Why Women Leave Their Husbands for Other Women

Why Women Leave Their Husbands for Other Women. Lauren Vinopal. Fatherly, Mar 04 2019. https://www.fatherly.com/health-science/women-bisexual-divorce/

Women are more likely to leave their husbands for other women because their sexual fluidity comes with fewer consequences.

Women are more likely than men to cheat on their spouses with a same-sex partner, studies suggest. It’s not that women are more likely to be homosexual or bisexual—it’s that women appear more willing than men to change their minds about what turns them on, throughout their lives. Men tend to choose a sexuality and stick with it, experts agree. Women are sexual wildcards.

“I think data are sufficient to suggest that more women are likely to change their reported sexual orientation depending on their circumstance, where men are more resistant to changing their identity from sexual behavior alone,” psychophysiologist and neuroscientist Nicole Prause, who studies women’s sexual responses, told Fatherly.

The phrase “sexual fluidity” was originally coined by psychologist Lisa Diamond in 2008. After following the same 100 women for a decade, she found that there were crucial differences between bisexuality and the sexual fluidity that otherwise heterosexual women experienced. Bisexuality is defined as being attracted to men and women. Many women, Diamond found, identified as gay or straight, but accepted the fact that they might change their minds at some point. When experts say that women are more likely to be sexually fluid, they mean that they’re more likely to make an exception to, or even update, their sexual identities.

Of course, this does not mean that women are more likely than men to cheat on their spouses and sexual fluidity is seldom the main cause of a relationship souring. As with any relationship problem, there are usually other, deeper relationship issues at play. But now, more than ever, married women are stepping away from problem heterosexual marriages, and into same-sex ones.

[...]

One hormonal explanation may be that women’s testosterone levels increase with age, and higher testosterone levels have also been linked with increased incidence of homosexuality and bisexuality in women. This might help explain why women may be more fluid in their thirties and forties, after having kids. Evolutionary psychologists have offered a number of theories as to why women may be more fluid as well, such as an adaptive way to decrease conflict in polygamous cultures. Another popular explanation is that, because saying yes to sex comes at a higher risk and reproductive cost to women, they tend to make sexual decisions more cautiously on a case by case basis, which could potentially allow for more deviation.

Still, it is likely that increased rates of sexual fluidity among women is primarily a social (rather than biological) phenomenon. Indeed, there is emerging evidence that men have the same potential as women to be sexually fluid, but that stigma prevents them from acting upon it. In most western societies, women still face fewer social costs for same-sex relationships than men. [...]

Psychological sex differences in humans are real, they can be large and even very large, and evidence suggests evolutionary origins for a broad range of sex differences

Archer, John  (2019) The reality and evolutionary significance of human psychological sex differences. Biological Reviews, https://doi.org/10.1111/brv.

Abstract: The aims of this article are: (1) to provide a quantitative overview of sex differences in human psychological attributes, and (2) to consider evidence for their possible evolutionary origins. Sex differences were identified from a systematic literature search of meta-analyses and large-sample studies. These were organized in terms of evolutionary significance as follows: (1) characteristics arising from inter-male competition (within-sex aggression; impulsiveness and sensation-seeking; fearfulness; visuospatial and object-location memory; object-centred orientations); (2) those concerning social relations that are likely to have arisen from women’s adaptations for small-group interactions and men’s for larger co-operative groups (person-centred orientation and social skills; language; depression and anxiety); (3) those arising from female choice (sexuality; mate choice; sexual conflict). There were sex differences in all categories, whose magnitudes ranged from (1) small (object location memory; negative emotions), to (2) medium (mental rotation; anxiety disorders; impulsivity; sex drive; interest in casual sex), to (3) large (social interests and abilities; sociosexuality), and (4) very large (escalated aggression; systemizing; sexual violence). Evolutionary explanations were evaluated according to whether: (1) similar differences occur in other mammals; (2) there is cross-cultural consistency; (3) the origin was early in life or at puberty; (4) there was evidence for hormonal influences; and (5), where possible, whether there was evidence for evolutionarily derived design features. The evidence was positive for most features in most categories, suggesting evolutionary origins for a broad range of sex differences. Attributes for which there was no sex difference are also noted. Within-sex variations are discussed as limitations to the emphasis on sex differences.

If emerging technologies are so impressive, why are interest rates so low, wage growth so slow, investment rates so flat, & total factor productivity growth so lukewarm? Lack of genius.

Digital Abundance and Scarce Genius: Implications for Wages, Interest Rates, and Growth. Seth G. Benzell, Erik Brynjolfsson. NBER Working Paper No. 25585, February 2019, https://www.nber.org/papers/w25585

Digital versions of labor and capital can be reproduced much more cheaply than their traditional forms. This increases the supply and reduces the marginal cost of both labor and capital. What then, if anything, is becoming scarcer? We posit a third factor, ‘genius’, that cannot be duplicated by digital technologies. Our approach resolves several macroeconomic puzzles. Over the last several decades, both real median wages and the real interest rate have been stagnant or falling in the United States and the World. Furthermore, shares of income paid to labor and capital (properly measured) have also decreased. And despite dramatic advances in digital technologies, the growth rate of measured output has not increased. No competitive neoclassical two-factor model can reconcile these trends. We show that when increasingly digitized capital and labor are sufficiently complementary to inelastically supplied genius, innovation augmenting either of the first two factors can decrease wages and interest rates in the short and long run. Growth is increasingly constrained by the scarce input, not labor or capital. We discuss microfoundations for genius, with a focus on the increasing importance of superstar labor. We also consider consequences for government policy and scale sustainability.

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Why then, if emerging technologies are so impressive, are interest rates so low, wage growth so slow and investment rates so flat? And why is total factor productivity growth so lukewarm? To resolve this paradox, we propose a model of aggregate production with three inputs. The third factor corresponds to a bottleneck which prevents firms from making full use of digital abundance. Bottlenecks are ubiquitous in economics. This paper is typed on a computer that is over 1000 times faster than those of the past, but our typing is still limited by our interface with the keyboard.
An assembly line that doubles the output, speed or precision of 1, 2 or 99 out of 100 of processes will still be limited by that line’s weakest link. In other words, no matter how much we increase the other inputs, if an inelastically supplied complement remains scarce, it will be the gating factor for growth.

Our model can explain why ordinary labor and ordinary capital haven’t captured the gains from digitization, while a few superstars have earned immense fortunes. Their contributions, whether due to genius or luck, are both indispensable and impossible to digitize. This puts them in a position to capture the gains from digitization.

In our digital economy technology advances rapidly, but humans and their institutions change slowly. Institutional, managerial, technological, and political constraints become bottlenecks (Brynjolfsson et al., 2017). Before a firm can make use of AI decision making, its leaders need to make costly and time-consuming investments in quantifying its business processes; before it can scale rapidly using web services it needs figure out how to codify its systems in software. Therefore, digital advances benefit neither unexceptional labor nor standard capital, at least insofar as they can be replicated digitally (Brynjolfsson et al., 2014). The invisible hand instead favors those who are a scarce complement to these factors.

The inputs in our model are traditional capital and labor and a relatively inelastic complement we dub ‘genius’ or G. When G is relatively abundant, the economy approximates a two-factor one. But as G becomes relatively scarce, it becomes a bottleneck for output and captures an increasing share of national income. We show that when traditional inputs are sufficiently complementary to G, innovations in automation technology can reduce both labor’s share of income and the interest rate.

This theory fits what we know about the limitations of digital technologies, including cutting-edge AI. While general artificial intelligence might someday lead to an economic singularity, contemporary AI technologies have clear limitations, making humans indispensable for many essential tasks. Agrawal et al. (2018a) and Agrawal et al. (2018c) observe that AI is good at prediction tasks, but struggles with judgment – often a close complement. Brynjolfsson et al. (2018) create a rubric for assessing which tasks are suitable for machine learning and use it to evaluate the content of over 18,000 tasks described in O-Net. They find that while the new technology delivers super-human performance for some tasks, it is ineffective for many others. In particular, despite their many strengths, existing computer systems weak or ineffective at tasks that involve significant creativity or large-scale problem solving. Even tasks amenable to automation may require large organizational investments before business processes can be automated.

The only essential feature of G in our model is that it is inelastically supplied, because, in part, it is not subject to digitization. For concreteness, our primary interpretation for G is superstar individuals. They may be exceptionally gifted with the ability to come up with an exciting new idea, sort through bad ideas for a diamond in the rough,3 or effectively manage a business. If these good ideas are owned by and accumulate within firms, they correspond to a kind of alienable genius.

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Many have the sense that intangible assets and superstar workers are more abundant than ever. Perhaps the most surprising thing then about our result is that these factors are increasingly scarce. We contend that this is due to confusion between the value and importance of these inputs, which are increasing, and their relative abundance, which is decreasing.

Laterality, or left–right discrimination (LRD) is assumed to be innate or acquired early, but in one study, a majority of students scored less than 77% on an objective LRD test

Challenging  assumptions of  innateness –leave nothing unturned . Jason J Han & Neha Vapiwala. Medical Education, Mar 3 2019, https://doi.org/10.1111/medu.13824

It was once common in various academic fields to assume that individuals possess certain fundamental abilities or intuitions (e.g. the assumption of rationality in the fields of economics and social sciences).1 However, the past half-century has overseen a transition towards a different model of human cognition, one which acknowledges the human brain as complex machinery that is vulnerable to systematic errors.

The pioneers of this paradigm shift, Daniel Kahneman and Amos Tversky, attributed this to the co-existence of two processing mechanisms.2 They described the first, aptly named System 1, as the fast, automatic, intuitive, unconscious approach and the second (System 2) as the slower, more deliberate, analytical and conscious mode. The purpose of this categorisation was not to assign a hierarchy, but rather to acknowledge that both systems have their respective pros and cons depending on the task. System 1 is efficient but more error-prone. System 2 is more thorough but requires greater resources and quickly drains our working memory and attention, thereby making it too susceptible to error. In this issue of Medical Education, Gormley et al. juxtapose these two systems in the context of one of the most commonly performed mental tasks –our ability to discern laterality or left–right discrimination (LRD). This ability is particularly critical in medicine, as errors in LRD can lead to wrong diagnoses and interventions, and ultimately patient harm. The authors note that although LRD is often assumed to be innate or acquired during early stages of human development, in reality LRD is a complex neuropsychological process with which 17% of women and 9% of men have reported difficulty.3 Medical students are not exempt from this challenge. In one study, a majority of students scored  less than 77% on an objective LRD test.4 In the interviews conducted by Gormley et al., students who had difficulty with LRD disclosed feelings of inadequacy, which led to greater efforts to conceal this difficulty and even influenced their career trajectories by steering them away from certain specialties. Undoubtedly, these f indings have important implications for the medical education community, suggesting the need to overthrow assumptions that LRD is an  innate human skill and to raise the importance of laterality training in the curriculum.5

This study inspires the realisation that no tacit assumption of innateness or intuitiveness should go unchecked. What else are we assuming is easy, innate or intuitive? The distinction between what is presumably innate and what merits attention and practice is somewhat arbitrary. Observing that we teach correct anatomic spatial orientation, such as anterior from posterior, superior from inferior, Gormley et al. asked, why not also left from right? Extrapolating further, we could apply the same line of questioning to other competencies in medical education, such as our ability to recognise personal cognitive biases or develop ‘soft’ skills such as empathy and clarity of communication. There are undoubtedly circumstances in which we assume we effectively and expertly broke bad news, disclosed error or obtained informed consent, but in the eyes of the patient our performance was lacking. As such, we can all stand to gain important insights into our own abilities with a more conscious and thoughtful approach.6,7 1

Short-run impacts of the 2018 trade war on the U.S. economy: Annual losses from higher costs, $68.8 bn (0.37% of GDP); after tariff revenue & gains to producers, welfare loss is $6.4 bn (0.03% of GDP)

The Return to Protectionism. Pablo D. Fajgelbaum, Pinelopi K. Goldberg, Patrick J. Kennedy, and Amit K. Khandelwal. Working Paper, Mar 2019, http://www.econ.ucla.edu/pfajgelbaum/RTP1.pdf

Abstract: We analyze the short-run impacts of the 2018 trade war on the U.S. economy. We estimate import demand and export supply elasticities using changes in U.S. and retaliatory war tariffs over time. Imports from targeted countries decline 31.5% within products, while targeted U.S. exports fall 9.5%. We find complete pass-through of U.S. tariffs to variety-level import prices, and compute the aggregate and regional impacts of the war in a general equilibrium framework that matches these elasticities. Annual losses from higher costs of imports are $68.8 billion (0.37% of GDP). After accounting for higher tariff revenue and gains to domestic producers from higher prices, the aggregate welfare loss is $6.4 billion (0.03% of GDP). U.S. tariffs favored sectors located in politically competitive counties, suggesting an ex ante rationale for the tariffs, but retaliatory tariffs offset the benefits to these counties. Tradeable-sector workers in heavily Republican counties are the most negatively affected by the trade war.


Check also Krugman on Sep 2018: Trump’s tariffs really are a big, bad deal. Their direct economic impact ***will be modest*** (?!), although hardly trivial https://www.bipartisanalliance.com/2018/09/krugman-trumps-tariffs-really-are-big.html