773 research outputs found

    Panama Papers' offshoring network behavior

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    The present study analyzes the offshoring network constructed from the information contained in the Panama Papers, characterizing worldwide regions and countries as well as their intra-and inter-relationships. The Panama Papers 2016 divulgence is the largest leak of offshoring and tax avoidance documentation. The document leak, with a volume content of approximately 2.6 terabytes, involves more than two hundred thousand enterprises in more than two hundred countries. From this information, the offshore connections of individuals and companies are constructed and aggregated using their countries of origin. The top offshore financial regions and countries of the network are identified, and their intra-and inter-relationship are mapped and described. We are able to identify the top countries in the offshoring network and characterize their connectivity structure, discovering the more prominent actors in the worldwide offshoring scenario and their range of influence.This work was funded by UDLA-SIS.MGR.20.0

    Information and modeling issues in designing water and sanitation subsidy schemes

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    In designing a rational scheme for subsidizing water services, it is important to support the choice of design parameters with empirical analysis that stimulates the impact of subsidy options on the target population. Otherwise, there is little guarantee that the subsidy program will meet its objectives. But such analysis is informationally demanding. Ideally, researchers should have access to a single, consistent data set containing household-level information on consumption, willingness to pay, and a range of socioeconomic characteristics. Such a comprehensive data set will rarely exist. The authors suggest overcoming this data deficiency by collating, and imaginatevily manipulating different sources of data to generate estimates of the missing variables. The most valuable sources of information, they explain, are likely to be the following: 1) Customer databases of the water company, which provide robust information on the measured consumption of formal customers, but little information on unmeasured consumption, informal customers, willingness to pay, or socioeconomic variables. 2) General socioeconomic household surveys, which are an excellent source of socioeconomic information, but tend to record water expenditure rather than physical consumption. 3) Willingness-to-pay surveys, which are generally tailored to a specific project, are very flexible, and may be the only source of willingness-to-pay data. However, they are expensive to undertake, and the information collected is based on hypothetical rather than real behavior. Where such surveys are unavailable, international benchmark values on willingness to pay may be used. Combining data sets requires some effort and creativity, and creates difficulties of its own. But once a suitable data set has been constructed, a simulation model can be created using simple spreadsheet software. The model used to design Panama's water subsidy proposal addressed these questions: a) What are the targeting properties of different eligibility criteria for the subsidy? b) How large should the subsidy be? c) How much will the subsidy scheme cost, including administrative costs? Armed with the above information, policymakers should be in a position to design a subsidy program that reaches the intended beneficiaries, provides them with the level of financial support that is strictly necessary, meets the overall budget restrictions, and does not waste an excessive amount of funding on administrative costs.Water Conservation,Environmental Economics&Policies,Health Economics&Finance,Economic Theory&Research,Decentralization,Water Supply and Sanitation Governance and Institutions,Economic Theory&Research,Environmental Economics&Policies,Town Water Supply and Sanitation,Health Economics&Finance

    Pricing Offshore Services: Evidence from the Paradise Papers

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    The Paradise Papers represent one of the largest public data leaks comprising 13.4 million con_dential electronic documents. A dominant theory presented by Neal (2014) and Gri_th, Miller and O'Connell (2014) concerns the use of these offshore services in the relocation of intellectual property for the purposes of compliance, privacy and tax avoidance. Building on the work of Fernandez (2011), Billio et al. (2016) and Kou, Peng and Zhong (2018) in Spatial Arbitrage Pricing Theory (s-APT) and work by Kelly, Lustig and Van Nieuwerburgh (2013), Ahern (2013), Herskovic (2018) and Proch_azkov_a (2020) on the impacts of network centrality on _rm pricing, we use market response, discussed in O'Donovan, Wagner and Zeume (2019), to characterise the role of offshore services in securities pricing and the transmission of price risk. Following the spatial modelling selection procedure proposed in Mur and Angulo (2009), we identify Pro_t Margin and Price-to-Research as firm-characteristics describing market response over this event window. Using a social network lag explanatory model, we provide evidence for social exogenous effects, as described in Manski (1993), which may characterise the licensing or exchange of intellectual property between connected firms found in the Paradise Papers. From these findings, we hope to provide insight to policymakers on the role and impact of offshore services on securities pricing

    An Effective and Efficient Graph Representation Learning Approach for Big Graphs

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    In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data

    The economic effects of special purpose entities on corporate tax avoidance

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    This study provides the first large‐sample evidence on the economic tax effects of special purpose entities (SPEs). These increasingly common organizational structures facilitate corporate tax savings by enabling sponsor‐firms to increase tax‐advantaged activities and/or enhance their tax efficiency (i.e., relative tax savings of a given activity). Using path analysis, we find that SPEs facilitate greater tax avoidance, such that an economically large amount of cash tax savings from research and development (R&D), depreciable assets, net operating loss carryforwards, intangible assets, foreign operations, and tax havens occur in conjunction with SPE use. We estimate that SPEs help generate over $330 billion of incremental cash tax savings, or roughly 6% of total U.S. federal corporate income tax collections during the sample period. Interaction analyses reveal that SPEs enhance the tax efficiency of intangibles and R&D by 61.5% to 87.5%. Overall, these findings provide economic insight into complex organizational structures supporting corporate tax avoidance.Accepted manuscrip

    Can the unsophisticated market provide discipline?

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    The authors question the widespread belief that market discipline on banks cannot be effective in less developed financial environments. There is no systematic tendency for low-income countries to lack the prerequisites for market discipline. Offsetting factors to the weaker market and formal information infrastructures are (1) the less complex character of banking business in low-income countries; (2) the growing internationalization of these markets through the presence of foreign banks, and through international trading of the debt and equity of locally-controlled non-government banks; and (3) the smaller size of the business and financial community. However, continuing dominance by public sector banks in some countries limits the likely development of market monitoring, which is clearly a cause for concern, given the disappointing record of governments around the world as monitors of their self-owned banks. Countries should build on this potential for market discipline by limiting the role of explicit deposit guarantees, reducing state ownership of banks where it is prevalent, and not putting all their eggs in the supervisory basket. Greater disclosure, for example, of how risk taking is rewarded and how rating agencies earn their fees would support the development of better market monitoring. Enhancing market discipline (pillar three) is much more likely to be of use in most developing countries than addressing the refinements of the risk-weighting system of Basel II's first pillar.Banks&Banking Reform,Payment Systems&Infrastructure,Financial Intermediation,Financial Crisis Management&Restructuring,Environmental Economics&Policies,Banks&Banking Reform,Financial Intermediation,Environmental Economics&Policies,Financial Crisis Management&Restructuring,Insurance&Risk Mitigation

    Metodologias para o uso de aprendizagem automåtica na classificação de entidades de rede com base em padrÔes de tråfego

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    For the last years, constant news about information and data leaks are raising public discussion of the safety of the systems that we all nowadays depend on. Communications are increasingly more private; hence next-generation security systems rely on pattern recognition techniques to detect and infer the safety without the need for scrapping its content. This dissertation proposes methodologies to infer entity patterns and their nature according to their network traffic: if they are running according to their previously known safe pattern or if its behavior is uncommon, an indication of a possible breach. There is a strong indication that behavioral pattern recognition will continue to lead the research of security solutions, not only for the network traffic but also for other measurable activities. Other examples are identity access management or programs running on a computer. This dissertation proposes modeling network OSI layers 3 to 5 metadata in features that are later processed by machine learning algorithms to classify the network activity. The classification itself is divided into two groups: the first level is recognizing active entities operating within a network domain and the second if each entity is acting according to each known pattern. The presented methods of inferring if something is acting according to known patterns are transversal to other domains. Although aggregation of metadata and modeling differ, the described process of solving the problem of inferring patterns is generic and can be applied to user use cases rather than to the network, or combined with more complex scenarios. The last chapter includes a proof of concept with a few evaluation metrics using synthetic data, to evaluate if the classification algorithms can successfully distinguish different patterns. The tests showed promising results, ranging from 99% for entity classification and 77% to 98% (depending on the entity nature) for abnormality detection.Nos Ășltimos anos notĂ­cias sobre roubos e perdas de informação e de dados tĂȘm sido constante, levantando discussĂŁo sobre a segurança dos sistemas dos quais hoje dependemos. As comunicaçÔes sĂŁo tambĂ©m cada vez mais privadas, pelo que os sistemas de segurança de Ășltima geração tĂȘm desenvolvido tĂ©cnicas de reconhecimento de padrĂ”es para detetar e inferir a segurança sem a necessidade de processar conteĂșdos. Esta dissertação propĂ”e metodologias para inferir os padrĂ”es de entidades considerando o seu trĂĄfego de rede: se estĂĄ enquadrado no comportamento de trĂĄfego previamente conhecido, ou se a atividade gerada Ă© incomum e, por isso, ser indicação de um possĂ­vel problema. HĂĄ uma forte indicação de que o reconhecimento de padrĂ”es de comportamento continuarĂĄ a liderar a investigação no domĂ­nio de soluçÔes de segurança, nĂŁo sĂł para o trĂĄfego de rede, mas tambĂ©m para outras atividades mensurĂĄveis. Outros exemplos englobam a gestĂŁo de acesso de identidade ou programas em execução em um computador. As metodologias propĂ”em a modelação de metadados da camada de rede OSI 3 a 5 em contagens que sĂŁo posteriormente processadas por algoritmos de aprendizagem automĂĄtica para classificar a atividade da rede. Esta classificação baseia-se em dois nĂ­veis: no primeiro o reconhecimento entidades ativas dentro de um domĂ­nio de rede e o segundo, se cada entidade corresponde ao padrĂŁo conhecido. As metodologias apresentadas para inferir se algo estĂĄ de acordo com padrĂ”es conhecidos sĂŁo transversais a outros domĂ­nios. Embora a agregação de metadados e modelação seja diferente, o processo descrito para inferir padrĂ”es Ă© genĂ©rico o suficiente para ser aplicado a outros casos de uso, de rede ou nĂŁo, ou ainda combinado em cenĂĄrios mais complexos. O Ășltimo capĂ­tulo inclui uma prova de conceito com dados sintĂ©ticos e algumas mĂ©tricas de avaliação, para perceber se os algoritmos de classificação podem distinguir com sucesso padrĂ”es diferentes. Os testes mostraram resultados promissores, variando de 99% para classificação de entidades e 77% para 98% (dependendo da natureza da entidade) para deteção de anormalidades.Mestrado em Engenharia de Computadores e TelemĂĄtic

    The role of experience in common sense and expert problem solving

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    Issued as Progress reports [nos. 1-5], Reports [nos. 1-6], and Final report, Project no. G-36-617 (includes Projects nos. GIT-ICS-87/26, GIT-ICS-85/19, and GIT-ICS-85/18
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