Belastingdienst

In the last decades the interst in the problem of comparing and harmonizing legislation has been steadily increasing. One reason is the increasing legal convergence between governments in the European Union, and the increasing traffic of people over borders of jurisdictions. Another reason is the increasing globalization of companies; Products and services are offered in many jurisdictions at the same time, and the product or service has to meet the provisions of all jurisdictions in which it is offered. In the E-POWER project relevant tax legislation and business processes are modeled in UML to improve the speed and efficiency with which the Dutch Tax and Customs Administration can implement decision support systems for internal use and for its clients. These conceptual models have also proven their usefulness for efficient and effective analysis of draft legislation. We are currently researching whether conceptual modeling can also be used to compare 'similar' legislation from different jurisdictions to improve the capacity of the Dutch Tax and Customs Administration to react to future consequences of increased movement of people, products, and money between EU member states and increased harmonization between tax authorities in Europe. In addition, addressing the problem of comparing models is also expected to improve our methodology for modeling legislation. This paper discusses problems and requirements of comparing legislation as we understand them now, and attempts to relate them to relevant research.

Public value creation is traditionally considered as the citizens' collective expectations with respect to government and public services. Recent e-government literature indicates that what exactly constitutes public value in digital government is still debated. Whereas previous research acknowledges aspects such as co-production and the orchestration role of government in the context of public value creation, there is only a limited understanding of how public value is created by the interactions between government and business actors, and the role digital technologies play in that process. Furthermore, so far, research into public value creation processes is limited to specific services that aim to meet a specific goal; for a more complete view, an integrative perspective is required to address the multiplicity of goals. Societal challenges including climate change, sustainability, and the transition towards circularity will require governments to play a crucial role. Businesses are also transforming their vision by adding societal goals to their economic objectives and contributing to these societal challenges. This necessitates even more the need to explicitly consider the role of business in public value creation processes. In this paper we argue that there is a need to understand public value creation as an interactive process, involving both government and business actors. In this process, voluntary information sharing enabled by digital infrastructures has the potential to contribute to the value creation processes, but the increased complexity of digital technologies obscures the effects they can have on value creation. Therefore, we develop a framework that allows to reason about public value creation as an interactive process, involving government and businesses, facilitated by voluntary information sharing. The framework also allows to reason about how the technological design choices of the underlying digital infrastructure influence this value creation process. For the framework development, we use an in-depth case study from the domain of international trade. We analyze the interactions between customs authorities and supply chain actors for jointly creating public value related to revenue collection, as well as safety and security of goods entering the European Union, using business data made available via a global blockchain-enabled infrastructure. In future research, the framework that we developed can be used to analyze more complex cases with additional public value aspects, such as sustainability and circularity.

Abstract Handheld Raman spectroscopy is an emerging technique for rapid on‐site detection of drugs of abuse. Most devices are developed for on‐scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0–100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography–mass spectrometry (GC–MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on‐screen results with combined partial least square regression (PLS‐R) and discriminant analysis (PLS‐DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built‐in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm −1 (cocaine HCl). Combined PLS‐R and PLS‐DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

With increasing global trade and growing emphasis on security, enhanced information sharing between actors in global supply chains is required. Currently, the data about cargo available in the supply chain does not provide a timely and accurate description of the goods. To solve this data quality issue, data should be captured upstream at the point where goods are packed for transport to the buyer. Without ICT, it was not possible to get timely access to the original trade data. The data pipeline concept is an IT innovation to enable capturing data at the source. The data pipeline accesses existing information systems used by the parties in international supply chains. This paper explores the data pipeline concept and the benefits that businesses and governments could obtain from such an innovation. This study also identifies the need for a public-private governance model that has to accompany the technical innovation.

Financial management differs across households with consequences for financial outcomes and well-being of partners in households. A large-sample study has been performed, investigating the relationship between financial management of households and the occurrence of financial problems. To our knowledge, this is the first study on this relationship. Data from both partners was collected on having joint and separate bank accounts, on financial decision making, on drivers of financial management, and on financial outcomes. Based on the data, four financial management styles were derived: syncratic/joint, male-dominant, female-dominant, and autonomous financial management. In the syncratic style, partners have a joint bank account and take most financial decisions together. In the male/female-dominant styles, one partner (husband or wife) takes the main financial decisions. In the autonomous style, both partners have their own bank accounts and make their own decisions. As a conclusion, we find that syncratic financial management and having a joint instead of a separate bank account correlates with fewer financial problems, as compared with male-dominant money management and having separate bank accounts. Deciding together as partners is beneficial for the quality of financial management and for avoiding financial problems.

The Dutch Tax and Customs Administration (DTCA in Dutch: Belastingdienst) conducts a research program POWER in which methods and tools are developed that support a systematic translation of (new) legislation into the DTCA's processes. The methods and tools developed help to improve the quality of (new) legislation and codify the knowledge used in the translation processes in which legislation and regulations are transformed into procedures, computer programs and other designs. Thereby the time-to-market of the implementation of legislation will be reduced. In this article we focus on the method we developed for modeling legislation. We will elaborate upon the principles behind the method and explain the use of Catalysis and UML/OCL in the modeling process. The coupling of models of legislation and task models originating from business policy is demonstrated and finally we will show the way knowledge-based components in function of applications are generated automatically.

On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.

Trust in government is foundering. Ethics codes have limited utility in bolstering public trust, and a clear correlation between such codes and changed behavior must still be established. They are a means for external oversight, but they do nothing in terms of providing an internal moral compass. To rebuild trust in government, employees must also act with integrity. Actions that are both ethical and carried out with integrity are necessary—neither is sufficient. Acknowledging this, the Dutch Tax Administration undertook a two‐pronged approach focused on the management of integrity as a means to codify the operational ethics of the organization, as well as to foster shared values and behaviors. This approach is noteworthy because it guides behaviors while retaining street‐level discretion. It also is the first step toward creating a bureaucracy of mutual relationships that creates an ongoing moral consciousness serving both democracy and efficiency not only through control, but also through self‐reflection, interaction, and association.

eCommerce, Brexit, new safety and security concerns are only a few examples of the challenges that government organisations, in particular customs administrations, face today when controlling goods crossing borders. To deal with the enormous volumes of trade customs administrations rely more and more on information technology (IT) and risk assessment, and are starting to explore the possibilities that data analytics (DA) can offer to support their supervision tasks. Driven by customs as our empirical domain, we explore the use of DA to support the supervision role of government. Although data analytics is considered to be a technological breakthrough, there is so far only a limited understanding of how governments can translate this potential into actual value and what are barriers and trade-offs that need to be overcome to lead to value realisation. The main question that we explore in this paper is: How to identify the value of DA in a government supervision context, and what are barriers and trade-offs to be considered and overcome in order to realise this value? Building on leading models from the information system (IS) literature, and by using case studies from the customs domain, we developed the Value of Data Analytics in Government Supervision (VDAGS) framework. The framework can help managers and policy-makers to gain a better understanding of the benefits and trade-offs of using DA when developing DA strategies or when embarking on new DA projects. Future research can examine the applicability of the VDAGS framework in other domains of government supervision.

Both the increasing number and diversity of illicit-drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on-site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain-of-evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near-infrared (NIR)-based analyzers may overcome these encumbrances by their compound-specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300-2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time-efficient model training. This calibration-light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non-invasive, operator-safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug-related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

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\nLa evasión tributaria es un gran problema para los países en vías de desarrollo debido a que afecta la equidad y eficiencia del sistema impositivo. Una de las soluciones que se proponen para lograr el cumplimiento tributario, tanto en países desarrollados como en aquellos en vías de desarrollo, es el uso de la tecnología. Específicamente, la factura electrónica, en el marco de la digitalización de las administraciones tributarias, presenta una alternativa prometedora para aumentar la recaudación. En América Latina existen implementaciones exitosas de sistemas nacionales de facturación electrónica, con millones de emisores que han generado decenas de documentos electrónicos. Argentina, Brasil-SP, Chile, Ecuador, México, Perú y Uruguay son los países de la región que poseen factura electrónica consolidada. No obstante, cada administración tributaria la implementó de forma distinta –cada una con características, procedimientos y reglas diferentes- para adecuarla de la mejor manera a las particularidades de su país. Entre las principales ventajas que se le atribuyen a la factura electrónica están: (i) que acorta los ciclos de tramitación, incluido el cobro; (ii) reduce errores humanos; (iii) disminuye costos de transacción (como impresión, espacio de almacenamiento, etc.); (iv) facilita la lucha contra el fraude; y (v) contribuye a la modernización de la economía y al fortalecimiento del sector tecnológico por el uso en grandes escalas de comunicaciones, firmas digitales y el desarrollo de servicios. Las ventajas de la factura electrónica generan expectativas sobre posibles efectos positivos en la recaudación. Sin embargo, la evidencia sobre estos efectos ha sido escaza. El objetivo de este capítulo es presentar 5 trabajos de investigación en la región que responden a esta pregunta en forma rigurosa en Argentina, Brasil-SP, Ecuador, México y Uruguay. El reto de aislar los efectos de la factura electrónica sobre variables relacionadas con la recaudación, tales como ventas y utilidades reportadas, y recaudación causada y efectiva, no es fácil de solucionar. Entre un periodo y otro de recaudación ocurren muchísimos cambios en otros factores que afectan las variables mencionadas. Esto hace que una simple comparación entre la recaudación de un periodo sin factura electrónica y otro con factura electrónica capture cambios en otros factores, lo que generan sesgos en la estimación del impacto de la facturación. Además, las empresas que entran al sistema en los primeros periodos no son similares a las empresas que quedan por fuera. Lo que hace que comparaciones entre empresas que entraron y que no entraron tampoco sean adecuadas para aislar los efectos de la factura electrónica. Para resolver este problema se utilizaron principalmente dos metodologías: diferencias en diferencias y regresión discontinua las cuales, en condiciones adecuadas, pueden aislar el efecto de la factura electrónica en forma más precisa. En estos estudios en general se encuentra evidencia de que la factura electrónica aumenta las ventas y utilidades reportadas y recaudación de impuestos. No obstante, es importante tener claro que los efectos pueden variar en el tiempo y por sector de la economía. En plazos cortos, se ven aumentos substanciales, sin embargo, conforme pasa el tiempo los efectos se van reduciendo (Artana y Templado, 2017). También varían según la actividad económica (Artana y Templado, 2017; Bergolo, Ceni y Sauval, 2017). Es de esperar, también, que actividades donde hay más probabilidad de denuncia por parte de los clientes van a ser más afectadas (Naritomi, 2015). La implementación de la factura electrónica es un instrumento prometedor para el aumento en el cumplimiento de parte de las empresas en sus deberes con el fisco. Esto es de especial importancia en la región donde los niveles de recaudación y cumplimiento son relativamente bajos comparados con los de los países más desarrollados.

This study examines and theorizes the effects of task challenge on skill utilization, affective wellbeing and intrapreneurial behaviour among civil servants through a real-life challenging assignment, which was part of a unique Dutch and Flemish bottom-up organized event called ‘Train Your Colleague’. Results of a short-term longitudinal study indicate that, as expected, task challenge is positively related to skill utilization and intrapreneurial behaviour but, unexpectedly, not to affective wellbeing. These results suggest that challenging assignments may be important tools to enhance employees’ skill utilization and intrapreneurial behaviour at the workplace. Implications for theory and practice are discussed.

The Dutch Tax and Customs Administration (DTCA) is one of many organizations that deal with a multitude of electronic legal data, from various sources and in different formats. In this paper, we describe the results of a study aimed at better access to these sources by having a supplier and format independent knowledge store that describes the sources and their interrelations in a semantic network. Furthermore we developed parsers to automatically detect the identity of sources and typed references within the sources to other legal documents. These parsers can be used to fill and update the semantic network as new documents are added.

Abstract Although there is little debate that Census data reveal declines in standard measures of segregation over the past several decades, depending on who you ask, racial residential segregation is either just about gone or is stubbornly persistent. In this study, we draw attention to how the murkiness in the conceptualization of what has replaced ‘segregation’ and the related question of what integration is, contributes to this disagreement. Through an analysis of attitudes toward racially integrated neighborhoods, we demonstrate the pitfalls of our lack of consistency and clarity about the conceptual and operational definition of integration. Our analysis reveals the diversity of attitudes toward integrated communities—depending on who is asked, and what kind of integration is considered—and points to a fragility of commitment to the ideals of integration. We do this by using an innovative survey dataset that includes both open and closed-ended questions asked of a large probability sample of Whites, African Americans, and Latinos living in the Chicago metropolitan area. The survey asked individuals to describe their ideal neighborhood racial/ethnic composition and explain why it was ideal; they were then asked to describe (and explain) their least desired neighborhood racial/ethnic composition. Juxtaposing the results, we reveal that integration is both enthusiastically endorsed and much maligned—even within the same person—and that whether it is good or bad very much depends on the type of integration. We argue that appreciating the diversity of integration attitudes is critical if we are to develop a more nuanced understanding of future patterns of residential stratification in our increasingly diverse nation.

As part of a new organisational development approach, the ‘Police Cafe’ method provided excellent opportunity to talk about a special student community, customs officer candidates and their future employer, the National Tax and Customs Administration (NTCA). Among the Hungarian authorities, this specific organisation combines administrative, law enforcement and investigative functions. At an outstanding location, in a comfortable atmosphere, with active dialogue and sincere expressions participants had a rare opportunity to learn about the expectations posed by the revenue authority for its new employees and also, how the cadets think about their chosen profession. Lecturers of the Law Enforcement Faculty of the National University of Public Service as well as senior managers from several sectors of the NTCA also attended the workshop where participants analysed very actual problems such as how to direct potential candidates to the organisation; how recruits should be prepared for the challenges of their service; how to integrate them into the staff and, in longer terms, how to keep new generation staff members within the agency? Feedbacks from the working groups indicated that not simply a change in our habits of thought is required, but additional development of effective training practices, the introduction of new communication channels, and improvements in the organisational culture are also needed for the success.

We tested the effects of prepopulated returns and accuracy confirmation on compliance. Participants were asked to report correct liabilities for different types of returns, whereby some had to confirm the accuracy of each reported liability and others not. Results showed that correctly prefilled returns yielded the highest rate of compliance, followed by returns that were not prefilled, followed by returns that overestimated liabilities, and with returns that underestimated liabilities displaying the lowest compliance. Moreover, accuracy confirmation increased compliance only for returns that overestimated liabilities. The present study indicates that both morality and defaults play a pivotal role in shaping the effects of prepopulated returns on compliance. Our findings imply that prepopulating tax returns should be done with care, because it can increase tax compliance when done correctly, but undermine it when done incorrectly.

Abstract Traditionally, tax authorities endeavour to resolve their tax treaty disputes among themselves, by amicable settlement through a mutual agreement procedure (commonly known as ‘MAP’ procedure), without involvement from any third parties—neither arbitrators nor mediators. In past years, due to globalization of countries’ economies and spread of tax treaty networks, the number of disputess, their complexity and revenue interest involved have gone up drastically, exceeding many authorities’ capacities, and resulting in MAP cases taking up increasingly more time, or remaining unresolved at all. It is generally expected that the recent OECD/G20 initiated ‘BEPS’ (short for: Base Erosion and Profit Shifting) measures against international tax avoidance will add further to this. Arbitration so far having been hardly tried in practice, the recent arbitration piece under the BEPS multilateral treaty (MLI) and EU Directive on dispute resolution in international tax matters, however, create new momentum. It is now up to tax authorities if they can accustom themselves to the use of arbitration as an ordinary, and in certain circumstances preferable tool for resolving their disputes.

BACKGROUND: Mixtures of internationally traded organic substances can contain parts of species protected by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). These mixtures often raise the suspicion of border control and customs offices, which can lead to confiscation, for example in the case of Traditional Chinese medicines (TCMs). High-throughput sequencing of DNA barcoding markers obtained from such samples provides insight into species constituents of mixtures, but manual cross-referencing of results against the CITES appendices is labor intensive. Matching DNA barcodes against NCBI GenBank using BLAST may yield misleading results both as false positives, due to incorrectly annotated sequences, and false negatives, due to spurious taxonomic re-assignment. Incongruence between the taxonomies of CITES and NCBI GenBank can result in erroneous estimates of illegal trade. RESULTS: The HTS barcode checker pipeline is an application for automated processing of sets of 'next generation' barcode sequences to determine whether these contain DNA barcodes obtained from species listed on the CITES appendices. This analytical pipeline builds upon and extends existing open-source applications for BLAST matching against the NCBI GenBank reference database and for taxonomic name reconciliation. In a single operation, reads are converted into taxonomic identifications matched with names on the CITES appendices. By inclusion of a blacklist and additional names databases, the HTS barcode checker pipeline prevents false positives and resolves taxonomic heterogeneity. CONCLUSIONS: The HTS barcode checker pipeline can detect and correctly identify DNA barcodes of CITES-protected species from reads obtained from TCM samples in just a few minutes. The pipeline facilitates and improves molecular monitoring of trade in endangered species, and can aid in safeguarding these species from extinction in the wild. The HTS barcode checker pipeline is available at https://github.com/naturalis/HTS-barcode-checker.

Over the last few decades, businesses have developed sophisticated information systems that allow the capture of vast amounts of data. Such data can be potentially useful for enabling government authorities to improve their processes and services. For example, access to business documents and track and trace information associated with supply chain activities is of great interest to customs administrations. Such information holds the potential to make customs risk assessment processes more efficient and effective and to enable faster clearance of goods crossing borders. Businesses, however, are often not willing to voluntarily share information with the government beyond what is strictly mandated to be shared by law (e.g. submitting customs declarations). There is only limited academic research and a general lack of understanding amongst practitioners about how voluntary business-government information sharing can be achieved. In this study, we present a framework to analyse the barriers, drivers, and enablers of voluntary business-government information sharing and the governance processes that make such voluntary information sharing possible. Our analysis shows that voluntary business-government information sharing can succeed when there are strong drivers and a government agency willing to take the lead in initiating the process.

This paper describes the possibilities of the translation of legislation, which is written in natural language, into a formal language, i.e. UML/OCL. The tool OPAL (Object-oriented Parsing and Analysis of Legislation) is developed to support the automatic modelling of legislation with the use of appropriate NLP techniques. The aim is not to perform this modelling in a batch fashion from legislation to final model, but interactively in dialogue with the knowledge engineer. The main components of OPAL are a parser (based on a chart-parser algorithm) and a model generator. A special component called modelling interface is added to OPAL to give the knowledge engineer the possibility to keep track of the modelling process and to make adjustments to the final model.