Annual Data Is Not Enough!

I confess. I am a bit of a geek for data – meaningful data to be precise. Often I come across reports that provide only annual data. While I typically find such reports to be interesting and well done and well-representative of the year in question, I find the impact of such data to be lacking more times than not.

I prefer annual data to not only capture the year’s activity but to be compared to previous years. Comparative data allows us to see what has changed, if progress has been made or conversely if the comparative data points to troubling news.

A while back I sat in on session of stakeholders trying to help an organization write their annual report of work and progress. Unfortunately, one year’s data tends to be silent on progress. One annual data set spoke to activities and impacts of a program I knew quite a bit about, including its historical data.

The annual data did identify strong information about the accomplishments of the program in that 12 month period. Folks lauded the program for its innovations and how it was helping disadvantaged people and there was a but of back patting going on. While being proud of annual accomplishments is not a bad thing in and of itself, I asked this question: How does this data compare to previous years?

No one knew. Due to my familiarity with the program, I knew that this year’s data identified a decline in the numbers of people helped and offered no insight into any material changes in the program itself. Had it changed its intake criteria; were the costs increasing and/or was the funding provided up or down? I had a host of other questions that required a look at past years, but you get the gist.

Given that this group wanted to demonstrate its effectiveness and impact, I think it would have been prudent (and transparent) to identify the trends that one typically can identify from data collected over time. Understanding where impacts had increased and where impact was declining are fundamental to community change aspirations. Without such comparable data, the risk is we just celebrate whatever it is we did in the given year.

I had been invited to the table as an advisor. So, I first acknowledged how important the work of this program was and then suggested it also deserved comparative analysis so that we could make sure we retained what was working and then devote attention to where things were not improving.

The group facilitator thanked me for my input and then turned to the person next to me for his comments. His remarks celebrated the success of the program. More pats on the back about a great program that was not advancing but rather declining in impact. The next person and the one after that joined the celebratory bandwagon. It was, for me, disheartening.

Not only is comparative data necessary to assess progress or change, we also need to look at more than one data set to truly understand what is going on. Let’s look at some data charts from the Edmonton Social Planning Council’s Tracking the Trends 2020, which typically offers strong comparative data.

There is a lot of data in this report about trends related to Cost of Living, Income, and Poverty, and in each data chart the Council does a pretty good job of describing the trends. Users of this data need to go further though if we are serious about fully understand what is going on.

“Since 2002, the average weekly cost of a nutritious food basket
for a family for four increased by $110.73, an increase of 83.8%” (page 40)
“The average monthly rent for a two-bedroom apartment in Edmonton CMA in October 2019
was $1,257. That is an increase of 109.2% since 2000″ (Page 41).

Both carts indicate significant rises in the cost of food and housing, which many of us have first hand experience. What is not evident, though, is if this data has been adjusted for inflation and been displayed in 2019 Constant Dollars. As well, the charts do not speak to the actual impacts of different cohorts of income earners.

“From the years 2000 to 2018, the median after-tax income after inflation increased by 24.9% for couple
families, 31.9% for lone-parent families, and 27.1% for single adults” (page 56).

In constant dollars, these gains in after-tax income demonstrate at best marginal progress. For Couple Families the overall percentage gain over 19 years represents an average gain of 1.3%; for lone parent families, 1.7%; and for Single Adults, 1.4%. Is that good? Does the data represent a vibrant economy?

“After adjusting for inflation, the top 1% of tax-filers saw a 27.6% increase in the real after-tax incomes
compared to a 7.0% increase for the bottom 99% of tax-filers from 1982 to 2017. The top 0.1% of tax-filers experienced a 56.8% increase in their real incomes from 1982 to 2017, compared to a 4.3% increase for the bottom 90% of tax-filers, and a 3.2% increase for the bottom 50% of tax-filers” (Page 62).

As you can see the average income data in the chart before this one paints a simple picture. In reality, the chart above indicates significant gains in wealth for those already wealthy. For the bottom 99%, their gain of 7% can hardly be identified as a gain in wealth. What does this data say about equity in what many would call a vibrant economy?

The questions continue to mount when one considers the Living Wage data (below) that indicates a decline in the living wage. Does the data mean that despite the steady increase in housing and food, living wage earners are getting by on less income?

“Edmonton’s living wage for a dual-income family of four in 2019 is $16.51.
This is up $0.02 from 2017, but down $0.85 from the first calculation in 2015″ (Page 48).

Then there is all the poverty data that indicates that the numbers of people living in poverty has decreased since 2000 (although it has increased since 2013)? Given the previous data sets, how can it be that poverty is not increasing more profoundly? Are we using the right cut off lines for poverty? Have transfer payments increased markedly

If we look at the poverty rates by family type, the data indicates Couple Families in poverty have decreased from 9.6% of the population to 6.9%. Single people saw a fraction of improvement since 2000, and Lone Parents have the same rate of poverty in 2018 as they did in 2000 (Page 73). And yet another chart indicates “the child poverty rate is down 6.5 percentage points from 22.7% in 2000” (Page 77).

That said, declines in percentages may not indicate a decline in numbers. In 2001, Edmonton’s population was 657,000 people and the number of people living in poverty was 98,600, a poverty rate of 15%. In 2019, the poverty rate had decreased to 13%. Good news, right? In 2018 Edmonton’s population was 972,000. The poverty rate of 13% indicates that just over 126,000 people were living in poverty in 2019. That’s an increase of 28,000 people or 28%. Not good news, is it?

Could it be that our desire to decrease poverty directs our eyes to the data that tells us we are winning the battle when the reality is the number of folks in poverty has increased markedly?

Average incomes are rising yet poverty is actually increasing. Those who have done well in the past financially are doing even better 20 years later while 99% of the population appears to be just getting by. And then there is the limitations on our thinking when we just look at the poverty- line. The Canadian Payroll’s annual survey indicates that in any given year between 45 to 50% of workers are living pay cheque to pay cheque and that losing that income for even a few weeks can spell ruin for them – causing homelessness or increased credit card debt, not to mention the mental health stresses of not having enough money to live on. Half the population living pay cheque to pay cheque indicates that there are hundreds of thousands of Edmonton workers living on the edge, living with significant economic vulnerability. What are we going to do about that?

My analysis provides more insight than just looking at chart by chart in isolation, but more analysis is required to fully understand what is going on. We may wish to consider breaking through our annual mindset and our desire to confirm our own biases about the progress we are making.

Data can help us get real about what we need to change.

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