Networks, Information, and Vote Buying Raúl Duarte Frederico Finan Horacio Larreguy Laura Schechter
Motivation ◮ Despite the secret ballot, vote buying remains pervasive throughout the developing world. ◮ Political brokers are thought to: ◮ enforce the exchange of targeted benefits for votes; ◮ exploit their social connections to sustain vote-buying exchanges; ◮ and acquire politically-relevant information about voters through their social networks. ◮ Question: Do social networks diffuse information about voters that brokers leverage to sustain vote buying?
Usual data challenges ◮ Lack of comprehensive data on social networks. ◮ Social network data from villages in which brokers and voters are embedded. ◮ Lack of data on brokers’ vote-buying decisions. ◮ Data on vote-buying targeting reported by multiple political brokers about overlapping voters in each village. ◮ Confoundedness of network measures. ◮ Voter characteristics as reported by both brokers and voters.
Our approach ◮ Compute a broker-voter network measure called hearing . This is the extent to which a given broker might hear information about a specific voter in his network. ◮ Use broker and voter fixed effects to deal with broker- and voter-specific confounders. ◮ Assess whether information about voters is diffused to brokers through the network. ◮ Asses whether this information is used for targeting. Main results ◮ Hearing significantly predicts: ◮ how much each political broker knows about each voter; ◮ who each political broker targets with vote buying; and ◮ whether a voter claims to support the broker’s party after the election.
Robustness ◮ Broker- and voter-level confounding variables. ◮ Include broker and voter fixed effects. ◮ Homophily - brokers might both target and be closer to voters who are similar to them. ◮ Control for the similarity between a voter and a broker in terms of their network position and their socio-demographic characteristics. ◮ Results are not driven by parametric choices when computing hearing , nor by potential bias due to partial network sampling.
Mechanisms ◮ The networks’ information-diffusion role: ◮ Hearing predicts how much the broker knows about the voter. ◮ Whether brokers target specific voters should not simply be a function of how much they hear about them, but also what they hear about them. ◮ Brokers are more likely to target voters who are reciprocal and who are not registered to their party, but only when the network allows brokers to hear information about them. ◮ Rule out that the network’s effect is explained by its enforcement role. ◮ Control for broker-voter network measures that the literature suggests increase people’s ability to enforce informal transactions. ◮ Rule out that the network’s effect is explained by brokers targeting voters who are good at convincing others. ◮ Control for voter fixed effects, and for network measures of how well-connected a voter is interacted with hearing .
Literature on targeting of vote-buying Who should be targeted? ◮ Voters with weak ideological attachment (Lindbeck and Weibull, 1987; Dixit and Londregan, 1995). ◮ Core supporters (Cox and McCubbins, 1986; Nichter, 2008). ◮ Reciprocal voters (Finan and Schechter, 2012; Lawson and Greene, 2014). ◮ Opinion formers (Schaffer and Baker, 2015). This paper extends Finan and Schechter (2012) in at least two important ways. ◮ How do brokers know which voters are reciprocal? Because social networks diffuse that information to them. ◮ Brokers are only more likely to target reciprocal voters about whom they can learn information through the network, and more so when these are not registered to their party.
Literature on social networks in political economy ◮ Political economy more generally ◮ Voter level - Voters spread information about unemployment, electoral violence, and elections. This affects their voting behavior. (Alt et al, 2019; Fafchamps and Vicente, 2013; Fafchamps et al, 2019) ◮ Vote buying ◮ Candidate level - Well-connected candidates get more votes. (Cruz et al, 2017) ◮ Voter level - Well-connected voters are more likely to (admit to) being targeted. (Calvo and Murillo, 2013; Cruz, 2019; Fafchamps and Labonne, 2019; Schaffer and Baker, 2015) They are more likely to be targeted. (Ravanilla et al, 2017) ◮ Broker level - Brokers’ central position in non-political networks explains their ability to influence vote choice. (Szwarcberg, 2012) ◮ We create a broker-voter level measure of connection, add two-way fixed effects, and show networks diffuse information from voters to brokers who use it to target vote buying.
Roadmap ◮ Background ◮ Data ◮ Empirical Strategy ◮ Results ◮ Conclusion
Roadmap ◮ Background ◮ Data ◮ Empirical Strategy ◮ Results ◮ Conclusion
Brief political history of Paraguay ◮ Paraguay was a dictatorship under the rule of Alfredo Stroessner of the Colorado party from 1954 to 1989. ◮ In 2008, an independent bishop won the presidency ending 61 years of Colorado rule. ◮ Paraguay remains largely a two-party country. ◮ 2006 elections: 66% Colorado mayors, 30% Liberal mayors. ◮ Political parties in Paraguay are not strongly ideological. ◮ “Policy has played little part in the campaigning for Paraguay’s top job." (The Telegraph, 2008) ◮ “Competition among candidates is very personalized and ideological differences are unclear." (Rizova, 2007)
Vote buying in Paraguay Given the small ideological differences across parties, vote buying can be effective. ◮ “Elections in Paraguay are decided by the voters who are mobilized with money. A very small percentage of the voters are loyal. The incentivized voters define [the election].” (A broker of the Liberal party in General Morínigo) Vote buying is becoming increasingly important to win elections. ◮ “There are three groups of voters: the captive, the thinkers, and those that can be bought. Relative to previous elections the captive voters have declined and the voters that can be bought have increased.” (A broker of the Colorado party in General Aquino)
Political brokers Political brokers (operadores políticos) act as intermediaries between candidates and voters, exchanging money and favors for promises to vote accordingly. ◮ “Political brokers are fundamental since they know their zone well.” (Liberal politician in San Lorenzo) Brokers’ central positions in the network allow them to learn about voters. ◮ “[Brokers] know who [their] party supporters are.” (Liberal official in Asunción) ◮ “[Brokers know] which Colorado and Liberal voters would sell their vote.” (Liberal official in Coronel Oviedo) ◮ “It’s all about ñe’embegue (gossip).” (Colorado broker in General Aquino) Brokers suggest that the voters who they target are likely to reciprocate with their vote: “While some voters take the money and vote for another candidate, the ◮ number of voters like that is small.” (Liberal broker in General Morínigo) “[The voters they target] always thank favors.” (Colorado broker in ◮ General Aquino).
Roadmap ◮ Background ◮ Data ◮ Empirical Strategy ◮ Results ◮ Conclusion
Sample ◮ Combine vote-buying data collected for Finan and Schechter (2012) with social network data collected for Ligon and Schechter (2012). ◮ Panel data collected in ten villages across two departments in Paraguay. ◮ 2002 - incentivized experiments measuring reciprocity. ◮ 2007 - added more households and collected social network data. ◮ 2010 - interviewed political brokers.
2007 social network data ◮ Collected from 30 to 48 households in each of 10 villages. Direct sampling rate between 12 and 91%, with a cross-village mean of 47%. ◮ Reach between 54 and 100% of all households directly or indirectly (with a cross-village mean of 88%). ◮ Social connections include: 1. One hhd provided assistance when a member of the other hhd fell sick. 2. One hhd provided monetary or in kind transfers to the other hhd. 3. One hhd lent money to the other hhd. 4. One hhd would ask to borrow from the other in times of need. 5. Any two members of the hhds belong to the same family (i.e., parents, children, siblings). 6. Any two members of the hhds are “compadres.”
Social network example ◮ 257 links btwn 81 hhds (39 directly surveyed) ◮ Brokers are #s 9 (direct, 14 links) and 73 (indirect, 10 links).
Hearing measure ◮ Hearing , H ib , is the expected number of times broker b would hear information originating from voter i (Banerjee et al. 2019). ◮ In t = 1, those directly connected to i find out the information with probability p . ◮ In t = 2, those who received information in the first period transmit it to nodes with which they are directly connected with probability p . ◮ In t = 3, those who received information in the second period transmit it to nodes with which they are directly connected with probability np , where n is the number of nodes from whom they received the information. ◮ Process goes on T periods. ◮ H ib is the ib th entry of the matrix H = � T t = 1 ( p g ) t , where g is the adjacency matrix (an element is 1 if the row and column households are socially connected and 0 if they are not). ◮ Set T equal to 7, the largest social distance between any voter and broker in our sample. ◮ Set p equal to the inverse of the largest eigenvalue of the adjacency matrix for each village’s social network. (Between 0.09 and 0.14.)
Hearing in period 0 Figure: H ib ( 0 ) = 0
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