Decision making on the job is becoming increasingly important in the labor market, in which there is an unprecedented rise in demand for workers with problem-solving and critical-thinking skills. This paper investigates how indoor air quality affects the quality of strategic decision making based on data from official chess tournaments.
Our main analysis relies on a unique data set linking the readings of air-quality monitors inside the tournament room to the quality of 30,000 moves, each of them objectively evaluated by a powerful artificial intelligence-based chess engine [Stockfish]. The results show that poor indoor air quality hampers players’ decision making.
We find that an increase in the indoor concentration of fine particulate matter (PM2.5) by 10 μg⁄m3 (corresponding to 75% of a standard deviation in our sample) increases a player’s probability of making an erroneous move by 26.3%. The decomposition of the effects by different stages of the game shows that time pressure amplifies the damage of poor air quality to the players’ decisions. We implement a number of robustness checks and conduct a replication exercise with analogous move-quality data from games in the top national league showing the strength of our results.
The results highlight the costs of poor air quality for highly skilled professionals faced with strategic decisions under time pressure. [Striking that CO2 has near-zero effect while particulate matter, including outside pollution, is much more predictive. Are all supposed CO2 effects actually PM-driven?]
[Keywords: indoor air quality, pollution, strategic decision making, chess]
…The data contain comprehensive information on more than 30,000 moves from 121 players in 609 games in 3 official tournaments held in Germany in 2017–22019. Each tournament comprised 7 rounds over a period of 8 weeks, which provides us with sufficient natural variation in air quality. In each round, the chess tournaments have a predefined system to allocate players to opponents. We use Stockfish, a powerful open source artificial intelligence chess engine to evaluate each move in our data set and generate our main performance indicators. The chess engine systematically evaluates the players’ actual moves by benchmarking them against moves deemed optimal based on the chess engine’s algorithm.5 Based on the output from the chess engine, we generate a binary indicator for moves annotated as an error by the engine and a continuous measure describing the differences in chances to win the game between the player’s and computer’s moves.
…Our identification strategy exploits the panel structure of the data. We evaluate the move quality of the same individual playing multiple games at the same venue on the same day of the week at the same time of day but under varying levels of indoor air quality, which are beyond the control of the players. To assess players’ exposure to poor air quality, we installed 3 sensors inside the tournament venue, and they continuously measure indoor environmental conditions. We evaluate air quality based on the concentration of fine particulate matter with a diameter smaller than 2.5 micrometers (PM2.5), which is one of the most common indicators for air pollution in health science and economic studies.
…Overall, our findings show that indoor concentrations of PM2.5 importantly worsen the ability of subjects in selecting the optimal move. Exploiting within-player variation in air quality and controlling for year, round, and move fixed effects and a set of control variables including other indoor and outdoor environmental factors (ie. temperature, humidity, and noise), we find that an increase in PM2.5 of 10 micrograms per cubic meter (75% of the standard deviation of PM2.5 in our sample) leads to a 2.1 percentage point increase in the probability of making a meaningful error. This corresponds to an increase of 26.3% relative to the average proportion of errors in our sample.
…Our results highlight that time pressure exacerbates the impact of poor air quality on performance. In our setting, each player has a fixed time budget for the first 40 moves. Time pressure arises as the game proceeds and the remaining time approaches zero. The high frequency of our data allows us to examine the presence of differential effects of air pollution under different levels of time stress.8 Our results indicate that the impact of PM2.5 increases with time pressure with the most pronounced effect shortly before the time control at move 40. This finding suggests that poor air quality harms performance of players, particularly when acting under time pressure. The results of a heterogeneity analysis indicate that weaker players were especially harmed by poor air quality in phases of the game with a limited time budget. This provides the first evidence that air pollution exacerbates inequalities among skilled individuals, particularly impacting initially disadvantaged groups in competitive settings.
…We document the role of outdoor pollution in shaping indoor conditions. The variation in indoor fine particles largely reflects levels of air pollution in the (outdoor) vicinity of the tournament site, coming from automobile exhaust or industrial emissions.9 Using outdoor air pollution measures from nearby air-quality stations, we find similar performance drops to those based on our indoor measures, suggesting the identified effects are indeed a result of particulate pollution rather than other potential sources. Exploiting intra-day variation in outdoor pollution, we find evidence for short-term and transitory effects of particulate matter.
In a final step, we conducted a replication exercise with analogous move-quality data from the top national league in Germany (ie. Chess Bundesliga). The replication data set combines data from tournament venues across the country with outdoor PM pollution measurements over the period 2003–162019. Consistent with our main results, the analysis in the sample of the top league displays an important and sizable increase in the likelihood of making meaningful mistakes, especially when players are in the stage of the game proceeding the time control. This emphasizes that our main results are valid beyond the studied tournament location and time period and are relevant for a cohort of players ranked among the strongest in the world. Finally, we implement an instrumental variable (IV) approach that exploits variation in air pollution exposure driven by changes in wind directions ( et al 2019). The IV estimates show the same pattern as those in our main analysis, highlighting that our estimated pollution damages are not driven by confounding factors, such as economic activity, traffic conditions, or any other change in the daily life of players that could bias our results.
…The research team collected the data on indoor air pollution during the chess tournament. During all editions of the tournament, the organizers allowed us to measure indoor environmental conditions throughout all tournament rounds inside the venue, a large church community hall in a suburban residential area. The tournament venue is located in a clean neighborhood with moderate levels of pollution. The average levels of outdoor concentration of fine particles during the tournament days are moderate. The average levels are equivalent to 34% of the average 24-hour concentration in US cities over the past decade (Environmental Protection 2020), and they are just below the average of pollution levels retrieved from stations in the largest cities in Germany during the time of the tournament (see Supplementary Figure A.4)…The indoor environmental quality measurements are retrieved from 3 real-time web-connected sensors located inside the tournament venue: two Foobot [discontinued] sensors and one Netatmo indoor sensor. The PM2.5 measurements come from the Foobot sensors. Previous studies in the field of atmospheric science show that Foobot yields precise estimates of PM2.5 concentrations in rooms.27 In addition, the Netatmo sensor measures CO2, temperature, and humidity and noise in the room.28 This indoor air-quality monitor is used by leading studies in the field of epidemiology and public health to evaluate the impact of ventilation rates on occupant cognitive outcomes (eg. et al 2016). The sensors measure the parameters of interest every minute and upload the measurements to a cloud server.
boottest.ado. All regressions include individual, year, round, and move fixed effects as well as the full set of control variables: (1) indoor CO2, temperature, humidity, and noise; (2) difference in the Elo rating score between the player and the opponent (as well as its squared term); (3) the number of points achieved during the tournament; and (4) the actual status of the game before the move, namely, the pawn metric describing the situation on the chess board before the player makes the move (Copponentjtrm−1).
…4.3.4. Impact of Indoor CO2. In our main analysis, we include the average CO2 levels (in ppm) in the tournament room as a control. Indoor CO2 is commonly used in the building science field to measure ventilation rates or air exchange in rooms.39 The levels of CO2 in the room change as a response to the settings in the ventilation system of the building, opening or closing windows in the room, or changes in the number or activity levels of occupants in the room because changes in breathing patterns generate changes in CO2 emissions. Therefore, this measure captures changes in numerous aspects of the room and occupants. Columns (9) & (10) in Supplementary Table A.3 present our main estimates without including CO2 as a control. The estimated coefficients are similar in magnitude and statistical-significance, suggesting that our main results are not influenced by the inclusion of CO2 as a control.