Introduction

Financial misconduct poses a significant threat to the efficiency and stability of financial markets, garnering increased attention from academics, practitioners, and lawmakers. One aspect of financial misconduct pertains to wrongdoings within the financial advisor industry.Footnote 1 Egan et al. (2019) document that financial advisor misconduct results in substantial costs, with a median settlement of $40,000 and a mean of $550,000, totaling nearly half a billion dollars annually for the financial advisory industry. Huber and Huber (2020) report that major U.S. financial institutions paid more than $12 billion in fines during 2013 and 2016. The costs of misconduct go beyond penalties and litigation expenses and include various indirect costs, such as damage to reputation, given the industry’s reliance on trust (Gurun et al., 2018).

The expenses associated with misbehavior in the financial advisor sector are substantial because a majority of individual investors seek advice from financial advisors for investment-related decisions.Footnote 2 More than half of American households rely on financial advisers (Egan et al., 2018), who service over $70 trillion (over four times the total assets of all commercial banks) for over 30 million clients in 2017 (Charoenwong et al., 2019). Similarly, as Foerster et al. (2017) note, about half of Canadian households consult financial advisers, and about $700 billion in retail investment assets are associated with adviser accounts. Recommendations from financial professionals are more accurate (Roth & Voskort, 2014) and considered credible sources of information (Jungermann & Fischer, 2005). Financial advisers, therefore, have a strong influence on household portfolio choices and performance (Baeckström et al., 2021; Danilov et al., 2013; Kramer, 2016; Stolper & Walter, 2017). Furthermore, misconduct committed by financial advisers is common, with, on average, one in thirteen advisers (or 7.7%) having a misconduct record in their career (Egan et al., 2019), and the number of financial misconducts has increased in recent years (Karpoff, 2021). Given its profound consequences, research investigating the driving forces behind financial advisor misconduct is of significant interest.

In this paper, we focus on the role of local corruption culture in shaping misconduct behaviors within financial planning and transaction services. Our paper is motivated by a growing number of instances showing that businesses are closely attached to their local communities and that locally held attitudes significantly drive corporate decisions and outcomes (e.g., Cai & Shi, 2019; Chen et al., 2014; Hilary & Hui, 2009; Kumar et al., 2011). We focus on the local attitudes toward corruption, an important local norm that can influence the decision-making of individuals in a society (e.g., Caprio et al., 2013; Smith, 2016; Dincer & Johnston, 2017; Dass et al., 2021; Khieu et al., 2022). Following the literature (e.g., Egan et al., 2019; Kowaleski et al., 2021), our definition of misconduct includes “criminal, regulatory, internal investigations, and customer events that were resolved against the adviser”.

The existing literature provides mixed implications on a possible association between local corruption culture and financial adviser wrongdoing. On the one hand, social identity theory and behavioral consistency theory suggest that individuals tend to adhere to their local norms as they increase social recognition and reduce the cost of social disregard (Hofmann & Schwaiger, 2020; Hogg & Abrams, 1988; Stavrova et al., 2013) and that individuals tend to incorporate their organizations’ norms into their decisions and performance (Epstein, 1979; Funder & Colvin, 1991; McAdams, 1995; McGregor & Doshi, 2015). In addition, moral seduction theory posits that individuals and organizations may gradually become desensitized to unethical conduct when exposed to an environment characterized by pervasive wrongdoing (Moore et al., 2006; Guiral et al., 2008, 2010). This strand of literature supports the notion that local corruption culture should play a role in individuals’ decisions to respect or break rules (Glaeser et al., 1996), which can affect the likelihood of adviser misconduct.

On the other hand, several studies in behavioral and psychological literature suggest that individuals do not always follow the crowd (Buchanan, 2010) and that personability traits, rather than surrounding factors, play an essential role in understanding important life outcomes (Aligica et al., 2021; Groves, 2005; Roberts et al., 2007). In addition, in their study on individual wrongdoing, Glaeser et al. (1996) find that variations in demographics cannot explain the different levels of crime among similar neighborhoods. This suggests that factors within the surrounding environment, such as local corruption, may hold limited relevance to the tendency of misconduct in the financial advisor industry. Given the mixed implications from the literature, the association between corruption and financial adviser misconduct is not conclusive, and hence, urges a thorough investigation.

Our study aims to address the inconvenient gaps in the literature. The U.S. market is a natural setting to conduct our empirical study, as the financial advisory market in the U.S. accounts for about 30% of the global market, according to Global Financial Advisory Industry 2021.Footnote 3 Using a novel data of more than 4.7 million employee-year observations of financial advisers and the U.S. Department of Justice data on political corruption convictions, we find that advisers and firms located in areas with greater corruption are more likely to commit misconduct. The effect is economically significant, with one standard deviation increase in corruption convictions resulting in 14.2% increase in the likelihood of financial adviser misconduct. The results suggest that local corruption culture plays a role in shaping financial advisers’ misconduct behavior.

To address potential endogeneity concerns, we adopt a number of approaches. First, we employ a combination of city, year, and firm fixed effects to address possible omitted variables that can affect the likelihood of committing misconduct. Second, we conduct both individual-level and firm employee analyses to draw comprehensive insights into the association between corruption and advisers’ wrongdoing. Third, we adopt a two-stage least square analysis that employs an instrumental variable. We use the isolation of the state capital city from the populace (Campante & Do, 2014; Smith, 2016) as an instrument for corruption and document that instrumented corruption is positively associated with financial adviser misconduct. Fourth and finally, we use several alternative measures of corruption-prone environments to ensure that our results are not driven by a specific measure. We find our results are robust after accounting for several identification approaches.

Motivated by an extant literature suggesting that whistleblowing can contribute to discouraging financial fraud and increase the expected costs of fraudulent activities (e.g., Allingham & Sandmo, 1972; Dyck et al., 2010), we investigate how the passage of the Dodd-Frank Whistleblower Provision in 2010 affects the association between local corruption culture and adviser misconduct. The provision can contribute to diminishing the positive correlation between local culture and adviser misconduct.

We further investigate the effect of the Dodd-Frank Whistleblower Provision on adviser misconduct after accounting for local corruption perception. Using the ex-post whistleblowing tips to proxy for local tolerance for corruption, we document that a lower tolerance for corruption intensifies the impact of whistleblowing on financial adviser misconduct. Overall, our study suggests the impact of corruption culture on individual ethics and the essential role of whistleblowing laws in reducing corruption-prone norms.

Our paper contributes to the literature in two significant ways. First, we contribute to a growing body of literature that examines the externalities of corruption on various social and economic outcomes (e.g., Levine & Zervos, 1998; Mauro, 1995, as well as many subsequent studies). Our study suggests that local corruption culture matters for employee misconduct, even in highly regulated and qualified professional environments such as the financial advisory industry.

Second, we enrich a strand of research aiming to identify determinants and consequences of financial misconduct (e.g., Dimmock et al., 2018; Egan et al., 2018, 2019; Charoenwong et al., 2019; Dimmock et al., 2021; Klimczak et al., 2021; Dong et al., 2018; Bai et al., 2021, among others). Our study provides fresh evidence suggesting corrupt environments as an important driving force behind financial misconduct. Our study, therefore, complements Dimmock et al. (2018), which suggests working with co-workers with a misconduct history increases the likelihood of advisor misconduct, and Law and Zuo (2021), which finds that the working environment in early career periods affects the probability of committing professional misconduct later in advisers’ careers. In addition, our study provides conclusive evidence suggesting the essential role of whistleblowing laws in reducing corruption-prone norms.

The remainder of the paper is organized as follows. “Background and Empirical Prediction” section discusses related literature and empirical predictions. “Data and Variable Description” section discusses the data, sample, and variables employed in the analyses. “Empirical Models and Results" discusses empirical findings. “Further Analyses” section presents additional analyses and “Conclusion” section concludes the paper.

Background and Empirical Prediction

Background

Corruption has been evident throughout history (Tanzi, 1998; Heidenheimer & Johnston, 2011) and remains a significant issue in recent years. The Organization for Economic Co-operation and Development (OECD), in its 2016 report, notes that “corruption is a severe impediment to sustainable economic, political and social progress for countries at all levels of development”.Footnote 4It is one of the most important global issues in recent decades, needing urgent attention and serious approaches to combat it, according to the United Nations’ Sustainable Development Goals.Footnote 5

Economic studies have been devoted to examining the nature and consequences of corruption. Early studies (e.g., Bardhan, 1997; Levine & Zervos, 1998; Mauro, 1995) show that corruption damages economic growth and development. Subsequent studies document the unfavorable impacts of corrupted environments on a variety of economic decisions, such as investments (Wei, 2000), mergers and acquisitions (Nguyen et al., 2020), or various firm outcomes (e.g., Al-Hadi et al., 2022; Dass et al., 2016; Smith, 2016). However, whether, and to what extent, local corruption culture affects the likelihood and magnitude of misconduct in the financial advisory industry remains under-studied.Footnote 6

The U.S. financial advisor market had a value of $25.4 billion as of 2022 and continues to grow rapidly. At the global level, the global financial advisor market had a value of $85.8 billion as of 2022 and is forecast to grow to $122 billion by the year 2030 according to the Global Financial Advisory Industry Report.Footnote 7 The increasing economic significance of the large investment-advisory industry and the prevalence of wrongdoing in broker-dealer services has attracted a growing body of studies on financial adviser misconduct. Egan et al. (2019), for example, show that over 7% of advisors have misconduct records. Using an experimental deception game, Angelova and Regner (2013) document that voluntary payments can contribute to enhancing the quality of financial advice. Cohn et al. (2014) find that the prevailing business culture in the banking industry undermines the honesty norms. Ismayilov and Potters (2013) show that disclosing advisors’ interests does not necessarily reduce misadvising issues. Danilov et al. (2013) document that when financial advisors have close social ties, team incentives can reduce the quality of their product recommendations, while Dimmock et al. (2018) employ changes to co-workers caused by mergers between firms and find that misconduct spreads across brokers from mergers. Kowaleski et al. (2020) document that ethics training helps reduce misconduct incidences in the financial sector. Dimmock et al. (2021) show that personal real estate shocks affect adviser misconduct likelihood. Gelman et al. (2021) document that competition and firm market power are related to the probability of advisor misconduct. Law and Zuo (2021) show that the economic conditions in an early career affect the likelihood of adviser misconduct, while Law and Zuo (2022) find that minority advisors tend to receive more complaints in periods of high public concern about immigration. Kowaleski et al. (2021) document that supervisors influence employee financial misconduct. Dong et al. (2018) and Bai et al. (2021) suggest that social capital influences corporate misconduct. Most recently, Clifford and Gerken (2021) suggest that after the property rights shock, advisers tend to take better care of their client relationships. Collectively, this body of research underscores the point that the financial advisory industry is deeply rooted in personal interactions, making it susceptible to the influences of its environment and those with whom advisors interact.

Empirical Prediction

Motivated by these two strands of literature (i.e., corruption-related studies and research on financial advisor misconduct), our paper examines whether local corruption culture matters for financial adviser misconduct. We explore how the prevalence of corruption within a society may influence the behavior of financial advisors, particularly whether they are more likely to engage in misconduct in regions with higher levels of corruption. Our research question is guided by insights from social identity theory, behavioral consistency theory, and the moral seduction framework, which suggest that local norms and organizational norms can influence individual decision-making in the context of financial misconduct.

Specifically, according to social identity theory, individuals tend to adhere to their local norms to increase social recognition and mitigate the cost of social disregard (see, for example, Hogg & Abrams, 1988; Stavrova et al., 2013). In the context of financial advisors, this theory suggests that their behavior is not solely influenced by individual ethical considerations but is also shaped by the prevailing norms in the society or region in which they operate. If corruption is widely accepted or tolerated within a local community, financial advisors in that area may be more inclined to engage in misconduct, as it may be seen as a socially acceptable behavior within their social context.

Behavioral consistency theory extends the idea that individuals tend to incorporate their organizations’ norms into their decisions (see, for example, Epstein, 1979; Funder & Colvin, 1991; McAdams, 1995). Therefore, financial advisors working within firms or institutions may internalize the ethical standards, or lack thereof, practiced within their organizations. If an advisor’s workplace has a culture that condones or overlooks unethical behavior, they may be more likely to engage in misconduct themselves, influenced by the prevailing organizational norms (Dimmock et al., 2018; Law & Zuo, 2021).

The moral seduction framework further explains the link between local corruption culture and individual/firm misconduct. Moral seduction refers to the process by which individuals or firms become gradually desensitized to unethical behavior due to their exposure to an environment filled with wrongdoing. This phenomenon is especially relevant in professions characterized by client-consultant relationships, such as law, accounting, auditing, pharmaceuticals, and financial advisor services, where conflicts of interest frequently arise. Within these fields, the internal dynamics of moral seduction often lead to a sense of complacency among practitioners (Moore et al., 2006; Guiral et al., 2008, 2010). In a morally seductive environment, financial advisors and financial advisor firms may initially resist engaging in misconduct, but over time, the normalization of corruption can erode their ethical boundaries.Footnote 8 This gradual desensitization to unethical behavior may lead to a higher likelihood of misconduct among financial advisors and within financial advisor firms operating in corrupt regions.

Drawing from these theoretical frameworks, we propose a “norms-induced hypothesis.” This hypothesis suggests that the level of corruption in a region can impact financial advisors’ ethical decision-making. Specifically, in areas characterized by high levels of corruption, financial advisors may perceive a lower social cost associated with engaging in misconduct. The normalization and gradual desensitization of unethical behavior in such societies might lead advisors to view misconduct as more acceptable, thus increasing the likelihood of their involvement in wrongdoing. To analyze the relationship between local corruption culture and advisor misconduct, we frame the decision to engage in wrongdoing as a utility-maximizing function of personal cost and benefit tradeoffs following Becker (1968) and Dimmock et al. (2021). In this framework, advisors weigh the potential benefits, such as financial gains, against the potential costs, including legal repercussions and damage to their reputation. In regions with a high tolerance for corruption, the social cost of being involved in misconduct may be perceived as lower, making the utility-maximizing choice more likely to favor misconduct.

Next, we propose the “attention-induced hypothesis” to investigate the role of media coverage as a moderator in the relationship between local corruption culture and financial advisor misconduct. Recent studies suggest that media coverage plays an important role in detecting and combating corruption and misconduct (OECD, 2018; Schauseil, 2019). Giglioli (1996) revealed that the media has the power to unearth instances of corruption that official agencies might overlook, bringing these issues to the attention of a vast public audience. Furthermore, Bebchuk et al. (2010) document that media coverage significantly increases the likelihood of litigation against wrongdoers by augmenting the expected payoffs from legal actions. In addition, recent research by Han et al. (2023) suggests that local press can act as an effective governance mechanism in deterring and inhibiting financial advisor misconduct.

Therefore, it is evident that when cases of corruption or financial advisor misconduct receive media exposure, it goes beyond merely raising public awareness. It exerts pressure on regulatory authorities, compelling them to act. Consequently, financial advisors and financial advisor firms operating in regions characterized by a historical prevalence of corruption incidents often find themselves under the scrutiny of both mainstream and investigative journalism. Such firms also tend to be included on the watch list of regulatory bodies like the SEC or FIRNA. This heightened visibility makes the costs of misconduct starkly apparent and severe for those contemplating unethical actions.Footnote 9

Given the insights from these studies, we argue that media, both mainstream and social, can serve as a powerful moderator in the relationship between local corruption culture and financial advisor misconduct. By reporting on cases of corruption convictions and financial misconduct, media outlets can disrupt the process of moral seduction. They do so by highlighting the negative consequences of corruption and misconduct, thus making the ethical costs and risks more apparent to individuals and organizations.

Finally, we propose a null hypothesis that local corruption might be irrelevant to adviser misconduct. This hypothesis is motivated by several studies which suggest that market and industry regulations as well as ethical training all shape behavior in the financial advisory sector, potentially counteracting the influence of unethical environments. Financial advisory services are highly regulated and financial advisers are often required to pass qualification exams that are rules- and ethics-focused. The market and legal disciplines (e.g., job loss, reputation destroyed, fines enforcement) and comprehensive training on ethics and responsible business conduct can affect labor market activities in financial planning and services industries (Egan et al., 2019; Hauser, 2019; Kowaleski et al., 2020; OECD, 2016), attenuating the effect of unethical environments on employee behavior. Furthermore, Glaeser et al. (1996) find that variations in demographics are less informative in understanding different levels of misconduct among seemingly identical neighborhoods, implying that local corruption and adviser misconduct might be irrelevant.

Figure 1 presents the hypothesized links between local corruption culture and financial adviser misconduct. Overall, the mixed implications from the existing literature and three opposing hypotheses suggest that the association between local corruption culture and financial adviser misconduct is far from conclusive and, hence, remains an open and important empirical question that we address in the following sections.

Fig. 1
figure 1

Hypothesized links between political corruption and financial adviser misconduct

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Data and Variable Description

Our study explores the impact of corruption culture on the misconduct behavior of financial advisers in the U.S. We provide detailed descriptions of the variables in the following sub-sections and present our variable summary in Table 1.

Table 1 Data and variable description
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Financial Adviser Misconduct

Information on financial advisors is obtained from the Financial Industry Regulatory Authority (FINRA) BrokerCheck database.Footnote 10 FINRA is the largest self-regulatory organization, authorized by the Congress, to protect America’s investors and oversees financial advisers across the country. Since FINRA mandates that all investment advisers must report these disclosures, FINRA’s BrokerCheck data allows us to construct misconduct measures based on the detailed disclosures reported to FINRA. The data includes the registered and licensed financial advisers in the U.S. and enables us to identify their location of employment, qualifications, tenure, and misconduct incidences.Footnote 11 Following the literature (e.g., Egan et al., 2019; Kowaleski et al., 2021), our definition of misconduct includes “criminal, regulatory, internal investigations, and customer events that were resolved against the adviser”.

To construct our sample, we start with the universe of financial adviser data covered in BrokerCheck database at the intersection of corruption data, individual advisor attributes, and several public datasets that allow us to collect and construct various local attributes. Our final sample includes 4,740,760 adviser-year observations, covering 692,820 unique advisers, from 1999 to 2021, the latest available data at the time of writing the paper.

Our study investigates two levels of adviser misconduct behavior, including individual and firm levels. For the former level, the dependent variable is Misconduct, a dummy variable that equals one if the adviser commits misconduct in a given year and equals zero otherwise. Regarding the latter level, the dependent variable is Firm Employee Misconduct, which is the total number of adviser misconducts of a firm in a given year.

Corruption Measure

Our primary corruption measure is based on the number of political corruption convictions collected from the annual reports to Congress by the Department of Justice’s Public Integrity Section.Footnote 12 These convictions are for crimes such as bribery, extortion, and election crimes (Glaeser & Saks, 2006; Smith, 2016). Following Parsons et al. (2018), each year we construct our city-level corruption measure (Corruption) as the total number of corruption-related convictions, as reported by the U.S. Department of Justice, scaled by city population (in 100,000 people).Footnote 13 The measure of corruption shows the average number of convictions per 100,000 inhabitants. Following the convention in culture and behavioral economic literature (e.g., Brown et al., 2021; Campante & Do, 2014; Dass et al., 2020; Ellis et al., 2020; Smith, 2016), we assume that a location with a high number of corruption convictions is deemed to have a culture of corruption that should impact individual advisors and firms operating in that location. The total number of conviction cases is, therefore, a proxy for the actual level of corruption in that location. Corruption culture has several dimensions and, hence, might not be captured by a single metric. We, therefore, employ several additional corruption measures, which are discussed in detail in “Further Analyses” section.

Financial Adviser Attributes

We consider a number of factors that may influence financial advisers’ misconduct behavior. Specifically, we follow Egan et al.’s (2019) and Kowaleski et al. (2020) and include several financial advisers’ characteristics, including gender, the number of years of experience, and several professional licenses. Gender matters in financial industries, given Klein et al. (2021) suggest gender affects the quality of investment advice, and Egan et al. (2019) show that female advisors tend to commit misconduct less often than their male peers. We follow Egan et al. (2019) and construct a dummy variable (Male) indicating if the advisor is male. We control for prior misconduct incidences as Egan et al. (2019) suggest that prior misconduct incidences can help explain the likelihood of an advisor conducting future fraud. We use an indicator (Prior misconduct dummy) indicating whether the advisor has a recorded misconduct disclosure in prior years.

We consider several professional licenses and qualifications that are required for a registered financial representative. For instance, to provide fee-based advice, advisers must pass either Series 65 (Uniform Investment Adviser Law license) or Series 66 (Uniform Combined Law license) (Kowaleski et al., 2020). Series 63 (Uniform Securities Agent State Law license), which covers state security regulations, is required for a registered financial representative (Egan et al., 2019). According to Financial Industry Regulatory Authority (FINRA) Rule 1210 and Rule 1220(b), the Series 6 examination qualifies an investment adviser to sell open-end mutual funds and variable annuities, while the Series 7 examination is a general securities examination mandatory for individuals who wishes to sell and trade any type of general securities products.Footnote 14 In addition, the Series 24 examination, the general securities principal qualification examination, qualifies an individual to operate in an officer or supervisory capacity at general securities broker-dealer firms. We also collect information for other qualifications that the advisor has and construct a variable, Other_Qualification, as a sum of the other adviser qualifications.

Prior studies provide mixed implications regarding the impact of work experience and misconduct behavior. Limited work experience can be positively associated with the likelihood of committing misconduct (Egan et al., 2019), while a high level of experience can contribute to mitigate misconduct behavior (e.g., Dadanlar & Abebe, 2020; Rozema et al., 2023). To account for non-linearities in the impacts of experience on employees’ behavior, we control for both the number of years of experience and the squared term of adviser experience, following the literature (e.g., Avolio et al., 1990; Bai et al., 2021; Wooldridge, 2015).

For the firm-level analyses, we generate firm-specific controls by aggregating individual adviser attributes. Specifically, we compute the average years of experience of all advisors in a given firm (Aver_experience), the percentage of male-gendered advisors (Aver_male_advisor), and the percentage of advisors who hold different licenses (e.g., Aver_Series_63, Aver_Series_65, Aver_Series_66, Aver_Series_6, Aver_Series_7, Aver_Series_24, and Aver_ Other_Qualification) in a firm in a given year.

Other Control Variables

We account for a number of local characteristics that may impact misconduct behavior as suggested by the literature (e.g., Egan et al., 2019; Parsons et al., 2018). Specifically, we obtain and construct the following variables. First, we account for the unemployment rate as it can be a driver for crime (e.g., Lochner & Moretti, 2004; Raphael & Winter-Ebmer, 2001). The local unemployment rate (Unemployment) is the annual unemployment rate collected from the U.S. Bureau of Labor Statistics.

Second, we control for the local educational background as business ethics can be improved through education and training (Kowaleski et al., 2020). We obtain education data from the Integrated Public Use Microdata Series (IPUMS) U.S. database drawn from annual American Community Surveys that collect the educational attainment of respondents in each state. The education data item has values between 0 and 11.Footnote 15 Following Call et al. (2018), our education variable (Education) is the weighted average education level from surveyed respondents.

In addition, we account for local economic conditions as they can affect corruption practices (e.g., Glaeser & Saks, 2006). We, therefore, control for local personal income (Personal_Income) and population growth (Population_Growth) (e.g., Parsons et al., 2018; Smith, 2016). We collect personal income data from the U.S. Bureau of Economic Analysis and annual population growth from US Census Bureau. Finally, we account for religious participation as it can be related to financial fraud (e.g., Dyreng et al., 2012). Following prior studies (e.g., Christensen et al., 2018; Kumar et al., 2011), we collect religion data from the American Religion Data Archive (ARDA).Footnote 16

Figure 2 shows the average number of financial advisor misconducts per population (in 100,000) across the States from 1999 to 2021. We construct the financial adviser misconduct data from the Financial Industry Regulatory Authority (FINRA) BrokerCheck database. Figure 3 shows the average number of corruption-related convictions per population across the States from 1999 to 2021, with corruption conviction data from the annual reports to Congress by the Department of Justice’s Public Integrity Section. According to Figs. 2 and 3, several states have a high median number of corruption records and also exhibit a high incidence of financial adviser misconduct, such as New York, Delaware, the District of Columbia, Maryland, New Jersey, Texas, Pennsylvania, Massachusetts, Illinois, Virginia, Floria, and Arizona. Overall, the two figures demonstrate significant geographical variations in corruption and misconduct behavior.

Fig. 2
figure 2

Financial advisor misconduct per population. Figure shows the median value of financial advisor misconduct per population across States from 1999 to 2021. We report the total number of advisor misconducts per 100,000 people. A darker color indicates a higher level of advisor misconduct. The financial adviser misconduct data is from the Financial Industry Regulatory Authority (FINRA) BrokerCheck database

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Fig. 3
figure 3

Corruption-related conviction per population. Figure shows the median corruption convictions per population across States from 1999 to 2021. We report the number of corruption convictions per 100,000 people. A darker color indicates a higher level of corruption. The data is from the US Department of Justice’s Public Integrity Section

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Summary Statistics

Our sample includes over 4.7 million adviser-year observations, covering 692,820 unique advisers, over the period of 1999 to 2021. Table 2 provides the summary statistics for the number of corruption convictions per population (in 100,000) for each year from 1999 to 2021. Table 3 presents the descriptive statistics for the variables employed in the study. Panel A and Panel B report the results for the individual and firm-level variables, respectively.

Table 2 Descriptive statistics
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Table 3 Summary statistics
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The mean value of Corruption is 0.309, indicating that there are 0.309 corruption convictions for every 100,000 people in an average city. This is consistent with the previous literature (Du & Heo, 2022; Ellis et al., 2020; Parsons et al., 2018; Smith, 2016). The annual incidence of misconduct among financial advisers is 0.01, suggesting that about 1% of the sample advisors committed misconduct behaviors each year. The median advisor has about 9 years of experience, which is consistent with Dimmock et al. (2021), The average incidence of misconduct at the firm level is about 0.271. Overall, these descriptive statistics are highly consistent with the literature (e.g., Egan et al., 2019; Gelman et al., 2021; El Ghoul et al., 2023).

Empirical Models and Results

Baseline Models

To test our first hypothesis (norms-induced hypothesis) involving the relation between local corruption culture and the likelihood of misconduct, we follow Egan et al., (2018, 2019) and Kowaleski et al. (2020) and estimate the following linear probability models.

Individual level analyses:

$$Y_{i,t} = \alpha_{1} Corruption_{st} + \alpha_{2} Advisor Characteristics_{i,t} + \alpha_{3} Controls_{s,t} + FE + \varepsilon_{tfs1}$$
(1)

Firm level analyses:

$$Y_{f,t} = b_{1} Corruption_{st} + b_{2} Firm Characteristics_{f,t} + b_{3} Control_{s,t} + FE + \varepsilon_{tfs2}$$
(2)

where: i is the advisor, f is the firm, s refers to the city, and t is the year. \(Y_{it}\) is a dummy variable that equals one if a financial adviser i has engaged in financial misconduct in year t. \(Y_{ft}\) indicates the total number of recorded advisor misconducts of firm f in year t. \(Corruption_{s,t}\) is the number of corruption convictions scaled by city population in year t. \(Advisor Characteristics_{i,t}\) and \(Firm Characteristics_{f,t}\) are individual and firm characteristics (i.e., prior misconduct, gender, Series 63 license, Series 65 license, Series 66 license, Series 6 license, Series 7 license, Series 24 license, other qualifications, experience and experience squared) in year t, respectively. \(Control_{s,t}\) represents individual local area characteristics (i.e., unemployment, population growth, education, average income, religion) at year t. FE refers to city-by-year fixed effects and firm fixed effects. \(\varepsilon_{tfs1}\) and \(\varepsilon_{tfs2}\) are the error terms. The sample spans 1999 to 2021, the latest available data at the time of writing the paper.

To address potential omitted variables that can affect the likelihood of committing misconduct, we adopt fixed effect models. Specifically, we use city-by-year fixed effects to account for geographical variations that affect all individuals and firms in a given city each year.Footnote 17 We further adopt firm fixed effects to control for within-firm time-invariant features that may affect financial advisers’ likelihood to engage in misconduct behavior. We employ robust standard errors clustered by both firm and year dimensions to account for dependence both across firm and time (Cameron et al., 2011; Petersen, 2008). This conservative clustering approach accounts for potential time-varying correlations in omitted variables that affect firms each year (e.g., Gow et al., 2010); Thompson, 2011). We report the results for these tests in Table 4.

Table 4 Corruption culture and financial adviser misconduct: Baseline results
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Panel A and Panel B of Table 4 show the results for individual-level and firm-level analyses, respectively. We present 3 regression specifications: (1) a model without any control variables, (2) a model with adviser characteristics, and (3) a model with both adviser and local area characteristics. We find that the coefficient estimates on Corruption are positive and statistically significant across different model specifications and are robust for both individual- and firm-level analyses. The results suggest that local culture plays a significant role in shaping financial advisers’ misconduct behavior. This effect is also economically significant, with a one standard deviation increase in corruption convictions resulting in a 14.2% increase in the likelihood of financial adviser misconduct.Footnote 18

Regarding control variables, Table 3’s results document the significant impacts of adviser characteristics on the likelihood of committing misconduct. Specifically, male financial advisers exhibit a greater incidence of wrongdoing. The results are consistent with Egan et al. (2019). In addition, financial advisers with prior misconduct are more likely to engage in new misconduct.Footnote 19

Instrumental Variable Approach

To address the concern that our linear probability estimates could be biased by omitted variables, we employ a two-stage least square analysis that employs an instrumental variable (e.g., Jiang, 2017). Following Campante and Do (2014) and Smith (2016), we use the isolation of the state capital city from the populace (Campante & Do, 2014; Smith, 2016) as an instrument for corruption. Specifically, our instrument is the gravity-based centered index for spatial concentration (denoted Concentration) from Campante and Do (2010). Prior literature suggests that isolated capital cities are associated with more state corruption (e.g., Campante & Do, 2014; Smith, 2016). In addition, there appears no direct link between capital city isolation and a firm’s financial decisions. We, therefore, argue that choosing capital city isolation as our instrument fulfills the relevance and exclusion conditions for an identification test (Roberts & Whited, 2013). We then perform a 2SLS regression as follows:

$$Concentration = {\text{ f }}\left( {Corruption,Controls,Fixed \, effects} \right),$$
(3)
$$Misconduct = {\text{ f }}(\widehat{Instrumeted Corruption},Controls,Fixed \, effects),$$
(4)

where Eq. (3) is the first-stage regression and Eq. (4) is the second-stage equation under our 2SLS regression framework; Controls refer to firm control variables as in Eqs. (1) and (2); Fixed effects refer to the fixed effects as in the baseline models. We present the results for these tests in Table 5. Panel A reports the results for individual-advisor analyses while Panel B reports the results for firm employee analyses.

Table 5 Instrumental variable approach
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In model (1), the first-stage regression reveals a negative correlation between the concentration of the state capital city and local corruption, which is consistent with Campante and Do (2010, 2014) and Smith (2016). The F-test that is associated with the addition of the instrumental variable to the first-stage model rejects the null of weak instruments (Panel A: F = 22.94, p = 0.01; Panel B: F = 168.34, p = 0.00), which supports the relevance of our instrumental variable.Footnote 20 In the second-stage regression (models (2)), we use the predicted values obtained from the first stage and regress these values against the misconduct measures, as described in Eq. (4). We observe a positive and significant coefficient for the instrumented variable in the second-stage model. These findings lend further support to our hypothesis that financial advisers’ misconduct is impacted by their local corruption culture.

Effect of Dodd-Frank Whistleblower Provision on Financial Adviser Misconduct

We further examine the effect of the Dodd-Frank Whistleblower Provision on t]he relation between local corruption and adviser misconduct. The Provision rewards whistleblowers, who provide the Securities and Exchange Commission (SEC) or the Commodity Futures Trading Commission (CFTC) significant information and help the SEC identify possible frauds and other violations, with financial bounties.Footnote 21 As suggested by the Chief of the SEC’s Office of the Whistleblowers, “these awards encourage others with specific, high-quality information regarding securities laws violations to step forward and report it to the SEC”.Footnote 22 A growing body of studies suggests that whistleblowing contributes significantly to detecting financial fraud and increases the expected costs of fraud and corruption for perpetrators (e.g., Allingham & Sandmo, 1972; Andon et al., 2018; Butler et al., 2020; Call et al., 2018; Dyck et al., 2010; Latan et al., 2019; Wilde, 2017). In addition, studies by the Certified Fraud Examiners that have indicated that tips account for approximate 40% of frauds found.Footnote 23 As the Provision can contribute to reducing the incentives for fraudulence, we investigate how the passage of the Dodd-Frank Whistleblower Provision in 2010 affects the association between local corruption and adviser misconduct. Specifically, we use the following models:

Advisor level analyses:

$$Y_{i,t} = \alpha_{1} Corruption_{st} + \alpha_{1} Corruption_{st} \times Provision + \alpha_{2} Controls_{s,t} + FE + \varepsilon_{tfs1}$$
(5)

Firm employee analyses:

$$Y_{f,t} = b_{1} Corruption_{st} + b_{2} Corruption_{st} \times Provision + b_{3} Control_{s,t} + FE + \varepsilon_{tfs2}$$
(6)

where i is the advisor, f is the firm, s refers to the city, and t is the year. \(Y_{it}\) is a dummy variable that equals one if a financial adviser i has engaged in financial misconduct in year t. \(Y_{ft}\) indicates the total number of recorded advisor misconducts of firm f in year t. \(Corruption_{s,t}\) is the number of corruption convictions scaled by city population in year t. Provision equals 1 if the given year is in or after 2010 when the Provision was enacted by the SEC and equals 0 otherwise. \(Control_{s,t}\) represents individual and firm advisor characteristics, and local area characteristics (e.g., unemployment, population growth, education, average income, religion) at year t. FE refers to fixed effects. \(\varepsilon_{tfs1}\) and \(\varepsilon_{tfs2}\) are the error terms. The variable of interest is the interaction term, Corruption × Provision, which captures the moderating effect of the Whistleblower Provision. We report the results for these analyses in Table 6.

Table 6 The effect of Provision on corruption and financial adviser misconduct
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Table 6 presents the results for the effects of local corruption culture on financial adviser misconduct conditioned on the passage of the Dodd-Frank Whistleblower Provision in 2010 for both the individual level (panel A) and firm employee level (panel B). Consistent with the previous findings, we find that the number of corruption convictions is significantly and positively associated with financial adviser misconduct. Interestingly, the coefficients on the interaction term, Corruption × Provision, are negative and statistically significant, suggesting that the Provision plays a significant role in reducing the positive association between local culture and adviser misconduct. We find these findings are robust to alternative model specifications and hold for both individual- and firm-level analyses. Our results are consistent with the literature suggesting that the SEC Whistleblower Program can contribute to reduce the likelihood of committing fraud such as financial misreporting (Wiedman & Zhu, 2023) or rent-seeking activities (Du & Heo, 2022).

Du et al. (2020) note, local corruption perception can affect the enforcement of whistleblowing laws, with lower (higher) tolerance of wrongdoing exacerbating (extenuating) the impact of whistleblowing. We, therefore, further investigate the effect of the Dodd-Frank Whistleblower Provision on adviser misconduct after accounting for local corruption perception.

We follow Du et al. (2020) and employ the ex-post whistleblowing tips to proxy for local tolerance for corruption.Footnote 24 Specifically, in high-tips areas, local residents are less tolerant of public corruption and exhibit stronger incentives for whistleblowing. Based on the number of whistleblowing tips data, we categorize states into high-tips and low-tips states based on the sample mean value of the tips data. We then run models (5) and (6) separately for each group based on the whistleblowing tips to further explore the impact of the Dodd-Frank Whistleblower Provision conditioned on the local corruption perception. We report the results for these tests in Table 7.

Table 7 The effect of Provision on misconduct conditioned on corruption tolerance level
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According to Table 7, the coefficients on the interaction term, Corruption × Provision, are only statistically significant among firms operating in states with lower corruption tolerance, suggesting that a lower tolerance for corruption intensifies the impact of whistleblowing on financial adviser misconduct. The results are robust for both individual and firm-level analyses. Our findings are in line with Du and Heo (2022) and Du et al. (2020), which suggest a more pronounced impact of the Provision among areas with lower local tolerance for corruption.

Further Analyses

To conduct a comprehensive investigation on the association between local corruption culture and financial adviser misconduct, we consider a range of additional analyses, which we discuss in detail in the following section.

Specifically, to ensure that our findings are not driven by specific measures, we consider alternative measures of corruption culture. First, we follow the literature (e.g., El Ghoul et al., 2023) and construct a high corruption culture measure. Specifically, we employ a dummy variable (High Corruption Dummy) that equals 1 for firms located in states where the corruption measure is in the top quartile of that year, and 0 otherwise. Second, we follow the approach of Du and Heo (2022) and construct the decile rank of the corruption measure (denoted Corruption Decile rank). We report the results for these analyses in Columns (1) and (2) in Table 8.

Table 8 Additional analyses
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Corruption culture has several dimensions and, hence, might not be captured by a single metric. We, therefore, employ two additional corruption measures, following Cordis and Milyo (2016) and Dincer and Johnston (2017). The first measure is a corruption conviction index which is sourced from the Justice Department’s Public Integrity Section. Corruption conviction index (CCI) uses data from the Justice Department’s Report to Congress on the Activities and Operations of the Public Integrity Section and reports the total convictions for crimes related to corruption annually. The second measure is the aggregate corruption measure by combining both the corruption conviction index (CCI) and the corruption reflections index (CRI). Each year corruption reflections index (CRI) is based on the corruption stories covered in Associated Press news wires and indicates the number of news stories that mention the terms “corrupt”, “fraud”, and “bribe” (and their variants), divided by the number of pages mentioning “politic” and its variants (Dincer & Johnston, 2017). As Dincer and Johnston (2017) suggest, CCI might suffer potential biases as it covers only those officials who are caught, and the data does not indicate the seriousness or consequences of a case. Using the aggregate corruption measure by combining both the CCI and CRI, therefore, allows us to capture both conviction number and its consequences.Footnote 25 The higher the value of the aggregate corruption measure, the more corruption-prone the local culture. We report the results for these analyses in Columns (3) and (4) in Table 8.

Next, Smith (2016) and Xie et al. (2023) suggest that corruption in the District of Columbia (DC) can be higher than in other districts. To ensure that our results are not driven by outlier districts, we follow Xie et al., (2023) and repeat our baseline regression by excluding DC-headquartered firms. We report the results for this test in Column (5) in Table 8.

Finally, we follow the literature (e.g., El Ghoul et al., 2023; Hossain et al., 2021) and construct a time-invariant corruption composite index (denoted Corruption Composite Index) that accounts for four corruption measures, including (i) corruption convictions, (ii) convictions per capita, (iii) reporter ratings, and (iv) lack of stringent laws.Footnote 26 The first two measures, corruption convictions and convictions per capita, are similar to our primary corruption measure in the baseline regressions. The third component, the reporter ratings, is obtained from a survey of 280 state public reporters in Dincer and Johnston (2020), in which each public reporter was asked how corrupt they thought their state governments were. The final component, the lack of stringent laws data, is obtained from the State Integrity Investigation, where the journalists rank each state based on various corruption risk indicators, including campaign finance, ethics laws, or lobbying regulations.Footnote 27 Taken together, the higher the value of the Corruption Composite Index, the more corruption-prone the local culture. We report the results for these analyses in Column 6 in Table 8.

According to Table 8’s results, the coefficient estimates on corruption measures are positive and statistically significant across different model specifications and are robust for both individual- and firm-level analyses. Thus, Table 8’s results suggest that our results are not driven by a specific measure and are robust after accounting for several identification approaches. Overall, the results lend further support to our hypothesis that financial advisers’ misconduct is driven by their local corruption environment.

Conclusion

Our study investigates the impact of local corruption culture on misconduct in the financial advisory industry. We find that advisors in areas with higher corruption levels are more prone to misconduct, underscoring the role of corruption culture in shaping unethical behavior. The enactment of the Dodd-Frank Whistleblower Provision has mitigated this effect, highlighting the importance of whistleblowing laws in curbing corruption-prone norms.

In terms of contributions to the literature, our study delves into the impact of political corruption on employee misconduct in regulated industries, adding to existing research on the externalities of corruption (e.g., Levine & Zervos, 1998; Mauro, 1995, as well as many subsequent studies). Furthermore, we provide evidence that corrupt environments significantly influence financial misconduct, complementing prior studies on the determinants and consequences of such misconduct, and highlighting the importance of whistleblowing laws in reducing corruption-prone norms. (e.g., Charoenwong et al., 2019; Dimmock et al., 2018; Egan et al., 2019; Klimczak et al., 2021).

From a business ethics perspective, our study reinforces the critical need for vigilance against corruption at both organizational and regulatory levels. The fact that financial advisers and firms located in areas with higher levels of corruption are more likely to engage in misconduct highlights the importance of instilling a strong ethical culture within financial institutions.Footnote 28 These findings underscore the urgency of implementing robust anti-corruption policies and ethical training programs within the financial advisory sector. Furthermore, our research emphasizes that corruption is not solely a problem of developing nations but is a concern even in developed economies like the United States. This underscores the global nature of the challenge and the need for a collective, international effort to combat corruption in business.

Our research offers several implications for future studies. It encourages extending our framework to international markets due to the global nature of corruption. Policymakers should consider our findings when designing regulations to promote ethical behavior and trust among corporate stakeholders. Investors can reduce information asymmetry by seeking third-party financial counseling to evaluate product recommendations and monitor advisor activities.