dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis We evaluated the Fragility Index for statistically significant and non-significant meta-analyses, by using for each iteration of the evaluation process the same measure and method used in the original systematic review to re-calculate the new 95% CIs of the pooled treatment effect after single eventstatus modifications in trials, i. e. if the original systematic review used RR to measure treatment effects, and Mantel-Haenszel with random effects to estimate the pooled treatment effect and its 95% CI, we consistently used the same effect measure and method to pool the treatment effects at each step of the iterative method to evaluate the Fragility Index. For each meta-analysis, we assessed the number of trials included as well as the total sample size and number of events, corresponding to the sum of the sample sizes and number of events in the included trials, respectively.
RESULTS From 2,796 Cochrane Systematic Reviews, 906 meta-analyses met our inclusion criteria (Figure  S2 ). These meta-analyses included data from 6,625 trials (median 4 trials [Q1-Q3: 3-8]). The median total sample size was 756 (301-2014) and median total number of events 127 (47-341) (Table  1 ). Overall, 400 (44. 2%) meta-analyses had statistically significant results, and 506 (55. 8%) had statistically nonsignificant results.
Data presented in this example corresponds to a real meta-analysis from a Cochrane Systematic Review. The measure used for evaluating the treatment effect (here risk ratio) and for deriving the pooled treatment effect (here Mantel-Haenszel with a fixed-effects model) was the same as that used in the Cochrane Systematic Review containing the data. The Fragility Index for the meta-analysis was then derived relative to the measure and method used in Cochrane Systematic Reviews for each individual meta-analysis. The method is based on an iterative re-evaluation of the statistical significance of the pooled treatment effect of modified meta-analyses, iteratively derived from the original meta-analysis by performing single event-status modifications in each arm of each trial in turn. The same method used in the original meta-analysis for estimating pooled treatment effects (Mantel-Haenszel, Peto, or inverse variance, as well as fixed or random effects) was used in each iteration to evaluate the statistical significance of the modified meta-analyses.
5 The results obtained in our application generate new insights into the extent to which MNEs rely -both directly and indirectly -on foreign versus local suppliers, and on how such engagement in GVCs varies across countries and industries. Specifically, while showing that MNEs' contribution to value-added exports is considerably smaller than what is suggested by traditional trade statistics, our findings also reveal that MNEs' higher import content of exports can go hand in hand with the creation of local backward linkages as a function of their much higher specialization in only parts of the production process. Although central to the IB field, these questions could not be addressed until now due to data limitations: both cross-country comparable firm-level microdata and international IO models are necessary to tackle them.
The adoption of this approach is in particular supported by large, newly developed datasets in academia (e. g. , the World Input-Output Database, see  Timmer, Dietzenbacher, Los, Stehrer, & de Vries, 2015)  7 and by international organizations (e. g. , the OECD-WTO Trade in Value Added initiative (TiVA), see OECD & WTO, 2011), 8 from which a plethora of new measures on GVCs can be derived (see, e. g. ,  Ahmad, Bohn, Mulder, Vaillant, & Zaclicever, 2017; Johnson & Noguera, 2012; Koopman, Wang, & Wei, 2014). Such indicators have supported new and insightful analyses of GVCs' features that have greatly contributed to the policy debate, highlighting for instance the importance of imports for export success, the need for well-developed domestic services industries that support manufacturing exports, and the changing relative importance of trading partners when looking at the ultimate sources and destinations of trade, rather than the immediate counterpart.
However, while analyses based on data derived from IO models have helped to significantly improve our understanding of inter-country and inter-industry economic interdependencies, they have until now been confined to viewing integration through the statistical prism of industries. This is despite the fact that it is well known that even within narrowly defined industries, firms show marked variations in terms of, for example, their productivity, capital, and skill intensity  (Bernard, Jensen, Redding, & Schott, 2007) , as well as major differences in their levels of integration in GVCs and the benefits that accrue from that (including for employees). A focus on industries alone has also been shown to introduce structural biases in GVC-related estimates. Among these three tools to study GVCs, IB scholars have typically privileged the use of survey data, also in view of the need to unveil the role of firm heterogeneities.
INTRODUCING FIRM HETEROGENEITY IN IO TABLES The main data sources used to develop the splits in the national IO tables were the official statistics on the structure and activity of foreign-controlled affiliates for value added and gross output by industry, collected by the OECD in the Activity of Multinational Enterprises (AMNE) Database, and the official national statistics on Trade by Enterprise Characteristics (TEC) for merchandise exports and imports by industry and firm ownership (collected in the OECD TEC database). However, as explained below and in Sections 1, 2, and 3 of the Appendix, several practical and methodological challenges need to be addressed to integrate these datasets for this purpose. It is, for example, necessary to align differences in industry classifications, to estimate missing data (e.
g. , due to confidentiality), and, most importantly, to sensibly adjust for methodological and conceptual differences between the various sources. National IO tables, with 34 industry breakdowns in ISIC Rev. 3 classification, were created by aggregating the inter-country IO tables that underpin the OECD-WTO TiVA databases 11 for the years 2011-2014 (for a discussion on the advantages of using TiVA measures to study GVCs, see  Suder et al. , 2015). We chose this time period because TEC by ownership is available from 2011 onwards and AMNE is available up to and including 2014. The TEC database contains annual international trade in goods data broken down by industry (ISIC Rev. 4) and enterprise characteristics -including ownership (i. e. , domestically controlled firms or foreign-controlled affiliates) -for 24 OECD and six non-OECD countries.
National Statistical Offices develop TEC statistics by attributing trade flows to firms by merging international trade (usually customs) data with business registers via common business identifiers. The OECD AMNE database provides harmonized information on the structure and activity of foreign-controlled affiliates by industry (ISIC Rev. 4). The shares of foreigncontrolled affiliates and domestically controlled firms in value added and output were directly calculated from the database. 12  AMNE and TEC statistics are both directly reported to the OECD and Eurostat by statistical offices, and are based on countries' official statistical business registers, enterprise surveys, and administrative records (including those from Customs Authorities), and provide statistical information on the complete population of firms of a given country.
DISCUSSION AND CONCLUSION Prior studies examining GVCs have shed light on this increasingly dominant aspect of the IB landscape  (Kano, 2018). Especially the ones utilizing data from IO models were able to capture important features of today's GVCs, as also noted by  Suder et al. (2015). Yet, the industry-level nature of IO tables hindered our understanding of the role of firm heterogeneities and thus of how firms differ in their engagement in GVCs depending on their characteristics. We precisely tackled this issue by introducing a novel methodology adopted by the OECD that integrates not only country-and industry-but also firm-level analysis, thus providing IB scholars who investigate GVCs with the unique opportunity to undertake analyses that are extensive and yet fine-grained. As discussed in this note (and, in greater detail, in the accompanying Appendix), it specifically allows for a disaggregation of IO tables using established official data sources on linked trade and business statistics in a transparent manner, i.
Hence, the application of this novel methodology to the phenomenon of MNEs' participation in GVCs proves very useful to illustrate its potential to further our understanding of GVCs, corroborating the notion that the IB field can greatly benefit from the adoption of this new method. It also generates new important insights that provide an answer to relevant IB questions on MNEs' engagement in GVCs that we could not adequately address before due to data limitations. An additional important contribution of this study involves the extensive tests for the robustness of the findings, as reported in the Online Appendix, and especially the sensitivity analysis which allows us to assess the reliability of the findings obtained through this new methodology. Stretching the assumptions that were made, by necessity, to disaggregate IO tables to their extremes, their impact on the key TiVA indicators by firm ownership was illustrated.
While, as expected, the variation in assumptions had a negligible effect on the overall import content of exports for domestically controlled firms and foreign-controlled affiliates, greater variation was observed for the domestic intermediate use relationships between different types of firms -even if the overall conclusions reported in this research note were not affected. The challenges for statistical offices in developing the data that would allow the analyses to move beyond these assumptions are being recognized by the OECD Committee of Statistics and Statistical Policy, which has established an Expert Group on this topic with subject matter specialists from national statistical offices. This indicates a strong potential that more data, for more countries, will become available in the near future, also for use by IB scholars, to support the types of analyses presented in this note.
Information on how businesses provide intermediate inputs to other firms forms the foundation for such more precisely disaggregated intermediate use matrices, as well as linked micro datasets. A further step that the OECD intends to make is to also include more detailed partner dimensions in these extended tables, to allow for an (improved) integration of extended SU and IO tables into global SU tables, and thus examine the differences in the geographical spread of different types of firms' engagement in GVCs (see OECD & Statistics Denmark, 2017, for an example). While our illustrative application focused on a 4-year time period and presented some key findings related to the most recent year for which data is currently available (2014), we note here that, as data covering additional years becomes available, future work embracing this new methodology will really be able to leverage the longitudinal aspect to then generate new important insights into how GVCs change over time.
Such follow-up studies might improve our understanding of the role of individual countries and specific firm typologies in these GVC configurations, and thus contribute to a much more nuanced picture of this important phenomenon in today's global economy. Moreover, as soon as it will be possible to split data on exports identifying the industry of origin and the country of destination by firm type, scholars will have the opportunity to move to truly multicountry analyses that can shed new light on how GVCs are organized. As national statistics offices of OECD countries are working towards the creation of datasets that also allow for combined splits, i. e. , using more than one firm dimension at a time, we expect that in the near future it will also be possible to examine the combined effect of specific firm dimensions, for instance by looking at firm ownership and firm size to identify four different categories of firms and unveil their specific contributions to GVCs.
This seems a particularly relevant avenue for future research as an in-depth examination of combined firm effects including size would unveil important features of the nature and configuration of GVCs, especially in view of the growing involvement of small and medium-sized MNEs in international supply networks. To offer a practical overview of both current and future data-related options available to IB researchers, Section 3 of the Online Appendix lists datasets that will hopefully become available in the near future as well or are already retrievable, with website links to the national statistics offices of the OECD countries through which IB scholars may connect with them to obtain access to apply the methodology that we introduced in this research note. Since the focus of this note was methodological, the empirical analysis presented in our illustrative application has been merely descriptive.
As also discussed in detail in the manuscript, the novel methodology presented here focuses on the extension of IO models (using of course international trade statistics) to look more carefully at international transactions by relying on firm-level information. 3 For more detailed information on (primarily national) IO models, see the seminal work by  Leontief (1966) , and the more recent one by  Miller and Blair (2009). 4 The OECD AMNE data collection, like the similar data collection by Eurostat, differentiates between ''foreign-controlled affiliates'' and ''domestically controlled firms. ''. An enterprise is classified as foreign-controlled when a foreign owner holds more than 50% of its capital, i. e. , the majority of ordinary shares or voting power. In this paper, we use ''foreign-controlled affiliate,'' ''foreign-controlled firm,'' and ''MNE'' interchangeably as they are all intended to refer to the previously identified typology of firms.
Before moving forward, it is important to highlight the few key assumptions made when adopting this methodology. First, the combination of AMNE and IO data presupposes consistency between the two. However, this is not typically the case in reality. Partly, this reflects the coverage of non-observed activities, which are usually not included in AMNE data but are estimated in the national accounts. Another source of difference is reflected in the statistical units used in constructing the different datasets, i. e. , 'enterprises' in AMNE versus 'establishments' in IO tables. 2 Third, non-market activities are not typically covered in AMNE data. As such, in the analysis that follows, activities with significant non-market output (e. g. , public administration services, health, education, and other social services) are excluded, which implies that these industries are not broken down by types of enterprise (foreign and domestic controlled), but treated separately (since in most OECD countries, the private sector plays a limited role in public administration services, this does not impact the relevance of the analysis).
Similarly, estimates of owner-occupied dwelling services are removed from the category of real-estate activities and included in non-market activities. Finally, no heterogeneity breakdowns are provided for the financial services sector because AMNE data for this sector are typically confidential. Step 2. Disaggregating IO columns: Imports. The next step involves splitting the industry columns of the import matrix (which records imports of products by using industries) by firm type. This is achieved using information available in the TEC database. The starting point is information on the value of total 'direct' imports by domestically controlled firms and foreign-controlled affiliates. However, as also noted in footnote 9 in the paper, many firms import 'indirectly' via wholesale intermediaries.
These are however not expected to form a significant source of difference as trade data is generally less affected by the non-observed economy (in most countries). Step 3. Disaggregating IO columns: Domestic intermediate use. Domestic purchases were calculated, by firm type, as the remainder of gross output less value added and less imports. This replicates the proportionality assumption introduced for imports, i. e. , the domestically purchased product basket between domestically controlled firms and foreign-controlled affiliates is the same. Step 4. Disaggregating IO rows: Exports. The export column of national IO tables is split using exports by firm type (within industries) available from TEC with adjustments for exports by wholesalers/retailers (as explained in Box 1). Step 5. Disaggregating IO rows: Intermediate and domestic final demand.
In practice this may generate a downward bias in the degree of upstream integration of domestically controlled firms as in many sectors these provide intermediate parts for foreign-controlled affiliates. While this methodology introduces data by firm ownership with respect to their value added, output, imports and exports, a few important assumptions have been made. The first assumption is that Box 1 Wholesale adjustment for imports. In IO tables, following the System of National Accounts, imports by firms are included as direct imports even if they pass through resident wholesale and retail industries first. In other words, imports of goods by wholesalers and retailers for subsequent sale without any further processing are not recorded as their imports in the SNA. The same holds for exports of goods that have not been the subject of any further processing by wholesalers and retailers.
In mathematic notion: X L i ¼ O L i X L S O L S where X L i = the share of foreign-controlled affiliates in exports of individual services industry i, O L i = the share of foreign-controlled affiliates in output of individual services industry i. A similar calculation was made for imports using output less value added instead of output. In addition to this imputation regarding the services industry, six other imputation methods were used to complete the dataset of shares of foreign-controlled affiliates (and as a consequence, domestically controlled firms) in value added, output, exports, and imports, in the following order: (1) simple linear interpolations; (2) using TEC export shares as a proxy for value added and gross outputs for the agriculture sector (01T03), as data in AMNE for the agriculture sector is missing in general; (3) mean imputation using country average share*industry average share/world average share for a specific variable; (4) correction applied if any imputed shares are greater than 1, then set to the nearest acceptable time series value; (5) logic checks: if the share of domestically controlled firms and foreign-controlled affiliates in output is zero, then value added, imports and exports are also set to zero to avoid data conflicts; and (6) if the entire series is still missing, then foreign-controlled affiliates' shares in gross output was used to impute all other variables.
For the purposes of the System of National Accounts, it is divided accordingly into local units and engaged in homogeneous activities, called local kind-of-activity units. The combination of location and kind of activity of an enterprise results in what is called an establishment. Establishments allow for the possibility of carrying out one or more secondary activities, although they should be on a small scale compared to the principal activity. Therefore, establishments are a refined definition of enterprises which are in the same location and produce one kind of primary outputs. Similar to TEC, STEC is designed to breakdown the service trade providers into groups with same business characteristics (e. g. , by firm ownership). Since trade in services is a very complex definition compared to trade in goods, it is taking longer to develop this database.
Abbreviations used in this Appendix, in alphabetical order: AMNE = Activity of Multinational Enterprises (database); CPC = Central Product Classification; GVC = Global Value Chain; IO = Input-Output; ISIC = International Standard Industrial Classification of all activities; OECD = Organization for Economic Cooperation and Development; SNA = System of National Accounts; STEC = Services Trade by Enterprise Characteristics (database); TEC = Trade by Enterprise Characteristics (database); VAT = Value Added Tax.
ACKNOWLEDGEMENTS The authors thank  Nadim Ahmad ,  Head of Division  at the OECD, for his helpful guidance and comments, and the participants of the OECD Expert Group on Extended SUTs, the contributors to the various Regional Trade in Value Added Initiatives including those of  APEC, Europe (Eurostat Figaro ) and  North America , and of the OECD Working Party on Trade in Goods and  Services Statistics , and the  OECD Working Party on National Accounts Statistics  for the productive discussions on previous versions of this work.
NOTES1 Abbreviations used in this Appendix, in alphabetical order: AMNE = Activity of Multinational Enterprises (database); CPC = Central Product Classification; GVC = Global Value Chain; IO = Input-Output; ISIC = International Standard Industrial Classification of all activities; OECD = Organization for Economic Cooperation and Development; SNA = System of National Accounts; STEC = Services Trade by Enterprise Characteristics (database); TEC = Trade by Enterprise Characteristics (database); VAT = Value Added Tax.
Apparatus We used a modified version of Canzoneri et al. 's audiotactile interaction task  [33]. Participants sat on one of two chairs placed side by side (figure  1 ) in a soundproofed room. Both of their hands were palmsdown on a table, aligned and in contact with their mid-sagittal plane. To control for the visual stimulation and gaze direction, participants were instructed to fix a permanent visual target located at 65 cm in front of them. Auditory stimuli were presented through Beyer Dynamic DT770 headphones. The auditory stimulus was a sound of bubbling water (32 bits, 44 100 Hz digitization), processed through binaural rendering using non-individual head related transfer functions (HRTF) of the LISTEN HRTF database (http://recherche. ircam. fr/equipes/salles/listen/). With this procedure, the virtual sound source location can be manipulated by rendering accurate auditory cues such as frequency spectrum, intensity, and inter-aural differences.
In order to simulate the looming source, the sound was processed in the Max/MSP(6. 1. 8) environment using the Spat library  [35]. The simulation consisted of a sound source moving in a virtual shoebox room (650 m 3 ), in which first reflections up to order 3, and late reverberation were rendered dynamically. The spatialization of the direct sound and of the first reflections was rendered using non-individual head related transfer functions (HRTF) taken from the LISTEN HRTF database (http://recherche. ircam. fr/equipes/salles/listen/). In order to enhance the looming effect of the direct sound, binaural nearfield correction filters were used  [36]. According to the direction of incidence, the sound pressure level delivered to the ears of the participant was ranging from 35 to 61 dBA and from 37 to 78 dBA, for the contralateral and the ipsilateral ears, respectively.
Acknowledgements. We are grateful to  Emmanuel Fléty  and  Arnaud Recher  for their help with the apparatus for tactile stimulation. We thank  Olivier Warusfel  for his help on the elaboration of spatialized auditory stimuli through binaural rendering. We thank  Philippe Nivaggioli  for his help during set-up installation, and  Anna Skrzatek  for her help with data collection. We thank  Alice Castan  for proofreading this manuscript for British English spelling.