dataesr/scientific-paragraphs-categorization huggingface dataset:
Data used to compute the pva series are extracted from the WDI database of the World Bank. The current and constant value added used to compute the six-sectors' value-added deators are taken from UNCTAD. The proxy BS_def 3 is available for the majority of countries until 2017, the starting date depending on the considered country. Turning to BS_def 6, it is available over the 1980-2017 period for the majority of countries (see TableA. 1). (! i;k ¢ pva k i;t ) (19) pva T i;t = kPT (! i;k ¢ pva k i;t ) (20) where pva k i;t , pva NT i;t and pva T i;t are expressed in logarithmic terms. Denoting BS_def 6 the BS measure based on six-sectors' value-added deators, we get for a country i at time t: BS_def 6 i;t = (pv a NT i;t pva T i;t ) N j=1 w ij;t (pv a NT j;t pva T j;t ) (21).
Recently, the IMF has constructed an annual database of productivity in the traded and non-traded goods sectors but the geographical coverage includes only 56 countries (Mano and Castillo, 2015). The Groningen Growth and Development Centre of the University of Groningen has also developed a database that provides comparisons of productivity at a detailed industry level, the GGDC Productivity Level database. This database is, however, restricted to a set of thirty OECD countries (Inklaar and Timmer, 2008).
In France, cost per quality-adjusted lifeyear (QALY) is the recommended type of analysis in most cases. Companies should provide QALY values related to treatments of interest using data collected in the French context and in French validated value sets  [3]. Presently, two French population-based value sets are recommended: EuroQol 5-Dimension 3-Level (EQ-5D-3L) tariffs, and the Health Utility Index (HUI) 3  [4] [5] [6]. Other ad hoc utility values are accepted, if fully justified and published in peer review journals. Hamers et al. [7]  have published a review article focused on utility measures, employed in submissions to the HAS to the end of 2015. A total of 32 submissions were assessed. Two submissions covered two indications, and 34 CEAs were analyzed. The EQ-5D-3L was used in 24 CEAs, while HUI 3 was only used in one submission.
Methods The study used the valuation protocol and its associated computer-assisted interview software (CAPI) EQ-VT version 2. 0 developed by EuroQol  [14]. Support from Euro-Qol included a training kit, a full script for interviewers, and a quality control module with specific criteria to adopt or reject interviews/interviewers. The French study team validated the French version of all documents, which were translated by a professional translation company. Interviews were performed by professional interviewers from a private market research company who had previously participated in the 3L valuation exercise.
Acknowledgements Open access funding was provided by  EuroQol. The authors thank  Elly Stolk  and  Arnd Jan Pause for EuroQol  for technical support;  Chantal Touboul ,  Christine Colin ,  Ramah Sellami ,  Marjorie Grimault ,  Kantar Health, France , for the field work ; and  Suzanne Gaudet ,  ESSEC , for secretarial support.
2019; Tiwari et al. 2019). However, the above literature ignores to differentiate the shock sources of oil prices and therefore cannot provide strong evidence on the impact of specific oil shocks. Unlike most of prior studies, we categorise oil shocks according to  Kilian (2009) 's approach and apply the connectedness measures of  Diebold and Yilmaz (2014)  with the aim of unravelling the different impacts of disaggregated oil shocks on real exchange rates. Our sample period includes three oil importers (India, Japan and South Korea) and three oil exporters [Canada, Norway and the United Kingdom (UK)]. It covers the period from February 1974 to December 2016, except for the case of South Korea, where data are only available from April 1981.
Data description We use monthly data on global crude oil production, the  Kilian (2009)  index of global real economic activity, real (US CPI deflated) oil prices and real (CPI deflated) exchange rates. Oil production data and real oil prices were extracted from the US EIA. We used the Kilian index, obtained from Kilian's website, to measure global real economic activity. Nominal exchange rates and CPI data were extracted from DataStream. In this paper, we measure the bilateral exchange rates by the quantity of foreign currency per unit of the US dollar, which means that an increase of the bilateral exchange rate indicates its depreciation against the US dollar. Our data span from February 1974 to December 2016, except for the case of South Korea, where data are only available from April 1981.
We include three oil exporters (Canada, Norway and the UK) and three oil importers (India, Japan and South Korea). In line with the existing literature (e. g. ,  Basher et al. 2016) , the choice of countries was influenced by data availability. 2 igure  1  depicts the data series over the full sample period, showing large fluctuations in most cases. In particular, the effect of the GFC is evident on oil prices and most of the real exchange rates. Large fluctuations in real exchange rates are apparent in Japan and South Korea in 1999, whereas currency depreciation is observed in India and South Korea in the early 1990s and in 1999, respectively. (5) DS i←j (H ) = N j=1,j� =i φ ij (H ) N i,j=1 φ ij (H ) × 100 = N j=1,j� =i φ ij (H) N × 100. ( 6 ) DS i→j (H ) = N j=1,j� =i φ ji (H ) N i,j=1 φ ji (H ) × 100 = N j=1,j� =i φ ji (H) N × 100.
Design and setting The study is a cross-sectional analysis to the baseline data of the french JEU cohort study (study protocol : Challet-Bouju et al, 2014). Recruitment (April 2009 to September 2011) involved clinicians and researchers from seven institutions that offer care for or conduct research on problemgamblers (PG). Participants were recruited in gambling places, and in care centres. Only participants who reported gambling in the previous year between 18 and 65 years old were included. Participants gave their written informed consent, it was approved by the French Research Ethics Committee. Participants The participants were 628 gamblers : 256 non-problem gamblers (NPG), 169 problem gamblers without treatment (PGWT) and 203 problem gamblers seeking treatment (PGST). results Six clustering models were tested, the one with three clusters displayed a lower classification error rate (7.
92%) and was better suited to clinical interpretation : 'Early Onset and Short Course' (47. 5%), 'Early Onset and Long Course' (35%) and 'Late Onset and Short Course' (17. 5%). Gambling characteristics differed significantly between the three clusters. Conclusions We defined clusters through the analysis of gambling variables, easy to identify, by psychiatrists or by physicians in primary care. Simple screening concerning these gambling characteristics could be constructed to prevent and to help PG identification. It is important to consider gambling characteristics : policy measures targeting gambling characteristics may reduce the risk of PG or minimise harm from gambling. trial registration number NCT01207674 ( ClinicalTrials. gov); Results.
MAterIAl AnD MethODs Participants The participants were 628 gamblers who took part in the JEU cohort study. The sample included 256 nonproblem gamblers (NPG), 169 problem gamblers without treatment (PGWT) and 203 problem gamblers seeking treatment (PGST). The JEU cohort study is a 5-year longitudinal national case-control cohort in France that seeks to identify protective and risk factors in gambling practice (for more information, please refer to the study protocol of the JEU cohort Challet-Bouju et al, 2014  32  ). The present study applied a cross-sectional analysis to the baseline data of the JEU cohort. Recruitment occurred between April 2009 and September 2011 and involved a group of French clinicians and researchers from seven institutions that offer care for or conduct research on problem gambling.
To identify a typology of gamblers based on the course of their gambling and their preferences, we led an exploratory analysis. We performed a clustering of gamblers with eight variables: age at initiation, age at onset of gambling problem, duration of gambling history, age at diagnosis of comorbid psychiatric disorder, history of a gambling-free period of at least 1 month's duration, familial and social support and preferred type of gambling and medium for gambling. We used the Latent GOLD software  43 44  to perform a latent class clustering (LCC) of the gamblers. LCC is a person-centred approach that seeks to identify homogeneous subgroups, each group being defined by a specific probability distribution. LCC has been found to be more likely to give superior classification results than more traditional methods such as k-means or hierarchical clustering,  45  particularly because it requires fewer assumptions.
17 The LOSC cluster might share similarities with the pathway of 'emotionally vulnerable problem gamblers'. 17 27 28 rengths and weaknesses of this study This study has several limitations, especially the relatively restricted amount of data collected. Characteristics related to the course of gambling (age at initiation, age at onset of gambling problems, etc) and lifetime psychiatric disorders were assessed in a retrospective manner and were self-reported, which could induce a recall bias. Moreover, we did not use DSM-5 criteria, and we did not analyse impulsivity and cognitive distortions in this population. Recruitment of patients occurred at the moment of the legalisation of online gambling in France (2010), which limits generalisation, as online gambling has since become widely available.
Acknowledgements We wish to sincerely thank all the staff who contributed to this study (the  JEU Group ) for their valuable assistance and significant investment. Special thanks goes to those who collected the data. The members of the JEU Group are:  Marie Grall-Bronnec ,  Gaëlle Challet-Bouju ,  Jean-Luc Vénisse ,  Lucia Romo ,  Cindy Legauffre ,  Caroline Dubertret ,  Irène Codina ,  Marc Valleur ,  Marc Auriacombe ,  Mélina Fatséas ,  Jean-Marc Alexandre ,  Pierre-Michel Llorca ,  Isabelle Chéreau-Boudet ,  Christophe Lançon ,  David Magalon ,  Michel Reynaud  and  Amandine Luquiens. This research was conducted at the initiative of and coordinated by the  Addictology and Psychiatry Department of the University Hospital of Nantes , who sponsored this study. Collaborators JEU Group:  Marie Grall-Bronnec ,  Gaëlle Challet-Bouju ,  Jean-Luc Vénisse ,  Lucia Romo ,  Cindy Legauffre ,  Caroline Dubertret ,  Irène Codina ,  Marc Valleur ,  Marc Auriacombe ,  Mélina Fatséas ,  Jean-Marc Alexandre ,  Pierre-Michel Llorca ,  Isabelle Chéreau-Boudet ,  Christophe Lançon ,  David Magalon ,  Michel Reynaud ,  Amandine Luquiens.
13 ) and in the work of  Burstein et al. (2017)  on the impact of immigration on wages in tradable and non-tradable occupations. Empirical resolution of this theoretical disagreement has been hindered by a shortage of panel data on wages and skill supplies in a wide spectrum of countries. A big step forward in this respect recently is the Socio-Economic Accounts of the World Input-Output Database (WIOD), which cover 40 countries during  [1995] [1996] [1997] [1998] [1999] [2000] [2001] [2002] [2003] [2004] [2005] [2006] [2007] [2008] [2009]. What these data show at first sight is set out in Figure  1 , which plots the wages of college-educated relative to other workers against their relative employment, both in logs, after averaging the data over time (Figure  1(a) ) and in changes over the period (Figure  1(b) ) -more information on the variables is in section III.
Section V applies a more stringent structural estimation approach to isolate and quantify the openness and price-ratio elasticity mechanisms. Section VI moves on to the second objective, bringing in the directed technical change and income elasticity of demand mechanisms. It deduces the likely size of the effect of these two mechanisms from the gap between the reduced-form and structural estimates in sections IV and V, and extends the structural estimation to assess whether in combination their impact is plausibly of the size suggested by this gap. Our data are from the World Input-Output Database (WIOD), described by its creators in  Timmer (2012). The core of WIOD is annual international input-output tables for 1995-2009 and 35 industries connecting 40 countries that produce 85% of world GDP, plus a composite rest of the world.
As in  Timmer et al. (2014)  and  Morrow and Trefler (2017) , we reduce the three WIOD skill categories to two by combining 'low-skilled' and 'medium-skilled' into 'unskilled' (those with less than a college degree). WIOD wage and employment data were assembled from national labour force surveys and censuses, not previously collated in this form. We derive wage rates for each skill level, country and year as labour compensation divided by hours worked. 16 n important maintained assumption of our analysis is that WIOD data on economy-wide employment by skill category are a good proxy for skill supplies (or endowments). Because of differences in participation rates, unemployment rates and working hours (the unit of employment in WIOD), there are differences across countries and periods in the relationship between total employment at specified levels of education and the numbers of adults with those levels of education (which is arguably the best measure of endowments).
On the other hand, the long duration of defence programs is corroborated by the fact that the main procurement programs signed by the governments in the 1990s and the early 2000s have been active along the whole sample period (Colom Piella 2016). Finally, we collect …nancial information retrieved from the Spanish business register through the System of Analysis of Iberian Balance Sheets (SABI) database, compiled by Bureau van Dijk. Our baseline regression is de…ned as: y it = + X it 1 + 0 Rank i + 1 P ost t + 2 (Rank i P ost t ) + u it : (7) The dependent variable y it takes the value of the measures of performance of …rm i during the year t included in the model: productivity (the operating income to employees, in logarithms to control for skewness) and pro…tability. The latter has been proxied using the return to sales (hereafter pro…tability) and the return on assets (ROA).
Economic Impact The financial mass committed in Ile-de-France for the treatment by immunotherapy of lung cancer amounts to tens of millions of euros, which weighs on health facilities. Anticancer drugs represent an increasingly important expense item. Thus, the INCa (French National Cancer Institute) estimated that in 2014, 55. 7% of the total 2. 87 billion euros of expenditure was dedicated to anticancer drugs  [12]. Based on data from the ATIH (Technical Agency for Information on Hospital Care), we estimated the expenditure related to nivolumab and pembrolizumab at around 100 million euros in 2018 in Ile-de-France, all indications combined, private and public healthcare facilities combined, and regardless of the administrative status of the ICI (temporary authorization for use = ATU or regulatory approval by the French Health Agency or European Health Agency = AMM).
Database PMSI (i. e. , program for medicalization information systems) data extraction was performed using the shared and mutualized decision-making tool called DIAMANT (Décisionnel Inter-Ars pour la Maîtrise et l'ANTicipation)  [25]. Patients included were those who received one of the two drugs to treat a metastatic NSCLC, whether in hospital or in a day case chemotherapy unit. PMSI data for Avicenne Hospital were compared with data from our prescription software CHIMIO ® in order to check the consistency of data. Data were collected from 1 January 2018 to 31 December 2018. This study covers all type of healthcare facilities located in Ile-de-France having medicine, surgery, and obstetrics activity, regardless of the tarification applied. The expenditure data for nivolumab and pembrolizumab were taken from the ScanSanté platform  [26].
An estimate of the population was calculated from PMSI data by selecting the active file of patients treated in 2018 with nivolumab and/or pembrolizumab in Avicenne Hospital then in Ile-de-France for a "Malignant tumor of the bronchi and lung" (code C34 of the International Classification of Diseases, ICD-10 or CIM-10 in France). This calculation of the analyzed population was completed with the data entered in CHIMIO ®. For this purpose, patients treated with nivolumab and pembrolizumab in 2018 at Avicenne Hospital were extracted according to the treatment line to assess prescription habits. For pembrolizumab, the distribution rate observed between 1st and 2nd line in 2018 in Avicenne Hospital was applied to the population of Ile-de-France. Among patients with NSCLC (85% of patients with lung cancer  [27] ), only those with advanced and/or metastatic stages are eligible, which represent, respectively, 30 and 40% of NSCLC.
Duration of Treatment The duration of treatment was estimated from the average number of cycles received per year by patients monitored and treated in Avicenne Hospital with ICI (since August 2015 for nivolumab). Patients treated with nivolumab in the 2nd metastatic line in our hospital received an average of 8. 5 cycles per year at the frequency of one cycle every two weeks. As pembrolizumab was available at the end of 2017, the decline in median patient follow-up is too short to calculate the average number of cycles and may not take long responders into account. Data from the pivotal studies KEYNOTE 024  [28]  in the 1st line and KEYNOTE 010  [31]  in the 2nd line were used. The average number of cycles of pembrolizumab received was 10. 5 cycles and 4. 9 cycles, respectively, at the frequency of one cycle every three weeks.
Sensitivity Analyses A sensitivity analysis was performed to assess the parameters for which the range had the greatest impact on cost savings. We used the Tornado chart as the graphical representation of the univariate deterministic sensitivity analysis. A Tornado graph is a specific form of a histogram that allows us to compare the uncertainty for each parameter by varying the values of each parameter in turn, the other parameters remaining at their value retained in the reference analysis  [32]. The sensitivity analysis was performed with all base care parameters: weight, treatment cost, number of cycles, and growth rate of the population. For all parameters we created a range by applying +/-10% to all base values. The Tornado chart is available in Figure  4. We used cost data in Ile-de-France for the high hypothesis in 2019 to create the diagram.
Finally, in 2019, Bayle and al. [49] performed a BIA comparing weight-based doses to fixed-doses for nivolumab and pembrolizumab, used for any type of cancer at the Gustave Roussy level for the year 2018. The study was conducted for a total of 978 perfusions (560 of nivolumab in 103 patients and 418 of pembrolizumab in 125 patients) mainly treated for lung cancer and melanoma at Gustave Roussy. Data were collected from January to April 2018. They estimated the annual extra cost at €477,120 for nivolumab and €1,613,898 for pembrolizumab. Moreover, they expected an annual budget impact of €55,162,211 at the French national level for the year 2017. Our results are consistent with all these studies found in the literature.
We combine a large and detailed occupations database with data on industrial robot deliveries from the International Federation of Robotics. The database on occupational employment from Reijnders and de Vries (2018) allows us to examine the share of employment in occupations with a high content of routine tasks -i. e. tasks that can be performed by following a well-defined set of procedures. We delineate occupations along two dimensions of the characteristics of tasks performed, namely  'analytic' versus 'manual', and 'routine' versus 'non-routine'. 1  We thus distinguish four key occupational group-ings, namely routine manual, routine analytic, non-routine manual, and non-routine analytic task-intensive occupations (as in Autor et al. 2003 ;  Reijnders and de Vries 2018 ; Cortes et al. 2020 ).
Occupations and robots We combine two datasets with information on occupations and robot purchases. The first dataset with occupational employment by countryindustry originates from Reijnders and de Vries (  2018 ) and was updated by Buckley et al. (2020). The data is constructed using detailed survey and census data from statistical offices for the period from 2000 to 2015. The sources used in constructing this dataset closely align with those from other studies. 15 The dataset provides employment for thirteen occupational groupings by country-industry pairs. It covers 40 countries, namely the 27 members of the European Union (per January 2007), Australia, Brazil, Canada, India, Indonesia, Japan, Mexico, the PRC, Russia, South Korea, Chinese Taipei, Turkey and the United States. For each of these countries, occupational employment shares by 35 ISIC revision 3.
1 industries that cover the overall economy are distinguished. They include 14 two-digit manufacturing industries (such as textile manufacturing and electronics manufacturing), as well as agriculture, mining, construction, utilities, finance, business services, personal services, trade and transport services, and public services industries. The dataset thus has dimensions of 13 occupational groupings × 35 industries × 40 countries × 16 years. Occupation data is intrinsically not exactly comparable across countries, and in practice will also vary due to differences in the type of sources and national data collection practices. Intertemporal changes within country-industries are likely more consistent because Reijnders and de Vries (2018) use data from the same national source for each country.
Our empirical analysis exploits this within-country variation. We examine the impact of robot adoption on tasks, which we distinguish into routine versus non-routine and manual versus analytic tasks. Our measurement strategy is to infer the impact of robot adoption on tasks from data on the occupational structure of the workforce. The distinction between occupations with different task intensities is based on the so-called Routine Task Intensity (RTI) index developed by Autor et al. (2003) and mapped into the International Standard Classification of Occupations (ISCO 88) by Goos et al. (2014). Table  1  provides the allocation of occupational groupings to tasks. The second database includes deliveries of industrial robots by country-industry from the International Federation of Robotics (IFR).
16 he IFR provides country data on the number of industrial robots delivered from 1993 onwards. Yet coverage varies and the breakdown of robot investment by country-industry is only consistently available for most countries after 2004. In addition, robot investments increased rapidly during the 2000s. We therefore build the dataset using information for all available years but focus on the period from 2005 to 2015 in the empirical analysis. 17 e use the perpetual inventory method to build robot stocks, assuming a depreciation rate of 10% as in Graetz and Michaels (2018). 18 We then define 'robot densification' or simply 'robot adoption' as the robot stock per thousand persons employed. We examine changes in robot adoption over time. The distribution of changes in robot adoption for the country-industries included in our analysis has mostly either zero or small positive values, with a long right tail.