(Over-)Measuring the Working Man

HughPickens.com writes: Tyler Cowen writes in MIT Technology Review that the improved measurement of worker performance through information technology is beginning to allow employers to measure value fairly precisely and as we get better at measuring who produces what, the pay gap between those who make more and those who make less grows. Insofar as workers type at a computer, everything they do is logged, recorded, and measured. Surveillance of workers continues to increase, and statistical analysis of large data sets makes it increasingly easy to evaluate individual productivity, even if the employer has a fairly noisy data set about what is going on in the workplace. Consider journalism. In the "good old days," no one knew how many people were reading an article, or an individual columnist. Today a digital media company knows exactly how many people are reading which articles for how long, and also whether they click through to other links. The result is that many journalists turn out to be not so valuable at all. Their wages fall or they lose their jobs, while the superstar journalists attract more Web traffic and become their own global brands.

According to Cowen, the upside is that measuring value tends to boost productivity, as has been the case since the very beginning of management science. We're simply able to do it much better now, and so employers can assign the most productive workers to the most suitable tasks. The downsides are several. Individuals don't in fact enjoy being evaluated all the time, especially when the results are not always stellar: for most people, one piece of negative feedback outweighs five pieces of positive feedback.

Comments

Activity or productivity

[DarkOx]

statistical analysis of large data sets makes it increasingly easy to evaluate individual productivity, even if the employer has a fairly noisy data set about what is going on in the workplace.

This is only true if you know what to measure. Otherwise you are measuring activity. For example one programmer may type out lots of quick lines to empirically discover the format of a string a library returns for a given inputs, another might go directly to the documentation. One will press more keys, but which is more productive? I don't think you can always expect the correct answer if the statistic you use is average key presses per hour.

If someones job is to paint unpainted widgets in bin A and paint them and put them in bin B, that we can pretty accurately measure their productivity by determine how many widgets are in bin B each day and comparing them with others who do the same work, or can we? What about the defect rate? Measuring is hard, knowing what to measure is harder.

How do measure the productivity of a corporate staff attorney? What about route / switch admin? Is one who puts in more change requests more productive or does that just mean (s)he fails to plan ahead?

Be careful what you measure you will probably get favorable results, but its the side effects that will hurt you.