Comparing the Finances and Operations of Professional Nonprofit Performing Arts Organizations Using National Service Organizations' Survey Data

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Comparing the Finances and Operations of
Professional Nonprofit Performing Arts
Organizations Using National Service
Organizations' Survey Data

• Roland J. Kushner, Kushner Management Advisory
  Services, Bethlehem, PA
• Thomas H. Pollak,The Urban Institute,
  Washington, DC
• (Special Thanks to Omolara Fatiregun)
• ARNOVA Annual ConferenceDenver, Nov. 2003

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The Performing Arts Research Coalitions (PARC)
Fiscal Survey Harmonization Project

• National service organizations (NSOs) for dance,
  opera, symphony orchestras, theatre, presenting
• A common template for financial and operational
  data
• Inform strategic management and policy issues for
  nonprofit professional live performing arts
  organizations (LPAs)
• Produce data that can be used to assess condition
  and performance of live, professional performing
  arts as a field

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NSO Surveys Strengths Mature Survey Collection
Operations

• Well developed and refined questionnaires (17
  pages!)
• Members accustomed to receiving responding on
  annual or biannual basis
• Opportunities for trend analysis
• In-house staff with deep knowledge of their
  fields infrastructure for collection and
  processing

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NSO Surveys vs. 990s Strengths

• FINANCIAL
• Revenue adds detailed breakouts by
• Fed., state, local govt revenue
• Foundation, corporate, board, individual contrib.
• Subscription, group, individual ticket sales
• Much more
• Expenses adds marketing, education
• Balance sheet current vs. long-term
• OPERATIONAL
• Num. of free paid performances, capacity
  attendance
• Volunteer use

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NSO Surveys vs. 990s Challenges

• Defining professional nonprofit performing arts
  organizations
• Embedded/hosted
• Unincorporated
• Is NSO membership representative?
• Variable response rates by NSO and organization
  size
• Data collection schedule, effort, and
  infrastructure differences
• Missing service organizations
• The challenge of harmonization of variables

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NSO Surveys vs. 990s Challenges

• Presenting vs. performing/producing
• Sustainability Building systems for efficiently
  combining data on an annual basis

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First Year Efforts Building Baseline Data

• Merged survey data the organizations collect
  annually from their members (2000-2001)
• 811 respondents

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The Data
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Outcome Variables of Interest

• What management factors lead to different levels
  of financial performance?
• Absolute Profitability
• Net Revenue gt 0
• Net Revenue / Total Revenue Return on Sales
• Relative Profitability small or large surpluses
  or deficits
• We set 5 as the line between small and large
• No measures for performing technique, aesthetic
  or beauty of art, impact on community

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Occurrence of Surplus and Deficit
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Independent Variables

• Focus on management decision-making
• Efficiency measures
• Development Efficiency (Total Contributions /
  Total Development Expense)
• Marketing Efficiency (Total Performance Revenue
  / Total Marketing Expense)
• Program Margin ((Total Performance Revenue
  Performance-Related Revenue )/( Total Artistic
  Expense Total Production Expense))

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Sources of Revenue
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The Independent Variables
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Determinants of LPAs Having a Surplus at the End
of FY 2001
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Determinants of LPAs Having a Large Surplus at
the End of FY 2001
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Determinants of LPAs Having a Large Deficit at
the End of FY 2001
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Summary of Results

• Artistic programming decisions and development
  expenditures are most significant in
  distinguishing between surplus and deficit
• No particular use of either programming,
  marketing, or development efforts leads to
  particularly high or low profitability for
  profitable LPAs.
• Programming decisions have a sizeable impact on
  if a money-losing organization loses a little or
  a lot.
• Cautions We are using expenditures within the
  bounds of a single season, so we do not account
  for any lag factors from prior years marketing,
  development, or programming activities.

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Things We Wish We Had Done With the Data (our
plan for more research)

• Alternative dependent variables (e.g., output,
  capacity utilization)
• More on the production function (balance of labor
  and capital)
• More analysis of the balance sheet (return on
  assets, degree of leverage)
• Connect this data set to the 990s
• Longitudinal study as the PARC project extends.