Observations with missing cells in the data - should they be dropped?

[Bruno Hoepers]

Hi all. I am organizing a panel data with information on the spending with government advertising by Brazilian municipalities for three 4-year periods (2005-2008, 2009-2012, 2013-2016). Here I reproduce the data for the 2013-2016 period. I am aggregating the amount spent with advertising per municipality for the years 2013, 2014, 2015, and 2016 (i.e., variables r_spend_com_social_13, r_spend_com_social_14, r_spend_com_social_15, and r_spend_com_social_16). My goal is to create a variable "r_spend_com_social_total", which will be the sum of the four aforementioned variables.

The problem: there is a considerable amount of missing cells in the data. That can be easily seen by inspecting which observations have missing cells with the variable "anymiss". For instance, observation #2 has a missing cell.

The question, then, is: What should I do with observations containing missing cells? Should a whole row of data be dropped if it contains at least one missing cell, as it is the case with observation #2?


Thank you.


Variables:
ibge_uf_code = state ID
ibge_mun_code = municipality ID
obs_number: observation's number
r_spend_com_social_13 = spending with govt ads at year 2013
r_spend_com_social_14 = spending with govt ads at year 2014
r_spend_com_social_15 = spending with govt ads at year 2015
r_spend_com_social_16 = spending with govt ads at year 2016

* Example generated by -dataex-. For more info, type help dataex
clear
input byte ibge_uf_code long ibge_mun_code float(obs_number r_spend_com_social_13 r_spend_com_social_14 r_spend_com_social_15 r_spend_com_social_16) byte anymiss
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21 2102101 444         0         0         0         . 1
21 2102150 445         0         0         0         0 0
21 2102200 446         0         0         0         0 0
21 2102309 447         0         . 290477.56 115744.62 1
21 2102325 448         0         0         0         0 0
21 2102358 449  177913.4 169102.56 208142.34 214742.38 0
21 2102374 450         0         0         0         0 0
end

[Carlo Lazzaro]

Bruno:
yes, Stata will drop all the observations with at least one missing value (listwise deletion).
That said, the recommended approach is diagnosing the mechanism underlying the missingness (MCAR, MAR, MNAR) and then deal them accordingly

[Nick Cox]

Cross-posted at https://www.reddit.com/r/stata/comme...s_in_the_data/ Please our policy on cross-posting, which is that you are asked to tell us about it.

This is hard. Looking at that listing I would guess that some of those missings are likely to be zero and some probably not. I know that doesn't help. The two simplest strategies are to ignore all observations with any missing values and to treat them all as zero. How different your results are with those strategies would give you a handle on the size of the data.

I am not sure that multiple imputation can help here. You are in effect thinking about imputing volatile time series.

[Bruno Hoepers]

Thank you Carlo Lazzaro and Nick Cox for the comments. For now, I think I will build two datasets: one treating missing cells as zeroes and another one excluding observations with any missing values. Then I will run the models and see what I get. I also think I need to talk more about this issue with people from my field.

PS: I didn't realize the Reddit community was somehow related with Statalist, or that there was a policy on cross-posting. I apologize.

[Carlo Lazzaro]

Bruno:
observations with any missing values will be automatically kicked out from calculation by Stata (listwise deletion).

[Nick Cox]

Thanks for the apology!

I didn't realize the Reddit community was somehow related with Statalist ,,,.

There is no formal connection. But clearly just as you can read and write on both, so can other people.

I didn't realize [...] there was a policy on cross-posting.

The home page advises

Please do read the Statalist FAQ for crucial advice before you try to post a message to Statalist. Knowledge of the FAQ will greatly improve the chance your question will be answered as you wish.

The new topic prompt advises

First read Advice on Posting.

and that document (one and the same) advises

8. May I cross-post to other forums?

People posting on Statalist may also post the same question on other listservers or in web forums. There is absolutely no rule against doing that.

But if you do post elsewhere, we ask that you provide cross-references in URL form to searchable archives. That way, people interested in your question can quickly check what has been said elsewhere and avoid posting similar comments. Being open about cross-posting saves everyone time.

If your question was answered well elsewhere, please post a cross-reference to that answer on Statalist.

Let's expand on that. Cross-posting arises because people want a good answer and soon. They know that different communities include Stata questions, so they sample two or more. I've seen Stata questions here, on Stack Overflow (SO), on Reddit, on Quora, on Twitter, on Facebook, and yet other communities exist. And many individuals have their own sites and a few offer to handle questions. (The practice is highly variable. Most people who want to showcase their own excellent work have no desire to become one-person helplines.) There is overlap between communities and it's hard to quantify meaningfully, or at all, but the overlap is neither here nor there for what follows.

Some people will read a question and not care about it or have nothing to say, so I think that the story ends there for them.

People who read your question might be interested in an answer In this case, how to handle missing values properly seems to me of essentially universal concern. But the same issues arise with any question, broadly interesting or highly specific. If you are interested in an answer, you should want to know about answers even in places you don't (often) visit. Highly competent people lurk almost everywhere and good answers might exist in surprising places.

People who might answer anywhere should want to know what has already been said. I post here on Statalist and on SO but I've often had the experience that I spent some time on an answer in one place and then found that there was already a fine answer somewhere else. That is frustrating. I wasted my time. Same story for anyone else who ever answers a question.

I've seen discussions asserting that cross-posting elsewhere is rude and insulting to any community X, as you're implying that you don't trust people in X to provide an answer and/or that you don't care about wasting their time. I don't sign up to that attitude but such suspicions aren't always wrong. Similarly, I've seen cross-posting likened to spamming. Regardless, know that some strong feelings do exist on this.

If everybody always scanned all possible places, you would not need to tell anyone about cross-posting, as they would already know. In practice, I suspect the practice is much nearer the opposite, that many people have a favourite community and spend little or no time watching anywhere else. It's precisely the lack of overlap between communities that makes it desirable and helpful that cross-posting is advertised, which is easy enough to do by giving a cross-reference to questions elsewhere.

Beyond that, it is of some interest, and occasionally even entertaining, to compare different communities and their pluses and minuses, but I will stop there. People don't need to be told what they already think.

[Bruno Hoepers]

Thanks for the comments Nick. The only reason I posted on Reddit is because at some point I thought nobody would answer my question at Statalist. Sometimes this happens, and it is fine. It's good to know that I can post the same question at other forums too. Sometimes my questions are not 100% Stata-related questions, and for this reason I think some of my questions are not answered. Next time, I'll be more patient and wait a little longer for an answer here before asking a question in other place.