dataesr/scientific-paragraphs-categorization huggingface dataset:
Total requirement coefficients We use a multi-regional input-output dataset, Exiobase 3 9  to compute the total requirements coefficients for each industrial sector pair at the 2-digit ISIC 3. 1 level. The Exiobase dataset is now in common use in the trade and industrial organization literatures, notably for the study of global value chains  (Wang et al. , 2013; Timmer et al. , 2015 Timmer et al. , , 2016)). Exiobase notably provides symmetric industry-by-industry input-output tables, as required by our empirical design. In the following, we only consider the national submatrices of these IO tables. Exiobase 3 provides a time series of input-output tables for each of the countries represented in our dataset over the whole 2000-2015 period. Compared to alternative MRIO options such as WIOD or EORA, Exiobase offers both a broader temporal and geographical coverage along with a more detailed disaggregation of the industrial sector.
The input-output accounts of Exiobase provide industry by industry direct requirements matrices, A it for each country i and year t in our dataset, from which we can derive the total requirements matrix using the familiar Leontief representation of the economy, L I A ( ) it it 1 =. For each sectoral pair (k, l), the coefficient l kl,it represents the sum of direct and indirect purchases from industry k required to produce a dollar of output in industry l -or the total requirements of sector k's output from sector l. In the context of this study, the total requirement coefficient aims at estimating the supply chain position 10  of the target sector in relation to the activity of the acquiring firm. For a given transaction, we therefore choose to use the coefficient computed in the country of the acquirer, for it better reflects the technology used by the acquiring firm.
Exiobase allows to compute time-varying total requirements coefficient through our entire sample period. However, cross-country industrial investments can have an impact on the technologies of the participating countries, particularly through knowledge and technology transfers. This raises a potential endogeneity concern, which we resolve by taking the average over time of the total requirement coefficients, for each acquiring country and pair of sectors observed in our transaction dataset. The sectoral means for these coefficients are reported in Table  1. Considering the joint coverage of all three datasets, M&A transactions, EPS index and total input requirements, we finally get a sample comprising 27 OECD, 5 BRICS countries and Indonesia over the period 2000-2015 (see Table  A. 1, in Appendix A).
Market size is measured by sectoral VA at the 2-digit ISIC rev. 4 level. To assess the POH empirically, we first classify transactions into four categories based on the degree of sectoral linkage (as defined in Section 3) between acquiring and target firms: • Horizontal: both firms have the same ISIC rev. 4 sector at the 4-digit level; • Vertical-upstream, High input: the target firm's sector accounts for at least 3 % of the total requirements of the acquiring firm's sector; • Vertical-upstream, Low input: the target firm's sector accounts for at least 1 % but less than 3 % of the total requirements of the acquiring firm's sector; • Conglomerate: the target firm's sector accounts for (strictly) less than 1 % of the total requirements of the acquiring firm's sector. The chosen thresholds of 1 % and 3 % are directly derived from our dataset: the former being the median and the latter is the 75th percentile of the total requirements coefficients between the acquiring and target sectors in our transactions.
Our original dataset allow us to focus this study on a particular internationalization strategy that has received little attention in the literature linking firm location and environmental policies: FDI through M&As. We recognize that this very originality may limit the external validity of our empirical results. Indeed, our methodological approach cannot account for POH scenarios whereby firms respond to environmental regulations by creating new foreign affiliates to produce their intermediate inputs (greenfield investment) or by subcontracting inputs to foreign suppliers (international outsourcing). Still, by focusing on M&As, we identify the conditions for PHH and POH for multinational companies that account for a significant share of global FDIs. Our results may prove to be underestimates because we only identify the effect of strict environmental regulations on international M&A, and cannot capture its effect on domestic transactions.
Stringent environmental regulations abroad discourage cross-border M&As of any kind provided the acquisition targets a highly polluting sector (GHG-emission intensive). However, we find the highest sensitivity to environmental policy stringency among upstream (vertical) high-input transactions. Environmental policy does not appear to be a significant factor for upstream low-input transactions, even in highly polluting sectors. None of the above-mentioned FDI strategies seem to be influenced by differences in environmental policies when the M&A transaction targets a low-polluting sector (defined as the first tercile of GHG-emission intensity in our dataset). Another interesting result concerns policy instruments. We find that international differences in environmental taxes stringency affect cross-border M&As twice as much as relative environmental standards stringency.
Finally, a few papers study whether the extent to which provided information is incorporated into beliefs depends on the cost and benefit of processing information. For example,  Fuster et al. (2019)  find that low-numeracy individuals incorporate the provided information less into their beliefs. Roth et al. (2020)  find that respondents who learn of a higher exposure to unemployment risk during recessions increase their demand for an expert forecast about the likelihood of a recession. Gaglianone et al. (  2019 ) also provide evidence that the choice of attention results from a costbenefit optimization. They estimate a model where professional forecasters face a fixed cost of updating their forecasts and an entropy-based cost of processing information. The authors exploit a unique dataset of Brazilian forecasters who update their inflation forecasts at days of their choice and participate in a monthly forecasting contest.
Objectives In this study, we address these limitations by using data from the UK Biobank (UKB) study to develop a novel 14-year dementia risk score specific to a mid-life UK population, which we refer to as the UK Biobank Dementia Risk Score (UKBDRS). We externally validate our score in the Whitehall II (WHII) study, another UK-based cohort with mid-life assessments. We focus on easily accessible sociodemographic and clinical data to enable the wider use of the score for routine care or large-scale screening for preventative trials. To combat the challenge of overfitting, we employ regularised regression methods to select variables used in the UKBDRS. We also compare the performance of our score to three previously published dementia risk scores developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index, ANU-ADRI  10  ), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score, CAIDE  11  ), and the UK (Dementia Risk Score, DRS  9  ).
Statistical analyses: development of the UKBDRS All continuous variables were standardised and outliers (ie, individuals with values <Q1 -3×IQR or values >Q3 + 3×IQR) were excluded (1. 8% of dataset). The first stage ('variable selection') used only the training set and involved submitting the 28 candidate predictors to a least absolute shrinkage and selection operator (LASSO) Cox regression (online supplemental methods), to identify a parsimonious model with dementia as the outcome. 18  Correlation between numerical variables was assessed before LASSO (online supplemental methods, online supplemental figure 2). LASSO selected variables were then.
Statistical analyses: evaluation of the UKBDRS The performance of the UKBDRS was compared with the DRS, CAIDE, and ANU-ADRI in the UKB train, test and WHII datasets. All models were additionally compared with a baseline model consisting of chronological age only, to examine the added predictive value of additional factors. The model discrimination was evaluated using the area under the curve (AUC). Based on available follow-up times, we evaluated AUC at a 14-year time horizon in UKB and 17-year in WHII (online supplemental methods). Wald's test  20  was used for pairwise comparisons of the AUCs of the UKBDRS with the other risk models. P values were corrected for multiple comparisons (false discovery rate (FDR) p value ≤0. 05 was considered significant). We used risk calibration to assess the agreement between the observed proportion of dementia cases and predicted probabilities of developing dementia as calculated from the risk score  21  (online supplemental methods).
This study is reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. Data analysis was conducted in R (3. 6. 3) and Python (3. 6), using FSL's funpack and using the following R packages: CARET,  22  glmnet,  23  cmprsk,  19  survival,  24  and riskRegression. 20.
Open access the UKBDRS model outperformed CAIDE but performed similarly to the DRS and ANU-ADRI in individuals over 60 (online supplemental table 7). Online supplemental table 8 presents sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of both UKBDRS scores at various thresholds. We have also presented these data as a UKBDRS Calculator, an excel sheet where users can enter responses for the 11 measures of UKBDRS and receive a 14-year risk of dementia (https://github. com/MelisAnaturk/dementia_risk_score/blob/ main/results/UKB-DRS_Calculator. xlsx). UKBDRS models were well-calibrated-that is, they had an intercept and slope close to 0 and 1, respectively (online supplemental table 9 and figure  5 ).
DISCUSSION We developed a risk score for predicting up to 14-year all-cause incident dementia in individuals 50-73 years old, and evaluated it against three published risk scores. [9] [10] [11]  A model consisting of age, education, diabetes, depression, stroke, parental history of dementia, material deprivation, hypertensive status, cholesterol status, sex, and household occupancy achieved a good-to-strong predictive accuracy and was well calibrated, with and without APOE4. Importantly, the discriminative performance of our score was consistently higher than three other established risk models, further supporting its utility in predicting individual-level risk of dementia in a mid-life UK population. We offer an excel sheet (https://github. com/MelisAnaturk/dementia_risk_score/blob/ main/results/UKB-DRS_Calculator.
49-0. 78  6 7 10  ; DRS 0. 56-0. 84  6 9  ). Both ANU-ADRI and CAIDE also performed worse in comparison to a model based solely on age, a striking observation but in line with their recent evaluation in the Rotterdam cohort. 6  This could be for several reasons. First, certain predictors may not map precisely from one dataset to another. For example, CAIDE defines physically active individuals as those who exercise at least twice a week, lasting at least 20-30 min each time. UKB participants were instead asked how many minutes were spent being physically active on a typical day. Second, ANU-ADRI was developed for older individuals (60+), while our cohort is slightly younger (50+). However, our sensitivity analysis found that the ANU-ADRI also performed poorly when restricting our cohort to an age range matching its development sample.
We used complete case analyses and did not impute missing data to mimic settings in clinical practice. This may have introduced certain biases in our results. Fourth, it is possible the samples of UKB and WHII data assessed introduced a healthy volunteer bias. While there were statistically significant differences between the included and excluded participants in terms of age, sex, and education, these were small (eg, age: 59. 97 vs 60. 71). The UKB and WHII cohorts are also healthier than the general population. For example, the prevalence of risk factors (eg, BMI, smoking) and rates of disease in the UKB are lower than in the general population. 30  We used electronic health records to derive diagnoses, which make this large sample analysis feasible but may be less sensitive than clinical adjudication.
Clinical implications We have developed and validated a novel dementia risk score, which outperformed other established risk scores in both an internal and external validation dataset. We recommend the UKBDRS for future studies interested in midlife dementia risk in a UK population. The UKBDRS includes age, education, material deprivation, history of diabetes, depression, stroke, parental history of dementia, hypertensive status, cholesterol status, sex, and household occupancy, making it easy to assess in large populations. We also present a version of the UKBDRS which includes APOE4 and can offer improved performance if this information is available. With further validation, the UKBDRS may be useful as a dementia screening tool for a wide range of middle-aged adults in either a clinical or research setting.
Data and code availability The data used in this study are available from the UK Biobank (https://www. ukbiobank. ac. uk/enable-your-research/apply-foraccess) and DPUK portal (https://portal. dementiasplatform. uk/Apply). As restrictions apply to the availability of these data, which were used under licence for the current study, the authors cannot publicly share these data. This research has been conducted using the UK Biobank Resource under Application Number 47279, while the use of data from the WHII study was approved by the DPUK Access Committee (Project No. 0346). Code is available at https://github. com/MelisAnaturk/dementia_ risk_score.
Data availability statement The data used in this study are available from the UK Biobank (https://www. ukbiobank. ac. uk/enable-your-research/apply-for-access) and DPUK portal (https://portal. dementiasplatform. uk/Apply). As restrictions apply to the availability of these data, which were used under licence for the current study, the authors cannot publicly share this data. This research has been conducted using the UK Biobank Resource under Application Number 47279, while the use of data from the WHII study was approved by the DPUK Access Committee (Project No. 0346). Code is available at https://github. com/MelisAnaturk/dementia_risk_score. Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed.
Electoral outcomes France is divided into several layers of governance, from municipalities ('communes'; N = 34,968) inserted in counties ('départements'; N = 101), themselves part of larger territories ('régions'; N = 18). Here, we examined voting shares for different parties during the French local (municipality) elections which took place in March of 2020. These elections are run on two-rounds, according to a proportional representation scheme with a majority premium for larger municipalities. Despite the COVID-19 pandemic, authorities maintained the first round on March the 15 th , but the second (March the 22 nd ) was subsequently cancelled. Our electoral outcome data represents the share of votes for each party at the end of the first round -for municipalities of more than 1,000 inhabitants.
Analytic Strategy Our final dataset (accessible at https://osf. io/pvmjf/) included 96 counties (101 minus oversea territories for which COVID-19 and election data was not available). Because these counties belong to several territories ('régions'), spatial clustering needed to be accounted for. Thus, we decided to test our hypotheses using mixed modelling  (Schielzeth, & Nakagawa, 2013). In addition, sensitivity analyses would be carried out by running models both with and without covariates, including potential confounds such as prior electoral outcomes. Overall, the study's biases due to its observational nature would be partly balanced by careful consideration of crucial confounds, and variable design (COVID-19 threat indicators temporally precede March 15 electoral outcomes).
Two year results from COAPT were published in 2018  [2]  and three year results made available in 2019  [3]  and in 2021  [4] ; four year results as yet have not been published (July 2022), per-protocol results are expected to five years (https://clinicaltrials. gov/ ct2/show/NCT01626079). COAPT data have been used in six cost-effectiveness (CE) analyses in publications spanning 2019 to 2022  [5] [6] [7] [8] [9] [10]  in studies that use a lifetime horizon (~30 years) so as to accommodate decision-makers' stipulation that analysis should fully capture benefits and harms. In estimating major benefits such as life years gained (LYG) mortality estimates need to span up to 30 years necessitating considerable extrapolation beyond in-trial observed mortality. Most published CE studies  [5] [6] [7] [8] [9] [10]  make use of 2-year mortality data from COAPT, and one  [11]  employed 3-year mortality for the GDMT arm to generate mortality in the MitraClip arm by applying a hazard ratio (HR).
The full factorial design of the CE, namely the number of unique choice cards that can be constructed from the selected number of attributes and levels, includes 186,192 choice cards. Using the Ngene software package (ChoiceMetrics, 2018), we selected an efficient design composed of 12 choice cards split into two blocks of six choice cards (each respondent being randomly assigned to one of the two blocks). 7 We added a status quo option to each choice card, which states "I prefer to keep my current practices". Note that current agricultural practices may vary from one winegrower to another and, 7. Efficient design has been deduced based upon "priors". The reasonableness of the design priors has been determined using information obtained from previous work, focus groups and pilot interviews.
Descriptive statistics We conducted an online survey that was sent out by our field partners to winegrowers in Languedoc-Roussillon in the spring of 2020. We collected the answers of 172 farmers (a response rate of about 10%), each completing six choice cards. Once protest answers have been dropped, we end up with 165 usable responses, equivalent to 2,970 observations (165 farmers x six choice cards x three alternatives). 8 Responses per block of choice cards are relatively balanced (47% for the first block, 53% for the second block). The quality of the data is good: the average reading time for the description of the attributes is more than one and a half minutes and the farmers' reported degree of certainty in their choices is quite high (average rating: 7. 6/10). Descriptive statistics of usable answers are presented in Table  2.
Acknowledgements We would like to thank the participants of the 2021 INRAE Seminar on Water and the Environment, the 2021  Environmental Economics Seminar of the CEEM , the 2021 XVI EAAE Annual Congress. We would also like to thank  Sophie Thoyer  and  Jens Rommel  for their very helpful comments. We are indebted to  Ira Helal  from  FREDON Occitanie  for her superb coordination and engagement of field partners, the network of agri-environmental technical advisers, all participants of the focus groups and the survey respondents. We are also grateful to  Shannon Harvey  for English editing. This work was supported by the  European Regional Development Fund  (Grant " AAP READYNOV 2017 / POLLDIFF CAPTAGE" ).
In this paper, we re-assess the relationship between (private and public) debt and growth with and without the influence of global shocks for a panel of 22 economies over the period 2000-2019. We propose an approach that accounts for the potential complex global factor structure of the data and estimate the debt to growth and growth to debt nexus for household, corporate and public debt from a purely idiosyncratic perspective. Our results confirm a complex factor structure with global shocks exerting strong traction on household and public debt, and with corporate debt being mostly idiosyncratic in nature. These global shocks influence the strength and significance of the idiosyncratic part of the relationship and emerge as critical in the identification of Granger causality, which (negatively) runs only from growth to debt once their influence has been filtered out.
They suggest that excess financial deepening, as manifested in a credit boom, can be problematic in both developing and developed countries because it weakens the banking system. Some scholars link financial development, measured by private credit, with the innovation-led growth hypothesis. In this regard,  Beck et al. (2016)  assess the relationship between financial innovation, bank development and growth. Using panel regressions on annual data for 32 countries over the period 1996-2010, they find that the relationship between growth opportunities and private credit is not significant. They argue that such a result is not surprising as their sample is limited to mostly high-income countries, for which the above-mentioned research has shown that the relationship between financial development and economic growth is not significant.
Data The analysis is based on a balanced panel dataset of quarterly series for 22 advanced and developing countries observed over the period 2000Q1-2019Q4, so that N=22 and T=80. 4  As it will be clear later, the need for a balanced panel is dictated by the approach used to account for common factors. 5  We consider public debt and two measures of private debt, i. e. , debt to households and debt to non-financial corporates. The source of debt variables is the Bank of International Settlements (BIS) database, "Credit to non-Financial Sector". The database provides a sample of countries for private debt that is potentially larger than those obtained from alternative sources. However, to obtain the largest possible balanced panel and preserve the time dimension, we exclude countries with debt series starting after the year 2000.
g. ,  Jordà et al. (2013) ;  Mian et al. (2017) ). We then gather the series for real GDP, consumer prices, and short-term interest rates from the Organization for Economic Co-operation and Development (OECD) database. Please refer to Table  A  in the online appendix for the summary statistics of the variables and Figures  A1-A3  for the debt and the other series of the countries under scrutiny.
Building on these findings, we turn to investigating the debt-growth nexus, after de-factoring the data to remove the influence of the significant common factors. Given the complex common factor structure of the data, this approach should be superior to the one based on panel settings augmented with CSAs. 18 For the evidence of global factors and comovement in inflation rates and interest rates, see, also  Rogoff (2006) ;  Borio and Filardo (2007) ;  Ciccarelli and Mojon (2010). Notes: This table examines the degree to which the optimal number of estimated common factors explains the variation in each country's macro variable, i. e. , debt, output, prices and short-term rates. Sample period 2000Q1-2019Q4 for 22 countries (N=22, T=80) using differenced and standardized data. The variance decomposition is calculated from Eq.