Sunday, November 6, 2022

The idea that conservatives are more sensitive to disgust than liberals is a basic tenet of political psychology — and it may be a mere artifact of self-reports

Investigating the conservatism-disgust paradox in reactions to the COVID-19 pandemic: A reexamination of the interrelations among political ideology, disgust sensitivity, and pandemic response. Benjamin C. Ruisch et al. PLoS One, November 4, 2022.

Abstract: Research has documented robust associations between greater disgust sensitivity and (1) concerns about disease, and (2) political conservatism. However, the COVID-19 disease pandemic raised challenging questions about these associations. In particular, why have conservatives—despite their greater disgust sensitivity—exhibited less concern about the pandemic? Here, we investigate this “conservatism-disgust paradox” and address several outstanding theoretical questions regarding the interrelations among disgust sensitivity, ideology, and pandemic response. In four studies (N = 1,764), we identify several methodological and conceptual factors—in particular, an overreliance on self-report measures—that may have inflated the apparent associations among these constructs. Using non-self-report measures, we find evidence that disgust sensitivity may be a less potent predictor of disease avoidance than is typically assumed, and that ideological differences in disgust sensitivity may be amplified by self-report measures. These findings suggest that the true pattern of interrelations among these factors may be less “paradoxical” than is typically believed.

General discussion

This research provides important insight into the conservatism-disgust paradox in responses to the pandemic, as well as the relations among each of these target constructs—disgust sensitivity, political ideology, and pandemic response. These studies identified multiple factors that influence the (apparent) strength of the relations among these variables, thereby pinpointing several factors that are likely to have contributed to this seemingly contradictory pattern of results (Table 3).

Table 3. List of hypotheses, the study in which each hypothesis was tested, and whether or not each hypothesis was supported.

One contributing factor appears to be the predominant use of self-report measures of pandemic response in past research. Indeed, using a behavioral measure of virtual social distancing, we found that the relations between pandemic response and both ideology and disgust sensitivity were significantly attenuated, compared with self-report pandemic response measures. These findings are consistent with the possibility that these self-report measures may suffer from IV-DV conceptual overlap, while also being more susceptible to social desirability and other reporting biases [3638]. Particularly given that this same virtual behavioral measure has been shown to out-predict self-reports in predicting who contracts the COVID-19 virus [21], these results suggest that behavioral measures of pandemic response may provide a more accurate estimate of the extent of ideological differences in responses to the COVID-19 pandemic, as well as of the predictive power of disgust sensitivity for pandemic response. We found a similar divergence between self-report and non-self-report measures in the domain of disgust sensitivity. In this case, however, it was our experiential measure of disgust sensitivity that was the more powerful predictor of pandemic response. These findings identify important additional caveats and considerations for research examining the impact of disgust sensitivity on real-world outcomes, suggesting, in line with some past research, that self-reports of disgust sensitivity may correlate only modestly with other, more experiential or indirect indices of sensitivity to disgust—and that these measures/operationalizations may have different predictive power for different kinds of attitudes and behavior.

These findings also provide a means of beginning to reconcile some of the puzzling associations uncovered in other research on the COVID-19 pandemic. In particular, recent work suggests that—despite the putative disease-protective function of disgust—individuals who scored higher on self-reported disgust sensitivity may actually have been more likely to contract COVID-19 than those who self-reported less disgust sensitivity [23]. As documented here, however, self-reported disgust sensitivity appears to be only a relatively weak predictor of behavioral responses to the pandemic (indeed, adjusting for our experiential disgust measure rendered this association effectively nonexistent). Thus, although questions remain, these findings may bring us a step closer to understanding how self-reported disgust sensitivity could be a positive predictor of contracting the COVID-19 virus.

Perhaps the most intriguing findings, however, concern the relation of political ideology to self-report and experiential measures of disgust sensitivity. Using the DS-R, we replicated the well-documented ideological differences in self-reported disgust sensitivity. However, using our more experiential measure of disgust sensitivity—which presented participants with visual stimuli that closely corresponded to those described in the DS-R vignettes—we found no evidence of liberal-conservative differences in sensitivity to disgust.

Taken together, the findings discussed above suggest that methodological features of past research—particularly the heavy reliance on self-report measures of disgust sensitivity and pandemic response—may have inflated the relations among these three variables, and, thus, contributed to this seemingly contradictory pattern of results. In identifying the influence of these methodological factors, this research brings us a step closer to resolving the conservatism-disgust paradox, suggesting that the true pattern of interrelations among these variables is not as “paradoxical” as is typically assumed. That is, if, as these findings suggest, (1) the true relation between disgust sensitivity and pandemic response is smaller than previously suggested, and (2) ideological differences in disgust sensitivity are overestimated, then it is less surprising that conservatives exhibit less concern about the virus—particularly given that (3) ideological differences in responses to the pandemic may not be as dramatic as has been suggested by past research. The relatively small size of these effects makes it more likely that they would be subsumed by other concerns and motivations such as ideological identification and elite cues.

More generally, these findings also pose some challenges for past research and theory—particularly work suggesting a general relation between disgust sensitivity and political ideology. At the very least, these findings appear to suggest that liberals and conservatives do not differ in the form of disgust sensitivity that is most predictive of pandemic response. A more pessimistic interpretation, however, is that ideological differences in disgust sensitivity may generally be overestimated. That is, consistent with some recent critiques, it may be that self-report measures such as the DS-R amplify the true degree of ideological differences in disgust sensitivity, at least compared with measures that rely less on self-reports and self-beliefs about one’s own sensitivity to disgust.

Of course, our findings stand in contrast to a large body of research that suggests a connection between ideology and disgust, and, clearly, liberals and conservatives do reliably differ on many measures of disgust sensitivity (in particular, the DS-R and similar vignette-based measures). However, our findings also seem to align with other recent failures to replicate ideological differences in sensitivity to disgust using more indirect or experiential measures (e.g., [45]). Particularly in light of other research suggesting that people may have limited introspective ability into their own level of disgust sensitivity (e.g., work showing that self-reports sometimes do not significantly correlate with more indirect measures of disgust sensitivity; e.g., [185758]) a closer examination of the nature and extent of ideological differences in disgust sensitivity may be warranted.

These findings therefore suggest that there may be a theoretical gap in our understanding of the relation between ideology and disgust sensitivity: Why is it that ideological differences reliably emerge on some measures of disgust sensitivity (e.g., the DS-R) but not others—even, as we found, measures that assess responses to closely related, or even identical, situations and stimuli? One possibility is that the ideological differences on the DS-R and similar vignette-based measures of sensitivity to disgust can in part be attributed to factors other than disgust sensitivity per se.

For example, forthcoming research suggests that conservatives tend to self-report greater interoceptive sensitivity—that is, to subjectively feel that they are more sensitive to the internal physiological states and signals of their own bodies—although by objective metrics they are actually less sensitive than are liberals [68]. Moreover, other research suggests that conservatives’ overconfidence may extend beyond interoception to experiences, judgments, and perceptions writ large [69]. Extending these past findings to the domain of disgust sensitivity would seem to suggest that conservatives may be likely to subjectively feel that they are more sensitive to disgust than they actually are, perhaps explaining why self-report measures of disgust sensitivity—which in part assess self-beliefs about one’s own degree of sensitivity to disgust—show more robust associations with conservatism than measures of disgust that are rooted in more immediate experience.

Less interestingly, another potential explanation for the weaker relation between ideology and our experiential disgust measure may be that previously documented ideological differences in personality traits such as conscientiousness [70] lead conservatives to complete survey measures more thoughtfully, perhaps reading more carefully or engaging more deeply with the material. This, too, could help explain why conservatives report experiencing greater disgust in response to these vignettes—which require a degree of cognitive effort to process and mentally represent—but do not appear to differ as greatly when these same stimuli are presented visually. Future research may wish to assess these possibilities to deepen our understanding of the nature of the relation between ideology and sensitivity to disgust.

More generally, these findings suggest that caution may be warranted in the development and use of measures to assess these constructs—disgust sensitivity, political ideology, and pandemic response—and, especially, their interrelations. Given the close connections among these factors, coupled with potential confounds such as self-presentational concerns that may be at play for such impactful and politicized issues as the COVID-19 pandemic, the use of self-report measures, in particular, should be subject to close scrutiny.

Finally, it is important to note that while our studies consistently show that using self-report scales may overestimate the strength of the interrelations among disgust sensitivity, pandemic response, and political ideology, some of these effects may be specific to the population that we sampled. Indeed, the sociopolitical context surrounding the COVID-19 pandemic in the U.S. was in many ways unique, and these factors are likely to have shaped some of our effects. In particular, as discussed above, the stark political polarization surrounding the pandemic in the U.S. is likely to have been at least partially responsible for the inflated ideological differences in self-reported (versus behavioral) responses to the pandemic. Future research will need to examine the degree to which these processes extend beyond the U.S. to other nations and cultural contexts.

Industrial Revolution. Why Britain? The Right Place (in the Technology Space) at the Right Time

Why Britain? The Right Place (in the Technology Space) at the Right Time. Carl Hallmann, W. Walker Hanlon, and Lukas Rosenberger. NBER, Jul 5 2022.

Abstract: Why did Britain attain economic leadership during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the 18th and early 19th century. We show that the network influenced innovation outcomes and then demonstrate that British inventors worked in more central technologies within the innovation network than inventors from France. Then, drawing on recently-developed theoretical tools, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, can help explain the more rapid technological growth in Britain during the Industrial Revolution.

Excerpts from the introduction:

In this study, we argue that there is one important British advantage that has been largely overlooked: the possibility that British inventors may have been working “at the right place” in the technology space. Our idea builds on emerging literature in growth economics which finds that innovation in some technologies generates more spillover benefits than innovation in others (Acemoglu et al., 2016; Cai and Li, 2019; Huang and Zenou, 2020; Liu and Ma, 2021). As a result, a country’s allocation of researchers across technologies can substantially impact the overall rate of economic growth. In particular, this literature shows that technological progress will be faster in economies where more research effort is focused on technologies that generate more spillovers for other technologies; in other words, technologies that are more central in the technology space. Translating these ideas into the context of the Industrial Revolution, we ask: did Britain experienced more rapid technological progress because British inventors were more focused on technologies, such as steam engines, machine tools, or metallurgy, that generated stronger spillover benefits for other technologies and were therefore more central in the technology space? In contrast, could it have been the case that Continental economies like France experienced slower technological progress because they specialized in developing technologies, such as apparel, glass, or papermaking, which were more peripheral in the technology space?1 Put another way, we aim to examine whether Britain’s differential growth during the eighteenth and early nineteenth centuries can be explained by the distinct position of British inventors in the technology space. By starting with ideas from modern growth economics, our analysis is less subject to the type of “post hoc, proper hoc” concerns that have been raised about some other explanations (Crafts, 1977, 1995). Moreover, we offer a theoretically-grounded quantification describing exactly how much of Britain’s differential growth experience can be attributed to this mechanism. These two features differentiate our study from most existing work that aims to understand Britain’s growth lead during the Industrial Revolution. To structure our analysis, we begin with a growth model, from Liu and Ma (2021), that incorporates an innovation network. In this network, each node is a technology type, while each edge reflects the extent to which innovations in one technology type increase the chances of further innovation in another. This model provides a framework for thinking about how the distribution of researchers across technology sectors relates to the growth rate in the economy. It also generates specific expressions that, given the matrix of connections across sectors, allow us to quantify how different allocations of researchers across technology sectors will affect growth. The upshot is that allocations in which more researchers are working in technology sectors with greater spillovers will generate higher overall growth rates than others. Therefore, the growth maximizing allocation of researchers will feature more researchers working in more central technology sectors: specifically, those sectors with higher eigenvalue network centrality. Furthermore, the model delivers precise analytical relationships that allow us to quantify the implications of different allocations of research effort for the rate of economic growth. To examine whether these forces operated during the Industrial Revolution, we utilize patent data for Britain, from 1700 to 1849, and for France from 1791-1844.2 These historical patent data cover a large number of inventors and their inventions, providing a rich source of information on innovation during the Industrial Revolution.3 We follow a long line of work, dating back at least to Sullivan (1989), using patent data to better understand innovation patterns during this period. A key challenge in our setting is measuring spillovers across technology categories. The innovation literature typically uses patent citations, but these are not available in our historical setting. Instead, we introduce a new approach based on the idea that if there are spillovers between two technology categories, then inventors working primarily in one area will occasionally file patents in the other. In particular, we measure the extent of spillovers from technology category j to i based on the propensity of inventors who patent in j to subsequently patent in i. Since our approach is new, we validate it using modern data. Specifically, using U.S. patents from 1970-2014, we construct innovation networks using our approach as well as the citation-based approach used in modern studies. Comparing these networks shows that the two approaches generate networks that are extremely similar. This suggests that our method does a good job of recovering the underlying innovation network. Using our approach, we document technology networks in Britain and France that feature a dense central core of closely related—and mainly mechanical—technologies. One important question about our estimated networks is, do they reflect fundamental features of the underlying technologies or simply reflect the local innovation environment in each country? One way to test this is to compare the networks obtained from the two countries. If they are similar, they likely reflect fundamental technological features rather than idiosyncratic conditions. Conducting a direct comparison, however, is challenging because the two countries use very different technology categorizations. Therefore, it is necessary to construct a mapping of technology categories from one country’s categorizations to the other. To do so, we carefully identify a set of inventions that were patented in both countries. We can then use the categorization of these inventions in each system to construct a crosswalk between the technology categorizations used in the two countries. Using this mapping, we construct technology spillover matrices derived from French patents but in terms of British technology categories, or derived from British patents but expressed in French technology categories. This allows us to regress the entries of the technology matrices of one country on the entries of the other country. We find they are strongly positively related, despite the noise that is inherent in any mapping between different systems of technology categorization. This indicates that our innovation matrices not just reflect the local economic environment, but that a significant part of each represents an underlying ‘global’ network of technology spillovers. Next, we establish that the shape of the technology spillover network matters for innovation outcomes. As a first step, we follow existing work on modern patent data by analyzing how patenting rates vary across technology categories depending on the lagged knowledge stock in other categories, weighted by the strength of connections through the innovation matrix. Consistent with the theory, and the results in previous studies of modern data, we find a significant positive associations of patenting with the lagged network weighted knowledge stock, shrinking toward zero as lags increase. However, the lack of exogenous variation in the lagged knowledge stock means that this result could be due to common shocks that affect connected technology categories. Thus, in the second step, we provide evidence based on a source of quasi-exogenous variation in the timing of increases in the knowledge stock at some nodes of the innovation network. Specifically, we use the unexpected arrival of “macroinventions.” These are inventions which Mokyr (1990) describes as “a radical new idea, without a clear precedent, emerges more or less ab nihilo.” Using a list of 65 macroinventions from Nuvolari et al. (2021), we study whether the arrival of a new macroinvention in one technology category leads to a subsequent increase in patenting in downstream technology categories within the innovation network. Here, the identifying assumption is not that the location of macroinventions were random, but that the timing of their arrival at a given location was unpredictable within the time frame of analysis. Using pooled difference-in-difference and event study analyses for a time frame of ten years before and after the arrival of each macroinvention, we show that macroinventions are followed by significant increases of the patenting rates in technology categories sharing stronger (downstream) connections from the technology category of the macroinvention. In addition, we find no evidence of an increase in technology categories as a result of being upstream from the macroinvention technology category within the innovation network. This second result provides a valuable placebo check that provides additional confidence that our results are picking up the impact of spillovers through the innovation network. Next, we look at whether there are notable differences in the allocation of British and French inventors within the innovation network. In particular, we focus on whether British inventors were patenting in technology categories that were more central within the innovation network than French inventors. We do this by studying, within the sets of British and French patents whether foreign inventors (of British or French origin) were patenting in more central technology categories than domestic inventors. We find that among French patents, patents by British-based inventors were significantly more central compared to the average patents by French domestic inventors—and all other foreign inventors—, whereas among British patents, patents by French-based inventors were less central compared to the average patent by British domestic inventors. The pattern indicates that British inventors were more likely to work in central technology categories than French inventors. As more central nodes have stronger spillover connections to other technology categories, the more central locations occupied by British inventors are consistent with a greater “bang for the buck” of British innovation on the aggregate rate of technological progress. Finally, we quantify the growth implications of the observed innovation network and different allocations of inventors in Britain and France through the lens of the model. Existing estimates for Britain suggest that industrial production grew by between 3 and 3.5% during the first half of the nineteenth century (Broadberry et al., 2015). In France, estimates indicate growth rates of between 1.7 and 2.5% in the same period (Crouzet, 1996; Asselain, 2007). (Preliminary) Results from our quantification exercise show that differences in the allocation of inventors across technology categories led to a technology growth rate in Britain that was between 0.5 and 2.9 percent higher than the French technology growth rate. Thus, our results indicate that Britain’s more advantageous position in the innovation network can explain a substantial fraction, and possibly the entire difference, in growth rates between the British and French economies during the first half of the nineteenth century. In sum, the evidence presented in this paper shows that Britain benefited from an advantageous distribution of inventors across technology sectors during the Industrial Revolution, and that this difference meaningfully contributed to Britain’s more rapid industrialization. Our analysis takes as given the differences in the distribution of inventors across sectors. Thus, our mechanism complements explanations for the British advantage during the Industrial Revolution, in particular those that can explain why British inventors were more likely than the French to work on technologies that happened to be more central 4 within the innovation network, in particular mechanical technologies. For example, it could be that Britain’s practical Enlightenment tradition and well-developed apprenticeship system (Mokyr, 2009; Kelly et al., 2014) contributed to the British inventors’ greater ability for working on mechanical technologies, or that high wages and access to cheap coal steered British inventors to focus on labor-saving mechanical devices (Allen, 2009). Put differently, the contribution of our paper lies in demonstrating that Britain was at the right place in the technology space at the right time, rather than explain why it was there but France was not. In addition to improving our understanding of one of the most important questions in economic history, our study also contributes to work by growth economists on the importance of innovation networks. Relative to studies in this area (cited above), we offer two main contributions. First, we offer new methods that can help researchers study innovation networks further back in history, when standard tools such as systematic patent citations are unavailable. This opens up the possibility of studying the influence of innovation networks in different contexts or over longer periods. Second, our analysis of macroinventions provides additional, more causal, evidence that innovation networks matter for technology development. Third, our application demonstrates empirically the value of recent theoretical advances integrating innovation networks into economic growth models. Our work builds on a long line of literature using patent data to examine innovation during the Industrial Revolution and into the nineteenth century. Early papers in this area include Sullivan (1989) and Sullivan (1990). More recent work includes MacLeod et al. (2003), Khan and Sokoloff (2004), Moser (2005), Khan (2005), Brunt et al. (2012), Nicholas (2011), Nuvolari and Tartari (2011), Moser (2012), Bottomley (2014b), Bottomley (2014a), Burton and Nicholas (2017), Khan (2018), Bottomley (2019), Nuvolari et al. (2020), Nuvolari et al. (2021), Hallmann et al. (2021), and Hanlon (2022). Relative to this extensive literature, we are the first to study the role of innovation networks in influencing inventive activity during the Industrial Revolution.

1 Hallmann et al. (2021) show that technological leadership in invention of Britain relative to France varied across technologies, with Britain leading, besides others, in steam engines and textile technologies, and France leading, besides others, in papermaking and shoemaking. Mokyr (1990, Chapter 5) provides a historical overview on British technological lead or lag in invention relative to Continental Europe.

2 Both of these were periods during which the patent systems were largely stable. We end just before the major British patent reform of 1852 and the French patent reform of 1844.

3  Of course, not every useful invention was patented, as (Moser, 2012) has shown. 

4   A stable institutional environment and well-developed patent system may have contributed in shifting inventors from technologies that can be protected by secrecy toward technologies as mechanical devices that are easily reverse engineered and thus profit the most from patents (Moser, 2005). However, as both Britain and France had strong patent protection, it is unclear how this mechanism could explain the differential focus of British vs. French inventors on mechanical devices.

We document that 85% of patent applications in the United States include no female inventors and ask: why are women underrepresented in innovation?

Try, try, try again? Persistence and the gender innovation gap. Gauri Subramani, Abhay Aneja, and Oren Reshef. Berkeley, Nov 2022.

Abstract: We document that 85% of patent applications in the United States include no female inventors and ask: why are women underrepresented in innovation? We argue that differences in responses to early rejections between men and women are a significant contributor to the gender disparity in innovation. We evaluate the prosecution and outcomes of almost one million patent applications in the United States from 2001 through 2012 and leverage variation in patent examiners’ probabilities of rejecting applications to employ a quasi-experimental instrumental variables approach. Our results show that applications from women are less likely to continue in the patent process after receiving an early rejection. Roughly half of the overall gender gap in awarded patents during this period can be accounted for by the differential propensity of women to abandon applications. We explore why this may be the case and provide evidence that the gender gap in outcomes is reduced for applications that are affiliated with firms, consistent with a role for institutional support in mitigating gender disparities.