dataesr/scientific-paragraphs-categorization huggingface dataset:
More specifically, we estimate a Markov Switching (MS) model to test the hypothesis of regime switching. This paper is the first contribution to specify the trading volume of the Forex through different (low and high volatility) regimes. Our empirical investigation looks at the EUR/USD currency market, that is, the most traded currency pair on the foreign exchange market. Our results show evidence of asymmetrical and nonlinear patterns for trading volumes and transaction costs on the Forex. The Tobin tax would not have a monotonic impact on trading activity across market conditions. However, the change in elasticity between low and high volatility regimes is slight (-0. 17 versus -0. 21). The paper is structured as follows. Section 2 sets out the econometric framework and section 3 presents the dataset and the descriptive analysis.
75 to -1. 5 whereas the French Ministry of the Economy (2000) and the Belgian Ministry of Finance (2001) assumed values of -0. 5 to -1. 5. In addition, these Forex elasticities are lower than equity market elasticities. However, as underlined by Schmidt and Bhushan (2011), the work of  Bismans and Damette (2008)  is based on a two-day microstructure dataset. Thus, elasticities are highly likely to change with market circumstances and structural features, making it a tenuous exercise to characterise these results as a robust feature of the markets. Schmidt (2008) estimated the elasticity in the dollar/yen market for the period 1986-2006. Using a simultaneous equations time series regression model to take into account potential endogeneity between trading volume and bidask spreads which he used as a proxy for transaction costs, he arrived at an elasticity of -0.
Data and Descriptive Analysis To assess the Tobin tax elasticity, we use exchange rate quotations and trading volume series for the Euro-Dollar currency pair. We collect an intradaily data set from Olsen Financial Technologies ranging from 15 September 2008 to 31 December 2010. In other words, our data set includes the period from the bankruptcy of Lehman Brothers to the end of the year 2010. This data set (600 observations) is therefore bigger than in most previous empirical studies of the Tobin tax. We expect to capture periods of stress alternating with periods of relative calm: for instance, the starting point of the sample is related to the onset of the subprime crisis, that is a period of stress, whereas spring 2009 saw a slow down on the foreign exchange market, with smaller trading volumes and low transaction costs.
Database and methodology The present study was conducted as a three-step analysis. First it was important to define the concept of performance to be assessed. Generally, the performance is related to the fulfilment of a target, therefore the (local) environmental performance can be seen as the extent to which the communes meet the expectations of their environmental responsibility. Gathering the data from the National Institute of Statistics (INS) as well as from the National Authority for Public Services (ANRSC), Ministry of Regional Development and Public Administration (MDRAP) and National Regulatory Authority for Energy Sector (ANRE) and organizing a database was the first task to accomplish for this study. It has to be mentioned that constructing a database from local level data was rather difficult, because on the one side, the data was not always available and on the other side the methods of the aggregation for the data of each county were sometimes different.
Secondly, indicators were calculated by using the available databases in order to assess the environmental performance in the studied area (table  1 ). Five indexes grouped in three categories were taken into account for this study: Forestry Cover Index (FCI), Land Degradation Index (LDI), Sanitation Services Index (SSI), Environmental Expenditures Index (EEI) and Utilities Modernization Index (UMI), each constructed by the aggregation of mean values for a given (recent) period of certain indicators presenting either a positive (the share of forest areas, access to water and sewage, renewable energy investments etc. ), or a negative (degraded areas) significance. LDI and the FCI are related to the way the natural capital of the units is managed, and even preserved in order to promote a sustainable development.
It should, however, be noted that the processes of land degradation in Western Moldavia are not only influenced by the existence or non-existences of forests, the agricultural and construction practices playing also an important part. SSI (Fig. 3 ) took into account,both the existence of a company which assures the management of the waste and, the provenience of the company. It was considered that the index relates to a good environmental performance when the sanitation company came from the same county as it limits other negatives impacts on the environment (for e. g. emissions due to transports). The database allowed the identification of 4 types of sanitation services: sanitation services assured by the commune hall, sanitation services assured by a company from the same county as the local territorial unit, sanitation services assured by a company from another county and communes without sanitation services at all.
Accounting for inventor career effects In our dataset we observe that there are some inventors with careers of patenting that span many years and others whose fifteen or more patents are all produced in a very short period. To account for this variation we measure the duration of an inventor's career (the number of years from the inventor's first patent applica-tion to the last) and adjust other effects for duration. We use duration to compute productivity, value, and inter-firm mobility on a per year of career basis. Some investigators (e. g. ,  Hoisl, 2006 Hoisl, , 2007;; Schankerman, Shalem and Trajtenberg, Shiff, and Melamed, 2006)  have tracked the numbers of patents and/or the numbers of citations that an inventor has prior to a move from one firm to another. Moves are assumed to be based only on past performance.
Still another way to measure inter-firm mobility is to list an inventor's patents chronologically and to count a move each time the assignee of the patent changes. Such a count results in the maximum possible measure of the number of moves that an inventor makes. Under this definition a single inventor in our dataset would be said to have moved 53 times. Such a high level of mobility may correctly represent the inventor, but certain patterns in a list of assignees seem to call that definition into question. For example suppose that Inventor # 1 assigns his first patent to firm a, the second to firm b, and the third to firm a, the fourth to firm b and so forth through the assignment of the tenth patent to firm b. Inventor # 2 assigns her first five patents to firm a and the next five to firm b.
IV. Empirical estimation issues All of our estimates use robust standard errors so that we do not have to be concerned about the effects of heteroscedasticity and, since our observations are in-ventors' careers, we do not have serial correlation problems with our estimates. The dependent variable for our productivity equation is the number of patents per year of an inventor's career within our dataset. The values are technically truncated at 0, but have enough variability and range so that we are able to estimate the parameters with ordinary least squares. Similarly the dependent variable for inter-firm mobility, moves per year, allows us to use ordinary least squares (ols). Our dependent variable for technical mobility is (1-a technical concentration ratio), which is strictly limited to the range from zero to one.
This research has two objectives. The first is to study the link between SMEs internationalizing at an early stage and the attitudes of their decision makers toward internationalization. The second is to analyze this relationship in an international context (India and France). We thus propose to contribute to a better understanding of the factors behind internationalization. To meet these objectives, we used an original methodology: decision makers' practices were studied using the scenario method. The assessment of attitudes toward internationalization was carried out using a new multidimensional scale of measurement. The measurement tool was distributed to decision makers in French and Indian SMEs. A database of 234 respondents was used. This research presents three main results: we found that the decision makers of SMEs with a very positive attitude towards internationalization are more conducive to internationalizing early than others but that the opposite is not always true.
! The data collection was carried out on SME decision makers from France and India. It is important to note, firstly, that the definition of SMEs differs between the two countries. In France, the European Union proposes a definition of SMEs. According to this definition, SMEs are firms with fewer than 250 employees whose turnover is less than 50 million (Recommendation 2003/361 / EC, OJEU  L124: 36, May 20, 2003). In India, the definition of SMEs is proposed by the Micro, Small and Medium Enterprise Development  Act (2006). According to this definition, an SME in the industrial sector is a firm whose investments (plant or machinery purchases) are valued lower than Rs. 10 crores 3  (approximately € 1. 2 million); an SME in the service sector is a firm whose investments in equipment are valued lower than Rs.
! Data collection was done in two stages. The first phase was completed in France in May 2012 (149 French decision makers participated in this study). A second phase was conducted in 2013 in India (98 Indian decision makers participated in this study). The first phase of data collection was mainly conducted through an email campaign from databases from the Chamber of Commerce and Industry and KOMPASS. The business sectors in these databases were selected services and industry. The companies contacted were drawn at random. It is difficult to accurately estimate the return rate of the email campaign, as many email addresses were incorrect. However, we believe that the return rate was between 5 and 7%. This result is satisfactory if we consider that the scope is very specific, that the target respondents have very little time to devote to outside work, and that we have implemented a number of actions to improve return rates  (Helgeson, Voss & Terpening, 2002).
! The data processing of the attitude measurement scales was performed using SPSS software. First, we calculated the average attitude scores to separate our respondents into groups. We distinguished between respondents who were most favorable to internationalization, those who were moderately favorable, and those who were least favorable. We used the cutoff point of one standard deviation. We then calculated a score for the behavioral responses of each group. Finally, the differences in scores between the groups were analyzed using the analysis of variance test (ANOVA). This type of processing data is recommended for researches using scenarios method  (Aguinis et al. , 2014).
Data for coastlines length and water-depth were collated from the local governmental planning department for each port including the Dalian, Yingkou, Qinhuangdao, Tianjin, Yantai, Qingdao, Rizhao, Shanghai, Suzhou, Ningbo, Fuzhou and Xiamen ports. Due to the inherently different backgrounds pertaining to port integration within specific regions, analysis of port integration is necessarily undertaken within two different contexts, namely dynamic mechanisms and spatial dimensions, thus permitting mode determination and further revelation of general trends and characteristics. The matrix presented in Table  1  and Fig. 2  has made it possible to ascertain the extent of differentiation pertaining to port integration in China, thus allowing the authors to explicitly define and verify several integration modes.
Waste related to poor planning and prioritization of research In 2009, Chalmers and Glasziou highlighted the enormous problem of waste in research, estimating that up to 85 % of research investment is wasted  [1]. Waste occurs at all stages of research  [2] [3] [4] [5] [6] [7]  and particularly affects planning and prioritization. We have increasing evidence that many trials address low-priority questions that are poorly related to the burden of disease  [8]  and patient or physician needs  [9, 10] , do not address patient-important outcomes  [11]  or use an inadequate comparator. For example, in rheumatology, few trials compare biologically active drugs against each other; comparisons against placebo represent 80 % of trials registered at ClinicalTrials. gov [12]. This lack of head-tohead trials does not allow for answering the pragmatic question raised by patients and their physicians: for this particular disease, which treatment is most effective? Also, it exposes patients to unnecessary risks  [12, 13].
It is very worrying that, despite the publication of around 25,000 randomized controlled trials each year, clinical practice guidelines continue to rely mostly on a poor level of evidence, with potentially serious consequences for patient care  [19]. Singh Ospina and colleagues used ClinicalTrials. gov to assess the response in terms of new, active studies conducted for research questions with a very low quality of evidence according to the Endocrine Society clinical practice guidelines  [15]. The authors found active studies for only one of five recommendations, which suggests that ongoing research does not sufficiently adapt to fill knowledge gaps in endocrinology.
Identifying gaps to decrease waste The research community is becoming increasingly concerned by these issues. We need to add incremental value to existing evidence by a better connection to future research. A first step is to systematically identify research gaps. As outlined by Singh Ospina and colleagues, clinical practice guidelines could be helpful. In the same way, systematic reviews, by synthesizing the available body of evidence, have a key role to play. The Cochrane Collaboration clearly recommends that review authors systematically comment on the need for further research in a separate section of the review, called "Implications for research"  [20]. Then, a second step would be to record research gaps. Some initiatives already exist. The UK Database of Uncertainties about the Effects of Treatments (UK DUETs), established by the National Institute for Health and Care Excellence (NICE), publishes treatment uncertainties reported by patients and clinicians and derived from research recommendations and systematic reviews  [21].
From POLES runs we draw sets of matching variations of factor intensities and prices for a range of carbon prices. Assuming constant non-energy prices allows interpreting as capital intensity shifts the variations of the monetary capital stock per physical unit of energy produced. We use the resulting dataset to calibrate the energy and capital intensities of energy production, α EE and k E , as functions of the ratio of their prices p EE and p K (Figure  2 )  16. As already hinted above, a non-negligible difficulty regards identifying actual capital expenses in the cost structure of the energy sector of national accounts. The remainder of value-added (VA) net of labor costs encompasses not only capital depreciation, but also elements as heterogeneous as interest payments, rents (on land, water, mineral, and fossil resources) and a mark-up reflecting static returns to scale and/or market characteristics.
It was picked up throughout the years to present times (cf. , e. g. , Messner and Schrattenholzer,  2000; Schäfer and Jacoby, 2006; Martinsen, 2011) where it appears to flourish (cf. the above 3 recent papers but also Igos et al. , 2015). bottom-up model as they are in the open-economy singleregion versions of IMACLIM-S 24. • Forcing, in the input-output framework of IMACLIM-S, bottom-up simulation results on (1) the international price of energy commodities; (2) the energy consumptions of households and the energy intensities of productions; (3) the capital intensities of energy supplies and, as far as possible, energy end-uses; (4) the volume of energy exports to foreign markets, together with that of domestic output of primary fossil energies, disaggregation permitting. • Running IMACLIM-S under constraint of these exogenous data to compute various economic indicators (such as GDP, output at various aggregation levels but also possibly relative price variations on non-energy markets), which are sent back to the bottom-up model to serve as drivers of energy demand in a renewed simulation.
Building from IEA Energy Balances and Energy Price Data 32 The most extensive energy/economy data hybridizing method developed for IMACLIM models builds on full-fledged energy balances and energy price data, typically that available from the International Energy Agency (IEA). Its starting point consists in reorganizing the disaggregated energy balance (in million tonsof-oil equivalent, Mtoe) into an input-output format compatible with that of national accounts. This is a much more timeconsuming and data-intensive procedure than it appears, as it entails, for dozens of energy products 33. • Correcting, when working on an IEA region that aggregates different countries, the reported imports and exports. For both accounts the IEA indeed only sums up the data of each country within the region, without subtracting intra-regional trade.
For, e. g. , the European Union this is an absolutely necessary step. • Absorbing statistical discrepancies, by e. g. , a homogeneous adjustment of all uses in one direction and of all resources in the other, to bridge the gap between the two totals. • Reallocating international bunkers and internal transport fuel consumptions to exports and domestic consumption. The IEA treats energy consumptions from a geographical perspective, whereas input-output data aggregate the economic accounts of resident businesses-two quite orthogonal perspectives. This is indeed one of the biggest data treatment challenges, considering how difficult it is to obtain the data required to perform it. The stakes are however high, with the energy intensity of transports recognized as one of the main deadlocks of energy transition.
In applied work so far we have hesitated between (1) stripping down auto-consumptions to genuine energy consumptions and consequently moving the value-added of commodity trading to non-energy sectors when re-balancing our hybrid national accounts, cf. below; or (2) acknowledging energy trade, but this implies breaking the link to energy balances and falling back on national accounting data only  35. The second step of our hybridizing procedure consists in complementing the obtained IO table of energy flows with a table of applying energy prices (in currency per volume, e. g. , Euros per ton-of-oil equivalent, e/toe). This is again a delicate step, as IEA databases provide energy price statistics with limited disaggregation and must therefore be augmented with other data sources, either national or international 36.
We implemented it to reconcile the base years of aggregate 2-sector models initially calibrated on the GTAP database with data for the corresponding year of, again, the POLES model 38. The procedure is similar but not identical to that of Section Building from IEA Energy Balances and Energy Price Data above. 37 National accounts treat the energy consumptions of public administrations as these of an aggregate public service sector, whose uses focus on the final consumption of public administrations. For this reason, the direct final energy consumptions of public administrations are nil. Understandably, gross fixed capital formation does not consume (immobilize) any energy flow either, at least notwithstanding stock variations (cf. above). 38 In the 8. 1 version we used, GTAP details harmonized 57-sector input-output tables for 134 countries or regions covering the globe, cf.
https://www. gtap. agecon. purdue. edu/databases/v8/default. asp (accessed July 2015). • A first step similar to Section Building from IEA Energy Balances and Energy Price Data is to substitute to GTAP energy consumptions of the composite (non-energy) sector and of households the corresponding price x volume statistics that can be aggregated from POLES data for the relevant year. • A second, quite specific step is to adjust the energy expenses of the energy sector prorata the adjustment of the sum of other uses induced by the first step. This rough procedure is prompted by the quite specific issue of commodity trading, which we introduced Section Building from IEA Energy Balances and Energy Price Data above (cf. also footnote 35). • A third and a fourth step, both identically shared with the approach of Section Building from IEA Energy Balances and Energy Price Data, consist in adjusting all non-energy resources of the energy sector prorata the adjustment of its energy expenses induced by the second step, and in compensating all induced changes of the uses and resources of the energy sector by adjusting the corresponding uses and resources of the non-energy sector, thereby guaranteeing conservation of all cross-sectoral accounting totals: intermediate, household and public consumption, investment and exports on the side of uses; intermediate consumption again, labor costs, capital costs, input and product taxes and imports on the side of resources.
Calibration of Agent-specific Prices Through Specific Margins Nothing forbids applying the standard uniform (normalized) pricing rule of CGE models to the hybrid accounts resulting from the methods of either Section Building from IEA Energy Balances and Energy Price Data or Section Building from BU Model Variables at Base Year. As we already stressed, this has the joint consequences of limiting, for each good in the model, price differences across consumers to tax specificities; and of biasing the distribution of energy volume consumptions. The only advantage of hybridizing data would therefore be to have pinpointed the economic  39  In the applied work of Section Sovereign Risk and Energy: The RISKERGY Program we crudely attribute to households all fuel consumptions of light duty vehicles.
A non-negligible part of such consumptions are paid for by firms, though. Conversely, some fuel consumptions attached to light trucks but also to boats and even airplanes (although statistically negligible) are paid for by households. value of the energy sectors and their actual cost shares in productions. It might appear counterproductive to thus discard all the collected information on the real distribution of consumption volumes and on agent-specific prices. However, this is indeed what modelers do when exploiting the hybrid matrixes derived from the widely used Global Trade Analysis Program (GTAP) database in standard CGE approaches. In our instance, having produced the hybrid tables ourselves naturally lead us to fully exploit them by departing from standard CGE modeling practice and introducing agent-specific prices.
Faced with this lack of information, in our applied work tackling this issue (that reported Sections An Outlook on the Macroeconomic Impacts of Electric Vehicles Penetration in EU28 and Sovereign Risk and Energy: The RISKERGY Program below) we decide to introduce a set of "specific margins" aggregating, for each economic user category, deviations from the average basic price emerging from the cost structure. By construction the aggregate margins compensate and thus do not alter the balance of each energy sector (or the energy aggregate) resulting from the hybridizing processes of either Section Building from IEA Energy Balances and Energy Price Data or Section Building from BU Model Variables at Base Year above. Let us underline at last that, to the best of our knowledge, no other modeling team has thus tackled the issue of agent-specific pricing.