Does Citizens’ Financial Literacy Relate to Bank Financial Reporting Transparency?

Abstract

In this study, we examine the relationship between financial literacy and bank financial reporting transparency for a sample of banks from the U.S. Following prior literature, we employ discretionary loan loss provisions (DLLP) as our primary measure of bank reporting transparency. We argue that the financial literacy of their customers can influence bank managers’ behaviors with respect to both the mechanics of the loan loss provisioning and their opportunistic actions. Financially literate customers represent more stable sources of funding and have more predictable loan loss provisioning that contributes to more persistent earnings. Financial literacy could also enhance customers’ ability to indirectly follow and monitor bank performance and risk-taking. Therefore, bank managers will be less likely to engage in opportunistic earnings manipulation. Following these arguments, we predict that citizens’ financial literacy is positively associated with bank financial reporting transparency. Consistent with our prediction, we find that the magnitude of bank DLLP is negatively related to state-level financial literacy. Moreover, the association between financial literacy and DLLP is higher for banks with more retail deposits and larger consumer loans, the two channels through which financial literacy could influence bank transparency.

Keywords:

• Financial literacy
• Financial reporting transparency
• Loan loss provisions
• Earnings quality

Supplemental data

Supplemental data for this article can be accessed https://doi.org/10.1080/09638180.2021.1965897.

Notes

1 Multiple imputation is a simulation-based statistical technique for handling missing data. Kofman and Sharpe (2003) suggest using multiple imputation in the analysis of incomplete observations in finance. To predict the missing values of FINLIT, a linear regression imputation method (i.e., regressing FINLIT on EDUC, INCOME, GENDER, MARIT, RACE, and state fixed effects) is used. This process of fill-in is repeated multiple times using Monte Carlo simulation by averaging each of these separate analyses. EDUC is the statewide proportion of the population receiving post-secondary education, INCOME is the natural logarithm of statewide per capita income, GENDER is the statewide proportion of the male population, MARIT is the statewide proportion of the married population, RACE is the statewide proportion of the white race. Prior literature documents that education level, marital status, income, and sex are associated with financial literacy (Lusardi & Mitchell, 2011a, 2011b, 2011c).

2 Although FASB has required the use of the incurred-loss model for loan loss provisioning (up to December 15, 2019), the complexity of loan portfolios allows a substantial magnitude of discretion within the prescribed rules (Dugan, 2009).

3 It seems that the extent of managerial discretion over loan loss provisions is low for consumer loans to begin with, as bank regulators have requirements for the timeline over which consumer loans are charged off, suggesting that the provisioning could also be mechanical. This is consistent with prior research arguing that banks have more discretion over commercial loans compared to consumer loans (e.g., Liu & Ryan, 2006). However, the Federal Reserve Bank of Kansas City (2018) emphasizes the allowance of discretion with pricing or underwriting decisions for consumer loans. Frequently observed discretionary pricing practices include lack of established rate sheets, reliance on unwritten pricing guidelines, reliance on vague and/or unwritten discretionary criteria when making adjustments to established rate sheets (e.g., good customer, large depositor), lack of guidance to select a rate from an established rate range, and inadvertent omission of pricing guidelines for certain credit requests. Therefore, the Fed advocates a compliance management system that includes an evaluation of the bank’s discretionary practices to determine the level of fair lending risk posed by such practices, as well as the controls in place to properly identify, measure, control, and monitor risks.

4 As an alternate proxy for financial literacy, we calculate the financial literacy score (FINLITW) for each multi-state bank by using the weighted average FINLIT based on state-level deposits (aggregated from branch-level data available from FDIC’s Summary of Deposits). We re-estimate all our regression models using FINLITW, and the untabulated results show that ABSDLLP_A and ABSDLLP_B are significantly and negatively associated with FINLITW at the 1% level.

5 As FINLIT is a state-level variable, this test raises the concern that state-level economic factors can affect both financial literacy and bank loan loss provisioning. To account for the different economic conditions, we compute ABSDLLP by estimating the first-stage model by state-year to allow the coefficients of the determinants to vary. In a robustness test, the second-stage model shows that FINLIT is significantly and negatively associated with ABSDLLP computed at the state-year level.

6 Bhat et al. (2019) identify that commonly used measures of LLP timeliness vary substantially across loan types. We examine the relationship between financial literacy and bank loan types, including consumer loans, real estate loans, and commercial loans studied by Bhat et al. (2019). The untabulated results show that the financial literacy of bank customers is significantly and negatively associated with consumer loans and real estate loans, but not commercial loans. This is consistent with prior literature documenting that financially literate consumers are less likely to take excessive amounts of debt (Lusardi & Tufano, 2015). In a robustness test, we incorporate bank loan types in the first-stage LLP estimation model. The untabulated results for the second-stage model show that ABSDLLP has a significantly negative relationship with FINLIT even after controlling for consumer loans, real estate loans, and commercial loans.

7 We also follow Beatty and Liao (2014) to exclude loan charge-offs in the first-stage LLP estimation Model (1b). The untabulated results of the second-stage regression remain robust after we exclude loan charge-offs in Model (1b).

8 To empirically control for income smoothing, we include the variable EBP (earnings before loan loss provisions divided by total assets) in our second stage of ABSDLLP regressions to account for the fact that management may manipulate LLP to achieve its desired level of net income.

9 As Hirshleifer et al. (2009) suggest, the decile rank has the advantage of reducing the influence of outliers. It also helps to linearize the relationship between ABSDLLP and FINLIT. We calculate DEPOSIT as the decile rank of the sum of RCON3485, RCONB563, RCON3486, RCON3487, RCONA529, and RCON3469 scaled by RCFD2170 (from Call Reports) or the sum of DPDC, CTTD, DPSC, and MMCD scaled by AT (from Compustat Bank database) if Call Reports data are missing. Similarly, we calculate CLOAN as the decile rank of RCFD1975 scaled by RCON2122 (from Call Reports) or LCACRD scaled by LNTAL (from Compustat Bank database) if Call Reports data are missing.

10 We control for bank fixed effects because many unobservable bank characteristics may affect banks’ discretion on LLP. LLP discretion is sticky within a bank, and FINLIT is likely to be sticky over time. Therefore, we run two separate regressions: (1) including only state and year fixed effects to control for within-state variation and (2) including bank and year fixed effects to control for within-bank variation.

11 The search-based index is obtained from Google Trends (https://trends.google.com/trends/) for a given state in a particular month from January 2009 to December 2018. The index is adjusted by the national average and divided by 100, resulting in the scale from −1 to 1. The greater the number on the scale is, the more intensive the search for the queried terms is. We then aggregate the monthly search index to annual level and calculate the mean value for each state-year.