This dataset is now updated annually here.

Context

This dataset contains the salary, pay rate, and total compensation of every New York City employee. In this dataset this information is provided for the 2014, 2015, 2016, and 2017 fiscal years, and provides a transparent lens into who gets paid how much and for what.

Note that fiscal years in the New York City budget cycle start on July 1st and end on June 30th (see here). That means that this dataset contains, in its sum, compensation information for all City of New York employees for the period July 1, 2014 to June 30, 2017.

Content

This dataset provides columns for fiscal year, employee name, the city department they work for, their job title, and various fields describing their compensation. The most important of these fields is "Regular Gross Pay", which provides that employee's total compensation.

Acknowledgements

This information was published as-is by the City of New York.

Inspiration

• How many people do the various city agencies employ, and how much does each department spend on salary in total?
• What are the most numerous job titles in civic government employment?
• Where does overtime pay seem to be especially common? How much of it is there?
• How do New York City employee salaries compare against salaries of city employees in Chicago? Is the difference more or less than the difference in cost of living between the two cities?

On 10 May you clarified: The dates I'm requesting are from 2010 to the present day as this was when this current government came into power Response I can confirm that the NHSBSA holds the information you have requested • 1,081,286 cases have paid the penalty charge in full • 219,940 cases have paid both the penalty charge and the surcharge in full. • No one has been taken to court. Please read the below notes to ensure correct understanding of the data: • We do not hold data for how many individual people have paid a fine. The data provided is based on the number of cases, rather than the number of individuals, where a fine has been paid. • We have included any cases that are classed as fully paid and have paid either the penalty charge or both the penalty charge and surcharge. • This data is correct as of 20th May 2024. • The Prescription Exemption Checking Service started in 2014. The data provided is therefore from 2014 to 20th May 2024. Publishing this response Please note that this information will be published on our Freedom of Information disclosure log at: https://opendata.nhsbsa.net/dataset/foi-01915

Project overview

The main goal of this model is to help me create an app that count How much money does a picture has.

Descriptions of each class type

I don't seperate country base money and don't seperate front and back

EUR-1-cent   dasdasd
EUR-2-cent
EUR-5-cent
EUR-10-cent
EUR-20-cent
EUR-50-cent
EUR-1-euro
EUR-2-euro

Current status and timeline

• Adding EUR (currently)
• Adding CHF

Links to external resources

Contribution and labeling guidelines

This dataset contains 10,000 simulated sales transaction records, each represented in natural language with diverse sentence structures. It is designed to mimic how different users might describe the same type of transaction in varying ways, making it ideal for Natural Language Processing (NLP) tasks, text-based data extraction, and accounting automation projects.

Each record in the dataset includes the following fields:

Sale Date: The date on which the transaction took place. Customer Name: A randomly generated customer name. Product: The type of product purchased. Quantity: The quantity of the product purchased. Unit Price: The price per unit of the product. Total Amount: The total price for the purchased products. Tax Rate: The percentage of tax applied to the transaction. Payment Method: The method by which the payment was made (e.g., Credit Card, Debit Card, UPI, etc.). Sentence: A natural language description of the sales transaction. The sentence structure is varied to simulate different ways people describe the same type of sales event.

Use Cases: NLP Training: This dataset is suitable for training models to extract structured information (e.g., date, customer, amount) from natural language descriptions of sales transactions. Accounting Automation: The dataset can be used to build or test systems that automate posting of sales transactions based on unstructured text input. Text Data Preprocessing: It provides a good resource for developing methods to preprocess and standardize varying formats of text descriptions. Chatbot Training: This dataset can help train chatbots or virtual assistants that handle accounting or customer inquiries by understanding different ways of expressing the same transaction details.

Key Features: High Variability: Sentences are structured in numerous ways to simulate natural human language variations. Randomized Data: Names, dates, products, quantities, prices, and payment methods are randomized, ensuring no duplication. Multi-Field Information: Each record contains key sales information essential for accounting and business use cases.

Potential Applications: Use for Named Entity Recognition (NER) tasks. Apply for information extraction challenges. Create pattern recognition models to understand different sentence structures. Test rule-based systems or machine learning models for sales data entry and accounting automation.

License: Ensure that the dataset is appropriately licensed according to your intended use. For general public and research purposes, choose a CC0: Public Domain license, unless specific restrictions apply.

Employee payroll data for all Cook County employees excluding Forest Preserves, indicating amount of base salary paid to an employee during the County fiscal quarter. Salaries are paid to employees on a bi-weekly basis. Any pay period that extended between quarters will be reported to the quarter of the Pay Period End Date. (e.g. If a Pay Period runs 02/21-03/05, that pay period would be reported in the Q2 period, as the end of the pay period falls in March - Q2) The county fiscal quarters are: Q1: December - February Q2: March - May Q3: June - August Q4: September - November The Employee Unique Identifier field is a unique number assigned to each employee for the purpose of this data set, that is not their internal employee ID number, and allows an employee to be identified in the data set over time, in case of a name change or other change. This number will be consistent within the data set, but we reserve the right to regenerate this number over time across the data set. ISSUE RESOLVED: As of 4/19/2018 there was an issue regarding employee FY2016 and FY2017 payroll in which records were duplicated in the quarterly aggregation, resulting in inflated base pay amounts. Please disregard any data extracted from this dataset prior to the correction date and use this version moving forward. KNOWN ISSUE: Several records are missing Bureau and Office information. We are working on correcting this and will update the dataset when this issue has been resolved. For data prior to Fiscal Year 2016, see datasets at https://datacatalog.cookcountyil.gov/browse?tags=payroll

Updated 30 January 2023

Version 14 of Dataset

License Update:

There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.

We provide a license to anyone who wishes to use this dataset for learning or teaching. For the purposes of sharing, please follow this license:

CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Codebook

https://rpubs.com/rhuebner/hrd_cb_v14

PLEASE NOTE -- I recently updated the codebook - please use the above link. A few minor discrepancies were identified between the codebook and the dataset. Please feel free to contact me through LinkedIn (www.linkedin.com/in/RichHuebner) to report discrepancies and make requests.

Context

HR data can be hard to come by, and HR professionals generally lag behind with respect to analytics and data visualization competency. Thus, Dr. Carla Patalano and I set out to create our own HR-related dataset, which is used in one of our graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. We created this data set ourselves. We use the data set to teach HR students how to use and analyze the data in Tableau Desktop - a data visualization tool that's easy to learn.

This version provides a variety of features that are useful for both data visualization AND creating machine learning / predictive analytics models. We are working on expanding the data set even further by generating even more records and a few additional features. We will be keeping this as one file/one data set for now. There is a possibility of creating a second file perhaps down the road where you can join the files together to practice SQL/joins, etc.

Note that this dataset isn't perfect. By design, there are some issues that are present. It is primarily designed as a teaching data set - to teach human resources professionals how to work with data and analytics.

Content

We have reduced the complexity of the dataset down to a single data file (v14). The CSV revolves around a fictitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score.

Recent additions to the data include: - Absences - Most Recent Performance Review Date - Employee Engagement Score

Acknowledgements

Dr. Carla Patalano provided the baseline idea for creating this synthetic data set, which has been used now by over 200 Human Resource Management students at the college. Students in the course learn data visualization techniques with Tableau Desktop and use this data set to complete a series of assignments.

Inspiration

We've included some open-ended questions that you can explore and try to address through creating Tableau visualizations, or R or Python analyses. Good luck and enjoy the learning!

• Is there any relationship between who a person works for and their performance score?
• What is the overall diversity profile of the organization?
• What are our best recruiting sources if we want to ensure a diverse organization?
• Can we predict who is going to terminate and who isn't? What level of accuracy can we achieve on this?
• Are there areas of the company where pay is not equitable?

There are so many other interesting questions that could be addressed through this interesting data set. Dr. Patalano and I look forward to seeing what we can come up with.

If you have any questions or comments about the dataset, please do not hesitate to reach out to me on LinkedIn: http://www.linkedin.com/in/RichHuebner

You can also reach me via email at: Richard.Huebner@go.cambridgecollege.edu

With respect to the NHS supporting early diagnosis of cancer (Community Pharmacy) pilot 1. How many pharmacy contractors registered for this pilot during June, July, August, September, October, November and December 2023? 2. How many pharmacy contractors are currently registered for this pilot? 3. How many claims for payment for the service have been received by community pharmacies during June, July, August, September, October, November and December 2023? 4. How many claims have community pharmacies made cumulatively for this pilot to date? 5. How much have community pharmacies been paid for their work as part of the pilot during June, July, August, September, October, November and December 2023? 6. How much have community pharmacies been paid cumulatively for this pilot to date? Response Question 1 Our User Research team have confirmed that 8 pharmacies registered for the service in July 2023- no other pharmacies have registered in June 2023, or between August and December 2023. Question 2 9 pharmacies are registered in total for the pilot as of January 2024. Questions 3 & 4 We are unable to answer these questions- This is because the NHSBSA only receive monthly confirmation of amounts to be paid to pharmacies participating in the Early Diagnosis of Cancer local scheme, as confirmed by the regional Integrated Care Boards the pharmacies are located in. The amounts confirmed are not broken down into the number of claims the pharmacies have made in order to be entitled to those payments. Therefore, we cannot definitively confirm how many claims for payment have been received, or made cumulatively, for the pilot to date. Questions 5 & 6 This information is held within the monthly Management Information Spreadsheet (MIS) Pharmacy Contractor reports, for which data is currently available up until October 2023. All the reports for the months requested (that are currently available) have been published in previous FOI’s; June 2023’s Pharmacy Contractor report was published in FOI-01426 - https://opendata.nhsbsa.net/dataset/foi-01426 July, August and September 2023’s Pharmacy Contractor report was published in FOI-01579 - https://opendata.nhsbsa.net/dataset/foi-01579 October 2023’s Pharmacy Contractor report was published in FOI-01673 - https://opendata.nhsbsa.net/dataset/foi-01673 Any payments made to a pharmacy contractor within the respective month are listed under the column Local_Scheme_21 within each pharmacy contractor spreadsheet. Please note that payments have only been made in June 2023, July 2023 and September 2023, based on the data within the MIS spreadsheets. We would also state as a caveat that any payments made may be subject to adjustments in further months.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Many. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Many, the median income for all workers aged 15 years and older, regardless of work hours, was $23,088 for males and $16,460 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in Many. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Many.

- Full-time workers, aged 15 years and older: In Many, among full-time, year-round workers aged 15 years and older, males earned a median income of $50,625, while females earned $33,250, leading to a 34% gender pay gap among full-time workers. This illustrates that women earn 66 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Many offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Many median household income by race. You can refer the same here

The dataset supports measure S.D.4.a of SD23. The Austin Municipal Court offers services via in person, phone, mail, email, online, in the community, in multiple locations, and during non-traditional hours to make it easier and more convenient for individuals to handle court business. This measure tracks the percentage of customers that utilize court services outside of normal business hours, defined as 8am-5pm Monday-Friday, and how many payments were made by methods other than in person. This measure helps determine how Court services are being used and enables the Court to allocate its resources to best meet the needs of the public. Historically, almost 30% of the operational hours are outside of traditional hours and the average percentage of payments made by mail and online has been over 59%. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/c7z3-geii Data source: electronic case management system and manual tracking of payments received via mail. Calculation: Business hours are manually calculated annually. - A query is run from the court’s case management system to calculate how many monetary transactions were posted. S.D.4.a: Numerator: Number of payments received by mail is entered manually by the Customer Service unit that processes all incoming mail. S.D.4.a Denominator: Total number of web payments is calculated using a query to calculate a total number of payments with a payment type ‘web’ in the case management system. Measure time period: Annual (Fiscal Year) Automated: No Date of last description update: 4/10/2020

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Ontario. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Ontario, the median income for all workers aged 15 years and older, regardless of work hours, was $32,277 for males and $25,837 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Ontario. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Ontario.

- Full-time workers, aged 15 years and older: In Ontario, among full-time, year-round workers aged 15 years and older, males earned a median income of $46,816, while females earned $42,018, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Ontario.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Ontario.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Ontario median household income by race. You can refer the same here

This study investigates whether people consider elements beyond health when valuing Quality-Adjusted Life-Years (QALYs) monetarily and the influence of inclusion on this value. A Willingness to Pay (WTP) experiment was administered among the general public in which people were asked to assign monetary values to QALYs. Our results show that (stated) UoC increases with quality of life but that instructing people to consider UoC does not impact their monetary valuation of the QALY. Furthermore, many respondents consider elements beyond health when valuing QALYs but the impact on the monetary value of a QALY is limited.This dataset includes the documents related to the construction of the (sub-versions of the) questionnaire, the raw data from the (subversions of the) questionnaire collected by and received from the sampling agency, and the data after merging the individual datasets for the subversions into one dataset, and the code to analyze the data.

Note, 7/10/2025: Please see this article for information on a change to the EMPLOYEE DATASET ID column and this article for information on a data correction. Welcome to the official source for Employee Payroll Costing data for the City of Chicago. This dataset offers a clean, comprehensive view of the City's payroll information by employee. About the Dataset: This has been extracted from the City of Chicago's Financial Management and Purchasing System (FMPS). FMPS is the system used to process all financial transactions made by the City of Chicago, ensuring accuracy and transparency in fiscal operations. This dataset includes useful details like employee name, pay element, pay period, fund, appropriation, department, and job title. Data Disclaimer: The following data disclaimer governs your use of the dataset extracted from the Payroll Costing module of the City of Chicago's Financial Management and Purchasing System (FMPS) or (FMPS Payroll Costing). Point-in-Time Extract: The dataset provided herein, represents a point-in-time extract from the FMPS Payroll Costing module and may not reflect real-time or up-to-date data. Financial Statement Disclaimer – Timeframe and Limitations: This dataset is provided without audit. It is essential to note that this dataset is not a component of the City's Annual Comprehensive Financial Report (ACFR). As such, it remains preliminary and is subject to the end-of-year reconciliation process inherent to the City's annual financial procedures outlined in the ACFR. Note on Pay Elements: All pay elements available in the FMPS Payroll Costing module have been included in this dataset. Previously published datasets, such as "Employee Overtime and Supplemental Earnings," contained only a subset of these pay elements. Payroll Period: The dataset's timeframe is organized into 24 payroll periods. It is important to understand that these periods may or may not directly correspond to specific earnings periods. Aggregating Data: The CIty of Chicago often has employees with the same name (including middle initials). It is vital to use the unique employee identifier code (EMPLOYEE DATASET ID) when aggregating at the employee level to avoid duplication. Data Subject to Change: This dataset is subject to updates and modifications due to the course of business, including activities such as canceling, adjusting, and reissuing checks. Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals. NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data. NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

This statistical release makes available the most recent Mental Health and Learning Disabilities Dataset (MHLDDS) final monthly data (June 2015). This publication presents a wide range of information about care delivered to users of NHS funded secondary mental health and learning disability services in England. The scope of the Mental Health Minimum Dataset (MHMDS) was extended to cover Learning Disability services from September 2014. Many people who have a learning disability use mental health services and people in learning disability services may have a mental health problem. This means that activity included in the new MHLDDS dataset cannot be distinctly divided into mental health or learning disability spells of care - a single spell of care may include inputs from either of both types of service. We will be working with stakeholders to define specific information and reporting requirements relating to specific services or groups of patients. Four new measures have been added to this release to help with interpretation of the data. At local level these contextual figures will provide an indication of the increased caseload that could be attributed to the extension of the dataset to cover LD services. Information on these measures can found in the Announcement of Change paper which accompanies this release. The Currencies and Payment file that forms part of this release is specifically limited to services in scope for currencies and payment in mental health services and remains unchanged. This information will be of particular interest to organisations involved in delivering secondary mental health and learning disability care to adults and older people, as it presents timely information to support discussions between providers and commissioners of services. The MHLDS Monthly Report also includes reporting by local authority for the first time. For patients, researchers, agencies, and the wider public it aims to provide up to date information about the numbers of people using services, spending time in hospital and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. The Currency and Payment (CaP) measures can be found in a separate machine-readable data file and may also be accessed via an on-line interactive visualisation tool that supports benchmarking. This can be accessed through the related links at the bottom of the page. This release also includes a note about the new experimental data file and the issuing of the ISN for the Mental Health Services Dataset (MHSDS). A consultation on changes to these statistics as a result of the change to the dataset has been opened. We welcome views on the proposed changes.

Context

This data set includes customers who have paid off their loans, who have been past due and put into collection without paying back their loan and interests, and who have paid off only after they were put in collection. The financial product is a bullet loan that customers should pay off all of their loan debt in just one time by the end of the term, instead of an installment schedule. Of course, they could pay off earlier than their pay schedule.

Content

Loan_id A unique loan number assigned to each loan customers

Loan_status Whether a loan is paid off, in collection, new customer yet to payoff, or paid off after the collection efforts

Principal Basic principal loan amount at the origination

terms Can be weekly (7 days), biweekly, and monthly payoff schedule

Effective_date When the loan got originated and took effects

Due_date Since it’s one-time payoff schedule, each loan has one single due date

Paidoff_time The actual time a customer pays off the loan

Pastdue_days How many days a loan has been past due

Age, education, gender A customer’s basic demographic information

Data Dictionary

The Data contains 1 file :

• loan.csv: In this file there are 18 columns:
  1. loanId: This is a unique loan identifier. Use this for joins with the payment.csv file
  2. anon_ssn: This is a hash based on a client’s SSN (Anonymous ssn). You can use this as if it is a SSN to compare if a loan belongs to a previous customer.
  3. payFrequency: This column represents repayment frequency of the loan:
     • B is biweekly payments
     • I is irregular
     • M is monthly
     • S is semi monthly
     • W is weekly
  4. apr: Annual Percentage Rate of the loan (%)
  5. applicationDate: Date of application (start date)
  6. originated: Indicates if the loan has been initiated (underwriting process started).
  7. originatedDate: Date of origination, day the loan was originated
  8. nPaidOff: Number of MoneyLion loans previously paid off by the client.
  9. approved: Indicates if the loan has been approved (final step of underwriting).
  10. isFunded: Whether or not a loan is ultimately funded. a loan can be voided by a customer shortly after it is approved, so not all approved loans are ultimately funded.
  11. loanStatus: Current loan status (this column is used for prediction). Most are selfexplanatory. Below are the statuses which need clarification:
      • Withdrawn Application: The applicant has withdrawn their loan application before it was approved or funded.
      • Paid Off Loan: The loan has been fully paid off by the borrower according to the repayment terms.
      • Rejected: The loan application was rejected, typically due to failure to meet underwriting criteria.
      • New Loan: A newly approved loan that has not yet been funded.
      • Internal Collection: The loan is being managed and collected internally by MoneyLion due to missed payments or delinquency.
      • CSR Voided New Loan: A new loan application was voided by a customer service representative (CSR) before funding.
      • External Collection: The loan has been transferred to an external collection agency for management and collection.
      • Returned Item: A payment on the loan has been returned due to insufficient funds in the borrower's account.
      • Customer Voided New Loan: The borrower voided a new loan application before funding.
      • Credit Return Void: The loan was voided due to a credit return, typically related to a refunded transaction.
      • Pending Paid Off: The loan is in the process of being paid off, but the process is pending completion.
      • Charged Off Paid Off: The loan has been charged off as a loss by MoneyLion but has also been paid off by the borrower.
      • Settled Bankruptcy: The loan has been settled as part of a bankruptcy proceeding.
      • Settlement Paid Off: The loan has been paid off through a settlement agreement.
      • Charged Off: The loan has been charged off as a loss by MoneyLion due to nonpayment.
      • Pending Rescind: The loan is pending rescission, meaning it may be canceled or reversed.
      • Customver Voided New Loan: Typo: Likely should be "Customer Voided New Loan". Similar to "Customer Voided New Loan", indicating the borrower voided a new loan application before funding.
      • Pending Application: The loan application is pending review and approval.
      • Voided New Loan: The loan application was voided before funding.• Pending Application Fee: The loan application is pending due to the application fee not being paid.
      • Settlement Pending Paid Off: The loan is pending being paid off through a settlement agreement.
  12. loanAmount: Principal amount of the loan ('Dollars') (for non-funded loans this will be the principal in the loan application)
  13. originallyScheduledPaymentAmount: This is the Initialy scheduled repayment amount ('Dollars') (if a customer pays off all his scheduled payments, this is the amount we should receive)
  14. state: State of the client
  15. Lead type: The lead type determines the underwriting rules for a lead.
      • bvMandatory: leads that are bought from the ping tree – required to perform bank verification before loan approval
      • lead: very similar to bvMandatory, except bank verification is optional for loan approval
      • california: similar to lead, but optimized for California lending rules
      • organic: customers that came through the MoneyLion website
      • rc_returning: customers who have at least 1 paid off loan in another loan portfolio. (The first paid off loan is not in this data set).
      • prescreen: preselected customers who have been offered a loan through direct mail campaigns
      • express: promotional “express” loans
      • repeat: promotional loans offered through ...

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Leslie. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Leslie, the median income for all workers aged 15 years and older, regardless of work hours, was $42,500 for males and $37,667 for females.

Based on these incomes, we observe a gender gap percentage of approximately 11%, indicating a significant disparity between the median incomes of males and females in Leslie. Women, regardless of work hours, still earn 89 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Leslie, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,375, while females earned $51,765, resulting in a 5% gender pay gap among full-time workers. This illustrates that women earn 95 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Leslie.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Leslie, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Leslie median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Depoe Bay. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Depoe Bay, the median income for all workers aged 15 years and older, regardless of work hours, was $44,167 for males and $41,000 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Depoe Bay. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Depoe Bay, among full-time, year-round workers aged 15 years and older, males earned a median income of $78,750, while females earned $61,767, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Depoe Bay offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Depoe Bay median household income by race. You can refer the same here

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932).

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016