Archive for January, 2010

Household Savings Rates, Longitudinal and Cross-Sectional

January 9, 2010

Recently, I asked a question on one of the discussion groups I subscribe to about any indicators in cultural changes they thought were significant.  The list owner suggested that  I look at changes in household savings rates, and how, in the U.S. it was increasing of late.

The implication was that with the current recession (which officially began in December, 2007), people were changing the way they thought of how they handled their money, and that was a change in cultural attitudes toward consumption, and materialism in general.  It seemed that this was a reasonable assumption, so I went off on a merry chase.

The first stop was the U.S. Federal Reserve, Saint Louis Regional Office. They collect and analyze data from many sources, but primarily from the U.S. Bureau of Economic Analysis, which, in turn is an aggregator of data from many other agencies.

But if this were an indicator of a change in American attitudes toward consumption and materialism, I wondered, what would a snapshot of other countries in the world look like?  Are some countries more prone to save their money than others?  And if so, what would those countries be?

Using just one variable doesn’t explain cultural habits toward savings, but it does give some clue about how, collectively, some countries behave differently than others.

So, to begin, here is a trend line of U.S. household savings rates.  There are two graphs; one by the Federal Reserve on a monthly basis from 1959 to the present, and one I annualized by averaging the months for each year to produce a trend on an annual basis.

The St. Louis graph below is a bit blurry, and the monthly trend data make it look rather jagged.  Still, you get the idea.  The vertical gray bars are recession periods.

For a clearer picture, Look Here

The next graph is the annualized trend from the raw monthly data, from 1959 to 2009.

You can clearly see that the savings rate is on the upswing, but it is still far below the overall average.

Looking at cross-sectional data by country:

The next step was to look at cross-sectional data.  In this case, I went to the OECD (Organization for Economic Cooperation and Development site and looked at several countries.  The list is not exhaustive, consisting of only 24 countries.

There was a trend line for each of these countries from 1992 to 2009 with projections to 2011.  In order to get a sense of the “character” of the savings habits in each of these countries, I averaged all the years and then sorted them on the average.

You can see the result below:

NOTE:  You can get a bigger, more readable picture by clicking on the graph.

Who saves the most?  Spain, followed by Belgium, France, Italy, Switzerland and Germany.

Who saves the least?  Denmark, followed by Australia, the United States, the Czech Republic,  Norway and the Slovak Republic.

Technical Notes:

I don’t think of this as a technical blog, but an informational one.  Sometimes, though, technical considerations cannot be avoided.  This is one of those times.

Here are some main considerations you should keep in mind while looking at these data:

  • Standards, quality control, and reliability of the data (more about that later)
  • The variability over time.  Some countries have very erratic trend lines and the average I used smooths all of those out.  Below are some illustrations of variability measures.

Standards, quality control and reliability of the data:

In general, the quality of  data since 1992 in the OECD has been very good, except for some former Soviet satellites.  The reason is because they adopted the uniform System of National Accounts since the breakup of the USSR.  Some of the former Soviet satellites are having difficulty reporting according to the SNA, mainly because of budget constraints.

Likewise, the data from north America has been improving rapidly.   This is because of the adoption of the National Income and Product Accounts system (NIPAs).  The result is that all product, income and expenditures are counted using the same criteria in Canada, Mexico and the United States.

In addition, there has been an ongoing effort to “harmonize” the SNA and NIPAs systems so that all items in the accounting systems mean the same thing on a worldwide basis, much the same way that the International Standards Organization (ISO) has standardized everything from Internet address, to the thread sizes of nut and bolts to the sizes of paper sheets.

Variability of the data:

Even though the graphs above show a ranking of countries in terms of their long-term average, the variability over the term of the trends can be considerable.  Generally, they can be categorized as follows:

  • The savings rate is high and the variability is erratic
  • The savings rate is high and the variability is small
  • The savings rate is low and the variability is erratic
  • The savings rate is low and the variability is small

Now, I’m afraid I have to get a little technical.  But not much…

Two of the most basic measures in statistics are the mean (average), and the standard deviation.  The average isn’t difficult; people use it all the time:  Add up the numbers and divide by the number of numbers.  So, the average of 5 and 3 and 7 is (3+5+7)/3  (there are three numbers) and the result is 5.  So far, so good.

The standard deviation is a little more tricky.   I launched into an explanation and then thought better of it.  Let’s just say it is an average of all the deviations from the mean.  If you want more detail, there is a very good explanation on Wikipedia.

Once the standard deviation is computed, then it can be taken as a percentage of the mean.  This is called the coefficient of variation, or C.V.  The advantage here is that it normalizes the variation regardless of the size of the numbers in the data.

Below is a graph of the same countries sorted on the coefficient of variation.  You can see which countries have been the most and least erratic over the trend.

If we plot the savings rate against the variability we can get a picture of those countries in each quadrant:  Low savings, erratic, high savings, erratic, low savings stable, and high savings stable.  The graph has the names of each of the countries at each data point, and the axes splitting them into the four quadrants.

If you can draw any coherent conclusions from this, I would sincerely welcome them.