Firm size and entrepreneurial characteristics: evidence from the SME sector in Argentina/Imones dydis ir verslumo vertinimai: Argentinos mazu ir vidutiniu imoniu tyrimo rezultatai.
Moreno, Justo de Jorge ; Castillo, Leopoldo Laborda ; Masere, Elio de Zuani 等
1. Introduction
There is systematic empirical evidence that entrepreneurship is
important for economic growth (e.g. Audretsch and Thurik 2000; Carree et
al. 2002; Wennekers and Thurik 1999). Over the last several decades, new
firm growth has become a popular research topic, especially since Birch
(1979) found that new firms had created the majority of new employment
in the U.S. In this respect, the SME sector's contribution to the
economy has attracted the attention of academics and policy makers in
both developed economies and those in transition (Krasniqi 2007).
The fact that entrepreneurs perform specialized functions that
directly or indirectly contribute to output and growth is not a novel
concept in the literature (Baumol 1968, 2004). As Salas and Sanchez
(2006) mentioned, the list of entrepreneurial activities reported
includes: innovation and creative destruction (Schumpeter, 1934; Acs and
Audretsch 1990); the creation of new firms and the resulting increased
competition (Nickell et al. 1997; Callejon and Segarra 1999); matching
supply and demand (Kirzner 1979); input co-ordination (Coase 1937);
monitoring input quality in team production (Alchian and Demsetz 1972);
and risk taking (Knight 1921; Kihlstrom and Laffont 1979).
The vast majority of studies have focused on the causes of firm
growth in the U.S. and in the most developed countries in Europe.
Evidence regarding the influence firms have on employment growth in
developing countries, especially in Latin America, is still very scarce.
In light of this shortcoming, the purpose of this study is to explore
the factors influencing firm size of new and established firms in
Argentina.
Why was Argentina special and what can we learn from it? According
Perry and Serven (2003) Argentina outperformed most other economies in
the region until 1997 in terms of growth per capita (see Table 1) in a
relatively benign external environment, in spite of a short-lived
interruption in 19951. But after the major slowdown in growth in 1999
that affected the whole region, mainly due to capital flow retrenchment
after the Russian crisis, other countries in the region began a modest
recovery, while Argentina plunged into a protracted recession, reversing
most of her previous gains at poverty reduction.
In this context, the bulk of papers are devoted to examine to which
extent and why was the Argentine economy more vulnerable to adverse
external shocks than other Latin American economies, and to what extent
were policy mistakes the main culprit. In Rou-bini (2001), it examines
the vulnerabilities associated with deflationary adjustments to shocks
under a hard peg. In Sachs (2002) the focus is the public debt, the
fragile fiscal position and the strength in the banking sector.
Although there were important vulnerabilities in each of these
areas, the impact was very different in the Argentina's firms.
Especially important was the size of the firm to overcome this crisis.
In this situation is very important try to understand the link between
entrepreneur's characteristics and firm size.
The primary information upon which this research is based was
collected during the period from April to September 2002, i.e. six
months after the beginning of the most serious political and social
crisis in Argentina's modern history (December 2001). In just one
month (December 2001--January 2002), President De La Rua resigned, three
new transition presidents came and went in quick succession, and the
Argentine currency was formally devalued by 40%. This resulted in
economic chaos and a real devaluation of the peso of over 200%, with the
currency dropping from 1 peso = $1 to almost 4 pesos per dollar.
This crisis worsened entrepreneurs' general distrust of
Argentina's public institutions, which was already poor the
previous year (2001) when, according to the Latinobaro-metro opinion
survey, only 17% of the Argentine population had confidence in the
Parliament or National Congress. Combined with the recession that had
been worsening since 1998 (convertibility plan 1 peso = $1), the result
was an unstable business environment with a high level of uncertainty
and hopelessness among small and medium-sized entrepreneurs. Figure 1
shows the evolution of GDP per worker in Argentina over more than 50
years.
As can be appreciated in Figure 1, the crisis is related to this
paper's period of analysis (2001-2002).
[FIGURE 1 OMITTED]
The contribution of our study to the existing literature is
twofold. First, quantile regression based on work by Mata (1996) is used
as a more suitable methodology to measure the determinants of firm size.
Second, the previous objective is carried out in a context of economic
and social of crisis where the lessons learned are even more important.
Our results show that the main sets of explanatory variables
related to entrepreneurial characteristics (age, experience, education,
and vocation) provide a full explanation of firm size. However, the
group of variables related to the strategy continued by entrepreneurs or
the environment was less representative.
The study is structured as follows. Section 2 examines previous
empirical research and hypotheses. Section 3 discusses data, variables,
and methodology. In Section 4, we discuss our empirical findings; and
Section 5 draws conclusions and implications.
2. Literature review and hypothesis
We can say that our work, on the characteristics of the
entrepreneur in Argentina, joins other empirical studies conducted--in
the last years--in other countries such as Mexico (Hernandez-Trillo et
al. 2005; Heino 2006), USA (Kim et al. 2006; Goetz and Rupas-ingha
2009), Germany (Wagner 2007; Fossen 2009), Japan (Masuda 2006), Sweden
(Nykvist 2008), Republic of Korea (Kang and Heshmati 2008), Irish
(Bhaird and Lucey 2009) , Italy (Bonaccorsi and Giannangeli 2008;
Gagliardi 2009), Lithuania (Milius 2008), and The Netherlands (Koster
2009).
Recent works (Mesnard and Ravallion 2006; Buera 2009; Quadrini
2009; Jaimovich 2010) stress the need to include the
entrepreneur's characteristics and others structural factors in the
traditional models of economic development. In relation with
entrepreneur's characteristics the literature considers different
factors like: Age (Mondragon-Velez 2009), Gender (Minniti and Nardone
2007; Startiene and Remeikiene 2008, Kobeissi 2010), Race (Fairlie and
Robb 2007), Education (Backes-Gellner and Werner 2007; Van der Sluis et
al. 2008), Risk Taking (Vereshchagina and Hopenhayn, 2009), etc.
Relative to structural factors the literature has focused in: Liquidity
Constraints (Oliveira and Fortunato 2006; Chapelle 2010), Credit
Rationing (Blumberg and Let-terie 2008; Gagliardi 2009), Regulations
(Capelleras et al. 2008), Institutional context (Henrekson and Johansson
1999; Bowen and De Clercq 2008; Nystrom 2008), Local Knowledge and
Innovation (Bae and Koo 2009; Braunerhjelm et al. 2010), etc.
In order to explain the firm growth and its determinants, this
paper uses Storey's (1994) analytical framework as its main guide.
This framework proposes three main factors that can be seen as a variety
of different elements: resources and characteristics of the entrepreneur
(individual), the firm (organizational), strategy and environment.
At the individual level, the entrepreneur's human capital is
often seen as a good indicator of their likely success. Becker's
(1964) theory of human capital extended micro-economic analysis to a
wide range of human behaviors and suggested that knowledge can increase
cognitive ability and lead to more effective activity. Many scholars
have examined the influence of human capital within the process of
entrepreneurship (Cooper et al. 1994; Honig 2001; Pena 2004) and the
positive effect human capital has on firm growth (McPherson 1996; Roper
1999; Walsilczuk 2000; Almus 2002).
For instance, the positive effect of formal education on firm
survival and growth has been extensively reported (e.g. Cooper et al.
1994; Gimeno et al. 1997; Pena 2004). Prior experience has also been
shown to influence firm growth, and entrepreneurs with some managerial
experience, normally in their previous job, are likely to form firms
which grow faster than firms started by individuals without such
experience (Stuart and Abetti 1990; Storey 1994).
However, there is no consensus about the influence of gender on
growth (Fischer et al. 1993; Du Rietz and Henrekson 2000; Liedholm
2002). Several psychological traits and motivations have been found to
influence firm growth (Roper 1999; Walsilczuk 2000; Baum et al. 2001;
Sadler-Smith et al. 2001).
In order to examine how human capital and motivations affect the
growth of SMEs, the following hypotheses will be tested:
H1: There will be a positive relationship between an
individual's level of general human capital and firm size.
H2: There will be a positive relationship between entrepreneurial
vocation and firm size.
Besides the characteristics related to entrepreneurs and firms,
size also depends on the strategies employed by entrepreneurs
themselves. In this respect, entrepreneurs consciously select strategies
and their choices, at least in part, reflect their views on what the
optimal strategy should be in a given environment. Porter (1980)
identifies three broad business-level choices: cost leadership,
differentiation, and focus. Focus refers to competitive strategies that
target a particular set of customers for a product line, or geographical
market. The low-cost strategy involves the construction of
efficient-scale facilities, the aggressive pursuit of cost reduction in
all functions of organizations. Differentiation strategies are designed
to create and market innovative, high-quality products and/or services.
The three competitive strategies are alternative, viable options to deal
with the environmental forces, and to outperform firms that implement
combined strategies. However, various authors suggest that strategy
should be adapted to the environment (Tushman and Romanelli 1985;
Sandberg and Hofer 1987). In this respect, McDougall et al. (1992) found
that, with regard to small firm growth, broad strategies were more
successful, thus questioning the otherwise common niche argument
(Davidsson et al. 2005).
As Capelleras and Rabetino (2008) mentioned, and according to
Storey (1994), there are other strategic variables, considered actions
taken by the entrepreneur after start-up, which are likely to have an
impact on growth. For example, the use of a formal business plan or
strategic planning. Delmar and Shane (2003, 2004) argue that business
planning is central to the organizational activities of new ventures and
firm growth.
Finally, many external factors may influence firm growth, such as
location-specific advantages, industry-specific factors, macroeconomic
conditions, and public policies. Authors like Shane and Kolvereid (1995)
found strategy to have little influence on firm growth, whereas
variations in national environments accounts for almost all performance
variation. In this context, the industry sector has been shown to be a
significant variable when analyzing firm growth (Davidsson et al. 2002).
Several scholars conclude that the more dynamic industries are, the more
firm growth there is (Jovanovic 1982; Audretsch 1995; Carroll and Hannan
1989, 2000). The location of the firm was also considered to have a
potential influence on firm growth.
However, evidence is not conclusive with respect to the effect firm
location has on growth (Birley and Westhead 1990; Storey 1994; Davidsson
et al. 2002).
In order to examine how strategy and environment affect the growth
of SMEs, the following hypotheses will be tested:
H3: The specific definition levels of entrepreneurial strategy (in
terms of competitive price and the knowledge of competitors'
prices) have a significant influence on firm growth.
H4: Location and industry activity will be significant variables in
explaining firm size.
3. Data, variables, and methodology
3.1. Data, variables, and descriptive statistics
The determinants mentioned in earlier studies have led us to use
two types of information sources together in this paper. One is of a
primary nature, using the entrepreneur (2) as a unit of analysis, and
the other is secondary, at the province level (3). With respect to the
sample selection for the primary analysis, as was mentioned above, we
have considered firms located in 14 provinces in Argentina (4), where
the total number of firms is 360,709 (data referring to July 2002), the
target population being firms with between 1 and 250 employees,
representing 99.63% of all firms in Argentina. A total of 1,690 firms
(0.36%) employ more than 250 workers.
The representativeness of the sample were determined by province
(see Table 2). Representativeness by sector was 0.29% taking into
consideration the total number of firms in the country (primary: 0.12;
industrial: 0.4; construction: 0.25; and services: 0.31).
We understand firm employment growth to be a multidimensional and
complex phenomenon. According to the literature review in the previous
section, three sets of factors can be used to explain firm growth. Each
of these components can be broken down into more detailed subset
variables, which will be used in our empirical analysis. In this
respect, firm growth (G) can be explained by the human capital of the
entrepreneur (HC); the entrepreneur's strategy behavior (ST); as
well as external factors or environmental influences ([E.sub.Regions] +
[E.sub.Sector]), where is a firm-specific stochastic variable that is
independent across firms:
G = f(HC,ST,[E.sub.regions] + [E.sub.Sector] ,[bar.u]). (1)
As was previously mentioned (Delmar 1997; Weinzimmer et al. 1998;
Wiklund 1998), a wide variety of different variable growth (G) measures
have been used in the literature, such sales and employment, or other
more subjective measures of growth. We favor measuring employment for
different reasons: i) In the Argentine context, it minimizes inflation,
currency, and accounting problems; ii) We are interested in researching
organic employment growth because this represents genuine job creation
and not simply growth through acquisition.
The size distribution of the sample is briefly described in Figure
2 and Table 3. The typical Latin-American firm is quite small.
[FIGURE 2 OMITTED]
It is clear that the size distribution of firms is highly skewed
and that 50% of them employ no more than five people, though average
firm size is about 12 people. The present distribution does not conform
to the statistical distributions that have been suggested in the
literature which underlie the firm size distribution. However, our data
do not have a normal distribution and thus dependent variables were
transformed into a logarithm.
The Shapiro-Wilk normality test computed for the log of the firm
size distribution gives a Z statistic of 7.39 significant at 1%. The
mean values of firm sample are: firm age = 16.5, entrepreneur age =
46.2, experience = 15.3 years, respectively. Males account for 75.27% of
the total and 24.73% are female. With respect to their educational
level, 35.02% of the entrepreneurs had university studies and 64.92% had
others.
As was mentioned before, one of the relevant factors to understand
managerial growth is knowing the type of actions carried out by
entrepreneurship. To determine this, three questions associated to the
strategic aspects were formulated. First, How often is the planning
process carried out? (annually, quarterly, monthly, or none). Second,
which competitive position does their company occupy with respect to
their most direct competitors? (1 = very weak to 5 = very strong).
Third, what is their cost strategy level with respect to their most
direct competitors? (1 = smaller, 2 = equal, 3 = larger, 4 = not known).
Likewise, market expectations play an important role mainly on the
manager's decisions which have a direct impact on company growth,
given the conditions during the Argentinean crisis to which this paper
refers. In this respect, besides controlling with dummy variables for
county and sector, the manager was asked about his/her market estimate
(1 = expansive, 2 = recessive, 3 = stable, 4 = not known). Finally, this
section ends by presenting the descriptive statistics of the variables
used which can be found below in Table 4.
3.2. Model
In this section, the model that will be used in this paper is
presented. The model is developed along the lines of small business
economic theory (see, for example, Evans 1987; Jovanovic 1982).
According to Basu and Goswami (1999), for the purposes of statistical
analysis, Equation (1) can be transformed into a double log linear
specification as follows:
Log [y.sub.1] = [alpha] + [beta]' + [x.sub.is] + [[mu].sub.is]
(2)
and [y.sub.i] = [y.sup.1/t.sub.t]), where [y.sub.i] refers to the
ith firm's sales for period t and [x.sub.is] is a previously
mentioned vector of variables [the human capital of the entrepreneur
(HC); the entrepreneur's strategy behavior (ST); external factors
or environmental influences ([E.sub.Regions] + [E.sub.Sector])].
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The empirical model specified in Equation (2) is estimated using
the Regression Quan-tiles (RQ) estimator as was introduced by Koenker
and Bassett (1978, 1982). Quantile regression allows the effects of
independent variables [x.sub.is] to be quantified at different points
along the conditional distribution of the dependent variable [y.sub.1].
Assuming that the distribution function F of y is continuous, this is
the [theta]th quantile (0 [greater than or equal to] [theta] [greater
than or equal to] 1), and [[PSI].sub.[theta]] is the value at which P(y
<[[PSI].sub.[theta]]) = F([[PSI].sub.[theta]]) = [theta]. While OLS
measures the effect of explanatory variables on the conditional mean of
y, quantile regression measures the effect at any point along the
conditional distribution, for example at the 50th percentile (i.e.,
median), 75th percentile, etc.
Mata and Machado (1996) point out a number of advantages of using
the RQ estimator instead of standard lest square regression models.
According to Gorg and Strobl (2002), one of the advantages is that RQ
enables different slope parameters to be estimated at different
quantiles along the conditional distribution of the dependent variable.
This may prove particularly valuable when estimating the effect of
industry covariates on the start-up size of firms, where one may expect,
for example, small firms (or entrants) to be affected differently by,
say, minimum efficient scale (MES) than large firms. This should then be
reflected in differences in the coefficients on the MES for the low and
high quantiles of the conditional distribution for start-up size.
There are also disadvantages associated with the RQ estimator.
Probably the main problem is that only asymptotic of the estimators are
known, which raises the issue of how parameters behave in finite
samples. This may not be too problematic in our case since we have a
large sample. Though estimating quantile regressions is computationally
demanding, this problem was less so because of the availability of
powerful computers and software programs such as Stata 9.0 which allow
estimates to be performed relatively easily.
4. Empirical findings
The results of estimating Equation (2) for two models in which
region variables are included where the company is located in Model 1
and GDP in Model 2 are reported in Table 5. For both models, the results
for five different quantiles of the size distribution are reported,
namely for the 0.1, 0.25, 0.5 (i.e. median), 0.75, and the 0.9
quantiles. Our choice of the lowest and highest quantiles, i.e., 0.10
and 0.90, was dictated by the nature of our data set.
Generally, an investigation of Table 4 reveals that there are
statically significant differences in the coefficients between and among
the various quantile regression estimates for most independent variables
(5). Specifically, it can be seen that the coefficients of the variables
are linked to the founder of the company. The coefficients of age,
experience, and the age*experience interaction varies significantly from
0.034 to -0.281, 0.042 to -0.779, and 0.010 to 0.132, respectively among
quantiles. In this respect, in the 0.1 and 0.25 quantiles, as the
manager's experience and age increases, firm growth increases,
although the effects that a combination of age and experience have are
negative. That is to say that the effects of these two variables help
the firm grow until the limits of age and experience are reached, with a
subsequent decline in growth.
However, in the 0.5 to 0.9 quantiles, the initial effects of age
and experience maintain an inverse relationship with growth, though the
combined effect of age and experience produces a positive relationship.
That is to say that starting at a limit, age and experience play a
relevant role affecting firm size. The gender variable is significant in
the 0.75 and 0.9 quantiles, indicating that firms created/managed by
women are smaller. As was expected, managers with university studies and
a larger vocation experience more growth than those who do not possess
these characteristics. Family firms have lower levels of growth linked
to the inferior quantile. These results allow us to accept hypotheses H1
and H2, therefore affirming that a positive relationship exists between
company size, human capital, and entrepreneur vocation.
However, the strategic factors that seek to capture entrepreneurial
behavior indicate that entrepreneurs who plan their decisions are
connected with companies of a larger size than those who do not plan.
This result is shown as statistically significant for all quantiles.
Likewise, there are no statistically significant differences for the
planning periods.
Similarly, managers who manifest to be in an unfavorable
competitive position run companies of a smaller size. However, when they
respond to the question regarding their cost strategy with respect to
the competition, the managers that indicated having smaller cost levels
are connected with larger companies compared to the rest. This fact is
reflected in the superior quantile (0.9). These results allow us to
accept hypothesis H3 although statistical significance is not shown in
all quantiles. The results reported in Table 4 are not as conclusive as
those with reference to human capital.
The explanatory factors of the environment do not have the same
importance as those relative to the human capital of entrepreneurs. As
is shown in the results for Model 1 (Table 5), only in the .75 quantile
is it reflected that entrepreneurs who perceive market expectations in
an expansionary way run larger firms compared to those with recessive
market expectations or who do not know how it will evolve. The companies
located in the counties of Salta, Cordoba, Neuquen, Misiones, and Buenos
Aires are larger than companies in San Juan. There are no statistically
significant differences with the rest of the counties. Finally, it
should be said that there are no significant differences at the sectoral
level.
With respect to Model 2, we observe the same behavior with regard
to the variables that measure entrepreneurial characteristics and
strategic behavior. The connection with LnGDP, the negative
coefficients, which is statistically significant at the 95% level, could
suggest that the economic situation in Argentinean firms as perceived by
entrepreneurs improves as GDP declines in the 0.9 quantile, and vice
versa in the 0.1 quantile. These results make it necessary to reject
hypothesis H4.
In sum, the determinant of firm size depends on entrepreneurs'
general and specific human capital and vocation as Becker (1964), Cressy
and Storey (1995), and Cooper (1981) have suggested. However, although
strategic factors and environment have had relative importance, they
were not as significant as those relative to entrepreneurship. In this
respect, authors like Mata and Machado (1996) and Gorg et al. (2000)
indicate the importance of industry characteristics in the determination
of size.
5. Conclusions
Four primary hypotheses are tested in this paper: a) Is the general
and specific human capital of the entrepreneur positively related to
firm size? b) Is the vocation of the entrepreneur positively related to
firm size? c) Does the specific definition level of the
entrepreneur's strategy (in terms of competitive price, knowledge
of competitors' price) have an important influence on firm size?
and d) Can location and type of industry be used as significant
variables to explain firm size?
A sample of 1314 firms in the manufacturing, agriculture,
construction, and service sectors operating in Argentina in 2002 was
used. The empirical answers provided by our analysis support the
theoretical proposition that the higher the degree of general and
specific Human this strongly related with the size of the firm. This
result is related to those reached by Mata (1996) and Almus (2002). A
positive effect of education on firm size has been extensively reported
(Cooper et al. 1994; Burke et al. 2002). Personality theories point to
the importance of personal predispositions for venture success. In this
context, a number of traits and motives of successful entrepreneurs have
been identified, but these concepts have typically produced weak
relationships with venture performance (Baum et al. 2001). However, our
results were able to confirm the positive influence of motivation on
firm size.
From a strategic point of view, we have been able to see the
importance that planning has on the resources of a company as well as on
its strategic behavior, concluding that larger firms are connected with
entrepreneurs who carry out an annual planning process compared to those
who do not plan. We have also found evidence that a better competitive
situation and knowledge of the competition is connected with larger.
This result is interesting because competitive strategies reflect the
choices of managers. Thus, the determinants of individual decision
making and behavior are among the determinants of strategy because
people choose plans in part on the basis of: a) what they are
predisposed to do, b) what they are motivated to do, and c) what they
think they can do (Bandura 1986; Hollenbeck and Whitener 1988).
As was mentioned earlier, the explanatory factors of the
environment have not had the same importance as those concerning the
human capital of entrepreneurs. The relatively low impact of the
environmental domain on firm size (market estimate, location, sectors)
is perhaps surprising. Although other studies have found similar results
in this direction, such as Baum et al. (2001). In our case, a possible
explanation could be derived from the widespread crisis that existed
during the period of analysis. However, De Jorge et al. (2007) indicated
differences in regional dynamism as well as the relevant heterogeneity
of the typology of different entrepreneurs in Argentina. In this
respect, future research should explore the role played by environment
on firm size and subsequent growth. It is also necessary to use
longitudinal data for firms in order to monitor their employment change.
Efforts should be made to further study the growth of new firms using
data sets that are as comparable as possible across different countries
in Latin America.
In terms of policy implications, these findings show that generic
public programs may not be the best way to increase the firm's
competitiveness. It would be better to design intervention strategies
targeted toward specific characteristics of firms. In the context of the
Latin American countries this is much more important since usually there
is a tendency to generate general incentive policies--at the sector or
regional level--that do not take into account the specific
characteristics of the firms (6).
doi: 10.3846/jbem.2010.13
Received 8 January 2010; accepted 15 April 2010
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(1) When it suffered severe contagion from the so-called Tequila
crisis.
(2) In this case, we have designed a specific survey to collect
information about the characteristics of the environment in which the
entrepreneurs operate, as well as their attitudes and behavior with
respect to the economy and the management of the company. The method
employed was a personal survey using a questionnaire that was specially
designed for our research and self-administered by the firms. The firms
were located within the borders of Argentina and came from all economic
sectors, with a workforce of between 1 and 250 employees (total
population 448.497 firms). The sample unit was the entrepreneur or
person designated by this figure, and from a planned sample of 2.300
firms of 1 to 249 employees, a real sample of 1.314 firms of under 250
employees was obtained.
The surveys were done by means of personal interviews to
entrepreneurship realized by auxiliary teachers and university students
advanced and coordinated by teachers in the following universities:
Universidad Nacional de Salta, Universidad Nacional de Jujuy,
Universidad Nacional de Tucuman, Universidad Nacional de Catamarca,
Universidad Nacional de Santiago del Estero, Universidad Nacional de
Buenos Aires, Universidad Nacional de Misiones, Universidad Nacional del
Nordeste, Universidad Nacional de San Juan, Universidad Nacional de
Cordoba, Universidad Nacional de Rio Cuarto, Universidad Nacional de la
Patagonia y Universidad Nacional del Comahue. The Association of
National Teachers of General Administration (ADENAG) was the institution
that helped in this project.
(3) Given the absence of official databases, we requested that the
Center for Statistical Services Special Works Division of the Argentine
National Institute of Statistics and Census--generate a database that
was specially designed for our research on the total population of
companies in the formal sector of the Argentine economy. The content of
the Report on Companies in Europe (Eurostat) was taken as its reference.
(4) The choice of this scope of analysis was motivated by our
interest in studying one of what is considered an emerging region (in a
particularly complex time period due to its substantial political and
economic instability), in contrast to those other studies referred to
above that center on regions.
(5) To further evaluate the importance of the differences in the
quantile parameter estimates presented in Table 4, we tested the
equality of coefficients for any two quantiles as well as jointly for
all quantiles. The tests were performed using the F-statistic. The
results confirm the importance of the variables related to human
capital, showing a major significance for most of the contrasts among
quantiles (seven contrasts), with the exception of the comparison
between the 0.1-0.25 and 0.75-0.9 quantiles (two contrasts). (The
results were not included to save space.)
(6) The OECD (2001) provides a framework allowing policy-makers to
identify strong and weak points in their country's business
environment. This report concludes that four micro-drivers (human
capital, information and communications technology, innovation and
entrepreneurship) are key drivers of performace and economic growth.
Justo de Jorge Moreno [1], Leopoldo Laborda Castillo [2], Elio de
Zuani Masere [3]
[1] Department of Economics and Business, University of Alcala,
Plaza de la Victoria, s/n Alcala de Henares, 28802 Madrid, Spain [2]
World Bank, Washington DC, USA [3] Department of Economics and Business,
National University of Salta, Salta, Argentina E-mails: [1]
justo.dejorge@uah.es; [2] llabordacastillo@gmail.com; [3]
erdezuani@yahoo.com.ar
Justo de JORGE MORENO. He is PhD from University of Alcala (Spain),
and professor of Business Sciences Department in Alcala. His work
focuses on problems of efficiency and productivity, entrepre-neurship
and growth, analysis of performance in Spanish retailing, and European
railway companies. He is author of articles and monographs on economic
and business about these issues. He has participated in diverse projects
on entrepreneurship for institutions in Spain (i.e Foundation Rafael del
Pino, City Council of Madrid) and International Organisms (i.e. IDB
Iberamericano of Development Bank)
Leopoldo LABORDA CASTILLO. He received his PhD. from University of
Alcala (Spain). His work focuses on problems of productivity and growth,
and he is author of articles and monographs on economic and business,
entrepreneurship, and entreprise restructuring in developing countries.
He works as a consultant at the World Bank and at the Inter-American
Development Bank.
Elio de ZUANI MASERE. He is PhD from University of Alcala (Spain),
and professor of Business Sciences Department in the National University
of Salta (Argentina). His work focuses on problems of business
organization and entrepreneurship, and he is author of articles and
monographs on economic and business, entrepreneurship, and entreprise
restructuring in public and private sector.
Table 1. Real GDP Growth Rate (Percentages)
Country 1991-97 1998 1999 2000-2001
Argentina 6.7 3.9 -3.4 -2.1
Bolivia 4.3 5.5 0.6 1.5
Brazil 3.1 0.2 0.8 3.1
Chile 8.3 3.9 -1.1 4.3
Colombia 4.0 0.5 -4.3 2.2
Costa Rica 4.9 8.4 8.2 1.3
Ecuador 3.2 0.4 -7.3 3.9
Mexico 2.9 4.9 3.8 3.3
Peru 5.3 -0.4 1.4 1.9
Venezuela 3.4 0.2 -6.1 3.3
Average 3.6 3.2 1.6 2.1
Source: World Development Indicators Database. World Bank (2010).
Table 2. Sample representativeness by province
Firms % of
No. Province Surveyed Sample
1 San Juan 102 7.76
2 Catamarca 110 8.37
3 Tucuman 105 7.99
4 Jujuy 110 8.37
5 Salta 97 7.38
6 Santiago del Estero 99 7.53
7 Chaco 97 7.38
8 Corrientes 95 7.23
9 Chubut 92 7.00
10 Cordoba 149 11.34
11 Neuquen 40 3.04
12 Rio Negro 53 4.03
13 Misiones 96 7.31
14 Buenos Aires 69 5.25
Totals 1314 100
Total % of Total
No. Province Population Population
1 San Juan 5204 1.96
2 Catamarca 1835 5.99
3 Tucuman 7565 1.39
4 Jujuy 3008 3.66
5 Salta 6303 1.54
6 Santiago del Estero 3204 3.09
7 Chaco 8312 1.16
8 Corrientes 5221 1.82
9 Chubut 6434 1.43
10 Cordoba 39315 0.38
11 Neuquen 4748 0.84
12 Rio Negro 6814 0.77
13 Misiones 6501 1.47
14 Buenos Aires 256245 0.02
Totals 360709 0.364
Table 3. Firm size: Descriptive statistics
Mean Standard Skewness Kurtosis Minimum
deviation
12.567 24.979 4.930 32.793 1
Lower Median Upper Maximum
quartile
2 5 142 245
Table 4. Quartile averages for key variables
10th 25th 50th
Variables
Age 43.21 44.63 44.86
(11.69) (11.71) (11.67)
Experience 12.07 13.58 13.91
(11.07) (11.54) (10.47)
Gender
Male 33.33% 26.18% 28.53%
Female 66.67% 73.82% 71.47%
Vocation
No 19.57% 21.46% 11.96%
Yes 80.43% 78.54% 88.04%
Education
University studies 31.16% 25.75% 28.53%
Others 68.84% 74.25% 71.47%
Family firm
No 62.32% 44.64% 44.23%
Yes 37.68% 55.36% 55.77%
Business Plan
No 26.09% 26.19% 30.43%
Yes 73.91% 73.82% 69.57%
Competitive position
Strong or very strong 26.08% 24.47% 28.81%
Critical, weak, average 73.92% 75.53% 71.19%
Market estimate
Expansive or stable 42.03% 38.2% 46.47%
Recessive or not known 57.97% 61.8% 53.53%
75th 90th
Variables
Age 48.20 48.84
(12.04) (11.87)
Experience 16.36 18.66
(11.65) (11.11)
Gender
Male 23.90% 16.36%
Female 76.10% 83.84%
Vocation
No 13.55% 8.33%
Yes 86.45% 91.67%
Education
University studies 38.65% 48.15%
Others 61.35% 51.85%
Family firm
No 46.22% 46.91%
Yes 53.78% 53.09%
Business Plan
No 26.30% 45.06%
Yes 73.7% 54.94%
Competitive position
Strong or very strong 30.28% 40.44%
Critical, weak, average 69.72% 59.56%
Market estimate
Expansive or stable 47.41% 55.56%
Recessive or not known 52.59% 44.44%
Table 5. Estimation results of firm size
Model 1
0.1 0.25 0.5
Variables
Constant -0 114 *** -0.278 *** 0.807 ***
0.056 0.102 0.106
Entrepreneurial characteristics
Lnage 0.034 ** 0.103 *** -0.123 ***
0.015 0.027 0.028
Lnexperience 0.042 * 0.094 ** -0.322 ***
0.024 0.040 0.042
Lnage x Lnexperience -0.010 * -0.031 *** 0.059 ***
0.006 0.010 0.011
Gender (male=1) -0.002 -0.009 -0.011
0.004 0.006 0.007
Vocation
(with vocation = 1) -0.002 0.003 0.019 ***
0.005 0.008 0.009
Education (university
studies = 1) 0.006 0.014 ** 0.015 ***
0.004 0.006 0.006
Family firm (family = 1) -0.012 *** -0.008 -0.006
0.004 0.006 0.006
Strategy
Business plan (four categories)
Quarterly 0.005 -0.005 -0.001
0.008 0.012 0.013
Monthly -0.0005 -0.007 -0.005
0.007 0.011 0.011
Improvised -0.015 *** -0.015 * -0.028 ***
0.006 0.009 0.009
Competitive position (five categories)
Weak 0.012 * 0.011 0.008
0.007 0.011 0.012
Average 0.013 ** 0.016 * 0.018
0.006 0.009 0.010
Strong 0.014 ** 0.008 0.006
0.006 0.010 0.011
Very strong 0.034 *** 0.011 0.020
0.012 0.023 0.025
Low-cost strategy (four categories)
Equal 0.005 0.017 0.027
0.007 0.027 0.029
Larger 0.009 0.022 0.032
0.007 0.027 0.029
Not known -0.009 -0.001 -0.007
0.009 0.029 0.031
Environment
Market estimate (four categories)
Stable -0.002 -0.007 -0.006
0.006 0.009 0.010
Recessive market -0.001 -0.011 -0.017
0.006 0.010 0.010
Not known 0.004 -0.001 -0.012
0.007 0.011 0.012
LnGDP -- -- --
Provinces (14 categories)
Catamarca -0.007 0.007 0.017
0.009 0.015 0.016
Tucuman -0.003 -0.007 0.02
0.010 0.014 0.015
Jujuy -0.012 -0.001 0.002
0.010 0.014 0.015
Salta 0.038 *** 0.055 *** 0.092 ***
0.01 0.015 0.016
Santiago del Estero 0.011 0.027 * 0.040 ***
0.011 0.016 0.017
Chaco -0.014 -0.009 0.011
0.009 0.015 0.015
Corrientes -0.0008 -0.011 0.009
0.01 0.015 0.016
Chubut 0.013 0.008 0.008
0.010 0.015 0.016
Cordoba 0.003 0.003 0.026 *
0.009 0.014 0.014
Neuquen 0.026 ** 0.036 * 0.025
0.013 0.02 0.021
Rio Negro 0.015 0.01 0.006
0.012 0.017 0.019
Misiones 0.002 0.006 -0.0005
0.011 0.015 0.016
Buenos Aires 0.006 0.008 0.028
0.011 0.017 0.017
Sectors (four categories)
D_Industrial 0.012 0.018 0.015
0.009 0.015 0.015
D_Construction 0.031 ** 0.023 0.022
0.013 0.021 0.022
D_Services -0.007 -0.0009 -0.003
0.008 0.013 0.014
F ([[beta].sub.i] = 0) 2.91 ** 16 79 *** 259.56 ***
F (equality) 4 59 *** 21 74 ***
No. observations 1302 1302 1302
Model 1
0.75 0.9
Variables
Constant 1.733 2.642 ***
0.233 0.465
Entrepreneurial characteristics
Lnage -0.281 *** -0.259 **
0.069 0.127
Lnexperience -0.676 *** -0 779 ***
0.090 0.188
Lnage x Lnexperience 0.131 *** 0.132 ***
0.023 0.048
Gender (male=1) -0.034 ** -0.065 **
0.015 0.032
Vocation
(with vocation = 1) 0.034 * 0.067 *
0.019 0.037
Education (university
studies = 1) 0.022 0.036
0.014 0.028
Family firm (family = 1) -0.005 -0.006
0.013 0.026
Strategy
Business plan (four categories)
Quarterly -0.056 ** -0.075
0.027 0.055
Monthly -0.039 -0.043
0.024 0.051
Improvised -0.076 *** -0.122 ***
0.020 0.042
Competitive position (five categories)
Weak 0.014 0.016
0.025 0.048
Average 0.013 0.007
0.020 0.041
Strong -0.005 0.011
0.023 0.046
Very strong -0.001 -0.084
0.052 0.106
Low-cost strategy (four categories)
Equal 0.040 -0.529 ***
0.061 0.119
Larger 0.047 -0.526 ***
0.060 0.117
Not known 0.050 -0.496 ***
0.064 0.129
Environment
Market estimate (four categories)
Stable -0.022 -0.015
0.021 0.043
Recessive market -0.043 ** -0.011
0.022 0.045
Not known -0.051 ** -0.010
0.025 0.052
LnGDP -- --
Provinces (14 categories)
Catamarca 0.017 0.016
0.033 0.048
Tucuman 0.054 0.007
0.033 0.041
Jujuy -0.001 0.011
0.032 0.046
Salta 0.156 *** -0.084
0.032 0.106
Santiago del Estero 0.070 ** -0.014
0.036 0.070
Chaco 0.033 0.010
0.033 0.064
Corrientes 0.030 -0.074
0.033 0.063
Chubut 0.024 0.099
0.032 0.067
Cordoba 0.059 ** 0.016
0.03 0.079
Neuquen 0.017 0.005
0.043 0.065
Rio Negro -0.002 0.010
0.039 0.070
Misiones 0.065 ** -0.065
0.033 0.067
Buenos Aires 0.043 0.174 **
0.036 0.075
Sectors (four categories)
D_Industrial 0.027 -0.027
0.031 0.060
D_Construction 0.018 -0.050
0.046 0.090
D_Services 0.004 0.052
0.028 0.055
F ([[beta].sub.i] = 0) 183.50 *** 88.16 ***
F (equality) 12.63 *** 5.20 ***
No. observations 1302 1302
Model 2
0.1 0.25 0.5
Variables
Constant -0.146 *** -0.281 *** 0.778 ***
0.050 0.103 0.106
Entrepreneurial characteristics
Lnage 0.036 *** 0.109 *** -0.110 ***
0.013 0.027 0.028
Lnexperience 0.057 ** 0.100 *** -0.298 ***
0.022 0.041 0.042
Lnage x Lnexperience -0.014 ** -0.033 *** 0.054 ***
0.006 0.011 0.011
Gender (male=1) 0.001 -0.009 -0.013 *
0.004 0.007 0.007
Vocation
(with vocation = 1) -0.002 0.002 0.021 ***
0.005 0.009 0.009
Education (university
studies = 1) 0.005 0.014 ** 0.014 **
0.004 0.006 0.007
Family firm (family = 1) -0.014 *** -0.013 ** -0.009
0.004 0.006 0.006
Strategy
Business plan (four categories)
Quarterly -0.007 -0.002 0.002
0.007 0.012 0.013
Monthly -0.018 *** -0.015 0.005
0.006 0.011 0.012
Improvised -0.026 *** -0.025 *** -0.024 ***
0.005 0.009 0.010
Competitive position (five categories)
Weak 0.015 *** 0.011 0.014
0.006 0.011 0.012
Average 0.019 *** 0.016 * 0.021 **
0.005 0.009 0.010
Strong 0.017 *** 0.008 0.012
0.006 0.010 0.011
Very strong 0.049 *** 0.011 0.035
0.013 0.023 0.025
Low-cost strategy (four categories)
Equal 0.017 0.017 0.033
0.015 0.027 0.029
Larger 0.021 0.022 0.034
0.014 0.027 0.029
Not known 0.011 -0.001 0.016
0.015 0.029 0.030
Environment
Market estimate (four categories)
Stable -0.007 0.009 -0.005
0.005 0.028 0.010
Recessive market -0.007 0.010 -0.008
0.006 0.027 0.010
Not known -2.4 E04 -0.006 -0.009
0.006 0.029 0.012
LnGDP 0.013 ** -0.002 -0.002
0.005 0.010 0.011
Provinces (14 categories)
Catamarca
Tucuman
Jujuy
Salta
Santiago del Estero
Chaco
Corrientes
Chubut
Cordoba
Neuquen
Rio Negro
Misiones
Buenos Aires
Sectors (four categories)
D_Industrial 0.016 *** 0.018 0.005
0.008 0.015 0.015
D_Construction 0.035 *** 0.023 0.011
0.011 0.021 0.022
D_Services -0.003 -0.0009 -0.023 *
0.007 0.013 0.014
F ([[beta].sub.i] = 0) 917 *** 16 79 *** 14.67 ***
F (equality) 4 59 *** 24.18 ***
No. observations 1302 1302 1302
Model 2
0.75 0.9
Variables
Constant 1 427 *** 3.070 ***
0.214 0.326
Entrepreneurial characteristics
Lnage -0.151 *** -0.321 **
0.057 0.088
Lnexperience -0 494 *** -0.855 ***
0.084 0.138
Lnage x Lnexperience 0.082 *** 0154 ***
0.022 0.035
Gender (male=1) -0.041 ** -0.068 **
0.015 0.023
Vocation
(with vocation = 1) 0.037 *** 0.071 ***
0.018 0.027
Education (university
studies = 1) 0.021 * 0.042 ***
0.013 0.019
Family firm (family = 1) -0.002 -0.016
0.012 0.019
Strategy
Business plan (four categories)
Quarterly -0.055 ** -0.084 ***
0.025 0.038
Monthly -0.054 *** -0.062 *
0.023 0.035
Improvised -0.089 *** -0.108 ***
0.019 0.029
Competitive position (five categories)
Weak 0.016 0.029
0.023 0.036
Average 0.016 0.011
0.019 0.030
Strong 0.017 0.015
0.021 0.033
Very strong 0.039 -0.064
0.047 0.060
Low-cost strategy (four categories)
Equal -0.060 -0.614 ***
0.056 0.083
Larger -0.057 -0.614 ***
0.055 0.081
Not known -0.049 -0 549 ***
0.058 0.084
Environment
Market estimate (four categories)
Stable -0.014 0.002
0.020 0.029
Recessive market -0.028 -0.006
0.020 0.030
Not known -0.029 -0.026
0.023 0.035
LnGDP -0.018 -0.075 **
0.021 0.032
Provinces (14 categories)
Catamarca
Tucuman
Jujuy
Salta
Santiago del Estero
Chaco
Corrientes
Chubut
Cordoba
Neuquen
Rio Negro
Misiones
Buenos Aires
Sectors (four categories)
D_Industrial -0.002 -0.006
0.030 0.047
D_Construction -0.017 -0.010
0.042 0.066
D_Services -0.012 0.066
0.027 0.043
F ([[beta].sub.i] = 0) 17 15 *** 10.52 ***
F (equality) 13 24 *** 5 67 ***
No. observations 1302 1302
Note: Standard errors in the second line of each value of the
estimated coefficient. ***, **, * represent significance at 1%, 5%,
and 10%, respectively. Omitted group: males, no vocation, without
university studies, non-family firm, annual planning, very weak
competitive position, smaller levels of costs, expansive market,
county of San Juan, agriculture sector.
