Rain and museum attendance: Are daily data fine enough?

Cuffe, Harold E.

doi:10.1007/s10824-017-9298-9

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…Paying attention to public good complementarity has important methodological implica- 1 The closest analyses we know of consider the relationship between amenities and private consumption, Connolly (2008) and Graff Zivin and Neidell (2014), examine the relationship between weather and time use, and thus leisure as a good. Cuffe (2017) examines how rainfall influences museum attendance.…”

Section: Introductionmentioning

confidence: 99%

Unlocking amenities: Estimating public good complementarity

Albouy

Christensen

Sarmiento-Barbieri

2020

Journal of Public Economics

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, for helpful comments and feedback. Yifang Zhang provided excellent research assistance. Data and replication files available at: https://github.com/uiuc-bdeep/Unlocking-Amenities. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Introductionmentioning

confidence: 99%

Unlocking amenities: Estimating public good complementarity

Albouy

Christensen

Sarmiento-Barbieri

2020

Journal of Public Economics

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“…Firstly, while year-on-year analysis is obviously necessary for linking museums with other year-on-year observed data, museums' activities are not restricted by, or contained within, discrete calendar years. In an optimal setting, data would be available in monthly, weekly, or daily intervals allowing for the minutiae analysis of factors influencing visit numbers, including those not considered in the present analysis , such as admission pricing, transport links, strikes, or other high impact events (Chen, 2007), and the weather (Cuffe, 2017). Ideally, one would consider how such variables interact with those specific to each museum, including its location (Waddop & Cutts, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Bailey et al, 1997;Steiner, 1997), whether or not there is special exhibition on (e.g. Skinner, 2006), whether or not it is raining (see Cuffe, 2017), or simply where the museum is located (Brook, 2016;Cutts & Widdop, 2017;Widdop & Cutts, 2012). These, should, of course, be seen in relation to other individual factors, such as an interest in culture, willingness to spend time in a museum, and overall preparedness to pay any other costs associated with the visit such as those associated with retail and catering.…”

Section: Introductionmentioning

confidence: 99%

Analysing visits to English museums 1850–2015: a research note

Hansen

2018

Cultural Trends

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Understanding why people visit museums is of interest to both cultural economics and museum studies. Most existing research relies on survey data concerned with visitors, their immediate background, and their experience of a particular museum. Very few studies (e.g. Chen & Della Change, 2016;Skinner et al., 2009) have taken a more general perspective and analysed macro-level societal factors, such as inflation, educational attainment and unemployment, and their influence on the number of visits to museums. The conventional approach being reliance on surveys of museum visitors used to understand what drives visits and general views on museums. In a departure from these conventional approaches, this article presents such an approach, using a unique dataset of visit counts for 40 English museums and visitor attractions spanning the period 1850-2015, unless otherwise specified the word "museum" shall in this article cover both types It examines the effect of socio-economic factors on visits using panel data analysis and macro-level variables. The results suggest that inflation rates, average earnings, and educational level (using the indicator of secondary school attendance) all significantly influence the number of visits made. However, the most important variable is the number of visits recorded for the previous year. These findings are discussed in relation to existing studies, and some suggestions for future research are proposed.

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“…Clearly, cinema attendance could be also related to weather conditions. Similarly, the negative effects of rain or snow have been recently found for museum attendance (Cuffe, 2018). However, given the very low quality of weather forecasts with a horizon longer than three days, a direct application for forecasting at one week ahead horizon is problematic.…”

Section: Limitations and Further Researchmentioning

confidence: 98%

“…It should also be underlined that the cinema industry is similar to other branches of the entertainment industry. In the literature, we can find studies that have been devoted to forecasting attendance, for example in a museum (Cuffe, 2018). The results of Cuffe's study show that rainfall during certain hours of the day can significantly affect museum attendance.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Baranowski

Korczak

Zając

2020

Business Systems Research Journal

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Background: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.

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Rain and museum attendance: Are daily data fine enough?

Cited by 12 publications

References 25 publications

Unlocking amenities: Estimating public good complementarity

Unlocking amenities: Estimating public good complementarity

Analysing visits to English museums 1850–2015: a research note

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

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