On Nancy MacLean’s Thesis

Nancy MacLean’s Democracy in Chains continues to yield surprises. Just a few days ago, Phil Magness now shows a “typo” that plays a significant role in MacLean’s thesis.

Despite all these detailed scrutiny of her work, it is not clear that MacLean understand the type of error is being pointed out about her book. There are two types of errors regarding a thesis: (1) the thesis is correctly defined, but the proof is flawed, or (2) the thesis is incorrectly defined, in which case there is no need to test the thesis. What MacLean and her supporters don’t seem to realize is that Democracy in Chains is built on the second error, not on the first one. Instead of ignoring her critics, MacLean should be up to the academic game and engage accordingly. Her behavior is very telling. If her research is so solid, what’s the problem?

Consider he following example. Let’s say you find a book built on the thesis that Milton Friedman was a french communist who lived in the 18th century. You don’t need to read this book to know that the author is wrong on her argument. This book on Friedman is both factually (Friedman did not live in the 18th century and was not French) and theoretically (Friedman was not a communist) wrong. This is how wrong MacLean’s thesis on Buchanan is for anyone with some minimal exposure to his work and Public Choice.

There a few reasons why someone would still read Democracy in Chains. For instance, if the book is a preach to the choir To try to understand how such a misguided thesis can actually be supported by by an author with so little knowledge and expertise on Buchanan and Public Choice. Etc. But a reason why MacLean thinks that their critics are unwilling to consider her thesis is because she is unaware her error is the second one mentioned above. Her thesis is just wrong from the go.

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How fast does populism destroy economic freedom in Latin America?

The turn of the twentieth century has seen an increase in populist government in Latin America. That populism is no friend of free markets is well known. And even if their movement against free markets if fairly quick, it is common for individuals to loose track of how fast they are loosing their economic freedoms.

There are five cases of populist governments in Latin America that can work as benchmarks for the region. In particular, we can look at the behavior of governments in Argentina, Bolivia, Brazil, Ecuador, and Venezuela for the time frames depicted in the following table.

Table 1

During this time period, populist governments failed to increase GDP per capita consistently faster than the region. The only exception is Argentina. But its fast increase in GDP is largely explained as recovery after the 2001 crisis and by consuming capital stock, not as an expansion of potential output. It is no accident that Argentina met stagflation in 2007. In the last three issues of the Economic Freedom of the World (Fraser Institute) Argentina ranks among the bottom 10 free economies in the world.

The following figure shows the fall in ranking of each country in the Economic Freedom of the World.

Figure 1

We can translate the information shown in the above into loss of ranking position per year of populist government. This is what is shown in the next table.

Table 2

This table offers a few readings:

  1. Argentina is the country that fall in the ranking of economic faster than its peers.
  2. Ecuador shows a very slow fall. This is due to two reasons: (1) Ecuador already starts from a low ranking position. (2) The last year of the index (2015) shows an improvement (without this improvement the fall is quite sharp as well.) Ecuador does not represent a case of “good populism.”

What this table is showing is that if an individual is born in any of these countries ranking 1st in economic freedom the same year a populist government takes office, then the same country will rank at the bottom of the world before he retires. In the case of Argentina, in 27.8 years the country will be at the bottom of the list, this means that by the time this individual starts to work, Argentina will already have a very repressed economy. By retiring time, this individual will have no experience of living and working in a free economy.

This numbers are not just descriptive of populism in Latin American countries. They also serve as a sort of warning for Europe and the United States, regions that have already seen some signs of populist behavior in their governments and political groups in the last few years. Populism can be emotionally attractive, but is very dangerous for our economic freedoms.

A Little More on “Price Gouging”

In my previous post on this subject I argued that the critics of “anti price-gouging laws” are mistakenly assuming that is possible to satisfy demand at the pre-natural disaster price. That is, sadly of course, fiction. It it not our reality anymore and we are better accepting the new situation than blindly deny it. As many economists are explaining these days, to not let prices increase after a natural disaster does more harm than letting prices increase. This can easily be seen in a demand and supply graph.

Prige gouging

Consider first just the lines in black. Those lines represent the pre natural disaster situation. What is considered “normal prices”. At price p0, a quantity q0 of a good is traded in the market (i.e. bottles of water.)

Now there is a shock. A hurricane hits this region and demand increases (shifts to the right). This is the demand line in color red. The red dotted line that extends to the right shows the size of the shortage (q2 – q1) at the “normal and fair” price.

Price gouging is an emotional loaded word, but it doesn’t have any specific economic meaning. How does “price gouging” show up in this graph? It is the increase in price from p0 to p1. This is the increase in price required to satisfy the higher demand and provide the extra number of goods (q1 – q0). No… supply is not horizontal.

What happens if price increases are banned? Then at the pre-crisis quantity (q0), consumers are willing to pay p2, a price even higher than price gouging. This means two things. First, a number of people in need will be unable to acquire the goods (the empty shelf problem). Second, that the actual total cost (to those who acquire the quantity q0) is p2, not p0. The difference between the price in the store and total cost falls into waiting in long lines, visiting a long number of stores, bribing producers (yes… with natural disaster price controls also lead to black markets), calling favors., etc. Any principles of microeconomic textbook has plenty of more examples under the price ceiling discussion.

There are three scenarios being discussed here.

  1. Quantity q0 at price p0
  2. Quantity q1 at price p1
  3. Quantity q0 at price p2

The natural disaster makes scenario 1 impossible. And it is not clear that scenario 3 is better for those in need than scenario 2. Less goods are provided at a higher total cost than in scenario 2.

One final remark. Note that in this analysis the natural disaster only affected demand. Of course, it is quite likely that supply would also be affected. The point, however, is to show that prices are not pushed up only by produces. As we can see in this case, it is consumers who are increasing the price and producers reacting to the new behavior of consumers.

Price Gouging: Reality vs Fiction

In a previous post I comment on a too common economic fallacy, that a natural disaster is good for the economy because of its alleged impact on GDP. Economic fallacies are not the only misconceptions gaining momentum during a natural disaster, but a confusion between reality and fiction becomes also quite common. The issue of price gouging provides a good example of this situation.

After a natural disaster, the price of certain goods such as water or gas, increases significantly. This is seen as an immoral exploitation by merchants who are taking advantage of the people affected by the natural disaster. Even though in this post I want to comment on another issue, it is worth mentioning that the now limited resources should be allocated to those in most need (rather than, for instance, to whoever happens to be the first one in line.) And unless someone has a crystal ball, there is no way of knowing who is in most need without changes in relative prices.

The mention to reality versus fiction refers to the fact that the critics of price gouging seem to (implicitly) assume that the natural disaster did not occur. It is plausible to assume that an event like this would (1) shift the supply to the left [reduce supply of goods] and (2) shift the demand to the right [increase the demand of goods.] At the usual (or “normal”) price these goods are in serious shortage.

This means that in the event of a natural disaster the option is between (1) having goods at a higher price or (2) not having goods at the “normal” price. This is the new reality. The old and normal reality does not exist anymore. To limit price gouging results in a lower price in the store, but not goods on the shelf. This would not help those in need. The fiction consists in thinking that a larger supply can be secured without an increase in the price (why should we assume supply is horizontal when these goods usually have a low elasticity?) An efficient policy would secure the provision of goods rather than secure a low price without the goods. Reality, rather than fiction, should be the first driver of a policy designed to assist during a natural disaster. As Milton Friedman insisted, a policy is to be valuated by its results (or design), not by its intentions.

The first rule for an efficient policy should be to not get in the way of changes in relative prices. Otherwise help will become erratic and inefficient. It might be more efficient, for instance, to make use of firms specialized in logistics (i.e. firms such as Walmart) and subsidize the demand than start a price control policy. For instance, a tax credit or a check can be sent to those affected by the natural disaster allowing them to pay the now higher prices. Similarly, a subsidy can be given to those firms bringing goods to the damaged areas (who says the government has the monopoly of charity or that the only one who can do it efficiently?) A policy on these lines would be more efficient than interfering with relative prices.

However, some opponents of price gouging seem to be more interested in damaging merchants than in making sure resources will be efficiently allocated among the ones affected by the natural disaster. Those who do not oppose price gouging do so because they have the affected ones first in line. It is not about merchant’s revenue, it is about allocating goods efficiently. Damaging the merchants should not be more important than worsening the situation of those in need.

No, natural disasters are not good for the economy.

Every time there is a natural disaster old economic fallacies make their appearance. And they are usually always the same. In particular, the argument that a natural disaster is good for the economy. This should make little sense. Wealth is not created by destroying things. A natural disaster destroys wealth, doesn’t create it. I doubt anyone affected by a hurricane would argue that he is better off after the natural disaster than before.

The argument that an event such as a natural disaster is good for the economy rests in the positive impact seen in GDP (as is argued) after the natural event. If GDP increases, then the economy is doing better. But this is a misreading of GDP. This variable is a flow of wealth, it is not a stock of accumulated wealth. It is possible that wealth creation (flow) increases at the same time the stock of wealth is decreasing. And this is what happens during a natural disaster.

Imagine that someone’s house caught fire and burns down. Because of this situation, this person decides to start working extra hours to increase his income and be able to buy a new one. The extra hours makes his income (GDP) increase. But his situation is considerable worst because he lost his stock of wealth (remember Bastiat’s broken window fallacy…?). Arguing that a natural disaster (or a war, etc…) is good for the economy is like arguing that this person is better of because he has to work extra hours to recover his loss.

This is just another case of a too common fallacy in economics. We know that if the economy is doing better the result will be better GDP and unemployment indicators. But from observing a better GDP and unemployment indicators we cannot, and should not, conclude that the economy is doing better. More important than observing what is happening to GDP is understanding why is changing its behavior.

It could be argued that one of the problem of the Keynesian view of the world is the focus on what happens to output and unemployment rather than why these variables are moving. Not surprisingly, we get to the conclusion that going to war (or having a natural disaster) would be a good way to achieve full employment.

Minimum Wages: Short rejoinder to Geloso

A few days ago I posted here at NOL a short comment on some reaction I’ve seen with regards to Seattle’s minimum wage study. Vincent Geloso offers an insightful criticism of my argument. Even if his point is quite specific (or so it seems to me), it offers an opportunity for some clarification.

But first, what was my argument? My comment was aimed at a specific point raised by advocates of increasing minimum wages. Namely, that even if Seattle’s study shows an increase in unemployment, a study with a larger sample may say otherwise. My point is that the way I’ve seen this criticism raised is missing the economic insight of minimum wage analysis, namely that jobs will be lost in less efficient employers and employees first. So far so good. The problem Geloso points out is with my example. I refer to McDonald’s as the efficient employers fast food chain (think of economics of scale) and as less efficient employers the neighborhood family-run little food place (neighborhood’s diner).

Geloso correctly argues that different employers react in different ways. It is expected, for instance, that a larger employer such as a fast-food chain would have more options to make a marginal adjustment when there is an increase in minimum wages. Of course, I agree, but the point I’m rising is about where jobs will be lost first (not the specific mechanism in each employer). Geloso flips my example and argues that a small diner has more (in relative terms) to lose by letting go one out of two employees than a fast food joint that has to let one employee go among maybe ten thousand. By letting one employee go, the small employer loses a larger share of its output. Therefore a small employer would be more inclined to keep all of his labor force and cut costs on another front (less hours work in average doesn’t cut it, that’s like a shared unemployment that would also cut output down).

A large employer like a fast food chain, however, can let one out of ten thousand employees go because the loss in output is not that significant. I have two issues with this example. The first one is that a fast food chain is facing the increase in minimum wage ten thousand times, not two. To cut even the rise in cost, the firm fast food chain has to cut down its labor force 15% (1,500 employees.) But I think the problem with this example does not end here. If it were the case that small diners don’t cut employment but fast food chains do, then we should see more unemployment in larger employers than in small neighborhood diners.

A second point I want to make is with Geloso’s argument that the study is about focusing “like a laser” on one out of multiple channels in the group most likely to respond in that manner (unemployment?). That the study, as long as the focus is on unemployment, should focus on the less efficient employers (and employees) first, and not just look at the unaffected employers because that’s where we just happen to have better statistics for is my point. There are two options. The first option is that what matters is focusing on the channel the increase in cost will be managed by employers. But this is neither a focus on unemployment nor on the criticism I’m replying to. Option number two, that the study should focus on the employers “most likely” to reduce unemployment, which is actually my point regardless of how many “channels” are included in the sample.

Minimum Wages: Where to Look for Evidence

A recent study on the effect of minimum wages in the city of Seattle has produced some conflicted reactions. As most economists expected, the significant increase in the minimum wage resulted in job losses and bankruptcies. Others, however, doubt the validity of the results given that the sample may be incomplete.

In this post I want to focus just one empirical problem. An incomplete sample in itself may not be a problem. The issue is whether or not the observations missing from the sample are relevant. This problem has been pointed out before as the Russian Roulette Effect, which consists in asking survivors of the increase in minimum wages if the increase in minimum wages have put them out of business. Of course, the answer is no. In regards to Seattle, a concern might be that fast food chains such as McDonald’s are not properly included in the study.

The first reaction is, so what? Why is that a problem? If the issue is to show that an increase of wages above their equilibrium level is going to produce unemployment, all that has to be shown is that this actually happens, not to show where it does not happen. This concern about the Seattle study is missing a key point of the economic analysis of minimum wages. The prediction is that jobs will be lost first among less efficient workers and less efficient employers, not equally across all workers and employers. More efficient employers may be able to absorb a larger share of the wage increase, to cut compensations, delay the lay-offs, etc. This is seen by the fact that demand is downward sloping and that a minimum wage above its equilibrium level “cuts” demand in two. Some employers are below the minimum wage (the less efficient ones) and others are above the minimum wage (the more efficient ones.) Let’s call the former “Uncle’s diner” and the latter “McDonald’s.” This how it is seen in a demand and supply graph:

minimum wage

Surely, there is some overlapping. But the point that this graph is making is that looking at the effects minimum wage above the red line is looking at the wrong place. A study that is looking for the effect on employment needs to be looking at what happens with below the red line. This sample, of course, has less information available than fast food chains such as McDonald’s; this is a reason why some studies focus on what can be seen even if the effect happens in what cannot be seen (and this is a value added of the Seattle study.)

This is why it is important to ask: “what do minimum wage advocates expect to find by increasing the sample size?” To question that minimum wages increase unemployment, then the critics also needs to focus on the “Uncle’s diner” part of the demand curve. If the objective is to inquire about something else, than that has no bearing on the fact that minimum wage increases do produce unemployment in the minimum wage market and at the less efficient (and harder to gather data) portion of it first.

PS: I have a previous post on minimum wages that can be found here.