dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Our data consists of monthly nominal and real stock returns and inflation for the UK -our main focus, and then also for two emerging markets (India and South Africa), and a developed market (US). The choice of these countries is based on availability of historical data, and provides a comparison of the results for UK with that of emerging and developed economies. Nominal stock prices are derived from Global Financial Database, and are converted to nominal stock returns by using the first differences of natural logarithms expressed in percentages by multiplying with 100; i. e. we consider log returns. For UK, we use the wholesale/producer price index as a measure of the price level, which in turn, is derived from the Three Centuries of Data (Version 2. 3) maintained by the Bank of England at: http://www.
bankofengland. co. uk/research/Pages/datasets/default. aspx. This data is available till April, 2016. The data is then updated until the end of our sample period from International Financial Statistics of the International Monetary Fund. We use this measure of price level for the UK rather than Consumer Price Index (CPI), since CPI data is only available from 1914. 3 For the remaining three countries used in comparison, we use the CPI as a measure of price level. The CPI data for the India and South Africa are derived from the Global Financial Database. For the US, the data is obtained from the data segment of Professor Robert J. Shiller's website: http://www. econ. yale. edu/∼shiller/data. htm. Inflation is computed as month on month changes in the natural logarithms of the measure of price level expressed in percentages.
Real stock return is then derived by subtracting the inflation rate from the nominal stock returns. Based on data availability the sample of data covered are: 1790:02-2017:02 for UK; 1920:08-2017:02 for India; 1936:01-2017:01 for South Africa; and 1871:02-2017:02 for US. The data on nominal and real stock returns, and inflation have been plotted in Figure  4  in the Appendix of the paper. The online version of this article offers supplementary material (DOI: https://doi. org/10. 1515/snde-2017-0049).
In addition, in the situation considered in this paper, the three elements did not have the same characteristics, indeed external data were only collected above a high threshold, scenarios were only representing by a few points in the right tail of the distributions underpinning quantifications and internal data were collected above different thresholds depending on the type of risks. Third, when one or more data elements for some categories of events are not numerous, the resulting capital estimate can be exorbitantly high and unrealistic depending on how the data elements are combined. In such challenging contexts, our methodology seems relatively more robust in producing sensible capital estimates. As a matter of fact, the sensitivity analysis we performed at the time was providing an increase of 400% of the initial value-at-risk (VaR) on some categories.
The Data Sets The results presented were obtained using OpBase, which is the external database developed by Aon Ltd (London, UK). It captures the losses corresponding to the operational risk claims and the losses in the public domain (PKM). The scenarios correspond to those that we obtained through a scenario workshop process. In this paper, the scenario analysis used was forward looking in the sense that none of the values considered were already captured in the internal loss data base and were different in terms of likelihood and magnitude to those captured in the external loss data base. Many different scenario approaches are compatible with our approach; therefore, this will not be discussed any further  (Guégan and Hassani 2014 ) in this paper. The internal loss data were provided by a first-tier European bank.
Acknowledgments: This work was achieved through the  Laboratory of Excellence on Financial Regulation (Labex ReFi ) supported by  PRES heSam  under the reference  ANR10LABX0095. It benefited from a French government support managed by the  National Research Agency (ANR)  within the  Q11 project Investissements d'Avenir Paris Nouveaux Mondes  (investments for the future Paris New Worlds) under the reference  ANR11IDEX000602.
Results The survey was first disseminated through a list of residents in the Lisbon Metropolitan Area (MA) who had previously agreed to answer to surveys related with transportation research initiatives. It resulted in 160 online surveys in July 2016, which after data cleaning were reduced to 146 surveys with a valid RP component, of which 129 had a valid SP component. These were complemented 3 months later with 266 face-to-face surveys (aimed at having a global sample similar to the LMA socio-demographics) using the exact same information, which were later reduced to 259 surveys with a valid RP component and 240 with a valid SP component. Globally the obtained commuting modal shares are similar to the ones collected for the LMA in the 2011 Census (www. ine. pt). Both samples were tested for differences in terms of socioeconomic characteristics, behavior and attitudinal aspects, using statistical tests and ordered probit models.
Differences in attitudes across both survey media The following comparison reports several questions asking for attitudes about perceptions on transport modes, transport policy, health and environmental concerns, transport impacts on health and climate change, and social concerns. Since attitudinal data was built using Likert scales, ordered probit models were built for each one of the statements to assess the role of survey mode on responses. The variables used include gender, age segments, education attainment, professional occupation and survey mode. Table  2  presents for each of the Likert scale question the coefficient relative to the survey mode (1 if web-survey) and its significance level. For 19 of the 30 attitudinal statements it can be concluded that survey mode doesn't condition the answers.
The logit model results indicate that information about CO 2 appears to be ineffective to guide the choice of commuting modes, but respondents appear to value the health benefits of a more active commuting choice. These results contradict the findings of  Gaker & Walker (2013)  who found negative coefficients for CO 2 emissions and positive for calories consumption. Our results also point to the existence of framing effects relative to the information on calories consumption, but conveying information in a more emotional format appears to not being more effective. Our results have several limitations, some of which could be addressed in future work with the same database and others would imply more data collection activities. Sample sizes in both subsamples could preferably be larger and more aligned in terms of sociodemographic attributes other than age and gender distributions, although this would only be possible with more resources.
We look at the data on three-month Eurodollar interest differentials for six countries, Canada, France, Germany, Italy, Japan and the UK, over the period ranging from the mid-1970s to 2012 (starting points vary by country). The dataset is the one used by  Engel and West (2005) , updated from Thomson Datastream. 11  Figure  5  plots the time series, and Panel B of Table  4  provides estimates of their memory parameters. We see that all series exhibit strong persistence with estimates of d greater than 0. 4, corroborating the results in  Maynard and Phillips (2001). A possible explanation for the strong persistence in the forward premium is the presence of an exogenous time-varying risk premium, see  Engel (1996). Under this explanation, the UIP equation becomes E t [s t+1 -s t ] = i t -i * t + ρ t , (20) where ρ t is an unobserved process that represents a time-varying risk premium.
Acknowledgements G. D. , A. R. and M. E. V. gratefully acknowledge the support by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 649186 -ISIGrowth , and M. C. P. the support by  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  under grant agreement No. 2015/09760-3.
The Mlomp Health and Demographic Surveillance System in Senegal was established in 1985 to study the demographic and health situation of a West African population with high mortality levels, to observe changes over time and to examine the factors involved. 1 The rationale was the need for reliable demographic information concerning sub-Saharan Africa, in particular in rural areas for which very few data were available. When the Mlomp project started in 1985, Senegal already had two rural areas under long-term health and demographic surveillance: Niakhar, in the centre-west, where surveillance started in 1962, 2 and Bandafassi, in the southeast, where surveillance started in 1970. 3 The objective was to add a third HDSS in another rural area of Senegal, in a region whose historic, economic, and ethnic characteristics are very different from those of the two other sites, thus providing the opportunity to better cover the diversity of demographic and epidemiological situations in the country.
What does the Mlomp HDSS cover now? The core Mlomp HDSS collects information on eventsbirths, deaths, marriages and migrations. The objective is to obtain accurate estimates of demographic levels and trends in the community, and to study changes in marriage and fertility patterns, household, family and kinship. Research on more specific topics is also conducted, making use of the data collected routinely by the Mlomp HDSS in combination with specific supplementary information on the study topic collected during one particular round. Examples of such research include studies of malaria, sexually transmitted diseases, contraceptive prevalence, breastfeeding, nutrition, etc. The Mlomp HDSS is now a joint project of the Institut de Recherche pour le Developpement (IRD) and the French Institute for Demographic Studies (INED), and is a member of the INDEPTH network [http://www.
Update rounds Once each year, usually in February, all households are visited by investigators who check the list of people present in each one the previous year, and who record all births, marriages, migrations and deaths (including their causes) since the preceding visit (Box 1). For each death identified, information on its cause is obtained from a close relative of the dead person, usually the mother in the case of a child's death, using a verbal autopsy questionnaire. Verbal autopsies collect detailed data on symptoms and signs during the terminal illness, so that the cause of death can be determined by a physician or a computer algorithm. Verbal autopsies have been performed for all deaths since the beginning of the study.
What has been measured and how have the Mlomp HDSS databases been constructed? The core data collected throughout the Mlomp HDSS include individual and household identifying information, parent identification and spousal relationships at each point in time. Sufficient data are collected to link individuals of all ages to both parents, facilitating household structure and kinship studies. Other data are collected at various times in the context of specific surveys often on population samples, such as serological, parasitological or resistance surveys for studies of sexually transmitted disease and malaria;  [5] [6] [7] [8] [9] [10] [11] [12] [13]  and contraceptive prevalence, breastfeeding, and nutritional surveys. Information collected during the baseline and follow-up surveys is coded and stored in databases designed in 1985 and subsequently modernized.
The data are held in multiple event tables in the database, with a single view table describing all Mlomp HDSS events. Each person who has been member of the resident population at one time, or has been mentioned as spouse or relative of a resident member, is described with an identity number enabling linkage between the different tables (a total of about 50 000 identity numbers have been attributed). The information collected during each annual survey is coded and entered with Access software and, after verification, added to the database using PostgreSQL software. 2. The population pyramid has a trough at the base of the pyramid related to the recent fertility decline, which has been rapid in the Mlomp community (see below). 6] [17]  A share of the women aged 20-34 years (53% in 2015), most of them unmarried, are employed as domestic servants in the main cities of Senegal and The Gambia; once married, women usually stay at home all year round.
Malaria mortality: a temporary increase in the 1990s The data collected in the Mlomp HDSS show that the risk for a newborn child of dying before age five was 89 per thousand over the period 1985-89, a relatively low level at that time compared with the national level, and levels in other rural areas of the country: in Bandafassi and Niakhar HDSS, for example, child mortality was three times higher (Table  1 ). 18 Its low level in Mlomp was linked to high child vaccination coverage, which resulted in a relatively low mortality from vaccine-preventable infectious diseases, and also to successful antimalaria campaigns organized by the health centre in the 1970s and 1980s (almost all children received chloroquine prophylaxis during the malaria season and all fever cases treated at the dispensary received malaria presumptive treatment).
Adult mortality: a large gender gap The Mlomp HDSS data show that the level of adult mortality has not changed much over the surveillance period. ] [36]  Maternal mortality is around 270 maternal deaths per 100 000 live births over the entire surveillance period, a relatively low level for a rural area of Senegal. Since well before the beginning of the surveillance period, nearly all women have given birth in maternity clinics (>95%). This is related to a tradition in the population to deliver outside the village, in huts specially built for this purpose and called 'kalambas'. 4  When a maternity clinic opened in Mlomp in 1968, located in the centre of the study area, women were encouraged successfully by missionaries and health workers to abandon the kalambas and use this modern facility.
Mlomp HDSS: an ideal site for methodological studies The Mlomp HDSS is regularly used for methodological studies to evaluate the quality of data collected by censuses and demographic surveys in countries with limited vital registration, and to test new 'unconventional' techniques to improve data collection. In collaboration with the Senegalese statistical office in charge of the census ('Agence nationale de la statistique et de la demographie'), an evaluation of the data collected by the two most recent national censuses of Senegal (those of 2002 and 2013) is currently ongoing by matching, at the individual level, the census data collected for the Mlomp villages with the data of the Mlomp HDSS. 40 The objectives are to measure the exhaustiveness of the census, and also study errors and bias in reporting of age, survival of parents, household deaths in the past 12 months and reporting of causes of death, especially maternal deaths and deaths from external causes.
Strengths and weaknesses A major strength of the Mlomp HDSS is the long duration of the follow-up which provides continuous time-series over 32 years, as the data collection method has remained practically unchanged, and a unique historical perspective on demographic changes in the study area. The main weakness of Mlomp HDSS is its size, which is often too small for trials. However, when combined with the information collected in the two other HDSS in rural Senegal (Niakhar 2 and Bandafassi 3 ), its data can be used to perform multisite studies. The three sites are well suited for comparative analysis: they are located in very different regions and environments in a country with contrasting population densities and health services, and the data collection methods are very similar.
In the static case, the Marxan software  [21]  is a well-known software which uses simulated annealing to find a near-optimal reserve network. In the dynamic case, there does not exist universally recognized approaches yet. Although the Marxan solution can be used in a sequential way, i. e. the optimal reserve for the static problem is computed and then sites are acquired progressively according to the yearly available budget, this approach is not ideal. In the dynamic case, the future state of the landscape is uncertain and as a consequence, the optimal static solution can exhibit poor efficiency if implemented dynamically  [22]. The major drawback of this approach is that sites can be lost before they can be purchased due for example to urban development. A natural way of framing the DRDP is to use the Markov Decision Process (MDP) framework.
Nonetheless, we use SPF = 1×10 6 to ensure that the computed network meets the target for every habitats. Indeed, using a low SPF value can lead to networks that are not necessarily meeting exactly the target when it allows a large cost saving. The Marxan software uses a simulated annealing approach to find a near-optimal reserve design N Ã Marxan maximizing r Marxan. More information on the reward function and the use of this method is available in the Marxan manual  [30]  or in  [29]. In all our experiments, Marxan solutions N Ã Marxan were computed using R and the R-package described in  [31]. In practice, N Ã Marxan represents a reserve network of minimal cost that meets the habitat targets, but a major issue is that this network cannot be implemented within a single year, due to budget limitation.
ARÁ , since the exact values are too costly to compute and • Discretized the domains D j of the variables λ j and used a genetic algorithm provided by Matlab, to compute the optimal discrete values. The genetic algorithm is simply searching for the effect of varying the value of the different weights and ultimately results in a near-optimal combination of these weights. For example, using a high value of λ 1 compared to the other weights creates a reserve design policy that first selects sites with a large quantity of the first habitat. If using this rule of thumb is beneficial (i. e. translates to a high value of the expected extended cost), as is the case, for example, when the first habitat is subject to a high conversion rate, the genetic algorithm will save this combination and potentially return it, if no better combinations are found.
We propose to base the site cost on the amounts of target habitats available in the site: cðsÞ ¼ $4; 000 Ã ðh 1 ðsÞ þ h 2 ðsÞÞ: The average price per site is $216,000 (range: $4,300-$790,000). We propose to simulate sites of random shape by first simulating the centroid of every sites using a uniform distribution and second determine site's boundaries by computing a voronoï diagram out of the centroids using Matlab. The simulated landscapes, conversion probabilities and costs are available in Fig 2. The first habitat is rare in the landscape and is mostly found on a long strip located West. The second habitat is present on most sites but preferentially in the North part of the landscape as well as in the South East part. For this experiment, compactness is not accounted for and we use BLM = 0.
On the example inspired from the extension of the EHNWR, the augmented greedy heuristic and dynamic Marxan exhibit similar performances, although the augmented heuristic allows much better performance in terms of boundary length and thus compactness. These encouraging results suggest that the augmented heuristics can provide useful reserve design policy to decision makers. Other solutions might also be investigated. For example, the principle that we used to extend the Marxan solution to a dynamic problem can also be used to extend the solution computed with integer linear programming. Indeed, the static reserve design problem can also be formulated as an Integer Linear Programming (ILP) problem (see for example  [10] ) to compute a solution of the static problem. Then, N Marxan can be replaced with N ILP , the solution provided by any software suitable for solving ILP problems.
We use the AMECO database to confirm that textbook multipliers are nearly always greater than one and that they increase during recessions. Propensity to import falls in recessions through the combined effects of the amplified fall in investment (Aftalion) and the high import content of investment (Palley). A change in saving behavior (Pasinetti) may also raise the multiplier. These findings strongly support the need for countercyclical fiscal policies during recessions.
First, we present a simple method for computing a proxy of the multiplier for a set of developed countries over the long run. Second, we identify in our database a rise in multiplier values during most recessions. Third, we use the investment accelerator  (Aftalion)  and import content  (Palley)  to explain why the Keynesian multiplier increases during recessions. Fourth, we provide insights into changing saving behaviors from Pasinetti, which may also influence multiplier values. Finally, we draw some conclusions.
Marginal or average propensities? By dividing first consumption and then imports by output, we are able to calculate the two average propensities, whereas equation 6 requires marginal propensities. What we call propensities are rather levels of consumption and imports as shares of GDP, and it is only because we assume very basic behavioral equations in (2) and (3) that our theoretical marginal and average propensities are equal. In the empirical part of the paper, our multiplier calculations may be viewed as a proxy for the "true" theoretical multiplier. Any more comprehensive study would require more sophisticated behavioral equations for consumption and imports. 3 We use average propensities to account for marginal propensities: the main issue here is that calculating marginal propensities to spend from one year to the next using the AMECO database 4  yields economically meaningless results.
Plugging (2') into (4) gives the value of the multiplier: /′ = 1/(1 -&3 + 4) with 3 = $ ) /$. Nevertheless, this formal model cannot be used in this article because our database does not contain statistics on gross disposable income since 1960. However, it clearly indicates that an increase in transfer payments increases our estimation of the multiplier, whereas an increase in income taxes decreases it. 8 do spend, it will depend on whether they spend mainly on domestic goods and services or on foreign ones. The value of fiscal multipliers is determined by just two parameters, and the differences between multiplier values therefore lie in different propensities to consume and/or import for different countries or different periods. Using the AMECO database, we calculate multipliers for 29 countries over the 1960-2014 period.
What happens with multipliers during recessions? We define a recession here simply as a negative GDP growth rate recorded in the database. We  Source: Ameco and authors' calculations 6 We thank Professor Ray Boddy for pointing out this correlation between our episodes of recessions and the timing of different SSAs. 7 It can also be observed that some of the early 1990s recessions correspond to the introduction of the European Monetary Union, while some of the post-2010 recessions stem from its contradictions.
The second factor refers to the institutional context in which the recession occurs. Due to income support programs, the fall in GDP may exceed the fall in households' disposable income, so that consumption expenditure may be partly supported by the size of current transfers, such as unemployment insurance. For example, in the United States, according to the AMECO database and our own calculations, the 2008-09 recession led to a contraction of 2. 8% in GDP, while households' gross disposable income only underwent a 0. 4% drop, especially because of a 13. 6% increase in current transfer received by households. 13 Even in the United States, where the Welfare State is not as developed as in Scandinavian countries, automatic stabilizers are sufficiently effective to maintain the propensity to consume during recessions.