The Trojan-horse mechanism: How networks reduce gender segregation

Abstract

The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse mechanism, which, in contradiction to the main tenet of previous research, suggests that network-based recruitment reduce rather than increase segregation levels. We identify the conditions under which networks are desegregating, and using unique data on all individuals and all workplaces located in the Stockholm region during the years 2000-2017, we find strong empirical evidence for the Trojan-horse mechanism and its role in the gender segregation of labor markets.

Fig. 1. Schematic representation of the Trojan-horse mechanism.

(t) With no prior mobility between organization j and k, a female moves from j to k (segregating move), increasing the probability for future mobility along the same path. (t + 1) Strong mixing constraints (8 of 9 males in j) makes it likely that the female that moved had more ties to males than females, despite homophily. Therefore, it is more likely that a male follows in her path (desegregating move). (t + 2) The combined effect of composition (7 of 8 males in j) and preference for same-gender ties makes it much more likely that another male will follow (desegregation move). While this example shows a stylized Trojan sequence of length three, it is important to note that, as the counteracting (desegregating) effect occurs already at the second step, a sequence of length-two also constitutes a realization of the Trojan-horse mechanism.

Fig. 2. Empirical evidence of the Trojan-horse mechanism.

(A) Fraction of the treated pairs j → k (with a prior move) and fraction of the control pairs i → k (without any prior move) where at least one individual moved to the destination (k) at time t + 1 (treatment occurs at time t). Error bars represent 95% confidence intervals obtained from estimated logistic regression models (see Supplementary Text for details). The x axis distinguishes between same- and opposite-gender movers (relative to the gender of the prior mover from j to k). The numbers shown alongside the vertical lines represent the estimated treatment effects, calculated as the risk ratio between the treated and the control group (i.e., by comparing the fraction of cases with at least one mover at t + 1 under each treatment regime). Control-R corresponds to random matching, and Control corresponds to coarsened exact matching. (B) Proportion of opposite-gender followers, relative to the initial mover, for all treated j → k dyads, plotted as a function of the same gender proportion (SGP) in the organization of origin (j). Three ranges of SGP are considered: less than 0.1, between 0.4 and 0.6, and more than 0.9. Error bars represent 95% binomial confidence intervals calculated using the normal approximation. (C) Distribution over differences in SGP between the target organization (k) and the organization of origin (j) for initiating moves, i.e., those that create new ties, for the same three ranges of SGP as in (B).

Fig. 3. Simulated trajectories of gender segregation as measured by the entropy index (Theil’s H) on the Stockholm labor market between 2000 and 2017.

Error bands represent 95% confidence intervals based on 10 repeated runs. (A) Counterfactual trajectories when the network coefficients for Trojan-initiating links are either increased (W2 and W3) or set at to 0 for opposite-gender colleagues (W4). (B) Counterfactual gender segregation level trajectories when the importance of the whole network is increased and the Trojan-horse mechanism is either operative (W5 and W6) or blocked (W7 and W8). (C) Relative number of followers along Trojan-initiating links for W2 to W4 compared to the baseline simulation, W1. Colors indicate the proportion of the followers who were of the opposite gender of the initial mover.