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«Annamaria Lusardi Dartmouth College and NBER Peter Tufano Harvard Business School and NBER December 22, 2008 We analyze a national sample of ...»

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This question asks about ―overall financial knowledge‖ and thus is more expansive. Second, we can evaluate and compare the answers to this self-reported measure of literacy with the answers to more objective measures of literacy and assess how they compare: do people know what they know? Third, it provides a simple and easy to answer question.11 Table 4 reports the answers to the self-reported literacy across the whole sample.

Contrary to the widespread debt illiteracy we find in the first three questions, most respondents think they are above average in term of their financial knowledge. The average score in the sample is 4.88 out of 7, and more than 50% of respondents chose a score as high as 5 or 6.

Conversely, only a little more than 10% of respondents chose a score below 4.

In general, self-reported literacy correlates with our measures of debt literacy, which indicates that people who think they know more generally do (although at a level lower than one might imagine.) For brevity we do not report how self-reported literacy varies across demographic groups, but we find a similar pattern as in the other measures of debt literacy in Tables 2. For example, women’s self-reported levels of literacy are much lower than are men’s levels. African-Americans and Hispanics also report lower self-confidence, even though differences in the self-reported measures across race and ethnicity are less sharp than across measures of debt literacy. Self-reported literacy increases steadily with income and wealth.

While self-reported literacy correlates strongly with debt literacy, there are some notable discrepancies between self-reported measures of literacy and actual measures of debt literacy across some specific groups. For example, while the elderly display very low levels of debt literacy across the three questions, they rank themselves highest in term of financial knowledge;

the average score among respondents older than 65 is 5.3. Similarly, those who are divorced/separated/widowed display very low levels of debt literacy but rank themselves rather high in term of self-reported literacy, with an average score of 4.79.

11 This question was asked to respondents before the three debt literacy questions.

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Individuals engage in many financial transactions that require careful consideration of interest rates and comparisons of alternatives. Those who are less knowledgeable may engage in higher-cost borrowing or less advantageous financial contracts; thus, we expect to see a negative relationship between financial skills and certain wealth-depleting financial behaviors.12 Experience measures. The TNS survey allows us to characterize a wide range of borrowing and investing experiences and transaction patterns of respondents. While we cannot measure their intensity or frequency, we can identify the types of transactions in which

individuals have engaged.13 This typology includes the four large related classes of transactions:

traditional borrowing, alternative financial service borrowing, saving/investing and credit card usage. The parenthetical headlines below were not part of the survey, but are given here to organize this information for the reader.

(1) (Experience with traditional borrowing) Have you ever…

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12 Financial experience could also affect financial knowledge, and we will discuss this issue in more detail in the empirical work.

13 The failure to engage in certain transactions could of course also be a function of individual choice or of supply constraints, i.e., the product was not available to the individual. For example, some may not have credit cards by choice, while others might be unable to obtain a card.

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While not exhaustive, this simple list contains many of the transactions in which a person might have needed to make a financial calculation regarding interest or fees.14 Table 5 provides the weighted incidence of the various transaction types for our sample population. Some activities are quite common—91% of the population have experience with checking accounts, 81% have experience with savings accounts or CDs, and 79% currently have credit cards. Other activities are fairly rare. For example, in our sample only 4.4% had ever gotten a refund anticipation loan, 6.5% had ever had an auto title loan and 7.8% had ever taken out a payday loan. As for credit cards, some (20%) do not have a card or do not use them.

However, a majority of respondents use credit cards and do not pay the balances in full each month.

Experience segments. A number of studies look at single activities, intensively studying consumers who use payday lending, refund anticipation lending, or credit cards. But these single-dimensional characterizations of consumer behavior cannot capture the fact that consumers engage in many activities simultaneously. Table 6 provides a two-way matrix of the incidence of each experience conditional on a second characteristic. For example, while the unconditional incidence of having used a payday loan is 7.8%, when conditioned on not having a credit card, the incidence is nearly double (15%). Further, conditional on paying off credit card balances on time each month, the incidence of having used a pay-day loan is less than half (3%).

This table is important as it shows that, focusing on one transaction only, as it has been done in many other studies, can give a narrow view of the behavior of individuals’ borrowing and saving 14 Because of space constraints, we could not include other choices, including the use of bank overdraft lines, car leases, variable annuity products, and other insurance products.

13 behavior. While it is possible to analyze each type of experience in Table 6 one at a time, or to consider dyads or triads, the large matrix contains a set of correlated activities. To reduce the dimensionality of this matrix, we rely on techniques used in marketing and market research. In particular, we use cluster analysis, a technique related to principal components analysis or factor analysis in that it reduces the dimensionality of a rich dataset. In this case, the cluster analysis is used to determine which groups of individuals have had similar financial experiences or could be considered ―market segments.‖ This segmentation is carried out solely on the basis of transaction activity, not with reference to demographics, literacy or self-judged indebtedness.

We first create the segments on the basis of common financial experiences, and then relate them to the other information.

Cluster analysis is a data analysis tool used to characterize high-dimensional data.15 This technique is used commonly in biology, linguistics and marketing. It is used to characterize a heterogeneous population into groups that are more homogeneous. Essentially, it uses orthogonal factors to parse the data into groups, testing for differences among groups as it divides the data into 2, 3, 4, or more groups.16 For our purposes, a key analytic question was which transaction types to include in the analysis. We include all of the transaction activity listed above in defining the cluster. The procedure groups the data into any arbitrary number of clusters. One must use statistics, judgment and sensitivity testing to ensure that the clustering is correct and sensible.

Based on the results of the cluster analysis, we reliably identify four main segments defined by common experiences. Table 7 identifies the transaction characteristics of four groups, which we describe hereafter and characterize with a name that summarizes their typology. Cluster 1, the ―in-control,‖ comprising about 26% of the sample, are people firmly engaged in the traditional financial system. These individuals all have credit cards, but do not carry any revolving balances (i.e., commonly called ―transactors‖). They have relatively high (but not the highest) levels of experience with mutual funds, stocks, and bonds. Among the four clusters they are most likely to have a mortgage, and fairly likely to have some experience with auto loans and home equity loans. However, among the four groups, they have the lowest levels of alternative financial services usage (payday lending, pawn shops, tax refund loans, etc.).

15 See Lehman, Gupta and Steckel (1998).

16 Cluster analysis is related to factor analysis; the latter identifies common traits and the former identifies similar populations of individuals on the basis of underlying factors.

14 At the other end of the spectrum (Cluster 4) are the 30% of our sample that one might consider “fringe‖ users of the financial service sector (―fringe‖ hereafter). Most (68%) do not have credit cards—although when they do have them, they pay them in full, as required by secured cards. When compared with the ―in control,‖ their usage of alternative financial services is considerably more frequent, using payday loans, tax refund loans and pawn shops 5, 16 and 9 times more frequently. At the same time, the likelihood that they have ever invested in a stock, a bond, or a mutual fund—or held a mortgage—is about one fifth that of the in-control group.

In between are two groups that comprise 43% of Americans. Almost all have credit cards and virtually all carry revolving balances most months. They are virtually all ―banked‖ with checking or debit accounts. The smaller subgroup, accounting for about 12% of the sample, is comprised of what we call the ―borrower/savers‖ (Cluster 2). This group has the highest level of experience with savings and investments of any of the four clusters, with 98% having experience with savings or CD products, 83% owning mutual funds, 83% owning stocks, and 65% owning bonds or savings bonds. At the same time, they have the highest levels of debt exposure too, with the most frequent experience with student loans (46%), home equity lines (54%), auto loans (94%) and virtually the highest levels of mortgage loans (77%). This group seems much more extended than the ―in control‖ group, with 95% carrying a revolving balance on their credit card, 27% paying the minimum balance only, 12% incurring late fees, and 6% going beyond their credit limit and incurring over-the-limit fees.

The final 31% of the sample are what we call the ―over-extended‖ (Cluster 3). In many ways they look like the borrower/savers, except that they have both less experience with savings and more markers of extended credit. Relative to all three other groups, this group has the highest likelihood of paying the minimum amount due on their credit cards (56%), running late fees on their credit cards (17%), incurring over-the-limit fees (11.8%) and using their card to get cash advances (16.1%). At the same time, they have far less experience than the borrower/savers or the in-charge group with respect to mutual funds, stocks, or bonds, as well as less experience than these other groups with home equity, mortgage and auto loans.

7. Characteristics by Experience Segment Our segmentation captures meaningfully different behaviors, even though the four clusters are defined only with respect to shared experiences, not on the basis of demographics, financial literacy, or perceived level of indebtedness. Nevertheless, we set out to examine 15 whether there is a relationship between demographics, debt literacy and these clusters: Are the ―in control‖ financially better off (e.g., in terms of income or wealth), more financially knowledgeable, and/or more secure in their level of indebtedness? Are the ―fringe‖ financially worse off, less financially literate, and/or less secure in their level of indebtedness? Finally, how bad off are the overextended? Table 8 provides descriptive statistics for these four clusters with respect to their demographics (panel A) and financial literacy (panel B). Following this discussion we report the results of a multinomial logit analysis which examines cluster assignment as a function of these factors.

With respect to demographics, the in-control have the highest incomes (53% over $75,000 per year) and wealth (74% with financial assets in excess of $50,000). They are more likely to be married and to be white than are members of the other three clusters.

Borrower/savers have incomes almost as high as the in-control, similar levels of marriage, are the second-oldest group, and tend to be men (62%). In terms of wealth, this group is not quite as wealthy as the in-control group, with only 52% having financial assets above $50,000. The fringe group has the lowest income (53% below $30,000 per year), and is most likely to be women (58%) who are single or separated (47%). Finally, the extended group looks most like the ―average‖ American, with income distributed roughly similar to the overall sample, and other demographics (age, gender, marital status and race) roughly comparable to the entire sample.

Both the fringe and over-extended have considerably fewer financial assets than do the other two groups, with only 24 and 28% having financial assets in excess of $50,000.

With respect to debt literacy (panel B), the in-charge and borrower/savers are both more knowledgeable than either the overextended or fringe segments. Looking across the three questions, the first two groups have considerably larger fractions correct on the three questions than do the latter two groups. A large fraction of the overextended and fringe admit to not knowing the answers to the questions. These patterns also are reflected in measures of selfreported financial literacy. The overextended and fringe each judge themselves to be much less knowledgeable than do members of the in-control and borrower/saver groups. We can see this both in the average scores as well as in the distribution of scores. For example, about 48% of those in-control and 53% of the borrower/savers ranked themselves in the top two scores with respect to their financial knowledge. In comparison, for the overextended and fringe, these percentages are 15.3 and 23.5% respectively. In short, from the univariate statistics, the two clusters that seem to pay the highest credit card fees and access the highest-cost borrowing 16 methods, i.e., the overextended and fringe, tend to be financially worse off and have lower levels of debt literacy.

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