dataesr/scientific-paragraphs-categorization huggingface dataset:
Cost data collection is expected to be completed before the end of the main trial in 2021. The societal perspective will include all costs associated with delivery of care, regardless of whether incurred by providers or by patients and their households. The cost-effectiveness analysis will not include any costs or savings associated with informal support networks in the community, other caregivers or production costs at a societal level. We will analyse and present costs incurred by the health system separately to inform future health system budgeting and allocation. We will also estimate the prevalence of catastrophic costs due to TB encountered at the household level in each trial arm. This analysis will help to inform whether the intervention facilitates progress towards the end TB target of 'zero catastrophic costs due to TB by 2020'.
Outcome data Event data from the trial for both intervention and control arms (for each patient type, age category, sex) will be collected on: number, reason (including but not limited to serious adverse events) and length of stay in hospital in each 24-week period over 108 weeks; mean number of outpatient visits per month in each 24-week period by reason over 108 weeks; mean number of monitoring tests in each 24-week period by reason over 108 weeks; duration and outcomes for serious adverse events, focusing on QT prolongation, indication of hepatotoxicity, hearing loss or nephrotoxicity and any service used to diagnose and treat adverse events as above. We will review the published literature (including data reported to WHO) on probabilities of relapse and life expectancy in each setting to calculate disability-adjusted life years (DALYs) averted by the intervention.
Cost data Provider perspective Primary data collection from the provider perspective will gather information on the resources used to provide TB care under the new regimens as compared with the SoC. We will collect the economic, real-world, full costs of provision of MDR-TB care in intervention and control arms, from each setting alongside the TB-PRACTECAL trial following best-practice costing methods. 12 We will employ standardised tools and instruments developed in the VALUE-TB study and in line with the Global Health Cost Consortium Reference Case for Estimating the Costs of Global Health Services and Interventions. 12 Data collection will involve interviews with healthcare workers, retrospective review of project and expenditure records, physical measurement of building space and observation of service delivery.
An ingredients approach will be taken, where the value of inputs is based on quantities and unit prices, including staff salaries, building space, training, supplies, drugs, equipment and overheads. Costs related to adverse events will be estimated from the literature; if not available, we will then estimate the costs using an ingredient approach based on treatment protocols. We will aim to exclude costs related to trial only activities; however, data collection at trial sites may produce biased observations in terms of non-standard service delivery. We will aim to account for this in the uncertainty analysis of the cost effectiveness results. For this analysis, it may be necessary to interview national programme-level staff or access national salary scales and other information to understand the likely cost if new regimens were to be provided through the public sector and scaled up to the national level.
Patient perspective From a patient's perspective, we will collect self-reported data on patient-incurred costs related to accessing care in the intervention and control arms through a survey of patients enrolled in the TB-PRACTECAL trial (patient costing questionnaires are available for download at: https:// doi. org/ 10. 17037/ DATA. 00001799). Costs related to accessing the standard TB services will then be estimated from the information collected in the control arm. Information on costs encountered by TB patients and their households will be collected. Patient and household costs will include direct and indirect costs incurred because of their TB diagnosis. Direct costs will be defined as out-of-pocket medical (including consultation fees and any out-of-pocket payment for medicines and diagnostics) and non-medical expenses (including travel costs of participants and guardians, food costs incurred while in hospital, money spent buying any special foods or dietary supplements due to illness and any interest incurred on loans taken out to meet the costs of out-of-pocket payments).
Patient/household cost data will be collected retrospectively during visits aligned with other substudies, at baseline and weeks 24 and 48 for all patients. For those patients already enrolled that are answering their first patient cost questionnaire in week 24, we will also administer sections 1 and 2 of the baseline questionnaire, and costs prior to week 12 will be estimated based on the average cost per visit. Collection of patient cost data within an economic evaluation of a clinical trial can be challenging and requires careful trade-off between available methodological approaches. 13 As cost data collection for this substudy will be nested within the main trial, our approach to collecting cost data aims to limit survey fatigue wherever possible; for example, we use a recall-based survey rather than a cost diary.
(eg, transport, special food or supplements), productivity loss associated with ill health (inability to work or reduced productivity while at work) and/or time costs associated with seeking care (eg, travel and consultation time for health facility visits). Patient costs data will be included as part of societal costs for the cost-effectiveness analysis. In addition, a separate analysis will be conducted to estimate the prevalence of catastrophic costs in intervention and control populations. The main trial is expected to report on its main outcomes in 2021; all cost information collected in the previous year will be inflated to 2021 currency and converted from local currency units to US dollars and purchasing-power parity using exchange rates for that year.
Expert opinion will be used where no data are available, for example, estimating the extent to which patients return to care after default. All model parameters will be reviewed by a panel of local experts. We will perform additional analyses disaggregated by patient type, sex, age and socioeconomic status, and the main determinants of cost-effectiveness, using a sensitivity analysis to inform decisions for other settings. We will also conduct a sensitivity analysis with intermediate durations, as cost estimates based on study subjects' lifetimes can be marked by very large confidence intervals.
Approval from the hospital management at each site will be sought and obtained before starting the provider cost data collection. We will seek written informed consent of patients before conducting the patient costing questionnaire. Following the end of the research project and publication of findings, cost data will be made available through an approved and curated open-access platform. All data will be fully anonymised before being placed in the public domain. The results of the economic evaluation will be published in a peer-reviewed journal. If the TB-PRACTECAL trial successfully identifies effective and safe regimens, the results of this study will inform a GRADE process by potentially answering a specific population, intervention, comparison, and outcomes (PICO) question on costeffectiveness of the PRACTECAL regimens, as applied by the WHO guidelines committee processes.
Main Results In this section, I document that the income gaps between the executives and average workers are higher for U. S. public firms engaged in the global market between 1992 and 2007. The empirical evidence is based on a linked data set that has three components: ExecuCompustat from the Standard & Poor, the Longitudinal Business Database (LBD) from the Census Bureau, and the Longitudinal Firm Trade Transactions Database (LFTTD) from the U. S. Customs and the Census Bureau. The Execu-Compustat provides data on executive compensation. It reports the total realized and estimated compensation of the CEO, CFO, and three other highly paid executives of U. S. public firms in the S&P Composite 1500 Index from 1992 onward. 4  The executive compensation consists of salary, bonus, stock options, long term incentive plans (LTIPs), restricted stock awards, and all others.
"Realized" compensation (variable name: TDC2) measures the value of stock option awards at the time of execution, while "estimated" compensation (variable name: TDC1) measures the value of stock options at the time of granting using the Black-Scholes formula. 5 he confidential Census Bureau databases provide the other key variables needed to measure within-firm inequality and exporting status. The LBD is compiled from the Census Bureau's Business Register, which covers the universe of U. S. firms at the establishment level. I aggregate it up to the firm level and extract annual employment and payroll variables, which are used to compute the average non-executive wage for each firm in a given year. The LBD is linked to the last component of the data set, the LFTTD, using the methods described in  McCallum (2013).
The LFTTD records the universe of individual international trade transactions made by U. S. firms based on the data collected by U. S. Customs from 1992 onward. It links each export transaction to the U. S. exporting firm and thus provides the base to identify exporting firms in each year. The final linkage between ExecuCompustat and the linked LBD-LFTTD is done through the Compustat-SSEL Bridge provided by the Census Bureau. This paper is the first to use census data in the study of executive compensations. The linked data set has several advantages relative to the data used in the existing literature. The first is the coverage of employment and payroll data. The U. S. public firms are not required to disclose non-executive compensations. As a result, the majority of firms do not report total payroll expenditure in SEC filings, making it almost impossible to compute wage at the firm level.
Multinational Firms The last extension introduces multinational firm indicators. The multinational firm indicators are constructed from the geographic segment data in Compustat. I classify a firm-year observation as multinational if a U. S. firm reports the existence of a non-domestic geographic segment. I then screen all the names of the segments and divisions to correct for apparent errors in the geographic location indicator. The multinational indicators from segment data are then linked with the ExecuCompustat-LBD. The resulting data set contains 12,943 firm-year observations and 1,606 unique firms. Out of these firm-year observations, 5,885 records are classified as non-MNE and the rest 7,058 as MNE. On average, the CEOto-worker pay ratio is 87. 4 among the non-MNE group and 100. 0 among the MNE group.
Survey-based data sets usually do not disclose firm identifiers, such as names and addresses, which makes the link to the census data impossible. The data set used in this paper comes from the Standard & Poor's Capital IQ (CIQ). Unlike the survey-based data set, CIQ collects data through regulatory filings, 14 news aggregators, and company websites. The "People Intelligence" part of the data set covers over 4. 5 million professionals across the globe, many of them working as executives in privately held companies. The unique advantage of CIQ data is that they provide the names, addresses, and telephone numbers for all the firms covered, making the linkage to the census data possible. I start with executives working in private U. S. firms between 2003 and 2007 from the CIQ data. This yields a data set that contains around 33,000 individuals working in 3,849 privately held firms and 11,706 firm-year level observations.
I then link this data set to the Standard Statistical Establishment List (SSEL) in the Census Bureau. 12 Total sales of U. S. firms come from the Census Bureau's Statistics of U. S. Businesses, 2007. Total sales of public firms come from Compustat. 13 See  Jensen (1997)  and  Hartzell and Starks (2003)  for details. 14 Contrary to common belief, some private firms are subject to executive disclosure rules similar to public firms by the SEC. They are usually large firms with more than 500 shareholders and more than $10 million in total assets. See  Gao, Lemmon, and Li (2012)  for more details. Unlike the ExecuCompustat, where the bridge files exist and firms can be matched using standardized identifiers, the CIQ data have not been linked to the census data sets before. Therefore, I carry out a fuzzy match based on name, street address, and zip code.
Overall, 51 percent of the CIQ records are matched with the census data. The sectoral distribution of the CIQ is preserved in the linked data set. For example, manufacturing firms constitute 33. 8 percent in the linked data and 34. 4 percent in the original data; financial firms are responsible for 22. 0 percent in the linked data and 18. 9 percent in CIQ. Instead of the CEO-to-worker pay ratio, I construct the ratio between the highestpaid executive and the non-executive wage as the benchmark measure of intra-firm inequality. The CIQ data does not report standardized title ,and therefore, constructing the CEO title from the raw data would introduce unnecessary noise. Nevertheless, most of the highest-paid executives are indeed CEOs: in ExecuCompustat, more than 98 percent of the highest-paid executives are the CEOs.
There is no strong reason to believe that this ratio will be significantly different in the CIQ sample. The summary statistics of the top-1-to-worker pay ratio are reported in Table  C. 2. 2. Overall, within-firm inequality is lower among private firms than among public firms. The top-1-to-worker pay ratio is 37. 6 in the private firm sample compared with 89 in the public firm sample. Again, the top-1-to-worker pay ratio varies with exporting status. The ratio is 41. 3 among exporters and only 32. 8 among nonexporters. The results of re-estimating equation (1) using the linked CIQ data are reported in Table  2. 2. 1. Overall, the results are similar to those based on the public firm sample. On average, the top-1-to-worker pay ratio is 41 percent higher among exporters than among non-exporters.
20 The shape parameter of the human capital distribution, λ, is set to 3. 81. This implies that the Pareto shape parameter of the firm employment distribution is λ/(ǫ -1) = 1. 27, an estimation based on the LBD in 2007 for all firms with more than 10 employees. 21  The fixed costs of operation and export are calibrated using the Doing Business database from the World Bank. Denote the days of starting a new business in each country i among the 110 countries as φ i. I use the days of starting a business in the U. S. as the measure of the fixed cost of operation in the home country (f 11 = φ USA ). The fixed cost of operation in the ROW(f 22 ) is the GDP-weighted-average of starting a business in each of the 109 countries: f 22 = 109 i=1 GDP i φ i 109 i=1 GDP i. The fixed costs of export are calibrated as follows: I first compute, among the 110 countries, the fixed costs of export from country i to country j as the sum of the days of starting a business in these two countries.
α = 28. 0 and β = 0. 7373. Firms with profit smaller than α are "sole proprietorship" firms while those with profit larger than α are "corporations. " income of a firm is distributed back to its shareholders, which is certainly an oversimplification. However, this approach provides a parsimonious way to capture the fact that the CEOs, both in the real world and the model world, are also significant owners of the firms they manage 24. I impose an upper bound, s, on the human capital distribution to prevent the creation of unrealistically large corporations. I first compute the ratio between the highest CEO compensation in ExecuCompustat and the average U. S. wage from national income and product accounts (NIPA) in each year between 1992 and 2007 25. I then calibrate s = 2. 8 so that the same ratio in the model is matched to the median of the data sequence, which is around 2,903.
All the above parameters are fixed and reported in Table  C. 3. 1. I jointly calibrate the iceberg trade cost and the fixed cost of starting foreign subsidiaries for each year between 1988 and 2008. I first assume that both cost matrices are symmetric: τ 12 = τ 21 and g 12 = g 21. Then I jointly calibrate the two costs {τ 21 , g 21 } to match the imports-to-GDP ratio and the multinational-firm-sales-to-GDP ratio in the U. S. in each year. The first moment condition can be directly estimated using the National Income and Product Accounts (NIPA). The data to estimate the second moment condition come from the Bureau of Economic Analysis's Direct Investment and Multinational Corporations data set. 26 These two parameters have to be jointly calibrated because iceberg trade costs affect not only the volume of trade but also the multinational sales through the extensive margin.
Attributes and Their Levels The basis for the selection of attributes was a targeted literature review on elements of ODs value  (12). The developed search strategy did not target price determinants or elements of ODs value specifically but focused on any matter related to HTA approaches and methodology, as well as on the social value of ODs. The search strategy was developed using relevant keywords and Medical Subject Headings (MESH) terms (see Supplementary Material). The search was conducted using the Medline bibliographical database and was run via the Ovid interface. The search was completed by a gray literature search. HTA agencies' and regulatory bodies' official websites were screened non-systematically to identify any publication of interest. No specific restriction based on geographical scope or publication data was applied.
These households were drawn from the list of addresses held by Royal Mail. At each sampled address, one dwelling was sampled. At each dwelling, one household was sampled. Respondents from this selection (approximately 17,000) comprised the household interview sample. The EHS team then chose a subsample of these dwellings (approximately 8,000 in 2008/2009), including vacant ones, and performed a physical inspection. This is called the "physical survey. " The subsample was constructed from the 17,500 by including all social housing 3  addresses and taking a subsample of private addresses. Private rental properties were oversampled. Finally, to construct the final "housing stock" sample, the EHS team combined data from two physical surveys. For instance, the housing stock sample in the 2011 wave is composed of the physical surveys from 2008/2009 and 2009/2010.
Weighting for the final sample is based on this two-year sampling window. We focus discussion on the 2011 wave of the EHS. Although we also analyze the 2012, 2013, and 2014 waves, these later waves have some limitations. In the later waves, property prices were top-coded at £1,000,000. Also, due to budget cuts, the later waves used smaller samples and collected information on a smaller range of topics. Despite these limitations, our results are robust across waves. Because the samples in each wave use data from a two year span, the samples overlap. For instance, the samples in the 2011 and 2012 waves both contain the same data collected in 2009/2010 as part of their samples. Owner-occupied property values recorded in the survey were obtained in one of two ways. For 82. 7% of properties, owners self-reported an estimate of the current market value.
For example, our calculations using UK Land Registry data show that, from 2009 to 2014, property prices within 10 km of London increased 42%, whereas prices further out increased only by 22%. 11  5. 2. Structure. 5. 2. 1. Size. Figure  4  (left panel) shows how rents and prices change with respect to the total floor space of the property. Compared to a baseline size of 40 m 2 , an otherwise identical 100 m 2 owner-occupied property will have a 73% higher value, whereas the same increase in size implies a 101% increase in rent in the rental sector. As a result, rent-to-price ratios increase with size. The right panel of Figure  4  shows how size affects the probability of being owner-occupied. Again, we compare the results from the selection model to an "unconditional" probit of ownership on size.
four anonymous referees, and  Randal Verbrugge  for helpful comments on an earlier draft. This research was supported by the  UK Economic and Social Research Council  through the  Centre for Microdata Methods and Practice  (CeMMAP grant number  ES/I034021/1 ), through an  ESRC  transformative research grant, grant number  ES/M000486/1 , and  ESRC   Large Research Grant   ES/P008909/1. The work is based on data from the English Housing Survey 2008-2014 (Department for Communities and Local Government, 2017). The data are Crown Copyright and are reproduced with the permission of the controller of  HMSO and Queen's Printer for Scotland. The use of the data in this work does not imply the endorsement of ONS, DCLG, the UK Data Service, nor the UK Data Archive in relation to the interpretation or analysis of the data.
Data The dataset we consider in our empirical exercise consists of daily closing prices in US Dollars, which are collected from CoinMarketCap (https://coinmarketcap. com/coins/). 4 We focus on the four most representative cryptocurrencies in terms of diffusion and market capitalization: Bitcoin, Ethereum, Ripple, and Litecoin. The returns are calculated by taking the natural logarithm of the ratio of two consecutive prices. Our data cover the period from April 28, 2013, to December 1, 2018, except for Ethereum for which data starts from August 07, 2015. The summary statistics of daily closing returns of our four cryptocurrencies are reported in Table  1. For comparison purposes, we consider the same daily period from August 07, 2015 to December 1, 2018, to examine their statistical properties.
We can then calculate some standard economic performance measures of research investment such as the IRR, and its modified version (MIRR), and the benefit-cost ratio (BC) (see Appendix B for details on the formula). 13 4 Data for the French case Due to data availability, our investigation focuses on the impact of public spending in agricultural research on the productivity of French agriculture and thus excludes the effects on the food processing industry and food consumption. We first build a TFP series for the period 1959-2012 on the basis of agricultural production and inputs (capital and labour). We then complete the series with public research spending in France for the period 1946-2013.
Agricultural productivity The estimated TFP and productivity surplus of French agriculture are based on data from national accounts for the period 1959-2012. The productivity index is defined as the ratio between the volume of output and the volume of production factors. The Fisher chained index is used with aggregated production output and input volumes (intermediate consumption, capital, land, and labour). 14 he relationship between research and productivity cannot be studied without taking into account improvements in human capital. As a consequence, human capital was incorporated directly into our measurements of productivity by using a labour quality index based on the training received by farm managers and the qualifications of agricultural workers. However, over inputs are not quality adjusted.