Financial Development and Vertical Integration:
Theory and Evidence
Rocco Macchiavello
August 2009
Abstract
Existing evidence is mostly inconclusive on the relevance of …nancial development
as a determinant of vertical integration. This paper presents evidence that, once industry heterogeneity in …rm size distribution is taken into account, …nancial development
is an important determinant of cross-country di¤erences in vertical integration. Financial development fosters entry of …rms and increases competition in the industry.
This reduces vertical integration of larger …rms, but also leads smaller, non-integrated,
…rms to exit the industry. As a result, higher …nancial development reduces vertical
integration in industries where a high share of output is produced by small …rms. The
positive e¤ect of …nancial development on entry also reduces vertical integration by
fostering the development of input markets.
Keywords: Vertical Integration, Borrowing Constraints, Contract Enforcement, Developing Countries, Industry Equilibrium.
JEL Codes: D23, L11, L22, O14.
Warwick University and CEPR. I am especially indebted to Tim Besley and Maitreesh Ghatak for their
support, Abhijit Banerjee for encouraging me at the beginning of this project, the Editor, Fabrizio Zilibotti,
and three anonymous referees for their comments. I also thank Oriana Bandiera, Marianne Bertrand, Robin
Burgess, Dave Donaldson, Mikhail Drugov, Aytek Erdil, Leonardo Felli, Sharun Mukand, Steve Pisckhe,
Xavier Ragot, Imran Rasul, Philipp Schmidt-Dengler, John Sutton, Eric Verooghen, Fabian Waldinger, participants at NEUDC 2005, ESSFM 2006 and CEPR/EUDN conferences, and seminars at Berkeley, Bocconi,
Essex, HEC Paris, IFS, IIES, LSE, Maryland, Munich, Oxford, PennState, PSE-Jourdan and UvA for useful
comments. All errors are mine. E-Mail: [email protected]¢ eld.ox.ac.uk
1
1
Introduction
Anecdotal evidence and theoretical considerations suggest the existence of cross-country differences in the organization of production in general, and in the degree of vertical integration
in particular. For instance, when contracts are di¢ cult to enforce, one in‡uential strand of
the literature has noted that vertical integration is more likely (see, e.g., Williamson (1975,
1985)). Since less developed countries are characterized by poor contractual enforcement,
…rms in those countries are often thought to be larger and more vertically integrated (see,
e.g., Khanna and Palepu (1997, 2000)). A parallel strand of the literature has noted that,
when contracts are di¢ cult to enforce, …nancial markets remain underdeveloped.1 These considerations, therefore, call for an analysis of the relationship between …nancial development
and vertical integration.
The existing evidence on the relationship between …nancial development and vertical
integration, however, is mostly inconclusive. On the one hand, a substantial body of work
documents the prevalence of subcontracting arrangements in the developing world, a fact
which seems to contrast with the intuitions above.2 On the other hand, business historians
have linked less developed …nancial markets with higher degrees of concentration and vertical
integration.3 The apparent lack of a systematic relationship between …nancial development
and vertical integration is con…rmed in the recent cross-country analysis by Acemoglu et al.
(2009). They …nd that, once industry e¤ects are controlled for, more developed …nancial
markets do not correlate with either higher or lower degrees of vertical integration.
This paper argues that, once industry heterogeneity in …rm size distribution is taken
into account, …nancial development is an important determinant of vertical integration. The
argument rests on two well-documented ingredients, formally presented by the model in
1
Across countries, measures of …nancial development strongly correlate with legal origin and other aspects
of the legal system (see, e.g., Levine (2005) and La Porta et al. (1998)).
2
See, e.g., the computer industry in Taiwan (Levy (1990)), the Guadalajara shoe cluster in Mexico
(Woodru¤ (2002)), the Sinos Valley in Brazil (Schmitz (1995)), the Tirupur cotton industry in India (Banerjee
and Munshi (2004)). Andrabi et al. (2006) study the subcontracting arrangements of a large tractor producer
in Pakistan.
3
See, e.g., Haber (1991), Temin (1988) and Brown (1992) studies of the 19th century textile industry and
Langlois and Robertson (1989) for the car industry. Porter and Livesay (1971) and Helper and Hochfelder
(1996), however, argue that access to …nance was a key determinant of vertical integration in the early days
of the textile and car industry respectively.
2
Section 2. First, …nancial development increases entry of …rms in the industry, and leads
to a more competitive environment (see, e.g., evidence in Haber (1991), Rajan and Zingales
(1998), and Aghion et al. (2007)). Second, within industry, vertical integration positively
correlates with size across …rms (see, e.g., evidence in Banerjee and Munshi (2004), Whinston
(2003), Hortaµcsu and Syverson (2009)).
The combination of the two ingredients implies that higher …nancial development, by
fostering entry of …rms in the industry, has two e¤ects on the degree of vertical integration.
On the one hand, since vertical integration is chosen by …rms that operate at a larger scale,
higher competition and lower pro…ts imply that fewer …rms can integrate vertically. On the
other hand, higher competition and lower pro…ts lead to higher exit rates among smaller,
non-integrated, …rms.
While the net impact of …nancial development on vertical integration appears to be ambiguous, the model provides an empirical proxy which can be used to uncover the heterogenous e¤ect across industries. Speci…cally, the model shows that higher …nancial development
reduces vertical integration in industries where small …rms generate a higher share of the
revenues. The opposite is true for industries in which large …rms generate a higher share
of the revenues. The empirical analysis shows strong evidence which is consistent with the
theoretical predictions in a cross section of countries.
The empirical analysis controls for several other channels through which contractual institutions in general, and …nancial development in particular, might a¤ect vertical integration.
In particular, the analysis shows that higher …nancial development fosters entry of …rms in
input markets as well, further reducing vertical integration in downstream industries that
use inputs that are heavily dependent on credit.4 The analysis also considers the separate
role of contractual imperfections with input suppliers, but fails to …nd strong evidence that
these frictions correlate with vertical integration.
Related Literature
This work is closely related to several strands in the literature. The theoretical section
assumes that vertical integration economizes on the needs for contracting with speci…c input
suppliers at the cost of higher …xed costs and is, therefore, closer in spirit to transaction
4
This argument was formally presented in a previous version of the paper.
3
costs theory of the …rm (see, e.g., Williamson (1975, 1985)), than to property rights theory
of the …rm (see, e.g., Hart (1995)). The model extends the industry equilibrium analysis
in Grossman and Helpman (2002) by considering …rm heterogeneity, modeled as in Melitz
(2003), and credit market imperfections at the entry stage. Both ingredients are essential
to disentangle the heterogenous e¤ects of credit market imperfections on vertical integration
across industries.
On the empirical front, Acemoglu et al. (2009) is the only other cross-country study on
the relationship between the institutional environment and vertical integration I am aware
of. Acemoglu et al. (2009) also do not …nd a signi…cant relationship between …nancial
development and vertical integration. They do …nd, however, that …nancial development
increases vertical integration in countries where contracts are harder to enforce. This is
consistent with a view in which better access to credit enables …rms to integrate vertically
in those contexts in which there is a demand for vertical integration. This paper, instead,
recovers a systematic relationship between …nancial development and vertical integration
by emphasizing the role of industry, rather than country, characteristics. The argument,
therefore, rests on a di¤erent economic mechanism, i.e., the role of well functioning credit
markets in fostering entry. Taken together, the evidence in the two papers suggests that
…nancial development is an important determinant of vertical integration. Because of the
multiple channels involved, however, knowledge of other properties of the industrial and
institutional environments in which the …rm operates are necessary to determine the overall
e¤ect. The complementarity of the two papers is further enhanced by the fact that this
paper replicates some of the results in Acemoglu et al. (2009) using a completely di¤erent
measures of vertical integration.5
The rest of the paper is organized as follows. Section 2 presents the theoretical model
and Section 3 presents the empirical evidence. Section 4 o¤ers a few concluding remarks.
The proofs of the results and details about the construction of the variables used in the
empirical analysis are in the Appendixes.
5
Acemoglu et al. (2009) and Aghion et al. (2006) are examples of cross-industry studies of vertical
integration. A large literature analyzes the determinants of vertical integration in speci…c industries (see,
e.g., Whinston (2003) for a survey), but with no focus on the e¤ects of …nancial market imperfections. Laeven
and Woodru¤ (2006) analyze the e¤ect of contract enforcement in Mexico on …rms size.
4
2
Theory
2.1
Set Up
Environment
I consider a small open economy populated by entrepreneurs and workers. There is a
mass L of identical workers and I + 1 sectors. One sector produces a single homogeneous
good. This good is used as the numeraire, and its price is set equal to 1. It is produced
under constant returns to scale, with a technology employing
1
w
units of labour to produce 1
unit of the homogeneous good. Provided that the economy produces the homogeneous good,
the wage in the economy is equal to w: In the remainder of the paper, I assume that this is
true.
Each of the remaining I sectors produces a measurable set of di¤erentiated varieties.
Each variety is produced by a …rm which is a monopolist over the single variety it produces.
Each …rm is run by a risk-neutral entrepreneur. In each industry there is an exogenously
given pool of entrepreneurs of mass also equal to L: Entrepreneurs only di¤er with respect
to their wealth a: Within each industry wealth is distributed across entrepreneurs according
to a continuous and strictly increasing cumulative function H(a) with associated continuous
density function h( ) de…ned over a 2 [0; 1) :
Workers are the only consumers and are endowed with 1 unit of inelastically supplied
labour. They have CES preferences over the di¤erentiated goods. Let
i
be the set of
varieties available for purchase in equilibrium in industry i. A consumer that receives q0
units of the homogeneous good, and q( ) of each variety
U=
1 I
q0 '
Z
I
Y
i=1
where
i 'i
= I' and "i =
1
1
i
q( ) i d
2
2
'i
i
i
;
gets a utility U
(1)
i
> 1 is the elasticity of substitution between any two varieties
of the di¤erentiated good in industry i:
If varieties in the set
i
are available at a particular price p( ); these preferences yield
5
aggregate demand functions
q( ) = Ai p( )
The monopolist of variety
"i
Ai = R
(
with
2
'i w
p( )
i
i "i
d )
:
(2)
in industry i treats Ai as a constant, and so perceives a constant
elasticity of demand "i :
Since the pool of potential entrepreneurs in each industry as well as the wage w are
exogenously given; industries can be treated independently. Therefore, in the remainder of
this section I suppress the subscript i from industry variables when it is not needed.
Production and Firm Organization
Within each industry, each entrepreneur receives an idea about a product to be developed:
There is uncertainty over the pro…tability of the idea and over other factors that a¤ect the
outcome of the …rm. Entrepreneurs independently draw their productivity from a distribution with associated continuous and strictly increasing cumulative function G( ); de…ned
over the support
2 [ ; 1) : In order to start a …rm in industry i and learn her productivity
; an entrepreneur has to pay a …xed cost Ki . Entrepreneurs can only start and, therefore,
run one …rm.
Upon observing her draw ; the entrepreneur decides whether to start production and,
conditional on starting production, whether to set up a vertically integrated …rm or a nonintegrated …rm. Production of each di¤erentiated good entails variable and …xed costs.
With a slight abuse of notation, I assume that after paying the appropriate …xed costs,
each di¤erentiated …nal product y( ) is produced under a constant marginal cost technology
according to
y= x
(3)
where x is a specialized component produced using only labour. The intermediate input x
is produced undertaking speci…c investments at constant marginal cost c = w; i.e., one unit
of the specialized component is produced employing one unit of labour.
Final goods may be produced by vertically integrated …rms or by specialized assemblers
that purchase their inputs at arm’s length from external suppliers. Under vertical integration
6
the entrepreneur decides how much to invest in x. In order to set up a vertically integrated
…rm, the entrepreneur has to pay set-up costs equal to kv :
Alternatively, the entrepreneur can outsource to an independent supplier the production
of the intermediate input. I assume a large number of homogenous suppliers of specialized
components. Therefore a non-integrated …rm always …nds a supplier. Ex-ante competition
to attract customers drives suppliers’ pro…ts down to zero. Once a match is formed, the
supplier decides the investments in the production of the specialized component x. I consider
a setting in which the investments required to produce x are speci…c to the requirements of
the customers and, while observable by suppliers and buyers, cannot be veri…ed by a court.
A non-integrated …rm, therefore, su¤ers from the classical hold-up problem associated with
contract incompleteness: once a supplier specializes its inputs to a particular customer, these
inputs have a higher value within the relationship than in any alternative use. Because of
incomplete contracts, the price for the intermediate input is negotiated ex-post in a context
of bilateral monopoly, once the non-contractible investments are sunk and the speci…c input
produced. For simplicity, I assume that the ex-post outside options in the bargaining game
are equal to zero for both customers and suppliers, and that the price of the intermediate
inputs is given by the Nash Bargaining solution with shares equal to (1
and
) for the buyer
for the supplier. In order to set up a non-integrated …rm the entrepreneur has to pay
set-up costs equal to ko < kv :
The model, therefore, captures the following simple trade-o¤: vertical integration limits
opportunism induced by imperfect contracting in input markets at the cost of larger set-up
costs. This trade-o¤ implies a positive correlation between …rm’s size and vertical integration
which is i) consistent with a large body of anecdotal as well as econometric evidence on
vertical integration6 , and ii) the key property required for the prediction that is tested in
the empirical section.
Financial Constraints and Financial Contract
Entrepreneurs borrow from external investors using their wealth a as collateral: in case
of default, the investors will seize wealth a from the entrepreneur. Entrepreneurs borrow in
order to …nance the set-up cost Ki and the …xed costs associated with production kd ; with
6
See, e.g., Hortaµcsu and Syverson (2009), Banerjee and Munshi (2004) and Whinston (2003).
7
d 2 fo; vg depending on which organizational form is chosen. Risk-neutral investors lend
funds at a risk-free interest rate equal to zero. I model contractual imperfections in the credit
market in a rather crude, but simple, way. I assume that the …xed costs Ki are composed
of a continuum of small investments. A fraction
c
of these investments is contractible:
external investors can easily make sure that the capital is e¤ectively invested in the project
(for instance, renting corporate buildings, acquiring speci…c machines, etc.). In contrast,
the remaining fraction 1
c
is not contractible, in the sense that external investors cannot
make sure that the capital is e¤ectively invested in production (e.g., hiring the appropriate
product designer, purchase of some speci…c services, etc.); this can instead be pocketed by
the entrepreneur. If the entrepreneur does not invest the entire sum Ki ; the …rm does not
produce any output and there is default. I interpret
c
as capturing characteristics of the
quality of contractual institutions in the …nancial market in the country. Countries with
more developed …nancial markets have higher
c:
I interpret Ki ; instead, as being a measure
of the external …nance requirements of the industry.
For simplicity, I assume that conditional on having completed investments Ki ; realized
productivity ; production …xed costs kd and realized pro…ts
d(
) are contractible. The …-
nancial contract with external investors, therefore, speci…es a re…nancing policy, i.e., whether
production should be started and, if so, under which organizational form, conditional on .
In the event in which production is started, external investors hold claims over the pro…ts
of the …rm.7
To summarize, the timing of events is as follows. Entrepreneurs decide whether to invest
and, if they do invest, approach external investors to receive funding to start a project. Upon
investing Ki ; they discover their productivity : If they do not invest Ki , they default and
their wealth is seized. Conditional on the realization of ; the …nancial contract prescribes
a re…nancing policy that leads to either the …rm being closed down, or production being
started as a …nal good assembler or as a vertically integrated …rm. Vertically integrated
…rms pay …xed costs kv and undertake production decisions. Assemblers pay …xed costs ko
and are matched to a supplier. The assembler and the supplier agree on an up-front fee to be
7
In other words, conditional on initial investment Ki , …nancial contracts are complete. Up to the adaptations required for industry equilibrium and heterogeneity, this gives the simplest textbook model of …nancial
constraints (see e.g., Tirole (2006), chapter 3).
8
paid by the supplier to the assembler. After this, the supplier undertakes non-contractible
investments: Bargaining over the surplus takes place, the …nal goods are produced and sold,
and pro…ts are realized. Finally, whenever appropriate, external investors are repaid out of
pro…ts according to the …nancial contract.
An equilibrium consists of a set of …nancial contracts C e and a mass of entrepreneurs M e
starting a …rm, such that there are no other contracts that satisfy the investor’s zero pro…ts
constraint and gives strictly higher utility to any entrepreneur.
2.2
Industry Equilibrium
The model has to be solved backwards. For a given mass of entrepreneurs M that has
started a …rm in the industry, I compute the pro…t functions for a vertically integrated and
for a non-integrated …rm respectively. I then derive the organizational form chosen by an
entrepreneur with productivity
and describe the industry equilibrium. I …nally roll back
the clock, and derive the mass of entrepreneurs M that start a …rm in the industry (see the
Appendix for details).
I focus on the more interesting cases in which the two organizational forms coexist in
equilibrium and the credit constraint binds. When the credit constraint binds, the marginal
entrepreneur that starts a …rm earns rents proportional to the product between the amount
borrowed, Ki ; and the contractual imperfections in the credit market, (1
c) :
The e¤ect
of contractual imperfections in the credit market on the industry equilibrium, therefore,
works exclusively through its interaction with the …nancial requirements, Ki : This property,
which is important for the identi…cation strategy in the empirical analysis, is captured by
the …nance index, Fic ; de…ned as
Fic = Ki
(4)
c:
The index captures the availability of external …nance to those industries where it is most
needed, and is given by the product between the external …nance requirements in industry
i; Ki ; and contractual e¢ ciency in the capital markets in country c,
c:
The following proposition characterizes the equilibrium in the cases of interest.
9
EÝSÞ
Non Integration
Integration
E vi ÝSÞ
E ni ÝSÞ
Se
Sv
S JP
F ni
k
F vi
1
Figure 1: Equilibrium Organizational Forms and Comparative Statics
Proposition 1
1. There is a unique wealth level, a (Fic ); such that only entrepreneurs with wealth a
a (Fic ) enter the industry. The mass of entrepreneurs entering the industry, i.e., M e =
1
H(a ); is such that
@M e
@Fic
2. There exist two thresholds
> 0:
e
and
start production, entrepreneurs with
products, and entrepreneurs with
Moreover,
@ e
@Fic
> 0 and
@ v
@Fic
v;
such that entrepreneurs with
2 [ e;
v
v)
<
e
do not
start production as assemblers of …nal
start production as vertically integrated …rms.
> 0:
The …rst part of the proposition links the level of entry in the industry to the degree of
imperfections in the credit market. When the …nancial constraint is binding for the marginal
entrant, the equilibrium level of expected pro…ts exceeds the investment requirement and
entrepreneurs earn rents. Those rents are inversely related to the …nance index Fic : A higher
e
level of the …nance index, Fic ; therefore, increases entry rates in the industry ( @M
> 0).
@Fic
The second part of the proposition describes the sorting of …rms with heterogeneous
productivity
into organizational forms: since limiting opportunism through vertical inte10
gration is especially important for more productive and, therefore, larger …rms, the model
implies a positive correlation between …rm’s size, measured either in terms of employment
or revenues, and vertical integration.8 This positive correlation is consistent with empirical
evidence, and is the key property for the results. Moreover, since higher entry rates lower
pro…ts in the industry, a higher level of the …nance index reduces the equilibrium dispersion
@ e
in productivity levels ( @F
> 0). Finally, since vertical integration is only pro…table if the
ic
…rm operates at a relatively large scale, a higher value of the …nance index increases the
@ v
productivity threshold at which vertical integration becomes pro…table ( @F
> 0). These
ic
e¤ects are illustrated in Figure 1.
2.3
Testable Prediction
Following the seminal contribution in Adelman (1955), the empirical literature has measured
vertical integration as the ratio of value added over sales. At the …rm level, the ratio captures
the percentage of the value of production that is carried out within …rm boundaries. In the
model, the ratio of value added over sales is equal to one for vertically integrated …rms and
to (1
) for non-integrated …rms.
The data available for the empirical exercise recommend the use of two di¤erent indexes
to measure vertical integration at the industry level. The …rst empirical measure of vertical
integration is given by the ratio of value added over output in the industry, computed using
data from UNIDO. This measure is a weighted average of …rm level indexes of vertical
integration, with weights given by …rm revenues (see equation (10) below). Let rd ( ) be
the revenues generated by a …rm with productivity
Denoting by
d
the set of productivity levels
and organizational form d 2 fo; vg:
for which …rms choose organizational form
d; the total revenues generated by …rms with organizational form d are given by Rd =
R
r ( )dG( ): The ratio of value added over output in the industry is, therefore, given by
2 d d
V IicU N IDO =
Rv + (1
)Ro
:
Rv + Ro
8
(5)
Note that, by de…nition, vertically integrated …rms are larger in terms of employment, but not necessarily
in terms of revenues. In the empirical part, …rm size is measured with respect to revenues, avoiding this
di¢ culty.
11
The second measure of vertical integration available in the data, instead, is the unweighted
average of …rm level indexes of vertical integration, computed from the WorldBase database,
as in Acemoglu et al. (2009).9 Denoting by Nd the number of …rms with organizational form
d 2 fo; vg; the second measure of vertical integration is, therefore, given by
V IicD&B =
Nv + (1
)No
:
Nv + No
(6)
As noted above, …nancial development a¤ects vertical integration in the industry exclusively through its impact on the entry of …rms. The …nance index, Fic ; captures the channel,
which works through the …nancial requirements Ki : The following proposition provides comparative statics results for both measures of vertical integration with respect to the …nance
index Fic :
Proposition 2
The e¤ects of the …nance index Fic on the two measures of vertical integration in the
industry is ambiguous, and such that
@V IicU N IDO
@Fic
0 ()
@V IicD&B
@Fic
where
1+
0 ()
1 Ro
"
Rv
g( v )
g( e )
; and
(7)
No
Nv
g( v )
g( e )
;
(8)
1+
is a positive constant that does not depend on Fic :
Both measures of vertical integration highlight that the …nance index, through its e¤ect
on the entry of …rms, has an ambiguous impact on the degree of vertical integration in the
industry. When should we expect either of the two e¤ects to dominate? The results in
Proposition 2 provide practical guidance on how to disentangle the two e¤ects in the data.
Both measures show that the e¤ect of better …nancial markets on vertical integration is
heterogenous across industries and depends on the shape of the …rm size distribution. In both
cases, the comparative statics suggests that, in response to better …nancial markets, vertical
integration decreases in industries where small …rms are relatively more important. The
9
Note, however, that the …rm level index of vertical integration is not the ratio of value added over sales.
12
proposition clari…es that when vertical integration is measured by V IicU N IDO the importance
of small …rms should be proxied by their share of revenues in the industry. When, instead,
vertical integration is measured by V IicD&B the importance of small …rms should be measured
by their relative frequency.
The next section exploits cross-country variation in …nancial development and crossindustry variation in …nancial requirements to shed light on the ambiguous relationship
between vertical integration and …nancial development. The main testable prediction is
summarized by:
Prediction: Regardless of which measure of vertical integration is used, a higher value
of the …nance index Fic reduces vertical integration in industries where small …rms are important.
The importance of small …rms is measured by their share of revenues when using the
measure V IicU N IDO and by their relative frequency when using the measure V IicD&B .
3
Empirical Evidence
3.1
Data
The main goal of the empirical exercise is to uncover systematic di¤erences in the organization of production across countries, and to relate them to variation in …nancial development.
A …rst challenge, therefore, is to …nd comparable measures of vertical integration across
countries.
The main measure of vertical integration in industry i in country c comes from the UNIDO
database.10 Following the industrial organization literature (see, e.g., Adelman (1955)), I
measure vertical integration in industry i in country c as the ratio of value added, V Aic ; over
output, Yic , i.e.,
V IicU N IDO =
10
V Aic
:
Yic
(9)
Section 3.7 reports results using an alternative measure of vertical integration constructed from the
Worldbase database and used in Acemoglu et al. (2009).
13
At the …rm level, the ratio of value added over output measures the proportion of the
production process that is carried out within …rm boundaries. A higher value of the index
is associated with a higher degree of vertical integration.
The UNIDO Database provides data for the manufacturing sector, divided into 28 industries. In constructing industry level variables, I have to consolidate two pairs of industries.
This leaves 26 industries (see Table 1.A). Due to data coverage, I focus on a sample of 84
countries using the years from 1990 to 1998 inclusive (see Table 1.B). I focus on empirical
speci…cations that do not exploit time variation by using the (log of the) average of the
variable of interest for the period between 1990 and 1998.
Two di¤erent sets of variables are used on the right-hand-side. First, a set of countrylevel measures of …nancial development and contractual institutions. The preferred measure
of …nancial development is the average ratio of bank credit over GDP during the nineties,
from Levine (2005). I use the natural logarithm for all country-level variables. Table 1.C
reports summary statistics and correlations among the main country level variables.
A second set of variables is given by industry characteristics in the United States. The
main ones are external …nancial dependency, which was …rst introduced by Rajan and Zingales (1998), as well as a proxy for the importance of small …rms in the industry which I
describe below in greater detail. I also use a measure of external …nancial dependency of
upstream industries as well as a measure of contractual needs at the industry level. In order
to attenuate concerns that industry characteristics in the United States are not representative of technologies in other countries, all the speci…cations use the ranking of the variables
across industries. Table 1.D provides rank-correlations among the industry variables in the
United States for the 26 industries in the sample.
3.2
From Theory to Data: Construction of Variables
The theoretical section derived comparative static results on the degree of vertical integration
in industry i in country c with respect to the …nance index, Fic . The index captures the
availability of external …nance to those industries where it is most needed, and is given by the
product between the …nancial requirements of the industry, Ki ; and a measure of contractual
14
e¢ ciency in the capital markets in the country,
c;
Fic = Ki
i.e.,
c:
I follow Rajan and Zingales (1998) and proxy Ki using the degree of external …nance dependency of industry i in the United States, EDi : To capture the development of …nancial
markets in country c; F Dc ; I use the ratio of bank credit over gross domestic product.
Therefore,
Fbic = EDi
F Dc :
The model predicts that the e¤ect of the …nance index Fbic depends on the shape of the
…rm size distribution in the industry. Proposition 2 shows that the appropriate proxy for
the shape of the …rms size distribution depends on whether the industry level measure of
vertical integration is a weighted average of …rm-level vertical integration, or an unweighted
average. Let vf ic =
vaf ic
yf ic
be the vertical integration index of …rm f in industry i and country
c: The UNIDO Index, V IicU N IDO ; is a weighted average of the vertical integration indexes of
the …rms in the industry,
V IicU N IDO
V Aic
=
=
Yic
X
vf ic
yf ic
f 2ic
X
:
(10)
yf ic
f 2ic
Proposition 2, therefore, recommends the use of a proxy that takes into account the share of
revenues of the large …rms in the industry. Equation (7) in the Proposition shows that the
e¤ect of the …nance index Fic on the degree of vertical integration in the industry depends
on the size distribution index, SDi ; given by
SDi =
1+
1 Ro
"
Rv
g( v )
:
g( e )
The model predicts that vertically integrated …rms are the largest in the industry. Information on …rm size distribution in the industry, therefore, can be used to construct the
proxy. In particular, the 1997 US census of industries provides information on employment
15
and revenues for …rms at the 4-digit SIC classi…cation, for ten groups de…ned according to
…rm size. I assume that within each group …rm size is distributed uniformly. Denote by g i (z)
the associated density at the z th percentile in industry i, and by
i
z
the share of revenues
generated by the …rms above the z th percentile in the size distribution in industry i. The
empirical proxy for the size distribution index in industry i; SDi is given by
di =
SD
2
1
i
80
g i (80)
:
g i (5)
(11)
To obtain the proxy, two important assumptions have been made. First, the shape of the
density function at the 5th percentile of active …rms is taken as a proxy of the density
function at the smallest surviving …rms in the industry, g i ( e ). Second,
v
is proxied using
the 80th percentile, i.e. set z = G( v ) = 80.11 I then match the 4-digit SIC industry codes
di within each ISIC code. The
with the 3-digit ISIC codes and take the median value of SD
di on industrial composition in the
median is taken to reduce the dependency of the index SD
United States. Table 5 reports results using alternative assumptions in the construction of
di :
the index SD
A previous version of the model showed that better …nancial markets may reduce vertical
integration by fostering entry of …rms in input markets. To control for this additional channel,
I exploit industry variation with respect to input requirements. The intuition is that better
…nancial markets decrease vertical integration relatively more in industries that use inputs
that are highly dependent on external …nance. I compute for each industry i a weighted
average of the …nance indexes of the industries that sell inputs to industry i: I construct the
weights using information from the input-output table of the United States. Denote by Fbjc
the …nance index in industry j in country c; and by zij the share of use of input j in the
d
production of output i: The upstream …nance index, U
F ic ; is given by
d
U
F ic =
j6=i vij
Fbj :
A third assumption is 1 " = 2: Since I use the ranking of the industry level variables in all the speci…cations, the choice of " does not a¤ect the results.
11
16
3.3
Reduced Form Speci…cation
The baseline speci…cation is given by the reduced form equation,
V IicU N IDO =
where
i
and
c
0
+
b +
1 Fic
d
2 SD i
Fbic +
d + Zic +
3 U F ic
i
+
c
+ "ic ;
(12)
are a set of industry and country dummies, Zic are country-industry controls
and "ic is the error term.
Since equation (12) includes country …xed e¤ects, the coe¢ cients
propensity towards vertical integration. The coe¢ cient
1;
identify relative
for instance, tells wether countries
with more developed …nancial markets are relatively more vertically integrated in industries
that depend on external …nance. Whether industries in countries with better …nancial markets are on average more or less vertically integrated cannot be identi…ed in the regression,
because of the inclusion of the country …xed e¤ects
c.
The controls Zic take the form of interactions between characteristics of industry i and
measures of economic, …nancial and institutional development in country c: A control variable that deserves special attention is the simple interaction between the size distribution
di ; and …nancial development in country c; F Dc : The comparative static in the
index, SD
model is taken on the …nance index, Fbic ; since the e¤ect of higher …nancial development
c
on vertical integration works through the …nancial requirements of the industry, Ki . The
di and Fbic amounts to a triple interaction and, therefore, would reinteraction between SD
di and F Dc to saturate the equation.
quire the inclusion of the simple interaction between SD
di and F Dc ; however, fails to capture the speci…c channel
The simple interaction between SD
through which …nancial development a¤ects vertical integration in the industry. Once appropriate controls are included, therefore, the simple interaction is expected to be insigni…cant.
The empirical analysis reports results for all the speci…cations when the simple interaction
is excluded, as well as when it is included. It also provides a falsi…cation test and shows
that the simple interaction between …nancial development and the distribution index does
not correlate with vertical integration, as suggested by the logic above.
The UNIDO database contains other variables at the industry-country level, such as number of establishments and total employment. These variables are endogenously determined
17
in equilibrium and, therefore, are not included in the reduced form equation. The use of
di and …nance
industry variables from the United States to construct the size distribution SD
d
indexes, Fbic and U
F ic ; is justi…ed by similar endogeneity concerns.12
Section 3.4 presents the main results from the reduced form equation. Section 3.5 shows
that the reduced form results are robust to: i) considering contractual imperfections with
di ; iii) alternative
input suppliers, ii) alternative de…nitions of the size distribution index SD
measures of …nancial development, F Dc ; iv) alternative samples. Taken together, the re-
duced form results document robust correlations which are consistent with the predictions
of the model.13 The model assumes that the …nance index Fic a¤ects vertical integration
exclusively by increasing entry of …rms in the industry. Based on this assumption, Section
(3.6) presents results in which the index Fbic is used to instrument entry. The …nance index
strongly predicts entry. Entry, in turn, a¤ects vertical integration as predicted by the model.
In the model, entry a¤ects vertical integration through a selection e¤ect which increases
average productivity in the industry. The section, therefore, presents direct evidence on the
selection e¤ect by considering speci…cations in which vertical integration and average productivity, measured by average output per worker, are jointly determined. Finally, Section
3.7, discusses concerns on whether the UNIDO Index genuinely captures vertical integration.
The section presents a set of falsi…cation tests and reproduces the reduced form results using
a di¤erent measure of vertical integration used in Acemoglu et al. (2009).14
3.4
Reduced Form Results
This section presents reduced form results on the relationship between …nancial markets
development and vertical integration. In particular, Proposition 2 predicts that a higher
value of the …nance index Fbic reduces vertical integration only in industries with high values
12
This implies omitting the United States from all the regressions. Furthermore, measures of the …rm size
distribution and input-output tables are not available for a large cross-section of countries and industries.
13
Following the logic in Rajan and Zingales (1998), the reduced form results can be given a causal interpretation if: i) industry variables in the U.S. re‡ect underlying technological characteristics inherent to
the production process of a given industry, ii) the ranking of industry characteristics in the United States
is correlated with the ranking in other countries. The validity of those two assumptions is however hard to
test empirically.
14
This index is an unweighted average of …rm level indexes of integration. Proposition 2 recommends the
use of a di¤erent proxy for the size distribution index, SDi : The results, therefore, also provides a further
robustness check with respect to the index.
18
di . With respect to equation (12), therefore, the model
of the size distribution index, SD
predicts
> 0 and
1
2
< 0: Furthermore, since better …nancial markets might reduce
vertical integration by fostering the development of input markets, we also expect
3
< 0:
Table 2 presents three sets of speci…cations. Columns I-III focus on the …nance index
Fbic : Columns IV-VI add the interaction of the …nance index Fbic with the size distribution
di . Finally, Columns VII-IX saturate the equation including the simple interaction
index, SD
di and …nancial development F Dc : All the speci…cations include the upstream
between SD
d
…nance index, U
F ic ; as control: Each set of speci…cations reports results from three di¤erent
estimations, allowing for an increasing number of controls.
The model has ambiguous predictions on the coe¢ cient for Fbic in the speci…cations in
Columns I-III. Better …nancial markets in industry i and country c can lead to higher or
lower degrees of vertical integration depending on the importance of small …rms in the industry. While the positive coe¢ cient indicates that, on average, countries with higher …nancial
development are relatively more vertically integrated in industries that heavily depend on
external …nance, Columns I-III show that the average relationship is not statistically di¤erent
from zero. Interestingly, this …nding replicates, using a di¤erent measure of vertical integration, the results in Table VIII of Acemoglu et al. (2009), where the interaction between
external …nance dependency and …nancial development is also positive, but not statistically
signi…cant. This (lack of) evidence motivates the remaining part of the analysis.
Consistently with the theoretical predictions, Columns IV-VI document that the e¤ect
di :
of the …nance index Fbic on vertical integration depends on the size distribution index, SD
di ; i.e.,
Better …nancial markets lower vertical integration for high values of SD
di >
for SD
1
j
2j
1+
d <0
2 SD i
di , better …nancial markets increase
. In industries with smaller values of SD
vertical integration, i.e.,
1
+
d > 0. Since I use the ranking of industry variables in all
2 SD i
the speci…cations, the economic signi…cance of the results depends on the ratio of the estimated coe¢ cients,
1
j
2j
: In particular, the ratio of the two coe¢ cients gives the approximate
share of industries for which the overall e¤ect of the …nance index on vertical integration is
positive. The estimates in Column VI, which includes the highest number of controls, suggest that the …nance index has a positive impact on vertical integration in
of industries in the sample.
19
1
j
2j
=
0:118
0:194
' 61%
Finally, to saturate the equation, Columns VII-IX include the simple interaction between
di and …nancial development, F Dc : The results show that the inclusion of this control has
SD
no e¤ect on the sign and statistical signi…cance of the coe¢ cients of interest
1
and
2.
Fur-
thermore, while the magnitude of the coe¢ cients is a¤ected, their economic signi…cance is
not. For comparison, taking again the estimated coe¢ cients from Column IX, which includes
the highest number of controls, gives a ratio
1
j
2j
' 59%: Moreover, once appropriate con-
trols for how cross-country variation in institutional environment a¤ects vertical integration
di¤erently across industries are included, the coe¢ cient on the simple interaction becomes
statistically insigni…cant. Consistently with the logic discussed above, the di¤erential e¤ect
of better …nancial development on vertical integration across industries only works through
the appropriate channel captured by external …nancial dependency.
Table 2 also shows that, consistently with the theoretical prior, countries with more
developed …nancial systems are relatively less vertically integrated in industries that use
inputs that heavily rely on external …nance, i.e.,
3
< 0. The evidence is consistent with the
e¤ect of higher …nancial development working through higher entry in upstream industries.
The second and third columns in each set of speci…cations include additional controls.
The second Column (i.e., Columns II, V, and VIII) adds two di¤erent sets of interactions.
First, it includes the interaction between the degree of vertical integration in the United
States in industry i and GDP per capita in country c: The interaction controls for the fact
that, within industries, richer countries produce mixes of goods that are relatively more
similar to the goods produced in the United States, and might be relatively more vertically integrated in those industries that are more integrated in the US.15 Second, it also
includes the interaction between GDP per capita, a proxy of institutional quality, with all
the industry-level variables that are interacted with …nancial development in the corresponding speci…cation. This controls for the possibility that …nancial development is capturing
the e¤ect of broader institutional quality on vertical integration.16
The third Column in each speci…cation (i.e., Columns III, VI and IX) adds interactions
15
The (unreported) coe¢ cient on this interaction is positive and statistically signi…cant, con…rming the intuition that product mixes within industries change systematically as countries achieve di¤erent development
stages.
16
None of the (unreported) interactions between the industry variables and GDP per capita is statistically
signi…cant. Financial development is not simply picking up the e¤ect of a better institutional environment.
20
between industry dummies and GDP per capita. This is done to capture the fact that there
may be broader omitted institutional factors that have di¤erential impact across industries.
These factors might work through channels which correlate, in an arbitrary way, with the
industry variables interacted with …nancial development. The table shows that results are
robust to the inclusion of these controls.
Figure 2 illustrates the estimated industry-speci…c elasticities with respect to changes in
…nancial development. Panel A reports the net e¤ect of the coe¢ cients of the …nance index,
di ; as estimated in Table 2, Column
Fbic ; and its interaction with the size distribution index, SD
V. Panel B, instead, reports the net e¤ect of all the four coe¢ cients estimated in Table 2,
Column VIII.17 The two panels show that there is substantial heterogeneity in the way
vertical integration responds to better …nancial markets. Furthermore, the inclusion of the
e¤ect working through the development of upstream industries changes quite substantially
the ranking of the e¤ects. Quantitatively, the estimated elasticities imply that a one standard
deviation change in the logarithm of …nancial development, which roughly corresponds to the
di¤erence between the United States and South Korea, changes the ranking of the vertical
integration index for roughly half of the industries in the sample.
Table 3 provides a falsi…cation test and shows that the simple interaction between …di ; does not correlate with
nancial development, F Dc ; and the size distribution index, SD
vertical integration. From a theoretical point of view, the simple interaction corresponds to
the derivative of the vertical integration index with respect to
the same sign of
2
c;
and should, therefore, have
in equation (12): Empirically, however, the variable fails to capture the
appropriate channel through which …nancial development a¤ects entry in the industry, i.e.,
the …xed costs Ki ; as proxied by external …nance dependency in the industry. As expected,
the table shows that the e¤ect of the simple interaction between …nancial development and
the size distribution index is insigni…cant across a broad range of speci…cations.
17
Note, however, that the inclusion of country …xed e¤ects prevents the identi…cation of the average e¤ect
of …nancial development on vertical integration.
21
3.5
Robustness Checks to the Reduced Form
This section reports robustness checks to the results of Table 2. A …rst set of robustness
checks is presented in Table 4, which includes controls for contractual imperfections with input suppliers. Across countries, contractual imperfections in …nancial markets are correlated
with di¢ culties in enforcing contracts with input suppliers as well. If the di¤erential e¤ect
of contract enforcement with input suppliers across industries correlates with the industry
variables interacted with …nancial development, the results in Table 2 might be biased. Furthermore, given the large theoretical literature on the subject (see, e.g., Williamson (1975,
1985)), it is of independent interest to study whether contract enforcement a¤ects vertical
integration. Table 4, therefore, repeats the exercise in Table 2 adding controls that capture
the importance of contractual frictions with suppliers. The results in the table disentangle
the e¤ect of the two di¤erent forms of contractual imperfections on the degree of vertical
integration.
I proxy the quality of contract enforcement in country c using the (log of one over the)
percentage costs of enforcing a contract, from the Doing Business Database at the World
Bank. I interact this measure of contractual enforcement with one minus the Her…ndahl
index of input use at the industry level in the United States. The rationale for using this
measure is that industries that rely on a less concentrated set of suppliers are more exposed
to hold-up problems and thus require more contractual provisions to mitigate these problems
(see e.g. Levchenko (2007)).18
Table 4 yields three main results. First, the results of Table 2 are robust to the inclusion
of contract enforcement with input suppliers. Not only the sign and statistical signi…cance of
the coe¢ cients of interest is not a¤ected, but also their economic signi…cance. Second, there
is some evidence that countries with better contractual enforcement tend to be relatively
more vertically integrated in industries that have higher contractual needs. This …nding is
consistent with the following logic. Since complete contracts are hard to enforce even in
countries with well functioning courts, industries exposed to severe hold-up problems are
18
Using the number of legal procedures mandated by law to enforce the contract in a court as alternative
proxy for the quality of contract enforcement in country c and/or a measure of contractual speci…city at the
industry level developed by Nunn (2007) does not a¤ect the results.
22
vertically integrated regardless of the quality of contract enforcement. Industries with lower
contractual intensity, however, are vertically integrated only in countries with worse contract
enforcement. The magnitude and the precision of the estimated coe¢ cient, however, depend
on the set of controls added in the equation.19 Finally, the Table shows that the role of
di . Implicitely,
contractual enforcement does not depend on the size distribution index, SD
this result provides a falsi…cation test which reassures about the fact that the …nance index
is capturing the channels predicted by the theory.20 The broad message emerging from
Table 4 is that, while the evidence on the relationship between …nancial market development
and vertical integration appears to be very robust, there is only weak evidence that better
contract enforcement a¤ects vertical integration di¤erently across industries.
Tables 5, 6 and 7 investigate the robustness of the results in Table 2 to di¤erent de…nitions
of the variables on the right hand side of equation (12).21 In particular, Table 5 considers
alternative de…nitions of the industry variables in the United States, Table 6 considers alternative measures of …nancial development, and Table 7 considers alternative samples. Each
speci…cation in the three tables is reported with, as well as without, the simple interaction
di and …nancial development F Dc as further control:
between the size distribution index SD
Across all the speci…cations, the set of controls includes the interactions between the industry variables and GDP per capita as well as the interaction between vertical integration in
the United States and GDP per capita, as in Columns II, V and VIII of Table 2.22
19
There are several possible explanations for this. Table 1.C shows that the index of contract enforcement
is highly correlated with GDP per capita, while Table 1.D shows that the index of contractual needs is
highly correlated with vertical integration in the United States. The inclusion of the interaction between
evrtical integration in the United States and GDP per capita might, therefore, accentuate the attenuation
bias caused by measurement error in the index of contractual enforcement. Acemoglu et al. (2009) …nds
that countries with worse contract enforcement are relatively more vertically integrated in capital intensive
industries. However, they also …nd that contracting costs do not correlate with vertical integration once
they are interacted with the measure of contractual needs developed by Nunn (2007). Unreported results
display a similar lack of evidence when contractual needs are measured through this alternative measure.
A possible reason is that the kind of contractual frictions captured by these industry measures cannot be
solved by vertical integration, an interpretation which is closer in spirit to property rights theories of the
…rms (see, e.g., Hart (1995)).
20
When included in the speci…cation, the cross-interactions between …nancial dependency and contractual
enforcement and between …nancial development and contractual intensity, are not statistically signi…cant.
Contractual imperfections with suppliers and investors are working (exclusively) through the appropriate
channels.
21
Concerns with the left hand side measure of vertical integration are adressed in Section 3.7.
22
Similar results are obtained using the speci…cations in Columns I, IV, and VII, or those in Columns III,
VI, and IX in Table 2.
23
The broad pattern emerging from the three Tables is that the reduced form results
are robust to modi…cations of the right hand side variables. The sign of the coe¢ cients
1;
2
and
3
are always as predicted by the theory. Their statistical signi…cance is only
marginally a¤ected by considering di¤erent de…nitions of the variables. Furthermore, the
di and …nancial development
simple interaction between the …rms size distribution index SD
F Dc is, as expected, (almost) always statistically insigni…cant and never a¤ects the economic
interpretation of the coe¢ cients of interest.
di is the choice of the 80th percentile
A …rst concern with the size distribution index SD
as the cuto¤ to compute the revenue share of the largest …rms. Columns I-IV in Table 5
present results that use the 70th and 90th percentile instead, and …nd similar results. In the
theoretical model, there is a one-to-one mapping between the …rm size distribution in terms
of revenues and employment. However, by de…nition, vertically integrated …rms employ
more workers, and, therefore, the empirical …rm size distributions measured with respect to
employment and revenues might di¤er. Columns V-VI in Table 5 present results in which the
di is computed using employment …gures, rather than sales …gures,
size distribution index SD
and …nd similar results. Finally, Columns VII-VIII in Table 5 show that the same pattern of
results holds when industry variables are classi…ed as dummies according to their ranking.
Speci…cally, I create dummies for all industry variables from the United States which are
equal to 1 if their ranking is higher than 13 (26 divided by 2) and zero otherwise. This
reduces concerns linked with measurement error as well as concerns arising from the use of
industry level variables from the United States. Results are, again, robust.
Results are also robust to alternative measures of …nancial development. To eliminate
concerns that results are driven by outliers, Columns I-II in Table 6 use the ranking of
countries with respect to their ratio of bank credit over GDP in the ‘90s. Columns III-IV
in Table 6 proxy …nancial development with the same ratio a decade earlier, to attenuate
endogeneity concerns. Columns V-VI consider the ratio of Bank Assets over GDP and
Columns VII-VIII in Table 6 present results using an inverse measure of bank concentration.
Results are robust to all these speci…cations.
Results are also robust to the use of alternative samples. Columns I-II in Table 7 show
that results are robust to consider only non-OECD countries. Results are, therefore, not
24
driven by variation in …nancial development between rich and poor countries alone. Conversely, Columns III-IV in Table 7 show that results are robust to exclude African countries
from the sample and are, therefore, not driven by the inclusion of countries with particularly
underdeveloped manufacturing sectors. Columns V-VI of Table 7 exclude the top and bottom 2.5% of observations and show that the results are not driven by outliers in the sample.
Finally, Columns VII and VIII exclude observations for which there are less than four years
of data in the UNIDO database. Results are, again, robust. In sum, Table 7 suggests that
the results on the relationship between …nancial development and vertical integration are
not driven by sample selection, country outliers, extreme values or measurement error.
3.6
Direct Evidence on the Mechanisms
Taken together, Tables 2 to 7 document a robust reduced form relationship between the
…nance index Fbic and vertical integration which is consistent with the predictions of the
model. Moving beyond reduced form results, this section exploits the assumptions of the
model to provide direct evidence on the channels linking …nancial development and vertical
integration.
In the model, higher …nancial development a¤ects vertical integration by increasing entry
of …rms in the industry. Because entry is endogenously determined in equilibrium, however,
investigating the relationship between vertical integration and entry by ordinary least squares
would yield biased estimates.
The model, however, assumes that the …nance index Fbic a¤ects vertical integration exclu-
sively through its e¤ect on the entry of …rms in the industry. If the …nance index correlates
with entry levels in the industry, the assumption of the model implies that the index is a
valid instrument for the entry of …rms.
Table 8 presents the results. Columns I and II report ordinary least square estimates of
the relationship between entry of …rms, proxied by the (log of the) number of establishments
in the industry, Nic ; and vertical integration. The estimated coe¢ cient on the e¤ect of entry,
di , show no
as well as the coe¢ cient on the interaction with the size distribution index SD
relationship between entry levels and vertical integration. As noted above, however, these
coe¢ cients do not consistently estimate the e¤ect of entry on vertical integration since entry
25
is endogenous.
For this reason, Columns III and IV instrument the level of entry in the industry with
the …nance index, Fbic : Before turning to the second-stage results, the lower part of Table 8
presents the results from the …rst stage. The …rst stage equation is given by
Nic =
0
b +
+
1 Fic
d +
2 U F ic
i
+
c
+ ! ic ;
(13)
where ! ic is an error term assumed to be independent of the error term in the second stage.
Four things are worth to be noted. First, the coe¢ cient
1
on the index Fbic is positive
and statistically signi…cant: in line with the theoretical assumptions, as well as with a large
empirical literature (see, e.g., Haber (1991), Rajan and Zingales (1998), and Aghion et al.
(2007)), better …nancial markets increase entry of …rms disproportionately more in industries
that depend on external …nance. Second, the R2 of the …rst stage is equal to 0.86, suggesting
that the instrument Fbic ; together with the variables included in the second stage are jointly
signi…cant in explaining entry. Third, the …rst stage includes the index of the importance
of …nancial markets in upstream industries, since this is a further control in the second
stage. This variable, however, turns out to have an insigni…cant e¤ect on entry. Finally, the
inclusion of country …xed e¤ects
c
in the second stage, implies that it is not possible to
instrument entry with …nancial development F Dc alone. This shows why, the …nance index
Fic provides the type of variation needed for identi…cation in the reduced form speci…cation,
while the simple interaction between the size distribution index and …nancial development
cannot capture the causal channels identi…ed by the model and, therefore, should not be
expected to have a signi…cant impact.
Turning to the second-stage results, Column III shows that, even when instrumented with
the …nance index Fbic , entry has an insigni…cant average impact on vertical integration across
industries, as predicted by the model. Column IV, instead, introduces the further interaction
di and …nds results which are in line with
between entry and the size distribution index SD
the predictions of the model. In particular, higher entry increases vertical integration in
industries with low levels of the size distribution index, and reduces vertical integration in
26
industries with a higher level of the index.23
The theoretical model shows that better …nancial markets a¤ect vertical integration in
the industry through the entry of …rms. This, in turn, increases competition and, through
a selection e¤ect, leads to an increase in the average productivity in the industry. In other
words, the model implies that the sign of the impact of entry on vertical integration dedi , while the sign of the impact of entry on average
pends on the size distribution index SD
productivity does not.
This feature of the theoretical model is exploited in Table 9, which reports results from
the estimation of a system of simultaneous equations with vertical integration and average
productivity as endogenous variables. Entry is instrumented with the …nance index Fbic ; as
in Table 8. Productivity is proxied with the (log of the) average output per worker in the
industry.24 The …rst equation in Table 9 shows that, even after taking into account average
productivity in the industry, the e¤ects of entry of …rms on vertical integration depends on
the …rm size distribution index as predicted by the model. The second equation in Table
9, instead, shows that entry of …rms increases productivity, consistently with the selection
e¤ect highlighted by the model. The Table also shows that vertical integration has a positive
e¤ect on productivity. While this is conistent with the model, the corresponding coe¢ cient
is not statistically signi…cant.25
In sum, taken together, the results in Table 8 and Table 9 provide empirical support
for the mechanism linking …nancial development and vertical integration identi…ed by the
theoretical model. Higher …nancial development increases entry in the industry. This leads
to higher competition which, in turn, raises productivity through a selection e¤ect. The
overall e¤ect on vertical integration is, instead, ambiguous, and depends on the importance
of small …rms in the industry.
23
Column IV reports bootstrapped standard errors. This is necessary because the interaction between
entry and the size distribution index is a predicted variable. Results are not a¤ected if the interaction is
instrumented using the corresponding reduced form variable.
24
Similar results are obtained using value added per worker as a measure of productivity.
25
The two equations include the interactions of industry variables in the United States with GDP per
capita. When this is not done, the statistical signi…cance of the coe¢ cients improves.
27
3.7
Concerns with the Index of Vertical Integration
Preliminary Discussion
The UNIDO Index of vertical integration presents some shortcomings. A part from the
high level of aggregation, the Index might also re‡ect variation in the average pro…tability
of (large) …rms in the industry. For example, in industries with high …xed costs, for reasons
other than vertical integration, …rms are likely to have more market power and charge a
higher markup, and thus have higher value-added relative to output. If the …nance index
increases competition more in industries where small …rms are relatively more important, this
might be a concern.26 In the model, since more pro…table …rms choose vertical integration
as their organizational form, pro…tability and integration are not separately identi…ed. The
data, however, might re‡ect both.
It is not possible to construct a convincing measure of pro…tability with the information
available in the UNIDO database. This section, therefore, …rst discusses indirect evidence
supporting the idea that the UNIDO Index captures organizational choices, and then replicates the reduced form results in Table 2 using an altogether di¤erent measure of vertical
integration used by Acemoglu et al. (2009).
Some indirect support to the use of the UNIDO Index to measure vertical integration
d
comes from the evidence on the e¤ects of the upstream …nance index, U
F ic : In particular,
d
the results have consistently shown that U
F ic has a negative impact on the UNIDO Index. If
the index was simply picking up pro…tability, the development of upstream industries should
be associated with a higher pro…tability in the downstream industry, rather than a lower one.
A negative e¤ect on pro…tability could be found if the development of upstream industries
had a large e¤ect on entry in downstream industries. This is not the case, as shown in the
…rst stage in Table 8. More likely, instead, entry and competition in upstream markets drive
input prices downwards, implying higher value added …gures, which should in turn lead to a
higher value of the Index. Strong evidence of a negative e¤ect, reported throughout Tables
2 to 9, suggests, instead, that the UNIDO Index is capturing vertical integration in the
industry.
26
I thank a referee for bringing this issue to my attention.
28
A Falsi…cation Test
Unfortunately, a measure of pro…tability cannot be computed using the UNIDO Database.
A variable which is related to pro…tability, however, is productivity. As noted above, the
model predicts that higher …nancial development, through its e¤ect on entry of …rms, always
increases productivity, regardless of the …rm size distribution in the industry. Table 10,
therefore, performs a falsi…cation test using output per worker as a proxy of the productivity
in the industry.27 Columns I and II report reduced form results and show that i) a higher
value of the …nance index, Fbic ; is associated with higher productivity, as predicted by the
di is
theory, and ii) the interaction of the …nance index with the size distribution index SD
not signi…cant, also consistently with the theory. Columns III and IV repeat the exercise
instrumenting entry as in Table 8, and …nd similar results.
The model suggests that …nancial markets have an ambiguous impact on vertical integration, but not on productivity. The falsi…cation test above shows that the interaction
e¤ects predicted by the model are only found for the measure of vertical integration, but
not for the measure of productivity. While not conclusive, this suggests that the UNIDO
Index captures vertical integration; the equilibrium variable for which the model predicts an
ambiguous e¤ect which depends on the importance of small …rms in the industry.
An Alternative Index of Vertical Integration
Finally, an alternative way of adressing concerns related to the use of the UNIDO Index
to measure vertical integration is to replicate the results using an alternative measure of
vertical integration. Table 11 reports results using a second measure of vertical integration
constructed from …rm-level information provided in Worldbase, a database maintained by
Dun & Bradstreet. This measure of vertical integration has been used by Acemoglu et al.
(2009).28
The Wordbase dataset reports, for each …rm, the primary 4-digit SIC code as well as up
to …ve other codes of secondary product lines for the …rm. I only have access to information
at the industry level, constructed as in Acemoglu et al. (2009) in the following way. For
27
Similar results are obtained using value added per worker.
I restrict attention to the manufacturing sector and to the same sample of countries as for the UNIDO
measure.
28
29
each …rm f in industry i in country c; the index of vertical integration, Vf ic , is given by
Vf ic =
1 1
2 jIf ic j
j vij
i
where vij is the input-output coe¢ cient between industry i and industry j in the U.S.,
and jIf ic j is the cardinality of the set of industries in which …rm f is active, If ic . Vertical
integration at the industry level is given by the unweighted average of the indexes of vertical
integration of …rms in industry i and country c; i.e.
V IicD&B =
1
Nic
f Vf ic
where Nic is the number of …rms in industry i and country c:
The index V IicD&B is an unweighted average of …rm level indexes in the industry. Equation
(8) in the Proposition suggests the use of a di¤erent proxy for the size distribution index.
In particular, the appopriate index is given by
SDiU =
1 g( v )
;
eUv g( e )
where eUv is the share of vertically integrated …rms in the industry. Following the same
di in (11), the proxy is given by
procedure used to construct the weighted index SD
where 4:5 =
0:95
0:20
dUi = 0:95
SD
0:20
gsi (80)
;
gsi (5)
is the inverse of the ratio of …rms above the 80th percentile in the industry.29
Table 10 replicates all the reduced form speci…cations in Table 2 using the alteranative
measure of vertical integration, V IicD&B ; as well as the appropriate size distribution index,
dUi : The results are robust to the use of the di¤erent measure of vertical integration,
SD
constructed from a di¤erent dataset, with a di¤erent methodology, and that requires the use
of a di¤erent proxy for the …rm size distribution in the industry.
The alternative measure of vertical integration, V IicD&B ; also su¤ers from important
29
U
d i ; the value of the ratio does not matter for the results.
Since I use the ranking of SD
30
shortcomings, which are best appreciated in relation to the UNIDO Index. In contrast
to the UNIDO measure, which is a well-known index of vertical integration obtained from
data originating from industrial statistics relying on Census information, in the WorldBase
dataset large …rms and large countries tend to be over-represented. The index V IicD&B is
constructed from a large …rm-level database, it exploits information on …rm activities and
allows for a …ner classi…cation of industries. However, its construction hinges on the use of
input-output information from the United States. This is problematic for two reasons. First,
the input-output table in the United States might not be representative of the input-output
tables in other countries. Second, the identi…cation strategy relies on the use of industry
variables from the United States on the right-hand side of equation (12). Having a measure of vertucal integration that relies on data from the United States on the left hand side
might introduce correlations that are confounded with the e¤ects of …nancial development
on vertical integration.
In sum, while neither of the two indexes provides a perfect measure of vertical integration,
and neither is unambigously superior to the other, in their di¤erences lie the strenght of their
complementarity. The reduced form evidence obtained from the two indexes is consistent
with the predictions of the model.
4
Conclusion
Existing studies are mostly inconclusive on the importance of …nancial development as a determinant of vertical integration. This paper shows that, once industry heterogeneity in …rm
size distributions is taken into account, …nancial development is an important determinant
of cross-country di¤erences in vertical integration. Financial development a¤ects vertical integration by increasing entry of …rms. The resulting increase in competition reduces vertical
integration of larger …rms, but also leads smaller, non-integrated, …rms to exit the industry.
As a result, better access to credit reduces vertical integration in those industries where
small …rms are important. Further evidence suggests that the same channel, i.e., higher
…nancial development increases entry, operates through the development of input markets.
This further reduces the incentives to vertically integrate.
31
Once combined with the …nding in Acemoglu et al. (2009), which suggests that better
access to credit is required to vertically integrate, the available evidence suggests that …nancial development is an important determinant of vertical integration.30 The relationship,
however, is quite complex and depends on both industry characteristics as well as other
characteristics of the institutional environment. This is in contrast with the lack of strong
evidence on the role of contracting costs as a determinant of vertical integration.
Since the seminal work of Coase and Williamson, it is well understood that organizational
forms are chosen to respond to market imperfections and are therefore crucial to understand
the allocation of resources and productivity in the economy. Systematic evidence that organizational forms in general, and vertical integration in particular, respond to variation in the
institutional environment has the potential to provide insights into which market imperfections constrain the operation of …rms in the developing world and, critically, shed light on
the relevant mechanisms. Investigating the relationship between organizational forms and
cross-country di¤erences in productivity is a priority area for future work.
30
The mechanism underlined by Acemoglu et al. (2009) can be included in the model above by letting credit
constraints also hinder the …nancing of the …xed costs k associated with production. A related possibility,
theoretically explored in Macchiavello (2009), is that vertical integration a¤ects the capacity to borrow, e.g.,
by altering the ability of investors to monitor the …rm. The theoretical predictions derived in this paper,
however, are likely to be robust to these extensions since they are derived from the free-entry condition.
32
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36
5
Appendix A
Derivation of Pro…t Functions:
Denote with
v(
) and
o(
) the variable pro…ts of a vertically integrated …rm and of a …nal
good assembler respectively. External investors hold claims on these pro…ts. Since the …nancial
contract can be made contingent on the realization of ; assume that if the …rm starts production
under vertical integration the external investor has a debt-like claim over the …rm’s pro…ts equal
to B( ) <
v(
), while, if the …rm starts production as a …nal assembler, external investors hold
an equity-like claim with share s( ) < 1 in …rm’s pro…ts. Below I show that this is an optimal
contract.
Vertical Integration:
Under vertical integration, the entrepreneur chooses investments to maximize pro…ts, taking as
given the …nancial contract B( ). The problem for the entrepreneur is
v(
)
B( ) = max A1
This gives optimal investments x =
yileds,
v(
)=
"
w
A
"
(1
x
x
"
w
A
"
wx
(14)
B( ):
; which, after substitution into the pro…t function,
).
Non Integration:
Under non-integration the sequence of events is as follows. First, …rms contract on the initial
transfer T ( ) from the supplier to the customer: Second, the supplier, anticipating ex-post bargaining, chooses x maximizing her share of pro…ts. Finally, taking as given x and the …nancial
contract s( ); the two …rms bargain. I solve for the subgame perfect equilibrium.
Taking as given the …nancial contract s( ) and x; the price for the intermediate input is negotiated ex-post according to Nash Bargaining. Since the intermediate input is useless outside the
relationship, the outside options in the bargaining process for both …rms are equal to zero. The
two …rms hence bargain over the revenues A1
p(s; ) 2 arg max
which implies p(s; ) = A1
(1
x : Denoting with p(s; ) the price, we have
s) (A1
x
p)
1
p
(15)
x : The ex-post price, and therefore the incentives to undertake
37
non-contractible investments, do not depend on the equity share s( ):
The supplier chooses x anticipating ex-post bargaining, i.e.,
x 2 arg max A1
x
(16)
wx:
1
This gives x =
1
w
A
. The assembler makes a take-it-or-leave-it o¤er to the supplier
1
that specify an initial transfer T ( ): Since ex-ante competition among suppliers drives their pro…ts
down to zero, the optimal contract solves
max(1
T
subject to (16) and
A1
x
)A1
s) (1
wx
T
x +T
0: This last constraint has to be binding. T can
therefore be substituted into the objective function of the assembler, and, using the solution to
(16), the assembler’s pro…ts are given by
h
(
)
=
A
o
"
"
i
) .
(1
w
Under both organizational forms, pro…ts are increasing in the productivity index
and decreas-
ing in the mass of …rms in the industry, as can be seen from the expression for A in (2). These
facts imply the following proposition.
Proposition 1 [Part 2]
For any mass M of entrepreneurs entering the industry,
<
(1
)
"
(1
)
kv
ko
i) if
; there exist two thresholds
do not start production, entrepreneurs with
e
…nal products, and entrepreneurs with
ii) if
<
v
kv
ko
<
(1
)
"
(1
)
v
e
v;
and
2 [ e;
v)
such that entrepreneurs with
start production as assemblers of
start production as vertically integrated …rms;
; there exists a unique threshold
do not start production, and entrepreneurs with
v;
such that entrepreneurs with
v
start production as vertically
integrated …rms:
Proof of Proposition 1 [Part 2]:
In proving Proposition 1, I restrict attention to renegotiation-proof …nancial contracts since
optimal contracts satisfy this property. We have that
@
v(
)
@
and (kv ko ) is a …nite number, there exists a unique threshold
v
>
v
@
o(
@
)
> 0, and, since
2 [ ; 1)
such that a …rm with productivity
earns higher pro…ts choosing vertical integration rather than non-integration. The opposite
38
is true for
<
v:
v( v)
The threshold
kv =
v
is implicitly de…ned by the equality
"
ko ()
o( v )
w
=
v
kv
"
ko
A
1
(1
"
)
(1
)
(17)
On the other hand, the threshold determining whether an entrepreneur can pro…tably start
production as a …nal assembler is determined in an analogous way, and is given by
o(
"
ko ()
)
w
"
1
"
e
=
,
kv
<
ko
(1
"
(1
; then
v
ko
A(1
(18)
)
Therefore, we have
e
We conclude that when
>
v
(1
)
"
(1
)
kv
ko
>
)
)
e;
and therefore in equilibrium the two
organizational forms coexist.
To see why the equilibrium is unique, note that, taking as given the mass of entrepreneurs that
received start-up …nance M; A is a function of
A=
1
h
M R
e
e
and
v
e
given by
'w
R
" dG( ) + 1 p ( )
v
v
po ( )
" dG(
i
)
(19)
(see equation (2)), where I have made explicit the fact that the price function depends on the
organizational form. The two thresholds
v
and
e
de…ne a system of two non-linear equations in
two unknown variables. This system has a unique solution since i ) (17) and (18) imply that the
ratio
e
v
is a positive constant, and ii ) total di¤erentiation of (19) implies
d
d
e
v
< 0: To obtain the
latter condition, note that from (19) we obtain
d
which implies
d
d
e
v
e
=
e
'
< 0 since
When, instead,
kv
ko
w
"
f
A(1
" e A( e ;
@A( e ; v )
>
@ v
(1
)
"
(1
)
()
)
v )[
@A( e ;
@ v
0 and
, then
@A( e ;
@ e
v
<
(20)
v)
v)
d
v
+
@A( e ;
@ e
v)
d e]
> 0:
e;
and only vertically integrated …rms enter
the industry. The uniqueness of the equilibrium follows from the fact that
39
v
is decreasing in A;
and that A is instead an increasing function of
v:
Properties of the Financial Contract:
I now consider the initial …nancing contract C 2 C
P
L: Let us denote by
possible realizations of ; R(i) the set of all possible realizations of
i(
the set of
) under production decision
i 2 fe; o; vg (where e stands for “exit”, o means that production is started as a …nal assembler,
and v as a vertically integrated …rm), and by
i(
i(
)) the set ( 1;
i(
transfers from the entrepreneur to the investor given pro…ts
i(
); i) : R(i) !
i(
i(
))
)] (the set of all feasible
)).
! fe; o; vg and a (net of re…nanc-
A …nancial contract speci…es a production decision P ( ) :
ing) claim over pro…ts L(
i(
fi for i 2 fe; o; vg: The optimal …nancial
contract C maximizes the (expected) utility of the borrower subject to the zero-pro…t constraints
of the investors. I start by proving the following Lemma.
Lemma 1:
The optimal8…nancial contract C is ex-post e¢ cient:
>
>
e if 2 [ ; e )
>
<
i) P ( ) =
o if 2 [ e ; v ) and
>
>
>
: v if 2 [ ; 1)
v
ii) if di 2 arg max i ( ; di ) Li ( i ( ; di )) than di 2 arg max
i(
; di ) for all actions di that
the entrepreneur takes under production decision i:
Proof of Lemma 1:
To prove the …rst part of the Lemma, suppose to the contrary that there exist a state
such that the contract does not pick up the optimal production decision. Consider …rst the case
P ( ) 2 fo; vg when 2 [ ;
e );
the investor makes a loss at least equal to f
o(
). By specifying
P 0 ( ) = e; and a lower repayment L0 ( 0 ) in some alternative state 0 , it would be possible to increase
the expected utility of the entrepreneur without violating the zero-pro…t constraints of the investor.
A contradiction.
The proof is similar if P ( ) 2 fe; vg when
2 [ e;
v)
or P ( ) 2 fe; og when
2 [ v ; 1):
To prove the second part of the Lemma, suppose to the contrary that di 2 arg max
Li (
i(
i(
; di )) is such that di 2
= arg max
i(
; di ): Since P ( ) is ex-post e¢ cient and Li (
; di ); it would be possible to choose d0 2 arg max
increasing the payo¤ of the entrepreneur by an amount
40
i(
; di ), set Li (
=
i(
; d0i )
i(
; di )
i(
; di ))
i(
; di )) = L0i (
i(
; di ): A contradiction.
i(
; d0i ))
This concludes the proof of Lemma 1.
The Lemma implies the following useful corollary:
Corollary:
The …nancial contract considered in the derivation in the proof of proposition 1 satis…es property
i) and ii) in the Lemma, and is therefore optimal. Consequently, expected pro…ts E ( ) do not
depend on the …nancial contract C :
The corollary implies that, given a mass M of …rms entering the industry, the ex-ante expected
pro…ts do not depend on the …nancial contract and are given by
E (M ) =
Z
v
(
o( )
ko ) dG( ) +
e
o(
where dependence of
),
v(
);
e
Z
1
(
v(
)
kv ) dG( ) ; with
v
and
v
dE (M )
< 0;
dM
(21)
on M is suppressed for notational simplicity. This
observation is useful to prove the following
Proposition 1 [Part 1]
Let MF = 1
E
1
H(K
F ); where F =
K is the …nance index de…ned in (4) and let
( ) stand for the inverse of the expected pro…t function. There exists a unique equilibrium
de…ned as follows:
i) if E (MF ) < K; a mass M e of entrepreneurs M e = E
a
K
1
(K) among those with wealth
F enter the industry;
ii) if E (MF ) > K; there exists a unique threshold a (F ) implicitly de…ned by E (1
H(a )) = 2K
and M e (F ) = 1
F
a such that only entrepreneurs with wealth a
a (F ) enter the industry,
H(a ).
Proof of Proposition 1 [Part 1]:
In order to prove proposition 2, we …rst prove the following Lemma:
Lemma 2: Consider a level of entry M : entrepreneurs with wealth a
and only if E (M )
E (M )
2K
F
K; while entrepreneurs with wealth a < K
K
F invest if
F invest if and only if
a:
Proof of Lemma 2:
Let us denote with EL(C) =
R
LP ( ) (
P ( )(
41
))dG( )
E (M ) the expected payment from
the entrepreneur to the investors; and with EF (C) =
investment induced by contract C .
R
kP ( ) dG( ) the expected production
The contract solves
maxE (M )
C
EL(C) s.t. EL(C)
K
where the constraint follows from investors’zero pro…t.
Without loss of generality, consider an entrepreneur that borrows K units of capital and signs
a …nancial contract C: Of the K units of borrowed funds, a fraction
has to be invested in the
project, since the investors can perfectly monitor such investments. The remaining fraction (1
)
can either be invested, or it can be diverted by the entrepreneur. If the entrepreneur invests,
she generates expected pro…ts E (M ); she repays EL(C) and keeps her wealth a: If instead she
diverts cash, she obtains K
F; but her wealth will be seized by the investors:
She abstains from diversion if and only if K
F
E (M )
EL(C) + a: Competition
among risk-neutral lenders implies that EL(C) = K: The former inequality can be rewritten as
2K
F
a
E (M ): The investment has a positive net present value if E (M ) > K: It
follows that an entrepreneur with wealth a invests if and only E (M )
maxfK; 2K
F
ag:
This concludes the proof of Lemma 2.
To complete the proof of proposition 2, simply note that the credit market reaches an equilibrium
when it is not possible to …nance any more entrepreneurs.
If E (1
with wealth a
H(K
K
F ))
K; the distribution of wealth H( ) is such that if all entrepreneurs
F invest, expected industry pro…ts E
the level of entry is such that E
Otherwise, if E (1
H(K
the industry, provided E (1
are driven below K: In equilibrium,
= K . The equilibrium is unique since E
is decreasing in M:
F )) > K; entrepreneurs with wealth a
K
H(a))
2K
F
F can invest in
a. The marginal entrant has wealth a such
that the latter expression is satis…ed with equality. The equilibrium is unique since the right-hand
side is decreasing in a; while the left-hand side is increasing (since
@M
@a
@E
@a
=
@E @M @E
; @M
@M @a
< 0). This concludes the proof of the proposition.
Proof of other results in Proposition 1:
R v @ o( )
R1
dE (M )
To see why = dM = e @M
dG( ) + v
42
@
v( )
dG(
@M
) note that
< 0 and
d e
dM
dE (M )
=
dM
o( e)
and since
dE (M )
dM
o ( e )dG( e )+
= ko and
= . Finally,
dE (M )
dM
d v
dM
o( v )
=
o ( v )dG( v )
d v
dM
v( v)
ko ); this expression can be rewritten as
(kv
< 0 immediately follows from
v ( v )dG( v )+
@
part of the Corollary follows by totally di¤erentiating E (1
da
d
> 0; and using the chain rule along with
d
d
=
e
@ e @M
@M @
> 0 since
@ e
@M
@M
@a
=
o( )
@M
< 0 and
H(a ))
@
2K
d v
d e
(kv ko )dG( v )+
ko dG(
dM
dM
v( )
@M
F
< 0: The …rst
a to obtain
h(a ) < 0: The second part follows from
> 0:
Proof of Proposition 2:
The proof of Proposition 2 follows from straightforward di¤erentiation. Consider …rst the
Ro
:
Rv +Ro
U N IDO
=1
UNIDO Index, V Iic
The derivative of the index with respect to the …nance
index Fic is given by
@Ro
@F
@V IicU N IDO
=
@Fic
Denote by
2
d
ed =
in Proposition 1: Denote by R
"
2
e d and Ro =
on d: Then, we have Rv = R
sign
@V IicU N IDO
= sign
@Fic
[Rv +
@Rv
@F
Ro ]2
Ro
:
that choose organizational form d 2 fo; vg; as described
the set of values of
R
Rv
v g( v )
d
dG( ); where
is a constant that does not depend
"e
Ro : This implies
@ v
@F
Ro
"
e g( e )
@ e
+
@F
v g( v )
@ v
@F
Rv :
(22)
Note that for j 2 fe; vg we have
@A
@F
< 0) and hence
d e
dF
d v
dF
=
e
v
=
d j
dF
=
@ j @A
:
@A @F
ko
(kv ko )
This implies
(1
)
"
(1
)
1
d j
dF
1
"
1
@A
=
1 j A1 @F
> 0 (since
p
=
: Substituting this terms into
equation (22) and rearraning yields
@V IicU N IDO
< 0 ()
@Fic
1+
1 Ro
"
Rv
g( v )
g( e )
;
which is equation (7) in Proposition 2.
D&B
Similarly, consider the measure V Iic
=1
43
No
:
Nv +No
The derivative of the index w.r.t. Fic
e
yields
@No
@F
@V IicD&B
=
@Fic
Noting that Nv
sign
1
G( v ) and No
@V IicD&B
= sign
@Fic
(Nv +
G( v )
e g( e )
Nv
@Nv
@F
No )2
No
:
G( e ); implies
@ e
@F
v g( v )
@ v
@F
Nv
v g( v )
@ v
@F
No ;
which, after some manipulation, yields
@V IicD&B
@Fic
0 ()
g( v ) No + Nv
>
g( e ) Nv
as in equation (8) in Proposition 2.
6
Appendix B: Data Description
Vertical Integration from UNIDO:
The data used to compute the index of vertical integration come from the 2001 edition of the
UNIDO Industrial Statistics Database, which classi…es industries at the 3-digit second revision ISIC
classi…cation system. The data are supplied by national statistical o¢ ces and are supplemented with
estimates generated by UNIDO whenever necessary. The 2001 edition of the database covers 175
countries for the period 1963-1999. However, since period coverage as well as item coverage di¤er
from country to country, I focus on a sample of 84 countries using years 1990 to 1998 inclusively.
Vertical Integration from Dun&Bradstreet:
The Wordbase dataset reports for each …rm the primary 4-digit SIC code, and up to …ve other
codes of secondary product lines for the …rm. I only have access to information at the industry
level, constructed in the following way. For each …rm f in industry i in country c; let Vf ic be the
index of vertical integration,
Vf ic =
1 1
2 jIf ic j
i
j vij
where vij is the input-output coe¢ cient between industry i and industry j in the U.S., and jIf ic j
is the cardinality of the set of industries in which …rm f is active, If ic . Vertical integration at the
44
industry level is given by the unweighted average of the indexes of vertical integration of …rms in
industry i and country c; i.e.
IN Tic =
1
Nic
f Vf ic
where Nic is the number of …rms in industry i and country c:
External Financial Dependency:
I rely on the data provided in Rajan and Zingales (1998) for the regressions using the UNIDO
database. For the results in Table 11, I computed the external …nancial dependency of 52 two-digit
Input-Output industries. Starting from Compustat data, I followed the methodology in Rajan and
Zingales (1998) to compute the external …nancial dependency of 4-digit SIC industries. I matched
4-digits codes with IO 2-digits codes and took median values.
External Financial Dependency of Upstream Industries:
From the 1992 input-output table for the US, I construct an average measure of External
Financial Dependency of upstream industries as follows. I construct input-output shares at the
3-digit ISIC level, using only ‡ows within the manufacturing sector. Denoting by EDj the external
…nancial dependency in the 3-digit ISIC industry j and by vij the share of inputs purchased by
(3-digit ISIC code) industry i from other (3-digit ISIC code) industries j; the measure is given by
EF DUi =
j6=i vij
EDj : I used the same procedure in Table 11, at the 2-digit IO level.
Contractual Needs:
Starting from the 1992 input-output table in the United States, I constructed for each 6-digit
IO industry the Her…ndahl index of input use. Letting sij be the share of input use of industry i
from industry j; the index is given by HIi =
2
j sij :
I then matched the 6-digit IO industry codes
with the 3-digit ISIC codes, and took the median value within industry groups to generate the
measure of contractual needs in Table 4.
45
FIGURE 2: FINANCIAL MARKETS AND VERTICAL INTEGRATION, ELASTICITIES
Panel A: Partial Effect
Panel B: Total Effect
metal products
plastic products
rubber prductus
iron and steel
non metal products
paper and products
textile
footwear
leather
wood products
nonferrous metal
pottery
machinery
petroleum raffineries
tobacco
apparel
beverages
other industrries
professional goods
food products
printing and publishing
furniture
electric machinery
other chemicals
transportation equipement
glass
tobacco
apparel
wood products
nonferrous metal
food products
paper and products
beverages
professional goods
machinery
rubber prductus
plastic products
metal products
iron and steel
petroleum raffineries
textile
other chemicals
non metal products
other industrries
pottery
transportation equipement
furniture
electric machinery
printing and publishing
footwear
leather
glass
-.05
0
.05
.1
-.05
0
.05
.1
.15
The figure reports the estimated industry-specific elasticities with respect to changes in financial development for each industry in the sample. Panel A reports the net
effect of the two coefficients on the finance index and its interaction with the size distribution index, estimated in Table 2, Column V. Panel B reports the net effects of
all the four coefficients involving interactions with financial development in Column VIII of Table 2. Because of the inclusion of country fixed effects in all the
specifications, the reported elasticities do not include the average effect of financial development on vertical integration.
TABLE 1A: AVERAGE VERTICAL INTEGRATION, BY INDUSTRY
Industry
food products
beverages
tobacco
textiles
apparel
leather
footwear
wood products
furniture
paper and products
printing / publishing
other chemicals
Code ISIC
311
313
314
321
322
323
324
331
332
341
342
352
Vertical Integration
0.29
0.48
0.56
0.38
0.41
0.33
0.39
0.38
0.40
0.35
0.47
0.38
Industry
rubber products
plastic products
pottery
glass
non-metal products
iron and steel
non-ferrous metal
metal products
machinery
electric machinery
transportation equip.
professional goods
Code ISIC
355
356
361
362
369
371
372
381
382
383
384
385
Vertical Integration
0.39
0.36
0.52
0.45
0.42
0.31
0.28
0.38
0.41
0.38
0.36
0.44
petroleum refineries
353
0.29
other industries
390
0.41
Note: to construct industry level variables, the following codes have been merged: ISIC 351 [Industrial chemicals] with ISIC 352 [Other
chemicals], and ISIC 353 [Petroleum refineries] with ISIC 354 [Miscellaneous petroleum and coal products].
TABLE 1B: LIST OF COUNTRIES IN THE SAMPLE
Algeria
Argentina
Australia
Austria
Bangladesh
Belgium
Bolivia
Botswana
Brazil
Bulgaria
Burundi
Cameroon
Canada
Centr. African Rep.
Chile
China
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Czech Republic
Denmark
Ecuador
Egypt
El Salvador
Ethiopia
Fiji
Finland
Note: USA are excluded from all the regressions.
France
Ghana
Greece
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kenya
South Korea
Kuwait
Latvia
Malawi
Malaysia
Mexico
Mongolia
Morocco
Namibia
Nepal
Netherlands
New Zealand
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Senegal
Sierra Leone
Singapore
Slovakia
Slovenia
South Africa
Spain
Sri Lanka
Sweden
Syria
Thailand
Tunisia
Turkey
UK
Venezuela
Zambia
Zimbabwe
TABLE 1C: DESCRIPTIVE STATISTICS, COUNTRY VARIABLES
Main Country Level Variables
N.
Mean
St.Dev.
Min.
Max
GDP per Capita ($)
84
8726
7516
526
24097
Bank Credit / GDP
84
0.37
0.29
0.03
1.45
Number of Procedures
84
29.29
11.32
11
58
Main Country Level Variables, pair-wise correlations
GDP per Capita (Log)
1.00
Bank Credit / GDP
0.68***
1.00
Number of Procedures
-0.43***
-0.44***
1.00
Sources: World Bank and Doing Business Database.
TABLE 1D: PAIRWISE CORRELATIONS OF INDUSTRY VARIABLES, RANKINGS
Ranking of Main Industry Variables in US [26 Obs.]
Vertical Integration
1.00
External Financial Dependency
0.11
1.00
Size Distribution Index [Weighted]
-0.24
-0.32
1.00
Size Distribution Index [Unweighted]
-0.21
-0.47**
0.37*
1.00
External Financial Dependency of Upstream
Industries
-0.06
0.14
-0.55***
0.08
1.00
0.47**
0.42**
-0.19
-0.32
0.05
Contractual Needs
Sources: UNIDO Database, Rajan and Zingales (1998), author’s calculations.
1.00
TABLE 2: FINANCIAL MARKETS AND VERTICAL INTEGRATION
Finance × Size Distribution
Indexes Interaction
Finance Index
Vertical Integration: UNIDO Measure
External Financial Dependency × Financial
Development
I
II
III
IV
V
VI
VII
VIII
IX
0.039
[0.030]
0.044
[0.037]
0.041
[0.036]
0.091***
[0.032]
0.122***
[0.043]
0.118***
[0.048]
0.189***
[0.056]
0.205**
[0.085]
0.203**
[0.083]
-0.119**
[0.048]
-0.200***
[0.062]
-0.194***
[0.061]
-0.296***
[0.081]
-0.344***
[0.110]
-0.344***
[0.113]
0.121*
[0.062]
0.113
[0.078]
0.111
[0.076]
External Financial Dependency × Financial
Development × Size Distribution [Weighted]
Financial Development × Size Distribution [Weighted]
External Financial Dependency of Upstream Industries
× Financial Development
Industry Dummies
Country Dummies
Vertical Integration U.S. × GDP Per Capita
Industry Characteristics × GDP Per Capita
Industry Dummies × GDP Per Capita
Observations
R-squared
Finance × Size Distribution Indexes
Interaction, Saturated Equation
-0.046**
[0.20]
-0.057*
[0.033]
-0.055*
[0.033]
-0.075***
[0.023]
-0.105***
[0.036]
-0.102***
[0.039]
-0.049**
[0.023]
-0.083**
[0.038]
-0.081**
[0.039]
yes
yes
yes
yes
yes
yes
yes
yes
--yes
yes
yes
yes
yes
yes
yes
yes
yes
--yes
yes
yes
yes
yes
yes
yes
yes
yes
--yes
1813
0.52
1813
0.52
1813
0.53
1813
0.52
1813
0.52
1813
0.54
1813
0.52
1813
0.52
1813
0.54
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the Industry
level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine (2005)).
Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)), “Size Distribution, [Weighted]” is described in the
text (source: author’s calculations), “External Financial Dependency of Upstream Industries” is described in the text (source: author’s calculations). I use the ranking across
industries for all industry variables. All industry variables are from the United States. Robust standard errors clustered at the country level are reported in parenthesis.
TABLE 3: INTERACTION BETWEEN SIZE DISTRIBUTION INDEX AND FINANCIAL DEVELOPMENT, PLACEBO TEST
Vertical Integration: UNIDO
Financial Development × Size Distribution
[Weighted]
I
0.016
[0.029]
External Financial Dependency ×
Financial Development
II
0.028
[0.032]
III
0.002
[0.039]
0.046
0.043
0.033
[0.032]
[0.033]
[0.039]
-0.043*
-0.073*
[0.025]
[0.038]
External Financial Dependency of
Upstream Industries × Financial
Development
Industry Dummies
Country Dummies
Industry Characteristics × GDP Per Capita
Industry Dummies × GDP Per Capita
Observations
R-squared
IV
0.010
[0.031]
V
-0.004
[0.036]
VI
-0.034
[0.046]
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
-yes
Yes
Yes
-Yes
1829
0.52
1829
0.52
1829
0.53
1829
0.52
1829
0.54
1829
0.54
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the Industry
level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine (2005)).
Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)), “Size Distribution, [Weighted]” is described in the
text (source: author’s calculations), “External Financial Dependency of Upstream Industries” is described in the text (source: author’s calculations). I use the ranking across
industries for all industry variables. All industry variables are from the United States. Robust standard errors clustered at the country level are reported in parenthesis.
TABLE 4: FINANCIAL MARKETS, CONTRACTS AND VERTICAL INTEGRATION
Finance & Contract Indexes
Vertical Integration: UNIDO
External Financial Dependency × Financial
Development
Finance, Contract and Size
Distribution Indexes Interactions
I
II
III
IV
V
VI
VII
VIII
IX
0.032
[0.030]
0.041
[0.036]
0.041
[0.036]
0.078**
[0.034]
0.104**
[0.042]
0.111**
[0.048]
0.168***
[0.060]
0.197**
[0.085]
0.197**
[0.085]
-0.109**
[0.047]
-0.163***
[0.058]
-0.181***
[0.061]
-0.271***
[0.090]
-0.331***
[0.111]
-0.335***
[0.113]
0.121
[0.082]
0.113
[0.077]
0.114
[0.078]
External Financial Dependency × Financial
Development × Size Distribution [Weighted]
Financial Development × Size Distribution
[Weighted]
Contractual Needs × Quality of Contract
Enforcement
Finance, Contract and Size Dist.
Indexes Interactions, Sat. Equation
0.155**
[0.069]
0.082
[0.075]
0.078
[0.078]
Contractual Needs × Qual. of Contr. Enforc. ×
Size Distribution [Weighted]
0.148**
[0.075]
0.104
[0.079]
0.096
[0.081]
0.134
[0.120]
0.051
[0.138]
0.053
[0.136]
-0.009
[0.066]
-0.090
[0.074]
-0.072
[0.078]
0.005
[0.222]
0.032
[0.281]
0.019
[0.272]
-0.031
[0.179]
-0.073
[0.215]
-0.073
[0.208]
Qual. of Contr. Enforc. × Size Distribution
[Weighted]
External Financial Dependency of Upstream
Industries × Financial Development
-0.043**
[0.021]
-0.056*
[0.033]
-0.055*
[0.033]
-0.070***
[0.025]
-0.098***
[0.035]
-0.100***
[0.039]
-0.048*
[0.026]
-0.081**
[0.038]
-0.081**
[0.039]
Industry Dummies
Country Dummies
Vertical Integration U.S. × GDP Per Capita
Industry Characteristics × GDP Per Capita
Industry Dummies × GDP Per Capita
yes
yes
yes
yes
yes
yes
yes
yes
--yes
yes
yes
yes
yes
yes
yes
yes
yes
--yes
yes
yes
yes
yes
yes
yes
yes
yes
--yes
1813
0.52
1813
0.52
1813
0.53
1813
0.52
1813
0.52
1813
0.54
1813
0.52
1813
0.52
1813
0.54
Observations
R-squared
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the Industry
level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine (2005)).
Quality of Contract Enforcement is the (log of 1 over the) number of legal procedures required to enforce a contract (source: Doing Business Database). Industry level
variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)), “Size Distribution, [Weighted]” is described in the text (source:
author’s calculations), “External Financial Dependency of Upstream Industries” is described in the text (source: author’s calculations). “Contractual Needs” is measured as
the Herfindahl index of input use (source: author’s calculations). I use the ranking across industries for all industry variables. All industry variables are from the United
States. Robust standard errors clustered at the country level are reported in parenthesis.
TABLE 5: ALTERNATIVE INDUSTRY VARIABLES
70 Percentile
Vertical Integration: UNIDO Measure
90 Percentile
80 Percentile
[Employment]
Industry Variables
as Dummies
I
II
III
IV
V
VI
VII
VIII
External Financial Dependency × Financial Development
0.093*
[0.055]
0.139*
[0.074]
0.102**
[0.043]
0.160**
[0.070]
0.092**
[0.044]
0.071
[0.064]
0.047**
[0.023]
0.062**
[0.030]
External Financial Dependency × Financial Development × Size
Distribution [Weighted]
-0.137*
[0.079]
-0.223*
[0.121]
-0.116**
[0.057]
-0.236**
[0.092]
-0.188***
[0.060]
-0.159*
[0.91]
-0.025*
[0.017]
-0.054*
[0.030]
0.053
[0.087]
Financial Development × Size Distribution [Weighted]
External Financial Dependency of Upstream Industries × Financial
Development
Industry Dummies
Country Dummies
Vertical Integration U.S. × GDP Per Capita
Industry Variables × GDP Per Capita
R-squared
Observations
0.095
[0.083]
-0.025
[0.078]
0.035
[0.030]
-0.041
[0.029]
-0.027
[0.029]
-0.088**
[0.038]
0.071*
[0.041]
-0.088**
[0.036]
-0.092**
[0.039]
-0.023
[0.022]
-0.015
[0.023]
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
yes
yes
yes
yes
0.52
1829
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the
Industry level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine
(2005)). Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)), “Size Distribution [Weighted, 70
Percentile]” (Columns I and II) and “Size Distribution [Weighted, 90 Percentile] (Columns III and IV) are as described in the text, using the corresponding percentiles
(source: author’s calculations). “Size Distribution [Weighted, 80 Percentile Employment] (Columns V and VI) is computed as described in the text using employment
figures instead of revenues. “Industry Dummy Variables’’ (Columns VII and VIII) are equal to 1 if the ranking of the industry for the corresponding variable is bigger
or equal to 13 (26/2). All industry variables are from the United States. Robust standard errors clustered at the country level are reported in parenthesis.
TABLE 6: ALTERNATIVE MEASURES OF FINANCIAL DEVELOPMENT
Bank Credit /
Bank Assets / GDP
Bank Credit / GDP [Ranking]
GDP [logs, av.
[logs]
1980s]
Vertical Integration: UNIDO Measure
External Financial Dependency × Financial Development
External Financial Dependency × Financial Development × Size
Distribution [Weighted]
I
II
III
IV
V
VI
VII
VIII
0.324*
[0.183]
0.634*
[0.337]
0.086*
[0.051]
0.189*
[0.099]
0.081**
[0.041]
0.202***
[0.078]
0.185*
[0.112]
0.557***
[0.196]
-0.550**
[0.231]
-1.103**
[0.446]
-0.068
[0.048]
-0.261*
[0.132]
-0.124**
[0.056]
-0.346***
[0.109]
-0.272*
[0.148]
-0.929***
[0.261]
0.409
[0.287]
Financial Development × Size Distribution [Weighted]
External Financial Dependency of Upstream Industries × Financial
Development
Industry Dummies
Country Dummies
Vertical Integration U.S. × GDP Per Capita
Industry Variables × GDP Per Capita
R-squared
Observations
1 - Bank
Concentration
0.139
[0.088]
0.159*
[0.094]
0.496*
[0.284]
-0.316**
[0.143]
-0.238*
[0.142]
-0.04
[0.042]
-0.014
[0.042]
-0.075**
[0.036]
-0.047
[0.037]
-0.104
[0.140]
-0.001
[0.137]
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
0.51
1813
0.53
1813
0.51
1813
0.53
1813
0.51
1813
0.53
1813
0.51
996
0.53
996
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the Industry
level in the 90s (source: UNIDO 2001 database). Financial Development is the ranking of Bank Credit over GDP in the 90s (Columns I and II), the log of Bank Credit over
GDP in the 80s (Columns III and IV), the ratio of Bank Assets over GDP in the 90s (Columns V and VI) and (log of one over the average degree of concentration in the 90s
(Columns VII and VIII). (source: Levine (2005)). Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)),
“Size Distribution [Weighted]” is described in the text (source: author’s calculations), “External Financial Dependency of Upstream Industries” is described in the text
(source: author’s calculations). I use the ranking across industries for all industry variables. All industry variables are from the United States. Robust standard errors
clustered at the country level are reported in parenthesis.
TABLE 7: ALTERNATIVE SAMPLES
Non-OECD Countries
Vertical Integration: UNIDO Measure
African Countries
Excluded
Excluding Top &
Bottom 2.5% of
observations
Excluding
observations with <4
years of data
I
II
III
IV
V
VI
VII
VIII
External Financial Dependency × Financial Development
0.131**
[0.054]
0.223**
[0.088]
0.086**
[0.043]
0.131*
[0.070]
0.069*
[0.042]
0.116*
[0.063]
0.087*
[0.048]
0.145*
[0.089]
External Financial Dependency × Financial Development
× Size Distribution [Weighted]
-0.245***
[0.068]
-0.406***
[0.119]
-0.166***
[0.066]
-0.246**
[0.100]
-0.177***
[0.059]
-0.256**
[0.111]
-0.176***
[0.066]
-0.277**
[0.116]
0.118
[0.079]
Financial Development × Size Distribution [Weighted]
External Financial Dependency of Upstream Industries ×
Financial Development
Industry Dummies
Country Dummies
Vertical Integration × GDP Per Capita
Industry Variables × GDP Per Capita
R-squared
Observations
0.059
[0.074]
0.059
[0.074]
0.076
[0.083]
-0.109***
[0.043]
-0.088**
[0.042]
-0.078***
[0.031]
-0.066*
[0.036]
-0.059**
[0.026]
-0.048*
[0.029]
-0.086**
[0.040]
-0.070*
[0.039]
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
0.5
1229
0.5
1229
0.58
1535
0.58
1535
0.51
1738
0.53
1738
0.55
1373
0.55
1373
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over Output at the
Industry level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine
(2005)). Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and Zingales (1998)), “Size Distribution [Weighted]” is
described in the text (source: author’s calculations), “External Financial Dependency of Upstream Industries” is described in the text (source: author’s calculations). I
use the ranking across industries for all industry variables. All industry variables are from the United States. Robust standard errors clustered at the country level are
reported in parenthesis.
TABLE 8: IV RESULTS
OLS
Vertical Integration: UNIDO Measure
Entry [Number of Establishments]
I
II
III
IV
0.002
[0.014]
-0.005
[0.017]
0.069
[0.052]
0.148***
[0.058]
0.012
[0.014]
Entry [Number of Establishments] × Size Distribution [Weighted]
External Financial Dependency of Upstream Industries × Financial
Development
R-squared
IV (Second Stage)
-0.181**
[0.087]
-0.049**
[0.020]
-0.042**
[0.020]
-0.056***
[0.020]
-0.082***
[0.022]
0.51
0.51
0.48
0.53
Dependent Variable: Entry [Number of Establishments]
IV (First Stage)
0.535***
[0.078]
External Financial Dependency × Financial Development
0.062
[0.058]
External Financial Dependency of Upstream Industries × Financial
Development
R-Squared
Industry Dummies
Country Dummies
Observations
0.86
yes
yes
1812
yes
yes
1812
yes
yes
1812
yes
yes
1812
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of Value Added over
Output at the Industry level in the 90s (source: UNIDO 2001 database). Entry [Number of Establishments] is the (log of the) average Number of
Establishments at the Industry level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank Credit
over GDP in the 90s (source: Levine (2005)). Industry level variables: “External Financial Dependency” is computed for the US (source: Rajan and
Zingales (1998)), “Size Distribution, [Weighted]” is described in the text (source: author’s calculations), “External Financial Dependency of Upstream
Industries” is described in the text (source: author’s calculations). I use the ranking across industries for all industry variables. All industry variables are
from the United States. Robust standard errors clustered at the country level are reported in parenthesis. In the Second Stage equation in Column IV,
standard errors are computed by bootstrap since the interaction Entry × Size Distribution is predicted. Using External Financial Dependency × Financial
Development × Size Distribution as instruments for the interaction yields qualitatively similar results.
TABLE 9: VERTICAL INTEGRATION, OUTPUT PER WORKER AND CREDIT MARKETS
Dependent Variable:
Entry [Number of Establishments]
Equation 1: Vertical Integration
Equation 2: Output Per Worker
0.218**
[0.044, 0.440]
0.423**
[0.030, 0.915]
Entry [Number of Establishments] × Size Distribution
[Weighted]
-0.343**
[-0.591, -0.101]
External Financial Dependency of Upstream Industries ×
Financial Development
-0.105**
[-0.207, -0.036]
Output Per Worker
-0.096
[-0.332, 0.328]
0.177
[-0.926, 1.641]
Vertical Integration
Industry Dummies
Country Dummies
Dependent Variable in the US × GDP Per Capita
Industry Variables × GDP Per Capita
R-squared
Observations
yes
yes
yes
yes
Yes
Yes
Yes
Yes
0.50
1812
0.93
1812
The Table reports results from a system of three simultaneous equations. Entry [Number of Establishments] is instrumented with the
interaction Financial Development × External Financial Dependency, as in the First Stage in Table 8, and is therefore not reported in
the Table. Since the interaction Entry × Size Distribution [Weighted] is predicted, bootstrapped bias corrected and accelerated 5%
confidence intervals are reported in parenthesis.
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) average ratio of
Value Added over Output at the Industry level in the 90s (source: UNIDO 2001 database). Entry [Number of Establishments] is the
(log of the) average Number of Establishments at the Industry level in the 90s (source: UNIDO 2001 database). Output per Worker is
the (log of the) average output divided by the number of workers employed at the Industry level in the 90s (source: UNIDO 2001
database). Financial Development is the (log of the) average ratio of Bank Credit over GDP in the 90s (source: Levine (2005)). Industry
level variables: “Size Distribution, [Weighted]” is described in the text (source: author’s calculations), “External Financial Dependency
of Upstream Industries” is described in the text (source: author’s calculations). I use the ranking across industries for all industry
variables.
TABLE 10: CREDIT MARKETS, ENTRY AND OUTPUT PER WORKER, PLACEBO TEST
Reduced Form Results
Dependent Variable: Output per Worker
External Financial Dependency × Financial
Development
I
0.215*
[0.121]
0.040
[0.195]
External Financial Dependency × Financial
Development × Size Distribution [Weighted]
Industry Dummies
Country Dummies
Output per Worker in the US × GDP Per Capita
Industry Variables × GDP Per Capita
R-squared
Observations
II
0.092**
[0.038]
Yes
Yes
Yes
Yes
0.93
1813
Yes
Yes
Yes
Yes
0.93
1813
IV Results
Entry [Number of Establishments]
III
0.364*
[0.223]
IV
0.439**
[0.181]
-0.269
[0.263]
Entry [Number of Establishments]
× Size Distribution [Weighted]
Yes
Yes
Yes
Yes
Yes
0.91
1812
0.91
1812
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Output per worker is the (log of the) average ratio of Output over
Employment in the Industry in the 90s (source: UNIDO 2001 database). Entry [Number of Establishments] is the (log of the) average Number of
Establishments at the Industry level in the 90s (source: UNIDO 2001 database). Financial Development is the (log of the) average ratio of Bank
Credit over GDP in the 90s (source: Levine (2005)). Industry level variables: “External Financial Dependency” is computed for the US (source:
Rajan and Zingales (1998)), “Size Distribution, [Weighted]” is described in the text (source: author’s calculations), “External Financial
Dependency of Upstream Industries” is described in the text (source: author’s calculations). I use the ranking across industries for all industry
variables. All industry variables are from the United States. Robust standard errors clustered at the country level are reported in parenthesis. In the
Second Stage equation in Column IV, standard errors are computed by bootstrap since the interaction Entry × Size Distribution [Weighted] is
predicted. Using External Financial Dependency × Financial Development × Size Distribution [Weighted] as instruments for the interaction yields
qualitatively similar results.
TABLE 11: FINANCIAL MARKETS AND VERTICAL INTEGRATION
Finance × Size Distribution
Indexes Interaction
Finance Index
Vertical Integration: D&B Measure
External Financial Dependency × Financial
Development
I
II
III
IV
V
VI
VII
VIII
IX
0.258**
[0.120]
0.333**
[0.144]
0.313
[0.147]
0.498***
[0.158]
0.549***
[0.194]
0.511***
[0.196]
0.681***
[0.251]
0.674**
[0.323]
0.611*
[0.342]
-0.811***
[0.228]
-0.718***
[0.262]
-0.693***
[0.266]
-1.387**
[0.575]
-1.098
[0.726]
-0.998
[0.775]
0.487
[0.455]
0.323
[0.547]
0.260
[0.584]
External Financial Dependency × Financial
Development × Size Distribution [Unweighted]
Financial Development × Size Distribution
[Unweighted]
External Financial Dependency of Upstream Industries
× Financial Development
Industry Dummies
Country Dummies
Vertical Integration U.S. × GDP Per Capita
Industry Characteristics × GDP Per Capita
Industry Dummies × GDP Per Capita
Observations
R-squared
Finance × Size Distribution Indexes
Interaction, Saturated Equation
-0.010
[0.184]
-0.066
[0.237]
-0.097
[0.246]
-0.126
[0.182]
-0.162
[0.231]
-0.188
[0.244]
-0.110
[0.188]
-0.153
[0.235]
-0.181
[0.247]
yes
yes
yes
yes
yes
yes
yes
yes
--yes
yes
yes
yes
yes
yes
yes
Yes
Yes
--Yes
yes
yes
yes
yes
yes
yes
yes
yes
--yes
2451
0.52
2451
0.52
2451
0.55
2451
0.53
2451
0.53
2451
0.55
2451
0.53
2451
0.53
2451
0.55
***, ** and * mean statistically significant at 1%, 5% and 10% respectively. Vertical Integration is the (log of the) ratio of the index of vertical integration from the Dun &
Bradstreet Worldbase (source: author’s calculations). Financial Development is the (log of the) ratio of Bank Credit over GDP (source: Levine (2005)). External Financial
Dependency (source: Rajan and Zingales (1998), author’s calculations). “Size Distribution [Unweighted]” is as described in the text (source: author’s calculations). External
Financial Dependency of Upstream Industries (source: author’s calculations). I use the ranking of the industry level variables. Robust standard errors clustered at the
country level are reported in parenthesis. Industries are classified as in Table 12
TABLE 12
List of Industries: IO Code
13
14
15
16
17
18
19
20+21
22+23
24
25
26A
26B
27A
27B
28
29A
29B
30
31
32
33+34
35
36
37
38
.
Ordnance and accessories
Food and kindred products
Tobacco products
Broad and narrow fabrics, yarn and mills
Miscellaneous textile goods
Apparel
Miscellaneous fabricated textile products
Lumber and wood products
Furniture and fixtures
Paper and allied products
Paperboard containers and boxes
Newspapers and periodicals
Other printing and publishing
Industrial and other chemicals
Agricultural fertilizers and chemicals
Plastics and synthetic materials
Drugs
Cleaning and toilet preparations
Paints and allied products
Petroleum refining and related products
Rubber and miscellaneus plastics products
Footwear, leather, and leather products
Glass and glass products
Stone and clay products
Primary iron and steel manufacturing
Primary nonferrous metals manufacturing
39
40
41
42
43
44+45
46
47
48
49
50
51
52
53
54
55
56
57
58
59A
59B
60
61
62
63
64
Metal containers
Heating, plumbing and fabric. Struct. Mat.
Screw machine products and stampings
Other fabricated metal products
Engines and turbines
Farm, construction and mining machinery
Materials handling machinery and equip.
Metalworking machinery and equipment
Special industry machinery and equipment
General industrial machinery and equipment
Miscellaneous machinery and equipment
Computer and office equipment
Service industry machinery
Electrical industrial equip. and apparatus
Household appliances
Electric lighting and wiring equipment
Audio, video and communication equipment
Electronic components and accessories
Misc. electrical machinery and supplies
Motor vehicles, passengers cars and trucks
Motor vehicles parts
Aircraft and parts
Other transportation equipment
Scientific and controlling instruments
Ophthalmic and photographic equipment
Miscellaneous
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Financial Development and Vertical Integration: Theory and Evidence Rocco Macchiavello August 2009