Selection of Study Period

Bloomberg supply chain relationship data provide a snapshot of existing supplier-customer relationships. The supply chain relationships are mostly reported for the 2012 fiscal year and have been continuously updated afterwards based on new sources of information, indi- cating newly formed relationships or terminations of prior relationships. Specifically, each supply chain relationship listed on Bloomberg terminal is appended with a status variable “As of date.” The “As of date” variable represents the most updated disclosure date of the relationship. Bloomberg only includes a relationship if the relationship is available in the current year as indicated by “As of date.” Relationships are typically updated four times a year based on newly filed financial reports and news sources. The “As of date” of the re- trieved relationship data concentrates in the last quarter of 2012 and the first quarter of 2013, as many of them are extracted from the companies’ 2012 annual reports. Bloomberg contin- uously updates the relationship status based on new sources of information such as quarterly reports following 2012 annual reports, press releases, news items, etc. The relationship data thus present those that existed in 2012 and likely continued to exist through 2013 Q4 (the time of the authors’ data collection) based on most updated information.

We use the Compustat longitudinal data to further assess the stability of supply rela- tionships. Specifically, we investigate how likely is a relationship observed in 2012 to have existed in 2011, and how likely to have continued to exist in 2013. As described earlier, the SEC requires a U.S. listed firm to disclose its major customers that comprise more than 10% of the firm’s revenue. Thirty years of time-series records are available through Compustat; we focus on the records of recent years because the level of stability of supply chain relation- ships are likely different now versus in earlier years (e.g., 1980s and 1990s). We thus retrieve Compustat segment data from 2009 to 2014. We followed the steps below to clean the re- lationship data: 1) we removed those relationships with non-identifiable customer names, e.g. “1 customer,” “others”; 2) we unified customer names for firms with multiple common appellations, e.g. IBM and International Business Machines Corporation; and 3) we stan- dardized company names, separated name suffixes such as Inc., Corp., and Ltd., and made the customer name case-insensitive.

After data cleaning, we end up with about 15,000 supply chain relationships across all industries, spanning five years. We find the average length of a relationship is 2.8 years, and around 65% of the relationships last for at least two years. This is an under-estimate of the average length of these supply chain relationships, because some relationships were formed before 2009 and others continue beyond 2014. This average length covers relation- ships across all industries; to focus on the supply chain relationships in the high-tech sector, we match customer names to firm identifiers (i.e., stock ticker) to identify the sector charac- terization under which a firm falls. We then focus on those supply chain relationships that include a customer in the high-tech sector (a comparable dataset to the Bloomberg data we acquired) and find that 77.0% of the high-tech supply chain relationships continue from 2012 to 2013 while 79.7% of the high-tech relationships in 2012 are inherited from 2011. For this reason, we consider the supply chain relationships observed in 2012 likely exist in adjacent years (one prior and one after). The three-year study period also limits the effect of common movements in firm volatility due to changes in firm size concentration (Kelly et al. 2013). To be conservative, we also conduct analyses on a two-year study period (2011Q3 to 2013Q2). All results are consistent as shown in Table3.9.

Table 3.9: Alternative Study Period DEPENDENT VARIABLE:

(a) (b) (c) (d) (e) (f)

Total Risk Idio Risk Idio Risk Total Risk Idio Risk Idio Risk on Total Risk on Total Risk on Idio Risk on Total Risk on Total Risk on Idio Risk

Tier-2 Supplier Risk 0.017 0.004 0.030 0.017 0.004 0.030 (0.020) (0.020) (0.022) (0.020) (0.020) (0.022) Diamond Ratio 0.443*** 0.555*** 0.560***

(0.159) (0.167) (0.167)

Cosine Commonality Score 0.378** 0.393** 0.409** (0.186) (0.201) (0.201) Market Risk 0.368*** 0.298*** 0.298*** 0.368*** 0.298*** 0.298***

(0.014) (0.013) (0.013) (0.014) (0.013) (0.013) Tier-1 Supplier Risk 0.073*** 0.076*** 0.082*** 0.072*** 0.075*** 0.081***

(0.014) (0.013) (0.013) (0.014) (0.013) (0.013)

Financial and Operational controls Yes Yes Yes Yes Yes Yes Sub-industry controls Yes Yes Yes Yes Yes Yes Random Effects Yes Yes Yes Yes Yes Yes Observations 16,434 16,434 16,432 16,432 16,432 16,432 Number of Firms 2,115 2,115 2,114 2,114 2,114 2,114 Overall R-squared 0.1969 0.1765 0.1760 0.1978 0.1778 0.1774 Tier-2 & Diamond Ratio Tier-2 & Cosine Commonality Score

Notes. Columns (a) and (d) regress total equity risk on suppliers' total equity risks; Columns (b) and (e) regress idiosyncratic risk on suppliers' total equity risks; Columns (c) and (f) regress idiosyncratic risk on suppliers' idiosyncratic risks. Standard errors adjusted for firm clusters are shown in parentheses. Market risk, tier-1 risk, and industry-adjusted (supplier) days in inventory are included with log transformation. Sub-industry dummies are included. To simplify the table, we do not report on controls. *** --- 0.01 level, **--- 0.05 level and *--- 0.1 level.

We acknowledge that no good data source is yet available to directly verify the length of each supply chain relationship. The stability analysis discussed above is generated using a different data set, that of the Compustat SEC filings, which captures only a subset of

relationships found in Bloomberg. As we show in the Data Section, large and significant suppliers are likely under-represented in the Compustat database compared to the Bloomberg database. For this reason, the likelihood that a relationship identified in the Bloomberg database has existed in the adjacent years could be even higher than that estimated using the Compustat database.

Unstable supply chain relationships may create an attenuation bias. That is, if some rela- tionships that we identify did not actually last for the three full years, there will be quarters in which the relationship no longer existed, making it more difficult to identify the associa- tion of risks between a focal firm and its immediate and sub-tier suppliers. In other words, the fact that we still find a statistically significant association indicates that the underlying association could be even stronger.