firm complexity: the ‘dark side’ of geographic diversification · 2018-01-22 · 1 1....
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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
References
Addoum, J., Kumar, A., Law, K., 2016, Slow diffusion of state-level information and return predictability, Working paper.
Aggarwal, R., Zhao, S., 1999. The diversification discount puzzle: evidence for a transaction-cost resolution. Financial Review 44(1), 113–135.
Almazan, A., de Motta, A., Titman, S., Uysal, V., 2010, Financial structure, acquisition opportunities, and firm locations. Journal of Finance 65(2), 529–563.
Ang, J., Nagel, G., Yang, J., 2014. The effect of social pressures on CEO compensation. Working paper.
Bae, K.-H., Stulz, R., Tan, H., 2008. Do local analysts know more? A cross-country study of the performance of local analysts and foreign analysts. Journal of Financial Economics 88(3), 581–606.
Bae, K.-H., Stulz, R.M., Tan, H., 2008. Do local analysts know more? A cross-country study of the performance of local analysts and foreign analysts. Journal of Financial Economics 88(3), 581–606.
Becker, B., Cronqvist, H., Fahlenbrach, R., 2011. Estimating the effects of large shareholders using a geographic instrument. Journal of Financial and Quantitative Analysis 46(4), 907–942.
Becker, B., Ivkovic, Z., Weisbenner, S., 2011. Local dividend clienteles. Journal of Finance 66(2), 655–683.
Berger, P., Ofek, E., 1995. Diversification's effect on firm value. Journal of Financial Economics 37(1), 39–65.
Bertrand, M., Mehta P., Mullainathan, S., 2002. Ferreting out tunneling: an application to Indian business groups. Quarterly Journal of Economics 117(1), 121–148.
Campa, J. M., Kedia, S., 2002. Explaining the diversification discount. Journal of Finance 57(4), 1731–1762.
Chang, S., Kogut, B., Yang, J.-S., 2016. Global diversification discount and its discontents: a bit of self-selection makes a world of difference. Strategic Management Journal 37(11), 2254–2274.
Chen, H., Gompers, P., Kovner, A., Lerner, J., 2010. Buy local? The geography of venture capital. Journal of Urban Economics 67(1), 90–102.
Chhaochharia, V., Kumar, A., Niessen-Ruenzi, A., 2012. Local investors and corporate governance. Journal of Accounting and Economics 54(1), 1–94.
Chung, K., Green, C., Schmidt, B., 2016. CEO home bias and corporate acquisitions. Working paper.
Coval, J., Moskowitz, T., 1999. Home bias at home: local equity preference in domestic portfolios. Journal of Finance 54(6), 2045–2073.
Coval, J., Moskowitz, T., 2001. The geography of investment: informed trading and asset prices. Journal of Political Economy 109(4), 811–841.
Cronqvist, H., Heyman, F., Nilsson, M., Svaleryd, H., Vlachos, J., 2009. Do entrenched managers pay their workers more? Journal of Finance 64(1), 309–339.
21
Custódio, C., 2014. Mergers and acquisitions accounting and the diversification discount. Journal of Finance 69(1), 219–240.
Dastidar, P., 2009. International corporate diversification and performance: does firm self-selection matter? Journal of International Business Studies 40(1), 71–85.
Degryse, H., Ongena, S., 2005. Distance, lending relationships, and competition. Journal of Finance 60(1), 231–266.
Deng, X., Gao, H., 2013. Nonmonetary benefits, quality of life, and executive compensation. Journal of Financial and Quantitative Analysis 48(1), 197–218.
Denis, D., Denis, D., Sarin, A., 1997. Agency problems, equity ownership, and corporate diversification. Journal of Finance 52(1), 135–160.
Denis, D., Denis, D., Yost, K. 2002. Global diversification, industrial diversification, and firm value. Journal of Finance 57(5), 1951–1979.
Dos Santos, M., Errunza, V., Miller, D., 2008. Does corporate international diversification destroy value? Evidence from cross-border mergers and acquisitions. Journal of Banking and Finance 32(12), 2716–2724.
Doukas, J., Kan, O., 2006. Does global diversification destroy firm value? Journal of International Business Studies 37(3), 352–371.
Doukas, J., Lang, L., 2003. Foreign direct investment, diversification and firm performance. Journal of International Business Studies 34(2), 153–172.
Engelberg, J., Gao, P., Parsons, C., 2013. The price of a CEO's rolodex. Review of Financial Studies 26(1), 79–114.
Francis, B., Hasan, I., Sun, X., 2008. Financial market integration and the value of global diversification: evidence for US acquirers in cross-border mergers and acquisitions. Journal of Banking and Finance 32(8), 1522–1540.
Francis, B., Hasan, I., John, K., Waisman, M., 2016. Urban agglomeration and CEO compensation. Journal of Financial and Quantitative Analysis 51(6), 1925-1953.
Francis, B., Hasan, I., Waisman, M., 2007. Does geography matter to bondholders? Federal Reserve Bank of Atlanta. Working paper.
Freund, S., Trahan, E., Vasudevan, G., 2007. Effects of global and industrial diversification on firm value and operating performance. Financial Management 36(4), 143–161.
Gao, W., Ng., L., Wang, Q., 2008. Does geographic dispersion affect firm valuation? Journal of Corporate Finance 14 (2008), 674–687.
Gao, W., Ng, L., Wang, Q., 2011. Does corporate headquarters location matter for firm capital structure? Financial Management 40(1), 113–138.
Garcia, D., Norli, 2012. Geographic dispersion and stock returns. Journal of Financial Economics 106(3), 547–565.
Graham, J., Lemmon, M., Wolf, J., 2002. Does corporate diversification destroy value? Journal of Finance 57(2), 695–720.
Hoechle, D., Schmid, M., Walter, I., Yermack, D., 2012. How much of the diversification discount can be explained by poor corporate governance? Journal of Financial Economics 103(1), 41–60.
22
Ivković, Z., Weisbenner, S., 2005. Local does as local is: information content of the geography of individual investors' common stock investments. Journal of Finance 60(1), 267–306.
Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3(4), 305–360.
Jiang, F., Qian, Y., Yonker, S., 2016. Home biased acquisitions. Working paper.
Jiraporn, P., Kim, Y. S., Davidson, W., Singh, M., 2006. Corporate governance, shareholder rights and firm diversification: an empirical analysis. Journal of Banking and Finance 30(3), 947–963.
John, K., Kadyrzhanova, D., 2009. Relative governance. Working paper.
John, K., Knyazeva, A., Knyazeva, D., 2011. Does geography matter? Firm location and corporate payout policy. Journal of Financial Economics 110(3), 533–551.
John, K., Litov, L., Yeung, B., 2008. Corporate governance and risk-taking. Journal of Finance 63(4). 1679–1728.
Kang, J.-K., Kim, J.-M., 2008. The geography of block acquisitions. Journal of Finance 63, 2817–2858.
Kedia, S., Panchapagesan, V., Uysal, V., 2008. Geography and acquirer returns. Journal of Financial Intermediation 17(2), 256–275.
Klein, P., Saidenberg, M., 2010. Organizational structure and the diversification discount: evidence from commercial banking. Journal of Industrial Economics 58(1), 127–155.
Knyazeva, A., Knyazeva, D. 2012. Does being your bank’s neighbor matter? Journal of Banking and Finance 36(4), 1194–1209.
Knyazeva, A., Knyazeva, D., Masulis, R., 2013. The supply of corporate directors and board independence. Review of Financial Studies 26(6), 1561–1605.
Korniotis, G., Kumar, A., 2013, State-level business cycles and local return predictability, Journal of Finance 68(3), 1037–1096.
Laeven, L., Levine, R., 2007. Is there a diversification discount in financial conglomerates? Journal of Financial Economics 85(2), 331–367.
Lamont, O., 1997. Cash flow and investment: evidence from internal capital markets. Journal of Finance 52(1), 83-–109.
Lamont, O., Polk, C., 2002. Does diversification destroy value? Evidence from the industry shocks. Journal of Financial Economics 63(1), 51–77.
Landier, A., Nair, V., Wulf, J., 2009. Trade-offs in staying close: corporate decision making and geographic dispersion. Review of Financial Studies 22(3), 1119–1148.
Lerner, J., 1995. Venture capitalists and the oversight of private firms. Journal of Finance 50(1), 301–318.
Lins, K., Servaes, H., 1999. International evidence on the value of corporate diversification. Journal of Finance 54(6), 2215–2239.
Loughran, T., 2008. The impact of firm location on equity issuance. Financiala Management 37(1), 1–21.
Loughran, T., Schultz, P., 2005. Liquidity: urban versus rural firms. Journal of Financial Economics 78(2), 341–374.
Malloy, C., 2005. The geography of equity analysis. Journal of Finance 60(2), 719–755.
23
Mansi, S., Reeb, D., 2002. Corporate diversification: what gets discounted? Journal of Finance 57(5), 2167–2183.
Martin, J., Sayrak, A., 2003. Corporate diversification and shareholder value: a survey of recent literature. Journal of Corporate Finance 9(1), 37–57.
Mathur, I., Singh, M., Gleason, K., 2004. Multinational diversification and corporate performance: evidence from European firms. European Financial Management 10(3), 439–464.
Matsusaka, J., Nanda, V., 2002. Internal capital markets and corporate refocusing. Journal of Financial Intermediation 11(2), 176–211.
Moeller, S., Schlingemann, F., 2005. Global diversification and bidder gains: a comparison between cross-border and domestic acquisitions. Journal of Banking and Finance 29(3), 533–564.
O’Brien, P., Tan, H., 2015. Geographic proximity and analyst coverage decisions: evidence from IPOs. Journal of Accounting and Economics 59(1), 41–59.
Pirinsky, C., Wang, Q., 2006. Does corporate headquarters location matter for stock returns? Journal of Finance 61(4), 1991–2015.
Pirinsky, C., Wang, Q., 2011. Geographic location and corporate finance, In: Eds. (A. Tourani-Rad, C. Ingley), Handbook on Emerging Issues in Corporate Governance, World Scientific, 23–38.
Rajan, R., Servaes, H., Zingales, L., 2000. The cost of diversity: the diversification discount and inefficient investment. Journal of Finance 55(1), 35–80.
Rudolph, C., Schwetzler, B., 2013. Conglomerates on the rise again? A cross-regional study on the impact of the 2008–2009 financial crisis on the diversification discount. Journal of Corporate Finance 22, 153–165.
Schoar, A., 2002. Effects of corporate diversification on productivity. Journal of Finance 57(6), 2379–2403.
Stowe, J., Xing, X., 2006. Can growth opportunities explain the diversification discount? Journal of Corporate Finance 12(4), 783–796.
Villalonga, B., 2004a. Does diversification cause the "diversification discount"? Financial Management 33(2), 5–27.
Villalonga, B., 2004b. Diversification discount or premium? New evidence from the business information tracking series. Journal of Finance 59(2), 479–506.
Yonker, S., 2016. Geography and the market for CEOs. Management Science 63(3), 609–630.
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
Mean credit quality:
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)
Mean credit quality:
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
Divisions away from HQ present
-5.610 ***
-20.41
Sales away from HQ
-11.262 ***
-42.03
Employees away from HQ
-11.147 ***
-41.61
Average distance to HQ
-1.306 ***
-28.36
Divisions away from HQ
-9.014 ***
-29.60
Geographic complexity
-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
Dep. var.: Credit quality (I) I
II
III
IV
V
VI
Divisions away from HQ present
-3.411 ***
-13.72
Sales away from HQ
-8.236 ***
-34.25
Employees away from HQ
-8.156 ***
-34.08
Average distance to HQ
-0.671 ***
-16.14
Divisions away from HQ
-4.843 ***
-17.69
Geographic complexity
-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
Dep. var.: Credit quality (I) I
II
III
IV
V
VI
Divisions away from HQ present
-3.582 ***
-15.45
Sales away from HQ
-8.323 ***
-38.08
Employees away from HQ
-8.228 ***
-37.77
Average distance to HQ
-0.736 ***
-18.90
Divisions away from HQ
-4.974 ***
-19.76
Geographic complexity
-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
Business diversification 1.127 *** 0.963 *** 0.988 *** 1.240 *** 1.124 *** 1.044 ***
6.49
5.69
5.83
7.14
6.49
6.10
Number of segments -11.493 *** -10.964 *** -10.989 *** -11.332 *** -11.288 *** -11.065 ***
-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
Product market concentration -3.325
-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
Dep. var.: Credit quality (I) I
II
III
IV
V
VI
Divisions away from HQ present
-5.256 ***
-19.79
Sales away from HQ
-10.552 ***
-29.35
Employees away from HQ
-10.437 ***
-29.79
Average distance to HQ
-1.187 ***
-22.35
Divisions away from HQ
-8.427 ***
-27.38
Geographic complexity
-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
Business diversification -3.080 *** -3.059 *** -3.045 *** -2.846 *** -2.970 *** -2.993 ***
-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
Dep. var.: Credit quality (I) I
II
III
IV
V
VI
Divisions away from HQ present
-3.986 ***
-8.70
Sales away from HQ
-9.382 ***
-19.32
Employees away from HQ
-9.334 ***
-19.53
Average distance to HQ
-1.354 ***
-13.90
Divisions away from HQ
-7.289 ***
-11.54
Geographic complexity
-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
Business diversification -4.356 *** -4.412 *** -4.397 *** -4.176 *** -4.339 *** -4.278 ***
-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
Divisions away from HQ present
-4.040 ***
-8.35
Sales away from HQ
-9.077 ***
-17.65
Employees away from HQ
-9.052 ***
-17.87
Average distance to HQ
-1.328 ***
-12.83
Divisions away from HQ
-7.370 ***
-10.93
Geographic complexity
-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
Business diversification -4.165 *** -4.230 *** -4.214 *** -3.994 *** -4.148 *** -4.095 ***
-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
Divisions away from HQ present
-3.513 ***
-13.75
Sales away from HQ
-9.321 ***
-36.65
Employees away from HQ
-9.432 ***
-36.43
Average distance to HQ
-0.955 ***
-22.01
Divisions away from HQ
-6.578 ***
-22.99
Geographic complexity
-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
Business diversification -4.921 *** -4.894 *** -4.888 *** -4.721 *** -4.833 *** -4.846 ***
-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
Divisions away from HQ present
-4.211 ***
-9.61
Sales away from HQ
-9.026 ***
-19.24
Employees away from HQ
-9.354 ***
-19.63
Average distance to HQ
-1.369 ***
-14.25
Divisions away from HQ
-7.344 ***
-11.78
Geographic complexity
-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
Business diversification -4.466 *** -4.530 *** -4.513 *** -4.289 *** -4.454 *** -4.395 ***
-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:
Divisions away from HQ present
Sales away from
HQ
Employees away from
HQ
Average distance to HQ
Divisions away from
HQ
Geographic complexity
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
Subsample: Firm size - Q2
Geographic complexity -1.831 *** -7.780 *** -7.582 *** -0.453 *** -2.944 *** -2.328 ***
-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
Subsample: Firm size - Q3
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
Subsample: Firm size - Q4
Geographic complexity -4.339 *** -7.351 *** -7.550 *** -0.990 *** -6.761 *** -3.576 ***
-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
Geographic complexity measure:
Divisions away from HQ present
Sales away from
HQ
Employees away from
HQ
Average distance to HQ
Divisions away from
HQ Geographic complexity
Dep. var.: Credit quality (I)
Subsample: Business diversification = 1
Geographic complexity -6.493 *** -9.090 *** -9.090 *** -1.163 *** -7.533 *** -3.923 ***
-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
Geographic complexity measure:
Divisions away from HQ present
Sales away from
HQ
Employees away from
HQ
Average distance to HQ
Divisions away from
HQ
Geographic complexity
Dep. var.: Credit quality (I)
Subsample: Industry growth - Q1
Geographic complexity -3.696 *** -7.961 *** -7.847 *** -0.713 *** -4.677 *** -2.866 ***
-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
Subsample: Industry growth - Q2
Geographic complexity -3.300 *** -8.482 *** -8.321 *** -0.681 *** -4.543 *** -2.978 ***
-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
Subsample: Industry growth - Q3
Geographic complexity -2.811 *** -7.690 *** -7.606 *** -0.666 *** -4.421 *** -2.760 ***
-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
Subsample: Industry growth - Q4
Geographic complexity -3.420 *** -7.604 *** -7.615 *** -0.693 *** -4.689 *** -2.877 ***
-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
Geographic complexity measure:
Divisions away from HQ present
Sales away from
HQ
Employees away from
HQ
Average distance to HQ
Divisions away from
HQ
Geographic complexity
Dep. var.: Credit quality (I)
Subsample: Area population density - Q1
Geographic complexity -3.115 *** -10.454 *** -10.404 *** -0.852 *** -5.059 *** -3.474 ***
-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
Subsample: Area population density - Q2
Geographic complexity -2.675 *** -6.921 *** -6.817 *** -0.599 *** -3.791 *** -2.457 ***
-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
Subsample: Area population density - Q3
Geographic complexity -2.853 *** -6.785 *** -6.642 *** -0.567 *** -3.637 *** -2.470 ***
-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
Subsample: Area population density - Q4
Geographic complexity -2.758 *** -6.549 *** -6.519 *** -0.440 *** -3.817 *** -2.390 ***
-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
Average distance to HQ (log)
Divisions away from
HQ
Geographic complexity
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
Average distance to HQ (log)
Divisions away from
HQ
Geographic complexity
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.:
Divisions away from HQ present
Sales away from HQ
Employees away from
HQ
Average distance to HQ (log)
Divisions away from
HQ
Geographic complexity
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.
Dep. var.: Credit quality (I) I
II
III
IV
V
VI
Divisions away from HQ present
-8.668 ***
-4.01
Sales away from HQ
-10.762 ***
-5.02
Employees away from HQ
-12.246 ***
-5.61
Average distance to HQ
-1.504 ***
-3.49
Divisions away from HQ
-11.115 ***
-4.17
Geographic complexity
-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
Business diversification 1.241 *** 1.182 *** 1.154 *** 1.472 *** 1.288 *** 1.223 ***
6.21
6.10
5.97
7.90
6.62
6.28
Number of segments -11.044 *** -10.627 *** -10.535 *** -10.803 *** -10.641 *** -10.592 ***
-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
Product market concentration -1.930
-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
Divisions away from HQ present
-0.003
-0.97
Sales away from HQ
-0.027 ***
-6.29
Employees away from HQ
-0.025 ***
-5.89
Average distance to HQ
-1.E-04
-0.21
Divisions away from HQ
-0.022 ***
-4.90
Geographic complexity
-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
Business diversification 0.038 *** 0.038 *** 0.038 *** 0.038 *** 0.038 *** 0.038 ***
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
Dep. var.: Sales growth I
II
III
IV
V
VI
Divisions away from HQ present
-0.003
-0.88
Sales away from HQ
-0.029 ***
-6.26
Employees away from HQ
-0.027 ***
-5.84
Average distance to HQ
-4.E-04
-0.50
Divisions away from HQ
-0.025 ***
-4.82
Geographic complexity
-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
Business diversification 0.038 *** 0.038 *** 0.038 *** 0.038 *** 0.038 *** 0.038 ***
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
Dep. var.: Sales growth I
II
III
IV
V
VI
Divisions away from HQ present
-0.039 **
-2.42
Sales away from HQ
-0.100 ***
-5.34
Employees away from HQ
-0.097 ***
-5.12
Average distance to HQ
-0.008 **
-2.22
Divisions away from HQ
-0.159 ***
-6.59
Geographic complexity
-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
Business diversification 0.053 *** 0.052 *** 0.053 *** 0.054 *** 0.055 *** 0.054 ***
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
42