Massage licence fees, sex services, and public health

I've finally gotten around to reading this job market paper, entitled "Optimal regulation of illegal goods: the case of massage licensing and prostitution", by Amanda Nguyen (who was on the job market at the start of the year, from UCLA). It's of interest for a few reasons: (1) it follows on from the paper I wrote about a couple of weeks ago about black market and white market goods; (2) it's a paper about the market for sex services; and (3) it may be one of the most clearly-written job market papers I've ever read (in the sense that even the most technical details in the theoretical section are clearly explained).

In the paper, Nguyen looks at how changes in the costs of licensing in the (legal) market for massage services affect the market for sex services and the associated externalities (in terms of health and crime). She distinguishes between two sectors: (1) the quasi-legal sector (e.g. erotic massage parlours); and (2) the illegal sector (escort services). Only the quasi-legal sector is subject to licensing costs.

So, first some economic theory. If the licensing costs for the quasi-legal sector decrease, we would expect an increase in supply in the quasi-legal sector, and a decrease in price (and increase in the number of services performed) in that sector. Some illegal sector suppliers will shift into the quasi-legal sector because of the lower costs. So, we would expect decreased supply in the illegal sector. Alongside this, because the price of quasi-legal services have reduced (and services from the quasi-legal and illegal sectors are imperfect substitutes for each other), we would expect a decrease in demand in the illegal sector. Overall, the quantity of services in the illegal sector will reduce, but the effect on the price of illegal services is ambiguous (the change will depend on the relative size of the shifts in demand and supply).

Now, because the sex services performed in the quasi-legal sector are typically less risky (more manual stimulation, less intercourse), then we would expect public health benefits from this shift. Nguyen does provide some support for this assertion in her paper. Overall the shift to less risky services should means less transmission of STDs, resulting in lower incidence of gonorrhea and chlamydia (as well as syphilis, HIV, etc.).

Nguyen uses an interesting natural experiment based on California data. Prior to 2009, the licensing fees varied widely for different cities, but in 2009 the fees were standardised. Some cities faced a substantial reduction in fees, while others faced no change. That provides the treatment (decreased fees) and control (no change) groups for the natural experiment. Then in 2015 California unwound the changes, which provides a second natural experiment.

You may be worried (and rightly so, it turns out) that the areas with the highest licensing fees prior to the change are systematically different from those with the lowest licensing fees, in ways that are important. So, Nguyen employs and instrumental variables approach (which I have earlier discussed here). Her instrument of choice is the fees for alternative business sectors (retail and professional services), which can plausibly be related to the fees for massage services but are unlikely to be directly related to the outcome variables (which are price and quantity of services, STD rates, and rape reports).

Nguyen finds support for the simple descriptive demand-and-supply analysis above. In terms of the health and crime externalities, she finds:

In the case of prostitution and massage, reducing licensing costs for the quasi-legal sector increased the total size but also reduced the overall riskiness of the black market for prostitution. For the average massage licensing fee reduction observed in California, gonorrhea rates fell by 16.3% for the general population and by 13.5% for the predominant sex worker demographic, Asian females. Chlamydia rates also fell by 1.73% for Asian females, while forcible rapes declined by 19% for the general population. These improvements to health and crime can be attributed to reductions in illegal prostitution consumption and risk-taking behavior in the quasi-legal prostitution sector.

The trade-off appears to be a significant impact on the legal massage sector (i.e. the massage sector that does not include sex services), which she attributes to competition from the quasi-legal sector. She writes:

...the consequent 138% growth in quasi-legal prostitution also reduced the supply of legal massage by 45.6%. Thus, reducing the barriers to entry makes the black market safer at the expense of the legal sector.

Overall though, the paper provides some food for thought, in terms of alternatives to full legalisation if harm reduction in the sex services sector is a policy goal but full legalisation is politically untenable. Or alternatively, read in reverse it provides some idea of the consequences of increasing licensing fees to try and prevent quasi-legal operations.

[HT: Marginal Revolution, back in December last year]

Try this: Broadway economics

The latest issue of the Journal of Economic Education has a short paper about the website Broadway Economics, by Matthew Rousu (Susquehanna University). From the paper:

Songs from musicals tell stories, and many of the concepts we strive to teach our principles of economics students are illustrated in songs such as “Stars” from Les Misérables (inelastic preferences) and “If I Were a Rich Man” from Fiddler on the Roof (inequality, economic growth). While titled Broadway Economics, the site also includes songs from non-Broadway musicals, such as “Let it Go” from Frozen (which illustrates sunk costs). Topics covered more often in upper-level courses such as signaling and screening and consumer time preferences are also well represented by Broadway musical songs.

I'm not much into show tunes, but perhaps you are or you know some economics students who are. The site has videos of the songs, with associated discussion questions that link the song lyrics or theme to economic concepts. For instance, for "Let It Go" from the Disney movie Frozen, the discussion questions are:

1.) What is a sunk cost?

2.) Why should sunk costs be ignored when considering future decisions?

3.) Provide one example where you’ve earned a sunk cost (Hint – the cost need not be a monetary one – it could be time you’ve invested).

 Enjoy!

Newsflash! Researchers in top departments publish in top journals

I just finished reading this new article in Applied Economics Letters by Tolga Yuret (Istanbul Technical University), titled "Is it easier to publish in journals that have low impact factors?" (sorry I don't see an ungated version online). The short answer to the titular question is yes, at least according to the data that was used.

However, I struggled to get past the 'so what?' question in this article. I guess maybe I was expecting the unexpected. Yuret's measure of difficulty of publishing was the proportion of the authors publishing in the journal who are affiliated with the top 125 departments. He argues:

A journal is less likely to be accepting papers from the researchers from lower ranked departments if most of the authors are from the top departments. Therefore the measure developed by Moore (1972) also reflects the difficulty in publishing in a journal. Therefore we label his measure as the difficulty measure.

I would argue that if you wanted a measure of difficulty of publishing in a journal, you probably want to start with the acceptance rate (the proportion of submitted papers that are eventually accepted). But then you would want to control for selection bias - authors don't send all papers to the top journals, because we know that not all papers will be accepted there and prefer not to waste our time (or that of the editors and reviewers). So, the more difficult journals to publish in may have low acceptance rates, but those low acceptance rates are actually likely to be biased upwards (they would be even lower if every researcher submitted every relevant paper to them).

When Yuret proceeds to show that there is a high correlation (0.62) between impact factor and his difficulty measure for economics journals, he is simply showing that faculty in top economics departments make up a higher proportion of the authors in the highest impact factor economics journals. Given that faculty in top economics departments are probably higher quality researchers, producing higher quality research, this should not be a surprise. This paper could clearly be filed under 'so what'.

A more interesting question to ask (and probably the question this article was trying to answer but really didn't) is, for a paper of a given quality, is it more difficult to get it accepted in a journal with a high impact factor than a journal with a lower impact factor? I think most researchers' experiences (and certainly mine) would suggest that it is - papers rejected at top journals usually eventually find a home at a lower-ranked journal.

What is perhaps more interesting is that the correlations between impact factor and proportion of authors from top departments are much smaller for the other disciplines that Yuret looked at: chemistry (0.49), physics (0.23), and mathematics (0.22). What's going on in those disciplines (especially physics and mathematics)? Do faculty outside the top departments in those disciplines have a better shot at publishing in the top journals? Given his data I suspect that the lower correlations (for physics and chemistry at least) may be an effect of the other disciplines simply having more journals with top impact factors - it's much harder for faculty at top departments to monopolise the pages of many top journals than it is to do so when there are only a few top journals. Still, the correlations are all positive - researchers in top departments publish in top journals. Surprise!

Predicting student success, and failure

Carrie Wells writes in the Baltimore Sun:

Officials at the University System of Maryland have begun to analyze student data — grades, financial aid information, demographics, even how often they swipe their ID cards at the library or the dining hall — to find undergraduates who are at risk of dropping out...

University system officials say the practice, called predictive analysis, will boost graduation rates by enabling educators to intervene with struggling students before failure becomes inevitable.

The whole story is well worth reading, covering how big data analytics can help identify at-risk students, but also the valid privacy concerns that this sort of data mining raise. I was interested in this because of two research projects I've recently been involved in. The first project was one I blogged about last April, and was somewhat similar to the work that Wells was looking at (but not nearly as sophisticated, mainly because we had less data available):

In the final (multivariate) specification of the logistic regression model (which only included data we would have known before the students commenced study, and data that are available for all students):

• Students aged 25 years and over (at first enrolment) had significantly lower odds of degree completion than those aged 19 years and under;

• Male students had significantly lower odds of degree completion than female students;

• Asian students had significantly higher odds of degree completion than all other ethnic groups, and Maori and Pacific Island students had the lowest odds of degree completion;

• Domestic students had significantly lower odds of degree completion than international students;

• Special admission (or provisional entrance) students had significantly lower odds of degree completion than other students; 

• Students who initially completed the Certificate of University Preparation (CUP) had significantly lower odds of degree completion than other students; and

• Students initially enrolled in conjoint degrees had significantly lower odds of degree completion than students enrolled in single degrees.

...at the least there is one take-away from Jacinda's work, which is that maybe we need to target more pastoral care or mentoring and role models for conjoint degree students.

Which brings me to this recent working paper by Papu Siameja and I. In the paper we first identify students at risk of failing ECON100, and then we used a simple randomised experiment to trial two very simple interventions. For the first intervention group (Treatment A), we sent them an email providing information about academic support. The second intervention group (Treatment B) received the email plus a follow up personal phone call. We ran the experiment in 2013 and 2014, but did not persist with it because my initial analyses showed little effect (on test results). However, when we look at pass rates, it appears there was an effect:

Both treatments appear to increase the odds of students passing the course, and the effect of Treatment B is statistically significant. Specifically, the results show that students who are part of the Treatment B group in 2014 had more than seven times higher odds of passing than the control group.

What is interesting is that the effect of Treatment B was only significant in 2014, and not 2013. In 2013 we had a staff member make the phone calls, but in 2014 we had a student (one of the tutors in the paper) make the calls. Maybe the (younger) tutor was simply better at connecting with the at-risk students and impressing on them the importance of remaining engaged in the course? Either way, this intervention turned out to be highly cost effective - I estimate the cost-per-failure-averted at about NZ$69. It's something we'll probably bring back next semester.

[HT for the Wells article: Marginal Revolution]