UNIVERSIDAD NACIONAL DE CHIMBORAZO
FACULTAD DE
CIENCIAS POLÍTICAS Y
ADMINISTRATIVAS
ISSN No. 2631-2743
KAIRÓS, Vol. (5) No. 8, pp. 144-165, enero - junio 2022
ECONOMIC STRUCTURE AND
POVERTY IN MEXICO,
2008-2018
ESTRUCTURA ECONÓMICA Y
POBREZA EN MÉXICO,
2008-2018
DOI:
https://doi.org/10.37135/kai.03.08.08
Carlos Padilla Moran
carlos_padilla@tecmilenio.mx
Universidad Tecmilenio
Centro de Investigación en Alimentación y Desarrollo AC
(Sonora, México)
ORCID: 0000-0002-2147-9124
Joaquín Bracamontes Nevárez
joaco@ciad.mx
Centro de Investigación en Alimentación y Desarrollo AC
(Sonora, México)
ORCID: 0000-0002-3219-9582
Recibido: 30/08/21
Aceptado: 13/12/21
ISSN No. 2631-2743
Resumen
This study analyzes the economic structure and whether the
changes it experiences are linked to and aect poverty in Mexico
and the states that comprise it. Therefore, the GDP and employment
sectoral participation, the intersectoral eect (EI), and the
reassignment eect (ER) of employment towards high productivity
sectors in the States that make up the country are estimated, and
Coneval poverty indices are also used in the analysis. The evidence
conrms that the sectoral participation with respect to GDP and
employment tends to increase in the services sector and that in the
country, the increase in productivity is mainly attributed to RE;
however, the activities to which employment is relocated in the
states are primarily non-industrial activities. Finally, it was found
that the poverty reduction is explained by economic growth, the
participation of the secondary and tertiary sectors of the economy,
but not by the reallocation eect, which denotes the non-existence
of a structural change in the Mexican economy.
Palabras clave: economic structure, structural change,
productivity, poverty.
Abstract
Este estudio analiza la estructura económica y si los cambios que
ésta experimenta están vinculados y afectan la pobreza de México
y los estados que lo integran. Por ello, se estima la participación
sectorial del PIB y el empleo, el efecto intrasectorial (EI) y el efecto
reasignación (ER) del empleo hacia sectores de alta productividad
en los Estados que conforman el país, también se utilizan los
índices de pobreza del Coneval en el análisis. La evidencia constata
que la participación sectorial respecto al PIB y el empleo tiende a
aumentar en el sector servicios y que en el país el aumento en la
productividad se atribuye principalmente al ER; sin embargo, las
actividades a las que se reubica el empleo en los estados son en su
mayoría actividades no industriales. Por último, se encontró que la
reducción de la pobreza se explica por el crecimiento económico,
la participación de los sectores secundario y terciario, pero no por
el ER lo cual denota la inexistencia de un cambio estructural en la
economía mexicana.
Key words: estructura económica, cambio estructural,
productividad, pobreza.
KAIRÓS, Vol. (5) No. 8, pp. 144-165, enero - junio 2022
ECONOMIC STRUCTURE
AND POVERTY IN MEXICO,
2008-2018
ESTRUCTURA ECONÓMICA
Y POBREZA EN MÉXICO,
2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08
Carlos Padilla Moran
146
1. Introduction
At present, poverty is one of the main problems to achieve development on a global scale, its
reduction by half was the rst of the eight Millennium Development Goals (MDGs) that the UN
(2014) agreed to achieve by 2015. It is the rst in the 2030 Agenda for Sustainable Development,
which includes 17 objectives in the eort to eradicate poverty, ght inequality, injustice and
confront climate change in the framework of globalization (ECLAC, 2016).
The World Bank (2018) indicates that in the last two decades, there have been important advances
in reducing poverty since the proportion registered worldwide in 2010 was reduced by half and
extreme poverty (people living with less than USD1.90) went from being almost 36% in 1990 to
represent 10% of the world population in 2015; however, far from ceasing to be worrisome, the
phenomenon of poverty is still valid, and currently millions of people continue to suer from it on
the dierent continents.
Thus, some countries stand out in reducing poverty, such as China and India in Asia and Brazil in
Latin America (WB, 2018). In these nations, there has been a change in the economic structure,
although before they were countries characterized by specialization in raw materials, industrial
activity currently holds more signicant weight. This process is linked to poverty reduction in
recent research (UNIDO, 2012; Haraguchi and Fang Chin Cheng, 2016).
However, within the framework of neoliberalism, as a theory that maintains that promoting welfare
consists of not restricting the free development of the individual’s entrepreneurial capacities and
freedoms, the recommendations of supranational organizations such as the World Bank or the
International Monetary Fund suggest reducing the government participation in the economy, which
implies denying the possibility of countries developing an industrial policy (Harvey, 2005; Storm,
2015).
Joseph Stiglitz (2009) points out that industrial policies are necessary and intrinsically fundamental
for all development processes and testimony of this is all successful industrialization for almost
two centuries in Germany and the United States until the recent cases of Korea, Taiwan, Brazil,
China, and India (cited in Cimoli et al., 2009). The secondary sector is considered the one with the
highest productivity and has characteristics that link it with reducing poverty, such as productive
chains, higher wages, more excellent distribution of benets (Kaldor, 1979; Dasgupta and Singh,
2006).
Recent studies show that greater participation in the industry leads to a considerable reduction in
poverty conditions in Brazil (UNIDO, 2012; Levinas and Somoes, 2016), South Korea (Lanzarote,
1991), China (UNIDO, 2012), Africa (Berthélemy, 2018) and Kazakhstan (Verme, 2010). However,
in cases such as India (Aggarwal, 2012) or Peru (Tello, 2015) when employment is directed from
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08 147
the agricultural sector to service activities, it has a positive impact on poverty reduction, which is
attributed to the productivity of the jobs that the labor force goes to and the wages they oer, hence
the interest of this study in investigating this relationship for Mexico.
In Latin America, studies on the economic structure nd that, for the Chilean case, Correa (2016)
nds that there are “virtuous” and “regressive” manufacturing sectors to reduce inequality. On the
other hand, Argentina (Longhi and Osatinsky, 2015) warns that the structural fragmentation of the
economy has generated employment problems and poverty that are more pronounced in the north
than in the Pampas provinces.
Since the beginning of the 1980s, the Mexican economy entered a slow growth phase (Loria, 2009).
Regarding the decrease in poverty explained by the economic growth of 2000-2014, Campos and
Monroy (2016) nd that a systematic relationship between growth and variations in poverty is not
observed. For their part, Ceballos and de Anda (2021) nd that in the south of the country, branches
such as transportation, communication services, education, health, government, and tourism are
associated with less poverty. In the center and north, the reduction decreases with occupations such
as machinery and equipment production, insurance, corporate services, professional, recreational,
and government activities.
On the other hand, Padilla-Pérez and Villarreal (2017) study the relationship between the change
in the economic structure and the increase in productivity from 1990 to 2015 in the Mexican
economy; for this, they decompose the increase in productivity. They nd the relocation of hours
worked in the industry signicantly, but its impact is hampered by workows from sectors with
high productivity to those with low productivity.
Therefore, this research is aimed at investigating how the economic structure impacts poverty levels,
that is, how the participation of the secondary and tertiary sectors inuences the low incidence of
poverty, knowing that the general trend of the economies is towards a decrease in the participation
of the primary sector and an increase in industry and services. The questions in this research are:
What have been the transformations in the economic structure of Mexico and its states during
the study period 2008-2018? In addition to determining if there is a link between the economic
structure and poverty in Mexico and its states?
The working hypothesis argues that those entities that are characterized by having higher levels
of productivity eventually have a lower incidence of poverty, particularly those entities identied
by their manufacturing vocation because industrial development favors better wages due to
productivity levels, as well as the higher proportion of jobs that contribute to the production of
consumer goods with higher added value.
Article is made up of the introduction and ve other parts. The second part briey reviews the
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08
Carlos Padilla Moran
148
structuralist approach and its relationship with poverty, then the methodology and data used
are explained. The fourth part analyzes the economic structure, the intersectoral eect (EI), the
reallocation eect (ER), and the situation of poverty in the states and the country; In contrast, the
fth part analyzes the determinants of poverty and, nally, the conclusions of the study are added.
2. The structuralist approach, Neo-structuralist and poverty
In the structuralist approach, the economic structure is strongly linked to the population’s living
conditions since the structural dierences between developed and undeveloped countries underlie
the socioeconomic contrasts of one type of country concerning the other (Dutt, 2019). The concept
of economic structure refers to the classication proposed by Fischer (1935 and 1939), which
suggests the division of production factors into three sectors: on the one hand, the primary sector
concentrated in agricultural activities, on the other hand, the sector secondary integrated by
industrial and mining activities. Finally, tertiary activities, dedicated to services (Moncayo, 2008).
In this logic, the economic structure is integrated, on the one hand, by the productive structure that
refers to the participation of each sector (primary, secondary, and tertiary) and its branches in the
Gross Domestic Product (GDP) registered in the national accounts. On the other hand, the relative
labor occupation in each sector or branch of the economy is also called the employment structure.
The composition of both refers to the economic structure of a country (Yoguel and Barletta, 2017;
Cimoli et al. 2005, and Lanzarotti, 1991).
The pioneers of structuralism point out that the primary-export structure, of services and little
industrialized, as well as the exchange of these products for machinery and technological products,
explains to some extent the conditions of backwardness in terms of consumption capacities and
the levels of poverty in Latin America and in the rest of the undeveloped countries. This, due to
the historical loss of the purchasing power of raw materials concerning industrialized products in
the international market (Prebisch, 1949, 1967; Furtado, 1961; Pinto, 1970; Cardoso and Faletto,
1979).
According to Moncayo (2008), the process that leads to economic growth is accompanied by
the change in the economic structure, which is perceptible from the relocation of the workforce,
altering the relative occupation of a sector or branch towards others, or starting from the change
in the proportion represented by primary, secondary or tertiary GDP (and their branches) in total
production; however, not every change in the economic structure implies a structural change.
The Latin American structuralist tradition denes structural change as one that induces socioeconomic
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08 149
improvement from the development of industrial and technological activities that promote greater
productivity and sustained growth, improving the conditions with which it participates in the
international market and, this being a change of By increasing general productivity, a virtuous nature
leads to a substantial improvement in the population’s living conditions, mitigating socioeconomic
problems in undeveloped countries (ECLAC, 2012; Cimoli et al. 2015; Yoguel and Barletta, 2017).
It is important to note that the structuralist approach arises in a context where productive structures
were mainly dual -in Lewis’s sense- and production was vertically and nationally integrated;
while, at present, multiple productive structures predominate with a high degree of production
fragmentation, due to the emergence of new knowledge-intensive services and the multiple
dependencies and interconnections between the dierent components of the global economic
system. This makes the debate on the type of specialization desirable to achieve socio-economic
development more complex (Yoguel and Barletta, 2017).
In this context, during the last decades and within ECLAC, the neo-structuralist approach has
emerged, for which not all sectors have the same potential to induce productivity increases, generate
productive chains, high-paying jobs, or attend to the socio-economic problems, so it is relevant to
study the eects on these variables of the changes in the participation of the dierent economic
sectors in each country (Cimoli et al., 2005; Cimoli et al. 2015).
Neo-structuralism criticizes that classical structuralism has not considered the weight of the State
and institutions as elements to achieve well-being via the use of other mechanisms such as income
transfers or progressive taxes, as well as their redistributive eect to reduce inequality and poverty
(Dutt, 2019); Therefore, this approach assumes that the State is a relevant economic actor in
promoting development and promoting change in the structure (production and employment) to
reduce the gaps in productivity and living standards between nations (Storm, 2015; Dutt, 2019).
Furthermore, for the renewed structuralist perspective, structural change does not simply suggest
the relocation of production towards the industrial sector and manufacturing activities, but towards
knowledge-intensive-diusing activities with high-income elasticity of export demand, in particular
contrast with those that are natural resources or work (Cimoli et al. 2005; Cimoli et al. 2010;
ECLAC, 2012).
Although these activities are located in the secondary sector of the economy, they are the activities
that make the most eective use of technology, as opposed to those that are labor-intensive or those
that are intensive in natural resources such as mining (ECLAC, 2012). For this reason, structural
change is suggested as the change in the pattern of specialization, in consideration of how the
composition of the sectors is related to technological change (Katz, 2000; Ocampo, 2005).
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
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Carlos Padilla Moran
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In this sense, to understand the link between economic structure, structural change, and well-being,
it is essential to point out that the economic dynamism that transforms the productive structure is
complementary to social equality, in such a way that structural change is understood as to how to
achieve synergy between both within an integrated vision of development that achieves economic
growth and increases in productivity, considering social inclusion and environmental sustainability
(Cimoli et al., 2005; ECLAC, 2012; Cimoli et al., 2015).
Consequently, a change in the economic structure towards industrial sectors and knowledge-
intensive activities would propitiate a structural change and, with it, the reduction of poverty
(Cimoli et al., 2005; Capdeville, 2005; ECLAC, 2012: however, if The economic structure tends
to specialize in activities that are intensive in natural resources and/or labor (with a lower income
elasticity of demand) would favor the opposite, although the sensitivity to this change will depend
on the institutional conditions of each country.
Regarding the relationship between the economic structure and the conditions of poverty, the
empirical evidence shows that the countries that are going through industrialization processes and
that direct the workforce in activities of this nature are those that historically register the highest
productivity and have seen a decrease in their poverty levels nding a high degree of causality. This
process is known as structural change (Cimoli et al. 2005; Lavopa, 2012; UNIDO 2012; Haraguchi
and Fang Chin Cheng, 2016; Berthélemy, 2018; Diao, McMillan and Rodrik, 2019).
3. Methodology applied, and data used
Recent studies on the change in the economic structure and industrialization processes in
developing countries highlight the measurement through the sectoral reallocation eect or simply
the reallocation eect (ER), which is dened as the contribution to the variation in the labor
productivity of the mobilization of workers between the dierent sectors of one period to another,
following the work of McMilllan and Rodrik (2011), widely cited and which is exposed below:
ΔYt= ∑θi,t-k·Δyi,t + ∑yi,t·Δθi,t (1)
i=1, …11 t=1,2
Where:
“Δ” refers to the increase in percentage terms of the variable in the period.
“Y” represents productivity, understood as the division of the GDP of each type of activity in a year
by the number of people employed in it.
“Θ” represents the participation of the employed population in the sector “i” in year “t” of total
employment.
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08 151
“I” represents economic activity, while “t” refers to the year within the period.
“∑θi,t-kΔY i,t” would represent the natural increase in productivity within that activity, also called
the intersectoral eect.
The second component, “∑yi,t·Δθi,t” is called the reallocation eect (RE), it would represent that
increase caused by the movement of employees from sectors of lower productivity If RE> 0 means
that employment has been relocated from activities with lower productivity towards those with
higher productivity.
For equation 1, labor productivity is understood as the increase in GDP at constant prices of each
activity or sector per person employed in it in each state or region in a year concerning the previous
one (ILO, 2015). Both data are provided by the National Institute of Geography and Statistics
(INEGI), GDP in the section of national accounts at 2013 prices, while for the employed population,
the Employed Population in the fourth quarter of the National Survey is considered of Occupation
and Employment for the period from 2006 to 2018.
To measure poverty, the poverty index by income and extreme poverty by income are taken,
estimated respectively by the National Council for the Evaluation of Social Policy (CONEVAL)
for the country and each state. These are presented for every two years, which are calculated based
on the National Household Income and Expenditure Survey (ENIGH) carried out biennially by the
INEGI.
CONEVAL uses the National Consumer Price Index (INPC) price indices to measure the Income
Poverty Lines (PL). These lines are constructed by measuring the minimum monthly income to
satisfy basic national needs (in urban or rural areas) from a food basket for extreme poverty lines
(EPL) and a non-food basket that, when added to the previous one, constitutes the PL. In such a
way, income poverty is calculated by determining the number of people who receive a monthly
income below the PL and the EPL, dividing each household by the number of people who inhabit
it according to the ENIGH (CONEVAL, 2020).
Subsequently, to explain the levels of moderate and extreme poverty from the economic structure
following the proposal of Aggarwal and Kumar (2012) as shown in equations 2 and 3, where both
poverty indices are explained by the participation in GDP of the secondary and tertiary sectors,
economic growth, and the reallocation eect. Investment in social programs is also used as a
control variable (because this variable is correlated with the levels of poverty in the states due to
their operating rules), since its contribution in reducing poverty is theoretically and empirically
recognized (Aggarwal and Kumar, 2012; Lavopa and Szirmai 2012; Tello, 2015):
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
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PM = β0 + β1EGi, t+ β2GDP2i, t+ β3GDP3i, t + β4REi, t + β5SPi, t + Ԑi (2)
PE = β0 + β1EGi, t+ β2GDP2i, t+ β3GDP3i, t + β4REi, t+ β5SPi, t + Ԑi (3)
i =1, …32 t= 1, …6
Where:
MP: represents the moderate-income poverty index presented by CONEVAL
EP: means the extreme income poverty index presented by CONEVAL.
β0: represents the constant.
EG: represents the economic growth registered in the year “t” with respect to the previous year in
the state “i”.
GDP2: represents the percentage share of the secondary sector in all GDP.
GDP3: means the share of the tertiary sector in GDP.
RE: represents the reallocation eect calculated for each state in each year calculated with equation
1.
PS: Represents investment in social programs as a percentage of GDP in each state and year.
Ԑ: represents the statistical error.
For the explanatory variables, the years that coincide with the poverty index (2008, 2010, 2012,
2014, and 2016) are considered, with respect to the variations, the biennial changes are considered
for the same reason between the same years.
Economic growth is considered as the relative increase in GDP registered in the year “i” with
respect to that registered two years before; the participation of the sectors in the economy refers to
the percentage that each GDP occupies (primary, secondary and tertiary) in each year and state; As
a proxy for the variable, the investment of social programs (PS) is taken, the amount allocated to
“transfers and support” as a percentage of GDP in each year is considered. The data are provided
by the National Institute of Geography and Statistics (INEGI). Excel software is used for data
management, and STATA 15.1 software for the application of statistical models.
4. Economic structure and poverty in the Country and States
Table 1 shows that in 2008 of the total economy, the primary sector only represented 3.38% of
GDP. For its part, the secondary sector makes up 35.27% of the generation of wealth, being the
manufacturing industry the one that participates to a greater extent (16.99%), followed by the
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
de 2022 (Ecuador). ISSN 2631-2743. DOI: https://doi.org/10.37135/kai.03.08.08 153
extractive industry and electricity (9.74%); and construction (8.53%). The tertiary sector is the
one with the highest participation in GDP this year (61.35%), where the highest representation is
held by professional, nancial, and corporate services; followed by trade (16.95%); while the least
represented are various services (2.21%) and restaurants and accommodation services (2.43%).
In 2018 the primary sector represented 3.34% of the production of that year. For its part, the secondary
sector has the participation of 30.55% in GDP; the highest participation is held by the manufacturing
industry (16.58%), followed by construction (7.28%), and the mining and quarrying industry and
electricity respectively (6.67%). The tertiary sector is the one with the highest participation in this
year, representing 66.11%, with commerce and professional nancial and corporate services being
the activities with the highest contribution (22.91% and 18.33% respectively); at the other extreme,
those with the lowest participation, as in 2008, are various services (2.06%); and restaurants and
accommodation services (2.37%).
Table 1. GDP by sectors and activities in Mexico from 2008 to 2018.
Economic sector or branch PIB 08 %* PIB 18 %* Δ**
Total 14,402,757 100% 17,739,437 100%
Primary sector. 486,465 3.38% 592,952 3.34% -0.04%
Secondary sector. 5,079,734 35.27% 5,418,536 30.55% -4.72%
Extractive and electricity industry. 1,403,235 9.74% 1,182,842 6.67% -3.07%
Manufacturing industry. 2,447,227 16.99% 2,941,823 16.58% -0.41%
Construction. 1,229,272 8.53% 1,293,871 7.29% -1.24%
Third sector. 8,836,558 61.35% 11,727,948 66.11% 4.76%
Comercio. 2,440,638 16.95% 3,251,896 18.33% 1.39%
Restaurants and accommodation services. 349,725 2.43% 419,787 2.37% -0.06%
Transportation, communications, mail and storage. 1,160,475 8.06% 1,742,447 9.82% 1.77%
Professional, nancial, and corporate services. 2,914,367 20.23% 4,063,909 22.91% 2.67%
Social services. 1,047,866 7.28% 1,158,280 6.53% -0.75%
Various services. 317,745 2.21% 365,793 2.06% -0.14%
Government and international organizations. 605,743 4.21% 725,836 4.09% -0.11%
*Percentage of participation in total GDP. ** Growth in percentage points in the participation of the activity or sector in the period.
Source: Own elaboration based on the National Accounts System of the INEGI in 2013 pesos.
Regarding the participation of the sectors in the economy, there is no substantial change in the
primary sector (-.04%). In contrast, the secondary sector registers a decrease in the total participation
of all sectors (-4.72% in the entire sector) that comprise it: -3.07% in the extractive industry,
-0.41% in manufacturing, and -1.24% in construction. Tertiary activities increase their participation
substantially (4.76%) Being in commerce (1.39%); transportation and communications (1.77%);
and professional, nancial, and corporate services (2.67%), which have registered the most
signicant increase in their participation in these ten years, which denotes a transparent process of
outsourcing of the Mexican economy.
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
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Table 2. Working population by sectors and activities in Mexico from 2008 to 2018.
Economic sector or branch PO* 08 %* PO* 18 %* Δ***
Total 44,798,686 100% 54,194,608 100%
Primary sector. 6,244,756 13.94% 6,874,691 12.69% -1.25%
Secondary sector. 11,046,708 24.66% 13,864,904 25.58% 0.92%
Extractive and electricity industry. 413,184 0.92% 398,788 0.74% -0.19%
Manufacturing industry. 6,997,919 15.62% 9,090,533 16.77% 1.15%
Construction. 3,635,605 8.12% 4,375,583 8.07% -0.04%
Third sector. 27,163,979 60.64% 33,170,241 61.21% 0.57%
Comercio. 8,735,487 19.50% 10,082,351 18.60% -0.90%
Restaurants and accommodation services. 2,843,647 6.35% 4,249,632 7.84% 1.49%
Transportation, communications, mail, and storage. 2,283,579 5.10% 2,832,600 5.23% 0.13%
Professional, nancial, and corporate services. 2,685,791 6.00% 3,955,199 7.30% 1.30%
Social services. 3,777,383 8.43% 4,321,009 7.97% -0.46%
Various services. 4,588,924 10.24% 5,449,702 10.06% -0.19%
Government and international organizations. 2,249,168 5.02% 2,279,748 4.21% -0.81%
* People working in the activity or sector. ** Percentage of participation of the total employed workers. Δ *** Growth in percentage
points of the participation of the activity or sector in the period.
Source: Own elaboration based on the ENOE.
According to table 2, in 2008, the employed population in Mexico was 44,798,686. Of these,
13.94% of the total national work is in the primary sector. On the other hand, the secondary sector
represents 24.66% of employment, manufacturing being where most of it is found (15.62%);
followed by construction (8.07%), and the extractive and electricity industry (0.92%), respectively.
The tertiary sector occupies most of the employment this year (60.64%), the largest participation
is found in commerce (19.50%) and various services (10.24%), while the minor occupation is
represented by the government and international organizations (5.02%).
In 2018, workers in Mexico reached 54,194,608, of which 12.69% were employed in the
primary sector. The secondary sector occupies 25.58% of national employment, being 16.77% in
manufacturing activities, 8.12% in construction, and 0.74% in the extractive industry and electricity.
Tertiary activities are those that occupy most of the workers this year, representing 61.21% of the
workers, most of which are in commerce (18.60%) and various services (10.06%). In contrast, the
activity with the lowest occupational occupation is the government and international organizations
(4.21%).
In the decade, the total employed population in Mexico has an increase of 20.97%, slightly less
than the growth of GDP production as indicated above (23.17%), which suggests an increase in
productivity. In this period, the participation of economic sectors is altered in the following way
in the employment structure: The primary sector is reduced (-1.25%). Since the share of these
activities in GDP remains the same, it is suggested that (in relative terms) workers have migrated
to higher productivity activities (because agricultural activities have the lowest productivity in the
entire economy).
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
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Table 3. Productivity in Mexico by sectors and activities in 2008 and 2018.
Economic sector or branch Prt*2008 Prt* 2018 Δ** %***
Total 321,500 327,328 5,829 1.81%
Primary sector. 77,900 86,251 8,352 10.72%
Secondary sector. 459,841 390,810 - 69,032 -15.01%
Extractive and electricity industry. 3,396,150 2,966,093 - 430,057 -12.66%
Manufacturing industry. 349,708 323,614 - 26,094 -7.46%
Construction. 338,120 295,702 - 42,418 -12.55%
Third sector. 325,304 353,568 28,264 8.69%
Comercio. 279,393 322,534 43,140 15.44%
Restaurants and accommodation services. 122,985 98,782 - 24,203 -19.68%
Transportation, communications, mail and storage. 508,183 615,141 106,958 21.05%
.Professional, nancial and corporate services. 1,085,106 1,027,485 - 57,620 -5.31%
Social services. 277,405 268,058 - 9,348 -3.37%
Various services. 69,242 67,122 - 2,120 -3.06%
Government and international organizations. 269,319 318,384 49,066 18.22%
* Productivity per working person. ** Absolute increase in productivity. ***
Source: Own elaboration based on the ENOE and the national accounts provided by INEGI. Percentage increase in productivity.
Table 3 shows that most stands out when analyzing productivity in the period is the low increase
of MXN 5,829. (1.81%) in the decade, which is in turn made up of disparate growth dynamics
between sectors and activities.
The primary and tertiary sectors show an increase higher than the national total, being 10.72% and
8.69% respectively, for its part, the secondary sector shows a decrease in the period in the three
activities that comprise it, the strongest fall occurs in the industry extractive and electricity, where
the decrease is 12.66% (- $ 430,057 MXN.) this is remarkable since this activity is the one with the
highest productivity in all the years of the period.
Although this decrease is explained by the fall in the prices of oil and other products derived from
mining, the fall in this activity does not fully explain that of the sector, since, at the same time,
productivity in manufacturing activities decreases considerably, thus as in construction; and both
represent a more signicant share in the national GDP than the other. Nor do they explain the fall
in national productivity since they do not represent more than 7% of national production.
On the other hand, in general, the activities of the tertiary sector increase their productivity; this
is due to the increase in trade, transportation, communications, mail and storage, and government
activities to a lesser extent. However, there is a decrease in the productivity of professional,
nancial, and corporate services; activity where this indicator is highest in the sector.
The fact that in the Mexican economy from 2008 to 2018, the secondary sector decreased in terms
of participation in GDP and productivity results in the low increase in productivity during the
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Carlos Padilla Moran
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period, even though the participation of the secondary sector in the structure of employment, which
is explained by ceasing to produce technology-intensive goods and specializing in labor-intensive
goods as mentioned in the studies mentioned above (Cimoli, 2005; CEPAL, 2012) as a result of the
outsourcing process (Carrillo and Cadena, 2019) and deindustrialization (Calderón-Villareal and
Hernández-Bielma, 2016) that the nation has experienced in the decade.
Table 4. Economic Structure in Mexico by State in 2008 and 2018.
Sector I Sector II Sector III
GDP* WP** GDP* WP** GDP* WP**
2008 2018 2008 2018 2008 2018 2008 2018 2008 2018 2008 2018
Mexico (Country) 3% 3% 14% 13% 35% 31% 25% 26% 61% 66% 61% 61%
Aguascalientes 4% 4% 7% 5% 38% 40% 28% 35% 57% 56% 65% 61%
Baja California 3% 3% 6% 4% 43% 38% 28% 32% 55% 59% 59% 60%
Baja California Sur 3% 3% 9% 7% 32% 37% 21% 18% 65% 60% 69% 75%
Campeche 0% 1% 19% 21% 92% 84% 22% 20% 8% 15% 58% 59%
Coahuila 2% 2% 5% 5% 54% 51% 32% 40% 44% 47% 62% 55%
Colima 6% 5% 12% 12% 26% 23% 20% 18% 67% 73% 68% 69%
Chiapas 8% 7% 39% 41% 29% 19% 13% 13% 63% 74% 47% 45%
Chihuahua 6% 6% 10% 9% 39% 39% 26% 38% 55% 54% 59% 52%
Mexico City 0% 0% 0% 0% 13% 9% 18% 16% 87% 90% 81% 83%
Durango 11% 10% 17% 14% 30% 30% 23% 28% 60% 60% 60% 57%
Guanajuato 4% 4% 14% 9% 34% 35% 33% 39% 61% 61% 53% 51%
Guerrero 6% 5% 30% 33% 19% 18% 17% 16% 75% 76% 53% 51%
Hidalgo 5% 4% 24% 20% 36% 32% 25% 25% 58% 64% 50% 55%
Jalisco 6% 6% 9% 8% 31% 31% 28% 27% 63% 64% 62% 65%
Mexico (State) 2% 1% 6% 5% 29% 26% 28% 27% 69% 73% 66% 67%
Michoacán 12% 13% 21% 24% 22% 16% 21% 18% 67% 71% 57% 58%
Morelos 3% 3% 15% 13% 34% 30% 21% 22% 63% 67% 64% 65%
Nayarit 8% 7% 19% 21% 23% 18% 18% 16% 68% 76% 63% 63%
Nuevo León 1% 0% 2% 1% 38% 35% 32% 32% 61% 64% 66% 66%
Oaxaca 6% 6% 33% 31% 26% 24% 20% 22% 69% 70% 47% 47%
Puebla 5% 4% 26% 19% 35% 35% 26% 27% 61% 61% 47% 53%
Querétaro 2% 2% 8% 4% 37% 40% 32% 34% 61% 58% 60% 62%
Quintana Roo 1% 1% 5% 5% 14% 11% 17% 16% 85% 88% 77% 79%
San Luis Potosí 4% 4% 20% 18% 36% 39% 24% 30% 61% 57% 55% 52%
Sinaloa 13% 12% 20% 16% 23% 20% 20% 20% 64% 68% 60% 63%
Sonora 6% 7% 11% 11% 44% 44% 28% 27% 49% 49% 59% 61%
Tabasco 2% 2% 17% 17% 68% 61% 20% 18% 30% 37% 63% 65%
Tamaulipas 3% 3% 6% 7% 45% 36% 29% 31% 51% 61% 63% 60%
Tlaxcala 4% 4% 18% 12% 35% 33% 32% 36% 61% 63% 50% 52%
Veracruz 6% 5% 22% 23% 36% 31% 20% 19% 58% 63% 57% 58%
Yucatán 4% 4% 11% 9% 27% 27% 27% 26% 69% 69% 62% 64%
Zacatecas 8% 9% 32% 25% 37% 33% 17% 24% 55% 57% 50% 50%
* Percentage of participation of the sector in GDP. ** Percentage of participation of the sector in the Working Population.
Source: Own elaboration with data from the National Accounts System of the INEGI in 2013 pesos and the ENOE.
As appears in table 4, the secondary sector tends to be the second with the highest participation at
the national level. In the states in both elements that make up the economic structure, this except
for Campeche and Tabasco, where it represents more than 50% of GDP, however, these The same
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
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activities employ a fth of the employed population in these two states, which is since in these the
oil activities have high participation, and this generates high levels of wealth. However, they do
not need a considerable amount of jobs. In this sector, the dynamics in the employment structure
tend to increase slightly; on the contrary, its participation in GDP tends to decrease slightly in most
entities.
Lastly, the tertiary sector turns out to be the one with the highest participation both in GDP and
in the employed population in most of the states apart from Coahuila, Campeche, and Tabasco. In
these states, the secondary sector has the largest share of GDP; however, tertiary activities occupy
most of the population. In contrast, the only state where the tertiary sector occupies less than 50%
of the population is Chiapas. In this sense, the general trend seen in this last sector is towards
increasing participation in GDP and employment.
Table 5. Economic Structure and Variations in Poverty from 2008 to 2018.
GDP1 * WP1** GDP2 WP2 GDP3 WP3 MP*** EP**** ΔYt*****
Aguascalientes = - + + + - - - $ 27,694
Baja California - - + + - + - - -$ 38,590
Baja California Sur - - + - - + - - $ 56,952
Campeche + + - - + = + = -$ 1,799,646
Coahuila - - - - + + - - $ 14,565
Colima - - - - + + + + $ 14,820
Chiapas - + - - + + + + -$ 8,013
Chihuahua + - - + + - - - $ 16,620
Mexico City - - - - + + + + $ 153,988
Durango - - = + = + - - -$ 16,720
Guanajuato - - + + + - + + $ 49,521
Guerrero - + - - + - - - -$ 42,644
Hidalgo - - - - + + - - $ 5,916
Jalisco = - - - + + - - $ 27,694
Mexico (State) - - - - + + + + $ 13,012
Michoacán + + - - + + - - $ 27,404
Morelos - + - - + + + + $ 6,061
Nayarit - + - - + - - - $ 27,694
Nuevo León - - - - + + - - $ 61,655
Oaxaca = - - + + + + + $ 2,157
Puebla - - + - + + - - $ 18,779
Querétaro - - + + - + - - $ 116,155
Quintana Roo - - - - + + - - $ 33,546
San Luis Potosí = - + + - - - - $ 27,694
Sinaloa - - = + + + + - $ 34,079
Sonora + + - - + + + + -$ 15,700
Tabasco = - - - + + - - -$ 69,929
Tamaulipas - - - + + - + + -$ 33,010
Tlaxcala - - - + + + - - -$ 19,664
Veracruz = - - - + + + + $ 15,886
Yucatán - - - - + + - - $ 17,594
Zacatecas = + + - - + - - $ 27,694
States where increased 4 8 8 11 26 24 12 10 23
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States where it decreases 21 24 22 21 5 7 20 20 9
States where it stays the same 7 0 2 0 1 1 0 2 0
* Gross domestic product by sector. ** Working Population by sector. *** Moderate poverty. ****Extreme poverty. *****Variation
in productivity in Mexican pesos of 2013
Source: Own elaboration with data from INEGI
The extreme poverty index increases in ten states: Colima, Chiapas, CDMX, Guanajuato, Mexico,
Morelos, Oaxaca, Sonora, Tamaulipas, and Veracruz. Moreover, it decreases by twenty-one.
Remaining the same in Campeche, for its part, the economic structure, both in the productive
structure and in employment, increase the participation of the tertiary sector to a greater extent in
most of the states. At the same time, primary and secondary activities decrease in their participation,
as shown in table 5.
Regarding the relationship between productivity and poverty, it is highlighted that six of the eleven
entities that had a considerable decrease in their moderate poverty index present an increase in
productivity greater than the national average, while the other six are found with an increment
below this. However, the states with the highest levels of productivity are those with the lowest
levels of moderate poverty in 2008 and 2018, apart from Campeche and Tabasco (oil states).
It should be noted that, in the three groups indicated above with high productivity (table 5), in
the case of tourist states and manufacturing states, all entities have poverty levels lower than the
national index. In Hidalgo and Durango, there is a high share of the manufacturing sector, and
poverty is below the national average despite not having productivity higher than the national
average.
The reallocation eect is understood as one of the two elements that result from the decomposition
of the productivity increase of a period of analysis. The rst element that makes up the equation
is the intersectoral eect (IE), which is understood as the natural increase in productivity. The
second element is called the reallocation eect (RE); this represents the increase in productivity
attributed to the relocation of employment from lower productivity activities to those with higher
productivity levels.
From 2008 to 2018, in ve years, the national average productivity increases by one year compared
to the previous one, while it decreases in the other ve. In such a way that the increase has not been
constant since, in most cases, when productivity increases from one year to another, it decreases
the next. The highest decrease was in 2009 ($ 26,992 MXN), which was an eect of the economic
crisis. In such a way that, although the balance of productivity is positive in these ten years, it is
widely held back by negative periods. This results in an increase in productivity in this decade,
being $ 5,829 MXN.
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
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Table 6. Reallocation eect in Mexico from 2008 to 2018.
Period ΔYt* IE** RE*** IE% RE%
2008-09 - 26,992 - 23,254 - 3,739 86.15% 13.85%
2009-10 18,100 19,763 - 1,663 109.19% -9.19%
2010-11 - 4,667 - 6,734 2,067 144.29% -44.29%
2011-12 8,125 4,787 3,338 58.92% 41.08%
2012-13 - 2,871 - 5,812 2,940 202.40% -102.40%
2013-14 9,155 13,708 - 4,553 149.73% -49.73%
2014-15 - 1,004 - 5,041 4,037 502.15% -402.15%
2015-16 5,239 9,350 - 4,111 178.46% -78.46%
2016-17 1,994 1,093 901 54.83% 45.17%
2017-18 - 1,249 - 5,578 4,329 446.60% -346.60%
2008-18 5,829 11 5,818 0.19% 99.81%
* Variation in productivity. ** Intrasectorial eect. *** Reallocation eect.
Source: Own elaboration based on the National Accounts System of INEGI and ENOE.
Following table 6, the intersectoral eect (IE) has a negative coecient in all cases where
productivity decreases, which suggests that the decrease in productivity responds to the productivity
that is naturally lost in the sectors. The EI perceives that productivity increases naturally to a lesser
extent since it only adds up to $ 11.00 MXN in the ten years, representing less than 1%.
On the other hand, the reallocation eect results in a positive coecient in six of the ten years,
of which in three of the decrease in productivity this serves to minimize the participation of the
intersectoral eect. However, in three years where there is an increase in productivity, the RE
subtracts from the increase by presenting a negative coecient.
The RE in the entire period of 2008-18 presents a high percentage (99.82%); This is explained
by the fact that, although a process of deindustrialization was going through during the period,
employment was relocated from agricultural and service activities (of low productivity) to services
with higher labor productivity.
On the other hand, the reallocation eect has a positive coecient in the years where poverty
(moderate and extreme) decreases. In contrast, the RE coecient is negative in those years where
it increases, apart from 2018, where the RE is negative while poverty levels decrease compared to
2016.
5. Results of the econometric model
To calculate equations regression 2 and 3 specied in the methodology, the relative levels of each
value are considered by state in biennial samples from 2008 to 2018. Robust linear regression is
estimated where the 192 observations are considered. In a set of panel data (since they combine a
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temporal and transversal dimension), to determine how much the levels of moderate poverty and
extreme poverty are explained by economic growth, the participation of the secondary and tertiary
sectors in GDP, the reallocation eect and investment in social programs as a percentage of each
state’s GDP.
Due to working with data that start from GDP, in addition to the presence of atypical data in
the calculation of the RE, robust regression is used, in this way, the traditional limitations of the
estimation by ordinary least squares are adjusted, anomalous data, lack of normality and symmetry
in errors (Andersen, 2008); in such a way that it is not necessary to apply a heteroscedasticity test.
Robust regression has potentialities in panel data empirical applications, does not require a
preliminary subjective cleaning of the data, and still produces reasonable parameter estimates even
when rough errors occur (Bramati and Croux, 2007).
Table 7. Results of the linear regression of equations 2 and 3
Number of obs=192 MP EP
Prob > F= 0 0
R-squared= 0.3725 0.4513
Coef. P>t Coef. P>t
EG -0.343925 0.197 -0.294333 0.088*
GDP2 -0.712271 0.013** -0.418239 0.042**
GDP3 -0.941943 0.001*** -0.595668 0.004***
RE -0.000155 0.158 -0.000107 0.142
SP 2.165849 0*** 1.976543 0***
β0 1.195724 0 0.5770818 0.004
MP: Moderate-income poverty index EP: Extreme income poverty index. EG: Represents the economic growth GDP2: The share
of the secondary sector in all GDP. GDP3: The share of the tertiary sector in GDP. RE: Reallocation eect SP: Social programs
β0: represents the constant.
As table 7 appears concerning moderate poverty, equation 2 denotes the high explanatory power
of the model with an R2 of 37%. In comparison, the probability of the “F” statistic is less than
5%, which indicates that together these variables can explain the levels of moderate poverty.
Furthermore, the estimated regression shows that all the variables observe a coecient with the
expected sign (except social spending). Secondary and tertiary GDP and social spending were
statistically signicant (but not the reallocation eect or economic growth) since the probability is
shown by the statistic “t” is less than 5%. It is important to note that the sign of the Reallocation
Eect is not statistically signicant, maybe a reection that increases in productivity are not
necessarily based on a structural change.
On the other hand, in equation 3, when running the model for extreme poverty, the explanatory
power increases, since it registers an R2 of 45%; while the probability of the “F” statistic is less
than 5%, which indicates that together these variables can explain the levels of extreme poverty and
Carlos Padilla Moran Economic structure and poverty in Mexico, 2008-2018
KAIRÓS, revista de ciencias económicas, jurídicas y administrativas, 5(8), pp. 144-165. Primer semestre
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economic growth, social programs secondary and tertiary GDP are statistically signicant since the
probability of the “t” statistic is less than 5%.
In summary, the econometric model indicates that the CE, GDP2, GDP3, and ER reduce poverty.
However, the ER is not signicant; that is, high levels of relocation do not translate into low levels
of poverty because states that have high levels do not increase the natural productivity of their
activities, and even though relocation is positive, It does not mean that work is migrating to higher
productivity activities in general.
In another sense, social spending, although it is statistically signicant, does not have the expected
sign. Since the programs tend to be directed to a greater extent at the entities with the highest levels
of poverty, consequently, in these, the spending is higher as a share of the GDP compared to the
states with the lowest poverty.
The results suggest that high levels of participation in the secondary and tertiary sectors result in
poverty levels below the national average, which is explained by the fact that the states that suer
from it to a lesser extent are identied with a manufacturing vocation with high participation of
the secondary sector (eleven entities) or with a tourist vocation and a strong weight of the tertiary
sector in its economic structure (six entities).
6. Conclusions
In this paper, Mexico’s economic structure is studied and if this experience’s changes correlate
and aect poverty during the 2008-2018 period. This is calculated using the relative sectoral
participation in GDP and employment, the intersectoral eect (IE), the reallocation eect (RE),
and the poverty indices estimated by Coneval. An econometric model is also estimated to identify
the determining factors of poverty.
Although the increase in productivity in the country was not very high in this period, it is suggested
that it was induced to a greater extent by labor relocation to higher productivity jobs than by the
natural increase in productivity. This is explained by the fact that the share of employment in the
industrial sector (the one with the highest productivity) is not reduced while employment decreases
in agricultural activities where the lowest productivity is found and increases considerably in
tertiary sector activities.
In addition, employment is relocated to higher productivity services. However, employment is
not relocated to the sector with the highest productivity (the secondary sector), and the increase
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Carlos Padilla Moran
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in productivity is feeble; even though the participation of the RE is high, it is not enough to arm
substantial improvements.
According to the econometric model, high levels of participation in the secondary and tertiary
sectors explain low poverty levels. On the other hand, the relocation eect and economic growth
were insignicant. The empirical analysis presented and the ndings of the econometric model
led to the acceptance of the research hypothesis since the entities characterized by having a more
extraordinary manufacturing vocation and thus high levels of productivity are those that register
poverty levels below the national average.
Based on these results, the following recommendations are derived:
First, the State must promote policies aimed at technological development by massifying higher
education and protecting strategic parastatal companies. Second, considering the structural
heterogeneity in the country’s states, it is essential to prioritize development in regions with high
levels of poverty; it would be necessary to promote the location of industrial activities and linkage
mechanisms with the rest of the country.
Acknowledgements
The work derives from the Master’s thesis in Regional Development carried out by the rst author
with advice from the second. We are grateful to the National Council of Science and Technology
(Conacyt) for the nancial support to study the postgraduate course at CIAD.
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