This paper used the six most recent waves of the Survey of Consumer Finances (2001 to 2016) to explore how household decision-making across five financial planning domains (portfolio risk level, savings habits, life insurance coverage, revolving credit card balances, and emergency savings) varied across four information sources: financial planners, transactional financial advisers, friends, or the Internet. By decomposing financial advisers into two types, it is possible to better understand if any differences exist by the type of the advice engagement.
The analysis focused on the soundness of various household financial decisions (e.g., does the household have any revolving credit card debt?) versus more outcome-oriented variables (e.g., wealth or level of savings). Focusing on decisions better captured differences in multiple domains, reduced issues associated with reverse causality (because clients with more wealth become increasingly attractive to financial advisers and it may be difficult to determine the role of the financial adviser with respect to the wealth creation), and controlled for the fact that higher wealth (or more savings) doesn’t necessarily imply the household is behaving optimally (e.g., adequate life insurance may reduce available savings, but it is a vital component of a sound financial plan for most households).
Households working with a financial planner were found to be making the “best” financial decisions, in the aggregate as well as in four of the five domains considered, while households working with a transactional adviser were making the “worst” financial decisions. Selection bias is a potential issue with the results, since the decision to work with a financial planner is a positive indicator of financial decision-making and potentially endogenous to variables considered; however, these findings do at least suggest financial planners are adding the most value among the information sources considered, especially compared to transactional advisers.
Households using the Internet scored second to financial planners on overall financial soundness. This is noteworthy given the growing use of the Internet as the primary information source for households included in the analysis, increasing from 3 percent in 2001, to 40 percent in 2016, as well as given its relatively low cost (especially compared to many financial advisers). However, the better outcomes associated with the Internet have been declining over time (from 2001 to 2016), so it is not clear to what extent this relation will persist in the future.
All financial advice is not the same; nor are adviser types. Thus, one shouldn’t expect the potential value of advice to be uniform, either, so research that does not attempt to control for advice type may likely produce biased results.
Overall, the basic question “Do financial advisers add value?” is not necessarily well-defined in the empirical literature, given the significant differences in the scope of services provided by financial advisers. It is likely that potential and realized benefits of financial advice vary by adviser type. This paper will explore this specific topic in greater detail.
The lack of financial literacy of U.S. households (Lusardi and Mitchell 2014) would suggest financial advisers have the potential to add significant value, both in investing and non-investing domains. For example, from an investment perspective, Odean (1998) found that investors tend to underperform by selling winners too soon and holding losers too long, a tendency labeled the “disposition effect” (Shefrin and Statman 1985). A financial adviser who is aware of this effect can either make clients aware of it to help mitigate it, or take discretion of the account (assuming the adviser is not disposed to the same effect).
Exploring the potential value of financial advice is a growing field of research. Theoretical research on the value of financial advice has focused on the potential value of making optimal financial decisions compared to some type of naïve benchmark (i.e., what the household would be assumed to do without the adviser). For example, Hanna and Lindamood (2010) and Blanchett and Kaplan (2013) both used utility-based models to explore the potential value of financial advisers. Both found that the value of financial advice can be significant and potentially exceed common financial adviser fees, although the true expected value will vary by client. Additional research by Kinniry, Jaconetti, DiJoseph, and Zilbering (2014) and Grable and Chatterjee (2014) also explored the potential value of financial advisers.
Households that work with a financial adviser tend to have higher incomes, be wealthier, more educated, older, and more financially literate (Burke and Hung 2015). These individuals also tend to have higher risk tolerance (Hanna 2011). Research on consumer financial decisions increasingly points to the importance of financial sophistication as a determinant of sound financial decision-making (Campbell 2006), therefore controlling for household demographics is an important aspect of any type of empirical analysis.
One problem with identifying any type of empirical benefit associated with working with a financial adviser is that the decision to hire a financial adviser is not random and is potentially endogenous to whatever outcome variable is considered. For example, it may be that wise and financially prudent decision-makers are more likely to hire financial advisers. Similarly, an investor who was already making sound financial decisions may hire a financial adviser with the goal of helping him or her make even better financial decisions. For these investors, it would be difficult to disentangle the actual impact of the adviser on decisions, had the investor not hired the adviser (i.e., correlation does not necessarily imply causation).
Early empirical evidence on the value of a financial adviser focused largely on investment-related domains and noted mixed findings. For example, research has noted positive (Grinblatt and Keloharju 2000; Shapira and Venezia 2001; and Barber, Lee, Liu, and Odean 2008), and negative (Bergstresser, Chalmers, and Tufano 2009; Mullainathan, Noeth, and Schoar 2012; Hackethal, Haliassos, and Jappelli 2012; and Chalmers, Johnson, and Reuter 2014) effects of advisers on investment outcomes. However, the majority of research has suggested investors using financial advisers are no better off (or potentially worse off, especially after fees) than those without.
The possible benefits of a financial adviser extend beyond investment domains, and some research has explored these areas. For example, Warschauer and Sciglimpaglia (2012) noted how advisers can assist with emergency fund management, debt management, insurable risk reduction, investment risk control, goal assessment, and tax and estate assessment. Engelmann, Capra, Noussair, and Berns (2009) suggested financial planners may help clients focus on long-term goals by reducing short-term anxiety from market volatility. Burke and Hung (2015) suggested that working with a financial adviser helps improve financial and savings habits.
Research by Martin and Finke (2014), Finke, Huston, and Waller (2009), Cho, Gutter, Kim, and Mauldin (2012), among others, has noted a positive relationship between the use of financial advisers and savings. Additional research on decisions surrounding life insurance (Finke, Huston, and Waller, 2009), emergency savings (Bhargava and Lown 2006), and disability insurance (Scott and Finke 2013) have also noted better outcomes for households working with financial advisers. Most of these studies, though, did not control for selection bias. Marsden, Zick, and Mayer (2011) attempted to control for simultaneity bias and reverse causation and found no statistically significant difference in self-reported retirement savings or short-term growth in retirement account asset values for those using a financial adviser. However, they did note that meeting with a financial adviser was associated with setting long-term goals, calculating retirement needs, retirement-account diversification, use of supplemental retirement accounts, retirement confidence, and higher levels of savings in emergency funds.
Recall that empirical evidence on the value of working with a financial adviser is weak for a variety of reasons, such as misaligned incentives, lack of general ability, and segmentation/identification.
With respect to incentives, depending on the domain explored, it may not actually be in the financial adviser’s best interest to help the client make the optimal decision, if that decision does not align with the adviser’s method of compensation. For example, Del Guercio and Reuter (2014) noted how brokers face a weaker incentive to generate alpha, and Christoffersen, Evans, and Musto (2013) suggested fee sharing alters broker incentives and can be particularly harmful to investors when brokers’ incentives are not aligned with their clients’ interests.
Financial advisers may also not be as capable as they should be. For example, Linnainmaa, Melzer, Previtero, and Foerster (2018) found financial advisers make the same poor investment decisions as their clients (such as frequent trading, return chasing, use of active funds, and under-diversification).
No state or federal law requires financial advisers to hold designations.1 Of the one million financial services professionals in the U.S. today, only approximately 80,000 financial advisers hold the Certified Financial Planner (CFP®) designation,2 the most popular financial advising designation, followed by the Chartered Financial Consultant (ChFC) designation, with 55,0003 designees (Raskie, Martin, Lemoine, and Cummings 2018). Job titles also often provide little insight into the scope of services provided by the adviser, at least partially due to lack of regulatory requirements.
Identifying the scope of the advice engagement (i.e., the type of financial adviser) can be difficult, especially when using well-known publicly available datasets (Heckman, Saey, Kim, and Letkiewicz 2016). Limited research documents how households fare using different types of financial advisers. Martin and Finke (2014) is one example. They noted households using more comprehensive financial advisers generated more wealth than those without any help, as well as versus those advisers providing less holistic services.
Sources of Financial Information for Households
Robust data on household financial information sources may be found in the Survey of Consumer Finances (SCF). The SCF is a triennial cross-sectional survey of U.S. families conducted by the Federal Reserve Board that includes information on families’ balance sheets, pensions, income, and demographic characteristics. Heckman, Saey, Kim, and Letkiewicz (2016) evaluated the validity of the measures of financial planner use in publicly available datasets and suggested the SCF was one of the two most promising datasets, as there are a variety available.4
This specific question in the SCF asks the respondent about the source of financial information:
The response to this question was used to determine a household’s source of financial information.5 If multiple sources were provided by the respondent, the first response provided was assumed to be the primary information source.
Financial advisers were classified into two types: financial planners and transactional advisers. If “financial planner” was the response, the financial adviser was deemed to be a financial planner. If “banker” or “broker” was selected, the financial adviser was deemed to be transactional. Lawyer or accountant responses were not included in either financial adviser group because these are not professions typically associated with financial planning.
The term “transactional adviser” was used, versus the actual response of banker or broker, to reflect the likely scope of services associated with the advice. Being a broker (or banker) and a financial planner is not mutually exclusive; many advisers work for a broker-dealer (who are technically brokers) that provides comprehensive financial planning services. Therefore, the response to the question was assumed to be based on the nature of services being provided, where the advisers providing more holistic services were referred to as “financial planners,” and advisers who are less holistic in nature, and likely more transaction-oriented (e.g., broker or banker) were “transactional advisers.” The “Internet” was also considered an information source (for those who selected the Internet) and a “friends” information source was created as a combination of the “call-around” and “friend/relative” responses.
Instead of including all available households, the test group was limited to households that were assumed to be potentially interested in considering financial advice, as well as those that would consider guidance among the five domains considered. To be included, the respondent must have been between ages 25 and 55;6 the household must have had at least $5,000 in financial assets and retirement assets (note these assets are not mutually exclusive); and the household had to have wage income and normal wage income above $25,000 annually (again, these definitions are not mutually exclusive). All values were converted to 2016 dollars. These filters created a dataset that was not representative of the entire U.S. population, yet likely better reflected the cohort of investors who would potentially be interested in working with a financial adviser (e.g., it is unlikely a household with no income and no savings would seek the services of a financial adviser).
Figure 1 includes information about the distribution of the use of these four advice sources for the six waves of the SCF included in the analysis. Household weights were included when estimating the percentages.
As shown in Figure 1, the Internet appears to be displacing the “Friends” and “Other” sources of financial information since 2001. For example, Friends and Internet were 45 percent and 3 percent of information sources in 2001, respectively, but changed to 19 percent and 40 percent, respectively, by 2016. This suggests that households who may have asked a (relatively unsophisticated) friend a financial question historically are increasingly going online to find an answer instead.
The growth in the use of the Internet has been relatively similar across age groups within this dataset. An additional analysis (not included in Table 1) was conducted where households were split based on respondent age—those above and below the age of 40. The results were very similar for both groups. One reason for the relatively large growth in the use of the Internet for this analysis was that only relatively young households were included (all are age 55 or younger). This relation may not hold at all ages (e.g., respondents over the age of 80).
The percentage of households using a financial planner increased over the study period, from 10 percent in 2001 to 18 percent in 2016, while the percentage using transactional advisers remained relatively unchanged. On average, approximately 34 percent households were using either type of financial adviser—a financial planner or a transactional adviser—over the entire period.
Collins (2012) noted financial advice usage in the U.S. was 20 percent to 33 percent based on different sources, while Hanna (2011), using data from SCFs from 1998 to 2007, noted advice usage from 21 percent to 25 percent. The likely reason this estimate of financial adviser use (34 percent) is higher than other research is because households that do not meet the previously noted income or asset requirements were excluded from the analysis (recall that the dataset is not representative of all U.S. households, rather households that are more likely to be investors).
Who Uses Each Information Source?
To better understand which household attributes were associated with the selection of each of the four potential information sources, a series of logistic regressions were performed. The dependent variable for the logistic regressions was the information source selected. The independent variables were respondent age, total household income, total household financial assets, respondent years of education, whether the respondent was female (this is a dummy variable that equals one if the respondent is female), whether the household was single (this is a dummy variable that equals one if the household is not married), and whether the household was non-white or Hispanic (also a dummy variable). All values were translated in 2016 dollars. Only households that met the previously noted criteria were included in these regressions. The results of the logistic regressions are included in Table 1.
Although it is common to use the repeated-imputation inference (RII) method to correct for underestimation of variances due to imputation of missing data (Montalto and Sung 1996) when running regressions using the SCF, the logistic regressions in this study were based on a single aggregated value for each household. (Additional information about the household-level aggregation approach is provided in the analysis section.) Using a single value for each household decreased the standard errors for the regression.
The logistic regression results in Table 1 are somewhat inconsistent with past research exploring who uses a financial adviser. For example, Burke and Hung (2015) noted that households with a financial adviser tended to be wealthier, have higher incomes, be more educated, older, and more financially literate (through a meta-analysis). The logistic regressions shown in this analysis (Table 1) suggest that households headed by a female and those who are married are more likely to use a financial planner or transactional adviser, but there is no statistically significant relation between age, income, or financial assets. With respect to the use of friends as an information source, these households tended to have higher income levels but lower levels of education. For the Internet, these households had lower levels of income, more education, and were more likely to be male and not white.
Determining the soundness of financial decision-making for a household is subjective. This analysis focused more on household decisions (the process), versus more outcome-oriented variables (wealth or savings levels). Focusing on decisions reduced potential issues associated with reverse causality, because clients with more wealth become increasingly attractive to financial advisers and it may be difficult to determine the role of the financial adviser with respect to the wealth creation, as well as the fact more wealth doesn’t necessarily imply the household has made (or is making) optimal financial planning decisions.
As noted previously, the analysis used data from the 2001, 2004, 2007, 2010, 2013, and 2016 waves of the SCF. To be included in the analysis, the respondent had to be between the ages of 25 and 55, have children, have a minimum of wage income and normal income of $25,000, and at least $5,000 in financial assets and retirement assets. Five financial decision-making domains were considered for the analysis: (1) portfolio risk appropriateness; (2) savings habits; (3) life insurance coverage; (4) revolving credit card debt; and (5) emergency savings. These tests are introduced below:
Portfolio risk appropriateness. This test determined if the household’s retirement assets were invested in a portfolio that had a risk level that would generally be considered prudent, given the respondent’s age. For the analysis, the equity level of retirement assets (e.g., 401(k)s, IRAs, etc.) was determined and compared with the Morningstar® Moderate Lifetime IndexSM based on the respondent’s age, assuming a retirement age of 65.
To be considered prudently invested, the equity level must be within 25 percentage points of the Morningstar Moderate Lifetime Index (25 points above or below the glide path, bounded by 100 percent and 0 percent, respectively). The glide path, or equity target, for the Morningstar Moderate Lifetime Index and the respective upper and lower bounds targets are included in Figure 2.
This was effectively a test that the portfolio was diversified and reasonably consistent with a general target risk level given the investor’s age. Only retirement assets were considered because these are typically savings directed toward a single goal (retirement) with a relatively similar begin date (approximately age 65). There will of course be situations where the allocations should deviate from the target; therefore, this was viewed more as a general test to ensure the household had their retirement assets invested in a reasonable manner. The 25-point band created a relatively wide range that would include virtually every target-date mutual fund family series in the U.S. market.
Savings habits. This test focused on whether the household had a savings plan in place. The specific text of the SCF question was: “Which of the following statements on this page comes closest to describing your (and your husband/wife/partner’s) saving habits?” There were six potential responses such as not saving at all, saving whatever is left over at the end of the month, or some type of savings plan (e.g., saving the income of one family member, saving non-regular income, and a regular savings program). For this analysis, so long as the household had some type of savings plan in place, it was considered to have good savings habits. Savings habits were the focus, versus the amount of savings, to simplify the analysis and because of SCF data limitations related to savings variables.
Life insurance coverage. This domain focused on whether the household had face value life insurance at least equal to the total wage income of the household. All households in this analysis had children; therefore, it is reasonable to assume that some level of life insurance would be desirable for most households. The ideal level of coverage was not estimated. The vast majority of households in this dataset should have more than the relatively low threshold of just one times wage income. This test showed whether the household had thought about life insurance enough to make even a relatively de minimis purchase. It is likely that some households may have no need life insurance; however, the analysis controlled for basic demographic data and the target was a relatively low threshold.
Revolving credit card debt. This question focused on whether the household had any revolving credit card debt at the end of the month. Interest rates on credit cards typically exceed 15 percent7—a “return” the household is highly unlikely to achieve through investing in the markets (especially on a risk-adjusted and after-tax basis). Therefore, it was assumed the households should not maintain any revolving credit balances. If the household did maintain any revolving credit, it was assumed the household was making a poor decision in this domain.
Emergency savings. The final test explored whether the household had adequate emergency savings. This was calculated by dividing total liquid savings—which included balances in checking accounts, savings accounts, money market mutual funds, and money market demand accounts—by average normal income. The goal was to have at least three months income set aside in emergency savings.
Each household in the SCF had five implicates, or observations. Each of the five tests were conducted for each implicate, resulting in 25 total tests for a household. The results of the test for each implicate were combined, based on implicate weights, to get a “pass rate” for the respective domain. Pass rates ranged from 0 percent, where none of the implicates passed, to 100 percent, where all the implicates passed. The scores at the individual domain level were then averaged to get the aggregate financial soundness score for the household. Demographic control variables (e.g., total financial assets) are also created using the implicate weights (i.e., the weighted average of the implicate values for that household) so that each household had a single set of values.
Figure 3 provides insight into the percentage of households that passed the respective tests for each of the six SCF datasets included in the analysis.
In Figure 3, most of the tests (except for savings habits) have approximately a 50 percent pass rate. This was somewhat intentional to ensure there was dispersion in each domain across households (i.e., all households were not passing or failing for a given domain). The fact that only approximately half of households passed each test would suggest there is a large potential benefit for financial advisers to help the households make better financial decisions.
Regarding results for the five individual planning domains, while there was significant variation in the aggregate results, the individual metrics were largely binary. For example, the percentage of households where all implicates either passed or failed the individual metric ranged from 76.8 percent (for portfolio risk) to 99.9 percent (for the savings test). Therefore, given the relatively binary nature of the individual results, the values were transformed and a logistic regression was performed.
For the logistic regression, for each domain, a value of 1 was assigned for that test if the pass rate for the household was 50 percent or greater, otherwise, it was assigned a value of zero. Note, this transformation was only performed for individual domain tests. The aggregate values were much more varied; only 9 percent of households had a score of zero or 1. Therefore, this transformation was not necessary when reviewing the aggregate results.
Similar to the logistic regressions exploring information source usage in Table 1, a number of independent variables were included in these next logistic regressions, including age, household income, total household financial assets, respondent years of education, whether the respondent is female, whether the household is single, and whether the household is non-white or Hispanic. In addition, the four sources of financial information were included as dummy variables, which is whether the household financial information source was a financial planner, a friend, the Internet, or a transactional adviser. For each information source, the coefficient was set to equal 1 if the household used that information source, otherwise it was zero. Weights for each household were included in logistic regressions. The results are included in Table 2.
The sign and statistical significance of the coefficients varied by test. The coefficients for years of education and financial assets were always positive and significant for four of the five tests (all but portfolio risk). This suggests households with more education and more financial assets tend to make better financial decisions.
The coefficient for age was negative (and statistically significant) for those same four domains (all but portfolio risk), which suggests older households are making worse decisions; however, the odds ratio was not that different from 1, which implies the economic impact of age is relatively low. There was quite a bit of inconsistency across some of the other variables. For example, the sign and statistical significance of the income variable varied across domains.
The financial planner coefficients were the most positive for all but the credit card metric (where it was second), but only statistically significant for three of the five domains. The Internet coefficients were the second best for all but the credit card metric (where it was first), while the transactional adviser and friend coefficients were generally the worst or second worst coefficients.
For the portfolio risk appropriateness domain logistic regression, only the financial planner coefficient was positive and statistically significant. This suggests the probability of having a portfolio that is even generally consistent with age was higher if the household used a financial planner, but effectively random for the other information sources.
Individual test results may be interesting; however, the aggregate financial soundness metric was the primary focus of this analysis. For this, an ordinary least squares (OLS) regression was performed where the dependent variable was the average pass rate across the five domains for each household. The same independent variables as past regressions were included in these regressions, and the regressions include household weights. Four separate regressions were performed, each including different sets of available independent variables. The results of the OLS regressions are shown in Table 3.
Households that make better financial decisions tended to be younger, have lower incomes, more financial assets, higher levels of education, have a male respondent, are married, and are white. The most significant variables were financial assets and years of education, both of which had positive coefficients (which is consistent with past research). The negative coefficients for age and income potentially warrant greater study, given that both are typically positively associated with financial sophistication.
The base demographic variables (e.g., Model 1) explained a significant degree more of household financial soundness than the financial information source (e.g., Model 2), as evidenced by the R² values (see Table 3). This suggests while the source of financial information is important, other household attributes were materially more so.
Households that used financial planners as their financial information source were making the best decisions of the groups studied, followed by those using the Internet. Households that were using a transactional adviser were making the worst decisions, and households using friends were the second worst.
It cannot be concluded that working with a financial planner is the reason those households were making better financial choices due to potential selection bias; the outcome could be endogenous to the selection of the information source. However, these findings at least imply that working with a financial planner can help households make better financial decisions, while working with a transactional adviser may actually result in worse decisions.
It is not known why households working with a transactional adviser were making the worst decisions of the groups studied; although one can speculate. One possibility is that these households may have a false sense of confidence about their financial soundness because they get advice in a few domains and think that are covered in all domains when they are, in fact, not. One problem with this hypothesis is that households working with a transactional adviser were doing the worst in effectively every domain considered. In other words, it’s not that households working with a transactional adviser were doing one thing really well and everything else poorly; they were doing everything poorly. It’s possible there are aspects of households that selected transactional advisers that were not controlled for in this analysis or other areas where they improve outcomes affecting these results. Future research may provide clarity here.
A secondary analysis was performed to see how the aggregate scores have changed across the four information sources. This analysis was similar to the information in Figure 3; however, instead of looking at the individual results, this analysis compared the average aggregate score for each household, based on household information source, and then controlled for the SCF year. This approach ensured the average score among the four sources for each SCF was zero. The results are shown in Figure 4.
Overall, the time varying results in Figure 4 are relatively similar to the regression results shown in Table 3, where the financial planner values were consistently the highest, and the transactional adviser and friend were typically and consistently the lowest.
Note, however, the reduction the average score among households that used the Internet. Households using the Internet as the primary information source scored almost 5 percent higher than the average in 2001, while households using the Internet in 2016 scored 2 percent below average (which was the worst among the four sources considered). There are a variety of potential reasons for this. One may be that the benefits associated with the Internet were due largely to “early adopters,” and as usage increased, the caliber and intentions of Internet users have declined. This gets to the fundamental issue around selection bias that is difficult to control for in this type of analysis. This topic is also likely worth exploring in future research.
Implications for Financial Advisers
The results of this analysis are consistent with the growing body of research that working with a financial adviser can result in better outcomes, as well as empirical research suggesting that financial advisers can actually make some households worse off. How it is possible that financial advisers can both help and hurt their clients? This is largely due to the relatively ambiguous nature of the term “financial adviser.” Financial advisers can provide significantly different scopes of services and advisers can be compensated in myriad ways. This heterogeneity creates significant issues when attempting to empirically assess the “value” of financial advice.
These findings strongly suggest that financial advisers who focus on financial planning are having a positive impact on households, especially compared to financial advisers that are more transactional in nature. These results should not be misconstrued to suggest financial advisers cannot provide value if they are paid primarily through commissions, or that certain types of adviser registration methods are worse than others. What matters are the services being provided to the client and consequently how the client perceives the nature of the relationship. Helping clients accomplish goals typically requires more than just selling a product, such as a mutual fund or annuity—it requires a financial plan with ongoing management. Financial advisers that provide these services are not likely to be described as transactional in nature; rather they are likely be described as financial planners.
This paper explored the quality of five household financial planning decisions (portfolio risk level, savings habits, life insurance coverage, revolving credit card balances, and emergency savings) across four information sources (financial planners, transactional financial advisers, friends, or the Internet). The quality of household decisions was found to vary across information sources. Households using a financial planner made the best decisions, followed by the Internet. Households using a transactional adviser made the worst decisions.
It cannot be concluded that using a financial planner entirely explains better decision-making of those households due to implications around selection bias. However, these findings do suggest that the potential value associated with working with a financial adviser could differ significantly by adviser type.
These findings also have important implications for future research exploring the value of financial advice, especially in an empirical setting. Any kind of analysis that focuses primarily on transactional advisers may yield significantly different conclusions on the value of financial advice than one focused on advisers that are comprehensive.
Additionally, there is significant evidence that households using the Internet are making better-than-average financial planning decisions, although the benefit does appear to be declining over time. The potential value of the Internet as a source of financial information and advice is notable given the significant increase in usage over the last 15 years or so, especially if its role as an information source continues to increase into the future.
- See the SEC’s “Investor Bulletin: Top Tips for Selecting a Financial Professional,” posted August 25, 2016 at sec.gov/investor/pubs/invadvisers.htm.
- See CFP Board professional demographics data at cfp.net/news-events/research-facts-figures/cfp-professional-demographics.
- See The American College of Financial Services data at theamericancollege.edu/designations-degrees/ChFC-CFP.
- The other is the National Longitudinal Study of Youth (bls.gov/nls/home.htm).
- A similar question in the SCF asks about sources of information for borrowing money or obtaining credit. This analysis only considered the savings and investment question because the majority of tests cover only these domains. Additionally, the household sources vary across the two questions, which would create additional classification groups that would complicate the analysis.
- This specific filter is important later in the analysis when determining life insurance coverage adequacy as well as savings habits.
- See current credit card interest rate data at bankrate.com/finance/credit-cards/current-interest-rates.aspx.
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