Showing posts with label labor force. Show all posts
Showing posts with label labor force. Show all posts

10/6/12

The rate of unemployment in the U.S. will fall to 6.2% by 2014

On March 1, 2012 we predicted (in a Seeking Alpha post) the rate of unemployment in the U.S. to fall down to 7.8% by 2013. The BLS announced 7.8% for September 2012. Here we present our basic model and predict the evolution of unemployment in 2013.

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 built a general relationship balancing all three variables simultaneously. 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.

On March 1, 2012 the monthly model predicted a drop from 8.3% in February to 7.8% by the end of 2012. Figure 1 depicts the original prediction (upper panel) and the observed fall in the rate of unemployment (lower panel). Figure 2 shows that the observed and predicted time series are well  correlated (Rsq.=0.81). This is a good statistical support to the model.

Figure 3 depicts the predicted rate of unemployment for the next 12 months. The model shows that the rate will fall to 6.2% by September 2013. For 105 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 6.2% [±0.4%].
 
 
Figure 1. Observed and predicted rate of unemployment in the USA as obtained in March and October 2012.


Figure 2.  Observed vs. predicted rate of unemployment between 1967 and 2012. The coefficient of determination   Rsq=0.81.



Figures 3. The predicted rate of unemployment. We expect the rate to fall down to 6.2% in September 2013.

10/5/12

7.8% unemployment was predicted in April 2012


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 built a general relationship balancing all three variables simultaneously. 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 is a follows:

u(t) = p(t-2) + 2.5dLF(t-5)/dtLF(t-5) + 0.0585 (1)

where inflation (CPI) leads unemployment by 2 years and the change in labor force by 5 years. We have already posted on the performance of this model several times.

Here a model with monthly estimates of CPI, u, and labor force is presented. 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) + 2.0dLF(t-5)/dtLF(t-5) + 0.07; between 1978 and 2003

u(t) = 0.90p(t-2) + 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 rate of unemployment became more sensitive 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.

All in all, the monthly model predicts the observed rate of unemployment which has recently dropped to 8.3%. We expect the rate to fall further to the level of 7.8% by the end of 2012.



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

10/1/12

Japan - my scenario of consumer price fall was too optimistic

I've found a new projection of labor force in Japan between 2010 and 2050. It says that the rate of labor force participation will be decreasing together with the depopulation of Japan. This means that the level of labor force will be falling much faster than it was used in my recent prediction. Considering the multiplication factor of 1.4, the rate of CPI inflation will reach -1% per year on average in 2020. By 2050, it will reach almost 2% per year (1.89%). The overall drop in consumer prices will be more than 2/3 by 2050.  This fall will be accompanied by the decrease in real GDP at a rate of 1% per year and the debt rise to 500% of GDP.

9/30/12

Exploring Japan: on dismal perspectives of consumer prices

In this post, we continue to validate our predictions of the rate of consumer price inflation (CPI) in Japan by the estimate for 2011. The Japan Bureau of Statistics has estimated the rate of CPI inflation as -0.3%. Now we have an estimate of labour force for 2011 and are able to compare the observed and predicted  figures.  
We have been following inflation in Japan since 2005 when our first paper on the Japanese economy was published and covered the period through 2003. We have revisited inflation in Japan in 2010 and confirmed the predictions of deflation as expressed by the negative GDP deflator. In this blog, we also reported on deflation (both CPI and GDP deflator) several times.  
The case of Japan is the best illustration of our concept linking inflation to the change in labour force. (In a sense, all developed countries stay on the brink of deflation because of the threat of falling labour force.) Therefore we do not suggest the liquidity trap in Japan or any mistakes in monetary policy (inflation does not depend on monetary policy as our model shows.). The evolution of inflation is completely driven by the change in labour force. This is an unfortunate situation for Japan since the level of labour force can only fall in the long run due to the decreasing working age population.   
Previously, we carried out an estimation of empirical relationship between the change rate of labour force, dlnLF(t)/dt, and inflation, p(t).  
First, we test the existence of a link between inflation and labour force. Because of the structural (likely related to definition and measurement procedure) break in the 1980s, we have chosen the period after 1982 for linear regression. By varying the lag between the labour force and inflation one can obtain the best-fit coefficients for the prediction of CPI inflation, p(t),  according to the following relationship (updated with new data since 2009): 
p(t) = 1.39dlnLF(t-t0)/dt + 0.0004                                (1)
where the time lag t0=0 years; standard errors for both coefficients are shown in brackets.  Figure 1 (upper panel) depicts this best-fit case. (The period after 2003 is highlighted.) 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 statistically undistinguishable from zero.
A more precise and reliable method to compare observed and predicted inflation consists in the comparison of cumulative curves. Short-term oscillations and uncorrelated noise in data as induced by inaccurate measurements and the inevitable bias in all definitions should be smoothed out in cumulative curves. Any actual deviation between two cumulative curves persists in time if measured values are not matched by the defining relationship.
The predicted cumulative values shown in the lower panel of Figure 1 are very sensitive to the free term in (1). For Japan, the cumulative curves are characterized by complex shapes. There are periods of intensive inflation and a deflationary period. The labour force change, defining the predicted inflation curve, follows all the turns in the measured cumulative inflation.
One can conclude that relationship (1) is valid and the labour force change is the driving force of inflation. Statistically, the evolution of the overall level of consumer prices in Japan is fully defined by the change in labour force. Hence, no other variable or process can affect the change in price. Otherwise, the statistically reliable link would not exist.  
Having the projection of labour force borrowed from the National Institute of Population and Social Security Research, one can predict the future of CPI inflation in Japan. It will be decreasing to the level of -1% per year in 2050.  
Conclusion: invite immigrants and start a baby boom today! Otherwise, the level of consumer prices in 2050 will be a half of that of today.  
 
Figure 1. Measured inflation (CPI) and that predicted from the change rate of labour force. Upper panel:  Annual curves. Lower panel: Cumulative curves between 1982 and 2011. A good agreement between the cumulative curves illustrates the predictive power of our model.
 
Figure 2. Scatter plot: predicted vs. measured rate of CPI inflation.
Figure 3. Projection of the labour force evolution between 2005 and 2050.

Figure 4. The rate of CPI inflation in Japan through 2050.

7/9/11

Modeling the change in unemployment rate: Canada, Australia and Spain

I continue model the change in unemployment rate as a linear function of the change rate  of real GDP per capita. (See my previous posts)

For Canada, I have estimated the following relation with a structural break in 1985
dlnG = -2.7du + 3.1, t<1995
dlnG = -2.7du + 1.2, t>1994
Figure 1 presents the observed dlnG curve and the scaled du, i.e. the change in GDP predicted from the change in the rate of unemployment.  The agreement is excellent, but both curves are volatile. I have smoothed them with MA(3).

For Spain, the result is really fantastic! the following relation is obtained with a structural break in 1987:
dlnG = -2.0du + 5, t<1987
dlnG = -0.8du + 2.1, t>1986

Figure 2 presents the observed and predicted dlnG. There might be another structural breal around 1970.

For Australia, the result might be not so exciting but is very good:
dlnG = -1.7du + 2.5, t<1995
dlnG = -3.0du + 1.3 t>1994

Figure 3 presents the observed and predicted curves smoothed with MA(3) and a structural breal in 1995. 
Figure 1. Annual growth rate of real GDP per capita, dlnG, and the scaled rate of unemployment, du. The lower panel shows the curves smoothed with MA(3)


Figure 2. Same as in Figure 1 for Spain .

Figure 3. Same as in Figure 1 for Australia




Bravo, Krugman!

A month ago I presented a graph linking the growth rate of real GDP per capita and the rate of unemployment. Figure 1 is borrowed from this post and shows that one can expect the rate of unemployment to  fall fast in the second half of 2011.

Figure 1. The annual change rate of real GDP, dlnGDP/dt, and the scaled rate of unemployment, UE.
Paul Krugman has modified this graph in order to prove that the current  rate of unemployment is a direct consequence of slow real growth. He plotted the change in unemployment rate against the change rate of real GDP.  In his scatter plot, correlation was very high.  I decided to repeat his result using GDP per capita instead of the overall GDP, which is a biased measure of growth in econometric assessments.  The only thing I have to say:
Bravo, Krugman!
This is almost the best economic graph I have ever seen. Figure 2 presents it in my interpretation and shows that the change in unemployment rate, du, almost coincides with the change rate of GDP per capita, dlnG. In Figure 2, we have scaled the du with the following relationship:
dlnG = -2.37du+2.25
or
du= -0.42 dlnG + 0.95
Figure 3 presents results of regression: Rsq.=0.81. For the scaled du, the slope is 1.0.

However, the current fall in the rate of unemployment exceeds the predicted one. Somehow, the sensitivity of the du to dlnG becomes lower after 2000, and the slope of -1.6 and the intercept of 2.1 better describe the link.  For these values of slope and intercept, the rate of real economic growth, dlnG, should be ~2% per year for the rate unemployment retained at 9.6%. For the rate of unemployment to fall, one needs the real growth rate above 2% per year.
Figure 1 looks a bit biased. We have also to reconsider this post on unemployment as based on the change in labor force, which predicted the rate of unemployment around 8% in 2012.
Figure 2. Annual growth rate of real GDP per capita, dlnG, and the scaled rate of unemployment, du.

Figure 3. Scatter plot of the curves in Figure 2 and linear regression.


Figure 4. Same as in Figure 2 with the slope -1.5 and intercept +2.0 for the period after 1995.

7/8/11

Is the employment situation really disappointing? II

This post almost completely repeats our post on employment situation a month ago.
The Bureau of Labor Statistics has published an “Employment Situation Summary” for June. The nonfarm payroll employment has increased by 18,000 (establishment data). The number of employed in the U.S. has decreased by 445,000; from 139,779,000 to 139,334,000 (household data).  As in May 2011, these low numbers have come as a surprise for many experts, who expected 105,000 for the nonfarm payroll employment in June.

We have already 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 2 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. As a month before we can conclude that the today’s BLS figures are 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).

An open letter to the U.S. Bureau of Labor Statistics

Five years ago I published a paper on the link between inflation and labor force in the U.S. There was one stupid problem with the estimates of labor force level provided by the BLS. They competely  ignored the changes in so called population controls after decennial censuses. Briefly, any census reveals the difference between projected and directly enumerated populations. The former figures are projected from the previous census using estimated birth and death rates and net immigration. After any census, this difference called "the error of closure"  is proportionally distributed by the U.S. Census Bureau  (CB) over the previous decade. This prodecure makes all population time series reported by the CB smooth.

The BLS does not address this problem at all. Therefore, its labor force time series has several "bumps" of a million an more people per one month. One should not use this time series as it is in statistical of econometric assessments.  I had to redistribute all known bumps back into their  past and obtained relatively smooth time series. This simple procedure did not work well in 1991 and additional investigation was needed to recover the reason of ~1,000,000 step in the labor force series.

Recently, I have found a paper written by Marisa Di Natale "Creating Comparability in CPS Employment Series"  from the BLS (no publication date is specified). The author used the same method of the error of closure redistribution. It is a good paper with a simple but correct methodology. But do not believe it. The original time series has not been smoothed. I downloaded the most recent version of labor force time series )July 1, 2011) and found no changes in 1991 and 2001. Same sharp spikes:

Figure. Monthly increment of labor force in the U.S.


Conclusion: Do not trust BLS!

6/6/11

On the slow growth of working age population

There is a discussion on the Seeking Alpha of my post on the last Employment Situation Summary issued by the BLS several days ago. Lee Adler asked about the evolution of working age population, WAP, in the U.S. during the past 50 years. This question has arisen because I had shown only the last ten years in the post. These were the years of a steady decrease in the annual increment of the working age population, which is defined as the number of people of 16 years of age and over.  Lee is right; the annual increment has two peaks - in the 1970s and between 1998 and 2003 as Figure 1 shows.  During the 1980s and 1990s, the increment was at the level of 2,200,000 per year, and in the 2000s it fell from 3,000,000 and more per year to ~2,000,000 per year in 2008 and 2009. One should not trust the peaks in 2000 and 2003. These are caused by one-sided revisions of the total population after the 2000 census. The numbers were corrected after 2000 and 2003 but not before what created severe steps in the WAP.
Figure 2 depicts the evolution of the change rate of the WAP, dWAP/WAPdt or dlnWAP/dt. This is to show that in relative terms (the rate of unemployment and employment-population ratio are defined in relative terms) the current growth of the WAP is not fast from the historical point of view. The 2008 through 2010 values are the smallest since the early 1950s when the aftermaths of the Great Depression and WWII were the most painful. Thus, the current decrease in the growth rate of working age population is one of the reasons behind the slow employment recovery.

Figure 1. Annual increment of the working age population (black line) and its 5-year moving average (red line).

Figure 2. The rate of growth of the working age population (black line) and its 5-year moving average (red line).

6/4/11

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.

6/3/11

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). 

6/1/11

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

5/22/11

Labour force participation in Sweden and the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel

We have published a number of models for the rate of participation in labour force, LFP. The intuition behind the model is very simple. The growth in real GDP influences the labour force supply through redistribution of personal incomes. Fluctuations in real GDP per capita relative to that defined by inertial economic growth, A1/G, provide variations in the distribution of personal income relative to some inertial (or neutral) growth rate. The influence of the growth in real GDP on the LFP has to be complicated by the presence of exponential distribution of personal inputs to real GDP. If the effect of real growth is based on the excess of the total personal income above its potential (inertia) level, then higher levels of LFP are more sensitive to real growth. Really, more people can be included in or excluded from the redistribution because of their smaller personal incomes for paid jobs, which are replaced by some other (not measured) mechanisms of personal income earning. It is reasonable to assume that the sensitivity of LFP to the difference between actual and potential (inertial) growth rates, e(t)=dG/GA/G, grows exponentially with increasing LFP. In addition, there might be a time delay between action and reaction and the LFP may lag behind the e(t). Now we are ready for a quantitative analysis with a tentative relationship: 

{B1dLFP(t)/LFP(t) + C1}exp{ a1[LFP(t) - LFP(t0)]/LFP(t0) =

          = {dG(t-T))/G(t-T) – A/G(t-T)}dt

Here we present the model of labour force participation in Sweden. Figure 1 shows that the LFP is very well predicted since 1975. This model is valid for all developed countries. No macroeconomic model can predict the observed changes in LFP using only one macroeconomic parameter. Since the presented model describes the case of Sweden I also mean the latter laureates of the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel awarded “for their analysis of markets with search frictions” have failed to model the labour market and predict its evolution at the same level of accuracy and forecast horizon.

Why we need the sophisticated model not describing reality if there exists a simple model predicting as accurately as one can only dream?

                  
Figure 1. Observed and predicted LFP in Sweden: T=0.

3/31/11

A win-win monetary policy in Canada

We have published a working paper on structural breaks related to the introduction of  inflation targeting in Canada. The intuition behind the Lucas (1976) crituque is correct. The reader may enjoy the beauty, i.e. the simplicity and clearness,  of the integral approach. 

The paper is available on arXiv::

A win-win monetary policy in Canada

Abstract
The Lucas critique has exposed the problem of the trade-off between changes  in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an
empirical model quantitatively linking the rate of inflation and unemployment to the change in the level of labour force in Canada. Inherently, our model belongs to the class of Phillips curve models, and the link between the involved variables is a linear one with all coefficients of individual and generalized models obtained by empirical calibration. To achieve the best LSQ fit between measured and predicted time series cumulative curves are used as a simplified version of the 1-D boundary elements (integral) method. The distance between the cumulative curves (in L2 metrics) is very sensitive to structural breaks since it accumulates true differences and suppresses uncorrelated noise and systematic errors. Our previous model of inflation and unemployment in Canada is enhanced by the introduction of structural breaks and is validated by new data in the past and future. The most exiting finding is that the introduction of inflation targeting as a new monetary policy in 1991 resulted in a structural break manifested in a lowered rate of price inflation accompanied by a substantial fall in the rate of unemployment. Therefore, the new monetary policy in Canada is a win-win one.

2/23/11

Inflation and unemployment in Switzerland: from 1970 to 2050

We have started writing a new monograph. As always, it’s an exciting process. This time we would like to collect all results on inflation and unemployment in OECD countries and to carry out a rigorous statistical analysis. We include only those OECD members who provide an extensive statistics on inflation, unemployment and labor force. As a result, some countries will not be modeled.

Switzerland was not fully modeled in our monograph on mechanomics. We have written a paper and submitted it to the MPRA. Now it is available via RePEc:


Inflation and unemployment in Switzerland: from 1970 to 2050
Abstract

An empirical model is presented linking inflation and unemployment rate to the change in the level of labour force in Switzerland. The involved variables are found to be cointegrated and we estimate lagged linear deterministic relationships using the method of cumulative curves, a simplified version of the 1D Boundary Elements Method. The model yields very accurate predictions of the inflation rate on a three year horizon. The results are coherent with the models estimated previously for the US, Japan, France and other developed countries and provide additional validation of our quantitative framework based solely on labour force. Finally, given the importance of inflation forecasts for the Swiss monetary policy, we present a prediction extended into 2050 based on official projections of the labour force level.

2/13/11

The Australian Phillips curve and more

This blog helps us to voice new ideas before they are formalized in an article or working paper. In many cases, original ideas are partially wrong and mathematics has to be changed severely. This was the case with labour force participation rate and productivity. As a rule, our initial ideas are good enough and do not suffer big changes.

In January 2011, we posted on inflation and unemployment in Australia. Meanwhile we prepared a formal working paper and submitted it to www.arXiv.org and MPRA. The major difference with the blog posts is a complete description of all models and thorough statistical assessment which includes successful tests for cointegration. However, it needs slight polish before we send it to a journal.

Now the paper on Australia is available:

Abstract
A quantitative model is presented linking the rate of inflation and unemployment to the change in the level of labor force. The link between the involved variables is a linear one with all coefficients of individual and generalized models obtained empirically. To achieve the best fit between measured and predicted time series cumulative curves are used as a simplified version of the 1-D boundary elements method. All models for Australia are similar to those obtained for the US, France, Japan and other developed countries and thus validate the concept and related quantitative model.

1/25/11

Unemployment in Australia

Following the previous post on inflation in Australia, we present a similar model for the rate of  unemployment .

It has been empirically revealed and statistically tested that the rate of unemployment, in developed countries is a linear function of the change in labor force. We expect the same relationship to be valid for Australia. A simple trial-and-error method applied to cumulative unemployment published by the Australian Bureau of Statistics at a monthly rate (see Figure 1) allows to accurately estimating both coefficient in the linear relation:

UE(t) = -2.1dLF(t)/LF(t) + 0.0977; t>1995
UE(t) = -2.1dLF(t)/LF(t) + 0.131; t<1996 (1)

Because of the change in monetary policy around 1995, we had to split the modeled period into two segments: before and after 1995. The above relationships show that only free term did change in 1996 from +0.131 to +0.099. The slope in the linear relationship is the same over the entire period. All in all, the agreement between the annual and cumulative curves is excellent. One can predict the rate of unemployment at any time horizon using labor force projections. We have failed to find any projection published by the Australian Bureau of Statistics except the one between 1999 and 2016. Unfortunately, this projection was all wrong and heavily underestimated the growth in labor force. It predicted the level of labor force in 2016 at 10,800,000. In December 2010, the level of labor force was 12,132,900. This is good news, however. According to (1), a higher rate of labor force results in a lower rate of unemployment.
Figure 1. Upper panel. Monthly estimates of the rate of unemployment in Australia and that obtained from labor force using (1). Due to high-amplitude fluctuations in the monthly estimates of dLF/LF, the predicted curve is smoothed by a twelve-month moving average, MA(12). Lower panel. Cumulative values of the observed and predicted curves in the upper panel. Notice excellent agreement between the cumulative curves.

1/11/11

The rate of participation in labor force: an accurately predicted fall

In August 2009, we made a short term (five years) prediction of the rate of labor force participation, LFPR, in the US as based on our model [1]. A prediction at a longer horizon is also available from the population age pyramid. The contemporary level of LFPR was 65.4%, as reported by the Bureau of Labor Statistics (http://data.bls.gov/cgi-bin/surveymost). We predicted a quick fall in the level of LFPR in 2010:

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.

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.
In 2011, the BLS reported the level of LFPR in December 2010. It is 64.3%, i.e. only 0.2% higher than predicted in August 2009. We consider this prediction as an excellent one and thus the model is validated and having a godd predcitive power. Has anybody made a better prediction?

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.

12/27/10

Cross-country comparison: labor force participation in Canada and Italy

One of the most important requirements to a sound macroeconomic model is the capability to explain the difference in evolution of modelled parameters across developed countries.  For example, a consistent model of the rate of participation in labour force, LFP, has to describe the striking difference observed in the long-term behaviour of LFP in Canada and Italy. Figures 1 and 2 depict the measured (open circless), as provided by the BLS: http://www.bls.gov/data/, and predicted LFP. The latter is obtained from the model linking LFP to real GDP per capita only [1]. The GDP estimates are taken from the Conference Board data base (at GK PPPs).

Our model shows an exceptional predictive power for both countries. This accurate prediction is obtained despite the measured LFP in Canada has been growing since 1960 and that in Italy has been on decline.  Moreover, even short-term deviations from the overall trend are well predicted in time and amplitude. In Figures 1 and 2 we added two new measurements made in 2008 and 2009 to the original curves published in [1]. 

One can conclude that the model does not contradict actual measurements in Sweden, Canada, and Italy.  We are going to extend the set of countries and the duration of relevant time series.

Figure 1. Measured and predicted LFP in Canada.                  

Figure 2. Measured and predicted LFP in Italy. 
References
1. Ivan O. KITOV, 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. 3(3(5)_Fall), pages 203-222.

Labor force participation in Sweden

The labor productivity model discussed in the previous post is based on the concept linking labor force participation rate, LFP, to real GDP per capita [1]. This is a primary model, which explains the dynamics and the long-term behavior of labor force level in developed countries. As before, the LFP model is extremely parsimonious and uses only one (!) defining parameter to explain all variations in the observed behaviou of labor force in developed countries. As a consequence, one needs no other macro- or micro-economic variable to explain the portion of labor in total population.  
In this post, we do not formally introduce the quantitative model since it is available in the paper and monograph. Our purpose is to extend the previous data set by two years (2008 and 2009) since new observations are now available. This is in line with our validation strategy – to test all models with new data.
Figure 3.13 is borrowed from our monograph and illustrates the predictive power of the model for Sweden. The agreement between the original LFP estimates (open circles) and those predicted by the model is excellent in timing and amplitude. Considering the fact that only one defining variable is used the prediction suggests the presence of long-term on-to-one link between LFP and real GDP. (More examples in the paper and monograph.)
Figure 1 extends the original data set by two estimates (real GDP per capita reported by the Conference Board). The agreement is also excellent.  This observation evidences in favor of our model.
We will continue reporting the accuracy of LFP predictions for Sweden and other developed countries.



Figure 3.13. Observed and predicted growth rate of LFP in Sweden: N(1959)=100000, A2=$310 (1990 U.S. dollars), B=2.2∙106, C=-0.0465, T=0. Lower panel depicts the original LFP, changing in the range from 67% in 1990 to 62.5 % in 1998, and the predicted LFP.


Figure 1. Same as in Figure 3.13, but extended with data in 2008 and 2009. The original LFP series is  reported by the BLS.

References
1. Ivan O. KITOV, 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. 3(3(5)_Fall), pages 203-222.

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