The tangible and intangible effects of social networks

In a nice new(ish) working paper, Anandi Mani and Emma Riley review the recent and expanding literature on social networks, role models, peer effects, and aspirations in low and middle-income countries. In this post, I will summarize Mani and Riley’s review of the literature and offer my own commentary along the way. I will also comment on some of the methodological challenges implicit in this literature and will end with a discussion of what this all means for development policy.

The authors organize the literature in two related but distinct strands, both considering how social networks influence social mobility.

  1. How social networks mediate access to material or tangible resources (e.g., credit and insurance markets, migration and trade, technology, and employment).
  2. How social networks can influence intangible resources such as our hopes, aspirations, and beliefs via role models and peers, and in turn, inform our choices and behavior.

This way of organizing the literature seems helpful to me. Although the first strand includes a familiar review of classic papers that are likely assigned readings for almost any development economics graduate student, the second strand represents literature filled with more recent research. For this reason, I will focus on the second strand of this literature.

Here is how the authors frame this strand of the literature (emphasis added):

[…] but for people to actually take advantage of an opportunity they must believe they are capable and that the desired outcome will follow from their efforts (Bandura 1977, 1997; Rotter 1966). Indeed, the outcomes realized from our current efforts shape our future aspirations too; failing to recognize this two-way feedback between aspirations and outcomes could contribute to low social mobility from an aspiration failure, especially among the poor (Dalton et al. 2016). Thus, people need a sufficient sense of self-efficacy and a strong internal locus of control to achieve social mobility.

I agree with this reading of the existing evidence. In fact, in my own work on aspirations and investment choices in Myanmar, I find evidence that aspirations that are ahead—but not too far ahead—of current levels provide the best incentive for investment on both the extensive and intensive margins. This finding aligns with a model of socially determined aspirations with a critical feedback loop between social outcomes, individual aspirations, and behavior (Genicot and Ray 2017).

If this is true, then the next question is: How are aspirations formed? Mani and Riley point out three broad findings in the literature to date. (a) real-life role models, (b) social peers, and (c) neighborhoods. The existing evidence on role models suggests that they play an important role in shaping individual-level beliefs, particularly when individuals can identify with the role model in some way. The same general finding follows in many cases for social peers.

At this point, I feel compelled to point out that empirically estimating the effect of peers on individual outcomes is really challenging. The identification challenges of peer-effect regression estimates (Manski 1993), as well as the issue of “exclusion bias” (Guryan et al. 2009; Caeyers and Fafchamps 2016), present analytical constraints on this literature. The “reflection problem” (Manski 1993) represents the challenge implicit when estimating peer-effects, of disentangling whether an individual is influenced by their peers or if the behavior and beliefs of both the individual and their peers are influenced by similar factors (i.e., common shocks, past experiences, personal characteristics, or social circumstances). The issue of “exclusion bias” (Guryan et al. 2009; Caeyers and Fafchamps 2016), exists because you cannot be your own peer. The challenge leads to a mechanical negative correlation, and corresponding bias, in most approaches used to estimate of peer effects. (Note: These analytical challenges are not directly discussed in the review by Mani and Riley. I understand the constraints on space, but I think these issues are important to consider because they influence the production function of this literature.)

So, what does all of this mean for development policy?

Mani and Riley start with (what I think is) an important foundation for thinking about the role of social networks. That is, although social networks may benefit members of the network, exclusion from these networks could be harmful to non-members. This dynamic may be especially strong in settings where social networks are formed primarily through caste, ethnicity, race, or gender. Therefore, the policy-relevant question is twofold: (1) How can we maximize the effects of social networks for members while minimizing the detriment for non-members? (2) How can policy help non-members gain access to beneficial social networks? The authors highlight several lessons.

  • Since aspirations, beliefs, and hopes are influenced by our peers or social network, development policies can be much more effective if a sufficient fraction of a given community is given access to some intervention.
  • Since non-members may be excluded from beneficial social networks based on factors outside of their control, technology can help bring role-models to excluded communities.

A number of studies have recently examined the impact of virtual role-models on a host of important outcomes (e.g., small business growth, education investment, education outcomes, fertility, divorce rates, domestic violence, etc.). I think these are exciting and important studies. Incorporating the insights from these studies into a larger and more systematic development policy seems promising.

I feel, however, that an important caveat is worthwhile. Just as failing to address the psycho-social (e.g., intangible) determinants of human behavior can lead to ineffective policy, failing to address the material (e.g., tangible) determinants of human behavior can also lead to ineffective policy. So, although much of this current literature is exciting, we must not forget the classic lessons of the importance of access to credit and insurance markets, migration and trade, technology, and employment. If my own work on the inverted U-shaped relationship between the aspiration gap and investment choices has any policy-relevant implications, it is this: That raising an individual’s aspirations all else being equal has an ambiguous effect on economic choices. Effective development policy needs to treat individuals as socially networked and economically motivated humans. This means we need to pay attention to both the tangible and intangible effects of social networks.


Bandura, A. (1977) Social Learning Theory. Englewood Cliffs, NJ: General Learning Press.

Bandura, A. (1997) Self-Efficacy: The Exercise of Control. New York: Freeman Press.

Caeyers, B. and Fafchamps, M. (2016) “Exclusion Bias in the Estimation of Peer Effects,” NBER Working Paper, No. 22565.

Dalton, P.S., S. Ghosal, and A. Mani (2016) “Poverty and Aspirations Failure.” Economic Journal, 126(590): 165–88.

Genicot, G., and D. Ray (2017). “Aspirations and Inequality,” Econometrica, 85(2): 489–519.

Guryan, J., Kroft, D., and Notowidigdo, J. (2009) “Peer Effects in the Workplace: Evidence from Random Groupings in Professional Golf Tournaments,” American Economic Journal: Applied Economics, vol. 44, no. 3, pp. 289-302.

Manski, C.F. (1993) “Identification of Endogenous Social Effects: The Reflection Problem,” The Review of Economic Studies, Vol. 60, no. 3, pp. 531-542.

Rotter, J.B. (1966) “Generalized Expectancies for Internal versus External Control of Reinforcement,” Psychological Monographs: General and Applied, 80(1): 1–28.

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