Deconstructing Econospeak

It’s been 20 years, but I still remember the feeling. It was a mix of curiosity and unease. I was curious because I was learning something new. But I was uneasy because something didn’t sit right. The place was Edmonton, Alberta, circa the year 2000. The situation? My first encounter with economics: Econ 101.

Interestingly, I can’t remember much of the course content. Instead, what I remember is the feeling. As I grappled with the language spoken by the economics textbook (what I’m calling econospeak), I felt that something was missing. But I couldn’t put my finger on what … and I was too busy to think much about it. So I ignored the feeling, memorized the course content, and moved on.

Today I’m a PhD-trained political economist and I know why I had a bad feeling in Economics 101. It’s because the course wasn’t teaching me about the real world. It was indoctrinating me in an ideology.

I’ve spent much of the last decade trying to understand this ideology. A key part of its appeal, I believe is the language that it uses. Of course, many people recognize that the language of econospeak is part of its ideological potency. And many people have analyzed this language. But what nobody has done (as far as I can tell) is to quantitatively deconstruct econospeak. That’s what I’m going to do here.

I’ve created a word-counting bot that compares the language found in economics textbooks to the English language at large. I’m going to use this bot to analyze econospeak. The results (which I’m only beginning to unwrap) are fascinating. Let’s dive in.

A word-counting bot

Before we get to the specifics of my word-counting bot, I’ll give it some context.

When someone asks you to ‘think critically’ about a text, they want you to compare what the author has said (or not said) to what other people have said. Here’s an example. When I read an economics textbook and see the words ‘rational utility maximizer’, I think of Thorstein Veblen’s phrase ‘homogenous globules of desire’ — a satire of the utility maximizing human. I also think of the novels I’ve read, and how the characters are filled with emotions — emotions that are absent from economics textbooks. In short, I compare the words in the economics textbook to everything else I’ve read.

This example illustrates why critical reading is difficult. To read critically, you have to read widely. The problem, though, is that there’s too much to read — far more than any human could digest. And that’s got me thinking. Is there a way to automate the act of critical reading?

Enter my word-counting bot. No, my bot is not an AI literary critic. It’s far simpler. My bot counts words. The idea is that the words you use (or don’t use) tell us about what you’re thinking. To ‘read critically’, we compare your vocabulary to the vocabulary of other people. We see how frequently you use certain words relative to everyone else. If, for instance, you have sex on your mind, you’ll likely use the word ‘sex’ more than other people. And conversely, you might use the word ‘love’ less than everyone else.

When we (as humans) read a text, we get an intuitive sense for what’s there and what’s not. But my word-counting bot can take this a step further. It can quantify word frequency … and it can do it on a massive scale.

Now let’s get to the specifics. My word-counting bot takes a sample of writing and quantifies the frequency of each word found in the text. It then compares this frequency to what’s found in the English language at large. The bot returns the ratio of the two frequencies — what I’m calling relative word frequency:

\displaystyle \text{relative word frequency} = \frac{\text{word frequency in sample text}}{\text{word frequency in English}}

The bot can take any sample of text as an input. Here, I feed it undergraduate economics textbooks. From Library Genesis, I’ve downloaded 43 economics textbooks that are standard fare in economics pedagogy. (Details here.) I’ve fed these books to my bot, and it spits out the frequency of each word in the text.

To measure ‘word frequency in English’ I’ve used data from the Google books database. According to Google, this database is the world’s largest repository of full-text books. Conveniently, Google has created the Ngrams database, which reports word frequency in its books corpus. I use word frequency in the Ngram database (which I’m calling the ‘Google English corpus’) to represent ‘word frequency in English’. I will revert, at times, to calling this Google sample ‘average English’. That doesn’t mean it’s what the ‘average person’ speaks. It’s the average of a huge sample of English text.

To summarize, my word-counting bot eats economics textbooks and spits out relative word frequency, defined as:

\displaystyle \text{relative word frequency} = \frac{\text{word frequency in economics textbooks}}{\text{word frequency in Google English corpus}}

In my sample of economics textbooks, there are about 34,000 unique words. My bot calculates relative frequency for all of them. But before we look at the whole output, let’s get a feel for the data. Table 1 shows the frequencies of four words found in the textbook sample.

Table 1: Examples of relative word frequency

Word Frequency in economics textbooks* Frequency in Google English* Relative frequency
price 13900 296 46.9
science 105 377 0.277
murder 3.65 81.8 0.0446
ditchdigger 0.13 0.015 8.71
* Frequency = occurrence per million words

The first thing to notice is that word frequency varies wildly. Economics textbooks use the word ‘price’ about 100,000 times more than they use the word ‘ditchdigger’. Now, for these specific words that frequency difference isn’t surprising. But the wild variation in word frequency is actually a feature of language in general. Some words (like ‘and’) get used a lot. Other words (like ‘mesonemertini’) are so rare that you’ve never heard of them.

This huge variation in word use is why looking at the absolute frequency of words in economics textbooks isn’t very useful. What matters is not absolute frequency, but relative frequency — word use relative to the average. And this relative frequency, it turns out, is not necessarily related to absolute frequency.

Table 1 illustrates this fact. Economists use the word ‘price’ a lot. And as you’d expect, they use it more than average — about 40 times more. Conversely, economists almost never use the word ‘ditchdigger’. You’d have to read about 10 million words of econospeak to see ‘ditchdigger’ once. And yet economists use the word ‘ditchdigger’ 9 times more than average. So even though ‘price’ and ‘ditchdigger’ have wildly different absolute frequencies, both are overused by economists.

Let’s continue. Economics textbooks use the word ‘science’ far more than the word ‘ditchdigger’. Yet it turns out that they’re using ‘science’ less than average. (Given economics’ pseudoscience state, I can’t help but laugh at this result.) And what about ‘murder’? Economics textbooks use it about 30 times more than ‘ditchdigger’. But this constitutes underuse. Economists use the word ‘murder’ 20 times less than average.

What’s important here is that a word’s absolute frequency (in economics textbooks) doesn’t predict its relative frequency. We’ll return to this fact later.

Now that you understand what my word-counting bot does, let’s dive into the data.

The ‘shape’ of econospeak

When we analyze language, most people are interested in the specific words that are used. (I will get to specific words, don’t worry.) The problem, though, is that my bot returns data for about 34,000 words. That’s far too many words to discuss individually. But what we can do is look at the ‘shape’ of these words. I’m calling this the ‘shape’ of econospeak.

I’ve plotted this shape in Figure 1. Here I show the distribution of relative word frequency in economics textbooks. The horizontal axis shows the ratio of textbook frequency to Google frequency. (Note that I’ve used a log scale, so each tick mark indicates a factor of 10). The vertical axis shows ‘word density’ — the portion of words with the given relative frequency.

Figure 1: The distribution of relative word frequency in economics textbooks. Here’s a histogram of relative word frequency in my sample of economics textbooks (plotted on a log scale). The vertical red line indicates ‘frequency parity’, which means that the given word has the same frequency in both the economic textbooks and the Google corpus. I’ve used color to show the most overused and underused words. [Sources and methods].

Let’s dissect this ‘shape’ of econospeak. We’ll start with the vertical red line that I call ‘frequency parity’. This line indicates that a word occurs at the same frequency in economics textbooks as it does in the Google corpus. (Its relative frequency is 1.) If the vocabulary in economics textbooks was identical to the vocabulary in the Google corpus, the distribution in Figure 1 would clump around the red line. But it doesn’t. That tells us that econospeak is different than average English. Hardly surprising.

Let’s talk about how econospeak is different. Notice that the peak of the econospeak distribution is to the left of frequency parity. That’s interesting. It suggests that economics textbooks use a large portion of English words less than average. I honestly didn’t expect this result, and am still trying to interpret it.

Here are two possibilities. First, the underuse of common English words could be a defining feature of econospeak. Alternatively, this underuse could be a feature of any branch of specialized writing. Either way, though, the result is important. It suggests that a key feature of econospeak is its underuse of a large chunk of the English language.

Let’s move on to the extremes of econospeak — the words that are most overused and most underused (relative to average English). These words live in the tails of the relative frequency distribution (shown in color in Figure 1). Notice that these tails are true extremes. In econospeak, some words are used 1000 times more than average. And other words are used 1000 times less than average.

I know you want to see these words. But before we get there, we need to do some statistics. Whenever we do empirical work, we need to make sure that our results aren’t caused by chance. We don’t want to get fooled by randomness. (Hat tip to Nassim Nicholas Taleb for this phrase). With randomness in mind, consider a thought experiment. Suppose that my econospeak data is actually a random sample of words taken from the Google English corpus. If this were true, what would the distribution of relative word frequency look like?

In statistics, this thought experiment is called the ‘null hypothesis’. To test the null hypothesis, we randomly draw words from the Google corpus and compare the result to our econospeak data. Figure 2 shows this comparison. Here, I’ve taken a random sample of 7.7 million words (the size of my econospeak sample) from the Google English corpus. For each randomly drawn word, I’ve calculated its frequency relative to the entire Google corpus. The red curve shows the resulting distribution of relative word frequency.

Figure 2: The distribution of relative word frequency — econospeak vs. the null hypothesis. The blue histogram shows the distribution of relative word frequency in economics textbooks — the same data as in Figure 1. The red distribution is the ‘null hypothesis’ — what happens when we randomly draw 7.7 million words from the Google English corpus. [Sources and methods].

Let’s dissect this result. The null hypothesis has a huge peak around frequency parity. That means that most words in our random sample occur at the same frequency as in the Google corpus. That’s unsurprising. (We are, after all, sampling from the Google corpus.) What is surprising, though, is that our random sample produces about the same number of overused words as found in econospeak. To see this fact, look at the right tails of the distributions in Figure 2. The right tail of the null hypothesis is similar to the right tail of the econospeak distribution. Does this similarity mean that economists are randomly overusing words? Yes and no. As you’ll see shortly, there’s more to the story.

What’s most important, in Figure 2, is not word overuse, but word underuse. Looking at the two distributions, we see that the left tail of the econospeak distribution far outreaches the left tail of the random sample. This tells us that econospeak’s underuse of many English words cannot be due to chance.

This result is fascinating, and I’ll return to it throughout the post. It suggests that econospeak is defined not by what it says, but by what it doesn’t say.

The most overused and underused words in econospeak

Now that we’ve looked at the ‘shape’ of econospeak, let’s get more concrete. Let’s look at the words that economics textbooks most overuse and most underuse. The results will surprise you.

We’ll start with the words that economics textbooks most overuse relative to average English. Figure 3 shows these words in a cloud. The larger the font, the more the word is overused.

Figure 3: The most overused words in econospeak. Here’s a wordcloud where font size indicates relative word frequency in economics textbooks. The larger the font, the more the word is overused. [Sources and methods].

If you’ve ever read an economics textbook, the words in Figure 3 are not what you’d expect. You’d think that the most overused words would be economics jargon — terms like ‘supply’, ‘demand’ and ‘market’. And yet this jargon is nowhere to be found. Instead, Figure 3 shows a collection of bizarre words. (‘Grasshopperish’ … seriously?) What’s going on here?

I’ll be honest. I didn’t anticipate that the most overused words in econospeak would be oddballs. But I understand (now) how it happens. Our intuition is that we overuse words by writing them many times. But this is only one path to overuse. The other path is to pick an extremely rare word and use it a few times. It’s this other path to overuse that explains why Figure 3 is filled with oddballs.

Take the word ‘outtell’. In the Google corpus, it appears once every 10 billion words. That’s so rare that you’d likely not see it in a lifetime of reading. The word ‘outtell’ is also rare in econospeak. It occurs just 4 times in my sample of textbooks. But that’s enough to constitute massive overuse. The same is true for many of the words in Figure 3. They’re rare words that got used a few times by economists.

Not all the words in Figure 3, however, are oddballs. Some of them are recognizable jargon (for instance, ‘loanable’, ‘monopolist’ and ‘oligopoly’). How do we distinguish this jargon from the quirks? We’ll get there shortly.

First, though, let’s look at the most underused words in econospeak. Figure 4 shows these words. Here, a larger font indicates that the word is more underused.

Figure 4: The most underused words in econospeak. Here’s a wordcloud where font size indicates the inverse of relative word frequency. The larger the font, the more the word is underused relative to the Google English corpus. [Sources and methods].

I could write an essay about the words in Figure 4. But I have other results to show you, so I’ll reflect on just a few of the words.

First, it seems that econospeak underuses many religious words (for instance, ‘jewish’, ‘jesus’, ‘god’, ‘gospel’, ‘islam’, ‘ritual’, etc.). This underuse is in some ways banal. We can think of English writing as having two sides — a secular side and a religious side. Secular writing will tend to underuse religious words. And religious writing will tend to underuse secular words. So what we’re seeing, in Figure 4, is that econospeak is secular. That’s no surprise.

Economics textbooks, however, are a very particular type of secular writing. They’re promoting a secular ideology. And that makes economists’ underuse of religious words more interesting. Framed this way, we can think of Figure 4 as showing two contrasting ideologies. The secular ideology of economics largely excludes the language used by religious ideologies. Fascinating.

Let’s move on to another important result. The most underused word in econospeak is … drum roll please … ‘anti’!

I didn’t expect this result. (Did you?) But I’ve had a few weeks to think about it, and I’ve realized that it’s quite revealing. Here’s why. The word ‘anti’ offers a succinct way of saying you’re opposed to something. As in:

Bob is anti slavery.

The near total absence of this word in economics textbooks speaks volumes about economics ideology. If you talk in econospeak, it’s difficult to voice opposition. That’s because economists frame opinions in terms of ‘preferences’. As in:

Bob has a preference for Cheerios.

Such banal opinions litter economic textbooks. What about more serious opinions? If you talk like an economist, it’s easy to voice support for something. For instance:

Bob has a preference for slavery.

But how do you use the language of ‘preferences’ to voice opposition? You must resort to a torturous double negative:

Bob has a preference for not having slavery.

Such indirect language, you’ll notice, defangs Bob’s opposition. Compare the turgid sentence above to the simple alternative:

Bob is anti slavery.

Now Bob’s opposition is clear. And that’s why it’s so revealing that econospeak almost never uses the word ‘anti’. Economic textbooks are selling an ideology that legitimizes the status quo. And the best way to do that is to mute any talk of opposition. Purge ‘anti’ from your vocabulary.

Quadrants of econospeak

Now that we’ve looked at the most overused and underused words in econospeak, let’s look again at the big picture. The most overused words tended to be oddballs (Figure 3). How do we separate these quirks from more common economic jargon?

Figure 5 shows one way to do so. Here I’ve divided econospeak into four quadrants. Before we talk about each quadrant, let’s discuss the whole chart. In Figure 5, each point is a word. The horizontal axis shows the word’s frequency in economics textbooks. The vertical axis shows the word’s frequency relative to the Google corpus.

Figure 5: Quadrants of econospeak. In this plot, each point is a word. The horizontal axis shows frequency in economics textbooks. The vertical axis shows the word’s frequency relative to the Google corpus. [Sources and methods].

Now to the quadrants. What’s important about the quadrants is that they identify different types of overuse and underuse. ‘Quirks’ and ‘jargon’ are both overused relative to the Google corpus. But they take different paths. ‘Jargon’ is used frequently in economic textbooks. But ‘quirks’ appear rarely. (I’ve used 50 occurrences per million words as the dividing line between quirks and jargon.)

Let’s start with ‘jargon’. You can see, in Figure 5, that the ‘jargon’ quadrant contains familiar words like ‘price’, ‘market’ and ‘demand’. These are among the most common words in econospeak. And they’re overused relative to the Google corpus. That’s not surprising.

Now to the ‘quirks’ quadrant. This is where the oddballs live. Sure, there are some jargony words here (like nonmonopolistic). But the further left we go, the odder the words become (i.e. ‘cumquat’). These quirks are rare in economics textbooks, and yet still overused relative to the Google corpus.

Now to the different types of underuse. The ‘under-represented’ quadrant contains words that are used frequently in economics textbooks, but are still under-represented relative to the Google corpus. Here you’ll find many words related to social groups and human institutions. (More on this later.)

Last, we have the ‘neglected’ quadrant. These are words that economists use rarely. But unlike ‘quirks’ (which are rare outside of economics), ‘neglected’ words are common in average English. So in the ‘neglected’ quadrant, we find words that are massively underused. This quadrant is a goldmine for economics critics. If something is missing from economic theory, its vocabulary is probably in the ‘neglected’ quadrant.

I know you want to see more of the words in each quadrant. (Skip ahead if you’d like.) But first we need to do more statistics. Let’s again compare econospeak to the ‘null hypothesis’. The null hypothesis, to remind you, is what happens when we randomly draw words from the Google corpus. Figure 6 shows how econospeak stacks up against this random sample. Again, each point is a word. Blue points are econospeak. Red points are the null hypothesis.

Figure 6: Econospeak vs. the null hypothesis, plotted on quadrants. Here I’ve replotted the econospeak data from Figure 5 (blue dots). I’ve added data for the ‘null hypothesis’ (red dots). The null hypothesis, to remind you, is what happens when we randomly drew 7.7 million words from the Google English corpus. [Sources and methods].

Here’s what the null hypothesis tells us. Many econospeak ‘quirks’, it seems, can be chalked up to chance. We know this because in the ‘quirks’ quadrant, many of the red dots (the null hypothesis) overlap blue dots (econospeak). This means we shouldn’t make too much of economists’ overuse of words like ‘cumquat’ and ‘decafs’. It’s probably just a matter of chance.

What’s important, though, is that all the other forms of overuse/underuse cannot be caused by chance. The null hypothesis does not create jargon. Nor does it create under-represented words, or extremely neglected ones. So in statistical terms, econospeak is significantly different than average English. Of course, if you’ve ever read an economics textbook, you already knew that. But here’s a quantification of your intuition.

Econospeak Jargon

Let’s get concrete again and talk about actual econospeak words. Figure 7 shows the top econospeak jargon. These are the words in the ‘jargon’ quadrant that are the most overused relative to average English. There aren’t many surprises here — just typical econospeak jargon.

Figure 7: Econospeak jargon. Here’s a wordcloud of the top ‘jargon’ found in economics textbooks. The larger the font, the more the word is used relative to the Google English corpus. [Sources and methods].

Let’s use some of this jargon to make a paragraph of econospeak:

The loanable funds staved off deadweight losses, brought on by firms acting monopolistically. Demanders, however, were not aware of the diseconomies of scale that caused recessionary trends away from equilibrium. But microeconomists knew that, ceteris paribus, prices were not respecting mpl or mpc. So they ate bushels of inelastic pizza.

(jargon in bold)

OK, you’re unlikely to find such turgid writing in an undergraduate economics textbook. But this sentence is a fitting parody of the neoclassical economics literature. To the outsider, it’s incomprehensible gibberish. Actually, neoclassical economics is gibberish. The point of the jargon is to stop you from figuring that out.

Econospeak Quirks

Now to the top econospeak ‘quirks’, shown in Figure 8. These are words that economists use rarely, but still overuse relative to average English. Here, a larger font means that the word is more overused.

Figure 8: Econospeak quirks. Here’s a wordcloud of the top quirks found in economics textbooks. Font size indicates the inverse of relative word frequency. The larger the font, the more the word is underused relative to the Google English corpus. [Sources and methods].

Unlike ‘jargon’, ‘quirks’ don’t jump out when you read economics literature. In fact, many of them are unique to a single textbook — they’re a quirk of a particular author. Lots of quirks result from non-hyphenation of usually hyphenated words. Some quirks may be typos. And a few of them, I’ll admit, could be an artifact of my word-counting bot. To analyze the textbooks, the bot converts PDF files to text files. The conversion isn’t perfect, and can introduce random errors. These show up in the ‘quirks’ quadrant.

Of the four quadrants of econospeak, ‘quirks’ are the least important, so I won’t analyze them much. Still, let’s try out a quirky paragraph:

Despite their grasshopperish legs, the superathletes tended to be homebodies. Their frontierlike, nondepreciating overdiscounting led to an underofficial refrainer.

(quirks in bold)

This paragraph has the flavor of econospeak. But the quirks are mostly just oddballs. I won’t pay much attention to them here.

Under-represented in econospeak

Now we’re getting to the meat of the analysis. What’s most interesting about econospeak is not what it includes, but what it excludes. Economics textbooks underuse a large portion of the English language. Let’s have a look at this underuse.

We’ll start with the ‘under-represented’ quadrant. These are words that are used frequently in economics textbooks, but still less than in average English. Figure 9 shows the most under-represented words. Here, a larger font indicates more underuse.

Figure 9: Under-represented words in econospeak. Here’s a wordcloud of ‘under-represented’ words found in economics textbooks. Font size indicates the inverse of relative word frequency. The larger the font, the more the word is underused relative to the Google English corpus. [Sources and methods].

Let’s write a sentence with some of these words. Unlike before, though, this sentence won’t be a parody of what economists say. It will capture what they don’t say. Here’s try number one:

Before his death, the man went to court. His child asked about the fire … but he looked away. Hope had no purpose.

(under-represented words in bold)

This is a sentence you’d expect in a novel. It’s personal. It deals with a life or death situation. And it has emotion. These are things that econospeak tends to exclude.

Here’s try number two:

The woman’s status in the committee was a matter of history. The commission on professional organizations had decided that evidence-based administration was essential.

(under-represented words in bold)

This sentence picks out bureaucratic language. The fact that such talk is under-represented in economics is telling. Economists pays attention to competition between groups, but not to the bureaucratic dynamics within groups.

Neglected by econospeak

Let’s now look at words that are neglected by econospeak. These are words that economists almost never use — and this rarity constitutes massive underuse relative to average English. Figure 10 shows the most neglected words. The larger the font, the more the word is neglected.

Figure 10: Neglected words in econospeak Here’s a wordcloud of the most ‘neglected’ words found in economics textbooks. Font size indicates the inverse of relative word frequency. The larger the font, the more the word is underused relative to the Google English corpus. [Sources and methods].

I’ll try my hand at a paragraph with these words:

The Jewish man was anti Islam. He believed he was God’s servant. His submission to the scriptures was based on his counselor’s teachings. God was his commander and savior. This was his eternal ritual.

(neglected words in bold)

What we get, when we use these neglected words, is religous speak. If we treat mainstream economics as a science, then this result isn’t very surprising. It would be astonishing to find a science textbook that read like the Bible. But mainstream economics is not a science. It is an ideology. And so the fact that this ideology neglects religion is important. It highlights that there are two competing ideologies here.

There are many other neglected words (in Figure 10) worth discussing. But I have more results to show you, so onward.

Not speaking about power

The purpose of an ideology is, in large part, to legitimize the powers that be. In this regard, the ideology of economics is a bit odd.

Most ideologies legitimize power explicitly. They effectively say ‘this person is powerful, and you should obey their command’. Take, as an example, feudal ideology (i.e. religion). Feudal rulers boasted openly about their power, proclaiming that it stemmed from God. It’s no surprise, then, that religion is laced with terms like ‘commandments’ and ‘submission’. The devotion to justifying power is overt.

With economics, though, things are different. Economists don’t overtly praise the powerful. Instead they hardly talk about power at all. That leads some people to conclude that economics isn’t an ideology. But that’s a mistake. Economics is an ideology, but it wraps its justification for power under a pretense — namely ‘freedom’. In capitalism, corporate rulers don’t have the ‘power’ to command. They have the ‘freedom’ to command.

I’ve written about this subterfuge in The Free Market as a Double Lie. I showed how free-market speak became more popular at the same time that corporate power became more concentrated. If you take free-market speak literally, this trend makes no sense. But if free-market speak is subterfuge for justifying power, then the pieces fit together.

Here I want to look at the flip side of the equation — not speaking about power. What defines econospeak is that power is conspicuously absent. It’s a linguistic turn that George Orwell noticed almost a century ago. Politicians of the time, Orwell observed, had started to speak in torturous euphemisms. When militaries committed massacres, politicians call it pacification. Today, we’re so used to this euphemistic language that we hardly notice it. What we would notice is if a politician spoke plainly. Imagine a politician proclaiming:

Let the slaughter begin! The sons of this king will die because of their ancestors’ sins. None of them will ever rule the earth or cover it with cities.

This morbid passage, if you’re wondering, is from the Old Testament. It’s the ‘Good News’ translation of Isaiah 14:20. Surrounded by the euphemisms of modernity, we forget that people ever spoke so plainly. They did so, presumably, because the justification for power was overt. God was on their side.

Today, God is (mostly) off table. And that means power is justified through subterfuge. Instead of praising power, you leave it unsaid. As the dominant secular ideology, economics reflects this subterfuge. In economics, talk about power is conspicuously absent.

If you’re a good critical reader, you can notice this absence. But here I’ll go a step further and quantify it. I’ve gone through the thesaurus and picked words that relate to wielding and submitting to power. Figure 11 shows their frequency in econospeak.

Figure 11: Not speaking about power. Here’s a sample of words related to wielding and submitting to power. I’ve plotted their frequency in economics textbooks on my econospeak quadrants. [Sources and methods].

The results, in Figure 11, are fascinating. The majority of words about power fall in the neglected quadrant. This speaks volumes about economics ideology. Economists don’t talk openly about power. That would ruin the subterfuge.

In simpler times, rulers boasted of their power. British imperialists, for instance, celebrated openly as they conquered the world. (For them, ‘imperialism’ was a good word.) But today, rulers talk in econospeak euphemisms. It’s not imperialism … it’s ‘free trade’!

Missing from econospeak

So far we’ve discussed words that are overused in econospeak, and words that are underused. Now let’s talk about words that are absent.

My sample of econospeak contains about 7.7 million words. In such a large sample, it’s no small feat for a word to be missing entirely. Unless the word is utterly obscure, its absence is important.

So what words are missing from econospeak? Many, obviously. But to frame the question, ask yourself — what is the most popular English word that economists don’t utter?

I’ve asked people on Twitter to take a guess. (See the responses here, here and here.) I’ve plotted these guesses in Figure 12. Notice that this is plot of words that are present in econospeak. That’s because almost no one managed to guess an actual missing word. Instead, the Twitterati were good at guessing neglected and under-represented words.

Figure 12: Twitter guesses for words that are missing from econospeak. I asked people on Twitter to guess the most popular English word that’s absent from economics textbooks. Nobody got the right answer. And most guesses weren’t actually missing words. Still, the guesses are interesting. I’ve plotted them here on my econospeak quadrants. [Sources and methods].

As with many of the plots in this post, I could write an essay about the words in Figure 12. But I have more results to show you. So let’s move on.

Let’s talk about the words that are actually missing from economics textbooks. I know you want to see the words themselves. (Skip ahead if you want.) But I first want to look at the structure of these missing words.

To understand this structure, it helps to have an analogy. Let’s think of the English language as a fully stocked buffet. The different foods represent words. Eating a food represents speaking a word. What we’re interested in here are the leftovers. These are the words that remain unspoken after economists finish talking.

Figure 13 shows one way of visualizing these ‘uneaten’ words. We start with the ‘English-language buffet’ (the red box). These are all the words in the English language (or in this case, a list of about 430,000 words from the Google English corpus). Before you’ve spoken anything, the language buffet is a square. The horizontal axis shows a word’s popularity, as indicated by its percentile in the Google corpus. The vertical axis is the portion of these words that you haven’t used.

Before you talk, the unused portion is 100% everywhere (you haven’t said anything). As you ‘speak’, you eat away at the buffet. If you speak in obscure prose, you eat away at the left side of the buffet. If you use only common words, you eat away at the right side.

What we’re interested in here are economists’ leftovers. When they speak (by writing textbooks), what words do they leave behind? Figure 13 shows the structure of these econospeak leftovers.

Figure 13: The shape of econospeak leftovers. Here’s one way of visualizing the words that are missing from economics textbooks. I represent the English language as a square. The horizontal axis indicates word popularity — the word’s percentile in the Google English corpus. The vertical axis indicates the portion of words not used. The dashed red square is the ‘whole’ English language (a list of about 430,000 words). The blue region shows what economists leave behind. [Sources and methods].

According to Figure 13, economics textbooks leave behind most of the English language — almost everything in the bottom 80% of words. The question is — what does this mean?

To interpret the econospeak leftovers, we’ll turn again to the ‘null hypothesis’. Recall that this is what happens when we randomly draw 7.7 million words from the Google corpus. Here, we’ll look at what the null hypothesis leaves behind. Figure 14 shows how the null hypothesis leftovers compare to what economists leave unspoken.

Figure 14: The shape of econospeak leftovers vs. null hypothesis leftovers. As in Figure 13, I represent the English language as a square. The horizontal axis indicates word popularity — the word’s percentile in the Google English corpus. The vertical axis indicates the portion of words not used. The dashed red square is the ‘whole’ English language (a list of about 430,000 words). The blue region shows what economists leave behind. The red region is what the null hypothesis leaves behind — the words not found in a random sample of 7.7 million words drawn from the Google corpus. Unlike the null hypothesis, econospeak leaves behind many words found in the top 10%. [Sources and methods].

Like econospeak, the null hypothesis leaves behind most of the English language. (The bottom 70% of words remain largely unused.) So the fact that economists don’t use obscure words is unremarkable. It’s a basic feature of language. What makes words obscure, after all, is that few people use them.

There is, however, an important difference between econospeak and the null hypothesis. Econospeak leaves behind many popular words found in the top 10%. In contrast, the null-hypothesis leaves behind virtually none of these top words. So the fact that economists leave many popular words unspoken is statistically significant. (That said, it’s not clear if this is a distinguishing feature of econospeak, or if it’s found in all types of specialized writing. Figuring that out will take more digging.)

Let’s have a look at econospeak’s top 10% leftovers. Figure 15 zooms in on this part of the distribution. The histogram shows the shape of some 16,000 words that economics textbooks omit. That’s far too many words to discuss. But to give you a sense for what words are there, I’ve labelled some examples (of my choosing). These words appear at their corresponding percentile in the Google corpus. (Their vertical position doesn’t mean anything — it’s purely aesthetic.)

Figure 15: Econospeaks’ top 10% leftovers. This figure plots the same data as in Figure 14, but zooms in on unused words in the top decile. I’ve labelled some words of my choosing. Their horizontal position represents their popularity. (Vertical position is aesthetic.) [Sources and methods].

From the examples in Figure 15, I think you’ll agree that there are many important words that economists don’t utter. And what the statistics tell us is that this non-utterance is a choice. It cannot be chalked up to chance.

What’s interesting — and worth looking at more rigorously — is the absence of words about conflict. I’ve shown some of these words in Figure 15. (I’m sure there are many others.) It seems that economists don’t speak about ‘racism’, ‘defiance’, ‘patriarchy’, ‘treachery’, ‘sexism’, or ‘dispossession’. The absence of these conflict words is fascinating. Far from being random, I believe it’s a core part of economics ideology. Economics legitimizes power relations by pretending they don’t exist.

The top missing words

Now to the results that many of you have been waiting for. Let’s look at the most popular English words that are missing from economics. Figure 16 shows these words. The larger the font, the more popular the word.

Figure 16: The most popular words that are missing from econospeak. Font size is proportional to word frequency in the Google corpus. [Sources and methods].

The most popular word that’s absent from economics is … ‘Christ’. That’s an interesting result. But it says more about culture outside of economics than it does about econospeak. Economics is a secular ideology, so it’s no surprise that the last name of a Christian prophet goes unmentioned. What’s interesting is that outside of economics, the word ‘Christ’ is hugely popular. Again, this is a sign of religion’s lasting influence. In capitalist societies, religion may not be the dominant ideology, but its influence remains significant. (It’s informative that no one in my Twitter circle guessed the word ‘Christ’. It suggests that I live in a bubble of atheists.)

There’s much to be said about the other words in Figure 16. But I’ll conclude with just one observation — a fitting irony. We can use words that are absent from econospeak to describe economics ideology:

Economists are the high priests of capitalist society who worship at the altar of the free market. But their doctrines are not based on evidence. Instead, economics is a type of secular theology based on scripture.

(absent words in bold)

Ideology as the unsaid

I started this word-counting project after skimming Gregory Mankiw’s textbook Principles of Economics. I noticed that he used the word ‘distort’ a lot. Peppered throughout his book were whoppers like this:

Almost all taxes distort incentives, cause people to alter their behavior, and lead to a less efficient allocation of the economy’s resources.

(Mankiw in Principles of Economics)

Mankiw’s love for the word ‘distort’ got me thinking — how much does he use this word compared to the average? And so my word-counting bot was born. My initial focus was on the words that were overused. This overuse, I thought, would quantify economics ideology. (FYI: Mankiw does say ‘distort’ a lot. He uses it about 50 times more frequently than average.)

As I started to crunch the numbers, though, I realized that what is most interesting about econospeak isn’t what is overused. What’s interesting are the words that are underused or left unsaid. It’s this (relative) absence, I now believe, that’s key to understanding the ideology of economics.

It reminds me of George Lakoff’s book Don’t Think of an Elephant! If you want somebody to not think about something, the last thing you should do is tell them so. (You’re thinking of an elephant, aren’t you?) Herein lies the genius of economics ideology. Its purpose is to legitimize the status quo. It does so by getting you to think about a free-market fairy tale. While that’s got your attention, you don’t notice that power (and its many injustices) aren’t discussed.

To deconstruct economics ideology, in turn, entails talking about these absences. That’s difficult. What’s in a text is obvious. What’s not there is harder to see. Hence my unease when I took Economics 101. My gut was telling me that something was missing. But exactly what eluded me. Now I know. … because I ran the numbers. The data shouts loud and clear that a large part of the English language is absent from economics textbooks. It’s ideology through the unsaid.

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Download the econospeak data and code

I know that many of you want to explore my econospeak data. To quench your thirst, I’ve included lists of the top 500 overused, underused, and missing words. See them here. I’ve also provided links (below) to my whole econospeak dataset. Lastly, I’m going to make an interactive chart that let’s you explore the structure of econospeak. Stay tuned for that.

Sources and methods

Economics textbooks

Table 2 shows my sample of economics textbooks. Although not exhaustive, this sample contains most of the standard textbooks used in undergraduate economics courses. When creating the sample, my restriction was that the textbooks should be published within roughly the same decade (here, 2004–2014) and that the books are available on Library Genesis. When possible, I tried to get the ‘micro’, ‘macro’ and ‘general’ versions of each book.

The resulting sample of econospeak contains about 7.7 million words, with a vocabulary of roughly 34,000 words.

Table 2: The sample of economics textbooks

Author Title Year
Arnold Economics 2008
Arnold Macroeconomics 2008
Arnold Microeconomics 2011
Blanchard & Johnson Macroeconomics 2012
Case, Fair & Oster Principles of Microeconomics 2008
Case, Fair & Oster Principles of Macroeconomics 2011
Case, Fair & Oster Principles of Economics 2012
Cowen & Tabarrok Modern Principles of Economics 2011
Frank & Bernanke Principles of Economics 2008
Frank & Bernanke Principles of Macroeconomics 2008
Frank & Bernanke Principles of Microeconomics 2008
Hubbard & O’Brien Economics 2009
Hubbard & O’Brien Macroeconomics 2011
Hubbard & O’Brien Microeconomics 2013
Krugman & Wells Macroeconomics 2005
Krugman & Wells Economics 2009
Krugman & Wells Microeconomics 2012
LeRoy Miller Economics Today: The Macro View 2011
LeRoy Miller Economics Today: The Micro View 2011
LeRoy Miller Economics Today 2011
Mankiw Principles of Economics 2008
Mankiw Principles of Macroeconomics 2011
Mankiw Principles of Microeconomics 2011
McConnell, Brue & Flynn Macroeconomics 2006
McConnell, Brue & Flynn Economics 2008
McConnell, Brue & Flynn Microeconomics 2011
Nicholson & Snyder Microeconomic Theory 2004
Nicholson & Snyder Microeconomic Theory 2007
Nicholson & Snyder Microeconomic Theory 2011
Parkin Microeconomics 2011
Parkin Macroeconomics 2011
Parkin, Powell & Matthews Economics 2005
Perloff Microeconomics 2011
Perloff Microeconomics 2014
Pindyck & Rubinfeld Microeconomics 2012
Pindyck & Rubinfeld Microeconomics 2014
Rittenberg & Tregarthen Principles of Economics 2009
Rittenberg & Tregarthen Principles of Microeconomics 2009
Rittenberg & Tregarthen Principles of Macroeconomics 2009
Samuelson & Nordhaus Economics 2009
Varian Intermediate Microeconomics 2005
Varian Intermediate Microeconomics 2010
Varian Intermediate Microeconomics 2014

Notes: I downloaded the textbooks as PDFs and extracted the text using the Linux function pdftotext. This conversion can sometimes induce errors (often due to non-standard fonts). It’s possible that some of the quirks in econospeak are caused by faults in the PDF-to-text conversion.

Google English corpus

I’ve used Google’s 2020 1-gram corpus, which measures the text frequency of one-word phrases in the Google Books database. You can download the data here. (Warning: the 1-gram dataset is about 46 GB).

If you’re interested in working with Ngram data, I’ve written a post about my data-wrangling experience. I’ve also written some custom R code for importing Ngram 2020 data. It’s available at Github.

I use Ngram data over the years covered by the textbooks (2004-2014). For each word, I calculated its mean frequency over these years, weighted by the portion of the textbook sample published in each year.


I restrict both the econospeak sample and Google English sample to words that are in a predetermined ‘dictionary’. My dictionary consists of the following:

From this word list I remove/change the following:

  • remove words with fewer than 3 letters
  • remove common first names (using R lexicon function freq_first_names)
  • remove common last names (using R lexicon function freq_last_names)
  • remove prepositions (using R lexicon function pos_preposition)
  • remove English numerals from 1 to 100 (i.e. one, two, three …)
  • remove ‘stop words’ (using R tm command stopwords)
  • change British spellings to American (as in labour → labor)
  • convert all words to lower case
  • remove acronyms (words containing ‘.’)
  • remove contractions (words containing apostrophes)
  • remove hyphenated words
  • remove words containing numbers 0-9

The resulting ‘dictionary’ contains about 500,000 words.

Econospeak word lists

Browse the top 500 overused, underused and missing words:

Top 500 overused words

Word Frequency relative to
Google corpus
nondissipated 2612.5
latinian 1553.0
instrengthen 1466.1
refrainer 1281.9
outtell 1093.2
nonmonopolistic 1003.8
unleads 993.1
reisted 973.0
underofficial 947.1
superathlete 819.3
inpayments 760.0
pretariff 734.2
outpayment 703.2
willingnesses 694.3
inworked 656.9
reveto 642.9
debitable 609.0
idahoes 573.1
condimented 558.4
monopolistically 552.9
grasshopperish 545.0
nondepreciating 528.0
nonrival 448.4
loanable 441.2
monopsonist 436.7
checkable 431.9
recessionary 406.0
srac 405.7
overdiscounting 384.1
monopsony 383.4
msbus 361.3
demanders 353.2
upsloping 342.5
dissaved 337.0
underproduces 311.1
theftproof 303.4
deadweight 300.7
kinetophonograph 297.0
yuckers 290.9
contractionary 288.4
macroeconomists 286.4
oligopolist 283.4
monopolist 267.6
mobilia 267.1
xed 258.9
groland 257.4
macroeconomist 247.2
avc 237.5
monopsonies 233.4
undersupplies 232.4
superstrain 225.8
slumpflation 223.7
noncollusive 222.9
nonrecession 222.1
nonsatiation 216.8
superathletes 216.1
oversale 214.6
hyperinflations 212.6
mrts 210.8
overallocation 206.0
dissaving 203.9
overprovide 202.8
oligopoly 198.3
diseconomies 197.1
expansionary 195.8
equiproportional 191.7
rightward 184.3
frictionally 183.0
microeconomics 182.9
nonprohibitive 179.5
repaves 178.2
restep 175.7
pfennings 175.1
finking 175.1
longrun 171.9
nonstrategically 170.4
regovernment 169.0
snowboards 168.0
ditchdigging 164.2
homothetic 161.2
inpayment 161.0
nonlabor 160.5
gardol 157.7
nonunionized 156.9
overfishes 154.5
leftward 153.0
debudded 152.8
ploughwright 152.4
underproduce 152.1
traylike 144.5
monopsonistic 144.4
duopolist 140.8
nairu 137.4
dissaves 135.4
monetarists 134.9
shlu 131.3
bananaland 130.9
marginal 129.2
overprovision 129.0
misestimating 128.4
oligopolies 128.4
noncredibility 127.6
macroeconomics 126.9
externality 126.9
inappropriable 126.2
nonmoney 125.7
epts 125.4
oversales 122.0
monopolistic 121.5
lossa 121.3
mpl 121.2
intercharge 119.2
mrsr 118.5
disinflation 117.8
hapte 117.3
berck 117.3
overproviding 116.7
repairperson 114.4
dissave 109.7
nonexportable 109.5
rebuying 109.4
underproduction 108.3
caff 108.1
isoelastic 107.9
tangencies 106.2
tangency 105.2
inelastic 102.4
nonlibrarian 101.9
homebodies 100.5
underproduced 100.0
elasticity 99.1
duopoly 99.1
overinformed 98.9
elasticities 98.7
nonpremium 98.0
paribus 93.9
unionizes 93.0
ceteris 92.0
prepriced 91.5
monopolists 90.9
noneconomists 90.7
switchgears 90.6
misallocated 90.6
monetarist 90.6
coalmaster 90.1
oligopolistic 88.2
quasilinear 87.7
oligopsonies 87.4
overinsure 86.9
cartelize 86.3
mpc 85.7
gerik 84.9
forgone 84.3
externalities 84.2
curve 84.2
mrp 83.5
noninflationary 83.2
stagflation 83.0
noneconomist 82.6
hoisters 79.9
depreciates 79.7
disinflations 79.5
takeaways 79.5
atc 79.1
demander 78.7
potterville 77.5
prot 77.1
recessions 76.8
diversifiable 76.5
multiplier 76.4
bushels 73.5
ungreat 73.2
underbids 72.2
dominiums 72.1
exhaustible 71.8
economists 71.6
dissavings 71.6
productivities 71.4
gien 71.4
netbooks 71.3
flyswatters 71.3
snowblowers 71.1
imperfectively 70.9
intis 70.4
telebanking 69.8
misallocations 69.7
quantity 69.0
seignorage 68.5
untillable 68.4
actuarily 68.3
kamika 68.0
superduper 67.6
gdp 67.5
nonprofitability 67.4
aggregate 67.3
underconsumed 67.0
windsurfs 66.9
antimerger 66.8
pizzas 66.6
overprovided 66.2
noncompeting 66.0
marketa 65.6
monopoly 65.3
inefficiently 65.3
mpp 65.1
deflator 64.9
monetarism 64.7
overproduces 64.4
nonexclusion 64.1
redeposits 64.0
tollbridge 64.0
dxt 63.8
tfc 63.7
undermeasured 63.6
logrolling 63.3
costlessly 62.9
surplus 62.9
nondiscount 62.8
disinflate 62.6
dcor 62.0
noncompulsive 61.9
rtsl 61.7
equilibrium 60.8
peasouper 60.8
oer 60.3
gristly 58.9
featherbedding 58.5
overplanting 57.6
nonreversibility 57.5
scalpers 57.4
prefreeze 57.3
inflation 57.1
microeconomic 56.6
keynesian 56.3
cornland 56.3
outfish 55.6
maximizes 55.1
autarky 54.7
precontrol 54.6
misallocates 54.3
collude 54.2
monopsonists 53.9
relends 53.7
retexturing 53.6
cartelizing 53.1
nautica 53.0
nonstandardization 52.7
nonsatisfaction 52.6
burritos 52.6
noncredible 52.6
superexpensive 52.5
shirking 52.5
stayover 52.4
bushel 52.4
minishing 52.3
noncooperative 52.3
marginalism 52.1
unrecoverably 52.1
decafs 51.8
nondiscriminating 51.6
earns 51.1
aggeration 50.9
xms 50.6
gonzlez 50.6
dowment 50.5
superfunction 50.4
demand 50.2
maximization 50.1
genos 50.0
grinches 49.7
calzones 49.3
bads 49.1
stairlike 48.7
duopolies 48.1
coconuts 48.0
precommit 47.8
hyperinflation 47.8
keynesians 47.5
averter 47.3
suers 47.2
price 46.9
remint 46.6
underpower 46.6
overinvest 45.9
overdifferentiation 45.8
costless 45.7
undertaxed 45.6
submarket 45.5
wahoo 45.5
misallocation 45.3
rms 45.3
collusive 45.2
repractice 45.1
kumquats 45.0
msb 44.5
cutto 44.5
inflationary 44.2
disinflationary 44.1
preannounce 44.0
korunas 43.9
peso 43.7
oligopsony 43.6
overcompensates 43.5
nonoptimal 43.5
djt 43.4
trillions 43.1
cappuccinos 43.0
dnx 43.0
cartelized 43.0
antipollution 42.8
deflation 42.6
sellers 42.5
thrifts 42.4
megadeal 42.0
unemployment 41.9
supersaver 41.7
antitheft 41.5
afc 41.4
excludability 41.1
cartel 41.0
inelasticity 40.7
indifference 40.6
carpetmaking 40.4
diminishing 40.3
postponable 40.2
backflows 40.1
preemergency 39.9
pesos 39.9
ricardian 39.9
nongraduates 39.9
excludable 39.9
chunnel 39.8
multiproduct 39.7
rebating 39.7
autoworker 39.5
postcontract 39.4
misestimated 39.2
mcpo 39.1
perfectively 39.0
payoffs 38.9
carlena 38.9
misestimate 38.8
haircutters 38.7
noncontrollable 38.6
overexpand 38.3
speat 38.0
cartelization 37.9
belion 37.7
redistributes 37.7
bailbond 37.6
rises 37.6
macroeconomic 37.5
overutilize 37.4
toyotas 37.2
overutilized 37.0
disposable 37.0
overvalued 37.0
hamburgers 36.9
bolivias 36.8
cpi 36.5
substitutes 36.4
curves 36.2
mpb 36.1
guilders 36.1
onethe 36.1
luters 35.9
subsidizes 35.9
dvc 35.8
reengineers 35.7
multiplant 35.7
overbook 35.7
vlor 35.4
poolers 35.4
macks 35.4
savers 35.3
preloan 35.2
pareto 35.2
righties 35.2
deadstock 35.1
firms 35.1
nonindexed 35.0
cokie 34.9
substitutability 34.7
kinked 34.7
summar 34.6
intersects 34.5
complier 34.5
rebegin 34.5
clich 34.4
proprietorships 34.4
rationing 34.3
monopolies 34.3
recession 34.2
lattes 34.1
depreciate 34.0
output 33.6
overconsumed 33.5
supply 33.5
wage 33.4
unforecasted 33.3
safekeeper 33.3
bundling 33.0
buyers 33.0
ringgits 33.0
inframarginal 33.0
disinvesting 32.9
quintile 32.9
basketballs 32.7
equals 32.7
billfolds 32.6
posttax 32.6
seigniorage 32.5
sml 32.5
qmc 32.5
pricefixing 32.3
inflations 32.2
antigrowth 32.1
underproducing 31.9
contraltos 31.9
countersue 31.8
starvers 31.7
overbidding 31.6
autoworkers 31.4
incomes 31.2
utility 31.2
decontrolling 31.2
frictional 31.2
mux 31.1
nominal 31.0
surpluses 31.0
uninsurable 31.0
ppc 31.0
snowboard 30.8
unexploited 30.8
overgraze 30.8
chevrolets 30.8
decontrolled 30.6
echikson 30.6
hyperrational 30.4
overutilizing 30.4
smoothies 30.4
yens 30.3
inefficiency 30.2
entres 30.1
diseconomy 30.0
randomizes 29.9
trustbusters 29.9
payoff 29.9
commercializes 29.8
bepress 29.8
nonlocals 29.7
buys 29.7
roundaboutness 29.7
nonneutrality 29.6
elysburg 29.4
contestable 29.3
supercompetitive 29.2
litterbugs 29.1
avocados 29.0
shiftability 29.0
damager 28.9
retime 28.7
overissuance 28.7
disutility 28.5
cleanings 28.4
propecia 28.4
hubbard 28.4
nonmarket 28.3
salsas 28.1
depletable 28.1
prices 28.0
maximized 28.0
haircuts 28.0
entrep 28.0
kiloliter 27.9
arbitrager 27.8
spendable 27.7
quintiles 27.6
edgeworth 27.5
substitution 27.4
outproduce 27.3
lobstermen 27.3
truckful 27.2
pengos 27.2
overprice 27.1
earfuls 27.0
complements 27.0
unrented 26.9
trilemma 26.9
prilosec 26.9
economizes 26.9
pepsi 26.8
substitutable 26.6
intramarginal 26.6
atter 26.5
colluding 26.5
nonstriking 26.3
birthrates 26.2
taxicabs 26.2
reselling 26.2

Top 500 underused words

Word Frequency relative to
Google corpus
anti 0.0009
jewish 0.0010
proteins 0.0018
god 0.0018
tumor 0.0019
neck 0.0020
damn 0.0023
gospel 0.0024
jesus 0.0024
islam 0.0024
paused 0.0026
bent 0.0026
renal 0.0026
ladies 0.0027
acids 0.0028
ritual 0.0028
servant 0.0028
poems 0.0030
moses 0.0030
wounded 0.0030
lips 0.0031
pursuant 0.0032
finger 0.0033
neural 0.0033
darkness 0.0033
hebrew 0.0033
chem 0.0033
ass 0.0034
enzyme 0.0034
soul 0.0034
atomic 0.0035
hello 0.0036
multi 0.0037
doi 0.0038
dare 0.0038
narrative 0.0039
sins 0.0039
activated 0.0040
stakeholders 0.0040
divine 0.0040
psychiatric 0.0040
sexuality 0.0041
physiological 0.0042
hormone 0.0042
warmth 0.0042
pathways 0.0042
prayer 0.0043
grin 0.0043
rape 0.0043
gods 0.0044
genre 0.0044
abdominal 0.0044
brave 0.0044
markers 0.0046
template 0.0046
cerebral 0.0047
pastor 0.0047
hypertension 0.0047
gay 0.0047
negro 0.0048
bishop 0.0049
archeological 0.0049
teaspoon 0.0050
clinical 0.0050
palace 0.0050
dysfunction 0.0050
echo 0.0051
supper 0.0051
elbow 0.0052
bibliography 0.0052
amplitude 0.0052
deployment 0.0052
organism 0.0053
gratitude 0.0053
hallway 0.0054
shining 0.0054
lord 0.0054
lit 0.0054
poets 0.0054
antibodies 0.0055
non 0.0055
murdered 0.0055
aloud 0.0056
ideals 0.0056
cheek 0.0057
eternal 0.0057
sofa 0.0058
thickness 0.0059
smiles 0.0059
waiver 0.0059
afterwards 0.0060
catholic 0.0060
tumors 0.0061
chest 0.0062
boiling 0.0062
mediation 0.0062
swear 0.0062
counselor 0.0062
grabbed 0.0062
gaze 0.0063
heavens 0.0063
salvation 0.0063
bleeding 0.0063
teachings 0.0063
naval 0.0064
manners 0.0064
christians 0.0064
yeah 0.0064
spirituality 0.0065
muscle 0.0065
stairs 0.0065
prose 0.0065
tissues 0.0065
kissing 0.0066
greeks 0.0066
whilst 0.0066
insertion 0.0067
hon 0.0067
serum 0.0067
sophie 0.0067
pillow 0.0067
commander 0.0067
delete 0.0067
forehead 0.0068
luke 0.0068
holy 0.0068
monk 0.0068
cheeks 0.0069
fairy 0.0069
glory 0.0069
goodbye 0.0069
scriptures 0.0069
gown 0.0070
gland 0.0070
sanctuary 0.0070
forgive 0.0070
detector 0.0070
prophet 0.0070
cave 0.0071
christianity 0.0071
amino 0.0071
winding 0.0071
breath 0.0071
textual 0.0071
mortal 0.0071
slept 0.0072
spine 0.0072
savior 0.0073
sensor 0.0073
sip 0.0073
biomass 0.0073
rebel 0.0074
bandwidth 0.0074
nausea 0.0075
appearances 0.0075
intellect 0.0076
biopsy 0.0077
herbs 0.0077
sensation 0.0077
superintendent 0.0077
font 0.0077
mentor 0.0077
acta 0.0077
sect 0.0077
mama 0.0078
dedication 0.0078
trauma 0.0078
muslim 0.0078
snapped 0.0078
patches 0.0079
lad 0.0079
lust 0.0079
appl 0.0079
semantics 0.0079
visualization 0.0079
marsh 0.0080
ribbon 0.0080
empowerment 0.0080
grammar 0.0080
soc 0.0080
int 0.0080
confidentiality 0.0081
fiction 0.0081
calibration 0.0081
asleep 0.0081
flung 0.0081
eur 0.0082
bible 0.0082
encoding 0.0082
antenna 0.0082
beloved 0.0083
twisted 0.0083
neurological 0.0083
upstairs 0.0083
smile 0.0084
madam 0.0084
dreaming 0.0084
sleeve 0.0084
intravenous 0.0084
heel 0.0084
knocked 0.0085
parted 0.0085
collaboration 0.0085
glowing 0.0085
verse 0.0086
memoirs 0.0086
polymer 0.0086
faint 0.0086
olivia 0.0086
submission 0.0087
der 0.0087
frowned 0.0087
annex 0.0087
habitats 0.0087
absorption 0.0087
disgust 0.0088
jaw 0.0088
preacher 0.0088
latino 0.0088
softly 0.0088
sensations 0.0088
pale 0.0089
apologize 0.0089
tense 0.0089
torture 0.0089
mourning 0.0089
deposition 0.0089
sexual 0.0090
impairment 0.0091
morrow 0.0091
beam 0.0091
pleading 0.0091
staring 0.0092
topical 0.0092
porch 0.0092
nerve 0.0092
sodium 0.0092
offenses 0.0093
kiss 0.0093
aye 0.0093
protocols 0.0094
cute 0.0094
eng 0.0094
horn 0.0095
farewell 0.0095
shone 0.0095
colonization 0.0095
pray 0.0095
inquiries 0.0095
glasgow 0.0095
remembers 0.0095
theatrical 0.0095
pity 0.0095
elder 0.0096
smiling 0.0096
activate 0.0096
screamed 0.0097
imperialism 0.0097
texture 0.0097
utmost 0.0097
irritation 0.0097
self 0.0097
battlefield 0.0098
jerusalem 0.0099
disbelief 0.0099
spiritual 0.0100
pulses 0.0101
humility 0.0101
colonel 0.0101
dna 0.0101
legitimacy 0.0101
policeman 0.0101
hum 0.0102
curtis 0.0102
believer 0.0102
tidal 0.0102
hesitated 0.0103
bristol 0.0103
viral 0.0103
aroused 0.0103
fungi 0.0103
laughing 0.0103
marching 0.0104
vessel 0.0104
feminist 0.0104
emergent 0.0105
thoracic 0.0105
pulse 0.0106
islamic 0.0106
rue 0.0106
sang 0.0106
signatures 0.0106
laughs 0.0106
implicated 0.0106
mask 0.0106
prophetic 0.0106
bodily 0.0107
epilepsy 0.0107
vols 0.0107
dances 0.0108
cowboy 0.0108
neuron 0.0108
ovarian 0.0108
endurance 0.0108
nos 0.0108
interpreter 0.0108
tel 0.0109
architecture 0.0109
unconscious 0.0109
consciousness 0.0109
synthesized 0.0110
rituals 0.0110
drowned 0.0110
glorious 0.0111
hereditary 0.0111
buddhist 0.0111
fraser 0.0111
cos 0.0111
sorrow 0.0111
modal 0.0111
shaft 0.0111
shrugged 0.0111
exile 0.0111
churches 0.0112
mythology 0.0112
clad 0.0112
porous 0.0112
intermittent 0.0113
tugged 0.0113
peeled 0.0113
inquired 0.0113
memorandum 0.0113
mouth 0.0114
comp 0.0114
teasing 0.0114
treasures 0.0114
downstairs 0.0114
della 0.0114
upright 0.0114
brook 0.0114
gonna 0.0115
commandments 0.0115
diagnosis 0.0116
homosexuality 0.0116
heroes 0.0116
jessie 0.0116
seminars 0.0116
exquisite 0.0116
brigade 0.0117
mortar 0.0117
startled 0.0118
hague 0.0118
kidding 0.0118
mutation 0.0118
sword 0.0119
youthful 0.0119
roared 0.0120
thyroid 0.0120
longed 0.0120
univ 0.0120
tongue 0.0120
travis 0.0120
cooler 0.0120
decoration 0.0120
legs 0.0120
shoulders 0.0121
muddy 0.0121
chin 0.0121
juvenile 0.0121
welcoming 0.0121
missionary 0.0121
singh 0.0122
cinnamon 0.0122
tone 0.0122
brow 0.0122
oath 0.0122
sequencing 0.0122
eyed 0.0123
accent 0.0123
bedside 0.0123
rifles 0.0123
sediment 0.0123
arterial 0.0123
relieved 0.0124
affirmation 0.0124
breasts 0.0124
indigenous 0.0124
charming 0.0124
coll 0.0125
tang 0.0125
surrendered 0.0125
canyon 0.0125
workflow 0.0125
leaped 0.0125
collaborative 0.0125
stationed 0.0125
charleston 0.0125
liaison 0.0125
grande 0.0126
prophets 0.0126
tick 0.0126
temperament 0.0126
girl 0.0126
pupils 0.0126
biochemistry 0.0127
beard 0.0127
counsel 0.0127
gill 0.0127
stat 0.0127
obstruction 0.0128
contempt 0.0128
amusing 0.0128
stout 0.0128
connor 0.0129
parkinson 0.0129
toes 0.0129
baptist 0.0129
companions 0.0129
angel 0.0129
muslims 0.0130
climax 0.0130
poem 0.0130
thou 0.0130
evelyn 0.0130
mater 0.0131
yelled 0.0131
neurology 0.0131
subdivision 0.0131
cicero 0.0131
skinny 0.0131
baltic 0.0131
intently 0.0131
romans 0.0132
cognitive 0.0132
reconstructed 0.0132
cranial 0.0132
knees 0.0132
paralysis 0.0132
dear 0.0132
dialog 0.0132
eyebrows 0.0133
skin 0.0133
trainer 0.0133
tcp 0.0133
psychiatry 0.0134
liberation 0.0134
gently 0.0134
expelled 0.0134
testament 0.0134
thee 0.0134
respectful 0.0134
rehabilitation 0.0134
categorical 0.0135
chased 0.0135
detention 0.0135
limp 0.0135
architectural 0.0135
symbolism 0.0135
backup 0.0135
savannah 0.0136
conquer 0.0136
recollection 0.0136
infiltration 0.0136
chronicles 0.0136
workload 0.0137
oppressive 0.0137
screw 0.0137
affinity 0.0137
che 0.0137
organisms 0.0137
censorship 0.0137
isaiah 0.0137
bless 0.0138
statutory 0.0138
learner 0.0138
celtic 0.0138
irene 0.0138
sounded 0.0138
legion 0.0138
incidents 0.0138
disciples 0.0138
termination 0.0138
jewel 0.0138
majesty 0.0139
orient 0.0139
tears 0.0139
posture 0.0139
church 0.0139
rusty 0.0140
handler 0.0140
molecular 0.0140
exhibitions 0.0140
monument 0.0140
alison 0.0141
genesis 0.0141
dialogs 0.0141
compassionate 0.0141
darcy 0.0141
dad 0.0141
delegation 0.0141

Top 500 missing words

Word Percentile
christ 0.998
smiled 0.997
nodded 0.997
laughed 0.996
jews 0.995
stared 0.995
throat 0.994
glanced 0.994
whispered 0.994
leaned 0.993
membrane 0.993
electron 0.993
worship 0.992
theology 0.992
lateral 0.992
receptor 0.992
kissed 0.992
activation 0.992
sighed 0.991
pulmonary 0.991
shit 0.991
para 0.991
shaking 0.991
laughter 0.991
lieutenant 0.990
lesions 0.990
verb 0.990
anterior 0.990
ieee 0.990
glucose 0.989
theological 0.989
retrieved 0.989
folder 0.989
scripture 0.989
cfr 0.989
sensory 0.989
proc 0.989
mediated 0.989
vascular 0.989
supra 0.989
alien 0.989
fuck 0.989
receptors 0.989
spinal 0.988
geog 0.988
waist 0.988
priests 0.988
learners 0.988
pathway 0.988
covenant 0.988
grinned 0.988
clin 0.988
fragments 0.988
subpart 0.988
rhythm 0.988
que 0.988
phys 0.988
metabolism 0.988
fracture 0.988
fatigue 0.988
cir 0.988
substrate 0.988
waved 0.988
inflammatory 0.987
narratives 0.987
fucking 0.987
ref 0.987
prayers 0.987
binary 0.987
adolescents 0.987
metabolic 0.987
urine 0.987
flame 0.987
chapel 0.987
belly 0.987
nucleus 0.987
shear 0.987
noun 0.987
specimens 0.987
custody 0.987
corpus 0.986
wounds 0.986
freud 0.986
biol 0.986
rna 0.986
gradient 0.986
hurried 0.986
stare 0.986
muttered 0.986
specimen 0.986
swallowed 0.986
enzymes 0.986
kant 0.986
hips 0.986
seq 0.986
meditation 0.986
silently 0.986
anglo 0.986
butler 0.986
anthropology 0.986
fetal 0.986
parliamentary 0.986
murmured 0.986
regiment 0.986
infantry 0.986
tribunal 0.986
inhibition 0.986
toxicity 0.986
saints 0.986
arabic 0.986
inflammation 0.986
skull 0.986
oxidation 0.985
scattering 0.985
rubbed 0.985
racism 0.985
maggie 0.985
meta 0.985
notification 0.985
satan 0.985
carcinoma 0.985
enlightenment 0.985
chambers 0.985
altar 0.985
lily 0.985
vitro 0.985
ventricular 0.985
vapor 0.985
parish 0.985
genome 0.985
spectral 0.985
feminine 0.985
spectra 0.985
prayed 0.985
modernity 0.985
sql 0.985
trustee 0.985
relational 0.985
cemetery 0.985
pathology 0.985
verses 0.985
mistress 0.984
verbs 0.984
artifacts 0.984
lodge 0.984
reactive 0.984
motions 0.984
cock 0.984
antibody 0.984
resurrection 0.984
luther 0.984
congregation 0.984
hindu 0.984
mol 0.984
distal 0.984
instructional 0.984
cathedral 0.984
nod 0.984
bladder 0.984
neo 0.984
lesion 0.984
goddess 0.984
cervical 0.984
reg 0.983
burial 0.983
inhibitors 0.983
ventilation 0.983
praying 0.983
morphology 0.983
obedience 0.983
syntax 0.983
discourses 0.983
cavalry 0.983
membranes 0.983
vivo 0.983
physiology 0.983
tablespoons 0.983
palestine 0.983
wavelength 0.983
temper 0.983
disciplinary 0.983
petitioner 0.983
archeology 0.983
psalm 0.983
tomb 0.983
battalion 0.983
trembling 0.983
tucked 0.983
urinary 0.983
chemotherapy 0.983
surg 0.983
deformation 0.983
nationalist 0.983
lattice 0.983
stirring 0.983
chromosome 0.983
sadness 0.983
hormones 0.983
potassium 0.983
augustine 0.983
fractures 0.983
invasive 0.983
anesthesia 0.983
gazed 0.983
ottoman 0.983
aboriginal 0.983
hug 0.982
bitch 0.982
narrator 0.982
medial 0.982
stained 0.982
flashed 0.982
malignant 0.982
jew 0.982
gasped 0.982
vibration 0.982
righteousness 0.982
chloride 0.982
intimacy 0.982
coating 0.982
buddha 0.982
antigen 0.982
angular 0.982
masculine 0.982
venous 0.982
righteous 0.982
demon 0.982
ashamed 0.982
lipid 0.982
airway 0.982
coarse 0.982
breathed 0.982
electrode 0.982
allah 0.982
huh 0.982
proximal 0.982
bowel 0.982
laden 0.982
judaism 0.982
fury 0.982
forensic 0.982
ankle 0.982
basal 0.982
soluble 0.982
palms 0.982
reinforcement 0.982
temples 0.982
blinked 0.982
hepatic 0.982
verlag 0.982
utter 0.982
innocence 0.981
seizures 0.981
congenital 0.981
schizophrenia 0.981
axial 0.981
demons 0.981
chuckled 0.981
precipitation 0.981
courtyard 0.981
dementia 0.981
appellate 0.981
bastard 0.981
thighs 0.981
lesbian 0.981
admiration 0.981
nasal 0.981
autism 0.981
buddhism 0.981
graves 0.981
shoved 0.981
intra 0.981
transverse 0.981
jealous 0.981
ontology 0.981
genus 0.981
slender 0.981
parenting 0.981
sermon 0.981
longing 0.981
affective 0.981
myocardial 0.981
auditory 0.981
coated 0.981
catheter 0.981
assay 0.981
spheres 0.981
authentication 0.980
vomiting 0.980
thigh 0.980
dorsal 0.980
subparagraph 0.980
commenced 0.980
skeletal 0.980
mammals 0.980
anemia 0.980
peptide 0.980
robe 0.980
baptism 0.980
holocaust 0.980
logan 0.980
danced 0.980
haired 0.980
hemorrhage 0.980
kinase 0.980
refugee 0.980
empathy 0.980
gastric 0.980
aortic 0.980
bishops 0.980
cohort 0.980
peered 0.980
adolescence 0.980
acknowledgments 0.980
supernatural 0.980
inhibitor 0.980
sperm 0.980
aquatic 0.980
judah 0.980
hegel 0.980
clinically 0.980
melbourne 0.980
bolt 0.980
socrates 0.979
fortress 0.979
squadron 0.979
cannon 0.979
modulation 0.979
situ 0.979
abu 0.979
lymph 0.979
aqueous 0.979
cortical 0.979
knot 0.979
appellant 0.979
foucault 0.979
communion 0.979
heidelberg 0.979
nietzsche 0.979
abdomen 0.979
reformation 0.979
landscapes 0.979
awe 0.979
tactical 0.979
summoned 0.979
commanders 0.979
arabs 0.979
intercourse 0.979
lied 0.979
genocide 0.979
blessings 0.979
ibn 0.979
earthly 0.979
perceptual 0.979
betrayed 0.979
duct 0.979
jerked 0.979
gorgeous 0.979
excitation 0.979
evangelical 0.979
lexical 0.979
conceptions 0.979
conductivity 0.979
spectroscopy 0.979
sucked 0.979
yoga 0.979
fixation 0.979
ghosts 0.979
antiquity 0.979
expressive 0.979
esthetics 0.978
tis 0.978
glands 0.978
cloak 0.978
dusty 0.978
coil 0.978
detached 0.978
sulfate 0.978
microbial 0.978
localization 0.978
epithelial 0.978
edema 0.978
potentials 0.978
cyclic 0.978
referral 0.978
microscopy 0.978
entropy 0.978
gothic 0.978
mhz 0.978
coded 0.978
clenched 0.978
lett 0.978
jealousy 0.978
lobe 0.978
groaned 0.978
atrial 0.978
cns 0.978
acad 0.978
queer 0.978
alzheimer 0.978
restless 0.978
flushed 0.978
mystical 0.978
bipolar 0.978
learnt 0.978
spectacle 0.978
jeremiah 0.978
conduction 0.978
notified 0.978
moist 0.978
daisy 0.978
militia 0.978
flap 0.978
webster 0.978
healed 0.978
tablespoon 0.978
stiffness 0.978
escort 0.978
apostles 0.978
fluorescence 0.978
glared 0.978
scar 0.978
grammatical 0.978
fetus 0.978
subjectivity 0.978
dante 0.978
messiah 0.978
viscosity 0.978
rupture 0.978
iris 0.978
spider 0.978
atp 0.977
roar 0.977
tho 0.977
riley 0.977
pelvic 0.977
sinus 0.977
dir 0.977
erotic 0.977
endothelial 0.977
skeleton 0.977
straightened 0.977
basil 0.977
sultan 0.977
spherical 0.977
mock 0.977
heidegger 0.977
assimilation 0.977
haunted 0.977
inscription 0.977
duly 0.977
lang 0.977
morphological 0.977
deity 0.977
pixels 0.977
rites 0.977
multicultural 0.977
terrestrial 0.977
archbishop 0.977
knelt 0.977
masculinity 0.977
torah 0.977
estrogen 0.977
aspiration 0.977
detachment 0.977
lace 0.977
marines 0.977
ligand 0.977
pagan 0.977
saxon 0.977
wolves 0.977
patted 0.977
cataloging 0.977
picnic 0.977
tenderness 0.977
almighty 0.977
jenkins 0.977
etiology 0.977
sabbath 0.977
firstly 0.977
carbohydrate 0.977
corinthians 0.977
interdisciplinary 0.977
sediments 0.977
intracellular 0.976
capillary 0.976
mrna 0.976
stain 0.976
metaphysics 0.976
writ 0.976
syntactic 0.976
sectional 0.976
cruelty 0.976
clinicians 0.976
permeability 0.976
uttered 0.976
docket 0.976
postmodern 0.976
penis 0.976
centerd 0.976


  1. >This is a sentence you’d expect in a novel.

    Exactly my thought: as I was looking through the under-represented word cloud, I was thinking “these are narrative and historical words.” I guess econ doesn’t do narrative, huh?


  2. This made me think about my filed of research. The problem is we use words that have meanings different from common usage. Organic (chemistry) is not natural. Structures are not made with bricks and mortar. Molecules can be cis or trans. Anti is not opposite of anything. A free radical is neither free nor antisocial. A solution is often a liquid. A reaction is not against an action. Strange!


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