The Economic Indictor Service (EIS) aims to deliver professional economic content to financial institutions on both the buy and sell side service providers. This service covers 136 countries and 43,000 recurring indicators, which are updated on a real-time basis.

We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. In addition, it provided data of over 1,700 non-recurring indicators in 2020.

The EIS service includes historic data on recurring economic indicators. Recurring events include GDP data, unemployment releases, PMI numbers etc. Information on economic indicators, includes details of issuing agency and historical data series is made available depending on its availability.

The two components available for the Economic Calendar are the following:

1. Live Calendar - updated 24/5 immediately after the data is released and with limited history for recurring indicators.

2. Historical Database - Database of all recurring indicators (with complete history) and non-recurring indicators

Live Calendar can be embedded on client's website using iFrame or API. Historical Database can be made available via API or FTP.

Additional Features of the Economic Indicator Service - Delivery of unique newsfeed by using algorithms and analysts - Feed to client’s website with customized branding - Automatic feed to social media accounts, such as: Twitter and Facebook - Desktop ticker updates - Mobile App integration - Bespoke dashboards for macro-economic & industry reports And most importantly, clients can customize filters to get the specific economic indicators (e.g. for specific countries) they need.

A good retail broker can gain advantage by minimizing the time lag in real time information flow to retail investors vis-à-vis institutional investors. One way to achieve this is by providing access to clients with timely and accurate access to all major economic and other market moving announcements / data. - In order to minimize this disadvantage, many broker dealers provide economic calendar and news flows on their trading platforms. - We have developed two distinct products – Economic Calendar and Economic News to meet this requirement.

Contact Ilze Gouws, i.gouws@africadata.com for more information.

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

Regional accounts give a description of the volume of the economic process in the various regions of a country consistent with national accounts. Elements in the economic process distinguished in national accounts are production, distribution of income, spending and financing. Regional accounts focus on the description of the production processes in the various regions.

Data available from: 1995

Status of the figures: The figures of the years 1995 to 2020 are final. Data of the year 2021 are also final, but the figures of the variables Full-time equivalent (fte), Employed persons and Hours worked are an exception, due to the late availability of annual data on self-employed persons. These final figures are published a year after. The figures of the year 2022 are provisional. Since this table has been discontinued, data of 2022 will not become final.

Changes as of December 9th 2024: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. For further information see section 3.

When will new figures be published? Not applicable anymore.

This table contains key figures from the environmental accounts and the National Accounts. It shows contributions to various environmental issues such as global warming, acidification, environmental costs and environmental taxes by industries. In addition, some economic characteristics of the National accounts are included for comparison, e.g. gross value added and the number of employee jobs converted to full-time equivalents. The environmental accounts are consistent with the concepts and definitions of the National Accounts. This implies that, for physical material flows, the direct relationship with the Dutch economy is the focal point. Material flows are attributed to the economic activities where the actions actually take place, they are registered according to the residence principle. This means that all air pollution caused by Dutch transport companies is taken into account for the Netherlands, but that air pollution caused by transport companies from abroad within the Dutch territory is not. The environmental accounts are based on figures from the environmental statistics. These data are based on the territory principle, however, everything that happens within the Dutch territory. Because of the consistency between the environmental accounts and the National Accounts, Dutch environmental indicators can be compared directly to the main economic indicators. Due to the difference in approach between environmental accounts and environmental statistics, results may vary somewhat.

Data available from: 2001-2013

Status of the figures: his table contains figures from various sources. For figures related to the National Accounts: most recent reference period has status Provisional, whereas the reference period prior to that has the status Revised Provisional. After two years the data become Definite. Data for 2001-2010 are still regarded as Provisional, as the Dutch National Accounts are currently being revised to comply with the European System of National and Regional Accounts 2010 (ESR 2010). Data based on the environmental accounts will be revised over a longer period of time, because of adjustments in the data sources used. This to maintain the closest relation possible to the environmental statistics.

Changes as of 8 June 2016: This table is discontinued.

When will new figures be published? Not applicable. This table is discontinued.

Context Diabetes is one of the most prevalent chronic diseases in the United States, affecting millions of Americans each year and placing a substantial financial burden on the economy. It is a serious chronic condition in which the body loses the ability to effectively regulate blood glucose levels, leading to a reduced quality of life and decreased life expectancy. During digestion, food is broken down into sugars, which enter the bloodstream. This triggers the pancreas to release insulin, a hormone that helps cells in the body use these sugars for energy. Diabetes is typically characterized by either insufficient insulin production or the body's inability to use insulin effectively.

Chronic high blood sugar levels in individuals with diabetes can lead to severe complications, including heart disease, vision loss, kidney disease, and lower-limb amputation. Although there is no cure for diabetes, strategies such as maintaining a healthy weight, eating a balanced diet, staying physically active, and receiving medical treatments can help mitigate its effects. Early diagnosis is crucial, as it allows for lifestyle modifications and more effective treatment, making predictive models for assessing diabetes risk valuable tools for public health officials.

The scale of the diabetes epidemic is significant. According to the Centers for Disease Control and Prevention (CDC), as of 2018, approximately 34.2 million Americans have diabetes, while 88 million have prediabetes. Alarmingly, the CDC estimates that 1 in 5 individuals with diabetes and about 8 in 10 individuals with prediabetes are unaware of their condition. Type II diabetes is the most common form, and its prevalence varies based on factors such as age, education, income, geographic location, race, and other social determinants of health. The burden of diabetes disproportionately affects those with lower socioeconomic status. The economic impact is also substantial, with the cost of diagnosed diabetes reaching approximately $327 billion annually, and total costs, including undiagnosed diabetes and prediabetes, nearing $400 billion each year.

Content The Behavioral Risk Factor Surveillance System (BRFSS) is a health-related telephone survey that is collected annually by the CDC. Each year, the survey collects responses from over 400,000 Americans on health-related risk behaviors, chronic health conditions, and the use of preventative services. It has been conducted every year since 1984. For this project, a XPT of the dataset available on CDC website for the year 2023 was used. This original dataset contains responses from 433,323 individuals and has 345 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

I have selected 20 features from this dataset that are suitable for working on the topic of diabetes, and I have saved them in a CSV file without making any changes to the data. The goal of this is to make it easier to work with the data. For more information or to access updated data, you can refer to the CDC website. I initially examined the original dataset from the CDC and found no duplicate entries. That dataset contains 330 columns and features. Therefore, the duplicate cases in this dataset are not due to errors but rather represent individuals with similar conditions. In my opinion, removing these entries would both introduce errors and reduce accuracy.

Explore some of the following research questions: - Can survey questions from the BRFSS provide accurate predictions of whether an individual has diabetes? - What risk factors are most predictive of diabetes risk? - Can we use a subset of the risk factors to accurately predict whether an individual has diabetes? - Can we create a short form of questions from the BRFSS using feature selection to accurately predict if someone might have diabetes or is at high risk of diabetes?

Acknowledgements It is important to reiterate that I did not create this dataset, it is simply a summarized and reformatted dataset derived from the BRFSS 2023 dataset available on the CDC website. It is also worth noting that none of the data in this dataset discloses individuals' identities.

Inspiration Zidian Xie et al for Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques using the 2014 BRFSS, and Alex Teboul for building Diabetes Health Indicators dataset based on BRFSS 2015 were the inspiration for creating this dataset and exploring the BRFSS in general.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Landline and cellular telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

This dataset includes interviews with educational personnel and policy-makers in the adult education sector in Hungary working with vulnerable young adults. The interviews discuss contents of study programmes provided by the organisation, pedagogical approaches, gender equality, life management, social problems, socioeconomic status, and active participatory citizenship. The dataset is a ten-interview sample of the interviews collected in Hungary for the Europe-wide EduMAP project (Adult Education as a Means to Active Participatory Citizenship). FSD's holdings also include data collected in the nine other EduMAP countries in their respective languages (the United Kingdom, Turkey, Spain, Romania, Latvia, Greece, Germany, Finland and Estonia). The EduMAP project was funded by the European Commission's "Horizon 2020" research and innovation programme (ID: 693388). Different interview schemes were used for interviews with educational personnel and educational authorities. In the interviews with educational personnel, the first questions concerned the interviewee's job responsibilities, practices in the organisation, and the types of vulnerable learners with whom the interviewee worked. Teaching practices were discussed as well as special education available at the organisation, the students' economic situation, and the concept of active participatory citizenship. Regarding the organisation's practices, the interviewees were asked about, for instance, the work processes for designing new educational programmes, pedagogical approaches, and learning/teaching methods that they had deemed particularly appropriate for vulnerable learners. Many facets of the organisation's practices were discussed in terms of gender, i.e. how possible gender differences were taken into consideration in teaching. Pedagogical details of the programmes were discussed in more detail with regard to, for instance, learning and teaching methods, contents of lessons, teaching processes, and the students' possibility of giving and receiving feedback. The interviewees were also asked what types of competences educational professionals working with vulnerable learners possess or should possess. Finally, the interviews examined the impact of the programmes from socioeconomic and sociocultural perspectives as well as the perspective of social activity and legal-political awareness. Attendance and graduation rates were also charted as well as suggestions for the improvement of the programmes. The final questions concerned collaboration and networking with other institutions. The interviews with educational authorities and policy-makers focused more on the national influence in educational policy, i.e. developing and implementing policies and legislation regarding vulnerable groups. In addition, these interviews discussed e.g. consulting representatives of vulnerable groups in developing policies, achievements and shortcomings of current educational policies, and the most important issues faced by vulnerable groups that should be taken into consideration in developing policies. Background information includes interview id, employment field and job description. The dataset is only available in Hungarian. Questionnaires and participant information sheets are only available in English. The data were organised into an HTML index at FSD.

With the deepening of population aging, the expenditure of basic endowment insurance in China is increasing. The urban employees’ basic endowment insurance(UEBEI) system for is an important part of China’s basic social endowment insurance system, which is the most important institutional guarantee for the basic needs of employees after retirement. It not only relates to the living standards of retired employees but also relates to the stability of the whole society. Especially considering the acceleration of urbanization process, the financial sustainability of the basic endowment insurance for employees is of great significance for safeguarding the pension rights of retired employees and realizing the normal operation of the whole system, and the operation efficiency of urban employees’ basic endowment insurance(UEBEI) fund inevitably becomes the focus of increasing attention. Based on the panel data of 31 provinces in China from 2016 to 2020, this paper established a three-stage DEA-SFA model, and compared the differences of comprehensive technical efficiency, pure technical efficiency and scale efficiency with radar chart, aiming to explore the operating efficiency of the UEBEI in China and how environmental factors affect it. The empirical results show that at present, the overall level of the expenditure efficiency of the UEBEI fund for urban workers is not high, and all provinces have not reached the efficiency frontier level, and there is still a certain space for efficiency improvement. Fiscal autonomy and elderly dependency ratio are negatively correlated with fund expenditure efficiency, while urbanization level and marketization level are positively correlated with fund expenditure efficiency. The regional difference of fund operation efficiency is significant, from high to low, it is East China, Central China and West China. Reasonable control of environmental variables and narrowing of regional economic development and fund expenditure efficiency differences can provide some enlightenment for better realization of common prosperity.

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The tourism sector GDP share in Tunisia was forecast to continuously increase between 2023 and 2028 by in total 4.8 percentage points. The share is estimated to amount to 16.8 percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the tourism sector GDP share in countries like Morocco and the Sudan.

Chad TD: Logistics Performance Index: 1=Low To 5=High: Efficiency of Customs Clearance Process data was reported at 2.150 NA in 2018. This records an increase from the previous number of 2.077 NA for 2016. Chad TD: Logistics Performance Index: 1=Low To 5=High: Efficiency of Customs Clearance Process data is updated yearly, averaging 2.114 NA from Dec 2007 (Median) to 2018, with 6 observations. The data reached an all-time high of 2.463 NA in 2014 and a record low of 1.860 NA in 2012. Chad TD: Logistics Performance Index: 1=Low To 5=High: Efficiency of Customs Clearance Process data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Transportation. Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.;Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.;Unweighted average;

Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data was reported at 113,074.640 BRL mn in Jun 2018. This records an increase from the previous number of 103,266.242 BRL mn for May 2018. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data is updated monthly, averaging 29,866.033 BRL mn from Jul 1994 (Median) to Jun 2018, with 288 observations. The data reached an all-time high of 218,686.067 BRL mn in Mar 2016 and a record low of 0.000 BRL mn in Jul 1999. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.KAA018: Money Supply. Brazilian Central Bank has made changes in methodology of Financial System Credit Data in February of 2013 after 13 years following the same methodology. These changes are necessary face the expansion of credit, favored by the improvement of the indicators of employment and income, continuous and sharp reduction of the interest rates and by important institutional advances. It is essential the availability of new information, in particular, which allows more detailed monitoring of credit arrangements with targeted resources, especially real estate financing, whose dynamism has contributed to reducing the housing deficit in the country. The main change includes coverage of data on concessions, interest rates, terms and default rates that were extended to the segment of directed credit and also became necessary to further detailing the statistical framework, to enable identification of the terms most relevant as well as reduce the relative share of loans not classified - embedded in 'other receivables'. The Money Supply statistics were revised in August 2018, incorporating methodological updates to increase compliance with international standards and consistency with other sets of macroeconomic statistics. The revision consists the inclusion of cooperatives among the institutions that meke up the money issuing system, resulting in M1 expansion, and the exclusion of non-residents assets, impacting mainly on M4. Replacement series ID: 408100927

India Natural Gas: Production: Offshore: Internal Use/Consumption data was reported at 4,045.480 Cub m mn in 2018. This records a decrease from the previous number of 4,131.610 Cub m mn for 2017. India Natural Gas: Production: Offshore: Internal Use/Consumption data is updated yearly, averaging 4,040.150 Cub m mn from Mar 2009 (Median) to 2018, with 10 observations. The data reached an all-time high of 4,451.990 Cub m mn in 2010 and a record low of 3,526.550 Cub m mn in 2009. India Natural Gas: Production: Offshore: Internal Use/Consumption data remains active status in CEIC and is reported by Ministry of Petroleum and Natural Gas. The data is categorized under India Premium Database’s Energy Sector – Table IN.RBM004: Natural Gas: Production: by Major States.