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Food industry represents an important part in the Mexican economy, including more than 400,000 companies and a Gross Domestic Product of around 16 billion of Mexican pesos in 2019. For that reason, this paper has the objective to analyze its productivity using data from the 2014 and 2019 Economic Censuses related to 2013 and 2018 economic indicators. The paper presents results of a productivity analysis of 1,672 municipalities from 32 Mexicans states grouped in eight regions using Data Envelopment Analysis. The results indicate significant differences between regions and, also, an important growth of the productivity in 2018.
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Productivity analysis in the Mexican food industry
Martin Flegl1, Carlos Alberto Jiménez Bandala2, Isaac Sánchez-Juá-
rez3, Edgar Matus4
Abstract. Food industry represents an important part in the Mexican economy, in-
cluding more than 400,000 companies and a Gross Domestic Product of around 16
billion of Mexican pesos in 2019. For that reason, this paper has the objective to ana-
lyze its productivity using data from the 2014 and 2019 Economic Censuses related
to 2013 and 2018 economic indicators. The paper presents results of a productivity
analysis of 1,672 municipalities from 32 Mexicans states grouped in eight regions
using Data Envelopment Analysis. The results indicate significant differences be-
tween regions and, also, an important growth of the productivity in 2018.
Keywords: Data Envelopment Analysis, Food Industry, Regional Development, Eco-
nomic Asymmetries, Regional Polarization.
JEL Classification: C44, E23, L66
AMS Classification: 90-08, 90C05
1 Introduction
The food industry in Mexico represents 4.6% of the national economy [9]. In the last trimester of 2020, the food
industry generated 4.35 billion of Mexican pesos (1 MXN is approximately .05 USD, thereafter pesos) in Gross
Domestic Product (GDP), representing a growth of 5.88% compared to the same period of the previous year. As
Figure 1 shows, the GDP of the Food industry has been constantly increasing during the last almost 20 years,
reaching its highest value in 2019 with 16.9 billion of pesos. In 2019, the whole industry included 433,370 eco-
nomic units (companies), where the highest number of the economic units were registered in Estado de México
(27,070), Oaxaca (21,493) and Puebla (17,958). The biggest gross production is reported in Jalisco (2.25 billion
of pesos), followed by Estado de México (1.92 billion of pesos) and Guanajuato (1,43 billion of pesos). Finally,
the industry employs 1.9 million of workers (47.4% of males and 52.6% of females), with an average monthly
salary of 4,370 pesos [4].
Figure 1 Evolution of the GDP in billions of pesos in the food industry in Mexico. Constant 2013 prices (own
elaboration based on data from [9]).
The Mexican economy is characterized by the fact that many of the companies are micro, small and medium
companies (MSMEs). The size of the companies is defined by a combination of the number of workers and total
sales; MSMEs have less than 250 workers and have annual sales of less than 250 million Mexican pesos [8]. In
1 Tecnologico de Monterrey, School of Engineering and Sciences, Calle Puente 222, Coapa, Arboledas del Sur, Tlalpan, 14380, Mexico
City, Mexico, martin.flegl@tec.mx. ORCID: 0000-0002-9944-8475
2 Universidad La Salle México, Facultad de Negocios, Benjamín Franklin 47, Col. Condesa, 06140, Mexico City, Mexico, carlos_jime-
nez@lasalle.mx. ORCID: 0000-0003-4431-0054
3 Universidad Autonoma de Ciudad Juarez, Department of Social Sciences, Avenida Universidad S/N, Zona Chamizal, Ciudad Juarez,
32310, Chihuahua, Mexico, isaac.sanchez@uacj.mx. ORCID: 0000-0002-1975-5185
4 Universidad La Salle México, Facultad de Negocios, Benjamín Franklin 47, Col. Condesa, 06140, Mexico City, Mexico, ma-
caed00@gmail.com.
100
the food industry, there were 420,862 (97.11%) companies with 0-10 employees, 9,312 (2.15%) companies with
11-50 employees, 1,134 (.26%) companies with 51-100 employees and 2,062 (.48%) companies with 101+ em-
ployees [4]. It should be noted that the Mexican economy is one of the most unequal in the American continent
[5][6]. The North of the country represents a more developed industry, while the development in the South lags
behind. This has been explained historically by a lack of investment resources in the South, but also by the exist-
ence of internal colonialist structures, in which the South transfers value to the North [10]. In this case, there are
two hypotheses of such difference. The first hypothesis is linked to a duality that is defined by socio-cultural
elements that suppose a low labor performance in the South and a lack of industrial productive vocations. This
duality has justified the non-intervention of the State to encourage productive chains in the South of the country,
which is why it has been assigned an eminent primary economic task with the intention of sending agricultural
products to the North for processing [12]. The second hypothesis assumes that Southern industries being more
labor intensive, transferring value to Northern industries. However, companies from the South would be just as
productive as those from the North and, therefore, require support and investment to grow just like those from the
North [14].
In this sense, the objective of the paper is to analyze the productivity in the Mexican food industry with an appli-
cation of the Data Envelopment Analysis model. The secondary objective is to verify whether we can observe
difference between Northern and Southern regions of the country. The analysis includes two periods 2014 and
2019 to observe how the productivity in the industry changed within a time. We selected the food industry because
it is the branch of manufacturing that is closest to the primary sector, which is the most developed sector in less
advanced regions and, therefore, is a branch with a lower demand for capital than other branches such as metal-
lurgy, chemicals, or electronics.
2 Materials and methods
2.1 Data Envelopment Analysis
The Data envelopment Analysis (DEA) allows to evaluate several decision-making units (DMU) regarding their
capabilities to convert multiple inputs into multiple outputs [3]. Each DMU can have different input quantities
to produce different outputs. If the model assumes variable returns to scale, you can use the so-called BCC model
[2]. The BCC output-oriented model for DMUO is formulated as follows:
Maximize
=
=1 (1)
subject to

=1 
=1 0, = 1,2, … , ,
 = 1
=1
,
,≥  , > 0, free in sign.
(2)
where  is the quantity of the input of the ,  is the amount of the output of the , and and
are the weights of the inputs and outputs = 1,2, … , , = 1,2, … , , = 1,2, … , and is the so-called non-
Archimedean element necessary to eliminate zero weights of the inputs and outputs. DMU is 100% efficient if
= 1, i.e., there is no other DMU that produces more outputs with the same combination of inputs, whereas DMU
is inefficient if > 1.
2.2 Data
For the analysis we used the economic indicators related to the Mexican food industry from the 2014 and 2019
Economic Censuses carried out by Instituto Nacional de Estadística y Geografía [7][8]. Each censuses includes
information linked to manufacturing, commercial and service activities from companies operating in Mexico. The
2014 Economic Census refers to the data for 2013 and the 2019 Economic Census refers to data for 2018. In the
food industry, we included the information related to the following subsectors in the food industry: agriculture-
related services; preparation of animal feed; grinding grains and seeds and obtaining oils and fat; manufacture of
sugars, chocolates, sweets and the like; preservation of fruits, vegetables and prepared foods; manufacture of dairy
101
products; slaughter, packing and processing of meat from cattle, poultry and other edible animals; preparation and
packaging of fish and shellfish; preparation of bakery products and tortillas; other food industries; and branches
grouped by the principle of confidentiality.
This information is linked to the Mexican municipalities as it is not possible to identify companies due to the
confidentiality of the Economic Censuses. Moreover, to be able to compare the productivity between 2013 and
2018, we only included municipalities that appear in both Economic Censuses. In the end, the analysis includes
1,672 municipalities that represents 67.91% of the whole Mexico. Table 1 displays the division of the municipal-
ities among the 32 Mexican states5. These 1,672 municipalities include information from 164,558 economic units
in 2013 and from 189,590 economic units in 2018. Moreover, the results from both Censuses are comparable as
we used constant prices of 2018 for the 2014 Economic Census.
State
# of municipalities
State
# of municipalities
Aguascalientes
9 (11) 81.82%
Morelos
28 (33) 84.85%
Baja California
5 (5) 100.00%
Nayarit
19 (20) 95.00%
Baja California Sur
5 (5) 100.00%
Nuevo León
31 (51)60.78%
Campeche
11 (11) 100.00%
Oaxaca
218 (570)38.25%
Chiapas
74 (122) 60.66%
Puebla
156 (217)71.89%
Chihuahua
34 (67) 50.75%
Querétaro
16 (18)88.89%
Ciudad de México
16 (16) 100.00%
Quintana Roo
7 (11)63.64%
Coahuila
25 (38) 65.79%
San Luis Potosí
42 (58) 72.41%
Colima
10 (10) 100.00%
Sinaloa
16 (18) 88.89%
Durango
30 (39) 76.92%
Sonora
33 (72) 45.83%
Estado de México
114 (125) 91.20%
Tabasco
17 (17) 100.00%
Guanajuato
41 (46) 89.13%
Tamaulipas
26 (43) 60.47%
Guerrero
67 (81) 82.72%
Tlaxcala
49 (60) 81.67%
Hidalgo
67 (84) 79.76%
Veracruz
159 (212) 75.00%
Jalisco
108 (125) 86.40%
Yucatán
75 (106) 70.75%
Michoacán
104 (113) 92.04%
Zacatecas
42 (58) 72.41%
Table 1 Division of the municipalities among the Mexican states.
2.3 Structure of the model
The input part of the DEA model summarizes the resources of each municipality in the food industry:
Personnel: Hours worked by the personnel in thousands of hours (HWP). This variable represents the
labor factor of the production, therefore, a greater number of hours worked by the personnel for the same
level of production would indicate lower productivity.
Material: Raw materials and materials in millions of pesos (RMM), Number of economic units (NEU).
These variables indicate the material inputs of the production necessary for the transformation. Higher
productivity is associated with less use of materials and economic units.
Finance: Total expenditures in millions of pesos (TE), Total personnel remunerations in millions of pesos
(TPR). These variables represent the salary expenses of the industry and all expenses used in the produc-
tion. Therefore, it is implicit that the higher the expenditure with the same level of production, the lower
the productivity is.
The output part of the DEA model includes:
Total gross production in millions of pesos (TGP). This variable measures the economic results of each
municipality in terms of volume.
The selection of the inputs and outputs follows the common structure of DEA models in the agricultural produc-
tivity analysis [1][11][15]. We used the BCC output-oriented model as the intention is to analyze the productivity
level of each municipality related to their economic results. The BCC model is used as we consider a direct com-
petition in the food industry. Finally, we used MaxDEA 7 Ultra software for all the calculations. The importance
of the inputs and outputs with = .5, which best balanced the model, is as follows: HWP 2.59%, RNM 6.67%,
NEU 3.96%, TE 83.60%, TPR 3.18%, and 100% in case of the 2014 model, and HWP 4.46%, RNM 19.11%, NEU
5.74%, TE 63.69%, TPR 6.99%, and TGP 100% in case of the 2019 model.
5 Mexico is divided into 2446 municipalities and Mexico City (Ciudad de México) is divided into 16 parts.
102
3 Results
The average productivity of the Mexican municipalities in 2013 is .3498 with standard deviation of .156 and 30
municipalities reached the productivity of 1.0 (representing 1.79% of the analyzed sample). This result indicates
very low productivity in the food industry in Mexico. As Figure 2 in the Appendix shows, we cannot identify a
region (state) with very high productivity. The municipalities with the 1.0 productivity are placed across Mexico.
To understand a little bit more the obtained results, we divide the municipalities according to their geographical
dependence6. Table 2 shows that the highest average productivity in the food industry in 2013 is reported in the
Southeast region (.4003), one of the biggest considering the number of municipalities, but it is also a region with
the highest variability measured by the standard deviation (.1895). What is more, the Southeast region is the only
one evaluated above the country average. On the other hand, the West region reported the lowest average produc-
tivity in Mexico (.3179) with the lowest variability (.1090). Applying the Games-Howell test7, the differences in
productivity between the regions are statistically significant (< .001). More specifically, the average productiv-
ity of the municipalities in the Southeast region is statistically higher compared to the rest of the regions (consid-
ering the confidence level of 99%). The rest of the differences are not statistically significance, except the differ-
ence between the East region and the Center North (= .083) and West (= .012) regions.
Regions
Mean
Center North
.3238
150
Center South
.3324
158
East
.3490
431
Northeast
.3248
82
Northwest
.3360
123
Southeast
.4003
377
Southwest
.3440
110
West
.3179
241
Average
.3498
1,672
Table 2 Average productivity by geographical regions in 2013
In 2018, the average productivity of the Mexican municipalities increased by +.2343 up to .5841 with a standard
deviation of .1163. In this case, 36 municipalities reached the 1.0 productivity (representing 2.15% of the analyzed
sample). As Figure 3 in the Appendix illustrates the improvement in the productivity of the industry can be seen
all around the country, which resulted that the difference between 2013 and 2018 is statistically significant (<
.001). The best evaluated region is now Northeast (.6342) whose average productivity increased by +.3094, fol-
lowed by the Northwest region (.6223, +.2863). The Southeast region that was evaluated as the best region in 2013
is evaluated as the 3rd worst region in 2018, with the average productivity of .5729, because its productivity im-
proved in the smallest proportion (+.1726). The worst evaluated region is the East region with the average produc-
tivity of .5586, with and improvement of +.2096 compared to 2013. Five out of the eight regions are evaluated
above the Mexican average. The Games-Howell test indicates statistically significant differences between the re-
gions. For example, the Northeast and Northwest regions compared to the rest of the regions (< .001), and West
and Center South regions compared to the East, Southeast and Southwest regions (confidence level of 95%) in
almost all cases (only few exceptions can be observed).
The changes in the productivity of the periods can be explained by the economic structure of Mexico itself. The
Southern regions, eminently agricultural, send their largest production to the Northern regions for processing. In
2013-2014, the international oil prices increased the gasoline prices and, as the major transportation of goods in
Mexico is done by roads, it is a reason why the Northern regions, further away from agricultural production, were
less productive than those in the South closer to the agricultural centers. With the above we can point out that
developing industrial centers, particularly food centers, in agricultural areas would have positive results in produc-
tivity. This, together with an active industrial development policy, would have a positive impact on regional eco-
nomic development and would combat regional asymmetries [13].
6 Mexico is divided into eight geographical regions: Northwest (Baja California, Baja California Sur, Chihuahua, Durango, Sinaloa and
Sonora), Northeast (Coahuila, Nuevo León and Tamaulipas), West (Colima, Jalisco, Michoacán and Nayarit), East (Hidalgo, Puebla,
Tlaxcala and Veracruz), Center North (Aguascalientes, Guanajuato, Querétaro, San Luis Potosí and Zacatecas), Center South (Ciudad de
México, Estado de México and Morelos), Southeast (Chiapas, Guerrero and Oaxaca) and Southwest (Campeche, Quintana Roo, Tabasco
and Yucatán).
7 Games-Howell is a nonparametric test that does not assume equal variances and the same sample size. In our case, there are significant
differences regarding the number of municipalities between the regions and the variances are different (Levene’s test < .001). All the
statistical tests presented in this article are based this test.
103
Regions Mean Std. Deviation N
Difference
2018-2013
Center North
.5880
150
+.2642
Center South
.6003
158
+.2679
East
.5586
431
+.2096
Northeast
.6342
82
+.3094
Northwest
.6223
123
+.2863
Southeast
.5729
377
+.1726
Southwest
.5701
110
+.2261
West
.6038
241
+.2859
Average
.5841
1,672
+.2343
Table 3 Average productivity by geographical regions in 2018
4 Conclusions
The objective of the paper was to analyze the productivity in the Mexican food industry using data from the 2014
and 2019 Economic Censuses. The results revealed that in 2013, the highest productivity was reported in the
municipalities of the South regions of Mexico, which did not confirm the historical observation of a lower devel-
opment of these regions in Mexico. However, this could have been caused by the rise of the 2013-2014 interna-
tional oil prices that negatively affected the transportation of goods in Mexico. Regarding the obtained results in
2018, we can observe significant improvements in the productivity in the food industry across the whole country.
But the improvements were higher in the municipalities of the Northern regions of Mexico, which correspond to
the historical development of Mexican economy, where the Northern regions concentrate more developed industry.
The further research could extend the analysis including data from level of investments in the industry. This piece
of information would explain whether higher productivity leads to higher investments in the Mexican food indus-
try.
5 Acknowledgements
This research was carried out within the framework of the project Patterns of success and failure in the economic
evolution of businesses identified from data mining and artificial neural networks”, A3-S-129311, of the CONA-
CYT-INEGI Sector Fund.
6 References
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7 Appendix
Figure 2 Productivity in the food industry by municipalities in 2013 (own elaboration using GeoNames, Mi-
crosoft tool).
Figure 3 Productivity in the food industry by municipalities in 2018 (own elaboration using GeoNames, Mi-
crosoft tool).
105
... The Data Envelopment Analysis (DEA) is widely used to assess the operational efficiency and performance [10] [13]. In public security, the DEA analysis focuses either on a single period, performs the analysis over several years, or the two-stage DEA models are applied. ...
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In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale (in multiple input and multiple output situations). The results are discussed and related not only to classical (single output) economics but also to more modern versions of economics which are identified with "contestable market theories."
Liberalización comercial, descentralización territorial y polarización económica en México
  • R García-Almada
García-Almada, R.: Liberalización comercial, descentralización territorial y polarización económica en México. Ciudad Juárez: Universidad Autónoma de Ciudad Juárez, 2012.