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The impact of temperature on gaming productivity: evidence from online games

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Abstract

This paper studies the short-run impacts of temperature on human performance in the computer-mediated environment using server logs of a popular online game in China. Taking advantage of the quasi-experiment of winter central heating policy in China, we distinguish the impacts of outdoor and indoor temperature and find that low temperatures below 5 \(^{\circ }\)C decrease game performance significantly. Non-experienced players suffered larger performance drop than experienced ones. Access to central heating attenuates negative impacts of low outdoor temperatures on gamers’ performance. High temperatures above 21 \(^{\circ }\)C also lead to drops in game performance. We conclude that expanding the current central heating zone will bring an increase in human performance by approximately 4% in Shanghai and surrounding provinces in the winter. While often perceived as a leisure activity, online gaming requires intense engagement and the deployment of cognitive, social, and motor skills, which are also key skills for productive activities. Our results draw attention to potential damages of extreme temperature on human performance in the modern computer-mediated environment.

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Notes

  1. The temperature in our sample ranges from a minimum temperature of − 22.1 \(^{\circ }\)C to a maximum temperature of 26 \(^{\circ }\)C.

  2. The missions in this game are mostly homogeneous. Even though gamers’ level in the game would rise based on rewards after accomplished missions, the difficulty level of missions would not escalate.

  3. Even though households would sparsely use air conditioners or small heaters in the unheated southern region, the usage rate is much lower in March than in other colder months. According to Wang et al. (2008), less than 5% of households use any heating facility during March in southern provinces. The ratio is above 60% in January.

  4. Ho, Louise. ‘Time to abolish the great divide for central heating’. Global Times. November 23, 2014. http://www.globaltimes.cn/content/893200.shtml

  5. Somanathan (2015) uses WetBulb Globe Temperature, which is adjusted for heat and humidity for overall average temperature.

  6. Players are encouraged to purchase game coins to enjoy more game features such as getting more game clothes, pets, etc. Those items make the character more attractive in the visual effects, but they would not help the players in respect of skills or power.

  7. Kali is an occupation in the game. The character is usually shown as a female dancer with certain combat skills.

  8. High-level players usually complete more missions and play longer, but are not necessarily more skilled players. It is common to see a low-level player defeat a high-level player. Here we measure the performance using the number of missions accomplished by a player, which is considered as comparable output by the players.

  9. This is unlike the classic platform video game Super Mario Brothers, in which missions become more difficult as players complete more missions.

  10. Players typically are attracted to the game by obtaining decorative equipment, cloth, pets, etc., by playing the game using repetition, iteration, and escalation in different stages.

  11. Blizzard Entertainment Inc. is the most influential video game developer based in Irvine, California. Some of the most famous games created by Blizzard includes StarCraft, Warcraft III: The Frozen Throne and World of Warcraft series.

  12. Prefecture is the lowest administrative level which the IP address can be matched.

  13. As our study period only covers 31 days in March, we assume that individuals’ income does not change during this period.

  14. Here, hdd(5) \(=5-\text{ temp }\) if temp\(<5\) and 0 otherwise. Similarly, cdd(21) \(=\text{ temp }-21\) if temp\(>21\) and 0 otherwise.

  15. The shape of the nonlinear curves of temperature and both dependent variables are stable, even for higher-order polynomials.

  16. Because we only cover gaming records in March, which is a cold month in China, cooling is rare.

  17. The correlation without heating or cooling is above 0.9 and approximately 0.2 if there is heating (Nguyen et al. 2014).

  18. In fact, the Standard for Heating, Ventilation, and Air Adjustment Design specifically indicates the indoor temperature to be maintained under heating. The central heating service should maintain an indoor temperature around 16–22 \(^{\circ }\)C (MOHURD 2012).

  19. The period might be extended if the average temperature dropped below 5 \(^{\circ }\)C for five consecutive days or if extremely cold days were expected after March 15. Source: Beijing Heating Administration Method (No. 216 of the Beijing Municipal Government).

  20. Because there are several cities with missing API data, the observation for column 4 while controlling for API is smaller than that in column 3.

  21. Because the Air Pollution Index is only available in most large prefectures or cities, the number of observations drops by around 32%.

  22. Schlenker and Roberts (2009) found a detrimental impact on crop yields when the temperature exceeds 28 \(^{\circ }\)C.

  23. The average diurnal temperature range, which is the difference between daily maximum temperature and minimum temperature, for Chinese prefectures in March 2011 is approximately 11 \(^{\circ }\)C. However, there are huge geographic variations. The daily temperature range varies from 0.6 to 31 \(^{\circ }\)C during the study period. Typically, the temperature difference is smaller in warmer days.

  24. The weighted weather in other cities for city i equals \(\sum _{c\ne i}\lambda _cW_c\), where \(\lambda _c=\frac{N_{ct}}{\sum _{c\ne i}N_{ct}}\), where \(N_{ct}\) is the number of gamers in city c on day t.

  25. A LA Times article: http://www.latimes.com/world/asia/la-fg-china-heat-20141115-story.html

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Authors and Affiliations

  1. Wang Yanan Institute for Studies in Economics (WISE) and School of Economics, Xiamen University, Xiamen, China

    Xiaojia Bao & Qingliang Fan

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  1. Xiaojia Bao
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  2. Qingliang Fan
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Correspondence to Qingliang Fan.

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Funding

Bao’s research was funded by the Social Science Foundation of Fujian Province in China (No. FJ2015C150), University Grant of Xiamen University (No. 20720151030), Science Foundation of Fujian Province in China (No. 2017J05117), and National Natural Science Foundation of China (No. 71703136). Fan’s research, in part, was supported by the National Natural Science Foundation of China Grants 71671149 and 71631004 (Key Project) and the Fundamental Research Funds for the Central Universities (No. 20720171042).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

We thank Robert M. Kunst (the Coordinating Editor) and the anonymous referees for their insightful comments, which have greatly improved the earlier version of this article. We are grateful to Jerome Adda, Alan Barreca, David Slusky, Jie Zheng, and seminar/conference participants from Xiamen University, the University of Kansas, National University of Singapore, the Chinese University of Hong Kong Business School, the Beijer Institute of Ecological Economics, 2017 Health and Development Conference, 2018 AMES, etc., for their helpful comments.

Appendix A: back-of-envelope calculation

Appendix A: back-of-envelope calculation

This section introduces the back-of-envelope calculation for the productivity gain from expanding central heating to south China. We assume that access to heating will be available to all regions which was exposed to daily average temperature below 5 \(^{\circ }\)C. The heating period is assumed to be the same as the current heating period for most regions in north China, that is November 15 to March 15, which lasted for 121 days in 2011. Because temperature records are at the prefecture level, we calculate productivity gain for each prefecture first. Following coefficients estimated in column 3 in Table 5 using a fixed effect Poisson model, the productivity gain for a player j in day t once being heated is:

$$\begin{aligned} g_{jt}=\beta _\mathrm{heat}+hdd5_{it}*\beta _\mathrm{interact}, \end{aligned}$$

where \(g_{jt}\) refers to the productivity gain of individual j. \(\beta _{heat}\) is the estimated coefficient from the dummy of heating, 1 (heating), and \(\beta _\mathrm{interact}\) is the estimated coefficient for the interaction term of heating and heating degree days below 5 \(^{\circ }\)C. \(hdd5_{it}\) refers to the heating degree below 5 \(^{\circ }\)C for prefecture i where individual j is located. According to the assumption that the heating policy is the same for all individuals of a prefecture, the productivity gain for prefecture i is the same as productivity gain for any individual. So

$$\begin{aligned} g_{it}=\beta _\mathrm{heat}+hdd5_{it}*\beta _\mathrm{interact}. \end{aligned}$$

The overall productivity gain for prefecture i during the heating period is the sum of productivity gain for all days with temperature below 5 \(^{\circ }\)C, divided by the heating period.

$$\begin{aligned} \begin{aligned} g_{i}&= \frac{\sum _t \left[ 1(hdd5_{it}>0)*(\beta _\mathrm{heat}+hdd5_{it}*\beta _\mathrm{interact})\right] }{T} \\&=\frac{hdd5days_i*\beta _\mathrm{heat}+\sum _t hdd5_{it}*\beta _\mathrm{interact}}{T}, \end{aligned} \end{aligned}$$

where \(hdd5days_i\) is the number of days with temperature blow 5 \(^{\circ }\)C and \(\sum _t hdd5_{it}\) is the accumulated heating degree below 5 \(^{\circ }\)C during the whole heating period. T is the length of the heating period.

For a province, which includes both heated and non-heated prefectures, the productivity gain during the heating period is the sum of gains of local prefectures weighted by population.

$$\begin{aligned} \begin{aligned} g_{p}&=\frac{\sum _i pop_{pi}*\left[ hdd5days_{pi}*\beta _\mathrm{heat}+\sum _t hdd5_{pit}*\beta _\mathrm{interact}\right] }{T\sum _i pop_{pi}} \\&= \frac{\sum _i pop_{pi}*hdd5days_{pi}}{T\sum _i pop_{pi}}\beta _\mathrm{heat}+\frac{\sum _i pop_{pi}\sum _t hdd5_{pit}}{T\sum _i pop_{pi}}\beta _\mathrm{interact}, \end{aligned} \end{aligned}$$

where \(\frac{\sum _i pop_{pi}*hdd5days_{pi}}{T\sum _i pop_{pi}}\) is the average days with hdd5 being positive, weighted by population in prefectures, and \(\frac{\sum _i pop_{pi}\sum _t hdd5_{pit}}{T\sum _i pop_{pi}}\) is the population-weighted cumulative heating degree below 5 \(^{\circ }\)C.

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Bao, X., Fan, Q. The impact of temperature on gaming productivity: evidence from online games. Empir Econ 58, 835–867 (2020). https://doi.org/10.1007/s00181-018-1523-7

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