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Journal of Business Ethics

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03-10-2017 | Original Paper

Do Religious Norms Influence Corporate Debt Financing?

Authors: Jay Cai, Guifeng Shi

Published in: Journal of Business Ethics | Issue 1/2019

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

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

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

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.

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.

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.

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.

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.

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

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).

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.

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

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

We find similar results using the SDC sample.

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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Title: Do Religious Norms Influence Corporate Debt Financing?
Authors: Jay Cai
Guifeng Shi
Publication date: 03-10-2017
Publisher: Springer Netherlands
Published in: Journal of Business Ethics / Issue 1/2019
Print ISSN: 0167-4544
Electronic ISSN: 1573-0697
DOI: https://doi.org/10.1007/s10551-017-3701-5

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