<p>The key question to ask here is this: <strong>is the data missing a random selection of the population/sample?</strong> If this is the case (a situation called Missing Completely At Random, MCAR, you can simply discard the observations with missing values and run your model on the balanced panel. Conversely, if that is not the case (Missing Not At Random, MNAR), an estimation from the balanced panel will be biased. There is also an intermediate case, Missing At Random (MAR), which depends on distinguishing between missing based on observables (in which case you can do simple imputation), or missing on unobservables, in which case you need to perform a two-step Heckman estimation. Read more about missing data <a href="https://en.wikipedia.org/wiki/Missing_data" rel="nofollow noreferrer">here</a>, and in <a href="https://us.sagepub.com/en-us/nam/missing-data/book9419" rel="nofollow noreferrer">this</a> very approachable book.</p>

<p>There are plenty of tests that you can do to evaluate the nature of missing data. See <a href="http://dwkwak.weebly.com/uploads/1/2/4/1/12419825/ht_tests.pdf" rel="nofollow noreferrer">here</a>. <a href="https://msu.edu/~kwakdo/kwak_slide.pdf" rel="nofollow noreferrer">These slides</a> show a hands-on example of how to deal with unbalanced panel datasets.</p>

<p>A very useful introduction to Unbalanced Panal Data is <a href="http://homepage.univie.ac.at/robert.kunst/pan2010_pres_mayer.pdf" rel="nofollow noreferrer">here</a>, based on the <a href="http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP003191.html" rel="nofollow noreferrer">textbook</a> on Panel Data by B. Baltagi.</p>

CC BY-SA 3.0
2017-09-27T06:19:47.677Z
Thanks for the detailed answers! Yes, I check whether missing values are attributed to some unobserved characteristics of respondents or not, once I construct panel data. But maybe it is better to keep attention on missing values while I am merging dataset.
Satoko
CC BY-SA 3.0
2017-09-23T06:44:20.797Z
2017-09-25T06:39:30.153Z
2017-09-25T06:39:30.153Z
luchonacho
<p>I would construct and maintain only the full (unbalanced) data set with many variables with many missing values. Why? We can always extract balanced panel data sets from the full one; we can't go the other way around. When a balanced set is needed (in order to reduce the file size or send it to others), I would use Stata or R and never do it manually. To err is human.</p>

CC BY-SA 3.0
2017-09-22T23:36:52.450Z
2017-09-22T23:49:37.060Z
2017-09-22T23:49:37.060Z
chan1142
<p>When I construct panel data from large household surveys (say, World Bank's Living Standard Measurement Surveys), I try to construct data set with as many potentially usable variables as possible. This creates a large unbalanced dataset and I don't know how many balanced data left until I run a regression. </p>

<p>I personally have not encountered any problems with this, but I wonder what other people think when they have to use dataset I construct. 
I have not worked with others so far, but I wonder how I should construct a good dataset to work on if I need to work with professors. 
Should I construct balanced dataset? Or unbalanced dataset with many variables with many missing values?</p>

CC BY-SA 3.0
2017-09-22T20:31:38.277Z
Why don't you create both?
london
<paneldata><micro-data>
Should I construct balanced data with only necessary variables or unbalanced data with many potentially usable variables?
Satoko
# Should I construct balanced data with only necessary variables or unbalanced data with many potentially usable variables?

(Asked by: username_0 on 2017-09-22)

When I construct panel data from large household surveys (say, World Bank's Living Standard Measurement Surveys), I try to construct data set with as many potentially usable variables as possible. This creates a large unbalanced dataset and I don't know how many balanced data left until I run a regression.

I personally have not encountered any problems with this, but I wonder what other people think when they have to use dataset I construct.
I have not worked with others so far, but I wonder how I should construct a good dataset to work on if I need to work with professors.
Should I construct balanced dataset? Or unbalanced dataset with many variables with many missing values?

**Tags:** paneldata, micro-data
> **Comments:**
> **username_1**: Why don't you create both?  

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## 2 answers

### Answer by username_2 on 2017-09-23. Score: 0 | Accepted answer

The key question to ask here is this: **is the data missing a random selection of the population/sample?** If this is the case (a situation called Missing Completely At Random, MCAR, you can simply discard the observations with missing values and run your model on the balanced panel. Conversely, if that is not the case (Missing Not At Random, MNAR), an estimation from the balanced panel will be biased. There is also an intermediate case, Missing At Random (MAR), which depends on distinguishing between missing based on observables (in which case you can do simple imputation), or missing on unobservables, in which case you need to perform a two-step Heckman estimation. Read more about missing data [here](https://en.wikipedia.org/wiki/Missing_data), and in [this](https://us.sagepub.com/en-us/nam/missing-data/book9419) very approachable book.

There are plenty of tests that you can do to evaluate the nature of missing data. See [here](http://dwkwak.weebly.com/uploads/1/2/4/1/12419825/ht_tests.pdf). [These slides](https://msu.edu/~kwakdo/kwak_slide.pdf) show a hands-on example of how to deal with unbalanced panel datasets.

A very useful introduction to Unbalanced Panal Data is [here](http://homepage.univie.ac.at/robert.kunst/pan2010_pres_mayer.pdf), based on the [textbook](http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP003191.html) on Panel Data by B. Baltagi.

> **Comments:**
> **username_0**: Thanks for the detailed answers! Yes, I check whether missing values are attributed to some unobserved characteristics of respondents or not, once I construct panel data. But maybe it is better to keep attention on missing values while I am merging dataset.  

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### Answer by username_3 on 2017-09-22. Score: 0

I would construct and maintain only the full (unbalanced) data set with many variables with many missing values. Why? We can always extract balanced panel data sets from the full one; we can't go the other way around. When a balanced set is needed (in order to reduce the file size or send it to others), I would use Stata or R and never do it manually. To err is human.


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