Myth Busting: The Productivity–Pay Gap (2021)

Did you know the productivity rates grew 56.9% in the period 1979–2019? However, the increase in the percentage for workers' wages in the same period turns out to be not nearly as high — only 15.8%.

There's an abundance of reasons for the productivity–pay gap, starting with the very definition of productivity. That's because there are several versions of what is considered to be "productivity".

This article will answer what is productivity-pay gap, why does it exist, what is the role of automation here, how said gap differs for the top 10% and the rest of earners, and debunk common myths, and answer if the link between productivity and wages is broken.

Myth Busting The Productivity–Pay Gap cover

The introduction to the productivity–pay gap

The productivity–pay gap is a complex concept that has many relevant components, but many different approaches to it.

As an introduction, we'll explain what the productivity–pay gap is, as well as provide answers to five relevant questions regarding the topic — so let's dive in.

What is the productivity–pay gap?

Simply put, the "pay–productivity gap" refers to the divergence between the increase of productivity rates and the increase of average worker's salary.

For example, EPI's data reveals that in the period 1948–1979, productivity growth was at 118.4%, while hourly compensation growth was 107.5%.

On the other hand, if we look at the period 1948–2019, productivity growth in total was 59.7% and hourly compensation growth was only 13.7%.

Let's see what were some of the relevant productivity–pay gap milestones from the start of each decade:

Productivity-pay gap milestones from start of each decade
Year Productivity growth rate Hourly compensation growth
1950 50.33% 53.14%
1960 66.62% 69.74%
1970 87.70% 91.20%
1980 97.34% 97.34%
1990 109.81% 93.64%
2000 126.39% 100.17%
2010 151.06% 109.63%

What's the result of the decline in the wages growth, specifically, in the period 1979-2020? The productivity growth rate was 3.5 times higher than the pay growth rate.

To translate into percentages, it's +61.8% vs. +17.5%, respectively.

It would be easiest to jump the gun and blame it all on a broken economic system where the richest are becoming richer — but bear in mind, this is only a part of the reason.

What is daily productivity?

Okay, before we deal with the relationship between productivity and median wages in more detail, we ought to define productivity.

There are several variations of the definition, depending on the industry — but we're currently interested in the economics definition, which goes as follows:

Productivity is calculated as a ratio of gross domestic product per hour worked, and is a measure of output per unit of input.

Simply put, productivity shows how efficiently one completes a certain task.

Therefore, we are talking about the productivity rate of a person, when it's measured on a daily basis. When it comes to personal productivity, it is often all about developing healthy and positive habits, which is further reflected on our personal lives as well.

In any case, in order for one to be productive, one must be motivated to be productive — either by internal or external factors.

Are wages proportional to productivity?

Productivity rates have been fluctuating in accordance with an abundance of factors, and heavily depend on the industry in question. There's a pretty simple explanation why, on a global level, the ratio between the growth of productivity and wages couldn't have been directly proportional.

Speaking about the past few decades, the core of the divergence is automation of processes. The vast majority of industries have been impacted by artificial intelligence and technological advancements, especially in the last decade.

Just think of the way technology and mass production made various jobs obsolete, for example — the positions such as switchboard operators, typesetters, milkmen, movie projectionists, etc.

But the role of automation is immense in terms of productivity, as the margin of human error was significantly reduced due to repetitive tasks being streamlined. So the vast majority of jobs have been improved, especially in terms of workers' productivity.

As you can already conclude, it's not a simple equation. Wages and productivity were not growing at a directly proportional pace, but for some industries, such as high-tech (especially nowadays), wages are proportional to productivity. It all depends on the choice of variables taken into account.

How does salary affect a worker's productivity?

For most people, external motivation in the financial form is the most common drive for productivity. It makes perfect sense that having financial freedom affects one's motivation, focus, and engagement at work.

The workers' productivity is directly related to the company's profits. Even though everyone should strive to achieve the best results because they want to be the best version of themselves, there should always be an initiative to reward the most productive workers on a company level.

Developing a strategy to increase the productivity of employees pays off — so let's give you a real-life example.

According to a study by the Workplace Research Foundation mentioned in a Forbes article, only a 10% increase in productivity results in a whopping $2,400 higher profit for the company per year. We are talking about an increase in profit per employee.

What happens to wages when productivity increases?

As we've just mentioned, when productivity increases, the company's profits increase as well — and all interested parties benefit from that.

Productive workers have a higher ability to focus and know when it's time to take a break. In other words, their time management skills are better, resulting in a better work-life balance.

When it comes to the workplace, the stronger the initiative on a company level and the higher the rewards, the more likely are employees to be engaged and motivated to find other ways to improve their skills.

When we examine the time-lapse over the last several decades, we can see that there were three important stages in the development of the productivity–pay gap:

If the growth rate of wages had kept pace with productivity growth, the Economic Policy Institute estimates that a median worker's hourly compensation would be $9 higher. To help you put this into perspective, it means average full-time workers would earn approximately $1,440 more on a monthly basis, compared to what they earn now.

What are some of the reasons for the wages–productivity gap?

There is not a single definitive answer to this question, especially if we want to speak about society as a whole. For one, different regions of the world, due to various specific natural, economic, and social aspects, favor different industries.

For example, it's only logical that Russia, the US, Iran, Qatar, Norway, and Saudi Arabia will heavily invest in the gas and oil industry. Why is that the case? It's because said regions are rich in reserves of natural gas resources — which are quite unevenly distributed across the globe, so they'll want to exploit the fact.

Another relevant reason is that technology was embraced and implemented much sooner in developed countries such as the US, the UK, or the whole Scandinavian region.

Overall, living standards have an immense impact on the minimum wage.

Another important thing to mention is that the productivity–pay gap depends on how you measure inflation. For example, according to CNBC and the Bureau of Labor Statistics, an average employee's wage in the US decreased by 2% in the last year because of inflation. Meaning that the average wages are increasing, but after we take into account the high levels of inflation, the "real wages" are actually lower in 2021 than in 2020.

A longitudinal study of the productivity–pay gap

With the abundance of factors that influence the pay—productivity gap, it's already challenging to precisely determine its exact ratio.

Not understanding the big picture and how everything is connected creates an additional issue — misconceptions and even urban myths regarding the topic. It's essential to understand that even economists can't agree about the exact definitions of many relevant variables, for starters, or how productivity is calculated.

One of the major reasons for emphasizing the gap and not adjusting the ideal ratio in accordance with several crucial factors, including automation, is the story about middle class stagnation. Don't get us wrong, inequality does exist — but this particular issue is a complex one.

Let's see what economists have to say about the productivity–pay gap and further examine some major methodological flaws in their reasoning — via a longitudinal study of the productivity–pay gap.

Productivity–pay gap: methodological flaws

The years 1979 and 2019 are interestingly suitable for comparison, marked as years with low employment rates — and, the whole 4-decade period is one of rising inequality.

Some of the explanatory factors EPI emphasize to be taken into account are the following:

We've also already mentioned many other aspects due to which results can vary. Another issue is the different approaches of the economists — and the Consumer Price Index change over time.

What do economists think of the productivity—pay gap?

Regarding this question, opinions vary between economists — and many would argue that the main reason is their political orientation.

There's a group of neoclassical economists who blame technology for the pay—productivity gap. This argument is called "skill-biased technological change", or SBCT, and has many flaws in its core — other major factors we've previously mentioned (inflation rates, diversity of industries, etc.) are not taken into account.

Technology and automation serve the purpose of improving productivity — therefore are essentially increasing the minimum wage per hour.

To debunk the SBCT notion, the Economic Policy Institute pointed out how the declining wage gap in the period 1987–2017 is inconsistent with the skills-gap explanation. In said period, the group of more educated middle-wage earners didn't see any advantage over the low-wage earners and the fact debunks the whole argument.

Moreover, even the high-skilled workers' wages didn't see a notable increase — meaning the level of education can't be a conclusive factor in the analysis of the pay—productivity gap.

The productivity—pay gap: the most significant factors

According to the MIT economist David Autor, cited in another relevant EPI's analysis, the pay–productivity gap is a combination of two major forces driving the inequality. Those are:

The inequality and divergence were discussed in a similar way in another paper. However, that paper is relevant because it includes several additional factors as the major drivers of the productivity–pay gap. Said factors are:

Following the same logic, Bloomberg also covered this important topic, and their article presented interesting results from a study by Stansbury and Summers. What they found was that the correlation between productivity and wages does exist — even though it isn't possible to categorize such a complex issue in a simple econ model.

Why was their approach innovative?

Well, unlike the vast majority of economists, their focus wasn't simply on the graph that depicts the long-term trend — instead, they examined short-term changes in the relationship between productivity and wages.

The examined periods weren't longer than 5 years and the analysis of results proves that when productivity levels rise, wages tend to increase as well.

What role does automation play in the productivity–pay gap?

We already talked about automation — now let's look into the role automation plays in the productivity–pay gap, in more detail. Since the late 1980s, automation has been one of the most controversial topics when it comes to the productivity–pay gap.

One thing is for sure — technological change in the workplace and the globalization driven by it not only influenced the gap, but also created another, closely related gap. We are talking about wage inequality between the less and the more educated people.

But we can't blame technology and say that the bias is in favor of workers with college degrees.

This other gap has always existed between high-level employees — or the 10th percentile — and the rest.

That being said, there's a significant difference in the impact of automation on the productivity–pay gap across the industries, even specific sectors within a specific industry.

In 2013, academics from the Oxford university estimated that close to half of the US jobs are at risk of being fully automated.

We already touched upon the jobs that underwent automation in the past. Let's now check out which industries have been at the highest risk lately:

So what's the logical conclusion? Automation brings change and implies the need for different skills for the future — but that's what progress is all about.

Distinctions in the productivity–pay gap

The productivity–pay gap can significantly vary by the variable which is the focus of research, such as by the industry type, which we have previously mentioned.

But, we can also approach the topic by analyzing specific factors such as the following ones:

The productivity–pay gap by job level

Another important thing to mention is that the wage inequality gap has been happening at a different pace for executives and people working at an entry-level job.

The data is significantly different for the top 10% of earners and the rest.

Moreover, there's a huge gap among the top 10% as well.

Since 1979, there's been an established pattern of faster growth of annual wages among the top 1% and even faster for the top 0.1%.

That being said, according to the thorough analysis by EPI, the productivity–pay gap is bigger within the 1%, as wages grew at a higher pace for those in the range of 90th-99th percentile.

Bottom 90% workers' point of view

The data from the EPI analysis shows the disparity in wage growth for the bottom 90%, whose annual wages grew by 26% in the period we've been mentioning — 1979–2019.

When we look at the results of the Social Security Administration for the period 1991–2019, the percentage for the real median annual wage is similar to the one for the bottom 90% — 23.8%.

Another thing is evident — there are two distinct periods of sustained low unemployment when wage growth was concentrated.

A whopping 90% of the bottom's 90% growth happened either between 1995–2000 or 2013–2019.

That's only 11 years out of 40 covered in the research. To help you grasp the concept better, we'll present the monetary value of these average wages per year.

Average annual wages for bottom 90% earners
Year 1979 2007 2009 2018 2019
Avg annual wage $30,880 $36,025 $35,806 $38,255 $38,923

As you can notice, the consequences of the Great recession are evident. The American earning among the bottom and middle class did not only stagnate in the post-recession period, but rather saw a decline.

Add to the fact that it happened in an already seriously damaged state of the economy, making the gap even more problematic. That's also the reason the years 2007 and 2009 are specifically included in the table.

Now, when it comes to the bottom group's share of total wages, the distribution is as shown in the table below.

Share of total wages from top 90% earners
Year 1979 2007 2009 2018 2019
Share of wages 69.8% 61.1% 62.3% 61% 60.9%

In the examined period, the distribution points to the constant decline in the bottom 90% group, excluding the period immediately following the Great recession.

It makes sense, as the global financial crisis burst the bubble of high-earners, people in the real estate and financial sectors.

So, while the bottom 90% group's total wages grew by around $8,000 in half a century, their share of the total wages declined by close to a whopping 9%.

The implication goes as follows — the pay–productivity gap was significantly lower for this group, making the wage inequality perhaps an even greater issue.

The decision-makers' perspective

There will always be discrepancies between the results of different studies, but we want to focus on the EPI's analysis. The main reason is that they depict the actual change in annual wages of the top 10%, including the underlying earnings, such as stock options and vested stock awards.

The period in question is still 1979–2019.

So, the pattern of upper redistribution of wages from the bottom 90% to the top 10% seems to be the only constant.

For example, during the said period, the share of all earnings almost doubled for the top earners.

Below is the table depicting the change in annual earnings for them.

Average annual wages for top 10% earners
Year 1979 2007 2009 2018 2019
90-99% average $101,001 $146,782 $146,401 $160,865 $165,782
Top 1% average $251,619 $495,880 $453,504 $516,850 $521,794
Top 0.1% average $648,725 $3,000,181 $2,215,819 $2,858,981 $2,888,192

The table above reveals that during the Great Recession, the higher the groups' earnings were, the harder the hit.

If we compare the data with the downturn for the bottom 90% (less than a $200 difference for 2007 vs. 2009) and the top 10% average (less than $400), there's not much difference.

However, going up the wage ladder, we can again notice a pattern — the higher the percentile, the harder the hit — a difference of over $42,000 for the top 1%, and close to a whopping $800,000 for the top 0.1%.

What about the share of the total earnings we've mentioned?

Here's the answer to that as well.

Share of total wages of top 10% earners
Year 1979 2007 2009 2018 2019
90-99% average 22.8% 24.9% 25.5% 25.7% 25.9%
Top 1% average 5.7% 8.4% 7.9% 8.2% 8.2%
Top 0.1% average 1.6% 5.7% 4.3% 5.1% 5.0%

Said rising inequality in the distribution of annual wages is also evident in the table above.

As you can see, while the portion for the bottom 90% was in a continuous decline, the percentages went in an opposite direction for the top 10%.

The most significant growth happened for the richest, 0.1% earners, as their portion of the total wages more than tripled over the course of 50 years.

The productivity–pay gap by country

Different countries focus on different industries and, as we've mentioned, that's one of the major reasons for the gap. Economic geographers often look at the fact from an evolutionary perspective, but what does that mean exactly?

Why would the region matter for the productivity–pay gap?

The explanation is that the growth path of a certain region depends on the preexisting industries, originally developed due to geographical factors such as climate, type of land, natural resources available, and similar.

So, while the implementation of changes and industrial transition may have happened at the same time, the result is still different for rural and urban areas, not to mention different continents.

Urban areas are more populated, have higher living standards, and tend to attract more technologically advanced sectors such as finance, real estate, IT, etc. For this reason, it's safe to conclude that the wages of urban-area residents are higher — making the productivity–pay gap smaller.

Another crucial consequence of the abovementioned, as well as many other, geopolitical factors, is the variation in the average number of hours worked by country, also impacting the pay–productivity gap.

For example, full-time employees in the US have up to 19% more working hours per year than full-time employees in Europe. If we didn't take into account any other factors, the logical conclusion would be that people in Europe are more productive.

But of course, as we've seen, it's not nearly as simple as that.

The current state of the productivity–pay gap in the world

Embracing the 4th industrial revolution seems to be one of the ways for a country to gain resilience to failure, as technological and other kinds of experimentation leads to higher productivity, wages, and living standards.

Let's check out the latest list of the top 10 most productive countries, and further try to establish a pattern of major economic sectors and industries in those. If you're wondering how one can measure a country's productivity, the answer lies in these two acronyms — GDP and PPP.

The top 10 most productive countries

Gross domestic product (GDP) is the monetary value of goods and services within a certain country, while purchasing power parity determines the relation between economic productivity and living standards of different countries.

So, the productivity list actually reflects an average worker's productivity per hour (PPP) and shows how much money workers contribute to the country's economy per one hour.

COUNTRY PRODUCTIVITY PER HOUR AVG HOURS PER WEEK BIGGEST ECONOMIC SECTORS
Ireland $99.13 39.7 Agriculture, fishing, tourism, trade, and service industries (pharmaceuticals, chemicals, computer hardware, and software, food and beverages)
Norway $80.83 38 Oil and gas production, hydropower, fish, forests, and minerals
Switzerland $69.26 40.5 Finance, banking, manufacturing, agriculture
Luxembourg $68.36 40 Banking, steel, information technology, tourism, agriculture
Germany $66.71 39.7 Machinery, automotive, and aviation, chemical and medical, consumers and service, energy and environmental, electronics and ICT
USA $65.51 41.5 Healthcare, technology, construction, retail, non-durable manufacturing
Denmark 64.71 37.2 Agriculture, tourism, energy, transportation
France $62.79 38.9 Energy, manufacturing and technology, transport, agriculture, tourism
Netherlands $61.43 37.3 Agriculture and food, energy, chemical, metallurgy, tourism
Belgium $59.65 38.8 Manufacturing, metallurgy, steel, textile, chemical, glass, paper, food processing

When we analyze the list and closely look at the biggest economic sectors of the most productive countries in the world, once again, it's evident that there are no fixed rules — nor any patterns for successfully lowering the productivity–pay gap.

Each country has a specific combination of factors that bring the pace of wage growth closer to the one of productivity, compared to the rest of the world.

Yet, there is one detail that caught our attention — more than half of the countries on the list have a weekly average lower than 40 hours.

Could the potential solution to reducing the gap, and a consequence of a higher increase in productivity level caused by automation be to start reducing the employees' hours?

There might be something right about the idea, provided that the workers' compensation stays the same for the lower number of hours.

The productivity–pay gap by gender

Last, but not least, when it comes to the main reasons for the very existence of the pay–productivity gap, we can't skip the "gender pay gap" topic. Nowadays, women are still paid less for the same position compared to their male counterparts, and this fact plays a significant part in the divergence of productivity and wage growth rates.

The gender pay gap in history

Throughout history, the role of women has drastically changed in comparison to men. The treatment of women was different in terms of their rights, from their education, lifestyle, choice of partners, voting rights, etc.

The vast majority of society as a whole functioned in the patriarchal system, originally due to some biological factors. And of course, there are and will be some distinctive factors in terms of female employees, such as maternity leave, for example.

The feminist movement did result in a more progressive attitude toward female workers, proving women are perfectly capable of being developers, decision-makers, and also firefighters. However, inequality is lingering, in terms of position, as there are still many male-dominated occupations.

To give you a few examples in terms of the lowest percentages of women in the US jobs, here's a list of those where female workers make up less than 10% of the workforce:

But what's even more important than the distribution of jobs between male and female workers within industries, there's a notable difference in terms of wages.

Current situation of the gender pay gap — the butterfly effect

Here's another shocking detail, as pointed out in the EPI's analysis of the gender pay gap — for every $1 a man makes, a woman in the same position earns $0.80. That's if we take an average full-time worker's median pay.

The situation is somewhat better when the data includes part-time workers — the ratio is $1 vs. $0.83.

If we examine the vertical scale, the wage distribution inequality is also evident. For example, there's a significant fluctuation in the top 10% of earners.

At the 10th percentile, female workers are paid $0.92 whereas male workers in the same position earn $1.

But, as women go up the corporate ladder, the gap increases, so in the 95th percentile, it's only $0.74 — again, relative to $1 of their male counterparts.

Lowering the gap by only 10% is estimated to result in a 3% labor productivity boost.

Objectively, there's no room for this kind of discrimination in the modern age, as women are just as valuable as men. This goes especially for working mothers, as the scope of their responsibilities requires perfecting their time management skills.

Wrapping it up

The truth is out there and the conclusion is that the productivity–pay gap does exist.

However, there is an explanation, at least for the largest part of it. We hope the article has shed some light on the most important aspects of the concept and further helped you understand just how many factors play a role in the difference of growth between productivity and pay.

One thing is for sure — we have to keep working on improving productivity levels if we want to evolve, both as individuals and the society as a whole.

Think of the butterfly effect we've mentioned earlier, because it will occur — and there will be numerous benefits for everyone, starting from the company you work for — benefits that can reflect on the industry sector, and further to the economy of a country.

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