This research aims to investigate the financial well-being (FWB) of retired male athletes in major professional North American team sports. Framed around Kempson’s (2017) FWB Model, the aim of this research is (1) to identify the key drivers that influence how retired athletes feel about their current financial situations and (2) to assess these findings in the context of how external constituents view the FWB of these athletes. The research addressed two questions: What is the current level of FWB for retired professional athletes? What are the key determinants that drive the FWB of retired professional athletes?
A primary survey of retired professional athletes (n = 65) provided the data for analysis. The sample of 65 retired professional athletes, primarily from the National Hockey League and the National Football League, is a unique sample of the hard-to-reach and small population of elite professional athletes who have high levels of wealth.
Results provide direction to professional sports leagues, players’ associations, players and player agents and/or advisors regarding the management of athlete wealth, both pre- and post-retirement.
Based on input directly from retired athletes, the findings dispute assertions of financial mismanagement on behalf of retired athletes and provide evidence that the opposite holds true in most cases, where retired athletes are satisfied about their finances.
Introduction
In the industrial age, workers have become increasingly interested in the post-work part of their lives, ensuring that they are financially prepared for retirement (Easterlin, 1974). More recently, in the post-pandemic world, the financial well-being (FWB) of professionals across various industries has become a focal point of much media and industry discussion. FWB is typically defined as the outcome of efforts by people or their advisors to manage their money to provide for their lives, their families and their future retirements. Muir et al. (2017, p. vi) define FWB as “when a person is able to meet expenses and has some money left over, is in control of their finances and feels financially secure, now and in the future”. Moolman (2023) adds that the individual must also have limited stress viz-á-viz their future financial position. Many scholars (e.g., Glidden and Brown, 2017; Huston, 2010) emphasize the importance of financial literacy in achieving FWB. Financial literacy is a complex construct itself that encompasses numerous variables regarding a person’s knowledge, experiences, and skills related to money management (Remund, 2010), including their ability to both control and comprehend their own finances (Louw et al., 2013).
These broader conversations about financial security often mirror debates in sport, where similar concerns arise around athletes’ financial well-being. Discussions related to the pension plans of teachers, government workers, pilots, and other groups are often topics of media reports, leading to numerous studies investigating the topic (American Academy of Actuaries, 2021; Stewart, 2021), including work specific to the sport industry and professional athletes (Hong and Fraser, 2021; Mogaji et al., 2021). Cases of retired athletes mishandling their significant fortunes have been widely noted despite their substantial earnings (Hong and Fraser, 2021; Moolman, 2020; Torre, 2009). Media reports frequently highlight business decisions by athletes that did not work out, extravagant purchases such as airplanes and sport cars, and expensive divorces. Major professional sport athletes’ careers could be described as unique with an increased need for planning for FWB early due to their professional sport careers being typically short and high risk, with elevated performance expectations and very high pay (Park et al., 2013), typically followed by a challenging transition to another career (or retirement) at a young age (Surujlal, 2016).
The life cycle hypothesis, which suggests that earnings grow throughout one’s life until retirement when those earnings support retirement, typically governs wealth accumulation. However, professional athletes deviate from this due to their relatively concentrated earning period early in their adult lives (Modigliani, 1986). For example, the average athlete in the major four North American sports earned USD $5.3 million per year and retires well before the age of 30 (Spotrac, 2023). Prudent money management can be paramount during an athlete’s playing years, as the sheer amount of savings needed to fund such a lengthy retirement can be enormous. Carlson et al. (2015) found that bankruptcy rates, a strong example of financial hardship amongst National Football League (NFL) players, were not affected by their total earnings or career length. In other words, the more an athlete earned and the longer they played had no bearing on the likelihood of going bankrupt in retirement.
While FWB is a relatively recent term, efforts to comprehend and quantify an individual’s financial status are not novel. The inception of the gross domestic product (GDP) concept by Kuznets (1934) was intended to gauge national performance from an output perspective. However, the initial measure overlooked the individual dimension, assuming that as a nation’s GDP increased, citizens’ FWB and associated happiness would similarly ascend (Kuznets, 1962). However, Easterlin’s work (1974) revealed that while wealthier individuals within a society tended to be happier, heightened prosperity in a country did not correlate with greater overall happiness. This phenomenon, termed the Easterlin Paradox, indicates a saturation point exists for the positive relationship between income and satisfaction. This led to a body of research regarding the drivers of FWB, with a prevailing view that increasing financial literacy—focused on knowledge and skills —was the most important driver (Bruggen et al., 2017; Glidden and Brown, 2017).
Building on this work, practitioner models are also common in FWB. For instance, Kempson (2017) published research that found that behaviors were paramount in influencing one’s FWB, often prevailing over financial expertise. A multitude of factors, both internal and external, were found to impact FWB, leading to a framework for assessing and quantifying FWB (Kempson, 2017).
This research builds upon the work of Easterlin (1974) and Kempson (2017), emphasizing factors beyond income. This study seeks to identify the drivers of FWB from the perspective of retired athletes. The Kempson (2017) framework is used to respond to two research questions: First, “What is the current level of FWB for retired professional athletes?” Second, “What are the key determinants that drive the FWB of retired professional athletes?”
Financial well-being
In the early 1970’s, Easterlin (1974) was one of the first to study happiness data, a subsection of overall well-being. Up to this point, an individual’s well-being was thought of as a function of a nation’s output (i.e., gross national product “GNP”), where individuals in wealthier nations were thought of as being happier than their poorer counterparts (Easterlin, 1974). In turn, poorer countries sought to increase outputs to improve standards of living and thus, FWB for its citizens (Easterlin, 1974). Cantril (1965) contradicted this assumption, finding that for most countries studied, a nation’s GNP had very little impact on the happiness of its citizens.
Easterlin (1974) found that increases in income can, in fact, improve happiness for individuals within a particular country but fails to do so when comparing between countries—a concept known as the Easterlin Paradox. This theory is based on the assumption that when individuals were asked to rate their happiness levels, they immediately compare their situation to a particular benchmark for reference, such as a peer group or society. This allows them to subjectively create an “average happiness” benchmark to which they can judge their own happiness. This applies only to within country analysis. When comparing happiness levels between countries, increases in real GNP tend to affect all of society evenly. As the GNP of poor countries increase, their citizens tend to benefit together. Therefore, increasing a nation’s output on average has very little significance on the overall happiness levels of its citizens (Easterlin, 1974). Easterlin’s findings represent some of the early attempts to understand the magnitude of factors influencing FWB.
In the years following the work of Easterlin (1974), the concept of FWB morphed from a focus on general satisfaction or happiness with one’s financial situation to include satisfaction with savings, income, and opportunity to make ends meet, sense of material security, and the fairness of reward distribution systems (Porter and Garman, 1992). Indeed, Porter (1990) set out to test a conceptual model and measurement of FWB, finding that FWB depends on more than just objective measures, such as demographics and socioeconomic status, and subjective measures, such as family financial management and household context. Porter (1990) found that personal characteristics and reference-point measures were also important drivers. Recent studies have focused on the sustainability (i.e., retirement) aspects of FWB (Muir et al., 2017), including lower stress about the longer term (Moolman, 2023) and the importance of financial literacy, skills and experience in money management (Huston, 2010; Remund, 2010).
The inclusions of evaluated attributes support the work of Davis and Helmick (1985) who found that reference-point variables need to be added to objective and subjective measures when evaluating FWB. These findings support Easterlin’s (1974) view that FWB is based on one’s perceived economic position compared to their peers, amongst other factors.
Measuring financial well-being
Despite advancements in understanding FWB, there is still no universally agreed upon definition or measurement method (Kempson and Poppe, 2018). Kempson (2017) proposed defining FWB as, “The extent to which someone is able to meet all their commitments and needs comfortably and has the financial resiliency to maintain this in the future” (p. 19). Therefore, it can be said that three components – meeting commitments, feeling financially comfortable, and financial resiliency – had the largest influence on FWB. Kempson further identifies the most important drivers of FWB as behaviors as well as social, economic and environmental factors. These drivers are primarily influenced by psychological factors and attitudes to spending, saving, and borrowing, with knowledge and experience playing a much smaller role (see Figure 1).
The model shows a total of eight text boxes in different sizes and scattered on the model. The largest text box labeled “Financial Wellbeing” is shown on the right, and the second largest box is labeled “Socioeconomic Environment” on the left. The third largest is the “Money Use Behaviors,” present on the right side of the “Financial Wellbeing.” Then the label of the boxes is in ascending order as follows: “Money Management Behaviors,” in the center. “Financial Confidence and Control” at the bottom center, “Financial Attitudes,” and “Personality Traits” have the same size. “Financial Attitudes” is present on the left of the “Socioeconomic Environment.” “Personality Traits” is present below “Socioeconomic Environment.” The last is “Knowledge and Experience” on the top left of “Socioeconomic Environment.” From “Socioeconomic Environment,” five arrows arise and point to “Financial Wellbeing,” “Personality Traits,” “Knowledge and Experience,” “Money Management Behaviors,” and “Financial Attitudes.” From “Personality Traits,” three arrows arise and point to “Financial Confidence and Control,” “Financial Attitudes,” and “Money Management Behaviors.” From “Knowledge and Experience,” two arrows arise and point to “Money Management Behaviors” and “Financial Confidence and Control.” From “Knowledge and Experience,” a dashed arrow arises and points to “Financial Attitudes.” From “Money Management Behaviors,” an arrow arises and points to “Financial Attitudes,” and an arrow arises and points to “Money Use Behaviors.” From “Financial Confidence and Control,” an arrow arises and points to “Financial Wellbeing.” Likewise, a dashed arrow arises from “Financial Confidence and Control,” and points to “Money Use Behaviors.” From “Money Use Behaviors,” an arrow points to “Financial Wellbeing.”Revised conceptual model of financial well-being. Source: Kempson and Poppe (2018). Understanding financial well-being and capability – a revised model and comprehensive analysis
The model shows a total of eight text boxes in different sizes and scattered on the model. The largest text box labeled “Financial Wellbeing” is shown on the right, and the second largest box is labeled “Socioeconomic Environment” on the left. The third largest is the “Money Use Behaviors,” present on the right side of the “Financial Wellbeing.” Then the label of the boxes is in ascending order as follows: “Money Management Behaviors,” in the center. “Financial Confidence and Control” at the bottom center, “Financial Attitudes,” and “Personality Traits” have the same size. “Financial Attitudes” is present on the left of the “Socioeconomic Environment.” “Personality Traits” is present below “Socioeconomic Environment.” The last is “Knowledge and Experience” on the top left of “Socioeconomic Environment.” From “Socioeconomic Environment,” five arrows arise and point to “Financial Wellbeing,” “Personality Traits,” “Knowledge and Experience,” “Money Management Behaviors,” and “Financial Attitudes.” From “Personality Traits,” three arrows arise and point to “Financial Confidence and Control,” “Financial Attitudes,” and “Money Management Behaviors.” From “Knowledge and Experience,” two arrows arise and point to “Money Management Behaviors” and “Financial Confidence and Control.” From “Knowledge and Experience,” a dashed arrow arises and points to “Financial Attitudes.” From “Money Management Behaviors,” an arrow arises and points to “Financial Attitudes,” and an arrow arises and points to “Money Use Behaviors.” From “Financial Confidence and Control,” an arrow arises and points to “Financial Wellbeing.” Likewise, a dashed arrow arises from “Financial Confidence and Control,” and points to “Money Use Behaviors.” From “Money Use Behaviors,” an arrow points to “Financial Wellbeing.”Revised conceptual model of financial well-being. Source: Kempson and Poppe (2018). Understanding financial well-being and capability – a revised model and comprehensive analysis
Taken directly from the work of Kempson and Poppe (2018), the graphic in Figure 1 provides a schematic of the drivers of FWB. It illustrates that the ability to meet financial commitments, feeling comfortable while doing so, and having the ability to withstand shocks financially are the most influential factors in determining FWB (Kempson, 2017). The figure includes updates from Kempson and Poppe’s (2018) later work, showing that behavioral factors have the most significant influence on FWB scores. Contrary to popular belief, knowledge and experience, as well as psychological factors, have very little direct influence on overall FWB. Instead, they indirectly influence FWB by operating through various behaviors (Kempson and Poppe, 2018).
The financial well-being of retired professional athletes
Recently, professional team sport leagues in North America have increased their commitment to helping athletes transition into retirement. Examples of such initiatives include the National Hockey League (NHL) Alumni Transition Program and the NFL Professional Athletes Foundation. These programs were developed following a 2009 Sports Illustrated article by Pablo Torre, which reported that 78% of former NFL players were under financial stress within two years of retirement, and that 60% of National Basketball Association (NBA) players have spent their earnings within five years of retirement. Although not formal research, Torre’s (2009) article highlighted concerns about the FWB of retired athletes, suggesting it was not at a level that is not consistent with league preferences.
An athlete’s earning power and career duration differ significantly from those of most professionals. Typically, people save during periods of high income for times when income is lower, such as retirement, a concept known as the life cycle hypothesis. Carlson et al. (2015) tested this hypothesis with individuals whose income spikes early in their careers and lasts only a few years, such as NFL players. NFL careers are typically short, with a small proportion of players continuing beyond the age of 30. Carlson et al. (2015) initially aimed to study the lifetime consumption patterns and overall wealth of NFL retirees but shifted focus to bankruptcy rates due to data limitations. They found that bankruptcy rates were unaffected by a player’s earnings or career length. For example, a player who earned $15 million over a 10-year career had the same probability of declaring bankruptcy as a player who played one year on a minimum (i.e., rookie) salary. Only 1.9% of retired NFL players file for bankruptcy within two years of retirement, much lower than Torre’s (2009) claim that 78% of NFL retirees are bankrupt or in financial distress at that point.
Time preference of retired professional athletes
Time discounting measures the preference for receiving a smaller amount of money now versus a larger amount in the future. Past research indicates that higher discount rates are associated with a preference for immediate rewards over delayed ones (Rosenboim et al., 2011). Rosenboim suggests that athletes are more likely to exhibit high discount rates. According to Carstensen et al.'s (1999) socio-emotional selectivity theory, the perception of time significantly influences goal selection and pursuit. The peak performance careers of major professional baseball, football, basketball, and hockey players typically last 4–5 years and this has been the case for many years (Foster, O’Reilly and Davila, 2020; Ogilvie and Howe, 1986). These short earning windows may psychologically drive athletes to act more in the moment (Heshmat, 2016).
Research indicates that athletes discount time more heavily than non-athletes, suggesting they are less likely to defer payments than their peers in the general population (Rosenboim et al., 2011). This is intuitive given the highly competitive, well-paying, and short-lived nature of professional sports careers. In the results section of the study, time orientation results are shared for the sample of retired athletes, exploring whether they, like current athletes, exhibit high time discount rates (i.e., low time orientation scores).
Method
To address the two research questions, retired athletes with at least one full year of playing experience in the NHL, NFL, NBA, or Major League Baseball (MLB) were surveyed. The survey gathered data to address the first two research questions regarding (1) the current level of FWB for retired professional athletes and (2) the key determinants driving FWB.
The survey was conducted from July 19, 2021, to September 16, 2021, targeting retired athletes. Recruitment involved random selection via social platforms and referrals from sports industry experts. The internet-based survey aimed to sample a representative group of retired athletes from the four leagues. The survey was divided into four sections: FWB, FWB components, FWB sections, and FWB section subcomponents, as depicted in Figure 2.
The vertical flowchart begins with a box labeled “F W B” at the top. “F W B” downward branches to three boxes in the “F W B components” category, arranged horizontally and labeled from left to right as follows: “Meeting Commitments,” “Feeling Comfortable,” and “Financial Resiliency.” Each of these three boxes has a small box attached at the bottom left with the number labeled. “Meeting Commitments” is labeled “3,” and “Feeling Comfortable” and “Financial Resiliency” are labeled “4.” Above the “F W B” sections, four boxes are arranged horizontally and labeled from left to right as follows: “Behavior Factors,” “Psychological Factors,” “Knowledge and Experience Factors,” and “Socioeconomic Factors.” Each of these four boxes also has a small box attached at the bottom left with a number labeled. “Behavior Factors” is labeled “26,” “Psychological Factors” is labeled “18,” “Knowledge and Experience Factors” is labeled “14,” and “Socioeconomic Factors” is labeled “5.” From each of the “F W B” sections, downward branches extend to the “F W B” subcomponents, except for “Socioeconomic Factors.” “Behavior Factors” downward branches to seven boxes arranged vertically and labeled from top to bottom as follows: “Planning and Budgeting,” “Spending Restraint,” “Not Borrowing for Daily Bills,” “Monitoring Finances,” “Active Saving,” “Informed Product Choice,” and “Informed Decision Making.” “Psychological Factors” downward branches to six boxes arranged vertically and labeled from top to bottom as follows: “Time Orientation,” “Impulsivity,” “Social Status,” “Self-control,” “Locus of Control,” and “Attitudes Towards Money.” “Knowledge and Experience Factors” downward branches to four boxes arranged vertically and labeled from top to bottom as follows: “Money Management Experience,” “Financial Product Experience,” “Financial Product Knowledge,” and “Understanding Risk.”FWB and subsections. Source: Authors’ created figure
The vertical flowchart begins with a box labeled “F W B” at the top. “F W B” downward branches to three boxes in the “F W B components” category, arranged horizontally and labeled from left to right as follows: “Meeting Commitments,” “Feeling Comfortable,” and “Financial Resiliency.” Each of these three boxes has a small box attached at the bottom left with the number labeled. “Meeting Commitments” is labeled “3,” and “Feeling Comfortable” and “Financial Resiliency” are labeled “4.” Above the “F W B” sections, four boxes are arranged horizontally and labeled from left to right as follows: “Behavior Factors,” “Psychological Factors,” “Knowledge and Experience Factors,” and “Socioeconomic Factors.” Each of these four boxes also has a small box attached at the bottom left with a number labeled. “Behavior Factors” is labeled “26,” “Psychological Factors” is labeled “18,” “Knowledge and Experience Factors” is labeled “14,” and “Socioeconomic Factors” is labeled “5.” From each of the “F W B” sections, downward branches extend to the “F W B” subcomponents, except for “Socioeconomic Factors.” “Behavior Factors” downward branches to seven boxes arranged vertically and labeled from top to bottom as follows: “Planning and Budgeting,” “Spending Restraint,” “Not Borrowing for Daily Bills,” “Monitoring Finances,” “Active Saving,” “Informed Product Choice,” and “Informed Decision Making.” “Psychological Factors” downward branches to six boxes arranged vertically and labeled from top to bottom as follows: “Time Orientation,” “Impulsivity,” “Social Status,” “Self-control,” “Locus of Control,” and “Attitudes Towards Money.” “Knowledge and Experience Factors” downward branches to four boxes arranged vertically and labeled from top to bottom as follows: “Money Management Experience,” “Financial Product Experience,” “Financial Product Knowledge,” and “Understanding Risk.”FWB and subsections. Source: Authors’ created figure
Figure 2 illustrates the drivers of FWB, highlighting that the ability to meet financial commitments, feeling comfortable, and having the financial resiliency to withstand shocks are the most influential factors (Kempson, 2017). The survey calculated FWB scores using the weighted average of the three FWB component scores. Behavior, psychological, and knowledge and experience FWB section scores were calculated using the weighted average of their subcomponent question scores. This segmentation allows for computing scores for each section, aiding in understanding the most and least influential factors. As shown in Figure 2, the gray boxes indicate the number of questions from the survey asked in each section. The FWB scores were calculated using the weighted average of the three FWB component scores. Behavior, psychological, and knowledge and experience section scores were calculated using the weighted average of their subcomponent question scores. Segmentation of the FWB framework in this manner allows for the computing of scores for each section, which better allows for understanding the most and least influential factors.
The questions were aligned with the corresponding level of the FWB framework, encompassing FWB, behaviors, psychological factors, and knowledge and experience. Approximately 200 invitations were sent out, facilitated by industry experts, resulting in a 100% male respondent pool, reflecting the male-only nature of the leagues. The survey was accessed by 65 respondents (32.5%). Due to the authors’ networks, respondents had retired from the NHL (76%), NFL (20%), and MLB (4%), with no NBA retirees participating. The sample size is considered sufficient given the small population size and challenging access. Given the similar and high levels of compensation and career duration amongst these leagues, the sample is assessed together for the purposes of addressing the research questions.
Among the surveys collected, ten were excluded due to limited responses. One respondent who failed to meet the minimum one-year playing requirement was excluded, while another who had indeterminable income was also omitted. Following Kempson’s (2017) thresholds, incomplete surveys were defined as those with 15 or more unanswered questions out of 91, leading to the elimination of three incomplete surveys. Thus, 50 complete and useable surveys were analyzed.
Adhering to Kempson’s (2017) approach, a comprehensive process involving each survey variable (i.e., question) was undertaken, incorporating each into distinct model components (Figure 2) to ensure that only valid cases were included for analysis. Missing responses, such as “don’t know” or blanks, were either assigned (1) a central value, (2) the most common value, or (3) a new category “not applicable” (N/A). All response categories were coded or scaled to facilitate data reduction via Principal Components Analysis (PCA), which identified and formulated FWB scores using the survey variables. The PCA method, using IBM SPSS software, was applied to the cleaned data, except for socioeconomic factors. PCA is used in exploratory data analysis to reduce the dimensionality of large datasets, transforming numerous variables into more concise groups while capturing the majority of variance from the original set. In this case, all variables identified within each subcomponent were reduced using PCA and rescaled to a score ranging from 0 to 100.
Next, a series of regression tests were performed for each section of the FWB framework, including overall FWB and FWB sections on each of its factors: behavior, psychological, knowledge and experience, and socioeconomic. The procedure for the regression tests included four steps involving many different regression models and tests to fully assess the FWB framework. First, three univariate regression tests on FWB components—meeting commitments, feeling comfortable, and financial resiliency—determined influences on FWB. Second, 16 multivariate regression tests on the variables within the FWB sections of behavior, psychological, knowledge and experience, and socioeconomic factors assessed their impact on FWB and component scores. Third, 92 regression tests were conducted on all FWB subcomponents against overall FWB and the components. Finally, 80 regression tests were performed on subcomponents found to have a high influence on FWB.
Reliability and validity tests were conducted, including Cronbach’s analysis and Kaiser–Meyer–Olkin (KMO) statistics for each FWB component and subcomponent, excluding all socioeconomic factors. Socioeconomic factors were excluded from the Cronbach’s analysis and KMO statistics because these factors are typically considered external variables that are not directly part of the psychological and behavioral constructs being measured. Specifically, the factors not included were variables resulting from questions about age, income, debt levels, and career length, which, in Kempson’s (2017) model, are typically used to contextualize the psychological and behavioral measures rather than being included within the internal consistency or factor structure analyses. Socioeconomic factors often serve as control variables or external influences in FWB studies, rather than internal components of the construct. Results indicated satisfactory inter-item reliability for each subcomponent.
Results
Demographic variables collected in the sample included league affiliation, age, income, debt levels, and changes in financial circumstances over the past year. The sample was comprised of 76% NHL, 20% NFL, and 4% MLB retirees, with no NBA participants. The distribution of respondents’ ages was relatively even, ranging from 28 to 68 with an average age of 47.
Table 1 presents the regression model results for overall FWB and FWB sections, including FWB subsections.
Regression – adjusted R2 score of FWB sections and subcomponents
| Sections of FWB | Overall well-being | |||
|---|---|---|---|---|
| Meeting commitments | Feeling comfortable | Financial resiliency | ||
| Financial behavior factors | 0.57 | 0.48 | 0.47 | 0.64 |
| Spending restraint (MUB) | 0.21 | 0.19 | 0.21** | 0.28** |
| Not borrowing for daily bills (MUB) | 0.54** | 0.22** | 0.38** | 0.45** |
| Active saving (MUB) | 0.25 | 0.44** | 0.30** | 0.47** |
| Informed product choice (MMB) | 0.14 | 0.20 | 0.05 | 0.17 |
| Informed decision-making (MMB) | 0.02 | 0.05 | 0.01 | 0.04 |
| Planning and budgeting (MMB) | 0.09 | 0.14 | 0.05 | 0.13 |
| Monitoring finances (MMB) | 0.13 | −0.02 | 0.02 | 0.02 |
| Psychological factors | 0.27 | 0.39 | 0.35 | 0.46 |
| Time orientation (PT) | 0.16 | 0.12 | 0.14 | 0.19 |
| Impulsivity control (PT) | 0.13 | 0.07 | 0.12 | 0.14 |
| Social status (PT) | −0.02 | −0.01 | −0.01 | −0.02 |
| Self-control (PT) | 0.03 | 0.03 | −0.003 | 0.03 |
| Locus of control (PT) | 0.27** | 0.32** | 0.10 | 0.29** |
| Attitudes towards money (PT) | 0.18 | 0.26** | 0.39** | 0.39** |
| Knowledge and experience factors | 0.01 | −0.01 | −0.02 | 0.004 |
| Money management experience | −0.01 | −0.001 | −0.0004 | 0.003 |
| Financial product experience | 0.01 | 0.00 | −0.004 | 0.01 |
| Financial product knowledge | 0.00 | 0.01 | −0.02 | −0.002 |
| Understanding of risk | 0.01 | −0.01 | −0.01 | 0.002 |
| Socioeconomic factors | 0.16 | 0.45 | 0.17 | 0.36 |
| League | −0.02 | −0.01 | −0.003 | −0.003 |
| Age | 0.07** | 0.006 | 0.06** | 0.05** |
| Debt levels | −0.01 | −0.01 | −0.02 | −0.01 |
| Income | 0.10** | 0.39** | 0.10** | 0.28** |
| Income changes | 0.04 | 0.16** | −0.008 | 0.08 |
| Expenditure changes | −0.01 | −0.02 | −0.004 | −0.01 |
Note(s): All figures reported in chart above are adjusted R2 values from regression tests. **Multivariate regression and highlights subcomponents at the p < 0.05 significance level. MUB stands for money use behaviors. MMB stands for money management behaviors. PT stands for personality traits
As indicated in Table 1, the FWB model is significant for the sample of retired athletes, highlighting variables such as spending restraint, not borrowing for daily bills, active saving, locus of control, attitudes towards money, social status, age, and income. While some subcomponents showed significance in univariate regressions, their effects diminished in larger multivariate models. Notably, monitoring finances negatively influenced comfortability, suggesting that sufficient income and savings reduce the need for constant financial vigilance. Those who struggled with active saving and borrowing for daily expenses experienced lower FWB. The ability to develop and implement saving strategies (active saving) had the most significant positive effect on FWB scores.
As noted in Table 1, three behavior factors related to money use—avoiding borrowing for daily expenses, active saving, and spending restraint played significant roles in determining retired athletes’ financial resiliency. In contrast, money management behaviors proved insignificant in predicting the ability to absorb financial shocks. Similar to the meeting commitments subcomponent, financial resiliency was primarily driven by avoiding debt for day-to-day expenses.
Tables 2–5 provide the FWB components, FWB subsections, variables (i.e., questions), and a weighted comparison of PCA scores against similar FWB studies in Ireland, Australia, and New Zealand. The Ireland study (Kempson and Poppe, 2018) aimed to measure FWB among the Irish population and utilized a survey to assess various dimensions of FWB, including the ability to meet commitments, feeling comfortable, and financial resiliency. The findings indicated that many individuals experienced challenges in managing their financial obligations and building financial resiliency. Key factors influencing FWB included income stability, financial behaviors such as saving and borrowing, and psychological factors like financial confidence and stress. The Australia and New Zealand study (ANZ Banking Group Ltd, 2018) focused on assessing FWB across different demographics. It used a comprehensive survey to evaluate factors like financial control, ability to absorb financial shocks, and financial freedom to make choices. The results highlighted significant variations in FWB linked to income levels, financial literacy, and money management behaviors. The study emphasized the importance of financial education and proactive financial planning in enhancing overall FWB.
FWB model – overall financial well-being scores
| Component | Variables (see Kempson, 2017) | Retired athletes | Ireland* | Australia* | New Zealand* |
|---|---|---|---|---|---|
| Meeting commitments | Three variables related to ability to meet financial commitments | 94 | 80 | 71 | 73 |
| Feeling comfortable | Four variables related to current level of comfort with financial situation | 84 | 61 | 55 | 54 |
| Financial resiliency | Four variables related to ability to meet unexpected financial needs via borrowing or drawing from savings | 88 | 52 | 54 | 52 |
| Overall Financial Well-being Score (weighted average) | 89 | 64 | 59 | 59 | |
Note(s): *Weighted PCA Scores; Ireland (Kempson and Poppe, 2018) and Australia and New Zealand (ANZ Banking Group Ltd, 2018). Used for comparison purposes. **Weighted PCA Score from available data
FWB model – behavior factors
| Sub-components | Variables (see Kempson, 2017) | Retired athletes | Ireland* | Australia* | New Zealand* |
|---|---|---|---|---|---|
| Planning and budgeting | Five variables related to budget planning | 76 | 59 | 60 | 60 |
| Spending restraint | Two variables related to ability to control spending | 83 | 67 | 74 | 74 |
| Not borrowing for day-to-day expenses | Three variables related to daily practice using credit | 92 | 86 | 83 | 82 |
| Monitoring finances | Four variables related to practices related to monitoring personal finances | 73 | 66 | 73 | 78 |
| Active saving | Four variables related to saving practices | 77 | 68 | 63 | 60 |
| Informed product choice | Three variables related to making informed choices of which financial products to use | 80 | 52 | 57 | 55 |
| Informed decision-making | Five variables related to financial decision-making based on research and expert support | 74 | 70 | 66 | 66 |
| Overall Behavior Score (weighted average) | 79 | 67 | 68 | 68 | |
Note(s): *Weighted PCA Scores; Ireland (Kempson and Poppe, 2018) and Australia and New Zealand (ANZ Banking Group Ltd, 2018). Used for comparison purposes. **Weighted PCA Score from available data
FWB model – psychological factors
| Sub components | Variables (see Kempson, 2017) | Retired athletes | Ireland* | Australia* | New Zealand* |
|---|---|---|---|---|---|
| Time orientation | Three variables related to financial decisions related to the future or the present | 77 | N/A | 60 | 59 |
| Impulsivity | Three variables related to behaviors around making decisions without much prior thought | 80 | N/A | 66 | 65 |
| Social status | Three variables related to importance of social status amongst people for the respondent | 42 | N/A | 50 | 50 |
| Self- control | Three variables related to ability to avoid bad decisions or habits related to spending | 74 | N/A | 58 | 58 |
| Locus of control | Two variables related to ability to avoid perceived ability to plan financially for future success | 86 | 67 | 60 | 61 |
| Attitude towards money | Four variables about attitude towards spending versus saving | 77 | 48 | 69 | 68 |
| Overall Psychological Score (weighted average) | 73 | 58** | 61 | 60 | |
Note(s): *Weighted PCA Scores; Ireland (Kempson and Poppe, 2018) and Australia and New Zealand (ANZ Banking Group Ltd, 2018). Used for comparison purposes. **Weighted PCA Score from available data
FWB model knowledge and experience factors
| Sub-components | Variables (see Kempson, 2017) | Retired athletes | Ireland* | Australia* | New Zealand* |
|---|---|---|---|---|---|
| Money management experience | Three variables related to ability to past experiences and time allocated to managing finances | 99 | 89 | 87 | 86 |
| Financial product experience | Two variables related to ability past usage of financial products (e.g. loans, mortgages, insurance) and comfort in their use | 73 | 28 | 35 | 36 |
| Financial product knowledge | Six variables related to understanding of the various financial products available | 72 | 61 | 57 | 56 |
| Understanding of risk | Three variables related to knowledge of risk in financial planning | 79 | 71 | 68 | 71 |
| Overall Knowledge and Experience Score (weighted average) | 73 | 62 | 62 | 62 | |
Note(s): *Weighted PCA Scores; Ireland (Kempson and Poppe, 2018) and Australia and New Zealand (ANZ Banking Group Ltd, 2018). Used for comparison purposes. **Weighted PCA Score from available data
Table 2 demonstrates the FWB of retired athletes exceeded that of comparable results, with an average FWB score of 89 out of 100. About half of the athletes scored over 90, and 32% achieved a perfect score of 100. Only one athlete scored as low as 42.
As noted in Table 2, the meeting commitments factor scored highest (94), with 88% of retired athletes never experiencing shortages for essential expenses, underscoring their ability to meet day-to-day financial commitments, a critical determinant of FWB. The feeling comfortable factor scored 84, with more than half surpassing 90, despite 20% never having money left over after essentials. The financial resiliency factor scored 88, with 82% able to cover unforeseen expenses equivalent to one month’s income, and 68% maintaining savings exceeding 12 months’ income.
Three univariate regression tests were further conducted on these factors, with adjusted R2 values for all three—meeting commitments (0.63), feeling comfortable (0.76), and financial resiliency (0.78)—found to be high and statistically significant (p < 0.05) with the components collectively explaining nearly 70% of the variance in retired athletes’ FWB. To further investigate this finding, we segmented each FWB component into two groups: those with low scores (<70) and those with high scores (>90), along with their ± differences for comparison. This analysis revealed that athletes with high scores exhibited an average FWB score 32 points higher than those with low scores. Notably, those adept at meeting commitments displayed higher FWB scores (94) than those who struggled (61). Similarly, those comfortable with their financial situation scored an average FWB of 98, contrasting with 69 for those who did not share this sentiment. Furthermore, respondents with strong financial resiliency had an average FWB score of 96, compared to 62 for those affected by sudden financial shocks. This 34-point gap represented the most substantial difference within all FWB components between low and high scorers.
Table 3 reports regression results for financial behavior subcomponents: planning and budgeting, spending restraint, not borrowing for daily expenses, monitoring finances, active saving, informed product choice, and informed decision-making.
Planning and budgeting averaged a score of 76 (range 34–100), with 68% frequently planning income and 10% struggling with daily finances. Spending restraint scored 83 (range 12–100), and not borrowing for daily expenses was the highest behavior subcomponent at 92, indicating that retired athletes have responsible saving habits and refrain from incurring debt for daily needs. Other factors— monitoring finances (73), active saving (77), informed product choice (80), and informed decision-making (74)—suggest overall positive FWB behavior. Few reported using debt to cover daily costs, most (84%) saved for unexpected expenses, and the majority (72%) considered alternative prior to purchase.
Table 4 covers psychological factors: time orientation, impulsivity, social status, self-control, locus of control, and attitudes towards money.
Time orientation scored 77, with 82% focusing on long-term goals, emphasizing delayed rewards over immediate gains, which correlated with higher FWB scores and aligns with the shift from the short-term mindset observed in active athletes (Rosenboim et al., 2011), possibly influenced by reduced career-related stress. Impulsivity scored 80, with only 8% highly impulsive in financial decisions. Other psychological factors showed that retired players generally desire positive public perception, feel in control of their finances, meet financial commitments, and prioritize saving overspending.
Table 5 presents results for the knowledge and experience section, which achieved the highest average score of 81, encompassing the four subcomponents of money management experience (99), financial product experience (73), product knowledge (72), and understanding of risk (79).
Respondents are well versed in money management, and an overwhelming majority (94%) demonstrated experience in handling financial products, highlighting their solid understanding of these financial instruments. Although most are aware of financial products, about 10% shared that they lack awareness of key financial variables, such as interest rates. However, knowledge and experience factors had a minimal impact on FWB scores (0.004) and were found to negatively influence feelings of comfort (−0.01) and financial resiliency (−0.02). Paradoxically, those who claimed higher knowledge and experience levels in financial matters reported lower confidence in their ability to withstand financial shocks and meet daily financial obligations. Compared to the results reported for behavior (Table 3) and psychological (Table 4) sections, knowledge and experience factors did not significantly influence FWB or its components in multivariate regressions.
Results suggest that higher financial knowledge did not necessarily translate to improved overall FWB. Retired athletes exhibited a solid understanding of financial principles, money management, and risk. While their familiarity with financial products and risk assessment was evident, a preference for moderate-risk investments was apparent.
Cohort analysis
The study divided respondents into five distinct groups based on the decade in which they initiated their professional league participation. This allowed for a detailed comparison of FWB across different career periods. The outcomes of this analysis are included in Table 6.
FWB model summary by decade entered as a professional athlete
| Decade entered | |||||
|---|---|---|---|---|---|
| 1970s | 1980s | 1990s | 2000s | 2010s | |
| Number of respondents | 6 | 8 | 11 | 19 | 5 |
| Financial well-being | 82 | 91 | 84 | 92* | 91 |
| Meeting commitments | 92 | 95 | 87 | 98* | 98 |
| Feeling comfortable | 78 | 87 | 83 | 87 | 88* |
| Financial resiliency | 79 | 91 | 83 | 92* | 89 |
| Financial behavior factors | 78 | 80 | 76 | 81* | 78 |
| Spending restraint | 83* | 76 | 72 | 77 | 69 |
| Not borrowing for daily bills | 69 | 89* | 84 | 84 | 80 |
| Active saving | 91 | 90 | 86 | 94 | 100* |
| Informed product choice | 93* | 70 | 66 | 73 | 62 |
| Informed decision-making | 68 | 81 | 69 | 84* | 75 |
| Planning and budgeting | 77 | 80 | 83 | 78 | 84* |
| Monitoring finances | 66 | 70 | 73 | 77 | 78* |
| Psychological factors | 68 | 73 | 68 | 76* | 76* |
| Time orientation | 74 | 80 | 66 | 82* | 77 |
| Impulsivity control | 86 | 78 | 72 | 81 | 88* |
| Social status | 32 | 32 | 47* | 46 | 47* |
| Self-control | 69 | 78 | 69 | 75 | 81* |
| Locus of control | 77 | 89 | 81 | 88 | 93* |
| Attitudes to money | 71 | 79 | 73 | 82* | 72 |
| Knowledge and experience factors | 80 | 80 | 80 | 82* | 78 |
| Money management experience | 98 | 100* | 98 | 100* | 100* |
| Financial product experience | 72 | 79* | 71 | 76 | 59 |
| Financial product knowledge | 67 | 65 | 77* | 75 | 65 |
| Understanding of risk | 82 | 77 | 75 | 78 | 87* |
| Socioeconomic factors | |||||
| Career length | 11 years | 12 years | 12 years | 7 years | 4 years |
| Age | 66 | 56 | 48 | 38 | 31 |
| Income | $50-100k | > $250k | $100-250k | $100-250k | $100-250k |
| Income changes | Same | Same | Same | Same | Same |
| Debt levels | $250-500k | $500-750k | $500-750k | $250-500k | $250-500k |
| Expenditure changes | Same | Same | Same | Same | Same |
Note(s): One participant left the enter/exit year blank; therefore, total sample count in above chart is 49 *Indicates highest score within section and subcomponent
The results show that retired athletes who began their professional careers in the 1970s exhibited the lowest average FWB, although this group did report high levels of money management experience, informed product choice, and impulsivity control. The 1980s cohort had the 2nd highest FWB and was currently making the highest incomes. Those who began their careers in the 2000s reported the highest levels of FWB. The most recent set of retirees (2010s) displayed the highest scores across the most sections, although they trailed the 1980 cohort in overall FWB. Differences between earlier retirees (1970s and 1980s) and more recent retirees (2000s and 2010s) were notable, where early retirees exhibit tendencies towards financial restraint, avoiding debt for bills, and actively saving, while recent retirees demonstrated stronger performance in influential psychological factors such as self-control, locus of control, and attitudes towards money.
Regression analysis found that current age and income are significant drivers of FWB. Both significantly affected the ability of retired athletes to meet financial commitments and navigate financial challenges. For instance, respondents currently earning over $250,000 annually exhibited an average FWB score of 97, whereas those with incomes below $25,000 had an average score of 64. Further, results indicate that a change in income upwards was linked to a FWB score of 94, while a significant decrease was a 77. Both income and changes in income significantly influenced respondents’ feelings of financial comfort. On debt, interestingly, higher FWB scores were correlated to larger debt amounts: those with over $1,000,000 in debt attained a FWB score of 98, in contrast to those with under $250,000 in debt, who scored 88. Similar patterns emerged when analyzing fluctuations in expenses: substantial increases corresponded to an average FWB score of 77, while decreases resulted in a score of 88.
Discussion
This study set out to explore the FWB of retired athletes from major professional sport in North America. A total of 65 responses on their FWB were obtained from retired North American major professional athletes, a data set unique to the literature to our knowledge. Studies that survey or interview professional athletes on compensation do exist, but are at the semi-professional level (e.g., Wicker et al. (2021) who compare male and female semi-professional athletes in Germany) or the low pay level (e.g., Mcleod and Chahardovali (2024) who interview low paid athletes in the US). Specific to FWB, a recent systematic search of the literature for FWB related articles found 94 publications (Nashruddin et al., 2025), of which only a few articles interviewed professional athletes on the topic of FWB. These included Moolman (2023, 2020) who interviewed agents (not athletes) in rugby and cricket about their role in their athletes’ FWB, and a sociology study by Law et al. (2021) who interviewed 27 professional football (soccer) players of various levels regarding their impression management. The current results, thus, provide an important contribution to the literature with interview data from retired major professional sport athletes about FWB.
Results show a set of retirees from the 1970s–2010s who exhibit higher FWB than other populations versus comparisons to previous studies (ANZ Banking Group Ltd, 2018). The often-cited view that professional athletes are “not good” or “reckless” with their money is shown to be wrong and, indeed, could be framed as a myth (Torre, 2009). Further, the retired athletes reported behaviors that aligned with positive FWB traits including being able to meet financial commitments, not using debt to cover daily expenses, planning and budgeting, exhibiting spending restraint, monitoring spending, and implement active saving strategies.
Notably, behavior subcomponents, including informed decision-making and planning/budgeting, had statistically insignificant impacts on retired athletes’ ability to meet financial obligations. Therefore, results suggest that improving the ability to meet financial commitments would be best achieved by concentrating on not borrowing for daily expenses, rather than focusing solely on management behaviors. The work of Law et al. (2021) also found that retired professional athletes did not exhibit irrational financial behavior and were able and focused on meeting their financial obligations.
Overall, the findings emphasize the importance of positive financial behaviors in influencing FWB and suggest that addressing specific behaviors, such as not borrowing for daily bills, can enhance FWB and resiliency. Further, retired athletes demonstrate a commendable level of financial knowledge and experience, particularly in money management and familiarity with financial products. Despite this, the impact of knowledge and experience on overall FWB is found to be limited, with behavior and psychological factors playing a more substantial role.
The results highlight the complex interplay between different factors in influencing retired athletes’ financial outcomes and emphasize the need for a comprehensive approach to enhancing FWB. This includes the finding that higher income levels were associated with increased FWB scores, aligning with the intuitive notion of improved savings capacity. Next, the ability to have money remaining after paying bills positively correlated with higher feeling comfortable scores, which further emphasized the role of income stability and surplus in shaping retirees’ sense of financial security.
The FWB survey examines socioeconomic factors and their interconnectedness with retirees’ FWB, including income, age, debt and expenses. Understanding these intricate relationships is crucial for developing effective strategies to enhance retired athletes’ FWB. Results reveal that FWB has been high for retired athletes since the 1970s, providing evidence that even those from a period where salaries were (relatively) lower (1980s) were amongst the highest in terms of FWB. Indeed, the cohort analysis provided valuable insights into the evolution of FWB over time. The study highlighted the impact of different career periods on FWB scores, which underscored the intricate relationship between debt, expenses, and overall FWB.
This research has furthered our understanding of the core determinants and their associated subcomponents that indirectly influence FWB; the Kempson and colleagues’ model was adapted and tailored specifically for retired athletes (see Figure 3). Figure 3 includes the psychological, behavior, knowledge and experience, and socioeconomic factors, all leading to FWB outcomes. This evolution of the Kempson (2017) and Kempson and Poppe (2018) approach into a conceptual model is one of the main contributions of this study and sets the stage for future research and theory development.
The figure shows six boxes on the top left, arranged in two columns. The first column has two boxes labeled from top to bottom as “Informed Decision Making” and “Informed Product Choice.” The second column has four boxes labeled from top to bottom as “Time Orientation,” “Impulsivity,” “Product Knowledge,” and “Planning and Budgeting.” In the center, five boxes are stacked vertically from top to bottom and labeled as follows: “Attitudes Towards Money,” “Locus of Control,” “Active Saving,” “Not Borrowing For Daily Bills,” and “Spending Restraint.” On the right, a large black box labeled “FINANCIAL WELLBEING” is present. From “Informed Product Choice,” an arrow extends rightward and points to “Spending Restraint,” “Active Saving,” “Locus of Control,” “Time Orientation,” “Income,” and “Attitudes Towards Money.” From “Time Orientation,” two arrows extend rightward and point to “Attitudes Towards Money” and “Active Saving.” From “Impulsivity,” an arrow arises and points to “Attitudes Towards Money.” From “Product Knowledge,” an arrow points to “Locus of Control.” From “Planning and Budgeting,” two arrows arise and point to “Locus of Control” and “Active Saving.” At the top of “FINANCIAL WELLBEING,” two boxes labeled “Age” and “Income” are present, arranged horizontally, with downward arrows pointing to “FINANCIAL WELLBEING.” On the right of “FINANCIAL WELLBEING,” three boxes are stacked vertically and labeled from top to bottom as “Meeting Commitments,” “Feeling Comfortable,” and “Financial Resiliency.” From each of these three boxes, an arrow extends leftward and points to “FINANCIAL WELLBEING.” From “Attitudes Towards Money,” “Locus of Control,” “Active Saving,” “Not Borrowing For Daily Bills,” and “Spending Restraint,” each arrow extends rightward and points to “FINANCIAL WELLBEING.”Revised FWB model. Source: Authors’ created figure
The figure shows six boxes on the top left, arranged in two columns. The first column has two boxes labeled from top to bottom as “Informed Decision Making” and “Informed Product Choice.” The second column has four boxes labeled from top to bottom as “Time Orientation,” “Impulsivity,” “Product Knowledge,” and “Planning and Budgeting.” In the center, five boxes are stacked vertically from top to bottom and labeled as follows: “Attitudes Towards Money,” “Locus of Control,” “Active Saving,” “Not Borrowing For Daily Bills,” and “Spending Restraint.” On the right, a large black box labeled “FINANCIAL WELLBEING” is present. From “Informed Product Choice,” an arrow extends rightward and points to “Spending Restraint,” “Active Saving,” “Locus of Control,” “Time Orientation,” “Income,” and “Attitudes Towards Money.” From “Time Orientation,” two arrows extend rightward and point to “Attitudes Towards Money” and “Active Saving.” From “Impulsivity,” an arrow arises and points to “Attitudes Towards Money.” From “Product Knowledge,” an arrow points to “Locus of Control.” From “Planning and Budgeting,” two arrows arise and point to “Locus of Control” and “Active Saving.” At the top of “FINANCIAL WELLBEING,” two boxes labeled “Age” and “Income” are present, arranged horizontally, with downward arrows pointing to “FINANCIAL WELLBEING.” On the right of “FINANCIAL WELLBEING,” three boxes are stacked vertically and labeled from top to bottom as “Meeting Commitments,” “Feeling Comfortable,” and “Financial Resiliency.” From each of these three boxes, an arrow extends leftward and points to “FINANCIAL WELLBEING.” From “Attitudes Towards Money,” “Locus of Control,” “Active Saving,” “Not Borrowing For Daily Bills,” and “Spending Restraint,” each arrow extends rightward and points to “FINANCIAL WELLBEING.”Revised FWB model. Source: Authors’ created figure
Regression analyses revealed that several behavior factors (e.g., active savings, spending restraint, and not borrowing money for daily expenses) had the largest influence, accounting for ∼44% of FWB scores in retired athletes. Psychological factors and socioeconomic factors also accounted for ∼31% and ∼25% of FWB scores, respectively. Knowledge and experience factors had little-to-no effect on FWB. In other words, the FWB of retired athletes does not appear to depend on their knowledge of financial products, experience making financial decisions, familiarity with managing money, or understanding of risk.
As noted in Figure 3, seven subcomponents were important in directly influencing the FWB of retired athletes’: three behavior subcomponents (active saving, spending restraint and not borrowing for daily bills), two psychological subcomponents (attitudes towards money and locus of control) and two socioeconomic subcomponents (age and income) highly influenced FWB directly. Subcomponents with direct influence are shown using direct lines to FWB, whereas other subcomponents with indirect influence are shown to connect to other subcomponents prior to FWB. Attitudes towards money, active savings and not borrowing for daily bills were shown to influence each other, with two-way significance. Active savings, not borrowing for daily bills and locus of control also exhibited two-way influence on one other. Notably, active savings, not borrowing for daily bills, and attitudes towards money – all money use behaviors – were the most influential subcomponents in determining the FWB of retired athletes.
A few other findings are reflected in Figure 3. First, the influence of age was one-directional, negatively influencing FWB directly while exhibiting little-to-no influence on any subcomponents. Second, income was significant in directly explaining variances in FWB, while also being responsible for influencing time orientation, informed product choice, attitudes towards money, locus of control, active saving, and spending restraint. Third, time orientation among retired athletes impacts attitudes towards money and active savings. Fourth, retired athletes who were inclined towards a longer-term perspective exhibited higher savings and perceived greater financial control.
Impulsivity also seemed to indirectly influence FWB through attitudes towards money. Being more thoughtful and less impulsive seemed to be associated with higher saving rates and a desire to use less debt for spending. Conversely, impulsive respondents were more likely to use credit cards and preferred spending over saving, both traits which negatively impact FWB. Informed decision-making and product knowledge directly influence locus of control. Retired athletes who considered multiple investment options, plan their spending, and better understand terms and conditions of financial products are more likely to feel in the driver seat of their financial situations. Informed product choice and planning and budgeting influence locus of control and active savings. No subcomponents have significant influence on spending restraint. Income was found to affect numerous subcomponents, including time orientation, attitudes towards money, locus of control, active savings, spending restraint, and informed product choice.
In summary, the refined FWB Model for retired athletes elucidates the pivotal role played by distinct behaviors, psychological attributes, and socioeconomic factors in shaping overall FWB. Directly, FWB is steered by three behaviors (active saving, spending restraint, and refraining from daily expense borrowing), two psychological factors (attitudes towards money and locus of control), and two socioeconomic factors (age and income). Moreover, indirect influence stems from six subcomponents (time orientation, impulsivity, informed decision-making, planning and budgeting, product knowledge, and informed product choice). This comprehensive model offers profound insights into the multifaceted dynamics governing FWB among retired athletes.
Recommendations for future research and practitioners
The learning provided in this study has considerable implications for practitioners. First, for players, players associations, player agents, and other members of a current athlete’s entourage, moving to very applied “how” to insure the FWB of the athlete in retirement with the understanding that athletes already have higher levels of FWB than other groups is recommended. This is a mind shift for many who have thought that most athletes, due to media reports, are not focused on their FWB. Second, for clubs, teams, brands, governments, leagues, associations, events, and federations, as well as any entity who remunerate athletes, our model (Figure 3) provides important direction as to the specific variables to focus on when building contracts, prize money plans, sponsorship investments, and other activities which provide financial resources to athletes. Third, for financial planners and investment advisors working with athletes, as per the athletes themselves, a focus on application as opposed to strategy is advised. Finally, for all stakeholders, the finding that a small proportion of retired athletes faced challenges in meeting bill and commitment deadlines means that there is still a group of athletes who require support and advice in building a plan towards FWB.
In terms of recommendations for retired athletes specifically seeking to enhance their FWB, the focus should primarily shift towards taking actionable steps rather than solely acquiring knowledge. Notably, the results underscore that behaviors and psychological factors play a more pivotal role in FWB than knowledge and experience factors. Thus, efforts should focus on (1) establishing and adhering to a consistent savings plan and budget, (2) avoid relying on credit cards and personal loans for everyday spending, (3) exercise restraint to prevent overspending, (4) ensure timely bill payments and loan obligations, (5) conduct thorough evaluations of various financial product options, and (vi) solicit advice before making significant financial decisions.
In terms of future research, the results of our study advocate for the expansion of socioeconomic variables in future research regarding athlete FWB. A deeper understanding of the demographic contexts (family status, relationship status, dependents, residential location, educational attainment, etc.) would add to the rigor of the research areas. Future research should also include diversity, equity and inclusion aspects (e.g., include variables related to race, culture, and language) and most detail on the post-retirement activities of the athlete (e.g., religion, work status, divorce, marital status, and health considerations). Another area for suggested future research is to extend the work to include psychological variables, such as the win-play orientation of the athlete and their mental health. The level of education and post-career success would also be valuable areas for future research, as would research on female professional athletes.
Divorce and marital status were found to have large impact on financial status of the athletes. Previous research finds that men and women held 86% and 87% less personal net worth, respectively, during the divorce proceeding compared to during marriage and at least 4 years prior to separation (Kapelle and Baxter, 2021). Other previous research indicates that retired NFL players are more likely to be married than their counterparts in the general population (Weir et al., 2009). Notably, 75% of younger NFL retirees and 81% of older retirees are currently married, respectively, compared to 64% and 74% of men in the general population. Thus, contrary to popular belief, retired NFL players are not more likely to be widowed or divorced than the general population. Additionally, they are less likely to have never married. Among retired players, 64% of younger and 53% of older players remain married to their first spouse. Lifetime divorce rates for retired NFL players are comparable to, or slightly lower than, those of the general population (Weir et al., 2009). Future research on athletes from the NHL, MLB and NBA on this topic would be of value. Finally, future research that segments retired athletes by their on the field success level (e.g., games played, championships, home runs, goals, etc.) and their total salary earned would provide deeper understanding of their FWB.
Limitations
This research has a number of limitations to acknowledge and address in future research. First, although the study was framed to focus on retired athletes from the four major team sports leagues in North America, the sample is primarily NHL (76%) and NFL (20%) retirees. Although athletes from these four leagues have similar compensation and career lengths, future research should include larger samples from MLB and NBA, as well as overall. Second, the fact that the majority of responses were from NHL players is noted as a potential limitation, as they may differ from NFL, MLB and/or NBA players. Third, the sampling frame (minimum 1 year playing in any period) limits the applicability of the findings. Future research could consider a more narrow sampling frame in terms of length of career and time frame of playing career, as player salaries have continued to increase over time and contract size tends to increase as a player’s career continues.
Conclusion
Building upon past literature (Hong and Fraser, 2021; Law et al., 2021; Moolman, 2020, 2023; Torre, 2009) and media reports, this research makes a contribution with a sample of retired major professional sport athletes who exhibit notably higher levels of FWB driven by behavior factors as the driving force behind FWB, closely followed by the array of psychological factors examined. Conversely, knowledge and experience factors, aligning with prior studies, demonstrated minimal impact on retired athletes’ sentiments towards their current financial state. The resulting model (Figure 3), building upon the original work of Kempson and Poppe (2018), advanced our conceptual understanding of FWB and showcases the key determinants influencing FWB both directly and indirectly. The five most influential FWB drivers for retired athletes, ranked in order, are active saving, refraining from borrowing for daily expenses, attitudes towards money, spending restraint, and locus of control. The model underscores the significant role played by behaviors, attitudes, motivations, and socioeconomic factors in shaping how retired athletes perceive their financial status.
