dataesr/scientific-paragraphs-categorization huggingface dataset:
This study focuses on the analysis of key indicators related to a panel of 28 European countries for a timeframe of seven years from 2009 to 2015. By analysing data from over half of the total number of countries in Europe, our findings are informed by a large percentage of the countries recognised by scholars and policymakers as highly entrepreneurial and innovation-driven nations  (Groth et al. , 2015). Since the nature of the data is longitudinal, this research adopts the methodology employed by Jafari-  Sadeghi (2019)  and employs a panel data analysis. To test developed hypotheses, following the literature (e. g. ,  Richard & Daria, 2006; Ross, et al. , 2015; Su et al. , 2015) , we use static panel data synthesis, comparing fixed-effects versus random-effects analysis. In this regard, the research relies on four databases as its source of data such as World Bank including World Development Indicators (WDI) and Ease of Doing Business Index (EDBI), Organisation for Economic Co-operation and Development (OECD) and Global Entrepreneurship Monitor (GEM).
Research method Source of data To test developed hypotheses, this research relies on four databases as its source of data. In this regard, World Development Indicators (WDI) and Ease of Doing Business Index (EDBI) have been taken from World Bank and other variables have been extracted from Organisation for Economic Cooperation and Development (OECD) and Global Entrepreneurship Monitor (GEM). We used WDI (2019) to collect information regarding technology exploration and technology exploitation as well as macroeconomic data (e. g. , the country's GDP). OECD (2019) has been utilised to collect data related to technology readiness such as ICT investment and internet access. Furthermore, EDBI is an important source of data about business's environmental institutions such as regulations, laws, and cost of doing business, etc.
(Pinheiro-Alves & Zambujal-Oliveira, 2012; Ruiz, Cabello, & Pérez-Gladish, 2018). Eventually, GEM (2019) is of the most reliable source for entrepreneurship information, hence the database has been employed to extract the regarding the value creation through entrepreneurship. For this research, the level of data collection is the country/national level. Therefore, a panel of 28 countries in the Europe area for a timeframe of 7 years from 2009 to 2015 has been employed. The logic behind analysing a group of nations (European countries) is adapted to  Xavier et al. (2012)  since they suggest that it considers variations between geographic regions and economic development levels of countries. The set of 28 countries include twenty-two EU members (Belgium, Germany, Denmark, Spain, Estonia, Finland, France, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Sweden) and six non-EU members (Bosnia & Herzegovina, Switzerland, United Kingdom, Macedonia, Norway, Russian Federation).
This research was restricted to specific perspectives. For instance, our analysis was limited to a sample of 28 nations, which may be perceived as a small-size sample. In this regard, we employed panel data analysis of 7 years that allowed for a resolution of issues related to the statistical validity of the method through inflating bootstrapping standard errors, as highlighted by  Castaño, Méndez and Galindo (2016). This was also addressed by limiting the timeframe of the research to 8 years from 2007 to 2015. Thus, future research can take advantage of more recent data. Furthermore, for this research, we were limited to use secondary datasets, in which the data were taken from several resources such as World Bank databases entitled "World Development Indicator" and "Ease of Doing Business Index" as well as Organisation for Economic Co-operation and Development and Global Entrepreneurship Monitor.
Firm-level variables The primary source of information is AIDA, a firm-level dataset administered by the Bureau van Dijk. AIDA is a commercial dataset which provides information on balance sheets and profit and loss statements for hundreds of thousands of Italian businesses. From this dataset we extract information on a number of variables including value added, sales, the number of workers, total tangible fixed assets, service purchases, firm age. We also retain information about firms' geographic localization and industry. An important advantage of the AIDA dataset is that it includes firms operating in all industries. This implies that our results apply to the whole spectrum of economic activity. 5 Analyzing service outsourcing with information collected at the firm-level rather than at the plant-level might be inaccurate if a firm runs multiple establishments.
The source of data for referendum turnout is the Ministry of the Interior. Blood donations: The number of blood donations per 1000 inhabitants, disaggregated by province. The data are collected from the health authorities of Italian regions. In each region, regional health authorities collect data on blood donations and subsequently send this information to the High Institute for Health (Istituto Superiore di Sanità) which, in turn, maintains a National and Regional Registry of Blood and Plasma. Provincial data on blood donations are not available for Apulia and Lazio. For the provinces of these two regions we take the total regional value. Data refer to the year 2002 and the source is  Cartocci (2007). Volunteers: It is the number of volunteers in non-profit organizations. Data refer to the year 2000 and the source is de  Blasio and Nuzzo (2010).
A. 1. 3 Other provincial explanatory variables Service workers: It is the number of total service workers in the province. We take this information from the universe of Italian enterprises coming from Archivio Statistico delle Imprese Attive (Statistical Archive of Active Enterprises) administered by Istat. The data refer to the years from 2001 to 2007 and are extracted for the interval of two-digit industries 50-74. Total workers: It is the number of total workers in the province (full-time equivalent of workers hired in every sector). We take this information from Istat. The data refer to the years from 2001 to 2007 (http://www3. istat. it/salastampa/comunicati/non calendario/20100114 00/). University graduates: It is the number of university graduates per 100 inhabitants, disaggregated by province.
GDP: It is the provincial nominal gross domestic product, expressed in thousands of euros. The source is Istat. Urbanization in 1861 : This variable is the share of total provincial population living in cities with more than 10,000 inhabitants in 1861. Data on city size come from the "Italian Urban Population Database 1300-1861", provided by Paolo Malanima (http://www. paolomalanima. it/default file/Page646. htm). The number of total provincial population in 1861 is taken from Populstat (http://www. populstat. info/). Note: The table provides descriptive statistics for the variables used in the regressions. The dependent variable, PSI (log), is the log of purchased service intensity, defined as the amount spent on external service provision divided by sales. Employees (log) is the log of the number of employees.
Data source We use data on foreign subsidiaries from Toyo Keizai's overseas investment (TK) dataset to test the hypotheses. This dataset, updated annually, provides information about Japanese firms' investment in foreign countries. Data are gathered through interviews with general managers, who provide information on each subsidiary of their parent company, including duration of the activity, start date, exit date, type of industry, parent firms, the purpose of investment, and so forth. The sample for this study consists of 6,082 observations and includes all Japanese firms that invested M u l t i n a t i o n a l B u s i n e s s R e v i e w 16 in the primary sector industries, in 65 foreign countries, from 1986 to 2013. After dropping observations with missing data, the final number of observations is 5,726 subsidiary-years.
Dependent variable The dependent variable for this study is subsidiary survival, which is operationalized as the conditional probability of exit/closure during the time of study of Japanese subsidiaries in the natural resources sector. This measure is a binary variable, coded as one when a subsidiary disappears from the panel prior to 2013 (i. e. exited). Those subsidiaries that survived to the 2013 edition of the dataset are considered as censored data (time to exit unknown) and hence are coded as zero.
Independent variable The main independent variable in this study is political stability, drawn from The PRS Group database. We rely on a comprehensive operationalization of political stability, consisting of factors related to both political risk and uncertainty  (Slangen and Van Tulder, 2009). The PRS Group dataset provides this information based on two methodologies. The first is based on the rate of change of policies, the general political environment, and tariff procedures. The second measures political events and considers how these events impact factors such as gross domestic product (GDP), inflation, and exchange rate stability. The combination of these two methodologies results in an overall metric for countries' risk which is highly relevant to FDI decisions. The content domain was selected by The PRS Group specifically to address the concerns of foreign direct investors.
Moderators The motives behind investing in host countries were obtained from the TK dataset. The dataset is compiled from an annual survey that is completed by senior managers, who select from a list of predefined investment purposes for each subsidiary of their MNE. We further categorize these purposes according to prior literature on the four motives  (Chrysostome and Lupton, 2011). Table  1  depicts the encoding of these objectives into the four 'seeking' categories.
The Weibull distribution exhibited the best fit for our data, according to AIC (Akaike Information Criterion), so this distribution was used to model the baseline function. Equation 2 formalizes the baseline hazard function  (Cleves et al. , 2010). Equation 1: Multilevel Mixed Effects Parametric Survival Model: 𝒉(𝒕 𝒊𝒋 ) = 𝒉 𝟎 (𝒕 𝒊𝒋 )𝐞𝐱𝐩 (𝒙 𝒊𝒋 𝜷 + 𝒛 𝒋𝒊 𝒖 𝒋 + 𝒗 𝒊𝒋 ) Equation 2: Weibull Distribution Baseline Hazard: 𝒉 𝟎 (𝒕) = 𝒑𝒕 𝒑 -𝟏 𝐞𝐱𝐩 (𝜶) To estimate the baseline hazard, this model was fit to the parameters (α,p,β i ), that is the hazard ratio of the coefficients of the i th covariate, with the shape of the baseline represented by p as one parameter and as the second parameter to estimate the baseline hazard  (Cleves et al. , 𝛼 2010). We selected a mixed-effects model because moderators that are reported in the TK dataset are subsidiary-level variables, while the independent variable (political stability) and control variables are country-level.
Limitations, Future Research and Conclusions Since the dataset is constrained to Japanese firms, the generalizability is limited beyond this sample. Governance of Japanese MNEs may be less risk-averse due to interlocking directorates  (Buckley and Strange, 2011). According to the logic of our study, this suggests that many Japanese MNEs would be less averse to investing in countries with high political instability. Future studies may attempt to determine the extent to which country of origin impacts the relationships we found between political instability and subsidiary survival, depending on investment motives. Also, the various types of policy changes related to environmental stewardship, financial reporting, and other relevant policies, and their impact on the decision to invest, is another research avenue for future study.
Acknowledgements. Thanks to  Laurent Terray  for useful conversations in developing this study. Dr Sanderson  was funded by the  French National Research Agency , grant number  ANR-17-MPGA-0016. This material is based in part upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation (NSF)  under Cooperative Agreement 1947282. Portions of this study were supported by the  Regional and Global Model Analysis (RGMA)  component of the Earth and  Environmental System Modeling Program  of the  U. S. Department of Energy's Office of Biological & Environmental Research (BER)  via  NSF   IA 1844590. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-17-MPGA-0016 ).
The combination of attribute levels is done using a so-called d-efficient model that seeks to maximize the information that can be derived from respondents' choices and thus reduces the sample size required to estimate the underlying decision model  [32]. A pre-test of the design was carried out on a simulated dataset to verify that it would be possible to test the hypotheses. Before the experiment, respondents were asked which of these two biodiversity p tection actions is most relevant to their forest. The most relevant was therefore used a protective measure in all subsequent commitment choices. The hypothesis is that the i pact of institutional factors does not depend on which of these two measures is chosen forest owners. The first attribute is the organization in which the forest owner is involved (see Ta 1).
The first part includ 12 choice situations and the second part 16 choice situations. A total of 28 choices w considered too numerous for an individual to agree to complete the questionnaire. T versions of the questionnaire were then developed with six and eight choice situatio assigned to each of the two versions, and then randomly distributed to the responden The combination of attribute levels is done using a so-called d-efficient model that see to maximize the information that can be derived from respondents' choices and thus duces the sample size required to estimate the underlying decision model  [32]. A pre-t of the design was carried out on a simulated dataset to verify that it would be possible test the hypotheses. The third step consists in formulating an introduction to the choice experiment a follow-up questions.
The questionnaire included relatively few questions about the forest and the forest owner since the forest owners interviewed had already participated in a telephone interview and provided this information. The questionnaire was tested within the project group (which included sociologists having carried out qualitative interviews with forest owners) and in face-to-face testing of the questionnaire with forest owners. The questionnaire was available on the Internet, programmed with LimeSurvey, free software, and hosted by an INRAE server. E-mails were sent to 214 forest owners with two reminders (postal and telephone). E-mail addresses were obtained by a previous telephone interview where the respondents were asked if they would participate in a follow-up survey. Forest owners who did not complete the Internet-based questionnaire were sent a letter with 14 choice cards from the DCE and told that they would be contacted by telephone.
Empirical Illustration We consider the 616 stocks from the MSCI U. S. Index and their market capitalizations as of June 2020. A set of 20 U. S. large cap stocks was randomly drawn from among the 100 largest stocks, with continuous monthly total returns over 20 years for the sample spanning from June 2000 to June 2020. Details of the dataset, including the description of the 20 stocks and their annualized returns and standard deviations, are displayed in Table  1. This standard methodology follows the literature, including -among others -  Michaud (1998) , or  Jobson and Korkie (1981). Figure  1  illustrates the optimal portfolio allocation map that is related to the traditional unconstrained MV optimization; displaying optimal allocations from minimum risk on the left-hand side, to maximum risk on the right-hand side.
Specifically, this study makes the following three contributions: First, an empirical study was conducted in Shanghai using a GWR model to explore the spatial heterogeneity of the MWTP for clean air in each census tract. Second, the latest data were used to quantify the economic value of urban clean air and to measure residents' MWTP in different census tracts, which is conducive to achieving a win-win situation between public health and economic development. Third, the actual losses caused by air pollution in Shanghai were computed from the perspective of asset value depreciation in the entirety of the Shanghai housing market. The results of this study can provide a reference for the government to formulate regional air pollution prevention and control policies. The remainder of this paper is structured as follows.
Data Description and Processing The data used in this study include housing price data, air quality data, and other built environment data in Shanghai. All the data used in this paper were collected in 2018 so that the time dimension can be unified. The housing price information of 27,608 residential areas in Shanghai constituted the housing price data in this study. These data were taken from a platform for real estate transactions in Shanghai (http://cd. lianjia. com/) and included the address, average housing price, construction time, number of housing units, and latitude and longitude coordinates of each community. The air quality data came from the Shanghai Municipal Bureau of Ecology and Environment. We collected data from 10 national controlled air quality monitoring stations of Shanghai in 2018.
These data included the hourly mean concentrations of CO, O 3 , SO 2 , NO 2 , PM 10 , and PM 2. 5 , the monitoring time, and the current air pollution levels, which can be used to calculate the annual mean concentrations of air pollutants in different census tracts of Shanghai. Figures  3 4 5 6 show that the air pollutants in Shanghai are diverse, mainly concentrated in the outer suburbs with heavy industries and the city center with heavy traffic. For example, the areas with the highest mean concentrations of SO 2 and PM 10 are the Baoshan District, Jinshan District, Yangpu District, and Jiading District, where there are many polluting factories, such as power plants, steel mills, and automobile factories. Furthermore, the large trucks that travel in these areas during the day also contribute to air pollution.
The areas with the highest mean concentrations of NO 2 and PM 2. 5 are mainly located in the inner ring area because of traffic congestion. The built environment variables can be measured by the "5 Ds, " namely, density, design, diversity, distance to transit, and destination accessibility  (42) (43) (44). In this study, the initial  population data came from the 6th Census of China. The urban road network data of Shanghai were extracted from the Open Street Map (OSM). The POI data were obtained from Baidu. com, which is one of the largest Chinese web mapping service applications. These data provided 14 types of locations, such as schools, restaurants, hospitals, parks, supermarkets, and transportation facilities  (45). The total number of POIs in this dataset is 603,085, and for each POI, the basic information includes the name, type, location, and latitude and longitude.
11 Supplementary File D 12  Becker (2017)  discusses quantitative and qualitative measures of burden-sharing in detail, and the ability of disaggregated quantitative data to capture the critical components of qualitative data. (NATO 2018). " 13 This data is the point of reference for actual transatlantic burden-sharing discussions, the source for the most widely used defense spending data set (SIPRI 2018), and for the most cited defense economics research  (Dunne, Smith, and Willenbockel 2005). The European Defence Agency (2018) replicated NATO's data collection methodology and presentation from 2006 to 2017. Supplementary File E discusses robustness checks using different measures for variables, as well as varying samples to ensure the results are not artifacts of varying alliance commitments, organizational memberships, data particularities, or other factors specific to groups of countries.
I expect states experiencing increased unemployment to burden-shift by reducing overall defense spending, while shifting defense resources out of equipment modernization and into personnel. I consider the World Bank's World Database of Indicators (WDI) unemployment data to be the best available source, as it incorporates the International Labor Organization's ILOSTAT data, and is the most comprehensive available in terms of country and year coverage. The OECD gathers separate unemployment data for its members, covering fewer country years. The correlation coefficient for the 627 observations for which there is both OECD and WB/ILO data available is 0. 9937, leaving me very confident in the utility of the data in my analysis. GDP it-1 is country i's rank within the sample in GDP in year t-1.
I t-1 is the party of the government in power  (Scartascini, Cruz, and Keefer 2018). 18 Party ideology may affect how states respond to unemployment, as well as how states allocate resources and pursue foreign policy. V it-1 is the Database of Political Institutions' "checks" score for country i in year t-1  (Scartascini, Cruz, and Keefer 2018) , a comprehensive measure of all actors with the ability to thwart policy change including independent executives, multiple legislative bodies, and number of parties in the ruling coalition. This variable addresses the effect of domestic political institutions, and executive autonomy on the transmission of macroeconomic variables to budgeting. 17 Russian capability (measured by defense spending)* intent (coded as 1. 5 from  1953-1989, 1 from 1990-2008, 1.
This assumption is appropriate for unemployment, which is strongly predicted by previous levels, but is not static. Figure  6  visualizes this: both unemployment and personnel 20 Supplementary Table  E3  summarizes data availability by country-year, for each model. The fully specified models comprise 2004-2018 for 30 of the 35 states. 21 Supplementary File E contains these robustness checks, many of which address the time-series properties of the data. I use panel corrected standard errors to address heteroskedasticity and contemporaneous correlation in errors  (Beck and Katz 1995). I test for cointegration using the  Westerlund (2007)  test. The p-value for the test of panel cointregation is 0. 864, indicating the null cannot be rejected. I test for stationarity within the panels using STATA's xtunitroot procedure, calculating an inverse chi-squared score of 107.
F o r P e e r R e v i e w   E11 , below, replicates the main OLS analysis using Whitten and Williams's measures of government positions along separate welfare and international policy dimensions, including the interaction terms they propose. In order to maximize sample size, I use welfare generosity data from the Comparative Welfare Entitlements Dataset  (Scruggs, Jahn and Kuitto 2014)  to capture the welfare dimension, and executive nationalism data from the Database of Political Institutions  (Beck, et al. 2001)   to the measurement of the DV as the total number of military personnel. While states do not add to the number of personnel they hire, they do reduce the share of resources they allocate to equipment. This decline in personnel numbers is to be expected during a period of secular decline in the size of armed forces.
Acknowledgements The authors thank  Francis Dennig  and  Maddalena Ferranna  for helpful comments, suggestions and support with the  NICE  model. We also thank  Christian Flachsland ,  Max Franks  and  Linus Mattauch  for reviewing earlier versions of the paper and for valuable comments and suggestions. Lastly, we thank the participants of the conferences EAERE 2017 and LAGV 2018 as well as participants of seminars at the University of Princeton and  UC Berkeley  for valuable discussions. Ulrike Kornek received funding from the  Deutsche Forschungsgemeinschaft (DFG)  ( Geschäftszeichen KO 5900/1-1 ).