Market concentration measures and investment decisions in mexican manufacturing firms

ACCOUNTING & TAXATION ♦ Volume 2♦ Number 1 ♦ 2010

MARKET CONCENTRATION MEASURES AND
INVESTMENT DECISIONS IN MEXICAN
MANUFACTURING FIRMS
Antonio Ruiz-Porras, University of Guadalajara
Celina López-Mateo, University of Guadalajara

ABSTRACT

We study how different measures of market concentration explain investment decisions of Mexican
manufacturing firms. The Herfindahl-Hirschman Index is the traditional measure of market structure
concentration. The Dominance Index is a competition measure used by Mexican regulators. The
econometric assessments suggest that investment decisions of Mexican firms can be better explained by
the Dominance Index than by the Herfindahl-Hirschman Index. Thus our results suggest that the
Mexican Dominance Index might be useful as a measure of market structure and competition. The results
also suggest that market concentration reduces investment. These conclusions are based on several
econometric assessments.

JEL: L40; L22; L60

KEYWORDS: Dominance Index, Herfindahl-Hirschman Index, Investment, Mexico, Manufacturing

INTRODUCTION

T raditional economic theory indicates that the maximization of profits explains the behavior and
decisions of firms. Particularly, from the view of financial economics, firms are considered as
flows of financial streams that depend on investments. Such view explains why the study of
optimal investment decisions and their determinants is considered an important research field for
economists.

Here we study the determinants of investment decisions in Mexican manufacturing firms because studies
for emerging economies are relatively scarce. Particularly, we focus on how market concentration, as a
proxy of market structure and competition, influences investment decisions. The assumption underlying
our study is that Mexican firms face constraints imposed by its competitors and by nature.

In the literature, competition constraints are analyzed with market concentration indexes. In this study we
follow this practice. The Herfindahl-Hirschman Index (HHI) is the usual measure of competition.
However it is not the only one. An alternative measure is the Dominance Index (DI) proposed by Garcia
Alba (1990). The main difference between these measures is that the DI explicitly accounts the size of
firms to measure competition.

We analyze how these two measures of market concentration may explain investment decisions of
Mexican manufacturing firms. We focus on micro, small, medium and large size firms. We control for
certain firm characteristics that capture the constraints that firms face by nature. They include firm size,
cash flow, capital intensity and investment opportunities.

The contributions of this research focus on two areas. The former contributions relate to the literature on
investment determinants. Traditional studies focus on developed economies, not in emerging ones. The
second contribution is methodological. To the best of our knowledge, econometric comparisons of the
HHI and the DI as market concentration measures do not exist.

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A. Ruiz-Porras, C. López-Mateo | AT ♦ Vol. 2 ♦ No. 1 ♦ 2010

The paper is organized as follows. Section 2 reviews the literature. Section 3 describes the
methodological design: data, variables and model specification. Section 4 shows our regression results.
Section 5 discusses them in terms of their implications for economic policy. Section 6 concludes.

LITERATURE REVIEW AND BACKGROUND

Here we review the economic literature about firm investment decisions. The review follows the
guidelines of the Structure-Conduct-Performance (SCP) paradigm. We begin our review by describing
the concentration indexes analyzed in this investigation. Then we indicate some studies that have
analyzed the determinants of investment decisions on empirical and theoretical grounds.

Traditional industrial organization studies analyze firms under the basis of the SCP paradigm. This
paradigm explains firms´ decisions and their performance in terms of the notion of market structure. In
such studies, the Herfindahl-Hirschman Index (HHI) is the standard measure of market structure and
concentration.

The HHI measures market structure under the assumption that firms of a market are identical and that
competition is symmetric. Thus the HHI is an adequate measure of concentration and competition when
big differences do not exist among the firms. Methodologically, the index is measured as the inverse of
the number of firms. Its construction only takes into account the concentration of output.

The Dominance Index (DI) is a measure used by Mexican regulators since the nineties. Garcia Alba
(1990) developed it to assess how differences in firms´ size may affect the strategic interactions in the
market. In fact, the DI assesses the capacity of two o more small firms to compete against large firms.
Thus it is an index that considers how total output is allocated among the firms

Market concentration indexes have been subject to criticism under methodological basis. Particularly, Ten
Kate (2006) argues that the DI is a hybrid between a concentration index and an inequality index. He also
argues that changes in strategic interactions may not be properly taken into account with the index.
Moreover he argues that identical firms are not necessarily better competitors than different ones.

The relevance of the discussion regarding market concentration indexes is not only methodological.
Some theoretical studies explicitly suggest that market structure modifies the behavior of firms. The
paper of Akdoğu and MacKay (2006) is relevant for our purposes because they argue that investment
decisions depend on the strategic interactions prevailing in the markets. Moreover, in a later study they
confirm that investment depends inversely on industry concentration (Akdoğu and MacKay, 2008).

Empirical evidence is not conclusive. For example, Lee and Hwang (2003) do not find any relationships
between market structure determinants and investment decisions in the Korean telecommunication
industry. Indeed they conclude that market structure (measured by the HHI) is not a determinant of
Research and Development (R&D) investment. However, in another study Escrihuela-Villar (2008)
concludes that investment depends directly on market concentration.

Interestingly both studies, Lee and Hwang (2003) and Escrihuela-Villar (2008), indicate that certain
determinants are necessary to understand the relationships between market structure and investment.
Concretely, both studies indicate that firm size and investment opportunities determine investment
decisions. Particularly, Escrihuela-Villar (2008) finds that large firms invest more than small ones.

Evidence from developed economies confirms that further determinants are necessary to analyze the
relationships between market structure and investment. Mishra (2007) and Czarnitzk and Binz (2008)
find direct relationships among investment intensity, market structure and firm size. Bøhren, Cooper and

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Priestley (2007), D’Erasmo (2007) and Ughetto (2008), also find direct relationships among investment
decisions and cash flow, firm size and capital intensity. De Marzo and Fishman (2007) find that
investments for small and medium firms are sensitive to cash flows.

Empirical research on the relationships between market structure and investment for emerging economies
are limited. Existing studies mostly focus on other determinants of investment decisions. For example,
Adelegen and Ariyo (2008) and Bokpin and Onumah (2009) find that firm size, cash flow and investment
opportunities may explain investment decisions. The first study focuses on the Nigerian economy. The
second one analyses manufacturing firms in several emerging markets.

We emphasize that further studies are necessary to understand the relationships among market structure
and investment decisions in emerging economies. Here we propose an econometric analysis with the HHI
and DI measures of market concentration to analyze such relationships. We include some complementary
determinants according the findings of previous studies. The methodological issues and outcomes
regarding such analysis are described in the following sections.

METHODOLOGY

Here we describe the methodological design of the investigation. Specifically, we describe the sources of
data and the indicators used in the econometric assessments. Furthermore we describe the econometric
modeling and testing procedure used to analyze the relationships among market structure and investment
decisions in the Mexican manufacturing firms.

Data Sources

We use data from the “Economic Census 2003” reported by the Mexican Bureau of Statistics (INEGI).
Such census is constructed accordingly to the North-American-Industry-Classification-System (NAICS).
We use a longitudinal data set because data of previous censuses are built with non-comparable
methodologies. In Mexico census data are collected every five years. Currently, data for the census
collected in 2008 is not available.

In the census, firm-level data are not available due to confidentiality reasons. We deal with such
constraint by constructing a set of four representative firms for each of the 182 industries. We build the
representative firms accordingly to the number of employees. A micro firm has no more than 10
employees. A small firm has between 11 and 50. A medium firm has between 51 and 250. A large firm
has at least 251 employees. This classification follows the one of the Mexican Economics Ministry for
manufacturing firms.

The census classifies firms of each industry into groups according to the number of employees. For
example, the first group includes firms with 0 to 2 employees. The second group includes firms with 3 to
5, and so on. The census has 12 classificatory groups for each of the 182 industries. As we have
indicated, the Mexican Economics Ministry uses a different classification for the firms. Table 1 shows
the relationships between both classifications.

The first step to build a variable that describes the behavior for a representative firm of size j of industry i
is to calculate a weight indicator. We use the mean of the number of employees by group to calculate it.
This is calculated as follows:

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n ijt M jt
Pijt =
∑n
t
ijt M jt
(1)
i = 1, ...,182
j = 1, 2, 3, 4
t = 1, ...,12

where Pijt is the weighted indicator of the industry i, size j, group t; nijt is the number of firms of the
industry i, size j, group t; Mjt is the mean of the number of employees of size j in group t; the subindex i
refers to the i-th industry; the subindex j refers to the firm of size j (micro, small, medium and large
firms); the subindex t refers to the t-th groups included in the size-j classification.

Table 1: The Census and the Mexican Economics Ministry Classifications for the Firms of an Industry

Census´ Classification Employees in the Firms Mean of Employees in Type of Firm According to Firms´ Size
of Firms in the Industry that Belong to Group t the Firms that Belong the Mexican Economics According to the
i(t) to Group t (Mjt) Ministry´ classification Type of Firm (j)

1 0-2 1 Micro 1
2 3-5 4 Micro 1
3 6-10 8 Micro 1
4 11-15 13 Small 2
5 16-20 18 Small 2
6 21-30 25 Small 2
7 31-50 40 Small 2
8 51-100 75 Medium 3
9 101-250 175 Medium 3
10 251-500 375 Large 4
11 501-1000 750 Large 4
12 1000+ Large 4
This table shows the relationships between the Economic Census´ classification and the one of the Mexican Economics Ministry. The census
classifies firms of each industry into groups according to the number of employees. The census has 12 classificatory groups for each of the 182
industries. Mexican Economics Ministry´ classification for manufacturing firms considers four types. A micro firm has no more than 10
employees. A small firm has between 11 and 50. A medium firm has between 51 and 250. A large firm has at least 251 employees. The mean of
employees for the firms of the twelfth group is the average of employees with respect to the total of firms in the twelfth group.

The second step is to use the weighted indicator of each one of the four representative firms of industry i
to estimate each variable assessed econometrically. We multiply Pijt by each variable included in the
census classification for each one of the twelve groups of firms Vijt (see Table 2 for a list of variables).
Such multiplications added accordingly to each subindex t will provide us with a variable each
representative firm of size j of the industry i.

RFij = ∑P t
ijt Vijt

i = 1, ...,182 (2)
j = 1, 2, 3, 4
t = 1, ...,12

where RFij is a variable associated to the representative firm of the industry i, size j; Pijt is the weighted
indicator of the industry i, size j, group t.

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Variables

Here we describe the main variables used in our study. We use the ones proposed by Bøhren, Cooper and
Priestley (2007) and Akdoğu and Mackay (2008). The variables used in the econometric assessments are
summarized in the following table:

Table 2: Investment and Its Determinants (Variables)

Variables Measures Indicator of the Census
Investment Fixed capital expenditures Gross fixed capital formation
(Value of fixed assets bought
during 2003 minus the value of
fixed assets sales)

Investment opportunities Ratio of output to capital Ratio of production value to fixed
capital stock

Market concentration Market concentration measures Herfindhal-Hirschman Index
Dominance Index

Cash flow Earnings Net earnings

Firm size Fixed assets Total value of fixed assets

Capital intensity Ratio of capital to labor Ratio of fixed capital stock to
number of employees

This table shows the variables and indicators used in the econometric assessments. The dependent variable is investment. The other variables
are the independent variables used in this investigation. The table includes the definitions of the variables (indicators) according to the
Economic Census of INEGI (Mexican Bureau of Statistics).

The measures of market concentration are the HHI and the DI indexes. We do not build indexes for each
industry because certain groups of industries can be considered, for practical purposes, as competitors in
the same market. We deal with this fact by grouping the industries in subsectors. We estimate 21
subsector level measures of market concentration. We use the total number of firms that belong to each
group of industries to build the measure that corresponds to each subsector.

The measure of market concentration assumes that all the firms in a subsector are in the same market.
Under that assumption, we define the HHI as follows:

n
HHI s = ∑m
k =1
2
ks (3)

where mks represents the share of the firm k in the total product of the subsector s; n is the number of
firms in the subsector s.
The Dominance Index is estimated in the same way as the HHI. Firms using similar raw material inputs,
similar capital equipment, and similar labor are classified in the same subsector. Thus, we estimate again
21 subsector level measures of market concentration. Again, the measure of market concentration
assumes that all the firms in a subsector are in the same market. Under that assumption, we define the DI
as:

DI s = ∑M ts Yts (4)

where Mts is the share of the production of the group t in the production of the subsector s; Yts is the firm
average production of the group t, subsector s.

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Modeling Specification and Econometric Techniques

We use a log-linear functional form specification to describe the relationships between market structure
and investment. Such specification allows the regression coefficients to measure the elasticity of
investment with respect to each independent variable (determinant). Moreover, the log transformation
reduces the possibility of heteroscedasticity problems. Thus the model specification is:

ln I ij = α 0 + α1 ln IOij + α 2 ln CFij + α 3 ln Sij + α 4 ln MC ij + α 5 ln KI ij + ε ij (5)

where Iij is investment; IOij represents the investment opportunities; CFij is cash flow; Sij is the size of the
firm; MCij is the market concentration; KIij represents the capital intensity; eij is the random error term.

The analysis relies on several estimations of the equation (5). Concretely it relies on two sets of
regressions. The first set includes estimations that use the HHI index as measure of market concentration.
The second set uses estimations with the DI index. Each set is conformed by four regressions that assess
how market concentration relates to investment for firms of a specific size (micro, small, medium and
large). We use Ordinary Least Squares (OLS) for estimation purposes in both sets of regressions. In
addition, we use specification-error Ramsey tests. The tests allow us to validate the econometric
assumptions regarding the functional specification form and to detect omitted-variable bias.

EMPIRICAL ASSESSMENT

Table 3 reports the summary of descriptive statistics of the variables. The variable means seem to depend
on the size of the firms. The means associated to micro firms are smaller than the ones of small firms.
The means associated to medium firms are smaller than the ones of large firms. These facts support the
necessity to differentiate firms by size.

Table 3: Summary Statistics

Std. Std.
Obs Mean Dev. Min. Max. Obs Mean Dev. Min. Max.
Variables Micro firms Medium firms
Investment 118 16.66 5.61 3.82 31.48 147 16.91 3.44 5.29 24.98
Cash flow 118 28.28 5.24 9.11 42.73 147 24.53 3.39 8.67 30.90
Firm size 118 26.45 5.01 12.76 40.00 147 22.79 3.40 7.48 31.60
Capital intensity 118 8.86 1.77 0.16 13.65 147 8.51 1.86 3.32 16.52
Investment
opportunities 118 -2.09 1.75 -14.01 1.11 147 0.24 1.17 -4.28 2.97
HHI 118 -5.65 0.77 -6.74 -2.04 147 -5.45 0.87 -6.74 -2.04
DI 118 -3.21 1.01 -5.35 -1.11 147 -3.16 1.10 -5.35 -1.11
Variables Small firms Large firms
Investment 107 24.10 6.18 5.25 38.00 118 22.04 8.57 5.86 37.63
Cash flow 107 40.43 5.67 10.04 51.46 118 31.04 11.11 10.32 47.82
Firm size 107 36.32 5.76 6.51 49.51 118 29.07 10.46 9.44 44.52
Capital intensity 107 12.42 2.44 3.17 21.33 118 10.32 3.72 3.14 19.97
Investment
opportunities 107 -1.82 1.60 -5.07 3.53 118 -0.46 1.87 -4.63 3.86
HHI 107 -5.53 0.92 -6.74 -2.04 118 -5.47 0.89 -6.74 -2.04
DI 107 -3.17 1.05 -5.35 -1.11 118 -3.28 1.14 -5.35 -1.16
This table shows summary statistics. It presents measures of central tendency. Also, this table shows the independent and dependent variables
used in model specification. The dependent variable is investment. Summary statistics is presented for micro, small, medium and large firms.
Values are expressed in natural logarithms.

Table 4 reports the regression outcomes for the first set of regressions. Apparently, the HHI coefficient is
positive and significant only for micro firms. Firm size coefficients are positive and significant,
independently of the type of firm. In most cases, the coefficients associated to cash flows and investment

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opportunities are significant. Investment opportunities and firm size coefficients are positive and
significant for small firms. The cash flow coefficient is negatively correlated with investment decisions
and is statistically significant. Medium and large firms show similar patterns. In all cases, the results
show high values of R2. In addition, the joint significance F tests suggest that the independent variables
are necessary to explain investment decisions.

Table 4: HHI Concentration Measures and Investment Decisions in Mexican Manufacturing Firms (OLS
Regressions)

Firm Size Micro Small Medium Large
Regression indicators
Investment opportunities 0.39 1.91*** 1.55*** 1.60***
(1.14) (5.36) (3.56) (4.86)
Herfindahl- Hirschman Index (HHI) 0.67*** 0.24 -0.056 -7.50
(2.98) (0.92) (-0.35) (-0.70)
Cash flow -0.40 -1.62*** -1.27*** -1.16***
(-1.21) (-4.60) (-2.90) (-3.55)
Firm size 1.47*** 2.70*** 2.26*** 2.15***
(4.63) (7.44) (4.75) (5.61)
Capital intensity 0.02 -0.06 0.02 0.02
(0.24) (-0.44) (0.19) (0.18)
Constant -6.57*** -2.84 -4.11*** -3.76***
(-2.69) (-1.09) (-3.45) (-4.91)
Observations 118 107 147 118
F 225.16*** 134.10*** 109.58*** 444.44***
Prob > F 0.00 0.00 0.00 0.00
R2 0.91 0.86 0.79 0.95
This table reports results for OLS regressions. They use the Herfindahl- Hirschman Index as a proxy of market structure. The dependent
variable is investment. The results are presented for firm size. The t-statistics are given in parenthesis. ***, **, and * indicate significance at
the 1, 5 and 10 percent levels respectively.

Table 5 reports the regression outcomes for the second set of regressions. Here we find that the DI
coefficient is a negative and statistically significant for medium and large firms. The coefficients
associated to investment opportunities are positive and significant in most cases. Cash flow coefficients
are negative and statistically significant. The coefficients associated to firm size are positive and
significant in all cases.

Table 5: DI Concentration Measures and Investment Decisions in Mexican Manufacturing Firms (OLS
Regressions)

Firm size Micro Small Medium Large
Regression Indicators
Investment opportunities 0.17 1.87*** 1.68*** 1.57***
(0.49) (5.23) (3.83) (4.80)
Dominance Index (DI) 0.11 -0.04 -0.20* -4.43*
(0.62) (-0.19) (-1.66) (-1.82)
Cash flow -0.21 -1.58*** -1.41*** -1.15***
(-0.64) (-4.48) (-3.18) (-3.57)
Firm size 1.27*** 2.64*** 2.40*** 2.13***
(3.92) (7.35) (5.01) (5.63)
Capital intensity 0.17 -0.03 0.02 0.05
(0.49) (-0.24) (0.18) (0.50)
Constant -10.50*** -4.34* -4.42*** -3.53***
(-4.82) (-1.81) (-4.38) (-4.62)
Observations 118 107 147 118
F 207.74*** 132.86*** 112.14*** 456.12***
Prob > F 0.00 0.00 0.00 0.00
R2 0.90 0.86 0.79 0.95
This table reports results for OLS regressions. They use the Dominance Index as a proxy of market structure. The dependent variable is
investment. The results are presented for firm size. The t-statistics are given in parenthesis. ***, **, and * indicate significance at the 1, 5 and
10 percent levels respectively.

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Like in the previous set of regressions, the results show high values of R2. Such values confirm that the
explanatory variables can explain investment decisions. Again the F tests confirm that the set of
independent variables explains them. So, apparently both sets of regression may provide similar
information. The only exception relies on the positive and significant coefficient associated to the market
concentration variable for micro firms in the first set of regressions.

We support the robustness of our previous results with specification-error Ramsey tests. Such tests allow
us to deal with the differences of information. Here we use two versions of the Ramsey test. The first
one, the traditional RESET test, uses powers of the estimated independent variable as regressors. The
second one uses powers of the RHS variables. The null hypothesis is that the model is adequately
specified in both versions of the test.

The outcomes of the tests of both sets of regressions suggest that the econometric assessments for small,
medium and large firms do not have specification errors. The modeled relationships between market
concentration and investment decisions seem adequate in most cases. However, the exception is referred
to micro firms. For these firms, the regressions suggest the existence of omitted variable-bias and/or
incorrect functional forms.

The Ramsey tests suggest that the differences reported between the two sets of regressions should not be
considered relevant. In fact, the comparison of the reported outcomes and tests suggest that the
regressions that include the DI index might be better than the ones that include the HHI index. We
support this statement on the basis that the only significant coefficients associated to the concentration
variables appear in the second set of regressions (see Table 5). As we have indicated, the regression of
the first set associated to the micro firms has specification errors (see Tables 4 and 6).

Here is important to point out that the outcomes suggest that how market concentration affects investment
decisions depends on the size of the firms. According to the regressions with the DI index, it seems that
concentration significantly reduces investment for medium and large size firms. When firms are micro or
small ones, the evidence is not conclusive due to specification errors and non significant variables (see
Table 5).

Table 6: Model Validation (Specification Tests)

Firm size Micro Small Medium Large

Models with Herfindhal-Hirschaman Index (HHI)
Ramsey test
(H0: Model has no specification error) 7.06*** 0.85 2.24* 0.82
Prob > F 0.0002 0.4720 0.0859 0.4875
Ramsey test, rhs
(H0: model has no omitted variables) 2.66*** 0.76 0.80 0.81
Prob > F 0.0020 0.7197 0.6788 0.6655

Models with Dominance Index (DI)
Ramsey test
(H0: model has no omitted variables 7.68*** 0.90 2.35* 0.43
Prob > F 0.0001 0.4465 0.0750 0.7287
Ramsey test, rhs
(H0: model has no omitted variables) 2.84*** 0.75 0.74 0.66
Prob > F 0.0011 0.7295 0.7434 0.8123
This table shows results of Ramsey test. It is used to detect specification errors. This table shows two versions of the of the Ramsey test. Ramsey
test (rhs) uses powers of the independent variables. Instead Ramsey test uses powers of the fitted values of the dependent variable. ***, **, and
* indicate significance at the 1, 5 and 10 percent levels respectively.

We conclude by indicating that the evidence supports the view that market concentration reduces
investment, at least in medium and large firms. Thus, according to our results, competition may promote

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investment. Furthermore the evidence provides elements to support the statistical adequacy of the DI
index as an adequate measure of market concentration. Moreover, the results suggest that the regressions
that include the DI index might be better than the ones that include the HHI index.

DISCUSSION

Here we have assessed the relationships between market structure and investment decisions in the
Mexican manufacturing firms. The assessments suggest that market concentration may reduce
investment, at least in medium and large firms. Thus, competition may promote investment.
Furthermore, they confirm that certain firm characteristics may be useful to explain investment decisions.
Particularly, firm size seems an important determinant.

However, it is interesting to point out that some findings seem counter intuitive. For example, capital
seems not to influence investment decisions. Furthermore, cash flows seem to have an inverse
relationship with investment. We believe that such findings may be explained on the basis that
manufacturing firms are intensive in labor. When firms are labor-intensive, investments may rely on new
“costly” workers that reduce cash flows.

Methodologically, the assessment procedure seems useful to explain the investment decisions of small,
medium and large firms. Furthermore, it supports the hypothesis that investment decisions in micro firms
may depend on other determinants, in addition to the market structure ones. Ekanem and Smallbone
(2007) include, among these determinants, the intuition, the social networks and the experience of the
entrepreneurs.

Empirically, we believe that the most interesting findings relate to the usefulness of the different market
concentration measures. Our econometric assessment suggests that the Dominance Index (DI) is a better
determinant of investment decisions than the Herfindahl-Hirschman Index (HHI). In practice, this finding
implies that the degree of competition can affected by differences in the size of the firms in the market.
Thus regulators may need to consider these differences when dealing with competition issues.

We conclude by indicating that our findings have implications for regulatory and policy purposes.
Probably, the most important one is associated to the necessity to promote the Dominance Index as an
alternative measure of market competition. Another one relates to the necessity to encourage competition
among the Mexican firms in order to increase investment. Finally, a third one relates to the necessity to
encourage studies on the determinants of investment in micro and small size firms because our evidence
is not conclusive.

CONCLUSIONS

We have studied how alternative measures of market concentration, as proxy indicator of market
structure, may explain investment decisions of Mexican manufacturing firms. Here we have focused on
the HHI and the DI measures. We have developed an econometric analysis that uses data for the last
census available in Mexico (2003). We have controlled by firm size, cash flow, capital intensity and
investment opportunities.

Methodologically, the empirical study has relied on two regression sets. The first set includes estimations
that use the HHI index as measure of market concentration. The second one includes estimations that use
the DI index. We have used OLS techniques for estimation purposes. In addition, we have used Ramsey
tests to validate the econometric outcomes. We have used data of the census to build the indicators of the
182 industries that integrate the Mexican manufacturing sector.

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Our findings confirm that market structure may influence investment decisions. Concretely they suggest
that concentration may reduce investment. Thus they confirm the findings of Akdoğu and MacKay
(2008). Our findings also suggest that the DI index is a better determinant than the HHI one.
Furthermore, they suggest that firm size and investment opportunities have a direct relationship with
investment. Cash flows, on the other hand, have an inverse one. Interestingly, capital intensity is not
related to investment decisions.

We believe that our study provides some ideas for further research. For example, extensions of our
analysis could be used to analyze investment decisions in firms that provide financial and non-financial
services. The “Economic Census 2008”, when available, may provide data useful for comparison
purposes. Finally, our results also suggest that further studies on the determinants of investments in micro
and small firms may be necessary.

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BIOGRAPHY

Antonio Ruiz-Porras is professor and researcher in the Department of Quantitative Methods at the
University of Guadalajara. Address for correspondence: Departamento de Métodos Cuantitativos.
Universidad de Guadalajara, CUCEA. Periférico Norte 799. Núcleo Universitario Los Belenes, 45140.
Zapopan, Jalisco, México. Telephone: ++ (52) (33) 3770 3300 ext. 5291. Fax: ++ (52) (33) 3770 3300
ext. 5227. Email: antoniop@cucea.udg.mx

Celina López-Mateo is student in the Doctoral Program of Economic and Managerial Sciences at the
University of Guadalajara. Address for correspondence: Programa Doctoral en Ciencias Económico-
Administrativas. Universidad de Guadalajara, CUCEA. Periférico Norte 799. Núcleo Universitario Los
Belenes, 45140. Zapopan, Jalisco, México. Email: celinalm@gmail.com

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