Trump vs. the ruble

During his tenure as U.S. president, Donald Trump tweeted angry tweets about Russia on many occasions (to be fair, he tweeted angry tweets against lots of targets). It would be fair to wonder whether those tweets conveyed any useful information about the U.S.'s stance towards Russia, in relation to economic or other sanctions. In a recent article published in the Journal of Economic Behavior and Organization (ungated version here), Dmitriy Afanasyev (JSC Greenatom), Elena Fedorova (Financial University Under the Government of Russian Federation), and Svetlana Ledyaeva (Aalto University) look at the impact of President Trump's tweets on the Russian ruble exchange rate, over the period from October 2016 to August 2018.

Over that period, Trump tweeted 5548 times, of which 296 tweets related to Russia. Afanasyev et al. used five different lexicons to code each tweet as positive, negative, or neutral, and tested seven different decay schemes (in terms of how the effect of a tweet fades over time). To overcome the problem of having lots of variables to test and determining which ones to include in their final model, they use an elastic net (which, for those of you who are pointy-headed, is a mix of LASSO regression and ridge regression - essentially a type of machine learning approach). Having established which variables to include in their model, they then move to a Markov regime-switching model, which is increasingly used in modelling time series where the researcher believes that there are multiple different relationships between the variables (different regimes) that occur at different points in time.

Overall, Afanasyev et al. find that:

...oil price (the only economic variable (fundamental) chosen by elastic net) remains the main long-term determinant of ruble exchange rate... The impact of Trump’s tweets’ sentiment tends to be episodic and short-term. Significant toughening of Trump’s Twitter Russia-related negative rhetoric can lead to short-term (around 3 days) "abruptions" in the process of ruble exchange rate’s formation based on oil price causing significant depreciation of the Russian ruble.

In other words, the Russian ruble exchange rate is mostly determined by the oil price (since oil is such a large component of the Russian economy). However, Trump's negative tweets caused short-term deviations in the exchange rate. However, I wasn't entirely convinced by this paper. The throw-everything-at-the-wall-and-see-what-sticks approach is dodgy, regardless of whether you use a fancy machine learning approach to help with variable selection or not. The results were only statistically significant for the variables that were chosen in the final model, and we don't know if the other measures of sentiment (as noted above, there were five measures) or different decay periods (there were seven) would have led to similar results. Those sorts of robustness checks are important to include, if you want readers to find your results convincing.

Of what we do know from the paper, Trump's negative tweets only affected the ruble some of the time, and there isn't a good explanation for why they affected the ruble those times, and not others (maybe the time of day mattered?). The research question is interesting and potentially important - I hope other researchers are looking at this as well.

The impact of epidemics on generalised trust

A few weeks ago, the Waikato Economics Discussion Group looked at this article by Arnstein Aasve, Guido Alfani, Francesco Gandolfi (all Bocconi University), and Marco Le Moglie (Catholic University of the Sacred Hearth), published in the journal Health Economics (ungated earlier version here). Aasve looked at a particularly timely topic - the impact of the Spanish Flu (1918-1919) on generalised trust.

This is a difficult topic to investigate, because there are no measures of generalised trust available for the 1918-1919 period (or immediately before and after, which is what you'd really want). Instead, Aasve et al. use data from the U.S. General Social Survey from 1978-2018 to infer measures of social trust for earlier generations. Specifically:

Survey respondents were also asked about their country of ethnic origin and a series of questions regarding their migration history: whether they were born in the United States or not, whether their mother and father were born in the United States and the number of grandparents born outside the country. Using this information, we group respondents on the basis of their country of ethnic origin and categorize them in three waves of immigration: second-generation Americans (i.e., people born in the United States with at least one parent and all the grandparents born abroad), third‐generation Americans (i.e., people with at least two immigrant grandparents and both parents born in the United States) and fourth‐generation Americans (i.e., people with more than two grandparents born in the United States and both parents born in the United States). We exploit different waves of immigration to measure the intergenerational path of social capital transmission by people migrated before and after the spread of the Spanish flu (i.e., 1918)...

It's quite an ingenious method, although it relies on a fairly strong assumption of intergenerational transmission of social trust. They have measures of trust from the GSS for 18 origin countries (Austria, Canada, Denmark, Finland, France, Germany, Hungary, Ireland, Italy, Mexico, the Netherlands, Norway, Portugal, Russia, Spain, Sweden, Switzerland, and the United Kingdom). Comparing levels of trust between countries at different levels of flu mortality, they find:

...a negative and significant effect of the Spanish Flu on trust. An increase in influenza mortality of one death per thousand resulted in a 1.4 percentage points decrease in trust.

Since mortality rates ranged between about 2 deaths per thousand and 20 deaths per thousand, moving from the bottom to the top of the distribution would decrease trust by around 25 percentage points, which is quite meaningful. Aasve et al. then go on to investigate potential mechanisms underlying their results:

A narrower resonance of the war within neutral countries, together with the specific lack of war censorship on media, might have led their respective citizens to internalize the extent and severity of the pandemic, and thus altered their social interactions accordingly... Consistently with this hypothesis, we do find a stronger reduction in social trust for the descendants of people migrating from countries heavily hit by the epidemic and that remained neutral during the war.

Of relevance to the ongoing coronavirus pandemic, they conclude that:

...if, during the Spanish Flu, the failure of government institutions and national health care services to contain the crisis led civil societies to experience a serious breakdown due to the climate of generalized suspicion (a situation further exacerbated by mistakes in communication, also due to war censorship) and this increased the persistent damage to social capital, then governments facing COVID‐19 today might have an additional reason to opt for strong policies of pandemic containment. While these are undoubtedly costly in the short run, it might be that they will contribute to minimize some economic costs to be paid in the long run.

However, as noted in the EDG group meeting where we discussed this paper, you could also interpret the results as suggesting that countries that did not heavily censor their media during the Spanish Flu pandemic suffered greater losses in generalised trust. Therefore censoring the media has a protective effect and governments that wanted to maintain high levels of trust should censor their media. Before we conclude that Russia or China has a better approach to maintaining the generalised trust of their population though, I think we need to see whether these results replicate for the current crisis.

The relationship between education and earnings for sex workers

In the basic supply and demand model of the labour market (as I teach in my ECONS102 class), workers with more education (or human capital) are more productive for their employer, and so they get paid a higher wage. That's because more productive workers generate a higher value of the marginal product of labour, which is the value generated for the employer, which determines the wage that the employer is willing to pay. This would also hold (perhaps even more strongly) in the case of self-employed workers. Search models of the labour market (which I teach in my ECONS101 class) also suggest that workers with more education (or human capital) get paid a higher wage. In this case, it is because more educated (and productive) workers have a better outside option, which gives them slightly more bargaining power in negotiating their wage with the employer.

Is it generally the case that more educated workers have higher earnings? Are there occupations where this doesn't hold? You might cite the case of jobs where luck is a big determinant of earnings, such as hedge fund managers. But what about unskilled jobs, where it is plausible that education won't make much of a difference to earnings (at least not within the occupation)?

One occupation that might fit into the latter cases is sex work (although, as I note a bit later, it may be incorrect to label it unskilled, and not for the reasons you may think). However, there is little in the way of analysis on sex workers' earnings and education, because of a paucity of good data. One exception is this 2017 article by Scott Cunningham (Baylor University) and Todd Kendall (Compass Lexecon), published in the journal Review of Economics of the Household (ungated earlier version here). Cunningham and Kendall use survey data on 685 sex workers from the U.S. in 2008-2009, and look at the relationship between education and earnings.

However, first they outline a theoretical model that shows that the relationship between education and earnings from sex work is ambiguous. This ambiguity stems from the marginal disutility of sex work. All work generates disutility (negative utility) to some extent, since working more involves giving up some leisure time, and workers would generally prefer to have more leisure (so, working more makes them worse off on one dimension, which they trade off for higher income). An important question is how much disutility is associated with sex work and, in this case, whether that disutility differs by education. Cunningham and Kendall note that:

Human capital may reduce the disutility associated with prostitution for several reasons. Better-educated women may be preferred by higher-quality clients who have lower disease and violence risks. In addition, better-educated women may be able to reduce arrest, violence, and disease risks by engaging in greater and more sophisticated screening of clients.

So, because sex work is lower risk for more educated sex workers, perhaps the disutility of sex work is lower for them than for less educated sex workers. Cunningham and Kendall then go on to show, theoretically, that:

...education has three separate potential effects on the propensity to engage in prostitution. First, if education is associated with higher legitimate market wages... and/or higher monogamous coupling returns... then education reduces prostitution participation, ceteris paribus. Second, if education is associated with lower marginal disutility from legitimate employment... and/or monogamous coupling... then education further reduces prostitution participation, ceteris paribus. Finally, if education is associated with lower marginal disutility from prostitution... then education increases prostitution entry, ceteris paribus.

And also:

...if education reduces the marginal disutility from prostitution work... and has no material effect on the marginal utility of consumption or leisure, then, conditional on participation, educated prostitutes will work more hours than those with less education.

So, based on the theoretical model, education either reduces engagement in sex work and the number of hours that sex workers will work (if education is not associated with lower marginal disutility from sex work), or education has an ambiguous effect on engagement in sex work but increases the number of hours that sex workers will work (if education is associated with lower marginal disutility from sex work).

Cunningham and Kendall then analyse their survey data, comparing college-educated and non-college educated sex workers, and find that:

...college-educated workers appear to work roughly 13.7% fewer weeks in the prostitution market... [and] conditional on working, college-educated workers see nearly 25% more clients.

...college-educated sex workers earned approximately 33% more in the last week than those with less education, conditional on working, but approximately the same amount unconditionally, accounting for the fact that they are less likely to work at all.

Those results seem to support a lower marginal disutility from sex work for sex workers with more education. Cunningham and Kendall then investigate why. Focusing on data from longer sessions with clients (which are more common among more educated sex workers), they find that:

...college completion is associated with a roughly 15% wage premium for these longer sessions. In other words, while college is not associated with statistically significant wage effects on average it is so for the longest sessions.

This leads them to conclude that:

These longer sessions... likely involve bundling of sexual services with non-sexual services such as companionship, for which college completion may be associated with higher productivity. Because sexual favors presumably form a smaller share of the total work time in these longer sessions, and because... college-educated workers are able to provide longer sessions, these results provide one means by which “job amenities” may be better, and therefore, the disutility of prostitution labor supply lower, for college-educated sex workers.

Cunningham and Kendall also note that:

...college-educated providers appear to be able to attract 33.5% more regulars... Regular clients generally involve lower violence and arrest risk since they are already known; moreover, sex workers may be able to form warmer, less “transactional,” relationships with regulars that may mitigate some of the disutility associated with prostitution labor supply. 

Taken altogether, the marginal disutility of sex work is clearly lower for more educated sex workers. These results also demonstrate that there are essentially (at least) two market segments here. High educated sex workers provide a meaningfully different bundle of services, of which sexual acts are only a part, than do low educated sex workers. This isn't an unskilled labour market for all sex workers. 

Overall, this research demonstrates that education does increase earnings, even in one occupation where a priori you might think that it wouldn't.

Incentivising coronavirus vaccination

A couple of weeks ago, my ECONS102 class covered externalities. An externality is the uncompensated impact of the actions of one person on a bystander. Externalities can be negative (and make the bystander worse off), or positive (and make the bystander better off). One of the examples I use for a positive externality is vaccines. A person who gets vaccinated makes themselves better off (by reducing their chance of getting sick), but also makes others better off (because there is at least one fewer person who they can get sick from) - that's a positive externality.

The problem with positive externalities is that the market, left on its own, will not ensure that enough is produced or consumed. That's because the market participants don't have an incentive to take into account the benefits that their actions confer on others. That market will produce too little, compared to the quantity that maximises societal welfare. In the case of vaccines, too few people would get vaccinated.

There needs to be some mechanism to encourage more people to purchase goods with positive externalities. One way is to subsidise them (for example, see this post about subsidising education). The subsidy effectively increases the benefits of selling the good or service (if it is paid to the sellers), or reduces the cost of the good or service (if it is paid to the buyers). Either way, it increases the amount that is produced and consumed, and can ensure the quantity is increased to the socially optimal quantity.

Alternatively, the government could find some other way to incentivise more production and consumption. Right now, we're in a situation where governments want to roll out coronavirus vaccines in the face of a substantial amount of vaccine hesitancy. Some governments have started to incentivise vaccines through more than just subsidising them and making them available for free. For example, the New York Times reported last month that:

West Virginia will give $100 savings bonds to 16- to 35-year-olds who get a Covid-19 vaccine, Gov. Jim Justice said on Monday.

There are roughly 380,000 West Virginians in that age group, many of whom have already gotten at least one shot, but Mr. Justice said he hoped the money would motivate the rest to get inoculated, as “they’re not taking the vaccines as fast as we’d like them to take them.”

Some people worry that giving monetary incentives reduces intrinsic motivation. Indeed, this famous research by Uri Gneezy and Aldo Rustichini (ungated version here) showed that fining parents for picking up their children late from a childcare centre encouraged more late pickups. When the moral incentive to pick up on time is replaced by a financial incentive, it turned out to be less effective. The corollary for vaccines is that paying people to get vaccinated could encourage fewer of them to do so.

However, to counter that argument UCLA has run some experiments showing that monetary incentives are effective, as reported by the New York Times a couple of weeks ago:

In recent randomized survey experiments by the U.C.L.A. Covid-19 Health and Politics Project, two seemingly strong incentives have emerged.

Roughly a third of the unvaccinated population said a cash payment would make them more likely to get a shot...

Similarly large increases in willingness to take vaccines emerged for those who were asked about getting a vaccine if doing so meant they wouldn’t need to wear a mask or social-distance in public, compared with a group that was told it would still have to do those things.

So, perhaps we don't need to worry so much about whether the monetary incentive would be effective. And, perhaps we wouldn't have to pay it to everyone. CBS News reported yesterday:

Health officials in Ohio have reported a surge in the amount of people getting their first COVID-19 vaccination shots, a week after Ohio Governor Mike DeWine announced the $5 million "Vax-a-Million" lottery.

Just days after DeWine said the state would award five vaccinated residents $1 million each in order to raise vaccination percentages, the Ohio Department of Health reported more than 113,000 people received their first dose of the vaccine.

Based on preliminary data, the department said the recent period showed a 53% week-to-week increase (May 13 to 18) compared to the time period before the announcement, where 74,000 people received their first dose (May 6 to 11). 

"We are seeing increasing numbers in all age groups, except those 80 and older, who are highly vaccinated already," said Ohio Dept. of Health director Stephanie McCloud. "Although the rate among that group is decreasing, it is doing so at a less rapid pace, demonstrating some positive impact even in that group."

Ohio residents 18 and older who have received at least one dose of the vaccine can enter to win one of the five $1 million prizes. Ohioans between the age of 12 and 17 who have received at least one dose of the COVID-19 vaccine can enter to win one of five four-year, full-ride scholarships to any state college or university in the state. So far, approximately one million entries have been collected, according to Ohio Lottery and Ohio Department of Health.

Gamifying vaccination by attaching it to a lottery is kind of inspired. If people who are the least risk averse are those who are least likely to get vaccinated, and also those who are most likely to play the lottery, then this could be incredibly effective in increasing vaccination rates. People constantly overestimate the chance of events happening that have small probabilities (this is one of the key features of what is called prospect theory), like winning the lottery. So, government wouldn't necessarily have to ensure that the lottery amount was high enough to ensure that it captures all of the social benefits of vaccination, making this solution more cost effective than paying everyone who got vaccinated. For example, paying 100,000 people $100 each to get vaccinated costs $10 million. But, government could possibly offer five prizes of $1 million each and get the same outcome of 100,000 people getting vaccinated for half the total cost.

Overall, New Zealand's approach to vaccination is slow and steady. We're ahead of target (see the New Zealand Herald's Vaccine Tracker), but there is a fair amount of concern about whether we will achieve the overall target (e.g. see comments here or here). There seems to be plenty of demand for vaccines right now, but if things start to slow up later, perhaps we need our own vaccine lottery?

[HT: Marginal Revolution for the NY Times article on incentives; The Dangerous Economist for the article on Ohio's lottery]