dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample and data Our sample is based on a novel dataset covering the 3 biggest ECF platforms in the UK with three different shareholder structures: direct model (Crowdcube), nominee model (Seedrs) and coinvestment model (SyndicateRoom). The crowdfunding data are sourced directly from TAB that was formerly known as Crowdsurfer. TAB Dashboard is described as the most comprehensive source of intelligence regarding the alternative finance market. 6 These data are augmented by firm-specific data sourced from Companies House that is a government agency website that makes available further information about all UK firms. Our study spans the period from April 2011 (date of Crowdcube's first successful campaign) to December 2018. The dataset consists of 709 firms with a successful initial campaign which were monitored to identify those that conducted at least one SECO.
Variables The study most closely related to ours is that of  Signori and Vismara (2018)  which focuses on the UK ECF market and studies the post-initial campaign life of 212 Crowdcube startups. We extend their study in two important directions. First, we focus not only on the determinants of SECOs (as theirs do) but also on the factors that impact on their success. Second our data covers the three major ECF platforms operating in the UK. This leads to a larger sample which requires additional variables to reflect the heterogeneity relating to platform ownership structures on which the campaigns take place. This enables us to study platform effects on the likelihood of a firm conducting a first SECO and of a first a successful SECO. Our first variable of interestnumber of investors -overlaps with that employed by  Signori and Vismara (2018).
We conjecture that it exerts a similar effect on conducting a first SECO and its probability of success. Our remaining variable of interest that we call valuation gains is novel and has not been previously used in the ECF context to the best of our knowledge. It is one of the very few variables that quantifies an ECF firm's financial performance and is also very timely as it is evaluated just prior to the SECO campaign going public. As such, it contributes substantially to lowering information asymmetries in the absence of any other up to date financial data for startups and ventures. As such, we would expect a positive impact of valuation gains on SECO success. This variable is measured by the annualised increase of the pre-money valuation amount (£m) of the startup over the period between of the start of the initial and first SECO campaigns.
Empirical results This section presents and discusses our probit results for a sample of 709 successful initial ECF campaigns and 105 first SECOs on the Big 3 UK platforms: Crowdcube, Seedrs, and SyndicateRoom. The data span the sample period from April 2011 to December 2018. It also includes a detailed analysis of the direct and indirect impact of platform shareholder structures on the probability of conducting (a successful) first SECO.
12. The results suggest that ECF firms employing the nominee model and coinvestment model in their initial campaign on average are 10. 9% and 25% more likely to conduct a first SECO campaign than those ECF firms employing a direct model (Crowdcube). These results can be interpreted as reflecting a significant a role for monitoring and also perhaps for crowd shareholder rights on both shareholder structures (platforms) which would mitigate moral hazard problems. This is consistent with the Signori and Vismara (2018) significant finding for their qualified investor variable in the context of follow-on campaigns for Crowdcube. Our results are also broadly consistent with  Rossi et al. (2019). They employ data from 185 ECF platforms across different countries and find that the direct ownership model lowers the likelihood of success in contrast with nominee model for initial ECF campaigns.
, 2020). In this context, our current serial SECO sample is very small. Another limitation is that lack of data prevents us from studying investment motivation. The latter may vary regarding involvement in SECOs as existing studies argue that ECF investment behaviour is heterogeneous  (Wallmeroth, 2019; Vismara, 2019). It would be interesting to extend our sample, but the study of cross platform effects would be more complex as the Crowdcube platform is now allowing nominee account campaigns whilst the Seedrs platform is permitting direct ownership campaigns. This is further supported by heterogeneity in local bias for venture capitalists  (Cumming and Dai, 2010). We leave the effect of these developments for future research. , 2019). The selection equation relates to the probability of conducting a first seasoned equity crowdfunded offering (SECO) after a successful initial campaign.
The study was registered at ClinicalTrials. gov (ID: NCT04321265) and adhered to the European Union General Data Privacy Regulation (GDPR). This investigation aimed to understand which factors are associated with survival and quality of life in older COVID-19 patients (the so-called COVIP-study, COVID-19 in VIP)  [2]. As in the previous VIP studies  [2, 9, 10] , national coordinators recruited the ICUs, coordinated national and local ethical permissions, and supervised patient recruitment at the national level. Ethical approval was mandatory for study participation and was obtained for each country separately. Usually, informed consent was mandatory for inclusion, but in some countries, this was not required with the reference to the critical condition and difficulties with patients consent.
Study population The COVIP-study recruited consecutive patients with a positive COVID-19 test and aged 70 years or older who were admitted to an ICU. The dataset was extracted from the COVIP study database on 14th of May 2021 and contained patients from 19th March to 4th February 2021. Prospective data collection commenced at ICU admission. The admission day was defined as day 1, and all consecutive days were numbered sequentially from that date.
Data collection The methods have been described in previous publications  [2]. In short, all centres used a uniform online electronic case report form (eCRF). Of each patient, we recorded (i) information present on admission (i. e. gender, weight, length, days in hospital prior to ICU admission, days with symptoms prior to hospital admission, comorbidities, medication use, habitat before admission, arterial blood gasses on admission, sequential organ failure assessment (SOFA), the clinical frailty scale (CFS) and the Katz activities of daily living), (ii) we collected data on treatments provided to (or withheld from) the patient while being admitted to ICU (i. e. mechanical ventilation, prone positioning, tracheostomy performed, vasoactive drugs used, use of renal replacement treatment used, use of non-invasive ventilation, use of extra-corporeal membrane oxygenation, use of antimicrobials, and life sustaining treatments withheld or withdrawn while being in the ICU) and (iii) outcomes of the patients (i.
e. survival to ICU discharge, ICU length of stay, vital status at 30 days, vital status at 3 months and EuroQoL quality of life questionnaire at 3 months). Records of patients for whom data were not complete were completed using multiple imputations (see Statistical section). For the SOFA score on admission, each element was entered and the eCRF calculated the total score. Furthermore, we assessed the need for non-invasive or invasive ventilation, prone positioning, tracheostomy, vasopressor use and renal replacement therapy. The CRF also documented any limitation of life-sustaining therapy during the ICU-stay.
Health-related quality of life The quality of life was assessed by the EurQol-5D-5L questionnaire at 3 months post-discharge. This is a self-reported assessment of generic health that consists of five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression), each with five levels of functioning (e. g. n 'no problems', 'slight problems', 'moderate problems', 'severe problems' and 'unable to'/'extreme problems' for all of these dimensions) (see Supplementary Table  1 , Supplementary data are available in Age and Ageing online). This health state classifier can describe 3,125 unique health states that are often reported as vectors ranging from 11,111 (full health) to 55,555 (worst health). Numerous societal value sets have been derived from population-based valuation studies around the world that, when applied to the health state vector, resulting in a preference-based score that typically ranges from states worse than dead (<0) to 1 (full health), anchoring dead at 0.
Statistical analysis Descriptive data were computed based on all case records, showing the amount of missing data per variable. Continuous data points were expressed as median ± interquartile range. Differences between independent groups were calculated using the Mann-Whitney U-test. Categorical data are expressed as numbers (percentage). The Chi-square test was applied to calculate differences between groups. Multiple variables showed missing data. For the multivariable analysis of associating CFS to HRQoL (see below), the complete cases considering all required variables numbered 585 of the total 1,224 patients (47. 8%). We used multiple imputation techniques to avoid the bias due to loss to followup when a complete case analysis would have been performed. A total of 100 imputed datasets were generated by multiple imputations through chained equations using the 'mice' package in R v4.
1. 0. All imputation models allowed for non-linear associations of any included continuous variables. Univariate and multivariable logistic regression analyses were performed to assess associations with baseline variables and the studied HRQoL outcomes. We chose the covariables for the multivariable model (age, gender, Katz, SOFA score and CFS) based on clinical experience and previous literature  [2, 9, 10]. In analyses where CFS was shown per level, CFS 7, 8 and 9 were combined because there were too few patients in CFS 8 and 9 to allow a proper analysis of these levels separately. Rubin's rules were used when pooling the results from the imputed datasets. All tests were two-sided, and a P-value of <0. 05 was considered statistically significant. R version 4. 1. 0 was used for all statistical analyses.
greatly appreciated. In Norway, the study was supported by a grant from the  Health Region West. In addition, the study was supported by a grant from the  European Open Science Cloud (EOSC). EOSCsecretariat. euhas received funding from the  European Union 's  Horizon Programme call   H2020-INFRAEOSC-05-2018-2019 , grant agreement number  831644. This work was supported by the  Forschungskommission of the Medical Faculty of the Heinrich-Heine-University Düsseldorf , No. 2018-32  to G. W. and No. 2020-21  to  R. R. B. for a Clinician Scientist Track.
Prolong Pharmaceuticals and Nycomed outside the submitted work. The money went into departmental funds. No personal financial gain applied. Declaration of Sources of Funding: No (industry) sponsorship has been received for this investigator-initiated study. This study was endorsed by the  European Society of Intensive Care Medicine (ESICM). Free support for running the electronic database and was granted from the  Department of Epidemiology, University of Aarhus, Denmark. The study was supported in France by a grant from  Fondation Assistance Publique-Hôpitaux de Paris pour la recherche  is.
We explore whether loan officer gender affects loan repayment performance in Cameroonian microfinance institutions (MFIs). After controlling for demand-side factors (borrower characteristics), lending methodology, loan contract terms, year, and industry fixed effects, we apply a pooled probit model to a unique loanlevel dataset including more than 7000 loans approved between 2007 and 2012 by two Cameroonian commercial MFIs. We find that loans granted and monitored by male loan officers perform better than those granted by female officers and that loans approved under joint liability contracts and monitored by male loan officers are less likely to fall into arrears. Our findings also show that the performance advantage of male loan officers over female officers is confirmed only during bad times.
These two greenfield MFIs serve as hold-out samples, allowing us to check whether the findings of previous studies are consistent in a different context. Our study also links to cross-country studies on the determinants of MFI loan portfolio quality  (Abdullah & Quayes, 2016; Boehe & Cruz, 2013; D'Espallier et al. , 2011)  and single country studies dealing with the determinants of repayment performance of loans granted by single MFIs. By investigating the gender difference in loan performance in two Cameroonian greenfield MFIs, our study also relates to  Cull et al. (2015)  who investigate whether sub-Saharan Africa greenfield MFIs grow faster, achieve better outreach and profitability than non-greenfield MFIs. Using a unique loan-level dataset of over 7000 enterprise loans approved by two Cameroonian commercial MFIs targeting mainly very small enterprises (VSEs) and small and medium enterprises (SMEs), we find that loans granted and monitored by male loan officers perform better than those granted by female loan officers.
Their activities are thus more likely to be impacted by crises and the business cycle. By accounting for the 2007-09 global financial crisis, we extend previous studies using loan-level data to investigate loan repayment performance and a sample of loan applications before  (Sharma & Zeller, 1997; Beck et al. , 2013; Agier, 2012)  and after (Van den  Berg et al. , 2015)  the 2007-09 global financial crisis. Based on a unique loan-level dataset including more than 7000 loans approved between 2007 and 2012 by two Cameroonian commercial MFIs, we find that loans granted and monitored by male loan officers perform better than those granted by female officers. Loans approved under joint liability contracts and monitored by male loan officer are less likely to fall into arrears. Our findings show that the performance advantage of male loan officers over female officers is confirmed only during bad times.
Using loan approved level data from VivaCred (a Brazilian Microfinance NGO),  Agier (2012)  documents that loan officer ability, strongly correlated with one loan officer attribute, namely experience, is likely to affect the success of microloans, especially at the screening stage. Scholars have also focused on the gender issue in the financial literature in relation to loan performance. Some studies have looked at the impact of gender on corporate financial decisions, including investments  (Barber & Odean, 2001) , asset allocation  (Sunden & Surette, 1998) , and capital structure  (Adusei & Obeng, 2019). Others have analyzed the impact of boardroom gender diversity or gender attributes on firm performance and earnings quality  (Adams & Ferreira, 2009; Bennouri et al. , 2018; Conyon & He, 2017; Gul et al.
Indeed, their findings suggest that male loan officers are better able to induce borrowers to repay compared to female loan officers. Based on MFI level data,  Blanco-Oliver et al. (2021)  show that female loan monitoring is associated with lower portfolio quality, suggesting that loans monitored by female loan officers perform less well. Some recent studies document that loan officer-borrower gender match (gender bias) and even gender affinity affect the likelihood of loans turning problematic. Using loan-level data from an Albanian lender,  Beck et al. (2018a)  investigate how gender interaction between loan officer and borrower improves loan condition and loan repayment performance. The authors document a gender bias in loan allocation, since borrowers assigned to a loan officer of the opposite sex receive loans at unfavorable conditions, that is, higher interest rate, lower duration, and small size, compared to borrowers assigned a loan officer of the same gender.
Data In this study, we use unique loan-level data from two Cameroonian 6 shareholder-based MFIs belonging to the second category (applying market-based principles and a size close to that of banks), namely greenfield MFI 1 and greenfield MFI 2. These two MFIs have a large distribution network at the national level in both urban and rural areas. Greenfield MFI 1 has 13 branches in seven regions including nine in the two main cities Douala (five branches) and Yaoundé (four branches), while greenfield MFI 2 has 24 functional branches in Yaoundé, Douala, and Bafoussam. We were able to obtain data on all loans approved between 2007 and 2012. 6 Using a sample of approved loans from two MFIs is not necessarily representative of the microfinance sector in Cameroon and may therefore raise concerns of the external validity of our results and their generalization to the microfinance sector in Cameroon and the microfinance industry as a whole.
CONCLUSION In this study, we used a unique loan-level dataset including over 7000 loans approved between 2007 and 2012 by two Cameroonian greenfield MFIs to analyze the determinants of loan repayment performance. Cameroon is a developing country with a weak institutional capacity for legal enforcement of loan repayments. This suggests that MFI-level factors such as loan officer gender and ability, and the lending technologies adopted to process and monitor loans, are more likely to explain loan repayment performance. We thus estimated a pooled Probit model controlling for both year and industry fixed effects. The results indicate that loans granted and monitored by male loan officers perform better than those granted and monitored by female loan officers. Loans approved using joint liability lending models perform less well.
(2018)  and computing CH 4 and N 2 O production from enteric fermentation and manure decomposition, it is possible to derive a more comprehensive analysis of the environmental-economic trade-offs in feedlot beef production systems. This paper uses a multiobjective method based on a nonlinear profitmaximizing diet model to assess the economic and environmental tradeoffs in diet formulation for feedlots in France. The analysis combines data from ingredients LCAfrom the ECOALIM database  (Wilfart et al. , 2016)  and Tier 2 equations for direct cattle emissions derived by  Escobar-Bahamondes et al. (2017) and the IPCC (2006). We also analyze the impacts of using a profit-maximizing objective function in contrast to using the least-cost diet, and the implications of weighting different environmental impacts in the multiobjective function.
Feedstuff properties We obtained the nutritional properties of feedstuffs from NASEM (  2016 ), which has a library with over 200 feeding ingredients. The mathematical model developed by  Marques et al. (2020)  requires the following properties to evaluate the nutritional requirements: metabolizable protein (MP), physically effective neutral detergent fiber (peNDF), fat content (FAT), ruminally degradable protein (RDP), crude protein (CP), net energy for maintenance and gain (CNEm and CNEg), and dry matter (DM) to "as fed" (AF) conversion for each ingredient. We obtained LCA information of each ingredient from the ECOALIM database  (Wilfart et al. , 2016). Analogous to Garcia-Launay et al. (  2018 ), we considered the following environmental impacts: phosphorus demand (PD, in kg P/kg of ingredient DM), non-renewable energy (NRE, MJ/kg of ingredient DM), climate change potential (CC, in kg CO 2 eq/kg of ingredient DM), acidification potential (AC, in mol H + -eq/kg of ingredient DM), eutrophication potential (EU, in kg PO 4 3-eq/kg of ingredient DM) and land occupation (LO, m 2 year/kg of ingredient DM).
The model and respective data used are available in  Marques (2021). We estimate animal full-cycle GHGs and assume in both the scenarios that the animals bring an overhead emission (i. e. emissions accumulated prior to the current process of interest) of 14. 3 kg CO 2 eq / kg LW (12. 24 kg CO 2 eq / kg CWE, at the final live weight of 620 kg) from birth and raise before being moved to the feedlot growing phase  (Desjardins et al. , 2012). We analyze two trade-off dynamic scenarios with different environmental impact weights β: "RCS" (reduced carbon scenario), and "BIS" (balanced impacts scenario). These scenarios offer alternative perspectives on sustainable intensification; the first focusing solely on GHG mitigation, the second targeting GHG mitigation that avoids impact transference. In the RCS, we run the multiobjective model with weights 1 for β CC and 0 for the rest.
Acknowledgments RdOS acknowledges the support of a  University of Edinburgh Chancellors Fellowship  funded by the  Data Driven Innovation initiative as part of the Edinburgh and South East of Scotland City Region Deal and CAPES Foundation  for the scholarship no. 10180/13-3. DM acknowledges UKRI support under grant number  BB/T004436/1. DM, RdOS and LB acknowledge support under  UKRI grant   ES/N013255/2.
The eclectic theory of entrepreneurship has identified several macro-determinants of national entrepreneurial activities. Taking advantage of the availability of new databases, several recent empirical studies have sought to test these determinants in multicountry studies using multivariate regression models. Due to the lack of consensus around their results, this paper posits that this empirical literature may be subject to endogeneity bias, which seriously threatens its accuracy, consistency, and reliability, as well as the effectiveness of the resulting management and policy recommendations. Consequently, we methodologically demonstrate why and how endogeneity occurs in these studies by analyzing their empirical and theoretical models. We also provide a step-by-step guide to help researchers understand how to detect and correct endogeneity using IV techniques applied to a panel data analysis of the macro-determinants of early-stage entrepreneurship in a sample of 48 countries between 2000 and 2019.
A 'toolkit' of generic STATA software commands specifying the tests, methods, and assumptions performed in this analysis is included. In doing so, we aim to raise awareness of endogeneity bias among researchers and to empirically guide future studies in order to avoid its hazards. Finally, after correcting for endogeneity, our analysis identifies the protection of property rights, entrepreneurial culture, income, and economic development as the most consistent macro-determinants of early-stage entrepreneurship, providing important policy and business insights.
(2017) ,  Ketokivi and McIntosh (2017) ,  Ullah et al. (2018) ,  Sande and Ghosh (2018) , inter alia. To the best of our knowledge, to date there have been no methodological studies that have addressed this issue in the entrepreneurship literature and more specifically in empirical research on the determinants of entrepreneurship at the macro level. This paper contributes to filling this gap. It establishes a comprehensive framework for dealing with endogeneity bias that is adapted to empirical studies on the macro-level determinants of entrepreneurship by (1) identifying endogeneity bias and its manifestations and sources, (2) providing a methodological procedure to detect and correct it with different IV techniques, (3) illustrating this step-by-step procedure through a panel data analysis of the macro-determinants of early-stage entrepreneurship in a sample of 48 countries between 2000 and 2019, and, finally, (4) developing a 'toolkit' of generic STATA software commands specifying the tests, methods, and assumptions performed in this analysis.