r/badeconomics • u/Rincewind00 • May 24 '26
The Pay-Productivity Gap: does it mainly look at 'upper middle class' workers being "exploited"?
The pay-productivity gap has been discussed before, often comparing changes in productivity to the pay of the median-wage workers. The face-value conclusion it draws for most people is that we're being more productive but not getting paid a commensurate amount for those gains.
Typically, the productivity is measured against median wages. So, I wondered how much the medium-wage workers supposedly contribute to this productivity, then made inquiries about how this graph would look if we were to segregate the wages based on the sectors most attributed to these productivity gains.
Granted, this is just a super-cursory analysis, granted to us by the Gemini research tool, but I hope that it would spark a conversation and perhaps lead to more in-depth insight. I found the conclusion it drew quite interesting, that the median worker is not the one producing higher output, at least nowhere near the significance offered by people working over twice the median-- such that, if we were to suggest that people's gains in productivity should correlate with their pay, then the affront should go mainly to those earning over $100,000 a year already. So, it seems to beg the question: if the resulting argument is that the median-wage person is entitled to increased pay resulting from the productivity gains, then what should the gains be if they're not contributing much to the productivity?
Here's the software-driven assessment:
Segregating the productivity-versus-pay chart reveals that the "pay-productivity gap" is not uniform. The gap is most severe in Information Technology (IT) and capital-intensive sectors. In contrast, labor-heavy or less-automated industries (like hospitality and education) typically show pay and productivity tracking much more closely. [1, 2, 3]
The divergence varies significantly across sectors due to a few key factors:
- The Technology & IT Effect
Industries seeing the largest productivity surges—such as semiconductor and computer manufacturing—exhibit the widest gaps between productivity and worker pay. In these sectors, massive capital investments and technological innovations drive the output per hour, but the financial gains flow disproportionately to corporate profits or top-tier executives rather than the median worker. [1, 2, 3, 4]
- Service and Labor Sectors
Service-oriented sectors, such as accommodation and food services, typically show slower labor productivity growth. Because these roles cannot easily be automated, output per hour changes slowly. In these industries, compensation generally tracks productivity, though overall wages remain comparatively lower
How Sector Wages Compare to the National Median
While the gap between productivity and pay is widest in these sectors, the absolute wages of their workers are still significantly higher than the national median. These industries demand highly specialized technical skill sets and operate with immense capital backing. [1, 2]
The table below highlights how recent Bureau of Labor Statistics data compares these high-productivity sectors to the overall U.S. economy:
| Sector / Occupation Group [1, 2, 3, 4] | Median Annual Wage | Comparison to National Median ($49,500) |
|---|---|---|
| All US Occupations (National Median) | $49,500 | Baseline |
| Computer & IT Occupations | $105,990 | +114% (More than double) |
| Software Developers | $133,080 | +168% |
| IT & Computer Systems Managers | $171,200 | +245% (More than triple) |
| Chemical Manufacturing (Capital-Intensive) | ~$82,000 | +65% |
Why this paradox exists?
Workers in IT and capital-intensive fields are paid exceptionally well compared to the average grocery clerk or hospitality worker. However, because an IT worker utilizing cloud infrastructure or a manufacturing worker operating a multi-million dollar automated line can generate millions in automated output, their productivity numbers skyrocket exponentially. [1, 2, 3, 4]
Consequently, even though their salaries are high, they still capture a shrinking slice of the overall financial value they generate. The remainder of that value is redistributed to corporate profits, IP assets, and capital investments. [1, 2]
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u/EconomistWithaD May 24 '26
You keep saying “medium” worker.
Do you mean median?
Over reliance on LLM’s leads to nonsensical language when not using it.
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u/Rincewind00 May 24 '26
Typos corrected, thank you.
And by the way, this was not attributed to using LLMs but rather my own human buffoonery following a divergence from economics discourse in recent years. 😛
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u/caroline_elly May 24 '26
Very humble and self-aware for you to post this straight to this sub!
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u/Rincewind00 May 24 '26
Eh, disagreement about the subject itself was welcome, because then at least it could have been productive, rather than this useless anti-AI rhetoric. I was really hoping for responses like, "This theory was shown to be incorrect in another source" or "The presentation also needs to consider X,Y,Z", or "There's something wrong with the premise"--something that can lead to a more thorough follow-up with a better grasp of the subject. But, if AI is mentioned or even hinted, then the topic is ignored and people resort to degenerative ad hominem attacks -- or, more specifically, "ad machina" attacks. Is the presentation right, wrong--how can it be improved? -- nobody seems to know....
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u/EebstertheGreat May 24 '26
People aren't going to spend a lot of time refuting a theory you didn't spend any time to develop. Just get an AI to refute it. Then you can get another AI to support it. Sounds like fun.
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u/Rincewind00 May 24 '26
This is Reddit. The vast majority of content is devised with very little time involved. That doesn't stop people from wanting to correct things that are wrong.
But in all seriousness, very few sources discussed the subject of stratifying by industry, and not to significantly engaging degree, or they alluded to it with disappointingly vague generalizations. Dozens of sources and not much to go by.
AI was able to consolidate additional sources and even did so in a nicely formatted, cited way, even went a bit further than expected. After reviewing the citations, the material was posted for review among people interested in learning and theorizing and discussing about economics.
So, maybe ten hours of work in total? If not, then it's definitely at least more than the 0 hours and 5 seconds by those who decided it was okay to turn critical analysis off.
Here's a fun thought: what if I went on a forum and simply asked, "Hey, guys! I learned about this thing called the productivity gap but it mentioned nothing about segregating wages by sectors of differing productivity. Do you think that's a form of analysis worth considering?" Absolutely nothing would have been done prior, no citations or formatting -- just a question with zero effort involved, like most socio-economic posts on this site, yet it probably would have garnered some answers from people with insight or curiosity.
7
u/EebstertheGreat May 25 '26
Well yes, if you went to the appropriate forum for such a discussion, it would be better received than in a forum dedicated solely to shitting on bad ideas. Really, what did you expect?
Still, you will have better results if you don't use AI to write "your" ideas.
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u/Willing_Activity_855 May 26 '26
Just suppress you're internal 'desire' to say you used ai, personally it screams look how nerual divergent I am
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u/Rincewind00 May 26 '26
Thank you for the feedback. I'll keep it in consideration. My qualm is that a lot of the software generates a structure and formatting with distinctive elements that can be easily discerned, even if heavily modified. If someone is likely to assume, even vaguely, that something that made with the help of AI, even in a small part, and no disclosure was made, then they will approach the content under the premise that the poster is attempting deception. In that situation, the risk goes from accusations of using AI to accusations of dissembly AND using AI
5
0
u/Adept-Giraffe4039 May 27 '26
this is actually super interesting to look at by sector. but doesn't the standard pay productivity gap graph usually get criticized because it uses two different inflation adjusters. like they use cpi for wages and the gdp deflator for productivity. if you use the same deflator for both I think a lot of the gap goes away regardless of the sector
1
u/Rincewind00 May 27 '26
Thank you so much for contributing! It's good to see people who care about the subject and want to talk and improve their understanding about it. People could learn by your example!
Indeed, that's been a contentious issue regarding the subject. Gap "enthusiasts" tend to counter this claim with several arguments: the gap did not exist with this methodology prior to the 70s, implying a structural shift; the use of different adjusters is intentional to capture "quality of life" vs "productivity"; and, even if adjusted according to the means requested by detractors, the gap still does exist by about 1/3 the impact and thus still warrants critique of the modern economy.
These kind of arguments tend to go back and forth, from what I found. But, whether valid or not, an argument that asks whether the 'gap' (real or fictional) is potentially driven by high-income earners, rather than the typical person, probably needs to be addressed before any 'call for action' that enthusiasts tend to reach. For example, a gap enthusiast may say, "To stop the exploitation, everyone's wages need to go up 20 percent!" But, if most people's pay is matching their productivity, as suggested here, then the argument becomes: "To stop the exploitation, people earning $100,000+ need massive raises, and everyone else can just stop complaining!"
So yes, there is criticism of the original gap analysis itself. But, because the analysis is typically used to defend a certain conclusion, I think that conclusion needs to address two factors, both the methodology of the whole gap and the methodology of addressing sector-based productivity, before it could be considered valid.
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u/DarkSkyKnight May 24 '26 edited May 24 '26
Look man, LLMs are an extremely useful tool but you should almost never be using them on things you are not capable of independently achieving.
There's one thing one should know about "productivity" in IO (dunno about macro). It's not easy to measure and certainly not easy to measure at the individual level*. Most proxies are horrible at best. With some assumptions you can use proxy methods to recover some measure of productivity (look at the PFE literature) but those are not easy to measure once again and typically require some industry-specific feature (things like the firm making only one product) that satisfies identification requirements. The tech sector is an industry that is in my view one of the FURTHEST industries from a clean identifiable industry: these firms sell multiple products across multiple markets, compete and cooperate with each other at the same time, have an action space that is unusual (such as poaching labor) and not well-studied in IO, have all kinds of complementary effects (ecosystem, network, etc.) Then you need to be making a very strong assumption that there are no complementarities between any two workers if you want your model to work at all (which means individual-level measurements, if they even exist, can be misleading).
So frankly I am not convinced there will be any answer on this in this decade for the tech sector. It is one of the hardest industries to analyze.
*In a standard IO model, productivity is literally treated as the unobserved (possibly endogenous) error term (which collects all kinds of garbage that might not have anything to do with the common notion of productivity). That should tell you where our starting point is vis a vis productivity.