Oil price in May

In 1 minute, the BLS will report a number of producer price indices, icluding  the price index of crude petroleum.  Oil price in May was fluctuating around $100 per barrel. In April, the average price was closer to $110. Hence, we expect a dramatic fall in the oil price index from its current level.

Update. June 14, 14:33
As expected, the oil index has fell from 309.8 to 275.8, i.e. by ~11%. In June, this trend is extended. As we forecasted, oil price and thus the price index of motor fuel will be decreasing into 2011. It may be the cause of employment-population ratio growth and fall in the rate of unemployment. 

The age of the highest income inequality

In Figure 1, we present the evolution of age dependent Gini ratio in the U.S. as reported by the Census Bureau. These estimates were obtained during the annual current population surveys. It is instructive to compare Gini ratios in various age groups.

Before 2003, the group with the highest ratio was between 55 and 64 years of age. Currently, the highest ratio belongs to the youngest (15 to 24 years of age) age group at the level of 0.51.  The smallest Gini ratio (~0.43) is shared by the age group between 25 and 34 years of age and the oldest group over 75 years.  The former group is characterized by a very stable Gini ratio between 1994 and 2009.

Figure 1. Evolution of Gini ratio in various age groups as reported by the Census Bureau.

Angry Bear on the relation between S&P 500 and GDP

A month ago Mike Kimel had a post on Angry Bear dealing with the relationship between the S&P 500 market index and nominal GDP. His naive regression showed correlation of ~94%. One should not forget that Clive Granger introduced the idea of spurious regression 30 years ago. (A surrogate Nobel Prize for this finding in 2003.) This correlation is a good example; both variables are nonstationary, I(1), and are not cointegrated. Hence, the above correlation is spurious.

Actually, the S&P 500 returns are coitegrated with the change rate of real GDP per capita and this correlation is not spurious as shown in this blog and our paper on S&P 500.

On decreasing income inequality in the US

Five years ago I published a working paper arguing that the measures of income inequality derived from the IRS data are highly biased as estimated from a varying share of population. Physics students are well aware that no conservation laws are applicable to open systems with unknown exchange with environment. For economists, it is not a good hint and they continue to present lots of inequality estimates associated with highly fluctuating population basis.  Figures 1 shows the evolution of the share of population with income and Figure 2 depicts the share of personal income in real GDP as determined by the IRS and by the Census Bureau during the Current Population Survey. One can see that the IRS population basis varying around 60% from the level of working age population and the CB covers around 90% of the population. Moreover, the share of personal income in real GDP is only 55% for the IRS and 70% for the CB. For a physicist, the estimates based on the CB data look more reliable than those from the IRS data. It should be noted, however, that people without income have to be included in the estimates of inequality to cover the whole working age population. (We have estimated the Gini ratio for the whole population.)

The Census Bureau has been reporting detailed results of the CPS since 1994. Figure 3 depicts the evolution of Gini ratio for the U.S.  It has been decreasing since 1994 with a clear minimum in 2007.   We also included the share of population without income in order to illustrate the decreasing basis for the Gini ratio estimate. When these people without income are included the Gini estimates should increase and the inequality should be slightly higher.    

Figure 1. Share of population with income as estimated by the IRS and Census Bureau.

Figure 2. Share of personal income in total GDP as estimated by the IRS and Census Bureau.

Figure 3. Evolution of Gini ratio and the share of population without income as estimated by the U.S. Census Bureau.  

People without income. Worrying trends.

The U.S. Census Bureau conducts a Current Population Surveys (CPS) every March. The CPS includes a number of questions related to personal incomes. A new questionnaire   was introduced in 1994 and many detailed tables have been published since. People not reporting any personal income during the previous year are considered as people without income. We have plotted the share of people without income as a function of time for various age groups. In all groups, the share has been growing over time. The most prominent increase was observed in the youngest group between 0 and 9 years of work experience marked in Figure 1 as “5”. (All other groups are marked by the relevant central points of 5 years bins of work experience.) The share grew from 25% to 38% between 1994 and 2009, i.e. by 0.9% per year, as the slope in Figure 1 shows. This is a worrying tendency.  

Young men do not feel real economic growth

The U.S. Census Bureau publishes many tables on income distribution. One of many reported features is the evolution of the age and gender distribution of real incomes. In several figures below we illustrate the time history of real mean income since 1974. The most striking feature is that young men between 15 and 24 do not see any improvement in the mean income – only $34 per year. Despite the level of mean income for young women is lower than that for young men the former experience a three times faster growth.   

An interesting feature, which is likely the legacy of the 1960s and 1970s, is a much faster growth of mean income for men over 65 years of age. Other age groups are characterized by steady improvements in income inequality.  The most successful women (in relative terms) are between 25 and 34 years of age. Their mean income has been increasing by ~$400 per year compared to only $70 per year for men of the same age. If this tendency holds beyond 2040, the mean income trends will intercept. For other age groups it may happen after 2050.

We have described these and many other phenomena of personal income distribution in our previous posts and papers.

Shimmy in economic gear. How far is the U.S. economy from a runaway?

Shimmy is a well-known mechanical effect in aircraft landing gear. During landing and take-off, the nose wheel oscillates about the vertical axis, sometimes with increasing amplitude. In the case of severe resonance oscillation, shimmy may result in the wheel  destruction.  Instructively, shimmy usually occurs in a specific band of aircraft velocities.  (A much safer but typical case of shimmy is observed in a shopping trolley.) The shimmy effect is well known and relatively well understood and modelled, although not completely.  

As an economic analogue of shimmy, we propose to take a look at the current oscillations in commodity prices.  Is it actually an economic shimmy? In several figures below we present the evolution of relative prices, pi, of selected commodities, iPPI. In order to remove the base effect we calculate the deviation from the overall PPI, PPI, and normalize it to the PPI:

pi(t)= (PPI-iPPI)/PPI

where i corresponds to iron&steel, gold ores, crude petroleum (domestic production),  copper ores, aluminium base scrap and grain. Four from these six basic commodities demonstrate a clear start of shimmy around 2005.  Aluminium base scrap and grain had higher oscillations in the past, but can be also characterized by an elevated volatility during the past 5 years.

Overall, a higher volatility is not a surprise for the market but since 2005 is has a coherent driving force behind all commodities. It is likely that there is a positive feed back in the loop of commodity pricing with money flooding into the commodity market without any restriction. For a nose wheel, similar mechanical feedback leads to shimmy and aircraft accidents. For the U.S. economy, one can expect a price runaway (gold is a candidate) if the positive feedback observed since 2005 is further retained. How far is the current situation from an accident?