Why compensating differentials might mean that inequality has been decreasing over time

John McGinnis wrote an interesting piece earlier in the month, on why compensating differentials likely temper any growth in income inequality. John writes:

This simple observation suggests that focusing only on earned income from employment can provide a misleading picture of  any growth in inequality.Overall satisfaction from a job comes not only from earnings but also from the amenities it provides and the risks it presents.  If our economy is improving lower income jobs by reducing risks and providing a more enjoyable environment this trend could compensate for at least some of any growth in income inequality.

Applying this idea to New Zealand might mean that inequality has been decreasing. Important note: "inequality", not "income inequality" (an important distinction, because we're not just talking about monetary income now).

The argument goes like this - first, our total compensation from any job includes both monetary and non-monetary components. The non-monetary characteristics of jobs include things like how annoying your work colleagues are (or how vivacious and enthusiastic, depending on your perspective), how boring or monotonous (or exciting) your job is, and how dangerous to life and limb your job is.

Think about two jobs that have the same human capital requirements. If the first job has attractive non-monetary characteristics (e.g. it is exciting) then more people will be willing to do that job. This leads the supply of labour to be higher, which leads to lower equilibrium wages. In contrast, if the second job has negative non-monetary characteristics (e.g. it comes with a high risk of death or injury) then fewer people will be willing to do that job. This leads the supply of labour to be lower, which leads to higher equilibrium wages. The difference in wages between the attractive job that lots of people want to do and the dangerous job that fewer people want to do is called a compensating differential. This is the reason why welders on oil rigs earn US$61,000-66,000, while spot welders in general earn US$24,000. Oil rig work comes with negative characteristics like lots of time away from family (for some families, this might be a positive characteristic!), and higher risk of death or injury.

Importantly, it is likely that compensating differentials have been changing over time. John McGinnis points out that in the U.S., "Since 1970 workplace deaths have plunged 65 percent and workplace illnesses 67 percent." Similarly, if we look at more recent data from New Zealand, the incidence of workplace injuries has fallen from 119 per 1,000 full-time equivalent employees in 2008, to 92 per 1,000 full-time equivalent employees in 2013.

How does that relate to inequality though? It depends. John argues that "These economic transformations differentially help those on the lower income part of the scale". Increases in inequality would be mitigated, if the improvements in non-monetary characteristics were greatest among the lowest-paid workers.

We can use some freely available data to check this. Using the injury data linked above and data from the NZ Income Survey by occupation, we can create a very crude analysis (which is made difficult by the change in occupational classifications over time). I picked three occupations that are reasonably consistently defined between the NZSCO95 classification (which is used in the injury statistics) and the NZSCO99 and ANZSCO classifications (that are used in the income statistics): (1) Professionals (median hourly wage of $30.17 was the highest of all occupations in June 2013); (2) Clerical and administrative workers ($21.58); and Machinery operators and drivers ($19.34).

For these three groups, between 2008 and 2013, injury incidence fell 19.5% for professionals, 41.9% for clerical workers, and 27.5% for machinery operators. So, injury incidence did fall by a greater proportion for the two lower-paid occupational groups when compared with professionals over this period, but it was the clerical workers who had the highest injury gains (note: they also had the highest income gains over this period, with median wages increasing by 21.7%). Perhaps there is something to this that could do with some further investigation - however, you would probably want to take into account how much people value reductions in injury incidence (since otherwise there is no easy way to combine change in wages with the change in injury incidence).

Now, why does this mean that inequality might have been decreasing in New Zealand? As Eric Cramption has pointed out on many occasions (e.g. see here or here), income inequality has been fairly static in New Zealand since the 1990s. So if income inequality has been static, but the non-monetary characteristics of occupations have changed in ways that favour occupations with lower median wages, then perhaps overall inequality has been decreasing. Of course, that assumes that there hasn't been a big shift in the occupational structure of the economy away from the lower-wage jobs (which there has - in the three occupations I used above, professionals increased from 17.3% to 19.5% of full-time equivalent employees between 2008 and 2013, and machinery operators and drivers decreased from 8.8% to 8.0%). Again, the relative effects on inequality of changes in wages, non-monetary characteristics, and changes in occupational structure of the economy, is something that bears further investigation.

[HT: Marginal Revolution]

Try this: The monkey economy and behavioural biases

Someone (I forget who, sorry) pointed me to this 2010 TED Talk by Laurie Santos (a psychologist at Yale). In it, Santos talks about creating an economy in a group of capuchin monkeys by introducing them to the concept of money. My ECON110 students might recognise it, as one of their assignments was based on this research. She also shows that the monkeys are subject to some of the same behavioural biases as humans, especially: (1) loss aversion (we value losses more than we value gains); and (2) thinking in relative rather than absolute terms (we compare any change we anticipate relative to our current position). Enjoy!

You might also like: Does exposure to markets make us more, or less, rational?

Why export quotas probably failed to help coffee farmers

Last year, my wife and I both read the same story from Daily Coffee News, entitled "A Brief History of Coffee Price Volatility in the Modern Era (1963-2013)". Probably unsurprisingly given our different disciplinary backgrounds, we had completely different takeaways from the story. On the one hand, you could take away that free market forces are a bad thing, because they led to volatility in the price of coffee on world markets - as the International Coffee Organization is quoted in the article as saying, this "makes it difficult for roasters to control processing costs and affects profit margins for traders and stockholders, making their activities less attractive".

What I took away from the article was how the International Coffee Organization ensured price stability in the period from 1963 to 1989 - by using a system of export quotas in producing countries. My overall comment was "wow, the coffee farmers were probably worse off, but I bet the middlemen were happy". And now I'll explain why (which I've been promising my wife I would do here for some time).

Let's start with an exporting country - a country that has a comparative advantage producing the product (coffee in this case). That means that the country can produce coffee at a lower opportunity cost than other countries. On a supply-and-demand diagram like the one below, it means that the domestic market equilibrium price of coffee (PD) would be below the price of coffee on the world market (PW). Because the domestic price is lower than the world price, if the country is open to trade there are opportunities for traders to buy coffee in the domestic market (at the price PD), and sell it on the world market (at the price PW) and make a profit (or maybe the suppliers themselves sell directly to the world market for the price PW). In other words, there are incentives to export coffee. The domestic consumers would end up having to pay the price PW for coffee as well, since they would be competing with the world price (and who would sell at the lower price PD when they could sell on the world market for PW instead?). At this higher price, the domestic consumers choose to purchase Qd0 coffee, while the domestic suppliers sell Qs0 coffee (assuming that the world market could absorb any quantity of coffee that was produced). The difference (Qs0 - Qd0) is the quantity of coffee that is exported. Essentially the demand curve with exports follows the red line in the diagram.

We can also use the diagram to demonstrate the gains from trade for an exporting country. Without trade, the market would operate at the domestic equilibrium, with price PD and quantity Q0. Consumer surplus (the gains to domestic coffee consumers) would be the area AEPD, the producer surplus (the gains to domestic coffee producers) would be the area PDEF, and total welfare (the sum of consumer surplus and producer surplus, or the gains to society overall) would be the area AEF. With trade, the consumer surplus decreases to ABPW, the producer surplus increases to PWCF, and total welfare increases to ABCF. Since total welfare is larger (by the area BCE), this represents the gains from trade. So, coffee farmers are better off with trade, because the producer surplus is larger than it is without trade.

What happens when there is an export quota? This is demonstrated in the diagram below. Whereas previously, we assumed that the world market could absorb any quantity of exports of coffee, now the quantity of exports is limited to the agreed quota amount. Let's say that the export quota is limited to the amount between B and G (about half the amount of unrestricted exports). Importantly, the export quota is implemented using licenses - only holders of export licenses are allowed to export.

Now that there is a quota on exports, consider what happens to the demand curve (including exports). The upper part represents the domestic consumers with high willingness-to-pay for coffee. Then there is a limited quantity of export demand, at the world price PW. After that, there are still profit opportunities for domestic suppliers (that is, there are still some domestic consumers who are willing to pay more than what it costs the suppliers to produce coffee). So, the demand curve (including the export quota) pivots at the point G, and follows a parallel path to the original demand curve (i.e. the demand curve including exports follows the red line in the diagram). The domestic price is the price where supply is equal to demand (P1). Export license holders can purchase coffee at this price, and then sell it on the world market and receive the higher world price (PW), and pocket a profit. The domestic consumers choose to purchase Qd1 coffee at the price P1, while the domestic suppliers sell Qs1 coffee at that price. The difference (Qs1 - Qd1) is the quantity of exports (which is also the quantity of the quota).

Now the consumer surplus is larger than it was without the export quota (it is now the area AJP1), the producer surplus is smaller than it was without the export quota (it is now the area P1HF). The export license holders now receive a surplus (profit), equal to the area KLHJ. Total welfare (which is now made up of the consumer surplus, producer surplus, and license holder surplus) is smaller than without the export quota (it is now the area AJHF+KLHJ). There is a deadweight loss (a loss of total welfare arising from the export quota) equal to the area [BKJ + LCH] - these areas were part of total welfare with trade and no export quota, but have now been lost.

Of most interest to us though is that the export quotas don't help the coffee farmers - producer surplus has fallen. In contrast, the export license holders (the middle men, who buy coffee from the farmers and sell it on the world market) are made better off by the export quota system.

But wait - what if the export quota system makes world coffee prices higher? That seems a reasonable possibility - if all coffee producing countries are restricting the supply of coffee to the world market, then that should raise prices for all. I'm sure that's what the International Coffee Organization was probably trying to do all along.

The diagram below demonstrates what happens, if the quota is kept the same size as the previous diagram, but the world price increases from PW to PX. The demand curve (including the export quota) now follows the purple path (since the license holders can now sell at the higher price PX instead of PW), but notice that the resulting domestic price is exactly the same (P1). In terms of welfare effects, the resulting consumer surplus and producer surplus are unchanged even though the world price is now higher. The license holder surplus increases to MNHJ.

So, even if the export quota system successfully raises the world price of coffee, it is the middle men who benefit, not the coffee farmers. Which is why, after the coffee export quota system collapsed in 1989, we would expect coffee farmers to have been made better off.

[Update: Fixed missing label in second diagram]

Could early identification of students at risk of failure be a bad thing?

One of my Summer Research Scholarship students this year, Jacinda Herring, was working on a project on identifying  the characteristics of Waikato Management School students at risk of not completing their degree. The point of the project is, essentially, that if we can identify high-risk students before they start their degree, then we can better target pastoral care or other interventions that might help increase students' chances of successful degree completion. It seems to me there is little to argue against this approach, which is why I am pursuing research in this area with my students.

However, a recent interview with Jeffrey Alan Johnson (Utah Valley University) published in the Christian Science Monitor argues convincingly that perhaps we should pause before we get carried away with 'profiling' our students. Johnson says:

We've got an early warning system [called Stoplight] in place on our campus that allows instructors to see what a student’s risk level is for completing a class. You don’t come in and start demonstrating what kind of a student you are. The instructor already knows that. The profile shows a red light, a green light, or a yellow light based on things like have you attempted to take the class before, what’s your overall level of performance, and do you fit any of the demographic categories related to risk. These profiles tend to follow students around, even after folks change how they approach school. The profile says they took three attempts to pass a basic math course and that suggests they’re going to be pretty shaky in advanced calculus...

When I told my students I have Stoplight data, they were worried about what I thought of them coming into the class. It definitely bothered them. They wondered if instructors will think they need help, or dismiss them because it looks like they won’t succeed and it’s better to prioritize other students.

So it seems that maybe there are valid concerns about making this data available to teaching staff. Lecturers' attention is a scarce resource, and if lecturers know which students are more likely to fail a given course, then they may divert their attention away from most-at-risk students to students who are more likely to pass. Of course, the counter-argument is that maybe some lecturers would divert their attention towards those who are on the margin of passing the course (to the extent that pass rates, or helping individual students to pass, are important to lecturers). But the risk of profiling isn't a good reason not to have a system for identifying at-risk students. Perhaps the data on risk level could be made available only to student advisors or those engaged in pastoral care, which would minimise the risk to students of their interactions with lecturers being biased by preconceptions of their likelihood of passing. In any case, my aim is to press on with research into at-risk students later this year. 

Coming back to Jacinda's project, she compiled administrative data from all Waikato Management School students who commenced study between 2008 and 2011 (n = 2033). Each student was then classified into three categories: (1) completed any degree (not necessarily the one they started in); (2) still studying (in 2014); and (3) did not complete any degree and not still studying. She then used chi-square tests and logistic regression models to compare the first group with the latter two (combined).

What did she find? In the final (multivariate) specification of the logistic regression model (which only included data we would have known before the students commenced study, and data that are available for all students):

• Students aged 25 years and over (at first enrolment) had significantly lower odds of degree completion than those aged 19 years and under;
• Male students had significantly lower odds of degree completion than female students;
• Asian students had significantly higher odds of degree completion than all other ethnic groups, and Maori and Pacific Island students had the lowest odds of degree completion;
• Domestic students had significantly lower odds of degree completion than international students;
• Special admission (or provisional entrance) students had significantly lower odds of degree completion than other students; 
• Students who initially completed the Certificate of University Preparation (CUP) had significantly lower odds of degree completion than other students; and
• Students initially enrolled in conjoint degrees had significantly lower odds of degree completion than students enrolled in single degrees.

The results are altogether not surprising if you have any experience with tertiary education, except perhaps for the last one. Most of the time it is top-achieving students who enrol in conjoint degrees. However, many students enrol in conjoint degrees because they can't decide on a single degree that they want to specialise in and so try to do a bit of everything. Conjoint degrees take longer to complete, and as such require a higher level of commitment to study - this may lead to discouragement and a higher level of non-completion. So, at the least there is one take-away from Jacinda's work, which is that maybe we need to target more pastoral care or mentoring and role models for conjoint degree students.

I expect that Jacinda and I will write up her analysis as a Department of Economics working paper in the near future.

[HT for the CSM article: Marginal Revolution]