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.

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.

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.

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.

The Paycheck Protection Program (PPP) established by the CARES Act, is implemented by the Small Business Administration (SBA) with support from the Department of the Treasury. The program provided small businesses with funds to pay up to 8 weeks of payroll costs including benefits. Funds could also be used to pay interest on mortgages, rent, and utilities

This dataset details New York State recipients of PPP funds.

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

United States US: Civil GBARD: Current PPP: General University Funds data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. United States US: Civil GBARD: Current PPP: General University Funds data is updated yearly, averaging 0.000 USD mn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. United States US: Civil GBARD: Current PPP: General University Funds data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Government Budgets for Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

UAB PPP Fund financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

China PPP: Number of Project: Signed and Implemented: Viability Gap Funding/Subsidy data was reported at 2,666.000 Unit in Mar 2019. This records an increase from the previous number of 2,179.000 Unit for Dec 2018. China PPP: Number of Project: Signed and Implemented: Viability Gap Funding/Subsidy data is updated quarterly, averaging 1,222.500 Unit from Dec 2016 (Median) to Mar 2019, with 10 observations. The data reached an all-time high of 2,666.000 Unit in Mar 2019 and a record low of 452.000 Unit in Dec 2016. China PPP: Number of Project: Signed and Implemented: Viability Gap Funding/Subsidy data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Investment – Table CN.OPPP: Number of Project: Payment Method.

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.

China PPP: Number of Project: Viability Gap Funding/Subsidy data was reported at 4,892.000 Unit in Mar 2019. This records an increase from the previous number of 4,721.000 Unit for Dec 2018. China PPP: Number of Project: Viability Gap Funding/Subsidy data is updated quarterly, averaging 3,401.000 Unit from Mar 2016 (Median) to Mar 2019, with 13 observations. The data reached an all-time high of 4,892.000 Unit in Mar 2019 and a record low of 1,839.000 Unit in Mar 2016. China PPP: Number of Project: Viability Gap Funding/Subsidy data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Investment – Table CN.OPPP: Number of Project: Payment Method.

Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed at constant purchasing power parity (constant PPP$). Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/

The gross domestic product (GDP) at PPP of Panama amounted to about 187.47 billion PPP dollars in 2024. Between 1980 and 2024, the GDP rose by approximately 177.74 billion PPP dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP will steadily rise by around 80.90 billion PPP dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product at current prices expressed in international dollars and adjusted for purchasing power parity. The gross domestic product refers to the total value of final goods and services produced during a year. For the indicator at hand, the GDP value has been adjusted for purchasing power parity to increase comparability regarding the costs for goods and services. The International Monetary Fund database provides further details on the utilized exchange rates.

The gross domestic product (GDP) at PPP of Chile stood at about 678.04 billion PPP dollars in 2024. Between 1980 and 2024, the GDP rose by approximately 639.16 billion PPP dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP will steadily rise by around 197.09 billion PPP dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product at current prices expressed in international dollars and adjusted for purchasing power parity. The gross domestic product refers to the total value of final goods and services produced during a year. For the indicator at hand, the GDP value has been adjusted for purchasing power parity to increase comparability regarding the costs for goods and services. The International Monetary Fund database provides further details on the utilized exchange rates.

Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/

The gross domestic product (GDP) at PPP of Peru amounted to about 609.11 billion PPP dollars in 2024. Between 1980 and 2024, the GDP rose by approximately 557.38 billion PPP dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP will steadily rise by around 193.57 billion PPP dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product at current prices expressed in international dollars and adjusted for purchasing power parity. The gross domestic product refers to the total value of final goods and services produced during a year. For the indicator at hand, the GDP value has been adjusted for purchasing power parity to increase comparability regarding the costs for goods and services. The International Monetary Fund database provides further details on the utilized exchange rates.

China PPP: Investment Amount: Viability Gap Funding/Subsidy data was reported at 8,715.000 RMB bn in Mar 2019. This records an increase from the previous number of 8,661.700 RMB bn for Dec 2018. China PPP: Investment Amount: Viability Gap Funding/Subsidy data is updated quarterly, averaging 5,979.300 RMB bn from Mar 2016 (Median) to Mar 2019, with 13 observations. The data reached an all-time high of 8,715.000 RMB bn in Mar 2019 and a record low of 3,207.300 RMB bn in Mar 2016. China PPP: Investment Amount: Viability Gap Funding/Subsidy data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Investment – Table CN.OPPP: Investment Amount: Payment Method.

View yearly updates and historical trends for Italy GDP Based on PPP Outlook. Source: International Monetary Fund. Track economic data with YCharts analyt…

The gross domestic product (GDP) at PPP of Nicaragua was estimated at about 60.23 billion PPP dollars in 2024. Between 1980 and 2024, the GDP rose by approximately 53.64 billion PPP dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP will steadily rise by around 22.97 billion PPP dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product at current prices expressed in international dollars and adjusted for purchasing power parity. The gross domestic product refers to the total value of final goods and services produced during a year. For the indicator at hand, the GDP value has been adjusted for purchasing power parity to increase comparability regarding the costs for goods and services. The International Monetary Fund database provides further details on the utilized exchange rates.