1. business and industrial

Financial Intermediation, Costly Information
Production, and Cross-Section of Firm Growth: Theory
and Evidence∗
Bongseok Choi†
Seon Tae Kim‡
March 16, 2015
Abstract
This paper studies the mechanism of financial intermediaries’ information production and its implications for the cross-section of capital allocation and firm growth. We
build a model of financial intermediation in which (i) both loan contracts and production of information on borrowing firms’ creditworthiness are endogenously determined,
and (ii) the smaller firm’s creditworthiness is more costly to assess. Analytic results
show that firm growth in the small-sized industry relative to that in the large-sized
industry is more sensitive to growth in the financial intermediary’s efficiency of information production, especially to the greater extent in the more financially developed
country. Using firm-level data, we find that within-country growth in the aggregate
provision of information on borrowers’ creditworthiness disproportionately increases
the smaller-sized industry’s firm-level capital growth—in terms of growth in capital in
place, liquidity and externally raised capital, respectively—, especially to the extent
significantly greater in the more financially developed country. All these results suggest
that in financially underdeveloped countries, the financial intermediaries’ improved efficiency disproportionately increases the large industry’s firm growth, opposite to the
case of financially advanced countries.
∗
We would like to thank Francisco P´erez-Gonz´alez, Kwanho Shin, Manjong Lee for helpful comments,
and seminar participants at the Korea University, SUNY Buffalo, Money, Macro and Finance Conference
2014, El III Congreso de Investigaci´
on Financiera IMEF 2013, and KMFA 2013. All errors are ours.
†
Korea Energy Economics Institute, Naesonsunhan-ro, Uiwang-si, Gyeonggi-do, 437-713, South Korea,
e-mail: bchoi4@keei.re.kr.
‡
Corresponding author. Department of Business Administration, ITAM Business School, Rio Hondo #1,
Col. Pregreso Tizapan, D.F., Mexico, C.P. 01080, e-mail: seon.kim@itam.mx.
JEL Classification: G14, G21, O16.
Keywords: Financial intermediation, Information production, Firm growth, Capital allocation, Financial development.
2
Financial systems can greatly affect the allocative efficiency and hence economic development (King and Levine; 1993; Buera et al.; 2011). However, less is known about the
mechanism of financial intermediaries’ information production and its implications for the
cross-section of capital allocation and firm growth (Giannetti and Yu; 2014). In this paper,
we explain and identify how the effect of the improved efficiency of financial intermediaries on
firm-level capital growth differs between the small and large industries (Beck, Demirguc-kunt,
Laeven and Levine; 2008; Beck, Demirguc-kunt and Maksimovic; 2008).1 In this literature,
much attention has been paid to the relative benefit of financial intermediation between the
small and large industries, while its relative cost between these two industries has been, even
though important, understudied. Our main contribution is to fill this gap.2
We aim to enhance understanding of implications of the fact that the financial-intermediation
cost is negatively correlated with the borrowing firm’s size—more costly to assess creditworthiness of a small firm than that of a large firm—(Peterson and Rajan; 2002), indicating that
relative to the large firm, the small firm has the disadvantage in terms of the greater cost
of financial intermediation.3 More specifically, we consider the case in which the financial
intermediary’s marginal cost curve of information production is much steeper for the small
firm than for the large firm. In certain cases, this difference in the slope of the marginal
cost curve might imply that for the small firm, the financial intermediary’s cost elasticity of
information production is even lower than for the large firm. In this case, the financial intermediaries’ improved efficiency (e.g., adoption of new information technologies) might not
necessarily bring about the disproportionate increase in the small industry’s firm growth,
1
This issue is also relevant to the optimal design of policies that are intended to boost growth of small
and medium-sized firms by various means of subsidies.
2
More broadly, this paper also contributes to the literature that studies financial intermediaries’ information production (Leland and Pyle; 1977; Campbell and Kracaw; 1980; Boyd and Prescott; 1986; Chemmanur
and Fulghieri; 1994; Fulghieri and Lukin; 2001; Stein; 2002; Giannetti and Yu; 2014).
3
See discussion and references in Beck, Demirguc-kunt, Laeven and Levine (2008).
1
contrary to the popular view (Beck, Demirguc-kunt, Laeven and Levine; 2008). The key
is the shape of the marginal cost curve of information production, especially its difference
between the small and large firms. Understanding of this equilibrium quantity calls for an
equilibrium model of the financial intermediary’s information production.
We develop a parsimonious model of financial intermediation in which both the real
sector’s production of goods/services and the financial sector’s information production are
fully flexible and endogenously determined (Giannetti and Yu; 2014). We consider a small
open economy in which domestic firms transform capital into the single final good by using the decreasing-returns-to-scale technology, where the firm-level productivity is a private
information known to only the firm itself (Arellano et al.; 2012). Firms belong to one of
two industries that differ in the (publicly known) distribution of firms’ productivities, which
would determine the technological firm-size distribution if there were no distortions. The industry’s productivity distribution is used as a proxy of all fundamental factors that determine
the industry’s “technological” composition of small firms.4 These firms finance investment
via one-period loans that are optimally designed by the financier, who engages in, prior to
designing loan contracts, costly production of information on such firms’ productivities so as
to reduce the conflict of interests between the borrowing firms and the international lenders.
In our model, the financier can raise, in the international capital market, as much amount
of capital as needed without default risk. Thus, the main source of distortions is the informational friction (i.e., borrowers’ private information) but not the country-level “capital endowment” constraint as in Giannetti and Yu (2014). Prior to providing domestic borrowing
firms with capital, the financier assesses the firm’s productivity that is in equilibrium tightly
4
Productivity is one, even though not all, of determinants of the industry’s technological firm-size distribution. For analytical tractability, we consider this one factor model. Otherwise, the model would become
too complicated to answer the main research question addressed in this paper.
2
related to the firm’s creditworthiness; then it optimally designs loan contracts by taking
into consideration the borrowing firms’ productivity/creditworthiness, incentive compatibility and participation constraints. Importantly, we assume that as in the data, production of
information on creditworthiness of a small firm is more costly than that of a large firm is.5
Using the truth-revealing mechanism, we solve for equilibrium: the equilibrium level of
information production and loan contracts that take into consideration the borrowers’ participation and incentive-compatibility constraints. The financier’s information production
determines firm growth and the allocative efficiency by mitigating the asymmetric information problem (its benefit) but incurs costs. The trade-off between these marginal benefit and
cost determines the equilibrium level of information production and hence the cross-section
of firm growth between the two industries that differ in the technological size.
Consider the technologically small- vs. large-sized (equivalently low- vs. high-productivity)
firms. On the one hand, the small firm is prone to the asymmetric information problem to
the greater extent and hence has the advantage in terms of the greater marginal benefit of
information production—the greater growth potential that would be realized if the current
financial constraint were relaxed. On the other hand, the small firm also has the disadvantage in terms of the financier’s greater marginal cost—more costly to assess the small
firm’s creditworthiness. These two opposing forces determine the relative level of equilibrium
information production and hence firm growth between the small- and large-sized industries.
Main analytical results are as follows: Consider two countries that differ in the financier’s
efficiency of assessing borrowers’ creditworthiness, the model-side measure of a country’s
financial development. The model predicts that in the more financially developed country,
the disproportionate impact of growth in the financier’s information-production efficiency on
5
The information-production cost is stochastic and not observed by the financier until the end of a period
so that it can not signal the borrowing firm’s productivity prior to designing the loan contracts.
3
the small industry’s firm growth is of magnitude greater than in the less financially developed
country. That is, in financially advanced countries, the financial intermediaries’ improved
efficiency of information production disproportionately increases the small industry’s firm
growth, but not necessarily in financially underdeveloped countries.
The economic mechanism is as follows: Consider the financier’s marginal cost curve of
information production for the small firm relative to that for the large firm. In a financially
advanced country, the difference in the cost of information production between the small and
large firms is negligible such that the marginal cost curve of information production is almost
identical between these two firms. By contrast, in a financially underdeveloped country, the
marginal cost curve of information production is much steeper for the small firm than for
the large firm. The steeper marginal cost curve of information production implies that in
response to one unit reduction in the overall cost of information production (i.e., parallel shift
of the marginal cost curve), the increase in information production would be of magnitude
smaller. Thus, in a financially underdeveloped country, the disproportionate effect of one
unit increase in the financier’s efficiency on the technologically small-sized industry’s firm
growth would be of magnitude substantially smaller than in a financially advanced country.
Using the cross-country firm-level data, we empirically examine the key mechanism. More
specifically, we investigate determinants of firm-level capital growth measured in three ways:
(i) growth in capital in place—e.g., growth in total assets, tangible fixed assets or investment
in intangibles, respectively—, (ii) liquidity growth—measured as growth in net working
capital—, and (iii) growth in externally raised capital.6 This firm-level data comes from the
Osiris database that provides firm-level information on balance sheet and income statement
6
Total assets refer to the sum of book values of tangibles and intangibles; tangible fixed assets refer to
property, plant and equipment; net working capital refers to the current assets minus current liabilities;
externally raised capital refers to the cumulative level of capital raised from sources other than the firm’s
own retained earnings.
4
for publicly traded firms over the world. Our sample covers publicly listed firms over 20
manufacturing industries and 31 (both developing and developed) countries during the period
2002-2007, where we exclude observations during the recent crisis period, from 2008 and
thereafter.7 The sample includes a total of 7,034 firm-industry-country observations.
We investigate how firm-level capital growth is systematically associated with withincountry growth in the financial intermediaries’ efficiency (i.e., inverse of the cost) of production of information on borrowers’ creditworthiness, which is measured as the growth rate of
the country’s credit bureau index on average during the sample period from 2004 to 2007.
For a given country, the credit bureau index measures the percentage of individuals and
companies of which past repayment history is provided, labeled coverage rate. Borrowers’
past repayment history is likely to provide valuable information on the likelihood of the
comparable borrowers’ future repayment, and hence to reduce the overall cost of production
of information on borrowers’ creditworthiness about the future repayment (Djankov et al.;
2007; Arellano et al.; 2012). Under this plausible assumption, the credit bureau index is
taken as our measure of the financial intermediaries’ efficiency of information production.
Our primary goal is to examine whether or not the effect of the improved efficiency of
financial intermediaries on the small-sized industry’s firm growth is substantially constrained
by the greater cost of financing such an industry. Our identification assumption is as follows:
the lower the country’s level of financial development, the greater the disproportionate cost
of financing the small-sized industry. The reason is that in financially advanced countries,
the overall cost of financial intermediation is small, and hence so is the difference in such a
cost between the small- and large-sized industries.
Thus, in running the cross-sectional regression of firm-level capital growth, we focus on
7
Data is available during the period from 2002 and thereafter.
5
estimating the coefficient of the triple interaction term among (i) growth in the credit bureau
index (i.e., per-year improvement in the financial intermediarie’s efficiency), (ii) industry’s
technological size that is not constrained by the financial obstacles and measured as the corresponding U.S. industry’s small-firms share in employment (Beck, Demirguc-kunt, Laeven
and Levine; 2008), and (iii) the level of a given country’s financial development that is measured as the country’s enterprise credit-to-GDP ratio so that it measures how easily firms
can access credit (Beck et al.; 2012). For identification, we use the country’s legal origin as
an instrumental variable for financial development (La Porta et al.; 1997, 1998) to avoid the
reverse-causality problem (Aghion et al.; 2005).
Our results for the IV (2SLS) cross-sectional regression of firm-level capital growth show
that in countries with the high enterprise credit-to-GDP ratio, credit bureau growth disproportionately increases the small industry’s capital growth, especially to the extent significantly greater than in countries with the low enterprise credit-to-GDP ratio, consistent with
results from the model. For instance, consider one percentage point increase in credit bureau
growth, and its disproportionate impact on total-assets growth of the small industry relative
to that of the large industry. Our estimate suggests that in Argentina of which enterprise
credit-to-GDP is low, about 8.7 percent, incremental total-assets growth is actually lower (by
about 0.17 percentage points) in the small industry than in the large industry. By contrast,
in U.K. in which the enterprise credit-to-GDP ratio is high, about 55.7 percent, incremental
total-assets growth is, as expected, higher (by about 0.81 percentage points) in the small
industry than in the large industry. That is, the difference between U.K. and Argentina
in the small industry’s disproportionate firm growth is about 0.98 percentage points. Note
that credit bureau growth is actually quite higher than one percent considered above, e.g.,
it is about 10.8 percent for Argentina and 3.7 percent for U.K., implying that the resulting
6
difference in the small industry’s disproportionate firm growth between these two countries
is actually substantial, about 4.8 percentage points.8
These implications are not limited to only total-assets growth but valid for a wide range
of other measures of capital growth: growth in tangible fixed assets, intangible investment,
liquidity, and externally raised capital, respectively. Furthermore, these findings are also
robust to controlling for many industry- and country-level traits variables.9
One might be concerned that our findings are inconclusive for privately held firms because our firm-level data is limited to publicly traded firms. Accordingly, we examine this
issue by running the same triple-difference regression of firm growth—measured as growth
in the industry-level average investment—by using the United Nations Industrial Statistics
database, which covers both publicly traded firms and privately held firms located in 31
countries over the world during the period 2002-2007; this data set covers 20 manufacturing
industries and includes 518 industry-country observations. In this case, regression results
are mainly the same with those for the Osiris publicly traded firms, indicating that our
empirical findings are relevant to both publicly traded and privately held firms.
One caveat is that the credit bureau index measures the total number of individuals and
companies but not separately the number of each of these two different types of borrowers.
Thus, evidence presented in this paper is limited to the presumed case in which credit bureau
growth, even if skewed for the individual borrowers rather than corporate borrowers, would
be positively correlated with the improvement of the financial intermediary’s efficiency of
production of information on corporate borrowers. This is likely the case in the data given the
8
The 4.8 percentage points difference in the small industry’s disproportionate firm growth between U.K.
and Argentina is calculated as follows: 4.8% = (0.81% × 3.7) − (−0.17% × 10.8).
9
As in Beck, Demirguc-kunt, Laeven and Levine (2008), we control for the industry-traits variables (such
as industry-specific components of global growth opportunities, dependence on external finance, degree of
competition, and so on) interacted with credit bureau growth and country-traits variables (e.g., per-capita
real GDP) interacted with credit bureau growth and country-specific industry’s share in value added.
7
plausible assumption that there exists the economy of scope in the financial intermediary’s
production of information on different types of borrowers, e.g., a feedback effect from the
assessment of creditworthiness of individuals to that of firms, and vice versa.
This paper contributes to the literature that studies financial intermediaries’ information production (Leland and Pyle; 1977; Campbell and Kracaw; 1980; Boyd and Prescott;
1986; Chemmanur and Fulghieri; 1994; Fulghieri and Lukin; 2001; Stein; 2002; Giannetti
and Yu; 2014) and, more broadly, the evolution of both technological and financial innovations (Greenwood et al.; 2010; Laeven et al.; 2012). While these aforementioned papers
focus mainly on the demand-side determinants of financial intermediation, we emphasize the
trade-off between the demand- and supply-side determinants.10 Importantly, our model is
featured that both information production in the financial sector and production of goods
and services in the real sector are fully flexible so that we can integrate the theory of financial intermediaries’ information production more tightly with new facts about financial
development and firm growth documented in this paper.
Theoretical analysis of this paper is closely related to Giannetti and Yu (2014) who
study the mechanism that determines formal financing vs. relationship-based financing,
where formal financing is more efficient but more costly. However, the key mechanism
differs between these two papers. In our model, the financier commits to pay back to
international lenders and hence can raise as much amount of capital as needed, making
the informational friction (i.e., borrowing firms’ private information) as essentially the main
friction that worsens the allocative efficiency. By contrast, in the model of Giannetti and Yu
(2014), the total amount of capital available to financial intermediaries is limited and hence
10
One exception is Giannetti and Yu (2014) who study how the cost of financial intermediaries’ information
production determines the formal vs. relationship-based financing depending on the country’s development
stages in terms of the size of financial intermediaries’ capital endowment.
8
a key factor, beyond the informational friction, in determining formal vs. relationship-based
financing, which determines the allocative efficiency.
This paper is also related with Pagano and Jappelli (1993). They show that sharing
information, which has been already produced, among banks mitigates the adverse-selection
problem, while this paper focuses on the equilibrium level of production of information.
This paper is also related to the literature that studies financial development and economic development. Many papers have studied consequences of improved financial intermediation by leaving the mechanism of financial intermediation itself as a black box (Buera
et al.; 2011), while this paper focuses on studying the mechanism of financial intermediation.
Empirical analysis of this paper is closely related to Beck et al. (2005), Beck, Demirguckunt and Maksimovic (2008), Beck, Demirguc-kunt, Laeven and Levine (2008) and Beck
et al. (2012); these papers document the effect of the cross-country difference in financial
development on relative firm growth between small- and large-sized firms/industries, which
this paper complements both empirically and theoretically. We identify the effect of withincountry growth in the financial intermediaries’ efficiency on relative firm growth between
small- and large-sized industries.
King and Levine (1993), Levine et al. (2000) and Aghion et al. (2005) document that aggregate measures of development in credit/financial markets are significantly associated with
per-capita GDP across countries, which this paper examines by using more disaggregated
firm-level data.11
The rest of the paper is organized as follows: Section 1 develops the model, and Section
2 discusses its analytic results. Section 3 discusses empirical evidence. Section 4 concludes.
11
For discussion of the feedback effect between financial and economic development, see, e.g., Greenwood
and Jovanovic (1990) and Fuente and Marin (1996).
9
1
Model
This section develops a model of financial intermediation in which (i) both information production in the financial sector and production of goods in the real sector are fully flexible,
and (ii) a financial intermediary’s production of information on a borrowing firm’s creditworthiness is key to determining firm-level capital growth and the allocative efficiency of capital.
More specifically, the model is a small open economy with access to the international capital
market and has features as follows: Domestic firms need to raise external funds and have private information on their own productivities that are in equilibrium tightly related to these
firms’ creditworthiness. A financier intermediates the international lenders and domestic
borrowers/firms. Prior to providing firms with capital, the financier assesses information on
a borrowing firm’s productivity/creditworthiness, which is costly. Importantly, production
of information on creditworthiness of a small firm is more costly than that of a large firm is.
1.1
Environment
We consider a small open economy with access to the international capital market in which
a risk-free asset of the (real) return rf > 0 is traded.
Technology There is a continuum of (domestic) firms indexed by i. Firm i can operate
the decreasing-return-to-scale technology producing the single final good yt (i) according to
the production function written as:
yt (i) = [zt (i)]1−α [kt (i)]α ,
10
α ∈ (0, 1)
(1)
where zt (i) > 0 refers to firm i’s (exogenous) idiosyncratic productivity, kt (i) ≥ 0 capital
used in production by firm i, and α the returns to scale. Idiosyncratic productivity shocks
zt (i) ∈ [z, z] are drawn from the distribution F (·) independently across individual firms i
where 0 < z < z and F (·) is constant over time. For the tractability of the analysis, we
assume that idiosyncratic productivity shocks zt (i) are independent over time.12
Firms are grouped by the (ex-ante publicly known) distribution of idiosyncratic productivity shocks. There are two groups of firms, and these two groups differ in their cumulative
distribution functions F (z; j) defined over z ∈ [z, z], which is indexed by j ∈ {S, L}; group
j = S stands for the technologically small-sized industry, and group j = L the technologically
large-sized industry, where an industry’s technological size refers to how large the industry’s
average firm size would be if there were no distortions. For each of two groups/industries,
there is a continuum of firms of measure one. In this paper, ‘group of firms’ and ‘industry’ will
be used interchangeably. It is public information whether firm i belongs to group/industry
j = S or j = L , which is thus incorporated into the terms of borrowing in the credit market.
And the firm i’s realized idiosyncratic productivity shock z(i) is private information known
to only firm i itself and in equilibrium tightly related the firm’s creditworthiness, indicating
that the asymmetric information problem is inherent.
More specifically, each distribution F (·; j) has finite mean and standard deviation, and
its probability density function is denoted by f (·; j). Let Fj ≡ F (·; j) denote the distribution
j ∈ {S, L}. Without loss of generality, we assume that the average productivity is higher in
12
The main reason for the assumed i.i.d. process of the idiosyncratic productivity shocks zt (i) is to keep
the tractability of the model so that we can obtain analytic results of the equilibrium outcome. The case of
a serial correlation for the process of zt (i) would raise the issue of dynamic contracting, which would greatly
sacrifice the tractability of the model without gaining much insight for the research question addressed in
this paper. The key in our model is the component of a borrowing firm’s productivity that is unpredictable
in the absence of information production in financial markets. Thus, idiosyncratic productivity shocks zt (i)
in our model are intended to capture the component of a borrowing firm’s profitability that can not be
assessed correctly by using only publicly available information.
11
industry j = L than in industry j = S: E[z|FL ] > E[z|FS ], essentially so that in industry j =
L, the average technological firm size in the absence of any distortion is larger than in industry
j = S. In addition, for a technical reason related to the optimal design of loan contracts,
we also assume that each distribution F (·; j) has a log-concave density as in the mechanism
design literature (Bagnoli and Bergstrom; 1997).13 Let φ(z; j) ≡ [1 − F (z; j)]/f (z; j) denote
the inverse of the hazard rate. We assume that φ(z; j) satisfies two properties as follows:
Assumption (A1) : φ0 (z; j) ≤ 0, ∀z ∈ [z, z], and φ(z; j) <
z
, ∀j ∈ {S, L}.
1−α
The first part of the assumption (A1) says that for both two distributions FS and FL , the
inverse hazard rate φ(z; j) is non-increasing in z, which essentially guarantees that for a given
group j, the capital allocation is in equilibrium non-decreasing in the firm’s productivity z
(as commonly assumed in the literature). The second part of the assumption (A1) essentially
states that for both two distributions FS and FL , the inverse hazard rate φ(z; j) is bounded
above by z/[1 − α].14
Firm i owns no capital and hence finances, at the beginning of a period t, capital kt (i)
via the one-period loan contract provided by the financier. For simplicity, we assume that
capital, if used in production, depreciates fully at the end of each period.15
The final good can be paid back to the international lenders at no delivery costs (i.e.,
zero iceberg cost), and its market is perfectly competitive. Without loss of generality, we
assume that in every period, the price of the final good is normalized to one.
13
Bagnoli and Bergstrom (1997) discuss merits of the log-concave density with numerous examples including the uniform, normal, exponential, logistic, Beta, and Gamma distributions.
14
This assumption of φ(z; j) being bounded above is technically needed to make the model tractable such
that as will be shown later, the financial intermediary’s problem of intermediating capital by designing loan
contracts is well defined so that every firm in need of external finance is served by the financial intermediary.
15
The assumption of 100 percent depreciation of capital does not hurt the generality of our analytic results
and helps to simplify the notation.
12
Private Information and Financial Intermediation Firm i observes its own ex-post
realized idiosyncratic productivity shock zt (i), while others do not. As a result, the allocation of capital via the capital market might be inefficient due to the asymmetric information
problem between lenders and borrowers, calling for information production so as to alleviate
such a problem. The domestic financial intermediary, labeled financier, which is the best
capable of acquiring and processing information on the domestic industrial firms’ productivities zt (i), does the job of intermediating borrowers/firms and lenders so that the conflict
of interests between them is reduced. The financier commits to repayment without default
risk. Thus, the financier can raise capital at the risk-free rate in the international capital
market and provide all (domestic) firms with as much amount of capital as demanded by
such borrowing firms, where firms face the terms of borrowing that depends, of course, on
their levels of borrowing (i.e., the larger the loan amount, the worse the terms of borrowing).
The financier is the monopolist in the domestic market of intermediation services. It
turns out that under this setting, the financier behaves in equilibrium as if the social planner
that maximizes the small open economy’s total output, and hence welfare, subject to the
same informational constraint, i.e., the equilibrium outcome is ‘constrained efficient’ subject
to the inherent informational constraint due to private information. Note that the financier
itself has no default risk. Thus, this economy suffers only from the informational friction but
not from the limited total amount of capital that can be raised. This feature of our model
enables us to clarify the mechanism that determines the financial intermediaries’ equilibrium
provision of information on borrowers’ creditworthiness, and hence equilibrium amount of
formal financing, which would mitigate the informational friction and hence improve the
allocative efficiency. By contrast, Giannetti and Yu (2014) study the setup in which the
total amount of capital available to financial intermediaries is limited and hence a key factor,
13
beyond the informational friction, in determining formal vs. relationship-based financing.16
Timing At the beginning of a period t, idiosyncratic productivity shocks zt (i) are realized;
the financier learns zt (i) stochastically by paying the stochastic information-production costs
that are realized but not observed by the financier; the financier designs loan contracts and
provides firms with capital. At the end of a period t, production takes place; firms pay back
to the financier, who then pays the international lender(s) capital plus interests.
Information Production Cost Consider borrowing firm i that belongs to group j. The
financier can learn firm i’s productivity zt (i; j) with probability µt (i; j) ∈ [0, 1] by paying the
information-production cost. If the financier fails to learn zt (i; j), then the financier simply
uses the group j’s distribution F (·; j) in designing the loan contract for firm i; in such a
case, the financier would optimally design the loan contract such that in equilibrium, firm i
is willing to voluntarily reveal zt (i; j) due to the “informational rent” paid by the financier.
Consider group j of firms. The financier’s information production takes place simultaneously across such firms. Note that prior to information production, the financier can
not distinguish individual borrowing firms who differ only in productivities, which are unknown to the financier. Thus, the financier chooses the same level of information production
µt (i; j) = µt (j) across firms that belong to the same group j.
For group j, the financier chooses µt (j), the probability of success in learning productivities of group j’s firms (i.e., the measure of information production) subject to the cost
function of information production c(µt (j); zt (i; j), at ) that is increasing and convex in µt (j).
More specifically, the cost function c(µt (j); zt (i; j), at ) is stochastic and negatively correlated
16
Thus, the mechanism explored in our setup is related to, but different from, the mechanism in Giannetti
and Yu (2014) regarding determinants of efficiency-enhancing formal financing.
14
with the borrowing/assessed firm’s productivity zt (i; j) and written as:
c(µt (j); zt (i; j), at ) =
i
1h
1
c · zt (i; j)−γ · µt (j) + [µt (j)]2 ,
at
2
∀µt (j) ∈ [0, 1],
γ>0
(2)
where c > 0 refers to the scale parameter for the information-production cost component that
is linear in µt (j) relative to the (normalized) quadratic component of the cost, and at > 0
is the financier’s efficiency of information production, which shifts down the informationproduction cost functions for borrowing firms in all industries.17 Thus, at is interpreted as
the level of this economy’s financial development.
Loan Contracts Let kt (zt (i)) denote capital supplied to firm i at the beginning of a period
t and dt (zt (i)) the firm i’s repayment at the end of the period t. The financier offers, at the
beginning of t, firm i the loan contract (kt (zt (i)), dt (zt (i))) depending on whether or not the
financier has learned zt (i). Given the choice of information production µt for group j of
firms, the financier, due to the law of large numbers, succeeds in learning zt (i) for µt fraction
of these firms and fails for (1 − µt ) fraction. For a given group j, suppressing group index
j, we let (ktN (zt (i)), dN
t (zt (i))) denote the loan contract for firm i conditional on that the
financier has succeeded in learning zt (i) and (ktO (zt (i)), dO
t (zt (i))) do the same conditional on
that the financier has failed to learn zt (i); similarly, let ytN (zt (i)) ≡ [zt (i)]1−α [ktN (zt (i))]α and
ytO (zt (i)) ≡ [zt (i)]1−α [ktO (zt (i))]α denote firm i’s end-of-period output conditional on whether
the financier has learned zt (i) or not, respectively.
17
There is ample evidence about the improvement of the efficiency in the financial sector. The financial
sector in the U.S. has grown steadily over the post-war period. In particular, the financial sector has
benefited greatly from innovation in computers and information technologies. For instance, from the early
1980s onward, the value added per employee has increased much faster in the financial sector than in the
rest of the U.S. economy, see, e.g., Philippon and Reshef (2007).
15
Discussion: Model Specification We discuss implications of the model specifications,
especially the information-production cost function specified above. We consider group j of
firms in what follows and suppress the group index j unless otherwise mentioned. Consider
the component c·zt (i)−γ ·µt that is linear in µt : it is negatively correlated with the borrower’s
productivity zt (i) given γ > 0. That is, for a given level of information produced µt , production of information on a low-productivity firm is more costly than that on a high-productivity
firm is.18 This assumption essentially captures the fact that information on a small firm is
“soft” and difficult to obtain and assess (Peterson and Rajan; 2002).19 This feature of the
information-production cost is intended to capture the supply-side determinants of financial
intermediation. There is ample evidence supporting that the spread of credit availability
or credit costs (proxied by the lending rate or required collateral) are negatively correlated
with a borrowing firm’s productivity (or loan size), see, e.g., Cressy and Toivanen (2001)
and Hanley and Girma (2006).20 The perfect correlation between the borrower’s productivity zt (i) and the information-production cost is assumed for simplicity and does not hurt the
generality of the main results given the fact that we focus on the industry-level equilibrium
outcome (due to the law of large numbers).21
18
The cost of production of information on an individual borrower is not observed by the financier until
the loan contract is designed, and hence can not be used as the signal of the borrower’s productivity in
designing the loan contract.
19
This assumption can be also thought of as a reduced-form approach to the fact that relative to an
entrant, an incumbent surviving firm tends to have higher productivity, e.g., the selection effect, and also
has a greater advantage in access to external funds, e.g., reputation effect. More generally, we could model
the case of a positive serial correlation for a firm’s productivity process and derive endogenously the property
of information-production cost function being decreasing in the borrowing firm’s productivity, which would,
as discussed earlier, sacrifice the tractability without gaining much insight.
20
In addition, it seems also plausible that the magnitude of the heterogeneity component of informationproduction cost is decreasing in the financial sector’s efficiency; for instance, Liberti and Mian (2010) estimate
the collateral spread of financing across riskiness and show that it is decreasing in the level of financial market
development.
21
For the case of the correlation between zt (i) and the information-production cost being imperfect, the
main results of the model would be the same as those for the perfect-correlation case due to the law of large
numbers. Moreover, as discussed earlier, the information-production cost is realized at the end of a period t
16
We discuss how we model the industry’s technological firm-size distribution. Note that as
discussed above, the financier’s cost function of information production is negatively correlated with the technological firm size, which is a critical component of our main hypothesis.
In this paper, the industry’s productivity distribution is used as a proxy of all fundamental
factors that determine the industry’s “technological” composition of small firms. Productivity is one, even though not all, of determinants of the industry’s technological firm-size distribution. We consider this one factor model mainly to keep the model analytically tractable.
Alternatively, we could consider multiple-factor model, which would then become too complicated to answer the main research question addressed in this paper.
The Financier’s Problem Consider group j of firms. Given the choice of information
production µt (j) for group j (which is analyzed soon), the financier designs optimal loan
contracts to maximize its own end-of-period profit from intermediation services V (µt (j); j):
(
V (µt (j); j) = max
Φ(µt (j))
Z
µt (j)
z
N
[dN
t (z; j) − (1 + rf )kt (z; j)]dF (z; j)
z
Z
+ [1 − µt (j)]
)
z
O
[dO
t (z; j) − (1 + rf )kt (z; j)]dF (z; j)
(3)
z
subject to the usual rationality and incentive-compatibility, if needed, conditions as follows:
For the case of the firm’s productivity z being successfully learned, labeled informed case
and denoted by the superscript ‘N ,’ the rationality condition is written as:
[z]1−α [ktN (z; j)]α − dN
t (z; j) ≥ 0,
∀z ∈ [z, z]
and hence can not signal the borrowing firm’s productivity prior to designing the loan contracts.
17
(4)
where the firm’s outside-option value is normalized to zero. For the other case in which the
financier has failed to learn z, labeled uninformed case and denoted by the superscript ‘O,’
the rationality and incentive-compatibility conditions are written as:
[z]1−α [ktO (z; j)]α − dO
t (z; j) ≥ 0,
∀z ∈ [z, z],
1−α O 0
0
[z]1−α [ktO (z; j)]α − dO
[kt (z ; j)]α − dO
t (z; j) ≥ [z]
t (z ; j),
(5)
∀z ∈ [z, z], ∀z 0 6= z.
(6)
Note that for this case in which the given firm’s productivity z is unknown to the financier,
the financier needs to provide such a firm with an incentive so that the firm voluntarily
reveals the firm’s productivity z, captured by the additional incentive-compatibility condition (6). That is, the firm in equilibrium reports its true productivity z despite the
available choice of lying and reporting z 0 6= z because the loan contract menu is designed
so that truth reporting does maximize the firm’s own value. The function Φ(µt (j)) ≡
O
O
{(ktN (z; j), dN
t (z; j)), (kt (z; j), dt (z; j))} denotes the loan contracts optimal to the financier’s
problem above for a given level of information production µt (j).
The financier, in turn, chooses the optimal level of information production µt (j) so as
to maximize its own beginning-of-period profit, i.e., the present value of the intermediation
profit V (µt (j); j)/(1 + rf ) net the current information-production costs:
(
W (at ; j) =
max
µt (j)∈[0,1]
1
1 + rf
Z
V (µt (j); j) −
)
z
c(µt (j); z, at )dF (z; j)
(7)
z
where W (at ; j) refers to the financier’s beginning-of-period profit function from producing
information and providing group j of firms with loan services.
18
1.2
Equilibrium
The equilibrium in this economy is a list of the policy and value functions of the loan
contracts that satisfy the condition that for each group/industry j ∈ {S, L}, both loan
O
O
contracts (ktN (z; j), dN
t (z; j)), (kt (z; j), dt (z; j)) for z ∈ [z, z] and information production
µt (j) solve for the financier’s problem.
2
Results
This section presents analytic results of the model. First, the financier’s problem is analyzed,
and the equilibrium allocation of capital across firms is discussed. Second, the equilibrium
level of information production is characterized. Third, we discuss how growth in the financier’s efficiency of information production affects relative firm growth between the smalland large-sized industries.
2.1
Allocation of Capital for Given Information Production
This section analyzes the industry-level equilibrium allocation of capital as a function of the
level of information production. We begin by characterizing the solution to the financier’s
problem of designing optimal loan contracts for a given group of firms. We suppress the
group/industry index j unless otherwise mentioned.
Lemma 1. Consider a firm of which realized ex-post productivity is z. The financier’s problem
of designing optimal loan contracts for such a firm (3) is equivalent to maximizing the “virtual
surplus” of the intermediation profit that is written as:
o
n
φ(z) 1−α h
[z] [kt (z)]α − (1 + rf )kth (z)
[z]1−α [kth (z)]α − 1{h=O} · (1 − α)
z
{kth (z)}
max
19
(8)
where the superscript ‘h’ indicates whether the financier has failed to learn z for h = O, in
which case the indicator function 1{h=O} equals one, or not for h = N , in which case the
indicator function 1{h=O} equals zero.22 The solution to the problem above is characterized
as follows:
1. Capital employed by the firm of productivity z is written as:
ktN (z)
=
α
1 + rf
1
1−α
· z,
ktO (z)
=
α
1 + rf
1
1−α
1
φ(z) o 1−α
.
· z · 1 − (1 − α)
z
n
(9)
2. The financier’s end-of-period profit is is written as:
πtN (z)
= (1−α)
α
1 + rf
α
1−α
·z,
πtO (z)
= (1−α)
α
1 + rf
α
1−α
α
n
φ(z) o 1−α
·z· 1−(1−α)
.
z
(10)
3. The participation constraint binds for the firm of the lowest productivity z = z.
Note that the term (1 − α)[φ(z)/z] · [z]1−α [k h (z)]α in equation (8) quantifies the degree
of the capital-allocation distortion due to the asymmetric information problem, essentially
caused by the incentive compatibility constraint, compared to the first best case of no asymmetric information. More specifically, it is the “informational rent” received by the borrowing
firm so that such a firm voluntarily reveals its private information on z. For every firm of
productivity z which the financier has failed to learn, such a degree of the capital-allocation
distortion should not be too large such that the amount of “informational rent” is smaller
than the borrowing firm’s output. Indeed, this is the case guaranteed by the second part
of the assumption (A1), so that the financier has an incentive to provide every firm with
22
It is obvious that the financier’s reformulated problem of designing optimal loan contracts (8) is identical
to the planner’s problem of the optimal allocation of capital subject to the same informational constraints.
20
the intermediation service. Otherwise, the financier’s payoff from providing these firms with
intermediation services would be negative, and hence the financier would not provide such
firms with the intermediation services, which is uninteresting and ruled out.23
As shown in Lemma 1, the solution to the financier’s problem of designing the optimal
loan contracts is independent of time t. From now on, the time index t is suppressed for the
solution to the financier’s problem, e.g., k h (z) stands for kth (z).
2.2
Information Production
This section characterizes the equilibrium level of information production. For group j of
firms, the first-order condition for equilibrium information production µt (j) is, if satisfied,
written as:
Z
z
Z
z
1
[HM C(z) + µt (j)]dF (z; j) = 0
(11)
z
z at
"
α
1−α
α #
π N (z) − π O (z)
1−α
α
φ(z) 1−α
where M R(z) ≡
=
·
· z · 1 − 1 − (1 − α)
1 + rf
1 + rf
1 + rf
z
(12)
M R(z)dF (z; j) −
and HM C(z) ≡ c[z −γ ].
(13)
Rearranging terms, we can simplify the first-order condition for µt (j) as:
µt (j) = at E[M R(z); j] − E[HM C(z); j] if 0 < at E[M R(z); j] − E[HM C(z); j] < 1 (14)
23
The second part of the assumption (A1) that φ(z) is bounded above kicks in here: for a firm of productivity z ∈ [z, z], the degree of the capital-allocation distortion (1 − α)[φ(z)/z] · [z]1−α [k h (z)]α is smaller than
the firm’s output [z]1−α [k h (z)]α essentially because (1 − α)[φ(z)/z] < 1. More specifically, consider a firm
of productivity z that the financier has failed to learn. For such a firm, the ratio of the degree of the distortion (1 − α)[φ(z)/z] · [z]1−α [k h (z)]α to the firm’s output [z]1−α [k h (z)]α is simply given by (1 − α)[φ(z)/z].
Below we show that (1 − α)[φ(z)/z] < 1 for every z ∈ [z, z]. We start by rewriting the second part of the
assumption (A1) as: [1 − α]φ(z)/z < 1. Note that φ(z)/z ≤ φ(z)/z ≤ φ(z)/z where the last inequality
follows from the first part of the assumption (A1) that φ(z) is non-increasing in z. Thus, we have shown
that [1 − α]φ(z)/z < 1, ∀z ∈ [z, z].
21
where E[M R(z); j] =
Rz
M R(z)dF (z; j) refers to the average M R(z), labeled marginal
Rz
revenue of information production for group j, and E[HM C(z); j] = z HM C(z)dF (z; j)
z
the average HM C(z), labeled marginal cost of information production for group j. These
two quantities, i.e., the marginal revenue and cost of information production E[M R(z); j]
and E[HM C(z); j], will be used later in explaining the mechanism of the relative level of
information production between the small- and large-sized industries µt (S) − µt (L).
From now on, at is assumed to be of a moderate level such that for each group j, the
first-order condition for equilibrium information production µt (j) is satisfied.
h
i
Assumption (A2) : at E[M R(z); j] − E[HM C(z); j] ∈ (0, 1),
2.3
∀j ∈ {S, L}.
Relative Firm Growth between Two Industries
This section discusses the effect of a marginal improvement in the financier’s efficiency of
information production on industry-level firm growth where ‘industry’ simply refers to ‘group
of firms’ as mentioned earlier. In particular, we focus on relative firm growth between
two industries differing in the technological size that is essentially determined by industrylevel average productivity E[z|F (:, j)]. Note that we have earlier assumed that in industry
j = L, the average productivity is higher than in industry j = S so that the industry
j = L’s average technological firm size is larger than that of industry S. More specifically,
we assume that the technologically large-sized industry j = L’s productivity distribution FL
first-order stochastically dominates (FOSD) the technologically small-sized industry j = S’s
distribution FS where Fj denotes the industry j’s productivity distribution: Fj ≡ F (:, j).
22
Assumption (A3): FL first-order stochastically dominates FS :
FL (z) < FS (z),
∀z ∈ [z, z].
(15)
The Assumption (A3) says that for any given level of productivity z, the large-sized
industry j = L’s cumulative probability FL (z) is smaller than the small-sized industry j = S’s
cumulative probability FS (z). This essentially implies that for any given lower bound z,
the large-sized industry’s fraction of firms of productivity higher than such a lower bound
(1 − FL (z)) is greater than the small-sized industry j = S’s counterpart (1 − FS (z)); that is,
(1 − FL (z)) > (1 − FS (z)) for any z ∈ [z, z].
We present results for the two key equilibrium quantities, i.e., the marginal revenue
and cost of information production E[M R(z)|Fj ] and E[HM C(z)|Fj ], especially relative
magnitude of each of these two equilibrium quantities between the two industries.
Lemma 2. Consider that assumptions (A1)-(A3) hold. In this case, the two results follow:
1. If [φ(z)/z − φ0 (z)] > 1/α and α < 0.5, then E[M R(z)|FS ] ≥ E[M R(z)|FL ].
2. E[HM C(z)|FS ] ≥ E[HM C(z)|FL ].
Lemma 2 provides sufficient conditions under which the industry-specific marginal revenue of information production E[M R(z)|Fj ] is greater in the technologically small-sized
industry j = S than in the technologically large-sized industry j = L. The reason is
essentially that the firm-level marginal benefit of information production M R(z) is decreasing in the firm-level size that is in equilibrium monotonic to the firm-level productivity z.
Results of Lemma 2 also show that the industry-specific marginal cost of information production E[HM C(z)|Fj ] is, by construction, also greater in the technologically small-sized
23
industry j = S than in the technologically large-sized industry j = L: the smaller firm’s
creditworthiness-related information is more costly to assess. Thus, it follows that the relative
industry-specific level of information production between the two industries is in equilibrium
determined by which one of these two opposing forces dominates the other.
From now on, we assume the two conditions as follows24 :
Assumption (A4):
1
φ(z)
0
− φ (z) > and α < 0.5.
z
α
The assumption (A4) says that the term [φ(z)/z]−φ0 (z) is bounded below by 1/α and that α
is smaller than 0.5 so that in the technologically small-sized industry j = S, the equilibrium
industry-specific marginal revenue of information production E[M R(z)|Fj ] is greater than in
the technologically large-sized industry j = L, consistent with the consensus in the literature.
Thus, the assumption (A4) is the sufficient condition under which our model is disciplined
to be consistent with the consensus in the literature about the smaller firm’s greater growth
potential that would be realized if the current financial constraint were relaxed.
The industry-level equilibrium average firm size, measured as average capital E[k(i)|Fj ],
is, not surprisingly, strictly increasing in the industry-level average productivity E[z(i)|Fj ].
Thus, indeed, in the technologically large industry, the industry-level equilibrium average
firm size is also greater than in the technologically small industry.25
The condition that [φ(z)/z −φ0 (z)] is bounded below holds for numerous examples of log-concave density
with reasonable parametrization. The condition of α < 0.5 seems also plausible. Note that the production
function specified in this paper can be thought of as the usual Cobb-Douglas function of the variable amount
of capital powered by α and fixed level of labor input (powered by 1 − α); in this case, α would correspond
to the capital income share in national income account and is typically smaller than 0.5 in most countries.
25
The industry’s technological size again refers to how large the industry’s average firm size would be if
there were no friction.
24
24
Industry-Specific Capital Growth This section discusses how growth in the financier’s
efficiency of information production would be propagated to firm growth in terms of capital
growth, especially its difference between the two industries differing in the technological size.
After discussion of capital growth, we will discuss firm growth in terms of output growth.
We define the relative equilibrium growth rate of information production, and hence
relative growth in the capital input, between two industries. Let g(µt (j)) ≡ dlog(µt (j))/dt
denote the (spontaneous) growth rate of information production for industry j.26 Then
g(µt (j)) is in equilibrium rewritten as:
g(µt (j)) = g(at )
at E[M R(z)|Fj ]
,
at E[M R(z)|Fj ] − E[HM C(z)|Fj ]
g(at ) ≡ dlog(at )/dt
(16)
where g(at ) denotes (spontaneous) growth in the financier’s information-production efficiency
at . Note that as shown by equation (16), the industry j-specific growth rate of information
production g(µt (j)) is in equilibrium proportional to industry-wide growth in the financier’s
information-production efficiency g(at ) with the industry-specific slope that is increasing
in the equilibrium quantity E[HM C(z)|Fj ]/[at E[M R(z)|Fj ]]. Thus, given an increase in
growth in the financier’s information-production efficiency g(at ), the relative industry-specific
growth in information production between the two industries is essentially determined by the
difference in the industry-specific sensitivity term E[HM C(z)|Fj ]/[at E[M R(z)|Fj ]], and so
would be the relative capital growth between these two industries. Accordingly, we introduce
one assumption below.
26
Here we consider the change in a variable xt over a short time interval [t, t + ∆] where ∆ > 0 is small.
In this case, the growth rate of xt+∆ is approximated as: xt+∆ /xt − 1 ≈ ∆ · [dxt /dt]/xt = ∆ · dlog(xt )/dt.
The short length of the time interval ∆ is constant and can be suppressed in discussion of main results.
25
Assumption (A5):
E[HM C(z)|FS ]
E[M R(z)|FS ]
>
.
E[HM C(z)|FL ]
E[M R(z)|FL ]
We proceed to discussing how the relative growth rate of capital between the smalland large-sized industries responds to growth in the financier’s efficiency. Let g(kt (j)) ≡
dlog(E[kt (z)|Fj ])/dt denote the (spontaneous) growth rate of the average level of capital for
industry j. Note that g(kt (j)) is in equilibrium rewritten as:
h
i
µt (j) E[k N |Fj ] − E[k O |Fj ]
g(kt (j)) =
µt (j)E[k N |Fj ] + (1 − µt (j))E[k O |Fj ]
· g(µt (j)).
(17)
Consider the sensitivity of industry j-specific percentage growth in capital to one percentage growth in the financier’s efficiency g(kt (j))/g(at ), especially, its difference between
the (technologically) small- and large-sized industries: [g(kt (S))/g(at )] − [g(kt (L))/g(at )].
We label it the relative effect of the improvement of the financial intermediary’s efficiency
(REIFIE) on industry-level capital growth between the two industries:
REIF IE(kt ) ≡
g(kt (S)) − g(kt (L))
g(at )
h
i
= at Ωt (S) − Ωt (L) ,
, Ωt (j) ≡ 1
E[HM C(z)|Fj ]
at −
E[M R(z)|Fj ]
+
1
E[k O (z)|Fj ]
×
E[M R(z)|Fj ] E[k N (z)|Fj ] − E[k O (z)|Fj ]
(18)
(19)
(20)
where equilibrium outcomes, equations (14), (16) and (17), are used. Similarly, the small-
26
sized industry’s disproportionate growth in information production is defined as:
REIF IE(µt ) ≡

= at  h
g(µt (S)) − g(µt (L))
g(at )
(21)

E[M R(z)|FL ] × E[HM C(z)|FS ] − E[M R(z)|FS ] × E[HM C(z)|FL ]
i h
i .
at E[M R(z)|FL ] − E[HM C(z)|FL ] × at E[M R(z)|FS ] − E[HM C(z)|FS ]
(22)
Note that the small-sized industry’s disproportionate capital growth REIF IE(kt ) would
be positive if the small-sized industry’s credit-cost disadvantage were negligible so that the
improved efficiency of financial intermediaries would help mostly the small-sized industry’s
capital raising via the small-size industry’s disproportionately increased information production REIF IE(µt ) > 0. In general, it is likely that the small-sized industry’s credit-cost
disadvantage is negatively correlated with the country’s level of financial development at
rather than it is negligible uniformly over all countries. In such a case, the small-sized industry’s disproportionate capital growth REIF IE(kt ) would systematically positively vary
over at : REIF IE(kt )/at > 0, because the small-sized industry’s credit-cost disadvantage is
of magnitude substantial in financially underdeveloped countries but negligible in financially
developed countries. It is of our main interest under which conditions REIF IE(kt )/at > 0
is an equilibrium outcome. To this end, we provide formal results; see Proposition 1.
Proposition 1. Consider that five assumptions (A1)- (A5) hold. In this case, the variation
in REIF IE(µt ) for growth in information production over a country’s financial development
at is positive: REIF IE(µt )/at > 0. Moreover, if we additionally assume that φ(z; j) ≤ z,
then the variation in REIF IE(kt ) for capital growth over a country’s financial development
at is also positive: REIF IE(kt )/at > 0.
27
Results of Proposition 1 show that under the sufficient conditions of assumptions (A1)(A5) and the additional restriction about the inverse hazard rate of productivity φ(z; j) ≤ z,
equilibrium quantities of the model can replicate one interesting pattern, which will be
shown later to be the case in the data: in the more financially developed country, the effect
of growth in the financial intermediary’s efficiency on capital growth in the (technologically)
small-sized industry relative to that in the large-sized industry is of magnitude greater than
in the less financially developed country.
More specifically, the economic mechanism is as follows: Consider the marginal cost curve
of information production for the small firm relative to that for the large firm. In a financially
advanced country, the difference in the cost of information production between the small and
large firms is negligible such that the marginal cost curve of information production is almost
identical between these two firms. By contrast, in a financially underdeveloped country, the
marginal cost curve of information production is much steeper for the small firm than for
the large firm. The steeper marginal cost curve of information production implies that in
response to one unit reduction in the overall cost of information production (i.e., parallel shift
of the marginal cost curve), the increase in information production would be of magnitude
smaller. Thus, in a financially underdeveloped country, the disproportionate effect of the
financial intermediary’s increased efficiency on the technologically small-sized industry’s firm
growth would be of magnitude substantially smaller than in a financially advanced country.
Industry-Specific Output Growth This section discusses how relative firm growth in
terms of output growth between the small- and large-sized industries responds to growth in
the financier’s efficiency of information production. We begin by discussing the relative level
of output between these two industries conditional on whether the financier has learned the
28
borrower’s productivity z or not.
Corollary 1. Consider that four assumptions (A1)- (A4) hold. In this case, two results
follow: (1) E[y h (z)|FL ] ≥ E[y h (z)|FS ] for ∀h ∈ {N, O} , and (2) E[y N (z) − y O (z)|FS ] ≥
E[y N (z) − y O (z)|FL ].
As shown by Corollary 1, industry-level average output conditional on whether the firm’s
productivity z is learned by the financier h = N or not h = O is in equilibrium higher in the
large-sized industry than in the small-sized industry, which is obvious.
Interestingly, the second result of Corollary 1 shows that in the small-sized industry,
the industry-specific marginal benefit of information production E[y N (z) − y O (z)|Fj ] is of
magnitude greater than in the large-sized industry. Consider two firms that (i) belong to
the same industry and (ii) have the same productivity but (iii) differ in whether or not the
financier has succeeded in learning the firm’s productivity. Then, the difference in expected
output between these two firms E[y N (z) − y O (z)|Fj ] would have been zero if there were no
informational friction, and hence represents the degree of the capital-allocation distortion
due to the informational friction, which can be reduced by increasing the level of information
production µt (j). Thus, E[y N (z) − y O (z)|Fj ] measures the industry-specific marginal benefit
of information production.
We turn to discussing how the industry-specific growth rate of output is in equilibrium related with common growth in the financier’s information-production efficiency. Let
g(yt (j)) ≡ dlog(E[yt (z)|Fj ])/dt denote the (spontaneous) growth rate of industry j’s average output.27 Note that industry j’s average output can be written as: E[yt (z)|Fj ] =
µt (j)E[y N (z)|Fj ] + (1 − µt (j))E[y O (z)|Fj ], for j ∈ {S, L}. Thus, we can simplify industry
27
As discussed earlier, we consider the change in a variable xt over a short time interval [t, t + ∆] where
∆ > 0 is small. In this case, the growth rate in xt+∆ would be approximated well by xt+∆ /xt − 1 ≈
∆ · [dxt /dt]/xt = ∆ · dlog(xt )/dt. The short length of the time interval ∆ is constant and suppressed.
29
j’s output growth rate g(yt (j)) as:
h
i
µt (j) E[y N |Fj ] − E[y O |Fj ]
g(yt (j)) =
µt (j)E[y N |Fj ] + (1 − µt (j))E[y O |Fj ]
· g(µt (j)).
(23)
As discussed earlier, it is of our interest to understand how, and under which conditions,
the sensitivity of firm growth to the improvement of the financial intermediary’s efficiency
differs between the (technologically) small- and large-sized industries. Accordingly, we define
and study the relative effect of the (one-percentage) improvement of the financial intermediary’s efficiency (REIFIE) on industry-level output growth between the two industries:
h
i
REIF IE(yt ) ≡ g(yt (S)) − g(yt (L)) /g(at )
(24)
h
(25)
i
= at Ψt (S) − Ψt (L) ,
, Ψt (j) ≡ 1
E[HM C(z)|Fj ]
at −
E[M R(z)|Fj ]
+
1
E[y O (z)|Fj ]
×
E[M R(z)|Fj ] E[y N (z)|Fj ] − E[y O (z)|Fj ]
(26)
where equilibrium outcomes, equations (14), (16) and (23), are used. In particular, we aim to
understand under which conditions REIF IE(yt ) would systematically positively vary over
the country’s level of financial development at : REIF IE(yt )/at > 0. Formal results about
this are presented in Proposition 2.
Proposition 2. Consider that five assumptions (A1)- (A5) hold. In this case, the variation
in REIF IE(yt ) for output growth over a country’s financial development at is positive:
REIF IE(yt )/at > 0.
Results of Proposition 2 for the relative output growth rate between the small- and large-
30
sized industries essentially follow from results of Proposition 1 for relative capital growth
between these two industries. That is, in the more financially developed country, the effect
of the marginal improvement in the financier’s information-production efficiency on output
growth in the technologically small-sized industry relative to that in the large-sized industry
is also of magnitude greater than in the less developed country.
We discuss one more result that without the heterogeneity cost component of information
production (i.e., c = 0), the qualitative relationship between the financier’s efficiency growth
and relative output growth between the two industries predicted by Proposition 2 can be
still an equilibrium outcome.
Lemma 3. Consider that four assumptions (A1)- (A4) hold and that c = 0. In this case,
the variation in REIF IE(yt ) for output growth over a country’s financial development at is
positive, but REIF IE(µt ) for growth in information production is constant equal to zero:
REIF IE(yt )
> 0, REIF IE(µt ) = 0, and g(µt (S)) = g(µt (L)) = g(at ).
at
Results of Lemma 3 say that even in the absence of the heterogeneity component of the
information-production cost function, i.e., the case of c = 0, the small-sized industry’s output
growth relative to that of the large-sized industry is sensitive to growth in the financial
intermediaries’ efficiency, especially more so in the more financially developed country.28
However, in such a case, the sensitivity of growth in information production to the financier’s
efficiency growth g(µt (j))/g(at ) is the same between the small- and large-sized industries,
especially independent of a country’s level of financial development: REIF IE(µt ) = 0,
which is in sharp contrast to the results for output growth: REIF IE(yt )/at > 0. That
28
In this case, it is also true that REIF IE(kt )/at > 0 if we additionally assume that φ(z) ≤ z.
31
is, the heterogeneity component of the information-production cost function is important
as long as the industry-specific growth in the financier’s information production is the key
in propagating the financial intermediaries’ improved efficiency into the real sector. Put
differently, the heterogeneity component of the information-production cost function does
the role of amplifying the magnitude of the effect REIF IE(yt )/at > 0.29
3
Empirical Analysis
This section discusses empirical evidence supporting the model’s key mechanism, e.g., previous results in Proposition 1 and 2. More specifically, we investigate how firm growth is associated with within-country growth in the financial intermediaries’ efficiency in terms of the
provision of information on borrowers’ creditworthiness, especially its systematic variations
over (i) the industry’s technological size and (ii) the country’s level of financial development
in terms of facilitating firms’ access to credit. To this end, we run triple-difference regression of firm growth on (i) within-country growth in the financial intermediaries’ efficiency
of production of information on borrowers’ creditworthiness, (ii) industry-level technological small-firms share, and (iii) country-level enterprise credit-to-GDP ratio, by controlling
for several industry-traits variables. Importantly, such a regression analysis is carried out
by considering the three different aspects of firm-level capital growth—growth in capital in
place, liquidity, and externally raised capital, respectively—as dependent variables so as to
decipher the link from the financial sector’s efficiency to real sector’s capital growth.
29
For instance, consider the case in which one of the four assumptions (A1)-(A4) is mildly violated so that
differently from results of Lemma 3, REIF IE(yt )/at would be close to zero if c were zero. In this case, the
heterogeneity cost component of information production c > 0 can play a quantitatively important role so
that REIF IE(yt )/at becomes significantly positive.
32
3.1
Data
Data Source We use the Osiris database that provides firm-level financial/accounting
information on the publicly traded firms, e.g., balance sheet components—short- and longterm assets, equity and debt—, and income statement—expenditures and earnings—in 20
manufacturing industries and 31 countries over the world. Throughout this section, all
monetary variables are in terms of the real 2013 U.S. dollars, and hence their growth rates
are also in real terms. The sample spans the period 2002-2007, annually, where observations
during the recent crisis period, from 2008 and thereafter, are excluded from the sample.
More specifically, the 20 sample industries (and their two-digit NACE codes) are as follows: Food (15), Textiles (17), Wearing (18), Leather (19), Wood (20), Paper (21), Publishing
(22), Chemicals (24), Rubber (25), Non-metallic (26), Basic metals (27), Fabricated metal
(28), Machinery (29), Office and computer (30), Electric (31), Communication (32), Precision (33), Motor (34), Other transport (35), and Furniture (36). And the 31 sample countries
are as follows: Australia, Austria, Canada, Costa Rica, Czech Republic, Estonia, Finland,
France, Germany, Hungary, Iceland, Indonesia, Ireland, Italy, Japan, Latvia, Lithuania,
Mexico, Netherlands, New Zealand, Poland, Portugal, Russian Federation, Slovenia, South
Africa, Spain, Sweden, Switzerland, Turkey, U.K.; these countries vary substantially, as will
be discussed later, in the levels of economic and financial development.
Measurement of Firm-Level Capital Growth and Industry Traits We investigate
the three aspects of firm-level capital growth: growth rates of (i) capital in place, (ii) liquidity,
and (iii) external capital raising. More specifically, we measure such three measures of firm
growth as follows: First, growth in capital in place is measured as the average annual growth
rate of assets in place: measured either as total assets (e.g., sum of book values of tangibles
33
and intangibles), as tangible fixed assets (e.g., book values of property, plant and equipment),
or as investment in intangibles. Note that differently from tangible fixed assets, the intangible
capital sock is missing (possibly due to no information on its depreciation rate), and hence we
use investment in intangibles. Second, liquidity growth is measured as average annual growth
in net working capital that is measured as current assets minus current liabilities. Third,
growth in externally raised capital is measured as average annual growth in the cumulative
level of capital raised from sources other than the firm’s own retained earnings.
We are interested in investigating how such various measures of firm-level capital growth
are systematically related with the within-country per-year improvement in the financial
intermediaries’ efficiency (IFIE), especially their differential sensitivities to IFIE between
the technologically small- and large-sized industries. Thus, we need to investigate the relationship between firm-level capital growth and the interaction term between IFIE and
industry-level firm size. Note that the industry-level firm size itself could be affected by the
financial obstacles that constrain firm growth. Therefore, we use the industry-level technological small-firms share (Beck, Demirguc-kunt, Laeven and Levine; 2008) as the inverse
measure of the industry’s technological size: for a given industry, we consider the corresponding U.S. industry, and measure the share in employment of small firms (as of 1997)
that have hired less than 20 employees in 1997.
We also control for other industry-level traits as follows: (i) industry-specific component
of global growth opportunities (Fisman and Love; 2007)—measured as real annual growth
in net sales in the corresponding U.S. industry on average over the period 2002-2007—,
(ii) technological dependence on external finance measured as the corresponding U.S. industry’s counterpart (Rajan and Zingales; 1998), (iii) industry-specific dependence on intangible
assets—measured as the ratio of intangible assets to fixed assets in the corresponding U.S. in-
34
dustry on average over the period 2002-2007 (Claessens and Laeven; 2003), (iv) the industrylevel degree of opaqueness in assessing firm performance in the stock market—measured as
one minus firm-specific variability of stock returns as in Durnev et al. (2004)—, and (v) the
industry-level four-firm concentration ratio—measured as the corresponding U.S. industry’s
counterpart in 2002 as in Beck, Demirguc-kunt, Laeven and Levine (2008).30
Financial Development, and Growth in the Financial Intermediaries’ Efficiency
We turn to discussing how to measure the two country-level variables: level of financial
development and growth in the financial intermediaries’ efficiency in terms of information
production. First, country c’s financial development F Dc is measured as the enterprise
credit-to-GDP ratio on average over the sample period as in Beck et al. (2012) and intended
to capture the country’s efficiency of financial intermediation that facilitate firms’ access to
credit. We focus on the role of the easiness of the business firm’s access to external finance
in determining firm growth rather than household welfare. Thus, our measure of financial
development includes enterprise credit but excludes household credit.31
Second, country c’s growth in the financial intermediaries’ efficiency IF IEc is measured in
terms of facilitating the provision of information on borrowers’ creditworthiness and proxied
by the average growth rate of the country’s credit bureau index during the period 2004-2007,
taken from the Doing Business publications of the World Bank. For a given country and
year, the credit bureau index measures the percentage of individuals and companies of which
past repayment history is provided, labeled coverage rate.32 Borrowers’ past repayment history is likely to provide valuable information on the likelihood of the comparable borrowers’
30
For industry-traits variables (i)–(iv), we use data from Compustat and CRSP.
Beck et al. (2012) find that enterprise credit is significantly associated with economic growth, but household credit is not.
32
Both private and public credit bureaus could exist; in such a case, we take the average of the two
credit-bureau coverage rates as the measure of credit bureau index for a given country-year observation.
31
35
future repayment, and hence to reduce the overall cost of production of information on borrowers’ creditworthiness about the future repayment (Djankov et al.; 2007; Arellano et al.;
2012). Under this plausible assumption, the credit bureau index is taken as our measure
of the financial intermediaries’ efficiency (i.e., inverse of the cost) of information production, and hence its average growth rate measures the per-year improvement in the financial
intermediaries’ efficiency of information production.
One caveat is that the credit bureau index measures the total number of individuals and
companies but not separately the number of each of these two different types of borrowers.
Thus, evidence presented in this paper is limited to the presumed case in which credit bureau
growth, even if skewed for the individual borrowers rather than corporate borrowers, would
be positively correlated with the improvement of the financial intermediary’s efficiency of
production of information on corporate borrowers. This is likely the case in the data given the
plausible assumption that there exists the economy of scope in the financial intermediary’s
production of information on different types of borrowers, e.g., a feedback effect from the
assessment of creditworthiness of individuals to that of firms, and vice versa.
Data Cleaning For each key variable, we delete observations in the bottom- and top-five
percentiles so that the regression results are not affected by extreme values. We also drop
the countries of which enterprise credit-to-GDP ratio differs too much from the country’s
economic development measured as per-capita real GDP. More specifically, we run regression
of a country’s enterprise credit-to-GDP ratio on the country’s per-capita real GDP and delete
countries that belong to bottom- and top-5 percentile in terms of the distance of the actual
enterprise credit-to-GDP ratio from the fitted line. This yields that two countries are deleted:
Malaysia and Thailand. U.S. is the benchmark country, especially for measuring industry-
36
traits in the absence of financial obstacles, and hence not included in the sample (Beck,
Demirguc-kunt, Laeven and Levine; 2008).
We also drop observations if the average growth rate of the country’s credit bureau is
extremely high, i.e., higher than 200 percent per year. As a result, the sample includes 31
countries. In particular, for the case of France, we delete observations during the period
from 2006 and thereafter; annual credit-bureau growth of France was about 2.9 percent
points until 2005, and thereafter peaked up as high as about 420 percent points.
The number of firm-industry-country observations used in the regression analysis of firmlevel capital growth is as follows: 7,034 observations are used for the regression of capital
growth (e.g., average growth rate of total assets), 5,435 observations for liquidity growth,
and 5,985 observations for growth in external capital raising.
Descriptive Statistics Table 1 presents summary statistics of the three measures of firmlevel capital growth and two country-level variables: enterprise credit-to-GDP ratio (measuring the level of financial development), and growth in the credit bureau index (measuring
the per-year improvement in the financial intermediaries’ efficiency).
[Insert Table 1.]
As shown by Panel D in Table 1, these countries vary substantially in the levels of
economic and financial development. For instance, per-capita real GDP (our measure of
economic development) has the mean of 15,951 U.S. dollars, and standard deviation of
11,409 U.S. dollars across countries, while the enterprise credit-to-GDP ratio (our measure
of financial development) has the mean 35 percent and standard deviation of 24 percent.
These countries also exhibit a great deal of heterogeneity in the per-year improvement in
the financial intermediaries’ efficiency (measured as credit bureau growth): average annual
37
growth in the given country’s credit bureau index has the mean of 19 percent and standard
deviation of 43 percent across countries. Panel E in Table 1 shows that in general, the
per-year improvement in the financial intermediaries’ efficiency is negatively correlated with
levels of both economic and financial development: credit bureau growth has a correlation
coefficient of -0.44 with per-capita real GDP and that of -0.34 with the enterprise creditto-GDP ratio. This might be due to the decreasing returns to scale of investment in the
efficiency of financial intermediaries. And the given country’s averages of key variables are
provided in Panel F in Table 1.
From Panel B in Table 1, we can see that country-level average firm-level growth in capital
differs significantly across countries: for instance, annual firm-level growth in total assets has
the mean of 18 percent and standard deviation of 11 percent. Similarly, firm-level growth
either in liquidity or in external capital raising also exhibits a great deal of variation compared
to its cross-country sample mean. Panel C in Table 1 shows that firm-level capital growth
(on average for a given country) is mildly negatively correlated with the country’s level of
financial development: the correlation coefficient between the country’s enterprise credit-toGDP ratio and firm-level growth in total assets is -0.101, and that with firm-level growth
in external capital raising is -0.134. Note that despite the negative relationship between the
level of financial development and per-year growth in the financial intermediaries’ efficiency,
the relationship between firm-level capital growth and the country’s per-year growth in
the financial intermediaries’ efficiency is negative rather than positive: for instance, the
correlation coefficient between firm-level total assets growth and the country-level credit
bureau growth is about -0.312. And Panel A in Table 1 shows that in terms of unconditional
statistics across firm-industry-country observations, firm-level capital growth exhibits a great
deal of variation: for instance, annual firm growth in total assets has the mean of 15 percent
38
and standard deviation of 29 percent.
[Insert Table 2.]
Table 2 provides statistics of industry-traits variables that are specific to industries arguably for technological reasons, but unrelated to the country-level financial/economic development and hence common across countries. As shown by Panel A in Table 2, our sample
manufacturing industries are substantially heterogeneous in their traits. For instance, the
industry-specific technological small-firms share (measured as the one in the corresponding
U.S. industry) has the mean of about 6.7 percent and standard deviation of about 5.4 percent. Similarly, other industry-specific traits also exhibit significant variations, especially
the dependence on external finance, dependence on intangible assets, and the four-firm concentration ratio. Their correlation coefficients are reported in Panel B in Table 2.
3.2
Findings: Regressions of Firm-Level Capital Growth
This section discusses the results for regressions of firm-level capital growth for publicly
traded Osiris firms. In particular, we focus on identifying the effect of the within-country
improvement in the financial intermediaries’ efficiency on relative firm-level capital growth
between the technologically small- and large-sized industries, especially its variation over
countries that differ in their levels of financial development. More specifically, we are interested in testing the main hypothesis about whether or not the sensitivity of the technologically small-sized industry’s firm growth to within-country growth in the financial intermediaries’ efficiency is of magnitude greater in the more financially developed country.
Provision of Information on Borrowers’ Creditworthiness and Firm Growth We
begin by running the cross-sectional regression of firm-level capital growth on the within-
39
country per-year improvement in the financial intermediaries’ efficiency of information production IF IEc where IF IEc is measured as the country c’s average growth rate of the credit
bureau index. Let gi,k,c denote firm-level capital growth, on average over the sample period,
for the observation of firm i, industry k, and country c. Importantly, capital growth is
measured in terms of three different aspects: growth in capital in place—measured either as
total assets, tangible fixed assets or as investment in intangibles—, liquidity—measured as
net working capital—, and externally raised capital, respectively.
For each of the three measures of firm-level capital growth gi,k,c , the cross-sectional regression equation of gi,k,c is written as:
gi,k,c =
h
i
α1 + α2 F Dc · Smallk + Ψ · Xk
!
· IF IEc
+ GDPc · Smallk + V ADk,c + γk + γc + γinit · InitLeveli,k,c + i,k,c
(27)
where the first line in the regression equation (27) controls for and decomposes the effect
on gi,k,c of the key independent variable, credit bureau growth IF IEc . The second line in
(27) controls for other industry- and country-level characteristics. F Dc refers to the country
c’s enterprise credit-to-GDP ratio that measures the country’s level of financial development
specific to firms’ access to credit, and Smallk the log of the industry k’s technological smallfirms share. The parameter vector Ψ refers to the coefficient vector of the interaction-term
vector between credit bureau growth IF IEc and the industry-traits vector Xk : industryspecific components of (i) global growth opportunities Growth Opportunity, (ii) dependence
on external finance Ext Dep, (iii) dependence on intangible assets Intangibility, (iv) the degree
of opaqueness in terms of assessing firm performance in the stock market Opaqueness, and
(v) the four-firm concentration ratio Concentration. These industry-traits variables Xk are
40
specific to industry k and common across countries c, and interacted with given country c’s
credit bureau growth IF IEc .
Following Beck, Demirguc-kunt, Laeven and Levine (2008), we also control for the two
additional factors: First, we control for the interaction term between the initial-period (in
2002) country-level real per-capita GDP and the industry-level logged technological smallfirms share GDPc · Smallk . This interaction term is intended to capture the small-sized
industry’s firm growth that is systematically correlated with the country’s economic development. Second, we also control for the initial-period country-specific industry k’s share in
value added across the sample industries for a given country c, denoted by V ADk,c . This
industry-country characteristic is intended to capture firm growth that is correlated with the
country-specific industrial features (proxied by the industry’s value-added share for a given
country) such as the country-specific industrial development stages.
Unobserved industry- and country-fixed effects, if any, are also controlled for, captured
by γk and γc . And the initial-period (in 2002) level of the dependent variable InitLeveli,k,c
is also controlled for; for instance, for the regression of total-assets growth, we control for
the level of firm i’s initial-period total assets. And i,k,c refers to the error term.
It is of our main interest to measure the small industry-specific effect of credit bureau
growth on firm growth, i.e., the coefficient α1 +α2 F Dc of the interaction term Smallk ·IF IEc .
We interpret [α1 + α2 F Dc ] as the relative effect of the per-year improvement of the financial
intermediaries’ efficiency (REIFIE) on firm-level capital growth between the two industries
that differ in the industry’s average firm size for the technological reason.
To this end, we need to control for the industry-specific growth potential (proxied by
Growth Opportunity) interacted with the country’s credit bureau growth. That is, the
small-sized industry might have higher growth potential, which would, if combined with
41
the increased credit bureau coverage, induce higher capital growth (i.e., demand-side determinants). Accordingly, we control for the interaction term Growth Opportunity × IFIE. For
the same reason, we also control for the interaction terms between credit bureau growth and
other industry characteristics to control for other demand-side determinants.
In measuring REIFIE (i.e., the coefficient α1 +α2 F Dc ), we aim to identify the component
of REIFIE that is systematically affected by a country’s financial development F Dc , i.e., the
coefficient α2 of the triple interaction term Smallk · IF IEc · F Dc . To this end, we use the
country’s legal origin as the instrumental variable for the country’s financial development
F Dc (Aghion et al.; 2005) so as to avoid the possible reverse causality problem (i.e., economic
development can cause financial development).
[Insert Table 3.]
Table 3 provides the instrumental variable (IV) estimation results of the cross-sectional
firm-growth regression equation (27), where the country’s legal origin is, in all regressions,
used as the instrumental variable for the country’s level of financial development F Dc . Standard errors—reported inside parentheses below the corresponding coefficients—are robust to
heteroskedasticity and industry- and country-level clustering.
Note that we are interested in testing the main null hypothesis that the coefficient α2 of
the triple interaction term Small×IF IE×F D is zero. We consistently find that the estimate
of α2 is, as expected, significantly positive. That is, as shown by Table 3, the estimate of α2
is positive for all five different measures of firm-level capital growth, and mostly significant
at the 5% significance level. More specifically, the estimate of α2 is significant at the 5%
level for the regression of growth in total assets, tangible fixed assets, intangible investment,
and externally raised capital, respectively, and at the 10% level for the regression of liquidity
growth (i.e., growth in net working capital).
42
These results indicate that in the country with the higher enterprise credit-to-GDP ratio, the technologically small-sized industry’s firm-level capital growth is associated with
growth in the credit bureau index to the extent significantly greater than in the country
with the lower enterprise credit-to-GDP ratio. Given that the coefficient α1 is estimated to
be significantly negative, our findings suggest that in financially underdeveloped countries,
the improved efficiency of financial intermediaries does not necessarily increase firm growth
in the small-sized industry relative to that in the large-sized industry, consistent with the
main results from the model discussed earlier. For instance, if a country’s level of financial
development is sufficiently low, then the estimated coefficient [α1 + α2 F Dc ] would be even
negative; in this case, one percentage point increase in credit bureau growth would disproportionately increase the large industry’s firm growth, opposite to the case of financially
advanced countries.
The estimation results are economically significant, too.
Consider the 3.7 percent-
age points increase in growth in the credit bureau index, its median across sample countries. In this case, we want to compare the associated incremental firm growth between
the bottom- and top-25 percentile industries in terms of the logged (percentage) smallfirms share, where this interquartile range (IQR) of the logged small-firms share is 1.238
(= Ln(10.11) − Ln(2.93)). In particular, we are interested in how much different such a
relative firm growth is between the two countries that belong to bottom- and top-25 percentile in terms of the enterprise credit-to-GDP ratio (i.e., 0.16 vs. 0.55). Note that in the
regression of growth in total assets (the column (1) in Table 3), the coefficient of the triple
interaction term ‘Small × IFIE × FD’ is 1.684. These results imply that in the financially
developed country (i.e., top-25 percentile country in terms of the enterprise credit-to-GDP
ratio), incremental output growth in the technologically small industry relative to that in
43
the technologically large industry is greater by 3.01 percentage points than in the financially underdeveloped country (i.e., bottom-25 percentile country in terms of the enterprise
credit-to-GDP ratio).33 This predicted variation in total-assets growth of 3.01 percentage
points is substantial: as shown by Panel B in Table 3, the given country’s average firm-level
growth rate of total assets has, across sample countries, the mean of 18 percentage points
and standard deviation of 11 percentage points.
Difference in Capital Growth b/w Small and
Large Industries (% Points)
Figure 1: Estimated Effect of Credit Bureau Growth on Firm-Level Capital Growth: Difference between Small vs. Large Industries
7.0
Japan
New
Zealand
5.0
Germany
Argentina
3.0
U. K.
Mexico
India
France
Canada
1.0
0
0.2
0.4
0.6
0.8
1
1.2
-1.0
-3.0
Poland
Russian
Federation
Enterprise Credit-to-GDP Ratio
Note: this figure plots, against the country’s enterprise credit-to-GDP ratio, the estimated effect of median
credit bureau growth of 3.7 percentage points (IF IE = 3.7%) on the difference in firm-level capital growth
(measured as the average growth rate, in percentage points, of total assets in place) between the small and
large industries, where the small and large industries, respectively, refer to the 75- and 25-percentiles in
terms of the industry’s technological small-firms share.
We further quantify implications of results for the regression of firm-level capital growth.
Figure 1 plots, against the country’s enterprise credit-to-GDP ratio, the estimated firm-level
total-assets growth that is associated with country’s credit bureau growth, especially its
33
For the presumed 3.7 percentage points increase in credit bureau growth IF IE = 3.7%, we obtain the
predicted difference in total-assets growth as: 3.01% ≈ 1.684 × [Ln(10.11) − Ln(2.93)] × [0.55 − 0.16] × 3.7%.
44
difference between the small and large industries (denoted by REIFIE and defined below).
More specifically, for country c, we compute the estimated relative effect of the improvement
in the financial intermediaries’ efficiency (REIFIE) on total-assets growth between the small
and large industries as:
h
i h
i h
i
REIF IEc = α
c1 + α
c2 × F Dc × Small75p − Small25p × IF IE = 3.7%
(28)
where IF IE refers to the improvement in the financial intermediaries’ efficiency and is
measured as credit bureau growth, of which median across sample countries is 3.7 percentage
points: IF IE = 3.7%, and α
c1 and α
c2 the estimated coefficient of the interaction term
Small × IF IE and Small × IF IE × F D, respectively, entering the regression of firmlevel growth in total assets; Small75p and Small25p refers to the 75- and 25-percentiles,
respectively, in terms of the log of the technological small-firms share. Thus, REIF IEc
measures, in response to the 3.7 percentage points increase in credit bureau growth, how
higher capital growth would be in the small industry than in the large industry. In particular,
we are interested in whether or not REIF IEc varies positively over the country’s enterprise
credit-to-GDP ratio F Dc .
As shown by Figure 1, indeed, in the country of the high enterprise credit-to-GDP ratio,
the disproportionate increase in the small industry’s firm growth is of magnitude grater
than in the country of the low enterprise credit-to-GDP ratio. For instance, for countries of
which enterprise credit-to-GDP ratio is below 16%, REIFIE is significantly negative: these
countries include several emerging market economies (e.g., Argentina, Mexico, India, Turkey,
Russian Federation, Lithuania, Latvia, and Poland) and developing countries (e.g., Costa
Rica). This implies that in such financially underdeveloped countries, improvements in the
45
financial intermediaries’ efficiency could increase firm growth disproportionately in the large
industry than in the small industry. Put differently, for such financially underdeveloped
countries, the overall improvement in the financial intermediation sector’s efficiency would
be utilized mainly for increasing capital growth in the already large industry rather than
that in the needy small industry that is likely to have the greater growth potential. Seen
through the lens of our model of financial intermediation and firm growth, this finding can
be explained by the fact that the cost of assessing borrowers’ creditworthiness is significantly
higher for the small industry than for the large industry, especially more so for the financially
underdeveloped countries.
In short, by using the firm-level data on balance sheet and income statement over 20
manufacturing industries and 31 countries over the world, we investigate the association of
firm-level capital growth with the within-country improvement in the financial intermediaries’ efficiency of information production (i.e., growth in the coverage of the credit bureau
that provides borrowers’ past repayment history), especially its difference (i) across industries that differ in the technological small-firms share and (ii) across countries that differ in
the level of financial development. We find that in the more financially developed country,
firm-level capital growth in the small-sized industry relative to that in the large-sized industry is associated with within-country growth in the financial intermediaries’ efficiency to the
extent significantly greater than in the less financially developed country. Importantly, these
empirical findings are robustly observed for the three aspects of capital growth: capital in
place, liquidity, and external capital raising. These findings are consistent with the key predictions of the model, indicating that in the financially underdeveloped countries, it would
be very costly to correctly assess creditworthiness of firms in the small-sized industry.
46
Discussion We discuss our regression results compared to the previous results in the literature that studies the relative effect of the improved financial intermediaries’ efficiency
on firm growth between the small- and large-sized firms/industries. The key issue in this
literature is how to measure the incremental growth rate either of capital or of output that
is associated with the variation in the efficiency of financial intermediaries. As for the variation in the efficiency of financial intermediaries/systems, while many papers in the literature
use its different levels across countries (Beck et al.; 2005; Beck, Demirguc-kunt, Laeven and
Levine; 2008), this paper uses its within-country growth rate. Thus, our regression analysis measures the firm-level incremental growth rate of capital that is associated with the
country’s actual changes—measured as the growth rate—in the efficiency of financial intermediaries, resulting in that the estimate is free from being contaminated by the constant
unobserved country-level characteristics. In particular, this paper focuses on within-country
growth in the specific aspects of the financial system’s effectiveness that facilitates financial
intermediation: the credit bureau coverage, which is likely to facilitate financial intermediaries’ production of information on borrowers’ creditworthiness about future repayments.
3.3
Robustness Check: Publicly Traded vs. Privately Held Firms
We have documented earlier the relationship between firm capital growth and within-country
growth in the credit bureau index for publicly traded firms. This section shows that such an
earlier finding is not limited to publicly traded firms but also applicable to non-listed private
firms. To this end, we run the same triple-difference regression of firm growth by using
the United Nations Industrial Statistics database that provides industry-level aggregated
information on both publicly traded firms and privately held firms over the world.
47
United Nations Industrial Statistics Data Now we use our second data set, the
United Nations Industrial Statistics database, which provides industry-level aggregated information on performance of both publicly traded and non-listed private firms. This data
set covers, after the data cleaning procedure that will be discussed soon, firms in the 20
NACE two-digit manufacturing industries and in 31 countries over the world, during the period 2002-2007 annually. Sample countries and industries are listed in Table 4, where both
sample countries and industries are almost the same with those covered by the previous
Osiris firm-level data. Given that industry-level aggregated information is provided, in this
database, less noise is contained but the scope of information is more limited than in the
Osiris firm-level data.
Given the industry-level aggregated information, we measure firm-level growth in capital
as the growth rate, on average over the sample period, in industry-level average investment.
Measured capital series are not available, and hence we take investment growth as a proxy of
firm-level growth in capital. Furthermore, we also measure growth in the industry-level real
value added, denoted by ‘VAD.’ In regressing industry-level growth either in investment or in
value added across industries and countries, we control for the same independent variables
as those in the earlier regression of Osiris firm-level capital growth, e.g., industry- and
country-traits variables. Every monetary variable is in terms of the real 2013 U.S. dollars.
Data Cleaning The procedure of deleting extreme-value observations is mainly the same
as that used for the previous analysis of the Osiris data on publicly traded firms. That is, we
drop the countries of which difference between the actual enterprise credit-to-GDP ratio and
that predicted by per-capita real GDP belongs to the bottom- and top-five percentiles; we
also drop the countries of which annual credit bureau growth exceeds 200 percentage points.
48
The only exception is that for each key variable, we now delete observations in the bottomand top-one percentiles rather than bottom- and top-five percentiles. The reason is that
for this industrial-level aggregated data, due to the very nature of aggregation, observations
contain less noise than for the previous firm-level data.
The number of industry-country observations used in the regression analysis of firm
growth is as follows: 518 observations are used for the regression of investment growth,
while 601 observations are used for the regression of value-added growth.
Descriptive Statistics Table 4 presents summary statistics of key variables of the U.
N. Industrial Statistics data together with country-level variables related to financial development. Descriptive statistics (across industry-country observations) of the industry-level
average firm behaviors (e.g., firm growth, employees, and investment) are summarized in
Panel A in Table 4. And Panel B in Table 4 provides statistics of the country-level variables
related with financial development and growth in the financial intermediaries’ efficiency of information production. More detailed statistics for a given country and industry are provided
in Panel D, E and F, respectively, in Table 4.
[Insert Table 4.]
As shown by Panel C in Table 4, credit bureau growth is negatively correlated with the
enterprise credit-to-GDP ratio (i.e., the correlation coefficient is -0.33), i.e., growth in the
financial intermediaries’ efficiency slows down as the country’s level of financial development
gets higher. We can also see that income level is positively correlated with financial development. And industry-level average investment growth is negatively correlated with the
enterprise credit-to-GDP ratio and positively correlated with credit bureau growth, and so
is industry-level value-added growth.
49
Regression Results for Both Publicly Traded and Privately Held Firms We proceed to running the cross-sectional triple-difference regression of industry-level investment
growth by controlling for the same independent variables with those controlled for in the
earlier regression for the Osiris publicly traded firms. That is, we regress industry k and
country c’s investment growth gk,c on within-country growth in the credit bureau index
IF IEc by controlling for the country’s level of financial development F Dc and the log of
the industry’s small-firms share Smallk , while we also control for the industry-specific traits
variables (common across countries): industry-specific components of global growth opportunity, dependence on external finance, intangibility, etc, which are interacted with credit
bureau growth IF IEc . We also control for the country-industry specific variables: the initial
level (in 2002) of investment, the industry’s share in value added for a given country, the
interaction term between the technological small-firms share Smallk and per-capita GDP.
We also run the same kind of regression of industry-level value-added growth, too.
[Insert Table 5.]
Table 5 presents results for the IV cross-sectional regression of industry-level investment
growth and value-added growth, respectively, where the country’s legal origin is, in all regressions, used as the instrumental variable for the country’s level of financial development F Dc
for the reason discussed earlier. In all of these industry-level regressions, standard errors are
robust to heteroskedasticity and country-level clustering, and reported inside parentheses.
As shown by the column (1) in Table 5, indeed, the estimated coefficient of the triple
interaction term Small × IF IE × F D is positive and significant at the 1% level. That
is, in the country with the high enterprise credit-to-GDP ratio, investment growth in the
technologically small-sized industry is associated with the country’s credit bureau growth
to the extent greater than in the country with the low enterprise credit-to-GDP ratio. Put
50
differently, in the financial underdeveloped country, the financial intermediaries’ increased
efficiency of information production disproportionately increases firm growth in the large
industry than in the needy small industry, consistent with the previous results for firm-level
capital growth of publicly traded Osiris firms. Interestingly, the regression of industry-level
value-added growth also delivers mainly the same message with that from the regression
of investment growth: the estimated coefficient of Small × IF IE × F D is positive and
significant at the 1% level, and that of Small × IF IE is negative and significant at the 5%
level. (See the column (3) in Table 5.) All these results suggest that our main findings are
relevant to both publicly traded and privately held firms.
4
Conclusion
This paper highlights the importance of understanding the mechanism of financial intermediaries’ information production and its implications for the cross-section of capital allocation
and firm growth. In particular, we highlight the trade-off between the cost and benefit of
financial intermediaries’ production of information on borrowing firms’ creditworthiness, especially the evolution of such a trade-off over a country’s level of financial development. We
build a model of financial intermediation in which financial intermediaries’ production of
information on borrowers’ creditworthiness and loan contracts are endogenously determined.
In particular, we consider the case in which as in the data, the smaller firm’s creditworthinessrelated information is more costly to assess. Analytic results show that firm growth in the
small-sized industry relative to that in the large-sized industry is more sensitive to growth in
the financial intermediary’s information-production efficiency, especially to the grater extent
in the more financially developed country. These results suggest that in financially under-
51
developed countries, the improved efficiency of financial intermediaries does not necessarily
improve firm growth in the small-sized industry relative to that in the large-sized industry.
Using the Osiris firm-level data on publicly traded firms over the world, we provide evidence supporting the key mechanism. More specifically, we investigate how firm-level capital
growth systematically varies with growth in the financial intermediaries’ efficiency of production of information on borrowers’ creditworthiness. We find that within-country growth
in the credit bureau coverage, which provides information on borrowers past repayment history, is associated with significantly higher firm-level capital growth in the technologically
small-sized industry than in the technologically large-sized industry, especially to the extent
significantly greater in the more financially developed country than in the less financially developed country. Importantly, these findings are consistently observed for the three aspects
of firm-level capital growth: capital in place (e.g., total assets, tangible fixed assets, and
investment in intangibles), liquidity and external capital raising, and hence strongly support
key predictions of the model. These findings are also consistently observed for the United
Nations Industrial Statistics database that covers industry-level aggregated information on
both publicly traded and privately held firms over the world.
As for the theoretical mechanism, we have considered the implications of heterogeneity in
the difficulty of assessing creditworthiness (i.e., hidden information) across borrowing firms
that differ in their sizes. It would be interesting to consider other factors related to the
supply-side determinants of financial intermediation, e.g., the costly mechanism to mitigate
the moral hazard problem due to borrowers’ hidden actions. As for the empirical study, it
would be also interesting to examine how differently the firm-level degree of financial constraint is relaxed in response to various improvements in the external financing opportunities,
e.g., improved efficiency in raising capital via equity financing and financial innovations.
52
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55
A
Theoretical Appendix
The proof of Lemma 1 follows from the results in Chapter 2 of Salanie (2005). We present
proofs of other results in what follows: Lemma 2, Proposition 1, Corollary 1, Proposition 2,
and Lemma 3.
• First, the proof of Lemma 2 is as follows, which is then used in proving subsequent
results.
Proof. The industry j-specific equilibrium marginal cost and revenue of information production µt (j) are written as:
Rz
Rz
α
E[HM C(z)|Fj ] = z c[z −γ ]dFj (z) and E[M R(z)|Fj ] = [(1−α)/(1+rf )[α/(1+rf )]( 1−α ) z λ(z)dFj (z)
α i
h
n
o 1−α
. To prove the first part of the results
where λ(z) ≡ z · 1 − 1 − (1 − α) φ(z)
z
Rz
E[M R(z)|FS ] ≥ E[M R(z)|FL ], we essentially need to show that z λ(z)dFS (z) is greater
Rz
than or equal to z λ(z)dFL (z).
Given that FL “first-order stochastically dominates (FOSD)” FS , it immediately follows
Rz
Rz
that z ϕ(z)dFS (z) ≥ z ϕ(z)dFL (z) for every function ϕ(z) that is non-increasing in z.
Thus, we proceed to showing that λ(z) is non-increasing in z. The first order derivative of
λ(z) is written as:
α
α −1
dλ(z)
φ(z) 1−α
φ(z)
φ(z) 1−α
0
= 1 − 1 − (1 − α)
−α
− φ (z) 1 − (1 − α)
.
dz
z
z
z
|
{z
}
(29)
≡ξ
Note that 1−(1−α)φ(z)/z is larger than zero under the assumption (A1) as discussed earlier
and smaller than one because φ(z)/z > 0 and α < 1. Thus, a sufficient, but not necessary,
condition for dλ(z)/dz < 0 is that the terms indicated by the underbrace denoted by ‘ξ’ is
larger than or equal to one, which is true under the two conditions: (i) α[φ(z)/z − φ0 (z)] > 1
56
and (ii) α/[1 − α] − 1 < 0. In fact, the condition (i) above is obviously satisfied by the
first part of the assumption (A4), and moreover, the condition (ii) above is also satisfied by
the second part of the assumption (A4) as follows: The second part of the assumption (A4)
states that α < 0.5, from which it follows that 1/[1 − α] < 2 and hence that the condition
α
i 1−α
h
−1
> 1, and hence
(ii) α/[1 − α] < 1 is satisfied. Thus, in this case, 1 − (1 − α) φ(z)
z
α
i 1−α
h
−1
φ(z)
0
α φ(z)
−
φ
(z)
1
−
(1
−
α)
> 1.
z
z
The second result follows from the property that d{c[z]−γ }/dz = −γc[z]−γ−1 < 0.
• Second, the proof of Proposition 1 is as follows.
Proof. Assume that five assumptions (1)-(5) hold. First, we prove the first part of the
results that in this case, REIF IE(µt )/at > 0. Note that under the assumption (A5),
E[HM C(z)|FS ]/E[HM C(z)|FL ] is larger than E[M R(z)|FS ]/E[M R(z)|FL ].
Thus, E[M R(z)|FL ]E[HM C(z)|FS ] is greater than E[M R(z)|FS ]E[HM C(z)|FL ]. Note that
REIF IE(µt ) = [g(µt (S))/g(at )] − [g(µt (L))/g(at )] is written as:


g(µt (S)) g(µt (L))
E[M R(z)|FL ] × E[HM C(z)|FS ] − E[M R(z)|FS ] × E[HM C(z)|FL ]
i h
i .
−
= at · h
g(at )
g(at )
at E[M R(z)|FL ] − E[HM C(z)|FL ] × at E[M R(z)|FS ] − E[HM C(z)|FS ]
(30)
Thus, [g(µt (S))/g(at ) − g(µt (L))/g(at )] /at is positive if and only if E[M R(z)|FL ]E[HM C(z)|FS ]
is larger than E[M R(z)|FS ]E[HM C(z)|FL ], and vice versa, because at E[M R(z)|Fj ]−E[HM C(z)|Fj ]
is positive ∀j ∈ {S, L}.
Second, assuming additionally that φ(z) ≤ z, we prove the second part of the results:
REIF IE(kt )/at > 0. We can rewrite REIF IE(kt )/at = Ωt (S) − Ωt (L) where Ωt (j) is
57
.
defined as in equation (21) as Ωt (j) = 1 [ψet (j) + ωt (j)] where
E[HM C(z)|Fj ]
ψet (j) ≡ at −
, and
E[M R(z)|Fj ]
1
E[k O |Fj ]
ωt (j) ≡
×
.
E[M R(z)|Fj ]
E[k N |Fj ] − E[k O |Fj ]
(31)
Note that Ωt (j) is strictly decreasing in each of ψet (j) and ωt (j). Thus, we will prove that
Ωt (S) > Ωt (L) by showing that ψet (S) < ψet (L) and ωt (S) < ωt (L) given that ψet (S) > 0 and
ωt (S) > 0.
First, ψet (S) < ψet (L) is immediately derived from the assumption (A5) that:
.
.
E[HM C(z)|FS ] E[HM C(z)|FL ] > E[M R(z)|FS ] E[M R(z)|FL ].
Second, ωt (S) < φt (L) is shown as follows: Note that 1/E[M R(z)|FS ] < 1/E[M R(z)|FL ]
.h
by Lemma 2. Thus, it suffices to show that (i) E[k O |FS ] ≤ E[k O |FL ] and (ii) 1 E[k N |FS ]−
i .h
i
E[k O |FS ] ≤ 1 E[k N |FL ] − E[k O |FL ] as follows:
1. We prove E[k O |FS ] ≤ E[k O |FL ] as follows: Note that FL FOSD FS ; thus, we prove
E[k O (z)|FL ] ≥ E[k O (z)|FS ] by showing that k O (z) is non-decreasing in z, where
i 1
h
φ(z) 1−α
O
. The first-order derivative of k O (z) is written as:
k (z) = z 1 − (1 − α) z
1
α
φ(z)
dk O (z)
φ(z) 1−α
φ(z) 1−α
0
= 1 − (1 − α)
+
− φ (z) 1 − (1 − α)
dz
z
z
z
(32)
which is strictly positive because 1 − (1 − α)φ(z)/z > 0, φ(z)/z > 0 and φ0 (z) ≤ 0 for
all z ∈ [z, z] by the assumption (A1).
.h
i
.h
i
N
O
N
O
2. We prove 1 E[k |FS ] − E[k |FS ] ≤ 1 E[k |FL ] − E[k |FL ] by showing
that E[k N (z) − k O (z)|FS ] ≥ E[k N (z) − k O (z)|FL ] as follows: Given that FL FOSD FS ,
it is sufficient to show that E[k N (z) − k O (z)|Fj ] is non-increasing in z.
58
Note that
E[ktN (z)
−
ktO (j)
=
h
α
1+rf
1
i( 1−α
)R
z
z
λk (z)dFj (z) where the integrand λk (z) is
defined as:
1 i
h
n
φ(z) o 1−α
λk (z) ≡ z 1 − 1 − (1 − α)
.
z
(33)
Thus, our goal is to prove that λk (z) is non-increasing in z. To this end, we want to
show that the first-order derivative of λk (z) is negative, where the first-order derivative
of λk (z) is written as:
α
1
1−α
dλ(z)
φ(z) 1−α
φ(z)
φ(z)
= 1 − 1 − (1 − α)
−
− φ0 (z) 1 − (1 − α)
.
dz
z
z
z
|
{z
}
(34)
≡ζ
Note that 1 − (1 − α)φ(z)/z is larger than zero under the assumption (A1) and smaller
than one because φ(z)/z > 0 and α < 1. Thus, a sufficient, but not necessary, condition
for dλk (z)/dz < 0 is that the terms indicated by the underbrace denoted by ‘ζ’ is larger
than or equal to one.
Consider the first term of ζ: [φ(z)/z − φ0 (z)], which is greater than 1/α due to the
assumption (A4). Therefore, it is sufficient to show that the second term of ζ is greater
h
iα/(1−α)
than or equal to α, i.e., 1 − (1 − α)φ(z)/z
≥ α, for which the additional
assumption φ(z) ≤ z will be used as follows:
Assume that φ(z) ≤ z, from which it immediately follows that φ(z)/z ≤ 1 for all
h
i
z ∈ [z, z] as discussed earlier. Thus, 1 − (1 − α)φ(z)/z ≥ α for all z ∈ [z, z]. Given
h
i
that 0 < 1 − (1 − α)φ(z)/z < 1 and that 0 < α < 0.5, we have:
φ(z) α
log(α) ≤ log 1 − (1 − α)
< 0 and 0 <
< 1.
z
1−α
59
(35)
Thus, it follows that:
i
log(α)
(36)
log 1 − (1 − α) φ(z)
z
(37)
h
log(α) <
≤
h
α
1−α
i
α
1−α
where the first inequality follows from that log(α) < 0 and that α/(1 − α) < 1, and the
second inequality is simply due to that α/(1 − α) > 0. Thus, it immediately follows
iα/(1−α)
h
.
that α < 1 − (1 − α) φ(z)
z
Therefore, we have proven that under the assumption of φ(z) ≤ z, ζ is larger than one.
This completes to prove that dλk (z)/dz < 0 and hence that λk (z) is non-increasing in
z, which shows that E[k N (z) − k O (z)|Fj ] is non-increasing in z.
Thus, we have proven that ωt (S) < ωt (L). Because of ψet (S) < ψet (L) and ωt (S) < ωt (L),
we have completed to prove that Ωt (S) > Ωt (L).
• Third, the proof of Corollary 1 is as follows:
Proof. It is obvious that E[y N (z)|FL ] ≥ E[y N (z)|FS ] because y N (z) =
α
1+rf
α
1−α
· z is
proportional to z with a positive slope, i.e., y N (z) is strictly increasing in z, and FL “firstorder stochastically dominates (FOSD)” FS . We proceed to proving that E[y O (z)|FL ] ≥
E[y O (z)|FS ]. Note that FL FOSD FS ; thus, we prove E[y O (z)|FL ] ≥ E[y O (z)|FS ] by showing
that y O (z) is non-decreasing in z. The first-order derivative of y O (z) is written as:
dy O (z)
=
dz
α
1 + rf
α
1−α
φ(z)
1 − (1 − α)
z
α
1−α
φ(z)
+ 1 − (1 − α)
z
α
1−α
−1
!
φ(z)
·α
− φ0 (z)
z
(38)
which is positive because 1 − (1 − α)φ(z)/z > 0 as shown earlier and φ0 (z) ≤ 0 by the assumption (A1). Last, we prove the remaining results that E[y N (z) − y O (z)|FS ] ≥ E[y N (z) −
60
y O (z)|FL ]. Because of E[y h (z)] =
1
·E[π h (z)]
1−α
for h ∈ {N, O}, it follows that for j ∈ {S, L},
h
i
E[y N (z)−y O (z)|Fj ] = E[y N (z)|Fj ]−E[y O (z)|Fj ] = [1/(1−α)]· E[π N (z)|Fj ]−E[π O (z)|Fj ] ,
which is strictly increasing in and proportional to E[M R(z)|Fj ] = E[π N (z)|Fj ]−E[π O (z)|Fj ]
because (1 − α) > 0. Note that E[M R(z)|FS ] ≥ E[M R(z)|FL ] by Lemma ??, which completes to prove that E[y N (z)|FS ] − E[y O (z)|FS ] ≥ E[y N (z)|FL ] − E[y O (z)|FL ].
• Fourth, the proof of Proposition 2 is as follows.
Proof. Assume that five assumptions (1)-(5) hold. We want to show that Ψt (S) > Ψt (L)
.
where Ψt (j) is defined as in equation (26). That is, Ψt (j) = 1 [ψet (j) + ψt (j)] where
E[HM C(z)|Fj ]
ψet (j) ≡ at −
, and
E[M R(z)|Fj ]
1
E[y O |Fj ]
ψt (j) ≡
×
.
E[M R(z)|Fj ]
E[y N |Fj ] − E[y O |Fj ]
(39)
Note that Ψt (j) is strictly decreasing in each of ψet (j) and ψt (j). Thus, we will prove that
Ψt (S) > Ψt (L) by showing that ψet (S) < ψet (L) and ψt (S) < ψt (L) given that ψet (S) > 0 and
ψt (S) > 0 as shown earlier. First, ψet (S) < ψet (L) is derived from the assumption (A5) that
.
.
E[HM C(z)|FS ] E[HM C(z)|FL ] > E[M R(z)|FS ] E[M R(z)|FL ]. Second, ψt (S) < ψt (L)
is shown below. Note that 1/E[M R(z)|FS ] < 1/E[M R(z)|FL ] by Lemma 2. Moreover,
.h
i .h
i
E[y O |FS ] < E[y O |FL ] and 1 E[y N |FS ] − E[y O |FS ] < 1 E[y N |FL ] − E[y O |FL ] by
Corollary 1. Thus, ψt (S) < ψt (L). Because of ψet (S) < ψet (L) and ψt (S) < ψt (L), we have
completed to prove that Ψt (S) > Ψt (L).
• Fifth, the proof of Lemma 3 is as follows:
h
i.
Proof. Assume that c = 0. First, we prove that [g(yt (S)) − g(yt (L))]/g(at ) at > 0. As
h
i.
.h
discussed earlier, [g(yt (S)) − g(yt (L))]/g(at ) at = Ψt (S) − Ψt (L) and Ψt (j) = 1 at +
61
i
ψt (j) , ∀j ∈ {S, L} where ψt (j) is the same as that defined earlier in the proof of Proposition
2:
E[y O |Fj ]
1
×
.
ψt (j) ≡
E[M R(z)|Fj ]
E[y N |Fj ] − E[y O |Fj ]
(40)
In this case, we can prove that Ψt (S) > Ψt (L) by showing that 0 < ψt (S) < ψt (L), which is
true in this case of c = 0 because the result for 0 < ψt (S) < ψt (L) is independent of whether
c is either zero or strictly positive as shown earlier in the proof of Proposition 2. Second,
the result of g(µt (S)) = g(µt (L)) = g(at ) for the case of c = 0 follows immediately from
equation (16) because c = 0 implies that E[HM C(z)|Fj ] = 0, ∀j ∈ {S, L}.
62
63
Total Assets: Level ($1,000 USD)
Total Assets: Growth
Tangible Fixed Assets: Growth
Intangible Investment: Growth
Net Working Capital: Growth
External Capital Raising: Growth
Total Assets: Level ($1,000 USD)
Total Assets: Growth
Tangible Fixed Assets: Growth
Intangible Investment: Growth
Net Working Capital: Growth
External Capital Raising: Growth
Panel A: Statistics Across Firm-Industry-Country Observations
Obs. Mean
SD
Min
p25
Median
p75
Max
7,034 5,023 27,992
1.4
54.2
441.6
1,800
1,091,996
7,034
0.15
0.29
-0.11
0.002
0.06
0.18
1.96
6,889
0.15
0.28
-0.14
-0.003
0.06
0.19
1.77
3,433
0.07
3.89
-9.83
-1.57
-0.37
4.11
14.86
5,435
0.18
0.57
-1.53
-0.02
0.08
0.29
2.97
5,985
0.08
0.16
-0.12
-0.03
0.03
0.14
0.69
Panel B: Cross-Country Statistics of Country-Level Averages of Variables
Obs.
Mean
SD
Min
p25
Median
p75
Max
31
4,687
5,079
131
1,172
3,112
6,864
20,449
31
0.18
0.11
0.008
0.11
0.16
0.22
0.54
31
0.19
0.12
-0.02
0.12
0.18
0.23
0.57
31
-0.07
0.82
-1.40
-0.41
-0.22
0.22
2.21
31
0.21
0.13
-0.06
0.14
0.29
0.31
0.45
31
0.15
0.11
0.005
0.07
0.13
0.18
0.59
Note: this table presents descriptive statistics of key variables of the Osiris data on publicly traded firms. ‘Panel A’ presents
descriptive statistics of variables across firm-industry-country observations unconditionally. By contrast, ‘Panel B’ presents the crosscountry statistics of country-level averages of the variables, and ‘Panel C’ their correlation coefficients. And ‘Panel D’ presents
cross-country statistics of country-level variables related to financial and economic development, and ‘Panel E’ their correlation
coefficients. ‘Enterprise Credit-to-GDP’ refers to the average ratio of enterprise credit to GDP, which measures a country’s level of
financial development in terms of facilitating firms’ access to credit, and ‘Credit Bureau Growth’ the average growth rate of the credit
bureau coverage over period 2004-2007, which measures the within-country improvement in the financial intermediaries’ efficiency
of production of information on borrowers’ creditworthiness. We measure firm-level capital growth as the per-year growth rates, on
average over the sample period 2002-2007, of five variables: (i) total assets—i.e., sum of tangibles and intangibles—, (ii) tangible
fixed assets—measured as net property, plant and equipment—, (iii) investment in intangibles, (iv) net working capital—measured as
the current asset minus the current liability—, and (v) external capital raising—measured as the accumulated level of capital raised
from sources other than the firm’s own retained earnings. ‘Obs.’ refers to the number of observations, ‘SD’ the standard deviation,
‘p25’ the 25 percentile, and ‘p75’ the 75 percentile. ‘*’ indicates significance at the 10% level, and ‘**’ at the 5% level.
Table 1: Summary Statistics: Osiris Data on Publicly Traded Firms
64
Panel C: Cross-Country Corr. Coefficients of Country-Level Averages of Variables
Tangible
Net
Enterprise
Credit
Total
Intangible
Fixed
Working
Credit-toBureau:
Assets:
Investment:
Assets:
Capital:
GDP
Growth
Growth
Growth
Growth
Growth
Enterprice Credit-to-GDP
1
Credit Bureau: Growth
-0.342
1
Total Assets: Growth
-0.101
-0.312
1
Tangible Fixed Assets: Growth
-0.023
-0.371*
0.951*
1
Intangible Investment: Growth
0.001
-0.170
0.344
0.381*
1
Net Working Capital: Growth
-0.021
-0.402*
0.790
0.759*
0.259
1
External Capital Raising: Growth
-0.134
-0.185
0.769*
0.830*
0.454*
0.500
Panel D: Statistics of Country-Level Financial Development
Obs.
Mean
SD
Min
p25
Median
p75
Max
Per-Capita GDP (in USD)
31
15,951 11,409
909
4,114
16,564
25,993
39,350
Enterprise Credit/GDP
31
0.35
0.24
0.09
0.16
0.25
0.55
1.07
Private Credit/GDP
31
0.73
0.46
0.16
0.29
0.73
1.21
1.57
Credit Bureau: Growth
31
0.19
0.43
-0.061
0.005
0.037
0.12
1.93
Credit Bureau: Level (%)
31
0.28
0.18
0.01
0.09
0.29
0.47
0.61
Panel E: Correlation Coefficients of Country-Level Financial Development
Enterprise
Private
Credit
Credit
Per-Capita
Credit to
Credit to
Bureau:
Bureau:
GDP
GDP
GDP
Growth
Level
Per-capita GDP
1.0
Enterprise Credit/GDP
0.70*
1.0
Private Credit/GDP
0.85*
0.79*
1.0
Credit Bureau: Growth
-0.44*
-0.34
-0.43*
1.0
Credit Bureau: Level
0.37*
0.34
0.41*
-0.26
1.0
Table 1 Cont’d
65
Argentina
Australia
Austria
Canada
Costa Rica
Czech Republic
Estonia
Finland
France
Germany
Hungary
Iceland
Indonesia
Ireland
Italy
Japan
Latvia
Lithuania
Mexico
Netherlands
New Zealand
Poland
Portugal
Russia
Slovenia
South Africa
Spain
Sweden
Switzerland
Turkey
U.K.
0.008
0.407
0.159
0.359
0.064
0.161
0.162
0.161
0.174
0.134
0.239
0.540
0.103
0.195
0.114
0.029
0.167
0.117
0.044
0.137
0.227
0.237
0.100
0.192
0.219
0.271
0.211
0.179
0.097
0.276
0.182
Total
Assets:
Growth
Panel F: Averages of Key Variables for a Given Country
Firm-Level Capital Growth
Tangible
Net
Externally
Intangible
Fixed
Working
Raised
Investment:
Assets:
Capital:
Capital:
Growth
Growth
Growth
Growth
-0.021
0.790
-0.055
0.077
0.395
-0.362
0.454
0.187
0.209
-0.412
0.228
0.182
0.344
0.025
0.394
0.164
0.047
-0.956
-0.054
0.133
0.149
-1.404
0.402
0.061
0.154
-0.224
0.292
0.122
0.146
-0.695
0.233
0.166
0.221
-0.398
0.176
0.132
0.179
-0.245
0.137
0.124
0.120
0.206
0.205
0.028
0.573
2.214
0.455
0.596
0.066
-0.006
0.219
0.065
0.204
-0.298
0.262
0.075
0.162
-0.143
0.121
0.126
0.038
0.293
0.083
0.005
0.189
1.181
0.318
0.148
0.125
-1.195
-0.062
0.122
0.026
-1.125
0.053
0.062
0.146
-0.362
0.171
0.088
0.213
-1.191
0.283
0.143
0.259
1.813
0.276
0.227
0.107
0.120
0.144
0.071
0.203
0.055
0.330
0.178
0.214
-0.352
0.272
0.288
0.272
-0.847
0.311
0.240
0.237
-0.352
0.229
0.192
0.235
0.085
0.183
0.160
0.101
0.521
0.240
0.066
0.369
0.712
0.344
0.279
0.182
0.227
0.169
0.113
Table 1 Cont’d: Country-Level Statistics
0.087
0.279
0.653
0.188
0.113
0.314
0.176
0.227
0.339
0.653
0.189
0.492
0.170
0.492
0.650
1.070
0.160
0.104
0.087
0.630
0.703
0.135
0.507
0.145
0.240
0.309
0.250
0.233
0.604
0.115
0.557
Enterprise
Credit to
GDP
0.108
0.016
0.018
0.000
1.937
0.299
0.000
0.002
0.029
0.047
0.285
0.000
1.407
0.000
0.085
0.037
0.636
0.770
0.194
0.066
0.008
0.118
0.032
0.000
0.005
-0.061
0.051
0.007
0.010
0.041
0.037
Credit
Bureau
Growth
Country-Level Variable
66
Small (<20)
Ext Dep
Growth Opportunity
Intangibility
Opaqueness
Concentration
Small (<20)
Ext Dep
Growth Opportunity
Intangibility
Opaqueness
Concentration
Panel A: Statistics of Sector-level Traits
Obs.
Mean
SD
Min
p25
Median
p75
20
6.66
5.43
1.20
2.93
3.07
10.11
20
-1.37
1.43
-3.05
-1.85
-1.37
-1.23
20
0.06
0.05
-0.03
0.03
0.05
0.09
20
1.05
0.63
0.30
0.61
0.90
1.48
20
0.19
0.09
0.07
0.11
0.16
0.27
20
17.85
11.22
3.20
9.68
15.60
22.20
Panel B: Correlation Coefficients of Sector-level Traits
Small
Ext
Growth
IntangiOpaqueness
(<20)
Dep
Opportunity
bility
1.0
-0.39
1.0
-0.45*
0.39
1.0
0.13
0.36
0.40
1.0
-0.45*
0.10
0.46*
0.02
1.0
0.31
-0.57*
-0.28
-0.25
0.00
1.0
Concentration
Max
19.50
4.11
0.17
2.76
0.35
48.20
Note: this table presents descriptive statistics and correlation coefficients of industry-level traits variables. ‘Obs.’ refers to the
number of observations, ‘SD’ the standard deviation, ‘p25’ the 25 percentile, and ‘p75’ the 75 percentile. ‘Small (<20)’ refers to
the inverse measure of the industry’s technological size, which is measured as the corresponding U.S. industry’s small-firms share in
1997 and defined as the share in employment of firms that hire less than 20 employees in 1997. ‘Ext Dep’ refers to the industry’s
technological dependence on external finance, ‘Growth Opportunity’ the industry-specific component of global growth opportunities,
‘Intangibility’ the industry’s dependence on intangible assets, ‘Opaqueness’ the industry’s degree of opaqueness in terms of assessing
firm performance in the stock market, and ‘Concentration’ the industry’s four-firm concentration ratio.
Table 2: Summary Statistics: Data on Industry-Level Traits
67
Small × GDP per capita
Small × IFIE × FD
Small × IFIE
Regression
Small
Dependent Variable
(1)
0.3385***
(0.0496)
-0.2815**
(0.1388)
1.6844***
(0.8305)
-0.0361***
(0.0072)
Total
Assets:
Growth
Capital in Place
Tangible
Investment in
Fixed
Intangibles:
Assets:
Growth
Growth
(2)
(3)
0.2857***
1.2244
(0.1082)
(1.3348)
-0.2261**
-0.9807
(0.1056)
(0.7681)
1.4385**
11.1580**
(0.6718)
(5.1851)
-0.0319***
-0.01255
(0.0112)
(0.1292)
(4)
0.6390***
(0.1958)
-0.3363**
(0.1515)
1.7787*
(0.9732)
-0.0005
(0.0010)
Net Working
Capital:
Growth
Liquidity
Financing
Externally
Raised
Capital:
Growth
(5)
0.0731
(0.0987)
-0.1040**
(0.0451)
0.5218**
(0.2385)
-0.0091
(0.0094)
The dependent variable gi,k,c refers to a number of different measures of firm-level capital growth on average over the sample period
2002-2007: (i) total assets—i.e., sum of tangibles and intangibles—, (ii) tangible fixed assets—measured as net property, plant and
equipment—, (iii) investment in intangibles, (iv) net working capital—measured as the current asset minus the current liability—,
and (v) externally raised capital—measured as the accumulated level of capital raised from sources other than the firm’s own retained
earnings. Industry k’s technological firm size is (inversely) measured as the log of the corresponding U. S. industry’s small-firms
share, denoted by ‘Small,’ and defined as the share in employment of firms that have less than 20 employees in 1997. Additional
industry-traits variables Xk are also controlled for as follows: ‘Growth Opportunity’ refers to the industry-specific component of
global growth opportunities, ‘Ext Dep’ the industry’s technological dependence on external finance, ‘Intangibility’ the industry’s
dependence on intangible assets, ‘Opaqueness’ the industry’s degree of opaqueness in terms of assessing firm performance in the
stock market, and ‘Concentration’ the industry’s four-firm concentration ratio. GDPc refers to the initial-period (in 2002) per-capita
real GDP, and V ADk,c the initial-period country-specific industry’s share in value added across the sample industries for a given
country. All regressions control for country- and industry-fixed effects, and the initial-period level of capital of which growth rate is
the dependent variable of the regression equation, e.g., the initial level of total assets of firm i. Standard errors are reported inside
parentheses and robust to the heteroskedasticity and country- and industry-level clustering. ‘*’ indicates significance at the 10%
level, ‘**’ at the 5% level, and ‘***’ at the 1% level.
Note: this table presents results for the IV (2SLS) regression of firm-level capital growth gi,k,c on the within-country per-year
improvement in the financial intermediaries’ efficiency (IFIE)—measured as the average growth rate of the credit bureau index—by
controlling for a number of industry- and country-level traits variables, where we use the country’s legal origin as the instrumental
variable for the country’s financial development ‘FD’ measured as the country’s enterprise credit-to-GDP ratio. The regression
equation of gi,k,c is written as:
!
h
i
gi,k,c =
α1 + α2 · F Dc · Smallk + Ψ · Xk · IF IEc + GDPc · Smallk + V ADk,c + Other Controlsi,k,c + i,k,c .
Table 3: Determinants of Firm-Level Capital Growth: Osiris Publicly Traded Firms
68
Industry-fixed effects
Country-fixed effects
Observations
Countries
Adj. R2
Constant
Concentration × IFIE
Opaqueness × IFIE
Intangibility × IFIE
Ext Dep × IFIE
Growth Opportunity × IFIE
Regression
Industry Share in Value Added
Dependent Variable
(1)
-0.0001
(0.0004)
-0.8879*
(0.5196)
0.0415*
(0.0213)
-0.0211
(0.0134)
-0.2634
(0.2482)
0.0021
(0.0020)
0.0416
(0.484)
Yes
Yes
7,034
31
0.33
Total
Assets:
Growth
Capital in Place
Tangible
Investment in
Fixed
Intangibles:
Assets:
Growth
Growth
(2)
(3)
-0.0002
-0.0085***
(0.0004)
(0.0025)
-0.8282**
-6.5769
(0.4045)
(5.8862)
0.0067
3.5637***
(0.0294)
(0.4067)
-0.0101
-1.7023*
(0.0205)
(0.9275)
-0.1210
1.5723
(0.2866)
(2.6589)
0.0012
0.0347
(0.0019)
(0.0685)
0.0254
1.2741
(0.0597)
(0.8148)
Yes
Yes
Yes
Yes
6,889
3,433
31
31
0.27
0.04
Table 3 Cont’d
(4)
-0.0004
(0.0010)
-0.0015
(0.8665)
-0.3679***
(0.1369)
0.0617
(0.0736)
-0.6634
(0.4053)
-0.0072**
(0.0030)
-0.1256
(0.1273)
Yes
Yes
5,435
31
0.08
Net Working
Capital:
Growth
Liquidity
Financing
Externally
Raised
Capital:
Growth
(5)
-0.0007***
(0.0002)
-0.2617
(0.2687)
-0.0566*
(0.0315)
0.0103
(0.0206)
-0.1326
(0.0808)
0.0011
(0.0014)
0.1340***
(0.0457)
Yes
Yes
5,985
31
0.29
69
Panel A: Unconditional Statistics Across Industry-Country Observations
Obs.
Mean
SD
Min
p25
Median
p75
Max
Investment: Growth
518
0.263
0.632
-0.543
0.022
0.095
0.272
6.257
Investment: Level (mil. USD)
518
3,912
7,936
1.3
248
1,050
3,562
65,036
Value Added: Growth
601
0.086
0.123
-0.146
0.021
0.066
0.118
1.173
Value Added: Level (mil.USD)
601
24,843 57,252
5.2
298
6,559
22,846
549,749
Panel B: Statistics of Country-Level Financial Development
Obs.
Mean
SD
Min
p25
Median
p75
Max
Per-Capita GDP (in USD)
33
16,297 10,524
909
5,310
16,546
25,679
32,344
Enterprise Credit/GDP
33
0.34
0.23
0.008
0.17
0.27
0.49
1.07
Private Credit/GDP
33
0.78
0.41
0.17
0.36
0.78
1.11
1.51
Credit Bureau: Growth
33
0.19
0.42
-0.06
0.01
0.03
0.18
1.93
Panel C: Correlation Coefficients of Country-Level Financial Development and Firm Growth
PerEnterprise
Private
Credit
InvestValue
InvestValue
Capita
Credit/
Credit/
Bureau:
ment:
Added:
ment:
Added:
GDP
GDP
GDP
Growth
Growth
Growth
Level
Level
Per-capita GDP
1.0
Enterprise Credit/GDP
0.58*
1.0
Private Credit/GDP
0.81*
0.73*
1.0
Credit Bureau: Growth
-0.44*
-0.33
-0.45*
1.0
Investment: Growth
-0.31
-0.26
-0.32
0.35
1.0
Value Added: Growth
-0.70*
-0.51*
-0.71*
0.25
0.60*
1.0
Investment: Level
0.21
0.55*
0.26
0.10
-0.19
-0.29
1.0
Value Added: Level
0.39*
0.66*
0.39*
-0.18
-0.33
-0.40
0.90*
1.0
Note: this table presents descriptive statistics of key variables of the UN Industrial Statistics data. Panel A presents statistics
of variables unconditionally across industry-country observations. And ‘Panel B’ presents cross-country statistics of country-level
variables related to financial and economic development, ‘Panel C’ their correlation coefficients, and Panel ‘D’ statistics of variables for
a given country. ‘Enterprise Credit-to-GDP’ refers to the average ratio of enterprise credit to GDP, which measures a country’s level of
financial development in terms of facilitating firms’ access to credit, and ‘Credit Bureau Growth’ the average growth rate of the credit
bureau coverage over period 2004-2007, which measures the within-country per-year improvement in the financial intermediaries’
efficiency of production of information on borrowers’ creditworthiness. ‘SD’ refers to the standard deviation, ‘p25’ the 25 percentile,
and ‘p75’ the 75 percentile. ‘*’, ‘**’, and ‘***’ indicate significance at the 10%, 5%, and 1% level, respectively.
Table 4: Summary Statistics: U. N. Industrial Statistics Data
Table 4 Cont’d: Country-Level Statistics
Panel D: Cross-Industry Statistics for a Given Country
Firm Growth
Enterprise
Credit
Investment Growth
Value-Added Growth
Credit-to- Bureau
Obs. Mean
SD Obs. Mean
SD
GDP Growth
Australia
20 0.164
0.166
19 0.108
0.085
0.279
0.016
Austria
19 0.036
0.085
19 0.055
0.056
0.653
0.018
Belgium
20 0.177
0.420
20 0.056
0.041
0.314
0.024
Canada
n.a.
n.a.
n.a.
20 0.032
0.059
0.188
0.000
Costa Rica
n.a.
n.a.
n.a.
16 0.086
0.093
0.113
1.937
Czech Republic
20 0.183
0.084
19 0.087
0.050
0.314
0.299
Denmark
19 0.059
0.094
19 0.020
0.053
0.133
0.192
Estonia
14 0.697
0.833
16 0.149
0.140
0.176
0.000
Finland
20 0.039
0.094
20 0.044
0.065
0.227
0.002
France
20 0.039
0.044
19 0.012
0.037
0.339
0.029
Germany
20 0.069
0.079
20 0.047
0.033
0.653
0.047
Hungary
20 0.071
0.134
20 0.073
0.111
0.189
0.285
Iceland
9 1.210
1.410
18 0.104
0.104
0.492
0.000
Indonesia
19 0.996
1.614
19 0.209
0.248
0.170
1.407
Ireland
19 0.176
0.413
17 0.059
0.044
0.492
0.000
Italy
20 0.037
0.075
20 0.036
0.046
0.650
0.085
Japan
19 0.057
0.044
19 -0.006
0.038
1.070
0.037
Latvia
20 0.427
0.273
20 0.106
0.123
0.160
0.636
Lithuania
20 0.318
0.287
19 0.191
0.153
0.104
0.770
Mexico
16 0.150
0.283
17 0.212
0.284
0.087
0.194
Netherlands
17 0.225
0.372
18 0.039
0.062
0.630
0.066
New Zealand
13 0.502
1.132
13 0.103
0.074
0.703
0.008
Poland
19 0.793
1.504
20 0.109
0.080
0.135
0.118
Portugal
17 0.087
0.147
17 0.042
0.053
0.507
0.032
South Korea
19 0.162
0.122
19 0.079
0.083
0.313
-0.041
Russian Fed.
n.a.
n.a.
n.a.
19 0.308
0.109
0.145
0.000
Slovenia
20 0.182
0.134
20 0.057
0.066
0.240
0.005
South Africa
n.a.
n.a.
n.a.
19 0.148
0.046
0.309
-0.061
Spain
20 0.091
0.073
20 0.029
0.037
0.250
0.051
Sweden
19 0.542
1.126
20 0.087
0.198
0.233
0.007
Turkey
20 0.605
0.313
20 0.151
0.093
0.115
0.041
U.K.
20 -0.010
0.074
20 -0.016
0.057
0.557
0.037
70
71
Panel E: Statistics of Industry-Level Investment Growth across Countries
NACE Industry
Obs.
Mean
SD
Min
p25
Median
15
Food
28
0.142
0.353
-0.088
-0.002
0.064
17
Textiles
27
0.290
0.749
-0.124
-0.017
0.075
18
Wearing
25
0.065
0.242
-0.307
-0.031
0.054
19
Leather
22
0.126
0.243
-0.493
-0.004
0.101
20
Wood
27
0.341
0.620
-0.063
0.045
0.156
21
Paper
28
0.420
0.964
-0.115
0.044
0.162
22
Publishing
26
0.196
0.252
-0.055
0.026
0.069
24
Chemicals
25
0.267
0.462
-0.097
0.034
0.070
25
Rubber
28
0.232
0.309
-0.072
0.039
0.069
26
Non-metallic
28
0.254
0.325
-0.032
0.080
0.135
27
Basic metals
27
0.422
0.831
-0.090
0.075
0.182
28
Fabricated metal
28
0.195
0.296
-0.282
0.042
0.087
29
Machinery
25
0.078
0.194
-0.360
0.033
0.085
30
Office and computer
20
0.119
0.357
-0.288
-0.043
0.070
31
Electric
25
0.216
0.509
-0.130
-0.033
0.068
32
Communication
24
0.178
0.271
-0.205
-0.009
0.124
33
Precision
25
0.360
1.170
-0.159
0.033
0.099
34
Motor
28
0.524
1.012
-0.079
0.033
0.194
35
Other transport
25
0.132
0.248
-0.543
0.026
0.089
36
Furniture
27
0.499
1.106
-0.044
0.025
0.095
p75
0.145
0.197
0.138
0.225
0.365
0.348
0.303
0.317
0.319
0.342
0.344
0.286
0.157
0.189
0.172
0.317
0.301
0.337
0.206
0.373
Max
1.866
3.765
1.034
0.731
3.012
4.200
0.937
1.985
1.151
1.497
4.174
1.270
0.581
1.593
2.210
1.198
6.257
4.270
1.033
4.815
Note: Panel E presents statistics of investment growth for a given industry. ‘SD’ refers to the sample standard deviation, ‘p25’ the
25 percentile, ‘p75’ the 75 percentile, ‘NACE’ the two-digit NACE industry code, and ‘Obs.’ the number of observations.
Table 4 Cont’d: Industry-Level Investment Growth
72
Panel F: Statistics of Industry-Level Value-Added Growth across
NACE Industry
Obs.
Mean
SD
Min
p25
15
Food
32
0.083
0.060
-0.020
0.053
17
Textiles
32
0.001
0.085
-0.091
-0.050
18
Wearing
29
0.041
0.218
-0.084
-0.042
19
Leather
26
0.071
0.136
-0.146
-0.015
20
Wood
32
0.089
0.106
-0.040
0.030
21
Paper
32
0.042
0.050
-0.074
0.009
22
Publishing
31
0.059
0.111
-0.105
0.009
24
Chemicals
30
0.085
0.097
-0.036
0.028
25
Rubber
32
0.093
0.085
-0.021
0.030
26
Non-metallic
32
0.102
0.093
-0.009
0.043
27
Basic metals
31
0.149
0.081
0.011
0.094
28
Fabricated metal
32
0.108
0.073
-0.012
0.053
29
Machinery
32
0.083
0.052
-0.081
0.051
30
Office and computer
20
0.120
0.149
-0.135
0.042
31
Electric
30
0.117
0.112
-0.033
0.044
32
Communication
27
0.076
0.175
-0.121
-0.017
33
Precision
30
0.135
0.206
-0.067
0.065
34
Motor
31
0.114
0.114
-0.044
0.039
35
Other transport
29
0.090
0.091
-0.059
0.030
36
Furniture
31
0.079
0.102
-0.033
0.016
Countries
Median
0.073
-0.011
-0.009
0.049
0.073
0.038
0.040
0.056
0.073
0.071
0.123
0.088
0.082
0.079
0.086
0.074
0.089
0.102
0.068
0.043
p75
0.090
0.006
0.044
0.110
0.106
0.065
0.075
0.121
0.141
0.176
0.179
0.163
0.112
0.157
0.163
0.115
0.143
0.144
0.126
0.109
Max
0.253
0.287
1.148
0.622
0.488
0.184
0.568
0.376
0.369
0.368
0.368
0.288
0.195
0.581
0.475
0.915
1.174
0.456
0.349
0.384
Note: Panel F presents statistics of value-added growth for a given industry. ‘SD’ refers to the sample standard deviation, ‘p25’ the
25 percentile, ‘p75’ the 75 percentile, ‘NACE’ the two-digit NACE industry code, and ‘Obs.’ the number of observations.
Table 4 Cont’d: Industry-Level Value-Added Growth
Table 5: Determinants of Firm Growth: U. N. Industrial Statistics IndustryLevel Data on Both Publicly Traded and Privately Held Firms
Note: this table presents results for the IV (2SLS) regression of industry-level annual firm growth gk,c —
in terms of investment and value added—in industry k and country c on average over the sample period
2002-2007 on the within-country per-year improvement in the financial intermediaries’ efficiency denoted
by ‘IFIE’—measured as the annual growth rate of the credit bureau index—and the country’s enterprise
credit-to-GDP ratio denoted by ‘FD’ where we use the country’s legal origin as the instrumental variable for
the country’s financial development ‘FD.’ The regression equation of gk,c is written as:
!
h
i
α1 + α2 · F Dc · Smallk + Ψ · Xk · IF IEc + GDPc · Smallk + V ADk,c + Other Controlsk,c + k,c .
gk,c =
Industry k’s technological firm size is (inversely) measured as the log of the corresponding U. S. industry’s
small-firms share, denoted by ‘Small,’ and defined as the share in employment of firms that have less than 20
employees in 1997. Additional industry-traits variables Xk are also controlled for as follows: ‘Growth Opportunity’ refers to the industry-specific component of global growth opportunities, ‘Ext Dep’ the industry’s
technological dependence on external finance, ‘Intangibility’ the industry’s dependence on intangible assets,
‘Opaqueness’ the industry’s degree of opaqueness in terms of assessing firm performance in the stock market,
and ‘Concentration’ the industry’s four-firm concentration ratio. GDPc refers to the initial-period (in 2002)
per-capita real GDP, and V ADk,c the initial-period country-specific industry’s share in value added across
the sample industries for a given country. All regressions control for country- and industry-fixed effects, and
the initial-period level of the dependent variable of which growth rate is the dependent variable of the regression equation, e.g., the initial level of investment of firm i. Standard errors are reported inside parenthesis
and robust to heteroskedasticity and country-level clustering. ‘*’, ‘**’, and ‘***’ indicate significance at the
10%, 5%, and 1% level, respectively.
Dependent Variable
Investment Growth
Regression
Small
(1)
0.921*
(0.49)
Small × IFIE
-0.475***
(0.145)
Small × IFIE × FD
1.888***
(0.298)
Small × GDP per capita
-0.104**
(0.049)
Industry Share in Value Added
0.015
(0.01)
Growth Opportunity × IFIE
-5.597*
(3.084)
73
Value-Added Growth
(2)
(3)
-0.102
0.043
(0.074)
(0.073)
-0.278 -0.069**
(0.235)
(0.028)
1.920** 0.244***
(0.906)
(0.079)
-0.007
(0.008)
0.001
(0.002)
-6.381
-0.371
(4.656)
(0.441)
(4)
-0.027***
(0.006)
-0.059**
(0.026)
0.215***
(0.055)
-0.385
(0.469)
Table 5 Cont’d
Dependent Variable
Regression
Ext Dep × IFIE
Intangibility × IFIE
Opaqueness × IFIE
Concentration × IFIE
Initial Level
Constant
Industry-fixed effects
Country-fixed effects
Observations
Countries
Adj. R2
Investment Growth
(1)
0.288
(0.173)
0.091
(0.066)
-2.891***
(0.857)
-0.011**
(0.004)
-0.105*
(0.059)
1.194**
(0.45)
Yes
Yes
518
31
0.33
74
Value-Added Growth
(2)
(3)
0.29
-0.002
(0.178)
(0.017)
0.102
0.019
(0.269)
(0.039)
-2.957**
-0.171
(1.429)
(0.133)
-0.009
-0.0004
(0.016)
(0.000)
-0.06
-0.040*
(0.043)
(0.019)
0.931*** 0.556***
(0.361)
(0.176)
Yes
Yes
Yes
Yes
518
601
31
31
0.32
0.46
(4)
-0.003
(0.017)
0.018
(0.039)
-0.178
(0.14)
-0.006
(0.001)
-0.035***
(0.011)
0.514***
(0.101)
Yes
Yes
601
31
0.45