For Richer for Poorer.

As probably many of you know, there is a cloud hanging over the future of the US and many other countries, the cloud of wealth concentration. There is nothing in life that will not be affected by this phenomenon. It obviously affects our allocation of resources and  it will have tremendous influence on who gets what. It would affect the relations between the social classes and could lead to social unrest if we allow the fissure between the haves and have nots to increase. The following is only one part of an excellent article that speaks to this issue.

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Growing inequality is one of the biggest social, economic and political challenges of our time. But it is not inevitable, says Zanny Minton Beddoes

Oct 13th 2012 | from the print edition of The Economist

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IN 1889, AT the height of America’s first Gilded Age, George Vanderbilt II, grandson of the original railway magnate, set out to build a country estate in the Blue Ridge mountains of North Carolina. He hired the most prominent architect of the time, toured the chateaux of the Loire for inspiration, laid a railway to bring in limestone from Indiana and employed more than 1,000 labourers. Six years later “Biltmore” was completed. With 250 rooms spread over 175,000 square feet (16,000 square metres), the mansion was 300 times bigger than the average dwelling of its day. It had central heating, an indoor swimming pool, a bowling alley, lifts and an intercom system at a time when most American homes had neither electricity nor indoor plumbing.

A bit over a century later, America’s second Gilded Age has nothing quite like the Vanderbilt extravaganza. Bill Gates’s home near Seattle is full of high-tech gizmos, but, at 66,000 square feet, it is a mere 30 times bigger than the average modern American home. Disparities in wealth are less visible in Americans’ everyday lives today than they were a century ago. Even poor people have televisions, air conditioners and cars.

But appearances deceive. The democratisation of living standards has masked a dramatic concentration of incomes over the past 30 years, on a scale that matches, or even exceeds, the first Gilded Age. Including capital gains, the share of national income going to the richest 1% of Americans has doubled since 1980, from 10% to 20%, roughly where it was a century ago. Even more striking, the share going to the top 0.01%—some 16,000 families with an average income of $24m—has quadrupled, from just over 1% to almost 5%. That is a bigger slice of the national pie than the top 0.01% received 100 years ago.
This is an extraordinary development, and it is not confined to America. Many countries, including Britain, Canada, China, India and even egalitarian Sweden, have seen a rise in the share of national income taken by the top 1%. The numbers of the ultra-wealthy have soared around the globe. According to Forbes magazine’s rich list, America has some 421 billionaires, Russia 96, China 95 and India 48. The world’s richest man is a Mexican (Carlos Slim, worth some $69 billion). The world’s largest new house belongs to an Indian. Mukesh Ambani’s 27-storey skyscraper in Mumbai occupies 400,000 square feet, making it 1,300 times bigger than the average shack in the slums that surround it.

The concentration of wealth at the very top is part of a much broader rise in disparities all along the income distribution. The best-known way of measuring inequality is the Gini coefficient, named after an Italian statistician called Corrado Gini. It aggregates the gaps between people’s incomes into a single measure. If everyone in a group has the same income, the Gini coefficient is 0; if all income goes to one person, it is 1.
The level of inequality differs widely around the world. Emerging economies are more unequal than rich ones. Scandinavian countries have the smallest income disparities, with a Gini coefficient for disposable income of around 0.25. At the other end of the spectrum the world’s most unequal, such as South Africa, register Ginis of around 0.6. (Because of the way the scale is constructed, a modest-sounding difference in the Gini ratio implies a big difference in inequality.)

Employment Conundrum

As soon as the US government released the unemployment results for September showing that nonfarm employment increased by 114,000 (anemic) but yet the rate of unemployment dropped substantially from 8.1 to 7.8 many of the conservative politicians and econimic analysts cried foul. Jack Welsh, the former GE CEO tweeted"If you can't debate then you fix the numbers"  and a CNBC personality said: "I told you that they would get the number below 8% just before the elections". All of the above comes under the category of sour grapes. No one who knows anything about the BLS would ever make such an accusation because it is baseless and is something that will be next to impossible to achieve . Over 50 different individuals work on these figures and not a single one has the power to manipulate them. The following is a great explanation of what the unemployment figures mean, as presented by Greg Manikw one of "star" economists in the US.


If you go to the recent release from the BLS, you can find these two sentences a few paragraphs apart:

Total employment rose by 873,000 in September.

Total nonfarm payroll employment increased by 114,000 in September.

To a layman, this may seem confusing.  The first statement suggests a robust labor market, the second a more lackluster one.  What is going on?

The issue is that there are two surveys.  The first estimate of employment comes from the survey of households; the second is from the survey of establishments.  I thought readers might like to hear what my favorite intermediate macro textbook says about this issue.  Here is an excerpt:

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Because the BLS conducts two surveys of labor-market conditions, it produces two measures of total employment. From the household survey, it obtains an estimate of the number of people who say they are working. From the establishment survey, it obtains an estimate of the number of workers firms have on their payrolls.

One might expect these two measures of employment to be identical, but that is not the case. Although they are positively correlated, the two measures can diverge, especially over short periods of time. A particularly large divergence occurred in the early 2000s, as the economy recovered from the recession of 2001. From November 2001 to August 2003, the establishment survey showed a decline in employment of 1.0 million, while the household survey showed an increase of 1.4 million. Some commentators said the economy was experiencing a “jobless recovery,” but this description applied only to the establishment data, not to the household data.

Why might these two measures of employment diverge? Part of the explanation is that the surveys measure different things. For example, a person who runs his or her own business is self-employed. The household survey counts that person as working, whereas the establishment survey does not because that person does not show up on any firm’s payroll. As another example, a person who holds two jobs is counted as one employed person in the household survey but is counted twice in the establishment survey because that person would show up on the payroll of two firms.

Another part of the explanation for the divergence is that surveys are imperfect. For example, when new firms start up, it may take some time before those firms are included in the establishment survey. The BLS tries to estimate employment at start-ups, but the model it uses to produce these estimates is one possible source of error. A different problem arises from how the household survey extrapolates employment among the surveyed households to the entire population. If the BLS uses incorrect estimates of the size of the population, these errors will be reflected in its estimates of household employment. One possible source of incorrect population estimates is changes in the rate of immigration, both legal and illegal.

In the end, the divergence between the household and establishment surveys from 2001 to 2003 remains a mystery. Some economists believe that the establishment survey is the more accurate one because it has a larger sample. Yet one recent study suggests that the best measure of employment is an average of the two surveys. [George Perry, “Gauging Employment: Is the Professional Wisdom Wrong?,” Brookings Papers on Economic Activity (2005): 2.]

More important than the specifics of these surveys or this particular episode when they diverged is the broader lesson: all economic statistics are imperfect. Although they contain valuable information about what is happening in the economy, each one should be interpreted with a healthy dose of caution and a bit of skepticism.