Complete set of loan-level data on the recipients of Paycheck Protection Program loans

An aggregated dataset of PPP (Paycheck Protection Program) SBA (Small Business Administration) loans involving 3 million businesses would be a comprehensive collection of financial information, aimed at analyzing the distribution and impact of these loans. This dataset would include key details such as the names of the businesses, loan amounts, loan disbursement dates, and the terms of the loans. Additionally, the dataset would contain information on board members of these businesses, providing insights into the governance structures and potential networks influencing the flow of SBA funds. This aspect of the dataset can be crucial for understanding the distribution patterns of PPP loans, identifying trends in funding allocation among different types of businesses, and examining any correlations between board composition and loan receipt. Such a dataset would be valuable for various analyses, including: Financial Analysis: Assessing the financial health and stability of businesses that received PPP loans, and understanding how these loans have impacted their operations during challenging economic times. Governance Analysis: Evaluating the role of board members in acquiring PPP loans, and whether certain types of governance structures were more successful in securing funds. Economic Impact Assessment: Measuring the broader economic impact of the PPP loans, such as job retention, business survival rates, and sector-wise distribution of funds. Network Analysis: Mapping the connections between different businesses and their board members to identify any potential networks or clusters that may have influenced the flow of funds. Policy Evaluation: Providing data-driven insights to policymakers for assessing the effectiveness of the PPP program and for planning future economic relief measures.

SBA Coronavirus (COVID-19) Relief Options: Paycheck Protection Program (PPP) Report and Data

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in Connecticut that received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient. This dataset includes loans under $150,000 and loans of $150,000 and above made to Connecticut businesses through August 8, 2020. Please see attached document for more details.

This dataset is published by the Atlanta Regional Commission Research & Analytics group to show Paycheck Protection Program Loans for Georgia. Source: US Department of Treasuryhttps://home.treasury.gov/policy-issues/cares-act/assistance-for-small-businesses/sba-paycheck-protection-program-loan-level-data

This dataset features PPP loan data distributed across the City of Los Angeles as of January 25, 2022. Data comes from SBA.gov.

Find the original dataset here

Pandas EDA with Plotly using this dataset here

Paycheck Protection Program (PPP) Loan Data – Key Aspects

SBA Values Transparency, Protecting Taxpayer Funds, and Protecting Proprietary Information of Small Businesses

In releasing PPP loan data to the public, SBA is maintaining a balance between providing transparency to American taxpayers and protecting small businesses’ confidential business information, such as payroll, and personally identifiable information. Small businesses are the driving force of American economic stability and are essential to America’s economic rebound from the pandemic. SBA is committed to ensuring that any release of PPP loan data does not harm small businesses or their employees.

PPP Is A Delegated Loan Making Process

PPP loans are not made by SBA. PPP loans are made by lending institutions and then guaranteed by SBA. Accordingly, borrowers apply to lenders and self-certify that they are eligible for PPP loans. The self- certification includes a good faith certification that the borrower has economic need requiring the loan and a certification that the borrower has applied the affiliation rules and is a small business, among other certifications The lender then reviews the borrower’s application, and if all the paperwork is in order, approves the loan and submits it to SBA.

PPP Loan Data Is Not Indicative of Loan Forgiveness or Program Compliance

A small business or non-profit organization that is listed in the publicly released data has been approved for a PPP loan by a delegated lender. However, the lender’s approval does not reflect a determination by SBA that the borrower is eligible for a PPP loan or entitled to loan forgiveness. All PPP loans are subject to SBA review and all loans over $2 million will automatically be reviewed. The fact that a borrower is listed in the data as having a PPP loan does not mean that SBA has determined that the borrower complied with program rules or is eligible to receive a PPP loan and loan forgiveness. Further, a small business’s receipt of a PPP loan should not be interpreted as an endorsement of the small business’ business activity or business model.

Cancelled Loans Do Not Appear In The PPP Loan Data

The public PPP data includes only active loans. Loans that were cancelled for any reason are not included in the public data release.

PPP Loan Demographic Data Is Voluntarily Submitted

PPP loan data reflects the information borrowers provided to their lenders in applying for PPP loans. SBA can make no representations about the accuracy or completeness of any information that borrowers provided to their lenders. Not all borrowers provided all information. For example, approximately 75% of all PPP loans did not include any demographic information because that information was not provided by the borrowers. SBA is working to collect more demographic information from borrowers to better understand which small businesses are benefiting from PPP loans. The loan forgiveness application expressly requests demographic information for borrowers.

Paycheck Protection Program loans are SBA loans that helps businesses keep their workforce employed during the Coronavirus (COVID-19) crisis, specific to Colorado. Includes < $150,000 loans and > $150,000 loans. Current law dictates that the Paycheck Protection Program (PPP) close at the end of August 8, 2020.

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in New Jersey who received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient.

Searchable directory of all Paycheck Protection Program loan recipients with fraud reporting capabilities.

Aggregated Paycheck Protection Program (PPP) Loan Data

  Aggregated Paycheck Protection Program (PPP) Loan Data 
  Geography Level: State, CountyItem Vintage: Not Available
  Update Frequency: UnknownAgency: SBAAvailable File Type: Excel with website link 

  Return to Other Federal Agency Datasets Page

Complete set of state-level aggregated data on the recipients of Paycheck Protection Program loans

SBA Coronavirus (COVID-19) Relief Options: Paycheck Protection Program (PPP) Report (Approvals through 05-16-2020)

PPP Loans of over $150k, as reported by SBA as of 6/30/21 Locations were geocoded by LA City Geocoder. Some locations may not have matched. https://www.sba.gov/funding-programs/loans/covid-19-relief-options/paycheck-protection-program/ppp-data

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in Utah who received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient.

Dataset

This dataset was created by Bruce Anders

Contents

Community banks have played an outsized role in the Paycheck Protection Program (PPP), disbursing 37 percent of all PPP loans despite holding only 18 percent of outstanding bank loans. Although participation boosted community banks’ revenue by supporting asset and interest income growth, it appears to have lowered their profitability, at least initially: low interest rates and deferred fee collection on PPP loans reduced banks’ earning margins.

United States SBP: IF: Received Fin Assistance: PPP Loan Forgiveness data was reported at 46.800 % in 10 Jan 2022. This records an increase from the previous number of 43.800 % for 03 Jan 2022. United States SBP: IF: Received Fin Assistance: PPP Loan Forgiveness data is updated weekly, averaging 33.200 % from Feb 2021 (Median) to 10 Jan 2022, with 36 observations. The data reached an all-time high of 47.100 % in 20 Dec 2021 and a record low of 11.100 % in 01 Mar 2021. United States SBP: IF: Received Fin Assistance: PPP Loan Forgiveness data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

United States SBP: FI: Received Fin Assistance: PPP Loan Forgiveness data was reported at 29.200 % in 10 Jan 2022. This records an increase from the previous number of 26.600 % for 03 Jan 2022. United States SBP: FI: Received Fin Assistance: PPP Loan Forgiveness data is updated weekly, averaging 18.250 % from Feb 2021 (Median) to 10 Jan 2022, with 36 observations. The data reached an all-time high of 29.500 % in 27 Dec 2021 and a record low of 5.100 % in 15 Feb 2021. United States SBP: FI: Received Fin Assistance: PPP Loan Forgiveness data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

Conventional politics approaches, emphasizing party ideology, electoral dynamics, committee member-ship, campaign donations and industry clout, exercise a powerful hold over assessments of public policies and their distributional effects. Emerging from pluralist and business power perspectives, such accounts see “who gets what and why” as the result of how politics and power shape policies, their implementation, and distributional outcomes. This pervades our understanding of the Paycheck Protection Program (PPP), the US government’s effort to avert mass unemployment during the pandemic by lending $786 billion to small businesses to keep employees on payroll. Yet contrary to prior studies of the PPP, we find that such factors were strikingly uncorrelated with distributional outcomes, revealing limits to such approaches to this case. Instead, we find that an institutional politics or politics-in-time (IP-PIT) approach better explains the program and its trajectories. IP-PIT revises the causal sequence by empha-sizing how institutions and policies generate politics, distributional outcomes and feedback loops. We engage both approaches via a mixed-methods analysis of the PPP and two new datasets. We anchor our study in a qualitative process-tracing of temporal variation in policy architectures, politics, policy revi-sions, and shifting access to loans within and across the program’s three periods before presenting quan-titative analyses of loan flows across congressional districts and periods using data on the entire corpus of PPP loans. We use one of the largest economic interventions since the New Deal as a case to advance research and debate over the dynamics and outcomes of US policy making during crises and the Ameri-can political economy in general. Ours is the first study of the PPP to conduct a mixed-methods analysis of loans across congressional districts or to use conventional and institutional approaches to address its politics, policy and outcomes. We document varieties of critical junctures, contribute arguments about what might shape policy or institutional innovation in those moments, and use the PPP to identify conditions under which systems are “their own grave diggers,” fueling negative-transformative rather than positive-reproductive feedback.