Aufkommens- und Entscheidungseffekte von Ertragsteuern

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Interest deductibility restrictions and organizational form

Using a theoretical model for risky investment decisions, we study the effect of interest deductibility restrictions on the choice of organizational form. We analyze the two most widely used approaches: first, rules that limit the interest deductibility if the firm’s leverage exceeds a specific level, and second, rules that restrict the interest deduction if the interest expenses exceed a specific percentage of the firm’s earnings. Although these restrictions apply uniformly for partnerships and corporations in many countries, we find that they usually distort the choice of organizational form. We demonstrate that only leverage-based interest deductibility restrictions can in theory be modified to achieve organizational form neutrality. However, this requires a legal form dependent application or absence of dividend taxation and in any case a full loss offset which is in contrast to current law in many countries. If one considers corporate loss offset limitations, both types of interest deductibility restrictions always distort the choice of organizational form. Thus, the introduction of interest deductibility restrictions is actually in conflict with the legislators’ often declared aim to design tax systems that are neutral with respect to the choice of organizational form

Preprocessing messages posted by dentists to an Internet mailing list: a report of methods developed for a study of clinical content

Objectives: Mining social media artifacts requires substantial processing before content analyses. In this report, we describe our procedures for preprocessing 14,576 e-mail messages sent to a mailing list of several hundred dental professionals. Our goal was to transform the messages into a format useful for natural language processing (NLP) to enable subsequent discovery of clinical topics expressed in the corpus. Methods: Preprocessing involved message capture, database creation and import, extraction of multipurpose Internet mail extensions, decoding of encoded text, de-identification, and cleaning. We also developed a Web-based tool to identify signals for noisy strings and sections, and to verify the effectiveness of customized noise filters. We tailored our cleaning strategies to delete text and images that would impede NLP and in-depth content analyses. Before applying the full set of filters to each message, we determined an effective filter order. Results: Preprocessing messages improved effectiveness of NLP by 38%. Sources of noise included personal information in the salutation, the farewell, and the signature block; names and places mentioned in the body of the text; threads with quoted text; advertisements; embedded or attached images; spam- and virus-scanning notifications; auto text parts; e-mail addresses; and Web links. We identified 53 patterns of noise and delivered a set of de-identified and cleaned messages to the NLP analyst. Conclusion: Preprocessing electronic messages can markedly improve subsequent NLP to enable discovery of clinical topics. Keywords: Electronic mail; data processing; natural language processing; dental informatic

Using Natural Language Processing to Enable In-depth Analysis of Clinical Messages Posted to an Internet Mailing List: A Feasibility Study

Background: An Internet mailing list may be characterized as a virtual community of practice that serves as an information hub with easy access to expert advice and opportunities for social networking. We are interested in mining messages posted to a list for dental practitioners to identify clinical topics. Once we understand the topical domain, we can study dentists’ real information needs and the nature of their shared expertise, and can avoid delivering useless content at the point of care in future informatics applications. However, a necessary first step involves developing procedures to identify messages that are worth studying given our resources for planned, labor-intensive research. Objectives: The primary objective of this study was to develop a workflow for finding a manageable number of clinically relevant messages from a much larger corpus of messages posted to an Internet mailing list, and to demonstrate the potential usefulness of our procedures for investigators by retrieving a set of messages tailored to the research question of a qualitative research team. Methods: We mined 14,576 messages posted to an Internet mailing list from April 2008 to May 2009. The list has about 450 subscribers, mostly dentists from North America interested in clinical practice. After extensive preprocessing, we used the Natural Language Toolkit to identify clinical phrases and keywords in the messages. Two academic dentists classified collocated phrases in an iterative, consensus-based process to describe the topics discussed by dental practitioners who subscribe to the list. We then consulted with qualitative researchers regarding their research question to develop a plan for targeted retrieval. We used selected phrases and keywords as search strings to identify clinically relevant messages and delivered the messages in a reusable database. Results: About half of the subscribers (245/450, 54.4%) posted messages. Natural language processing (NLP) yielded 279,193 clinically relevant tokens or processed words (19% of all tokens). Of these, 2.02% (5634 unique tokens) represent the vocabulary for dental practitioners. Based on pointwise mutual information score and clinical relevance, 325 collocated phrases (eg, fistula filled obturation and herpes zoster) with 108 keywords (eg, mercury) were classified into 13 broad categories with subcategories. In the demonstration, we identified 305 relevant messages (2.1% of all messages) over 10 selected categories with instances of collocated phrases, and 299 messages (2.1%) with instances of phrases or keywords for the category systemic disease. Conclusions: A workflow with a sequence of machine-based steps and human classification of NLP-discovered phrases can support researchers who need to identify relevant messages in a much larger corpus. Discovered phrases and keywords are useful search strings to aid targeted retrieval. We demonstrate the potential value of our procedures for qualitative researchers by retrieving a manageable set of messages concerning systemic and oral disease