Why Bad Weather Means Good Productivity 

Categories: Weather

Lee, Gino, and Staats (2014) designed a study to test the hypothesis that answers if good weather conditions, such as lack of rain, will decrease worker productivity on tasks that require sustained attention and focus, compared to bad weather conditions. Although many studies were conducted I will be focusing my critique on the first study that was conducted. In Study 1, they examined the proposed link between weather conditions and productivity by matching data on employee productivity from a mid-size bank in Japan with daily weather data.

The sample consisted of 111 workers who completed 598,393 transactions. Workers at the bank conducted data-entry tasks required to move from a paper loan application to a loan decision.

This study used a correlational research method because the researchers were trying to find the correlation between bad weather and productivity in the work environment. The independent variable is the weather in Tokyo, Japan which is where the workers worked. We begin our examination of the impact of bad weather on worker productivity by considering three measures of weather: Rain, temperature, visibility.

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The dependent variable is the worker’s productivity (completion time). Starting on June 1, 2007, the measurements started to be taken. Workers at the bank began conducting 17 data entry tasks that were required to change the status of their application from just an application to a decision.

The procedure ran as follows: the tasks that were completed included steps like entering a customer’s personal information, entering information from a real estate appraisal, and ordering the customer’s credit scores and reports.

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Workers sat at a desk with a two-screen monitored computer. One on monitor, the work to be completed was shown, on the other monitor, is where the worker completed and entered the information into the company’s system. All tasks by workers were completed one at a time (all 17 of them). Once all 17 tasks were completed the worker would be assigned a new task which was usually the same tasks they had just completed. While all the workers were completing their tasks, the office they were completing them in had windows all over the room so the weather could be observed. The income of all the workers were a flat rate; there was no greater incentive given out for faster completion. This was observed until December 30, 2009.

The results of this study confirmed the hypothesis. Those days that the weather was worse, workers had higher productivity. At first while analyzing the rain they found that the coefficient is negative and significant (coefficient = -0.01284). In terms of the effect size, we find that a one standard deviation increase in rain is related to a 0.7% decrease in worker completion time. Next with the temperature, they saw evidence of an inverted U-shaped relationship between temperature and the completion time of the tasks. Worker completion time is fastest at low and high temperatures. Finally, we examine the relationship between visibility and worker productivity, and find that the coefficient on visibility is positive and statistically significant (coefficient = 8.483e-04). Interpreting the coefficient, we find that a one standard deviation increase in visibility is related to a 1.2% increase in completion time. Thus, on days with greater visibility, workers have slower completion times. In conclusion, using a within-subject design, Study 1 shows that worse weather is related to better worker productivity.


Overall this study was well designed to test the given hypothesis that if good weather conditions, such as lack of rain, will decrease worker productivity on tasks that require sustained attention and focus, compared to bad weather conditions. The timeline of two and a half years that was used for this study is a good enough timeline to have received valid and reliable results. Reliability is the measure to which assessments are consistent while validity refers to whether the study is measuring what it purports to measure. The goal of this study was to conduct if workers were more productive on days that the weather was not good and it succeeded with valid and reliable results. Lee, Gino, and Staats (2014) conducted all the research of this study with workers from a Japanese bank in Tokyo which is an appropriate ethical safeguard being that for this study all that was needed was adults in the work force.

Although this study does seem to be valid and reliable, in my opinion it always seems like a good idea to do a follow up experiment in a couple of years to make sure that the results remain valid and reliable. A follow-up study I would conduct would be to go ahead and conduct the same study but this time with another group of people. Being that the study was conducted in a Japanese bank in Tokyo, that means that most of the workers that completed the tasks for the studies were Japanese as well. Conducting a study with workers from a western culture might get different results than the researchers received in Tokyo. This proposal requires actual testing before making further assumptions, but it does show the need for a more diverse sample of participants.

Lee, Gino, and Staats (2014) also predict that workers will be less productive on good weather days than on bad weather days. More specifically, argued that on a bad weather day, individuals will have a higher ability to focus on a given work task not because of the negative mood induced by the weather but because fewer distracting thoughts related to outdoor options will be readily available in their minds. Even if individuals did have more ability to focus on their jobs and tasks because they did not have any outdoor options roaming in their minds does not mean that they completed the tasks faster because of the bad weather. Other factors could be the reasoning behind that such as: personal preference of weather, smoother or easier applications to complete, or the specific person’s well-being on that specific day. All these factors that should have been taken as control variables can come into play on why workers happened to complete their work more efficiently on a day with bad weather other than having fewer distracting thoughts related to outdoor options available in their minds.

On another hand as stated in the article “Interpreting the coefficient, we find that a one standard deviation increase in visibility is related to a 1.2% increase in completion time.” Although 1.2% is an increase, is it a big enough increase?

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Why Bad Weather Means Good Productivity . (2021, Aug 18). Retrieved from https://studymoose.com/why-bad-weather-means-good-productivity-essay

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