Wednesday, 25 May 2022

Husbands vs. wives

On the Development Impact blog back in March, Markus Goldstein pointed to this fascinating NBER Working Paper by John Conlon (Harvard University) and co-authors, innocuously titled "Learning in the Household". Despite the title, the working paper reveals a study of how husbands and wives treat information revealed by each other differently (a finding that will no doubt come as no surprise to my wife).

Conlon et al. recruited 400 married couples and 500 unrelated strangers (with equal numbers of men and women) for their study, in Chennai (India) in 2019. In the experiment, research participants: five rounds - with different treatments - of a balls-and-urns task... The goal in each round is to guess the number of red balls in an urn containing 20 red and white balls. Participants are informed that the number of red balls is drawn uniformly from 4 to 16 in each round...

In each round, participants receive independent signals about the composition of the urn. Concretely, they privately draw balls from the urn with replacement. Depending on the round, they either play the game entirely on their own or else can learn some of the signals from their teammate. Comparing learning across these rounds allows us to test for frictions in communication and information-processing which may interfere with social learning.

Seems straightforward so far. The experiment involves several rounds, which proceed somewhat differently from each other:

Individual round. The Individual round proceeds as follows. First, the participant draws a set of balls from the urn, followed by a guess of how many red balls are in the urn. Then, they draw a second set of balls from the urn and make a second (and final) guess. All drawing and guessing is done privately, without any opportunity to share information. This round serves as a control condition - a benchmark against which we compare the other conditions.

Discussion round. The Discussion round differs from the Individual round in that, for each participant, their teammate’s draws - accessible through a discussion - serve as their ‘second’ set of draws. Each person first makes one set of draws followed by a private guess, exactly as in the Individual round. Next, the couple are asked to hold a face-to-face discussion and decide on a joint guess. After their discussion and joint guess, each person makes one final, private guess.

By comparing the final private guesses in the individual round and the discussion round, Conlon et al. can test whether learning your teammate’s information through a discussion is just as good as receiving the information directly yourself. There are two further rounds as well:

Draw-sharing round. This round is identical to the Discussion round except that, after participants receive their first set of draws and enter their first guess, they are told their teammate’s draws (both number and composition) directly by the experimenter, e.g. “Your spouse had five draws, of which three were red and two were white.” They then make an additional private guess which can incorporate both sets of draws before moving on to the discussion, joint guess and final private guess...

Guess-sharing round. The Guess-sharing round is the same as the Draw-sharing round except that the experimenter informs each person of their spouse’s private guess (made based on their own draws only), rather than their spouse’s draws. The experimenter also shares the number of draws this guess was based on, e.g. “Your spouse had 5 draws and, after seeing these draws, they guessed that the urn contains 12 red balls.”

By comparing the final private guesses in the individual round and the draw-sharing round, Conlon et al. can test whether the identity of who learns the information matters (aside from who shares that information). By comparing the final private guesses in the discussion round and the draw-sharing round, Conlon et al. can test the extent to which communication frictions affect decision-making (since there are no frictions in the draw-sharing round, because the information is shared by the experimenter, and the teammates cannot discuss at all). Comparing the draw-sharing round and the guess-sharing round allows Conlon et al. to test whether beliefs about the competence of the teammate matters. Finally, having teams made up of spouses or made up of mixed-gender strangers or same-gender strangers allows Conlon et al. to see if spouses share information (or act on information) differently than strangers do.

Having run these experiments, Conlon et al.:

...first compare guesses in the Individual and Discussion rounds, played in randomized order. Husbands put 58 percent less weight (p<0.01) on information their wives gathered - available to them via discussion - than on information they gathered themselves. In contrast, wives barely discount their husband’s information (by 7 percent), and we cannot reject that wives treat their husband’s information like their own (p=0.61). The difference in husbands’ and wives’ discounting of each other’s information is statistically significant (p=0.02).

The lower weight husbands place on their wives’ information is not due of a lack of communication from wives to husbands. In another experimental treatment - the Draw-sharing round - husbands put less weight on their wife’s information even when it is directly conveyed to them by the experimenter (absent any discussion). In this case, husbands discount information collected by their wives by a striking 98 percent compared to information collected by themselves (p<0.01), while wives again treat their spouses’ information nearly identically to their own. Lack of communication between spouses or husbands’ mistrust of (say) wives’ memory or ability thus cannot explain husbands’ behavior. Rather, husbands treat information their wives gathered as innately less informative than information they gathered themselves. In contrast, wives treat their own and their husbands’ information equally.

The guess-sharing round doesn't appear to reveal too much of interest, with results that are similar to the draw-sharing round. Finally, comparing the results from teams of spouses with the results from teams of strangers, Conlon et al. find that:

In both mixed- and same-gender pairs, men and women both respond more strongly to their own information than to their teammate’s. Thus, the underweighting of others’ information appears to be a more general phenomenon. Husbands treat their wives (information) as they treat strangers; wives instead put more weight on their husband’s information than on strangers’ information.

There is a huge amount of additional information and supporting analysis in the working paper (far too much to summarise here), so I encourage you to read it if you are interested. Conlon et al. conclude that there is:

...a general tendency to underweight others’ information relative to one’s ‘own’ information, with a counteracting tendency for women to weight their husband’s information highly.

Now of course, this is just one study that begs replication in other contexts with other samples. However, I'm sure there is a large section of the population who would find that conclusion meets their expectations (and/or their lived experience).

[HT: Markus Goldstein at Development Impact]

Sunday, 22 May 2022

The global inequality boomerang

Overall, global inequality has been decreasing over the last several decades. That may contrast with the rhetoric about inequality that you are familiar with from the media. However, the global decrease in inequality has mostly been driven by the substantial rise in incomes in China. However, China's contribution to decreasing global inequality may be about to change. In a new working paper, Ravi Kanbur (Cornell University), Eduardo Ortiz-Juarez and Andy Sumner (both King’s College London), discuss the possibility of a 'global inequality boomerang'.

Focusing on between-country inequality (essentially assuming that within-country inequality doesn't change), and using a cool dataset on the income distribution for every country scraped as percentiles from the World Bank's PovcalNet database, Kanbur et al. find that:

...there will be a reversal, or ‘boomerang’, in the recent declining (between-country) inequality trend by the early-2030s. Specifically, if each country’s income bins grow at the average annual rate observed over 1990–2019 (scenario 1), the declining trend recorded since 2000 would reach a minimum by the end-2020s, followed by the emergence of a global income inequality boomerang...

This outcome is illustrated in their Figure 4, which shows the historical decline in inequality since the 1980s, along with their projections forward to 2040:

Scenario 1 assumes an almost immediate return to pre-pandemic rates of growth. Scenario 2 assumes slower growth rates for countries with lower rates of vaccination. Neither scenario is particularly likely, but the future trend in global inequality is likely to be somewhere between them, and likely to be moving upwards. Why is that? Between-country inequality has been decreasing as China's average income has increased towards the global average. In other words, Chinese household incomes have increased, decreasing the gap between Chinese households and households in the developed world. However, once China crosses the global average, further increases in Chinese average income will tend to increase between-country inequality.

Of course, this might be a pessimistic take, because if other populous poor countries (including India, Indonesia, Pakistan, Nigeria, and Ethiopia) grow more quickly, then their growth might reduce inequality enough to offset the inequality-increasing effect of Chinese growth. However, that is a big 'if'. According to World Bank data, China's GDP per capita growth rate averaged 8.5 percent per year between 1991 and 2020. Compare that with 4.2 percent for India, 3.2 percent for Indonesia, 1.5 percent for Pakistan, 1.4 percent for Nigeria, and 3.9 percent for Ethiopia. These other countries would have to increase their growth rates massively to offset Chinese growth's effect on inequality.

This isn't the first time that Chinese growth has been a concern for the future of global inequality. Branko Milanovic estimated back in 2016 that China would be contributing to increasing global inequality by 2020 (see my post on that here). Things may not have moved quite that quickly (a pandemic intervened, after all). Kanbur et al. are estimating under their Scenario 1 that the turning point will be around 2029 (and around 2024 under their Scenario 2). Slower Chinese growth, and faster growth in the rest of the world, have no doubt played a part in this delay. However, it's likely that the turning point in global inequality cannot continue to be delayed for much longer.

Read more:

Saturday, 21 May 2022

Ian Pool, 1936-2022

I was very saddened to hear of the passing of Ian Pool at the end of last month. I have held off on posting about this, waiting for a good obituary to show up online. I was not to be disappointed - Stuff published a beautiful obituary by Brian Easton earlier today. For those who don't know, Ian Pool is widely regarded as the founding father of New Zealand demography. He set up the Population Studies Centre (PSC) at the University of Waikato, which remains a national centre of excellence in demography and population studies (in its latest incarnation, renamed as Te Ngira - Institute for Population Research). Ian was well known for his work on African demography, as well as Māori demography, among many other contributions to the field.

I had many interactions with Ian over the years, including as a co-author and a co-researcher. When I first met him, sometime in the mid-2000s, I was working as a research assistant for Jacques Poot at the PSC, and completing my PhD in economics. Ian initially struck me as one of those infuriating people who have the habit of constantly name-dropping famous people they have met. However, Ian's name-dropping wasn't merely a cynical attempt to big-note himself - he really did know and had closely interacted with Joseph Stiglitz, Thomas Piketty, and others. And I have to admit feeling a bit of a warm glow some years later when Ian name-checked me in a seminar or presentation (more than once) for my work on stochastic population projections.

My interactions with Ian also resulted in what is possibly one of the big missed opportunities of my career. Ian was very keen to have me work on a new research idea, distributional national accounts. I was mildly interested, but didn't have the time to devote to it immediately. I also had other priorities, especially in trying to establish a longitudinal ageing study based at Waikato (an initiative that eventually proved to be a dead end, as despite a lot of positive end-user engagement, we couldn't secure sufficient funding to make the study feasible). Anyway, I hadn't realised at the time just how important the idea of distributional national accounts was to become, as well as its centrality to the work of Piketty, Emmanuel Saez, and others. Distributional national accounts were also a key contribution to the work of the High-Level Expert Group on the Measurement of Economic Performance and Social Progress, co-chaired by Stiglitz, Fitoussi, and Durand, as noted in the book Measuring What Counts (which I reviewed here). Ian was at the cutting edge, but sadly, I don't believe that he did find someone to work on New Zealand's distributional national accounts.

I was never a student of Ian's, but I did sit in on a workshop that he gave some years ago, on how to derive and interpret life tables. He was an excellent communicator, and clearly would have been a great teacher and mentor to students at all levels. I have had the pleasure of working with a number of his excellent former PhD and graduate students, including Natalie Jackson and Tahu Kukutai. I'm sure that a more complete roll call of Ian's students would reveal just how much of an impact he has had, and continues to have, on population studies and demography both in New Zealand and internationally.

Aside from his research contributions, Ian was just a lovely, generous, and sincere man. He was patient and kind to colleagues and students alike, and a fountain of knowledge on many things. He will be greatly missed.

Friday, 20 May 2022

The problem with studies on the relationship between alcohol outlets and sexually transmitted diseases

I've done a fair amount of research on the impacts of alcohol outlets on various measures of alcohol-related harm. That work has focused predominantly on violence, property damage, and crime generally. One thing I haven't looked at is the relationship with sexually transmitted diseases. That is for good reason. Crime is an acute outcome associated with drinking, and is measurable almost immediately. That distinguished crime from longer-term negative consequences of drinking such as liver cirrhosis, for example.

However, in-between those two extremes are some alcohol-related harms that we could refer to as medium-term harms. For example, the theoretical link between alcohol consumption and risky behaviour, including risky sex, seems clear. So, if having more alcohol outlets in a particular area is associated with greater alcohol consumption (following availability theory, as I discussed in this post earlier this week), then alcohol outlets should be positively associated with greater prevalence of sexually transmitted diseases. Sexually transmitted diseases do not become immediately apparent in the way that violence or property damage does, but they don't take years to manifest in the way that cirrhosis does.

There have only been a few studies on the relationship between alcohol outlets and sexually transmitted diseases. So, I was interested to read two studies recently related to this topic, which had been sitting on my to-be-read pile for some time. The first study was reported in this 2015 article by Molly Rosenberg (Harvard School of Public Health) and co-authors, published in the journal Sexually Transmitted Diseases (ungated NLM version here). They look at the relationship between alcohol outlets and Herpes Simplex Virus Type 2 (HSV-2) prevalence among young women (aged 13-21 years) in the Agincourt Health and Demographic Surveillance System site in South Africa. The Agincourt sample has predominantly been used as an HIV surveillance site, and there are dozens of studies based on this sample. However, they didn't look at HIV as an outcome in this study:

...because of the small number of prevalent infections at baseline and the likelihood that at least some of the cases were a result of perinatal, as opposed to sexual, transmission.

Fair enough. Perinatal transmission of HIV (transmission at or around the time of birth) has been a serious problem, but I guess it must be less of a problem for HSV-2. In their analysis, Rosenberg et al. essentially counted the number of alcohol outlets (both on-licence and off-licence combined) in each village, and related that number to HSV-2 prevalence for the 2533 young women in their sample. They found that:

Treating the alcohol outlet exposure numerically, for every 1-unit increase in number of alcohol outlets per village, the odds of prevalent HSV-2 infection increased 8% (odds ratio [OR; 95% CI], 1.08 (1.01–1.15]). The point estimate changed minimally after adjustment for village- and individual-level covariates (OR [95% CI], 1.11 (0.98–1.25]); however, this adjusted estimate was less precise.

Not only was it less precise, but it becomes statistically insignificant (barely), which they don't note. So, this doesn't provide strong evidence of a link between alcohol outlets and sexually transmitted diseases, although the evidence is suggestive. The problem is that the analysis essentially assumes that all young women in the same village have the same exposure to alcohol. This marks the number of outlets as an imperfect proxy for the real exposure variable, and suggests that the real effect might be larger. Again, this is suggestive evidence at best.

The second study was reported in this 2015 article by Matthew Rossheim (George Mason University), Dennis Thombs, and Sumihiro Suzuki (both University of North Texas), published in the journal Drug and Alcohol Dependence (sorry, I don't see an ungated version of this one online). This study did look at HIV as an outcome, relating zip-code-level HIV prevalence to the number of alcohol outlets (of different types) across 350 cities in the US. Perinatal transmission of HIV is not much of a problem in the US (certainly not compared to South Africa at the time that the Agincourt sample were born). Based on their data for a little over 1000 zip codes, Rossheim et al. found that:

...the presence of one additional on-premise alcohol outlet in a ZIP code was associated with an increase in HIV prevalence by 1.5% (rate ratio [RR] = 1.015). In contrast, more beer, wine, and liquor stores and gas stations with convenience stores were associated with lower HIV rates (RR = 0.981 and 0.990, respectively). Number of pharmacies and drug stores was not associated with HIV prevalence (p = 0.355).

On-premise outlets (predominantly bars and nightclubs) were associated with higher HIV prevalence, while liquor stores and gas stations were associated with lower HIV prevalence. Rossheim et al. don't have a good explanation for why, although they note a number of obvious limitations with their study. The literature on the impacts of alcohol outlets is littered with these sorts of inconsistent findings.

The real problem with a study like this is the time lapse between the alcohol consumption and the measured outcome variable. As I noted at the start of this post, with acute harm (like violence or property damage), the effect is immediately seen and can be measured, and likely occurred close to the location of alcohol consumption. With HIV prevalence, there is only a small chance that HIV was contracted as a result of activity within the local area. People move about over time, they 'interact' with people in many locations, and they can migrate from city to city. So, all we can say with this study is that people living with HIV tend to live in areas that have lots of bars and night clubs, and tend to live in areas that have fewer liquor stores and gas stations. Call this the gay-men-live-near-night-clubs effect, if you want to evoke a bunch of stereotypes. This effect is correlation, and it is difficult to say with any certainty if there is any causal relationship here.

Now, the Agincourt study has this problem as well, but the young women there probably still live in the same village they grew up in, so in that case the exposure to alcohol can be (imperfectly, as I noted above) proxied by the number of outlets in the village. And the symptoms of HSV-2 appear within a week, rather than weeks or months later as can be the case for HIV. So, moving about is less of an issue, although not eliminated entirely.

Anyway, these two studies are interesting, but they mainly highlight the problems with this broader literature. When we move beyond measuring acute harms associated with alcohol outlets, it isn't clear that the associations that are being measured are anything more than spurious correlation.