More-men-more-violence association holds particularly for male violence against other men, but is insignificant for violence against women; significant among childless men, but not fathers; robustness checks question causality of associations

Are skewed sex ratios associated with violent crime? A longitudinal analysis using Swedish register data. Andreas Filser et al. Evolution and Human Behavior, Volume 42, Issue 3, May 2021, Pages 212-222. https://doi.org/10.1016/j.evolhumbehav.2020.10.001

David Schmitt's take: more-men-more-violence association holds particularly for male violence against other men, but is insignificant for violence against women...significant among childless men, but not fathers...robustness checks question causality of associations

Abstract: There is widespread concern in both the popular and academic literature that a surplus of men in a population intensifies mating competition between men, particularly unpartnered men, resulting in increased violence towards both men and women. Recent contributions challenge this perspective and argue that male mating competition and levels of violence will be higher when sex ratios are female-skewed. Existing empirical evidence remains inconclusive. We argue that this empirical ambiguity results from analyses of aggregate-level data, which put inferences at risk of ecological fallacies. Our analysis circumvents such problems by using individual-level, longitudinal demographic register and police data for the Stockholm metropolitan area, Sweden (1990–2003, n = 758,498). These data allow us to investigate the association between municipality-level sex ratios and violent offending (homicide, assault, threat, and sexual crimes) while adjusting for sociodemographic factors. Results suggest that aggregated offending rates are negatively associated with male-skewed sex ratios, whereas individual-level violent offending correlates positively with male-skews. We find that the more-men-more-violence association holds particularly for male violence against other men, but is insignificant for violence against women. Moreover, the association is significant among childless men, but not among fathers. However, robustness checks question the causality of these associations. Female violent offending is positively, albeit due to a low number of cases, insignificantly associated with male-skews. Moreover, both male and female non-violent offending is higher in male-skewed municipalities. We discuss the implications with regard to the theoretical debate and problems of unobserved heterogeneity in the sex ratio literature.

Keywords: Sex ratioViolent crimeMating marketSweden

4. Discussion

In this study, we use Swedish register data to investigate the link between local sex ratios and violent criminal offending. Existing theoretical approaches are contradictory and empirical evidence remains inconclusive, suggesting both negative and positive associations between male-skewed sex ratios and violence (Schacht et al., 2014, Schacht et al., 2016; Schnettler and Filser, 2015, Schnettler and Filser, 2020). To our knowledge, this study is the first to use individual-level, longitudinal data to circumvent an ecological fallacy, a common problem of previous research on the issue (Filser & Schnettler, 2018; Pollet et al., 2017; Schacht et al., 2014). The detail of our data enable us to disentangle associations of intra- and inter-sexual violence with the sex ratio and to adjust for a number of socio-demographic confounders. Moreover, we investigate hypothesized, but largely untested differences in these associations by socio-economic, marital, and parental status.

On the surface, our results seem to provide additional support for the more-men-more-violence hypothesis. We find that male-on-male violence is positively associated with male-skewed sex ratios. Results for male-on-female violence suggest a similar positive association with male-skews, which is somewhat weaker and not statistically significant, due to a smaller sample size for these offenses. Both findings are compatible with the hypothesis that an abundance of men will particularly result in high levels male-male violence. Furthermore, our results suggest that intra-male violence correlates significantly with local sex ratios among childless men, but not among fathers. This may appear as further support for the hypothesis that local male-skews particularly instigate violent rivalry among men competing for partners.

However, the full set of results, including the results from models examining female-on-female violent offenses, and non-violent offenses, suggests caution before drawing any firm conclusions. First, when comparing our findings on male and female offending, we would expect either no association between sex ratios and female offending or one that is opposite to the association with male offending (cf. Stone, 2015). Yet, we find that the association between the sex ratio and female violent offending resembles that for male offending. The association for female offending is not significant, but this may be largely due to the low number of offenses by women in our data. The number of offenses is even lower for analyses of female offending split by victims' sex, which precludes any meaningful interference from results on female-on-male and female-on-female violent offending.

Second, we find that municipality-level sex ratios are not only associated with violent offending, but also with a general indicator of any non-violent offending. Property and white-collar offenses might correlate with sex ratios, as individuals are more pressurized to obtain resources and resort to scramble competition (Benenson & Abadzi, 2020; Edlund et al., 2013). However, given the broadness of the indicator, we would expect these associations to be weaker, compared to violent offenses. While this is only true for non-violent offending by men, we find that the association of sex ratios with female non-violent offending is even stronger than the one for violent offending. Furthermore, we find that the associations of sex ratios with non-violent offending are in the same direction for both male and female offenders. This contradicts theoretical expectations related to scramble competition as women should become less and not more likely to engage in non-violent offending as the sex ratio increases, that is, as the environment becomes less female-skewed.

In sum, these findings prompt us to suspect that there are still potential confounders that might drive the association between sex ratios and violent crime that we are not able to account for. We are able to adjust our models for contextual and individual-level socio-economic deprivation in a more comprehensive way than previous studies. Socio-economic deprivation is a key confounder of the association between sex ratios and violent crime, given that young women are more likely to migrate to more economically thriving regions (Leibert, 2016) and levels of violent crime are correlated with prosperity (Hooghe et al., 2011). We address this issue by including municipality-level fixed effects to account for time-constant unobserved heterogeneity on the municipality level. Moreover, we include a range of time-varying socioeconomic and demographic status variables on both the individual and context level. These adjustments should take care of socio-economic unobserved heterogeneity, yet some limitations remain.

Beyond socioeconomic factors, sex-selective migration patterns might be a source of unobserved heterogeneity. Empirical evidence suggests that women out-migrate from male-biased areas to more strongly female-biased areas than men (Uggla & Mace, 2017). With regard to violence and crime, one potential explanation could be that men are less concerned about falling victim to a crime (Jackson, 2009). Consequently, sex-selective migration might drive the association of sex ratios and violent crime independently of economic deprivation. Unfortunately, we are not able adjust our models for migration patterns in our analysis, particularly migration from outside our study area.

Another limitation of our study is the comparatively small geographical scope of our data. An underlying assumption of our analysis is that individuals are sensitive to cues of the municipality-level sex ratio and that municipalities meaningfully represents the local ecology which impacts individual behavior. However, individuals might have committed offenses in different contexts than their municipality of residence, resulting in a mismatch of the contextual sex ratio at the offense and our sex ratio measure. Moreover, municipalities might be too small entities to measure sex ratios in a way that also correlates closely with individuals' perceptions of partner markets (Filser & Preetz, 2020; Fossett & Kiecolt, 1991; Gilbert, Uggla, & Mace, 2016). Furthermore, our study area consists of a metropolitan area with an urban center, Stockholm city. Municipalities are a meaningful social entity in Sweden, because they organize schools and municipality centers serve as local hubs. However, individuals still commute and move between municipalities. With a size of 7150 km2, the area is well connected by public transport and roads. Consequently, municipalities are not as separate as calculating specific municipality-level sex ratios suggests them to be.

Nevertheless, the level of detail in our data allow us to elucidate a number of aspects previous studies have not been able to explore. A key contribution of our paper is to support concerns about studying the association of sex ratios and aggregated rates of individual social outcomes, as it is commonly done in the existing sex ratio literature (cf. Pollet et al., 2017 for an in-depth critique). Specifically, our findings demonstrate how, based on the same data, sex ratios and aggregated violent offending rates can suggest a negative association, even when individual probabilities for violent offending are actually positively associated with sex ratios. This illustrates the importance of individual-level analyses to further establish a coherent empirical basis in the sex ratio literature.

Moreover, our paper illustrates that detailed offending data are necessary to generate clearer evidence with regard to which types of violent offenses are associated with sex ratio skews. We wish to remain cautious with too much emphasis on the differential levels of significance due to diverging sample sizes in offenses. Yet, the weaker association for male-on-female offending compared to male-on-male offending puts predictions of increased male-on-female intimate partner violence (D'Alessio & Stolzenberg, 2010; Daly & Wilson, 1998; Uggla & Mace, 2015b; Vandello, 2007) or higher levels of sexual harassment in male-skewed environments into perspective (Malamuth et al., 2005; Trent & South, 2012; Trent, South, & Bose, 2015). However, our data do not differentiate between violence against (intimate) partners and other female victims and thus our results on male-on-female violence can only serve as a combined indicator of violence against women by both partners and other men. Additional research is necessary to disentangle these different types of male-on-female violence and their associations with the sex ratio.

Moreover, our results reveal that the association of skewed sex ratios with violence may differ across individual demographic characteristics, as has been shown for other outcomes (Uggla & Mace, 2017). Specifically, sex ratios are positively associated with violent offending in childless men, but not among fathers. While we outlined above that this finding should be treated with caution, it still serves as an illustration for the yet untapped potential of individual-level data for the literature on sex ratios and violent offending. Future studies should further explore this aspect.

In sum, our findings demonstrate the need for studies relying on more detailed data and advanced causal identification strategies when exploring the association of sex ratios with violence and aggression. Observational studies, no matter how detailed, might be unable to overcome unobserved heterogeneity problems at both the individual and the aggregate level. Experimental studies have generated evidence for sex ratio effects on aggression by human participants (Arnocky, Ribout, Mirza, & Knack, 2014). We encourage future research to evaluate whether these effects vary across parental status groups as our results indicate.

Finally, our findings should be considered within their specific socio-sexual context (see Schacht et al., 2014). Our study population fits the WEIRD definition (Henrich, Heine, & Norenzayan, 2010), with high levels of acceptance for uncommitted sexual relationships (cf. Widmer, Treas, & Newcomb, 1998). Therefore, our paper complements the literature in that it comes from a sexually liberal society, while previous individual-level analyses use data from more sexually restrictive contexts (Diamond-Smith & Rudolph, 2018; South et al., 2014). Such societies might not be suitable test cases for the more-men-more-violence hypothesis, since this perspective emphasizes uncommitted sexual relationships as a main mediator for the link between sex ratios and male violence (Schacht et al., 2014, Schacht et al., 2016). This limitation does not apply to our study. While we cannot provide evidence of a counterfactual causal effect of male-skewed sex ratios on violent crime, our findings at least cast doubt on the more-men-less-violence hypothesis vis-à-vis the more-men-more-violence hypothesis in this context.

CRED Louvain climate disaster events “This is creating weather-related disasters that are completely unprecedented”

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Gore Pulls Slide of Disaster Trends
BY ANDREW C. REVKIN FEBRUARY 23, 2009 12:31 PM February 23, 2009 12:31 pm 183
COLELLAPHOTO.COM via AAAS.org Al Gore addresses the American Association for the Advancement of Science.

Former Vice President Al Gore is pulling a dramatic slide from his ever-evolving global warming presentation. When Mr. Gore addressed a packed, cheering hall at the annual meeting of the American Association for the Advancement of Science in Chicago earlier this month, his climate slide show contained a startling graph showing a ceiling-high spike in disasters in recent years. The data came from the Center for Research on the Epidemiology of Disasters (also called CRED) at the Catholic University of Louvain in Brussels.

The graph, which was added to his talk last year, came just after a sequence of images of people from Iowa to South Australia struggling with drought, wildfire, flooding and other weather-related calamities. Mr. Gore described the pattern as a manifestation of human-driven climate change. “This is creating weather-related disasters that are completely unprecedented,” he said. (The preceding link is to a video clip of that portion of the talk; go to 7th minute.)

Now Mr. Gore is dropping the graph, his office said today. Here’s why.

Two days after the talk, Mr. Gore was sharply criticized for using the data to make a point about global warming by Roger A. Pielke, Jr., a political scientist focused on disaster trends and climate policy at the University of Colorado. Mr. Pielke noted that the Center for Research on the Epidemiology of Disasters stressed in reports that a host of factors unrelated to climate caused the enormous rise in reported disasters (details below).

Dr. Pielke quoted the Belgian center: “Indeed, justifying the upward trend in hydro-meteorological disaster occurrence and impacts essentially through climate change would be misleading. Climate change is probably an actor in this increase but not the major one — even if its impact on the figures will likely become more evident in the future.”

Officials at the disaster center, after reviewing what Mr. Gore showed and said, sent a comment to Dr. Pielke’s blog and to me. You can read their full response below. I sent it to Mr. Gore’s office and asked for his interpretation. Kalee Kreider, Mr. Gore’s spokeswoman on environmental matters, wrote back today:


I can confirm that historically, we used Munich Re and Swiss Re data for the slide show. This can be confirmed using a hard copy of An Inconvenient Truth. (It is cited if you cannot recall from the film which is now several years old!). We became aware of the CRED database from its use by Charles Blow in the New York Times (May 31, 2008). So, it’s a very new addition.

We have found that Munich Re and other insurers and their science experts have made the attribution. I’m referring you particularly to their floods section/report [link, link] Both of these were published in a series entitled “Weather catastrophes and climate change-Is there still hope for us.”

We appreciate that you have pointed out the issues with the CRED database and will make the switch back to the data we used previously to ensure that there is no confusion either with regards to the data or attribution.

As to climate change and its impacts on storms and floods, the IPCC and NOAA among many other top scientific groups have indicated that climate change will result in more extreme weather events, including heat waves, wildfires, storms and floods. As the result of briefings from top scientists, Vice President Gore believes that we are beginning to see evidence of that now.
Reproduced with permission, from Guha-Sapir, D., Vos, F.; Quantifying Global Environmental Change Impacts: Methods, Criteria and Definitions for Compiling Data on Hydro-Meteorological Hazards in Coping with Global Environmental Change, Disasters and Security – Threats, Challenges, Vulnerabilities and Risks. Edited by H. Brauch et al Hexagon Series on Human and Environmental Security and Peace, vol. 5 (Berlin – Heidelberg – New York: Springer-Verlag, 2009). Click on the image for a full-sized version of the graph, which shows storm trends as a green band and flooding and related landslides in blue.

At right is a link to the Belgian center’s graph of disaster trends through 2008 (click to see the whole thing). And here is the center’s statement (highlight added):


CRED is fully aware of the potential for misleading interpretations of EM-DAT figures by various users. This is a risk all public datasets run…. Before interpreting the upward trend in the occurrence of weather-related disasters as “completely unprecedented” and “due to global warming”, one has to take into account the complexities of disaster occurrence, human vulnerabilities and statistical reporting and registering.

Over the last 30 years, the development of telecommunications, media and increased international cooperation has played a critical role in the number of disasters that are reported internationally. In addition, increases in humanitarian funds have encouraged reporting of more disasters, especially smaller events. Finally, disasters are the convergence of hazards with vulnerabilities. As such, an increase of physical, social, economic or environmental vulnerabilities can mean an increase in the occurrence of disasters.

We believe that the increase seen in the graph until about 1995 is explained partly by better reporting of disasters in general, partly due to active data collection efforts by CRED and partly due to real increases in certain types of disasters. We estimate that the data in the most recent decade present the least bias and reflect a real change in numbers. This is especially true for floods and cyclones. Whether this is due to climate change or not, we are unable to say.

Once again, we would like to point out that although climate change could affect the severity, frequency and spatial distribution of hydro-meteorological events, we need to be cautious when interpreting disaster data and take into account the inherent complexity of climate and weather related processes — and remain objective scientific observers.

[UPDATE: 5:10 p.m.: I’ve posted on a more measured effort at climate risk communication.]

Also on the disaster-climate front, there is an interesting story in the Washington Post today describing a variegated assemblage of efforts to flee in the face of climate-related threats. Matthew Nisbet pondered how a global warming story without a hot political element made it onto a front page.

It’s pretty clear it was the climate-disaster link. There were some things missing from the article, however. As the folks in Belgium explained above, the connection between human-driven climate change and recent trends in disasters remains highly uncertain, even as most climate scientists foresee intensification of floods and droughts and, of course, more coastal flooding with rising sea levels.

So while the climate hook might have given this story its “front-page thought,” there’s no examination in the article of simultaneous trends in population growth in poor places, urbanization (people are leaving marginal lands for many reasons) and the like.

In the absence of that hook, it’s basically a story about people moving out of harm’s way, something that’s been happening throughout human history.183

From 2019... Middle‐aged women with a greater number of recent stressful life events demonstrate memory decline over a decade later

Stressful life events and cognitive decline: Sex differences in the Baltimore Epidemiologic Catchment Area Follow‐Up Study. Cynthia A. Munro  Alexandra M. Wennberg  Nicholas Bienko  William W. Eaton  Constantine G. Lyketsos  Adam P. Spira. International Journal of Geriatric Psychiatry, March 22 2019. https://doi.org/10.1002/gps.5102

Abstract

Introduction: The reasons why women are at higher risk than men for developing dementia are unclear. Although studies implicate sex differences in the effect of stress on cognitive functioning, whether stressful life events are associated with subsequent cognitive decline has received scant research attention.

Methods: In Wave 3 (1993–1996) of the Baltimore Epidemiologic Catchment Area study, 337 men and 572 women (mean age = 47 years) reported recent (within the last year) and remote (from 1981 until 1 year ago) traumatic events (eg, combat) and stressful life events (eg, divorce/separation). At Waves 3 and 4 (2004–2005), they completed the Mini Mental State Examination (MMSE) and a word‐list memory test. Multivariable models were used to examine the association between traumatic and stressful life events at Wave 3 and cognitive change by Wave 4.

Results: A greater number of recent stressful life events at Wave 3, but not of more remote stressful events, was associated with greater verbal memory decline by Wave 4 in women but not in men. Stressful events were not associated with change in MMSE, and there were no associations between traumatic events occurring at any time and subsequent memory or MMSE decline in either sex.

Conclusions: Unlike men, middle‐aged women with a greater number of recent stressful life events demonstrate memory decline over a decade later. Sex differences in cognitive vulnerability to stressful life events may underlie women's increased risk of memory impairment in late life, suggesting that stress reduction interventions may help prevent cognitive decline in women.

From “NASA Lies” to “Reptilian Eyes”: Mapping Communication About 10 Conspiracy Theories

From “Nasa Lies” to “Reptilian Eyes”: Mapping Communication About 10 Conspiracy Theories, Their Communities, and Main Propagators on Twitter. Daniela Mahl, Jing Zeng, Mike S. Schäfer. Social Media + Society, May 12, 2021. https://doi.org/10.1177/20563051211017482

Abstract: In recent years, conspiracy theories have pervaded mainstream discourse. Social media, in particular, reinforce their visibility and propagation. However, most prior studies on the dissemination of conspiracy theories in digital environments have focused on individual cases or conspiracy theories as a generic phenomenon. Our research addresses this gap by comparing the 10 most prominent conspiracy theories on Twitter, the communities supporting them, and their main propagators. Drawing on a dataset of 106,807 tweets published over 6 weeks from 2018 to 2019, we combine large-scale network analysis and in-depth qualitative analysis of user profiles. Our findings illustrate which conspiracy theories are prevalent on Twitter, and how different conspiracy theories are separated or interconnected within communities. In addition, our study provides empirical support for previous assertions that extremist accounts are being “deplatformed” by leading social media companies. We also discuss how the implications of these findings elucidate the role of societal and political contexts in propagating conspiracy theories on social media.

Keywords: conspiracy theory, social media, Twitter

Discussion

Conspiracy theories are a fast-changing phenomenon and highly responsive to external events. In light of the ongoing COVID-19 pandemic, a plethora of conspiracy theories abound online. Going beyond previous studies on either the general phenomenon of conspiracy theories (e.g., Del Vicario et al., 2016) or specific conspiracy theories (e.g., Broniatowski et al., 2018), our study provides an empirically informed comparison of the most visible conspiracy theories on Twitter by shedding light on the interplay of platform affordances and the dissemination of conspiracy theory content.

Regarding the diversity of conspiracy theories, our results reveal a variety of prevalent conspiratorial explanations circulating on Twitter: Agenda 21, Anti-Vaccination, Chemtrails, Climate Change Denial, Directed Energy Weapons, Flat Earth, Illuminati, Pizzagate, Reptilians, and 9/11. While most of these conspiracy theories are directed against the establishment and elite, referring to secret machinations of influential people or institutions acting for their own benefit (e.g., Agenda 21, Illuminati), others construct narratives challenging science, epistemic institutions, or scientists (e.g., Anti-Vaccination, Flat Earth).

Concerning communities evolving around conspiracy theories on Twitter as well as main propagators within these communities, our results reveal two loosely connected clusters of pro- and anti-conspiracy theories. Both anti-conspiracy theory communities, Anti-Flat Earth and Pro-Vaccination, are centered around scientists and medical practitioners. Their use of pro-conspiracy theory hashtags likely is an attempt to directly engage and confront users who disseminate conspiracy theories. Studies from social psychology have shown that cross-group communication can be an effective way to resolve misunderstandings, rumors, and misinformation (e.g., DiFonzo, 2013). By deliberately using pro-conspiracy hashtags, anti-conspiracy theory accounts inject their ideas into the conspiracists’ conversations. However, our study suggests that this visibility does not translate into cross-group communication, that is, retweeting each other’s messages. This, in turn, indicates that debunking efforts hardly traverse the two clusters.

Finally, our study lends support to previous assertions that social media platforms are taking increasingly proactive measures to systematically crack down on accounts promoting conspiracy theories (e.g., Rogers, 2020). As our results show, users banned from Twitter are predominantly those who propagate conspiracy theories.

Alignments Between Conspiracy Theories and Their Communities

In line with recent research demonstrating that conspiracy beliefs tend to “stick together” (Douglas et al., 2019, p. 7; van Prooijen, 2018), our study reveals a general proximity between several conspiracy theories. We argue that three factors help us to explore these overlaps in more depth. First, the closely aligned conspiracy theories Climate Change Denial, Pizzagate, and 9/11 share structural and thematic features. They provide alternative rationales and explanations for national or international policies, political affairs, or events, challenging accounts from governments and official authorities (Huneman & Vorms, 2018; Räikkä, 2009). Other conspiracy theories such as Chemtrail, Reptilians, and Illuminati emphasize a conspiracy of powerful groups and non-human entities, claiming that, for instance, aliens or secret societies rule the world (Uscinski, 2018). In contrast to political conspiracy theories, these conspiracies mingle reality with fiction and are often closely tied to popular culture such as Dan Brown’s novel “The Da Vinci Code.” Second, factors such as ideological and geographic proximity further help explain alignments between conspiracy theories. A shared characteristic of several conspiracy theories is that they are disseminated by people with conservative political views who support Donald Trump and that they are mostly popular in the United States. For instance, conspiracy theories around Climate Change, Agenda 21, and Directed Energy Weapons have their roots in anti-environmentalist and anti-globalist ideologies, both of which are aligned with conservative political ideology and values (Kirilenko & Stepchenkova, 2014) and popular among the political right and populists in the United States (Harris et al., 2017). 9/11 and Pizzagate conspiracy theories are also widely promoted by conservative and right-wing politicians in the United States and often supported by individuals who hold conservative beliefs (Stempel et al., 2007). In contrast, believers in Anti-Vaccination, Flat Earth, Reptilians, Illuminati, and Chemtrail conspiracy theories can be found across the political spectrum, and their respective communities are less US-centric. For instance, Anti-Vaccination sentiment is on the rise around the globe and the movement finds supporters on both the political left and right (Holt, 2018). In addition, some of the most influential propagators of Illuminati and Reptilians conspiracy theories are based outside the United States (Robertson, 2013), which underlines the importance of societal and political contexts to understand the propagation patterns of conspiracy theories.

Limitations and Future Research

As all studies, ours comes with some limitations as well. A general limitation resides in the way we built our sample. First, hashtag-based approaches to collect tweets leave out ancillary discussions by participants who have chosen not to use these hashtags or any hashtags at all (Burgess & Bruns, 2015). Future studies should dive deeper and make use of alternative sampling methods, such as including specific user-defined keywords, utilizing topic-related dictionaries or classifiers, or examine recent tweeting history and follower network information of participating accounts to capture further communication (Burgess & Bruns, 2015). Second, our analysis was limited to a relatively small sample of English-language Twitter only, limiting the generalizability of our findings. As prior research suggests that conspiracy theories are communicated differently according to national and regional contexts (e.g., Gray, 2008), studies on other languages and linguistic regions would be recommendable.

To further enhance our understanding of conspiracy theories in digital environments, future research should incorporate more cross-platform, cross-lingual, and cross-regional comparative perspectives in general. Furthermore, we argue that future research of online conspiracy theories should not be limited to mainstream platforms, such as Twitter, Facebook, or YouTube. These platforms, as indicated in both literature (e.g., Rogers, 2020) and our current study, have been systematically cracking down on accounts that promote conspiracy theories. As more and more conspiracy theorists and their followers migrate to “alternative” social media, such as Gab, BitChute, and Parler, more research will be required to investigate the impacts of this trend. 

Since it is difficult to hold politicians accountable for personal welfare changes, to protect our self-image we tend to take personal responsibility for positive changes & hold the government responsible for negative changes

How Do Voters Hold Politicians Accountable for Personal Welfare? Evidence of a Self-Serving Bias. Martin Vinæs Larsen. The Journal of Politics, Volume 83, Number 2, May 2021. https://www.journals.uchicago.edu/doi/abs/10.1086/710325

Abstract: Examining a government’s record is difficult. This is a problem for voters who want to hold governments accountable. One solution is for voters to hold governments accountable for changes in their personal welfare. Yet, it is often unclear whether changes in personal welfare are caused by government policies or voters’ own actions. Since voters have a desire to protect their self-image, this ambiguity might fuel a self-serving bias in attribution. That is, voters might take personal responsibility for positive changes in personal welfare and hold the government responsible for negative changes. Using data from election surveys and survey experiments, this article shows that voters attribute responsibility for personal welfare in this self-serving way. This hurts democratic accountability because voters do not reward governments (enough) for improving their personal welfare.

We strongly overestimate the power of self-interest on others' blood donation willingness and smoking policies

Self-interest Is Overestimated: Two Successful Pre-registered Replications and Extensions of Miller and Ratner (1998). Cameron Brick et al. Collabra: Psychology (2021) 7 (1): 23443, May 2021. https://doi.org/10.1525/collabra.23443

Abstract: Self-interest is a central driver of attitudes and behaviors, but people also act against their immediate self-interest through prosocial behaviors, voting incongruously with their finances, or punishing others at personal cost. How much people believe that self-interest causes attitudes and behaviors is important, because this belief may shape regulation, shared narratives, and institutional structures. An influential paper claimed that people overestimate the power of self-interest on others’ attitudes and behavioral intentions (Miller & Ratner, 1998). We present two registered, close, and successful replications (U.S. MTurk, N = 800; U.K. Prolific, N = 799) that compared actual to estimated intentions, with open data and code. Consistent with the original article, participants overestimated the impact of payment on blood donation in Study 1, ds = 0.59 [0.51, 0.66], 0.57 [0.49, 0.64], and overestimated the importance of smoking status for smoking policy preferences in Study 4, ds = 0.75 [0.59, 0.90], 0.84 [0.73, 0.96]. These replications included two extensions: 1) communal orientation as a moderator of overestimation and 2) a more detailed measure of self-interest in Study 4 (ordinal smoking status). Communal orientation did not predict overestimation, and the ordinal smoking measure yielded similar results to the main study. Verifying the overestimation error informs behavioral theories across several fields and has practical implications for institutions that require trust and cooperation. All materials, data, and code are available at osf.io/57mdc/

Keywords: self-interest, judgment, bias, decision making, attribution, pre-registered replication

General Discussion

The results in both samples and both studies strongly supported the original findings. Individuals overestimated the impact of self-interest on intentions to donate blood, and also how much smoking status determined support of smoking regulations (ds > 0.58). The overestimation effects may have been smaller than the original paper, but original effect sizes could not be precisely calculated because the variances were not reported. Any discrepancies in effect size from the original could be attributed to noise from their small sample size, an estimation error due to the lack of their reported statistics, or differences in the context or manipulation strength. For example, because of currency inflation, $15 was less incentive in 2019 than in 1998, which could lead to smaller perceived incentive in the replication.

In Study 4, the original study did not find significant effects of self-interest for four out of eight policies in self-ratings, perhaps due to lack of statistical power. We found support for self-interest effects for 13 out of 16 tests (smokers endorsed the policies less; eight policies in two samples), with particularly large effects in the MTurk sample (Table S4). Replications often focus on replicating the significant original effects, but finding support for non-significant effects in the original article is also informative (Chandrashekar et al., 2020; LeBel et al., 2019). Here, these additional findings suggest strong generalizability of the overestimation effect across different types of smoking policies (e.g., restriction and taxation).

To evaluate a more granular measure of self-interest, a random half of participants in a Study 4 extension gave responses for five categories of smoking frequency rather than just two. The ordinal smoking status scale did not yield enough smokers within each category for inferential tests. However, it appears from visual analysis that overestimation may be most pronounced when individuals consider others with stronger vested interests. In the extension, that pattern could be partially due to an expectancy effect. Participants may have assumed that being asked about multiple categories of smoker implied that each category would be different in policy support.

The other extension investigated individual differences that predict overestimation. The social norm in Western individualistic cultures that self-interest powerfully determines behavior may be relevant to overestimation (Ratner & Miller, 2001). Beliefs about self-interest may become self-fulfilling by influencing social institutions and individual decision-making processes, which in turn could reinforce the original idea of self-interested human nature. Therefore, communalism was tested in predicting donation, policy support, estimates of each, and overestimation of self-interest. As expected, communality was positively associated with more prosocial behavior and endorsement of smoking restrictions, and was also positively associated with higher estimates of others' prosociality in both studies. However, we found no support for a relationship between overestimation and communality in either study. Exploratory correlations with other demographics revealed mostly null effects, but being younger was associated with more overestimation in Study 1, perhaps because younger individuals have less money. It remains valuable to identify other individual differences associated with overestimation.

Limitations and Future Directions

Alternative Explanations

Self-reported willingness to donate blood or endorse smoking policies is not equivalent to objective behaviors like blood donation or voting. The main narrative in this paper is that people over-estimate others' self-interest, but the results are also consistent with the pattern that such estimates are accurate and that self-reported willingness is inaccurate; that in actual behavior people would manifest more self-interest than they expect or are willing to report. Further studies with observed behavior would be valuable for testing this account.

The experimental paradigms were copied from the original manuscript and not validated before testing the hypotheses. The vignettes and manipulations might have confounds or unknown effects orthogonal to the theory and predictions used here. Additionally, the participants were only given very sparse information about the targets, e.g., that they were smokers or nonsmokers. This could have created an expectancy effect or at the least an ecologically unusual focus on a single attribute when predicting how individuals would evaluate policies. By failing to provide rich, complex targets with varied mental experiences, the paradigms here may have encouraged individuals to focus on external behaviors like smoking, which could alter attributions and perceived self-interest (Vuolevi & Van Lange, 2009). Future studies could consider richer, more life-like vignettes, or paying participants for their accuracy.

Attitudes versus Behaviors

The original article and the current replications hinge on outcomes that may be better characterized as intentions rather than behaviors. This is important because self-interest may predict behavior better than attitudes (Ratner & Miller, 2001). For instance, one study found that people who owned property or had school-age children did not oppose school busing policy more than those without material stake in the policy, but they were much more likely to join anti-busing organizations (Green & Cowden, 1992). Another key paper found that people overestimated their likelihood of acting generously but accurately predicted other's behaviors (Epley & Dunning, 2000). Perceived self-interest may be higher when people face immediate, concrete outcomes (Boninger et al., 1995), and people's sensitivity to their self-interest increases after self-interest is made salient (Ratner & Miller, 2001). Thus, future research on the overestimation of self-interest could focus on consequential behaviors rather than hypotheticals. This could help resolve conflicting findings (Epley & Dunning, 2000; Vuolevi & Van Lange, 2009) and provide better generalizability to real-world contexts.

Constraints on Generality

The current findings and their interpretation are based on sampling and measurement choices that limit their generalizability as with any study (Simons et al., 2017).

Sample. The participants were recruited from MTurk (USA) and Prolific (UK). Both samples were more representative of their countries than university student samples, but the results may have limited generalizability to populations that are not Western, Educated, Industrialized, Rich, and Democratic (Cheon et al., 2020; Henrich et al., 2010). In particular, overestimation of others' self-interest may be inflated by social norms of self-interest in individualistic societies. There is a strong need for studies on overestimation of others' self-interests in non-Western samples. Cross-cultural, multi-lab studies such as through consortia like the Psychological Science Accelerator (Moshontz et al., 2018) could replicate and extend this phenomenon particularly in collectivistic cultures with weaker norms of self-interest.

Method, Measures, and Contexts. We closely replicated the original studies across two medical topics—blood donation and smoking—measuring attitudes and intentions but not objective behavior. Our results appeared to contradict Epley & Dunning (2000), but were consistent with Vuolevi & Van Lange (2009), which both measured behavior. These discrepancies could be due to differences in measures or topics. Future replication studies could focus on consequential behaviors and consider other decision contexts such as financial or environmental decisions.

Overestimating self-interest may also be higher when participants lack information about the other people making decisions. When study vignettes refer to unspecified others and only provide limited information, e.g., the decision maker is a smoker or not, participants may base their estimates on generalized perceptions of norms of self-interest (Vuolevi & Van Lange, 2009). Therefore, future studies could investigate contexts in which participants have more specific information or richer interactions with the estimation targets.

Additionally, there was a possible ceiling effect in self-reported policy endorsement in Study 4. This could have led to an artificially smaller difference between estimates and self-reported preferences due to the specific policies. That is, for a different set of policies, one might observe even more overestimation without this restriction in range.

Negative events and experience, although lower in frequency, are more salient and have a higher urgency; negative ties are more consequential for individual outcomes than positive ones

Negative Social Ties: Prevalence and Consequences. Shira Offer. Annual Review of Sociology, Volume 47, July 2021, online May 3, 2021. https://doi.org/10.1146/annurev-soc-090820-025827

Abstract: Recent decades have seen a surge of interest in negative ties and the negative aspects of social relationships. Researchers in different fields have studied negative ties and their consequences for various individual outcomes, including health and well-being, social status in schools and other organizations, and job performance and satisfaction, but they have mainly done so in disconnect. The result is a dearth of theoretization, manifested in a multitude of concepts and measures, that has made synthesis difficult and left numerous questions unanswered. By critically assessing these literatures, this review maps unresolved issues and identifies important lacunae in current investigations of negative ties. It is organized around three key issues: What are negative ties? How prevalent are they and where do they come from? And what are their consequences? The review concludes by proposing an agenda for future research.

We identify several methodological & conceptual factors—in particular, an overreliance on self-reports—that likely inflated, or even wholly created, the apparent associations between disgust sensitivity, ideology, & pandemic response

Ruisch, Benjamin, Shelby Boggs, Courtney Moore, Javier G. Samayoa, Jesse T. Ladanyi, Steffen Steinert, and Russell Fazio. 2021. “Investigating the Conservatism-disgust Paradox in Reactions to the COVID-19 Pandemic: A Critical Reexamination of the Interrelations Between Political Ideology, Disgust Sensitivity, and Pandemic Response.” PsyArXiv. May 11. doi:10.31234/osf.io/yn23v

Abstract: Research has documented robust associations between greater disgust sensitivity and (1) concern 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 aim to resolve this “conservatism-disgust paradox” and address several outstanding theoretical questions regarding the interrelations between 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 likely inflated, or even wholly created, the apparent associations between these constructs. Using non-self-report measures, we find that disgust sensitivity is a far less potent predictor of disease avoidance than is typically believed, and that ideological differences in disgust sensitivity may be limited to self-report measures. These findings help resolve this paradox, while providing important insight into the nature of these associations.

We identify weaknesses and inconsistencies in the Sperm Count Decline hypothesis; sperm count varies within a wide range, much of which can be considered non-pathological and species-typical

The future of sperm: a biovariability framework for understanding global sperm count trends. Marion Boulicault et al. Human Fertility, May 10 2021. https://doi.org/10.1080/14647273.2021.1917778

Abstract: The past 50 years have seen heated debate in the reproductive sciences about global trends in human sperm count. In 2017, Levine and colleagues published the largest and most methodologically rigorous meta-regression analysis to date and reported that average total sperm concentration among men from ‘Western’ countries has decreased by 59.3% since 1973, with no sign of halting. These results reverberated in the scientific community and in public discussions about men and masculinity in the modern world, in part because of scientists’ public-facing claims about the societal implications of the decline of male fertility. We find that existing research follows a set of implicit and explicit assumptions about how to measure and interpret sperm counts, which collectively form what we term the Sperm Count Decline hypothesis (SCD). Using the study by Levine and colleagues, we identify weaknesses and inconsistencies in the SCD, and propose an alternative framework to guide research on sperm count trends: the Sperm Count Biovariability hypothesis (SCB). SCB asserts that sperm count varies within a wide range, much of which can be considered non-pathological and species-typical. Knowledge about the relationship between individual and population sperm count and life-historical and ecological factors is critical to interpreting trends in average sperm counts and their relationships to health and fertility.

Keywords: Sperm count declinemale reproductive healthmale fertilitysemen analysisandrologyhuman reproduction

Contrasting the sperm count decline and sperm count biovariability hypotheses

In this paper we contrast the Sperm Count Decline and Sperm Count Biovariability hypotheses. We understand a hypothesis as not only a set of propositions open to empirical testing, but also as a set of implicit and explicit model-theoretic assumptions about the world that provides a framework for collecting and interpreting new and existing data and setting research agendas.

The SCD hypothesis interprets data on sperm count over time as a metric of men’s potential fertility, a proxy for men’s health, and an assay of environmental quality. According to SCD, a decline in ‘Western’ sperm counts from 1970s levels indicates a decline in male fertility, health, and a sign of a degrading environment. By contrast, the SCB hypothesis allows for the possibility of both pathological and non-pathological variation in sperm counts across populations and time. SCB begins with the premise that, above the threshold necessary for fertility, there is no basis to assume that high average population sperm counts are optimal. Nor is there any reason to believe that sperm counts in the 1970s are a species-typical baseline. SCB posits that sperm count varies within and across bodies in ways that are compatible with health such that a decline in an individual or population may not necessarily signal danger to fertility or well-being. We emphasise that while SCB invites a wider explanation and interpretation of sperm count trends, it does not exclude the possibility of sperm count decline or that decline may carry implications for men’s health and fertility. The SCB hypothesis provides a framework for exploring the trends identified by Levine et al. (2017) that considers the possibility that these trends can be explained by benign or adaptive variation in sperm counts in relation to diverse contexts and factors. Rather than treat nations or regions of global wealth as proxies for stable populations or biologically meaningful environments, SCB calls for testing links between specific developmental and proximate stimuli and sperm count outcomes, recognising human biological variation as local and situated (Lock, 2017; Niewohner & Lock, 2018).

From an SCB perspective, the data points that make up the 2017 meta-analysis simply demonstrate that sperm count varies across bodies, ecologies, and time periods. Examining the same data and background literature with a different set of assumptions, SCB argues that the interpretation that population sperm counts vary within a wide optimum with little consequence for fertility is at least as plausible as the interpretation that steady decline occurs.

We argue in favour of the SCB as a framework for interpreting population trends in human sperm counts. It identifies testable hypotheses that include both pathological and non-pathological explanations for and outcomes of observed variation in sperm counts. Table 2 contrasts the propositions of the SCD and SCB hypotheses. In the following sections, we analyse each proposition pair in turn.

1. Sperm count and men’s fertility

The SCD hypothesis contends that lower average population sperm counts portend higher rates of male infertility, positioning sperm count decline as a marker or cause of reproductive crisis for the human species. Levine et al. (2017) for example, infer that ‘declining mean [sperm count] implies that an increasing proportion of men have sperm counts below any given threshold for sub-fertility or infertility’. Levine et al. (2017) link this to claims of increasing ‘economic and societal burden of male infertility’ (p. 649).

There is little evidence that this is true. Levine et al. (2017) contend that the high circa 1973 numbers represent normal, healthy, and natural levels, while today's numbers represent a crisis and decline from a prior optimum. But current Western average sperm counts reported by Levine et al. (2017) for men unselected for fertility are well within the ‘normal’ range, defined by the World Health Organisation (WHO) as 15–259 million per mL for individuals (World Health Organization, 2010, p. 224). That is, the Levine et al. (2017) study reports a population average decline from ‘normal’ (99 million sperm per ml) to ‘normal’ (47 million sperm per ml). Furthermore, in absolute numbers, the 2009–2011 Unselected Western sperm counts (47.1 million/mL), which are ostensibly cause for alarm, are in fact relatively close to absolute sperm counts in ‘Other’ countries back in the 1978–1983 period (66.4 million/mL for Fertile Other, 72.7 million/mL for Unselected Other) and in the 2010–2011 period (75.7 million/mL for Fertile Other, 62.6 million/mL for Unselected Other).

Male infertility is a complex biological and social phenomenon that cannot be understood in terms of the single metric of sperm count (Guzick et al., 2001). Though azoospermia (sperm count of zero) guarantees infertility, researchers have found that some men with low sperm counts can conceive, while others with higher counts cannot (Patel et al., 2018; Wang & Swerdloff, 2014). Guzick et al. (2001) demonstrate that even sperm concentrations in the so-called sub-fertile range of less than 13.5 million/mL ‘do not exclude the possibility of normal fertility’ (p. 1392). Of note, the 2010 WHO reference values for semen parameters do not predict infertility, as the values were determined by studying fertile men; therefore, while the top 95% of sperm concentrations in the sample were taken to be the reference range, all of the men with sperm concentrations below the 5th centile were also fertile (Chiles & Schiegel, 2015; Cooper et al., 2010). Other studies from across the world have similarly confirmed the fertility of men below the WHO reference values (Haugen et al., 2006; Tang et al., 2015; Zedan et al., 2018).

Clinicians do not report proportionate increases in infertile men presenting for clinical consultation over Levine et al.’s study period (Inhorn & Patrizio, 2015). As urologist Peter Schlegel remarked in The New York Times in reference to the Levine et al. (2017) meta-analysis, ‘If you had a decrease in sperm count in the 50 to 60 percent range, we would expect the proportion of men with severe male infertility to be going up astronomically. And we don’t see that’ (Bowles, 2018). There is insufficient evidence to support claims of increasing rates of male subfertility in recent decades (Inhorn & Patrizio, 2015).

We note that there exists no species optimum in many other measures of reproductive function in men and women. As a concrete example, the gonadal steroid hormones testosterone, oestradiol, and progesterone are necessary to fertility (Dohle et al., 2003; Laufer et al., 1982; Welt et al., 2003). Researchers have documented significant variation in these hormones within and across populations and within individuals over time (Bjørnerem et al., 2006; Stanton et al., 2011; Vitzthum et al., 2004). As the WHO does with sperm count, researchers validate and publish non-pathological ranges for gonadal steroid hormones for use in clinical evaluation (Bhasin et al., 2011; Elmlinger et al., 2002). Within those ranges, higher levels are not considered absolute signals of better fertility or health (Bribiescas, 2016).

2. Sperm count as an assay of men’s health

SCD interprets sperm count decline as a biomarker of declining overall health status among men. Citing studies associating reduced sperm count and ‘increased all-cause mortality and morbidity’ (Levine et al., 2017, pp. 647, 649, 654), Levine et al. (2017) hypothesise that average population declines in sperm counts represent ‘a ‘canary in the coal mine’ for male health across the lifespan’ (p. 654; see also Skakkebaek et al., 2001). This metaphor suggests that low sperm counts are not only a barometer of men’s current health, but also a warning sign of future risks.

While there is evidence of a relationship between abnormal semen parameters and poor health status (Eisenberg et al., 2014), there is little evidence that average sperm count by itself is a valid summary measure of health status of men within a population. Recent work in a population in Córdoba, Argentina, suggests that, while semen parameters decline with age, lifestyle and health factors such as obesity, alcohol, and smoking have only modest associations with decline (Veron et al., 2018). Similar findings exist with respect to other semen parameters: a 1999 study of 939 UK men found no relation between sperm motility and common lifestyle factors such as consumption of alcohol, use of tobacco or recreational drugs, or high body mass index (Povey et al., 2012).

Specific relationships between sperm parameters and developmental and current conditions, including health status, remain to be established. Sperm variability can reflect endogenous and exogenous stimuli on both short and longer time scales. Spermatogenesis is a 42–76 day process (Misell et al., 2006). Interventions can occur at any point from the first division of the spermatogonia to the mature sperm’s journey through the epididymis (Chenoweth & Lorton, 2014). Research on livestock indicates that, depending on the developmental stage of their influence, effects can be permanent or may resolve. For example, enhanced nutrition in early life increased adult sperm production in bulls, but later-life nutrition could not compensate for early-life nutritional deficits (Kastelic, 2013). Seasonal climate variation, however, had only a transient effect on sperm parameters in bulls (Valeanu et al., 2015). Further research is needed to establish whether the same range of developmental and transient effects can be found in humans.

Prospective study models that use repeat individual measures in combination with a wide variety of social and biological measures are needed to identify potential confounders and causal variables in sperm biovariation. Such variables include transient exposures such as heat or tight clothing; the stimulus conditions under which the sample was collected, including available arousal material and duration of arousal pre-ejaculation; lifestyle factors including activity and diet; and developmental or environmental exposures like maternal smoking, pollutants, and endocrine disruptors (for examples of existing cross-sectional studies along these lines, see Gaskins et al., 2015; Inhorn et al., 2008; Priskorn et al., 2018). Without longitudinal individual and population data with sufficient ecological granularity, causal claims about the relationship between average population sperm counts and environments or lifestyles cannot be empirically substantiated.

In any case, the connection between sperm count and health is mediated by the individual’s recent experience and prior life history. For example, increased exercise does not have a stable relationship to sperm production, in part because the effect of exercise is mediated by current fitness level (Ibañez-Perez et al., 2019; Jóźków & Rossato, 2017; Rosety et al., 2017). Factors such as seasonal temperature and illness do not have uniform effects on the sperm production process for similar reasons. Given the range of relationships between stimulus and effect in spermatogenesis, sperm count is not an independent metric of human well-being.

3. Sperm count and environmental pollutants

In line with the TDS hypothesis (Bay et al., 2006; Skakkebaek, 2016), SCD asserts that the likely causes for sperm count declines among ‘Western’ populations are endocrine disruptors and other environmental pollutants introduced by industrialisation, as well as changes in men’s lifestyles. Levine et al. (2017) write that, ‘sperm count and other semen parameters have been plausibly associated with multiple environmental influences, including endocrine disrupting chemicals, pesticides, heat and lifestyle factors, including diet, stress, smoking and BMI’ (Levine et al., 2017, p. 649). In a Guardian article titled, ‘Sperm counts are on the decline - could plastics be to blame?,’ Levine identifies endocrine-disrupting chemicals (EDCs) such as plastics as a major cause of dropping sperm counts (Carr, 2019).

While environmental context undoubtedly affects men’s health, empirical research to date does not support a stable causal relationship between EDCs – exogenous chemicals that interfere with hormone action, typically through mimicking endogenous hormones and binding to protein receptors – and any indices of sperm health, including sperm count, sperm motility, and fertility (Bonde et al., 2016; Zamkowska et al., 2018). Scientists have approached questions about the impact of EDCs on reproductive function through animal models and human studies. In animal studies, male rodents are exposed to specific quantities of EDCs in a controlled environment, and systematically examined for effects on their reproductive health. In contrast, human clinical and epidemiological studies are primarily observational, studying the sperm of human males in the general population who were accidently exposed to unspecified levels of EDCs.

The strongest evidence for the impact of EDCs on human populations lies in their action as somatic carcinogens (Soto & Sonnenschein, 2010). Although reproductive cancers could plausibly lower sperm count, this pathway cannot explain the patterns reported by Levine et al. (2017) as they exclude cancer patients from their study. EDC exposure is also associated with risk for a wide range of health conditions outside of its effects on reproductive health, including non-reproductive cancers, diabetes, thyroid disorders, and neurological conditions (Gore et al., 2015). Some scientific research suggests that EDCs can have reproductive, neurological, and immunological effects on developing human foetuses (of both males and females), but more research is required to establish the exact relationship (Abaci et al., 2009; Bonde et al., 2016).

Even if EDCs cause a decline in sperm count, higher levels of industrial pollutant exposure in the West cannot explain the divergent trends in Levine et al.’s categories of ‘West’ versus ‘Other.’ Scientists have used the global distribution of plastics as a geological indicator of the extent of human altered landscapes (Zalasiewicz et al., 2016). It is widely established that the inequities of global capitalism disproportionately burden the global poor and indigenous peoples with the consequences of toxic pollution (Martinez-Alier et al., 2016). Substantial evidence suggests that pesticide poisoning is an equal or greater problem in low- and middle-income countries as in high-income countries (Jørs et al., 2018); the World Health Organization (2016) reports that 98 percent of people in urban low- and middle-income countries are exposed to unhealthy levels of toxic pollution.

The study period of 1973 to 2011 included in Levine et al. (2017) accompanied increasing global levels of industrial pollution (He et al., 2002; Karan & Bladen, 1976; Ramakrishnan, 2018). As a detailed example, consider two studies from Hyderabad, Andhra Pradesh, both included in Levine et al. (2017): one from a lead-acid battery manufacturing facility in Patancheru District and another at an anonymous lead welding facility (Danadevi et al., 2003; Vani et al., 2012). The 1970s initiated a rapid intensification of environmental pollution, in the form of waste disposal, air pollution, wastewater effluents, and occupational exposures in this region (Danadevi et al., 2003; Vani et al., 2012). Half of India’s rivers today are polluted by industrial wastewater effluents introduced in the 1970s; in Hyderabad, water sources were contaminated with industrial metals as early as 1983 (Karan & Bladen, 1976; Prahalad & Seenayya, 1988). Vegetables grown and consumed in urban Hyderabadi areas are packed with lead, cadmium and chromium, and bodies of water sampled are saturated with EDCs (Kiran Kumar et al., 2016; Ramakrishnan, 2018; Srikanth & Papi Reddy, 1991). In summary, evidence does not support the claim that increased exposure to environmental pollutants in the nations categorised by Levine et al. (2017) as ‘Western’ could be a plausible driver of distinctions between average population sperm count in ‘Western’ compared to ‘Other’ nations.

4. ‘West,’ ‘Other,’ and nations as units of population

Levine et al. (2017) extracted 244 estimates of average sperm counts from human sperm samples collected over the period 1973–2011 and reported in English-language publications, representing 42,935 individual men across the globe. They report that the average sperm concentration across Unselected ‘Western’ populations was 99 million/ml in 1973, declining to 47.1 million/ml in 2011, or 52.4% overall (Table 3). Sperm declines were only statistically significant in studies in ‘Western’ countries (Levine et al., 2017, p. 654), while ‘[n]o significant trends in SC [sperm concentration] or TSC [total sperm count] were seen in ‘Other’ countries overall, or for Unselected or Fertile men separately’ (Levine et al., 2017, p. 652).

Table 3. Sperm concentration in the first and last years of the Levine et al. (2017) meta-regression analysis, for all men and by fertility and geographic groups ‘Western’ and ‘Other.’

CSVDisplay Table

The study design of Levine et al. (2017) separated men along axes of fertility and geography. First, they categorised men as ‘Unselected,’ meaning that it was not known whether or not they had conceived a pregnancy, or as ‘Fertile,’ meaning that they had conceived a pregnancy. They next disaggregated men by the nation of the study in which they participated: Europe/Australia; North America; and ‘Other,’ which included South America, Asia, and Africa.

Constituting ‘Western’ and ‘Other’

Notably, the model used by Levine et al. (2017) generated statistically significant declines in sperm concentration over time for both Unselected and Fertile Europe/Australia cohorts, and for the Unselected North America Cohort, but not for the Fertile North America cohort (p = 0.29) or either of the Other cohorts (p = 0.30 for Unselected Other, 0.41 for Fertile Other) (Levine et al. (2017) Table S3). In the final published study, Levine et al. (2017) aggregated North American and Europe/Australia data to create a ‘Western’ cohort. Their justification was the similarity of effect magnitude in the data (despite one subgroup – Fertile North America – being statistically insignificant) and that North American data comprised only 16% of the estimates. In the final model, both Unselected and Fertile Western had statistically significant negative effects. In other words, sperm count declines in North America among Fertile men, which were not previously significant (p = 0.29), gained manufactured significance (p = 0.033) by being weighted with the European/Australian data in the final model.

It is justifiable to explore multiple aggregations of data along hypothesis-driven inquiries. However, the reframing of a statistically insignificant decline in fertility among Fertile North American men implies a level of certainty that the data do not support. When this certainty is adopted by public-facing reporting, it not only contributes to unfounded panic over ‘Western’ fertility, but also may influence the course of future research programs.

The data included in the meta-analysis are sparse by any measure. Global coverage is mottled and asymmetric. Levine et al. (2017) recognise that data on sperm count in ‘Other’ countries is much sparser than in ‘Western’ countries, as illustrated by Figure 1 and Table 3. Less evident yet still important is the quantitative and qualitative variation in the data points at the level of the nation and the region. For example, the preponderance of sperm count studies over a range of time periods from several major Danish cities included in the meta-analysis might allow researchers to describe general population trends in sperm count within Denmark. However, the same number of studies, or fewer, conducted at disparate times in such large and heterogeneous countries such as India or China cannot hope to capture the same granularity of data by averaging sperm counts.

Figure 1. Number of sperm samples per country over the period 1973–2011 included in the 2017 meta-analysis.

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Studies of men unselected by fertility (i.e. men assumed to be representative of the general population in a given geographic area) included in the meta-analysis vary in study design and sample composition across geographic location. For example, control groups in studies of the impact of an environmental exposure on sperm quality were extracted for inclusion in Levine et al. (2017). Studies conducted in this way contribute more samples to some national data pools than others. For example, of 10 separate studies conducted in Denmark, four (40%) were interested in the impact of a specific exposure (e.g. pesticides or maternal folic acid) on sperm quality. By contrast, 13 of the 16 studies in the United States (81%) are exposure studies, looking at the effects of a chemical exposure, smoking, stress, or a medical condition such as cryptorchidism on sperm quality. And 100% of the five studies used for Unselected samples in all of Central and South America were designed to study sperm quality in the context of a specific exposure, whether pesticides, contaminants, or a medical condition. This is important because the controls in these exposure studies are often convenience samples relative to study subjects. For example, a study in Mexico City on rubber factory workers exposed to hydrocarbons used a control group consisting of employees working in the factory’s administrative offices (De Celis et al., 2000), and a study in San Francisco on sperm quality in anaesthesiologists had anaesthesia residents serve as controls (Wyrobek et al., 1981). As Fleiss and Gross (1991) explain, factors other than exposure may affect whether a sample is a case or a control, and these confounding variables can obscure the associations of interest.

Interpreting average sperm count in a nation

The SCD treats nations and continents as bounded populations, with men unselected by fertility described as ‘more likely to be representative of the general population’ in that nation or continent (Levine et al., (2017), p. 655). That is, continents or nations are conceptualised as population samples that can be used to compare, for example, average 1973 sperm counts to average 2011 sperm counts.

Within this paradigm, the categories ‘West’ and ‘Other’ rely on a particular vision of a static national population that obfuscates the role of several types of migration in continually redefining how these populations are constructed. Since the 1970s, repeated, large-scale, highly varied movements of populations have occurred across national borders, and in particular between nations categorised by Levine et al. (2017) as ‘Other’ to ‘Western’ countries. Yet these movements, from East and South to North and West, from rural to urban, and across many kinds of differently polluted and polluting ecologies, are lost entirely in the racial/national geopolitical categories of difference uncritically embraced by current instantiations of the SCD hypothesis. If biological variables such as sperm count are to be understood as ecologically dependent at the population level, patterns of migration since the 1970s that have fundamentally reshaped nations categorised as ‘West’ and ‘Other’ must be taken into consideration.

During the time period covered by the Levine et al. (2017) meta-analysis, patterns of migration have redistributed formerly concentrated populations into a contingent of increasingly heterogeneous cities and states, predominantly in the Global North and West. In Western Europe, decolonisation as well as the proliferation of guest worker programmes to meet the needs of a broadly booming post-war economy brought individuals from former colonies distributed over three continents, combined with workers from North Africa and Southern Europe, into Northern and Western Europe (Moch, 2003; Van Mol & de Valk, 2016). Sweden, for instance, which historically had higher emigration than immigration, saw a rapid population change after World War II. Immigration rates peaked in 2013. As a result, 24% of the population is now foreign-born, and its ethnic composition has also shifted. Migration from Africa again rose in the 1990s and migration from across Asia and Latin America into Western Europe rose significantly from the turn of the 21st century (Pellegrino, 2004; Van Mol & de Valk, 2016). Meanwhile in North America, the United States has also seen significant demographic shifts precipitated by evolving migration patterns. Due to alterations to US migration law after 1965, most immigrants after 1970 were of Latin American or Asian origin, whereas previously they had been predominantly of European origin. Simultaneously, total immigration in the US increased dramatically – from approximately 5% to nearly 15% of the total population (Martin, 2014).

Within the nations broadly categorised by Levine et al. (2017) as ‘Other,’ which comprise the large majority of the world by aggregate population, migration has also played a formative role in shaping demographic distribution since the 1970s. Large-scale internal migration in large and populous nations such as China and India, predominantly from rural to urban settings, was generated by rapid industrialisation and entry into global markets by many states in the 1970s that created the need for more robust industrial workforces (Liang & Ma, 2004; Lusome & Bhagat, 2006). In the same states during the same period, international migration played a similarly important role in structuring demographics; the Opening of China in the later-1980s saw a renewed wave of emigration of diverse individuals, and the oil boom in the Gulf States in the 1970s saw the efflux of labourers from India and into new ‘Other’ nations (Ecevit, 1981; Ganeshan, 2011; Khadria, 2006; Xiang, 2016). In summary, particularly if the SCD assumes that the influence of interest is the individual’s developmental rather than current environment, country of residence is a poor proxy for a sample population, because populations have not stayed within their borders during the study period.

Attending to the globalising processes of migration, development, and pollution reveals that the differences assumed between so-called ‘West’ and ‘Other’ countries do not apply to the study period covered in Levine et al. (2017). South India can act as an exemplar of a region that has undergone momentous shifts in ecology and demography since the 1970s, brought about by ongoing internal and international migration and environmental pollution from globalising industrialism. The 1970s Gulf oil boom contributed to an increase in Indian emigration (particularly among males), as did India’s growing global economic presence, which led to a ‘brain drain’ migration among educated and skilled Indians to the global West (Chacko, 2007; Ganeshan, 2011). Now, across India, rural-to-urban migrants account for more than half of the population of cities (Ganeshan, 2011; Irudaya Rajan & Sumeetha 2020). Hyderabad too has seen decades of cyclical immigration from rural areas towards urban industrialisation, emigration abroad both to ‘West’ and ‘Other’ nations, and a recent reversal of this process wherein Western-trained Indians return as Hyderabad grows in its transnational, globally connected contemporary networks (Chacko, 2007). The social and environmental experience of growing up and living in South India in the 1970s, for example, is not comparable to that of South India in the 2010s. It is unclear how Levine et al. (2017) locate ‘India’ in their analysis, whether as a place with a set of defined ecological conditions, as a group of people, or as a place that might have changed over time ecologically but where the population has remained constant enough to allow for disambiguation of the effects of place on sperm production from any other effects. We suggest that it is not obvious that the genetic composition of a population in a given place remains the same over time. Nor is it certain that the people in a given place have experienced the developmental environment of that place, or that the place has remained ecologically stable (or not) in predictable or documented ways.

A biovariability framework emphasises that the appropriate unit of analysis to understand relationships between ecology and average population sperm count is a spatiotemporally continuous population, in which bio-environmental and socio-cultural exposures and their outcomes can be tracked over time within and among individuals at multiple time points. Levine et al. (2017) assume that geographic regions such as nations and continents over the period from 1973 to the present day represent such populations. Although it may sometimes be the case that geopolitical boundaries can meet these criteria, the highly dynamic history of migration and environmental change since 1973, wrought by increasingly globalised processes, indicates that the nation-level sperm count averages utilised by Levine et al. (2017) are inappropriate categories for understanding sperm count epidemiology.