dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, as empirical research on R&D policy has shown that a firm's level of effort in learning from a collaborative project may significantly depend on its motives for participation  (Veugelers, 2012) , we have also controlled for firms' FP7 project participation motives. The survey asked firms about their motives for participating in that FP7 project. A factor analysis identified three motives driving firms in this project, as shown in Table  3  (nine items). Based on this information, we created three composite dichotomous variables using the same procedure as the composite variables for strategies in equations 4-5. The variable finance motive refers to the motivation to access financial support; network motive refers to the motivation to build and improve collaboration networks; while technology motive refers to the motivation to acquire support for developing and maintaining technological leadership.
DISCUSSION We used data on 601 European firms that participated in the EU's FP7 program to examine how the national institutional context influences the benefits a firm may derive from that public sponsored R&D collaboration. We distinguish three types of benefits a firm may obtain from participating in open innovation based on collaborative contribution: building or expand a network of partners (network benefits), reorganizing work and process structures (organizational benefits), and developing or refining products and technologies (product development benefits). We examined the direct effect of the design of public financial incentives for business expenditure on R&D, and of the degree of diffusion of knowledge-revealing practices across different these three types of benefits. We analyzed the data using multilevel models and we put our results to several sensitivity tests.
To examine the effect of national embedding on the benefits derived from collaborative innovation, this study relies primarily on data from a 2011 survey of firms that took part in the 7 th Framework Programme for Research and Technological Development (FP7), funded by the European Commission (EC). FP7 provided funding for precompetitive research and technological development projects. The program aimed to support capacity building and the development of external networks amongst different types of organizations -primarily firms, universities, public research organizations and 75%) for research and technological development activities; other organizations benefited from 50% co-funding. SMEs also received a special flat rate of 60% of direct costs to cover their overheads. Hence, FP7 attracted firms, including new technology-based firms, looking for opportunities to access finance, get support for technological development, and develop and expand existing network linkages.
consultants. FP7 had a budget of over €50 billion for the period covering 2007 to 2013, and was a key part of the EC's strategy to make the EU a global leader in R&D. Our use of data from FP7 allowed us to analyze project benefits for firms that had been exposed to largely the same supranational treatment across countries. While data has been collected in 2011, international R&D collaborations are nowadays still sponsored in quite similar terms as in FP7 under the EU's Horizon 2020 project. In addition, the European Commission is in the process of developing its successor, Horizon Europe. It is therefore clear that programs like FP7 will continue to remain the normalcy in the EU for the foreseeable future. Hence, examining benefits from participation in international R&D collaboration is still quite relevant to policymakers developing the next European innovation program, who are no doubt seeking to learn from past experiences.
The documented predictive power is distinct from that of alternative well known financial predictors. We estimate our model on data for the S&P 500 index. Our specification is strongly supported by the data, the filtered variance predictions and the risk neutral variance expectations match the level and the dynamics of their observable counterparts along the entire sample. The filtered variance risk premium satisfies naturally the positivity constraint, is highly persistent and exhibits changes in level and variability. We show that there is no substantial return predictability from the smooth component of the variance risk premium. In fact, the predictability attains a maximum value of 1. 3% at the five month horizon. Including our sentiment indicators, the R 2 s in the predictive regressions increase dramatically at every horizon and surpass 10% at the four month horizon.
Data S&P 500 data is sampled on non overlapping one month intervals with a monthly horizon for the computation of the physical and risk neutral variance expectations (i. e. τ = 1). The , i = RW, AR(1), AR(2), ARX1 and ARX2, is computed using (3) where the expectation under the physical measure is obtained using model i and the risk neutral expectation is approximated by the squared VIX. The first order sample autocorrelation coefficient is denoted by ρ(1). The sample used is monthly data from January, 1990, to September, 2015, totalling 309 observations. data runs from  January, 1990 , to September, 2015. This setup is similar to  Drechsler and Yaron (2011)  and  Bollerslev, Marrone, Xu, and Zhou (2014) , among others. The variable SW t,t+1 is the CBOE volatility index (VIX), see  CBOE (2015).
When we regress the future aggregate market excess return on Π C t,t+1 , we find no substantial return predictability at any horizon. In fact, the predictability 1. 17 2. 68 3. 78 5. 02  5. 43 5. 36 3. 41 2. 56  Notes: This table reports the adjusted R 2 s in percentage from the predictive return regressions in (33) with VRP, sentiment indicators and their interaction with the state of the market as regressors. The sample used is monthly data from January, 1990, to September, 2015, totalling 309 observations. attains a maximum value equal to 1. 3% at the five-month horizon. Although a persistent Π C t,t+1 variable, we do not find increasing predictability, as documented e. g. for the dividend yield by  Boudoukh, Richardson, and Whitelaw (2008) , over the horizons we consider. The size of R 2 is in line with  Huang, Jiang, Tu, and Zhou (2015) , when using the investor sentiment index results of  Baker and Wurgler (2006) , who size the predictability of the monthly aggregate market excess return in the order of 1.
5%. Thus, the results of Table  8  suggest that Π C t,t+1 indeed measures the economic uncertainty under normal market conditions. This finding suggests that much of the return predictability ascribed to the variance risk premium computed as Π RW t,t+1 is effectively coming from the part of the premium related Notes: This table reports parameter estimates from the predictive return regressions in (33) with VRP, sentiment indicators and their interaction with the state of the market as regressors. Newey-West t-statistics are in brackets and adjusted R 2 s are expressed in percentages. The sample used is monthly data from January, 1990, to September, 2015, totalling 309 observations. to how agents gauge extreme variance events, their prediction and compensation, see also  Bollerslev, Todorov, and Xu (2015).
Data and parameter estimates The monthly data for the panel of international indices spans the period from January, Table  12  presents parameter estimates of our model for the seven indices. Generally speaking, we find similar results to the S&P 500 index case. Regarding the dynamics of Σ t-1,t , we find values ranging from 0. 754 to 0. 914, with SMI and AEX being the least and the most persistent, respectively. The idiosyncratic component of Π C t,t+1 is generally very persistent with mean reversion coefficients between 0. 830 and 0. 962 for SMI and DJIA respectively. In terms of regime duration, the first regime lasts on average 6 months, the second regime on average 1. 5 months, and the third regime about 3 months with small variability across indices. The model isolates the smooth component well as can be seen from the distribution of J P t-1,1 and E Q t [J Q t,t+1 ] exhibiting a large location and variance.
31 -0. 29 -0. 35 -0. 05 -0. 49 -0. 56 -0. 56 -0. 36  Notes: This table reports the adjusted R 2 s in percentage from the predictive return regressions in (33) with VRP, sentiment indicators and their interaction with the state of the market as regressors. The sample used is monthly data from  January, 2000 , to March, 2015, totalling 189 observations. with the state of the market as explanatory variables largely dominates over most if not all horizons for all indices. The results reported for DJIA, which shows stronger gains at the short and long horizons, displays very similar patterns and levels as observed for the S&P 500 index. For FTSE, the extra degree of flexibility provided by the sentiment indicators, produces gains in predictability from 3. 36 to 6. 71 and 2. 38 to 5. 44 at the three and six month horizon, respectively.
Bachelier and the  French National Research Agency (ANR) , under grant  ANR-17-CE26-0001  (project  BREAKRISK ). Lars Stentoft acknowledges support from  CREATES , funded by the  Danish National Research Foundation  ( DNRF78 ). Francesco Violante acknowledges support by the grant of the  French National Research Agency (ANR) ,  Investissements d'Avenir (LabEx Ecodec/ ANR-11-LABX-0047 ), and of the  Danish Council for Independent Research  ( 1329-00011A ).
Data To analyze the effect of opening a new high school on pupils' school choices and academic achievement, we use exhaustive micro-level data, at both pupil and school level, sourced through the statistical department of the French Ministry of Education. We use exhaustive annual individual datasets of French secondary education pupils (called "fichiers anonymisés d'élèves pour la recherche et les études" or 'FAERE'). These annual databases are composed of every pupil enrolled in a secondary school every year from 2004-2005 to 2013-2014. Here we focus on the ninthgrade pupils enrolled in a middle school in France. Each of these pupils are observed in year t (the year of their ninth grade) and up to year t+4. The data provides the school and track of each pupil in each year. We know whether they are enrolled in a private or a public school, whether it is a middle school or a high school, and whether it is a general or a vocational high school.
For each pupil, we observe key socio-demographic characteristics: gender, age, origin, family background through the parents' occupations, and whether or not he/she receives a scholarship. Pupils' scores at the end-of-middle-school national exam (Brevet) are also observed. In addition, we know each year whether the pupil graduates. By the time of the analysis, the 2011 to 2013 cohorts could not be followed for four years, and so they could not be used for the regressions on long-term outcomes (graduation after ninth grade). Note that the data cover all schooling institutions except agricultural-vocation ones. Moreover, because there was a reform of the vocational track in 2007, there is a clear break in the data for pupils following this type of track. For that reason, estimations will be made on the cohorts enrolled in ninth grade from the 2007 school year only.
A second source of data comes from an exhaustive school-level panel dataset, which provides information on every French school. As each school's postal address is known, we can observe their exact geographic location. The exact administrative date when they opened (and, if they ceased to exist, the date when they closed) is also known. Working with these exhaustive datasets enables us to identify high schools that appear for the first time in the data, for every year. For a given year t, a high school is considered as a new high school if some pupils are enrolled in that high school in year t while no pupils had been enrolled there in previous years. We also check that this year corresponds to the administrative date of first opening. A high school that appears only one year in the dataset is not considered as an opening.
7 ConclusionThis paper analyzes the causal effect of a change in local school supply on pupils' track choices at the end of lower secondary education. We exploit data on high school openings to highlight the constraint school supply exerts on individual schooling choices. We use an exceptionally rich dataset in which we observe every single pupil enrolled in ninth grade in mainland France every year from 2004 to 2013. The data enables us to retrieve the key information on new high schools for each school year. A generalized difference-in-differences model makes use of the variation in time and location of opening high schools to identify the causal effect of a change in local school supply on the allocation of pupils at the end of middle school. We show that pupils are constrained by the local school supply, as opening a new high school increases the proportion of pupils who continue in upper secondary education.
DID estimates of the effect of opening a new public high school on school social composition -Closest middle school FAERE dataset, ninth-grade pupil cohorts from 2007-2008 to 2012-2013. Note: *** p-value<0. 001, ** p-value<0. 05, * p-value<0. 1. A treated school is defined as the closest middle school to a new public high school. All estimations use year, middle-school fixed effects, and heterogenous time trends. Standard errors in parenthesis account for the autocorrelation of the residuals between observations on the same middle school.
029) T(t >= ts) LPR 0. 116** 0. 035 0. 002 0. 033 0. 003 -0. 038** 0. 019 (0. 054) (0. 032) (0. 016) (0. 041) (0. 019) (0. 017) (0. 023) Intercept -0. 008 0. 829*** 0. 511*** 0. 319*** 0. 090*** 0. 080*** 0. 760*** (0. 047) (0. 016) (0. 023) (0. 023) (0. 010) (0. 012) (0. 030) Controls Yes Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Yes Heterogenous trends Yes Yes Yes Yes Yes Yes Yes Nbr obs 31,213 34,129 34,129 34,129 34,129 34,129 21,557 Nbr clusters 47 47 47 47 47 47 45 Source: FAERE dataset, ninth-grade pupil cohorts from 2007-2008 to 2012-2013. Note: *** p-value<0. 001,** p-value<0. 05,* p-value<0. 1. A treated school is defined as the closest middle school to a new public high school. T(t >= ts) refers to the treatment dummy which equals one after a new high school opened.
016) (0. 011) (0. 012) (0. 026) T(t >= ts) LPR 0. 154** 0. 037 0. 012 0. 026 -0. 001 -0. 037* -0. 000 (0. 062) (0. 035) (0. 015) (0. 038) (0. 022) (0. 019) (0. 026) Intercept -0. 052 0. 814*** 0. 531*** 0. 283*** 0. 098*** 0. 088*** 0. 750*** (0. 043) (0. 020) (0. 028) (0. 028) (0. 013) (0. 015) (0. 033) Controls Yes Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Yes Heterogenous trends Yes Yes Yes Yes Yes Yes Yes Before/after time trends Yes Yes Yes Yes Yes Yes Yes Nbr obs 31,213 34,129 34,129 34,129 34,129 34,129 21,557 Nbr clusters 47 47 47 47 47 47 45 Source: FAERE dataset, ninth-grade pupil cohorts from 2007-2008 to 2012-2013. Note: *** p-value<0. 001, ** p-value<0. 05, * p-value<0. 1. A treated school is defined as the closest middle school to a new public high school.
Data, variables and descriptive statistics For our empirical analysis, we use data from the French Labor Force Surveys (LFS) for the period from 2003 to 2012. For this period we have consistent and reliable information on occupations, on whether the individual has found the job through social interactions, country of birth and year of arrival in France for immigrants. The LFS was launched in 1950 and established as an annual survey in 1982. We start our analysis in 2003 which is the year when the LFS was redesigned as a continuous survey with quarterly interviews of people. Participation is compulsory and all individuals living in the same dwelling and older than 15 are surveyed. 11 The main topics covered by the LFS concern employment, unemployment, underemployment, hours of work, wages, duration of employment and unemployment (length of service), discouraged 11 The collection method has always been a face-to-face interview.
Since 2003, a telephone interview has been employed for intermediate surveys (2nd to 5th). The sampling method consists of a stratification of mainland France into 189 strata (22 French regions x 9 types of urban unit) and a first stage sampling of areas in each stratum (with different probabilities, average sampling rate= 1/600). Areas contain about 20 dwellings and among them only primary residences are surveyed. Access to the LFS data is possible for researchers using the Quetelet Prodego website (http://quetelet. progedo. fr/). workers, industry, occupation, status in employment, education/qualifications and secondary jobs. The French LFS provides the four-digit level occupation for each employed individual. 12. We will refer to occupations as jobs all along the paper. There are two limitations of the French LFS concerning our topic of interest.
First, we have to stop our analysis in 2012, since afterwards there is no detailed information on the respondent's country of birth. Since we exploit individual's geographical origin to estimate the relationship between the network matching rate and the job finding rate, our analysis cannot go beyond 2012. Second, even if the LFS allows for consideration of new hirings by isolating people who have been in the job for less than a year, the facts of working with survey data and not census data makes this approach not possible due to the lack of observations, particularly when we consider immigrants from different origins. 13 We will, however, focus on people with less than 3 years of seniority in the job (i. e. 2 years of seniority or less in the job), which corresponds to hirings over the past two years.
INSERT TABLE 1 After pooling all the year-specific datasets, we obtain a sample comprising 232,803 respondents for the period 2003-2012. As shown in Table  1 , this workforce is very unequally distributed across regions, with Ile de France being by far the most workforce abundant region, followed by Rhone Alpes, Nord-Pas-de-Calais, Provence-Alpes Cote d'Azur (PACA) and Pays de la Loire. The 12 When implementing robustness tests using a cell approach, we will consider the two digit occupation classification (provided by the two first digits of the code associated with each occupation defined at four digit level), in order to ensure a sufficient number of observations for every occupation. This will lead us to consider 24 occupations. For both, the individual data analysis and the cell analysis, we exclude from our sample farmers, civil servants, military and clergymen.
13 In contrast with most papers on migration (see  Ortega and Verdugo, 2014 or Patel and Vella, 2013) , we rely on survey data and not census data. Indeed, the French database resulting from matching French Census with individual social security data ("Declaration Annuelle Donnees Sociales") fails to provide some of the information we require for our analysis. For example, occupations are not consistently reported and there is no information on the use of social networks to find a job. average share of immigrants in the employed population equals 10. 5%. In our study, the job finding rate is used as an indicator of the economic cycle. Based on the estimations of  Hairault et al. (2015)  and given the strictness of the employment protection legislation in France, we argue that the job finding rate at the regional level is a good indicator of the economic cycle, i.
e. it summarizes well the evolution of labor market conditions. The job finding rate in period t, which is defined as the probability of transition from unemployment to employment, is calculated using quarterly data (which is the highest frequency available in the French LFS) at the regional level on the flows of workers into and out of unemployment between t -1 and t. 14 In order to reduce time aggregation biases, we calculate the job finding rate following Shimer (2012) so as to take into account the problem that, while data is available only at discrete dates the underlying environment keeps changing over time. Taking yearly averages, we obtain an average job finding rate per region. 15   The positive correlation between the economic cycle and the job finding rate is clearly displayed on the left-hand side panel of INSERT FIGURE  1  14 We thank Idriss Fontaine for providing us with all the prepared data to compute the job finding rates.
First, due to the fact that we are considering survey data and not census data, we cannot exactly estimate equation (2). We employ the French LFS since it is the unique population representative database which precisely asks the individual how she found her job in the firm. Included among possible answers is "through family relations, personal relations or professional relations". The French LFS is a sequence of non-exhaustive quarterly cross-sections that does not allow for rigourously computing hirings per period t, which stands for the denominator in equation (  2 ). For the same reason, we are unable to compute the numerator, since it corresponds to the number of new hirings during period t that took place through social networks. We propose then an indicator of the network matching rate which proxies equation (2) using stock variables, which are available in the LFS.
Open access information for decision makers who need to consider its economic value along with clinical efficacy when planning for introduction. TB-PRACTECAL is a randomised controlled trial to evaluate the safety and efficacy of investigational regimens containing bedaquiline and pretomanid for the treatment of MDR-TB (see figure  1 ) ( ClinicalTrials. gov study registration NCT02589782). It has been designed in two stages; stage 1 is a phase II trial aiming to identify two regimens containing bedaquiline and pretomanid for further evaluation based on safety and efficacy outcomes after 8 weeks of treatment. Stage 2 is a phase III trial to evaluate the safety and efficacy of the two investigational regimens containing bedaquiline and pretomanid, selected in stage 1, compared with the standard of care (SoC) at 72 weeks post randomisation.
METHODS AND ANALYSIS Study design This substudy is a cost-effectiveness analysis alongside a clinical trial. We will evaluate the total costs and effects of the study regimen (intervention) against the locally used WHO-approved regimens for treatment of multidrugresistant or extensively drug-resistant TB (M/XDR-TB) (control) using a Markov model populated with primary cost and outcome data collected at each study site. This substudy will take place in three settings: Uzbekistan, South Africa and Belarus. The selection of countries and facilities in each country is based on their participation in the TB-PRACTECAL trial. In Uzbekistan, the trial is taking place in Tashkent city and six rayons (districts) in Karakalpakstan, in Western Uzbekistan (Nukus city, Takhiatash, Chimbay, Kegeily and Xodjeli rayons) implemented by the Republican Specialised Scientific-Practical Medical Centre for Phthisiology and Pulmonology of the Republic of Uzbekistan.
The sample size for the overall trial is 630. Patient enrolment in TB-PRACTECAL is currently underway, and cost data collection is anticipated to start in October 2020. The distribution of patient recruitment by country in the TB-PRACTECAL trial is expected to be approximately 280 patients in South Africa, 100 patients in Belarus and 250 patients in Uzbekistan. For cost data collection from the patient perspective, the numbers recruited into PRACTECAL-EE will be proportional to the overall trial, depending on the starting dates and numbers consenting. The level of patient costs and the expected change as a result of the intervention is unknown, therefore we have not done a sample size calculation for the patient costing survey. 5] [6] [7] [8]  Data collection for costings from the provider perspective will be done at all trial sites in each country.