Second-Order Peer Instrumentation (G²X Identification)
RCTReview
In social network econometric models, identifying direct peer influence is obstructed by simultaneous outcome determination (reflection) and unobserved homophily in link formation.
Picture this
Imagine trying to prove whether a student's study habits directly improve a classmate's grades. Instead of measuring the classmate directly, researchers look at the background traits of the classmate's friends from another school—people who affect the classmate but have no direct contact with the original student.
What the evidence says
In Large-Country Virtual-Within networks, peer outcomes increase proposal submission rates ($\beta = 0.533, p < 0.01$). In Small-Country Virtual-Across networks, peer outcomes increase intensive business proposal quality ($\beta = 0.476, p < 0.01$).
- Who was studied
- N = 1,016 active Milestone 0 completers across virtual interaction arms in 49 African countries.
- How
- Two-stage Instrumental Variable (IV) OLS regression using baseline characteristics of second-degree network neighbors ($G^2X$) as instruments for direct peer outcomes ($Gy$).
What to do
Instrument direct peer influences using exogenous baseline traits of two-step network neighbors when estimating social spillover effects in non-saturated social networks.
From the source
"The approach we pursue here to tackle the reflection problem follows Bramoullé et al. (2009) and De Giorgi et al. (2010)... It relies on using the variables in G²X (the total influence weights associated to the peers lying two links away) as respective instruments for Gy."
Peer Networks and Entrepreneurship- a Pan-African RCT.pdf