DeLong on the pace of evolution

Any theory that seeks to invoke human evolution as a factor in the Industrial Revolution needs to deal with how quickly humans can evolve and whether this rate of change is fast enough to be a factor.

I was recently browsing Gregory Clark’s web-page for his book A Farewell to Alms and came across a video of a seminar in 2007 involving Clark, Brad DeLong and Tyler Cowen. There were some interesting points throughout the session (and it is worth watching it all) but one interesting point was an argument by DeLong on the pace of evolution.

His argument was based on the following example. Suppose there is a patience gene in the population. Assume that each person with the patience gene has a two-thirds chance of being patient while those without the gene have a one-third chance. Of those who are patient, they have a two-thirds chance of being rich, versus one-third for the others. Finally, assume that those who are rich have a two-thirds chance of having children, while the rest have a one-third chance.

None of the numbers in the example seem implausible, although there is plenty of room to debate the specifics. Based on these numbers, DeLong noted that those with the patience gene have a 14 in 27 chance of having children, while those without have a 13 in 27 chance. DeLong translated this to a proportional growth rate of 1/27 or approximately 0.04 for those with the patience gene. Assuming 25 years per generation, it would take about 500 years to double the proportion of the patience gene in the population from, say, 1 per cent to 2 per cent.

The following table indicates how he came to that conclusion (the numbers are how many of each type):

DeLong took this slow rate of change to be a challenge for any genetically based theory of the Industrial Revolution. If those numbers were the last word, I would be inclined to agree. However, I would not rule out a scenario where a change in a relatively small part of the population could have large effects if s small group of individuals were responsible for a large proportion of innovation in an economy or there were positive feedback loops.

More importantly, a closer look at the numbers can change the assessment. The first issue is DeLong’s interpretation of his own example. While DeLong’s estimate of 0.04 for the rate of growth is approximately right when the population is composed of equal numbers of each genotype, it underestimates the growth rate when there are proportionally less patient genotypes. Take the situation where the population has 1 per cent patient genotypes. In such a case, the increase of patient genotypes is effectively their absolute growth rate as they make little difference to the total population. Therefore, they increase as a proportion of the population at a rate of 1/13, or approximately 8 per cent per generation.

This would see the patient genotypes increase to 2 per cent of the population in less than 10 generations, or around 250 years. They would quadruple their proportion of the population in 500 years. As their proportion grows, their proportional growth rate slows. However, an argument that patient genotypes increased from 5 to 20 per cent of the population over 500 years is certainly a basis for significant macroeconomic effects.

Further playing with the numbers gives us some other possibilities. If instead of using two-thirds, one-third as the basis of our calculations, we could use three-quarters-one quarter, giving the following:

These calculations yield us a proportional growth rate of 12 per cent when there is a low proportion of patient genotypes, and 6 per cent when the population is composed of around 50 per cent patient genotypes. That is a doubling in proportion every 6 generations or 150 years when there is a low proportions of patient genotypes.

Another alternative is to simply cut out a step and assume that patient genotypes have a two-thirds chance of being rich, while the others have a one-third chance. This dramatically increases the potential growth rates:

At low prevalence, the patient genotypes increase in proportion of the population at a rate of 25 per cent per generation. Every three to four generations, patient genotypes would double in proportion of the total population.

All of the above is fairly crude and open to debate. However, it seems to indicate that genetically based hypotheses about the Industrial Revolution are robust to this particular back of the envelope calculation.

The speed of cities – afterthoughts

Having recently discussed cross-country variation in time preference and the pace of life, I have found it interesting reconciling the conclusions.

Richer countries tend to have residents with lower rates of time preference and a higher pace of life. Wang, Rieger and Hens noted this relationship and showed that pace of life is strongly and positively correlated with propensity to wait, an indication of time preference. The residents of the country where everyone is scurrying around have a higher ability to delay gratification. What might be a sign of impatience (the fast walking) is actually the opposite. I have a suggestions on how to reconcile these facts, but I am not yet convinced which of them are true or more important.

The first is that the subjects whose walking speed was measured were downtown during office hours. If they were employed, they were likely going to or from a work task. If work is unpleasant, an impatient person who cannot delay gratification (or conversely, tries to delay pain) might walk more slowly. Conversely, a patient person who is able to delay reward for greater returns might walk faster to take advantage of the returns from work.

The second point is that patient people tend to have higher incomes (as shown with Mischel’s marshmallows) and their time is worth more. As a result, they might rush due to the incentive effects (and despite their patience). In the richer city, more people will be in this situation.

Another possibility comes down to definitions. The way economists use the ideas such as foresight, delay of gratification and rate of time preference may not coincide with the common concept of “patience”. In my case, I consider that I have the ability to delay gratification and make decisions in a relatively time consistent manner with a low discount rate. However, I could also be considered impatient as I don’t like waiting and often rush others (and myself) through tasks (I am also a very fast walker). If we drop the common understanding of patience and limit ourselves to economic concepts such as self-control and ability to delay self-gratification, we may not need to reconcile this anomaly in the first place.

A final and related possibility is that the ability to delay gratification is actually a result of other traits, such as intelligence. A more intelligent person might have better judgement of when they should exercise patience and when not. Whether they seem patient will depend on which facet of their life we are examining.

The speed of cities, part II

As I described in my last post, there is a strong relationship between the size of cities and the residents’ speed of walking. The larger the city, the quicker its residents scamper from A to B. A number of studies have confirmed this relationship and have broadened the relationship to the speed of other activities (such as betel nuts changing hands quicker in Port Moresby than in rural centres in Papua New Guinea).

As the relationship between city size and speed has been shown to be surprisingly robust, it took some time before this research was extended to other factors that may influence the speed of walking. Do people walk faster in richer cities? In colder cities? Where these other factors were examined (such as by Levine et al), the samples had tended to be limited to cities within the same country or region, limiting the variation of the explanatory factors in the sample.

This gap was addressed by Robert Levine (a guest on the Radiolab podcast that triggered this series of posts) and Ara Norenzayan in a 1999 paper in which they examined the “pace of life” in 31 countries. The pace of life measure was composed of three elements: average walking speed, the speed with which postal clerks completed a simple request (a stamp purchase) and the accuracy of public clocks. The sample included cities in North and South America, Asia and Europe, plus one African city. Generally, they took measurements in the largest city in the country.

At the top of the pace of life score rankings were Japan (4th) and the Western European countries. In fact, the nine Western European countries all placed among the top 11 for pace, split only by Japan and Hong Kong (10th). In the middle of the rankings were the Eastern European countries, the United States (New York), Canada and newly industrialised Asian countries (such as Guangzhou, China). The slowest were those from the Middle East, Latin America and Asia.

The apparently faster pace of life in the Western European countries compared to the United States and Canada is surprising and seems to go against New York stereotypes. I would suggest that one reason for the apparently faster pace of the Western European countries is that two of the three measures, postal speed and the accuracy of public clocks, may be more weakly linked with speed of life than walking speed and are heavily influenced by the nature of the public service. When we look at only walking speed, the rankings are changed. The United States jumps to 6th (from 16th overall) and Kenya is 9th (compared to 22nd overall). Conversely, Austrians have the 23rd fastest walking pace, versus their overall place of 8th. The Irish are the fastest walkers and have the second highest pace of life score.

Given the lack of earlier analysis into factors besides population size, Levine and Norenzayan sought to test a range of hypothesis about what might affect the pace of life. These were:

  1. The more economic vitality a city has, the faster its pace of life. They used the country’s GDP, purchasing power parity (PPP – a measure of economic well-being) and average calorific intake as measures of the vitality.
  2. Hotter places are slower (using the average annual maximum).
  3. Individualistic cultures are slower.
  4. Bigger cities are faster. As 23 of the 28 cities for which population size was available had more than 1 million residents, the sample did not allow them to fully test this hypothesis.

An examination of the correlation between the pace of life and the characteristics used to test the hypotheses showed that the first three hypotheses could be supported. For the overall pace of life and walking speed, there was a strong correlation with GDP (0.74 and 0.61) and PPP (0.72 and 0.59). Climate (-0.58 and -0.47) and collectivism (-0.59 and -0.60) were strongly negatively correlated. Calorific intake had a weaker but still positive correlation (0.51 and 0.39).

The correlation between walking speed and the community characteristics was generally stronger than that between the postal service or clock accuracy measures and those characteristics. Walking speed had a stronger relationship with GDP, PPP and collectivism than the other pace-of-life measures, while it had a similar relationship to the rest. This could be considered another sign that the postal and clock measures of pace-of-life have more variation due to idiosyncratic characteristics of the country than the straight measure of walking speed.

The big question that comes out of these findings is the question of causation. We have a strong correlation, but which is causing which? Does the higher value of time in rich cities result in the residents walking faster or do cities with more active residents become richer? Is there a selection effect, whereby dynamic, rich cities attract dynamic, fast walking residents? A question might also be asked about the permanence of these traits. Do residents speed up as the city gets richer? Do residents of a hot city walk faster on a cold day (or when they are in another, colder city)?

My instinct is that it is a mix of both, but that the selection effect is a significant player. However, there would be expected to be clear incentive effects of higher valued time (although which wins out – the substitution or income effect as the city gets richer?).

Unlike earlier studies, the authors found no significant link between walking speed and population size. This could be representative of the lack of variation in the sample. The authors also suggested that this could be evidence of a threshold effect in that once a city exceeds a certain size, additional population growth does not affect the pace of life. This makes some sense, in that there is a limit to the speed with which someone can realistically walk. Further, once there is a certain threshold of environmental stimulus (if we adopt the hypothesis of Milbrand and Bornstein and Bornstein discussed in my last post) or alternative forms of entertainment, it is unlikely to spur faster walking.

One interesting suggestion by the authors was the potential for reinforcement. If a large city has more economic opportunity, it may attract migrants, further increasing its size. They noted the potential for future study into which factors are mutually reinforcing. This ties back to the issue of selection effects. A city that is slightly richer may become significantly so if it can attract a certain type of resident.

As a last note,the authors also sought to look at some consequences of the pace-of-life and hypothesised that faster cities have higher rates of death from heart disease, higher smoking rates and higher subjective well-being. These were all found to be supported in the predicted directions (although the relationship was less strong than that between the other community characteristics and the pace of life). I won’t go into these findings here, but the heart disease and smoking elements make sense. The subjective well-being finding also seems reasonable if you assume that higher income and other city benefits deliver well-being. It also suggests that if there is any “over-stimulation” that residents are trying to avoid through a fast-walking speed, the cost of this is outweighed by the benefits of city life.

The speed of cities

Over the weekend, I listened to a great Radiolab podcast in which Bob Levine was interviewed about the pace of walking in cities. Bob spoke about how people tend to walk faster in larger cities, with this relationship surprisingly consistent. Where does this walking pace comes from. As the host Jad asked, do we make the city, or does the city make us?

The early movers in this area of research were Bornstein and Bornstein, who between 1972 and 1974 went to 15 countries across Europe, North America and Asia and measured the speed of pedestrians. They took a 50 feet stretch in similar downtown areas of each city and measured the speed of single, unencumbered walkers traversing that distance.

The slowest walkers were from Itea, Greece (population 2,500), who took an average of 22 seconds to cover the 50 feet. In Prague, a city of over 1 million, the pedestrians covered the distance in a flying average of 8.5 seconds. Walking speed and the log of the population were strongly correlated (with a correlation coefficient of 0.91 that was significant beyond the 0.001 confidence level). Particularly surprising is the consistency of the results and the absence of any large outliers. For example, the five largest cities sampled all had higher average walking speeds than the slowest five.

This high level of consistency raises some obvious questions. While there were no severe outliers in the sample of 15 cities included in the paper, are there any cities that are different? If so, why? Also, where there is variation in the sample, can this be explained? Some other researchers have considered this since the Bornstein and Bornstein paper was published, and I hope to post about those in the near future.

Bornstein and Bornstein based their explanation for the consistency on the total number of people in the city as opposed to density. Borrowing from Milgram, they considered that a higher number of people in a city causes increased stimulation. A person seeking to control the sensory overload will have a higher walking speed as a protection from this excessive environmental stimulation. The variance in walking speeds between cities is the manifestation of this adaptation.

While this is an interesting and possibly correct explanation, two other effects strike me as relevant. The first is the selection effect. Why do people choose to live in cities? If people who have a preference for a faster paced lifestyle wish to live in cities, and those who prefer a slower pace tend to leave, this will tend to drive the results we see. There is an element of the people making the city. This may tend to reduce any “stimulation effect” as residents who live in the city may be there as they wish to be stimulated. The easiest way to reduce stimulation would be to simply leave the city.

The second effect concerns the opportunity cost of time. For the pedestrians, what is the value of an earlier arrival? If incomes are higher in the city, as they tend to be in larger cities, their time has higher monetary value. If there is more or better entertainment in the city, there is more value in being at the destination than dawdling along the way.

Clark on violence

In Greg Clark’s excellent book A Farewell to Alms, Clark posited that there was only one important event in human history – the Industrial Revolution. Before that time, per capita income was effectively flat, with no discernible trend. That all changed around 1800 AD with the Industrial Revolution. Clark saw the Neolithic revolution and the move to settled agriculture as simply an extension of hunting and gathering and symptomatic of the steadily improving efficiency that had occurred over the previous tens of thousands of years.

Following publication of the book, a series of articles by reviewers were published in the European Review of Economic History (which are unfortunately gated for those without university access). In Clark’s response (which he has helpfully placed on his website), he concedes that there is another event to the Industrial Revolution that we should note. This event is not the Neolithic revolution but is the move to societies where violence was limited (and centralised). It is in such societies the competition for reproductive success shifts towards economic means, meaning that those traits conducive to economic growth can spread.

Clark noted the poor understanding that we have of this transition. Following from my posts of the last two days on violence (here and here), if a violent society favours genotypes which have a tendency to violence, what is the trigger to exit that violent state? The Neolithic revolution appears to be a necessary but not sufficient step.

Clark uses the example of the Huli of the southern highlands of Papua New Guinea. They had established a settled system of agriculture and men accumulated wealth through the accumulation of pigs and control over gardens. However, 20 per cent of male deaths and 6 per cent of female deaths were from violence or warfare. Rather than social status being attained through wealth, causation was reversed, with high social status leading to wealth and that status often coming from being distinguished in fighting. Success in this society was built on war and social intercourse and not skill in production.

More on violence

Following yesterday’s post on female preference for masculine men, a couple of old articles came to mind.

The first (and I am not sure why this did not come into my head yesterday) is the work by Napoleon Chagnon on the Yanomamo. From his paper Life Histories, Blood Revenge, and Warfare in a Tribal Population:

Studies of the Yanomamo Indians of Amazonas during the past 23 years show that 44 percent of males estimated to be 25 or older have participated in the killing of someone, that approximately 30 percent of adult male deaths are due to violence, and that nearly 70 percent of all adults over an estimated 40 years of age have lost a close genetic relative due to violence. Demographic data indicate that men who have killed have more wives and offspring than men who have not killed.

The reproductive advantage to being unokais (having killed) was significant, with an average of 4.91 children compared to 1.59 for those in Chagnon’s sample who had not killed. To the extent that traits making someone more likely to kill are heritable, they would shortly dominate this population (assuming they do not already).

There is a gap between Chagnon’s study and the preference for masculinity that I wrote about yesterday. The unokais were reproductively successful with women who knew that they had killed. There is no evidence that the unokais appear more masculine and that this was behind their reproductive success. However, preference for masculinity in a violent society may be an indicator or proxy of what was Chagnon noted – that in violent societies there is benefit to being aggressive.

The second article was by Edward O Wilson, titled Competitive and aggressive behavior. After some very interesting discussion on the rate of behavioural change in humans, the closing paragraph was as follows:

Some degree of aggressiveness in man is nevertheless probably adaptive – that is, genetically programmed by means of natural selection to contribute to fitness in the narrow reproductive sense. This complex trait cannot be assumed to be due to a useless or harmful genetic residue left over from prehistoric times. It is more plausibly viewed as a trait that has been adaptive within the past few hundreds or, at most, thousands of years. Some of its components might have even originated during historical times, since both theoretical considerations and empirical studies on animal populations show that some behavioural traits can evolve significantly within ten generations or less.

If we accept this point, we might find genetically based variations in aggressiveness across modern populations. Extension of the research into female preferences for masculine males could shed some light on the strength of this adaptive advantage in modern populations and whether a cycle between violence and adaptive advantage for aggressiveness is a feasible scenario.

Selection for aggression

Masculine appearance in a man is an indicator of their health, which in turn leads to more viable offspring. On this basis, one might assume that women prefer masculine men. However, empirical research into whether women prefer men with more masculine physical features has not shown the strong positive preference we might expect. While masculine appearance is linked to health, that masculine partner may be less interested in a long-term relationship and be unlikely to provide for the child over the long-term. In that case, a woman is likely to weigh up the benefits of a healthy child against the likelihood of provision of care by the father of that child.

As reported in The Economist last month, there has been an interesting exchange in the Proceedings of the Royal Society about what factors may influence this trade-off and lead to variations in preferences for masculinity. In the first paper, DeBruine et al proposed that where health in a country is poor, a woman’s preference for masculinity will be strong as it is important to have healthier offspring. Across about 4,800 women resident in 30 developed countries, they found a significant relationship between health and preference for masculinity that was robust to controls for age and wealth. However, they suggested that other factors such as violence in the society should be researched.

Brooks et al took up this suggestion and used data from the first study to examine whether women are attracted to more masculine men in environments where there are greater benefits to dominance. Using income inequality as an indicator of violence, they found that income inequality may be a better predictor than health of preferences for masculinity. They did add the proviso, however, that health and inequality may be correlated, and that other correlates may mediate the relationship. Brooks et al also tested murder rates as an explanatory variable and found that if they included both health and murder rates in the regression, only the murder rate was significant (although income inequality was a stronger predictor than the murder rate).

DeBruine et al responded that the finding by Brooks et al that male-male competition is responsible for the preference for masculinity was reliant on income being excluded from the murder rate and health regressions. Using new United States data, they also called into question the finding that income inequality was a better predictor than health.

Leaving aside which analysis of the data we should prefer, the relationship suggested by Brooks et al could have some interesting dynamics. If a society is violent, Brooks et al would suggest that more masculine, dominant men would have a fitness advantage. If that were the case, they would come to form a larger part of the population. Does a larger proportion of more masculine men in a population make it inherently more violent? If so, a cycle could emerge where violent societies have a larger proportion of violent men and stay violent. Conversely, peaceful societies would stay peaceful with characteristics linked to greater propensity to violence not being rewarded.

To look at whether that scenario might hold, greater analysis of the strength of the preference variation would be required. Is the fitness advantage large enough that it could have dynamic implications? To analyse this we would need to expand the analysis to countries with greater extremes of violence, with the DeBruine et al dataset restricted to people in developed countries who had identified their ethnicity as white. What is the relative strength of the preference in Iraq or Sudan? Another source of data would be the level of masculinity of men within a population. Is there a higher proportion of masculine men in populations with a long history of violence?

World population 500BC

Spurred by this chart, a number of bloggers (such as Robin Hanson and Razib Khan) have asked what was happening around 500 BC to cause the jump in population. Was this an almost industrial revolution (with population in the Malthusian state the primary indicator of the level of technology)?

The data in the chart is based on the low estimate by the United States Census Bureau, which in turn comes from a range of sources. Looking at the way the Bureau put the low estimate together, I am not sure that there was a major event around 500 BC as the chart suggests. The main source of data between 5,000 BC and 700 AD is the Atlas of World Population History by Jones and McEvedy. They provide all the data points between those two dates, except they have no measure for 400 BC, which is then sourced from Biraben’s 1980 paper, An Essay Concerning Mankind’s Evolution. This combination of the two data sources is what causes the appearance of a rapid acceleration of population growth into 400 BC. Splitting out these two data sources, the  sudden surge into 400 AD becomes a gentle rise:

The growth in population is not beyond that which might be expected from a scale effect in the development of technology (more people, more ideas – such as that proposed by Kremer). In such a case, you would expect the population to increase at greater than an exponential rate (an exponential increase would be a straight line with the logarithmic scale on the y-axis).

One point of interest which does remain is why population growth slowed after this point, and looking at the Biraben data, there was a marked decrease in population. At this time, Europe entered the Dark Ages. Was the technological stagnation and economic decay through this period responsible for the population decline?

My top 10 books in 2010

As is the fashion for this time of year, here are my top ten books of 2010. As I tend to read books that are both old and new, these are the top 10 books I have read this year.

1. The Enlightened Economy: An Economic History of Britain 1700-1850 by Joel Mokyr – Although I don’t agree with the underlying hypothesis, it is a great analysis of the Industrial Revolution.

2. This Time Is Different: Eight Centuries of Financial Folly by Carmen Reinhart & Kenneth Rogoff – It is nice to put the recent crisis in perspective.

3. Too Big to Fail: The Inside Story of How Wall Street and Washington Fought to Save the Financial System — and Themselves by Andrew Ross Sorkin – As a critic of the bailouts during the crisis, this book put a seed of doubt in my mind as to whether I would have stuck to my guns if I was the one pulling the levers. It is one thing to criticise from the ivory tower. It is another to be in the midst of the panic with the responsibility on your shoulders. Sorkin puts you in the room like no other.

4. Charles Darwin – Voyaging by Janet Browne – I am only halfway through this book, but it adds some great colour to Darwin’s life. I have read a number of Darwin biographies and (so far) this is comfortably the best.

5. Krakatoa: The Day the World Exploded: August 27, 1883 by Simon Winchester – I read this sitting on the beach in Bali. I could not have picked a better introduction to Indonesia (apart from possibly Wallace’s The Malay Archipelago).

6. Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism by George Akerlof and Robert Shiller – As with most books that advocate the use of psychology in economics, I agree with the concept that we need an economics that incorporates real humans. As is also usually the case, I am uncomfortable with the extent that Akerlof and Shiller advocate the use of government power to constrain the “animal spirits”.  I am not sure it is so easy. However, the book is a great read (in plain English) and raises plenty of interesting ideas.

7. The Invisible Hook: The Hidden Economics of Pirates by Peter Leeson – Of all the books applying economics to new areas, this was the most fun.

8. The Bonfire of the Vanities by Tom Wolfe – A bit dated, not particularly subtle and it is hard  to like any of the characters, but I couldn’t put it down.

9. Born to Run: A Hidden Tribe, Superathletes, and the Greatest Race the World Has Never Seen by Christopher McDougall – This book had possibly the greatest practical effect on me. I have moved to bare foot (or near bare foot) running and years of shin pain have gone away. And as an aside, the story is great.

10. The Big Short: Inside the Doomsday Machine by Michael Lewis – Lewis finds the humour in the chaos better than most. The question that hangs in the air through this book is what is the social purpose behind this gambling.

Trading fish

Alex Tabarrok has posted on Marginal Revolution a piece on the expansion of “catch shares” as a fisheries management tool. Under catch shares (also called individual tradeable quotas or ITQs), each fisher owns a percentage of the quota set for the fishery. The fisher can trade the share and it provides flexibility to the fisher about when and how they choose to catch their quota.

The use of catch shares as a method of allocating fishing rights has many benefits. Beyond the information obtained from the prices of the quotas and the opportunity for environmental groups to buy shares, there are some important incentives that they offer. The owners of the shares know that they have an established right into the future and it is in their interest that the stock over which that right exists is maintained at a reasonable level.

As pointed out by Quentin Grafton, Tom Kompas and Ray Hilborn in Science, a maximised economic yield (and maximised catch share value) generally occurs when there is a larger stock size. This is mainly due to the stock effect, whereby it is easier to catch fish from a larger stock. Catching the last fish in the ocean is very expensive. If fisherman have certainty in their future catch rights through their catch share, they will be more likely to support restoration of stock size to that which delivers the maximum economic yield.

Like John Tierney, I find the opposition to catch shares by some environmental groups perplexing from a strategic point of view. Catch shares have the potential to widen the group of allies who wish to preserve the stock. As noted by  John, research suggests that catch shares are superior to alternative allocation measures (although they are not perfect and require appropriate quota levels etc). I would suggest that the opposition is because many environmentalists do not trust markets. This  extends to doubt about the effectiveness of cap-and-trade systems for pollutants or the response to incentives provided by prices such as a carbon tax. To Oceana, ITQs are like the collateralised debt obligations at the centre of the global financial crisis. Despite the differences, evidence of one market crisis (whatever the cause) condemns all markets.

Many environmentalists are also opposed to the “corporatisation” of  fisheries, with catch shares seen as a pathway to ownership of the fisheries by large corporate interests. While this might result, it would not be significantly different to the current state of affairs. This would also depend on the initial allocation. If small fishers were given catch shares, it would be a benefit of the scheme that they have the ability to sell their share if they wished to use that money for an alternative use.

Having said this, some environmentalists are more actively advocating their use (such as the Environmental Defence Fund) as it becomes clear that fisheries with allocated catch shares have better conservation outcomes. Based on the examples in Alex’s post, they are being heard.