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.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from Community Reinvestment Act (CRA) to show total amount and number of small business loans, by loan size, for 2015, by census tract in the Atlanta region.

Attributes:

GEOID10 = 2010 Census tract identifier (combination of FIPS codes for state, county, and tract)

County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

Area_Name = 2010 Census tract number and county name

Total_Population_ACS_2016 = # Total population estimate, 2016 (American Community Survey)

Total_Population_ACS_MOE_2016 = # Total population estimate (Margin of Error), 2016 (American Community Survey)

Planning_Region = Planning region designation for ARC purposes

AcresLand = Land area within the tract (in acres)

AcresWater = Water area within the tract (in acres)

AcresTotal = Total area within the tract (in acres)

SqMi_Land = Land area within the tract (in square miles)

SqMi_Water = Water area within the tract (in square miles)

SqMi_Total = Total area within the tract (in square miles)

TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.

CountyName = County Name

Num_SBloans_lessEq_100k_2015 = Number of Small Business (SB) Loans Originated <=$100k, 2015

Num_SBloans_100k_250k_2015 = # SB Loans Originated $100-250k, 2015

Num_SBloans_250k_1M_2015 = # SB Loans Originated $250k-$1M, 2015

Num_SBloans_Rev_lessEq_1M_2015 = # SB Loans Originated With Gross Annual Revenue <=$1M, 2015

TotNum_SBloans_Orig_2015 = Total

SB Loans Originated, 2015

PctNum_SBloans_lessEq_100k_2015 = % of

SB Loans Originated <=$100k, 2015

PctNum_SBloans_100k_250k_2015 = % of # SB Loans Originated $100k-$250k, 2015

PctNum_SBloans_250k_1M_2015 = % of

SB Loans Originated $250k-$1M, 2015

PctNum_SBlns_Rev_lessEq_1M_2015 = % of

SB Loans Originated With Gross Annual Revenue <=$1M, 2015

TotAmt_Sbloans_lessEq100k_2015 = Total Amt SB Loans Originated <=$100k, 2015 (in $000s)

TotAmt_Sbloans_100k_250k_2015 = Total Amt SB Loans Originated $100-250k, 2015 (in $000s)

TotAmt_Sbloans_250k_1M_2015 = Total Amt SB Loans Originated $250k-$1M, 2015 (in $000s)

TotAmt_inKs_SBlnsRevless1M_2015 = Total SB Amt Loans Originated With Gross Annual Revenue <=$1M, 2015 (in $000s)

TotAmt_inKs_SBloans_2015 = Total AMT SB Loans Originated, 2015 (in $000s)

PctTotAmt_SBlns_less_100k_2015 = % of Total Amount SB Loans Originated <=$100k, 2015

PctTotAmt_SBloans_100k250k_2015 = % of Total Amount SB Loans Originated $100k-$250k, 2015

PctTotAmt_SBloans_250k_1M_2015 = % of Total Amount SB Loans Originated $250k-$1M, 2015

PctAmt_SBlns_Rev_lessEq_1M_2015 = % of AMT SB Loans Originated With Gross Annual Revenue <=$1M, 2015

Num_SblnsPur_lessEq_100k_2015 = # SB Loans Purchased <=$100k, 2015

Num_SblnsPur_100k_250k_2015 = # SB Loans Purchased $100-250k, 2015

Num_SblnsPur_250k_1M_2015 = # SB Loans Purchased $250k-$1M, 2015

Num_SblnsPur_Rev_less1M_2015 = # SB Loans Purchased With Gross Annual Revenue <=$1M, 2015

Num_Sbloans_Pur_2015 = Total

SB Loans Purchased, 2015

PctNum_SblnsPur_less_100k_2015 = % of

SB Loans Purchased <=$100k, 2015

PctNum_SblnsPur_100k_250k_2015 = % of # SB Loans Purchased $100k-$250k, 2015

PctNum_SblnsPur_250k_1M_2015 = % of

SB Loans Purchased $250k-$1M, 2015

PctNum_SblnsPur_Rev_less1M_2015 = % of

SB Loans Purchased With Gross Annual Revenue <=$1M, 2015

TotAmt_SblnsPur_lessEq100k_2015 = Total Amt SB Loans Purchased <=$100k, 2015 (in $000s)

TotAmt_SblnsPur_100k_250k_2015 = Total Amt SB Loans Purchased $100-250k, 2015 (in $000s)

TotAmt_SblnsPur_250k_1M_2015 = Total Amt SB Loans Purchased $250k-$1M, 2015 (in $000s)

TotAmt_SblnsPur_Rev_less1M_2015 = Total SB Amt Loans Purchased With Gross Annual Revenue <=$1M, 2015 (in $000s)

TotAmt_Sbloans_Pur_2015 = Total AMT SB Loans Purchased, 2015 (in $000s)

PctTot_SBlnsPur_lessEq100k_2015 = % of Total SB Loans Purchased <=$100k, 2015

PctTot_SBlnsPur_100k_250k_2015 = % of Total SB Loans Purchased $100k-$250k, 2015

PctTot_SBlnsPur_250k_1M_2015 = % of Total SB Loans Purchased $250k-$1M, 2015

PctAmt_SBlnsPur_Rev_less1M_2015 = % of AMT SB Loans Purchased With Gross Annual Revenue <=$1M, 2015

Chg_TotNum_SBloans_Orig_2014_15 = Change in the Total # of SB Loans Originated, 2014-2015

Chg_Amt_SBlns_Orig_2014_15_inKs = Change in the Total Amount of SB Loans Originated, 2014-2015 ($000s)

last_edited_date = Last date the feature was edited by ARC

Source: Community Reinvestment Act (CRA), Atlanta Regional Commission

Date: 2015

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Abstract copyright UK Data Service and data collection copyright owner.

In January 2004, a consortium of public and private sector organisations commissioned Warwick Business School to carry out the United Kingdom Survey of Small- and Medium-sized Enterprises' (SME) Finances, 2004. This was the first representative survey of SMEs to offer a close analysis of businesses with fewer than 250 employees, their main owners and their access to external finance. A second survey was conducted in 2008, where business owners were interviewed by telephone about the finances they have used or applied for in the last three years, their financial relationships, the characteristics of the business and personal details.

In 2007, another consortium of UK public sector bodies, small business representative organisations and finance providers agreed to sponsor a similar survey to the 2004 survey, conducted by the Centre for Business Research based at the University of Cambridge. This study is held at the UKDA under SN 6049, with the title United Kingdom Survey of Small- and Medium-Sized Enterprises' Finances, 2007. It aimed to compile another benchmark and to identify any changes or trends that had emerged since 2004, but made a number of changes to the 2004 questionnaire, so that it is not a direct member of the UKSMEF series, but stands alongside it as a separate cross-sectional survey. The UKSMEF 2008 survey was conducted by the same Principal Investigator as the 2004 survey, based at Warwick Business School, and the 2008 report provides direct comparison between the 2004 and 2008 surveys.

The aims of the 2009 survey were to:

• provide benchmarking data on the availability of credit to SMEs and the types of finance used
• collect information on the relationship between SMEs and their providers of finance
• develop a general purpose micro database for quantitative research on business finance (offering, for example, scope for comparisons with the US Survey of Small Business Finances)

The 2009 sample consisted of 1,250 follow up interviews with businesses interviewed for the 2008 survey. Telephone interviews were conducted by IFF Research Ltd during autumn 2009. These interviews focused on the cost and availability of overdrafts and term loans to businesses in the previous year due to policy makers concerns about the affect of the Credit Crisis on bank lending to SMEs. The data can be used for panel data analysis, in conjunction with UKSMEFs 2004 and 2008, or for standalone cross-sectional analysis. A set of population weights is included in the dataset so that this analysis can be weighted to the UK SME population. These weights were calculated using statistics provided by the Department of Business, Innovation and Skills Enterprise Directorate - see Business population estimates, formerly 'SME Statistics'.

Further information may be found on the ESRC UK Survey of SME Finances 2009 Follow On Study award webpage.


Main Topics:

Topics covered in the survey included

• personal characteristics of the owner/manager
• firm demographics
• providers of finance
• use of current accounts, deposit accounts, trade credit, commercial loans and mortgages, assets and asset-based finance, credit cards and equity finance
• income and profits and balance sheet information

To improve access to capital, the City of Chicago seeded a $2MM revolving loan fund and partnered with Accion to create the Chicago Microlending Institute (CMI). CMI helped train two new local microlenders, Chicago Neighborhood Initiatives (CNI) and Women’s Business Development Corporation (WBDC), to help connect small businesses around the city to affordable access to capital. These microloans vary in size from $500 to $25,000 and the average loan size is around $10,000. This dataset reflects the lender, location, business industry, and borrower demographics for small businesses supported by the City’s revolving loan fund. Certain data elements could not be included on a per-loan basis for privacy reasons but are summarized in the https://data.cityofchicago.org/id/4s8s-adbr dataset.

As of January 2024, the value of outstanding credit card loans granted by universal and commercial banks in the Philippines reached roughly 728 billion Philippine pesos. Meanwhile, salary-based general purposed consumption loans reached about 129 billion Philippine pesos as of this period. Bank account ownership in the Philippines Based on Statista estimates, the credit card penetration rate in the Philippines has gradually increased since 2018. However, this accounts for only a minimal share of the population, as the country remains to have one of the lowest banked population share in the entire Asia-Pacific region. Among the population with a formal account from a financial provider, a larger share of the population has an e-money account than a bank account. Leading universal and commercial banks  Universal and commercial banks offer vast financial services, including deposit and checking services, investment and mutual funds, and housing loans, among others. These types of banks also had the highest bank footprint in the Philippines, which was higher than thrift banks and rural and cooperative banks combined. As of the fourth quarter of 2023, BDO Unibank Inc (BDO) emerged as the largest universal bank in the Philippines based on the value of deposits.