dataesr/scientific-paragraphs-categorization huggingface dataset:
Dependent variable Consistent with existing studies on location choice for foreign investments  (Georgallis et al. , 2021; Maggioni et al. , 2019; Nachum et al. , 2008) , our dependent variable, R&D investment, is binary and assigns 1 to the foreign country where a firm makes an R&D investment and 0 to other potential host countries for such an investment. To build the investment choice sets, we consider a country as a potential target for an investment if IPR regulations data, as well as data on all other factors used in our models, were available. As a result, the choice sets in our sample include up to 79 countries 2 for which all the information used in our models is complete. Our final sample for analysis comprises 95,201 data points.
Indeed, 65 of the 79 countries making up the choice sets for analysis received at least one of the 1,341 investments in our sample. protection, enforcement mechanisms, and restrictions. This index is available in five-year intervals from 1960 to 2015. We used the 2000 index for investments carried out in the 2003-2005 period, the 2005 index for investments carried out in the 2006-2010 period, the 2010 index for investments carried out in the 2011-2015 period, and the 2015 index for investments carried out in 2016. This index varies from 0 to 5 with higher numbers indicating more stringent protection of IPR. We thus reversed each country's score to derive our measure of IPR weakness. To test H1, we developed a measure of host country inclination toward a firm's home country, based on the Global Database on Events, Location and Tone (GDELT).
The open-source and free GDELT database collects information about the interactions that a wide variety of actors all around the world have with other actors, as reported in the media  (Odziemkowska and Henisz, 2020; Yiu et al. , 2021). The information in GDELT is taken from published articles and is broken down into what they call "events". An event is a subject-verb-object sequence, which is machine-coded using textual analysis in order to assign it a grade, depending on the level of conflict/cooperation conveyed by the verb. This grade can vary from -10, such as with "military attack" and "clash", to +8, such as in "military assistance". GDELT classifies all events based on the nature of the involved actors, location, time, etc. (Goldstein, 1992). Because we theorize on how host government authorities at all levels may discretionarily protect the intellectual property of a foreign investor depending on the country of origin of that investor, we focus on those events involving any actors from the two countries  (Yiu et al.
Control variables We controlled for other factors that have been shown to influence the choice of location for foreign R&D investments or that could affect the main relationships we investigate. Following prior literature, we controlled for three factors capturing the political and economic relations between home and host country. First, we included a measure of IGO connections  (Alcacer and Ingram, 2013; Jandhyala and Phene, 2015; Rangan and Sengul, 2009)  using the moving average of the number of intergovernmental organizations in which both home and host country governments are members at the same time. This variable captures the extent and strength of the diplomatic ties between the two countries and was calculated using data from the Correlates of War project  (Pevehouse et al. , 2004).
Second, we added a measure of bilateral trade, using the sum of exports and imports in dollars taking place between the firm's home and a potential host country  (Duanmu, 2014). We calculated this variable as a moving average of bilateral trade from year minus 3 to minus 1. We gathered trade data from the Correlates of War project  (Barbieri et al. , 2009; Barbieri and Keshk, 2012). Finally, we accounted for the political affinity between home and host countries  (Bertrand et al. , 2016; Li et al. , 2018) , i. e. the similarity in views on global affairs held by high level government officials on both sides. This variable was measured using UN General Assembly voting patterns  (Gartzke, 1998; Voeten, 2000)  in the year prior to the focal investment. We also controlled for several dimensions of cross-national distance between a firm's home country and the potential host country  (Berry et al.
, 2010; Ghemawat, 2007). We captured geographic distance with a measure of the great circle distancein thousand kilometersbetween the largest city in the firm's home country and the largest city in a potential host country. We controlled for administrative distance and for cultural distance using two binary variables, indicating whether a colonial relationship existed between the firm's home country and a potential host country at any time in the past, and whether a common spoken or official language is shared between the two countries, respectively. We obtained data for these variables from the center d'Etudes Prospectives et d'Informations Internationales (CEPII). We controlled for economic distance with a measure based on how different home and host country are with regards to income, inflation and foreign trade.
We rely on data provided by  Berry et al. (2010)  to measure this variable. These four measures cover the main dimensions of cross-national distance as in the CAGE framework  (Ghemawat, 2007). Because our aim is to investigate the influence of non-market factors on innovation-related investments, we also controlled for two additional distance measures. We accounted for the political distance between home and host country, a measure based on intercountry differences related to policy-making uncertainty, democracy, size of the state and membership in global and regional agreements. Finally, we controlled for knowledge distance using a measure based on countries' production of patents and scientific articles. Both measures are also provided by  Berry et al. (2010). Besides intercountry relations and cross-national distance, the literature on location choice also emphasizes the importance of several host country features, which we also included as controls.
We added four variables to capture the quality of the institutional environment in a potential host country. First, we added a dummy variable capturing whether the host country has an independent judiciary, using data from the World Bank's Database of Political Institutions. Second, we controlled for how difficult it is for individual policy makers to discretionarily change policy in a potential host country, using the measure of political constraints, based on data from POLCONV  (Henisz, 2000). Third, because the incidence of corruption in particular countries can substantially affect firms' ability to protect their property  (Cuervo-Cazurra, 2006; Veracierto, 2008) , we also included a measure to capture the control of corruption in a host country, using data from the World Governance Indicators  (Kaufmann et al.
, 2011). Finally, we included a measure to capture the easiness to trade in a particular country, using data from The Heritage Foundation. We also control for the economic attractiveness of particular countries through multiple factors. To account for a potential host country's market size, we used variables measuring its GDP (in millions of constant 2005 US$), population (in millions of inhabitants), and GDP per capita (in thousands of constant 2005 US$). We controlled for the market potential of a host country using GDP growth (%, annual). We controlled for a potential host country's trade openness using the ratio of total trade (exports plus imports) to GDP. We used data from the World Bank World Development Indicators (WDI) to create these variables. We also controlled for a potential host country's technological intensity based on patenting; we measured this with the number of patent applications filed by residents using data from PATSTAT.
We used three variables to account for potential cluster and bandwagon effects  (Belderbos et al. , 2011). First, we controlled for a potential host country's FDI specialization with the ratio between the total inflow of FDI in that country and the country's GDP. We also controlled for a potential host country's R&D industry specialization with a count variable capturing the number of prior inward foreign R&D investments made in that potential host country in the same industry as the focal R&D investment. Finally, we controlled for a potential host country's R&D specialization with a count variable capturing the number of prior R&D investments made by foreign firms in that potential host country. We used data from WDI to create the first variable and from fDiMarkets to create the latter two variables.
Many of these correlations were to be expected. For example, population is highly correlated with the dummy variables denoting investments in China and in India. All the variables capturing features of a host country's institutional environment are correlated with each other. 5 These features of the host country institutional environment are also correlated with GDP per capita. Overall, the pattern of correlations in our data does not appear to raise particular concerns about multicollinearity: the VIF measures are all well below the accepted cutoff value of 10, with the average at 2. 59. Our sample includes investments in 65 host countries made by firms originating in 14 home countries. The largest contingent of investments was made by firms from the United States, followed by Japan and Germany.
)  and low-technology investments (low-technology and medium-low-technology). We find that, indeed, IPR weakness is no longer a significant antecedent of location choice when considering R&D investments in low-technology industries (see Appendix Table  AV ). R&D investments in high-technology industries, on the other hand, lead to results consistent with our theorizing. In other words, our theory appears to apply primarily to firms choosing the location of their high-technology R&D investments. Fourth, the fDiMarkets database further distinguishes new R&D investments from expansion R&D investments. We believe some of our arguments primarily apply to new investments and, therefore, used only those investments in our main analyses. However, firms may not use different processes when deciding on the location of expansion versus new investments.
Acknowledgment We thank  Editor Elisa Giulani  for her guidance during the review process and the two anonymous reviewers for their insightful comments and constructive suggestions. We also thank  Grazia Santangelo  and  Marc van Essen  for helpful comments, criticisms and suggestions, and  William Brown ,  Pauline Asmar  and  Debtanu Lahiri  for research assistance. Joao Albino-Pimentel  acknowledges support from the  University of South Carolina CIBER and Copenhagen Business School. Pierre Dussauge  is grateful for support from the  HEC Foundation.
Introduction Most countries impose two types of regulation on Internet Service Providers (ISPs): Net Neutrality (NN) and Universal Service Obligations (USOs). The first prohibits ISPs from "speeding up, slowing down or blocking Internet traffic based on its source, ownership or destination" (Kramer et al. [21] ). It aims to promote investment, innovation and competition among content providers (CPs) and more generally, to ensure free speech  (Katz [20] ). The latter forces ISPs to cover a given percentage of the territory with a minimum broadband standard, set in terms of download and upload speeds. Its goal is to avoid a "digital divide" among citizens of different regions. Because of the growth of data intensive content on internet, ISPs argue that it is nowadays counterproductive to treat in the same way CPs that require high speed of transmission and that do not tolerate delays, like streaming, from those that are far less demanding on those counts, like emails.
The overall impact depends on the network bandwitdth. The greater is the bandwidth, i. e. the greater is the network data transfer rate, the lesser is the impedement of priority on unprioritized content and the more likely it is that prioritization will increase total consumption, ISP profit and/or social welfare. However, because the detrimental effect of prioritization on unprioritized content impacts on welfare but not on the ISP profit, there is a bandwidth range over which the repeal of NN increases the ISP profit while it decreases welfare. A regulation mandating net neutrality is then called for. The intuitive property that the P regime favors the prioritized content is a basic feature of the NN literature. Across models, however, this property is ensured by using different mechanisms to implement prioritization.
Baffes (2007)  investigates the effect of crude oil prices on the prices of 35 commodities over the 1960-2005 period, and finds that fertilizers exhibit the largest pass-through, followed by food commodities. Kao and Wan (2009)  use daily closing prices  (from June 26, 1998 to December 31, 2007)  to examine the price discovery process among the U. S. and U. K. natural gas markets. Their main findings suggest (i) that the U. S. futures market is the most efficient in processing information, (ii) that the U. S. and U. K. spot markets are less efficient than their corresponding futures markets, and (iii) that the volatility spillovers across markets exhibit asymmetric responses to news. Zhang and Wei (2010)  use a monthly dataset, from January of 2000 to March of 2008, to analyze the cointegration and causality relationships between crude oil and gold markets.
Their results suggest a unidirectional Granger causality running from crude oil to gold. Based on panel cointegration and Granger causality methods and a monthly dataset ranging from January 1980 to February 2010,  Nazlioglu and Soytas (2012) , examine the linkages between oil prices and twenty-four agricultural commodity prices. Their results suggest that oil prices exert a significant impact on agricultural commodity prices. Gardebroek et al. (2016)  employ a multivariate GARCH model on different data frequencies to explore volatility transmission among major agricultural commodities returns in the United States over the 1998-2012 period. Their results reveal important volatility spillovers across commodities, particularly at the weekly and monthly frequencies. More interestingly, despite the supposed higher financial market integration of agricultural commodities, their results do not indicate that agricultural markets have become more interdependent in recent years.
Selecting different maturities is motivated by  Büyükşahin and Robe (2014)  and  Isleimeyyeh (2020). For instance, Büyükşahin and Robe (2014) demonstrate that excess speculation increases in short-term maturities as well as all maturities. Isleimeyyeh (2020)  investigates the integration between commodity futures risk premium and stock market for different maturities. His results show that futures risk premium is attributed to stock market, represented by financial investors, more than to traditional hedging pressure for long-dated maturity. Second, our selected datasets cover critical and important period between 1995 and 2020 (approximately 26 years). Indeed, as the past 26 years witnessed three uneven recessions in the United States and in the Euro area, as well as several well-known episodes of global political and financial concerns that increased uncertainty, and, more importantly, the financialization of commodities, our sample period is informative in terms of market development and, therefore, allows us to investigate the connectedness among commodities during tranquil and stress periods.
Moreover, the frequency decomposition reveals that most of periods of high total connectedness are driven mostly by high frequency components, which may indicate that commodity markets process information rapidly, except for the COVID-19 crisis period where total connectedness has been driven by lower frequency components. Our findings are important for market participants, both investors and policy makers. Indeed, in a context of increasing economic openness and financial integration, a better understanding of the magnitude of the interdependence among commodities as well as of the transmission mechanisms of shocks among them is crucial for investors and regulators to deal with financial risks. The remainder of this paper is organized as follows. Section 2 explains the empirical strategy and describes the dataset; section 3 presents the results; section 4 concludes this study.
We select datasets cover critical period between 1995 and 2020, which witnessed several uneven recessions; global, political, and financial concerns; and more importantly, the financialization of commodities. For obtaining robust and reliable results, we implement our estimations in different dimensions: the static connectedness over the full sample selected, the static connectedness over several subperiods that highlight certain episodes in the global economic and financial system, the time-varying connectedness, the connectedness between commodities in each basket using different time-frequency domains, and the connectedness across different maturities in each step. Regarding results, we provide a set of stylized facts on the extent to which commodity markets are integrated or segmented.
On the other side,  Grafton, Kompas, & Lindenmayer (2005)  highlight that the effect of protected areas becomes positive in presence of ecological uncertainty, i. e. a random evolution for fish populations. Protected areas are predicted to benefit from non-protected areas through the main mechanism of net fish migration across bor-ders. This effect is sometimes called spillover, see for instance  ( Gell & Roberts, 2003; Halpern, 2003; Halpern et al. , 2010; Micheli et al. , 2004 ). Lester et al. (2009)  compile a database of studies, based on a comprehensive survey of literature, in order to document biological effects of marine reserves and conclude with a statistically significance that even small reserves can produce significant biological responses, although more data are needed to test whether reserve effects scale with reserve size.
ACKNOWLEDGMENTS Funding was received from the  Agence Nationale de la Recherche  (grant  ANR-18-CE26-0018 ),  Spanish Ministerio de Ciencia, Innovación y Universidades  (grant  RTI2018-096927-B-100 ),  Generalitat Valenciana  ( AICO/2021/005 ), and  Universitat Jaume I  (grant  UJI-B2021-23 ).
Universitat Jaume I, Grant/Award Number:  UJI-B2021-23 ;  Ministerio de Ciencia y Tecnología , Grant/Award Number:  RTI2018-096927-B-100 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-18-CE26-0018 ;  Generalitat Valenciana , Grant/Award Number:  AICO/2021/005.
This paper employs provincial data to study the spatial and intersectoral spill-overs in aggregate failure rates in Spain, by using an Integrated Nested Laplace Approximation. The analysis is based on NUTS3 data over the time span 2005Q1-2013Q4. By speculating on the effects of the Spanish financial crisis, we document empirical evidence of the presence of spatial spill-overs among neighboring counties. Furthermore, some intersectoral spill-overs are also detected: we observe that Industry and Agriculture exhibit a positive impact on the Service sector. These results can be useful to design proper policy rules to better manage the spread of bankruptcies over time and space.
Study area The dataset is at the NUTS3 level over the time span 2005Q1-2013Q4. More precisely, Spain is made up of 17 autonomous communities and 2 autonomous cities (Ceuta and Melilla) which together represent the first-order administrative division (NUTS2) of the country. Autonomous communities comprise provinces, i. e. , groups of municipalities recognized by the constitution, of which there are 50 in total (NUTS3). In this work we use Spanish data at province level (i. e. 48 provinces 1 of 17 autonomous communities) from 2005 to 2013, obtained from the INE (2015).
Models are estimated by employing the Integrated Nested Laplace Approximation (INLA) algorithm. This innovative and recent approach is used within a Bayesian framework and makes possible the transformation from a Gaussian field (GF) to a Gaussian Markov Random Field (GMRF). In addition, when dealing with Bayesian inference for GMRFs it is possible to use the INLA algorithm proposed by  (Rue et al. 2009 ) as an alternative to Markov chain Monte Carlo (MCMC) methods for latent Gaussian field models giving rise to addition computational advantages. This class of space-time models have the remarkable feature to be flexible and time-efficient. All analyses are carried out using the R freeware statistical package (version 4. 1. 0) (2011) and the R-INLA package (2019).
Acknowledgements MS was funded by a  Wellcome Trust   Clinical PhD Fellowship  (Grant number  203919/Z/16/Z ). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the WHO or the  Wellcome Trust. SJ is funded by a  National Institute for Health Research (NIHR) Clinical Lectureship. BN was funded by the  MRC DTP Programme at Liverpool School of Tropical Medicine, Liverpool, UK. Conflicts of interest: The authors report no conflicts of interest. KM reports advisory board fees from  AstraZeneca (Cambridge, UK). JRH has received personal payment and payment to his employer for educational and advisory work from pharmaceutical companies that make medicines to treat COPD. AY reports advisory board fees from  AstraZeneca ,  Novartis  ( Basel, Switzerland), Abdi İbrahim (Istanbul, Turkey) ,  GlaxoSmithKline  ( GSK; Brentford, UK), Bilim (Istanbul, Turkey ).
Stellio Del Campo  gratefully acknowledges funding from the  CHIPS  project. CHIPS is part of AXIS, an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  DLR/BMBF  ( DE, Grant  No. 01LS1904A-B ),  AEI (ES)  and  ANR (FR)  with co-funding by the  European Union  (Grant No. 776608 ). Bob Cairns  was supported by  FRQSC.
This allows us to present xtþh as a convex combination of y t , y tÀh and EðyÞ from the sum S and the product P: xtþh ¼ Sy t À Py tÀh þ ð1 þ P À SÞy: (15) Using what has just been presented above and in Appendix B, the optimal values of a and K can be determined by minimizing the MSE of ARTU forecasts. Based on the results therein, the optimal a and K can be written in functions of a triplet [R, r(h), r(2h)], where r(h) and r(2h) are two correlation coefficients that have already defined previously, and R ¼ s 2 v =s 2 x is linked to the quality of the measurement. The ARTU prediction is described by the pseudocode in Appendix D. 5. There are, nevertheless, some hypotheses that have been formulated, especially concerning the stationarity of the studied time series. Indeed, wide-sense stationary assumption (mean and variance are time-independent and autocovariance and autocovariance can be expressed as functions of the time-lag) is necessary to obtain a closed-form in B.
We computed a and K for values of r(h) and r(2h) from À1 to þ 1 (with an incremental step of 0. 01) and for 4 values of R (0, 0. 01, 0. 05 and 0. 1) solving Eq. (B. 6). An example is given in Fig. 1  and other values are available in https://github. com/cyrilvoyant/ARTU. git (via Matlab® codes). What is important to remember is that once these values are known, the forecasts become relatively simple to implement by knowing the autocorrelations of the series studied. The forecast is direct, fast and does not require modeling or learning. The parameters are obtained by data mining approach and the method can then be qualified as naïve although a minimum of historical data is necessary. A potential limitation on the use of the ARTU approach can be enacted from Eq. (11). Indeed, by multiplying by x c tÀh and taking the expectation value, it comes Eq.