Context

I found this dataset on the US Office of Government Ethics website, but all the financial reports were in the PDF format. I wanted to make it more easily accessible for data analysis & data science; hence, I converted all the PDF files to the Excel format that is much easier to use.

Content

It contains the Annual & on-termination financial reports for the entire Execution branch & it's administration from 2013 to 2020 including those of the President & vice-president. So, it covers the Obama Administration & the Trump Administration between those dates. It includes financial Assets, transactions, Retirement Accounts, Employments, Liabilities, Gifts & Travel Reimbursements, etc. with their values, income amounts, dates, name/description, etc.

I have seen inaccuracies in the data when converting from the PDF to Excel for the President & Vice-president's (Obama, Biden, Trump, Pence) files. I have tried to fix the numerical errors as much as I can. Also, I am attaching the raw PDF files so you can compare it with the excel & fix your analysis. I haven't seen any inaccuracies between the PDF & Excel file for the rest of the administration files (which is easily the bulk of this dataset).

Acknowledgements

This dataset, ofcourse, would not be possible without the US Office of Government Ethics collecting this & making it available for downloads. So, thanks to them! You can find the original PDF files on their website at : https://www.oge.gov/web/oge.nsf

The data also comes with Terms of Use that I have uploaded as the LICENSE.txt file. I am pasting it here too for easy access. By using this dataset, you are acknowledging & accepting these terms.

Public Financial Disclosure Reports Title 1 of the Ethics in Government Act of 1978, as amended, 5 U.S.C. app. § 105(c), states that: It shall be unlawful for any person to obtain or use a report: (A) for any unlawful purpose; (B) for any commercial purpose, other than by news and communications media for dissemination to the general public; (C) for determining or establishing the credit rating of any individual; or (D) for use, directly or indirectly, in the solicitation of money for any political, charitable, or other purpose. The Attorney General may bring a civil action against any person who obtains or uses a report for any purpose prohibited in paragraph (1) of this subsection. The court in which such action is brought may assess against such person a penalty in any amount not to exceed $11,000. Such remedy shall be in addition to any other remedy available under statutory or common law.

Inspiration

I don't know exactly what questions to ask, but feel free to use your imagination or follow your inspiration. Some interesting things might be how people's finances have evolved over time, does anyone seemingly have any conflict of interest based on their investments & their role

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

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Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Some communes in the Agadez region and Diffa region were excluded because of insecurity. In addition PSUs with fewer than 25 households were also excluded. The excluded area represents approximately 8% of the population.

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Niger is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Content

Member countries are allocated votes at the time of membership and subsequently for additional subscriptions to capital. Votes are allocated differently in each organization.

Each member receives the votes it is allocated under IDA replenishments according to the rules established in each IDA replenishment resolution. Votes consist of subscription votes and membership votes.

Latest information about voting power is available at http://www.worldbank.org/en/about/leadership/votingpowers

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore World Bank's Financial Data using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under a Creative Commons Attribution 3.0 IGO license.

Cover photo by Brandon Mowinkel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under Creative Commons Attribution 3.0 IGO

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

This datasets contains information about NYCHA residents’ use of: a) NYC Financial Empowerment Centers: a program that provides free, one-on-one professional financial counseling and coaching to all NYC residents. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; b) EmpoweredNYC: is an initiative to assist New Yorkers with disabilities and their families to better manage their finances and become more financially stable. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; c) Student Loan Debt clinic: is an initiative to help New Yorkers understand their student loans and how to repay them. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; and d) Ready to Rent: a program providing free one-on-one financial counseling to New Yorkers seeking to apply for affordable housing units through HPD’s Housing Connect lottery. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service. The dataset is part of the annual report compiled by the Mayor’s Office of Operations as mandated by the Local Law 163 of 2016 on different services provided to NYCHA residents. See other datasets in this report by searching the keyword “Services available to NYCHA Residents - Local Law 163 (2016)” on the Open Data Portal.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

See Methodology document for country-specific geographic coverage details.

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

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 Money Creek township. 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 Money Creek township, the median income for all workers aged 15 years and older, regardless of work hours, was $42,604 for males and $39,643 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 Money Creek township. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

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 Money Creek township median household income by race. You can refer the same here

Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers

At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.

By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.

What Makes Lucror’s Fixed Income Data Unique?

Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.

Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.

Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.

How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:

• Public Sources: Includes issuer filings, bond prospectuses, financial reports, and market data.
• Proprietary Analysis: Leveraging proprietary models, our team enriches raw data to provide actionable insights.
• Expert Review: Data is validated and adjusted by experienced analysts to ensure accuracy and relevance.
• Regular Updates: Models are continuously updated to reflect market movements, regulatory changes, and issuer-specific developments.

This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.

Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.

1. Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.

2. Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.

3. Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.

Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.

Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.

By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...

A. SUMMARY This dataset contains data from financial statements of campaign committees that file with the San Francisco Ethics Commission and (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC. Financial statements are included for a committee if they meet any of the three criteria for each election included in the search parameters and are not primarily formed for the election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the output of the second step for committees that file with the San Francisco Ethics Commission. The data comes from a nightly search of the Ethics Commission campaign database. A second dataset includes committees that file with the Secretary of State. C. UPDATE PROCESS This dataset is rewritten nightly and is based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions from FPPC Form 461 and Form 465 filings are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F461P5, F465P3, F496, or F497P2 represent expenditures, or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts, or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are presented in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on 496/497 filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the C

About this dataset

Context

The dataset tabulates the Money Creek township household income by age. The dataset can be utilized to understand the age-based income distribution of Money Creek township income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Money Creek Township, Minnesota annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Money Creek Township, Minnesota household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Money Creek township income distribution by age. You can refer the same here

This datasets contains information about NYCHA residents’ use of: a) NYC Financial Empowerment Centers: a program that provides free, one-on-one professional financial counseling and coaching to all NYC residents. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; b) EmpoweredNYC: is an initiative to assist New Yorkers with disabilities and their families to better manage their finances and become more financially stable. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; c) Student Loan Debt clinic: is an initiative to help New Yorkers understand their student loans and how to repay them. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; and d) Ready to Rent: a program providing free one-on-one financial counseling to New Yorkers seeking to apply for affordable housing units through HPD’s Housing Connect lottery. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service. The dataset is part of the annual report compiled by the Mayor’s Office of Operations as mandated by the Local Law 163 of 2016 on different services provided to NYCHA residents. See other datasets in this report by searching the keyword “Services available to NYCHA Residents - Local Law 163 (2016)” on the Open Data Portal.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Money Creek township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Money Creek township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Money Creek township, the median household income stands at $95,500 for householders within the 25 to 44 years age group, followed by $93,958 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $76,875.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Money Creek township median household income by age. You can refer the same here

This dataset reports summary state-by-state total expenditures by program for the Medicaid Program, Medicaid Administration and CHIP programs. These state expenditures are tracked through the automated Medicaid Budget and Expenditure System/State Children's Health Insurance Program Budget and Expenditure System (MBES/CBES). For more information, visit https://medicaid.gov/medicaid/finance/state-expenditure-reporting/expenditure-reports/index.html.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Money Creek township: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 4(1.75%) households where the householder is under 25 years old, 35(15.35%) households with a householder aged between 25 and 44 years, 138(60.53%) households with a householder aged between 45 and 64 years, and 51(22.37%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the township of Money Creek township, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Money Creek township median household income by age. You can refer the same here

This dataset contains a list of all campaign finance reports (C3 and C4) for the last 10 years including attached schedules. It includes reports that have been superseded by an amendment. The primary purpose of this dataset is for data consumers to track report amendments and to examine the reporting history for a filer. Refer to other datasets to get actual values for any of the reports referenced herewith. For candidates, the number of years is determined by the year of the election, not necessarily the year the report was filed. For political committees, the number of years is determined by the calendar year of the reporting period. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

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Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. The data includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. Unlike prior years' where the audited financial information was compiled by OPM, the FY 2020 and beyond information in this edition was based upon the self-reporting by municipalities of their own audited data. Note: This dataset includes annually reported data using three types of years: calendar year, fiscal year, and grand list year. The calendar year spans January 1 to December 31. In Connecticut, the state fiscal year runs from July 1 to June 30, with the numerical year indicating when the fiscal year ends (e.g., fiscal year 2022 ended on June 30, 2022). The grand list year refers to the year municipalities assess property values, which occurs annually on October 1. For example, the property values assessed on October 1, 2020, are referred to as "Grand List Year 2020." However, these values are used to levy property taxes for the next fiscal year, spanning July 1, 2021, to June 30, 2022. In this context, grand list year 2020 corresponds to fiscal year ending 2022. Similarly, mill rates for each year are based on the grand list from two years prior. The most recent edition is for the Fiscal Years Ended 2018-2022 published in September 2024. For additional data on net current expenditures per pupil, see the State Department of Education website here: https://portal.ct.gov/sde/fiscal-services/net-current-expenditures-per-pupil-used-for-excess-cost-grant-basic-contributions/documents For additional population data from the Department of Public Health, visit their website here: https://portal.ct.gov/dph/health-information-systems--reporting/population/annual-town-and-county-population-for-connecticut The most recent data on the Municipal Fiscal Indicators is included in the following datasets: Municipal-Fiscal-Indicators: Financial Statement Information, 2020-2022 https://data.ct.gov/d/d6pe-dw46 Municipal-Fiscal-Indicators: Uniform Chart of Accounts, 2020-2022 https://data.ct.gov/d/e2qt-k238 Municipal Fiscal Indicators: Pension Funding Information for Defined Benefit Pension Plans, 2020-2022 https://data.ct.gov/d/73q3-sgr8 Municipal Fiscal Indicators: Type and Number of Pension Plans, 2020-2022 https://data.ct.gov/d/i84g-vvfb Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2020-2022 https://data.ct.gov/d/ei7n-pnn9 Municipal Fiscal Indicators: Economic and Grand List Data, 2019-2024 https://data.ct.gov/d/xgef-f6jp Municipal Fiscal Indicators: Benchmark Labor Data, 2020-2024 https://data.ct.gov/d/5ijb-j6bn Municipal Fiscal Indicators: Bond Ratings, 2019-2022 https://data.ct.gov/d/a65i-iag5 Municipal Fiscal Indicators: Individual Town Data, 2014-2022 https://data.ct.gov/d/ej6f-y2wf Municipal Fiscal Indicators: Totals and Averages, 2014-2022 https://data.ct.gov/d/ryvc-y5rf

A. SUMMARY Transactions from FPPC Forms 460, 461, 496, 497, and 450. This dataset combines all schedules, pages, and includes unitemized totals. Only transactions from the "most recent" version of a filing (original/amendment) appear here.

B. HOW THE DATASET IS CREATED Committees file campaign statements with the Ethics Commission on a periodic basis. Those statements are stored with the Commission's data provider. Data is generally presented as-filed by committees.

If a committee files an amendment, the data from that filing completely replaces the original and any prior amendments in the filing sequence.

C. UPDATE PROCESS Each night starting at midnight Pacific time a script runs to check for new filings with the Commission's database, and updates this dataset with transactions from new filings. The update process can take a variable amount of time to complete. Viewing or downloading this dataset while the update is running may result in incomplete data, therefore it is highly recommended to view or download this data before midnight or after 8am.

During the update, some fields are copied from the Filings dataset into this dataset for viewing convenience. The copy process may occasionally fail for some transactions due to timing issues but should self-correct the following day. Transactions with a blank 'Filing Id Number' or 'Filing Date' field are such transactions, but can be joined with the appropriate record using the 'Filing Activity Nid' field shared between Filing and Transaction datasets.

D. HOW TO USE THIS DATASET
Transactions from rejected filings are not included in this dataset. Transactions from many different FPPC forms and schedules are combined in this dataset, refer to the column "Form Type" to differentiate transaction types. Properties suffixed with "-nid" can be used to join the data between Filers, Filings, and Transaction datasets. Refer to the Ethics Commission's webpage for more information. Fppc Form460 is organized into Schedules as follows:

• A: Monetary Contributions Received
• B1: Loans Received
• B2: Loan Guarantors
• C: Nonmonetary Contributions Received
• D: Summary of Expenditures Supporting/Opposing Other Candidates, Measures and Committees
• E: Payments Made
• F: Accrued Expenses (Unpaid Bills)
• G: Payments Made by an Agent or Independent Contractor (on Behalf of This Committee)
• H: Loans Made to Others
• I: Miscellaneous Increases to Cash

RELATED DATASETS

• San Francisco Campaign Filers
• Filings Received by SFEC
• Summary Totals
• Transactions

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.