The Market Financial

Krugman and damned lies about income inequality. No politics

Staff and Wire Reports — Sun, 05 Feb 2012 19:01:00 +0000

Paul Krugman and a bigger company have been speculating on the increasing economic inequality in the US. They do not trust any data from the BLS (income measurements obtained during Current Population Surveys) and deny that income data from censuses can be used to characterize Gini coefficient since these data sets do not contain higher incomes. They claim that the most interesting processes have been evolving at very high incomes. In this post, I am going to justify the estimates of Gini reported by the BLS. My goal is to extend the distribution of personal incomes to as high level as possible and to demonstrate that this distribution follows up the Pareto distribution, i.e. is well described by a simple power law. This observation allows replacing (interpolate) actual measurements with a simple function when calculating the Lorenz curve and thus Gini coefficient.

Following this direction, we have recently reported that the personal income distribution, PID, in the USA does not change with time when normalized to the total population and total income. In other words, the relative distribution of personal income in the United States has not been changing since the start of income measurements in 1947. The accuracy of early measurements is not good enough, however, and we have to rely of the most recent results.

The US Census Bureau routinely reports income estimates obtained during the Annual Social and Economic Supplement of the Current Population Surveys. We begin with the higher income range as reported by the BLS and have retrieved the population distribution over mean income in the range from $0 to $250,000. These distributions are available only from 2000. The relevant measurements of the number of people in a given income range were carried out in $2500 bins between $0 and $100,000 and $50000 bins between $100,000 and $250,000.

The personal income distributions, as reported by the BLS in current dollars, are affected by the change in population (working age population), and nominal GDP growth. Also the width of income bins varies with income level. Therefore, one cannot directly compare PIDs obtained in different years. In order to suppress the influence of the width we have calculated the population density, i.e. the ratio of the number of people in a given bin and its width. Since the personal income is measured in current dollars we have to reduce all incomes by the total change of the GDP deflator since 2000 to a given year. Figure 1 shows the result of normalization for 2000, 2005, and 2010. In relative terms, the income distribution has not been changing since 2000. At higher incomes, all three curves are practically identical. This observation is validated by the estimates of Gini coefficient provided by the Census Bureau. There is a high income cap of $250,000 (all incomes above the cap are gather in one group), which is used by Krugman and company to deny the BLS estimates.

Let’s take a look the data they used to prove the increasing inequality. The IRS measured incomes are usually referred to. Without loss of generality, we have retried “Table 1.1 Selected Income and Tax Items, by Size and Accumulated Size of Adjusted Gross Income, Tax Year 2009”. (Any other year between 1996 and 2009 is good as well.) This Table lists individual incomes in various income bins from $1 to $10,000,000. There are also 8274 reports of income above $10,000,000. We cannot use the latter incomes but definitely can plot the population density function for all incomes below $10,000,000. Figure 2 depicts the whole PID and Figure 3 its high income portion. The higher incomes are well approximated by a power low with an exponent of -3.07. (The difference of ~1.0 from the exponent for the BLS PDF (-4.1) is completely explained by the normalization to the total personal income reported by the BLS. It means that both exponents are identical.) It is likely that the same power law is valid at incomes higher than $10,000,000. Hence, there is no significant deviation (except measurement errors) from the Pareto distribution even at very high incomes and our extrapolation of the BLS incomes along the power law is valid for the calculations of Gini coefficients.

Conclusion: there is no growth in income inequality. Krugman et al. definitely exaggerate. As a Russian physicist, I have no political or any other emotional prejudice to the income distribution in the USA. I just calculate it.

Figure 1. The population density function, PDF, as a function of mean income as normalized to the total personal income for a given year. At higher incomes, the curves are practically identical.

Figure 2. Population density function reported by the IRS.

Figure 3. Population density function reported by the IRS for high incomes. The Pareto distribution is obvious. Fluctuations are likely related to measurement error.

Krugman and damned lies about income inequality. No politics

Staff and Wire Reports — Sun, 05 Feb 2012 19:01:00 +0000

Figure 1. The population density function, PDF, as a function of mean income as normalized to the total personal income for a given year. At higher incomes, the curves are practically identical.

Figure 2. Population density function reported by the IRS.

Figure 3. Population density function reported by the IRS for high incomes. The Pareto distribution is obvious. Fluctuations are likely related to measurement error.

S&P 500 in 2012

Staff and Wire Reports — Sun, 05 Feb 2012 07:47:00 +0000

In August 2011, we routinely revisited our model of the S&P 500 returns where the driving force of the stock market is real GDP. Our quantitative model predicted a negative correction of the S&P 500 level in August-October 2011, as shown in Figure 1 borrowed from the post in August. Figure 2 shows that our prediction was accurate and the annual S&P 500 returns dropped to the level of 0.005 and even below. Hence, the model does predict major turns in the evolution of S&P 500. From Figure 2, we expect the index to grow in the beginning of 2012. This prediction is supported by the rate of unemployment reported for January 2012. According to the link between real GDP per capita and the rate of unemployment in the US (8.3%) we expect real GDP to grow at a rate above 3% (SAAR) in the first and second quarters.

Here, we update our model with the revised GDP estimates and include the advance GDP estimate for the fourth quarter of 2011. The monthly closing prices through January 2012 are used. As discussed in our working paper on the S&P 500 index, there exists a trade-off between the growth rate of real GDP, G(t), and the S&P 500 return, R(t). The predicted returns, R_p(t), can be obtained from the following relationship:

R_p(t) = 0.0054dlnG(t) - 0.03 (1)

where G(t) is represented by the Q/Q (annualized) growth rate, because only quarterly readings of real GDP are published by the BEA.

Figure 2 displays the observed S&P 500 returns and those obtained using real GDP. As before, the observed returns are MA(12) of the monthly returns. For the predicted curve, we use the same GDP value for all three months in a given quarter.

The period after 2003 is relatively well predicted. The updated GDP estimates highlighted two strong deviations from the observed trajectory started in February 2010 and February 2011. During the first excursion, the predicted curve returned to the observed one in May 2010. One might speculate that these excursions were caused by quantitative easing. In any case it was a transitory deviation.

One of the sources of controversial information is the BEA. It routinely revises all historical estimates of real GDP and introduces significant changes affecting any model referring to GDP data. Figure 3 depicts the prediction of S&P 500 returns carried out in March 2011 in this blog. The predicted curve fits the measured returns with a high accuracy for the period between 2007 and 2011. After the comprehensive GDP revision published in July 2011, the fit disappeared due to much smoother time series. This is not the final revision, however, and some of the fit in Figure 3 can be still recovered in the future.

Figure 4 illustrates the increasing volatility in the monthly closing S&P 500 returns since 2009. This is a clear sign that the economy and financial market are far from the stability observed in the mid-1990s and between 2004 and 2007.

Figure 1. The prediction given in August 2011. The predicted curve is smoothed by MA(4). The annual S&P return was predicted to drop to 0.005 and below by October 2011 as shown by red diamonds.

Figure 2. The observed S&P 500 returns and that predicted from real GDP through January 2012. The predicted fall did happen and the current expectation is that the S&P return will grow into 2012. This prediction is supported by the fall in unemployment reported for January 2012.

Figure 3. The S&P returns predcited from real GDP in March 2011. The peak in 2010 was well described by contemporary GDP estimates. The peak disappeared (was ironed out) after the comprehensive GDP revision in July 2011.

Figure 4. The S&P monthly (closing price) returns since 1990. It should be noted that the overal volatility has been increasing since 2009.

S&P 500 in 2012

Staff and Wire Reports — Sun, 05 Feb 2012 07:47:00 +0000

R_p(t) = 0.0054dlnG(t) - 0.03 (1)

where G(t) is represented by the Q/Q (annualized) growth rate, because only quarterly readings of real GDP are published by the BEA.

Figure 1. The prediction given in August 2011. The predicted curve is smoothed by MA(4). The annual S&P return was predicted to drop to 0.005 and below by October 2011 as shown by red diamonds.

Figure 4. The S&P monthly (closing price) returns since 1990. It should be noted that the overal volatility has been increasing since 2009.

The rate of unemployment in Italy – a well predicted rise

Staff and Wire Reports — Sat, 04 Feb 2012 17:33:00 +0000

A new estimate of unemployment rate in 2011 is now available for Italy. In December 2011, it almost touched 9.0%. Here we validate our model of unemployment as a function of the change in labour force.

We introduced the model of unemployment in Italy in 2008 with data available only for 2006. The rate of unemployment was near its bottom at the level of 6%. The model predicted a long-term growth in the rate unemployment to the level of 11% in 2013-2014.

The agreement between the measured and predicted unemployment estimates in Italy validates our concept which states that there exists a long-term equilibrium link between unemployment, u_t, and the rate of change of labour force, l_t=dLF/LFdt. Italy is a unique economy to validate this link because the time lag of unemployment behind l_t is eleven (!) years.

The estimation method is standard – we seek for the best overall fit between observed and predicted curves by the LSQR method. All in all, the best-fit equation is as follows:

u_t = 5.0l_t-11 + 0.07 (1)

As mentioned above, the lead of l_t is eleven years. This defines the rate of unemployment many years ahead of the current change in labour force. Figure 1 presents two versions of unemployment as defined by the U.S. Bureau of Labor Statistics (BLS) and the OECD. We describe the estimates provided by the OECD (labour force estimates also obtained from the OECD) but have to emphasise that the divergence before 1994 makes it difficult to find a unique model for both agencies.

Figure 2 presents the observed unemployment curve and that predicted using the rate of labour force change 11 years ago and equation (1). Since the estimates of labour force in Italy are very noisy we have smoothed the annual predicted curve with MA(5). All in all, the predictive power of the model is excellent and timely fits major peaks and troughs after 1988. The period between 2006 and 2011 was predicted almost exactly. This is the best validation of the model – it has successfully described a major turn in the evolution of unemployment near its bottom. No other macroeconomic model is capable to describe such dramatic turns many years ahead. As four years ago, we expect the peak in the rate of unemployment in 2013-2014 at the level of 11%.

The evolution of the rate of unemployment in Italy is completely defined ten year ahead. Since the linear coefficient in (1) is positive one needs to reduce the growth in labour force (see Figure 3) in order to reduce unemployment in the 2020s. For the 2010s everything is predefined already and the rate of unemployment will be high, i.e. above 9%.

Figure 1. The rate of unemployment in Italy as measured by the BLS and OECD.

Figure 2. Observed and predicted rate of unemployment in Italy.

Figure 3. The rate of growth in labour force.

Employment Situation: the effect of population controls and seasonal adjustment

Staff and Wire Reports — Sat, 04 Feb 2012 16:34:00 +0000

When interpreting labor statistics one should be very careful with Januaries. This is the month when major changes to the population estimates (including so called population controls, i.e. the distribution of population over age/sex/race) are introduced. Briefly, all corrections to the overall population and its components gathered during the previous year, or ten years after decennial censuses, are introduced in January as a step in the relevant times series by the Bureau of Labor Statistics. The Bureau explicitly explains this trick in its documents.

Figure 1 shows how big were these corrections in January 2012. We have displayed the first differences of several time series. The population corrections (the updated population controls) are applied to the civilian population (16 years and over), CP. The level of labor force, LF, the employment, E, and the number of unemployed, UE, is recalculated accordingly as the portions of the CP measured in the household surveys, e.g. the rate of unemployment. The 2012 correction is the highest since 2003, when the 2000 census was inserted in the time series. The adjustments to the population estimates change these rates only slightly. For example, the unemployment rate does not change and the employment-population ration rose due to the adjustment to the population controls. In January 2012, the civilian noninstitutional population rose by 1685000 including 1510000 due to the change in the population controls. This number is higher than in 2003, when the 2000 census was included in the CP estimates.

Another issue is the rate of unemployment. The population controls do not change this rate much. Figure 2 compares the rates as obtained with and without seasonal adjustment. The NSA rate for January is 8.8% due to peaks in this rate in January 2010 (10.6%) and 2011 (9.8%). The NSA rate will be also above the SA rate in February as well.

Figure 3 shows the number of unemployed. In January 2012, it increased by 849,000 in absolute values and decreased by 330,000 in seasonally adjusted representation. The absolute growth is partially related to the change in population estimates (controls) and partially to the seasonal adjustment.

Figure 1. The first differences of the civilian population (16 years and over), CP, the level of labor force, LF, the employment, E, and the number of unemployed, UE, time series. The 2012 correction is the highest since 2003, when the 2000 census was inserted in the time series.

Figure 2. The rate of unemployment, UER, as measured with seasonal adjustment, SA, and without seasonal adjustment, NSA. The NSA value for January is 8.8%.

Figure 3. The number of unemployed, UE, as measured with seasonal adjustment, SA, and without seasonal adjustment, NSA.

Suppressing Volatility and The Black Swan of Cairo

Staff and Wire Reports — Fri, 03 Feb 2012 17:30:00 +0000

First published in the May/June 2011 issue of Foreign Affairs, The Black Swan of Cairo: How Suppressing Volatility Makes the World Less Predictable and More Dangerous is a thought-provoking effort by co-authors Nassim Nicholas Taleb and Mark Blyth to advance the idea the efforts of policy-makers to smooth out the peaks and troughs of volatility actually has the unintended consequence of making the world a more volatile place.

I was reminded of the Taleb and Blyth article when I recently read Suppressing Volatility Makes the World More Dangerous, by Kurt Cobb of Resource Insights. Here Cobb extends the thinking of Taleb and Blyth and argues that not only do efforts to suppress volatility backfire in the economic and political realms, but also in areas such as agriculture and public health.

Of course, I could probably argue that Jeff Goldblum’s ranting against the instability of complex systems in Jurassic Park some two decades ago outflanked Taleb, Blyth and Cobb, but on a week when a low VIX seems to have many vexed, ruminating on the ideas of Taleb, Blyth and Cobb may help readers flesh out some insights into what may lie ahead. Along the same lines, I believe the links below might also contain some provocative and related thought starters.

Disclosure(s): none

Casino Stocks Are A Wild Card

Nicholas Santiago — Fri, 03 Feb 2012 16:01:43 +0000

Wynn Resorts Ltd (NASDAQ:WYNN) is considered the leading casino stock in the market. This morning, WYNN stock is trading lower by $5.27 to $115.59 a share. Yesterday after the closing bell, the company released earnings that were below analyst expectations. WYNN stock has been struggling to trade above the December 2011 highs which tell us that the stock has weak relative strength. Traders can watch for intra-day support around the $114.00, and $112.00 levels.

MGM Resorts International (NYSE:MGM), and Las Vegas Sands Corp (NYSE:LVS) are two leading casino stocks that are trading higher despite the poor reaction to WYNN stock. It is important to note that MGM and LVS are getting extended on the daily charts and may need to pull back soon.

Nicholas Santiago
InTheMoneyStocks.com

The Levels To Know In The Stock Market

Nicholas Santiago — Thu, 02 Feb 2012 19:16:54 +0000

Tomorrow morning, prior to the stock markets open, Non Farm Payrolls will be reported along with the Unemployment Report. This number will guide the markets for one day but have little long term impact. In 2012, volume has been missing. This is mainly due to the lack of buying by institutions. If institutions are not buying, we should all be aware and on alert. The SPDR S&P 500 ETF (NYSEARCA:SPY) is trading at $132.62, +0.15 (0.11%).

I have said it before but will say it again. The $133.30 level on the SPY is the master pivot. One week ago it was tagged. Since then, the markets have not hit that level again. As long as we do not take that point out on a closing basis, I remain slightly bearish, regardless of the light volume float.

The only thing that will slam the markets hard is an eruption out of Europe. Something that shocks Wall Street and the world. As of now, economic news is a one day event and mostly factored in. As long as the markets stay below the $133.30 level on the SPY, and have no major news from Europe, expect a slow steady consolidation pull back. Should the markets close above the $133.30 level, they can easily head to $135.50.

Gareth Soloway
InTheMoneyStocks.com

Slaying the Natural Gas Contango Dragon

Staff and Wire Reports — Thu, 02 Feb 2012 17:57:00 +0000

Yesterday’s post on Natural Gas, Contango and UNG appears to have generated a fair amount of interest across a broad base of readers, so for an encore I have decided to forego the typical collection of dazzling Liszt miniatures and skip directly to more on the ways to benefit from the persistent contango and negative roll yield in natural gas.

Starting with the graphic below, I have plotted the performance of natural gas (red line) and three natural gas ETPs since June 16, 2011:

United States Natural Gas Fund (UNG) – blue line
United States 12 Month Natural Gas Fund (UNL) – green line
UBS ETRACS Natural Gas Futures Contango ETN (GASZ) – pink(ish) line

The reason the graph begins in June 2011 is that it marks the launch of GASZ; the other two ETPs have a much longer track record.

First, note that UNG does not attempt to minimize its exposure to contango. Like many other futures-based ETPs, its objective is to hold a one-month weighted average constant maturity in its portfolio and it does this by buying second month futures and selling front month futures. UNL, launched after UNG, was an attempt by the same issuer to minimize contango by holding twelve months of natural gas futures contracts on the assumption that contango is likely to be steepest at the front end of the futures curve and flatter in the more distant months. As the chart below shows, the recent performance differential between UNG and UNL has been minimal.

The UBS ETRACS product, GASZ, takes a completely different approach and is based on a natural gas futures spread index that shorts the front month and is long some of the more distant months. In other words, this ETP is specifically designed to take advantage of contango. According to UBS:

“The ISE Natural Gas Futures Spread™ Index, through a series of investments in natural gas sub-indices, effectively provides short exposure in front month natural gas futures contracts and long exposure in mid-term natural gas futures contracts. This is achieved by taking a 100% long position in the components of the ISE Short Front Month Natural Gas Futures™ Index, which provides short (or inverse) exposure to the ISE Long Front Month Natural Gas Futures™ Index and an aggregate 100% long position in the components of the ISE Twelfth Month Natural Gas Futures™ Index, ISE Thirteenth Month Natural Gas Futures™ Index and ISE Fourteenth Natural Gas Futures™ Index (33.33% per index), which provides long exposure to the mid-term Henry Hub Natural Gas Futures (NG) futures contracts. The index is rebalanced monthly before the Sub-Indices’ roll process to maintain the 1:1 ratio.”

For more information, check out the GASZ web site and prospectus.

The results, at least as seen in the chart below, show that the GASZ approach has some promise insofar as the last eight months are concerned. To be fair, GASZ is very thinly traded and has yet to inspire a broad group of investors, but here is an approach that is not likely to be correlated with any strategies investors are currently running and has been racking up profits in a sideways (at least for equities) market.

Of course investors can always short UNG, but I believe that in much the same manner that ZIV is undeservedly neglected as an inverse VIX futures contango play, so is GASZ overlooked for the same reasons. These are two ETPs with a lot of potential that deserve a broader audience.

Finally, as a side note, UNG announced late yesterday that it will undergo a reverse 1-4 split following the market close on February 21. Here is a product that is down more than 40% in each of the last three years and is already down more than 21% in 2012. Don’t be surprised if this is not the last reverse split.

[source(s): United States Natural Gas Fund]

Disclosure(s): long GASZ and ZIV, short UNG at time of writing