Césaire Chiatchoua et al., Resilience and Transformation: Analysis of the Mexican Economy in Times of Pandemic and Post-Pandemic (2025)

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A research article by Cesaire Chiatchoua, Ernest Yasser Nuñez Betancourt and Carlos Ernesto Luquez Gaitan, Resilience and Transformation: Analysis of the Mexican Economy in Times of Pandemic and Post-Pandemic, published in the Journal of Posthumanism, Vol. 5, No. 5, 2025, pages 3627–3643 (DOI 10.63332/joph.v5i5.1802). Using segmented regression models on data from February 2020 to March 2023, the study finds a positive correlation (r=0.9026622) between confirmed COVID-19 cases and deaths in the first period, while in the second period only the IGAE index showed a significant relationship. With 93.2% of companies in Mexico reporting pandemic impacts and 91.3% reporting lower income, the authors recommend expanded regulated credit for micro and small businesses.

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2025
Volume: 5, No: 5, pp. 3627–3643
ISSN: 2634-3576 (Print) | ISSN 2634-3584 (Online)
posthumanism.co.uk

DOI: https://doi.org/10.63332/joph.v5i5.1802

Resilience and Transformation: Analysis of the Mexican Economy in
Times of Pandemic and Post-Pandemic
Cesaire Chiatchoua1, Ernest Yasser Nuñez Betancourt2, Carlos Ernesto Luquez Gaitan3

Abstract
The emergence of the pandemic has surprised the world, generating a significant impact on macroeconomic variables and impacting
the quality of life of citizens. The objective of this research is to analyze economic performance in the pandemic and post-pandemic
scenario through the use of segmented regression models in order to formulate political strategies that contribute to improving the
living conditions of the citizens of Mexico. Using data from February 2020 to March 2023, it was found that, in the first period, a
significant and positive correlation ( r=0.9026622) between confirmed cases of COVID-19 and deaths. In the second period, only
the IGAE showed a significant relationship with confirmed cases and deaths. From the above, the need arises to strengthen the
mechanisms and programs for access to regulated credit for micro and small businesses, access to social security, access to public
health services, as well as the incorporation of women into the formal labor market.
Keywords: Wellbeing, Growth, Disease, Employment; Segmented Regression.

Introduction
The emergence of the pandemic has surprised the world, generating a significant impact on
macroeconomic variables and impacting the quality of life of citizens (Niembro and Calá, 2021;
Matailo Pinta; Romero Ramon and Dávila Herrera, 2022). This phenomenon has transcended
the field of health and has affected growth rates globally. According to Mejía Reyes, Reyes
Hernández, and Vergara Gonzalez (2022), the world has experienced the worst health crisis in
the last hundred years during the years 2020 and 2021. During the first months, confinement and
mobility restriction measures were implemented with the objective of containing infections,
which has caused a drastic contraction in the world economy and strong distortions in the supply
and demand of products, affecting the balance in various markets. However, by 2022, a gradual
normalization of economic activities and a rebound in the global economy has been observed.
In Mexico, the arrival of the health crisis has caused humanitarian, economic and health losses.
According to Santamaría Velasco, Montañez Moya and Gutiérrez Olvera (2021, pp. 1886-4171),
93.2% of companies in the country have experienced some type of impact on their economic
activity and labor market. The decrease in income has had the most notable impact, with 91.3%
of companies reporting it, while 41.4% have had to carry out temporary closures or technical
stoppages. Other adverse effects include reduced sales, reduced staff, and reduced wages.
1
International Trade/Postgraduate Studies and Research Section, Escuela Superior de Economía/Instituto Politécnico Nacional,
Plutarco Elías Calles, Miguel Hidalgo, 11350 Ciudad de México, México.
2
Academy of administration, logistics and commerce, Polytechnic University of Texcoco, Carretera Federal los Reyes-Texcoco
14.200 San Miguel Coatlinchan, 56250 Texcoco de Mora, Mexico
3
Division of Economic-Administrative Sciences, Autonomous University of Chapingo, 56227 Estado de México, Mexico, Email:
carlosluquezgaitan@gmail.com, (Corresponding Author)
3628 Resilience and Transformation: Analysis of the Mexican
However, with the total reopening of different economic activities in 2022, a growing recovery
of the economy in Mexico has been observed. According to the OECD (2022), the country
experienced a solid rebound in economic activity in the first part of 2021, although it has been
weakened due to supply pressures and new outbreaks of the SARS-CoV-2 virus. It is estimated
that the Mexican economy will grow 2.3% in 2022 and 2.6% in 2023, after having experienced
an increase of 5.3% in 2021.
The analysis of the two crucial moments mentioned reveals the importance of two different
stages: the pandemic (February 2020 - February 2022), in which countries were forced to
develop resilience to understand and effectively address the abrupt situation; and the post-
pandemic stage (March 2022 to May 2023), in which governments have transformed with the
aim of adapting to the new normal. In light of the previously mentioned events, the objective is
to analyze economic performance in the pandemic and post-pandemic scenario through the use
of segmented regression models. The fundamental purpose of this analysis is to formulate
political strategies that contribute to improving the living conditions of the citizens of Mexico.
The development of this research will allow us to test the hypothesis that resilience and
transformation capacities have been the determining factors that have allowed the Mexican
economy to overcome the effects caused by the pandemic and the post-pandemic.
After the introduction, the first section of this study is dedicated to exhaustively detailing the
evolution of the Mexican economy during the pandemic and post-pandemic periods. Next, the
second section focuses on the detailed description of the methodology used in this research. The
third section is intended to present the results obtained and carry out a detailed discussion of
them. Finally, the conclusions reached in this study are presented. It is worth highlighting the
importance of the model used as a valuable contribution to research, providing a solid analytical
basis and facilitating a deeper understanding of the economic phenomena studied.
Resilience and Transformation of the Mexican Economy During the Pandemic and Post-
Pandemic
Since the declaration of a health emergency by the World Health Organization (WHO), the
SARS-CoV-2 virus has been recognized as a public problem with consequences for both health
and the economy, both globally as well as in the specific context of Mexico. In this section, the
relationship between the health crisis and the Mexican economy is addressed in two stages: the
first covering the period from February 2020 to February 2022, and the second covering the
period from March 2022 to May 2023. In the analysis, examines the behavior of key variables
such as deaths, positive cases, the Global Indicator of Economic Activity (IGAE),
unemployment rate, National Consumer Price Index (INPC), Price and Quotations Index (CPI),
Monthly Consumption Indicator Private in the Internal Market (IMCPMI) and the exchange rate.
Through a gamma regression model, we seek to identify the relationship between these variables,
demonstrating how an increase in deaths and a decrease in cases of contagion contribute to the
changes observed in the country's economic activity.
Resilience: February 2020-February 2022
In the first stage, which corresponds to the pandemic period, governments were surprised by the
appearance of the disease and had to develop strategies to contain the rapid increase in infections
and stabilize the economy, without having previous experience in this situation. The World
Health Organization (WHO) recommended the implementation of the "great confinement" as
the main measure.
Journal of Posthumanism
Chiatchoua et al. 3629
Although confinement was essential to contain the spread of the virus, the temporary closures
of activities negatively affected the performance of the country's economy. Due to the increase
in positive cases of SARS-CoV-2 (Figure 1a) and deaths (Figure 1b) and the restriction measures
on non-essential activities, a significant drop in the Gross Domestic Product (GDP) is observed
in the second quarter of 2020, Figure 2.
Figure 1
Impact of the pandemic in Mexico: Confirmed cases and deaths from COVID-19 from 2020 to
2022

Positive cases Deaths

80,000
1,400
70,000
1,200
60,000
50,000 1,000

40,000 800

30,000 600
20,000 400
10,000
200
0 2/26/2020
3/26/2020
4/26/2020
5/26/2020
6/26/2020
7/26/2020 0 3/17/2020
8/26/2020
9/26/2020
10/26/2020 4/17/2020
5/17/2020
6/17/2020
11/26/2020
12/26/2020
1/26/2021 7/17/2020
8/17/2020
9/17/2020
2/26/2021
3/26/2021
4/26/2021 10/17/2020
11/17/2020
12/17/2020
5/26/2021
6/26/2021
7/26/2021 1/17/2021
2/17/2021
3/17/2021
8/26/2021
9/26/2021
10/26/2021 4/17/2021
5/17/2021
6/17/2021
11/26/2021
12/26/2021
1/26/2022 7/17/2021
8/17/2021
9/17/2021
2/26/2022 10/17/2021
11/17/2021
12/17/2021
1/17/2022
2/17/2022

1 a) Positive cases from February 2020 to 1 b) Deaths from February 2020 to February
February 2022. 2022.
Note: Own elaboration with data from the Ministry of Health (2023).
Furthermore, an increase in the unemployment rate and a decrease in the National Consumer
Price Index (INPC) and the consumer price index (CPI) are evident (Figures 3 a, 3 b and 3 c).

Figure 2
The economic impact of the pandemic: variation in Mexico's GDP

Gross domestic product

16.00

11.00

6.00

1.00
2020|T1 2020|T2 2020|T3 2020|T4 2021|T1 2021|T2 2021|T3 2021|T4 2022|T1
-4.00

-9.00

-14.00

-19.00

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3630 Resilience and Transformation: Analysis of the Mexican
Note: Own elaboration with data from Expansión México
https://datosmacro.expansion.com/pib/mexico
Finally, in Private Consumption in the Internal Market (IMCPMI) (Figure 3 d).
Figure 3
Key indicators: Unemployment, INPC and CPI in the economic scenario from 2020 to 2022.

Unemployment rate National consumer price index
5.60 8.00

7.00
4.60
6.00
3.60
5.00

2.60 4.00

1.60 3.00

2.00
0.60
1.00
-0.40
0.00

Figure 3 a) Unemployment rate during resilience. Figure 3 b) Variation of the INPC rate

CPI IMCPMI

8.00
7.00 24.00

6.00
14.00
5.00
4.00
4.00
3.00
2.00 -6.00
1.00
0.00 -16.00
2020 2021 2022
-26.00

Figure 3 c) Variation of the CPI rate Figure 3 d) Variation of the IMCPMI
rate
Note. Own elaboration with data from INEGI (2023).
For its part, an increase was obtained in the peso-dollar exchange rate (Figure 4).
Figure 4
Evolution of the peso-dollar exchange rate from February 2020 to February 2022.

Journal of Posthumanism
Chiatchoua et al. 3631

Exchange rate
35.00

30.00

25.00

20.00

15.00

10.00

5.00

0.00

-5.00

-10.00

-15.00

-20.00

Note: Own elaboration with data from Banco de México (2023).
In summary, during this pandemic stage, the negative impacts on various economic indicators
are evident as a consequence of the confinement measures implemented to contain the spread of
the virus.
The global economic outlook for 2021 presented a significant challenge related to access to
vaccines, as the vaccination process became a crucial factor in restoring mobility and economic
reactivation. This advance allowed a rebound in the Gross Domestic Product (GDP) worldwide,
with a growth of 5.9 %, being 5.3% for advanced economies and 6.7% for emerging markets,
according to the World Bank (2023).
However, despite these advances, uncontrolled risks persisted, including the emergence of new
strains of the virus, problems in supply chains, difficulties in accessing vaccines, virus mutations,
and continued wear and tear on health systems. These factors contributed to the imminent
prolongation of the pandemic and a growing loss of purchasing power in various contexts.
In the case of Mexico, the Organization for Economic Cooperation and Development (OECD,
2022) noted a solid rebound in economic activity during the first part of 2021. However, this
growth has been weakened due to pressures in the supply and the appearance of new outbreaks
of the SARS-CoV-2 virus. So, despite the advances in vaccination and economic recovery
observed in certain periods, the persistence of risks and challenges related to the pandemic has
significantly impacted the global economic scenario, including Mexico.
The economic recovery observed in 2021 has been the result of the gradual reopening of essential
activities in various sectors. Furthermore, the public policies implemented have sought to
promote sustained growth and strengthen the domestic market with the aim of minimizing the
negative effects of the economic crisis.
In this sense, various actions have been implemented by the government. Hernández (2020)
highlights the implementation of a fiscal policy of austerity and the search to avoid incurring
excessive debt to maintain the sustainability of public finances. For their part, Mejía Reyes,
Reyes Hernández and Vergara González (2022) mention the implementation of a monetary
policy through the Bank of Mexico, which has included measures such as accelerating the cut
of the reference interest rate with the objective to boost the economy.
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3632 Resilience and Transformation: Analysis of the Mexican
These policies and actions by the government have sought to generate an environment conducive
to economic reactivation, stimulate investment and promote consumption. However, it is
important to mention that the effectiveness of these measures has been the subject of analysis
and debate, and their impact may vary depending on various economic and contextual factors.
Transformation: March 2022-May 2023
By 2022, the outlook will undergo a substantial transformation with the complete resumption of
economic activities and a high rate of immunization in the population, which translates into
greater mobility and a notable decrease in the incidence of infections and mortality, as is evident
in Figure 5. In this sense, Ugalde, Hellmann and Homedes (2022) recognize that the progress
achieved in the development of vaccines is undeniable, although it should be noted that their
global distribution has been marked by notable inequalities.
Figure 5
Impact of the pandemic in Mexico: Confirmed cases and deaths from COVID-19 from 2022 to
2023
Confirmed cases Deaths

5,000 140
4,500
120
4,000
3,500 100
3,000 80
2,500
2,000 60

1,500 40
1,000
20
500
0 0

5 a) Positive cases from March 2022 to May 5 b) Deaths from March 2022 to May 2023.
2023.
Note: Own elaboration with data from the Ministry of Health (2023).
Of course, here's a rewritten form of the idea:
The values presented in Table 1 show the evolution of the Gross Domestic Product from the
second quarter of 2022 to the first quarter of 2023. According to the OECD (2022), Mexico's
economic recovery is due to a solid macroeconomic policy supported by innovative management
of debt, an adequate monetary policy and a flexible exchange rate that allowed macroeconomic
stability to be protected and the economy to quickly return to the path of recovery, although
challenges still exist.

Quarter Percentage
Q2 2022 18.30
Q3 2022 18.47
Q4 2022 18.58

Journal of Posthumanism
Chiatchoua et al. 3633
Q1 2023 18.76
Table 1: Gross Domestic Product (GDP) Of Mexico from the 2nd Quarter of 2022 to the 1st Quarter of
2023 (In Billions of Mexican Pesos)
Note: INEGI. (May 15, 2023). Gross domestic product (GDP) of Mexico from 1st quarter 2010
to 1st quarter 2023 (in trillion Mexican pesos).
Furthermore, an increase in the unemployment rate and a decrease in the National Consumer
Price Index (INPC) is evident (Figures 6 a, 6 b and 6 c). The consumer price index (CPI) during
2022 had a rate of 7.82%, while in 2023 it was 5.03%. All these events allowed the OECD (2022)
to estimate that Mexico will grow 2.3% in 2022 and 2.6% in 2023.
Figure 6 d shows an evolution of the dollar-peso exchange rate, a historical appreciation of the
currency and a record collection of remittances is observed. For the Center for Public Finance
Studies (2023), the wide differential between the interest rates of Mexico and the United States
is a factor that has contributed to the exchange rate appreciation, by increasing demand for the
national currency. This fact has placed the national currency in one of the most valued in the
world and has made it the favorite of operators, or traders , who want to bet for or against
emerging countries around the world according to Suárez (January , 2023).
Figure 6
Key indicators: Unemployment, INPC and CPI in the economic scenario from 2022 to 2023

Unemployment rate INPC

5.00 10.00
9.00
4.00 8.00
3.00 7.00
6.00
2.00 5.00
1.00 4.00
3.00
0.00 Marzo 2.00
Abril
Mayo 1.00
Junio
Julio
Agosto
Septiembre 0.00

2022/02 2022/03 2022/04 2022/05 2022/06 2022/07 2022/08 2022/09 2022/10 2022/11 2022/12 2023/01 2023/02 2023/03 2023/04 2023/05
Octubre
Noviembre
Diciembre
Enero
Febrero
Marzo
Abril

Figure 6 a) Variation of the IMCPMI rate Figure 6 b) Variation of the INPC rate

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3634 Resilience and Transformation: Analysis of the Mexican

IMCPMI Exchange rate

10.00 4.00
9.00 2.00
8.00
0.00
7.00

Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2022 Mar 2023 Mar 2023 Mar 2023 Mar 2023 Mar 2023
6.00 -2.00

5.00 -4.00
4.00
-6.00
3.00
-8.00
2.00
1.00 -10.00
0.00 -12.00

-14.00

Figure 6 c) Variation of the IMCPMI rate Figure 6 d) Exchange rate.
Note. Own elaboration with data from INEGI (2023).
Materials and Methods
In this research, segmented regression models are proposed with the purpose of describing the
behavior of several economic indicators in relation to key variables related to the context of
COVID-19 during two different time intervals. The main objective is to examine the existence
of relationships between these indicators and the aforementioned variables, as well as to
determine the nature of these relationships. In addition, it seeks to analyze the impact of both the
emergence of the pandemic and the beginning of the administration of vaccines on these
economic indicators. This will allow us to evaluate whether there have been significant changes
in the trends of economic indicators when comparing the two time periods.
From a statistical point of view, a change in the trend can be reformulated as a change in the
slope of the fitted line by means of a regression model. In the opinion of Chen et al. (2011),
segmented regression models are regression models in which the dependence of a response
variable on a covariate is piecewise linear. In other words, the dependency can be represented
by several straight line segments.
The work considered four variables that describe the impact of the pandemic: the number of
confirmed cases ( 𝑥1 ), the number of deaths from COVID-19 ( 𝑥2 ), the number of vaccine doses
administered ( 𝑥3 ) and the number of people vaccinated ( 𝑥4 ). In addition to these variables, time
𝑡measured in months was taken into account.
One of the fundamental assumptions of the multiple linear regression model states that there is
no high linear correlation between the independent variables, that is, there is no perfect
multicollinearity in the model. If the possible multicollinearity of the regressor variables is not
taken into account, an overfitted model could be obtained in which part of the explanation
obtained by the coefficients reflects the underlying linear relationship in the independent
variables.
Therefore, multicollinearity between the regressor variables was first analyzed. If the correlation
is high ( |𝑟| > 0.7), a dimensionality reduction is carried out through principal components
analysis to express the variation of the regressor variables in a way that avoids the problems that
arise from multicollinearity. Principal components analysis is extremely useful as a hypothesis-
generating tool, summarizing patterns in multivariate data and reducing the number of variables
Journal of Posthumanism
Chiatchoua et al. 3635
in analyses. It is suitable for data sets where the dependent variables are linearly related to each
other but can still provide useful results even if this condition is not completely met (Syms,
2019).
Several approaches have been proposed to address the challenge of the presence of
multicollinearity among the independent variables in the linear regression model, represented by
the equation.
𝑦𝑖 = 𝛽0 + 𝛽1 𝑥1𝑖 + ⋯ + 𝛽𝑝 𝑥𝑝𝑖 + 𝑒𝑖 ,
where 𝑖 = 1, 2, … , 𝑛, 𝑦𝑖 is the output variable, 𝑥𝑛𝑖 is the value of the nth output variable, 𝑒𝑖 is
the error between the current output and the estimated output 𝑦̂𝑖 . The method that was applied
was regression on principal components (del Valle Moreno & Guerra Bustillo, 2012). That is,
in this study, the variables obtained from this analysis were used as the new regressor variables
of the model.
The time interval in which the events that were examined take place was divided into two periods
in which different linear equations were proposed following a segmented regression model. Said
model can be expressed in the following way, for each of the estimated economic indicators
𝑦̂,𝑖 𝑖 = 1, … ,7, we have the following segmented model
𝛽1,0 + ∑𝑖=1,2,3,4 𝛽1,𝑖 𝑥𝑖 , 1 ≤ 𝑡 < 11
𝑦̂𝑖 (𝑥1 , 𝑥2 , 𝑥3 , 𝑥4 , 𝑡) = {
𝛽2,0 + ∑𝑖=1,2,3,4 𝛽2,𝑖 𝑥𝑖 , 11 ≤ 𝑡 ≤ 39
The indicators studied are coded using subscripts 𝑖. The summary of the coding of the variables
is found in Table 2. The first of the periods (called Period 1) includes from the beginning of the
pandemic in February 2020 (corresponding to) until December 2020 when only the variables are
available 𝑡 = 1. time 𝑡, and 𝑥1 and 𝑥2 and the second period (named Period 2) is from the first
vaccination records in December 2020 (corresponding to 𝑡 = 11) until March 2023.
The validity of the model was measured first by reviewing the values 𝑝of the test 𝑡applied to the
coefficients and the value 𝑝 of the Fischer test performed on the set of coefficients. Finally, it
was verified whether or not there is cointegration between the variables by applying the Dickey
-Fuller test to the residuals of the regression model obtained (Chiapinotto, 2021). It should be
noted that the values 𝑝are important in determining the statistical significance of the results in
the context of the model. In this case, test 𝑡values are being calculated 𝑝to evaluate the model
coefficients (Stella, et al., 2023).

Variable Coding Variable type Units
Time 𝑡 Returner Months
confirmed_cases 𝑥1 Returner People
Deaths 𝑥2 Returner People
Administered_doses + 𝑥3 Returner People
Vaccinated_people 𝑥4 Returner People
GDP 𝑦1 Answer
IGAE 𝑦2 Answer
Unemployment rate 𝑦3 Answer
INPC 𝑦4 Answer
CPI 𝑦5 Answer
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3636 Resilience and Transformation: Analysis of the Mexican
IMCPMI 𝑦6 Answer
Exchange rate 𝑦7 Answer
To carry out the test, the imputation of 11/18/2022 was carried out with the previous value
22315899.
Results
First Period
The first period is from February 2020 to December 2020 (10 months). Initially, linear models
were proposed with regressor variables number of confirmed cases and deaths. However, they
were found to be correlated with a correlation coefficient of 𝑟 = 0.9026622, with a 𝑝 <
0.0003487significance value for hypothesis testing with alternative hypothesis 𝜌 ≠ 0.
Therefore, a linear combination was carried out with both variables using principal components.
In principal component analysis one of the key steps is to calculate the covariance matrix and
then find its values and eigenvectors (eigenvalues and eigenvectors). The covariance matrix of
the regressor variables for Period 1 is given in Table 3.

Confirmed cases Deaths
Confirmed cases 6717.7805 619.90052
Deaths 619.90052 70.20495
The values and eigenvectors associated with the covariance matrix of the data that represent the
variance explained by each principal component were calculated following the principal
components method. The eigenvalues and eigenvectors associated with the covariance matrix
are 𝜆1 = 6775.09343with associated vector 𝑣⃗ = (0.99575324, −0.09206237) corresponding
to more than 99 % the variation. The remaining percentage corresponds to the value 𝜆2 =
12.89204with associated eigenvector 𝑣⃗ = (0.09206237,0.99575324). Considering these
results, the transformation was applied.
𝑥5 = 0.9957𝑥1 − 0.0920𝑥2
This new variable was then used for the Gaussian regression model. Table 4 summarizes the
results obtained for this model. It should be noted that for the positive variables, Gaussian and
gamma distribution regression models were used without finding clear differences in the results.
Gamma regression is a type of regression analysis that is used when the response variable
follows a gamma distribution, that is, instead of using the normal density function for errors, the
gamma density function is used (Brown, Griffin, 2010). This allows modeling response variables
that are non-negative and asymmetric.

Variable Intercept 𝑥5 Cointegration (value 𝑝)
GDP 17441426* -2614 0.7273
IGAE -8,980** -0.0007 0.3971
Unemployment rate 3.82*** 0.0059** 0.89
INPC 3.0268** 0.003 0.99
CPI 37054.584** 4,870 0.97
IMCPMI -8.07* 0.0351** 0.10

Journal of Posthumanism
Chiatchoua et al. 3637
Exchange rate 14.79** -0.009 0.01
Table 4: Linear Regression with Gaussian
Significance level * less than 0.1, ** less than 0.05, *** less than 0.01.
From the regression analysis it was concluded that for Period 1 only in two economic indicators
did the presence of the variable 𝑥5 result in a significant contribution. This means that in this
period the variable 𝑥5 had a significant impact on these two economic indicators, while it did
not have a significant impact on the others. As a result, the models of these equations were
obtained after replacing the variable 𝑥5 with its definition:
𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒 = 3.82 + 0.0058 𝐶𝑜𝑛𝑓𝑖𝑟𝑚𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 − 0.0005 𝐷𝑒𝑎𝑡ℎ𝑠
𝐼𝑀𝐶𝑃𝑀𝐼 = −8.07 + 0.034 𝐶𝑜𝑛𝑓𝑖𝑟𝑚𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 − 0.0032 𝐷𝑒𝑎𝑡ℎ𝑠
Figure 7 shows the linear regression graph for the IMCPMI and unemployment rate for the first
period.
Figure 7
Result of the linear regression for the Monthly Indicator of Private Consumption in the Internal
Market (IMCPMI) and unemployment rate

IMCPMI. Period 1 Unemployment rate. Period 1
10 7.00

5 6.00

0 5.00
0 50 100 150 200 250 4.00
-5
3.00
-10
2.00
-15
1.00
-20
0.00
-25 0 100 200 300

Note: Own elaboration based on the results.
In these models, the coefficients 0.0058, 0.0005, 0.034 and 0.0032 represent how the economic
indicator changes in response to changes in the predictor variables (Confirmed Cases and
Deaths). The constant values 3.82 and -8.07 represent the base level of the economic indicators
when all predictor variables are equal to zero. These models allow predictions to be made about
the Unemployment Rate and IMCPMI based on the values of Confirmed Cases and Deaths. If
the values of these variables change, the models provide an estimate of how economic indicators
will change accordingly.
Period 2
For the second period (starting in December 2020), in addition to the number of confirmed cases
and deaths, the number of doses applied and the total number of vaccinated people, regressor

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3638 Resilience and Transformation: Analysis of the Mexican
variables were used as regressor variables. For this case, with the objective of detecting
multicollinearity, the correlation matrix shown in Table 5 was first constructed.

Confirmed Deaths.thousands Administered Vaccinated
cases in doses people
thousands
Confirmed cases in 1,0000000 0.34609706 -0.09544956 -
thousands 0.05653765
Deaths.thousands 0.34609706 1,0000000 -0.7385928 -0.7303022
Administered doses -0.09544956 -0.7385928 1,0000000 0.97855647
Vaccinated people -0.05653765 -0.7303022 0.97855647 1,0000000
Table 5: Correlation Matrix
From this table it is obtained that the variables corresponding to deaths, doses administered, and
people vaccinated have a high correlation. Due to this and to avoid the effects of collinearity,
similar to the previous case, two variables were proposed that are a linear combination of the
previously mentioned ones obtained from the principal components’ method. Table 6 a shows
the covariance between all possible combinations of the three mentioned variables
(Deaths.thousands, Doses.administered.millions and People.vaccinated.millions ). For example,
the value in the row " Deaths.thousands " and the column " Doses.Administered.millions " is -
61.06935, suggesting a negative relationship between the number of deaths and the number of
doses administered; That is, as the doses administered increase, the number of deaths tends to
decrease.

Deaths.thousand Doses People.vaccinated.milli
s administere ons
d in millions
Deaths.thousands 93.62600 -61.06935 -25.30519
Doses administered in -61.06935 73.01970 29.94434
millions
People.vaccinated.million -25.30519 29.94434 12.82383
s
Table 6a: Covariance Matrix
Table 6 b shows the eigenvalues (eigenvalues) and eigenvectors (eigenvectors) of the covariance
matrix. As mentioned above, principal component analysis involves decomposing the variability
of the original variables into its principal components, namely the eigenvalues and eigenvectors.

Eigenvalue Eigenvector Variability
percentage
155.7351790 (−0.7287879, 0.6847358, −0.002236796) 86.77%
23.2692305 (0.6327843, 0.6747351, 0.379890247) 12.96%
0.4651154 (−0.2616337, −0.2754440, 0.925028863) 0.005%

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Chiatchoua et al. 3639
Because the first two eigenvectors represent more than 99% of the variability of the information
provided by these three variables, the following two new variables were proposed.
𝑥5 = −0.7287𝑥2 + 0.6847𝑥3 − 0.002𝑥4
𝑥6 = 0.6327𝑥2 + 0.6747𝑥3 + 0.3798𝑥4
Using as regressor variables 𝑥1 , 𝑥5 and 𝑥6 regression models were proposed for economic
indicators. Table 7 summarizes the regression model for the second period and provides
information on how the predictor variables 𝑥1 affect 𝑥5 the 𝑥6 response variables GDP, IGAE,
Unemployment Rate, INPC, CPI, IMCPMI and Exchange Rate.

Variable Intercept 𝑥1 𝑥5 𝑥6 Cointegration
(value 𝑝)
GDP 7.07e+06 1.56 2.404e+05 -1.87e+05 -
IGAE 0.004** 0.945 0.036** 0.037** 0.01**
Unemployment 4,047*** -3.99e- 9.37e-02 -1.43e-01 0.17
rate 07
INPC*** 5.37*** 1.89e-07 -3.60e-01 5.11e-01 0.78
CPI** 4.602e+04*** -1.33e- -2.63e+03 2.99e+03 0.2703
03
IMCPMI 4.19e-02 -5.58e- -4.31e-01 4.68e-01 0.072*
07
Exchange rate -7,304 4.3e-06 -2.38 2.58 0.8017
Significance level * less than 0.1, ** less than 0.05, *** less than 0.01.
The coefficients 𝑥1 , 𝑥5 and 𝑥6 indicate how much the dependent variable changes for each unit
change in the corresponding predictor variable, holding the other predictor variables constant.
For example, for the IGAE, the coefficient for "Confirmed Cases" is 0.945, which means that
an increase of one unit in "Confirmed Cases" is associated with an increase in 0.945the value of
IGAE, holding the other variables constant.
In this second period, only in the IGAE was a significant effect of the regressor variables shown.
The equation obtained after substituting 𝑥5 and 𝑥6 by its definitions is.
𝐼𝐺𝐴𝐸 = 0.0040 + 0.945 𝐶𝑜𝑛𝑓𝑖𝑟𝑚𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 − 0.0028 𝐷𝑒𝑎𝑡ℎ𝑠
+ 0.0496 𝐷𝑜𝑠𝑒𝑠 𝑎𝑑𝑚𝑖𝑛𝑖𝑠𝑡𝑒𝑟𝑒𝑑 + 0.0139 𝑉𝑎𝑐𝑐𝑖𝑛𝑎𝑡𝑒𝑑 𝑝𝑒𝑜𝑝𝑙𝑒
This equation represents how the IGAE relates to the specific predictor variables in the second
period.

Below is a summary of the results. In the first period, which covers from February 2020 to
December 2020, a significant and positive correlation was found 𝑟 = 0.9026622between
confirmed cases of COVID-19 and deaths. This suggests that as confirmed cases increased,
deaths also increased, as Mejía Reyes, Reyes Hernández, and Vergara Gonzalez (2022) point
out, the world has experienced the worst health crisis in the last hundred years during the years
2020 and 2021.
To address multicollinearity between variables, principal components analysis was applied. The
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3640 Resilience and Transformation: Analysis of the Mexican
first principal component, termed 𝑥5 , captured more than 99% of the variability of the original
data. This component was used in linear regression models with Gaussian distribution. Among
these models, only two economic indicators, namely, the Unemployment Rate and the IMCPMI,
showed significant relationships with the variable 𝑥5 . The resulting regression equations indicate
how the Unemployment Rate and IMCPMI change in response to changes in confirmed cases
and deaths during the first period.
The above translates according to Santamaría Velasco, Montañez Moya, and Gutiérrez Olvera
(2021, pp. 1886-4171), 93.2% of companies in the country have experienced some type of
impact on their economic activity and labor market. The decrease in income has had the most
notable impact, with 91.3% of companies reporting it, while 41.4% have had to carry out
temporary closures or technical stoppages. Other adverse effects include reduced sales, reduced
staff, and reduced wages.
In the second period, starting in December 2020, two new predictor variables, 𝑥5 and 𝑥6 , were
introduced to overcome the problem of multicollinearity between the variables related to deaths
and vaccination. The correlation matrix revealed a high correlation between these variables.
Principal components analysis was again applied, and the variables 𝑥5 y 𝑥6 captured more than
99% of the variability of the original data. However, in this second period, only the IGAE
showed a significant relationship with the predictor variables. 𝑥1 , 𝑥5 and 𝑥6 . The resulting
regression equation for the IGAE provides information on how this economic indicator relates
to confirmed cases, deaths, doses administered, and people vaccinated during the second period.
The above denotes two things. One, the application of vaccines and the reduction of deaths. For
Ugalde, Hellmann and Homedes (2022) the progress achieved in the development of vaccines
is undeniable. And two, the reopening of economic activities. The economic recovery in Mexico
is due, according to the OECD (2022), to a solid macroeconomic policy supported by innovative
debt management, an adequate monetary policy and a flexible exchange rate that made it
possible to protect macroeconomic stability and quickly return to economic stability. road to
recovery, although challenges still exist.
Conclusions, Recommendations, and Final Considerations
In this analysis, economic performance was studied in the pandemic and post-pandemic scenario
using segmented regression models with the purpose of formulating political strategies that
contribute to improving the living conditions of the citizens of Mexico. Therefore, data from
February 2020 to March 2023 were used. This research was developed in two different stages:
the pandemic (February 2020 - February 2022), in which countries were forced to develop
resilience to understand and effectively address the abrupt situation; and the post-pandemic stage
(March 2022 to May 2023), in which governments have transformed with the aim of adapting
to the new normal.
The results show that, in the first period, a significant and positive correlation ( 𝑟 = 0.9026622)
was found between confirmed cases of COVID-19 and deaths. This suggests that as the
thousands of confirmed cases increase, the deaths of thousands of people also increased. In the
second period, starting in December 2020, only the IGAE showed a significant relationship with
the predictor variables. 𝑥1 , 𝑥5 and 𝑥6 (confirmed cases, deaths, doses administered, and people
vaccinated). The resulting regression equation for the IGAE provides information on how this
economic indicator relates to confirmed cases, deaths, doses administered, and people
vaccinated.
Journal of Posthumanism
Chiatchoua et al. 3641
Thus, in the first period, as confirmed cases increased, deaths also increased and, consequently,
an increase in the unemployment rate and a fall in the Monthly Index of Household Private
Consumption. Faced with this problem, the Mexican government applied certain measures (
support to older adults, the construction of the Santa Lucia airport, the Maya Train and the Dos
Bocas refinery, the cancellation of ten undersecretaries, support for 2.1 million MyPES), in
addition to confinement to reduce the rate of infections, as financial support to micro and small
businesses, progressive reopening of essential activities, generation of temporary and permanent
jobs, construction of infrastructure, debt freezing, among others.
In a second period, the results show that the application of vaccines allowed the reduction of
cases of infections on the one hand and the reduction of the number of deaths. The direct
consequence was the economic recovery through a solid macroeconomic policy supported by
innovative debt management, an adequate monetary policy and a flexible exchange rate that
allowed macroeconomic stability to be protected, although there are still challenges such as
inflation, stagnant wage bill, informality and underemployment. Additionally, this recovery has
not been the same in all states of the republic, nor does it translate into greater well-being.
The results of this study allow us to suggest some public policy interventions. One of the main
recommendations revolves around the need to strengthen mechanisms and programs for access
to regulated credit for micro and small businesses, access to social security, access to public
health services, as well as the incorporation of women into the market. formal work. Other
mechanisms and programs for access to education, mainly basic education (preschool, primary
and secondary), gender parity, the use of mobile phones and homes with internet connection,
access to the rule of law, as well as encouraging investment to generate jobs and decent wages.
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