income inequality

Some Sunshine on the Ontario Job Hierarchy

Published by Anonymous (not verified) on Tue, 04/02/2020 - 7:03am in

Income, I’ve come to believe, is shaped largely by rank within a hierarchy. If you’re at the top of a hierarchy, you’ll earn a handsome sum. But if you’re at the bottom of a hierarchy, you’ll earn a pittance.

As a hard-nosed scientist, I’m always looking for ways to test this hypothesis. The problem is that it’s difficult to do. Although hierarchy surrounds us, we have almost no data about it. So if we want to study how income grows with hierarchical rank, we need to be creative.

In this post I use an unlikely source to study hierarchy — the Ontario Sunshine List. Maintained by the Canadian province of Ontario, the Sunshine List reports the income of public-sector employees who earn more than $100,000. On the face of it, the Sunshine List has nothing to do with hierarchy. It’s a database of individual income. But with a little creativity, we can use it as a window into the world of hierarchy.

Our looking glass will be job frequency. Here’s how it works. Imagine we could take everyone in a society and line them up from lowest to highest income. Then, starting at the lowest earner, we ask each person their job title. As income grows, we track the changing frequency of different jobs.

To make things concrete, suppose we track two jobs — ‘nurse’ and ‘CEO’. How might the frequency of these jobs change with income?

Here’s what you probably expect. Nurses will be common among low earners. But they will be rare among top earners. CEOs, in contrast, will be rare among low earners. But they will be common among top earners.

Now here’s the question. Why do you expect this behavior?

You expect it, I believe, because you have an intuitive understanding of hierarchy. You know that nurses work mostly at the bottom of hierarchies where they get paid relatively little. And you know that CEOs work at the top of hierarchies where they get paid a lot. Because you know this, you expect that nurses will become less frequent as income grows, while CEOs will become more frequent.

In this post, I test this intuition. I first use a model of hierarchy to predict how the frequency of differently-ranked jobs should change with income. Then I compare the model’s prediction to real-world trends.

The results are exciting.

The trends on the Ontario Sunshine List closely match what the model predicts. Low-ranking jobs (like ‘nurse’) become less frequent as income grows. But top-ranking jobs (like ‘CEO’) become ubiquitous as income grows.

I’m thrilled by this result because it means that the income effects of hierarchy are hiding in plain sight. They’re waiting to be teased out from any database that reports both income and job descriptions.

Using new theory to rethink old evidence

Good scientific theories often give new meaning to old evidence. Take Darwin’s theory of evolution. It gave new meaning to the fossil record. Before Darwin, fossils were just the bones of long-dead creatures. But after Darwin, fossils were a testament of life’s evolution.

On a less grand scale, I propose here that a banal public-sector database (the Ontario Sunshine List) is actually a record of how hierarchy shapes income. As with the reinterpretation of the fossil record, this reinterpretation of public-sector pay depends on new theory — a theory of how hierarchy affects income.

In a series of recent papers (here, here and here) I’ve argued that income is shaped largely by one’s hierarchical rank. As part of this theory, I’ve developed a model of hierarchy. The model uses evidence from a variety of sources (firm case studies, CEO pay) to simulate the hierarchical structure of the US private sector.

Figure 1 shows what the model looks like. Here each pyramid represents a firm. Moving up the pyramid represents moving up the hierarchy. Color indicates individual income.

mod_landscape
Figure 1: The US hierarchy model as a landscape. Each pyramid represents a firm. Size indicates the number of employees. Moving up the pyramid represents moving up the hierarchy. Color indicates individual income.

The purpose of the model is to indirectly study how hierarchy affects income. It works like this. First, we use the model to predict an income effect that is caused by hierarchy. Then we look for this effect in the real world. If we find it, we infer that real-world income grows with hierarchical rank as it does in the model.

We can use the model to make many different types of predictions. In a recent paper, for instance, I predicted that top-earning individuals should work for large firms. Then I showed that in the real-world, top-earning individuals actually do work for large firms, just as predicted.

In this post, I study how job frequency changes with income. I first use the model to predict how the frequency of three classes of employees should vary by income. Then I look for the predicted trends in real-world data.

Three classes of employee

Large hierarchies have many ranks. But because I’m going to use coarse-grain information (job titles) to infer hierarchical rank, here I’m interested in three broad classes:

  1. Low-ranking employees
  2. Mid-ranking employees
  3. Top-ranking employees

As the names suggest, these classes relate to position in a hierarchy. Low-ranking employees are at the bottom of the hierarchy. Mid-ranking employees are in the middle. And top-ranking employees are at the top.

With these classes in mind, here’s the road map ahead. First, I’m going to use the hierarchy model to predict how the frequency of each class of employee should vary with income. Then I’ll show that this variance is due to hierarchy. Last, I’ll look for the predicted trends in real-world data (on the Ontario Sunshine List).

Predictions

Low-ranking employees are less frequent as income grows

Low-ranking employees are the poor saps (like me) who work at the bottom of hierarchies. They’re the red individuals in Figure 2.

entry_level_hierarchy
Figure 2: Low-ranking employees in a hierarchy

Before getting to quantitative predictions, let’s first think qualitatively. How might the frequency of low-ranking employees change with income? If income grows rapidly with rank, low-ranking employees should be mostly at the bottom of the distribution of income. So low-ranking employees should become less frequent as income grows.

Figure 3 shows our quantitative prediction. On the horizontal axis I’ve plotted income percentile. For each percentile, the vertical axis shows the relative frequency of low-ranking employees.

mod_entry_percentile 
Figure 3: Frequency of low-ranking employees by income percentile. The horizontal axis shows income percentile in the model. The vertical axis shows the relative frequency of low-ranking employees within each percentile.

As expected, the model predicts that low-ranking employees become less frequent as income increases. Almost everyone in the bottom 1% is a low-ranking employee. But almost no one in the top 1% is a low-ranking employee.

Figure 4 shows a different way of looking at the same prediction. Instead of plotting income percentile, on the horizontal axis I plot income size (relative to the average income). Note that I’ve used a logarithmic scale, so each axis tick indicates a factor of 10.

mod_entry_norm_income
Figure 4: Frequency of low-ranking employees by income size. The horizontal axis shows income in the model (relative to the mean). The vertical axis shows the relative frequency of low-ranking employees at the given income.

Again, we find that low-ranking employees become less frequent as income increases. Almost everyone who earns less than 10% of the average income is a low-ranking employee. Conversely, almost no one who earns more than 10 times the average income is a low-ranking employee.

Mid-ranking employees are most frequent in the middle

Mid-ranking employees work in the middle of hierarchies. While the exact rank of these workers is open to interpretation, here I assume that they work in the second hierarchical rank.

mid_level_hierarchy 
Figure 5: Mid-ranking employees in a hierarchy

Although mid-ranking employees are only one step above low-ranking employees, our model predicts that they are dispersed quite differently in the distribution of income.

Figure 6 shows the prediction for mid-ranking employees. These workers are most frequent in the middle 80% of incomes. They’re rare below the 10th percentile and above the 90th percentile.

mod_mid_percentile
Figure 6: Frequency of mid-ranking employees by income percentile. The horizontal axis shows income percentile in the model. The vertical axis shows the relative frequency of mid-ranking employees within each percentile.

Figure 7 shows the same prediction, but this time plotting income on the horizontal axis. The model predicts that mid-ranking employees are most frequent around the average income. Again, this tells us that mid-ranking employees are the middle class.

mod_mid_norm_income
Figure 7: Frequency of mid-ranking employees by income size. The horizontal axis shows income in the model (relative to the mean). The vertical axis shows the relative frequency of mid-ranking employees at the given income.

Top-ranking employees are more frequent as income grows

Top-ranking employees work at the top of their respective hierarchies. Figure 8 shows a top-ranking employee in a small hierarchy.

top_level_hierarchy
Figure 8: A top-ranking employee in a hierarchy

Moving from the middle to the top of the hierarchy may seem like a small shift. Yet it drastically changes how individuals are dispersed in the distribution of income.

Our model predicts that top-ranking employees are located overwhelmingly at the top of the distribution of income. As Figure 9 shows, few people below the 99th percentile are top-ranking employees. But above the 99th percentile, almost everyone is a top-ranking employee.

mod_top_percentile
Figure 9: Frequency of top-ranking employees by income percentile. The horizontal axis shows income percentile in the model. The vertical axis shows the relative frequency of top-ranking employees within each percentile.

Figure 10 (below) shows how this explosion of top-ranking employees relates to income size. Below the average income, almost no one is a top-ranking employee. This changes at about double the average income, where the frequency of top-ranking employees begins to grow.

mod_top_norm_income
Figure 10: Frequency of top-ranking employees by income size. The horizontal axis shows income in the model (relative to the mean). The vertical axis shows the relative frequency of top-ranking employees at the given income.

At 100 times the average income, half the people are top-ranking employees. At 1000 time the average income, virtually everyone is a top-ranking employee.

In hindsight, this prediction is easy to understand. We’ve assumed that income grows rapidly with hierarchical rank. Flipping things around, this means that if you have a large income, you likely have a high rank. And the higher your rank, the more likely it is that you sit at the top of your hierarchy. [1] The result is that top-ranking employees become more frequent as income grows.

Yes, these predictions stem from hierarchy

Before we test our predictions against real-world evidence, we want to be sure that these predictions actually stem from hierarchy. The way we’ll do this is by simulating a counterfactual world. In this world there are no returns to hierarchical rank. So CEOs earn no more than bottom-ranked employees.

As Figure 11 shows, the results of this counterfactual model are strikingly different than the original model.

mod_percentile_null
Figure 11: A counterfactual world with no returns to hierarchical rank. This figure shows the results of two models. One model has income returns to hierarchy, the other does not.

In a world with no returns to hierarchical rank, our model predicts that job frequency shouldn’t vary by income. Our three classes of employees are equally frequent for all incomes. This stands in marked contrast to our original model. It’s only when there are returns to hierarchical rank that our predictions hold. Only then do top-ranking jobs explode among top incomes.

The Ontario Sunshine List

To test our predictions, I’m going to use the Ontario Sunshine List. Created in 1996, the Sunshine List discloses the salaries of all public-sector employees in Ontario who earn more than $100,000.

The Sunshine List is unique for two reasons. First, it’s a complete list of top-earning workers in the Ontario public sector. Second, the database isn’t ‘top coded’. Top coding is the practice of capping the size of incomes that you report. In many databases, for instance, incomes are top coded at $100,000. So anyone who earns more than this amount gets reported as earning ‘more than $100,000’.

Top coding is used to shield the identity of survey respondents. But because the Ontario Sunshine List was created to reveal the identity of top earners, it reports top incomes in full. This is important because we’ve predicted that the most spectacular effects of hierarchy occur among top earners.

To test our predictions, I pick three jobs that appear on the Sunshine List and equate them with the three classes of workers used in the model. Here are my choices:

Class in Model
Sunshine List Job

Low-Ranking Employee
‘Nurse’

Mid-Ranking Employee
‘Professor’

Top-Ranking Employee
‘President/CEO’

‘Nurses’ on the Ontario Sunshine List

I use ‘nurses’ to represent low-ranking employees. The caveat here is that nurses in Canada are paid fairly well — far better than other low-ranking jobs like ‘janitor’. I choose ‘nurse’ because it’s a low-ranking job that pays well enough to appear on the Sunshine List.

Figure 12 shows the results. I find that the frequency of nurses declines as income increases — just as the model predicts.

sunshine_entry_percentile
Figure 12: Frequency of ‘Nurse’ on the Ontario Sunshine List by income percentile. The horizontal axis shows income percentile on the Sunshine List. The vertical axis shows the relative frequency of ‘nurses’ within each percentile. The inset plot shows the model predictions.

A caveat is that the empirical data in Figure 12 isn’t directly comparable to the model. In the model, income percentiles rank all individuals. But in the empirical data, income percentiles rank only the members of the Sunshine List (public-sector employees earning more than $100K).

Figure 13 (below) shows the same data, but plots job frequency against income size. The frequency of nurses declines rapidly as income grows. Most Ontario nurses (on the Sunshine List) earn close to the average Canadian income. Almost none earn more than twice the average income. Our model predicts a similar trend — the frequency of low-ranking employees should decline rapidly as income grows.

sunshine_entry_norm_income
Figure 13: Frequency of ‘Nurse’ on the Ontario Sunshine List by income size. The horizontal axis shows income relative to the Canadian average. The vertical axis shows the relative frequency of ‘nurses’ on the Ontario Sunshine List. The inset plot shows model predictions. [2]

‘Professors’ on the Ontario Sunshine List

I use ‘professors’ to represent mid-ranking employees. In Canadian universities, professors often command a few subordinates (in the form of teaching assistants and post docs). Professors also have some administrative power through faculty senates.

Figure 14 shows how the frequency of ‘professors’ changes with income on the Sunshine List. Unlike nurses, the frequency of professors is roughly constant with income percentile. This is similar to the predicted behavior of mid-ranking employees (inset).

sunshine_mid_percentile
Figure 14: Frequency of ‘Professor’ on the Ontario Sunshine List by income percentile. The horizontal axis shows income percentile on the Sunshine List. The vertical axis shows the relative frequency of ‘professors’ within each percentile. The inset plot shows model predictions.

Figure 15 (below) shows the same data, but plots job frequency against the size of income. Most Ontario professors (on the Sunshine List) earn between 1 and 5 times the Canadian average. In this case, the model isn’t particularly accurate. It predicts the tapering of mid-ranking employees for large incomes, but it doesn’t predict the tapering (evident among professors) for incomes close to the average.

sunshine_mid_norm_income
Figure 15: Frequency of ‘Professor’ on the Ontario Sunshine List by income size. The horizontal axis shows income relative to the Canadian average. The vertical axis shows the relative frequency of ‘professors’ on the Ontario Sunshine List. The inset plot shows model predictions. [2]

‘Presidents/CEOs’ on the Ontario Sunshine List

I use ‘presidents/CEOs’ to represent top-ranking employees. Figure 16 shows the trends on the Ontario Sunshine List. Among the bottom 99%, almost no one is a CEO. But among the top 1% (of Sunshine earners), CEOs are ubiquitous. This explosion of top-ranked employees is exactly what our model predicts (inset).

sunshine_top_percentile
Figure 16: Frequency of ‘President/CEO’ on the Ontario Sunshine List by income percentile. The horizontal axis shows income percentile on the Sunshine List. The vertical axis shows the relative frequency of ‘presidents/CEOs’ within each percentile. The inset plot shows model predictions.

Figure 15 (below) shows the same data, but plots job frequency against the size of income. Below average income, CEOs are basically non-existent. But as income reaches 10 times the Canadian average, CEOs become ubiquitous — approaching 100% of of Sunshine-List members.

sunshine_top_norm_income
Figure 17: Frequency of ‘President/CEO’ on the Ontario Sunshine List by income size. The horizontal axis shows income relative to the Canadian average. The vertical axis shows the relative frequency of ‘presidents/CEOs’ on the Ontario Sunshine List. The inset plot shows model predictions. [2]

The model (inset) predicts this explosion of top-ranking employees. However, the model predicts the saturation of top-ranking employees at about 500 times the average income (see Figure 10). In contrast, saturation on the Sunshine List happens at 10 times the average income.

Why the discrepancy? It’s because the model is based on the US private sector, where pay is far more unequal than in the Canadian public sector. Top US CEOs often earn hundreds of times the average income. In contrast, top public-sector CEOs in Canada rarely earn more than 10 times the average income.

The key here is that top-ranking employees become ubiquitous among the largest incomes — however large these may be.

A new window into hierarchy?

I’m excited by these results for a few reasons. First, there’s something tantalizing (and insidious) about knowing that CEOs become ubiquitous among top earners. It shows that all jobs are not created equal.

What’s more exciting is that we can predict this trend using a simple model of hierarchy. If income grows with hierarchical rank, then top-ranking employees will become ubiquitous as income grows. There’s no way around this prediction — it’s a basic consequence of hierarchy.

But what’s most exciting are the doors opened for future research. Hierarchy surrounds us. Yet we know virtually nothing about it. The results here suggest that evidence for how hierarchy affects income is staring us in the face. It’s sitting there (waiting to be analyzed) in any dataset that records income and job titles.

Notes

[1] As rank grows, why is it more likely that you sit at the top of your hierarchy? This results from a joint property of hierarchies and the size distribution of firms.

Imagine two people, Alice and Bob. They both have a rank of 8. But Alice is the CEO of her firm, while Bob is a Vice President in his firm. How common are our hypothetical Alice and Bob?

It turns out that someone like Alice is far more common than someone like Bob. This is because hierarchies tend to grow exponentially with the number of hierarchical levels. Because Bob’s firm has one more hierarchical level than Alice’s firm, we’ll guess that its roughly double the size.

Now, the size distribution of firms follows a power law. The probability of finding a firm of size x is roughly proportional to the inverse square of x (see this post for more details). This means that Bob’s firm, which is twice as large, is about 4 times rarer than Alice’s firm.

So even though Alice and Bob have the same rank, our hypothetical Alice is about 4 times more common than our hypothetical Bob (because the size of her firm is about 4 times more common). The result is that as your rank grows, it becomes increasingly probable that you occupy the top rank in your firm.

[2] I calculate average Canadian income by dividing GDP by the size of the labor force (using World Bank series NY.GDP.MKTP.CN and SL.TLF.TOTL.IN).

Further reading

Fix, B. (2018). Hierarchy and the Power-Law Income Distribution Tail. Journal of Computational Social Science, 1(2), 471–491. SocArXiv Preprint.

Fix, B. (2019). How the Rich Are Different: Hierarchical Power as the Basis of Income Size and Class. SocArXiv Preprint.

Fix, B. (2019). Personal Income and Hierarchical Power. Journal of Economic Issues. 2019; 53(4): 928-945. SocArXiv Preprint.

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Debunking the ‘Productivity-Pay Gap’

Published by Anonymous (not verified) on Sat, 18/01/2020 - 3:41am in

Have you heard of the ‘productivity-pay gap’? It’s the (apparently) growing gap between the productivity of US workers and their pay. Here’s what it looks like:

epi_pay_gap

Figure 1: The Productivity-Pay Gap. Source: Economic Policy Institute.

In this post, I debunk the ‘productivity-pay gap’ by showing that it has nothing to do with productivity. The reason is simple. Although economists claim to measure ‘productivity’, their measure is actually income relabelled.

As a result, the ‘productivity-pay gap’ isn’t what it appears. It claims to be a gap between productivity and wages. But it’s not. It’s really a gap between two types of income — (1) the wages of workers and (2) the average hourly income of all Americans. This gap is an important measure of inequality. But it has nothing to do with ‘productivity’.

How economists measure productivity

To understand the problem with the ‘productivity-pay gap’, we first need to understand how economists measure productivity. Economists define ‘labor productivity’ as the economic output per unit of labor input:

\text{Labor Productivity} = \displaystyle \frac{\text{Output}}{\text{Labor Input}}

To use this equation, we’ll start with a simple example. Suppose we want to measure the productivity of two corn farmers, Alice and Bob. After working for an hour, Alice harvests 1 ton of corn. During the same time, Bob harvests 5 tons of corn. Using the equation above, we find that Bob is 5 times more productive than Alice: [1]

Alice’s productivity: 1 ton of corn per hour

Bob’s productivity: 5 tons of corn per hour

When there’s only one commodity, measuring productivity is simple. But what if we have multiple commodities? In this case, we can’t just count commodities, because they have different ‘natural units’ (apples and oranges, as they say). Instead, we have to ‘aggregate’ our commodities using a common unit of measure.

To aggregate economic output, economists use prices as the common unit. They define ‘output’ as the sum of the quantity of each commodity multiplied by its price:

\text{Output} = \displaystyle \sum   \text{Unit Quantity} \times \text{Unit Price}

So if Alice sold 1 ton of corn at $100 per ton, her ‘output’ would be:

Alice’s output: 1 ton of corn × $100 per ton = $100

Likewise, if Bob sold 5 tons of potatoes at $50 per ton, his ‘output’ would be:

Bob’s output: 5 tons of potatoes × $50 per ton = $250

Using prices to aggregate output seems innocent enough. But when we look deeper, we find two big problems:

  1. ‘Productivity’ becomes equivalent to average hourly income.
  2. ‘Productivity’ becomes ambiguous because its units (prices) are unstable.

‘Productivity’ is hourly income relabelled

By choosing prices to aggregate output, economists make ‘productivity’ equivalent to average hourly income. Here’s how it happens.

Economists measure ‘output’ as the sum of the quantity of each commodity multiplied by its price. But this is precisely the formula for gross income (i.e. sales). To measure gross income, we multiply the quantity of each commodity sold by its price:

\text{Gross Income} = \displaystyle \sum   \text{Unit Quantity} \times \text{Unit Price}

To find ‘productivity’, we then divide ‘output’ (gross income) by the number of labor hours worked:

\text{Productivity} =  \displaystyle  \frac{\text{Gross Income}}{\text{Labor Hours}}

When we do so, we find that ‘productivity’ is equivalent to average hourly income:

Productivity = Average Hourly Income

So economists’ measure of ‘productivity’ is really just income relabelled. The result is that any relation between ‘productivity’ and wages is tautological — it follows from the definition of productivity.

Ambiguous ‘productivity’

In addition to making ‘productivity’ equivalent to average hourly income, using prices to measure ‘output’ also makes ‘productivity’ ambiguous. This seems odd at first. How can ‘productivity’ be ambiguous when income is always well-defined?

The answer has to do with prices.

We expect prices to play an important role in shaping income. Suppose I’m an apple farmer who sells the same number of apples each year. If the price of apples doubles, my income doubles. That’s how prices work.

If ‘output’ is equivalent to income, it seems that my ‘output’ (of apples) has also doubled. But here economists protest. Your apparent change in ‘output’, they say, was caused by a change in price. To find the ‘true’ change in output, you need to hold prices constant. When you do, you’ll find that your ‘output’ remains the same.

On the face of it, this ‘adjustment’ for price change seems reasonable. But it actually leads to a measurement quagmire. To see this quagmire, we’ll return to Alice and Bob.

Suppose that Alice grows 1 ton of corn and 5 tons of potatoes. Bob grows 5 tons of corn and 1 ton of potatoes. Whose output is greater? The answer is ambiguous — it depends on prices.

Suppose that corn sells for $100 per ton and potatoes sell for $20 per ton. We find that Bob’s output is about 250% greater than Alice’s:

Alice’s Output: 1 ton corn × $100 per ton + 5 tons potatoes × $20 per ton = $200

Bob’s Output: 5 tons corn × $100 per ton + 1 ton potatoes × $20 per ton = $520

Now suppose that corn sells for $20 per ton and potatoes sell for $100 per ton. We now find that Bob’s output is about 60% less than Alice’s:

Alice’s Output: 1 ton corn × $20 per ton + 5 tons potatoes × $100 per ton = $520

Bob’s Output: 5 tons corn × $20 per ton + 1 ton potatoes × $100 per ton = $200

What’s going on here? When we aggregate output using prices, these prices determine the relative weighting given to corn and potatoes. When this weighting changes, the measurement of ‘output’ changes.

As a result, our measure of ‘output’ depends on the particular prices we choose to hold constant. This is a big problem. It means that standard measures of productivity are inherently ambiguous. (For more details about this ambiguity, see my work with Jonathan Nitzan and Shimshon Bichler and with Erald Kolasi.)

To summarize, using prices to aggregate ‘output’ leads to bizarre problems. On the one hand, it causes ‘productivity’ to be equivalent to average hourly income. This means that any connection between ‘productivity’ and wages is circular. On the other hand, the same decision causes ‘productivity’ to be ambiguous. Our measure of ‘productivity’ depends on arbitrary choices about how to adjust for price change. As a result, productivity trends (like the one in Figure 1) are riddled with uncertainty.

Dissecting the ‘productivity-pay gap’

Now that you understand the problems with how economists measure productivity, let’s return to the ‘productivity-pay gap’. I’m going to dissect the evidence in Figure 1.

This chart comes from the Economic Policy Institute (EPI). By dissecting it, I don’t mean to pick on the EPI authors. They use methods that are standard in economics. Instead, I want to show why these standard methods are flawed.

We’ll start with how the EPI measures productivity. They write:

“Net productivity” [of workers] is the growth of output of goods and services less depreciation per hour worked.

To non-economists, this sounds like the EPI is measuring some physical quantity of output. But they’re not. Instead, the “output of goods and services” is economists’ code for the value of goods and services, as measured by Gross Domestic Product (GDP). ‘Depreciation’ is code for the financial depreciation of capital.

When we subtract capital depreciation from GDP, we get something called ‘Net Domestic Product’:

Net Domestic Product = GDP – Capital Depreciation

So the EPI defines ‘economic output’ in terms of Net Domestic Product.

Now here’s the rub. The national accounts are based on the principles of double-entry bookkeeping. This means that for every sale there is a corresponding income. So when you build a house and sell it for $1 million, you record the sale in one ledger as ‘output’. On the opposite ledger, you record the same sale as ‘income’. So ‘output’ is formally equivalent to income.

In the national accounts, Net Domestic Product is the sales side of the ledger, recorded as ‘output’. It’s equivalent to the income side of the ledger, which we call ‘National Income’ — the income of all individuals in the country:

Net Domestic Product ≈ National Income

I’ve put the ‘≈’ here to mean ‘almost equivalent’. There are some small differences between Net Domestic Product and National Income (some business taxes, for instance). But in practice, the two quantities are nearly identical, as shown in Figure 2.

ndp_ni
Figure 2: US Net Domestic Product And National Income. Data is from the Bureau of Economic Analysis Table 1.7.5.

To calculate workers ‘productivity’, the EPI divides Net Domestic Product by the number of labor hours worked:

\text{Productivity} = \displaystyle \frac{\text{Net Domestic Product}}{\text{Labor Hours}}

But this is equivalent to dividing National Income by the number of labor hours worked:

\text{Productivity} = \displaystyle \frac{\text{National Income}}{\text{Labor Hours}}

When we divide National Income by total labor hours, we’re actually measuring average hourly income. So the EPI’s measure of ‘productivity’ is identical to average hourly income:

Productivity = Average Income per Hour

Given this equivalence, any connection between ‘productivity’ and average hourly income isn’t surprising. It’s a tautology.

How can wages diverge from ‘productivity’?

If productivity is equivalent to average hourly income, how can wages diverge from ‘productivity’? In other words, how can the ‘productivity-pay gap’ exist?

Let me explain.

‘Productivity’ (as measured by the EPI) is equivalent to the average hourly income of all US earners. Since average income can’t diverge from itself, average income and ‘productivity’ can’t diverge. However, if we select a subpopulation of US citizens, their income can diverge from the average. This is just a mathematical truism. If I select a non-random sample from a population, the properties of this sample need not match the properties of the whole population.

To make this thinking concrete, suppose we select only CEOs. Must CEO income track with the national average? The answer is no. CEOs are a unique subpopulation, so their income can diverge from the national average. And as you probably know, CEO income has done just that. Over the last 40 years, the income of US CEOs has grown drastically relative to average income.

Wages of production workers

In Figure 1, the EPI studies the wages of ‘production/nonsupervisory workers’. Because these workers are a subpopulation of the US, their income can (and does) diverge from the national average. Over the last 40 years, the wages of production workers have declined relative to the average hourly income.

This decline, however, has nothing to do with productivity. Instead, it owes to a redistribution of income — a redistribution that has two parts. First, the labor share of national income has declined over the last 40 years. Second, over the same period, US wages and salaries have become increasingly unequal.

The declining labor share of income

In the national accounts, there are two basic types of income. If you earn income from property, you earn ‘capitalist income’. If you earn income from wages and salaries, you earn ‘labor income’. The two types of income sum to National Income:

National Income = Capitalist Income + Labor Income

If we select only ‘laborers’, it’s possible for the average hourly income of this subpopulation to diverge from the average income of the population. For instance, if capitalist income grows relative to workers’ income, it pulls up the average income. This causes a gap between the wages of workers and the hourly income of the whole population.

Looking back at Figure 1, we see that the ‘productivity-pay gap’ emerges after 1970. Not surprisingly, it’s around this time that the labor share of US income began to drop:

lab_share
Figure 3: Labor’s Share of US National Income. Data is from the Bureau of Economic Analysis Table 1.12. Labor’s share is calculated as the ‘compensation of employees’ as a fraction of national income.

This decline of labor’s share of income is partly why the EPI finds a ‘productivity-pay gap’. Remember that ‘productivity’ (as measured by the EPI) is equivalent to the average hourly income in the US. Since 1970, US workers have received a declining share of this income. Consequently, their wages have declined relative to the average US income.

The growing inequality of labor income

The other reason that the EPI finds a ‘productivity-pay gap’ is because US wages and salaries have become increasingly unequal. Since 1970, the income share of the top 1% of wage/salary earners has grown steadily:

lab_top_1
Figure 4: Top 1% of Wage/Salary Earners, Share of US Labor Income Data is from the World Inequality Database (average of series flinc and plinc).

It may not be clear how wage inequality would affect the relative income of production workers. To help understand, we’ll divide labor income into two parts:

Labor Income = Production Workers Income + Non-Production Workers Income

Suppose that the income of non-production workers increases relative to the income of production workers. This increase pulls up the average labor income, causing it to outpace the average income of production workers. Still, it’s not clear how this redistribution relates to wage inequality.

This is where hierarchy comes in.

I propose that ‘production workers’ occupy the bottom two ranks in firm hierarchies. The bottom rank consists of ‘shop floor’ workers. The second rank consists of ‘working supervisors’. Everyone in ranks three and above is a ‘non-production worker’ (i.e. manager).

hierarchy_production_workers
Figure 5: Production workers in a hierarchy. Production workers (blue) occupy the bottom two ranks in a hierarchy. The first rank contains ‘shop floor’ workers. The second rank contains ‘working supervisors’. Ranks three and above are ‘managers’.

In this simple model, ‘production workers’ make up about 77% of total employment. That’s not far from the actual US figure of 82%.

prod_workers
Figure 6: Production workers’ share of US private employment. Data is from the Bureau of Labor Statistics, series CES0500000001 and CES0500000006.

What does our hierarchy model tell us about the income of production workers? In hierarchies income increases steeply with hierarchical rank. (I review the evidence here and here.) So if production workers occupy the bottom of the corporate hierarchy, they should also occupy the bottom of the income distribution.

Let’s suppose that production workers occupy the bottom 80% of US labor incomes. If labor income inequality increases, we expect the relative income of production workers to decline.

Figure 7 shows a simple model of what this might look like. Here I’ve defined ‘production workers’ as everyone in the bottom 80% of a hypothetical distribution of income. I then calculate the average income of these production workers and compare it to the average income in the whole population.

wage_mod_plot
Figure 7: A model of the relative income of production workers. Here I model ‘production workers’ as the bottom 80% of earners in the population. As inequality (measured by the top 1% share of income) grows, the average income of production workers declines relative to the average income of the population. For the math people, I’ve modeled the distribution of income with a lognormal distribution.

Because production workers are at the bottom of the income distribution, we expect their income to be below the population average. (That’s why the y-axis values in Figure 7 are below 100%.) But just how far below depends on income inequality.

As we increase inequality in the population (shown on the horizontal axis in Figure 7) the relative income of production workers declines. When inequality is minimal, production workers’ relative income approaches the population average. When inequality is extreme, production workers’ relative income approaches zero.

In Figure 7, the vertical red lines show the US top 1% share of labor income in 1970 and 2012. Given this growing inequality, our model predicts that the relative income of production workers should drop by about 50%. This is on par with the pay gap shown in Figure 1.

In short, the growing inequality of labor income can explain a large part of the apparent ‘productivity-pay gap’. Again, this gap isn’t about productivity. It’s about the declining relative income of production workers.

The price-index problem

While most of the apparent ‘productivity-pay gap’ has been caused by income redistribution, part of this gap is caused by price index shenanigans. In Figure 1, the EPI uses two different price indexes to ‘adjust’ for inflation.

To understand the problems with the EPI’s method, we need to backtrack a bit. I’ve already noted that ‘productivity’ is equivalent to average hourly income. But this wasn’t quite correct. ‘Productivity’ is equivalent to real average hourly income:

Productivity = ‘Real’ Average Hourly Income

Unlike ‘nominal’ income, ‘real’ income adjusts for inflation. To get ‘real’ income, we divide ‘nominal’ income by a price index — a measure of average price change:

\text{Real Income} = \displaystyle \frac{\text{Nominal Income}}{\text{Price Index}}

There are many different types of price indexes. Some track a few commodities. Others track many commodities. Because price change varies wildly between commodities, different price indexes can vary wildly.

Here’s where the EPI errors. It uses the (implicit) Net Domestic Product deflator to measure ‘productivity’ (i.e. real average income per hour). But it uses the Consumer Price Index (CPI) to measure the ‘real’ wage of production workers.

This is a problem. The two price indexes have diverged since 1970 — the very period where the EPI finds a growing ‘productivity-pay gap’. Here’s what the divergence looks like:

ndp_cpi_ratio

Figure 8: The US Net Domestic Product deflator relative to the Consumer Price Index. CPI data is from Federal Reserve Economic Data, series CPIAUCSL. The implicit NDP deflator data is from BEA Table 1.17.6 (the ratio of nominal NDP to real NDP).

To put this in perspective, the EPI’s method is like using different price indexes to compare the ‘real’ income of two people. Suppose Alice and Bob both start out with $100. Over 40 years, both of their incomes grow to $200. We then use the NDP deflator to find Alice’s real income. But we use the CPI to find Bob’s real income. Although their nominal incomes are identical, we find that Alice’s real income outpaced Bob’s by 20%.

The crime here is that we don’t need price indexes to compare incomes. We can compare Alice and Bob’s incomes directly. Similarly, the EPI could have compared the nominal income of production workers directly to the nominal hourly income in the US.

The declining relative income of workers

The problem with the ‘productivity-pay gap’ is that it proclaims to be something it’s not. It’s not a gap between workers’ productivity and their income. Instead, it shows the declining relative income of workers.

The best way to look at this decline is to measure the relative income of production workers:

\text{Relative Income of Production Workers} =  \displaystyle \frac{\text{Average Wage of Production Workers}}{\text{Average Hourly Income of Population}}

Figure 9 shows this relative income over the last 50 years. In 1964, US production workers earned 60% of the average hourly US income. By 2015, this declined to 35%.

differential_income
Figure 9: The relative income of US production workers. Average hourly earnings of production workers is from FRED series AHETPI. Average hourly US income is calculated by dividing National Income (BEA Table 1.7.5) by the number of labor hours worked by US persons engaged in production (FRED series EMPENGUSA148NRUG × series AVHWPEUSA065NRUG).

What’s important here is that we haven’t dressed up income as ‘productivity’. We’re explicitly comparing two types of income — the income of production workers relative to the national average.

The relative income of production workers has nothing to do with ‘productivity’. It’s actually a measure of income inequality. As shown in Figure 10, production workers’ relative income correlates strongly with the income share of the top 1%. As income inequality increases, the relative income of production workers decreases.

differential_income_top1
Figure 10: The relative wages of production workers decline as US inequality increases. For the sources for relative wages, see Figure 8. Data for the top 1% share of income comes from the World Inequality Database.

Is ‘productivity’ still increasing?

The tale told by the ‘productivity-pay gap’ (Figure 1) is that workers’ productivity has increased steadily but wages have not. This is a powerful piece of propaganda. It says to workers “look, the tide has risen, but it didn’t lift your boat”.

The problem, though, is that it’s not clear that the tide has actually risen. We can say for certain that workers relative wages have declined (Figure 9). But what about their productivity? Has it gone up as Figure 1 suggests?

To believe the ‘productivity’ trends in Figure 1, you have to put on a brave face. You have to believe that the myriad of subjective decisions made by statistical agencies (reviewed here) are the ‘correct’ decisions. You have to believe that prices ‘reveal’ utility, and that monetary income is the same as economic ‘output’.

I, for one, don’t believe these things. Consequently, I treat official measures of ‘productivity’ as garbage.

How should we measure productivity? It depends on what we think the economy ‘does’. Personally, I like the view taken by atmospheric scientist Tim Garrett. He treats the economy as a heat engine. Garrett uses the analogy of a growing child. It takes energy to maintain the child’s body. And if the child is to grow, it needs to consume increasing amounts of energy. The same is true of the economy.

When you think this way, you realize that ‘useful work’ (the amount of energy put to an end use) is a good indicator of economic output. I propose that we treat useful work per labor as an alternative measure of labor productivity.

How does this alternative measure compare with the standard measure of productivity? Figure 11 shows a comparison. Here I use real GDP per labor hour as the standard measure of productivity. I contrast this with Benjamin Warr and Robert Ayres’ estimate for useful work per labor hour.

useful_work
Figure 11: How a physical measure of US productivity compares to the official measure of productivity. Data is from Benjamin Warr’s REXS Database.

It’s not hard to spot the difference between the two series. The standard measure of productivity tells a tale of steady growth. In contrast, our physical measure suggests that productivity has stagnated since 1970. Interestingly, this is the period when the relative wages of production workers began to decline (i.e. when the apparent ‘productivity-pay gap’ appears).

Here’s an interesting question. Is the stagnation in useful work output related to the decline of workers’ wages? Biophysical economist Carey King thinks so. He recently built a model to investigate this connection.

The important point is that it’s far from clear that US productivity has increased steadily over the 20th century. In energetic terms, productivity has stagnated since 1970. I, for one, think that this physical measure of productivity is far more meaningful than the official measure. ‘Useful work’ is based on the laws of thermodynamics. The standard measure of productivity, in contrast, is based on the dubious assumptions of neoclassical economics.

The productivity problem

‘Productivity’ is used by both major schools of economic thought. Neoclassical economists use productivity to claim that the distribution of income is just. They argue that in a competitive economy, workers get what they produce. Marxists, in contrast, use productivity to claim that the distribution of income is unjust. They argue that in a capitalist economy, workers receive less than they produce (because capitalists extract a surplus).

What’s interesting is that these two opposing theories commit the same sin. They define productivity in terms of income. Neoclassical economists do so explicitly, as I’ve described in this post. Marxists do so implicitly because they haven’t developed their own system of national accounts. Instead, Marxists who do empirical work use neoclassical measures of productivity (As an example, see this fascinating exchange between Paul Cockshott, Shimshon Bichler and Jonathan Nitzan.)

The result of this circular definition is that the analysis of productivity is a sleight of hand. ‘Productivity’ is just income relabelled.

The ‘productivity-pay gap’ is a textbook example of this relabelling. It claims to show a growing gap between what workers ‘produce’ and what they get paid. But workers’ ‘productivity’ is actually measured in terms of income — the average hourly income.

This relabelling of income gives the analysis ideological potency. Instead of saying that workers’ relative wages have declined, it says that workers don’t get paid what they produce. The latter, as Marx long ago realized, is far more potent propaganda.

Productivity propaganda cuts both ways

The problem with productivity propaganda is that it cuts both ways. The EPI uses income to measure ‘productivity’ at the national level. But why stop there? Why not equate income and productivity at the sector level, or at the individual level? Curiously, the EPI warns against doing so (see the technical appendix here).

The problem is that the more finely we equate income with productivity, the more we’ll find that everyone ‘gets what they produce’. This is because as we study smaller and smaller groups, we remove the possibility of sampling subgroups whose income diverges from the group’s average income.

As a progressive think tank, the EPI wants to show that workers do not get paid what they produce. So it warns against equating income and productivity at the sector and individual level.

The problem is that the EPI wants to have its cake and eat it too. It wants to equate productivity with income when the results suit it — when the analysis shows a productivity-pay gap. But the more fine grain the analysis, the more this gap will disappear. And so the EPI warns against equating income and productivity at lower levels of analysis.

To be fair, the EPI is doing what many heterodox economists do. They reject the ‘crude’ neoclassical assumption that individual income is equivalent to productivity. Yet they then equate income and productivity at the national level.

This double standard is unjustifiable. Either we side with neoclassical theory and equate income and productivity wholesale. Or we reject neoclassical theory and so reject the accounting system that economists use to measure productivity.

Many heterodox economists are uncomfortable with the latter choice. And it’s not hard to see why. When you reject equating income and productivity, you reject the heart of macroeconomics. You reject the entire suite of measures that macroeconomists use to measure economic output and productivity. In so doing, you reject almost all that you (as a macroeconomist) are taught to hold dear. That’s a scary prospect.

The uncomfortable fact, though, is that if we want to create an alternative to neoclassical economics, we can’t use methods that have neoclassical assumptions baked into them. So a major part of being a heterodox economist is looking for new ways to quantify the economy.

Let’s bring this post to a close. I’m all for reducing inequality. And I think that workers’ wages have grown increasingly unfair. But I’m also a hard-nosed scientist who dislikes analysis with dubious assumptions baked into it. For that reason, I think the ‘productivity-pay gap’ needs to be called what it actually is — a decline of workers’ relative income.

Notes

[1] “Bob is more ‘productive’ than Alice”. Note that this doesn’t mean that Bob caused his greater output of corn. Maybe Bob had better land. Or maybe he had a bigger tractor. Our measure of productivity says nothing about Bob’s abilities.

Further reading

Ayres, R. U., & Warr, B. (2010). The economic growth engine: How energy and work drive material prosperity. Edward Elgar Publishing.

Bivens, J., Gould, E., Mishel, L. R., & Shierholz, H. (2014). Raising America’s Pay: Why It’s Our Central Economic Policy Challenge. Economic Policy Institute.

Bivens, J., & Mishel, L. (2015). Understanding the Historic Divergence Between Productivity and a Typical Worker’s Pay: Why It Matters and Why It’s Real. Economic Policy Institute.

Cockshot, P., Shimshon, B., & Nitzan, J. (2010). Testing the Labour Theory of Value: An Exchange. Nitzan & Bichler Archives.

Fix, B. (2019). Personal Income and Hierarchical Power. Journal of Economic Issues, 53(4), 928–945. SocArXiv preprint.

Fix, B. (2019). The Aggregation Problem: Implications for Ecological and Biophysical Economics. BioPhysical Economics and Resource Quality, 4(1), 1. SocArXiv preprint.

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Anand Giridharadas in a Dutch interview: Their Parliament’s Finance Committee called him

Published by Anonymous (not verified) on Fri, 27/12/2019 - 9:37am in

I found this interesting.  Mr. Giridharadas was invited to discuss his perspective regarding his themes of his book Winners take all.  He was invited by the Dutch Parliament’s Finance Committee to discuss his book.  All 6 parties showed up.  All had been given the book prior and several had read it.   This is a […]

Which way from here? That depends on where we want to go. Our choices now will determine our future.

Sign on a fence with and arrow logo and the word votePhoto via PxHere

We are in the last few days of the election campaign. An election which, without doubt, will be a defining one for the future of this country and possibly even the planet. It will determine whether we carry on with the economic and political status quo or whether we choose a different path towards a socially just and fairer economic system which also addresses as a matter of priority the challenges posed to the future survival of our species.  Growing political unrest caused by the last forty years of market-driven dogma has created huge wealth inequalities and is driving dangerous right-wing populism worldwide.

This might be just a national election, but the world is watching. Where we put our X in the voting booth this time around will be crucial. It matters as never before.

The ancient Greek philosopher Aristotle wrote:

“For the duty of the truly democratic politician is just to see that people are not destitute; for destitution is the cause of deterioration of democracy’

Of course, he lived in a time very different to our own, but he believed that the best form of democracy was one with a more equal income distribution and that greater economic equality would increase the stability of the state and thus that of citizens.

The State has a crucial role to play in serving the public purpose or in other words creating the fundamental frameworks for a healthy society and economy which benefits everyone.  However, for the last forty and more years, economic power has become increasingly concentrated in the hands of a few people. This has been facilitated by successive governments whose policies have been informed by an ideologically based dogma of privatisation, deregulation and an emphasis on ‘sound finance’ which, over the last nine years, has been at the heart of Conservative austerity.

It has also been enabled by politicians who have acted less in the service of the nation and more in the interests of corporations and excessively wealthy people who have influenced government policies in their favour through a network of lobbying and special advisors. Democracy has been undermined by those with the power and wealth to influence politicians and a media which continues to play a huge role in that subversion.

The ideological premise of trickle-down has been that the rich are the wealth creators, that tax cuts encourage investment in the economy and jobs which benefit working people and then, in their turn, brings in taxes to pay for our public services. We have been deceived with the lie trotted out over the years and even during this election campaign by Conservative ministers and even some on the progressive left that our public services are dependent on bringing in tax revenue. When in fact it is quite the reverse.

A healthy economy and all that means, from citizens having access to good education, quality healthcare and a protective welfare system, (not to mention other vital public services or businesses which rely on access to an educated and healthy workforce and the physical infrastructure for their businesses to flourish) depends on a government which has made a political decision to invest sufficiently in that public and social infrastructure to benefit both today’s and tomorrow’s citizens. It does not depend on a government checking on whether there is enough in the public purse to do so.

For well over a year now, GIMMS has charted the consequences of austerity in its MMT blogs. Yet, now we are now witnessing on a daily basis, like never before, its damaging effects on the very foundations of economic and social life.

Economic data published last month showed that the services sector slowed in the last quarter and the manufacturing and construction sectors contracted in November. The economy just avoided recession, with the weakest growth in a decade.  Whilst clearly the uncertainty over Brexit will have played a part, cuts in government spending over the last 9 years will have also played a significant role as businesses lose investment confidence and households tighten their belts due to rising household debt.

A study published by the Office for National Statistics on 5th December 2019 found that whilst Britain’s total wealth grew by 13% between 2016 and 2018, the wealth of the richest 10% increased four times faster than those of the poorest 10%. It also found that the poorest 10% of households had debts three times larger than their assets, compared with the richest 10% who have accumulated a stash of wealth which was 35 times larger than their total debts. The Wealth and Assets Survey carried out by the ONS also showed that in 2018 the top 10% finished up with 45% of national wealth while the poorest 10% held just 2%.

The shocking data underlines the growing wealth divide. A divide between those at the top who barely noticed the 2008 Global Financial Crash (or indeed profited from it) and those on low incomes whose real earnings have barely risen since the crash and who have seen their economic share of productivity decrease over decades. The very people who have paid the real price for austerity have, in fact, suffered a double whammy.  They not only are facing an enormous and increasing burden of household debt (putting huge stress on their finances exacerbated for those on low incomes and in precarious employment), but they are also reaping the consequences of brutal cuts to the public service sector.

Huge inequalities that have arisen as a result of the pursuit of this pernicious market-focused ideology along with a deceitful balanced public accounts narrative have not only driven a steam roller through our public services and vital welfare systems but have also impoverished millions leaving them floundering in insecure and low paid employment.

In the week that the Liberal Democrat leader Jo Swinson apologised for backing the Coalition’s austerity policies during the Coalition years and whose economic spokesman claimed in a speech very recently that they are the only party of ‘sound finance’ (which sounds very much like more of the same), the news has been ever more damning about its consequences for the lives of working people, families, children and the elderly and our public infrastructure.

Shelter’s ‘Generational Homeless’ report found that a child becomes homeless every eight minutes; that’s 183 children losing their homes every day. It found that at least 135,000 children will be living in temporary accommodation on Christmas day.

‘Life in a B&B is horrible. There’s no room to do anything. I’ve been told off … for running in the small corridor. You can’t do much, you can’t play much. I don’t get to play that much. Sometimes me and my little brother Harry fight for the one chair because we both want to sit at the table. I find it really hard to do my homework’ says Will whose family was made homeless and now lives in a single room in a bed and breakfast in Ilford.

A leading charity Action for Children warned this week that some of the youngest children are facing a childhood crisis as almost one million under 10s from low-income families face a bleak Christmas lacking basics such as a heated home, warm winter coat or fresh food.

Research from the charity shows that after a decade of austerity and ongoing problems with universal credit, parents below the breadline are able to spend just £2 a day per child on food and struggle to afford nutritious food which is vital for their health and development.

The Dispatches programme ‘Growing up Poor; Britain’s breadline kids’ which aired on Channel 4 earlier this week exemplified the shocking poverty that exists in one of the wealthiest countries in the world. Children sleeping in their coats in the middle of winter because they can’t afford heating; parents counting the pennies to see if there is enough money to feed the meter; a family living in Cambridge surviving on £5 a day in a wealthy city that houses eight of the 2000 food banks that have been set up across the UK in the last decade to alleviate hunger; and a teenager Danielle who is studying for her GCEs and self-harming housed with her family in a bedsit, with no savings and relying on a local soup kitchen and food bank to survive.

This is happening in 21st century Britain and yet it feels like we are being transported in Dr Who’s Tardis back to the streets of Dickensian times.  Our children are being denied a future by a government which has put balancing the public accounts above the health of the nation, its children who represent the future and the environment upon which they will depend for their survival.

At a hustings last week, the Conservative MP John Whittingdale was applauded by the audience when he claimed that Labour had left the economy in a perilous state and close to bankruptcy. Perpetuating the lie that austerity had been necessary to get the public finances in order, he said that careful economic management by the Conservatives meant that they could now spend on the NHS, policing and education. No acknowledgement was made about the damage that austerity had caused to our public services; those on low incomes and in insecure working; the huge rise in homelessness or the 73% increase in supplies being distributed in the 2000 food banks across the UK; the increasing numbers of hospital admissions for scurvy, vitamin D deficiency and other maladies associated with economy inequality and child food poverty; and no mention of the systemic problems with welfare reforms and the introduction of Universal Credit, along with a punitive assessment system which have led to many deaths.

We must continue to challenge the false assumptions about how modern monetary systems operate and demonstrate to the public that contrary to common belief government spending is not constrained by monetary resources.

Tackling existing and future inequality and saving the planet will not be constrained by the state of the public accounts or the national debt or whether government can raise sufficient tax or borrow on the markets but rather how it will manage the finite resources it has at its disposal to create the public frameworks and infrastructure to sustain a healthy economy and environment.

It is both a moral question about how a civilised nation should behave towards its neighbours near or far and how we organise our societies to create the optimum environment for all to live with dignity and without fear.

It is regrettable that creating fear and hate has been the modus operandi of governments, extreme political movements and the press. Without a fundamental shift in our attitudes we cannot hope to make the radical changes we need to create a fairer society and more importantly to survive.  A challenge to the political and economic status quo is vital if we care about our children’s future and that of many others around the world.

To reiterate the final paragraph in last week’s MMT Lens:

What are we so afraid of? A better future for our children? A more sustainable and fairer economy for all? Indeed, a planet for us to live and breathe on? What is not to like?

 

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The post Which way from here? That depends on where we want to go. Our choices now will determine our future. appeared first on The Gower Initiative for Modern Money Studies.