Have We Passed Peak Capitalism?
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Fix, Blair Article — Accepted Manuscript (Postprint) Have We Passed Peak Capitalism? Real-World Economics Review Provided in Cooperation with: The Bichler & Nitzan Archives Suggested Citation: Fix, Blair (2022) : Have We Passed Peak Capitalism?, Real-World Economics Review, ISSN 1755-9472, World Economics Association, Bristol, Iss. 102, pp. 55-88, https://bnarchives.yorku.ca/759/ This Version is available at: https://hdl.handle.net/10419/267775 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/4.0/
real-world economics review, issue no. 102 subscribe for free 55 Have We Passed Peak Capitalism? Blair Fix [York University, Toronto, Canada] Copyright: Blair Fix, 2022 You may post comments on this paper at http://rwer.wordpress.com/comments-on-rwer-issue-no-102/ Abstract This paper uses word frequency to track the rise and potential peak of capitalist ideology. Using a sample of mainstream economics textbooks as my corpus of capitalist thinking, I isolate the jargon of these books and then track its frequency over time in the Google English corpus. I also measure the popularity of feudal ideology by applying the same method to a sample of christian bibles. I find that over the last four centuries, biblical language fell out of favor and was replaced by the language of economics. Surprisingly, however, I find that since the 1980s, the trend has reversed. Today, the language of , . passed the peak of capitalist ideology? The end is (not) near Among leftists, predicting the end of capitalism is a favorite parlor game. For example, as a graduate 2010, 1976 Capital and being struck by the introduction. Written by the Belgian Marxist Erne , -century (Mandel, 1976). ( ) . social orders rarel . , . - , . B a peak. The goal of this post is to chart the rise (and potent ) . . , . , ( ). , , talism is primarily an ideology B (2009). Capitalism is a set of ideas that justify the modern social order. Although there are many ways to chart the rise of capitalism, what interests me here is that it was the . (and potential peak) should be visible in the word frequency of written language. F , , that capitalist jargon should become more common. And feudal jargon should become . , . B , o . , in question (capitalism or feudalism). And from there, I let the jargon of the text speak for itself. The
real-world economics review, issue no. 102 subscribe for free 56 basic idea is that jargon words are those that are both frequently used in a text corpus and overused relative to mainstream English. The first step of the analysis, then, is to select a corpus of ideological texts. To capture feudal ideology, I use a sample of 22 modern English bibles. I use mo ( ). A B of European feudalism. 32 To capture capitalist ideology, I use a sample of 43 introductory economics textbooks. My claim is that these textbooks deal mostly in capitalist metaphysics; they describe a fantasy world of self-equilibrating markets in which each person earns what they produce. 33 With my samples of biblical and economics text, I first isolate the jargon words of each corpus. Then I use the Google English corpus to measure how the frequency of this jargon has changed over time. (As a consistency check, I also analyze the text in paper titles on the Sci-Hub database and book titles in Library Genesis.) I find that over the last several centuries, biblical jargon became less popular and was slowly replaced by economics jargon. I also find evidence that the popularity of economics language peaked during the 1980s, and has since declined. Ominously, this peak coincides with an uptick in the popularity of biblical language. In simple terms, it seems that we (anglophones) are in the midst of an ideological transition. Why ideology matters , . B , emphasizing why I think the study of ideology is important. 32 One could argue that since the various books of the Bible were written over many centuries, the text advocates . (F , .) , B . foundation of class relations in medieval Europe. For example, here is an oath of fealty used by the Emperor Charlemagne in 802: By this oath I promise to be faithful to the lord Charles, the most pious emperor, son of King Pepin and Bertha, as a vassal should rightfully be to his lord, for the preservation of his kingdom and of his rights. And I will keep and hope to keep this oath which I have sworn as I know it and understand it, from this day henceforward, with the help of God, the creator of heaven and earth, and of these . ( G, 1952) Notice the use of biblical language, both in the explicit referenc G, a biblical synonym for God. The hallmark of christian ideology is its use of a heavenly hierarchy to legitimize earthly ones. 33 . , about the values it advocated. With the neoclassical revolution of the late 19th century, however, these values were purged and what was a supposedly scientific description of a market economy. . in neoclassical economics. Generations of critics have shown that its foundations are either untenable or untestable. (For example, see Keen (2001), Mirowski (1991), and Bichler & Nitzan (2021).) Like the Bible, neoclassical economics deals almost completely with metaphysics. Thus, it seems fitting to call . , . However, like the God of the Bible, these competitive markets are nowhere to be found in the real world. Instead, we find a world fille .
real-world economics review, issue no. 102 subscribe for free 57 Karl Marx was likely the first political economist to exhaustively study the transition from feudalism to . , to be understood in material terms. Ideology was not an : Just as our opinion of an individual is not based on what he thinks of himself, so we . contrary, this consciousness must be explained from the contradictions of material life. (Marx, 1980) , . , have a life of their own within our collective consciousness. In a sense, Marx proved so himself by writing an ideological tome about capitalism that went on to inspire many anti-capitalist revolutions. In broader terms, humans are a cultural species, which means that our behavior is driven in large part by our collective ideas. How and why these ideas evolve is poorly understood. But what seems clear is that the evolution of our ideological milieu is an important topic of inquiry. To turn Marx on his head, we can and should judge a period of transformation by its own consciousness. Dissecting an ideology G , ? . ical change, we analyze the meaning of a belief-system. Then, we try to map this meaning onto the changing social zeitgeist. A . meaning, we break the belief system into its smallest components namely, words. The idea is that the features of an ideology can be discerned through the frequency of its vocabulary. Words that are frequent tell us what the ideology emphasizes. And words that are infrequent tell us what the ideology ignores. The main advantage of this statistical approach is that it allows us to parse massive quantities of text, and so gauge the written zeitgeist in a way that no philosopher ever could. 34 To analyze the rise of capitalist ideology, I will use the statistical approach. The core of my method is illustrated in Figure 1, which demonstrates how I use word frequency to classify the vocabulary of a text. 34 To give you a sense of the analytic scale we can achieve with the statistical approach, consider the size of the Google English database. It reports the frequency of about 800 billion words (written over more than four centuries) and can be parsed by a modern computer in a few minutes. (Compiling this database took far longer.) If a person attempted to read the same volume of text, they would die long before they finished. For example, supposing you read 300 words per minute for 8 hours a day, 365 days a year, you would need about 15,000 years to parse 800 billion words.
real-world economics review, issue no. 102 subscribe for free 58 Figure 1: Categorizing the vocabulary of a corpus of text. This figure illustrates my method for classifying words within a sample of text. On the horizontal axis I measure . , E. B , : , , -. On the horizontal axis in Figure 1, I plot word frequency within the text itself. As an example, in , (an organic compound) will be rare. Although it is tempting to judge a text by its word frequency alone, we need to account for a more general feature of language, which is that some words get used more than others. For example, in the E , . A , . , compare their frequency to w E. : relative frequency = text frequency frequency in mainstream English
real-world economics review, issue no. 102 subscribe for free 59 F 1, . B two dimensions of frequency, we can classify the vocabulary of a text into four quadrants: 1. Jargon: words that are common in the text corpus and overused relative to mainstream English. 2. Quirks: words that are rare in the text corpus, yet overused relative to mainstream English. 3. Under-represented: words that are common in the text corpus but underused relative to mainstream English. 4. Neglected: words that are rare in the text corpus and underused relative to mainstream English. , . tell us about the concepts that define a text. And neglected words tell us about the omissions that define a text. I will use this classification scheme to measure the changing ideological landscape of the English language. In what follows, I assemble a corpus of economics textbooks and a corpus of bibles. Then I F 1. is that we do not need to choose the jargon words ourselves. Instead, we let the text speak for itself. That said, my classification scheme still requires some subjective decisions. Most importantly, to construct our quadrants, we need to compare word frequency in the text to the frequency found in E. A writing. However, given that there are many forms of writing (books, newspapers, websites, email, text ), E / . things, I will focus only on the language found in books. A E, G E . dataset, which is derived from G , 800 . E, E. G s both popular books as well as technical monographs. So it does not tell us about the language heard in a typical English conversation. Instead, the Google corpus quantifies the average linguistic patterns found across the whole spectrum of anglophone books. 35 The other major decision that goes into my word classification system is the choice of threshold that separates the left and right quadrants (in Figure 1). There is no linguistic feature that tells us exactly where this threshold should lie. However, when we plot word frequency on a logarithmic scale (see the horizontal axes in Figures 2 and 5), I find that the value of 50 words per million lies roughly in the middle of the frequency range. So I use this number as my dividing line between left and right quadrants. 35 Some scientists are skeptical of using the Google English corpus to gauge the social zeitgeist (for example, Pechenick, Danforth, & Dodds, 2015). One problem is that the corpus contains one of each book, so it does not . , measuring what authors write, not what people read. Another criticism is that the Google books corpus contains a , E. A, , since I am concerned here with the state of ideas exp E, .
real-world economics review, issue no. 102 subscribe for free 60 Analyzing the language of economics H , . A , . To analyze the language of economics, I have collected a sample of 43 undergraduate economics textbooks, shown in Table 1. Although not exhaustive, this sample contains most of the standard texts used in undergraduate economics courses (taught in English). When po, , . , of front matter, and then fed the resulting text into a word-counting algorithm. (For details about the text processing, see Sources and methods.) The resulting language sample contains about 10.7 million words, with a vocabulary of roughly 35,000 unique words. Table 1: A corpus of capitalist ideology from 43 economics textbooks Author Title Year Arnold Economics 2008 Arnold Macroeconomics 2008 Arnold Microeconomics 2011 Blanchard & Johnson Macroeconomics 2012 Case, Fair & Oster Principles of Economics 2012 Case, Fair & Oster Principles of Macroeconomics 2011 Case, Fair & Oster Principles of Microeconomics 2008 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 H & B Economics 2009 H & B Macroeconomics 2011 H & B Microeconomics 2013 Krugman & Wells Economics 2009 Krugman & Wells Macroeconomics 2005 Krugman & Wells Microeconomics 2012 LeRoy Miller Economics Today 2011 LeRoy Miller Economics Today: The Macro View 2011 LeRoy Miller Economics Today: The Micro View 2011 Mankiw Principles of Economics 2008 Mankiw Principles of Macroeconomics 2011 Mankiw Principles of Microeconomics 2011 McConnell, Brue & Flynn Economics 2008 McConnell, Brue & Flynn Macroeconomics 2006 McConnell, Brue & Flynn Microeconomics 2011 Nicholson & Snyder Microeconomic Theory 2004 Nicholson & Snyder Microeconomic Theory 2007 Nicholson & Snyder Microeconomic Theory 2011 Parkin Macroeconomics 2011 Parkin Microeconomics 2011 Parkin, Powell & Matthews Economics 2005 Perloff Microeconomics 2011 Perloff Microeconomics 2014
real-world economics review, issue no. 102 subscribe for free 61 Author Title Year Pindyck & Rubinfeld Microeconomics 2012 Pindyck & Rubinfeld Microeconomics 2014 Rittenberg & Tregarthen Principles of Economics 2009 Rittenberg & Tregarthen Principles of Macroeconomics 2009 Rittenberg & Tregarthen Principles of Microeconomics 2009 Samuelson & Nordhaus Economics 2009 Varian Intermediate Microeconomics 2005 Varian Intermediate Microeconomics 2010 Varian Intermediate Microeconomics 2014 In Figure 2, I take the vocabulary in these economics textbooks and plot it on my quadrant system. In this chart, each point . textbooks. On the vertical axis, I plot relative frequency the ratio of textbook frequency to the frequency in the Google English corpus. In each quadrant, the colored points represent the 1000 words that are most overused (for quirks and jargon) or most underused (for neglected and underrepresented). Looking ahead, it is the red- . Figure 2: Dissecting the language found in economics textbooks. This figure analyzes word frequency in a sample of 43 economics textbooks. Each point represents a word, with its frequency in economics textbooks plotted on the horizontal axis. On the vertical axis, I compare this textbook frequency to word frequency in the Google English corpus (averaged over the years 20002019). In each quadrant, the colored points represent the 1000 words that are most overused (for quirks and jargon) or most underused (for neglected and under-represented). For more details about the data, see Sources and methods.
real-world economics review, issue no. 102 subscribe for free 62 Some things to note about Figure 2. If the language in economics textbooks was identical to the Google English corpus (something we do not expect), then all the points would cluster around the horizontal axis. But that is not what happens. Instead, we find a large vertical spread in relative frequency. This spread indicates that economics textbooks overuse some words (those which appear above the horizontal axis) and underuse others (those which appear below the horizontal axis). When interpreting the data in Figure 2, note that both axes use logarithmic scales. These scales allow us to visualize the entire vocabulary of our text corpus on one chart; however, they have the effect of compressing frequency variation, which is actually enormous. For example, in my sample of economics , () ( ) factor of 100,000. (Note that preposition words would be even more frequent. But since they are uninteresting for ideological analysis, I have removed them. For more details about the text processing, see Sources and methods.) Just as word frequency varies immensely, so does relative frequency. For instance, economics 370 G E . A 1000 G E . Because there are about 35,000 words plotted in Figure 2, i . H, the word cloud in Figure 3 gives you a sense of the words that define economics writing. Here I show . , the words that stand out. Figure 3: Economics jargon. This cloud shows the most overused words from the economics jargon quadrant in Figure 2. These are words that are frequent in economics textbooks and overused relative to mainstream English.
real-world economics review, issue no. 102 subscribe for free 69 Figure 9: The location of biblical jargon in the Bible and in economics textbooks. This figure shows the top 1000 jargon words selected from m . ( , ). of the same words in my sample of economics textbooks. The vast majority of these words are found in the , . F , see Sources and methods. Because our bibles and economics textbooks neg , ideologies are mutually distinct. Moreover, if the two ideologies remain distinct, their jargon must move in opposite directions. Therefore, with no understanding of history, we can make a simple prediction: if economics jargon becomes more popular, biblical jargon will become less popular (and vice versa). As we will see in a moment, this is exactly what happened historically. Pausing for reflection, I suspect that the mutual exclusion shown in Figures 8 and 9 is a general feature of all opposing ideologies. In short, if two ideologies conflict, we expect that they will not share each . The changing frequency of biblical and economics jargon in the Google English corpus on to look at the changing ideological landscape that is written in the English language. To quantify ideological change, I measure how the frequency of biblical/economics jargon has changed with time in the Google English corpus.
real-world economics review, issue no. 102 subscribe for free 70 Figure 10 shows my results. Here the blue line shows the annual frequency of biblical jargon. The red line shows the annual frequency of economics jargon. Note that the frequency is expressed per thousand words, so you can interpret it like a batting average. For example, during the 1950s, for every 1000 words contained within the Google corpus, about 130 of them were economics jargon. (If our 1000, E jargon.) Figure 10: The changing frequency of biblical and economics jargon in the Google English corpus. E / G E . 1000 F 2. A 1000 most overused words from the jargon quadrant of Figure 6. In each year, I sum the frequency of these words in the Google corpus, and plot the pattern over time. To smooth the trends, each line shows the 10-year trailing average of jargon frequency. For more details about the data, see Sources and methods. F 10, . , al jargon fell out of favor while economics jargon became more popular. This is exactly what we expect for the transition from feudalism to capitalism. To give you a sense of the scale of this linguistic transformation, note that in the 17th century, English writing was overwhelmingly religious. For every 1000 words written during that period (and captured by the Google English corpus), on average about 200 of them were biblical jargon. That may not sound significant, until you realize that my sample of bi 300. , 1000 , 300 . E literature of the 17th century was dominated by theology.
real-world economics review, issue no. 102 subscribe for free 71 From the 18th century onward, however, this christian hegemony steadily waned. At the same time, the jargon of economics grew more popular, becoming the dominant ideology around the turn of the 20th century. While we should be cautious about interpreting these trends (because they depend in part on my analytic assumptions), they seem consistent with what we know both about the history of capitalism and about the history of economic thought. We know, for example, that the transition to capitalism has deep roots, but that the change accelerated during the late 19th century, as Western countries began to industrialize. We also know that the origin of modern-day economics dates back centuries, but that these ideas were not mainstream until after the marginal revolution of the late 19th century. So over all, the long-term pattern in Figure 10 meets our expectations. That said, something conspicuous happened after 1980: the popularity of economics jargon began to wane, and the popularity of biblical jargon began to rise. What should we make of this reversal? Does it indicate that we (anglophones) have passed the peak of capitalist ideology? I think the answer is yes. Reinforcing evidence There is a saying in science that extraordinary claims require extraordinary evidence. I admit that I canno ideology. However, I can muster several independent lines of linguistic evidence that point to the same conclusion. In what follows, I look at different ways of tracking the popularity of biblical and economics language. Most of these measurements suggest that capitalist ideology (as measured by the language of economics) peaked in the 1980s. The language similarity index In Figure 10, I tracked the frequency of b . method is that it is (in my opinion) simple to understand. The disadvantage of the jargon approach is that it tracks only a small portion (about 3%) of the vocabulary of each corpus. To address this , . similarity between two language samples using all of the words common to both vocabularies. The idea behind the similarity index is that we can quantify language similarity by comparing the . F , frequency in two text samples, this would indicate that the . , one word. So what we should do is compare the frequency of every word that is contained within both samples of text. The closer the frequency of each word, the more similar the texts. In more technical terms, the similarity index is defined as follows. Let 𝑓 be the frequency of word 𝑖 in language sample 𝑎. And let 𝑓 be the frequency of the same word in language sample 𝑏. For the intersection of all words found in both samples, the similarity index is defined as: = 100 ⌊ ln𝑓 ln𝑓 1 ⌋ H, | | , ⌊ ⌋ nearest integer. The resulting similarity index varies from 0 (no similarity between language samples)
real-world economics review, issue no. 102 subscribe for free 72 to 100 (the language samples have identical word frequency). The advantage of the similarity index is that it measures all words in a text. The disadvantage is that it requires a large text sample to give accurate results. (See Sources and methods for a discussion.) In Figure 11, I apply the similarity index to measure the similarity between the Google English corpus and: (a) my sample of bibles (blue line); and (b) my sample of economics textbooks (red line). Because the similarity index requires a large text sample to be accurate, I restrict the analysis to the last two centuries. Figure 11: The similarity index between the Google English corpus and my sample of bibles and economics textbooks. frequency. An index of 0 indicates no similarity. An index of 100 indicates that the word frequency is identical. The blue curve compares the similarity of the Google English corpus to my sample of bibles. The red curve compares the similarity of the Google English corpus to my sample of economics textbooks. To smooth the trends, each line shows the 5-year trailing average of the similarity index. For more details about the data, see Sources and methods. Looking at Figure 11, we see that the trends are not identical to those found using jargon (in Figure 10). Using the similarity index, we find that the decline of biblical language is less pronounced, while the increase of economics language is more rapid. That said, the overall pattern is similar. Over the last two centuries, mainstream English became less similar to modern bibles and more similar to economics textbooks. And around 1980, this pattern reversed.
real-world economics review, issue no. 102 subscribe for free 73 The frequency of biblical and economics jargon in other samples of English writing Although the Google English corpus is the largest sample (by far) of historical English writing, it is wise to check that our results hold across multiple sets of data. With that in mind, I turn now to the word frequency found in two other sources of historical text: 1. The titles of academic articles on Sci-Hub 2. The titles of books on Library Genesis For some context, Sci-Hub and Library Genesis are two databases that make copyrighted material . ( . .) - Hub has amassed a collection of some 90 million scientific papers. And Library Genesis holds a collection of over 20 million books. Since these articles and books are available in full text, we could in principle analyze their word frequency as Google has done with its corpus of books. However, the scale of the task is enormous; it would involve downloading and processing many terabytes of data. 37 While certainly possible, I leave this intensive task as a possibility for the future. Here, I look only at the titles within these two databases. At first glance, studying titles alone seems like a poor source of English text. Keep in mind, however, that Sci-Hub and Library Genesis host many millions of entries. So even when we look only at titles, we are still dealing with a substantial body of writing. (For details about the sample size, see Figure 17 in Sources and methods.) The caveat is that the Sci-Hub and Library Genesis datasets are not large enough to apply the language similarity index. So I look only at the frequency of jargon. Figure 12 shows the frequency of biblical and economics jargon within article titles on Sci-Hub. Consistent with the trends found in the Google corpus (Figure 10), it seems that over the last two centuries, biblical jargon fell out of favor, while economics jargon grew more popular. We also see a plateau in this pattern circa 1980. H, -Hub data that the popularity of economics jargon has actually peaked, since after 2010, we see an uptick in the trend. 37 For example, public domain advocate Carl Malamud recently released an ngram database which analyzes the full text of some 107 million academic articles, many of which are suspected to have come from Sci-Hub (Public Resource, 2021). The problem is that this dataset is an unwieldy 38 Terabytes. Crunching data of this scale is beyond the scope of the current analysis.
real-world economics review, issue no. 102 subscribe for free 74 Figure 12: The frequency of economics jargon and biblical jargon in article titles on Sci-Hub. The red curve shows the frequency of economics jargon in the titles of articles in the Sci-Hub database. The blue . 1000 words from the respective jargon quadrants in Figures 2 and 5. I show data over the period during which the SciHub dataset contains more than 10,000 words per year. To smooth the trends, each line shows the 5-year trailing average of jargon frequency. For more details about the data, see Sources and methods.
real-world economics review, issue no. 102 subscribe for free 75 Turning to the book titles in Library Genesis, Figure 13 shows the frequency of economics and biblical jargon over the last century. Again, we see that until 1980, biblical jargon waned in popularity, while economic jargon grew more popular. After 1980, the pattern reversed. Figure 13: The frequency of economics jargon and biblical jargon in book titles on Library Genesis. The red curve shows the frequency of economics jargon in the titles of books in the Library Genesis database. The . 1000 words from the respective jargon quadrants in Figures 2 and 5. I show data over the period during which the Library Genesis dataset contains more than 10,000 words per year. To smooth the trends, each line shows the 5-year trailing average of jargon frequency. For more details about the data, see Sources and methods. Looking at the results in Figures 1013, the trends from the various measurements/datasets appear consistent with one another. Until about 1980, biblical language became less popular and economics language grew more popular. After 1980, these trends reversed. (For in-depth consistency checks, see Figures 19 and 20 in Sources and methods.) The peak of capitalist ideology Having collected several independent measures of E , judge if and when capitalist ideology peaked (as measured by the frequency of economics language). Figure 14 analyzes the timing of this peak. In panel A, points indicate the date when economics language peaked in popularity, as judged by the corresponding metric on the vertical axis. The error bars show the range for the top ten years. Using the same convention, panel B shows the date when
real-world economics review, issue no. 102 subscribe for free 76 biblical language reached its minimum popularity. The evidence suggests that economics ideology peaked around 1980, with biblical ideology reaching a minimum around the same time (or shortly thereafter). Figure 14: The timing of peak economics. Panel A analyzes the date at which economics language peaked in popularity in my samples of English writing. Panel B analyzes the date when the popularity of biblical language reached a minimum. Points indicate the date of maximum (for economics) or minimum (for biblical language) popularity. Error bars show the range for the 10 years with maximal/minimal popularity. For more details about the data, see Sources and methods. Before discussing the significance of this peak, a note of caution. Technically, the evidence demonstrates that there has been a peak in the popularity of economics language. Because the future is unwritten, we cannot be certain that we have observed the peak. 38 However, assuming there is no 38 US oil production provides a good example of a peak that was not the peak. In 1970, US oil production peaked, and then proceeded to decline for many decades. Many analysts thought they had witnessed the peak of oil
real-world economics review, issue no. 102 subscribe for free 77 reversal of current trends, we are left with a fascinating conclusion: capitalist ideology (as I measure it) seems to have peaked around 1980. The date of this peak is interesting, because it coincides with the so- anglophone politics. In 1980, Ronald Reagan became US President, running on a platform that heralded the benefits of free-market capitalism. In Britain, Margaret Thatcher ran on a similar platform, and was elected Prime Minister in 1979. -market movement (which extended far beyond Reagan and Thatcher) hearkened back to the laissez faire policies of the 19th century. The linguistic evidence, however, suggests that this way of characterizing the neoliberal turn is not entirely accurate. Yes, free-market ideas existed in the 19th century and were promoted by neoclassical economists. But . , economics achieved its maximum appeal. And so perhaps a more appropriate way of thinking about neoliberalism is that it was not a return to the past; it was a statement of ideological supremacy the moment when there really was no alternative to the doctrines of economics. . F 1990 , omics ideology waned. I can think of many reasons for this decline, all of which relate to the fact that economics promises a free-market road to utopia. The longer this myth fails to pan out in reality, the less convincing it becomes. As an example, take the problem of inequality. Beginning in the 1980s, anglophone countries experienced an explosion of income inequality, likely caused by neoliberal policies (Piketty, 2014). Apart from being socially corrosive, the problem with inequality is that it tends to undermine the economic doctrine that income stems from productivity. For example, if a CEO earns 5 times more than the , CE productivity. But if the CEO earns 500 times more than the average worker, attributing this windfall to . The 1980s also marked the period when climate change became a growing concern. People discovered that mainstream economists paid virtually no attention to the environment, other than to say that the market would solve our problems. But the market did not solve our ecological problems, which are today more severe than ever. Broadly speaking, I suspect that since the 1980s, there has been a wave of hostility to neoclassical economics. Part of this hostility has come from the left, and from social scientists seeking to construct a more realistic theory of human interaction. How impactful this left-leaning movement has been, I do not know. (An interesting research project would be to use the linguistic methods developed here to study the extent of progressive ideology.) What we do know is that a major source of hostility towards economics seems to have come from christian reactionaries. Indeed, this is the most plausible explanation for why biblical language is on the rise. And yet here we have a puzzle. Many evangelical christians proclaim a faith in both God and free markets (Rae & Hill, 2010). So you would think that if evangelical ideology became more prominent, biblical and economics language might both become more popular. And yet there is little evidence for such a trend. production. However, during the 2010s, there was an explosion of shale oil extraction that reversed the decline, and eventually pushed oil production to new heights. The lesson: we can never be certain that a peak is permanent.
real-world economics review, issue no. 102 subscribe for free 78 , puzzle. But I suspect that given its appeal to biblical literalism, the evangelical movement has less in common with the secular doctrines offered in economics textbooks, and more in common with the religious doctrines of the Bible. An age of ideological discord The linguistic evidence suggests that anglophones are in the middle of an ideological transition a period when economics ideology is losing its dominance but has not yet been replaced by another hegemonic belief system. Assuming this inference is true, then there should be many signs of this ongoing ideological struggle. In his book Ages of Discord, biologist-turned-historian Peter Turchin provides a rich set of evidence that , (, 2016). What Turchin means is that the US seems to oscillate between periods of social harmony and . , . Might this discord relate to the ideological transition that is written in the linguistic data? To test this possibility, I propose something called . , an interesting (almost paradoxical) feature of ideology. Humans discern ideology in much the same way as we smell things. For example, when you walk into a bakery, you immediately smell the aroma. Yet , , . new scents. When an aroma becomes ubiquitous, it quickly loses its odor. . A , . , , . , . H, , . wrong ideology and it . . the degree that no ideology dominates. Conversely, as one ideology becomes hegemonic, the world . Figure 15 illustrates this thinking. In the top panel, the red and blue lines plot the popularity of two opposing ideologies. Over time, ideology A falls out of favor and is replaced by ideology B. The bottom panel shows the corresponding ideological discord index. When ideology A is dominant, there is little discord. But as the two ideologies approach equal popularity, discord rises. Then, as ideology B becomes dominant, discord falls.
real-world economics review, issue no. 102 subscribe for free 85 Figure 18: How the language similarity index is affected by sample size. This figure shows the result of an experiment where I sample words from the economics textbooks corpus and then calculate the similarity index between this sample and the full corpus. The horizontal axis shows the sample size. The vertical axis shows the corresponding similarity index. We reach the true similarity (100) only when the sample size is comparable to the database itself. Testing the consistency of the various measures of economics/biblical language I have assembled four measures of the popularity of economics/biblical language: 1. Jargon frequency in the Google English corpus (Figure 10) 2. Language similarity index between bibles/economics textbooks and the Google English corpus (Figure 11) 3. Jargon frequency in Sci-Hub article titles (Figure 12) 4. Jargon frequency in Library Genesis book titles (Figure 13) , F 19 . Each box shows the correlation between the corresponding time series labeled on vertical and horizontal axis. (Darker red indicates a stronger correlation.) Panel A correlates the various measure of economics language. Panel B correlates the measure of biblical language. The correlations are generally high, indicating that the measurements are consistent with each other.
real-world economics review, issue no. 102 subscribe for free 86 Figure 19: Cross correlating the four metrics of economics/biblical language. E -series correlation between the corresponding metrics on the horizontal and vertical axes. For example, in panel A, the top right box shows the time-series correlation between economics jargon in Library Genesis and in Sci-Hub. The top right box in panel B shows the equivalent correlation for biblical jargon. A deeper shade of red indicates greater correlation. Figure 20 illustrates that for each of these measurements, the popularity of biblical language correlates negatively with the popularity of economics language.
real-world economics review, issue no. 102 subscribe for free 87 Figure 20: The popularity of economics language correlates negatively with the popularity of biblical language. Each panels shows a different measurement of word frequency in English, with the popularity of biblical language plotted on the horizontal axis and the popularity of economics language plotted on the vertical axis. Color indicates the year of the data. The correlations are strong and negative. When biblical language becomes less popular, economics language becomes more popular, and vice versa. Polarization of US Federal Government Data for the ideological stance of Federal politicians comes from voteview.com, series nokken_poole_dim1. It quantifies the ideological stance of each US president, senator and member of congress on / 0 ( ) 1 ( liberal). For details about this data, see Boche, Lewis, Rudkin, & Sonnet (2018).
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