dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources and sample Our data come from several sources. We first use Crowdcube's website to identify and collect data on the firms that (successfully or not) searched for equity crowdfunding during the 2012-2015 period (inclusive). Then, we used multiple online resources, including the Internet archive Wayback Machine and TechCrunch, to reconstruct the sample of firms that unsuccessfully applied for equity crowdfunding on Crowdcube. 3 Using additional data sources is important because firms with unsuccessful campaigns are gradually removed from Crowdcube's website. Disclosure requirements in Europe (including the UK) require privately held firms to publish annual accounting data. We obtained accounting data from the Orbis Europe (previously, Amadeus) database managed by Bureau Van Dijk and Companies House.
Orbis Europe contains high-quality accounting data on privately held and publicly traded European firms  (Faccio et al. , 2011; Vanacker et al. , 2017). We use Orbis Europe to identify the matched firms that did not search for equity crowdfunding but share basic characteristics with firms that searched (regardless of their success) for equity crowdfunding. However, the financial statements submitted to Companies House include more information than what Orbis Europe captures, so we use Companies House as our primary data source to subsequently collect accounting data on all firms in our sample. The sample selection process consisted of several steps. First, we identified firms that sought equity crowdfunding for the first time through Crowdcube between 2012 and 2015. Second, consistent with our focus on equity crowdfunding in entrepreneurial firms, we excluded firms active in real estate.
To form the matched sample, we start from Orbis Europe, which has data on more than two million UK firms. Similar to other researchers, we use the nearest-neighbor matching algorithm (e. g. ,  Bertoni et al. , 2015). 4 We perform all matching with replacement to reduce possible biases (see  Abadie and Imbens, 2012). Accordingly, we matched the 277 firms that searched for equity crowdfunding at the period of getting funding using three a priori defined characteristics: 3-digit industry classification, firm age, and firm size (e. g. ,  Puri and Zarutskie, 2012). If there are multiple matches to the same firm that searched for equity crowdfunding, we select the first one as the matched counterpart. The matching on these basic characteristics makes the accounting data and ratios more comparable between firms.
We consider two one-to-one matched samples (Samples 1 and 2) to show that our results are independent of the matching sample employed. First, we form a matched sample based on a random set of independent firms from the population of UK firms (equivalent to prior work that compared venture-capital-backed to non-venture-capital-backed firms; e. g. ,  Puri and Zarutskie, 2012). Firms could not have raised or searched for (equity) crowdfunding on other crowdfunding platforms. For this purpose, we (1) checked other prominent UK equity crowdfunding platforms (e. g. , Seedrs, Angelsden), (2) verified for any presence on online crowdfunding databases (e. g. , Crowdsurfer, Dealroom, Techcrunch), and (3) determined any changes in capital and tapped into the details of the shareholders. On this random set of firms, we performed our matching procedures as described previously.
An alternative approach to capture non-linear effects is to include both the debt ratios and their squared terms. However, the interpretation of squared terms in logistic and probit regressions models is complex (e. g. ,  Hoetker, 2007). searching for equity crowdfunding. Specifically, only firms that search for equity crowdfunding had registered patent applications, but the firms in the matched samples did not have any patent applications registered in the PATSTAT database. This worldwide database contains bibliographical and legal status patent data and is maintained by the European Patent Office. Thus, in our multivariate regressions, we focus on the intangible fixed assets ratio. In addition, we control for a range of variables that might drive firms to search for equity crowdfunding. 7 Specifically, at the firm level, we control for firm size and firm age.
Third, it is important to consider the possibility that firms with worse characteristics (i. e. , no internal funds and no additional debt capacity) may end up doing better. For example, firms with worse characteristics tend to get venture capital, but they tend to do better (e. g. ,  Cole et al. , 2016). Puri and Zarutskie (2012)  show that cumulative failure rates of venture-capital-backed firms are lower than non-venture-capital-backed firms, with the difference driven largely by lower failure rates in the initial years after receiving venture capital. To assess this possibility in the equity crowdfunding context, we conducted a post hoc test. We also focus on firm failure because failure is a key concern for entrepreneurs and investors in early-stage firms  (Damodaran, 2009). We use data from Companies House that reflect the status of all firms in our samples up to the end of November 2017.
Conclusion Through a pecking order theory lens, we investigated the factors that drive firms to list on equity crowdfunding platforms. For this purpose, we used data from 277 firms that searched for equity crowdfunding on Crowdcube-a leading UK equity crowdfunding platform-and two matched samples of firms that did not list on (equity) crowdfunding platforms. We find that firms list on equity crowdfunding platforms as a last resort (i. e. , when their internal funds and debt capacity are exhausted). These findings might also explain the relatively high failure rate of firms that unsuccessfully searched for equity crowdfunding. We hope that this study will further stimulate scholars to continue investigating entrepreneurs' demand for equity crowdfunding and its implications for subsequent firm performance and growth.
Objectives To characterise postmarketing studies for drugs that were newly approved by the US Food and Drug Administration and the European Medicines Agency. Design and setting Cross-sectional analysis of postmarketing studies registered in ClinicalTrials. gov until September 2014 for all novel drugs approved by both regulators between 2005 and 2010. Regulatory documents from both agencies were used. Primary and secondary outcome measures All identified postmarketing studies were classified according to planned enrolment, funding, status and geographical location, and we determined whether studies studied the originally approved indication. results Overall, 69 novel drugs approved between 2005 and 2010 were eligible for inclusion. A total of 6679 relevant postmarketing studies were identified; 5972 were interventional (89.
Data sources and study sample We identified all novel drugs approved between 1 January 2005 and 31 December 2010 by both the FDA and EMA through its Centralised Authorisation Procedure. For the FDA, Drugs@FDA is a publicly accessible database listing relevant regulatory actions for all approved drugs. 16 For the EMA, information was accessible in the European Public Assessment Reports, which provide a summary of scientific review and list notable regulatory events for all drug submissions. 17 Generic drugs, reformulations, combination therapies and non-therapeutic agents such as radiographic dye were not included. This first search led to a sample of 71 novel drugs approved by both regulators between 2005 and 2010. Two drugs, everolimus and temsirolimus, were excluded because they were associated with an abnormally high number of postmarketing studies involving drug-eluting stents.
Drug and manufacturer characteristics The following data were retrieved for each drug: agent type (small molecule or biological), dates of regulatory submissions for both the FDA and EMA, orphan status according to the FDA, orphan designation from the EMA, therapeutic class according to the Anatomical Therapeutic Chemical classification,  18  initial label from both regulators, degree of novelty (first-in-class, advance-inclass, addition-to-class) as previously described in a paper from FDA officials  19  and size of the marketing-authorisation holder (ie, manufacturer). This latter information was obtained by personal communication with EMA officials (Constantinos Ziogas, Small and Medium-sized Manufacturer Office, EMA), who classified manufacturers as large pharmaceutical companies, intermediated-size companies or small and medium-size companies according to the European Union definition based on headcount and financial turnover or balance sheet total.
Postmarketing studies On 24 September 2014, we extracted all studies that were registered at ClinicalTrials. gov for each drug of our sample, regardless of dates and other details. We then excluded studies with the following characteristics: included in the FDA regulatory submission (by a manual review of Drugs@FDA), with inadequate registered status (expanded-access studies, withdrawn studies, suspended studies), and mistakenly extracted (ie, studies actually not assessing the drug of interest). For our main analysis, we decided that all studies whose starting date had preceded the first regulatory submission (to the FDA or EMA) by 1 year or less would be classified as postmarketing studies. However, we also performed most calculations with a slightly different set of studies, namely only those whose launch started after the first regulatory approval of any agency.
For all remaining postmarketing studies, the following data were collected: condition studied, starting date, study sponsors (as a primary sponsor or a collaborator), status at the date of extraction (not yet recruiting, recruiting, active yet not recruiting, enrolling by invitation, completed, terminated), number and list of countries, number of centres, study phase, study type (observational or interventional), randomisation and planned enrolment. In addition, studies were classified as assessing the drug for its originally approved indication or not, depending on the initial label. When the initial label differed between the FDA and EMA, we accepted both labels as defining the originally approved indication. One of us (J-DZ) performed this classification after careful review of each primary label.
statistical analysis Using descriptive statistics, we characterised the premarket characteristics of the novel drugs included in our sample (drugs approved by both the FDA and EMA between 2005 and 2010). Next, we used descriptive statistics to characterise features of all identified postmarketing studies registered at ClinicalTrials. gov for all novel drugs. We used a series of trend charts representing the annual number of postmarketing studies over the life-cycle of the drugs according to off-condition and on-condition studies. All statistical tests were two-tailed, with a type I error rate of 0. 05. We used SAS V. 9. 4 (SAS Institute; Cary, North Carolina, USA) for all statistical analyses.
Planned enrolment was also highly variable, with studies only including one patient, and one study intending to recruit 904 585 patients (actually a prospective population-based cohort study examining risk of congenital malformations after use of varenicline, a tobacco-use cessation drug, in pregnant women). However, the median number of patients to be enrolled per study was 60 (IQR 28-183). Data on the total population to be enrolled in all postmarketing studies for a given drug was also highly varied, with a median total sample of 15 418 patients (IQR 4932-37 523). Velaglucerase alfa, an orphan medication indicated for Gaucher disease, was associated with the lowest population size to be included in studies (n=67), and varenicline was associated with the greatest population to be enrolled (>1 million patients overall).
This latter finding is somewhat reassuring because postmarketing studies for an already approved indication aim to refine knowledge regarding the long-term effect and/or safety and should therefore include more patients than preapproval pivotal trials. Our study has limitations. The first may be a registration bias at ClinicalTrials. gov, which would alter the exhaustiveness of our assessment. Some studies are not registered by researchers  27 28  and were therefore not included in our study. Others are imperfectly registered, with some information missing. However, Clinical-Trials. gov is widely recognised as a benchmark registry, and recent reports showed that compliance might have improved over time. 29 Another limitation is the definition of postmarketing studies, in that clinical studies are designed and launched according to a continuous timing and a single threshold might be lacking for distinguishing premarketing and postmarketing trials.
Acknowledgements The authors are grateful to EMA officials who graciously helped them classify drug manufacturers. They also thank  Geoffroy Beraud-Chaulet  for his useful work on  Drugs@FDA  and  Elise Diard  for her invaluable help on figures. contributors  J-DZ  and PR were responsible for the conception and design of this work. J-DZ  drafted the manuscript and was responsible for most of the data acquisition. IA was responsible for data exportation and structuration. AV was responsible for collection of some data and contributed to categorisation of studies. NSD was responsible for some of the data acquisition. GB conducted the statistical analysis. JSR and PR provided supervision. All authors participated in the analysis and interpretation of the data and critically revised the manuscript for important intellectual content.
This temporal weights matrix is then after multiplied, element-by-element, with a general spatial weights matrix, W, to obtain a general weights matrix that enables us to isolate multidirectional spatial effect: S = W T, where is a Hadamard product. The individual elements of the spatial weights matrix are defined as usual, i. e. , as a function of the distance separating two observations i and j such that w ij = f(d ij ), where f(•) could be any decreasing function, assigning a higher value to closer observations and a lower value to more remote observations. Another spatial effect is also possible for such a database. A spatial unidirectional effect, i. e. , how previous observations were spatially located can influence actual observations, is possible. To account for such a possibility, it is possible to generate a second temporal weights matrix.
Empirical Investigation The empirical investigation is based on a change in the mass transit system in metropolitan suburbs (The estimation has been performed with Stata software and the spreg command. More detail on the estimation procedure can be found in appendix of the book of  [24] ). The case study examines two separate commuter rail trains (CRT) in the Montreal (QC, Canada) metropolitan area. The first CRT serves the north shore part of the metropolitan area and was inaugurated in May 1997, when the first station opened in the municipality of Blainville and Sainte-Thérèse (the green line-Figure  2 ). Two more stations opened after that. The first was in Rosmère, a few months after the We now turn to explore the impact of using such a decomposition of the marginal effect on empirical applications, or how should the information provided so far for analyzing a practical empirical case be used.
Empirical Investigation The empirical investigation is based on a change in the mass transit system in metropolitan suburbs (The estimation has been performed with Stata software and the spreg command. More detail on the estimation procedure can be found in appendix of the book of  [24] ). The case study examines two separate commuter rail trains (CRT) in the Montreal (QC, Canada) metropolitan area. The first CRT serves the north shore part of the metropolitan area and was inaugurated in May 1997, when the first station opened in the municipality of Blainville and Sainte-Thérèse (the green line-Figure  2 ). Two more stations opened after that. The first was in Rosmère, a few months after the inauguration (1 January 1998), while the next stations only opened in January 2007 in St-Jérôme, the municipality located in the northern part of the metropolitan area.
Descriptive Statistics The pairs of transactions used in the analysis consider 6 years before the opening of the first station of the new mass transit system. In the case of the south-east CRT, the database includes transactions from 1995 until 2009 (Table  1 ), while in the case of the north CRT, transactions run from 1992 until 2009 (Table  2 ). In both cases, the number of transactions in the grey shaded part of the tables indicates houses that may experience a change in mass transit supply between sale and resale. These data are considered to form the treatment group, while transactions occurring both before and after the change in mass transit supply are considered to form the control group. The main idea of the DID, SDID, and STDID estimators is to use these treatment and control groups to infer the possible effect related to the opening of new CRT stations.
One advantage of the case study here is the fact that the new mass transit service operates on an existing railway system. The railway was mainly used to transport freight before the authorities decided to develop a CRT line on these existing lines. Consequently, there is no possible anticipation effect or any other cross-effect related to the implantation of the lines before the implementation of the new service. In both cases, the number of transactions (sale and resale) are comparable, with a larger number of first transactions (sale) occurring at the beginning of the database that progressively decreased over time, while having a small number of second transactions (resale) occurring at the beginning of the database, but where the number progressively increases as years go by (Figure  3 ).
The total number of transactions is also quite comparable within the database, with a total of 18,320 pairs of transactions recorded for the south-east shore of the metropolitan area for the east CRT line, and with a total of 19,808 pairs of transactions for the north shore. Buildings 2017, 7, 51; doi:10. 3390/buildings7020051 www. mdpi. com/journal/buildings In both cases, the number of transactions (sale and resale) are comparable, with a larger number of first transactions (sale) occurring at the beginning of the database that progressively decreased over time, while having a small number of second transactions (resale) occurring at the beginning of the database, but where the number progressively increases as years go by (Figure  3 ). The total number of transactions is also quite comparable within the database, with a total of 18,320 pairs of transactions recorded for the south-east shore of the metropolitan area for the east CRT line, and with a total of 19,808 pairs of transactions for the north shore.
Conclusions The paper proposes developing a spatio-temporal difference-in-differences (STDID) estimator to account for two separate effects in the calculation of the marginal effect of an exogenous change in environmental amenities: (i) a spatial multidirectional effect; and (ii) a spatial unidirectional effect. Taking into account those two effects, the paper shows how one can take into account these two spatial dimensions in the calculation of the marginal effects, and how such effects may change the global portrait of the calculation of the economic impact of landscaping policies on real estate prices. The STDID allows us to decompose the effect to incorporate spatial spillover effects, but also shortand long-term dynamic spatial effects. The originality of this paper is twofold. First, it is the first time that such a framework has been applied to spatial data pooled over time, and it is the first time that the calculation of the marginal effects has been discussed for such a database.
Acknowledgments: This research was funded by the  Agence Métropolitaine de Transport (AMT)  in Montreal and undertaken through JDMD Groupe conseils inc. , an independent consulting firm based in Québec City and Rimouski. The authors would like to thanks the  Altus Group  for allowing us to use their database on single family houses sold in the Montréal area between 1992 and 2009 as well as the  Canadian real Estate Association of Canada (CREA).
This paper presents new findings on global inequality dynamics from the World Wealth and Income Database (WID. world), with particular emphasis on the contrast between the trends observed in the United States, China, France, and the United Kingdom. We observe rising top income and wealth shares in nearly all countries in recent decades. But the magnitude of the increase varies substantially, thereby suggesting that different country-specific policies and institutions matter considerably. Long-run wealth inequality dynamics appear to be highly unstable. We stress the need for more democratic transparency on income and wealth dynamics and better access to administrative and financial data.
Rising inequality has attracted considerable interest among academics, policy-makers and the general public in recent years, as shown by the attention received by an academic book recently published by one of us  (Piketty, 2014 ). Yet despite this endeavor we still face important limitations in our ability to measure the changing distribution of income and wealth, both within and between countries, and at the world level. In this paper, we present new findings about global inequality dynamics from the World Wealth and Income Database (WID. world), which aims at measuring income and wealth inequality on a consistent basis over time and across countries. We start with a brief history of the WID. world project. We then present selected findings on income inequality, private vs. public wealth to income ratios, and wealth inequality, with particular emphasis on the contrast between the trends observed in the United States (US), China, France, and the United Kingdom (UK).
History of WID. world The WID. world project started with the construction of historical top income share series for France, the US, and the UK, and then extended to a growing number of countries  (Piketty 2001 (Piketty , 2003;; Piketty and Saez 2003; Atkinson and Piketty 2007, 2010; Atkinson et al. 2011; Alvaredo et al. 2013). These projects generated a large volume of data, intended as a research resource for further analysis, as well as a source to inform the public debate on inequality. The World Top Incomes Database-WTID  (Alvaredo et al. 2011 (Alvaredo et al. -2015) )  was created in January 2011 with the aim of providing convenient and free access to all the existing series. Thanks to the contribution of over a hundred researchers, the WTID expanded to include series on income concentration for more than thirty countries, spanning over most of the 20 th , early 21 st centuries, and, in some cases, going back to the 19 th century.
Following the pioneering work of  Kuznets (1953) , the WTID combined tax and national accounts data in a systematic manner to estimate longer and more reliable top income shares series than previous inequality databases (which generally rely on self-reported survey data, with under-reporting problems at the top, and limited time span). These series had a large impact on the discussion on global inequality. In particular, by making it possible to compare over long periods of time and across countries the income shares captured by top income groups (e. g. the top 1%), they contributed to reveal new facts and refocus the discussion on rising inequality. In December 2015, the WTID was subsumed into the WID, The World Wealth and Income Database. In addition to the WTID top income shares series, this first version of WID included an extended version of the historical database on the long-run evolution of aggregate wealthincome ratios first developed by  Piketty and Zucman (2014).