Thursday, December 29, 2022

Greedy people generate more household income (not personal one), have more sexual partners, fewer long-term relationships, and less offspring (partly due to the relationaships, partly because of greed), and are less satisfied with life

Greed: What Is It Good for? Karlijn Hoyer et al. Personality and Social Psychology Bulletin, December 28, 2022.

Abstract: What is greed good for? Greed is ubiquitous, suggesting that it must have some benefits, but it is also often condemned. In a representative sample of the Dutch population (N = 2,367, 51.3% female, Mage = 54.06, SD = 17.90), we examined two questions. First, inspired by Eriksson et al., we studied whether greedy people generate more personal and household income (economic outcomes), have more sexual partners, longer relationships, and more offspring (evolutionary outcomes), and are more satisfied in life (psychological outcomes). We found that greedy individuals had higher economic outcomes, mixed evolutionary outcomes, and lower psychological outcomes. Second, we compared greed and self-interest. We found that they differed in terms of economic outcomes, and partly in terms of evolutionary outcomes, but that they were similar in terms of psychological outcomes. This research provides insights into what greed is and does. Directions for further research are discussed.


What is greed good for? In a representative sample of the Dutch population, we studied relationships between greed and a number of economic, evolutionary, and psychological life outcomes, similar to the approach that Eriksson et al. (2020) recently used to test the possible benefits of self-interest. We examined whether individual differences in dispositional greed (assessed by the Dispositional Greed Scale of Seuntjens, Zeelenberg, Van de Ven, & Breugelmans, 2015) are related to personal and household income (economic outcomes), to number of biological children, sexual partners, and duration of romantic relationships (evolutionary outcomes), and to life satisfaction (psychological outcomes). For comparison, we performed similar analyses for self-interest (assessed via both the Prosocial Motivation scale of Eriksson et al., 2020, and the SVO-slider of Murphy et al., 2011). What did we find?
With the exception of personal income, which we will come back to later, all (preregistered) hypothesized relations with greed were found to be significant: being higher in dispositional greed correlated with having a higher household income and having had more sexual partners, and with having fewer children, shorter lasting romantic relationships and having lower well-being. Importantly, these patterns are different from those of self-interest, where fewer significant relationships were found and where, with the SVO measure, there was a negative correlation with household income and a positive correlation with length of romantic relationships. Greed and self-interest (measured as Prosocial Motivation) where similar in their negative relation with the number of children. The general picture that emerges is that dispositional greed may be good for the purposes of acquisition, but that in a contemporary Western society, in this case, the Netherlands, it confers few other benefits. This largely negative view of greed aligns well with the general condemnation of greed as a sin and with the undesirability of being called a greedy person. However, at closer scrutiny, the results may hint at a more nuanced picture.
To start with economic outcomes, the data show a mixed picture: greedier people did not have a higher personal income than less greedy people, but they did report a higher household income. As was discussed in the introduction, findings on dispositional greed and personal income in previous studies have been mixed (Seuntjens et al., 2016Seuntjens, Zeelenberg, Van de Ven, & Breugelmans, 2015Van Muijen & Melse, 2015Zeelenberg et al., 2020). Interesting in this regard is that in their study among 120.000 Dutch employees, Van Muijen and Melse did report positive relationships for specific occupations, such as sales managers, where greedy individuals earned substantially more than their less-greedy coworkers. This could suggest that the economic benefits of greed are dependent on the specific situation people find themselves in. Unfortunately, our data do not include information on participants’ occupations or the specific branches they were working in. We believe further differentiation along different occupations to be an interesting avenue for future research.
Personal income can come from various sources, such as employment, social benefits, and pensions. Interestingly, exploratory analysis revealed that, among the (self)employed, there was a slight positive relationship between greed and personal income suggesting that greed may indeed be beneficial for personal income in specific situations (i.e., employment). Over all sources of personal income together, however, our data showed a net null effect of personal income (though in the SEM there was a positive correlation).
The positive relationship between greed and household income could go different ways. It might be that greedy individuals contribute to higher household income by, for example, stimulating their partners to work harder, or that greedy individuals select partners that are economically better off, or it could be that there is a third variable, such as greedy individuals taking care of fewer children which contributes to household income because both partners can work more. Context effects may also be important in this regard. Recently, evidence has been found that growing up in more wealthy circumstances is associated with higher greed at a later age (Hoyer, Zeelenberg, & Breugelmans, 2021Liu, Sun, & Tsydypov, 2019). Thus, it might be that the opportunities that the environment presents breeds higher greed which in turn creates a later preference for environments that are more conducive to greed. Of course, such mechanisms are mere conjecture at the time, but we feel that the question as to when (rather than whether) greed is related to more income is worthy of further attention. It is also interesting that we did not find a relation between self-interest and personal income, while SVO self-interest showed a negative relation with household income, suggesting that there is something specific to greed in this regard.
With regard to evolutionary outcomes, the data suggest that greedy people are more likely to follow an r-strategy (MacArthur & Wilson, 1967), having more sexual partners (though in the SEM this relation was not significant) but less long-lasting relationships. In contemporary societies, this may lead to having fewer children, as is evident in our data. However, like the economic outcomes, this effect may be dependent on context. In other social or historical circumstances, an r-strategy may actually lead to having increased reproductive opportunities by having more sexual partners, and as a possible consequence, more genetically diverse offspring. From a more psychological perspective, there may also be other reasons why greedy people have fewer children. It could be a deliberate choice but also the result of unsuccessful relational bonding. Interestingly, an exploratory analysis revealed that greedy individuals more often reported having a partner, r(2,367) = .05, p = .016, but these relationships did not seem to last. In either case, the data suggest that greed transcends mere material goods and acquisitions in that it is related to different ways in which people approach relationships as well. Indeed, this was also suggested by Hoyer (2022), who found that, among other things, greedy individuals objectify their friends more and feel less close to them.
With regard to psychological outcome, the data are quite clear and very much in line with previous research (Krekels & Pandelaere, 2015Li et al., 2021Masui et al., 2018Seuntjens, Zeelenberg, Van de Ven, & Breugelmans, 2015Zeelenberg et al., 2020); higher dispositional greed was related to a lower satisfaction-with-life. This could be an intrinsic property of greed: the constant dissatisfaction of never having enough and the endless pursuit of more which are core characteristics of greed may by necessity imply lower life satisfaction in general. The relationship could also be more indirect, with greedy people being less satisfied with life due to the fact that, for example, their relationships are shorter lasting or their families are smaller. Having good social relationships is crucial to well-being (e.g., Amati et al., 2018), even more so than having a good income (Powdthavee, 2008).
A secondary goal of this study was to compare greed with self-interest. The reasons for including this comparison were two-fold. First, self-interest and greed are clearly related constructs, both theoretically and empirically. Second, the design of our study was inspired by the study of Eriksson et al. (2020). For a complete comparison, we not only used the Prosocial Motivation Scale that was used by Eriksson et al. to measure self-interest, but also the more commonly used SVO-slider (Murphy et al., 2011). Both measures gave slightly different results.
When comparing the bivariate correlational results, it is notable that greed and self-interest share many of the negative relationships, although self-interest shows overall fewer significant relationships. One salient difference is the relationship with household income, which is positively related to greed but negatively to self-interest. A second difference is the relationship with duration of the longest romantic relationship. Greed was related to shorter romantic relationships, while for self-interest the effects depended on the scale: self-interest as measured by Prosocial Motivation was negatively correlated with relationship length, but self-interest as measured by SVO was positively correlated. This makes the interpretation of the results in relation to greed complicated. A third difference is that greed was positively related to the number of sexual partners whereas there was no significant relationship with self-interest. Thus, being greedy appears to be somewhat more advantageous than being self-interested, both economically and evolutionarily.
Because greed and self-interest were correlated, we also looked at partial correlations. Here, unique effects of greed and self-interest remain, albeit only for the SVO measure. When it comes to greedy and self-interested individuals having fewer children, partial correlations suggests that this effect may better be explained by greed than by self-interest. The same holds for the negative correlation with relationship length of the prosocial motivation measure. The positive correlation of the SVO measure with relationship length remained significant after controlling for greed. Also, the negative correlation with life satisfaction remained significant for both greed and SVO-self-interest, suggesting that being greedy and being self-interested makes you unhappy in their own way.
Taken together, these results clearly show the usefulness of distinguishing between greed and self-interest when it comes to studying economic, evolutionary, and psychological outcomes. All in all, greed appears to have positive and negative relationships with life outcomes, whereas self-interest tends to be negative across the board for the outcomes that we examined.
To account for measurement error, we used SEM to further explore the relationship between greed and self-interest as latent variables and the economic, evolutionary, and psychological outcomes as manifest indicators. The results changed somewhat, indicating that we should interpret the results with caution. The most notable changes are the following. Using SEM, we found a negative relationship between greed and personal income. For household income, SEM revealed a positive relationship with self-interest (measured as prosocial motivation), and the negative relationship with self-interest (measured as SVO) disappeared. The positive correlation between greed and the number of sexual partners disappeared (p = .077) in SEM.
Like any study using correlational, cross-sectional panel data, there are limitations to this study. Ideally, a future, longitudinal study should investigate the underlying mechanism of differences in greed in socioeconomic success over the years. The results obtained for income already suggest that there might be differences over the course of people’s lives. Furthermore, future research could investigate why the greedy have lowered evolutionary outcomes by examining the mating practices of the greedy. Finally, future research should investigate why greedier individuals feel less satisfied with life, in order to design interventions to increase their mental well-being and reduce possible severe side effects such as depression.
A second limitation that would warrant more research is the observation of a relatively strong correlation between greed and age in our data. When we explored the effect of age as a control variable many relationships between greed (as well as self-interest) and life outcomes were no longer significant. Given the limited literature on such effects, it is hard to provide a strong interpretation as to what this means. Both Liu, Sun, and Tsydypov (2019) and Hoyer, Zeelenberg, and Breugelmans (2021) speculated that a relationship between age and greed might be curvilinear, following an inverted U-shape. This would mean that greed reaches a maximum in early adulthood. In favor of such a relationship are findings of a positive correlation between greed and age with adolescent samples (Liu, Sun, & Tsydypov, 2019Seuntjens et al., 2016), and findings of negative relationships with adult samples (Liu, Sun, Ding, et al., 2019Seuntjens, Zeelenberg, Van de Ven, & Breugelmans, 2015). With regard to self-interest and age, we found somewhat mixed evidence: Age correlated positively with self-interest measured with SVO but negatively with self-interest measured with Prosocial Motivation. The literature seems to be more in line with the latter, suggesting that people become more prosocial later in life (e.g., Matsumoto et al., 2016). Van Lange et al. (1997) refer to this phenomenon as the prosocial-growth hypothesis. It would appear to be worthwhile to further investigate the relationships among greed, self-interest and age, especially from a developmental, longitudinal perspective.
In this research, we used two existing measures of self-interest: the inverse of prosocial motivation (Eriksson et al., 2020) and SVO (Murphy et al., 2011). Both measures correlated, but not highly, and correlations with the different life outcomes differed somewhat between measures. This raises questions about the convergent validity of both self-interest measures. Eriksson et al. analyzed a large number of existing data sets, so in their search for indicators of self-interest, they were bound by what was available. The prosocial motivation scale was used in Study 1, but in other studies, they employed different indexes. In retrospect, we believe that the operationalization as self-interest as the inverse of prosocial motivation might be criticized from a psychological perspective. In organizational research, people have argued for treating self-interested and other-interested orientations as distinct dispositions (e.g., Meglino & Korsgaard, 2004). Research of Gerbasi and Prentice (2013) shows that indeed self-interest and other-interest were moderately positively correlated, rather than negatively correlated. For this reason, we included the SVO measure of Murphy et al. (2011), which is based on decomposed games, as a more traditional measure of a continuum between prosociality and self-interest. This measure is closer to how self-interest is usually assessed in psychological research. However, because our study was not designed to distinguish between different indicators of self-interest, we refrain from speculating on the difference between the two measures in too much detail. Most important for this article is that the patterns of both self-interest measures were distinct from that of greed.
A question that could be asked is to what extent the relationships we found for greed are unique to this construct or whether they could be explained by other constructs. Previous research by Seuntjens, Zeelenberg, Van de Ven, and Breugelmans (2015) revealed four constructs that most strongly correlate with greed: in decreasing order materialism, envy, maximization, and self-interest. The latter construct was the comparison standard in this article but could the other constructs explain the effects of greed? We believe that this is not plausible. First, in a multistudy prototype analysis, Seuntjens, Zeelenberg, Breugelmans, and Van de Ven (2015) found that although these constructs were mentioned, they did not belong to the core of features of greed. It is this core that is assessed by the DGS that we used in this study. In addition, Seuntjens, Zeelenberg, Van de Ven, and Breugelmans (2015) extensively mapped the nomological network of the DGS and the other constructs. Greed emerged as being clearly distinct. Furthermore, there are theoretical reasons why the other constructs cannot explain the full pattern that we found for greed. For example, the research by Crusius and Lange (2021) suggests that greed predicts envy, and not the other way around. Likewise, while materialism might well be related to income, the relationship with number of children, relationships and number of sexual partners is not at all evident. Finally, maximization might be related to more sexual encounters but should rather relate to having more rather than fewer children. Thus, we are somewhat confident that the patterns we found for greed are unique to greed in comparison to related constructs.
Another question that might arise is whether certain conditions that might be unique to a particular country or culture has a significant effect on the outcome variables in our study. Although we do not have direct evidence for cross-cultural equivalence, we have quite a bit of evidence for the cross-cultural validity and invariance of structural relations for the Dispositional Greed Scale from Seuntjens, Zeelenberg, Van de Ven, and Breugelmans (2015). This scale has been applied in (and validated for use in) various countries from different continents. Furthermore, many effects of greed are structurally the same between these samples, for instance positive associations between the greed and envy, psychological entitlement, materialism, and impulsive buying behavior, and negatively associations between greed and self-control, self-esteem, and life satisfaction. As a further case in point, recent evidence for the luxury hypothesis (that growing up wealthy is related to higher levels of adult greed) as found in a Chinese sample (Liu, Sun, & Tsydypov, 2019) has been replicated in Dutch and American samples (Hoyer, Zeelenberg, & Breugelmans, 2021). Of course, none of this is direct evidence for cultural invariance, and we cannot exclude that there are global conditions that would lead to different relations. However, given the extant evidence, we believe that we can be reasonably confident that our findings are not limited to the Netherlands as a country or a culture.
Let us return to the question that motivated the current research, is there anything good about greed? Despite the clear condemnation of greed in philosophical, religious, and popular writings, our results show that greed is (somewhat) beneficial for economic outcomes (supporting claims put forward by some economists). However, our results also show that greed is mixed for evolutionary outcomes and unfavorable for psychological outcomes. A secondary goal of this study was to disentangle the relationship between greed and self-interest. On the basis of the current findings, we can say that greed and self-interest differ in their relation to economic outcomes and are mostly similar in their relation to evolutionary outcomes (with greed being somewhat more advantageous) and well-being. In short, greed may be good for income but bad for happiness.

The Lead-Crime Hypothesis: When we restrict our analysis to only high-quality studies that address endogeneity the estimated mean effect size is close to zero

The Lead-Crime Hypothesis: A Meta-Analysis. Anthony Higney, Nick Hanley, Mirko Moro. Glasgow Univ. Paper No. 2021 – 02, February 21 2021.

Abstract: Does lead pollution increase crime? We perform the first meta-analysis of the effect of lead on crime by pooling 529 estimates from 24 studies. We find evidence of publication bias across a range of tests. This publication bias means that the effect of lead is overstated in the literature. We perform over 1 million meta-regression specifications, controlling for this bias, and conditioning on observable between-study heterogeneity. When we restrict our analysis to only high-quality studies that address endogeneity the estimated mean effect size is close to zero. When we use the full sample, the mean effect size is a partial correlation coefficient of 0.11, over ten times larger than the high-quality sample. We calculate a plausible elasticity range of 0.22-0.02 for the full sample and 0.03-0.00 for the high-quality sample. Back-of-envelope calculations suggest that the fall in lead over recent decades is responsible for between 36%-0% of the fall in homicide in the US. Our results suggest lead does not explain the majority of the large fall in crime observed in some countries, and additional explanations are needed.

Keywords: Meta-analysis; Publication selection bias; pollution; lead; crime

JEL: C83, K42, Q53

In-Person Schooling Seems to Raise Youth Suicide, possibly because of bullying victimization

In-Person Schooling and Youth Suicide: Evidence from School Calendars and Pandemic School Closures. Benjamin Hansen, Joseph J. Sabia & Jessamyn Schaller. NBER Working Paper 30795, December 2022. DOI 10.3386/w30795

Abstract: This study explores the effect of in-person schooling on youth suicide. We document three key findings. First, using data from the National Vital Statistics System from 1990-2019, we document the historical association between teen suicides and the school calendar. We show that suicides among 12-to-18-year-olds are highest during months of the school year and lowest during summer months (June through August) and also establish that areas with schools starting in early August experience increases in teen suicides in August, while areas with schools starting in September don’t see youth suicides rise until September. Second, we show that this seasonal pattern dramatically changed in 2020. Teen suicides plummeted in March 2020, when the COVID-19 pandemic began in the U.S. and remained low throughout the summer before rising in Fall 2020 when many K-12 schools returned to in-person instruction. Third, using county-level variation in school reopenings in Fall 2020 and Spring 2021—proxied by anonymized SafeGraph smartphone data on elementary and secondary school foot traffic—we find that returning from online to in-person schooling was associated with a 12-to-18 percent increase teen suicides. This result is robust to controls for seasonal effects and general lockdown effects (proxied by restaurant and bar foot traffic), and survives falsification tests using suicides among young adults ages 19-to-25. Auxiliary analyses using Google Trends queries and the Youth Risk Behavior Survey suggests that bullying victimization may be an important mechanism.

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics. Nicholas Judd, Bruno Sauce & Torkel Klingberg. npj Science of Learning volume 7, Article number: 33, Dec 15 2022.

Abstract: Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9–11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.


Schooling showed substantial and independent effects for each intelligence domain tested: cIQ, fIQ, and WM. In line with previous research, we found the raw effect of schooling on cIQ to be larger than for fIQ, though this difference was not significant13. This was also true for the relative influence of schooling compared to the effect of chronological age, as that ratio for cIQ was almost double that of fIQ (1.1 vs 0.54), showing almost near equal influences per year of schooling and age. Surprisingly, WM had the highest ratio (2.2), with the effect of schooling being more than double that of age. While this is in agreement with a previous study in younger children21, it should be interpreted with caution as the WM measure comprised of only one task59.

As expected, SES and cogPGS were highly correlated with each other, highlighting the need to isolate the independent effects of each. Both had large, independent effects on cIQ, fIQ, and WM. In a follow-up analysis, we estimated the contribution of each SES component separately. Notably, all SES components were significant for each intelligence domain, with the effects from parental education and income being similar in size while neighborhood quality was roughly half.

A child’s SES is not independent of their cogPGS31, which makes it difficult to support causal inferences of these factors as well as interpret the interaction between them (i.e., the endogeneity problem)60. This gene-environment dependence can cause spurious gene-environment interactions41,61. Our sibling analysis sheds light on this issue. We found the within-family effect of cogPGS to be roughly half of the between-family effect for cIQ, in line with the literature51,52. This indicates the presence of passive genotype-environment correlations—whereby parents create family environments consistent with their genotypes, which in turn facilitate the development of their children’s intelligence. Since we only had data from 392 families, a lack of statistical power is most likely the reason for our null findings for fIQ and WM. While our cogPGS estimate in the full sample should be interpreted with caution, previous research has shown SES to be the major source of these between-family effects51,62.

Predominant theories of GE-interplay imply a positive sign—genetically endowed cognition influences one’s proximal environment, and that environment, in turn, influences one’s cognition in continuous, reciprocal interactions, such as the multiplier theory34, the transactional model35 and the bioecological model36. In line with this, a meta-analysis on twin research found the heritability of intelligence to increase with higher SES in the United States63. But this effect is far from consistent. The same meta-analysis did not replicate this finding on data collected outside the United States. Furthermore, a large twin study found negative and null results for heritability by SES interaction regarding mathematics and reading in Florida64. Two studies using a similar polygenic marker to ours did not find evidence of GE-interplay for EA65,66, yet a study with 130,000 adults in the UK found a very small negative interaction of SES with neighborhood quality for fIQ and EA40. Crucially, the standardized effect size of this interaction was (β < 0.02 SD)—translating to less than a third IQ point throughout one’s entire life—in turn having no practical effect for the individual.

We did not find any significant interaction between schooling and SES or cogPGS. One strength of our design is that schooling is, in principle, independent from cogPGS and SES. We are not aware of any other research looking at GE-interplay with schooling. A recent study did find low PGS children in high-SES schools to continue with mathematics much longer than genetically similar children in low-SES schools39. However, we see their result as more relevant to inform a cogPGS-SES effect rather than the gene-by-schooling interaction.

We expected our interaction terms to either compensate or accelerate preexisting differences. Schooling, for example, could increase (i.e., Mathew’s effect) economic/genetic inequality or lessen these differences between children (i.e., catch-up effect). The Coleman Report, a seminal study with more than half a million students and over 3000 schools in 1966, controversially concluded schools did not contribute to widening achievement gaps between children29. Conversely, there is some evidence that schooling might lessen socioeconomic disparities between children (i.e., a catch-up effect) for cognitive skills67.

Our study indicates schooling to not be a major driving force for either increasing or decreasing differences due to SES or cogPGS. Yet, we emphasize caution in interpreting these null effects as our range of schooling (3rd–5th grade) was limited, and Bayesian analysis showed that an effect of less than 0.02 SD could not be ruled out (Fig. 3). Since any interaction effect with schooling could accumulate—that is, continue to increase each year—a very small (<0.05 SD) effect size could be of practical relevance54,55. For example, an interaction with schooling as small as 0.02 SD could accumulate over five years to 0.1 SD or roughly 30% of the largest SES main effect (i.e., cIQ = 0.29). This is, of course, a simplistic scenario assuming no counteracting mechanisms, yet it illustrates how very small effect sizes can become consequential68,69. In contrast, cogPGS–SES’ interaction is a lifelong effect and does not have the potential to accumulate in the same way. However, our sample had a slightly lower SES (Cohen’s d = −0.23) than the average for the United States, therefore, we cannot rule out an interaction at the lower tail of the SES distribution.

One limitation of this study is that the 1.1 million individuals used to estimate the cogPGS are heavily biased towards those of European descent and from higher SES areas30,70. This means our results regarding genetics should only be generalized to white populations. Furthermore, GWAS methods cannot detect certain types and sizes of GE interactions since they are intended to detect additive effects71. Another consideration is how to interpret findings with multi-trait GWAS’s—in our case cognitive ability, mathematics, and educational attainment—since one of the supplementary phenotypes could be driving the results72. The extent of this issue depends on (1) the relative sample size differences between the GWAS included and (2) the genetic correlation of these traits. In our case, there are sample size differences between educational attainment and cognitive ability, yet the very high (r ~ 0.75) genetic correlation between these traits most likely mitigates this issue73. Lastly, a strength of our study is that we controlled for ancestry-based genetic PCs in the full model, rather than just correcting cogPGS. While this means SES’s relationship with cognition is controlled for population stratification, it also brought the limitation that we had to exclude subjects without DNA, resulting in the average level of SES increasing.

We found that schooling causes relatively large increases in children’s intelligence. The two years of schooling (3rd to 5th grade) caused a larger difference in intelligence than either SES or cogPGS. However, schooling did not change the rank order of individuals’ intelligence. This was shown by the lack of significant two-way interactions between Schooling, SES, and cogPGS, although our power to detect potentially meaningful small effects for schooling was limited. Intriguingly, we did not find any interaction between SES and cogPGS, this means that children’s genetic differences do not matter more, or less, for intelligence dependent upon their SES background.