Are Crowdfunding Platforms Active and Effective Intermediaries? Douglas Cumming Professor and Ontario Research Chair York University - Schulich School of Business 4700 Keele Street Toronto, Ontario M3J 1P3 Canada http://ssrn.com/author=75390 [email protected]Yelin Zhang York University - Schulich School of Business 4700 Keele Street Toronto, Ontario M3J 1P3 Canada http://www.schulich.yorku.ca/ [email protected]June 25, 2016 * We are indebted to the crowdfunding platforms for providing their detailed data. Also, we are indebted to Craig Asano, Sofia Johan, and the seminar participants at the 2016 National Crowdfunding Association of Canada Annual Summit and York University for helpful comments. This paper is scheduled for presentation at the EMLyon Conference on Entrepreneurial Finance, July 2016.
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Are Crowdfunding Platforms Active and Effective Intermediaries?
Douglas Cumming Professor and Ontario Research Chair
York University - Schulich School of Business 4700 Keele Street
June 25, 2016 * We are indebted to the crowdfunding platforms for providing their detailed data. Also, we are indebted to Craig Asano, Sofia Johan, and the seminar participants at the 2016 National Crowdfunding Association of Canada Annual Summit and York University for helpful comments. This paper is scheduled for presentation at the EMLyon Conference on Entrepreneurial Finance, July 2016.
third party proof, and register checks) was 1.2, with a median of 0 and a maximum of 7.
The data indicate the services available to subscribers, such as pre-evaluation before
listing on the platform, strategic fundraising guidance, business or financial planning, facilitation
3 As of December 2015, there were 72 crowdfunding platforms in Canada. There were fewer platforms in earlier
years, implying we have coverage of more than 72% of the market for earlier years.
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in crowdfunding contract design, and marketing or promotional services. A total of 35.3% of
platforms provided regular updates to users (investors and entrepreneurs), while 29.4% of
platforms offered pre-evaluation to start-ups before listing, 27.5% offered fundraising guidance
to entrepreneurs, 17,7% offered marketing and promotional services, 15.7% offered business and
financial planning services, and 7.8% offered contractual help to start-ups.
The data comprise information on each platform’s operating conditions: number of
projects launched by year, average successful fund-raising rate, average fund-raising duration,
total amount of money raised on platform by year, and the industry composition of listed projects.
The median platform has projects that take between 7-9 weeks for funding, and the median
platform has entrepreneurs that achieve 21-30% of their funding goal. The median platform
spends between $2,500 to $10,000 on compliance annually, has 10 employees, 501-1000
investors per year, has 51-100 entrepreneurial projects listed per year, and total capital raised
between $50,000 - $100,000 per year. The more common industries include non-profit (median
25%), business and professional services (median 15%), education and research (median 10%),
art (median 10%), life science (median 10%), cleantech and energy (median 5%), and hardware
and software (median 5%), followed by manufacturing, media, real estate, and social enterprise
each with medians per platform at 0% but averages across platforms between 2-5% per year
(details are in Table 1).
Fee structure/revenue models of the platform are in the data, including information on
whether or not the platform charges a one-time platform listing fee, periodical subscription at
different levels/tiers, fixed percentage of total amount raised (whether funding is successful or
not), fixed percentage of total amount raised (only if funding is successful), and management
fees and carry percentages. There are 11.8% of platforms with a one-time listing fee, 19.6% of
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funds with a periodical subscription at different levels, 13.7% of funds with a fixed percentage of
the total amount raised regardless of success, 15.7% of funds with a fixed percentage raised only
if funded was successful, and 11.8% of funds with a mixed management fee and a carry
percentage fee structure.
Other variables and detailed summary statistics (means, medians, standard deviations,
minimum, and maximums) are shown in Table 1.
Table 1 About Here
The comparison tests in Table 2 provide a first impression of some distinct patterns in the
dataset. Although comparison tests do not show the precise relationship among variables
controlling for other things being equal because variables are analyzed separately and in
isolation, the comparison tests nevertheless present a general picture on relationships in the data
and the relationships between some key variables of interest. The joint effect of the same
variables is discussed later in the regression analysis in the next section. The data in Table 2 are
based on the 2013 information provided by the platforms. We applied the same tests on data in
later years; the results are consistent with what we report for 2013, although the magnitude of the
differences and statistical significance slightly varies.
Table 2 About Here
Table 2 Panel A shows that there is a higher probability of due-diligence application
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among platforms with a smaller number of projects (when the number of campaign projects is
greater than the median across platforms only 17.9% of platforms carry out due diligence, and
when the number of campaign projects is below the median then 87.0% of platforms carry out
due diligence), consistent with Hypothesis 1. Similarly, there is a higher probability of due-
diligence among platforms with a great number of employees (when the number of employees is
greater than the median across platforms there are 86.4% of platforms carrying out due diligence,
and when the number of employees is below the median then 20.7% of platforms carry out due
diligence), again consistent with Hypothesis 1. There is a higher probability of due-diligence
among platforms that spend more on compliance (when compliance expenditures are greater than
the median across platforms there are 95.5% of platforms carrying out due diligence, and when
the compliance expenditures are below the median then 13.8% of platforms carry out due
diligence). Due diligence is more likely among platforms that have fee models with periodical
subscription and different levels (73.5% with these fees, versus 30.7% without this fee structure),
consistent with Hypothesis 2. There is a higher probability of due-diligence among equity
crowdfunding platforms (80.1% of equity crowdfunding platforms carry out due diligence, while
41.5% of non-equity crowdfunding platforms carry out due diligence), consistent with
Hypothesis 3. There is a higher probability of due-diligence among platforms with art, life-
sciences, and non-profit and charities, which is in part consistent with Hypothesis 3 at least for
the life-sciences industries. Each of these differences are statistically significant at at least the
5% level, with the exception of the differences for art industries which is significant only at the
10% level.
Table 2 Panel B presents the possible advantages of due-diligence application, consistent
with Hypothesis 4. There is a higher level of fully funded projects and larger amount of money
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raised through platforms that carry our due diligence, and these differences are significant at the
1% level. Due-diligence does not have a significant effect on fundraising duration.
Table 3 reals the correlations among variables of interest. Table 3 Panel A shows that due-
diligence application is positively correlated with resource on compliance, employee numbers,
equity crowdfunding, user subscription at different levels, and a higher industry composition of
art, life science and charities. Due diligence application is negatively correlated with the number
of campaign projects on a platform. Table 3 Panel B shows the correlation among due-diligence
application, platform performance, and different types of platform services. Consistent with
comparison test results in Table 2 Panel B, due-diligence application is positively associated with
higher percentage of fully funded projects and larger amount of money raised through a platform
and does not exhibit strong correlation with fund-raising duration. The number of types of due-
diligence is positively correlated with percentage of fully funded project, total amount of money
raised through the platform, and several platform services. The number of projects per employee
ratio is negatively correlated with total amount of money raised through the platform, periodical
updates, and strategic guidance. Further detailed correlations among platform services are also
presented in Table 3.
Table 3 About Here
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4. Multivariate Analyses
In this section, we use detailed analysis to reveal the factors influencing platform due-
diligence application and demonstrate how due-diligence application benefits crowdfunding
platforms. We used two methods in the analysis: logit regressions to examine the factors that
influence platform due diligence in Tables 4 and 5, and ordered logit regressions to examine
whether due-diligence, among other things, influences platform performance in Table 6,
accounting for the non-random application of due diligence in the first step. For reasons of
conciseness, we do not show regressions of all types of due diligence one by one. Instead, we
first report regressions for the overall due-diligence (all kinds of due-diligence combined), and
then report regressions for the three most common due-diligence subcategories: background
check, site visit and cross check.
Tables 4 and 5 About Here
Table 4 shows the factors influencing due-diligence application in general. Due-diligence
is applied when at least one of the following actions is taken: background check, site visit, credit
check, cross-check from social media connections, monitor account activities, and request third
party certificates or proof. We applied the same logit regressions on data in different years; the
results are consistent over time. The data indicate that number of projects in each year is
negatively correlated with due-diligence application, and this effect is statistically significant at
the 10% level in Models 1 and 3 for 2013 and 2015, respectively, and at the 5% level in Models
2 and 4 for 2014 and 2016, respectively. The economic significance is such that on average, an
increase by one categorical unit (the number of projects is an ordinal variable; see Table 1) in
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the number of projects is associated with a 13.85% (Model 4) to 23.79% (Model 1) reduction in
probability of due-diligence, consistent with Hypothesis 1. Similarly, the number of employees is
positively and significantly correlated with due-diligence: a one standard deviation increase in
the number of employees results in a 2.52% (Model 1) to 6.75% (Model 4) increase in the
probability of due-diligence, again consistent with Hypothesis 1. These results remain even when
controlling for resources spent on compliance, which is positively and significantly correlated
with due-diligence: an increase by one unit in resources spent on compliance (resources is an
ordinal variable; see Table 1) leads to an 8.55% (Model 2) to 10.35% (Model 4) increase the
probability of due-diligence.
Platforms with periodical subscription at different levels are more likely to carry out due
diligence by 4.28% (Model 2) to 5.85% (Model 4), consistent with Hypothesis 2. Also,
platforms that offer equity crowdfunding are more likely to carry out due-diligence, consistent
with Hypothesis 3. On average, a platform with equity crowdfunding is 14.68% to 16.98% more
likely to carry out due-diligence than a platform without equity crowdfunding. These effects are
robust across each of the years, and robust to controls for industry (the industry effect, although
not explicitly reported in Table 4 for conciseness, are consistent with that which was reported in
Table 2 above).
Table 5 further analyzes three main types of due-diligence application: background
checks (Panel A), site visits (Panel B), and cross-checks from social media connections (Panel
C). The data in Table 5 Panel A indicate the following. A one unit increase in the number of
campaign projects reduces the probability of background checks by 13.36% (Year 2013) to
16.95% (Year 2014). A one unit increase in the amount of resources spent on compliance
annually increases the probability of background checks by 3.49% (Year 2016) to 5.30% (Year
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2015). A one standard deviation increase in the number of employees increases the probability of
background checks by 2.55% (Year 2013) to 4.06% (Year 2015). Equity crowdfunding increases
the probability of background checks by 8.24% (Year 2013) to 12.24% (Year 2014). Advanced
fee structures increase the probability of background checks by 1.94% (Year 2013) to 4.56%
(Year 2016).
Table 5 Panel B indicates that a one unit increase in the number of projects reduces
probability of site visits by 7.88% (Year 2015) to 10.46% (Year 2014). A one standard deviation
increase in the number of platform employees increases the probability of site visit by 1.82%
(Year 2013) to 3.13% (Year 2014). Equity crowdfunding is associated with an increase in the
probability of site visit by 2.12% (Year 2013) to 3.23% (Year 2015). The fee structure, however,
does not have statistically significant impact on site visits.
Table 5 Panel C indicates that a one unit increase in resources spent on compliance
increases the probability of cross-checks by 9.58% (Year 2013) to 16.49% (Year 2015). A one
standard deviation increase in the number of employees increases the probability of cross-checks
by 3.39% (Year 2013) - 5.80% (Year 2016). Equity crowdfunding is associated with an increase
in the probability of cross-checks by 2.85% (Year 2016) to 4.53% (Year 2013). Advanced fee
structures increase the probability of cross-checks by 3.80% (Year 2016) to 6.06% (Year 2015).
The number of campaign projects and number of investors do not have a noticeable impact on
cross-checks from social media connections. Also, platform age has no influence on due-
diligence application, likely because most platforms are very young in our sample.
Table 6 presents the results for the impact of due-diligence application on platform
performance (controlling for the determinants of due diligence from Table 4), measured by
percentage of fully funded projects, total amount of money raised annually, and average fund-
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raising duration. Since it is costly for platforms to apply due-diligence, the according benefit
should outweigh the cost to justify for the expenses. We use the fitted values of due diligence
from Table 4, with the instruments that include the fee structure and resources spent on
compliance. Fee structures and resources spent on compliance will be directly connected with
due diligence, but only indirectly related (if at all) to project outcomes since fee structures and
compliance expenditures are unknown to the crowd investors. We note that the results without
fitted values for the potentially endogenous due diligence variables are not materially different
when we use the non-fitted raw variables, and likewise similar with different controls in the first
stage regressions.
Table 6 About Here
Table 6 Panel A shows that due-diligence application is associated with higher percentage
of fully funded projects, controlling for all types of services offered by the platform. Specifically,
the application of due-diligence increases the percentage of fully funded projects by 40.96%
(Year 2014) to 60.45% (Year 2016). This effect is statistically significant at 5% level in each
Model in Table 6. Also, notice that project/employee ratio has a negative impact on the
percentage of fully funded projects: the higher the ratio, the less resource will be devoted to each
project , the more competition there is across projects, and the lower the success of projects on
average.
Table 6 Panel B shows that due-diligence application is associated with larger amount of
money raised through a platform. Specifically, the application of due-diligence increases the total
amount of money raised by 27.62% (Year 2014) to 39.06% (Year 2015), controlling for number
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of projects listed on the platform and services provided by the platform.
Table 6 Panel C does not present robust negative relationship between due-diligence
application and fund-raising duration (the effect is only marginally significant at the 10% level in
Model 4 and insignificant in the other models); nevertheless, the negative coefficient for due-
diligence application shows that as due-diligence applied, fund-raising becomes quicker, which
is consistent with what we would expect. Notice that promotion and marketing service does have
a significant impact on the efficiency of fund-raising: on average, fund-raising becomes 20.88%
(Year 2013) to 29.51% (Year 2014) quicker when promotion and marketing service is offered.
5. Conclusion
The past decade leading up to 2016 has witnessed a massive growth in the popularity of
crowdfunding as a viable form of entrepreneurial finance. In Canada, thousands of new projects
are launched on different fund raising websites every year. Connecting donors and investors with
beneficiaries, borrowers and entrepreneurs, crowdfunding platforms help idle money find its
value. But exactly what do crowdfunding platforms do? Do they simply provide a cheap online
spot for business soliciting? Or, do they apply due-diligence on listed projects and help reduce
information asymmetry between projects initiators and subscribers? What advantage can
platforms obtain through carrying out due-diligence?
In this paper, we assess the factors that influence the application of due diligence, as well
as whether or not due diligence by platforms is associated with success of projects. The scope of
crowdfunding due diligence comprises background checks, site visits, credit checks, cross
checks, monitoring accounts, third party proof, and register checks. For the first time ever, we
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examine empirical data on topic, made possible from the innovation data collection efforts of the
National Crowdfunding Association of Canada.
The summary statistics and comparison tests showed a transparent picture in the data, as
do the regression results controlling for other things being equal. The application of due-
diligence is associated with more affluent platform resources, either in compliance expenditure
or in employee number; and more sophisticated management structure, indicated by different
levels of subscription service. Due-diligence is also more likely to be applied when a platform
contains projects pertaining to higher intangible assets or information asymmetry, including the
life science industries, as well as in the arts and charities. Due-diligence is less likely to be
applied when platform employees’ expected working load is heavier, as shown by larger number
of campaign projects launched on a platform.
The data further indicate that the application of due-diligence in general has very strong
positive influence on the fund-raising successful rate and amount in the platform, controlling for
all service offered by a platform. Among all service offered by platforms, only strategic fund
raising guidance is significantly positively related with fund-raising successful rate and total
amount raised through platform. The strong positive association between due diligence and
fundraising success shows an important value for crowdfunding platforms in limiting the number
of lower quality projects on a platform through active due diligence.
The evidence herein suggests that there is a strong case for policymakers to impose
standards on platforms to act with greater stringency in carrying out due diligence. Also, the
evidence suggests that further research on crowdfunding could play careful attention to
differences across platforms, as there appears to be massive heterogeneity in respect of what it is
that platforms actually do.
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Table 1. Definitions and Summary Statistics This table provides definitions of the main variables, as well as summary statistics.
Variable Definition Obs Mean Median Std. Dev Min Max
Any Due Diligence is applied? (Yes=1)
Dummy Variable: Is Any of the Following Due Diligence Regularly Applied: Background Check, Site Visit, Credit Check, Cross-check from Social Media Connections, Monitor
Account Activities, Request Third Party Certificates or Proof? 51 0.4902 0 0.5049 0 1
(Yes=1, No=0)
Platform Provides Periodical Updated Information to Users
Dummy Variable: Does a Platform Provide Periodical (weekly, bi-weekly, monthly) Platform Updates and Activities to Users (Investors and Startups)? 51 0.3529 0 0.4826 0 1
(Yes=1, No=0) Platform offers pre-evaluation before
listing Startups Dummy Variable: Does a Platform Offer Pre-evaluation to Startups before Their Listing?
(Yes=1, No=0) 51 0.2941 0 0.4602 0 1
Platform offers strategic fundraising guidance
Dummy Variable: Does a Platform Offer Strategic Fundraising Guidance to Startups? (Yes=1, No=0) 51 0.2745 0 0.4507 0 1
Platform helps with business and financial planning
Dummy Variable: Does Platform Helps Startups with Business and Financial Planning? (Yes=1, No=0) 51 0.1569 0 0.3673 0 1
Platform offers contractual help to Startups
Dummy Variable: Does a Platform Offer Contractual Help to Startups? 51 0.0784 0 0.2715 0 1
(Yes=1, No=0) Platform offers marketing or promotion
service Dummy Variable: Does a Platform Offer Marketing or Promotion Service to Startups?
(Yes=1, No=0) 51 0.1765 0 0.3850 0 1
Total Number of types of Due Diligence applied
Total Number of Types of Due Diligence Applied by a Platform. Types of Due Diligence refer to: Background Check, Site Visit, Credit Check, Cross-check from Social Media
Connections, Monitor Account Activities, Request Third Party Certificates or Proof 51 1.1961 0 1.6495 0 7
Average Fund-raising Duration Ordinal Variable: Average Fund-raising Duration Level 1: 1-3 Weeks; Level 2: 4-6 Weeks; Level 3: 7-9 Weeks; Level 4: 10-12 Weeks; Level 5: More than 12 Weeks 51 2.4706 3 1.6729 1 5
Percentage Composition of Fully Funded Project
Ordinal Variable: the percentage of fully funded project with respect to all projects launched on the platform. Level 1:0-10%; Level 2:11-20%; Level 3: 21-30%; Level 4: 31-
40%; Level 5:41-50%; Level 6: More than 50% 51 2.7451 3 1.6823 1 6
Resource Spent on Compliance Annually
Ordinal Variable: Total Resource Spent on Compliance Annually? Level 1: less than $2500; Level 2: $2501-10000; Level 3: $10001-30000; Level 4: $30001-50000; Level 5:
Larger than $50000 51 2.2353 2 1.2503 1 5
Number of Employees Number of Employees Working for a Crowdfunding Platform 51 14.9412 10 9.5716 2 50 Equity Crowdfunding on the Platform?
(Yes=1) Dummy Variable: Does a Platform Handles Equity Crowdfunding? (Yes=1, No=0) 51 0.1961 0 0.401 0 1
Platform Age Number of Months between Platform Setup Month and February 2016 51 61.3725 38 18.347 2 238 Fee Structure: One-time Platform
Listing Fee Dummy Variable: Is the Main Service Charge a One-time Listing Fee?
51 0.1176 0 0.3254 0 1 (Yes=1, No=0)
Fee Structure: Periodical Subscription at Different Levels/Tiers
Dummy Variable: Is the Main Service Charge based on Periodical Subscription at Different Levels/Tiers? (Yes=1, No=0) 51 0.1961 0 0.3476 0 1
Fee Structure: Fixed percentage of total amount raised, whether funding is
successful or not
Dummy Variable: Is the Main Service Charge based on Fixed Percentage of Total Amount Raised, whether Funding is Successful or Not? (Yes=1, No=0) 51 0.1373 0 0.4154 0 1
Fee Structure: Fixed percentage of total amount raised, only if funding is
successful
Dummy Variable: Is the Main Service Charge based on Fixed Percentage of Total Amount Raised, only if Funding is Successful? (Yes=1, No=0) 51 0.1569 0 0.3673 0 1
Fee Structure: Management fee and carry percentage
Dummy Variable: Is the Main Service Charge based on Management Fee and Carry Percentage? (Yes=1, No=0) 51 0.1176 0 0.3254 0 1
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Table 1. (Continued)
Variable Definition Obs Mean Median Std. Dev Min Max
Number of Investors in Respective Year
Ordinal Variable :Total Number of Investors in Each Year from 2013 to 2016 (estimated) Level 1: less than 100; Level 2: 101-500; Level 3: 501-1000; Level 4: 1001-2500; Level 5: 2501-5000; Level 6: 5001-10000; Level 7: 10001-20000; Level 8: 20001-50000; Level 9:
Larger than 50000
(Number of Investors in 2013) 51 2.2745 3 1.1574 2 8 (Number of Investors in 2014) 51 2.3137 3 1.3611 2 8 (Number of Investors in 2015) 51 2.549 3 1.5401 2 8
(Number of Investors in 2016: estimated) 51 2.7059 4 2.3416 2 9
Number of Projects in Respective Year Ordinal Variable :Total Number of Projects/Financings/Loans Launched in Each Year from 2013 to 2016 (estimated) Level 1: less than 20; Level 2: 21-50; Level 3: 51-100;
(Number of Projects in 2013) 51 2.3529 3 1.0016 2 6 (Number of Projects in 2014) 51 2.6078 3 1.2217 3 6 (Number of Projects in 2015) 51 3.7843 4 1.307 3 6
(Number of Projects in 2016: estimated) 51 4.8235 5 1.6294 4 6
Project/Employee Ratio in Respective Year
Ordinal Variable: Total Number of Projects Launched in Each Year from 2013 to 2016(estimated) divided by Number of Employees for Each Platform: Level 1: less than 10
projects per employee; Level 2: 11-20 projects per employee; Level 3:21-50 projects per employee; Level 4: 51-100 projects per employee; Level 5: Larger than 100 projects per
employee
(Project/Employee Ratio in 2013) 51 2.5294 3 1.2467 1 5 (Project/Employee Ratio in 2014) 51 2.8431 3 1.1358 1 5 (Project/Employee Ratio in 2015) 51 2.9412 3 1.2109 1 5
(Project/Employee Ratio in 2016: estimated) 51 3.0392 3 1.3345 1 5
Total Amount of Money Raised
Ordinal Variable: Total Amount of Money Raised in Each Year from 2013 to 2016(estimated) Level 1: Less than 2.5K; Level 2: 2.5K-10K; Level 3: 10K-50K;Level 4:
50K-100K; Level 5: 100K-500K; Level 6: 500K-1 M; Level 7: 1M-5M; Level 8: More than 5M
(Total Amount in 2013) 51 3.6863 4 2.0356 1 8 (Total Amount in 2014) 51 3.8431 4 1.9452 1 8 (Total Amount in 2015) 51 4.1765 4 2.0723 1 8
(Total Amount in 2016: estimated) 51 4.9216 5 2.1681 1 8 Industry Composition in respective year: Art Percentage of Projects Launched on the Platform Categorized as Art
(Art in 2013) 51 13.92% 10% 0.1404 0% 100% (Art in 2014) 51 14.51% 10% 0.146 0% 100% (Art in 2015) 51 10.59% 10% 0.1465 0% 100%
(Art in 2016:estimated) 51 9.41% 10% 0.1569 0% 100% Industry Composition in respective year: Business and
Professional Service Percentage of Projects Launched on the Platform Categorized as Business and Professional
Service (Business and Professional Service in 2013) 51 16.27% 15% 0.0588 0% 30% (Business and Professional Service in 2014) 51 16.37% 15% 0.042 0% 30% (Business and Professional Service in 2015) 51 16.08% 15% 0.1074 0% 70%
(Business and Professional Service in 2016: estimated) 51 17.16% 15% 0.1209 0% 70%
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Table 1.(Continued) Variable Definition Obs Mean Median Std. Dev Min Max
Industry Composition in respective year: Education and Research Percentage of Projects Launched on the Platform Categorized as Education and Research
(Education and Research in 2013) 51 9.02% 10% 0.0196 0% 10%
(Education and Research in 2014) 51 11.18% 10% 0.044 0% 10%
(Education and Research in 2015) 51 10.98% 10% 0.0985 0% 30%
(Education and Research in 2016: estimated) 51 10.59% 10% 0.1376 0% 30%
Industry Composition in respective year: Clean Tech and Energy Percentage of Projects Launched on the Platform Categorized as Clean Tech and Energy
(Clean Tech and Energy in 2013) 51 5.29% 5% 0.0204 0% 10%
(Clean Tech and Energy in 2014) 51 4.90% 5% 0.0257 0% 10%
(Clean Tech and Energy in 2015) 51 7.45% 5% 0.0238 0% 10%
(Clean Tech and Energy in 2016: estimated) 51 8.04% 5% 0.0383 0% 15%
Industry Composition in respective year: Life Science Percentage of Projects Launched on the Platform Categorized as Life Science
(Life Science in 2013) 51 10.39% 10% 0.0216 0% 20%
(Life Science in 2014) 51 11.57% 10% 0.0274 0% 25%
Industry Composition in respective year: Manufacturing Percentage of Projects Launched on the Platform Categorized as Manufacturing
(Manufacturing in 2013) 51 4.12% 0% 0.0103 0% 5%
(Manufacturing in 2014) 51 2.75% 0% 0.0247 0% 10%
(Manufacturing in 2015) 51 3.53% 0% 0.0401 0% 20%
(Manufacturing in 2016: estimated) 51 2.94% 0% 0.0465 0% 20%
Industry Composition in respective year: media Percentage of Projects Launched on the Platform Categorized as Media
(Media in 2013) 51 2.35% 0% 0.014 0% 10%
(Media in 2014) 51 3.63% 0% 0.0458 0% 30%
(Media in 2015) 51 3.82% 0% 0.0651 0% 30%
(Media in 2016: estimated) 51 3.24% 0% 0.0682 0% 30%
26
Table 1.(Continued)
Variable Definition Obs Mean Median Std. Dev Min Max Industry Composition in respective year: Non-
Profit and Charities Percentage of Projects Launched on the Platform Categorized as Non-Profit and Charities
(Profit and Charities in 2013) 51 26.47% 25% 0.2079 0% 100%
(Profit and Charities in 2014) 51 23.92% 25% 0.2388 0% 100%
(Profit and Charities in 2015) 51 26.86% 25% 0.239 0% 100%
(Profit and Charities in 2016: estimated) 51 25.49% 25% 0.2536 0% 100% Industry Composition in respective year: Real
Estate Percentage of Projects Launched on the Platform Categorized as Real Estate
(Real Estate in 2013) 51 2.16% 0% 0.0427 0% 15%
(Real Estate in 2014) 51 2.35% 0% 0.0459 0% 25%
(Real Estate in 2015) 51 2.84% 0% 0.1404 0% 100%
(Real Estate in 2016:estimated) 51 2.75% 0% 0.1453 0% 100% Industry Composition in respective year: Social
Enterprise Percentage of Projects Launched on the Platform Categorized as Social Enterprise
(Social Enterprise in 2013) 51 4.90% 0% 0.0901 0% 50%
(Social Enterprise in 2014) 51 4.51% 0% 0.0808 0% 50%
(Social Enterprise in 2015) 51 3.63% 0% 0.0815 0% 50%
(Social Enterprise in 2016: estimated) 51 3.33% 0% 0.1263 0% 50% Industry Composition in respective year:
Hardware and Software Percentage of Projects Launched on the Platform Categorized as Hardware and Software
(Hardware and Software in 2013) 51 5.10% 5% 0.0272 0% 10%
(Hardware and Software in 2014) 51 4.31% 5% 0.028 0% 20%
(Hardware and Software in 2015) 51 4.02% 5% 0.1782 0% 100%
(Hardware and Software in 2016: estimated) 51 6.86% 5% 0.1833 0% 100%
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Table 2. Comparison Tests on Variable Impact This table shows the impact of different platform characters on due diligence application (Panel A) and the impact of due diligence application on platform performance (Panel B) using comparison tests. The table is based on platform activities in year 2013. We observe similar results when analyzing platform activities in year 2014, 2015 and 2016(estimated). *, **, *** Significant at the 10%, 5%, and 1% levels, respectively. Panel A: Factors Affecting Due Diligence Application (Year 2013)
Project Number (Below Median)
Project Number (Above Median) Z Value4 Compliance Expenditure
(Below Median) Compliance Expenditure
(Above Median) Z Value
Probability of Due Diligence Application 0.8696 0.1786 4.91*** 0.1379 0.9545 -5.78***
Employee Number (Below Median)
Employee Number (Above Median) Z Value Equity Crowdfunding
(Not Available) Equity Crowdfunding
(Available) Z Value
Probability of Due Diligence Application 0.2069 0.8636 -4.65*** 0.4146 0.8012 -2.19**
Periodical Subscription at Different Levels/Tiers
(No)
Periodical Subscription at Different Levels/Tiers
(Yes) Z Value Industry Composition:
Art (Below Median) Industry Composition: Art (Above Median) Z Value
Probability of Due Diligence Application 0.3065 0.7348 -2.40*** 0.3517 0.7029 -1.86*
Industry Composition: Life Science
(Below Median)
Industry Composition: Life Science
(Above Median) Z Value
Industry Composition: Non-Profit and Charities
(Below Median)
Industry Composition: Non-Profit and Charities
(Above Median) Z Value
Probability of Due Diligence Application 0.3496 0.7783 -2.26** 0.3065 0.8461 -3.83*** Panel B: Impact of Due Diligence Application on Platform Performance (Year 2013)
Due Diligence (Not Applied)
Due Diligence (Applied) T Value5
Average Percentage of Fully Funded Project (in Levels/Ordinal Scale) 1.3769 2.4231 -4.97***
Average Amount of Money Raised (in Levels/Ordinal Scale) 2.6400 4.7692 -5.36***
Average Fund-raising Duration (in Levels/Ordinal Scale) 2.8800 3.1923 -0.74
4 Two-sample test of proportions 5 Two-sample t test with equal variances
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Table 3. Correlation Matrix for Key Variables This table shows the correlation among different variables used in regressions in Table 4 and Table 6. The correlation matrix shown below is based on platform activities in year 2013. We observe similar correlations among the listed variables under platform activities in year 2014, year 2015 and year 2016 (estimated). . *, **, *** Significant at the 10%, 5%, and 1% levels, respectively. Panel A: Factors Affecting Due Diligence Application (Year 2013)
Due Diligence Dummy Project Number Resource on
Compliance Employee Number
Equity Crowdfunding
Dummy
Subscription at Different Levels
Industry Composition:
Art
Industry Composition: Life Science
Due Diligence Dummy 1 Project Number -0.506*** 1 Resource on Compliance 0.704*** 0.339* 1 Employee Number 0.424** 0.225 0.472*** 1 Equity Crowdfunding Dummy 0.306* 0.0957 0.417** 0.00307 1 Subscription at Different Levels 0.144** -0.0867 0.144 -0.0737 -0.0698 1 Industry Composition: Art 0.130* 0.0613 -0.0292 -0.0481 -0.0766 -0.0219 1 Industry Composition: Life Science 0.0433* -0.00190 0.134 -0.0492 0.123 -0.0339 -0.0372 1 Industry Composition: Non-Profit and Charities 0.263** 0.597*** 0.246 0.467*** -0.146 -0.0418 -0.0321 -0.0709
Panel B: The Influence of Due Diligence Application on Platform Performance (Year 2013)
Percentage of Fully Funded Project
Total Amount of
Money Raised
Average Fund-raising
Duration
Due Diligence Dummy
Periodical Update
Pre-listing Evaluation
Strategic Guidance
Business Planning
Contract Help
Promotion Service
Number of Types of Applied
Due Diligence
Percentage of Fully Funded Project 1 Total Amount of Money Raised 0.749*** 1 Average Fund-raising Duration 0.484 0.593 1 Due Diligence Dummy 0.375** 0.396** 0.581 1 Periodical Update 0.387** 0.547*** 0.670*** 0.671*** 1 Pre-listing Evaluation 0.228 0.196 0.409** 0.486*** 0.514*** 1 Strategic Guidance 0.493*** 0.605*** 0.530 0.452*** 0.649*** 0.471*** 1 Business Planning -0.0174 -0.0476 0.289* 0.440** 0.246 0.0766 0.0971 1 Contract Help 0.0282 0.0520 0.310 0.298* 0.395** 0.292* 0.311* 0.275 1 Promotion Service 0.468*** 0.491*** -0.436** 0.266 0.412** 0.378** 0.637*** -0.0582 0.248 1 Number of Types of Applied Due Diligence 0.288* 0.337* -0.494 0.747*** 0.615*** 0.634*** 0.357* 0.245 0.278* 0.228 1 Number of Projects/ Number of Employees 0.358 -0.394** 0.290 0.154 -0.507** -0.100 -0.375* 0.168 -0.247 0.182 0.0676
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Table 4. Factors Affecting Platform’s Due-Diligence Application This table shows the factors affecting a crowd-funding platform’s due diligence application. Logit regression models are applied to evaluate the influence of different platform characters and activities. Due diligence is applied when at least one of the following actions is taken: background check, site visit, credit check, cross-check from social media connections, monitor account activities, and request third party certificates or proof. Model 1, Model 2, Model 3 and Model 4 are based on platform characters and activities in year 2013, 2014, 2015 and 2016(estimated) respectively. Dependent variables equal 1 if due diligence is applied; 0 otherwise. *, **, *** Significant at the 10%, 5%, and 1% levels, respectively.
Model 1 (2013) Model 2 (2014) Model 3 (2015) Model 4 (2016: estimated)
Number of Investors in Respective Year -0.419 -0.878 -1.060 -1.207
(-0.48) (-1.00) (-1.20) (-1.39)
Number of Projects in Respective Year -2.151* -1.536** -1.348* -1.252**
(-1.73) (-2.10) (-1.91) (-2.13)
Resource Spent on Compliance Annually 0.908** 0.773* 0.851** 0.936**
(1.99) (1.85) (2.03) (2.10)
Number of Employees 0.228** 0.319* 0.459* 0.610*
(2.58) (1.90) (1.86) (1.71)
Equity Crowdfunding on the Platform? (Yes=1) 1.443** 1.327** 1.535** 1.424*
Table 5. Application of Main Types of Due Diligence This table shows the application of three main types of due diligence: background check, site visit and cross check from social media connections. Logit regression models are applied to evaluate the influence of different platform characters and activities. Panel A shows the application of background check; Panel B, site visit; Panel C, cross check. , Models are based on platform characters and activities in years specified in brackets. Dependent variables equal 1 if the type of due diligence is applied; 0 otherwise. *, **, *** Significant at the 10%, 5%, and 1% levels, respectively. Panel A. Dependent Variable: Background Check in Each Year
Background Check (2013)
Background Check (2014)
Background Check (2015)
Background Check (2016 estimated)
Number of Investors in Respective Year 0.136 0.742 -0.669 0.113
(1.09) (1.02) (-0.21) (0.45)
Number of Projects in Respective Year -1.291* -1.638** -1.597* -1.358**
(-1.90) (-2.08) (-1.82) (-2.33)
Resource Spent on Compliance Annually 0.401 0.399* 0.512* 0.337**
(1.47) (1.74) (1.93) (2.33)
Number of Employees 0.246** 0.267* 0.392* 0.384*
(2.19) (1.81) (1.91) (1.69)
Equity Crowdfunding on the Platform? (Yes=1) 0.796** 1.183** 0.845** 0.927*
(2.14) (2.09) (2.11) (1.75)
Platform Age 0.0479 0.0602 0.0426 0.0231*
(0.09) (1.26) (1.55) (1.69)
Fee Structure: Periodical Subscription at Different Levels/Tiers 0.187** 0.296** 0.267* 0.441**
(2.39) (2.03) (1.95) (2.15)
Fee Structure: Fixed percentage of total amount raised, whether funding is successful or not -0.559* -0.458 -0.940 0.693
(-1.76) (-1.33) (-0.68) (1.42)
Fee Structure: Fixed percentage of total amount raised, only if funding is successful 0.0235 -0.0589 0.0357 0.0534
Fee Structure: Periodical Subscription at Different Levels/Tiers 0.0423 0.0426 0.0353 0.0431*
(1.33) (0.41) (1.17) (1.84) Fee Structure: Fixed percentage of total amount raised, whether funding is successful or not -0.0632 -0.0346 -0.0413 0.0357
(-0.35) (-0.40) (-0.55) (0.04) Fee Structure: Fixed percentage of total amount raised, only if funding is successful 0.0653 -0.0169 0.0334 0.0581
Table 6. Impact of Due Diligence Application on Platform Performance This table shows the impact of due diligence application on the percentage composition of fully funded project, total amount of money raised through a crowd-funding platform, and the average duration of the projects launched on a platform. Due diligence is applied when at least one of the following actions is taken: background check, site visit, credit check, cross-check from social media connections, monitor account activities, and request third party certificates or proof. The due diligence variable is based on fitted values from Table 4 in each respective year. Ordered logit regressions are used to evaluate the influence of due diligence application on platform performance in year 2013, 2014, 2015 and 2016 (estimated). Different services offered by crowd-funding platforms are used as controls. Cutpoints and constants are not reported for conciseness. *, **, *** Significant at the 10%, 5%, and 1% levels, respectively. Panel A. Dependent Variable: Percentage of Fully Funded Project in Each Year
Percentage of Fully Funded Project 2013
Percentage of Fully Funded Project 2014
Percentage of Fully Funded Project 2015
Percentage of Fully Funded Project 2016 (estimated)
Predicted Due Diligence Application? (Yes=1) 1.274** 1.147** 1.315** 1.693**
(2.29) (2.52) (2.60) (2.60)
Platform Provides Periodical Updated Platform Information to Startups -0.775 -0.414 -0.102 -0.0695
(-1.09) (-0.73) (-0.19) (-0.12)
Platform offers pre-evaluation before listing Startups 0.896 0.228 0.117 -0.101