A thematically clustered map of wealth inequality research in sociology

Figure 1 displays the co-citation network for sociology. Considered are cited references that have at least five co-citation links. It turns out that 110 out of 12,251 cited references identified by VOSviewer fulfill this selection criterion. Therefore, the network contains 110 vertices, with each vertex standing for a single publication.

Publications are visualized by circles and labels. The size of the publication’s circle and label depends on the total strength of links of a given publication. To avoid overlapping labels, some labels may not be visible. The labels used contain only the first author and the publication date. The label “Oliver (1995),” for example, stands for the monograph Black Wealth/White Wealth published by Melvin L. Oliver and Thomas M. Shapiro in 1995. All labels and their corresponding full publication titles are to be found in the Online Appendix.

The color of an item is determined by the cluster to which the publication belongs. The distance between two publications indicates the strength of their relatedness in terms of co-citation links. The closer two publications are located to each other, the more often these publications tend to be listed in the investigated bibliographies. “Easterlin (1974),” for example, tends to be cited in the same bibliographies as ‘Wilkinson (2010),” but is never mentioned together with “Lauterbach (1996).” In contrast, publications in the center of the co-citation network, such as “Oliver (1995)” or “Keister (2000c)” tend to have co-citation links with many and thematically different publications. The same holds for clusters as well. Cluster 2 (in green) is located closer to Cluster 1 (in red) than to Cluster 3 (in blue-purple) because of the greater number of co-citation links between Clusters 2 and 1.

The left part of the map in Fig. 2 represents what can be referred to as literature on the racial wealth gap best represented by such works as “Conley (1999),” “Massey (1993),” or “Oliver (1995).” While this literature documents wealth disparities between different ethnic groups, the literature at the right part of the map is mostly contributed by economists such as Easterlin or Stiglitz and is concerned with social inequality as a societal macro trend affecting the social conditions of the many.

Fig. 2 Notes: Number of vertices: 110; min. cluster size: 15; resolution parameter: 2.0; max. lines: 1300. (Color figure online) Visualization of the sociology of wealth inequality (co-citation network) Full size image

There are two other subfields. A significant number of sociologists specialized in research on the importance of intergenerational transmission of wealth (e.g., “Beckert (2008)”). Finally, classical studies (e.g., “Bourdieu (1984)”) on inequality and more theoretical contributions that investigate cumulative advantage processes as well as mechanisms underlying inequality (e.g., “Becker (1964)”) constitute a field on their own. Interestingly, Piketty’s contributions on the topic of the evolution of incomes (e.g., “Piketty (2006)”) belong to the very same sociological cluster. An overview of all four clusters is given in Table 1.

Table 1 Summary of the four thematic clusters in sociology Full size table

It is worth noting that the structure of the network is partly determined not only by content but also by exogenous factors such as common language and publication date. “Lauterbach (1996),” for example, is only loosely related to other publications in Cluster 1 because the article mainly refers to other German literature that is clearly underrepresented in the data set analyzed. Only recently published contributions such as Piketty’s Capital in the Twenty-First Century (“Piketty (2014)”) are more likely to have fewer co-citation links than work from earlier time periods. That is most likely the main reason why “Piketty (2006)” is more central to the network than “Piketty (2014).” As can be seen from Fig. 2, “Piketty (2014)” is mapped with Cluster 3 but (mistakenly) assigned to Cluster 4. Apparently, VOSviewer has difficulties allocating the right place to recent publications in a two-dimensional space, especially if these contributions additionally cross-cut different research domains, as “Piketty (2014)” does.

A thematically clustered map of wealth inequality research in economics

Figure 3 shows the result of a combined mapping and clustering of cited references in economics that have a total strength of five—which applies to 146 out of the 7344 references identified by VOSviewer. What catches the eye immediately are the different overall structures of the two co-citation networks (Figs. 1, 2):

Fig. 3 Notes: Number of vertices: 146; min. cluster size: 20; resolution parameter: 2.0; max. lines: 1300. (Color figure online) Visualization of the economics of wealth inequality (co-citation network). Full size image

While for sociology, we almost see a “citation island” of economic work (“Easterlin (1974)”) that relates only loosely to other clusters, only a few selected publications (“Atack (1981),” “Steckel (1990)”) occupy distant network positions in economics—which is mostly explained by this rather isolated work’s particular interest in past episodes (e.g., wealth inequality before the American Civil War).

Anthony Atkinson and Thomas Piketty turn out to be the leading figures in contributions that measure inequality by mainly focusing on the top of the affluence distribution (Cluster 1 in Table 2). A major topic in this research stream, therefore, is the “top one percent share” measure of wealth inequality.Footnote 7

Table 2 Summary of the five thematic clusters in economics Full size table

A second cluster gravitates around Franco Modigliani’s seminal contribution (“Modigliani (1988)”) on the relative magnitude of the two main sources of wealth: life-cycle savings and inter vivos transfers/bequests. Like the bulk of contributions in this cluster, Modigliani discusses the role of intergenerational transfers.Footnote 8

The third cluster consists of highly associated publications that share similar topics with the second cluster (see Table 2). Representative for this research are models that link parents and children by (voluntary and accidental) bequests and by transmission of earnings ability (“overlapping-generational model”). These stylized mathematical models that focus on few causes and seek to show how their effects function in the system are then mapped against reality.Footnote 9 In contrast, most authors in Cluster 2 tend to rely much more on inferences from (survey) data on savings and inheritance.

In the fourth cluster, research is mainly concerned with the trade-off between macroeconomic trends and the unequal distribution of material resources (see Table 2). “Galor (1993),” for example, reports evidence that cross-country differences in macroeconomic adjustment to aggregate shocks can be attributed to differences in wealth distributions across countries.

Publications in the fifth cluster are not concerned with top wealth shares (Cluster 1), empirical evidence on inheritance (Cluster 2), life-cycle models of savings (Cluster 3), or the relationship between economic growth and inequality (Cluster 4), but with macro models of wealth inequality. The “model builders” that belong to Cluster 5 try to come up with theory-based mathematical frameworks on the determinants of real wealth inequality (e.g., entrepreneurship, intergenerational links, rate of return, heterogeneity in savings rates, public policies). A typical workhorse model is the “Bewley model,” in which people (“agents”) save in order to self-insure against earnings shocks to smooth their consumption.

As already observed in Fig. 2, publications that cross-cut different research domains are positioned between the various clusters. Obvious examples in Fig. 3 are “BeckerG. (1979)” and “Kuznets (1955).”

Validating cluster results using computer-aided content analysis

Different researchers employ different vocabulary in academic writing. It is likely that we will find a higher word similarity between the work of social scientists writing on the same topic than between scientists with different research specializations. Text analysis is thus another appropriate tool to identify distinct research domains (Griffiths and Steyvers 2004).

In the following section, we will examine the distinctiveness of each cluster’s semantic profile by applying purely descriptive word frequency counts.Footnote 10 Considered are English-language journal articles (of similar length) only. If a given journal article is included in the JSTOR archive, we made use of the “JSTOR Data for Research”Footnote 11 service, which generates n-grams (contiguous sequences of n words) from archived texts. All other articles had to be converted from a pdf to an ASCII (text) format before conducting text analysis with Yoshikoder.Footnote 12

What is counted is the number of times a given word (1-g) such as “inheritance” appears in the texts belonging to a given cluster. We assume here that such a simple approach, which treats text as “bags of words” and thus ignores where in the text words occur, is able to reveal the overall topic contained in already clustered texts.

The most recurrent words which are likely to be indicative of topics are listed in descending order in Tables 3 and 4. The especially high frequency of terms such as “family,” “children,” “inheritance,” or “bequests” suggests that Cluster 1 in sociology is mainly concerned with the transmission of wealth from parents to children. Interestingly, the word “housing” is even more characteristic of Cluster 2 than words such as “race” or “ethnic”—which can be explained by the fact that the white-black wealth (or other ethnic wealth disparities) are closely intertwined with topics such as residential segregation or housing inequality (see, for example, “Massey (1993)”).

Table 3 The 30 most frequent terms in the sociology of wealth inequality Full size table

Table 4 The 30 most frequent terms in the economics of wealth inequality Full size table

The neglect of (sociological) books leads to a list of keywords for Cluster 3 that features, among other things, word combinations such as “top income” or “top 1 percent” (as several articles by Piketty are contained in the very same cluster). One has to treat these results with caution, as the inclusion of sociological books contained in the cluster would certainly reveal different keywords. As expected, words and word combinations such as “health,” “happiness,” “subjective well-being,” and “life satisfaction” dominate Cluster 4.

On a more general level, one can conclude that while word frequency analysis confirms the fractal divisions detected by network analysis, no cluster is monothematic. Text analysis reveals, for example, that different research streams such as articles on the happiness-wealth and the health-wealth nexus are clustered together. The many commonalities of these specialized literatures are, however, self-evident.

While content analysis clearly characterizes Cluster 1 in economics as containing work based on time series data and visualizing the changing top shares of income and wealth, the results show fewer differences in keywords between Cluster 2 and Cluster 3, as expected. The obvious reason is that both thematic clusters feature research on the role of life-cycle and inherited wealth. However, the different usage of word combinations such as “utility function” or “parameter value” hint at substantially different methodological approaches pursued in both clusters. Research contained in Cluster 3 uses economic models to uncover, for example, the significance of inherited wealth. These models are supposed to mimic reality, and parameters are typically calibrated based on key assumptions. A second step tests whether these models can produce aggregate wealth statistics observed in the real world. In contrast, research in Cluster 2 is marked by a different epistemological preference: Conclusions are derived inductively from empirical evidence. “Wolff (1994)” in Cluster 2, for example, reports results on trends in household wealth without reference to any economic model.

The outstanding salience of co-occurrences such as “economic growth,” “human capital,” “growth rate,” or “political instability” characterize Cluster 4 as featuring texts that discuss politico-economic differences between countries and how these relate to the distribution of wealth. Cluster 5 contains fewer unique keywords. What becomes evident is that this cluster of texts, which use models to explore the underlying causes of wealth inequality, is substantially less concerned with top wealth shares (Cluster 1), the role of life-cycle and inherited wealth (Clusters 2 and 3), and the trade-off between economic growth/development and inequality (Cluster 4). In essence, research in Cluster 5 features various models that are supposed to unearth the underlying forces (i.e., heterogeneity in rates of return) that drive wealth inequality.

In general, one can thus conclude that the simplest of all text-mining algorithms proves the distinctiveness of all thematic clusters.

The joint co-citation network: who bridges gaps in the social fabric of wealth inequality research?

The analysis has so far revealed different research paradigms and a few communalities between economics and sociology. We will continue by examining in detail whose research connects the otherwise mostly disconnected discipline-specific literatures. Scholars acting as intermediaries between research traditions are crucial in enabling knowledge transmission between disciplines, which is a necessary condition for any (future) interdisciplinary endeavor to address the complex problem of wealth inequality.

To do so, we merged both literatures which yielded a total of 18,977 references and extracted publications with 10 or more co-citation links which left us with a total of 70 publications. All co-citations between these publications are depicted in Fig. 4. As expected, the network separates into the economic literature on the left and in the center and the sociology literature on the right. Literature which appears in the co-citation network for both disciplines (see Figs. 2, 3) functions as a linchpin. Interestingly, we see three different ‘bridges’ between both disciplines.

Fig. 4 Notes: Number of vertices: 70; max. lines: 500; literature in grey appears only in the co-citation network of economics (see Fig. 3), literature in red appears only in the co-citation network of sociology (see Fig. 2), and literature in green is part of both disciplinary co-citation networks (see Figs. 2, Fig. 3). (Color figure online) A joint visualization of the economics and sociology of wealth inequality (co-citation network). Full size image

First, the work of the economist Edward N. Wolff made the most inroads into sociology. Wolff contributed to different topics such as the historical evolution of wealth trends, wealth top shares and the role of inherited wealth that are all of utmost interest to sociologists. What is more, his quantitative analyses of the Survey of Consumer Finances (SCF) do not rely on model assumptions and can be understood by non-economists. Thus, both the wide thematic scope and his intellectual style make Wolff´s work attractive to sociologists.

Second, the debate on the quantitative importance of bequests in wealth accumulation sparked by economists such as Laurence J. Kotlikoff, Franco Modigliani and continued by others such as William G. Gale became a cornerstone in the sociology literature. The many cross-links to this type of economic literature indicates that sociologists imported methodological insights on how to best measure inherited wealth from economics.

Third, Piketty´s work appears to contain significant resources for sociologists. The sociologist Savage (2014) identified three reasons for the Piketty´s outstanding reception in sociology.Footnote 13 His repertoire of assembling vast data into a powerful visual template, his historical orientation, and finally his conceptualization of a ‘elite’ class (“the top 1 percent”) chime closely with dominant research paradigms in contemporary sociology.

In line with previous work on interdisciplinary citation patterns (Fourcade et al. 2015), we find sociologists citing economists rather than the other way around. Clearly, work originating from sociologists is rarely, if at all, taken up by economists, which in turn makes interdisciplinary learning one-sided. The reasons for the limited reception of sociological work in economics appear to be not only due to different research interests but are also attributable to different methodological standards. The sociological literature does not build on the mathematical models that are the ‘golden standard’ in economics, making knowledge transfer from sociology to economics more difficult.