## 1/19/14

### 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.

## 1/5/14

### 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.

## 1/4/14

### 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 POPOVIÄ†, Faculty for Business Studies, Mediterranean University, Podgorica, Montenegro, milenko.popovic@unimediteran.net, Kruna RATKOVIÄ†, Faculty for Business Studies, Mediterranean University, Podgorica, Montenegro, kruna.ratkovic@gmail.com; Keywords: household production function, allocation of time, arts, expected benefits.

### The mean income gap between white males and black females grows during the democratic presidencies

Two days ago, we compared the mean income evolution of the white and black population and demonstrated that the difference did not change mu...