Mexico

127.5 Population 2022 (Millions)

0.76 HDI Score
2021 (Max. 1)

69.7 SDG Score
2023
(Max. 100)

0.31 Gender Inequality
Index Score
(Max. 1)

36 Internet Inclusivity
Index 2022
(100 countries)

Sources: 1. World Bank (2022), 2, UNDP (2021), 3. Sustainable Development Report (2023), 4. UNDP (2021), 5. Economist Impact (2022)

Overview

As the only Latin American country located in North America, Mexico has intrinsic and deep linkages with its northern neighbor (the United States). With a diverse population that includes those of European descent, mestizos and Indigenous peoples, Mexico enjoys an abundance of natural resources. However, despite many natural and geographic advantages, the country has been racked with instability, both politically and as a result of drug cartel violence. The latter has caused human suffering on a vast scale, with thousands killed every year. Additional challenges include high levels of inequality, government corruption, and the struggle by Indigenous groups to secure their rights.

Projects

Disarming Disinformation: How Migrants Fall Victim to Disinformation

To fill the information gap brought on by complex migratory processes, poor institutional communication, and lack of trust in authorities, Latin American migrant communities transiting through Mexico on their way to the USA often resort to the use of social media and instant messaging apps. While sometimes a source of helpful information, these are also polluted with disinformation and scams, ranging from alleged “opening” days/hours of Mexico’s northern border, to fallacious legal advice, to false job and visa offerings. In collaboration with Conexión Migrante, VerificadoMX, PolitiFact and The Associated Press, the DPA team investigated the business models that leverage and finance the disinformation that can put migrants’ lives at risk. The investigation was published by multiple media outlets, including our partner Proceso.

Feminist Urban Design: A Gender-Inclusive Framework for Cities

Data-Pop Alliance (DPA), in collaboration with Open Data Watch (ODW), was tasked with creating a pioneering gender data framework for urban environments, commissioned by the City Hub and Network for Gender Equity (CHANGE). This framework was intended for implementation across CHANGE’s network cities, starting with Barcelona, Buenos Aires, Mexico City, Nairobi, and Quezon City as the initial participants. Capitalizing on DPA’s and ODW’s specialized knowledge in data analytics and gender-focused research, the project’s core objective was to provide a robust measurement of gender equity within varied city landscapes. The project’s deliverables included: A comprehensive blueprint outlining the essential requirements and stages for cities to prepare and deploy the framework; The framework itself, featuring specific dimensions and indicators that assessed both the processes and physical form of the cities concerning gender equity; and a specialized data collection tool, complete with training materials, to enable cities to gather and process all necessary data to effectively utilize the framework. This multi-faceted approach ensured that the participating cities were equipped with the knowledge, tools, and methods required to drive meaningful progress in the realm of urban gender equity.

Design and Development of the Central Database & CRM for Abogadas MX’s “Standards for Diversity and Inclusion within Law Firms in Mexico” Program.

DPA, in collaboration with Abogadas MX, led the design and development of the Central Database and Customer Relationship Management (CRM) software for the “Standards for Diversity and Inclusion in Law Firms in Mexico” Program. This included the creation of a survey to collect gender-focused data from law firms. This initiative enabled the organization to systematically gather and manage information from specific law firms about gender equality and the implemented policies, facilitating the generation of recommendations aimed at improving internal policies to address gender inequality issues and promote the mainstreaming of gender perspectives in the legal sector.

Open Algorithms (OPAL) for Public Data and Good

“Open Algorithm (OPAL) for Public Data and Good” seeked to merge different “privacy enhancing techniques” (PETs), such as federated learning, differential privacy, and negative databases, to allow trusted third parties such as researchers or official institutions to analyze censuses or national surveys’ microdata produced by national statistical offices (NSOs), as well as other administrative records, to derive indicators using these data, while avoiding privacy risks. A pilot was expected to be conducted in Mexico, and DPA plans to expand to additional NSOs and other public data holders in the future.

Professional Training Program “Big Data for Measuring the Digital Economy”

In partnership with the United Nations Economic Commission for Latin America and the Caribbean (ECLAC), DPA offered a series of workshops focused on Big Data and the Digital Economy in the Latin American and Caribbean region. These workshops were designed for development practitioners, policymakers, and researchers. Five editions were delivered in: Santiago de Chile (March 2016), São Paulo (September 2017) —in partnership with Cetic.br—, Mexico City (October 2017) —in collaboration with the National Digital Strategy (EDN) program and the MIT Sloan School of Management—, Santo Domingo (April 2019), and Bogotá (May 2019) —in partnership with DANE.

DataMex

Data-Pop Alliance and Oxfam México formed a partnership through “DataMex”, with the goal of advancing research and the application of Big Data for sustainable development in Mexico. This agreement, signed in late 2018, has since led to a consultancy and scoping study to identify the areas in which Big Data could be employed to better understand inequalities that have not been analyzed in depth. The resulting project was “Mundos Paralelos“, which focused on areas lacking in understanding and new perspectives that could provide critical insights into the work of Oxfam Mexico.

EmpoderaData

EmpoderaData built upon the success of the “Quantitative Step” (Q-Step) program, which was developed as a strategic response to the shortage of quantitatively-skilled social science graduates in the United Kingdom. Together, University of Manchester and Data-Pop Alliance expanded upon the program’s excellent results, exploring this model in the Global South as the “EmpoderaData Project”. The project aimed to promote a virtuous cycle of social transformation by fostering data literacy skills applied to addressing our society’s most pressing issues within the framework of the Sustainable Development Goals (SDGs).

Big Data to Address Global Development Challenges (Paper Series)

Colombia, Latin America and the Caribbean (LAC), Mexico, Namibia, Senegal, Sub-Saharan Africa (SSA)
2016 - 2019
Resilient Livelihoods and Ecosystems
Partner(s): Agence Française de Développement - AFD (Funder), Cloud to Street, Flowminder.org, Harvard Humanitarian Initiative, MIT Media Lab, Overseas Development Institute

Four research papers were developed in collaboration with and funded by the Agence Française de Développement (AFD) between 2016 and 2019 under a joint program with Data-Pop Alliance and research partners (Cloud to Street, Flowminder, Harvard Humanitarian Initiative, MIT Media Lab, Overseas Development Institute) titled “Strengthening the evidence-base for leveraging Big Data to address global development challenges”. This research program and papers were designed with the following objectives and criteria in mind: to focus on various development challenges in different local contexts to ensure relevance; to work with trusted partners to ensure academic quality; and to both reflect and promote key determinants of sustainable development, including smoother, fairer, and safer access to data and stronger links between analysts, local decision-makers, and communities. Individually, these papers outlined specific cases and examples of how computational analysis of behavioral data (combined with other datasets) can paint a finer-grained, more complex, and dynamic picture of human reality than ‘traditional’ data allows. Collectively, they sketched the contours of a world where public decisions, in the form of policies and programs, might someday be designed, implemented, and evaluated using the best available data and approaches.

Crime and Inequality in Mexico

In collaboration with Banorte (Mexican bank), and following DPA’s previous work on the impact of crime on consumption across gender and income groups, this assessment focused on the quantification, at a fine-grain and large scale, of the disruption and recovery dynamics induced by crime across socioeconomic groups. This project showed the progress of DPA’s findings at the zip code level analysis in Mexico City, where a significant correlation between increased crime rates and decreased consumption were found.

Scoping Study for a Comparative Research on Crime Risk Factors Using Big Data

A study published by the Open Society Foundations has reported numerous “atrocity crimes” perpetrated in Mexico against the civilian population since 2006. Against this backdrop, with United Nations Office on Drugs and Crime (UNODC) support, DPA sought to gain better insights into organized and interpersonal crime, by undertaking a scoping study to build a comparative research study in two cities: one heavily affected by organized crime violence and another with low organized crime rates, where violence is mostly interpersonal. To this end, this scoping study aimed at building a research proposal for two cities, according to data availability (i.e. traditional and Big Data sources) and crime dynamics; suggested methodology, and potential partners.

Parallel Worlds: Big Data and Inequality in Mexico City

“Parallel Worlds” was a project developed by Data-Pop Alliance and Oxfam México to analyze inequality in Mexico City using mobility data provided by Cuebiq’s Data for Good program. The project aimed to inform and influence public policy actors in making decisions that contribute to reducing social and economic segregation based on the privilege and marginalization associated with certain spaces in the city. More specifically, DPA analyzed urban inequality in Mexico City through the mapping of movement patterns in the city, using mobile data to identify segregation patterns in terms of where people live, work, and consume. The report analyzed three dimensions of inequality: 1) access to education, 2) the right to the city by analyzing exclusive spaces, and 3) access to culture. The results demonstrated that spatial inequality in Mexico City was marked by unequal access to government services and cultural sites, translating into unequal experiences of urban life and biased access to the city. The paper concluded with a series of public policy recommendations to foster a more equitable and inclusive appropriation of public space. A version of this paper was published in English by Projections, the Journal of the MIT Department of Urban Studies and Planning.

Reporting and Registering Domestic Violence Against Women and Girls in Mexico City, Bogota and Sao Paulo: A Data-Driven Model

This project, developed in partnership with UNIDAS and GIZ Data Lab, leveraged traditional and non-traditional data sources to assess the reporting capability of women and girls in Mexico City, Bogota and Sao Paulo. The analytical model estimated the probability of registering domestic violence at the locality or municipal level, taking into account personal (e.g. age, educational attainment) and environmental factors (e.g. access to support services, human mobility during the COVID-19 quarantine). The report for Mexico is not available.

Mexico

127.5

Population 2022 (Millions)

0.76

HDI Score 2021 (Max. 1)

69.7

SDG Score 2023 (Max. 100)

0.31

Gender Inequality Index Score (Max. 1)

36

Internet Inclusivity Index 2022 (100 countries)

Overview

Projects

HDI Score
2021 (Max. 1)

SDG Score
2023
(Max. 100)

Gender Inequality
Index Score
(Max. 1)

Internet Inclusivity
Index 2022
(100 countries)