Audits Reporting Municipal Malfeasance

A key discretionary program at a mayor’s disposal is the Municipal Fund for Social Infrastructure (FISM), which constitutes 24% of the average municipality’s budget. According to the 1997 Fiscal Coordination Law, FISM funds are direct federal transfers earmarked exclusively for infrastructure projects benefiting citizens living in localities designated as impoverished.^{Footnote 5} Eligible projects include investments in water supply, drainage, electrification, health and education infrastructure, housing, and roads.

Mayors’ use of FISM transfers has been subject to independent audits by the Federal Auditor’s Office (ASF) since 1999. The ASF has constitutionally enshrined autonomy to audit federal funds spent by federal, state, and municipal governments, and is generally perceived to be neutral, autonomous, and professional (De La O and Martel García Reference De La O and Martel García2015). Each year, the ASF selects approximately 150 municipalities for audit based on the relative contribution of FISM transfers to their municipal budget, their history of malfeasance, factors that increase the likelihood of mismanagement, and whether the municipality has recently been audited (Auditoría Superior de la Federación 2014). ASF audits cover spending from the previous year, and are announced after spending has occurred. Audit reports are presented to Congress in February of the year after the audit was conducted (i.e., two calendar years after the spending occurred) and are publicly available on the ASF’s website.

Although ASF reports examine various aspects of performance, we focus on the two main dimensions of mayoral malfeasance: the share of FISM funds spent on infrastructure projects that do not directly benefit the poor (the program’s intended beneficiaries), and the share of funds diverted to unauthorized projects (e.g., personal expenses and election campaigns, or expenditures that cannot be accounted for). The latter constitutes what is often regarded as corruption (e.g., Ferraz and Finan Reference Ferraz and Finan2008). Our study’s sample consists mostly of rural precincts with high rates of poverty, and thus both measures capture misallocation away from the majority of voters.

While many municipal governments comply with the FISM rules, malfeasance can often be substantial. Between 2007 and 2015, 8% of audited funds were spent on projects that did not benefit the poor and 6% on unauthorized projects. For example, the municipal government of Guadalajara used most of its audited FISM funds to cover projects that did not benefit the poor in 2009.^{Footnote 6} Regarding instances of unauthorized spending, municipal governments across the state of Tabasco diverted significant FISM resources to fund the 2012 electoral campaigns of their parties’ candidates,^{Footnote 7} and the mayor of San Pedro Pochutla used millions of FISM pesos in 2008 to make unjustified payments to his wife and others, as well as to buy furniture for his house.^{Footnote 8} According to Chong et al. (Reference Chong, De La O, Karlan and Wantchekon2015), 45% of voters do not believe that municipal governments use public resources honestly and 54% are dissatisfied with public services.

Municipal Political Competition

Most municipalities in Mexico’s party-centric political system are characterized by competition between two of the country’s three largest parties. Due to its local strength and relatively nationwide appeal, the populist Institutional Revolutionary Party (PRI) typically competes against either the right-wing National Action Party (PAN) or the PRI’s left-wing offshoot, the Party of the Democratic Revolution (PRD). The two dominant parties in the municipality often subsume smaller parties on their electoral ticket. Accordingly, most elections are de facto two-party races; the average effective number of party coalitions in municipal elections is 2.5.

Most voters are poorly informed about the resources available to mayors and their responsibility to provide public services (see Chong et al. Reference Chong, De La O, Karlan and Wantchekon2015). Awareness of ASF malfeasance revelations is relatively low, and concentrated in urban areas (Larreguy, Marshall, and Snyder Reference Larreguy, Marshall and Snyder2018). Nevertheless, the vast majority of voters know the party of the incumbent mayor and, while reelection is not possible, are willing and able to hold incumbent parties to account for their performance in office (Chong et al. Reference Chong, De La O, Karlan and Wantchekon2015). Consequently, there is significant scope for voters to respond to the provision of incumbent malfeasance information, which is likely to be novel to them.

The Importance of Social Networks

A burgeoning literature argues that interactions within social networks shape political outcomes across the globe. For example, networks have been shown to influence turnout in the United States (Bond et al. Reference Bond, Fariss, Jones, Kramer, Marlow, Settle and Fowler2012; Gerber, Green, and Larimer Reference Gerber, Green and Larimer2008; Nickerson Reference Nickerson2008; Sinclair, McConnell, and Green Reference Sinclair, McConnell and Green2012), electoral performance and the targeting of public services in the Philippines (Cruz, Labonne, and Querubín Reference Cruz, Labonne and Querubín2017), and protest participation in Russia, France, and the Middle East and North Africa (Enikolopov, Makarin, and Petrova Reference Enikolopov, Makarin and Petrova2016; Larson et al. Reference Larson, Nagler, Ronen and Tucker2017; Steinert-Threlkeld Reference Steinert-Threlkeld2017).

In many contexts, social networks are built around family ties. This is particularly true of Mexico, where the notion of family is much more extensive and inclusive than in other cultures. Grandparents, uncles, and aunts play an important role in the upbringing of younger generations, and the extended family meets regularly (Belausteguigoitia Reference Belausteguigoitia2007). The structure of the Mexican family initially followed the Spanish tradition of an extended family, which assigned uncles and cousins on both sides of the family a similar degree of closeness as parents and siblings. This structure persisted over time, especially in rural areas like those that this article focuses on (Sabau García and Jovane Reference Sabau García and Jovane1994). According to the 2014 wave of the World Values Survey, 97.6% of respondents stated that the family is “very important,” and Mexico ranked sixth among the 40 countries included in the survey and second within Latin America in this measure.

The strength of extended family ties is particularly prevalent in Mexican politics. For example, the 2009 Comparative Study of Electoral Systems (CSES) survey found that 47% of respondents discussed politics with their household members during the week before being interviewed. Moreover, the 2012 CSES survey indicates that, out of the 20% of respondents that reported attempts to persuade them to vote for a specific political party or candidate, 42% identified family members as the source of those attempts. We report evidence below that electoral precincts where extended familial networks exhibit high connectedness experience greater civic participation and political efficacy. This suggests that extended family ties are relevant and may signal greater community connectedness more generally.

Recent work in Mexico similarly highlights the important role that social networks play in explaining individual behavior. Examples include the effect of social networks on incentives to migrate (McKenzie and Rapoport Reference McKenzie and Rapoport2010), remittance flows (Woodruff and Zenteno Reference Woodruff and Zenteno2007), and on student academic performance (Ramirez Ortiz, Caballero Hoyos, and Ramírez-López Reference Ramirez Ortiz, Caballero Hoyos and Ramírez-López2004). Of particular relevance to our study, Angelucci et al. (Reference Angelucci, De Giorgi, Rangel and Rasul2009) use data from Prospera beneficiaries to show that—consistent with the extended family being a source of informal insurance to its members—localities with more extensive family networks experience lower levels of out-migration and inequality.

Setup

Voter Preferences and Actions

Voters derive utility from three sources. First, they receive expressive disutility from voting for malfeasant politicians. Specifically, the expressive disutility that a risk-neutral voter receives from voting for party p ∈ {I, C}, after receiving a signal s, is given by:

(1)$${e^I}\left(s \right) = {\pi ^1}\left(s \right){\theta ^H} + \left[ {1 - {\pi ^1}\left(s \right)} \right]{\theta ^L},$$

(2)$${e^{\rm{C}}} = {\lambda ^0}{\theta ^H} + \left( {1 - {\lambda ^0}} \right){\theta ^L},$$

where θ^S > 0 represents the disutility that a candidate of type S yields to voters, and θ^H > θ^L. To ease notation, we define Δe(s): = e ^I(s) − e ^C as the expected difference in expressive disutility of voting for I’s candidate relative to C’s candidate. Since information provision can only affect e ^I(s), lower values of Δe(s) indicate a relatively stronger preference for voting for I’s candidate because voters believe that party I contains less malfeasant candidates than they originally believed.

Second, voters are connected within a politically engaged social network and can coordinate with those they are connected to in response to receiving an informative signal (either s = l or s = h). We clarify our notions of individual connectedness below. Information provision could serve as a coordination device within social networks in two ways. First, information provision through networks could induce voters to discuss the information and politics more generally, and stimulate agreement upon a common response (Larson Reference Larson2017). Second, even without such explicit coordination, communication with others may reveal to voters that others also received the signal, and believe that this common signal will stimulate coordinated behavior (Morris and Shin Reference Morris and Shin2002). We assume that uninformative signals (i.e., $s = \emptyset$) do not facilitate coordination.^{Footnote 12}

When voters coordinate, we assume that they do so around the party that they believe to be less malfeasant. In our model, this is manifested in voters receiving utility $\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta}} {u_j}\left( {p|s} \right) \ge 0} \right]}$ from voting for the party p that they believe is less malfeasant (party I if Δe(s) ≤ 0 and party C if Δe(s) > 0), where ${{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ \cdot \right]$ denotes the indicator function, N _i is the set of voters connected to voter i, and Δu _j(p|s) := u _j(p|s) − u _j(p′|s) is the difference in voter j’s utility from voting for party p over party p′. We fully characterize u _j(p|s) below, but for now Δu _j(p|s) ≥ 0 implies that voter j votes for party p’s candidate. This formulation captures the idea that voters gain utility from coordinating around (what they perceive to be) a less malfeasant candidate with the voters they are connected to, where such utility increases with the number of other voters—i.e., $\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta {u_j}\left( {p|s} \right) \ge 0} \right]}$—with whom they are coordinating their vote. This is in line with rule-utilitarian models in which individuals derive utility from acting according to a strategy that maximizes social welfare (Feddersen Reference Feddersen2006).

Our model does not take a specific stance on the micro-foundations of voters’ utility derived from coordination. However, it could reflect a lower probability that parties will sanction individual voters when a group of voters deviates from a party’s preferred behavior (e.g., Medina Reference Medina2007), a desire to be part of a group signaling discontent (e.g., Lohmann Reference Lohmann1993), or simply a preference to conform (e.g., Bernheim Reference Bernheim1994). None of these interpretations requires that voters believe that their community’s voting behavior will change the election outcome.

We consider two common notions of network connectedness—average degree and the largest eigenvalue (see Alatas et al. Reference Alatas, Banerjee, Chandrasekhar, Hanna and Olken2016). Intuitively, average degree is the average number of other voters that a voter i is locally connected to and can directly coordinate with. In turn, the largest eigenvalue defines the extent to which the average individual is central in the sense that they are connected to other central individuals, with centrality being recursively determined. The largest eigenvalue then captures the extent to which the information required for coordination can flow from and to the average individual in the network. The largest eigenvalue lies between the network’s average degree and its maximal degree. Section A.3 in in the Online Appendix provides technical definitions of both measures. To significantly simplify computations, we restrict our analysis to regular graphs, where average degree n is constant and coincides with the largest eigenvalue. This is reasonable in our empirical context, where the correlation between the average degree and largest eigenvalue is 0.98. In our model, we thus interpret n as the number of other voters in—or the cardinality of—the set N _i.

Third, voters are also subject to a (possibly negative) partisan bias δ_i toward I’s candidate. In particular, δ_i is an independently and identically uniformly distributed shock across the electorate over support $\left[ {b - {1 \over {2\phi }},\;b + {1 \over {2\phi }}} \right]$, with density $\phi \in \left( {0,\;{1 \over {2\left[ {b - \Delta e\left(s \right) + n} \right]}}} \right)$.^{Footnote 13} Although this distribution is common knowledge, each realization is private information for individual voters. The average voter thus has a bias b > 0 toward I’s candidate, which could reflect material inducements—such as vote buying or targeted future transfers—that I can better provide (e.g., Magaloni Reference Magaloni2006).^{Footnote 14} For simplicity, we assume perfect enforcement such that individuals voting for I’s candidate receive b, regardless of the election outcome. This could reflect voters’ reciprocity or brokers’ willingness to target only those that they have learned are likely to reciprocate (e.g., Duarte et al. Reference Duarte, Finan, Larreguy and Schechter2018; Finan and Schechter Reference Finan and Schechter2012; Lawson and Greene Reference Lawson and Greene2014).

Combining these sources of utility, and abstracting from the decision to turn out,^{Footnote 15} voters then decide whether to vote for party I’s or party C’s candidate. The utility of voting for I for individual i receiving signal s is:

(3)$$\eqalign{{u_i}\left( {I{\rm{|}}s} \right) = - {e^I}\left(s \right) + {\delta _i} + {{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {s \in \left\{ {l,h} \right\}} \right]{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta e\left(s \right) \,\le \,0} \right]\cr\mathop\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta {u_j}\left( {I{\rm{|}}s} \right) \,\ge \,0}\right]} .$$

Similarly, voting for C yields:

(4)$$\eqalign{{u_i}\left( {C{\rm{|}}s} \right) = - {e^C} + {{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {s \in \left\{ {l,h} \right\}} \right]{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta e\left(s \right) > 0} \right]\cr\mathop\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}[\Delta {u_j}\left( {C{\rm{|}}s} \right) > 0]} .$$

A voter votes for I when u _i(I|s) ≥ u _i(C|s).

Equilibrium and Comparative Statics

We solve for a rational expectations equilibrium by first calculating I’s vote share as a function of its expected vote share, ${\mathbb{E}}\left[ {{v_I}\left(s \right)} \right]$. We then set expectations to the true vote share to recursively derive equilibrium behavior under rational expectations about the behavior of other voters.

Integrating over partisan biases, the vote share among voters that receive signal s is implicitly defined by:

(5)$$\eqalign{ {v_I}\left(s \right) \cr= & {1 \over 2} + \phi \left( {b - \Delta e\left(s \right) + {{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {s \in \{ l,h\} } \right]{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta e\left(s \right) \,\le \,0\,} \right]n{\mathbb{E}}\left[ {{v_I}\left(s \right)} \right]} \right. \cr & \left. { - {{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {s \in \{ l,h\} } \right]{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta e(s) > 0} \right]n{\mathbb{E}}\left[ {1 - {v_I}\left(s \right)} \right]} \right) }$$

which follows from ${\mathbb{E}}\left[ {\scriptstyle\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta {u_j}\left( {I{\rm{|}}s} \right) \,\ge\, 0} \right]} } \right] = n{\mathbb{E}}\left[ {{v_I}\left(s \right)} \right]$ and ${\mathbb{E}}\left[ {\scriptstyle\sum\limits_{j \in {N_i}} {{{\tf="Geostar-Regular (OpenType)"\char"006C}}\left[ {\Delta {u_j}\left( {C{\rm{|}}s} \right) > 0} \right]} } \right] = n{\mathbb{E}}\left[ {1 - {v_I}\left(s \right)} \right]$, by virtue of partisan biases being distributed independently across voters. Due to voter coordination, I’s support increases (decreases) with I’s vote share when voters’ posterior beliefs about I’s malfeasance are below (above) their belief about C’s malfeasance.

We then derive the equilibrium vote shares by applying rational expectations (i.e., ${\mathbb{E}}\left[ {{v_I}\left(s \right)} \right] = {v_I}\left(s \right)$), and solving recursively. This yields:

Proposition 1. In a rational expectations equilibrium, the candidate of incumbent party I receives the following vote share in a given community:

(6)$$\eqalign{{v_I}\left(s \right) = \cr\left\{ {\matrix{ {{1 \over 2} + \phi \left( {b - \Delta e\left( {\emptyset} \right)} \right)} \hfill & {\rm{if}}{\;s = \emptyset} \hfill \cr {{{{1 \over 2} + \phi \left( {b - \Delta e\left(s \right)} \right)} \over {1 - \phi n}}} \hfill & {\rm{if}}{\;s \in \left\{ {l,h} \right\}\;\quad{\rm{and}}\quad\;\Delta e\left(s \right) \le \,0} \hfill \cr {{{{1 \over 2} + \phi \left( {b - \Delta e\left(s \right) - n} \right)} \over {1 - \phi n}}} \hfill & {\rm{if}}{\;s \in \left\{ {l,h} \right\}\;\quad{\rm{and}}\quad\;\Delta e\left(s \right) > 0} \hfill \cr} } \right.$$

for any $s \in \left\{ {\emptyset,l,h} \right\}$.

Proof: follows from derivation in the text. ■

Unsurprisingly, party I’s equilibrium vote share increases with the partisan bias in their favor and the extent to which voters update their posterior beliefs to believe that I is less malfeasant than they originally believed (i.e., when Δe(s) decreases). When coordination is around C’s candidate, the final component of the numerator in the last case captures coordination against I’s candidate. The denominator also illustrates coordination’s multiplier effect, such that the preceding effects in the numerator are inflated by the capacity to coordinate vote choices with n others.

We focus on the comparative statics that motivate our empirical analysis. In particular, in line with our empirical specification, we compare the case in which voters receive an informative signal (s ∈ {l, h}), which corresponds to voters in treated experimental precincts, to the case in which they do not $\left( {s = \emptyset} \right)$, i.e., control precincts. For any given informative signal s, the difference in vote share between these cases is given by:

(7)$$\eqalign{{v_I}\left(s \right) - {v_I}\left( {\emptyset} \right) \cr= \left\{ {\matrix{ {{{ - \phi \left[ {\Delta e\left(s \right) - \Delta e\left( {\emptyset} \right)} \right] + \phi n\left[ {{1 \over 2} + \phi b - \phi \Delta e\left( {\emptyset} \right)} \right]} \over {1 - \phi n}}} \hfill & {\rm{if}}{\;s \in \left\{ {l,h} \right\}\;{\rm{and}}\;\Delta e\left(s \right) \le \,0} \hfill \cr {{{ - \phi \left[ {\Delta e\left(s \right) - \Delta e\left( {\emptyset} \right)} \right] - \phi n\left[ {{1 \over 2} - \phi b + \phi \Delta e\left( {\emptyset} \right)} \right]} \over {1 - \phi n}}} \hfill & {\rm{if}}{\;s \in \left\{ {l,h} \right\}\;{\rm{and}}\;\Delta e\left(s \right) > 0} \hfill \cr} } \right..$$

Regardless of the party that voters coordinate around, the effect of providing information is ambiguous. This reflects two potentially competing forces. Through the first term in the numerators, voters update their beliefs about I’s malfeasance, becoming more favorable toward I when their posterior belief that I is malfeasant is below their corresponding prior belief. This is more likely when voters initially believed I to be malfeasant, which could reflect an accurate assessment of I’s malfeasance (i.e., high σ_H − σ_L) or generally low expectations (i.e., high π ⁰), and when the signal suggests low malfeasance (i.e., s = l). The second term in the numerators captures an individual’s coordination incentives, which vary depending on whether Δe(s) ≤ 0. Intuitively, coordination benefits (harms) I when Δe(s) ≤ (>)0, and is increasing in n, ϕ, and the baseline (i.e., uninformative signal) vote share of the party around which voters coordinate. This may or may not agree with the direction of belief updating about I because learning depends on the change in beliefs, while coordination depends on comparing the levels of posterior beliefs. Both effects are multiplied by the incentive to bandwagon within social networks.^{Footnote 16}

While the sign of the difference depends on the relative roles of belief updating and coordinated behavior, the difference changes unambiguously with n:^{Footnote 17}

(8)$$\eqalign{{{\partial \left[ {{v_I}\left(s \right) - {v_I}\left( {\emptyset} \right)} \right]} \over {\partial n}} \cr= \left\{ {\matrix{ {{{\phi \left[ {{1 \over 2} + \phi b - \phi \Delta e\left(s \right)} \right]} \over {{{\left[ {1 - \phi n} \right]}^2}}} > 0} & {{\rm{if}}\;s \in \left\{ {l,h} \right\}\;{\rm{and}}\;\Delta e\left(s \right) \,\le \,0} \cr {{{ - \phi \left[ {{1 \over 2} - \phi b + \phi \Delta e\left(s \right)} \right]} \over {{{\left[ {1 - \phi n} \right]}^2}}} \lt \,0} & {\rm{if}}\;s \in \left\{ {l,h} \right\}\;{\rm{and}}\;\Delta e\left(s \right) > 0} \cr} } \right.$$

Intuitively, an increase in network connectedness n accentuates the coordination component, and thus increases the reward to (punishment of) I when voters believe that I is less (more) malfeasant than C. This is because coordination in more-connected networks increases the expectation that others will decide to coordinate on the less malfeasant candidate, which in turn increases the return for any individual to do so. Again, the direction of this effect does not necessarily match the direction of voters’ belief updating. For example, voters could become less likely to believe that I is malfeasant yet still believe that I’s candidate is more malfeasant in general, and thus coordinate more on C. However, for sufficiently large changes in beliefs, the learning and coordination effects will coincide, and coordination will compound learning.

Empirical Implications of Coordination within Social Networks

Various studies have shown that belief updating can drive voting behavior, including in the Mexican context that we study (Arias et al. Reference Arias, Larreguy, Marshall and Querubín2019). We instead focus on testing whether information provision can also generate voter coordination. To understand the model’s predictions relating to coordination in a particular context, we must first determine whether voters are more likely to coordinate around the incumbent or challenger party: we must identify whether Δe(s) ≤ 0 or Δe(s) > 0.

A crucial element of our particular empirical context is that voters believe challenger parties are less malfeasant than the incumbent party (i.e., $\Delta e\left( {\emptyset} \right) > 0$). Comparing average voter posterior perceptions of incumbent party malfeasance on a five-point scale with the analogous perception for the challenger party that came second in the previous election, 65% of treated precincts in our sample believed the challenger to be less malfeasant.^{Footnote 18}

Because challenger parties are generally perceived to be less malfeasant than incumbent parties, when the voters in our sample coordinate, our model predicts that they will usually do so against the incumbent party. The analysis above thus implies that, if networks indeed moderate the effect of providing information about incumbent malfeasance by facilitating voter coordination, the provision of information should increasingly harm incumbent parties as network connectedness (i.e., n) increases. The comparative static in equation (8) entails:

Hypothesis 1 (H1). The effect of information provision on the incumbent party’s vote share decreases with network connectedness.

Although this heterogeneous effect is well-defined for our sample of precincts, the average effect of providing information—which reflects countervailing updating and coordinating forces—remains ambiguous. However, in our specific empirical context, where voters often update favorably about the incumbent party but still believe the challenger to be less malfeasant, equation (8) establishes that information provision can reduce the incumbent’s vote share in sufficiently connected networks.

Furthermore, we can directly test several of the model’s key assumptions using survey data. First, we assumed that networks facilitate voter coordination around the information provided by our informational treatment. If this is indeed the case, we expect to observe that voters in communities with more-connected networks will report higher levels of individual and collective engagement with, and understanding of, the treatment information:

Hypothesis 2 (H2). The effect of information provision on voter engagement with the information provided increases with network connectedness.

Second, given that H2 is not unique to the coordination mechanism, a more direct test examines our expectation that network connectedness increases both tacit and more explicit coordination among voters after information is provided:

Hypothesis 3 (H3). The effect of information provision on voter coordination increases with network connectedness.

Although the model assumes, for simplicity, that all voters receive the common signal, our theory also implies that coordination should be greater where a larger share of the voters received the information treatment. This is because networks are more likely to explicitly coordinate around the information and there is greater common knowledge of information provision. We thus hypothesize that:

Hypothesis 4 (H4). The magnitude of the differential effects predicted by H1, H2, and H3 increases with the share of voters that received the information.

Before turning to the research design, we also highlight how the defining features of our empirical context help us to empirically differentiate voter coordination from the potentially confounding effects of belief updating amplified by information diffusion through social networks. While our model abstracted from information diffusion within networks by assuming that all voters received the common signal, network connectedness is likely to increase the diffusion of information within communities where some voters do not receive the information. In the context of our model, this could entail seeding the information with a subset of voters that probabilistically transfer the information to those they are connected to. The diffusion mechanism thus implies effects that are observationally equivalent to the effects of voter coordination where voters, on average, update unfavorably about an incumbent party already believed to be more malfeasant than the challenger. This is because diffusion increases the probability that voters receive unfavorable information and update their posterior beliefs about the incumbent party’s malfeasance accordingly. However, because malfeasance information in our empirical context at least as often causes voters to update favorably about the incumbent party, even though they continue to perceive the incumbent party as relatively more malfeasant than challengers, networks’ diffusion and coordination functions produce opposing or orthogonal predictions. Similarly, information diffusion within networks predicts the opposite of H4, given that there are fewer opportunities for diffusion where more voters already have access to the information.

Sample of Rural Mexican Precincts

The experiment was conducted over the month before Mexico’s municipal elections held on Sunday 7th June 2015. The study covered 26 Mexican municipalities from the central Mexican states of Guanajuato, México, San Luis Potosí, and Querétaro. These states were selected for three reasons: (1) they held local elections in 2015, (2) they vary in their incumbent parties, and (3) they satisfied our safety and logistical protocols. The 26 municipalities were selected from among the 56 municipalities in these states for which an audit report was released in 2015. We oversampled municipalities for which reported incumbent malfeasance was particularly high or low and contrasted with that of other parties in the state. Figure 1 maps the location of these municipalities.

FIGURE 1.

Municipalities Included in Our Experimental Sample

We selected 356 rural and 322 urban electoral precincts—Mexico’s smallest electoral unit—for our experimental sample.^{Footnote 19} This sample prioritized accessible rural precincts, where informational spillovers are least likely and where voters are unlikely to receive the information from other sources, and precincts in municipalities with high or low levels of incumbent malfeasance and stark contrasts with other parties. To minimize effects on municipal election outcomes, at most one-third of electoral precincts were treated in any municipality.

In this article, we focus on the almost-exclusively rural 296 precincts for which reliable social network data are available (see network construction details below). The 17 municipalities containing this final sample of precincts are shown in blue in Figure 1. Of these, four were governed by the PAN, 12 by the PRI, and one by the Citizen’s Movement. The summary statistics in Table 1 show that, compared to the national average, this sample has a lower population density and is less economically developed.

TABLE 1.

Precinct-Level Comparison of 2010 Census Characteristics Between Our Sample and the Nation

Note: All variables are unweighted.

Provision of Information on Incumbent Malfeasance

Our informational treatment, which was designed in partnership with the nonpartisan transparency nongovernment organization (NGO) Borde Político, sought to inform voters of ASF audit report outcomes for their municipality. We provided citizens with information about either the share of FISM expenditures that did not benefit the poor or the share of unauthorized FISM expenditures. Figure 2 shows a sample leaflet from the municipality of Guanajuato. The leaflet explains that the municipal government received 29.2 million pesos from the FISM fund to spend on social infrastructure projects benefiting the poor, and that (in this case) 28% of funds were spent on projects that did not benefit the poor. Figure 3 shows the distribution of reported malfeasance in our final sample of precincts. To minimize the risk that the information was perceived as political propaganda, the leaflet emphasized Borde Político’s nonpartisan status and explained the data source, referred to the government rather than particular parties, and used black and white to avoid colors associated with particular political parties.

FIGURE 2.

Example of Local Information Leaflet in Salamanca, Guanajuato

FIGURE 3.

Distribution of Incumbent Malfeasance in Our Final Sample

Although this core information was constant across treatment conditions, we also subtly varied the mode of information dissemination along two dimensions. First, in some precincts we provided a comparison with the average malfeasance of incumbents from different parties in the state. Second, to facilitate common knowledge about the information treatment, leaflet delivery was accompanied by a loudspeaker announcing the information’s dissemination. There is no evidence that either treatment variant influenced voters,^{Footnote 20} so we henceforth pool all information treatments. As discussed below, the lack of differential effects between treatment variants suggests that information provision serves primarily as a coordinating device in more-connected precincts.

Treatments were randomly assigned using a block randomization procedure in which four precincts from blocks containing six or seven precincts received an information treatment. Blocks include only rural or only urban precincts from within a particular municipality, and were otherwise assigned to maximize within-block similarity.^{Footnote 21} Within our 53 predominantly rural blocks, malfeasance information pertains to the same municipal incumbent party for all precincts within a block. Table A.1 in the Online Appendix shows that receiving an information treatment remains well balanced across precinct- and individual-level covariates for our subsample where reliable social network data are available. This indicates that the sample restriction maintains the randomization.

In each treated precinct, up to 200 leaflets were delivered to households by hand during the month before the election—either in person, or left in a mailbox or taped to the door if nobody was home—on behalf of Borde Político. Delivery occurred with few problems.^{Footnote 22} The exact locations where leaflets were delivered were logged, enabling enumerators to only visit leaflet recipients in treated precincts to administer the postelection survey.

Measuring Network Connectedness

A key challenge for researchers studying social networks is accurately mapping ties between individuals (Chandrasekhar and Lewis Reference Chandrasekhar and Lewis.2016). We address this challenge by using family ties to construct individual-level networks, which are aggregated to produce precinct-level proxies for a precinct’s connectedness. Unlike other societies, where friends and colleagues represent the primary sources of social interaction (e.g., Alt et al. Reference Alt, Jensen, Larreguy, Lassen and Marshall2017), extended families capture a substantial component of social interaction in rural Mexico, as noted above.

Following Angelucci et al. (Reference Angelucci, De Giorgi, Rangel and Rasul2009), we exploit Spanish naming conventions to link individuals from the list of Prospera beneficiaries. Prospera is a major nationwide conditional cash transfer program (previously called Oportunidades, and based on Progresa), that provides cash to around seven million impoverished beneficiaries in exchange for meeting school attendance and health requirements for their children. We obtained the list of individual Prospera beneficiaries and their localities for the first quarter of 2017 from catalogo.datos.gob.mx. Like other Spanish-speaking countries, Mexicans typically have two last names: a paternal last name passed on by their father and a maternal last name passed on by their mother.

We denote a node as an individual Prospera beneficiary, and define two nodes as connected if they share at least one last name and reside in the same precinct. As illustrated in Figure 4, a beneficiary named Juan López Fernández is connected to a second beneficiary named María Medina López, who indirectly connects Juan López Fernández to Pedro Pérez Medina. While our baseline specifications consider individuals as nodes, we show that our findings are robust to defining family names as nodes instead.^{Footnote 23}

FIGURE 4.

Example of Three Linked Individual Prospera Beneficiaries

To link the localities of Prospera beneficiaries to electoral precincts, we use 2010 Census data on the spatial distribution of all individuals living in each locality and the boundaries of electoral precincts. The procedure explained in Section A.2 in the Online Appendix ensures that Prospera beneficiaries are only used to characterize social networks when there is a sufficiently large voter overlap between their localities and an electoral precinct. Ultimately, this procedure yielded maps of linked individuals for 296 predominantly rural precincts containing 95,199 beneficiaries.

This approach to mapping social networks is appropriate for the rural precincts that we examine. First, because 31% of registered voters in our final sample of precincts are Prospera beneficiaries, our network maps are relatively comprehensive. While the use of sampled networks can upwardly bias estimates from network-level regressions due to nonclassical measurement error, this bias declines dramatically once sampling rates reach 30% (Chandrasekhar and Lewis Reference Chandrasekhar and Lewis.2016). Second, since rural communities are generally more tight-knit and experience lower levels of migration than urban areas, a shared surname in a rural area is more likely to indicate a genuine family tie. Nevertheless, like most network studies, there remains a risk of measurement error arising from false or missing connections between individuals. Fortunately, measurement error is likely to be reduced by aggregating our network measures at the precinct level, and there is little reason to believe that common surnames producing false ties are correlated with political behavior (e.g., Cantú Reference Cantú2014), or that political behavior is systematically associated with the probability of within-community marriage. To further mitigate the concern that the results reflect spurious ties, we control for the share of Prospera beneficiaries sharing a common surname as a robustness check.

To test our hypotheses, we use the network data to construct the two aforementioned precinct-level measures of network connectedness—average degree and the largest eigenvalue of the adjacency matrix describing the network. We standardize both measures to facilitate interpretation. Despite differences in their definitions, the high correlation suggests that these measures capture a similar underlying dimension, as our model assumed. Figures A.1 and A.2 in the Online Appendix provide examples of two similarly sized networks that vary significantly in their average degree and largest eigenvalue, respectively. Figure 5 shows the distribution of network connectedness in our sample.

FIGURE 5.

Histogram of (Standardized) Measures of Network Connectedness

We validate that these measures of network connectedness indeed capture characteristics of the locality that are likely to support voter coordination by matching our network measures to survey data from the 2006 and 2011 National Social Capital Surveys (ENCAS).^{Footnote 24} The two cross-sectional ENCAS waves comprise 219 questions gauging different aspects of community life and include a special module for respondents who identify themselves as Prospera beneficiaries. Our main outcome—an index of overall community connectedness—is based on subindices capturing two aspects of connectedness: participation and efficacy.^{Footnote 25} The participation index is composed of up to three variables: participation in social organizations, participation in social activities with other Prospera beneficiaries (if a Prospera beneficiary), and informal associations with other Prospera beneficiaries (if a Prospera beneficiary). The efficacy index is composed of up to four variables: perceived influence, cooperation, problem-solving involvement (if a Prospera beneficiary), and problem-solving experience. Due to the rural nature of our final sample, we restrict attention to the 376 rural localities across the country sampled in the ENCAS, and construct family networks of individual beneficiaries at the locality level comprising almost a million individuals.

Table 2 reports a strong positive correlation between average degree and the largest eigenvalue and the community connectedness index, as well as the participation and efficacy subindices. In all instances, these associations are statistically significant and suggest that our network measures based on family ties among Prospera beneficiaries meaningfully capture broader features of communal life that may help sustain cooperation and coordination in political behavior. By contrast, we show in Table A.2 in the Online Appendix that other common network metrics—such as average clustering, average path length, closeness, and link density^{Footnote 26}—are uncorrelated with the community connectedness index. For this reason, we focus throughout on our two theoretically-driven measures of network connectedness that positively correlate with proxies for the strength of social ties and coordination capacity—average degree and largest eigenvalue.

TABLE 2.

Correlation Between Locality-Level Network Connectedness Measures and Locality-Level Community Connectedness

Notes: All specifications are estimated using OLS. Both measures of network connectedness are standardized. Standard errors clustered by municipality are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Outcomes

We use two data sources for our main outcomes: official electoral returns and a survey we conducted in the three weeks following the election. First, we use precinct-level election results from state electoral institutes to measure the incumbent party’s vote share, both as a share of those that turned out and as a share of registered voters in the precinct. Second, precinct-level electoral outcomes are supplemented by individual-level survey data gauging beliefs about different parties, engagement with the treatment, and coordinated vote choices. These variables, which test the central mechanisms underpinning the model, are introduced later on when we present evidence on mechanisms. We conducted surveys with ten randomly sampled voters who received a leaflet in all treated precincts, and ten surveys of randomly selected voters in one control precinct per block.^{Footnote 27}

Empirical Strategy

To test hypotheses H1, H2, and H3, we estimate baseline specifications of the following form:

(9)$$\eqalign{\displaylines{ {Y_{pbm}}{ \,= \beta_1}\;Information\;provisio{n_{pbm}} \cr+ {\beta _2}\;Networ{k_{pbm}}} \cr + \,{\beta _3}\left( {Information\;provisio{n_{pbm}} \,\times\, Networ{k_{pbm}}} \right) \cr+ \,{\mu _{bm}} + {\varepsilon _{pbm}}, \cr}$$

where Y _pbm is an outcome for electoral precinct p within randomization block b in municipality m. For individual-level survey outcomes, Y _ipbm includes an i subscript. Information provision _pbm and Network _pbm are, respectively, a randomized precinct-level information provision indicator and one of our two measures of network connectedness. The block fixed effects, μ_bm, adjust for the differential treatment assignment probabilities across blocks arising from different block sizes, and enhance efficiency by exploiting variation in treatment assignment only within blocks of similar precincts. Standard errors are clustered at the municipality-treatment level, and precinct-level observations are weighted by the share of registered voters that received a leaflet (or would have received a leaflet, among control precincts). Additional specifications control for the interaction between information provision and the following (standardized) variables that could potentially confound the interaction between information provision and network connectedness: (log) population density; an urban indicator; an index of socioeconomic development; the distance from the precinct’s center to the municipality head; the share of Prospera beneficiaries; and the PAN, PRD, and PRI, and incumbent vote shares in 2012.

Since we are principally interested in how social networks moderate the effects of providing voters with information about incumbent performance, our main coefficient of interest is β₃. This captures the heterogeneous effect of information provision by network connectedness. Given that voters generally perceive challenger parties to be less malfeasant than the incumbent party, the model’s prediction in H1 implies that β₃ < 0. In other words, provision of information concerning incumbent malfeasance should have a decreasing effect on incumbent party vote share as network connectedness increases. In contrast, following H2 and H3, we expect β₃ > 0 for outcomes related to engagement with and coordination around the information. This implies that voters’ reaction to the treatment should increase with network connectedness.

To test H4, we include a further interaction with the share of registered voters that received the information in equation (9). In such regressions, we expect that the share receiving the leaflet would accentuate the interaction between information provision and network connectedness, because a greater share of the network is aware of our treatment leaflet and that other voters also received it.