dataesr/scientific-paragraphs-categorization huggingface dataset:
ENTR = f (CULT, INST, GDPG, FINDEV, INTEG, INNOV, GDPC, UNEMP, HUMCAP) (18) We use unbalanced panel data by matching three different sources: the GEM, World Development Indicators (WDI), and the Heritage Foundation (HF). The sample covers 48 developing and developed countries 2 spanning a period ranging from 2000 to 2019, facilitating the capture of year-to-year, cross-country, and intra-country variations in variables. Table  2  presents the names, definitions, and sources of the variables and illustrates their main descriptive statistics. The entrepreneurial literature has reached a quasi-consensus -at least theoretically -on the meaningful impact of 2. Argentina, Australia, Belgium, Brazil, Canada, Chile, China, Colombia, Croatia, Denmark, Ecuador, Finland, France, Germany, Greece, Guatemala, Hungary, Iceland, India, Iran, Islamic Rep.
Next, we study the main IV techniques, i. e. , 2SLS, LIML, GMM, and SGMM, used to deal with endogeneity bias and explain how they function, the assumptions supporting their use, and the extent to which they are adapted to address this bias in the context of empirical research on the macro-level determinants of entrepreneurship. Thus, we provide a methodological procedure to detect and correct endogeneity with different IV techniques and then illustrate it with a panel data analysis of the macro-determinants of early-stage entrepreneurship in a sample of 48 countries between 2000 and 2019. In addition, we develop a 'toolkit' of generic STATA software commands specifying the tests, methods, and assumptions performed in this analysis. In doing so, we aimed to provide a comprehensive reference not only to assist empirical studies investigating the macro-level determinants of entrepreneurship to select the most suitable method to deal with the issue of endogeneity, but also to improve their quality by avoiding biased results and interpretations.
The organization of the paper is as follows: In Section 2, the local active COVID-19 data structures and its descriptive statistics are provided. Section 3 emphasizes on the INAR model construction with some novel innovations and ZI distributions. The inferential properties of the INAR process are also discussed. Section 4 focuses on the fitting of the various INAR models and providing the possible short term forecasts. This section also comprises of the discussions on the several significant factors. The concluding remarks and some limitations are provided in Section 5.
The SARS-CoV-2 series data for Mauritius The daily new COVID-19 infection and death series for Mauritius, covering the period from 18 March 2020 to 25 April 2021, summing to a total of 404 observations, were extracted from the official portal for European data (See https://data. europa. eu/data/datasets/covid-19-coronavirus-data?locale=en). The evolution of the COVID-19 new infection cases series and the death series are displayed below: From Fig  1 , it can be deduced that at the beginning of COVID-19 pandemic, the situation was worrisome with the climbing number of daily deaths cases(in red), associated with the increasing trend in the number of new daily COVID-19 infection cases (in blue). As from April 2020 till December 2020, the spread of the SARS-CoV-2 in the local community has plunged and a long sequence of zero daily locally acquired new COVID-19 cases and deaths cases were reported.
The relationship between the COVID-19 Stringency index in Mauritius (Refer to https:// ourworldindata. org/covid-stringency-index) and the number of daily new locally acquired COVID-19 active cases also plays a significant role, as proven in  [4]  and as shown in In Table  1  below, we provide the descriptive statistics and preliminary test results to confirm the nature of both data series along with their respective Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots to determine the orders: Based on the qcc. overdispersion. test in R statistical software via the 'qcc' package, it is confirmed that the data is over-dispersed and due to the excess zeros in the data, the Vuong test, refer to Table  5  in S1 Appendix, and Van den Broek tests were also significant, proving that the series is zero-inflated as well.
Data on vaccination was obtained from https://ourworldindata. org/covid-vaccinations  [26]  and from the official COVID-19 platform in Mauritius, falling under the aegis of the Ministry of Health and Wellness https://covid19. mu/. • The reproduction rate (ReR): This covariate refers to the degree of propagation of SARS--CoV-2 from one person to another. The data on ReR was obtained from https:// ourworldindata. org/covid-cases and the logarithm of the nominal value of ReR (log(ReR)) was considered. To note that this reproduction rate relates to the degree of transmissibility of the "original" SARS-CoV-2 and was used as a proxy to understand the severity of the coronavirus, considering that nowadays, constant mutation of the SARS-CoV-2 has been observed. In terms of proactive health related measures, this rate can be highly indicative.
The logarithm of the nominal value of ReR (log(ReR)) was considered. • The Relative COVID-19 Risk due to Weather and Air Pollution (CRW): This variable represents some environmental factors and explains their impact on COVID-19 transmission. Weather factors like average and diurnal temperature, ultraviolet (UV) index, humidity, pressure, precipitation and air pollutants (SO2 and Ozone) were considered while computing this index. In this paper, the CRW was categorised into 0 and 1 (binary) where 0 (ref) is when an index is below 1 referring to relatively lower impact of weather factors on spread of COVID-19 and 1 is when an index is above 1, indicating otherwise. Data has been extracted from https://projects. iq. harvard. edu/covid19 and imputation based on observations were done for missing values.
The Zero-inflated poisson mixture models The Zero Inflated (ZI) models, introduced by  [6] , are suitable for over-dispersed count data that exhibit excessive zeros. These data are commonly encountered in social sciences, likewise in the analysis of drug addicts  [27] , crimes  [28] , adolescents' drinking patterns  [29] , counselling session attendance  [30] , or in the financial sectors such as in the modelling of insurance claims  [31] , and in health studies such as in dental caries  [32] , in injection cessation in HIV patients  [33]  and among many other applications areas mentioned in  [7]. Basically the ZI models is a mixture of two distributions: Firstly, a probability distribution that degenerates at zero and on the second stage, mixed with a standard probability model such as the Poisson or Negative Binomial (NB) model.
The PGF of WCG is given by: G R t ðsÞ ¼ C l � t ;n 2 1 1 À l � t s þ 1 À l � t s cosð2nÞ 1 À 2l � t s cosð2nÞ þ ðl � t sÞ 2 " # ; s < À lnðl � t Þ; where l � t ¼ l t 1þl t and l � t 2 ð0; 1Þ and n 2 0; p 2 � �. The PGF of the ZI-WCG is p t þ ð1 À p t Þ � G R t ðsÞ. In the event we have some explanatory variables, given by the vector x t , which are known to influence the t th response variable y = y t , then x t = [x 1 , x 2 ,. , x p ], and l t ¼ expðx t T bÞ, for the t th term. In this context, p = 4, with x 1 = ReR,. , x 4 = CRW. For the zero inflated part, we assume the probability of zero is denoted by π t for y = y t , where p t ¼ expðx t T bÞ 1þexpðx t T bÞ where η = [η 1 , η 2 ,. , η p ] T. Overall, for the interested reader, the ZI data can easily be generated in R using ifelse(rbinom(n, size = 1, prob = π)>0, 0, rdis(n, λ � , ν)), where for the Poisson model, 'rdis' is rpois(n,λ = μ), for NB model, 'rdis' is rnbinom(n, size ¼ 1 n , mean=λ) and for COM-Poisson, 'rdis' is rcmp (n,λ,ν), (similar for the PT model, refer to the poistweedie package in R) or alternatively, the data can be obtained from ZIM  [42] , iZID  [43] , bZinb  [44]  and ZiC packages  [45].
below: The results in Tables  2  and 3  and Table  6  in S1 Appendix, were obtained assuming the training dataset from 18 March 2020 to 25 April 2021. It can be deduced that the Zero-Inflated Negative Binomial model (ZI-NB), given its lowest Akaike Information Criteria (AIC), outperformed the other competing ZI-PT, ZI-WCG and Poisson mixture models (See the results of Poisson mixture models in the S1 Appendix). Referring to results in Tables  2  and 3 , the variables 'ReR', 'SI', and 'Vaccine' were highly significant in reducing the number of infection in Mauritius, as compared to 'CRW'. The ReR is directly associated with the number of new active cases  [57]. This is because by observing the evolution of the series, it can be deduced that in October 2020 when there were an increase in international mobility  [19]  following opening of frontier and in March 2021 when the second wave has resurfaced, an exponential increase in the number of active COVID-19 cases was reported.
In fact, given the constant mutation of the SARS-CoV-2 in different regions and Mauritius having a comparatively restricted regional disparity, possibly a larger dataset on 'CRW' will allow better exploration of its association with the number of infection  [66, 67]. Table  3  confirms that the estimates of the over-dispersion parameters in the ZI models are significant. In addition, the death series has also been analysed using the ZI-NB model due to its lower AIC. Below, the results have been presented. From above Table  4 , using the death series from 18 March 2020 till 25 April 2021, it can be concluded that all covariates except 'CRW' is highly significant in reducing the number of deaths related to COVID-19. The most important point to note is that in line with the results in Table  2  and as discussed in  [58] , the event of vaccination and the COVID-19 Stringency index have a substantial impact on the mortality rate related to COVID-19.
Datasets used are from: 1) https://ourworldindata. org/covidstringency-index 2) https://data. europa. eu/data/ datasets/covid-19-coronavirus-data?locale=en 3) https://ourworldindata. org/covid-cases 4) https:// projects. iq. harvard. edu/covid19 5) https:// ourworldindata. org/covid-vaccinations 6) https:// covid19. mu/.
In our framework, the effect of frictions is ambiguous at the individual level but not at the aggregate level. See  Petropoulou (2011)  for a model where search frictions may have a non-monotonic impact on aggregate trade flows. country datasets. 8 In  Bernard et al. (2018b)  and  Carballo et al. (2018) , the heterogeneity is studied in monopolistic competition models with two-sided heterogeneity. A distinctive feature of our model in comparison with theirs is the distortive effect of frictions that we confirm prevails in the data. The distortion also characterizes the model in  Eaton et al. (2022) , in which the matching of exporters and importers is also governed by random search in a firm-tofirm framework. Eaton et al. (2022)  focus on how country-level frictions affect individual firms' decisions to oursource some productive tasks, with an endeffect on the labor market.
Data The empirical analysis is conducted using detailed data covering the universe of French exporting firms. The data are provided by the French Customs and are described in  Bergounhon et al. (2018). The dataset covers each single transaction that involves a French exporter and an importing firm located in the European Union, in 2007. Using another reference year, such as 1997 or 2016, does not alter the results. Firms with annual export sales in Europe below 150,000 euros are allowed to fill a simplified form that does not contain information on the product category. The restriction concerns 31% of firms who accumulate less than 1% of aggregate exports. 10 For each transaction, the dataset contains the identity of the exporting firm (its SIREN identifier), an identifier for the importer (an anonymized version of its VAT code), the date of the transaction (month and year), the product category (at the 8-digit level 10.
Core products are defined as those that represent at least 10% of the firm's export sales plus all products that constitute at least 10% of sales for at least one firm in the same 4-digit NAF sector. Information frictions are expected to be less of a problem for non-core products, that the firm sells occasionally. The restriction reduces the number of exporter × product pairs covered by almost 50% without having much of an impact on the aggregate value of exports (-8%), on the population of importers (-4%), and on the population of exporters (which is left unaffected). The restriction thus helps focus the analysis on exporters that actually compete for serving foreign markets with their core products. We have also reproduced the estimation of search frictions on the full dataset with most results being qualitatively unchanged.
The density of trade networks is low, on average. The firm-to-firm dataset is complemented with several product-level and aggregate variables used to run gravity regressions. Distance data are taken from CEPII  (Mayer and Zignago, 2011). We control for the market's overall demand using HS6-specific imports in the destination, less the demand for French products. Multilateral import data are from the CEPII-BACI database  (Gaulier and Zignago, 2010). Finally, we use three alternative proxies for information frictions at country-level: the stock of French migrants in each destination, taken from the UN database on Trends in International Migrant Stock, the probability that individuals in France and the destination speak the same language (constructed by  Melitz and Toubal (2014) ) and a measure of social connectedness between France and the destinations, computed by  Bailey et al.
12   We close this Section with an empirical analysis using the gravity framework to show how the buyer margin correlates with the geography of French exports. Table  1  summarizes the results. The gravity equation is run at the product level (columns (1)-(  4 )) and within a firm (columns (  5 )-(  7 )). Our dataset covers French exports to 26 EU countries. As a consequence, standard gravity variables such as distance, the border effect and the common language dummy end up strongly correlated. This explains that our specification has less control variables than a typical gravity regression based on multilateral data. Namely, bilateral trade is explained by distance to France, two proxies for market size, the country's (product-specific) import demand and GDP per capita, and a proxy for search and information frictions, namely the likelihood that citizens 12.
The only practical difficulty concerns the measurement of S k and B k i in the data. Indeed, the theoretical moment in (8) identifies λ k i conditional on B k i. Moreover, the total number S k of potential suppliers is needed to compute both the optimal weights entering the objective function and the asymptotic variance of the estimator. We recover measures of the population of buyers in each destination country and sector using predictions of the model regarding trade shares. Under the assumptions of the model, π k i is both the share of French products in country 22. Ω k i = g(λ k i )Σ k i g(λ ij ), where g is the variance function and Σ k i is the variancecovariance matrix of the random variables 1{B k i (z) = M } for M = m 1 , m 2 , m 3. i's absorption of product k and the ratio of the number of buyers from i buying their consumption from a French producer divided by the total number of buyers in i (π Information on the number of potential suppliers by hs6 product is not available in any administrative dataset.
We measure S k based on information on the number of firms in each sector available from the INSEE-Repertoire Siren database. All firms belonging to a sector in which at least one firm makes 10% of its exports in a product are considered potential suppliers of the product. k i = B k iF /B k i ). π k i can Atalay et al. (  2014 ) use a comparable strategy to proxy for the number of firms susceptible to purchasing a firm's output. 23. We use bilateral trade flows from the CEPII-BACI database  (Gaulier and Zignago, 2010)  and production data from the World Input-Output Database. π k i is defined as the ratio of bilateral trade from France to country i over absorption in country i. 24. In sectors and countries in which the market share of French firms is very low, our empirical strategy implies very high values for B k i , above a million firms.
π k ij thus measures the share of producers from country j in country i's consumption of product k. In this equation, the exporter-product fixed effect F E k j absorbs the impact of Ricardian technological advantages that affect a country's sales in all export destinations. A positive correlation between this term and estimated search frictions is thus indicative of magnified distortions, i. e. search frictions that are particularly high in those product markets in which French exporters have a Ricardian comparative advantage. To test whether it is the case in the data, we first estimate equation (  11 ) using the CEPII-BACI multilateral trade database available at the product level for 2007. Estimated revealed comparative advantages for France are then correlated with the product-specific average of estimated search frictions.
"). To test this prediction of the model, we leverage upon external balance-sheet data on French firms to measure the relative productivity of exporters. We use two measures of productivity, the size of domestic sales and the firm's apparent labor productivity. We then estimate heterogeneity in export performances across firms within a product and destination and how it varies depending on the strength of estimated search frictions. According to our model, we shall see the relative export performance of high-productivity firms being dampened in more frictional markets. Results are summarized in Table  5. Columns (1)-(  3 ) use domestic sales to characterize firms' heterogeneity while columns (  4 )-(  6 ) use the firm's apparent labor productivity. Our measure of a firm's export performance is based on the value of annual exports but we have reproduced the same analysis using the number of buyers instead and results are qualitatively unchanged.
This heterogeneous impact of frictions along the productivity distribution is a distinctive feature of our model. In highly frictional markets, the export premium of high-productivity firms is lowered and the export probability of small and medium firms increased. We provide direct evidence of such distortion in our data. Bilateral search frictions are first estimated structurally using firm-to-firm trade data at the product and destination level. For each French firm and each product it sells, we can measure the size of its customer base in a particular destination. In the model, heterogeneity across firms in this number is explained by firms' heterogeneous productivity and the magnitude of search frictions in the destination. Intuitively, more frictional markets induce more distorsions, which reduces the export premium of high-productivity firms.
Acknowledgements : We thank  A. Cheskidov ,  C. Doering ,  R. Shvydkoy ,  J. T. Stuart  and  D. Vincenzi  for useful comments. B. D. acknowledges support from the  ANR , project  EXPLOIT , grant agreement no. ANR-16-CE06-0006-01. This research was supported in part by the  International Centre for Theoretical Sciences (ICTS) ,  Bengaluru , for the online program Turbulence : Problems at the  Interface of Mathematics and Physics , (code:  ICTS/TPIMP2020/12 ).
Case Study China and India with the help of eviews software as an analytical tool that is able to see the relationship between independent variables on the dependent variable both in the short and long term. (Luzzati, 2015)  explain that EKC theory is a theory that explains that the level of environmental damage will increase along with economic development and at a certain point (turning point) in achieving economic growth, then the level of environmental damage will decrease with increasing economic development marked by U-reverse curve (U-inverted curve). (Shahbaz & Lean, 2011)  state that sustainable economic development will be achieved along with the preservation of a sustainable environment.
ACKNOWLEDGMENTS This research work fully supported by  Department of Computer System, (STEKOM) Semarang, FEB Universitas Diponegoro , and under supervision of  Université Paris 13 Nord | Paris 13 Nord CEPN Centre d'Economie de l'Université Paris-Nord -UMR 7234 CNRS-UP13-USPC -Université Sorbonne Paris Cité, France.
| DATA AND METHODS The main objective of the paper is to investigate the influence of immobility and mobility activities on the spread over time of the COVID-19 pandemic in 29 European countries. 1 The study used daily data for the time span from 12 March 2020 to 31 August 2021. Co-integration tests and pooled mean group-autoregressive distributed lag (PMG-ARDL) models were applied to investigate the impact of immobility and daily mobility activities on the spread of the COVID-19 pandemic. As the relationship between the two variables is complex, a Granger causality test was applied to identify the existence and direction of this causality.
| Data sources Four sources of data were used in this study of the 29 countries. The first is the number of new confirmed COVID-19 cases per million per day. The up-to-date OxCGRT data and documentation are available via the project GitHub repository at https://github. com/OxCGRT/covid-policy-tracker (Ritchie et al. , 2020). The second source is the daily time series indicators on Google mobility. Six indicators are obtained using aggregated, anonymized POIs data from GPS tracking of mobile devices, for users who opted in to 'Google Location History'. These data measure the change in the number of visits for four place categories (grocery and pharmacies, parks and beaches, transit stations, retail and recreation) and length of stay at home (residential) compared with a baseline (the median value calculated during the 5-week period between 3 January and 6 February 2020).
In our article, the first four indicators represent mobility activities, while the fifth (residential) represents immobility. The third source on policy measures also comes from the OxCGRT data. The stringency index is a composite measure of nine of the response metrics, each taking a value between 0 and 100: school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls  (Hale et al. , 2021). These data from the three sources mentioned can be viewed for each country over the entire analysis period via the associated dashboards  (Ritchie et al. , 2020). The last data source for temperature is from the daily dataset of the twentieth-century surface air temperature and precipitation series for the European Climate Assessment  (Klein Tank et al.
As we will see in the next section, the mobility variables are highly correlated with each other. Different models will therefore be estimated, each time introducing a mobility variable (MOB) i = 1, 2 …. , N = to the basic model composed of the stringency index (STR) and the mean temperature (TEM) variables. T A B L E 1 Description of the data series used  Constrained by these measures or by fear of contamination, mobility has been strongly impacted. The time spent at home increased by 7% on average, and by more than 30% in Luxembourg, Italy, France, Portugal and Spain, compared with the baseline period. Considering that the baseline is in winter, when the time spent at home is already higher owing to shorter and colder days, an increase of 30% can represent several hours. The comparison between France and the Netherlands, in the following example, shows the difference in the variation of the immobility level (Figure  1 ).