A couple of weeks ago, Razib Kahn wrote a post in which he argued that “you don’t need to know the exact gene of major effect to conclude that a trait is genetic.” Where a lot of research is invested in finding the specific genes behind traits, and with a media hungry for these kinds of stories, many people have forgotten how much can be understood without knowledge of the specific genes. As Razib points out, much of genetics predates molecular biology and the discovery of the structure of DNA.

From the perspective of integrating evolutionary biology and economics, a few of the papers I have posted about this year illustrate Razib’s point. Ashraf and Galor’s soon to be published paper on genetic diversity and economic development uses DNA-level data. While there is a statistically significant relationship between genetic diversity and economic development, few people seem convinced that there is a causative relationship between the two. How does the genetic diversity manifest itself into the economic outcomes? In developing the evidence for that causative relationship, it is not clear where you would start. Cross-species analysis might yield some insight on the cooperative effects of diversity, but how would you show the positive effects on innovation of higher levels of diversity? Linking the molecular biology to economic outcomes is difficult.

We’re also seeing an increasing number of genoeconomics papers on the genetic basis for economic traits, such as time and risk preference, that use molecular data. Unfortunately, many studies find spurious relationships and the low size of the effect of most alleles has resulted in this work being of limited use in economic analysis.

However, this is not the point to give up on molecular biology as a tool for analysing economic traits and outcomes. The genoeconomics enterprise is in its early days and may bear fruit. And in the interim, we already have a lot of information that can already be used. Take the estimates of heritability of economic preferences we have from twin studies. We cannot pinpoint specific genes to account for even a small fraction of the heritability, but those estimates can still be useful for increasing our understanding of the role of genetics in economic outcomes, and may even be useful in policy development.

My last post on Gregory Clark and Neil Cummins’s use of surnames to track social mobility across the generations is another case in point. Combining  data about life outcomes across the generations could yield insight into the genetic factors underlying the transmission of socioeconomic status, adding to that already obtained through twin studies and shorter term analysis of intergenerational transmission. The data lend itself to quantitative genetic analysis.  And in this analysis, there is not a gene in sight.

There have been a few recent pointers to Gregory Clark and Neil Cummin’s work on long-term social mobility using surnames (papers here, here and here). The basic method used in these studies is to examine the share of rare surnames in high or low status occupations and compare it to the overall prevalence of that surname in the population. By tracking the relative status of the rare surname through time (effectively treating those with the same surname as a large family), the change in status through the generations can be measured.

The abstract of the paper presented by Clark at a Becker-Friedman Institute conference on intergenerational mobility earlier this year gives a good summary of the general results:

What is the true rate of social mobility? Modern one-generation studies suggest considerable regression to the mean for all measures of status – wealth, income, occupation and education across a variety of societies. The β that links status across generations is in the order of 0.2-0.5. In that case inherited surnames will quickly lose any information about social status. Using surnames this paper looks at social mobility rates across many generations in England 1086-2011, Sweden, 1700-2011, the USA 1650-2011, India, 1870-2011, Japan, 1870-2011, and China and Taiwan 1700-2011. The underlying β for long-run social mobility is around 0.75, and is remarkably similar across societies and epochs. This implies that compete regression to the mean for elites takes 15 or more generations.

The lack of social mobility is consistent across cultures, social systems and times. Clark’s conclusion from this (although he does not actively discuss the basis for his conclusion) is that “Social status is likely mainly of genetic origin.”

This contrasts with Dylan Matthews’s interpretation at the Washington Post:

[G]enetics likely has little to do with those results. Clark and Cummins studied surnames across eight generations. So, two people with the same surname in 1800 and 2011 would only share 0.5⁸ = 0.4 percent of their DNA.

What Matthews misses, however, is the reason that Clark attributes to the very high value of β – assortative mating. Thus, while a person may contribute only 0.4 per cent of the genome of a descendant 8 generations down the track (assuming no intermarriage between relations in that time), the descendant’s genome will largely consist of DNA contributed by other high-socioeconomic status people.

In a National Review article a couple of months ago, Kevin Williamson questioned Obama’s status relative to Mitt Romney’s because Obama’s children were daughters, while Romney had sons.

It is a curious scientific fact (explained in evolutionary biology by the Trivers-Willard hypothesis — Willard, notice) that high-status animals tend to have more male offspring than female offspring, which holds true across many species, from red deer to mink to Homo sap. The offspring of rich families are statistically biased in favor of sons — the children of the general population are 51 percent male and 49 percent female, but the children of the Forbes billionaire list are 60 percent male. Have a gander at that Romney family picture: five sons, zero daughters. Romney has 18 grandchildren, and they exceed a 2:1 ratio of grandsons to granddaughters (13:5). When they go to church at their summer-vacation home, the Romney clan makes up a third of the congregation. He is basically a tribal chieftain.

Professor Obama? Two daughters. May as well give the guy a cardigan. And fallopian tubes.

From an evolutionary point of view, Mitt Romney should get 100 percent of the female vote. All of it.

In response, Steve Mirsky called Robert Trivers to ask whether Williamson’s use of the Trivers-Willard hypothesis was correct. Trivers notes a couple of problems with the analysis. First, voting is not the relevant decision:

“Maybe the guy should be saying that all women should try to f— [Romney]. Look, the f—er’s rich. Can you f— him and get some of the money? Or are you just voting for him? They’re two different decisions.” …

“They [women] should all want a man with money. That’s so obvious we don’t need to talk about the sex ratio of the progeny.

Of course, if you want to measure their evolutionary success, raw numbers are also a better measure:

“There’s no way of looking at the sex ratios of progeny of these two couples and predicting anything about their relative superiority over time. It would be better put as an evolutionist arguing about the five-versus-two ratio [of the total number of children born to each candidate].

A five to two gap would be hard to make up, regardless of the advantages sons may accrue from their high status.

The committee for selecting the 2012 winners of the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (remember, it is not an original Nobel Prize) seems to have done a better job than the Peace Prize Committee. Alvin Roth and Lloyd Shapely have been awarded the prize “for the theory of stable allocations and the practice of market design”, and they are worthy winners.

There is plenty of commentary about their contribution in the media and blogosphere, so I’ll draw attention to just one element of Shapely’s work – the 1962 development of the Gale-Shapely algorithm with David Gale, and its application to marriage markets. The Information for the Public provided by the Royal Swedish Academy of Sciences gives a good summary of this work:

How should ten women and ten men be matched, while respecting their individual preferences? The main challenge involved designing a simple mechanism that would lead to a stable matching, where no couples would break up and form new matches which would make them better off. The solution – the Gale-Shapley “deferred acceptance” algorithm – was a set of simple rules that always led straight to a stable matching.

The Gale-Shapley algorithm can be set up in two alternative ways: either men propose to women, or women propose to men. In the latter case, the process begins with each woman proposing to the man she likes the best. Each man then looks at the different proposals he has received (if any), retains what he regards as the most attractive proposal (but defers from accepting it) and rejects the others. The women who were rejected in the first round then propose to their second-best choices, while the men again keep their best offer and reject the rest. This continues until no women want to make any further proposals. As each of the men then accepts the proposal he holds, the process comes to an end. Gale and Shapley proved mathematically that this algorithm always leads to a stable matching.

If we suppose that the men are making the offers, and the women considering them, the woman should reject all the offers except for her favourite, and keep stringing them along for the possibility that someone even better will come along. However, Gale and Shapely also pointed out a more important consideration in achieving the optimal result:

The specific setup of the algorithm turned out to have important distributional consequences; it matters a great deal whether the right to propose is given to the women – as in our example – or to the men. If the women propose, the outcome is better for them than if the men propose, because some women wind up with men they like better, and no woman is worse off than if the men had been given the right to propose. Indeed, the resulting matching is better for the women than any other stable matching. Conversely, the reverse algorithm – where the men propose – leads to the worst outcome from the women’s perspective.

It’s not an entirely intuitive result, but you are better off if you are the one making the offers. No prizes for shrinking violets! However, if the relative rankings of the men and women are consistent between different men and women, which party is making the offers becomes less important.

This is, of course, a simplified model and can fall apart under all sorts of conditions (such as offers flying in both directions, search costs, time constraints and the entry of new potential partners), which provided Alvin Roth with plenty of scope to further develop the work.

I should also note that despite the prize being for economics, Shapely prefers the label of mathematician to economist.

As I noted in a postscript to my last post, Quamrul Ashraf and Oded Galor have prepared a response [Update: the response is no longer online] to the Harvard academic critique of their paper on genetic diversity and economic development (I recommend having a look through the comments on that post, where Jade d’Alpoim Guedes, Nick Patterson (both authors of the critique), Henry Harpending and others continue the debate).

Apart from the broader question of whether this work should even be undertaken, the Harvard critique focused on two issues: causation and the statistical foundations of the work. Ashraf and Galor are quick to dismiss the statistical critique:

[O]ur critics have falsely suggested that we treat socioeconomic and genetic data as if populations are independent of one another. On the contrary, our empirical analysis accounts for the possibility of spatial dependence across observations, including analytical methods that correct for spatial autocorrelation in “error terms” and bootstrapping. This criticism of our work thus reflects either a misunderstanding of the techniques that we employ or a superficial reading of our work.

The response on causation is more detailed, and one of Ashraf and Galor’s arguments is one that I did not expect to see. They write:

The key is that the measure of intra-population genetic diversity that we employ should be interpreted as a proxy (i.e., a correlated summary measure) for diversity amongst individuals in a myriad of observable and unobservable personal traits that may be physiological, behavioral, socially-constructed, or otherwise. …

A careful reading of our research should make it apparent that our use of the measure of genetic diversity from the field of population genetics does not imply that our hypothesis is one of biological determinism, nor does it imply that DNA material is directly important for economic outcomes or that some genes are more important than others for economic success. The fact that the measure of genetic diversity we use is based on variation across individuals in non-protein- coding regions of the genome (and, thus, in genomic characteristics that are not necessarily phenotypically expressed so as to be subject to the forces of natural selection) is clear reason why our findings should be interpreted through the lens of our measure serving as a proxy for diversity more broadly defined.

The more relevant question to ask therefore is to what extent the measure we use can reasonably be considered a proxy for diversity in unobserved phenotypic or socially-constructed characteristics. There is indeed an emerging body of scientific evidence that establishes remarkable correlations in this regard.

Ashraf and Galor are also quoted running this line in a Nature News piece on their paper:

 Galor and Ashraf told Nature that, far from claiming that genetic diversity directly influences economic development, they are using it as a proxy for immeasurable cultural, historical and biological factors that influence economies.

After reading this, I went back to the paper to confirm my previous understanding of it, and if Ashraf and Galor intended to use genetic diversity as a proxy, it is not clear. The paper appears to finger genetic diversity and the phenotypic expression of that diversity as the relevant causal factors, with no suggestion it is a proxy. For example, they write:

The hypothesized channels through which genetic diversity affects aggregate productivity follow naturally from separate well-established mechanisms in the field of evolutionary biology and from experimental evidence from scientific studies on organisms that display a relatively high degree of social behavior in nature (e.g., living in task-directed hierarchical societies and engaging in cooperative rearing of offspring). The benefits of genetic diversity, for instance, are highlighted in the Darwinian theory of evolution by natural selection, according to which diversity, by permitting the forces of natural selection to operate over a wider spectrum of traits, increases the adaptability and, hence, the survivability of a population to changing environmental conditions. On the other hand, to the extent that genetic diversity is associated with a lower average degree of relatedness amongst individuals in a population, kin selection theory, which emphasizes that cooperation amongst genetically related individuals can indeed be collectively beneficial as it ultimately facilitates the propagation of shared genes to the next generation, is suggestive of the hypothesized mechanism through which diversity confers costs on aggregate productivity.

I would like to see a more direct defence of their argument about the causal mechanisms. However, Ashraf and Galor do suggest in their response that further research on the causal mechanisms is required.

The timing of this debate has highlighted the extent of continued disciplinary divides. Ashraf and Galor released the working paper a couple of years ago, and they have since presented it in a raft of conferences and seminars. It was then accepted for publication in the American Economic Review, but the current debate was only triggered when the paper was mentioned in Science (gated). The pre-publication of working papers so prevalent in economics, and which is starting to gain traction in other fields, still relies on the working paper getting in front of people who might be interested in commenting. The reality is, however, that publication in a reputable journal remains the point at which a paper comes to others’ attention – or becomes “important” enough that it deserves a response.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:

1. A summary of the paper methodology and findings
2. Does genetic diversity increase innovation?
3. Does genetic diversity increase conflict?
4. Is genetic diversity a proxy for phenotypic diversity?
5. Is population density a good measure of technological progress?
6. What are the policy implications of the effects of genetic diversity on economic development?
7. Should this paper have been published?

Other debate on this paper can also be found here, here, here and here.

A group of Harvard academics have penned a short response to Ashraf and Galor’s forthcoming American Economic Review paper, The Out of Africa Hypothesis, Human Genetic Diversity and Comparative Economic Development.

Ashraf and Galor argue that economic development is affected by genetic diversity, which increases innovation but also increases conflict and distrust. This leads to an optimum “goldilocks” level of diversity, with genetically diverse Africans and less genetically diverse native Americans falling on either side of that optimum.

The Harvard academics suggest that the findings of the paper are scientifically flawed and that Ashraf and Galor “misuse genetic, evolutionary, archaeological, historical and cultural data”. They question the causal mechanism proposed by Ashraf and Galor and their statistical treatment. I will give my views on the causative mechanisms when I write a full post on the paper (probably to coincide with its publication). However, the statistical issue is interesting. The academics write:

The argument is also statistically flawed by treating genetic data as each population having an entirely independent history both from a genetic and from a historical point of view, when in fact, they are highly correlated and inextricably entangled with genetic population structure and with contingent historical events. Such haphazard methods and erroneous assumptions of statistical independence could equally find a genetic cause for the use of chopsticks.

This argument is similar to the statement that the various independent origins of agriculture are not actually “independent”. Populations in each region may have developed the specific idea of agriculture themselves, but they had a shared cultural and evolutionary history and their state at the time of the development of agriculture reflected elements of that shared history. However, in the limit, there is little of interest in the social sciences that is truly independent, so the question is what level of independence is required and whether statistical techniques can draw out relationships that are not spurious. Still, the risks presented by population structure in research such as this is very real.

In the last part of the letter, the Harvard academics reflect on the implications of the research and raise the common argument that this area should not be investigated due to “the potential to be misused with frightening consequences to justify indefensible practices such as ethnic cleansing or genocide.” I do not find this argument compelling, and consider that there is a need for robust, scientific exploration of an area that will continue to be debated with or without that research.

*Postscript: I somehow missed it, but Ashraf and Galor have written a response (Update: which now seems to have been taken down). Thanks Vincenzo for the pointer.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:

1. A summary of the paper methodology and findings
2. Does genetic diversity increase innovation?
3. Does genetic diversity increase conflict?
4. Is genetic diversity a proxy for phenotypic diversity?
5. Is population density a good measure of technological progress?
6. What are the policy implications of the effects of genetic diversity on economic development?
7. Should this paper have been published?

Other debate on this paper can also be found here, here, here and here.

I recently came across a review of Robert Frank’s The Darwin Economy by Ted Bergstrom. Frank’s argument is largely based on the concept that a person is made worse off when they respond to someone else’s consumption choices, as it often turns into a winner takes all arms race. Bergstrom makes an important point that people may wish for someone else to increase their level of conspicuous consumption or competitive output. Bergstrom writes:

Suppose, for example, that neighbors A and B each consume two goods, x whose consumption is publicly observed and y whose consumption is not. Suppose that A gets an income windfall and buys more x. Shortly thereafter, we observe that B buys more x, although his income hasn’t changed. Can we conclude that B has been made worse off by A’s good fortune? We know that B now chooses a bundle that he rejected before A’s windfall, which would be bad for him if he were indifferent about A’s consumption. But, in fact, he is not indifferent about A’s consumption. It is not hard to construct “realistic” vignettes in which B would be pleased to see the increase in A’s consumption of x and pleased to increase his own x in response. For example, A if paints his house, or improves the appearance of his garden, B might enjoy the neighborhood improvement and although he could still afford his old combination of x and y he would prefer to complement his neighbor’s action by his own home improvements. Other examples come from athletic endeavors. Suppose that A and B frequently play tennis together and traditionally win about equally often. For some reason, A’s game improves, and he begins to win more than half the time. This induces B to play harder or perhaps purchase costly tennis lessons so that once again they win about equally often. Are we to conclude that B is worse off and A is no better off than before the improvement of their games? Not necessarily. Both may be evolutionarily programmed by our hunter-gather past to enjoy the challenge of succeeding at a difficult task. After all, they do not play each other for prize money, they play for the pleasure of competing.

I have some sympathy for Bergstrom’s argument, although I wonder how many examples of this type might be explained by competition in other domains. For example, if someone is pleased that their neighbour renovates their house and then increases the upkeep on theirs, is this because there are people besides their neighbour with whom they are engaging in positional competition? Does it increase the prestige of their neighbourhood?

I am also not convinced that the pleasure of competing is materially improved through your playing partner in social tennis becoming better than you, particularly given experimental evidence on changes in testosterone and cortisol from losing “fun” competitions. Being at the bottom of the pile is bad for your health. Is the additional training simply intended to increase the probability of winning against the improved player? Or is the training against the better competitor yielding benefits in more success against other potential competitors?

Despite my doubt about whether these examples are useful or representative, I am hesitant to ignore them in drawing policy conclusions that may affect competition. As I suggested recently, there may be spillovers from competitive activity that yield broader benefits. Plus, do I have the required level of insight about someone’s enjoyment of tennis to be making assessments on their behalf? It’s a reasonable assumption that the mismatch between private and social benefits means that private investment in winner takes all competition exceeds the socially optimal level, but quantifying or controlling that mismatch with imperfect information about a third party’s intentions is a difficult task.

While I find the occasional Ayn Rand (or Ayn Rand fan) bashing amusing, critics of Rand typically mis-characterise her writings (as many of her ardent fans also do). A Slate article by Eric Michael Johnson continues this tradition, where Johnson sets Rand up as the representative of selfish individualists against the altruists of hunter-gather tribes.

Johnson’s altruistic case study is the Mbuti hunters of the Congo:

The Mbuti employed long nets of twined liana bark to catch their prey, sometimes stretching the nets for 300 feet. Once the nets were hung, women and children began shouting, yelling, and beating the ground to frighten animals toward the trap. As Turnbull came to understand, Mbuti hunts were collective efforts in which each hunter’s success belonged to everybody else. But one man, a rugged individualist named Cephu, had other ideas. When no one was looking, Cephu slipped away to set up his own net in front of the others. “In this way he caught the first of the animals fleeing from the beaters,” explained Turnbull in his book The Forest People, “but he had not been able to retreat before he was discovered.” …

Faced with banishment, a punishment nearly equivalent to a death sentence, Cephu relented. “He apologized profusely,” Turnbull wrote, “and said that in any case he would hand over all the meat.” … Cephu was bound to support the tribe whether he chose to or not.

The “altruistic” behaviour of the Mbuti in conducting their hunt is only one example of wider altruism in hunter-gatherer societies. In research by Christopher Boehm, he found that sharing and cooperation are the most commonly named moral values in these societies.

Johnson contrasts this tribal collectivism with Ayn Rand’s worship of the individual:

“Collectivism,” Rand wrote in Capitalism: The Unknown Ideal, “is the tribal premise of primordial savages who, unable to conceive of individual rights, believed that the tribe is a supreme, omnipotent ruler, that it owns the lives of its members and may sacrifice them whenever it pleases.” An objective understanding of “man’s nature and man’s relationship to existence” should inoculate society from the disease of altruistic morality and economic redistribution.”

The problem with the dichotomy set up by Johnson is that, at least as it relates to his Mbuti example, he has it backwards. Rand rails against people who did not pull their weight and who loot rather than relying on their own productive efforts. In Johnson’s example, it was Cephu who was the looter who sought to take advantage of the hard work of others. If someone turned up in John Galt’s town and sought to skim off the rewards of his effort, Galt would withdraw his cooperation with them. And that is the beauty of the picture painted by Johnson – the association was voluntary (it may not classify as euvoluntary, however). Cephu was offered the choice to stay and cooperate, or he could leave. Tribe members are free to cooperate with each other and reap the rewards of that cooperation as they see fit.

Of course, Rand’s philosophy was not to ignore others. If helping them or providing them services is valued by you, go ahead and do it. The heroes of Atlas Shrugged did not seek to become self-sufficient. They sought to succeed by providing goods and services valued by others. Dagney Taggart’s trains ran on steel provided by Hank Reardon. Hank Reardon used coal provided by Ken Dannager. The gains from specialisation and trade make it in your interest to care about the welfare of others.

If Rand makes an error, it may be her understanding of primordial savages. She saw the tribe as dominating the lives of its members, when it is closer to a cooperative pact. Johnson’s story also suggests that Rand may have been too pessimistic. Rand saw a world where the looters were winning, where second-class talent prospered at the expense of the truly talented and where the wealthy maintain their wealth through political patronage. In contrast, Johnson paints a picture of the looter getting his due, so at least in this small part of the world, Rand’s nightmare has not come true.

Johnson goes as far as noting the benefits that come from “altruistic” behaviour (hence my use of inverted commas around “altruism” during this post). Gossip is a primary form of communication within tribes. Reputations rapidly spread and those with bad reputations can be quickly marginalised. Further, altruistic behaviour is a signal to the opposite sex as it may be a reliable signal of your quality. Is something still altruistic when it allows the continued propagation of your genes?

From a recent letter in Nature by Rand, Greene and Nowak:

We find that across a range of experimental designs, subjects who reach their decisions more quickly are more cooperative. Furthermore, forcing subjects to decide quickly increases contributions, whereas instructing them to reflect and forcing them to decide slowly decreases contributions. Finally, an induction that primes subjects to trust their intuitions increases contributions compared with an induction that promotes greater reflection.

The interesting part of the article is the mechanism that the authors propose as being behind the intuitive cooperative response:

[P]eople develop their intuitions in the context of daily life, where cooperation is typically advantageous because many important interactions are repeated, reputation is often at stake, and sanctions for good or bad behaviour might exist. Thus, our subjects develop cooperative intuitions for social interactions and bring these cooperative intuitions with them into the laboratory.

They tested this mechanism by checking whether cooperation was favoured where people were from a cooperative environment:

Even in the presence of repetition, reputation and sanctions, cooperation will only be favoured if enough other people are similarly cooperative. We tested this prediction on AMT with a replication of our baseline correlational study. As predicted, it is only among subjects that report having mainly cooperative daily-life interaction partners that faster decisions are associated with higher contributions.

Thus, there are some people for whom the intuitive response is more cooperative and the reflective response is less cooperative; and there are other people for whom both the intuitive and reflective responses lead to relatively little cooperation. But we find no cases in which the intuitive response is reliably less cooperative than the reflective response. As a result, on average, intuition promotes cooperation relative to reflection in our experiments.

In many of the debates about cooperation and why it occurs, we forget that there is often a direct benefit to being cooperative. In most of life today, cooperative behaviour is the path to success.

The effect of the external environments highlights an important point on trust, a core part of the cooperative behaviour in the experiments. Trustworthiness is important generating trust. As Garett Jones recently wrote:

When people are trustworthy, when cultures and laws make honorable behavior common, when people so fully take it for granted that promises are kept that they use the passive voice–because it just doesn’t matter who made the promise–that’s when trust blossoms.

The Economist notes a new working paper (pdf) by Bloom and colleagues of the Harvard School of Public Health, which shows that a short-term implication of reduced fertility in poor countries may be increased inequality. As fertility declines initially among the richer residents of the country, they are the first to reap the demographic dividend. From The Economist:

The three countries in the Harvard study which saw the largest declines in child dependency were Côte d’Ivoire (with a GDP per head in 2011 of $1,800), Namibia ($6,800) and Peru ($10,300). The pattern of their demographic change shows a clear progression. Poor Côte d’Ivoire saw its child-dependency ratios fall most among the rich and least among the poor. In Namibia child dependency fell furthest in the middle of the income range; the decline in the second-poorest group was largest of all. In middle-income Peru, the pattern was different again. There, child dependency fell across the board by roughly equal amounts.

What seems to happen is that falling fertility widens demographic differences in countries with a per-person GDP of around $2,000; that the forces of inequality and convergence are balanced in countries where GDP per person is $5,000; and that by the time this figure has reached $10,000 per person, the forces of convergence are dominant. To put it another way, the rich lead the decline in fertility, producing a short-term increase in income inequality as they capture the benefits of demographic change first. Then the middle catches up as they educate daughters and plan families, followed by the poor, so that eventually fertility is lower across the board and the economic benefits of the demographic dividend are spread more evenly.

As fertility is typically highest among the poor before the fertility transition, they would seem to have the most to gain, but are the last to transition. The paper would suggest that increased inequality should not, in itself, be seen as a negative thing in countries undergoing a demographic transition. However, there might be a large dividend if the exit of the poor from the low income-high fertility state could be accelerated.

HT: Economics and Psychology Research blog