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Do Religious Norms Influence Corporate Debt Financing?

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Abstract

Previous studies substantiate that religious social norms influence individual and organizational decisions. Using debt financing settings, we examine whether a firm’s religious environment influences outside parties’ perceptions in contracting with the firm. We document that firms located in the more religious areas use less debt financing and receive better credit ratings. Bond investors require lower yields and impose fewer covenants on such firms. Using the 2002 revelation of sex abuse by Catholic priests as an exogenous shock, we verify that these findings are not driven by endogeneity issues. Our study highlights the role of social norms in financial transactions.

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Notes

  1. See Sect. 2 for a detailed review of the literature.

  2. We focus on the Biblical teaching on debt because the religious population in the USA is made up of primarily Catholics and Protestants.

  3. In Sect. 5.2, we find that firms located in more religious areas are likely to have more religious CEOs.

  4. For example, Numbers 30:2 declares that “If a man vows a vow to the Lord, or swears an oath to bind himself by a pledge, he shall not break his word. He shall do according to all that proceeds out of his mouth.” See also Joshua 23:14, Numbers 23:19, Psalm 89:34, Matthew 5:33, 37, 2 Peter 3:9 ESV, among many others.

  5. Gennaioli et al. (2015) argue that investor trust in portfolio managers reduces perceived risk in investments and allows managers to charge higher fees. Using financial frauds by local firms as an instrument, Parsons et al. (2015) document higher loan spreads and more restrictive covenants for borrowers perceived as less trustworthy.

  6. Less borrowing by the religious firms, however, is not irrational. By definition, the expected utility of an individual with a higher-level risk aversion decreases more than that of an individual with lower risk aversion when the risk of future outcomes increases. If individuals can choose the risk level of future outcomes, utility maximization leads to the choice of a lower risk level by a more risk-averse person even if risk taking is rewarded by expected returns. A more risk-averse manager, therefore, optimally takes a lower level of risk to maximize her expected utility function.

  7. Two recently published papers examine the relation between religion and capital structure, but the evidence is inconclusive. Baxamusa and Jalal (2014) find that firms located in Catholic-majority counties have higher leverage, while Baxamusa and Jalal (2016) document lower leverage for firms with Catholic CEOs.

  8. For example, religious and risk-averse employees may require a higher salary to work for a heavily indebted firm. Religious and risk-averse suppliers may grant less generous trade credit to the same firm, and customers may be less willing to sign long-term contracts with the firm. All of these represent additional non-interest cost of debt that firms located in the more religious areas have to consider.

  9. For examples, see Weber (1905), Iannaccone (1998), Stulz and Williamson (2003), Kumar et al. (2011), Chen et al (2014) and Adhikari and Agrawal (2016b).

  10. For example, religious belief is found to be related to higher credit scores, lower consumer debt, and few incidents of personal bankruptcy (Hess 2012), risk taking (Shu et al. 2012), investment decisions (Anderson, Fedenia, Hirschey, and Skiba, 2011), unethical business dealings (Liu, 2016), and household financial decisions (Renneboog and Spaenjers, 2012).

  11. To the extent that firm risk is controlled in our empirical tests, the findings we document are net of the religiosity’s effect on a firm’s risk taking behavior.

  12. For example, Chintrakarn et al. (2017) find a complementary relation between religion and anti-takeover provisions.

  13. For empirical evidence on the relation between religious belief and ethical behavior, see Conroy and Emerson (2004), Longenecker et al. (2004), Li (2008), among others.

  14. ARDA provides statistics for all religious faiths by county, providing information on the number of organizations and members. These data originally appear in Religious Congregations & Membership in the United States, 2000, published by the Glenmary Research Center. See details at http://www.thearda.com/Archive/ChCounty.asp.

  15. We focus on the number of adherents rather than the number of members because ARDA indicates that members include only those who are designated as “full members” by the congregation. Congregational “adherents” include all full members, their children, and others who regularly attend services or participate in the congregation. Using adherents allows for consistency between groups that count children as members (e.g., Catholics) and those that do not (e.g. Baptists).

  16. Hilary and Hui (2009) argue that linear interpolation increases the power of the tests and provides the opportunity to test time series properties of religion.

  17. To address the possibility that the religiosity ratio may be a proxy for other demographic variables, we estimate a regression of the religiosity ratio on other demographic variables and use the residual as the main independent variable in our analyses. Our results are similar.

  18. The income data were available up to 2007 when we accessed the data. We, therefore, use the income data in year 2007 for the three years afterward.

  19. See Coval and Moskowitz (1999, 2001), Ivkovic and Weisbenner (2005), Loughran and Schultz (2004, 2005), Pirinsky and Wang (2006), Kang and Kim (2008), and Hilary and Hui (2009), among others.

  20. Following extensive capital structure literature (Antoniou et al. 2008; Kayhan and Titman 2007; Frank and Goyal 2009; Hovakimian et al. 2004; Leary and Roberts 2005; Lemmon et al. (2008), and Leary and Roberts 2014), we include a firm’s growth opportunity, profitability, asset tangibility, firm size, R&D, operating leverage, dividend policy, and non-debt tax shield as well as industry and time-fixed effects in the target leverage regressions as independent variables. In a robustness test, we estimate the excess leverage ratios with the industry median-adjusted leverage ratios. Our findings are similar.

  21. The GIM index is available for the years 1990, 1993, 1995, 1998, 2000, 2002, 2004, and 2006 during the sample period. For intermediate years and the years after 2006, we use the GIM index from the most recent year.

  22. The results are similar when we follow Ashbaugh-Skaife et al. (2006) and classify credit ratings in seven categories: 7 for AAA; 6 for AA +, AA, and AA −; 5 for A +, A, and A −; 4 for BBB +, BBB, and BBB −; 3 for BB +, BB, and BB −; 2 for B +, B, and B −; and 1 for CCC + and below. Results are also similar if we use the average (rather than the median) credit ratings over a fiscal year.

  23. Our results are also robust to clustering at the firm level.

  24. In unreported robustness tests, we include the market leverage ratio as well as indicator variables of whether a leverage ratio is above or below the sample median, respectively, as control for capital structure. All our results are similar.

  25. We do not control for board characteristics in our main results because nearly half of our sample firms are not available from the RiskMetrics Director database. In a sensitivity test for those firms with available board data, we control for board size, board independence, and the CEO-Chairman duality. Our main results are similar.

  26. In comparison, the coefficient of social capital equals 0.276 and is statistically significant at the 1% level. For an inter-quartile increase of 1.39 in social capital, the credit rating of the firm increases by 38% (0.276 × 1.39) of a rating tier.

  27. One may argue that the religiosity ratios in 2000 are correlated with those in 2010 and consequently may drive the results in Panel B of Table 5. The nonsignificant coefficients of the predicted Protestant ratio in 2010, however, suggest that this interpretation is unlikely to be the case.

  28. The 10 most conservative states include Mississippi, Idaho, Alabama, Wyoming, Utah, South Dakota, Louisiana, North Dakota, South Carolina, Arkansas, as defined by a 2010 Gallup poll. See http://www.gallup.com/poll/146348/mississippi-rates-conservative-state.aspx/ for details.

  29. Because Boardex database is available only for the years after 2000, we perform this sensitivity test for a sample during the period of 2000–2010.

  30. See Kumar et al. (2011), Shu et al. (2012), Renneboog and Spaenjers (2012) and Baxamusa and Jalal (2014, 2016).

  31. For companies issuing multiple bonds in a year, we use the average bond yields and the average number of bond covenants for all the bonds issued in the same year as the dependent variables in the bond yield and debt covenant regressions, respectively. Similarly, we also average the values of the other bond characteristics as the control variables. As a result, the numbers of observations in the bond yield and debt covenant regressions are smaller than those in Table 4.

  32. We find similar results using the SDC sample.

  33. Following Cremers et al. (2007), we obtain one-year, three-year, five-year, seven-year, 10-year, and 30-year constant maturity Treasury yields from the Federal Reserve’s H-15 Release, which we interpolate into a piecewise linear term structure.

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Acknowledgements

A part of this study was completed, while Guifeng Shi was visiting Wharton. Guifeng Shi acknowledges funding from the National Nature Science Foundation of China (NSFC-71002036), Shanghai Pujiang Program (13PJC078), and the China Scholarship Council. We thank Matt Billett, Greg Shailer (the Editor), and two anonymous referees for their constructive comments.

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Authors and Affiliations

  1. LeBow College of Business, Drexel University, Philadelphia, PA, 19103, USA

    Jay Cai

  2. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China

    Guifeng Shi

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Correspondence to Jay Cai.

Appendix: Variables Definitions

Appendix: Variables Definitions

Religiosity ratio equals the number religious adherents in a county (from ARDA) divided by the total population in the county (from the US Census). Data on religiosity are available for years 1990, 2000, and 2010. We linearly interpolate the data to obtain the values for the years in between. Source: ARDA.

Firm and Bond Characteristics

Book leverage ratio equals total debt (Compustat #9 + #34) divided by book value of total assets (Compustat #6). Source: Compustat.

Excess Book leverage ratio equals the residual from a panel regression of the book leverage ratio on market-to-book ratio, ROA, tangible assets/total assets, log sales, R&D expenses/Sales, SG&A expenses/sales, dividend, depreciation/assets, and industry (Fama–French 48 industry classification) and year fixed effects.

Market leverage ratio equals total debt (Compustat #9 + #34 divided by market value of total assets, which equals the book value of total assets (Compustat #6) minus the book value of common equity (Compustat #60) plus the market value of common equity (Compustat #25* Compustat #199). Source: Compustat.

Excess Market leverage ratio equals the residual from a panel regression of the market leverage ratio on market-to-book ratio, ROA, tangible assets/total assets, log sales, R&D expenses/Sales, SG&A expenses/sales, dividend, depreciation/assets, and industry (Fama–French 48 industry classification) and year fixed effects.

Credit rating equals the median S&P long-term domestic issuer credit rating (Compustat #280) over the 12 months in a fiscal year. We convert the credit ratings into numbers as follows: 21 for AAA, 20 for AA +, 19 for AA, 18 for AA −, 17 for A +, 16 for A, 15 for A −, 14 for BBB +, 13 for BBB, 12 for BBB −, 11 for BB +, 10 for BB, 9 for BB −, 8 for B +, 7 for B, 6 for B −, 5 for CCC +, 4 for CCC, 3 for CC, 2 for C, and 1 for D and SD. Source: Compustat.

Yield spread for fixed rate bonds is defined as the difference between the yield-to-maturity of a corporate bond and the yield-to-maturity of its duration equivalent Treasury bond. The yield-to-maturity at the time of the bond issue on a corporate bond is the discount rate that sets the present value of its future payments equal to its offering price. The Treasury bond yield is measured by the yield on the constant maturity Treasury security series provided by the Federal Reserve. If there is no duration equivalent Treasury security available to match the duration of the corporate bond, the yield-to-maturity of the Treasury security is calculated as the linear interpolation between the two securities with the closest maturity.Footnote 33 The yield spread for a floating rate bond equals the difference between the reference index and the yield on its duration equivalent Treasury security over the first interest period plus the bond’s basis point spread over the reference index. Source: SDC.

Log yield spread is the natural logarithm of yield spread. Source: SDC.

Covenant index equals the sum of 15 indicator variables, each of which equals one if a bond has at least one covenant in the given category of covenants as defined in Billett et al. (2007) and zero otherwise. The 15 categories of covenant restrictions include dividend payment restrictions, share repurchase restrictions, funded debt restrictions, subordinated debt restrictions, senior debt restrictions, secured debt restrictions, total leverage tests restrictions, sale and lease-back restrictions, stock issuance restrictions, credit rating and net worth triggers, cross-default provisions, poison put, asset sale clauses, investment policy restrictions, and merger restrictions.

Subsequent financing covenant index equals the sum of seven indicator variables that are associated with the following categories of covenants: funded debt restrictions, subordinated debt restrictions, senior debt restrictions, secured debt restrictions, total leverage tests restrictions, sale and lease-back restrictions, and stock issuance restrictions.

Investment covenant index equals the sum of three indicator variables that are associated with the following categories of covenants: asset sale clauses, investment policy restrictions, and merger restrictions.

Dividend covenant index equals the sum of two indicator variables that are associated with the following categories of covenants: dividend payment restrictions and share repurchase restrictions.

Event covenant index equals the sum of three indicator variables that are associated with the following categories of covenants: credit rating and net worth triggers, cross-default provisions, and poison put.

Bond rating is the average bond rating of S&P, Moody, and Fitch as reported by SDC or FISD and is coded between 21 (AAA) and 1 (D). If the specific bond rating is missing from SDC or FISD, we use the issuing firm’s credit rating from Compusat.

Floating bond indicator (1/0) equals one if the bond’s coupon is floating, and zero otherwise. Source: SDC and FISD.

Callable bond indicator (1/0) equals one if the bond is callable, and zero otherwise. Source: SDC and FISD.

Putable bond indicator (1/0) equals one if the bond is putable, and zero otherwise. Source: SDC and FISD.

Private placement indicator (1/0) equals one if a bond issue is privately placed (Rule 144A). Source: FISD.

Log (maturity) equals the natural logarithm of a bond’s time to maturity in years. Source: FISD.

Log (offering amount) equals the natural logarithm of the offering amount of a bond. Source: FISD.

Issuing amount/Net fixed assets equals the issuing amount of a bond divided by the issuing firm’s net fixed assets (Compustat variable PPENT) in the previous year. Source: FISD and Compustat.

Convertible indicator (1/0) equals one if a bond can be converted into other securities, and zero otherwise. Source: FISD.

Q equals the market value of assets divided by book value of assets, where the market value of assets equals the book value of assets minus the book value of equity plus the market value of equity. Source: Compustat.

ROA equals the net income before extraordinary items (Compustat #18) divided by total assets. Source: Compustat.

Negative net income (1/0) equals one if the net income before extraordinary items (Compustat #18) is negative in the current and prior fiscal year, and zero otherwise. Source: Compustat.

Return volatility equals the standard deviation of the daily return during a fiscal year.

ROA volatility equals the standard deviation of ROA from year t-5 to t + 5. Source: Compustat.

Investment rate equals capital expenditure (Compustat # 128) divided by the net PPE (Compustat # 8) of previous fiscal year.

Interest coverage equals the operating income before depreciation (Compustat #13) divided by interest expense (Compustat #15). Source: Compustat.

Subordinated debt indicator (1/0) equals one if the firm has subordinated debt, and zero otherwise. Source: Compustat.

PPE/Assets ratio equals the net PPE (Compustat #8) divided by total assets. Source: Compustat.

Institutional holdings equal the number of shares held by institutions divided by shares outstanding in the quarter prior to the bond issuance. Source: Thomson Reuters Institutional (13f) Holdings.

Number of block holders equals the number of financial institutions that hold over 5% of a firm’s outstanding shares as of the quarter prior to the bond issuance. Source: Thomson Reuters Institutional (13f) Holdings.

GIM index equals the sum of 24 anti-takeover provisions from the IRRC database and is available for the years 1990, 1993, 1995, 1998, 2000, 2002, 2004, and 2006. For intermediate years, we use the GIM index from the most recent year. Source: Gompers et al. (2003) and IRRC Governance database.

R&D/sales equals Research and development expenses (Compustat #46) divided by sales (Compusta #12). We set the variable value to zero if R&D expense is missing.

SGA/sales equals SG&A expenses (Compustat #189) divided by sales (Compustat #12).

Dividend dummy equals one if the firm pays common dividend (Compustat #21) in a fiscal year, and zero otherwise.

Demographical Variables

Total population equals the total population in a county. Source: US Census.

Male-to-female ratio equals the male population in a county divided by the female population. Source: US Census.

Minority equals the minority populations (total population minus white population) divided by the total population of a county. Source: US Census.

Married equals the number of married households divided by the total number of households in a county. Source: US Census.

Age is the median age of a county’s population. Source: US Census.

Education equals the number of people above 25 who have a bachelor degree or higher education divided by the number of people above 25 in a county. Source: US Census.

Income is the per capita personal income. Source: US Census.

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Cai, J., Shi, G. Do Religious Norms Influence Corporate Debt Financing?. J Bus Ethics 157, 159–182 (2019). https://doi.org/10.1007/s10551-017-3701-5

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