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Business Groups and Tunneling: Evidence from Corporate Charitable Contributions by Korean Companies

Abstract

This paper investigates whether corporate philanthropic decisions are associated with a firm’s listing status and business group affiliation. Analyzing a large sample of public and private firms in Korea, we find that (1) public firms make more charitable contributions than private firms and (2) business group-affiliated firms make more charitable contributions than non-affiliated firms. The results suggest that public firms, owing to greater public scrutiny, and business groups, owing to higher political costs, are encouraged to make more corporate charitable contributions. Further, we find that (3) greater corporate giving by public firms than private firms is more pronounced for business group-affiliated firms, compared with non-affiliated firms. The result is consistent with business groups’ strategic coordination of their affiliates’ philanthropic decisions to tunnel business group resources out to controlling shareholders who hold a larger portion of private affiliates than public affiliates.

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Fig. 1

Notes

  1. Hillman and Keim (2001) argue that researchers need to exploit the individual components of CSR. Carroll (2004) depicts corporate philanthropy as one of the most important components of CSR.

  2. Note that Barnea and Rubin (2010) do not directly examine charitable contributions. Rather, they examine CSR ratings assuming that the rating is associated with CSR-related expenditure level.

  3. Of the remaining 55 papers, 7 studies found a negative relationship, 28 studies brought insignificant associations, and 20 studies reported mixed results.

  4. Details are available at KFTC’s business group information portal (http://groupopni.ftc.go.kr/).

  5. The total asset requirement of KRW 7 billion has changed to KRW 10 billion in 2009, and KRW 12 billion in 2015.

  6. In untabulated tests, we verify that our results remain qualitatively similar when we change the minimum number of affiliated firm in a business group from five to two or to ten.

  7. Prior literature uses Heckman procedure instead of the often used two-stage least squares (2SLS) method when the variable of interest is not a continuous variable, but a binary variable (e.g., Ball and Shivakumar 2005; Kim and Yi 2006; Givoly et al. 2010). Wooldridge (2010) shows that non-OLS regression models as the first-stage equation of 2SLS estimation do not provide consistent coefficient estimates.

  8. The Herfindahl–Hirschman Index (HHI) is a measure of the market concentration of an industry. Thus, highly competitive industries have a relatively low HHI.

  9. Our prediction based on H3 (δ3 > 0) is also consistent with greater charitable contributions by group-affiliated firms than non-affiliated firms being more pronounced for public firms than for private firms.

  10. Our results are qualitatively similar when we add the excluded observations back to the sample.

  11. Recently, researchers have raised concerns and criticism over the misuse of the propensity-score matching in addressing endogeneity issues (see DeFond et al. 2016; Shipman et al. 2016). That is, seemingly innocuous changes in PSM design can significantly affect sample composition and inferences from the results. DeFond et al. (2016) suggest using CEM as an alternative matching approach to PSM.

  12. In the first-stage estimates of Heckman approach, we find that the coefficients on FirmSize, Leverage, ExportRatio, QuickRatio, Age, Industry_PubPr, and HHI (except SalesGrowth) are highly significant, indicating that our prediction model for a firm’s listing status is well specified.

  13. To address concern about the heteroscedasticity of error terms, we also apply the White adjustment for standard errors, but the results are qualitatively similar to what we present in the paper.

  14. We find that the mean values of most firm characteristics are similar between public and private firms, indicating an effective matching between the two groups (untabulated).

  15. The sample size of CEM differs from that of PSM because CEM allows for a 1:N matching. Further, unlike PSM that condenses various dimensions of the covariates into a single dimension (that is, a summary score), CEM considers every aspect of the covariates. Thus, CEM is less vulnerable to the so-called random-matching problem of PSM.

  16. Analyses are conducted for a sample of large business group affiliates over the period from 2001 to 2013 for which ownership structure data are available from KFTC.

  17. We are very grateful to an anonymous reviewer to point out this perspective.

  18. In untabulated tests, we verify that our results remain qualitatively the same when using log change variables [e.g., log(Givingt−1/Givingt−2)] instead of change variables [e.g., ΔGivingt−1] to measure various growth variables.

  19. In untabulated tests, we verify that our results, while become weak partly owing to the loss of data, remain qualitatively similar when we use a longer 10 years of capitalization period.

  20. In untabulated tests, we verify that our results remain qualitatively similar when we control for Advertising Intensity (advertising expense as a percentage of total sales) instead of Unrecorded-Marketing Assets.

  21. Korean Corporate Governance Index (KCGI) developed by Black et al. (2006) is most widely used proxy for corporate governance in Korean evidence. However, KCGI is not feasible for our study, since it does not cover private firms.

  22. We are very grateful to an anonymous reviewer to suggest isolating the impact of societal relations from that of political corruption.

  23. We are very grateful to an anonymous reviewer for pointing out this market segregation in Korea.

  24. Bae et al. (2008) and Kim and Yi (2006) highlight the economic importance of thirty largest Chaebols in the Korean economy.

  25. Defining a Chaebol as “a group of companies of which more than 30% of shares are owned by the group’s controlling shareholder and its affiliated companies,” KFTC regularly releases the list of Chaebols and ranks the business groups by their total assets.

  26. We find the same results from Logit regression estimates (untabulated).

  27. We are very grateful to an anonymous reviewer for the suggestion to examine the initiation effect of corporate giving.

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Acknowledgements

We thank Steven Dellaportas (Accounting Section Editor), two anonymous referees, Dominic H. Chai, Yong Jin Hyun, Jun-Koo Kang, Woojin Kim, Hyun-Han Shin, Chae-Yeol Yang, and the seminar participants at 2016 Korean Securities Association Conference, Korea Economic Research Institute, and 2016 Association for Fair Trade Research Summer Symposium for helpful comments and suggestions. Jinhan Pae acknowledges the financial support from Korea University Research Grant.

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Appendices

Appendix 1: Variable Descriptions

Variable Definition
Giving The amount of charitable contributions as a percentage of sales
GivingD An indicator variable that equals one if a firm makes charitable contributions
Public An indicator variable that equals one if a firm is listed on KRX
Affiliate An indicator variable that equals one if a firm belongs to a business group
FirmSize The natural log of total assets
Age The number of years elapsed since the incorporation
Leverage The book value of short- and long-term debt scaled by the book value of equity
ROA Net income divided by total assets
Advertising Advertising expense scaled by total assets
PreGivingD An indicator variable that equals one if a firm made charitable contributions in the preceding year
Big4Audit An indicator variable that equals one if a firm has a big 4 auditor
ExportRatio The ratio of the amount of exports to total sales
QuickRatio The quick ratio measured by current assets less inventory divided by current liabilities
Industry_PubPr Proportion of public firms in a firm’s industry
HHI Herfindahl–Hirschman index, measured as the sum of squares of the market shares (firm sales/industry sales) of the firms in the industry
ΔSALE Percentage change in sales
ΔGiving Change in the charitable contributions, scaled by sales
ΔPublicGiving Change in the sum of the charitable contributions of public affiliates, scaled by the sum of the sales of public affiliates
ΔPrivateGiving Change in the sum of the charitable contributions of private affiliates, scaled by the sum of the sales of private affiliates
ΔGroup-wide Giving Change in the sum of the charitable contributions of all the group affiliates, scaled by the sum of the sales of group affiliates
ΔGroup-wide Advertising Change in the sum of the advertising expense of all the group affiliate, scaled by the sum of the sales of group affiliates
Wedge The difference between control rights and cash flow rights held by the controlling shareholder
Unrecorded-Marketing Asset (2.5/3) × Advertisingt + (1.5/3) × Advertisingt1 + (0.5/3) × Advertisingt2
CGVD An indicator variable that equals one if a firm is not classified as a small and medium size enterprise according to Framework Act on Small and Medium Enterprises
G_KFTC An indicator variable for large business groups or Chaebols as designated by KFTC
KOSPI An indicator variable for firms on the KOSPI market
KOSDAQ An indicator variable for firms on the KOSDAQ market
Big30 An indicator variable that equals one if a firm belongs to top 30 Chaebol
Initiation An indicator variable for the initiation of corporate giving by firms that have not made charitable contributions in the preceding three years

Appendix 2: Description on the Statistical Model

$$Giving_{{i,t}} = {\mkern 1mu} \delta _{0} + {\mkern 1mu} \delta _{1} Public_{{i,t}} + {\mkern 1mu} \delta _{2} Affiliate_{{i,t}} + {\mkern 1mu} \delta _{3} Public_{{i,t}} \times {\mkern 1mu} Affiliate_{{i,t}} + {\mkern 1mu} \delta _{4} FirmSize_{{i,t}} + {\mkern 1mu} \delta _{5} Age_{{i,t}} + {\mkern 1mu} \delta _{6} Leverage_{{i,t}} + {\mkern 1mu} \delta _{7} ROA_{{i,t}} + {\mkern 1mu} \delta _{8} Advertising_{{i,t}} + {\mkern 1mu} \delta _{9} PreGivingD_{{i,t}} + {\mkern 1mu} \delta _{{10}} Big4Audit_{{i,t}} + {\mkern 1mu} \delta _{{11}} IMR{\mkern 1mu} + {\mkern 1mu} \left( {{\text{Year dummies}}} \right){\mkern 1mu} + {\mkern 1mu} \left( {{\text{Industry dummies}}} \right){\mkern 1mu} + \varepsilon,$$
(4)
  Public Private
Business Group Public × Affiliate [A] Private × Affiliate [C]
Non-Group Public × Non-affiliate [B] Private × Non-affiliate [D]
  1. A: δ0 + δ1 + δ2 + δ3, B: δ0 + δ1, C: δ0 + δ2, D0, A − B = δ2 + δ3, A − C = δ1 + δ3, B − D  = δ1, C − D  = δ2

Equation (4) includes two variables of interest, Public and Affiliate, and an interaction variable of the two. In effect, the sample is partitioned into four subgroups: Public and affiliated firms [A], Public, but not affiliated firms [B], Private but affiliated [C], and Private and not affiliated firms [D]. δ1 is an estimate of the effect of listing status on corporate charitable contributions and δ2 is an estimate of the effect of group affiliation on corporate charitable contributions. δ3 is an estimate of the excessive effect of subgroup A on corporate charitable contributions. The impact of subgroup A (B) on charitable contributions, on average, is computed by summing up δ0, δ1, δ2, and δ3 (δ0 and δ1), while the impact of subgroup C (D) on charitable contributions is sum of δ0 and δ20). The different impact on charitable contributions between A and B is the sum of δ2 and δ3, while it between A and C is the sum of δ1 and δ3. δ1 (δ2) shows the different influence on charitable contributions between B and D (C and D).

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Kim, B., Pae, J. & Yoo, CY. Business Groups and Tunneling: Evidence from Corporate Charitable Contributions by Korean Companies. J Bus Ethics 154, 643–666 (2019). https://doi.org/10.1007/s10551-016-3415-0

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Keywords

  • Listing status
  • Business group
  • Corporate giving
  • Tunneling
  • Public scrutiny