PEDL Research PapersThis research was partly or entirely supported by funding from the research initiative Private

Enterprise Development in Low-Income Countries (PEDL), a Foreign, Commonwealth & Development Office (FCDO) funded programme run by the Centre for Economic Policy Research (CEPR).

This is a PEDL Research Paper which emanates from a PEDL funded project. Any views expressed here are those of the author(s) and not those of the programme nor of the affiliated organiiations. Although researchdisseminated by PEDL may include views on policy, the programme itself takes no institutional policy positions.

PEDL Twitter

Managerial Practices and Performance of Small and

Medium-sized Enterprises: Evidence from Burkina Faso∗†

Sugrinoma Aristide Ouédraogo*1 and Soumaila Gansonré2

1Université de Ouahigouya, Burkina Faso, E-mail: ouedarist@gmail.com

2Université Joseph Ki-Zerbo, E-mail: soumailagansonre@gmail.com

Abstract

Despite the growing interest in private enterprises' contribution to income growth and

employment, Small and Medium-sized Enterprises (SMEs) in developing countries are

still experiencing a low performance. The aim of this study is therefore to examine the

importance of management practices in explaining the performance of SMEs based on

survey data collected over 850 SMEs in Burkina Faso. Three components of manage-

ment practices such as the monitoring, the setting of targets, and incentive schemes

are used to compute a management practice score for each enterprise. The estimation

of a Cobb-Douglas production function shows a positive and signi�cant e�ect of the

management practice score on the level of �rms' sales and pro�ts. Thus, the quality

of managerial practices improves the level of performance of �rms. This suggests that

there is a need for policymakers to support the improvement of managerial skills of

entrepreneurs in order to increase the private sector's contribution to economic growth

and employment, particularly in African Economies.

Keywords: Small and Medium-sized Enterprise, Managerial Practices, Firm Productivity,

Pro�tability, Burkina Faso

JEL Codes: D22, L23, O14

∗†The authors are grateful to Ramatu Mahama Al-Hassan and Denis Akouwerabou for their

supports. This work receives comments from Joshua Deutschmann and Louise Guillouet and from

the participants of the dissemination conference. This research was supported by funding from the

Private Enterprise Development in Low-Income Countries (PEDL), a research initiative program

run by the Centre for Economic Policy Research (CEPR). Any errors and any views expressed here

are the sole responsibility of the authors.

1

1 Introduction

The universe of private enterprises in developing countries is dominated by Small and

Medium-sized Enterprises (SMEs) which account for 60% of Gross Domestic Product (GDP)

and employ over 70% of active population (Ayyagari et al. 2007; OECD 2004). Aside the

low survival rate among the SMEs, most of these enterprises are characterized by low level

of productivity and growth as well as limited innovation in management and organizational

practices. The di�erence in �rm performances are usually explained by the di�erence in

access to physical and �nancial capitals and the di�erences in input, price and idiosyncratic

shocks (Aiello and Ricotta 2016; Aiello et al. 2015; Yang and Huang 2005). However, these

factors alone are not su�cient in explaining the observed high heterogeneity in �rm per-

formances within industries and across countries. The endogenous economic growth models

initially developed by Romer (1990) and Lucas (1993) recognize the importance of knowledge

and technological innovations such as investment in Research& Development (R&D), and

information technologies as important factors of productivity growth.

Recent studies showed that di�erences in �rms' performances persist even after controlling

for these conventional factors and cannot be reduced to measurement error of innovation

technology as it has often been assumed (Atkin et al. 2017). In contrary, this calls for a

wider de�nition of technology that should encompass managerial and organizational sys-

tems. However, the empirical studies that assess the extent to which management practices

in�uence �rms' performance are still limited. The existing work generally shows that better

management practices can have both direct and indirect e�ects on �rm productivity and

growth (Bloom et al. 2013; Bloom and Van Reenen 2010; Bruhn et al. 2010; Chong and

Rundus 2004).

Models that consider management as capital or as technology highlight the direct e�ect

of management practices on �rm performance. Considered as capital or technology, good

management practice can raise production like any other production factor such as labor

and physical capital. Thus, Bloom et al. (2016) showed that, in developing countries, man-

2

agement practices accounts for about 30% of di�erences in total factor productivity across

countries. Lemos et al. (2016) found a similar magnitude of the in�uence of management

scores on �rm performances in Pakistan. Evidence in Indian textile �rms using randomized

control trial (RCT) showed that increasing management scores by one standard deviation

causes 10% increase in Total Factor Productivity (Bloom et al. 2013). Manufacturing �rms

that adopt better management practices are also found to be more performing in Australia

(Agarwal et al. 2014).

Indirectly, better management input raises �rm performances by improving the marginal

productivity of other inputs such as capital and labor but also a�ect the level of investment

in physical capital (Bruhn et al. 2010). In addition, technology is found to be more e�cient

for �rms with good managerial and organizational systems. Managerial quality also in�u-

ences production by increasing �rm ability to cope with shocks to worker productivity. This

prediction was tested by Adhvaryu et al. (2016) in the context of Indian garment industry.

The authors found that line supervised by better quality managers are more likely to diag-

nose and mitigate the deleterious impact of worker productivity shocks.

The quality of �rms' management is not always found by empirical studies as driver of �rm

performance. Bloom et al. (2016) showed that management may have di�erential e�ects de-

pending on the environment in which �rms are operating. In this sense, there is no common

de�nition of what can be identi�ed as better management practices, because management

is viewed as an optimal design which is contingent to �rm's environment, limiting compara-

bility of management practices across �rms (Bloom et al. 2016).

In many sub-Saharan African countries, numerous projects have been initiated to reduce

unemployment rate and promote decent jobs for the youth and women. In Burkina Faso, for

instance, various public initiatives provide �nancial support for the creation and expansion

of SMEs. However, the majority of SMEs are still characterized by low performances and

low survival rate. A survey undertaken on private enterprises in Burkina Faso by the World

Bank reveals that over 40% of �rms identi�ed �nancial constraints and tax rate as the main

3

obstacles of growth (World Bank 2009). However, in addition to limited access to �nance,

technology and energy as well as poor transportation conditions, SMEs operate under weak

managerial and organizational systems. Limited number of studies exist on the relation-

ship between management practices and performances of �rms in sub-Saharan Africa (SSA).

Most of the existing studies have been conducted in developed countries where economic

environment is characterized by high level of competition and technological change. Thus,

understanding the barriers and drivers of growth of SMEs in Africa is important to guide

policy formulation and the promotion of private sector development. This study contributes

to the literature on �rms' organization by �lling the gap of the lack of empirical investigation

on the relationship between management practices and �rms' performance in SSA.

The objective of this paper is therefore to analyze the relationship between managerial prac-

tices and the performance of SMEs . Speci�cally, the study assesses the quality of managerial

practices and examines the extent to which managerial practices explain di�erences in pro-

ductivity and pro�t of SMEs in Burkina Faso. This investigation is undertaken using survey

data collected in 2018-2019 in the two largest cities of Burkina Faso: Ouagadougou and Bobo

Dioulasso. The structural questionnaire used is inspired by the Management and Organiza-

tional Practices Survey (MOPS) developed by the US census bureau (Bu�ngton et al. 2017).

Management practices encompass three components that are monitoring, setting targets and

incentives. Based on these components, a score of management practices is computed for

each SMEs and the empirical results indicate that better management practices increase the

level of sale and pro�tability of SMEs. These �ndings suggest that promoting good manage-

rial skills within SMEs is important to speed enterprise expansion.

The rest of the paper is organized as follow. The next section develops the theoretical frame-

work. The third is consecrated to the empirical strategy. It describes the data collection

and sampling procedure and presents the econometric method of estimation. The results are

presented and discussed in the fourth section and �nally conclusion and policy implications

are given in the �fth section.

4

2 Theoretical Framework of the relationship between man-

agerial practices and �rms' productivity

The endogenous growth model presents technological progress as the key driver of productiv-

ity growth across countries and industries (Romer 1990; Lucas 1993). In this growth model,

observed heterogeneity in productivity is explained by innovation that is driven by the level

of �rms' investment in Research and Development (R&D). However, empirical investigation

highlights the fact that, not only do productivity di�erences persist after all measures of

technology have been controlled for, but also the fact that the e�ect of technology on pro-

ductivity across �rms and countries is heterogeneous (Syverson 2004a;b). The di�erence in

managerial ability, considered as unobservable �xed e�ect in panel data estimation, is seen

by Lucas (1978) as plausible explanation of the heterogeneous e�ect of innovation on pro-

ductivity.

We present in this study a theoretical framework where managerial practices appears as an

explanatory factor of �rm performances and a technology that can be adopted and improved

by entrepreneurs. Thus, following Bloom et al. (2016), the relationship between management

practices and �rm performances can be expressed in the production function as:

Y = F (A,K,L,M) (1)

Where Y represents the level of production, A is an e�ciency term, L represents the labor,

K is physical capital and M , the management capital.

Considering management as technology, it can be established a positive and direct relation-

ship between management and performance. This means that higher quality of management

practices is expected to yield higher level of performance. We consider a Cobb-Douglas

production model, as in Bloom et al. (2016), and specify Equation (1) as follow:

Yi = AiKαi L

βi G(Mi) (2)

5

Where G(Mi) is a function that explains the endogenous change of management practices

related to entrepreneurs' choice to adopt new or better management practices. The manage-

ment ine�ciency model, also known as �management as technology� model, considers that

�rm performance is an increasing function of the level of management quality (Bloom et al.

2016; Bloom and Van Reenen 2006). Since G(Mi) is increasing in Mi, for simplicity, we can

write

G(Mi) = M ci (3)

Therefore, the production function can be re-written as follow:

Yi = AiKαi L

βiM

ci (4)

Alternatively, the optimal choice model considers that an increase in management practices

does not always increase the level of output. Depending on the speci�c characteristics of

each industry and the environment in which �rms are operating, managerial practices can

have di�erentiated e�ect on �rms' output. In this sense, optimal management practices may

vary across industries and countries.

To formalize the optimal choice model (known as management as design model), let us de�ne

G(Mi) =1

1 + θ|Mi −M |(5)

Where θ ≥ 0 and G(Mi) ∈ (0, 1] is decreasing in the absolute deviation ofM from its optimal

level.

The optimal choice model and managerial ine�ciency are generally used to discuss the re-

lationship between management practices and �rm's performances. In the optimal choice

model, management practices are considered as a costly activity. In this case, depending

on the environment, each �rm chooses the optimal level of management that maximizes the

pro�t. Therefore, management practices will not necessary be correlated to �rm e�ciency

and performances. On contrary, managements as technology or management ine�ciency

model argues that di�erence in management practices will simply be re�ected in �rm ef-

6

�ciency. The empirical investigation of this study on the relationship between managerial

practices and �rm performance provide evidence that relevance of both theories, manage-

ment as technology model (MAT) and management as design in the context of sub-Saharan

African, especially in Burkina Faso.

3 Empirical methods

3.1 Estimation strategy

Based on the theoretical framework and following Bloom and Van Reenen (2007), we specify

the empirical model as an augmented Cobb-Douglas production function. The trans-log

speci�cation is expressed as:

ln(Yit) = αln(Kit) + β1ln(LPit) + β2ln(LNPit) + λMit + δXit + ωit + εit (6)

Yit is the dependent variable measuring the performance of enterprise i at time t. M is the

score of management practices, LP indicates the number of permanent workers, LNP repre-

sents the number of non-permanent workers, K the level of capital (in million of CFA), X is

a set of control variables that include the experience of the respondent of the interview, the

location dummy of the enterprise, the number of visits made before getting the interview,

the industry branch the enterprise belongs to, and the Juridical status of the enterprise. ωit

and εit represent the shocks that are not observable by the econometrician.

However, ωit represent shocks that are potentially observed or predictable by �rms when they

make input decision. εit are shocks that are not observable or predictable by �rms when

making input decision. ωit are also referred to as productivity shock while εit represent the

measurement error of the output variable or error term. The performance of the enterprises

is measured by two alternative indicators, that is the value of sale (in million of CFA) and the

amount of pro�t (in million of FCFA). Due to the existence of negative pro�t for some SMEs,

we compute the logarithm of the pro�t using the Inverse Hyperbolic Sine Transformation.

7

Therefore, the logarithm of the pro�t is calculated as follow:

ln(profitit) = ln(profitit + (profit2it + 1)0.5) (7)

Production functions relate productive inputs (e.g. capital, labor) to outputs. The major

econometric issue confronting estimation of production functions is the possibility that there

are determinants of production that are unobserved to the econometrician but observed by

the �rm. If this is the case, and if the observed inputs are chosen as a function of these

determinants(as will typically be the case for a pro�t-maximizing or cost-minimizing �rm),

then there is an endogeneity problem and OLS estimates of the coe�cients on the observed

inputs will be biased.

In addition, the management practices of the enterprises is likely to be endogenous because

of problem of simultaneity and measurement errors. In fact, if even management can in�u-

ence �rm sale and level of pro�tability, it is also likely that �rms that experience higher level

of sale and pro�tability present more ability to upgrade their level of management (Bloom

et al. 2016; Bloom and Van Reenen 2006). Therefore, the Ordinary Least Square (OLS)

estimation may not be consistent.

Some studies also use the Olley and Pakes method to correct for the simultaneity and se-

lection bias in the estimation of production function. This approach is a semi-parametric

method that estimate the parameters of the production function consistently (Yasar et al.

2008). However, this method is adequate for dynamic panel where there exists �rms that

exit and enter in the market. As our sample does not contain exit �rms, the Olley and Pakes

method is not appropriates.

The instrumental variable regression approach represents another approach that can be used

to controlled for the endogeneity. The variables that are used as instruments must satisfy

8

the conditions of relevance and validity. These conditions mean that instruments should not

have direct e�ects on the performance of the �rms. They should a�ect �rm performance only

indirectly through the management practices score Thus, if the joint e�ect of instruments

used are signi�cant in explaining the level of management, then these instruments can be

considered as relevant. The validity of the instruments is related to the exogeneity of these

variables. Thus, instruments are exogenous if they do not show signi�cant e�ect on the

dependent variable (i.e. the performance). In addition, the number of valid instruments

that are exogenous should excess the number of endogenous variables. The drawback of this

method resides in the identi�cation of valid and relevant instruments.

In this context, the �xed e�ect estimator remains an adequate and feasible approach that

we adopt in the study. Fixed-e�ect estimation requires the additional assumption that

ωit = ωit−1,∆t. Assume that εit is independent across time and the productivity shock ωit−1

constant across time ωit = ωit−1 = ωi

Therefore, applying the �rst di�erence to Equation 6 yields

ln(Yit)− ln(Yit−1) = α[ln(Kit)− ln(Kit−1)] + β1[ln(LPit)− ln(LPit−1]

+β2[ln(LNPit)− ln(LNPit−1)] + λ[Mit −Mit−1] + δ[Xit −Xit−1]

+[ωi − ωi] + [εit − εit]

(8)

ln(Yit)− ln(Yit−1) = α[ln(Kit)− ln(Kit−1)] + β1[ln(LPit)− ln(LPit−1]

+β2[ln(LNPit)− ln(LNPit−1) + λ[Mit −Mit−1] + δ[Xit −Xit−1] + [εit − εit](9)

Since the unobservable ωit have been di�erenced out of these expressions, the equation can

be estimated with OLS

0Wused the age of the enterprise and the competition level as candidate instruments for the IV regression,but the tests of validity and the relevance have not been satisfying

9

3.2 Data collection and sampling procedure

3.2.1 De�nition of SMEs

Small and Medium-sized Enterprises (SME) play an important role in both developed and

developing countries. They are characterized by a high heterogeneity in terms of employ-

ment and growth rate and can be identi�ed in various sectors of activities. SMEs contribute

to over the half of formal employment in the world and bring e�cient solution in various

sector such as energy, water supply, industries, health and education (World Bank 2009).

However, there is no common de�nition of what is SME. The de�nition of SME varies by

country and is usually based on the number of employees, the value of sales and/or value of

assets. The most commonly used variable to de�ne SMEs is the number of employees. Thus,

in European Union and in many OECD countries, SMEs are enterprises that do not employ

more than 200 to 250 workers. In USA, this upper limit of employees for SMEs is set at 300.

In Burkina Faso, the statistical de�nition of SMEs is based on both the number of employees

and the annual level of sale. Thus, Small and Medium-sized Enterprise is de�ned as a formal

enterprise employing less than 100 permanent workers with a value of sale estimated at less

than one billion CFA per year (which represent about two million dollars (2 000 000USD)1.

This de�nition is therefore used throughout this study. Among the SME, there are micro-

enterprises that are establishment with less than 10 permanent workers and an annual sale

estimated at less than 15 million of FCFA.

3.2.2 Study areas and sampling procedure

The Management and Organizational Practices Survey was conducted from August to De-

cember 2018 in Ouagadougou and Bobo Dioulasso which are the two biggest cities of Burkina

Faso. The choice of these towns is motivated by their importance in the national economy

and by the fact that headquarter of most enterprises can be found in these cities. In fact,

among the 66 044 formal enterprises recorded by the chamber of Trade and Industry in 2010,

1Exchange rate 1 USD=500USD (Approximation)

10

about 80% are established in Ouagadougou, the capital of the country and only 11% are es-

tablished in Bobo Dioulasso (Institut National des Statistiques de la Démographie (INSD)

2010). We therefore surveyed 850 enterprises whose 75% are chosen in Ouagadougou and

25% in Bobo Dioulasso. We purposely decided to over-sample the enterprises established in

Bobo in order to increase their representativeness in the �nal sample. After the cleaning,

the �nal data set contains 651 enterprises with 27% from Bobo and the remaining 73% from

Ouagadougou (Table 1).

Within each city, the number of enterprises assigned has been randomly chosen based on a

list obtained from the Direction of Small and Medium Enterprises . The enterprises surveyed

was randomly selected within four sectors of activities that are 1) Manufacturing industry, 2)

Buildings and Civil Engineering (B&CE), 3) Intellectual service providers, 4) General trade.

The survey was designed to take into account the importance of each sector in the economy.

The distribution of the sample per area and per sectors of activities is showed in Table 1.

Table 1: Distribution of the sample per activity branch

Location Bobo-Dioulasso Ouagadougou Total

Manufacturing 19 62 81

Services 56 123 179

Buildings and Civil Engineering (B&CE) 25 84 109

Commerce &Others 75 207 282

Total 175 476 651

In order to observe the change in the variables over the years, the questionnaire is designed

to collect data for the years 2012 and 2017. Therefore, only enterprises that are established

before 2012 are retained for the survey. To reduce the recall bias, interviewers were asked to

use the year 2014, that is the date of the insurrection in Burkina as reference and to consider

the situation before and after this event. The remaining usable data set after the cleaning is

constituted of a sample 651 enterprises distributed across the four sectors and the two cities.

11

3.2.3 Management and Organizational Practices Survey (MOPS)

The Management and Organizational Practices Survey (MOPS) that we conducted is in-

spired by the questionnaire developed in Bu�ngton et al. (2017) and also used by Lemos

et al. (2016) in Pakistan. Sixteen (16) questions are developed to assess three main aspects

of management practices that are monitoring, target setting and incentives. The �rst �ve

questions which concern the monitoring aspects, seek to understand how �rms collect data

to monitor and improve production processes. The following three questions concern the

setting of targets. They assess the accuracy, the realism and the transparency of targets set

by �rms. Finally, the eight questions on incentives describe the practices of bonus, promo-

tion, reassignment and dismissal in the �rms. The response of each management question

is normalized to be on a scale of 0 and 1 and the results are then aggregated into a single

measure of management practices. Therefore, the structural management score is computed

as the average of the score of each of the 16 questions. The score is scaled from 0 to 1 with

0 representing the lowest quality of management practices of �rm in terms of monitoring,

targets and incentives and 1 representing the best practices. This means that a score close

to 1 indicates a good quality of management practices of the �rm.

4 Results and Discussion

4.1 Descriptive statistics

The average management practice score (MPS) of the sample is estimated at 0.38 in 2012

and 0.41 in 2017 which corresponds to an increase of 0.03 points, that is, a growth of roughly

8% (Figure 1a). This management score is similar to the score found by Lemos et al. (2016)

in a study on Pakistan's �rms. In addition, the magnitude of increase in management score

during the �ve years in Burkina Faso is found to be also similar to the magnitude of increase

in management score of Pakistan's establishments in �ve years period (between 2005 and

2010). In fact, Lemos et al. (2016) found that between 2005 and 2010, management score in-

creases from 0.43 to 0.45 in Pakistan corresponding to an increase of 0.02 point (i.e. 4.65%).

12

The lower level of management practices among SMEs in Burkina Faso can be explained by

the low competition in the environment. Furthermore, adoption of management practices

may be di�cult for entrepreneurs with a low education level as it is the case in Burkina Faso.

(a) Average management score per year (b) Average management score per city

Figure 1: Average management score

Figure 1b shows no signi�cant di�erence between SMEs in Bobo Dioulasso and those estab-

lished in Ouagadougou. The average management score is estimated at 0.39 for enterprises

surveyed in Ouagadougou and 0.38 for enterprise surveyed at Bobo Dioulasso. The incen-

tive component of management practices remains the lowest among the tree component of

management (Figure 2). This is followed by monitoring and then target setting. This result

suggests that the more the adoption of management practices is costly, more enterprises are

unwilling to adopt it. Thus, the incentive component captures the �nancial motivation such

as bonus and promotion which is costly than setting target to achieved. In addition, moni-

toring the various activities necessitates also human and �nancial resources. This situation

tends to be in line with the management as design model, where the adoption is seen as

costly for many entrepreneurs. Thus, some components of management may be di�cult to

adopt by some SMEs as regard to the skills, education level and �nancial costs that it may

involve.

The descriptive statistics, reported in Table 2, show that the average experience of

respondent is 9 years in the enterprise. This suggest that the respondents have su�cient

13

Figure 2: Distribution of the management practice score (MPS)

knowledge about the enterprise to give accurate answers to the questions asked during the

interview. The average number of full time employers is about 14, while the sale value is

estimated at 171 millions of FCFA. In addition the capital is evaluated at 80 millions of FCFA

on average. However, education level of managers and non-manager workers estimated at

12.9 and 7.9 years respectively is very low. These represent the characteristics of business

environment in Burkina that is still dominated by owners with limited education level. This

situation may partly be a constraint to upgrade managerial skills in the SMEs.

14

Table 2: Descriptive Statistics of variables used

(1)Mean SD Min Max

Number of permanent employees 8.508 11.212 1 157Value of capital in millions of FCFA 30.187 96.692 0.0109 2000Value of sale in million of FCFA 107.506 168.335 0.2 965Amount of wage in million of FCFA 13.934 30.958 0.144 390Amount of credit received 99.416 474.245 0 5688.117Enterprise applied for credit (1=yes) 0.445 0.497 0 1Average education level of non-managers 7.865 3.788 0 18Number of competitors in the market 71.482 44.125 0 300Pro�t of the enterprises in million of FCFA 91.157 154.173 -346.6 926Proportion of permanent female employees 0.239 0.230 0 1Age of the enterprise 9.722 8.985 0 69Experience of the respondent 9.0165 6.0082 0 42Management Practice Score 0.387 0.142 0.0364 0.779Observations 1026

4.2 Econometric results of the relationship between managerial prac-

tices and performances

This section presents the estimation results of the e�ect of management on �rm performances

measured by the level of sale value and pro�t. For each dependent variable, we successively

estimate the model with the ordinary least square (OLS) and Fixed E�ect. As discussed

in the previous section, the �xed e�ect estimation removes the time-invariant individual

characteristics and provides more robust estimates than the OLS.

4.2.1 Management practices and production

The results of OLS estimation shows a positive and signi�cant e�ect of management on the

value of sale of enterprises (Table 3). When additional explanatory variables are included in

the regression, the signi�cation of the management coe�cient become low (10%). This may

suggest the presence of unobserved �xed e�ect that biases the estimates. The column 4 and

5 provide the Fixed e�ect estimation of the model.

The estimation obtained with FE shows a positive and signi�cant e�ect of management on

15

sale value of SMEs. When the e�ect of capital and labor are controlled in the model, R-

squared increases to 39% making the estimate more adequate. The coe�cient is established

in this case at 1.19 and signi�cant at 5%. As the standard deviation is 0.14, the marginal ef-

fect of the change in standard deviation is computed as exp(coef ∗sd) = exp(1.19∗0.14)−1 =

0.1813. This means that a one unit change in the standard deviation of management prac-

tices results in 0.18 percent increase in the level of sale value of SMEs. In a study on the

causes of productivity di�erence across countries, Bloom and Van Reenen (2010) �nd that a

one unit change in the standard deviation of management practices results in in 38% increase

in the �rm sale holding employment constant.

Table 3: Management practices and �rms' production

Dependent variable: Log of value of sale(1) (2) (3) (4) (5)

VARIABLES OLS OLS OLS FE FE

MPS 2.2226*** 0.4554 0.5621* 4.0730*** 1.1907**(0.328) (0.293) (0.315) (0.613) (0.526)

Log of capital 0.4285*** 0.4341*** 0.4205***(0.033) (0.036) (0.040)

Log of labor 0.6068*** 0.6140*** 0.4556***(0.059) (0.061) (0.068)

Location 0.1427(0.088)

Respondent exp -0.0184**(0.007)

Visits 0.0494***(0.017)

Working Sector YesJuridical status YesConstant 2.6335*** 1.3256*** 1.2827*** 1.9144*** 1.3258***

(0.134) (0.132) (0.226) (0.239) (0.197)

Observations 1,225 1,225 1,102 1,225 1,225R-squared 0.034 0.344 0.385 0.068 0.390Number of id 622 622

MPS indicate the management practice scoreRobust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

16

4.2.2 Management and Pro�t

The results of the OLS estimation of the e�ects of managerial practices on SMEs' pro�t is

reported in Table 4. The �ndings show a positive e�ect of management practice score on the

level of pro�t. However, this e�ect become no signi�cant when other explanatory variables

are controlled for (column 2 and 3).

In column 4 and 5, the �xed e�ect estimation provides more robust estimates. The coe�cient

in the column 5 is estimated at 1.78. This means that a one standard deviation change in

management score will almost double the pro�t of the �rms (that is exp(coef ∗ sd) − 1 =

exp(1.78 ∗ 0.14)− 1 = 1.00.

Therefore, enterprises that set targets at beginning of each year and develop tools to fre-

quently evaluate the evolution of the indicators are more likely to experience higher produc-

tivity and pro�tability. The combination of managerial e�orts has therefore the potential to

strengthen the growth of SMEs in Burkina Faso and to increase their contribution to overall

economic growth.

These �ndings corroborate the results of the majority of recent empirical studies on man-

agement practices. Bloom and Van Reenen (2010) estimated at 21.3% the magnitude of

one standard deviation change of management practices on productivity of enterprises in

developed countries and Lemos et al. (2016) found 21.8% for the case of Pakistan. In India

garment industry, management investment is also found as having a positive relationship with

�rm performances (Bloom et al. 2013). These �ndings which represent one of rare studies in

Sub-Sahara Africa highlight that despite the di�erence in the environment of African �rms

compared to �rms operating in Asia and US in terms of technological gaps, the management

remains an important factor of performance. Furthermore, better management practices in-

creases the ability and the incentive to adopt good technology which improve e�ciency and

market share (the level of sale). Thus, the e�ect of management on �rms' performance are

not context speci�c even if the extent of the e�ect may vary across countries. The relatively

17

Table 4: Management practices and �rms' pro�t:

Dependent variable: Log of pro�t(1) (2) (3) (4) (5)

VARIABLES OLS OLS OLS FE FE

Management Practice Score 1.4600*** 0.3353 0.4105 4.4218*** 1.7812**(0.485) (0.457) (0.491) (0.853) (0.835)

Log of capital 0.4111*** 0.4829*** 0.3852***(0.060) (0.054) (0.063)

Log of labor, full time 0.4176***(0.108)

Log of labor, part time 0.3394*** 0.2742***(0.064) (0.068)

Location 0.1323(0.132)

Respondent experience -0.0184*(0.011)

Number of visits 0.0407(0.028)

Working Sector YesJuridical status YesConstant 3.0959*** 2.2083*** 2.3866*** 1.9449*** 1.4057***

(0.184) (0.197) (0.328) (0.332) (0.313)

Observations 1,225 1,225 1,102 1,225 1,225R-squared 0.008 0.139 0.197 0.043 0.186Number of id 622 622

MPS indicate the management practice scoreRobust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

18

lower gain in the increase of management practices in Burkina Faso compared to the �ndings

in developed world may be explained by the low level of competition faced by �rms which re-

duces the gain in market share associated with e�ciency gain in management improvement.

However, the positive and signi�cant relationship established between managerial practices

and productivity con�rms the model of management as technology (MAT).

In addition, the results show that the level of capital positively in�uence the performance of

SMEs. Thus, a 1% increase in the level of capital results in 0.42% increase in the level of sale

and 0.38% increase in the level of pro�t. This �nding indicates that the di�cult access to

�nance represents a major constraint to the growth of SMEs in Burkina and in Sub-Africa

in general. A survey of the World Bank showed that many entrepreneurs in Burkina Faso

identify the �nancial constraints as the main barriers of their performance (World Bank

2009).

5 Conclusion

Managerial skills are important for entrepreneurs to ensure the growth and the survival of

their businesses. In economic literature, though management ability is seen as explanatory

factor of productivity di�erence among �rms, it was generally associated with unobserved

�xed e�ect. Recent empirical studies that attempt to assess the level of management prac-

tices of enterprises and the extent to which they a�ect productivity, consider management

as a form of capital or technology that can be adopted and accumulated. However, most

of empirical researches have been conducted for enterprises established in developed coun-

tries where the environment is highly competitive. In Sub-Saharan African, adoption of new

technology is sometime costly, exacerbated by lack of information and the low education

level of many SMEs managers. Environment is also less competitive and highly corrupted

which impedes the innovation and adoption of better management practices. Therefore, this

study contributes to the existing literature by analyzing the relationship between manage-

ment practices and SMEs in Burkina Faso, considering management practices as a form of

technology.

19

The study uses survey data collected over a sample of 850 SMEs selected in two cities of the

countries that are Ouagadougou and Bobo Dioulasso. Recall method is used to collect data

in two points, that is 2012 and 2017. Based on the representativeness of each cities, 25% of

enterprises was selected in Bobo Dioulasso and 75% in Ouagadougou distributed across four

sectors of activities. The descriptive statistics indicate a score of management practices of

0.39 on average. The statistics also showed an improvement of 8% of management practices

between 2012 and 2017. Furthermore, an augmented Cobb-Douglas production function is

estimated to analyze the e�ect of managerial practices on �rms' performances using �xed

e�ect estimation method. The results showed a positive and signi�cant e�ect of management

practices on the level of sale and the level of pro�tability. Particularly, a one standard devia-

tion increase in the management score is associated with a raise in �rms' output sold by 18%

and has the potential of doubling the pro�t of the �rms. Thus, the quality of managerial

practices is associated with a higher level of SMEs' performances.

These empirical �ndings provide important policy implications for the promotion of vibrant

private enterprises in sub-Saharan Africa. The various initiatives that support enterprises

creation should not be limited to �nancial grants. Entrepreneurs need to be supported to

improve their managerial skills in order to lower the probability of bankruptcy and increase

their performance in terms of sale and pro�t. Policy need also to support entrepreneurs in

developing realistic indicators and design adequate strategies to follow up the evolution of

these indicators. There is also a need for entrepreneurs to develop adequate strategies to an-

ticipate any deviation of the indicators from predicted paths. Finally, entrepreneurs should

promote a culture of excellence such as competence-based promotion and reward scheme

based on the performances of employees.

Aside the �nance and management constraints, �rms in African countries also face a low

level of innovation which is important to remain competitive. Even if management can be

seen as a soft technology, it may have a crucial in�uence on the adoption of hard technol-

ogy. Therefore, further research should focus on how the quality of management can a�ect

20

innovation and increase the bene�t of new technology among SMEs.

References

Adhvaryu, A., Kala, N., and Nyshadham, A. (2016). Management and Shocks to Worker

Productivity. PEDL Research Papers.

Agarwal, R., Brown, P. J., Green, R., Randhawa, K., and Tan, H. (2014). Management

practices of Australian manufacturing �rms : why are some �rms more innovative ? In-

ternational Journal of Production Research, 52(21):6496�6517.

Aiello, F., Pupo, V., and Ricotta, F. (2015). Firm heterogeneity in TFP , sectoral innovation

and location. Evidence from Italy. International Review of Applied Economics, 29(5):579�

607.

Aiello, F. and Ricotta, F. (2016). Firm heterogeneity in productivity across Europe : Evi-

dence from multilevel models. Economics of Innovation and New Technology, 25(1):57�89.

Atkin, D., Chaudhry, A., Chaudry, S., Khandelwal, A. K., and Verhoogen, E. (2017). Or-

ganizational Barriers to Technology Adoption : Evidence from Soccer-Ball Producers in

Pakistan. The Quarterly Journal of Economics, 132(3):1101�1164.

Ayyagari, M., Beck, T., and Demirguc-Kunt, A. (2007). Small and Medium Across Enter-

prises the Globe. Small Business Economics, 29(4):415�434.

Bloom, N., Eifert, B., Mahajan, A., Mckenzie, D., and Roberts, J. (2013). Does Management

Matter? Evidence from India. The Quarterly Journal of Economics, 128(1):1�51.

Bloom, N., Sadun, R., and Van Reenen, J. (2016). Management as a Technology? PEDL

Research Papers.

Bloom, N. and Van Reenen, J. (2006). Management Practices, Work-Life Balance, and

Productivity: A Review of Some Recent Evidence. Oxford Review of Economic Policy,

22(4):445�456.

21

Bloom, N. and Van Reenen, J. (2007). Measuring and Explaining Management Practices

across Firms and Countries. The Quarterly Journal of Economics, CXXII(4).

Bloom, N. and Van Reenen, J. (2010). Why Do Management Practices Di�er across Firms

and Countries? Journal of Economic Perspectives, 24(1):203�224.

Bruhn, B. M., Karlan, D., Schoar, A., Bartelsman, E., and Haltiwanger, J. (2010). What

Capital is Missing in Developing Countries ? American Economic Review: Papers &

Proceedings, 100:629�633.

Bu�ngton, C., Foster, L., Jarmin, R., and Ohlmacher, S. (2017). The Management and

Organizational Practices Survey ( MOPS ): An Overview. Working Paper, U.S. Census

Bureau, pages 1�53.

Chong, V. K. and Rundus, M. J. (2004). Total quality management , market competition

and organizational performance. The British Accounting Review, 36:155�172.

Institut National des Statistiques de la Démographie (INSD) (2010). Repertoire Statistique

Des Entreprises (Rse). Technical report, Ministère de l'Economie et des Finances, Oua-

gadougou, Burkina Faso.

Lemos, R., Choudhary, A., Reenen, J. V., and Bloom, N. (2016). Management in Pakistan:

First Evidence from Punjab. Working Paper, International Growth Centre.

Lucas, R. E. J. (1978). On the Size Distribution of Business Firms. The Bell Journal of

Economics, 9(2):508�523.

Lucas, R. E. J. (1993). Making a Miracle. Econometrica, 61(2):251�272.

OECD (2004). Promoting Entrepreneurship and Innovative SMEs in a Global Economy: To-

wards a more Responsible and Inclusive Globalisation. Technical report, Istanbul, Turkey.

Romer, P. M. (1990). Endogenous Technological Change. The Journal of Political Economy,

98(5):71�102.

Syverson, C. (2004a). Product substitutability and productivity dispersion. The Review of

Economics and Statistics, 86(2):534�550.

22

Syverson, C. (2004b). Product substitutability and productivity dispersion. The Review of

Economics and Statistics, 86(2):534�550.

World Bank (2009). Enterprise Survey: Burkina Faso. Technical report, Washington, DC

20433.

Yang, C.-H. and Huang, C.-H. (2005). R&D, Size and Firm Growth in Taiwan`s Electronics

Industry. Small Business Economics, 25(5):477�487.

Yasar, M., Raciborski, R., and Poi, B. (2008). Production function estimation in Stata using

the Olley and Pakes method. The Stata Journal, 8(2):221�231.

6 Appendix

Table 5: Descriptive statistics of sample

(1) (2) (3)2012 2017 Average

Variables Mean sd Mean sd Mean sdLabor 7.423 8.574 9.609 13.287 8.507 11.212Capital 23.490 67.399 36.989 118.988 30.186 96.692Sale 94.322 153.979 120.897 180.930 107.506 168.334Wage 12.082 28.294 15.815 33.369 13.934 30.958Credit 103.735 486.675 95.0285 461.714 99.416 474.245Credit dummy 0.445 0.497 0.446 0.497 0.445 0.497Education of non-managers 7.827 3.823 7.904 3.755 7.865 3.788Numb. competitors 65.452 42.123 77.607 45.296 71.482 44.125Pro�t 80.361 141.224 102.123 165.718 91.157 154.173Female employees (%) 0.236 0.235 0.243 0.225 0.239 0.230Age of the enterprise 7.263 8.658 12.220 8.622 9.722 8.985Respondent Experience 9.00967 5.998 9.0235 6.0235 9.0165 6.00818Management practice score 0.376 0.140 0.398 0.144 0.387 0.142Observations 517 509 1026

Capital, sale, wage and credit are measured in millions of FCFA

23

Table 6: Management practices and �rms' production

(1) (2) (3) (4) (5)VARIABLES OLS OLS OLS FE FE

Management Practice Score 2.2226*** 0.4554 0.5621* 4.0730*** 1.1907**(0.328) (0.293) (0.315) (0.613) (0.526)

Log of capital 0.4285*** 0.4341*** 0.4205***(0.033) (0.036) (0.040)

Log of labor 0.6068*** 0.6140*** 0.4556***(0.059) (0.061) (0.068)

Location 0.1427(0.088)

Respondent Experience -0.0184**(0.007)

Number of visits 0.0494***(0.017)

Working Sector = Services 0.1792(0.149)

Working Sector = B&CE 0.2671(0.168)

Working Sector = Trade -0.2588*(0.144)

Juridical status = SARL -0.0690(0.140)

Juridical status = Individual Enterp. -0.0093(0.155)

Juridical status = Other -0.2083(0.259)

Constant 2.6335*** 1.3256*** 1.2827*** 1.9144*** 1.3258***(0.134) (0.132) (0.226) (0.239) (0.197)

Observations 1,225 1,225 1,102 1,225 1,225R-squared 0.034 0.344 0.385 0.068 0.390Number of id 622 622

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

24

Table 7: Management practices and �rms' pro�t

(1) (2) (3) (4) (5)VARIABLES OLS OLS OLS FE FE

Management Practice Score 1.4600*** 0.3353 0.4105 4.4218*** 1.7812**(0.485) (0.457) (0.491) (0.853) (0.835)

Log of capital 0.4111*** 0.4829*** 0.3852***(0.060) (0.054) (0.063)

Log of labor, full time 0.4176***(0.108)

Log of labour, part time 0.3394*** 0.2742***(0.064) (0.068)

Location 0.1323(0.132)

Respondent experience -0.0184*(0.011)

Number of visits 0.0407(0.028)

Working Sector = Services -0.1407(0.194)

Working Sector = B&CE -0.0858(0.224)

Working Sector = Trade -0.5311***(0.197)

Juridical status = SARL -0.0426(0.207)

Juridical status = Individual enterp. 0.1659(0.224)

Juridical status = Others -0.0474(0.346)

Constant 3.0959*** 2.2083*** 2.3866*** 1.9449*** 1.4057***(0.184) (0.197) (0.328) (0.332) (0.313)

Observations 1,225 1,225 1,102 1,225 1,225R-squared 0.008 0.139 0.197 0.043 0.186Number of id 622 622

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

25

(a) Management score and Production (b) Management score and Employment

(c) Management score and Pro�t

Figure 3: Management score and PerformancesThe three graphs show the relationship between the score of management practices and theindicators of �rms' performances that are the value of sale, the number of employees andpro�t of the SMEs. These three graphs show positive link between the management and

performances.

26