Workplace and Employee Survey (WES) - Custom Tab Request

Question:
I am helping a researcher interested in using the employee version of the (WES) to explore “within-organization discrimination against parents who take maternity/paternity leave”, “does leave results in discrimination?” and “is the person in a leadership position” (this last question seems hardest to answer). I discovered the WES pdf reports on archived Stat Can pages and saw the synthetic files on . I am wondering if any additional tables exist? Would this be something that could be requested via a custom tab?

For the time being, I will be recommending exploration (and sub setting) of the GSS and LFS.

Answer:
In regards to the WES, the data is solely available through the RDC - custom tabulations aren’t possible.

Ways to Contact DLI

I wanted to take a moment to remind you of the different avenues available to contact us here at the DLI.

The listserv is meant to serve as a collaborative venue to a) ask questions in which other members of the community may be able to help with, b) ask questions to the DLI team at Statistics Canada which may be of interest to the entire community, and c) to share other bits of interesting information including upcoming training dates, webinars and presentations, notices, suggestions, etc.

In order to help cut down the amount of messages sent through the listserv, we are asking that if you have a question intended specifically for us here at Statistics Canada (that you don’t feel is beneficial to the entire community) or if you have a custom tabulation request, you contact us directly at statcan.maddli-damidd.statcan@canada.ca.

We also request that you limit the number of emails sent directly to our personal email addresses. While we love hearing from you, it’s always a possibility that we may not be in the office for an extended period of time and your questions may go unnoticed!

2016 Labour Force Survey - missing NOCs

Question:
A researcher is looking for NOC occupation groups for the monthly 2016 Labour Force Survey PUMFs.  Is this the way it should be represented: NOCS_01_25 STC Nesstar and NOCS_01_47 STC Nesstar when there actually are no such variables in 2016? 

Is it correct that the values are 100% missing for in 2016? It would be helpful to have NOC values for 2016.

What was the intention in 2016 when there are NOC values for the PUMF for these variables, e.g., on Odesi see January 2015: NOCS_01_25 link and NOC_01_47 link and for January 2017: NOC_10 LINK and NOC_40 LINK?  Would this be considered a problem with the data files?  

As an aside, there are SOC80_21 and SOC80_49 variables (with values) in 2015 and 2016.  Revisions to the 2015 LFS

Follow-Up Question:
I’d like to make a correction please to my question, with thanks to Scholars Portal for reviewing the occupation variables from 2015 – 2017 LFS monthly PUMFs.

Is it correct that there are supposed to be placeholders for a number of occupation variables but no data as highlighted in yellow  below?

1. All the LFS monthly PUMFs, 1987-2015, have data for NOCS-01-25 and NOCS_01_47, but as for the variables SOC80_21 and SOC80_49, there is no data.  
2. The monthly 2016 LFS PUMFs have  variables named NOCS_01_25, NOCS_01_47, SOC80_21 and SOC80_49 but with no data.
3. The monthly 2017 LFS PUMFs have two occupation variables only, the new NOC_10 and NOC_40.

Answer:

The short answer to this is simply that the labels have changed from the older years to the newer ones. For example, SOC80_21 was replaced with an NOC listing instead. From what I’ve been told, the descriptions of the variables should remain the same, it’s just a matter of needing to match them up from year to year (not necessarily the answer you were looking for I’m sure!)

So yes, it is correct that there is no data.

Where are the PCCF Files?

Question:
I was looking for the most recent version of the PCCF which is supposed to be under MAD_PCCF_FCCP_DAM/ROOT/2019 but this directory does not show up (there are folders for 1986 all the way to 2018, but no 2019). Also, if I look under /MAD_PCCF_FCCP_DAM/Root/2018/pccf-fccp for the previous version, this folder is empty!

Answer:
We moved it to reflect the most recent Census release! See attached. Sorry for the confusion!

*Original email included attachment*

PCCF Question - Rural vs Urban FSAs

Question:
I have a question from a grad student in Political Studies and, given my lack of expertise dealing with the PCCF, I thought I’d post it here for help. The problem stems largely from the fact that some FSAs are classified as both urban and rural.:

"I am using survey data to determine whether or not feelings of western alienation are higher in rural places relative to urban places in Western Canada. My survey asked respondents for the first three digits of their postal code (FSA), which I plan to code as either rural or urban. To do so, I plan on using the Postal Code Conversion File (PCCF) to code the FSAs of Western Canada as rural or urban, according to the "PopCntr_RA_size_class" variable. Unfortunately, each FSA has multiple values for the "PopCntr_RA_size_class" variable, making it difficult to code each FSA as either rural or urban. How can I effectively sort each FSA into the geographic categories?

Also, once I classify each FSA as rural or urban, how can I integrate that information into the survey data set? Is there a way to create an SPSS syntax file that sorts the respondents into geographic categories based on their reported FSA?"

Answer:
I’ve received the following from subject matter: 

“There is no easy way to classify the FSAs as either rural/urban. FSAs are designed as part of Canada Post Corporation’s mail delivery system, which conceptually is operational, not geographical. That makes it difficult to match it with conceptually geographic systems, like StatCan’s census geographies. The PCCF does the best it can, and at the lowest levels of geography, it’s not too bad. However, moving to higher levels of geography, like Population Centres, or higher in the delivery system, like FSAs, causes more gray areas. To facilitate the sorting of a lot of mail, they cover delivery to many different types of geography: villages, indian reserves, cities, towns, rural routes, and so on. It is not surprising that they cannot be classified as either rural or uban.

The best that can be done is some analysis of how the FSAs link up with PopCentres in the PCCF. Then, the researcher will need to make some decisions as to how to define each FSA, based on that analysis.

So, given all that, we have done the following:

— selected all non-retired FSAs from the western provinces (Manitoba to British Columbia) à 455
— found those that overlapped more than 1 PopCentreSizeClass or more than 1 PopCentreRAType à 304

and then we provided further information on those with the postal code counts. See the attached table. With that, the client can decided which way to classify each FSA.

This is only one example of the types of analysis that can be done. There are also the SAC and SACType attributes that may be useful, as they indicate whether the FSA is influenced by a CMA / CA or not. Hmm, I just took a quick look at that, and that may prove more useful. I’ve included that table in the workbook as well.

Anyway, there is no easy way to classify the FSAs are rural/urban with the PCCF. The client will have to do some sort of analysis of the attributes (PopCentre or SAC or perhaps something else that makes sense to them), then make some decisions as to how they will classify each FSA.”

PCCF in csv Format

Question:
Until we complete hiring a new data librarian, I’m the contact for anyone in our institution needing the PCCF and PCCF+ files and my knowledge is very limited. Usually I just provide the files and all is good, but now a grad student who has received the files sent me the following: “I am not familiar with SAS. Could you please send me a csv or Excel file which contains the postal codes and corresponding regions”.

Answer:
I’ve received the following response from the PCCF team:

“We do not provide the PCCF or PCFRF files in csv or Excel format. The files are quite large, and would take up even more room if they were provided is some application format. They are provided as a flat, fixed width, ASCII text file (.txt), which is readable/importable into a wide variety of applications. This is not a format specific to SAS. The client should be able to use it in whatever data application they are familiar with.

The record layout is provided in the Reference Guide (page 11 in the 2019 February version). That explains the format of the text file, and how the client needs to define the file in their application.

It’s not the most convenient way to provide the files. However, it is the format that is usable in the most applications, and has the smallest file size.”

Crime Data at Census Tract Level

Question:
A researcher is looking for crime data at the CT level for 2016 and 2011. As far as I know, data from the Uniform Crime Reporting Survey is only available at the provincial or CMA level. I have seen some CT tabulations for Montreal (2001) and Toronto (2006) but that’s all. Short of custom tabulation, is there any other source for this data?

Answer:
Subject matter has responded with the following:

“The Montreal and Toronto reports in question were really one off special analytical products, outside the normal geographical scope of the UCR.  


The lowest geography that the UCR is published at is the police service boundary level, then it goes to CMA, then CANPROV.”