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When Will Arts Attendance Return?

How Vaccination Rates May Impact Performing Arts Ticket Sales Through March 2022

In this update, we reexamine the impact of COVID-19 on performing arts ticket sales for 51 organizations, using actual purchase data for January 1, 2018 – September 30, 2021.  The analysis updates the results we reported last month, which examined ticket sales data through June 30, 2021.  At that time, we estimated the total losses in the nonprofit performing arts industry attributable to the pandemic through December 2021 to likely exceed $3.2B, which dwarfs the $400M allocated as part of the Paycheck Protection Program.[1]  That estimate is unchanged by the current results.  We also estimated that lagging vaccination rates cost this industry around $10M per month for every unrealized percentage point in vaccination rates.  This negative effect for lagging vaccination rates is also largely unchanged in our analyses, even though the results clearly point to diminishing effects as vaccination rates increase towards 100%.


How SMU DataArts Researchers Built a Model to Predict Arts Ticket Sales

Behind-the-scenes with Karthik Babu Nattamai Kannan, Ph.D. and Glenn B. Voss, Ph.D.

Tuesday, February 8 | 1 - 2 PM EST | Free Admission

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Our model predicts purchase transactions at 51 performing arts organizations across the country, which ranged from $1 million to $167 million in total annual expenses pre-COVID (see Model Details and Limitations section below for additional information).  Figure 1 plots the historical values that are normalized on a scale of 0-100 for the characteristics, or “variables,” that vary by month. This way, you can see when each variable was at its maximum (100%) over the period as well as its level of volatility.

For example, ticket purchases were historically lower in the summer months and January.  After the arrival of COVID, ticket purchases plummeted to about 5% of their peak level in August and September 2020, whereas restaurant employment trends bottomed out in April 2020 at roughly 50% of their peak level.  State vaccination rates started climbing and COVID case rates declined in February 2021; in response, household ticket purchases, average ticket prices, and the number of performances offered rebounded, as did restaurant employment levels.  But then COVID cases shot up in August and September, and ticket sales dropped again.


Figure 2 provides a comparison of results from the June 2021 analysis (Panel A) and the current September 2021 analysis (Panel B). Each plot shows the expected change in average ticket sales per household census tract (HHCT) associated with a -20% and +20% change in price discount, number of performances, COVID vaccination rates, and COVID case rates.  Originally, we did not include COVID case rates because the effect was small compared to the vaccination rates; for example, a 20% increase in cases created only a 2% drop in ticket sales in the June 2021 simulation.  Ticket sales increased by 15% with a 20% increase in vaccination rates, by 6% with a 20% increase in number of performances, and by 5% with a 20% price discount.


Figure 2: Simulating Changes in Price, Performances, and COVID Vaccination Rates June vs September 2021

The simulation in Panel B uses data through September 2021 and produces some interesting changes.  The first thing to note is the large drop-off in average ticket sales from almost 7.5 tickets per HHCT in Panel A to just over 5.8 tickets per HHCT in Panel B, a 23% drop.  The trend data in Figure 1 suggest that a significant increase in COVID cases between July and September played a role in that drop-off, and Panel B offers additional support for an increased COVID case rate effect, with a 20% increase in COVID cases leading to a 3% drop in ticket sales.   


As we predicted last month, the upside impact of vaccination rates has diminished as average vaccination rates increased to 58% of the total population, including children, in September 2021.  An additional increase in vaccination rates from 58-69% (a 20% increase) produces only a 6% increase in ticket sales.  But a 47% vaccination rate (i.e., 20% below average) leads to 14% fewer ticket sales.  Thus, lagging vaccination rates still have a strong effect on ticket sales, even as increasing vaccination rates are associated with a diminishing impact on sales.


The number of performances had the largest upside effect on ticket sales in September, with a 20% increase producing a 9% increase in ticket sales.  The price effect also increased a bit, with a 20% price discount increasing ticket sales by 7%.  We expect these trends to continue.  As vaccination rates increase, the effect on tickets sales will diminish, but COVID case rates will likely have a stronger impact on ticket sales.  And the impact of managerially controlled variables like programming and price will continue to increase.


We also see the diminishing impact of COVID vaccinations in simulations that predict future ticket purchase levels under different assumptions.  We show three updated simulations in Figure 3.  In each scenario, we hold organizational and household demographic characteristics constant at their September 2021 levels to isolate the effects of COVID-19. 


  1. The realistic worst-case scenario (Panel A) shows predicted ticket sales reaching 59% of the 4-year high by March 2022:
    • using actual levels of COVID cases, vaccination rates, traffic counts and restaurant employment for October 2021 and
    • continuing those trends through the end of the calendar year, with vaccination rates eventually reaching 69% of the total population (including children).
  2. The realistic best-case scenario (Panel B) shows predicted ticket sales reaching 66% of the 4-year high by March 2022:
    • using actual levels of COVID cases, vaccination rates, traffic counts and restaurant employment for October 2021 and
    • assuming that COVID cases drop and vaccination rates improve significantly through the end of the calendar year, with vaccination rates reaching 76% of the total population in March 2022.
  3. The idealized best-case scenario (Panel C) shows predicted ticket sales reaching 70% of the 4-year high by March 2022:
    • assuming vaccination rates escalate to 82% in March 2022, which is approximately equal to the current vaccination rate in Spain (81%) and still below the current vaccination rate in Portugal (88%),[2] and
    • assuming COVID cases remain at October 2021 levels.

The first takeaway is that the three scenarios produce far less dramatic differences in ticket sales than the simulations produced last month.  The difference in overall predicted ticket sales for the six months from October 2021–March 2022 is only 6% between the realistic worst-case and realistic best-case scenarios. The idealized best-case scenario is only 10% better than the realistic worst-case scenario.  These results suggest that vaccines have stabilized industry demand to some extent.  Unfortunately, our predictions suggest that ticket sales will remain depressed, even under the best-case scenarios. 


The pandemic has changed our lives in many ways, but few if any industries have been more affected than the performing arts.  Our model-free data visualizations and econometric model confirm the dramatic impact that COVID-19 has had.  The simulations calculate the ongoing impact of low vaccination rates and resurging COVID cases and offer insights for performing arts leaders trying to manage supply and demand in the time of COVID-19.  Quantifying the impact of the pandemic on this industry provides context for better understanding the future for live performances and the important role that vaccinations play.


Model Details and Limitations

Our initial predictive model used historical ticket data ending in June 2021 to predict changes in ticket sales as a result of changes in COVID indicators.  This update uses aggregated ticket data provided by our partner TRG Arts through September 30, 2021. Transactions for each organization were summed up for each household census tract (HHCT) on a monthly basis.  We then ran a model predicting the number of transactions each month in each HHCT that incorporates the following influences:

  • organization characteristics (budget size, number of performances, prices)
  • household characteristics associated with the transactions inferred from census data for the household tract (number of households, income, education, age)
  • market characteristics (e.g., traffic flows, number of competitors and complements), and
  • COVID-related measures (local case counts, vaccination rates, restaurant employment levels)


The statistician, George Box is often quoted as saying, “All models are wrong, but some are useful.”  Our model is surely wrong.  This is especially likely in predicting sales levels outside the range of the data used, which currently peaks at a 75% vaccination rate.  This suggests that the simulations offered in Figure 2 are more reliable than the simulations offered in Figure 3, especially the idealized, best-case scenario offered in Panel C.  Figure 2 shows how the model fits actual data for organizations operating in counties with vaccination rates that ranged from 43-72% in September 2021.  Figure 3, on the other hand, extrapolates the model results to scenarios that feature vaccination rates rising to 69-82%.  Our model results indicate that the effect of vaccination rates is diminishing, as would be expected for a construct that has a theoretical maximum of 100%.  Moreover, the rate at which the effect diminishes has accelerated in the 3 months since our first analysis (see the comparison in Figure 2).  We will continue to update our model as new data become available and share the new results.

[1] See

[2] accessed 12/07/2021.

Expansion of the COVID-19 Benchmark Dashboard, an initiative led by international arts management consultants TRG Arts and U.K. arts data specialists Purple Seven, is supported in part by a grant from the National Endowment for the Arts to SMU DataArts, TRG Arts’ long-time partner in advancing the arts and culture sector.


How SMU DataArts Researchers Built a Model to Predict Arts Ticket Sales

Tuesday, February 8, 2022 | 1:00 PM - 2:00 PM EST

Register Now

When the COVID-19 virus began spreading across the U.S., researchers at SMU DataArts responded by integrating datasets and building a framework for predicting ticket purchasing demand. Continually refined for over a year, this framework takes into account ticketing purchases, census data, COVID cases, vaccine rates, restaurant employment, and arts ticket prices to help organizations across the nation predict demand for in-person ticket purchases. Join our leading researchers for a behind-the-scenes look at how the model was developed and how early actual results compare with predictions. Participants will also receive a first look at a new tool that will allow cohort groups of organizations to submit their data for analysis within the model.

Glenn B. Voss, Ph.D., is research director for SMU DataArts and Professor Emeritus from the Cox School of Business at Southern Methodist University. His research – focusing on innovation and organizational learning, customer satisfaction and relationship management, and retail pricing strategies – has appeared in leading academic journals in marketing (e.g., Journal of Marketing, Journal of Marketing Research) and management (e.g., Academy of Management Journal, Organization Science).

Karthik Babu Nattamai Kannan, Ph.D., is an Assistant Professor of Information Technology and Operations Management at Cox School of Business, Southern Methodist University and the Donna Wilhelm Research Fellow at SMU DataArts. He studies how technological innovations are changing how people access the internet, consume digital entertainment and participate in e-commerce platforms. He also studies how retailers and art institutions can leverage mobile location data to improve their service operations. His research interests include location analytics, electronic/mobile commerce, and social media analytics. In his research, he uses empirical methods such as advanced econometrics, machine learning, field/natural experiments, etc., and optimization models to study large-scale datasets.

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