dataesr/scientific-paragraphs-categorization huggingface dataset:
The estimated coefficient b o gives the average responsiveness of country i's fiscal policy to all other countries of the sample, while the responsiveness of country i's corporate tax rate to country j 's tax rate is given by b om $M. 7 This regression will be tested on a database that covers (up to) 26 OECD countries for the period from 1982 to 2006. 8 These countries are: Australia, Austria, Canada, Czech Republic, Denmark, Germany, Hungary, Iceland, Spain, Finland, France, Great Britain, Greece, Italy, Ireland, Japan, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Sweden, Switzerland, Turkey, and the United States of America. 9 Data definitions, summary statistics, and sources are given in Table  1  of appendix A. 2.
However, with more sophisticated models we can show that the decline in trade costs strengthens tax interactions. This is true, for example, if we include infra-national trade costs, asymmetric trade costs across countries, or if we assume an endogenous labour supply so that the wage becomes dependent on the level of trade integration (see Exbrayat et al. , 2012). Thus, we also  7  Thus, even if we consider that each country adjusts its tax policy to the average tax rate of the other countries, it reacts more or less strongly to each one of them as far as we define non-uniform weight matrices. 8 The starting point follows from the availability of corporate tax rate data (source: Loretz, 2008), while the endpoint is due to the lack of market potential data after 2006 (source: CEPII Market Potential Database).
The final dataset is unbalanced since the data regarding some new EU member states is only available in the second part of the time period. As a robustness check (see section 4. 2), we provide results using a smaller but balanced sample. 9 We included all countries in the tax dataset by Loretz (2008), except Republic of Korea and Mexico for which there are many missing values for taxes or bilateral trade, as well as Belgium and Luxembourg for which the CEPII does not provide data on bilateral trade and market potential. 10 Note that because of the row standardization, this is equivalent to using a weighting scheme based on the absolute population of each neighbouring country. However, this is not a problem. By expressing weights as a function of relative (rather than absolute) population, we can easily check that the derivative of m sA > with respect to the relative population (epe sA epe A ⁄ ) is positive.
Specifically, we rely on the real market potential variable provided by the CEPII, which has several advantages. 15 irst, it is estimated from a theory-grounded gravity equation, so that the endogeneity bias of such a variable is controlled for (see Head and Mayer, 2011). Secondly, it takes into account that the market potential in a country is not only increasing with demand coming from other countries but also decreasing with the average price in those countries, and thereby captures the market-crowding effect at work in the model. Thirdly, it is estimated using a dataset covering more than 150 countries, and thus allows us to indirectly take account of thirdcountry effects. Finally, let us discuss the choice of control variables that compose the vector h ij. While these variables do not matter for the location of capital, they influence the government's tax policy through other channels.
The size of the public sector is approximated by a variable for public consumption. Following Davies and Voget (2011), we limit the potential endogeneity bias of the public sector size in two ways: by expressing public consumption expenditures as the share over GDP and by taking the lagged value of this 14 By contrast, ex post measures of corporate taxation are defined as a ratio of tax revenues over the tax base. Thus, they are backward-looking measures that cannot capture the impact of taxes on the behaviour of firms. 15 For more information on the CEPII dataset, see http://www. cepii. fr/anglaisgraph/bdd/marketpotentials. htm. This dataset provides estimates of real market potential until 2003. In order to extend the data to 2006, we replicated what Head and Mayer (2011) call the "RV04" method.
Moreover, recall that tax competition for firms occurs if and only if firms are mobile and responsive to tax differentials. Following Devereux et al. (2008), we further investigate the importance of capital mobility for tax interactions by allowing the coefficient α to vary with the degree of capital mobility of countries. We approximate capital mobility by the Index of Capital Mobility Constraints (ICMC) from the Economic Freedom of the World database. This index combines information about foreign investment restrictions (taken from the Global Competitiveness Report) and international capital controls (taken from the IMF) into a 10point scale, where increasing numbers indicate higher capital mobility. We cannot allow the coefficient of tax interaction to vary by country, due to lack of degrees of freedom.
A. 2. Data sources and summary statistics Definitions and data sources €• c M , €d c M and = • • M : effective average tax rate, effective marginal tax rate and statutory tax rate (Loretz, 2008). u{z M : top marginal income tax rate (World Tax Database and OECD tax database). etu $M : trade phi-ness index (our calculations, sources: Trade and Production database). |p} M : proportion of population less than 14 years old (World Development Indicators). •ye•c M : GDP growth from year t-1 to year t (World Development Indicators).
xyu M : sum of inward and outward FDI flows over GDP (in current $) (World Development Indicators). ezp{= M : general government final consumption expenditures in percentage of GDP (World Development Indicators). cde M : real market potential (U 10 ) (CEPII market potential database, and own calculations). }c~ M : proportion of population living in urban areas (World Development Indicators). p y M : proportion of population more than 65 years old (World Development Indicators).
This interpretation is also in line with the findings ofBenassy-Quéré et al. (2007). Using a dataset of FDI flowing from the United States to 18 European countries in the period 1994-2002, their estimates reveal that funding a marginal increase in public capital through corporate taxation induces capital outflow, because public capital is not productive enough to compensate for the required increase in corporate taxation. Klemm and van Parys (2012)  also point out that government consumption expenditures have a negative impact on FDI in Latin American, Caribbean and African countries for the period1985-2004.
ACKNOWLEDGEMENTS The authors confirm that this article content has no conflict of interest. The authors would like to thanks the students of Reims University (from the  Management Logistics and Transport department of the IUT)  who conducted the survey concerning firms' location factors, as well as developers and investors who accepted to be interviewed. The authors appreciate the previous economic support of the  Regional Council of Champagne-Ardenne  and the current economic support of  PREDIT  ( French program  of research, experimentation and innovation in land transport).
As a result of evolving clinical trial designs and the changing patient populations selected, data from earlier clinical trials of biologics are not likely to be comparable with more recently conducted RCTs. As a simple marker of this phenomenon, consider that the time needed to enroll and/or complete a given IBD trial has changed dramatically (Tables  1  and 2 ). For example, in 1999, the 54-week ACCENT 1 trial enrolled 573 anti-tumor necrosis factor (TNF)-therapy naive patients with CD from 55 centers in 12 countries and was completed in approximately 2 years. 8 In contrast, by 2008 the 52-week GEMINI 2 trial required 1115 patients with CD (62% prior anti-TNF exposed) from 285 centers in 39 countries and took almost 3. 5 years to complete. 14 ubstantial changes have taken place in IBD trials over the past 20 years.
ACKNOWLEDGMENTS Juliette Allport of Leading Edge Medical Education provided medical writing support to the authors in the development of the first draft of this manuscript;  AbbVie paid Leading Edge Medical Education  for this work. Alessia Piazza, PhD , of  Excerpta Medica , provided editorial assistance in the preparation of this manuscript, which was funded by  AbbVie Inc. S. G. received financial support for research from  AbbVie , received consulting fees from  AbbVie ,  Shire ,  Pfizer ,  Receptos ,.
Overview of the Common Methodology Deployed in the Case Studies The methodology deployed in the two case studies comprises the four following steps  (Herivaux 2011; Rinaudo 2008 ): (1) preliminary social survey; (2) questionnaire design and test; (3) survey implementation; and (4) data analysis. Step 1 consists of a series of qualitative interviews to analyse people's perception and understanding of the groundwater resource under study. In the LTS, a total of 72 semi-structured face-to-face interviews were conducted to capture the lay vision of the reservoir, its characteristics and geographic extension; and to assess the level of understanding of the water cycle underground, with specific attention being paid to the understanding of exchanges between ground and surface water. Respondents were also asked to identify the services provided by groundwater to society.
). The quantitative survey was then completed, with respectively 530 and 650 respondents in the MAA and LTS case studies (step 3). Face-to-face interviews were used in the MAA case study and a mail survey in the French LTS case study. Both methods have their advantages and their limits. For the MAA case study, the in-person survey seemed to be the most appropriate to collect answers to openended questions on groundwater and to minimize the non-response rate which was expected to be particularly high in this "non-use context". The mail survey method was chosen for the LTS case study to ensure that respondents would have sufficient time to get to know an unfamiliar subject and think about their preferences. The return rate was about 11 %. Data obtained were then statistically analysed to check the consistency of responses and to identify factors determining stated WTP for groundwater protection (step 4).
Acknowledgements The authors wish to thank the  Belgian Federal Science Policy Programme BELSPO  for the financial support provided for the FRAC-WECO research project, and the  European Commission  for the financial support provided for the  BRIDGE-WFD research project. The chapter has been prepared with the financial support of  BRGM  research program 30 (Environmental and Risk economics). Open Access This chapter is distributed under the terms of the Creative Commons Attribution-Noncommercial 2. 5 License (http://creativecommons. org/licenses/by-nc/2. 5/)which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.
We build an endogenous growth model to analyze the relationships between taxation, corruption, and economic growth. Entrepreneurs lie at the center of the model and face disincentive e¤ects from taxation but acquire positive bene…ts from public infrastructure. Political corruption governs the e¢ ciency with which tax revenues are translated into infrastructure. The model predicts an inverted-U relationship between taxation and growth, with corruption reducing the optimal taxation level. We …nd evidence consistent with these predictions and the entrepreneurial channel using data from the Longitudinal Business Database of the US Census Bureau. The marginal e¤ect of taxation for growth for a state at the 10th or 25th percentile of corruption is signi…cantly positive; on the other hand, the marginal e¤ects of taxation for growth for a state at the 90th percentile of corruption are much lower across the board.
In Section 3, we provide empirical evidence on the relationships between taxation, corruption, and economic growth using state-and county-level variations within the United States. Our employment and …rm count data are primarily from the Longitudinal Business Database (LBD) of the US Census Bureau. We measure corruption through convictions of local public o¢ cials (e. g. ,  Glaeser and Saks 2006) , and we collect data on tax revenues from US tax records. Our state-level analysis …rst considers how lagged tax revenues and corruption in ‡uence future growth in state GDP and employment. Over the 1983-2007 period, our panel analysis …nds evidence that is consistent with the model's predicted relationships for taxation and corruption on economic growth. Most important, taxation's marginal impact for growth depends sharply on local corruption.
In more practical terms, the business press frequently connects a state's business climate to its ability to attract …rms and workers from other states, which is not present in an endogenous growth model with …xed populations. We thus complement our model's growth in GDP per worker with a broader set of metrics that include growth in GDP, workers, establishment counts, and so on. We also consider drivers connected to growth like entry/exit, patenting, etc. Our goal is to provide a broad and robust depiction of these novel patterns, even where they extend beyond the model, to establish a comprehensive perspective. We collect estimates of state GDP from the Bureau of Economic Analysis (BEA). We draw our employment and establishment data from the Census Bureau's Longitudinal Business Database (LBD).
The LBD is the business registry for the United States and contains annual observations for every private-sector establishment with payroll from 1976 onward. Sourced from US tax records and Census Bureau surveys, the micro-records document the universe of establishments and …rms rather than a strati…ed random sample or published aggregate tabulations. Jarmin and Miranda (2002)  describe the LBD construction. As a representative year, the data include 108 million workers and 5. 8 million establishments in 1997. This data platform provides great ‡exibility for disaggregating employment growth e¤ects into separate parts. Our empirical estimations consider the 1983-2007 period, and we structure our data into …ve-year time periods that run from 1983-1987 to 2003-2007. 5 Within each time period, we take the average of our economic variables like GDP and employment.
g. , incompetence, laziness). While some think tanks are now "grading" states on their e¤ectiveness of government, these report cards are only very recent and do not o¤er a long history to analyze. We follow prior work by considering federal convictions of corrupt public o¢ cials by state. These data are collected and published by the Public Integrity Section of the Department of Justice for 93 districts, including some US territories like Guam and Puerto Rico, which we aggregate to states. 6 The corruption cases include local, state, and federal o¢ cials and stretch back to the 1970s. There are some instances of unreported conviction totals for small districts in a given year, and we code these and zero convictions to be a single conviction minimum. These choices are not very important as the major bouts of corruption can see a district jump from 4-5 cases per year to over 50.
Conclusion In this paper we have analyzed the e¤ect of taxation and corruption on growth, innovation, and entry. First, we have built an endogenous growth model with quality-improving innovations that lead to the new product lines, and then calibrated this model to assess the welfare e¤ects of the interaction between taxation and corruption. Then, using cross-state and then crosscounty panel data from the Longitudinal Business Database (LBD) of the US Census Bureau, we have obtained evidence that is consistent with the theoretical prediction that the e¤ect of taxation on growth and innovation should be increasing and concave, and that higher local corruption should weaken the positive e¤ect of taxation on growth, innovation, and entry. Evidence also suggests this e¤ect operates through entrepreneurial channels consistent with the model's structure.
Study population Autonomous (Barthel test >90 points), not institutionalised individuals (living at their home or with a family member) aged 70 and over, who agree to participate in the study. Subjects in a terminal situation defined according to the guidelines of the Spanish Society of Palliative Care  [23] , those who reside more than 6 months per year in a different area and those with difficulty communicating in Spanish or Basque (in Gipuzkoa area only), will be excluded from the study. Patients will be randomly selected from the administrative databases of the participating healthcare services. The sample will be representative of the populations of interest in terms of age and sex. Essential socio-demographic information related to all patients who comply with the selection criteria will be collected with the purpose of comparing subjects who finally participate to those who refuse or abandon.
This N should also allow for possible subgroup analyses if so indicated by the data. It is estimated that death will occur in a much lower percentage, not more than 5 %. Because of its expected low-frequency, this result will not be studied individually. In addition, it is expected that approximately 20 % of the recruited subjects will not attend the arranged appointment with the study personnel or will not provide informed consent, and therefore will have to be excluded from the study. Similarly, another 20 %, will not provide data at the end of the study for various other reasons. Considering these follow-up losses the initial sample size has to be increased by 40 %, recruiting a total of 900 subjects (450 in each setting). Genetic analyses will be performed in a subsample of 100 subjects (50 in each setting) randomly selected considering their frailty status measured by the TFI.
Data management All selected subjects will be contacted by letter and phone and will receive information about the project. Those interested in participating will undergo the Barthel test. Subjects with a Barthel ≤90 will be excluded. Basic sociodemographic data of subjects who decline participation will be collected and analysed with the purpose of identifying possible selection or participation bias. Those interested will be informed in detail about the study; will sign an informed consent and will undertake a baseline assessment performed by a trained nurse. Blood samples will be extracted in the selected subsample. Information on current clinical diagnoses and consumption of medicines will be obtained from the clinical records. The variables related to the use of healthcare resources will be obtained from the electronic clinical records and the administrative databases of the healthcare services.
During this analysis phase the external validation of the KoS model, applied to this cohort of subjects, will also be examined. The predictive effect of each index, both univariate and multivariate will be studied. The aim is to study whether the simultaneous consideration of more than one of the included indices can improve the prediction of this adverse event and also quantify this improvement in terms of AUC, sensitivity and specificity of the derived probabilities. 3. Differences in terms of resources consumption by frail and non-frail subjects: Frequency of consumption as well as duration of possible hospital admissions will be compared between the two groups. These data usually follow skewed distributions therefore comparisons will most likely be performed by means of median differences.
In this healthcare setting, the identification of frailty should be simple and require little time  [36]. The main limitation of this work is related to the possibility of having a representative sample of the nondependent elderly population in our setting given the tendency of subjects with better heath to be more likely to participate. This can limit the incidence of adverse events sought during follow-up. For this reason, a sample of advance age was selected in which the likelihood of occurrence of these events is greater. Another limitation is the expected rate of losses to follow-up. To reduce these losses, contact data will be verified and additional contacts (carer, spouse or descendants) will be requested in order to maintain close contact with the participants. The main focus of this study is the evaluation of instruments and strategies for identifying frail subjects, a goal that has major potential for direct application on primary care settings.
Study design and population This case-control study was conducted using the clinical database of the Drug Development Department, Gustave Roussy Cancer Campus, Villejuif. The Ethics Committee of Gustave Roussy Cancer Campus approved this retrospective study. Patient records were anonymized and de-identified prior to analysis, as consent could not be obtained. All patients who were referred to the department medical team and who were included in any P1 clinical trial from 2008 to 2013 were eligible. Cancer patients who were considered as fully eligible during the enrolment visit but who were no longer eligible at the time of C1D1, and therefore never received the experimental drug, were considered as SFs. SFs due to the absence of the protocolstipulated molecular aberration were excluded from the study.
Data collection The electronic medical records were reviewed retrospectively by one single investigator. We collected relevant data at patient inclusion in P1. Demographic characteristics included patient gender, age, geographical distance between home and the Gustave Roussy Cancer Treatment Center, referral or not from outside the cancer center and the number of concomitant medications. Collected tumor-related characteristics were as follows: primary tumor site, number and type of metastatic sites, ECOG PS, number of previous chemotherapy lines, progression-free survival (PFS) during the last chemotherapy line. The Royal-Marsden Hospital (RMH) prognostic score was determined for each patient based on the clinical and biological data available when the patient signed the informed consent form (ICF).
Medication nonadherence is a persistent issue in schizophrenia and has been associated with greater risk of relapse and hospitalization, lower quality of life, and higher healthcare costs  [4, 7]. Nonadherence has also been associated with increased use of health-care resources, poorer mental functioning, and less satisfaction with basic needs, social life, and life in general  [8, 9]. In a recent large retrospective claims database analysis, adherence to second-generation antipsychotics (vs nonadherence) significantly decreased total costs by almost $20,000 USD per year and reduced the risk of hospitalization by 27%  [10]. Adherence to antispychotic therapy may be improved by using a long-acting injectable (LAI) formulation. Use of LAI antipsychotic agents relieves the patient from the responsibility of a daily medication schedule  [11, 12]  and also requires regular contact between the patient and health-care professionals, allowing better monitoring of adherence.