Monday, 28 April 2014

Why study economics? NZ graduate earnings edition

I have posted before on why studying economics is a good idea.

Recently though, I was pointed to the Careers NZ website, which has a useful tool ("Compare Study Options") that compares the earnings of graduates of various disciplines. It uses data from the excellent Integrated Data Infrastructure from Statistics New Zealand, which is a relatively new and exciting database combining datasets from many sources.

I had a bit of a play with the Compare Study Options tool, and this is what I found when comparing Economics and Econometrics graduates with graduates from the Management and Commerce disciplines:

Now, I have no idea what the standard errors are like in these median salaries (and the full 2013 report on the Education Counts website from which these median salaries are taken doesn't say either), but I imagine that there isn't much to choose between many of the majors. But there are some clear things to see here:
  • Tourism is clearly on the bottom in terms of expected salary for graduates. Slightly above Tourism are Sales and Marketing and Business and Management. The other majors are clearly above those three.
  • All majors increase salaries with more study with the exception of Banking, Finance and Related Fields, where Honours graduates earn more than Masters graduates. Maybe that's a small sample size issue?
  • Economics and Econometrics improves in ranking and relative earnings with more study, compared with the other majors.
The latter bullet point is the important one. Studying economics is associated with higher graduate salaries, particularly at higher levels of study. Note that we can't say for sure that there is a causal mechanism here. Economics and Econometrics might lead to higher earnings, but there are a number of reasons why Economics and Econometrics graduates may earn more than graduates in Management and Commerce disciplines. Jonah Sinick recently outlined the reasons in a pretty comprehensive way (see here for the general considerations, and here for further exploration of the data). In short, he suggested three reasons (drawn from Bryan Caplan here):
  • Human capital acquisition: Education develops students’ employable skills. 
  • Ability bias: Obtaining an educational credential reflects greater or lesser pre-existing ability (that exists independently of what’s learned in school), which is later reflected in earnings. 
  • Signaling: An educational credential signals pre-existing ability (which as before, can be independent of what’s learned in school) which makes employers more likely to hire one
Under the first reason, if Economics and Econometrics creates greater human capital than other majors, then that higher human capital is rewarded in the labour market with higher salaries. Under the second reason, Economics and Econometrics graduates may have higher ability than graduates of other majors, and that higher ability is rewarded in the labour market with higher salaries. Under the third reason, a degree with an Economics and Econometrics major signals to employers that graduates are high quality, perhaps because Economics and Econometrics is a more difficult major than other majors. So Economics and Econometrics majors find it easier to get higher-paying jobs because of the value of the signal to employers.

Like Jonah, I believe the publicly available data (such as on the Careers NZ website or in the related report) on their own don't give us enough to be able to disaggregate these effects. As a teacher of economics, I would hope it is a human capital thing, but having taught both first-year and graduate economics for a number of years I strongly suspect that ability bias is significantly more at play than human capital. And signalling is ever-present in markets with incomplete information. At this point, I don't see any reason to disagree with Bryan Caplan, who suggests 10% Human Capital, 50% Ability Bias, and 40% Signalling.

[HT: Dan Marsh; also Guido Stark of Statistics New Zealand]

Tuesday, 22 April 2014

Changes in the drinking age and hospitalisations - big effect or small effect?

I've been meaning to blog on this for a while. Last year there were two papers published both investigating the effect of the decrease in the legal drinking age [*] from 20 to 18 on alcohol-related hospitalisations in New Zealand. The decrease in drinking age kicked in on 1 December 1999, so there are plenty of data available now to evaluate its effects.

The first paper is this one in the Journal of Health Economics by Emily Conover of Hamilton College and Dean Scrimgeour of Colgate University (earlier ungated version here). The second paper is this IZA Discussion Paper by Stefan Boes of the University of Lucerne and Steve Stillman of the University of Otago (hereafter B&S).

Remarkably, both papers use almost the same dataset and almost the same methods. While it's not unusual for several research teams to be working on the same research question at the same time, it's surprising to see too such similar papers appear in quick succession. And with similar results, though interpreted in meaningfully different ways.

Conover and Scrimgeour (hereafter C&S) look specifically at the health impacts of the law change. They used NZ Health Information Service (NZHIS) data covering all hospitalisations in New Zealand over the period 1993 to 2006. They use both a difference-in-difference approach (DiD), and a regression discontinuity design approach (RDD), to evaluate the difference in alcohol-related hospitalisations for those aged 18-19 years before/after 1 December 1999 (they also look at impacts on those aged 15-17 years). They estimate their models separately for each sex, and in both cases their control group is those aged 20-23 years (though they also present results that use a control group of those aged 20-21 years).

Boes and Stillman (hereafter B&S) look at a broader set of impacts than simply hospitalisations, including self-reported alcohol consumption, and alcohol-related motor vehicle accidents. They also use NZHIS data, over the period 1996 to 2007, and use similar DiD and RDD approaches to C&S. The key age group is again those aged 18-19 years (and they also look at those aged 15-17 years), but they don't present separate results by sex, and their control group is those aged 20-21 years.

The results are interesting. Using their preferred DiD approach, C&S find:
...a significant increase in hospitalizations as a consequence of passage of the Sale of Liquor Amendment Act (1999). Among eighteen and nineteen year old males difference-in-difference estimates indicate a 24.6% (s.e. = 5.5%) increase in alcohol-related hospitalizations. For females in the same age group, the estimated effect is 22% (s.e. = 8.1%).
They find qualitatively similar results using RDD, and conclude (in the abstract) that this shows "a substantial increase in alcohol-related hospitalisations among those newly eligible to purchase liquor". And you'd have to agree, the relative effects seem quite large - more than a 20% increase in hospitalisations for both young men and young women is pretty substantial.

B&S find even larger relative effects:
Overall, our results imply that the reduction of the [drinking age] from 20 to 18 led to a 75-91% increase in alcohol related hospital admission rates for 18-19 year olds.
These are BIG effects, right? In relative terms, yes. But in absolute terms, B&S point out just how many additional hospitalisations their effects translate into:
Translating the relative impacts using population figures from 1999 implies that the reduction in the [drinking age] from 20 to 18 led to approximately an additional... 2.1-2.6 [admissions] per month for 18-19 year olds... in the immediate aftermath of the law change.
So, across the country as a whole that would be an additional 25-31 alcohol-related hospitalisations per year. And remember, C&S found smaller effects than B&S. So, the effect is only very SMALL in absolute terms.

But that's not the end of this story. Aside from the period of the data and the control group, part of the difference in the two papers lies in their choice of what constitutes an 'alcohol-related hospitalisation'. C&S use all ICD-9 or ICD-10 codes that mention alcohol. B&S use a smaller subset of the ICD-9 codes, limited to alcohol use disorder (code 3050). In comparing their results with C&S, B&S argue that "...we use a much narrower definition of alcohol-related admissions that intentionally excludes more chronic conditions, which we show below are unresponsive to the reduction in the [drinking age]."

But there is a problem with both studies. In both cases, the hospitalisation data used was based on a limited set of ICD-9 or ICD-10 codes that are directly related to alcohol. However, there are a large number of ICD codes that could be related to alcohol, but are not alcohol use disorder and don't explicitly mention alcohol. For instance, the most obvious example are injuries or assaults that would be coded to S or T or X codes, none of which mention alcohol. Having spoken to a couple of clinicians, they pointed out that the ICD codes are very specific - unless they are sure the symptoms arise directly from alcohol, then the alcohol codes are not used.

Given that, it is likely that the small effects (in absolute terms) described by B&S may in fact be big effects if all of the hospitalisations that result from alcohol (including injuries, assaults, etc.) were included. Some estimates suggest that an average of 18 percent of emergency room admissions at peak party time are alcohol related. So, the jury has to remain out on the question of whether the decrease in the drinking age had a big effect or small effect on hospitalisations. More research required. [**]


[*] Technically, there is no legal drinking age in New Zealand. Instead there is a minimum age for purchasing alcohol. I'm using the term 'drinking age' in this post purely for simplicity.

[**] Wellington Hospital has been collecting data on alcohol-related emergency room admissions for a number of years, even when the primary diagnosis is not alcohol-related (see here for example). As I understand it, most hospitals do this now, although completeness might be an issue. These data could be leveraged (along with associated time/day of admission data) to get an estimate of the proportion of ICD codes that are not included in the C&S study that might be alcohol-related. Imperfect of course, but possible.

Saturday, 19 April 2014

What do Mexican maize farmers do when maize prices are low?

My ECON100 and ECON110 students often raise their eyebrows at the idea that many suppliers will change what they produce in response to price signals. If producers are able to easily switch between one good and another, the idea is that when prices are low they will switch to producing alternative higher priced (and higher profit) goods. Obviously the key caveat here is that they must be able to easily switch. For instance, it's unlikely that soft drink makers can easily switch to making T-shirts, because their plant and equipment are not suited to T-shirt production. However, crop farmers are a good example of producers that can easily switch to alternative crops, since the major inputs into production of different crops are land and labour, and these inputs can easily be transferred to alternative crops.

Which brings us to the question - what do Mexican maize farmers do when maize prices are low? According to this CGD Working Paper by Oeindrula Dube, Omar Garcia-Ponce and Kevin Thom (all of New York University), maize farmers shift production to more lucrative marijuana cultivation when maize prices fall. The theoretical effect is explained as follows:
Maize price fluctuations will impact rural households in different ways depending on their production and labor supply choices. First and foremost, such changes directly impact households that initially produce and sell maize. These households must decide how much labor to allocate to crop cultivation, market labor, and leisure. Jointly with this time allocation problem, they must decide how much land and time to devote to each possible crop. A fall in the price of maize will tend to increase drug crop cultivation as the result of both a substitution and an income effect. It will provide agricultural households an incentive to substitute the production of other crops for the production of maize. At the same time, this will make households poorer, increasing their incentives to spend more time and effort on income-generating activities as the marginal value of wealth increases. As the price of maize falls, both forces will push maize-producing households in the direction of greater drug production.
The effect of the maize price changes on marijuana and opium poppy cultivation is large:

Our estimates imply that the 59% fall in maize prices between 1990 and 2005 resulted in 8 percent more marijuana eradication and 5 percent more poppy eradication in municipios at the 90th percentile of the maize suitability distribution, as compared to municipios at the 10th percentile of this distribution.

And there are flow-on effects on cartel activity and violence as well:
In addition, we estimate a negative relationship between maize prices and cartel presence, as well as killings perpetrated by these groups in connection with the drug war over 2007-2010.
The authors draw some interesting conclusions, particularly around the effects of free trade. In 1994, the North American Free Trade Agreement (NAFTA) opened up the North American economies (Canada, the U.S., and Mexico) to free trade, lowering tariffs and leading to substantially lower maize prices for Mexican crop farmers. This in turn may have directly contributed to increases in marijuana cultivation in Mexico (Mexico is now one of the top three marijuana producting countries, alongside Morocco and Afghanistan - see here (PDF)).

One of the gains of freer trade is that it encourages countries to specialise in the production of goods and services where they have a comparative advantage, and leads to firms and workers shifting increasingly into export-oriented sectors. What's more export-oriented than the Mexican marijuana industry?

Wednesday, 9 April 2014

Fancy a margarita? Why it'll cost you more

I ran a special workshop for high school students in Tauranga on Friday. One of the students, very perceptively, asked why a firm would reduce supply now if they believed that prices would be higher in the future (or alternatively, why they would increase supply now if they believes that prices would be lower in the future). The textbook answer is that, by delaying supply (or bringing supply forward), they make higher profits. They student asked whether there were real-world examples of this.

And it turns out there are many, including this one in the New York Times last week (see also here in the NY Daily News). The story is about the market for limes in the U.S., which is dominated by Mexican suppliers. This bit in particular answers the student's query well:
Farmers have already been stripping their trees to cash in on sky-high prices, said David Krause, president of Paramount Citrus, which grows Persian limes in Tabasco State for the United States market. Such premature harvesting exacerbates the shortage because the fruit never grows to normal size and is 20 to 40 percent lower in volume, he added.
So, the farmers are supplying now in order to profit from the high prices, knowing the prices will be lower later. This is more than just an increase in the quantity supplied, because they are 'harvesting prematurely', i.e. shifting some of their future supply to now instead.

While we're thinking about this market, let's go through all of the effects. First, let's assume there are two markets here for substitutes - Key limes (the small, seeded, highly aromatic type preferred in Mexico) and Persian limes (the large, seedless type exported to the United States). The two markets are described in the diagrams below.

First, note that supply is relatively inelastic. This is because you can't easily jump into the lime market, or easily increase production - it takes time for new trees to mature. Second, the bacterial disease HLB has been killing Key lime trees, and Mexican drug cartels have been hijacking lime trucks. Both of these effects reduce supply in the Key lime market, raising the price from P0 to P1. Since Key limes and Persian limes are substitutes, at least some of the demand for limes shifts to the Persian lime market (Persian limes are now relatively cheaper), raising demand there and increasing the price from Pa to Pb. Some Persian lime farmers shift their supply forward (because they anticipate lower prices in the future and want to take advantage of the high price now), so supply increases. However, because yields are lower if you pick the fruit early, the increase in supply isn't large enough to fully offset the price increase. The resulting price is Pc (higher than Pa, but a bit less than Pb).

What does that mean for margaritas? Well, since limes are a key ingredient in margaritas and they now cost more, the cost of producing a margarita has increased which will almost certainly increase the cost for consumers (or lower quality, by substituting lemon juice in place of lime juice). Maybe time to switch to a substitute (not likely these though!).

[HT: Marginal Revolution]

Sunday, 6 April 2014

Low price but high cost: The economics of queues

How do you know that the price of a good is too low? This happens. Or this. Or this (which maybe goes against my earlier post on the high cost of weddings). If the price of a good is 'too low', what economists often mean is that the price is below the market clearing or equilibrium price. At this low price, the quantity demanded (the amount of the good that the consumers want to buy) exceeds the quantity supplied (the amount of the good that is available at that price), leading to a shortage, or excess demand. One of the ways that excess demand is manifest is in the form of waiting lists, or queues.

Now, you might think that low prices are a great thing for consumers. I'm going to argue that they aren't necessarily a great thing for consumers at all, because there are hidden costs that go along with the low prices.

First, let's look at a market with a price that is set below the equilibrium price (let's assume for the moment that we are talking about a competitive market, i.e. one that we can represent easily with supply and demand. This would be the case if there was a government-mandated maximum (ceiling) price that is below the equilibrium price). At the low price (P1 in the diagram below), there is excess demand (quantity demanded is Q2, and quantity supplied is much lower at Q1). So, there are a number of consumers who would be willing to purchase at the price P1, but are unable to find a supplier.

Consumers know that the price is really low, and they worry about missing out. So, to ensure that they get the product before it runs out, they show up at the store early, i.e. before it opens, and queue for the product. This increases the total cost to the consumer, because they face opportunity costs (foregone sleep perhaps, if they are getting up early to go and wait in line at the store). The total cost to the consumer is the price they pay plus the opportunity cost of waiting in line.

How long should the customers wait? That depends on their willingness to pay. If they are willing to pay a high price for the good, they will be willing to wait in line for longer (i.e. they will get to the store earlier). At the extreme, they will be willing to go early enough so that the total cost (price of the good plus the opportunity cost of waiting) is exactly equal to their willingness to pay.

What does that do to the graph above? The price remains P1, but the total cost to the consumers is actually P2. In fact, the queueing makes consumers worse off because they end up paying a much higher cost (including opportunity costs) than without queueing. In economic welfare terms, the consumer surplus (the difference between what the consumers are willing to pay and what they actually pay, shown by the area between the demand curve and the price) is actually lower with the low price (it is the area ABP2), than it would have been with the higher equilibrium price (there it is AEP0). And the deadweight loss is way bigger than expected (with queueing it is the large area P2BECP1, but if there was not queueing it would only be the area BEC).

The irony is that the government often implements maximum (or ceiling) prices in order to help consumers. If the shortage is managed by queues, it actually makes consumers worse off both in welfare terms (consumer surplus of ABP2 instead of AEP0) and intuitively (now they pay a lower price, but including the opportunity costs of time spent queueing the total cost is higher).

Of course, there could be a better way. Instead of queueing, the firms could adopt some other way of managing the shortage, like using a lottery to allocate the good. That way there is no additional opportunity cost.

Or, maybe the market will adapt, which is what we have seen recently. Some firms have set themselves up as professional line sitters, such as those described in this article from Racked last week. These guys can be hired to sit and wait in line for you, so that you avoid the opportunity costs of waiting (of course, instead you now face the added monetary cost of paying some guy to sit in line for you). Provided the amoung the line sitters charge (according to the article, US$25 for the first hour and US$10 for each additional half hour) is less than the difference between the consumer's willingness-to-pay and the price, the consumer should be happy to pay for the line sitter. And guess what? In welfare terms this is way better, since at least part of the large deadweight loss of queueing is converted into back into economic welfare gains (for the line sitter).

The above analysis relates to a competitive market, but what about firms with market power, i.e. those firms that can set their own price? As we discuss in ECON100, these firms want to set the price that will maximise their profits, but setting a profit-maximising price is very difficult in practice. Firms may not know exactly what demand is going to be, so there is a chance that they set the price too low (as above, this leads to a shortage which can be settled through queueing).

Also, firms are playing a complex long-run strategic game with their customers. If you set the price low and encourage queueing for your product, that attracts publicity. Also, consumers might see queues as a signal of high product quality (after all, why would so many people be willing to wait in line to get the product?). So, the next time that firm releases a new product, demand may well be higher. So, even though encouraging shortages and queueing might not be profit maximising in the immediate future, over the longer term it might be an optimal strategy for a profit-maximising firm with market power.

In either case (competitive or less competitive markets), queueing inevitably makes the consumer face a low price but a high cost for whatever good they are waiting for.

[HT: Marginal Revolution]

Thursday, 3 April 2014

Try this: Tradeoffs for the poor

The beautiful future-Mrs-Cameron shared this link to an online game called Spent with me, from one of her students. In the game, you have to try and survive for one month as an unemployed American with no savings.

It's a tough game, but underlines the sorts of tough choices and trade-offs that real people have to make every day. It's very Americentric (as you would expect), but if you have a spare few minutes, I suggest you try it out for yourself.

How did I go? Apparently, I could survive as a poor American since I got through all 30 days unscathed, but with what would have been a pretty wretched standard of living.