Under My Umbrella:
Fiscal Sponsorship in New York State
SECTION
Acknowledgements “Under My Umbrella” is funded by a generous contribution from the New York State Arts & Culture Research Fund, administered by the New York Community Trust. By Sarah K. Lenigan and Ian David Moss Design by Monroe&Co. www.monroeand.co December 2013 Special thanks to the Fractured Atlas fiscal sponsorship staff, Katherine Ingersoll, Heather Bryant, Katya Vasilaky, Matthew Steele, Ryan Tully-Doyle, Eli Draluk, Stacey Finkelstein, Andres Gomez, and members of the Cultural Research Network and EVALTALK listservs who responded to methodological inquiries. The data pertaining to arts nonprofits used for this report was provided by the Cultural Data Project (“CDP”), an organization created to strengthen arts and culture by documenting and disseminating information on the arts and culture sector. Any interpretation of the data is the view of Fractured Atlas and does not reflect the views of the Cultural Data Project. For more information on the Cultural Data Project, visit www.culturaldata.org.
2
Introduction Fractured Atlas is a national arts service organization that empowers artists, arts organizations, and other cultural sector stakeholders by eliminating practical barriers to artistic expression. It pursues this mission by offering a variety of programs, including insurance, technology solutions for artists, and the country’s largest arts-focused fiscal sponsorship program. Founded in 2002, Fractured Atlas’s fiscal sponsorship program currently represents more than 3400 projects nationwide in all arts disciplines. Fiscal sponsorship is a legal arrangement that allows Fractured Atlas to extend its tax-exempt nonprofit status to individuals and groups who would rather spend their time producing art than filing paperwork. Under the fiscal sponsorship model, Fractured Atlas members may apply for grants and solicit donations for arts-related projects as if they were a nonprofit arts group, making them eligible for more awards and making support from individuals tax-exempt. An association with Fractured Atlas, according to many members, also adds an air of legitimacy to a project. In 2011, Fractured Atlas redesigned the annual report required from its sponsored projects to better align with the Cultural Data Project (CDP). The CDP collects data from arts organizations in a number of states, including New York, and allows them to generate a variety of reports for both internal analytics and external grant applications. The CDP also makes its data available to researchers for projects such as this one. A “test run” of this integration took place in 2012, when Fractured Atlas analyzed
3
fiscally sponsored dancemakers in New York City on behalf of local arts service organization Dance/NYC. This project yielded several insights, suggesting that fiscally sponsored projects use limited fundraising and marketing dollars with remarkable effectiveness and spend more money on programs as compared to similar-sized nonprofits. The objective of “Under My Umbrella” is to better understand fiscal sponsorship’s relationship to and place within the arts ecosystem, using the most recent year available of CDP and Fractured Atlas fiscal sponsorship data. In particular, this effort sheds light on the characteristics of sponsored projects as compared with arts nonprofits of similar size, discipline, geography, and location. We are particularly interested in comparing organizations and projects of similar scale, and employ linear regression as a tool to illuminate correlations between parameters. The core research question of our project is whether, controlling for other factors, fiscally sponsored status predicts specific relevant organizational characteristics such as earned income, total attendance, and fundraising efficiency. This report represents the first opportunity to analyze fiscally sponsored projects alongside their peer independent nonprofit organizations across disciplines. This study should be considered a first pass of Fractured Atlas’s data and an exploration of appropriate analysis methods. As such, it was as useful in identifying logistical challenges to be resolved and new questions to be answered as it was in recognizing and illustrating patterns and relationships.
Key Findings
4
1 2 3
Sponsored projects are less reliant on contributed income than nonprofits, all else being equal. Whether that’s by choice or by necessity is unclear.
4
Discipline categorization is an important factor that may be underexamined in arts research efforts. Discipline effects showed up in every analysis undertaken for this study, and there were particularly strong relationships between theater and attendance (theater entities had lower attendance, holding other factors constant) and between music and occupancy expenses (music groups paid less for occupancy).
5
As expected, organizations and projects located in New York City pay more for occupancy— space rental, utilities, and the like. But in other respects location does not appear to play a particularly important role.
In other respects, sponsored projects are a lot like nonprofits. It is not one of the most important factors driving outcomes like attendance, occupancy expense, or the proportion of expenses going to programs. Organizations and sponsored projects allocate resources differently at different budget levels. In particular, large-budget entities spend a smaller proportion of their expenses on occupancy, while the very smallest groups spend less on programs and earn less of their income.
INTRODUCTION
Methodology This is the first research report to explore the potential of regression analysis to understand the differences between nonprofits and fiscally sponsored projects using data from the Cultural Data Project, and one of the first to use regression in connection with the CDP more generally. Previous research in this area has tended to rely on descriptive statistics (counts, percentages, etc.) to illuminate cross-sections of the arts and cultural ecosystem as it exists in the CDP. The limitation of this approach is that it doesn’t account for the interaction of factors with each other. For example, it’s no secret that organizations look and behave differently by budget size. But large-budget music organizations and large-budget visual arts organizations may also differ in no less important ways; similarly, small-budget big city organizations may have little in common with small-budget organizations in rural areas. What regression does is isolate these factors from each other so that they can be examined individually. It allows us to answer the question, how much of an impact does budget size (or any other variable of interest) have, all else being equal?
5
Adding fiscal sponsorship status to this mix, in turn, enables us to answer some important questions about fiscal sponsorship. To date, our sector has operated mostly in the dark about how sponsored projects differ from nonprofits in the aggregate, and what little insight has been available to us has been limited by the fact that sponsored projects tend to be smaller than nonprofits on average. Are the differences we’re seeing simply a product of that small size, or is there something specific about being a sponsored project that is causing them? This study empowers us to start exploring that question.
The Data Our analysis is made possible by the combination of two data sets: the Cultural Data Project, representing 501(c)(3) nonprofits, and Fractured Atlas’s internal database of fiscally sponsored projects. The CDP data set consisted of 743 organizations with reviewcomplete profiles for fiscal years ending between September 1, 2011 and December 31 2012; in the instances where more than one report was generated in this timeframe, the later report was used. The Fractured Atlas data set includes 703 sponsored projects that submitted an annual report corresponding to Fractured Atlas’s fiscal year spanning September 1, 2011 through August 31, 2012. Because it was outside the scope of this study to analyze the projects of more than one fiscal sponsor, the 15 fiscally sponsored projects that were present in the CDP data set (including three that were also Fractured Atlas projects) were removed.
We have combined and segmented these data sets in several ways to further our analysis. Since we were interested in the effects of location, we divided the nonprofits and sponsored projects in to three location categories based on the principal address on file: • New York City (Bronx, Kings, New York, Queens, and Richmond counties) • Metro Region (Dutchess, Nassau, Orange, Putnam, Rockland, Suffolk, Ulster and Westchester counties)
Distribution of Sample: Location Sponsored
Nonprofit
700 600
653
500
509
400 300 200 179 100
City
• Upstate (all other counties) We also segmented arts entities by discipline, as indicated in the left column under ‘Report’ in the table below. Note that all entities falling into the ‘Other’ category are not considered comparable to each other.
55
34
16
0 Metro Region
Upstate
Distribution of Sample: Arts Discipline Sponsored
Nonprofit
300
301
250
257
200
150
50
71
58 61 33
13
0 Dance
139
134
131
123
100
Film & Electronic Media
Music
42
26
Literary Arts
Theater
57
Visual Arts
Other
Report
Fractured Atlas*
CDP
Dance
Dance
Dance
Film & Electronic Media
Film
Media Arts
Music
Music
Music Opera/Musical Theatre (Opera groups only)
Literary Arts
Publishing
Literature
Theater
Theatre
Theatre Opera/Musical Theatre (Musical Theatre groups only)
Visual Arts
Visual Arts Gallery
Visual Arts Photography
Other
Other Multimedia Performance Art
Crafts Design Arts Folklife/Traditional Arts Humanities storytelling Interdisciplinary Multidisciplinary Non-Arts/Non-Humanities
* Note: Fractured Atlas adopted the discipline system used in this report (first column) for its sponsored projects in September 2013. However, the sponsored project data set uses an older discipline categorization scheme (second column).
6
METHODOLOGY
Finally, we ranked the arts entities by budget size from smallest to largest and broke them into 10 equal bands, or “deciles.” Budget size was defined as the total expenses of a given organization or project. Though a blunt tool, this strategy allowed some insights into how the influence of budget size varied at different levels. The breakdown of budget bands is as follows: Expense Band
Expense bottom value
Expense top value
Count
Sponsored
Nonprofit
0–10%
$14
$1,076
145
143
2
10–20%
$1,078
$3,926
145
140
5
20–30%
$3,992
$7,775
145
136
9
30–40%
$7,821
$15,709
145
117
28
40–50%
$15,726
$30,006
145
86
59
50–60%
$30,270
$66,212
145
52
93
60–70%
$66,444
$142,124
144
22
122
70–80%
$142,234
$342,464
144
4
140
80–90%
$353,455
$1,143,145
144
2
142
90–100%
$1,145,324
$501,005,684
144
1
143
Distribution of Sample: Budget Range Sponsored
Nonprofit
160
140
143
140
120
143
142
140
136 122
117 100 93 86
80
60 59 52 40
28
20
0
22 2 0–10%
7
5 10–20%
9 20–30%
30–40%
40–50%
50–60%
60–70%
4
2
70–80%
80–90%
1 90–100%
METHODOLOGY
The Analysis Structure The primary analysis tool used in this study was linear regression analysis, which is a way of statistically estimating relationships between variables and isolating the influence of different factors from each other. The regression analyses used for “Under My Umbrella” examined several measures of fiscal health and operational efficiency while controlling for arts discipline, location, budget size and entity type (nonprofit or fiscally sponsored). To examine the role of location, art discipline, budget size and organizational type we employed linear regression on five dependent variables:
Analysis of the final two dependent variables each involved an additional control variable to increase statistical fit. In the case of contributed income, we controlled for the proportion of expenses devoted to fundraising. Similarly, for analysis of attendance, we controlled for the proportion of expenses devoted to marketing.
Data Definitions In order to run analyses on the variables described above, we needed to define certain concepts as consistently as possible between the two data sets. Below is a crosswalk of data fields as they pertained to our dependent variables.
• Earned Income • Program Expense • Occupancy Expense • Contributed Income • Attendance
Report Variable
Fractured Atlas Field
CDP Field
Earned Income
Sum of the following: Revenue Admissions Revenue Tuition Revenue Events Revenue Publications Revenue Dues Revenue Contracts Revenue Advertising Revenue Other
Total Earned Revenue (section 3 line 20)
Program Expense
Percent Programs
Total Program Expenses (section 6 line 45)
Occupancy Expense
Expenses Rent
Total Rent Expenses (section 6 line 38) Total Telephone Expenses (section 6 line 41) Total Utility Expenses (section 6 line 44) Total Repair and Maintenance Expenses (section 6 line 8) Total Major Repair Expenses (section 6 line 29)
Expenses Utilities Expenses Repairs
8
Contributed Income
Revenue Grants Revenue Donations
Total Support Revenue (section 3 line 33)
Fundraising Expense
Percent Fundraising
Total Fundraising Expenses (section 6 line 45)
Attendance
Paid Attendance Free Attendance
Total Attendance Paid (section 11 line C1) Total Attendance Free (section 11 line C2)
Marketing Expense
Expenses Public Relations Expenses Advertising
Public Relations Expense (section 6 line 37) Advertising/Marketing (section 6 line 3) Printing (section 6 line 33) Marketing Salaries and Fringe (section 7 line 5)
METHODOLOGY
Data Exclusions In order to fit the assumptions of the regression method used, we needed to transform each of our dependent variables via the natural logarithm, which required us to exclude any values of zero in those dependent variables. What this means is that, for example, our regressions looking at attendance only apply to entities that reported any attendance. These zero values do not necessarily indicate a lack of activity; for example, due to the frequently erratic nature of sponsored project lifecycles, a value of zero can often indicate a lack of activity in a particular area during the arbitrary-to-theproject reporting timeframe. Because it was not possible to determine the meaning behind zero values in every case, we removed the corresponding entities from the regressions and described them separately. For regression analysis of program expense and occupancy expense, the total sample included 743 independent nonprofit organizations and 703 fiscally sponsored projects. All organizations that reported program or occupancy expenses were included in regressions, resulting in an analysis sample of 1342 cases for program expenses and 1068 cases for occupancy expense. Groups that reported no program or occupancy expenses were not included in their respective regressions.
9
Earned income and support income excluded the 11 501(c)(3) organizations that reported negative income of any kind as well as the 58 sponsored projects that reported no income from any source. In addition, some groups reported no income from earned or donated sources, in which case they were removed from the corresponding regression analysis. This resulted in an analysis sample of 1038 cases for earned income and 1364 cases for support income. For analysis of attendance, organizations and sponsored projects that reported zero attendance or whose primary purpose was not to offer activities open to the public (e.g., educational groups, service organizations, arts councils, professional associations) were omitted, resulting in a sample size of 964. For analysis of contributed income that controlled for fundraising expense, 70 entities were eliminated because they reported zero funds raised (100% earned income). This resulted in an analysis sample of 1376 cases. While not eliminated from regression analysis, 543 of these cases reported no spending on fundraising.
METHODOLOGY
Limitations Despite the wealth of data collected by arts groups and curated by Fractured Atlas and the Cultural Data Project, there are some limitations arising from both sources that must be taken into account. These limitations affect both the validity of the comparison between the two data sets as well as the generalizability of the results to groups that were not a part of the study. Regarding the first issue, the CDP is not necessarily representative of all nonprofit arts groups in New York State, as the organizations that fill out reports are doing so in most cases in order to apply for a grant, and thus represent an active and engaged cross-section of the total arts nonprofit population. By contrast, every fiscally sponsored project at Fractured Atlas is required to submit an annual report if it released any funds from its sponsored account during the year, so levels of engagement and activity may not be entirely comparable. It’s important to keep in mind that in the case of many smaller projects and organizations, reporting is undertaken by nonprofessionals who may not be following strict accounting standards or correctly interpreting instructions. While the Cultural Data Project maintains a well-staffed help desk to assist nonprofits with understanding the data profile, neither it nor Fractured Atlas can guarantee the accuracy of individual records. As a rule of thumb, accuracy is likely to increase with budget size and data from the smallest entities should be considered least reliable.
10
One additional limitation uncovered by this examination of data is that sponsored projects frequently operate on cycles that span more than one year with periods of heavy and light activity, sometimes causing a delay between when money is raised and when it is spent (or when money is spent and activity takes place). This irregular cycle can result in reports with skewed numbers because activity that bridges two reporting years is reported in one year or another, breaking up patterns such as earning and spending. Regarding the ability to generalize results beyond the study, there are a few caveats to keep in mind. First, since nonprofits are required to fill out their CDP profiles to receive funding from certain sources, there may be geographic biases embedded within that data set aligning with the geographic scope of funders that require the CDP. Second, Fractured Atlas is only one of many organizations with sponsored projects in the state, and we have no way of knowing to what extent projects affiliated with other sponsors would reflect the findings presented here.
METHODOLOGY
Analysis We observed meaningful relationships between our control variables and each of the five dependent variables analyzed. In general, our models were strong predictors of these variables. Budget size, arts discipline, location, organizational structure and other variable-specific controls explained between 68-97% of the variance in what an individual company would spend on program and occupancy expense, how much earned or donated income it could be expected to generate, and what attendance figures it could expect. Not surprisingly, for all of these categories, budget size played by far the largest role in determining outcomes. Fiscal sponsorship influenced the calculations more than anything but budget size for earned income and contributed income (controlling for fundraising expense), and was a factor to some extent in the percent of funds spent on occupancy. Specific disciplines influenced outcomes in all instances to a small but measurable degree, and location played a similar small role in determining earned income and occupancy expenses. We ran stepwise linear regressions in two different ways: one with the budget size (expenses) as a continuous log-transformed variable, and one with the budget size broken into 10 deciles as explained in the Methodology section. In all cases using the deciles yielded lower prediction accuracy, so the version using the continuous budget variable is used as the default model and quoted throughout this report, with differences between the two models noted as appropriate.
11
Variable
R2
Adjusted R2
With Budget Separations
No Budget Separation
With Budget Separations
No Budget Separation
Earned Income
.678
.732
.675
.730
Program Expense
.933
.974
.932
.974
Occupancy Expense
.725
.766
.721
.764
Contributed Income
.832
.855
.830
.854
Attendance
.662
.681
.657
.679
In each regression, we controlled for organizational type (i.e., nonprofit or fiscally sponsored), arts discipline, budget size, and location, using the definitions detailed in the Methodology section above. The results of these analyses are below, presented by control. To help illustrate the findings, we make use of a consistent example case that we refer to throughout. This arts organization has nonprofit status, is located in New York City, has an annual budget of $50,000 and has a discipline categorized as “other.” At various points, we will manipulate this theoretical group’s characteristics, showing how changing its budget, location, discipline and entity type changes the predicted results for each variable analyzed.
Summary of Significant Results: Fiscal Sponsorship
Variable
We found several notable differences between groups that were fiscally sponsored and those that had their own nonprofit status. Sponsorship status made a difference in predicting earned income and contributed income, though was secondary to the importance of budget size. Sponsorship was also a factor in determining occupancy expense, but not as important as budget size or arts discipline. Our results suggest that sponsored projects derive quite a bit more of their income from earned rather than donated sources compared to their nonprofit peers. Our example case would be expected to earn $9,471 against a $50,000 expense budget as a nonprofit, but as a sponsored project this number would rise to $17,623. This could be because sponsored projects are not eligible for funding to the same extent as nonprofit groups, or because they lack the expertise to fundraise effectively. It could also mean that they are simply better at or more positively oriented toward earning money than their peer nonprofits. Whatever the explanation, the results are even more striking considering the fact that because Fractured Atlas receives donations and grants on behalf of its projects, it can more reliably set a lower bound for contributed income than earned; if anything, we would expect earned income to be slightly underreported among sponsored projects.
12
Coefficient
Beta
P-value
Contributed Income
-.505
-.094
.000
Earned Income
.621
.114
.000
Occupancy Expense
.276
.057
.007
While at first glance being fiscally sponsored appears to make a difference in fundraising efficiency based on the results of the contributed income analysis, because fiscal sponsorship is such a strong predictor of earned income, it is not clear whether the control for the proportion of fundraising expenses adds any explanatory power. That control is only statistically significant at a 94.8% confidence level, and its exclusion from the model makes barely any difference in the regression results. Put in less technical terms, it seems likely that the strong negative relationship between fiscal sponsorship and contributed income we see here is simply the mirror image of the strong positive relationship between sponsored project status and earned income discussed above. Finally, sponsored projects appear to pay more of their expenses towards occupancy than nonprofit groups do, though budget and discipline have a more reliable influence on the cost of occupancy. Our example nonprofit group would be predicted to pay $4,559 annually towards rent, utilities, and so forth; as a sponsored project, those occupancy costs would be $6,009.
ANALYSIS
Summary of Significant Results: Budget Size As anticipated, budget size played a very large role in the analysis results and was consistently the most important factor in the regressions. However, in the cases of earned income, attendance and occupancy expense, the version of the regressions where deciles were used showed insignificant results for the lowest two budget deciles, suggesting instability in those figures for the smallest companies (the vast majority of which are sponsored projects). For this reason, any calculation of these variables for entities with budgets below around $4,000 should not be considered reliable.
Average % of Expenses Devoted to Programs, Full Sample 80% 70% 60% 50% 40% 30% 20% 10% 0% 0–10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Deciles Represented as Percentiles
Program expense tracks very strongly with budget size when broken out by deciles. Starting at a very low budget threshold represented at the second decile (between $1,000 and $4,000), the percent of expenses going to programs rises to 68% of total expenses and increases gradually, staying mostly within the 73-76% range at higher budget levels. Budget size is an excellent predictor of expenses devoted to programs, and there is not a great deal of variation in the relationship. When points are graphed, we can see the strong relationship between (log-transformed) budget size (x-axis) and (log-transformed) program expense (y-axis):
13
ANALYSIS
Earned income also tracks closely with budget size, and averages 30% of income overall. As budget sizes reach decile 4 (the 30th-40th percentiles), which ranges from approximately $8,000 to $16,000, earned income levels stabilize within a narrow band of 31-37% of total income. In budget deciles 4-7, which all have sufficient sponsored projects and nonprofit cases to draw conclusions about both, on average sponsored projects derive more of their income from earned revenue than nonprofits do, suggesting greater efficiency that should be explored further. Especially in the 7th decile, which includes budgets ranging from $66,444 to $142,124, sponsored projects’ income patterns deviate greatly from nonprofit groups’. Where nonprofits see a small dip in percent of income from earned sources, sponsored projects’ earned income jumps 17 percentage points.
Average % Earned Revenue, Full Sample 40% 35% 30% 25% 20% 15% 10% 5% 0% 0–10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Deciles Represented as Percentiles
Average Earned Revenue as a % of Total Revenue by Entity Type Sponsored
Nonprofit
60%
50%
40%
30%
20%
10%
0% 30–40%
40–50%
50–60%
60–70%
Deciles Represented as Percentiles
14
ANALYSIS
Occupancy expenses are also closely tied to budget size, increasing as budget sizes increase. Unlike program expense and earned income, however, the scatterplot below is more diffuse, showing that there is not as direct a relationship between occupancy costs and budget size as there is between program expenses and budget size. The proportion of expenses devoted to occupancy drops by as much as half as a percentage of overall expenses after hitting a high of around 18% in the $8,000 to $30,000 budget range, possibly the point where space outside of a home office is first utilized by many groups. These results are consistent with a hypothesis that space rental is often one of the first things that small arts groups need to pay for, but that as budget sizes get bigger, those groups diversify their spending and are able to achieve some economies of scale with respect to space usage.
Average % of Expenses Devoted to Programs, Full Sample 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 0–10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Deciles Represented as Percentiles
15
ANALYSIS
Budget size strongly predicted the amount of income derived from donations and grants, though the proportion of the budget devoted to fundraising also influenced that figure. As expenses grow, organizations and projects tend to spend more to raise each incremental dollar, especially at the high end of the budget scale. Overall, nonprofits averaged $0.09 in spending for every dollar raised while sponsored projects averaged $0.07. However, this result can be explained by sponsored projects’ smaller size overall. Grouped with entities of similar scale, sponsored projects consistently spent more to raise each dollar than nonprofits. Further analysis could focus on the effectiveness of increased spending on fundraising at different budget levels.
Average % of Expenses Devoted to Programs, Full Sample 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 0–10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Deciles Represented as Percentiles
Average Earned Revenue as a % of Total Revenue by Entity Type Sponsored
Nonprofit
12%
10%
8%
6%
4%
2%
0% 30–40%
40–50%
50–60%
60–70%
Deciles Represented as Percentiles
16
ANALYSIS
Similarly, budget size predicted most strongly how large attendance would be for any group, though the proportion of the budget spent on marketing had the second–strongest effect on audience size. Not surprisingly, groups that spent a larger proportion of their budgets on marketing could expect larger attendance figures. While average spending per audience member stays within a narrow band of 75 cents to $3 for the most part, organizations in the largest decile, with budgets above $1.1 million, spend significantly more to reach each audience member. Comparing nonprofit groups and sponsored projects in the middle budget deciles which contain a mix of each, we see the two entity types roughly tracking each other in marketing expense per audience member. However, the increase in this ratio at the 7th decile (budgets of $66,444 to $142,124) is mostly driven by sponsored projects.
Marketing Expense per Audience Member, Whole Sample $8.00 $7.00 $6.00 $5.00 $4.00 $3.00 $2.00 $1.00 $0.00 0–10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Deciles Represented as Percentiles
Average Marketing Expense Per Audience Member Sponsored
Nonprofit
$3.50
$3.00
$2.50
$2.00
$1.50
$1.00
$0.50
$0 30–40%
40–50%
50–60%
60–70%
Deciles Represented as Percentiles
17
ANALYSIS
Summary of Significant Results: Discipline Which discipline an arts entity works in has an effect on all of the variables we examined, according to our analysis. However, only one regression analysis, attendance, was an individual discipline the strongest influencer on a score after budget size. The strongest discipline-specific relationships we saw were in the attendance calculations. It appears that theater and dance companies bring in smaller audiences than their peers in other disciplines after controlling for marketing expenses, budget, entity type, and location. If our example group was a theater or dance company, this model predicts it could expect lower attendance rates. Assuming the average marketing expense rate for the entire sample, our fictional company could expect to bring in total audiences of 2,840 people annually. However, if it were a theater group, our model predicts a total audience of only 1,736 people. This particular model explains slightly less than 70% of the variance in attendance, so these figures should be taken with a grain of salt.
18
Variable
Discipline
Attendance
We observed both positive and negative relationships with specific disciplines as they related to occupancy expenses. Music–related groups paid less for occupancy than comparable organizations or projects working in other disciplines. By contrast, both theater and visual arts devote more of their resources to occupancy than their peers in other disciplines, holding other factors constant. An identity as a visual arts or dance group was a good predictor of a greater proportion of earned income relative to the other disciplines. In addition, music, visual arts and theater organizations and projects were likely to have a higher percentage of expenses devoted to programs (as opposed to general and fundraising expenses) than their peers in other disciplines. Finally, the results of our analysis of contributed income showed small differences for theater, dance and literary arts groups, suggesting they derive slightly less of their income from contributions than their peers when controlling for fundraising expenses.
Coefficient
Beta
P-value
Dance Theater
-.316 -.492
-.037 -.094
.047 .000
Occupancy Expense
Theater Visual Arts Music
.354 .431 -.486
.068 .045 -.071
.005 .001 .000
Earned Income
Dance Visual Arts
.495 -.379
.055 -.037
.001 .023
Program Expense
Music Visual Arts Theater
.179 -.140 .084
.024 -.013 .014
.000 .004 .002
Contributed Income
Dance Literary Arts Theater
-.227 -.402 -.182
-.023 -.024 -.030
.030 .022 .005
ANALYSIS
Summary of Significant Results: Location
Variable
Location
Co-efficient
Beta P-value
Earned Income
Upstate
.359
.052
.002
Location was a factor in regression analysis for two of the five dependent variables analyzed. Location was not the most important factor in any of the calculations. There were no conclusive location effects observed on contributed income, program expenses, or attendance.
Occupancy Exp.
Metro
-.373
-.037
.014
Upstate organizations tended to earn more of their income when compared with their peers in and closer to New York City. It would be interesting to compare these results with similar studies in other regions of the country to determine if New York City has particularly high support for arts groups or if cities in general are generating more fundraising activity than less urban areas. Meanwhile, entities in the metropolitan region appear to pay less for their physical space and its upkeep than their peers in New York City, and a larger sample might show a similar finding with entities located upstate as suggested by the version of the regression using budget deciles (for upstate, coefficient = -.274, Beta = -.042, p-value = .013). This makes intuitive sense considering the higher costs of occupancy associated with New York City rents. Using our model to calculate occupancy expense, our example company could expect to pay $4,560 for occupancy in New York City. If it were located in the suburbs instead, it could expect to pay only $3,139 annually.
19
ANALYSIS
Exploring the Excluded Data As explained earlier, the regression analyses above pertain only to organizations and sponsored projects with nonzero values in the respective dependent variables – so, an organization with no occupancy expenses would not have been included in the occupancy expense regression. Below, we take a look at what the entities with zero values have in common.
Earned Income More than 90% of the 338 entities that reported no earned income were sponsored projects. Though 23% of all cases reported no earned income, 59% of film and electronic media groups had no earnings. All other disciplines ranged from 10% (literary arts) to 23% (visual arts). The overall budget size of these entities ranged from $43 to $1.7 million, all from support income. The median income of these groups was $3,996.
Program Expense A total of 104 cases were removed from regression analysis of program expenses because they reported zero program expenses. All but nine of these were sponsored projects. The most common discipline excluded for lack of reported program expenses was film and electronic media; 17% of all such entities reported zero program expenses compared to 7% across disciplines. The median budget size of these excluded entities was $1,050.
Occupancy Expense The majority (82%) of the 378 cases that reported zero occupancy expense were sponsored projects. Film and electronic media groups were the ones most likely to report zero occupancy expense, representing more than half of all groups in that discipline. By contrast, only 16% of dance organizations and sponsored projects reported zero occupancy expenses. The average for all disciplines was 26%. The median budget size was around $4,311. 20
Contributed Income Seventy cases, or 5% of the whole sample, reported zero income from contributed sources (grants and donations). Of these, 68 were sponsored projects. Ten percent of film and electronic media arts groups reported no contributed income, while only 3% of music groups did. Location of these groups is roughly proportional to the overall sample, and the median budget size was $763. The analysis of contributed income included a control for fundraising expenses. After removing the 70 entities with no contributed income, nearly 40% of the remaining groups, 209 nonprofits and 344 sponsored projects, reported zero expenses devoted to fundraising. The budgets of these groups, which were not removed from the regression analysis, averaged $121,000, whilethe median was only $11,750. Only 21% of literary arts groups reported zero fundraising expenses, whereas 51% of film and electronic media arts groups reported the same. Sponsored projects’ fundraising costs may be a reflection of reporting year, where funds expended in one year yielded support in the next, skewing the results. It is also possible that all fundraising activities were performed by volunteers and thus incurred no financial costs.
Attendance Of the 213 or 15% of all entities that reported zero attendance, 191 were sponsored projects and 22 were nonprofits. Eighty were film and electronic media groups (79 of them sponsored projects) which represented 51% of all film groups in the sample. The median budget size was $2,712. While not excluded from regression analysis, 168 entities reported attendance figures but had no marketing expenses, representing about 12% of the sample. Of these, 21 were nonprofits and 147 were sponsored projects with a median budget size of $4,383. Some disciplines are over or underrepresented: 7% of music groups reported zero marketing expenses while 18% of dance groups did. ANALYSIS
Conclusion Fiscal sponsorship is touted as a viable option for artists to structure their activity for a variety of reasons. It allows them to solicit funds with the same tax benefits as a nonprofit and gives them greater access to grants. It has been posited that sponsorship allows for greater administrative efficiency due to less time spent on paperwork, but those theories have not been studied in any depth. Fractured Atlas has sought to systematically reflect on its offerings to better understand how artists and the arts are and could benefit from innovative organizational structures and other targeted programs. For the last several years Fractured Atlas has been taking steps to allow for more study of fiscal sponsorship and this report represents the first major comparative analysis of sponsored projects and nonprofit organizations. Metrics typically used to assess nonprofit health were employed to begin to gauge the impact of fiscal sponsorship on art making when compared to similar arts groups operating as traditional 501(c)(3)s. Most notably, we discovered that fiscally sponsored projects derive more of their revenue from earned sources than their nonprofit peers. When looking at the corollary, support income, there was an even stronger relationship, and controlling for the proportion of expenses devoted to fundraising efforts did nothing to change that interpretation. Fiscally sponsored projects appear to derive less of their income from donations and grants than nonprofit groups. What we don’t know is why. Perhaps it is because
21
sponsored artists are not as skilled fundraisers, or because they are not eligible for as many grants as nonprofits, or perhaps it a conscious choice to earn rather than ask for money. Whatever the reason, fiscally sponsored projects are more selfsufficient than comparable nonprofits. This study proved inconclusive as to whether or not fiscally sponsored groups were able to devote more of their resources to programs than nonprofit groups. Analysis of program expenses with budgets segmented suggested a very small negative relationship (meaning sponsored projects devoted slightly less spending to programs than nonprofits), but the relationship disappeared when analyzed with the budget as a continuous variable. If there is a relationship, it appears to be borderline at best.The biggest takeaway of this exercise as it relates to fiscal sponsorship appears to be that sponsored project status simply doesn’t mean that much on its own. Other than the propensity to earn a greater proportion of their income, sponsored projects are pretty similar to nonprofits all things considered. By contrast, discipline appears to play a greater role in determining organizational structure and spending/earning patterns than is typically acknowledged. We found discipline-based relationships in every model we constructed. Research efforts in the arts often do not break out results by discipline, but in neglecting to do so it is clear that researchers are missing out on a rich array of analysis possibilities.
As expected, location in hyper-expensive New York City played a role in determining occupancy expenses, but in most other respects location was a virtual non-factor, at least as defined by this study. One important note to keep in mind is that our way of segmenting location was based on regional identity rather than urban/rural divide. Though Upstate New York is considerably more rural than Downstate, it is quite possible that most of the Upstate organizations in our data set nevertheless operate in urban contexts in cities like Buffalo, Syracuse, and Albany. Further investigation of how a truly rural context affects arts activity would be a worthy area of inquiry. Finally, while our models generally did a good job of explaining variance in outcomes along various metrics, they were not able to explain everything. One factor that was left out of the analysis but that could have explanatory value is organizations’/projects’ age. For the most part, Fractured Atlas projects are less than five years old, and have had limited opportunity to build up a long-term base of support, a loyal audience, and so forth. By contrast, many nonprofits have been around for decades and presumably have accrued some advantages by way of longevity. Furthermore, organizations that survive over many decades typically are those that have been able to attract a solid base of supporters and are doing work their communities find relevant and valuable. While many of these advantages are no doubt captured in budget size, it’s quite possible that not all of them are, and that two organizations of equal size, discipline and location that are twenty years apart in age will have different characteristics. We recommend including this variable in any future study that seeks to build upon this work.
22
CONCLUSION