Oil price will be decreasing through the rest of 2011. CPI vs core CPI

The U.S. Bureau of Labor Statistics has reported the estimates of various consumer price indices for May 2011. According to our schedule, we have to revisit the difference between the headline and core CPI only in July 2011. However, the new estimates likely manifest a short-term turn in the difference which is worth mentioning.

Figures 1 and 2 briefly introduce our concept of sustainable (quasi-linear) long-term trends in the difference between the headline and core CPI in the U.S. There were two clear periods of linear behaviour: between 1981 and 1999 and between 2002 and 2009. A natural assumption of the future evolution of the difference was that a new trend has to emerge around 2010 after a short period of very high volatility. 

Figure 1. Linear regression of the difference between the core CPI and CPI for the period from 1981 to 1999 (R²= 0.96 the slope is 0.67) and linear regression of the difference between the core CPI and CPI between 2002 and 2009 (R²=0.91, and the slope is -1.59). 

Accordingly, Figure 2 illustrate this hypothesis with the reversion (like mirror reflection) of the trend between 2002 and 2009. We expected this new trend with a positive slope to be developed between 2008 and 2011, as shown by the solid red line. Against our expectations, after a year of “right” evolution in 2010 the difference fell to the zero line again.

Figure 2. The evolution of the difference between the core and headline CPI since 2002.

The May 2011 estimates suggest the end of the fall in the difference and a pivot to the long-term trend (solid red line in Figure 2). Figure 3 depicts the most recent period with a clear turn in May 2011. Thus, we expect the difference will return to the long-term trend by the end of 2011. This return should be accompanied by a remarkable drop in the index price of energy which was the driver of the headline CPI in 2011.  Hence, ol price will be falling during the rest of 2011.

Figure 3. The evolution of the difference between the core and headline CPI since 2010.

Recession? In 2012-2013!

Is a new recession coming? This is currently one of hot questions in economic blogosphere. We expect it in 2012 and 2013. Our prediction is based on a quantitative growth model.

The first post in this blog was devoted to real GDP growth and its relation to the change in a specific age population. We have presented a number of growth models for various developed counties and validated them by new data. The original model  for the U.S. links the change rate of real GDP per capita, dlnG/dt, to the change in the number of 9-year-olds, dlnN9/dt, and the reciprocal value of the attained level of GDP per capita, A/G:

dlnG/dt= A/G + 0.5dlnN9/dt (1)

where A is an empirically derived constant. One can rewrite (1) relative to N9 and obtain the following equation in a discrete form:

N9(t) = N9(t-1)[2.0( dlnG - A/G) + 1] (2)

where dt=1 year.

Figure 1 presents the result of the N9 modeling between 1960 and 2005. The agreement between the measured and predicted N9 is excellent and we have shown that these time series are cointegrated. Our model has passed all rigorous econometric tests and can be used for GDP forecasts when the quality of population estimates is good enough.

Figure 1. Measured number of 9-year-olds in the U.S. and that predicted from real GDP per capita.

After 2003, the U.S. Census Bureau has been publishing extremely smoothed and thus biased population estimates, which are not appropriate for the purposes of real GDP prediction. This unfortunate situation might be resolved only after the 2010 census. We do not have quantitative estimates of the 9-year-old population yet but can use the age pyramid presented in Figure 2, which we borrowed from the U.S. Census Bureau.

At first glance, the 2008-2009 recession was induces by a negative value of dlnN9/dt, as one can judge from the number of 12- and 11-year olds. These people were 9-year-olds three and two years ago. One should not forget that younger cohorts accumulate more and more people with time due to intensive immigration and thus the numbers of people above 12 years of age are all biased up relative to the younger generations.

 

The number of 10- and 9-year-olds is slightly higher than in two older cohorts, and thus, we observe a period of positive real economic growth in 2010 and in 2011(the growth rate of real GDP per capita is about 1% per year lower than that of the overall GDP). However, the fall in N8 and N7 (male) almost guarantees a new recession in 2012-2013. Hence, a new recession is around the corner. We will present a more accurate quantitative estimate when the 2010 census data are available.

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.