Representation in American Cities: Who Runs for Mayor and Who Wins?

Abstract

Business leaders emerge as key players in canonical accounts of urban politics, but data limitations have hampered efforts to quantify their role in city politics. Drawing on an original dataset that includes gender, race, occupational, and political experience for over 3,500 mayoral candidates from 259 cities over fifty years, I document who runs for office and who serves as mayor, with a focus on candidates who are business owners and executives. Overall, the data indicate that mayors tend to be White and male with prior political experience and white-collar careers. Business owners and executives account for nearly one-third of the candidates in the sample, but I find no indication that they win elections at higher rates than other candidates overall. However, my results do suggest that business owners and executives have better electoral prospects in more conservative cities, especially those that hold nonpartisan elections.

Keywords

representation mayors local elections

HELP WANTED: CEO for financially distressed 146 year-old limited partnership drowning in long term debt and enough past due bills to choke a goat. Successful applicant will be responsible for managing the needs, wants, safety and endless complaints of 74,000 customers while juggling chronic deficits, anemic revenues, suffocating union contracts and crippling legacy costs using a business model that hasn’t evolved since the advent of indoor plumbing. ANNUAL SALARY: $50,000. Seriously. That is not a typo (Kelly 2013)

—excerpt from op-ed in the Scranton Times-Tribune, January 13, 2013.

As the end of his third term approached, Scranton (Pennyslyvania) mayor, Chris Doherty, announced he would not seek a fourth term. In his comments, Mayor Doherty emphasized his major accomplishments, namely the revitalization of the city’s parks and downtown and a reduction in crime (Krawczeniuk 2013). However, local media coverage also included speculation that failed policy initiatives to control spending precipitated property tax increases which undermined the mayor’s reelection prospects. Days after the mayor’s announcement, the Scranton Times-Tribune published an op-ed advocating higher pay for the mayor, reasoning that in light of the demands and challenges of the job, the best option for attracting a qualified, capable candidate was to offer a higher salary and the potential for performance bonuses (Kelly 2013). The op-ed included a mockup of a classified advertisement characterizing the city as a struggling business in need of a new executive (quoted above). Of course, a city is not a business, but the op-ed vividly captures the challenges facing mayors across the country. At the same time, it is not uncommon to find a CEO in city hall. In fact, as a former city council member and a partner in a marketing firm, Doherty had quite a bit in common with mayors from cities across the United States and over time.

Dating back to the early sociological studies of Middletown (Lynd and Lynd 1929; 1937) and Floyd Hunter’s Community Power Structure (1953), business leaders and business interests are often depicted as potent actors in city politics. Indeed, business leaders are at the center of Hunter's elite theory. With a strong emphasis on promoting growth and economic development, the theory of the city as a “growth machine” casts business interests as the dominant force in local politics even when they operate behind the scenes (Logan and Molotch 1987). An alternative perspective portrays business leaders as senior partners in a durable governing coalition. Within the framework of Stone’s (1989) regime theory, business leaders have significant political power but not unchecked influence. Business leaders were key advocates of the reform movement and in some cities emerged as dominant political actors postreform (Bridges 1997; Welch and Bledsoe 1988). Yet, local business interests were also both supporters and beneficiaries of the political machines the reformers sought to undermine (Erie 1988; Shefter 1976). However, the notion that business leaders and interests are preeminent forces in local politics has not gone unchallenged (cf., Dahl 1961).

Questions about race, ethnicity, and representation have long been central themes of urban politics research, and for good reason. Immigrants and racial and ethnic minorities often were key players in or excluded by political machines (e.g., Erie 1988; Shefter 1976), and there are compelling arguments that a key goal of the municipal reform movement was to limit the political influence of racial and ethnic minorities and the working class (e.g., Bridges 1997; Welch and Bledsoe 1988). In cities across the United States, African-Americans, ethnic minorities, and immigrants worked for decades to achieve political incorporation (e.g., Browning, Marshall and Tabb 1984; Jones-Correa 1994; Rogers 2006), yet the underrepresentation of minorities and women persists. Scholars of urban politics have long studied factors that affect representation, the implications of the representation of racial and ethnic minorities and women, and how municipal institutions impact who serves in city hall.¹ Compelling new research also examines questions about the emergence of women and minority candidates at the local level, leveraging novel data and innovative research designs to develop and examine new theoretical insights (e.g., Crowder-Meyer and Smith 2015; Crowder-Meyer 2020). Recent studies have also returned to questions about social class and representation (e.g., Carnes 2013; Schaffner, Rhodes and La Raja 2020), but few empirical studies have examined the role of business owners and executives as politicians (but see Carnes 2013; Kirkland 2019; Witko and Friedman 2008).

Despite the prominent role of business interests in the study of urban politics, many claims have not been rigorously tested. Although rich case studies have provided valuable insights on mayors and their influence (see, e.g., DeLeon 1995; Ferman 1985; Fuchs 1992; Stone 1989), we know relatively little about who runs for mayor and who serves as mayor in cities across the United States. At the same time, considerable evidence from a variety of political contexts suggests that who serves in office can influence public policy (e.g., Besley and Case 2003; Carnes 2013; Chattopadhyay and Duflo 2004; Whitby 1997). This paper investigates descriptive representation in American cities, providing new details on who serves as mayor focusing on the role of business leaders in local politics. Perhaps, not surprisingly, business owners and executives are especially well represented in American city halls. But, conditional on running for office, are business leaders more likely to win?

In this paper, I focus on business owners and executives who run for and who serve as mayor. Using an original dataset of 3,582 candidate backgrounds, I provide an account of descriptive representation in American cities, covering 259 cities in forty-four states over more than fifty years. My data reveal that U.S. mayors are not very diverse. The vast majority are White and male with white-collar occupations and prior political experience. Business owners and executives are especially well represented, accounting for nearly a third of the candidates in the sample. Despite their numbers, basic descriptive statistics indicate that overall, business owners and executives do not win at higher rates than candidates with other types of occupational experience. However, I do find evidence to suggest that business owners and executives may have better electoral prospects in cities that are more conservative than average, especially in the presence of nonpartisan electoral rules. While I cannot fully account for unobservable factors that may influence voters’ choices or candidates’ strategic entry decisions, additional results suggest business owners and executives also may be more likely to run for mayor in more conservative cities.

Taken together, these findings provide new evidence of the prominence of business owners and executives in city politics. Keeping in mind both canonical accounts and descriptive theories of urban politics, we might not be surprised by the analyses presented here. Nonetheless, important questions remain unanswered. Why do business executives seek office? Does electing business owners and executives have policy consequences? Renewed attention to the role of social class in representation suggests that the overrepresentation of the affluent shapes public policy at all levels of American politics (Carnes 2012; 2013; Schaffner, Rhodes and La Raja 2020), and business executive mayors, in particular, may preside over fiscal policy changes that shift city spending away from redistributive programs (Kirkland 2019). These recent studies combined with the new data presented here suggest that the role and influence of business owners and executives as politicians warrants additional attention from scholars of politics and public policy.

Why are There so Many Business Executive Mayors?

Explaining how he was elected as a Republican mayor in a Democratic city, former New York mayor and billionaire business executive Michael Bloomberg invoked Fiorello La Guardia’s famous declaration that there is no Democratic or Republican way to pick up garbage (Tholl 2014). “People care about services, not ideologies $\dots$ There are virtually no Republicans in New York City and yet I won because people care about services” (Tholl 2014). When business executives run for office, they tend to emphasize their leadership experience, business knowledge, and management skills. More specific claims about the value and relevance of business experience tend to revolve around efficiency, effectiveness, and accountability on the one hand and stimulating local economic activity on the other hand. Candidate Robert J. Foley’s comments in a 1994 mayoral debate in Waukesha, Wisconsin, nicely sum up the former:

Greater accountability is called for. We need to increase efficiency. We need to force government to adjust to the concept of saying that this is what we have to spend … as opposed to this is what we want and let’s go get it to spend (Waukesha Mayoral Candidates Detail Priorities for City 1994).

Foley also referenced his “orientation to marketing,” citing the need to market the city, and his “ability to motivate and lead” (Waukesha Mayoral Candidates Detail Priorities for City 1994). Wesley Hammonds, candidate for mayor of Corpus Christi, Texas, in 1963 promised to lower taxes for homeowners, attract new businesses, and reduce costs by increasing efficiency (Hammonds 1963). These examples depict candidates making the case that their business experience affords them skills and knowledge that equip them to competently lead a city.

Perhaps, these claims resonate with voters and they elect business owners and executives because they believe that business candidates are well qualified and highly competent. Adopting a principal–agent framework, Fearon (1999) makes a strong theoretical case that elections primarily serve as a selection mechanism (see also Besley 2006). Voters lack the time and knowledge to adequately monitor elected officials, so “it might be entirely reasonable to imagine that the best available solution is to try to elect good types of candidates” (Fearon 1999: 69). For incumbents, retrospective evaluations provide at least a noisy measure of quality, but voters also might make inferences from a variety of candidate attributes, such as appearance and demeanor or education and job experience. For many years, scholars of American politics have used prior office-holding experience as a measure of quality (see, e.g., Jacobson and Kernell 1983). Prior political experience may signal electability, policy expertise, or competence. Similarly, political experience or owning or running a business might simply be a sign of a highly successful person.

Are business executives and owners so well represented because their occupation marks them as high-quality candidates? If so, we should expect business owners and executives to win elections at higher rates than other candidates. At the same time, nonpartisan elections might make candidates’ occupations more salient. However, this relationship may not be so straightforward if voters’ partisanship shapes their evaluations of business executive candidates. It may also be that voters see prior political experience, especially incumbency, as the clearest signal of a candidate’s quality and competence.² These possibilities lead to my first hypothesis ( $H_{1}$ ) that business owners and executives will win more often, on average, than mayoral candidates with other types of occupational backgrounds.

By some accounts, ideology and partisanship should, as Mayor Bloomberg claims, be of little consequence in city politics. Perhaps most notably, Peterson (1981) argues that the formal and informal constraints on cities leave a little role for party politics. Oliver, Ha and Callen (2012) suggest that in small cities, as long as elected officials maintain an existing balance of services and taxes, voters are likely to retain incumbents. On the other hand, Kaufmann (2004) focuses on how elections in large cities differ from state or national elections, notably in the diminished salience of partisan identification. Instead, she argues, voters may rely on the heuristic of racial or ethnic group identity, particularly under conditions of heightened group conflict. The majority of local elections are nonpartisan, and local issues, including basic service provision, infrastructure, and zoning, may be less clearly associated with partisan or ideological positions.

At the same time, however, recent research poses strong challenges to the notion that partisanship and ideology are inconsequential in city politics and elections. Local public policy tends to vary with mayoral partisanship (de Benedictis-Kessner and Warshaw 2016; Einstein and Kogan 2016, cf., Ferreira and Gyourko 2009; Gerber and Hopkins 2011). For example, cities that elect Democratic mayors may see an increase in the size of government, compared to cities that elect Republicans (de Benedictis-Kessner and Warshaw 2016). Leveraging advances in methods of estimating subnational public opinion, Tausanovitch and Warshaw (2013) generate estimates of city level ideology, which they use to examine the extent to which local public policy is correlated with a measure of city conservatism. They find that more conservative cities tend to have more conservative policies compared to relatively liberal cities (Tausanovitch and Warshaw 2013; 2014).

As de Benedictis-Kessner and Warshaw (2016) note, policy change with mayoral partisanship may reflect the role of elections as a mechanism for policy responsiveness. When voters select a mayor, they likely look beyond competence in search of a candidate who shares their policy preferences and will address issues they find important. Learning about the details of municipal finances or local public policy, however, would be complex and time consuming. Instead, voters likely use a variety of information shortcuts to guide their choices at the ballot box (Downs 1957; Lupia 1994; Popkin 1991). A candidate’s political party affiliation tends to be the most influential heuristic (Rahn 1993), but other characteristics, such as a candidate’s race, gender, or social class background, may also impact voters’ evaluations of candidate ideology (e.g., Brady and Sniderman 1985; Carnes and Sadin 2015; Huddy and Terkildsen 1993; McDermott 1998). For example, Black candidates and women are perceived to be more liberal and more Democratic than White men (Huddy and Terkildsen 1993; McDermott 1998) while voters may mistakenly infer that candidates from working-class families take more liberal positions on economic policy compared to candidates with more affluent backgrounds (Carnes and Sadin 2015).

When ballots include candidates’ party affiliations, voters have quick access to a powerful information shortcut that typically provides reliable information about candidates’ ideology and issue positions (Popkin 1991; Rahn 1993), but nonpartisan elections are quite common in U.S. cities, an enduring component of the municipal reform movement. Although much of the rhetoric of municipal reform addressed the corruption and waste typically associated with machine politics, reformers sought to fundamentally alter politics to limit representation and circumscribe government activity (see, e.g., Bridges 1997; Welch and Bledsoe 1988). Amy Bridges (1997) argues that reformers wanted to weaken the linkages between politicians and their constituents, and she notes that there was also a debate about the role of government that “often resembled the argument between New Deal Democrats and Hoover Republicans, the former arguing government should do more and the latter arguing it should do less” (p. 23). In particular, reformers sought to curtail the influence of poor, working-class, and minority voters to create a more homogeneous White, middle-class electorate with little appetite for redistribution. To accomplish these goals, reformers worked to change the rules of the game by altering municipal institutions. The indirect election of mayors, the council-manager form, nonpartisan ballots, and at-large elections were all designed to dampen participation and the influence of non-White and working-class voters.

In the absence of party labels, voters may look for alternative indicators of candidate ideology and competence. In addition to candidates’ occupational backgrounds and political experience, attributes such as race, ethnicity, and gender may be more influential in nonpartisan elections (Citrin, Green and Sears 1990; McDermott 1998; 2005; Pomper 1966; Schaffner, Streb and Wright 2001). For example, voters with little information about candidates might rely on names and photos to infer gender, race, or ethnicity. Survey experimental evidence indicates that when respondents have no additional information, they are less likely to vote for non-White candidates, a penalty that is diminished or eliminated with additional information, such as a candidate’s occupation (Crowder-Meyer, Gadarian and Trounstine 2019). With data from an experiment embedded in California election polls, McDermott (1998) argues that when candidates’ occupations are provided, voters may use this information to make inferences about a candidate’s competence, particularly when their job provides experience that seems relevant for the office they seek.

In a nonpartisan election, voters might rely on candidates’ demographics and occupations to make inferences about not only their competence but also their partisanship or ideology. A variety of evidence from observational data, surveys, and survey experiments suggests that in nonpartisan electoral settings, voters use information or signals about candidates’ partisanship when they are available (Schaffner, Streb and Wright 2001; Sen 2017; Squire and Smith 1988). Recent studies of local elections suggest that voters do indeed learn about candidates’ relative ideological positions over the course of a campaign—even when some common information shortcuts are absent or uninformative—and that evaluations of ideology influence voting behavior (Holman and Lay 2020; Sances 2018). For example, Holman and Lay (2020) focus on the 2017 mayoral elections in New Orleans, a race between two Black women, both Democrats. While voters could not differentiate the candidates based on partisanship, gender, or race, the authors find that the candidates signaled their ideology via endorsements and the issues they emphasized, which allowed voters to judge which candidate was more or less liberal.

Voters may judge candidates’ ideology on the basis of their occupational backgrounds. Several studies use occupation as a proxy for social class (e.g., Carnes 2012; 2013; Sadin 2014), and there is evidence to suggest that voters may rely on candidates’ occupations to make inferences not only about competence but also about candidates’ policy positions. For example, Sadin (2014) finds a positive relationship between an upper-class occupation and perceptions of competence. However, respondents in her survey experiment also differentiated between occupations, anticipating that a candidate who works as an investment banker would be more conservative on economic issues than a candidate employed as a cardiologist. At the same time, Carnes (2012; 2013; 2016) finds that members of Congress and state legislative candidates with backgrounds as business owners are more conservative than their counterparts with working-class or service-oriented occupations.

Political and occupational experience may be especially salient to voters at the local level where a predominant shortcut, party label, may be either unavailable or less informative. A candidate’s prior political experience and occupation tend to be readily available not only in media coverage and campaign materials but also on the ballot in some nonpartisan cities. Using a conjoint survey experiment to examine the impact of nonpartisan ballots, Kirkland and Coppock (2018) find that Democrats and Republicans may rely on different shortcuts in the absence of party labels. In nonpartisan matchups, prior political experience had the largest effect on vote choice for Democratic respondents, while Republican respondents’ preferences were more heavily influenced by candidates’ occupations. Notably, without a party cue, Democratic and Republican respondents share a common perception of business owners and executives as more conservative than candidates with other occupations such as attorney or educator. Perhaps, not surprisingly, Republicans did (and Democrats did not) prefer candidates described as business owners and executives, and this difference is most pronounced in nonpartisan contests.

If business owners and executives tend to be more conservative ideologically than candidates with other types of career experience—or if voters perceive business owners and executives to be more conservative, occupation might serve as a signal to voters who are trying to evaluate the relative ideological positions of candidates. This possibility leads to the hypothesis ( $H_{2}$ ) that business owners and executives will be favored by conservative voters. Prior research on nonpartisan elections suggests that candidates’ occupational backgrounds are likely to be more impactful when voters have to choose a candidate in the absence of party labels because voters may rely on other information to evaluate candidates when a party cue is unavailable (or uninformative). This insight leads to the hypothesis ( $H_{3}$ ) that a candidate’s experience as a business owner or executive is likely to be more consequential when elections are nonpartisan compared to those where candidates’ party affiliations appear on the ballot.

It is possible that an outsize share of business executive mayors reflects voters’ preferences—or the interaction of voters’ preferences and electoral institutions, but mayoral candidates are not randomly assigned to cities. Business owners and executives may have a variety of motivations for seeking local public office, and they may be in greater supply than candidates with other occupational backgrounds. Typical local policy domains, such as zoning, land use, and development are of particular interest to those who own or operate businesses within the city. Local tax policies, services, and amenities can directly shape the climate for commerce in a city, and to the extent that these features attract or repel affluent taxpayers and business activity, they may also contribute indirectly to the cost of doing business (Logan and Molotch 1987; Peterson 1981). Indeed, Peterson (1981) argues that competition for high-income taxpayers and businesses creates informal but powerful constraints on cities. Logan and Molotch (1987) go further, describing the city as a “growth machine” where economic growth and value-free development are preeminent goals, regardless of the potential ramifications for citizens and the environment. In theorizing the city as a growth machine, Logan and Molotch (1987) portray business leaders and business interests as dominant and exclusively (or very nearly so) self-interested political actors. Stone (1989) offers a more nuanced view in his regime theory, portraying the business community as powerful but not necessarily dominant, and he notes an element of civic-mindedness in addition to self-interest. However, a common thread that runs through these descriptive theories is the characterization of local businesspeople as key players in local politics, working to shape the agenda and policy to their benefit whether or not they hold elected office.

The central position of business interests in descriptive theories of urban politics suggests that we should not be especially surprised to observe a large number of business owners and executives serving in local elected offices. Perhaps, counterintuitively, research that investigates the underrepresentation of racial and ethnic minorities and women offers valuable insights for thinking about the supply of business candidates and the types of places where we might expect to observe business owners and executives running for mayor. Studies of the political incorporation of racial and ethnic minorities often emphasize the importance of the local political context, including both formal and informal institutions, the ethnic and racial makeup of a city, and historical factors in shaping the degree of minority political incorporation (e.g., Browning, Marshall and Tabb 1984; Jones-Correa 1994; Rogers 2006). For example, Browning, Marshall and Tabb (1984) find that a substantial bloc of liberal White voters could improve prospects for Black representation while Jones-Correa (1994) argues that the remnants of political machines can undermine prospects for Latino incorporation.

A key feature of more recent studies of gender and representation is an explicit and careful focus on candidate emergence, ambition, and strategic entry (e.g., Kanthak and Woon 2015; Lawless and Fox 2005; 2010). Some experimental evidence suggests that women may be less likely to run for office if they are uncertain about their likelihood of winning (Kanthak and Woon 2015; Preece and Stoddard 2015). In the context of local legislative elections, Crowder-Meyer and Smith (2015) argue that more women in higher-level offices can create a favorable environment in which women would run and win more frequently, but they also suggest that women are more likely to find electoral success where they have greater socioeconomic resources. Shah (2014) argues that a similar focus on the factors that determine who runs for office could advance our understanding of the underrepresentation of racial and ethnic minorities. She finds that context, including demographic factors and institutions, impacts the likelihood that a Black candidate even appears on the ballot. While many studies have often focused on how demographics, institutions, and other contextual factors affect the likelihood that women and minority candidates will win elections (e.g., Abott and Magazinnik 2020; Browning, Marshall and Tabb 1984; Bullock 1984; Cargile and Pringle 2019; Citrin, Green and Sears 1990; Crowder-Meyer et al. 2018; Trounstine and Valdini 2008), these considerations likely also influence the supply of candidates—i.e., who opts to run for office.

In contrast to women and racial and ethnic minorities, business owners and executives appear to be overrepresented in local politics—they also tend to be White and male, so their political calculus may be different. However, business owners and executives likely consider the local political context when they decide whether to run for office. Stone (1989) emphasizes their resources as a source of business leaders’ influence in city politics, and business interests tend to be well funded, well organized, and influential (see, e.g., Baumgartner and Leech 2001; Gilens and Page 2014; Schattschneider 1960; Schlozman, Verba and Brady 2012). Business owners and executives may find considerable support for a candidacy, but they might also think about their prospects for electoral success. Given some evidence that business owners may be especially conservative (Carnes 2013; 2018), they might expect that they will be more likely to win in places with more conservative voters.

A candidate’s experience as a business owner or executive may influence voters’ behavior, but business owners and executives may have a variety of motivations for seeking local public office. One reason for the overrepresentation of business owners and executives might be that they make up a large share of the supply of candidates. Prior research suggests business owners and executives share distinctive ideological preferences, and they are likely to have access to considerable resources and support. Because businesspeople may have strong motivations and resources to seek a formal role in local policymaking, I hypothesize ( $H_{4}$ ) that business owners and executives will make up an outsize share of the candidates that run for mayor. If business owners and executives take their electoral prospects into account and make strategic decisions about whether to run for office, they may be more likely to run in cities with more conservative voters.

Data: Candidates, Cities, and Institutions

Data availability presents a significant challenge in documenting who seeks and wins the office of mayor in U.S. cities. There is no central government database of candidates and election returns, and the accessibility and format of election information vary across places and time. Earlier studies have chronicled some mayoral characteristics, such as race, gender, and party affiliation (e.g., de Benedictis-Kessner and Warshaw 2016; Ferreira and Gyourko 2009; 2014; Gerber and Hopkins 2011; Hopkins and McCabe 2012; Karnig and Welch 1980; Pelissero, Holian and Tomaka 2000), but the existing data have been limited in two key ways. First, some studies and surveys cover only mayors, excluding other candidates. Second, datasets that include more detailed information about mayors or candidates often include only a small sample of cities and cover a relatively short time period. Notable exceptions include recent efforts to build large datasets of mayoral elections that include multiple candidates for large numbers of cities and cover most of the post-World War II time period (de Benedictis-Kessner and Warshaw 2016; Ferreira and Gyourko 2009; 2014; Gerber and Hopkins 2011). Although these datasets represent a significant advance, they still provide only limited information about candidates—typically name, party affiliation, and incumbency status.

To provide a richer, more comprehensive account of representation in American cities, I compiled an original dataset of mayoral candidate backgrounds. Working from an existing dataset of U.S. mayoral elections, I researched the top two mayoral candidates in each election, building detailed candidate profiles.³ Relying on a variety of sources, including government records, contemporaneous news stories, historical archives, and obituaries, I collected information about candidates, including age, race, gender, occupational background, and political experience. I focus on a sample of cities with a population of 50,000 or greater as of the 2000 U.S. Census. The cities in the sample vary quite a bit in terms of population, geographic region, and political institutions but are, on average, quite similar to the universe of U.S. cities with populations of at least 50,000. Table 1 presents summary statistics for cities in the sample as well as all U.S. cities of at least 50,000 residents as of the 2000 U.S. Census. Although the mean population is higher for cities in the sample, on other demographic and socioeconomic measures, including median household income, home ownership rate, and home values, the sample closely resembles the universe of U.S. cities of 50,000 or more.

Table 1.

Sample of Cities.

	Cities with $> 50, 000$	Current sample—
	population	mayors data
Number of cities	600	259
Population	165,042	214,640
	(410,737)	(575,273)
White population share	68.86%	69.49%
	(18.47)	(19.00)
Unemployment rate	6.43%	6.40%
	(2.69)	(2.63)
Median household income	$43,642	$43,516
	(13,674)	(13,384)
Home ownership	58.46%	58.25%
	(12.44)	(11.53)
Median house value	$143,000	$141,642
	(87,496)	(77,483)

Note. From 2000 U.S. Census, mean values with standard deviations in parentheses.

The candidate backgrounds dataset includes race, gender, political experience, and occupational backgrounds for a total of 3,582 candidates. Because some candidates’ background information is missing or incomplete, I focus on the subset of data that covers elections with complete information for the top two candidates. The resulting dataset includes 2,802 candidates (1,401 elections) from 259 U.S. cities across forty-four states and covers the time period of 1950–2007. From the raw data, I coded candidates’ occupational backgrounds and political experience. Indicators of political experience include incumbency and prior experience as mayor, city council member, county legislator, state legislator, and member of Congress. I recorded a primary occupation for each candidate, as well as an occupational category. These categories include business owner or executive, nonexecutive business occupations, other white-collar occupations (e.g., educators and health care professionals), public sector employment, and blue-collar occupations.

To examine these candidates in the context of local politics, I compiled data on local government institutions as well as demographic and socioeconomic characteristics of their home cities. Information about cities’ populations and their characteristics comes from the U.S. Census Bureau. These data include measures of population, racial diversity, median household income, and home ownership rates.⁴ Details about the form and institutions of local governments come from the International City/County Management Association (ICMA). The ICMA routinely surveys municipalities to determine the form of government, electoral rules, city council size, and mayors’ formal powers. Data are available in electronic format for surveys conducted at five-year intervals beginning in 1981. To cover earlier years, I collected similar data available in printed volumes of The Municipal Year Book published by the ICMA.⁵ There are some changes in the variables measured by ICMA over the years, but key features of municipal governing institutions, such as the form of government, use of nonpartisan or partisan ballots, the size of the city council, and whether its members are elected by districts or at large, are consistently observed over time.

Operationalizing ideology or partisanship at the city level presents a serious challenge—in particular, finding measures that are comparable across places and over time is exceedingly difficult. Many cities hold nonpartisan elections, so municipal election results are of little help. Election returns for higher-level offices typically are not easily aggregated at the city level, and election rules and procedures vary across states. To address these challenges, I use estimates of city conservatism from Tausanovitch and Warshaw (2013). This measure has several significant advantages. Tausanovitch and Warshaw (2013) pool data from multiple national public opinion surveys to estimate respondents’ ideology using a Bayesian Item-Response model (Clinton, Jackman and Rivers 2004) and then use multilevel regression and poststratification (MRP) (Park, Gelman and Bafumi 2004) to generate estimates of city level conservatism.⁶

The result is an estimate of city level ideology that is based on public opinion data and readily comparable across U.S. cities. The main limitation of these estimates of city conservatism is that each city has a single estimate generated from public opinion data collected between 2000 and 2011, so this measure of ideology is static and reflects data on mass attitudes from the most recent years covered by my mayoral candidate background data. I believe the strengths of the city conservatism far outweigh this potential concern. Erikson, Wright and McIver (1993) pool more than a decade of polling data and use disaggregation to generate a static measure of state-level ideology, and crucially, they find that in the aggregate, ideology is relatively stable over time. Research on public opinion and its link to public policy also consistently implies that subnational measures of mass attitudes and ideology that rely on public opinion survey data perform better than demographic or socioeconomic predictors sometimes used as a proxy for mass ideology (see, e.g., Erikson, Wright and McIver 1993; Lax and Phillips 2009; Tausanovitch and Warshaw 2013; 2014).

Who Runs for Mayor? Who Serves?

The candidate background data indicate that mayors, such as elected officials at higher levels of government, are not an especially diverse group. The vast majority are White and male with white-collar occupations and prior political experience. Table 2 displays details on the race, ethnicity, and gender of candidates. In each panel, counts and shares of winning candidates in each category are included in the first column, and the second column displays the counts and shares of runners-up. Strikingly, over 90% of mayors are White, 89% are male, and 80% are both White and male. Approximately 5% of mayors are African-American, while 2.6% are Latino and $<$ 1% are Asian. It seems important to note that these data cover more than fifty years. In my dataset, there are only five women candidates prior to the 1970s when about 5% of candidates were women. This figure doubles to just over 10% in the 1980s and virtually doubles again in the 1990s and 2000s when about 20% of mayoral candidates are women. Non-White candidates begin to emerge in the 1960s when they make up about 1% of all candidates. The share of non-White candidates increases to 6% in the 1980s and 8% in the 1990s. By the 2000s, non-White candidates make up 17% of the candidates in the dataset.

Table 2.

Race, Ethnicity, and Gender.

Race and ethnicity	Winners		Runners-up
White	1,264	90.2%	1,244	88.8%
Black	74	5.3%	76	5.4%
Latino	37	2.6%	43	3.1%
Unknown	16	1.1%	28	2.0%
Asian	10	0.7%	10	0.7%
$n = 2, 802$ , $χ^{2} = 3.909$ , $p = .418$ .
Gender
Men	1,246	88.9%	1,233	88.0%
Women	155	11.1%	168	12.0%
$n = 2, 802$ , $χ^{2} = 0.504$ , $p = .478$ .

Women and non-White candidates gain greater representation over time, but the disparities remain quite profound—even in the 2000s, almost two-thirds (66.2%) of mayors are White and male. Table 2 shows that representation along race and gender lines is quite similar for winning candidates and runners-up. Because these data are categorical, I use $χ^{2}$ tests to formally evaluate the differences in the distributions of winning and losing candidates across racial, ethnic, and gender categories. For both race and gender, the resulting test statistics provide little reason to think the small differences are systematic (for race, $χ^{2} = 3.909, p = .418$ ; for gender, $χ^{2} = 0.504, p = .478$ ). These results, however, do not necessarily imply that these traits are insignificant. For example, I cannot account for candidates’ strategic decisions to run for office or for other characteristics of candidates (or cities) that might influence a candidate’s prospects for electoral success.

Moving on to examine candidates’ occupational backgrounds, we see more diversity, although mayors tend to have white-collar backgrounds. Table 3 shows the distribution of mayoral candidates across occupation categories. Again, the column on the left (right) indicates the count and share of winning (runner-up) candidates in each category. As anticipated, business owners and executives are very well represented—making up about 32% of mayors. By way of comparison, 31% of the Members of Congress who served during the 106th to 110th Congresses had experience as a business owner or executive (as per CLASS dataset, Carnes 2016). Data for other offices tend to be quite sparse, but the National Conference of State Legislatures (NCSL) has collected information about state legislators’ occupations sporadically. These data indicate that anywhere from about 16% (1993) to just over 21% (1976) of state legislators are business owners or executives.⁷ Among mayors, business owners and executives constitute the largest occupational group. Attorneys comprise another 18% of mayors, public employees make up 8%, and 5.7% of mayors are managers or supervisors, while sales professionals and educators each account for about 6% of mayors. Although business owners and executives are the largest single occupational group, they do not appear to win at higher rates—in fact, business executives make up a slightly larger share of losing candidates (33%). Even focusing on races where a business owner or executive faces a nonbusiness opponent, business executive candidates won about 48% of these races. For the most part, differences in the distribution of winners and runners-up by occupation appear to be negligible. The difference in win rates is greatest for attorneys, who are more common among winning candidates than runners-up (18% and 15.5%, respectively). This initial test cannot rule out the possibility that these differences are due to chance ( $χ^{2} = 14.439$ and $p = .108$ ).⁸

Table 3.

Occupational Backgrounds.

Occupational experience	Winners		Runners-up
Business owner/executive	443	31.6%	467	33.3%
Other occupations	258	18.4%	300	21.4%
Attorney	256	18.3%	209	14.9%
Public employee	113	8.1%	131	9.4%
Sales	85	6.1%	82	5.9%
Educator	85	6.1%	61	4.4%
Manager/supervisor	80	5.7%	74	5.3%
Other professional	37	2.6%	37	2.6%
Homemaker	26	1.9%	22	1.6%
Health care professional	18	1.3%	18	1.3%

$n = 2, 802$ , $χ^{2} = 14.439$ , $p = .108$ .

In sharp contrast to the analyses of candidates’ race, gender, and occupation, it appears that mayors and runners-up do differ in terms of political experience. Table 4 displays the prior political experience of mayoral candidates, and the differences between those who win the election and those who lose seem quite stark. As above, the first column includes the count and share of winning candidates with analogous figures for runners-up in the second column. Overall, nearly 80% of winning candidates have previously served in elected office compared to only 63.5% of runners-up. The data indicate that incumbents and other candidates who have served as mayor, in particular, win at much higher rates than those who lack prior mayoral experience. Just under 46% of winning candidates ran as incumbents, compared to about 19% of losing candidates. The gap is slightly narrower among all candidates with prior mayoral experience (including incumbents), but half of the winning candidates had already served as mayor. Here, I find evidence of a systematic difference in the distribution of political experience among winning candidates and runners-up ( $χ^{2} = 207.125$ and $p < .001$ ).⁹

Table 4.

Political Experience.

Political experience	Winners		Runners-up
No experience	282	20.1%	512	36.5%
City council	763	54.5%	652	46.5%
Mayor	704	50.2%	357	25.5%
Incumbent	641	45.8%	269	19.2%
State legislator	121	8.6%	87	6.2%
County legislator	35	2.5%	40	2.9%
US legislator	16	1.1%	9	0.6%

Note. Recoded into mutually exclusive categories to estimate $χ^{2}$ test statistic.

$n = 2, 802$ , $χ^{2} = 207.125$ , $p < .001$ .

These differences may reflect a variety of factors. Voters may simply prefer candidates with previous political experience over inexperienced candidates. However, political experience might reflect other unobserved variables that translate into victory at the polls. It may be, for example, that previous mayors are more likely to have well-funded campaigns. These results also may be the result of a straightforward selection problem: weaker candidates may never have won the election, so could not have accumulated any political experience. Nonetheless, this analysis does yield some evidence that mayoral candidates with political experience win elections at higher rates than inexperienced candidates.

A Closer Look at Business Executive Candidates

The mayoral candidate background data reveal that business owners and executives are extraordinarily well represented among U.S. mayors. They constitute the largest occupational category overall. It seems worthwhile to consider whether this descriptive finding is driven by a certain time or place. Figure 1 shows that business owner or executive consistently is the most common occupation among mayoral candidates over time and across regions. Figure 1(a) shows the share of candidates, measured on the $y$ -axis, that fall into the most common occupational categories over time, denoted on the $x$ -axis. The points indicate the share of candidates in each occupation by five-year increments. The solid line traces the share of business owners and executives over time while the dotted and dashed lines plot the shares of attorneys and public employees, respectively. Figure 1(a) shows that business owners and executives make up the largest occupational category over time, though the share of business executive candidates has fluctuated a bit over time from between 34% and 45% in the 1950s and early 1960s before dropping to remain closer to 30% the remainder of the time period. The shares of candidates who are attorneys or public employees have also shifted a bit over time, but never exceed the share of business owners or executives.

Figure 1(b) shows, by the census region, the share of candidates who are business owners and executives. The vertical axis denotes the share of candidates, black bars indicate winning candidates, and gray bars indicate runners-up. This plot suggests that no particular region is driving the overall share of mayors who have business executive experience. While business owners and executives are more common in the South, accounting for about 42% of winning candidates and 40% of runners-up, they make up just over or under 30% of candidates from other regions of the country. These candidates win a bit more often in the South and the Northeast and lose more often in the Midwest and the West, but differences in win rates are substantively small (about 2 to 5 percentage points). Moreover, business owner or executive is the most common occupational background in all regions. Similarly, I find no reason to think that the frequency of business executive candidacy is related to the population of cities. If I simply divide sample cities into quartiles based on population, the smallest cities have a slightly larger share of business executive candidates (37%) compared to larger cities, which range from 30% to 33%, but again business owners and executives make up the largest category for each group.

Of the 1,401 elections covered by the candidate background data, 771 (55%) include at least one candidate with experience as a business owner or executive. In 159 of these, both of the top two candidates had a background as a business owner or executive. Table 5 provides details on candidates’ party affiliations. Because nonpartisan elections are so common, party affiliations are unknown for nearly one-third of candidates. In some cases, I was able to collect party affiliations for candidates in nonpartisan elections, but partisanship was often more difficult to find than details about careers and political experience. About 33% of business candidates are Republicans, while another 26% are Democrats. Among candidates without a business background, this relationship is reversed—39% are Democrats and 25% are Republicans. Candidates with unknown party affiliations are also more common among those with executive business experience.

Table 5.

Political Party Affiliations

	All candidates		Nonbusiness candidates		Business candidates
Democrat	971	34.7%	730	39.0%	241	25.9%
Republican	774	27.6%	463	24.7%	311	33.4%
Other	141	5.0%	93	5.0%	48	5.2%
Unknown	916	32.7%	586	31.3%	330	35.5%

Business Owners and Executives on the Ballot

Mayoral candidate background data affirm that business owners and executives are fixtures in mayoral politics not only over time but in cities across the United States. At the same time, the descriptive statistics above do not support the hypothesis that business owners and executives are more likely to win elections across the board. Questions remain about the conditions under which—or the types of cities where—candidates with business backgrounds are likely to win elections. In particular, do business owners and executives fare better at the ballot box in more conservative cities or in cities with nonpartisan elections? Assessing these hypotheses proves to be quite challenging. The characteristics of candidates and cities are not randomly assigned, and there are a number of factors that could confound this relationship. However, the first challenge is to determine the most appropriate dependent variable.

The question of whether a business background improves a candidate’s electoral prospects suggests modeling vote share as a function of candidate and city characteristics. One option might be to operationalize the dependent variable in terms of Democratic (or Republican) vote share with an election as the unit of analysis, but the significant number of nonpartisan elections leaves party affiliations unknown for nearly one-third of candidates. Another option is to use vote share (or an indicator for winning) with a candidate as the unit of analysis. This option results in two symmetric observations for each election which complicates the interpretation of some estimates. With these complexities in mind, I opt to focus on elections where a business candidate faces a nonbusiness candidate and operationalize the dependent variable as the business candidate’s vote share, taking an election as the unit of analysis. I think this approach offers the most straightforward test of my hypotheses, but alternative specifications that use vote share or election outcome as the dependent variable generate substantively similar results, which are included in the Supplemental Material.

To assess the hypothesis that business owners and executives fare better in more conservative cities, I specify a series of ordinary least squares (OLS) regression models. The dependent variable is the business candidate’s vote share, and the key explanatory variable is an indicator for above average conservatism (i.e., is the city conservatism measure above the average city conservatism of all cities in the sample?).¹⁰ In an effort to account for potential confounders, I present multiple specifications which include additional variables that may influence whether or not a business executive candidate runs for office. Given evidence from prior research that incumbents and candidates with political experience tend to perform better (e.g., Jacobson and Kernell 1983; Trounstine 2011), I include indicators for incumbency and prior political experience for both the business candidate and their opponent. As discussed above, business experience may be more salient in nonpartisan elections, so I add an indicator for nonpartisan elections.

Demographic and socioeconomic characteristics of cities may influence who runs for mayor and who wins for a variety of reasons. For example, these factors may shape voters’ preferences over the form of government, candidates, and public policy. In additional specifications, I add measures of city characteristics, including population, median household income, median house value, and racial diversity. Given considerable evidence that partisanship is a key factor in vote choice (e.g., Campbell et al. 1960; Green, Palmquist and Schickler 2002; Rahn 1993) it seems crucial to address candidates’ partisanship, but nonpartisan elections and unknown party affiliations complicate the operationalization of this variable. As a result, I use an indicator for a party–ideology match. This variable takes a value of 1 if the city has an above average conservatism score and the candidate is a Republican (or below-average city conservatism and a Democratic candidate).

While covariates can account for factors that are readily measurable, it is possible that the unobserved characteristics of candidates or cities could bias the results. For example, local economic conditions, labor costs, or tax policies could shape business owners’ decisions to enter a mayoral race. City fixed effects could help by accounting for unobserved, time-invariant characteristics of cities. However, in this case, the key explanatory variable—city conservatism—is a single measure that does not vary over time, so within-city estimates are not useful. Here, cross-city variation is crucial information but leaves unresolved concerns about unobserved heterogeneity. Largely for this reason, I estimate models that include fixed effects for the region (rather than a city) in addition to covariate-adjusted models.

In addition to the expectation that business candidates will perform better in elections in more conservative cities, I also hypothesize that a background as a business owner or executive will be more consequential in cities with nonpartisan elections where voters may rely on occupation as a heuristic for candidates’ ideology or policy positions in the absence of a party cue. This hypothesis implies that electoral rules may condition the relationship between candidate occupation and vote share. To evaluate this hypothesis, I run the covariate-adjusted model described above separately for cities with nonpartisan and partisan elections, and I also specify a model that includes the interaction of above average city conservatism and nonpartisan elections.

Results

Table 6 presents the initial results. The first model, which includes the city conservatism measure and candidate characteristics, indicates that business executive candidates garner a vote share that is about 2 percentage points higher in cities with above average measures of city conservatism, an estimate that is statistically significant at the 10% level. These results also suggest that incumbents and politically experienced candidates perform better. Business candidates’ vote shares are higher when they run as incumbents (by about 5.4 percentage points), but lower by a similar margin when their opponent is an incumbent. Facing a politically experienced opponent compared to one without prior political experience is associated with a vote share that is almost 4 percentage points lower. Interestingly, prior political experience appears not to be systematically related to a business owner or executive’s vote share. The estimate is positive but small in magnitude and not statistically significant.

Table 6.

Business Vote Share.

	Dependent variable:
	Business candidate vote share
	(1)	(2)	(3)	(4)
Above average city conservatism	0.019 $^{*}$	0.024 $^{* *}$	0.020 $^{* *}$	0.019 $^{*}$
	(0.010)	(0.010)	(0.009)	(0.010)
Incumbent	0.054 $^{* *}$	0.049 $^{* *}$	0.047 $^{* *}$	0.048 $^{* *}$
	(0.013)	(0.012)	(0.012)	(0.012)
Political experience	0.005	0.009	0.008	0.008
	(0.011)	(0.012)	(0.012)	(0.012)
Incumbent opponent	$-$ 0.053 $^{* *}$	$-$ 0.059 $^{* *}$	$-$ 0.056 $^{* *}$	$-$ 0.055 $^{* *}$
	(0.013)	(0.013)	(0.013)	(0.013)
Experienced opponent	$-$ 0.039 $^{* *}$	$-$ 0.041 $^{* *}$	$-$ 0.041 $^{* *}$	$-$ 0.041 $^{* *}$
	(0.012)	(0.013)	(0.013)	(0.013)
Nonpartisan city		0.007	0.017	0.017
		(0.012)	(0.012)	(0.013)
Party–ideology match			0.043 $^{* *}$	0.042 $^{* *}$
			(0.010)	(0.010)
Large city		0.011	0.003	0.004
		(0.012)	(0.011)	(0.010)
Above average median income		0.001	0.003	0.004
		(0.011)	(0.010)	(0.010)
Above average median house value		$-$ 0.002	$-$ 0.003	$-$ 0.001
		(0.011)	(0.011)	(0.012)
Above average White population		0.011	0.009	0.012
		(0.010)	(0.009)	(0.010)
Constant	0.513 $^{* *}$	0.497 $^{* *}$	0.478 $^{* *}$	0.470 $^{* *}$
	(0.012)	(0.016)	(0.015)	(0.017)
Fixed effects	NA	NA	NA	Region
Observations	609	564	564	564
$R^{2}$	0.207	0.238	0.267	0.268
Adjusted $R^{2}$	0.200	0.224	0.252	0.250
Residual std. error	0.109 ( $d f = 603$ )	0.106 ( $d f = 553$ )	0.104 ( $d f = 552$ )	0.104 ( $d f = 549$ )
$F$ statistic	31.456 $^{* *}$ ( $d f = 5$ ; 603)	17.250 $^{* *}$ ( $d f = 10$ ; 553)	18.244 $^{* *}$ ( $d f = 11$ ; 552)	14.370 $^{* *}$ ( $d f = 14$ ; 549)

Note: Dependent variable is the business owner or executive candidate’s vote share (sample includes elections where a business candidate faced a nonbusiness candidate). Ordinary least squares (OLS) regression results with errors clustered by city. Two-tailed test.

$^{*} p < .10;^{* *} p < .05$ .

Model 2 in Table 6 adds an indicator for nonpartisan elections as well as variables that account for city demographic and socioeconomic characteristics. While none of the additional variables appear to be systematically related to business candidate vote share, the estimates for above average city conservatism, incumbency, and political experience are quite similar to those in model 1. The third model adds an indicator for a match between the business candidate’s party and the city conservatism measure. Consistent with considerable prior evidence, these results suggest a strong link between partisanship and vote share. Specifically, when a Republican business owner or executive runs in a city with above average city conservatism (or when a Democrat runs in a city with below-average city conservatism), the business candidate vote share is about 4.3 percentage points larger compared to the vote share for business candidates in the absence of a party–ideology match. Notably, the estimates for city conservatism and candidates’ political experience and incumbency remain quite consistent even accounting for partisanship while nonpartisan elections seem to have no systematic relationship to business candidate vote share. Finally, model 4 adds region-fixed effects. The within-region estimates are nearly identical, and the estimate for above average city conservatism is statistically significant at the 10% level.

Results presented in Table 7 suggest that nonpartisan elections may in fact be a consequential factor in determining how business owners and executives fare in mayoral elections. Model 1 includes only nonpartisan elections and produces results that are consistent with those in Table 6. In nonpartisan elections, business candidates win a larger share of the vote in conservative cities—about 2.3 percentage points larger compared to cities that are more liberal than average. Incumbents also tend to garner a greater share of the vote as do business candidates whose party affiliation matches the city’s ideology, while facing an incumbent or politically experienced opponent is associated with a lower vote share. Model 2 includes only partisan elections, and the results differ noticeably. In particular, the point estimate for above average city conservatism is smaller in magnitude and negative, and it fails to reach conventional levels of statistical significance. In contrast, the estimate for the party–ideology match is larger in magnitude while the results for incumbency and political experience are mixed. There are only a small number of partisan elections (seventy) where a business candidate faces a candidate without experience as a business owner or executive, so these results should be viewed with caution. However, model 3, which includes both partisan and nonpartisan elections with an interaction term for nonpartisan elections and above average city conservatism, adds support for the notion that experience as a business owner or executive is more consequential in nonpartisan elections. Indeed, the null result for above average city conservatism combined with a positive and statistically significant coefficient on the interaction term suggests that business owners and executives may be advantaged in more conservative cities—but only when elections are nonpartisan.

Table 7.

Business Vote Share—Nonpartisan Versus Partisan Elections.

	Dependent variable:
	Business candidate vote share
	Nonpartisan	Partisan	All
	(1)	(2)	(3)
Above average city conservatism	0.023 $^{* *}$	$-$ 0.002	$-$ 0.030
	(0.011)	(0.031)	(0.022)
Nonpartisan city			$-$ 0.011
			(0.018)
Incumbent	0.051 $^{* *}$	0.010	0.049 $^{* *}$
	(0.013)	(0.022)	(0.012)
Political experience	0.008	0.011	0.007
	(0.013)	(0.021)	(0.012)
Incumbent opponent	$-$ 0.057 $^{* *}$	$-$ 0.048 $^{*}$	$-$ 0.056 $^{* *}$
	(0.014)	(0.027)	(0.013)
Experienced opponent	$-$ 0.054 $^{* *}$	0.024	$-$ 0.041 $^{* *}$
	(0.014)	(0.027)	(0.013)
Party–ideology match	0.040 $^{* *}$	0.077 $^{* *}$	0.044 $^{* *}$
	(0.011)	(0.026)	(0.010)
Large city	0.001	0.011	0.003
	(0.012)	(0.030)	(0.011)
Above average median income	$-$ 0.002	0.005	0.002
	(0.011)	(0.031)	(0.010)
Above average median house value	0.005	$-$ 0.075 $^{* *}$	0.002
	(0.013)	(0.029)	(0.012)
Above average White population	0.017 $^{*}$	0.010	0.015
	(0.010)	(0.025)	(0.010)
Above average city conservatism $\times$ nonpartisan city			0.054 $^{* *}$
			(0.022)
Constant	0.488 $^{* *}$	0.447 $^{* *}$	0.492 $^{* *}$
	(0.019)	(0.034)	(0.021)
Fixed effects	Region	Region	Region
Observations	494	70	564
$R^{2}$	0.298	0.254	0.273
Adjusted $R^{2}$	0.279	0.097	0.253
Residual std. error	0.105 ( $d f = 480$ )	0.088 ( $d f = 57$ )	0.104 ( $d f = 548$ )
$F$ statistic	15.644 $^{* *}$ ( $d f = 13$ ; 480)	1.615 ( $d f = 12$ ; 57)	13.716 $^{* *}$ ( $d f = 15$ ; 548)

Note. Dependent variable is the business owner or executive candidate’s vote share (sample includes elections where a business candidate faced a nonbusiness candidate). Ordinary least squares (OLS) regression results with errors clustered by city. Two-tailed test.

$^{*} p < .10;^{* *} p < .05 .$

Basic descriptive statistics suggest that business owners and executives do not necessarily win elections at higher rates than candidates with other types of occupational experience. However, they may have better electoral prospects in cities that are more conservative than average, especially those with nonpartisan electoral rules. These results should be interpreted with some caution in large part because neither business experience nor candidates are randomly assigned. For example, business owners might opt to run for mayor in more conservative cities—or opt not to run in more liberal cities—based on their expectations about their likelihood of winning the election. While I cannot account for all of the factors that influence candidates’ strategic entry decisions, I do consider whether business owners and executives are more likely to appear on the ballot in more conservative cities. To do so, I specify a series of regression models where the dependent variable is an indicator for the presence of at least one business owner or executive candidate in an election. The key explanatory variable again is an indicator for above average city conservatism.

Table 8 reports the results of a series of OLS regression models examining the link between business executive candidates and the form of government.¹¹ The first model includes only the key explanatory variable, whether a city’s conservatism score is above average, and produces a substantively large and statistically significant positive coefficient of 0.167, which implies that the likelihood of observing a business executive candidate for mayor is about 17 percentage points higher in conservative cities (compared to cities of below-average conservatism).

Table 8.

Business Executive Candidates.

	Dependent variable:
	Business executive candidate
	(1)	(2)	(3)	(4)
Above average city conservatism	0.167 $^{* *}$	0.166 $^{* *}$	0.149 $^{* *}$	0.155 $^{* *}$
	(0.042)	(0.042)	(0.052)	(0.052)
Nonpartisan elections		0.007	0.009	0.017
		(0.064)	(0.069)	(0.066)
Council-manager form			0.087 $^{*}$	0.052
			(0.051)	(0.051)
Percent White			$-$ 0.0004	0.00003
			(0.001)	(0.001)
Population			$-$ 0.028	$-$ 0.029
			(0.025)	(0.026)
Median household income			0.370 $^{* *}$	0.402 $^{* *}$
			(0.122)	(0.117)
Median house value			$-$ 0.192 $^{* *}$	$-$ 0.180 $^{* *}$
			(0.062)	(0.066)
Home ownership rate			$-$ 0.004	$-$ 0.003
			(0.003)	(0.002)
Constant	0.457 $^{* *}$	0.452 $^{* *}$	$-$ 0.694	$-$ 1.349
	(0.034)	(0.069)	(0.952)	(0.976)
Fixed effects	NA	NA	NA	Region
Observations	1,401	1,371	1,077	1,077
$R^{2}$	0.028	0.028	0.051	0.065
Adjusted $R^{2}$	0.027	0.026	0.044	0.055
Residual std. error	0.491 ( $d f = 1,399$ )	0.491 ( $d f = 1,368$ )	0.487 ( $d f = 1,068$ )	0.484 ( $d f = 1,065$ )
$F$ statistic	40.037 $^{* *}$ ( $d f = 1$ ; 1,399)	19.398 $^{* *}$ ( $d f = 2$ ; 1,368)	7.203 $^{* *}$ ( $d f = 8$ ; 1,068)	6.688 $^{* *}$ ( $d f = 11$ ; 1,065)

Note. Dependent variable an indicator for the presence of at least one business owner or executive candidate. Ordinary least squares (OLS) regression results with errors clustered by city. Two-tailed test.

$^{*} p < .10;^{* *} p < .05$ .

The second model adds an indicator for nonpartisan elections and yields nearly identical results. In addition to institutional variables, model 3 adds demographic and socioeconomic variables, population, the share of the population that is White, median household income, median house value, and home ownership rate. The point estimate for above average conservatism is a bit smaller in magnitude (0.149) but remains statistically significant. The coefficient of the council-manager variable is statistically significant at the 90% level, suggesting that the probability of observing a business executive candidate is, on average, about 9 percentage points higher in cities with the council-manager form. Interestingly, results from model 3 also suggest that the likelihood of observing a mayoral candidate with executive business experience may increase with household incomes but decrease with home values. Within-region estimates from model 4 are substantively quite similar, but the estimate for a council-manager form of government is smaller and not statistically significant.

Discussion

Questions about the power and influence of business interests and the affluent have long been at the heart of the study of urban politics. One key obstacle to rigorously assess the role of business executives and owners in city politics has been a lack of comprehensive data about local office holders. Relying on an original data set of mayoral candidate backgrounds, this paper provides a detailed picture of descriptive representation in American cities. What emerges is a striking deficit of descriptive representation. Indeed, these data affirm that women and people of color are vastly underrepresented. In line with the hypothesis that business owners and executives are especially well represented in mayoral politics, business executives account for about 32.5% in the sample of candidates for mayor.

Uncovering the factors that determine descriptive representation in American cities is a daunting task, but the analyses presented here do provide some suggestive evidence on three fronts. First, contrary to my first hypothesis, I find little evidence to indicate that business executives win elections at higher rates across the board compared to candidates with other types of occupational experience. Second, business owners and executives seem to have better electoral prospects in cities that are more conservative than average, a finding largely driven by cities with nonpartisan elections. Third, candidates with executive business experience may be more likely to run for mayor in more conservative cities. An important limitation of these results, however, is that I cannot fully account for all potential sources of endogeneity—particularly unobserved characteristics of candidates or cities that may affect the behavior of voters or candidates.

My results are consistent with the hypothesis that business owners and executives are more likely to run for office and win in conservative cities, and they also fit with broader claims that reform institutions may affect who serves in elected office. Yet, we must be cautious in interpreting these results and especially in drawing conclusions about the causal effects of institutions. A city’s electoral rules are not randomly assigned, which raises the threat of endogeneity. Despite these methodological concerns, it is reassuring to note that the findings presented here are consistent with earlier research on nonpartisan elections, including observational and experimental studies. Scholars have long argued that in the absence of party labels, voters would rely on other information about candidates, such as race, ethnicity, or political experience (e.g., Pomper 1966; Schaffner, Streb and Wright 2001) to guide their choices. My results echo those of recent survey experimental research suggesting that candidate characteristics serve as heuristics not only for competence but also for ideology in nonpartisan elections (e.g., Crowder-Meyer et al. 2018; Kirkland and Coppock 2018).

These findings touch on longstanding questions about electoral institutions and representation but also raise new ones. Do nonpartisan elections contribute to the overrepresentation of the affluent? How does class or occupational background influence voters in primary elections where voters cannot differentiate candidates by party? At the same time, additional results which indicate that business owners and executives may be more likely to run for office in more conservative cities affirm the notion that to better understand representation, we must go beyond who wins to consider who runs—i.e., the supply of candidates.

The results here provide new details and insights on representation in American cities and suggest that the overrepresentation of business executives in local politics warrants further exploration. Questions remain about why so many business executives serve as mayor. Under what conditions do business owners and executives decide to run for office rather than supporting other candidates or lobbying to advance their policy preferences? Why do voters select these candidates? Beyond understanding their numerical representation among American mayors, a pressing question is whether and how business owners and executives influence public policy. Given evidence to suggest that electing a business executive mayor may lead to changes in local fiscal policy, we should consider whether business executive mayors shape policy in other domains. What are the long-term consequences of business owners’ and executives’ role in city politics?

Supplemental Material

sj-pdf-1-uar-10.1177_10780874211021688 - Supplemental material for Representation in American Cities: Who Runs for Mayor and Who Wins?

Supplemental material, sj-pdf-1-uar-10.1177_10780874211021688 for Representation in American Cities: Who Runs for Mayor and Who Wins? by Patricia A. Kirkland in Urban Affairs Review

Footnotes

Acknowledgments

I thank Fernando Ferreira and Joseph Gyourko for sharing mayoral election data and to Nick Carnes for sharing data on state legislators and members of Congress. Many thanks to Justin Phillips, Shigeo Hirano, Ester Fuchs, Megan Mullin, Bob Erikson, Dan Alexander, Leah Stokes, Leeann Bass, and Mary Kroeger for helpful comments and suggestions and to Alina Dunlap for invaluable research assistance.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Data collection for this study was funded in part by a grant from the National Science Foundation (grant no. SES–1647503).

ORCID iD

Patricia A. Kirkland

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biography

Patricia A. Kirkland is an assistant professor of Politics and Public Affairs at Princeton University.

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