in the benefits it provides for consumers and the way it engenders a pro-competitive market environment. The labor protection arrangements for sharing firms’ laborers should be more flexible and diversified. In order to recognize whether an Uber-Driver is an employee or independent contractor, a new standard taking into account a range of factors should be established through collective negotiations between the participants of the sharing economy, and dialogues between members of the judiciary, academics, and the policymakers. Further, consumer protection law and personal data protection provisions should apply when sharing firms misuse their distinctive algorithmic management model to compete unfairly to the detriment of consumers and other users. Ex ante regulatory measures designed to protect the personal data of users should be introduced for deployment in the context of the sharing economy. When enforcing these rules, a balance should be struck ensuring free data flow that is essential to sharing firms’ innovation and competition, and the need to ensure the level of data security required to underpin a well-functioning sharing society.     

From fragile to smart consumers: Shifting paradigm for the digital era

The use of digital technologies, functioning thanks to data processing, has been conquering many sectors of the world economy and it is possible that, in the near future, only a few markets will still be excluded from this industrial revolution. Therefore, even if one chose unreasonably to disregard the many innovations that the digital economy has brought about, its development seems quite inexorable, although it is true that this new stage in human progress raises some concerns. In particular, many worry about the millions of passive and powerless digital consumers who, facing a few huge and influential companies without any education or awareness, could succumb and find themselves poorer, victimized, and manipulated. The paper proposes to react to this state of affairs without further fueling the fear of the digital revolution and without the thought that regulation can be used only as a shield to protect fragile digital consumers. Rather, by taking inspiration from some regulatory actions undertaken by the European Union, the paper bears in mind that regulation can be used as a sword in the hands of consumers to finally assign them a lead role in digital markets. New rules to empower consumers and to make them take autonomous and independent decisions as to the management of their personal data as well as to the merits of digital firms can be envisaged. After all, one of the cultural roots of Western societies is that every individual should be enabled to be faber ipsius fortunae.     

Application of Enhanced Point Estimators to a Sample of In Vivo CT‐derived Facial Soft Tissue Thicknesses

Facial approximations based on facial soft tissue depth measurement tables often utilize the arithmetic mean as a central tendency estimator. Stephan et al. (J Forensic Sci 2013;58:1439) suggest that the shorth and 75‐shormax statistics are better suited to describe the central tendency of non‐normal soft tissue depth data, while also accommodating normal distributions. The shorth, 75‐shormax, arithmetic mean, and other central tendency estimators were evaluated using a CT ‐derived facial soft tissue depth dataset. Differences between arithmetic mean and shorth mean for the tissue depths examined ranged from 0 mm to +2.3 mm (average 0.6 mm). Differences between the arithmetic mean plus one standard deviation (to approximate the same data points covered by the 75‐shormax) and 75‐shormax values ranged from ?0.8 mm to +0.7 mm (average 0.2 mm). The results of this research suggest that few practical differences exist across the central tendency point estimators for the evaluated soft tissue depth dataset.     

Forensic Analysis of Digital Dynamic Signatures: New Methods for Data Treatment and Feature Evaluation

This study explored digital dynamic signatures containing quantifiable dynamic data. The change in data content and nature necessitates the development of new data treatment approaches. A SignPad Omega digitizing tablet was used to assess measurement reproducibility, as well as within‐writer variation and the occurrence of correctly simulated features. Measurement reproducibility was found to be high except for pressure information. Within‐writer variation was found to be higher between days than on a same day. Occurrence of correct simulation was low for features such as signature size, trajectory length, and total signature time. Feature discrimination factors combining within‐writer variability and the occurrence of correctly simulated features were computed and show that signature size, trajectory length, and signature time are the features that perform the best for discriminating genuine from simulated signatures. A final experiment indicates that dynamic information can be used to create connections between simulation cases.