Reading 5:What Is Productivity

What Is Productivity? By Steve Pavlina In this post we’re going to take a deep look into the concept of productivity. Here’s my personal definition of productivity: Productivity = Value / Time (productivity equals value divided by time) By this definition there are two primary ways of increasing productivity: 1) Increase the value created 2) Decrease the time required to create that value You can complicate this definition by including other factors like energy and resources, but I prefer the simplicity of time because in most cases factors like energy and resources are reducible to time anyway. Time also makes it very easy to compare different levels of productivity, such as output per hour or per day. Apparently you can make some significant gains on the time side. There are many personal productivity optimizations which, especially if you introduce them in your youth, will produce a massive net savings of time over the course of your life. Consider your typing speed, for instance. If you invest the time to get your speed up to 90 words per minute or faster, it will be well worth the initial time investment if you happen to do a lot of typing over your lifetime, compared to allowing your speed to linger at 50 wpm or slower year after year. The extra hours of practice will be nothing compared to the time you save typing emails, letters, or blog entries over the next few decades. Other time-based optimizations include improving your sleeping habits, minimizing commute time, or dropping time-wasting habits like smoking. The main limit of time-based optimizations is that the optimization process requires an input of time itself. It takes time to save time. So the more time you invest in optimizing time usage, the greater your initial time investment, and the greater your need for a long-term payoff to justify that investment. This limit creates an upper bound for any time-based optimizations you attempt, in accordance with the law of diminishing returns. The more time you invest in any optimization attempt, the lower your net return, all else being equal. This law of diminishing returns points us back to the value side. While we might be stuck with diminishing returns by trying to optimize the time side alone, we may notice that working to optimize the value side is less limiting and more open-ended.