Is abenomics working?

Here we present  quantitative evidences that the effect of abenomics on CPI in Japan is invisibly weak, if any. Our quantitative model, which we presented at the conference “Inflation Developments after the Great Recession” hosted (December 2013) by the Deutsche Bundesbank and sponsored by the EABCN, is available as a CEPR working paper “Inflation, Unemployment, and Labor Force: The Phillips Curve and Long-term Projections for Japan”.   This model shows that the current surge in consumer prices is  an abenomics achievement, but the result of labour force increase started in 2011, i.e. before the start of the new monetary (and economic) policy introduced by Abe. Moreover, when the effect of increasing labour force fades away in a few months deflation in consumer prices will be back. It is worth noting that the GDP price deflator is still in the negative zone and that the headline CPI is a highly biased (up) measure of inflation not to be used by sane researchers.

 

At first, we present our model in detail. In this blog, we have already mentioned that Japan is the best illustration of our concept linking inflation/unemployment to the change in labour force. In our previous posts on the GDP deflator in Japan, we showed two cumulative curves for observed and predicted inflation since 1980. Here we revisit similar CPI curves also two more readings and conclude that our concept is quantitatively excellent. The underlying data have been borrowed from the OECD and Japan Statistics.

 

Using stata9 and allowing a structural break, we sought for the best-fit (in RMS sense) coefficients in the linear and lagged link between inflation and labour force. Because of the structural (measurement related) break in the 1980s, we have chosen the period after 1981 for linear regression, which is common for almost all economic studies related to Japan. By varying the lag and coefficients we have found the following relationship for consumer price inflation (CPI):

 

CPI(t) = 1.39dLF(t-t₀)/LF(t-t₀) + 0.0004            (1)

 

where the time lag t₀=0 years; Figure 1 depicts this best-fit case. There is no time lag between the inflation series and the labour force change series in Japan. Free term in (1), defining the level of price inflation in the absence of labour force change, is close to zero but negative.

 

A more precise and reliable representation of the observed and predicted inflation consists in the comparison of cumulative curves (a version of CUSUM technique) shown in the lower panel of Figure 1. We always stress that the cumulative values of price inflation and the change in labour force are the levels of price and labour force, respectively. Therefore, the summation of the annual reading gives the original estimates of price and workforce, which when are converted into rates.

 

Another advantage of the cumulative curves is that all short-term oscillations and uncorrelated noise in data as induced by inaccurate measurements and the inevitable bias in all definitions are effectively smoothed out. Any actual deviation between these two cumulative curves persists in time if measured values are not matched by the defining relationship. The predicted cumulative values are very sensitive to free term in (1).

 

For Japan, the CPI cumulative curves are characterized by very complex and unusual for economics shapes. There was a period of intensive inflation growth and a long deflationary period. The labour force change, defining the predicted inflation curve, follows all the turns in the measured cumulative inflation with the coefficient of determination R²=0.99. The predicted and observed cumulative CPI curves are cointegrated and thus this estimate is consistent. For the annual estimates: R²=0.73. With shrinking population, and thus, labour force, the level of CPI will be falling through 2050 and likely beyond.

 

Now, we return to the current rise in the headline CPI and apply (1) to monthly readings of labour force and inflation. Figure 2 shows the change rate in labour force. One can observe the positive trend started in the middle of 2011 and will likely extend into the first quarter of 2014. Figure 3 compares the predicted and observed rate of inflation (year-on-year estimates for the monthly estimates) since 2010 and demonstrates that the current rise in the headline CPI is likely a short deviation from the long-term trend fully related to a small rise in the level of labour force, not the abenomics tools. Because of the overall ageing and depopulation this positive trend will not last long and the level of labour force will definitely fall. This fall will induce price deflation, as Figure 1 predicts.  

 

 

Figure 1. Measured CPI inflation and that predicted from the change rate of labour force in Japan. Upper panel:  Annual curves smoothed with MA(3). Lower panel: Cumulative curves between 1981 and 2012. The extremely accurate agreement between the cumulative curves illustrates the predictive power of our model. The cumulative curves are I(1) processes and we thus it was instructive to test them for cointegration. This test was successful.

Figure 2. The rate of change in labour force, dLF/LF, since 2010. Monthly readings are used and the year-on-year rate is calculated. 

Figure 3. Comparison of the measured inflation rate (y-o-y) and that predicted from the change in labour force.

Towards cheaper food and energy in 2014-2016

We found sustainable linear trends in the difference between the headline and core CPI in 2007. We also were the first to suggest that the trend between 2002 and 2008 to be reversed to the opposite after an extended period of strong fluctuations. These findings are currently validated by five years of observations – the studied difference is on a sustainable linear trend since 2011. When extended into the second part of the 2010s, this trend implies that the joint consumer price of food and energy will be falling against other consumer goods and services in the headline CPI.

We have been routinely reporting on the difference between the headline and core CPI since 2008. Figure 1 illustrates our general finding that this deference can be well approximated be a set of linear trends. The last trend likely finished in 2009. That’s why we expected a new trend to evolve since 2011 into the late 2010s.

The U.S. Bureau of Labor Statistics has reported the estimates of various consumer price indices for December 2013. Figure 2 shows the predicted trend and the actual difference since 2002. The studied difference has been fluctuating around the zero line between in 2009 and 2011 and then showed a turn to the early predicted trend (Figure 3).  Essentially, the zero difference suggests that the core and headline CPI are practically equal and evolve at the same monthly rate, i.e. the joint price index of energy and food has been following the price index of all other good and services (the core CPI) one-to-one.  

Currently, the price index of energy slowly falls together with oil price. We expect them to fall deeper and thus the headline CPI to decelerate a bit together with energy. If the core CPI will retain its current cohesion with the headline CPI, we will have a period of very low inflation in all goods and services less energy and food. 

Figure 1. Two trends in the difference between the healine and core CPI.

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

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

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.