Impacts on Information Networks A Randomized Controlled Test of Strategic Network Change Scott McNiven UC Davis PacDev March 17, 2012 Impacts on Information Networks A Randomized Controlled Test of Strategic Network Change Scott McNiven UC Davis PacDev March 17, 2012

1.The big picture: The diffusion of innovations drives economic growth. The rural poor face pervasive market failures. Networks may substitute for formal markets. 2.Strategic network formation models look at it “from the ground up”. Such models do not directly address changes in existing networks due to policy shocks. My results concern “strategic network change”. 3. Find policy instruments that promote technology diffusion. Motivation

The Introduced Agricultural Technology: Orange-Fleshed Sweet Potato (OSP) Intro OSP: Vitamin A dense Orange Bred in Uganda, not a GMO <0.5% cultivating at baseline All sweet potatoes in Uganda: Vine-propagated Vines are given, not sold, to other farmers Two seasons per year Primarily-subsistence staple crop Higher yielding Dries out more easily in dry season or in sun Rots more easily when left in the ground past maturity Traditional varieties: Little or no vitamin A White or yellow 75% cultivating at baseline

Cluster-randomized controlled trial In Central and Eastern Uganda From August 2007 to June 2009 The HarvestPlus Program The goal: Fight vitamin A deficiency The treatment: 20 kg of OSP vines (enough to plant 1/4 acre) Trainings on OSP cultivation, cooking, nutrition, and marketing. Intro Kampala

Sample and Research Design Intro 48 Treated Communities 36 Control Communities Farmer Group Members 14 sampled per community Nonmembers 5 sampled per community All farmer group (FG) members in treated Communities were treated NM in control communities Nonmembers (NM) in treated Communities No farmer group (FG) members in control Communities were treated

Two types of policies: 1.Policies that “assign” links. E.g., roommates or to classrooms. What is the impact of network tie assignment on node characteristics? 2.Policies that change agents’ characteristics but do not directly manipulate social relations. What is the impact of the assignment of a characteristic to nodes on network ties? How might networks change under the policy? We expect agents to make ties to treated agents. Untreated agents might break ties to other untreated agents if there are costs to maintaining ties. Do new links become more likely to form? Do existing links become more likely to be retained? Motivation: Document “strategic network change” (not formation) Intro

Network tie definition: Information neighbor Survey question: – In the 12 months before the baseline, have you or anyone else in your household had a conversation about farming or health with someone in [other HH]? If the respondent answers “Yes” with respect to the [other HH], then it is an “information neighbor” to the respondent’s HH. When I say “link” or “tie” I’m referring to an “information neighbor link”. Links are “directed” – HH “A” may be a neighbor of “B” without “B” being a neighbor of “A”.

Three channels of peer-to-peer diffusion: Do technologies diffuse through new links in addition to existing links? Intro Solid white lines indicate links between agents. A and B are offered a technology. “D” might acquire the technology: 1.Directly through an existing link: B  D 2.Directly through a new link (dashed white line): A  D 3.Indirectly: A  C  D A A B B C C D D

Econometric Specification

FG and NM gain FG neighbors Coefficient: Impact of living in a treated communityDependent variable: Sample population  reference population # of FG/NM neighbors Added ≥ 1 FG/NM Neighbors Implied change among those adding >=1 (1)(2)(3) FG  FG neighbor (N=1090) 0.556***0.098***5.6x (0.102)(0.020) FG  NM neighbor (N=1090) 0.871*0.052**17.2x (0.484)(0.024) NM  FG neighbor (N=370) 0.329**0.117**2.8x (0.161)(0.059) NM  NM neighbor (N=370) (1.065)(0.042) Each pair of cells presents the estimate of the impact of living in a treated community on the outcome named in the column header. Community-clustered standard errors are in parentheses. OLS estimates are reported in Column (1). Marginal effects from logit regressions are reported in Column (2). All regressions include network, sample, and household covariates. *, **, and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels.

A few FG members gained many NM neighbors Treated Control

New links are formed; smaller effects on old links Coefficient: Impact of living in a treated community Dependent variable: Number of FG/NM information neighbors at endline Sample pop  reference popNot linked at baselineLinked at baseline (1)(2) FG  FG neighbor 0.379***0.198* (0.099)(0.106) N=813N=862 FG  NM neighbor (0.589)(0.407) N=902N=591 NM  FG neighbor 0.270**0.172 (0.126)(0.205) N=321N=225 NM  NM neighbor 1.829*-1.663** (1.052)(0.829) N=329N=198 Each triad of cells presents the estimate of the impact of living in a treated community on the number FG/NM information neighbors at endline. Coefficients from OLS regressions are reported. Community- clustered standard errors are in parentheses. All regressions include network, sample, and household covariates. *, **, and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels.

Surprisingly, no direct association with OSP Cultivation in the Fourth Season Dependent variable: Cultivated OSP in the Fourth Season?Sample: Farmer Group Members (N=622) Nonmembers (N=208) Coefficient:(1)(2) Change in Number of Farmer Group Member Information Neighbors (0.012)(0.026) Change in Number of Nonmember Information Neighbors (0.002)(0.003) Number of Farmer Group Member Information Neighbors at Baseline *** (0.007)(0.012) Number of Nonmember Information Neighbors at Baseline * (0.002)(0.003) Each column is a separate logit regression reporting marginal effects. Community-clustered standard errors are in parentheses. Regressions include network and sample covariates. *, **, and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels.

Households adding adopting (fewer non-adopting) ties were more likely to cultivate OSP in the Fourth Season Dependent variable: Cultivated OSP in the Fourth Season?Sample: Farmer Group Members (N=622) Nonmembers (N=208) Coefficient:(1)(2) Change in Number of Adopting Farmer Group Member Information Neighbors ** (0.008) Change in Number of Non-adopting Farmer Group Member Information Neighbors ** (0.006)(0.005) Change in Number of Adopting Nonmember Information Neighbors ** * (0.006)(0.005) Change in Number of Non-adopting Nonmember Information Neighbors ** (0.011)(0.005) Each column is a separate logit regression reporting marginal effects. Community-clustered standard errors are in parentheses. Regressions include network and sample covariates. *, **, and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels.

In Summary Increased ties among farmer group members and between members and nonmembers – Consistent with strategic network formation models Significant “churn” in nonmember-nonmember links and conversations – Nonmembers face incentives form new links to other nonmembers. – Suggests that links are costly to maintain. A few farmer group members began talking with many nonmembers. – Some individuals may be “central” to diffusion. Households adding adopting (fewer non-adopting) ties were more likely to cultivate OSP in the Fourth Season – New links are associated with diffusion. Are they causal?