CC BY
21
(«DI
RESEARCH ARTICLE
https://doi.org/10.17059/ekon.reg.2022-1-5 UDC: 332.1
Robert Ng Henaoa), Isabel Cristina Betancur Hinestrozab), Abraham Allec Londoño Pinedac)
ab c) Universidad de MedelUn, MedelUn, Colombia a) https://orcid.org/0000-0002-9228-2193, e-mail: robertng@udem.edu.co
b) https://orcid.org/0000-0001-9661-5146
c) https://orcid.org/0000-0002-2419-0211
Geographic Concentration of Economic Activities in Aburrá Sur in Antioquia (Colombia) - A Reinterpretation of the Industrialisation Coefficients of the Stochastic Independence Approach1
In terms of economic geography, spatial and industrial concentration represents an increasingly important input for the design of public policies that foster local economic development and productive transformation of a territory. This analysis aims to identify the geographic concentration indices of different sectors in which the economic activity is distributed in each of the five municipalities that are part of the territorial agglomeration of Aburrá Sur in the department of Antioquia (Colombia). The methodology is based on a reinterpretation of the industrialisation coefficients used in the stochastic independence approach, which explains the divergences between specialisation levels of production and geographical concentration by using entropy indices that consider the reference distribution as uniform distribution, as is the case with maximum dispersion. We start from the hypothesis that in the municipalities belonging to the Aburrá Sur predominates the high concentration in the manufacturing industry, although some of them could have medium and even low concentration because they have different sector indicators. Results show that the manufacturing industry is the most concentrated sector in the territorial agglomeration. The research findings can be used by political and economic actors in the territory for designing strategies and decision-making on sectoral strategic development commitments.
Keywords: local economic development, public policies, business competitiveness, territorial localisation, territorial productivity, Aburrá Sur, economic geography, Stochastic Independence Approach, territorial concentration, smart specialisation
Acknoledgement
This article is the result of a research project titled "Sectoral validation and strategic commitments of the business sector in Aburrá Sur in the framework of smart metropolitan specialization" carried out between the University of Medellín and the Aburrá Sur Chamber of Commerce.
For citation: Henao, R. N, Hinestroza, I. C. B. & Pineda, A. A. L. (2022). Geographic Concentration of Economic Activities in Aburrá Sur in Antioquia (Colombia) - A Reinterpretation of the Industrialisation Coefficients of the Stochastic Independence Approach. Ekonomika regiona [Economy of regions], 18(1), 64-77, https://doi.org/10.17059/ekon.reg.2022-1-5.
1 © Henao R. N, Hinestroza I. C. B., Pineda A. A. L. Text. 2022. Ekonomika Regiona [Economy of Region], 18(1), 2022
ИССЛЕДОВАТЕЛЬСКАЯ СТАТЬЯ
Р. Н. Энаоа), И. К. Б. Хинестроза б), А. А. Л. Пинедав)
а б> в) Университет Медельина, Медельин, Колумбия а) https://orcid.org/0000-0002-9228-2193, e-maU: robertng@udem.edu.co
б) https://orcid.org/0000-0001-9661-5146
в) https://orcid.org/0000-0002-2419-0211
Пространственная концентрация экономической деятельности в Абурра-Сур в Антьокии (Колумбия): пересмотр коэффициентов индустриализации с использованием подхода
стохастической независимости
С точки зрения экономической географии в государственной политике большую роль играют факторы пространственной и промышленной концентрации, способствующие экономическому развитию и эффективной трансформации территорий. В этой статье определяются индексы географической концентрации экономической деятельности в каждом из пяти муниципалитетов, входящих в состав территориальной агломерации Абурра Сур в департаменте Антьокия (Колумбия). В ходе исследования дана новая интерпретация коэффициентов индустриализации, используемых при подходе стохастической независимости. Этот подход объясняет расхождения между уровнями специализации производства и пространственной концентрацией с помощью индексов энтропии, в которых в качестве эталонного рассматривается равномерное распределение, как в случае с максимальной дисперсией. Проверена гипотеза о преобладании в муниципалитетах Абурра Сур высокой концентрации обрабатывающей промышленности, несмотря на то, что в некоторых муниципалитетах отмечаются средние и даже низкие показатели концентрации производства (в зависимости от отрасли). Проведенные расчеты показали, что обрабатывающая промышленность является наиболее концентрированным сектором территориальной агломерации. Результаты исследования могут быть использованы руководством государственных органов и промышленных предприятий для разработки стратегий и решений в области отраслевого развития.
Ключевые слова: экономическое развитие территорий, государственная политика, конкурентоспособность бизнеса, территориальная локализация, территориальная производительность, Абурра Сур, экономическая география, подход стохастической независимости, территориальная концентрация, умная специализация
Благодарность
Статья является результатом исследовательского проекта «Отраслевая оценка и стратегические обязательства делового сектора в Абурра-Сур в рамках «умной специализации» городов», проведенного Медельинским университетом и Торговой палатой Абурра-Сур.
Для цитирования: Энао Р. Н., Хинестроза И. К. Б., Пинеда А. А. Л. Пространственная концентрация экономической деятельности в Абурра Сур в Антьокии (Колумбия): пересмотр коэффициентов индустриализации с использованием подхода стохастической независимости // Экономика региона. 2022. Т. 18, вып. 1. С. 64-77. https://doi.org/10.17059/ekon. reg.2022-1-5.
1. Introduction
Territorial localisation of businesses that are part of a particular economic sector is an aspect related to productivity and business competitiveness, which is further studied in the field of economic geography (Dueñas, Morales, Olmos, 2009). From Marshall (1890) to Romer (1990), several studies have tried to substantiate the benefits of territorial localisation for businesses in a particular sector using the following points in their explanations: (i) ease of recruiting trained and specialised labour in the sector, (ii) ease in accessing necessary and specific information, and (iii) wide availability of specialised providers (Marshall, 1890; Arrow, 1962; Romer, 1990; Krugman, 1991). In this sense, economic literature points to a sig-
nificant and largely positive net effect of the concentration of businesses and labour on the sectoral and territorial productivity in such a way that the effects of concentration enhance productivity and competitiveness when it is confined to specific economic sectors (Ciccone, Hall, 1996; Ciccone, 2002; Lall, Deichmann, Shalizi, 2004; Bogetic, Sanogo, 2005; Lin, Yun, Hai, 2011).
Since, traditionally, the economy of the municipalities in the analysed territory has been based on a manufacturing industry, this paper will try to confirm whether a high concentration in this industry actually predominates through the Stochastic Independence Approach (SIA). The research aims to identify the economic sectors in each municipality that is part of the Aburra Sur,
which have a certain level of business concentration. Reinterpretation and calculation of industrialisation indices used to determine the industrial specialisation of territories and geographic concentration of economic activities is based on the methodology developed by Haedo and Mouchart (2018), which will be addressed in the methodology section.
This paper is divided into three sections. The first describes the methodology used to calculate the geographic concentration index (GCIs) of the economic sectors in the municipalities of Aburrá Sur, using as a reference the SIA model, with data aggregated by territory, developed by Haedo and Mouchart (2015) and applied in the configuration of Colombian industrial geography maps (Donato, Haedo, 2019). The second section presents the calculations resulting from measuring the GCIs by sector and the interpretation of results based on the methodological description section. Lastly, the third section establishes some conclusions related to the description and interpretation of the results according to the economic sectors of each municipality, which yielded a certain level of spatial concentration through the proposed methodology.
2. Methodology
The Aburrá Valley is one of the nine sub-regions of the department of Antioquia (Colombia), which covers an area of 1156 km2 (Área Metropolitana del Valle de Aburrá, 2007), with an estimated population of 4,256,997 inhabitants (DANE, 2018). This sub-region comprises the municipality of Medellín as its central axis in addition to neighbouring municipalities to the north (Barbosa, Bello, Copacabana, Girardota) and to the south (Caldas, Envigado, La Estrella, Itagüí and Sabaneta), which according to Law 1625 of 20131, form the Metropolitan Area of Aburrá Valley.
The method used to prove the high concentration in the manufactory industry in the municipalities belonging to the south of the Aburrá Valley, consists of reinterpretation of the industrialisation coefficients of the stochastic independence approach applied in the studied territory. The contributions developed by Donato and Haedo (2019) are used as a reference and define the following variables from the SIA model. The methodology developed for this study starts from a finite set AS2 of I territories, i, for the territorial grouping, which is the subject of study wherein i : i e AS = {1, ..., I }, and a finite set J of J sectors, j,
of economic activity wherein j e J = {1, ..., J }. Tags i e AS and j e J are not informative given that they are not arbitrary and do not include any information. For each territorial data point (i, j) e AS x J, there is a number N of primary units of analysis, which refer to the number of companies and employees in territory i in sector j. This yields a matrix of I x J N = [Nj] wherein the table rows and columns contain information for a finite set AS in the following manner:
N =ZNij ; Nj =ZNij ;
j=i i=i
N _¿ ¿N -¿N _¿Ni •
(i)
=i j=i
j=i
Data is represented by the total number of observations N.., and the relative frequencies3:
* - Nu . P - N.. P _ Nl . P _ Nu. P _ Nl (2)
Pi _ N 'Pi _ N ' Pi _ N 'Pi/i _ N. 'Pi/ j _ N. ' C)
A
N
.Nl
N
N4
The focus of this analysis will be on the geographic concentration of economic sectors based on the classification of all economic activities established by the fourth edition of the International Standard Industrial Classification (ISIC, Rev. 4) of the United Nations4. Given the hierarchical structure of information organisation established by ISIC Rev. 4 according to four category levels (sections, divisions, groups, and classes), the category used in this study is the highest category level corresponding to the sections identified through an alphabetical code that subdivides the economic activities of any territory in 21 large groups. These are equivalent to sectors of the economy through the methodology used in Colombia to measure production via GDP by supply (Lora, Prada, 2016). The analysis of the aggregated territorial data by the SIA model is not exempt from discrepancies among territorial data distributions (i, j) (Basseville, 2013). To consider and evaluate the possible effects of these discrepancies on the information, the principle of the estimated Hoover-Balassa index5 or location quotient (LO) (Florence, 1939) will be used for each territorial data point (i, j) in the manner proposed by Donato and Haedo (2019):
1 "Law by which Organic Law 128 of 1994 is repealed and the Regime for Metropolitan Areas in Colombia is enacted."
2 Understood as Aburra Sur
3 Where N.. represents the total number of observations.
4 https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev 4s.pdf.
5 The estimated Hoover-Balassa coefficient is commonly recognised in the applications of location theory and New Economic Geography as the Location Quotient.
Fig. 1. Location of Aburrâ Sur (AS) (source: Prepared by the authors based on https://sites.google.com/site/seriescol/shapes (Date
of access: 20.03.2020))
lqNINl
N.i / N.. _Ni / N.._ Pi
Ni./ N. i P, Pi
.a=
"n,/ N..
= Eil- = Eut. (3)
The measurement of the relative territorial concentration d (p— | p) is derived from the difference of the distributions that measure the relative and general association between territories i and sectors j of the finite set of territories AS. The methodology employed takes as a reference Aiginger and Davies' approach (2004), which explains the diver-
gences between specialisation levels of production and geographical concentration by using entropy indices that consider the reference distribution as uniform distribution, as is the case with maximum dispersion. In the same methodological line, (Wu, Zhu, 2017) analysed the trend determinants of geographic concentration and industrial specialisation in China between 1999 and 2010 using the reference distribution as the area distribution of territories and taking the Kullback and Leiber divergence model (1951) as a pattern for discrepancies, wherein the relative concentration of sector j in territory i will be determined by Relative territorial concentration
Экономмка peruoHa, T. 18, Bun. 1 (2022)
d(Pm I Pi) = Yßi j H ^
t. y
lQg ( LQ ) •
Relative territorial concentration =
(
= Pj log
V P
Vf e J •
(4)
(5)
O..
INCO.. = =f-
' Oh o.
(6)
Reinterpreting (6) based on (4) and (5), the GCI by sector can be obtained through
O,
GCI.. = S°' O
(7)
TO
Indicators Used
The indicators used to represent the information in Tables 2, 3, 4, 5, and 6 are described below:
— Area (km2) of the municipal i: AREAMU.
— Population (inhabitants) in the municipality i: POP,
MU;'
— Population density of municipality i: Indicator that determines the quantity of inhabitants per km2 in each municipality MU..
Thus, the geographic concentration index (GCI) by sector can be calculated for Aburra Sur based on the reinterpretation of the industrialisation coefficient (INCO), which is used in economic geography as a measurement of industrial concentration in a territory (Kiely, 1994). This industrialisation coefficient is developed based on the number of employed people, considering the participation of people employed in industry in a territory as a proportion of the total number of employed people in that territory in comparison with the level of industrialisation in the region. The latter is assumed to be the regional average of people employed in industry (Polese, Rubiera, 2009).
POPDEN,„, = -
POPmu AREA,
(8)
— Quantity of businesses in municipality i in sector j: B...
— Quantity of employees in municipality i in sector j: E...
— Availability of business resources in municipality i in sector j: Indicator that determines the number of businesses in each municipality MU of each economic sector j per 1,000 inhabitants.
B„
AVAILBUS,„, = -
POP,
-x1000.
(9)
— Relative business participation of municipality i in sector i in %: Indicator that determines the business participation percentage of sector i in municipality MU{ with respect to the total number of businesses in municipality MU..
BUSPART
B„
Relative^
x100. (10)
LPuv,
— Total business participation of municipality i in sector i in %: Indicator that determines the business participation percentage of sector i in municipality i MU{ with regard to the total number of businesses in Aburra Sur MU „.
BUSPART
Bs
TotalM
TBm
-x 100. (11)
This study reinterprets Donato and Haedo's proposal (2019) on a national scale for the territorial grouping of municipalities that form Aburra Sur in a manner such that
— when the value of GCL > 1.1 (10 % above the average of Aburra Sur), the territory is classified as high concentration (HC);
— when the value of 0.9 < GCL < 1.1 (+/-10 % above the average of Aburra Sur), the territory is classified as medium concentration (MC);
— when the value of 0.6 < GCI{j < 1.1 (between 10 % — 40 % below the average of Aburra Sur), the territory is classified as low concentration (LC); and
— when the value of 0.6 > GCI, the territory is classified as no concentration (NC).
— Relative employee participation of municipality i in sector i in %: Indicator that determines the percentage of employees in sector i in municipality MU with regard to the total number of employees in MUi. This indicator is the result of reinterpreting what economic geography considers as the territorial industrialisation level, which is obtained by calculating the participation of employed people as a proportion of the total amount of employed people (Donato, Haedo, 2019).
EMPLPART
E,
RelativeM
x100. (12)
— Total employee participation in municipality i in sector i in %: Indicator that determines the employee participation percentage of sector i in
¡I
Table 1
General Information about each Municipality of Aburrá Sur
Territory AREA POP POPDEN B E AVAILBUS Total BUSPART, % Total EMPLPART, % 0mUAS
Caldas 135 80 538 596.5 2 319 7 188 28.8 8.1 3.2 342.2
Envigado 78.8 238173 3023.2 9 696 68 336 40.7 33.7 30.7 3254.1
Itagüí 21.1 276936 13 131.1 10477 84 914 37.8 36.4 38.2 4043.5
La Estrella 35 65 303 1865.8 2 230 19 362 34.1 7.7 8.7 922.0
Sabaneta 15 53 913 3594.2 4 049 42 203 75.1 14.1 19 2009.6
Total in Aburrá Sur 284.8 714863 2509.4 28 771 222 003 40.2 100 100 10 571.4
Source: Calculated by the authors based on information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
municipality MU{ with regard to the total number of employees in MUS As with the indicator above, this participation level is obtained by reinterpreting the indicator known as territorial industrialisation level. The difference in this case is that the territorial base is broader and determined by the total number of employees in the Aburra Sur AS territorial grouping.
EMPLPART
E
TotaM
-x100. (13)
— Average number of employees in Aburra Sur AS in sector j: Indicator that determines the average number of employees for each of the 21 economic sectors according to the ISIC Rev. 4 classification for the municipalities of Aburra Sur AS.
E,,
21
(14)
— Geographic concentration level of municipality i in sector j in %: The indicator that results from reinterpreting the industrialisation coefficients used in the SIA model, with data aggregated by territory in (4), (5), and (6).
E
^MU^
m YEmuh ,nn
GCL„, = —=—- x100.
MUij E
A C
3. Results
(15)
Results of the calculation of the GCIs for Aburrá Sur's municipalities were developed from the database of the Aburrá Sur business structure as of December 31, 2018 and obtained from the Aburrá Sur Chamber of Commerce (CAS — Cámara de Comercio del Aburrá Sur, 2018). Employment data was gathered by the Public Employment Service through the Statistical Directory of Companies (DEST) by DANE (2018).
Table 1 provides general information about each municipality of the Aburra Sur based on applied basic indicators related to the area measured in km2 (AREA), population (POP), and population density (POPDEN). Inventories of the total amount of businesses (B) and employees (E) are also presented, which enables the calculation of basic indicators related to the availability of business resources (AVAILBUS) and levels of business (BUSPART) and employment (EMPLPART) participation, with regard to the entirety territory and average number of jobs in Aburra Sur (Om ).
Tables 2, 3, 4, 5 and 6, which are presented below, show the GCIs by sector for each municipality of Aburra Sur, calculated through the proposed methodology and indicators throughout this research study.
Based on Table 1, the municipality of Caldas presents the lowest level of labour participation, in the same analytical line, in 2018, it had 2,319 registered companies, higher than the municipality of La Estrella in number and business participation and in terms of availability of business resources, the municipality of Caldas has an added value of 28.8, with the lowest indicator in Aburra Sur.
The calculation of the correlation coefficient p of Pearson (Edwards, 1976) between the indicators of business participation of the different sectors in the municipality [EMPLPARTJRelative and the employment participation of each sector in the municipality [BUSPARTJRelative shows a low level of correlation with a result of p = 0.440572. This can be explained in greater detail by noting that the vehicle sales and repair sector (Section G) has the largest number of related businesses (1,045) but produces only 12.28 % of total municipal employment. On the other hand, the sectors of manufacturing industries (Section C) and transport and storage (Section H) produce 27.43 % and 16.07 %, respectively, of employment in the municipality. However, its participation in the total
Table 2
GCI and Basic Indicators per Economic Sector for the Municipality of Caldas
Economic Sector B E AVAILBUS BUSPART Relative, % BUSPART Total, % EMPLPART Relative, % EMPLPART Total, % GCI, % GCI Categorisation
A. Agriculture, Livestock, Hunting, Forestry, Fishing 35 531 0.4 1.5 0.12 7.39 0.23 0.00 NC
B. Mining & Quarrying 6 213 0.1 0.3 0.02 2.96 0.09 2.01 NC
C. Manufacturing Industries 293 1972 3.6 12.6 1.02 27.49 0.88 18.65 NC
D. Electricity & Gas Supply 3 64 0.0 0.1 0.01 0.89 0.02 0.60 NC
E. Water Distribution & Treatment 13 31 0.2 0.6 0.05 0.43 0.01 0.29 NC
F. Construction 104 517 1.3 4.5 0.36 7.19 0.23 4.89 NC
G. Vehicle Sales & Repair 1045 883 12.8 45.1 3.63 12.28 0.39 8.35 NC
H. Transportation & Storage 97 1155 1.2 4.2 0.34 16.07 0.52 10.92 NC
I. Lodging & Food Services 344 120 4.2 14.8 1.20 1.67 0.05 1.13 NC
J. Information & Communication 25 92 0.3 1.1 0.09 1.28 0.04 0.87 NC
K. Financial & Insurance Related Activities 17 11 0.2 0.7 0.06 0.15 0.00 0.10 NC
L. Real Estate Activities 32 83 0.4 1.4 0.11 1.15 0.03 0.78 NC
M. Professional, Scientific & Technical Activities 65 241 0.8 2.8 0.23 3.35 0.10 2.28 NC
N. Administrative & Support Services Activities 55 31 0.7 2.4 0.19 0.43 0.01 0.29 NC
O. Public Administration 0 122 0.0 0.0 0.00 1.70 0.05 1.15 NC
P. Education 9 483 0.1 0.4 0.03 6.72 0.21 4.56 NC
Q. Human Health Care Activities 21 424 0.3 0.9 0.07 5.90 0.19 4.01 NC
R. Artistic & Recreational Activities 27 9 0.3 1.2 0.09 0.13 0.00 0.08 NC
S. Other Service Activities 128 206 1.6 5.5 0.44 2.87 0.09 1.94 NC
T. Household activities as Employers 0 0 0.0 0.0 0.00 0.00 0.00 0.00 NC
Total 2319 7188 28.8 100 8.06 100 3.238
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
employment levels of the five municipalities of Aburra Sur does not even reach 1 %.
The calculation of the geographic concentration of economic sectors in the municipality through the GCI in 2018 shows that the municipality of Caldas does not have an economic sector with evidence of concentration in its territory. This is because under the classification scale of the territorial sectoral concentration proposed by this research, none of the 21 sectors, in which economic activity and employment are distributed, shows a coefficient higher than 60 %.
The analysis of the data in Table 1 identifies Envigado as the second municipality with the highest business participation and labour participation. In 2018, the municipality produced 33.70 % of companies and 30.78 % of jobs in the territorial grouping, only less than the municipality of Itagui. Regarding the availability of business resources, the municipality of Envigado had a value of 40.7 in
2018, being, after the municipality of Sabaneta, the territory with the second highest indicator.
The vehicle sales and repair sector (Section G) registered the highest level of business participation in the municipality. The second with the highest relative business participation was the manufacturing industry (Section C).
The calculation of the Pearson correlation coefficient p (Edwards, 1976) between the business participation indicators of the different economic sectors and employment participation yielded the result p = 0.860120, establishing a positive impact between the economic sectors that register the largest number of companies and generation of employment in the territory. This situation can be verified by stating that the vehicle sales and repair sector (Section G) is the one with the most registered companies (3,140) and generates the largest number of jobs in the municipality (17,954). Likewise, the manufacturing industry
Table 3
GCI and Basic Indicators per Economic Sector for the Municipality of Envigado
Economic Sector B E AVAILBUS BUSPART Relative, % BUSPART Total, % EMPLPART Relative, % EMPLPART Total, % GCI, % GCI Categorisation
A. Agriculture, Livestock, Hunting, Forestry, Fishing 161 7890 0.68 1.7 0.56 11.55 3.55 74.63 LC
B. Mining & Quarrying 19 851 0.08 0.2 0.07 1.25 0.38 8.05 NC
C. Manufacturing Industries 1168 9716 4.90 12.0 4.06 14.22 4.38 91.90 MC
D. Electricity & Gas Supply 6 5 0.03 0.1 0.02 0.01 0.00 0.04 NC
E. Water Distribution & Treatment 18 455 0.08 0.2 0.06 0.67 0.20 4.30 NC
F. Construction 517 5874 2.17 5.3 1.80 8.60 2.65 55.56 NC
G. Vehicle Sales & Repair 3140 17954 13.18 32.4 10.91 26.27 8.09 169.83 HC
H. Transportation & Storage 228 2460 0.96 2.4 0.79 3.60 1.11 23.27 NC
I. Lodging & Food Services 1061 2617 4.45 10.9 3.69 3.83 1.18 24.75 NC
J. Information & Communication 293 3662 1.23 3.0 1.02 5.36 1.65 34.64 NC
K. Financial & Insurance Related Activities 285 553 1.20 2.9 0.99 0.81 0.25 5.23 NC
L. Real Estate Activities 427 1210 1.79 4.4 1.48 1.77 0.55 11.44 NC
M. Professional, Scientific & Technical Activities 958 3257 4.02 9.9 3.33 4.77 1.47 30.80 NC
N. Administrative & Support Services Activities 464 4220 1.95 4.8 1.61 6.18 1.90 39.91 NC
O. Public Administration 5 1152 0.02 0.1 0.02 1.69 0.52 10.89 NC
P. Education 99 2900 0.42 1.0 0.34 4.24 1.31 27.43 NC
Q. Human Health Care Activities 147 1776 0.62 1.5 0.51 2.60 0.80 16.80 NC
R. Artistic & Recreational Activities 125 410 0.52 1.3 0.43 0.60 0.18 3.87 NC
S. Other Service Activities 575 1374 2.41 5.9 2.00 2.01 0.62 12.99 NC
T. Household activities as Employers 0 0 0.00 0.0 0.00 0.00 0.00 0.00 NC
Total 9696 68336 40.71 100 33.70 100.00 30.78
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
(Section C) has the second largest number of businesses (1,168) and job creation (9,716).
Regarding the geographic concentration according to the ICG as of 2018, the municipality of Envigado has three sectors that present levels of territorial concentration based on the scale applied in this research study. In turn, these sectors are the ones that contribute the most in the municipality in terms of labour participation and correspond to the vehicle sales and repair sector with a high territorial concentration categorisation where ICG = 169.83 %; the manufacturing industry sector with medium concentration categorisation where ICG = 91.90 %; and the agriculture, livestock, hunting, forestry and fishing sector with low concentration where ICG = 74.63 %.
Table 1 shows that, in aggregate terms, Itagui is the municipality of Aburra Sur characterised by the best performance in terms of business participation and labour participation. It can be established that although it is the territory of Aburra Sur with the largest population, it has the largest
number of registered companies and employees. For this reason, the relationship between the total number of companies and the population of the municipality per 1,000 inhabitants, produces the indicator of availability of business resources that places Itagui with the third best performance in the region with 37.8.
The calculation of the Pearson correlation coefficient p (Edwards, 1976) between the indicators of business participation of the different economic sectors and participation in employment for the municipality showed the results of p = 0.788980, establishing an acceptable level of correlation between the economic sectors that register the greater number of companies and generation of employment in the territory. This is verified by analysing that of the five economic sectors with the most businesses registered in the municipality, only the vehicle sales and repair sectors (Section G) and the manufacturing industry (Section C) are among the top five in the municipality in terms of employment.
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
Table 4
GCI and Basic Indicators per Economic Sector for the Municipality of Itagui
Economic Sector B E AVAILBUS BUSPART Relative, % BUSPART Total, % EMPLPART Relative, % EMPLPART Total, % GCI, % GCI Categorisation
A. Agriculture, Livestock, Hunting, Forestry, Fishing 82 1590 0.30 0.8 0.28 1.87 0.72 15.04 NC
B. Mining & Quarrying 10 310 0.04 0.1 0.03 0.37 0.14 2.93 NC
C. Manufacturing Industries 2027 28458 7.32 19.3 7.04 33.51 12.82 269.19 HC
D. Electricity & Gas Supply 5 25 0.02 0.0 0.01 0.03 0.01 0.23 NC
E. Water Distribution & Treatment 35 283 0.13 0.3 0.12 0.33 0.13 2.67 NC
F. Construction 359 6166 1.30 3.4 1.24 7.26 2.78 58.32 NC
G. Vehicle Sales & Repair 4504 20284 16.26 43.0 15.65 23.89 9.14 191.87 HC
H. Transportation & Storage 400 4699 1.44 3.8 1.39 5.53 2.12 44.44 NC
I. Lodging & Food Services 1211 1233 4.37 11.6 4.20 1.45 0.56 11.66 NC
J. Information & Communication 146 1823 0.53 1.4 0.50 2.15 0.82 17.24 NC
K. Financial & Insurance Related Activities 132 195 0.48 1.3 0.45 0.23 0.09 1.84 NC
L. Real Estate Activities 214 485 0.77 2.0 0.74 0.57 0.22 4.58 NC
M. Professional, Scientific & Technical Activities 327 4195 1.18 3.1 1.13 4.94 1.89 39.68 NC
N. Administrative & Support Services Activities 292 7740 1.05 2.8 1.01 9.12 3.49 73.21 LC
O. Public Administration 2 2097 0.01 0.0 0.00 2.47 0.94 19.83 NC
P. Education 79 732 0.29 0.8 0.27 0.86 0.33 6.92 NC
Q. Human Health Care Activities 78 1999 0.28 0.7 0.27 2.35 0.90 18.90 NC
R. Artistic & Recreational Activities 116 635 0.42 1.1 0.40 0.75 0.29 6.00 NC
S. Other Service Activities 458 1965 1.65 4.4 1.59 2.31 0.89 18.58 NC
T. Household activities as Employers 0 0 0.00 0.0 0.00 0.00 0.00 0.00 NC
Total 10477 84914 37.83 100.0 36.415 100.00 38.25
Only three economic sectors have some level of territorial concentration according to the proposed methodology. The first sector is the manufacturing industry (Section C) with a high concentration supported by GCI = 269.19 %. The second highest is the vehicle sales and repair sector (Section G) with a high concentration according to GCI = 191.87 %. The last sector with territorial concentration is that of administrative and support services activities (Section N) with a low level of concentration of GCI = 73.21 %.
According to the data in Table 1, La Estrella, after Caldas, has the lowest labour participation. Regarding business participation, in 2018, the municipality of La Estrella registered the least number of companies in Aburra Sur. Regarding the availability of business resources, the municipality of La Estrella has a value of 34.1, surpassing the municipality of Caldas.
The vehicle sales and repair sector (Section G) had the highest level of business participation. In
2018, the manufacturing industries (Section C) and hospitality and food services (Section I) sectors had the second and third highest participation percentages in the same period with 23.4 % and 10 %, respectively.
The calculation of the Pearson correlation coefficient p (Edwards, 1976) between the business participation indicators of the different sectors of the municipality and the labour participation of each sector in the municipality yields a correlation level of p = 0.680720. It is explained in greater detail by highlighting that the vehicle sales and repair sector (Section G), which has the largest number of related businesses (787), generates only 12.90 % of total municipal employment.
The spatial concentration by sectors of the municipality of La Estrella obtained from the GCI for the year 2018 establishes that of the 21 economic sectors, only the manufacturing industries sector (Section C) can be framed within the lev-
Table 5
GCI and Basic Indicators per Economic Sector for the Municipality of La Estrella
Economic Sector B E AVAILBUS BUSPART Relative, % BUSPART Total, % EMPLPART Relative, % EMPLPART Total, % GCI, % GCI Categorisation
A. Agriculture, Livestock, Hunting, Forestry, Fishing 32 165 0.49 1.4 0.11 0.85 0.07 1.56 NC
B. Mining & Quarrying 4 199 0.06 0.2 0.01 1.03 0.09 1.88 NC
C. Manufacturing Industries 522 9264 7.99 23.4 1.81 47.85 4.17 87.63 LC
D. Electricity & Gas Supply 2 0 0.03 0.1 0.01 0.00 0.00 0.00 NC
E. Water Distribution & Treatment 19 234 0.29 0.9 0.07 1.21 0.11 2.21 NC
F. Construction 99 1450 1.52 4.4 0.34 7.49 0.65 13.71 NC
G. Vehicle Sales & Repair 787 2498 12.05 35.3 2.74 12.90 1.13 23.62 NC
H. Transportation & Storage 94 1430 1.52 4.2 0.33 7.39 0.64 13.52 NC
I. Lodging & Food Services 222 314 3.40 10.0 0.77 1.62 0.14 2.97 NC
J. Information & Communication 31 272 0.47 1.4 0.11 1.40 0.12 2.57 NC
K. Financial & Insurance Related Activities 26 328 0.40 1.2 0.09 1.69 0.15 3.10 NC
L. Real Estate Activities 71 245 1.09 3.2 0.25 1.27 0.11 2.31 NC
M. Professional, Scientific & Technical Activities 105 521 1.61 4.7 0.36 2.69 0.23 4.92 NC
N. Administrative & Support Services Activities 69 1103 1.06 3.1 0.24 5.70 0.50 10.43 NC
O. Public Administration 1 197 0.02 0.0 0.00 1.02 0.09 1.86 NC
P. Education 21 432 0.32 0.9 0.07 2.23 0.19 4.08 NC
Q. Human Health Care Activities 17 299 0.26 0.8 0.06 1.54 0.13 2.82 NC
R. Artistic & Recreational Activities 21 16 0.32 0.9 0.07 0.08 0.01 0.15 NC
S. Other Service Activities 87 395 1.33 3.9 0.30 2.04 0.18 3.73 NC
T. Household activities as Employers 0 0 0.00 0.0 0.00 0.00 0.00 0.00 NC
Total 2230 19362 34.15 100.0 7.75 100.00 8.72
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
els established for territorial concentration conditions with low concentration according to the proposed methodology, which is supported by GCI = 87.631 %.
According to the data in Table 1, La Estrella, after Caldas, has the lowest labour participation with 19,362 employed persons, that is, 8.72 % of the total employment generated in the five municipalities, in Aburra Sur. Regarding business participation, in 2018 the municipality of La Estrella registered 2,230 companies in total, equivalent to 7.75 % of the companies in the region, being the municipality with the least number of companies registered in Aburra Sur. Regarding the availability of business resources, AVAILBUS, the municipality of La Estrella has a value of 34.1, surpassing the municipality of Caldas as the territory of Aburra Sur with the lowest indicator in terms of the relationship between the total number of
companies and the population of the municipality per 1000 inhabitants.
The vehicle sales and repair sector (Section G of the ISIC Code, Rev. 4) had the highest level of business participation with 35.3 % of the total businesses in the municipality (787), that is, 2.74 % of all businesses in Aburrá Sur. In 2018, the manufacturing industries (Section C) and accommodation and food services (Section I) sectors had the second and third highest participation percentage in the same period with 23.4 % and 10 %, respectively, among the total number of companies registered in the five municipalities that make up the territorial grouping.
The correlation analysis by calculating the Pearson correlation coefficient p (Edwards, 1976) between the business participation indicators of the different sectors of the BUSBAR!}_Relative municipality and the labour participation of each
Table 6
GCI and Basic Indicators per Economic Sector for the Municipality of Sabaneta
Economic Sector B E AVAILBUS BUSPART Relative, % BUSPART Total, % EMPLPART Relative, % EMPLPART Total, % GCI, % GCI Categorisation
A. Agriculture, Livestock, Hunting, Forestry, Fishing 40 3247 0.73 1.0 0.13 7.69 1.46 30.71 NC
B. Mining & Quarrying 13 656 0.24 0.3 0.04 1.55 0.30 6.20 NC
C. Manufacturing Industries 591 16654 10.83 14.6 2.05 39.39 7.49 157.25 HC
D. Electricity & Gas Supply 2 5 0.04 0.0 0.00 0.01 0.00 0.02 NC
E. Water Distribution & Treatment 14 39 0.26 0.3 0.04 0.10 0.02 0.38 NC
F. Construction 238 4235 4.36 5.9 0.82 10.03 1.91 40.02 NC
G. Vehicle Sales & Repair 1320 5340 24.19 32.6 4.58 12.69 2.41 50.66 NC
H. Transportation & Storage 149 4210 2.73 3.7 0.51 10.01 1.90 39.97 NC
I. Lodging & Food Services 486 947 8.91 12.0 1.68 2.25 0.43 8.96 NC
J. Information & Communication 115 1992 2.11 2.8 0.40 4.68 0.89 18.70 NC
K. Financial & Insurance Related Activities 86 95 1.58 2.1 0.29 0.33 0.06 1.30 NC
L. Real Estate Activities 166 302 3.04 4.1 0.57 0.71 0.13 2.81 NC
M. Professional, Scientific & Technical Activities 277 1380 5.08 6.8 0.96 3.29 0.63 13.14 NC
N. Administrative & Support Services Activities 167 1252 3.06 4.1 0.58 2.98 0.57 11.90 NC
O. Public Administration 0 289 0.00 0.0 0.00 0.68 0.13 2.73 NC
P. Education 48 775 0.88 1.2 0.16 1.82 0.35 7.28 NC
Q. Human Health Care Activities 58 202 1.06 1.4 0.20 0.48 0.09 1.91 NC
R. Artistic & Recreational Activities 45 32 0.82 1.1 0.15 0.07 0.01 0.28 NC
S. Other Service Activities 234 528 4.29 5.8 0.81 1.23 0.23 4.90 NC
T. Household activities as Employers 0 0 0.00 0.0 0.00 0.00 0.00 0.00 NC
Total 4049 42203 74.21 100 14.073 100 19.00
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
sector in the EMPLIPARJJRelative yields a correlation level of p = 0.680720, which is explained in greater detail by highlighting that the vehicle sales and repair sector (Section G), which has the largest number of related businesses (787), generates only 12.90 % of total municipal employment. Additionally, the manufacturing industries sector (Section C) with 522 companies has 9,264 registered jobs, that is, 47.85 % of municipal employment and 4.17 % of total employment in Aburra Sur.
The information in Table 1 highlights a set of particularities regarding the business dynamics of the municipality of Sabaneta, since it is the smallest territory in Aburra Sur. It is the municipality of the territorial group with the best result in terms of availability of business resources (AVAILBUS) with a 2018 result of 75.1, surpassing the municipalities of Envigado (40.7) and Itagui (37.8), which are the territories-leaders in terms of the number of companies and jobs in Aburra Sur.
The vehicle sales and repair sector (Section G) has the largest number of registered companies and contributes 12.69 % of the employment generated in the territory. The manufacturing industries sector (Section C) exceeds the first in this category of analysis, since it generated 39.39 % of municipal employment
Other sectors with registered companies outside the described ranking but with optimal performance in terms of job creation were the following: construction (Section F) with a 10.03 % share of municipal employment and transport and storage (Section H) with 10.01 %. The correlation analysis of the indicators of business participation and participation in employment for the different economic sectors yielded a result p = 0.533832 for the municipality which, after the result registered by the municipality of Caldas, is the lowest value of the five municipalities of Aburra Sur.
The calculation of the geographic concentration by sector establishes that in the municipal-
Table 7
Primary Results
CALDAS ENVIGADO ITAGUi LA ESTRELLA SABANETA
Employment Participation in the Region 3.24 % 30.78 % 38.25 % 8.72 % 19.10 %
Lower Second highest Highest Fourth highest Third highest
Business Participation in the Region 2.319 9.696 10.477 2.230 4.049
8.06 % 33.70 % 36.42 % 7.75 % 14.10 %
Availability of Business Resources (va) 28.8 40.7 37.8 34.1 75.1
Five Main Sectors by Number of Businesses i. Vehicle Sales and Repair ii. Lodging and Food Services iii. Manufacturing Industries iv. Other Service Activities v. Construction i. Vehicle Sales and Repair ii. Manufacturing Industries iii. Lodging and Food Services iv. Professional, Scientific, and Technical Activities v. Other Service Activities i. Vehicle Sales and Repair ii. Manufacturing Industries iii. Lodging and Food Services iv. Other Service Activities v. Transportation & Storage i. Vehicle Sales and Repair ii. Manufacturing Industries iii. Lodging and Food Services iv. Professional, Scientific, and Technical Activities v. Construction i. Vehicle Sales and Repair ii. Manufacturing Industries iii. Lodging and Food Services iv. Professional, Scientific, and Technical Activities v. Other Service Activities
Participation of Businesses in the Five Main Sectors in the Municipality, % 82.50 71.30 82.08 77.80 71.8
Pearson Correlation Coefficient (p) 0.44 0.86 0.78 0.68 0.533
Geographic Concentration Index Sectors with no concentration Three sectors with territorial concentration Three sectors with territorial concentration One sector with territorial concentration One sector with territorial concentration
Source: Calculated by the authors based on the information provided by (CAS-Camara de Comercio del Aburra Sur, 2018) and DANE (2018).
ity of Sabaneta, only the manufacturing industries economic sector has a categorisation of territorial concentration, indicated by ICG = 157.25 % and showing high concentration.
4. Conclusions
The industrial sector is the economic sector with the highest levels of territorial concentration in Aburra Sur, given that it has high concentration categorisation in the municipalities of Itagui and Sabaneta, medium concentration in the municipality of Envigado, and low concentration in the municipality of La Estrella. The results of this study prove the hypothesis that in the municipalities studied predominates the high concentration in the manufacturing industry and provide an important input for local administrations and other development actors in Aburra Sur to guide their plans and programs for more efficient resource allocation in the region.
When analysing the industrial concentration of each municipality, the municipality of Itagui has two sectors with high industrial concentration (manufacturing industries and vehicle sales & repair) and one with low concentration (administrative & support services activities). In this sense, it is understood that the focus of public investment should be geared toward the first two, since they have the greatest impact on both economic growth and job creation. The municipality of Envigado has a highly concentrated sector in vehicle sales & repair; a medium concentration sector in the manufacturing industries; and a low concentration sector in agriculture, livestock, hunting, forestry, and fishing.
In the municipalities of Sabaneta and La Estrella, the manufacturing industries sector should receive the highest proportion of public investment, given that it achieved high and low industrial concentration levels, respectively.
Экономмка peruoHa,T. 18, Bun. 1 (2022)
There are no industrial concentration sectors in the municipality of Caldas. This could be related to Pearson's low correlation coefficient (p) (see Table 7), as its business participation is one of the lowest in the region; additionally, it has the lowest level of employment participation and the lowest availability of business resources. Notably, the municipal administration should focus its public policy efforts on promoting and generating incentives that increase the creation, growth, and/ or consolidation of existing companies among the five main sectors according to the number of companies and incentivise job creation.
Due to economic activities in the territories being concentrated in one space in most cases, the SIA model is useful when analysing this type of data as the prevalence of reported sectors with concentration levels does not depend on one another or among others, since they are different and one sector does not depend on or influence the other. This is supported by the application of this approach in several studies on corporate specialisation and concentration. In terms of public policy, with regard to business decisions and regional interests, it is important to prioritise ef-
forts and resources regarding investment and assertive decisions to increase business productivity and regional development. The sectors with little or no concentration are no less important, as they also contribute to the regions in terms of employment and as potential sectors for business growth and diversification.
This type of analysis is relevant for local authorities, since it enables them to focus business strategies and public resources in a more efficient and accurate manner, in addition to identifying specific needs and opportunities, such as in the municipality of Caldas, where it would be relevant to promote its main sectors through a more structured analysis, thus providing more robust and complete statistical information and analysis.
Sectoral concentration must be addressed by not only businesspersons but also all the competent local authorities that implement an institutional structure necessary to create an environment conducive to territorial development. This will help them become economically self-sustainable, coincide with the economic activities of the regions, and stay updated with the results of analytical studies of the territories.
References
Aiginger, K. & Davies, S. (2004). Industrial Specialisation and Geographic Concentration: Two Sides of the Same Coin? Not for the European Union. Journal of Applied Economics, 7(2), 231-248. DOI: 10.1080/15140326.2004.12040610.
Área Metropolitana del Valle de Aburrá. (2007). 2008-2020 Metrópoli: Hacia la integración regional sostenible [2008-2020 Metropolis: Towards sustainable regional integration]. Medellín: Área Metropolitana del Valle de Aburrá. (In Spanish)
Arrow, K. (1962). The economic implications of learning by doing. Review of Economic Studies, 29(3), 155-173. DOI: 10.2307/2295952.
Basseville, M. (2013). Divergence measures for statistical data processing — An annotated bibliography. Signal Processing, 93(4), 621-633. 10.1016/j.sigpro.2012.09.003
Bogetic, Z. & Sanogo, I. (2005). Infrastructure, Productivity and Urban Dynamic in Cote D'Ivoire. Africa Region Working Paper Series, 86, 49-97.
CAS — Cámara de Comercio del Aburrá Sur. (2018). Cuadros de Salida — Estructura empresarial de los municipios del Aburrá Sur [Output Tables — Business structure of the municipalities of Aburrá Sur]. Itagüí: Cámara de Comercio del Aburrá Sur. (In Spanish)
Ciccone, A. & Hall, R. (1996). Productivity and the density of economic activity. American Economic Review, 86(1), 54-70. Retrieved from: http://www.jstor.org/stable/2118255 (Date of access: 20.03.2020).
Ciccone, A. (2002). Agglomeration effects in Europe. European Economic Review, 46(2), 213-227. DOI: 10.1016/ S0014-2921(00)00099-4.
DANE. (2018). Directorio Estadístico de Empresas [Statistical Directory of Companies]. Bogotá D.C.: DANE. (In Spanish)
Donato, N. & Haedo, C. (2019). Atlas de la geografía industrial de Colombia: especialización sectorial, concentración y competitividad territorial de la industria manufacturera colombiana [Atlas of Industrial Geography of Colombia]. Buenos Aires: Fundación Observatorio Pyme, 488. (In Spanish)
Dueñas Esterling, M. A., Morales Rubiano, M. E., & Olmos Sánchez, L. E. (2009Industrial Aglomeration In Bogotá City. Revista Facultad de Ciencias Económicas: Investigación y Reflexión, 17(2), 99-118. https://revistas.unimilitar.edu. co/index.php/rfce/article/view/4441 (In Spanish)
Edwards, A. (1976). An Introduction to Linear Regression and Correlation. San Francisco: W. H. Freeman.
Florence, P. (1939). Report on the Location of Industry in Great Britain. Political and Economic Planning, 49(194), 331-335. DOI: 10.2307/2225106.
Haedo, C. & Mouchart, M. (2015). Specialized agglomerations with Lattice data: Model and detection. Bologna: Universita di Bologna.
Haedo, C. & Mouchart, M. (2018). A stochastic independence approach for measuring regional specialization and concentration. Papers in Regional Science, 97(4), 1151-1118. DOI: 10.1111/pirs.12294.
Kiely, R. (1994). Development theory and industrialisation: Beyond the impasse. Journal of Contemporary Asia, 24(2), 133-160. DOI: 10.1080/00472339480000101.
Krugman, P. (1991). Geography and Trade. Cambridge: MIT Press, 156.
Kullback, S. & Leiber, R. (1951). On Information and Sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
Lall, S., Deichmann, U. & Shalizi, Z. (2004). Agglomeration Economies and Productivity in Indian Industry. Journal of Development Economics, 73(2), 643-673.
Lin, H.-L., Li, H.-Y. & Yang, C.-H. (2011). Agglomeration and productivity: Firm-level evidence from China's textile industry. China Economic Review, 22(3), 313-329. DOI: 10.1016/j.chieco.2011.03.003.
Lora, E. & Prada, S. (2016). Técnicas de Medición Económica, Metodología y Aplicaciones [Economic Measurement Techniques, Methodology and Applications]. Cali: Universidad Icesi, 418. (In Spanish)
Marshall, A. (1890). Principles of Economics. London: Macmillan, 754.
Polése, M. & Rubiera, F. (2009). Urban and regional economy. Introduction to economic geography. Investigaciones Regionales [Journal of regional research], 18, 221-227. Retrieved from: http://hdl.handle.net/10017/29521 (Date of access: 20.03.2020) (In Spanish)
Romer, P. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5), 71-102. Retrieved from: http://www.jstor.org/stable/2937632 (Date of access: 20.03.2020).
Wu, Y. & Zhu, X. (2017). Industrial policy and economic geography: evidence from China. Journal of the Asia Pacific Economy, 22(1), 173-190. DOI: 10.1080/13547860.2016.1261485.
About the authors
Robert Ng Henao — Master in Administration, Coordinator of Master in Management and Transformation of Smart and Sustainable Cities, Professor of the School of Economic and Administrative Sciences, Universidad de Medellín; Researcher ID: N-3280-2014; https://orcid.org/0000-0002-9228-2193 (Carrera 87 N° 30-65, Medellín, Colombia; e-mail: robertng@udem.edu.co).
Isabel Cristina Betancur Hinestroza — PhD student in Scientific Modeling and Computing, Coordinator of Master in Government, Professor of the School of Economic and Administrative Sciences, Universidad de Medellín; Researcher ID: N-4187-2014; https://orcid.org/0000-0001-9661-5146 (Carrera 87 N° 30-65, Medellín, Colombia; e-mail: icbetancur@ udem.edu.co).
Abraham Allec Londoño Pineda — PhD in Sustainable Development, Coordinator of the Business and International Relations Research Group, School of Economic and Administrative Sciences, Universidad de Medellín; Researcher ID: N-3813-2014; https://orcid.org/0000-0002-2419-0211 (Carrera 87 N° 30-65, Medellín, Colombia; e-mail: alondono@ udem.edu.co).
Информация об авторах
Энао Роберт Нг — магистр администрирования, координатор магистратуры в области управления и трансформации умных и устойчивых городов, профессор Школы экономических и административных наук, Университет Медельина; Researcher ID: N-3280-2014; https://orcid.org/0000-0002-9228-2193 (Колумбия, г. Медельин, Каррера 87 N° 30-65; e-mail: robertng@udem.edu.co).
Хинестроза Изабель Кристина Бетанкур — аспирант в области научного моделирования и вычислений, координатор магистратуры в области государственного управления, профессор Школы экономических и административных наук, Университет Медельина; Researcher ID: N-4187-2014; https://orcid.org/0000-0001-9661-5146 (Колумбия, г. Медельин, Каррера 87 N° 30-65; e-mail: icbetancur@udem.edu.co).
Пинеда Абрам Аллек Лондоньо — PhD в области устойчивого развития, координатор исследовательской группы по бизнесу и международным отношениям, Школа экономических и административных наук, Университет Медельина; Researcher ID: N-3813-2014; https://orcid.org/0000-0002-2419-0211 (Колумбия, г. Медельин, Каррера 87 N° 30-65; e-mail: alondono@udem.edu.co).
Дата поступления рукописи: 12.03.2020.
Прошла рецензирование: 23.09.2020.
Принято решение о публикации: 24.12.2021.
Received: 12 Mar 2020.
Reviewed: 24 Sep 2021.
Accepted: 24 Dec 2021.
