Project Center, Graduate School of Economics, Kyoto University, Working Paper No.10-12

Cultural determinants of countries management efficiency:
A random coefficients stochastic frontier approach

Maria del Pilar Baquero Foreroa, Toshifumi Kurodab, and Takanori Idaca

Corresponding author. Affiliation: Graduate School of Economics, Kyoto University.

Address: Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Telephone number: +81-80-3789-4707.
E-mail address: baquero.maria@ax7.ecs.kyoto-u.ac.jpbAffiliation: Faculty of Economics, Tokyo Keizai University.

cAffiliation: Graduate School of Economics, Kyoto University.

Cultural determinants of countries management efficiency:
A random coefficients stochastic frontier approach

Abstract
From a country’s perspective, management refers to the organization of inputs,
such as national capital or labor. In this paper, we investigate i) the mechanism inwhich countries management impacts national income, and ii) the cultural sources ofdifferent management levels among countries. We found that countries management
mainly affects income due to its interaction with physical capital, rather than throughits relation with labor or education. Furthermore, management levels are shown to bepositively correlated to language and religious homogeneity, the existence of Britishstyle institutions and the degree of individualism. Our methodology is twofold. First,
using data of 62 countries from 1980 to 2004, we estimated a management input without the use of proxies. For this purpose, we employed a stochastic production frontierwith random coefficients. Second, we regressed by OLS the estimated management
input on cultural variables, for a sub-sample of 33 countries with available culturaldata.
Key words: Random coefficients stochastic production frontier, countries
management, language and religious diversity, British-style institutions,individualism, JEL CODE O43 - Institutions and Growth

Preprint submitted to Elsevier

December 13, 2010

Cultural determinants of countries management efficiency:
A random coefficients stochastic frontier approach

1. Introduction
Management refers to the planning, organization and monitoring of other inputs
of production, such as physical capital, labor or human capital. The importance ofcontrolling for management in the estimation of production or cost functions of firms,
is well known. Since management is unobservable, earlier approaches to control for
it include the use of proxy variables [Mefford, 1986; Dawson et al., 1985], covarianceanalysis or within transformations. However, since imposing strong assumptions may
yield biased results, recent work has analyzed firms management by using a methodthat does not require the use of proxies [Barros et al., 2008; Alvarez et al. , 2004;
Alvarez et al., 2003].
Although, at the micro-level, there are a number of studies regarding the role ofmanagerial skills on cost performance, output and efficiency of firms, at the macro level
there is a lack of research on the role of countries management on national outcome
and productivity. Consequently, the first objective of this paper is to study the mechanisms through which management affects countries output and technical efficiency. We
analyze, in particular, the interaction between management and other production inputs
such as physical capital, labor and human capital.
For this first purpose, we employed a random coefficients stochastic production
frontier model[Tsionas, 2002; Greene, 2005]. The stochastic production frontier includes a management input, without the need for proxy variables [Alvarez et al. ,2004]. The assumptions placed on management are that (i) it is a normally distributed
random variable, (ii) it interacts with the rest of the production inputs (K,L,E) and (iii)
it also appears in the model with a squared term. We estimated this model using data

Preprint submitted to Elsevier

December 13, 2010

of 62 countries from 1980 to 2004, and we found that country-level management affects income mainly due to the interaction with physical capital, rather than throughthe relation with labor or education. Our results suggest that countries can boost theirproductivity if they dedicate special effort to improve the management of physical capital.
The second aim of this paper is to investigate the underlying cultural factors that determine different levels of management among countries. Although previous research
has focused on the direct relationship between income differences and cultural or institutional variables such as ethnolinguistic fractionalization [Alesina et al. , 2005], governance[Huynh et al., 2009], market creating, stabilizing, regulating and legitimising
institutions [Bhattacharyya , 2009] , British institutions, trade and geography, [Rodrik
et al., 2004], private property (British) and extractive (Spanish) institutions [Acemoglu
et al. , 2002, 2001], antidiversion policies [Hall et al., 1999]; or on the relationshipbetween general values and economic systems [Pryor , 2008], or between individualism and British institutions, and governance [Licht et al. , 2007], to the best of ourknowledge, this is the first study that attempts to unveil the relationship between cultural factors and country-level management differences. The overall motivation of thispaper is that disentangling the relation between management and other inputs, as wellas understanding the cultural determinants that affect national management can help
countries issuing policies to increase their management level and therefore their productivity.
Concerning the second goal, our paper found that management efficiency is positively correlated to countries cultural variables such as language and religious homogeneity, as well as the existence of British-style institutions and the degree of individualism in the society. An OLS regression shows that the above cultural variables explain
around 50% of the difference in management levels accross countries. Our results imply that the more linguistic and religious diversity, the larger the transaction costs tomanage firms, create institutions or form social networks that contribute to a country’s
general output. In contrast, British-style institutions encourage a more productive useof physical capital mainly because it guarantees private property; and individualism
implies a higher working motivation that leads to more efficient management of human3capital. After controlling for cultural variables, geography had no significant impact onmanagement. These results are similar to those of previous research studying the effectof geography on income after controlling for macroeconomic or institutional variables[Rodrik et al., 2004; Acemoglu et al. , 2001].
This paper is organized as follows. Section 2 summarizes the relevant literature on
management and on the relationship between culture, institutions and income. Section
3 explains the random-coefficients stochastic production frontier with a fixed management parameter. Section 4 presents the employed database and the estimated empiricalmodels. Section 5 analyzes the estimation results concerning management, technicalinefficiency and its relationship with cultural variables. Finally, Section 6 gives theconclusions and final remarks.
2. Relevant Literature and Contributions
2.1. Fixed Management
Incorrect specification of a production function, such as a model excluding management levels, leads to omitted-management-variable bias. This bias occurs because
management is, not only, a determinant of output, but also, is likely to be correlated toother inputs of production. However, a major issue is that management is unobserved
and the use of assumptions or proxies brings inaccuracy and measurement error. Because of this additional error, there is a need to control for management in a different
way. A novel econometric approach has been employed by recent studies to measure
the impact of management on firms output or cost functions without using proxy variables. In this paper, we followed this method, known as random coefficients stochastic
frontier approach [Tsionas, 2002; Greene, 2005] with a fixed management parameter[Alvarez et al. , 2004].
Using this methodology, the firm-related literature has reached the following conclusions about management. Barros et al. [2008] analyzed the impact of managementon the cost function of 117 airports from 2001 to 2004 and showed that management
contributes to cost control, as do regulatory procedures, such as the rate of return or
incentive regulation. Alvarez et al. [2004] examined the effect of management on
the production function of 247 farms in Spain from 1993 to 1998 and found that (i)4management has a positive but decreasing effect on production, and (ii) not accounting
for management leads to overestimation of firms technical inefficiency. Lastly, using
data of 84 farms in Spain from 1987 to 1991, Alvarez et al. [2003] pointed out thatan increase in farm size while holding managerial ability constant can be an importantsource of diseconomies of size, and can lead to a decrease in firms profits. Thus, theseprevious studies emphasize the importance of controlling for management and show
empirical evidence of the impact of management on the productive activities of firms.
2.2. Culture and economic output
This paper postulates that culture impacts a country’s income because it influences
the management of physical capital, labor and human capital. Although previous literature shows that the prevalence of institutions and cultural values influences economic
development [Licht et al. , 2007]; that sets of national values match the specific economic systems (institutions) of OECD countries [Pryor , 2008]; and that cultural factors affect economic performance through their impact on organization of firms, theattitudes towards consumption and work, and the creation of institutions and social
networks [Fukuyama , 2001]; there are no studies that analyze empirically the specificrole of culture on national management levels.
In particular, we make three hypotheses related to the cultural variables under study.
First, we infer that religious and linguistic diversity affect economic growth by meansof the national management input, because the more linguistic and religious heterogeneity, the larger the transaction costs. Related literature has found that increasing
ethnolinguistic heterogeneity, as well as ethnic fractionalization, reduces the growthrate of countries [Alesina et al. , 2005], but it does not analyze the impact on national
management.
Second, we hypothesize that British-style institutions impact countries management because they guarantee private property and this encourages a more productive
use of physical capital. Related studies indicate that, in general, there is a positive effect of British rule on governance, economic performance and efficiency [Licht et al. ,2007], and specifically, on financial development [Dehejia et al., 2005] and favourable
investment environment [Acemoglu et al. , 2002; Dehejia et al., 2005] which is vi-

5tal to increase productivity, output growth and capital stock at the firm level 1 [Dollar
et al. , 2005]. Nevertheless, there is no previous study on the relationship between
British-style institutions and management.
Third, we expect individualism to affect countries management level, because it
is likely to contribute to higher working motivation and to lead to more efficient administration of human capital 2 . This idea complements the study by Licht et al.
[2007] which argues that autonomous individuals are important to achieve higher economic performance, because autonomy leads to democratic accountability and, in turn,democracy and rule of law exert a positive influence on economic development.
3. Stochastic production frontier approach
In order to estimate the national management input and per country technical efficiency, we employed a random coefficients stochastic production frontier model which
includes a random management parameter. After the unobservable fixed management
input was estimated, it was regressed by OLS on cultural variables such as language
and religious diversity, British-style institutions and individualism.
The broad stochastic frontier approach (SFA) is a method for estimating frontierfunctions, in our case of production, and measuring productive or Technical Efficiency(TE). Technical Efficiency (TE) is the degree of success countries achieve in allocating
resources, by obtaining maximum outputs from given inputs [Kumbhakar et al., 2000].
TE is defined in terms of distance to the production frontier. To measure TE using
the SFA, we add to the production function a random error vi and an additional nonnegative random variable ui that accounts for technical inefficiency.
A common assumption, in the SFA framework, has been that the inefficiency term
ui includes, among other things, differences in the level of management across countries. However, Alvarez et al. [2004] introduced an additional fixed input, mi , to themodel to capture managerial ability. This management input is assumed to be a random effect, distributed standard normal, and is part of the stochastic element of the
production function. The unobservable maximum level of management m∗i can be inserted as a random effect in a panel data model with a trans logarithmic function andmultiple inputs in the following way:6

∗Kln yit =α + βm m∗i + 0.5βmm m∗2
i + Σk=1 βk + βkm mi ln xitk

(1)

K
K
+ 0.5Σk=1
Σl=1
βkl ln xitk ln xitl + vit − uit

where ln yit is the natural log of the observed output for the ith country at the tth time
period; α is a constant; m∗i is the maximum level of management which appears (i)independently, (ii) multiplied by each of the other production inputs k and (iii) in aquadratic form, where a negative sign indicates that management has a positive but
decreasing effect on production; ln xitk is a (1xK) vector of the natural log of the production inputs k of the ith country at the tth time period; β is a (K x1) vector of unknown
scalar parameters to be estimated; and vit , or random error, is distributed i.i.d (0, σ2v ).
It encompasses measurement error and other random factors that affect the output ln yit
and accounts for the combined effects of unspecified input variables in the production
function.
uit is a non-negative variable, associated with technical inefficiency in the production process. It is the amount by which the sample countries fail to reach the frontier.
For the ith country, the time-variant technical inefficiency effects, uit , are independently
N + (µ, σ2u ) distributed with truncation point at 0. In addition, uit corresponds to thestandard technical inefficiency definition in the SFA framework, so that T E = exp uit. We assume absence of correlation between uit and the input levels (random-effects
assumption).3 Additional characteristics of the model are that (i) (βmm ) and first orderterms (βm ) and [ΣKj=1 (βk )] are random normally distributed; and (ii) the random component of each random parameter is the same, m∗i . This means that each country has its
own production function with parameters βk that imply heterogeneity of countries intheir technology and parameters(βm ), (βmm ) that reflect management variation among
countries. This specification makes it feasible to separate technical inefficiency fromtechnological differences and managerial heterogeneity across countries.
The marginal impact of management in this model is:∂ ln yit= βm + βmm m∗i + βkm ln xitk > 0
∂m∗i

7

(2)

and the link between TE and management is:
ln T Eit = ln yit − ln yit ∗ = (βm + βkm ln xitk ) mi − m∗i + 0.5βmm [(mi )2 − m∗i 2 ](3)where ln yit ∗ is maximum output for given xitk achieved with the maximum level ofmanagerial input m∗i . As shown by the above equation, the Technical Efficiency (TE)
is composed by (i) an individual time-invariant effect: (βm ) mi − m∗i + 0.5βmm [(mi )2 − 2m∗i ] and (ii) a time-varying component: (βkm ln xitk ) mi − m∗i . This specification implies that the change in managerial input necessary to increase TE, by a given amount,differs according to input use. Therefore, the effect of changes in management andinput use on TE can be written as:∂ ln T Eit= βm + βkm ln xitk + βmm mi
∂mi

(4)


∂ ln T Eit= βkm mi − m∗i∂ ln xitk(5)

Since ∂ ln T Eit /∂mi = ∂ ln yit /∂mi , an increase in management increases TE given
the other inputs, if the production function is monotonic in managerial ability. Consequently, the model shows that (i) TE is not a fixed effect and can vary over timeand (ii) the relationship between TE and management depends on the amount of the
management input and the rest of the inputs.
The joint density for T observations on country i, or the contribution to the conditional likelihood for country i, Li |m∗i , is defined as:f (i1 , ..., iT |m∗i ) = ΠTt=1 f (it |m∗i )and the unconditional contribution to the likelihood function can be written as:
Z


Li =
ΠTt=1 f it |m∗i g m∗i dm∗i

(6)

(7)

m∗i

 
where g m∗i is the marginal density of m∗i . The log likelihood is:N
log L (δ) = Σi=1
log Li (δ)

where δ denotes the full vector of parameters in the model

8(8)

The maximum likelihood estimates of the parameters are obtained by maximizing
equation 8 with respect to δ. Since the integral in equation 7 does not have a closed
form, it is not possible to maximize equation 8 directly. Therefore, the maximum
likelihood is simulated. In order to achieve a good approximation to the true likelihood
function, a large number of random draws are required. The process can be accelerated
by using intelligent draws, such as Halton sequences. In this paper we have used 300
Halton draws.
In order to estimate the efficiency of individual countries the best predictor for uit is
the conditional expectation of uit , given the value of the compound error: it = vit − uit .
The conditional expectation is used since uit is unobservable. Even if the true value ofthe parameter vector β in the stochastic frontier model was known, only the difference,it = vit − uit , could be observed. For the particular distributional assumptions imposedon the technical inefficiency effects, the expected value of T Eit can be calculated byusing the equation by Jondrow et al. (1982):  ( |m∗ )λ    ϕ − it i
∗


|m
σ
it i λ σλ 

E uit |it , m∗i =
−



σ
1 + λ2  φ − (it |m∗i )λ

σ

(9)

where ϕ (·) and φ (·) denote the density and cumulative density function of the standard
normal variable, respectively; and
λ=



σu
; σ = σ2u + σ2v
σv

(10)

4. Models Specification and Panel Data
The main model in this paper is a random coefficients stochastic production frontier with a management parameter. For comparison we also show the results, using
the same data, of a non random coefficients stochastic production frontier without the
management input. The two models assume random-effects, or absence of correlation
between uit and the input levels, and estimate a trans logarithmic production function.
The trans logarithmic is a flexible functional form, because it does not impose any
assumptions about elasticities of production nor elasticities of substitution between
production inputs. In addition to the inclusion of the management input, the random
9coefficient model relaxes the restrictive assumption imposed by the non random coefficients model that all countries share the same technological possibilities [Tsionas,
2002].
The estimated random coefficients model with four production inputs, physical capital stock K, labor force L, education E and management M, is of the form:ln yit =α + βK ln Kit + βL ln Lit + βE ln Eit + βKL ln Kit ln Lit +βKE ln Kit ln Eit + βLE ln Lit ln Eit + 0.5βKK ln Kit2 +(11)

0.5βLL ln Lit2 + 0.5βEE ln Eit2 + βK M ln Kit m∗i +βLM ln Lit m∗i + βEM ln Eit m∗i + β M m∗i + 0.5β M m2∗
i + vit − uit
The estimated non random coefficients model, with the three conventional inputs(K, L, E) and without management M, is of the form:ln yit =βi + βK ln Kit + βL ln Lit + βE ln Eit + βKL ln Kit ln Lit +βKE ln Kit ln Eit + βLE ln Lit ln Eit + 0.5βKK ln Kit2 +(12)

0.5βLL ln Lit2 + 0.5βEE ln Eit2 + vit − uiIn order to estimate the above models, we used a panel data set of 62 countries, from1980 to 2004, with available data on output and inputs of production. The list of oursample countries is given in Table 1. The countries marked with (*) are a subsample of
33 countries with available data on the cultural variables used for the OLS regressionof management.
INSERT TABLE 1 HERE
The output used, in the production frontier, is the real gross domestic product inconstant 2000 US Dollars and was taken from the World Development Indicators [WDI
, 2007]. The input data was obtained as follows. The physical capital stock in 2000 US
Dollars, from 1980 to 2000, was calculated based on the physical capital to output ratio
obtained from Klenow et al. [1997, p.78]; and, from 2001 to 2004, it was calculated
based on the methodology described in Klenow et al. [1997, p.78] and using the annual
gross capital formation data in 2000 US Dollars [WDI , 2007]. The total labor force,10

or economically active population, was extracted from the WDI [2007]. Finally, thehuman capital, defined as years of educational attainment for population aged between
15 and 64 was taken from the Barro et al. [2001] database. The data employed in
the stochastic production function, both inputs and output, were normalized aroundthe sample mean before applying the logarithmic transformation, in order to define thepoint of approximation to the trans logarithmic function[Chung , 1994].
After having estimated the random coefficients stochastic frontier and obtained the
management input per country, the following OLS regression of management on cultural variables was performed for the sub-sample of 33 countries, with available cultural data, and marked with (*) in Table 1:
M̂anagement = β0 +βLanguage.Diversity +βBritish.S tyle.Instit. +βReligious.Diversity +βIndividualism +
(13)
The definitions and sources of the cultural variables used in the OLS regression
are as follows. The muller index is defined as the probability from 0 to 1 that two
randomly selected individuals in a country speak different languages [Muller, 1964].
British-style institutions is a dummy variable that takes the value of 1 if the country isGreat Britain or was a British colony, and therefore has British-style institutions, and0 otherwise ( Rodrik et al. [2004] argues that colonial past is a valid instrument for
current institutions). Religious fractionalization, ranging from 0 to 1, was calculated as
1 − Σs2i j where s is the share of religious group i in country j. The data was taken from
Alesina et al. [2005]. Individualism (100) versus collectivism (0) measures the degree
to which individuals are integrated into, or define themselves apart from, groups suchas extended families that provide protection in exchange for loyalty. It was taken fromthe Geert Hofstede Cultural Dimensions [Hofstede , 2009].
INSERT TABLE 2 HERE
An additional regression was estimated to test whether geographic conditions havea significant impact on countries management input level. The regression is:M̂anagement = β0 +βT ropical.Dummy +βLanguage.Diversity +βBritish.S tyle.Instit. +βReligious.Diversity +βEmphasis.T echnology +
(14)

11

The tropical dummy takes the value of 1 if the country has more than 50% of its
land mass between the tropics of cancer and capricorn and 0 otherwise. The source isthe 1986 list of the International Union of Conservation of Nature. Emphasis on technology, extracted from the 1981-2008 aggregate of the World Values Survey [2009],
indicates the importance placed by a country’s population on future technology development. All the models, including the random and non random coefficients stochastic
frontier and the OLS regressions, were estimated using Limdep 9.0.
5. Estimation Results and Discussion
Except for their composed error term, the random and non random coefficientsstochastic production frontier models are linear regression models. Therefore, themarginal effects in the production function are the β parameters. In both models, theestimated constant β0 and the first-order coefficients -the parameters of physical capital
βK , labor force βL and educational attainment βE - have the expected positive signs andare statistically significant at the 1% level. All the second-order parameters are also
statistically significant.
INSERT TABLE 3 HERE
Concerning the random coefficients model, the three interaction coefficients between the traditional inputs and the unobservable fixed management β MxK , β MxL andβ MxE are positive. This means that an increase in fixed management raises the contribution to output of the other three factors of production K, L and E. According to our
results, an increase in management raises the contribution of physical capital (0.19) the
most, followed by labor (0.08) and human capital (0.01). As the fixed management
input augments, it exhibits a decreasing marginal contribution to output as shown by
the negative coefficient of αmm (-0.09).
In the non random coefficients model, the estimated parameter of the capital factor
is underestimated (0.64) when compared to the random coefficients model result (0.72).
In contrary, the estimated parameters of the remaining factors are overestimated by thenon random coefficients model, labor (0.45) and human capital (0.23), in comparison
with the results of the random coefficients model (0.29 and 0.11 respectively). The

12difference in the results of both models can be explained as follows. When the unobservable fourth production input or fixed management is not estimated and countries
are assumed to have the same technology, the effect of management and technology isallocated to the labor and human capital factors. However, when estimated separately,
the heterogeneity in management and technology lowers the contribution of labor andhuman capital and increases the one for capital.
INSERT TABLE 4 HERE
Table 4 shows a summary of basic statistics of TE per country estimated by the
random and non random coefficients production frontiers, as well as the fixed management estimated by the random coefficients model. According to Table 4, the randomcoefficients model efficiency ranking ranges from 0.93 to 0.80, while the ranking of
the non random coefficients model lies within a much wider range, between 0.98 and0.18. These results suggest that when the management input is not taken into accountand the assumption of homogeneous technology among countries is imposed, the estimated country inefficiency is overestimated. Therefore, we can interpret the inefficiency component in a stochastic frontier model as being composed by management
and technology effects, as well as other unexplained factors.
INSERT TABLE 5 HERE
Finally, the 1980-2004 average marginal impact of fixed management on output per
country ∂ ln yit /∂m∗i (See equation 2) is shown on Table 5. As expected from the model
specification, countries with larger amounts of inputs of production, such as developed
countries, obtain a higher marginal output from management.
Once the management input per country was estimated, we used an OLS regression on the cultural variables expected to impact national economic output through theorganization of inputs of production. The results of the main OLS model are shownin Table 6. The coefficients of the cultural variables in the regression are statisticallysignificant, have the expected signs and in total explain about 50% of the management
input (the adjusted R-squared is 0.46).
INSERT TABLE 6 HERE
The negative coefficients of the Muller Index (-0.72) and Religion Franctionalization (-1.61) indicate that the more languages or religions that coexist in a single coun13

try, the less management a given country will have as a production input. The reason isthat the more linguistic and religious diversity it exits, the larger the transaction costs
to manage firms and institutions that contribute to a country’s output. These results
are consistent with the study by Alesina et al.

[2005], which found that increased

ethnolinguistic fractionalization reduces countries growth.
On the other hand, the estimated positive coefficient of the individualism index(0.009) confirms our hypothesis that individualism encourages a higher working motivation that leads to more management input directed to human capital. This result
supports the findings of previous research showing that the place of individuals in thegroup as autonomous is important for economic development, because higher autonomy implies more democratic accountability [Licht et al. , 2007].
The positive parameter of the British-style institutions dummy variable (0.71) also
supports our inference that British rule encourages a more productive use of physicalcapital and a higher management input because it guarantees private property. This
results complements the findings of previous studies on the relation between British
institutions and governance [Licht et al. , 2007], as well as British institutions andinvestment incentives and economic performance4 [Acemoglu et al. , 2002, 2001].
After controlling for language and religious diversity, British-style institutions andthe emphasis on technology of the society, geography (defined as a Tropical dummy
variable) has no significant impact on management. The results of this additionalmodel, shown in Table 7, are similar to those of previous research that state that there isno significant effect of geography on income after controlling for institutional variables[Rodrik et al., 2004; Acemoglu et al. , 2002].
INSERT TABLE 7 HERE
6. Conclusions
Since countries management is likely to be correlated with both output and other
inputs, not accounting for it in the production function yields biased results. However, management cannot be included directly into the production function because it
is unobservable, and the use of proxies causes measurement error. Moreover, although
there are a number of studies on the role of management on cost performance, output
14

and efficiency of firms, there is no previous research on management at the country
level. For these reasons, this paper analyzed the impact of national management on
countries output, as well as on physical capital, labor and human capital. By means of
a novel econometric method, that does not require the use of proxies, we estimated arandom coefficients stochastic production frontier with a fixed management input for
62 countries for the period 1980-2004.
Our results offer empirical evidence on the bias caused by the lack of control ofcountry-level management and the restriction on technology heterogeneity of countries
in the production function. For our sample data, the bias consisted on underestimationof the physical capital coefficient and overestimation of the labor and human capital
parameters, as well as overvaluation of countries technical inefficiency. Additionally,
we showed that country-level management affects national income mainly due to its
interaction with physical capital, rather than through its relation with labor or education. Therefore, countries should particularly direct their policy efforts to improving
the management of physical capital in order to increase their output.
The second goal of this paper was to shed light on the cultural determinants ofvarying management levels among countries. OLS analysis showed that management
efficiency is positively correlated to cultural variables such as language and religious
homogeneity, the existence of British-style institutions and the degree of individualism.
Our results suggest that linguistic and religious diversity increase the transaction costs
to manage firms, create institutions and form social networks that contribute to national
output. In addition, individualism encourages higher working motivation, which leads
to a more efficient management of human capital; and British-style institutions affectpositively the management decisions in a given country, since it guarantees private
property. Therefore, in order to improve their management efficiency, countries should
issue policies to reduce the transaction costs derived from ethnolinguistic heterogeneity, promote the working motivation of its labor force by encouraging individual behaviour and guarantee private property to increase national investment projects.
Lastly, a number of issues are left for future research. In this paper, management
was treated as an exogenous variable. However, a further analysis should attempt tohandle management as an endogenous input in the production function. In addition, fu15

ture research on this topic should study the impact on national management of historicalfacts such as countries independence, democratization, transition to market economy
or membership of economic or political zones such as the European Union, among
others. Finally, it would be interesting to test whether factors of production, such as
physical capital and labor, move from countries with lower management levels, andtherefore lower productivity, to countries with higher management levels.
Notes
1 For instance, the lack of guarantee of private property in Colombia is shown to be one of the causes oflow farms efficiency [Gonzalez et al., 2007].
2 Lack of autonomy, or embededness, has been found to be negatively correlated to the Western European
economic system [Pryor , 2008]
3 The explicit definition of is u is:
it
 2



K
(βkm ln xitk ) m∗i − mi + 0.5βmm m∗i − (mi )2
uit = ln y∗it − ln yit uit = βm + Σk=1
(15)



while ln xitk does appear in uit we assume that they do not influence m∗i − mi . Thus, uit is of the form
ai + k(mi − m∗i )g(xkit , and each term m∗i − mi g(xitk ) will, by virtue of the presence of the freely varyingm∗i − mi be uncorrelated with xitk . [Alvarez et al. , 2004]
4 On the contrary, extractive institutions, such as those introduced by Spain in its colonies in Latin America, which concentrate power in the hands of a small elite and create a high risk of expropriation for the
majority of the population, are likely to discourage investment and economic development [Acemoglu et al.
, 2002, 2001].

16

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19

Table 1: List of Countries

62 Sample Countries1
AlgeriaEgypt (*)

LesothoSenegalArgentina (*)

El Salvador (*)

MalawiSpain (*)

Australia (*)

Finland (*)

Malaysia

Sweden (*)

Austria (*)

France (*)

Mali

Switzerland (*)

Bangladesh

Ghana

Mauritius

Syria

Belgium (*)

Greece (*)

Mexico (*)

ThailandBenin

IcelandMozambiqueTunisia

Botswana

India (*)

Netherlands (*)

Turkey (*)

Cameroon

Indonesia (*)

New Zealand (*)

UgandaCanada (*)

Iran (*)

Nicaragua

United Kingdom (*)

Chile (*)

Ireland (*)

Niger

United States (*)

China (*)

Italy (*)

Norway (*)

Venezuela (*)

Hong KongJapan (*)

PanamaZambiaDenmark (*)

JordanPeru (*)

Zimbabwe

D. Republic

Kenya

Philippines (*)

Ecuador

Korea,South

Portugal (*)

1 The countries marked with (*) are a subsample of 33 countries with available data on the cultural variables used for the OLS regression of management.

20

Table 2: Data Sources 1980-2004

Fixed-management random-coefficients stochastic production frontier
Variable

Definition

SourceY

Real Gross Domestic Productin 2000 US Dollars

WDI [2007]

K

Physical Capital Stockin 2000 US Dollars

Klenow et al. [1997]
and WDI [2007] 2

LTotal Labor force
Economically Active Population

WDI [2007]

E

Human Capital
Years of Educational Attainment
population 15 and over

Barro et al. [2001]

Cultural variables used in OLS regression on management
Variable

Definition

SourceMuller Index
range 0-1

Probability of two randomly selectedindividuals speaking
different languages

Muller [1964]

Britain and British
Colony 0 or 1

Dummy variable forformer British colonies and Britain

Religious
Fractionalization
range 0-1

1 − Σs2i j where s is the shareof religious group i in country j

Alesina et al. [2003]

Individualism
range 0-100

Degree to which individuals
are integrated into groups

Hofstede [2009]

Tropicaldummy (0 or 1)

A country’s tropical territory
is more than 50% (list 1986)

IUCN [1998]

Emphasis on
Technology

More emphasis in technology
in the future is goodWorld Values Survey [2009]

2 Physical capital from 1980-2000 was calculated from the physical capital to output ratio k/y obtained
from Klenow et al. Physical capital from 2001-2004 was calculated based on the methodology described in
Klenow et al. [1997, p.78] and using the WDI [2007] annual Gross Capital Formation in 2000 US Dollars

21

Table 3: Random-effects stochastic production function 1980-2004 - 62 Countries

Estimated parameters

Random coefficientsNon-random coefficientsNon-random parameters
βCapitalxLabor

0.0188***3-0.0179***

βCapitalxEducation-0.0311***-0.0266***

βLaborxEducation-0.0335***

0.0176*βCapital2

0.0308***0.0460***βLabor2-0.0410***0.0847***βEducation2

0.0903***0.1127***

Parameters with random means

Non-random parameters

β00.1869***0.4438***βCapital

0.7202***0.6443***βLabor

0.2966***0.4542***βEducation

0.1100***0.2331***

Unobservable fixed managementβmanagement

0.9478***———-βmanagementxCapital

0.1932***———-βmanagementxLabor

0.0845***———-βmanagementxEducation

0.0142***———-αmanagement2-0.0933***———-

Variance parameters for compound error
λ2.7636***6.8877***

OthersNumber of Observations

1550

1550

Log likelihood Function

1166.767

1283.755

AIC-1.483-1.6409

BIC-1.424-1.5995

3 *** Indicates that the parameter is statistically significant at 1% level, ** at 5% level and * at 1% level.

22

Table 4: Summary of estimated technical efficiency and fixed management

Estimated technical efficiency (1980-2004)
Model

Mean

Standard Deviation

Minimum

Maximum

Non random coefficients

0.649

0.219

0.179

0.980

Random coefficients

0.892

0.027

0.801

0.930

Estimated management (1980-2004)
Maximum management m∗i

Mean

Standard Deviation

Minimum

Maximum

Random coefficients

0.180

0.926

-2.3402.952

23

Table 5: Marginal impact of fixed management on output per country ∂ ln yit /∂m∗i Average 1980-2004∂ ln yit /∂m∗i Average 1980-2004
Rank

Country

Average

Rank

Country

Average

1

United States

1.634

2

Japan

1.550

32

Malaysia

0.434

33

Bangladesh

0.394

3

China

1.216

34

Algeria

0.375

4France

1.178

35

New Zealand

0.337

5

United Kingdom

1.137

36

Ecuador

0.316

6

Italy

1.128

37

Chile

0.310

7

Canada0.976

38

Zimbabwe

0.270

8

India

0.968

39

Egypt

0.179

9

Spain

0.965

40

Ireland0.116

10

Mexico0.934

41

Cameroon

0.096

11

Korea (south)

0.908

42

Niger

-0.015

12

Australia

0.822

43

Syria

-0.02613

Netherlands

0.798

44

Dominican Republic-0.068

14

Argentina

0.761

45

El Salvador-0.07815

Thailand

0.746

46

Tunisia-0.105

16

Switzerland

0.730

47

Iceland-0.14917

Indonesia

0.711

48

Panama-0.191

18

Sweden0.680

49

Jordan-0.207

19

Belgium

0.661

50

Kenya

-0.24320

Turkey

0.639

51

Benin

-0.261

21

Austria0.630

52Lesotho-0.376

22

Iran

0.609

53

Nicaragua-0.41623

Norway0.590

54Senegal-0.41724

Philippines

0.582

55

Ghana

-0.42025

Greece0.573

56

Botswana-0.44026

Denmark

0.558

57

Zambia-0.46827

Venezuela

0.534

58

Uganda-0.48828

Finland0.516

59

Mauritius-0.494

29

Peru

0.496

60

Mozambique-0.50130

Portugal

0.476

61

Mali

-0.52531

Hong Kong

0.452

62

Malawi-0.60424

Table 6: OLS Regression of estimated Management on cultural variables

OLS Regression of Management
on cultural variables
Variable

Coefficient

β0

0.36

βLanguage.Diversity-0.67**3βBritish.Institutions

0.71***

βReligious.Diversity-1.70***

βIndividualism

0.008**

Fit
R-squared

0.53

Adjusted R-squared

0.46

Observations

33

Diagnostic
Loglikelihood-12.99AIC-1.75

3 *** Indicates that the parameter is statistically significant at 1% level, ** at 5% level and * at 1% level.

25

Table 7: OLS Regression of estimated Management on cultural variables

OLS Regression of Management
on cultural variables
Variable

Coefficient

β0

-0.62

βT ropical.+50%-0.154βLanguage.Diversity-0.64**βBritish.Institutions

0.55***

βReligious.Diversity-0.75**βEmphasis.T echnology

0.73**

Fit
R-squared

0.35

Adjusted R-squared

0.25

Observations

40

Diagnostic
Loglikelihood-20.36AIC-1.51

4 *** Indicates that the parameter is statistically significant at 1% level, ** at 5% level and * at 1% level.

26