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Wal-Mart and Disaster Supplies

A few days ago I commented on an article about how Wal-Mart provides emergency supplies to disaster areas at their regular everyday low prices. I pointed out that libertarians like those at Café Hayek should note these developments and see that some guy in the back of a pick-up truck selling supplies at high prices was not the only way to provide disaster victims additional supplies.

Russ Roberts took exception to what I said and we went though a long discussion of it over at his blog. He started out claiming that Wal-Mart had to face higher costs because of the higher cost of carrying inventories. He finally conceded that my explanation of how Wal-Mart had revolutionized inventory management and that when you looked at how the computer revolution had altered the way big-box retailers managed inventories that I was right in arguing that the cost of inventory management did not mean that Wal-Mart had to charge higher prices.

http://cafehayek.typepad.com/hayek/

But he was still unwilling to concede that Wal-Mart did not have to raise prices, claiming that this is what theory shows and we have a long history of the world working this way.

I think the difference stems from the way academic economist view supply curves. They look at supply – demand diagrams and see supply curves as continuous curves rising from the lower left to the upper right of the diagram. It is a beautiful theory and is a great tool for explaining to students how the system works in the long run, especially when suppliers have to create new capacity to increase supply. Moreover, continuous curves are required to do the advance math modern academic economist do. So someone seeped in this methodology obviously and correctly believes that higher prices are needed to attract additional supply. If this is always true the guy in his pick up truck charging storm victims very high prices is doing a good thing.

But in the real world in the short to intermediate run supply curve are not always continuous curves sloping from the lower left to the upper right – especially in the short run. In the short run most industries do not operate a full capacity and do not have to bring new capacity on line to increase supply – they normally have excess capacity.

Because of this most businesses in the US economy typically operate on some form of posted price approach. For example if you want to buy a pizza you call the pizza shop and they tell you the price of a pizza is $10. Moreover, the price will be $10 per pizza if you want to buy one pizza or ten pizzas or anything in between . Over this portion of the supply curve it is flat, it is not a continuous curve with an upward slope. If you said to the pizza owner next Wednesday I am having a kids party and want 20 pizzas delivered at three in the afternoon, what kind of a discount would you give me. The owner would think, at that time most of my ovens are sitting unused and my employees are idle. So sure, I’ll give you a discount and sell you 20 pizzas at $8 each. Just don’t try this for the pizzas to be delivered at half-time of the Super Bowl. Actually, if might even work then because Super Bowl Sunday is one of the worse days of the year for restaurants. But anyway, what we have here is a supply curve that actually slopes downward over portions of the curve.

Think of the typical small retailer. They look at the catalog or price list the vendor provides and finds that if they want one or two cases of something the price is set at one level. But normally, if they buys 20 or thirty cases they will get a volume discount. So again, we typically have a supply curve that over certain portions of the curve actually slopes downward. It is not a continuous curve with a constant upward slope.

Imagine you are the regional manager for Wal-Mart and as you do your planning you estimate that next year you should sell 1,000 crates of six-ounce bottled water each week. So you go to the manufacturer and negotiate a contract. But you do not negotiate a contract to buy 1,000 bottles a week. What you do is negotiate a contract with minimum and maximums. You commit to buying at least 800 bottles per week and up to potentially 1,200 bottles per week at the agreed on price. The contract provides a great deal of detail about exact specifications and how the contract can be changed. But it will also call on Wal-Mart to tell the bottler each Tuesday how many bottles from 800 to 1,200 they want next week and calls for them to ship about 20% of the order each day from Monday to Friday. The manufacturer now has several days to arrange his supplies and production schedule. Wal-Mart is not the vendors only customer and typically Wal-Mart will buy from multiple vendors. So it is a good deal for both. But it means that between a volume of 800 to 1,200 bottles a week Wal-Mart faces a flat supply curve. It is not upward sloping.

If you are a academic economist and long accustomed to thinking about supply curves having a continuous upward slope and you see that a natural disaster creates a temporary surge in the demand for bottled water at some location it is perfectly natural to apply your theory and believe that it will take higher prices to attract the additional supply. The big retail chains like Wal-Mart, Home Depot, 7-11, CVS Drug, etc, sell several hundred thousand bottles of water around the country every day and they each have contracts similar to the one Wal-Mart has with bottled water manufacturers . Compared to this total national supply the few thousand bottles needed at a natural disaster site is insignificant. And the amount the proverbial guy in the pick-up truck could supply is infinitesimal . It is very easy for big-box retailers to redirect a small portion of their supply and remain well within their existing contracts where they face a flat supply curve.

When you look at the world like this and realize that supply curves can be flat or actually downward sloping over certain portions of the curve it is very easy to understand why and how modern retailers can quickly increase supplies to a disaster area at regular every day low prices. This does not mean there is anything wrong with the economic theory. It is a good first approximation of how the world works. In the next year if Wal-Mart wanted to sell 1,500 six ounce bottles of water rather then 1,000 bottles the theory based on an continuously upward sloping supply curves should work very well.

Well, maybe not. Ever year since 1992, retailers like Wal-Mart, Home Depot, etc, have actually experienced deflation. The deflator for GAO type products – think department store products or roughly what Wal-Mart sells – has been falling at a 2% to 3% annual rate every year since 1992 . So actually, there is a very good chance that the per bottle price for 1,500 bottles of water may be cheaper next year. So even the long run supply curve may be downward sloping.

So what evidence do I have that this is the way the world works? Experience is the best evidence I have. But there is also a body of research that finds that most firms tend to change prices only once a year. Firms do work on a posted and/or contract price approach and find that in a low inflation world it does not pay to frequently change prices. Most firms have the flexibility built into their systems to moderately change their supplies in the short to intermediate run without changing prices. In the short run the overwhelming bulk of the items sold and the vast majority of firms in the US economy face a flat supply curve. This is not inconsistent with economic theory. Upward sloping supply curves are a long run phenomenon, not a short run phenomenon.

So I will stick with my argument that modern retail chains like Wal-Mart can and do quickly and easily provide extra supplies to disaster areas at regular every day low prices. Consequently, the proverbial guy in his pick-up truck charging the victims higher prices is not doing them a favor. Academics teaching this to college undergraduates are teaching both bad economics and bad morality.

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INCOME INEQUALITY

Jarad Berstein at
http://www.tpmcafe.com/blog/coffeehouse/2007/dec/13/boy_have_we_got_an_inequality_problem just reported that the

The Congressional Budget Office (CBO) just updated their invaluable data series on income inequality.

In looking at them I came up with a couple of very interesting charts.


In recent years there has been one big factor in this redistribution of income that
has not been discussed much. That is the massive increase in profits share of GDP
that happened this cycle.

The primary factor behind this large increase in profits has been firms ability to
capture the large productivity gains this cycle in higher profit margins rather
than in labor compensation.

Standard economic theory says this large gain in profits should be reflected in higher
savings and/or higher capital spending. But we have not seen either response.
However, the much higher income inequality and large cyclical increase in profits
have generated somewhat higher tax receipts than we had at the bottom of the cycle.
So through these mechanisms we do seem to be getting something of a supply side
impact on tax receipts, but given these other developments it is amazingly small.

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NOVEMBER RETAIL SALES DATA

The November retail sales data clearly do no support a recession scenario.

Nov(3)

Oct

sept

total

385,753

381,088

380,231

grocery

43,417

43,003

42,805

gas

39,438

36,919

35,806

Nonstore retailers………………………….

25,611

25,127

25,016

REMAINDER

277,287

276,039

276,604

About half of the nonstore retailers is fuel oil deliveries. So if you take out the price driven
large increases in food,gas, and nonstore retail there is still about a 0.5% gain. Moreover, for most of this it is real gains as prices for most retail goods are actually flat to down.

%

total

1.2

grocery

1.0

gas

6.8

nonstore retail

1.9

remainder

0.5

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How Modern Capitalism Really Works

This is from the Oregonian via Division of labor

Big Boxes to the Rescue

On Wal-Mart et al. in the recent Northwestern deluge:

In cases of extreme weather and natural disasters, some of the nation’s largest retailers now behave like municipalities — sometimes better.

Retailers have created specialized divisions — or hired outside firms — to gird for emergencies. The goal: to speed recovery for customers, employees and ultimately sales.

No one is clear how many retailers operate internal emergency units, but the practice is now standard among the biggest players, including Target Corp. and Lowe’s Cos. Inc.

This past week, Wal-Mart donated a 40-foot tanker of potable water to Vernonia, [Oregon,] while up north Home Depot opened its still-waterlogged Chehalis store for the town’s Chamber of Commerce to pick up face masks and cleaning supplies free of charge.

Such coordination became clear during Hurricane Katrina in 2005, when local governments praised initial responses from retailers as more expedient than those of the Federal Emergency Management Agency.

http://divisionoflabour.com/archives/004257.php

http://www.oregonlive.com/news/oregonian/index.ssf?/base/business/1197095130228920.xml&coll=7&thispage=1

Division of Labor does not allow comments, but I so wanted to make a common not to send this article to Don Boudreau at Café Hayek of other libertarians . Because they seem to be believe the only way to get supplies to natural disaster victims is some guy in the back of a pick-up truck gouging them with high prices.

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EMPLOYMENT REPORT

Just a quick follow up on the employment report.

Not only did wage growth come in weak, but the prior data was revised down.
People had been looking at the downward revisions of the personal income data
and expecting to to see employment revised down. But rather than employment,
it was average hourly earnings that were revised lower. In particular, look at
sharp slowdown for the three month growth rate.

Interestingly, the new experimental measure of average hourly earnings had
been reporting weaker wage growth. This revision bring the standard measure
down to what the experimental had been reporting.

This has significant negative implications for consumer spending prospects.

The slow down in wage growth should be expected as growth weakens.
Surprisingly, my estimate of inflation expectations — a moving average of
the last three years trailing headline CPI — is the main reason my equation
implies that wages should be higher. This suggest that labors ability to off-set
higher inflation with higher wages is weaker then it use to be.

But it also implies that the Fed is likely to continue cutting rates.

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IMPORTS AND A WEAK DOLLAR

Tyler Cowen has an interesting article on the weak dollar in the Times today that is also discussed at economistsview.

Just so everyone would have a good view of imports market share I though I would publish this chart.

Discussion on the dollar often center on the impact of a weak dollar on imports. But this chart raises the question of how much the currency matters. Maybe the strength of the domestic economy is much more important then the dollar on this issue. Just something to think about.

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income mobility

The WSJ took the data from this study and created this graphic:

The reason that I posted the data below on how age distorted this study is simply because
this data massively overstates the degree of income mobility in the US.

It is one of the most blatant misrepresentations I have ever seen on the WSJ editorial page.

All I trying to do is show why the WSJ article is a misrepresentation because it never said a word about how aging distorts the data.

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income mobility

The Treasury recently came out with a study of income mobility in the US that is receiving a lot of play in the Wall Street Journal and in several blogs.

Income mobility studies are very difficult to do properly and often are very misleading because they incompletely adjust for different factors.

The biggest factor driving income mobility in the US is age. People start working as young people and as they gain experience and earn promotions they receive higher incomes. The Census Bureau reports data that shows how this process. According to their data in 2006 median incomes by age bracket were:

MEDIAN INCOME

AGE

2006

% ch

15-24

$30,937

25-34

$49,164

58.9

35-44

$60,405

22.9

45-54

$64,874

7.4

55-65

$54,592

-15.8

0VER 65

$27,798

-49.1

2006 is typical or normal. If you looked at the data in 1975, 1985 or 1995 or any year in-between you would find a very similar pattern of income growing sharply as people age and starting to fall after the age of 55 and falling very sharply after age 65 when most individuals retire.

The Treasury study looked at income tax returns of people over 25. In this way they avoided the problem of showing the Doctors daughter making $5,000 at age 20 with her summer job as a life guard at the country club and $50,000 at age 30 as a drug company representative.

Income mobility is commonly described as an escalator moving everyone on the escalator up over time. It is a good analogy, and the three factors drive this move. One is economic growth. If you take a snapshot of everyone on the escalator in 1995 and another snapshot in 2005 you can see how the average has changed and get a picture of how economic growth influence the location of the escalator.

The second factor is the aging of the population. This is what dominates this Treasury study. They took a sample of the population in 1995 and came back and looked at the same individuals in 2005 when everyone in the sample was ten years older. This approach deals with some of the problems in the first methodology where the comparison between 1995 and 2005 would look at different people. In the first methodology in 2005 the people in the 15-24 year old age bracket were not included in the 1995 snapshot and the 15-24 year olds are now in the 25-34 year bracket.

There is nothing wrong with this methodology and it deals with some of the problems in the first approach. It is a valid approach. But you have to be careful in evaluating what the results mean. For example, if you look at what happens to the income of the lowest quintile you see that their income grows very rapidly. Their incomes will grow rapidly because of three factors. One is overall economic growth that moves everyone higher. A second is age which will move everyone in the 25-34 age bracket into the 35-44 age bracket, etc (See below).. A third is what we normally think of as economic mobility as individuals through hard work, education and good fortune or luck improve themselves. But since this study is dominated by people aging the lowest income group of the 25-34 age bracket in 1995 and in the 35-44 age bracket in 2005. Given that it is normal for the average income of the 35-44 age bracket to be around 25% higher than the 25-34 age bracket the study finding that over half the individuals in the lowest quintile moved to another quintile is not surprising. What is surprising to me is that about half of the lowest quintile were still in the lowest quintile ten years later.

AGE

POPULATION

CHANGE

1995-05

(%)

15-24

1.2

25-34

-6.5

35-44

-2.7

45-54

33.3

55-65

45.9

over 65

10.7

The problem with the Wall Street Journal and several blogs analysis of this data is that they tend to credit all the change from 1995 to 2005 to overall economic growth and/ or
individual economic mobility. But this is misleading as the dominant factor driving the changes reported in the Treasury study is the aging of the population. But we do not have the data to age adjust the data to see how much of the reported improvement was due to this factor.

This is not to say that there is anything wrong with the Treasury study, It is a common shortcoming of many such studies. That is the reason why the best studies on income mobility are the new series of studies that look at lifetime earnings and compare lifetime earnings of one generation to the lifetime earnings of their parents generation.

But it does mean that many of the advances in average income reported and incomes of the lowest income quintiles emphasized in the Wall Street Journal discussion of the study are misleading. They are attributing the sharp growth in the income of the lowest income brackets to overall economic growth and income mobility when it is largely due to people moving into the next age bracket. But they conveniently fail to point this out.

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FDR and CAPITAL SPENDING IN THE DEPRESSION

I see the history rewriters are tying to develop a new meme about FDR and the depression. They are now trying to argue that FDR scared businessmen and capitalist so badly that they were unwilling to invest and this was why the recovery was so weak and the depression lasted so long.

As usual, they take a historic fact, that investment in the recovery was weak, and blame it on the New Deal policies and conclude that the New Deal had a negative impact. As the chart below shows, real nonresidential fixed investment in the depression was very weak when you compare it to post war cycles. Note, that in the chart I have rescaled the post-war cycles so you can easily compare the differences. The data is rescaled in two ways. One, the post-war data is quarterly data while the depression data is annual data. Two, the post-war data is on the right scale and the depression data is on the left scale and the scales are set so that the magnitude of the downturns appears similar even though the depression downturn was much larger.

The argument is that the weaker depression rebound was because FDR scared investors. It ignors the possibility that other factors that normally drive investment might have been responsible. Over the years as a business economist I have concluded that three factors dominate business fixed investment. They are corporate profits, capacity utilization, and the stock market. The stock market is earnings times the market PE. So using stocks as a variable captures the cost 0f capital as the market PE is it’s inverse. But it also means the variables double counts profits. For capacity utilization I use the gap between potential real GDP published by the Congressional Budget Office and actual real GDP. Not to get into a detailed discussion, but the following chart shows how well changes in the three variables have explained changes in real nonresidential fixed investment since WW II.

So the next step is to see how well these variables explain real nonresidential fixed investment during the depression. If they do a poor job it might imply that New Deal did scare business and capitalist into not investing. There is one problem in doing an exact comparison. The CBO does not calculate a real potential GDP series for the pre-war era so I can not calculate the GDP Gap for the earlier era in the same way. But it is an easy problem to surmount. From 1900 to around 1975 trend real gdp growth in the US was 3.5%. as this chart shows. So it is easy to substitute this trend real GDP series for potential real GDP to calculate the GDP gap.

So what did I find? I find that these three variables, profits, the GDP gap and the stock market do a great job of explaining real nonresidential fixed investment during the 1930s. One of the interesting points to make is that the equation does an outstanding job of capturing the turning points at the 1933 bottom and around the 1937-38 second recession.

In two years, 1935 and 1936 actual real capital spending was right at the bottom of the one standard error band and in one year, 1937, capital spending was moderately higher then the equation implied it should have been. But it has to be obvious that overall, the three factors of profits, capacity utilization and the stock market do an outstanding job of explaining business fixed investment in the depression just as they do in the post-WW II era. On balance this work clearly implies that the argument that FDR scared businessmen and capitalist into not investing is strictly a myth that is not at all supported by the data. Compared to post WW II cycles capital spending was weak in the late 1930s, but it was weak largely because of the massive excess capacity created by the economic collapse from 1929 to 1933, not anything FDR did.

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