pág. 3506
HOW CORRUPTION AFFECTS THE DEVELOP-
MENT OF A NATION: CASE STUDY MEXICO
CÓMO AFECTA LA CORRUPCIÓN AL DESARROLLO DE
UNA NACIÓN: CASO DE ESTUDIO MÉXICO
Gerardo Mario Ortigoza Capetillo
Instituto de Ingeniería Universidad Veracruzana
William Alejandro Castillo Toscano
Instituto de Ingeniería Universidad Veracruzana
pág. 3507
DOI: https://doi.org/10.37811/cl_rcm.v9i4.19007
How corruption affects the development of a nation: case study Mexico.
Gerardo Mario Ortigoza Capetillo1
gortigoza@uv.mx
https://orcid.org/0000-0002-5899-0938
Instituto de Ingeniería Universidad Veracruzana
Av Juan Pablo II, S/N, Costa Verde, 94292 ,
Boca del Río Veracruz, Mexico.
William Alejandro Castillo Toscano
wcastillo@uv.mx
https://orcid.org/0009-0003-1385-4523
Facultad de Ingeniería Universidad Veracruzana
Bv. Adolfo Ruíz Cortines 475, Costa Verde,
94294 Veracruz, Veracruz, Mexico.
ABSTRACT
In this work we analyze the correlations between indexes that quantify corruption and indexes used to
estimate the measure of wealth and well-being of a nation. Following the identification of the corruption-
affected indexes, some strategies for lowering these indexes are put forth, and time estimates (six years
in the future) are used to forecast the advantages of these strategies' execution on indexes that measure
a country's wealth and well-being. Our statistical study demonstrates the detrimental effects that
corruption has on peace, climate change performance and social progress of the Mexican nation. In this
sense, the adverse impacts of corruption on a country's growth might be mitigated by policies that lower
its rates; seeking to reduce its negative impacts on indexes related to the development of a nation.
Machine learning projections show us scenarios where measures are implemented to reduce corruption
and the benefits on the development indexes of a nation are visualized.
Keywords: corruption, correlation, social progress, peace, climate change performance
1 Autor principal
Correspondencia: gortigoza@uv.mx
DOI: https://doi.org/10.37811/cl_rcm.v9i4.19007
How corruption affects the development of a nation: case study Mexico.
Gerardo Mario Ortigoza Capetillo1
gortigoza@uv.mx
https://orcid.org/0000-0002-5899-0938
Instituto de Ingeniería Universidad Veracruzana
Av Juan Pablo II, S/N, Costa Verde, 94292 ,
Boca del Río Veracruz, Mexico.
William Alejandro Castillo Toscano
wcastillo@uv.mx
https://orcid.org/0009-0003-1385-4523
Facultad de Ingeniería Universidad Veracruzana
Bv. Adolfo Ruíz Cortines 475, Costa Verde,
94294 Veracruz, Veracruz, Mexico.
ABSTRACT
In this work we analyze the correlations between indexes that quantify corruption and indexes used to
estimate the measure of wealth and well-being of a nation. Following the identification of the corruption-
affected indexes, some strategies for lowering these indexes are put forth, and time estimates (six years
in the future) are used to forecast the advantages of these strategies' execution on indexes that measure
a country's wealth and well-being. Our statistical study demonstrates the detrimental effects that
corruption has on peace, climate change performance and social progress of the Mexican nation. In this
sense, the adverse impacts of corruption on a country's growth might be mitigated by policies that lower
its rates; seeking to reduce its negative impacts on indexes related to the development of a nation.
Machine learning projections show us scenarios where measures are implemented to reduce corruption
and the benefits on the development indexes of a nation are visualized.
Keywords: corruption, correlation, social progress, peace, climate change performance
1 Autor principal
Correspondencia: gortigoza@uv.mx
pág. 3508
Cómo afecta la corrupción al desarrollo de una nación: caso de estudio
México
RESUMEN
En este trabajo analizamos las correlaciones entre los índices que cuantifican la corrupción y los
utilizados para estimar la riqueza y el bienestar de una nación. Tras identificar los índices afectados por
la corrupción, se proponen estrategias para reducirlos, y se utilizan estimaciones temporales (a seis años
vista) para pronosticar las ventajas de la ejecución de estas estrategias en los índices que miden la riqueza
y el bienestar de un país. Nuestro estudio estadístico demuestra los efectos perjudiciales de la corrupción
sobre la paz, el desempeño frente al cambio climático y el progreso social de México. En este sentido,
los impactos adversos de la corrupción en el crecimiento de un país podrían mitigarse mediante políticas
que reduzcan sus tasas, buscando reducir sus impactos negativos en los índices relacionados con el
desarrollo nacional. Las proyecciones de aprendizaje máquina muestran escenarios donde se
implementan medidas para reducir la corrupción y se visualizan los beneficios en los índices de
desarrollo de una nación.
Palabras clave: corrupción, correlación, progreso social, paz, desempeño en materia de cambio
climático
Artículo recibido 15 julio 2025
Aceptado para publicación: 19 agosto 2025
Cómo afecta la corrupción al desarrollo de una nación: caso de estudio
México
RESUMEN
En este trabajo analizamos las correlaciones entre los índices que cuantifican la corrupción y los
utilizados para estimar la riqueza y el bienestar de una nación. Tras identificar los índices afectados por
la corrupción, se proponen estrategias para reducirlos, y se utilizan estimaciones temporales (a seis años
vista) para pronosticar las ventajas de la ejecución de estas estrategias en los índices que miden la riqueza
y el bienestar de un país. Nuestro estudio estadístico demuestra los efectos perjudiciales de la corrupción
sobre la paz, el desempeño frente al cambio climático y el progreso social de México. En este sentido,
los impactos adversos de la corrupción en el crecimiento de un país podrían mitigarse mediante políticas
que reduzcan sus tasas, buscando reducir sus impactos negativos en los índices relacionados con el
desarrollo nacional. Las proyecciones de aprendizaje máquina muestran escenarios donde se
implementan medidas para reducir la corrupción y se visualizan los beneficios en los índices de
desarrollo de una nación.
Palabras clave: corrupción, correlación, progreso social, paz, desempeño en materia de cambio
climático
Artículo recibido 15 julio 2025
Aceptado para publicación: 19 agosto 2025
pág. 3509
INTRODUCTION
The misuse of authority for personal benefit is what we refer to as corruption. Over $1 trillion is thought
to be spent in bribes annually worldwide, benefiting the unscrupulous and depriving future generations
of opportunity.
Corruption is a worldwide occurrence that hinders progress, discourages investment, and results in
poverty. Additionally, it weakens the political and legal structures, which ought to serve the general
welfare. It is not unexpected that public confidence in public servants and national institutions declines
when the rule of law is undermined and the voice of the people is not heard. Corruption worsens social
division, poverty, inequality, and the environmental catastrophe while undermining democracy and
economic growth. It is only possible to expose corruption and hold those responsible for it accountable
if we comprehend the mechanisms that allow it to occur (Transparency 2024).
Many people believe that within the past 20 years, corruption in Latin America has increased. While
bribery is not new in the area, incidents in Argentina, Brazil, Mexico, Venezuela, and other places seem
to indicate that large-scale graft has increased. What are the primary causes of this growth, assuming
that the public's improved awareness and better reporting aren't the only factors contributing to the
perception of rising corruption? Explanations that are now in place typically emphasize more chances
than incentives to extract bribes. Regarding the former, several academics have highlighted the rise in
state interventionism, which grants politicians and bureaucrats wide latitude over resources that seem
enticingly abundant. On the other hand, several academics have brought out the recent wave of
neoliberal reforms, in which politicians and bureaucrats sold off large amounts of public property,
frequently with little to no transparency. Even while these circumstances occasionally played a role,
they are insufficient to explain the current increase in corruption.
The global bank (world bank 2024) claims that because corruption raises prices and restricts access to
essential services like health, education, social programs, and even justice, it mostly affects the weak
and impoverished. It hurts markets, jobs, and economies by increasing inequality and decreasing private
sector investment. Moreover, corruption can impede a nation's ability to respond to crises, causing
needless suffering and, in the worst case scenario, fatalities. Corruption has the potential to erode public
confidence in institutions and leaders over time, causing rifts in society and, in some situations, raising
INTRODUCTION
The misuse of authority for personal benefit is what we refer to as corruption. Over $1 trillion is thought
to be spent in bribes annually worldwide, benefiting the unscrupulous and depriving future generations
of opportunity.
Corruption is a worldwide occurrence that hinders progress, discourages investment, and results in
poverty. Additionally, it weakens the political and legal structures, which ought to serve the general
welfare. It is not unexpected that public confidence in public servants and national institutions declines
when the rule of law is undermined and the voice of the people is not heard. Corruption worsens social
division, poverty, inequality, and the environmental catastrophe while undermining democracy and
economic growth. It is only possible to expose corruption and hold those responsible for it accountable
if we comprehend the mechanisms that allow it to occur (Transparency 2024).
Many people believe that within the past 20 years, corruption in Latin America has increased. While
bribery is not new in the area, incidents in Argentina, Brazil, Mexico, Venezuela, and other places seem
to indicate that large-scale graft has increased. What are the primary causes of this growth, assuming
that the public's improved awareness and better reporting aren't the only factors contributing to the
perception of rising corruption? Explanations that are now in place typically emphasize more chances
than incentives to extract bribes. Regarding the former, several academics have highlighted the rise in
state interventionism, which grants politicians and bureaucrats wide latitude over resources that seem
enticingly abundant. On the other hand, several academics have brought out the recent wave of
neoliberal reforms, in which politicians and bureaucrats sold off large amounts of public property,
frequently with little to no transparency. Even while these circumstances occasionally played a role,
they are insufficient to explain the current increase in corruption.
The global bank (world bank 2024) claims that because corruption raises prices and restricts access to
essential services like health, education, social programs, and even justice, it mostly affects the weak
and impoverished. It hurts markets, jobs, and economies by increasing inequality and decreasing private
sector investment. Moreover, corruption can impede a nation's ability to respond to crises, causing
needless suffering and, in the worst case scenario, fatalities. Corruption has the potential to erode public
confidence in institutions and leaders over time, causing rifts in society and, in some situations, raising
pág. 3510
the possibility of violence, conflict, and instability.
According to Deloitte (2021), the average cost of a corruption act in Mexico is MXN $2,799 for an adult
victim and MXN $12,243 for a corporation victim. The influence of corruption on commerce among the
three North American nations is represented in the recent Treaty between Mexico, the United States,
and Canada (T-MEC), which includes a chapter on the subject (Deloitte, 2021).
A nation's level of corruption may be estimated using a variety of indexes. Three of these primary
corruption indexes are covered in this work: Political corruption index, corruption perception index and
control of corruption.
The Political Corruption Index (PCI) is obtained as the best estimate of the extent to which a country
is affected by political corruption. It was retrieved from the website Our World in Data, Source V-Dem
(2024) and processed by Our World in Data (2024). The directionality of the V-Dem corruption index
runs from less corrupt to more corrupt. The corruption index includes measures of six distinct types of
corruption that cover both different areas and levels of the polity realm, distinguishing between
executive, legislative and judicial corruption.
The Corruption Perceptions Index (CPI) is an index that scores and ranks countries by their perceived
levels of corruption at public sectors, as assessed by experts and business executives. The CPI generally
defines corruption as an "abuse of entrusted power for private gain". The index is published annually by
the non-governmental organization Transparency International since 1995 (Corruption Perception
Index, 2023). Ratings range from 0 to 100, with 100 signifying behavior that is impressively free from
corruption and 0 signifying severely corrupt behavior.
Perceptions of the degree to which public authority is used for personal benefit, including both small-
scale and large-scale corruption, as well as the "capture" of the state by elites and corporate interests,
are captured by the control corruption indicator. This is one of the Worldwide Governance Indicators
provided by the world bank group (world bank group, 2024). It ranges from 0 to 100 (percentile rank):
the higher its value, the better in its fight against corruption (Hamilton and Hammer, 2018).
Scoring 180 countries around the world, the Corruption Perceptions Index is the leading global indicator
of public sector corruption. Mexico got a CPI score of 31 on 2023 year, with a change of 0 since preview
year, meaning it ranks 126 out of 180 countries. With respect to the other two indexes, in 2023 Mexico
the possibility of violence, conflict, and instability.
According to Deloitte (2021), the average cost of a corruption act in Mexico is MXN $2,799 for an adult
victim and MXN $12,243 for a corporation victim. The influence of corruption on commerce among the
three North American nations is represented in the recent Treaty between Mexico, the United States,
and Canada (T-MEC), which includes a chapter on the subject (Deloitte, 2021).
A nation's level of corruption may be estimated using a variety of indexes. Three of these primary
corruption indexes are covered in this work: Political corruption index, corruption perception index and
control of corruption.
The Political Corruption Index (PCI) is obtained as the best estimate of the extent to which a country
is affected by political corruption. It was retrieved from the website Our World in Data, Source V-Dem
(2024) and processed by Our World in Data (2024). The directionality of the V-Dem corruption index
runs from less corrupt to more corrupt. The corruption index includes measures of six distinct types of
corruption that cover both different areas and levels of the polity realm, distinguishing between
executive, legislative and judicial corruption.
The Corruption Perceptions Index (CPI) is an index that scores and ranks countries by their perceived
levels of corruption at public sectors, as assessed by experts and business executives. The CPI generally
defines corruption as an "abuse of entrusted power for private gain". The index is published annually by
the non-governmental organization Transparency International since 1995 (Corruption Perception
Index, 2023). Ratings range from 0 to 100, with 100 signifying behavior that is impressively free from
corruption and 0 signifying severely corrupt behavior.
Perceptions of the degree to which public authority is used for personal benefit, including both small-
scale and large-scale corruption, as well as the "capture" of the state by elites and corporate interests,
are captured by the control corruption indicator. This is one of the Worldwide Governance Indicators
provided by the world bank group (world bank group, 2024). It ranges from 0 to 100 (percentile rank):
the higher its value, the better in its fight against corruption (Hamilton and Hammer, 2018).
Scoring 180 countries around the world, the Corruption Perceptions Index is the leading global indicator
of public sector corruption. Mexico got a CPI score of 31 on 2023 year, with a change of 0 since preview
year, meaning it ranks 126 out of 180 countries. With respect to the other two indexes, in 2023 Mexico
pág. 3511
obtained a score of 46 for the political corruption index while a score of 17.45 for control of corruption
in 2022.
Below we introduce some indexes related to the development of a country. The Mexican Institute for
Competitiveness A.C. (IMCO for its acronym in Spanish), created in 2003, is a non-profit, non-partisan
research center whose actions to solve Mexico's most critical challenges are based on evidence. Its
mission is to “develop public policy proposals that improve the competitiveness of the country's
companies”. IMCO introduced the Urban Competence Index (UCI) (Urban Competence Index by
IMCO, 2024 ), this index is based on 10 subindexes o dimensions: I.-Reliable and objective law system,
II.- Sustainable management of the environment, III.- Inclusive, educated and healthy society, IV.-
Stable and functional political system, V.- Efficient and effective governments, VI.-Efficient Factor
market, VII.- Stable economy, VIII.- World-class precursor sectors, IX.- Taking advantage of
international relations. X.- Innovation and sophistication in the economical sectors. Another two indexes
related to the competitiveness are the World Competitiveness index (WCI) and the Global
Competitiveness Index. The World Competitiveness Yearbook is an annual report published by the
Swiss-based International Institute for Management Development (IMD) on the competitiveness of
nations. The yearbook benchmarks the performance of 63 countries based on 340 criteria measuring
different facets of competitiveness. It uses two types of data: 2/3 hard statistical data provided by
international/national sources and 1/3 survey data based on executive opinion survey (World
Competitiveness index by IMD, 2024). Since 2005, the World Economic Forum has based its
competitiveness analysis on the Global Competitiveness Index (GCI), a comprehensive framework
that measures the microeconomic and macroeconomic foundations of national competitiveness, grouped
into 12 categories (Global Competitiveness Index, 2024). Global Peace Index (GPI), which ranks 163
independent states and territories according to their level of peacefulness. Produced by the Institute for
Economics and Peace (IEP) since 2007, the GPI is the world’s leading measure of global peacefulness
(Global Peace Index, 2024), another peace related index is given by the Fragile States Index (FSI). FSI
is an annual report mainly published and supported by the United States think tank the Fund for Peace.
It aims to assess states' vulnerability to conflict or collapse, ranking all sovereign states with membership
in the United Nations where there is enough data available for analysis (Fragile States Index, 2024).
obtained a score of 46 for the political corruption index while a score of 17.45 for control of corruption
in 2022.
Below we introduce some indexes related to the development of a country. The Mexican Institute for
Competitiveness A.C. (IMCO for its acronym in Spanish), created in 2003, is a non-profit, non-partisan
research center whose actions to solve Mexico's most critical challenges are based on evidence. Its
mission is to “develop public policy proposals that improve the competitiveness of the country's
companies”. IMCO introduced the Urban Competence Index (UCI) (Urban Competence Index by
IMCO, 2024 ), this index is based on 10 subindexes o dimensions: I.-Reliable and objective law system,
II.- Sustainable management of the environment, III.- Inclusive, educated and healthy society, IV.-
Stable and functional political system, V.- Efficient and effective governments, VI.-Efficient Factor
market, VII.- Stable economy, VIII.- World-class precursor sectors, IX.- Taking advantage of
international relations. X.- Innovation and sophistication in the economical sectors. Another two indexes
related to the competitiveness are the World Competitiveness index (WCI) and the Global
Competitiveness Index. The World Competitiveness Yearbook is an annual report published by the
Swiss-based International Institute for Management Development (IMD) on the competitiveness of
nations. The yearbook benchmarks the performance of 63 countries based on 340 criteria measuring
different facets of competitiveness. It uses two types of data: 2/3 hard statistical data provided by
international/national sources and 1/3 survey data based on executive opinion survey (World
Competitiveness index by IMD, 2024). Since 2005, the World Economic Forum has based its
competitiveness analysis on the Global Competitiveness Index (GCI), a comprehensive framework
that measures the microeconomic and macroeconomic foundations of national competitiveness, grouped
into 12 categories (Global Competitiveness Index, 2024). Global Peace Index (GPI), which ranks 163
independent states and territories according to their level of peacefulness. Produced by the Institute for
Economics and Peace (IEP) since 2007, the GPI is the world’s leading measure of global peacefulness
(Global Peace Index, 2024), another peace related index is given by the Fragile States Index (FSI). FSI
is an annual report mainly published and supported by the United States think tank the Fund for Peace.
It aims to assess states' vulnerability to conflict or collapse, ranking all sovereign states with membership
in the United Nations where there is enough data available for analysis (Fragile States Index, 2024).
pág. 3512
Two environmental aspect-related indices are taken into consideration: the Environmental Progress
Index and the Climate Change Performance. The Environmental Performance Index (EPI) provides
a data-driven summary of the state of sustainability around the world. Using 58 performance indicators
across 11 issue
categories, the EPI ranks 180 countries on climate change performance, environmental health, and
ecosystem vitality (Environmental Performance Index, 2024). One tool to promote openness in climate
politics at the national and international levels is the Climate Change Performance Index (CCPI). The
CCPI compares the climate performance of the EU and 63 nations, which together are responsible for
more than 90% of the world's greenhouse gas emissions, using a standardized framework. Four criteria
are used to evaluate the effectiveness of climate mitigation: greenhouse gas emissions, renewable
energy, energy use, and climate policy (Climate Change Performance Index, 2024). Since the
Sustainable Development Goals were adopted by the 193 UN Member States in 2015, annual progress
on them is reviewed in the Sustainable Development Report (SDR). The Sustainable Development
Report 2024, which was released on the eve of the UN Summit of the Future, makes several important
reform recommendations to the UN system in order to address the problems of the twenty-first century
and also reports the Sustainable Development Goals Index (SDGI) (Sustainable Development Goals,
2024). The Global innovation Index (GII) measures innovation in the context of an unpredictable
geopolitical and economic landscape. By rating the innovation performance of over 132 economies and
stressing the advantages and disadvantages of innovation, it identifies the most innovative economies in
the world (Global Innovation Index, 2024). One of the most accurate sets of social and environmental
data in the world is the Social Progress Index. It focuses only on the non-economic facets of global
social performance, offering clear, useful information as well as in-depth understanding of the actual
condition of our society ( Social Progress Index,2024 ). A long and healthy life, knowledge, and a
reasonable level of living are three important aspects of human development that the Human
Development Index (HDI) measures in summary form. The normalized indices for each of the three
dimensions' geometric means make up the HDI ( Human Development Index, 2024). Designed by the
world bank Development Research Group, the GINI index measures the extent to which the distribution
of income among individuals or households within an economy deviates from a perfectly equal
Two environmental aspect-related indices are taken into consideration: the Environmental Progress
Index and the Climate Change Performance. The Environmental Performance Index (EPI) provides
a data-driven summary of the state of sustainability around the world. Using 58 performance indicators
across 11 issue
categories, the EPI ranks 180 countries on climate change performance, environmental health, and
ecosystem vitality (Environmental Performance Index, 2024). One tool to promote openness in climate
politics at the national and international levels is the Climate Change Performance Index (CCPI). The
CCPI compares the climate performance of the EU and 63 nations, which together are responsible for
more than 90% of the world's greenhouse gas emissions, using a standardized framework. Four criteria
are used to evaluate the effectiveness of climate mitigation: greenhouse gas emissions, renewable
energy, energy use, and climate policy (Climate Change Performance Index, 2024). Since the
Sustainable Development Goals were adopted by the 193 UN Member States in 2015, annual progress
on them is reviewed in the Sustainable Development Report (SDR). The Sustainable Development
Report 2024, which was released on the eve of the UN Summit of the Future, makes several important
reform recommendations to the UN system in order to address the problems of the twenty-first century
and also reports the Sustainable Development Goals Index (SDGI) (Sustainable Development Goals,
2024). The Global innovation Index (GII) measures innovation in the context of an unpredictable
geopolitical and economic landscape. By rating the innovation performance of over 132 economies and
stressing the advantages and disadvantages of innovation, it identifies the most innovative economies in
the world (Global Innovation Index, 2024). One of the most accurate sets of social and environmental
data in the world is the Social Progress Index. It focuses only on the non-economic facets of global
social performance, offering clear, useful information as well as in-depth understanding of the actual
condition of our society ( Social Progress Index,2024 ). A long and healthy life, knowledge, and a
reasonable level of living are three important aspects of human development that the Human
Development Index (HDI) measures in summary form. The normalized indices for each of the three
dimensions' geometric means make up the HDI ( Human Development Index, 2024). Designed by the
world bank Development Research Group, the GINI index measures the extent to which the distribution
of income among individuals or households within an economy deviates from a perfectly equal
pág. 3513
distribution (GINI, 2024). The percentage at risk of poverty, estimated as the share of population
living on less than $6.85 a day, 2000 to 2022 was retrieved from the website (our world in data, 2024).
Based on respondents' assessments of their own lives, the World Happiness Report is a publication that
includes articles and rankings of country happiness. The report also compares these rankings with a
variety of aspects related to the quality of life. The Gallup World Poll is the main source of data used in
this study. Data is collected from people in over 150 countries. Each variable measured reveals a
populated-weighted average score on a scale running from 0 to 10, thus the Global Happiness Index
(GHI) is calculated ( Global Happiness Index, 2024).
MATERIAL AND METHODS
Yearly data for the Mexican nation corresponding to the three above introduced corruption related
indexes were collected: Corruption Perception Index ranges from 2012 to 2023, Political Corruption
Index ranges from 2000 to 2023, while control Corruption ranges from 2002 to 2022.
As a first step, the correlation coefficients between the three indexes related to corruption are calculated.
The results obtained show us that there is no correlation (statistically significant) between the Political
Corruption Index and the Corruption Perception Index or between the Political Corruption Index and
Control of Corruption. However, Control of Corruption and Corruption Perception Index show a
correlation. Figure 1 shows these three corruption related indexes. Here PCI was reshaped (100-value)
to bigger is better in order to the make the comparison with the other two indexes.
Figure 1. Three corruption related indexes.
Source: Figure drawn based on retrieved data.
distribution (GINI, 2024). The percentage at risk of poverty, estimated as the share of population
living on less than $6.85 a day, 2000 to 2022 was retrieved from the website (our world in data, 2024).
Based on respondents' assessments of their own lives, the World Happiness Report is a publication that
includes articles and rankings of country happiness. The report also compares these rankings with a
variety of aspects related to the quality of life. The Gallup World Poll is the main source of data used in
this study. Data is collected from people in over 150 countries. Each variable measured reveals a
populated-weighted average score on a scale running from 0 to 10, thus the Global Happiness Index
(GHI) is calculated ( Global Happiness Index, 2024).
MATERIAL AND METHODS
Yearly data for the Mexican nation corresponding to the three above introduced corruption related
indexes were collected: Corruption Perception Index ranges from 2012 to 2023, Political Corruption
Index ranges from 2000 to 2023, while control Corruption ranges from 2002 to 2022.
As a first step, the correlation coefficients between the three indexes related to corruption are calculated.
The results obtained show us that there is no correlation (statistically significant) between the Political
Corruption Index and the Corruption Perception Index or between the Political Corruption Index and
Control of Corruption. However, Control of Corruption and Corruption Perception Index show a
correlation. Figure 1 shows these three corruption related indexes. Here PCI was reshaped (100-value)
to bigger is better in order to the make the comparison with the other two indexes.
Figure 1. Three corruption related indexes.
Source: Figure drawn based on retrieved data.
pág. 3514
Table 1. Statistical description of the three corruption related indexes
Index Mean
Standard
Deviation Min. Max.
Correlation
with
Political.
Correlation
with
Perception.
Correlation
with
Control
Political
Corruption 62.5 6.7 51 76 1 None None
Corruption
Perception 31.16 2.07 28 35 None 1 0.735856
Control of
Corruption 32.46 12.38 16.19 47.62 None 0.735856 1
Table 1 summarizes basic statistics of the three corruption related indexes, here the label None indicates
that no correlation either Pearson or Spearman have been found (statistically significant ). Let us point out that
high values of the perception and control corruption indexes represent positive efforts, while high values of
the political corruption index correspond to a more corrupt country (lower values are preferable). The value
of 0.735856 obtained for the Pearson correlation coefficient between the control of corruption and corruption
perception index indicates that: the more controls are implemented to reduce corruption, the perception of the
fight against corruption improves, and when the perception of the fight against corruption decreases, it is
indicative that the controls used to combat corruption are decreasing.
RESULTS
In what follows we investigate the impact that these three indexes related to corruption have on other
indexes related to the development of a country, such as competitiveness, innovation, environmental
progress, progress in climate change, GINI, poverty, happiness, human and social development among
others. Thus, we compute the correlation coefficients between the three corruption related indexes and
the fourteen development related indexes: UCI, WCI, GCI , GPI* (rescaled to range 0 to 100, high
values more peaceful), FSI, EPI,CCP SDGI,GII, SPI, HDI, GINI, Poverty Risk, and GHI. Table II
summarizes the correlation coefficients obtained between the three political related indexes and other
indexes related to the development of a country (only indexes correlated with at least one corruption
Table 1. Statistical description of the three corruption related indexes
Index Mean
Standard
Deviation Min. Max.
Correlation
with
Political.
Correlation
with
Perception.
Correlation
with
Control
Political
Corruption 62.5 6.7 51 76 1 None None
Corruption
Perception 31.16 2.07 28 35 None 1 0.735856
Control of
Corruption 32.46 12.38 16.19 47.62 None 0.735856 1
Table 1 summarizes basic statistics of the three corruption related indexes, here the label None indicates
that no correlation either Pearson or Spearman have been found (statistically significant ). Let us point out that
high values of the perception and control corruption indexes represent positive efforts, while high values of
the political corruption index correspond to a more corrupt country (lower values are preferable). The value
of 0.735856 obtained for the Pearson correlation coefficient between the control of corruption and corruption
perception index indicates that: the more controls are implemented to reduce corruption, the perception of the
fight against corruption improves, and when the perception of the fight against corruption decreases, it is
indicative that the controls used to combat corruption are decreasing.
RESULTS
In what follows we investigate the impact that these three indexes related to corruption have on other
indexes related to the development of a country, such as competitiveness, innovation, environmental
progress, progress in climate change, GINI, poverty, happiness, human and social development among
others. Thus, we compute the correlation coefficients between the three corruption related indexes and
the fourteen development related indexes: UCI, WCI, GCI , GPI* (rescaled to range 0 to 100, high
values more peaceful), FSI, EPI,CCP SDGI,GII, SPI, HDI, GINI, Poverty Risk, and GHI. Table II
summarizes the correlation coefficients obtained between the three political related indexes and other
indexes related to the development of a country (only indexes correlated with at least one corruption
pág. 3515
related index are reported).
Table 2. Statistical description of the indexes correlated with the pci, cc and cpi
Index Mean
Standard
Deviation Min. Max.
Correlation
with
Political
Corruption
Correlation
with
Control of
Corruption
Correlation
with
corruption
Perception
World
Competitiveness 57.18 7.13 43.11 67.3 0.77167678 none none
Global
Innovation 33.32 3.38 24.67 38.03 0.62374322 -0.60540549 none
Global
Peace* 64.89 4.48 55.5 72.12 0.53734113 0.63377071
Urban
Competitiveness 45.89 0.99 43.19 47.09 0.51832251 none none
Climate
Change
Performance 58.11 5.17 47.01 64.91 0.61581826 0.590177522 none
GINI 47.63 2.33 43.5 51.66 None 0.820172083 0.63861352
Global
Competitiveness 61.81 1.71 59.84 64.95 None -0.75958078 none
Modified
Fragile
State 40.07 1.92 36.58 44 None -0.64374888 None
Sustainable
Development
Goals 66.96 1.66 64.09 69.28 None -0.932725 -0.58167244
Human
Development 75.2 2.24 70.9 78.1 None -0.93015821 -0.84972258
Social
Progress 67.23 1.33 64.77 69.15 -0.6525253 -0.80222858 None
Poverty 43.83 2.51 36.3 46.2 None 0.60610654 None
Happiness 66.16 2.85 63.12 71.87 0.86972178 None None
related index are reported).
Table 2. Statistical description of the indexes correlated with the pci, cc and cpi
Index Mean
Standard
Deviation Min. Max.
Correlation
with
Political
Corruption
Correlation
with
Control of
Corruption
Correlation
with
corruption
Perception
World
Competitiveness 57.18 7.13 43.11 67.3 0.77167678 none none
Global
Innovation 33.32 3.38 24.67 38.03 0.62374322 -0.60540549 none
Global
Peace* 64.89 4.48 55.5 72.12 0.53734113 0.63377071
Urban
Competitiveness 45.89 0.99 43.19 47.09 0.51832251 none none
Climate
Change
Performance 58.11 5.17 47.01 64.91 0.61581826 0.590177522 none
GINI 47.63 2.33 43.5 51.66 None 0.820172083 0.63861352
Global
Competitiveness 61.81 1.71 59.84 64.95 None -0.75958078 none
Modified
Fragile
State 40.07 1.92 36.58 44 None -0.64374888 None
Sustainable
Development
Goals 66.96 1.66 64.09 69.28 None -0.932725 -0.58167244
Human
Development 75.2 2.24 70.9 78.1 None -0.93015821 -0.84972258
Social
Progress 67.23 1.33 64.77 69.15 -0.6525253 -0.80222858 None
Poverty 43.83 2.51 36.3 46.2 None 0.60610654 None
Happiness 66.16 2.85 63.12 71.87 0.86972178 None None
pág. 3516
Namely three entries of table II support the sand the wheels hypothesis (corruption has negative effects on
the development of a nation). Global Peace and Climate Change performance indexes are improved when
control of corruption increases, while the social progress index increases when then political corruption
index decreases. At first glance, shaded correlation coefficients in table 2 seem to suggest contradictory
relationships between the three indexes related to corruption and the indexes: WCI, GII, GPI, UCI, CCP,
GINI, GCI, MFSI, SDGI, HDI, SPI, Poverty and happiness. That is, by increasing controls of corruption,
poverty percentage and wage inequality (measured by GINI) increase; while Global Innovation, Global
Competitiveness, Modified Fragile State (100-FSI), Sustainable Development Goals, Human
Development and Social Progress indexes are reduced. Political Corruption increases seem to improve
World Competitiveness, Global Innovation, Global Peace, Urban Competitiveness, Climate Change
Performance and happiness indexes. Finally corruption perception increases seem to improve the salary
inequality (GINI) while reducing the Sustainable Development Goals and the Human Development
indexes. This peculiar occurrence has already been noted and explained by the hypothesis that corruption
greases the wheels of financial sector development. It argues that corruption and bribery promote the
avoidance of inefficient policies, leading to investment, trade, and economic growth in countries with weak
institutions (Cooray & Schneider, 2018). In the case of corruption affecting the GINI, as stated by Andres
and Ramlogan. From an empirical perspective, corruption reduces inequality in Latin America by virtue
of its redistributive effect in the informal sector (Andres and Ramlogan-Dobson, 2011). Therefore,
whereas institutional measures aimed at reducing corruption would likely to raise income disparity,
corruption itself encourages wealth redistribution among the poor within the informal sector, resulting
in a fall in inequities. Regarding the negative correlation between control of corruption and the global
innovation index (also the positive correlation between political corruption and global innovation); in
his master thesis, Bernier asserts that: In Latin America, corruption has a special effect on business
activity and ensuing economic progress. His research (Bernier, 2020) provides an explanation that
establishes a direct link between increasing corporate innovation and the existence of bribery. His
research specifically suggests that there are three primary methods via which this happens: the habit of
exporting, the general absence of business rivalry, and the development of tight ties with government
officials. In fact, by using principal component analysis methodologies and structural equation
Namely three entries of table II support the sand the wheels hypothesis (corruption has negative effects on
the development of a nation). Global Peace and Climate Change performance indexes are improved when
control of corruption increases, while the social progress index increases when then political corruption
index decreases. At first glance, shaded correlation coefficients in table 2 seem to suggest contradictory
relationships between the three indexes related to corruption and the indexes: WCI, GII, GPI, UCI, CCP,
GINI, GCI, MFSI, SDGI, HDI, SPI, Poverty and happiness. That is, by increasing controls of corruption,
poverty percentage and wage inequality (measured by GINI) increase; while Global Innovation, Global
Competitiveness, Modified Fragile State (100-FSI), Sustainable Development Goals, Human
Development and Social Progress indexes are reduced. Political Corruption increases seem to improve
World Competitiveness, Global Innovation, Global Peace, Urban Competitiveness, Climate Change
Performance and happiness indexes. Finally corruption perception increases seem to improve the salary
inequality (GINI) while reducing the Sustainable Development Goals and the Human Development
indexes. This peculiar occurrence has already been noted and explained by the hypothesis that corruption
greases the wheels of financial sector development. It argues that corruption and bribery promote the
avoidance of inefficient policies, leading to investment, trade, and economic growth in countries with weak
institutions (Cooray & Schneider, 2018). In the case of corruption affecting the GINI, as stated by Andres
and Ramlogan. From an empirical perspective, corruption reduces inequality in Latin America by virtue
of its redistributive effect in the informal sector (Andres and Ramlogan-Dobson, 2011). Therefore,
whereas institutional measures aimed at reducing corruption would likely to raise income disparity,
corruption itself encourages wealth redistribution among the poor within the informal sector, resulting
in a fall in inequities. Regarding the negative correlation between control of corruption and the global
innovation index (also the positive correlation between political corruption and global innovation); in
his master thesis, Bernier asserts that: In Latin America, corruption has a special effect on business
activity and ensuing economic progress. His research (Bernier, 2020) provides an explanation that
establishes a direct link between increasing corporate innovation and the existence of bribery. His
research specifically suggests that there are three primary methods via which this happens: the habit of
exporting, the general absence of business rivalry, and the development of tight ties with government
officials. In fact, by using principal component analysis methodologies and structural equation
pág. 3517
modeling, Alarcon (Alarcon, 2024 ) concluded that corruption facilitates innovative activity and
economic growth in Mexico.
Next, we investigate, within the sand the wheels theory, how changes in the Control of Corruption and
Political Corruption indexes affect peace, climate change, and social progress. Figure 2 shows second
order polynomial fitted curves to approximate relations of control of corruption with peace and climate
change indexes, while Figure 3 displays the second order polynomial curve fitted for the social progress
index as a function of political corruption index. Table 3 summarizes the fitting information of these
polynomial approximation models.
Figure 2.
No linear relation between control of corruption and peace and climate change performance indexes.
Source: Figure drawn based on retrieved data.
modeling, Alarcon (Alarcon, 2024 ) concluded that corruption facilitates innovative activity and
economic growth in Mexico.
Next, we investigate, within the sand the wheels theory, how changes in the Control of Corruption and
Political Corruption indexes affect peace, climate change, and social progress. Figure 2 shows second
order polynomial fitted curves to approximate relations of control of corruption with peace and climate
change indexes, while Figure 3 displays the second order polynomial curve fitted for the social progress
index as a function of political corruption index. Table 3 summarizes the fitting information of these
polynomial approximation models.
Figure 2.
No linear relation between control of corruption and peace and climate change performance indexes.
Source: Figure drawn based on retrieved data.
pág. 3518
Figure 3. No linear relation between political corruption and social progress index.
Source: Figure drawn based on retrieved data.
Figure 3. No linear relation between political corruption and social progress index.
Source: Figure drawn based on retrieved data.
pág. 3519
Table 3. Goodness of the fitted p_1 x²+p_2 x+p_3 models
Dependent
variable
Independent
variable
𝑝1
Confidence
interval
𝑝2
Confidence
interval
𝑝3
Confidence
interval
SSE R²
Adjusted
R²
RMSE
GPI CC 0.0075 [-0.0235
,0.0384]
-0.2305 [-2.0996,
1.6385]
65.6616 [41.1509 ,
90.1723]
126.7571 0.4151 0.3177 3.2501
CCP CC -0.0250 [-0.0668 ,
0.0169]
1.7801 [-0.7460
4.3062]
31.7032 [-1.4245
64.8308]
231.5473 0.4288 0.3336 4.3927
SPI PCI 0.0035 [-0.0086 ,
0.0156
]
-0.5579 [-2.1079,
0.9921
]
88.4501 [39.2532 ,
137.647]
12.8344 0.4489 0.3387 1.1329
Table 3. Goodness of the fitted p_1 x²+p_2 x+p_3 models
Dependent
variable
Independent
variable
𝑝1
Confidence
interval
𝑝2
Confidence
interval
𝑝3
Confidence
interval
SSE R²
Adjusted
R²
RMSE
GPI CC 0.0075 [-0.0235
,0.0384]
-0.2305 [-2.0996,
1.6385]
65.6616 [41.1509 ,
90.1723]
126.7571 0.4151 0.3177 3.2501
CCP CC -0.0250 [-0.0668 ,
0.0169]
1.7801 [-0.7460
4.3062]
31.7032 [-1.4245
64.8308]
231.5473 0.4288 0.3336 4.3927
SPI PCI 0.0035 [-0.0086 ,
0.0156
]
-0.5579 [-2.1079,
0.9921
]
88.4501 [39.2532 ,
137.647]
12.8344 0.4489 0.3387 1.1329
pág. 3520
Figure 4 shows time machine learning projections of political corruption and control of corruption
indexes (solid lines); the projections indicate growth for both indexes (good news for the control of
corruption but not for the political corruption), constrained PCI and CC are also plotted. These
constrained data are obtained by assuming that some preventive measures have been implemented in
order to increase in 10 percent each year the control of corruption index while decreasing in the same
rate the political corruption index. Here we notice that if nothong is done: political corruption index will
increase, on the other hand control of corruption index is projected to decrease to vey low values and
start an increase after 2025, in both cases the constrained ten percent per year offers a better strategy
that doing nothing. Figure 5 presents time domain machine learning projections of the Global Peace and
the climate change performance indexes (solid lines); labeled as simulated GPI and CCP are the
simulated values for these indexes when the control of corruption is constrained by a ten percent increase
( the effects of the control of corruption over the global peace and climate change performance were
obtained by the second order fitted polynomial models). Remarkable improvements of the Global Peace
and Climate change performance indexes are obtained by imposing constrains over the control of
corruption. In a similar way figure 6 shows time domain projections of the social progress index (solid
line), also the improvement obtained over the social progress index by imposing a ten percent reduction
on the political corruption index (social progress index was fitted as a second order function of the
political corruption index) is plotted.
Figure 4. Time domain projections of political corruption and control of corruption indexes.
Figure 4 shows time machine learning projections of political corruption and control of corruption
indexes (solid lines); the projections indicate growth for both indexes (good news for the control of
corruption but not for the political corruption), constrained PCI and CC are also plotted. These
constrained data are obtained by assuming that some preventive measures have been implemented in
order to increase in 10 percent each year the control of corruption index while decreasing in the same
rate the political corruption index. Here we notice that if nothong is done: political corruption index will
increase, on the other hand control of corruption index is projected to decrease to vey low values and
start an increase after 2025, in both cases the constrained ten percent per year offers a better strategy
that doing nothing. Figure 5 presents time domain machine learning projections of the Global Peace and
the climate change performance indexes (solid lines); labeled as simulated GPI and CCP are the
simulated values for these indexes when the control of corruption is constrained by a ten percent increase
( the effects of the control of corruption over the global peace and climate change performance were
obtained by the second order fitted polynomial models). Remarkable improvements of the Global Peace
and Climate change performance indexes are obtained by imposing constrains over the control of
corruption. In a similar way figure 6 shows time domain projections of the social progress index (solid
line), also the improvement obtained over the social progress index by imposing a ten percent reduction
on the political corruption index (social progress index was fitted as a second order function of the
political corruption index) is plotted.
Figure 4. Time domain projections of political corruption and control of corruption indexes.
pág. 3521
Figure 5. Time domain projections of global peace and climate change performance indexes compared
with simulated obtained when control of corruption is constrained
Figure 6. Time domain projections of social progress index compared with the simulated SPI obtained
by constraint political corruption.
Table4 reports the goodness of the fitted machine learning methods for the time domain projections of
the indexes: CC, PCI, GPI, CCP and SPI. Seven machine learning methods methods were tested:
DecisionTree, Gradient Boosted Trees, Linear Regression, Nearest Neighbors, Neural Network,
Random Forest, and Gaussian Process, here only the best fitted for each index were reported.
Figure 5. Time domain projections of global peace and climate change performance indexes compared
with simulated obtained when control of corruption is constrained
Figure 6. Time domain projections of social progress index compared with the simulated SPI obtained
by constraint political corruption.
Table4 reports the goodness of the fitted machine learning methods for the time domain projections of
the indexes: CC, PCI, GPI, CCP and SPI. Seven machine learning methods methods were tested:
DecisionTree, Gradient Boosted Trees, Linear Regression, Nearest Neighbors, Neural Network,
Random Forest, and Gaussian Process, here only the best fitted for each index were reported.
pág. 3522
Table 4. Goodness of the fitted machine learning models for time domain projections
Index Method Mean Squared R²
Political
Corruption
neural network 1.64875 0.963361
Control of
Corruption
Gaussian
process
3.39234 0.977874
Global Peace Gaussian
process
1.29359 0.935736
Climate Change neural network 5.18387 0.806706
Social Progress neural network 0.0337255 0.981174
DISCUSSION
Three corruption related indexes have been analyzed in order to investigate how corruption affects
indexes related to the development of a country. The linear and nonlinear correlation coefficients
obtained support both effects of corruption theories: grease the wheels and sand the wheels. We have
decided to simulate scenarios assuming that corruption decreases to see its effects within the sand the
wheel theory, since we consider it not appropriate (ethically) for the case of Mexico, which already has
high rates of corruption, to assume that corruption continues to increase and estimate its benefits within
sticking to the theory grease the wheels. As stated by Lucarelli et al., in the short term, corruption may
be seen as a factor that promotes economic growth by expediting bureaucratic procedures; however,
over time, corruption carries significant social costs that make it difficult to bear the political, economic,
and social burdens. Consequently, corruption levels rise, which has a negative impact on economic
growth.
CONCLUSIONS
Seeking to improve the indexes of global peace, climate change performance and social progress, we
have assumed scenarios where restrictions are imposed to reduce corruption (sand the wheels theory).
The hypothetical measures are implemented as an increase in control of corruption index by 10 percent
as well as reduction of the political corruption index by the same percentage, the simulations allow us
Table 4. Goodness of the fitted machine learning models for time domain projections
Index Method Mean Squared R²
Political
Corruption
neural network 1.64875 0.963361
Control of
Corruption
Gaussian
process
3.39234 0.977874
Global Peace Gaussian
process
1.29359 0.935736
Climate Change neural network 5.18387 0.806706
Social Progress neural network 0.0337255 0.981174
DISCUSSION
Three corruption related indexes have been analyzed in order to investigate how corruption affects
indexes related to the development of a country. The linear and nonlinear correlation coefficients
obtained support both effects of corruption theories: grease the wheels and sand the wheels. We have
decided to simulate scenarios assuming that corruption decreases to see its effects within the sand the
wheel theory, since we consider it not appropriate (ethically) for the case of Mexico, which already has
high rates of corruption, to assume that corruption continues to increase and estimate its benefits within
sticking to the theory grease the wheels. As stated by Lucarelli et al., in the short term, corruption may
be seen as a factor that promotes economic growth by expediting bureaucratic procedures; however,
over time, corruption carries significant social costs that make it difficult to bear the political, economic,
and social burdens. Consequently, corruption levels rise, which has a negative impact on economic
growth.
CONCLUSIONS
Seeking to improve the indexes of global peace, climate change performance and social progress, we
have assumed scenarios where restrictions are imposed to reduce corruption (sand the wheels theory).
The hypothetical measures are implemented as an increase in control of corruption index by 10 percent
as well as reduction of the political corruption index by the same percentage, the simulations allow us
pág. 3523
to estimate the growth in the Global Peace, Climate Change Performance and Social Progress indexes.
As a result of imposing these restrictions on the control of corruption and on the political corruption
index, by 2030 we would obtain increases of 3%, 28.9% and 18% for each of the Global Peace, Climate
Change Performance and Social Progress indexes respectively with respect to the values obtained
without imposing restrictive measures. We have determined which corruption-related indexes are
connected with a country's development indicators by computing the Pearson and Spearman
coefficients. Next we have been able to quantify the association between the PCI and SPI as well as the
relationship between the CC and the GPI and CCP indexes by fitting curves. Machine learning methods
have been useful to estimate time domain projections of the indexes. Although we have presented results
for the Mexican nation; all calculations can be carried out for another country if the information on its
indexes is available. Thus each country can identify the main indexes affected by corruption and design
its own strategy to reduce its effects.
As future work, clusters of countries can be defined, thus similar calculations can be carried out to
estimate the effects of corruption on the indexes related to the development of the whole region/cluster.
Some scenarios could be simulated where restrictions (collaborative efforts, bilateral and multilateral
treaties) on corruption are imposed on some or all countries of the region and the benefits on the
development indexes of the region can be estimated
REFERENCES
Alarcon M.A., (2024), The effects of corruption on innovation and growth in Mexico, Prob. Des vol.55
no.216
Andres A.,Ramlogan-Dobson C., (2011), Is corruption really bad for inequality? Evidence from Latin
America, Journal of Development Studies, vol 1, iss 7, pp 959-976.
Bernier-Chen E., (2020), The effect of bribery on firm innovation in Latin America, master thesis,
Georgetown University, Washington DC. Available at
https://repository.library.georgetown.edu/bitstream/handle/10822/1059652/BernierChen_geor
getown_0076M_14606.pdf?sequence=1
Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., et al. (2024). 2024
Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law &
to estimate the growth in the Global Peace, Climate Change Performance and Social Progress indexes.
As a result of imposing these restrictions on the control of corruption and on the political corruption
index, by 2030 we would obtain increases of 3%, 28.9% and 18% for each of the Global Peace, Climate
Change Performance and Social Progress indexes respectively with respect to the values obtained
without imposing restrictive measures. We have determined which corruption-related indexes are
connected with a country's development indicators by computing the Pearson and Spearman
coefficients. Next we have been able to quantify the association between the PCI and SPI as well as the
relationship between the CC and the GPI and CCP indexes by fitting curves. Machine learning methods
have been useful to estimate time domain projections of the indexes. Although we have presented results
for the Mexican nation; all calculations can be carried out for another country if the information on its
indexes is available. Thus each country can identify the main indexes affected by corruption and design
its own strategy to reduce its effects.
As future work, clusters of countries can be defined, thus similar calculations can be carried out to
estimate the effects of corruption on the indexes related to the development of the whole region/cluster.
Some scenarios could be simulated where restrictions (collaborative efforts, bilateral and multilateral
treaties) on corruption are imposed on some or all countries of the region and the benefits on the
development indexes of the region can be estimated
REFERENCES
Alarcon M.A., (2024), The effects of corruption on innovation and growth in Mexico, Prob. Des vol.55
no.216
Andres A.,Ramlogan-Dobson C., (2011), Is corruption really bad for inequality? Evidence from Latin
America, Journal of Development Studies, vol 1, iss 7, pp 959-976.
Bernier-Chen E., (2020), The effect of bribery on firm innovation in Latin America, master thesis,
Georgetown University, Washington DC. Available at
https://repository.library.georgetown.edu/bitstream/handle/10822/1059652/BernierChen_geor
getown_0076M_14606.pdf?sequence=1
Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., et al. (2024). 2024
Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law &
pág. 3524
Policy. epi.yale.edu
Climate Change Performance Index, (2024), available at https://ccpi.org/
Cooray, A., Schneider, F., (2018), Does corruption throw sand into or grease the wheels of financial
sector development?. Public Choice 177, 111–133 . https://doi.org/10.1007/s11127-018-0592-
7
Corruption Perception Index, (2023), available at
https://transparencia.org.es/wp-content/uploads/Report_CPI2023_English.pdf
Deloitte, (2021), Asi se mide la corrupcion en Mexico.
https://www2.deloitte.com/mx/es/pages/dnoticias/articles/asi-se-mide-corrupcion-mexico.html
Fragile States Index, (2024), available at https://fragilestatesindex.org/about/
Gini Index, (2024), available at
https://data.worldbank.org/indicator/SI.POV.GINI?name_desc=false&year=2020
Global Happiness Index,( 2024), available at https://worldhappiness.report/
Global Innovation Index, (2024), available at https://www.wipo.int/global_innovation_index/en/
Global Peace Index, (2024), available at https://www.economicsandpeace.org/global-peace-index/
Hamilton A., Hammer C., (2018), Can We Measure the Power of the Grabbing Hand? A Comparative
Analysis of Different Indicators of Corruption , world bank group, available at
https://documents1.worldbank.org/curated/en/113281515516828746/pdf/WPS8299.pdf
Human Development Index, (2024), available at https://hdr.undp.org/data-center/human-development-
index#/indicies/HDI
Lucarelli, Stefano, Klodian Muço, and Enzo Valentini. (2024), Short Run and Long Run Effects of
Corruption on Economic Growth: Evidence from Balkan Countries, Economies 12, no. 4: 86.
https://doi.org/10.3390/economies12040086
Our world in Data, (2024) available at https://ourworldindata.org/grapher/political-corruption-index
Poverty Data Explorer, (2024), our world in data, available at https://ourworldindata.org/poverty
Social Progress Index, (2024), available at https://www.socialprogress.org/social-progress-index
Sustainable Development Goals, (2024), available at https://dashboards.sdgindex.org/
The global competitiveness index by World Economic Forum, 2024, available at
Policy. epi.yale.edu
Climate Change Performance Index, (2024), available at https://ccpi.org/
Cooray, A., Schneider, F., (2018), Does corruption throw sand into or grease the wheels of financial
sector development?. Public Choice 177, 111–133 . https://doi.org/10.1007/s11127-018-0592-
7
Corruption Perception Index, (2023), available at
https://transparencia.org.es/wp-content/uploads/Report_CPI2023_English.pdf
Deloitte, (2021), Asi se mide la corrupcion en Mexico.
https://www2.deloitte.com/mx/es/pages/dnoticias/articles/asi-se-mide-corrupcion-mexico.html
Fragile States Index, (2024), available at https://fragilestatesindex.org/about/
Gini Index, (2024), available at
https://data.worldbank.org/indicator/SI.POV.GINI?name_desc=false&year=2020
Global Happiness Index,( 2024), available at https://worldhappiness.report/
Global Innovation Index, (2024), available at https://www.wipo.int/global_innovation_index/en/
Global Peace Index, (2024), available at https://www.economicsandpeace.org/global-peace-index/
Hamilton A., Hammer C., (2018), Can We Measure the Power of the Grabbing Hand? A Comparative
Analysis of Different Indicators of Corruption , world bank group, available at
https://documents1.worldbank.org/curated/en/113281515516828746/pdf/WPS8299.pdf
Human Development Index, (2024), available at https://hdr.undp.org/data-center/human-development-
index#/indicies/HDI
Lucarelli, Stefano, Klodian Muço, and Enzo Valentini. (2024), Short Run and Long Run Effects of
Corruption on Economic Growth: Evidence from Balkan Countries, Economies 12, no. 4: 86.
https://doi.org/10.3390/economies12040086
Our world in Data, (2024) available at https://ourworldindata.org/grapher/political-corruption-index
Poverty Data Explorer, (2024), our world in data, available at https://ourworldindata.org/poverty
Social Progress Index, (2024), available at https://www.socialprogress.org/social-progress-index
Sustainable Development Goals, (2024), available at https://dashboards.sdgindex.org/
The global competitiveness index by World Economic Forum, 2024, available at
pág. 3525
https://www.weforum.org/publications/the-global-competitiveness-report-2020/
The World Competitiveness Index by the IMD, (2024), available at
https://www.imd.org/centers/wcc/world-competitiveness-center/rankings/world-
competitiveness-ranking/
Transparency International, the global coalition against corruption, (2024), available at
https://www.transparency.org/en/what-is-corruption
Urban Competitiveness Index by IMCO, (2024), available at https://imco.org.mx/indices/
World Bank Group,Corruption is a Global Problem for Development. To Fight It, We All Have a Role
to Play, (2024), available at
https://www.worldbank.org/en/news/opinion/2023/06/13/corruption-is-a-global-problem-for-
development-to-fight-it-we-all-have-a-role-to-play
World Bank Group, (2024), worldwide governance indicators, control of corruption, available at
https://databank.worldbank.org/databases/control-of-corruption
https://www.weforum.org/publications/the-global-competitiveness-report-2020/
The World Competitiveness Index by the IMD, (2024), available at
https://www.imd.org/centers/wcc/world-competitiveness-center/rankings/world-
competitiveness-ranking/
Transparency International, the global coalition against corruption, (2024), available at
https://www.transparency.org/en/what-is-corruption
Urban Competitiveness Index by IMCO, (2024), available at https://imco.org.mx/indices/
World Bank Group,Corruption is a Global Problem for Development. To Fight It, We All Have a Role
to Play, (2024), available at
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