Abstract
We test two potential hypotheses regarding the effects of major customer concentration on firm profitability. Under the collaboration hypothesis, customer power facilitates collaboration, and both the supplier firm and its major customers obtain benefits. Under the competition hypothesis, customer power results in rent extraction, and the major customers benefit at the expense of the supplier firm. We document that major customer concentration is negatively associated with the supplier firm’s profitability but positively associated with the major customers’ profitability. We demonstrate that these effects weaken as the supplier firm’s own power grows over its relationship with major customers, supporting the competition hypothesis. We carefully reconcile our results with prior studies’ findings that focus only on the supplier firm’s profitability and identify their research design and interpretation problems. We obtain similar inferences in a setting of major customers’ horizontal mergers and when we use an alternative measure of major customer power.
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Notes
Casual evidence confirms that powerful customers tend to squeeze the supplier firm and that supplier are deeply concerned about powerful customers. For instance, see reports on the Wall Street Journal “Apple squeeze parts suppliers to protect margins” (September1, 2016), “Big manufacturers tighten supply chain as low growth forecasts spread” (December 23, 2015), and “UnitedHealth, Anthem seek to buy smaller rivals” (June 16, 2015).
Irvine et al. (2016) do not eliminate unprofitable firms, so their analyses does not suffer from the truncation bias. However, they do not tabulate the average effect of customer concentration on firm profitability. The reported interaction results in their Table 5 (as well as our replication analyses tabulated in Appendix 1.2) suggest that it is negative.
In the textbook case of perfect competition among the supplier firms, the normal selling price of a supplier is the competitive price, and the customer power is monopsony power.
This follows the same procedure to remove supplier firms in financial services.
Results are similar if return on equity (ROE) is the dependent variable. We consider the ROE results redundant because we control for financial leverage (FLEV) in the regression of ROA.
Specifically, firms are ranked annually and assigned to deciles. The raw rankings are replaced by the corresponding annual decile ranks scaled to be between 0 (lowest rank) and 1 (the highest rank). Inferences are similar when we use unranked CC.
Similar to Patatoukas (2012), we report the time-series means of the estimated coefficients and statistical inferences based on Fama and MacBeth (1973) t-statistics with Newey and West (1986) adjustment at three-lags. Inferences are similar if we run pooled regressions with two-way clustered standard errors by firm and year.
Following prior work, we measure the dependent variable using data from the same year. Results are similar when all dependent variables are measured using one-year-ahead data.
Although the estimated coefficients in Panel B of Table 3 are the same as the sum in the F-tests, the statistical significances differ in the Fama-MacBeth framework, which assumes that estimated coefficients from yearly regressions represent independent draws from a single distribution and that there is no cross-variable dependence. While the F-test follows this assumption (i.e., independence between the estimated coefficient of the CC’s main effect and that of the CC’s interaction with LINKAGE quintile), the t-test does not. Thus the inferences from t-tests and F-tests are not exactly the same in the Fama-MacBeth regressions for the total effects of CC in each LINKAGE quintile as F-tests generate higher standard errors in our sample (i.e., more conservative inferences).
For ease of interpreting the coefficients, decile ranks of LINKAGE, pred_LINKAGE, and res_LINKAGE are converted between zero and one.
The insignificant results cannot be explained by the low power of res_LINKAGE, because supplier firm sales and market value collectively explain only 20% of the variation in LINKAGE. If the relationship life-cycle really matters (i.e., the stage of the relationship is important beyond simply supplier firm size), then we should still observe a significant impact of res_LINKAGE.
Note that the coefficient of CC on Customer GM (0.029) is much smaller in magnitude than that on GM (−0.304), which is not surprising, given that major customers are much larger than the average supplier firm.
The 20% cutoff is used, following Ahern and Harford (2014). Results are similar when we use other cutoffs (0, 15%, or 50%).
One-digit SIC industry is used because narrower industry classification sharply decreases the pool of match candidates.
Because of ties in the value of ∆CC in this sample, the average rank is 0.488, instead of 0.500.
The first-difference of variable AGE is not meaningful, so we continue to use log(AGE) in these regressions.
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Acknowledgments
We thank Russell Lundholm (editor) and an anonymous referee for helpful comments. We also appreciate comments from Paul Ma (discussant at the 2016 AAA FARS Mid-Year Meeting), Haifeng You (discussant at the 2016 MIT Asian Conference), and workshop participants at Cornell University, Southwest Jiaotong University, Tsinghua University, Western Australian University, The University of Hong Kong, and Hong Kong University of Science and Technology.
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Appendix 1
Appendix 1
1.1 Appendix 1.1: Replication of Patatoukas’s (2012) Main Results
In Appendix 1.1, we replicate Patatoukas’s (2012) main results (Table 2) and show they go away after fixing the sample truncation problem. Results are reported in Tables 10 and 11 below. Untabulated descriptive statistics show that the variable distribution and industry composition of the sample are similar to those of Patatoukas (2012).
1.2 Appendix 1.2: Replication of Irvine et al.’s (2016) Main Results
In Appendix 1.2, we replicate Irvine et al.’s (2016) main results (Table 5 Panel A) and re-interpret their results by focusing on the total effects of CC over supplier-customer relationship. Results are reported in Table 12 below. Untabulated descriptive statistics are similar to those of Irvine et al. (2016).
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Hui, K.W., Liang, C. & Yeung, P.E. The effect of major customer concentration on firm profitability: competitive or collaborative?. Rev Account Stud 24, 189–229 (2019). https://doi.org/10.1007/s11142-018-9469-8
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DOI: https://doi.org/10.1007/s11142-018-9469-8
Keywords
- Fundamental analysis
- Customer concentration
- Supply-chain competition
- Firm profitability
- Mergers and acquisitions