financial advice: what about low- income consumers? · 2015-07-16 · 121 financial advice: what...

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FINANCIAL ADVICE: WHAT ABOUT LOW-INCOME CONSUMERS?

Ning Tang (corresponding author) San Diego State University

Marie-Eve Lachance

San Diego State University

This paper uses data from the National Financial Capability Study (NFCS) to analyze the determinants and benefits of financial advice use, with a special emphasis on the low-income group. While, as expected, this group fares worse financially, we also find that they have different needs and priorities. For example, they use less investment advice than insurance advice. A discriminant analysis reveals that cost plays a lesser role in their decision to seek advice. Using an instrument variable strategy, we conclude that certain types of advice improve clients’ financial behaviors, with greater benefits for the low-income group.

Introduction

Historically, low-income communities have had limited access to financial services, affordable credit, and investment capital (Rubin, 2007). This is especially the case in the professional financial advice industry, which has targeted mainly the higher end of the market. From the supply perspective, pursuing clients with modest accounts is not profitable when compensation is based on a percentage of the assets under management. However, as Rubin (2007) points out, the rich and the poor both have the need and the desire to build financial assets to enable them to meet important life goals. Access to capital and basic financial services is a critical component of achieving such goals. Numerous products, programs, organizations, and policies have been designed to

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address the financial exclusion of low-income individuals and communities. It is more and more clear that, given incentives and provided with financial education, low-income individuals can change their financial behavior, and they need such changes (Schreiner, Clancy, & Sherraden, 2002; Mills, Patterson, Orr, & DeMarco, 2004). To better address the problem of low-income consumers' limited access to financial services and products, to serve them more efficiently, and to effectively advocate for a relatively new low-income financial service market, we need more insights into the financial behavior of low-income consumers.

In particular, we need to know how the financial condition of low-income individuals compares to that of people with higher incomes, their financial advice-seeking behavior, and the effect of financial advice on their financial literacy and behavior. Unfortunately, the literature has not provided much information on these areas. Although there are a number of studies exploring who uses financial advice and what factors contribute to consumers’ advice-seeking behavior, none of these examine the difference in advice-seeking behavior between low- and high-income groups. Considering that low-income consumers have different financial exposures and needs, findings from the previous literature on wealthier individuals may not readily apply to them. In addition, due to the limitation of data and methodology, very little is known about the effects of various kinds of financial advice on individuals’ financial literacy and behavior and whether these effects are the same among different income groups.

The unique National Financial Capability Study (NFCS) dataset allows us to fill the gap in the literature by addressing the above issues. Specifically, the paper finds that

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low-income individuals fare worse financially than high-income ones: they have lower levels of financial knowledge and ability, achieve fewer financial planning goals, and have less access to financial services and products. Meanwhile, they are using less financial advice and have less favorable attitudes towards the industry than their higher-income counterparts.

We find that low-income consumers have different demand compositions of financial advice and consider different factors than high-income consumers when seeking such advice. Six percent more low-income consumers use insurance advice than use savings/investments advice, while the advice most demanded by high-income consumers is savings/investments advice. In addition, by incorporating demographic, socioeconomic, psychosocial factors and financial literacy and capability in one model, discriminant analysis shows that the cost of seeking financial advice is less important to low-income earners than to high-income earners.

Last, we examine whether financial advice is beneficial in terms of improving financial knowledge (objective and self-assessed) and a broad set of financial behaviors. When performing this type of exercise, a common limitation is that reverse causality and selection may cause biased estimation. We use an instrument variable strategy to address the problem. We observe that those who received financial advice report higher levels of self-assessed financial knowledge, but they do not score higher on a set of objective financial literacy questions. In most cases, all types of advice help clients improve positive behaviors, but only savings/investments and tax advice help avoid negative behaviors. In a second set of regressions, we interact financial advice with a low-income


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dummy and find greater improvements in certain financial behaviors for the low-income group.

The paper is structured as follows. Section 2 describes the data, and Section 3 compares the financial condition of low-income and high-income individuals. Section 4 analyzes the determinants of financial advice adoption and how they differ between low- and high-income groups. Section 5 uses the instrument variable strategy to examine the effects of financial advice on clients’ financial literacy and behavior, and Section 6 concludes.

Data

This paper uses the National Financial Capability Study (NFCS) dataset, which was commissioned by the Investor Education Foundation of the Financial Regulatory Authority (FINRA). Interviews were conducted between May and October 2009. The dataset collected information from about 500 respondents in each state (28,146 observations in total) regarding their demographic characteristics, use of financial advice, financial literacy and other aspects of financial planning. 19 In particular, the survey asks respondents “In the last 5 years, have you asked for any advice from a financial professional about any of the following: 1) debt counseling, 2) savings or investments, 3) taking out a mortgage or a loan, 4) insurance of any type, 5) tax planning?” 20 In addition, the survey records consumers’ attitudes towards financial advisors. In terms of financial literacy and capability, the survey features

19 Personal-planning topics include cash management, retirement planning, home and auto acquisition, credit management, and insurance planning. 20 We do not use the question for mortgage and loan advice in our analysis because mortgages and loans are very different in nature, which makes the interpretation of the results difficult.

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five questions that measure financial literacy and asks the respondents to self-assess their financial knowledge and capability, including the ability to deal with day-to-day financial matters, their math skills, and whether or not they keep up with economic news (see Appendix for exact question wording). The rich dataset covering various aspects of financial well-being at the individual level allows us to explore the financial conditions and use of financial advice among low-income and high-income consumers separately. The demographic composition of the sample is similar to that in the 2010 American Community Survey, with the exception of an underrepresentation of the group with less than a high-school degree.

Financial condition of low- and high-income consumers

To explore the use of financial advice and its effect on consumers’ behavior among low- and high-income consumers, it is necessary to first investigate the different financial conditions within these two income groups. This is necessary because their financial condition and what they believe about financial services will largely determine their choice of professional financial advice and the effects of such advice. With the unique dataset, we can examine financial literacy and capability and financial conditions and attitudes towards professional financial advice among low- and high-income earners separately. We define low-income consumers as those with an annual household income lower than $25,000 (7,013 observations) and high-income consumers as those with an annual income higher than $75,000 (7,779 observations). These low- and high- income groups correspond, respectively, to the first and fourth quartiles of the income distribution in our sample.


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Table 1 Financial Condition of Low-Income and High-Income Consumers Mean Mean Difference

Financial literacy Financial literacy score 0.49 0.75 -0.26 *** Self-assessed financial knowledge 0.59 0.73 -0.14 *** Good at day-to-day financial matters 0.71 0.82 -0.11 *** Good at math 0.71 0.82 -0.11 *** Keep up with financial news 0.55 0.72 -0.17 *** Cash flow management Have emergency fund 19.4% 59.9% -40.5% *** Spend more than income 27.4% 13.4% 14.0% *** Overdraw checking account 31.5% 20.1% 11.4% *** Savings and investments Save for children's college 15.5% 55.3% -39.8% *** Have 529 plan 14.0% 45.2% -31.2% *** Retirement planning Figured out need for retirement 23.8% 64.9% -41.1% *** Use Soc. Sec. to decide retirement age 27.5% 26.6% 0.9% Calculate withdraw amt. after ret. 44.3% 74.5% -30.2% *** Contribute regularly to retirement 46.4% 85.5% -39.1% *** Rebalance retirement account 24.9% 57.9% -33.0% *** Hardship withdrawal from ret. account 12.9% 4.9% 8.0% *** Credit management Checked credit score in past one year 35.9% 61.6% -25.7% *** Compare auto loan providers 39.0% 50.6% -11.6% *** Compare mortgage providers 48.2% 70.9% -22.7% *** Consider mtg. pmt. as % of income 80.5% 81.7% -1.2% Have high cost loans 35.0% 11.1% 23.9% *** Late with mtg. pmt. in past 2 years 33.1% 12.9% 20.2% *** Foreclosure process in past 2 years 2.7% 2.0% 0.7% *** Credit card penalty 34.9% 21.9% 13.0% *** Insurance planning Compare insurance providers 67.4% 70.7% -3.3% *** Review insurance coverage 53.8% 76.0% -22.2% *** General Drop in income in past year 48.9% 28.3% 20.6% *** Savings and investments Have checking account 82.2% 98.8% -16.6% *** Have savings account 56.3% 93.1% -36.8% *** Investment outside ret. account 14.9% 65.9% -51.0% *** Investment outside ret. account (in $) 4,188 69,271 -65,083 ***

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Retirement planningHave employer-sponsored ret. plan 18.2% 84.8% -66.6% *** Non employer-sponsored ret. account 7.5% 53.2% -45.7% *** Retirement account amount (in $) 24,956 125,560 -100,604 *** Stock in retirement account (yes=1) More than half 38.7% 62.0% -23.3% *** Less than half 29.7% 33.4% -3.7% * None 31.6% 4.7% 26.9% *** Invest in lifecycle fund 37.8% 28.2% 9.6% *** Take loan from retirement account 9.3% 8.7% 0.6% Retirement funding Social Security 88.9% 69.8% 19.1% *** Pension plan payments 33.5% 73.6% -40.1% *** Savings, investments, or retirement accounts 24.0% 37.8% -13.8% ***

Dividends or interest income 11.6% 42.4% -30.8% *** Salary, wages, or self-emp. income 7.5% 42.6% -35.1% ***

Rental income or sale of real estate 4.0% 14.9% -10.9% *** Reverse mortgage 1.6% 0.1% 1.5% *** Family support 9.6% 1.4% 8.2% *** Change in response to current economic conditions 52.2% 35.2% 17.0% ***

Real estate Homeowner 30.9% 86.7% -55.8% *** Other real estate ownership 8.2% 39.0% -30.8% *** Have a mortgage among homeowners 44.3% 77.7% -33.4% *** Have a home equity loan among homeowners 12.1% 29.0% -16.9% ***

Credit Number of credit cards 1.55 4.40 -2.85 *** Credit card balance 3,513 5,569 -2,056 *** Have an auto loan 17.9% 45.3% -27.4% *** Declare bankruptcy in past two years 2.5% 1.2% 1.3% *** Insurance Have health insurance 62.6% 95.8% -33.2% *** Have life insurance 33.8% 83.4% -49.6% *** Have auto insurance 70.8% 97.7% -26.9% *** Have homeowners or renters insurance 36.1% 92.7% -56.6% *** Financial cost Mortgage interest rate 7.3% 5.9% 1.4% *** Credit card interest rate 12.7% 11.9% 0.8% *** Auto loan interest rate 9.9% 6.1% 3.8% ***


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To give an overview of how the two income groups differ in their financial condition, Table 1 presents a variety of summary statistics for the low-income (column 1) and high-income (column 2) groups. The differences between the two groups are reported in the third column, and asteriske used to indicate whether these differences are significantly different when performing a t-test. The variables in Table 1 are divided into three panels: financial literacy and ability, financial management results, and financial status.

Panel A shows the results on financial literacy and ability. We construct a scaled financial literacy score by adding up the number of correct answers for the five financial literacy questions and dividing the total score by 5.21 The self-assessed financial knowledge and capability scores are rescaled between 0 and 1 in the presentation of our results. The results show that low-income earners, on average, have lower levels of financial knowledge than high-income earners. Their self-assessed scores on financial knowledge and ability are also lower than those for the high-income group. However, it is worth noting that for the low-income group, average self-assessed knowledge is quite a bit higher than actual financial knowledge (0.59 vs. 0.49), while that is not the case for the high-income group (0.73 vs. 0.75). Possibly, low-income consumers are not as aware of their lack of financial knowledge, but we do not have sufficient data to test the hypothesis at this stage. It might also be that, because low-income workers are less exposed to investments, their perception of financial knowledge is less affected by investment knowledge than the financial literacy score is. Since self-assessed knowledge may capture some aspects of financial knowledge more related to day-to-day 21 Those who answered “don’t know” or “prefer not to say” to financial literacy questions are coded as zeros.

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matters, in the later analysis we will use both self-assessed financial knowledge measures and the objective financial literacy scores.

Panel B summarizes how individuals achieve personal financial planning goals in terms of cash flow management, savings and investments, retirement planning, credit management, and insurance planning by income groups. It shows that low-income consumers achieve less than high-income consumers. For example, under “cash flow management” there are fewer low-income earners setting up an emergency fund, and more of them are outspending their incomes or overdrawing on their checking accounts. The differences between the two income groups are statistically significant. However, it is worth noting that results here are simply summary statistics without controlling for individuals’ characteristics and financial exposures, such as wealth, education, etc. Thus, differences may reflect not only behavior but also economic circumstances and financial needs.

Panel C contrasts the current financial status of the low- and high-income groups. Low-income individuals have experienced an unexpected drop in income in the past year at a higher rate than their wealthier counterparts. They have less access to checking and saving accounts and have fewer investments both inside and outside their retirement accounts. They allocate less to equities in retirement accounts and rely more on social security and family support as retirement funding than high-income individuals do. More of the low-income earners have changed their withdrawal amount or frequency from retirement accounts in response to the recent financial crisis, which reflects the fact that they are more vulnerable to economic shocks. In terms of real estate, fewer


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low-income consumers own their homes or other real estate properties. Low-income homeowners are less likely to have a mortgage or a home equity loan. Low-income consumers have fewer credit cards with lower card balances and are less likely to have auto loans. Low-income earners are less likely to be insured, especially for life insurance and homeowners or renters insurance: 34% of them have life insurance and 36% have homeowners or renters insurance, while the figures for their high-income counterparts are 83% and 93%, respectively. Last, the results indicate that low-income groups pay higher interest rates on mortgages, credit cards, and auto loans. Therefore, we conclude from the results in Panel C that low-income individuals have less accumulated wealth through retirement and non-retirement savings and real estate, they face higher interest rates for borrowing, are less likely to have insurance, and are more vulnerable to financial shocks.

Knowing that low-income consumers have lower levels of financial knowledge and ability, achieve fewer financial planning goals and have less access to financial products, more assistance is needed to improve their financial situation. Professional financial advice can be one solution. In the remainder of this section, we explore the prevalence of financial advice use among low- and high-income groups and their attitudes towards such professional services.

Figure 1 shows the prevalence of financial advice among consumers. First, to give an overview of financial advice use, we find that 32% of respondents use savings/investments advice, 19% tax advice, 35% insurance advice, and 10% debt advice. Then, we investigate the use of different types of financial advice by income group. The use of financial advice increases with income, except for debt advice.

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Figure 1. Use of Financial Advice by Advice Types

0%10%20%30%40%50%60%70%80%90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<15 15~25 25~35 35~50 50~75 75~100 100~150 150+

Tax adviceAverage: 19%

Income (in thousand dollars)


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0%

20%

40%

60%

80%

100%

0%

20%

40%

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80%

100%

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Although low-income people need more help to improve their personal financial planning situation, we show that they are currently less likely to use financial advice than high-income people to solve the problem. The skewed supply of professional financial advice and products towards wealthy people and the less-favorable attitudes towards the industry among low-income communities could explain why low-income consumers use less financial advice to improve their financial situation. We collect data on Certified Financial Planners (CFPs) and Certified Public Accountants (CPAs) with a Personal Financial Specialist designation, two major financial advice suppliers. In Figure 2, we show the average number of these suppliers by quartile of average income in the zip code. The results clearly indicate that CFPs and CPAs are located mainly in the top-quartile neighborhoods.22

To examine the role of attitudes towards financial advisers, Figure 3 compiles the answers to the three perception questions (trust financial professionals; financial professionals too expensive; hard to find the right financial professional) by income group. More low-income people believe that it is hard to find the right financial professionals and that advice is too expensive for them. Trust generally increases with income, except at the tail ends of the distribution. Overall, low-income consumers display less favorable attitudes towards the financial advice industry.

22 Of course, this is not to say that financial advisers work only for clients in their zip code. The result, however, is reflective of the skewed nature of supply in this industry.


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Figure 2. Supply of CFPs and CPAs by Income Quartile

Notes: Authors’ computation. The zip code income data comes from the annual file by IRS Statistics of Income Division for tax year 2008 (available at http://www.irs.gov/taxstats/indtaxstats/article/0,,id=242739,00.html). Number of CFPs by zip code (total 7,521) was derived from the Financial Planning Association directory http://www.fpanet.org/PLANNERSEARCH/PlannerSearch.aspx. Number of CPAs with a Personal Financial Specialist designation (total 4,688) by zip code was derived from the addresses in the American Institute of CPAs directory http://apps.aicpa.org/credentialsrefweb/PFSCredentialSearchPage.aspx.

0%10%20%30%40%50%60%70%80%90%

First incomequartile

Second incomequartile

Third incomequartile

Fouth incomequartile

CFP supply CPA supply

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Figure 3. Attitudes towards Financial Professionals

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0.47

0.49

0.51

0.53

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0.2

0.4

0.6

0.8

<15 15~25 25~35 35~50 50~75 75~100 100~150 150+

Financial professionals too expensive

Income (in thousand

0.48

0.5

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0.56

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Figure 4. Ranks of Financial Advice Use by Income Group

9%

10%

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24%

0% 5% 10% 15% 20% 25%

4. Tax advice

3. Debt advice

2. Savings/investments advice

1. Insurance advice

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8%

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44%

47%

0% 10% 20% 30% 40% 50%

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3. Tax advice

2. Insurance advice

1. Savings/investments advice

High income

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Before leaving this section, we further explore the demand compositions of financial advice among low- and high-income clients. Figure 4 ranks the prevalence of each type of financial advice among the two income groups separately. We find that low- and high-income individuals have very different demand compositions of financial advice. Insurance advice is the one that low-income consumers demand most (24%), followed by savings/investments (18%), debt (10%) and tax (9%) advice. For their part, high-income consumers use savings/investments advice most (47%), followed by insurance (44%), tax (31%) and debt (8%) advice. For policymakers, financial planners and educators to better fill the gap and serve the low-income community, there is a need to understand that group's special financial needs and exposures. For example, the low-income group needs insurance advice more than savings/investments advice. This makes sense given that they do not have as many assets to invest and are more exposed to economic insecurity.

Determinants of financial advice adoption

In the previous section, we found that the prevalence of using financial advice varies among income groups. In this section, we use discriminant analysis to explore the factors affecting consumers’ decision to use financial advice and how these factors influence low- and high- income individuals differently.

There is a rich literature on who seeks financial advice. The characteristics that have been found to affect individuals’ financial advice-seeking behavior can be grouped into four categories: demographic, socioeconomic, psychosocial and financial literacy and capability. In the first category -


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demographic factors - older married females are found to be more likely to use financial advice (Bluethgen, Ginschel, Hackethal, & Müller, 2008; Finke, Huston, & Winchester, 2011; Gerhardt & Hackethal, 2009; Hackethal, Haliassos, & Jappelli, 2012; Hung & Yoong, 2010; Joo & Grable, 2001). As for the socioeconomic characteristics, education, homeownership, wealth and working status also affect financial advice-seeking behavior significantly (Bluethgen et al., 2008; Collins, 2010; Elmerick, Montalto, & Fox, 2002; Finke et al., 2011; Grable & Joo, 1999; Gerhardt & Hackethal, 2009; Hackethal et al., 2012; Haslem, 2010; Grable & Joo, 2001). Effects of psychosocial factors and financial literacy are relatively new topics in the literature. Risk tolerance, financial behaviors, attitudes towards retirement, financial stress and levels of financial satisfaction are found to affect financial advice adoption significantly (Grable & Joo, 1999 and 2001; Bluethgen et al., 2008; Gerhardt & Hackethal, 2009; Joo & Grable, 2001). Finke et al. (2011) shows that people with lower self-reported financial knowledge are more likely to pay for financial advice, and Collins (2010) finds that higher financial literacy increases the use of financial advice. We extend this literature by incorporating all these factors into one model and studying how they work together to affect individuals’ decision to pursue professional financial advice. Namely, we perform a discriminant analysis based on the following regression:

iii XAdvice 1

where iAdvice is a dummy variable indicating whether

individual i uses advice. Since Elmerick et al. (2002) point out that regression results vary by categories of advice, we run four regressions for savings/investments, tax, insurance and debt

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advice separately. iX includes demographic characteristics such as gender, age, marital status, and race; it also has socioeconomic characteristics comprising education, homeownership, business ownership, income level, investment amount outside retirement accounts, working status and experiencing an unexpected income drop in past year.

Additionally, psychosocial factors are incorporated in iX , including being willing to take investment risk, being satisfied with current financial condition, and attitudes towards financial professionals (trust financial professionals, believe financial professionals are too expensive, and find it hard to find right

financial professionals). Last, iX considers the following financial literacy and capability variables: financial literacy score, self-assessed financial knowledge, good at dealing with day-to-day financial matters, good at math, and keeps up with economic financial news.23 We also control for other financial exposures and regional factors in the regression.24

We use discriminant analysis instead of traditional Probit regression because this approach can statistically test the relative importance of independent variables when explaining the decision to use financial advice. As mentioned by Hair, Anderson, Tatham and Black (1995), discriminant analysis can be used to determine which of the independent variables accounts the most for the differences in the average score 23 Satisfied with current financial condition, attitudes towards financial professionals, financial literacy and capability levels are scaled to a 0-1 range. 24 Financial exposure controls include dummy variables indicating if the individual claims it is difficult to pay bills, overdraws on checking account, has a defined-contribution plan, has been involved in foreclosure process in past two years, declared bankruptcy in past two years, has high-cost loans, and has health, homeowners or renters, life and auto insurance.


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profiles of two or more groups. Magnitudes of coefficients from discriminant regressions can show the relative explanatory power of each independent variable. Grable and Joo (1999) follow the same strategy to investigate the most important factors affecting individuals’ decision to seek professional financial help.

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Tab

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58

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6

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-0.2

4

Invo

lved

in f

orec

losu

re p

roce

ss

in p

ast 2

yea

rs

-0.2

0

Dif

ficu

lt to

pay

bill

s -0

.21

Dif

ficu

lt to

pay

bill

s -0

.17

E

duca

tion

-0.1

8

Sat

isfi

ed w

ith c

urre

nt f

inan

cial

co

nditi

on

0.18

N.

18,8

19

3,51

7

6,

072

Not

es: T

he ta

ble

show

s th

e in

depe

nden

t var

iabl

es w

ith th

e to

p fi

ve d

iscr

imin

ant s

tand

ardi

zed

coef

fici

ents

in e

ach

regr

essi

on. T

he

full

sam

ple

cont

ains

28,

146

obse

rvat

ions

, out

of

whi

ch 7

,013

are

cla

ssif

ied

in th

e lo

w-i

ncom

e gr

oup

and

7,77

9 in

the

high

-inc

ome

grou

p. T

he n

umbe

rs o

f ob

serv

atio

ns in

this

tabl

e ar

e sm

alle

r be

caus

e of

mis

sing

obs

erva

tions

for

som

e of

the

ques

tion

s.

143

Column (1) in Table 2 presents the top five canonical discriminant coefficients in each regression, which reflect the variables’ relative significance in contributing to the discriminant function. We find that the factors explaining the most variance in advice-seeking behavior among all income level respondents vary by type of financial advice. For example, wealth (investment amount outside retirement accounts) and believing that financial professionals are too expensive are the top two factors affecting consumers’ use of savings/investments and tax advice. Having life insurance and running a business are the top two determinants for insurance-advice adoption. The two factors contributing the most to the use of debt advice are “declared bankruptcy in past two years” and “overdraw checking account.” In addition, factors that are shown to have significant effects and that have been the focus of much of the previous literature such as gender and homeownership, do not affect the use of financial advice so significantly after controlling for other factors. Factors such as trust in financial professionals and believing that financial advice is too expensive, not studied much in the previous literature, play very important roles in financial advice-adoption decisions. For example, trust in financial professionals is among the top five factors affecting savings/investments and tax-advice adoption. Lachance and Tang (2012) study the relationship between financial advice and trust and conclude the same.

Next, we explore how these factors affect low- and high-income consumers’ decisions to use financial advice differently. We run the four regressions among low- and high-income individuals and show the results in columns (2) and (3) separately. We find that even for the same type of advice, low-


144

and high-income consumers’ concerns are different. For example, for savings/investments, tax and insurance advice, believing that financial professionals are too expensive is the top concern among high-income consumers. However, low-income consumers put other factors such as wealth, age, and having high-cost loans ahead of expense when considering using such advice. For example, in low-income consumers’ decisions to adopt savings/investments advice, investment outside retirement accounts with the standardized coefficient of 0.58 plays a more important role than believing that financial professionals are too expensive with a coefficient of -0.26. While it is commonly assumed that cost is the main concern when consumers decide whether to seek financial advice, our results indicate that this is only the case for high-income consumers.

Effects of financial advice

The effects of professional financial advice on individuals’ financial matters have been a very important topic in the literature since any positive effects that exist can justify the expense of seeking professional advice. To find an efficient way to improve society’s overall personal financial condition and financial literacy level, it is necessary to know if and how financial professionals, as one potential solution, can help improve consumers’ financial well-being. However, so far the literature has been limited in two ways. First, most studies focus only on the effects of investment advice on portfolio performance (Hackethal et al., 2012; Bluethgen et al., 2008; Jansen, Fischer, & Hackethal, 2008; Gerhardt & Hackethal, 2009) or credit management (Staten, Elliehausen, & Lundquist, 2003; Hirad & Zorn, 2001). Investment and credit advice is only a subset of professional financial advice offered in today's

145

market. In addition, financial advice not only affects individuals’ portfolio performance or credit management in a direct way, but can also affect consumers’ behavior and financial literacy level, which potentially have a bigger impact on consumers’ financial well-being. In the current “professionals” era of financial services, financial advisors do more than just suggest financial products or manage portfolios for their clients, they work “with” the clients. During the process, the education and coaching function can do more than just fix the current financial problem for which the client is seeking help (Warschauer, 2002). Very few studies have looked at the effects of professional advice on clients’ behavior and financial literacy level. Second, reverse causality and selection caused by unobserved characteristics can lead to biased estimations of causal effects of financial advice on individual financial behavior using linear regressions. Lyons (2005) concludes that, while the general consensus from the literature is that financial education positively affects financial outcomes, it is important to acknowledge that the findings are far from conclusive, especially with respect to the direction of causation. Well-designed experiments can be a solution to this problem. For example, to rule out the problem of reverse causality and selection bias, Hung and Yoong (2010) designs and implements a hypothetical choice experiment to study the effect of financial advice on defined-contribution plan holders’ financial behavior. However, the question that can be explored by one experiment is very limited. Currently, no such experiment allows us to investigate the effects of financial advice on consumers’ financial behavior from a broad perspective.


146

Another possible solution is to use instrument variables. Hackethal et al. (2012) employ this strategy to study the impact of advice on advised accounts’ performance in Germany, and Yoong (2010) uses instrument variables to examine the effect of financial literacy on stock participation. However, very little has been done to study the effect of financial advice on individuals’ financial behavior in the United States due to the limitations of the data and unavailability of qualified instrument variables. The NFCS dataset offers new variables that can be used as instruments, and we consider the following six candidates: trust in financial professionals, believing that financial advice is too expensive, finding it hard to find the right professional, having defined contribution plans, and number of CFPs and CPAs in the zip code. These variables are expected to have effects on individuals’ decision to use financial advice but not on their financial literacy and behavior directly. To select the preferred instrument set for each regression, we follow Yoong’s (2010) criteria by checking the relevance, weakness and exogeneity of the instrument variables. In particular, we use Anderson canonical correlations test, a likelihood-ratio test of whether the excluded instruments are correlated with the endogenous regressors to check for relevance. The null hypothesis needs to be rejected to meet the relevance requirement. We test for weakness by using the Cragg-Donald Fstatistics from the first-stage regression, which must be higher than the Stock-Yogo critical values to pass the test. We also check for exogeneity with the Sargan-Hansen test of over-identifying restriction, which tests the joint null hypothesis that the additional instrument is uncorrelated with the error term and the excluded instruments are correctly excluded from the estimated equation. We need to fail the null to pass the exogeneity test.

147

We explore the effects of professional financial advice on individuals’ financial matters in three areas: financial literacy (both self-assessed and objectively measured); negative behaviors (including spending more than one's income over the past year, using high-cost loans,25 overdrawing on checking accounts, and being charged credit card penalties),26 and positive behaviors (including trying to figure out how much to save to retire, setting up emergency funds, and reviewing insurance coverage).

We implement the instrument variable approach by running the following two-stage least squares regressions for each type of advice (savings/investments, tax, insurance, and debt):

iii XInstrumentAdvice 211

iiii XAdviceEffect

211

In equation (2), the first-stage regression, iAdvice is a

dummy variable indicating if individual i uses the relevant

type of advice. iInstrument denotes the instrument variable

used for each type of financial advice. Control variables iX in (2) and (3) include gender, age, race, education, income, and working status. In addition, scaled financial literacy scores are

used to test effects on positive and negative behaviors. iEffect 25 High-cost loans include auto title loans, short-term payday loans, refund anticipation loans, loans from pawn shops, and using rent-to-own stores. 26 One is considered to have been charged credit card penalties if he or she was assessed a fee for late payment or an over-the-limit fee for exceeding the credit line.


148

in the second-stage regression (3) include individual i ’s self-assessed and objectively measured financial literacy scores, indicators of each of the negative and positive behaviors in the nine regressions, respectively. To explore the causal effect of

financial advice on iEffect , we use the instrumented variable

iAdvice estimated from the first-stage regression.

Table 3 shows the results among all income level respondents. All the regressions pass the relevance, weak-identification, and exogeneity tests.27 All four types of financial advice are associated with greater self-assessed knowledge. However, advice does not improve the objective measure of financial literacy. No significant effect is found for savings/investments or tax advice, and insurance and debt advice are associated with lower financial literacy scores.28 In most cases, all types of advice help clients improve positive behaviors, but only savings/investments and tax advice help avoid negative behaviors. For example, clients taking insurance

27 We use Anderson canonical correlations LM statistics to test for the relevance of instrument variables, Cragg-Donald Wald F-statistics for weakness test and Sargan-Hansen statistics are for exogeneity test. χ2(2) P-value for Anderson canonical correlations LM statistics needs to be lower than the significance level to meet the relevance requirement. Cragg-Donald Wald F-statistic must be higher than the Stock-Yogo critical values to pass the weakness test and χ2(1) P-value for Sargan-Hansen statistics should be higher than the significance level to pass exogeneity tests. Regressions of debt advice on “have emergency fund” pass the weak identification test at the 15% level instead of the 10% level, as in other regressions. 28 Financial literacy scores from the five questions are titled to investment knowledge, which may not truly reflect the influence of insurance/debt advice on clients’ financial knowledge related to insurance and debt matters, while self-assessed financial knowledge measures may capture some aspects of financial knowledge related to insurance and debt issues. Such disconnect between the two financial knowledge measures may explain the positive effects of insurance/debt advice on self-assessed financial knowledge, but negative effects on financial literacy scores.

149

Tab

le 3

. Eff

ects

of

Fin

anci

al A

dvic

e on

Fin

anci

al L

iter

acy

and

Beh

avio

r am

ong

All

Inco

me

Gro

ups

F

inan

cial

li

tera

cy

N

egat

ive

beh

avio

r

Pos

itiv

e b

ehav

ior

S

elf-

asse

ssed

L

iter

acy

scor

e

Sp

end

>

inco

me

Hig

h c

ost

loan

Ove

rdr.

fr

om

chec

kin

g

Cre

dit

ca

rd

pen

alty

C

alcu

late

re

tire

am

t.

Em

erge

ncy

fu

nd

Rev

iew

in

sura

nce

Sav

ings

/inve

stm

ents

adv

ice

0.19

0.

01

-1

.22

-1.0

4 -0

.86

-0.8

0

0.88

2.

30

0.49

**

*

**

* **

* **

* **

*

***

***

***

N.

24,5

87

24,9

13

24

,326

24

,608

22

,938

18

,854

24,1

05

26,7

74

23,1

17

Inst

rum

ent v

aria

bles

test

s A

nder

son

cano

n. c

orr.

LM

361.

8

365.

1

35

8.9

36

9.6

37

5.69

361.

78

363.

92

14

0.84

368.

79

Cra

gg-D

onal

d W

ald

F

183.

42

185.

11

18

1.94

18

7.46

19

0.78

18

4.21

184.

57

70.7

3 18

7.20

χ

2 (1)

P-v

alue

0.

23

0.19

0.39

0.

33

0.20

0.

37

0.

38

0.23

0.

35

Tax

adv

ice

0.35

0.

02

-2

.29

-1.8

7 -1

.53

-1.5

1

1.64

2.

82

0.91

**

*

**

* **

* **

* **

*

***

***

***

N.

24,5

49

24,8

76

24

,302

24

,578

22

,914

18

,798

24,0

69

24,1

64

23,0

83

Inst

rum

ent v

aria

bles

test

s A

nder

son

cano

n. c

orr.

LM

145.

97

14

5.48

13

9.70

146.

81

14

5.43

139.

33

146.

18

14

8.07

143.

71

Cra

gg-D

onal

d W

ald

F

73.3

6 73

.10

70

.19

73.7

8 73

.11

70.1

0

73.4

7 74

.42

72.2

3

χ

2 (1)

P-v

alue

0.

12

0.27

0.23

0.

17

0.11

0.

25

0.

28

0.35

0.

28

Insu

ran

ce a

dvi

ce

0.20

-0

.12

0.

65

0.19

0.

79

0.54

1.67

4.

22

0.79

150

Jour

nal o

f P

erso

nal F

inan

ce

150

**

* **

*

***

**

* **

***

***

***

N.

25,7

82

26,1

33

25

,493

25

,805

24

,019

19

,619

25,2

91

24,2

03

24,2

01

Inst

rum

ent v

aria

bles

test

s A

nder

son

cano

n. c

orr.

LM

176.

53

17

9.12

17

6.32

182.

11

15

7.91

134.

02

178.

89

60

.58

17

0.01

C

ragg

-Don

ald

Wal

d F

88

.80

90.1

0

88.6

9 91

.62

79.4

0 67

.39

90

.00

30.3

4 85

.52

χ

2 (1)

P-v

alue

0.

30

0.19

0.68

0.

67

0.88

0.

90

0.

37

0.25

0.

19

Deb

t ad

vice

0.

47

-0.2

9

1.49

0.

34

1.79

1.

00

3.

94

-7.2

9 1.

87

**

* **

*

**

**

*

**

* **

* **

*

N.

25,7

85

26,1

41

25

,502

25

,811

24

,033

19

,622

25,2

93

24,2

01

24,1

99

Inst

rum

ent v

aria

bles

test

s A

nder

son

cano

n. c

orr.

LM

73.8

9

75.4

7

68

.17

75

.30

62

.57

53

.94

71.5

9

35.6

0

69.3

3 C

ragg

-Don

ald

Wal

d F

37

.02

37.8

1

34.1

5 37

.73

31.3

4 27

.01

35

.86

17.8

1 34

.73

χ

2 (1)

P-v

alue

0.

16

0.28

0.91

0.

64

0.61

1.

00

0.

17

0.46

0.

70

Not

es: T

he ta

ble

show

s co

effi

cien

ts o

f fi

nanc

ial a

dvic

e on

fin

anci

al li

tera

cy, n

egat

ive

and

posi

tive

fina

ncia

l beh

avio

rs in

the

seco

nd-s

tage

reg

ress

ion.

Con

trol

var

iabl

es n

ot s

how

n in

the

tabl

e in

the

seco

nd-s

tage

reg

ress

ion

incl

ude

gend

er, a

ge, r

ace,

ed

ucat

ion,

inco

me

and

wor

king

sta

tus.

In

addi

tion,

sca

led

fina

ncia

l lite

racy

sco

res

are

used

as

cont

rol v

aria

bles

to te

st e

ffec

ts o

n po

sitiv

e an

d ne

gativ

e be

havi

ors.

And

erso

n ca

noni

cal c

orre

latio

ns L

M s

tati

stic

s ar

e us

ed to

test

for

the

rele

vanc

e of

inst

rum

ent

vari

able

s; C

ragg

-Don

ald

Wal

d F

sta

tistic

s ar

e fo

r w

eakn

ess

test

and

Sar

gan-

Han

sen

stat

isti

cs a

re f

or e

xoge

neit

y te

st. T

he s

ampl

e

incl

udes

all

the

resp

onde

nts

who

hav

e co

mpl

ete

set o

f in

form

atio

n fo

r th

e re

gres

sion

. ***

indi

cate

s st

atis

tical

sig

nifi

canc

e at

the

1% le

vel,

** a

t the

5%

leve

l and

* a

t the

10%

leve

l.

151

advice will pursue more positive behaviors, but they are also more likely to spend more than income, overdraw from checking accounts and get credit card penalties. Therefore, we conclude from Table 3 that there are some positive effects from taking advice, but it depends on the type of advice. Savings/investments and tax advice serves better to correct clients’ behavior and promote positive behavior than does insurance and debt advice. The reason for such a phenomenon is worth investigating in further study. One explanation could be that savings/investments and tax advice is more likely to be provided in a comprehensive plan,29 with financial advisors working with clients to define the problem and work through the process. Insurance and debt advice, however, focuses more on solving one specific problem and referring products, which decreases the probability that advisors will help clients improve their overall financial behavior. In addition, none of the advice helps to improve clients' financial literacy, although it does make them perceive that they know more. Thus, our results indicate that, currently, professional financial advisors are not an effective channel to improve consumers’ financial literacy.

Next, we examine whether our previous results are different for the low-income group. To do so, we modify the regressions in (2) and (3) as follows:

iiii XInstrumentAdvice 211

Lowiii XAdviceAdviceEffect

3211

.

29 We find that those who use savings/investments or tax advice are more likely to use savings/investments, tax and insurance advice together than those who use insurance or debt advice.


152

The first-stage estimation (4) is the same as in (2), but

in the second stage, we include a new variable

LowiAdvice ,

which is the instrumented interaction between the low-income dummy and use of financial advice to test the additional effect

of iAdvice on iEffect among low-income consumers. As

there is one more endogenous variable

LowiAdvice in the second

stage, we need a new instrument. By following Wooldridge (2010), we create a new instrument variable by interacting the

low-income dummy with the estimated value of

iAdvice in the first stage.

Table 4 shows the effects of financial advice on financial literacy and behavior and the additional effects among low-income consumers. All the regressions pass the three identification tests on instrument variables.30 As for financial literacy, low-income consumers are more likely to believe that their financial knowledge level is improved after receiving savings/investments, insurance, and debt advice, but their objective financial literacy level is not significantly higher than high-income clients after receiving the advice. Financial advice helps low-income clients more by avoiding most negative behaviors and improving some positive behaviors to a greater extent than the high-income clients. For example, receiving savings/investments advice lowers the chance to overdraw from checking accounts among all income-level respondents with a coefficient of -0.81, and it helps the low-income ones

30 Regressions of debt advice on “have emergency fund” pass the weak identification test at the 15% level instead of the 10% level, as in other regressions.

153

more with a coefficient of -1.27 (-0.81-0.46=-1.27). Considering the limited access to financial advice in low-income communities, low-income consumers’ lower financial literacy level and their worse financial condition, it is expected that, with the same input, the marginal benefit of financial advice to low-income consumers will be higher than it would be to higher-income groups. However, as we discussed in the introduction, the current professional financial service industry is skewed towards high-end consumers, mainly due to the compensation system. To improve the overall financial capability level of the society, policymakers, financial counselors, planners, and educators should find ways to promote more financial advice to low-income groups, as we have shown that the marginal benefit from providing such a service is higher for low-income clients.

Conclusion

Using a unique rich dataset from the NFCS, the paper first compares the financial condition of low- and high-income consumers. We find that low-income individuals have less financial knowledge and ability, achieve fewer financial planning goals, and have less access to financial services and products. Meanwhile, they are not using as much financial advice as wealthier individuals as a channel to solve the problem. The skewed supply of professional financial advice and products towards wealthy people and the less-favorable attitudes towards financial professionals among low-income communities could explain this.

We provide insights to those who want to advocate financial advice services to low-income communities by showing that the unique demand compositions among low-


154

income clients, and their various considerations, differ from those of high-income consumers when seeking financial advice. We find that low-income earners demand insurance advice most, followed by savings/investments advice, debt advice and tax advice, which is quite different from the demand compositions of high-income clients. Discriminant analysis shows that the top concern among low-income consumers seeking financial advice is not cost as among high-income consumers; rather, for low-income consumers, factors such as wealth, age and having high-cost loans appear of greater concern. Therefore, financial advice providers should focus on different sets of financial services and pay attention to the unique considerations among low-income consumers to serve them better and attract more potential clients.

Using an instrument variable strategy to solve the problem of reverse causality and selection, we find that savings/investments and tax advice improves clients’ positive behaviors and corrects their negative behaviors. In addition, low-income consumers’ financial behavior is improved more than that of high-income ones. Such findings might help justify the cost of subsidizing financial advice for low-income households with key stakeholders such as policymakers, financial planners and educators. In addition, our analysis indicates that financial advice has positive effects on clients’ financial behavior, which could potentially benefit the clients in the long term.

The financial situation within the low-income community has gained a lot of attention as we learn more about increases in income and wealth gaps. As a group of consumers who need more resources to improve their financial well-being and who represent a large potential market to financial advice

155

suppliers, low-income consumers’ advice-seeking behavior has not yet been studied extensively. Today’s financial advice industry is more focused on wealthy clients, and the literature on advice-seeking behavior is biased towards higher-income individuals. This paper contributes to the literature by showing that financial exposures and advice-seeking considerations among low-income individuals differ from those of their higher-income counterparts. We also show that professional financial advice has a larger positive effect on the low-income group. However, we recognize that more research is needed to better understand the financial behavior of low-income consumers in order to serve them better.

Appendix

Financial Literacy Questions: The NFCS includes five standard questions on financial literacy developed by Lusardi and Mitchell (2009):

Compound interest question. Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow? 1) more than $102, 2) exactly $102, 3) less than $102, 4) don’t know, and 5) prefer not to say. Inflation question. Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account? 1) more than today, 2) exactly the same, 3) less than today, 4) don’t know, and 5) prefer not to say.


156

Bond question. If interest rates rise, what will typically happen to bond prices? 1) they will rise, 2) they will fall, 3) they will stay the same, 4) there is no relationship between bond prices and the interest rate, 5) don’t know, and 6) prefer not to say. Mortgage question. A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less. 1) true, 2) false, 3) don’t know, and 4) prefer not to say. Diversification question. Buying a single company’s stock usually provides a safer return than a stock mutual fund. 1) true, 2) false, 3) don’t know, and 4) prefer not to say.

Self-Assessed Financial Knowledge Question: On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge? Self-Assessed Ability Questions: How strongly do you agree or disagree with the following statements? Please give your answer on a scale of 1 to 7, where 1 = “Strongly Disagree,” 7 = “Strongly Agree,” and 4 = “Neither Agree nor Disagree”. You can use any number from 1 to 7.

1) I am good at dealing with day-to-day financial matters, such as checking accounts, credit and debit cards, and tracking expenses 2) I am pretty good at math 3) I regularly keep up with economic and financial news

157

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