dataesr/scientific-paragraphs-categorization huggingface dataset:
All regressions include country and sector fixed effects as well as the input tariff regressor x. Robust (country-clustered) standard errors in parenthesis. Statistical significance: * p<0. 1; ** p<0. 05; *** p<0. 01. Governance variables are from the World Bank Governance Indicators. Services trade policies from the World Bank Services Trade Restrictiveness Database. Labour productivity (output per worker) from the UNIDO industrial statistics database. Panel A: as control of corruption 0. 055* 0. 259 0. 097 0. 074* -0. 018 (0. 030) (0. 303) (0. 227) (0. 042) (0. 047) x -0. 037*** -0. 204* -0. 132* -0. 037** -0. 004 (0. 011) (0. 121) (0. 078) (0. 016) (0. 019) Adjusted R-squared 0. 591 0. 589 0. 589 0. 589 0. 586 Panel B: as regulatory quality 0. 073** 0. 211 -0. 026 0. 116** -0. 051 (0. 032) (0.
In recent years, the tax and accounting literature at-large has gained important new insights through larger-scale parent and affiliate entity data that became available via Amadeus (Europe) and Orbis (Global), both Bureau van Dijk products. These databases bring the advantage that they allow investigations of corporate groups, including information on reporting sub-entities from the same ultimate owners, and it has proven to be useful in several international tax studies including the work of  Huizinga and Laeven (2008) ,  Beuselinck et al. (2015) ,  Markle (2015) , De Simone (2016), De  Simone et al. (2017)  and  Alexander et al. (2020). We also rely on the Orbis database to identify the global network of European headquartered multinational groups and propose a novel calculation approach to identify the importance of and dynamics in local tax strategies vis-a-vis profit shifting.
Consequently, we propose that a group level versus income-weighted subsidiary level ETR correlation can serve as an inverse measure of profit shifting and that a lower correlation coefficient indicates more profit shifting, relative to local tax planning as part of the overall tax planning strategy. Empirically we proceed as follows. We compile subsidiary-level financial data (154,022 subsidiary-year observations) from the Bureau Van Dijk Orbis database to calculate GAAP ETRs of subsidiaries and group-level data (32,849 multinational group-year observations) to calculate GAAP ETRs of European multinational groups over the period of 2006-2014. Next we regress the group consolidated GAAP ETR on the pre-tax income weighted GAAP ETR for the entirety of its observable subsidiaries. A low association between the pre-tax income-weighted ETR of the subsidiaries and the group-level ETR indicates profit shifting, whereas a high association indicates affiliate entity ETRs being more aligned with the overall group tax planning.
We can perform this type of analysis since our dataset (described in more detail in Section 5. 1) allows us to observe unconsolidated (subsidiary-entity) financial statements of domestic and foreign affiliates that are majority-owned by global multinationals. The pretax income that is reported in unconsolidated financial statements is the source-country income that is subject to local tax. Notably, this is the income reported in a country after potential profit shifting into or out of that country. Next we compute the weighted average (by pretax income, PTI) ETR for all subsidiaries of a given multinational to obtain one measure of tax planning of all its subsidiaries in year t. This measure can be interpreted as the weighted local tax planning within jurisdictions where the subsidiaries are located (wSubETR g,t ) and where the weight is formed by the level of the subsidiary taxable income.
Sample and descriptive statistics The sample contains all nonfinancial groups from 27 EU member states and their global subsidiaries from the Bureau van Dijk Orbis database covering the period 2006 to 2014. 12 This database contains information on the (most recent) ultimate owner of each corporation, which we use to construct corporate groups. We exclude purely domestic (i. e. , single-jurisdictional) groups, since these firms can only avoid taxes locally and therefore cannot trade off local tax planning with cross-jurisdictional income shifting. For each EU member state, we download the consolidated parent financial data and the unconsolidated subsidiary-level data. Subsidiaries are defined as such if the parent company directly or indirectly owns at least two-thirds (67%) of the shares. 13 This search strategy allows us to combine all unique subsidiary observations with their ultimate parent.
We exclude observations with missing data on pretax income and total assets and for which we have missing data on control variables, for firm-years with negative pretax income or negative tax expense, firm-years with a tax rate above 100% of pre-tax income, and subsidiaries with net income of exactly zero. The final subsidiary-level dataset consists of 154,022 subsidiary-year observations from 64 countries from both within and outside the European Union. This sample also corresponds to 32,849 multinational group-year observations headquartered in the European Union. In our sample, we capture a significant portion of the overall groups' activities. For example, the sum of all subsidiary assets to consolidate assets is around 38%, the sum of all subsidiary pretax income to consolidated pretax income is around 63%, and the sum of all subsidiary tax expense to consolidated tax expense is around 58%.
Acknowledgements An earlier draft of this paper was titled " Multinational Tax Avoidance -Is  it all about profit shifting?"(2018). We thank  Jennifer Blouin  (editor) and an anonymous referee. This study has also benefited from comments by  Anna Alexander ,  Kathleen Andries ,  Jennifer Glen ,  Kenneth Klassen ,  Stacie Laplante ,  Nico Lehmann ,  Lillian Mills ,  Benjamin Osswald ,  John Robinson , and participants of the  WHU Tax Readings Group. We thank participants of the  University of Illinois Symposium on Tax Research XV (2017) in Chicago (USA)  and  Tom Neubig  (discussant) and  Steven Shay  (discussant) and participants of the  Norwegian Tax Accounting Symposium 2018 in Bergen (Norway). Furthermore, we thank workshop participants at the  Free University of Bozen-Bolzano (Italy) ,  University of Bristol (UK) ,  University of Essex (UK) ,  University of Gothenburg (Sweden) ,  University of Goettingen (Germany) ,  Hong Kong University of Science and Technology, University of Paderborn (Germany) ,  Tilburg University (Netherlands) ,  University of Wisconsin (USA) , the Research Day in Accounting hosted by the  University of Antwerp (Belgium) , the 1 st ERIM Accounting Day at  Erasmus University Rotterdam  (Netherlands), and conference participants at the 40 th Annual Congress of the  European Accounting Association in Valencia (Spain)  for their valuable comments.
The total number of academic articles published every year is somewhere on the order of 2. 5 million (size of the Web of Science database in 2018) or 3. 5 million (size of the Dimensions database in 2018; see also  Schimmer, Geschuhn, and Vogler 2015). The overall publication bill should therefore be on the order of 1 billion dollars per year, but research organizations actually pay roughly 10 times more than this in subscriptions and APCs  ( Johnson, Watkinson, and Mabe 2018). About a third of this amount is profit for publishers, and about half covers unwanted costs required to support the forprofit system, such as paywalls, lobbying, marketing fees  (Grossmann and Brembs 2021) , and million-dollar lawsuits against open access initiatives  (Shiermeier 2017). All in all, academia could save about $10 billion of public money every year if the publishing system were reformed.
g. , see the lists of repositories in the OpenDOAR and ROAR databases). Indeed, an embargofree rights retention strategy is a key component of the Plan S initiative for open access (https: //www. coalition-s. org/rights-retention-strategy/). Additionally, the practice of uploading manuscripts in preprint servers-like arXiv, Zenodo and bioRxiv-has skyrocketed in the last two decades  (Abdill and Blekhman 2019; N. Fraser et al. 2021; Wang, Chen, and Glänzel 2020). This, in turn, has opened up new possibilities for subsequent open and transparent review of manuscripts by community members, without the participation of the dominant publishers-effectively a form of social niche construction. For example, commons-based manuscript-recommendation models like the Peer Community In (PCI) initiative  (Guillemaud, Facon, and Bourguet 2019)  provide a way for peer review to occur at no cost to reviewers, authors, or readers.
How can we best inform ourselves about the different publishing models and how they prioritize ethical publishing practices? Some initiatives exist to guide scientists and provide valuable information. For example, in the field of ecology and evolutionary biology, DAFNEE (https://dafnee. isem-evolution. fr) is a database intended to inform scientists about the economic models, publication fees, and partnerships of different journals. DAFNEE only includes journals that have a minimum set of credentials to be qualified as "academia-friendly", i. e. , owned or controlled by public institutions, nonprofit organizations, or groups of scientists such as learned societies. Some of these organizations partner with for-profit publishers, while others are entirely nonprofit and/or non-commercial. The user can query journals by topic, economic model, Box 1: The multidimensional landscape of academic publishing.
It is often suggested that publishing in such journals is beneficial to a scientist's career. We assessed the validity of this claim in the field of Ecology and Evolutionary Biology by querying the DAFNEE database, which contains publishing information for this field. We sampled nine cities/states having recently hosted a major conference in this field: the Joint Evolution meeting (Society for the Study of Evolution, European Society for Evolutionary Biology, Society of Systematic Biologists, American Society of Naturalists) or the SMBE meeting (Society for Molecular Biology and Evolution). These were located in cities across three continents: Austin, Ottawa, Québec (America), Auckland, Queensland, Tokyo (Asia), Manchester, Montpellier, Vienna (Europe). In each of these cities, a department of ecology, evolutionary biology or biology was identified.
Acknowledgments We thank  Corina Logan  for helpful information in the early stages of the manuscript,  Khalid Belkhir  and members of the  DAFNEE work group  for their help in generating the data analyzed here, and the  Gen-Ethics  workshop organizers at the  Globe Institute , for facilitating discussions about the ethics of research practices, which motivated the writing of the manuscript. We also thank  Rasmus Nielsen ,  Moisès Coll Macià ,  Sébastien Puechmaille , the members of the  Racimo group , and two anonymous reviewers for helpful feedback on the text.
Conflict of interest disclosure TG and DB are co-founders of PCI and members of ESEB and SFE 2. FR is a member of the managing board of PCI Evol Biol and a member of SMBE. NG is a recommender for PCI Evol Biol and a curator of the DAFNEE database. BP is a member of the managing board for PCI Ecology and a recommender for PCI Evol Biol. As any other scientists, the authors face a dilemma when selecting journals for publications, linked to the discrepancy between professional constraints/opportunities and ethical considerations on journal venues. The authors have published in for-profit journals in the past, and might do so in the future. This dilemma is part of what motivated the authors to write the paper, to help increase debate in the scientific community concerning the systemic constraints that researchers face, and on paths to changing these constraints moving forwards.
Data collection and analysis Data collection took place throughout 2017, except in Togo, where it began in 2016 as part of a pilot study to test the approach and methods. We conducted semistructured interviews with key informants, including policy dialogue participants and representatives of organizations involved in the policy dialogue (n = 121), analyzed policy documents, and observed policy dialogue events in Burkina Faso and Cabo Verde. Table  2  presents the number of respondents and observation opportunities by case and indicates the types of analyses conducted. We performed five types of analyses, to various extents depending on the availability and quality of data and expertise, and on the researchers' availability in each of the six countries. Since we could not perform a realist analysis in some case studies, we were unable to conduct the transversal analysis using CIMO configurations, as set out in the protocol.
Acknowledgements We would like to thank researchers  Eufémia Vicente Rocha  ( University of Cabo Verde, Cabo Verde ),  Kodjo Segniagbeto  (consultant, Liberia),  Mahaman Moha  (LASDEL, Niger),  Bruno Lapika  ( University of Kinshasa, Democratic Republic of the Congo ),  Célé Manianga  ( University of Kinshasa, Democratic Republic.
Computational experiments In this section we aim at evaluating the performance of the aggregated formulations presented in Section 3. Tests are performed on benchmark instances for the IRP (see  Archetti et al. , 2014 ). The characteristics of the instances are the following: • Time horizon H: 3 and 6; • Number of customers n : 5 l, with l = 1 ,. , 6 for H = 6 and l = 1 ,. , 10 for H = 3. • Number of vehicles m : from 2 to 5. • High and low inventory cost. For each combination of the above parameters, 5 instances were generated, thus having 640 instances in total. We refer to  Archetti et al. (2014)  for more details on the instances. Computational tests were made on a Windows 64 machine equipped with Intel(R) Xeon(R) CPU E5-1650 v2, 3050 GHz, 64. 0 GB RAM. The code was written in C++, compiled with MS Visual Studio 2019 Express Edition in release mode, and CPLEX 12.
Thus, with this setting, we replace the combinatorial separation of constraints (GSEC) , by internal separation provided by Cplex using the annotated Benders setting. Benders subproblems are separated by t ∈ T and l ∈ N as they are fully independent. A time limit of two hours has been set. For the two competing approaches, CL was run on a grid of Intel Xeon T M processors running at 2. 66 GHz with up to 48 GB of RAM installed per node, with the Scientific Linux 6. 1 operating system and a time limit of 2 hours. Cplex 12. 5 was used as exact solver. DRC was run on an Intel Core i7-2600 processor clocked at 3. 4 GHz with 8 cores and 16 GB RAM, with a time limit of 2 hours. Cplex 12. 2 was used as exact solver. We point out that we did not separate inequalities (MS) and we kept them explicitly in the model using (  14 ).
Databases are also used as knowledge repositories to disseminate knowledge across functions. In other phases, however, solution providers mainly rely on employees to develop and coordinate solution activities manually, for example, collecting information to understand customer value drivers, developing rules for structuring solutions, proposing value to customers and working with industrial associations to create visibility, using working methodologies such as configuration tools to develop specific customer solutions and quantifying the value of the specific solutions for the customers. Thus, while valuable in mapping the complexities of interorganizational coordination in each phase of the solution process, Storbacka's framework does not take account of the respective roles and interactions between digital interfaces and boundary spanners.
Data collection The research process consisted of two phases. The first phase was conducted from July to August 2016 as part of a broader research study  (Wei et al. , 2019). In this phase, 22 semistructured interviews were conducted (a further eight interviews were conducted in phase two, as detailed below). The interviews lasted from approximately 30 to 100 min. The option of using semi-structured interviews was consistent with our aim of theory building and offered us the flexibility to include the topics and concepts that we might have missed  (Patton, 1990). To gain a deeper insight into the companies' context, business models and platform designs, we interviewed senior executives from different departments within the organizations, as detailed in Table  3. These interviews enabled us to gain an understanding of the overall picture of the solution process from the firm's perspective.
We also conducted interviews with managers focused on how the solution providers to design and use their platforms to manage the interactions among module providers and customers in each solution phase. The interviewees included managers responsible for different phases in a solution, such as supply chain, operations, community and printed circuit board (PCB) managers. During this research phase, the importance of boundary spanners and their interactions with digital interfaces emerged, which prompted us to review the literature on boundary spanning and interfaces. To complement the interview data, we collected data from the firms' websites and digital platforms, such as the designs of information systems, digital contracts, digital forms, process descriptions posted online, to understand the use these companies made of boundary resources.
Archival records and internal firm documents about stakeholders such as module providers were collected to supplement interview data. Such documents included product books, process descriptions, quality management procedures, customer feedback, contract templates, regulations, as well as screenshots from websites to identify the functions of the interfaces. Observation notes were also taken in a research diary during the fieldwork, for instance, when studying the digital boundary interfaces. These different kinds of data collected allowed us to maintain a clear chain of evidence from the empirical data to the final analytical framework  (Yin, 2003).
Data analysis Aligned with similar prior studies  (Storbacka, 2011; Kindström and Kowalkowski, 2009) , our unit of analysis is the solution process, due to our focus on how digital interfaces and boundary spanners interact in different solution phases. Adopting a solution process framework as our research framework also enhances the internal validity of our findings  (Gibbert and Ruigrok, 2010). To gain an in-depth understanding of current processes, thematic analysis was used on all data, supported by the software NVivo 11  (Miles and Huberman, 1994). A systematic data reduction process was conducted following  Miles and Huberman's (1994)  recommendations. When segmentation and coding began, a Following the solution process framework  (Storbacka, 2011) , these codes were subsequently merged into higher-order topics (the functions of boundary spanners and interfaces) in each solution phase.
To increase reliability and transparency, we built a case database which includes the interview transcripts, the documents, observation notes and analysis  (Yin, 2003). To improve data quality, we triangulated our findings across different data sources  (Creswell and Miller, 2000). This also ensured construct validity in our study. For example, the interview transcripts were compared with our observation notes and process description documents, which helped examine consistency among them. Further questions were asked to the interviewees for explanations of any inconsistency. We also ensured that our interviewees had sufficient familiarity with the solution process by asking initial interview questions to evaluate their experience  (Miles and Huberman, 1994). To validate the findings and ensure internal validity, the findings were sent back to key informants who agreed that the findings were valid and recommended small modifications, for instance, technical terms in the service processes  (Lindgreen, 2008).
Customers can upload a list of components and their designs so that the algorithms can automatically match component providers and service providers for them. Partnering with a components database, ICT1 offers data analytics to its customers to optimize their product designs. With an online community on its digital platform, customers can gain advice and knowledge from technical experts and interact with each other. Communication and payments tools are embedded in the digital platform to manage transactions. Information sharing is enabled on the platform to help customers monitor parts of the solution process. Chatbots are used to answer frequently asked questions raised by customers. ICT2 also operates in the ICT industry. Originally a PCB manufacturer, ICT2 has developed into a solution provider offering end-to-end solutions for new product development.
The data are also complemented by feedback from online surveys, aftersales calls and online reviews, as revealed by the records we collected. While Light1 mainly relies on internal data, ICT1 and ICT2 conduct research on competitor performance, including sales data and prices. Rather than using traditional research methods, technical consultants gather these competitor data by using web scraping bots to scan competitors' websites or platforms so that they understand the market dynamics and identify potential module providers. Web scraping bots automatically collect and synthesize large amounts of competitor data. These interfaces suffice for these tasks since task complexity is moderate, which means these tasks only involve some analytical thinking, such as data synthesis. However, the web scraping bots can only perform these tasks with some human interventions and without advanced technological capabilities to further analyze the data, so their intelligence levels are moderate.
Our system will recommend our customers to switch. " (CEO, ICT2) For value quantification, the algorithms predict results in terms of the delivery time, the cost and the performance level of the requested modules due to data linking across departments. As such, the solution providers do not need to rely on business control employees to support sales teams by manually calculating the data for each solution. Additionally, in partnership with a large industry database, the algorithms in ICT1 also provide further information about product modules, such as product lifecycle, compatibility and price fluctuation. By sharing these data in the matching results, the algorithms quantify the value of the solutions and identify risks for the customers. Therefore, the intelligence level of these algorithm interfaces is high, as they analyze customer information and module providers' information to automate most of the module reconfiguration process.
The python package NetworkX (version 2. 6. 3) is used for implementing the directed graph  [23]. Apart from the "KnowledgeBase" and "State" class, the proposed simulation framework also provides an "Action" class to capture the actual situation that a collaborative network might face disruptions and interventions. The "Action" class has a method "execute()" that takes a "State" as input and returns a "State" as the result after the disruption or intervention has been executed. This class can be used to break the stable state of the system during a simulation or to test the impact of risk management measures on the system. Aggregating and depending on the above three class, the "Experiment" class is designed for implementing the simulation experiment given a specific collaborative network. The "knowledgeBase" attribute provides knowledge of the considered system for driving the inference, the "initialState" and "currentState attribute represents the starting point and the status of the simulated system respectively, while the "trajectory" attribute is composed of a series of states and records the state evolution over time.