dataesr/scientific-paragraphs-categorization huggingface dataset:
With increased transparency, the line between flexibility and discretion becomes more explicit, and the message of implementing and complying with the rule becomes more piercing. For this reason, the institutional aspect of the rules is fundamental to overall compliance since they enhance government effectiveness and accountability  (Blume and Voigt, 2013). The remainder of the paper is organized as follows. Section 2 presents an overview of existing datasets on compliance with fiscal rules. Section 3 explains how the dataset was constructed in the LAC context and presents some descriptive statistics. Section 4 introduces the methodology for calculating numerical compliance with fiscal rules. Section 5 presents the adjusted index of compliance. Section 6 concludes.
Existing datasets A fiscal rule is commonly defined as a long-lasting constraint on fiscal policy through numerical limits on budgetary aggregates  (Eyraud et al. , 2018). Numerical targets are usually set on budget balance, debt, expenditure, or revenue. They differ significantly from country to country because of their numerical objective, design, and implementation characteristics. As more and more countries opt to implement fiscal rules, various efforts have been devoted to collecting information, mainly on the design of the rules. The IMF FAD Fiscal Rules Dataset  (Davoodi et al. , 2022)  has been particularly influential given its effort to compile cross-national information on fiscal rules from 1985 to 2021. It covers 105 countries and details the rules' characteristics, such as type of rule, legal basis, coverage, escape clauses, and enforcement procedures.
Researchers use this dataset widely as the best data available for comparisons across countries and over time. Although the IMF FAD Fiscal Rules Dataset groups similar information for a large sample of countries, it does not delve into specific features of rules and their workings at the country level. Because of this, several studies analyze the experience of particular countries with their rules. For example, several articles study Latin American countries' fiscal framework and fiscal rules. Some examples include Argentina  (Artana et al. , 2021) , Brazil  (Bonomo et al. , 2021) , Chile  (Fuentes et al. , 2021) , Colombia  (Arbeláez et al. , 2021 ), Peru (Mendoza Bellido et al. , 2021) , or more broadly, the book Reglas Fiscales Resilientes en América Latina  (IDB, 2020). In addition, the secretariat of the European Fiscal Board presents updated and detailed information on the fiscal rules implemented by the members of the European Union (EU) derived from the Stability and Growth Pact (SGP).
This information is compiled and summarized in a compliance tracker for four fiscal rules included in the SGP from 1998 to 2020 for 28 countries. This is the first public dataset that is updated annually, which provides information on compliance with each country's rules. In its technical note,  Larch and Santacroce (2020)  present key facts and trends of average numerical compliance that assess the performance of implemented rules relative to the objectives set. The compliance tracker and its assessment show that this is a key indicator when studying the performance of fiscal rules. However, the coverage of this indicator is limited to EU member countries. Although the methodology proposed by the European Commission is an advance in the analysis of numerical compliance with fiscal rules, it cannot be extended precisely to LAC countries.
Constructing a dataset to monitor compliance This dataset aims to provide updated information about LAC countries' design features and compliance with fiscal rules. By contrasting the objectives or targets set by the rules with their executed or observed values, we can characterize the compliance behavior of LAC and assess the extent to which they have deviated from their targets. The approach is to compute compliance rates using as much information as possible from official sources in each country. We focus on numerical compliance with national-level rules and exclude subnational fiscal rules from the analysis. The process of building the dataset is detailed below. Most rules were introduced on a statutory basis with fiscal responsibility laws. The design of the rule described in each law provides information about the macroeconomic aggregate it seeks to constrain, the numerical target or the procedure to set it, as well as escape clauses (if any) and the level of government it covers.
Does the design of the rule include escape clauses? 4. Was the escape clause invoked? 5. To which level of government does the target apply? 6. What is the legal numerical target of the rule? 7. What was the executed value for the corresponding macroeconomic aggregate? 8. Did the executed result meet the target? 9. What was the deviation between the executed value and the target? 10. What were the reasons why the target was not met? The answers to these questions provide a dataset of country-year observations about the implemented fiscal rules for LAC countries. This information makes it possible to understand how each rule has worked over the years. Additionally, by comparing the definitions of each rule with the values executed for the respective macroeconomic objectives, we can calculate compliance rates.
Descriptive statistics The first set of questions used to construct the database gathers information on the design and implementation of fiscal rules. Our sample includes 14 2 LAC countries that have implemented at least one fiscal rule between 2000 and 2020. Furthermore, Barbados and El Salvador 3 recently implemented fiscal rules. The fact that 16 LAC are implementing at least one fiscal rule by 2022 reflects the increasing number of countries using fiscal rules in the region. Fig. 2  shows that from those 14 countries that have implemented one rule for at least one period, four have implemented a single rule, seven have implemented a combination of two rules, and three have implemented a combination of three rules. The literature focuses on four types of fiscal rules restricting four macroeconomic aggregates: the fiscal deficit, the debt ratio, fiscal expenditure, or fiscal revenues.
The remaining questions on the design of fiscal rules deal with coverage, that is, the level of government to which the rule applies and the fiscal aggregate that it constrains. Regarding this, it is important to offer some clarifications. As mentioned above, this database does not consider the fiscal rules applied at the subnational level. This is because the purpose is to calculate overall compliance rates. For this, the numerical objective and the executed value of the macroeconomic aggregates are obtained from the official reports of each country. However, ease of access to this type of information and transparency in reporting both fiscal rule objectives and compliance is quite heterogeneous. For this reason, we refer to the historical series of fiscal aggregates to obtain compliance information in many countries.
It is also possible that the information provided in some reports does not coincide with that published in the series. This may be because many rules consider additional accounts in the calculation that may vary from period to period but are not detailed, nor does the report specify how the calculation has been made. Finally, in some countries, the initial goal is established based on projections, such as, for example, GDP. These projections are adjusted throughout the fiscal year and the goal. These practices leads to the situation where government compliance can only be reported at the end of the period. In this database, we seek to monitor numerical compliance with the rules. Although we consider the modifications that governments may have made throughout the period, we always adhere to the established objective to determine compliance.
Defining compliance Compliance directly answers one of the abovementioned questions and is included in the database as a dummy variable for each country and year. It takes the value of one when there is compliance and zero otherwise. However, compliance in many cases is not absolute and is subject to the type of rule. For instance, the expenditure rule is simple in the sense that it is directly observed whether the executed expenditure exceeded the limit or not. But we observe reduction targets for specific periods for both the debt and the balance rule. On the one hand, if the target is set to reduce the level of debt over a certain period, it is possible that within that period, the debt level increased in some years and decreased in others but without exceeding the target set. In this case, we assume compliance.
This dataset is a starting point in monitoring LAC countries' fiscal rules. Average numerical compliance rates show that LAC countries have largely complied with the objectives set in their fiscal rules. We find that compliance with rules aiming to constrain debt ratios and structural balances is highest, while compliance with fiscal balance and expenditure rules is the lowest. However, combining rules improves average compliance, especially when combining a balance rule with a spending rule or with a debt rule. We also calculated the deviations of the executed values from the target. Although each deviation must be read in context, our results indicate how realistic the objectives that countries have set for their rules are, as well as the adjustments needed to comply with the targets. An essential finding of this paper is that compliance with fiscal rules is not absolute, nor does its implementation imply immediate fulfillment.
Synthetic Data Set The anomaly detection task is performed on N = 20 stocks simultaneously. The stock prices are diffused according to the Black and Scholes model. Each stock has its own drift and volatility and the 20 stocks are correlated, as described in Section 3. Each time series represents T = 1, 500 daily stock prices, split into two sets. The first 1, 000 observations, corresponding to the train set, are used to learn the model parameters, i. e. the PCA transfer matrix and the NN weights and biases. The last 500 observations, corresponding to the test set, are used to assess the quality of the estimated parameters when applied to unseen samples. For the application of the sliding window technique, we set the length of the resulting time series p to be 206. We recall that both steps of the approach are preceeded by a features extraction step, for which the latent space dimension k needs to be calibrated.
To assess the quality of the imputation value suggested by each approach, we consider the following metrics. The imputation errors are computed on each time series i as  where S i refers to as the i-th path price with imputed values. The error on the covariance matrix is computed as ImputationError i = j S i t j -S i ErrCov = Σ -Σ Frob , where • Frob is the Frobenius norm, Σ the sample covariance matrix, and Σ the covariance matrix estimated on the data after the anomalies have been replaced by their respective imputation values. Each stock path was diffused and contaminated 100 times. The anomalies were then imputed following two baseline approaches (BF and LI). Figure  9  clearly shows that imputation using the reconstructed values with PCA reduces the imputation error but not as much as basic imputation techniques (LI and BF) as it is shown on Figure  10.
Numerical Results on Real Data We consider a labelled real data set including stock prices, bonds yields, CDS spreads, FX rates, and volatilities. These data were collected from a financial data provider for the period between 2018 and 2020. These data sets were labelled by experts. They are provided on https: //github. com/MadharNisrine/PCANN in the form of 132,000 time series (after augmentation). The train set is balanced, while the test set is imbalanced with 20% of contaminated time series. To ensure the independence of the train and test data sets, we calibrate the PCA NN approach considering the time series of 2018 and 2019, while the 2020 time series are dedicated to model evaluation. The performance evaluation of the PCA NN approach after its calibration on real data sets is shown in Table  22.
The anomaly score of an observation is computed as function of its distances to K-nearest neighbours, weighted average of the distances for instance. The points with the highest anomaly scores are considered as anomalies. Support Vector Machines (SVM) The ultimate aim of SVM  (Cortes and Vapnik, 1995)  is to define a hyperplan that separates the data. The specificity of this hyperplan is that it maximizes the distances to the set of features representing each class. When the data is not linearly separable, a map φ is applied to the initial features vector so the data became linearly separable in the new space in which it was projected.
Acknowledgments This research was funded by the  French National Agency for Territorial Cohesion  and the  Ministry of Territorial Cohesion (ANCT, PUCA  and DGALN). The authors would like to thank the partners of these institutions, notably the participants of the seminar organized by  Ariella Masboungi  as well as all the researchers involved in this project:  Adrián Pablo Mañas Gomez ,  Agim Kerçuku Joanna Koszewska  and  Louna Villain. The two referees provided valuable comments for which the authors are grateful.
This so-called path dependency (the fact that past decisions affect the current decision) is clearly a highly undesirable phenomenon, especially when court outcomes have farreaching consequences (for example death sentences, or asylum decisions in immigration courts). In this paper, we investigate whether caseload exposure plays a role when judicial decisions are made "simultaneously". In such a procedure a judge also hears all cases of a given session one after the other, but makes a decision on each of them only at the end, i. e. , after the last case of the session has been heard. 1 The objective is thus to test for the presence of the analogue of path dependency, namely simultaneity in decision making. Our data come from the Paris Labor Court. While the differences between these two procedures have drawn no attention in studies on judicial decisionmaking (nor are there any papers on simultaneous court decisions), they have been explored in other contexts.
Data collection Our dataset is constructed by combining two sources. The first source contains detailed information on the decisions made by the adjudication panels at the Paris Labor Court. We had access to this information thanks to a partnership with this court, a partnership approved by the Ministry of Justice under the condition that all collected data would remain strictly confidential and anonymous. We compiled data on all decisions taken by the adjudication panels in the three main sections (Commerce, Diverse Activities, and Executives), 16 This article states that "[. ] in the determination of his civil rights [. ] everyone is entitled to a fair and public hearing within a reasonable time by an independent and impartial tribunal established by law". 17 Hearings at the Paris Labor Court are held in the afternoon.
*    We thank the  GIP Ministère de la Justice  and  ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047  for financial support. We are also grateful to  Joshua Teitelbaum , seminar participants at the  University of Paris  2 and the  39th EALE conference , for helpful remarks, and to  Isabelle Lapeyronnie  for her explanations of the practical aspects of the Paris Labor Court.
Study design and settings This is a cross-sectional analysis of the COVID-19 data reported from the start of the pandemic through 29 November 2020 (inclusive) among the 47 Member States comprising the WHO African region. We analysed the data gathered by the WHO  9  on the daily number of new cases and deaths published by national health ministries through official channels. This period captures the initial wave of the pandemic for most countries in the region, ending prior to complications driven by the festive season and roll-out of vaccines. While members of our team were responsible for compiling these data with the WHO during this time, all data were systematically published to the WHO COVID-19 dashboard  9  and are thus freely accessible. We note that groups of confirmed cases marked as 'probable' due to sole availability of rapid diagnostic (viral antigen) tests-representing 70 cases from Comoros and one case from the Democratic Republic of Congo-are now also considered part of the official case counts and were included as such in the present study.
We included the cases with information about patient outcome status (alive, recovered or dead), excluding those with missing values (eight from Burundi, four from Comoros and three from Rwanda). Because data from the Republic of Tanzania were not shared during this period, our analysis focused on the remaining 46 countries (figure  1 ). Predictor variables were pulled from publicly available datasets (WHO, World Bank, IndexMundi and so on), for which the complete set of specific sources is provided in online supplemental information table S1. Data were processed using both Python (V. 3. 7. 10) and R (V. 4. 0. 2) (https://www. r-project. org/). All statistical analyses were performed in R (V. 4. 0. 2).
Statistical analysis Missing data and imputation We identified 11 missing values in our dataset corresponding to (1) initial and mean government response stringency index for Comoros, Equatorial Guinea, Guinea-Bissau and Sao Tome and Principe and (2) per capita tourist arrivals from Equatorial Guinea, Liberia and South Sudan. To preserve inclusion of these countries in our analysis, we used a mean value data imputation method to artificially complete the missing records by substituting the missing values corresponding to the explanatory variables. Due to heavy skew in the data, median (rather than mean) values were imputed for missing instances of per capita tourist arrivals and mean government response stringency. Since square-root transformation was more appropriate for the distribution of the initial government response stringency values (detailed below), we substituted the missing values with the value computed using equation (  1 ): Initial Stringency = [median( √ Initial Stringency)] 2 (1) For the cumulative attack rate in neighbouring countries for which data were not available via the WHO database, we sourced this information from the freely available Our World In Data COVID-19 database for the total number of positive cases (https://ourworldindata.
Robustness to variation in data quality In order to check the robustness of our results to data quality issues, we assumed countries with fewer than 10 reported deaths as of 29 November 2020 to be suspected of under-detection or under-reporting of cases and deaths. Thus, we repeated the above analyses after removing the four countries that had reported fewer than 10 deaths (ie, Burundi (deaths=1), Comoros (deaths=7), Eritrea (deaths=0) and Seychelles (deaths=0)) to check for qualitative differences in the results. As with the full dataset, the negative binomial model was the most appropriate model for each of the response variables.
Additionally, the availability of individual patient data could have been useful, but such studies are limited in the context of Africa and have so far only addressed the impacts of a few individuallevel factors on disease frequency and outcomes. 46 oreover, we relied on non-contextual (average value) data imputation methods where values were missing, as was the case for tourism and stringency variables for some countries. We also note that some indicators were not reported with consistent frequency for all countries, such as population size and density estimates. There are other potential variables not accounted for in our study such as air pollution, climatic variation, economic inequality (GINI index), diet and so on, that could affect the spread and severity of COVID-19 in the African region.
BMJ Health of personnel, and public health education campaigns during non-crisis periods can help improve preparedness. Finally, surveillance and testing capacities remain a key challenge in the region. Robust surveillance and testing capacities are needed to ensure that public health decisions are based on data that depict the epidemiological situation accurately. The quality and timeliness of data are essential to better evaluate and adjust control measures implemented during the course of an outbreak, to help limit the reliance on blanket or prolonged measures that can have harmful social and economic impacts. We thus urge decision-makers to improve these capacities to ensure rapid response to future threats to public health and economic stability.
Data availability statement The script for the statistical analyses described in this article as well as the assocated datafile can be found here: https://github. com/ Ananthu89/James-Dalal-Kousi-et-al-BJMGH-2022. COVID-19 data used in this manuscript is available at https://github. com/JyotiDalal93/COVID-19_SSA/blob/ main/Data_sub_saharan_Africa. zip. 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. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Introduction Copulas express the dependence structures of multivariate random vectors independently of their marginal distributions. Therefore, they often allow insightful two-step model specifications and inference. Copulas have induced many academic papers during the last decades and have become very popular in many applied fields. If several multivariate datasets of the same nature are available, then the natural question that arises is whether their dependence structures coincide. This problem was first tackled in  [19]  for the case of two samples. A general testing procedure for several samples has been proposed in  [2]. A similar question arises for conditional dependence structure in multivariate datasets. The key point is now to state whether some conditional copulas differ or not, given different conditioning events.
In Section 5, we propose an algorithm to generate a relevant collection of sets AJ. The empirical properties of these methods are discussed in a simulation study (Section 6). Two applications on real datasets are provided in Section 7: first, the conditional dependence between the S&P500 and the Eurostoxx is studied in a Copula-GARCH model. Through some properties of the tree of conditioning events that has been built, we highlight some contagion phenomenons across stock indices. The second application focuses on the dependence between different coverages in an insurance dataset. Proofs have been postponed into the appendix.
Algorithm 1: Recursive algorithm for building a set of relevant boxes for conditional Kendall's taus def CutCKT(a dataset D ∈ R n×d , a subset A def ault = R |J| , minCut ≥ 0, minSize ≥ 0): for i ← 1 to p -1 do for j ← i + 1 to p do for k ← p + 1 to d do foreach t ∈ R do Diff[i, j, k, t] ← τi,j|X J ∈A∩(-∞,t] k -τi,j|X J ∈A∩(t,+∞) k ; (i * , j * , k * , t * ) ← arg max i,j,k,t Diff[i, j, k, t] ; Box1 ← A ∩ (-∞, t * ] k * ; Box2 ← A ∩ (t * , +∞) k * ; if min P n (X J ∈ Box1) , P n (X J ∈ Box2) < minSize or Diff[i * , j * , k * , t * ] < minCut then return τ I|X J ∈A. else Child -← CutCKT(D, A = Box1, minCut, minSize) ; Child + ← CutCKT(D, A = Box2, minCut, minSize) ; return τ I|X J ∈A , (i * , j * , k * , t * ), Child -, Child + Afterwards, the procedure is going on with the two datasets that are corresponding to the conditioning subsets (X k 1 ≤ t1) and (X k 1 > t1) respectively, etc.