The employment/population ratio may rise to 63% by the end of 2011

Our model links the rate of participation in labor force, LFP, to the change in real GDP per capita. The latter leads by two years, and we have successfully predicted the fall in LFP in 2009. For short time intervals, one can replace labor force with employment, E, and GDP per capita with GDP. Figure 1 shows the evolution of dGDP/GDPdt and E/P (employment population ratio) in the U.S. after 1990. The latter variable is reduced by 60% and shifted 12 months back in order to fit the level of dGPG/GDP between 1990 and 2010.

The overall agreement between the curves is excellent and allows forecasting the E/P, the dGDP/GDPdt curve leads by 12 months. Then, the current E/P value corresponds to May 2010. Therefore, the E/P should jump to the level of 63% by the end of 2011.

Figure 1. Annual change rate of real GDP, dGDP/GDP, and the monthly estimated ratio of employment and working age population, E/P.

Why the employment situation is not disappointing

The Bureau of Labor Statistics has published an “Employment Situation Summary” for May. The nonfarm payroll employment has increased by 54,000. The number of employed in the U.S. increased by 105,000; from 139,674,000 to 139,779,000.  These low numbers have come as a surprise for many experts, who predicted 170,000 (http://online.wsj.com/mdc/public/page/2_3064-446888.html) for the nonfarm payroll employment in May. Therefore, the market and general public feel some disappointment> Should they?

In the previous post, we demonstrated that the level of labor force in the U.S. has been experiencing an unprecedented fall since 2008. Figure 1 reminds us that the reason for the fall is not the current financial crisis and recession but rather a new trend in the rate of labor force participation, LFP. This is not a short- or mid-term transient process but the change in the long-term tendency. The LFP had been growing between 1955 and 2000, when it reached its peak. One can consider 2001 as a pivot point manifesting a fundamental change in the labor market behavior in the U.S. It is worth noting that the change in LFP behaviour started ten years ago, not in 2008. (The reader might be interested in the explanation of this phenomenon. We had accurately predicted the 2010/2011 fall in the LFP many years before it happened.)

As a result of the new long-term tendency, one should not expect the same pace of employment growth as it was between 1960 and 2000. In addition to the fundamental shift in the secular LFP evolution, one should not forget another source of employment growth – the level of working age population. Figure 2 depicts monthly increments of the working age population, i.e. 16 years old and over.  One can clearly see that the influx of the population has been decelerating since 2000 as well. The deep negative corrections in Figure 3 are associated with annual revisions to population controls. It is not wise to wait that the growth in employment will exceed the influx of working age population in the situation with the falling LFP.   

It is important that even decreasing unemployment can not compensate the effects of LFP and population. Figure 3 shows the evolution of monthly increments in employment, E, after 2003 with MA(12). One should not expect that E will be growing at a pace which was considered as a healthy one before 2000 any time soon. In that sense, the today’s BLS news is not disappointing. Really disappointing is the unjustified expectation of any large increase in the U.S. employment.  

Figure 1. Measured LFP in the U.S.

Figure 2. Monthly increment in working age popualtion (16 years of age and over) in the U.S.

 

Figure 3. Monthly increment of employment in the US with its MA(12). 

Catastrophic fall in labor force in the U.S.

Labor force in the U.S. experiences unprecedented fall. With total population growing at a healthy pace of ~1% per year, the number of people in labor force has been physically decreasing since 2009. The reason behind this effect is the labor force participation rate, LFP, plummeting down. Figure 1 shows that LFP dropped from 66.4% in 2008 to 63.9% in the first quarter of 2011. This 2.5% is equivalent to 6,000,000 people out of the working force in 2011 relative to 2008.  Event the growth in the total working age population from 235,000,000 to 239,000,000 has failed to compensate the fall in the LFP. Figure 2 shows that the decline in the labor force, LF, is a unique feature since the very beginning of observations in 1948.  Except the current fall, there were only two short intervals with dLF/LFdt<0 after WWII, in 1951 and 1962, as Figure 3 shows.

The negative growth rate of labor force is the cause of a higher rate of unemployment and lower rate of price inflation. It should be noted that we predicted the current decline in the LFP many years ago. 

However, the fall in LFP is not the cause but a consequence of the low rate of real GDP  (per capita) growth after 2008. When the growth rate of real GDP per capita regains its normal pace of 2% per years the LFP will start to increase, with a two-year delay.

 

Figure 1. Measured LFP in the U.S.

Figure 2. Labor force in the US.

Figure 3. The change rate of labor force, dLF/LFdt

Motor fuel price to fall in the near future

Our task is to estimate relative growth in a given price with time.  We use the ratio of price index, P(t), and GDP per capita in current prices, Y(t) (the idea borrowed from V.Kossov): 

Z(t)=P(t)/Y(t)

Figure 1 presents the evolution of the price index of motor fuel since 1935 (obtained from the BLS) and Figure 2 – nominal GDP per capita.   The share of motor fuel price in GDP per capita can be presented as a function of Y as well as time.  Figure 3 shows that there exist a long-term negative trend for Z(t) (notice the log-log scale) with two major fluctuations.  The trend looks sustainable and deviations seem to be of transient character.  Therefore, one can expect the fall in Z in the near future – motor fuel will be falling against GDP per capita. Oil price is likely to fall as well.  Figure 4 presents log(Z) as a function of time.

Figure 1. The consumer price index of motor fuel (not seasonally adjusted).

Figure 2. Nominal GDP per capita

Figure 3. LogZ vs. GDP per capita.

Figure 4. LogZ vs. time

Food is getting cheaper

Food is getting more and more expensive. Everybody knows that.  Figure 1 illustrates the evolution of the price index of food since 1913. At the same time, the US economy also grows including the growth in real GDP per capita which is shown in Figure since 1929 (chained, in 2005$).  One can easily estimate which of these two variables grows faster. Figure 3 depicts the ratio of CPI and GDP per capita relative to that in 1929. Overall, the food price falls relative to the GDP per capita, i.e. one has to pay a lower share of income (a fixed portion of GDP per capita)  for the same amount of food (we do not consider nomenclature and quality of food here).  Food is getting cheaper with time. It is interesting that the ratio in Figure 3 has not been falling much since 1975.

Figure 1.

Figure 2.

Figure 3.

The New Keynesian Phillips Curve – methodological dead-end

Couple days ago we presented a Phillips curve for Germany.  When unemployment leads inflation (the GDP deflator) by one year in the model, one can explain about 80 per cent of the variability in the inflation time series. The model residual error can be explained by measurement errors and with increasing accuracy one could reach a much higher predictive power. This is a simple way of explanation which meets general requirements of scientific methodology. Economics and econometrics are likely to violate this methodology in order to fit own understanding of reality.

The new Keynesian Phillips curve (NKPC) and many other economic and econometric models are based on an assumption that the future inflation value must depend on its current and/or past values and additional variables related to economic activity. Among many others, it might be unemployment , output gap or marginal labor cost.  To define the input of the activity variable one has to apply an econometric model which is similar (but not equivalent) to linear regression and calculate relevant coefficients in the relationship:

P(t+1)=a0P(t) +a1P(t-1)+ ….anP(t-n) + b0U(t)+b1U(t-1) ….

where P(t) is the inflation time series and U(t) is the rate of unemployment.  Instead of using advanced VAR models we apply simple linear regression to the German inflation (Figure 1) and unemployment (Figure 2) time series. There is a series of models with increasing complexity. In model M1, the original time series is regressed against itself with lag 1. The slope of 0.86 and R²=0.744 in table 1 demonstrate a high level of correlation which is well expected. The inflation time series varies with a period larger than 1 year. A crucial characteristic of the model is its accuracy as expressed as RMSE=0.00955. Thus, the uncertainty of one year ahead forecast is 0.96% in Germany between 1973 and 2010. For a purely naïve model, which does not include the intercept in the regression, RMSE=0.0097.  
In model M2, we use lags 1 and 2. This model is even worse than model 1 with R²=0.738 and RMSE=0.00967. Therefore, lag=2 does not help much and we include U(t) in model 3. This new term dramatically change the model. Coefficient b0=-0.34 steals some input from a0, which is now only 0.57. It means that one can explain same variations in the DGDP time series using its lagged values or the unemployment series. In model 3, individual inputs are shared almost proportionally, as required for collinear parts of regressed time series. Is it a fair division of influence?  Let’s look closer.
The input of U(t) can be masked by  the influence of the lagged values of inflation. In order to estimate the true effect of unemployment on inflation one needs to exclude all past values of inflation.  Models 5 and 6 try the unemployment time series and its lagged version. We have expected the outcome since it was obtained previously and described in our post on the Phillips curve in Germany. Model M6 with unemployment lagged by one year has all merits: R²=0.80 and RMSE=0.0084. Why should one use the NKPC if it does not reach the predictive power of the original Phillips curve? The explanation is simple and sad. Economics and, in part, econometrics are the hostages of prejudice and unjustified assumptions (rational expectations and likes). 
Mathematically, any student knows that one must not decompose a function into any set of functions which are not orthogonal. Otherwise, the decomposition cannot be completely resolved, and thus, is unreliable.  The NKPC makes this school-level mistake and decomposes inflation into a set of non-orthogonal functions. This is a methodological dead-end. It will always mask real influence of true inflation drivers, such as unemployment as models M3 and M4 demonstrate. One can check that the VAR models with the same lags give almost the same coefficients as in table 1.

Table 1

model	a0	a1	b0	b1	Rsq.	RMSE
M1	0.86				0.744	0.00955
M2	0.81	0.05			0.738	0.00967
M3	0.57	-0.04	-0.34		0.821	0.00799
M4	0.43	-0.04	-0.1	-0.31	0.828	0.00783
M5			-0.66		0.691	0.01050
M6				-0.65	0.804	0.00840

 Figure 1. The GDP deflator in Germany between 1971 and 2010

Figure 2. The rate of unemployment in Germany.

Real economic growth. The importance of being … small

Here, we compare real economic growth based on real GDP per capita, G. In developed countries, annual increment of GDP per capita is constant over time with all fluctuations caused by the change in the age pyramid. The average value of the annual increment of GDP per capita varies between countries, however. Among large economies, the USA grows with the highest annual increment.  In that sense, it is the most efficient economy.

Lately, we presented several posts showing the difference between real GDP per capita in the USA, Gusa,  and select countries, Gi:

dG = Gusa-Gi

When the difference dG has a positive trend, the gap with the USA increase with time. When dG has a negative trend, this country grows faster than the USA. There are not many economies outperforming the U.S. since 1990. Six developed countries deserve special consideration: Ireland, Norway, Luxembourg, Hong Kong, Singapore, and Trinidad and Tobago which joined recently.  Figure 1 demonstrates that these six economies all have negative trend in the dG time series. Ireland, the biggest among them, has been experiencing problems since 2006.

Hence, one can conclude that small countries have higher probability to grow fast. To be small is not enough, however!  

Figure 1. The differences between real GDP per capita in the USA and six select countries