Velázquez-Salazar, Marisol
Main Affiliation
Preferred name
Velázquez-Salazar, Marisol
Official Name
Velázquez Salazar, Marisol
ORCID
0000-0002-2434-8760
Researcher ID
HPD-6060-2023
Scopus Author ID
57221855410
17 results
Now showing 1 - 10 of 17
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Item type:Publication, Assertiveness in Coffee Agri-Food Chain Business(Emerald Publishing Limited, 2025); ; 20 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Colored heirloom corn as a public good : the case of Tlaxcala, Mexico(2021); ; Byker Shanks, CarmenValorization of territories with diverse cultures and heritage has multiplied in recent years. This study analyzes the case of colored heirloom corn in Tlaxcala, Mexico, as a potential public good associated with the region’s biocultural heritage. The analysis conducted herein relies on a wide range of literature from relevant theory, including Geographical Indications, Global Value Chains, Community-Based Entrepreneurship, Public Goods, and Sustainable Development, in order to employ case study methodology. We leverage a novel approach to analyze the heirloom corn chain and its publicness. This chain reveals its status as a potential public good that clearly influences biocultural heritage, which has been preserved by several generations. To preserve colored heirloom corn in Tlaxcala, Mexico, a development strategy is needed that links actors and resources, involves the public sector, and furthers expansion of the private sector. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Scopus© Citations 1 1 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Financial education and internal governance on short food supply chains: evidence from vulnerable rural communities in Latin America(Springer Science and Business Media LLC, 2026-08-12); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Corporate Social Responsibility and Financial Performance: Evidence from Public Companies Listed on the Mexican Stock Exchange(Springer Nature Switzerland, 2026); ; The research on how corporate social responsibility (CSR) affects the financial performance (FP) of companies is limited, especially in the case of Mexico. There is a lack of studies that measure the financial impact of socially responsible strategies in Mexican companies. This study introduces an innovative approach by proposing a hybrid model that combines three quantitative methodologies: principal component analysis (PCA), cluster analysis, and an ordered logit model. The goal is to assess the impact of sustainability practices on financial performance. The study considers financial data from the past decade for 91 companies listed on the Mexican Stock Exchange. The results indicate that CSR practices have a positive effect on FP. The proposed hybrid model serves as a valuable methodological tool for evaluating the impact on FP of Mexican public companies listed on the BMV and holding the ESR seal granted by CEMEFI (Mexican Center for Philanthropy). This approach will help monitor the evolution of FP through the adoption of various CSR-related practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimating Conditional Labor Productivity Penalties from Non-Communicable Diseases Using Double Machine Learning: Evidence from Mexican Household Data(MDPI AG, 2026-07-31); Non-communicable diseases (NCDs) represent a growing challenge for developing countries, as they are among the leading causes of morbidity and mortality within the working-age population. Beyond their effects on health outcomes, NCDs are associated with reductions in labor productivity, which may influence long-term development dynamics through human capital. This study estimates the conditional labor productivity penalty associated with Non-Communicable Diseases (NCDs) among employed workers in Mexico using an observational high-dimensional framework. By addressing high-dimensional confounding under the Conditional Independence Assumption (CIA), the analysis isolates the relationship between NCD status and hourly labor earnings. Using microdata from the Mexican National Survey of Household Income and Expenditure, labor productivity is defined as the ratio of labor income to hours worked. To obtain robust causal estimates, a Double Machine Learning (DML) framework is implemented, integrating regularized regression and machine learning algorithms to control for high-dimensional confounding factors. These covariates include socioeconomic, demographic, and health-related variables, allowing for flexible adjustment of observable heterogeneity across individuals. The empirical findings demonstrate a statistically significant conditional negative penalty of −11.44% (p = 1.03 × 10−11, 95% CI: [−14.59, −8.40]) on hourly labor earnings under the cross-fitted DML specification using XGBoost and Random Forest. To resolve an observational measurement artifact where chronic individuals report artificially inflated hourly wages due to severe hours contraction, a presenteeism adjustment factor ((Formula presented.)) was calibrated. Incorporating this presenteeism correction, the aggregate national economic burden among the employed population is estimated at approximately 496.82 billion pesos annually. The findings suggest that NCDs constitute a relevant constraint on labor productivity at the microeconomic level. The study highlights the importance of prevention-oriented health strategies, workplace health promotion, and early disease management. From a methodological perspective, the results illustrate the usefulness of Double Machine Learning for purging high-dimensional confounding in cross-sectional survey data, providing robust conditional empirical benchmarks for public health policy. © 2026 by the authors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Influence of NCD on the Health Habits of the Mexican Working Population(Springer Nature Switzerland, 2026); 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Transformation and Innovation in Organizations : A Latin American Perspective(Springer Nature Switzerland, 2025); ; 30 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling the Relation Between Non-Communicable Diseases and the Health Habits of the Mexican Working Population: A Hybrid Modeling Approach(MDPI AG, 2025); The impact that Non-Communicable Diseases (NCDs) have on the health status of the population has generated the need for an in-depth analysis of health habits and NCDs. In addition to its significant impact on population health, this phenomenon also translates into substantial economic consequences for countries. This study delves into the analysis of the relationship between health habits and NCDs among the economically active population of Mexico. Through a hybrid approach that integrates the use of machine learning (ML) models and a structural equation model (SEM), we seek to quantify the direct and indirect causal effects between health habits and NCDs. For this study, information from the 2022 National Health and Nutrition Survey carried out in Mexico for the working-age population is used. According to the results obtained in the first stage of analysis using ML, the most relevant variables (health habits) that impact the probability of individuals presenting with NCDs were identified (random forest precision of 78.66% and Lasso with 71.27%). The second stage of analysis through SEM using the most relevant variables, which were selected through ML, allowed us to measure the direct and indirect causal effect of health habits on NCDs. The SEM model was statistically significant (Chi-square: 449.186; p-value = 0.0000) and revealed that negative health habits, such as a poor diet, physical inactivity, smoking and alcohol consumption, significantly increase the risk of NCDs in the working-age population in Mexico (0.23), while vigorous physical activity and salary has a negative impact (−0.17 and −0.23, respectively) on the presence of NCDs. This study highlights the ability of machine learning and SEM approaches to model the impact of health habits on NCDs for the economically active population in Mexico. ©The authors © Mathematics ©MDPI.20 114 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sustainability and Quality of Life in Marginalized Areas: An Impact Evaluation of a Community Center in Santa Fe, MexicoThe aim of this paper is to present the results of an impact evaluation of a community center in health, capacity building, and digital access, which form an approximation of quality of life, in the population of Santa Fe town in Mexico City, from 2022 to 2024. The methodology is quantitative, using an impact index and the differences in differences (DD) technique. The data were obtained from primary sources with surveys undertaken via questionnaires. The center is operated by a private university and funded by private firms. The results show a positive impact of 0.287127 out of 1 on the weighted impact index, which allows us to consider this program successful in improving the quality of life of the target population. Through impact evaluation, the effectiveness of interventions and opportunities for improvement are identified, fostering collaboration among local actors, including community members, state-run public programs, and community centers. This collaborative effort improves the quality of life, creating a sustainable community wherein each actor addresses specific needs. Impact evaluation plays a crucial role in measuring sustainability because it is a continuous improvement process that, when combined with other actions, enhances the community’s overall well-being. ©2024 MDPI, The authors.Scopus© Citations 2 27 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small Coffee Companies and the Impact of Geographical Indications as Productive Innovation in Mexico in the New Reality(2021); Pérez Akaki, PabloThis paper analyzes the Protected Designation of Origin (PDO) as a factor of innovation in the Coffee Pluma geographical region in Oaxaca, Mexico, a vital tool to solve the problem of the actual crisis in the chain and for the new context of business and markets in the post-COVID 19 era due to the need for new marketing methods. Two case studies are presented under the Global Value Chain (GVC) methodology proposed by Gereffi et al. (1994, 2005, 2018) with a contribution from the conceptual framework of Geographical Indications (GI) used by Belletti et al. (2017) to analyze the PDO as an innovation. The first are small-size producers and the second are medium-size producers, both considered as small companies by the number of people employed. Even on a small scale, the coffee sector, through the appellation of origin, has the potential to generate economic benefits in the place of origin by promoting the development of two other economic sectors such as tourism and retail marketing. Likewise, it gives a comprehensive answer considering the business economic field and incorporating, as required by the current reality, other capitals such as social, cultural and environmental. The aim of this chapter is to evaluate the benefits that coffee sector, will obtain and generate through this sectorial and territorial development tool, considering that the G.I. emerges as an option to improve production by acquiring the exclusivity of producing coffee within that region to achieve sustainable development faced with the new reality. © Springer1 34Scopus© Citations 1