Consumers can't tell the difference in audio quality between high-end audio cables and a banana

Consumers are not very good judges of quality. They can't tell the difference between bottled water and tap water. They can't even tell the difference between pâté and dog food. And now, according to this article by Futurism last month, they can't tell the difference between audio cables and a banana:

High-quality cables have long been marketed as a key way to get the most out of high-end equipment, such as expensive studio-grade monitor speaker cables and gold-plated HDMI cables for cutting-edge TVs.

In the high-end audiophile world, which is renowned for eye-bulging prices, cables can cost tens of thousands of dollars for ultra-pure copper with silver plating, specialized insulation, and dozens of individual conductors that manufacturers claim will squeeze the most out of a luxury-grade sound system aimed at the uber-wealthy.

The laws of physics, however, have long dictated that spending that kind of cash on cables simply isn’t worth it in the vast majority of circumstances — as long as you don’t go for the cheapest option from the dollar store, of course.

To put the decades-long debate to the ultimate test, a moderator who goes by Pano at the audiophile enthusiast forum diyAudio conducted an eyebrow-raising experiment back in 2024, which was rediscovered by Headphonesty late last month and Tom’s Hardware last week.

Pano ran high-quality audio through a number of different mediums, including pro audio copper wire, an unripe banana, old microphone cable soldered to pennies, and wet mud. He then challenged his fellow forum members to listen to the resulting clips, which were musical recordings from official CD releases run through the different “cables.”

The results confirmed what most hobbyist audiophiles had already suspected: it was practically impossible to tell the difference.

Consumers are not fully informed about the quality of the products that they buy. When they lack quality information before they buy, but that information is revealed after the consumer buys the good, we say that quality is an experience characteristic (and goods like that are called experience goods). A used car is an example of an experience good - the consumer doesn't really know if it is a high-quality car until they drive it. However, for some goods, the quality isn't revealed even after the good is purchased. In that case, quality is a credence characteristic (and goods like that are called credence goods). Health care is a credence good, because patients don't know for sure what would have happened to them without treatment, so it is impossible to judge the quality of the treatment.

Coming back to using an unripe banana as an audio cable, it appears that the quality of audio cable may also be a credence characteristic. At least, that's what this research tells us.

Why does this matter? The thing about credence goods is that the buyer may be reliant on the seller telling them about the quality. In the case of audio cables, the industry has a strong incentive to convince buyers that a 'high-quality' audio cable matters for sound quality, even if the consumer can't tell the difference. That changes the nature of competition in the industry. When buyers cannot verify quality for themselves, sellers can't compete on quality, and instead rely on reputation, branding, expert language, and the seller’s ability to sound convincing. They aren't going to want to sell banana cables, even if the banana cable would be produce audio of equivalent quality to a 'fancier' cable. Overall, this is a good reminder that in some markets, what consumers pay for is not better quality, but a more persuasive story about quality.

[HT: Marginal Revolution]

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Perceptions of inequality and satisfaction with democracy

Last week, my ECONS101 class covered (among many other things) the faulty causation fallacy. This occurs when we observe two variables that appear to be related to each other (they are correlated), but a change in one of the variables does not actually cause a change in the other variable (there is no causal relationship). We might observe a relationship between two variables (call them A and B), and it might be because a change in A causes a change in B, in which case the relationship is causal. But even if we can tell a really good story explaining why we think a change in A causes a change in B, that in itself doesn't make it true. We might observe that relationship because a change in B causes a change in A (we call this reverse causation). Or, we might observe that relationship because a change in some other variable causes a change in both A and B (we call this confounding). Or, the two variables might be completely unrelated, and the observed relationship happens by chance (we call this spurious correlation).

To illustrate this, I'm going to use the example of the research in this 2024 discussion paper by Nicholas Biddle and Matthew Gray (both Australian National University). They also wrote a non-technical summary of their paper on The Conversation. Biddle and Gray look at the relationship between perceptions of income inequality and faith in democratic institutions. To be fair to them, they do say in the paper that "This does not, however, demonstrate a causal relationship from views on inequality to views on democracy". However, most of their interpretations and their policy recommendations assume that the relationship is causal. For example, they conclude that:

The fundamental issue identified in this paper is that the Australian population has identified the income distribution in Australia as being unfair, and that this appears to be impacting views on democracy.

First though, let's take a step back and look at the research. Biddle and Gray use data from Waves 5 and 6 (from 2018 and 2023 respectively) of the Asian Barometer Survey (with a sample size of over a thousand in each wave for Australia), as well as from the ANUPoll surveys, which is a quarterly survey of public opinion run by the Social Research Centre at ANU. For the ANUPoll, they use the January 2024 data, which includes data from over 4000 respondents.

First, from the Asian Barometer, Biddle and Gray find that there is substantial concern about inequality:

In both waves 5 and 6 of the survey, respondents were asked ‘How fair do you think income distribution is in Australia?’... more Australians think that the income distribution is unfair or very unfair (60.5 per cent) than think it is fair or very fair. This gap has widened slightly since 2018, particularly in terms of those who think the distribution is very unfair as opposed to just unfair.

Second, in the ANUPoll data, they find that:

Combined, 30.3 per cent of Australians were not at all or not very satisfied with democracy in January 2024 (compared to 34.2 per cent in October 2023). This is still well above the January 2023 levels of dissatisfaction (22.9 per cent) and even more so the March 2008 levels (18.6 per cent).

So over time, Australians' perceptions of inequality have gotten worse (they think the income distribution is less fair), and they are less satisfied with democracy. It is reasonable, then, to ask whether those concerns about inequality affect people's faith in democratic institutions. Biddle and Gray next look at that relationship, using to the ANUPoll data, and find that:

There is a very strong relationship between views on income inequality in Australia and views on democracy...

Their model (shown in Table 1 in the paper [*]) shows that the most negative views of the income distribution are associated with negative satisfaction with democracy, while the more positive views of the income distribution are associated with positive views of democracy.

So, there is a strong correlation between perceptions of inequality and satisfaction with democracy. But is that just a correlation, or is there a causal relationship? We can tell a good story here (and Biddle and Gray do that). People who are less satisfied with the income distribution may lay some blame on government, and therefore their satisfaction with democracy falls.

Before we conclude that this relationship is causal though, let me lay out some alternatives. First, perhaps people who are less satisfied with democracy become less satisfied in general with many aspects of society, including the income distribution. In this case, there could be reverse causality. Second, perhaps people who are less satisfied with life in general express less satisfaction with many aspects of life and society, and so they answer more negatively when asked about the satisfaction with democracy, and they answer more negatively when asked about their views of the income distribution. In this case, there would be confounding. Third, perhaps satisfaction with democracy is declining over time for some reason, and views about the income distribution are becoming more negative for some completely different reason. But they look like they are related because they are both trending downwards. In this case, there would be a spurious correlation between perceptions of inequality and satisfaction with democracy.

It isn't straightforward to see two variables that appear to be related, and assume that a change in one of those variables causes a change in the other variable. Economists and other researchers have developed a number of statistical tools and experimental methods to try and tease out when a correlation really is demonstrating a causal relationship. Biddle and Gray haven't done that. It might be that negative perceptions of inequality reduce satisfaction with democracy. By itself, this research doesn't allow us to conclude that.

[HT: The Conversation]

*****

[*] Table 1 in the paper actually has an error. The explanatory variable in the table is labelled as satisfaction with democracy, when that is actually the dependent variable. It is perceptions of inequality that is the explanatory variable.

This week in research #116

Here's what caught my eye in research over the past week:

• Numa and Zahran (with ungated earlier version here) show that W.E.B. Du Bois made enduring contributions to economics (and may be one of the most under-rated economists of the early 20th Century)
• Federle et al. (with ungated earlier version here) study 150 years of the economic cost of war, and find that a war of average intensity is associated with an output drop of close to 10 percent in the war-site economy, while consumer prices rise by approximately 20 percent
• Passaro, Kojima, and Pakzad-Hurson (with ungated earlier version here) find that when there are more men than women in a labour market, 'equal pay for similar work' policies increase the gender wage gap
• Strulik and Trimborn (open access) show analytically that higher world population could causally lead to a lower long-run temperature increase under optimal carbon taxation (though I think that the optimal carbon taxation might be doing a lot of the work there)
• Arellano-Bover et al. (with ungated earlier version here) look into the initial job-matching of US graduates by major, and find significant variation in callback rate returns to majors, with Biology and Economics majors receiving the highest rate, particularly in occupations involving high intensity of analytical and interpersonal skills
• Xu et al. develop a geographically weighted autoregressive model with an adaptive spatial weights matrix (a bit pointy-headed for many readers of this blog, but of interest to me!)
• Li, Liu, and Si find in a meta-analysis that minimum wages actually increase female employment (showing that the question of the employment effects of minimum wages is still not solved)

This is not how generative AI should be used in research

I've been using ChatGPT Pro to help with drafting research papers this year, as I noted that I would do in this post from January. It has amped up my productivity a lot, allowing me to finish writing up two papers already, with a third on the way. These were papers where the analysis was already done, but it was the writing that was holding up the process. Having ChatGPT to help with the drafting seems to kickstart my writing, even though I have ended up extensively re-writing everything that ChatGPT produces. I find it a good disciplining tool as much as anything. Several colleagues have asked whether I am disclosing my generative AI use to journal editors when I submit. And I do. I have a standard 'generative AI use statement' that I include in my papers, that notes how it was used, and that I remain responsible for all of the content. You can see an example in this recent working paper.

However, not everyone is as careful with their generative AI use, or as transparent. Consider this example:

That is both infuriating and a sad indictment of the reviewing, editing, and publishing process, not least because, as on Reddit commenter noted, many authors see high-quality work rejected by journals, whereas a paper like this, with obvious flaws, has successfully been published. And it's not an isolated incident. This 2025 article by Artur Strzelecki (University of Economics in Katowice), published in the journal Learned Publishing (open access), catalogues over 1300 instances of likely unacknowledged and frankly stupid use of ChatGPT, up to September 2024.

Strzelecki's approach is to search for text strings that are almost certainly ChatGPT responses to a prompt asking it to generate text. The main example Strzelecki uses, which is in the title of the article, is "as of my last knowledge update". No human author is going to say that in a research paper. Similarly, "as an AI language model", "I don't have access to", and "certainly, here is" are highly indicative of ChatGPT use. There are circumstances where a human might use those phrases in a research paper, but it seems unlikely. Strzelecki screens out papers that mention ChatGPT, and manually checks each paper to ensure the text was not in some way legitimate, and that leaves 1362 articles.

How do these articles get published with this content intact? There are lots of stopping points where this could be caught and corrected (or prevented), but these articles have gotten through all of them. Strzelecki outlines the process. First, perhaps it is only one of the authors (and not all of them) that used ChatGPT. In which case, why didn't the other co-authors pick it up? Next, the paper is submitted to a journal, and often goes through a text review by the publisher. And then the editor or editors (including associate editors) looks at it, and decides whether it should be sent out for peer review. And then the peer reviewers (usually more than one, sometimes four or more) look at the paper in detail and provide comments. Then the editor receives the review reports and makes a decision. The paper may go through more than one round of review and editorial decision. And then, once accepted for publication, the article may be copy-edited. And at any of those stages, this text could be picked up. And yet, for over 1300 articles as of September 2024, the ChatGPT-generated text has not been picked up.

Strzelecki particularly focuses on 89 articles that have been published in journals indexed by Scopus or Web of Science, which should be the most credible journals. Of these:

...as many as 28 of them are in journals with Scopus percentile values of 90 and above. Two journals have a 99th percentile, indicating that they are the top journals in their field...

In total, 64 articles were found in journals considered to be in Q1, top quartile, recognized as the group of the best journals in their respective fields. Twenty-five articles are in the percentile range between 50 and 75, indicating that the journals in which these articles are found belong to Q2.

So, this phenomenon is not limited to low-ranked 'predatory' journals. In fact, looking at the list, there are several journals published by MDPI and Frontiers (for more on those publishers, see here). However, there are a whole lot published by Elsevier and Springer, publishers that we should expect much better of. Although, those are also publishers that publish a lot of journals, and a lot of articles, so perhaps that accounts for their higher numbers within the 89 articles that Strzelecki focuses on. Fortunately, I don't see any reputable journals in economics in the list, but I could be wrong.

Anyway, the takeaway is not so much that generative AI use is widespread in the write-up of research. It is that authors are using generative AI, not being transparent in their use of it, and that the quality control system by journals, even high-ranking journals, is terrible. Strzelecki makes a good point in the conclusion of his article that 89 out of over 2.5 million articles indexed in Scopus is only 0.000035% of the total indexed articles. However, this analysis is only picking up the really, really obvious cases. There will be far more use of generative AI that has not been adequately checked or acknowledged by authors, and not picked up in quality control.

I'm not against using generative AI in the write-up of research. Obviously, because I am doing the same thing. What needs to happen is that researchers need to be transparent and honest when they use generative AI, so that editors, reviewers, and the readers of research can see how it was used. That way, the users of research can evaluate for themselves whether they should believe, discount, or discard research depending on the ways and the extent of generative AI use. Without transparency, that important evaluation step is lost.

[HT: Artur Strzelecki]

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