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Risk-Taking and Air Pollution: Evidence from Chess

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Abstract

Medical research suggests that particulate matter (PM) increases stress hormones, therefore increasing the feeling of stress, which has been hypothesised to induce individuals to take less risk. To examine this, we study whether \(\hbox {PM}_{{10}}\) increases the probability of drawing in chess games using information from the Dutch club competition. We provide evidence of a reasonably strong effect: A \(10\mu \hbox {g}\) increase in \(\hbox {PM}_{{10}}\) (33.6% of mean concentration) leads to a 5.6% increase in draws. We examine a range of explanations for these findings. Our preferred interpretation is that air pollution causes individuals to take less risk.

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  1. Duflo and Banerjee (2011) observe that poorer individuals have more stress and make less risky investment decisions.

  2. Graff Zivin and Neidell (2012) show that agricultural workers are less productive on days with high ozone levels. Lichter et al. (2017) identify a small effect of PM on some productivity indicators of professional players in football. Chang et al. (2016), Chang et al. (2019) study productivity of pear packers and call centre employees and find adverse effects of PM pollution on productivity. Klingen and van Ommeren (2020) show that ozone reduces cycling speed.

  3. An alternative explanation is that these traders lose or adapt their discount rate. PM increases car accidents (Sager 2019) as well as crime (Bondy et al. 2019), but these results are shown to be unlikely due to higher levels of risk-taking.

  4. The benefits of health insurance and lottery gains are in the future, so an alternative explanation is that PM affects the discount rate. Projections bias may also play a role. Projection bias is the tendency for individuals to exaggerate the degree to which their future tastes will resemble current tastes, which is likely affected by pollution.

  5. We will discuss alternative explanations for finding an increased number of draws due to PM, such as the length of a game or reduced cognitive performance.

  6. For that reason, we concentrate on the Dutch national competition, but ignore information from other countries (e.g. Germany, UK), where players tend to play at the same location, hence there is little or no spatial variation in those contexts.

  7. The probability of a draw depends on the strength of the players. Therefore, we improve the efficiency of our estimates by controlling for the so called Elo rating, which is a very accurate measures of a player’s strength at the time of playing (Regan and Haworth 2011).

  8. It is plausible that the instrument affects a range of pollutions, and not only PM, so it is difficult to interpret the IV estimate as a causal estimate of PM. In addition, confidence intervals of the IV estimates are much larger (and tend not to differ from OLS estimates using Hausman tests).

  9. We use a daily measure of \(\hbox {PM}_{{10}}\) rather than a measure of \(\hbox {PM}_{2.5}\) observed during the game. \(\hbox {PM}_{2.5}\) may be a slightly better measure from a theoretical point of view as it is roughly 1/30 of the diameter of a human hair, and may go through walls. However, data on \(\hbox {PM}_{2.5}\) is not sufficiently available in our context and time window. At the same time outside and inside concentration levels are usually very similar for both measures, with a correlation between \(\hbox {PM}_{{10}}\) and \(\hbox {PM}_{2.5}\) of 0.90 for days and locations with both data available.

  10. Players are often categorised as those with a high risk attitude (e.g., the 1985-2000 world champion Kasparov) or with a less risky attitude (e.g., the 1963-1969 world champion Petrosian).

  11. The outcome variance is equal to (1 - proportion of draws)/4.

  12. The estimates results hardly change when estimating using similar specifications with a logit model.

  13. This specification implies that we control for the rating of the strongest player and the rating of the weakest player, where we allow the effects of these variables to differ.

  14. Because our results also hold using only one-way time fixed effect, we do not have the issue that two-way fixed effects models have difficulties addressing heterogeneity of estimates, resulting in inconsistent estimates (de Chaisemartin and d’Haultfoeuille 2020).

  15. For example, the distance between Amsterdam and Rotterdam, the two largest cities of the Netherlands, is only 65 km, whereas a number of cities, such as the Hague, Delft and Leiden, are located in between.

  16. This makes sense. Stronger players are better able to calculate the consequences of their moves, and therefore have more control over the game outcome.

  17. It is not an issue that we do not measure \(\hbox {PM}_{{10}}\) inside buildings, as environmental policies use information from outside monitoring stations, so the preferred measures, from a policy point of view, is the measure used by us.

  18. In this study, participants are treated with PM for a number of days, but dynamic treatment effects are not investigated.

  19. The Dutch league follows the rules of the World Chess Federation FIDE: players receive 90 minutes for the first 40 moves, and an additional 30 minutes for the rest of the game. For each move played, the player receives an additional 30 seconds. A player who exceeds the time limit loses the game.

  20. When chess clubs have several teams in the national competition, and the team plays at home, then all games are played at the same location.

  21. The correlation between \(\hbox {PM}_{{10}}\) measurement stations for a sample with the same average distance as our main sample, is about 0.77, suggesting that attenuation bias will be about 40%. Here we use the formula \(1-\rho ^2= 1-0.77^2=0.40\), derived from Cameron and Trivedi (2005), where \(\rho\) is the correlation between \(\hbox {PM}_{{10}}\) at the measured location and \(\hbox {PM}_{{10}}\) at the chess location.

  22. We have also estimated models with different restrictions on the distance between home and visitors team locations. The results are not sensitive to that.

  23. Consistent with this reasoning, the point estimate of visitor’s \(\hbox {PM}_{{10}}\) is negative (but not statistically significant).

  24. On theoretical grounds, one may expect a convex function, for example as \(\hbox {PM}_{{10}}\) has to surpass a certain threshold, or a concave function, for example because a saturation level of \(\hbox {PM}_{{10}}\) kicks in.

  25. We have also estimated logit models using the same specification. The average marginal effects are almost identical to those in the linear model. Because the difference in Elo ratings between players strongly reduces the probability of a draw, the relative effect of \(\hbox {PM}_{{10}}\) becomes stronger when the absolute difference in Elo ratings increases.

  26. We have also estimated a multinomial logit models with three outcomes (stronger player wins, draw, weaker player wins). Results are almost identical to the results in Table 4.

  27. Conversely, the weaker player should not win more often due to PM. It is however possible that there is no effect on the number of wins of weaker player, as increased draws are favourable for the weaker player.

  28. We have classified risky play using several measures distinguishing between opening risk, where risk is based on the opening’s share of draws, opposite castling, and white plays G4 in the opening. We also demonstrate that these measures are valid measures of risk-taking as they are strongly related to the probability of making a draw. Results can be received upon request.

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Authors and Affiliations

  1. Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081, HV, Amsterdam, The Netherlands

    Joris Klingen & Jos van Ommeren

  2. Tinbergen Institute Amsterdam, Gustav Mahlerplein 117, 1082, MS, Amsterdam, The Netherlands

    Joris Klingen & Jos van Ommeren

Authors
  1. Joris Klingen
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  2. Jos van Ommeren
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Correspondence to Joris Klingen.

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We would like to thank Koos Stolk of the Royal Dutch Chess Federation for help with the data. Moreover we would like to thank Hans Koster, Erik Verhoef, Francis Ostermeijer, Devi Brands, Jesper de Groote, Sebastian Yap, and seminar participants at University of Birmingham and VU Amsterdam.

Appendices

Additional descriptives

See Figs. 4, 5, 6.

Fig. 4
figure 4

Frequency of Elo rating and draw per rating

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Fig. 5
figure 5

Effect of players strength (mean rating per game) on draw, conditional on difference in rating

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Fig. 6
figure 6

Histogram difference in rating and results stronger player

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Additional results

See Tables 5, 6, 7.

Table 5 Regression results with various specifications for weather controls
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Table 6 Regression results with player fixed effects
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Table 7 Regression results with higher order polynomial terms
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Table 8 Regression results with co-pollutants
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Klingen, J., van Ommeren, J. Risk-Taking and Air Pollution: Evidence from Chess. Environ Resource Econ 81, 73–93 (2022). https://doi.org/10.1007/s10640-021-00618-1

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