1

Customer Concentration and Employment Risk in Supplier Firms

Yanan Zhang

Central University of Finance and Economics

School of Accountancy

E-mail: ynzhang@cufe.edu.cn

Yun Ke

Brock University

Goodman School of Business

E-mail: yke@brocku.ca

Woo-Jong Lee

Seoul National University

Business School

E-mail: woojong@snu.ac.kr

This version: January 1, 2018

2

Customer Concentration and Employment Risk in Supplier Firms

ABSTRACT

Employment risk is an important type of operational risk in the supply chain. Based on a sample

of supplier firms that disclose their major customers, we posit and find that supplier firms’

employment risk increases with customer concentration. The evidence suggests that both

customer firms’ strategic outsourcing and supplier firms’ relationship-specific considerations

play a role in driving the relation. Cross-sectional analyses reveal that the adverse impact is

pronounced mainly when suppliers have less bargaining power, a less complex business, more

customer-specific investment, greater operational uncertainty, and a poor information

environment. We also provide some evidence that employment risk associated with customer

concentration yields poor firm performance. Our results are robust to alternative measures of

employment risk and potential endogeneity concerns.

Keywords: Customer concentration; Employment risk; Major customers.

JEL Classifications: M51; M41

3

1. Introduction

Supply chain risk has been one of the most studied topics in operations management research.

The conventional approach in operations management research focuses more on a “focal” firm,

which frequently locates in the customer of supply chain (Chopra and Sodhi 2004; Fahimnia et al.

2015; Heckmann et al. 2015; Lewis 2003; Seshadri and Subrahmanyam 2005). In contrast,

accounting and finance studies focus more on supplier firms and generally document a

significant influence of economically important customers on suppliers’ major corporate

decisions (e.g., Ak and Patatoukas 2016; Campello and Gao 2017; Dhaliwal et al. 2016;

Patatoukas 2012).1

Extending these studies, we examine whether the presence of a major customer imposes

operational risk to supplier firms in terms of employment (i.e., employment risk).2 We expect

supplier firms with major customers to bear nontrivial employment risk for several reasons. First,

suppliers with a major customer often make relationship-specific investments (RSIs) by retaining

unnecessary capacity exclusively for the customer. In particular, customers usually outsource to a

vendor that specializes in a given function and performs that function more efficiently than the

customers themselves could, which, in turn, forces the supplier to customize the function. Such

customization works as an operational constraint in supplier firms. In contrast, suppliers are

usually restricted from opportunities for outsourcing compared to customer firms, despite

outsourcing being an effective means of mitigating such operational risk (Holzhacker et al. 2015).

Therefore, suppliers may end up with excessive labor that they otherwise would not retain.

1 The difference is partly attributable to the popular use of disclosure requirements in accounting and finance. We

refer to Statement of Financial Accounting Standards (SFAS) No. 131 and U.S. Securities and Exchange

Commission (SEC) Regulation S-K, which require suppliers to report any individual customers that comprise 10%

or more of firm sales. 2 In a similar spirit as that of Falato and Liang (2016) and Jung et al. (2014), we define employment risk as the

likelihood of suboptimal employment and measure it as the difference between actual net hiring and expected levels.

In doing so, we assume that operational constraints induced by a major customer relationship hinder suppliers from

achieving optimal employment.

4

Second, once RSIs are made, suppliers continue to bearing costs, even when customers

renege on implicit and explicit contractual obligations for their own benefits (e.g., Dou et al.

2013). Research on such “hold-up” problems indicates that suppliers then attempt to avoid costly

RSIs due to uncertainty regarding their customers’ future performance and payments, which

possibly results in underinvestment in RSIs (Drake and Haka 2008). More specifically, in terms

of labor force, the asymmetrically greater costs of adjusting labor downward than upward can

aggravate the hold-up problem (Anderson et al. 2003). Furthermore, the amplified propagation of

uncertainty distortion toward suppliers adversely affects their operations in various ways (Lee et

al. 1997; Lee and Whang 2002). Such a “bullwhip effect” exposes suppliers with major

customers to employment risk to a greater extent. In sum, theoretical predictions suggest that

employment risk in supplier firms will be greater in the presence of a major customer than

otherwise.

Anecdotal evidence suggests that customers can indeed affect supplier firms’ employment

decisions. When Walmart adjusts wage levels upward for the welfare of its employees, its

vendors are forced to lay off employees amid the increased fees. 3 Moreover, customers’

financial distress or bankruptcy clearly triggers suppliers’ layoffs. Aecon laid off workers in its

nuclear division based in Cambridge after its major U.S.-based customer, Westinghouse Electric

Co., filed for bankruptcy protection in 2017.4 Suppliers also fear the strikes of major customers,

since work stoppage in customer firms also creates substantial uncertainty in their own

operations. For example, auto part suppliers have expressed concerns regarding possible strikes

3 See http://www.zerohedge.com/news/2015-09-11/wal-mart-wage-hike-debacle-continues-suppliers-forced-

layoff-employees-amid-new-fees. 4 See https://www.therecord.com/news-story/7227842-aecon-lays-off-cambridge-nuclear-workers-in-wake-of-

westinghouse-bankruptcy/.

5

at GM.5 Therefore, suppliers with a more concentrated customer base should bear greater

employment risk than suppliers with a more diverse customer base. Furthermore, major

customers could take advantage of their bargaining power by pressuring dependent suppliers into

maintaining high levels of product availability at low cost. Cooperative suppliers should then

retain slack resources in an attempt to entertain such requests, hoping to avoid the risk of losing a

key customer.

However, given the merits of supply chains documented in extant literature, the link between

customer concentration and employment risk may not be ex ante clear as predicted. Major

customers can help suppliers streamline their business activities and thus achieve greater

operating efficiency through collaboration and information sharing along the supply chain

(Kalwani and Narayandas 1995; Kinney and Wempe 2001; Kumar 1996). Prior studies support

this view based on shorter cash conversion cycles, leaner balance sheets with fewer working

capital accruals, and lower accrual estimation errors for more dependent suppliers (e.g.,

Patatoukas 2012). Ak and Patatoukas (2016) document a positive association between

customer-base concentration and inventory management efficiency. Such efficiency advantages

in supply-chain practices via collaboration and information sharing ultimately enable dependent

suppliers to better cope with demand uncertainty and thus manage their resources in a more

efficient way. Based on this view, employment efficiency can be high for suppliers with a more

concentrated customer base.6 In sum, whether customer concentration and employment risk are

positively or negatively related is an empirical question.

5 See http://www.torontosun.com/2016/09/16/worst-case-scenario-gm-auto-parts-supplier-fears-unifor-strike-

would-lead-to-layoffs. 6 Note that it is not uncommon for customers and suppliers to align their production elements. For example, a

supply-chain practice known as vendor-managed inventory delegates customers’ inventory replenishment decisions

to suppliers. Suppliers then monitor and maintain customers’ inventories beyond buffer stock levels. Although this

mitigates demand uncertainty that would greatly affect suppliers otherwise, customers could simply take advantage

6

We address this question by analyzing a sample of 13,690 firm–years for U.S. suppliers from

1992 to 2015. Following prior study (e.g., Patatoukas 2012), we identify supplier firms with

major customers based on their disclosure of major customers that represent 10% or more of

their total sales. Specifically, we use the Compustat Segment Customer database to identify

customer–supplier information to construct two measures capturing the extent of a supplier’s

customer concentration.7 One measure is based on sales to major customers and the other is

based on an application of the Herfindahl–Hirschman index.

We document our main findings as follows. First, relying on a recently developed empirical

construct of employment efficiency (Jung et al. 2014), we find that the employment of suppliers

with a more concentrated customer base deviates from the optimal level to a greater extent. This

finding indicates that a customer-dependent supplier often makes employment decisions that are

not fully based on its economic needs, implying customer dependency creates suboptimal labor

investment in supplier firms. By partitioning the sample into two subgroups based on over- or

underinvestment in labor, respectively, we show that customer concentration is associated with

both types of labor employment risk. Further tests reveal that changes in employment are

positively associated with customer concentration, indicating that suppliers with a major

customer indeed tend to hire more employees to accommodate the customer. The results are

robust to alternative measure of employment risk and to an instrumental variable (IV) approach

to address potential endogeneity.

Second, we examine whether the low employment efficiency associated with customer

concentration is driven by customers’ needs for outsourcing. If a major customer exerts its of this practice to save their own inventory management costs by transferring the function to suppliers. Therefore,

trust along the supply chain is critical for the successful alignment of production functions. 7 Note that, following prior studies (Banerjee et al. 2008; Campello and Gao 2017; Patatoukas 2012), we restrict our

sample to firms with major customers because doing so ensures sufficiently asymmetric levels of bargaining power

between suppliers and customers. We thus do not contrast suppliers with and without major customers; instead, we

examine a cross-sectional variation of customer concentration among suppliers with major customers.

7

bargaining power to transfer its labor-intensive modules to supplier firms in an attempt to

improve its own operational efficiency, employment in supplier firms is then naturally affected

by the outsourcing motives of customer firms (Ramanna and Roychowdhury 2010). We find that

the positive relation between customer concentration and employment inefficiency is more

pronounced when customer outsourcing is more likely, implying that outsourcing motives

mediate the relation between customer concentration and employment risk.

Third, following Irvine et al. (2016) which maintain that selling, general, and administrative

(SG&A) expenses best reflect the RSIs of suppliers, we further find that customer concentration

is negatively associated with the elasticity of SG&A costs and the relation is mediated by

excessive employment. This finding indicates that relationship-specific employment is at least

partly a channel through which customer concentration is linked to excessive employment.

Fourth, additional analyses further reveal that the adverse impact of customer concentration

on supplier employment efficiency is contingent on certain firm characteristics. Specifically, we

find that customer concentration plays a more significant role in aggravating employment

inefficiency in supplier firms when 1) the supplier’s bargaining power is particularly weak due to

low customer switching costs, 2) the business is less complex (i.e., revenue sources are less

diversified), 3) customer-specific investment is greater, 4) operational uncertainty is greater, and

5) the information environment is poor.

Lastly, to explore the consequence of employment risk associated with customer

concentration, we investigate firm performance and provide some evidence that higher

employment risk induces suppliers with a more concentrated customer base to perform

worse—that is, with lower returns on assets (ROA)—than suppliers with a more diverse

8

customer base. This result is consistent with inefficient employment working as an operational

constraint by imposing rigid labor costs via customer concentration.

This study contributes to the literature in three important aspects. First, to our knowledge,

we are the first to empirically document employment risk transferred between suppliers and

customers. Whereas the supply chain risk literature has evaluated the overall performance impact

of supply chain risk, we consider employment risk as a specific channel through which the

operational risk of suppliers with major customers is binding. Second, we consider employment

as reflecting RSIs that suppliers with a major customer may strategically pursue. That is,

although the labor investment of supplier firms is far from optimal, it can be viewed as a way to

retain major customers. Our empirical findings on the negative association between customer

concentration and employment efficiency put an emphasis on that suppliers should make balance

between potential benefits and related costs of having a major customer. Third, we present direct

evidence that suppliers’ employment decisions are affected by their customers. This finding

therefore complements the findings of Campello and Gao (2017) and Dhaliwal et al. (2016), who

commonly report the negative consequences of concentrated customer bases in terms of

financing ease (i.e., the cost of raising equity and debt capital). Our study differs from these

studies by presenting the adverse effects from an operational view (i.e., employment).

The remainder of the paper is organized as follows. In Section 2, we review the literature

and develop our hypothesis. Section 3 presents the sample selection and the research

methodology, followed by our empirical results in Section 4. We conduct several additional

analyses in Section 5. Finally, Section 6 concludes the study with implications for practitioners

and academics and future research avenues.

9

2. Literature review and hypothesis development

Supply chain operational risk

Customers and suppliers establish and maintain economic links via various implicit and explicit

arrangements, such as long-term contracts, strategic alliances, and RSIs (Hui et al. 2012). The

supply chain’s contractual nature imposes a certain risk to both parties, known as supply chain

risk. In operations or management research, supply chain risk has typically been categorized in

many different ways and from many perspectives (Christopher and Peck 2004). For example,

Thun and Hoenig (2011) distinguish between internal and external supply chain risks that

encompass purchasing, demand, and environmental issues, whereas Guertler and Spinler (2015)

subdivide supply chain risk into supply, demand, product, and process risks. Recently,

operational risk has gained growing attention (Guertler and Spinler 2015; Hora and Klassen 2013;

Heckmann et al. 2015; Mitra et al. 2015). Because operational risk reflects the complexity,

uncertainty, and diversity of risk sources that are valid for supply networks; operational risk is

considered a better conceptual basis for the notion of supply chain risk compared to financial risk,

which is understood as market, credit, currency, and liquidity risk (Heckmann et al. 2015).

Therefore, the conventional approach in operations management research analyzes the benefits

and costs of management of reliable suppliers, with the customer firm at the center.

In contrast, accounting and finance studies focus instead on supplier firms and assess the

potential impact of their major customers (e.g., Ak and Patatoukas 2016; Campello and Gao

2017; Dhaliwal et al. 2016; Patatoukas 2012).8 In particular, the requirement to disclose major

customers allows related studies in accounting and finance to apprehend supply chain risk of

suppliers in the context of operational constraints. This stream of literature examines the impact

8 The difference is partly attributable to the popular use of disclosure requirements in accounting and finance. We

refer to SFAS No. 131 and SEC regulation S-K, which require a supplier with an individual customer that comprises

10% or more of firm sales to report on the major customer.

10

of the customer–supplier relationship on firm performance and the cost of equity (Dhaliwal et al.

2016; Patatoukas 2012), capital structure (Banerjee et al. 2008; Titman and Wessel 1988), bank

loan contracting (Cen et al. 2016; Kim et al. 2011), earnings management and accounting

conservatism (Hui et al. 2012; Raman and Shahrur 2008), analysts’ forecasts (Guan et al. 2015),

and tax avoidance (Cen et al. 2017). However, research on the influence of major customers on

the investment strategy of supplier firms is relatively scarce. Our study aims to fill part of this

gap by documenting the impact of customer–supplier relationships on corporate employment.

Sources of operational risk for supplier firms – demand uncertainty and outsourcing

Drawing from operations management and other management literature, we specifically consider

two sources of operational risk for supplier firms with major customers: demand uncertainty and

the lack of outsourcing opportunities. First, demand uncertainty can put suppliers in supply

chains in a disadvantageous position. Lee et al. (1997) and Lee and Whang (2002) analyze the

bullwhip effect, both empirically and theoretically, and show that demand information distortion

being exaggerated towards the suppliers can cause longer lead times and lower their supply chain

efficiency. 9 Particularly, Garavelli (2003) suggests that the bullwhip effect translates into

business uncertainty, cost rigidity, and operational risk suppliers of the supply chain.

Furthermore, the literature relates outsourcing opportunities to operational risk. Various

advantages of outsourcing in terms of lower manufacturing costs, reduced investment in plant

and equipment, capacity flexibility, enhanced focus on core competencies, and the promotion of

suppliers’ competition have long been widely documented across multiple areas of management

(Beach et al. 2000; D’Aveni and Ravenscraft 1994; Gilley and Rasheed 2000; Lei and Hitt; 1995;

Prahalad and Hamel 1990). Therefore, a stylized factor in the operations management literature

9 The bullwhip effect prevails for a majority of firms along the supply chain (Bray and Mendelson 2013) and can

arise in various relationships, such as between a retailer and a manufacturer or between a manufacturer and its

supplier (Lee et al. 1997).

11

is that an outsourcing strategy benefits customers with tighter supply chain integration, while

some recent studies introduce into this relation several contingency factors such as product

complexity, the complexity of business environment, uncertainty, and other country-level

variables (Kim 2009; van der Vaart et al. 2012; Wiengarten et al. 2016).

By contrast, suppliers are characterized by limited outsourcing options, which is one of the

most common policies for reducing operational risk (Chen and Xiao 2015; Holzhacker et al.

2015; Wiengarten et al. 2016). Suppliers consist mostly of manufacturers of parts and interim

goods, as well as natural raw material providers. In such cases, supplier firms must invest in

manufacturing facilities and have few outsourcing opportunities due to the inherent nature of

their operations, whereas customer firms can easily adopt a flexible multiple-sourcing strategy to

mitigate risk (Chen and Xiao 2015). We therefore anticipate that, when a major customer

attempts to outsource to a supplier and thus transfer its own operational constraints to the

supplier, the supplier will end up retaining excessive capacity and related labor that would have

been curtailed otherwise. This is, in fact, the spirit of the relationship-specific investment, whose

costs in terms of labor have not yet been studied in the literature.

Employment risk as part of supply chain risk

Rigidity in employment works as an operating constraint, given that labor is a key production

element. In addressing this issue, prior studies have primarily focused on labor unions (e.g.,

Bronars and Deere 1991; 1993; Hirsch 1992) or country-level employee protection laws (Van

Long and Siebert 1983). In general, prior studies suggest that the constraints of employment

decisions, such as firm-level labor unions or employment protection legislation, provide

managers with nontrivial adjustment costs in employment and thus prompt inefficient labor

investment decisions.

12

We consider customer concentration as another determinant of employment efficiency. As

documented above, suppliers with major customers suffer from significant operational

constraints induced by supply chain risk. We therefore hypothesize that such suppliers are also

exposed to employment risk to a great extent. In the presence of operational constraints,

suppliers’ employment can deviate from optimal levels and remain off-equilibrium due to the

high adjustment costs. On the one hand, over-hiring (i.e., employment levels above optimum) is

likely for suppliers with major customers, because they unavoidably retain excessive labor that

would not be otherwise needed. The sticky nature of employment due to its greater downward

than upward adjustment costs will exacerbate labor over-investment among such suppliers. On

the other hand, under-hiring (i.e., employment levels below optimum) can also arise, because

resources for new employment will be limited due to excessive capacity exclusively devoted to

major customers (Jensen and Meckling 1976; Montgomery 1989). Such a “hold-up” problem

deters managers from making sufficient commitment in employment. We therefore expect that

employment efficiency will be lower as customer base is more concentrated in supplier firms.

In contrast, the alignment theory indicates some merits of supply chains for suppliers. Major

customers can help suppliers streamline their business activities and thus achieve greater

operating efficiency through collaboration and information sharing along the supply chain (e.g.,

Kinney and Wempe 2001). Consistent with the idea, Ak and Patatoukas (2016) document a

positive association between customer-base concentration and inventory management efficiency.

Such efficiency advantages in supply-chain practices via collaboration and information sharing

ultimately enable dependent suppliers to better cope with demand uncertainty and thus manage

their resources more efficiently. Accordingly, employment efficiency does not have to be always

low for suppliers with a concentrated customer base. In sum, whether customer concentration

13

and employment risk are positively or negatively related is an empirical question. We therefore

set up our hypothesis in a null form.

HYPOTHESIS: Customer concentration is not associated with the employment risk of

supplier firms.

3. Sample, research design, and descriptive statistics

Sample

We construct our sample by intersecting a number of data sources. Since this study focuses on

supplier firms with major customers, we follow prior literature (e.g., Ak and Patatoukas 2016;

Irvine et al. 2016; Patatoukas 2012) and obtain customer–supplier data from the Compustat

Segment database, which provides the name of each major customer along with the sales

amounts. We then retrieve financial statement data from Compustat’s annual data sets, stock

return and stock price data from the Center for Research in Security Prices monthly stock files,

institutional ownership data from the Thomson Reuters Institutional Holdings database, and

industry unionization data from the Union Membership and Coverage Database.10 Our sample

period is from 1992 to 2015. We begin in 1992 because five years of operating cash flow data are

required to calculate cash flow volatility and the cash flow data first became available in 1988.

We exclude utilities (Standard Industrial Classification, or SIC, codes 4900–4999) and financial

firms (SIC codes 6000–6999) and require firm–year observations with non-missing data in

estimating our test variables. These procedures yield a final sample of 13,690 supplier–year

observations.

10 The Union Membership and Coverage Database is available at www.unionstats.com.

14

Measure of employment risk

In this section, we describe our approach of measuring labor employment risk. We define labor

employment risk as the extent to which a firm’s employment level deviates from its optimum.

Specifically, following prior studies (Jung et al. 2014; Li 2011; Pinnuck and Lillis 2007), we

measure labor employment risk as firms’ abnormal net hiring, defined as the absolute difference

between actual net hiring and the expected level of net hiring. Firms’ actual net hiring is reflected

as the change in their numbers of employees. To estimate expected net hiring, we construct a

regression model based on previous research (Jung et al. 2014; Pinnuck and Lillis 2007). The

regression is the percentage change in employees on a set of variables that represent underlying

economic fundamentals, written as follows:

𝑁𝐸𝑇_𝐻𝐼𝑅𝐸𝐼 = 𝛽0 + 𝛽1𝑆𝐴𝐿𝐸𝑆_𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽2𝑆𝐴𝐿𝐸𝑆_𝐺𝑅𝑂𝑊𝑇𝐻𝑡 + 𝛽3𝑅𝑂𝐴𝑡 +

𝛽4𝛥𝑅𝑂𝐴𝑡−1 + 𝛽5𝛥𝑅𝑂𝐴𝑡 + 𝛽6𝑅𝐸𝑇𝑈𝑅𝑁𝑡 + 𝛽7𝑆𝐼𝑍𝐸_𝑅𝑡−1 + 𝛽8𝑄𝑈𝐼𝐶𝐾𝑡−1 +

𝛽9𝛥𝑄𝑈𝐼𝐶𝐾𝑡−1 + 𝛽10𝛥𝑄𝑈𝐼𝐶𝐾𝑡 + 𝛽11𝐿𝐸𝑉𝑡−1 + 𝛽12𝐿𝑂𝑆𝑆𝐵𝐼𝑁1𝑡−1 + 𝛽13𝐿𝑂𝑆𝑆𝐵𝐼𝑁2𝑡−1 +

𝛽14𝐿𝑂𝑆𝑆𝐵𝐼𝑁3𝑡−1 + 𝛽15𝐿𝑂𝑆𝑆𝐵𝐼𝑁4𝑡−1 + 𝛽16𝐿𝑂𝑆𝑆𝐵𝐼𝑁5𝑡−1 + 𝜀𝑡, (1)

where NET_HIRE represents the percentage change in the number of employees (Compustat

variable EMP); SALES_GROWTH is the percentage change in sales revenue; ROA is the net

income scaled by beginning-of-year total assets; RETURN represents the annual stock return

during fiscal year t; SIZE_R is the natural logarithm of the market value of equity, ranked in

percentiles; QUICK is the quick ratio, defined as the ratio of cash and short-term investments

plus receivables to current liabilities; LEV is the sum of debt in current liabilities and total

long-term debt divided by total assets; and LOSSBINX is a dummy variable that indicates each

0.005 interval of the prior year’s ROA, from zero to -0.025 (i.e., LOSSBIN1 equals one if the

prior year’s ROA is less than 0.00 and greater than or equal to -0.005 and zero otherwise). We

15

also include year and industry fixed effects in our regression model. A detailed definition of the

variables is provided in Appendix A.

We calculate all required variables and perform the above regression model. The results of

this regression are reported in Appendix B. The coefficients of the variables are largely

consistent with those reported by Jung et al. (2014). For instance, SALES_GROWTH (both

lagged and current) is positively and significantly related to the level of net hiring, while

LOSSBINX is negatively associated with it. We then use the estimated coefficients to calculate

the expected level of net hiring. Finally, we construct our primary measure of labor employment

risk, ABS_AB_NET_HIRE, which is the absolute value of the difference between actual and

expected net hiring (i.e., the absolute value of the residual from regression model (1)).

Since generalization of the findings in our study hinges critically on the measure of labor

employment risk, we also consider an alternative measure of abnormal net hiring. Following

prior studies (e.g., Cella 2014; Jung et al. 2014), we use the industry median as the normal level

of net hiring. The absolute difference between each firm’s actual net hiring and its industry

median level (ABS_EMP_CHG) is our alternative measure of abnormal net hiring.

Measures of customer concentration

We follow prior literature to construct our measure of customer concentration, which is our main

variable of interest. SFAS No. 131 requires firms to disclose information about major customers

that represent 10% or more of total sales. We use Compustat’s Segment Customer database to

identify customer–supplier information and the supplier’s sales to each major customer. We then

calculate two measures to capture the importance of a supplier’s major customers (i.e., the extent

to which a supplier has concentrated customers).

16

Our first measure follows Banerjee et al. (2008) and Campello and Gao (2017), who define

customer concentration (CC_SALES) as the sum of the percentage of sales coming from all the

firm’s major customers. Thus, CC_SALES is computed as follows:

𝐶𝐶_𝑆𝐴𝐿𝐸𝑆𝑖𝑡 = ∑𝑆𝑎𝑙𝑒𝑠𝑖𝑗𝑡

𝑆𝑎𝑙𝑒𝑠𝑖𝑡

𝑛𝑖𝑗=1 ,

where ni is the number of supplier i’s major customers, Salesijt is suppler i’s sales to its major

customer j in year t, and Salesit is supplier i’s total sales in year t. A higher value of CC_SALES

represents a larger percentage of sales to major customers.

Our second measure follows Patatoukas (2012), who develops a customer concentration

measure based on an application of the Herfindahl–Hirschman index. More specifically, we

calculate the customer concentration based on the notion of the Herfindahl–Hirschman index

(CC_HHI) as follows:

𝐶𝐶_𝐻𝐻𝐼𝑖𝑡 = ∑ (𝑆𝑎𝑙𝑒𝑠𝑖𝑗𝑡

𝑆𝑎𝑙𝑒𝑠𝑖𝑡)2𝑛𝑖

𝑗=1 ,

where ni, Salesijt, and Salesit are defined as above. This alternative measure captures not only the

number of major customers, but also their importance to the supplier’s total sales. The variable

CC_HHI ranges from zero to one. It takes the value of zero when a supplier does not have any

major customers and a value of one when all the supplier’s revenues come from only one major

customer. Therefore, a higher value of CC_HHI indicates a larger customer concentration base.

The same measure is used by Dhaliwal et al. (2016) and Huang et al. (2016).

Regression model

To investigate the relation between customer concentration and labor employment risk, we

employ the following multivariate regression model:

17

𝐴𝐵𝑆_𝐴𝐵_𝑁𝐸𝑇_𝐻𝐼𝑅𝐸𝑡 = 𝛼0 + 𝛼1𝐶𝐶𝑡−1 + 𝛼2𝐴𝑄𝑡−1 + 𝛼3𝑀𝑇𝐵𝑡−1 + 𝛼4𝑆𝐼𝑍𝐸𝑡−1 +

𝛼5𝑄𝑈𝐼𝐶𝐾𝑡−1 + 𝛼6𝐿𝐸𝑉𝑡−1 + 𝛼7𝐷𝐼𝑉𝐷𝑈𝑀𝑡−1 + 𝛼8𝑆𝑇𝐷_𝐶𝐹𝑂𝑡−1 + 𝛼9𝑆𝑇𝐷_𝑆𝐴𝐿𝐸𝑆𝑡−1 +

𝛼10𝑇𝐴𝑁𝐺𝐼𝐵𝐿𝐸𝑡−1 + 𝛼11𝐿𝑂𝑆𝑆𝑡−1 + 𝛼12𝐼𝑁𝑆𝑇𝐼𝑡−1 + 𝛼13𝑆𝑇𝐷_𝑁𝐸𝑇_𝐻𝐼𝑅𝐸𝑡−1 +

𝛼14𝐿𝐴𝐵𝑂𝑅_𝐼𝑁𝑇𝐸𝑁𝑆𝐼𝑇𝑌𝑡−1 + 𝛼15𝑈𝑁𝐼𝑂𝑁𝑡−1 + 𝛼16𝐴𝐵𝑆_𝐴𝐵_𝐼𝑁𝑉𝐸𝑆𝑇_𝑂𝑇𝐻𝐸𝑅𝑖 +

𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡 + 𝜀𝑡, (2)

where the dependent variable, ABS_AB_NET_HIRE, is our proxy for labor employment risk,

measured as the absolute value of the residual estimated from the regression model (1), and CC

is one of our two measures of customer concentration. We expect a positive coefficient for CC,

that is, 𝛼1 > 0, if customer concentration increases suppliers’ labor employment risk, as we

hypothesize.

To ensure that the results are not driven by other factors that could also affect labor

employment risk, we include a number of control variables based on prior studies in Eq. (2)

(Biddle and Hilary 2006; Biddle et al. 2009; Jung et al. 2014). Since Jung et al. (2014) find a

positive relation between financial reporting quality and labor employment risk, we control for

accounting quality (AQ). Following Biddle and Hilary (2006) and Biddle et al. (2009), we

include a set of variables that could generally be related to firms’ investments, such as the

market-to-book ratio (MTB), firm size (SIZE), the quick ratio (QUICK), the leverage ratio (LEV),

dividend payouts (DIVDUM), cash flow volatility (STD_CFO), sales volatility (STD_SALES),

tangibility (TANGIBLE), and the incidence of losses (LOSS). Considering the monitoring effect

of institutional owners (Cella 2014), we also control for the proportion of institutional ownership

(INSTI). Since firms’ net hiring volatility (STD_NET_HIRE) and labor intensity

(LABOR_INTENSITY) are related to firms’ labor adjustment flexibility (Jung et al. 2014), these

are included in the model. Moreover, we add the bargaining power of organized labor (UNION)

in the regression and predict its negative effect on labor employment risk. Firms’ non-labor

18

investments could have an indirect effect on abnormal net hiring. Therefore, based on the model

of Biddle et al. (2009), we include ABS_AB_INVEST_OTHER to account for non-labor

investment efficiency. Finally, industry and year fixed effects are included and the regression is

estimated with all standard errors clustered at the firm level. Detailed definitions for all the

variables are provided in Appendix A.

Descriptive statistics

Table 1 presents descriptive statistics of the regression variables. To mitigate the potential

influence of outliers, we winsorize all continuous variables at their first and 99th percentiles. The

mean and median values of ABS_AB_NET_HIRE, our dependent variable, are 0.117 and 0.071,

respectively. These numbers are very close to those reported in prior studies (e.g., Jung et al.

2014). The mean and median values of CC_SALES are 0.266 and 0.210, respectively. Prior

studies also report similar numbers (e.g., Campello and Gao 2017). The mean and median values

of CC_HHI are 0.080 and 0.034, respectively, which, again, are similar to previously reported

levels (e.g., Dhaliwal et al. 2016; Patatoukas 2012). For brevity, we do not discuss control

variables; however, they are largely consistent with those reported in prior studies (e.g., Biddle et

al. 2009; Jung et al. 2014).

[Insert Table 1 here]

Table 2 reports the Pearson correlation matrix. We focus our discussion on the correlation

between labor employment risk and customer concentration. The table shows that labor

employment risk (ABS_AB_NET_HIRE) is positively correlated with both measures of customer

concentration, indicating that a concentrated customer base is associated with the supplier’s

higher employment risk. The correlations among the other variables are largely consistent with

19

prior studies’ findings (e.g., Jung et al. 2014). For example, ABS_AB_NET_HIRE is positively

related to MTB, STD_CFO, and STD_SALES, and negatively related to DIVDUM and SIZE.

[Insert Table 2 here]

4. Empirical results

Main results

We report our main results in Table 3. Columns (1) and (2) show the results for the whole sample.

The dependent variable is ABS_AB_NET_HIRE, while the independent variables in columns (1)

and (2) are CC_SALES and CC_HHI, respectively. For both measures of customer concentration,

the coefficients are positive and statistically significant at the 1% level (0.041 with t-value =

6.713 and 0.084 with t-value = 5.770, respectively). The results are consistent with the view that,

for supplier firms, a concentrated customer base is positively associated with employment risk.

The signs of the other coefficients of the control variables measuring firm characteristics are

generally consistent with prior studies’ findings (e.g., Jung et al. 2014).

[Insert Table 3 here]

Next, we separate our sample into two subgroups based on the sign of abnormal net hiring so

that we can investigate the type of labor employment risk associated with customer concentration

(i.e., labor over- and/or underinvestment). Note that overinvestment means that actual net hiring

is greater than expected levels and underinvestment corresponds to the case in which actual net

hiring is less than expected levels. Columns (3) and (4) of Table 3 report the results for the

overinvestment subsample, while columns (5) and (6) present the results for the underinvestment

subsample. The dependent variable is still the absolute value of abnormal net hiring

(ABS_AB_NET_HIRE) in both subsamples. In both cases, the coefficients of the customer

20

concentration measures are significantly positive at the 1% level. Thus, the results clearly

indicate that customer concentration is associated with labor employment risk, resulting in both

labor over- and underinvestment.

To ensure the robustness of our findings, we also use the alternative measure of employment

risk, which is industry-adjusted abnormal hiring (ABS_EMP_CHG), to rerun the regression

model. Specifically, we measure ABS_EMP_CHG as the absolute value of the percentage change

in the employment numbers from years t to t - 1, adjusted by the two-digit SIC industry median

value. Table 4 reports the results of using this alternative measure. We find that the results are

largely consistent with those reported in Table 3. The coefficients of the customer concentration

measures are all significantly positive (0.032 with t-value = 4.229 and 0.061 with t-value =

3.407), suggesting again that customer concentration is associated with higher employment risk

(i.e., lower employment efficiency or higher abnormal net hiring). Moreover, subsample analyses

show that this happens in both labor over- and underinvestment cases.

[Insert Table 4 here]

Potential channels

Given our main findings above, in this section we attempt to explore the underlying reasons why

customer concentration increases labor employment risk (i.e., reducing labor investment

efficiency). We conduct tests based on two perspectives: customer firms’ strategic outsourcing

and suppliers’ RSI.

Customer firms’ strategic outsourcing

To examine this perspective, we need to determine a measure of the level of customer firms’

outsourcing and include it and its interaction with customer concentration in the regression

21

model. If the increase in labor employment risk with customer concentration is partially due to

customers’ outsourcing, we should observe a positive coefficient for the interaction term.

Empirically, following Ramanna et al. (2010), we measure the level of customer firms’ outsourcing

(OUTSOURCING) as the negative value of major customers’ weighted average abnormal net

hiring for each supplier–year and then rank them into deciles, so that a higher value indicates

greater levels of outsourcing activities for suppliers’ customers. We next interact this outsourcing

measure with customer concentration. The results are presented in Table 5.

[Insert Table 5 here]

For both measures of customer concentration, the coefficients of the interaction are

significantly positive at the 1% level (0.011 with t-value = 3.186 and 0.030 with t-value = 2.865

in columns (1) and (2), respectively), suggesting that the effect of customer concentration on

labor employment risk is more pronounced in suppliers subjected to more outsourcing from their

customer firms. The result is consistent with the notion that, for suppliers with concentrated

customers, customer firms’ outsourcing activity largely exacerbates suppliers’ employment risk.

Supplier firms’ RSI

For the second perspective, based on RSI, we follow Irvine et al. (2016) and examine the

elasticity of supply firms’ SG&A expenses. Irvine et al. (2016) argue that SG&A expenses can be

considered the RSIs of suppliers. We examine how customer concentration affects SG&A

elasticity and how labor investment inefficiency could moderate the relation. Empirically, we

regress SG&A elasticity on customer concentration and its interaction with abnormal net hiring.

The results are shown in Table 6. Columns (1) and (2) present the results for two different

customer concentration measures. In both columns, we find that customer concentration is

22

negatively associated with the elasticity of SG&A costs. More importantly, the relation is

mediated by excessive employment since the interaction terms are significantly positive.

[Insert Table 6 here]

Together, the findings reported in this section corroborate our main findings and show that

customer outsourcing and supplier relationship-specific employment are two potential channels

through which customer concentration is related to labor employment risk.

Cross-sectional analysis

Next, we perform several cross-sectional tests to explore the heterogeneity of the relation

between customer concentration and labor employment risk. Specifically, we investigate whether

the effect of customer concentration on labor employment risk varies with (1) customers’

switching costs, (2) supplier’s revenue diversification, (3) suppliers’ relationship-specific capital

investment, and (4) suppliers’ operational uncertainty and information environment.

Effect of customers’ switching costs

For a supplier with a concentrated customer base, when a major customer chooses to switch to

another supplier, the original supplier usually incurs material financial losses. Thus, when a

supplier makes its labor investment decision, it will consider the likelihood of such an event. For

example, if it is very easy to lose a major customer, a supplier facing greater uncertainty in future

cash flow and potential financial distress could hesitate to invest optimally in labor capital,

causing labor inefficiency. On the other hand, when a customer is highly dependent on a supplier,

the supplier is more likely to invest in labor to satisfy the customer’s demands. If that is the case,

the likelihood of customers switching suppliers should affect the relation between customer

concentration and labor employment risk. Prior studies already document that customer

23

switching costs can, to some degree, moderate the adverse effects associated with customer

concentration (e.g., Dhaliwal et al. 2016; Huang et al. 2016).

To empirically test our prediction, we use customers’ switching costs to proxy for the

likelihood of customers’ switching suppliers. We follow Dhaliwal et al. (2016) and use a

supplier’s market share in its industry to measure its customers’ switching costs. Specifically,

these are calculated as the ratio of the supplier’s sales to the total sales of its two-digit SIC

industry. A higher number (i.e., higher market share) suggests a higher switching cost for its

customers. We rank suppliers’ market share each year into deciles (SWITCH_COST) and then

interact it with our customer concentration measures.

[Insert Table 7 here]

We report the testing results in Panel A of Table 7. Columns (1) to (2) report the results for

two measures of customer concentration. The coefficients of SWITCH_COST are negative and

significant at the 1% level in both columns (-0.004 with t-value = -2.817 and -0.005 with t-value

= -3.580), suggesting that a higher customer switching cost mitigates suppliers’ labor

employment risk. We are interested in the interaction between customer concentration and

customer switching costs. For both measures of customer concentration, the coefficients of the

interaction term between customer concentration and switching costs are significantly negative at

the 1% level (-0.009 with t-value = -3.983 and -0.018 with t-value = 3.723). Overall, the results

suggest that customers’ high switching costs can help mitigate suppliers’ labor employment risk

associated with customer concentration.

Effect of supplier revenue diversification

We next examine whether a supplier being diversified could affect the relation between customer

concentration and labor employment risk. Hann et al. (2013) have suggested that operating in

24

multiple business segments helps firms reduce the adverse effect of losing business in one

segment. Thus, for a supplier operating in only one business segment, the consequences of losing

a major customer are more severe and the supplier might be more hesitant to invest in labor

capital. Consequently, we expect the relation between customer concentration and labor

employment risk to be more pronounced for non-diversified suppliers.

Following Dhaliwal et al. (2016), we employ an indicator variable (BUS_SEG) that equals

one if a supplier has only one business segment and zero otherwise. We then interact it with our

measures of customer concentration. Panel B of Table 7 reports the testing results. We focus our

discussion on the interaction term. When customer concentration is measured by sales (column

(1)), the coefficient is positive and marginally significant at the 10% level (0.024 with t-value =

1.881). When customer concentration is measured by HHI (column (2)), the coefficient remains

positive and becomes more significant at the 5% level (0.063 with t-value = 2.103). Therefore,

across the two measures of customer concentration, we find evidence that the positive relation

between customer concentration and labor employment risk is more pronounced when a supplier

is non-diversified (i.e., operating only in one business segment).

Effect of RSIs

A supplier often makes capital investments that are specifically related to its major customers but

worth little or nothing to either party (Titman and Wessels 1988). Ex ante, it is not clear how

these RSIs could affect the relation between customer concentration and labor employment risk.

On one hand, a supplier with significant RSIs could choose to invest optimally in labor capital to

please its customer and avoid losing the customer’s business, which could partially mitigate the

supplier’s labor risk. On the other hand, the supplier might hesitate to invest in labor because

such an investment can also be relationship specific and the potential loss by losing the customer

25

would be even bigger, suggesting that RSIs can exacerbate the negative effect of customer

concentration.

To test these two different predictions, we follow Campello and Gao’s (2017) approach and

use industry-level input specificity to calculate customers’ input specificity (Nunn 2007).

Specifically, Nunn (2007) defines an input as differentiated if it is not sold on an exchange or it

does not have a published reference price. We calculate a supplier’s customers’ weighted average

of non-homogeneous inputs over sales (CSPECIFICITY) as follows:

𝐶𝑆𝑃𝐸𝐶𝐼𝐹𝐼𝐶𝐼𝑇𝑌𝑗 = ∑ %𝑠𝑎𝑙𝑒𝑠𝑖𝑗 × 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦𝑗𝑛𝑖𝑗=1 ,

where specificityj is the input specificity of customer j’s industry and specificity is the proportion

of differential inputs or non-homogeneous inputs used by a certain industry. The industry

classification is defined according to the input and output of commodity flows used by the 1997

U.S. Bureau of Economic Analysis. Suppliers with higher values of CSPECIFICITY are more

likely to make RSIs.

We report the effect of RSIs on the relation between customer concentration and labor

employment risk in Panel C of Table 7. For both measures of customer concentration, the

coefficients of the interaction terms with CSPECIFICITY are positive (0.014 with t-value =

2.604 and 0.035 with t-value = 3.478) and statistically significant at the 1% level. Thus,

consistent with the latter view, our results suggest that RSIs exacerbate the negative effect of

customer concentration on labor employment risk.

Effect of suppliers’ operational uncertainty and information environment

We next explore whether the relation between customer concentration and labor employment risk

can also be affected by a supplier’s operational uncertainty and information environment.

Intuitively, uncertainty should play an important role in firms’ investment decisions (Abel 1983;

26

Biddle and Hilary 2006). For example, when a supplier faces huge uncertainty about future

product demand, it will likely underinvest in labor capital to avoid potential future losses. To test

this prediction, we use the decile ranking of the standard deviation of earnings in the past five

years to proxy for operational uncertainty (STD_EARN) and interact it with our measures of

customer concentration. The results in Panel D of Table 7 show that, for both measures of

customer concentration, the coefficients of the interaction are positive and statistically significant

at the 1% or 10% level (0.006 with t-value = 2.850 and 0.001 with t-value = 1.840, respectively),

suggesting that, for a supplier, the effect of customer concentration on labor employment risk

increases with operational uncertainty.

A firm’s information environment is important for its investment decision making as well.

Arguing that high accounting quality can mitigate information asymmetry and improve

information quality, Jung et al. (2014) show that higher accounting quality is associated with

better labor investment efficiency. Building on their findings, we predict that a better information

environment, as measured by higher financial reporting quality, can mitigate the negative effects

of customer concentration on labor employment risk. To test our prediction, we use the decile

ranking of accrual quality (AQ) to interact with customer concentration. A higher ranking means

better accounting quality. The testing results are shown in Panel E of Table 7. Consistent with our

prediction, we find that the coefficients of the interaction terms are both significantly negative

(-0.006 with t-value = -3.039 and -0.011 with t-value = -2.255).

5. Additional analyses

Governmental customer concentration and labor employment risk

So far we have focused our analysis on customer concentration based on corporate customers. In

the United States, however, many firms also have the government as important and sometimes

27

major customers. According to Dhaliwal et al. (2016), the U.S. federal government’s spending

accounted for 23.2% of the gross domestic product in 2011. More importantly, concentration in

government customers typically means lower risk for suppliers for several reasons, which could

affect suppliers’ labor employment risk. First, the government is more stable than corporate

customers and unlikely to default and go bankrupt. Second, governmental contracts are usually

longer term and the government is less likely to change suppliers during the contract. Finally,

many government contracts use cost-plus pricing, which actually shifts operational risk to the

government (e.g., Berrios 2006; Reichelstein 1992).11 Thus, when suppliers have business with

the government, they may worry less about downside risk and are more likely to fully invest in

labor forces to fulfill government orders. In this case, we might be less likely to observe any

relation between government customer concentration and suppliers’ labor investment

inefficiency.

To test this prediction, we follow Dhaliwal et al. (2016) and Huang et al. (2016) to construct

a government customer concentration measure. Also from the Compustat Segment database, we

identify suppliers reporting a federal government customer as their major customer (i.e., this

revenue is at least 10% of total sales). Two measures (GOV_SALES and GOV_HHI), similar to

those for the corporate customer concentration measure, are calculated to capture government

customer concentration. Detailed definitions are provided in Appendix A.

We then run the regression model with government customer concentration replacing

corporate customer concentration. Untabulated results show that government customer

concentration is not associated with suppliers’ labor employment risk, since none of the

coefficients of the government customer concentration measures is statistically significant.

11 For example, the government can pay the supplier total costs plus a 10% margin. Total costs can include all

material and labor costs. In this case, the supplier can hire at the project’s full capacity and be covered by the

government for all costs.

28

Address potential endogeneity with instrumental variable approach

We next discuss potential endogeneity concerns and our approach in addressing them. One type

of endogeneity concern arises from unobservable omitted variables. It is conceivable that certain

characteristics could be correlated with customer concentration and labor employment risk,

which can affect or even drive our results. For example, it is possible for a manager to prefer

both a concentrated customer base and overhiring. Therefore, we next employ an IV approach to

mitigate this concern. Prior studies (e.g., Larcker and Rusticus 2010; Roberts and Whited 2013)

point out that the success of the IV approach hinges on the selection of the IVs and prescribe that

they should satisfy two requirements: the relevance condition and the exclusion restriction.

Recent studies (Cohen and Li 2016; Dhaliwal et al. 2016) validate the use of lagged industry

averages as IVs. Thus, we follow these studies and use both the two-year and three-year lagged

industry averages as our IVs.12 Annual industry averages are calculated based on the suppliers’

three-digit SIC industry by excluding the suppliers’ customer concentration.

We report the first-stage results in Panel A of Table 8. We regress each customer

concentration measure on the IVs and the other control variables. Diagnostic test results suggest

that the selection of IVs is valid to some extent. Specifically, the Wu–Hausman test rejects the

null hypothesis that the customer concentration measures are exogenous. We then report the

second-stage results in Panel B. Again, the results show a positive relation between customer

concentration and labor employment risk. Therefore, to the extent that our IVs are valid, the

results suggest that greater customer concentration causally leads to higher suppliers’ labor

employment risk.

12 Dhaliwal et al. (2016), in their footnote 36, provide several prior studies that use lagged industry average as

instrumental variables.

29

[Insert Table 8 here]

Employment risk and firm performance

Finally, in this section we conduct a simple analysis to examine whether employment

inefficiency is associated with poor firm performance. We use the ROA (ROA) to measure firm

performance and regress it on labor inefficiency (ABS_AB_NET_HIRE), customer concentration,

and their interaction term. Other control variables are adopted from Ak and Patatoukas (2016).

The results are reported in Table 9. For both measures of customer concentration, the coefficients

of labor inefficiency are significantly negative (-0.098 with t-value = -5.200 and -0.096 with

t-value = -5.071), suggesting that inefficient employment is indeed associated with poor firm

performance. More importantly, the coefficients of the interaction terms are also negative and

statistically significant at the 5% level (-0.075 with t-value = -2.434 and -0.078 with t-value =

-2.526). The findings are consistent with our prediction that customer concentration operationally

constrains suppliers by imposing rigid labor costs and leads to inefficient employment.

[Insert Table 9 here]

6. Conclusion

While supply chain risk has been extensively studied in the operations management, accounting,

and finance literature, employment risk has received little attention from academia. We focus on

a set of supplier firms with major customers and examine how a concentrated customer base

affects supplier firms’ employment risk. Empirical evidence suggests a positive association

between customer concentration and employment risk. We find that both customer firms’

strategic outsourcing and supplier firms’ relationship-specific considerations can partially explain

our findings. Furthermore, we show that the adverse impact is concentrated in supplier firms

30

with lower bargaining power, a less complex business, more customer-specific investments,

greater operational uncertainty, and a poorer information environment.

Although the presence of major customers benefits supplier firms by creating manageable

revenue streams, it also imposes nontrivial inefficiency to suppliers. Our paper documents

potential costs of having a concentrated customer base in terms of employment. In so doing, this

paper sheds some light on the extant literature which has been silent about customers’ influence

in suppliers’ investment efficiency in general, and employment efficiency in particular. We call

for further research taking a holistic view on the implication of customer concentration.

31

REFERENCES

Abel, A. B. 1983. Optimal investment under uncertainty. The American Economic Review 73

(1):228-233.

Ak, B. K., and P. N. Patatoukas. 2016. Customer-base concentration and inventory efficiencies:

evidence from the manufacturing sector. Production and Operations Management 25

(2):258-272.

Anderson, M. C., R. D. Banker, and S. N. Janakiraman. 2003. Are selling, general, and

administrative costs "sticky"? Journal of Accounting Research 41 (1):47-63.

Banerjee, S., S. Dasgupta, and Y. Kim. 2008. Buyer-supplier relationships and the stakeholder

theory of capital structure. The Journal of Finance 63 (5):2507-2552.

Beach, R., A. P. Muhlemann, D. H. R. Price, A. Paterson, and J. A. Sharp. 2000. Manufacturing

operations and strategic flexibility: survey and cases. International Journal of Operations &

Production Management 20 (1):7-30.

Berrios, R. 2006. Government contracts and contractor behavior. Journal of Business Ethics 63

(2):119-130.

Biddle, G. C., G. Hilary, and R. S. Verdi. 2009. How does financial reporting quality relate to

investment efficiency? Journal of Accounting and Economics 48 (2-3):112-131.

Biddle, G. C., and G. Hilary. 2006. Accounting quality and firm-level capital investment. The

Accounting Review 81 (5):963-982.

Bray, R. L., and H. Mendelson. 2013. Information transmission and the bullwhip effect: an

empirical investigation. Management Science 58 (5):860-875.

Bronars, S. G., and D. R. Deere. 1991. The threat of unionization, the use of debt, and the

preservation of shareholder wealth. Quarterly Journal of Economics 106 (1):231-254.

———. 1993. Unionization, incomplete contracting, and capital investment. The Journal of

Business 66 (1):117-132.

Campello, M., and J. Gao. 2017. Customer concentration and loan contract terms. Journal of

Financial Economics 123 (1):108-136.

Cella, C. 2014. Institutional investors and corporate investment. Working paper, Stockholm

School of Economics.

Cen, L., S. Dasgupta, R. Elkamhi, and R. S. Pungaliya. 2016. Reputation and loan contract terms:

the role of principal customers. Review of Finance 20 (2):501-533.

Cen, L., E. L. Maydew, L. Zhang, and L. Zuo. 2017. Customer-supplier relationships and

corporate tax avoidance. Journal of Financial Economics 123 (2):377-394.

Chen, K., and T. Xiao. 2015. Outsourcing strategy and production disruption of supply chain

with demand and capacity allocation uncertainties. International Journal of Production

Economics 170:243-257.

Chopra, S., and M. S. Sodhi. 2004. Managing risk to avoid supply-chain breakdown. Mit Sloan

Management Review 46 (1):53-61.

Christopher, M., and H. Peck. 2004. Building the resilient supply chain. International Journal of

Logistics Management 15 (2):1-13.

Cohen, D. A., B. Li, N. Li, and Y. Lou. 2016. Major government customers and loan contract

terms. Working paper, University of Texas at Dallas, and HEC Paris.

D'Aveni, R. A., and D. J. Ravenscraft. 1994. Economies of integration versus bureaucracy costs:

does vertical integration improve performance? Academy of Management Journal 37

(5):1167-1206.

32

Dechow, P. M., and I. D. Dichev. 2002. The quality of accruals and earnings: the role of accrual

estimation errors. The Accounting Review 77 (Supplement):35-59.

Dhaliwal, D., J. S. Judd, M. Serfling, and S. Shaikh. 2016. Customer concentration risk and the

cost of equity capital. Journal of Accounting and Economics 61 (1):23-48.

Dou, Y., O. Hope, and W. B. Thomas. 2013. Relationship-specificity, contract enforceability,

and income smoothing. The Accounting Review 88 (5):1629-1656.

Drake, A. R., and S. F. Haka. 2008. Does abc information exacerbate hold‐up problems in

buyer‐supplier negotiations? The Accounting Review 83 (1):29-60.

Fahimnia, B., C. S. Tang, H. Davarzani, and J. Sarkis. 2015. Quantitative models for managing

supply chain risks: a review. European Journal of Operational Research 247 (1):1-15.

Falato, A., and N. Liang. 2016. Do creditor rights increase employment risk? Evidence from loan

covenants. The Journal of Finance 71 (6):2545-2590.

Francis, J., R. LaFond, P. Olsson, and K. Schipper. 2005. The market pricing of accruals quality.

Journal of Accounting and Economics 39 (2):295-327.

Garavelli, A. C. 2003. Flexibility configurations for the supply chain management. International

Journal of Production Economics 85 (2):141-153.

Gilley, K. M., and A. Rasheed. 2000. Making more by doing less: an analysis of outsourcing and

its effects on firm performance. Journal of Management 26 (4):763-790.

Guan, Y., M. H. F. Wong, and Y. Zhang. 2015. Analyst following along the supply chain.

Review of Accounting Studies 20 (1):210-241.

Guertler, B., and S. Spinler. 2015. When does operational risk cause supply chain enterprises to

tip? A simulation of intra-organizational dynamics. Omega 57:54-69.

Hann, R. N., M. Ogneva, and O. Ozbas. 2013. Corporate diversification and the cost of capital.

The Journal of Finance 68 (5):1961-1999.

Heckmann, I., T. Comes, and S. Nickel. 2015. A critical review on supply chain risk - definition,

measure and modeling. Omega 52:119-132.

Hirsch, B. T. 1992. Firm investment behavior and collective bargaining strategy. Industrial

Relations: A Journal of Economy and Society 31 (1):95-121.

Holzhacker, M., R. Krishnan, and M. D. Mahlendorf. 2015. The impact of changes in regulation

on cost behavior. Contemporary Accounting Research 32 (2):534-566.

Hora, M., and R. D. Klassen. 2013. Learning from others' misfortune: factors influencing

knowledge acquisition to reduce operational risk. Journal of Operations Management 31

(1-2):52-61.

Huang, H. H., G. J. Lobo, C. Wang, and H. Xie. 2016. Customer concentration and corporate tax

avoidance. Journal of Banking and Finance 72:184-200.

Hui, K. W., S. Klasa, and P. E. Yeung. 2012. Corporate suppliers and customers and accounting

conservatism. Journal of Accounting and Economics 52 (1):115-135.

Irvine, P. J., S. S. Park, and Ç. Yıldızhan. 2016. Customer-base concentration, profitability, and

the relationship life cycle. The Accounting Review 91 (3):883-906.

Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: managerial behavior, agency costs

and ownership structure. Journal of Financial Economics 3 (4):305-360.

Jung, B., W. J. Lee, and D. P. Weber. 2014. Financial reporting quality and labor investment

efficiency. Contemporary Accounting Research 31 (4):1047-1076.

Kalwani, M. U., and N. Narayandas. 1995. Long-term manufacturer-supplier relationships: do

they pay off for supplier firms? Journal of Marketing 59 (1):1-16.

Kim, J., B. Y. Song, and Y. Zhang. 2015. Earnings performance of major customers and bank

33

loan contracting with suppliers. Journal of Banking and Finance 59:384-398.

Kim, S. W. 2009. An investigation on the direct and indirect effect of supply chain integration on

firm performance. International Journal of Production Economics 119 (2):328-346.

Kinney, M. R., and W. F. Wempe. 2002. Further evidence on the extent and origins of jit's

profitability effects. The Accounting Review 77 (1):203-225.

Kumar, N. 1996. The power of trust in manufacturer-retailer relationships. Harvard Business

Review 74 (6):15-19.

Larcker, D. F., and T. O. Rusticus. 2010. On the use of instrumental variables in accounting

research. Journal of Accounting and Economics 49 (3):186-205.

Lee, H. L., V. Padmanabhan, and S. Whang. 1997. Information distortion in a supply chain: the

bullwhip effect. Management Science 43 (4):546-558.

Lee, H., and S. Whang. 2002. The impact of the secondary market on the supply chain.

Management Science 48 (6):719-731.

Lei, D., and M. A. Hitt. 1995. Strategic restructuring and outsourcing: the effect of mergers and

acquisitions and LBOs on building firm skills and capabilities. Journal of Management 21

(5):835-859.

Lewis, M. A. 2003. Cause, consequence and control: towards a theoretical and practical model of

operational risk. Journal of Operations Management 21 (2):205-224.

Li, F. 2011. Earnings quality based on corporate investment decisions. Journal of Accounting

Research 49 (3):721-752.

McNichols, M. F. 2002. Discussion of the quality of accruals and earnings: the role of accrual

estimation errors. The Accounting Review 77 (s-1):61-69.

Mitra, S., A. Karathanasopoulos, G. Sermpinis, C. Dunis, and J. Hood. 2015. Operational risk:

emerging markets, sectors and measurement. European Journal of Operational Research

241 (1):122-132.

Montgomery, E. 1989. Employment and unemployment effects of unions. Journal of Labor

Economics 7 (2):170-190.

Nunn, N. 2007. Relationship-specificity, incomplete contracts, and the pattern of trade. The

Quarterly Journal of Economics 122 (2):569-600.

Patatoukas, P. N. 2012. Customer-base concentration: implications for firm performance and

capital markets. The Accounting Review 87 (2):363-392.

Pinnuck, M., and A. M. Lillis. 2007. Profits versus losses: does reporting an accounting loss act

as a heuristic trigger to exercise the abandonment option and divest employees? The

Accounting Review 82 (4):1031-1053.

Prahalad, C. K., and G. Hamel. 1990. The core competence of the corporation. Harvard Business

Review 68 (3):275-292.

Raman, K., and H. Shahrur. 2008. Relationship-specific investments and earnings management:

evidence on corporate suppliers and customers. The Accounting Review 83 (4):1041-1081.

Ramanna, K., and S. Roychowdhury. 2010. Elections and discretionary accruals: evidence from

2004. Journal of Accounting Research 48 (2):445-475.

Reichelstein, S. 1992. Constructing incentive schemes for government contracts: an application

of agency theory. The Accounting Review 67 (4):712-731.

Roberts, M. R., and T. M. Whited. 2013. Chapter 7 - endogeneity in empirical corporate finance1.

In Handbook of the Economics of Finance, edited by G. M. Constantinides, M. Harris and R.

M. Stulz: Elsevier, 493-572.

Seshadri, S., and M. Subrahmanyam. 2005. Introduction to the special issue on "risk

34

management in operations". Production and Operations Management 14 (1):1-4.

Thun, J., and D. Hoenig. 2011. An empirical analysis of supply chain risk management in the

German automotive industry. International Journal of Production Economics 131

(1):242-249.

Titman, S., and R. Wessels. 1988. The determinants of capital structure choice. The Journal of

Finance 43 (1):1-19.

van der Vaart, T., D. Pieter Van Donk, C. Gimenez, and V. Sierra. 2012. Modelling the

integration-performance relationship: collaborative practices, enablers and contextual

factors. International Journal of Operations & Production Management 32 (9):1043-1074.

Van Long, N., and H. Siebert. 1983. Lay-off restraints and the demand for labor. Journal of

Institutional and Theoretical Economics 139 (4):612-624.

Wiengarten, F., P. Humphreys, C. Gimenez, and R. McIvor. 2016. Risk, risk management

practices, and the success of supply chain integration. International Journal of Production

Economics 171:361-370.

35

Appendix A

Variable definitions

Variable Definition

Variables in Model (1)

NET_HIREt Percentage change in the number of employees (EMP) from years t - 1 to t.

SALES_GROWTHt-1 Percentage change in sales revenue (SALE) from years t - 2 to t - 1.

SALES_GROWTHt Percentage change in sales revenue (SALE) from years t - 1 to t.

ROAt Net income (NI) scaled by beginning-of-year total assets (AT).

ΔROAt-1 Change in ROA from years t - 2 to t - 1.

ΔROAt Change in ROA from years t - 1 to t.

RETURNt Annual stock return during fiscal year t.

SIZE_Rt-1 Natural logarithm of the market value of equity in year t - 1, ranked into percentiles.

QUICKt-1 Quick ratio in year t - 1, defined as the ratio of cash and short-term investments plus

receivables to current liabilities ((CHE + RECT)/LCT).

ΔQUICKt-1 Percentage change in the quick ratio from year t - 2 to t-1.

ΔQUICKt Percentage change in the quick ratio from year t - 1 to t.

LEVt-1 Leverage ratio in year t - 1, measured as the sum of debt in current liabilities and total

long-term debt divided by total assets ((DLC + DLTT)/AT).

LOSSBIN1t-1 Indicator variable equals one if -0.005 ≤ ROAt-2 < 0.000 and zero otherwise.

LOSSBIN2t-1 Indicator variable equals one if -0.010 ≤ ROAt-2 < -0.005 and zero otherwise.

LOSSBIN3t-1 Indicator variable equals one if -0.015 ≤ ROAt-2 < -0.010 and zero otherwise.

LOSSBIN4t-1 Indicator variable equals one if -0.020 ≤ ROAt-2 < -0.015 and zero otherwise.

LOSSBIN5t-1 Indicator variable equals one if -0.025 ≤ ROAt-2 < -0.020 and zero otherwise.

Variables in Model (2)

ABS_AB_NET_HIREt Absolute value of abnormal net hiring, defined as the absolute magnitude of the

residual from regression model (1).

CC_SALESt-1 Fraction of a supplier’s total sales to all customers that account for at least 10% of total

sales.

CC_HHIt-1 Customer sales-based Herfindahl–Hirschman index.

AQt-1 Accounting quality measure based on the Dechow and Dichev (2002) model as

modified by McNichols (2002) and Francis et al. (2005). We estimate the model

cross-sectionally by industry–year and collect the residuals. Then, we compute the

standard deviation of the residuals over the years t - 5 to t - 1. Finally, we multiply that

standard deviation by one (so that it increases with accounting quality) and rank the

resulting measure into deciles by year.

MTBt-1 Market-to-book ratio (CSHO*PRCC_F/SEQ) in year t - 1.

SIZEt-1 Natural logarithm of the market value (CSHO*PRCC_F) at the end of year t - 1.

QUICKt-1 Quick ratio ((CHE + RECT)/LCT) at the end of year t - 1.

LEVt-1 The sum of debt in current liabilities and total long-term debt (DLC + DLTT) at the end

of year t - 1, divided by total assets (AT) at the end of year t - 1.

DIVDUMt-1 Indicator variable equals one if the firm pays dividends (DVPSP_F) in year t - 1.

36

STD_CFOt-1 Standard deviation of cash flows from operations (OANCF) from years t - 5 to t - 1.

STD_SALESt-1 Standard deviation of sales (SALE) from years t - 5 to t - 1.

TANGIBLEt-1 Property, plant, and equipment (PPENT) at the end of year t - 1, divided by total assets

(AT) in year t - 1.

LOSSt-1 Indicator variable equals one if the firm had a negative ROA (NI/lag(AT)) in year t - 1.

INSTIt-1 Institutional shareholdings at the end of year t - 1.

STD_NET_HIREt-1 Standard deviation of a firm’s change in the number of employees (EMP) from year

t - 5 to t - 1.

LABOR_INTENSITYt-1 Labor intensity, measured as the number of employees (EMP) divided by total assets

(AT) at the end of year t - 1.

UNIONt-1 Industry-level rate of labor unionization in year t - 1.

ABS_AB_INVEST_OTHERt Absolute value of abnormal other (non-labor) investments, defined as the absolute

magnitude of the residual from the model INVEST_OTHERt = b0 +

b1SALES_GROWTHt-1 + ɛt, where INVEST_OTHER is the sum of capital expenditures

(CAPX), acquisition expenditures (AQC), and research and development expenditures

(XRD), less cash receipts from the sale of property, plant, and equipment (SPPE), all

scaled by lagged total assets (AT).

37

Appendix B

Model of expected net hiring

Dep. Var. = NET_HIREt

SALES_GROWTHt 0.449***

(71.21)

SALES_GROWTHt-1 0.019***

(5.04)

ΔROAt -0.250***

(-20.75)

ΔROAt-1 0.042***

(3.74)

ROAt 0.177***

(17.19)

RETURNt 0.037***

(18.57)

SIZE_Rt-1 -0.010***

(-3.17)

QUICKt-1 0.008***

(8.47)

ΔQUICKt-1 0.025***

(10.72)

ΔQUICKt -0.022***

(-8.83)

LEVt-1 -0.048***

(-8.10)

LOSSBIN1t-1 -0.028***

(-4.10)

LOSSBIN2t-1 -0.025***

(-3.66)

LOSSBIN3t-1 -0.029***

(-4.30)

LOSSBIN4t-1 -0.017**

(-2.24)

LOSSBIN5t-1 -0.027***

(-3.39)

Constant 0.008**

(2.48)

Observations 81,941

Adjusted R-squared 0.310

Year Fixed Effects YES

Industry Fixed Effects YES

This table reports the coefficients of estimating normal net hire. All the variables are defined in Appendix

A.

38

TABLE 1

Descriptive statistics

Variable N Mean Std. Dev. Median 25th 75th

ABS_AB_NET_HIREt 13690 0.117 0.144 0.071 0.032 0.145

CC_SALESt-1 13690 0.266 0.257 0.210 0.000 0.430

CC_HHIt-1 13690 0.080 0.121 0.034 0.000 0.104

AQt-1 13690 5.197 2.832 5.000 3.000 8.000

MTBt-1 13690 2.662 2.604 1.925 1.183 3.164

SIZEt-1 13690 5.906 2.382 5.847 4.037 7.684

QUICKt-1 13690 1.584 1.268 1.220 0.845 1.832

LEVt-1 13690 0.218 0.155 0.203 0.091 0.318

DIVDUMt-1 13690 0.383 0.486 0.000 0.000 1.000

STD_CFOt-1 13690 0.065 0.052 0.050 0.031 0.082

STD_SALESt-1 13690 0.258 0.230 0.189 0.107 0.329

TANGIBLEt-1 13690 0.257 0.190 0.207 0.114 0.355

LOSSt-1 13690 0.271 0.445 0.000 0.000 1.000

INSTIt-1 13690 0.436 0.341 0.444 0.079 0.748

STD_NET_HIREt-1 13690 0.229 0.364 0.138 0.077 0.246

LABOR_INTENSITYt-1 13690 0.008 0.009 0.005 0.003 0.009

UNIONt-1 13690 0.092 0.081 0.068 0.033 0.117

ABS_AB_INVEST_OTHERt 13690 0.168 0.409 0.088 0.041 0.167

This table presents descriptive statistics for the variables used in the main tests. The sample period is from

1992 to 2015. We exclude observations missing the financial data required to compute the variables used in

our regression analyses. To alleviate potential problems associated with extreme outliers, we winsorize all

continuous variables at the first and 99th percentiles. All the variables are defined in Appendix A.

39

TABLE 2

Pearson correlations

[1] [2] [3] [4] [5] [6] [7] [8] [9]

[1] ABS_AB_NET_HIREt 1

[2] CC_SALESt-1 0.161*** 1

[3] CC_HHIt-1 0.151*** 0.851*** 1

[4] AQt-1 -0.164*** -0.214*** -0.183*** 1

[5] MTBt-1 0.048*** 0.010 0.026*** -0.045*** 1

[6] SIZEt-1 -0.172*** -0.257*** -0.218*** 0.421*** 0.284*** 1

[7] QUICKt-1 0.099*** 0.183*** 0.175*** -0.054*** 0.053*** -0.051*** 1

[8] LEVt-1 -0.018** -0.051*** -0.057*** 0.117*** 0.034*** 0.009 -0.349*** 1

[9] DIVDUMt-1 -0.156*** -0.212*** -0.177*** 0.349*** 0.041*** 0.486*** -0.190*** 0.017** 1

[10] STD_CFOt-1 0.180*** 0.254*** 0.228*** -0.486*** 0.193*** -0.358*** 0.174*** -0.173*** -0.316***

[11] STD_SALESt-1 0.128*** 0.098*** 0.078*** -0.299*** 0.040*** -0.201*** -0.068*** -0.058*** -0.165***

[12] TANGIBLEt-1 0.012 0.108*** 0.102*** 0.199*** -0.077*** 0.003 -0.178*** 0.262*** 0.111***

[13] LOSSt-1 0.116*** 0.161*** 0.147*** -0.246*** -0.005 -0.314*** 0.056*** 0.068*** -0.288***

[14] INSTIt-1 -0.189*** -0.204*** -0.182*** 0.313*** 0.042*** 0.612*** -0.028*** 0.008 0.280***

[15] STD_NET_HIREt-1 0.179*** 0.107*** 0.102*** -0.199*** -0.004 -0.167*** -0.007 0.076*** -0.188***

[16] LABOR_INTENSITYt-1 0.029*** 0.059*** 0.049*** -0.112*** -0.067*** -0.305*** -0.097*** -0.028*** -0.096***

[17] UNIONt-1 -0.019** 0.014 -0.007 0.109*** -0.094*** 0.016* -0.144*** 0.138*** 0.207***

[18] ABS_AB_INVEST_OTHERt 0.073*** 0.044*** 0.041*** -0.037*** 0.053*** 0.004 0.062*** -0.007 -0.035***

[10] [11] [12] [13] [14] [15] [16] [17] [18]

[10] STD_CFOt-1 1

[11] STD_SALESt-1 0.300*** 1

[12] TANGIBLEt-1 -0.106*** -0.159*** 1

[13] LOSSt-1 0.217*** -0.028*** 0.019** 1

[14] INSTIt-1 -0.334*** -0.154*** -0.070*** -0.236*** 1

[15] STD_NET_HIREt-1 0.155*** 0.296*** -0.010 0.120*** -0.187*** 1

[16] LABOR_INTENSITYt-1 0.076*** 0.211*** 0.002 0.011 -0.214*** 0.093*** 1

[17] UNIONt-1 -0.105*** 0.005 0.253*** -0.079*** -0.004 -0.038*** 0.007 1

[18] ABS_AB_INVEST_OTHERt 0.048*** 0.025*** -0.032*** 0.023*** -0.034*** 0.031*** -0.037*** -0.099*** 1

This table presents the Pearson correlation coefficients for the variables used in the main tests. The sample period is from 1992 to 2015. We exclude firms missing

the financial data required to compute the variables used in our regression analyses. To alleviate potential problems associated with extreme outliers, we winsorize

all continuous variables at the first and 99th percentiles. All the variables are defined in Appendix A. Here, *, **, and *** indicate, respectively, the 10%, 5%, and

1% significance levels (two tailed).

40

TABLE 3

Effect of customer concentration on labor employment risk

Absolute abnormal net hiring

(ABS_AB_NET_HIREt)

Overinvestment in labor (positive

abnormal net hiring subgroup)

Underinvestment in labor (negative

abnormal net hiring subgroup)

(1) (2) (3) (4) (5) (6)

CC_SALESt-1 0.041*** 0.048*** 0.033***

(6.713) (4.285) (5.986)

CC_HHIt-1 0.084*** 0.079*** 0.082***

(5.770) (3.115) (5.666)

AQt-1 -0.002*** -0.002*** -0.002** -0.002** -0.002*** -0.002***

(-4.042) (-4.139) (-2.070) (-2.117) (-4.346) (-4.416)

MTBt-1 0.003*** 0.003*** 0.004*** 0.004*** 0.001 0.001

(3.681) (3.644) (3.459) (3.484) (1.325) (1.229)

SIZEt-1 -0.002** -0.002*** -0.003 -0.003 -0.002** -0.002**

(-2.502) (-2.601) (-1.531) (-1.637) (-2.216) (-2.206)

QUICKt-1 0.008*** 0.008*** 0.006** 0.007*** 0.009*** 0.009***

(5.757) (5.905) (2.549) (2.663) (6.298) (6.370)

LEVt-1 0.002 0.003 -0.014 -0.014 0.012 0.013

(0.236) (0.323) (-0.747) (-0.746) (1.287) (1.415)

DIVDUMt-1 -0.007** -0.008** -0.012* -0.013** -0.003 -0.003

(-2.223) (-2.448) (-1.905) (-2.050) (-0.968) (-1.134)

STD_CFOt-1 0.080** 0.082** 0.082 0.089 0.069* 0.066*

(2.102) (2.141) (1.132) (1.235) (1.921) (1.836)

STD_SALESt-1 0.023** 0.023*** 0.046*** 0.047*** -0.005 -0.005

(2.551) (2.624) (2.891) (2.982) (-0.660) (-0.650)

TANGIBLEt-1 -0.018* -0.017 -0.016 -0.013 -0.022** -0.023**

(-1.649) (-1.628) (-0.809) (-0.664) (-2.211) (-2.370)

LOSSt-1 0.011*** 0.011*** -0.011* -0.011* 0.025*** 0.024***

(3.320) (3.295) (-1.825) (-1.795) (7.745) (7.661)

INSTIt-1 -0.030*** -0.030*** -0.038*** -0.039*** -0.026*** -0.026***

(-5.883) (-5.819) (-3.868) (-3.877) (-5.688) (-5.613)

41

STD_NET_HIREt-1 0.038*** 0.038*** 0.040*** 0.040*** 0.038*** 0.037***

(5.116) (5.092) (3.420) (3.374) (4.555) (4.564)

LABOR_INTENSITYt-1 -0.518** -0.496** -1.374*** -1.315*** 0.206 0.208

(-2.428) (-2.332) (-3.249) (-3.142) (1.210) (1.224)

UNIONt-1 -0.013 -0.012 -0.021 -0.021 -0.019 -0.017

(-0.382) (-0.358) (-0.321) (-0.319) (-0.687) (-0.605)

ABS_AB_INVEST_OTHERt 0.018*** 0.018*** 0.039*** 0.039*** -0.002 -0.002

(4.285) (4.315) (4.042) (4.041) (-0.864) (-0.825)

Constant 0.126*** 0.133*** 0.178*** 0.187*** 0.100*** 0.105***

(4.916) (5.256) (3.164) (3.347) (3.968) (4.228)

Industry fixed effects YES YES YES YES YES YES

Year fixed effects YES YES YES YES YES YES

Observations 13,690 13,690 5,403 5,403 8,287 8,287

Adjusted R-squared 0.110 0.110 0.106 0.104 0.154 0.156

This table reports the main results of the impact of customer concentration on abnormal net hiring based on regression model (2). The sample period is from

1992 to 2015. Columns (1) and (2) show the full-sample results. Columns (3) and (4) present the results for the overinvestment subsample, where actual net

hiring is above expected levels. Columns (5) and (6) demonstrate the results of the underinvestment subsample, where actual net hiring is under expected

levels. In both subsamples, the dependent variable is ABS_AB_NET_HIRE. All the variables are defined in Appendix A. Here, *, **, and *** indicate,

respectively, 10%, 5%, and 1% significance (two tailed). The t-values in parentheses are based on standard errors clustered by firm.

42

TABLE 4

Alternative measure of labor employment risk

Absolute change in employment

(ABS_EMP_CHGt)

Positive change in employment

(subsample where EMP_CHGt > 0)

Negative change in employment

(subsample where EMP_CHGt < 0)

(1) (2) (3) (4) (5) (6)

CC_SALESt-1 0.032***

0.023***

0.037***

(4.229)

(3.342)

(2.780)

CC_HHIt-1

0.061***

0.044***

0.071**

(3.407)

(2.800)

(2.160)

AQt-1 -0.002*** -0.002***

-0.001** -0.001**

-0.003** -0.003**

(-2.930) (-3.003)

(-2.068) (-2.085)

(-2.374) (-2.448)

MTBt-1 0.003*** 0.003***

-0.001 -0.001

0.005*** 0.005***

(3.178) (3.156)

(-1.191) (-1.215)

(3.123) (3.100)

SIZEt-1 -0.002 -0.002

-0.003** -0.003***

-0.002 -0.002

(-1.422) (-1.506)

(-2.526) (-2.614)

(-0.780) (-0.792)

QUICKt-1 0.007*** 0.007***

0.001 0.002

0.009*** 0.009***

(4.055) (4.188)

(0.801) (0.850)

(3.293) (3.379)

LEVt-1 -0.013 -0.013

0.005 0.006

-0.019 -0.018

(-1.059) (-1.011)

(0.431) (0.471)

(-0.841) (-0.811)

DIVDUMt-1 -0.016*** -0.017***

-0.002 -0.003

-0.019*** -0.020***

(-3.999) (-4.153)

(-0.612) (-0.720)

(-2.737) (-2.840)

STD_CFOt-1 0.035 0.037

0.046 0.047

0.048 0.051

(0.740) (0.783)

(1.121) (1.136)

(0.562) (0.603)

STD_SALESt-1 0.059*** 0.060***

0.018* 0.018*

0.062*** 0.063***

(5.379) -5.422

(1.778) (1.814)

(3.359) (3.386)

TANGIBLEt-1 -0.026** -0.026*

-0.043*** -0.043***

-0.015 -0.014

(-1.972) (-1.915)

(-3.528) (-3.507)

(-0.637) (-0.574)

LOSSt-1 0.017*** 0.017***

0.050*** 0.050***

-0.014* -0.014*

(4.115) (4.106)

(12.573) (12.541)

(-1.818) (-1.798)

INSTIt-1 -0.037*** -0.037***

-0.040*** -0.040***

-0.041*** -0.040***

(-5.813) (-5.777)

(-6.529) (-6.501)

(-3.452) (-3.435)

43

STD_NET_HIREt-1 0.038*** 0.038***

0.021*** 0.021***

0.065*** 0.065***

(4.354) (4.327)

(2.837) (2.832)

(4.150) (4.107)

LABOR_INTENSITYt-1 -0.610** -0.591**

0.663*** 0.672***

-1.734*** -1.701***

(-2.242) (-2.177)

(3.119) (3.168)

(-3.326) (-3.272)

UNIONt-1 -0.033 -0.032

-0.084** -0.084**

-0.009 -0.010

(-0.826) (-0.819)

(-2.361) (-2.334)

(-0.125) (-0.133)

ABS_AB_INVEST_OTHERt 0.027*** 0.027***

0.003 0.004

0.046*** 0.046***

(5.097) (5.111)

(1.009) (1.046)

(4.238) (4.232)

Constant 0.143*** 0.148***

0.146*** 0.151***

0.163** 0.170**

(4.457) (4.653)

(5.154) (5.271)

(2.416) (2.530)

Industry fixed effects 13,690 13,690

6,973 6,973

6,550 6,550

Year fixed effects YES YES

YES YES

YES YES

Observations YES YES

YES YES

YES YES

Adjusted R-squared 0.093 0.093 0.152 0.152 0.102 0.102

This table reports the main results of the impact of customer concentration on labor employment risk using an alternative proxy (ABS_EMP_CHG). We

measure this variable as the absolute value of the percentage change in the employment numbers from years t to t - 1, adjusted by the two-digit SIC industry

median value. The sample period is from 1992 to 2015. Columns (1) and (2) show the full-sample results. Columns (3) and (4) present the results of a positive

change in the employment numbers in the subsample where EMP_CHG > 0. Columns (5) and (6) present the results of a negative change in the employment

numbers in the subsample where EMP_CHG < 0. In both subsamples, the dependent variable is ABS_EMP_CHG. All the variables are defined in Appendix

A. Here, *, **, and *** indicate, respectively, 10%, 5%, and 1% significance (two tailed). The t-values, in parentheses, are based on standard errors clustered

by firm.

44

TABLE 5

Channel test: Outsourcing

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 -0.034

(-1.586)

CC_SALESt-1 × OUTSOURCINGt-1 0.011***

(3.186)

CC_HHIt-1

-0.059

(-1.254)

CC_HHIt-1 × OUTSOURCINGt-1

0.030***

(2.865)

OUTSOURCINGt-1 -0.005*** -0.004**

(-2.938) (-2.562)

AQt-1 -0.005*** -0.004***

(-3.529) (-3.486)

MTBt-1 0.002** 0.002*

(1.999) (1.888)

SIZEt-1 -0.000 0.000

(-0.118) (0.059)

QUICKt-1 0.005* 0.004*

(1.955) (1.699)

LEVt-1 0.010 0.012

(0.410) (0.500)

DIVDUMt-1 -0.010 -0.010

(-1.372) (-1.426)

STD_CFOt-1 0.073 0.059

(1.065) (0.879)

STD_SALESt-1 0.005 0.007

(0.311) (0.459)

TANGIBLEt-1 -0.054** -0.058**

(-2.282) (-2.444)

LOSSt-1 0.006 0.005

(0.867) (0.746)

INSTIt-1 -0.031*** -0.028**

(-2.701) (-2.443)

STD_NET_HIREt-1 0.023*** 0.022***

(3.300) (3.170)

LABOR_INTENSITYt-1 -0.087 -0.044

(-0.253) (-0.131)

UNIONt-1 -0.009 -0.017

(-0.124) (-0.225)

ABS_AB_INVEST_OTHERt 0.016** 0.016**

(2.383) (2.490)

Constant 0.199*** 0.187***

(3.285) (3.028)

Industry fixed effects YES YES

45

Year fixed effects YES YES

Observations 2,576 2,576

Adjusted R-squared 0.098 0.107

This table reports the channel effect of customers’ outsourcing activities on the relation between suppliers’

customer concentration and labor investment efficiency. In a similar vein as that of Ramanna et al. (2010), we

measure outsourcing activity (OUTSOURCING) as the negative value of major customers’ weighted average

abnormal net hiring over sales for each supplier–year, ranked into deciles. A higher value of OUTSOURCING

indicates a higher level of outsourcing activities. All the other variables are defined in Appendix A. Here, *, **, and

*** indicate, respectively, 10%, 5%, and 1% significance (two tailed). The t-values in parentheses are based on

standard errors clustered by firm.

46

TABLE 6

Channel test: RSI

Dep. Var. = SGA_ELASTICITYt

(1) (2)

CC_SALES_Rt -0.356***

(-3.262)

CC_SALES_Rt × AB_NET_HIRE_Rt 0.434***

(3.386)

CC_HHI_Rt

-0.345***

(-3.153)

CC_HHI_Rt × AB_NET_HIRE_Rt

0.444***

(3.447)

MVEt 0.000** 0.000**

(2.055) (2.066)

AGEt 0.003 0.003

(1.484) (1.517)

GROWTHt 0.201*** 0.200***

(6.062) (6.053)

CONGLOt -0.023 -0.021

(-0.389) (-0.367)

FLEVt -0.004 -0.004

(-0.299) (-0.303)

Constant 2.009*** 1.995***

(3.400) (3.374)

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 26,372 26,372

Adjusted R-squared 0.003 0.003

This table investigates the channel effect of RSI. In detail, the results demonstrate the impact of abnormal net hiring

on the relation between customer concentration and SG&A elasticity. The variable CC_SALES_R is the decile rank

of CC_SALES, CC_HHI_R is the decile rank of CC_HHI, and AB_NET_HIRE_R is the decile rank of the raw value

of abnormal net hiring. Following Irvine et al. (2016), we calculate the elasticity of SG&A expenses with respect to

sales (SGA_ELASTICITY) as the change in the logarithmic value of SG&A expenses from years t to t - 1, divided by

the change in the logarithmic value of sales from years t to t - 1. Several control variables are included. The variable

MVE is the logarithm of the market value of equity, AGE is the logarithm of firm age, GROWTH is the annual sales

growth rate, CONGLO is a dummy variable that equals one if the firm reports more than one business segment, and

FLEV is calculated as the book value of assets divided by the book value of equity. All the other variables are

defined in Appendix A. Here, *, **, and *** indicate, respectively, 10%, 5%, and 1% significance (two tailed). The

t-values in parentheses are based on standard errors clustered by firm.

47

TABLE 7

Cross-sectional analysis

Panel A: Effect of supplier bargaining power (measured by customers’ switching costs)

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 0.079***

(5.820)

CC_SALESt-1 × SWITCH_COSTt-1 -0.009***

(-3.983)

CC_HHIt-1

0.148***

(5.071)

CC_HHIt-1 × SWITCH_COSTt-1

-0.018***

(-3.723)

SWITCH_COSTt-1 -0.004*** -0.005***

(-2.817) (-3.580)

Control variables YES YES

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 13,613 13,613

Adjusted R-squared 0.114 0.114

Panel B: Effect of supplier revenue diversification

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 0.026**

(2.334)

CC_SALESt-1 × BUS_SEGt-1 0.024*

(1.881)

CC_HHIt-1 0.036

(1.424)

CC_HHIt-1 × BUS_SEGt-1 0.063**

(2.103)

BUS_SEGt-1 -0.013*** -0.011***

(-3.327) (-3.083)

Control variables YES YES

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 12,801 12,801

Adjusted R-squared 0.112 0.111

48

Panel C: Effect of customer-specific investment

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 -0.044

(-1.353)

CC_SALESt-1 × CSPECIFICITYt-1 0.014***

(2.604)

CC_HHIt-1 -0.110

(-1.639)

CC_HHIt-1 × CSPECIFICITYt-1 0.035***

(3.478)

CSPECIFICITYt-1 -0.004* -0.003*

(-1.721) (-1.791)

Control variables YES YES

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 1,750 1,750

Adjusted R-squared 0.106 0.115

Panel D: Effect of supplier operational uncertainty

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 0.001

(0.072)

CC_SALESt-1 × STD_EARNt-1 0.006***

(2.850)

CC_HHIt-1

0.017

(0.464)

CC_HHIt-1 × STD_EARNt-1

0.009*

(1.647)

STD_EARNt-1 0.000 0.001*

(0.497) (1.840)

Control variables YES YES

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 13,141 13,141

Adjusted R-squared 0.110 0.110

49

Panel E: Effect of the supplier’s information environment

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

CC_SALESt-1 0.070***

(5.490)

CC_SALESt-1 × AQt-1 -0.006***

(-3.039)

CC_HHIt-1 0.129***

(4.600)

CC_HHIt-1 × AQt-1 -0.011**

(-2.255)

AQt-1 -0.001 -0.002**

(-1.017) (-2.464)

Control variables YES YES

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 13,690 13,690

Adjusted R-squared 0.111 0.111

This table reports the cross-sectional results of the impact of customer concentration on labor employment risk.

Panel A presents the effects of customer’s switching costs, SWITCH_COST, measured as the supplier’s sales

divided by two-digit SIC industry sales, ranked into deciles by year. Panel B presents the effect of supplier

diversification with the proxy of BUS_SEG, an indicator variable equal to one if the supplier firm has only one

business segment and zero otherwise. Panel C reports the effect of investor-specific investment as proxied by the

customer’s input specificity (CSPECIFICITY). Following Nunn (2007), we measure CSPECIFICITY using

customers’ weighted average level of non-homogeneous inputs over sales. Panel D presents the effect of suppliers

operational uncertainty as measured by sales volatility, STD_SALES, where STD_SALES is the standard deviation

of sales from years t - 5 to t - 1, ranked into deciles by year. Panel E presents the effect of the supplier’s information

environment as proxied by AQ, the accounting quality measure, ranked into deciles by year. All the other variables

are defined in Appendix A. Here, *, **, and *** indicate, respectively, 10%, 5%, and 1% significance (two-tailed).

The t-values in parentheses are based on standard errors clustered by firm.

50

TABLE 8

Instrumental variables regressions

Panel A: First-stage results

Industry concentration measures as IVs

Dep. Var. = CC_SALESt-1 CC_HHIt-1

(1) (2)

IND_CC_SALESt-2 0.675***

(66.975)

IND_CC_SALESt-3 0.166***

(16.490)

IND_CC_HHIt-2 1.096***

(48.824)

IND_CC_HHIt-3 0.469***

(21.068)

AQt-1 0.000 -0.001*

(0.068) (-1.889)

MTBt-1 -0.000 -0.001***

(-0.368) (-3.287)

SIZEt-1 0.001** 0.002***

(2.153) (3.829)

QUICKt-1 0.002*** 0.002***

(3.210) (2.592)

LEVt-1 -0.008 -0.019***

(-1.478) (-2.754)

DIVDUMt-1 0.000 0.000

(0.041) (0.027)

STD_CFOt-1 0.051** 0.067***

(2.487) (2.677)

STD_SALESt-1 -0.001 0.002

(-0.360) (0.377)

TANGIBLEt-1 0.030*** 0.041***

(6.650) (7.341)

LOSSt-1 0.002 0.005**

(1.075) (2.164)

INSTIt-1 -0.010*** -0.021***

(-3.499) (-5.990)

STD_NET_HIREt-1 0.006** 0.011***

(2.139) (3.508)

LABOR_INTENSITYt-1 -0.130 -0.331***

(-1.409) (-2.923)

UNIONt-1 0.023** 0.102***

(2.309) (8.300)

ABS_AB_INVEST_OTHERt 0.007*** 0.008***

(3.518) (3.339)

51

Constant 0.025*** 0.116***

(5.331) (21.018)

Observations 9,412 9,412

Adjusted R-squared 0.683 0.522

Test of endogeneity, weak instruments, and overidentification

Wu–Hausman F-statistic 3.47364 (p = 0.0624) 1.89399 (p = 0.1688)

F-Statistic 5820.6 (p < 0.001) 2496.24 (p < 0.001)

Partial R-squared 0.6525 0.5327

Hansen’s J-test (Pr > chi2) 0.3601 0.8469

Panel B: Second-stage results

Dep. Var. = ABS_AB_NET_HIREt

(1) (2)

Predicted CC_SALESt-1 0.052***

(3.858)

Predicted CC_HHIt-1 0.116***

(3.492)

AQt-1 -0.001** -0.001**

(-2.101) (-2.179)

MTBt-1 0.003*** 0.003***

(3.789) (3.681)

SIZEt-1 -0.004*** -0.004***

(-4.407) (-4.373)

QUICKt-1 0.009*** 0.009***

(5.925) (5.822)

LEVt-1 0.010 0.010

(0.964) (0.914)

DIVDUMt-1 -0.007** -0.007**

(-2.174) (-2.172)

STD_CFOt-1 0.113** 0.112**

(2.367) (2.344)

STD_SALESt-1 0.044*** 0.044***

(4.926) (4.963)

TANGIBLEt-1 0.031*** 0.030***

(3.383) (3.291)

LOSSt-1 0.010*** 0.010***

(2.741) (2.802)

INSTIt-1 -0.029*** -0.029***

(-5.362) (-5.422)

STD_NET_HIREt-1 0.048*** 0.048***

(6.467) (6.456)

LABOR_INTENSITYt-1 -0.364** -0.368**

(-2.055) (-2.080)

UNIONt-1 0.018 0.025

52

(0.960) (1.299)

ABS_AB_INVEST_OTHERt 0.023*** 0.023***

(4.227) (4.160)

Constant 0.081*** 0.086***

(8.904) (9.618)

Observations 9,412 9,412

Adjusted R-squared 0.090 0.090

This table reports the main results based on IV regressions. Panel A presents the first-stage regression

results. In columns (1) and (2), the dependent variables are industry-level customer concentration

measures. Specifically, IND_CC_SALES and IND_CC_HHI are the average values of CC_SALES and

CC_HHI, respectively, for each supplier’s three-digit SIC industry and year (except that supplier).

Panel B shows the second-stage results. The predicted CC_SALES and CC_HHI values are from the

first-stage regressions. All the other variables are defined in Appendix A. Here, *, **, and *** indicate,

respectively, 10%, 5%, and 1% significance (two tailed). The t-values in parentheses are based on

standard errors clustered by firm.

53

TABLE 9

Effect on employment risk on firm profitability

Dep. Var. = ROAt

(1) (2)

ABS_AB_NET_HIREt -0.098*** -0.096***

(-5.200) (-5.071)

CC_SALES_Rt -0.001

(-0.239)

ABS_AB_NET_HIREt × CC_SALES_Rt -0.075**

(-2.434)

CC_HHI_Rt

-0.000

(-0.045)

ABS_AB_NET_HIREt × CC_HHI_Rt

-0.078**

(-2.526)

MVt 0.021*** 0.021***

(22.568) (22.591)

AGEt 0.027*** 0.027***

(11.466) (11.512)

SGt 0.122*** 0.122***

(18.540) (18.532)

SGVt -0.076*** -0.076***

(-11.204) (-11.195)

CONGLOt 0.002 0.002

(0.583) (0.608)

LEVt -0.071*** -0.071***

(-7.362) (-7.361)

GMt 0.120*** 0.120***

(16.827) (16.836)

ACCLOSSt -0.002 -0.002

(-0.625) (-0.633)

CAPt 0.040*** 0.040***

(4.111) (4.107)

Constant -0.128*** -0.129***

(-8.391) (-8.502)

Industry fixed effects YES YES

Year fixed effects YES YES

Observations 27,812 27,812

Adjusted R-squared 0.306 0.306

This table reports the results of regressions of firm profitability on the interaction term between

employment efficiency and customer concentration. The dependent variable, ROA, is measured as

operating income divided by total assets, CC_SALES_R is the decile rank of CC_SALES, and

CC_HHI_R is the decile rank of CC_HHI. The control variables are included following Ak and

54

Patatoukas (2016), including the logarithm of the market value of equity (MV), the logarithm of firm

age (AGE), the sales growth rate (SG), sales growth volatility in the preceding three years, the

leverage ratio (LEV), an indicator variable that equals one if, at the beginning of the year, the firm

reported a book value of equity that was either negative or below market value (ACCLOSS); and net

PP&E divided by total assets (CAP). All the other variables are defined in Appendix A. Here, *, **,

and *** indicate, respectively, 10%, 5%, and 1% significance (two tailed). The t-values in parentheses

are based on standard errors clustered by firm.