Dimensions and items obtained from external sources for the construction of a Country similarity index related to mortality in life insurance.

The database contains index measures of linguistic similarity both domestically and internationally. The domestic measures capture linguistic similarities present among populations within a single country while the international indexes capture language similarities between two different countries. The 8 indices reflect three different aspects of language: common official languages, common native and acquired spoken languages, and linguistic proximity across different languages. This database has many uses, such as in models of bilateral flows—including FDI, migration, and international trade—as well as in regional or country level analyses. Extensive and detailed coverage: Bilateral indexes for 242 countries Based on 6,674 individual languages

Measurement invariance indices from multi-group confirmatory factor analysis for disclosure, similarity, and intimacy scales, analyzed by target, in Study 2.

European Macaroni, Noodles, Couscous and Similar Farinaceous Products Production Index by Country, 2022 Discover more data with ReportLinker!

Fish stocks have declined rapidly over the past half-century due to the increased demand for seafood and unsustainable fishing practices. The incidental capture of non-target species (bycatch) is a pervasive issue in fisheries management and has led to population declines in non-target species worldwide. The fisheries sector in Guyana currently supports the livelihoods of over 10,000 Guyanese and contributes approximately 2% to the country’s GDP. Bycatch is believed to be a major threat to Guyana’s marine fisheries, especially the small-scale sector, due to a lack of management infrastructure and limited data and monitoring. Here, we assessed bycatch in Guyana’s artisanal gillnet and Chinese seine fisheries through vessel observations and semi-structured interviews with local fishers. Most of the discarded species documented had no commercial importance to the fisheries in Guyana. Although no statistical difference was observed among the bycatch rates in the gillnet and Chinese seine fisheries, the latter generally had more discarded individuals, most of which were juveniles. The Shannon-Weiner diversity index showed a greater diversity of bycatch species in the gillnet fisheries compared to the Chinese seine. Jaccard’s similarity index indicated a low similarity among the gear types. Even though most fishers were aware of bycatch, they did not view it as a major issue and were not interested in reducing their discards. We recommend a collaborative approach in exploring solutions to ensure the ecological and socioeconomic sustainability of the fisheries sector.

In 2024, Russia had the largest population among European countries at ***** million people. The next largest countries in terms of their population size were Turkey at **** million, Germany at **** million, the United Kingdom at **** million, and France at **** million. Europe is also home to some of the world’s smallest countries, such as the microstates of Liechtenstein and San Marino, with populations of ****** and ****** respectively. Europe’s largest economies Germany was Europe’s largest economy in 2023, with a Gross Domestic Product of around *** trillion Euros, while the UK and France are the second and third largest economies, at *** trillion and *** trillion euros respectively. Prior to the mid-2000s, Europe’s fourth-largest economy, Italy, had an economy that was of a similar sized to France and the UK, before diverging growth patterns saw the UK and France become far larger economies than Italy. Moscow and Istanbul the megacities of Europe Two cities on the eastern borders of Europe were Europe’s largest in 2023. The Turkish city of Istanbul, with a population of 15.8 million, and the Russian capital, Moscow, with a population of 12.7 million. Istanbul is arguably the world’s most famous transcontinental city with territory in both Europe and Asia and has been an important center for commerce and culture for over 2,000 years. Paris was the third largest European city with a population of ** million, with London being the fourth largest at *** million.

This dataset was used in the publication of "All Roads Lead to Paris: The Eight Pathways to Renewable Energy Target Adoption" in the journal of Energy Research & Social Science. The objective was to compile data on the first national adoption of a renewable energy target in each country to analyze its mechanisms of diffusion (learning, economic competition, emulation, and coercion). The data were compiled for 187 countries for the period ranging from 1975 to 2017. The list of countries was gathered from the Annex I of IRENA's "Renewable Energy Target Setting" report. We used primarily the IEA policies database (https://www.iea.org/policies) to identify the first adoption of a renewable energy target in each country. Other sources were used when data was unavailable in such repository for specific countries. Additionally, we include the data gathered from various sources, as they were used in our paper for measuring variables. The variables in this dataset include: target adoption (or “Target”, from various sources listed in the dataset); year of adoption (or “Year”, from various sources listed in the dataset); cumulative membership to energy-related international environmental agreements (or “IEA”, with data from Mitchell’s International Environmental Agreements Database Project); net energy imports as a percentage of energy use (or “Energy”, with data from the World Bank); a similarity index (or “Similarity”, created with data from the Polity Index, population and GDP per capita from the World Bank, and revenue from the World Bank); official development assistance as a percentage of gross national income (or “ODAGNI”, with data from the World Bank and OECD); income level (“Income”, with data from the World bank); and the international price for oil (“Oil”, with data from the Federal Reserve Bank of St. Louis). For more details, refer to the manuscript. Note that in 2018 the “IEA’s policy database” was actually the “IEA/IRENA RE Policies and Measures database”. The links for the sources for renewable energy target adoption for Norway and Albania were lost in the transition from one to the other; all other sources could be retrieved by the authors.

This is a replication data for my paper under blind review. Abstract This paper develops a new prediction model for media content presence on a website. It analyses a new corpus of one million articles from five countries: Poland, Russia, Belarus, Kazakhstan and Ukraine, in two languages, Polish and Russian. These articles were scraped daily from seventeen websites in 2017-2020 period. The research applies a wide range of natural language processing methods to automatically derive several properties of each article: its topic, sentiment, basic emotions, mentions of influential domestic politicians. The articles’ embeddings and their cosine similarity are used to calculate the news context, such as how an article differs from the daily issue main themes. These features are used to estimate a logistic regression assessing the likelihood that the same or slightly modified, as measured by cosine similarity, article will remain on the main web page the next day. The key, and somewhat unexpected result is that articles with negative sentiment polarity are less likely to be published for more than one day. This result holds for all countries analyzed. It means that the negative news bias documented in the literature is partly offset by their shorter life cycle. Data is in the Python pickle format. Should be read into Python using the pickle.load() function. Each element (row) is the data frames or list represents one news article. Each file has the same format. Loading a pickle file returns a list of four elements: 1. A dummy variable equal to 1 when the article was published the next day, with the text being identical 2. A dummy variable equal to 1 when the article was published the next day, but we allow for small text modifications (cosine similarity > 0.99) 3. Dataframe with extracted features, described below. 4. List with texts of articles in Polish or Russian Ad 3. The columns of the dataframe are as follows (we refer to row number i in description): - pandas index (may appear once or twice in the datafame) - maxcosine: maximum cosine similarity between art i and all articles published next day - cosine_diff: cosine similarity between article i and the elementwise average of embeddings of all articles in the current issue. Measure how similar is the article i to the core narrative of the current issue - cosine_std: std. dev. of cosine similarity measures between all pairs of articles in the current issue. Measures how focused or dispersed is the current issue news coverage - thirteen LDA topic groups: politics, legislation and legal affairs (POL); economy, finance, various sectors of the economy (ECO); military, war, protests, crime, security threats (MIL); international affairs, specific issues concerning foreign countries (INT); technology (TECH); family issues, culture, sport, education (FAM); regional issues and housing (REG); health issues and the Covid-19 pandemic (HEA); media (MED); accidents (ACC); religion (REL); the Soviet Union (USSR); and articles for which no topic could be determined (MISC). - rsent.c: relative sentiment that is dictionary based sentiment of articles i minus the average sentiment of the newspaper. This approach eliminates newspaper or country idiosyncratic sentiment factors. c stands for Covid, the sentiment lexicon was augmented with Covid related terms - dip_*: Variable measuring if influential domestic politicians are mentioned in article i, * represent a country acronym. If N is equal to the number of occurrences of the names of influential domestic politicians in the article i, dip_* = 0 if N=0, dip_* = 1+ log(N) if N>0. - three or four names of news portals from which the data was scraped. - names of six basic emotions and the article i emotion scores calculated using zero-shot learning and the large version of the XLM (Conneau et al., 2019) model from the huggingface transformers library available at https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli Names of the politicians used to calculate dip variables Russia "putin" "medvedev" "vaino" "shoigu" "bortnikov" "lavrov" "mishustin" "kirienko" "sechin" Ukraine "zelensky" "shmygal" "akhmetov" "avakov" "ermak" "poroshenko" "medvedchuk" "groisman" Kazakhstan "sagyntaev" "mamin" "tokayev" "nnazarbayev" "dnazarbayeva" "kulibayev" "masimov" Belarus "alukashenko" "vakulchik" "vlukashenko" "kobyakov" "makei" "myasnikovich" [37] "rumas" "golovchenko" Poland "kaczynski" "duda" "morawiecki" "ziobro" Data coverage Country, news portal, numbr of articles Russia iz.ru 43,782 Russia kommersant.ru 46,070 Russia novayagazeta.ru 29,357 Russia vedomosti.ru 27,797 Kazakhstan informburo.kz 29,375 Kazakhstan nur.kz 67,350 Kazakhstan tengrinews.kz 44,285 Kazakhstan zakon.kz 109,442 Belarus bdg.by 33,447 Belarus be... Visit https://dataone.org/datasets/sha256%3A5391ea6800f30b4dd138164c36af415099e84010c4a8d536d81edecab0f25d4c for complete metadata about this dataset.

European Sold Production of Filing Cabinets, Card-Index Cabinets, Paper Trays, Paper Rests, Pen Trays, Office-Stamp Stands and Similar Office or Desk Equipment by Country, 2023 Discover more data with ReportLinker!

Similarity indices between relative web search popularity and Covid-19 time-series.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Despite the significance of economic freedom in tourism dynamics, especially from a spatial standpoint, its nuanced influence remains unexplored mainly in current research. To fill this gap, our study introduces a novel spatial panel data analysis to investigate how various components of the economic freedom index affect tourist arrivals in 41 European countries from 2005 to 2018. By employing this innovative approach, we uncover the complex interdependencies between economic freedom and tourism and highlight the significance of regional economic characteristics on the tourism sector’s health. Our findings reveal that a one percent increase in GDP per capita of neighboring nations corresponds to a 0.4 percent increase in tourist arrivals to the home country. In comparison, a similar rise in neighboring countries’ prices leads to a 0.4 percent decrease in inbound tourists. Most economic freedom variables, including the Business Freedom Index, Investment Freedom Index, Labor Freedom Index, Trade Freedom Index, and Government Integrity Index, demonstrate statistically significant positive effects. However, a one percent increase in the Monetary Freedom Index of neighboring countries results in a 0.747 percent reduction in homebound tourists. Notably, enhancements in the country’s and neighboring countries’ Investment Freedom Index and Government Integrity Index contribute to increased arrivals. This research contributes to the broader understanding of economic policies’ impact on tourism, offering valuable insights for policymakers aiming to leverage economic freedom for tourism development. The application of a spatial panel data approach marks a significant methodological advancement in tourism studies, opening new avenues for analyzing economic influences on tourism at a regional level.

Despite the significance of economic freedom in tourism dynamics, especially from a spatial standpoint, its nuanced influence remains unexplored mainly in current research. To fill this gap, our study introduces a novel spatial panel data analysis to investigate how various components of the economic freedom index affect tourist arrivals in 41 European countries from 2005 to 2018. By employing this innovative approach, we uncover the complex interdependencies between economic freedom and tourism and highlight the significance of regional economic characteristics on the tourism sector’s health. Our findings reveal that a one percent increase in GDP per capita of neighboring nations corresponds to a 0.4 percent increase in tourist arrivals to the home country. In comparison, a similar rise in neighboring countries’ prices leads to a 0.4 percent decrease in inbound tourists. Most economic freedom variables, including the Business Freedom Index, Investment Freedom Index, Labor Freedom Index, Trade Freedom Index, and Government Integrity Index, demonstrate statistically significant positive effects. However, a one percent increase in the Monetary Freedom Index of neighboring countries results in a 0.747 percent reduction in homebound tourists. Notably, enhancements in the country’s and neighboring countries’ Investment Freedom Index and Government Integrity Index contribute to increased arrivals. This research contributes to the broader understanding of economic policies’ impact on tourism, offering valuable insights for policymakers aiming to leverage economic freedom for tourism development. The application of a spatial panel data approach marks a significant methodological advancement in tourism studies, opening new avenues for analyzing economic influences on tourism at a regional level.

Despite the significance of economic freedom in tourism dynamics, especially from a spatial standpoint, its nuanced influence remains unexplored mainly in current research. To fill this gap, our study introduces a novel spatial panel data analysis to investigate how various components of the economic freedom index affect tourist arrivals in 41 European countries from 2005 to 2018. By employing this innovative approach, we uncover the complex interdependencies between economic freedom and tourism and highlight the significance of regional economic characteristics on the tourism sector’s health. Our findings reveal that a one percent increase in GDP per capita of neighboring nations corresponds to a 0.4 percent increase in tourist arrivals to the home country. In comparison, a similar rise in neighboring countries’ prices leads to a 0.4 percent decrease in inbound tourists. Most economic freedom variables, including the Business Freedom Index, Investment Freedom Index, Labor Freedom Index, Trade Freedom Index, and Government Integrity Index, demonstrate statistically significant positive effects. However, a one percent increase in the Monetary Freedom Index of neighboring countries results in a 0.747 percent reduction in homebound tourists. Notably, enhancements in the country’s and neighboring countries’ Investment Freedom Index and Government Integrity Index contribute to increased arrivals. This research contributes to the broader understanding of economic policies’ impact on tourism, offering valuable insights for policymakers aiming to leverage economic freedom for tourism development. The application of a spatial panel data approach marks a significant methodological advancement in tourism studies, opening new avenues for analyzing economic influences on tourism at a regional level.

With a Gross Domestic Product of over 4.18 trillion Euros, the German economy was by far the largest in Europe in 2023. The similarly sized economies of the United Kingdom and France were the second and third largest economies in Europe during this year, followed by Italy and Spain. The smallest economy in this statistic is that of the small Balkan nation of Montenegro, which had a GDP of 5.7 billion Euros. In this year, the combined GDP of the 27 member states that compose the European Union amounted to approximately 17.1 trillion Euros. The big five Germany’s economy has consistently had the largest economy in Europe since 1980, even before the reunification of West and East Germany. The United Kingdom, by contrast, has had mixed fortunes during the same period and had a smaller economy than Italy in the late 1980s. The UK also suffered more than the other major economies during the recession of the late 2000s, meaning the French economy was the second largest on the continent for some time afterward. The Spanish economy was continually the fifth-largest in Europe in this 38-year period, and from 2004 onwards, has been worth more than one trillion Euros. The smallest GDP, the highest economic growth in Europe Despite having the smallerst GDP of Europe, Montenegro emerged as the fastest growing economy in the continent, achieving an impressive annual growth rate of 4.5 percent, surpassing Turkey's growth rate of 4 percent. Overall,this Balkan nation has shown a remarkable economic recovery since the 2010 financial crisis, with its GDP projected to grow by 28.71 percent between 2024 and 2029. Contributing to this positive trend are successful tourism seasons in recent years, along with increased private consumption and rising imports. Europe's economic stagnation Malta, Albania, Iceland, and Croatia were among the countries reporting some of the highest growth rates this year. However, Europe's overall performance reflected a general slowdown in growth compared to the trend seen in 2021, during the post-pandemic recovery. Estonia experienced the sharpest negative growth in 2023, with its economy shrinking by 2.3% compared to 2022, primarily due to the negative impact of sanctions placed on its large neighbor, Russia. Other nations, including Sweden, Germany, and Finland, also recorded slight negative growth.

The split of the susceptibility results by country is given in Table A2 in S1 Appendix. The definition of an “antibiotic” was linked to the data given so is at different levels (e.g., separate aminoglycosides were included as well as an antibiotic category of “aminoglycoside”). Fluroquinolones resistance was labelled the same across species though there were species-specific definitions. Age given in years. MRSA covers oxacillin and cefotoxin. 3G = third-generation. Pip-taz = piperacillin-tazobactam. SD = standard deviation.

Based on the Temporal Exponential Random Graph Models (TERGM), this paper applies global intellectual property trade data between countries to investigate the impact and mechanism of institutional distance on the intellectual property trade network. The study finds that the smaller the institutional distance between countries, the more conducive it is to build an intellectual property trade network. This conclusion remains valid after controlling for geographical adjacency, use of a common language, existence of colonial relationships, and characteristics of the intellectual property trade network. Moreover, through regression by year, it is found that this impact increases year by year. Further, after regressing on sub-indicators of institutional distance, it is found that the smaller the distance in political stability, government efficiency, and regulatory quality, the greater the probability of generating an intellectual property trade relationship. Mechanism analysis reveals that economies with smaller institutional distances are more likely to sign trade agreements, thereby generating trade relationships and promoting the establishment of intellectual property trade networks. In order to deeply participate in the intellectual property trade network, countries should actively align with international institutional norms and sign bilateral or multilateral trade agreements with countries with similar institutional levels to enhance the production level and export of intellectual property.

Mission sub-sample to assemble non-government mission-driven innovation semantic network.

The division of the EU countries into similar groups based on the examined indicators for small enterprises together with activation values (distance from the center of the cluster).