dataesr/scientific-paragraphs-categorization huggingface dataset:
Column 8 further adds a bank x rating category fixed effect designed to capture the differential effect of bank funding shocks across observable risk categories. Error terms are clustered at the bank-year level. Stars indicate statistical significance (* for 10%, ** for 5% and *** for 1%). Standard errors are in bracket Table A. 1: Persistence of exposure across years and datasets Log of (1+Exposure at Default) (1) (2) (3) (4) (5) (6) Capital requirement -0. 087** -0. 066* -0. 023 0. 021 -0. 069*** -0. 063** (0. 037) (0. 034) (0. 050) (0. 046) (0. 014) (0. 029) Observations 207,834 207,834 207,834 207,834 207,834 207,834 Adjusted R-squared 0. 056 0. 085 0. 288 0. 309 0. 324 0. 292 Year FE YES YES YES YES YES YES Firm Size FE YES YES YES YES YES YES Firm Rating FE YES YES YES YES YES YES Bank FE NO YES NO YES YES YES Firm FE NO NO YES YES YES YES Bank*Year FE NO NO NO NO YES YES Firm*Year FE NO NO NO NO NO YES Cluster CIB*.
We use simulated data instead of real data for three reasons. First, household data sets that combine accurate information on household water use and monthly water bills with information on household income are rare (Whittington et al. ,  2015). Second, the large number of datasets and studies on residential water demand around the world, as well as numerous income studies, provide sufficient information to calibrate distributions of water use and income among a hypothetical population of households connected to the piped water distribution system. Third, simulated data allow us to study a range of IBTs designs and to check how their performance in terms of equity and economic efficiency is affected by characteristics of the IBT, including the size of i) a positive, fixed charge, ii) the first (lifeline) block, and iii) the price in different blocks.
This study adds to the empirical literature on subsidy targeting in the water sector. A number of authors have investigated how IBTs perform in terms of distributing subsidies to the poorest households but fewer have considered the trade-off between redistribution and economic efficiency. Borenstein (2012) asks similar questions for the residential electricity sector. He explores trade-offs between wealth transfer and economic efficiency using household billing data provided by three large Californian electric utilities combined with block-level income data provided by the United States Census Bureau, and finds that IBT tariffs for electricity do redistribute income from wealthier to poorer households but that transfers are fairly modest in comparison to substantial losses in economic efficiency.
We combined data from several sources (see Table  1 ). Evidence from industrialized countries mainly comes from the 2008 OECD Environmental Policy and Individual Behaviour Change (EPIC) survey, which includes eight OECD countries (Australia, Canada, France, Italy, South Korea, Netherlands, Norway, and Sweden). 6 About 1000 households were interviewed in each country about their environmental behavior and attitudes in different sectors (water, energy, waste, food, and personal transport) and their household income. For a subset of households in each country, the survey collected data on the household's annual water bill and annual water use. 7 Whittington et al. (2015) provide a description of the survey data covering the cities in four developing countries (Sri Lanka, El Salvador, Senegal, and Kenya) in Table  1.
Individual household data on water use and income are obtained by draws from two log-normal distributions calibrated using the OECD household survey data described in the previous section. From the reported household-specific data on water use and income from these eight OECD countries, we estimate a log-normal distribution for household monthly water use with location parameter We use a procedure proposed by Johnson and Tenenbein (1981) and described in Whittington  et al. (2015) to draw household-specific pairs of income and water use data that maintain an assumed overall correlation for the 5000 households. Thus, an important assumption embedded in our model is this assumed correlation between water use and income. We run simulations under two different assumptions about the correlation between water use and income.
What happens if both the cost recovery constraint is high (100%) and the correlation between income and water use is high (+0. 8)? To the best of our knowledge, this combination of parameters does not exist in any water utility today. A small portion of water utilities in industrialized countries may be recovering close to full long run supply costs, but most are not. The majority of water utilities in developing countries are recovering less than 50% of total costs, many much less. And although the data on the correlation between household income and water use are sparse, there is no indication from any of our datasets (Table  1 ) that the correlation is above +0. 5, certainly not as high as +0. 8. Nevertheless, it is important to consider this case because it is here that the IBT does in fact offer some advantages in terms of equity compared to the UP design, and this is the case that many water professionals (mistakenly) have in mind when they recommend IBTs.
We had access to the 2011 Global Water Intelligence database on water and wastewater utilities worldwide. 150 out of the 308 utilities listed in the database used IBTs. For these 150 utilities we calculated the following ratio: price in lifeline block/price in the second block. This ratio varied from 0 (when water in the lifeline block is free) to 0. 97. The mean was 0. 47 and the median was 0. 52. For more information on Global Water Intelligence utility surveys, see https://www. globalwaterintel. com/; accessed 5 October 2016.
Five phases of data collection were utilized: three longer faceto-face interviews spaced at one year intervals, and two shorter telephone interviews to track and subjectively assess changes in health status. The collected data included: socio-demographic variables (age, sex, marital status, social network, income, education, etc. ); lifestyle (sleep patterns, alcohol and tobacco use); health status (frailty, comorbidities, cognitive status, depression, obesity, physiological impairments (sight, hearing, or lower-limb disability), and self-rated health); functional disabilities (Katz index (activities of daily living (ADL)), Lawton scale (instrumental ADL (IADL)), urinary and fecal incontinence); social network and support (help received, relationships with family and friends); and socio-psychosocial characteristics (locus of control, and satisfaction with life).
Materials Our study involved a secondary analysis of data from the FréLE cohort. We focused on the following four frailty screening tools, which are commonly used in community-dwelling care: the abbreviated Comprehensive Geriatric Assessment (aCGA)  [16] ; the Vulnerable Elders Survey (VES)-13  [17] ; the GFI  [18] ; and the Fried scale  [2]. We chose these tools because of their usefulness and previous use, and because they were easily adapted using our database. Also, they are frequently cited in the literature. For example, Buta et al. [10]  found that the VES-13 had been cited more than 200 times. Fried's phenotype is a definition of frailty that has been used differently in previous epidemiological studies. For practical operationalization, we used proxy data to build our scales (see Additional file 1).
The value of the area under the curve (AUC) can range from 0 to 1, with values over 0. 8 indicating good predictive accuracy  [20]. Third, odds ratios (ORs) were calculated to investigate the relationship between frailty, disability, institutionalization and mortality. ORs adjusted for age and sex were calculated using logistic regression models. For mortality analyses, we performed Cox regression model analyses. Statistical analyses were performed using SAS software (version 9. 3; SAS Institute Inc. , Cary, NC).
Acknowledgements The authors thank the  Canadian Institutes of Health Research (CIHR)  and the  Ministry of Health and Social Services of Quebec (MSSS)  for their financial support of the FRéLE study. The authors also thank  Mr. Nobert Deville  (Director of CETAF),  Dr. Alain Colvez  (CETAF) and  Mrs. Catherine Sass  ( CETAF ) for their support.
3 percent, while the computer applications penetration rate in the Implementation phase (the product or service is actually carried out according to the envisaged plan and in accordance with the requirements of the applicant) reached more than 70 percent. This paper focuses on the relationship between the decision-making factors of innovation strategy and the productivity of the healthcare projects. The data was collected over a period from 2005 to 2015 in a cross section of 31 hospitals in Canada. We applied the Cobb-Douglas production function as the basic model, followed with the Hausman test. We set up the adjustable panel model for the empirical analysis of the impact of innovation factors on productivity. In terms of variables, we selected the innovation productivity index (IPI). We performed an analysis of the healthcare projects in three Canadian provinces (Quebec, Ontario, and Alberta), amongst which there exists a project productivity gap.
However, if we reject the null hypothesis and accept the alternative hypothesis, the estimator of RE will no longer be the consistent estimator, and only FE will be available. We used annual panel data from 31 hospitals over the period from 2005 to 2015. The data was retrieved from the Canadian National Statistical Office. We used the annual provincial gross domestic product the indicator of the healthcare project productivity in each hospital, and the total investment in fixed assets (units: billion) was used as the comprehensive indicator of the scale of investment in fixed assets in all the projects. Taking into account the differences between economic growth levels among provinces, we have divided the project geographically to Alberta, Ontario, and Quebec, in order to analyze the relationship between the implementation of IT innovation mechanisms and the healthcare project productivity in each region.
We assess the role of ethnic ties in the diffusion of technical knowledge using a database of patents filed by US-resident inventors of foreign origin, identified by name analysis. We consider 10 leading source countries, both Asian and European, of highly skilled migration to the USA and test whether foreign inventors' patents are disproportionately cited by (i) co-ethnic migrants ('diaspora' effect), and (ii) inventors residing in their country of origin ('brain gain' effect). We find evidence of the diaspora effect for the Asian but not the European countries, with the exception of Russia. A diaspora effect does not necessarily translate into a brain gain effect, most notably for India where no such effect is detected. Neither does a brain gain effect occur solely in conjunction with a diaspora effect.
6 million people, the combined stock of these three countries was 60% higher than that of India (top of the ranking) and more than twice that of China (third in the ranking, after the Philippines)  (OECD 2015). According to the same source, Germany, Italy, and France have greater highly skilled emigration rates than China and India (between 6 and 9% compared to less than 5%), while the rates for the UK, Poland, and Romania stand at 11, 17, and 21%, respectively  (Mayr and Peri 2008, Schiff and Wang 2013). Finally, official statistics do not provide details on the specific skills or jobs of the highly educated. Hence, the gathering of micro-evidence at the cross-country level is essential. We contribute to this emerging literature by producing and analysing an extensive dataset of foreign-origin, US-based inventors from five Asian (China, India, Iran, Japan, and South Korea) and five European countries A -2  (France, Germany, Italy, Poland, and Russia).
All data are novel and come from EP-INV, a database of uniquely identified inventors listed on patent applications at the European Patent Office (EPO), combined with name analysis based upon IBM-GNR © , a commercial database. Foreign-origin inventors include both foreign nationals and first or subsequent generation migrants who have acquired the US nationality, but still may contribute to knowledge diffusion, based on ethnic affiliation. We analyse the knowledge flows generated by these inventors, as measured by forward citations to their patent applications. We state a "diaspora effect" to exist when US-resident inventors of the same foreign origin have a higher propensity to cite one another's patents, compared to patents by other inventors, other things being equal. We state a "brain gain effect" to exist when US-resident foreign-origin inventors are disproportionately cited by inventors in their home countries, so that the latter stand to gain from high skilled migration.
Other localized citations occur between individuals located at short geodesic distances in co-inventorship networks. Agrawal et al. (2006)  show that social ties of this kind, once established locally, may be resistant to physical distance. A -4 Subsequent studies have sought to uncover other forms of social ties besides those established through collaboration. Agrawal et al. (2008)  assess the importance of ethnic ties in the US-resident Indian community, described as a close-knit diaspora  (Kapur 2001). Drawing on a database of Indian surnames, the authors identify a large number of ethnic Indian, US-resident inventors of patents issued by the US Patent and Trademark Office (USPTO). They then extend the JTH method by including inventors' co-ethnicity among their explanatory variables. Indian inventors are found to be more likely to cite one another's patents than patents by non-Indians.
2015) ; (iii) Returnees' direct contributions. Highly skilled migrants may decide to move back to their home countries or set up entrepreneurial activities there, while maintaining contact with knowledge sources in their countries of destination  (Wadhwa, 2007, and  references therein; for a critique:  Kenney et al. 2013). While case studies on these phenomena abound, large-scale quantitative evidence is scant and almost entirely focused on the US as destination and on China and India as origins. This ignores the fact that highly skilled migration to the US also originates from Western Europe, South Korea, and Japan  (Docquier and Marfouk 2006, Widmaier and Dumont 2011 ; see also  Freeman, 2010). Some progress has been made by William Kerr and co-authors, who combine information from the USPTO patent database with that from the Melissa database, a commercial repository of names and surnames of US residents, classified according to nine broad ethnic groups.
Patent and inventor data Our data result from matching the names and surnames of inventors in the EP-INV inventor database  (Coffano and Tarasconi, 2014)   Cowan and Zinovyeva, 2013; Sterzi, 2013; Nathan, 2015; Akcigit et al. , 2016). Appendix 1 succinctly describes both the algorithm and how it was adapted to the needs of the present study. Using USPTO data, as opposed to EPO, would appear a more natural choice for a study on US-resident inventors. In fact,  Li et al. (2014)  provide disambiguated data. For our purposes, however, the EP-INV dataset has richer information contents, especially on inventors' addresses, which come complete of harmonized street and zip code, from the OECD REGPAT database  (Maraut et al. 2008 ). This provides crucial information for our disambiguation algorithm.
In addition, mastering our own disambiguation algorithm allows us to calibrate it according to our needs (see Appendix 1). 3 Harmonization of applicant names is performed using the OECD HAN Database, as employed in recent PatStat releases. However, as this is far from perfect, we also carried out an ad hoc reconstruction of business groups, using Bureau van Dijk's Zephyr database on Mergers & Acquisitions. 4 The IBM-GNR system is a commercial product using information collected by the US immigration authorities in the first half of the 1990s. When fed with either a first name or a surname, IBM-GNR returns a list of Countries of Association (CoA) plus statistical information on the strength of the association. Consider for instance the inventor Rajiv Laroia. His first name, Rajiv, is associated with seven countries, including India, the UK and the Netherlands.
Our algorithm assigns a specific weight to each of our three indicators (a)-(c), which we obtain by calibration against a benchmark dataset on the nationality of inventors resident in the US, based on PCT patent applications  (Miguelez and Fink 2013). We retain the weights for a "high recall" calibration, that is, one that minimizes false negatives (foreign-origin inventors from the selected CoO mistaken for locals), albeit at the price of low precision. We do so in order to avoid a bias in favour of positive co-ethnicity effects in equations (  1 ) and (  2 ). When no CoO can be selected (no association is sufficiently strong), inventors are treated indifferently as locals or as foreigners from an unknown CoO. Nationality, however, is not the ideal benchmark, as it tends to be overly restrictive.
Table1 HERE Table  1  shows the percentage of inventors in our database from each of the ten selected CoO (column 1) alongside the analogous percentage of inventors listed as nationals from the same countries in PCT data (column 2). Figures in column (  1 ) are always higher than those in column (2), as expected. Columns (3) and (4) report the results of z-test on proportions, which indicates these differences always to be significant. They can also be very large, as for Germany, Italy, Poland, and, above all, Iran. This latter case is highly instructive since, as Iranians have very distinctive names and surnames, so no large error can be attributed to our algorithm. More likely, we explain the difference with the Iranian inventors from the generation that fled the Islamic revolution in the 1980s and their descendants.
Sampling We select all patent applications from the EP-INV database, with priority years between 1990 and 2010, and for which at least one inventor, resident in the US, reported a CoO among the ten selected. Our initial sample includes 88,522 inventors and 174,160 patents. Of these we retain only those applications receiving at least one forward citation from another EPO patent application (either directly, or indirectly, via an equivalent patent in its family). 6 In this way, we build a "national" and an "international" sample, which we use to investigate the diaspora and brain gain effects, respectively. For the national sample, we retain all cited-citing pairs in which the citing patent comprises at least one USresident among its inventors. We then exclude all self-citations at the applicant level, as well as all selfcitations at the inventor level, where the self-citing inventor belongs to one of the 10 CoO selected.
In the international sample, in fact, the share of applicant citations drops dramatically, since patent offices outside the US do not impose any duty of candor rule (see appendix 3 for a detailed discussion). In addition, when the USPTO examines patent applications according to the PCT procedure -which is quite likely in the international sample -it treats all citations inserted by foreign patent offices as they were from applicants, while in reality most come instead from examiners of such offices  (Alcacer et al. , 2009). Therefore, the distinction between applicant-added and examiner-added citations becomes too blurred to be useful. country and on five Asian and five European countries of origin, selected from among the main sources of highly skilled migration to the US. Our empirical exercise has exploited a large, original dataset, based on disambiguated inventor data and the linguistic analysis of names and surnames.
Additionally, we wish to extend the analysis conducted herein and consider Europe as a region of destination. This, among other things, should help shedding light on the policy-sensitive topic of the comparative attractiveness of Europe and the US as destinations for migrant scientists and engineers  (Cerna and Chou 2014, Guild 2007). i)   foreign nationality, 1995-2005 (ii)  z (iii)  p-value (iv)   (  (ii) source: WIPO-PCT dataset (see  Miguelez and Fink, 2013 ). ( iii) Normalized difference between (1) and (2) (iv) p-values for z-test on H0 : (1) = (  2 )  (i) For regression analysis we did not retain all observations related to Iranian and Polish inventors  (43,308 obs). This reduces the sample size from 1,048,258 to 1,004,950 observations   (i) Co-ethnicity in columns 1 and 2 ; co-nationality in columns 3 and 4 -Clustered robust standard errors (at the cited patent level) in parentheses, *** p<0.
The second consists in using different algorithms to produce more than one datasets, each of which with different combinations of precision and recall, and using them to test the robustness of results. The third one consists in calibrating the disambiguation algorithm by collecting information on linguistic specificities of each country of origin, and exploit them at the matching stage. The information retrieval and computational costs increase when moving from the first to the third strategy. For this reason, Massacrator 2. 0 does not follow the third one. Massacrator 2. 0's matches inventors on the basis of edit distances between all tokens comprised in the inventors' name-and-surname text strings, and then filters the matches by exploiting information on both the inventors and their patents.
19   Massacrator 2. 0 does not produce a unique dataset, but several ones, each of which is calibrated against a benchmark dataset in order to return a different combination of precision and recall. For this paper we started from the "balanced" calibration (which returns a precision rate of 88%, and a recall of 68%, when tested against a benchmark of French inventors) and slightly modified it. The modification consists in considering as positive cases (that is, the same person) all matched inventors whose patents are linked by at least one citation, irrespective of other filter criteria. This presumably allows for higher recall, and directly address the problem of over-estimation of ethnic citations. To the extent that this modification induces higher recall at the price of lowering precision, it may lead to over-estimating the phenomenon of returnee inventorship (when the same inventor is first found to be active away from her country of origin, and then back to it).
IV. We set six possible threshold values for indicator n. 1 (from 10 to 100, with steps of 20), eleven threshold values for indicator n. 2 (from 0 to 10000, with steps of 1000), and six threshold values for indicator n. 3 (from 50 to 100, with steps of 10). We consider 102 combinations of such threshold values ("calibrations"), and for each combination we assign each inventor to one or another CoO (or to no CoO at all). Each inventor is therefore associated to one vector of 102 dummies (one for each calibration) and a specific CoO, with dummy=1 indicating that the inventor comes for that CoO, and dummy=0 that she does not (no CoO assigned). 25 V. We apply steps I. to IV. also to inventors in the WIPO-PCT database by  Miguelez and Fink (2013) , which report the inventors' nationality, which we use as benchmark to evaluate the precision and recall rates obtained by each calibration, for each CoO.