A few days ago I tweeted about data manipulation, and how it may appear that Canada’s household debt levels are too high. You can check out my “tweet-storm” here.
I thought I would also publish a post about data manipulation and do a quick analysis comparing national household debt to quality of life measures for member states of the Organization for Economic Co-Operation and Development (OECD). Among it’s primary tasks as a forum to stimulate economic development, the OECD also collects and disseminates a wealth of data about the economic performance of its member states, and is often the main source for national household debt to income data and information.
Many who keep up to date with the housing market in Canada and its cities have seen this chart before –
Figure 1 – Household Debt to Disposable Income, 1995-2014
Based on this chart, Canada has the highest household debt to disposable income when compared to some of the most industrialized and wealthiest countries in the world. And to further intensify fears of a housing bubble, Canada’s household debt levels have been growing, in contrast to these industrialized countries where debt levels have stagnated or have been declining in recent years.
BUT keep in mind that I individually selected which countries to display in Figure 1. I made an identical chart (shown in Figure 2), but I chose a few other industrialized countries to compare to Canada.
Figure 2 – Household Debt to Disposable Income, 1995-2014
Evidently Canada has the lowest household debt to income levels relative to countries like Denmark, Norway, Sweden, Switzerland, Australia, Netherlands, etc.
When you compare the 2014 household debt levels of the OECD member states I have shown in the Figure 1 and 2, Canada’s household debt levels are somewhere in the middle.
Figure 3 – Household Debt to Income Levels, 2014
The next chart shows the household debt to income for all the OECD member states.
Figure 4 – Household Debt to Income Levels (all OECD member states), 2014
What I find most interesting is how some of the wealthiest and most industrialized countries in the world appear to have the highest household debt levels.
I wanted to explore that notion further so I decided to correlate the debt level data with national quality of life or Human Development Index (HDI) data. HDI data is a composite of three major quality of life indicators – Life expectancy, mean years of schooling, and Gross National Income per capita, and it is collected by the United Nations Development Programme. It is a measure used to rank countries based on human development, and not just economic growth alone. The HDI data was collected from here.
Figure 5 – Household Debt to Income levels and Human Development Index measures of OECD member states, 2014
Interestingly enough, the wealthier, and more developed and industrialized the country, the higher its household debt levels (with a few outliers like Portugal, Slovenia, Germany and the US).
Why is that?
Wealthy, developed and industrialized countries are likely to have resilient financial institutions, and their residents are generally highly financially literate. As I’ve written previously, some of the world’s safest banks are from countries with higher debt levels. Debt in these industrialized countries is used to create wealth. Investment in education, a house, and/or a business, combined with a higher cost of living often requires a line of credit or a mortgage. Borrowing money to make money is the principle here.
The final chart shows a scatter plot of household debt to income correlated with human development index data. Applying a logarithmic trend line, it is evident that there is a modestly strong positive correlation between a nation’s debt levels and quality of life (R² = 0.6085).
Figure 6 – Correlation Analysis of Household Debt to Income levels and Human Development Index measure of OECD member states, 2014
People who work with data a lot know about manipulation very well. It is important to question what you see and hear, and supplement information from a variety of sources.
I hope this post was helpful in explaining about data manipulation, and the hazards and vulnerabilities of it.