WATCH OUT FOR MISLEADING DATA!

The Social Security Administration, the agency managing the largest Ponzi scheme in the world that some would call the "mother of all Ponzi schemes", has just released data on the wage and income compensation paid to workers in 2014. The data is somewhat interesting, mostly from a curiosity angle since its analytical value is minimal. Nonetheless, I think it's worthwhile reviewing it, and pointing to its limitations since the data may easily lead the unwary to incorrect conclusions. In fact, I've seen a couple of references to this data drawing incorrect comparison to poverty levels.

The data shows the total wage income of individuals that is subject to social security payments. You can look at the raw data for last year here: https://www.ssa.gov/cgi-bin/netcomp.cgi?year=2014. Data for previous years is also available in the same web page.

The chart to the right shows on the horizontal axis the wages and income paid to all workers in the U.S. in 2014, in ranges of five thousand dollars up to those who earned $200,000. From that point the data are grouped into $50,000 ranges up to the million dollar mark, etc. On the vertical axis is the number of workers falling within each range. We can see the first point on the left represents 22.5 million workers, earning each under $5,000 a year; this is just over 14% of the workers. Wow, is the data telling me that one out of seven workers in the U.S. made less than $5,000 last year? Things are pretty bad then.

Well, not really. This is where the data is misleading if it's interpreted wrongly. First of all, the data includes all workers; teenagers working part time in the Summer, retired workers who also work part time in temporary jobs, housewives and others who take temporary jobs around the Christmas season, etc. Secondly, many of these workers are not the primary income earners in a household, they may have a spouse who is the main income earner in the family. That is, this is not the normal household or family income data that is more meaningful for analysis. For instance, the median household income is currently about $53,000, but using the Social Security data we find that the median compensation per person is about $44,500- the difference is made by other working persons living in the same household who presumably contribute to the household's economic wellbeing.

Comparisons to other data, such as poverty levels for instance, should not be made using these data. Poverty levels are usually meaningful only in terms of the number of persons who live within a family, while the social security data above does not provide a hint whether the person is living alone or with other members. If one were to use these data to estimate the number of people who are below the poverty level, we get that about 40.5 million individuals fall below the poverty line of $11,670 for one individual- this is 25% of the wage earners. But this is the wrong conclusion, as I said, because that individual may or may not be part of a larger family.


However an interesting comparison is to see how the data changes over time. The chart to the right displays the changes in the number of wage earners over the ten years ended in 2014. The horizontal axis displays data for individuals making less than $100 thousand, split again in groups of $5,000 each. The bars highlight changes over two 5-year periods; one is the change from 2004 to 2009 shown in the blue bars, and from 2009 to 2014 in the red bars.
It is immediately apparent that the number of individuals earning less than $35,000 fell over the ten-year period; it fell by nearly 8.4 million workers (although it's hard to discern that from the graph alone.) The graph suggests that most of that change occurred between 2004 and 2009, when in fact the number of individuals making less than $35,000 fell by 6.9 million.

Another caution with the data can be inferred from the last paragraph and chart. Yes, the number of workers earning less than $35,000 fell sharply; this is not necessarily because their incomes improved but because many of them lost their jobs as a consequence of the 2008-2009 recession.