This record contains data on: the number of sponsors registered on the points based system; the number of new sponsor applications; the percentage of sponsors with highly trusted sponsor status; the breakdown by time taken to process an application; the sponsor notifications in potential non-compliance categories received; sponsors (by tier) which had pre registration visits; sponsors (by tier) which had follow up visits including “Unannounced” visits sponsorship requests; sponsorship service standards; action taken against sponsors; sponsor notifications regarding potential non-compliance; number where leave has been curtailed.

We conducted a cross-sectional study of the publicly available 2022 Open Payments data to characterize and quantify sponsored events (available for download at: https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads). Data sources We downloaded the 2022 dataset ZIP files from the Open Payments website on June 30th, 2023. We included all records for nurse practitioners, clinical nurse specialists, certified registered nurse anesthetists, and certified nurse-midwives (hereafter advanced practiced registered nurses (APRNs)); and allopathic and osteopathic physicians (hereafter, ‘physicians’). To ensure consistency in provider classification, we linked Payments data to the National Plan and Provider Enumeration System data (June 2023) by National Provider Identifier (NPI) and the National Uniform Claim Committee (NUCC) and excluded individuals with an ambiguous provider type. Event-centric analysis of Open Payments records: Creating an event typology We included only payments classified as “food and beverage” to reliably identify distinct sponsored events. We reasoned that food and beverage would be consumed on the same day in the same place, thus assumed that records for food and beverage associated with the same event would share the date of payment and location. We also assumed that the reported value of a food and beverage payment is the total cost of the hospitality divided by the number of attendees, thus grouped payment records with the same amount, rounded to the nearest dollar. Inferring which Open Payment records relate to the same sponsored event requires analytic decisions regarding the selection and representation of variables that define an event. To understand the impact of these choices, we undertook a sensitivity analysis to explore alternative ways to group Open Payments records for food and beverage, to determine how combination of variables, including date (specific date or within the same calendar week), amount (rounded to nearest dollar), and recipient’s state, affected the identification of sponsored events in the Open Payments data set. We chose to define a sponsored event as a cluster of three or more individual payment records for food and beverage (nature of payment) with the following matching Open Payments record variables: • Submitting applicable manufacturer (name) • Product category or therapeutic area • Name of drug or biological or device or medical supply • Recipient state • Total amount of payment (USD, rounded to nearest dollar) • Date of payment (exact) After examining the distribution of the data, we classified events in terms of size (≥20 attendees as “large” and 3-<20 as “small”) and amount per person. We categorized events <$10 as “coffee”, $10-<$30 as “lunch”, $30-<$150 as “dinner”, and ≥$150 as “banquet”.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for data analyst in the U.S.

H-1B visa sponsorship trends for Data Engineer, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

This record contains data on: the number of sponsors registered on the points based system; the number of new sponsor applications; the percentage of sponsors with highly trusted sponsor status; the breakdown by time taken to process an application; the sponsor notifications in potential non-compliance categories received; sponsors (by tier) which had pre registration visits; sponsors (by tier) which had follow up visits including “Unannounced” visits sponsorship requests; sponsorship service standards; action taken against sponsors; sponsor notifications regarding potential non-compliance; number where leave has been curtailed.

United States Assets: Flow: PP: Misc: Claims of Pension Fund on Sponsor data was reported at -9.858 USD bn in Sep 2018. This records a decrease from the previous number of 3.343 USD bn for Jun 2018. United States Assets: Flow: PP: Misc: Claims of Pension Fund on Sponsor data is updated quarterly, averaging 2.026 USD bn from Dec 1951 (Median) to Sep 2018, with 268 observations. The data reached an all-time high of 48.661 USD bn in Mar 2001 and a record low of -21.182 USD bn in Dec 1985. United States Assets: Flow: PP: Misc: Claims of Pension Fund on Sponsor data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB022: Funds by Sector: Flows and Outstanding: Private Pension Funds.

The (CACFP) provides reimbursements for nutritious meals and snacks served in family day care homes, child care centers, and other participating facilities and programs. This assessment examines the accuracy of the classification of Family Day Care Homes (FDCHs) participating in the U.S. Department of Agriculture's (USDA) Child and Adult Care Food Program. The assessment provides estimates of the number of FDCHs misclassified by sponsoring agencies into the wrong tier and the resulting erroneous payments for meals and snacks reimbursed at the wrong rate for program year 2013.

United States Assets: Outs: DS: DF: Government-sponsored data was reported at 407.525 USD bn in Mar 2018. This records an increase from the previous number of 403.769 USD bn for Dec 2017. United States Assets: Outs: DS: DF: Government-sponsored data is updated quarterly, averaging 8.088 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 1,537.862 USD bn in Sep 2003 and a record low of 0.428 USD bn in Dec 1951. United States Assets: Outs: DS: DF: Government-sponsored data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB043: Funds by Instruments: Flows and Outstanding: Debt Securities.

The United Nations General Assembly (UNGA) represents a microcosm of global politics that offers a valuable snapshot of interstate relations and state preferences. In this context, roll-call votes and measures of voting affinity often receive the bulk of scholarly attention. However, even though techniques such as ideal point estimation have grown more sophisticated over time when applied to voting data, they remain grounded by an original selection bias that discards 2/3 of the UNGA yield. This share of disregarded output can prove highly informative if drafting and sponsorship procedures receive a closer look instead. This research note applies ideal point estimation to UNGA sponsorship data for the first time for every member from 2009 to 2019. It advances a cutting-edge approach to better estimate state preferences over a contested policy space, while correcting for the narrow focus of previous UNGA analyses on voting data. The results detect an underlying issue space that bears external validity with the inclination of states towards multilateralism.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for data science in the U.S.

H-1B visa sponsorship trends for Senior Data Analyst, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Most nonelderly Americans purchase health insurance through their employers, which sponsor a limited number of plans. Using a panel dataset representing over ten million insured lives, we estimate employees' preferences for different health plans and use the estimates to predict their choices if more plans were made available to them on the same terms, i.e., with equivalent subsidies and at large-group prices. Using conservative assumptions, we estimate a median welfare gain of 13 percent of premiums. A proper accounting of the costs and benefits of a transition from employer-sponsored to individually-purchased insurance should include this nontrivial gain. (JEL G22, I13, J32)

H-1B visa sponsorship trends for Data Scientist Ii, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Graph and download economic data for Government-Sponsored Enterprises; Total Financial Assets Held by REFCORP, Level (BOGZ1FL404090075A) from 1945 to 2024 about GSE, assets, and USA.

Graph and download economic data for Government-Sponsored Enterprises; GSE Issues; Liability, Level (GSEIL) from Q4 1945 to Q1 2025 about GSE, issues, liabilities, and USA.

To address the global extinction crisis, both efficient use of existing conservation funding and new sources of funding are vital. Private sponsorship of charismatic ‘flagship’ species conservation represents an important source of new funding, but has been criticized as being inefficient. However, the ancillary benefits of privately sponsored flagship species conservation via actions benefiting other species have not been quantified, nor have the benefits of incorporating such sponsorship into objective prioritization protocols. Here, we use a comprehensive dataset of conservation actions for the 700 most threatened species in New Zealand to examine the potential biodiversity gains from national private flagship species sponsorship programmes. We find that private funding for flagship species can clearly result in additional species and phylogenetic diversity conserved, via conservation actions shared with other species. When private flagship species funding is incorporated into a prioritization protocol to preferentially sponsor shared actions, expected gains can be more than doubled. However, these gains are consistently smaller than expected gains in a hypothetical scenario where private funding could be optimally allocated among all threatened species. We recommend integrating private sponsorship of flagship species into objective prioritization protocols to sponsor efficient actions that maximize biodiversity gains, or wherever possible, encouraging private donations for broader biodiversity goals.

Graph and download economic data for Government-Sponsored Enterprises; Debt Securities; Asset, Level (GSEDSA) from Q4 1945 to Q1 2025 about GSE, debt, securities, assets, and USA.