Consumer & Community Context
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Figure 1. Use of trackers on online lender websites
Lender | Essential | Site analytics | Customer interaction | Social media | Advertising | Total trackers |
---|---|---|---|---|---|---|
Company 1 | 3 | 5 | 2 | 1 | 7 | 18 |
Company 2 | 3 | 4 | 0 | 1 | 8 | 16 |
Company 3 | 2 | 5 | 2 | 1 | 6 | 16 |
Company 4 | 1 | 5 | 1 | 1 | 5 | 13 |
Company 5 | 3 | 3 | 1 | 1 | 3 | 11 |
Company 6 | 3 | 3 | 1 | 2 | 2 | 11 |
Company 7 | 2 | 5 | 0 | 1 | 2 | 10 |
Company 8 | 1 | 3 | 1 | 1 | 3 | 9 |
Company 9 | 2 | 2 | 1 | 1 | 2 | 8 |
Company 10 | 2 | 4 | 0 | 1 | 0 | 7 |
Note: Company names have been anonymized and relabeled to indicate that the order in which they are listed here does not correspond with the order in table 1.
Essential includes tag managers, privacy notices, and technologies that are critical to the functionality of a website.
Site analytics collects and analyzes data related to site usage and performance.
Customer interaction includes chat, email messaging, customer support, and other interaction tools.
Social media integrates features related to social media sites.
Advertising provides advertising or advertising-related services such as data collection, behavioral analysis, or retargeting.
Source: Analysis by Scott Colgate, Federal Reserve Board, as of July 16, 2019.
Figure 1. Likelihood of approval for at least some financing at lending source, by race/ ethnicity (2018)
Race/ethnicity | Average adjusted predictions (percent) | |||
---|---|---|---|---|
Overall | Large banks | Small banks | Online lenders | |
Non-Hispanic white | 81.7 | 64.8 | 75.9 | 84.6 |
Non-Hispanic black | 74.4 | 44.8 | 58.8 | 83.0 |
Hispanic | 78.0 | 67.0 |
Note: The likelihood of approval overall refers to approval at any lender source for all types of credit. The likelihood of approval at each respective lending source refers to approval only for loan or line of credit products. Results are from a series of logistic regressions controlling for revenue size, credit score, profitability, urban/rural location, age, industry, state, veteran-owned, woman-owned, and employee size. Estimates are displayed as average adjusted predictions. Asterisks on minority-owned firm estimates denote statistical differences from white-owned firms: *** p<0.01, ** p<0.05, * p<0.1
Source: The authors’ analysis based on 2018 Small Business Credit Survey (SBCS) data.
Figure 2. Likelihood of reporting reason for not submitting credit application, by race/ethnicity of firm ownership (2018)
Race/ethnicity | Discouraged from applying | Sufficient financing in place |
---|---|---|
Non-Hispanic white | 12.4 | 55.9 |
Non-Hispanic black | 12.9 | 46.6 |
Asian | 18.1 | 46.4 |
Hispanic | 12.6 | 45.3 |
Note: The results are from a series of logistic regressions controlling for revenue size, credit score, profitability, urban/rural location, age, industry, state, veteran-owned, woman-owned, and employee size. Estimates are displayed as average adjusted predictions. Asterisks on minority-owned firm estimates denote statistical differences from white-owned firms: *** p<0.01, ** p<0.05, * p<0.1
Source: The authors’ analysis based on 2018 SBCS data.
Figure 3. Likelihood of applying at lending source, by race/ethnicity of firm ownership (2018)
Race/ethnicity | Average adjusted predictions (percent) | ||||
---|---|---|---|---|---|
Overall | Large banks | Small banks | Online lenders | CDFIs | |
Non-Hispanic white | 44.6 | 48.2 | 47.4 | 28.9 | 6.2 |
Non-Hispanic black | 44.8 | 53.3 | 36.6 | 31.0 | 15.9 |
Asian | 43.1 | 55.4 | 47.3 | 27.6 | 8.1 |
Hispanic | 47.0 | 48.1 | 32.8 | 33.6 | 4.9 |
Note: Results are from a series of logistic regressions controlling for revenue size, credit score, profitability, urban/rural location, age, industry, state, veteran-owned, woman-owned, and employee size. Estimates are displayed as average adjusted predictions. Asterisks on minority-owned firm estimates denote statistical differences from white-owned firms: *** p<0.01, ** p<0.05, * p<0.1
CDFIs Community development financial institutions.
Source: The authors’ analysis based on 2018 SBCS data.
Figure 1. Average number of financial service providers by income level of zip code
Zip code median household income quartile | Banks | Credit unions | AFS providers |
---|---|---|---|
Lowest | |||
2007 | 2.311564 | 1.124274727 | |
2008 | 2.33032 | 1.169207934 | |
2009 | 2.330185 | 1.134124949 | |
2010 | 2.309 | 0.432465254 | 1.119821886 |
2011 | 2.301309 | 0.508298475 | 1.12643368 |
2012 | 2.271758 | 0.500472271 | 1.152071245 |
2013 | 2.235326 | 0.486843881 | 1.229118877 |
2014 | 2.194576 | 0.480636891 | 1.252732425 |
2015 | 2.148698 | 0.472810687 | 1.23127783 |
2016 | 2.108892 | 0.466468763 | 1.207799217 |
2017 | 2.058831 | 0.459452166 | |
2018 | 2.019566 | 0.450141681 | |
Lower-middle | |||
2007 | 2.856202 | 0.95562286 | |
2008 | 2.896892 | 1.017382144 | |
2009 | 2.897287 | 0.972214907 | |
2010 | 2.868712 | 0.495786147 | 0.954437714 |
2011 | 2.855412 | 0.589939426 | 0.94521991 |
2012 | 2.818804 | 0.57334738 | 0.94745852 |
2013 | 2.783777 | 0.566368185 | 1.013695022 |
2014 | 2.728206 | 0.561495918 | 1.039768238 |
2015 | 2.680932 | 0.555306821 | 1.03713458 |
2016 | 2.637214 | 0.548195944 | 1.006715828 |
2017 | 2.579273 | 0.546484066 | |
2018 | 2.523308 | 0.542006848 | |
Upper-middle | |||
2007 | 2.908576 | 0.748517982 | |
2008 | 2.963773 | 0.789224081 | |
2009 | 2.975497 | 0.746541958 | |
2010 | 2.944935 | 0.483335529 | 0.721380582 |
2011 | 2.930707 | 0.579633777 | 0.711763931 |
2012 | 2.911869 | 0.578052957 | 0.719536293 |
2013 | 2.881965 | 0.575681728 | 0.762745356 |
2014 | 2.836385 | 0.57014886 | 0.7798709 |
2015 | 2.801212 | 0.57093927 | 0.787116322 |
2016 | 2.764326 | 0.566723752 | 0.76603873 |
2017 | 2.704387 | 0.572915294 | |
2018 | 2.644974 | 0.573310499 | |
Upper | |||
2007 | 4.449631 | 0.683544304 | |
2008 | 4.590454 | 0.712552743 | |
2009 | 4.647943 | 0.632515823 | |
2010 | 4.628296 | 0.559994726 | 0.609968354 |
2011 | 4.646888 | 0.698839662 | 0.590849156 |
2012 | 4.633966 | 0.660733122 | 0.630142405 |
2013 | 4.610232 | 0.65664557 | 0.644382911 |
2014 | 4.551292 | 0.659150844 | 0.668908228 |
2015 | 4.503165 | 0.660733122 | 0.682093882 |
2016 | 4.445807 | 0.659941983 | 0.65585443 |
2017 | 4.356804 | 0.655986287 | |
2018 | 4.283096 | 0.667721519 |
Note: “Average number of financial institutions” includes main and branch locations. Only zip codes with an associated geographic area are included in the analysis.
Source: Federal Deposit Insurance Corporation, Summary of Deposits, Branch Office Deposits for June of given year; National Credit Union Administration, Call Report Quarterly Data for June of given year except 2010 and 2013, where September data were used due to data unavailability; U.S. Census Bureau, 2007 through 2016 County Business Patterns Complete ZIP Code Industry Detail Files; and U.S. Census Bureau, 2013–17 American Community Survey 5-Year Estimates, Table S1901.
Figure 2. Number of small loans to businesses by FFIEC income designation of business's census tract (Loans under $1M)
FFIEC income designation of business's census tract | Year | ||||||||
---|---|---|---|---|---|---|---|---|---|
2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
Low | 188,483 | 127,617 | 161,433 | 262,543 | 237,476 | 264,078 | 292,381 | 336,796 | 338,904 |
Moderate | 941,304 | 609,079 | 759,714 | 935,924 | 817,942 | 932,446 | 1,027,076 | 1,219,281 | 1,154,374 |
Middle | 2,824,515 | 1,813,131 | 2,231,301 | 2,319,400 | 1,951,490 | 2,179,723 | 2,369,036 | 2,858,125 | 2,436,121 |
Upper | 2,076,290 | 1,554,004 | 1,892,932 | 2,084,512 | 1,859,295 | 2,087,476 | 2,262,521 | 2,881,893 | 2,497,246 |
Source: Federal Financial Institutions Examination Council (FFIEC) Community Reinvestment Act lending data, National Aggregate Reports.