Advani, Hughson and Tarrant (2021) model the revenue that could be raised from an annual and a one-off wealth tax of the design recommended by Advani, Chamberlain and Summers in the Wealth Tax Commission’s Final Report (2020). This deposit contains the code required to replicate the revenue modelling and distributional analysis. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.Around the world, the unprecedented public spending required to tackle COVID-19 will inevitably be followed by a debate about how to rebuild public finances. At the same time, politicians in many countries are already facing far-reaching questions from their electorates about the widening cracks in the social fabric that this pandemic has exposed, as prior inequalities become amplified and public services are stretched to their limits. These simultaneous shocks to national politics inevitably encourage people to 'think big' on tax policy. Even before the current crisis there were widespread calls for reforms to the taxation of wealth in the UK. These proposals have so far focused on reforming existing taxes. However, other countries have begun to raise the idea of introducing a 'wealth tax'-a new tax on ownership of wealth (net of debt). COVID-19 has rapidly pushed this idea higher up political agendas around the world, but existing studies fall a long way short of providing policymakers with a comprehensive blueprint for whether and how to introduce a wealth tax. Critics point to a number of legitimate issues that would need to be addressed. Would it be fair, and would the public support it? Is this type of tax justified from an economic perspective? How would you stop the wealthiest from hiding their assets? Will they all simply leave? How can you value some assets? What happens to people who own lots of wealth, but have little income with which to pay a wealth tax? And if wealth taxes are such a good idea, why have many countries abandoned them? These are important questions, without straightforward answers. The UK government last considered a wealth tax in the mid-1970s. This was also the last time that academics and policymakers in the UK thought seriously about how such a tax could be implemented. Over the past half century, much has changed in the mobility of people, the structure of our tax system, the availability of data, and the scope for digital solutions and coordination between tax authorities. Old plans therefore cannot be pulled 'off the shelf'. This project will evaluate whether a wealth tax for the UK would be desirable and deliverable. We will address the following three main research questions: (1) Is a wealth tax justified in principle, on economic or other grounds? (2) How should a wealth tax be designed, including definition of the tax base and solutions to administrative challenges such as valuation and liquidity? (3) What would be the revenue and distributional effects of a wealth tax in the UK, for a variety of design options and at specified rates/thresholds? To answer these questions, we will draw on a network of world-leading exports on tax policy from across academia, policy spheres, and legal practice. We will examine international experience, synthesising a large body of existing research originating in countries that already have (or have had) a wealth tax. We will add to these resources through novel research that draws on adjacent fields and disciplines to craft new solutions to the practical problems faced in delivering a wealth tax. We will also review common objections to a wealth tax. These new insights will be published in a series of 'evidence papers' made available directly to the public and policymakers. We will also publish a final report that states key recommendations for government and (if appropriate) delivers a 'ready to legislate' design for a wealth tax. We will not recommend specific rates or thresholds for the tax. Instead, we will create an online 'tax simulator' so that policymakers and members of the public can model the revenue and distributional effects of different options. We will also work with international partners to inform debates about wealth taxes in other countries. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

To lend money to someone and to later ask this same person to pay the money back should be relatively unproblematic in modern, monetized societies. Still, some people find it difficult to ask for lent money to be paid back, even though it is in their own interest that this happens and they have the legitimate right to ask their money back. In this article, we examine one reason why people might experience such difficulties: the anticipation of guilt. In Study 4.1, the majority of participants from 3 different countries indicated that they sometimes did not ask money back because doing so would make them feel guilty. Study 4.2 found that the more people anticipated guilt about asking their money back, the less willing they were to do this. Study 4.3 found that the effect of guilt became less strong when more money was at stake. Study 4.4 found that people anticipated more guilt and were less likely to ask money back when the other person was poor compared to rich. Studies 4.5 and 4.6 found that the amount of harm people anticipated by asking the money back mediated the effect. Taken together, we interpret these studies (Ntotal = 2988) to showcase the social nature of guilt, in that it can motivate people to sacrifice their (financial) self-interest in order to protect relationships with others. Additional documentation and metadata can be found in the files Data Report Chapter 4.pdf, Documentation of all author responsibilities.pdf, Documentation of Data Exclusions.pdf, and the metadata files in the rawdata folders. This research has preregistered all materials, hypothesis and sample size through: https://aspredicted.org/blind.php?x=2qa52h (for Study 3); https://aspredicted.org/blind.php?x=fs3qj9 (For Study 4); https://aspredicted.org/blind.php?x=7867r4 (For Study 5); https://aspredicted.org/blind.php?x=ni3y2a (For Study 6). The present data package includes Raw data files (Raw data+ metadata information+ Final Data, both in EXCEL), Syntax file (SPSS) and Materials (questionnaires in pdf from MTurk). The packages are primarily organized based on the raw data, SPSS (or R) code, and materials(questionnaires).

Hello! I was searching for a dataset for my own analysis project, and I found a pretty good one, but I found that the Budget and Revenue values had some errors, so I updated it personally and used it for the project. I thought I might as well upload it here to help others. DISCLAIMER: The author of the original dataset is Shruthi(https://www.kaggle.com/datasets/shruthiiiee/studio-ghibli-dataset/data). They have not reviewed or endorsed this updated version.

This dataset contains the following information about the Studio Ghibli Films.

Column Description:

1. Name: Full Film Name 2. Year: Release Year 3. Director: Director of the Movie 4. Screenplay: Screenplay writer 5. Budget: Budget of the movie, as per TMDB. Represented as a txt type with '$' at the front. 7. Revenue: Box Office Revenue from IMDB Pro's Box Office Mojo. Represented as a txt type with '$' at the front. 8. Genre 1: Primary Genre 9. Genre 2: Secondary Genre 10. Genre 3: Tertiary Genre 11. Duration: Length of the film.

Sources for changes to 'Budget' and 'Revenue': Revenue numbers from IMDb Pro's Box Office Mojo. Link: https://www.boxofficemojo.com/?ref_=bo_nb_tt_mojologo Budget numbers from TMD Link: https://www.themoviedb.org/list/4309-the-studio-ghibli-collection except for 'When Marnie Was There', 'My Neighbors the Yamadas', 'The Cat Returns', which is from Wikipedia. Link: https://www.wikipedia.org

A major study conducted by the Young Foundation and funded by the Economic and Social Research Council investigating the use of high cost credit in Wales. The research was carried out throughout 2015 with the final report - Credit where credit’s due? Understanding experiences of high cost credit in Wales – published in May 2016. An estimated 12 million people across the UK lack access to affordable credit, something the majority take for granted in everyday life. The alternatives for many are high cost lenders – payday loans, doorstep lenders, or expensive rent-to-own stores on the high street. This financial exclusion is harmful to individuals and families, our communities and the wider economy. Objectives: We set out to understand the scale of the issue, pathways into and journeys through high cost credit, and the impact this has. We aimed to identify opportunities and offer suggestions for new and improved products, services and ways of engaging consumers – and to outline what might work as alternatives to high cost credit. Key Findings: • Six per cent of the Welsh population have used one or more of rent-to-own stores, home credit and payday loans in the last year. • Customers come from all walks of life but are most likely to be young families. • Reasons for using high cost credit range from paying for Christmas or buying new items for the home, to simply paying the bills and making ends meet. For most, these represent essential purchases. • The majority of people turn straight to high cost credit without considering different types of credit or comparing offers between lenders. • High cost credit customers are typically extremely aware of their income and outgoings, often using ‘jam jar’ and other informal money management solutions. • Many see high cost credit options as being ‘for people like me’ and one of a very limited set of financial options. • The majority of high cost credit customers live in communities where these types of borrowing are normal. • Home credit providers especially are in a strong position to encourage repeat borrowing. • Customer perceptions of payday loans still firmly reflect the pre-cap market. • By contrast, rent-to-own and home credit have largely slipped through the net of negative publicity. • Regulating all forms of high cost credit out of existence is not the answer. • There is a clear need for market growth in the affordable credit market. The Young Foundation took a mixed-methods approach combining robust survey data with deep qualitative insights: (1) A nationally representative survey of 1,000 members of the Welsh population (conducted in person, June 2015). (2) A survey of 134 customers of high cost credit and/ or credit unions across Wales (conducted in-person, October 2015). (3) In-home depth interviews with 24 high cost credit customers. (4) Nine focus groups with 77 high cost credit and affordable credit customers. (5)Telephone interviews with 26 expert stakeholders. The research focused on three kinds of credit – home credit, rent-to-own and payday loans. (1) Home credit – Often known as doorstep loans, repayments on cash loans are collected by an agent from the customer’s home. Leading home credit lenders include Provident and Morses Club. (2) Rent-to-own – Sometimes referred to as hire-purchase, the customer typically pays a weekly amount for a fixed term. At the end of the term the customer owns the product but until that point it is only leased, allowing the customer to return it if they wish, or the lender can repossess the goods if payments are not made. Leading providers include Brighthouse, Perfect Home, Family Vision and Buy-As-You-View. (3) Payday loans – Payday loans are a form of short-term credit, typically for small amounts of money. They are available online and in high street shops. Interestingly our research found that payday loans were not perceived well due to the pre-cap market, reinforced by media portrayals and past experiences. Payday loan lenders include Wonga, Quick Quid and Sunny.

Context

This dataset contains all the stats of Gender Statistics 2022 - World Bank.

Details

The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work. Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. It is usually measured for women ages 15-49 who are married or in union. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.

Number of male sole proprietors is the number of newly registered sole proprietors owned by female individuals in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable.

Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households. Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered. Women who own house both alone and jointly (% of women age 15-49): Q4 is the percentage of women age 15-49 who alone as well as jointly with someone else own a house which is legally registered with their name or cannot be sold without their signature. "Both alone and jointly" Implies a woman owns a house alone and another house jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households.

Number of infants dying before reaching one year of age. Male population between the ages 75 to 79.

The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a financial institution account or through a mobile money account in the past 12 months, male (% age 15+).

Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Metadata

Coverage & Extent

• Granularity List : National
• Temporal Coverage : 1959 - 2021
• Periodicity : Annual
• Acronym : Gender Stats
• Recommended Citation: Gender Statistics, The World Bank
• Languages Supported : English
• Source Type : World Bank Group
• Source: : Gender Statistics, The World Bank
• Harvest Source : World Bank Data API
• Dates
  • First Published Date : Jul 18, 2010
  • Last Updated on : Jun 22, 2022
• Update Frequency : Quarter

Download

kaggle API Command !kaggle datasets download -d azminetoushikwasi/gender-statistics-wb

Disclaimer

The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

https://datacatalog.worldbank.org/search/dataset/0037654

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Sex for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01A.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Sex Female Male Equally male-owned and female-owned Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. ...

Whenever we are confronted with action opportunities in everyday life, e.g., when passing an opening, we rely on our ability to precisely estimate our own bodily capabilities in relation to the environmental conditions. So-called affordance judgments can be affected after brain damage. Previous studies with healthy adults showed that such judgments appeared to be trainable within one session. In the current study, we examined whether stroke patients with either right brain damage (n = 30) or left brain damage (n = 30) may similarly profit from training in an aperture task. Further, the role of neuropsychological deficits in trainability was investigated. In the administered task, stroke patients decided whether their hand would fit into a presented opening with varying horizontal width (Aperture Task). During one training session, patients were asked to try to fit their hand into the opening and received feedback on their decisions. We analyzed accuracy and the detection theory parameters perceptual sensitivity and judgment tendency. Both patients with right brain damage and patients with left brain damage showed improved performance during training as well as post training. High variability with differential profiles of trainability was revealed in these patients. Patients with impaired performance in a visuo-spatial or motor-cognitive task appeared to profit considerably from the target-driven action phase with feedback, but the performance increase in judgments did not last when the action was withdrawn. Future studies applying lesion analysis with a larger sample may shed further light on the dissociation in the trainability of affordance judgments observed in patients with versus without visuo-spatial or motor-cognitive deficits.

Family and living conditions as well as conduct and attitudes of young people. Besides a survey compositions were evaluated and encoded for content analysis. Topics: 1. Questionnaire for all respondents: social origin and occupation; occupations of parents; occupational career of parents; number of siblings and position in sibling sequence; one´s own bedroom; local residency; refugee; bombed out; whereabouts or living together with parents; possession of a car; number of living-rooms and one´s own room; house ownership; greatest concerns of parents; desired occupation; consent of parents with desired occupation; participation of parents in parents meetings; membership in a youth group and participation in social evenings; ideas about an ideal youth group; club membership; attitude of parents to youth groups and clubs. 2. The following additional questions were posed to schoolchildren in the 13th grade: favorite subjects; the route to school; conflicts in school; conduct of parents with school difficulties; homework; concept of an ideal teacher; preferred education goals for elementary schoolchildren and schoolchildren at higher levels; grades skipped and repeating a year; type of rewards for good grades; conduct of parents with bad grades; school grades and fairness; satisfaction with personal school achievements; judgement on the job of the class spokesman; duties of the pupils´ and parents´ council; participation of parents in parents meetings; further employment; earning money after school; preferred education style; greatest concerns and difficulties; judgement on the school and concepts of an ideal school; greatest concerns and difficulties of parents; judgement on the child-raising effort of parents; frequency of punishment; pocket-money and use of the money; marriage desire and desire for children; assessment of the future of Germany; leisure activities and friendships; perceived influence of the individual on the structuring of public life. 3. Encoding of compositions written by schoolchildren in the 8th grade for content analysis: the recording for content analysis was oriented on the description given in ZA Study No. 1575. Demography: age; sex; religious denomination; size of household; place of residence; place of birth; school; class. Familien- und Lebensverhältnisse sowie Verhalten und Einstellungen von Jugendlichen. Neben einer Befragung wurden Aufsätze ausgewertet, die inhaltsanalytisch verkodet wurden. Themen: 1.)Fragebogen an alle Befragten: Soziale Herkunft und Beruf; Tätigkeiten der Eltern; beruflicher Werdegang der Eltern; Geschwisterzahl und Stellung in der Geschwisterreihe; eigener Schlafraum; Ortsansässigkeit; Flüchtling; ausgebombt; Verbleib bzw. Zusammenleben der Eltern; Autobesitz; Anzahl der Wohnräume und eigenes Zimmer; Hausbesitz; größte Sorgen der Eltern; Berufswunsch; Einverständnis der Eltern mit dem Berufswunsch; Teilnahme der Eltern an Elternversammlungen; Mitgliedschaft in einer Jugendgruppe und Teilnahme an Heimabenden; Vorstellungen über eine ideale Jugendgruppe; Vereinsmitgliedschaft; Einstellung der Eltern zu Jugendgruppen und Vereinen. 2.)Die Schüler der Oberprima wurden zusätzlich gefragt: Lieblingsfächer; Schulweg; Konflikte in der Schule; Verhalten der Eltern bei Schulschwierigkeiten; Schularbeiten; Vorstellung von einem idealen Lehrer; präferierte Ausbildungsziele für Volksschüler und höhere Schüler; übersprungene Klassen und sitzenbleiben; Art der Belohnungen für gute Noten; Verhalten der Eltern bei schlechten Noten; Schulnoten und Gerechtigkeit; Zufriedenheit mit den eigenen Schulleistungen; Beurteilung der Arbeit des Klassensprechers; Aufgaben des Schüler- und Elternbeirats; Teilnahme der Eltern an Elternversammlungen; Weiterbeschäftigung; Gelderwerb neben der Schule; präferierter Erziehungsstil; größte Sorgen und Schwierigkeiten; Beurteilung der Schule und Vorstellungen von einer idealen Schule; größte Sorgen und Schwierigkeiten der Eltern; Beurteilung des Erziehungsaufwands der Eltern; Bestrafungshäufigkeit; Taschengeld und Verwendung des Geldes Heiratswunsch und Kinderwunsch; Einschätzung der Zukunft Deutschlands; Freizeitbeschäftigungen und Freundschaften; perzipierte Einflußnahme des Einzelnen auf die Gestaltung des öffentlichen Lebens. 3.)Inhaltsanalytische Verkodung der von den Schülern der Untertertia geschriebenen Aufsätze: Die inhaltsanalytische Erfassung orientiert sich an der in der ZA-Studien-Nr. 1575 gegebenen Beschreibung. Demographie: Alter; Geschlecht; Konfession; Haushaltsgröße; Wohnort; Geburtsort; Schule; Klasse.

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

Family and living conditions as well as conduct and attitudes of young people. Besides a survey compositions were evaluated and encoded for content analysis. Topics: 1. Content of the questionnaire: father missing, in captivity or deceased; occupation and activity of father as well as of mother; social origins; working hours of mother; number of siblings; position in sibling sequence; person responsible for housework and child care; one´s own child´s bed; local residency; refugee; bombed out. 2. Content analysis encoding of schoolchildren´s compositions with 5 different topics: 1. playing, 2. how I spend the day, 3. my life in school, 4. how we live at home, 5. what gives me the most fun and what I do not like (each child received only one of the topics). Encoded was: number of statements about father, mother, family, siblings, friends, school, teachers, playing, residence, Organizations; critical faculty; number of statements about prohibitions, desires and pleasure-oriented emotional states; adventures reported; formal recording of type as well as extent of report; number of mistakes and script; optimistic statements about the future; number of mentions of playing and manners of play as well as toys; mention of money, food and social situation. On topic of playing: description of the play situation and preferred playmate. Course of the day: occupations during the course of the day. School: favorite subjects; the route to school; judgement on teachers; criticism of school friends; criticism of material conditions at school; mention of punishments; judgement on the school. At home: occupation with siblings and relationship to them; desire for siblings; pocket-money and use of money; playmates and friends; helping in household. Pleasure and fun: pleasure with manual activity and particular inclinations; sympathy with others; concerns and desires; political observations. Future plans: desired occupation of respondent and concepts of parents; further school attendance and desires for life in the future; marrying and having children; assumed attitude of parents to personal plans. Demography: age; sex; religious denomination; size of household; place of birth; place of residence; school; class. Familien- und Lebensverhältnisse sowie Verhalten und Einstellungen von Jugendlichen. Neben einer Befragung wurden Aufsätze ausgewertet, die inhaltsanalytisch verkodet wurden. Themen: 1.)Inhalt des Fragebogens: Vater vermisst, in Gefangenschaft oder gestorben; Beruf und Tätigkeit des Vaters sowie der Mutter; soziale Herkunft; Arbeitszeit der Mutter; Geschwisterzahl; Stellung in der Geschwisterreihe; zuständige Person für Hausarbeit und Kinderbetreuung; eigenes Kinderbett; Ortsansässigkeit; Flüchtling; ausgebombt. 2.)Inhaltsanalytische Verkodung von Schüleraufsätzen mit 5 unterschiedlichen Themen: 1.Spielen; 2.Wie verbringe ich meinen Tag?, 3.Mein Leben in der Schule, 4.Wie wir zu Hause leben, 5.Was mir am meisten Spaß macht und was mir nicht gefällt (Jedes Kind erhielt nur eins der Themen). Verkodet wurde: Anzahl der Aussagen über den Vater, die Mutter, die Familie, die Geschwister, Freunde, Schule, Lehrer, Spielen, Wohnung, Organisationen; Kritikfähigkeit; Anzahl der Aussagen über Verbote, Wünsche und lustbetonte Gefühlslagen; berichtete Erlebnisse; formale Erfassung der Art sowie des Umfangs des Berichts; Fehlerzahl und Schriftbild; optimistische Aussagen über die Zukunft; Anzahl der Erwähnungen von Spielen und Spielarten sowie Spielsachen; Erwähnung von Geld, Essen und sozialer Lage. Zum Thema Spielen: Beschreibung der Spielsituation und präferierte Spielpartner. Tagesablauf; Beschäftigungen im Tagesablauf. Schule: Lieblingsfächer; Schulweg; Beurteilung der Lehrer; Kritik an Schulkameraden; Kritik an materiellen Schulverhältnissen; Erwähnung von Strafen; Urteil über die Schule. Zu Hause: Beschäftigung mit den Geschwistern und Verhältnis zu ihnen; Geschwisterwunsch; Taschengeld und Verwendung des Geldes; Spielkameraden und Freunde; Helfen im Haushalt. Freude und Spaß: Freude an manueller Betätigung und besondere Neigungen; Mitleid mit anderen; Sorgen und Wünsche; politische Bemerkungen. Zukunftspläne: Berufswunsch des Befragten und Vorstellungen der Eltern; weiterer Schulbesuch und Wünsche für das fernere Leben; Heiraten und Kinder haben; vermutete Einstellung der Eltern zu den eigenen Plänen. Demographie: Alter; Geschlecht; Konfession; Haushaltsgröße; Geburtsort; Wohnort; Schule; Klasse.