Selecting Clients: Partisan Networks and the Electoral Benefits of Targeted Distribution M. Victoria Murillo Columbia University [email protected] Ernesto Calvo University of Maryland [email protected] Version 4.2 December/2010 Abstract: To whom should political parties distribute public and private goods if they seek to maximize electoral returns? In this article, we measure the benefits accrued from the programmatic and non‐programmatic distribution of resources to voters and explain the critical role played by partisan networks in the delivery of excludable goods. We propose a model where parties with efficient distribution network target non‐programmatic goods to in­network voters and move centripetally in the policy space. Meanwhile, parties with less efficient distribution networks are crowded out in the policy space. We test our model using survey data from Argentina and Chile. "I have the municipality divided in two regions, and each of those two regions is sub‐
divided in other 10 or 15 quadrants. Then, I have coordinators by area, from this street to that street. Any problem that occurs, the coordinator needs to go there, talk, inform, report to his or her boss who will then let me know. If the problem is serious, I will be notified immediately" Manuel José Ossandón, Mayor of Puente Alto, Chile, Interview with the authors, July 2009. "We call it 'multiplicative work': each of us has acquaintances in the street where we live, friends. We tell each of them to get out, to speak, and publicize our political work. Thanks to this 'multiplicative work' we are known around here, because we do not control any media outlet. Groundwork [trabajo de base], wherever we are needed we go. They call us from some community and say: 'we have a problem, the street needs repairing, the water, the septic tanks, we call the municipality and their are taking 2, 3 days.' " Carlos Bringas, PJ Activist, San Isidro, Argentina, Interview with the authors, August, 2009. 1. Introduction A crucial question on distributive politics is how political parties should select policies and allocate particularistic goods among voters to maximize electoral returns. Should they take on the policy positions of loyal voters that are less likely to defect or favor critical voters that can swing the election in their favor? Should they target supporters embedded in party networks, whose needs are well known to activists, or allocate goods to lure new supporters towards the party? In this article we take on one of the most active agendas in the field of comparative politics, targeted distribution, to assess the electoral benefits of delivering policies and goods to voters. In doing so, we provide an integrated model that measures the importance of partisan networks for allocating programmatic and non‐programmatic benefits to voters and to maximize electoral returns. Research on distributive politics has evolved along two distinct paths. First, a responsible party
literature has argued that parties maximize electoral support by distributing benefits to voters using
general criteria or group categories (Adams 2001; Downs 1957; Schattschneider 1942). Informative party
labels and ideological markers, it is said, help voters anticipate likely beneficiaries of future policies and
to locate themselves on the policy space. Accordingly, in the responsible party literature, politicians 1 attach value to the ideological content of their message and build policy oriented reputations that are credible to voters. A different framework has been proposed to explain the non‐programmatic distributive orientation of parties. In contrast to the responsible party literature, scholars have argued that elites deliver excludable goods to voters through existing partisan channels (Auyero 2001; Scott 1972). In this framework, party‐voter linkages are non‐ideological, unconstrained in the policy space, and information markers are exchanged in networks connecting voters to parties. Because higher income reduces the marginal value of private goods and expands the time horizon of voters, modernization scholars expected non‐ideological parties to decline over time and responsible parties to become the only game in the electoral town. Evidence is mounting, however, that political parties in both new and old democracies adopt distributive portfolios that combine programmatic and non‐programmatic goods (Diaz‐Cayeros 2008; Dixit and Londregan 1996). The question of how parties allocate programmatic and non‐programmatic resources among voters, however, has not received a fully satisfactory answer. In what follows, we propose a model to explain the decision to distribute resources among voters and a methodology to measure the electoral benefits of delivering private, semi‐private, and public goods. Our analysis begins with the assumption that the delivery of excludable goods to voters is constrained by the parties’ organizational capacity to know their clients and deliver the goods (Cox and McCubbins 1986; Dixit and Londregan 1998, 1996). Political networks,1 built around party activists, supply politicians with information about the voter’s needs and the expected returns from targeting different types of voters. Networks, we argue, allow politicians and voters to exchange 1 In recent years, there has been a wealth of research analyzing the role of political networks in areas such as participation (Siegel 2009; Nickerson 2008), congressional behavior (Tam Cho and Fowler 2010; Zhang et al. 2008; Fowler 2006), political information and communication (Baker, Ames, and Renno 2006; Huckfeldt and Sprague 1995). For an overview of recent developments see Heaney and McClurg (2009). Our research contributes to this burgeoning literature by exploring the effect of partisan networks on targeted distribution. 2 critical information to ensure the delivery of goods as well as a mechanism to deal with moral hazard and adverse selection problems in the delivery of goods.2 Our research brings together the responsible party and clientelistic literatures, with party elites pursuing both programmatic and non‐programmatic distributive strategies and voters developing expectations in regards to the delivery of both policies and excludable goods. Political elites, as described by the responsible party literature, advertise consistent policy messages to cultivate long term ideological support from voters. However, they also rely on distribution networks to target clients that are “well known” to party activists and give higher returns to targeted investments (Dixit and Londregan 1996). Over time, ideologically consistent messages and informational advantages drawn from partisan networks facilitate more stable electoral coalitions (Cox and McCubbins 1986) and the development of voter loyalties that maximize long‐term electoral returns (Diaz‐Cayeros, Estevez, and Magaloni 2007). Because partisan networks disseminate information and reduce dead weight losses in the delivery of goods, we expect parties to allocate more resources to in network voters. Selecting clients for non‐programmatic distribution, however, affects the decision process by which parties decide their programmatic choices. To model the Party’s decision process, we present a theory than brings together insights from Dixit and Londregan’s (1996) model of targeted distribution and Adams, Merrill, and Grofman (2005) equilibrium model for multiparty competition. The proposed model hypothesizes those parties with efficient distribution networks will target non­programmatic goods to in­network voters and move centripetally on the policy space. By 2 Moral hazard and adverse selection problems are pervasive in democratic settings where the secrecy of the ballot precludes direct observation of the voters' behavior. Networks serve an important role both for monitoring the voter's behavior (moral hazard) or gathering information that reveals the voter's type (adverse selection). In this article we focus on how networks solve the latter problem, adverse selection, with parties seeking to maximize their electoral returns by increasing their distributive efficiency in the delivery of private goods (Dixit and Londregan, 1996). 3 contrast, parties with less efficient distribution networks are expected to target out of network voters and move centrifugally on the policy space. To test our model, we use a novel survey instrument that measures the proximity of respondents to party networks and propose a statistical design to estimate the electoral benefits of programmatic and non‐programmatic distribution, conditional on network proximity. Data collection was carried out in Argentina and Chile, two countries whose party systems have been respectively described as clientelistic and programmatic by the extant literature. We then estimate the proposed model, measuring vote choice for respondents that are (i) ideologically closer to a party, (ii) closer to a party’s networks; and conditional on (iii) the expected delivery of handouts, public sector jobs, and pork. Results support the view that non­programmatic distribution to in network voters provides larger electoral benefits than distribution to out of network voters. Results also provide consistent evidence that electoral gains from programmatic and non‐programmatic distribution vary by party and by the type of resource being delivered—handouts, jobs, pork—, conditional on the proximity of voters to partisan networks, and the ideological distance to the parties location. Evidence supporting the hypothesis that parties draw larger electoral benefits from targeting in‐network voters is particularly strong for more excludable goods, such as handouts or patronage. More moderate benefits result from allocating local public goods such as pork to in‐network voters. Our results hold across electoral institutions and controlling for other determinants of vote choice, such as valence assessments of incumbent parties and the socio‐economic status of voters. Findings highlight the importance of estimating a unified model with both programmatic and non‐
programmatic determinants of vote choice to understand the distributive strategies of political parties.. The order of presentation of this article is the following: in the next section we revisit recent debates on distributive politics and provide a justification for our theoretical set up. In the third 4 section we describe the importance of partisan networks and ideological cues to assess both non‐
programmatic and programmatic linkages between voters and parties in Argentina and Chile. In the fourth section we test our hypothesis about electoral returns from targeting in‐network voters with programmatic and non‐programmatic goods in both countries and for all five major parties in each of the two countries. We conclude discussing some comparative implications from our analysis. 2. A Unified Theory of Targeted Distribution in Multiparty Elections Targeted distribution is just one among the many strategies available to parties and one among the many factors that determine vote choice. Voters weigh parties according to a variety of traits such as the policies they propose, the goods they deliver, their performance in office, as well as other identity and socio‐economic characteristics (Adams, Merrill, and Grofman 2005; Schofield and Sened 2006). Voters, consequently, perceive the distribution of excludable goods such as handouts, patronage, or pork, not as substitutes but as complements to other programmatic benefits delivered by party elites. In turn, party elites consider programmatic and non‐
programmatic strategies as related investments on constituencies whose demands include broadly defined policies as well as particularistic goods.3 While parties implement distributive portfolios for the delivery of programmatic and non‐
programmatic goods, researchers have focused until recently on a single dimension at a time. A broad literature on economic policymaking analyzing the electoral incentives for programmatic distribution has proposed two sets of theories –electoral competition and partisan models— with models that either benefits the median voter of an election or that target a party’s own core constituency. Both targets of programmatic distribution, median voters or core voters, are defined 3 An important insight that is forcefully expressed in the joint model of global and tactical redistribution of Dixit and Londregan (1996). 5 in ideological terms, with parties cultivating informative policy reputations and voters slowly updating their ideological positions over time. 4 The earlier literature on clientelism, by contrast, developed without much consideration for spatial (programmatic) models of voting. Instead, scholars focused on the development of party‐
voter loyalty linkages explained by identity and socio‐economic traits. Keen to the earlier literature on clientelism and targeted distribution, consequently, was the notion of a core voter whose loyalty was impervious to the programmatic choices of parties.5 In recent years, a new literature on targeted distribution has begun to bridge the theoretical gap between these different traditions. With the development of more sensible models of voting that include both spatial and non spatial components (Adams et.al. 2005, Schofield and Sened 2006) as well as formal models that include both programmatic and non‐programmatic incentives (Dixit and Londregan 1996; Diaz‐Cayeros et.al. 2006, Stokes 2005), it is now possible to explicitly define and estimate unified models of distribution. Informed by these new theories, a debate has emerged in regards to the targets of distribution, with some authors arguing in favor of models where parties distribute resources to core voters –i.e. voters that are more responsive or sensitive to distribution carried out by particular parties (Cox 2007)‐ and others purporting that parties should instead allocate goods to swing voters –i.e. ideologically uncommitted or moderate voters that may decide an election (Lindbeck and Weibull 1987; Stokes 2005). Political Networks and Targeted Distribution There is considerable ambiguity in the literature on the proper definition of core voters. While most scholars readily identify core voters as a subgroup of the electorate that consistently and over the course of repeated elections supports the same party or coalition, there is little 4 For an excellent summary of these traditions from an institutional perspective see Franzese (2002). 5 See the collection of essays edited by S. Schmidt, Guasti, Lande and Scott ( 1977) for an excellent overview of this literature. 6 consensus on the particular traits that explain such behavior. As core voters defend, promote, and assimilate the party’s programmatic positions (ideological affinity), develop ties with acquaintances that hold similar preferences (political homophily or identity), and become politically informed by engaging their friends and families (political socialization), a single definition that explains vote loyalty has proven elusive. Differences in how scholars understand core voters have also led to competing models of programmatic and non‐programmatic distributive politics. Some authors, for example, define core voters by an adherence or identity dimension, based on a special understanding or personal connection between the party and a well defined group of voters (Cox and McCubbins 1986; Dixit and Londregan 1996, 1998). Other authors understand core voters as a subset of the electorate that maximizes long term benefits by building a reputation for loyalty that smoothes electoral business cycles and brings higher returns to both parties and their voters over the course of multiple elections (Diaz‐Cayeros, Estevez, and Magaloni 2007). 6 Yet other authors emphasize ideological or issue proximity as the mechanism that prevents vote defection and guarantee loyalty at the ballot box (Lindbeck and Weibull 1987; Stokes 2005). Having as a point of departure different definitions of core voters, it is not surprising that competing models of targeted distribution have produced inconclusive –and oftentimes contradictory‐ results. In our research, consequently, we make explicit (and defend) the choice of an adherence dimension that is orthogonal to the programmatic and non‐programmatic preferences of voters. We then measure the electoral benefits of distribution conditional on this adherence dimension. Our research begins with a description of the voter behavior determined by programmatic (e.g. ideological proximity) and non‐programmatic preferences (e.g. preferences for particularistic 6 As described by Diaz‐Cayeros et.al. “core vote support is unconditional today but conditional on the history of tactical redistribution tomorrow. This is what we call conditional partisan loyalty” (Diaz‐Cayeros, Estevez, and Magaloni 2007) 7 goods and performance valuations). We consider that voters are connected to parties through networks, with linkages constrained by a party’s organizational capacity to finance and mobilize their activists. More dense networks ‐a larger number of ties‐ facilitate the exchange of information between voters and party activists. This information exchange allows parties to invest resources more efficiently, both when formulating policy and when delivering particularistic goods. In our framework, consequently, core voters are characterized by an adherence dimension that is separate to their programmatic and non‐programmatic preferences.7 We use the term core­N voters to describe the type of party‐voter attachment or linkage through partisan network ties. Partisan networks facilitate the (patron’s) delivery and the (client’s) access to targeted goods, where activists have a crucial role gathering information and brokering deals with their clients (Auyero 2001; Szwarcberg 2009; Stokes and Dunning 2010). Through these exchange networks (Friedkin 1992), party elites gather critical information about the voters’ mood, needs, and wants and develop expectations in regards to the voter’s performance. Hence, partisan networks allow politicians to assess the value of their investment, disseminate risk, and provide voters with a mechanism to reveal their type and declare their eligibility to receive goods (Omitted Reference). The Conditional Benefits of Programmatic and Non­Programmatic Distribution Our analysis begins revisiting Dixit and Londregan’ (1996) model of targeted distribution, where two parties ‐L(eft) and R(ight)‐ offer programmatic benefits through general policies as well 7 Our definition follows Cox & McCubbins (1986) and Dixit and Londregan (1996), with core voters defined by informational advantages that minimize dead weight losses in the delivery of resources. They latter state: “… differences in the parties’ abilities to target redistributive benefits to different groups are the key determinants of the outcome. Such differences can arise when each party has its core groups of constituents whom it understands well. This greater understanding translates into greater efficiency in the allocation of particularistic benefits: patronage dollars are spent more effectively while taxes may impose less pain per dollar” (Dixit and Londregan, 1996:1134). 8 as targeted (non‐programmatic) goods. In their framework, ideological proximity explains the voter’s taste for programmatic distribution, where voter i prefers when her/his preferred policy is closer to the proposal P of party R, so that 0. Each party k also offers voters a vector of discretionary transfers …
, subject to budget constraints, with promises revealed ex ante by parties and honored ex post.8 Actual transfers perceived by voters, however, vary by the level of information available to efficiently identify the needs of different groups: 1
0 As it was described by Dixit and Londregan: “We allow the transfers to occur via a leaky bucket —of the dollars offered by party to each member […] , only a fraction may get through. Moreover, the fraction may depend on the identity of the group and the party; this captures the possibility that each party has some ‘core support groups’ it understands better, and it can deliver benefits to them with greater efficacy.” (Dixit and Londregan, 1996: 1139) Prior linkages or special attachments between voters and parties –e.g. different values of —, yield different electoral returns per transfer , driving party elites to select different distributive strategies. Such attachment can take a multitude of forms –identity politics, information networks, etc.‐, each of which affects the efficiency of targeted distribution. Both programmatic and non‐programmatic expectations enter the utility function of voters: 1
(1) with parties jointly selecting policies and a vector of transfers to maximizing electoral returns. A most important corollary of the model proposed by Dixit and Londregan (1996) is that the allocation of targeted benefits should also drive parties to change their policy proposals e.g. their preferred location in the policy space. 8 Stokes (2001) and (2005) challenges this assumption and provides a model under moral hazard, where parties need to spend resources to monitor and enforce the clientelistic contract. This may introduce a new source of heterogeneity, as parties face further difficulties to monitor voters that are removed from the party. Moral hazard, however, can be also modeled within the Dixit and Londregan framework. 9 Targeted Distribution in Multiparty Systems: Defining Core­N Voters More recently, Adams, Merrill, and Grofman (2005), from now on AMG, proposed a unified model of competition in multiparty elections, where 2 parties maximize separate spatial and non‐spatial components. As in Dixit and Londregan, voters use informational shortcuts to vote for parties that are ideologically proximate –e.g. closer on the policy space‐ and attach value to a non‐
spatial or valence component –e.g. a their assessment of a party’s performance (Schofield and Sened 2006) and/or other strategic or identity traits (Kedar 2005; Adams, Merrill, and Grofman 2005). The model proposed by Dixit and Londregan is mathematically equivalent to a restricted version of AMG unified model of party competition when there are only two parties, the non‐spatial component is substituted by a targeted distribution term, and parameters are conditioned on a relevant attachment dimension. In fact, after adding non‐spatial terms to account for the different sensitivity to distribution by voters and other performance assessments, we can write our workhorse model as: (2) and the voter probability of selecting alternative k with the familiar conditional logit model: ∑
, (3) The first term in the right hand side of equation (2) describes the respondent’s income , with different voters having a different taste, , for distribution. The second term, , describes programmatic redistribution which “reflects the prevailing ideological beliefs about equality and is carried out using income taxes (sometimes and in some countries wealth taxes) and the general social welfare system” (Dixit and Londregan, 1996: 1132). As in AMG, describes the declining utility of being further distant from a party’s policy proposal. The third term on the right hand side of equation (3), , describes the utility voter i perceives from non‐programmatic 10 distribution by party k, subject to dead‐weight losses as voters become further removed from the network of a party . This "leaky bucket" describes information loses that make distribution less efficient. Differences in the sensitivity of voters to non‐programmatic distribution affect how parties decide which clients should be targeted. As it was described by Cox (2006): “Dixit and Londregan show that, when the parties have no special relationships with any groups (e.g., = ), the parties’ allocations are driven by the density of swing voters in each group—
as in the Lindbeck‐Weibull model. As larger and larger asymmetries in the parties’ abilities to deliver benefits arise, however, the parties’ allocations are driven more and more by the core voter logic of promising benefits to those groups to which the party can most effectively deliver benefits.” (Cox, 2006: 7). Following Dixit and Londregan, we include measures of ideological proximity and a special adherence dimension that defines core­N voters, reflecting proximity to partisan networks. Differences in the efficiency of partisan networks, we argue, should affect the capacity of parties to deliver resources. Constraints in the delivery of goods through political networks will also limit the set of strategies available to parties—suggesting further variation across political systems. Using the Multinomial/Conditional Logit specification in equations (2) and (3), we can assess the importance of targeted distribution on vote choice, conditional on network proximity. After measuring the effect of distributive networks on vote choice, we need to estimate the optimal policy choices from different parties. To this end, we use AMG iterative algorithm and find the equilibrium location of parties in the policy space. Their model searches for party equilibria, maximizing the parties expected vote share9 ,
, conditional on the respondents self reported placement , the importance they attach to policy proximity , and the policy location of parties at each iteration: ,
∑
,
(4) After substituting equation (2) into (4), AMG differentiate for the last occurrence of Pk in equation (2) to find the location at which party k maximizes its vote share: 9 Please note that our notation differs from Adams, Merrill and Grofman (2005) for consistency with Dixit and Londregan (1996). 11 0
∑
∑
,
,
,
,
(5) By iteratively solving for each party preferred location, max( k), AMG provide an algorithm to find the Nash equilibria in multiparty elections. 10 As shown by Calvo and Hellwig (2010), parties with a performance advantage have an incentive to move into more moderate policy locations (centripetal movement), while parties that are at a disadvantage are crowded out into more extreme policy positions (centrifugal movement). In our vote choice model, consequently, we expect that parties with more efficient distribution networks will move to more moderate policy positions while parties that are at a comparative disadvantage will be crowded out into more extreme policy positions. Because positive returns to distribution move parties to more moderate positions, we expect party systems with efficient distribution networks to produce central clustering while party systems with less efficient distribution networks show wider dispersion on the policy space. Distributive Expectations and Political Networks A number of theoretical implications can be derived from the proposed model, both in regards to the behavior of party elites as well as for explaining overall competition among parties. We summarize the theoretical implication here. 1) Parties will benefit from allocating non‐programmatic resources to core voters when there are informational advantages – e.g. a special understanding of a group of voters or clients. If closer ties to party activists provide such a better understanding of the voters’ needs and a more efficient delivery of goods, parties should target non‐programmatic resources to core­N voters. Overall, we expect political parties to derive higher electoral returns from distributing private goods to in­network voters. 10 Estimation details in Appendix C. 12 2) Because partisan networks draw more precise information when targeting individual voters than collective ones, we expect their efficiency to increase when delivering excludable goods to individual voters than when delivering club and public goods to groups. We expect proximity to partisan networks to have larger effects when delivering handouts and jobs rather than non­excludable public works or policies. 3) Party networks are neither equally efficient, nor are all voters are equally attuned to the distribution of private goods. Consequently, we expect within country variation in the performance of partisan networks and in the sensitivity of voters to targeted distribution. 4) As previously described, parties jointly decide how to allocate clientelistic resources and what type of policies to promote. Because parties with more efficient networks have performance advantages in the delivery of goods, per AMG’s equilibrium model we expect them to crowd out parties with less efficient networks and move centripetally on the policy space.11 To summarize, the most important implication of this research is that informational advantages in the delivery of non‐programmatic goods will result in parties targeting core­N voters to maximize their electoral returns. These informational advantages should be more pronounced for more excludable goods and generate centripetal policy adjustments for parties enjoying a comparative advantage in the supply of non‐programmatic goods. In the next section we present our research cases and describe survey results that test for the proposed model of programmatic and non‐programmatic distribution. Let us preface our analysis with a succinct description of the ideological and partisan network context in our two cases, Argentina and Chile. 11 Parties with less efficient networks, by contrast, should be crowded out into more marginal policy locations. 13 3. Ideological Attachment and Party Networks in Argentina and Chile Argentina and Chile, the two southernmost countries of the Americas, share a common colonial past, roughly similar levels of development, populations with comparable socio‐
demographic characteristics, and stable but relatively young democratic institutions. Both countries have presidential executives, bicameral legislatures, and competitive multi‐party elections. Political competition in these two countries, however, has been described in starkly different terms. While Chile is generally portrayed as the poster child of programmatic party competition, predictable electoral environment, and policy stability (Carey 2002; Scully and Valenzuela 1993); scholars consistently depict Argentina as a democracy with weak programmatic parties, extensive clientelistic networks, electorally volatile, and prone to frequent policy change (Jones and Hwang 2005; Levitsky 2003). These contrasting images, however, provide a somewhat distorted view of competition in these two countries. As we will show, contrary to what is today conventional wisdom among Comparative scholars, ideology remains a relevant determinant of vote behavior among Argentine respondents and the non‐programmatic delivery of goods increases party vote among Chilean respondents.12 Ideological Cues and Party­Voter Linkages in Chile and Argentina Our 2007 survey confirms that ideological identification is easier for Chilean than Argentine voters. Over two thirds of Chilean respondents readily identify the ideological location of Chile’s main political parties on a left‐right continuum.13 A remarkable 87% of survey respondents provided an ideological location for the then incumbent Socialist Party (PS), with over seventy percent of respondents placing it on the left of the political spectrum. Respondents identified the 12 An emerging literature is starting to take notice of the importance of clientelism in Chile. See Barozet (2004, 2006) and Luna (2006). 13 We asked respondents to identify the location of five major Chilean parties, which are organized into two coalitons: the progressive Concertacion (PS, DC, and PPD) and the conservative Alianza (RN and DC). 14 Christian Democrats (DC) at the center of the ideological spectrum, the Party for Democracy (PPD) on the center‐left, and Renovación Nacional (RN) and Democratic Union (UDI) on the right.14 The main two parties in Argentina are the Peronist PJ and the moderate UCR. In contrast with Chile, only 58% of survey respondents reported an ideological score for the Peronists (FPV‐PJ) on our ten point ideological scale while only 54% reported a location for the main opposition party, the Union Civica Radical, confirming their “catch‐all” character as described by the extant literature (Freidenberg, Llamazares, and García Díez 2007; Saiegh 2009). While catering to voters with different socio‐economic profiles,15 both parties host factions on the left and right of the political spectrum. Significant policy swings by Peronist and UCR presidents make it difficult for voters to use ideological markers to assess the programmatic stance of candidates (Levitsky and Murillo 2005). Survey data reflects these difficulties, with much higher variance in the reported ideological location of parties among Argentine voters. Since 2001, as a result of a political crisis that led to the resignation of former UCR president Fernando de la Rua, new parties have emerged with more defined programmatic stances. In particular, two parties have attracted a significant number of UCR voters: the center‐left Alliance for a Republic of Equals (ARI) and the center‐right Republican Proposal (PRO). Both the ARI and the PRO are recognized by voters as being respectively on the center‐left and center‐right of the political spectrum. A Description of Partisan Networks in Argentina and Chile Partisan networks in both countries have activists (militantes) as the most encompassing party category.16 Party activists represente close to 1.4 percent of the Argentine population and 1.2 14 The high response rate is explained by long‐term voter stability. See Torcal and Mainwaring (2003). 15 The UCR has consistently draw support among better‐off voters and professional middle‐classes whereas the Peronists combined extensive labor‐based roots with relatively conservative constituencies in the hinterlands. 16 In our research we measure the size and structure of different partisan networks, including networks of candidates, activists, supporters, and volunteers. Networks of activists included the largest number of members for each and every one of the parties in each country. 15 percent of the Chilean population (Omitted Reference). 17 Because networks of activists take time to develop and benefit from broader access to state resources, the strength of party organizations also varies significantly within each country. Whereas the main Chilean political parties have relatively similar contingents of activists, large asymmetries characterize party competition in Argentina. In Chile, the Socialists have the largest partisan networks, with activists representing 0.356 percent of the total population or 53,880 individuals, closely followed by the Christian Democrats (54,221 activists), the PPD and the UDI (around 30,000 activists), and trailed by the smaller RN (approximately 22,000 activists). Whereas the three Concertación parties, which headed the national executive between 1990 and 2010, had larger networks than those of the Alianza; the difference was not very large. By contrast, the Peronist network of activists, representing 0.766 percent of the population or 290,930 activists, is significantly larger than of all others parties combined. The Peronist activist network is almost twice the size of its UCR counterpart (which includes 0.42 percent of the population or approximately 160,000 activists). Still, the size of the Radical network remains impressive given its paltry 17% of votes in the 2007 presidential election, and is a testament to the resilience of organizations built over many decades. As described by Radical representative (and former presidential candidate) Leopoldo Moreau (personal interview with authors on July 20, 2009) when asked about what brought people to be Radical activists: 17 Online Appendix for a full description of the survey sampling design and instruments. The surveys included a combined total of 5600 registered voters, with 2800 respondents from each country, including in the sample cities with populations over 10,000 in Argentina and 40,000 in Chile. The survey was designed using questions of the form “how many X do you know whom also know you,” asking each respondent to provide counts of reference groups whose frequencies in the population are known ‐i.e. professions, names, etc‐ and counts of groups whose frequencies in the population we sought to estimate ‐ i.e. Socialist Party activists, candidates, etc‐. Using a battery of questions about populations whose frequencies we know we estimated the size of each respondent’s personal network. A different set of questions asked about populations whose frequencies we were interested in retrieving. From such data we estimate the prevalence of party members in the population (number of activists, candidates, volunteers, etc). 16 “…the Radicalism is a party with a long trajectory…with a network that developed over a
hundred years [and] cannot collapse overnight. The basic structure of the party committee (e.g.
territorial office) survives…pursuing community roles that range from providing kids with school
support, nursing services, doctor services, etc… The party committee is a reference point [among
voters]. In every town of the Buenos Aires province you can ask for the Radical committee and
everyone knows where it is, just as when asking for the police station, the church… Not only in
the Province of Buenos Aires in almost all the country”
The considerable smaller networks of the PRO and ARI, roughly twenty times smaller than those of the Peronists are a testament to how difficult it is to translate electoral success into organizational power. Peronists and Radicals, consequently, enjoy considerable advantages to mobilize supporters (Auyero 2001; Nichter 2008; Szwarcberg 2009), allocate public sector jobs to political allies (Calvo and Murillo 2004), distribute goods as well as social benefits (Brusco, Nazareno, and Stokes 2004; Giraudi 2007; Weitz‐Shapiro 2006; Stokes 2005). Chilean candidates face considerable more institutional restrictions in the access and use of publicly‐funded benefits for non‐programmatic distribution. While there is significant allocation of targeted resources to Chilean voters, from cash transfers to broad workfare programs, distribution is generally carried out by bureaucratic agencies with little partisan involvement (Luna and Mardones 2009). Similar restrictions affect the allocation of public sector jobs, tightly regulated by the 2003 Chilean civil service reform which explicitly sought to depoliticize hiring in the national public sector (Bau Aedo 2005; Rehren 2000). Restrictions in the discretionary allocation of public resources to voters, and in the capacity of partisan networks to deliver goods, have resulted in Chilean politicians relying more heavily on the private financing of targeted distribution and a more intense focus on advertising programmatic goals. As described by PPD representative Marco A. Nunez, who is a medical doctor (personal interview with authors, March 2009): “we provide medical services to people, ‘please, come in; let me know where it hurts?’,
pharmacies distribute medicines that I either buy or receive from friends who are doctors.
Veterinarians deparasite pets, lawyers provide legal advice, and teachers play with the kids. They
paint the kids faces while radio personalities or karaoke machines provide entertainment. All of it
on Saturday morning in my headquarters.”
17 Similarly, UDI representative Felipe Salaberry (personal interview with authors, March 2009)
described his work with constituents:
“We made a law…at the proposal of UDI representatives, that allows the sale or gift of glasses
for farsightedness without a medical prescription as a transitory solution…we adopted this
program that allows us to have a daily contact, almost the obligation to be in permanent contact
with the voter…[and we deliver the glasses]…in their homes, in the sports clubs, in the
neighborhood associations, in the parks. I have a mobile office that offers the program…”
While there are significant differences in the use of ideological markers and in network structure, most Argentine and Chilean voters are able to place parties on the policy space and connected to their activists’ networks. In the next section we analyze how voters in Argentina and Chile use these ideological markers and network linkages when deciding their vote. We then show that these differences should also drive parties into different targeting strategies for non‐
programmatic benefits as well as changes in their policy preferences. 4. Distribution, Party Networks, and Vote Choice In this section we test the vote choice model of equations (2) and (3), where voters select among competing candidates by minimizing the ideological distance to parties and delivering non‐
programmatic goods. Our dependent variable is the vote choice of survey respondents if a legislative election "were to take place next week." We deleted observations where the dependent variable yield non‐responses, resulting in a sample of 1647 respondents in Argentina and 1497 in Chile. We imputed missing values for all independent variables using multivariate imputation by chain equations (MICE).18 We estimate a model with five party alternatives in Argentina –PJ,19 UCR, 18 We did not provide a closed menu of parties to respondents and non‐responses prompted a one‐time insistence. Undecided voters represented 27% and 20% of respondents in Argentina and Chile. Blanc votes represented another 10% and 14% respectively. Finally, votes for smaller parties represented 3.3% of the vote in Argentina and 11% of the vote in Chile. While we deleted non‐responses, blank, and small party votes in both countries; we replicated all analyses with a full dataset, drawing votes randomly to replace missing observations with multivariate imputation by chained equation (MICE in R 2.9). Results of alternative models are very similar to those with deleted observations and are available upon request. 18 ARI, PRO, and the main provincial party (PPP)—20; and five party alternatives in Chile –the Socialist Party (PS), the Christian Democrats (DC), the PPD, UDI and the RN. Our three main independent variables report on the (i) ideological distance between voters and parties, the (ii) proximity of respondents to the network of activists of a party, and the (iii) distributive expectations for handouts, public sector jobs, and pork. Ideological distance (i) measures the squared distance from the self‐reported ideological location of each respondent to the reported location for each party, . Ideological placements were measured on a ten point scale, from 1 to 10, with low numbers describing locations on the left of the political spectrum and high numbers representing placement on the right. The ideological distance variable is alternative specific, assessing the ideological distance between each respondent i and each party k. We expect a negative effect on vote choice, as further ideological distance reduces the probability of voting for a party. The second set of independent variables reports the normalized (ii) proximity between respondents and the network of activists of each party using survey questions of the form "how many people do you know, and they know you, who are activists of party x?"21 Following Gelman and Hill (2007), individual parameters reporting distances from respondents to each party network 19 The survey was structured so that voters could select their preferred Peronist faction. This included the Frente para la Victoria (FPV) of former President Kirchner, allies of Carlos Menem, Rodriguez Saa, and a generic Peronist party. Because the survey question was “undirected,” we recode as Peronists all responses that describe any of the party factions. 20 Due to the importance of local parties in many Argentine provinces, the fifth party choice was worded such that in every province we could retrieve information about the most important provincial party as well as information about their partisan network and distributive intent. For example, in the Province of Neuquén the most important local party is the MPN (Movimiento Popular Neuquino), which has had a firm control of the governorship for over twenty years. In each Argentine province, the survey requested information about a different “fifth” party. We coded such parties as PPP when conducting our analyses. 21 Respondents were instructed that knowing someone meant that "you know them, they know you, that you may contact them by phone, letter, or in person and that you have had some contact during the last two years." Further survey details in the appendix. 19 were estimated using a negative‐binomial design with individual and group specific over‐
dispersion parameters (Gelman and Hill 2007; McCarty et al. 2000; McCarty, Killworth, and Rennell 2007). As described in Gelman and Hill (2007), the over‐dispersion parameters report that the respondent knows more/less members of a group than the prevalence rate (in standard deviations).22 As with ideological distance, network proximity is alternative specific for respondent i and party k. We expect this variable to have a positive effect on vote choice, where a respondent who knows more activists from a party will be more likely to vote for that party. A third set of key independent variable is the self‐reported unconditional expectation of (iii) receiving handouts, a public sector job, or public works in the community from an elected member of party k. The question read: "In a scale from 1 to 10, where 1 is very unlikely and 10 is very likely, How likely would it be that an elected member of the (party) would provide you with (type of good)." The question was worded as to solicit unconditional expectations of receiving goods, and did not imply a quid­pro­quo exchange before, during, or after an election.23 The respondent was presented with this question prior to questions on vote behavior, in a survey module that assesses party performance. Consequently, survey instruments did not prompt respondents to assume that the delivery of goods was conditional on voting for a candidate whom, once in office, would deliver the benefits. These questions are also alternative specific, inquiring on the likelihood of perceiving different goods from members of each party k. We expect a positive effect from distributive expectations into vote choice. 24 22 Further references in Appendix A. A detailed description of our strategy to estimate the size and structure of networks can be found in _____________(2010). 23 The survey instrument was also worded to prevent voters from disclosing information under that false assumption that the interviewer could provide any link or connection to a party that was in position to deliver goods. The question was inserted in a module assessing the expected performance of parties in office, together with questions about the parties’ capacity to manage the economy, unemployment, being responsive to the voter preferences, etc. 24 In a separate article we show that ideological distance and network proximity also shape the distributive expectations of voters. Consequently, we expect ideology and network proximity to have a direct effect on 20 The most important parameter for this research measures the expected party vote conditional on expectations about the delivery of goods and network proximity. Consequently, our model specification interacts the (ii) network proximity and (iii) the expected goods variables to obtain predictions about the marginal change in vote choice when goods are targeted to voters that are comparatively closer or further removed from a party's network. We expect this interaction to have a positive effect, suggesting larger electoral gains from targeting goods to in­
network voters. That is, we expect the core­N voter model to be confirmed. We also include a number of control variables in our analyses, measuring the (iv) relative size of the respondents’ personal network (log), (v) performance assessments describing the party’s capacity to manage the economy, the (vi) attitudinal view of respondents on distribution, and socio‐economic variables such as (vii) status (to assess the marginal value of distribution) and (viii) gender. The size of the personal network was measured using a battery of questions of the form "how many x do you know," with a mean personal network size of 200 and 203 in Argentina and Chile respectively. The alternative specific performance question asks respondents "how capable is [party] to manage the economy." To control for biases on the self‐reported expectation of receiving goods, we ask respondents to indicate, in a scale from 1 to 10, "how adequate is for the government to provide [type of good]" to citizens. Finally, we use the standard measure of socio‐
economic classification of the Argentine and Chilean national statistics agencies, entered into the statistical model as a battery of dummy variables—as we expect them to affect the marginal value of distributed private goods for individuals. vote choice and also an indirect effect, as they shape the distributive expectations of voters. Estimates of the direct and indirect effect of network proximity and ideology can be estimated explicitly (and are available upon request), and does not affect the validity of our estimates as these variables are not endogenous to the vote choice model estimated here. 21 Our Results: the Conditional (Multinomial) Logit Model To measure the effect of targeted distribution on voting, we estimate a conditional (multinomial) logit model with alternative (and respondent) specific variables. Alternative specific variables incorporate distinct information for each choice (each party k in our model). For example, the ideological distance between a respondent i and the PS enters into the utility function of voting for the Socialists, but not in the utility function of voting for other parties such as the UDI or the RN. Each survey respondent, consequently, reports different ideological distances to each of the parties. Respondent specific variables, on the other hand, take the same value across alternatives. For example, wealthy voters may display different propensity to vote for the Peronism than less well off voters. The same wealth score, however, enters into the utility function of a voter when deciding among all different alternatives. The conditional (multinomial) model, consequently, includes alternative specific and respondent specific variables. We estimate six restricted models and six full models. The restricted models provide a single alternative specific variable per country, while the unrestricted models provide estimates both by party and by country. Country­Level Results: the Restricted Model Table 1 presents results of the restricted models, with the Peronists and the Socialists as the base categories in Argentina and Chile respectively. Because the alternative specific parameters describe overall changes in the log‐odds ratio of vote choice for any party, the direction and significance of the estimates is meaningful. Readers may assess the direction and significance of the results labeled "Alternative Specific Variables" (the shaded area) in Table 1. The variables labeled "individual Specific Variables," on the other hand, need to be interpreted relative to the base categories (PJ and PS) or after being transformed into probabilities. <<Insert Table 1>> Our findings provide a more nuanced picture of party politics in Argentina and Chile than conventional wisdom suggests. The estimates of ideological distance have the expected negative 22 sign and are statistically significant; indicating that being ideologically distant from a party decreases the probability of voting for that party. That remains the case in Argentina, defying the conventional view of a political system with poorly defined ideological parties. However, the linear effect of ideological distance remains more pronounced among Chilean respondents when compared to their Argentine counterparts.25 Additionally, proximity to the network of activists, the expectation of receiving private goods (column 1 for handouts, column 2 for jobs and column 3 for public works), and the interactions between these variables are all positive, thereby indicating that network proximity increases the odds of voting for a party as does the expectation of receiving private goods from elected officials of that party. That remains the case in Chile, defying the conventional view of a political system with strictly programmatically oriented parties. The positive effect of the interaction confirms the core‐N vote model, indicating larger benefits from distributing goods to in­network voters. The magnitude of the effect, however, is difficult to gauge directly from the coefficients. Consequently, we plot the joint marginal effects of distributing goods, conditional on network proximity to allow for further comparison of our estimates. Figure 1 provides a more intuitive view of the model estimates, plotting the marginal change in the log‐odds ratio of voting for a party k conditional on both the expectation of targeted distribution and network proximity (Brambor, Clark, Golder 2006). The horizontal axis in Figures 1 describes network proximity, with ‐2 indicating that the respondent knows considerable fewer activists than the prevalence rate in the population (2 standard deviations further away from the prevalence rate), a value of 0 indicating that the respondents knows an average number of activists, and a value of +2 indicating that the respondents knows considerable more activists than the prevalence rate (2 standard deviations closer than the prevalence rate). The vertical axis in Figures 1 describes the marginal change in the log‐odds ratio of voting for a party k (the linear change or slope that a one unit of change in targeted distribution has on party vote). 25 Notice that the log‐odds linear predictions do not allow us to compare the substantive impact across models. We provide a more intuitive description in Figures 1 through 3. 23 The first plot in Figure 1, row 1 column 1, describes the marginal change in the log‐odds ratio of voting for a party k in Argentina per unit of increase in the expectation of receiving handouts and conditional on network proximity. This plot shows that if a voter knows exactly the prevalence rate of activists in the population (a 0 on the horizontal axis), a one unit change in the likelihood of receiving a handout leads to a statistically insignificant increase of 0.05 in the log‐odds ratio of voting for that party. When voters are two standard deviations further removed from the network of activists of a party, we observe a decrease of 0.05 in the log‐odds ratio of voting for that party, but the impact remains statistically insignificant. By contrast, being two standard deviations closer to the network of activists results in a statistically significant increase of 0.2 in the log‐odds ratio of voting for that party. Aggregate results for all Argentine parties, consequently, suggest that the delivery of handouts significantly increases party vote among in network voters but fails to increase the party vote among out of network voters. The plot in row 1 column 2 describes the effect of handout delivery on vote choice among Chilean voters, finding again a statistically significance benefit when targeting in network voters but no returns for out of network voters. However, the core‐N effect is not as sharp as in Argentina, as shown by the flatter slope in the linear prediction. That is, while in both Argentina and Chile parties get positive returns from delivering handouts, benefits from targeting core­N voters are less dramatic in Chile. <<Insert Figure 1>> Taken together, all six plots in Figure 1 allow us to compare the aggregate effects of delivering handouts, public sector jobs, and public works among voters in Argentina and Chile. As it is possible to observe, benefits from targeting in network voters vary by type of goods and across countries. As shown by the flattening of the slopes when comparing rows 1 through 3, electoral returns from targeting in­network voters is more pronounced for handouts. Different from handouts, the delivery of pork provides roughly similar electoral benefits whether we target in­ or out of network voters. Because public works are locally non‐excludable, the information provided 24 by partisan networks to distinguish core‐N voters is less relevant than when delivering handouts and public jobs. Hence, our results indicate that parties should follow different strategies when delivering handouts and jobs, which provide a larger boost among in­network voters, than when allocating pork. Results, consequently, suggest that “excludability” is an important determinant of non‐programmatic distribution by partisan networks, with benefits from targeting core‐N voters decreasing for less excludable goods. Figure 1 shows that non‐programmatic distribution of goods has a more pronounced effect on party vote in Argentina than in Chile across all three types of goods, irrespective of decisions to target in­ or out­network voters. On the other hand, although in both countries the delivery of goods to in­network voters guarantees larger electoral benefits to parties, the estimated boost in votes is more pronounced in Argentina. The substantive benefits are also more pronounced in Argentina, given the larger number of voters reporting at least some probability of receiving handouts, a public sector job, or pork. Other estimates in Table 1 deserve further attention. Results show that the expected performance of a party vis­à­vis the economy has a very large and positive effect on party vote, consistent with much research that has identified performance as an important albeit less studied determinant of vote choice (Adams, Merrill, and Grofman 2005; Schofield and Sened 2006). Interestingly, the magnitude of change explained by performance is similar across countries, e.g. a 1.1 and 1.03 in the log‐odds ratio of voting for a party respectively for Argentina and Chile. To summarize, results from our restricted models show that both programmatic and non‐
programmatic distribution increase the probability of voting for a party. More importantly, returns to non‐programmatic distribution are higher for core‐N voters in both political systems, but become less relevant for less excludable goods such as pork. 25 Partisan benefits from Targeted Distribution After estimating the overall effect of targeted distribution, we now describe party specific estimates of network proximity and targeted distribution on vote choice. We use the same variables to estimate unrestricted models, but run a different specification to capture party specific effects. As in the results presented in Table 1, the Peronists in Argentina and the Socialists in Chile are the baseline categories. Table 2 presents the estimates of a multinomial (conditional logit) model with respondent (and choice specific) variables, including interactive terms to measure the joint effect of network proximity and targeted distribution. As in Table 1, both ideological distance and physical proximity to parties are significant and in the expected direction, with ideological distance reducing party vote and network proximity increasing party vote. <<Insert Table 2>> However, the party specific estimates of distribution on vote choice conditional on network proximity present a more nuanced picture. Again, to better visualize our results we present Figures 2 and 3, which plot marginal changes in the log‐odds ratio of voting for a party k conditional on both targeted distribution and network proximity (Brambor, Clark, and Golder 2006). As in figure 1, the horizontal axis in figures 2 and 3 describes network proximity, with ‐2 indicating that the respondent knows two standard deviations fewer activists than the prevalence rate, a value of 0 indicating that the respondents knows an average number of activists, and a value of +2 indicating that the respondents knows two standard deviations more activists than the prevalence rate. As in figure 1, the vertical axis in Figures 2 and 3 describes the marginal change in the log‐odds ratio of voting for party k. Let us first describe a single plot in Figure 2 to facilitate further interpretation of the results. The first row and first column in figure 2 describes the marginal change in the log‐odds ratio of voting for party k for each increase of one unit in the expected delivery of handouts for the PJ.26 In 26 As indicated before, the range for the expectation of receiving goods goes from 1 to 10. 26 analyzing changes in the log‐odds ratio of voting for the PJ, the delivery of handouts has no statistically significant electoral effect either for those voters who know the average number of Peronist activists or for those that are two standard deviations away in either direction (in network and out of network voters). While results defy the conventional wisdom of a positive relationship between the delivery of handouts as a strategy to foster electoral support for the Peronists, results are very robust to alternative specifications). By contrast, results shows that respondents increase their probability of voting for the UCR when receiving handouts (second row, first column plot in Figure 2). The electoral benefits for the UCR, when distributing handouts, are roughly similar when targeting in‐network or out of network voters –e.g. the predicted change in the log‐odds ratio of voting for the UCR goes from a minimum of 0.19 when the respondent knows two standard deviations fewer UCR activists than average to a change of 0.27 when the respondent knows two standard deviations more activists than the prevalence rate. The second plot in row one of Figure 2 provides an intuitive representation of the benefits of allocating public sector jobs to voters. If a voter knows a number of Peronist activists equal to the prevalence rate in the population (a 0 in the horizontal axis), a one unit change in the likelihood of receiving a public sector job leads to a statistically significant increase of 0.16 in the log‐odds ratio of voting for the PJ. Knowing considerable more PJ activists than average (two standard deviations above the mean) increases the log‐odds of voting Peronists roughly 50% (from 0.16 to 0.246). By contrast, a respondent that is further removed from the PJ network of activists (two standard deviations below the prevalence rate), is 50% less likely (a change from 0.16 to 0.082). <<Insert Figure 2>> <<Insert Figure 3>> The comparison of all plots in figures 2 and 3 permits us to assess within country variation in the linear estimate of electoral returns to non‐programmatic distribution. Argentine parties endowed with extensive partisan networks, such as the PJ and the UCR, show substantive and 27 statistically significant benefits from targeting in­network voters with public sector jobs. The benefits from targeting in‐network voters are larger when allocating public sector jobs, more moderate for handouts, and negligible when allocating pork. A dominant strategy for Peronists' candidates, consequently, should target in­network voters with public sector jobs and use pork spending on competitive districts, irrespective of network capacity.27 Figure 3 shows our results for Chile and provide important insights into the differences across political parties regarding non‐programmatic distribution. A most important finding is that, against most prior research on targeted distribution in Chile, all four major parties in Figure 3 benefit from distributing jobs and pork to voters. Results are more moderate for handouts, with positive slopes for all parties but failing to achieve statistical significance at the .05 for the RN. Our results are consistent with emerging research that highlights the importance of targeted distribution in Chile, even if it serves a more limited role than that observed among Argentine voters (Luna 2006). Like the incumbent Argentine PJ, the incumbent PS has a moderate advantage in targeting core‐N voters when delivering both public sector jobs and pork even though the magnitude of the effects are smaller than for Peronists' voters. Finally, a most interesting result is that the delivery of patronage and pork increases the log‐odds ration of voting for the UDI and the RN irrespective of the respondent's position in the network of activists. In fact, results show the electoral benefits of distribution to be moderately higher when targeting out‐of‐network voters, although differences are not statistically significant. In short, different from the restricted model, the individual party results provide a more nuanced view of the effects of redistribution on vote choice. While the restricted model showed that on average parties in Argentina and Chile draw larger benefits from targeting in­network voters; party specific results only support the core‐N voter model for the PJ and UCR in Argentina as well as for the PS in Chile—the three parties with the most extensive organizations. The lack of significance 27 Exploring the equilibrium strategies of parties requires a different modeling strategy, which we explore in a separate article. 28 in the expectations for receiving handouts for in‐network voters of the incumbent PJ and PS is remarkable. The strong impact of performance on vote propensity along with the fact that two popular incumbents from these parties held the presidency in both countries, may help us understand the effect. The literature on the PJ has shown effects of handouts on rally attendance (Sczwarzerberg 2010) and turnout (Nichter 2008) –which are easy to monitor— but we find no evidence that handouts significantly affect vote behavior for incumbent parties. In short, our results support the core‐N model for the two main Argentine political parties and the Chilean PS, which have the most extensive partisan networks. However, we do not find support for the core‐N model for other Chilean parties that rely on smaller partisan networks. Measuring the Centripetal and Centrifugal Effect of Targeted Distribution In this section we test the corollary of our argument, which describes adjustments in the policy location of parties in response to targeted distribution. As proposed in the second section, we expect positive returns to targeted distribution to drive parties into more centripetal locations, crowding out parties with lower returns to distribution and/or less efficient distribution networks.28 We test this hypothesis using the specification of AMG’s iterative algorithm described in the second section, which computes the equilibrium location of parties taking as input the self‐
reported ideology of voters, the respondent assessments of the performance of parties, and the distribution network parameters reported in Tables 3 and 4. For reasons of space, we concentrate on estimating changes in the equilibrium location of Chilean parties in response to the delivery of patronage jobs.29 At each iteration, the algorithm maximizes the vote share of a party holding all other parties fixed at their previous ideological location. After solving for the location at which this party 28 Assuming roughly normal distributions of voters as observed in both Chile and Argentina. 29 All equilibrium estimates by country and type of good are available upon request. The behavior of the equilibrium model, however, is well described by the example of Patronage jobs in Chile as described in the text. 29 k maximizes its vote share, the algorithm sets the current location of party k to the newly estimated value, moves to the next party, and repeats the procedure. Beginning with arbitrary locations in the policy space, the algorithm iterates until no party can improve its vote share by moving to a different location. AMG programmed their algorithm to stop iterating once changes in the equilibrium location of parties fall below a specified threshold. Our code instead uses an MCMC algorithm –a random walk over the policy space—that iterates until all parties converge at their equilibrium locations.30 We run three different specifications of AMG’s equilibrium algorithm: firstly, we estimate the equilibrium location of parties with the full set of programmatic and non‐programmatic parameters set to the values reported in Table 4. Secondly, we estimate a model where a single party ‐the PS in Chile‐ is bestowed with positive returns to targeted distribution (parameters for the PS set to their estimated values in Table 4) while all other parties’ distribution networks are set to zero. Finally, we compare the shift in location of parties from a restricted model (with all network and distributive expectation parameters set to zero) to the unrestricted model (with all programmatic and non‐programmatic terms set to their observed values in Table 4). <<Insert Figure 4>> Results of the algorithms are reported in two plots in Figure 4. The plot on the left of Figure 4 describes changes in the equilibrium location of parties when only the PS benefits from efficient distribution networks. In this plot, dotted lines describe the initial equilibrium when no party benefits from distribution networks e.g. all distribution network parameters are set to zero. In turn, solid lines describe the location of parties when only the PS benefits from an efficient distribution network. As it is possible to observe by comparing the dotted and solid lines, while in the baseline model the equilibrium location of the PS is at around 4.3 (to the left of all other parties), the PS 30 All code and replication materials may be downloaded from www.____.edu. We write the iterative algorithm in WinBUGS 1.4 (Spiegelhalter et al. 2003) with three different chains starting at randomly drawn locations for each party k. All models converge quickly, with Gelman‐Rubin statistics smaller than 1.2 for all estimated equilibrium locations and no significant change in the series after the first few hundred iterations. We discarded the first 2000 iterations (burn in) and save the next 4000 iterations when reporting results. 30 moves centripetally to 4.4 when its distribution network parameters are set to the reported values of Table 4. Meanwhile, all other parties are crowded out, with both allies (PPD and DC) and opposition (UDI and RN) parties moving further to the right. 31 A more realistic model, however, allows for cross‐pressures from all parties, each of them characterized by very different returns to targeted distribution as shown in Figures 2 and 3. To this end, the plot on the right of Figure 4 shows the change from an initial location (dotted lines) when all distribution networks are set to zero to the equilibrium location of parties with all distribution networks set at their estimated values (solid lines). The more realistic equilibrium model shows vote gains for the DC, the PS, and the UDI (longer solid lines when compared with the same parties’ dotted lines) at the expense of the PPD and RN. Furthermore, as the DC moves towards the center of the voter distribution, all parties except the PPD moved to more extreme positions. This centripetal shift in the equilibrium location of the PPD is explained by its position vis‐à‐vis his coalition partners –the DC and the PS‐, beneficiaries of larger and more efficient distribution networks. Overall, results show that parties with larger returns to targeted distribution crowd out parties with smaller and less efficient networks. Parties with clear distributional advantages move to more centrist ideological locations while parties that derive lower returns to distribution are crowded out into more extreme policy locations and more severely penalized in votes.32 Results also provide a rational for the more centrist position of Argentina’s main parties, endowed with larger and more efficient distribution networks. 5. Concluding Remarks Recent research on distributive politics has begun to question the long held belief that the programmatic and non‐programmatic delivery of resources represents mutually exclusive electoral 31 Further positive increases in the importance of networks or in the delivery of goods would drive the PS. If the positive returns are sufficiently high, the PS eventually dominates the center of the ideological spectrum while the PPD and DC are crowded out to the left of the ideological space. Simulations for the full range of feasible values are available upon requests. 32 Parties whose voters fall within those of parties with larger and more efficient networks, as the PPD in Chile, have little room to maneuver and will be more fiercely penalized in votes. 31 strategies. This conceptual change is most welcome, as evidence mounts showing that party elites invest in both policies and goods, rather than single‐mindedly specializing in either distributive strategy. As parties allocate a portfolio of resources to heterogeneous groups of voters, distributive models of politics have grown in complexity and raised new theoretical questions. With these new models, scholars have begun to deal with the joint decision making process by which elites decide the policies to be enacted and the voters to whom goods should be delivered. In this research, we bring together and extend prior research by Dixit and Londregan (1996) and Adams, Merrill, and Grofman (2005) to respond a central question in current debates on distributive politics: to whom should the goods be delivered? We argue that a proper response to this question needs to model partisan distribution networks and jointly assess how the delivery of goods shapes the programmatic choices that parties make. To this end, we measure the benefits parties perceive from targeting in­ or out­network voters. This article shows that the party’s decision to target core voters depends critically on the choice of an adherence dimension by researchers. In our case, following Dixit and Londregan (1996), we propose that networks serve a critical role in communicating preferences and delivering resources. Conditional to this adherence dimension, we provide a rational to expect larger electoral returns when targeting in­network voters e.g. core­N voters. We then test this hypothesis in Argentina and Chile, finding common support for the core‐N hypothesis and significant inter‐party variation in the electoral returns to targeted distribution. After showing the benefits from targeting in­ and out­network voters, we use a variation of AMG’s iterative algorithm to model the policy choices of parties. We show that parties drawing larger electoral benefits from distribution networks crowd out less successful parties, moving centripetally on the policy space. Overall, our research provides a rational for the reported centrist position of parties in the case of Argentina and the more extreme distribution of party preferences in Chile. 32 Two theoretical implications of our analysis deserved to be highlighted: first, the parties’ decision to simultaneously enact policies and deliver goods is able to explain bait­and­switch political strategies that in the past have been deemed inconsistent. The strategy of President Carlos S. Menem, who delivered goods to the Peronists core constituency and formed a policy coalition with the conservative UCeDe in the early 1990s inspired an extensive literature on accountability (Stokes 2001; Gibson 1997). More recently, Nestor Kirchner again followed the strategy of targeting the Peronists core while embracing a policy coalition with former UCR and center‐left politicians. In both cases, maintaining the integrity of the party coalition through targeted distribution provided elites with room to maneuver, both when enacting public policies and for broadening electoral support for the party. Our model show that these strategies, far from being inconsistent, can be explained by an investment portfolio that includes policies and goods, allowing Peronists presidents to maximize electoral support. Policy consistency in Chile, by contrast, should be expected when less efficient distribution networks provide for more limited policy divergence. On a methodological note, our research shows that more realistic models of targeted distribution result from designs that combine survey data, formal modeling, and computational simulation. The proposed integrated model of targeted distribution, beginning with the revealed policy preferences of survey respondents, measuring the relative proximity of voters to parties, assessing the effect of distributive networks on vote behavior, and jointly assessing the equilibrium locations at which all parties maximize their expected vote; provides a well defined blue print for future research. While we do not expect all scholars to agree on each of the choices we made along the way, we consider that challenges to each of the stages described before will get us closer to fulfilling the common goal of a standard model of targeted distribution. Eventually, we hope that such research agenda will pay off, unifying the responsible party and clientelism literatures to provide for a broader and deeper understanding of politics. 33 References Adams, James. 2001. Party competition and responsible party government : a theory of spatial competition based upon insights from behavioral voting research. Ann Arbor: University of Michigan Press. Adams, James, Samuel Merrill, and Bernard Grofman. 2005. A unified theory of party competition : a cross‐national analysis integrating spatial and behavioral factors. Cambridge ; New York: Cambridge University Press. Auyero, Javier. 2001. Poor People's Politics: Peronist Survival Networks and the Legacy of Evita. Durham, NC: Duke University Press. Baker, Andy, Barry Ames, and Lucio R. Renno. 2006. Social Context and Campaign Volatility in New Democracies: Networks and Neighborhoods in Brazil's 2002 Elections. American Journal of Political Science 50 (2):382‐399. Barozet, Emmanuelle. 2004. Elementos Explicativos de la Votación de los Sectores Populares en Iquique: Lógica y Eficiencia de las Redes Clientelares. Politica 43:205‐251. ———. 2006. Relecturas de la noción de clientelismo: una forma diversificada de intermediación política y social. Debate (Ecuador) 69:77‐101. Bau Aedo, Catalina. 2005. Experiencias exitosas en la profesionalizacion de la funcion publica en America Latina. El caso Chile. In Foro Iberoamericano: Revitalizacion de la administracion publica. Estrategias para la Implantacion de la Carta Iberoamericana de la Funcion Publica. Mexico City, Mexico. Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. Understanding Interaction Models: Improving Empirical Analyses. Political Analysis 14 (1):63‐82. Brusco, Valeria, Marcelo Nazareno, and Susan C. Stokes. 2004. Vote Buying in Argentina. Latin American Research Review 39 (2):66‐88. Carey, John M. 2002. Parties, Coalitions and the Chilean Congress in the 1990s. In Legislative Politics in Latin America, edited by S. Morgenstern and B. Nacif. Cambridge: Cambridge University Press. Cox, Gary W. 2007. Swing Voters, Core Voters and Distributive Politics. In Elections and Redistribution. Yale University. Cox, Gary W., and Mathew D. McCubbins. 1986. Electoral Politics as a Redistributive Game. The Journal of Politics 48 (2):370‐389. Diaz‐Cayeros, A., Federico Estevez, and Beatriz Magaloni. 2007. The Core Voter Model: Evidence from México. Yale University. Diaz‐Cayeros, Alberto. 2008. Electoral risk and redistributive politics in Mexico and the United States. Studies in Comparative International Development 43 (2):129‐150. Dixit, Avinash, and John Londregan. 1996. The Determinants of Success of Special Interests in Redistributive Politics. The Journal of Politics 58 (4):1132‐1155. ———. 1998. Ideology, Tactics, and Efficiency in Redistributive Politics. The Quarterly Journal of Economics 113 (2):497‐529. Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper Collins. Fowler, James H. 2006. Connecting the Congress: A Study of Cosponsorship Networks. Franzese, Robert J. 2002. Macroeconomic policies of developed democracies, Cambridge studies in comparative politics. Cambridge: Cambridge University Press. Freidenberg, Flavia, Iván Llamazares, and Fátima García Díez. 2007. The Determinants of Intraparty Ideological Differences. In Politicians and politics in Latin America, edited by M. A. Sáez. London: Lynne Rienner Publisher. Friedkin, Noah E. 1992. An expected value model of social power: predictions for selected exchange networks. Social Networks 14 (3‐4):213‐229. 34 Gelman, Andrew, and Jennifer Hill. 2007. Data analysis using regression and multilevel/hierarchical models, Analytical methods for social research. Cambridge ; New York: Cambridge University Press. Gibson, Edward. 1997. The Populist Road to Market Reform: Policy and Electoral Coalitions in Mexico and Argentina. World Politics 59 (N° 3):339‐70. Giraudi, Agustina. 2007. The Distributive Politics of Emergency Employment Programs in Argentina (1993‐2002). Latin American Research Review 42 (2):33‐55. Heaney, Michael T., and Scott D. McClurg. 2009. Social Networks and American Politics. American Politics Research 37 (5):727‐741. Huckfeldt, R. Robert, and John D. Sprague. 1995. Citizens, politics, and social communication : information and influence in an election campaign, Cambridge studies in political psychology and public opinion. Cambridge England ; New York: Cambridge University Press. Jones, Mark P., and Wonjae Hwang. 2005. Party Government in Presidential Democracies: Extending Cartel Theory Beyond the U.S. Congress. American Journal of Political Science 49 (2):267‐83. Kedar, Orit. 2005. When Moderate Voters Prefer Extreme Parties: Policy Balancing in Parliamentary Elections. American Political Science Review 99 (2):185‐199. Levitsky, Steven. 2003. Transforming labor‐based parties in Latin America : Argentine Peronism in comparative perspective. Cambridge, U.K. ; New York: Cambridge University Press. Levitsky, Steven, and María Victoria Murillo. 2005. Argentine democracy : the politics of institutional weakness. University Park, Pa.: Pennsylvania State University Press. Lindbeck, Assar, and Jorgen W. Weibull. 1987. Balanced‐budget redistribution and the outcome of political competition. Public Choice 52 (3):273‐297. Luna, Juan Pablo. 2006. Programmatic and non‐programmatic party‐voter linkages in two institutionalized party systems: Chile and Uruguay in comparative perspective, Department of Political Science, University of North Carolina, Chapel Hill. Luna, Juan Pablo, and Rodrigo Mardones. 2009. Distributive Politics in a Non‐Machine Party System: The Allocation of Targeted Social Funds and Subsidies in Chile (2000‐2008). Universidad Catolica de Chile, Santiago. McCarty, Christopher , Peter D. Killworth, H. Russell Bernard, Eugene Johnsen, and Gene A. Shelley. 2000. Comparing two methods for estimating network size. Human Organization 60:28‐39. McCarty, Christopher, Peter D. Killworth, and James Rennell. 2007. Impact of methods for reducing respondent burden on personal network structural measures. Social Networks 29 (2):300‐315. Nichter, Simeon. 2008. Vote Buying or Turnout Buying? Machine Politics and the Secret Ballot. American Political Science Review 102 (01):19‐31. Nickerson, David W. 2008. Is Voting Contagious? Evidence from Two Field Experiments. American Political Science Review 102 (01):49‐57. Rehren, Alfredo J. 2000. Clientelismo polâitico y reforma del estado en Chile. Santiago, Chile: Centro de Estudios Pâublicos. Saiegh, Sebastian M. 2009. Recovering a Basic Space from Elite Surveys: Evidence from Latin America. Legislative Studies Quarterly 34:117‐145. Schattschneider, E. E. 1942. Party government, American government in action series. New York: Farrar and Rinehart. Schmidt, Steffen, Laura Guasti, Carl Lande Lande, and James Scott, eds. 1977. Friends, Followers and Factions. A Reader in Political Clientelism. Berkeley: University of California Press. Schofield, Norman, and Itai Sened. 2006. Multiparty democracy : elections and legislative politics, Political economy of institutions and decisions. Cambridge ; New York: Cambridge University Press. 35 Scott, James C. 1972. Patron‐Client Politics and Political Change in Southeast Asia. The American Political Science Review 66 (1):91‐113. Scully, Timothy, and J. Samuel Valenzuela. 1993. From democracy to democracy : continuities and changes of electoral choices and the party system in Chile. Notre Dame, Ind.: Helen Kellogg Institute for International Studies University of Notre Dame. Siegel, David A. 2009. Social Networks and Collective Action. American Journal of Political Science 53 (1):122‐138. Spiegelhalter, David, Andrew Thomas, Nicky Best, and Dave Lunn. 2003. WinBUGS user Manual 1.4. http://www.mrc‐bsu.cam.ac.uk/bugs. Stokes, S. C. 2005. Perverse accountability: A formal model of machine politics with evidence from Argentina. American Political Science Review 99 (3):315‐325. Stokes, Susan Carol. 2001. Mandates and democracy : neoliberalism by surprise in Latin America, Cambridge studies in comparative politics. Cambridge ; New York: Cambridge University Press. Stokes, Susan, and Thad Dunning. 2010. How does the Internal Structure of Political Parties Shape their Distributive Strategies. Szwarcberg, Mariela. 2009. The political effects of party rallies: Clientelism and electoral competition. New Heaven. Tam Cho, Wendy K., and James H. Fowler. 2010. Legislative Success in a Small World: Social Network Analysis and the Dynamics of Congressional Legislation. The Journal of Politics 72 (01):124‐135. Torcal, Mariano, and Scott Mainwaring. 2003. The Political Recrafting of Social Bases of Party Competition: Chile, 1973&#8211;95. Cambridge Journals Online. Weitz‐Shapiro, Rebecca. 2006. Partisanship and Protest: The Politics of Workfare Distribution in Argentina. Latin American Research Review 41 (3):122‐147. Zhang, Yan, A.J. Friend, Amanda L Traud, Mason A Porter, Fowler James A., and Peter J. Much. 2008. Community structure in Congressional cosponsorship networks. Physica 387:1705–1712. 36 Table 1: Ideological Distance, Network Proximity, and the Determinants of Vote Behavior in Argentina, Multinomial Logit (Restricted Models) with Alternative Specific Variables. Model2
Model3
Model1
Model2
Handouts
Patronage
Pork
Handouts
Patronage
Pork
‐0.0189***
(0.0034)
1.0833***
(0.0655)
0.3944***
(0.0735)
0.1876***
(0.0381)
0.0421*
(0.0218)
‐0.1634
(0.4674)
‐0.7545*
(0.3996)
‐0.3657
(0.4181)
‐1.4441***
(0.5520)
‐0.431
(0.4388)
‐1.0348***
(0.3759)
‐1.3756***
(0.4118)
‐1.5073***
(0.4628)
‐0.7783*
(0.4505)
‐1.4209***
(0.3979)
‐1.9978***
(0.4472)
‐2.2819***
(0.5210)
‐0.6279
(0.4342)
‐2.2276***
(0.4411)
‐2.1122***
(0.4573)
‐2.4162***
(0.4975)
‐0.0158***
(0.0034)
1.012***
(0.0669)
0.341***
(0.0967)
0.2298***
(0.0280)
0.0261
(0.0187)
0.0203
(0.4724)
‐0.6954*
(0.4021)
‐0.4148
(0.4325)
‐1.3176**
(0.5536)
‐0.2789
(0.4444)
‐0.9454**
(0.3743)
‐1.2011***
(0.4185)
‐1.3552***
(0.4660)
‐0.6079
(0.4562)
‐1.3187***
(0.3987)
‐1.814***
(0.4544)
‐2.1647***
(0.5267)
‐0.5435
(0.4401)
‐2.129***
(0.4396)
‐1.9602***
(0.4626)
‐2.2293***
(0.5022)
‐0.7589
NSE E (UCR)
(0.6196)
‐2.0161**
NSE E (ARI)
(0.8128)
‐2.8391***
NSE E (PRO)
(1.0861)
‐1.5364**
NSE E (PPP)
(0.6961)
0.3457***
Personal Network (UCR)
(0.1227)
0.2068
Personal Network (ARI)
(0.1455)
‐0.0798
Personal Network (PRO)
(0.1675)
0.1855
Personal Network (PPP)
(0.1900)
‐0.0433
Positive view of Redistribution (UCR)
(0.0327)
‐0.0775**
Positive view of Redistribution (ARI)
(0.0378)
‐0.0554
Positive view of Redistribution (PRO)
(0.0415)
‐0.0182
Positive view of Redistribution (PPP)
(0.0450)
‐0.0632
Women (UCR)
(0.1920)
‐0.503**
Women (ARI)
(0.2154)
‐0.5139**
Women (PRO)
(0.2393)
‐0.069
Women (PPP)
(0.2819)
‐2.6945***
Constant (UCR)
(0.7909)
‐1.1044
Constant (ARI)
(0.8482)
0.1728
Constant (PRO)
(0.9490)
‐1.5604
Constant (PPP)
(1.1217)
‐0.7654
(0.6069)
‐2.0054**
(0.8149)
‐2.7725**
(1.0865)
‐1.6608**
(0.6920)
0.3347***
(0.1236)
0.2239
(0.1470)
‐0.0412
(0.1679)
0.2082
(0.1895)
‐0.0127
(0.0283)
‐0.1015***
(0.0320)
‐0.0538
(0.0353)
0.0178
(0.0433)
‐0.028
(0.1943)
‐0.4606**
(0.2181)
‐0.5004**
(0.2406)
‐0.0762
(0.2849)
‐2.6387***
(0.7998)
‐0.8369
(0.8658)
0.1179
(0.9658)
‐1.7393
(1.1355)
‐0.6518
(0.6170)
‐2.0422**
(0.8191)
‐2.6803**
(1.0879)
‐1.5091**
(0.6924)
0.3466***
(0.1241)
0.2399
(0.1471)
0.0425
(0.1708)
0.2645
(0.1894)
0.0123
(0.0318)
‐0.1118***
(0.0341)
0.0038
(0.0413)
0.0229
(0.0479)
‐0.0348
(0.1950)
‐0.4957**
(0.2192)
‐0.5159**
(0.2452)
‐0.0514
(0.2857)
‐2.9744***
(0.8181)
‐0.6995
(0.8741)
‐0.7043
(1.0022)
‐2.1184*
(1.1418)
‐1204.4
0.91176
‐1191.4
0.91271
‐1168
0.91442
‐0.0189***
Ideological Distance
(0.0034)
1.1152***
Performance (Economy)
(0.0649)
0.3555***
Network of Activists
(0.0721)
0.067**
Distribution of Goods
(0.0334)
Network of Activists*Distribution 0.0661***
of Goods
(0.0212)
‐0.0236
NSE AB (UCR)
(0.4659)
‐0.7766*
NSE AB (ARI)
(0.3968)
‐0.3929
NSE AB (PRO)
(0.4113)
‐1.2751**
NSE AB (PPP)
(0.5399)
‐0.3184
NSE C1 (UCR)
(0.4383)
‐1.0898***
NSE C1 (ARI)
(0.3719)
‐1.4421***
NSE C1 (PRO)
(0.4054)
‐1.4656***
NSE C1 (PPP)
(0.4643)
‐0.7201
NSE C2 (UCR)
(0.4515)
‐1.4692***
NSE C2 (ARI)
(0.3939)
‐2.0294***
NSE C2 (PRO)
(0.4400)
‐2.2063***
NSE C2 (PPP)
(0.5231)
‐0.5555
NSE C3 (UCR)
(0.4362)
‐2.1906***
NSE C3 (ARI)
(0.4366)
‐2.1856***
NSE C3 (PRO)
(0.4546)
‐2.3596***
NSE C3 (PPP)
(0.5033)
Individual Specific Variables (Multinomial Logit)
Individual Specific Variables (Multinomial Logit)
Alternative Specific Variables (Conditional Logit)
Argentina
Model1
Log Lik
McFaden R2
Model3
Note: Conditional/Multinomial Logit models estimated in R 2.11, using library MLOGIT. Grey label describe alternative specific variables. All other variables are respondent specific. Wald statistics used to assess statistical significance. Joint significance for interacted variables described in marginal effect plots of Figure 1. * p < 0.1, ** p < 0.05, *** p < 0.01. 37 Table 2: Ideological Distance, Network Proximity, and the Determinants of Vote Behavior in Chile, Multinomial Logit (Restricted Models) with Alternative Specific Variables. Model2
Model3
Model1
Model2
Model3
Handouts
Patronage
Pork
Handouts
Patronage
Pork
‐0.0396***
(0.0030)
1.0223***
(0.0561)
0.2701***
(0.0587)
0.1613***
(0.0359)
0.0199
(0.0168)
‐0.219
(0.4261)
‐0.7542*
(0.3969)
‐0.4556
(0.3991)
‐0.4364
(0.3659)
0.4179
(0.3962)
‐0.4399
(0.3708)
‐0.1557
(0.3822)
‐0.1971
(0.3524)
0.3743
(0.3861)
‐0.2477
(0.3528)
‐0.1179
(0.3656)
‐0.3506
(0.3365)
‐0.0384***
(0.0030)
1.0075***
(0.0563)
0.3064***
(0.0685)
0.1643***
(0.0265)
0.0034
(0.0141)
‐0.1656
(0.4274)
‐0.6997*
(0.3976)
‐0.4277
(0.3977)
‐0.3862
(0.3659)
0.4809
(0.3972)
‐0.3876
(0.3708)
‐0.0745
(0.3808)
‐0.0943
(0.3523)
0.4192
(0.3867)
‐0.2001
(0.3528)
‐0.0434
(0.3637)
‐0.2644
(0.3364)
‐0.0403***
Ideological Distance
(0.0030)
1.0328***
Performance (Economy)
(0.0558)
0.2778***
Network of Activists
(0.0598)
0.0698**
Distribution of Goods
(0.0330)
0.0176
Network of Activists*Distribution of Goods
(0.0174)
‐0.2271
NSE 1 (DC)
(0.4259)
‐0.7121*
NSE 1 (PPD)
(0.3954)
‐0.4023
NSE 1 (UDI)
(0.3965)
‐0.3671
NSE 1 (RN)
(0.3644)
0.3952
NSE C1 (DC)
(0.3956)
‐0.4195
NSE C1 (PPD)
(0.3696)
‐0.1344
NSE C1 (UDI)
(0.3795)
‐0.1547
NSE C1 (RN)
(0.3504)
0.3825
NSE C2 (DC)
(0.3866)
‐0.1874
NSE C2 (PPD)
(0.3526)
‐0.0473
NSE C2 (UDI)
(0.3641)
‐0.2525
NSE C2 (RN)
(0.3362)
Personal Network (DC)
Personal Network (PPD)
Personal Network (UDI)
Personal Network (RN)
Positive view of Redistribution (DC)
Positive view of Redistribution (PPD)
Individual Specific Variables (Multinomial Logit)
Individual Specific Variables (Multinomial Logit)
Alternative Specific Variables (Conditional Logit)
Chile
Model1
Positive view of Redistribution (UDI)
Positive view of Redistribution (RN)
Women (DC)
Women (PPD)
Women (UDI)
Women (RN)
Constant (DC)
Constant (PPD)
Constant (UDI)
Constant (RN)
Log Lik
McFaden R2
0.0167
0.0083
0.0132
(0.1071)
(0.1079)
(0.1084)
0.0378
0.0466
0.0353
(0.1125)
(0.1126)
(0.1133)
0.1745
0.1724
0.1581
(0.1188)
(0.1189)
(0.1199)
0.2923** 0.2846** 0.2878**
(0.1137)
(0.1140)
(0.1145)
0.0139
0.0354
0.0335
(0.0291)
(0.0259)
(0.0254)
‐0.0483
0.0264
0.0355
(0.0322)
(0.0270)
(0.0264)
‐0.0288
0.0006
0.0165
(0.0342)
(0.0289)
(0.0278)
‐0.0568*
‐0.0124
‐0.0143
(0.0337)
(0.0277)
(0.0265)
0.0837
0.0823
0.0963
(0.1718)
(0.1725)
(0.1730)
‐0.045
‐0.0771
‐0.0523
(0.1799)
(0.1803)
(0.1809)
0.4712** 0.4864** 0.4472**
(0.1909)
(0.1914)
(0.1919)
0.1572
0.146
0.1105
(0.1810)
(0.1814)
(0.1817)
‐0.849
‐0.9289
‐1.0028
(0.6817)
(0.6872)
(0.6933)
‐0.2777
‐0.5162
‐0.5731
(0.6939)
(0.6984)
(0.7020)
‐1.1288
‐1.1358
‐1.1763
(0.7324)
(0.7360)
(0.7396)
‐1.2669* ‐1.2276*
‐1.261*
(0.6969)
(0.7001)
(0.7031)
‐1819.2
(0.8573)
‐1811.6
(0.8579)
‐1802.6
(0.8586)
Note: Conditional/Multinomial Logit models estimated in R 2.11, using library MLOGIT. Grey label describe alternative specific variables. All other variables are respondent specific. Wald statistics used to assess statistical significance. Joint significance for interacted variables described in marginal effect plots of Figure 1. * p < 0.1, ** p < 0.05, *** p < 0.01. 38 Table 3: Ideological Distance, Network Proximity, and the Determinants of Vote Behavior in Argentina, Multinomial Logit with Alternative Specific and Individual Specific Variables. Argentina
Performance (Economy)
Network of Activists
Distribution of Goods
Alternative Specific Variables (Conditional Logit)
Network of Activists*Goods (Baseline, PJ)
Network of Activists (UCR)
Network of Activists (ARI)
Network of Activists(PRO)
Network of Activists(PPP)
Distribution of Goods (UCR)
Distribution of Goods (ARI)
Distribution of Goods (PRO)
Distribution of Goods (PPP)
Activists*Goods(UCR)
Activists*Goods(ARI)
Activists*Goods(PRO)
Activists*Goods(PPP)
Individual Specific Variables (Multinomial Logit)
NSE AB (UCR)
NSE AB (ARI)
NSE AB (PRO)
NSE AB (PPP)
NSE C1 (UCR)
NSE C1 (ARI)
NSE C1 (PRO)
NSE C1 (PPP)
Model2
Patronage
Model3
Pork
‐0.0186***
(0.0034)
1.1116***
(0.0660)
0.2283**
(0.1110)
0.0217
(0.0343)
0.0228
(0.0298)
0.3946***
(0.1447)
0.0475
(0.2188)
0.1922
(0.2169)
0.2936
(0.2029)
0.207***
(0.0416)
0.002
(0.0654)
0.0761
(0.0641)
0.1841***
(0.0596)
0.0107
(0.0397)
‐0.0354
(0.0994)
0.1202
(0.1078)
‐0.0094
(0.0581)
‐0.1587
(0.4790)
‐0.7995**
(0.3898)
‐0.3853
(0.4157)
‐1.4543***
(0.5541)
‐0.3747
(0.4486)
‐1.1588
(0.3663)
‐1.4614
(0.4119)
‐1.6095
(0.4748)
‐0.0186***
(0.0034)
1.0728***
(0.0661)
0.1812
(0.1120)
0.1554***
(0.0404)
0.0344
(0.0345)
0.4705***
(0.1431)
0.1382
(0.2266)
0.4125**
(0.1785)
0.3312
(0.2067)
0.1207**
(0.0472)
0.0347
(0.0637)
‐0.0596
(0.0860)
0.1719**
(0.0687)
‐0.0193
(0.0422)
‐0.0434
(0.0811)
‐0.0074
(0.0500)
‐0.0257
(0.0657)
‐0.2198
(0.4754)
‐0.7237*
(0.3917)
‐0.3556
(0.4195)
‐1.5868***
(0.5648)
‐0.4088
(0.4421)
‐1.0551
(0.3696)
‐1.3346
(0.4123)
‐1.5894
(0.4669)
‐0.0154***
(0.0034)
1.0169***
(0.0677)
0.1268
(0.1483)
0.2172***
(0.0331)
0.0274
(0.0302)
0.4196**
(0.2033)
0.1995
(0.2864)
0.3009
(0.2604)
0.5641**
(0.2781)
0.0707
(0.0432)
‐0.0024
(0.0497)
‐0.0458
(0.0508)
0.0488
(0.0587)
‐0.008
(0.0404)
‐0.0476
(0.0644)
0.0069
(0.0480)
‐0.0651
(0.0558)
0.0003
(0.4837)
‐0.7191*
(0.3957)
‐0.4167
(0.4292)
‐1.3253**
(0.5620)
‐0.22
(0.4534)
‐1.0020
(0.3700)
‐1.2511
(0.4202)
‐1.3207
(0.4789)
NSE C2 (UCR)
NSE C2 (ARI)
NSE C2 (PRO)
NSE C2 (PPP)
NSE C3 (UCR)
NSE C3 (ARI)
NSE C3 (PRO)
NSE C3 (PPP)
NSE E (UCR)
NSE E (ARI)
NSE E (PRO)
Individual Specific Variables (Multinomial Logit)
Ideological Distance
Model1
Handouts
NSE E (PPP)
Personal Network (UCR)
Personal Network (ARI)
Personal Network (PRO)
Personal Network (PPP)
Positive view of Redistribution (UCR)
Positive view of Redistribution (ARI)
Positive view of Redistribution (PRO)
Positive view of Redistribution (PPP)
Women (UCR)
Women (ARI)
Women (PRO)
Women (PPP)
Constant (UCR)
Constant (ARI)
Constant (PRO)
Log Lik
McFaden R2
‐1175.8
0.91385
‐1173.4
0.91402
‐1154.4
0.91542
Constant (PPP)
Model1
Handouts
Model2
Patronage
‐0.8491*
(0.4649)
‐1.5232***
(0.3883)
‐2.1103***
(0.4493)
‐2.3381***
(0.5316)
‐0.7939*
(0.4497)
‐2.2861***
(0.4328)
‐2.2532***
(0.4629)
‐2.6157***
(0.5179)
‐0.9632
(0.6408)
‐2.0554**
(0.8082)
‐2.846***
(1.0892)
‐1.8359**
(0.7157)
0.1999
(0.1277)
0.1773
(0.1445)
‐0.1536
(0.1708)
0.1535
(0.1949)
‐0.0549
(0.0344)
‐0.0784**
(0.0377)
‐0.0645
(0.0426)
‐0.0222
(0.0462)
0.0479
(0.2005)
‐0.4627**
(0.2156)
‐0.4484*
(0.2425)
0.0051
(0.2872)
‐2.4459***
(0.8128)
‐0.9116
(0.8451)
0.3713
(0.9692)
‐1.7215
(1.1534)
‐0.7736*
(0.4552)
‐1.4351***
(0.3909)
‐2.0102***
(0.4498)
‐2.3159***
(0.5221)
‐0.6792
(0.4380)
‐2.2652***
(0.4358)
‐2.1116***
(0.4577)
‐2.5713***
(0.5047)
‐0.7181
(0.6140)
‐2.0106**
(0.8095)
‐2.7533**
(1.0876)
‐1.8274***
(0.7064)
0.2142*
(0.1285)
0.1947
(0.1461)
‐0.1054
(0.1715)
0.1631
(0.1950)
‐0.0083
(0.0292)
‐0.1***
(0.0318)
‐0.0523
(0.0359)
0.0148
(0.0443)
0.0984
(0.2017)
‐0.4155*
(0.2180)
‐0.459*
(0.2423)
‐0.0214
(0.2898)
‐2.4835***
(0.8176)
‐0.7554
(0.8602)
0.4622
(0.9982)
‐1.8134
(1.1679)
Model3
Pork
‐0.5546
(0.4665)
‐1.374***
(0.3931)
‐1.9053***
(0.4563)
‐2.1202***
(0.5359)
‐0.5437
(0.4492)
‐2.1994***
(0.4363)
‐2.0446***
(0.4655)
‐2.2297***
(0.5136)
‐0.5404
(0.6317)
‐2.0673**
(0.8142)
‐2.7374**
(1.0883)
‐1.4773**
(0.7044)
0.2383*
(0.1300)
0.2315
(0.1463)
‐0.008
(0.1715)
0.2399
(0.1949)
0.0138
(0.0330)
‐0.1109***
(0.0343)
0.0051
(0.0414)
0.0213
(0.0490)
0.0787
(0.2027)
‐0.4538**
(0.2183)
‐0.459*
(0.2451)
0.0019
(0.2877)
‐2.9041***
(0.8507)
‐0.6108
(0.8722)
‐0.3025
(1.0207)
‐2.2419*
(1.1936) Note: Conditional/Multinomial Logit models estimated in R 2.11, using library MLOGIT. Grey label describe alternative specific variables. All other variables are respondent specific. Wald statistics used to assess statistical significance. Joint significance for interacted variables described in marginal effect plots of Figure 1. * p < 0.1, ** p < 0.05, *** p < 0.01. 39 Table 4: Ideological Distance, Network Proximity, and the Determinants of Vote Behavior in Chile, Multinomial Logit with Alternative Specific and Individual Specific Variables. Chile
Performance (Economy)
Network of Activists
Distribution of Goods
Alternative Specific Variables (Conditional Logit)
Network of Activists*Goods (Baseline, PS)
Network of Activists (DC)
Network of Activists (PPD)
Network of Activists(UDI)
Network of Activists(RN)
Distribution of Goods (DC)
Distribution of Goods (PPD)
Distribution of Goods (UDI)
Distribution of Goods (RN)
Activists*Goods(DC)
Activists*Goods(PPD)
Activists*Goods(UDI)
Activists*Goods(RN)
Individual Specific Variables (Multinomial Logit)
NSE 1 (DC)
NSE 1 (PPD)
NSE 1 (UDI)
NSE 1 (RN)
NSE C1 (DC)
NSE C1 (PPD)
NSE C1 (UDI)
NSE C1 (RN)
Log Lik
McFaden R2
Model2
Patronage
Model3
Pork
‐0.0404***
(0.0031)
1.0391***
(0.0561)
0.2793***
(0.0974)
0.0347
(0.0439)
0.0401
(0.0312)
0.1077
(0.1333)
‐0.3473**
(0.1632)
0.2259
(0.1532)
‐0.044
(0.1565)
0.1011**
(0.0441)
0.0538
(0.0475)
0.0535
(0.0476)
0.0184
(0.0456)
‐0.0124
(0.0388)
0.0456
(0.0477)
‐0.0648
(0.0428)
‐0.0682
(0.0452)
‐0.1586
(0.4334)
‐0.7005*
(0.3967)
‐0.4709
(0.4026)
‐0.4443
(0.3664)
0.4966
(0.4023)
‐0.3798
(0.3719)
‐0.1388
(0.3838)
‐0.2412
(0.3526)
‐0.0397***
(0.0031)
1.029***
(0.0563)
0.2206**
(0.1001)
0.1491***
(0.0438)
0.0569*
(0.0312)
0.1634
(0.1355)
‐0.1512
(0.1542)
0.2164
(0.1535)
‐0.0151
(0.1522)
0.0506
(0.0420)
0.0021
(0.0467)
0.0244
(0.0481)
‐0.0487
(0.0472)
‐0.0314
(0.0380)
‐0.0191
(0.0413)
‐0.0607
(0.0464)
‐0.0688
(0.0444)
‐0.1893
(0.4319)
‐0.7718*
(0.3968)
‐0.5111
(0.4070)
‐0.5091
(0.3684)
0.471
(0.4017)
‐0.4454
(0.3717)
‐0.1434
(0.3889)
‐0.2847
(0.3558)
‐0.0384***
(0.0030)
1.0104***
(0.0565)
0.1425
(0.1191)
0.1686***
(0.0343)
0.0524**
(0.0248)
0.3378**
(0.1661)
0.0643
(0.1806)
0.3173*
(0.1795)
0.1637
(0.1846)
0.0274
(0.0380)
‐0.0212
(0.0397)
‐0.0009
(0.0412)
‐0.0288
(0.0395)
‐0.0583*
(0.0334)
‐0.0554
(0.0351)
‐0.0597
(0.0377)
‐0.0858**
(0.0383)
‐0.0914
(0.4352)
‐0.6876*
(0.3991)
‐0.4529
(0.4050)
‐0.4014
(0.3677)
0.5215
(0.4046)
‐0.4055
(0.3730)
‐0.0617
(0.3871)
‐0.1882
(0.3552)
‐1803.6
0.85851
‐1798.7
0.85889
‐1789.8
0.85958
Model1
Handouts
Model2
Patronage
Model3
Pork
0.4695
0.4179
0.4752
(0.3932)
(0.3915)
(0.3946)
‐0.1738
‐0.2824
‐0.239
NSE C2 (PPD)
(0.3552)
(0.3540)
(0.3555)
‐0.0602
‐0.1076
‐0.0254
NSE C2 (UDI)
(0.3686)
(0.3723)
(0.3710)
‐0.3591
‐0.4531
‐0.3648
NSE C2 (RN)
(0.3385)
(0.3397)
(0.3397)
Personal Network 0.0042
‐0.0066
‐0.0024
(DC)
(0.1084)
(0.1093)
(0.1101)
0.0644
0.0705
0.0663
Personal Network (PPD)
(0.1134)
(0.1134)
(0.1147)
0.157
0.1594
0.1469
Personal Network (UDI)
(0.1201)
(0.1204)
(0.1218)
Personal Network 0.3102*** 0.3177*** 0.3154***
(RN)
(0.1138)
(0.1142)
(0.1149)
Positive view of 0.002
0.0336
0.0325
Redistribution (DC)
(0.0304)
(0.0267)
(0.0264)
Positive view of ‐0.0621*
0.024
0.0414
Redistribution (PPD)
(0.0338)
(0.0277)
(0.0272)
‐0.0342
‐0.0054
0.016
Positive view of Redistribution (UDI)
(0.0352)
(0.0297)
(0.0288)
‐0.0629*
‐0.0056
‐0.004
Positive view of Redistribution (RN)
(0.0343)
(0.0281)
(0.0273)
0.0713
0.0688
0.1135
Women (DC)
(0.1740)
(0.1744)
(0.1752)
‐0.0326
‐0.0792
‐0.0464
Women (PPD)
(0.1802)
(0.1801)
(0.1809)
0.481** 0.4935** 0.4657**
Women (UDI)
(0.1930)
(0.1939)
(0.1939)
0.1634
0.1406
0.1178
Women (RN)
(0.1809)
(0.1813)
(0.1815)
‐1.0917
‐1.024
‐1.0875
Constant (DC)
(0.6927)
(0.6944)
(0.7026)
‐0.4762
‐0.5654
‐0.6166
Constant (PPD)
(0.7044)
(0.7038)
(0.7093)
‐1.1444
‐1.1217
‐1.1416
Constant (UDI)
(0.7464)
(0.7477)
(0.7486)
‐1.2586* ‐1.1974*
‐1.248*
Constant (RN)
(0.7068)
(0.7043)
(0.7073)
NSE C2 (DC)
Individual Specific Variables (Multinomial Logit)
Ideological Distance
Model1
Handouts
Note: Conditional/Multinomial Logit models estimated in R 2.11, using library MLOGIT. Grey label describe alternative specific variables. All other variables are respondent specific. Wald statistics used to assess statistical significance. Joint significance for interacted variables described in marginal effect plots of Figure 1. * p < 0.1, ** p < 0.05, *** p < 0.01.
40 -0.2
Chile, Handouts
Argentina, Patronage
Chile, Patronage
Argentina, Pork
Chile, Pork
-0.2
0.0
0.2
0.4 -0.4
-0.2
0.0
0.2
0.4 -0.4
Argentina, Handouts
-0.4
Marginal Effect of Tactical Redistribution on Party Vote
0.0
0.2
0.4
Figure 1: Effect of Targeted distribution on Party Vote, All Parties, Argentina and Chile. Solid Line describes marginal change in log‐odds ratio of party vote, by type of good and conditional on proximity to the party network of Activists. -2
-1
0
1
2
-2
-1
0
1
2
Proximity to the Network of Activists
Note: Estimates from Table 3, with 90% confidence intervals in dotted grey lines. Simulated confidence intervals extracted from the Var‐Cov matrix as described in Brambor, Clark, and Golder (2006). 41 0.0 0.2 0.4 -0.4
0.0 0.2 0.4 -0.4
0.0 0.2 0.4 -0.4
-0.4
Marginal Effect of Tactical Redistribution on Party Vote
0.0 0.2 0.4
Figure 2: Effect of Targeted distribution on Party Vote, Argentina 2007. Solid Line describes marginal change in log‐odds ratio of party vote, by type of good and conditional on proximity to the party network of Activists. -2
PJ, Handouts
PJ, Patronage
PJ, Pork
UCR, Handouts
UCR, Patronage
UCR, Pork
ARI, Handouts
ARI, Patronage
ARI, Pork
PRO, Handouts
PRO, Patronage
PRO, Pork
-1
0
1
2
-2
-1
0
1
2
-2
-1
0
1
2
Proximity to the Network of Activists
Note: Estimates from Table 3, with 90% confidence intervals in dotted grey lines. Simulated confidence intervals extracted from the Var‐Cov matrix as described in Brambor, Clark, and Golder (2006). 42 0.0 0.2 0.4 -0.4
0.0 0.2 0.4 -0.4
0.0 0.2 0.4 -0.4
-0.4
Marginal Effect of Tactical Redistribution on Party Vote
0.0 0.2 0.4
Figure 3: Effect of Targeted distribution on Party Vote, Chile 2007. Solid Line describes marginal changes in log‐odds ratio of party vote, by type of good and conditional on proximity to the party network of Activists. -2
PS, Handouts
PS, Patronage
PS, Pork
DC, Handouts
DC, Patronage
DC, Pork
UDI, Handouts
UDI, Patronage
UDI, Pork
RN, Handouts
RN, Patronage
RN, Pork
-1
0
1
2
-2
-1
0
1
2
-2
-1
0
1
2
Proximity to the Network of Activists
Note: Estimates from Table 3, with 90% confidence intervals in dotted grey lines. Simulated confidence intervals extracted from the Var‐Cov matrix as described in Brambor, Clark, and Golder (2006). 43 Figure 4: Changes in the Equilibrium Location of Parties in Chile. AMG Iterative Algorithm with Spatial and Non‐Spatial Terms. Nash Equilibria and Targeted Distribution, Chile
0.30
0.4
Nash Equilibria when only the PS has Network Support
0.25
0.3
Nash with Targeted Goods
Nash Baseline
PS
Nash with Targeted Goods
Nash Baseline
PS
PS
RN UDI
UDI
PPD
UDI
PPD
0.20
0.15
UDI
RN
DC
DC
RN
DC
PPD
PPD
0.0
0.00
0.05
0.10
0.2
PS
Percent Party Vote
RN
0.1
Percent Party Vote
DC
4.5
5.0
5.5
4.5
6.0
5.0
Ideology
Ideology
5.5
6.0
Note: Equilibrium location of Parties estimated using AMG iterated algorithm, with Conditional Logit Parameters from Table 4. Left plot describes the change from a Baseline Nash equilibrium (no targeted distribution) to equilibrium locations when a single party (the PS) has positive gains from distribution networks. Left plot describes changes in the equilibrium location of all parties from a baseline with no targeted distribution (dotted lines) to the location of parties when distribution networks are set to their estimated values in Table 4 (solid lines). 44