Context

Getting proper data for survival analysis is often difficult.

Content

This data represents entry dates, departure dates and other information about fictional clients of a life insurance company. You have the age at which the insured entered the contract, the age at which he left, and the reason : either death or withdrawal, equivalent for us to right-censorship since the actual age at death of the person will no longer be observed. The data are left-truncated at the 1st of January 1820 : you only know if a client was present before that date, but you have no idea for how long he's been there.

Acknowledgements

Entirely generated using the numpy.random module, source code attached. For the survival analysis notebooks to come, my theoretical basis is the excellent course of Duration Models by Olivier Lopez at ENSAE Paris.

Inspiration

Develop some survival analysis and duration models tools to estimate death or departure of your clients as accurately as possible !

Abstract copyright UK Data Service and data collection copyright owner.To provide information on consumers' experience, knowledge and attitudes in relation to insurance policies of five different types. The types of policy covered are: 1. Life assurance policies 2. Endowment assurance policies 3. Home insurance (buildings) (Householders survey only) 4. Home insurance (contents) (Householders survey only) 5. Motor vehicle insurance Main Topics: Attitudinal/Behavioural Questions a) How people have taken out their present policy(ies) b) Who advised them and what advice they were given c) Policy holders' awareness of specific clauses in their policies d) Problems that have arisen when making a claim on their policy e) Other difficulties members of the public have had to face in connection with insurance f) Attitudes of non-policy holders to each type of policy and the way in which they would obtain such a policy. Survey consists of 4 separate questionnaires. Variables common to all include: whether informant holds a policy or whether he/she has ever held a policy - if applicable; reason for no longer having one is given. If respondent has never considered taking out a policy, reason' is stated and a record is made of how he/she would go about obtaining one should he/she so decide. Particulars of policy held: company with which policy is held; annual yearly premium on policy; method of payment (e.g. by Giro); whether paid by instalments; if so, frequency of payment is recorded; total sum assured on policy; date policy was taken out. Procedures followed when taking out policy: whether and by whom prompted to take out policy; objectives in taking out policy; decision stages involved in first taking out policy; from whom received advice (8 categories). There is a separate section on insurance brokers, where applicable. Knowledge of policy: particularly, cover of policy; facilities linked with policy (e.g. life insurance policy and mortgage facility); amount which would be received if respondent stopped paying premiums before policy maturation date. Also, familiarity with policy document is tested (e.g. the last time that holder read or even looked at the document, and where it is normally kept). Claims (except in life insurance questionnaire): number of claims made on policy; value of claim; whether handled by self; circumstances leading to claim; difficulties experienced; eventual outcome (e.g. claim met in full) and knowledge of the termaveraging' on claims. Attitudes: circumstances in which respondent might decide to increase the value of his/her policy; whether he/she has ever contacted the insurance company with a view to modifying policy (if yes, who contacted and number of times is recorded); whether respondent thinks that the company should contact policy holders from time to time or whether it should be left to the holder to contact the company. Satisfaction with people who may have been contacted at some stage in connection with policy is gauged on a 7-point scale (people listed are: insurance broker; insurance agent; bank manager; solicitor; other professional adviser). Background Variables Sex, age cohort, marital status, occupational details (including industry, job description and status, qualifications obtained), social grade, household status (i.e. sex, ages, number, occupational status, marital status, relationship to informant), home tenure and area of residence.

The Iowa Insurance Division is responsible for issuing licenses or authority for many types of regulated individuals dealing with insurance related products. Individuals interested in becoming either a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the National Insurance Producer Registry (NIPR) online system. Those wishing to become a resident insurance producer licensed in the state of Iowa must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

To add additional lines of authority, a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the NIPR online system. Resident insurance producers wishing to add a line of authority must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

This dataset provides a listing of resident and non-resident insurance producers licensed to sell to Iowans.

Individuals save for future uncertain health care expenses. This is less efficient than pooling health risk through insurance. The provision of comprehensive health insurance may raise welfare by providing the missing market to smooth out consumption through the life cycle. We employ a semiparametric smooth coefficient model to examine the effects of the introduction of the National Health Insurance in Taiwan in 1995 on savings and consumption over the life cycle. The idea is to estimate the coefficients of health insurance which vary with age. Our results suggest that younger households are more sensitive to the risk reductions, and that they demonstrate a greater response in the reduction of their precautionary saving.

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

Information on employment and income situation as well as pension payments of older persons. Topics: most important hopes and fears for the immediate future; self-assessment of condition of health; time of last vacation trip; judgement on one´s own current economic situation and comparison with the situation 10 years ago as well as in comparison with all West Germans; residential status; possession of a telephone; education and occupational training; number of children and year of birth; marital status; current employment or desire for employment; reason for employment; number of hours worked each week; age at start of employment; reason and point in time of interruptions of employment; total number of years of employment as well as profession in the individual working years; refund of contributions or retroactive insurance payments; reasons for termination of employment; company size and type of business of the company for which respondent last worked; duration of employment in this company; last professional position; monthly pension payments from company retirement program or supplementary insurance; amount of pension; form of pension; support for children; receipt of pension for inability to work or conduct one´s occupation prior to receipt of retirement income; basis of pension entitlement; begin and level of payments of pensions from the Altershilfe der Landwirte {farmers´ retirement program}, retirement program for self-employed or support for victims of the war; pension from the burden-sharing program, accident pension and pensions from private life insurance policies; further income from work, leases, commercial enterprise, assets, welfare, unemployment support or sick pay; financial support from private individuals; type of health insurance; amount of premium to the health insurance company; tax class; total net income; saving; receipt of housing benefit; widow pension. After consent of respondent information on insurance premiums was obtained from the responsible insurance provider. Interviewer rating: estimated respondent income. Angaben zur Erwerbstätigkeit und Einkommenssituation sowie zu den Rentenbezügen von älteren Personen. Themen: Wichtigste Sorgen und Hoffnungen für die nächste Zukunft; Selbsteinschätzung des Gesundheitszustands; Zeitpunkt der letzten Urlaubsreise; Beurteilung der eigenen derzeitigen wirtschaftlichen Lage und Vergleich zur Lage vor 10 Jahren sowie im Vergleich zu allen Bundesbürgern; Wohnstatus; Telefonbesitz; Schul- und Berufsausbildung; Anzahl und Geburtsjahr der Kinder; Familienstand; derzeitige Erwerbstätigkeit bzw. Wunsch nach einer Erwerbstätigkeit; Grund für die Erwerbstätigkeit; Anzahl der Wochenarbeitsstunden; Alter bei Beginn der Erwerbstätigkeit; Grund und Zeitpunkt von Unterbrechungen der Erwerbstätigkeit; Gesamtjahre der Erwerbstätigkeit sowie Berufsstand in den einzelnen Erwerbsjahren; Beitragsrückerstattungen bzw. Nachversicherungen; Gründe für die Beendigung der Erwerbstätigkeit; Betriebsgröße und Betriebsart des Unternehmens, bei dem der Befragte zuletzt gearbeitet hat; Beschäftigungsdauer in diesem Betrieb; letzte berufliche Stellung; monatliche Rentenbezüge aus betrieblicher Altersversorgung oder Zusatzversicherung; Pensionshöhe; Rentenform; Kinderzuschuß; Bezug von Erwerbs- bzw. Berufsunfähigkeitsrente vor dem Bezug von Altersruhegeld; Rentenbemessungsgrundlage; Beginn und Höhe der Zahlungen von Renten aus der Altershilfe der Landwirte, Altersversorgung von Selbständigen bzw. Kriegsopfer versorgung; Lastenausgleichsrente, Unfallrente und Renten aus privaten Lebensversicherungen; weitere Einkünfte aus Arbeit, Verpachtung, Gewerbebetrieb, Vermögen, Sozialhilfe, Arbeitslosengeld oder Krankengeld; finanzielle Unterstützung durch Privatpersonen; Krankenversicherungsart; Beitragshöhe zur Krankenkasse; Steuerklasse; Gesamtnettoeinkommen; Sparen; Wohngeldbezüge; Witwenrente. Nach Einwilligung des Befragten wurden auch Beitragsinformationen vom zuständigen Versicherungsträger eingeholt. Demographie: Geburtsjahr; Geschlecht; Nettohaushaltseinkommen; Einkommensquellen des Haushaltes; Haushaltsgröße; Haushaltszusammensetzung. Interviewerrating: Geschätztes Befragteneinkommen.

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

Introduction

Welcome to the UK English Call Center Speech Dataset for the BFSI domain designed to enhance the development of call center speech recognition models specifically for the BFSI industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the BFSI domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the

The Texas Department of Insurance (TDI) is responsible for licensing, registering, certifying, and regulating people who sell insurance or adjust property and casualty claims in Texas. This data set includes a row for each license held by a person. A person with more than one license will be listed in multiple rows. To view the list of agencies and business licensed by TDI, go to the Insurance agencies data set. To learn more about the type of licenses in this data set, go to TDI’s agent and adjuster licensing webpage.

Introduction

Welcome to the Punjabi Call Center Speech Dataset for the BFSI domain designed to enhance the development of call center speech recognition models specifically for the BFSI industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the BFSI domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each spending unit (usually the husband, the main earner, or the owner of the home) was interviewed. The basic unit of reference in the study was the spending unit, but some family data are also available. The questions in the 1957 survey covered the respondent's attitudes toward national economic conditions and price activity, as well as the respondent's own financial situation. Other questions examined the spending unit head's occupation, and the nature and amount of the spending unit's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of cars and other major durables. The survey also elicited respondent's attitudes about different methods of using income remaining after their expenses were met, e.g., investing in stocks or putting money in savings. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Regarding financial assets, the respondent was asked questions on attitudes toward financial assets, minimum balance in checking accounts, and common stock ownership and changes. Also included were questions on life insurance coverage and premiums, and whether the spouse had a full-time job and how much of the year he or she worked. Personal data include number of people in the spending unit, age, sex, and education of the head, and the race and sex of the respondent. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03616.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada; Atlantic Region; Newfoundland and Labrador; Prince Edward Island; ...);  Statistics (3 items: Value; Distribution of value; Value per household);  Characteristics (1 item: All households);  Wealth (11 items: Total assets; Financial assets; Life insurance and pensions; Other financial assets; ...).

COVID-19 Treatment Locator

This dataset displays pharmacies, clinics, and other locations with safe and effective COVID-19 medications. These medications require a prescription from a healthcare provider. Some locations, known as Test to Treat sites, give you the option to get tested, get assessed by a healthcare provider, and receive treatment – all in one visit. COVID-19 medications may be available at additional locations that are not shown in this dataset.

The locations displayed have either self-attested they have inventory of Paxlovid (nirmatrelvir packaged with ritonavir), Lagevrio (molnupiravir), or Veklury (Remdesivir) within at least the last two months and/or reported participation in the Paxlovid Patient Assistance Program. Sites that have not reported in the last two weeks display a notification, "Inventory has not been reported in the last 2 weeks. Please contact the provider to make sure the product is available." Outpatient COVID-19 medications may be available at additional locations not listed on this website.

All therapeutics identified in the locator not approved by the FDA must be used in alignment with the terms of the respective product’s Emergency Use Authorization. Visit COVID-19 Treatments and Therapeutics for more information on all treatment options.

This website identifies sites that have commercially purchased inventory of COVID-19 treatments and, in some cases, may identify sites that have remaining, no-cost U.S. government distributed supply. Some sites may charge for services not covered by insurance. Some sites may offer telehealth services. This website is intended for informational purposes only and does not serve as an endorsement or recommendation for use of any of the locations listed on the sites.

Clarification for DoD Facilities: Those individuals eligible for care in an MTF include Active Duty Service Members (ADSMs), covered beneficiaries enrolled in TRICARE Prime or Select, including TRICARE Reserve Select (TRS), TRICARE Retired Reserve (TRR) and TRICARE Young Adult (TYA) participants, TRICARE for Life beneficiaries, and individuals otherwise entitled by law to MTF care (e.g., regular retired members and their dependents who are not enrolled in TRICARE but who are otherwise eligible for MTF space-available care, certain foreign military members and their families registered in DEERS, and others).

Introduction

Welcome to the French Call Center Speech Dataset for the BFSI domain designed to enhance the development of call center speech recognition models specifically for the BFSI industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the BFSI domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each spending unit (usually the husband, the main earner, or the owner of the home) was interviewed. The basic unit of reference in the study was the spending unit, but some family data are also available. The questions in the 1953 survey covered the respondent's attitudes toward national economic conditions and price activity, as well as the respondent's own financial situation. Other questions examined the spending unit head's occupation, and the nature and amount of the spending unit's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of cars and other major durables. The survey also elicited respondent's attitudes about different methods of using income remaining after expenses were met, e.g., investing in stocks or putting money in savings. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Further questions concerned life insurance premiums and coverage and common stock ownership and value. The 1953 survey included a separate questionnaire for farmers that contained differing questions on sources of income and housing. Personal data include number of people in the spending unit, age, sex, and education of the head, and the race and sex of the respondent. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03613.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Change of subjective attitudes of the people in Eastern Germany. Topics: Development of personal situation in life in the next few weeks; importance of areas of life; time consciousness; general contentment with life; satisfaction with areas of life; comparison of retrospective satisfaction with current; eligibility to vote and election participation at the last Federal Parliament election; the question of memory of the election; self-classification on a left-right continuum; assessment of socialism as an idea; satisfaction with democracy in the Federal Republic; preferred forms of political participation (scale); political commitment; goals in life (scale); strength of influence of various groups of persons on municipal politics; importance of support by family, relatives, friends, colleagues, government and church facilities in situations of need; expectations of family and friends of the conduct of respondent; opinion on the adaptation of living conditions in Eastern and Western Germany and expected time period; judgement on one's own economic situation; preferred leisure activities; activities to structure one's own situation in life; expected social changes in the next few years; self-assessment of condition of health; relationship to various parties; membership in trade union, club, association, citizen initiative; discussion about the affairs of the municipality in these organizations; frequency of activity in these organizations; gatherings such as group of regulars, hen party, sociable circle of friends, environment and peace groups; sources of information on municipal affairs; participation in citizen initiatives or collecting signatures; readiness for participation in municipal politics; criticism of conditions in the new states within the circle of friends; cultural gains and losses since the turning point; postmaterialism questions; existence of building loan contract, life insurance, other capital insurance policies, loans; residency at place of residence and in part of town; length of residence in current residence; solidarity with place of residence; interest in municipal politics; relatives in same place of residence; subjective classification of social class; possession of a telephone and entry in telephone book; personal jeopardy to job; religious affiliation; solidarity with church; place of birth in the new states; time of move to the new states. Wandel der subjektiven Einstellungen der Menschen in Ostdeutschland. Themen: Entwicklung der persönlichen Lebenssituation in den nächsten Wochen; Wichtigkeit der Lebensbereiche; Zeitbewußtsein; allgemeine Lebenszufriedenheit; Zufriedenheit mit den Lebensbereichen; Vergleich der retrospektiven Zufriedenheit mit der aktuellen; Wahlberechtigung und Wahlbeteiligung bei der letzten Bundestagswahl; Wahlrückerinnerungsfrage; Selbsteinstufung auf Links-Rechts-Kontinuum; Einschätzung des Sozialismus als Idee; Zufriedenheit mit der Demokratie in der Bundesrepublik; präferierte Formen der politischen Partizipation (Skala); politisches Engagement; Lebensziele (Skala); Stärke des Einflusses verschiedener Personengruppen auf Kommunalpolitik; Wichtigkeit der Unterstützung durch die Familie, Verwandtschaft, Freunde, Arbeitskollegen, staatliche und kirchliche Einrichtungen bei Notsituationen; Erwartungen der primären Umwelt an das Verhalten der Befragten; Meinung über die Anpassung der Lebensverhältnisse in Ost- und Westdeutschland und erwarteter Zeitraum; Beurteilung der eigenen wirtschaftlichen Lage; präferierte Freizeitaktivitäten; Aktivitäten zur Gestaltung der eigenen Lebenssituation; erwartete gesellschaftliche Veränderungen in den nächsten Jahren; Selbsteinschätzung des Gesundheitszustandes; Verhältnis zu verschiedenen Parteien; Mitgliedschaft in Gewerkschaft, Verein, Verband, Bürgerinitiative; Diskussion über die Angelegenheiten der Gemeinde in diesen Organisationen; Häufigkeit der Betätigung in diesen Organisationen; Treffen wie Stammtisch, Kaffeekränzchen, geselliger Freundeskreis, Umwelt- und Friedensgruppen; Informationsquellen für kommunale Angelegenheiten; Beteiligung an Bürgerinitiativen oder Unterschriftensammlungen; Bereitschaft zur Mitarbeit in der Kommunalpolitik; Kritik an den Zuständen in den neuen Bundesländern innerhalb des Freundeskreises; kulturelle Gewinne und Verluste seit der Wende; Postmaterialismus-Fragen; Existenz von Bausparvertrag, Lebensversicherung, anderen kapitalbildenden Versicherungen, Kredit; Ansässigkeit am Wohnort und im Ortsteil; Wohndauer in jetziger Wohnung; Verbundenheit mit dem Wohnort; Interesse an der Kommunalpolitik; Verwandtschaft im gleichen Wohnort; subjektive Schichteinstufung; Telefonbesitz und Telefonbucheintrag; eigene Arbeitsplatzgefährdung; Religionszugehörigkeit; Verbundenheit mit Kirche; Geburtsort in den neuen Bundesländern; Zeitpunkt des Umzuges in die neuen Bundesländer.

This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealing with nonhousehold members/work-related expenses contains items on regular support payments for nonhousehold members and expenses associated with a job such as union dues, licenses, permits, special tools, uniforms, or travel expenses. Information is supplied in the Wave VII Topical Module file on (1) assets and liabilities, (2) pension plan coverage, and (3) real estate property and vehicles. Variables pertaining to assets and liabilities are similar to those contained in the topical module for Wave IV. Pension plan coverage items include whether the respondent will receive retirement benefits, whether the employer offers a retirement plan and if the respondent is included in the plan, and contributions by the employer and the employee to the plan. Real estate property and vehicles data include information on mortgages held, amount of principal still owed and current interest rate on mortgages, rental and vacation properties owned, and various items pertaining to vehicles belonging to the household. Wave VIII Topical Module includes questions on support for nonhousehold members, work-related expenses, marital history, migration history, fertility history, and household relationships. Support for nonhousehold members includes data for children and adults not in the household. Weekly and annual work-related expenses are documented. Widowhood, divorce, separation, and marriage dates are part of the marital history. Birth expectations as well as dates of birth for all the householder's children, in the household or elsewhere, are recorded in the fertility history. Migration history data supplies information on birth history of the householder's parents, number of times moved, and moving expenses. Household relationships lists the exact relationships among persons living in the household. Part 49, Wave IX Rectangular Core and Topical Module Research File, includes data on annual income, retirement accounts, taxes, school enrollment, and financing. This topical module research file has not been edited nor imputed, but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08317.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This dataset presents information about superannuation income. The data covers the financial years 2011-12 to 2017-18, and is based on Greater Capital City Statistical Areas (GCCSA) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Superannuation income includes the following data items on the Individual Tax Returns (ITR):

• Australian annuities and superannuation income streams taxable component taxed element

• Australian annuities and superannuation income streams taxable component untaxed element

• Australian annuities and superannuation income streams lump sum in arrears taxable component taxed element

• Australian annuities and superannuation income streams lump sum in arrears taxable component untaxed element

• Australian superannuation lump sum payments taxed element

• Australian superannuation lump sum payments untaxed element

• Bonuses from life insurance companies and friendly societies

A change to legislation relating to superannuation, taking effect from 1 July 2007, meant that people aged 60 years and over who receive superannuation income in the form of a lump sum or income stream (such as a pension) from a taxed source, receive that income tax free. Therefore, if a person has no other income, or their total income is below the tax-free threshold, or any tax payable is mitigated by a tax offset (such as Senior Australian Tax Offset), then this person is not required to lodge a tax return. Due to such changes, the superannuation statistics (persons, income) included in this release are regarded as partial, subject to under-coverage.

All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the Australian Bureau of Statistics (ABS) to closely align to ABS definitions of income.

The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.

Please note:

• All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

• To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.

• Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.

For further information please visit the Australian Bureau of Statistics.

AURIN has made the following changes to the original data:

• Spatially enabled the original data.

• Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.

The Survey on Household Income and Wealth (SHIW) is conducted every two years by the Bank of Italy on a sample of about 8,000 households and 24,000 individuals. In 2014 8,156 households and 19,366 individuals are interviewed. The households interviewed for the first time in this survey were 3,697 and the other 4,549 were panel households. The questionnaire consists of several thematic sections, which are further articulated within, and it is structured in such a way to ensure comparability with the information collected in the previous surveys: Family structure: sociodemographic characteristics of family members Employment and incomes: salary, work characteristics, sector of professional activities for each member Payment instruments and forms of saving: relations with financial institutions, use of payment instruments, savings and investments, expectations toward the future Principal residence, other property and debts: characteristics of property owned, mortgage loans, rents, property expenditures, other debts not in connection with business activity Household expenditures: purchase and sales of goods, average monthly spending on all consumption (food and non-food), perception of the expenditures compared to the past and the future, savings propensity Supplementary pension plans and insurance policies: characteristics of supplementary pension plans owned, life insurance, health insurance, household insurance The questionnaire has also a in-depth focus (annexes) for each professional condition (payroll employees, self-employed worker, family business, working shareholder/partner, pensions and other income sources) and loans (loans for main residence, for other properties than principal residence, for consumer credit, for business purposes). For each section and annex of the questionnaire is released a single file. The Bank of Italy has constructed a set of addictional files concerning to the household and individual incomes, household expenditure and savings, household wealth and about individuals that left the panel household. Since 2008, the survey is part of the project coordinated by the European Central Bank on the harmonized Eurosystem's Household Finance and Consumption Survey. 8,156 families and 19,366 individuals. Two-stage stratified random sample Computer-Assisted Personal Interviewing (CAPI)

Data Dictionary:

1. Title: Credit data

2. Source: Credit One Bank

3. Number of Instances: 5000

4. Name of Dataset: Analysis_of_Default

5. Number of Attributes: 20 (7 numerical, 13 categorical)

6. Attribute description

Attribute 1: (Qualitative / Categorical) Status of existing checking account A11: ... < 0 USD A12: 0 <= ... < 10000 USD A13: ... >= 10000 USD A14: no checking account

Attribute 2: (numerical) Duration in month

Attribute 3: (Qualitative / Categorical) Credit history A30: no credits taken/all credits paid back duly A31: all credits at this bank paid back duly A32: existing credits paid back duly till now A33: delay in paying off in the past A34:critical account/other credits existing(not at this bank)

Attribute 4: (Qualitative / Categorical) Purpose A40: car (new) A41: car (used) A42: furniture/equipment A43: radio/television A44: domestic appliances A45: repairs A46: education A47: (vacation - does not exist?) A48: retraining A49: business A410: others

Attribute 5: (numerical) Credit amount

Attribute 6: (Qualitative / Categorical) Savings account/bonds A61: ... < 1000 USD A62: 1000 <= ... < 5000 USD A63: 5000 <= ... < 10000 USD A64: .. >= 10000 USD A65: unknown/ no savings account

Attribute 7: (Qualitative / Categorical) Present employment since A71: unemployed A72: ... < 1 year A73: 1 <= ... < 4 years
A74: 4 <= ... < 7 years A75: .. >= 7 years

Attribute 8: (numerical) Installment rate in percentage of disposable income

Attribute 9: (Qualitative / Categorical) Personal status and sex A91: male : divorced/separated A92: female: divorced/separated/married A93: male : single A94: male : married/widowed A95: female: single

Attribute 10: (Qualitative / Categorical) Other debtors / guarantors A101: none A102: co-applicant A103: guarantor

Attribute 11: (numerical) Present residence since

Attribute 12: (Qualitative / Categorical) Property A121: real estate A122: if not A121: building society savings agreement/ life insurance A123: if not A121/A122: car or other, not in attribute 6 A124: unknown / no property

Attribute 13: (numerical) Age in years

Attribute 14: (Qualitative / Categorical) Other installment plans A141: bank A142: stores A143: none

Attribute 15: (Qualitative / Categorical) Housing A151: rent A152: own A153: for free

Attribute 16: (numerical) Number of existing credits at this bank

Attribute 17: (Qualitative / Categorical) Job A171: unemployed/ unskilled - non-resident A172: unskilled - resident A173: skilled employee / official A174: management/ self-employed/ highly qualified employee/ officer

Attribute 18: (numerical) Number of people being liable to provide maintenance for

Attribute 19: (Qualitative / Categorical) Telephone A191: none A192: yes, registered under the customer’s name

Attribute 20: (Qualitative / Categorical) foreign worker A201: yes A202: no

1. Default on Payment due

1 (Defaulted) 0 (No Default)