Inequality was similar, or higher, in ancient times than it is today

I'm really enjoying my study leave, because it's giving me the opportunity to catch up on reading some papers that have been sitting on my must-read-soon pile, in some cases for years. This paper (pdf), by Branko Milanovic (World Bank), Peter Lindert (University of California, Davis), and Jeffrey Williamson (Harvard), entitled "Measuring Ancient Inequality" is one example.

If you're interested in inequality, or interested in economic history, the paper is a good read and provides some interesting insights (which I note that the authors have followed up in some subsequent publications, which I might talk about in a later post). The authors use data from social tables for 14 societies spanning from Rome (14 C.E.) to British India (1947), and a combination of Gini mesaures of inequality along with two new measures they term the inequality possibility frontier and the inequality extraction ratio.

They find:

First, as measured by the Gini coefficient, income inequality in still-pre-industrial countries today is not very different from inequality in distant pre-industrial times. In addition, the variance between countries then and now is much greater than the variance in average inequality between then and now. Second, the extraction ratio – how much of potential inequality was converted into actual inequality – was significantly bigger then than now. We are persuaded that much more can be learned about inequality in the past and the present by looking at the extraction ratio rather than just at actual inequality...

Third, differences in lifetime survival rates between rich and poor countries and between rich and poor individuals within countries were much higher two centuries ago than they are now, and this served to make for greater lifetime inequality in the past. Fourth, unlike the findings regarding the evolution of the 20th century inequality in advanced economies, our ancient inequality sample does not reveal any significant correlation between the income share of the top 1 percent and overall inequality. Thus, an equally high Gini could and was achieved in two ways: in some societies, a high income share of the elite coexisted with a yawning gap between it and the rest of society, and small differences in income amongst the non-elite; in other societies, the very top of income pyramid was followed by only slightly less rich people and then further down toward something that resembled a middle class... 

The frequent claim that inequality promotes accumulation and growth does not get much support from history. On the contrary, great economic inequality has always been correlated with extreme concentration of political power, and that power has always been used to widen the income gaps through rent-seeking and rent-keeping, forces that demonstrably retard economic growth. 

In other words, inequality within societies was similar in the past to what it is today, even though incomes were much lower then. But when you take into account life expectancy, inequality is much lower now than in the past. Although I'm not sure that a long life in abject poverty is necessarily an improvement on a short life in abject poverty. The final point is important though - there is little support for the conjecture that inequality promotes growth, and the reverse is likely.

Drunk people are more impatient and less generous

Understanding the effects of alcohol on our behaviour is important for policy. Do people make more risky decisions when under the influence? Are they more impulsive? Are they more, or less, affected by systematic biases? Answering these questions would help with designing appropriate harm-minimising policies, or at least better assessing the costs and benefits of such policies.

But assessing the impacts of alcohol is also hard, and not helped by garbage research like this. In contrast, a paper last year (pdf) by Luca Corazzini (University of Padua), Antonio Filippin (University of Milan), and Paolo Vanin (University of Bologna) takes a much more robust approach involving lab experiments, different from the traditional observational or field experimental approaches.

Why is it important to find a robust approach? The authors explain:

First, empirical studies of alcohol intoxication based on field data, whether collected from directly observed or from self-reported behavior, typically suffer from self-selection into drinking... Any correlation between blood alcohol concentration and certain behavioral traits may reflect a true causal effect, but could also stem from different propensity to drink alcohol by individuals with those traits.

Second, and relatedly, individuals usually choose at the same time whether, when, where, with whom and how much to drink alcoholic beverages. This means that it is usually hard to disentangle the effects of alcohol from those of the context in which drinking takes place.

Third, the behavioral effects of alcohol intoxication are partly pharmacological and partly triggered by a psychological reaction to the subjective perception of being under the influence of alcohol. Disentangling the two effects requires independent variations of actual and perceived blood alcohol concentration (with implied relevant misperceptions).

In other words, people who drink may be systematically different from those who don't drink, the drinking context matters, and people may behave differently not because of the alcohol itself, but because of how they think they should be behaving under the influence of alcohol.

The paper uses a neat experimental design to get around these problems. They had three experimental groups: (1) received no alcohol and was never told the experiment had anything to do with alcohol; (2) drank no alcohol before the experiment and didn't know whether they had been given alcohol; (3) drank alcohol before the experiment  but didn't know whether they had been given alcohol. Comparing the first and second group gives an indication of the placebo effect of alcohol on behaviour, while comparing the second and third groups gives an indication of the pure pharmacological effect of alcohol on behaviour. They then ran their subjects through a battery of different lab experiments to identify their risk tolerance, impatience, and pro-social behaviour.

What they found was interesting:

Concerning risk preferences, after controlling for optimism, the willingness to pay and other subjective controls, we only detect a marginal positive effect of alcohol intoxication on risk aversion for female subjects.

On the contrary, we find a strong pharmacological effect of alcohol consumption on time preferences: it makes subjects more impatient. The pure impact of alcohol consumption on time preferences remain substantially large even after taking into account its interplay with subjects’ risk attitude. In this respect, net of the pharmacological effect of alcohol intoxication and in line with previous studies, we detect a negative and significant relationship between impatience and risk aversion.

Finally, concerning altruism, our results suggest that alcohol makes subjects more selfish, as we observe a negative and significant relationship between alcohol intoxication and donations to NGOs.

Now, their results might be sensitive to some selection bias, in that the participants in the second and third groups knew that the experiment had something to do with alcohol (which was in the advertisement for participants), whereas those in the first group did not. The no-alcohol group was slightly older and had a much higher proportion of women. Which makes the results on risk aversion a little shaky. The sample size was a little small too - an opportunity for replication beckons (though I suspect your institutional review board would take some convincing).

What do the results tell us (other than that drunk people are more impatient and less generous)? The authors suggest that "alcohol intoxication makes decisions more determined by emotions and less by deliberation", which should not be a surprise to any of us, and provides good reason for policy to place some moderate restrictions on alcohol availability.

[HT: Steve Tucker]

Why study economics? Even if you won't be an economist edition...

Last year there was an interesting article in the Journal of Economics Education by Thomas Carroll, Djeto Assane, and Jared Busker (all University of Nevada, Las Vegas) entitled "Why it pays to major in economics" (ungated earlier version here). In it, the authors use data from the 2009-2012 American Community Survey to look at the earnings of degree holders, by major. Importantly, because of the size of the dataset (millions of observations) they are able to control for not only the demographic characteristics of each person, by their job type (a combination of occupation and industry) and location. That means that their results show the relationship between college major and earnings for people in the same job.

They find that overall both male and female economics majors earn more than other majors (which other studies have found too), but importantly:

When we added occupational controls, we found that the average male Bachelor of Arts in economics major earns 8.64 percent more than his non-economics-major counterpart working in the same type of job. The advantage for female economics majors is 5.37 percent more than their counterparts of the same age, ethnicity, and job, who have different majors...

Essentially, about two-thirds of the bachelor's degree premium for economics major can be attributed to the type of job economics majors perform, and about one-third is a premium that economics majors earn over other workers within the same job.

In other words, economics majors tend to be employed in jobs that pay more on average than other jobs, but this only explains two-third of the extra earnings that economics majors receive. So, even if you aren't employed as an economist, these results suggest that there is an earnings benefit to having an economics undergraduate major (the authors also show there are benefits for economics majors who have done further study as well).

Is there something intrinsic about economics majors that lead to these higher earnings? Carroll et al. quote this earlier paper by Black, Sanders and Taylor (ungated here):

In a good undergraduate economics program, students develop an ability to think critically: They gain broadly applicable analytic and quantitative skills that improve decision making in a wide range of tasks. In short, it may be that economics majors are better trained than many other majors in skills that have returns in the marketplace.

I would agree with that - economics does provide students with skills that are both widely applicable, and in demand. Of course, that attributes causality to the relationship between economics major and earnings, which may be problematic. Maybe it is that economics students are naturally more intelligent, harder working, or just generally better employees and are rewarded with higher wages as a result? More research needed, but I would still argue that the case for completing an economics major is very strong.

Read more:

• Why study economics? PhD edition...
• Why study economics? It could make you rich...
• Why study economics? Sheepskin effects edition
• Why study economics? NZ graduate earnings edition
• Why study economics?

Do we need a price-comparison-website comparison website?

Last month The Economist reported on price-comparison websites (where consumers can compare prices between different providers of insurance, for example, or electricity - like Powerswitch in New Zealand, or other products and services). The article made for interesting reading:

Comparison sites, whether for insurance or something else, introduce a new layer of costs, including their own splashy advertising campaigns. In theory, competition in the market for comparison sites ought to keep those costs down. But in a recent paper, David Ronayne of Warwick University argues that consumers often lose out from comparison sites. They earn a commission for each shopper who uses them to buy insurance. That referral cost is incorporated into the price the consumer ends up paying. If the increased costs outweigh the saving the comparison enables, consumers end up worse off.

For instance, suppose some consumers are loyal to a single comparison site, and do not use any others to compare prices. The lucky website can crank up its referral fees, safe in the knowledge that insurers must pay up if they want access to its captive market. Those fee hikes are then passed on to consumers in the form of higher premiums.

Having a few loyal consumers would not be enough for a price-comparison website to have a high degree of market power. It would need a lot of loyal consumers - enough so that other price-comparison firms would find it difficult to make a profit, and so would choose not to enter the price-comparison website market. In other words, having a high number of loyal consumers constitutes a barrier to entry for other price-comparison website firms. It is hard to say what proportion of loyal consumers you would need to effectively lock out potential competitors, but it may be a lot. Are consumers likely to be loyal to a single price-comparison website? It's hard to say - by definition by using a price-comparison website they aren't loyal to their insurer, so why would we believe they would be loyal to a single price-comparison website?

Anyway, let's assume that there are a lot of loyal consumers, which provide a single dominant price-comparison website with market power. The higher the proportion of consumers who use that site for comparing prices, the more market power the site will have. Firms with market power are able to raise prices above marginal costs - in this case the price of referral fees to the insurance companies (or electricity retailers, or whoever). This leads to higher costs for those firms, who respond by raising prices.

At the extreme, if there is only one price-comparison website, they will (almost) have a monopoly on providing price information to consumers (I say almost because consumers could shop around themselves, albeit incurring higher search costs in the process). The single price-comparison website could increase the referral fee greatly, making consumers much worse off in the process.

How could this problem be tackled? Government could intervene in the market for price-comparison websites through price regulation (regulating the referral fees that price-comparison websites can levy on firms), or the government could run the price-comparison website itself (then it can set a low referral fee, or even none at all if the taxpayer is happy to subsidise the effort). Or the government could try to increase competition between different price-comparison websites, although that will be tricky if consumers are very loyal.

An alternative solution is to turn the price-comparison websites own business against them, and create a price-comparison-website comparison website. Then consumers could investigate to determine which price-comparison website will give them the best deal. That should increase competition between the price-comparison websites, lowering their referral fees and the prices for consumers. But would the price-comparison website comparison website then charge referral fees to the price-comparison websites? Maybe that just shifts the market power problem one step further up the chain.

Your workload impacts your grade more than you think it does

One of the most well-established cognitive biases is that people are over-confident in their own abilities. We believe that we are better than we are. This holds for students' expectations about grades as well - on average, at the beginning of a course students expect a higher grade than they end up receiving.

A recent paper by Belayet Hossain and Panagiotis Tsigaris (Thompson Rivers University) takes a closer look at students' grade expectations (sorry, I don't see an ungated version). The authors collected data on grade expectations from students across six semesters of a second-year statistics for business and economics course. Their overall test is whether students expectations are rational (unbiased, and incorporating all available information relevant to grades), which is a pretty high standard to meet. Not surprisingly, they find that expectations are not rational. However, it is some of the other results that are most interesting:

Like other studies in the field, we found that students are mostly overconfident. The unbiasedness hypothesis is rejected for most students. Expectations change sluggishly during the semester. The study suggests that students' expectations improve as more information regarding actual performance becomes available. Hence, expectations carry valuable information, and adjustments are made... Course load has a negative impact on a student's final grade after controlling for grade expectations.

That last point is important. It suggests that students who have a heavier course load are more over-confident about their grades than students with a lighter course load. Which has been my experience too - even top students over-estimate their ability to maintain top grades when over-loading their studies. All of which suggests that you should be more realistic about the degree to which study workload is going to have a negative effect on your grades.

Finally, a couple of gripes about the analysis in the paper. First, there are six semesters of students, so observations of students within each semester are not independent of each other. So, when pooling the sample the standard errors should probably be clustered at the level of the semester (Jeremy Miles gives a good, though technical, explanation of why here). Not clustering the standard errors leads to results that are more likely to show up as statistically significant than they 'should'. Second, there are likely to be unobserved differences between semesters, so semester fixed effects should probably have been included too. Omitting these fixed effects may lead to biased coefficient estimates in the models. The combination of these two problems should lead us to doubt the robustness of the results, even though they are mostly similar to those in other studies.

Philosophers suffer the same cognitive biases as everyone else

Behavioural economics is essentially founded on the principle that decision-makers are affected by a range of cognitive biases. In his book "Thinking Fast and Slow" Daniel Kahneman distinguishes between two systems of thought: 'System 1' is fast, instinctive, emotional and subject to many of the observed cognitive biases; and  'System 2' is slower, logical and more deliberative and able to avoid at least some of the biases that System 1 is subject to. The obvious implication is that if you could train people to slow down their thinking and apply more logical reasoning and be more deliberative, you could help them avoid many of the common cognitive biases.

Which brings me to this recent paper (ungated version here) by Eric Schwitzgebel (University of California at Riverside) and Fiery Cushman (Harvard). In the paper, the authors test whether academic philosophers are subject to some common cognitive biases to the same extent as similarly-educated non-philosophers. They use two common experiments: (1) the trolley problem (a common ethics problem); and (2) the 'Asian disease' problem described by Kahneman and Tversky (which is explained here and here, and which I use in my ECON110 class each year).

Essentially the authors were looking for two cognitive biases: (1) Ordering effects, where the order that scenarios are presented affects how they are evaluated; and (2) Framing effects, where the way that different options are framed (or presented) affects how they are evaluated. They find:

...substantial order effects on participants’ judgments about the Switch version of trolley problem, substantial order effects on their judgments about making risky choices in loss-aversion-type scenarios, and substantial framing effects on their judgments about making risky choices in loss-aversion-type scenarios.

Moreover, we could find no level of philosophical expertise that reduced the size of the order effects or the framing effects on judgments of specific cases. Across the board, professional philosophers (94% with PhD’s) showed about the same size order and framing effects as similarly educated non-philosophers. Nor were order effects and framing effects reduced by assignment to a condition enforcing a delay before responding and encouraging participants to reflect on “different variants of the scenario or different ways of describing the case”. Nor were order effects any smaller for the majority of philosopher participants reporting antecedent familiarity with the issues. Nor were order effects any smaller for the minority of philosopher participants reporting expertise on the very issues under investigation. Nor were order effects any smaller for the minority of philosopher participants reporting that before participating in our experiment they had stable views about the issues under investigation.

In other words, even academic philosophers are subject to the same cognitive biases as non-philosophers, and even when they are familiar with the problems they are being asked to evaluate. Scary stuff, particularly as the authors conclude:

Our results cast doubt on some commonsense approaches to bias reduction in scenario evaluation: training in logical reasoning, encouraging deliberative thought, exposure to information both about the specific biases in question and about the specific scenarios in which those biases manifest.

All of which suggests that nudging may be one of the few solutions to cognitive bias.

[HT: Marginal Revolution]

Lions are dying to be saved

You would have to have been living under a rock not to have heard about the furore set off by the killing of Cecil the lion last month (Wikipedia summarises the reactions to the killing here). From my reading though, the best report is this one by Norimitsu Onishi in the New York Times:

Despite intensifying calls to ban or restrict trophy hunting in Africa after the killing of a lion named Cecil in Zimbabwe, most conservation groups, wildlife management experts and African governments support the practice as a way to maintain wildlife. Hunting, they contend, is part of a complex economy that has so far proven to be the most effective method of conservation, not only in Africa but around the world as well.

While hunting is banned in government parks here in South Africa, animals inside their boundaries are routinely sold to game ranches when their populations are considered excessive, generating money to maintain habitats and fight poachers.

And because trophy hunting is legal in private game reserves, the animals end up fetching higher prices than they would in being killed for food or other reasons, conservationists contend.

In other words, hunting helps to save threatened species like lions. How does that work? There are two ways to explain this argument.

First, the trouble with most threatened or endangered species (like lions) is that they are a common resource - rival and non-excludable. Rival goods are those where one person's use of the good reduces the amount available to everyone else, i.e. in this case killing one lion reduces the number of lions available to everyone. Non-excludable goods are those where you cannot easily prevent a person from obtaining the benefit from them, i.e. in this case it is difficult to stop the hunters from killing lions. In contrast, if only hunters with an appropriate licence are allowed to hunt lions, that makes lions a private good - rival but excludable (because only licensed hunters can kill them). Making threatened species a private good creates incentives for local communities (or farmers) to conserve the species - it is valuable to sell the rights to hunt to hunters, and this value can only be realised in the long run if the species is managed sustainably.

Second, consider the opportunity costs of killing a lion. If there are hunting licences and the licence to kill a lion can be sold for between $24,000 and $71,000 (according to the Onishi article linked above), then the opportunity cost of killing a lion is the tens of thousands of dollars in income foregone. This provides incentives for locals not to kill lions, since they are very valuable as hunting trophies. On the other hand, if there are no licences to kill lions that can be sold to hunters, then the value of a lion falls to the value of the meat (less than 60 cents per pound according to the article, or about $300 for a mature male lion). The opportunity cost of locals killing lions for food is much lower, so more lions may be killed (a simple result of the Law of Demand - the 'price' (opportunity cost) of killing lions is lower, so more lions are likely to be killed).

Either way, hunting is a solution to the problem of threatened species, in a similar way that farming could also be a solution. So, many of the arguments against hunting threatened species may well be misplaced.

More on endangered species:

• Can technology save the rhinos?
• Because crushing ivory is so much better than burning it
• Poisoning rhino horns to save the rhinos
• This couldn't backfire, could it?... Ivory burning edition

The drug dealers' dilemma

Probably the most famous example in game theory is the prisoners' dilemma. The general story goes something like this (with lots of variants; this is the version I use in ECON100):

Bonnie and Clyde are two criminals who have been captured by police. The police have enough evidence to convict both Bonnie and Clyde of the minor offence of carrying an unregistered gun. This would result in a sentence of one year in jail for each of them.

However, the police suspect Bonnie and Clyde of committing a bank robbery (but they have no evidence). The police question Bonnie and Clyde separately and offer them a deal: if they confess to the bank robbery they will get immunity (and be set free) but the other criminal would get a sentence of 20 years. However, if both criminals confess they would both receive a sentence of 8 years (since their testimonies would not be needed).

The outcome of the game is that both criminals have a dominant strategy to confess. Confessing results in a payoff that is always better than the alternative, no matter what the other criminal decides to do.

The New York Times reports on a new real-world take on this game:

When the sheriff in Franklin County, Ky., posted a flier on Facebook asking local drug dealers to snitch on their competition, the response was more than a little incredulous.

That is, until a tip sent to a phone number on the flier led to an investigation that helped the sheriff arrest a local drug dealer...

“Asking drug dealers to turn in other drug dealers,” Sheriff Melton said. “It’s comical, and it’s working.”

Here's the flier they used:

And here's why it works, for drug dealers who have short time horizons. For simplicity, let's assume that there are two drug dealers only (A and B). In this short-run non-repeated game each drug dealer has a simple choice: Snitch on their competition, or stay silent. If both drug dealers stay silent, then make low profits (because they have to compete with each other). If one stays silent and the other snitches, then the snitch makes high profits while the other goes to jail. If both snitch, then both go to jail. The game in normal (payoff table) form is as follows:

What happens in this game? Consider Drug Dealer A first. They have a weakly dominant strategy to snitch. This is because the payoff is never worse than snitching (and sometimes it is better). If Drug Dealer B stays silent, Drug Dealer A is better off snitching (because high profits are better than low profits). If Drug Dealer B snitches, either strategy is equally good for Drug Dealer A (since they go to jail either way). So Drug Dealer A shouldn't choose to stay silent, because the better option (at least some of the time) is to choose to snitch.

Now consider Drug Dealer B. They also have a weakly dominant strategy to snitch. This is because the payoff is never worse than snitching (and sometimes it is better). If Drug Dealer A stays silent, Drug Dealer B is better off snitching (because high profits are better than low profits). If Drug Dealer A snitches, either strategy is equally good for Drug Dealer B (since they go to jail either way). So Drug Dealer A shouldn't choose to stay silent, because the better option (at least some of the time) is to choose to snitch.

So, both drug dealers acting in their own best interests leads both to snitch. If you're working out the Nash equilibriums, there are three - the only outcome that isn't a Nash equilibrium is the outcome where both drug dealers remain silent (to see why, take any of those outcomes and consider whether either player would be willing to change strategy - since they wouldn't (or would be indifferent to the change in strategy) those three outcomes are Nash equilibriums).

Now, the game isn't quite that simple of course, because the game isn't played just once (a non-repeated game, which is what the payoff table shows), but is played many times. You could think of the two drug dealers as making the choice to snitch or stay silent every morning when they wake up. The payoffs to the repeated game are the sums of the profits (low or high) or jail time from every play of the game.

So what happens in a repeated game? The 'best' choice for each drug dealer may be to cooperate (i.e. to remain silent). If either drug dealer snitches, then that is likely to cause the other drug dealer to snitch the next time the game is played. Snitching only has a short-term payoff, but ultimately leads to a worse outcome for everyone. So, the threat of reciprocal snitching may be enough to ensure an uneasy alliance results for drug dealers who have long time horizons (those who recognise this as a repeated game). Which suggests that there is probably such a thing as 'honour among drug dealers', and this police strategy will not work for long.

[HT: Marginal Revolution]

Your smartphone knows if you will pass or fail this semester

Tyler Cowan at Marginal Revolution pointed me to this new paper (pdf) by Rui Wang (Dartmouth College), Gabrielle Harari (University of Texas at Austin), Peilin Hao, Zia Zhou and Andrew Campbell (all Dartmouth College). The authors used continuous sensing data (like location, audio) collected from students' smartphones over a semester (apparently over 53GB of data per student!) to infer the times students spent studying (smartphone stationary in a location like the library, with no audio), in conversations (smartphone stationary, with audio), partying (smartphone in a location like a fraternity, with loud audio), etc. They also collected data on the students' personality, stress, sleep, and a bunch of other things.

What did they find? Nothing too surprising, according to the paper:

Our prediction model indicates that students with better grades are more conscientious, study more, experience positive moods across the term but register a drop in positive affect after the midterm point, experience lower levels of stress as the term progresses, are less social in terms of conversations during the evening period, and experience change in their conversation duration patterns later in the term.

In this age of worries about the data that is collected from us, I wonder how they got their 30 students to agree to this data collection exercise (and they must have had a really good story for their institutional review board!). Two takeaways from the paper though:

1. Students' behaviours really do matter for their chances of passing - studying more makes a difference! Though apparently attendance in class does not - that might be an artefact of the small sample size and the homogeneity of the sample. Note that they do find that students who increase their attendance after the mid-point of the semester do perform better; and
2. Student smartphones potentially contain a wealth of unexploited data that could be used to develop study aids. How long before there is an app that prompts you to decrease your time spent partying (or encourages you to party more early in the semester, and less later in the semester)?

Finally, this suggests that perhaps universities could use this data to identify students at risk of failure, and intervene. Provided students let their university have access to continuous sensing data from their phones, of course!

Related reading:

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

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.

Bride price and the returns to education for women

Continuing the development economics theme this week, one of the most important and most studied development projects is the massive school building program in Indonesia in the 1970s, the Sekolah Dasar INPRES. Over 61,000 primary schools were constructed - about one for every 500 children aged 5-14, which roughly doubled the number of primary schools in the country, and increased the enrolment rates of children aged 7 to 12 from 69 percent in 1973 to 83 percent in 1978.

The effects of this program on educational attainment and wages for men were evaluated by Esther Duflo in this AER paper (ungated here). She found:

The INPRES program led to an increase in educational attainment in Indonesia. On average, the estimates indicate that the program led to an increase of 0.25 to 0.40 years of education (0.12 to 0.19 years for each new school built per 1,000 children), and increased by 12 percent the probability that an affected child would complete primary school. The estimates also suggest that the program led to an increase of 3 to 5.4 percent in wages.

A subsequent NBER working paper by Lucia Breierova and Esther Duflo (ungated here) showed that the effects on women's education were much smaller. Both studies make use of the fact that date and region of birth determine each person's exposure to the increased schooling (which overcomes the identification problem that would usually arise - since family characteristics determine the choice of schooling, and subsequent labour market outcomes).

Which brings me to this new paper (PDF) by Navaa Ashraf, Natalie Bau, and Nathan Nunn (all from Harvard) and Alessandra Voena (University of Chicago). Using the same dataset and identification, they set out to investigate the effect of bride price on education attainment among women. Bride price is the custom of the groom's family making a payment to the wife's family at the time of the marriage (it is the opposite of dowry). It's not about buying a bride - it is typically interpreted as rewarding the parents of the bride for their years of investment in their daughter, and compensating them for her no longer contributing to their household (directly). Bride price is (still) a reasonably common practice in parts of Africa and Asia.

The paper's findings are interesting:

We show that among ethnic groups that practice bride price, the amount that the bride’s family receives as a bride price payment increases with the level of education of the bride. Completing primary school is associated with a 100% increase in the bride price payment, completing junior secondary is associated with a further 40% increase, and completing college with another 100% increase. These relationships are very robust and remain strong even when conditioning on a large set of observable characteristics, as well as potentially endogenous characteristics like the groom’s education.

Essentially, they find that the impact of the school building program in Indonesia was much greater for ethnic groups that practice bride price, and was virtually zero for other ethnic groups. Moreover, they show similar results for Zambia (which had a similar, albeit smaller, wide-scale school building program in the 1990s and early 2000s). The reason why bride price had such positive effects on educational attainment for girls is that:

...bride price provides a greater incentive for parents to invest in girls' education, and it is these parents that are more likely to take advantage of the increased supply of schools by educating their daughters.

Bride price often gets and unfair rap, but this is one situation where it appears to have had a positive impact - young girls would have had less schooling in Indonesia had it not been for the existence of the bride price custom.

[HT: Kevin Grier at Cherokee Gothic]

El Nino, climate change, and agriculture in the tropics

Following on from yesterday's post on developing countries, today I read an interesting article from the AER Papers and Proceedings issue earlier this year (ungated version here; PDF), by Solomon Hsiang (University of California, Berkeley) and Kyle Meng (University of California, Santa Barbara). In the article, the authors investigate the relationship between the El Nino Southern Oscillation (ENSO) and agriculture in tropical and temperate countries. ENSO has been shown to cause large and systematic changes in local climatic conditions around the world, but these effects have the greatest impact in tropical countries.

The authors use as a measure of ENSO an index of sea surface temperature in the eastern Pacific Ocean (a commonly used measure). They find:

that ENSO systematically affects country level temperature and rainfall in the tropics... A rise in the ENSO index by +1°C increases local temperatures in the tropics by +0.27°C and lowers rainfall by -4.6 cm on average (combined over two years). For temperate countries, temperatures actually fall due largely to changes in atmospheric and ocean circulations, but only by half as much, and there is a small but insignificant positive effect on rainfall.

More importantly though, they find:

A +1°C increase in the ENSO index lowers cereal yields -2%, total cereal production -3.5%, and agricultural income -1.8% on average across the tropics. These effects are highly statistically significant and suggest that rises in prices do not fully compensate countries for declines in agricultural output... crop yields increase in temperate countries when the tropical Pacific warms, albeit with a smaller magnitude that is less significant.

Given that some (but not all) research suggests that future climate change will be linked to stronger ENSO events (for a great review see here), these results suggest that poor countries (which are disproportionately located in the tropics) could face decreased agricultural output, decreased food security, and decreased economic growth in the future. Perhaps it also helps to explain in part the poor past growth trajectories of these countries as well.

How love conquered marriage

One of the key differences between developed and developing countries is the presence (or absence) of complete and functional markets. In my graduate development economics class, we spend a fair amount of time talking about the causes and implications of incomplete labour markets, credit markets, and insurance markets. The absence of functional insurance markets is a big problem for people in developing countries and as you would expect, they have developed various strategies to insure themselves (either individually, as a household/family, or as a community) against adverse shocks such as bad weather. These strategies include share-cropping (which shares the risk of weather shocks between the tenant farmer and the landowner), choosing low-yield but low-risk crop varieties, temporary rural-urban migration to diversity sources of income, or diversifying geographically through marriage of family members into families located in other villages (or regions, or even countries).

One of the ways that a family can ensure that they diversify geographically is for parents to arrange marriages for their children. Late last year, Marginal Revolution pointed me to this interesting paper (PDF) by Gabriela Rubio (University of California, Merced), and I've been meaning to write about it for a while. Rubio uses data from the Indonesian Family Life Survey (IFLS) to investigate whether improved agricultural outcomes have reduced the need for arranged-marriage-as-insurance, and have led to the observed decline in arranged marriage in Indonesia (and in many other countries, though Rubio notes that India, Pakistan and Bangladesh are exceptions). From the paper:

The goal of this paper is to understand the main driver(s) of the transition by proposing and testing empirically a model of marital choices. I first show that this transition away from arranged marriages in favor of self-choice or “love” marriages is correlated with increases in education, formal employment, urbanization, and declines in agriculture. These trends are common in all the countries where micro-data is available, suggesting that despite having different institutions at work, there is a fundamental economic explanation behind these changes in marriage institutions.

Based on these patterns, I build a simple model of marriage choice. I assume that arranged marriages serve as a form of informal insurance (as suggested in the literature of sociology, anthropology and economics, e.g. Rosenzweig and Stark (1989)) whereas other marriages (outside one’s networks) do not...

The model predicts that arranged marriages disappear when the net benefits of the insurance arrangement decrease relative to the (unconstrained) returns outside of the social network. When this is the case, parents invest in more education for the child, effectively increasing her outside option and, thus, the probability that she will reject the arranged marriage.

Rubio first demonstrates that arranged marriage acts as a form of (imperfect) insurance. She then uses the progressive introduction of the Green Revolution across Indonesia from the 1960s to the 1980s as a quasi-experiment, to test her theory about the impact on arranged marriage. She finds:

...that the Green Revolution increased returns to schooling by an additional 2.1% to 4.7% per additional year of schooling, it increased mean income of agricultural households, and importantly, it decreased their income variance by 8.1% and 8.3%, respectively.

Importantly, because the income variance decreased, that made insurance less necessary, which in turn led to the main results:

As predicted, the Green Revolution resulted in a decline of 9 to 20 percentage points in the probability of having an arranged marriages [sic] for the cohort exposed to the Green Revolution, and in an increase in education of 0.3 to 0.5 years of schooling for the same cohort.

The results are robust to various alternative specifications that Rubio tries. Which may lead us to conclude that the Green Revolution conquered arranged marriage, leaving love to take over.

However, it does leave one important question unanswered - what is so different about India, Pakistan and Bangladesh? They've had similar increases in agricultural incomes to Indonesia (and other countries that Rubio identifies as having declining rates of arranged marriage), and they've had a reduction in variability of incomes as well due to the Green Revolution. So the need for arranged-marriage-as-insurance is lower in these countries too, and yet arranged marriage persists. Are South Asians simply more intent on maintaining marriage traditions than Indonesians, do they use arranged marriage to signal their group affiliations and maintain social capital, or are they much more risk averse (such that insurance is still necessary even in the wake of improved incomes)? These are questions to be answered in future research.

A collection of the best bits from "Dear Undercover Economist"

I've been reading through the back catalogue of Tim Harford books. Earlier this year I read "Dear Undercover Economist", which is a collection of Dear Economist columns Harford wrote for the Financial Times. Tim Harford is a great author (and a great speaker - he was a keynote speaker at the New Zealand Association of Economists conference a few years back), and many of these columns (written in the style of an 'agony aunt' column) are hilarious.

This book is well worth reading, although since most of these columns are available online (either at the Financial Times (which may require you to sign up or subscribe) or on Harford's own site), I've collected a few of my favourites here (the short titles in the bullet points are mine):

• Do the costs outweigh the benefits of getting a girlfriend?
• Should children get sex education at school?
• Peltzman effects and driving like a lunatic
• Should you go along with an arranged marriage?
• Explaining the credit crunch if it was a fairy tale
• An answer to those pesky Nigerian scammers
• Where do missing socks go?

Enjoy!