China buying NZ dairy farms doesn't affect the price of dairy products

I enjoy reading the occasional opinion pieces of former University of Waikato vice-chancellor Bryan Gould. Invariably the opinion pieces are on economic issues - Gould fancies himself an armchair economist. Unfortunately, occasionally he gets some things wrong. As he did in this opinion piece earlier this week. Gould writes:

We, however, seem unaware of what is happening. It is no accident that this direct supply to the Chinese market has accompanied a fall in the proportion of New Zealand dairy production handled by Fonterra. While the proportion of our dairy production under Chinese control is still quite small, there can be little doubt that it will grow.

Low dairy prices will force the sale of a number of farms to foreign owners. As the Chinese increasingly control their own sources of supply, their reduced requirements for dairy produce on international markets will inevitably mean downward pressure on prices.

The problem is, that China buying New Zealand farms to supply dairy produce directly to the Chinese market (bypassing Fonterra and other New Zealand exporters) probably has no effect at all on the international price of dairy products.

To see why, consider the diagram below, which represents the international market for dairy products. With China owning no farms in New Zealand, demand is D0 and supply is S0, leading to an equilibrium price of P0, and the equilibrium quantity of dairy products traded is Q0. Now, China buys some New Zealand farms, to supply its market directly. This reduces the amount of dairy products China demands from the international market (because that part of their demand is being satisfied by the Chinese-owned New Zealand farms), reducing demand to D1. However, it also reduces the supply of dairy products to the international market to S1 (because those New Zealand farms are supplying China directly rather than supplying the international market). Notice that the quantity demanded and the quantity supplied in the international market both reduce to Q1. That is because the quantity of dairy products that the Chinese-owned New Zealand farms are supplying to China matches the quantity of dairy products that the Chinese-owned New Zealand farms are no longer supplying to the international market. What happens to the price? Nothing.

Of course, a more thorough analysis would consider more general equilibrium trade impacts. And the reduction in processing might limit the gains from economies of scale to dairy producers in New Zealand, increasing the costs of production. But if your worry is that Chinese ownership of New Zealand farms will dampen international dairy prices, your fears are probably misplaced.

More on the dairy sector:

• The cobweb model and the boom-bust cycle in the NZ dairy market

Nothing good happens after midnight when using the CVM

Some goods and services that have value to people are not traded in markets (e.g. clean air) or are not traded because they don't exist yet (e.g. vaccines for HIV). These goods and services have value (we like clean air, and we want more of it), but direct estimates of the value cannot be made. However, if we want to evaluate the costs and benefits of different project alternatives, or if we want to evaluate the demand for products that don't exist yet, we need to get some sense of these values.

Economists often use non-market valuation techniques to derive estimates of value for goods and services that are not actively traded in markets. One non-market valuation technique that we discuss in ECON110 is the contingent valuation method (CVM) - a survey-based stated preference approach. We call it stated preference because we essentially ask people the maximum amount they would be willing to pay for the good or service (so they state their preference), or the minimum amount they would be willing to accept in exchange for foregoing the good or service. This differs from a revealed preference approach, where you look at the actual behaviour of people to derive their implied willingness-to-pay or willingness-to-accept.

I've used the CVM in a number of past studies, including this one on landmine clearance in Thailand (ungated earlier version here), this one on landmines in Cambodia (ungated earlier version here), and a still incomplete paper on estimating demand for a hypothetical HIV vaccine in China (which I presented as a poster at this conference). And I have a current PhD student who may use the CVM in evaluating the intensity of preferences for institutional quality by return migrants to Vietnam.

While the CVM is attractive because it is fairly intuitive and easy to use, we know that there are a number of issues with it. Not the least is that people just aren't very good at estimating what they are willing to pay (or accept) for hypothetical goods or services, or in hypothetical scenarios. And people can be quite inconsistent in their responses (which violates the common economic assumption of static preferences, which you may or may not adhere to). Either way, that leads to very noisy measures of value.

A recent paper by David Dickinson and John Whitehead (Appalachian State University) demonstrates the challenge of obtaining 'good' estimates of value quite clearly (sorry I don't see an ungated version anywhere). They use data from an online survey of Appalachian State students, who were asked whether they would vote for a student-funded group to purchase, install, and operate a wind turbine as part of a Renewable Energy Initiative. If the wind turbine was purchased, students would face a fee of $X. The student fee ($X) varied between $4 and $56 in the survey, and students could vote "For" or "Against". The key element of the study though, was that the students were randomised to complete the survey at different times of the day or night. Now, the time of day (or night) that you complete a survey shouldn't in theory affect how you feel about renewable energy, or how much you would be willing to contribute to this initiative. But it turns out it does matter:

During morning, afternoon, and evening time blocks, students vote rationally with "for" votes declining as the student fee rises... During the night time, students are completely insensitive to the student fee, at least in the standard way of thinking; the student fee has no statistically significant effect on "yes" votes.

In other words, the students demonstrated a downward-sloping demand curve during the morning, afternoon, and evening, but price didn't seem to matter at night (between Midnight and 6am). The lack of price sensitivity at night leads the authors to conclude that "Nothing good happens after midnight when using the CVM". I would add that it suggests we should all avoid watching the Shopping Channel late at night.

Why rent controls have worse effects over longer time horizons

There are many topics on which economists disagree. One of the topics on which there is broad agreement is the negative effects of price controls. One common example is the case of rent controls.

A government (local or national) that feels that rents are too high and hurting low income tenants might enact rent controls. There are several ways these might be implemented, but the simplest way is a maximum price in the market for rental housing (alternatives include maximum absolute or relative (percentage) annual increases in rents). Regardless of how the rent controls are implemented, they lead to the same main effect – shortages of (or excess demand for) rental housing.

The ‘usual’ examples we use include the market for New York City apartments, or houses in San Francisco or some other jurisdictions in California. Alex Tabarrok at Marginal Revolution recently pointed to this letter from a resident of Stockholm, to the city of Seattle:

Stockholm City Council now has an official housing queue, where 1 day waiting = 1 point. To get an apartment you need both money for the rent and enough points to be the first in line. Recently an apartment in inner Stockholm became available. In just 5 days, 2000 people had applied for the apartment. The person who got the apartment had been waiting in the official housing queue since 1989!

Rent controls reduced my housing supply and promoted a lack of availability in my existing housing stock. This was a surprise because the Swedish Government didn’t cap rents directly- it limited rises to those negotiated between the landlord and tenant associations; based on analysis of costs and apartment ‘utility values’ that described location and apartment quality.

I’m in Copenhagen this week, and talking to one of the locals there are similar rent control mechanisms at work here in Denmark as well. With similar effects – she mentioned that the waiting lists on rent-controlled housing can be around 30 years or more.

To see why rent control leads to shortages (excess demand) and long waiting lists, consider the diagram below. If the market operated without rent controls, the equilibrium rent (R0) and quantity (Q0) would prevail. There would be no excess demand for rental housing. However, rent control keeps the price below equilibrium (R1) – it can’t rise to R0 because of the rent control rules. The lower rent makes renting more attractive relative to owning your own home. Some people would find it cheaper or more convenient to be a renter at this lower rent, so the quantity of rental housing demanded increases (to QD1). However, the lower rents make rental housing a less attractive investment for landlords. Perhaps they convert that rental housing into commercial rentals instead (e.g. offices) or maybe they choose to live there themselves (the opportunity cost of living in the house is now lower). Either way, the quantity of rental housing decreases (to QS1). The difference between QD1 and QS1 represents the excess demand for rental housing at the controlled rent – there are fewer houses available than the quantity people want to rent.

This excess demand can have a range of negative effects, depending on how it is managed. Perhaps the excess demand is managed by waiting lists of various flavours (as in Stockholm or Copenhagen), which means that potential tenants have to wait years for a rent-controlled space to become available. Instead, perhaps landlords are left to manage the excess demand on their own, in which case the rent-controlled housing is more likely to be rented to higher income tenants. Why? The landlord has a lot of choice over tenants now (because of the excess demand). If they can choose to rent their house to the professional couple with two incomes, or the solo mother with no job and three young children, it doesn’t take an economics PhD to work out who is going to miss out. So in this case the rent control actually hurts the very people (low income tenants) that it was designed to help.

On top of that, landlords might be willing to accept side-payments (bribes) to ensure access to rental housing. Tenants are willing to pay the bribes to ensure they don't miss out on a place to live. This further stacks the rental market against low-income tenants. And on top of that, increased competition between tenants lowers the incentives for landlords to maintain their properties. Since tenants can't be choosy or they will miss out on a place to live, landlords don't need to offer high-amenity living. So the quality of housing available to rent may decline over time, which definitely doesn't make tenants better off.

In the long run, the situation is even worse, as we discuss in ECON100 each semester and as shows in the diagram below. Longer time horizons are associated with more elastic demand and supply. So in the long run, demand is more elastic (D2) than in the short run (D1). In the context of rent control, in the long run potential tenants (who may be homeowners) have more time to adjust to the change in rent. Note that the long run and short run demand curves both pass through the equilibrium point - this reflects that the difference between the two demand curves is only the elasticity of demand. That is, there is no "increase in demand" here between the short run and long run, just an "increase in quantity demanded" (because potential renters are part of the market, as well as those who actually do rent at the market rent). In the long run, supply is also more elastic (S2) than in the short run (S1). Landlords also have more time to adjust to the change in rent in the long run. And note again that both curves pass through the equilibrium point - there is no "decrease in supply" here, just a "decrease in quantity supplied" (because landlords are free to move houses between available to rent and not available, depending on the market rent).

From the diagram, you can see that the excess demand for housing arising from the rent control is larger in the long run (QD2 - QS2) than in the short run (QD1 - QS1). Which explains why rent controls have negative effects, and those negative effects just get worse over time. Leading to decades-long waiting lists for rental property in Stockholm and Copenhagen.

Does education in economics make politicians corrupt?

There is a fair amount of evidence that economics students behave differently in laboratory and field experimental settings. Studies often find that economics students act more rationally, are less altruistic, and less pro-social. However, it is difficult if not impossible to tell whether it is economics that causes students to be less pro-social, or whether less pro-social people are more likely to choose to study economics. Moreover, people may act differently in experimental settings than they do 'in the real world'. Which means that finding real-world tests for whether economics leads to less pro-social behaviour important.

Back in April, Tyler Cowan at Marginal Revolution pointed us to this paper by Rene Ruske (University of Muenster), entitled "Does Economics Make Politicians Corrupt? Empirical Evidence from the United States Congress" (sorry, no ungated version). Since corruption can be considered not-pro-social behaviour, I thought this paper might give us some insight. Unfortunately, the paper doesn't actually answer the question in the title. And neither does it necessarily tell us anything about economics at all.

This is because of the methods that have been employed. First, Ruske takes data on the 695 members of the 109th-111th U.S. congresses (2005-2009), on their corrupt behaviour (taken from a dataset compiled from CREW (PDF)), and on their higher education degrees (the data don't seem to be available online any more). She then essentially compares the degrees of corrupt politicians with the non-corrupt politicians (controlling for other demographic characteristics). The first problem is that the data is purely correlational - it can only show whether politicians with economics degrees engaged in more or less corruption than those with other degrees, but it can't tell us whether the relationship is causal. Maybe more easily corruptible people tend to take economics majors? Or maybe politicians with economics majors are presented with more opportunities for corruption? Or maybe politicians with economics majors are less adept at hiding their corrupt behaviour? To her credit, Ruske doesn't claim that the relationship is causal - except in the title to her paper, which is at best misleading.

Second, she isn't looking at economics degrees at all. She combines economics with business administration. Given that a lot of senior business people and politicians have MBAs, and MBAs are not an economics degree, then the results of the paper tell us little about the relationship between economics and corruption. Instead, maybe they show the correlation between business degrees and corruption instead. So it is disingenuous to claim that the results say much about economics or economists.

Maybe sometime in the future we could randomise students into economics classes and then follow up later with those that become politicians? However, it's likely that the self-selection into politics would still create a problem.