The rate of unemployment in December is 6.7%, 0.6% lower than in October

In the USA, the rate of unemployment in December 2013 is 6.7%. It is 0.6% lower than in October. According to our model, this dramatic fall during the last two months was expected. Actually, two years ago we predicted the level of unemployment to fall between 6.0% and 6.4% by the end of 2013 or the beginning of 2014. 

So, we have been reporting on the decline in the rate of unemployment in the US since the beginning of 2012. We predicted a dramatic period of unemployment falling down to the level of 6.2% (=-0.4%) in the fourth quarter of 2013. This prediction was made after we accurately forecasted (on March 1, 2012) the rate of unemployment in the US to fall down to 7.8% by the end of 2012. Here we update our model and present the evolution of the unemployment rate in the second quarter of 2013. Overall, the measured rate has been following our prediction. We foresee the rate to fall down to 6% [±0.4%] in the fourth quarter of 2013 or in the first quarter of 2014.

In 2006, we developed three individual empirical relationships between the rate of unemployment, u(t), price inflation, p(t), and the change rate of labour force, LF(t), in the United States. We also revealed a general relationship balancing all three variables. Since measurement (including definition) errors in all three variables are independent it may so happen that they cancel each other (destructive interference) and the general relationship might have better statistical properties than the individual ones. For the USA, the best fit model for annual estimates was a follows:

u(t) = p(t-2.5) + 2.5dLF(t-5)/dtLF(t-5) + 0.0585   (1)

where inflation (CPI) leads unemployment by 2.5 years (30 months) and the change in labor force leads by 5 years (60 months). We have already posted on the performance of this model several times.

For the model in this post, we use monthly estimates of the headline CPI, u, and labor force, all reported by the US Bureau of Labor Statistics. The time lags are the same as in (1) but coefficients are different since we use month to month-a-year-ago rates of growth. We have also allowed for changing inflation coefficient. The best fit models for the period after 1978 are as follows:

u(t) = 0.63p(t-2.5) + 2.0dLF(t-5)/dtLF(t-5) + 0.07; between 1978 and 2003

u(t) = 0.90p(t-2.5) + 4.0dLF(t-5)/dtLF(t-5) + 0.30; after 2003

There is a structural break in 2003 which is needed to fit the predictions and observations in Figure 1. Due to strong fluctuations in monthly estimates of labor force and CPI we smoothed the predicted curve with MA(24).

The structural break in 2003 may be associated with the change of sensitivity of the rate of unemployment to the change of inflation and labor force. Alternatively, definitions of all three (or two) variables were revised around 2003, which is the year when new population controls were introduced by the BLS. The Census Bureau also reports major revisions to the Current Population Survey, where the estimates of labor force and unemployment are taken from. Therefore, the reason behind the change in coefficients night be of artificial character - the change in measuring units.

Figure 1 depicts the predicted and observed in the rate of unemployment since the beginning of the 1960s. Figure 2 depicts the observed and predicted rate of unemployment since 2006, including  a forecast for the next 12 months. The model showed that the rate will fall to 6.0 % by December 2013. For 114 observations since 2003, the modelling error is 0.4% with the precision of unemployment rate measurement of 0.2% (Census Bureau estimates in Technical Paper 66). Hence, one may expect 6.0% [±0.4%]. So far, our model was accurate in major changes, with all observed short-term deviations returning to the predicted curve.

 

Figure 1. Observed and predicted rate of unemployment in the USA. 

Figures 2. The predicted and observed  rate of unemployment since 2006. We expect this rate to fall down to 6.0%  (and likely below) in the beginning 2014. The red and  black curves have to intercept somewhere in 2014.  

Lie, big lie, and increasing income inequality

Economists are not physicists. Most visible economists tend to manipulate data in a way to be more visible by obtaining politically biased results to please lay public. Income inequality is the hottest topic of 2013. Almost all economists focus on increasing income inequality as reported by the BEA. The top 1% snatch more and more money from poor working people. When taking a closer look, the BEA tells a different story, however. Figure 1 displays the cumulative increase in GDP, Gross Personal Income (GPI), and Compensation of employees (CE) since 1929. All curves are normalized to 1960, i.e. all cross 1 in 1960.  The most remarkable feature is that the GPI has been growing much faster than GDP since 1977 (by the way, the year of dramatic changes in income statistics).  Therefore, the share of personal income has been growing. This tendency is still on and one can expect further gains in personal income.

The share of labor money or compensation of employees in the GDP has not been changing much, however. The working population gets practically  the same share of GDP since 1929. So to say, the labor part of production is rock solid. And the capital part of production has been melting out since 1977. It is not a surprize that the increment in personal income obtained by the top 1% is extracted from the capital part of GDP or Gross Domestic Income, which this 1% ... owns anyway. Figure 2 gives some more details on the period since 1960.

The distribution of income reported by the Bureau of Labor Statistics proves that the CE (labor share) does not indicate any change in income inequality.

 

Krugman and Co actually complain that the capital part of GDP involved in production is consumed now by the top 1% in a greater proportion. But this is a different story absolutely not related to income inequality.

Figure 1 . The net increase in GDP, Gross Personal Income (GPI), and Compensation of employees (CE) since 1929. All curves are normalized to their respective values in 1960.

 

Figure 2. Same as in Figure 1 but since 1960.

 

 

New Issue of Theoretical and Practical Research in Economic Fields

Theoretical and Practical Research in Economic Fields
CURRENT ISSUE:    Volume IV, Issue 2(8), Winter, 2013

ARTICLE: DOES INFLATION INCREASE THE EXPORT? CASE STUDY TURKEY

Author: Ergin AKALPLER, Near East University, North Cyprus, akalpler@yahoo.com; Keywords: export, Turkey, inflation, international trade, trade balance.

ARTICLE: AN EARLY WARNING SYSTEM FOR INFLATION IN THE PHILIPPINES USING MARKOV-SWITCHING AND LOGISTIC REGRESSION MODELS

Authors: Christopher John F. CRUZ, Bangko Sentral ng Pilipinas, Philippines, cruzcf@bsp.gov.ph, Claire Dennis S. MAPA, University of the Philippines School of Statistics, Philippines, cdsmapa@yahoo.com; Keywords: inflation targeting, Markov switching models, early warning system
ARTICLE: THE PHILLIPS CURVE AND A MICRO-FOUNDATION OF TREND INFLATION
Author: Taiji HARASHIMA, Department of Economics, Kanazawa Seiryo University, Japan, harashim@seiryo-u.ac.jp: Keywords: trend inflation, inflation persistence, central bank independence, the New Keynesian Phillips curve, the fiscal theory of the price level.
ARTICLE: WHO CONTROLS INFLATION IN AUSTRIA?
Author: Ivan KITOV, Institute of Geosphere Dynamics, Russian Academy of Sciences, Russia, ikitov@mail.ru; Keywords: inflation, unemployment, labor force, Phillips curve, forecasting, monetary policy, Austria.
ARTICLE: AN EMPIRICAL STUDY OD FACTORS AFFECTING INFLATION IN REPUBLIC OF TAJIKISTAN
Author: Nigina QURBANALIEVA, Ritsumeikan Asia Pacific University, Japan, nigiqu12@apu.ac.jp; Keywords: inflation, Tajikistan, cost push, demand pull, ARDL, cointegration.
ARTICLE: OVERSUPPLY OF LABOR AND OTHER PECULIARITIES OF ARTS LABOR MARKET
Authors: Milenko POPOVIĆ, Faculty for Business Studies, Mediterranean University, Podgorica, Montenegro, milenko.popovic@unimediteran.net, Kruna RATKOVIĆ, Faculty for Business Studies, Mediterranean University, Podgorica, Montenegro, kruna.ratkovic@gmail.com; Keywords: household production function, allocation of time, arts, expected benefits.

PPI v. core PPI

Six years ago we first reported on the presence of sustainable trends in the difference between various components of PPI [1]. Figure 1 illustrates the concept by highlighting two quasi-linear trends in the difference between the overall PPI and the core PPI, i.e. the PPI less food and energy. Both indices are not seasonally adjusted ones and represent finished goods (http://www.bls.gov/data/). We predicted that the trend observed between 2001 and 2008 had to come to end. A new trend had to develop and to define the prices of commodities in the 2010s. This new trend was expected to have a positive slope, i.e. the price indices of energy and food should grow at a lower rate than those for other commodities.  

Figure 1 displays the time history of the difference and two slopes of the relevant trends. Between 1980 and 2000, the difference was growing at a rate of 0.79 per year. Between 2001 and 2008, the difference fell at a rate of 3.4 units of index per year. Since 2008, this trend, which was reigning between 2001 and 2008, started to fade away and a new trend have been emerging. This period is characterized by very high volatility. The fall in the difference observed in 2008 was followed by a positive spike in 2009 and again by a fall in 2010. In 2011, the difference stabilized and has been following the expected trend ever since. This is the pattern we accurately foresaw in 2008.

The concept of sustainable trends allows predicting the future evolution of the difference. The new trend is likely defined. Figure 2 depicts the period after 2000 and highlights the new trend with a slope 0.77 units of index per year. This slope is the same as between 1980 and 2000. Initially we put forward two naïve assumptions that the new trend has to repeat the previous one with a positive sign or the one between 1980 and 2000. The latter hypothesis is likely right. 

Figure 1. The difference between the core PPI and the overall PPI between 1974 and 2013. There are two distinct period of quasi-linear trends: 1980-2000 and 2001-2008.

Figure 2. A new sustainable trend has been emerging since 2011. Green line - an assumption on the new trend.

On further fall in copper price

Since 2008, we have been reporting that the evolution of various components of CPI and PPI in the United States is not a random process but rather a predetermined one with long-term sustainable trends [1, 2]. Using these trends, one can predict consumer and producer price indices for various goods, services and commodities.  For example, in [3, 4], we presented the evolution many goods and services with varying weights in the CPI. There are more goods, services, and commodities of interest for producers, consumers, and investors. Here we revisit the index for copper ores. This is an example showing that some commodity prices are not well predictable.

Figure 1 displays the difference between PPI and the index for copper ores since 1988. This difference has a remarkable history: no big change between 1988 and 2003, and then a sudden surge in the copper index started. The peak was reached in the middle of 2006. It survived before the second quarter of 2008. Then the copper index dropped by almost 300 units back to the PPI level. In 2009, the PPI of copper increased above 500.  One may consider these changes as associated with the rise-fall cycles in oil price, but there is no one-to-one correspondence.

We have to admit that there is no sustainable trend in the copper index and the future of the copper ores index cannot be predicted in the long run. Currently, the difference is right in the middle between the previous trough and zero line. Moreover, it has reached the level of the previous local peak in 2007 (see Figure 2 for relative prices). Therefore, the PPI of copper may go any direction in 2014. I would refrain from buying/selling this commodity before the next clear sign of the future evolution. Considering the overall fall in commodities (oil, various metals, grains, etc.), I would not exclude further fall in the PPI of copper.

Figure 1. Evolution of the price index of copper ores relative to the PPI. 

 

Figure 2. Evolution of the difference of the PPI and price index of copper ores normalized to the PPI. Both troughs have the same depth.  

Food is getting cheaper

This is an annual update. We continue reporting on and predicting the evolution of the difference between the core consumer price index (CPI) and the index for food (less beverages).  Previously, we confirmed in many posts (see this blog) and papers [1, 2] that this difference had been following a long-term negative and linear (time) trend since 2001.  Originally, we predicted a turn to a positive trend in 2014. Two years ago, we expected the turn to a positive trend in 2012. Currently, we have new estimates of the core and food CPI through October 2013and can re-estimate the duration of the negative trend and its bottom value. For an investor dealing with commodities, the index of food, which continues to grow at a rate higher than the core CPI, is an important reference for any action. Food price affects not only economic but also social and political processes.

Figure 1 depict the most recent period. In 2008, when we first addressed the issue of sustainable trends in CPIs, the trend line was much steeper than now and intersected the zero line in 2014.  This was our initial estimate of the turning point for the negative trend. The zero line was considered as a natural level of resistance.  In the beginning of 2009, the difference reached the bottom and turned to a positive one, although not for long. The growth in food prices restarted in 2010. In the end of 2011, the difference had a short stop which we likely misinterpreted as a manifestation of the transition to a positive trend. Since October 2011, the difference has not been changing much with just a slight positive trend. This segment might manifest the major turn to the overall food price fall.

There are three possibilities of the future evolution. Firstly, we consider the probability of the turning point in 2013-2014 to be high.  Secondly, it is not excluded that the difference may suffer a further slight fall before it reaches its absolute historical minimum observed in 1979. Figure 2 illustrates this assumption and implies that with the current values at the level -3.0 and the bottom was at -8.5. Thirdly, the bottom value may be expressed in relative values. Figure 3 displays the difference between the core and food CPI normalized to the core CPI. In relative terms, the minimum was in 1974 and much deeper than in absolute terms. Falling along the current trend the normalized difference will reach the bottom only in the 2020s. This is the worst case scenario involving a significant rise in food prices through the 2010s. 

The first version is supported by the evolution of the producer price of grains in Figure 4. This price has been falling relative to the PPI since August 2013. The price of grain should affect the overall consumer price of food, likely with some delay. This facilitates the realization of the first scenario. Then food will be getting cheaper relative to the headline CPI.

Figure 1. The difference between the core CPI and the price index of food since 2002.  

Figure 2. The difference between the core CPI and the price index of food between 1960 and October 2013.

Figure 3. The difference between the core CPI and the price index of food normalized to the core CPI.  

Figure 4. The difference between the PPI and PPI of grains normalized to the PPI. The PPI of grains rapidly falls relative to the PPI since August 2012.

Price of steel and iron will be declining another three years

Five months ago we revisited the previously predicted fall in the producer price index of steel and iron in the first half of 2013 and formulated the hypothesis on the evolution in 2013-2016: “We foresee that the difference will be growing fluctuating around the green line till 2016. The price of iron and steel will be declining further before the difference reach ~10 to 20. ”  It is time to revisit our prediction.  

Originally, we reported on the difference between the overall PPI and the PPI of steel and iron in 2008. Then we revisited the difference in 2010, February 2012, December 2012, and August 2013. We predicted the index of steel and iron to return to the long term trend, which express a higher rate of growth of the producer price index than that of steel and iron. Our general approach is based on the presence of long-term sustainable (linear and nonlinear) trends in the evolution of the CPI and PPI in the United States [1, 2]. The difference between various components of these indices is not a random one but is rather a predetermined process. Using these trends, one can predict consumer and producer price indices for select goods, services and commodities.  

Figure 1 displays the difference between the PPI and the index for iron and steel (BLS code 101). The difference is characterized by the presence of a sharp decline between 2001 and 2008. Between 1985 and 2000, the curve fluctuates around the zero line, i.e. there was no linear trend in the absolute difference. In 2008, our main assumption was that the negative trend observed before 2008 should start transforming, after a short period of large fluctuations, into a positive trend after 2010. In Figure 1, the (originally expected) new trend is shown by green line. This trend suggests that the PPI grows faster than the index of steel and iron by approximately 2 units of index per year.  

Figure 2 demonstrates the most recent period and confirms that our prediction for 2013 was correct – the difference fluctuates around the green line. Therefore: 

We confirm our early prediction that the price of iron and steel will be falling through 2016  before the discussed difference reach ~10 to 20.  Investments in iron and steel related assets are likely not profitable.  

 

Figure 1. The difference of the PPI and the index of steel and iron updated for the period between November 2012 and October 2013. The green line was first introduced in 2008.

Figure 2. Same as in Figure 1 for the period between January 2005 and October 2013. Green line predicts the evolution of the difference after 2008.