The economics of sex robots

In my ECONS101 class, we cover search models of the labour market. Unlike the supply and demand model, search models do not rely on a concept of market equilibrium. Instead, it is the relative bargaining power of the parties (employers and workers) that determine the wage.

The simple explanation works like this. Each matching of a worker to a job creates a surplus that is shared between the worker and the employer. Because job matching creates a surplus, this provides the worker with a small amount of market power (or bargaining power). That is because if the worker rejects the job offer, the employer has to start looking for someone else to fill the vacancy. The employer is somewhat reluctant to start their search over, so the worker can use that to their advantage. The division of the surplus created by the match, and therefore the wage, will depend on the relative bargaining power of the worker and employer. If the worker has relatively more bargaining power, the wage will be higher. And if the employer has relatively more bargaining power, the wage will be lower.

This search model doesn't just apply to the labour market. You can also apply it to many situations that involve matching two or more parties. Which brings me to this post on sex robots by Diana Fleischman. Sex involves matching (unless you go it alone). The agreement on the what-where-how of sex will depend on the relative bargaining power of the sexual partners. The increasing availability of increasingly realistic sex robots looks likely to shake things up, because sex robots and women are substitutes (see also this earlier post on pornography and marriage as substitutes). As Fleischman explains:

What does this mean for women? When the sex ratio changes, so too do sexual norms; sex robots are going to emulate an increase in the ratio of women to men. Contrary to a prediction based on the idea that men would wield greater patriachal [sic] control if they were in higher numbers, a larger percentage of women relative to men on University campuses is associated with women who are more likely to have casual sex and less likely to be virgins. When there are more men than women, women are much less likely to have casual sex. The majority sex (in this case men) competes for the minority sex (in this case women) and the minority sex calls the shots. When there is a female majority in the population, women compete for access to mates with casual sex. Whereas a male majority competing for access to scarce women compete with long-term commitment.

Sex robots will emulate a majority women ratio, shifting women to compete for men’s attention by requiring less courtship and commitment in exchange for sex.

Taking a heteronormative perspective, the availability of sex robots reduces the relative bargaining power of women, and therefore increases the relative bargaining power of men. That means that men may be able to extract more of the surplus from potential sexual liaisons. That is, men may be able to get more of what they want. Fleischman notes that:

The long-term ramifications are unclear, especially the way long-term technologies and cultural norms will interact. Perhaps women will discover they have to make the costs of courtship both low and transparent to compete with sex robots.

Women, having to compete with sex robots, may have to offer men more. But not so fast:

Or, perhaps, new technology could enable women to recombine their genes with one another, making men enamored with sex robots (or men generally) totally redundant.

New technology for recombining genes and completely excluding men won't rebalance bargaining power back towards women. The technology necessary to reproduce without involving sex has existed for some time. Fleischman is conflating the reproductive goal of sex, with the pleasure goal of sex. To rebalance bargaining power back towards women, women need their own sex robots. Sex robots for all!

[HT: Marginal Revolution]

Charles Plott's strategies for getting published

There are plenty of critiques of peer review, one of which is that it is incremental and leads to the most novel research failing to get published. If you are a researcher doing incredibly novel research, using methods that reviewers don't fully understand because those methods are not yet widely employed, then you could have real difficulty in getting your best work into top journals. That has been the case for many emerging fields. In economics, in (relatively) recent times (prior to the 1990s), that applies to behavioural economics and to experimental economics. The challenge, then, is how to get your work accepted if you are one of these researchers employing novel or misunderstood methods.

In a new article published in the journal Oxford Economic Papers (ungated version here), Andrej Svorencik (University of Mannheim) documents how one of the pioneers of experimental economics, Charles Plott, overcame the reluctance of editors and journal reviewers to accept the validity of laboratory experiments. As Svorencik writes:

Whereas there were no public detractors of experimentation in economics, the early and most prolific experimenters, such as Charles Plott and Vernon Smith, encountered skeptics and systematic rejections of their submitted papers. Getting them published required tenacity on the writers’ part to go through several rounds of often heated discussions with editors and referees. These iterations present a unique perspective on the arguments raised against experiments in economics and the specific strategies developed by experimental economists to counter them.

Svorencik uses the 'research corpus' of Plott, covering letters and responses to editors and reviewers dating from the mid-1970s to the mid-1990s, and establishes nine different strategies that Plott employed to disarm reviewers and convince editors that his publications using experimental economics should be published:

S1 Asking for knowledgeable referees because previous referees were ignorant of experimental economics;
S2 Claiming that results are interesting, relevant for theory, and have applications;
S3 Claiming that the experiments present real situations;
S4 Claiming that the theory applies to simple cases;
S5 Citing basic research;
S6 Conducting more experiments;
S7 Shifting of the burden of proof;
S8 Steering clear of a specialized journal;
S9 Claiming that field has been confused with method.

One particular example of strategy S4 struck me as particularly important. From a 1979 letter that Plott wrote to George Borts, the editor of the American Economic Review:

The laboratory processes are simple and very special markets... but they are nevertheless real markets which should be governed by the same principles that are supposed to govern all markets. The justification for studying them is the same as the justification for studying the simple special cases and special types of any complicated phenomenon.

In order to see why these markets are real, one need only apply directly the theory of derived demand. It works as follows. Let Ri(xi) be the revenue received by individual i from some source expressed as a function of the number of units (xi) he has to sell. Standard derived demand theory tells us that δRi/δxi is limit price (inverse demand) function for this individual. It is important to note that the theory places no restriction upon the source of the revenue so when the source is an experimenter the derived limit price function for this individual is just as real as when the source is a business. Furthermore, the theory places no restriction on what x is called (unless the individual gets consumption pleasures from it) so the theory applies equally as xi becomes baseball cards, shirts, food, or ‘commodities’ created especially for the purposes of an experiment. There are no ‘side payments’ or incidental sources of enjoyment so as long as the individual prefers more money to less we can be assured the preferences for units of x have been induced. The individual is indeed a ‘demander.’

The supply side of the market is handled similarly. Each supplier, j, faces an individualized cost function Cj(xj) which indicates what j must pay the experimenter as a function of units purchased for resale. Profits to j, which are j’s to keep, are simply the revenues received by j over costs Cj(xj). Clearly that δCj(xj)/δxj is a real marginal cost function. The fact that it was constructed by the experimenter makes the concept no less relevant because the concept is intended to apply universally.

We have then a valued and scarce resource. Almost any textbook will say that those conditions are sufficient for the existence of an economic problem. The laboratory markets are thus real markets and the principles of economics should apply to them as readily as they are supposed to apply to any other market.

Plott's responses to editors and reviewers was very forceful, and it appears that more often than not, he got his way. And generations of experimental economists have benefited from his efforts, as by the 1990s economics research using laboratory experiments had been broadly accepted and was regularly being published in top journals. Svorencik's article provides a key insight into how this process happened, and is a really interesting contribution to the history of economic thought.

Book review: Plagues and the Paradox of Progress

When we went into lockdown earlier this year, I quickly gathered together some books from my office, not knowing how long it would be before I could return. One of those books was Plagues and the Paradox of Progress by Thomas Bollyky. It seemed quite relevant (perhaps too relevant) at the time, but I've only just gotten finished with reading it now.

The premise of the book is relatively simple. High-income countries took a long time to tackle their own problems of infectious disease (like cholera, typhoid, etc.), and in the process of tackling infectious diseases, those countries developed the types of institutions (like health systems) that have contributed to high income countries' ongoing success. In contrast, modern-day low-income countries haven't had to go through the same long process of institution building in order to combat infectious diseases, as they have been able to import the solutions from high income countries, often with the support of international aid or philanthropic donors. So, while infectious disease no longer kills millions of people in low income countries, the lack of effective institutions keeps those countries poor - that is what Bollyky terms the paradox of progress. As Bollyky summarises:

...the world has gotten better in ways that should make us worry. The last two decades have brought dramatic reductions in endemic infectious disease and child mortality, but not the improvements in health-care systems, responsive governance, and employment opportunities that accompanied these changes in wealthier nations in the past. 

On the face of it, it's a compelling narrative, supported by what is among the most extensively researched evidence base I've seen in a book (for instance, Chapter 4 is 34 pages, but has 180 endnotes of references). However, the narrative is not entirely convincing, because it lacks strong causal evidence. It is all very well to present lots of evidence of how bad infectious diseases were in the past, and describe how high income countries dealt with them, but quite another to export that experience to low income countries. If you're not able to first establish the causal links, then your evidence base is at best partial. This is one of the problems that the Kuznets Curve faces - it looks good based on the experience of high income countries, but falls apart when exposed to data from low income countries.

I feel like the book also dramatically oversimplifies the issues. The role of institutions in economic development involves a huge literature, on which Bollyky barely touches the surface. To some extent, that's because that literature would broaden the scope of the book to an unmanageable extent. However, it also leaves the book without a clear relationship to a pretty important section of the literature.

It also leads to a policy prescription in the concluding chapter that can best be described as banal. I think almost anyone could have recommended secure property rights over land, and increased investment in education and primary health care, as important policy goals for low income countries, regardless of any link to infectious disease. And the links between those recommendations that the evidence in the preceding chapters is pretty tenuous, especially in the case of property rights.

Aside from those major gripes, the book is wonderfully well written and easy to read. And as I said, it is incredibly well sourced, so an interested reader can easily follow up on any of the evidence that Bollyky presents. The book also had some prescient moments that call to mind the current pandemic crisis:

Several years after the Ebola outbreak, coordination and funding of international epidemic and pandemic preparedness and response remain ad hoc and dependent on media attention. "We still are not ready for the big one," said Ron Klain, the former US Ebola czar, to the Washington Post in October 2017.

Indeed. Also this:

The United States was (and remains) woefully ill-prepared for the threat of antibiotic resistance and future pandemics.

As Americans have been discovering this year, to the dismay of many. However, Bollyky does make some clear errors, one of which is among my pet peeves - comparing stocks with flows:

The market capitalization of Philip Morris International at $187 billion (as of July 2017) is larger in nominal terms than the economies of most of the 180 countries where that company sells Marlboro and its other brands of cigarettes.

It may be technically true, but it's a worthless comparison, because a market capitalisation is a stock (based on fundamentals, it would be the present value of all future cash flows of the corporation), while GDP is an annual flow.

Overall, while the book was an enjoyable read, I don't feel like I learned much from it, and it certainly falls short of providing a convincing argument to me.

Professor gender and the gender gap in STEM

I've written a number of posts on the gender gap in economics, but also in STEM (science, technology, engineering, and maths) (see here for example). In some of those posts on economics, the research I've been discussing has highlighted the potential effect of role models on the gender gap.

If role models matter, then you'd expect the gender of professors to matter. The problem with evaluating that empirically is that students often have some choice over who their professors are, particularly in large classes early in their studies, where there may be many sections to choose from, or the same papers may be taught in multiple semesters. This 2010 article by Scott Carrell, Marianne Page (both University of California at Davis), and James West (US Air Force Academy), published in the Quarterly Journal of Economics (ungated earlier version here), is able to overcome that problem.

Carrell et al. use data from the US Air Force Academy, where:

...students are randomly assigned to professors for a wide variety of mandatory standardized courses.

That means that there will be no problems of selection bias, where students choose their own professors. In addition:

...course grades are not determined by an individual student’s professor. Instead, all faculty members teaching the same course use an identical syllabus and give the same exams during a common testing period.

That means that there is also no bias arising from differential grading between different professors. Carrell et al. have data from 9015 students from the USAFA graduating classes of 2001 through 2008. They focus their attention on mathematics, physics, chemistry, engineering, history, and English classes, and look at how the gender of the professor in each introductory class affects students' grades, the likelihood of taking further classes in the discipline, and the likelihood of graduating with a STEM degree. They find that:

...professor gender has only a limited impact on male students, it has a powerful effect on female students’ performance in math and science classes, their likelihood of taking future math and science courses, and their likelihood of graduating with a STEM degree. The estimates are robust to the inclusion of controls for students’ initial ability, and they are substantially largest for students with high SAT math scores.

Specifically, for female students:

...having a female professor reduces the gender gap in course grades by approximately two-thirds.

That is quite substantial, given that after controlling for mathematical ability, female students on average perform 15 percent of a standard deviation worse than male students, in STEM courses. However, it is notable that they also find that:

...at the top of the distribution... having a female professor completely closes the gender gap...

The effect on taking additional STEM classes and graduating with a STEM degree is also concentrated amongst female students with greater mathematical ability, and eliminates the gender gap in those measures as well.

However, the same effect of professor gender are not apparent in the humanities:

 In contrast, the gender of professors teaching humanities courses has, at best, a limited impact on students’ outcomes.

 All of this suggests that, if we want to narrow the gender gap in STEM, particularly amongst the most able female students, universities would need to employ more female teaching staff. However, Carrell et al. bury a very important caveat in a footnote on the last page of their article:

Note that the impact of female professors may reflect the high quality of faculty at the USAFA, and that substituting lower-quality female professors for high-quality male professors is not a policy that would be recommended by the authors.

Universities need to fully understand the trade-offs before they jump into a policy response. And also, USAFA is a pretty unique environment, so it would be good to know if similar results are obtained in other contexts. However, this research adds to the increasing evidence that the impact of professor gender on the performance and academic decision-making of female students.

Read more:

• STEM-readiness and the gender gap in STEM at university
• Comparative advantage and the gender gap in STEM
• More on comparative advantage and the gender gap in STEM