To view the May 15th recorded webinar, visit: http://www.niagaraknowledgeexchange.com/work-employment/better-business-outcomes-through-a-more-secure-workforce-event-recording/
To view the May 15th recorded webinar, visit: http://www.niagaraknowledgeexchange.com/work-employment/better-business-outcomes-through-a-more-secure-workforce-event-recording/
Building and maintaining an engaged and productive workforce is a challenge for employers, made more difficult by employment precarity. The impacts of precarious employment can be serious not only for workers but also for businesses.
Reducing the incidence of precarious employment and mitigating its effects are essential for building workforce security and growing business value. Fortunately, there are many good practices available for adoption.
During this webinar, presenters will discuss:
This event is hosted by Niagara Connects in partnership with Poverty and Employment Precarity in Niagara (PEPiN). A follow-up resource package will be distributed after the event.
To register for the webinar, click here .
Check out our blog on the Niagara Knowledge Exchange:
Getting Left Behind was prepared by the Poverty and Employment Precarity in Southern Ontario (PEPSO) research group, a university-community joint initiative. This report is one component of a larger PEPSO research program.
Niagara, ON: Poverty and Employment Precarity in Niagara (PEPiN) has completed a research study on precarious employment and its impact in Niagara. Unemployment rates may be lower than previous years, however, a little over half of working-age Niagara residents have some form of precarious employment. We define precarious work as work that is typically part-time, temporary, or contract, and often without longer term job security and employment based benefits. It is often called non-standard employment.
PEPiN is an initiative between United Way and Brock University made possible with funding from the Ontario Trillium Foundation. The report, “Uncertain Jobs, Certain Impacts: Employment Precarity in Niagara”, suggests that precarious employment has a significant impact on the individuals, families and communities it touches, and that these impacts are indeed widespread and pervasive in Niagara. Moving forward, lead researcher, Dr. Jeff Boggs, Associate Professor, Department of Geography and Tourism Studies at Brock University, would like to see an even larger sample of Niagara’s population in order to more precisely identify the extent and impacts of precarious employment.
“We are reasonably confident that roughly half of those working in Niagara between April 1st 2016 and March 31st 2017 are engaged in some type of non-standard or precarious work. While some Niagara residents choose such work to accommodate their lifestyle and family situations, far more find themselves suffering negative social and economic effects that are linked to not having standard, full-time jobs with benefits” says Dr. Boggs.
This research project is an important first step in an evidence-informed decision-making process to encourage meaningful action. The report also includes a list of recommendations, aimed at government and employers, to reduce the incidence of precarious employment and to mitigate its impacts.
In an effort to replicate similar studies conducted in other Ontario cities by the Poverty and Employment Precarity in Southern Ontario (PEPSO) research group, the PEPiN study analyzed survey results from a random sample of 713 employed Niagara residents. Study participants answered questions about their backgrounds, family situations, current employment, health, and economic standing. PEPiN contracted Leger, the same survey research group which conducted the other PEPSO studies, to survey participants on their experience with precarious work, mental health, general health, and overall quality of life. The report can be found online at www.pepniagara.ca.
After evaluating answers to survey questions completed by phone interviews, results indicated the following:
So how exactly does one calculate their score on the Employment Precarity Index?
As noted in PEPiN Nerd Blog Post #4, the Employment Precarity Index (EPI) is a measure created from responses to ten (10) close-ended questions. Each question’s possible response is then weighted. These ten scores are then added up to create a single Employment Precarity Index score of 0 to 100.
Because the Employment Precarity Index is more complicated to calculate than the Secure Employment variable, we’ve provided a tutorial to explain how we calculate the EPI.
Step 1: Answer the following questions. Then, score each of them. Finally, add up all your scores to come up with your Employment Precarity Index score.
|Question, closed-ended responses and associated score||Your score|
|1. Do you usually get paid if you miss a day’s work?
|2. I have one employer, whom I expect to be working for a year from now, who provides at least 30 hours of work a week, and who pays benefits.
|3. Between April 1st of 2016 and March 31st of 2017, how much did your income vary from week to week?
A GREAT DEAL (+10)
A LOT (+7.5)
|4. How likely will your total hours of paid employment be reduced in the next six months?
VERY LIKELY (+10)
|5b In the first three months of 2017, how often did you work on an on-call basis? (That is, you have no set schedule, and your employer calls you in only when there is work.)
ALL THE TIME (+10)
|6. Do you know your work schedule at least one week in advance?
MOST OF THE TIME (+2.5)
|7. In the first 3 months of 2017, what portion of your employment income was received in cash?
|8. Which of the following best describes the job/contract that paid you the most in the first 3 months of 2017?
CASUAL (on call, day labour) (+10)
TEMPORARY / SHORT TERM CONTRACT (less than a year) (+10)
SELF-EMPLOYED – NO EMPLOYEES (+7.5)
SELF-EMPLOYED – OTHERS WORK FOR HIM/HER (+0)
PERMANENT FULL-TIME – HOURS VARY WEEK TO WEEK AND COULD SOMETIMES BE LESS THAN 30 (+2.5)
|9.* Other than your Canada Pension Plan contributions, does your current employer(s) provide:
A COMPANY PENSION PLAN OR CONTRIBUTIONS TO YOUR RRSP (If NO, +5)
ANY OTHER EMPLOYMENT BENEFITS(S) ( drug plan, vision, dental, life insurance etc.) (If NO, +5)
|10. Would your employment in the first 3 months of 2017 have been negatively affected if you raised a health and safety concern or raised an employment rights concern with your employer(s)?
|Total score (to find your Employment Precarity Index score)|
NB: In the telephone survey, Question #9 is actually asked using two separate survey questions. In the 2011 and 2013 versions of the Employment Precarity Index, Question #10 uses a five-point Likert response of Very Likely (+10), Likely (+7.5), Neither likely nor unlikely (+5), Unlikely (+0) and Very Unlikely (+0).
In cases where any of the respondents did not know or refused to answer any of these ten questions, we could not use their response to calculate the Employment Precarity Index. As a result, they were dropped from the calculation. Of the 713 survey respondents, 684 answered these ten questions. The minimum score possible is 0 and the maximum score possible is 100.
|Practice by scoring Judy and Bob’s responses:
Judy Q1 NO, Q2 YES, Q3 SOME, Q4 UNLIKELY, Q5 MOST OF THE TIME, Q6 ALWAYS, Q7 NONE, Q8 PERMANENT PART-TIME, Q9 NO & YES, Q10 YES.
Bob Q1 YES, Q2 YES, Q3 A LITTLE, Q4 UNLIKELY, Q5 NEVER, Q6 ALWAYS, Q7 NONE, Q8 PERMANENT FULL-TIME, Q9 YES & YES, Q10 NO.
Scroll to the bottom for the answers.
Step 2: We then take everyone’s Employment Precarity Index scores, and rank the scores from smallest to largest.
Thus, if your EPI score is zero, then you are ranked first (or equivalently with all the other persons who scored a zero). Next, we rank all the persons who have an EPI score of 2.5 (as that is the next lowest possible score. For instance, Answer d “Most of the time” to Question 6: “Do you know your work schedule at least one week in advance?” is worth 2.5 points). We then move on to all those person whose EPI score was a 5. We continue ranking these scores until we reach 100.
When we are done with all this ranking, we have what is called a distribution. If we make a relative frequency histogram or absolute frequency histogram (it resembles a bar graph but is not a bar graph), we can see the spread of the scores making up the Employment Precarity Index.
Step 3: We then divide this distribution into quartiles (or categories). This creates four categories. The first quartile includes the lowest EPI scores, the second and third quartiles follow, and the fourth quartile contains the highest EPI scores. Following the convention established in the 2011, 2013 and 2015 PEPSO studies on precarious employment in the GTA and Hamilton, we call the first quartile secure, the second stable, the third vulnerable and the fourth precarious.
More importantly, we have identified cut-points for these four categories. This, in conjunction with our original distribution, provides us a baseline against which to compare future surveys, and in turn determine if overall Niagara’s workers are becoming more or less secure over time.
|ANSWERS: Judy’s score on the Employment Precarity Index is 40. Bob’s is 2.5.|
How else does PEPiN measure precarious employment?
Measure #2: The Employment Precarity Index
As noted in PEPiN NERD BLOG POST #2, precarious employment is often measured with proxies, and not direct measures of precarious employment. The problem with using proxies is that they do not necessarily correspond to what one really wants to measure. In the previous blog post , we introduced the first measure of employment precarity. This post introduces our second measure.
As noted in previous posts, McMaster’s Dr. Wayne Lewchuk and his colleagues created a survey specifically designed to measure employment precarity: the Employment Precarity Index. With their permission, PEPiN uses this index.
An index is a measure created from two or more other measures. The Employment Precarity Index is created by adding the weighted results from the following ten questions:
Question #1 Do you usually get paid if you miss a day’s work?
Question #2 I have one employer, whom I expect to be working for a year from now, who provides at least 30 hours of work a week, and who pays benefits.
Question #3 In the last 12 months, how much did your income vary from week to week?
Question #4 How likely will your total hours of paid employment be reduced in the next six months?
Question #5 In the last three months, how often did you work on an on-call basis?
Question #6 Do you know your work schedule at least one week in advance?
Question #7 In the last three months, what portion of your employment income was received in cash?
Question #8 What is the form of your employment relationship (short-term, casual, fixed-term contract, self-employed, permanent part-time, permanent full-time)?
Question #9 Do you receive any other employment benefits from your current employer(s), such as a drug plan, vision, dental, life insurance, pension, etc.?
Question #10 Would your current employment be negatively affected if you raised a health and safety concern or raised an employment-rights concern with your employer(s)?
Source: PEPSO (2015) The Precarity Penalty Executive Summary, pages 4-5.
Surveys usually rely on close-ended questions. Close-ended questions require the respondent to select from pre-defined answers such as YES or NO. These ten questions are no different. (The reader may also note that four of these questions are the questions used in constructing our first measure of employment precarity, described in blog post #3 ).
After these data are collected, responses to these ten questions are tallied for each survey. Each question is worth a specific number of points. The points from these ten questions are added together to create a single score ranging from 0 to 100. A low score indicates more secure employment whereas a higher score indicates less secure employment.
Next, each respondent’s scores are then compared to all the other respondents’ scores, and ranked from highest to lowest, that is, from the most precarious to the most secure. Of all the scores, the top 25% of the scores correspond to the ‘precarious’ category. This does not mean that the scores in the first quartile ranges from 76 to 100. Instead, it means that the survey respondents whose scoring on the Precarious Employment Index place them in the top quarter of all the surveys are defined as precariously employed. This process is then repeated three more times, each time placing another 25% of the population into a category.
This produces four employment categories — secure, stable, vulnerable and precarious — with the same number of people in each. These quartiles (so called because each contained 25% of the survey respondents) are useful in three ways. First, these identify the range of employment precarity and security in Niagara. This allows us to see what precarious and secure employment look like in Niagara, and the combinations these forms of employment take.
Second, when combined with future surveys in Niagara, the current findings will provide a benchmark to measure if Niagara’s employment is becoming more or less precarious. This would involve comparing the distribution of scores of new survey results with old survey results. If, overall, the precarity scores in a new survey tend to be lower than in the original survey, this implies that overall Niagara’s workforce is better off than before in that survey respondents report fewer of the conditions associated with employment precarity.
Finally, when calibrated with the scores used in PEPSO’s 2013 It’s More Than Poverty study, they provide a benchmark against which we can compare Niagara’s results with those in the GTA-Hamilton in 2011 (reported in the 2013 report) and 2014 (reported in PEPSO’s 2015 report).
Now that the reader has a sense of how we measure precarious employment using the Employment Precarity Index, their next question is likely this: What can this index tell us besides how many people are precariously employed? The short version is that this index allows us to then see if precarious employment tends to be associated with other good or bad outcomes. In our Nerd Blog #6, we will discuss the kinds of questions we asked to measure these good or bad outcomes. But before we do that, let’s look at some examples to show how the Employment Precarity Index is actually calculated in Nerd Blog #5.
What are cross-tabulations?
This is a perfectly reasonable question. To illustrate what we mean by cross-tabulations in PEPiN Nerd Blog Post 3 , let’s have an example. We could compare Secure Employment with a variable we (sadly) did not measure, ‘Owns One Or More Pets’ in a two-by-two (2 x 2) cross-tabulation. It is a called a 2 x 2 cross-tabulation because it reflects the fact that each variable can only have two outcomes. Either you are or are not in secure employment, and either you are or are not an owner of one or more pets.
|Owns one or more pets||YES||Cell 1||Cell 2||= Cell 1+2|
|NO||Cell 3||Cell 4||= Cell 3+4|
|TOTAL||= Cell 1+3||= Cell 2 +4||= Cell 1+2+3+4|
All cross-tabulations have rows and columns. Columns go up and down, like columns on a building. Rows go left and right, like rows in a stadium or movie-theatre.
Collectively, these rows and columns create a series of boxes. The boxes that contain numbers are called cells. In this example, there are nine cells, but we’ve named them in a weird way. You will see why in a moment. Cells can contain numbers that are counts, percentages or both.
A count is the number of persons ending up in a particular cell, whereas the percentage is that count converted to a percentage by dividing the count by the total number of responses.
In this example, Cell 1 includes all the people who have secure employment and who own one or more pets. Cells 2 includes all the people who do not have secure employment, but do own one or more pets. Cells 3 is the reverse of Cell 2, as it contains all the people who have secure employment but who own no pets. Cell 4 is the reverse of Cell 1, as it contains all the people who lack secure employment and pets.
The bottom and right-most cells of a cross-tabulation always contain the row, column or total totals (this last one is not a typo). These totals are taken by adding their corresponding row or column. The special case is the bottom-most, right-most cell, as it is the total of the row totals and column totals. This total of the totals should always equal the total number of persons who answered both questions.
So now you have had an accidental lecture on the construction of cross-tabulations. Why would we ever bother to use these funny looking tables?
Cross-tabulations are useful because they allow us to see if one outcome varies with another. If we think that lots of people with secure employment are likely to have pets, we would expect to see that in this table, especially given our random sampling procedure (to be discussed in another blog post). To use some examples for which we do have data: Do people with secure employment tend to have children? Do they tend to be partnered (common-law or otherwise)? Do they tend to have health problems? These kinds of questions can be answered using cross-tabulations of the PEPiN survey data.
Ultimately, this — constructing a variable to measure precarious employment and comparing it in a cross-tabulation with another variable — allows us to see if the things alleged to accompany precarious employment actually happen more to someone who is precariously employed in Niagara.
How does PEPiN measure precarious employment?
Measure 1: Secure Employment
As noted in PEPiN NERD BLOG POST #2, precarious employment is often measured with proxies, and not direct measures of precarious employment. The problem with using proxies is that they do not necessarily correspond to what one really wants to measure.
We rely on two related measures of precarity. The first — Secure Employment — is simpler, while the second — the Employment Precarity Index — is more complicated (and will be discussed in PEPiN NERD BLOG POST #4). We are indebted to McMaster’s Dr. Wayne Lewchuk and his colleagues for both of these measures. By explaining the simpler measure of employment precarity first, we hope that our explanation of the second measure will be more clear.
This first measure — Secure Employment — is constructed from four questions asked in the PEPiN telephone survey. A respondent is assumed to have secure employment if they answer affirmatively to all four (4) of these questions:
Question #1 Do you have one employer?
Question #2 Is your job permanent, full-time?
Question #3 Do you expect to be in the same job in 12 months?
Question #4 Do you have benefits?
Source: PEPSO (2017) Personal correspondence with Dr. Wayne Lewchuk to Jonah Butovsky. 25 August 2017.
All respondents who answered YES to these four questions are coded as having secure employment in terms of this variable, Secure Employment. If a respondent answered NO to one or more of these four questions, they are coded as not having secure employment. This is a binary variable, meaning in this case that a respondent either does or does not have secure employment.
On the one hand, this makes it easy to identify who has secure employment. As we will see in later blog posts, this means we can compare outcomes on a second variable by secure employment. We can show this in a cross-tabulation, which is a special kind of table and its results. If you want to know more about cross-tabulations, click here. If not, read on.
But is Secure Employment really a measure of precarious employment?
The reader will likely have just asked themselves this question: is the variable Secure Employment actually a measure of precarious employment?
To this we respond: that depends on how you define precarious employment. Secure Employment is a binary variable. Either one has secure employment or one does not. As you will recall from the box above, the Secure Employment variable measures whether or not the respondent answered YES to those four questions. And this can be a problem if it is our only measure of employment precarity.
Why? All other things being equal, persons answering YES to any three of those four questions are probably better off than persons answering YES to any two of those four questions. And in turn, they are probably better off than persons answering YES to any one of those four questions. And persons answering YES to just one of those four questions is still probably better off than someone answering NO to any of those four questions. Relying only on a Secure Employment variable, however, conceals this spectrum of differences.
With this in mind, we need a more complex measure of precarious employment. We need a measure that captures the spectrum of employment conditions. And that measure — the Employment Precarity Index — is the topic of NERD BLOG POST #4.
As an update to our project, we have received our phone survey data and are in the process of analyzing it. To ensure we’re getting the most from the data, we’re inviting community experts to participate as part of an advisory committee. If you are interested in contributing to this work, please feel free to contact us to discuss in more detail.
We seek advisory committee members who:
a) have an identifiable stake (such as representing employees, industries, or the public/non-profit sector elements that support either) in the desired long-term outcome of a more secure workforce with all the economic and social benefits this implies;
b) by dint of some balance of position, experience and knowledge, are able to comment and make suggestions that will contribute to the success of this project’s investigative and/or persuasive abilities;
c) collectively represent a balance and diversity of several identifiable entities in the region that have such a stake; and
d) collectively are of a (reasonable enough) size to balance that diversity with clarity of collective voice.
We anticipate our first meeting taking place in the final week of September 2017. This will include a focus group directed at improving our explanations of the measures of employment precarity and identifying which subgroup(s) in our sample should be the focus of follow-up interviews.