This dataset holds the keys for understanding global financial landscapes. From the size of economies (GDP) and how fast they're growing (GDP Growth Rate), to the wealth per person (GDP Per Capita. Explore how countries earn from abroad (Gross National Income - GNI) and how they're doing per citizen (GNI Per Capita). Dive into overall production (Gross National Product), economic performance (Economic Growth), and prices ups and downs (Inflation Rate). Finally, peek into a vital sector (Manufacturing Output Rate). Perfect for learners and analysts curious about the world's economic heartbeat.

The flash report aims to update​ monthly on the Cambodian economy situation regarding various indicators including consumer price index, finance, trade, budget execution, private investment, tourism, and construction as well as the international commodity price.

Abstract

Socio-Economic Conditions Survey 2018 is a key Palestinian official statistical aspects; it also falls within the mandate of the Palestinian Central Bureau of Statistics (PCBS) to provide updated statistical data on the society conditions and provide data on the most important changes in socio-economic indicators and its trends. The survey came in response to users' needs for social and economic statistical data, and in line with the national policy agenda and the sustainable development agenda. The indicators of Socio-Economic Conditions Survey 2018 covers many socio-economic and environmental aspects, and establishes a comprehensive database on those indicators. Its coverage of a set of sustainable development indicators that are considered as a national and international entitlement. The objective of this survey is to provide a comprehensive database on the most important changes that have taken place in the system of social and economic indicators that PCBS works on, which covers many socio-economic and environmental indicators. It also responds to the needs of many partners and users.The indicators that have been worked on in this survey cover the demographic characteristics of household members, characteristics of the housing unit where household lives, household income, expenses, and consumption, agricultural and economic activities of households, methods used by households to withstand and adapt to their economic conditions, availability of basic services to Palestinian households, assistance received by households and assessment of such assistance, the needs of the Palestinian households to be able to withstand the conditions, the reality of the Palestinian individual's suffering and the quality of life, sustainable development objectives for the survey's relevant indicators.

Geographic coverage

National level: State of Palestine. Region level: (West Bank, and Gaza Strip).

Analysis unit

Households, and individuals

Universe

The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2018).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling and Frame The Sample of the survey is a three-stage stratified cluster systematic random sample of households residing in Palestine.

Target Population The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2018). Focus was given to individuals aged 18 years and above to complete an annex to the questionnaire, designed for this age group.

Sampling Framework In previous survey rounds, sampling was based on census 2007, which includes a list of enumeration areas. An enumeration area is a geographic region with buildings and housing units averaging 124 housing units. In the survey design, they are considered as Primary Sampling Units (PSUs) at the first stage of selecting the sample. Enumeration areas of 2007 were adapted to the enumeration areas of 2017 to be used in future survey rounds. Target sample buildings were set up in 2015 electronically by using Geographic Information Systems (GIS), where the geospatial join tool was used within ArcMap 10.6 to identify the buildings selected in the first stage of the sample design of 8,225 households taken from the general frame buildings for enumeration areas of 2007 which falls within the boundaries of enumeration areas that were updated during the population, housing and establishments census 2017. Only the buildings for the year 2017 were used to link the sites of the sample buildings to the targeted enumeration areas, to ensure tracking households that moved after 2015.

Sample Size The survey sample comprised 11,008 households at the total level, where 9,926 households responded, they are divided as follows: 1. Fixing the sample of the survey on the Impact of Israeli Aggression on Gaza Strip in 2014 and Socio-Economic Conditions of the Palestinian Households - Main Findings, which was conducted in 2015, with a sample of 8,225 households in the previous round (household-panel),where 7,587 households responded. 2. Sample of new households that consisted of separated individuals (split households) totaled 2,783 households, where 2,339 households responded.

Sample Design

Three-stage stratified cluster systematic random sample: - Stage I: Selection of enumeration areas represented in the previous round of the survey on the socioeconomic conditions 2015 including 337 enumeration areas, in addition to enumeration areas in which individuals separated from their households and formed new households and households that changed their place of residence and address to other enumeration areas. - Stage II: Visit the same households from previous round of survey on socioeconomic conditions 2015 (25 households in each enumeration area). Households that changed their place of residence or registered address will be tracked in the existing database to search for the updated data registered in questionnaire. Individuals separated from their households from the previous round and formed new households or joined new households were tracked. - Stage III: A male and female member of each household in the sample (old and new) were selected for stage III among members aged 18 years and above, using Kish (multivariate) tables to fill in the questionnaire for household members aged 18 years and above. Taking into account that the household whose number is an even number in the sample of the enumeration area, we choose a female and the family whose number is an odd number we choose a male.

Sample Strata The population was divided into the following strata: 1. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967 (J1) as a separated stratum, and the Gaza Strip). 2. Locality type (urban, rural, camp). 3. Area C (class C, non-C) as an implicit stratum.

Domains 1. National level: State of Palestine. 2. Region level: (West Bank, and Gaza Strip). 3. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967, and Gaza Strip). 4. The location of the Annexation wall and Isolation (inside the wall, outside the wall). 5. Locality type (urban, rural, camp). 6. Refugee status (refugee, non-refugee). 7. Sex (male, female). 8. Area C (class C, non-C).

Sampling deviation

There are no deviations in the proposed sample design.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is the key tool for data collection. It must be conforming to the technical characteristics of fieldwork to allow for data processing and analysis. The survey questionnaire comprised the following parts: - Part one: Identification data. - Part two: Quality control - Part three: Data of households' members and social data. - Part four: Housing unit data - Part five: Assistance and Coping Strategies Information - Part six: Expenditure and Consumption - Part seven: Food Variation and Facing Food Shortage - Part eight: Income - Part nine: Agricultural and economic activities. - Part ten: Freedom of mobility - In addition to a questionnaire for individuals (18 years old and above): Questions on suffering and life quality, assessment of health, education, administration (Ministry of the Interior) services and information technology.

The language used in the questionnaire is Arabic with an English questionnaire

Cleaning operations

Data Processing

Data processing was done in different ways including:

Programming Consistency Check 1. Tablet applications were developed in accordance with the questionnaire's design to facilitate collection of data in the field. The application interfaces were made user-friendly to enable fieldworkers collect data quickly with minimal errors. Proper data entry tools were also used to concord with the question including drop down menus/lists. 2. Develop automated data editing mechanism consistent with the use of technology in the survey and uploading the tools for use to clean the data entered into the database and ensure they are logic and error free as much as possible. The tool also accelerated conclusion of preliminary results prior to finalization of results. 3. GPS and GIS were used to avoid duplication and omission of counting units (buildings, and households).

In order to work in parallel with Jerusalem (J1) in which the data was collected in paper, the same application that was designed on the tablets was used and some of its properties were modified, there was no need for maps to enter their data as the software was downloaded on the devices after the completion of the editing of the questionnaires.

Data Cleaning 1. Concurrently with the data collection process, a weekly check of the data entered was carried out centrally and returned to the field for modification during the data collection phase and follow-up. The work was carried out through examination of the questions and variables to ensure that all required items are included, and the check of shifts, stops and range was done too. 2. Data processing was conducted after the fieldwork stage, where it was limited to conducting the final inspection and cleaning of the survey databases. Data cleaning and editing stage focused on: - Editing skips and values allowed. - Checking the consistency between different the questions of questionnaire based on logical relationships. - Checking on the basis of relations between certain questions so that a list of non-identical cases

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

The Organisation for Economic Co-operation and Development (OECD) Social and Welfare Statistics (previously Social Expenditure Database) available via the UK Data Service includes the following databases:

The OECD Social Expenditure Database (SOCX) has been developed in order to serve a growing need for indicators of social policy. It includes reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at programme level. SOCX provides a unique tool for monitoring trends in aggregate social expenditure and analysing changes in its composition. The main social policy areas are as follows: old age, survivors, incapacity-related benefits, health, family, active labour market programmes, unemployment, housing, and other social policy areas.

The Income Distribution database contains comparable data on the distribution of household income, providing both a point of reference for judging the performance of any country and an opportunity to assess the role of common drivers as well as drivers that are country-specific. They also allow governments to draw on the experience of different countries in order to learn "what works best" in narrowing income disparities and poverty. But achieving comparability in this field is also difficult, as national practices differ widely in terms of concepts, measures, and statistical sources.

The Child Wellbeing dataset compare 21 policy-focussed measures of child well-being in six areas, chosen to cover the major aspects of children’s lives: material well being; housing and environment; education; health and safety; risk behaviours; and quality of school life.

The Better Life Index: There is more to life than the cold numbers of GDP and economic statistics. This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

The Social Expenditure data were first provided by the UK Data Service in March 2004.

The Pew Research Center’s American Trends Panel – Wave 34 (April 23–May 6, 2018) is a nationally representative survey of U.S. adults that examines political, social, and economic attitudes. Conducted in spring 2018, it provides valuable insights into public opinion leading up to the 2018 midterm elections and serves as a pre-2020 and pre-COVID-19 benchmark.

Trend Market Economy is calculated based on data of Market Economy status index for 2016 and 2018 whether it is increasing or decreasing. Market Economy Status index is calculated taking account indicators like Level of Socioeconomic Development, Organization of the Market and Competition, Currency and Price Stability, Private Property, welfare regime, economic performance and sustainability. Market Economy Status index is one of indicators contributing for Bertelsmann Stiftung’s Transformation Index (BTI). The short description of Bertelsmann Stiftung’s Transformation Index (BTI)is given below :- Advocating reforms aimed at supporting the development of a constitutional democracy and a socially responsible market economy, the BTI provides the framework for an exchange of good practices among agents of reform. The BTI publishes two rankings, the Status Index and the Management Index, both of which are based on in-depth assessments of 129 countries. The Status Index ranks the countries according to the state of their democracy and market economy, while the Management Index ranks them according to their respective leadership’s management performance. Distributed among the dimensions of democracy, market economy and management, a total of 17 criteria are subdivided into 49 questions. BTI countries are selected according to the following criteria: They have yet to achieve a fully consolidated democracy and market economy, have populations of more than two million (excepting seven states chosen as particularly interesting cases), and are recognized as sovereign states. Quality/Lineage: The data is downloaded from the above link https://www.bti-project.org/en/data/ and manipulated only table format keeping the value same for all the countries as the requirement of the Strive database. The map is created based on the values of the country using rworldmap package in R.

Abstract

Socio-Economic Conditions Survey 2020 is a key Palestinian official statistical aspects; it also falls within the mandate of the Palestinian Central Bureau of Statistics (PCBS) to provide updated statistical data on the society conditions and provide data on the most important changes in socio-economic indicators and its trends. The survey came in response to users' needs for social and economic statistical data, and in line with the national policy agenda and the sustainable development agenda. The indicators of Socio-Economic Conditions Survey 2020 covers many socio-economic and environmental aspects, and establishes a comprehensive database on those indicators. its coverage of a set of sustainable development indicators that are considered as a national and international entitlement. The objective of this survey is to provide a comprehensive database on the most important changes that have taken place in the system of social and economic indicators that PCBS works on, which covers many socio-economic and environmental indicators. It also responds to the needs of many partners and users.The indicators that have been worked on in this survey cover the Demographic characteristics of household members, Characteristics of the housing unit where household lives, Household income, expenses, and consumption, Agricultural and economic activities of households, Methods used by households to withstand and adapt to their economic conditions, Availability of basic services to Palestinian households, Assistance received by households and assessment of such assistance, the needs of the Palestinian households to be able to withstand the conditions, the reality of the Palestinian individual's suffering and the quality of life, Sustainable development objectives. for the survey's relevant indicators.

Geographic coverage

National level: State of Palestine. Region level: (West Bank, and Gaza Strip).

Analysis unit

Households, and individuals

Universe

The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2020). The focus was given to individuals aged 18 years and above to complete an annex to the questionnaire, designed for this age group.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sample of the survey is a three-stage stratified cluster systematic random sample of households residing in Palestine

Sampling Framework The sampling frame consist of the Rule of Law and Access to Justice Survey in Palestine 2018 which originally based on the list of enumeration areas of the Population, Housing and Establishments Census 2017, with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first sampling selection stage.

Sample Size 3,623 families were reached at the national level, 2,461 households in the West Bank, and 1,162 households in the Gaza Strip. These households were contacted using the phone, 3,122 households responded to the survey.

Sample Design Three-stage stratified cluster systematic random sample:

Stage I: Selection of a stratified cluster systematic random sample consisting of (161) enumeration areas. Stage II: Selection (09-25) households from each enumeration area in the first stage in a stratified cluster systematic random. (Lists of the heads of households).

Stage III: A male and female member of each household in stage II were selected for among members aged 18 years and above, using Kish (multivariate) tables to fill in the questionnaire for household members aged 18 years and above. Taking into account that the household whose number is an even number in the sample of the enumeration area, we choose a female and the family whose number is an odd number we choose a male.

In Jerusalem (j1) area, a survey sample of 25 households is selected from each enumeration area in the first stage.

Sample Strata The population was divided into the following strata: 1. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967 (J1) as a separated stratum, and the Gaza Strip). 2. Locality type (urban, rural, camp). 3. Area C (class C, non-C) as an implicit stratum.

Domains 1. Region level: (North of the West Bank, Middle of the West Bank and South of the West Bank). 2. The location of the Annexation wall and Isolation (inside the wall, outside the wall). 3. Locality type (urban, rural, camp). 4. Refugee status (refugee, non-refugee). 5. Sex (male, female). 6. Area C (class C, non-C).

Sampling deviation

There are no deviations in the proposed sample design

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is the key tool for data collection. It must be conforming to the technical characteristics of fieldwork to allow for data processing and analysis. The survey questionnaire comprised the following parts:

· Part one: Identification data. · Part two: Quality control · Part three: Data of households' members and social data. · Part four: Housing unit data · Part five: Assistance and Coping Strategies Information · Part six: Expenditure and Consumption · Part seven: Food Variation and Facing Food Shortage · Part eight: Income · Part nine: Agricultural and economic activities. · Part ten: Freedom of mobility · In addition to a questionnaire for individuals (18 years old and above): it includes questions related to the Food Insecurity Experience Scale (FIES), assessment of health, education, administration (Ministry of the Interior) services, and tobacco use.

The language used in the questionner is Arabic with an English questionner

Cleaning operations

Data processing was done in different ways including:

Programming Consistency Check 1. Tablet applications were developed in accordance with the questionnaire's design to facilitate collection of data in the field. The application interfaces were made user-friendly to enable fieldworkers collect data quickly with minimal errors. Proper data entry tools were also used to concord with the question including drop down menus/lists. 2. The application was examined by all members of the technical committee, and all comments were modified in addition to updates, and the transition between questions. It was also ensured that all audit rules were applied to the survey program, and the final version of the application was provided on time. 3. Develop automated data editing mechanism consistent with the use of technology in the survey and uploading the tools for use to clean the data entered into the database and ensure they are logic and error free as much as possible. The tool also accelerated conclusion of preliminary results prior to finalization of results. 4. In order to work in parallel with Jerusalem (J1) in which the data was collected in paper, the same application that was designed on the tablets was used to enter their data as the software was downloaded on the devices after the completion of the editing of the questionnaires.

Data Cleaning 1. Concurrently with the data collection process, a weekly check of the data entered was carried out centrally and returned to the field for modification during the data collection phase and follow-up. The work was carried out thorough examination of the questions and variables to ensure that all required items are included, and the check of shifts, stops and range was done too. 2. Data processing was conducted after the fieldwork stage, where it was limited to conducting the final inspection and cleaning of the survey databases. Data cleaning and editing stage focused on: · Editing skips and values allowed. · Checking the consistency between different the questions of questionnaire based on logical relationships. · Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, and reviewed toward identifying the source of the error case by case, where such errors were immediately modified and corrected based on the source of the error with the documentation process for the checks occurred on the questionnaire. 3. The SPSS program was used to extract and modify errors and discrepancies, to prepare clean and accurate data ready for scheduling and publishing.

Tabulation After finishing from checking and cleaning any errors of data, tabulation was prepared for this purpose and extracted accordingly.

Response rate

3,623 representative households was reached. Number of responded households (3,122) including (2,104) in the West Bank and (1,018) in Gaza Strip. Weights were adjusted with the design strata to compensate for the rate of refusal and non-response.

Sampling error estimates

Those errors result from studying part (sample) of the society and not all society units. Since the socio-economic conditions survey 2020 was conducted on a sample, sampling errors are expected to occur. To minimize sampling errors, a properly designed probability sample was used to calculate errors throughout the process. This means that for every unit of the society there is a probability to be selected in the sample. The variance was calculated to measure the impact on sample design for Palestine.

Data appraisal

This standard is linked to the statistical product, since statistics must have comparative advantage with other sources and with other time periods. Many analyses are based on comparison. The data of the survey of 2020 were compared to the previous

GDP: Trend: Compensation of Employees: Employers' Social Contributions data was reported at 24,523.000 AUD mn in Mar 2019. This records an increase from the previous number of 24,084.000 AUD mn for Dec 2018. GDP: Trend: Compensation of Employees: Employers' Social Contributions data is updated quarterly, averaging 8,588.000 AUD mn from Sep 1983 (Median) to Mar 2019, with 143 observations. The data reached an all-time high of 24,523.000 AUD mn in Mar 2019 and a record low of 2,170.000 AUD mn in Sep 1983. GDP: Trend: Compensation of Employees: Employers' Social Contributions data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A: SNA08: Gross Domestic Product: by Income: Current Price: Trend.

The Pew Research Center’s American Trends Panel – Wave 31 (January 29–February 13, 2018) is a nationally representative survey of U.S. adults examining political, social, and economic attitudes. Conducted in early 2018, it provides valuable insights into public opinion in the first months of the year, ahead of the 2018 midterm elections, and serves as a pre-2020 and pre-COVID-19 benchmark.

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

This report presents the preliminary findings for Norway as part of the Global Media and Internet Concentration Project (GMICP). Covering the years 2018 to 2022, the report examines media and internet market trends with a focus on concentration and revenue developments across various communication and media sectors. Its scope extends beyond traditional media to include newer, converging domains of IT, telecommunications, and digital media. While not all service segments are represented due to data limitations, selected areas were included for cross-national comparison. Although not among the largest economic sectors, the communications and media industry is vital to public discourse and democratic infrastructure. The report underscores the importance of understanding market structures, ownership patterns, and revenue flows to assess the shifting dynamics and power within the networked media landscape.

Australia GVA: Trend: Health Care & Social Assistance data was reported at 33,065.000 AUD mn in Sep 2018. This records an increase from the previous number of 32,729.000 AUD mn for Jun 2018. Australia GVA: Trend: Health Care & Social Assistance data is updated quarterly, averaging 21,182.000 AUD mn from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 33,065.000 AUD mn in Sep 2018 and a record low of 10,097.000 AUD mn in Sep 2002. Australia GVA: Trend: Health Care & Social Assistance data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A077: SNA08: Gross Value Added: by Industry: Current Price.

This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

Use Cases:

Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

BackgroundsRobust evidence have demonstrated the beneficial effect of Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) in T2D patients with cardiovascular diseases and chronic kidney disease. Multiple studies analyzed patterns and predictors of SGLT2i and GLP-1RA in the US, Europe and worldwide. However, there is no study about the utilization of these two classes of drugs in real-world in China.MethodA total of 181743 prescriptions of SGLT2i and 59720 GLP-1RA were retrospectively pooled from Hospital Prescription Analysis Cooperation Project from 2018 to 2021. The social-economic characteristics of patients and prescribers, including age, gender, residency, hospital level, insurance type, department visited, and payment amount, were collected and analyzed to study trends and risk factors associated with preference among two antidiabetics.ResultsAnnual number of prescriptions of SGLT2i significantly increased to approximately 140 folds, while GLP-1RA increased to about 6.5 folds. After adjustment for socio-economic information, several patients or physician characteristics were positively associated with the preference of GLP-1RA, including female gender (OR 1.581, 95% CI 1.528-1.635), residents in second-tier cities (OR 1.194, 95% CI 1.148-1.142), visiting primary or secondary hospital level (OR 2.387, 95% CI 2.268-2.512); while other factors were associated with the preference of SGLT2i, including older adults (OR 0.713, 95% CI 0.688-0.739), uncovered by insurance (OR 0.310, 95% CI 0.293-0.329), visiting other departments compared with endocrinology. In addition, the share of SGLT2i and GLP-1RA was low but in an increasing tendency.ConclusionsSGLT2i and GLP-1RA prescription significantly increased from 2018 to 2021. The socio-economic risk factors in choosing SGLT2i or GLP-1RA highlight an effort required to reduce disparities and improve health outcomes.

Abstract

The Labour Force Survey is a survey that collects data on the main characteristics of labour force, which are used to estimate the total labour force in our country. It is primarily aimed at obtaining data on three basic, contingent and mutually exclusive categories of population: employed, unemployed and economically inactive persons. The data serve to monitor, measure and evaluate economic and social trends in the Republic of Serbia. The main areas that are measured are employment and unemployment, economic inactivity, as well as demographic, socio-economic, educational and other characteristics of population in each of the mentioned areas.

Geographic coverage

National coverage

Analysis unit

The unit of observation is every member of every randomly selected household, and the unit of selection is the household selected in the sample.

Universe

The survey covers all persons in individual, (non)institutional households on the territory of the Republic of Serbia, without the Region Kosovo and Metohija, which represent usual population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The LFS uses a two-stage stratified cluster sampling approach. The sample of enumeration areas (cluster of households) was selected at the first stage. A sample of households was selected in each enumeration area at the second stage.

Enumeration areas as primary sampling units are stratified according to the type of settlements (town and other) and territory at NSTJ 3 level (25 areas: Beogradska oblast, Zapadnobacka oblast, Južnobanatska oblast, Južnobacka oblast, Severnobanatska oblast, Severnobacka oblast, Srednjobanatska oblast, Sremska oblast, Zlatiborska oblast, Kolubarska oblast, Macvanska oblast, Moravicka oblast, Pomoravska oblast, Rasinska oblast, Raška oblast, Šumadijska oblast, Borska oblast, Branicevska oblast, Zajecarska oblast, Jablanicka oblast, Nišavska oblast, Pirotska oblast, Podunavska oblast, Pcinjska oblast and Toplicka oblast).

Mode of data collection

Face-to-face [f2f]

The original surveys have been designed to monitor trends in attitudes, behavior, and societal change in the Federal Republic of Germany. The main topics of this cumulative study are:

1.) Economy 2.) Politics 3.) Social inequality 4.) Ethnocentrism and minorities 5.) Family 6.) Lifestyle and personality 7.) Health 8.) Religion and world view 9.) Personal and collective values 10.) Social networks and social capital 11.) Deviant behavior and sanctions 12.) ALLBUScompact-Demography 13.) Geographic data 14.) Added value

Topics:

1.) Economy: assessment of the present and future economic situation in Germany and in one´s own federal state, assessment of present and future personal economic situation.

2.) Politics: satisfaction with the federal and state government, with German democracy and with the performance of the German political system (political support);

basic political attitudes: self-placement on left-right continuum, placement of political parties on a left-right-continuum, political interest, party inclination;

voting intention (Sonntagsfrage), participation in last federal elections, recall of vote in last federal elections, party-sympathy-scales, likelihood of voting for different political parties;

political participation: personal participation and willingness to participate in selected forms of protest and other political activities, norms for political participation; frequency of discussing politics with friends, acquaintances, strangers, and family;

political issues: attitudes towards nuclear energy, the death penalty for terrorists, towards the privatization of publicly owned companies; support for less government interference in the economy, for stricter environmental protection measures, for harsher punishment of criminals, for making social security government´s top priority, for a redistribution of income in favor of the common people; for the view that immigrants are good for the economy, for access to abortion without legal limitations, for more global free trade; attitude towards expanding or cutting budgets for social services and defense, perceived position of the federal government in these matters;

democracy scale;

political knowledge questions (party affiliation of top-level politicians, functioning of democratic institutions etc.);

political efficacy: perception of individual influence on politics, gap between politicians and citizens, self-assuredness with regard to political group work, too much complexity in politics, perception of politicians´ closeness to constituents, participation in the vote as a civic duty;

perceived strength of conflicts between social groups;

confidence in public institutions and organizations;

identification with various political entities: identification with own municipality, the federal state, the old Federal Republic or the GDR, unified Germany and the EU;

attitudes relating to the process of German unification: attitude towards the demand for increased willingness to make sacrifices in the West and more patience in the East, unification is advantageous, for East and West respectively, future of the East depends on the willingness of eastern Germans to make an effort, strangeness of citizens in the other part of Germany, performance pressure in the new states, attitude towards dealing with the Stasi-past of individuals, evaluation of socialism as an idea;

evaluation of administration services and assessment of treatment by the administration;

national pride and right-wing extremism: pride in German institutions and German achievements, pride in being a German, extremism scale.

3.) Social Inequality: fair share in standard of living, self-assessment of social class and classification on a top-bottom-scale, evaluation of personal occupational success, comparison with father´s position and personal occupational expectations for the future, attitudes towards the German economic system and evaluation of policies supporting the welfare state, assessment of access to education, perceived prerequisites for success in society, income differences as incentive to achieve, acceptance of social differences, evaluation of personal social security.

4.) Ethnocentrism and minorities: attitude towards the influx of eastern European ethnic Germans, asylum seekers, labor from EU or non-EU countries; perceived consequences of presence of foreigners in Germany, attitudes towards refugees, treatment of foreigners by the administration, ranking in terms of...

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

The Pew Research Center’s American Trends Panel – Wave 33 (March 27–April 9, 2018) is a nationally representative survey of U.S. adults focusing on political, social, and economic attitudes. Conducted in early spring 2018, it offers insights into public opinion several months before the 2018 midterm elections, serving as a key pre-2020 and pre-COVID-19 benchmark.

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.