dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of background data was determined as 20% of the grids which contain the occurrence record of the target taxonomic group. In the model development procedure, the background data were extracted within the native range for each species. • Errors and biases: Occurrence records GBIF database are likely to be spatially biased towards more easily accessed or better surveyed areas, which are affected by various social constraints. Therefore, we took the bias background approach as described in the "Background data" section. whose predictions are consistent with the presence distribution in the test set. We used models with CBI > 0 based on the 95 % confidence interval for the subsequent analyses.
Prediction Prediction output • Prediction unit: Predictions bounded by clamping in MaxEnt. The probability of occurrence was converted into the binary map by using the threshold (see section Threshold selection), then we calculated the area of potentially suitable habitat. • Post-processing:  Current: Predicted suitable habitats may appear beyond the species' native ranges. For each species, we discarded projected suitable habitats if (1) they were beyond the current native regions recorded in the IUCN species database, or (2) if they are on a landmass that has not been connected to other landmasses with occurrence points since the last glacial maximum period. In this case, the threshold value for the paleocoastline was set to -130 m below the current coastline, estimated by using seafloor topography data (ETOPO1).
acknowledge support from the  US NSF Long-Term Ecological Research (LTER) Network Communications Office  ( DEB-1545288 ). ASM and KO acknowledge support from the  Ichimura New Technology Foundation. ASM, KO, TM and HO were funded by the  Environment Research and Technology Development Fund  (ERTDF;  JPMEERF15S11420 ) of the  Environmental Restoration and Conservation Agency (ERCA) of Japan. ASM was supported by the  Grants-in-Aid for Scientific Research  of the  Japan Society for the Promotion of Science  (JSPS;  15KK0022 ). FI acknowledges support from a  US National Science Foundation (NSF)   CAREER award  ( DEB-1845334 ). AG was supported by the  Liber Ero Chair in Biodiversity Conservation. ML was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ). TM and HO were funded by the  ERTDF  ( JPMEERF20202002 ) of the ERCA.
Data The main data source used in the analysis is the British Household Panel Survey (BHPS). 8 This is a nationally representative random sample of over 10,000 household, containing almost 30,000 unique adult individuals, conducted between September and Christmas of each year from 1991 to 2008 (see  Taylor et al. , 2002). Data on self-employment have been collected from 1991, whilst data on lottery wins were collected for the first time in 1997. The exact question asked about lottery win is, "About how much in total did you receive? Win on the football pools, national lottery or other form of gambling?". In modern Britain, the national lottery is overwhelmingly the main form of gambling relevant to this question, so for succinctness we shall refer to this as lottery wins. For the design of this study, any form of gambling windfall would be suitable as a quasiexperimental income shock since the amount received is randomly distributed across winners.
The linear expenditure system (LES) is a popular option for modeling consumer preferences in computable general equilibrium (CGE) models when data are scarce, since its underlying functional form is parsimonious in parameters. The goal of this paper is to compare the performance of LES against the indirect addilog system (IAS), a hardly known alternative, in terms of their theoretical properties and in a case study. Both systems are equally easy to implement and require the same information for parameter calibration. IAS, however, offers a richer description of consumer preferences. On the basis of an expenditure survey of Statistics Palestine in 1998, we find overwhelming statistical evidence that the IAS demand equations perform better than those of the LES. Simulations with a CGE model developed for disaster impact analysis applied to the intifada of the early 2000s show that the absolute value of the equivalent variation is larger for IAS than for LES.
In many practical applications, like the model used in this paper, this prevents AIDADS and CDE 2 from being used. In a situation of data scarcity one has to resort to a simplification; in case of AIDADS that is the linear expenditure system (LES), whereas in case of CDE that is the indirect addilog system (IAS). For both systems only the expenditure elasticities and Frisch's parameter or one own-price elasticity are required so that the information required for calibration is reduced from 2n to (n+1). Although  Hanoch (1975)  and  Hertel et al. (1991)  explore the theoretical implications of IAS to the best of our knowledge a systematic theoretical and empirical comparison of the performance of IAS and LES has never been performed. To fill in that knowledge gap in this paper we investigate whether the household behavior in CGE models should be modeled according to LES or to IAS.
In Section 3. 3 we summarize both test procedures. As said before, in our empirical example we use the Palestinian Expenditure and Consumption Survey of 1998 (PECS1998) constructed by Statistics Palestine (1998) and the SAM of the economy of WBG (SAM1998) constructed by the World Bank (1998). Unfortunately, the only level at which both datasets are compatible is the three-commodity level adopted in this paper: ' Agrifood', 'Manufacturing' and 'Services'. In Section 3. 4 we present our empirical findings of estimation, testing and calibration of the parameters of LES and IAS.
De Boer and Paap (  2009 ) derived two different tests, a parametric one developed by  Vuong (1989)  with a test statistic that is asymptotically standard normally distributed, and a non-parametric one developed by  Clarke (2007). It is the sign-test on the log-likelihoods with a test statistic that follows a binomial distribution with a number of trials equal to the number of respondents T and a probability of success p = 0. 5. In their Appendix C they give the Gauss 6. 0 code for the estimation of the parameters and standard errors of the demand equations according to LES and IAS, (i. c. (  28 ) and (33), respectively), and for testing LES versus IAS by means of the two just mentioned tests. They applied them to the data on the ten main expenditure groups of the consumer price index (CPI) of the  PECS of 2005 (Statistics Palestine, 2005).
They gave a so-called intifada shock to update 35 the 1998 SAM to 2002 which served as the basis for their policy simulations. Later on, the results of the expenditure survey for 1998 (PECS1998) conducted by Statistics Palestine (1998) became available. This enabled us to estimate expenditure elasticities rather than assigning a value to them. The only level at which the SAM and the PECS datasets are compatible is the three-commodity level adopted in this paper: ' Agrifood', 'Manufacturing' and 'Services'. The focus of this paper is not on a policy issue (such as 'Food-for-Work versus Cash-for-Work'), but on a methodological one: the comparison of the outcomes of the same shock given to an IAS-version of the model and an LES-version 31 Missaglia and de Boer (2004) reproduced this Summary in their Table  1.
Acknowledgements This paper is dedicated to  Professor W. H. Somermeyer , Director of the  Econometric Institute of the Erasmus University Rotterdam  from 1966 until his untimely death on 31 May  1982. Wim Somermeyer  was friend and guide, as well as the supervisor of the Ph. D. theses of  Paul de Boer  and  Jan van Daal. We are indebted to  Marco Missaglia , formerly at the  University of Pavia, Italy  for carrying out the simulations reported upon in Section 4, and to  Wim van Veen of the Amsterdam Centre for World Food Studies  for his stimulating comments on this and earlier versions of the paper. The revised version greatly benefitted from remarks of three anonymous reviewers.
The three energy transition targets Our proposal aims to establish the trajectories of the EU and its 28 countries with respect to the three energy transition objectives. We consider three comparable variables across countries, described in Table  A1  in appendix, namely the Greenhouse Gas Emissions (GHGE), Primary Energy Intensity (PEI)  [4] , and the Share of Renewable Energy Consumption (SREC). These three active variables will hereafter be called the three components of the energy transition (ET). We use annual data extracted from Eurostat databases over the period 2000-2015. To get an overview of the differences between the 28 EU countries, in Table  1  we present some summary descriptive statistics computed from the national averages of the three ET components of the EU over the period 2000-2015.
Data The study's empirical design employs a unique dataset drawn from an international research project on competitiveness (Global Competitiveness Project, GCP: www. sme-gcp. org) developed by the Polytechnic University of Catalonia (UPC Barcelona Tech, Spain) and the E u r o p e a n B u s i n e s s R e v i e w 8 University of Pécs (Hungary). In 2020, universities from ten European and Latin American countries participated in the GCP-i. e. , Bosnia, Czech Republic, France, Hungary, Spain, Russia, Brazil, Colombia, Costa Rica, and Mexico. The objective of the GCP is to analyse business competitiveness by using composite indicators  (Lafuente et al. , 2020). Recent work by Alonso-Ubieta  and Leiva (2019), Moreno-Gómez  and Lafuente (2019), Bayon  and Aguilera (2020), Horváth  and Lafuente (2020)  Researchers from the participating universities supervised the entire data collection process.
The selection process of the surveyed firms in each country was conducted in two different phases. First, each participating team identified businesses operating in different industries. Owners and/or top managers are the relevant respondent group at this phase. After an initial telephone call to gain approval from the owners or a top manager, an appointment was arranged. In the second phase, a face-to-face interview was held with one of the owners (only if he/she is part of the top management team) in the case of businesses with less than 20 employees, while a top manager was interviewed in firms with more than 20 employees. Self-administrated, structured interviews were conducted during the data collection process in which managers were asked to answer essentially closed questions. The survey was conducted by members of the participating teams, and the data was collected between March and June 2019.
Multilevel analysis model The first stage of our analysis is to determine the possible presence of country level influence over firm competitiveness and the competitive efficiency of the way firms exploit their resources. To address this point, which is related to this study's first hypothesis, a multilevel mixed-effects linear regression is used. As a consequence of the nested nature of the dataset by countries, a conventional regression model would be likely to produce inefficient estimates and biased standard errors  (Snijders & Bosker, 2012)  In equation (3), i indexes business and c countries, is the overall mean of the dependent  00 variable (i. e. , BOD scores) and is the randomly distributed country effect, that is, the  0 variance of the mean value of the BOD scores for each country (c) around the overall average BOD score.
In addition, we investigate the determinants of derivative usage by emerging country banks. Numerous studies have examined the relationships between financial derivatives and bank risk, stability, and value for developed market banks  (Li and Marinč, 2014; Mayordomo et al. , 2014; Bliss et al. , 2017; Chang et al. , 2017) , but few studies have been dedicated to analyzing these relationships in emerging market banks  (Keffala et al. , 2011 (Keffala et al. , , 2013;; Keffala, 2015; Titova et al. , 2018). To further our understanding of emerging market bank derivative usage, we analyze a unique dataset by hand collecting derivative data from the financial statements of listed banks and classifying derivatives according to their main purpose, namely, hedging or speculation. In the context of emerging markets, to estimate the effect of derivative usage on bank stability, risk, and bank value, we match this information with data on bank-and country-specific variables.
(2017)  findings regarding systematic risk but are different from their findings for bank value and total risk 2. Finally, we find that derivative usage by emerging market banks reduces their instability as measured by the Z-score. The paper contributes to the literature on different levels. First, our unique, hand-collected dataset allows us to add to the derivative usage literature by exploring the determinants of bank derivate usage and the effects of this usage on bank risk and value for a wide sample of emerging market banks, a topic that until now has remained unexamined in the literature. Prior research was conducted mainly on U. S. banks or European markets and to our knowledge, our dataset has a much wider emerging market bank coverage than previous studies. Second, this is the first paper that investigates the effects of derivative usage on both value, risk and stability measures for a large sample of emerging market banks in the post-financial crisis era.
Data source Our sample covers publicly listed 4 emerging market commercial banks for the period 2010-2017. To identify emerging markets, we first use the Morgan Stanley MSCI emerging market index, which classifies 54 countries as emerging. The disclosure of information by banks on financial derivative usage is voluntary; however, when disclosing, most banks explain their derivative usage and report the notional and/or fair values in the notes on financial statements of their annual reports. However, as stated by  Acharya and Bisin (2014) , the lack of common standards on derivative reporting limits the ability to use derivative data in research. In response to this and to ensure derivatives reporting is as consistent as possible across countries and banks, we sample banks exclusively from countries adopting IFRS accounting standards.
After excluding countries that do not adopt IFRS accounting standards 6 and others with underdeveloped banking sectors with no listed banks, our final sample includes banks from 24 emerging market countries 7. For these 24 countries, we initially identified 435 listed financial firms, but after the exclusion of non-commercial banks, Islamic banks, 8 subsidiaries and banks that do not report on financial derivative usage in their financial statements, we are left with 115 entities. We further need to discard 10 banks with unclear information on financial derivatives 9 and achieve a final sample of 105 listed domestic banks over the time period 2010-2017. Where available, data from unconsolidated statements is used to avoid double counting. Financial statement data on derivative usage are then matched with bank-and country-level data from Bloomberg, the World Bank and DataStream.
org/use-around-the-world/useof-ifrs-standards-by-jurisdiction/. 6 Some of the countries that do not follow the IFRS standards include North African countries, China, India, Indonesia, and some other countries. For other countries no information on derivatives can be found and those, such as Kenya, Bangladesh, Benin, Burkina Faso no listed banks can be found. 7 The dataset contains banks from 24 Middle Eastern, East Asian, African, East European and Latin American countries. Domestic commercial banks are identified by the availability of data on derivative usage as indicated by notional amounts reported in annual reports and financial statements. Table  1  presents the countries and number of banks in each country. 8 We exclude Islamic banks since "Islamic" derivatives are still not standardized and follow ununified legal provisions and accounting standards.
Bank risk and stability. To measure bank risk, consistent with  Brewer et al. (1996) ;  Bartram et al. (2011)  and  Chang et al. (2017) , we use the standard deviation of weekly stock returns (σ return ). We follow the literature in assuming that net risk exposure can be estimated by using bank stock price volatility. The data on the weekly stock price for banks were collected from Bloomberg. Furthermore, in addition to its impact on overall volatility, we also evaluate the effect of derivative usage on bank stability or insolvency risk. As a proxy of bank insolvency, we use the Z-score  (Laeven and Levine, 2009; Duran and Lozano-vivas, 2013; Ashraf et al. , 2016 Ashraf et al. , , 2020)). Based on accounting data, this indicator is calculated as follows: Zscore = Ei,t TAi,t + ROA i,t σROA i,t where ROA i,t and σROA i,t denote the return on assets and its standard deviation, respectively, for Bank i at time t and E and TA denote the total equity and total assets, respectively, for bank i at time t.
Furthermore, we observe the effect of the flow of deposits (DEP_ASSETS), calculated Fig. 3. IFRS adopting countries (Dark Gray) vs. non IFRS adopting countries (Light Gray). 10 We use the exchange rates reported by the BIS to convert the notional derivative usage from the local currency value reported in the annual reports, to millions of USD and match it with the bank-specific control variables collected from the Bloomberg database. as the ratio of deposits to total assets. This flow acts as a natural hedge against liquidity risks  (Chang et al. , 2017)  and encourages banks to engage more in speculative behavior such as derivative trading  (Minton et al. , 2009). Another variable that may affect financial derivatives usage is bank capital (EQRAT), calculated as a ratio of equity capital to total assets  (Minton et al.
In times of distress, banks can reduce dividend payments as an alternative to engaging in risky assets, such as financial derivatives. To control for cross-country differences in financial openness, we use the variable (KAOPEN) 11 developed by  Chinn and Ito (2008). This variable, which is based on the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER), captures the extent of financial liberalization through cross-border financial transactions. To capture the effect of competition, we use the three-bank concentration ratio (CNTRN) from the Financial Development and Structure Database maintained by  Cihak et al. (2012). This variable is calculated as the ratio of the assets of the three largest banks in a country as a share of the assets of all commercial banks.
We use this variable as a proxy for competition in the banking sector, in which banks in a highly competitive environment may engage more in financial derivatives to gain an edge over competitors and increase their profit. We also control for the strength of the institutional environment (WGI_AVR) by using an average of the World Government Indicators from the World Bank database. As the six indicators are highly correlated, we consider the average as a proxy of institutional strength. Furthermore, we use the World Uncertainty Index (WUI) 12 as a proxy of political and economic uncertainty. "Uncertainty" affects the pricing of financial derivatives  (Pan et al. , 2020)  in addition to the trading volume and bank liquidity  (Berger et al. , 2020). Finally, we control for the annual rate of inflation, as the literature argues that through excessive competition, high inflation can affect bank behavior  (Kasman and Yildirim, 2006)  and can affect bank margins and overhead costs (Demirgüç-Kunt and Detragiache, 2005).
The dataset is built by  Ahir et al. (2018)  and provided by the IMF. See https://worlduncertaintyindex. com/data/ 13 Details on the variables' description are reported in the Appendix model usage also controls for the problem of endogeneity that can occur because of unobserved variations. We also control for time by using year dummies, and we correct for heteroscedasticity by using bank clustered standard errors. Risk i,t = Ɣ 0 + Ɣ 1 DER it + Ɣ 2 ROE it + Ɣ 3 NIM it + Ɣ 4 DEP_ASSETS it + Ɣ 5 EQRAT it + Ɣ 6 LNTASS it + Ɣ 7 CCLOAN it + Ɣ 8 DIV it +Ɣ 9 KAOPEN it + Ɣ 10 CNTRN it + Ɣ 11 WGI_AVR it + Ɣ 12 WUI it + Ɣ 13 INF it +v i + E it. (2) TOB_Q = Ƞ 0 + Ƞ 1 DER it + Ƞ 2 ROE it + Ƞ 3 NIM it + Ƞ 4 DEP_ASSETS it + Ƞ 5 EQRAT it + Ƞ 6 LNTASS it + Ƞ 7 CCLOAN it + Ƞ 8 DIV it + Ƞ 9 KAOPEN it + Ƞ 10 CNTRN it + Ƞ 11 WGI_AVR it + Ƞ 12 WUI it + Ƞ 13 INF it + v i + E it.
545133 KAOPEN 2. 19 0. 456693 KAOPEN 2. 34 0. 427908 WGI_AVR 2. 93 0. 341253 WGI_AVR 3. 03 0. 330187 WUI 1. 42 0. 704041 WUI 1. 47 0. 678693 INF 1. 98 0. 505154 INF 1. 99 0. 502426 Mean VIF 1. 93 Mean VIF 2. 41 Independent variables ROA Return on assets ROE Return on equity NIM Difference between total interest income and total interest expense to total assets DEP_ASSETS Ratio of total deposits to assets CNTRN Three-bank concentration ratio from the Financial Development and Structure Database maintained by Anginer et al. (2014) EQRAT Ratio of book value of equity to total assets LNTASS Natural logarithm of total assets CCLOANS Total commercial loans normalized by total assets DIV Dividends paid by total assets KAOPEN A measure of Determinants Beta Stock Volatility Variable VIF 1/VIF Variable VIF 1/VIF Variable VIF 1/VIF ROE 1.
The detailed questions used to assess each domain are presented in Table  1. The questionnaire also collected information on age, gender, marital status, religion, educational attainment, type of occupation, and monthly income. To assess the validity and reliability of the questionnaire, average variance extracted (AVE), composite reliability, factor loading and Cronbach's alpha were measured for each domain of variable. Our data suggested that each item within each domain had standard loadings value higher than 0. 5 and AVE higher than 0. 5, suggesting that the question within the domain had acceptable convergence and all items used within domain are valid (Table  1 ). In addition, both composite reliability and Cronbach's alpha were greater than 0. 7 indicating that items within the domain were reliable (Table  1 ).
The indicators used for absolute best fit analysis were df and RMSEA. The value of df and RMSEA were acceptable since it met the requirement of the cut-off value. Five indicators were used for the incremental goodness of fit analysis: GFI, AGFI, CMIN/DF, TLI, and CFI. Our data suggested that all indicators met the requirements suggesting that the model was fit and acceptable (Table  3  and Figure  2B ). The results of SEM analysis of the modified model are shown in Table  4. Our data suggested that perceived usefulness significantly affected the acceptance for a COVID-19 vaccine (p < 0. 001). Data revealed that perceived religiosity did not significantly affect the vaccine acceptance (p = 0. 217). Lastly, SEM analysis indicated that perceived ease of use significantly affected the perceived usefulness (p < 0.
Report 16 August 2021 https://doi. org/10. 5256/f1000research. 56895. r91709 © 2021 Hariyanto T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Timotius Ivan HariyantoFaculty of Medicine, Pelita Harapan University, Tangerang, Indonesia Thank you for inviting me to review this manuscript. This manuscript provides evidence regarding the COVID-19 vaccine acceptance rate and analyzed the factors which influence COVID-19 vaccine acceptance in Indonesia. Overall, this study has good methodological quality and was presented in a descriptive and informative manner. I only have a few comments/concerns regarding this manuscript:This study was conducted in November 2020 where there was no COVID-19 vaccine available in Indonesia.