Hello, my name is Jacob Klerman.
Today, I want to share with you an orientation to the public use file -- what we call the
PUF -- for the 2018 FMLA surveys.
This is work that was done by Abt Associates under contract for the Department of Labor.
The standard disclaimer applies.
Views expressed here are only those of the authors and should not be attributed to the
Department of Labor.
All of the material I'm gonna show you today and talk about, including this briefing, are
available at the URL at the bottom of the screen.
The 2018 FMLA surveys actually included two surveys.
The first one was the employee survey.
The sample size was 4,470 and domains covered included: eligibility for, and knowledge of
the FMLA, leave taking and unmet need for leave.
The second survey was a worksite survey.
It included 2,206 work sites.
Domains included FMLA coverage, leave policies and experiences implementing the FMLA.
There are a variety resources available to help you to use the public use file.
They start with a set of resources that are narrowly focused on the PUF -- the public
use file.
The first are a set of documents that all begin Family and Medical Leave in 2018:...including
the public use file documentation guide, the employee survey public use file code book
and the worksite survey public use file code book.
And then, of course, there are files, there were sample code and corresponding output
for SAS and for STATA and there is survey data for the employee survey and for the worksite
survey.
And again, all this information is available on the DOL Chief Evaluation Office website.
See the URL at the bottom of the screen.
In addition to the files there are a set of reports.
There were a set of summary documents.
An executive summary from main report, three briefs: Who is Eligible?
Employee Leave Taking Patterns, and Paid Leave, and two presentations on the substance of
the findings.
And then there are a set of detailed documents.
The first one is the main report results of the 2018 survey, which includes an appendix
volume.
The second is a methodology report for the 2018 surveys, also including an appendix volume.
And then there are two short papers, which, as of now are forthcoming.
A comparison of lead taking experiences by gender and leave experiences of low-wage-workers.
So, with that, I'm going to turn to the code and the sample output.
So the first example here is an example of how to use the employee survey in SAS.
So the goal here is to compute frequencies of FMLA eligibility on the full sample, the
outcome you would get from SAS is in the lower right.
You can see that it gives the number and percentage of people who are not FMLA eligible, FMLA
eligible and the totals.
In order to do this you want to use the SAS command PROC SURVEYFREQ.
These are not simple survey data.
There are complicated weighting schemes for non response.
You must use the survey commands both in SAS and in STATA in this case not FREQ, but SURVEYFREQ.
You want to use insert your local library name in the angled brackets.
Don't put in the angle brackets there and then you want to use the file name.
FMLA_2018_employee_PUF which should be common no matter where you're running.
And then you want to include VARMETHOD brr balanced replication.
Want to use the weights, which are given here.
That's the name of the weight variable and the replication weights.
There are two hundred of them, and then the TABLES command will give you the variable
that you want to create tables for.
In this case fmla_eligible.
That was the example for the running analysis, the employee survey in SAS.
The equivalent example, for running analysis of the worksite survey in SAS are on this
slide.
So, in this case, we want to run industry frequencies on the full sample.
The variable is called Q4_CAT and in the lower right you can see distributions of manufacturing
retail services other and total.
Then in the upper left, you can find the corresponding commands again you wanna use the survey command,
in this case, PROC SURVEYFREQ you want to include the local lib name, the file name
will be common FMLA_2018_Worksite_PUF.
VARMETHOD is again brr.
The WEIGHT in this case is Estab_Wt.
There're gonna be 250 replication weights, and then tables with the variable name, which
is Q4_CAT.
That was the example for the worksite survey in SAS.
Now we're going to do the equivalent examples for STATA.
So here is the example for the employee survey in Stata again in the lower right you can
see the output that Stata would create.
The fraction that are not FMLA eligible, the fraction that are FMLA Eligible and the total.
So, again, in Stata on the upper left, we have the commands.
You want to be sure to use the survey commands.
In Stata that would be svyset S-V-Y set.
The group of options and then you want to run survey svy: tab, which is the frequency
command and again, the name of the variable in this case fmla_eligible.
That was analyzing the employee survey in Stata.
Here is the equivalent example for analyzing the worksite survey in Stata.
So, again, in the lower right we have the output fraction of work sites in manufacturing,
retail services and other and the total.
We want to use the svyset command to set up the survey analysis.
With the appropriate options.
One is a svy: to make sure you're actually running the survey analysis.
tab to run a frequency and then q4_cat is the name of the variable.
So, those are our four examples employee survey, worksite survey, in SAS and in Stata.
I want to give you a couple of pointers about analyzing these data.
The first pointer is that there is considerable non-response in these data and therefore you
must use the weights.
And in fact, also, the appropriate survey commands.
Second point is that the samples for these surveys are not small, but you still need
to test.
Even the pooled results are often much less precise than you would expect.
Certainly, when you slice and dice the data for subgroups the samples get real small,
real fast.
As is always true and even more so here, you must consider confidence intervals and test
for differences across groups.
The final analysis point is to remember as is always true with cross sectional surveys
that correlation does not imply causation.
You can compare, for example, covered versus not covered by the fmla or paid leave state
versus not paid leave state but lots of other things vary.
So, you need to be real careful about inferring causation.
We've now come to the end of our presentation.
Here are the next steps that we advise that you use.
First print off the Family and Medical Leave in 2018 Public Use File Documentation Guide.
Download the files and programs and the code books and reports.
Run the test programs and check that the output matches so, you can be sure you're doing everything
right.
Then run your own analysis.
We're looking forward to seeing your results and what we're gonna learn about the FMLA
and leave taking in the United States.
If you have more questions you can contact Radha Roy, who is the project director at
Abt or Kuang-chi Chang who is the project officer at the Chief Evaluation Office at
the Department of Labor.
Thank you very much.