firm complexity: the ‘dark side’ of geographic diversification · 2018-01-22 · 1 1....

Firm Complexity: The ‘Dark Side’ of Geographic Diversification

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Anzhela Knyazeva2

Securities and Exchange Commission

Diana Knyazeva

Securities and Exchange Commission

This version: October 1, 2017

Abstract

In this paper we analyze firm geographic complexity and its implications for credit risk using a unique new dataset with granular geographic segment information and credit quality scores. After accounting for determinants of geographic diversification, we find that geographically complex firms are characterized by significantly lower credit quality than their focused peers. Overall, greater geographic complexity increases credit risk, consistent with geographically disperse firms facing higher information and monitoring costs that may exacerbate information asymmetries and intra-firm capital allocation inefficiencies. The evidence is inconsistent with geographic diversification decreasing credit risk through diversification of cash flows. The identified effects are economically important for potential lenders. The results hold for firms of varying size and cannot be explained by business diversification or other firm, industry, and local area factors and firm fixed effects. Keywords: firm complexity, geographic diversification, information frictions, location, credit quality JEL: G30, G32, G34

1 The authors are grateful to the Institute for Exceptional Growth Companies (IEGC) for granting access to the National Establishment Time Series database. IEGC is a project of the University of Wisconsin Extension Division of Entrepreneurship and Economic Development (DEED). The authors acknowledge the support of Simon School of Business at the University of Rochester and helpful comments from Ariell Reshef and participants at the NYU Economics Alumni Conference. The Securities and Exchange Commission disclaims responsibility for any private publication or statement of any SEC employee or Commissioner. The article expresses the authors’ views and does not necessarily reflect those of the Commission, the Commissioners, or other members of the staff.

2 Corresponding author. Anzhela Knyazeva, Securities and Exchange Commission, 100 F Street NE, Washington, DC 20549. E-mail: [email protected].

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1. Introduction

We argue that geographic complexity is a critical dimension of a firm’s organizational

structure and by consequence, credit quality. A large body of existing work has examined the

impact of business diversification in multi-segment firms, arriving at mixed conclusions. A

growing literature has highlighted the role of local factors and information frictions associated

with distance for firm behavior and soft information flows in capital markets. Studies

demonstrate the significance of geographic effects for financing and other corporate decisions, as

well as for explaining the behavior of investors and other market participants. Although frictions

associated with geographic structure of firms appear to be an important factor in a firm’s

information environment, the issue of intra-firm geographic complexity has received scarce

attention in prior work, largely due to significant limitations on the availability of data about the

locations of divisions of US firms.

In this study, we focus on this crucial yet understudied dimension of firm organizational

structure and examine the implications of within-firm geographic diversification for credit

quality. Critically, we use a novel dataset which incorporates privately held firms and small

businesses in addition to larger firms and allows us to draw broader inference and offer evidence

beyond existing work on large firms. Evidence obtained from a sample that incorporates small

and private companies as well as larger firms is more informative about information frictions that

may be offset through numerous sources of external scrutiny in samples dominated by large

publicly listed firms (institutional blockholders, rating agencies, auditors, analysts etc.). Our

empirical analysis focuses on credit quality as the outcome variable, which is particularly well-

suited for contrasting the hypothesis of information frictions and the alternative hypothesis of

diversification-related risk reduction, as described below.

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Our main hypotheses are discussed below. Because distance can limit soft information

flows between divisions and headquarters, we expect intra-firm geographic complexity to

increase a firm’s aggregate information costs and the overall severity of intra-firm information

asymmetry problems. More geographic complex firms are likely to be both more difficult to

evaluate for investors seeking to understand company operations (as opposed to merely one of

the divisions), as well as for executives located at headquarters that are tasked with monitoring

all of the firm’s geographically disperse divisions (higher monitoring costs may in turn result in

less efficient internal capital allocation and suboptimal investment decisions). To the extent that

geographic complexity increases the external information costs as well as the intra-firm

monitoring costs, it is likely to decrease a firm’s credit quality, all else equal. Empirically, this

hypothesis predicts a negative relation between geographic complexity and credit quality.

The alternative hypothesis is that geographic diversification acts as a natural hedge for

local business risk, allowing the company to diversify internal cash flow risk across markets in

various regions and to benefit from a more efficient internal capital market, which reduces the

impact of external financing frictions in the event of a negative shock to one geographic region.

Empirically, this suggests that we should observe a positive relation between geographic

complexity and credit quality.

Lastly, it is possible that within-firm geographic complexity is irrelevant for a firm’s

information environment and credit quality. Increased sophistication and adoption of information

technology and internet communications enables fast and low-cost dissemination of information,

both facilitating observability of division operations by management at headquarters and the

observability of regional investment decisions and outcomes by the firm’s lenders. The adoption

of standardized credit scoring and the lower cost of implementing computationally intensive

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internal risk analytics facilitate delegated bank monitoring of borrowers, including complex and

geographically versified firms. Greater access to low-cost air travel can further promote

information gathering and monitoring, where appropriate. If these and related trends have

resulted in decreased relevance of geographic factors, including geographic complexity, we

would see a lack of significance of geographic complexity on credit quality. That said, in line

with other work on geographic frictions in capital markets, we believe there likely remain local

advantages with respect to the gathering and transmission of soft information, which cannot be

readily conveyed via technology, unlike hard information on financial conditions and

performance metrics. Ultimately, the relevance of within-firm geographic complexity is an

empirical question.

This paper relates to several strands of existing work. A large literature has analyzed

business diversification, focusing on whether firms with multiple business segments exhibit a

valuation discount, either because managers overinvest out of free cash flow and excessively

diversify at the expense of positive-NPV projects (e.g., Denis, Denis, and Sarin, 1997; Hoechle,

Schmid, Walter, and Yermack, 2012) or because excessive business diversification entails

inefficiencies in internal capital allocation that lead to cross-subsidization of underperforming

divisions (e.g., Rajan, Servaes, and Zingales, 2000). Various studies find a business

diversification discount (e.g., Berger and Ofek, 1995; Bertrand, Mehta and Mullainathan, 2002;

Lamont, 1997; Lamont and Polk, 2002; Laeven and Levine, 2007; Stowe and Xing, 2006). Some

other studies show that the discount decreases or becomes a premium after accounting for

endogeneity and measurement concerns, 1 consistent with business diversification mitigating

1 See Martin and Sayrak (2003) for a survey. For example, Campa and Kedia (2002) and Villalonga (2004a) find that the discount can disappear after accounting for endogeneity and selection. Graham, Lemmon, and Wolf (2002) also provide evidence of selection bias in conglomerate acquisitions. Custodio (2014) finds that the discount is attenuated after accounting for upward bias in q due to M&A accounting. Mansi and Reeb (2002) find that the discount is sensitive to the bias from using the book value of debt. Villalonga (2004b) finds a business diversification premium in a large establishment-level dataset which provides more

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external financing frictions (e.g., Aggarwal and Zhao, 1999; Matsusaka and Nanda, 2002; Klein

and Saidenberg, 2010) and/or offering tax advantages. While business diversification studies

uncover important evidence, geographic diversification is a distinct dimension of a firm’s

structure. In particular, the agency conflict argument that is commonly used to explain business

diversification discount need not generalize to geographic diversification (e.g., Jiraporn et al.,

2006). It is much more likely that managers focused on positive-NPV investment opportunities

within a core business segment may need to expand geographically to access new markets.2

Existing work on geographic diversification has focused on the presence of a foreign segment

(global diversification by large multinationals), arriving at mixed results.3 Importantly, global

diversification by US multinationals into foreign countries typically brings with it exposure to a

host of potentially significant country-level macroeconomic, political, and institutional risks,

which cannot be disentangled from the effects of increased intra-firm complexity.

A growing body of research has also demonstrated the role of local factors and frictions

associated with headquarters locations for various firm decisions, suggesting that greater distance

may increase monitoring costs and information asymmetries.4 In a related vein, a number of

accurate business diversification measurement. Hoechle, Schmid, Walter, and Yermack (2012) find that the business diversification discount remains after a correction for endogeneity but can be explained by managerial entrenchment and poor governance quality. Using plant-level data, Schoar (2002) shows that conglomerates have higher productivity in the cross-section but that over time, diversification is associated with a reduction in productivity, due in part to higher wages. Various studies show that diversification discount varies internationally and over time (e.g., Lins and Servaes, 1999; Rudolph and Schwetzler, 2013).

2 The agency argument based on an excessively conservative manager seeking to diversify to reduce risk may extend to geographic diversification. Regardless of whether geographic diversification is driven by the manager’s or shareholders’ interests, from the standpoint of lenders, risk reduction may reduce credit risk (see, e.g., John, Litov, and Yeung, 2008). However, to the extent that geographic diversification is a risk-reduction technique to which conflicted managers resort when they are no longer able to diversify across industry segments, we should see differential effects in subsamples of firms with and without multiple business segments. For instance, geographic diversification may be relatively more detrimental when the firm already has multiple business segments. We find negative effects across business diversification subsamples.

3 Some studies finding a valuation discount for globally diversified firms (e.g., Denis, Denis, and Yost, 2002; Moeller and

Schlingemann, 2005; Freund, Trahan, and Vasudevan, 2007). However, others find either no discount or a premium for global diversification (e.g., Dos Santos, Errunza, and Miller, 2008; Mathur, Singh, and Gleason, 2004; Francis, Hasan, and Sun, 2008; Chang, Kogut, and Yang, 2016; Doukas and Lang, 2003; Dastidar, 2009). Doukas and Kan (2006) show that global diversification improves bondholder value but reduces shareholder value.

4 For example, studies have considered geography and payout policy (John, Knyazeva, and Knyazeva, 2011; Becker, Ivković, and

Weisbenner, 2011), financing decisions (Gao, Ng, and Wang, 2011; Loughran, 2008), acquisitions (Kedia, Panchapagesan, and

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studies have demonstrated the effects of local factors and distance in capital markets, concluding

that geographic distance increases information asymmetries and that proximity is important for

soft information flows.5 For instance, institutional investors favor, and generate higher abnormal

returns, from investments in local firms (e.g., Coval and Moskowitz, 1999, 2001). Sell-side

security analysts exhibit a preference for, and offer more precise earnings forecasts, when

covering local firms (e.g., Bae, Stulz, and Tan, 2008; Malloy, 2005). While the above studies

establish the importance of firm locations for the decisions of firms and investors, due to data

availability, these studies have focused the locations of headquarters of large and mid-sized

publicly listed firms.6 However, for such firms, headquarters locations are likely to be noisy in

capturing locations of different units within the firm and therefore may mismeasure location-

related information asymmetries. Most data sources do not provide disaggregated location

information for geographic segments within the US. Thus, evidence on intra-firm geographic

complexity within the US and its implications is extremely scarce as mentioned earlier. We

present novel evidence from a large panel of over 31,500 small and medium-sized businesses

Uysal, 2008; Kang and Kim, 2008; Jiang, Qian, and Yonker, 2016; Chung, Green, and Schmidt, 2016; Almazan, de Motta, Titman, and Uysal, 2010), executive compensation (Francis, Hasan, John, and Waisman, 2016; Ang, Nagel, and Yang, 2014; Deng and Gao, 2013; Engelberg, Gao, and Parsons, 2013), CEO appointments (Yonker, 2016), corporate governance (John and Kadyrzhanova, 2009; Chhaochharia, Kumar, and Niessen-Ruenzi, 2012; Cronqvist et al., 2009), and board composition (Knyazeva, Knyazeva, and Masulis, 2013) (see Pirinsky and Wang (2011) for a survey of the literature).

5 For example, studies have considered evidence of geographic factors in stock returns (Pirinsky and Wang, 2006; Korniotis and

Kumar, 2013; Addoum, Kumar, and Law, 2016), liquidity (Loughran and Schultz, 2005), institutional ownership and institutional investor behavior (Loughran and Schultz, 2005; Coval and Moskowitz, 1999, 2001), individual ownership and investor behavior (Ivković and Weisbenner, 2005; Becker, Cronqvist, and Fahlenbrach, 2011), research coverage (Malloy, 2005; Bae, Stulz, and Tan, 2008; O’Brien and Tan, 2015), bondholder behavior (Francis, Hasan, and Waisman, 2007), bank lending (Degryse and Ongena, 2005; Knyazeva and Knyazeva, 2012), and venture capital (Lerner, 1995; Chen, Gompers, Kovner, and Lerner, 2010).

6 We are aware of very few studies of geographic dispersion within the US. Their research questions and empirical designs differ significantly from ours. Landier, Nair, and Wulf (2009) find that geographically disperse firms are less employee-friendly. Garcia and Norli (2012) show that firms with that are more local in nature (proxied by having fewer different states named in their Form 10-K) have a lower level of investor recognition and higher stock returns. Gao, Ng and Wang (2008) find lower firm value for firms with subsidiaries across multiple Census regions in 1993-2003. Differently from these papers, our main focus is on credit quality rather than valuation, stock returns or employee-friendliness. In terms of measurement, our sample spans a longer time period and a significantly larger universe of companies, including small and privately held firms, which reduces potential confounding by the complex host of business and agency conflicts within large US multinationals and enables us to use granular information about locations at the county level relative to the generally less complete and more coarse geographic segment data for US public firms.

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with multiple divisions for 1990–2010. More generally, our evidence on the frictions stemming

from the organizational structure of the firm informs the literature on the boundaries of the firm

and intra-firm agency conflicts (e.g., Jensen and Meckling, 1976).

Our main findings are as follows. We present evidence on the credit quality effects of

geographic complexity, defined along several key dimensions (presence of divisions outside the

headquarters area, share of divisions, sales and employees outside the headquarters area, average

distance between divisions and headquarters, and a factor analysis-based index of geographic

complexity). Using payment history scores that capture ex post credit quality, we find that

disperse firms have significantly lower credit quality than their less disperse peers. In essence,

rather than experience reductions in risk or improvements in bottom line performance due to

geographic diversification of cash flows, which can serve as a natural hedge, geographically

disperse firms appear to face higher information and monitoring costs. The ensuing information

frictions and inefficiencies in capital allocation undermine such firms’ credit quality. The effects

are economically important, not explained by industry and regional factors or common

observable characteristics, are not limited to small versus large firms, periods of poor versus

strong industry performance, or firms located in urban versus rural areas. The hypothesis of risk

reduction due to geographic diversification is not supported by our evidence.

As our analysis focuses on a new factor that can be used to predict credit risk, our

inference is less affected by the common concern about causality, and evidence of associations

between credit quality and geographic complexity by itself can be informative. Moreover, to the

extent that geographic expansion is more likely to be undertaken mainly by successful firms,

reverse causality would most likely predict the opposite sign of the effect (a positive relation

between geographic expansion and credit quality) relative to the one we find. However, we

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account for a number of controls and perform robustness analyses to rule out likely sources of

omitted variables that could affect both credit quality and geographic diversification. Besides

controls for differences that may be associated with firm credit quality as well as location (such

as firm size, maturity, industry, state, and year fixed effects, market share, competition in the

industry, local business density, growth opportunities, etc.), we also confirm that the results

continue to hold after the inclusion of county-level fixed effects and firm effects, as well as after

explicitly accounting for the choice of geographic complexity.

The rest of the paper is organized as follows. Section 2 describes the data and variables.

Section 3 presents the main results and robustness analyses. Section 4 concludes.

2. Data

2.1. Sample

We use the National Establishment Time Series (NETS) data for 1990-2010. We use

headquarters identifiers supplied by NETS to group establishments with the same owner, defined

as a firm. NETS data is constructed by Walls & Associates based on archival establishment

information from Dun and Bradstreet (D&B) using “twenty-one annual (January) snapshots…of

the full Duns Marketing Information (DMI)” between 1990 and 2010.

As our analysis focuses on intra-firm geographic complexity, we only include firms that

have two or more establishments. The main sample uses firms headquartered in the continental

US (as the patterns of economic activity as well as intra-firm geographic complexity may vary

dramatically, due to exogenous reasons, for Alaska and Hawaii firms). In line with related work,

the main sample also excludes financial firms (primary SIC codes 6000-6999). The analysis of

credit risk for financial institutions, particularly, commercial banks, may differ from that for non-

financial firms. In a robustness test we reintroduce those firms back into the sample. Our main

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variables of interest revolve around intra-firm geographic complexity. The dataset provides data

on credit quality scores. Many financing decisions and credit quality metrics are poorly

observable for small businesses, and this D&B score enables us to use a single metric reliant on

payment history – observable and verifiable information about ex post outcomes – to compare a

broad range of businesses, including small businesses. The NETS series provides disaggregate

information about individual establishment locations, sales, employees, industry of operation,

parent owner, and parent firm headquarters locations. We use two-digit SIC industry definitions

and geographic analysis at the county level (as headquarters information is reported at the county

level). Geographic coordinates for counties and population density data are obtained from the US

Census. The sample is described in more detail in Appendix A.

2.2. Variables

Variable definitions are described in more detail in Appendix A. We use several

measures of intra-firm geographic complexity: (1) indicator for the presence of divisions outside

the headquarters county; (2) share of sales in divisions outside the headquarters county; (3) share

of employees in divisions outside the headquarters county; (4) log of one plus the average

distance between divisions and headquarters in miles (measured based on county coordinates

reported in Census Gazetteer); (5) share of divisions outside of the headquarters county. Higher

values indicate greater intra-firm geographic complexity. Further, as all of the measures all have

high positive statistically significant correlations in the 0.7-0.8 range, we construct a factor-based

geographic complexity index. Credit quality is measured using PayDex scores reported by Dun

& Bradstreet (D&B) for small business establishments, with firm-level average scores used in

the analysis. Higher values of credit quality scores indicate better credit quality based on past

payment history.

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We use several control variables based on the data available in the time series NETS

dataset, such as log of firm sales, log of firm age (to proxy firm maturity), business

diversification indicator (to proxy the firm’s business diversification complexity along industry

lines and potential conflicts of interest and information asymmetries ensuing from it), the firm’s

market share (as a measure of the firm’s relative strength in the product market), industry

concentration index (to capture the competitive environment in the firm’s industry), local

business density (to capture local business activity and vibrancy), local density of industry firms

(to capture local competition) etc. The variables are defined in Appendix A. Univariate statistics

for the main variables are presented in Table 1.

[Table 1]

The average (median) firm in our sample is small or medium-sized, with firm sales of

$116 mln ($11 mln) and 940 (115) employees, indicating considerable positive skewness, as

expected. Almost eighty percent of parent firms have a division operating outside the county of

headquarters, with over half the sales and employees allocated to such divisions at the average

firm. The average credit quality score is just under 30 on a 100-point scale. The average

(median) age is 20 (12.5) years, suggesting that a number of firms are relatively mature in their

lifecycle and the sample is not dominated by very small startups. The average (median) firm in

the sample has 12 (2) segments.

2.3. Methodology

We present several univariate tabulations to summarize unconditional differences in

credit quality between geographically complex and geographically focused firms. Credit quality

tests regress credit quality scores on intra-firm geographic complexity measures and firm-level

controls above. To account for the remaining heterogeneity in investment and growth

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opportunities, risk, and economic conditions, we incorporate fixed effects based on the state of

the firm’s headquarters location, the firm’s primary two-digit SIC industry (identified based on

the largest share of sales), and year. Robustness tests use county and firm-level fixed effects. To

address the possible correlation in standard errors over time for individual firms, we use robust

standard errors clustered at the firm level.

While incorporating a number of standard control variables and using industry, state and

year fixed effects is likely to filter out a significant share of unobservable variation correlated

with firm geographic complexity decisions as well as credit quality, the potential issue of

endogeneity of intra-firm geographic complexity choices due to the presence of some omitted

variables is virtually impossible to eliminate. One source of omitted variable bias is local

economic conditions, such as the availability of human capital, household wealth, population

density, business-friendliness of local regulations, and local investment opportunities, etc., which

may contribute to credit risk. For robustness we therefore include county fixed effects, in order

to capture time-invariant unobservable variation in local business and investment conditions.

Another potential source of omitted variable bias is that firms sort into the high versus low

geographic complexity group based on some firm-level characteristic that is related to their

growth opportunities, quality, or level of risk. For example, companies may elect to diversify

geographically when they have exhausted local opportunities, which may result in a spurious

finding of lower credit quality. Alternative, high-growth companies may elect to expand

geographically, which may result in either high credit quality (if growth opportunities translate

into a strong cash flow outlook) or low credit quality (if growth opportunities are associated with

greater cash flow volatility and fewer assets in place). To account for this issue, we use firm

fixed effects in robustness tests, as well as controls for sales growth.

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Further, we perform two-stage least squares estimation to partially alleviate the concern

about time-varying omitted variable bias. In the first stage we predict geographic complexity

with several characteristics of the firm’s area that may affect the firm’s decision to locate

divisions farther from headquarters. First-stage predictors include size of the geographic area

where the firm is headquartered, the level of intra-firm dispersion common in the firm’s industry

and the firm’s state in a given year.7 Being headquartered in a larger county makes it more likely

that other divisions will be located in the same county (because of greater availability of land, as

well as local knowledge, customers, regulatory permits, supplier relationships etc. that make it

more efficient to locate the firm in the same county). Industry and state-specific practices

regarding intra-firm dispersion serve as a proxy for the remaining determinants of intra-firm

dispersion that could factor in industry-specific variation in supply chains or distribution

channels or state-specific variation in transportation and labor costs, which makes the tradeoff

involved with disperse locations more or less attractive.

3. Results

We begin with several univariate tabulations of credit quality by geographic complexity

summarized in Table 2 and Figures in Appendix B. Tests in Table 2 shows univariate

comparisons of mean credit quality for firms with high and low levels of intra-firm geographic

complexity. For the purposes of this analysis, firms with a division outside the county of

headquarters and firms with other geographic complexity measures above sample median are

classified as geographically complex. Univariate comparisons reveal lower credit quality scores

for geographically complex firms. The differences are statistically significant and economically

7 By construction, second-stage controls, including firm size and age, business diversification, local business density, and the presence of multiple divisions, are also used in the first stage and may also affect geographic complexity, but they may be relatively less excludable.

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important, amounting to between fifty and sixty-five percent of a standard deviation of credit

quality, depending on the complexity measure. So far the evidence does not appear to support the

hedging hypothesis but is consistent with the intra-firm information cost hypothesis.

[Table 2]

However, univariate analyses do not account for a variety of other firm characteristics

that could be correlated with firm location decisions as well as credit quality. Therefore, next we

proceed to multivariate tests of the effects of intra-firm geographic complexity for firm credit

quality. The main multivariate analyses are performed in Table 3. Intra-firm dispersion enters

with a significant negative coefficient in all specifications. The effect is economically important.

All else equal, presence outside the county of headquarters is associated with a decrease in credit

quality that is approximately twenty-eight percent of a sample standard deviation of credit

quality. Other geographic complexity measures, as well as the geographic complexity factor, also

enter with significant negative effects. All else equal, a one-standard deviation increase in

geographic complexity is associated with a decrease in credit quality by approximately 17%-23%

of a standard deviation of credit quality. The effects’ magnitude is greater in absolute terms than

the magnitude of the effects of the presence of multiple business segments or a one-standard-

deviation change in firm age and is approximately half of the magnitude of the effect of a one-

standard deviation in firm size. Larger, diversified firms, which are generally likely to be more

complex, have lower credit quality. However, being a better established business, as proxied by

firm age, is associated with lower credit risk.

[Table 3]

In Table 4 we perform various robustness and sensitivity tests to rule out several sources

of potential confounding effects. Panel A repeats the main tests with additional controls. Our

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main tests account for firm size and business diversification. However, to account for the

possibility that other dimensions of greater overall complexity of a firm, rather than geographic

complexity, are driving the result, we add controls for the overall number of segments

(irrespective of their geographic location). The effect of the number of segments (in log terms)

enters with a negative sign, consistent with greater complexity having a negative effect on

creditors. Joint inclusion of the number of segments and business diversification causes the

incremental effect of business diversification to change to a positive effect, suggesting that,

overall complexity has a negative effect, but business diversification has potentially beneficial

risk hedging effects, holding overall complexity constant. In contrast, geographic complexity

continues to have a significant and negative effect. We also account for the presence of local

firms in the same industry, which may either reflect potential local investment opportunities or

the level of local competition. The effect on credit quality is negative, consistent with the local

density of industry firms measuring potential competition. We also control for overall product

market measures not tied to location, including the firm’s market share and the Herfindahl

concentration index in the firm’s primary industry. The negative effect of geographic complexity

remains significant after the inclusion of these controls. The economic magnitude of the effect of

out-of-county divisions is approximately seventeen percent of a standard deviation of credit

quality and of the effect of a one-standard-deviation change in other measures of geographic

complexity – approximately nine to seventeen percent of a standard deviation of credit quality.

The economic magnitudes of geographic complexity are greater in magnitude than the economic

effects of other controls, except the overall number of segments of any type.

[Table 4]

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Panel B relaxes some of the sample filters used in the main tests, reintroducing firms

headquartered in Alaska and Hawaii and financial industry firms into the sample, which does not

change the results.

A potential source of concern is that our analysis of geographic complexity is picking up

systematic variation in local economic and business conditions, which may also affect credit

quality. For example, attributes of local consumers, workers, suppliers, the extent of local

regulatory burden, the overall level of vibrancy or the extent to which the area is economically

developed can potentially increase a firm’s willingness to remain geographically focused in the

area of headquarters, rather than expand to other areas. Being located in such an area can also

generally benefit a firm’s credit quality. This channel could lead to a spurious negative relation

between geographic complexity, as we measure it, and credit quality. The main tests incorporate

state fixed effects. To further account for this potential concern, in Panel C we include local area

(county-level) fixed effects to account for potential unobservable variation in local economic and

business conditions. The effects remain significant. The economic magnitude of the effects also

remains high and generally comparable to the main tests in Table 3 (the effect of having out-of-

county divisions is approximately twenty-six percent of a standard deviation of credit quality; the

effect of a one-standard-deviation change in other measures of geographic complexity is

approximately fifteen to twenty-two percent of a standard deviation of credit quality).

Another potential concern is that unobservable heterogeneity, for instance, in investment

and growth opportunities or risk exposure, is responsible for variation both in geographic

complexity and credit risk. For example, growing firms may be expanding geographically, but

while this may benefit shareholders, it may lower credit quality to the extent that it coincides

with higher firm risk. Alternatively, growing firms may have better overall quality, resulting in

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both a larger geographic footprint, holding size constant, thus better credit quality. The main

tests incorporate two-digit SIC industry fixed effects. Tests in Panel D include firm fixed effects

to further address this issue. In Columns VII-IX, in addition to firm fixed effects, we also control

for sales growth to account for time-varying differences in firm-specific investment and growth

opportunities. Of interest, sales growth enters with a positive sign in the credit quality regression,

suggesting that the firm quality channel dominates the risk channel in the relation between

growth opportunities and credit quality. The geographic complexity effects remain highly

significant after these robustness checks, alleviating some of the concerns about omitted variable

bias and associated endogeneity. The results retain their economic magnitude (approximately

twenty percent of a standard deviation of credit quality for the presence of out-of-county

divisions and approximately fourteen percent to twenty-three percent of a standard deviation of

credit quality for a one-standard-deviation change in other measures of geographic complexity).

Panel E uses an alternative credit quality measure (weighting establishment scores based

on employee rather than sales distribution). The results are robust to these tests, both with and

without the inclusion of firm fixed effects.

In Table 5 we evaluate the sensitivity of our finding in various subsamples. We reproduce

the main results, reporting only the coefficients of geographic complexity effects on credit

quality for brevity, in subsamples based on quartiles of firm size (Panel A), business

diversification status (Panel B), industry sales growth (Panel C), and local population density per

square mile (Panel D). For instance, small firms might be characterized by more pronounced

information asymmetries. Alternatively, firms diversified along industry lines may have more

severe conflicts of interest. Firms in declining industries may suffer from lower credit quality

overall. Firms in rural and other scarcely populated locales may face economic constraints

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associated with credit quality. Alternatively, geographic diversification among such firms may

be a necessity, yielding a benign relation with credit quality. We find that statistically significant

and economically large effects of geographic complexity on credit quality continue to hold

across the various subsamples. In sum, the tradeoffs of geographic complexity are significant

regardless of boom versus downturn industry conditions, firm size, business diversification or

location in an urban versus nonurban area. This conclusion is consistent with our earlier

conjecture that geographic complexity is a distinct dimension of a firm’s organizational structure

and information risk.

[Table 5]

Next, in Table 6 we explicitly analyze potential determinants of intra-firm geographic

complexity. As could be expected, larger firms are more geographically complex, consistent with

such firms experiencing the need to expand into new areas as they grow in size and exceed the

capabilities of their original market, workforce or physical location. More complex firms with a

larger number of divisions overall are more likely to be geographically disperse, which is not

surprising as the firm’s expansion needs and growth potential likely drive the need to build

operations or customers in several different markets at once. Consistent with the growth

intuition, holding size and complexity constant, younger firms are more likely to expand. Finally,

in line with our earlier discussion of how pre-existing geographic area characteristics could

influence geographic expansion decisions, firms located in larger counties (by land area) are

more likely to locate entirely within the county, which is intuitive given that in larger locations

parent firms are more likely to find the necessary physical, workforce and knowledge resources

and reach a broad customer base without having to move outside the main location.

[Table 6]

17

In Panel B, we add other controls for robustness. In this table we add a control for

business diversification, which captures the presence of divisions in different two-digit SIC

industries. We find that business diversification has a negative relation with geographic

dispersion, holding overall complexity constant. Intuitively, firms focused in a single industry

segment may need to expand geographically to continue growing in new markets or sourcing

materials as they exceed the capabilities of their existing locations, whereas diversified,

conglomerate businesses may utilize a single location for multiple lines of business, especially if

production or workforce synergies may be increased by co-location. In addition, we control for

characteristics of the firm’s product market, using a Herfindahl concentration index (not

significant after other determinants are included) and local business density in the county of

headquarters. Consistent with the notion of general clustering in business activity, we find a

negative relation between general local business density and the firm’s geographic expansion. In

economically vibrant counties with dense business activity, firms with multiple divisions may

locate them nearby. We also replace state and industry fixed effects with time-varying average

geographic complexity in the primary industry and state. In Panel C, for robustness we modify

sample selection criteria to include financials and Alaska and Hawaii firms, which does not

affect the results.

Although geographic complexity may be a relatively pre-determined characteristic of a

firm’s organizational structure and we have incorporated a number of controls to account for

potential omitted variation, as well as industry, state, local, and firm fixed effects to capture

potential omitted variable bias that may give rise to endogeneity, in the final set of analyses we

perform two-stage least squares estimation to explicitly model geographic complexity. The

results are shown in Table 7.

18

[Table 7]

We find that negative effects of geographic complexity on credit quality continue to hold

in two-stage estimation results. The economic magnitude of the geographic complexity effect is

generally in line with the previous results (about twenty percent of a standard deviation decrease

in credit quality for a one-standard-deviation increase in geographic complexity) and some of the

effects gain in economic magnitude.8 Overall, the findings of univariate and multivariate tests

and robustness analyses support the hypothesis that within-firm geographic distance increases

the costs of monitoring and information exchange that interfere with monitoring and efficient

allocation of resources to investment projects when firms are geographically complex.

4. Conclusions

This paper has examined firm complexity from the standpoint of within-firm geographic

diversification and presented evidence on the bottom line implications of complexity for credit

quality. Using a large new sample of firms, we find that geographically complex firms exhibit

significantly lower credit quality than their geographically focused counterparts. Our evidence

rejects the hypothesis that geographic diversification helps firms reduce risk through

diversification of cash flows. Instead, our results suggest that geographically complex firms pose

higher information costs and result in lower credit quality.

The results remain robust across a set of alternative geographic complexity measures and

after the inclusion of a number of firm-specific, industry-level, and local characteristics, as well

as firm fixed effects, and after accounting for the determinants of the choice to diversify

geographically. The results continue to hold across various subsets of the sample, including both

8 This is intuitive. Reverse causality would suggest that financially healthy firms with high credit quality are more likely to expand into new regions and would bias OLS estimates towards a positive or a less negative coefficient. Addressing reverse causality focuses on the negative effect of geographic complexity predicted by our hypothesis.

19

small and large firms, firms with and without multiple business segments, firms in stagnant and

in fast-growing industries, and firms both in dense urban and rural areas.

The findings of our analysis both inform the large diversification literature, which has

mostly focused on business or multinational diversification, and the literature on the importance

of geographic locations in finance. Most existing finance research has focused on firm

headquarters locations. Our results based on a large new sample and granular information on the

locations of individual establishments enables us to refine the understanding of the geographic

dimension of complexity of a firm’s organizational structure and provide new inference on the

effects of firm complexity on financial risk and credit quality. Our study corroborates the

importance of geographical distance for monitoring and information costs for small businesses.

The findings therefore suggest that the optimal decision to diversify geographically must weigh

the benefits of new regional markets and cash flow diversification against the well-defined

tradeoff of greater information frictions stemming from geographic diversification. On balance,

this translates into an adverse impact on credit quality. The results may inform prospective

lenders, and to the extent that lower credit quality affects borrowing costs, they may provide

another tradeoff to be considered in a firm’s organizational structure decision.

20

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24

Appendix A. Sample and variable definitions

Sample

The sample period based on the data available to us is 1990-2010. We begin with the entire National Establishment Time Series (NETS) dataset. The unit of our analysis is the firm-year. Headquarters identifiers are used to group establishments owned by the same firm. For the purposes of our analysis of intra-firm dispersion, we exclude firms that have only one establishment and focus on firms with multiple geographic divisions. The following selection criteria are applied to construct the main sample (where specified, some robustness and sensitivity tests modify these sample selection criteria): firms headquartered outside the continental US (including Alaska, Hawaii and foreign firms) are excluded; financial firms (primary SIC codes 6000-6999) are excluded; firms with missing credit quality scores or missing data on any of the main geographic complexity measures or controls are excluded; firms with zero sales or firms with identical sales values recorded for all establishments are excluded as those represent filled in data when disaggregate data is missing. We use two-digit SIC industry definitions unless specified otherwise. Primary SIC codes are identified based on the industry that accounts for the largest share of firm sales. Geographic coordinate data for counties and population density data are obtained from the US Census.

Variables

Geographic complexity

Divisions away from HQ present – indicator variable equal to 1 if the firm has establishments outside the county where the firm is headquartered, and 0 otherwise.

Sales away from HQ – share of firm sales by establishments located outside the county where the firm is headquartered.

Employees away from HQ – share of firm employees of establishments located outside the county where the firm is headquartered.

Average distance to HQ – log of one plus the average distance between establishments and headquarters, using the latitudes and longitudes of counties where they are located.

Divisions away from HQ – share of divisions outside the county where the firm is headquartered in the total number of divisions.

Geographic complexity – factor based on factor analysis with regression scoring of the above variables.

Outcomes

Credit quality (I) – average PayDex score for various firm establishments, weighted by sales (the PayDex score for each establishment is calculated as the average of minimum and maximum PayDex scores reported for a given year)

Credit quality (II) - average PayDex score for various firm establishments, weighted by employees

25

PayDex scores are Dun & Bradstreet (D&B)’s credit quality “indexing system that represents trade experiences reported to D&B, compares payment to terms of sale, and scores the overall manner of payment. The index is dollar-weighted by high credit. A PayDex Score of 80 indicates that, on average, the business pays its bills in a "Prompt" manner.” (source: NETS database description).

Controls

Firm size – log of total firm sales across all establishments

Business diversification – indicator variable equal to 1 if the firm has establishments in more than one two-digit SIC industry, and 0 if it is focused in a single two-digit SIC industry.

Firm age – log of the average age of the firm’s establishments in years.

Number of segments – log of the number of the firm’s establishments.

Market share – average share of the firm’s sales in the total sales of the industry/industries in which it operates.

Product market concentration – sales-based Herfindahl index of concentration in the firm’s primary two-digit SIC industry.

Geographic area size – log of the county land area in square miles

Local business density – log of the number of other establishments located in the county of the firm’s headquarters

Local industry business density – log of the number of other establishments in the firm’s two-digit SIC industry located in the county of the firm’s headquarters

Sales growth – annual sales growth rate based on total sales across all of the firm’s establishments, winsorized at the 1st and 99th percentiles of the distribution.

26

Appendix B. Figures This figure compares means of credit quality across subsamples based on intra-firm dispersion measures. Firms with divisions away from HQ present (for the first measure) and with intra-firm dispersion measure above median (for the other three measures) are classified as “disperse”. The remaining firms are classified as “concentrated”. Sample and variable definitions are presented in Appendix A.

0

5

10

15

20

25

30

35

40

45

Divisions away

from HQ

present

Sales away

from HQ

Employees

away from HQ

Average

distance to HQ

(log)

Credit quality (I) of disperse and concentrated firms

Concentrated

Disperse

0

5

10

15

20

25

30

35

40

Divisions away

from HQ

present

Sales away

from HQ

Employees

away from HQ

Average

distance to HQ

(log)

Credit quality (II) of disperse and concentrated firms

Concentrated

Disperse

27

Table 1. Summary statistics This table presents summary statistics for the main variables. Sample and variable definitions are presented in Appendix A.

Obs. Mean Med SD

Divisions away from HQ present 213656 0.79 1.00 0.40

Sales away from HQ 213656 0.56 0.64 0.41

Employees away from HQ 213656 0.56 0.63 0.41

Average distance to HQ 213656 4.21 4.89 2.56

Divisions away from HQ 213656 0.60 0.67 0.37

Geographic complexity 213656 0.04 0.25 0.95

Credit quality (I) 213656 29.42 29.82 19.92

Credit quality (II) 213656 28.59 29.14 19.09

Firm size 213656 16.37 16.24 1.98

Firm size ($ mln) 213656 116.45 11.24 643.96

Employees 213656 939.54 115.00 6039.88

Business diversification 213656 0.51 1.00 0.50

Firm age 213656 2.67 2.60 0.86

Firm age (no log) 213656 19.92 12.50 19.66

Number of segments 213656 1.41 0.69 1.05

Number of segments (no log) 213656 11.60 2.0 53.07

Market share 213656 0.00 0.00 0.01

Product market concentration 213628 0.04 0.02 0.04

Geographic area size 213656 6.34 6.42 1.14

Local business density 213656 5.72 5.97 1.33

Local business density (no log) 213656 584.18 392.00 660.09

Local industry business density 213656 2.23 2.26 1.23

Local industry business density (no log) 213656 19.13 9.57 27.01

Sales growth 191848 0.12 0.01 0.64

28

Table 2. Univariate evidence

This table reports tabulations and univariate tests of means of credit quality across subsamples based on intra-firm dispersion measures. The table reports means for each subsample, differences in means and their statistical significance based on a t-test, and differences in means expressed as a percentage of the sample standard deviation of credit quality (computed based on the full sample). Sample and variable definitions are presented in Appendix. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Dep. var.: Mean of

Credit quality (I) Mean of

Credit quality (II)

Criterion:

Divisions away from HQ present

Mean credit quality: if there are no divisions away from HQ 39.02 36.47

if there divisions away from HQ are present 27.11 26.84

Δ -11.91 *** -9.63 ***

Δ/SD -60% -50%

Sales away from HQ

Mean credit quality:

if Sales away from HQ ≤median 35.63 34.31

if Sales away from HQ >median 23.30 23.18

Δ -12.33 *** -11.13 ***

Δ/SD(credit quality) -62% -58%

Employees away from HQ


if Employees away from HQ ≤median 35.71 34.42

if Employees away from HQ >median 23.24 23.08

Δ -12.47 *** -11.34 ***

Δ/SD -63% -59%

Average distance to HQ (log)


if average distance to HQ ≤median 35.88 34.59

if average distance to HQ >median 22.96 22.83

Δ -12.92 *** -11.76 ***

Δ/SD(credit quality) -65% -62%

29

Table 3. Geographic complexity and credit quality

This table reports regressions of intra-firm dispersion effects on credit quality. Sample and variable definitions are presented in Appendix A. All specifications include year, industry and state fixed effects. Robust t-statistics with clustering by firm are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Dep. var.: Credit quality (I) I

II

III

IV

V

VI


-5.610 ***

-20.41

Sales away from HQ

-11.262 ***

-42.03


-11.147 ***

-41.61

Average distance to HQ

-1.306 ***

-28.36

Divisions away from HQ

-9.014 ***

-29.60


-4.505 ***

-37.68

Firm size -4.882 *** -4.668 *** -4.673 *** -4.547 *** -4.610 *** -4.548 ***

-92.24

-90.17

-90.27

-83.42

-85.80

-86.48

Business diversification -2.970 *** -2.922 *** -2.915 *** -2.688 *** -2.841 *** -2.863 ***

-14.58

-14.74

-14.68

-13.20

-14.03

-14.31

Firm age 3.201 *** 1.751 *** 1.765 *** 2.589 *** 2.289 *** 1.934 ***

26.15

14.47

14.57

20.85

18.15

15.76

Obs. 213628

213628

213628

213628

213628

213628

R2 0.31

0.34

0.34

0.32

0.32

0.33

Adj. R2 0.31

0.34

0.34

0.32

0.32

0.33

30

Table 4. Geographic complexity and credit quality: additional tests and sensitivity analyses

This table reports additional tests and sensitivity analyses of intra-firm dispersion effects on credit quality. Panel A includes additional controls. Panel B reintroduces into the sample financial firms (primary SIC 6000-6999) and firms headquartered in Alaska and Hawaii. Panels A and B use year, two-digit SIC industry and state fixed effects. Panel C uses two-digit SIC industry, county and year fixed effects. Panel D uses firm and year fixed effects. Panel E uses Credit quality (II) as the dependent variable; year, industry, and state fixed effects in Columns I-VI and year and firm fixed effects in Columns VII-XII. Sample and variable definitions are presented in Appendix A. Robust t-statistics with clustering by firm in Panels A, B, D and E and by county in Panel C are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Panel A: Additional control variables


II

III

IV

V

VI


-3.411 ***

-13.72

Sales away from HQ

-8.236 ***

-34.25


-8.156 ***

-34.08


-0.671 ***

-16.14


-4.843 ***

-17.69


-2.975 ***

-27.63

Firm size -0.713 *** -0.687 *** -0.681 *** -0.627 *** -0.654 *** -0.614 ***

-9.79

-9.66

-9.58

-8.60

-9.01

-8.56

Business diversification 1.378 *** 1.230 *** 1.246 *** 1.468 *** 1.384 *** 1.302 ***

7.49

6.85

6.93

7.97

7.54

7.17

Number of segments -11.301 *** -10.784 *** -10.813 *** -11.174 *** -11.102 *** -10.892 ***

-73.07

-71.33

-71.69

-71.93

-71.37

-71.34

Firm age 0.458 *** -0.556 *** -0.553 *** 0.236 ** 0.047

-0.314 ***

4.22

-5.12

-5.10

2.15

0.42

-2.86

Product market concentration -3.465

-3.974 * -3.964 * -3.268

-3.689 * -3.684 *

-1.54

-1.80

-1.80

-1.46

-1.65

-1.66

Market share 28.100 * 23.180

23.499

27.393

27.296

24.458

1.66

1.50

1.51

1.64

1.64

1.54

Local industry business density -1.005 *** -1.179 *** -1.178 *** -0.889 *** -1.060 *** -1.105 ***

-11.44

-13.80

-13.76

-10.20

-12.05

-12.76

Obs. 213628

213628

213628

213628

213628

213628

R2 0.46

0.48

0.48

0.47

0.47

0.47

Adj. R2 0.46

0.48

0.48

0.46

0.47

0.47

31

Panel B: Alternative sample definitions


II

III

IV

V

VI


-3.582 ***

-15.45

Sales away from HQ

-8.323 ***

-38.08


-8.228 ***

-37.77


-0.736 ***

-18.90


-4.974 ***

-19.76


-3.050 ***

-30.78

Firm size -0.708 *** -0.742 *** -0.737 *** -0.600 *** -0.662 *** -0.640 ***

-10.18

-10.96

-10.89

-8.67

-9.57

-9.38


6.49

5.69

5.83

7.14

6.49

6.10


-80.08

-78.09

-78.35

-78.68

-78.20

-78.07

Firm age 0.646 *** -0.292 *** -0.287 *** 0.404 *** 0.270 *** -0.080

6.40

-2.91

-2.86

3.96

2.60

-0.78


-3.822 * -3.789 * -3.250

-3.614

-3.571

-1.50

-1.76

-1.74

-1.47

-1.63

-1.63

Market share 36.183 ** 30.876 * 31.078 * 34.604 ** 35.445 ** 32.138 *

2.06

1.93

1.94

2.00

2.04

1.95

Obs. 246354

246354

246354

246354

246354

246354

R2 0.47

0.48

0.48

0.47

0.47

0.48

Adj. R2 0.47

0.48

0.48

0.47

0.47

0.48

32

Panel C: County fixed effects


II

III

IV

V

VI


-5.256 ***

-19.79

Sales away from HQ

-10.552 ***

-29.35


-10.437 ***

-29.79


-1.187 ***

-22.35


-8.427 ***

-27.38


-4.235 ***

-30.23

Firm size -4.718 *** -4.563 *** -4.568 *** -4.449 *** -4.483 *** -4.444 ***

-71.74

-66.14

-66.35

-62.56

-66.44

-63.81


-14.67

-15.02

-15.04

-13.72

-14.50

-14.81

Firm age 2.997 *** 1.681 *** 1.694 *** 2.480 *** 2.165 *** 1.845 ***

22.68

13.40

13.60

19.25

16.49

14.50

Obs. 213628

213628

213628

213628

213628

213628

R2 0.28

0.31

0.30

0.28

0.29

0.30

Adj. R2 0.28

0.31

0.30

0.28

0.29

0.30

33

Panel D: Firm fixed effects


II

III

IV

V

VI


-3.986 ***

-8.70

Sales away from HQ

-9.382 ***

-19.32


-9.334 ***

-19.53


-1.354 ***

-13.90


-7.289 ***

-11.54


-4.721 ***

-18.75

Firm size -4.337 *** -4.567 *** -4.546 *** -4.253 *** -4.310 *** -4.404 ***

-36.59

-39.11

-38.96

-35.83

-36.46

-37.60


-19.78

-20.31

-20.22

-18.93

-19.73

-19.62

Firm age 2.269 *** 1.642 *** 1.634 *** 1.928 *** 1.817 *** 1.550 ***

11.27

8.22

8.18

9.55

9.08

7.76

Obs. 213656

213656

213656

213656

213656

213656

R2 0.10

0.11

0.11

0.10

0.10

0.11

Adj. R2 0.10

0.11

0.11

0.10

0.10

0.11

Dep. var.: Credit quality (I) VII

VIII

IX

X

XI

XI


-4.040 ***

-8.35

Sales away from HQ

-9.077 ***

-17.65


-9.052 ***

-17.87


-1.328 ***

-12.83


-7.370 ***

-10.93


-4.647 ***

-17.33

Firm size -4.316 *** -4.539 *** -4.518 *** -4.232 *** -4.284 *** -4.379 ***

-33.66

-35.89

-35.75

-32.95

-33.49

-34.52


-18.17

-18.70

-18.62

-17.40

-18.13

-18.05

Firm age 2.958 *** 2.281 *** 2.276 *** 2.588 *** 2.452 *** 2.171 ***

12.85

9.97

9.94

11.21

10.72

9.49

Sales growth 0.276 *** 0.243 *** 0.245 *** 0.272 *** 0.258 *** 0.244 ***

5.17

4.59

4.63

5.11

4.85

4.61

Obs. 191848

191848

191848

191848

191848

191848

R2 0.09

0.11

0.11

0.10

0.10

0.10

Adj. R2 0.09

0.11

0.11

0.10

0.10

0.10

34

Panel E: Alternative credit quality measure

Dep. var.: Credit quality (II) I

II

III

IV

V

VI


-3.513 ***

-13.75

Sales away from HQ

-9.321 ***

-36.65


-9.432 ***

-36.43


-0.955 ***

-22.01


-6.578 ***

-22.99


-3.551 ***

-31.53

Firm size -4.507 *** -4.272 *** -4.268 *** -4.232 *** -4.279 *** -4.197 ***

-89.90

-87.76

-87.63

-81.75

-84.05

-84.26


-25.59

-26.10

-26.06

-24.61

-25.28

-25.60

Firm age 3.419 *** 2.116 *** 2.092 *** 2.919 *** 2.701 *** 2.338 ***

29.04

18.39

18.15

24.58

22.46

19.97

Industry FE Y

Y

Y

Y

Y

Y

State FE Y

Y

Y

Y

Y

Y

Year FE Y

Y

Y

Y

Y

Y

Obs. 213628

213628

213628

213628

213628

213628

R2 0.31

0.33

0.33

0.31

0.31

0.32

Adj. R2 0.31

0.33

0.33

0.31

0.31

0.32

Dep. var.: Credit quality (II) VII

VIII

IX

X

XI

XI


-4.211 ***

-9.61

Sales away from HQ

-9.026 ***

-19.24


-9.354 ***

-19.63


-1.369 ***

-14.25


-7.344 ***

-11.78


-4.697 ***

-18.96

Firm size -4.128 *** -4.354 *** -4.340 *** -4.045 *** -4.103 *** -4.198 ***

-36.14

-38.62

-38.53

-35.42

-36.06

-37.19


-20.53

-21.11

-21.03

-19.66

-20.50

-20.40

Firm age 2.280 *** 1.687 *** 1.649 *** 1.940 *** 1.830 *** 1.572 ***

11.38

8.50

8.30

9.66

9.18

7.91

Firm FE Y

Y

Y

Y

Y

Y

Year FE Y

Y

Y

Y

Y

Y

Obs. 213656

213656

213656

213656

213656

213656

R2 0.10

0.11

0.11

0.10

0.10

0.11

Adj. R2 0.10

0.11

0.11

0.10

0.10

0.11

35

Table 5. Geographic complexity and credit quality: subsample analyses

This table reports analyses of geographic complexity effects on credit quality within subsamples. In Panel A, the subsamples are based on the quartiles of firm size. In Panel B, the subsamples are based on whether the firm’s business diversification indicator. In Panel C, the subsamples are based on the quartiles of industry median sales growth. In Panel D, the subsamples are based on the quartiles of population density per mile in the county of the firm’s headquarters. Sample and variable definitions are presented in Appendix A. Included in the specifications but not reported in the table for brevity: firm size, business diversification indicator, firm age, industry concentration, firm market share. All specifications include year, industry and state of headquarters location effects. Robust t-statistics with clustering by firm are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Panel A: Firm size

Geographic complexity measure:


Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ


Dep. var.: Credit quality (I)

Subsample: Firm size - Q1

Geographic complexity -2.504 *** -7.870 *** -7.565 *** -0.470 *** -3.035 *** -2.256 ***

-5.18

-13.96

-13.66

-5.14

-5.12

-9.74

Obs. 28460

28460

28460

28460

28460

28460

R2 0.18

0.21

0.20

0.18

0.18

0.19



-4.08

-17.16

-17.00

-5.90

-6.04

-11.96

Obs. 45567

45567

45567

45567

45567

45567

R2 0.29

0.31

0.31

0.29

0.29

0.30


Geographic complexity -1.042 ** -7.220 *** -7.147 *** -0.355 *** -2.686 *** -2.232 ***

-2.53

-17.65

-17.56

-5.20

-5.91

-12.41

Obs. 64301

64301

64301

64301

64301

64301

R2 0.41

0.43

0.43

0.41

0.42

0.42



-8.38

-18.59

-18.81

-12.46

-13.09

-17.81

Obs. 75136

75136

75136

75136

75136

75136

R2 0.55

0.56

0.56

0.55

0.55

0.56

Panel B: Business diversification



Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ Geographic complexity


Subsample: Business diversification = 1


-16.00 -25.89 -26.11 -18.19 -17.74 -23.71

Obs. 108919 108919 108919 108919 108919 108919

R2 0.51 0.53 0.53 0.52 0.51 0.52

Subsample: Business diversification = 0

Geographic complexity -0.361 -6.470 *** -6.258 *** -0.139 *** -1.191 *** -1.716 ***

-1.22 -21.21 -20.49 -2.74 -3.70 -13.21

Obs. 104709 104709 104709 104709 104709 104709

R2 0.37 0.39 0.39 0.37 0.37 0.38

36

Panel C: Industry conditions



Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ



Subsample: Industry growth - Q1


-11.68

-25.27

-24.96

-13.06

-13.25

-20.53

Obs. 48081

48081

48081

48081

48081

48081

R2 0.48

0.50

0.50

0.48

0.48

0.49



-10.70

-27.98

-27.44

-13.08

-13.34

-22.07

Obs. 52310

52310

52310

52310

52310

52310

R2 0.46

0.48

0.48

0.46

0.46

0.47



-8.72

-24.36

-24.33

-12.20

-12.26

-19.43

Obs. 51746

51746

51746

51746

51746

51746

R2 0.46

0.48

0.48

0.46

0.46

0.47



-9.11

-22.20

-22.19

-11.22

-11.49

-18.15

Obs. 53263

53263

53263

53263

53263

53263

R2 0.46

0.47

0.47

0.46

0.46

0.47

Panel D: Urban and nonurban areas



Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ



Subsample: Area population density - Q1


-6.56

-20.46

-20.66

-9.47

-8.94

-15.59

Obs. 52576

52576

52576

52576

52576

52576

R2 0.41

0.43

0.43

0.41

0.41

0.42



-5.59

-14.72

-14.72

-7.28

-7.15

-11.79

Obs. 52411

52411

52411

52411

52411

52411

R2 0.45

0.46

0.46

0.45

0.45

0.46



-5.35

-14.58

-14.28

-6.68

-6.70

-11.49

Obs. 52474

52474

52474

52474

52474

52474

R2 0.49

0.50

0.50

0.49

0.49

0.50



-5.64

-14.37

-14.15

-5.70

-7.36

-11.63

Obs. 54322

54322

54322

54322

54322

54322

R2 0.49

0.50

0.50

0.49

0.49

0.50

37

Table 6. Determinants of geographic complexity

This table reports regressions of intra-firm geographic dispersion on its determinants. Sample and variable definitions are presented in Appendix A. Panel A reports the main tests. Panel B replaces state and industry fixed effects with annual means of the dependent variable within the state and the industry and introduces additional determinants on the right hand side. Panel C expands the sample to include observations with missing credit quality scores, financial firms (primary SIC codes 6000-6999) and firms headquartered in Alaska and Hawaii. The specifications include year, two-digit SIC industry, and state fixed effects, except as specified otherwise. Robust t-statistics with clustering by firm are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Panel A: Main tests

Dep. var.: Divisions away from HQ present

Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ


I

II

III

IV

V VI

Firm size 0.028 *** 0.011 *** 0.012 *** 0.285 *** 0.031*** 0.060 ***

14.18

6.24

6.86

26.46

19.80 14.67

Firm age -0.076 *** -0.152 *** -0.154 *** -0.734 *** -0.137*** -0.343 ***

-22.99

-52.54

-53.73

-41.60

-54.87 -52.23

Number of segments 0.052 *** 0.079 *** 0.076 *** 0.494 *** 0.076*** 0.189 ***

17.39

26.37

25.88

28.23

30.01 28.68

Geographic area size -0.026 *** -0.033 *** -0.034 *** -0.123 *** -0.025*** -0.072 ***

-8.61

-10.90

-11.26

-6.86

-9.68 -10.76

Obs. 213628

213628

213628

213628

213628 213628

R2 0.22

0.26

0.27

0.38

0.35 0.33

Adj. R2 0.22

0.26

0.27

0.38

0.35 0.33

38

Panel B: Additional controls and alternative specifications

Dep. var.: Divisions away from HQ present

Sales away from

HQ

Employees away from

HQ


Divisions away from

HQ


I II III IV V VI

Firm size 0.026*** 0.023*** 0.024*** 0.243*** 0.035*** 0.074***

15.60 14.28 14.89 25.99 25.69 20.82

Firm age -0.067*** -0.129*** -0.130*** -0.646*** -0.119*** -0.296***

-20.93 -45.48 -46.54 -37.74 -49.25 -46.12

Number of segments 0.055*** 0.065*** 0.063*** 0.483*** 0.071*** 0.169***

20.86 22.95 22.40 29.87 29.81 27.49

Geographic area size -0.017*** -0.025*** -0.025*** -0.059*** -0.020*** -0.052***

-8.04 -11.22 -11.38 -4.68 -10.26 -10.62

Business diversification -0.027*** -0.015*** -0.013*** -0.008 -0.009** -0.031***

-5.05 -3.03 -2.76 -0.28 -2.24 -2.89

Product market concentration 0.002 -0.005 -0.009 -0.022 -0.022 -0.032

0.04 -0.10 -0.19 -0.08 -0.52 -0.29

Industry mean 0.937*** 0.910*** 0.912*** 0.911*** 0.925*** 0.933***

50.93 55.46 56.53 70.47 62.06 62.90

State mean 0.450*** 0.555*** 0.557*** 0.402*** 0.466*** 0.506***

8.19 12.44 12.71 11.36 11.50 12.08

Local business density -0.001 -0.007*** -0.008*** 0.151*** -0.010*** -0.009**

-0.64 -3.80 -3.93 12.40 -5.66 -2.06

Obs. 213628 213628 213628 213628 213628 213628

R2 0.22 0.24 0.25 0.37 0.34 0.31

Adj. R2 0.22 0.24 0.25 0.37 0.34 0.31

Panel C: Alternative sample definitions

Dep. var.:


Sales away from HQ

Employees away from

HQ


Divisions away from

HQ


I

II

III

IV

V VI

Firm size 0.027 *** 0.006 *** 0.007 *** 0.279 *** 0.028 *** 0.051 ***

14.89 3.33 3.85 27.90 19.65 13.35

Firm age -0.074 *** -0.144 *** -0.145 *** -0.690 *** -0.129 *** -0.324 ***

-23.88 -52.64 -53.69 -42.47 -54.15 -52.27

Number of segments 0.055 *** 0.084 *** 0.082 *** 0.508 *** 0.081 *** 0.200 ***

20.02 30.31 30.10 31.51 34.31 33.02

Geographic area size -0.026 *** -0.033 *** -0.033 *** -0.124 *** -0.026 *** -0.073 ***

-9.43 -11.61 -12.01 -7.50 -10.89 -11.70

Obs. 246354 246354 246354 246354 246354 246354

R2 0.21 0.25 0.25 0.37 0.34 0.31

Adj. R2 0.21 0.25 0.25 0.37 0.34 0.31

39

Table 7. Geographic complexity and credit quality: two-stage estimation

This table reports analyses of intra-firm dispersion effects on credit quality using two-stage least squares estimation. The first stage equation includes the following predictors in addition to the second-stage controls: log of county land area (in square miles) for the county of the firm’s headquarters, and means of intra-firm dispersion in the firm’s primary two-digit SIC and in the firm’s state of headquarters location in a given year. Sample and variable definitions are presented in Appendix A. All specifications include year, industry and state of headquarters location effects. Robust t-statistics with clustering by firm are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.


II

III

IV

V

VI


-8.668 ***

-4.01

Sales away from HQ

-10.762 ***

-5.02


-12.246 ***

-5.61


-1.504 ***

-3.49


-11.115 ***

-4.17


-4.729 ***

-4.66

Firm size -0.522 *** -0.625 *** -0.591 *** -0.362 ** -0.409 *** -0.470 ***

-5.31

-7.98

-7.44

-2.55

-3.52

-4.75


6.21

6.10

5.97

7.90

6.62

6.28


-56.67

-45.49

-45.56

-41.70

-40.67

-42.23

Firm age 0.051

-0.937 *** -1.181 *** -0.370

-0.825 ** -0.917 **

0.25

-2.69

-3.31

-1.11

-2.12

-2.48

Market share 27.313 * 24.954 * 24.088

25.636

26.520 * 24.615

1.70

1.65

1.62

1.60

1.67

1.61


-2.628

-2.641

-1.843

-2.346

-2.326

-0.86

-1.20

-1.20

-0.82

-1.05

-1.05

Local business density -1.161 *** -1.264 *** -1.279 *** -0.920 *** -1.294 *** -1.213 ***

-14.25 -15.90 -15.98 -8.40 -15.16 -15.23

Obs. 213628

213628

213628

213628

213628

213628

R2 0.46

0.48

0.48

0.46

0.46

0.47

Adj. R2 0.46

0.48

0.48

0.46

0.46

0.47 ==

40

Table 8. Geographic complexity and sales growth

This table reports analyses of intra-firm dispersion effects on sales growth. In Panel A year, industry and state fixed effects are included. In Panel B, year, industry and local (county) fixed effects are included. In Panel C year and firm fixed effects are included. Robust t-statistics with clustering by firm in Panels A and C and by county in Panel B are italicized. Statistical significance at 1%, 5%, and 10% levels is denoted with ***, **, and *, respectively.

Panel A. Main tests

Dep. var.: Sales growth I

II

III

IV

V

VI


-0.003

-0.97

Sales away from HQ

-0.027 ***

-6.29


-0.025 ***

-5.89


-1.E-04

-0.21


-0.022 ***

-4.90


-0.009 ***

-5.02

Firm size 0.038 *** 0.039 *** 0.039 *** 0.038 *** 0.040 *** 0.039 ***

37.02

37.78

37.75

35.32

37.09

37.23


12.60

12.56

12.57

12.65

12.65

12.62

Firm age -0.069 *** -0.073 *** -0.073 *** -0.068 *** -0.072 *** -0.072 ***

-30.40

-30.33

-30.19

-29.30

-29.75

-29.94

Obs. 191825

191825

191825

191825

191825

191825

R2 0.04

0.04

0.04

0.04

0.04

0.04

Adj. R2 0.04

0.04

0.04

0.04

0.04

0.04

41

Panel B. County fixed effects


II

III

IV

V

VI


-0.003

-0.88

Sales away from HQ

-0.029 ***

-6.26


-0.027 ***

-5.84


-4.E-04

-0.50


-0.025 ***

-4.82


-0.010 ***

-4.93

Firm size 0.040 *** 0.041 *** 0.041 *** 0.040 *** 0.042 *** 0.042 ***

30.84

31.53

31.58

30.90

31.54

31.61


12.82

12.79

12.81

12.90

12.90

12.87

Firm age -0.072 *** -0.076 *** -0.076 *** -0.072 *** -0.075 *** -0.075 ***

-25.83

-26.14

-26.14

-25.68

-25.99

-26.07

Obs. 191825

191825

191825

191825

191825

191825

R2 0.04

0.04

0.04

0.04

0.04

0.04

Adj. R2 0.04

0.04

0.04

0.04

0.04

0.04

Panel C. Firm fixed effects


II

III

IV

V

VI


-0.039 **

-2.42

Sales away from HQ

-0.100 ***

-5.34


-0.097 ***

-5.12


-0.008 **

-2.22


-0.159 ***

-6.59


-0.056 ***

-5.93

Firm size 0.274 *** 0.271 *** 0.271 *** 0.274 *** 0.275 *** 0.273 ***

49.31

49.01

49.06

49.09

49.49

49.37


5.53

5.48

5.49

5.58

5.71

5.65

Firm age -0.174 *** -0.182 *** -0.182 *** -0.176 *** -0.187 *** -0.184 ***

-17.88

-18.44

-18.39

-17.76

-18.53

-18.48

Obs. 191848

191848

191848

191848

191848

191848

R2 0.08

0.08

0.08

0.08

0.08

0.08

Adj. R2 0.08

0.08

0.08

0.08

0.08

0.08

firm complexity: the ‘dark side’ of geographic diversification · 2018-01-22 · 1 1....

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