Tuesday, 21 January 2020

Pre-drinking and the night-time economy

I'm a little unusual among economists, in that I really like to get out and do fieldwork (ok, maybe not that unusual any more, given the Nobel Prize winners of last year do it as well). Some of my most interesting fieldwork experiences have come overseas, or interviewing drunk people in the night-time economy. In fact, I posted about some of my work on the latter back in 2018.

I was lucky enough to secure some research funding from the Health Promotion Agency to repeat similar work last year (in fact, given the number of my students I encountered, probably some of the readers of this blog remember seeing me out in town late at night, breathalyser in hand). That research, which is joint work with Matthew Roskruge (Massey University), Nic Droste and Peter Miller (both Deakin University), is now published on the Health Promotion Agency website.

This time around, we had three research questions in mind:
  1. Where and when do pre-drinkers (people drinking before a night out or event) obtain their alcohol?;
  2. What is the difference in the level of intoxication of pre-drinkers vs. non- pre-drinkers, and how does this difference vary over the course of a night?; and
  3. Is the level of intoxication of pre-drinkers related to where and when they obtain their alcohol?
We also looked at the motivations for pre-drinking, and at the prevalence of side-loading behaviour (side-loading is the consumption of alcohol during a night out or event, occurring at a location other than a licensed venue). In this post, I'm just going to focus mostly on the first and third research questions, which I think have the most policy relevance (if you're more interested in the other research questions, then read the report).

The reason for looking at those research questions is easy to explain. There are lots of intoxicated people out and about in the night-time economy, and there's a lot of alcohol-related harm that arises from this. If you want to reduce alcohol-related harm, then one way is to try to reduce the amount of drinking. However, it isn't clear where policy should be directed. The bar owners will tell you that the problem is pre-drinking - people get drunk before they come into town for the night, and then cause problems. In that case, you may want to target policy at the off-licence outlets, since they are the main cause of the problems. However, the off-licences argue that making them close earlier wouldn't be effective, because people plan ahead and buy their drinks for pre-drinking ahead of time.

So, which side is correct? It turns out both. Pre-drinking is a big contributor to the level of intoxication in the night-time economy (we showed that in our earlier research, and again in this work). So, the bar owners are right.

However, when we looked at where and when pre-drinkers were buying their alcohol for pre-drinking, we found that the majority of pre-drinkers purchase their alcohol for pre-drinking on the day that they consume it. However, more than half of them were purchasing sometime before 6 p.m., so to have an appreciable impact on pre-drinking by modifying off-licence trading hours, you'd have to make the off-licences stop selling alcohol awfully early in the evening. It would be hard to make the case for that as a policy solution.

One of the biggest motivations for pre-drinking is price, which we found here, and which has also been found many times in international research. Alcohol is much less expensive when purchased from an off-licence and consumed at home, or on the way into town, than it is when purchased at a club or bar. If curbing pre-drinking is an important means of reducing alcohol-related harm, then it seems more feasible to try to reduce the price differential between bars and off-licences, than to mess with off-licence trading hours.

One of the great ironies of the Sale and Supply of Alcohol Act, which came into force in December 2013, was that it prohibited alcohol outlets from selling low-priced drinks. On the surface, this makes sense. People drink more when alcohol is less expensive (that's the simple downward-sloping demand curve at work). However, in practical terms, prohibiting low-priced drinks killed the 'happy hour' at bars and clubs, which was often early in the evening. Being able to buy cheap drinks in happy hour encouraged at least some people to come into town early. [*] Without happy hours, the incentives change in favour of drinking at home and coming into town later in the evening. I'd argue that this change has probably contributed to a continuing increase pre-drinking behaviour.

So, if reducing alcohol related harm through curbing pre-drinking is a policy goal, looking at how alcohol is priced is important. I'm not necessarily arguing for a return of the happy hour, in order to reduce the price differential between bars and off-licences. However, the obvious alternatives are either: (1) increasing excise taxes at off-licences but not bars, which seems unnecessarily complicated (especially since there are bars that also have an off-licence); or (2) minimum unit pricing, which would affect off-licences but be unlikely to affect bars. More on that in a future post.

*****

[*] Not everyone, obviously. And let's be clear - pre-drinking is not a new behaviour, as it was common when I was an undergraduate student.

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Monday, 20 January 2020

Marijuana legalisation and local crime rates

In my final post of last year, I talked about a research paper on the effect of marijuana legalisation on drug dealers. The overall conclusion of the paper was that marijuana legalisation increased recidivism of marijuana dealers, inducing them to switch to crime related to harder drugs:
Following legalization, marijuana offenders become 4 to 5 percentage points more likely to re-enter prison within 9 months of release. The effect is sizable, corresponding to a near 50% increase from a baseline rate of 10 percent. When decomposed by crime categories, I find the overall increase masks two countervailing effects. One, marijuana offenders became less likely to commit future marijuana offenses. Two, this reduction is offset by the transition to the trafficking of other drugs. As a result, the observed criminality of former marijuana traffickers increased.
However, that is definitely not the end of the story. I just read an article by Jeffrey Brinkman and David Mok-Lamme (both Federal Reserve Bank of Philadelphia), published in the journal Regional Science and Urban Economics (ungated earlier version here), where the conclusions seem to almost be the opposite. Brinkman and Mok-Lamme look at crime data at the census tract level in Denver, over the period from 2013 to 2016 (retail marijuana for recreational use became available in Colorado on 1 January 2014). They use an interesting identification strategy:
While the legalization of recreational marijuana in 2014 applied to the entire state, many municipalities within Colorado prohibit sales within their own jurisdictions. Residents living in municipalities near Denver that prohibit recreational sales often travel to Denver to purchase marijuana. Therefore, locations within Denver that have more access to demand from neighboring municipalities show more growth in their dispensary density, ceteris paribus. In addition, out-of-state tourists could purchase marijuana starting in 2014, further increasing the demand for dispensaries in locations with access to broader outside markets. In the empirical analysis, we use two geospatial variables to proxy for access to outside demand: a neighborhood’s proximity to municipal borders and proximity to major roads or highways. These variables are then used to instrument for changes in locations of dispensaries over time.
I was initially sceptical of this, because areas close to the outer border of Denver are further from the central business district, and consequently suffer less crime. However, their supplementary analyses, including where they show that the effect is unique to the period after recreational marijuana became available, convinced me. They find that:
...an additional dispensary per 10,000 residents is associated with a reduction of 17 crimes per 10,000 residents per month. The average number of crimes per 10,000 residents in Denver is 90 per month, so an additional dispensary is associated with roughly a 19 percent decline in crime.
The results from the supplementary analysis I mentioned (which is just one among many) suggest a smaller effect, on the order of 14 fewer crimes per 10,000 residents per month (a reduction of 16 percent). Some of their other results are interesting as well. For instance:
Dispensary densities after 2014 increased more in neighborhoods with higher poverty rates, with higher levels of employment, that are closer to the central business district, and where there is more useable land.
To be honest, we see something similar with off-licence alcohol outlets. In my own work, we have reasoned that more outlets locate in poorer areas because rents are lower, and because poorer residents are unable to (or unable to afford to) travel long distances to obtain alcohol. The latter effect leads to markets that are much more localised in poorer areas.

That marijuana dispensaries tend to locate in poorer areas leads to perverse effects on the standard OLS (ordinary least squares) regression model, which shows that marijuana dispensaries are in areas with more crime, even controlling for poverty and other neighbourhood characteristics. That raises questions about a lot of the research linking off-licence alcohol outlets with crime, where similar effects might be at play.

Finally, coming back to the overall results, this research shows that marijuana legalisation is associated with lower crime. That is the opposite conclusion to the paper by Heyu Xiong I discussed last month. They use different data sources and cover different regions (Xiong had Oregon and Washington states in his analysis, as well as Colorado). This study was purely based on the urban area of Denver (although a supplementary analysis they report at the county level for all of Colorado was suggestive of a negative effect as well).

Brinkman and Mok-Lamme test for spatial spillovers into surrounding neighbourhoods, and don't find any. One explanation that might link both studies is if the spatial spillovers are wider than that. Perhaps, in addition to moving to harder drug crime, former marijuana dealers are forced to move to other areas as well? Certainly, there is more work to be done in this area.

[HT: Marginal Revolution, last August]

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Sunday, 19 January 2020

Happiness inequality, revisited

At the start of the month, I wrote a post about happiness inequality. The research paper I reference there did a poor job (I think) of measuring happiness inequality (using the standard deviation of happiness). I just finished reading this 2013 article, by Indranil Dutta (University of Manchester) and James Foster (George Washington University), published in the journal Review of Income and Wealth (appears to be gated, but there is a working direct link to the paper here), which does a much better job.

Dutta and Foster use data from the US General Social Survey from 1972 to 2010, which has 49,433 observations of happiness, all measured as responses to the question: "Taken all together, how would you say things are these days - would you say that you are very happy, pretty happy or not too happy?"

They discard the standard deviation as a measure, much as I did in my earlier post:
Variance or standard deviation is an unsatisfactory measure of inequality under a cardinal scale...
Instead, they use measures based on the median, rather than the mean or the standard deviation. It's possible that basing an inequality measure on the median may go some way to reducing the problems with happiness measures I highlighted in this post last week, because the median-based measure is scale invariant (it doesn't depend on how you weight the different 'levels' of happiness). I'm sure someone who is much more mathematically inclined than I am can get to the bottom of that question, and given the strength of the conclusions drawn by Bond and Lang against happiness measures, I'd say that it is already a priority for someone.

Anyway, using their median-based measures Dutta and Foster find that:
...happiness inequality decreased from its highest level in the 1970s, through the 1980s and 1990s. Only in the 2000s did it start to rise again. However, in 2010 there has been a remarkable decline in inequality, making it the year with the lowest inequality under the linear scale of the AF measure. This achievement is offset, to some extent, by the fact that the average level of happiness in 2010 turns out to be the lowest among all the years.
This is an interesting result, when you place it alongside the fact that over this period, inequality in incomes has been increasing in the U.S. So, there is increasing inequality in incomes alongside decreasing inequality in happiness, and a moderate decline in median happiness. It would be interesting to consider a model that fits those stylised facts.

They also find that:
...on average, women have higher happiness inequality relative to men.
Add that to the list of stylised facts to explain in a model of happiness and inequality. Why is happiness both declining and converging over time in the U.S.?

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Friday, 17 January 2020

The gender gap in reviewing and editing for top economics journals

I've written a number of posts about the gender gap in economics (most recently, this one; see the list at the end of this post for more). So, I was interested to read this article by David Card, Stefano DellaVigna (both University of California, Berkeley), Patricia Funk (Universita Della Svizzera Italiana), and Nagore Iriberri (University of the Basque Country), published in the Quarterly Journal of Economics (ungated earlier version here). In the paper, they look at the reviewing and editing process for four top economics journals (Journal of the European Economics Association, Quarterly Journal of Economics, Review of Economics and Statistics, and Review of Economic Studies), in terms of gender bias.

They have data on nearly 30,000 submissions to those four journals, which they use to:
...analyze gender differences in how papers are assigned to referees, how they are reviewed, and how editors use referee inputs to reach a revise and resubmit (R&R) verdict.
There is both good and bad in their results. First, they find that:
...female-authored papers receive 22 log points (std. err. = 0.05) more citations than male-authored papers, controlling for the referee evaluations. Our estimate of this gender gap is robust to alternative measures of citations and to a variety of alternative specifications...
What this means is that:
...female-authored papers would have to be of 28 log points (32%) higher quality than male-authored papers to receive the same referee assessment.
That gender gap is then perpetuated through the publication process:
 On average editors tend to follow the referees’ recommendations, putting essentially no weight on author gender in their R&R decisions. This means that they are overrejecting female-authored papers relative to a citation-maximizing benchmark. 
There are at least a couple of interpretations for what is going on here:
There are two main explanations for our finding that female-authored papers receive more citations, conditional on the referee evaluations. The first is that referees hold female authors to a higher bar, perhaps because of stereotype biases. The second is that female-authored papers have characteristics that lead to higher citations but are not as highly rewarded in the review process. For example, female authors may tend to write more empirically oriented papers, or concentrate on certain topics within broad field categories that referees undervalue relative to expected citations.
Card et al. find evidence that is suggestive that paper characteristics play some role. That is, female authors do concentrate on different fields than male authors, and those fields tend to attract more citations. The news isn't all bad though:
We find no gender differences in the time that referees take to return a recommendation, in the time that editors take to reach a decision, or in the time between submission and acceptance for published papers.
There's also no difference between female and male reviewers, so if male reviewers are holding female-authored papers to a higher bar, then so are female reviewers. However, they couldn't assess differences between female and male editors, because of a lack of female editors (!).

This paper also made me wonder whether there is a third explanation, which the authors did not identify. Female authors might self-censor, sending only their very best papers to these top journals, but sending their second-tier papers to other, lower-ranked journals. In contrast, male authors might send those second-tier papers to the top journals anyway. That would lead the average quality (as measured by citations) of female-authored papers to be higher than male-authored papers.

Regardless of explanation, the results have clear and negative implications for female economists, who have to work harder to get the same outcomes as male economists. And the solutions are not as straightforward as equalising gender participation. Card et al. note in their conclusion that:
One potential remedy to help female economists — using more female referees — is unlikely to help, given that female referees hold female-authored papers to the same higher bar as do male referees.
Their preferred solution is disappointingly vague (although admittedly, I'm not sure I can offer anything better):
It appears to us that a simpler path is to increase the awareness of the higher bar for female-authored papers. The referees and editors can then take it into account in their recommendations and decisions.
Of course, that means moving away from the double-blind reviewing process (which is a fiction in any case - but that's a topic for another post).

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