Abstract
I investigate the intraday effects of rain on the demand for indoor leisure. To do so, I use sub-daily museum attendance and weather data to reveal a dynamic response to precipitation that would be obscured using day-level data. I find that the magnitudes and signs of the effects of rainfall vary significantly throughout the day. In some hours, the predicted increase in visitors is nearly three times larger than would be expected from estimates using daily measures. Many individuals appear to actively adjust their plans throughout the day in response to rain, while others’ attendance depends upon prior weather forecasts of rain. Further analysis reveals that visit duration also increases during rainy periods, and visitors are more likely to attend pay-to-enter special exhibits. International visitors make up a greater share of total visitors during periods of observed precipitation. More broadly, this paper establishes the viability and value of working with widely available sub-daily rainfall data to uncover these dynamics.
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Notes
A full description of this literature is beyond the scope of this paper. Readers are directed to Dell et al. (2014) for a recent survey of the economics of the weather.
The potential for intraday effects of outdoor temperature on individuals is only slightly more researched. A series of papers beginning with Cohn and Rotton (1997) considers temperature’s effects on crime in a large US city over a 2-year period. The results indicate that “past failures to uncover nonlinear relations [between temperature and crime] were due, at least in part, to attenuation that accompanies aggregation”. Graff Zivin and Neidell (2014) consider temperature’s effects on individuals’ daily labor/leisure choices. Separately estimating daily maximum temperature’s effects on activities during daylight versus twilight hours reveals that non-working individuals may shift outdoor activities to cooler hours.
Besides the regular hours of operation, the museum remains open to the public until 21:00 on Thursdays. While the analysis focuses on admissions data from 10:00 to 18:00, the empirical results are robust to dropping Thursday observations, with just the expected modest drop in precision.
I consider precipitation to be forecasted if a symbol for “showers,” “few showers,” “drizzle,” “hail,” “snow,” “rain,” or “thunder” is shown, or any of the following words appear in the written description: “drizzle,” “rain,” “shower,” “hail,” “snow,” “wet.”
As daily leisure planning may be especially responsive to observed conditions after waking, I include precipitation data from 1 h prior to the museum’s opening when defining my rainfall variables. The results are not sensitive to this decision.
Results in “Appendix” show that the results are not sensitive to the choice of a log-linear specification.
In “Appendix” investigate the potential insights gained from greater disaggregation of admissions data. Table 9 gives estimates stratified by hour, rather than just by morning and afternoon. Since the morning/afternoon division of hours well summarizes the finer results, I present the more parsimonious approach. In further unreported results, greater disaggregation of the rainfall variables also leads to results consistent with the findings presented here. While not presented here, hourly rainfall data may provide additional insights in other contexts.
In the analysis that follows, including daily maximum temperatures imparts no substantive effects on estimates, and the estimated coefficient for temperature is not statistically significant. This may be due to the city’s temperate climate with temperatures rarely in the tails of results in Graff Zivin and Neidell (2014) (the lowest and highest temperatures on record in the city are 29 and 88\(^{\circ }\)F, respectively), or because the rainfall indicators already capture the effects of temperature changes. Unfortunately, sub-daily temperature data at the museum are not available for the time period. http://about.metservice.com/our-company/learning-centre/climate-summary.
In all regressions, standard errors account for potential clustering that occurs at the calendar week-of-year level.
Studies using daily data find evidence for interday substitution and estimate distributed lag models accounting for lagging and leading weather (for examples, see: Jacob et al. 2007; Connolly 2008; Deschenes and Moretti 2009; Simonsohn 2010; Stafford 2015; Zivin and Neidell 2009). My results are robust to including indicators for rain occurring 1 and 7 days prior.
An alternative specification could interact the forecast and rain indicators or stratify estimation by forecast weather. While in principle, this approach could yield greater information on situations where expected rain never materializes, or unexpected rain occurs, there is insufficient variation in rainfall within days with no forecasted rain to retrieve stable parameter estimates. Note from Table 1 that 56% of days include a forecast for rain, yet rain is actually observed during the museum’s hours of operation on only 24% of days. Likewise, there exist only 31 instances of rainfall on days where no rain is forecasted.
From survey responses, tourists appear to be significantly less likely to attend special exhibits than residents. From the subsample of 9046 surveys which include information on exhibition visits, 37% of domestic residents report visiting the exhibition, compared to only 14% of international visitors.
A helpful referee has suggested that sensitivity to rain may decline in the closing weeks of an exhibit because it is the “last chance” to see the exhibition. In “Appendix” Table 15, I stratify the effects of rain to separately estimate the effects before and during the closing two weeks of an exhibition. In line with expectations of reduced responsiveness, precipitation does not impart a statistically significant effect on daily exhibit attendance in the final two weeks, yet overall event attendance across all days in this period rises.
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Appendix
Appendix
See Tables 8, 9, 10, 11, 12, 13, 14 and 15.
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Cuffe, H.E. Rain and museum attendance: Are daily data fine enough?. J Cult Econ 42, 213–241 (2018). https://doi.org/10.1007/s10824-017-9298-9
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DOI: https://doi.org/10.1007/s10824-017-9298-9