Party Activists: Brokers Prioritize Their Party’s Victory

Consider a party comprised of n brokers. Each of these brokers produces votes from a distinct group of voters for her party by expending effort. If a broker does not contribute effort, then voters in the broker’s group will either not vote or vote for the opposing party. Each broker i has a vote production function of:

where L _i ∈[0,1] is the effort expended by the broker, θ∈[0,1] measures brokers’ skill level, and p∈[0,1] is the proportion of the total electorate that could be organized by a party’s current brokers. Normalizing the total labor that a broker can expend to 1, makes the product of broker’s effort and skill level equal to the share of her constituency that she produces for her party. For example if L _i =0.5 and θ=0.5, then the broker will turn out 25 percent of her constituency to vote for her party.

The total vote share that is produced by a machine’s brokers is the sum of the vote shares that each broker earns from her constituency. Formally, this is:

From Equation 2 we see, for example, that if p=0.7, then the brokers’ party would earn a vote share of 70 percent if all of the brokers contributed maximum effort and had a maximum skill level.

First, consider the case when brokers are party activists and only care about their party’s victory and their own time. The utility function for brokers is then defined as a Cobb–Douglas function:

where α∈[0,1] measures the weight that a broker places on her political goals relative to other interests.

Each broker independently chooses a level of effort that maximizes her utility. This level of effort is defined by the maximization problem:

Using the first order condition and using the fact that each broker contributes the same amount of labor, we can solve for L _i in terms of α and n:Footnote ¹²

The machine’s vote share is:

When brokers participate in politics only to help their party win elections, they face a collective action problem. Equation 5 indicates that each broker’s effort declines as the number of brokers increases. Equation 6 shows that a machine suffers when these brokers organize constituencies that are small relative to the size of the electorate. It shows that a machine’s vote share decreases as the number of brokers required to organize a fixed share of the electorate increases. The basic problem facing these brokers is that every broker’s effort enhances a party’s victory, and so each activist broker is inclined to free-ride on the efforts of others.

The model indicates that this collective action problem becomes especially pressing when brokers organize small groups of voters relative to the total number of voters in the electorate, which forces a party to depend upon many brokers to win elections. This is problematic for machines since the activities that brokers undertake to make clientelist exchange work are extremely time-intensive and prevent brokers from organizing large groups.

Careerists: Brokers Prioritize Their Own Power

Now consider a broker who simply wants to advance her own power and career by expanding her local following. A broker’s vote production function is still defined by Equation 1 and the machine’s vote share is defined by Equation 2. Yet now the broker is only concerned with the voters that she can produce from her own constituency. Thus, her utility function is:

Solving for the first order condition of this maximization problem defines broker i’s labor contribution as:

Now a broker’s effort is entirely determined by α, which measures how much a broker values her political power and career relative to leisure or non-political objectives. Unlike the previous case, a broker’s effort is not affected by the number of brokers that the machine employs. When brokers prioritize their own power, they do not suffer from a collective action problem.

The machine’s vote share is now defined as:

Comparing Equations 6 and 9, we can see that generally a machine produces fewer votes when it is comprised of brokers who prioritize the machine’s vote share rather than their own power. These two machines win the same number of votes only if one of four conditions holds: brokers derive no utility from non-political activities, so α=1; brokers derive no utility from political activities, so α=0; the machine only consists of one broker, so n=1; or the machine does not organize any voters, so p=0. Moreover, the difference between Equations 6 and 9 increases as n increases.Footnote ¹³ This indicates that a machine comprised of party activists would dramatically underperform one comprised of careerists when brokers organize small groups relative to the electorate, a situation commonly documented by scholars, and supported in the empirical portion of this article.

Figure 1 illustrates the advantages that parties derive by relying on careerist brokers. This figure allows us to consider the counterfactual of what happens to a party’s vote share when the party must rely on brokers who organize small groups of voters. The figure shows a party’s vote share as a function of the number of brokers that are required to organize 75 percent of the electorate.Footnote ¹⁴ If brokers are limited by their economies of scale and, thus, end up organizing small groups of voters, then a party will be forced to rely on a large number of brokers to organize a fixed share of the electorate. A party’s vote share declines dramatically with an increase in the number of brokers, when it is comprised of activists. When a party is comprised of careerists, its vote share is unaffected. Empirically, brokers organize small groups of voters, which means that political machines are often comprised of thousands of intermediaries. Under these circumstances, this figure indicates that the collective action problem facing activists could potentially decimate the machine’s vote share.

Fig. 1

A machine’s vote share as a function of the number of brokers required to organize 75 percent of the electorate

Mixed Motivations

By making extreme assumptions about a broker’s motivation, I showed that brokers who only pursue their party’s victory would experience a collective action problem, and that this collective action problem is completely resolved when brokers pursue careerist objectives. Yet, brokers may value both their party’s victory, and their own power and career. After all, a broker’s power and career might be partially contingent upon her party’s victory. For example, a broker could experience a cost associated with abandoning her party when it loses an election. Even if this is the case, a slightly more complicated model still shows that the individual pursuit of one’s power and career softens any collective action problem that brokers might face. Thus, the model anticipates that party leaders will utilize individual career motivations to extract effort from brokers.Footnote ¹⁵

Broker Exit and Bargaining Strength

To win elections, political machines often need to reach voters who are not directly organized by brokers who work for the machine. Stokes et al. present extensive evidence that shows a minority of voters receive goods or help from brokers.Footnote ¹⁶ These authors find that as little as 5 percent of voters in Argentina receive goods from party operatives, and that party operatives distribute goods in 30 percent of the voters’ neighborhoods. They also find that 20 percent of voters in eighteen sub-Saharan African countries and 7 percent of voters in seventeen Latin American countries reported that they were offered a gift or were pressured by someone to vote a certain way. Using a list experiment that was designed to elicit honest responses, Gonzalez-Ocantos et al. find that 24 percent of registered voters in Nicaragua were offered a gift or service in exchange for their vote.Footnote ¹⁷ Together these results indicate that resources distributed through brokers play a significant role in many electoral democracies, but that parties must also reach voters who do not have relationships with their current brokers.

If party leaders have surplus resources, they may want to use the resources to reach voters who are not organized by the machine’s brokers. Party leaders could use surplus resources in two ways to attract these voters. First, they could begin building party organizations in districts where the party does not currently have brokers, which could consist of finding neighborhood leaders and training them to organize voters. Alternatively, they could dedicate the resources to media and alternative outreach that attracts voters through non-clientelist strategies, such as emphasizing policy positions. This latter strategy may become increasingly important as voters become wealthier and are less persuaded by resources that address basic needs.Footnote ¹⁸

To capture the idea that party leaders want to use surplus resources to expand the reach of their machine, I redefine the party’s vote share so that it accounts for voters who are organized by brokers and voters who are not. The party’s vote share and the party leader’s utility function can be written as:

where p∈[0,1] is still the percentage of voters in the electorate who are organized by the machine’s brokers. The share of resources that a party leader uses to reach voters who are not organized by the machine’s brokers is r _pl ∈[0,1]. The party’s vote share that it earns from constituencies that are currently not organized by the machine’s brokers is π _pl (r _pl )=σr _pl ; the party’s vote share that it earns from constituencies that are organized by the machine’s brokers is $$\pi _{b} (L)=\mathop{\sum}\nolimits_{i=1}^n {{1 \over n}\theta L_{i} } $$ . A measure of the inefficiencies associated with using resources to capture votes from voters who are not organized by the machine is provided by σ∈[0,1]. The value of σ would likely be a function of the effectiveness of media strategies or the ease of building party organizations in neighborhoods that lack them.

The problem facing political machines is that brokers who focus on developing their local following as a means to develop their own power and career have an incentive to maximize the resources that they can procure from a party. Brokers who secure large amounts of resources for their followers gain reputations of effectively looking after their voters.Footnote ¹⁹ Careerist brokers may also consume these resources privately, or hoard them to expand their following further in future periods. Below, I provide substantial empirical evidence that many brokers prioritize the resources that they can procure from their party. In sharp contrast to careerist brokers, I assume that party activist brokers want party leaders to have full discretion over resources as long as the party leaders use the resources to maximize the party’s vote share.

Now a careerist broker has a utility function of:

where r _i is the value of the resources that broker i receives. I normalize the total amount of surplus resources as equal to 1, and so $r_{{pl}} {\plus}\mathop{\sum}\nolimits_{i=1}^n {r_{i} =1} $ .

To capture the potential costs of careerist brokers, I provide brokers with an exit option and add a second stage to the model. In the first stage of this framework, brokers still choose their effort level to maximize their utility, and they undertake this effort to build their local following. In the background, I am assuming that the party’s current brokers already have sufficient resources to organize their own constituency.

But now, during the second stage, each broker and the party leader bargain over surplus resources that could be used to attract voters who are not currently organized by brokers. A party leader bargains to retain full discretion over the resources, which she will use to expand the reach of the machine.Footnote ²⁰ In contrast, careerist brokers want the resources to develop their own careers and, therefore, bargain for as many resources as possible. I employ a subgame perfect Nash equilibrium concept; I assume that brokers recognize that the resources each will secure are endogenous to the second-stage bargaining process and choose their labor accordingly.

If the party leader and each broker can come to an agreement over the distribution of resources, then each broker delivers her voters to the party. Therefore, the party leader gets $r_{{pl}}^{{\asterisk}} $ resources and all of the votes that the brokers produce, and each broker receives $r_{i}^{{\asterisk}} $ resources. In contrast, if a party leader cannot come to an agreement with a broker, then the party leader receives all of her share of the resources $r_{{pl}}^{{\asterisk}} $ , and the resources that would have been distributed to the broker in equilibrium, $r_{i}^{{\asterisk}} $ . But the broker withholds her votes, and so the party leader only earns a vote share of $\prod(r_{{pl}}^{{\asterisk}} {\plus}r_{i}^{{\asterisk}} ,L){\minus}{p \over n}\theta L_{i} $ .

If the broker and party leader cannot come to an agreement, I assume that the broker delivers her voters to a competing party. For tractability, I assume that the competing party simply provides the broker with resources that value $$\rho {p \over n}\theta L_{i}$$ . Thus, the resources that a broker could earn from an outside option are endogenous to the votes that the broker can produce multiplied by ρ, which measures the resources available to the opposing party.

There is an intuitive and substantive interpretation for ρ. For example, a broker who works for the governor’s party in the Argentine province of San Luis would have a low-valued ρ. The Saá family has dominated San Luis’s provincial government since Argentina democratized in 1983.Footnote ²¹ So if a broker abandons the governor’s party in this province she will be hard-pressed to leverage her local following to build a political career and advance her power. In situations where parties are more competitive, like in the Argentine province of Córdoba or in late nineteenth-century Britain, ρ is likely to be higher. In these cases, a broker can leave the incumbent party, and still use her local following to build a career. Finally, a broker working for an opposition party in a province like San Luis should have an extremely high-valued ρ. This broker’s local following might be more valuable to her if she abandons her current party and works for the governor’s party.

Given a set of parameter values, an equilibrium in this model constitutes a vector of $L_{i}^{{\asterisk}} $ for each broker i, a vector of $r_{i}^{{\asterisk}} $ for each broker i, and $r_{{pl}}^{{\asterisk}} $ , which solve the following maximization problems. Since all of the brokers are engaged in the same bargaining and maximization problems, the equilibrium vectors can be solved using a system of equations that consists of three first order equations for the bargaining problem and one first order equation for the effort maximization problem.

Equation 13 represents the solution to the Nash bargaining problem that occurs in the second stage, where β measures the bargaining ability of the party leader relative to each broker.Footnote ²²

Solving the system of equations yields the following interior equilibrium outcomes.

These results capture a fundamental dilemma for party leaders. Party leaders need effective brokers to make clientelism work, and one way that party leaders cultivate effective brokers is by rewarding those brokers who build large local followings with career opportunities and political power. Yet, careerist brokers who build large local followings can then use the threat of leaving the party with their local following to extract additional resources from the party leader. This forces a party leader to over-invest in conserving her organization and the voters organized through it, rather than using resources to attract new voters and expand the reach of her machine.Footnote ²³

This dilemma is captured by the model, and the model indicates that parties with relatively less resources will be even more adversely affected by this dilemma. Party leaders earn more votes through their machine as brokers contribute more effort, an increase in α,Footnote ²⁴ or as brokers become more skilled, an increase in θ. Yet, a broker earns more resources and a party leader earns less resources with an increase in α or an increase in θ. This dilemma becomes even more severe when competing parties can offer brokers valuable exit options, since a broker also earns more resources and a party leader earns less resources with an increase in ρ.

Party leaders benefit when brokers undertake more effort by earning a higher vote share from the constituencies organized by brokers, but are harmed by earning a lower vote share from the constituencies not organized by brokers. In the bargaining environment, an increase in α or in θ increases a party leader’s total vote share when $\rho \,\lt {1 \over {(1{\minus}p)\sigma }}$ . But when $$\rho \,\gt {1 \over {(1{\minus}p)\sigma }},$$ an increase in α or in θ decreases the party’s total vote share. When brokers have valuable exit options, effective-careerist brokers harm the party. Also effective brokers are more likely to harm the party when the party’s machine does not organize many voters, p is small, or when strategies of reaching voters outside of the machine are effective, σ is large. Moreover, the party leader’s utility declines at a faster rate with a decline in ρ when the value of α or θ is larger. Finally, if ρ is sufficiently large brokers cannot benefit by bargaining with their party leaders, and they will exit the party en masse.

Generally, then the model indicates that opposition parties incur higher costs from bargaining than competing incumbent parties, which can offer brokers valuable exit options. These opposition parties may have incentives to devise alternative mobilizing strategies that rely on fewer brokers, who are activist rather than careerist brokers. Thus, the model also provides insights into why ideological parties that rely less on clientelist exchange or even organize against this form of distributive politics may rise up against political machines. For example, the progressive movement in the early twentieth century in the United States, the PAN and PRD in Mexico, and third parties in Argentina, all relied heavily on non-clientelist electoral strategies, especially when they experienced resource disadvantages.Footnote ²⁵

Extracting Broker Effort

The survey indicates that brokers organize small groups relative to the electorate, which would make the collective action problem a challenge for activist brokers. The average number of brokers, who worked for a city councilor, ranged from about twenty-three brokers per councilor in Buenos Aires to fourteen brokers per councilor in Córdoba and San Luis.Footnote ²⁸ A majority of brokers in every province reported that they can individually transport less than 200 voters to a rally. A broker’s ability to turn people out for political rallies is one of the primary metrics that party leaders use when evaluating a broker’s capacity and effort.Footnote ²⁹ Municipal electoral data from seven provinces and 1,154 municipalities in Argentina show that the average number of voters who participated in a municipal election is 11,088.Footnote ³⁰ Even a councilor who employs a team of brokers and controls 300 voters would control only 2.71 percent of the votes in an average municipality, and would control much less in the larger municipalities.

Since party leaders must depend upon many brokers who organize small groups relative to the electorate, the model predicts that careerist brokers will outperform activist brokers. To test this prediction, I test whether careerist brokers report a larger capacity to mobilize voters for political rallies. In addition to providing a measure of a broker’s capacity, mobilizing for rallies also contributes to a party’s electoral success. Over 95 percent of the brokers believed that at least half of all the voters who turned out to political rally would vote for the broker’s candidate. Over 70 percent of the brokers believed that at least 70 percent of all the voters who turned out to political rally would vote for the broker’s candidate.

The brokers’ survey provides a measure of a broker’s mobilizing capacity: the number of voters a broker can turn out for a rally. The survey asks respondents: Speaking of political rallies, approximately how many people would you say you can transport: fewer than 50 people, between 50 and 100 people, between 100 and 200 people, between 200 and 300 people or more than 300 people? The variable, termed Rally Capacity, is an interval variable; its values range from 1 to 5.

Although this measure might suffer from a social desirability bias as brokers overestimate their capacity, careerist brokers should not be any more likely to overestimate their capacity than party activist brokers. For careerist brokers, their capacity to mobilize voters for a rally enhances their career and power. For party activist brokers, their capacity to mobilize voters enhances their contribution to their party’s victory. Moreover, this measure is one of the most systematic measures of brokers’ mobilizational capacities ever collected for such a large sample.

The simplest way to distinguish between careerist and activist brokers would be to directly ask the brokers whether they work in politics to empower themselves or to help their party win elections. Unfortunately, such a direct question would not be likely to elicit honest responses. Careerist brokers may believe that expressing strong support for their current party is a good strategy for advancing their career. The survey provides three less direct measures that indicate a broker is prioritizing her own power and career.

The first measure is a variable entitled Career Ambition that distinguishes brokers who express an ambition to advance their own careers within a party. This dummy variable is coded as 1 if a respondent expressed a preference to run for a powerful office even if she was not certain of winning. The variable is coded as 0 if the respondent expressed a preference to run for a less powerful office that she was certain of winning. By assigning more risk to the more powerful office, this question creates a cost for the higher office and prevents all brokers from taking the default answer of preferring the more powerful office. Thus, Career Ambition distinguishes the brokers who place a stronger priority on advancing their own power and political career, and are even willing to pay the cost of more risk to advance their careers.

A variable entitled Career Reward distinguishes brokers who are paid for their political activities. This is a dummy variable that is coded as 1 if the broker reported that she is paid for her political work. It is coded as 0 if the broker reports that she has never been paid for her political work. This measure identifies brokers who have achieved private gain from their political career.

Finally, a variable entitled Competitive distinguishes between brokers who view competition from brokers within their own party as presenting more risk than brokers who work for opposing parties. In the survey, brokers were asked to imagine two brokers compete for support from voters who live in the same neighborhood. This variable is a dummy variable that is coded as 1 if brokers reported that competition from a broker who works for the same party presents a risker situation than a broker from a competing party. The variable is coded as 0 if the broker responded that a broker from a competing party presents more risk. Careerist brokers should view brokers within their own party as the primary source of risk since they are competing with these individuals for career opportunities, resources, and power. Party activists should view brokers from the opposing party as the primary source of risk, since brokers from the opposing party can cause an electoral loss for the respondent’s party.

I evaluate the model with six linear regression models, three of which contain control variables that are defined in the appendix. All three independent variables should have a positive and significant effect on Rally Capacity. A positive and significant relationship effect for each of these variables would indicate that brokers who are seeking or have achieved private career objectives have a greater mobilizing capacity.

Table 1 reports the results of the linear regression models. Brokers who seek or have achieved private career objectives have a larger mobilizing capacity. Competitive has the weakest effect on Rally Capacity, but in the model with controls the coefficient increases in magnitude and becomes significant. These results indicate that party leaders can use the career objectives of brokers to extract effort.

Table 1

Broker Motivation and Rally Turnout

Notes: Significance levels: †10%, *5%, **1%, ††Includes controls. The standard errors are adjusted to account for the design effects of the survey sampling method. Controls include: Provinces, Municipalities, Parties, Age, Councilor Dummy, Gender, Income, Education, Survey Version.

I now test two counterarguments to the model. The most direct counterargument is that brokers who are particularly loyal to a party will exert more effort than other brokers to help their party win elections. A less obvious counterargument is that brokers who reside in swing neighborhoods will not experience a collective action problem and will contribute more effort since the brokers believe their effort will have a larger effect on their party’s victory. Objectively, brokers who live in swing neighborhoods will not have a more meaningful effect on their party’s probability of victory, since municipal elections utilize closed-list proportional representation systems, which do not correspond to neighborhoods. But inter-party competition should be more visceral for these brokers, and they may believe that their efforts are more likely to have an effect on an electoral outcome. Importantly, finding that brokers undertake more effort when they live in competitive neighborhoods would indicate that inter-party competition can motivate brokers, which challenges my theory.

To identify brokers who are loyal to their party, I develop a variable titled No Exit. This dummy variable assumes the value of 1 if the broker has never abandoned her party in her career, and assumes the value of 0 if the broker has abandoned her party at least once in her career. Brokers who have not abandoned their party should on average be more committed to their party’s victory.Footnote ³¹

To measure the inter-party political competition that a broker faces, I develop a variable entitled Core. In the survey, each respondent was asked how many brokers work in her neighborhood. The respondent was then asked how many brokers from her party work in her neighborhood. Core is defined as the absolute value of:

If more than half of the brokers in a neighborhood share the respondent’s party, then Core increases with an increase in the percentage of the respondent’s co-partisans. If less than half of the brokers share the respondent’s party, then Core increases with a decrease in the percentage of the respondent’s co-partisans. If Core has a negative and significant effect on Rally Capacity, this would indicate that brokers contribute more effort when their neighborhood is highly contested with inter-party competition.

Table 2 reports the results of four linear regression models, in which I regress No Exit and Core on Rally Capacity. The results indicate that party activist brokers do not have a greater mobilizing capacity than other brokers. Together Tables 1 and 2 indicate that private motivations can be utilized to extract effort from political brokers, but a stronger commitment to a political party or high levels of inter-party competition does not necessarily cause a broker to undertake more effort.

Table 2

Broker Partisan Loyalty and Rally Turnout

Notes: None of these values are significant. ††Includes controls. The standard errors are adjusted to account for the design effects of the survey sampling method. Controls include: Province, Municipality, Party, Age, Councilor Dummy, Gender, Income, Education.

Broker Exit and Withdrawal of Support

Although careerist brokers exert substantial effort for their parties, they also make parties vulnerable to bargaining problems and broker exit. Unlike all other theories that model political machines as multi-actor organizations, this theory emphasizes the effect of a broker’s exit options by assuming that a broker can withdraw a proportion of the voters in her constituency from her party. In doing so, this approach identifies a key organizational vulnerability in political machines, and a reason why party leaders may pursue alternative forms of distributive politics and electoral strategies.

The survey data provide substantial evidence of broker exit. Across all four provinces 23.65 percent of the brokers abandoned a party at least once in their career.Footnote ³² In the province with the highest rate of broker exit, Missions, 48.41 percent of the brokers abandoned at least one party. In the province with the lowest rate, San Luis, 19.74 percent of the brokers abandoned at least one party.Footnote ³³ Moreover, 57.75 percent of the brokers indicated that a broker would either stop organizing, or abandon his current party boss or party, if his party boss transferred resources from him and awarded the resources to another party broker to increase vote share.Footnote ³⁴ Clearly the threat of exit is credible, and the high rates of exit indicate that brokers are not entirely bound by partisan attachment. Moreover, scholars have observed similar rates of exit by party actors in other settings where clientelism is pervasive such as Brazil.Footnote ³⁵ Still, the model also indicates that the value of exit options will affect a broker’s bargaining power, and that the threat of broker exit will force parties to over-invest in voters who are already captured by the machine’s brokers.

Overinvestment in Effective Brokers

The theory predicts that effective brokers who mobilize many voters can use the threat of exit to encourage party leaders to over-invest in their constituencies. This prediction identifies an important vulnerability in political machines that has been overlooked by most of the formal literature on clientelism. Most theories assume parties can distribute resources in any manner that best serves the party’s electoral goal. Even those models that consider multi-actor parties do not model intra-party bargaining dynamics and do not predict that effective brokers will capture resources that could be better invested elsewhere.Footnote ³⁶

The respondents were asked to:

The experimental design of this question allows me to test whether effective brokers are more able to procure resources, even if doing so adversely affects their party. Half of the respondents were randomly assigned a treatment that asked them to consider a broker who had many voters and the other half were randomly assigned a treatment that asked them to consider a broker who had few voters. An effective broker should have many voters opposed to few voters. In terms of the model, the size of broker’s local following and the broker’s share of resources increases with effort, α, and skill, θ. If the respondents say that it is just as easy to take resources away from a broker with a large number of voters as it is from a broker with few voters, then the prediction from my model is incorrect.

My theory further anticipates that a broker with many voters all of whom are loyal should have more bargaining strength than brokers who have few voters. Yet my theory does not anticipate that a broker with many voters of whom very few are loyal will have more bargaining strength than a broker who has few voters. In this case, a broker may have many voters, but she would be less able to withdraw them to punish a party leader. In the survey, half of the respondents were asked to consider a broker with voters who are all loyal and half were asked to consider a broker who has only a few loyal voters. Below I use the interaction between loyalty and the number of voters that a broker has to test whether a broker should cultivate many voters, who are also loyal, to procure more resources.

To test the predictions, I perform three difference-in-means tests. The dependent variable consists of the five answers very easy, …,very difficult measured on an interval scale of 1 to 5. In the first test, I subtract the mean response of those who were asked to consider a broker who has few voters from the mean response from those who were asked to consider a broker who has many voters. In the subsequent tests, I make the same calculation, but I divide the respondents into those who were asked to consider a broker all of whose voters were loyal, and those who were asked to consider a broker who had only a few loyal voters. Table 3 summarizes the Sample Average Treatment Effects (SATE) by reporting the 95 percent and 90 percent confidence intervals for the difference in mean responses between the various treatments.

Table 3

Confidence Intervals for the Difference in Means of Difficulty between Treatments

The tests support the prediction that an effective broker is able to procure more resources, even when doing so adversely affects her party’s vote share. Brokers who have many voters are able to procure more resources than brokers who have few voters. Moreover, it is more difficult to take resources from a broker who has many voters than from a broker who has few voters only if these voters are loyal to the broker. When voters are not loyal to a broker, the effect of the broker’s group size is no longer significant.

During my fieldwork, many brokers articulated dynamics that support this result. For example, one broker argued:

Overinvestment in Captured Neighborhoods

If parties are forced to award resources to brokers who have large followings to prevent defection, then parties will be forced to overinvest in constituencies that already provide strong support for the party. Effective brokers earn more resources from a party by making voters more responsive to resources, but they also have more bargaining power, which causes party leaders to give them more resources than they would without intra-party bargaining. To test this prediction, I present evidence that shows a considerable discrepancy between the distributive strategies that party members think parties should use and the strategies that are actually used. The respondents were given the following scenario:

The follow-up question asked respondents ‘Which of the brokers do you think will be more successful in obtaining resources for their neighborhood?’Footnote ³⁸

The model predicts that effective brokers force parties to over-invest in neighborhoods that are already captured by the party. If this prediction is correct, then more respondents should report that brokers in captured neighborhoods will be more successful in obtaining resources, than those who say it would be ideal to invest in captured neighborhoods. To test this prediction I perform a difference-in-means test. The respondent’s answer to the first question is coded as 1 if the respondent prefers that her party distributes resources to the captured neighborhood, and 0 otherwise. The respondent’s second answer is coded as 1 if the respondent believes that the broker working in the captured neighborhood is more likely to be successful in obtaining resources, and 0 otherwise. I then calculate a difference between the mean response for each question by subtracting the mean response of the second question, the respondent’s belief about their party’s actual distribution, from the mean response to the first question, the respondent’s ideal distribution.

Table 4 shows that the difference in means is significantly less than 0, which indicates that a larger number of brokers believe that brokers working in captured neighborhoods will be successful in obtaining resources than the number of brokers who would prefer that these brokers actually receive the resources. While an estimated 54.1 percent of brokers believed a broker in a captured neighborhood will be more successful in obtaining resources, only 25.2 percent of brokers preferred that their party prioritize the captured neighborhoods. The finding indicates substantial overinvestment in neighborhoods that already support the party.

Table 4

Confidence Intervals for the Difference in Means of Difficulty between Ideal and Actual Distribution

Notes: The confidence intervals use Linearized Standard Errors to account for the sampling method.

This finding stands in contrast to other multi-actor theories of political machines. For example, Stokes et al. predict that parties target swing groups, while brokers target core voters.Footnote ³⁹ In particular, Stokes et al. predict that parties will target ‘ideologically “mobile” groups,’ that have many voters who could be won over by a small increase in resources distributed to this group.Footnote ⁴⁰ Without explicitly attributing bargaining power to brokers, these authors predict that parties, seeking to maximize the probability of victory, will distribute resources to constituencies with maximum efficiency. Although brokers seem to share these authors’ views about the efficient distributive strategy of a party, brokers depart from this theory’s prediction when considering how a party actually distributes resources.

This finding is also inconsistent with a theory of party building that predicts that parties should invest in captured neighborhoods to take advantage of the efficient vote-getting operations in these neighborhoods. In contrast to a theory of party building, a large majority of brokers agree with theories that predict parties should target swing neighborhoods as the most efficient strategy. Yet in spite of preferences regarding an ideal distribution, brokers believe parties end up investing in captured neighborhoods, which is consistent with a theory of intra-party bargaining.