Hourly Wage Discrepancies Between Males and Females in the Trades Occupation: A Policy Issue

The trades are historically known as a male dominated occupation. This is due to women not being exposed to the trades as a career option, a general lack of a lack of awareness of the trades and a lack of encouragement for them to work in the trades. To ensure diversity in trades occupations there needs to be a diversity strategy created, training support to organizations and employment supports to increase hiring and retention of females in trades occupations.

Please see an info-graphic at ‘A Comparison of Hourly Wages for the Trades by Sex – Statistics Canada Data Set’ detailing the hourly wages of trades between 1997-99 and 2017-19, and also a comparison for those time frames sub-categorized by sex. The visual representation of data shows an increase to base hourly wages of trades over the last 20 years. It is interesting to note the total hourly wage shown in the top set of data is almost identical in the lower set of data. This is due to the abysmal numbers of women being in the trades.

This information was found in a data set downloaded from the Statistics Canada website and represents the ‘Hourly wage distribution by occupation, monthly, unadjusted for seasonality (x 1,000)’. Tableau was then utilized to design the info-graphic.

Persuasive Presentation vs. Manipulation of Decision-Makers by Policy Analysts

In the public service the role of a policy analyst is to provide unbiased information to a decision-maker to ensure decisions made in the public interest reflect public needs which are moral and ethical. This means focusing on statistical facts attained through analyzing data. There are two approaches which could be used to deliver this information to a decision-maker. The first is considered an ethical and moral approach, also known as persuasive presentation. The second approach would be to manipulate the data so it is presented in a format which the decision-maker finds appealing, but is ethically and morally corrupt.   

To achieve persuasive presentation one must know who their audience is, what their goals are, what motivates them and what they value. The main difference between information being presented as persuasive or as manipulative is on whether the representation of facts is neutral or influencing. To be neutral, a policy analyst must ensure the facts are represented as an expression of the goals of the audience with obstacles stated and explored.

Manipulation, on the other hand, is the act of influencing an audience to change their behaviour or their perception of others or situations, through direct, deceptive, or underhanded tactics; all for your own, or their own, personal gain. This approach by a policy analyst would be seen as unethical and immoral as the best interests of the public are not being considered. As it is not the role of a policy analyst to respond to the needs and wants of the public, policy analysts should present all facts and information in a neutral manner. The interpretation of the facts should remain in the hands of the decision-maker voted for by the public.


Understanding Probability Statements Allows for Informed Public Policy

You are always better off when you are more informed. Understanding probability statements like”1 in 500 chance” or terms such as “possibility”, “likelihood” or a “balance of probabilities” is important for good public policy. For example, the term “on a balance of probabilities” in the court system, refers to a neutral third party, a hearing officer, judge, or adjudicator, who is determining the outcome of evidence presented to them. It means the neutral third party believes by at least fifty-one percent that what is being stated is the truth. This also means there is a forty-nine percent chance it is not the truth.

What is key here is the idea of understanding the probability statement. If there is a balance of probabilities that something is likely to occur, we are inclined towards the argument because the terminology is vague. When the terminology changes to something along the lines of “80 percent likelihood of being true” we are inclined to be persuaded. 

For there to be good policy, we all need to understand what probability statements mean or are actually stating. Do your research on the topic; become informed on the topic. By allowing yourself to understand the true depth of the topic you will allow yourself to make accurate decisions related to the topic. Should you buy life insurance? How likely is it that you will die tomorrow? 1 in 500 chance? This means there is a .2 percent chance you may die tomorrow. Or alternatively a 99.8 percent chance you will live for another day. This example portrays the notion that there are no guarantees in life. We can make educated guesses and follow trends but ultimately there will be outside factors which are completely unpredictable. The key takeaway is to think about the background of an issue, topic or situation, analyze any probability statements and verify with facts before believing everything which you read.

What is the ideal level of well-being?

The Human Development Index (HDI) is used to calculate the “Wellness Index”, also known as human well-being. The standard HDI is comprised of three dimensions:

  1. A long and healthy life;
  2. Knowledge; and
  3. A decent standard of living. 

These three dimensions are then broken down into indicators; these indicators portray life expectancy at birth, expected years of schooling/mean years of schooling, and the Gross National Income per capita (PPP US$). I decided to switch out one indicator to see if there was any way the “Wellness Index” could improve. Let me tell you, my experiment failed miserably. I switched out Knowledge (yes, I see the irony here) for various alternative indicators such as the percentage of females in science, mathematics, engineering, manufacturing, and construction. The various indicators I selected were proving only one thing… many countries do not collect data on females and their well-being.

So, scratch female empowerment. The next indicator theme I thought of was the environment. It turns out a lot of countries do not collect data on sustaining the environment. If you find this disappointing, I am nodding my head in agreement with you. How are there so many countries who are not considering women or the environment as fruitful statistical analyses?

I ended up selecting something entirely generic, hoping the results would improve from the statistics of women and the environment. They did, but not by much. It turns out exports and imports show higher levels of global human development than statistics which are on women or the environment. Unfortunately, when comparing the results of my new HDI calculation and the standard HDI calculation my results were almost 50% lower than the standard HDI “Wellness Index”. Based on the low numbers I would say exports and imports are not a good indicator of well-being; however, it is better than how statistics are tracked for women and also for the environment. 

The Analysis

I will keep this brief. I had to download and then transfer the data from http://hdr.undp.org/en/data. I separated out three indicators into three separate spreadsheets and filtered the countries into alphabetical order with their corresponding information moved too. Once filtered alphabetically, I transferred this information into my master copy (Google spreadsheet) and calculated the Indices for the various dimensions. 

After attempting to find a better “Wellness Index” I believe the standard approach is used because it yields the highest well-being for most countries. 

NOTE: Monaco and San Marino did not yield any results.

‘Uber Game’ Simulation: An Inspiration for Policy Development in Healthcare Patient Safety

Uber Game is a simulation designed to help an individual understand what an Uber driver may experience during a regular work week. There is an initial goal of $1000 to be met for an entire week of work to pay your mortgage bill. Throughout the game you are given options to select from such as buying cleaning supplies, going out with friends, driving to a destination for more fare, taking on a fare, or going home to sleep when you are getting tired. If you are energetic and willing to work longer hours you have the opportunity to complete ‘Uber Quests’. These quests can earn you a bonus at the end of the week if you meet the required amount of fares. Depending on the situation you are placed in, will depend on how much money you can make. If you fail to repair a windshield, because you want to get as many fares as possible, and it breaks, you can incur a hefty bill. 

Overall the game is effective at making you consider your options in various situations. Do you pick the potential for more money over vehicle maintenance and repairs? Do you go and socialize or continue earning money? Do you chit chat with the passenger or stay silent? With a goal of meeting your mortgage payment and unknown costs it is questionable on which option you will select. 

Playing this game allowed me to appreciate the balance of give and take. This gaming experience has positively influenced my thoughts on the governments’ facilitation of the sharing economy. Individuals are able to make money at their own pace and the public are gaining another mode of transportation in a city where public transportation can take hours and cabs cost too much. Uber allows individuals to make their own decisions on income and transportation while connecting people directly to a service. 

For those who are curious, I was able to earn $1000 in one week to pay for my mortgage. My income was $1475 before any costs were calculated. After costs I was at $1145. See the picture below for the details on my costs incurred over a week of driving time. Please also note, my income was rated at $17.09 an hour which was high above the minimum hourly wage in California.

‘Uber Game’ – Total after costs incurred.

Patient Safety in Healthcare

One public policy problem I can see benefiting from a simulation game would be patient safety in healthcare. Awareness of patient safety could be increased by using a simulation to see the perspective of all parties involved in a patient’s experience. These perspectives could be from a doctor, a nurse, another healthcare provider, and the patient themselves. Depending on which character you choose to be would depend on what actions you could take. The goal would be to have the patient released in good health, but the situations and choices made would have some form of reaction. By calculating the most common responses by people attempting the simulation the developer would have the opportunity to determine areas where new policies could be designed and implemented to design policies that could affect the direction of choices commonly made. Other data needed to make the simulation more realistic could be the ages of the characters, type of illness, and personal sources of influence (family, friends, emotional state, etc.). Patient safety is a broad topic and will have many influencing factors. A simulation of this scale would need to be detailed enough to be of use, but not too detailed as to be overly complicated.

How Much Do University of Regina Employees Earn? 2018/2019 Annual Salaries

Have you been to university and ever questioned how much money your teacher makes? Did you know many universities have this information available for the public to view? As a University of Regina student (both undergraduate and master’s) I have always been curious about just how much money these people who are moulding me into a leader of tomorrow actually earn. This post explores the 2018/2019 University of Regina Staff Salaries (click the link to see a spreadsheet of the $100,000+ numbers showing how much these knowledge bearers earn per year). If your interest has been piqued, please continue to read. If your interest is not piqued, I am not sure why you read this far… but you might as well continue.

The Spreadsheet

The “Original 2018/2019 Data” tab is the initial spreadsheet I was working with. It is structured to detail the names of University of Regina employees with salaries over $100,000 for 2018 and 2019. Employee names are listed alphabetically and the two years are divided into further categories of: 

  1. Salary: what the employee actually earns for the year before incentives and is determined based on experience, qualifications, and reputation.
  2. Administrative/research stipend: the administrative stipend is earned by the Head of an academic department and is based on length of time in the role over one month; and, the research stipend is given to research chairs, and is based on qualifications, experience, and reputation.
  3. Market supplement: Market supplements are an incentive given to academic staff to either entice them into working for the university, or to keep them working at the university. The idea behind the market supplement is that wages at the University of Regina may not be competitive with other universities and the University of Regina then has the opportunity to make itself a competitive option.

At the far right side of the “Original 2018/2019 Data” tab spreadsheet you will find the totals for 2018 and 2019 salaries. 

Error Check

To ensure accurate comparison of total data I created a second tab labeled “Data”. In this new tab I added a column beside each of the yearly total columns; they are labelled “2018 Verification” and “2019 Verification”. Using the calculation “=sum(F2, H2, J2)” for the 2018 total, and then dragging the formula down, I was able to find the total values for each employee. I then repeated this process for the 2019 information. Once I had these new total columns I added a further two columns labelled “2018 Match/No match” and “2019 Match/No match” where I input “=IF(L2=M2,”Match”,””)” to determine whether or not the original data matched my new calculations. These columns showed an inaccurate sum found in cell “Q201”. This one inaccuracy of a couple thousand dollars does not concern me for this one spreadsheet. If this one error were to skew overall data and sway the public into feelings of mistrust I would be concerned. Regardless of the significance of the error, I still believe the error should be noted. 

Exploration of the Data

From the data I can see recruitment efforts (incentives) per person and new employees without incentive increased in 2019 as compared to 2018; as well as hiring new employees in general without incentives. Employees who worked in 2018 received little incentive to stay in 2019.

To see how much money the University of Regina puts towards market supplements I totaled both the 2018 (see “J542”) and 2019 (see “K542”) results. In 2018 over $1.03 million was spent and in 2019 over $1.15 million was spent on market supplements.

The simple growth rate can be calculated as =(N542-L542)/L542 * 100, and totals 9.4%.

At the bottom of the spreadsheet I added a row called “Totals”, here you will see I totaled each of the columns. The data represented here shows there has been an increase in base salary expenses from 2018 to 2019 of over $5 million. The trend continues for administrative expenses and market supplements. The higher expenses could be due to many things. For example, the University of Regina could be focusing on gaining higher caliber academic instructors and require more competitive wages to entice these highly sought after individuals.

Two determine the average salary of an employee I have included a chart that shows the mean, median, mode. I also included the minimum and maximum wages in the spreadsheet. It should be noted the outlier maximum is the President and Vice-Chancellor for the University of Regina. See tab “Measures of Central Tendencies” for specific input calculations.

MEASURE2018 TOTALS2019 TOTALS
Mean$135,473$137,316
Median$130,005$131,972
Mode$106,571$103,385
Minimum$100,007$101,196
Maximum$365,998$388,025

The average did not decrease if being calculated as a mean or a median from 2018 to 2019. If calculating as a mode average then there was a decrease from 2018 to 2019 of $3,186.

Columns which would help to analyze the information in this spreadsheet are “Employment status”, “Employment length”, “Education Status”. Having this information would help establish salary range for years of employment. The effect that the level of education has on the amount the university will pay and will help determine if the employee is currently employed or has fallen below the $100,000 range. This should be released in the salary data as it would help potential new hires know the range they can expect an offer at depending on their personal qualifications. These new columns are identified on the “Data” tab.

Final Thoughts

The University of Regina is a non-profit that receives funds from the provincial government. As a bulk of the funds are from public finances the money used should be documented. Showing the salaries of any employees earning over $100,000 is integral in maintaining transparency with the public and how their money is being used. While I can see there may be instances where the privacy and safety of an individual may take precedence over publication of their name, this will not be of concern to the majority of people whose salaries are displayed. With these salaries being published online the public is able to see transparency from the University of Regina in their financial records. By being available online, instead of only being accessible in the library with a paper copy, the university is showing they are able to evolve with the times and utilize technologies.

A drawback from individuals being able to request their name be redacted is the information is not complete; however, a few names out of over 500 should not skew the results too severely.

If you made it this far, I hope you were able to learn something through this post.

The Importance of Statistical Analysis in Good Public Policy

Statistical analysis is essential for public administration to create good policies that are based on impartial and trustworthy information. The public should be able to trust data being collected, interpreted and then used to create positive change for the public by the public administration, is done ethically and accurately. The use of data collected from the public needs to focus on the areas where services are lacking. By using statistical analysis the public administration is able to fill gaps in public services with the implementation of new programs based on public policies founded in accurate interpretation and presentation of current conditions. 

Enterprise 2.0: The Dawn of Emergent Collaboration – Article Review

By Hannah Gregory March 14, 2019

Andrew McAfee wrote a concise and compelling article called “Enterprise 2.0: The Dawn of Emergent Collaboration“.

An overview of the article:

This article explores McAfee’s investigations into informal, spontaneous, less structured and knowledge-based work of companies. He believes blogs, wikis, and tagging should be implemented into businesses for more effective and creative collaboration.

  • Channels & Platforms: channels are where digital information is created such as emails and instant messaging; Platforms are developed by small groups but can be made widely accessible, such as intranets, corporate web sites, and information portals.
  • Enterprise 2.0:
    • Search – easily find key words
    • Links – more readily available based on frequency of use
    • Authoring – desire to be heard, contribute knowledge, provide insight and share experience
    • Tags – create labels to create ease of searching
    • Extensions – “I see you like this, here is something else you may like”
    • Signals – alerts to new content
  • Ground Rules: new platforms must be easy to use and should not have preconceived notions attached; a blank slate.
  • Role of Managers in Enterprise 2.0:
    • Create a receptive culture;
    • Provide a common platform for effective collaboration;
    • Have an informal roll-out of the new platform;
    • Provide managerial support.
  • Threats to implementing Enterprise 2.0:
    • Busy workplace and workers – reluctance to move to a new way of thinking and doing.
    • Use of the new platform – if used there is less ability to control the content shared, this may have negative consequences.
  • Other points of interest:
    • Wikipedia is surprisingly accurate and show how effectively people can collaborate ideas.
    • Tagging and bookmarking applications such as Pocket (archive version del.icio.us)

Relevance to Public Sector Management:

  • ease of project sharing
  • timely responses
  • ability to collaborate more effectively
  • save on resources as less manpower would be sent responding to emails
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