Beauty pays. Why?

Are handsome people more productive?

In 1976, an attractive young secretary to a powerful US Congressman, Wayne Hays, acknowledged that she couldn’t type, file or answer the phone.  Elizabeth Ray's skills were rather more tactile than that.

In the past 20 years, economists have studied whether good looks lead to good wages.  The answer might help explain why fashion is a booming industry in Kazakhstan.  Production of clothes in January and February was 79% above that for the same period in 2014, according to the government’s committee on statistics.  The trick is to find an industry in which beauty adds nothing to productivity and yet earns a premium.  Congressmen may not be the only employers to discriminate in favor of knockouts.

The task is not simple.  Even in industries where beauty is not a work skill, it may enable the beautiful to produce more, because they are more self-assured.  In central banking, good looks don’t directly make inflation forecasts more accurate; but comely forecasters may have the confidence to present their tentative results to colleagues, gathering feedback that improves their work.  We must disentangle two effects of beauty on wages: The direct one, which reflects the employer’s tastes; and the indirect one, which reflects the employee’s confidence.  

In a 2005 experiment, Markus Mobius and Tanya Rosenblat construct a workplace in which beauty cannot directly affect productivity – the solving of computer mazes, reminiscent of those in white-rat studies.  In the experiment, each "worker" estimates the number of mazes that she thinks that she can complete in 15 minutes; this is a measure of her confidence.  A separate panel of high school students computes a beauty score for each worker by examining her photo.

Beauts and bias

Each "employer" estimates, for each worker, the number of mazes that she can solve, given what he knows of her, including her beauty.  These estimates are used to determine the worker’s expected wage.  Employers may learn about workers through resumes, photos, and interviews by phone or face-to-face.  If beauty matters to the employer, then he may estimate a higher productivity for a worker by examining her photo than he would have done had he only interviewed her by phone.

The researchers conclude that beauty raises the worker’s estimate of his own productivity – as well as the employers’ estimates.  Of the latter, roughly 80% relate to the bosses’ own observations of beauty, and 20% to the greater confidence of more beauteous workers. 

Beauty affects the employer’s estimate of productivity when he learns about the worker through photos, phone interviews, and -- above all -- through face-to-face interviews.  However, Mobius and Rosenblat find no evidence that employers discriminate in favor of the handsome.  They conclude this by looking at whether beauty increases the employer’s estimate of productivity when he knows that this calculation will determine the worker's wage.  Beauty has no such effect. 

In the study, good looks affect the employer’s estimate of productivity even when he only interviews her over the telephone.  Mobius and Rosenblat speculate that beauty may relate to “certain oral communication skills” aside from influencing confidence.  Maybe Ms. Ray should have learned how to answer the phone. 

Curiously, gender does not affect the worker’s confidence.  But men solve 30% more mazes than women do, perhaps because men are naturally rats.

The experiment involved 165 “employers” and 165 “employees” at La Universidad de Tucuman in Argentina.  –Leon Taylor tayloralmaty@gmail.com  

References

Markus M. Mobius and Tanya S. Rosenblat.  Why beauty matters.  American Economic Review 96(1): 222-35.  2006.  The working paper, upon which I draw, was published by the National Bureau of Economic Research in 2005.

Kazakhstan Ministry of National Economy, Committee on Statistics.  Socio-economic development of the Republic of Kazakhstan.  February 2015.  Online.

Wikipedia.  Elizabeth Ray.  Accessed March 25, 2015.

Russian roulette

Is the Bank of Russia stoking inflation?

When the leaders of Russia’s central bank met last week, they had to choose between their monetarist principles and the Kremlin.  The outcome was never in doubt.

The bank’s ostensible duty is to contain inflation, which may peak at 20% this year.  This would mean printing fewer rubles, which are feeding high prices.   But contracting the money supply would also curtail spending.  That would destroy jobs and deepen a recession that already may shrink the economy as much as 4% to 7% this year.  So the Kremlin prefers more rubles to fewer.  And the Bank of Russia, under Elvira Nabiullina, is playing along by cutting interest rates – this time by 1% on the key rate, to 14%; last January, by 2%. 

Like most large central banks, Russia’s governs the money supply by tweaking the interest rate on securities – in Moscow’s case, on “repo” loans that mature within a week.  When the bank buys more repos, it forces up their price, which means a fall in the return that they pay to their holder.  That return is an interest rate.  By buying a lot of securities, the central bank can force interest rates to fall throughout the economy. 

The Bank of Russia also affects the money supply directly:  When it buys the security, it pays out rubles.

The Bank says that won't create inflation since this is due not to abundant money but to weakening of the ruble, which raises domestic prices for foreign products, and to "external trade restrictions."  These factors will disappear by the end of the year, it claims.       

Certainly, money growth in 2014 did not cause much concurrent inflation.  M2 -- "broad money," which includes cash and most monetary accounts except for credit -- rose only 2.2%, while consumer prices rose 11.4%, according to the Bank of Russia.  For 2010 through 2013, the correlation between M2 growth and inflation in the same year was only .53.  And as long as the Russian economy remains below capacity, inflation is unlikely to soar.  Sellers won't raise prices when they're losing their shirts.  

Nevertheless, money may fuel inflation, although it may may take more than a year to show up.  Over the long run, Russia has had both high inflation and rapid money growth.  For 2010-13, inflation averaged 6.8%; M2 growth, 20%.  When the Russian economy is running again at full speed, sellers won't be shy to raise prices -- and buyers, flush with rubles, won't be shy to pay them.

Incredible

Even if the Bank manages to mop up the excess rubles in time, it may not be able to repair the damage done by easy money to its credibility as an inflation fighter.  When the economy begins surging, sellers may doubt that the Bank will curtail impending inflation.  So they will raise prices immediately, since inflation will raise their input costs and erode the purchasing power of their earnings.  Similarly, households may buy goods right away rather than wait for prices to get out of hand.  So even if the Bank can tighten the money supply, the rate of spending (the jargon is "velocity") may rise, pressuring prices to rise.  
     

Easy money may soften the recession, but it probably won’t address the real problem.  The Kremlin seems to think that the recession is driven by a fall in demand for Russian products.  Were this true, prices as well as output would fall.  In reality, both unemployment and prices are climbing -- the classic symptoms of stagflation.  This is usually due to a decline in supply:  When input costs increase, firms produce less than before at given prices for their goods and services.  This will eventually drive up those prices.

The most obvious cause of the stagflation is the Western sanction against Russia.  This has cut off some foreign inputs to Russia -- and raised the domestic price of others, via a 40%-plus plunge in the ruble since last summer.  To end the recession, the Kremlin could come to terms with the US and Europe over its intervention in Ukraine.  Fat chance.  So the best that the Russian people can hope for is some relief from unemployment, bought at the expense of accelerated inflation.  Twenty percent?  You ain’t seen nothing yet. –Leon Taylor tayloralmaty@gmail.com

Notes 

Data on Russian consumer inflation and M2 money come from the Bank of Russia and from the World Bank.  Curiously, the Bank of Russia does not provide English translations for recent inflation statistics, although it does post them in Russian.   

References

Howard Amos.  Political pressure fears as Russia’s central bank set to cut rates.  Moscow Times.  March 13, 2015.

Bank of Russia.  Interest rates on the Bank of Russia operations.  Accessed March 15, 2015. Online. 

Bank of Russia Press Service.  On Bank of Russia key rate.  March 13, 2015.  Online.

Bank of Russia.  Inflatsiia na potrebitel'skom rynke Dekabr' 2014 god.  [Inflation in consumer markets, December 2014.]  Accessed March 15, 2015.  Online.

Olga Tanas and Anna Andrianova.  Russia lowers key rate to 14% as inflation eases amid slump.  Bloomberg.  March 13, 2015.  Online.

World Bank.  World Development Indicators.  Online.

The trouble with infinity

Can we estimate the value of a life?

Government programs that try to keep people alive longer, such as policies curtailing pollution and accidents, must somehow value the ensuing benefits in order to know how much to spend.  If we have only $500,000 to spend on either of two programs, with one worth $800,000 and the other only $300,000, then the choice is clear.  But since the usual benefit is an extension of life, we cannot avoid considering the economic value of a life.    

Assume that a life is worth an infinite number of dollars.  It’s certainly reasonable.  Now consider two programs: Plan A would save 50 million lives, and Plan B would save 1 life.  Both would cost the same amount.  Which would we adopt?

Surely, this is a no-brainer: Plan A.  But look again.  Since both programs have the same cost, we would prefer the plan with the higher value.  Apparently, Plan A is worth 50 million times infinity, and Plan B is worth 1 times infinity.  But these estimates are absurd, since infinity is not a cardinal number.  We cannot conclude that 50 million times infinity is greater than infinity, because infinity is incomparable.  If we are to choose between the programs, then we must assume a finite value of life.

The problem does have a practical solution although it is not fully satisfying.  Rather than consider the value of a life per se, think about the value of a slight reduction in the risk to life.  The value of a 1% reduction may be $10,000; of a 2% reduction, $30,000, and so on.  Each of these decreases of risk has a finite value.  And yet life itself has an infinite value, because the value of going from a 100% risk of death to a 99% risk is infinite.  The value of a life is roughly the sum of the values of all the slight reductions, including the one from 100% to 99%, the one from 99% to 98%, and so forth, down to the last one, from 1% to 0%.  This sum is infinite since part of it is infinite. 

No way out? 

This approach is workable, because most programs do attempt small reductions of risk when life is already pretty safe – for example, the avoidance of fatal fires, by requiring a fire detector in each apartment.  It’s sensible to think that such a small decrease of risk has a finite value since you are not willing to pay your entire income for a fire detector; $10 or $15 should do it.  This is like going from a 2% risk of death to a 1% risk. 

Yet the approach is not entirely satisfying. In principle, we should have some way to also evaluate the value of cutting risk from 100% to 99%, since some programs may attempt this reduction.  But we don’t have any such practical method.

Another problem with the marginal approach is that it implies a vastly disproportional change in the value of a reduction of risk.  A decrease in the risk of death from 100% to 99% is worth far more than 100 times the value of a decrease in risk from 2% to 1%.  In fact, the first risk is worth any number of the second risk or even of most larger risks.  But are we truly willing to devote all our time and money to cutting the risk of death to 99% for one advanced cancer patient rather than to reduce risks from 90% to near zero for a million patients in earlier stages of cancer?  Perhaps we must either assume a finite value of life or compare programs without using values.  

One possibility is to prefer the program that saves more lives at the same total cost as other programs.  This rule is enticing but arbitrary -- and too specialized to be of much use, since it applies only to the case of equal costs.  If costs differ between programs, then we must compare them to the various values, which is the dilemma that we were trying to avoid. –Leon Taylor tayloralmaty@gmail.com 


 Notes

Define p as the risk of an early death in percentage terms, varying from 0 through 100.  Define MV(p) as the marginal value of avoiding an early death – that is, of continuing  a life – for given p.  The variables p and MV are continuous.  Integrating MV(p) over the full range of p gives us the total value of a life, which is an improper integral.