Unemployment in Japan is approaching 6.0%

In June and August 2009, we published two articles at Seeking Alpha devoted to the evolution of unemployment in Japan [A1] and [A2], which predicted the rate of 6.0% in August 2009. On August 28, a new reading for July 2009 was reported by the Statistics Bureau of Japan. The (seasonally adjusted) rate for July was measured at 5.7%, i.e. 0.3% higher than in June. It is also 0.3% left to match our prediction of 6.0% in August, which is based on the model linking the rate of unemployment, UE(t), to the rate of change of labor force, dLF(t)/LF(t):
UE(t)= -1.5*dLF(t)/LF(t) +0.045 (1)
According to the results of labor survey presented on August 28, the level of labor force in for July was 66280000, i.e. 200,000 lower than in June. The number of unemployed people increased by 11,000 from June.
Figure 1 updates the observed and predicted curves for 2009. Because the accuracy of short-term estimates provided by labor surveys is not high, the monthly estimates of unemployment and labor force are prone to large measurement errors. So, the discrepancy between the observed and predicted curves likely manifests the problems with measurements, because in the long run the curves fit much better, as presented in Figure 2.
From 1, one can conclude that the rate of unemployment in August still may rise to 6.0%. On the other hand, this likely to be the peak of the unemployment growth and it will be declining to the long-term level between 4.5% and 5% [1,2] in 2009 and 2010.

[1] Kitov, I., (2006). The Japanese economy, MPRA Paper 2737, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/2737.html
[2] Kitov, I., (2007). Exact prediction of inflation and unemployment in Japan, MPRA Paper 5464, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/5464.html



Figure 1. Observed and predicted rate of unemployment in Japan in 2009.


Figure 2. Observed and predicted rate of unemployment in Japan between 1998 and 2008.

Figure 3. Prediction of the evolution of unemployment rate in Japan between 1990 and 2050.

Looking into the (gold) crystal ball: the evolution of the iron and steel price index

Introduction

The presence of long-term trends, both linear and nonlinear, in the differences between various expenditure categories of producer and consumer prices has been reported in several papers [1-7]. These trends allow predicting prices of many commodities at time horizons of several years. Such predictions are of fundamental importance for all stock market participants because of their anchoring and stabilizing effect. A striking feature associated with the trends is high volatility during the transition periods between adjacent trends. At first glance, the transient processes in the differences between price indices are driven by some stochastic forces, and thus, can not be predicted. It was found, however, that during the transition to new trends, some differences mimic trajectories of a pendulum-like oscillation [3,4]. Therefore, these trajectories are predictable ones and have been actually used in forecasting crude oil and gold ore prices [4,6]. The next natural step is to compare individual trajectories in order to evaluate their similarity in shape and timing of major peaks and troughs. If a price difference lags behind others, it would be possible to predict its evolution at a time horizon of corresponding lag.

1. The model
The model derived in [1,4] implies that the difference between the headline PPI, PPI, and the index for an individual commodity, iPPI, can be described by a linear time function over time intervals of several years:

iPPI(t) – PPI(t) = A + Bt (1)

where A and B are empirical constants, and t is the elapsed time. Therefore, the “distance” between the PPI and the studied index is a linear function of time, with a positive or negative slope B.
As an example, Figure 1 presents the difference between the iron and steel index and the PPI (all PPI indices are retrieved from the BLS web-site: http://www.bls.gov/data on 20.08.2009). There is a transition period between 2000 and 2001. This turning point is characterized by a slightly elevated volatility. Since 2008, the difference has been also passing a turning point, but this time with a very high volatility caused by the uncertainty in the characteristics of the following trend. A naive assumption about the future trend is that it will repeat the predecessor but with an opposite sign. The green line in Figure 1 represents the hypothetical new linear trend.
From Figure 1, the fundamental feature of the difference is that all deviations from the trends were only short-term ones. This implies that the current or future deviations from the new trend, which has been under development since 2008, must be promptly compensated. This feature allows a short-term (months) price prediction along the trend. The principal task of this study is different - to predict the evolution of the difference during the current transition period.

Figure 1. Illustration of linear trends in the difference between the headline PPI and the producer price index of iron and steel in the U.S. There are two quasi-linear segments with a turning point near 2000. Currently, the difference passes second transition period. Two linear trends with relevant linear regression lines are also shown. A tentative future trend is shown by a green line.

2. Price prediction

Relationship (1) describes the evolution of a given difference. The absolute value of such a difference may vary between commodities due to variation in start years. This might complicate the cross-commodity comparisons. So, it is instructive to use the differences normalized to the PPI: (iPPI(t)-PPI(t))/PPI(t). In a sense, the normalized differences represent the evolution of the rate of deviation from the PPI over years. For historical and logical reasons, we have chosen the following commodities: iron and steel, crude petroleum (domestic production), and gold ores. Figure 2 depicts corresponding time histories of the normalized deviation from the PPI. The initial inspection reveals the following apparent features: the (normalized deviation from the PPI of the) index for gold ores is characterized by a higher volatility; the index for iron and steel lags by several months behind the other two indices; the deviation of the crude oil index is comparable to the PPI itself.

Figure 2. The deviation of the iron and steel price index, the index of crude oil, and the index for gold ores from the PPI, normalized to the PPI.

In order to refine the pattern we additionally normalized the curves in Figure 2 to their peak values between 2005 and 2009:

(iPPI(t)-PPI(t))/[PPI(t)*max{iPPI-PPI)}]

The normalization allows a direct comparison of corresponding shapes. In Figure 3, we display the normalized index for iron and steel shifted by six and eight months back in the past for the synchronization of its peak with that observed in the normalized index for crude petroleum (upper panel) and gold ores (lower panel), respectively. The (normalized) index for crude petroleum demonstrates larger discrepancies from the index of iron and steel in the overall shape and timing of the current trough. On the other hand, despite its higher volatility, the index of gold ores is very similar to that of iron and steel. Simple smoothing with a weighted MA(3) makes the curves resemblance even better. As an invaluable benefit of the resemblance, one can use the eight-month lag to predict the future of the iron and steel price index.


Figure 3. The deviation of the iron and steel price index from the PPI, normalized to the PPI and the peak value after 2005 as compared to the normalized deviations of the index for crude petroleum (upper panel) and gold ores (lower panel). The normalized index for iron and steel is shifted six and eight months back in the past, respectively.

Conclusion
Between 2006 and 2010, the deviation of the price index for iron and steel from the PPI in the USA repeats the trajectory of the deviation of the index of gold ores with an eight-month lag. Therefore, the prediction of price for iron and steel at this horizon is straightforward – it will be growing during the next six to eight months. It is likely that in 2010 the index of iron and steel will approach closely the level attained in August 2008. From this level, it will be declining in the long run following the new trend, as shown in Figure 1.

References
1. Kitov, I., Kitov, O., (2008). Long-Term Linear Trends In Consumer Price Indices, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ), pp. 101-112.
2. Kitov, I., (2009). Apples and oranges: relative growth rate of consumer price indices, MPRA Paper 13587, University Library of Munich, Germany.
3. Kitov, I., Kitov, O., (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany,
4. Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, Journal of Applied Research in Finance, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. I(1(1)_ Summ), pp. 43-51
5. Kitov, I., Kitov, O., (2009). PPI of durable and nondurable goods: 1985-2016, MPRA Paper 15874, University Library of Munich, Germany
6. Kitov, I., (2009). Predicting gold ores price, MPRA Paper 15873, University Library of Munich, Germany
7. Kitov, I., (2009). Predicting the price index for jewelry and jewelry products: 2009-2016, MPRA Paper 15875, University Library of Munich, Germany

Prediction of price of gas at the pump: August-December 2009

Four months ago we made a prediction of price of gas at the pump [1]. In essence, we drew a straight line shown in Figure 1 as the future gas price for the period between March and December 2009. The line represents the difference between core CPI and the motor fuel index. The line says that the price for gas will be growing faster than the price of all goods and services less food and energy (the definition of core CPI). A midterm review is a natural step in tracking the following prediction [1]:

In March 2009, the difference was at the level of +45, i.e. much higher than the level predicted by the new trend. As happened in the past with numerous individual price indices [9,10], such a strong deviation (one might call it “dynamic overshoot”) should be compensated in the near future. Without loss of generality, we have restricted the recovery to the trend by the end of 2009. As a result the index for motor fuel should growth by 90 units during the next 9 months, or by 10 units per month. Red filled circles represent the evolution of the difference from April to December 2009. In 2010, the difference may undergo an overshoot in the opposite direction with additional rise in the index for motor fuel.
Translating indices into prices, the rise in the difference by 90 units (from 173 in March to 263 in December) means an increase in price by 50%. Therefore, it is very likely that the price for motor fuel in the beginning of 2010 will be 60% to 70% larger than in March 2009 due to the overshoot.

Having the latest estimates of the core CPI and the motor fuel index, as published by the BLS, we have calculated the difference for July at +13 units of index instead of predicted +5. Figure 2 displays relevant curves and confirms that the overall trend in the difference holds. Considering high volatility in relevant price indices one could not expect a one-to-one correspondence between the observed and predicted curve, and a small delay observed in June will be compensated in August or September.
We have been also reporting the evolution of crude oil price, which obviously affects the price of motor fuel. Despite high resemblance, these prices have no one-to-one correspondence and it is instructive to model them separately. So, we will continue tracking gas price at the pump. The delay in June allows to predict a surge in the price in August and likely in September. We also retain the mid-term prediction, i.e. the price in December 2009 and in the beginning of 2010, at 70% higher than in March 2009. Bearing in mind our prediction of oil price at $100 by December 2009, we might put the motor fuel price higher with probability increasing in time, since our predictions were accurate.

Figure 1. The difference between the core CPI and the index for motor fuel. Red filled circles predict the evolution of the difference between March and December 2009. Total increase in the difference is +90 units of index or +50%: from 173 in March to 263 in December. Solid red line represents the “mirror” trend for that between 2002 and 2008, which is shown by solid black line.

Figure 2. The difference between the core CPI and the index for motor fuel. Red filled circles predict the evolution of the difference between August and December 2009.


References

[1] Kitov, I., Kitov, O., (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15039/01/MPRA_paper_15039.pdf

Will the rate of unemployment in the USA decline?

In economics, many measured variables inherit poor understanding of an economy as a system, which was developed in the past. Unemployment is one of the worst defined economic variables, which ignored the presence of a complete and closed (even if it is considered in many models as an open one) economic system. The rate of unemployment is defined as a share the labor force, i.e. as the percentage of people of the total available working force actively seeking jobs, but remain unhired. As an example, students are not available during scholar terms and can not be considered as unemployed despite they might think about some job position.
The inherent weakness of the unemployment definition roots in the fact that the labor force is a varying portion of the total working age population, as defined by all people of 16 years of age and over. (As mentioned above, this varying portion of the working age population is the denominator in the unemployment rate.) The portion is called labor force participation rate (LFPR). The presence of the varying basis in the definition of unemployment rate results in a strong bias in the interpretation of unemployment rate estimates by an unprofessional audience and even many researchers.
At first, labor force participation rate in developed countries varies in a wide range and thus the same portion of the total working age population announcing itself as " unemployed" may define quite different "unemployment rates" - compare Italy and Canada from Figure 1. Hence, before saying “unemployment rate is high” in a given country, one should mention relevant LFPR. Otherwise, the statement could be considered as an intentionally biased one.

Figure 1. The evolution of labor force participation rate in select developed countries.

Figure 1 also demonstrates that, when applied to one country, the definition of unemployment misses actual long-term variations in LFPR. These variations in participation rate are tremendous, as it has been actually observed in the USA since the 1960s. In 1963, the participation rate was below 58.7%, and between 1997 and 2000 it was 67.1%. The latter level was the peak and since 2000 the rate has been falling. As a result, during the 2000s people have been likely moving first into the unemployment “pot” and then out of the labor force at all. It is worth noting that the rate of unemployment in the US was low in the 2000s despite the decline in LFPR. In 2006, the rate was 66.2%, i.e. ~1 percentage point less than in 2000. One percent of the participation rate or 1% of the working age population comprises ~2,300,000 people leaving labor force, also through unemployment. I would like to stress that this is an observed (actual) process with some fundamental economic, social, demographic, and etc. forces behind it. As one can see, the effect of varying participation rate can not be neglected in the discussion of the current unemployment. When some disputants claim that the current rate of unemployment is high relative to that observed in the late 1960s, they put aside the fact that with the current labor force participation rate the labor force in the 1960s would be ~15,000,000 larger. This is a big question yet – could that more than a dozen million get some paid job when sought for it? On the contrary, with the participation rate observed in the 1960s (~60%), what all extra 20,000,000 people currently in the labor force would do? Would they comprise the unemployment? Then the rate of unemployment would be 20+%.
Figure 2. The evolution of unemployment rate in the USA between 1960 and 2007.

As a facultative part of this article, we present our model describing the evolution of labor force in developed countries. Skipping technical details and boring formulas, we provide couple illustrations borrowed from our article [1]. Figure 3 shows the evolution of observed and predicted LFPR in the United States. The latter is obtained directly from real GDP per capita. In other words, our model relates LFPR in developed countries solely to the evolution of real GDP. In the US, the rate of real GDP growth above the trend, which is defined in the model as the inverse value of the attained level of real GDP per capita, causes a decrease in the participation rate. When the rate of GDP growth is below the trend the LFPR is increasing. So to say, when the US economy is successful it does not need too many people to work. In poor years, more and more people must join the labor force in order to get incomes which would be obtained without work during the bright years.
The curves in Figure 3 almost coincide between 1960 and 2007. The largest deviations are observed in the years of biggest revisions to the LFPR after decennial censuses. Therefore, they can be neglected as having artificial character. The predicted curve shows that the LFPR should decrease after 2006 - the last year with the LFPR estimates available when the model was developed.

Figure 3. Observed and predicted LFP in the U.S. Notice the largest deviation between the curves is associated with the years of major revisions to the LFP - 1980 and 1990.


In order to predict the evolution of the LFPR we used projections of real GDP based on the projections of population. Figure 4 depicts the predicted and observed LFPR curves for the years between 2000 and 2014. In 2010, the rate should drop by approximately 1.3%. When translated into absolute numbers, it gives more than 2,500,000 people leaving the labor force in 2010 at once. Really, the wave of the boomer’s retirement has just started and it is likely that nobody will replace many of them in the labor force. Then the unemployment in 2010 will fall to its long-term level around 5%. The effect of the change in the LFPR is neglected by all researchers interpreting current level of unemployment. As a rule, nobody believes in a quick fall in the unemployment rate. Following our own model, we expect a dramatic decrease in unemployment in the near future.

Figure 4. Prediction of the LFPR evolution in the USA between 2000 and 2014 from the number of 3-year-olds. Flat segment between 2004 and 2009 will end up in a rapid drop by 1.3% after 2010. This is the effect of an elevated (above potential) real economic growth in 2010.

References
[1] Kitov, I., Kitov, O., (2008). The Driving Force of Labor Force Participation in Developed Countries, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. III(3(5)_Fall), pp. 203-222. http://www.jaes.reprograph.ro/articles/3_TheDrivingForceofLaborForceParticipationinDevelopedCountries.pdf

Unemployment in Japan at 5.4% in June

Here we are following our previous article on unemployment in Japan, where we predicted the rate to be at 6.0 in August. A new reading 5.4% for June 2009 was published on July 31. At the same time, a new estimate of labor force is also available for June and it is possible to quantitatively predict the unemployment rate using the empirical relationship introduced in the previous article:

UE(t)= -1.5*dLF(t)/LF(t) +0.045 (1)

where UE is the unemployment rate at time t, LF is the level of labor force at the same time. There is no time delay between the change in the LF and UE. Figure 1 updates the observed and predicted curves in 2009. Both unemployment and labor force are estimated in labor force survey, which is not usually an accurate procedure in the short-term. So, the discrepancy between the observed and predicted curves likely manifests the problems with measurements, because in the long run the curves fit much better.

Figure 1. Observed and predicted rate of unemployment in Japan.

All in all, the rate of unemployment in Japan has been increasing since the mid 2008. Results for August 2009 will be published in the end of September. Meanwhile, the estimate for July should show another uptick.

Unemployment situation

Three simple graphs illustrating the unemployment situation

Figure 1 presents monthly growth in civilian (non-institutional) population (dCP) and in labor force (dLF) between the January 2007 and July 2009. All estimates are obtained in the monthly Current Population Surveys (household data) conducted by the US Census Bureau for the BLS and are seasonally adjusted ones. One can observed three benchmark revisions to the civilian population, which are carried out every January. The estimates of labor force are also affected by the CP revisions. Otherwise, the change in labor force is very volatile. The last three months demonstrate a decrease in the rate of labor force growth from +653 in April to -422 in July. One can expect that the labor force will start to grow again soon.

Figure 1. Monthly growth in civilian non-institutional population (dCP) and labor force (dLF).

Figure 2 compares the change in labor force and in the number of employed people. The latter has also been volatile over the last three years but has clear trends before and after January 2009. These trends are obviously associated with the current recession, with the bottom of the employment market in December 2008. Since January 2009, the number of employed has been decreasing at a decelerating rate. This trend implies that the employment will start to grow in August or September 2009.

Figure 2. Monthly growth in employment (dE) and labor force (dLF).



Figure 3 addresses the change in unemployment. It has been increasing in absolute number since April 2008. However, in July 2009 the unemployment first showed a decrease by 267,000. Considering the long-term behavior of all time series one can make some assumptions about the near future:

The labor force will be growing due to the growth in employment.
The number of unemployed will be decreasing. Due to the increase in the labor force the rate of unemployment will be falling faster than the absolute number of unemployed.

Figure 3. Monthly growth in the number of unemployed (dUE) and labor force (dLF).