Partner choice does not predict prosociality across countries

Abstract

Why does human prosociality vary around the world? Evolutionary models and laboratory experiments suggest that possibilities for partner choice (i.e. the ability to leave unprofitable relationships and strike up new ones) should promote cooperation across human societies. Leveraging the Global Preferences Survey (n = 27,125; 27 countries) and the World Values Survey (n = 54,728; 32 countries), we test this theory by estimating the associations between relational mobility, a socioecological measure of partner choice, and a wide variety of prosocial attitudes and behaviours, including impersonal altruism, reciprocity, trust, collective action and moral judgements of antisocial behaviour. Contrary to our pre-registered predictions, we found little evidence that partner choice is related to prosociality across countries. After controlling for shared causes of relational mobility and prosociality - environmental harshness, subsistence style and geographic and linguistic proximity - we found that only altruism and trust in people from another religion are positively related to relational mobility. We did not find positive relationships between relational mobility and reciprocity, generalised trust, collective action or moral judgements. These findings challenge evolutionary theories of human cooperation which emphasise partner choice as a key explanatory mechanism, and highlight the need to generalise models and experiments to global samples.

Keywords: cooperation; cross-cultural; partner choice; prosociality; relational mobility.

Graphical abstract

Figure 1.

Directed acyclic graph of the causal model justifying the inclusion of covariates in our statistical models. Thomson et al. (2018) show that environmental harshness and subsistence style are antecedents of relational mobility, but other evidence also suggests that environmental harshness and subsistence style directly affect prosociality (Cronk et al., ; Talhelm et al., 2014). Environmental harshness and subsistence style are thus third variables that confound the direct path from relational mobility to prosociality. Moreover, all four of these variables are confounded by unmeasured factors (U), such as ecology, climate, institutions and norms. We cannot directly condition on unmeasured factors, but since these factors are themselves predicted by geographic and linguistic proximity between countries, we can account for them by allowing countries to covary according to geographic and linguistic proximity.

Figure 2.

Posterior predictions from a Bayesian multilevel regression predicting prosocial preferences from country-level relational mobility, without control variables. (a) The overall effect of relational mobility on prosociality. (b–d) The individual effects of relational mobility on altruism, positive reciprocity and generalised trust. Lines and shaded areas indicate median posterior regression lines and 95% credible intervals. Points indicate average prosociality levels and relational mobility scores for each of the 27 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.

Figure 3.

Posterior predictions from a Bayesian multilevel regression predicting prosocial preferences from country-level relational mobility, controlling for environmental harshness and subsistence style. (a) The overall effect of relational mobility on prosociality. (b–d) The individual effects of relational mobility on altruism, positive reciprocity, and generalised trust. Lines and shaded areas indicate median posterior regression lines and 95% credible intervals. Points indicate average prosociality levels and relational mobility scores for each of the 27 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.

Figure 4.

Posterior predictions from a Bayesian multilevel logistic regression predicting charitable organisation membership from country-level relational mobility, without controls. The line and shaded area indicate the median posterior regression line and 95% credible intervals. Points indicate the proportion of individuals belonging to charitable organisations on the y-axis and relational mobility scores on the x-axis, for each of the 32 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.

Figure 5.

Posterior predictions from a Bayesian multilevel logistic regression predicting generalised trust from country-level relational mobility, without controls. The line and shaded area indicate the median posterior regression line and 95% credible intervals. Points indicate the proportion of individuals stating that ‘most people can be trusted’ on the y-axis and relational mobility scores on the x-axis, for each of the 32 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.

Figure 6.

Posterior predictions from a Bayesian multilevel ordinal regression predicting trust in specific groups from country-level relational mobility, without controls. Higher numbers on the y-axis indicate higher levels of trust. Lines and shaded areas indicate median posterior regression lines and 95% credible intervals. Points indicate average trust and relational mobility scores for each of the 32 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.

Figure 7.

Posterior predictions from a Bayesian multilevel ordinal regression predicting moral justifiability of different scenarios from country-level relational mobility, without controls. Higher numbers on the y-axis indicate lower justifiability ratings for each scenario, such that higher values reflect higher levels of prosociality. Lines and shaded areas indicate median posterior regression lines and 95% credible intervals. Points indicate average justifiability (reversed) and relational mobility scores for each of the 32 countries, with error bars representing ±1 standard error. Letters represent country ISO codes.