with a little help from my friends (and my financial planner)

With a Little Help from My Friends (And My Financial Planner)Author(s): Mariko Lin ChangSource: Social Forces, Vol. 83, No. 4 (Jun., 2005), pp. 1469-1497Published by: Oxford University PressStable URL: http://www.jstor.org/stable/3598401 .

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With a Little Help from My Friends (and My Financial Planner)*

MARIKO LIN CHANG, Harvard University

Abstract

Despite the tremendous implications thatfinancial decisions havefor socioeconomic well-being, the study offinancial decision-making has been left largely to economists. This paper places this topic firmly within sociological terrain and demonstrates that the search for financial information is embedded within broader systems of social inequality. Analyses of data from the 1998 Survey of Consumer Finances reveal that social networks are byfar the mostfrequently used source of saving and investment information; however they are used most often by those with the least wealth. Wealthier households are more likely to turn to paid financial professionals and to certain forms of media for saving and investment information. Results indicate that those at the top of the socioeconomic ladder do gather information from multiple sources possibly to minimize the risk of making a poor decision; yet as socioeconomic status increases, networks are decreasingly likely to be among the sources consulted.

Whereas the study of financial decision-making has largely been left to economists, social structures such as networks have been shown to be conduits of financial information (Baker 1984; Uzzi 1999) and networks have been shown to influence the nature of market transactions (Abolafia 1996; DiMaggio and Louch 1998; Podolny 2001). One key area of economic decision-making that has been largely neglected by sociologists is the potential role of social networks in shaping saving and investment decisions. Yet, the choice of saving and investment vehicles

* An earlier version of this paper was presented at the 2003 meetings of the American Sociological Association in Atlanta, Georgia. I wish to thank Kenneth Andrews, Emilio Castilla, Mark Granovetter, David Grusky, Lowell Hargens, Bob Kaufman, Jason Kaufman, Lisa Keister, Peter Marsden, Paul DiMaggio, Barbara Reskin and anonymous reviewers for providing valuable criticisms and suggestions. Direct correspondence to Mariko Lin Chang, Department of Sociology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138. E-mail: [email protected].

? The University of North Carolina Press Social Forces, June 2005, 83(4):1469-1498



1470 / Social Forces 83:4, June 2005

may have a tremendous impact on the eventual rate of return and, hence, the accumulation of wealth across households. During the 1989-1998 period, for instance, stocks had an average annual rate of return of 10%, bonds 4.7%, and savings accounts, money market funds, and certificates of deposit combined only 1.4% (Wolff 2000). The average rate of return between stocks (a much riskier asset) and savings accounts (a much safer asset) is dramatic, but consequential differences in rates of return also exist among low-risk assets such as bank accounts, money-market deposit accounts and certificates of deposit (CDs). Analyses of Federal Reserve data reveal that on an aggregate level, the American public is losing $30 to $50 billion every year in interest by relying heavily on bank accounts for saving money instead of other vehicles such as CDs or money-market deposit accounts that are just as safe (Reuters 2000).

In a perfect world, information about each asset type and savings option would be available to all, regardless of ascribed characteristics. However, the flow of financial information is imperfect. Financial information is complex, financial knowledge may require a substantial investment of time or resources to acquire, and separating the relevant information from the irrelevant (or even inaccurate) information is difficult. Under such circumstances, people are likely to seek additional information or assistance from others when making financial decisions.

This article explores how socioeconomic status influences whether people use social networks to search for savings and investment information, or whether they prefer alternative sources of information. It begins with an overview of the reasons why people may or may not use networks to obtain financial information generally and then addresses how the use of social networks may vary by socioeconomic status. After discussing the data and methods, this article presents results from bivariate probit analyses that examine how indicators of socioeconomic status influence whether people get saving and investment information from social networks or from alternative sources, such as bankers, the media or paid financial professionals. The final section of the paper discusses the conclusions and how the findings contribute to our understanding of wealth inequality.

Social Networks

A thriving literature addresses the centrality of social networks in the job- matching process (e.g., Fernandez, Castilla and Moore 2000; Granovetter 1995; Lin 2000; Marsden and Gorman 2001), in shaping consumer transactions (DiMaggio and Louch 1998; Engel, Blackwell and Miniard 1995), and in the operation of financial markets (Abolafia 1996; Baker 1984; Podolny 2001; Uzzi 1999). The importance of networks in shaping both consumer decisions and socioeconomic outcomes suggests that social networks would also play an important role in the search for financial information. Drawing upon the networks literature, I expect



Financial Planning with a Little Help / 1471

that people will prefer to use social networks as sources of financial information for two reasons: cost and trust.

First of all, people may prefer to use networks because they cost less. Unlike financial professionals, friends and family do not charge a fee for their advice. In addition, obtaining information from friends and family is often easier than locating alternative sources of information. Friends and family are accessible and opportunities for exchanging information may arise in the context of other informal interactions. A second reason people may prefer networks is trust. Since most people are not confident of their level of financial knowledge (Lach 1999) and because it is often difficult to determine the quality of financial advice, the risk of receiving misleading or false information is particularly salient in the search for saving and investment information. Networks are embedded in shared norms of mutual obligation and trust, minimizing the risk of opportunism. In other words, people may prefer to get information from social networks because people assume that friends and family members will treat them fairly, will not knowingly steer them into the purchase of an inferior product or service, and will have no economic incentive for offering their opinions.

WHY WOULD PEOPLE NOT USE NETWORKS?

With all else being equal, people would probably prefer to receive saving and investment information from friends and relatives. Why conduct an extensive search for information or pay for advice if you don't have to? But what if the type of information people seek requires knowledge, information or expertise that their networks do not possess? In such situations, people may turn to sources outside their networks.

The primary reason that people turn to financial professionals is because they believe that professionals are more knowledgeable (Larson 1993). In matters such as personal finance, in which people lack information or expertise, professional advice might be especially sought out - particularly as the degree of complication or the need for specialized knowledge increases.

Furthermore, people may prefer not to use their social networks if the information is sensitive and they do not feel comfortable discussing it with people close to them. In fact, many people feel uncomfortable revealing their financial situation to friends and family members (Rubenstein 1981). And perhaps for good reason. Money is frequently the medium through which people express struggles over love and feelings of anger, resentment and envy (Millman 1991; Zelizer 1997, 2002). People may feel that if they share the details of their own financial situation with friends and family members, they may respond with jealousy, pity or embarrassment.

And finally, exchange theory (Blau 1964) suggests another reason that people may not prefer to use their social networks. The exchange of information within personal networks involves the expectation of reciprocity and, therefore, asking




others for advice or information may make one indebted to the person providing the information.

Therefore, although social networks may be easier, cheaper and more trusted sources of information, people may search outside their networks if they cannot obtain the information they need from their networks, if they wish to keep financial affairs private, or if they wish to avoid feeling obligated to the person providing the information.

Socioeconomic Status and the Use of Networks

Extrapolating upon the use of networks for job information, I expect that the use of networks for obtaining saving and investment information varies by socioeconomic status. Socioeconomic status is positively related to access to resource-rich networks (Lin 1999). Because networks are economically and educationally homogeneous (Lin 1999; Marsden 1987), the higher one's socioeconomic status, the more resource-rich one's networks are. For example, economic elites have access to important types of financial information through their business, political and social ties (Domhoff 1974, 1998; Mills 1956; Useem 1984). Boards of directors are in powerful positions with respect to knowledge that may be translated into financial gain and are often linked to others in similar positions through interlocking directorates or indirect interlocks. In fact, Useem (1984) found that corporate leaders use interlocking directorates to exchange information that could be used for economic gain. Even if one is not a member of the corporate elite, members of the upper class tend to belong to the same social clubs and tend to marry other members of the upper class (Domhoff 1974). Therefore, one would expect that the wealthy not only have more money, but also have networks whose members are financially knowledgeable. One might argue that this tendency exists, to a lesser degree, in the middle class as well. Accountants, lawyers and others in the business and financial industries are also likely to have access to financial knowledge or information that may be useful when making saving and investment decisions.

If socioeconomic status is positively related to financial information or access to financial expertise more generally, and if social networks are fairly homogeneous with respect to socioeconomic status, then we would expect people in socioeconomically advantaged positions to have networks that contain individuals with more extensive financial knowledge. The literature on the use of social capital in job searches suggests the same relationship between social standing and access to others in "high" positions. (For a review, see Lin 1999.)

Nevertheless, one cannot assume that access to social resources implies the use of social resources (Lai, Lin and Leung 1998). For example, although those of higher socioeconomic status are more likely to have resource-rich networks, research on job searches has shown that they make less use of their networks in




acquiring a job (De Graaf and Flap 1988; Granovetter 1995). An explanation for this finding is that people with resource-rich networks are also likely to possess the human capital necessary to render other avenues of a job search fruitful. In other words, they may have alternative paths or options available to them when acquiring jobs. The same processes may operate in the search for financial information. Those with more beneficial networks are also likely to have the option of seeking information from other sources - such as the ability to pay a financial professional for advice. Consequently, the use of networks for financial information is hypothesized to decline as socioeconomic status increases because socioeconomic status is positively related to one's ability to obtain saving and investment information from alternative sources, rendering one less reliant on networks as sources of information.

The same hypothesis is evident when the issue is approached from the opposite end of the socioeconomic hierarchy. Some studies have found that the poor rely on social networks for both emotional and economic support. Auslander and Litwin argue that the poor rely on informal support from family and friends because they have no other options: they have "heavy burdens, small circles of support already fully tapped, and few additional resources from which to seek informal help" (1988:237). Despite the heavy reliance on networks among the poor, research suggests that their networks are smaller and more heavily comprised of family members (Auslander and Litwin 1988; Marsden 1987).

We are left with the impression that the poor are less likely to have extensive networks to tap for financial information. But once again, we must bear in mind the distinction between having and using networks. Despite the fact that the poor are less likely to have networks containing people with access to financial expertise, the poor may be more likely to use networks because they have few alternative sources of financial information.

To summarize, the literature on the use of networks for job information suggests that although socioeconomic status should be positively related to access to networks of individuals with extensive financial information or expertise, those with the most resource-rich networks may be least likely to use them to search for financial information because they have greater access to alternative sources of information, such as financial professionals. As socioeconomic status decreases, individuals become increasingly reliant upon networks because they have less access to alternative sources of information. Therefore, variation in the composition and use of social networks by socioeconomic status suggests that access to saving and investment information is embedded in broader systems of social inequality. By examining how people search for financial information, we can better understand the ways in which such individual-level processes are related to economic inequality.




Data and Method

DATA

I use data from the 1998 Survey of Consumer Finances to examine socioeconomic differences in the sources of information used when making saving and investment decisions. The Survey of Consumer Finances is a comprehensive triennial national survey carried out by the Federal Reserve Board. Households for the main survey are selected from a multi-stage area probability design that was intended to represent the population with respect to broad demographic characteristics. In addition, the Federal Reserve Board oversamples high-income households who are less likely to show up in the main sample. (See Kennickel, Starr-McCluer and Surette [2000] for an overview of the Survey of Consumer Finances). To correct for the differential probability of being included in the sample, sample weights are used when appropriate.'

The main question of interest revolves around the respondent's answer to the following question:

"How do you (and your spouse/partner) make decisions about saving and investments? Do you call around, read newspapers, material you get in the mail, or use information from television, radio, an online service or advertisements? Do you get advice from a friend, relative, lawyer, accountant, banker, broker or financial planner? Or do you do something else?"

Answers were open-ended and coded, in the order given, to a maximum of 10 responses. Respondents reported using an average of 1.83 sources of information. I created a set of dummy variables to indicate whether a respondent reported consulting each particular source for financial information, regardless of the order reported by the respondent. However, because the survey recorded responses in the order given, I was able to compare analyses of the respondents' first responses with the analyses presented here and did not find any substantive differences.

The primary explanatory variables are indicators of socioeconomic status. Three variables are used to examine effects of socioeconomic status: level of education, income and liquid assets.2 Education refers to the highest level of educational attainment of the respondent or spouse, which ever is greater. This variable is coded into the following ordinal categories: less than high school, high school diploma or GED, some college, college degree and graduate school.3 Income is measured as the log of the total household income from all sources (i.e., wages, capital gains, etc.). An indicator of wealth,4 liquid assets is measured as the log of the total market value of all household liquid assets (savings and checking accounts, certificates of deposit, bonds, mutual funds and stocks).5

To properly specify the model, it is necessary to control for attributes that may influence the search for saving and investment information besides socioeconomic status. These control variables include the following: marital status, race, age and




financial risk tolerance. Marital status is represented by dummy variables - one dummy variable indicating that the household is headed by a single female and the other dummy variable indicating that the household is headed by a single male. Married and cohabitating households are treated as the reference category.6 Race is measured using two dummy variables indicating whether the respondent is black or whether the respondent is of another race, with white as the reference category.7 Age refers to the respondent (or, if married or cohabitating, the age of the older partner is used). Risk tolerance refers to the self-reported preference for taking financial risks. Answers range from 1: "not willing to take any financial risks" to 4: "take substantial financial risks expecting to earn substantial returns."8 Means and standard deviations for all variables are presented in Appendix A.

METHOD

Since the dependent variable of interest is the source(s) of information when saving and investment decisions are made (and therefore only applies to those households who save or invest), one must model the possibility of sample selection bias. Selection bias occurs when cases that are non-randomly related to the dependent variable are excluded from analyses. In the context presented here, the dependent variable is the source of information used for saving and investment decisions and hence includes only those households that save or invest. The potential source of selection bias is that households who do not save or invest are excluded from the analyses. Heckman's (1979) maximum-likelihood probit estimation with selection is an appropriate method for addressing this issue. This model assumes that the likelihood of consulting each source of information is a function not only of the independent variables in the model, but also upon the likelihood that a household saves or invests in the first place. The Heckman model first estimates the selection model (to predict whether a household saves or invests), calculates the expected error, and then uses the estimated error from the selection equation in the calculation of the analyses predicting whether a household uses a particular source of information when making saving or investment decisions.9 A separate model was run for each source of saving and investment information.

The selection criteria is whether or not a household saves or invests. To identify households who do not save or invest, I used respondent answers to the question of how they make decisions about saving and investments (see the first page of this article's "Data and Method" section). If respondents answered this question by stating that they don't save or invest (and listed no sources of saving or investment information), they were coded as households that do not save or invest. Using this definition, only 10% of households reported that they do not save or invest.10 This definition is liberal and most likely includes households that save regularly as well as those that rarely save. It may also refer not only to current saving and investment decisions, but perhaps to decisions that were made many




years ago. Nevertheless, analyses that utilized less liberal definitions of whether the household saves or invests did not differ from those presented here.

The primary question of interest refers to both saving and investment decisions; but because "saving" and "investing" may involve different processes, I also conducted parallel analyses using Heckman's probit model with different "selection" criteria - one in which I selected only households who were likely to be "investors" (as evidenced by their ownership of stocks, mutual funds or bonds) and another in which I selected only households engaged in "savings" (those with checking accounts, savings accounts or CDs, but without stocks, mutual funds or bonds). Results were consistent when each of these different selection criteria were used, suggesting that socioeconomic status affects "saving" and "investing" in similar ways. Consequently, I present the results that do not distinguish between saving and investing because doing so fits most closely with the wording of the questionnaire.

Descriptive Results

Of the 90% of households reporting that they save or invest, friends and relatives" are the most frequently utilized sources of information for saving and investing decisions, as shown in Figure 1.12 Approximately 41% of households that save or invest consult friends and relatives, revealing the importance of social networks in the exchange of financial information. In fact, no other single source of information comes close to matching the frequency by which households consult friends and relatives when making saving and investment decisions. After friends and relatives, the most commonly reported sources of information were bankers (27.1%), magazines and newspapers (20.8%), and financial planners (20.5%). Other sources reported include brokers, advertisements, material in the mail, television and radio, accountants, the Internet and lawyers.

Collectively, these various sources can be grouped into four general types: (1) social networks: friends and relatives; (2) paid financial professionals: financial planners, accountants, brokers and lawyers; (3) bankers; and (4) media: magazines and newspapers, television and radio, advertisements, material in the mail13 and the Internet. Figure 2 presents the percentage of respondents who report each of these four general types of sources.

Whereas 41.3% of households that save or invest reported consulting friends or relatives, 35.5% reported consulting a paid financial professional, and 27.1% reported consulting a banker. Here, I am assuming that "banker" refers to someone who works at a bank - such as an account manger or specialized financial counselor - not the CEO.14 What separates bank employees from other paid financial professionals in this paper is the key distinction that this person is not paid a fee directly by the consumer for their advice. People who work at a bank are also much more accessible than the other financial professionals listed




Friend, Relative Banker

Magazines, Newspapers Financial Planner

Broker Advertisements

Material in the Mail

Television, Radio Accountant

Internet

Lawyer

No Source Other than Self/Spouse

27

20.8

20.5

12.1

11.4

10.0

9.9

9..3

9.2

4.1

.1

15.4

0 5 10 15 20 25 Percent

30 35 40 45

Figure 1. Sources of Information Used When Making Saving and Invest- ment Decisions (Only Households that Save or Invest; N = 3975)

Friend, Relative 41.3

Paid Professionals 35.5

Banker 27.1

Media 35.2

0 1 15 0 15 0 25 30 35 40 45 Percent

Figure 2. Aggregated Sources of Information Used When Making Saving and Investment Decisions (Only Households that Save or Invest; N = 3975)

41.3

I I . , . , i . I




because it is often possible to speak with a bank employee without making an appointment. Consequently, the effects of socioeconomic status on consulting people who work at a bank are likely to differ from the effects of socioeconomic status on consulting the other types of financial professionals.

About 35% of households use some form of media to gather information about saving or investing. Of all five media sources, the category that includes magazines and newspapers is the most frequently cited source of information. Unfortunately, it is not possible to discern whether the respondent is referring to advertisements in local newspapers or specialized financial publications such as Fortune or the Wall Street Journal. Therefore, it is important to bear in mind the tremendous diversity in the range of content and quality of information likely received by respondents who report consulting magazines and newspapers for information.

Approximately 15% of respondents who save or invest report consulting no source other than themselves (or a spouse or partner).,Of the 85% of households that consult other sources, friends and relatives are the most frequently utilized sources of information. These findings reveal that social networks are key mechanisms for the exchange of financial information, providing a unique opportunity to observe how sociological factors intersect with economic decisions. This article will next address how socioeconomic status affects whether information is obtained from networks or from alternative sources (i.e., paid financial professionals, bankers and the media).

SOCIOECONOMIC DIFFERENCES

Of all three socioeconomic measures (i.e., education, income and liquid assets), liquid assets best signify a household's overall socioeconomic resources. As an indicator of wealth, liquid assets is more encompassing than income or education, and more closely tied to a household's ability to save and invest.15

Table 1 displays the frequencies of using particular sources of information by liquid asset quartile for those households that save or invest.16 The tendency to consult friends and relatives declines as liquid asset quartiles increase. In contrast, the percentage of households consulting paid financial professionals increases quite dramatically as liquid assets increase.17 More than half of the households in the top quartile report consulting a paid financial professional when making saving and investment decisions. The use of media also generally increases as liquid assets increase, although to a lesser degree. The use of bankers does not follow a linear trajectory, as bankers are consulted most often by those in the top and bottom quartiles.

At this point, one might reasonably conclude that friends and relatives are the most frequently used source of information for those in the bottom half of the economic distribution and that financial professionals are the favored




Table 1. Percent of Households Reporting Different Sources of Saving and Investment Information by Liquid Asset Quartile.

Liquid Asset Quartile 1st 2nd 3rd 4th

$0-$600 $601- $4,691- $29,101- Informational Source $4,690 $29,100 highest Chi2

Friend, Relative 45.7 44.8 42.4 33.7 p <.001

Paid Financial Professional 19.4 25.1 41.0 52.3 p < .001 Financial Planner 10.9 15.5 24.2 28.9 p <.001 Broker 4.8 7.2 11.5 22.7 p <.001 Accountant 4.4 5.7 10.4 15.4 p <.001 Lawyer 3.8 2.8 3.7 6.0 p <.001

Banker 27.4 25.5 25.8 29.8 p<.l

Media 31.4 34.9 37.8 35.9 p <.001 Magazines, Newspapers 15.1 17.0 22.3 27.3 p<.001 Advertisements 10.1 13.0 11.6 10.6 p<.001 Material in the Mail 9.9 10.2 9.5 10.4 p<.l Television, Radio 10.3 10.6 9.2 9.6 p <.001 Internet 4.1 7.7 11.5 12.3 p<.001

Unweighted N (Total=3975) 632 742 833 1768

Note: Data are for households that save or invest and are weighted.

information source for the wealthiest households.l8 Nevertheless, more than one-third of the wealthiest households report consulting friends and relatives for advice, suggesting that networks are a significant source of information across the socioeconomic spectrum.

Results of Multivariate Analyses

I utilize Heckman's (1979) probit model with selection to examine the likelihood that a household uses each particular source of information. As mentioned previously, the bivariate probit model with selection was chosen in order to address possible selection bias since not all households save or invest. The particular model tested here assumes that the likelihood of consulting each source for information is a function of a household's level of education, marital status, race, age, financial risk tolerance, income and liquid assets;'9 whereas the likelihood of saving and investing (the selection criteria) is a function of a




household's level of education, income, marital status, race, age and financial planning period.20

For purposes of simplification, the tables in the main text present only the results from the substantive equation that predicts whether or not each source was consulted. The coefficients from the first selection equation (which predicts whether a household consults friends and relatives) are presented in Appendix B. The coefficients for the selection portion of the remaining equations are virtually identical and, therefore, it would be redundant to list them independently; however, they are available from the author by request. The results from the selection portion of the analyses reveal that the most significant predictors of whether a household saves or invests in the first place is respondents' level of education, income and the financial planning period that they feel is most important when making saving and spending decisions, ranging from (1) "the next few months" to (5) "more than 10 years."

Table 2 presents the results of the bivariate probit analysis with selection for each source of saving and investing information.21

FRIENDS AND RELATIVES

Friends and relatives are used primarily by those with the least financial resources, as represented by both income and liquid assets. Therefore, the hypothesis that socioeconomic status is negatively related to the use of personal networks for financial information is supported.22 Although networks have the advantage of being easier, cheaper and embedded within norms of trust, as socioeconomic status increases these benefits appear to either decline or are replaced by advantages offered by other search methods.

Besides socioeconomic status, the use of social networks for information is greater for households headed by single females and for younger households. These findings are consistent with previous findings that network range is greater for younger individuals (Marsden 1987).

OTHER SOURCES OF INFORMATION

Paid Financial Professionals

Two indicators of socioeconomic status (education and liquid assets) increase the likelihood that a household will consult a paid professional for saving and investment information. When households have more money to save and invest - and more money to pay for professional advice - it is not surprising that they seek information from a professional.

A look at the detailed categories that comprise the aggregated paid financial professional category provides more information on socioeconomic differences (see Table 3). Consistent with the results of the aggregated financial professional




Table 2. Coefficients for Bivariate Probit Models with Selection Predicting Sources of Information Consulted When Making Saving and Investment Decisions.

Friends/Relatives Paid Professional Banker Media

Socioeconomic Status:

Education

(log) Income

(log) Liquid Assets

Controls:

Marital Status:

Single Female-Headed

Single Male-Headed

Race:

Black

Other

Age (.0153 (.002)

.001 -.010*** (.002) (.002)

.218*** (.029)

-3411.9 285.1***

-.349* (.194)

4305

-.055* .140*** (.028) (.027)

-3223.9 -3469.7 35.0*** 175.0*** -.153 -.187 (.345) (.277)

4305 4305

Note: Standard errors are in parentheses and coefficients for the constant term are not presented. * p 1.0 ** p< .05 **p*p.001 (two-tailed tests)

.023 (.024) -.024* (.015) -.029***

(.009)

.309** (.057) .068

(.065)

-.031 (.078) .087

(.074)

.120*** (.023) .002

(.014) .076***

(.009)

.019 (.059) -.067 (.065)

.175** (.080) -.253** (.0781

.003* (.002)

(.09126) (.026) -.026

(.017) .020**

(.009)

.015 (.061) -.098 (.070)

.112 (.083) -.234** (.082)

.130*** (.025) -.040** (.015) -.005 (.009)

-.015 (.059) .123*

(.064)

.217** (.079) .193**

(.075)

Financial Risk Tolerance

Log likelihood Wald Chi2 Rho

N

.001 (.028)

-3470.7 254.5***

-.053 (.262)

4305



Table 3. Bivariate Probit Analyses with Selection Predicting Sources of Information For Saving and Investment

Decisions, Non-Aggregated.

Financial Broker Accountant Lawyer Magazine, Ads Material Internet TV, Radio ?

Planner Newspaper in Mail

Socioeconomic Status: Education .048** .105*** .041 .071** .110*** .016 .072** .242*** .028 >

(.024) (.028) (.031) (.032) (.026) (.035) (.031) (.037) (.035) .

(log) Income -.029** .012 .002 .036* -.017 -.059*** -.030 -.021 -.045**

(.013) (.016) (.018) (.019) (.016) (.018) (.020) (.020) (.019)

(log) Liquid Assets .047*** .076*** .067*** .026* .031*** -.003 .001 .044*** -.007 o

(.009) (.015) (.012) (.014) (.010) (.011) (.011) (.013) (.011) m

Controls: Marital Status:

Single Female-Headed .127** -.052 -.064 .247** .001 -.051 -.087 -.024 -.164**

(.060) (.074) (.076) (.089) (.065) (.078) (.079) (.092) (.083)

Single Male-Headed -.087 .195** -.175** .011 .085 .085 .078 .187** .235**

(.069) (.074) (.085) (.107) (.070) (.080) (.082) (.086) (.079)

Race: Black .280*** -.143 .220** .390*** .143 .240** .208** .097 .175*

(.080) (.117) (.100) (.116) (.088) (.093) (.100) (.117) (.102)

Other -.222** -.121 -.058 -.243** .186** -.062 -.053 .061 .294**

(.087) (.102) (.101) (.121) (.082) (.099) (.104) (.110) (.094)

Age -.002 .008*** -.002 .009*** -.005** -.008*** -.006** -.022*** -.0004

(.002) (.002) (.002) (.002) (.002) (.002) (.002) (.003) (.002)



.158*** .182*** .076** (.027) (.031) (.032)

.108**

(.042) .135*** .078** .093**

(.029) (.034) (.035)

.242*** .081**

(.037) (.035)


N 4305

-3054.0 -2719.4 -2528.9 -1834.1 -3048.2 -2271.7 -2249.7 -2145.6 -2225.8 105.9*** 182.2*** 58.0*** 73.9*** 99.9*** 66.9*** 44.3*** 204.7*** 49.9***

-.767*** -.443** -.489** .689 .061 -.401 .135 -.150 -.240 (.098) (.193) (.232) (672) (.336) (.292) (.449) (.328) (.346)

4305 4305 4305 4305 4305 4305 4305 4305 4305

Note: Standard errors are in parentheses and coefficients for the constant term are not presented. * p< 1.0 ** p <.05 *** p<.001 (two-tailed tests)

_.

:s p

_.

,

OQ

-.-

f-?

oo

o

W 4-+ 00+ <^





category, as socioeconomic status increases so does the likelihood that a household will consult each type of financial professional.

In addition to socioeconomic differences, there are statistically significant racial differences in the use of paid financial professionals. Black households are more likely than economically similar white households to seek the advice of paid professionals when making saving and investment decisions (financial planners, accountants and lawyers in particular). Since many blacks are newer to the investment arena (Conley 1999; Oliver and Shapiro 1997), they may feel more of a need to turn to professionals for information. As a financial consultant quoted in a 1997 Money magazine article explained, "Since many blacks are first- or second-generation investors at best, there is no pattern of behavior to follow, so they often feel more secure relying on professional help" (Smith 1997). And unlike other minority groups, blacks have been the focus of marketing by the financial services industry, such as Ariel Capital Management (a black-owned financial services company) and many others, who have targeted the black community as potential clients. Furthermore, organizations ranging from churches to the Coalition of Black Investors have been actively reaching out to current and potential black investors to increase financial literacy and forge connections between blacks and financial professionals (Kong 2000; Mabry 1999; Weems 1998:107).

Bankers

A less expensive alternative to paid financial professionals are people who work at a bank. Interestingly, education is negatively related whereas liquid assets are positively related to the likelihood that a household will consult a banker for saving or investment information. In comparing the effects of socioeconomic status across the banker and paid professional categories, results indicate that education and risk aversion, rather than financial assets, per se, are the determinants of which type of financial industry representative (paid professional or banker) a household chooses. A banker is the favored information source of less-educated, more risk-averse households; a paid professional is the favored information source of higher-educated, less risk-averse households.

Media

Interestingly, the use of media increases with education, but decreases as income increases. However, as mentioned previously, the media category could potentially include things as diverse as the local news, direct-mail advertisements, the Wall Street Journal or financial sites on the Internet. Given the diversity of types of media, it is prudent to examine the bivariate probit analyses of each individual media category, which are presented in Table 3.




Higher education attainment increases the likelihood that households will consult several forms of media: material in the mail, the Internet and magazines or newspapers. Those with more wealth are likely to consult the Internet as well as magazines and newspapers. One interesting benefit of consulting the media (compared to networks or paid professionals) is that the search is private and there is no risk of offending anyone if the advice or information is disregarded.

But it is important to bear in mind that the content and quality of information varies across media forms and even within the same form of media. Without knowing more about the media form or content, the implications of any socioeconomic differences in media use are unclear at this point and can be resolved only in future studies.

Are Alternative Sources used at the Expense of Networks?

Results thus far indicate that social networks were used most often by those with the least wealth, while wealthier households were more likely to gather savings and investment information from paid professionals and from certain types of media. However, is the reliance on professionals and the media at the expense of social networks? After all, it seems reasonable that one might want to use multiple sources of advice, thus combining their strengths and minimizing their inherent weaknesses. To elaborate, networks have the advantage of being embedded in norms of mutual trust, thereby minimizing the risk of opportunism. However, people in one's networks may not be as financially knowledgeable as paid experts. In contrast, paid professionals have the advantage of being knowledgeable, but there is the risk that the financial professional will behave opportunistically rather than provide advice that is in the customer's best interest. Combining information from networks with information from other sources (such as paid professionals) may help to minimize risk by tapping both sources of information - trustworthy (but perhaps not as knowledgeable) and knowledgeable (but perhaps looking to further their own interests over yours).

To further explore how socioeconomic status impacts whether people use networks in combination with other sources of information, I conducted a multinomial logistic regression for those households that save or invest,23 in which there were three possible outcomes: (1) networks used as one's only source of information, (2) networks used in conjunction with other sources of information, and (3) networks are not used. In the results presented in Table 4, people using networks in conjunction with other sources of information are the reference category, which allows me to determine (a) how they compare to those who use networks exclusively and (b) how they compare to those who do not use networks.

The first column of Table 4 compares those who use networks exclusively with those who use networks in conjunction with other sources of information (the




reference category). Results indicate that those who use networks exclusively have lower socioeconomic status (as measured by education, income and liquid assets) than those who use networks in conjunction with other sources. Those with the least socioeconomic resources are more likely to rely exclusively on networks.

The second column of Table 4 compares those who do not use networks with those who use networks in conjunction with other sources. Although education is positively related to the use of networks in conjunction with other sources, those who use networks in conjunction with other sources have lower liquid assets, suggesting that as wealth increases, people are more likely to exclude networks as a potential source of information.

These results suggest that the use and exclusivity of networks as a source of information shifts as socioeconomic status increases. The exclusive use of networks is most likely for those at the bottom of the socioeconomic ladder. People who use networks in conjunction with other sources have higher economic status than those using networks exclusively. But those who do not use networks have the highest economic status of all. Apparently, as socioeconomic status increases, one is less likely to use their networks at all, even to cross-check the information acquired from outside sources.

A second way of assessing how socioeconomic status may be related to whether people are possibly gathering information from multiple sources is to examine how many different sources of information people use when making savings and investment decisions. To empirically assess how socioeconomic status is related to the number of sources people utilize for making saving and investment decisions, I conducted a Heckman selection model that estimates regression models with sample selection. Like the Heckman probit model with selection (discussed previously), it addresses the possible selection bias that not all households save or invest; but whereas the Heckman probit model is appropriate for discrete outcomes, the Heckman selection model is appropriate for regression equations.

The dependent variable for the Heckman selection model is the number of sources people report using when making saving and investment decisions. The independent variables for the regression equation and the selection model are identical to previous models. The results of the Heckman selection model predicting number of sources of information when making savings and investment decisions are presented in Table 5. (And, like models in Tables 2 and 3, the coefficients for the selection portion of the analyses are virtually identical to those presented in Appendix B.)

As shown in Table 5, socioeconomic status is positively related to the number of sources consulted when making saving and investment decisions. It seems logical that those individuals with more wealth would perform more extensive searches for information since they have more wealth to manage. Taken in conjunction with the previous analyses, it appears that as socioeconomic status increases, people are more likely to consult multiple sources of information except




Table 4. Coefficients for Multinomial Logistic Regression Model Compar- ing Characteristics of Those Using Networks in Conjunction with Other Sources (Reference Category) with those Using Networks

Exclusively and those Not Using Networks.

Used Networks Exclusively Did Not Use Networks

Socioeconomic Status: Education

(log) Income

(log) Liquid Assets



Single Male-Headed

Race: Black

Other

Age


Log likelihood Wald Chi2

N

-.298*** (.063)

-.110** (.037)

-.023 (.034)

.034 (.023)

.026* (.015)

-.075*** (.023)

.112 (.157)

-.467*** (.102)

.328* (.187)

-.028 (.116)

-.096 (.207)

.036 (.137)

.169 (.196)

-.079 (.133)

.014*** (.004)

.030*** (.003)

-.049 (.047)

-.244** (.083)

-3188.2 438.8***

3,975

-3188.2 438.8***

3,975

Note: Standard errors are in parentheses and coefficients for the constant term are not presented. * p < 1.0 ** p<.05 *** p .001 (two-tailed tests)




for their social networks. In other words, those of the highest socioeconomic status are likely to be combining multiple sources of information, but as socioeconomic status increases, networks are decreasingly likely to be one of the sources.

Discussion and Directions for Further Research

Social networks are by far the most frequently used source of information for making saving and investment decisions among the 11 sources examined in this paper. Social networks are clearly key mechanisms by which financial information is exchanged in households of all socioeconomic levels. However, networks are used most often by households with the fewest economic resources. The negative relationship between socioeconomic status and the use of networks for saving and investment information may be a result of the types of information sought by lower socioeconomic status households. Members of these households are not likely to be flush with cash or investing in the stock market. Therefore, they are most likely interested in finding out information about account fees, interest rates or other types of information that can be acquired successfully by asking people in one's network.

But the greater reliance on networks may also hinder poorer households' access to information regarding all possible saving and investment vehicles. Because networks are fairly socioeconomically homogeneous, people in the lowest socioeconomic groups are turning to others of similar socioeconomic status. This reliance on networks may be problematic if financial information does not flow equally across all socioeconomic classes or if the information obtained from networks is inferior to information obtained from other sources. In that case, those most in need of financial planning experts and access to financial information are least likely to receive it.

Households with greater wealth are more likely to turn to paid financial professionals and to certain forms of media. Interestingly, as socioeconomic status increases, so does the number of sources of information consulted. However, as socioeconomic status increases, networks are less likely to be one of the sources of information. Results indicate that those at the top of the socioeconomic ladder do gather information from multiple sources - possibly as a way of cross-checking or combining information to minimize the risk of making a poor decision - however, networks are likely to be left out of this process. Apparently, as socioeconomic status increases, networks are less likely to be used for saving and investment information because (a) networks are less useful sources of information, (b) people are more resistant to sharing their personal financial information with others in their networks, or (c) people are hesitant to accrue obligations to others in their networks.

An alternative explanation for the finding that socioeconomic status is negatively related to the use of networks for information is measurement error. If




Table 5. Coefficients for Heckman Selection Model Predicting the Total Number of Sources of Information Consulted When Making Saving and Investment Decisions.

Number of Sources Consulted

Socioeconomic Status: Education .152***

(.023)

(log) Income

(log) Liquid Assets



Single Male-Headed

Race Black

Other

Age



N 4305

Note: Standard errors are in parentheses and coefficients for the constant term are not presented.

*p< 1.0 **p<.05 ***p<.001 (two-tailed tests)

-.042** (.016)

.044*** (.011)

.075 (.072)

.081 (.082)

.294** (.099)

.034 (.095)

-.012*** (.002)

-.241** (.034)

-8610.1 240.9***

-.133 (.091)




financial information is exchanged routinely in the course of other conversations and without being actively sought out, then the recipient of the information may not recognize that relevant information has been exchanged. As explained by Lin, "embeddedness in resource-rich social networks increases the likelihood of receiving useful information in the routine exchanges and without actively seeking such information.... Only when such useful information is not available and not forthcoming would activation of social capital become necessary" (2000:792). It

may well be the case that people in positions of higher socioeconomic status are more likely to receive information relevant to saving and investment decisions in routine exchanges with members of their social networks. Because the information is exchanged within the context of other conversations or in small incremental pieces throughout multiple conversations, the recipients may not even realize that they received information. The flow of information may therefore constitute "the invisible hand of social capital" (Lin 2000:792). Because those without such resource-rich networks do not receive information via the invisible hand, they must actively seek information from networks.

In many ways, the findings here parallel the findings regarding the use of networks for job searches. In both cases, we would expect that those in higher socioeconomic positions would have access to beneficial networks; and yet findings suggest that those who have the most resource-rich networks are less likely to use social networks for information. There are two main explanations for this finding: (1) those with beneficial networks have other advantages (e.g., human

capital, economic capital, etc.) and are less reliant on networks for information, and (2) those with beneficial networks do not need to actively seek it from their networks because they routinely receive it. Further research is needed to adjudicate between these two explanations.

Further research is also needed to address several key questions brought to the fore by this paper that could not be examined due to data limitations. First, to better understand the implications of different search methods, future research should address how the content and quality of information received from these different sources varies. If financial professionals generally provide high-quality information, then it is likely that the rich will become richer, further exacerbating wealth inequality. Second, future research should also address how the source of information impacts savings and investment decisions. For example, it is likely that those seeking information from financial advisors and brokers are more likely to invest in stocks and bonds, both because the people who use paid financial professionals are likely to have higher wealth to begin with, but also because these types of financial professionals specialize in (and make money from) recommending these types of investments. Third, the collection of data over time is necessary to tease out the causal relationships involved in the process of financial decision-making and the acquisition of wealth.

The research reported here is intended to bring financial behavior to the attention of sociologists because saving and investment decisions have important




implications for understanding wealth inequality. As stated recently by several scholars (Conley 1999; Keister 2000; Oliver and Shapiro 1997; Shapiro 2004; Shapiro and Wolff 2001; Sherraden 1991; Spilerman 2000; Tilly 2000), measures of economic well-being must move beyond income to include wealth because wealth inequality is much more extreme than income inequality, and also because wealth confers benefits that income does not: it can be transmitted across generations fairly easily, it can provide the ability to weather common financial crises (e.g., illness or unemployment), and it can be used to help generate more income and wealth. Consequently, in order to understand past, present and future economic inequality, it is necessary to understand how wealth is created and maintained and the ways that financial decision-making may reinforce wealth inequality.

Notes

1. Sample weights are used in all descriptive tables and figures, but not in the multivariate analyses. Since the sampling weights are derived in part from the independent variables in the analyses, it is not appropriate to use weighted data in the multivariate analyses. See Winship and Radbill (1994) for a more detailed discussion of the use of weights in multivariate analysis.

2. Among the independent variables the only correlation coefficient above .5 was for liquid assets and education (.577). Omitting education from the analyses did not change the main findings.

3. Results did not differ when I treated education as a series of dummy variables.

4. An alternative measure of wealth would be net worth. Net worth consists of the total household assets (i.e., real estate, vehicles, businesses, liquid assets) minus debts (i.e., home mortgage, credit card debt, loans). Since net worth can sometimes obscure subtle but important differences in wealth that may be relevant for the types of saving and investment decisions people make (for example, a person with $15,000 equity in their home but no other assets is likely to be in a different saving and investment situation than a person with $15,000 in liquid assets), liquid assets are used in the analyses. Nevertheless, results of additional analyses using net worth instead of liquid assets did not differ substantially from those reported here.

5. Following the lead of other researchers, I excluded retirement plan assets that the holders cannot borrow against.

6. The survey questions were designed to treat cohabitating couples as married couples and consequently most variables in the analyses are based on this assumption. However, the results did not differ when cohabitating couples were treated as distinct from married couples. 7. The data do not contain the racial identity of both spouses in the household. The race variable only refers to the respondent and therefore it is not possible to ascertain whether the racial category refers to both spouses or partners (in the case of married or cohabitating households) or just the respondent. However, in 1998 interracial married couples constituted only 2.4% of all married couples (U.S. Bureau of the Census 2000).

8. The question refers to the amount of risk that the respondent and spouse/partner (if applicable) are willing to take when they save or make investments. Because it is possible




that one's financial risk tolerance has been constructed with a financial advisor, I have run additional models that exclude this variable from the analyses and the results are consistent with those presented here.

9. For a more detailed description of the Heckman estimator, see Greene (1997) and Winship and Mare (2000).

10. This percentage is much lower than another indicator of savings in the Survey of Consumer Finances that asks respondents whether, over the preceding year, their household

spent less than its income, more than its income, or about as much as its income. Fifty-six percent of households reported spending less than their income, an indicator of saving. However, when asked for their motivations for saving (even though they may not be saving currently), only 4.9% of respondents reported that they do not save (Kennickell, Starr- McCluer & Surette 2000).

11. In the data, friends and relatives are combined into a single category, rendering it

impossible for me to separate the two sources of information.

12. Sources reported by less than 3% of the households are not included. In addition, "calling around" is not analyzed due to the tremendous ambiguity of this category; it may include

responses as diverse as calling friends, banks or a stock broker. Nevertheless I conducted a bivariate probit analysis for this category (not shown) and found that the variables used in the analyses were extremely poor predictors of whether or not someone "called around," further indicating the conceptual ambiguity of this category.

13. While material in the mail most likely contains items ranging from direct-mail advertisements to newsletters from financial institutions, its content is most likely similar to information in the other forms of media in this category.

14. Characteristics of households that consult bankers support this assumption-they tend to have lower levels of education (see Table 2).

15. Results for the other indicators of socioeconomic status suggest similar bivariate

relationships as those presented in Table 1.

16. Liquid asset quartiles were calculated using the entire sample and are weighted to account for the oversample of wealthy households. Therefore, quartiles are designed to reflect the U.S. population and not the survey sample.

17. Patterns in the use of lawyers, however, deviate somewhat from the clear pattern mentioned here.

18. The likelihood of having friends and family members who are financial professionals most likely increases as socioeconomic status increases. It is impossible to discern whether

respondents classified such individuals as a financial professional or as a friend/relative. Since we have no reason to believe that a classification bias exists (i.e., respondents are neither more nor less likely to report these individuals as either friends/relatives or as financial

professionals), the error is most likely random. Nevertheless, if we assume that financial

professionals who are friends or relatives are disproportionately classified as financial

professionals, then the observed effect of socioeconomic status on the use of friends/relatives and on paid financial professionals may be weakened. On the other hand, if we assume that these same individuals are more likely to be classified as friends or relatives, then the observed

relationship between socioeconomic status and both the use of networks and the use of paid financial professionals is further strengthened. However, we have no reason to believe that either of these two scenarios is operating here.




19. It is possible that there may be a reciprocal relationship between source of information and liquid assets. For example, households might have greater (or less) wealth because they followed the advice of a friend or a broker. Because the data is cross-sectional, I cannot determine the causal order of the variables in the models. However, I do not think this issue poses a serious challenge to the findings. When people answer the question of how they acquire information when making saving and investment decisions, they probably think back to whom or what sources they consulted the last time they made a decision. Since amounts of liquid assets change very slowly for most households (most households have very little wealth), it is unlikely that the value of a household's liquid assets changed dramatically since their last saving or investment decision.

20. The survey asks the following: "In planning your (your family's) saving and spending, which of the time periods listed on this page is most important to you?" Answers range from 1: "the next few months" to 5: "more than 10 years." The "planning period" variable is used in the selection equation as an instrumental variable in order to identify the model. Nevertheless, the inclusion of this variable as a predictor of both saving and of the likelihood of consulting each particular source did not change the conclusions presented here.

21. After restricting the sample to respondents who save or invest, logistic regressions were also conducted. The results did not differ in any meaningful way from the results obtained using the bivariate probit model with selection, which is not surprising given that many of the values of Rho in Tables 2 and 3 are not significantly different from zero.

22. This does not, however, exclude the possibility that the wealthiest households are using their networks as sources of information regarding which financial planner, accountant, lawyer or broker to use.

23. Unfortunately, it is not possible to control for the selection of households that save or invest using multinomial logistic regression.

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Appendix A. Means and Standard Deviations for Variables in the Analysis

Mean (S.D.)

Dependent Variables:a Friends and Relatives .37 (.48)

Exclusively .12 (.32) In Conjunction with Other Sources .25 (.44)

Paid Financial Professional .32 (.47) Financial Planner .18 (.39) Broker .11 (.31) Accountant .08 (.28) Lawyer .04 (.19)

Banker .24 (.43) Media .32 (.47)

Magazines, Newspapers .19 (.39) Advertisements .10 (.30) Material in the Mail .09 (.29) Internet .08 (.28) Television, Radio .09 (.29)

Total Number of Sources of Information 1.83 (1.65)

Independent Variables: Socioeconomic Status:

Level of Education 2.90 (1.25) (log) Income 10.11 (1.98) (log) Liquid Assets 7.92 (3.41)


Single Female-Headedb .27 .45 Single Male-Headedb .14 .35

Race: Blackc .12 (.32) Other Non-Whitec .10 (.31)

Age Financial Risk Tolerance 1.89 (.87) Planning Periodd 3.00 (1.32)

Data are weighted and refer to the entire sample. a Variables indicate whether a household consults or gets advice from each source (no = 0, yes = 1). b Dummy variables; reference category is "married or cohabitating." c Dummy variables; reference category is "white." d This variable is used only in the selection equation of Heckman's probit model with selection as an instrumental variable to identify the model (see note 20).




Appendix B. Results from Bivariate Probit Analysis, predicting whether a household saves or invests in the first place (taken from the full model predicting whether a household consults friends or relatives)t

Household Saves or Invests

Socioeconomic Status: Education .205 ***

(.028)

.104 ***

(.014) (log) Income

Marital Status:


Single Male-Headed

Race: Black

Other

Age

Financial Planning Period

Rho

N

Note: Standard errors are in parentheses and coefficients for the constant term are not presented. * p< 1.0 * p<.05 *** p<.0Ol (two-tailed tests)

tAs mentioned in the second paragraph of the Results of Multivariate Analyses section of this article, the coefficients for the selection portion of the remaining analyses are virtually identical, except that the value of Rho is statistically significant in the models predicting seeking information from financial professionals, financial planners, brokers and accountants (as indicated in Tables 2 and 3).

-.132 *

(.074)

-.093 (.089)

-.117 (.091)

.115 (.105)

-.0002 (.002)

.141 **

(.024)

-.053 (.262)

4305



with a little help from my friends (and my financial planner)

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