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Review of Quantitative Finance and Accounting

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12.05.2017 | Original Research

Suppliers’/customers’ production efficiency uncertainty and firm credit risk

verfasst von: Tsung-Kang Chen, Hsien-Hsing Liao

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2018

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Abstract

This study explores the effects of business counterparties’ (i.e. suppliers’/customers’) production efficiency uncertainty (PEU) on corporate credit risk by employing American bond observations of manufacturing firms. Empirical results of this study show that customers’ PEU is positively related to corporate bond yield spreads whereas suppliers’ has an opposite effect. The former result shows the importance of demand uncertainty while the latter one suggests that the benefits of supply chain integration or information sharing exceed the costs of supply chain uncertainty. We also find that the effects of suppliers’/customers’ PEUs on bond yield spreads are significantly affected by the information flow risk within the supply chain. In addition, the customer-side effect becomes weaker during the financial crisis period, whereas the supplier-side one is insignificantly affected. These empirical results are robust when controlling for potential endogeneity problems and employing an alternative sample which consists of the bond observations with both supplier and customer identification information. Finally, it has to be noticed that our conclusions are only applicable to manufacturing industries.

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In addition, Chen et al. (2013b, c) also provide the evidences that a manufacturing firm’s credit risk is affected by the risks of firm’s business counterparties.

Lee et al. (1997a) demonstrate that the information flows play an important role for the coordination in a supply chain relationship and have a direct impact on a firm's operations, such as production scheduling, inventory policy and shipping plans of the firm’s suppliers and customers. Tsai (2008) also describes the variations of inventory flow and cash flow within the supply chain relationship by employing cash conversion cycle (operating cycle).

Chen et al. (2013c) also show that the effects of customers’ internal liquidity risks (ILRs) are greater and the ILR effect becomes greater upwardly along the business counterparties.

Davis (1993) identifies three sources of supply chain uncertainty, including demand, manufacturing process, and supply uncertainty. The model of Davis (1993) suggests that demand and supply uncertainty have an effect on manufacturing process uncertainty, which in turn affects timely order fulfilment. The above three uncertainties of demand, manufacturing process, and supply are closely related to the inventory flow risk. In addition, manufacturing process uncertainty could be reasonably viewed as PEU since production efficiency is an essence of manufacturing process.

In addition, some studies also discuss whether productivity affects a firm’s capital structure. For example, Chen et al. (2013a) address the issue of the relationship between financing decisions and productivity growth for venture capital industry.

Kahn (1987) proposes a production counter-smoothing hypothesis to explain the stylized fact of inventory behavior. Blinder (1986) and West (1986) also have similar arguments.

Chen et al. (2013b, c) demonstrate that suppliers’/customers’ trading-based information asymmetry and internal liquidity risk increase a firm’s credit risk, respectively. Chen et al. (2014b) also demonstrate that suppliers’/customers’ macroeconomic risks positively relate to firm credit risk.

Kale and Shahrur (2007) found that a firm’s leverage is positively related to the concentration levels in its supplier and customer industries. In addition, firms that deal with R&D-intensive suppliers (customers) and firms with high intensities of strategic alliances and joint ventures with suppliers (customers) have lower credit risk.

In addition, Eshleman and Guo (2014) demonstrate that the quarterly earnings announcements of supplier firms contain information about their customer’s earnings. That is, supplier’s earnings is helpful for assessing the customer firm’s future cash flows.

Lee et al. (1997a, 1997b) analyzed four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.

Huang et al. (2014) show that supply chain integration has a significant positive effect on the suppliers’ performance. In addition, Huang et al. (2014) also show that the demand uncertainty weakens the positive relationship between supply chain integration and supplier’s performance.

Chiang and Feng (2007) find that information sharing is more beneficial for the manufacturer than for the retailers in the presence of supply uncertainty and demand volatility. The value of information sharing to the retailers is relatively insensitive to the production yield uncertainty.

Following Brynjolfsson and Hitt (2003), this study also assumes 2040-hour work year for each employee. In addition, the average sector labor cost is computed using annual sector-level wage data (salary plus benefits) from the Bureau of Labor Statistics (BLS) from 1993 to 2008.

In practices, although public firms have to disclose the identity of any customer that contributes at least 10% to the firm’s revenues due to the requirement of the Statement of Financial Accounting Standards (SFAS) No. 14 and 131, some firms also choose to report customers that contribute less than 10% in fact.

This is mainly because the estimation of the primary variable in this study, the TFPV (the volatility of a firm’s total factor productivity), is only applicable for the manufacturing industry (e.g. Chen et al. 2014a). Hence, firms in the retail and wholesale industries, and firms whose main supplier/customer is a retailer or a wholesaler are removed from the sample. In addition, financial and utility firms are highly regulated so that this study also excludes these sample observations. Moreover, firms that are headquartered in foreign countries are not viewed as the sample observations in United State market so that this study also excludes these sample observations.

The reason why the denominator of C_IC is a firm's total sale rather than a firm's total purchase is that the information about a firm’s total purchase and the firm’s all suppliers is not completely disclosed. In practices, a firm (supplier) is only required to disclose each sales information to its each main customers (firm). That is, the information about a firm’s all purchases from suppliers is incomplete and limited. Therefore, the study follows Kale and Shahrur (2007) and adopts a firm's total sales as the denominator of C_IC.

The fact that production information is less disclosed in financial statements, and the stylized fact that the variance of production exceeds the variance of sales with demand uncertainty (Kahn, 1987).

For the estimation of the ADJPIN variable, this work follows the EM-algorithm first developed by Chen and Chung (2007) (later followed by Lu et al. 2010).

Douglas et al. (2016) demonstrate that cash flow volatility is positively associated with firm credit risk.

The operating cash flow in this study is defined as the ratio of the sum of operating cash flow in financial report and interest expense to total asset market value.

In addition, Chen et al. (2017) demonstrate that CEO ability heterogeneity and board’s recruiting ability affect corporate bond yield spread. Shaw (2012) addresses the relation between CEO incentives and the cost of debt.

For the RAT variable, its value is set to 1 for bonds with Aaa rating, 2 for Aa1, 3 for Aa2, and so on.

This study considers the possible econometric problem that the error terms in the system regressions could have cross-equation contemporaneous correlations by employing the seeming unrelated regressions to estimate the variance–covariance matrix.

The lagged variable is commonly employed as one candidate of instrument variables (Sovey and Green 2011). In this study, the lagged variable has a relatively low correlation with the error term and a greater correlation with current PEU proxies of suppliers and customers. In addition, due to the past one-year S_TFPV as a weak instrument variable for suppliers’ PEU proxy, this study employs the past one-year S_HHI variable as another appropriate instrument variable for suppliers’ PEU proxy.

To examine the validity of the employed instrument variable, this study follows Stock et al. (2002) to conduct the F-test on the null hypothesis that the instrument variables are jointly zero in the first-stage regression (or a partial F-test with the presence of other control variables). As shown in column (1) and (4) of Table 8, the partial F-statistics in the first-stage regression are all higher than the critical value of the partial F-statistic (8.96) for the situation of one instrumental variable mentioned by Stock et al. (2002).

This study conducts the over-identifying restrictions test and finds that the model is exactly identified. It implies that the instrumental variables are exogenous and valid. However, although the over-identifying restrictions test may not be useful under some circumstances (Roberts and Whited 2012), the lagged variables are common and approvable instruments in the literature (Sovey and Green 2011).

IV_S_TFPV and IV_C_TFPV satisfy the relevance condition and the exclusion condition (Roberts and Whited 2012) based on partial F-statistics (Stock et al. 2002) and over-identifying restrictions test, respectively.

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Titel: Suppliers’/customers’ production efficiency uncertainty and firm credit risk
verfasst von: Tsung-Kang Chen
Hsien-Hsing Liao
Publikationsdatum: 12.05.2017
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 2/2018
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0637-x

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