# Using CPS "CPI99" to adjust for inflation

Hi,

I have wage data from the monthly CPS from 2012-2017 that I would like to adjust for inflation (I think up to 2017 dollars). I’ve read the instructions on how to do so using the CPI99 variable here: https://cps.ipums.org/cps/cpi99.shtml. I understand this in theory but do not know how to actually use the CPI99 variable in Stata. This is my first time using Stata and I’m new to analysis in general so if anyone can provide a simple explanation that would be great!

The CPI99 inflation adjustment variable will bring all dollar values into consistent 1999 dollar values. As outlined on Table 1 on this page, if you multiply the dollar value from a given year by the value given in CPI99 the resulting product will be the dollar value converted into 1999 dollars. Once you’ve done this and you want to bring your dollar values into another base year, then follow the procedure outlined on Table 2. For example, say you want to convert into 2016 dollars, then you can multiply the 1999 dollar values by 1.440. In STATA this can all be done using the generate command.

So, the process is as follows:
(1) Convert dollar values in all years into 1999 dollars, using CPI99. (e.g. gen incwage_99 = incwage*CPI99)
(2) Convert 1999 dollar values into any other year, using Table 2. (e.g. gen incwage_16 = incwage_99*1.440)

I hope this helps. Let me know if you have any additional questions.

Thank you so much for your answer! I have a very delayed follow-up question (I set this project aside for a few months and am revisiting it now).

I tried using the method you outlined in your post (which was very clear, thank you!), but noticed that it left me with a ton of missing values for my adjusted wage variable. I looked at the data editor, and cpi99 has no values for the vast majority of the rows in my data.

I think this is probably because I’m using monthly data (and btw my wage variables come from the EARNER supplement) and the cpi99 is a yearly variable.

Is there a corresponding monthly variable for adjustment, or do I need to do it “by hand” using the factors provided in Table 1. (In other words, I would use 0.741 for 2012 data, 0.726 for 2013 data, 0.715 for 20124 data, etc.)