You don’t need to create a new variable to do this; instead, you can use the “Comparison of Means” program in the online tabulator by clicking on the “Means” tab. If you enter HOURWAGE as the dependent variable, the contents of the table will be the mean value of HOURWAGE for the subpopulations of your table (as defined by your row and column variables–age and industry based on your original question). You can limit the size of the table by using the “Selection Filter(s)” field to only include persons of a certain age (e.g., entering “age(30,60)”).
I do want to share a few other comments on using the tabulator and CPS data.
First, for analyses using HOURWAGE, you should weight with EARNWT. Additionally, you may want to apply the selection filter to exclude missing/out of universe cases for HOURWAGE (e.g., entering “hourwage(0-998)”). Finally, values for HOURWAGE are not adjusted for inflation; if you are pooling multiple years of data, you should use Consumer Price Index adjustment factors.
Another reason to consider using a single year of data is to avoid breaks in the industry coding scheme. Modifications to how industry is classified and coded over time limit the comparability of the variable IND across years of the CPS; more information on changes to industry classifications over time are available from the Census Bureau.
Finally, I would encourage you to look at the unweighted counts in your table cells; some of them may end up quite small, which increases the sampling error around that estimate and limits the ability to interpret the data. A common way to get around this is to pool multiple years of data; however, there are other approaches too. As I noted in my earlier response, you might consider collapsing industries into fewer categories by using the recoding functionality of the SDA tabulator. In addition to reducing the size of your table, it will also increase the unweighted count in cells. You might also consider looking at slightly wider age ranges rather than exactly 30 and 60 years old to address small cell sizes.