How do I get quarterly data?

CPS
1

Hi, I have realized that data available from IPUMS-CPS are monthly, do you know how to get quarterly data or to convert the monthly data into quarterly ones? Thanks very much for your answer

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2

You can pool together 3 months of the basic monthly samples in order to create quarterly data. If you intend to calculate representative population statistics with this pooled sample you’ll need to adjust the sampling weight appropriately. Since you are pooling together 3 samples, you’ll want to divide the sampling weight by 3 (the number of samples you are pooling together).

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3

Thanks so much, your answer has been very helpful

4

If I want to pool the entire 2020 year, would I then divide by 12?

5

That is correct.

6

I have a related question. When we pool 3 months of the basic monthly samples to get quarterly (or annual) one, shouldn’t we consider repeated household and thus make adjustments for duplicated observations? How can we address the issue of duplicated observation in this case? Should we limit the sample to a certain MISH (e.g., MISH=1)?

I think a similar issue was discussed previously but wanted to make sure I am understanding correctly. I am attaching link to the previous discussions. https://forum.ipums.org/t/can-i-pool-monthly-cps-data-into-years-to-look-at-share-of-population-by-educational-attainment-level/875

Thank you for your support!

7

IPUMS doesn’t have any specific recommendations on how to adjust your standard errors in pooled analysis of the CPS. One option is to only use households in a single rotation group, as you mentioned. Unfortunately that reduces your sample by almost 90%. I recommend reading some papers in your field using CPS data and see how they have approached this problem.

A couple other things to note on this:

  1. There is another issue with standard error calculations with basic monthly CPS data, which is that the design variables (clusters and strata) are not available in the public use microdata. You can get fairly close to the actual standard errors by using the smallest geographic level available as strata and SERIAL as the cluster in your estimation. See Davern et al. (2006, 2007) for details on this.

  2. You might also consider clustering your standard errors at the level of household, using CPSID as the identifier, since this will treat all observations of a given household as having correlated errors. You may also consider using household fixed effects. The particular model you use depends on the assumptions you’re willing to make about the data. Some good sources on adjusting standard errors for clustering include Wooldridge’s Econometrics of Cross Section and Panel Data, and most other graduate econometrics textbooks. Good sources for variance estimation in complex surveys include Cochran (1977), Sampling Techniques, and Kish (1995), Survey Sampling.