Contains cross-reference SSN data.

Database storing data for all transactions in the Enumeration process.

This dataset provides data at the national level from federal fiscal year 2006 onwards for the accuracy of the assignment of Social Security numbers (SSN) based on an end-of line sample review of transactions that result in the release of SSN cards.

An Enumeration Operational Data Store (ODS) in DB2 using SUMS standards and architecture. MISF version for ad hoc reporting and standard MI reports.

Database for several Social Security Number Verification Systems that verify SSNs against the Numident.

Provides ad hoc query and standard report data on the measure for preventing the issuance of SSN cards to non-existent children.

Database that logs enumeration transactions processed by the SSNAP process.

The SSN Verification Service is used by Java applications to execute the GUVERF02 service using the WebSphere/CICS Interface. It accepts several input data fields like First Name, Last Name, and Mother's Maiden Name and compares the data to the corresponding fields on the Numident record. SSN Verify Service can be invoked from WebSphere only through the CMM infrastructure. The Numident record is retrieved by calling the GUNSCHIP general utility. This service is currently being used by the appointment application to match input data with the information on the Numident.

Contains information about the enumeration process.

Database list of unassigned numbers.

Description:This data deposit contains the Numerical Identification Death Files (National Archives Identifier 23845618), the NUMIDENT SS-5 Application Files (National Archives Identifier 23845613), the NUMIDENT Claims Files (National Archives Identifier 23852747), and the associated technical documentation. Data Acquisition:These files were e-delivered to Anthony Wray via secure link by the Electronic Records Division of the National Archives and Records Administration (NARA) on 17 October 2019, as per a digitized reproduction order (Quote QO1-525370500 and Quote QO1-528389077). The packing slip is included in the data deposit (docs/Packing Slip.PDF).Rights to Publish:The data are in the public domain, as confirmed by emails received from NARA on 28 December 2023 and 3 January 2024 (see docs/permission_to_publish_email.pdf).How to Cite: Please adhere to the citation and data usage guidelines when using this dataset. See the included LICENSE.txt and README.md files for details. Details:The Numerical Identification Files (NUMIDENT), 1936–2007, series contains records for every Social Security number (SSN) assigned to individuals with a verified death or who would have been over 110 years old by December 31, 2007. There are three types of entries in NUMIDENT: application (SS-5), claim, and death records. A NUMIDENT record may contain more than one entry. Information contained in NUMIDENT records includes: each applicant's full name, SSN, date of birth, place of birth, citizenship, sex, father's name, mother's maiden name, and race/ethnic description (optional). NUMIDENT includes information regarding any subsequent changes made to the applicant's record, including name changes and life or death claims. The death records in NUMIDENT do not include any State reported deaths in accordance with the Social Security Act section 205(r). There are 72,182,729 SS-5 records entries; 25,230,486 claim record entries; and 49,459,293 death record entries.See https://catalog.archives.gov/id/12004494 for more information.Related Data:Visit the CenSoc Project for public micro datasets linked to NUMIDENT: https://censoc.berkeley.edu/.

Files sent to Department of Treasury to pay SSI Recipients.

Brazil Social Security: Number of Conceded Benefits: Urban: Northeast data was reported at 51,746.000 Unit in Apr 2019. This records an increase from the previous number of 43,349.000 Unit for Mar 2019. Brazil Social Security: Number of Conceded Benefits: Urban: Northeast data is updated monthly, averaging 49,508.500 Unit from Jan 2008 (Median) to Apr 2019, with 136 observations. The data reached an all-time high of 71,211.000 Unit in Aug 2017 and a record low of 24,810.000 Unit in Sep 2015. Brazil Social Security: Number of Conceded Benefits: Urban: Northeast data remains active status in CEIC and is reported by Ministry of Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBE003: Social Security: Number of Conceded Benefits: by Region and State.

Brazil Social Security: Number of Required Benefits: Other data was reported at 341,177.000 Unit in Dec 2019. This records a decrease from the previous number of 369,069.000 Unit for Nov 2019. Brazil Social Security: Number of Required Benefits: Other data is updated monthly, averaging 326,850.500 Unit from Jan 2008 (Median) to Dec 2019, with 144 observations. The data reached an all-time high of 747,723.000 Unit in Sep 2019 and a record low of 177,231.000 Unit in Feb 2008. Brazil Social Security: Number of Required Benefits: Other data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Global Database’s Brazil – Table BR.GBE001: Social Security: Summary.

Social Security: No of Conceded Benefits: Rural: South data was reported at 10,897.000 Unit in Apr 2019. This records an increase from the previous number of 8,537.000 Unit for Mar 2019. Social Security: No of Conceded Benefits: Rural: South data is updated monthly, averaging 12,383.000 Unit from Jan 2008 (Median) to Apr 2019, with 136 observations. The data reached an all-time high of 17,896.000 Unit in Mar 2010 and a record low of 5,979.000 Unit in Aug 2015. Social Security: No of Conceded Benefits: Rural: South data remains active status in CEIC and is reported by Ministry of Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBE003: Social Security: Number of Conceded Benefits: by Region and State.

Brazil Social Security: Number of Emitted Benefits: Urban: Northeast: Rio Grande do Norte data was reported at 223,334.000 Unit in Apr 2019. This records an increase from the previous number of 222,963.000 Unit for Mar 2019. Brazil Social Security: Number of Emitted Benefits: Urban: Northeast: Rio Grande do Norte data is updated monthly, averaging 253,903.000 Unit from Jan 2008 (Median) to Apr 2019, with 136 observations. The data reached an all-time high of 312,723.000 Unit in Dec 2018 and a record low of 206,943.000 Unit in Jan 2008. Brazil Social Security: Number of Emitted Benefits: Urban: Northeast: Rio Grande do Norte data remains active status in CEIC and is reported by Ministry of Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBE011: Social Security: Number of Emitted Benefits: by Region and State.

Brazil Social Security: Number of Required Benefits: South: Santa Catarina data was reported at 40,798.000 Unit in Apr 2019. This records an increase from the previous number of 36,453.000 Unit for Mar 2019. Brazil Social Security: Number of Required Benefits: South: Santa Catarina data is updated monthly, averaging 34,329.000 Unit from Jan 2008 (Median) to Apr 2019, with 136 observations. The data reached an all-time high of 48,218.000 Unit in Apr 2018 and a record low of 22,606.000 Unit in Mar 2008. Brazil Social Security: Number of Required Benefits: South: Santa Catarina data remains active status in CEIC and is reported by Ministry of Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBE022: Social Security: Number of Required Benefits: by Region and State.

The purpose of this agreement is for SSA to verify SSNs and provide citizenship status, as recorded in SSA's records, of individuals applying to serve in approved national service positions.

The digitalised social safety net programmes of Bangladesh are evaluated with a sample of 531 beneficiaries and non-beneficiaries.

Case Studies in Public Interest Technology Today, there are multiple ways where digital technologies adversely impacts the public interest, whether that’s the spread of misinformation online, the loss of privacy, the threat of algorithmic discrimination, and more. Public interest technology is an emerging field that seeks to use cross-disciplinary techniques to research and address these issues in order to advance the public interest. For this dissertation, I present three different case studies of public interest tech research projects, each of which focuses on a different technology and relevant public interest. In Chapter 2, I research how Facebook’s advertising algorithms can discriminate by race and ethnicity. In Chapter 3, I test how the predictability of Social Security Number (SSN) assignment based on easily accessible data about Americans presents a risk of identity theft. In Chapter 4, I demonstrate how TraceFi, a Wi-Fi based collocation detection technology, can be deployed for COVID-19 contact tracing. I propose how we can adapt Lawrence Lessig’s pathetic dot model as the “Three Forces Model of Public Interest Tech” to understand the current dysfunctional state of relationships between technology, society, and the public interest, where the public interest is often affected as an output of technology but not fully considered as an input. The three forces of the law, norms, and market can affect a given technology or vice versa which in turn affects the public interest. For different combinations of technologies and public interests, the amount of force exerted by the law, norms, or market could also differ and so could the degree of feedback between the technology and each of the forces. Since the normative goal of public interest tech as a field is to ultimately advance the public interest, the goal state of the Three Forces Model demonstrates how the public interest can be an input for the law, norms, and market in how they affect a technology’s design and usage, which would in turn affect the public interest. Stakeholders relevant to each of the forces can consider the public interest as a priority in how they interact with a technology and its designer. In Chapter 5, I present how we can apply the Three Forces Model for Public Interest Tech to each case study to describe the current state and the ideal goal state. In order to effectively respond to the multiple ways of how digital technologies have adversely impacted the public interest, we need a “whole-society” strategy that coordinates our laws, norms, and markets in how they interact with our technologies to prioritize the public interest. As public interest technologists, we need to work across disciplines to advance the public interest. Let’s get started.