Human resource software is used by more than just HR specialists. According to O*Net ( https://www.onetonline.org/ ), there are 78 occupations that interface with some sort of human resource software–from CEOs to retail sales persons, from surgeons to crossing guards. Furthermore, there are 256 different examples of human resource software on the market according to O*Net.

Note that according to Software Advice (https://www.softwareadvice.com/hr/p/all/), there are currently 784 pieces of HR Software, but O*Net only found 256 examples in its survey of occupations. I take O*Net’s list of HR software as a list of most commonly used HR software. (See notes for a list of all HR software on O*Net and a matrix of which occupations use which software.)

The 78 occupations that O*Net found that used HR software combined for a total of 42 million employed persons in 2018. These 42 million employees earned approximately 2.6 trillion dollars in wages. The average median annual earnings across the 78 occupations equaled $78,000 a year.

Human resource software can streamline work in two major ways–one is to facilitate data entry, the other is to facilitate data analytics. I analyze how the importance of data entry for an occupation and the importance of data analytics for an occupation impacts median hourly wages. The point is to show which skill–data entry or data analysis–has a premium in the labor market and, therefore, provide the biggest opportunity and challenge for automation.

The first step is to code how important, on a scale of 1 to 100, is data entry and data analytics for an occupation. I do this for each of the 78 occupations that use human resource software. Data entry is defined as: “Entering, transcribing, recording, storing, or maintaining information in written or electronic/magnetic form.” Data analytics is defined as: “Identifying the underlying principles, reasons, or facts of information by breaking down information or data into separate parts.”

Turning first to data entry, I run a regression of median hourly wages on importance of data entry and I control for the number of total employees in the occupation and the number of projected openings between 2018 and 2028. Figure 1, below, shows the impact of a one unit increase in importance of data entry on median hourly wage. Not only is the coefficient, or the impact, close to zero, it is also highly insignificant. This means that the result is likely to be due to chance.

Figure 1. Importance of Data Entry Skills for Median Hourly Wage | |||

Coefficient | T-statistic | Signficant? | |

Importance of Data Entry | 0.0021683 | 0.01 | No |

Turning next to the importance of data analytics for median hourly wage, Figure 2, below, shows that a one unit increase in the importance of data analytic skills corresponds to a $0.54 increase in hourly wages. This translates into a $1,132 increase in annual earnings based on full-time hours of 2,080 hours a year. The result is very statistically significant, which means the effect is not likely to be due to chance.

Figure 2. Importance of Data Analytic Skills for Median Hourly Wage | |||

Coefficient | T-statistic | Signficant? | |

Importance of Data Analysis | 0.54427 | 5.53 | Yes |

The reason why data entry has no impact on wages and data analysis has a large impact on wages is because data entry is a skill that all employees have. In other words, people are more perfect substitutes for each other on this dimension and data entry skills are easily replaced. Therefore, it has no cache on wages. Data analytics, on the other hand, is a scarcer skill and not as easily replaced. Therefore, those who have analytic skills earn a premium.

If human resource software can facilitate data analytics, it will assume some of the premium that employees earn in wages.

Notes: