The statistic shows the growth rate in the real GDP in the United Kingdom from 2020 to 2024, with projections up until 2030. In 2024, the rate of GDP growth in the United Kingdom was at around 1.1 percent compared to the previous year.The economy of the United KingdomGDP is used an indicator as to the shape of a national economy. It is one of the most regularly called upon measurements regarding the economic fitness of a country. GDP is the total market value of all final goods and services that have been produced in a country within a given period of time, usually a year. Inflation adjusted real GDP figures serve as an even more telling indication of a country’s economic state in that they act as a more reliable and clear tool as to a nation’s economic health. The gross domestic product (GDP) growth rate in the United Kingdom has started to level in recent years after taking a huge body blow in the financial collapse of 2008. The UK managed to rise from the state of dark desperation it was in between 2009 and 2010, from -3.97 to 1.8 percent. The country suffered acutely from the collapse of the banking industry, raising a number of questions within the UK with regards to the country’s heavy reliance on revenues coming from London's financial sector, arguably the most important in the world and one of the globe’s financial command centers. Since the collapse of the post-war consensus and the rise of Thatcherism, the United Kingdom has been swept along in a wave of individualism - collective ideals have been abandoned and the mass privatisation of the heavy industries was unveiled - opening them up to market competition and shifting the economic focus to that of service.The Big Bang policy, one of the cornerstones of the Thatcher government programs of reform, involved mass and sudden deregulation of financial markets. This led to huge changes in the way the financial markets in London work, and saw the many old firms being absorbed by big banks. This, one could argue, strengthened the UK financial sector greatly and while frivolous and dangerous practices brought the sector into great disrepute, the city of London alone brings in around one fifth of the countries national income making it a very prominent contributor to wealth in the UK.

The statistic shows the gross domestic product growth rate in Canada from 2020 to 2024, with projections up until 2030. In 2024, Canada’s real GDP growth was around 1.53 percent compared to the previous year.Economy of CanadaAs an indicator for the shape of a country’s economy, there are not many factors as telling as GDP. GDP is the total market value of all final goods and services that have been produced within a country within a given period of time, usually a year. Real GDP figures serve as an even more reliable tool in determining the direction in which a country’s economy may be swaying, as they are adjusted for inflation and reflect real price changes.Canada is one of the largest economies in the world and is counted among the globe’s wealthiest nations. It has a relatively small labor force in comparison to some of the world’s other largest economic powers, amounting to just under 19 million. Unemployment in Canada has remained relatively high as the country has battled against the tide of economic woe that swept across the majority of the world after the 2008 financial meltdown, and although moving in the right direction, there is still some way to go for Canada.Canada is among the leading trading nations worldwide, owing to the absolutely vast supplies of natural resources, which make up a key part of the Canadian trading relationship with the United States, the country with which Canada trades by far the most. In recent years, around three quarters of Canadian exports went to the United States and just over half of its imports came from its neighbor to the south. The relationship is very much mutually beneficial; Canada is the leading foreign energy supplier to the United States.

Abstract

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) -the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “… the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) Annual panel surveys.

The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN

Panel sample, with LSMS 2002 and 2004 The APS 2004 collects information on 1,797 valid observations at household level and 7,476 at individual level. The sample of the second and third waves of the panel (APS) has been selected from the LSMS 2002 in order to be representative of Albanian households and individuals at national level. The LSMS 2002 differs from the APS 2003 and 2004 in that the former is designed to be representative at regional level (Mountain, Central, Coastal and Tirana) as well as for urban and rural domains, while the latter are for last domains only (urban and rural) LSMS 2002 sample design The LSMS is based on a probability sample of housing units (HUs) within the 16 strata of the sampling frame. It is divided in three regions: Coastal, Central, and Mountain Area. In addition, urban areas of Tirana are also considered as a separate region/stratum. The three regions are further stratified in major cities (the most important cities in the region), other urban (other cities in the region), and rural. The city of Tirana and its suburbs have been implicitly stratified to improve the efficiency of the sample design. Each stratum has been divided in Enumeration Area (EA), in accordance with the 2001 Census data, and each Primary Sampling Unit (PSU) selected with probabilities proportional to the number of occupied HUs in the EA. Every EA includes occupied and unoccupied HUs. Occupied rather than total units have been used because of the large number of empty dwellings registered in the Census data. The Housing Unit, defined as the space occupied by one household, is taken as sampling unit because is more permanent and easier to identify compared to the household. 10 EAs for each major city (75 for Tirana) and 65 EAs for each rural region -with the exception of the mountain area which is over-represented (75 EAs)- are selected. 8 households, plus 4 eventual substitutes, have been systematically selected in each EAs. As the LSMS consists of 450 EAs, total sample size is 3,600 households.

(b) STRATIFICATION

The panel component selected from the LSMS is designed to provide a nationally representative sample of households and individuals within Albania. It consists of roughly half of the households in the 2002 LSMS, interviewed both in 2003 and 2004. Contrarily to what done for the LSMS, no over-sampling in the Mountain Area has been performed for the panel survey. The sample is designed to minimize the variability in households' selection probabilities. It ensures national representativeness by matching the sample distribution across strata with the population distribution drawn from 2001 Census data. In Table 3 the ex-ante sampling scheme of the 2003-2004 APS is shown. Compared to the LSMS design, statistical precision has improved. Under equal stratum population variances hypothesis, sample design effects are expected to be around 1.02, compared to the 1.28 of the LSMS sample. Moreover, further precision is obtained by keeping all 450 EAs of LSMS in the panel sample, thus reducing the eventual bias due to clustering because of new design. Finally, the panel survey has a number of peculiar features that should be considered when using the data. The sample is designed to focus on individuals, who have been also traced when moving from the original household to a new one. This possibility represents the only way a household can enter the panel sample if it has not been already interviewed in the wave 1 (or in wave 2 for the APS 2004). If an original survey member (OSM) moves to a new household, his/her old and new household -and their members- are both included in the panel sample. Though a moved OSM will be present in the roster of both sampled households, he/she is a valid member only in the new one. In the old household he/she is considered as "moved away", hence not a valid member. This might generate some confusion. Three modalities exist to classify an individual in the third wave. First, when he/she is an OSM, that is a respondent interviewed both in wave 1 and 2. Second, when he is a re-joiner from 2002, that is an OSM not interviewed in 2003 (i.e. because temporarily absent) who returns in 2004. Third, when he/she is a new member, whenever he/she is a newborn of an original household, a member joined by an OSM or a person who co-resides with an original survey household. So, the APS is an indefinite life panel study, without replacement by drawing new sample units. From wave 2, only individuals aged 15 years and over are considered valid members, hence eligible for the interview. Individuals moved out of Albania are not accounted as valid for this survey year, though they are still eligible for future waves.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

A first data cleaning took place in Albania and implemented by INSTAT in collaboration with ISER and Government of Albania consultants. The cleaning process has involved following activities: 1. defining data checking routines and writing the syntax code of the cleaning programs; 2. generating lists of outliers and inconsistencies for each module to be checked against paper questionnaires; During the first few days, data cleaning operators have been working on the Export Procedure of the Data Entry Program to check if data export succeeded and to finalize the English version of the dictionaries and error messages. Some changes were made to the Export Procedure due to a problem on the “Agriculturea2” file conversion and to the

1st column: Elasticity of the quantile dummy variables to the probability that satisfaction  =  10, the maximum level. 2nd column: column: elasticity of the quantile dummy variables estimated using a linear model (OLS with country specific effect). The base level is the last quantile (the 15th), grouping the countries with per capita GDP larger than 36.81K. The coefficients are derived from the estimation of the baseline specification of model (1). GDP is reported in 10K, 2005 USD, PPP adjusted. Standard errors are clustered at country and wave levels (in brackets); *** p

Dependent variable: life satisfaction. Country data refer to waves 1981–1984, 1989–93, 1994–99, 1999–04, 2005–08. Dummy of the last quantile (the 15) is omitted. GDP is the per capita GDP in PPP, in 10K, 2005 UDS. The countries excluded in column 3 are Luxembourg and Singapore. Standard errors clustered at country and wave levels (in brackets); *** , ** , * .

Due to the outbreak of coronavirus (COVID-19), the gross domestic product of France could decrease by 11.4 to 14.1 percent in 2020. The largest decrease might be registered if a second wave of infections, with renewed lock-downs, hits the country before the end of 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Ordered Probit Estimation. Data refer to waves 1994–99, 1999–04, 2005–08. Dummy of the last quantile (the 5) is omitted. Reg.GDP is the per capita regional GDP in PPP, in 10K, 2005 USD. Standard errors are clustered at country and wave levels; *** , ** , * .

In January 2021, Italy's gross domestic product (GDP) growth volume decreased by 7.8 percent compared to January 2020. However, from March to September 2021 Italy's GDP was higher than in the same months of 2020, when the pandemic hit Italy with the first wave of infections.

For most of the 20th century, Ireland stood out as one of the poorest countries in Western Europe, not experience the same post-war boom in prosperity that was felt by virtually all other countries in the region. At the onset of the 1973-1975 Recession, Ireland's GDP per capita was less than 60 percent of GDP per capita in the European Union and less than a quarter of GDP per capita in the U.S. Catching up in the 1980s By the 1980s, a wave of foreign investment saw Ireland's export sector grow exponentially, and between 1975 and 1990, Ireland had the second-fastest growth of exports in the world (behind Japan). Additionally, as Ireland joined the European Communities in 1973, it became more integrated into the European economy; before 1973, around three-quarters of Ireland's exports went to the United Kingdom, but this fell to one-third by the 1990s. Ireland's period of industrialization was relatively short in comparison to its neighbors, as it transitioned from an agriculture-based economy to a producer of high-tech products and services. Ireland's low tax rate and other incentives also attracted many American tech companies in the 1980s, such as Apple, Intel, and Microsoft, who were keen on establishing a presence in the European Union. The Celtic Tiger Named after the Four Asian Tigers (Hong Kong, Singapore, South Korea, and Taiwan), which experienced rapid economic growth in the 1970s and 1980s, the period of prosperity between the 1990s and 2000s in Ireland has been dubbed the "Celtic Tiger." Over this time, Ireland's GDP per capita grew to exceed the average in the EU by 10 percent in 2000, and it would eventually surpass that of the U.S. in 2003. Ireland was severely impacted by the financial crisis of 2008 due to the instability of its property sector and extensive lending by banks, and it was the first European economy to go into recession. By the late 2010s, most sectors of the economy had returned to pre-recession levels, and today, Ireland's GDP per capita remains among the top in the world, second in the EU only to Luxembourg.

India's quarterly GDP was estimated to grow by 8.4 percent in the second quarter of financial year 2022 compared to the same quarter in the previous fiscal year. While continuing to be a positive change, it was a significant reduction from the performance during the first quarter of fiscal year 2022 when GDP growth peaked by 20 percent.

Cost of the pandemic

As a result of the various lockdowns enforced since the onset of the coronavirus pandemic in 2020, the Indian economy has been reeling from a multibillion dollar setback. The GDP contribution as well as the employment rate among most major sectors, especially services and trade, had taken a hit. The agriculture sector was an exception, having experienced positive changes on both these fronts.

A slowly recovering economy

With the outbreak of the second wave of the pandemic in March 2021, the government redirected financial support to boost India’s vaccination campaign. As of February 2022, over a billion vaccine doses had been administered across the country. Furthermore, inflation within the country was expected to decline 2021 onwards. However, the stagnation of employment continued to remain a matter of concern with protests erupting across different states in 2022.

Ordered Probit Estimation. Dependent variable is individual life satisfaction; data refer to wave 1996–06, townsize includes dummy variables controlling for 8 different town sizes. Per capita regional GDP and personal income is in 10K 2005 USD and is PPP adjusted. is set to 0 if , and is set to 0 if . Standard errors are clustered at regional level (in brackets); ***, ** , * .

Description:
This dataset accompanies the empirical analysis in Legality Without Justice, a study examining the relationship between public trust in institutions and perceived governance legitimacy using data from the World Values Survey Wave 7 (2017–2022). It includes:

• WVS_Cross-National_Wave_7_csv_v6_0.csv — World Values Survey Wave 7 core data.

• GDP.csv — World Bank GDP per capita (current US$) for 2022 by country.

• denial.ipynb — Fully documented Jupyter notebook with code for data merging, exploratory statistics, and ordinal logistic regression using OrderedModel. Includes GDP as a control for institutional trust and perceived governance.

All data processing and analysis were conducted in Python using FAIR reproducibility principles and can be replicated or extended on Google Colab.

DOI: 10.5281/zenodo.16361108
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Authors: Anon Annotator
Publication date: 2025-07-23
Language: English
Version: 1.0.0
Publisher: Zenodo
Programming language: Python

🔽 How to Download and Run on Google Colab

Step 1: Open Google Colab

Go to https://colab.research.google.com

Step 2: Upload Files

Click File > Upload notebook, and upload the denial.ipynb file.
Also upload the CSVs (WVS_Cross-National_Wave_7_csv_v6_0.csv and GDP.csv) using the file browser on the left sidebar.

Step 3: Adjust File Paths (if needed)

In denial.ipynb, ensure file paths match:

Step 4: Run the Code

Execute the notebook cells from top to bottom. You may need to install required libraries:

The notebook performs:

• Data cleaning

• Merging WVS and GDP datasets

• Summary statistics

• Ordered logistic regression to test if confidence in courts/police (Q57, Q58) predicts belief that the country is governed in the interest of the people (Q183), controlling for GDP.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(APA) APA Stock: Riding the Energy Wave?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Australia's tourism gross domestic product (GDP) bounced back strong in 2023, recording an increase of 90.8 percent. In 2024, the country's tourism GDP increased by around 9.1 percent. After witnessing a significant decline in tourism GDP in 2020 and 2021, with tourism GDP taking a massive plunge of 36.2 percent in 2021 as a result of the coronavirus outbreak, the industry appears to be on the road to recovery. Economic contribution and employment trends Australia's tourism sector recovery is reflected in its substantial economic contribution in 2024. In the year ending June 2024, Australia's direct tourism GDP rose to approximately 75 billion Australian dollars. New South Wales continued to be a key player in the industry, with its tourism gross value added (GVA) reaching about 20 billion Australian dollars. The state also led in employment, with around 195,000 people directly employed in the tourism sector. These figures underscore the tourism industry's significance in driving economic growth and job creation across the country. International visitors fuel industry recovery The revival of Australia's tourism sector is closely tied to the return of international tourists. In 2024, the country welcomed over 7.3 million international visitor arrivals, a significant increase from the mere 140,000 visitors recorded during the height of pandemic restrictions in 2021. New Zealand residents led the way, with over 1.2 million visitors, followed by tourists from China numbering just below 750,000. This influx of international travelers contributed substantially to the Australian economy in 2024, with total trip expenditure reaching approximately 47.8 billion Australian dollars, surpassing pre-pandemic levels for the first time.

Based on 2015 as the base year, this data set selects population density, distribution of high-risk population and GDP as the evaluation indicators to complete the assessment of high temperature heat wave exposure at 34 key nodes. Exposure refers to the degree that a certain area may be affected by the disaster when the disaster occurs. In the extreme high temperature, human and economy are the two most obvious factors affected by the high temperature heat wave. The high-risk population is defined as children younger than five years old and the elderly older than 65 years old respectively. Equal weight overlapping plus method is adopted in the assessment. In order to eliminate the influence of unit difference, the data of each indicator layer is normalized before the assessment. The spatial resolution of the assessment result is 100m, covering 34 key nodes of the third pole.

According to Cognitive Market Research, the global Surface Acoustic Wave SAW Filters market size will be USD 1758.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.80% from 2024 to 2031. North America held the major market share for more than 40% of the global revenue with a market size of USD 703.2 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031. Europe accounted for a market share of over 30% of the global revenue with a market size of USD 527.4 million. Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 404.3 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2024 to 2031. Latin America had a market share of more than 5% of the global revenue with a market size of USD 87.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031. Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 35.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031. The RF SAW Filters Type category is the fastest growing segment of the Surface Acoustic Wave SAW Filters industry Market Dynamics of Surface Acoustic Wave SAW Filters Market Key Drivers for Surface Acoustic Wave SAW Filters Market Increasing Demand for Wireless Communication to Boost Market Growth The emergence of wireless mobile phone systems has spurred remarkable growth and development in the wireless industry in recent years. In 2022, the U.S. wireless and mobile industry invested $11.9 billion to expand capacity and enhance coverage across the nation’s wireless networks. This infrastructure, spanning 50 states and nearly 3.8 million square miles, plays a crucial role in supporting the country’s $23 trillion GDP. Surface acoustic wave (SAW) devices provide key advantages, including low power consumption, high reliability, and compact size, making them ideal for use in sensors, filters, oscillators, and transducers. SAW filters are vital for maintaining signal integrity and reducing interference in mobile devices, base stations, and other wireless communication systems. Growth of the Automotive Sector to Drive Market Growth The increasing demand from the defense and aerospace sectors is expected to significantly boost revenue growth in the surface acoustic wave (SAW) filters market. The aerospace and defense (A&D) industry contributed $391 billion to the U.S. economy, accounting for 1.7% of the total nominal GDP. Between 2020 and 2021, A&D exports rose by 11.2%, reaching a total value of $100.4 billion, with exports going to 205 countries in 2021. SAW filters play a vital role in improving signal quality for defense communication systems, radar, satellite communication, and military radios. By utilizing acoustic waves, these filters effectively eliminate specific frequencies and reduce signal interference, making them essential for defense and aerospace communication systems. Additionally, governments and defense organizations worldwide are investing in military space communications to enhance the resilience and security of military, civilian, and commercial space operations. Restraint Factor for the Surface Acoustic Wave SAW Filters Market High Manufacturing Costs Will Limit Market Growth The production of SAW filters involves complex manufacturing processes and high-precision equipment, which can lead to elevated costs. These high manufacturing expenses may limit market entry for new players and restrict the overall growth of the market, particularly in price-sensitive segments. SAW filters face competition from alternative technologies such as Bulk Acoustic Wave (BAW) filters and micro-electromechanical systems (MEMS). These alternatives may offer comparable performance with different advantages, such as smaller size or lower cost, which can hinder the adoption of SAW filters in certain applications. Impact of Covid-19 on the Surface Acoustic Wave SAW Filters Market The outbreak of COVID-19 has significantly affected the electronics and semiconductor sector. Business and manufacturing units across various countries were closed, owing to the increase in several COVID-19 cases, and are expected to remain closed in 2021. Furthermore, partial or complete lockdown has disrupted the global supply chain posing challenges for manufacturers to reach customers. Lock...

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National.

Analysis unit

Household Individual

Universe

National Population, both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 3401

The different stages in the sampling procedure were: - Self representative PSU´s (provinces) - Selected provinces (PPS selection with implied stratification according to income) - Districts within provinces - Urban and rural locations within districts (villages selected PPS within rural areas; neighbourhoods and streets selected within urban locations, households identified with systematic random selection, age and gender quotas used in the final selection of individuals).

The final numbers of clusters or sampling points were 22 PSU´s. The sampled unit we got from the office sampling was the address and the selection method that was used to identify a respondent was move on the next address until quota is filled. The quota control was 3 age groups and 2 gender groups were used as quotas. The stratification factors that was used: Per capita gdp. There were some limitations in the sample. It was a quota sample in the last stage as explained above. Overall, the design yields very satisfactory results.

Remarks about sampling: 3 age groups (18-27; 28-40; 41+) and 2 gender groups were used as quota controls. There were stratification factors per capita gdp. A known limitation of the realized sample: this was a quota sample in the last stage as explained. Overall, the design yields very satisfactory results

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was translated from the English questionnaire by a member of the research team. The translated questionnaire was not back-translated into English and also was pre-tested. There were some questions that caused problems when the questionnaire was translated especially questions assuming a church organisation were problematic. Also some of the irrelevant questions were omitted; some were asked nevertheless. There have not been any optional WVS questions and/or items been included, however country-specific questions were included. They were mostly included at the end of the questionnaire but some country specific additions were made to a confidence in institutions and b) neighbours questions. A number of questions were omitted because they sounded totally irrelevant for the local population or because previous surveys indicated that they did not work for this population. The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 18 and there was not any upper age cut-off for the sample.

Sampling error estimates

Estimated error: 1.7

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Union Bankshares (UNB): Riding the Wave of Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

MIP23 - European Gross Domestic Product (GDP) per capita in Purchasing Power Standards (PPS). Published by Eurostat. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).European Gross Domestic Product (GDP) per capita in Purchasing Power Standards (PPS)...

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) –the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS)– drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “[...] the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.